Methods for developing the fishing effort regime for demersal fisheries in western Mediterranean Sea. Part III (STECF-19-01).
Date: 2019-06-17
Institutions: Joint Research Centre, Scientific, Technical and Economic Committee for Fisheries
Subjects: Mediterranean Sea, catch by species, fishery management, fishery research, fishery resources, management of resources, research report, scientific report, sea fish, statistics

Scientific, Technical and Economic 
Committee for Fisheries (STECF) 
- 
Methods for developing the 
fishing effort regime for demersal 
fisheries in western 
Mediterranean Sea – Part III 
(STECF-19-01)

Edited by Clara Ulrich and Cecilia Pinto 

 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  policy-making  process.  The  scientific  output  expressed  does  not  imply  a  policy 
position of the European Commission. Neither the European Commission nor any person acting on 
behalf of the Commission is responsible for the use which might be made of this publication. 

Contact information  

Name: STECF secretariat 

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

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

Tel.: +39 0332 789343 

JRC Science Hub 

https://ec.europa.eu/jrc 

JRC116968 

EUR 28359 EN 

PDF 

ISBN 978-92-76-08330-6 

ISSN 1831-9424 

doi:10.2760/249536 

STECF 

ISSN 2467-0715 

Luxembourg: Publications Office of the European Union, 2019 

© European Union, 2019 

The  reuse  policy  of  the  European  Commission  is  implemented  by  Commission  Decision 
2011/833/EU of 12 December 2011 on the reuse of Commission documents (OJ L 330, 14.12.2011, 
p. 39). Reuse is authorised, provided the source of the document is acknowledged and its original
meaning  or  message  is  not  distorted.  The  European  Commission  shall  not  be  liable  for  any 
consequence  stemming  from  the  reuse.  For  any  use  or  reproduction  of  photos  or  other  material 
that is not owned by the EU, permission must be sought directly from the copyright holders. 

For  any  use  or  reproduction  of  photos  or  other  material  that  is  not  under  the  EU  copyright, 
permission must be sought directly from the copyright holders. 

All content © European Union 2019 

How  to  cite:  Scientific,  Technical  and  Economic  Committee  for  Fisheries  (STECF)  –  Methods  for 
developing fishing effort regimes for demersal fisheries in Western Mediterranean-Part III  (STECF-
19-01).  Publications  Office  of  the  European  Union,  Luxembourg,  2019,  ISBN  978-92-76-08330-6, 
doi:10.2760/249536, JRC116968 

All images © European Union 2019 

Authors: 

STECF advice:  

Ulrich,  C.,  Abella,  J.  A.,  Andersen,  J.,  Arrizabalaga,  H.,  Bailey,  N.,  Bertignac,  M.,  Borges,  L., 
Cardinale,  M.,  Catchpole,  T.,  Curtis,  H.,  Daskalov,  G.,  Döring,  R.,  Gascuel,  D.,  Knittweis,  L.,  
Lloret,  J.,  Malvarosa,  L.,  Martin,  P.,  Motova,  A.,  Murua,  H.,  Nord,  J.,  Prellezo,  R.,  Raid,  T., 
Sabatella,  E.,  Sala,  A.,  Scarcella,  G.,  Soldo,  A.,  Somarakis,  S.,  Stransky,  C.,  van  Hoof,  L., 
Vanhee, W., van Oostenbrugge, H. , Vrgoc, N. 

EWG-19-01 report:  

Ulrich,  C.,  Accadia,  P.,  Bitetto,  I.,  Certain,  G.,  Garcia,  D.,  Konrad,  C.,  Martin,  P.,  Maynou,  F., 
Merzereaud,  M.,  Murenu,  M.,  Pinto  C.,  Russo,  T.,  Sanchez  Lizaso,  J.L.,  Sbrana,  M.,  Spedicato, 
M.T., Vaz, S. 

3 

3 

 
 
 
 
 
 
 
 
 
 
 
 
TABLE OF CONTENTS 

Abstract 9 

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

developing fishing effort regime for demersal fisheries in Western Mediterranean – Part 
III (STECF-19-01) .................................................................................... 10 

Background provided by the Commission ................................................................ 10 

Request to the STECF ........................................................................................... 10 

STECF observations .............................................................................................. 10 

STECF comments ................................................................................................. 10 

STECF conclusions ................................................................................................ 11 

References .......................................................................................................... 12 

Contact details of STECF members ......................................................................... 12 

Expert Working Group  EWG-19-01 report ............................................................... 18 

1 

1.1 

1.1 

1.2 

1.3 

2 

2.1 

2.2 

Introduction ............................................................................................. 19 

List of acronyms ....................................................................................... 19 

Background ............................................................................................. 19 

Terms of Reference for EWG-19-01 ............................................................. 19 

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

Main elements of the West Med MAP to be supported by scientific advice ......... 21 

MAP elements .......................................................................................... 21 

Overview of the MAP coverage ................................................................... 24 

2.2.1 

Percentage of MAP stocks caught by the regulated trawler fleets .................... 24 

2.2.2 

Percentage of MAP fleets catches from the MAP species ................................. 28 

2.3 

3 

3.1 

Requirements to the bioeconomic mixed-fisheries models in relation to the ToRs28 

FLBEIA in EMU1 ........................................................................................ 33 

Overview of the model’s generic characteristics and use ................................ 33 

3.1.1  Main features ........................................................................................... 33 

3.1.2  References ............................................................................................... 34 

3.2 

3.3 

3.4 

3.5 

4 

4.1 

Application of the model to the West Med MAP ............................................. 35 

Comparison of model’s short-term forecast with the single-stock advice predictions
 .............................................................................................................. 35 

Baseline Run 2020-2024 ............................................................................ 35 

Summary on the model: achievements, gaps still to be addressed, work plan from now to 
STECF 19-14 (October 2019) ..................................................................... 37 

FLasher in EMU1 ....................................................................................... 38 

Overview of the model’s generic characteristics and use ................................ 38 

4.1.1  References ............................................................................................... 38 

4.2 

Application of the model to the West Med MAP ............................................. 38 

4.2.1  State of completion and additional development work specially undertaken before and 

during EWG 19-01 .................................................................................... 38 

4 

4 

 
 
4.2.2  Space and time scale ................................................................................ 39 

4.2.3  Stocks (which stocks, which assessment data etc) ........................................ 39 

4.2.4 

Fleets ...................................................................................................... 39 

4.3 

4.4 

4.5 

Comparison of model’s short-term forecast with the single-stock advice predictions
 .............................................................................................................. 39 

Baseline Run 2020-2024 ............................................................................ 40 

Alternative Runs ....................................................................................... 40 

4.5.1  Modellers’ list of possible runs that can be implemented with the model and considered of 
direct relevance for the purpose of the West Med MAP ................................... 40 

4.5.2  Runs performed and analysed during EWG 19-01 ......................................... 40 

4.6 

5 

5.1 

Summary on the model: achievements, gaps still to be addressed, work plan from now to 
STECF 19-14 (October 2019) ..................................................................... 40 

NIMED in EMU 2 ....................................................................................... 41 

Overview of the model’s generic characteristics and use ................................ 41 

5.1.1  Main features ........................................................................................... 41 

5.1.2  References ............................................................................................... 41 

5.2 

Application of the model to the West Med MAP ............................................. 41 

5.2.1  State of completion and additional development work specially undertaken before and 

during EWG 19-01 .................................................................................... 41 

5.2.2  Space and time scale ................................................................................ 41 

5.2.3  Stocks (which stocks, which assessment data etc) ........................................ 41 

5.2.4 

Fleets ...................................................................................................... 42 

5.3 

5.4 

5.5 

Comparison of model’s short-term forecast with the single-stock advice predictions
 .............................................................................................................. 43 

Baseline Run 2020-2024 ............................................................................ 43 

Alternative Runs ....................................................................................... 43 

5.5.1  Modellers’ list of possible runs that can be implemented with the model and considered of 
direct relevance for the purpose of the West Med MAP ................................... 44 

5.5.2  Runs performed and analysed during EWG 19-01 ......................................... 44 

5.6 

6 

6.1 

Summary on the model: achievements, gaps still to be addressed, work plan from now to 
STECF 19-14 (October 2019) ..................................................................... 46 

BEMTOOL in EMU2 .................................................................................... 47 

Overview of the model’s generic characteristics and use ................................ 47 

6.1.1  Main features ........................................................................................... 47 

6.1.2  References ............................................................................................... 47 

6.2 

Application of the model to the West Med MAP ............................................. 48 

6.2.1  State of completion and additional development work specially undertaken before and 

during EWG 19-01 .................................................................................... 48 

6.2.2  Space and time scale ................................................................................ 48 

6.2.3  Stocks (which stocks, which assessment data etc) ........................................ 48 

6.2.4 

Fleets ...................................................................................................... 49 

5 

5 

 
6.3 

6.4 

6.5 

Comparison of model’s short-term forecast with the single-stock advice predictions
 .............................................................................................................. 50 

Baseline Run 2020-2024 ............................................................................ 51 

Alternative Runs ....................................................................................... 51 

6.5.1  Modellers’ list of possible runs that can be implemented with the model and considered of 
direct relevance for the purpose of the West Med MAP ................................... 53 

6.5.2  Runs performed and analysed during EWG 19-01 ......................................... 53 

6.6 

7 

7.1 

Summary on the model: achievements, gaps still to be addressed, work plan from now to 
STECF 19-14 (October 2019) ..................................................................... 58 

IAM in EMU1 ............................................................................................ 60 

Overview of the model’s generic characteristics and use ................................ 60 

7.1.1  Overview ................................................................................................. 60 

7.1.2  References ............................................................................................... 60 

7.2 

Application of the model to the West Med MAP ............................................. 60 

7.2.1  State of completion and additional development work specially undertaken before and 

during EWG 19-01 .................................................................................... 61 

7.2.2  Space and time scale ................................................................................ 61 

7.2.3  Stocks (which stocks, which assessment data etc) ........................................ 61 

7.2.4 

Fleets ...................................................................................................... 61 

7.3 

Comparison of model’s short-term forecast with the single-stock advice predictions
 .............................................................................................................. 61 

7.3.1  Short-Term Forecast IAM-GSA7 .................................................................. 63 

7.3.2  Short-Term Forecast IAM-GSA1,5,6,7 ......................................................... 65 

7.4 

Results of the Status Quo Scenario,  IAM-GSA1,5,6,7 ................................... 67 

7.5 Quantitative results for 2024 ............................................................................ 68 

7.6 Summary: achievements, gaps still to be addressed, work plan from now to STECF 19-14

 .............................................................................................................. 69 

8 

8.1 

MEFISTO in EMU1 ..................................................................................... 70 

Overview of the model’s generic characteristics and use ................................ 70 

8.1.1  Main features ........................................................................................... 70 

8.1.2  References ............................................................................................... 70 

8.2 

Application of the model to the West Med MAP ............................................. 70 

8.2.1  State of completion and additional development work specially undertaken before and 

during EWG 19-01 .................................................................................... 70 

8.2.2  Space and time scale ................................................................................ 70 

8.2.3  Stocks (which stocks, which assessment data etc) ........................................ 70 

8.2.4 

Fleets ...................................................................................................... 71 

8.3 

8.4 

8.5 

Comparison of model’s short-term forecast with the single-stock advice predictions
 .............................................................................................................. 72 

Baseline Run 2020-2024 ............................................................................ 73 

Alternative Runs ....................................................................................... 75 

6 

6 

 
8.5.1  Modellers’ list of possible runs that can be implemented with the model and considered of 
direct relevance for the purpose of the West Med MAP ................................... 75 

8.5.2  Runs performed and analysed during EWG 19-01 ......................................... 76 

8.6 

9 

9.1 

Summary on the model: achievements, gaps still to be addressed, work plan from now to 
STECF 19-14 (October 2019) ..................................................................... 77 

SMART in EMU2 ........................................................................................ 79 

Overview of the model’s generic characteristics and use ................................ 79 

9.1.1  Main features ........................................................................................... 79 

9.1.2  References ............................................................................................... 79 

9.2 

Application of the SMART model to the West Med MAP .................................. 79 

9.2.1  State of completion and additional development work specially undertaken before and 

during EWG 19-01 .................................................................................... 79 

9.2.2  Space and time scale ................................................................................ 80 

9.2.3  Stocks ..................................................................................................... 82 

9.2.3.1  MEDITS data ............................................................................................ 82 

9.2.3.2  Demographic structure of the commercial data ............................................. 84 

9.2.4 

Fleets ...................................................................................................... 84 

9.3 

9.4 

9.5 

Comparison of model’s short-term forecast with the single-stock advice predictions
 .............................................................................................................. 84 

Baseline Run 2020-2024 ............................................................................ 84 

Alternative Runs ....................................................................................... 85 

9.5.1  Modellers’ list of possible runs that can be implemented with the model and considered of 
direct relevance for the purpose of the West Med MAP ................................... 85 

9.5.2  Runs performed and analysed during EWG 19-01 ......................................... 85 

9.6 

Summary on the model: achievements, gaps still to be addressed, work plan from now to 
STECF 19-14 (October 2019) ..................................................................... 88 

10 

ISIS-FISH in the Gulf of Lions (GSA 7, EMU1) .............................................. 89 

10.1 

Overview of the model’s generic characteristics and use ................................ 89 

10.1.1  Main features ........................................................................................... 89 

10.1.2  References ............................................................................................... 89 

10.2 

Application of the model to the West Med MAP ............................................. 89 

10.2.1  State of completion and additional development work specially undertaken before and 

during EWG 19-01 .................................................................................... 89 

10.2.2  Space and time scale ................................................................................ 89 

10.2.3  Stocks (which stocks, which assessment data etc) ........................................ 89 

10.2.4  Fleets ...................................................................................................... 89 

10.3 

Summary on the model: achievements, gaps still to be addressed, work plan from now to 
STECF 19-14 (October 2019) ..................................................................... 89 

11 

Main issues and gaps ................................................................................ 91 

11.1 

Estimation of fishing effort (compatibility of the different databases)............... 91 

11.1.1  What is the problem .................................................................................. 91 

11.1.2  Where are we in addressing this issue ......................................................... 91 

7 

7 

 
11.1.3  What are the next steps ............................................................................ 91 

11.2 

Mixed-metiers vs. deep water metiers ......................................................... 91 

11.2.1  What is the problem .................................................................................. 91 

11.2.2  Where are we now in addressing this issue .................................................. 92 

11.2.3  What are the next steps ............................................................................ 93 

11.3 

Multiple assessments and differences in stock definitions for hake .................. 94 

11.3.1  What is the problem .................................................................................. 94 

11.3.2  Where are we with addressing this issue...................................................... 94 

11.3.3  What are the next steps ............................................................................ 94 

11.4 

One broad vs. several detailed models in the EMU1 ...................................... 94 

11.4.1  What is the problem .................................................................................. 94 

11.4.2  Where are we with addressing this issue...................................................... 94 

11.4.3  What are the next steps ............................................................................ 95 

11.5 

Other species ........................................................................................... 95 

11.5.1  What is the problem .................................................................................. 95 

11.5.2  Where are we in addressing this issue ......................................................... 95 

11.5.3  What are the next steps ............................................................................ 96 

11.6 

Other gears ............................................................................................. 97 

11.6.1  What is the problem .................................................................................. 97 

11.6.2  Where are we with addressing this issue...................................................... 99 

11.6.3  What are the next steps ............................................................................ 99 

11.7 

Various scales of models .......................................................................... 100 

11.7.1  What is the problem ................................................................................ 100 

11.7.2  Where are we with addressing this issue.................................................... 100 

11.7.3  What are the next steps .......................................................................... 100 

11.8 

Requirements for the stock assessment (consistency between stock object and 
assessment report) ................................................................................. 100 

11.8.1  What is the problem ................................................................................ 100 

11.8.2  Where are we now with addressing this issue ............................................. 102 

11.8.3  What are the next steps .......................................................................... 103 

11.9  What happens in 2025? ........................................................................... 103 

11.10  Balancing different objectives ................................................................... 103 

12 

Summary in relation to the ToRs .............................................................. 106 

ToR 1: Selection of models .................................................................................. 106 

ToR 2: Stocks and forecasts ................................................................................ 106 

ToR 3: Scenarios and displays .............................................................................. 106 

13 

14 

15 

Conclusions and next steps ...................................................................... 107 

Contact details of EWG-19-01 participants ................................................. 108 

List of Background Documents ................................................................. 110 

8 

8 

 
 
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  third expert  working  group  (EWG)  report  dealing  with  methods  for  developing 
the  fishing  effort  regime  for  demersal  fisheries  in  western  Mediterranean  Sea,  after  EWG  18-09 
and  18-13.  The  group  was  requested  to  select  the  most  appropriate  model(s)  to  carry  out  a 
mixed-fisheries  advice  for  the  western  Mediterranean  demersal  fisheries,  and  to  analyse  the 
ability of the model(s) to be compatible with the latest single-stock scientific advice provided for 
western Mediterranean demersal stocks Finally, the group was requested to discuss and suggest 
possible mixed-fisheries scenarios and type of results for future developments.  

The EWG reviewed 8 different models, of different complexity levels and covering different GSAs 
and EMUs. For EMU2 (East side of Western Med, which mainly includes fisheries from one single 
Member  State),  3  models  were  presented,  which  are  fully  parameterised  and  operational.  The 
situation is more challenging in EMU1 (West side of Western Med, which covers fisheries from two 
Member  States),  where  five  models  were  presented  but  none  of  them  is  directly  operational  at 
the scale of the EMU. Progresses were reached during the EWG, but further intersessional work, 
involving scientists from the two Member States, is necessary before reaching the desired level of 
completion for the evaluation of management scenarios.  

During  the  EWG,  all  models  presented  were  updated  to  the  most  recent  information  available. 
Where  possible,  short-term  forecasts  were  run,  assuming  a  reduction  in  fishing  effort  in  2019 
equivalent to the reduction necessary to achieve the target F (Fmsy, proxy F01). For all models, 
the  aggregated  results  of  the  short  term  forecast  were  largely  similar  between  the  single-stock 
and the mixed-fisheries models. Then most models were able to run the basic MAP scenario of a 
gradual  reduction  of  fishing  effort  between  2020  and  2024.  Several  alternative  runs  and  model 
capabilities were discussed.  

Finally, the EWG discussed a number of remaining issues and gaps that are important in relation 
to the scientific support to the MAP, and agreed on some future work.  

9 

9 

 
 
 
 
 
 
 
 
SCIENTIFIC, TECHNICAL AND ECONOMIC COMMITTEE FOR FISHERIES (STECF) - 
Methods for developing fishing effort regime for demersal fisheries in Western 
Mediterranean – Part III (STECF-19-01) 

Background provided by the Commission 

TERMS OF REFERENCE 

As a follow-up of the STECF Expert Working Group 18-13 (October 2018, Copenhagen), the group 
is requested to: 

TOR 1.  
Select the most appropriate model(s) to carry  out a mixed-fisheries advice for the 
western  Mediterranean  demersal  fisheries.  It  should  be  taken  into  account  the  analyses  done  in 
the STECF Report 18-13 , the coverage in terms of stocks and fleets segments at the scale of the 
multi-annual plan and the availability of data requirements. 

TOR 2.  
Analyse the ability of the model(s) to be compatible with the latest scientific advice 
provided  for  western  Mediterranean  demersal  stocks  in  the  STECF  Report  18-12  .  The  group 
should  also  make  any  appropriate  comments  and  recommendations  to  the  stock  assessments’ 
group in order to ensure adequacy of the single-stock advice and the mixed-fisheries model(s). 

TOR 3.  
displayed to be tested at the STECF EWG 19-14 concerning the multi-annual plan. 

Discuss  and  suggest  possible  mixed-fisheries  scenarios  and  type  of  results 

Request to the STECF 

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

STECF observations 

EWG 19-01 was held in Barcelona, Spain, from 18 to 22 March 2019. The EWG was a follow-up of 
the EWG 18-09 held in June 2018 and the EWG 18-13 held in October 2018. 
As  the  EWG  19-01  took  place  the  week  before  the  STECF  plenary,  the  EWG  report  was  not 
finalised.  The  STECF  commented  on  a  draft  version  of  the  report  and  the  presentation  and 
discussion held at the STECF plenary. The EWG had the following TORs: 
TOR 1. Select the most appropriate model(s) to carry out a mixed-fisheries advice for the western 
Mediterranean demersal fisheries. It should be taken into account the analyses done in the STECF 
Report  18-13,  the  coverage  in  terms  of  stocks  and  fleets  segments  at  the  scale  of  the  multi-
annual plan and the availability of data requirements. 
TOR  2.  Analyse  the  ability  of  the  model(s)  to  be  compatible  with  the  latest  scientific  advice 
provided  for  western  Mediterranean  demersal  stocks  in  the  STECF  Report  18-12.  The  group 
should  also  make  any  appropriate  comments  and  recommendations  to  the  stock  assessments’ 
group in order to ensure adequacy of the single-stock advice and the mixed-fisheries model(s). 
TOR 3. Discuss and suggest possible mixed-fisheries scenarios and type of results displayed to be 
tested at the STECF EWG 19-14 concerning the multi-annual plan. 

STECF comments 

STECF  notes  that  the  EWG  19-01  is  part  of  the  roadmap  defined  by  the  STECF  (PLEN  18-03) in 
2018 and the TORs or this EWG responds to this roadmap. They have been fully covered by the 
EWG. 
STECF notes that, as required by the TORs, several models were tested in the EWG 19-01. These 
models can be categorized in terms of the model scale and complexity (fleet-based models with a 
simple  annual  setting,  models  adding  more  complex  features  and  models  with  finer  time-spatial 

10 

10 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
scale)  and  the  Effort  Management  Unit  (EMU)  in  which  they  are  to  be  applied  (EMU  1  including 
GSAs  1,  2,  5,  6  and  7  and  EMU  2  including  GSAs  8  to  11).  STECF  notes  that  both  EMUs  had  a 
model  of  each  scale  presented,  although  not  all  at  the  same  readiness  level  to  perform  the 
simulations  required.  However,  it  should  be  further  noted  that  none  of  the  models  cover  GSA  1 
(Alboran Sea) and GSA 5 (Balearic Islands). 
STECF  notes  that  almost  all  the  models  are  compatible  with  the  stock  assessments  provided  by 
the  EWG  18-12,  in  the  sense  that  they  are  able  to  replicate  the  short-term  forecast  for  hake 
performed in the EWG 18-12. STECF further notes that these short-term projections require some 
working assumptions to be made, although STECF agrees with the EWG that the compatibility is 
sufficient to perform further mid-term projections. 

STECF  notes  that  for  EMU2,  the  BEMTOOL  model  is  almost  ready  for  performing  simulations  of 
different  scenarios.  However,  in  the  EWG  19-01  report  the  results  presented  did  not  include 
confidence intervals. 
STECF  also  notes  that  results  from  BEMTOOL  could  be  additionally  supported  by  the  outputs 
obtained  from  the  SMART  model.  This  last  model  can  simulate  effort  reallocation  following  a 
reduction  in  effort  within  the  historical  fishing  areas,  and  therefore,  evaluate  fishing  mortality 
reductions  after  fleets  have  spatially  reallocated  their  fishing  effort.  STECF  notes  that  although 
the  results  of  this  model  are  still  preliminary,  they  showed  signs  of  hyperstability,  that  is,  that 
effort can be reallocated to maintain similar levels of fishing mortality and economic performance. 
For EMU 1 STECF notes that the work is more preliminary. However, STECF also notes that three 
candidate  models  were  identified  by  the  EWG  (FLBEIA,  FLASHER  and  IAM)  although  the 
development of the application of these three models to cover the entire EMU 1 is still preliminary 
and therefore the simulations provided in the EWG 19-01 have to be considered with caution. 
STECF  notes  that  the  MEFISTO  and  ISIS-FISH  models  are  not  likely  to  be  retained  for  further 
simulations in the frame of the demersal fisheries Western Mediterranean MAP due to the existing 
trade off among their complexity and the value added from more “simple” settings. 
STECF  notes  that  there  is  a  problem  on  how  to  differentiate in  the  simulations  the  two  types  of 
activities  foreseen  in  the  plan,  i.e.  the  mixed  demersal  metier  and  the  deep-sea  shrimp  metier. 
These two métiers are not well identified in the current datasets available. 
STECF notes that some of the simulations included fishing effort regimes characteristics identified 
in EWG 18-13 and EWG 18-09 such as hyperstability and technology creep. 
STECF notes that while the effort baseline is clearly defined in the MAP (2015-2017 fishing days), 
the value of this baseline in terms of actual number of days are not provided yet. STECF further 
notes that these absolute values might differ depending on how the trip data (e.g. logbooks) are 
being  computed  and  aggregated  by  different  people  or  for  different  purposes  in  different 
databases. 
Finally,  STECF  notes  that  EWG  19-01  also  discussed  on  how  to  present  the  results.  The  EWG 
discussed two main approaches: the first approach is a web-based results-display app, where the 
user  can  select  the  indicator(s)  to  be  displayed  and  the  scenarios  to  be  compared.  The  second 
approach is a multi-criteria approach in where each dimension (biological, economic and social) is 
weighted  and  a  utility  metric  calculated  based  on  these  weights  and  the  utility  functional  form 
itself. However, the value of this single metric is dependent on how the weights are selected and 
on the form of the utility function (additive, multiplicative, Rawls, max-min,…). 

STECF conclusions 

STECF  concludes  that  EWG  19-01  proved  the  capacity  of  the  models  tested  to  produce  a  bio-
economic assessment of different scenarios in the frame of the demersal fisheries West Med MAP. 
STECF also concludes that models differ in  the readiness level to produce results and that those 
models to be used in the EMU 2 are at a more advanced readiness level than those to be used in 
EMU 1. 
STECF concludes that given that the MAP only applies to trawlers, there is a potential risk of effort 
being transferred from trawlers to those gears not covered by the plan (i.e. gillnetters). This can 
cause  that  fishing  mortality  remains  high  under  regulated  effort  reduction.  Furthermore,  due  to 
the different overall selectivity a change in the productivity of the stocks could occur in this case, 
which will require new  calculations of reference points such as FMSY. STECF also concludes that 

11 

11 

 
 
 
 
even  if  this  effort  shifting  does  not  occur,  gears  not  covered  by  the  plan  will  be  clearly 
advantaged.  These  gears  are  likely  to  obtain  higher  catches  benefited  from  the  higher  future 
stock  abundance.  These  higher  landings  of  the  gear  not  covered  by  the  plan  could  affect  the 
market prices and outweigh, at least partially, the likely higher prices that trawlers would receive 
from their lower supply to the market. 
STECF concludes that setting the correct baseline effort in terms of actual number of fishing days 
is critical for the simulations. In that sense it is important that the fleet’s productivity estimations 
or  calibrations  (i.e.  catchability)  refer  to  the  baseline  values  agreed  by  the  Member  States. 
Building  on  the  suggestion  from  EWG  19-01,  STECF  suggests  holding  a  scoping  meeting  before 
the next EWG planned in October. Such a scoping meeting involving Member States and scientists 
would be beneficial to discuss the data issues in relation to the baseline and to agree on a set of 
scenarios for the bio-economic simulations. 
Regarding the problem of the identification of demersal mix and deep-sea shrimp métiers, STECF 
concludes  that  the  metier  identification  will  require  an  alignment  between  the  recommendation 
given by the DCF Métier Workshop (2018) and the definitions applied by Member States in their 
monitoring  of  fishing  effort.  In  this  workshop  it  was  recommended  to  use  catch  composition  in 
value  (instead  of  volume)  as  the  metric  to  be  used  if  the  distinction  between  metiers  is  to  be 
based on target assemblage reflecting fishers intentions. . 
STECF  encourages  that,  when  possible,  final  results  should  include  uncertainty  of  estimates, 
considering the uncertainty of both the stock assessment and of the projections. 
Regarding  the  display  of  the  results,  STECF  concludes  that  the  multi-criteria  approach  proposed 
can be a step ahead, given that scenarios could be compared using a single dimensionless metric 
(utility).  However,  the  value  of  this  single  metric  is  dependent  on  how  the  weights  are  selected 
and  on  the  form  of  the  utility  function.  These  are  likely  to  differ  according  to  the  different 
priorities from different actors (Commission, Member States, NGOs, fishing firms, etc.). 

References 

DCF Métier Workshop: Sub-group of the RCGs - North Sea and Eastern Arctic and North Atlantic. 
DTU Aqua, Lyngby, Denmark. January 2018. 

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 

Address1 

Tel. 

Email 

STECF members 

Abella, 
Alvaro 

J. 

Independent consultant 

Tel. 0039-3384989821 

aabellafisheries@gmail.com 

12 

12 

 
 
 
 
 
 
Name 

Address1 

Tel. 

Email 

STECF members 

Tel.dir.:    +45  35  33  68 
92 

jla@ifro.ku.dk 

Andersen, 
Jesper Levring  

Department of Food and 
Economics 
Resource 
(IFRO) 

Section  for  Environment 
and Natural Resources 

University 
Copenhagen 

of 

Rolighedsvej 25 

1958 Frederiksberg 

Denmark 

Arrizabalaga, 
Haritz 

AZTI / Unidad de 

Tel.: +34667174477 

harri@azti.es 

Investigación  Marina, 
Herrera 

kaia 
20110 Pasaia 

portualdea 

z/g 

(Gipuzkoa), Spain 

Bailey, 
Nicholas  

Independent consultant 

nickbailey2013@btinternet.com 

Bertignac, 
Michel  

Laboratoire  de  Biologie 
Halieutique 

IFREMER  Centre 
Brest 

de 

BP 70 - 29280 Plouzane, 
France  

tel  :  +33  (0)2  98  22  45 
25  -  fax  :  +33  (0)2  98 
22 46 53  

michel.bertignac@ifremer.fr 

Borges, Lisa 

FishFix, 
Belgium 

Brussels, 

info@fishfix.eu 

Cardinale, 
Massimiliano 

Föreningsgatan  45,  
330 Lysekil, Sweden 

Catchpole, 
Thomas 

CEFAS 
Laboratory, 

Lowestoft 

Tel: +46 523 18750 

massimiliano.cardinale@slu.se 

thomas.catchpole@cefas.co.uk  

Pakefield Road, 

Lowestoft 

Suffolk, UK 

NR33 0HT 

Sea 
Authority 

Fish 

18 Logie Mill 

Curtis, Hazel  

Logie Green Road 

Edinburgh 

EH7 4HS, U.K. 

Industry 

Tel:  +44  (0)131  524 
8664 

Hazel.curtis@seafish.co.uk 

Fax:  +44  (0)131  558 
1442 

13 

13 

 
 
 
 
 
 
 
 
 
Name 

Address1 

Tel. 

Email 

STECF members 

Daskalov, 
Georgi 

Laboratory  of  Marine 
Ecology, 

Tel.: +359 52 646892 

Georgi.m.daskalov@gmail.com 

Döring, 
(vice-chair) 

Ralf 

Institute  of  Biodiversity 
and 

Ecosystem 
Bulgarian 

Research, 

Academy of Sciences 

for 
Forestry 

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

Tel.:  +49  471  94460-
378 

Fax.:  +49  471  94460-
199 

ralf.doering@thuenen.de 

Gascuel, 
Didier  

AGROCAMPUS OUEST 

Tel:+33(0)2.23.48.55.34 

65  Route  de  Saint 
Brieuc, 

Fax: 
+33(0)2.23.48.55.35 

Didier.Gascuel@agrocampus-
ouest.fr 

CS 84215, 

F-35042 RENNES Cedex 

France 

Department of Biology 

Leyla.knittweis@um.edu.mt  

University of Malta 

Msida, MSD 2080 

Malta 

Associate 
(Professor 
University 
(UdG), Spain 

Professor 
Agregat), 
of  Girona 

Fishery 

NISEA, 
and 
Aquaculture  Research, 
Via 
Irno,  11,  84135 
Salerno, Italy 

josep.lloret@udg.edu 

Tel: +39 089795775 

malvarosa@nisea.eu  

Knittweis, 
Leyla 

Lloret, Josep 

Malvarosa, 
Loretta  

Martin, 
Paloma   

CSIC 
Instituto 
Ciencias del Mar  

de 

Tel: +34.93.2309500 

paloma@icm.csic.es 

Fax: +34.93.2309555 

Passeig Marítim, 37-49 

08003 Barcelona 

Spain 

14 

14 

 
 
 
 
 
 
Name 

Address1 

Tel. 

Email 

STECF members 

Motova, Arina 

Sea 
Authority 

Fish 

18 Logie Mill 

Industry 

Tel.: +44 131 524 8662 

arina.motova@seafish.co.uk 

Logie Green Road 

Edinburgh 

EH7 4HS, U.K 

Murua, Hilario  AZTI / Unidad de 

Tel: 0034 667174433 

hmurua@azti.es 

Investigación  Marina, 
Herrera 

kaia 
20110 Pasaia 

portualdea 

z/g 

(Gipuzkoa), Spain 

The  Swedish  Agency  of 
Marine 
and  Water 
Management (SwAM)  

Nord, Jenny  

Fax: +34 94 6572555 

Tel. 0046 76 140 140 3 

Jenny.nord@havochvatten.se 

Prellezo, Raúl   AZTI 

-Unidad 

de 

Tel: +34 667174368 

rprellezo@azti.es 

Investigación Marina 

Txatxarramendi  Ugartea 
z/g 

48395 
(Bizkaia), Spain 

Sukarrieta 

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

Raid, Tiit 

Tel.: +372 58339340 

Tiit.raid@gmail.com  

Fax: +372 6718900 

Sabatella, 
Evelina 
Carmen 

Fishery 

NISEA, 
and 
Aquaculture  Research, 
Irno,  11,  84135 
Via 
Salerno, Italy  

TEL.: +39 089795775 

e.sabatella@nisea.eu  

Sala, 
Antonello 
(vice-chair) 

Italian 
National 
Research Council (CNR) 

of  Marine 
Institute 
Sciences (ISMAR), Largo 
Fiera della Pesca, 1 

60125 Ancona - Italy 

Tel: +39 071 2078841 

a.sala@ismar.cnr.it 

Fax: +39 071 55313 

Mob.: +39 3283070446 

15 

15 

 
 
 
 
 
 
 
 
 
Name 

Address1 

Tel. 

Email 

STECF members 

Scarcella, 
Giuseppe 

Soldo, Alen 

Somarakis, 
Stylianos 

Stransky, 
Christoph 

Ulrich,  Clara 
(chair) 

van Hoof, Luc 

Tel: +39 071 2078846 

g.scarcella@ismar.cnr.it 

Fax: +39 071 55313 

Tel.: +357 99664694 

gscarcella@apmarine.com.cy 

Tel.: +385914433906 

soldo@unist.hr  

Tel.: +30 2810 337832 

somarak@hcmr. gr 

Fax +30 6936566764 

Italian 

1) 
National 
Research Council (CNR), 
of  Marine 
Institute 
Sciences 
- 
(ISMAR) 
Fisheries  Section,  Largo 
Fiera  della  Pesca,  1, 
60125 Ancona – Italy 

AP 

2) 
Environmental 
Consultancy 
ACROPOLEOS 
AGLANJIA, 
26728 

Marine  

Ltd, 

2, 
ST. 
P.O.BOX 

1647 Nicosia, Cyprus 

Department  of  Marine 
Studies,  University  of 
Split, 
5, 
21000 Split, Croatia 

Livanjska 

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

P.O. 

of 

for 
Forestry 

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

Technical  University  of 
Denmark, 
National 
Aquatic 
of 
Institute 
Resources,  (DTU  Aqua), 
Charlottenlund 
Slot, 
JægersborgAllé  1,  2920 
Charlottenlund, 
Denmark 

IMARES,  Haringkade  1, 
Ijmuiden, 
The 
Netherlands 

Tel. +49 471 94460-141 

christoph.stransky@thuenen.de 

Fax:  +49  471  94460-
199 

clu@aqua.dtu.dk 

Tel.: +31 61061991 

Luc.vanhoof@wur.nl 

Vanhee, Willy  

Independent consultant 

wvanhee@telenet.be 

16 

16 

 
 
 
 
 
 
 
 
 
Name 

Address1 

Tel. 

Email 

STECF members 

van 
Oostenbrugge, 
Hans 

Vrgoc, Nedo 

Fisheries 
Economics, 
Wageningen  Economic 
Research,  formerly  LEI 
Wageningen  UR,  The 
Hague, The Netherlands 

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

Hans.vanOostenbrugge@wur.nl 

Tel.: +385 21408002 

vrgoc@izor.hr 

17 

17 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
EXPERT WORKING GROUP  EWG-19-01 REPORT 

REPORT TO THE STECF 

EXPERT WORKING GROUP ON 
methods for developing the fishing effort regime 
for demersal fisheries in western Mediterranean 
Sea – Part III 
(EWG-19-01) 

Barcelona, Spain, 18-22 March 2019 

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 

18 

18 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1 

INTRODUCTION 

1.1  List of acronyms 

DTS   

EMU   

EWG  

MAP   

MCDA 

PGP   

Demersal Trawls and Seines 

Effort Management Unit 

Expert Working Group 

Multi-Annual Management Plan 

Multi-Criteria Decision Analysis 

Vessels using polyvalent passive gears only 

1.1  Background 

This Expert Working Group is the third EWG dealing with methods for developing the fishing effort 
regime for demersal fisheries in western Mediterranean Sea 

1.2 

Terms of Reference for EWG-19-01 

As a follow-up of the STECF Expert Working Group 18-13 (October 2018, Copenhagen), the group 
is requested to: 

TOR 1. 
Select the most appropriate model(s) to carry  out a mixed-fisheries advice for the 
western  Mediterranean  demersal  fisheries.  It  should  be  taken  into  account  the  analyses  done  in 
the STECF Report 18-13 , the coverage in terms of stocks and fleets segments at the scale of the 
multi-annual plan and the availability of data requirements. 

TOR 2.  
Analyse the ability of the model(s) to be compatible with the latest scientific advice 
provided  for  western  Mediterranean  demersal  stocks  in  the  STECF  Report  18-12.  The  group 
should  also  make  any  appropriate  comments  and  recommendations  to  the  stock  assessments’ 
group in order to ensure adequacy of the single-stock advice and the mixed-fisheries model(s). 

TOR 3.  
displayed to be tested at the STECF EWG 19-14 concerning the multi-annual plan. 

Discuss  and  suggest  possible  mixed-fisheries  scenarios  and  type  of  results 

1.3  Main findings 

ToR1 reviewed 8 different models, of different complexity levels and covering different GSAs and 
EMUs.  For  EMU2  (East  side  of  Western  Med,  which  mainly  includes  fisheries  from  one  single 
Member  State),  3  models  were  presented,  which  are  fully  parameterised  and  operational.  By 
operating at different levels of complexity and scale, they appear complementary, allowing them 
to test different scenarios.  

The situation is more challenging in EMU1 (West side of Western Med, which covers fisheries from 
two Member States), where five models were presented but none of them is directly operational 
at  the  scale  of  the  EMU.  Progresses  were  reached  during  the  EWG,  but  further  intersessional 
work, involving scientists from the two Member States, is necessary before reaching the desired 
level of completion for the evaluation of management scenarios.  

During  the  EWG,  all  models  presented  were  updated  to  be  compatible  with  the  latest  stock 
assessment data coming from STECF EWG 18-12. Where possible, short-term forecasts were run, 
assuming  a  reduction  in  fishing  effort  in  2019  equivalent  to  the  reduction  necessary  to  achieve 
the target F (Fmsy, proxy F01). For all models, the aggregated results of the short term forecast 
were largely similar between the single-stock and the mixed-fisheries models.  

Then most models were able to run the basic MAP scenario of a gradual reduction of fishing effort 
between 2020 and 2024. Several alternative runs and model capabilities were discussed and are 
reported in the various models’ section. Future scenarios and outcomes would be best agreed in 
discussions with MARE and Member States / stakeholders before the next meeting.  

19 

19 

 
 
 
 
Finally, the EWG discussed a number of remaining issues and gaps that are important in relation 
to the scientific support to the MAP, and agreed on some future work.  

20 

20 

 
 
2 

MAIN ELEMENTS OF THE WEST MED MAP TO BE SUPPORTED BY SCIENTIFIC ADVICE 

2.1  MAP elements 

DG Mare focal person presented the main elements of the West Med MAP.  

The Commission adopted the MAP proposal on March 8th, 2018 and the European Parliament and 
Council  reached  an  agreement  on  February  4th,  2019.  The  Official  Journal  of  the  EU  will  publish 
the  regulation  establishing  the  plan  in  early  May  2019,  and  the  plan  will  enter  into  force 
afterwards.  

Scope 

The scope is the Western Med, i.e. GSAs 1, 2, 5, 6, 7, 8, 9, 10 and 11, divided into two spatial 
units EMU (Effort Management Units). 

Figure 1. MAP region. Blue= EMU1. Red: EMU2 

The  Western  MAP  includes  6  species  as  category  1  stocks.  In  this  report,  these  are  referred  to 
either  as  their  common  or  latin  name,  or  sometimes  using  the  FAO  3-letter  code.  The 
correspondence between the three denomination is given below: 

Common name 

Latin name 

FAO 3-letter code 

Hake 

Merluccius merluccius 

Red mullet 

Mullus barbatus 

HKE 

MUT 

deep-water rose shrimp 

Parapenaeus longirostris 

DPS 

Norway lobster 

Nephrops norvegicus 

NEP 

giant red shrimp 

Aristaeomorpha foliacea 

ARS 

21 

21 

 
 
 
 
 
 
blue and red shrimp 

Aristeus antennatus 

ARA 

The main target of the plan is Maximum Sustainable Yield MSY. The plan states that “The target 
fishing  mortality,  in  line  with  the  ranges  of  FMSY  defined  in  Article  2,  shall  be  achieved  on  a 
progressive, incremental basis by 2020 where possible, and by 1 January 2025 at the latest, for 
the stocks concerned, and shall be maintained thereafter within the ranges of FMSY.” 

Effort limitations 

The  main instrument  for  reaching  Fmsy  is  the  effort  regime.  Annual  effort  quotas  will  be  set  by 
the Council, starting on 1.01.2020. The main characteristics of the regime are as follows:  

  Effort regime applicable to all trawl vessels targeting demersal stocks in WMed.  

  Two effort groups: mixed demersal fisheries; deep-shrimp fisheries. 

  Four sub-groups of vessels: < 12m; 12-18m; 18-24m; and >24m. 

  Effort quotas in terms of fishing days. 

  Fishing day is limited to 15 hours (from port to port). 

  Baseline: average fishing days between 1.1.2015 and 31.12.2017 

Effort quotas will be managed within 16 categories across three criteria: fishing type (“regime”), 
fishing area (“effort management unit EMU”) and vessel group.  

Figure 2. Fishing effort categories 

22 

22 

 
 
 
 
 
 
 
Table 1. Fishing effort group codes 

Throughout this report, the effort regime for trawls fishing for hake, red mullet, deep-water rose 
shrimp and Norway lobster is referred to as “Mixed demersal métier” and the effort regime for 
trawls fishing for giant red shrimp (ARS) and blue and red shrimp (ARA) is referred to as “deep-
water shrimp metier”.   

During the five-year transitional period, fishing effort reductions shall be as follows:  

  YEAR 1 (2020): 10% reduction compared to the 2015-2017 baseline 

  YEAR  2-  5  (2021  –  2024):  up  to  30%  reduction.  The  effort  reduction  may  be 

supplemented with other technical measures in order to achieve MSY by 2025. 

Closures 

An Annual closure is foreseen: trawl vessels are prohibited within 6nm from the coast except in 
areas  deeper  than  100m  during  3  months  each  year.  Member  States  may  though  ask  for  a 
derogation and establish other closure areas provided that a reduction of at least 20% of catches 
of juveniles hake in each GSA is achieved.  

Other  closures  areas  shall  be  established  two  years  after  the  entry  into  force  (by  2021)  for  the 
protection of juveniles, individuals under MCRS and spawning grounds. 

Recreational fisheries 

Where  the  scientific  advice  indicates  that  recreational  fisheries  is  having  impact  on  the  fishing 
mortality, the Council may set non-discriminatory limits for recreational fisheries. 

  Technical measures (under regionalisation) may be adopted. 

23 

23 

 
 
 
 
 
 
  Where  possible,  Member  States  shall  take  necessary  and  proportionate  measures  for  the 

monitoring and collection of data. 

Timeline 

After  entry  into  force  end  of  May  2019,  the  first  3  months-closure  will  take  place  between  June 
and  December  2019.  The  Commission  will  make  its  proposal  on  effort  levels  by  end  of  October 
2019, and the effort regime will enter into force on January 1st, 2020.  

2.2  Overview of the MAP coverage 

2.2.1  Percentage of MAP stocks caught by the regulated trawler fleets 

Table 2. Percentages of landings of MAP species caught by the regulated trawler fleets 
(average  values for  2015-2016). Percentages are calculated over the total landing  per 
vessel length within one unit of management. 

VL 
VL0612 
VL1218 
VL1824 
VL2440 
VL0612 
VL1218 
VL1824 
VL2440 
VL0612 
VL1218 
VL1824 
VL2440 
VL0612 
VL1218 
VL1824 
VL2440 
VL 
VL0612 
VL1218 
VL1824 
VL2440 
VL0612 
VL1218 
VL1824 
VL2440 
VL0612 
VL1218 
VL1824 
VL2440 
VL0612 
VL1218 
VL1824 
VL2440 

ARA  ARS 

- 
3 
4 
3 
- 
0 
3 
1 
1 
2 
9 
15 
- 
0 
1 
2 

- 
0 
0 
0 
- 
0 
0 
0 
- 
1 
1 
0 
- 
- 
0 
0 

ARA  ARS 

- 
- 
- 
- 
- 
2 
2 
1 
- 
1 
2 
- 
- 
1 
0 
12 

- 
- 
- 
- 
- 
1 
1 
0 
0 
3 
5 
- 
- 
1 
0 
20 

DPS 
1 
5 
2 
1 
- 
0 
0 
0 
10 
4 
6 
3 
- 
0 
0 
1 
DPS 
- 
0 
0 
0 
6 
14 
19 
7 
19 
14 
8 
- 
- 
1 
0 
5 

0 
1 
1 
0 
- 
0 
1 
0 
4 
6 
7 
5 
- 
0 
0 
1 

HKE  MUT  NEP 
29 
2 
7 
5 
2 
3 
0 
1 
- 
- 
1 
2 
1 
1 
0 
1 
37 
16 
36 
25 
17 
26 
12 
27 
- 
- 
0 
0 
2 
12 
1 
25 
HKE  MUT  NEP 
- 
- 
- 
- 
34 
21 
24 
9 
17 
9 
5 
- 
- 
8 
2 
5 

1 
0 
0 
0 
9 
10 
20 
7 
11 
5 
7 
- 
- 
5 
1 
29 

7 
1 
0 
1 
0 
4 
2 
2 
- 
0 
0 
- 
- 
0 
0 
3 

24 

24 

GSA 

EMU 

1 

5 

6 

7 

1 

GSA 

EMU 

8 

9 

10 

11 

2 

 
 
 
 
 
Landings  values  by  FAO  Geographic  Sub  Area  (GSA),  to  create  dependency  tables  and  graphs, 
were  obtained from the Annual Economic Report (AER) as in STECF 18-13. During STECF 18-13 
dependencies  were  calculated  also  considering  species  and  gears  not  considered  in  the  MAP.  In 
this  report  dependencies  reported  in  Table  2  are  instead  calculated  only  for  species  and  gears 
(Fig. 2.2.1 and 2.2.2) considered by the MAP. Table 3 was taken from STECF 18-12 to show the 
gear.
percentages 

landings 

stock 

per 

by 

of 

25 

25 

 
Figure 3 Total landings per GSA, gear, country and year within EMU 1  

26 

 
 
 
 
 
Figure 

4. 

Total 

landings 

per 

GSA, 

gear 

and 

year 

within 

EMU 

2 

27 

 
 
2.2.2  Percentage of MAP fleets catches from the MAP species 

NB the EWG 19-01 found some minor issues in the way the dependencies and percentages were 
calculated  in  previous  reports  of  EWG  18-12  and  18-13.  This  shall  be  solved  before  the  next 
meeting.  

Table  3  Percentage  of  landings  of  each  stock  across  metiers.  The  columns  summed  to 
100% for each line.  

Bottom 
nets 

trawl 

Gillnets 

Trammel nets 

Other 

Stock 

HKE 1_5_6_7 

HKE 9_10_11 

MUT 1 

MUT 6 

MUT 7 

MUT 9 

MUT 10 

NEP 5 

NEP 6 

90% 

60% 

77% 

92% 

99% 

6% 

23% 

0% 

0% 

1% 

2% 

8% 

23% 

8% 

0% 

96.6% 

0.8% 

2.6% 

91% 

100% 

100% 

DPS 9_10_11 

100% 

ARA 1 

ARA 6 

100% 

100% 

ARS 9_10_11 

100% 

8% 

0% 

0% 

0% 

0% 

0% 

0% 

1% 

0% 

0% 

0% 

0% 

0% 

0% 

2% 

9% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

2.3  Requirements to the bioeconomic mixed-fisheries models in relation to the ToRs 

A comprehensive review of the available models and their current state of development had been 
performed  in  EWG  18-13  (West  Med  Part  II)  ToR  5.  The  main  conclusion  from  this  review  was 
that  many  models  exist  already  in  the  Western  Med  area,  but  that  none  of  them  was  directly 
operational for providing timely annual advice at the scale of the plan, i.e. across several stocks, 
GSAs and fleets.  

Making  the  models  operational  was  thus  the  main  aim  of  the  EWG  19-01.  In  advance  of  the 
meeting, modellers were  asked to investigate the feasibility of their models to address the gaps 
listed in EWG 18-13 and in particular 

  The coverage of several GSAs (models by the two EMU sub-areas of the plan) 

  The inclusion of at least all the stocks of the 6 MAP species within the two sub-areas (and 

possibly more stocks if already included in the existing models) 

28 

28 

 
 
 
 
 
  The ability to be updated with the most recent data (e.g. stock and transversal data up to 

2017) 

  The ability to match the most recent single-stock short-term advice (e.g. advice for 2019, 

for the stocks assessed in 2018 on 2017 data).  

The EWG started with a presentation of each of the available models, and the progresses reached 
since  EWG  18-13  in  relation  to  the  ToRs  and  above-mentioned  gaps  issues.  Most  models  were 
those  already  described  in  EWG  18-13  report,  but  new  approaches  (such  as  FLR  Flasher)  were 
also presented. Then the EWG agreed on the requirements for the rest of the week.  

The report is thus structured as follows: 

Sections 3 to 10 are presentations of the various models:  

  Model  characteristics  and  use  following  the  typology  and  scoring  criteria  developed  by 
https://onlinelibrary.wiley.com/doi/full/10.1111/faf.12232 

2018 

Nielsen 
(summarised in Table 4 below) 

al., 

et 

  Case study application for the West Med MAP 

  Short-Term  forecast  (projection  2018-2019)  to  assess  the  compatibility  of  the  mixed-

fisheries model with the single-stock forecast from EWG 18-12 

  Deterministic  Medium-Term  baseline  projection  over  the  transitional  period  2020-2024, 
simulating  the  basic  provision  of  the  plan,  i.e.  10%  reduction  in  fishing  days  in  2020 
compared  to  the  average  2015-2017  and  30%  additional  reduction  between  2021  and 
2024, corresponding to a 7.5% reduction per year uniformly applied to all fleet segments 

  A discussion on which alternative scenarios are possible to run with this model 

A  Total  of  8  models  were  presented  and  discussed  during  the  EWG,  of  different  setup,  spatial 
coverage and complexity.   

Fleet- 
and 
models, “simple setup” 

–year 

based 

Fleet-based models with more 
complex features 

Models  with  finer  time-  and 
spatial scale 

FLBEIA (EMU1)        Section 3  BEMTOOL  (EMU2)          Section 

SMART (EMU2)        Section 9 

6 

FLasher (EMU1)       Section 4 

IAM (EMU1)             Section 7 

ISIS-FISH  (EMU1)      Section 
10 

NIMED (EMU2)        Section 5  MEFISTO  (EMU1)            Section 

8 

29 

29 

 
 
 
 
 
 
 
Table  4.  Models’  characteristics  and  use  following  the  typology  and  scoring  criteria  developed  by  Nielsen  et  al.,  2018.  More 
comprehensive model descriptions are found in STECF 18-13 and/or in Nielsen et al., 2018 (self-evaluation by modellers) 

CRITERIA 

SCORING 

NIMED 

FLBEIA 

Flasher 

IAM 

BEMTOOL 

MEFISTO 

ISIS-
FISH 

SMART 

Model capabilities 

Panel 
design 

1—model 

1= Short-term advice,  
2= Medium-term MSE 
3= Long-term strategic 

1 =TACs 
2= Effort 
3= ITE 
4= ITQ 
1= data collection 
2= Single Stock 
3= Multispecies 
4= Mixed fishery 
5= Bioeconomic 
6= Ecosystem 
1= Single/multispecies only 
2= single stock/economic 
3= Multispecies/economic 
4= Multispecies/ecosystem/economic 

Panel 
2—
management advice  

3—model 

Panel 
structural 
characteristics 
terms of advice on  

in 

Panel  4—model  use 
in  terms  of 
index 
modules 
included 
and 
linkages 
for  biology  (stocks), 
economic 
and 
ecosystems 

their 

Model characteristics  

Panel 1—fishing fleet 
characteristics 

1= Full Fishery 
2= Single Métier 
3= Multiple Metiers 
4 = Agent-Based (IBM) 

Panel 
resolution 

2—spatial 

Panel  3—  biological 
characteristics 

1= Ecosystem 
2= Region 
3= Stock Area 
4= Stock sub-area 
5= VMS track 
1= Biomass 
2= Size-structured 
3= Age‐structured 

1 

2 

3 

5 

3 

1 

3 

3 

1 
2 
3 

1 
2 

1 
2 
3 
4 
5 

1 
2 
3 

1 
2 

3 
4 

1 
3 

1, 2 & 3 

1, 2 

1,  2,  3, 
4, 5 

1, 2, 3 

1, 2, 3 

3, 4 

1, 3 

30 

1, 2 & 3 

1, 2, 3 

1-2-3 

1,2,3 

1,2 

1, 2 & 4 

1, 2 

2 

1,2,3,4 

2,3,4 

2, 3, 4 & 5 

1, 2, 3, 4, 5 

2-3-4-5 

2,3,4,5 

1,3,4,5 

3 

2, 3 

3 

3 

3 

1, 2, 3 & 4 

1, 2, 3 

2 & 3 

2, 3, 4 

3 

2, 3 

3 

3 

3 

1,2,3 

2,3,4 

2,3, 4 

2,3,4,5 

1,2,3 

1,2,3 

 
 
 
 
 
 
Panel 4—time step  

Panel 
dynamic 

5— 

time 

Panel 6— Process 

Panel  7  —  fishing 
sector components   

Panel 8 — estimation 
of model parameters  

Panel 9 — revenues  

Panel  10  —  type  of 
embedded 
interactions; 

Panel 11 — nature of 
embedded  economic 
behavioural model 

Panel  12  —  included 
functions  

Model trade-offs 

Panel 
1—expertise 
required  to  conduct 
model runs  

1= Year 
2= Multiple Years 
3= Season 
1= Static 
2= Equilibrium 
3= Dynamic 

1= Simulation 
2= Optimisation 
3= Both 
1= Catch sector 
2=Fishery  system  including  processing  and 
distribution 
3= Communities 
4=  Multi-sector  of  a  local  or  regional 
economy 
1= qualitative indicators 
2= deterministic 
3= stochastic 

1= market prices 
2= consideration of the value chain 
3= inclusion of non‐market values; 
1= linear 
2= nonlinear 
3= both 

1= tactical 
2= strategic 
3= no behavioural module 

1= recruitment 
2= catchability 
3= fish prices 
4= harvest costs 

1= developer 
2= specialized expertise or training 
3= general expertise 

Panel  2  —  model 
applications 

1= specialized 
2= simple 
3= flexible 

Panel  3  —  model 
accessibility  to  end 

1= software required 
2= open access 
3= Manual/Website 
4= User-friendly 

1 

3 

1 

1 

2 

1 

3 

3 

3 

4 

1 

3 

1 

1 
2 
3 
3 

3 

1 

2 
3 

1 

3 

1 

1 
2 
3 
4 

2 

3 

2 
4 

1, 3 

3 

1 

1 

2, 3 

1 

3 

1, 2 

1,  2,  3, 
4, 

2, 3 

1,2,3 

1,2,3,4 

31 

1 

3 

3 

1 

1, 3 

1-2-3 

1,2,3 

1,3 

3 

1 

1 

3 

1 

1-2-3 

1,3 

3 

2 

3 

3 

1,2 

2 & 3 

2, 3 

2-3 

1,2,3 

2,3 

1 

3 

2 

1 

3 

1 

1 

2 

2 

1 

3 

2,3 

1 

3 

2 

1, 2, 3 & 4 

1, 3, 4 

1-2-3-4 

1,2,3,4 

1,2,3,4 

1 

3 

2 

2, 3 

3 

1, 2, 3 

2, 3. 4 

1-3 

2-4 

2 

3 

1,2 

2,3 

2,3,4 

2,4 

 
 
users  

4 

— 
Panel 
relationship  between 
model 
complexity 
and data needs  

1= simple with low data needs 
2= simple with high data needs 
3= complex with high data needs 

1 

1,2,3 

Summary of model use 

Panel  1  —  model 
implementation  

Panel 
use   

2—academic 

Panel  3—level  of 
advice for models  

1= none 
2= low 
3= medium 
4= high 
1= models that only have technical reports 
2= models that have been published in the 
peer‐reviewed literature 
3= models that have been widely cited 
1= National 
2= EU (STECF) 
3= Nation + EU (STECF) 
4= EU (STECF) + ICES/GFCM 
5= Nation + EU + ICES/GFCM 

2 

1 

3 

3 

2,3 

5 

1 

2 

1 

5 

3 

3 

2 

5 

3 

3, 4 

2, 3 

2, 3, 5 

3 

3 

2 

3 

3 

3 

4 

3 

5 

3,4 

2 

1,2,3,4,5 

32 

 
 
3  FLBEIA IN EMU1 

3.1  Overview of the model’s generic characteristics and use 

3.1.1  Main features 

FLBEIA  is  a  generic  tool  to  conduct  Bio-Economic  Impact  Assessment  of  fisheries  management 
strategies in a management strategy evaluation framework (Garcia et al., 2017). FLBEIA can be 
categorized  as  a  ‘Models  of  Intermediate  Complexity  for  Ecosystem  assessments’  (MICE, 
(Plagányi  et  al.,  2014))  which  is  focused  on  the  fishing  activity  in  a  muti-stock  and  multi-fleet 
context. 

FLBEIA  has  been  built  using  R-  FLR  packages  (Kell  et  al.,  2007)  and  beneficiate  automatically 
from the new developments in those packages.  As any MSE algorithms it is formed by two main 
blocks,  the  Operating  Model  (OM)  and  the  Management  Procedure  (MP)  (Figure  5).  The  OM  has 
three  components  that  interact  among  themselves,  the  stocks,  the  fleets  and  the  covariates.  In 
turn  the  MP  is  divided  in  other  three  components,  the  observed  data,  the  perceived  stocks  and 
the management advice. 

Figure 5. Conceptual diagram of FLBEIA (taken from Garcia et al. (2017)) 

The  stocks  can  be  age  or  biomass  structured.  Trophic interactions  have  never  been  modelled  in 
FLBEIA  but  it  could  be  done.  There  is  also  a  development  version  where  Gadget  (Begley  and 
Howell, 2004) can be used as operating model. The activity of the fleet is divided in metiers and 
four  processes  are  modelled.  The  short-term  dynamics  (total  effort  and  its  distribution  along 
metiers), long term dynamics (entry-exit of new vessels in the fishery), price formation and catch 
production. The covariates can be used to store any variable not included in the stocks and fleet 
components.  

The  link  between  the  OM  and  the  MP  is  done  through  the  observation  model  that  generate  de 
observed data. Two types  of data can be generated, the  stocks and the abundance indices.  Any 
observable  variable  can  be  subject  to  observation  error  and  the  error  is  divided  in  two 
components,  the  aging  error  component  and  a  multiplicative  error.  As  the  errors  are  introduced 
as  input  data  they  can  be  conditioned  using  any  distribution,  bootstrap  or  other  analysis.  The 
perceived  population  is  generated  using  an  assessment  model.  There  is  the  possibility  of  using 
the  ‘short-cut’  approach  or  any  assessment  model  available  in  R/FLR.  What  is  needed  is  a 
wrapper  that  generates  the  input  and  output  of  the  model  in  the  right  shape.  Wrappers  are 
already available for SPiCT, XSA, sca in Fla4a and FLSAM. The management advice is generated 
using  a  harvest  control  rule.  Two  types  are  available,  model-free  HCRs  and  model-based  ones. 
The model-free HCRs use the abundance indices generated by the observation model and do not 

33 

 
 
 
 
 
 
require  to  apply  any  assessment  model.  In  turn,  the  model-based  HCRs  use  the  output  of  the 
short-cut approach of an assessment model to generate the advice.  

The  adaptative  management  advice  based  on  catch  can  be  accompanied  by  technical  measures 
like  changes  in  selectivity,  implicitly  simulated  spatiotemporal  closures  or  effort  restrictions  for 
example.  

The stochasticity is introduced using montecarlo approximation and the iterations run in parallel. 
The  results  can  be  analysed  and  presented  using  the  Shiny  application  available  in  the 
FLBEIAShiny package (https://github.com/flr/FLBEIAshiny).  

The  model  is  constructed  in  a  modular  way.  The  fishery  system  is  discomposed  in  processes 
(recruitment, catch production, population growth…) and several models are provided to simulate 
each  of  them.  Alternatively,  new  models  can  be  coded  and  call  from  the  function  with  no  extra 
coding.  

The model documentation is extensive. There is a research paper describing the model (Garcia et 
al.,  2017).  A  manual  which  describes  in  detail  all  the  models  available  is  provided  within  the  R 
library.  And  there  is  a  set  of  dedicated  tutorials in  the  FLR  web-site http://www.flr-project.org/. 
The  source  code  can  be  downloaded  from  github  (https://github.com/flr/FLBEIA)  and  the 
compiled package from the FLR website  (http://www.flr-project.org/). There is a support mailing 
list flbeia@azti.es. 

3.1.2  References 

Garcia,  D.  2018.  A  multi-stock  harvest  control  rule  to  support  a  consistent  management  along 
stocks in the framework of fishing mortality ranges. ICES annual sciencitic conference, Hamburg. 

García, D., Prellezo, R., Sampedro, P., Da-Rocha, J. M., Castro, J., Cerviño, S., García-Cutrín, J., 
et  al.  2017.  Bioeconomic  multistock  reference  points  as  a  tool  for  overcoming  the  drawbacks  of 
the landing obligation. ICES Journal of Marine Science, 74: 511-524. 

Garcia, D., Sánchez, S., Prellezo, R., Urtizberea, A., and Andrés, M. 2017. FLBEIA : A simulation 
model  to  conduct  Bio-Economic  evaluation  of  fisheries  management  strategies.  SoftwareX,  6: 
141-147. 

Garcia,  D.,  Urtizberea,  A.,  Diez,  G.,  Gil,  J.,  and  Marchal,  P.  2013.  Bio-economic  management 
strategy  evaluation  of  deepwater  stocks  using  the  FLBEIA  model.  Aquatic  Living  Resources,  26: 
365-379. 

Kell, L. T., Mosqueira, I., Grosjean, P., Fromentin, J.-M., Garcia, D., Hillary, R., Jardim, E., et al. 
2007.  FLR:  an  open-source  framework  for  the  evaluation  and  development  of  management 
strategies10.1093/icesjms/fsm012. ICES J. Mar. Sci., 64: 640-646. 

Nielsen, J. R., Thunberg, E., Holland, D. S., Schmidt,  J. O., Fulton, E. A., Bastardie, F., Punt, A. 
E.,  et  al.  2018.  Integrated  ecological–economic  fisheries  models—Evaluation,  review  and 
challenges for implementation. Fish and Fisheries, 19: 1-29. 

Prellezo,  R.,  Carmona,  I.,  and  García,  D.  2016.  The  bad,  the  good  and  the  very  good  of  the 
landing obligation implementation in the Bay of Biscay: A case study of Basque trawlers. Fisheries 
Research, 181: 172-185. 

Prellezo,  R.,  Carmona,  I.,  García,  D.,  Arregi,  L.,  Ruiz,  J.,  and  Onandia,  I.  2017.  Bioeconomic 
assessment  of a change in fishing gear selectivity: the case of a  single-species fleet affected by 
the landing obligation. Scientia Marina, 81: 10. 

Sánchez, S., Ibaibarriaga, L., Uriarte, A., Prellezo, R., Andrés, M., Abaunza, P., Jardim, E., et al. 
2018.  Challenges  of  management  strategy  evaluation  for  small  pelagic  fish:  the  Bay  of  Biscay 
anchovy case study. Mar Ecol Prog Ser. 

34 

 
 
 
3.2  Application of the model to the West Med MAP 

The model was applied to the available data for the West Med. Six stocks were included "HKE_1-
5-6-7," "MUT_1",  "MUT_6",  "MUT_7", "NEP_5",  "NEP_6". The fleets included only one fleet that 
accounted for all the catch of the stocks.  

All  the  stocks  were  simulated  using  the  exponential  survival  equation  with  a  geometric  mean 
recruitment.   

Regarding fleet dynamics two scenarios were  simulated. A scenario were the effort in the whole 
projection was equal to historical effort and an alternative scenario where the effort was reduced 
according to the MAP. 

The model was projected until 2025 in a deterministic simulation. 

The  model  results  were  analyzed  using  a  Shiny  App  that  can  be  directly  used  in  the  outputs  of 
FLBEIA. This app can also be used with other model outputs if they have the right shape (R data 
frames with certain columns). 

3.3  Comparison  of  model’s  short-term  forecast  with  the  single-stock  advice 

predictions 

No comparisons were made 

3.4  Baseline Run 2020-2024 

The  spawning  stock  biomass  of  the  two  scenarios  simulated  with  FLBEIA  are  shown  in  Figure  6. 
For  all  the  stocks  the  spawning  stock  biomass  obtained  in  the  MAP  scenario  increased  sharply, 
while  in  the  constant  effort  scenario  the  biomass  stayed  constant  in  the  case  of  hake  or 
decreased in the case of the other stocks. 

35 

 
 
 
Figure  6.  FLBEIA.  Spawning  stock  biomass  of  the  "HKE_1-5-6-7",  "MUT_1",  "MUT_6",      
"MUT_7",  "NEP_5"  and    "NEP_6"  stocks  obtained  with  FLBEIA  in  constant  effort  and 
MAP scenarios. 

36 

 
 
 
3.5  Summary on the model: achievements, gaps still to be addressed, work plan from 

now to STECF 19-14 (October 2019) 

The  model  was  implemented  with  the  available  data.  The  implementation  took  one  morning.  To 
conduct a bio-economic impact assessment it would be necessary to disaggregate the exploitation 
of the fishing activity by fleet and metier. If the data were in the right shape, the implementation 
could be done in a short time using the functions available in FLBEIA to facilitate the conditioning 
of complex model implementations.  

37 

 
 
 
 
4  FLASHER IN EMU1 

4.1  Overview of the model’s generic characteristics and use 

4.1.1  References 

Only working report of STECF and IOTC 

4.2  Application of the model to the West Med MAP 

4.2.1  State  of  completion  and  additional  development  work  specially  undertaken  before  and 

during EWG 19-01 

FLasher  is  able  to  simulate  the  EMU  1  Western  Med  Demersal  mixed  metier  using  the  stock 
objects  of  previous  assessments  and  the  AER  data  for  4  stocks  (HKE_1567,  MUT_1,  MUT_6, 
MUT_7).  The  reduction  of  the  stock  numbers  was  a  proof  of  concept  as  it  is  easily  extended  to 
encompass  all  the  required  stocks.  The  main  difficulty  is  the  discrepancy  between  the  stock 
assessments  and  the  effort  data:  The  catches  in  2017  are  known,  but  the  effort  is  not.  With 
FLasher this should be straight forward as it is possible to set the harvest control rule in terms of 
effort,  fishing  mortality,  catch,  and  economic  metrics.  The  HCR  for  this  working  group  was 
parameterised as follows:  

2017 – the same catches for all stocks as reported by the stock assessments (which should lead 
to a effort estimate) 

2018-2019 – catches at the same effort level as in 2017 

2020:2024 – reduction in effort levels compared to 2017  

2025 – fishing at Fmsy level 

FLasher  exhibited  an  unusual  result  for  the  effort  estimate  in  2017  which  needs  further 
investigation. Thus the current results are not accurate and should not be used.  

38 

 
 
 
 
Figure  1  -  Diagram  of  the  relationship  of  the  fisheries  and  stocks.  As  the  catches  are 
estimated  and  accounted  for  separately,  the  simulation  keeps  them  separately.  They 
are connected to one single fisheries, which means that the catch effort is the same for 
the 4 catches/stocks.  

4.2.2  Space and time scale 

The model updates yearly and has no spatial resolution.  

4.2.3  Stocks (which stocks, which assessment data etc) 

STECF assessment of Hake (GSA 1,5,6,7; stock object: GSAs_1-5-6-7_HKE ), Red mullet (GSA 
1,6,7 (separately); stock objects: Red Mullet GSA1, ESP_6_MUT, ESP_6_MUT ), Nephrops (GSA 
5,6 (separately); stock objects: ESP_5_NEP, ESP_6_NEP). 

4.2.4  Fleets 

EMU 1 level aggregated, AER data 

4.3  Comparison  of  model’s  short-term  forecast  with  the  single-stock  advice 

predictions 

The forecast of catches with single stock advice works as long as the catches are set individually 
and  not  as  a  single  aggregated  fishery.  A  current  bug  causes  the  effort  estimation  to  fail in  the 
year 2017. I am currently working closely with the developers to alleviate the issue 

39 

 
 
 
 
 
 
4.4  Baseline Run 2020-2024 

The  baseline  run  was  completed  for  4  stocks  (see  above).  It  can  be  extended  without  much 
difficulty to incorporate all the stocks and catches. It cannot model the switch between the deep 
and shallow fisheries as the highest resolution is fisheries and not metier. As the fisheries are not 
directly linked, ie. they will have their own effort quota, it is possible to model them in separately. 
No economic data has been used (apart from effort) in the baseline run, but it can be included for 
the  next  meeting.  The  implemented  HCR  follows  the  MP  agreed  by  the  Commision.  With  the 
exception of the implementation of Fmsy in 2025 as agreed to by the STECF meeting.  

4.5  Alternative Runs 

4.5.1  Modellers’ list of possible runs that can be implemented with the model and considered of 

direct relevance for the purpose of the West Med MAP 

Exhaustive  scenario  testing  is  possible  using  FLasher.  Possible  options  are  selectivity 
shifts,  natural  mortality  changing,  recruitment  changes,  different  stock  recruitment 
relationships.  Depending  on  the  performance  statistics  and  the  goal  of  the  forward 
projections,  different  selection  of  scenarios  would  be  more  sensible.  Care  needs  to  be 
taken  as  strong  reductions  in  effort  will  lead  to  simulations  that  provide  results  outside 
the current data  

4.5.2  Runs performed and analysed during EWG 19-01 

Presentation and results 

4.6  Summary on the model: achievements, gaps still to be addressed, work plan from 

now to STECF 19-14 (October 2019) 

 

Implement changes relevant to MAP in Flasher – end June 

  Fixing effort estimations – mid June  

  Fully implement all the Fisheries and metiers – mid July 

  Full runs both metiers – end of July 

  Scenarios runs– end August 

  update model runs with new SA – mid september 

  organising results to present to EWG  - mid september 

  Make code available (via Gitlab/hub) – end of end of August 

40 

 
 
 
 
 
 
 
 
 
 
5  NIMED IN EMU 2 

5.1  Overview of the model’s generic characteristics and use 

5.1.1  Main features 

Cf Table 4 above and STECF EWG 18-09 and 18-13 reports. 

NIMED is a multi-species and multi-fleet bio-economic model developed within the Horizon 2020 
research  project  “Science,  Technology,  and  Society  Initiative  to  minimize  Unwanted  Catches  in 
European  Fisheries  (MINOUW)”.  The  model  was  used  in  the  project  MINOUW  to  simulate 
management  scenarios  based  on  changes  in  selectivity  and  fishing  effort  for  the  demersal 
fisheries in GSA 9. During the EWG 18-09, additional scenarios on the reduction of fishing effort 
in GSA 9 demersal fisheries were simulated. Fleets included in the model were demersal trawlers 
divided in three length classes  – 12-18m, 18-24m and 24-40m - and passive polyvalent vessels 
divided in two length classes, lower and greater than 12m. The model simulated the dynamics of 
five stocks: European hake, Norway lobster, surmullet, red mullet and deep-water rose shrimp. 

5.1.2  References 

Scientific,  Technical  and  Economic  Committee  for  Fisheries  (STECF)  –  Fishing  effort  regime  for 
demersal fisheries in the western Mediterranean Sea – Part II (STECF-18- 13). Publications Office 
of  the  European  Union,  Luxembourg,  2018,  ISBN  978-92-79-79396-7,  doi:10.2760/509604, 
JRC114702. 

5.2  Application of the model to the West Med MAP 

5.2.1  State  of  completion  and  additional  development  work  specially  undertaken  before  and 

during EWG 19-01 

During the EWG 19-01, the model dimensions were extended to be adapted to the requirements 
of  the  Multi-Annual  Management  Plan  for  the  western  Mediterranean  fisheries.  Data  for  the 
Management Unit 2 (EMU2) was collected and economic parameters were estimated for  the fleet 
segments  involved  in  these  fisheries.  Furthermore,  the  stocks  included  in  the  model  for 
simulations  were  changed  according  to  the  data  available  from  the  last  stock-assessment.  Two 
management  scenarios  were  simulated:  Status  Quo  scenario  (no  change  in  fishing  effort)  and 
Scenario 1 (10% reduction in fishing effort in 2020 and 7.5% reductions each year from 2021 to 
2024). 

5.2.2  Space and time scale 

The model was used to simulate the effects of the management measures on all the stocks with 
known dynamics (available stock-assessment results) included in the western Mediterranean MAP 
and related to the Italian GSAs. The geographical area covered by the model consists in the EMU2 
(GSAs 9, 10 and 11).  

Regarding the time scale, the model works with an annual simulation step and projections can be 
run  for  an  indefinite  number  of  years.  However,  as  the  model  used  short-term  forecasts  for 
projecting stock population and catches, outcomes reliability decreases over time. 

5.2.3  Stocks (which stocks, which assessment data etc) 

The stocks included in the model are reported in Table 5. These are the stocks considered for the 
western Mediterranean MAP. Regarding the EMU2, the Aristeus antennatus is not considered. For 
the other species, stock-assessment outcomes are available at the geographical level reported in 
the column GSAs,  except for  Norway lobster.  European hake, deep-water  rose  shrimp and giant 
red shrimp were analysed as single stocks in the EMU2, while two stocks are considered for both 
red mullet (separated stocks for GSAs 9 and 10) and Norway lobster (separated stocks for GSAs 
9 and 11). However, stock-assessment results for Norway lobster were not available. Therefore, a 
total of 5 stocks was included in the model. 

41 

 
 
 
 
Table 5 – NIMED. Stocks included for the western Mediterranean MAP for the EMU 2 

Common name  
European hake  

Scientific name  
Merluccius merluccius  

FAO code 
HKE 

Red mullet  

Mullus barbatus  

Deep-water rose shrimp   Parapenaeus longirostris  

Norway lobster  

Nephrops norvegicus  

Giant red shrimp  

Aristaeomorpha foliacea  

Blue and red shrimp  

Aristeus antennatus  

MUT 

DPS 

NEP 

ARS 

ARA 

GSAs 
(9-10-11) 

9 - 10 

(9-10-11) 

9 – 11 

(9-10-11) 

none 

Model 
Y 

Y - Y 

Y 

N - N 

Y 

N 

5.2.4  Fleets 

The  selection  of  the  fleet  segments  to  be  included  in  the  model  for  simulations  was  based  on  a 
combination of two criteria: 1) the relevance of the fleet segment in terms of contribution to the 
total landings of each of the stocks included in the model; 2) the relevance of the stocks included 
in the model on the revenues of the fleet segments. 

Based on the first criterium, 13 fleet segments catching more than 2% of total landings for each 
stock were selected. Based on the second criterium, the fleet segment DTS VL0612 in GSA 9 was 
added  to  the  list  because  the  stocks  selected  impacted  by  more  than  10%  on  its  revenues.  The 
combination  of  the  two  criteria  determined  the  selection  of  the  14  fleet  segments  reported  in 
Table 6.  

In  2017,  the  selected  fleet  segments  contributed  to  the  95%  of  the  total  landings  of  European 
hake in the area (GSAs 9, 10 and 11) and 99% of the total landings of red mullet in GSA 9. The 
landings  of  the  other  stocks  included  in  the  model  are  completely  covered  by  the  selected  fleet 
segments. Even if the coverage of the total landings by stock is almost complete, the coverage of 
the total revenues by fleet segment is very limited. The 5 selected stocks represent a percentage 
of the total revenues varying from 4% for the PGP VL0612 in GSA 9 to 52% for the DTS VL2440 
in GSA 11. Clearly, a low percentage of revenues covevered by the selected stocks produces also 
a low reliability of the simulated economic outcomes. 

Table 6. – Coverage of total landings and total revenues in the NIMED model 

GSA 

Tech 

LFT 

HKE  MUT 9  MUT 10  DPS  NEP 9  NEP 11  ARS 

   Revenues 

9 

9 

9 

9 

9 

9 

10 

10 

10 

10 

10 

11 

11 

11 

DTS 

DTS 

DTS 

DTS 

PGP 

PGP 

DTS 

DTS 

PGP 

PGP 

PGP 

DTS 

DTS 

DTS 

VL0612 

VL1218 

0% 

5% 

1% 

45% 

VL1824 

19% 

48% 

3% 

3% 

1% 

VL2440 

VL0612 

VL1218 

VL1218 

VL1824 

VL0006 

1% 

3% 

3% 

4% 

6% 

3% 

VL0612 

29% 

VL1218 

VL1218 

VL1824 

VL2440 

3% 

7% 

5% 

5% 

0% 

0% 

23% 

44% 

35% 

54% 

2% 

0% 

0% 

2% 

0% 

0% 

51% 

21% 

36% 

15% 

5% 

7% 

1% 

0% 

0% 

0% 

1% 

1% 

2% 

0% 

2% 

3% 

0% 

0% 

0% 

21% 

32% 

0% 

0% 

0% 

4% 

9% 

25% 

35% 

40% 

28% 

COVERAGE 

95% 

99%  100%  100%  100%  100%  100% 

23% 

29% 

32% 

35% 

4% 

13% 

44% 

47% 

9% 

17% 

5% 

14% 

19% 

52% 

42 

 
 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
a4a 

% 

a4a 

% 

5.3  Comparison  of  model’s  short-term  forecast  with  the  single-stock  advice 

predictions 

The comparison of the NIMED model’s short-term forecast with the single-stock advice predictions 
was carried out in terms of total catches in the period 2017-2020. In the model, the assumptions 
regarding the age classes used for the calculation of F as well as the methods for estimating the 
weight at age and the  recruits were set accordingly to those used in the stock-assessment. The 
values of catch for each of the 4 years by stock and the differences in percentage are reported in 
Table  7.  The  differences  are  very  small  for  all stocks,  except  for  red  mullet in  GSA  10.  For  that 
stock,  as  it  was  not  clear  which  variation  in  F  was  applied  during  the  stock-assessment  for 
simulating the short-term projections, a comparison was carried out considering both an increase 
in F by 120% (v1), from 0.25 to 0.54, and an increase by 84% (v2). 

Table 7 – Comparison of NIMED and stock-assessment predictions of catch. 

HKE 

2017 

2018 

2019 

2020 

ARS 

2017 

2018 

2019 

2020 

Nimed 

1778 

1703 

510 

1782 

1696 

494 

764 

762 

Nimed 

a4a 

399 

399 

283 

283 

172 

171 

216 

215 

100% 

100% 

103% 

100% 

   % 

100% 

100% 

101% 

100% 

DPS 

2017 

2018 

2019 

2020 

MUT 9 

2017 

2018 

2019 

2020 

Nimed 

1508 

847 

1508 

858 

640 

644 

861 

865 

Nimed 

1773 

1683 

a4a 

1773 

1675 

808 

812 

1093 

1081 

100% 

99% 

99% 

100% 

   % 

100% 

100% 

99% 

101% 

MUT 10 v1  2017 

2018 

2019 

2020 

  MUT 10 v2  2017 

2018 

2019 

2020 

Nimed 

a4a 

% 

606 

596 

659 

646 

1259 

1056 

984 

881 

  Nimed 

  a4a 

606 

596 

659 

646 

1095 

1056 

912 

881 

102% 

102% 

119% 

112% 

  % 

102% 

102% 

104% 

104% 

5.4  Baseline Run 2020-2024 

The MAP proposal includes the following for the five-year transitional period: 

  Average fishing days 2015-2017 

  10 % reduction in 2020 

  7.5% reduction per year between 2021 and 2024 

The  model  was  used  to  simulate  two  scenarios  related  to  the  points  listed  above:  Status  Quo 
(SQ),  where  no  change  in  fishing  effort  compared  to  the  average  2015-2017  is  applied,  and 
Scenario  1  (S1),  where  the  additional  two  points  in  the  MAP  proposal  are  simulated  (10% 
reduction in fishing effort in 2020 and 7.5% reductions each year from 2021 to 2024). 

5.5  Alternative Runs 

No alternative scenario to SQ and S1 were simulated through NIMED during the EWG 19-01. 

43 

 
 
 
  
  
  
  
 
 
 
 
  
  
 
 
 
 
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
5.5.1  Modellers’ list of possible runs that can be implemented with the model and considered of 

direct relevance for the purpose of the West Med MAP 

The NIMED model can be used to simulate management scenarios based on changes in selectivity 
and fishing effort. Scenarios on alternative paths to achieve the same reductions in fishing effort 
or stronger reductions in fishing effort can be simulated. 

5.5.2  Runs performed and analysed during EWG 19-01 

During  EWG  19-01,  two  management  scenarios  were  simulated:  Status  Quo  (SQ:  no  change  in 
fishing  effort)  and  Scenario  1  (S1:  10%  reduction in  fishing  effort  in  2020  and  7.5%  reductions 
each year from 2021 to 2024). The main outcomes of the simulations are reported in Tables and 
Figure  below.  The  tables  show  the  values  for  the  baseline  (average  2015-2017)  and  the  two 
scenarios in 2024, and the variations of the two scenarios compared to the baseline. 

Table 8 shows that the reductions in fishing effort (days at sea), amounting to a total of 40% in 
2024 compared to the baseline, which are assumed to be applied equally to all fleet segments. 

Table 9 shows the potential impact of the two scenarios on the total landings in value. Under SQ, 
limited variations between -3% and +1% in revenues are expected in 2024. Under S1, landings 
value for the whole demersal fleet is expected to decline by 14%. Around this value, variations by 
fleet segment depend on the specific composition of landings. 

The  ratios  between  current  revenues  and  break-even  revenues  (CR/BER),  which  measure  the 
ability of the  fleets to produce  enough income  to cover  fixed and variable costs, are  reported in 
Table  6.  Values  in  red  (the  indicator  is  lower  than  1)  indicate  that  revenues  are  not  enough  to 
cover the costs. This condition, registered on the baseline for DTS VL2440 in GSAs 9 and 11, PGP 
VL1218  in  GSA  9  and  DTS  VL1824  in  GSA  11,  is  expected  to  be  held  also  in  2024  under  both 
scenarios.  The  CR/BER  shows  a  negative  economic  performance  under  both  scenarios.  The 
indicator calculated for the whole fleet indicates reductions by 3% and 4% under the SQ and S1 
respectively. Almost all fleet segments are expected to register a decrease in the indicator under 
SQ, while some cases of relevant increases are expected under S1. These are related to the DTS 
VL2440 in GSAs 9 and 11. 

The average salary per employee for the whole demersal fleet is also expected to register limited 
reductions  by  3%  and  4%  under  SQ  and  S1  respectively.  Improvements  in  this  indicator  are 
expected for the larger trawlers, DTS VL2440, in GSAs 9 and 11, while the worse performance is 
expected for PGP VL1218 in GSA 10. 

The  net  profits  are  expected  to  register  a  reduction  by  10%  under  SQ  and  14%  under  S1.  As 
reported  in  Figure  7,  the  strongest  reduction  under  S1  is  expected  for  the  trawlers  operating  in 
GSA  11  (-39%),  followed  by  the  polyvalent  vessels  in  GSAs  9  and  10  (-13%  and  -24% 
respectively). On the contrary, trawlers in GSAs 9 and 10 are expected to increase their profits by 
3%  and  9%  respectively.  Under  SQ,  profits  are  expected  to  decline  for  all  groups  of  vessels 
except for trawlers in GSA 10, where the model projects an increase by 7%. 

Table 8  – NIMED. Days at sea by fleet segment 

Fleet Segment 

Baseline 

SQ 2024 

S1 2024 

SQ 2024 %  S1 2024 % 

9_DTSVL0612 

9_DTSVL1218 

9_DTSVL1824 

9_DTSVL2440 

9_PGPVL0612 

9_PGPVL1218 

10_DTSVL1218 

10_DTSVL1824 

10_PGPVL0006 

1687 

22586 

23777 

1676 

1687 

22586 

23777 

1676 

101559 

101559 

6172 

23097 

11621 

68499 

6172 

23097 

11621 

68499 

44 

1012 

13552 

14266 

1005 

60935 

3703 

13858 

6973 

41099 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

-40% 

-40% 

-40% 

-40% 

-40% 

-40% 

-40% 

-40% 

-40% 

 
 
 
10_PGPVL0612 

10_PGPVL1218 

11_DTSVL1218 

11_DTSVL1824 

11_DTSVL2440 

170350 

11613 

9428 

3711 

3455 

170350 

11613 

9428 

3711 

3455 

102210 

6968 

5657 

2227 

2073 

0% 

0% 

0% 

0% 

0% 

-40% 

-40% 

-40% 

-40% 

-40% 

Table 9  – NIMED. Total landings in value by fleet segment. 

Fleet Segment 

9_DTSVL0612 

9_DTSVL1218 

9_DTSVL1824 

9_DTSVL2440 

9_PGPVL0612 

9_PGPVL1218 

10_DTSVL1218 

10_DTSVL1824 

10_PGPVL0006 

10_PGPVL0612 

10_PGPVL1218 

11_DTSVL1218 

11_DTSVL1824 

11_DTSVL2440 

Baseline 

845249 

22129859 

30862934 

1978964 

22415454 

3960861 

11959964 

14843281 

9770039 

32737828 

9872119 

8551014 

5318423 

6386650 

SQ 2024 

S1 2024 

SQ 2024 %  S1 2024 % 

824192 

21715070 

30475893 

1955244 

736136 

18709681 

26624284 

1772175 

21534293 

19872740 

3821755 

12083684 

14930341 

9448724 

3585587 

10348542 

12886965 

8584205 

31626751 

29578061 

9538126 

8410305 

5284878 

6416775 

7175582 

7086796 

4203834 

5638002 

-2% 

-2% 

-1% 

-1% 

-4% 

-4% 

1% 

1% 

-3% 

-3% 

-3% 

-2% 

-1% 

0% 

-13% 

-15% 

-14% 

-10% 

-11% 

-9% 

-13% 

-13% 

-12% 

-10% 

-27% 

-17% 

-21% 

-12% 

Table 10  – NIMED. Current Revenues on Break Even Revenues by fleet segment 

Fleet Segment 

Baseline 

SQ 2024 

S1 2024 

SQ 2024 %  S1 2024 % 

9_DTSVL0612 

9_DTSVL1218 

9_DTSVL1824 

9_DTSVL2440 

9_PGPVL0612 

9_PGPVL1218 

10_DTSVL1218 

10_DTSVL1824 

10_PGPVL0006 

10_PGPVL0612 

10_PGPVL1218 

11_DTSVL1218 

11_DTSVL1824 

11_DTSVL2440 

2.18 

2.61 

1.06 

0.42 

1.42 

0.85 

1.62 

1.05 

2.80 

1.55 

1.91 

2.79 

0.95 

0.57 

2.10 

2.54 

1.03 

0.41 

1.34 

0.81 

1.65 

1.07 

2.69 

1.48 

1.82 

2.72 

0.94 

0.58 

1.97 

2.44 

1.14 

0.52 

1.36 

0.89 

1.62 

1.09 

2.51 

1.46 

1.39 

2.47 

0.78 

0.67 

-4% 

-3% 

-3% 

-3% 

-6% 

-6% 

2% 

1% 

-4% 

-4% 

-5% 

-3% 

-1% 

2% 

-10% 

-6% 

8% 

23% 

-4% 

4% 

0% 

4% 

-10% 

-5% 

-27% 

-11% 

-17% 

17% 

Table 11  –NIMED. Average salary by fleet segment 

Fleet Segment 

Baseline 

SQ 2024 

S1 2024 

SQ 2024 %  S1 2024 % 

9_DTSVL0612 

9_DTSVL1218 

9_DTSVL1824 

10154 

19135 

17958 

9388 

17990 

19188 

-3% 

-3% 

-2% 

-8% 

-6% 

7% 

9851 

18638 

17556 

45 

 
 
 
 
9_DTSVL2440 

9_PGPVL0612 

9_PGPVL1218 

10_DTSVL1218 

10_DTSVL1824 

10_PGPVL0006 

10_PGPVL0612 

10_PGPVL1218 

11_DTSVL1218 

11_DTSVL1824 

11_DTSVL2440 

12639 

6167 

9749 

5827 

11129 

4779 

5283 

8897 

11534 

12592 

14599 

12335 

5860 

9258 

5918 

11234 

4606 

5075 

8530 

11281 

12478 

14719 

15077 

5937 

10123 

5834 

11451 

4336 

5031 

6716 

10435 

11000 

15966 

-2% 

-5% 

-5% 

2% 

1% 

-4% 

-4% 

-4% 

-2% 

-1% 

1% 

19% 

-4% 

4% 

0% 

3% 

-9% 

-5% 

-25% 

-10% 

-13% 

9% 

Figure  7  –  NIMED.  Net  profits  by  fleet  segments  groups  (GSA  and  fishing  technique) 
under different scenarios. 

5.6  Summary on the model: achievements, gaps still to be addressed, work plan from 

now to STECF 19-14 (October 2019) 

The model was used to estimate the expected economic outcomes  of two scenarios, Status Quo 
and the scenario including the fishing effort reductions of the MAP proposal, for the most relevant 
fleet  segments  operating  in  the  demersal  fisheries  in  EMU2.  However,  the  standard  version  of 
NIMED  needs  catch  at  age  by  stock  and  fishing  gear  (at  least  for  the  most  relevant  ones)  as 
inputs.  During  the  EWG  19-01,  data  provided  from  the  stock  assessments  did  not  achieve  this 
level of detail. Catch at age was provided by stock without the quotas from each fishing gear.  

The gap was overcome using an “equivalent effort” measure. However, this solution shows some 
limitations  because  it  does  not  consider  the  different  selectivity  of  the  fleet  segments.  As  the 
same data format on the stock assessment outcomes is expected for the STECF EWG 19-14, an 
adaptation of the model will be carried out. Furthermore, the outputs of the NIMED model will be 
compared with the outputs of other models proposed during the EWG 19-01, like BEMTOOL, for a 
validation of the results. 

46 

0200040006000800010000120009_DTS9_PGP10_DTS10_PGP11_DTS'000 €Net ProfitsBaselineSQ 2024S1 2024 
 
 
 
 
6  BEMTOOL IN EMU2 

6.1  Overview of the model’s generic characteristics and use 

6.1.1  Main features 

BEMTOOL is a multi-species multi-gear bio-economic simulation model for mixed fisheries, which 
resumes  and  integrates  the  different  bio-economic  models  and  biological  modelling  tools 
developed  for  Mediterranean  fisheries1.  It  consists  of  six  operational  modules  characterized  by 
components  communicating  by  means  of  relationships  and  equations:  Biological  (age/length 
structured dynamic model, Lembo et al., 2009), Impact, Economic, Behavioural, Policy and Multi 
Criteria  Decision  Analysis  (MCDA)  (Rossetto  et.al,  2014;  Russo  et.al,  2017).  BEMTOOL  follows  a 
multi-fleet  approach  simulating  the  effects  of  management  options  on  stocks  and  fisheries  on  a 
fine  time  scale  (month).  The  model  accounts  for  length/age-specific  selection  effects,  discards, 
economic  and  social  performances,  effects  of  compliance  with  landing  obligation  and  reference 
points. The model can consider a large number of fleet groups. The implementation of a decision 
module  (Multi-Criteria Decision  Analysis  and Multi-attribute  utility  theory)  allows  stakeholders  to 
weight  model-based  indicators  and  rank  different  management  strategies.  The  model  can 
simulate  management  scenarios  based  on  changes  in  fishing  pattern,  fishing  effort,  fishing 
mortality  and  TAC.  A  wide  set  of  biological,  pressure  and  economic  indicators  is  the  default 
output.  The  uncertainty  (process  error)  implemented  in  the  model  following  Monte  Carlo 
paradigm allows a risk evaluation in terms of biological sustainability of the different management 
strategies accounting for the economic performances.  

In BEMTOOLv.3 (Spedicato et.al, 2017) the uncertainty component has been expanded, allowing 
an  approach  comparable  to  MSE.  The  process  error  is  implemented  on  recruitment,  individual 
growth  and  natural  mortality,  while  the  model  error  on  maturity  ogive  and  selectivity  functions. 
Uncertainty  can  be  applied  according  to  three  different  probability  distributions:  normal, 
lognormal and uniform. BEMTOOLv.3 platform allows also the implementation of a scenario based 
on a TAC set according to an MSE approach (GFCM, 2018). Every year the model checks that the 
SSB level and the fishing mortality are within safe biological limits, so the TAC is set accordingly. 
Further information on the model applications can be found in STECF (2018). 

6.1.2  References 

GFCM.  2018.  Scientific  Advisory  Committee  on  Fisheries  (SAC)  Report  of  the  Workshop  on  the 
assessment of management measures (WKMSE) Zagreb, Croatia, 9–11 April 2018 

Lembo  G.,  Abella  A.,  Fiorentino  F.,  Martino  S.,  Spedicato  M.T.  (2009)  -  ALADYM:  an  age  and 
length-based  single  species  simulator  for  exploring  alternative  management  strategies.  Aquat. 
Living Resour. 22, 233–241. DOI: 10.1051/alr/2009024 

Rossetto M., Bitetto I., Spedicato M. T., Lembo G., Gambino M., Accadia P., Melià P. 2014. Multi-
criteria  decision-making  for  fisheries  management:  A  case  study  of  Mediterranean  demersal 
fisheries. Marine Policy, 53, 83–93. 

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, https://doi.org/10.3389/fmars.2017.00193 

Spedicato, M.T., Bitetto, I., Carlucci, R., Cataudella, S., Facchini, M.T., Fiorentino, F., Lembo, G., 
Maiorano,  P.,  Mariani,  A.,  Piccinetti,  C.,  Russo,  T.,  Santojanni,  A.,  Scardi.  M.  2017.  Basi 
scientifiche e strumenti a supporto dei Piani di Gestione delle risorse della pesca nell’ambito della 

1 

 Tender  DGMARE  2009/05/Lot1.  MAREA  Framework  Contract.  Accadia  P.,  Bitetto  I.,  Facchini  M.T., 
Gambino M., Kavadas S., Lembo G., Maynou F., Melià P., Maravelias C., Rossetto M., Sartor P., Sbrana M., 
Spedicato M.T., BEMTOOL Deliverable D10: BEMTOOL FINAL REPORT. February 2013. 46 pp. 

47 

 
 
 
 
                                           
Politica  Comune  della  Pesca  e  delle  politiche  ambientali  ed  economiche”  -  (Rete3).  CoNISMA, 
Roma. 129 pp. 

STECF,  2018  –  Fishing  effort  regime  for  demersal  fisheries  in  the  western  Mediterranean  Sea  – 
Part II (STECF-18-13). Publications Office of the European Union, Luxembourg, 2018, ISBN 978-
92-79-79396-7, doi:10.2760/509604, JRC114702 

6.2  Application of the model to the West Med MAP 

6.2.1  State  of  completion  and  additional  development  work  specially  undertaken  before  and 

during EWG 19-01 

Before  and  during  the  EWG  19-01,  DCF  data  (landings,  discards,  fishing  effort,  biological  and 
economic  parameters)  and  results  from  the  assessments  carried  out  in  the  EWG  18-12  were 
analysed,  to  allow  the  parameterization  of  the  mixed  fishery  bio-economic  simulation  model 
BEMTOOL.  This  was  first  parameterized  in  the  hindcasting  mode  for  4  species  (European  hake, 
red  mullet,  deep-water  rose  shrimp  and  giant  red  shrimp)  of  the  Multiannual  Management  Plan 
(MAP) in the eastern part of the western Mediterranean (GSAs 9-10-11), to compare the assessed 
fishing  mortality,  spawning  stock  biomass  and  the  observed  catches  with  the  simulated  ones. 
Runs  of  the  model  in  the  short-terms  were  done  for  comparison  to  the  short-term  forecasts  of 
EWG 18-12. Results were in agreement. Three scenarios referred to the baseline run 2020-2024 
have  been  implemented.  Four  additional  scenarios  that  included  also  the  simulation  of 
hyperstability (STECF 18-03) were also run for the transition MAP period 2020-2024. These tests 
were  only  deterministic  to  get  a  first  feedback  on  the  model  settings  and  scenarios’  design  and 
feasibility.  Stochastic  runs  were  left  to  the  next  phase.  Estimates  of  stock-recruitment 
relationships were initiated using the Eqsim approach, to test their possible use in the next STECF 
EWG 19-14. 

6.2.2  Space and time scale 

The space scale the eastern side of the western Mediterranean. This area is belonging 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.  

The  time  scale  of  the  available  DCF  data  goes  from  2006  to  2017.  The  time  scale  of  the  model 
encompass  the  same  time  range  for  the  hindcasting.  For  2018  and  2019  an  invariant  situation 
compared  to  2017  is  assumed.  The  forecasts  are  covering  the  period  from  2020  to  2024, 
corresponding to the transitional phase of the Multiannual Management Plan. The time unit of the 
model is the month.  

6.2.3  Stocks (which stocks, which assessment data etc) 

The stocks taken into consideration in BEMTOOL simulations are:  

-  European hake in GSAs 9, 10 and 11; 

-  Red mullet in GSA9; 

-  Red mullet in GSA10; 

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

2 

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

48 

 
 
 
 
 
 
                                           
-  Giant red shrimp in GSAs 9, 10 and 11 

These are the ones for which stock assessment results from EWG 18-12 are available and can be 
used to parameterize BEMTOOL model.  

Norway  lobster  is  a  stock  considered  in  the  MAP  for  GSA9  and  GSA11,  but  it  has  not  been 
included in the present exercise, as an analytical assessment was not available.  

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 the Table 12.  

This table also reports the upper and lower range of FMSY, according to the formulas used in EWG 
18-02: 

and the needed reduction to reach F0.1. 

Considering the ratio between the current fishing mortality and the reference point (Fcurr/F0.1 and 
Fcurr/F0.1upper)  European  hake  is  the  stock  more  at  risk  (ratio=3.9;  2.75),  while  giant  red  shrimp 
(ratio=1.96;  1.45)  is  the  stock  less  impacted,  after  red  mullet  in  the  GSA  10,  which  is  a  stock 
actually less impacted, because sustainably exploited (ratio=0.46; 0.34).  

Table  12  Results  of  the  assessments  from  EWG  18-02  relevant  for  BEMTOOL 
parameterization.  The  computation  of  the  reduction  by  stock  to  reach  F0.1  is  also 
reported. 

Stock 

Fcurr 

F0.1  Fcurr/F0.1  F0.1lower  F0.1upper  Fcurr/Fupper 

Hake9-10-11 

RedMullet10 

RedMullet9 

Deep-water rose 
shrimp9-10-11 
GiantRedShrimp9-
10-11 

0.55  0.14 

0.25  0.54 

1.57  0.54 

3.93 

0.46 

2.91 

0.10 

0.36 

0.36 

0.20 

0.74 

0.74 

1.68  0.74 

2.27 

0.49 

1.01 

1.12  0.57 

1.96 

0.38 

0.77 

2.75 

0.34 

2.12 

1.66 

1.45 

% red to 
F0.1 

75 

0 

66 

56 

49 

6.2.4  Fleets 

In the simulation and forecast scenarios the following fleet segments have been considered:  

GSA9_DTS_1824 
GSA10_DTS_VL1218  GSA11_DTS_VL1218 
GSA9_DTS_VL1218  GSA10_DTS_VL1824  GSA11_DTS_VL1824 
GSA9_DTS_VL2440  GSA10_PGP_VL0006  GSA11_DTS_VL2440 
GSA9_PGP_VL0012  GSA10_PGP_VL0612  GSA11_PGP_VL0012 
GSA11_PGP_VL1218 
GSA9_PGP_VL1218    

The dependency of landings and revenues on the pool of the assessed/target species in the MAP 
proposal is differentiated according to the fleet segment and it is generally more marked for the 
fleets  targeting  European  hake  and  crustaceans  (Table  13)  If  the  geographical  areas  are 
considered, dependency in GSA9 and GSA10 is more marked compared to GSA11, where a higher 
dependency is observed only for the bigger trawlers (OTB_VL2440). 

49 

 
 
 
 
 
 
 
Table 13 - Percentage of landings and revenues of the pool of the target species of the 
MAP  in  respect  to  the  total  landing  (demersal  and  bento-pelagic  species)  and  total 
revenues  by  GSA  and  fleet  segment  (source:  authors  from  the  data  of  the  Annual 
Economic Report 2018, data refer to 2016). 

% target species 
landings/total landings 

% target species revenues /total 
revenues 

GSA9 

GSA10  GSA11 

GSA9 

GSA10 

GSA11 

OTB_VL0612 

18.57 

44.61 

- 

13.49 

48.84 

- 

OTB_VL1218 

41.06 

37.09 

10.98 

44.26 

44.67 

14.77 

OTB_VL1824 

36.37 

37.51 

5.86 

35.09 

40.47 

7.88 

OTB_VL2440 

49.71 

- 

33.74 

49.99 

- 

41.76 

GNS_VL0006  10.35 

16.73 

- 

12.56 

15.35 

- 

GNS_VL0612  16.78 

30.92 

0.16 

23.28 

28.53 

GNS_VL1218  23.99 

44.27 

6.14 

24.26 

39.79 

GTR_VL0006 

0.75 

7.08 

4.12 

GTR_VL0612 

8.94 

14.81 

2.26 

0.86 

7.23 

8.59 

18.86 

0.12 

5.68 

2.78 

1.42 

GTR_VL1218 

2.19 

- 

17.68 

1.38 

- 

10.62 

LLS_VL0006 

- 

3.66 

LLS_VL0612 

1.07 

25.10 

- 

- 

- 

2.35 

2.58 

38.92 

- 

- 

LLS_VL1218 

17.71 

41.05 

0.66 

30.54 

57.82 

0.44 

Total 

31.02 

28.52 

11.59 

31.02 

31.63 

13.15 

6.3  Comparison  of  model’s  short-term  forecast  with  the  single-stock  advice 

predictions 

Table 14 reports the results of the comparison between the short terms forecasts in EWG 18-02 
with  the  results  from  BEMTOOL  in  this  working  group.  The  level  of  change  of  catches  between 
2017  and  2019  agrees  between  the  short-term  forecasts  and  BEMTOOL  for  the  five  stocks 
assessed also the status of SSB showed the same direction in the two approaches. 

Table 14 – Comparison of the short terms forecasts of EWG 18-02 with the results from 
BEMTOOL (EWG 19-01). 

Catch 2017 

Catch 2019 

% Catch 
change 

SSB status 

Stock 

F red.  F0.1 

EWG 
18-02 

BEMTOOL 

EWG 
18-02 

BEMTOOL 

EWG 
18-02 

BEMTOOL 

EWG 
18-02 

BEMTOOL 

Hake 
Red 
mullet9 
Red 

-75%  0.14  1782 

1825 

494 

488 

-72% 

-73%  Decreas.  Decreas. 

-66%  0.54  1601 

1743 

812 

959 

-49% 

-45% 

Increas. 

Increas. 

mullet10  84%  0.54 

596 

353 

1056 

763 

- 

116% 

Increas. 

Increas. 

Deep-
water rose 
shrimp9-
10-11 

-56%  0.74  1507 

1487 

644 

659 

-57% 

-56%  Decreas.  Decreas. 

50 

 
 
 
 
 
 
 
 
 
 
Giant red 
shrimp 

-49%  0.57 

399 

575 

171 

257 

-57% 

-55%  Decreas.  Decreas. 

6.4  Baseline Run 2020-2024 

Three scenarios have been implemented to test the possibility of matching the MAP requirements. 
For sake of available time the running were deterministic to get a first feedback on:  

1)  the completeness and coherence of inputs and of the BEMTOOL parameterization;  
2)  the different scenarios settings to restrict the focus on a more suitable set in a next 

phase.  

Given the computation time, stochastic run can be performed in the second step.  

Scenarios related to the baseline run 2020-2024 were the following: 

S0: StatusQuo, with the days at sea in 2019 equal to the average of 2015-2017; 

S1:  Baseline,  with  days  at  sea  in  2019  equal  to  the  average  of  2015-2017,  reduction  of 
trawlers’  activity  (days  at  sea)  equally  distributed  among  the  fleet  segments:  10%  in 
2020; 30% by 2024 

S2: Baseline differentiated by DTS Fleet Segments.  

For  S2  the  basis  was  given  by  the  different  impacts  that  the  fleet  segments  have  in  terms  of 
fishing mortality on the pool of the target species of the MAP (on the basis of the overall impact 
on the MAP species, excluding N. norvegicus; see Figure 8) 

Figure  8  –  BEMTOOL.  Relative  impact  of  the  different  fleet  segments  on  the  fishing 
mortality pooled for the different species of the MAP (excluding N. norvegicus). 

6.5  Alternative Runs 

Four scenarios have been implemented:  

S3: Baseline differentiated by DTS Fleet Segments focusing the reductions in the months 
of European hake and giant red shrimp recruitment; 

S4: As S2 + Fishing Ban of PGP in winter months; 

S5: As S2, assuming hyperstability of F;  

S6: As S2 + closure of areas within 100 m.  

51 

 
 
 
 
 
 
 
 
For  S3  the  basis  was  given  by  the  knowledge  of  the  recruitment  peaks  of  European  hake  and 
giant red shrimp in the region (Figure 9). 

Figure  9.  BEMTOOL.  Scheme  of  the  calendar  in  which  the  reductions  at  month  level 
were applied in S3. 

Regarding scenario S4 the basis was given by the fact that the production of PGP is representing 
40% of European hake production; the ban was placed in February-March, which are considered 
the main spawning months of European hake. 

In  scenario  S5,  the  basis  was  given  by  the  assumption  of  hyperstability  in  the  relationship 
between fishing mortality and fishing effort taking, as an example, for all the stocks and DTS fleet 
segments,  the  following  coefficients:  a=1  and  b=0.5  in  a  relationship  of  the  form:  𝐹=𝑎∗𝐸𝑏 
(STECF EWG 18-03, 2018). 

In BEMTOOL the coefficient b has been applied to the factor fact,f of the following equation, which 
internally the model recalculates the fishing mortality: 

𝐹𝑓(𝑎) = (𝑍inp − mean(𝑀)) ∗ Sel𝑓(𝑎) ∗ 𝑓act,𝑓 ∗ 𝑝𝑓; 

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, which 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). 

Regarding  the  scenario  S6  the  basis  was  given  by  the  knowledge  on  the  distribution  of  the 
juveniles of key species and the fleet activity by month in the depth range 50-100 m (VMS data, 
Figure 10): 

- 
- 

for DTS GSA 9 in June and July (to still protect recruitment of red mullet in GSA 9)  
for DTS GSA 10 from April to June (to partially protect recruitment of deep-water 
rose shrimp, considering that in GSA10 red mullet is exploited sustainably).  

In  addition,  the  activity  within  50-100  m  depth  of  the  different  fleet  segments  in  the  GSA9  and 
GSA10 was taken into account to tune the selectivity of the involved fleets. 

52 

 
 
 
GSA 9 
DTS 1218: 27 % 
DTS 1824: 69 % 
DTS 2440: 0% 

GSA 10 
DTS 1218: 49 % 
DTS 1824: 47 % 
DTS 2440: 0% 

Figure  10  –  BEMTOOL.  Basis  for  changing  the  exploitation  pattern  in  scenario  S6. 
Activity within 100m depth of the different fleets  according to the month, as observed 
in the last two years. 

The recruitment used for the projection was the same as for the short-term forecasts  

6.5.1  Modellers’ list of possible runs that can be implemented with the model and considered of 

direct relevance for the purpose of the West Med MAP 

Other  possible  runs  that  can  be  implemented  using  BEMTOOL  and  considered  relevant  for  the 
MAP are:  

- Exploring the effects of changes in the exploitation pattern of European hake associated to the 
closure of nurseries and/or changes in gear selectivity (e.g. mesh size) to rescale the ratio of the 
current F to the reference point;  

-  Still  severe  reductions  should  be  implemented  (a  situation  close  to  a  moratoria),  because  for 
European hake the reduction of the fishing activity in the transition phase of the MAP will be not 
enough to bring the stock to the FMSY; 

- Management measures on the whole fleet exploiting the relevant stocks of the MAP; indeed, in 
the  application  of  BEMTOOL  explored  during  the  STECF  EWG  19-01,  also  fleet  segments  using  
gears  different  from  trawlers  were  included,  because  were  found  to  impact  on  European  hake 
stock;    

- Scenarios of reaching FMSY upper of European hake and giant red shrimp, simulate combinations 
of the abovementioned scenarios.  

6.5.2  Runs performed and analysed during EWG 19-01 

Regarding  the  reduction  of  fishing  mortality,  the  higher  value  for  the  more  vulnerable  stock 
(European  hake)  is  obtained in  the  scenario  S4,  while  the lowest  percentage  of  reduction in  the 

53 

 
  
 
 
 
 
scenario  with  hyperstability  S5  (Table  15).  For  the  less  vulnerable  stock  (except  red  mullet  in 
GSA10  which  is  currently  exploited  sustainably)  the  higher  change  was  achieved  in  the  S1 
scenario  (Figure  11).  The  stock  of  red  mullet  in  GSA10  would  be  negatively  impacted  by  all 
scenarios. 

For the stock of A. foliacea the reduction of fishing activity applied in the transition phase of the 
MAP  would  contribute  to  get  close  to  the  reference  point.  For  European  hake,  instead,  even  the 
higher  reduction  of  activity  (not  assuming  hyperstability)  would  not  reach  the  reference  point. 
The  exploitation  of  this  stock  would  though  be  quite  far  from  this  target  (F  at  2024  around  0.4 
compared to F0.1 upper of about 0.2). This would mean the needing of introducing changes in the 
exploitation pattern to mitigate a possible severe reduction of fishing activity to reach FMSY in the 
phase following the transition. 

Table 15 – BEMTOOL. Changes (in percentage) of the fishing mortality F of the 5 stocks 
in the 6 tested scenario compared to the status quo S0. This is referred to 2024. 

Stock 

A. fol 
M. 
bar10 
M. 
bar9 
M. 
mer 

S0 – 
Status 
Quo F 

S1 – 

Baseline 

S2 – 
Baseline 
Diff 

S3 – 
Baseline 
Diff_FS_period 

S4 - 
Baseline_ 
FB_PGP 

S5 - 
Baseline_ 
Hyp 

S6 – 
Baseline 
Closure100 

1.11 

-40% 

-24% 

-23% 

-40% 

-23% 

-40% 

0.20 

-35% 

-31% 

-15% 

-35% 

-20% 

-35% 

1.38 

-32% 

-36% 

-37% 

-32% 

-18% 

-35% 

-19% 

-36% 

-27% 

-14% 

-24% 

-40% 

-23% 

-42% 

0.55 

-24% 

-18% 

P. lon 

1.60 

-40% 

-41% 

54 

 
 
 
 
Figure  11.  BEMTOOL.  Trajectories  of  the  Fishing  mortality  for  the  2  stocks  in  the 
hindcasting  phase  (until  2018)  and  in  the  forecast  phase  (after  2018).  The  horizontal 
dotted line represents the reference point F0.1. 

Regarding  SSB  all  scenarios  gave  better  results  than  S0.  For  European  hake  the  better  results 
would be achieved in the scenario S4, when also the PGP fisheries are reduced. For A. foliacea te 
better ressults would be achieved n S1, S4 and S6 that for this stock would be equivalent (Table 
16 and Figure 12). 

55 

 
 
 
 
 
 
Table 16. BEMTOOL. Changes (in percentage) of the SSB of the 5 stocks in the 6 tested 
scenarios  compared  to  the  status  quo  S0.  This  is  referred  to  2024  (the  SSB  in  S0  is 
reported in tons). 

Stock 

S0 – 
Status 
Quo SSB 

S1 – 

Baseline 

S2 – 
Baseline 
Diff 

S3 – 
Baseline 
Diff_FS_period 

S4 - 
Baseline_ 
FB_PGP 

S5 - 
Baseline_ 
Hyp 

S6 – 
Baseline 
Closure100 

A. fol 

111 

89% 

M. bar10 

2024 

M. bar9 

M. mer 

P. lon 

1551 

3011 

1145 

14% 

38% 

25% 

28% 

45% 

12% 

46% 

20% 

30% 

39% 

13% 

55% 

17% 

32% 

89% 

14% 

39% 

35% 

28% 

41% 

7% 

19% 

13% 

14% 

89% 

14% 

44% 

25% 

35% 

Figure  12.  BEMTOOL.  Trajectories  of  the  SSB  for  the  2  stocks in  the  hindcasting  phase 
(until 2018) and in the forecast phase (after 2018). 

56 

 
 
 
 
The  reduction  of  the  activity  would  imply  a  reduction  of  the  catches  for  the  DTS  fleets  and  a 
stability or improvement for the PGP fleets. (Table 17). The two months of ban for the PGP in the 
scenario S4 would not affect negatively the catches. The scenario with hyperstability would have 
the  lower  influence  on  the  catches  reduction,  but  this  was  due  to  a  lower  reduction  of  fishing 
mortality.  The  lower  reduction  of  European  hake  catches  for  DTS  would  result  in  S3.  The 
trajectories  of  the  predicted  catches  for  the  6  scenarios  appear  more  similar  for  European  hake 
than for giant red shrimp (Figure 13). 

Table  17.  BEMTOOL.  -  Changes  (in  percentage)  of  the  catches  of  the  5  stocks  by  fleet 
groups (DTS and PGP) in the 6 tested scenarios compared to the status quo S0. This is 
referred to 2024 (the catches in S0 are reported in tons). 

S0 - 
StatusQ
uo 

S1 - 
Baseli
ne 

S2 - 
Baseline
Diff 

S3 - 
BaselineDiff_FS_p
eriod 

S4 - 
Baseline_FB_
PGP 

S5 - 
Baseline_
Hyp 

S6 - 
BaselineClosure
100 

DTS Fleets 

400 

-13% 

-6% 

-4% 

-13% 

-6% 

-13% 

356 

-34% 

-29% 

-29% 

-33% 

-18% 

-34% 

1629 

-19% 

-23% 

-27% 

-18% 

-9% 

-21% 

1222 

-24% 

-17% 

-14% 

-20% 

-12% 

-24% 

1246 

-16% 

-17% 

-18% 

-16% 

-8% 

-18% 

PGP Fleets 

S0 - 
StatusQ
uo 

S1 - 
Baseli
ne 

S2 - 
Baseline
Diff 

S3 - 
BaselineDiff_FS_p
eriod 

S4 - 
Baseline_FB_
PGP 

S5 - 
Baseline_
Hyp 

S6 - 
BaselineClosure
100 

42 

15% 

13% 

232 

52% 

63% 

722 

29% 

22% 

14% 

77% 

18% 

1% 

43% 

21% 

8% 

25% 

15% 

15% 

61% 

29% 

Stoc
k 

A. 
fol 
M. 
bar1
0 
M. 
bar9 
M. 
mer 
P. 
lon 

Stoc
k 

M. 
bar1
0 
M. 
bar9 
M. 
mer 

57 

 
 
 
 
Figure  13  –  BEMTOOL.  Trajectories  of  the  catches  (in  tons)  for  the  2  stocks  in  the 
hindcasting phase (until 2018) and in the forecast phase (after 2018). 

A  more  clear  picture  of  the  comparison  among  the  different  scenarios  is  obtained  by  the  MCDA 
analysis  in  BEMTOOL  (see  chapter  11.10),  that  show  a  similar  utility  among  the  different 
scenarios  compared  to  the  status  quo  that  has  the  lower  utility.  Considering  the  ensemble  of 
stocks,  fleets  and  the  different  indicators  of  the  biological,  economic  and  social  domain  the 
scenarios with a slight better performance are S1, S4 and S6.  

6.6  Summary on the model: achievements, gaps still to be addressed, work plan from 

now to STECF 19-14 (October 2019) 

The availability of the socio-economic data at GSA-fleet segment level is crucial to parameterize 
the  model.  This  data  have  to  be  available  to  the  next  STECF  19-14  to  allow  to  run  updated 
projections of the fleet socio-economic performances.  

58 

 
 
 
 
After  the  next  EWG  on  stock  assessment,  the  stock-recruitment  relationships  preliminarily 
estimated  for  this  EWG  19-01  can  be  updated  and  applied  in  the  projections  of  BEMTOOL,  thus 
runs can be also done with different assumptions on the recruitment; 

During this EWG 19-01 only the deterministic runs have been carried out for time constrains. The 
workplan for the next STECF 19-14 is to include uncertainty in the simulations (through process 
and model errors) to take it into account in the evaluation of the different strategies. 

In  case  in  the  next  EWG  on  stock  assessment  the  biomass  reference  points  will  be  defined (i.e. 
Bpa,  Blim),  the  probability  to  have  an  SSB  below  the  reference  point  could  be  derived  in  the 
STECF 19-14, alternatively an empirical limit, as the lower  level of the SSB observed in the time 
series could be considered to make a risk evaluation. 

The  comparison  of  the  scenarios  using  the  MCDA  analysis  to  synthetise  the  results,  taking  into 
account the biological, economic and social components, is also planned in the STECF 19-14. This 
tool  represents  also  a  basis  for  developing  a  participatory  approach  with  stakeholders,  making 
them aware of the consequences of different management strategies. 

The  application  of  BEMTOOL  to  EMU1  was  also  considered  feasible,  given  the  availability  of  the 
relevant  input  well  in  advance  (at  least  3  weeks  before),  this  also  because  the  model  with 
uncertainty requires time for running and thus the basic model should be available in advance. 

59 

 
 
 
7 

IAM IN EMU1 

7.1  Overview of the model’s generic characteristics and use 

7.1.1  Overview 

See Table 4, description in Nielsen et al, and STECF reports 18-09 and 18-13. 

7.1.2  References 

STECF  (Scientific,  Technical  and  Economic  Committee  for  Fisheries)  (2015).  Multiannual 
management plans SWW and NWW ( STECF - 15 - 08 ). CSTEP/STECF, Ref. EUR XXXX EN, 
JR C XXXX,, 80p. 

Merzéréaud  Mathieu,  Biais  Gerard,  Lissardy  Muriel,  Bertignac  Michel,  Biseau  Alain  (2013). 
Evaluation  of  proposed  harvest  control  rules  for  Bay  of  Biscay  sole.  CIEM,  Ref.  ICES  CM 
2013/ACOM:75, 18p. 

STECF (Scientific, Technical and Economic Committee for Fisheries) (2011) Impact Assessment 
of  Bay  of  Biscay  sole  (STECF-11-01).  European  Commission,  Ref.  Publications  Office  of  the 
European Union, Luxembourg, EUR 24814 EN - 2011, 41p. 

Merzéréaud  Mathieu,  Macher  Claire,  Bertignac  Michel,  Fresard  Marjolaine,  Le  Grand  Christelle, 
Guyader  Olivier,  Daures  Fabienne,  Fifas  Spyros  (2011).  Description  of  the  Impact 
Assessment bio-economic Model for fisheries management (IAM).  

Bellanger  Manuel,  Macher  Claire,  Merzéréaud  Mathieu,  Guyader  Olivier,  Le  Grand  Christelle 
(2018). Investigating trade-offs in alternative catch-share systems: an individual-based 
bio-economic  model  applied  to  the  Bay  of  Biscay  sole  fishery  .  Canadian  Journal  Of 
.  Publisher's  official  version  : 
,  75(10),  1663-1679 
Fisheries  And  Aquatic  Sciences 
: 
Open 
, 
https://doi.org/10.1139/cjfas-2017-0075 
https://archimer.ifremer.fr/doc/00416/52779/  

version 

Access 

Macher Claire, Bertignac Michel, Guyader Olivier, Frangoudes Katia, Fresard Marjolaine, Le Grand 
Christelle,  Merzéréaud  Mathieu,  Thebaud  Olivier  (2018).  The  role  of  technical  protocols  and 
partnership  engagement  in  developing  a  decision  support  framework  for  fisheries 
management 
. 
https://doi.org/10.1016/j.jenvman.2018.06.063  

Environmental  Management 

Journal  Of 

503-516 

223, 

, 

. 

Guillen Jordi, Macher Claire, Merzéréaud Mathieu, Bertignac Michel, Fifas Spyros, Guyader Olivier 
(2013).  Estimating  MSY  and  MEY 
fisheries, 
consequences and limits: an application to the Bay of Biscay mixed fishery . Marine Policy 
,  40,  64-74  .  Publisher's  official  version  :  https://doi.org/10.1016/j.marpol.2012.12.029  ,  Open 
Access version : https://archimer.ifremer.fr/doc/00129/24000/  

in  multi-species  and  multi-fleet 

7.2  Application of the model to the West Med MAP 

Two applications of the IAM model have been implemented; one specifically designed for the GSA 
7,  and  another  encompassing  GSAs  1,5,6,7.  Both  models  focus  on  Hake  population  dynamics, 
consider  explicitly  5  fleet  categories:  French  trawlers  (<18m,  18-24m  and  >24m),  French  non-
trawlers  (<12m  and  >12m),  while  spanish  vessels  are  all  pooled  together.  For  GSA  7,  it  is 
assumed  that  spanish  vessels  are  only  trawlers.  For  GSA  1,5,6,7,  trawlers  are  responsible  for 
94%  of  the  catch  –  based  on  the  catch  data  available  to  the  working  group.  The  models  are 
initially set-up with 2017 values issued from the parameters of the stock assessment models, i.e. 
the  CGPM/GFCM  stock  assessment  carried  out  for  the  GSA  7  (XSA  model,  Certain  et  al.  2018), 
and the STECF stock assessment for the GSA 1,5,6,7 (a4a model, version made available to the 
working  group).  In  this  specific  application,  the  models  are  used  to  produce  short-term  and 
medium-term forecasts (up to 2031), tracking hake biomasses and catch.  

For  the  short-term  forecasts,  we  use  a  scenario  based  on  the  recommendation  of  the 
management plan, i.e.  a reduction of the 2019 fishing mortality (1.81) of 10%  on the first year 
(in  2020),  then  a  succession  of  7.5%  per  year  (still  based  on  the  2019  fishing  mortality)  until 
2024,  and  finally  a  drastic  reduction  in  2025  to  reach  the  fishing  mortality  expected  under 

60 

 
 
 
 
sustainable  harvesting  (Fmsy  =  0.23).  According  to  the  management  plan,  fishing  reduction 
should solely apply to trawlers.  

In  a  preliminary  exercise,  we  compare  the  short-term  forecast  directly  issued  from  the  stock 
assessment model (XSA – GSA7) to the short-term forecast obtained by the IAM-GSA7 model, in 
order to ensure that the Hake dynamics simulated by both models is consistent. For that exercise, 
the reduction in fishing effort is applied uniformly to all fishing fleets, for the sake of simplicity.  

Then,  we  apply  the  fishing  reduction  scenario  to  the  IAM  short-term  forecasts  to  propose  a 
preliminary assessment of the potential effects of the management plan on catches and spawning 
stock  biomasses.  Here,  we  use  both  IAM  –  GSA7  and  IAM  –  GSA1,5,6,7  models,  and  we  apply 
fishing mortality reduction only to the trawlers.  

Finally, as a control, we run the short-term forecast according to a “status quo” scenario, in which 
fishing mortality is kept constant through time. 

7.2.1  State  of  completion  and  additional  development  work  specially  undertaken  before  and 

during EWG 19-01 

The  current  set-up  for  the  IAM  model  is  rather  simple,  and  its  capacity  to  explore  the 
consequences of the management plan could be increased in many ways, such as the addition of 
further  stocks  (red  mullets  and  shrimps),  the  further  specification  of  fleets  (e.g.  explicitly 
separating spanish trawlers, gillnetters, longliners, etc..), or the use of economic information for 
all fleets to build economic indicators. 

7.2.2  Space and time scale 

Our  model  set-up  allows  comparing  a  relatively  localized  (GSA  7)  with  a  broad  (GSA  1,5,6,7) 
spatial  scale.  Our  short-term  forecast  extends  up  to  2031,  but  we  will  only  extract  quantitative 
results up to 2024 – further trends will only be interpreted qualitatively.   

7.2.3  Stocks (which stocks, which assessment data etc) 

The  current  implementation  of  the  IAM  model  focuses  only  on  Hake  (Merluccius  merluccius)  as 
stock dynamics.  

7.2.4  Fleets 

Input  data  on  a  disaggregated  typology  of  french  fleets  for  IAM  on  GSA7  was  already  available 
before  the  group  (cf  STECF  18-09),  but  here  a  simpler  typology  was  used,  that  could  answer 
particularly  the  questions  asked,  testing  at  the  same  time  the  ability  of  the  model  to  easily  be 
updated  with  the  most  recent  data.  This  current  implementation  of  the  IAM  model  explicitly 
consider  the  French  trawler  size  categories  of  the  management  plan,  i.e.  <12m,  12-18m,  18-
24m, and >24m. However, no trawler in France has a size <12m so there are only 3 categories. 
Conversely, due to a lack of information on the fleet composition, especially in GSA 1,5,6, all the 
spanish  fleet  together  were  pooled  together.  In  GSA  7,  we  assumed  that  it  was  composed  of 
spanish  trawlers  only,  while  when  considering  GSA1,5,6,7  together,  we  assumed  that  trawlers 
represented 94% of the catch, applying this sharing rate to Spanish vessels fishing mortality.    

7.3  Comparison  of  model’s  short-term  forecast  with  the  single-stock  advice 

predictions 

The outputs of the short-term forecasts produced by XSA-GAS7 and IAM-GSA7 are very similar, 
in terms of fishing mortality, spawning stock biomass and landings.  

61 

 
 
 
 
 
 
 
Figure  14.  IAM.  –  Comparison  between  IAM  and  XSA  short-term  forecast:  fishing 
mortality 

Figure  15.  IAM  –  Comparison  between  IAM  and  XSA  short-term  forecast:  spawning 
stock biomass 

62 

 
 
 
 
 
Figure 16. IAM.– Comparison between IAM and XSA short-term forecast : Hake landings 

7.3.1  Short-Term Forecast IAM-GSA7 

The results of the run for GSA-7 only are displayed below (mean effort per vessel/fleet, in nb of 
trips, ssb HKE, and total HKE landings per fleet) : 

Figure  17.  IAM  –  Short-Term  Forecast  IAM-GSA7:  Mean  effort  per  vessel  per  fleet  (nb 
trips) 

63 

 
 
 
 
 
Figure 18. IAM– Short-Term Forecast IAM-GSA7 : Hake Spawning Stock Biomass (tons) 

Figure 19. IAM – Short-Term Forecast IAM-GSA7 : Total Hake Landings per fleet (tons) 

64 

 
 
 
 
 
 
When  running  the  model  in  GSA  7  only,  the  fishing  mortality  reduction  envisioned  by  the  plan 
result  in  a  drastic  reduction  in  trawlers  effort,  while  other  fishing  vessels  effort  remains 
unchanged.  SSB  recovers  slowly  in  the  first  year  of  the  plan  (2020-2024),  while  the  fishing 
mortality  reduction  remains  progressive,  and  then  recovers  quickly  once  fishing  mortality 
approached  Fmsy  in  2025.  However,  it  should  be  noted  that  in  this  set-up,  we  could  not  reach 
Fmsy  (0.23)  by  solely  applying  the  fishing  mortality  reduction  to  trawler,  as  the  other  fleets 
(which are french gillnetters for the most part) continue to fish as usual. As a result, the landings 
of these other fleets increased significantly, together with the Spawning stock biomass. 

7.3.2  Short-Term Forecast IAM-GSA1,5,6,7 

Only the results of the run for GSA-1,5,6,7 are displayed below: 

Figure  20.  IAM  –  Short-Term  Forecast  IAM-GSA1567  :  Mean  effort  per  vessel  per  fleet 
(nb trips) 

65 

 
 
 
 
 
Figure  21.  IAM  –  Short-Term  Forecast  IAM-GSA1567  :  Hake  Spawning  Stock  Biomass 
(tons) 

Figure  22.  IAM  –  Short-Term  Forecast  IAM-GSA1567  :  Total  Hake  Landings  per  fleet 
(tons) 

The  results  displayed  by  the  IAM  –  GSA  1,5,6,7  model  show  a  very  similar  trend  than  with  the 
IAM - GSA 7 run, with the difference that some levels of trawling effort can be maintained in 2025 

66 

 
 
 
 
when  reaching  Fmsy.  Other  fishing  vessels  belonging  to  the  <12m  category  (mostly  gillnetters) 
are still the one benefitting mostly from the management plan. 

7.4  Results of the Status Quo Scenario,  IAM-GSA1,5,6,7 

Figure  23.  IAM  –  Short-Term  Forecast  IAM-GSA1567-SQ  :  Mean  effort  per  vessel  per 
fleet (nb trips) 

67 

 
 
 
Figure  24.  IAM  –  Short-Term  Forecast  IAM-GSA1567-SQ  :  Hake  Spawning  Stock 
Biomass (tons) 

Figure 25. IAM – Short-Term Forecast IAM-GSA1567-SQ : Total Hake Landings per fleet 
(tons) 

The forecasts of the status quo scenario points towards a collapse of the fisheries, with both SSB 
and landings pushed towards very low values if fishing effort is maintained at its current level. 

7.5 Quantitative results for 2024 

In  both  IAM-GSA7  and  IAM-GSA1,5,6,7,  the  drastic  effort  reduction  required  to  reach  Fmsy  in 
2025 does not seem fully realistic from a management point of view. Hence, for this preliminary 
exercise,  we  will  concentrate  on  providing  quantitative  estimates  of  the  consequence  of  the 
management  plan  for  the  French  stocks  and  fleet,  as  forecasted  by  the  model  in  2024,  that  is, 
after 5 years of consecutive effort reductions. 

Regarding the stock status, our forecasts envision an increase in the spawning stock biomass of 
around 20%, while total landings (ldgs) would only have decreased by either 3,5% (in GSA 7) or 
9%  (when  grouping  GSA  1,5,6,7).  In  both  cases,  hake  fishing  mortality  should  be  reduced  by 
~30-35%.  

68 

 
 
 
 
The forecasted consequences of the management plan for the French fleet are summarized in the 
table below:  

The  forecasts of  our models suggests that a global  reduction of trawling effort of 40%  operated 
between  2019  and  2024  should  result  in  a  moderate,  but  still  substantial  decrease  of  trawler 
landings (from -1% to -15%, depending on the spatial scale considered), while landings of other 
fishing vessels (gillnetters for the most part) would increase significantly (from +50% to +67%, 
again depending on scale).         

7.6 Summary:  achievements, gaps still to  be  addressed,  work  plan from now to STECF 
19-14  

Our  modelling  exercise,  while  preliminary,  highlights  unbalanced  consequence  of  the  current 
management  plan  between  the  trawlers  and  the  other  fishing  vessels.  Therefore,  any  further 
modification or revision of the current management plan may need to address this issue. Indeed, 
such  discrepancy  of  consequences  between  fleet  segments  may  undermine  the  fishermen 
compliance to the management plan. 

The  current  implementation  of  the  IAM  model  is  rather  preliminary  and  could  be  extended  in 
many  ways  (more  detailed  economic  informations,  explicit  consideration  of  spanish  fleet 
segments, inclusion of more fish stocks). To explore in which way the model should be extended, 
it would be necessary to organize a joint French/Spanish workshop on this issue. 

69 

 
   
 
 
 
8  MEFISTO IN EMU1 

8.1  Overview of the model’s generic characteristics and use 

8.1.1  Main features 

See Table 4 above, previous descriptions in STEF EWG 18-09, 18-13 and references below 

8.1.2  References 

Lleonart,  J.,  F.  Maynou,  L.  Recasens  and  R.  Franquesa.  2003.  A  bioeconomic  model  for 
Mediterranean  Fisheries,  the  hake  off  Catalonia  (Western  Mediterranean)  as  a  case 
study. Scientia  Marina 67  (suppl.  1):  337-351. In:  Fisheries  stock  assessments  and  predictions: 
Integrating relevant knowledge, Ø. Ulltang, G. Blom 
 [eds.] http://dx.doi.org/10.3989/scimar.2003.67s1337  

Merino,  G.,  A.  Quetglas,  F.  Maynou,  A.  Garau,  H.  Arrizabalaga,  H.  Murua,  J.  Santiago,  M. 
Barange, R. Prellezo, D. García, J. Lleonart, G. Tserpes, N. Carvalho, M. Austen, J.A. Fernandes, 
P.  Oliver,  A.M.  Grau.  2015.  Improving  the  performance  of  a  Mediterranean  demersal  fishery 
toward economic objectives beyond MSY. Fisheries Research 161: 131–14. 

8.2  Application of the model to the West Med MAP 

8.2.1  State  of  completion  and  additional  development  work  specially  undertaken  before  and 

during EWG 19-01 

The MEFISTO v.4.0 model was parameterized with biological and economic data for the demersal 
fisheries  in  EMU1  (west  Western  Mediterranean  or  GSAs  1,  5,  6,  7).  The  biological  data  was 
obtained  from  EWG  18-12,  which  included  analytical  stock  assessments3.  Economic  data  was 
derived from DCF 2018 data facilitated by the JRC through the repository (2018-07_STECF 18-07 
-  EU  Fleet  Economic  and  Transversal  data_fs  level_final.xlsx  and  2018-07_STECF  18-07  -  EU 
Fleet Landings FAO Gear levels_final.xlsx).   

8.2.2  Space and time scale 

The model was applied over the period 2017 to 2024, considering the following: 

-  2017 year for which assessment data was available 

-  2020 first year of the implementation of the MAP 

-  2021-2024 second phase of the implementation 

The  model  application  was  run  at  annual  scale,  although  the  MEFISTO  model  can  work  at  time 
steps smaller than one year (quarter, month, week). 

MEFISTO does not allow for analysis of spatial scale. 

8.2.3  Stocks (which stocks, which assessment data etc) 

The biological data was obtained from EWG 18-12, which included analytical stock assessments, 
for 8 species or stock combinations, as shown in the following table: 

3 

 Note that MEFISTO can work with stocks assessed with age-structured models only, and 
not with index derived or biomass pool stock assessments. 

70 

 
 
 
 
 
 
 
 
 
                                           
HKE 
MUT 
NEP 
DPS 

ARA 

GSA1 

GSA5 

GSA6 

GSA7 

X 
- 

X 

X 

no advice 

X 

index advice 

index 
advice 

X 
X 

X 

X 
- 

- 

For hake (HKE) a stock assessment combining the four GSAs was available. For red mullet (MUT), 
separate  stock  assessments  were  available,  except  for  GSA5.  For  Norway  lobster  (NEP) 
assessment  results  were  available  for  the  two  GSAs  for  which  it  is  a  relevant  stock  (GSA5  and 
GSA6). For the deep-water rose shrimp (DPS) only index advice was produced in EWG 18-12. The 
blue  and  red  shrimp  (ARA)  is  an  important  fishery  species  in  the  three  Spanish  GSA,  but  only 
index advice was available for GSA5. 

8.2.4  Fleets 

The five species concerned by the Multi-Annual Plan are exploited by three fishing techniques in 
EMU1:  DFN,  DTS  and  HOK.  DFN  comprises  passive  set  net  fishing  gear  (GTR  and  GNS).  DTS 
includes only OTB in the three Spanish GSAs, but includes OTB, OTT and OTM in GSA7 (France). 
HOK comprises set longlines (LLS). Fishing techniques DFN and HOK are employed by small scale 
fishing vessels, usually in VL classes VL0006 and VL0612, while DTS comprise VL1218 and larger. 
The  model  runs  carried  out  during  the  meeting  were  based  on  an  exhaustive  list  of  30  fleet 
segments: 

GSA1 

GSA5 

GSA6 

GSA7 France 

GSA7 Spain 

DFN 

2: VL0612 VL1218 

2: VL0612 VL1218 

DTS 

HOK 

4: VL0612 VL1218 
VL1824 VL2440 

3: VL1218 
VL1824 VL2440  

4: VL0612 VL1218 
VL1824 VL2440 
2: VL0612 VL1218 

3: VL0006 
VL0612 VL1218 
2: VL1824 
VL2440 
2: VL0006 
VL0612 

3: VL1218 
VL1824 VL2440 
3: VL0612 
VL1218 VL1824 

71 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
The  landings  of  the  8  stocks  comprising  the  biological  basis  of  the  MEFISTO  model  application 
represent  between  5  and  20%  of  the  landings  of  the  main  three  demersal  fleets  (DFN,  DTS, 
HOK),  as  shown  in  the  following  table  (from  data  in  2018-07_STECF  18-07  -  EU  Fleet  Landings 
FAO Gear levels_final.xlsx): 

HKE 

MUT 

NEP 
DPS 

ARA 
Total 
landings 

GSA1 

GSA5 

GSA6 

GSA7 

157,164  

60,539  

         1,622,841  

973,068  

153,395  

         1,190,048  

130,471  

20,455  

310,971  

187,092  

579,997  

82,552  

    3,507,429  

    1,558,359  

      18,226,110  

9,881,378  

assessed 
prop. 

497,650  
14% 

80,994  

5% 

3,703,857  
20% 

1,186,091  

12% 

This  means  that,  depending  on  the  GSA,  between  80  and  95%  of  the  landings  are  not 
represented  by  a  population  dynamics  model  in  the  MEFISTO  application,  but  considered  as  a 
pool  of  secondary  species.  In  MEFISTO  the  landings  resulting  from  the  secondary  species  are 
related  to  the  population  dynamics  of  the  main  species  by  means  of  a  linear  or  non-linear 
regression model; in our case a simple linear regression was used. However, note that due to the 
short  span  of  the  landings  data  series  in  2018-07_STECF  18-07  -  EU  Fleet  Landings  FAO  Gear 
levels_final.xlsx  (2013-2016  only),  the  relationship  between  main  and  secondary  species  could 
only be estimated with very low accuracy. 

The value of the main economic parameters calculated for the model application from are given in 
the following table: 

fishing 
technique 

GSA 

1  DFN 
1  DTS 
5  DTS 
6  DFN 
6  DTS 
6  HOK 
7  DFN 
7  DTS 
7  HOK 

Number of 
vessels 
262 
64 
40 
761 
220 
197 
369 
36 
89 

Average annual fuel 
consumption by vessel  (l/yr) 
1900 
193000 
193000 
1900 
193000 
1600 
8700 
383800 
3760 

Average annual total 
costs by vessel (€/yr) 

Average Capital 
by vessel (€) 

14489 
48333 
60272 
14489 
48333 
23833 
18255 
96548 
21452 

20346 
69644 
85411 
20346 
69644 
38109 
66970 
253702 
33598 

8.3  Comparison  of  model’s  short-term  forecast  with  the  single-stock  advice 

predictions 

The following table summarizes changes in the relevant indicators, Catch and SSB, over the short 
term period as shown in the short term forecast of EWG 18-12 or from MEFISTO. Four scenarios 
were  compared:  immediate  reduction  of  fishing  mortality  to  achieve  F01  as  proxy  for  Fmsy,  a 
bracket of F(lower) and F(upper) around F01, and continuing with the status quo current fishing 
mortality. Note the following: 

- 

the R object containing the results of the a4a stock assessment for HKE 1-5-6-7 did 
not match the results shown in table 6.1.5.1 of EWG 18-12. In particular, Fbar(0-2) is 
given as 1.14 in the report, but can be estimated 1.129 

72 

 
 
         
            
                     
         
 
                     
 
            
              
 
 
 
 
 
         
 
              
                        
                
   
     
   
       
 
 
 
-  Table 6.1.5.1 reports catches of 2017 as derived from the SOP correction, while the R 

object does not seem to incorporate that.  

MODEL 
DIFF 
(MEFIST
O / EWG 
18·12) 

MODEL 
DIFF 
(MEFIST
O / EWG 
18·12) 

CATCH2019 

CATCH2017 

EWG 
18·1
2 
3171 

MEFISTO 4.0 

MEFISTO 4.0 

EWG 
18·1
2 

2890 

91% 

819 

899 

110% 

% CHANGE IN 
CATCH 

SSB TREND (2019-
2020) 

EWG 
18·1
2 
-74% 

MEFIST
O 4.0 

EWG 
18·12 

MEFIST
O 4.0 

-69%  226% 

149% 

0% 

3171 

2890 

91% 

2661 

72% 

3171 

86% 

3171 

289
0 
289
0 

91% 

1083 

91% 

574 

299
3 
120
7 
639 

112% 

-16% 

4%  28.32% 

102% 

111% 

-66% 

-58%  195.19

105% 

% 

111% 

-82% 

-78%  254.45

148% 

% 

HKE 1·5·6·7 
RATIONAL
E 

Ffacto
r 

Fba
r 

Fre
d 

80% 

F01 

SQ 

0.2 

1 

FUPP 

0.281 

FLOW 

0.137 

0.2
3 
1.1
4 
0.3
2 
0.1
6 

The MEFISTO estimate of catches for 2017 was 9% lower than the value reported in EWG 18-12, 
but the estimate for 2019 was 10 to 12% higher. The trend in SSB between 2020 and 2019 was 
similar and in the same direction (increasing) but the magnitudes of change were quite different. 

8.4  Baseline Run 2020-2024 

Average  fishing  days  2015-2017.  The  average  fishing  days  by  fleet  segment  over  2015-2017  is 
given in the following table considered in the baseline run are the following: 

fleetname  Number of Fishing Days (2015-2017) 

DTS0612_1 
DTS1218_1 
DTS1824_1 
DTS2440_1 
DFN0612_1 
DFN1218_1 
DTS1218_5 
DTS1824_5 
DTS2440_5 
DTS0612_6 
DTS1218_6 
DTS1824_6 
DTS2440_6 
DFN0612_6 
DFN1218_6 
HOK0612_6 
HOK1218_6 
DTS1824_7FR 
DTS2440_7FR 

150 

190 

200 

200 

150 

170 

190 

200 

200 

150 

190 

200 

200 

150 

190 

150 

190 

200 

200 

73 

 
 
 
 
 
 
 
 
 
DFN0006_7FR 
DFN0612_7FR 
DFN1218_7FR 
HOK0006_7FR 
HOK0612_7FR 
DTS1218_7ES 
DTS1824_7ES 
DTS2440_7ES 
HOK0612_7ES 
HOK1218_7ES 
HOK1824_7ES 

100 

150 

170 

100 

150 

190 

200 

200 

150 

170 

170 

10  %  reduction  in  2020.  A  linear  10%  reduction  in  the  number  of  days  per  fleet  segment  was 
applied, resulting in the following effort values: 

fleetname  Number of Fishing Days (2020) 
135 
171 
180 
180 
135 
153 
171 
180 
180 
135 
171 
180 
180 
135 
171 
135 
171 
180 
180 
90 
135 
153 
90 
135 
171 
180 
180 
135 
153 
153 

DTS0612_1 
DTS1218_1 
DTS1824_1 
DTS2440_1 
DFN0612_1 
DFN1218_1 
DTS1218_5 
DTS1824_5 
DTS2440_5 
DTS0612_6 
DTS1218_6 
DTS1824_6 
DTS2440_6 
DFN0612_6 
DFN1218_6 
HOK0612_6 
HOK1218_6 
DTS1824_7FR 
DTS2440_7FR 
DFN0006_7FR 
DFN0612_7FR 
DFN1218_7FR 
HOK0006_7FR 
HOK0612_7FR 
DTS1218_7ES 
DTS1824_7ES 
DTS2440_7ES 
HOK0612_7ES 
HOK1218_7ES 
HOK1824_7ES 

7.5%  reduction  per  year  between  2021  and  2024.  The  following  linear  reduction  of  7.5%  each 
year between 2021 and 2024 was applied: 

74 

 
 
 
 
fleetname   

2021 

2022 

2023 

2024 

DTS0612_1 
DTS1218_1 
DTS1824_1 
DTS2440_1 
DFN0612_1 
DFN1218_1 
DTS1218_5 
DTS1824_5 
DTS2440_5 
DTS0612_6 
DTS1218_6 
DTS1824_6 
DTS2440_6 
DFN0612_6 
DFN1218_6 
HOK0612_6 
HOK1218_6 
DTS1824_7FR 
DTS2440_7FR 
DFN0006_7FR 
DFN0612_7FR 
DFN1218_7FR 
HOK0006_7FR 
HOK0612_7FR 
DTS1218_7ES 
DTS1824_7ES 
DTS2440_7ES 
HOK0612_7ES 
HOK1218_7ES 
HOK1824_7ES 

125 
158 
167 
167 
125 
142 
158 
167 
167 
125 
158 
167 
167 
125 
158 
125 
158 
167 
167 
83 
125 
142 
83 
125 
158 
167 
167 
125 
142 
142 

116 
146 
154 
154 
116 
131 
146 
154 
154 
116 
146 
154 
154 
116 
146 
116 
146 
154 
154 
77 
116 
131 
77 
116 
146 
154 
154 
116 
131 
131 

107 
135 
142 
142 
107 
121 
135 
142 
142 
107 
135 
142 
142 
107 
135 
107 
135 
142 
142 
71 
107 
121 
71 
107 
135 
142 
142 
107 
121 
121 

99 
125 
132 
132 
99 
112 
125 
132 
132 
99 
125 
132 
132 
99 
125 
99 
125 
132 
132 
66 
99 
112 
66 
99 
125 
132 
132 
99 
112 
112 

8.5  Alternative Runs 

8.5.1  Modellers’ list of possible runs that can be implemented with the model and considered of 

direct relevance for the purpose of the West Med MAP 

The MEFISTO model application was parameterized for 8 stocks and 30 fleet segments. However, 
the  simulation  of  this  large  problem  proved  impractical  during  EWG  19-01  and  only  the  subset 
with HKE as main species and all fleet segments was run and is presented in the next section. 

In  addition  to  the  effort  reduction  specified  in  the  MAP,  the  policy  of  implementing  F01  in  2020 
was  also  simulated.  In  the  two  scenarios,  a  model  with  constant  catchability  and  a  model  with 
catchability increasing at 1% per annum were also run. 

75 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8.5.2  Runs performed and analysed during EWG 19-01 

Presentation and results 

The figures below represent the trajectory of two indicators, catch and spawning stock biomass of 
HKE  in  GSAs  1·5·6·7,  comparing  the  results  of  the  application  of  an  F01  regime  in  2020,  the 
progressive  reduction  of  effort  established  in  the  MAP,  against  the  status  quo.  The  scenarios 
considering a 1% in catchability did not result in important differences. 

76 

 
 
 
 
 
8.6  Summary on the model: achievements, gaps still to be addressed, work plan from 

now to STECF 19-14 (October 2019) 

Achievements: 

A full bioeconomic model application of MEFISTO was parameterized from stock assessment data 
and economic data facilitated by the JRC. Although the biological and economic data used are far 
from  perfect,  the  exercise  shows  that  MEFISTO  can  be  used  to  answer  the  bioeconomic 
consequences  of  the  change  in  effort  regime  proposed  in  the  MAP,  at  least  regarding  the  non-
spatial management measures foreseen in the plan. However, due to the short data series in the 
economic  data  set  and  the  fact  that  the  assessed  stocks  typically  represent  20%  or  less  of  the 
production  of  the  demersal  fishing  gear  in  the area  the  precision  of  the  results is  relatively low. 
The following gaps can be identified: 

Shortcomings  of  MEFISTO  as  a  model  application  to  assess  the  proposed  effort  regime  and  to 
provide advice on mixed fisheries in general 

*  The  definition  of  economic  variables  in  MEFISTO  does  not  match  perfectly  with  the 
definitions in the DCF; 

*  Complex  problems  such  as  the  one  attempted  (8  stocks  and  30  fleet  segments)  are 
difficult and time-consuming to parameterize with DCF official data; 

*  Complex  problems  such  as  the  one  attempted  (8  stocks  and  30  fleet  segments)  will 
take a few hours to run in stochastic mode. 

Limitations  in  the  application  of  the  integrated  bioeconomic  model  MEFISTO  to  official  European 
Commission data 

* Not all concerned stocks had analytical assessment results; 

77 

 
 
 
 
* Inconsistencies between assessment results in the published report EWF 18·13 and the 
"RData objects" provided by the JRC; 

*  Economic  data  incomplete  or  inconsistent  for  certain  fleet  segments,  particularly  in 
VL0006 and VL0612; 

* MEFISTO requires a model to relate the total production of each fleet segment with the 
target species. This model cannot be computed with accuracy from the short data series 
available in the DCF. 

For the next EWG 19-14, the utilization of FLBEIA or BEMTOOL, or the extension of IAM, in EMU1 
will be explored as an alternative to MEFISTO. 

78 

 
 
 
9  SMART IN EMU2 

9.1  Overview of the model’s generic characteristics and use 

9.1.1  Main features 

See previous descriptions in STECF 18-13, in Table 4 and in references below 

The  rationale  of  the  SMART  model,  as  well  as  the  workflow  of  the  smartR  package,  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; 

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.  

9.1.2  References 

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 
e86222. 
doi:10.1371/journal.pone.0086222. 

Trawlers 

Sicily. 

Strait 

9(1): 

PLoS 

ONE 

the 

of 

in 

Russo  T,  Morello  EB,  Parisi  A,  Scarcella  G,  Angelini  S,  Labanchi  L,  Martinelli  M,  D’Andrea  L, 
Santojanni  A,  Arneri  E,  Cataudella  S  (2018).  A  model  combining  landings  and  VMS  data  to 
estimate landings by fishing ground and harbor. Fisheries Research 199, 218-230. 

D’Andrea  L,  Parisi  A,  Fiorentino  F,  Garofalo  G,  Gristina  M,  Russo  T,  Cataudella  S.  (submitted). 
smartR: an R package for spatial modelling of fisheries and simulation of effort management. 
Journal of Statistical Software.  

9.2  Application of the SMART model to the West Med MAP 

9.2.1  State  of  completion  and  additional  development  work  specially  undertaken  before  and 

during EWG 19-01 

An almost complete application of SMART to the case study of West Med was prepared before the 
EWG.  In  particular,  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. 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. 

79 

 
 
 
 
 
commercialization); 4) yield and revenues (using the prices at market of the different species by 
size class) were collected and integrated into the model. 

During the EWG, the data about biological sampling of catches were integrated in order to split, 
at the monthly scale and for each cell of the grid, the total catch by species in its component in 
terms of age classes.  

9.2.2  Space and time scale 

For this application of SMART to the case study of West Med, the GFCM standard grid (composed 
by 30 x 30 nm cells) for the GSAs 9, 10 and 11 was selected (Figure 26). The cells covering the 
area  deeper  than  800m  depth  were  excluded  to  reduce  complexity  and  computational  time 
required for the simulations. From a temporal point of view, the set up of the model was limited 
to the years 2015-2016.  

Figure 26 – 30 x 30 nm square grid (defined by the GFCM) used for the implementation 
of SMART on the case study of the three Italian GSAs in the Tyrrhenian Sea.  

Thus, 24 months’ temporal series of LPUE (Figure 27) and AFD (proportion of age classes/length 
by species –Figure 28) were estimated for the cells of the grid, together with accessory economic 
models.  These  represent  the  basis  for  the  simulation  of  different  effort  scenarios,  including  the 
status quo. In fact, 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.  

80 

 
 
 
 
Figure  27  –  Representation  of  the  mean  LPUE  (in  red  scale)  as  grams  of  catch  per 
meter  of  LOA  and  hour  of  fishing,  for  the  24  months’  temporal  series  (years  2015-
2016). 

Figure 28 – Distribution (frequence into catch) of the Age 0 (cohort 1) for the HKE. This 
kind  of  result  represents  the  LFD  (proportion  of  age  classes/length  by  species) 
estimated for each cell and time in order to allow distributing total catch by age/length 
class 

81 

 
 
 
 
9.2.3  Stocks  

Four  species  of  the  MAP  were  considered  for  this  implementation  of  SMART.  Namely:  the  Giant 
red shrimp (Aristeomorpha foliacea - ARS), the Deep-water rose shrimp (Parapenaeus longirostris 
– DPS), the Hake (Merluccius merluccius – HKE), and the Red mullet (Mullus barbatus – MUT).  

Beyond the fishing activity of trawlers on the stocks of this species in the GSA9, 10 and 11, the 
following  trophic  interactions  were  considered:  1)  the  cannibalism  between  the  different  age 
classes of the hake and 2) the predation of the hake on deep-water rose shrimp (Figure 29). 

Figure 29 – Representation of the trophic and technical interactions modelled with the 
MICE approach integrated into SMART. 

9.2.3.1  MEDITS data 

Abundance and biomass indices by age class coming from the MEDITS trawl surveys for the years 
2015  and  2016  have  been  utilised  for  tuning  the  SMART  model.  Based  on  the  Data  Collection 
Framework  data  (DCF),  abundance  and  biomass  indices  for  GSAs  9,  10  and  11  combined  were 
calculated.  For  red  mullet,  the  indices  are  reported  for  GSAs  9  and  10  as  the  last  stock 
assessment  was  performed  separately  in  the  two  GSAs.  In  Table  18  the  number  of  hauls  was 
reported per depth stratum in each GSA. 

Table  18  Number  of  hauls  per  year  and  depth  stratum  in  GSAs  9-10-11,  period  2015-
2016. 

GSA 

9 

9 

10 

10 

11 

11 

STRATUM 

2015  2016 

2015 

2016 

2015 

2016 

10-50 

50-100 

100-200 

200-500 

500-800 

14 

19 

30 

35 

22 

14 

18 

31 

36 

21 

Total 

120 

120 

7 

8 

14 

18 

23 

70 

7 

8 

14 

18 

23 

70 

18 

19 

24 

21 

17 

99 

18 

19 

24 

21 

17 

99 

82 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Data  were  assigned  to  strata  based  upon  the  shooting  position  and  average  depth  (between 
shooting and hauling depth). Catches by haul were standardized to 60 minutes hauling duration. 
Hauls noted as valid were used only, including stations with no catches of hake, red mullet, giant 
red shrimp or pink shrimp (zero catches are included).  

The  abundance  and  biomass  indices  by  GSA  were  calculated  through  stratified  means.  This 
implies weighting of the average values of the individual standardized catches and the variation of 
each stratum by the respective stratum areas in each GSA: 

Yst = Σ (Yi*Ai) / A                  

V(Yst) = Σ (Ai² * si ² / ni) / A² 

Where: 

A=total survey area                                                   Ai=area of the i-th stratum 

si=standard deviation of the i-th stratum                  ni=number of valid hauls of the i-th stratum 

n=number of hauls in the GSA                                 Yi=mean of the i-th stratum 

Yst=stratified mean abundance                                 V(Yst)=variance of the stratified mean 

The  variation  of  the  stratified  mean  is  then  expressed  as  the  95  %  confidence  interval:  
Confidence interval = Yst ± t(student distribution) * V(Yst) / n 

GSA 

Species  Year 

Index 

Age 0 

Age 1 

Age 2 

Age 3  Age 4*  Age 5  Age 6+ 

9-10-11 

HKE 

2015  N/km2 

762.6 

476.5 

63.7 

19.6 

9-10-11 

HKE 

2016  N/km2 

789.8 

152.1 

31.9 

9-10-11 

HKE 

2015  Kg/km2 

9-10-11 

HKE 

2016  Kg/km2 

6.9 

7.9 

21.4 

6.7 

8.8 

4.3 

MUT 

2015  N/km2 

2627.5 

648.5 

58.6 

MUT 

2016  N/km2 

491.4 

531.9 

65.8 

MUT 

2015  Kg/km2 

26.4 

MUT 

2016  Kg/km2 

MUT 

2015  N/km2 

9.6 

1.5 

21.5 

18.1 

4.0 

4.8 

MUT 

2016  N/km2 

1368.4 

557.2 

136.8 

33.6 

0.8 

0.6 

0.6 

0.5 

0.5 

0.6 

0.8 

1.0 

1.6 

1.6 

0.8 

0.8 

0.5 

1.3 

0.1 

0.2 

5.4 

5.8 

1.6 

2.8 

1.2 

0.3 

0.1 

MUT 

2015  N/km2 

0.0 

MUT 

2016  Kg/km2 

11.4 

9-10-11 

ARS 

2015  N/km2 

45 

9-10-11 

ARS 

2016  N/km2 

30.9 

9-10-11 

ARS 

2015  Kg/km2 

0.54 

9-10-11 

ARS 

2016  Kg/km2 

0.40 

88.0 

249.2 

49.6 

16.1 

9.2 

5.1 

20.6 

18 

3.4 

2.2 

3.5 

5.2 

6.0 

1.7 

0.7 

0.6 

0.9 

0.80 

0.02 

0.05 

0.61 

0.27 

0.06 

1.6 

10.4 

27.3 

42.8 

0.74 

1.20 

9 

9 

9 

9 

10 

10 

10 

10 

9-10-11 

DPS 

2015  N/km2 

63.9 

711.1 

100 

9-10-11 

DPS 

2016  N/km2 

34.9 

595.9 

84.3 

2.8 

2.3 

0.2 

0.1 

9-10-11 

DPS 

2015  N/km2 

0.128 

3.556 

1.500 

0.056 

0.004 

9-10-11 

DPS 

2016  Kg/km2 

0.070 

2.980 

1.265 

0.058 

0.003 

*Age 4+ for MUT, ARS and DPS 

Table 19 the abundance and biomass indices by age 

83 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9.2.3.2  Demographic structure of the commercial data 

The data collection Framework provides for demographic structures of the commercial catches at 
level 5 of Table 2 of the Commission Implementing Decision (EU) 2016/1251. The data collection 
is  carried  out  for  a  defined  number  of  samples  for  each  GSA,  subdivided  in  observations  at  the 
landing  points  and  embarks  of  scientists  on  board  of  professional  vessels.  The  observations  on 
board allow collecting detailed information by haul on the quantities and demographic structure of 
the commercial and discarded fractions. The importance of this typology of sampling is related, in 
addition to the collection of discard, to the fact that the data are georeferenced.  

The  length  frequency  distributions  of  the  catches  in  each  haul  have  been  transformed  in  age 
classes by sex applying the growth parameters estimated in the three GSAs. 

9.2.4  Fleets 

The  fleet  included  in  the  analyses  is  composed  by  the  Italian  trawlers  with  LOA  equal  or  larger 
than  15m,  that  is  the  portion  of  the  fleet  equipped  with  VMS.  The  native  VMS  pings  were  pre-
processed  using  the  VMSbase  platform  (Russo  et  al.,  2014)  and  coupled,  at  the  level  of  single 
vessels  and  at  a  monthly  scale,  with  logbook,  landings  and  economic  data  (fuel  consumption, 
etc.) 

9.3  Comparison  of  model’s  short-term  forecast  with  the  single-stock  advice 

predictions 

Figure  30  summarizes  the  output  of  the  prediction  in  terms  of  catch  by  species.  Given  that  the 
values  from  EWG18-12  are  referred  to  all  the  gears  and  length  classes,  we  integrated  into  the 
plot  the  corresponding  DCF  values  of  catch  for  the  trawlers  in  the  LOA  range  15-40m.  The 
catches returned by SMART at the year 2017 are close to the DCF ones, in particular for HKE and 
MUT.  

Figure  30  –  Fitted  and  predicted  (SMART)  against  official  (DCF)  and  estimated 
(EWG_18-12) catch for the four MAP species considered by SMART 

9.4  Baseline Run 2020-2024 

84 

 
 
 
 
 
 
 
The  baseline  run  2020-2024  and  the  corresponding  progressive  decreases  of  the  effort  (7.5% 
reduction  per  year  between  2021  and  2024)  was  not  simulated  during  this  EWG,  given  that  it 
requires more time and a longer temporal series to return robust estimates. 

9.5  Alternative Runs 

It  could  be  interesting  to  test  the  decline  of  fishing  effort  together  with  some  spatial  closures, 
such as the main nurseries and spawning areas.  

9.5.1  Modellers’ list of possible runs that can be implemented with the model and considered of 

direct relevance for the purpose of the West Med MAP 

The  application  of  SMART  to  the  case  study  of  Italian,  French  and  Spanish  GSA  was  discussed. 
The main issue toward this hypothesis could be related to the long pre-processing of input data, 
which  could  be  characterized  by  different  structures  and  levels  of  aggregation.  However,  a 
tentative roadmap was defined as follows: 

1.  Development of the present SMART application with the integration of the temporal series 

used for training (to cover the period 2013-2017); 

2.  Possible extension to the French and Spanish GSAs; 

3.  Integration of the fleet segment 12-15 (using some modelling approach); 

4.  Integration of the other species in the MAP; 

5.  Simulation  of  10%  decrease  in  2019  and  successive  decreases  of  the  effort  (7.5% 

reduction per year between 2021 and 2024); 

6.  Simulation of effort regime combined with spatial closures. 

9.5.2  Runs performed and analysed during EWG 19-01 

It  is  important  to  notice  that  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 that to directly set a desired value of F for the target stocks. Thus, the 
SMART model was used to assess the potential effect of a 10% decrease of trawling effort, which 
is  rather  different  from  the  F  reduction  in  single-stock  advice  used  as  benchmark  for  the  EWG. 
However,  the  10%  decrease  of  effort  at  year  2019  is  likely  to  determine  a  reduction  of  catch 
between 11% (ARS) and 17% (HKE). This result could be commented with the observation that 
high  value  species  (ARS)  are  still  targeted  whereas  some  accessory  ones  (HKE)  are  more 
preferentially  avoided.  However,  the  estimated  catch  is  far  from  the  EWG18-12  targets 
corresponding  to  a4a  MSY.  The  corresponding  impact  in  term  of  F  is  represented  in  Figure  31, 
together with the reference from a4a. It seems that the 10% decrease of effort could determine 
lower levels of F for the four species, although it is not sufficient to reach the FMSY. 

85 

 
 
 
 
 
Figure 31 – Comparison between the EWG_18-12 estimates (from a4a) and the values 
of F predicted by SMART. Year 2019 corresponds to the 10% decrease of fishing effort.  

The predicted pattern of fishing effort was obtained as consequence of the 10% reduction of the 
total  yearly  effort  of  each  vessel  (Figure  32).    The  model  predicted  some  positive  (increment  of 
the  effort)  and  negative  (decrease  of  the  effort)  displacements.  In  particular:  some  fishing 
grounds  far  from  the  coast  are  likely  to  be  abandoned  (especially  in  the  southern  part  of  the 
GSA10) while some other areas (northern part of the Sardinia, north of the GSA10, etc.) attract 
more effort. However, the whole pattern is similar to the observed one. 

From an economic point of view, SMART returned the aggregated data of Figure 33 .  

86 

 
 
 
 
 
 
Figure 32 – SMART. Observed fishing effort (upper left panel) for the years 2015-2016; 
predicted fishing effort (bottom left panel) after the 10% reduction of the total effort; 
difference in percentage, for each cell, between observed and predicted patterns (right 
panel).  

87 

 
 
 
 
 
Figure  33  –  Main  economic  parameters  summarizing  the  performance  of  the  trawling 
fleet before and after the 10% decrease of the effort. 

9.6  Summary on the model: achievements, gaps still to be addressed, work plan from 

now to STECF 19-14 (October 2019) 

This  preliminary  application  of  SMART  to  EMU2  is  still  in  his  infancy,  but  it  is  possible  to 
summarize that: 

  The  estimates  of  the  baseline  are  partially  coherent  with  DCF  data  end  EWG18-12,  even 
considering  that  the  modelled  segments  are  related  to  trawlers  equipped  with  VMS,  and 
thus they represent only a subset of the whole Italian fleet operating in the area; 

  The management measures in the MAP are likely to determine different consequences for 
the  four  demersal  stocks  considered.  In  particular,  the  displacement  of  the  fishing  effort 
from less productive areas to the main fishing grounds is expected to partially compensate 
the MAP measures. In this way, SMART predicted that the new F by species will be lower 
than before the establishment of the MAP but still higher of the target values.   

The next step towards STECF 19-14 will be: 

  The extension of the historical time series used to train the model; 

 

If possible, the extension of the model to other (French of Spanish) GSA; 

  The integration of additional species; 

  The  exploration  of  additional  management  scenarios  and  the  preparation  of  middle-term 

forecast of the effect on the Spawning Stock Biomass of the different stocks. 

88 

 
 
 
 
 
 
10  ISIS-FISH IN THE GULF OF LIONS (GSA 7, EMU1) 

10.1  Overview of the model’s generic characteristics and use 

10.1.1 Main features 

See Table 4 above, previous descriptions in Nielsen et al., 2018 and references below 

10.1.2 References 

Mahevas, 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(1–2), 65–84. 

Pelletier, D., Mahevas, S., Drouineau, H., Vermard, Y., Thebaud, O., Guyader,O., & Poussind, 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. 

10.2  Application of the model to the West Med MAP 

10.2.1 State  of  completion  and  additional  development  work  specially  undertaken  before  and 

during EWG 19-01 

ISIS-FISH  model  is  being  used  to  model  the  trawlers  fleets  targeting  hake  in  the  Gulf  of  Lions 
(GSA 7). Its main objectives are to simulate and compare impacts of effort reduction and spatial 
and/or  seasonal closures on Hake stock dynamics and age  structure  of catches. At present, this 
model  is  not  yet  operational  and  needs  improved  calibration  to  properly  reproduce  observed 
biomass  dynamics.  A  framework  including  calibration  and  uncertainty  analysis  has  been 
developed  to  assess  model  skills  and  to  provide  a  robust  diagnostic  of  management  scenarios 
consequences. 

10.2.2 Space and time scale 

It  is  spatially  structured  and  uses  a  3’x3’  regular  grid  covering  the  whole  Gulf  of  Lion.  It 
reproduces  monthly  dynamics  and  allows  investigating  intra-annual  and  inter-annual  stock  and 
fleet dynamics and the response to effort reductions and seasonal closures. 

10.2.3 Stocks (which stocks, which assessment data etc) 

Hake stock dynamics is detailed based on GFCM (Hake, GSA 7) stock evaluation parameters and 
MEDITS data. Other species catch value and their static distribution may be added in the future to 
investigate short-term income change in case of spatial closure scenarios. 

10.2.4 Fleets 

French and Spanish trawlers’ fleets are described. French trawlers are further detailed depending 
on main landing harbour, vessel size (above or below 24m), gear used (OTM, OTT, OTB). Fishing 
ground  and  hake  catch  distribution  are  obtained  from  data  aggregating  daily  VMS  data  and 
landing data. 

This model was not used during the STECF EWG 19-01 meeting 

10.3  Summary on the model: achievements, gaps still to be addressed, work plan from 

now to STECF 19-14 (October 2019) 

More  efforts are  still needed to  make this model operational. At present it should be considered 
as a research object and planned work aims at improving its realism in the next few months. If 
model  calibration  is  achieved  by  October  2019,  it  will  be  possible  to  simulate  effort  reduction  in 
2019 corresponding to the F reduction in single-stock advice and investigate its effect on catches 

89 

 
 
 
 
 
 
 
and  biomass.  Model  outputs  comparison  with  IAM  short-term  forecast  may  also  be  undertaken 
using a common aggregation level. Providing that these preliminary results are satisfactory, base-
line  scenario  of  effort  reduction  over  2020-2025  period  may  be  forecasted.  These  however,  will 
still only cover the hake population in GSA 7. 

90 

 
 
 
 
11  MAIN ISSUES AND GAPS  

This  section  summarises  the  main  issues  that  have  been  identified  by  the  EWG  regarding  the 
implementation  of  an  operational  mixed-fisheries  advice  for  the  West  Med  MAP,  and  the  next 
steps needed to progress.  

11.1  Estimation of fishing effort (compatibility of the different databases)  

11.1.1 What is the problem 

The issue of the estimation of fishing effort was discussed extensively in the two previous reports 
(EWGs  18-09  and  18-13).  The  two  main  questions  were  i)  what  would  be  the  most  appropriate 
unit  to  measure  effort  and  ii)  which  would  be  the  primary  source  of  information  for  monitoring 
this  measure?  The  first  question  relates  to  the  fact  that  fishing  effort  can  be  measured  in  many 
different  ways  (e.g.  days,  hours  etc).  The  second  question  relates  to  the  fact  that  EWGs  18-09 
and  18-13  identified  differences,  sometimes  substantial,  for  the  same  unit  of  effort  extracted 
from different databases available at JRC. 

11.1.2 Where are we in addressing this issue 

STECF 18-09 and 18-13 reviewed the quality and availability of several fishing effort descriptors. 
A  good  measure  shall  be  easy  to  measure  (i.e.  how  to  monitor  the  fulfilling  of  the  measure 
compared  to  the  agreed  ceiling),  easy  to  control  (i.e.  what  happens  once  the  ceiling  has  been 
reached), and reflect the true activity of the fleet. This last one is the most important, since it is 
expected that managing the effort measure at the agreed level should have a tangible effect on 
the  stocks.  Days  at  sea  are  easiest  to  measure,  but  can  easily  be  biased  if  reduced  numbers  of 
days are compensated by longer trips. Therefore, monitoring also the number of hours at sea is 
of crucial importance, in order to track and if necessary correct against this potential substitution. 
The MAP contains already some elements against the increase of hours at sea above 15 hours per 
day.  

11.1.3 What are the next steps 

Regarding  the  first  issue,  hours  at  sea  are  now  already  recorded  in  the  FDI  database,  and  this 
should be maintained. Hours at sea are becoming increasingly to measure using VMS or AIS, and 
these data provide also very useful information for more in-depth analyses of how fishing effort is 
targeted towards the various species.  

Regarding  the  second  issue,  the  inconsistency  between  databases  needs  to  be  investigated 
further,  checking  the  procedures  involved  to  answer  the  different  data  calls  in  the  different 
member states, and aligning them with each other where necessary.  

There is also a need for dialogue between the scientific institutes and the national authorities to 
make  sure  that  the  procedures  are  also  fully  aligned  between  the  scientific  databases  and  the 
ways member states will calculate the effort baselines and the monitoring of fishing effort.  

Similar  adjustments  took  place  in  the  Atlantic  EU  waters  in  the  previous  decade  when  effort 
regimes were put in place, and it is reasonable to believe that such alignements can be overcome 
without major hindrances.  

11.2  Mixed-metiers vs. deep water metiers  

11.2.1 What is the problem 

In the Management Plan of western Mediterranean, the fisheries are distinguished by species and 
gear: the continental shelf and upper slope group is defined by catches of red mullet, deep water 
rose shrimp, hake and Norway lobster; while the deep waters group is defined by catches of blue 
and red shrimp and giant red shrimp. It should be clarified by the Management Plan if the metiers 

91 

 
 
 
 
 
 
fishing for red shrimps (MDD and the DWS) utilised in the Data Collection Framework (DCF) will 
be both classified as deep fisheries (independently by the share of deep water species caught). In 
fact,  this  could  risk  overestimating  the  fishing  effort  on  deep  water  fisheries,  as  during  trips 
classified as mixed fisheries (MDD) often hauls on shallow areas are carried out, but, according to 
the Management Plan, will fall anyway on the deep fisheries effort group (Table 20). 

Table  20.  The  table  shows  the  allocation  of  the  fishery  in  the  management  plan, 
depending on the allocation done in the data collection process. 

Continental shelf and 
upper slope fishery 

DEF 

No deep water species 

Deep waters fishery 

MDD 

Deep waters fishery 

DWS 

Deep water species 
<40% or not the first 
species in the catch 

Deep water species 
>40% or the first 
species in the catch 

11.2.2 Where are we now in addressing this issue 

The Data Collection on Fisheries (DCF) provides for the collection of demographic and landing 
data  of  the  catches  obtained  from  different  target  assemblages  (level  5)  for  the  gear  OTB 
(level 4) as reported in Appendix IV (Fishing activity by region) of the Commission Decision of 
18  December  2009  adopting  a  multiannual  Community  programme  for  the  collection, 
management  and  use  of  data  in  the  fisheries  sector  for  the  period  2011-2013  (2010/93/EU). 
Other  gears,  classified  as  trawling  and  included  in  the  Management  Plan,  are  OTT  (multi-rig 
otter trawl), PTB (bottom pair trawl), TBB (beam trawl), OTM (midwater otter trawl) and PTM 
(midwater  pair  trawl).  For  those  gears,  landings  were  registered  in  the  DCF  database  for  the 
western  Mediterranean.  However,  they  operate  in  shallower  waters  and  do  not  exploit  deep 
water  species.    The  Management  plan  reports  also  other  gears  but  currently  they  are  not 
utilised in the western Mediterranean.  

The Mediterranean Planning Group for Methodological Development (PGMed, Report of the 2nd 
Meeting  in  2008)  reported  that  the  term  “deep  water  species”  for  the  Mediterranean  area 
referred  only  to  red  shrimps,  Aristaeomorpha  foliacea  and  Aristeus  antennatus,  species  not 
included in the definition of deep sea species given by the Council Regulation (EC) 2347/2002. 

The Mediterranean Planning Group for Methodological Development (PGMed, Report of the 3rd 
Meeting in 2009) reported that for the Mediterranean the basic métier for which a threshold is 
required for allocating the target assemblages at level 5 is the bottom otter trawl (OTB), with 
three  target  assemblages  (demersal  species,  deep  water  species,  mixed  demersal  and  deep 
water species). PGMed reported that different approaches are applied by the MS for allocating 
the target assemblages of a bottom otter trawl at a trip level: either  a quantitative threshold 
(e.g. 40% contribution in the total catch in weight) or a qualitative one (presence/absence of 
species) was used. The Group suggested that, when deep water species occur in the catch, the 
catch  should  be  sorted  to  demersal  and  deep  water  species  and  be  ranked  by  value.  In  the 
case  the  deep  water  species  are  ranked  first,  the  target  assemblage  should  be  assigned  to 
deep water species (DWS); in the case the deep water species are ranked second, the target 
assemblage should be assigned to mixed demersal and deep water species (MDD). The PGMed 
was informed that a study for allocating metiers based on VMS and logbook data was granted 
by the Commission by the end of 2010. It was expected that the outcomes of the study should 
provide support to MS for setting common thresholds for allocating target assemblages but this 
did not happen as the study was finally not carried out. 

92 

 
 
 
 
 
ITALY 

The  distinction  among  the  metiers  level  5  for  OTB  is  based  on  the  presence/absence  and 
relative  importance  in  the  landing  of  the  two  species  of  red  shrimps  (Blue  and  red  shrimp, 
Aristeus antennatus and giant red shrimp, Aristaeomorpha foliacea). The first metier related to 
bottom trawling is defined DEF (demersal species) and it is attributed to the trips in which red 
shrimps  are  not  present  in  the  landing.  The  second  metier,  MDD  (mixed  demersal  and  deep 
water species) is attributed to the fishing trips in which the red shrimps (one or both species) 
are  present  in  the  landing  but  they  represent  less  of  the  40%  of  the  total  biomass  landed  in 
that trip. The third metier, DWS (deep water species) is characterised by the presence of red 
shrimps  (one  or  both  species)  in  the  landing  in  a  percentage  higher  than  40%  of  the  total 
landing. Italy has adopted this scheme of attribution of level 5 of the metier starting from the 
National Program 2009-2010.  

The sampling unit for the collection of the demographic and landing data is represented by the 
fishing trip that could be constituted by one or more fishing days. It could happen that, during 
the  fishing  trip  to  which  the  target  assemblage  MDD  or  DWS  is  attributed,  some  hauls  are 
performed  in  deep  waters  to  target  red  shrimps  and  other  hauls  in  shallower  waters  where 
other species than red shrimps are caught. 

The Italian fleet operating in the western Mediterranean does not utilise other types of bottom 
trawling than OTB with the only exception of OTM in GSA 10. 

FRANCE 

The French fleet operating in GSAs 7 (Gulf of Lyons) and 8 (waters around Corsica) does not 
fish  for  red  shrimps.  In  GSA  7,  the  wide  extension  of  the  continental  shelf  makes  the  deep 
fishing grounds very far from the fishing ports. In this area, the red shrimps are exploited only 
by some Spanish vessels coming from GSA6. In GSA 8, the trawling fleet is composed by very 
few small vessels not equipped for fishing in deep waters.  In  this area, deep water resources 
are not exploited. 

Since  2002,  PTB  has  never  been  used  by  the  French  fleet  operating  in  the  western 
Mediterranean  waters.  Landing  of  TBB  have  been  registered  in  GSA7  only  in  2013.  OTT  is 
utilized by French vessels in GSA7 starting from 2014. OTM is commonly used in the GSA. 

SPAIN 

The  attribution  of  the  target  assemblage  (Level  5)  is  done  for  each  single  trip  that  in  Spain 
corresponds to one fishing day (12 hours). The metier demersal species (DEF) is attributed to 
the  fishing  trips  of  OTB  when  red  shrimps  are  not  landed.  Mixed  demersal  and  deep  water 
species (MDD) represents the trips in which the catch of red shrimps is accompanied by other 
demersal species. As in the case of the Italian fleet, some hauls are performed in deep waters 
to  target  red  shrimps  and  other  hauls  in  shallower  waters  where  other  red  shrimps  are  not 
caught. The deep sea species target assemblage (DWS) is attributed to the trips in which red 
shrimps constitute the large predominance of the total landing. At the present there is no limit 
in effort allocation between metiers and it is extremely variable between boats but also for the 
same  boat.  There  are  differences  between  ports  related  with  the  extension  of  the  continental 
shelf. Moreover, it is not rare that boats may do fishing sets at both metiers in the same day. 

Since 2002, PTB, TBB, OTM and PTM have never been used by the Spanish fleet in the western 
Mediterranean.  

11.2.3 What are the next steps 

The  Management  Plan  provides  for  a  management  of  fishing  effort  in  relation  to  the  target 
species.  It  is  very  important  that  the  classification  of  the  fisheries  is  compatible  with  the  one 
applied in the DCF. In the western Mediterranean the fishery is typically multispecific and the 
trips are characterized by hauls performed at different depths even during the same day. It will 

93 

 
 
 
 
therefore  be  important  to  define  a  method  to  correctly  attribute  the  catches  and  the  related 
fishing effort to one of the two groups identified by the Management Plan. 

It  is  a  concern  that  at  the  moment  Member  States  do  not  use  the  same  process  to  allocate 
metier depending on the composition of the catch. There should be an agreement among the 
member  states  on  which  should  be  the  standardized  method  to  do  so,  so  that  the  data 
collected can be comparable and effort allocation will not vary depending on the data collection 
but on the actual catch compositions. 

11.3  Multiple assessments and differences in stock definitions for hake  

11.3.1 What is the problem 

The  Hake  stock  is  currently  being  assessed  at  several  spatial  scales.  Most  stock  assessment 
models  are  focusing  on  the  GSA  scale  (which  is  the  scale  at  which  GFCM  is  currently  providing 
advice), while in STECF hake is assessed at the level of the combined GSAs. The GSA scale may 
not  be  the  most  convenient  to  propose  management  measures  at  the  European  level. 
Furthermore,  biological  information  is  still  uncertain  regarding  the  degree  of  connection  of  hake 
populations  between  GSAs  (see  among  other  discussions  in  STECF  EWG  18-12,  18-13  and 
STOCKMED report) 

11.3.2 Where are we with addressing this issue 

Hake stock assessment in the western Mediterranean will undergo a benchmark in 2020, during 
which  options  for  grouping  GSAs  for  stock  assessment  purposes  will  be  explored.  We  hope  that 
the benchmark will result with proposal for grouping that makes sense from both a biological and 
a management perspective. 

11.3.3 What are the next steps 

Perform the benchmark. 

11.4  One broad vs. several detailed models in the EMU1  

11.4.1 What is the problem 

EMU1  covers  the  GSAs  bordering  two  Member  States,  Spain  and  France,  and  a  number  a  while 
EMU2 covers mainly one. Different Member States means different access to data, and different 
developments according to the national and/or regional priorities. As a result, the existing models 
developed in Italy covers GSAs 9,10 and 11, while, at contrary, the models available in Spain and 
France do not cover the entire EMU1. IAM and ISIS-FISH, developed in France, cover only GSA 7. 
MEFISTO, developed by CSIC in Barcelona, covers mainly GSA6, although some applications have 
also been developed for GSA5 in the past. No models cover the GSAs 1 and 2.  

11.4.2 Where are we with addressing this issue 

During the EWG, some trails were made both with MEFISTO and with IAM to extend the current 
models’  set  up  to  additional  GSAs.  Also,  simpler  models  like  FLBEIA  and  Flasher  were 
implemented for the first time during the EWG. FLBEIA showed that it was very easy to deal with 
several  stocks  once  supplied  in  the  appropriate  FLStock  format,  and  that  similar  ease  of  use  is 
expected  for  fleet  data  once  formatted  as  FLFleets  objects  (but  this  was  not  done  during  the 
EWG).  

94 

 
 
 
 
 
 
 
11.4.3 What are the next steps 

During  the  EWG  it  was  concluded  that  MEFISTO  was  not  appropriate  for  running  scenarios  over 
several  GSAs,  being  too  complex  to  parameterise  and  slow  to  operate.  Three  options  for  future 
work were thus considered by the experts:  

  Extending  IAM  to  the  GSAs  along  the  Spanish  coasts,  with  appropriate  stocks  and  fleets 

data 

  Further developing the FLBEIA application with appropriate fleets data 

 

Implementing a BEMTOOL application from scratch, to be consistent with EMU2.  

After the EWG, the conclusion reached by the experts was that the preferred option would be the 
first  one  (extending  IAM),  on  the  basis  that  this  is  this  model  where  most  expertise  is  in  the 
region.  It  was  understood  that  further  work  should  take  place  intersessionally,  depending  on 
funding options for supporting the work before the next EWG.  

11.5  Other species 

11.5.1 What is the problem 

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 produce 
of the order of 60 species in significant quantities, but only 5 are concerned by the Multi-Annual 
management  Plan.  These  5  species  are,  naturally,  the  main  species  in  terms  of  landings  and 
economic  importance  and  stock  assessments  are  regularly  produced.  However,  they  represent 
20%  or  less  (depending  on  the  GSA)  of  the  total  demersal  fisheries  production.  Hence  the 
population  dynamics  of  the  majority  of  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. 

11.5.2 Where are we in addressing this issue 

There  are  two  common  approaches  to  relate  the  production  of  secondary  species  to  the 
production,  and  hence  the  population  dynamics,  of  main  species:  constructing  a  mathematical 
relationship  between  main  stocks  and  secondary  species  in  terms  of  catches  or  using  fishing 
effort as a proxy for the production of secondary species. Both approaches are simplistic and can 
be  the  source  of  large  uncertainty  in  the  bioeconomic  results,  especially  when  the  models  are 
fitted with short data series.  

Compare the following relationships between a main species (ARA) and the bycatch of DTS across 
the  four  GSAs  in  EMU  1.  The  official  data  available  to  establish  these  relationships  was  just  a 
short  data  series  (2013-2016)  in  the  2018-07_STECF  18-07  -  EU  Fleet  Landings  FAO  Gear 
levels_final.xlsx. 

95 

 
 
 
 
11.5.3 What are the next steps 

The  correct  parameterization  of  models  of  target  species  with  bycatch,  or  secondary  species, 
needs  to  be  done  with  sufficiently  long  data  series.  Additionally,  it  is  not  necessary  to  limit  the 
analysis  to  a  single  pool  of  accessory  species  per  main  species.  A  detailed  species-by-species 
analysis  of  catch  data  series  would  suggest  groups  of  secondary  species  that  follow  the  same 
trend  of  the  target  species  or  groups  of  secondary  species  that  follow  a  different  trend.  And 
naturally  the  relationship  between  main  and  secondary  species  does  not  need  to  be  a  linear 
model. The following figure shows the monthly landings of the Barcelona trawl fleets in the period 
2000-2018, considering ARA as the target species and the two main species of commercial (GFB: 
Phycis  blennoides  or  greater  forkbeard,  and  WHB:  Micromesistius  poutassou  or  blue  whiting)  by 
catch as secondary species. There is an overall decreasing relationship between the target species 
and  the  secondary  species,  but  the  large  variability  suggests  that  many  other  factors  (e.g. 
seasonality, year to year variation, market price of secondary species) should be considered.  

96 

 
 
 
 
 
 
 
11.6  Other gears 

11.6.1 What is the problem 

The MAP for the western Mediterranean is aimed at the management of the towed gears fisheries. 
However, we must take into account the fact that Mediterranean fishing is not only multi-specific 
but also multi-gear, in the sense that some  species are exploited by different gears that can be 
used  in  different  periods  of  the  year,  in  different  areas  and  targeted  to  catch  different 
demographic portions of a certain stock. From the analysis of the landing data (DCF database as 
used  in  STECF  EWG  18-12)  of  the  period  2015-2017  in  the  two  effort  management  units,  it 
appears  that  the  four  target  crustaceans  species  of  the  MAP  (ARS,  ARA,  DPS  and  NEP)  are 
exclusively  exploited  by  the  towed  gears.  On  the  other  hand,  gears  used  by  the  small  scale 
fishery,  essentially  gillnet  (GNS)  and  trammel  net  (GTR)  for  demersal  species,  also  significantly 
contribute to the commercial landing of the two species of bony fish included in the MAP, HKE and 
MUT. In the management unit east western Mediterranean (EMU2), almost 40% of the total catch 
of  hake  comes  from  small-scale  fisheries.  In  particular,  there  is  a  fishery  in  the  GSA9  that  uses 
GNS to catch adults of this species. The nets used are highly size-selective and catch exclusively 
specimens above the Minimum Reference Conservation Size. In the GSA10, the landings of hake 
are  essentially  due  to  the  small  scale  fishing  gears  (70-80%)  that  land  much  more  than  the 
towed  ones.  In  this  area,  GNS,  GTR  and  bottom  longline  (LLS)  are  important.  In  the  GSA8, 
according  to  the  different  years  analysed,  from  2  to  24%  of  the  hake  landing  is  due  to  the  set 
nets,  GNS  and  GTR.  Information  on  GSA11  is  not  available  in  the  DCF  data;  however  from  the 
national database the landing of hake from small scale fishery in this area resulted around 10% 
of the total.  

As  concerns  red  mullet  in  the  east  western  Mediterranean  management  unit,  the  landings  from 
the small scale fisheries (mostly GTR) is about 5-7% of the total. Also in this case, the specimens 
caught by GTR are adults of medium-large size, given the high selectivity of the gears used. The 
fishery  targeting  this  species  with  GTR  is  particularly  developed  in  the  GSA10,  but  it  is  also 
presents in the GSA9 and GSA11. The landing of red mullet in GSA8 are not reported in the DCF 
database.  

In the west western Mediterranean effort management unit, the hake and red mullet landings by 
small-scale  fisheries  represent  about  7-10%  of  the  total  landings  of  the  two  species.  Hake  is 
captured  mainly  with  GNS  and,  to  a  lesser  extent,  with  GTR  and  LLS.  Fishing  with  GNS  is 
particularly  important  in  GSA7,  while  LLS  are  more  utilised  by  the  Spanish  fleets  operating  in 
GSA1  and  GSA6.  Red  mullet  is  caught  by  the  small  scale  fishery  exclusively  with  GTR  that 
represents an important fishery in GSA1 and GSA6. 

From  the  analysis  of  the  available  landings  and  the  relative  demographic  structures,  it  appears 
that  in  the  case  of  the  two  species  of  bony  fish,  hake  and  red  mullet,  the  importance  of  small 
scale fishery gears is not negligible, contributing to 5 to 40% of the total landing of each species. 
This  fraction  of  the  landings  is  of  high  importance  from  a  management  point  of  view  if  we 
consider  that  it  is  composed  exclusively  by  adult  individuals,  given  the  high  selectivity  of  the 
gears  used.  In  the  evaluation  on  the  exploitation  status  of  these  two  species,  it  is  therefore 
important  to  include  also  the  catches  due  to  these  gears.  Their  exclusion  can  in  fact  lead  to 
inaccurate conclusions, especially for the estimation of spawning stock biomass. The fact that the 
MAP  does  not  take  into  consideration  the  management  of  these  gears  as  a  major  gap,  as  the 
effect that an increase in the catches by those gears cannot be taken into account. 

97 

 
 
 
Table  21  –  Contribution  of  the  different  gears  to  the  landing  of  hake  in  tons  and  in 
percentage of biomass landed (data from EWG 18-12, Med&BS data call). 

98 

speciesHKEOther gearsGNSGTRLLSOTB-OTM-OTT-PTMTotalOther gearsGNSGTRLLSOTB-OTM-OTT-PTMTotal2015GSA 102451721830123941002015GSA 2111001002015GSA 51021021001002015GSA 639217215941726214921002015GSA 72153167940111801411841002015GSA 1-2-5-6-721944185280831300613901002016GSA 1223169176112961002016GSA 2111001002016GSA 567671001002016GSA 622244517191810112951002016GSA 7212424899103101200871002016GSA 1-2-5-6-721482952285430840512931002017GSA 101732882990021961002017GSA 2221001002017GSA 572721001002017GSA 624384916171728123941002017GSA 701485668884701711811002017GSA 1-2-5-6-701735058266729480622901002015GSA 80011915761002015GSA 9158358551048153821002015GSA 10236319020228710430351819271002015GSA 112202201001002015GSA 8-9-10-11252222520213622312023109591002016GSA 800000019121771002016GSA 98337661782115851002016GSA 102827520822731410523262022301002016GSA 11026526501001002016GSA 8-9-10-1128359245227124121001171211591002017GSA 80001415002981002017GSA 9085134745720152831002017GSA 10132661131291336532411720201002017GSA 113043041001002017GSA 8-9-10-111335012612992615451238860100Landings (tons)Landings (in percentage) 
 
 
 
 
Table 22. – Contribution of the different gears to the landing of red mullet in tons and 
in percentage of biomass landed (data from EWG 18-12, Med&BS data call). 

11.6.2 Where are we with addressing this issue 

The models presented during EWG 19-01 already include to some extent the non-trawlers fleets. 
Examples  of  this  are  given  for  e.g.  BEMTOOL,  NIMED,  IAM  or  MEFISTO.  The  data  needs  for 
including  these  non-trawlers  fleets  in  the  model  are  the  same  as  for  the  trawlers,  and  there  is 
thus no specific challenges in including these other fleets.  

11.6.3 What are the next steps 

The  final  models  to  be  used  in  the  future  shall  all  include  the  non-trawls  fleets,  either  in 
disaggregated  form  or  aggregated  into  a  single  “others”  fleets.  The  main  requirement  is  that  all 
catches from the single-stock assessment must be allocated to some fleets, in order to have the 
full adequacy between the fleets’ module and the stocks’ module, and to account for all sources of 
fishing mortality.  

This will also allow testing management scenarios for the non-regulated fleets, and to assess their 
role in the achievement of the MAP objectives.  

99 

speciesMUTOther gearsGNSGTRLLSOTB OTT OTM TBBTotalOther gearsGNSGTRLLSOTB OTT OTM TBBTotal2015GSA 1109126136107931002015GSA 2000001002015GSA 50112028721002015GSA 6111301138715190090911002015GSA 7010031331400001001002015GSA 1-2-5-6-7221401182719710070931002016GSA 110781812600030691002016GSA 2000001002016GSA 50101001001002016GSA 61920158116740060941002016GSA 703216388436074891002016GSA 1-2-5-6-72321860216023810180911002017GSA 1064211275023771002017GSA 50131301001002017GSA 61110113381449080921002017GSA 70426827201991002016GSA 1-2-5-6-7141741183020090090911002015GSA 9085411211183015951002015GSA 10101337362421239861002015GSA 11019719701001002015GSA 9-10-111021910168018021150931002016GSA 90117011401222016931002016GSA 100231319353019911002016GSA 11023223201001002016GSA 9-10-110131020169218060160941002017GSA 90123814101461013971002017GSA 1001292092400112871002017GSA 110919101001002017GSA 9-10-1101368017101791014095100Landings (tons)Landings (in percentage) 
 
 
 
 
11.7  Various scales of models  

11.7.1 What is the problem 

For  EMU2,  3  models  (BEMTOOL,  NIMED  ad  SMART)  were  presented  as  parameterized  and 
operational  to  test  different  scenarios  aimed  at  developing  the  fishing  effort  regime.  The  three 
models are characterized by different level of complexity and different time and spatial scales. 

11.7.2 Where are we with addressing this issue 

NIMED has been used in chapter 5 in association with the short term.  

BEMTOOL has been used in chapter 6 to predict the impact of MAP measures on a fine time scale 
(temporal  closure)  and  taking  into  account  implicitly  spatial  considerations  (simulating  spatial 
closure  of  areas  within  100  m).  The  reductions  in  effort  was  differentiated  by  fleet  segment, 
according to the impact on the stocks and also assuming hyperstability. Due to time constraints, 
the simulations did not include the uncertainty, but this will be included in the next steps. 

A  preliminary  application  of  SMART  presented  in  chapter  9  was  carried  out,  parameterizing  only 
the training phase. 

11.7.3 What are the next steps 

The next steps to address the problem in EMU2 are: 

-  Update  BEMTOOL  application  to  EMU2,  using  the  outcomes  of  the  next  EWG  on  stock 
assessment in the area and exploring the scenarios that will be considered useful for the 
aims of the MAP, including uncertainty; 

-  SMART model, can be coupled to BEMTOOL to derive, in the short term, the actual fishing 
mortality  most  likely  to  occur  from  effort  reduction  considering  that  fishing  vessels  may 
change their fishing pattern and spatial effort allocation. 

11.8  Requirements  for  the  stock  assessment  (consistency  between  stock  object  and 

assessment report) 

11.8.1 What is the problem 

The  working  group  analysed  the  output  of  the  STECF  18-12  working  group  to  be  used  for  the 
purposes of the EWG 19-01. The assessment procedures were analysed to check the consistency 
between the information reported in the text and the data generated as output of the assessment 
provided as R stock object. 

Outputs  of  the  assessment  have  been  used  to  set  the  target  for  both  F  and  catches  and  to 
parametrise the models in order to evaluate the effort regime in the western Mediterranean Sea 
as requested in the ToRs. 

The  short  term  forecast  (STF)  estimates  catch  in  year  Y+1  based  on  the  assessment  with  final 
year Y-1 to predict 2 years forward and define the catch options to target the F0.1 as a proxy of 
Fmsy. The EWG 19-01 checked the STF output from the assessments against the output from the 
mixed fisheries models used in the WG. 

Moreover  in  the  report  STECF-18-12,  both  the  stock  specific  summary  sheets  (section  5)  and 
assessment results (section 6) by stock were checked. Inconsistencies were found in the report, 
since the text did not always correspond to the outputs. In addition, the information in the report 
was not always the same as in the stock objects. 

Inconsistence were for both EMU1 and EMU2, and listed below and reported on the table below. 

West Western Mediterranean Sea - EMU1 (GSAs 1,2,5,6,7) 

100 

 
 
 
 
 
 
Stock numbers at age and F at age are required as input for MEFISTO, that was applied to GSAs 
1-5-6-7 to HKE. Furthermore, in some cases these values are not presented in the report. 

HKE1-5-6-7 

Small differences in F at  age, that result in different Fbar (0-2) values and small difference in F0.1 
(see table below) 

Small differences in the recruitment (geometric mean of the last 6 years):  115880 in the report 
and 116039 in the stock object. 

Small differences in numbers at age 

MUT1 

F at age in the report higher than in the stock object. 

Stock numbers at age not presented in the report. 

R much higher in the stock object than in the report. 

ARA1 

F at age and catch at age not presented in the report. 

ARA6 

F at age and catch at age not presented in the report. 

East Western Mediterranean Sea - EMU2 (GSAs 8,9,10,11) 

EWG19-01  detected  some  contradictory  information  in  the  stock  assessment  of  Hake  and  Giant 
red shrimp in the eastern management unit (GSA 9_10_11). 

For hake, the F target had been calculated as an average of all age classes while in the report is 
clearly  stated  that  Fbar  0-2  was  used  (Table  below).  Moreover  by  reproducing  the  STF  with  the 
provided stock object a slight discrepancy on terminal F in the assessment (2017) and catch were 
found. 

When  the  Fbar  was  set  to  0-2  as  indicate  in  the  report,  the  output  of  STF  replication  showed  a 
slight different value for the target F and a different % of change between target F in 2019 and 
the estimated F for 2017. 

For Giant red shrimp (ARS), the replication of the STF using the provided stock object (Fbar 0-4) 
gives the same numbers listed in the report. When the Fbar range was set to the values indicated 
in  the  report  (Fbar  1-3)  to  replicate  the  STF  a  10%  change  in  the  estimation  of  terminal  and 
target F was found. 

101 

 
 
 
 
 
 
 
 
Table  23.  Comparison  of  stock  objects  and  STF  assumptions  and  results  listed  in  the 
report. 
F status quo is the Fbar estimated as reported in the report; F 2017 is terminal F in the 
assessment. Change in F is the difference (as a fraction) between target F in 2019 and 
the estimated F for 2017. Change in catch is from catch 2017 to catch 2019. (in red are 
highlighted discrepancies on Fbar settings). 

stock 

source 

Fbar 
range 

F  
status quo 

F 2017 

F 2019 

Change 
in F 

catch 
2017 

catch 
2019 

Change in 
catch 

Fbar/Fmsy 

hke 9-10-11  EWG18-02 report  

0-2 

hke 9-10-11  Check stock object 

0-6 

hke 9-10-11  EWG19-01 new STF 

0-2 

ars 9-10-11 

EWG18-02 report  

1-3 

ars 9-10-11 

Check stock object 

0-4 

ars 9-10-11 

EWG19-01 new STF 

1-3 

hke 1-5-6-7 

EWG18-02 report  

0-2 

hke 1-5-6-7  Check stock object 

0-2 

hke 1-5-6-7 

EWG19-01 new STF 

0-2 

ara 1 

ara 1 

ara 1 

ara 6 

ara 6 

ara 6 

mut 1 

mut 1 

mut 1 

EWG18-02 report  

0-2 

Check stock object 

0-2 

EWG19-01 new STF 

0-2 

EWG18-02 report  

0-2 

Check stock object 

0-2 

EWG19-01 new STF 

0-2 

EWG18-02 report  

0-2 

Check stock object 

0-2 

EWG19-01 new STF 

0-2 

0.6 

0.6 

0.71 

1.12 

1.12 

1.37 

1.14 

1.13 

1.13 

0.73 

0.73 

0.73 

0.96 

1.04 

1.04 

1.47 

1.19 

1.19 

0.55 

0.54 

0.62 

1.12 

1.12 

1.37 

1.14 

1.13 

1.13 

0.73 

0.57 

0.57 

0.96 

0.96 

0.96 

1.47 

1.19 

1.19 

0.14 

0.14 

0.17 

0.57 

0.57 

0.69 

0.23 

0.22 

0.22 

0.42 

0.42 

0.42 

0.32 

0.32 

0.32 

0.26 

0.26 

0.26 

-75  1782 

494 

-74  1778 

506 

-73  1778 

506 

-50 

399 

171 

-50 

399 

172 

-50 

399 

172 

-80  3172 

819 

-80  3166 

466 

-80  3166 

466 

-42 

99 

-27 

115 

-27 

115 

97 

88 

88 

-67 

527 

223 

-67 

526 

216 

-67 

526 

216 

-82 

231 

35 

-78 

278 

122 

-78 

278 

122 

-72 

-72 

-72 

-57 

-57 

-57 

-74 

-85 

-85 

-2 

-23 

-23 

-58 

-59 

-59 

-85 

-56 

-56 

4.29 

4.22 

4.22 

1.96 

1.98 

1.98 

4.96 

5.06 

5.06 

1.74 

1.73 

1.73 

3.00 

3.3 

3.3 

5.65 

4.55 

4.55 

11.8.2 Where are we now with addressing this issue 

Some  models  need  some  specific  raw  data  to  improve  the  analysis,  and  others  need  different 
format of data output to facilitate an easier parametrization of models. EWG 19-01 suggest some 
improvement to be taken in to account in the next coming stock assessment EWG. 

1)  Data and parameters used for the STF should be made available for reproducibility of the 

forecast 

2)  Assessors should upload the STF object and the code done to implement it to allow using it 

in the following working group 

3)  Input  catch  data  used  for  the  assessment  should  be  reported  as  landings  and  discard 

separated matrices and disaggregated by gear (NIMED) 

4)  Abundance index of the tuning fleet should be available by length age class (SMART) 
5)  F should be also provided by metiers as an output of the stock assessment 
6)  MEFISTO is an age structured model, therefore only results from analytical assessments 

can be used as input to the biological sub-model 

7)  Ensure that the results in the report are presented at least as tables, not only as figures. 

102 

 
 
 
 
 
 
11.8.3 What are the next steps 

Update  the  models  with  the  new  outputs  of  stock  assessment  and  test  the  usefulness  for  the 
purposes of West Med MAP 

11.9  What happens in 2025? 

All simulations presented below shows that while effort reductions are expected to have a positive 
effect on the status of the West Med stocks, they will likely not be sufficient for reaching Fmsy in 
2025.  Additionally,  the  simulations  presented  here  are  deterministic,  and  do  not  include  major 
sources of uncertainty –and, first of all, the uncertainty regarding the actual relationship between 
fishing  effort  and  fishing  mortality  that  has  been  discussed  at  length  in  STECF  EWG  18-09  and 
18-13.  

As  such,  it  is  important  to  keep  in  mind  that  when  reaching  the  end  of  the  transition  period  in 
2024,  it  might be  necessary  to  call  for  larger  adjustments  of  fishing effort  in  order  to  reach  the 
objectives stated in the plan.  

11.10  Balancing different objectives  

The  main  challenge  in  fisheries  management  is  that  the  different  objectives  point  often  out  to 
different  direction,  with  the  long-term  conservation  objectives  having  short-term  economic  and 
social consequences.  

In  BEMTOOL,  a  conceptual  framework  was  developed  to  support  a  multi-criteria  evaluation  of 
alternative management scenarios. By combining different multi-criteria techniques, the approach 
allows comparing alternative management scenarios on the basis of their ability to achieve a set 
of biological and socio economic goals. The analysis involved: 

(1)  the  identification  of  appropriate  biological  and  socio-economic  indicators  and  their 
organization into a proper hierarchy; 

(2)  the  definition  of  a  set  of  mathematical  functions  to  evaluate  the  satisfaction  (utility) 
associated with each level of the different indicators (Figure 34); and  

(3) the determination of a set of weights that represent the relative importance of each indicator 
to the overall utility. 

The  multi-criteria  decision  analysis  (MCDA)  in  BEMTOOL  is  combining  two  multi-criteria 
techniques: multi-attribute utility theory (MAUT) and the analytic hierarchy process (AHP). MAUT 
relies on the idea that decision-makers attempt to maximize their utility with respect to a number 
of  independent  attributes,  each  one  representing  a  management  objective.  The  weighting  set in 
MAUT were set by using the AHP, a method that facilitates the elicitation of individual preferences 
toward the different attributes and their conversion into a set of weights (Table 24). 

103 

 
 
 
 
 
Figure 34 - Utility functions for the eight selected indicators 

The  indicators  used  are:  MGVA:  maximum  GVA;  CW:  current  wage;  CE:  current  employment; 
FMSY:  fishing  mortality  at  maximum  sustainable  yield;  SSB0:  spawning  stock  biomass  in 
unexploited conditions (F=0); MSY: maximum sustainable yield; MD: maximum discard rate. 

Table 24 -Final weights associated with the indicators obtained through an AHP within 
a panel of experts. 

The output of the MCDA in BEMTOOL is a combination of three graphs (Figure 35) that synthetize 
the  utility  of  each  scenario,  taking  into  account  the  set  of  indicators  estimated  by  the  model.  A 
different  weighing  set,  reflecting  a  different  perception  about  the  relative  importance  of  the 
different  indicators  by  a  stakeholder  panel  could  be  implemented  following,  for  example,  a 
consultation and a survey with stakeholders. 

104 

 
 
 
 
 
 
Figure  35  –  Examples  of  the  MCDA  results  obtained  in  BEMTOOL  for  the  scenarios 
performed in EWG 19-01. 

105 

 
 
 
 
 
12  SUMMARY IN RELATION TO THE TORS 

ToR 1: Selection of models 

ToR1 reviewed 8 different models, of different complexity levels and covering different GSAs and 
EMUs. For EMU2, the 3 models (BEMTOOL, NIMED and SMARTFISH) are fully parameterised and 
operational,  and  by  operating  at  different  levels  of  complexity  and  scale,  they  appear 
complementary,  allowing  them  to  test  different  scenarios.  For  example,  the  expected  short-,  
medium- and long term impacts of various options for effort reduction can be tested in BEMTOOL 
assuming  a  constant  catchability  and  different  types  of  relationships  between  fishing  effort  and 
fishing  mortality  (e.g.  hyperstability),  while  SMART  can  mechanistically  estimate  what  is  the 
actual  fishing  mortality  most  likely  to  occur  from  effort  reduction  in  the  short-term,  considering 
that fishing vessels may change their fishing pattern and spatial effort allocation.  

The  situation  is  more  complex  in  EMU1,  where  five  models  were  available  but  none  of  them  is 
directly operational at the scale of the EMU.  

No  global  model  with  a  simple  setup  (fleet-  and  yearly  based),  was  available  prior  to  the  EWG. 
Trials  were  started  during  the  EWG  to  implement  FLR  models  (FLBEIA,  Flasher)  from  scratch. 
FLBEIA  was  easy  to  setup  in  terms  of  stocks,  but  the  fleet  data  were  not  made  available  in  an 
appropriate  format  and  compatible  with  the  catch  (e.g.  the  sum  of  catches  by  fleet  shall  be  the 
same as the total catches used in the stock assessment), so more work wold be required to get 
the  fleet  data  in  an  appropriate  and  accurate  dataset.  FLasher  could  not  be  parameterised 
correctly during the EWG itself, and more work is needed to fix the coding issues encountered.  

Two  models  with  a  more  complex  set  up  were  presented,  covering  only  a  limited  part  of  the 
EMU1: IAM in GSA 7 and MEFISTO in GSA 6. Trials to update and expand both models (in ToR2) 
demonstrated  that  IAM  was  rather  flexible  and  operational,  while  MEFISTO  was  deemed  more 
challenging to be extended to the entire EMU1. This model will not be investigated further in the 
context of the MAP. Finally, the fifth model, ISIS-FISH, was not ready to be used yet.  

For progressing further on EMU1, more work is thus necessary to extent IAM model to the whole 
EMU1 and/or to complete the set-up of the FLBEIA model.  

ToR 2: Stocks and forecasts 

This TOR has been satisfactorily achieved by the EWG. All models presented have been updated 
to incorporate the latest stock assessment data coming from STECF EWG 18-12. A few models in 
EMU 1 were initially parameterised with the most recent GFCM assessments, which may involve a 
different stock definition, in particular for hake (single-GSA assessment vs. combined GSAs 1-2-
5-6-7  assessment).  These  models  were  adapted  accordingly  and  comparative  runs  with  the  two 
assessments were run.  

For  a  number  of  models  (NIMED,  BEMTOOL,  IAM,  MEFISTO),  short-term  forecasts  were  run, 
assuming  a  reduction  in  fishing  effort  in  2019  equivalent  to  the  reduction  necessary  to  achieve 
the target F (Fmsy, proxy F01). For all models, the aggregated results of the short term forecast 
were largely similar between the single-stock and the mixed-fisheries models, though with larger 
discrepancies observed in MEFISTO.   

ToR 3: Scenarios and displays 

All  models  except  ISIS-FISH  and  SMART  were  able  to  run  the  basic  MAP  scenario  of  a  gradual 
reduction  of  fishing  effort  between  2020  and  2024.  Several  alternative  runs  and  model 
capabilities  were  discussed  and  reported  in  the  various  models’  section,  with  some  examples 
given  mainly  for  BEMTOOL.  Actual  scenarios  and  outcomes  would  be  best  agreed  in  discussions 
with MARE and Member States / stakeholders before the next meeting.  

In  addition,  the  EWG  discussed  a  number  of  options  for  the  easy  display  of  complex  results 
involving many stocks and many fleets. Two aspects are worth putting forward: 

106 

 
 
 
 
  FLBEIA  has  developed  a  generic  R  shiny  application  (FLBEIAShiny  package, 
the  standard  display  of  mixed-fisheries 
https://github.com/flr/FLBEIAshiny) 
bioeconomic projections. These do not require  the models to be run with FLBEIA or even 
with  FLR,  as  the  R  shiny  involves  only  standard  R  data  frames,  with  pre-defined  column 
headers related to e.g. biomass and fishing mortality by stock and year or catches by fleet 
and year. This quick and easy display can thus advantageously be used with any model’s 
outcomes 

for 

  BEMTOOL  has  a  functionality  for  comparing  the  outcomes  of  scenarios  across  multiple 
objectives, allowing trade-offs to be highlighted in a simple and elegant way (see section 
11.10).  This  option  can  be  a  very  useful  manner  to  convey  complex  information,  and  its 
use should also be discussed with managers and stakeholders 

13  CONCLUSIONS AND NEXT STEPS 

In  conclusion,  the  EWG  achieved  its  ToRs  and  progressed  towards  providing  operational  models 
for running bioeconomic mixed-fisheries scenarios in support of the MAP.  

The EWG suggests that a scoping meeting with DG Mare and Member States / managers could be 
held before the  next EWG planned at the end of September, to agree on the desired next steps 
and scenarios.  

The  main  problem  identified  in  the  short  term  is  the  need  to  progress  on  the  setup  and 
parameterisation  of  one  or  more  models  covering  all  of  EMU1,  bring  together  data  on  both 
Spanish and French fleets. It remains to be decided with DG Mare and the experts involved how 
this progress can take place before the next EWG.  

107 

 
 
 
 
 
 
14  CONTACT DETAILS OF EWG-19-01 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 

Address1 

Email 

MARTIN Paloma 

CSIC, Barcelona, Spain 

paloma@icm.csic.es 

ULRICH Clara (Chair) 

DTU Aqua, Copenhagen, Denmark 

clu@aqua.dtu.dk 

Invited experts 

Name 

Address 

Email 

ACCADIA Paolo 

NISEA, Salerno, Italy 

accadia@nisea.eu 

BITETTO Isabella 

COISPA, Torre al Mare (BA), Italy 

bitetto@coispa.it 

CERTAIN Gregoire 

IFREMER, Sete, France 

gregoire.certain@ifremer.fr 

GARCIA Dorleta 

AZTI, Spain  

dgarcia@azti.es 

MAYNOU Francesc 

CSIC, Barcelona, Spain 

maynouf@icm.csic.es 

MERZERAUD Mathieu 

IFREMER, Sete, France 

mathieu.merzereaud@ifremer.fr 

MURENU Matteo 

University of Cagliari, Cagliari, Italy 

mmurenu@unica.it 

RUSSO Tommaso 

University of Rome of Tor Vergata, 
Roma, Italy  

Tommaso.Russo@Uniroma2.it 

SANCHEZ  LISAZO  Jose 
Luis 

University of Alicante, Alicante, Spain 

SBRANA Mario 

CIBM, Livorno, Italy 

jl.sanchez@ua.es 

msbrana@cibm.it 

SPEDICATO Maria Teresa  COISPA, Torre al Mare (BA) , Italy 

spedicato@coispa.it 

VAZ Sandrine 

IFREMER, Sete, France 

sandrine.vaz@ifremer.fr 

108 

 
 
 
 
 
JRC experts 

Name 

Address 

Email 

KONRAD Christoph 

JRC, Ispra (VA), Italy 

Christoph.Konrad@ec.europa.eu 

PINTO Cecilia 

JRC, Ispra (VA), Italy 

Cecilia.Pinto@ec.europa.eu 

European Commission 

Name 

Address 

Email 

PEREZ-PERERA Amanda 

DGMARE, Brussels, Belgium 

Amanda.PEREZ-PERERA@ec.europa.eu 

JRC, Ispra (VA), Italy 

Cecilia.Pinto@ec.europa.eu 

PINTO Cecilia 

(JRC secretariat) 

Observers 

Name 

Address 

Email 

CARBAJO LLORENTE Elisa  Spanish  Ministry  of  Agriculture, 

Madrid, Spain 

ecarbajo@mapama.es 

GRAU  Antonio MaríA 

Govern  de  les  Illes  Balears,  Balearic 
Islands, Spain 

agrau@dgpesca.caib.es 

MARTÍNEZ 

POSSE

LucíA 

Directorate-general  of  Fisheries  and 
Maritime  Affairs  of  Generalitat  de 
Catalunya, Barcelona, Spain 

lucia.martinezp@gencat.cat 

SAINZ-TRÁPAGA Susana 

Directorate-general  of  Fisheries  and 
Maritime  Affairs  of  Generalitat  de 
Catalunya, Barcelona, Spain 

ssainztrapaga@gencat.cat 

109 

 
 
 
 
 
 
 
 
 
 
15  LIST OF BACKGROUND DOCUMENTS  

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

List of background documents: 

EWG-18-09 – Fishing Effort regime of demersal fisheries of the western Mediterranean – Part I 

EWG-18-13 – Fishing Effort regime of demersal fisheries of the western Mediterranean – Part II 

110 

 
 
 
 
 
 
 
 
 
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 

Europe Direct is a service that answers your questions about the European Union. You can contact this 
service: 

- by freephone: 00 800 6 7 8 9 10 11 (certain operators may charge for these calls), 

- at the following standard number: +32 22999696, or 

- by electronic mail via: https://europa.eu/european-union/contact_en 

FINDING INFORMATION ABOUT THE EU 

Online 

Information about the European Union in all the official languages of the EU is available on the Europa 
website at: https://europa.eu/european-union/index_en 

EU publications 
You can download or order free and priced EU publications from EU Bookshop at: 
https://publications.europa.eu/en/publications. 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). 

111 

 
 
 
 
 
 
 
K
J
-
A
X
-
1
9
-
0
0
4
-
E
N
-
N

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. 

112 

doi:10.2760/249536 

ISBN 978-92-76-08330-6