Fishing effort regime for demersal fisheries in the western Mediterranean Sea. Part II (STECF-18-13).
Date: 2018-12-14
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) 
- 
Fishing effort regime for demersal 
fisheries in the Western 
Mediterranean Sea – Part II  
(STECF-18-13) 

Edited by Clara Ulrich and Ernesto Jardim 

 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 

JRC114702 

EUR 28359 EN 

PDF 

ISBN 978-92-79-79396-7 

ISSN 1831-9424 

doi:10.2760/509604 

STECF 

ISSN 2467-0715 

Luxembourg: Publications Office of the European Union, 2018  

© European Union, 2018 

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 

How  to  cite:  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 

Abstract 

This report presents advice and discussions about a fishing effort regime for demersal fisheries in 

the  western  Mediterranean  Sea.  It  constitutes  a  complementary  and  updated  approach  to  that 

presented in April 2018.  

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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-18-13 report: 

ULRICH,  Clara;  ABELLA,  Alvaro;  ACCADIA,  Paolo;  ACOSTA  CAMACHO,  Daniel;  BASTARDIE, 
Francois;  CARBAJO  LLORENTE,  Elisa;  FIORENTINO,  Fabio;  MARTIN,  Paloma;  MURENU,  Matteo; 
ORDINAS,  Francesc;  PULCINELLA,  Jacopo;  RAID,  Tiit;  RUSSO,  Tommaso;  SALA,  Antonello; 
SBRANA, Mario; JARDIM, Ernesto.  

3 

3 

 
 
 
 
 
 
 
 
 
 
 
TABLE OF CONTENTS 

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

regime for demersal fisheries in the western Mediterranean Sea – Part II (STECF-18-13)
 ............................................................................................................. 7 

Request to the STECF ........................................................................................... 7 

STECF observations .............................................................................................. 7 

STECF comments ................................................................................................. 7 

STECF conclusions ................................................................................................ 8 

Contact details of STECF members ......................................................................... 9 

Expert Working Group EWG-18-13 report ...............................................................13 

1 

1.1 

1.2 

1.3 

2 

2.1 

2.2 

2.3 

Introduction............................................................................................14 

Terms of Reference for EWG-18-13 ............................................................14 

Data available and sources of information ...................................................14 

Summary of EWG 18-13 outcomes .............................................................16 

ToR 1: dataset review ..............................................................................18 

Comparison of datasets coverage ..............................................................18 

Definition of fishing effort .........................................................................21 

Comparison of fisheries data .....................................................................22 

2.3.1 

The variables considered in the review .......................................................22 

2.3.2  Comparison of the fishing statistics between the three data calls ....................26 

2.3.2.1  Spain .....................................................................................................27 

2.3.2.2  France ...................................................................................................28 

2.3.2.3  Italy ......................................................................................................30 

2.4 

2.5 

Summary conclusion ................................................................................32 

Baseline information about demersal fisheries by the Management Units of western 
Mediterranean Sea ..................................................................................32 

2.5.1  Western management unit (GSA1-5-6-7) ....................................................33 

2.5.2 

Eastern management unit .........................................................................34 

3 

3.1 

ToR 2 relationship between effort and mortality ...........................................36 

Additional linear regressions analyses .........................................................36 

3.1.1  Western GSAs (1-2-5-6-7) - Management Unit 1. .........................................36 

3.1.1.1  Trend in nominal effort .............................................................................36 

3.1.1.2  Stock assessment data .............................................................................37 

3.1.1.3  Fishing mortality – nominal effort relationship .............................................39 

3.1.2 

Eastern GSAs (9-10-11) - Management Unit 2. ............................................40 

3.1.2.1  Trend in nominal effort .............................................................................41 

3.1.2.2  Stock assessment data .............................................................................42 

3.1.2.3  Fishing mortality – nominal effort relationship .............................................42 

3.1.3  Conclusions ............................................................................................48 

3.2 

Alternative non-linear relationships ............................................................48 

4 

4 

 
 
3.2.1  Using a regression model ..........................................................................48 

3.2.2  Using a mixed effect model .......................................................................50 

3.3 

Exploration of alternative measures of fishing effort to try to improve the F-E relationship
 ............................................................................................................51 

3.3.1  Using hours fished and account for targeting ...............................................51 

3.3.1.1  Principles ...............................................................................................51 

3.3.1.2  Examples of use of specific effort information related to directed effort and a more 

precise effort quantification (effective hours fishing). ....................................52 

3.3.2 

Estimating the swept areas. ......................................................................57 

3.4 

4 

4.1 

Conclusion on ToR 2 ................................................................................62 

ToR 3 vessel performance .........................................................................63 

Data by country ......................................................................................63 

4.1.1  Data for ITALY ........................................................................................64 

4.1.2  Data for FRANCE .....................................................................................65 

4.1.3  Data for SPAIN (CATALONIA) ....................................................................67 

4.2 

GAM modelling results ..............................................................................69 

4.2.1 

ITALY ....................................................................................................69 

4.2.2 

FRANCE .................................................................................................74 

4.2.3  CATALONIA (SPAIN) ................................................................................78 

4.2.4  General conclusions .................................................................................83 

4.3 

Mixed fisheries and quantiles analyses for the French fleet ............................83 

5 

6 

6.1 

ToR 4. Partial F .......................................................................................89 

TOR 5. Road map ....................................................................................90 

A summary of the mixed-fisheries advice process in European North-East Atlantic waters 
(ICES area) ............................................................................................90 

6.1.1  What is the ICES mixed fisheries advice and which information it provides .......91 

6.1.2  Brief summary of historical development of the ICES mixed-fisheries advice since 
inception in 2006 .....................................................................................94 

6.1.3 

The current scientific process ....................................................................95 

6.1.3.1  Which data are used and how they are updated every year ...........................95 

6.1.3.2  The FCube model ....................................................................................95 

6.1.3.3  ICES working groups and intersessional work done in the research institutes ...96 

6.1.3.4  Scenarios and questions asked by managers/recipients of advice ...................96 

6.2 

Available and potential Mixed-fisheries models for the Western Mediterranean demersal 
fisheries. ................................................................................................96 

6.2.1 

Fleet-based Models presented in EWG 18-09 ...............................................97 

6.2.1.1  MEFISTO ................................................................................................97 

6.2.1.2  IAM ..................................................................................................... 101 

6.2.1.3  NIMED (Nisea Model) ............................................................................. 103 

6.2.2  Other existing fleet-based models for the West Med demersal mixed fisheries not 
presented in 18-09 ................................................................................ 105 

6.2.2.1  BEMTOOL ............................................................................................. 105 

5 

5 

 
6.2.2.2  Other MEFISTO applications .................................................................... 106 

6.2.2.3  Others ................................................................................................. 107 

6.2.3 

Individual-based and spatially explicit mixed-fisheries models ...................... 107 

6.2.3.1  SMART ................................................................................................. 107 

6.2.3.2  DISPLACE ............................................................................................. 109 

6.2.3.3  SWOT Analysis of individual-based models ................................................ 111 

6.2.4  Summary  - availability of mixed-fisheries models and identified gaps ........... 112 

6.3 

Other gaps and issues to consider in order to have an operational mixed-fisheries advice
 .......................................................................................................... 113 

6.3.1 

Improvement of fishing effort definition .................................................... 113 

6.3.2  Definition of fleets and metiers ................................................................ 113 

6.3.2.1  Previous STECF reports dealing with catch composition in mixed-fisheries ..... 113 

6.3.2.2  Spain ................................................................................................... 116 

6.3.2.3  Italy .................................................................................................... 117 

6.3.3  Species and stocks issues ....................................................................... 118 

6.3.3.1  Overview .............................................................................................. 118 

6.3.3.2  Species included in the plan .................................................................... 119 

6.3.3.3  Species not included in the plan but which have a stock assessment/forecast . 121 

6.3.3.4  Other species (not in the plan, no assessment) .......................................... 121 

6.3.3.5  How these other species are already included in the existing models ............. 121 

6.3.3.6  Role of other species in the achievement of the other objectives of the MAP ... 121 

6.3.4 

Timing of availability of new data ............................................................. 122 

6.4 

Road Map for the mixed-fisheries advice ................................................... 124 

6.4.1  Summary of the main gaps and issues to be addressed ............................... 124 

6.4.1.1  With regards to models .......................................................................... 124 

6.4.1.2  With regards to data .............................................................................. 124 

6.4.1.3  With regards to experts .......................................................................... 124 

6.4.2  Suggestion for a roadmap in 2019-2020 ................................................... 124 
6.4.2.1  Step1 (1st semester 2019): MODEL AND DATA SCOPING, AND FUNDING PLAN125 
6.4.2.2  Step2 (2nd semester 2019): DRAFT RUN ................................................... 125 

6.4.2.3  Step3 (1st semester 2020): UPDATE AND DEVELOPMENT ........................... 126 

6.4.2.4  Step4 (Months 19-24, 2nd semester 2020): FINAL OPERATIONAL SETUP ...... 126 

7 

8 

9 

References ........................................................................................... 127 

Contact details of EWG-18-13 participants ................................................ 131 

List of Annexes ..................................................................................... 133 

10 

List of Background Documents ................................................................ 133 

6 

6 

 
 
 
 
SCIENTIFIC,  TECHNICAL  AND  ECONOMIC  COMMITTEE  FOR  FISHERIES  (STECF)  - 
FISHING  EFFORT  REGIME  FOR  DEMERSAL  FISHERIES  IN  THE  WESTERN  MEDITERRANEAN SEA – PART 
II (STECF-18-13) 

Request to the STECF 

The  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 

The working group was held in Copenhagen, Denmark, from 8 to 12 October 2018.  

The EWG 18-13 was a follow-up of the EWG 18-09 held in June 2018 and was largely attended by 
the  same  experts.  STECF  observes  that  the  terms  of  reference  had  been  discussed  internally 
before  the  beginning  of  the  EWG  but  were  not  published  and  made  available  to  the  EWG 
participants other than EWG chair and JRC expert prior its start.  

As the EWG report was not finalised before the STECF plenary, the STECF commented on a draft 
version of the report and the presentation held at the plenary on the 13th of November 2018. 

STECF comments 

STECF notes that the EWG ToRs requested to expand the outcomes of EWG 18-09 concerning the 
relationship  between  effort  and  fishing  mortality  and  the  analysis  on  the  differences  in  fishing 
pattern and LPUE by fleets. These updates were based on additional data for the French fisheries 
made available during the meeting. 

STECF notes that EWG 18-13 conducted the analysis on the basis of three datasets: Med&Black 
Sea, AER, FDI. STECF also observes that an additional effort has been made to complement the 
aggregated  data  with  ad  hoc  datasets  request  by  DGMARE  to  the  relevant  Member  States.  The 
additional  datasets  are  referred  to  haul-by-haul  data  collected  through  observer  on-board 
programmes and trip-by-trip data compiled from VMS, logbooks and sales notes. These datasets 
were provided by France while Italy and Spain (Catalonian fisheries) had provided the trip-by-trip 
dataset in June and no update was required. 

An  in-depth  comparison  of  the  completeness,  coverage  and  consistency  of  the  datasets  was 
carried out by EWG. STECF notes that the data are overall considered more reliable than in EWG 
18-09,  but  a  number  of  inconsistencies  between  the  datasets  still  remain,  making  it  difficult  to 
provide  a  robust  quantitative  description  of  the  activity  of  the  fleets  in  the  Western 
Mediterranean.  

STECF  notes  that  EWG  attempted  to  fit  linear  relationships  between  fishing  effort  and  fishing 
mortality  for  a  number  of  stocks  and  fleet  segments.  The  results  obtained  were  very  similar  to 
those of EWG 18-09, showing weak relationships in most cases. However, STECF notes that since 
the  EWG  18-12  stock  assessment  data  were  not  made  available  to  the  EWG  18-13,  these  fits 
could only be applied on older assessments with F estimates up to 2014, not 2017.  

STECF  appreciates  the  EWG  exercises  aimed  at  testing  non-linear relationship  and  at  discussing 
alternative ways to measures fishing effort like hours fished or swept area. However, STECF notes 
that  even the  fits  of  non-linear relationship  remain  poor  and  that  it  was  not  possible  during  the 
EWG  to  provide  a  full  time  series  of  alternative  effort  descriptors,  so  a  relationship  with  fishing 
mortality could not be tested. 

STECF observes that TOR 4 (calculate the average partial fishing mortality of trawls exploiting the 
demersal  stocks  concerned  by  the  MAP,  by  type  of  fisheries,  effort  unit,  fleet  segment  and 

7 

7 

 
 
 
 
 
 
 
country, as estimated in the latest stock assessments) was not addressed mainly because of lack 
of most recent assessment results. STECF notes that a number of stock assessments for the MAP 
stocks have  been  performed  in  2018  (EWG  18-12), but  the  report  and  assessment  results were 
not  considered  final  enough  at  the  time  when  EWG  18-13  started  and  the  stock  assessment 
results were not available and could not be used by 18-13. Additionally, the analysis of datasets 
performed in ToR 1 did not allow for a single robust estimate of transversal data (catch and effort 
data by fleet and metier) and the differences between the different datasets remain unclear and 
poorly  explained.  STECF  notes  however  that  calculating  these  partial  fishing  mortalities  is 
straightforward  once  the  updated  datasets  of  fishing  mortality  and  transversal  data  (catch  and 
effort)  are  available,  and  this  step  can  thus  be  undertaken  as  part  of  Step  1  of  the  Road  Map 
below.  

STECF notes that EWG addressed TOR 5, defining a 2-year roadmap to set-up a mixed fisheries 
advice for western Mediterranean demersal fisheries. The plan outlines the priorities in the short 
and  medium  term,  any  potential  gaps  in  knowledge/data/modelling  and  the  actions  that  can  be 
taken  to  overcome  it.  STECF  notes  that  the  EWG  also  considered  the  skills  and  tools  needed, 
including the actors to be involved, but the actual selection of models and experts’ commitment 
should better be decided during a dedicated scoping meeting, suggested by the EWG 18-13 to be 
held in March 2019. 

STECF conclusions 

STECF  recalls  the  conclusions  on  the  opportunities  and  challenges  in  the  use  of  fishing  effort 
regimes  as  a  management  tool  for  mixed  fisheries  reported  in  PLEN  17-02,  PLEN-18-01  and 
STECF 18-09. 

STECF stresses the need to have consistent data as a basis to carry out the assessment and the 
monitoring  of  the  effects  of  effort  management  plans  in  the  western  Mediterranean  Sea.  STECF 
concludes that the results of the analysis carried out by the EWG on the completeness, coverage 
and  consistency  of  the  various  datasets  available  should  be  brought  to  the  attention  of  the 
Member States concerned to urge the improvement of the quality of available data sets. 

STECF  concludes  that  the  proposed  Management  Plan  indicates  general  reductions  in  effort  and 
that  for  the  stocks  and  time  periods  analysed,  in  both  EWGs  (18-09  and  18-13)  relationship 
between  effort  and  fishing  mortalities  cannot  be  determined  and  quantified.  However,  STECF 
concludes that even if the current data of fishing effort and fishing mortality do not show a clear 
correlation, this would not prevent the application of procedures to simulate the impacts of effort 
management  measures  under  alternative  assumptions.  This  exercise  may  use  one  or  more 
models that have been already applied and tested in the assessment of management  options in 
the Mediterranean. Indeed, STECF recognizes that there are several models available even if with 
some  various  levels  of  coverage,  development,  completeness,  update,  complexity  and  user-
friendliness.  

Building on the suggestion from EWG 18-13, STECF proposes the following roadmap to set-up a 
mixed fisheries advice for western Mediterranean demersal fisheries: 

MODEL SCOPING 

I STEP (2019) 

Hold a dedicated STECF EWG in (March) 2019 which 
the main purpose of testing the suitability of various 
candidate models and the availability of modellers  

Model(s)  selection,  on  the  basis  of  identified  criteria 
(compatibility  with  DCF  data  sets  and  STECF/GFCM 
assessment  results  should  be  a  prerequisite  for 
selection)  and  conditioned  on  the  availability  of 
modellers.  A  list  of  minimum  requirements  for  what 
a  model  for  the  mixed-fisheries  advice  should  be 
able  to  do  should  be  agreed  in  advance  of  the 

8 

8 

 
 
 
 
DRAFT RUN 

scoping. 

Identification  of  the  financial  options  supporting  the 
development of the work in 2019 and 2020. 

Agreement  on  scenarios,  results,  and  draft  mixed-
fisheries advice on data available in 2018 (reference 
year 2017) 

II STEP (2020) 

UPDATE AND DEVELOPMENT 

Update to 2019 datasets (reference year 2018)  

Model(s) improvements/extension  

Discussion  and  possible  overcoming  of  other  gaps 
and  issues  related  to  an  operational  mixed-fisheries 
advice:  inclusion  of  other  types  of  fishery  that 
exploit  stocks  in  the  MAP,  adoption  of  specific  sub-
regional fleets/metiers. 

FINAL OPERATIONAL SETUP 

Actual  mixed-fisheries  advice  for  2021  (reference 
year 2019) 

STECF concludes that this roadmap will focus mainly on the Western Med in the first place. But, 
as  it  also  the  case  in  the  ICES  area,  it  should  not  be  excluded  to  expand  it  to  a  more  global 
mixed-fisheries approach for the EU Mediterranean demersal fisheries. 

Contact details of STECF members 

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

Name 

Address1 

STECF members 

Tel. 

Email 

Abella, 
Alvaro 

J. 

Independent consultant 

Tel. 
3384989821 

0039-

aabellafisheries@gmail.c
om 

Tel.dir.:    +45  35 
33 68 92 

jla@ifro.ku.dk 

Andersen, 
Jesper Levring  

for  Environment 

Department  of  Food  and 
Resource 
Economics 
(IFRO) 
Section 
and Natural Resources 
University of Copenhagen 
Rolighedsvej 25 
1958 Frederiksberg 
Denmark 

9 

9 

 
 
 
 
 
 
Tel. 

Email 

Tel.: 
+34667174477 

harri@azti.es 

Name 

Address1 

STECF members 

Marina, 

AZTI / Unidad de 
Investigación 
Herrera 
kaia  portualdea  z/g  20110 
Pasaia 
(Gipuzkoa), Spain 

Independent consultant 

Arrizabalaga, 
Haritz 

Bailey, 
Nicholas  

Bertignac, 
Michel  

Laboratoire  de  Biologie 
Halieutique 
IFREMER Centre de Brest 
BP  70  -  29280  Plouzane, 
France  

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

nickbailey2013@btintern
et.com 

michel.bertignac@ifreme
r.fr 

Borges, Lisa 

FishFix, Brussels, Belgium 

info@fishfix.eu 

Cardinale, 
Massimiliano 

Föreningsgatan  45,    330 
Lysekil, Sweden 

Tel:  +46 
18750 

523 

massimiliano.cardinale@
slu.se 

Lowestoft 

thomas.catchpole@cefas
.co.uk  

CEFAS 
Laboratory, 
Pakefield Road, 
Lowestoft 
Suffolk, UK 
NR33 0HT 

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

Tel:  +44  (0)131 
524 8664 
Fax:  +44  (0)131 
558 1442 

Hazel.curtis@seafish.co.
uk 

of 

Marine 

Research, 

Laboratory 
Ecology, 
Institute  of  Biodiversity 
and 
Ecosystem 
Bulgarian 
Academy of Sciences 
Thünen  Institute  [TI-SF] 
Federal  Research  Institute 
for  Rural  Areas,  Forestry 
and  Fisheries,  Institute  of 
Sea  Fisheries,  Economic 
  Herwigstrasse 
analyses 
D-27572 
31,  
Bremerhaven, Germany 

Gascuel, Didier   AGROCAMPUS OUEST 

Knittweis, 
Leyla 

Lloret, Josep 

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

Department of Biology 
University of Malta 
Msida, MSD 2080 
Malta 

Professor 
Associate 
Agregat), 
(Professor 
University of Girona (UdG), 
Spain 

Tel.:  +359 
646892 

52 

Georgi.m.daskalov@gm
ail.com 

Tel.:  +49 
94460-378 

471 

ralf.doering@thuenen.de 

Fax.:  +49  471 
94460-199 

Tel:+33(0)2.23.48
.55.34 
Fax: 
+33(0)2.23.48.55.
35 

Didier.Gascuel@agroca
mpus-ouest.fr 

Leyla.knittweis@um.edu
.mt  

josep.lloret@udg.edu 

Catchpole, 
Thomas 

Curtis, Hazel  

Daskalov, 
Georgi 

Döring, 
(vice-chair) 

Ralf 

10 

10 

 
 
 
 
 
 
 
 
 
 
 
 
 
Name 

Address1 

STECF members 

Tel. 

Email 

Malvarosa, 
Loretta  

Fishery 

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

Tel: 
089795775 

+39 

malvarosa@nisea.eu  

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

Tel: 
+34.93.2309500 
Fax: 
+34.93.2309555 

paloma@icm.csic.es 

Motova, Arina 

Murua, Hilario 

Nord, Jenny  

Prellezo, Raúl  

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

Marina, 

AZTI / Unidad de 
Investigación 
Herrera 
kaia  portualdea  z/g  20110 
Pasaia 
(Gipuzkoa), Spain 

The  Swedish  Agency  of 
Marine 
Water 
and 
Management (SwAM)  

Tel.:  +44  131  524 
8662 

arina.motova@seafish.c
o.uk 

Tel: 
667174433 
Fax: 
6572555 

+34 

0034 

hmurua@azti.es 

94 

Tel.  0046  76  140 
140 3 

Jenny.nord@havochvatt
en.se 

de 

Tel: 
667174368 

+34 

rprellezo@azti.es 

-Unidad 

AZTI 
Investigación Marina 
Txatxarramendi 
z/g 
48395 
(Bizkaia), Spain 

Ugartea 

Sukarrieta 

Raid, Tiit 

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

of 

Tel.: 
58339340 
Fax: 
6718900 

+372 

Tiit.raid@gmail.com  

+372 

Sabatella, 
Evelina 
Carmen 

Fishery 

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

TEL.: 
089795775 

+39 

e.sabatella@nisea.eu  

Sala, Antonello 
(vice-chair) 

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

Tel:  +39 
2078841 
Fax:  +39 
55313 
Mob.: 
3283070446 

071 

a.sala@ismar.cnr.it 

071 

+39 

Scarcella, 
Giuseppe 

Italian 

1) 
National 
Research  Council  (CNR), 
Marine 
of 
Institute 
Sciences 
- 
(ISMAR) 
Fisheries  Section,  Largo 
Fiera della Pesca, 1, 60125 
Ancona – Italy 
2) 
Marine  
Environmental  Consultancy 
Ltd,  2,  ACROPOLEOS  ST. 
AGLANJIA, P.O.BOX 26728 
1647 Nicosia, Cyprus 

AP 

Tel:  +39 
2078846 
Fax:  +39 
55313 
Tel.: 
99664694 

071 

g.scarcella@ismar.cnr.it 

071 

+357 

gscarcella@apmarine.co
m.cy 

11 

11 

 
 
 
 
 
 
 
 
 
 
 
Name 

Address1 

STECF members 

Soldo, Alen 

Somarakis, 
Stylianos 

Stransky, 
Christoph 

Ulrich, 
(chair) 

Clara 

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

Institute 
Marine 
of 
Biological  Resources  and 
Inland  Waters  (IMBRIW), 
Hellenic  Centre  of  Marine 
(HCMR), 
Research 
Thalassocosmos  Gournes, 
P.O.  Box  2214,  Heraklion 
71003, Crete, Greece 
Thünen  Institute  [TI-SF] 
Federal  Research  Institute 
for  Rural  Areas,  Forestry 
and  Fisheries,  Institute  of 
Sea 
Fisheries, Herwigstrasse 
31,  
Bremerhaven, Germany 

D-27572 

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

Tel. 

Email 

Tel.: 
+385914433906 

soldo@unist.hr  

Tel.:  +30  2810 
337832 

somarak@hcmr. gr 

Fax 
6936566764 

+30 

Tel.  +49 
94460-141 

471 

christoph.stransky@thue
nen.de 

Fax:  +49 
94460-199 

471 

clu@aqua.dtu.dk 

van Hoof, Luc 

IMARES,  Haringkade  1, 
Ijmuiden, The Netherlands 

Tel.: 
61061991 

+31 

Luc.vanhoof@wur.nl 

Vanhee, Willy  

Independent consultant 

wvanhee@telenet.be 

van 
Oostenbrugge, 
Hans 

Vrgoc, Nedo 

Fisheries 
Wageningen 
Research, 
Wageningen 
Hague, The Netherlands 

Economics, 
Economic 
LEI 
The 

formerly 

UR, 

Hans.vanOostenbrugge
@wur.nl 

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

Tel.: 
21408002 

+385 

vrgoc@izor.hr 

12 

12 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
EXPERT WORKING GROUP EWG-18-13 REPORT 

REPORT TO THE STECF 

EXPERT WORKING GROUP ON  
Fishing effort regime for demersal fisheries 
in the Western Mediterranean Sea – Part II  
(EWG-18-13) 

Copenhagen, Denmark, 8-12 October 2018 

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 

13 

13 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1 

INTRODUCTION 

1.1  Terms of Reference for EWG-18-13 

TOR 1. In light of new data available, review the dataset on the trawl fleet exploiting demersal 
stocks in the western Mediterranean Sea as prepared by the STECF EWG 18-09. 

TOR 2. In light of new data, review the outcomes concerning the relationship between effort and 
fishing mortality as prepared by the STECF EWG 18-09. 

Given the STECF-18-09 considered that ‘a linear relationship is overoptimistic and that actual 
changes in F will be likely lower than changes in nominal effort’, estimate proxy linear relations 
under three different scenarios: optimistic (i.e. current linear relationship), conservative and 
pessimistic. 

TOR 3. In light of new data, review the analysis on the differences in fishing pattern and LPUE by 
fleets as prepared by the STECF EWG 18-09. 

TOR 4. Calculate the average partial fishing mortality of trawls exploiting the demersal stocks 
concerned by the MAP, by type of fisheries, effort unit, fleet segment and country, as estimated 
in the latest stock assessments. 

TOR 5. Create  a  2-year  roadmap  to  set-up  a  mixed  fisheries  advice  for  western  Mediterranean 
demersal  fisheries.  The  plan  should  provide  a  detailed  description  of  all  the  steps  needed  to 
achieve  this  goal.  It  should  outline  the  priorities  in  the  short-  and  medium-term,  any  potential 
gaps  in  knowledge/data/modelling  and  the  actions  that  can  be  taken  to  overcome  it.  It  should 
also  specify  the  skills  and  tools  needed,  including  the  actors  to  be  involved  (e.g.  for  scoping 
decisions). The starting date of the roadmap should be 1 January 2019. 

1.2  Data available and sources of information 

To make progress on the ToRs the EWG had access to several datasets.  

As  for  EWG  18-09,  the  primary  data  sources  for  aggregated  data  come  from  the  databases 
hosted  by  JRC  and  populated  with  different  data  calls  (https://stecf.jrc.ec.europa.eu/data-
dissemination). These data are aggregated by quarter and fleet segment (AER) or metier (FDI). 
With regards to stock assessment results the data are aggregated at the stock level. 

To complement the aggregated data, EWG 18-13 defined two more detailed datasets (Table 1.1) 
which  formed  the  basis  for  a  data  request  by  DGMARE  to  the  relevant  Member  States.  These 
datasets  referred  to  haul-by-haul  data  collected  through  observers  on-board  programmes,  and 
trip-by-trip data compiled from VMS, logbooks and sells notes. 

France  sent  both datasets  as required  for years 2012-2017  the  day before  the meeting  started, 
which  limited  its  analysis.  Nevertheless,  the  main  objectives  of  the  EWG  for  ToR  3  were 
accomplished. 

Spain didn’t send either dataset for the EWG, because that the data requests sent by the EU to 
the  Spanish  central  administration  did  not  reach  the  persons  in  charge  in  time.  Two  observers 
from  the  Spanish  authorities  participated  though  the  EWG,  and  tried  to  gather  a  useful  dataset 
during  the  EWG.  But  ultimately,  this  tentative  could  not  be  pursued  by  lack  of  time.  A  partial 
dataset of trip-by-trip data was sent by the Catalonian authorities, referring to an update of the 
data provided to EWG1809. This dataset allowed the group to run the analysis to support ToR 3, 
although partially. 

Italy had provide the trip-by-trip dataset in June and no update was required. 

14 

14 

 
 
A number of stock assessments for the MAP stocks have been performed in 2018 (EWG  18-12), 
but  the  report  and  assessment  results  were  not  considered  final  enough  and  the  stock 
assessment results were not made available to 18-13. 

Table 1.1 Data request in support of EWG 18 13 

Dataset  1  –  haul 
level,  based  on  on-board 
observations.  Each line refers to a species caught in a 
haul by a vessel  

Variable 
Member State 
Vessel anonymised 
Lenght over All 
Gear 
Meshsize 
Gear  length  (fixed  gears 
only) 
Date 
Haul No 
Effort 
Hour start 
Longitude start 
Latitude start 
Depth start 
Hour end 
Longitude end 
Latitude end 
Depth end 

Species 
Species (latin) 
Landings 
Discards 
Price per kg 
Fuel costs 

Format 
character 
character 
numeric 
character 
numeric 

numeric 
DD/MM/YYYY 
integer 
numeric 
HH:MM 
numeric 
numeric 
numeric 
HH:MM 
numeric 
numeric 
numeric 

character 
character 
numeric 
numeric 
numeric 
numeric 

Units 

meter 

Mm 

meter 

hour 

degree 
degree 
meter 

degree 
degree 
meter 

Kg 
Kg 
Euro 
Euro 

Code 

Priority 
1 
1 
1 
DCMAP T2  1 
1 

1 
1 
1 
1 
1 
1 
1 
1 
2 
2 
2 
2 

1 
1 
1 
1 
2 
2 

FAO 
3alpha 

Dataset 2 – day level, based on logbooks, sales notes. 
Each line refers to a species caught in a day by a vessel 

Format 
character 
character 
numeric 
character 
numeric 

Variable 
Member State 
Vessel anonymised 
Lenght over All 
Gear 
Meshsize 
Gear  length  (fixed  gears 
numeric 
only) 
DD/MM/YYYY 
Date 
Effort 
numeric 
Longitude averaged by day  numeric 
numeric 
Latitude  averaged by day 
numeric 
Depth  averaged by day 

Units 

meter 

Mm 

meter 

hour 
degree 
degree 
meter 

Species 
Species (latin) 

character 
character 

Code 

Priority 
1 
1 
1 
DCMAP T2  1 
1 

1 
1 
1 
1 
1 
1 

1 
1 

FAO 
3alpha 

15 

15 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Landings 
Discards 
Price per kg 
Fuel costs 

numeric 
numeric 
numeric 
numeric 

kg 
kg 
euro 
euro 

1 
1 
2 
2 

1.3  Summary of EWG 18-13 outcomes 

The EWG 18-13 was a follow-up of the EWG 18-09 (STECF, 2018a) held in June 2018, and was 
largely attended by the same experts. The Terms of reference had been discussed internally but 
were not available to the EWG prior its start.  

The main outcomes were as follows: 

ToR  1:  Updated  dataset.  STECF  (2018a)  had  highlighted  a  number  of  issues  and  gaps  in  the 
transversal  data  (catch  and  effort  data  by  fleet  segment)  coming  from  different  data  sets.  In 
October 2018, a number of new data were available, owing to several other STECF EWGs having 
been  held  between  June  and  October.  The  Med&Black  Sea  (MBS)  data  have  been  used  in  the 
stock  assessment  EWG  18-12  (STECF,  2018b);  the  Annual  Economic  Report  (AER)  data  from 
EWG  18-07  (STECF,  2018c)  were  completed;  and  the  FDI  data  (STECF,  2018d)  were  also 
available. EWG 18-13 conducted thus an in-depth comparison of the completeness, coverage and 
consistency  of  the  three  datasets.  The  data  are  overall  considered  more  reliable  than  in  STECF 
(2018a), but a number of inconsistency between the three datasets remain, making it difficult to 
provide a robust quantitative description of the fleets activity in the Western Mediterranean.  

ToR 2: The work in ToR 2 was split into several sub-sections. In a first part, the work undertaken 
in  EWG  18-09  ToR  4  was  pursued,  fitting  linear  relationships  between  fishing  effort  and  fishing 
mortality  for  a  number  of  stocks  and  fleet  segments,  based  on  older  assessments.  2018 
assessments (STECF, 2018b) were not available at the time where EWG 18-13 met.  

Considering the single GSA within the two Management Units no significant relationships between 
fishing  effort  and  F  on  deep  water  rose  shrimps  and  hake  were  found.  On  the  contrary  linear 
relationship  derived  assuming  the  stock  unit  inside  the  two  Management  Units  and  negligible 
differences  in  catchability  between  fishing  systems  used  in  the  different  GSAs  inside  each 
Management  Unit  were  significant  but  not  considered  very  reliable  by  the  WG  due  to  expected 
differences in size of the standing stock in each GSA that could affect the catchability.  However, 
considering the lack of contrast in the data within the single GSA, this approach could be worthy 
for further investigations. 

A  second  subsection  tried  to  fit  non-linear  relationship  over  the  same  data.  In  most  case  the 
power  relationships  obtained  (b  parameter)  is  less  than  1,  implying  that  fishing  mortality  has 
historically decreased less than fishing effort. However, the fits remain poor.  

In  a  third  sub-section,  alternative  ways  to  measures  fishing  effort  are  discussed,  using  hours 
fished,  targeted  effort  or  swept  area.  These  metrics  are  considered  to  be  better  descriptors  of 
effort  than  fishing  days,  and  could  also  potentially  be  measured  or  estimated  with  the  standard 
information available, owing among other to a better use of VMS/AIS data. However,  it was not 
possible during the EWG itself to provide a full time series of such alternative effort descriptors, 
so a relationship with fishing mortality could not be tested.  

Finally, some results from EWG 18-09 were recalled, highlighting the importance of gears design 
and size as important descriptors of the fishing effort, but not well known and monitored. There is 
a huge potential for efficiency increase and technical creep in the Western Mediterranean.  

Ultimately,  these  analyses  remain  preliminary  and  there  would  be  scope  for  analysing  this 
relationship further. Nonetheless, the management implications of the current state of knowledge 
remain,  as  stated  previously  by  EWG  18-09,  are  that  i)  it  cannot  be  expected  that  fishing 
mortality  will  decrease  by  exactly  the  same  amount  as  fishing  effort  in  the  first  years  of  effort 
reduction in the MAP, but will likely reduce at a lesser rate and ii) it might take a few years before 
the effects of effort reductions can be seen in the stock assessment outcomes.  

ToR 3: The work in ToR 3 was an update of the results described in sections 5 and 8 of the EWG 
18-09 report. The main addition was the application of the GAM and mixed-fisheries analyses to 

16 

16 

 
 
 
 
  
 
the  French  data.  The  French  fleet  is  characterised  by  a  very  different  structure  in  vessel  size 
compared to Spain and Italy.  

ToR 4: ToR 4 could not be completed during EWG 18-13. The updated stock assessments results 
from EWG 18-12 were not considered sufficiently robust and finalised at the time where EWG 18-
13 met, and the data were not readily available. Additionally, the analysis of ToR 2 did not allow 
for a single robust estimate of transversal data (catch and effort data by fleet  and metier), and 
the differences between the different datasets remained unclear and poorly explained. Decision is 
therefore  still  to  be  made  on  which  dataset  to  base  the  actual  estimates  of  partial  fishing 
mortality. 

Performing this ToR would otherwise be straightforward once the relevant data are collected, so 
EWG 18-13 decided to postpone the exercise and to include it as part of Step 1 of the RoadMap. 

ToR 5: ToR 5 was about establishing a roadmap for the work to be performed over the next two 
years  in  order  to  provide  an  operational  mixed-fisheries  advice  for  the  Western  Med  MAP.  To 
address this, EWG 18-13 established a detailed review of the current issues and gaps in i) mixed-
fisheries models, ii) data and iii) experts’ availability and human resources limitations. EWG 18-
13  provide  also  an  overview  of  the  mixed-fisheries  advice  annually  published  by  ICES  and  its 
corresponding procedures for model and data updates and choices of scenarios.  

From  this,  the  EWG  18-13  recognised  that  the  highest  chance  to  obtain  an  operational  mixed-
fisheries  advice  is  to  combine  a  suite  of  expert  working  groups  or  workshops  (around  one  per 
semester) together with additional funding dedicated to intersessional developments. This funding 
would secure  the  necessary  time  and  commitment  of  the  relevant  experts, while  the  workshops 
would  act  as  useful  milestones  reviewing  the  progresses  achieved  and  framing  their  outcomes 
into the desired operational setup. On this basis, the EWG suggested 4 steps, corresponding more 
or less to the four semesters in 2019 and 2020. The most important step is the first one, which is 
the  scoping  of  which  model(s)  to  use,  and  the  selection  of  the  appropriate  stocks  and 
fleet/metiers definition and corresponding transversal data. This step will also establish the plan 
for the financial support to this scientific work. This will define the details of the following steps, 
recognising  that  the  actual  development  of  the  advice  is  directly  linked  to  the  question  of  who 
gets funded to do the work, how much and when. EWG 18-13 recommends thus that a dedicated 
scoping  meeting  is  organised  in  the  early  period  of  2019,  in  order  to  agree  on  the  following 
developments.  

17 

17 

 
 
2  TOR 1: DATASET REVIEW 

“In light of new data available, review the dataset on the trawl fleet exploiting demersal stocks in 
the western Mediterranean Sea as prepared by the STECF EWG 18-09. Considering the limitations 
of  the  datasets  previously  used  to  provide  baseline  information  on  the  trawl  fleets  exploiting 
demersal  stocks  in  the  western  Mediterranean  Sea,  the  EWG  is  requested  to  review  the 
consistency of the new datasets that are provided to the experts. The analysis focused mainly on 
effort and landings data collected under the Data Collection Framework (DCF – EC 199/2008) up 
to 2018.” 

2.1  Comparison of datasets coverage 

Three datasets were available to the experts during the EWG: 

- 

- 

- 

The  2018  Mediterranean  and  Black  Sea  data  call  (MBS  –  Ares  (2018)2385889  - 
04/05/2018).  The  MBS  data  call  requests  data  for  a  considerable  number  of  tables 
specifically  aimed  at  allowing  stock  assessments.  Table  reporting  effort  and  landings  are 
available since 2002 (STECF, 2018b).  

The  2018  New-Fishery  Depend  Information  data  call  (FDI  –  Ares  (2018)  2607160  - 
18/05/2018).  The  STECF  FDI  database  was  developed  to  support  the  management  of 
fishing  effort  management  regimes.  Time  series  are  available  for  the  period  2015-2017 
(STECF, 2018d).  

The 2018 AER (STECF, 2018c). The report covers a nine-year time series (2008-2016) and 
includes  information  on  the  EU  fleet’s  fishing  capacity,  effort,  employment,  landings, 
income and costs. Economic and transversal variables were not included in the review, a 
detailed  description  of  economic  features  of  fleets  involved  in  the  effort  regime  are 
available  in  EWG  18  09.  Effort  and  landing  data  reported  at  country  level,  without 
specification of sub areas and gear, are considered as a different dataset (AER2).   

The  exploratory analysis  of  the  datasets  reveals  that  most  of  the  problems observed  during  the 
EWG 18 09 are fixed in the most updated version of the dataset provided to the EWG, increasing 
the  quality  and  temporal  coverage  of  the  time  series  available.  However,  inconsistencies  across 
data  calls  are  still  found  for  all  countries.  A  complete  comparison  between  the  variables  is 
possible only for fishing days and landings weights that are reported in all data calls; most of the 
remaining  variables  are  reported  in  2  out  of  the  3  datasets.  The  EWG  also  focused  on  the 
difference  in  the  methods  used  by  Member  States  in  the  collection  and  estimation  of  effort 
variables  (mainly  fishing  days  and  days  at  sea).  The  comparison  of  the  three  datasets  revealed 
that: 

  The  most  complete  period  covers  from  all  the  3  data  calls  is  the  2015-2017.  In  these 
years,  effort  and  landings  data  are  available  from  all  the  3  data  calls  (Figure  2.2). 
However,  temporal  coverage  of  the  MBS  and  AER  datasets  vary  across  countries. 
Temporal coverage of the MBS and AER starts from 2008 for Italy and Spain and no lack 
of data is found. Data for France reported in the MBS data call are available only in the 
period 2015-2017 while temporal gaps are present in the AER.  

  A good agreement is found between fishing days reported in the 3  data calls for France 
and Italy, while considerable differences are found between in the FDI and the other two 
data calls for Spain.  

  Landings data are similar in the AER and FDI data calls for France and, between MBS and 
FDI  for  Spain  and  Italy.  However,  cross  checking  of  the  gear  revealed  that  there  are 
problems in  gear  specification  between  landings  and  effort  data.  Landings  for gear  OTT, 
PTB, and TBB are reported in the AER for Spain without a correspondence in FDI and MBS 
data calls (Figure 2.3). Landings for TBB are available in MBS dataset for France without 
correspondence in the AER and FDI data calls. 

  The 2018 Annual Economic Report on (AER) the EU Fishing Fleet (AER – EWG 18-07). The 
report  covers  a  nine-year  time  series  (2008-2016)  and  includes  information  on  the  EU 
fleet’s  fishing  capacity,  effort,  employment,  landings,  income  and  costs.  Economic  and 
transversal variables were not included in the review, a detailed description of economic 

18 

18 

 
features  of  fleets  involved  in  the  effort  regime  are  available  in  EWG  18  09.  Effort  and 
landing  data  reported  at  country  level,  without  specification  of  sub  areas  and  gear,  are 
considered as a different dataset (AER2). 

  Effort  indicators  as  hours  at  sea,  estimated  using  georeferenced  data  (VMS  –  Vessels 

Monitoring System) collected under the FDI, are not available for Spain. 

Figure  2.1:  Correspondences  between  effort  and  landing  variables  is  explored  by  tiles  plot.  Red 
cells  indicate  the  presence  of  a  variable  (y  axis)  in  the  correspondence  dataset  (x  axis).  The 
AER2  dataset  refers  to  the  economic  and  transversal  data  reported  at  supra  region  level  and  it 
was reported in the analysis to highlight the difference in the aggregation level for diseases and 
no vessels (vessels number) 

19 

19 

 
 
Figure 2.2: Time coverage of the considered datasets explored by Tiles plot. For each dataset (y 
axis  on  the  right),  red  cells  indicate  the  presence  of  a  variable  (top  x  axis)  in  a  year  (lower  x 
axis) for the considered countries (left y axis) 

20 

20 

 
 
 
Figure  2.3:  Cross  checking  of  fishing  gears  belonging  to  DTS  fishing  technic  reported  in  the 
considered datasets. Correspondences across variables, data sources and country were explored 
by Tiles plots. Red cells represent the presence of a variables (y axis) respect to the gear (x axis) 
belonging to the DTS fishing technic in each country. 

2.2  Definition of fishing effort 

When  discussing  which  variables  to  include  in  the  effort  scheme,  the  experts  first  focused  on 
fishing days and days at sea. The EWG discussed the use of these metrics in the proposed effort 
scheme focusing on the reliability of the data collected by member state. Although in 2016 some 
member  states  implemented  a  system  to  collect  logbooks  data  haul  by  haul,  the  experts  noted 
that problems related on the compilation of the logbook can influence the quality of the data.   

Under  the  Implementing  Decision  (EU)  2016/1251  adopting  multiannual  programme  for  the 
collection,  management  and  use  of  data  in  the  fisheries  sector,  the  following  definition  are 
reported: 

  Days at sea: any continuous period of 24 hours (or part thereof) during which a vessel is 

present within an area and absent from port. 

  Fishing  days:  any  calendar  day  at  sea  in  which  a  fishing  operation  takes  place,  without 
prejudice to the international obligations of the Union and its Member States. One fishing 
trip  can  contribute  to both  the  sum of  the  fishing  days for  passive  gears  and  the  sum  of 
the fishing days for active gears on that trip. 

In  the  EC  1224/2009,  which  establishes  a  community  control  system  for  ensuring  compliance 
with the rules of the common fisheries policy (EC 1224/2009), is also reported a generic definition 
of “days”:  

  A  day  present  within  an  area  shall  be  any  continuous  period  of  24  hours  or  part  thereof 
during which a fishing vessel is present within the geographical area and absent from port 
or where appropriate deploying its fishing gear. The time from which the continuous period 
of a day present in the area is measured is at the discretion of the Member State whose 
flag  is  flown  by  the  fishing  vessel  concerned.  A  day  absent  from  port  shall  be  any 
continuous  period  of  24  hours  or  part  thereof  during  which  the  fishing  vessel  is  absent 
from port.  

Generally,  in  the  annual  economic  report  of  economic  and  transversal  data  days  at  sea  are 
reported at gear, fishing area and economic zone level. Fishing days is reported at gear, fishing 
area,  rectangle  and  economic  zone  level.  However,  in  the  case  of Mediterranean  Sea,  the  finest 
spatial  resolution  for  fishing  days  is  reported  at  geographical  sub  areas  (GSA)  level  (STECF, 
2018c).  In  the  case  of  Spain,  the  experts  noted  that  due  to  a  12  hours-rule  currently 
implemented for trawl fishery there is no difference between values of fishing day and days at sea 
and they can be considered equivalent. The results obtained in the analysis showed that also in 
case of France similar values are observed between the two metrics. In the case of Italy, values 
of days at sea reported in the MBS data call resulted ten time higher if compared to fishing days 
and it possible to hypothesize that an error occurred during data transmission.  

The EWG also discussed the use of indicators based on georeferenced data (such as hours at sea) 
to  better  address  the  relationship  between  effort  and  fishing  mortality.  These  variables  are 
available in the new FDI data call for the period 2015-2017 and a good agreement is found with 
fishing  days  and  days  at  sea  collected  under  the  other  2  data  calls  considered  for  all  countries. 
Although standardized methods are proposed to estimate effort from the combination of logbook 
and VMS data, alternative methods were used by France and Italy to accommodate the request of 
FDI  data  call  (STECF,  2018d).  Italian  experts  stressed  that  several  problems  related  to  a  poor 
consistency  of  logbooks  and  VMS  data  are  present  in  the  data  collected  in  the  FDI  for  Italy 
(STECF,  2018d).  In  addition,  hours  at  sea are  not  available  for  Spain,  while  values  reported  for 
days at sea resulted not comparable with those reported in the other 2 data calls. 

21 

21 

 
2.3  Comparison of fisheries data  

2.3.1  The variables considered in the review 

The  list  of  the  variables  that  are  reviewed  and  their  relative  codification  is  reported  in  Table 
2.1.To  facilitate  comparison  between  datasets,  variables  are  extracted  at  the  same  aggregation 
level  using  filtering  criteria  that  are  consistent  with  the  fisheries  and  the  areas  involved  in  the 
effort regime. Factors used to subset data and their codification are reported in Table 2.2. Under 
the  proposed  effort  scheme,  vessel  length  categories  VL0006,  VL0010  and  VL0612  are  merged 
into  a  category  VL0012,  other  segments  considered  are  the  standard  DCF  categories  VL1218, 
VL1824, VL2440 and VL40XX. Since most of effort and landings data are specified at gear level, 
the experts decided first to select all fishing gear belonging to the DTS fishing technic (‘Demersal 
trawlers  and/or  demersal  seiners’)  and  after,  distinguish  bottom  trawling  gears  from  the  other 
gears. Bottom trawling gears (OTB, OTT, PTB, TBB) were flagged as “BOTTOM_TRAWLERS”, while 
all other gear types as “OTHERS”. Data collected under the MBS data call were provided without 
specification of fishing technics, then data were subset considering only the type of gear. 

Table 2.1: List of variables taken in consideration.  

Landings variables 

Source  Name 

AER2 

totalling 

Original 
codification 
landings value 

Standardized 
codification 
Landings value 

AER 

totwghtlandg 

landings_weight  Landings weight 

MBS 

landings 

landings_weight  Landings weight 

FDI 

totwghtlandg 

landings_weight  Landings weight 

Effort variables 

Source  Name 

MBS 

Days at sea 

Original 
codification 
days_at_sea 

Standardized 
codification 
days_at_sea 

FDI 

Days at sea 

totseadays 

days_at_sea 

22 

Definition 

Source 

Total  values 
of landings 

EWG 18-07 

EWG 18-07 

landings 

Total weight 
of 
per 
species 

landings 

Total weight 
of 
per 
species 

Implementing 
Decision  (EU) 
2016/1251 

EWG 18-11 

landings 

Total weight 
of 
per 
species 

Definition 

Source 

Implementing 
Decision  (EU) 
2016/1251 

Any continuous 
period  of  24 
hours  (or  part 
thereof)  during 
which  a  vessel 
is 
present 
within  an  area 
and 
absent 
from port. 
Any continuous  EWG 18-11 

Aggregation 
level 
Country-
year-supra 
region-
fishing 
technic-gear-
vessel  length 
-species 
Country-
year-sub 
region-
fishing 
technic-gear-
vessel  length 
- species 
Country-
year-sub 
region-
fishing 
technic-gear-
vessel  length 
- species 
Country-
year-sub 
region-
fishing 
technic-gear-
vessel  length 
- species 

Aggregation 
level 
Country-
year-sub 
region-
fishing 
technic-
gear-vessel 
length 

Country-

22 

 
AER2  Days at sea 

totseadays 

days_at_sea 

MBS 

Fishing days 

fishing_days 

fishing_days 

FDI 

Fishing days 

totfishdays 

fishing_days 

year-sub 
region-
fishing 
technic-
gear-vessel 
length 

Country-
year-supra 
region-
fishing 
technic-
gear-vessel 
length 

Country-
year-sub 
region-
fishing 
technic-
gear-vessel 
length 

Implementing 
Decision  (EU) 
2016/1251 

Implementing 
Decision  (EU) 
2016/1251 

Implementing 
Decision  (EU) 
2016/1251 

Country-
year-sub 
region-
fishing 
technic-
gear-vessel 
length 

period  of  24 
hours  (or  part 
thereof)  during 
which  a  vessel 
is 
present 
within  an  area 
absent 
and 
from port. 

to 

place, 

Any continuous 
period  of  24 
hours  (or  part 
thereof)  during 
which  a  vessel 
is 
present 
within  an  area 
and 
absent 
from port. 
Any 
calendar 
day  at  sea  in 
which  a  fishing 
operation 
takes 
without 
prejudice 
the 
international 
obligations  of 
the  Union  and 
Member 
its 
States. 
One 
fishing  trip  can 
contribute 
to 
both  the  sum 
of  the  fishing 
days 
for 
passive  gears 
and the sum of 
fishing 
the 
days  for  active 
gears  on  that 
trip. 

to 

place, 

any 
calendar 
day  at  sea  in 
which  a  fishing 
operation 
takes 
without 
prejudice 
the 
international 
obligations  of 
the  Union  and 
Member 
its 
States. 
One 
fishing  trip  can 
contribute 
to 
both  the  sum 
of  the  fishing 
days 
for 
passive  gears 
and the sum of 
the 
fishing 
days  for  active 

23 

23 

 
 
 
AER 

Fishing days 

totfishdays 

fishihg_days 

EWG 18-07 

Country-
year-sub 
region-
fishing 
technic-
gear-vessel 
length 

gears  on  that 
trip. 

to 

place, 

any 
calendar 
day  at  sea  in 
which  a  fishing 
operation 
takes 
without 
prejudice 
the 
international 
obligations  of 
the  Union  and 
Member 
its 
One 
States. 
fishing  trip  can 
contribute 
to 
both  the  sum 
of  the  fishing 
for 
days 
passive  gears 
and the sum of 
the 
fishing 
days  for  active 
gears  on  that 
trip. 

MBS 

GT  days  at 
sea 

gt_days_at_sea  gt_days_at _sea 

FDI 

GT  days  at 
sea 

totgtdaysatsea 

gt_days_at_sea 

Total  days  at 
sea  times  the 
Gross  tonnage 
of  the  active 
vessel 

Implementing 
Decision  (EU) 
2016/1251 

gross tonnage 
times  days  at 
sea 

EWG 18-11 

FDI 

GT 
days 

fishing 

totgtfishdays 

gt_fishing_days 

gross tonnage 
times 
days 

fishing 

EWG 18-11 

AER 

GT 
days 

fishing 

totgtfishdays 

gt_fishing_days 

gross tonnage 
times 
days 

fishing 

EWG 18-07 

FDI 

GT  hours  at 
sea 

gthrsea 

gthrsea 

gross tonnage 
times  hours  at 
sea 

EWG 18-11 

FDI 

Hours at sea 

hrsea 

hrsea 

Total 
spent at sea 

hours 

EWG 18-11 

24 

Country-
year-sub 
region-
fishing 
technic-
gear-vessel 
length 
Country-
year-sub 
region-
fishing 
technic-
gear-vessel 
length 
Country-
year-sub 
region-
fishing 
technic-
gear-vessel 
length 
Country-
year-sub 
region-
fishing 
technic-
gear-vessel 
length 
Country-
year-sub 
region-
fishing 
technic-
gear-vessel 
length 
Country-
year-sub 

24 

 
 
 
FDI 

kW  days  at 
sea 

totkwdaysatsea  nominal_effort 

kW  times  days 
at 
sea 

EWG 18-11 

FDI 

kW 
days 

fishing 

totkwfishdays 

kw_fishing_days 

kW 
fishing days 

times 

EWG 18-11 

AER 

kW 
days 

fishing 

totkwfishdays 

kw_fishing_days 

kW 
fishing days 

times 

EWG 18-07 

FDI 

kW  hours  at 
sea 

kwhrsea 

kwhrsea 

times 

EWG 18-11  

kW 
hours at 
sea 

MBS 

Nominal effort  nominal_effort 

nominal_effort 

kW  times  days 
at 
sea 

AER 

Number 
fishing trip 

of 

tottrips 

fishing_trip 

EWG 18-07 

MBS 

Vessels 
number 

no_vessels 

no_vessels 

Total  of  active 
vessels 

Implementing 
Decision  (EU) 
2016/1251 

FDI 

Vessels 
number 

totves 

no_vessels 

Total  of  active 
vessels 

EWG 18-11 

AER2 

Vessels 
number 

totves 

no_vessels 

Total  of  active 
vessels 

EWG 18-07 

region-
fishing 
technic-
gear-vessel 
length 
Country-
year-sub 
region-
fishing 
technic-
gear-vessel 
length 
Country-
year-sub 
region-
fishing 
technic-
gear-vessel 
length 
Country-
year-sub 
region-
fishing 
technic-
gear-vessel 
length 
Country-
year-sub 
region-
fishing 
technic-
gear-vessel 
length 
Country-
year-sub 
region-
fishing 
technic-
gear-vessel 
length 
Country-
year-sub 
region-
fishing 
technic-
gear-vessel 
length 
Country-
year-sub 
region-
fishing 
technic-
gear-vessel 
length 
Country-
year-sub 
region-
fishing 
technic-
gear-vessel 
length 
Country-
year-supra 
region-
fishing 

25 

25 

 
 
 
Table 2.2: Factors used in the aggregation of data. Codification reported in bold are those used to 
report results of the analysis 

technic-
gear-vessel 
length 

Source of data 

AER 

Annual  Economic  Report  (sub 
region 
gear 
specification) 

level 

with 

AER2  Annual  Economic  Report  (country 
level without gear specification on 
fishing technic) 

MBS  Mediterranean and Black Sea data 

Information 

FDI 

call 
Fishing  Dependent 
data call 
Sub area - Management unit 

GSA1    Northern Alboran Sea 

GSA2 

Alboran Island 

GSA5 

Balearic Island 

GSA6 

Northern Spain 

GSA7 

Gulf of Lion 

GSA8 

Corsica 

GSA9 

Ligurian Sea and North Tyrrhenian Sea 

GSA10  Southern and Central Tyrrhenian Sea 

GSA11   Western Sardinia & Eastern Sardinia 

Fishing technic 

Demersal  trawlers  and/or  demersal 
seiners 

DTS 

western 

western 

western 

western 

western 

eastern 

eastern 

eastern 

eastern 

Country 

FRA 

ITA 

ESP 

France 

Italy 

Spain 

Vessel length  

VL0012  LOA < 12m 

VL1218  12 ≤ LOA > 18m 

VL1824  18 ≤ LOA > 24m 

VL2440  24 ≤ LOA > 40m 
VL40X
X 

LOA ≥ 40m 

Gears  included  in  DTS  fishing  technic  – 
gear type 

OTB 

OTT 

PTB 

Bottom 
trawl 

otter 

Otter twin trawl 
pair 
Bottom 
trawl 

TBB 

Beam trawl 

Bottom 
trawler 
Bottom 
trawler 
Bottom 
trawler 
Bottom 
trawler 

Species 

ARA 

ARS 

DPS 

HKE 

Blue  and 
antennatus) 
Giant 
red 
foliacea) 
Rose 
longirostris)  
European 
merluccius) 

red 

shrimp 

(Aristeus 

shrimp 

(Aristomorpha 

shrimp 

(Parapenaeus 

hake 

(Merluccius 

MUT  Red mullet (Mullus barbatus) 

Norwegian 
norvegicus) 

NEP 

lobster 

(Nephrops 

2.3.2  Comparison of the fishing statistics between the three data calls 

The  description  below  is  based  in  EWG  18-09.  Time  series  of  effort  and  landings  data  are 
analysed  at  country  level  considering  only  areas  (GSA)  and  gear  involved  in  the  effort  regime. 

26 

26 

 
 
 
 
 
 
 
 
 
 
 
  
Landings  data  are  reported  also  at  species  level.  The  analysis  of  the  relative  differences  across 
data  calls  is  explored  for  each  variable  estimating  the  ratio  between  pairs  of  values  and  results 
are showed at sub region and gear level. Resulting plots are manually adjusted to facilitate their 
evaluation  and  values  greater  than  1.5  are  set  to  2,  if  the  denominator  is  not  available  or  it  is 
very small, the ratio is forced to 2.5.  

2.3.2.1  Spain 

Trends  of  time  series  of  effort  data  reported  at  country  level  showed  similar  values  in  the  MBS 
and AER2 data calls (Figure 2.4). In both datasets, the values of fishing days are equal to days at 
sea  and  their  values  result  comparable  with  slight  differences  across  dataset.  Aggregated 
landings  data  are  similar  in  MBS  and  FDI  while  lower  values  are  reported  in  the  AER.  Relevant 
differences  in  MBS  and  FDI  data  calls  are  also  found  for  nominal  effort  and  number  of  vessels. 
However, the number of vessels is different in all 3 data calls. 

Considering, landings by species reported in Figure 2.5, a good agreement is found between the 
MBS  and  FDI  data  calls  for  MUT,  DPS  and  HKE.  The  more  severe  differences  are  observed  for 
ARS,  MUT  and  NEP  in  MBS  and  AER  data  call.  For  these  species  values  and  trends  between  the 
AER and the other 2 data calls are very different. For all other species (ARA, HKE and DPS), lower 
differences and more similar trends are observed between the 3 data calls. 

The ratio of mean values for the data reported at sub region and gear level are reported in Figure 
2.6. Similar values are reported in the FDI and the MBS data calls for OTB in GSA1, GSA2, GSA5, 
GSA6 and GSA7. For the same areas, fishing days are similar in AER2 and MBS. Lack of values for 
GSA9, GSA10 and GSA11 are due to a mismatch between the data calls, gear TBB, OTT and PTB 
are reported in this area in FDI but no data are available in the MBS and AER.  

Figure  2.4:  Time  series  of  effort  and  landings  data  reported  in  the  3  data  calls  aggregate  at 
country level for Spain. 

27 

27 

 
 
Figure 2.5: Time series of landings by species aggregate at country level for Spain 

Figure 2.6: Relative differences between data calls for Spain are explored using tiles geometry at 
sub region and gear level. Cell colour represent the ration between mean values of the variables. 
Ratio  are  estimated  between  pairs  of  data  calls  and  only  for  those  variables  that  are  present  at 
least in 2 of them. 

2.3.2.2  France 

Trends  of  time  series  of  effort  and  landings  data  are  reported  in  Figure  2.7  at  country  level.  A 
good agreement is found between values reported in the 3 data calls.  Negligible differences and 
similar  trends  are  found  between  the  time  series  of  effort  variable  (i.e.  days_at_sea, 
fishing_days, gt_fishing_days, kw_fishing_days) and total landings of the 3 data calls.  Number of 
vessels  reported  in  the  AER  data  call  are  greater  than  those  reported  in  the  other  2  data  calls. 
Similar values and trends are found for nominal effort between the FDI and the MBS data calls 

Time  series  of  landings  by  species  are  different  across  dataset,  Figure  2.8.  Although,  similar 
trends are reported in the 3 data calls for DPS and HKE, considerable differences are present for 

28 

28 

 
 
 
NEP, MUT and HKE. Values of MUT and HKE reported in the MBS data call are very different from 
those reported in the other 2 data calls.  

The ratio of mean values for the data reported at sub region and gear level are reported  Figure 
2.9. In GSA5 a good agreement is found between FDI and MBS. In GSA7 similar values are found 
for fishing days and days at sea between all 3 data calls.  

Figure  2.7:  Time  series  of  effort  and  landings  data  reported  in  the  3  data  calls  aggregate  at 
country level for France. 

Figure 2.8: Time series of landings by species aggregate at country level for France 

29 

29 

 
 
 
Figure 2.9: Relative differences between data calls for France are explored  using tiles geometry 
at  sub  region  and  gear  level.  Cell  colour  represent  the  ration  between  mean  values  of  the 
variables.  Ratio  are  estimated  between  pairs  of  data  calls  and  only  for  those  variables  that  are 
present at least in 2 of them. 

2.3.2.3  Italy 

Trends  of  time  series  of  effort  and  landing  at  country  level  are  reported  in  Figure  2.10.  Time 
series  of  the  variables  reported  in  the  AER2  dataset  for  Italy  are  not  considered  in  the  review 
because  they  were  collected  considering  GSA  not  involved  in  the  effort  scheme.    A  good 
agreement is found in time series of fishing days for which a complete correspondence between 
the  FDI  and  AER  datasets  is  found,  while  negligible  differences  with  the  MBS  are  present.  The 
number of vessels reported in the MBS and FDI data calls showed relevant difference with values 
reported  in  the  first  greater  than  those  reported  in  the  second.  On  the  contrary,  nominal  effort 
reported in the two data calls is similar in 2017. Landings at country levels are very similar in the 
MBS and AER data calls but they differ in the FDI. Days at sea reported in the MBS resulted to be 
too high respect than those reported in AER and FDI data calls. Days at sea of the entire Italian 
trawler fleet as reported in the AER2 dataset is showed in Figure 2.10 to highlight the possibility 
of error in the values of days at sea reported for the considered areas.  

Trends  of  landings  by  species  at  country  level  are  reported  in  Figure  2.11.  A  good  agreement 
between  all  3  datasets  are  found  for  DPS  and  NEP.    Time  series  of  HKE  and  MUT  resulted  very 
similar between AER and MBS while differences are found respect the FDI data calls. Time series 
of ARA and ARS resulted very similar in the AER and MBS data calls until 2015, in the last 2 years 
different values are observed in all 3 data calls. 

A  good  agreement  between  the  data  calls  is  also  confirmed  by  the  ratio  of  the  mean  values 
reported in Figure 2.12. In all areas, similar values in all 3 data callas are found for fishing days. 
Landings  reported  in  the  three  data  calls  are  similar  in  GSA  10  while  differences  are  found  in 
GSA9 and GSA10. The number of vessels, is similar between FDI and MBS in GSA10 and GSA11, 
while it is slightly different in GSA9. 

30 

30 

 
 
Figure  2.10:  Time  series  of  effort  and  landings  data  reported  in  the  3  data  calls  aggregate  at 
country  level  for  Italy.  Value  of  days  at  sea  for  the  AER2  are  reported  to  confront  the  values 
reported in the MBS respect to those for the whole Italian trawler fleets 

Figure 2.11: Time series of landings by species aggregate at country level for Italy 

31 

31 

 
 
 
Figure 2.12: Relative differences between data calls for Italy are explored using tiles geometry at 
sub region and gear level.  Cell colour represent the ration between mean values of the variables. 
Ratio  are  estimated  between  pairs  of  data  calls  and  only  for  those  variables  that  are  present  at 
least in 2 of them. 

2.4  Summary conclusion 

When discussed on the dataset to use to provide baseline information about the fleet involved in 
the effort regime, the experts noted that there are differences between the value reported in the 
available datasets. Although the great majority of problems reported in STECF (2018a) regarding 
quality and temporal coverage of data were fixed, several issues are still present. The most 
relevant are: 

MBS data for France are available only for 2015-2017. In the case of landings data for 

• 
Spain, the vessel length categories are missing. 

Data reported in the AER are not available for 2017. Landings data for VL1218 are not 

• 
available for 2015 GSA1-2-5-6-7. In addition, the number of vessels and days at sea reported for 
Italy are available only at country level and they cannot be used in the baseline information 
because they also include fleets operating in areas outside of the management plan (e.g. Adriatic 
Sea). 

• 

The number of vessels and hours at sea in the FDI for Spain are not available. 

Taking  in  consideration  the  limits  of  the  datasets,  the  EWG  decided  to  provide  baseline 
information  basing  on  data  reported  in  the  AER.  Indeed,  considering  the  aggregation  level 
required  for  the  baseline  information  (sub_area,  gear,  vessel  length),  the  AER  dataset  resulted 
the most detailed in both landings and effort data for Spain and France.  For Italy, the number of 
vessels,  that  is  not  available  at  sub  region  level  in  the  AER,  can  be  obtained  from  the  MBS.  On 
the  contrary,  days  at  sea  reported  in  the  MBS  for  Italy  cannot  be  used  because  the  values 
reported for 2015 and 2016 are not reliable.  

Finally, when a direct comparison of the AER and the other two datasets was possible, trend of 
the time series resulted similar or with negligible differences in most cases. 

2.5  Baseline  information  about  demersal  fisheries  by  the  Management  Units  of 

western Mediterranean Sea 

According  to  the  spatial  division  of  the  effort  regime  controls,  the  western  and  eastern 
management  units  were  described  aggregating  data  from  GSA1-5-6-7  and  GSA8-9-10-11 
respectively. For each variable, values from the GSAs which belong to a management unit were 

32 

32 

 
 
summed  by  years  and  fleet  segments  and  after,  the  average  value  of  the  last  three  years  was 
estimated. 

2.5.1  Western management unit (GSA1-5-6-7) 

Effort  and  landings  data  used  to  describe  trawling  fleet  in  the  western  management  unit  come 
from  the  AER  and  they  refer  to  the  period  2015-2016.  Table  2.3  reports  the  main  features  of 
trawl  fisheries  in  the  western  management  unit.  In  the  period  2015-2016,  647,  on  average, 
vessels were involved in the demersal fishery in this area. The fleet was mostly composed by the 
segment VL1824 (51%), followed by VL2440 (25%) and VL1218 (24%).  

In  the  period  2015-2016,  landings  were  about  19000  tonnes,  on  average,  and  the  great  part 
coming from VL1824 (43%), followed by VL2440 (35%) and VL1218 (17%).Considering only the 
target species, the most landed was HKE (1845t), followed by ARA (938t) and MUT (875t) (Figure 
2.13). Altogether, target species account for about 20% of total landing. Target species represent 
22% of total landings of VL2440, 20% of VL1824 and 15% of VL1218 (Figure 2.14).  

Table  2.3:  Main  features  of  trawl  fisheries  in  GSA1-5-6-7.  The  values  reported  represent  the 
average values during the period 2015-2016. 

Fleet segment 

VL1218  VL1824  VL2440 

Vessels number 

(#) 

153 

331 

163 

Days at sea 

Fishing days 

18970 

44933 

22781 

18812 

44264 

22314 

Total 
weight 

Target 
weight 

landings 

(tonne) 

4362 

8084 

6613 

species 

(tonne) 

656 

1646 

1481 

Landings 
dependency 

% 

15 

20 

22 

Figure 2.13: Species average landing weight across fleet segments in western management unit 
during the period 2015-2016 

33 

33 

 
 
 
 
 
 
Figure  2.14:  Landings  dependency  of  fleet  segments  by  species  estimated  on  average  landings 
value for the period 2015-2016 in western management unit. 

2.5.2  Eastern management unit 

Effort  and  landings  data  used  to  describe  trawling  fleet  in  the  eastern  management  unit  come 
from  the  AER  and  they  refer  to  the  period  2015-2016.  Table  2.4  reports  the  main  features  of 
trawl  fisheries  in  the  eastern  management  unit.  In  the  period  2015-2016,  736,  on  average, 
vessels were involved in the demersal fishery in this area. The fleet was mostly composed by the 
segment VL1218 (60%), followed by VL1824 (35%) and VL2440 (5%).  

In  the  period  2015-2016,  landings  were  about  14000  tonnes,  on  average,  and  the  great  part 
coming  from  VL1824  (52%),  followed  by  VL1218  (40%)  and  VL2440  (8%).Considering  only  the 
target  species,  the  most  landed  was  MUT  (1671t),  followed  by  DPS  (1357t)  and  HKE  (1300t) 
(Figure  2.15).  Altogether,  target  species  account  for  about  36%  of  total  landing.  Target  species 
represent 43% of total landings of VL2440, 35% of VL1824 and 37% of VL1218 (Figure 2.16) 

Table  2.4:  Main  features  of  trawl  fisheries  in  GSA8-9-10-11.  The  values  reported  represent  the 
average values during the period 2015-2016. 

Fleet segment 

VL1218  VL1824  VL2440 

Vessels number 

(#) 

441 

256 

39 

Days at sea 

Fishing days 

- 

- 

- 

55065 

40922 

8062 

Total 
weight 

Target 
weight 

landings 

(tonne) 

5625 

7182 

1067 

species 

(tonne) 

2070 

2536 

458 

Landings 
dependency 

% 

37 

35 

43 

34 

34 

 
 
 
 
 
 
Figure  2.15:  Species  average  landing  weight  across  fleet  segments  in  eastern management  unit 
during the period 2015-2016 

Figure  2.16:  Landings  dependency  of  fleet  segments  by  species  estimated  on  average  landings 
value for the period 2015-2016 in eastern management unit. 

35 

35 

 
 
 
 
3  TOR 2 RELATIONSHIP BETWEEN EFFORT AND MORTALITY 

“In light of new data, review the outcomes concerning the relationship between effort and fishing 
mortality as prepared by the STECF EWG 18-09.” 

3.1  Additional linear regressions analyses 

During  the  EWG18-09  the  relationship  between  fishing  mortality  and  fishing  effort  for  some 
Western  Mediterranean  stocks  was  analysed.  However,  the  updated  stock  assessments  results 
from  EWG  18-12  were  yet  available  and  could  not  be  used  by  EWG  18-13,  so  the  analyses  are 
based on older STECF assessments.  

In particular, the stocks of Deep-water rose shrimp (DPS) in GSAs 1 and 5 and the stocks of Blue 
and red shrimp (ARA) in GSAs 5 and 6 were analysed in the western GSAs. In the eastern GSAs, 
the  relationship  between  fishing  effort  and  fishing  mortality  has  been  investigated  for  Norway 
lobster  (NEP)  and  giant  red  shrimp  in  GSA9,  DPS  and  hake  (HKE)  in  the  GSAs  9,10  and  11 
combined. 

The EWG analysed stocks for which stock assessments for combined GSAs were available: HKE in 
GSAs  1,  2,  5,  6  and  7  in  the  western  areas  and  HKE  and  DPS  in  GSAs  9,  10  and  11.  Effort 
(nominal  effort  times  fishing  days)  were  extracted  from  the  MEDBS  Data  call  2018.  For  GSA7, 
only  Spanish  data  were  available.  French  data  were  extracted  from  the  Annual  Economic  report 
for  the  period  2008-2016.  Fishing  mortalities  were  obtained  from  the  last  stock  assessments 
performed  on  the  target  species  by  the  STECF  stock  assessment working  groups.  The  results  of 
the  stock  assessments  report  a  fishing  mortality  for  age  classes,  not  split  between  the  various 
GSAs.  The  subdivision  was  made  using  as  a  factor  the  ratio  between  the  number  of  individuals 
per  age  class  caught  in  each  GSA  and  the  total  number  of  individuals  caught  in  the  combined 
GSAs. It was not possible to analyse the relationship between fishing mortality and fishing effort 
by  fleet-length  segments  as  the  catch  by  age  class  per  fleet  segment  was  not  available  to 
EWG18-13. 

3.1.1  Western GSAs (1-2-5-6-7) - Management Unit 1. 

3.1.1.1  Trend in nominal effort  

The  trends  of  nominal  trawl  effort  for  the  species  and  areas  under  study  are  reported  in  Figure 
3.1 and Table 3.1. The level of fishing effort is particularly high in the case of GSA6 compared to 
the other GSAs, being the fishing effort very low in the small GSA 2 including the Alboran Island. 
In the western geographical area, there is an evident tendency to a reduction over the years. In 
GSA7, data for France are not available for the entire time series. 

36 

36 

 
 
Figure  3.1  -  Trends  of  the  nominal  fishing  effort  of  the  bottom  trawling  for  the  fleet  fishing  in 
GSAs 1, 2, 5, 6 and 7 and in the management Unit 1 (whole western GSAs).. 

Table  3.1  -  Nominal  fishing  effort  of  the  bottom  trawling  for  the  fleet  fishing  in  GSAs  1,  2,  5,  6 
and 7 and in the management Unit 1 (whole western GSAs). 

count
ry 

gs
a 

SPAIN  1 

SPAIN  5 

SPAIN  6 

SPAIN  7 

FRAN
CE 

all 

7 

all 

2003 

2004 

2005 

2006 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

2017 

59154
85 

639604
3 
291174
1 
335612
73 
179833
7 

593961
3 
269471
3 
314466
73 
169188
8 

446673
94 

417728
87 

565438
4 
250939
4 
310800
81 
164582
3 
341740
6 
443070
88 

542729
6 
293908
2 
279661
30 
165707
6 
356889
1 
415584
75 

488383
7 
303558
2 
299568
99 
169503
3 
327235
1 
428437
02 

509589
9 
278417
5 
283393
56 
162365
1 
389230
4 
417353
85 

526903
5 
292765
0 
263060
47 
145605
4 
332283
0 
392816
16 

507897
5 
269439
9 
248058
84 
163029
8 
309373
4 
373032
90 

467458
8 
267559
1 
235539
25 
139236
5 
298527
9 
352817
48 

437155
0 
274596
7 
228219
90 
130280
3 
288043
3 
341227
43 

395365
2 
282855
0 
234228
70 
138605
9 
302189
6 
346130
27 

378029
4 
282128
6 
205131
26 
454957
2 
327459
9 
349388
77 

380763
3 
227557
1 
213522
82 
430799
2 

398709
6 
233043
3 
205930
59 
368844
4 

317434
78 

305990
32 

Nominal  trawl  effort  for  France  in  GSA  7  in  missing  years  (2004,  2005,  2016  and  2017)  was 
reconstructed as the mean effort in the available time series (2006-2015). 

3.1.1.2  Stock assessment data 

The catch numbers at age and the overall fishing mortalities at age for HKE in Management Unit 1 
and  the  corresponding  Fbar  (1-4)  estimated  during  the  STECF  EWG15-18  stock  assessment  for 
the  GSAs  1,5,6  and  7  combined  are  reported  in  Table  3.2  and  Table  3.3.  The  period  covered  is 
2006-2014. 

37 

37 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 3.2 – Fishing mortalities  at age for HKE in GSAs 1, 5, 6 and 7 combined. 

age  2003  2004  2005  2006  2007  2008  2009  2010  2011  2012  2013  2014 

0 

1 

2 

3 
4 
5 

0.48  0.52  0.43  0.5  0.22  0.38  0.38  0.28  0.09  0.08  0.1  0.11 

1.42  1.1  1.09  1.12  0.86  1.16  1.36  1.24  1.52  1.33  1.23  1.39 

1.63  1.13  1.23  1.29  1.47  1.12  1.56  1.62  1.99  1.78  1.92  1.25 

2.17  1.56  1.2  1.49  1.31  0.94  1.67  2.15  1.59  2.11  1.09  0.74 
1.89  1.81  1.57  0.58  1.28  0.74  1.22  2.49  2.14  0.71  1.52  0.2 
1.89  1.81  1.57  0.58  1.28  0.74  1.22  2.49  2.14  0.71  1.52  0.2 

Table 3.3 – Catch in number at age by GSA and by the whole western geographical area. 

gsa  age  2004 

2005 

2006 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

1 

1 

1 

1 

1 

1 

5 

5 

5 

5 

5 

5 

6 

6 

6 

6 

6 

6 

7 

7 

7 

7 

7 

7 

0 

1 

2 

3 

4 

5 

0 

1 

2 

3 

4 

5 

0 

1 

2 

3 

4 

5 

0 

1 

2 

3 

4 

5 

2009.9  1237.2  2729.4  84.4 

960.7 

1730.9  652.3 

497.5 

1320.7  672.3 

2095.8 

3019.6  1137.7  729.9 

1293.6  1020.4  2971.2  1586.2  3442.1  2409.2  1386.6  1174.3 

373.1 

347.8 

345.1 

247.8 

303.3 

418.1 

625.6 

491.4 

334.7 

356.3 

229.5 

43.9 

56.5 

48.8 

42.9 

44.7 

80.9 

78.4 

41.1 

26.2 

39.8 

29.0 

5.4 

0.7 

5.0 

1.0 

1.7 

1.9 

5.6 

1.8 

7.8 

3.9 

5.4 

1.3 

8.3 

0.4 

3.3 

0.5 

1.8 

0.3 

2.4 

0.2 

1.6 

0.2 

712.8 

475.7 

563.7 

134.4 

334.9 

67.2 

243.5 

100.9 

117.4 

257.0 

198.7 

556.3 

802.0 

964.6 

605.0 

461.3 

659.9 

792.3 

698.0 

329.3 

865.1 

750.6 

34.2 

59.7 

57.7 

3.4 

0.4 

0.0 

3.7 

0.6 

0.0 

4.7 

0.8 

0.2 

80.8 

14.6 

2.2 

0.5 

49.7 

12.4 

3.4 

0.5 

41.9 

114.0 

62.1 

35.7 

37.9 

70.6 

5.4 

2.3 

0.2 

4.5 

2.1 

0.1 

8.0 

1.1 

0.3 

7.1 

1.4 

0.2 

6.6 

2.4 

1.2 

7.7 

1.2 

0.3 

72261.9  54160.7  65725.4  45033.6  65573.8  56617.3  32119.6  8399.7  9332.2  6821.2  6236.6 

16972.8  18214.0  19361.4  11033.3  17188.6  27930.6  15818.5  17885.1  18416.9  19764.8  10043.1 

1394.8  2166.8  2471.8  2341.5  1963.0  1944.9  2025.3  2351.5  1757.4  2001.9  1890.4 

196.9 

281.7 

396.8 

350.1 

222.8 

216.9 

179.1 

224.0 

169.8 

161.7 

117.3 

14.4 

12.0 

27.0 

28.4 

9.7 

8.0 

54.6 

12.7 

46.4 

3.1 

94.5 

74.0 

52.1 

19.1 

25.7 

10.1 

5.9 

5.8 

1.3 

2.0 

8.0 

2.6 

6225.4  5826.3  2816.2  3210.9  12079.3  3840.9  7289.1  2678.6  2911.9  6287.1  6475.9 

5268.5  5690.6  4452.1  6096.8  16922.9  7803.9  9620.7  6188.1  6558.2  10374.0  10590.6 

1284.1  1565.1  1615.8  1821.2  1595.3  2371.4  1923.8  1403.3  915.1 

1439.6  1952.6 

161.9 

177.3 

239.8 

231.5 

148.3 

374.8 

209.7 

163.1 

100.8 

13.3 

23.6 

11.9 

15.4 

27.9 

21.2 

13.2 

15.4 

11.8 

2.8 

2.9 

6.2 

7.2 

5.1 

3.7 

1.5 

5.1 

0.9 

4.2 

1.0 

2.7 

0.2 

1.1 

0.1 

The F bar(1-4) by single GSA was estimated according two steps: 

1)  first  the  F  at  age  by  each  GSA  was  obtained  as  the  overall  F  at  a  given  age  (Table 
3.2)  divided  by  the  ratio  of  catch  in  number  at  given  age  of  the  given  GSA  out  the 
overall catch in number at the same age (Table 3.3); 

2)  then the Fbar (1-4) by GSA was estimated as arithmetic average on F at age 1 to 4 of 

each GSA (Table 3.4) 

Table 3.4 – Fbar (1-4) by GSA and overall western area (GSAs 1, 5, 6, 7) of HKE. 

gsa  age  2003  2004  2005  2006  2007  2008  2009  2010  2011  2012  2013  2014 

1 
5 
6 

1-4  0.141  0.184  0.111  0.064  0.078  0.075  0.124  0.230  0.202  0.133  0.159  0.072 
1-4  0.018  0.017  0.020  0.019  0.029  0.023  0.018  0.041  0.044  0.029  0.074  0.030 
1-4  0.874  0.700  0.736  0.675  0.704  0.523  0.780  0.991  1.072  0.911  0.853  0.452 

38 

38 

 
 
 
 
7 
1-4  0.743  0.496  0.404  0.364  0.417  0.368  0.532  0.610  0.489  0.410  0.353  0.339 
all  1-4  1.776  1.398  1.271  1.122  1.229  0.989  1.454  1.873  1.808  1.483  1.438  0.892 

3.1.1.3  Fishing mortality – nominal effort relationship 

HKE in GSAs 1, 2, 5, 6 and 7 

The relationship between the nominal effort of bottom trawling and the fishing mortality for HKE 
in  each  GSA  is  reported  in  Figure  3.2.  The  points  are  distributed  in  a  cloud  of  values.  The  lines 
reported  in  the  graph  hypothesize  a  linear  relationship  between  fishing  effort  and  fishing 
mortality. The red line represents the linear regression on the observed values. The black dashed 
line  represents  the  linear  regression  forced  to  pass  from  the  origin  according  to  the  reasonable 
assumption that F is nihil when no fishing effort is exerted on the stock. 

In the same way as the single GSAs, the values for the overall western Management Unit MU1 are 
distributed  in  a  cloud  that  does  not  allow  to  highlights  any  clear  relationship  between  fishing 
mortality and the nominal fishing effort for GSA 5, 6 and 7 (Figure 3.3). 

a 

c 

b 

d 

Figure 3.2 – Relationship between total nominal effort and Fbar for HKE in a) GSA1, b) GSA5, c) 
GSA6,  d)  GSA7.  Red  dashed  line:  linear  regression  on  the  observed  points.  Black  dashed  line: 
linear regression forced through the origin. 

An attempt to derive an overall relationship between nominal effort and F was tried to combined 
the values by GSA assuming that the HKE of western subareas belong to a single stock according 
to the EU STOCKMED project (Fiorentino et al., 2015) and that differences in catchability between 
fishing systems used in the different GSAs are negligible. 

39 

39 

 
 
 
 
 
 
Figure 3.3  – Relationship between nominal effort and Fbar(1-4) for hake in GSAs 1, 5, 6 and 7. 
Red  line:  linear  regression  for  each  GSA  and  for  the  GSAs  combined.  Black  dashed  line:  linear 
regression  forced  through  the  origin  for  each  GSA  and  GSAs  combined.  Data  for  the  individual 
GSAs are the same as in Figure 3.2 

The  main  parameters  of  the  estimated  relationships,  keeping  the  GSAs  separated  and  as  the 
whole Management Unit 1 (overall and combined) are reported in table 3.5. 

Table  3.5    –  Parameters  of  the  relationship  between  nominal  effort  and  Fbar  for  the  European 
Hake in GSAs 1, 5, 6 and 7. 

gsa1 

gsa5 
0.044 

gsa6 
1.222 

gsa7 
Variable 
Fbar = a+b*effort 
0.426 
(Intercept)  0.054 
1.48E-08  -4.66E-09  -1.66E-08  2.02E-09  -1.46E-08  Fbar = a+b*effort 
Effort 
Fbar = a+b*effort 
0.001 
r.squared  0.036 
0.556 
Pr(>F) 
Fbar = a+b*effort 
0.929 
2.49E-08  1.12E-08  2.70E-08  9.45E-08  3.38E-08  Fbar = 0+b*effort 
Effort 
4.30E-06  0.000101  1.10E-06  3.62E-06  1.81E-07  Fbar = 0+b*effort 
Pr(>F) 

gsa 1-7  model 
1.942 

0.107 
0.326 

0.034 
0.585 

0.002 
0.901 

3.1.2  Eastern GSAs (9-10-11) - Management Unit 2. 

40 

40 

 
 
 
 
 
 
 
 
 
3.1.2.1  Trend in nominal effort  

The  trends  of  nominal  fishing  effort  for  the  bottom  trawling  in  the  Management  Unit  2  was 
reported  in  Figure  3.4  and  Table  3.6.  A  notable  decreasing  trend  in  fishing  effort  was  observed 
from 2005 to 2011. In the last years, the effort remained quite constant with small fluctuations 
with exception of GSA 10. 

Figure  3.4  -  Trends  of  the  nominal  fishing  effort  of  the  bottom  trawling  for  the  fleet  fishing  in 
GSAs 9, 10 and 11 and in the management Unit 2 (whole eastern GSAs). 

Table 3.6 - Nominal fishing effort of the bottom trawling for the fleet fishing in GSAs 9, 10 and 11 
and in the management Unit 2 (whole eastern GSAs). 

9 

10 

gsa  2004  2005  2006  2007  2008  2009  2010  2011  2012  2013  2014  2015  2016  2017 
1058
1122
7373 
8001 
6556
5603
181 
044 
3832
4036
107 
734 
2097
2086
5661 
7779 

1077
5882 
5724
631 
4326
313 
2082
6826 

1072
4881 
6154
030 
3139
044 
2001
7955 

1470
0599 
8029
362 
7324
728 
3005
4689 

1240
4787 
7500
584 
5752
588 
2565
7959 

1278
2144 
7287
211 
5867
826 
2593
7181 

1069
6166 
5234
759 
3788
057 
1971
8982 

9997
907 
6051
158 
3824
269 
1987
3334 

1097
5696 
8797
448 
3298
194 
2307
1338 

1109
5335 
5510
629 
3087
757 
1969
3721 

1060
0947 
7647
088 
3254
088 
2150
2123 

1482
0339 
8070
376 
7706
431 
3059
7146 

1217
2751 
5997
764 
4370
758 
2254
1273 

11 

all 

To  verify  the  consistency  of  effort  data,  the  relationship  between  nominal  effort  expressed  as 
engine  power  (kW)  per  fishing  days,  and  the  fishing  days  as  supplied  by  the  MEDBS  data  call 
2018  was  compared.  In  all  the  three  GSAs  analysed  the  two  indices  of  fishing  effort  resulted 
highly correlated (Figure 3.5). 

41 

41 

 
 
 
 
 
Figure  3.5  –  Relationship  between  nominal  effort  (kW*fishing  days)  and  fishing  days  for  the 
bottom trawling fleet fishing in GSAs 9, 10 and 11. 

3.1.2.2  Stock assessment data 

The fishing mortalities for the analysed species are reported in Table 3.7. The fishing mortality for 
hake was estimated by STECF (2015a) for the GSAs 9, 10 and 11 combined. The period covered 
is 2006-2014, the Fbar was computed on the 1-4 age classes. 

The fishing mortality for deep-water rose shrimp was estimated by STECF (2016a) for the GSAs 
9, 10 and 11 combined. The period covered is 2006-2014, the Fbar was computed on the 0-2 age 
classes. 

Table  3.7  –  Fishing  mortalities  for  the  analyzed  stocks  in  the  western  eastern  Mediterranean 
(GSA 9,10,11). 

species  2006  2007  2008  2009  2010  2011  2012  2013  2014  2015 
1.22  1.05  1.07  0.89  1.04  1.22  0.97  0.86  1.05 
HKE 
1.08  0.78  0.64  0.54  0.59  0.56  0.8  0.77  0.83  0.87 
DPS 

3.1.2.3  Fishing mortality – nominal effort relationship 

Hake in GSAs 9, 10 and 11 

In  table  3.8  the  relationship  between  the  nominal  effort  of  bottom  trawling  and  the  fishing 
mortality for HKE in GSAs 9, 10 and 11 combined is reported. 

42 

42 

y = 214.49xR² = 0.888202000000400000060000008000000100000001200000014000000160000001800000001000020000300004000050000600007000080000Nominal effortFishing daysNominal effort vs Fishing days OTB GSA9 All fleet segmentsy = 183.23xR² = 0.852701000000200000030000004000000500000060000007000000800000090000001000000005000100001500020000250003000035000400004500050000Nominal effortFishing daysnominal effort vs fishing days OTB GSA10 All fleet segmentsy = 322.16x -2E+06R² = 0.9430100000020000003000000400000050000006000000700000080000009000000050001000015000200002500030000Nominal effortFishing daysnominal effort vs fishing days OTB GSA11 All fleet segments 
 
 
 
 
 
 
 
 
 
Table 3.8 – Catch in number at age by GSA and by the whole western geographical area. 

gsa  Age  2006 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

9 
9 
9 
9 
9 
9 
9 

0 
1 
2 
3 
4 
5 
6 

10  0 
10  1 
10  2 
10  3 
10  4 
10  5 
10  6 

11  0 
11  1 
11  2 
11  3 
11  4 
11  5 
11  6 

23197.4  32439.2  35592.9  60804.2  11959.7  41216.3  12689.7  13083.2  30613.1 
5961.2  7990.1  5752.0  6327.5  5038.5  5913.9  4275.2  7206.2  5584.2 
514.1 
1351.8  691.8 
132.5 
170.6 
53.8 
59.4 
25.8 
1.7 
5.5 
0.0 

319.6 
82.4 
34.3 
7.6 
0.9 

439.3 
77.0 
11.6 
2.8 
0.7 

326.8 
40.3 
18.3 
3.1 
0.5 

529.4 
96.1 
52.5 
13.0 
2.5 

403.2 
105.1 
39.8 
9.2 
1.9 

383.2 
92.5 
15.5 
11.6 
3.6 

73.1 
10.5 
0.0 
1.1 

15744.1  20385.0  13856.9  24960.6  13062.0  10180.3  15987.9  10749.6  6604.1 
6355.5  4805.3  3864.8  4205.5  6267.7  3711.8  4895.6  4711.3  4488.5 
723.7 
561.9 
65.8 
89.1 
6.7 
34.8 
8.9 
19.0 
6.3 
0.0 

448.7 
117.4 
17.6 
5.0 
1.1 

847.2 
105.7 
25.5 
19.1 
19.1 

506.6 
175.4 
46.2 
23.2 
5.9 

326.1 
77.5 
28.0 
3.3 
3.3 

367.6 
138.0 
54.3 
22.1 
4.2 

317.0 
57.6 
34.4 
10.4 
7.3 

450.8 
121.9 
41.1 
9.3 
1.5 

43482.2  9196.0  15671.6  5895.6  17472.6  15350.9  1290.9  2221.6  5346.4 
2286.3  1180.6  2506.2  1827.4  1216.3  3194.7  1100.6  981.9 
385.6 
38.9 
8.7 
8.7 
0.001 

515.9 
44.5 
4.2 
2.6 
2.1 
0.001 

112.1 
9.4 
1.7 
1.4 
0.001 

167.9 
13.8 
2.0 
1.7 
0.001 

91.5 
16.3 
7.0 
5.3 
0.001 

92.0 
8.8 
11.6 
5.6 
0.001 

171.4 
14.0 
3.2 
3.2 
0.001 

119.5 
20.5 
10.7 
3.9 
0.001 

213.7 
26.9 
7.7 
2.0 
0.001 

As  in  the  case  of  HKE  in  the  Management  Unit  1  an  overall  relationship  between  nominal  effort 
and  F  was  derived  combining  the  values  by  GSA  assuming  that  the  HKE  of  eastern  subareas 
belong to a single stock and that differences in catchability between fishing systems used in the 
different GSAs of Management Unit 2 are negligible. 

In  Figure  3.6  the  relationship  between  the  nominal  effort  of  bottom  trawling  and  the  fishing 
mortality for HKE in GSAs 9, 10 and 11 combined is reported. The red lines are the regressions 
between  F  and  effort  data  for  the  period  2006-2014  for  each  GSA  and  for  the  combined  GSAs. 
The  black  dashed  lines  are  the  regressions  forced  through  the  origin.  Most  of  them  was 
significant. 

43 

43 

 
 
 
Figure  3.6  –  Relationship  between  nominal  effort  and  Fbar  for  hake  in  GSAs  9,  10  and  11.  Red 
line:  linear  regression  for  each  GSA  and  for  the  GSAs  combined.  Black  dashed  line:  linear 
regression  forced  through  the  origin  for  each  GSA  and  for  the  GSAs  combined.  Data  for  the 
individual GSAs are the same as in Figure 3.7 

In Figure 3.7 the relationships between nominal effort and fishing mortality by keeping the GSAs 
separated and considering the overall values  in the whole management Unit 2 are displayed. In 
this case it is possible to identify a trend with values of fishing mortalities generally lower in the 
GSAs with low levels of fishing effort. 

a 

44 

b 

44 

 
 
 
 
c 

Figure  3.7  –  Relationship  between  nominal  effort  (kW*fishing  days)  and  fishing  days  for  the 
bottom trawling fleet fishing in GSAs 9 (a), 10 (b) and 11 (c). 

The  main  parameters  of  the  linear  relationships,  keeping  the  GSAs  separated  and  as  the  whole 
Management Unit 1 (overall and combined) are reported in Table 3.9 while catch numbers at age 
are shown in Table 3.10. 

Table  3.9  –  Parameters  of  the  relationship  between  nominal  effort  and  Fbar  for  the  European 
Hake in GSAs 9, 10 and 11. 

gsa9 

variable 
(Intercept)  -0.032 
effort 
r.squared  0.309 
Pr(>F) 
effort 
Pr(>F) 

gsa10 
0.299 

gsa11 
0.035 

gsa 9-11  model 
0.717 

Fbar = a+b*effort 
4.55E-08  2.19E-08  1.92E-08  1.47E-08  Fbar = a+b*effort 
Fbar = a+b*effort 
0.12 
Fbar = a+b*effort 
4.27E-08  6.68E-08  2.71E-08  4.68E-08  Fbar = 0+b*effort 
Fbar = 0+b*effort 
1.41E-08  2.84E-07  3.10E-05  0.465 

0.125 
0.351 

0.166 
0.276 

0.079 
0.465 

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

Table 3.10 – Catch in number at age by GSA and by the whole western geographical area. 

0 

622 

3390 
0 

2007 
4872 

1595  1511  947 

2271  3395  4044  2322  6787  3801  4422  4262 
1391  693 
0 

2008  2009  2010  2011  2012  2013  2014  2015 
gsa  age  2006 
0 
4924 
9 
9717  13071  7504  33199  7619  12103  26556  57461 
1 
26312  13339  20689  22068  40079  39825  44708  42176  37541  55556 
9 
2 
6957 
9 
1760 
9 
3 
104647  94380  43768  36865  36746  50686  56509  66703  55029  93574 
10  0 
53918  15897  20582  21409  18727  17949  21276  30173  28043  17970 
10  1 
1570 
10  2 
0 
10  3 
4925 
11  0 
5461 
11  1 
737 
11  2 
29 
11  3 
2006 
all  0 
114495  102255  55218  50273  45223  86293  65615  79661  82006  152400 
all  1 
85691  32567  43193  44554  59624  60002  67345  73293  66354  74571 
all  2 
9263 
all  3 

561 
488 
328 
3 
3 
0.3 
1733  337 
973 
1921  1077  819 
171 
157 
259 
10 
1 
0 
2008  2009  2010  2011  2012  2013  2014  2015 

256 
470 
0 
34 
2408  1486  855 
2227  1361  944 
56 
228 
43 
21 

2858  4039  4776  3020  7099  4022  5339  4671 

1157 
16 
3003 
3331 
449 
18 
2007 

362 
0 
1365 
1045 
48 
5 

878 
9 
421 
770 
38 
2 

146 
1 

75 
2 

4996 

45 

45 

 
 
 
 
 
 
 
The relationship between F and effort for DPS in GSAs 9, 10 and 11 separated and combined is 
showed in Figure 3.8 and 3.9. 

a

b 

c 

Figure  3.8  –  Relationship  between  nominal  effort  and  Fbar  for  Deep-water  rose  shrimp  in  GSA 
9(a),  10  (b)  and  11  (c).  Red  line:  linear  regression  for  each  GSA  and  for  the  GSAs  combined. 
Black  dashed  line:  linear  regression  forced  through  the  origin  for  each  GSA  and  for  the  GSAs 
combined. 

46 

46 

 
 
 
 
Figure 3.9 – Relationship between nominal effort and Fbar for Deep-water rose shrimp in GSAs 9, 
10  and  11.  Red  line:  linear  regression  for  each  GSA  and  for  the  GSAs  combined.  Black  dashed 
line:  linear  regression  forced  through  the  origin  for  each  GSA  and  for  the  GSAs  combined.  Data 
for the individual GSAs are the same as in Figure 3.8 

The  main  parameters  of  the  linear  relationships,  keeping  the  GSAs  separated  and  as  the  whole 
Management Unit 1 (overall and combined) are reported in table 3.11. 

Table 3.11 – Parameters of the relationship between nominal effort and Fbar for Deep-water rose 
shrimp in GSAs 9, 10 and 11. 

variable  gsa9 

gsa10 

gsa11 

gsa  9-
11 

model 

(Intercep
t) 
effort 

r.square
d 
Pr(>F) 

effort 

Pr(>F) 

1.112 

-0.148 

-0.066  0.059 

-5.96E-
08 
0.242 

6.70E-
08 
0.401 

2.24E-
08 
0.859 

3.15E-
08 
0.203 

0.149 

0.049 

0 

0.191 

3.84E-
08 
1.87E-
06 

7.20E-
09 
0.00039
8 

7.20E-
09 
0.0004  0.191 

3.41E-
08 

Fbar 
a+b*effort 
Fbar 
a+b*effort 
Fbar 
a+b*effort 
Fbar 
a+b*effort 
Fbar 
0+b*effort 
Fbar 
0+b*effort 

= 

= 

= 

= 

= 

= 

47 

47 

 
 
 
 
 
 
3.1.3  Conclusions 

The analyses carried out during STECF EWG18-13 allowed to deepen the results obtained during 
the previous meeting STECF (2018a). The fishing mortality values of the main demersal stocks in 
western Mediterranean are not clearly correlated to the nominal effort exerted by the trawl fleets 
exploiting the stocks. 

These results are in accordance with the results showed by Cardinale et al. (2017). According to 
the authors, the lack of a significant relationship between F and effort was observed and should 
be the effect of a combination of factors such as; 

i) 

ii) 
iii) 

iv) 

short  time  series  of  the  assessments  and  lack  of  sufficient  contrast  F/effort  (i.e.  the  F 
values are concentrate in a period of high fishing pressure on the stock);  
reduction in effort mostly due to the decommissioning of the less efficient vessels;  
temporal change in the spatial pattern in fishing effort in relation to the abundance of the 
main target stocks, the markets demands, etc.;  
nominal effort trend not reflecting the real fishing effort. 

Furthermore the absence of clear pattern might be also due in low accuracy in measuring fishing 
mortality. 

During  STECF  EWG18-13  the  three  stocks  analyzed  did  not  show  a  strong  correlation  between 
fishing effort and fishing mortality. However in all  the 3 cases analysed the lowest F values were 
obtained in the GSAs featured by lowest effort values. 

The  relationship  derived  assuming  the  stock  unit  of  HKE  and  DPS  inside  the  two  manangement 
Units  according  to  the  EU  STOCKMED  project  (Fiorentino  et  al.,  2015)  and  that  differences  in 
catchability between fishing systems used in the different GSAs inside each Management Unit are 
negligible were not considered very reliable by the WG due to expected differences in size of the 
standing  stock  in  each  GSA  that  could  affect  the  catchability.  However,  considering  the  lack  of 
contrast  in  the  data  within  the  single  GSA,  this  approach  could  be  worthy  for  further 
investigations. Because of the differences in hours of fishing per fishing day in different GSAs, the 
WG  recommended  to  increase  accuracy  in  measuring  the  fishing  activity  (e.g.  as  fishing  hours 
instead  of  fishing  days)  would  be  probably  necessary  to  estimate  a  more  reliable  fishing  effort 
exerted on each stock. 

3.2  Alternative non-linear relationships 

The  second  paragraph  of  TOR  2  requested  the  EWG-18-13  to  estimate  three  different 
relationships  between  changes  in  F  and  changes  in  nominal  effort.  As  the  linear  relationship 
generally  used  was  considered  by  the  EWG-18-09  as  overoptimistic,  non-linear  relationships 
should be estimated to cover the conservative and pessimistic scenarios. 

The  EWG-18-13  explored  the  potential  use  of  the  non-linear  relationship  𝐹 = 𝑎 ∗ 𝐸𝑏,  which  can 
cover different scenarios from optimistic to pessimistic depending on the value of the parameter 
b, which is generally called elasticity coefficient. The elasticity coefficient indicates the percentage 
change that will occur in one variable (F) when another variable (E) changes one percent.  

3.2.1  Using a regression model 

The equation reported above can be expressed also in terms of variations from time t-1 to time t 
as follows:  

𝐹𝑡
𝐹𝑡−1

= (

𝑏
)

𝐸𝑡
𝐸𝑡−1

A value of b equal to 1 means a linear relationship where a percentage change by 1% in nominal 
effort  would  produce  a  1%  variation  also  in  F.  A  value  lower  (higher)  than  1  means  that  the 
percentage variation in F would be lower (higher) than the percentage variation in nominal effort. 
As  b  =  1  is  considered  optimistic,  the  estimation  of  this  model  on  real  data  was  expected  to 
produce values of the elasticity coefficient b between 0 and 1. 

Data on the demersal fisheries in GSA 9 were used to test the relationship. The fishing mortality 
at age from 2006 to 2015 of vessels using OTB were collected for European hake (HKE), Norway 
lobster  (NEP),  Deep-water  rose  shrimp  (DPS),  striped  mullet  (MUT)  and  surmullet  (MUR)  (see  

48 

48 

 
 
https://www.politicheagricole.it/flex/cm/pages/ServeBLOB.php/L/IT/IDPagina/12478  for  details). 
Regarding the nominal effort, expressed in terms of average KW by days at sea, data on KW and 
days at sea for the same period were collected for the three fleet segments using OTB in GSA 9: 
demersal trawlers of length classes 12-18, 18-24 and 24-40 (DTS1218, DTS1824 and DTS2440). 
Data on nominal effort is consistent with the dataset from the AER. Days at sea from the AER for 
the  period  2008-2015  were  integrated  with  2006  and  2007  data  provided  by  the  EWG  experts. 
The average KW of the active vessels, which is not available by GSA from the AER, was provided 
by  the  EWG  experts.  To  overcome  the  problem  of  the  different  level  of  aggregation  between  F 
and E, fishing mortality at age was split by fleet segment using the landings proportions. Finally, 
a time series of partial Fbar was calculated for each stock and fleet segment by using the stock 
specific ranges adopted for the stock-assessment procedures.  

Regressions of Fbar on E were carried out to estimate a total of 15 elasticity coefficients, one for 
each  combination  stock-fleet  segment.  Regressions  were  performed  following  two  different 
approaches: 

1)  Estimating a and b for each combination stock-fleet; 
2)  Estimating a by stock and b for each combination stock-fleet. 

The results obtained on the coefficient b by the two approaches are reported in Table 3.12. The 
use  of  the same  a  for all  fleet  segments  (approach  2)  determined also  a higher  homogeneity  in 
the  values  of  b  for  the  different  stocks.  However,  some  unacceptable  values  for  b  are  present 
under  both  approaches.  There  are  some  coefficients  at  zero  (or  negative)  in  approach  1  (4 
values)  and  approach  2  (3  values).  The  zero  value  would  mean  that  there  is  no  relationship 
between F and E for those stock-fleet combinations, while a negative value would be in contrast 
with the theoretical expected relationship as an increase in E would produce a decrease in F.  

The  other  estimated  values  are  all  acceptable  even  though  in  some  cases  the  coefficients  are 
greater than 1. A value greater than 1 would mean that a percentage reduction in nominal effort 
by 10% would determine a reduction in F higher than 10%. This is in contrast with an expected 
value of b between 0 and 1. 

Figure 3.10 reports the actual vs. fitted data for each of the 15 combinations analysed. Quadratic 
trend  lines  are  designed  on  the  fitted  data  estimated  under  the  two  approaches  for  each 
combination  stock-fleet:  blue  line  relates  to  the  approach  1  and  grey  line  to  approach  2.  The 
trend  lines  allow  the  reader  to  have  an  idea  of  the  similarity  among  the  outputs  from  the  two 
different approaches. 

Table 3.12. Estimates of model’s b parameter. 

Estimates of b based on the approach 1 

Estimates of b based on the approach 2 

HKE 
NEP 
DPS 
MUT 
MUR 

DTS1218  DTS1824  DTS2440 
0.00 
0.22 
0.00 
0.98 
0.00 

1.23 
0.36 
0.00 
1.11 
1.30 

0.55 
0.82 
0.81 
0.58 
1.44 

HKE 
NEP 
DPS 
MUT 
MUR 

DTS1218  DTS1824  DTS2440 
0.55 
0.40 
-0.29 
0.97 
1.73 

0.59 
0.43 
-0.23 
0.95 
1.70 

0.48 
0.34 
-0.43 
0.87 
1.75 

49 

49 

 
  
 
  
 
 
 
Figure 3.10 – Relationship F-E: fitted vs. actual data with trend lines for each estimation methods 
for  each  of  the  15  combinations  between  stock  and  fleet  segment.  blue  line  relates  to  the 
approach 1 and grey line to approach 2. 

3.2.2  Using a mixed effect model 

An alternative to estimate the non-linear relationship between fishing mortality and fishing effort 
was explored using mixed effects models. These models allow the fit to all the stocks and fleets 
simultaneously, borrowing information across each.  

Preliminary  results  are  presented  in  Annex  01  and  Figure  3.11.  Results  show  a  steeper 
relationship between fishing mortality and effort for MUT caught by DTS 1218 and 1824, and HKE 
caught  by  DTS  1824.  On  the  other  hand  MUR  shows  the  flattest  relationship,  with  the  other 
species and fleet segments somewhere in between these two groups.  

Note that this approach can’t be simply converted into the exponential model used in the previous 
section due to the inclusion of the random effect on intercept and slope by stock. To work within 
a  bio-economic  model  these  results  would  have  to  be  embedded  in  such  model  and  fishing 
mortality predicted from the LME model. 

50 

50 

0.000.200.400.600.801.001.200500010000150002000025000300003500040000HKE DTS18240.000.050.100.150.200.250.300.350.400.450.5005000100001500020000250003000035000HKE DTS1218-0.010.000.010.020.030.040.050.060.0705001000150020002500HKE DTS24400.000.050.100.150.200.250.300.350.400.450.500500010000150002000025000300003500040000NEP DTS18240.000.050.100.150.200.2505000100001500020000250003000035000NEP DTS12180.000.010.020.030.040.050.0605001000150020002500NEP DTS24400.000.050.100.150.200.250.300.350.400.450.500500010000150002000025000300003500040000DPS DTS18240.000.050.100.150.200.250.3005000100001500020000250003000035000DPS DTS12180.000.010.020.030.040.050.060.070.080.090.1005001000150020002500DPS DTS24400.000.200.400.600.801.001.200500010000150002000025000300003500040000MUT DTS18240.000.100.200.300.400.500.600.700.8005000100001500020000250003000035000MUT DTS1218-0.010.000.010.020.030.040.050.0605001000150020002500MUT DTS2440-0.020.000.020.040.060.080.100.120.140.160.180500010000150002000025000300003500040000MUR DTS1824-0.010.000.010.020.030.040.050.060.070.0805000100001500020000250003000035000MUR DTS12180.000.000.000.000.000.000.010.010.0105001000150020002500MUR DTS2440 
 
 
 
 
 
Figure  3.11  Non-linear  relationship  between  fishing  mortality  and  fishing  effort  for  the  relevant 
species and fleet segments. Above: all together. Below: separated by stock and by fleet 

3.3  Exploration  of  alternative  measures  of  fishing  effort  to  try  to  improve  the  F-E 

relationship  

With regards to gear size and nominal effort interactions the EWG recalls what was done by EWG 
18-09 (STECF, 2018a) which is still considered valid. 

3.3.1  Using hours fished and account for targeting 

3.3.1.1  Principles 

While discussing ToR 2 results in the plenary of EWG 1813, it was observed that the fishing days 
used  in  the  nominal  effort  measurements  were  not  the  same  depending  on  the  country.  Each 
country  or  regional  legislations  define  different  limits  of  daily  fishing  operations,  including  hours 

51 

51 

 
 
 
 
per day allowed for fishing. In this sense, a fishing day in Spain means that the boat has been out 
of the port no more than twelve hours, although there are the exceptions of the fleets exploiting 
the Ibiza and Alboran Island crustacean fisheries in the slope which can be out of the port sixteen 
hours due to the distance to those fishing grounds. On the other hand, in Italy a more elastic and 
variable  or  no  limitations  at  all  does  occur,  and  in  some  cases  daily  fishing  activity  is  deployed 
day  and night. There  is  also an  exception  that  allows  some  boats  to  stay  at  sea  more  than  one 
day. For these differences in the time allowed at sea between countries, the measure of the effort 
is not standardized throughout the countries involved in the future MAP. It was also pointed out 
that  the  other  variable  included  in  the  effort  measure,  the  KW  of  each  of  the  vessels,  is  an 
uncertain value and that perhaps the effort measure should be more simple and reliable by just 
using  fishing  days.  An  option  that  was  proposed  during  the  discussion  was  to  use  hours  of 
effective  fishing  instead  of  fishing  days,  which  would  be  a  standardized  measure  of  effort 
throughout all the countries involved. It is pointed out that the calculation of the effective fishing 
hours  can  be  done  either  by  using  VMS,  not  currently  requested  in  the  official  data  calls,  or  by 
using the information in the log-books which is currently requested not by day but by haul and is 
requested  in  the  official  data  calls.  Both  data  sources  would  allow  computing  the  effort  as 
effective  fishing  hours.  Having  more  fine  measures  of  effort  could  allow  improving  in  the 
exploration of the relationship between F and effort in the core of the proposal of the MAP. VMS 
have  the  advantage  that  could  provide  a  longer  time  series  of  effort  as  their  implementation 
started in 2006.  

The so-called nominal effort is the sum of the fishing effort units exerted on a resource in a given 
time  period.  It  can  only be  considered  effective effort  when  effort has been  standardized  taking 
into account the differences in fishing power and efficiency, and likely also differences in skipper 
skill or any technological difference among vessels or fleets that may change the vessel’s fishing 
power . Only under such conditions a direct proportionality with fishing mortality can be expected.  

In  the  Mediterranean  Sea  different  fractions  of  the  fleets  target  different  species  assemblages 
with  very  different  species  composition  and  with  fishing  grounds  spatially  separated.  In 
consequence,  not  all  the  fishing  effort  of  a  fleet  can  be  computed  to  all  the  species  or  species 
assemblages because they not remove individuals of all the populations but only for those which 
are present in the fishing ground where they are operating. Fishing effort should be calculated in 
consequence  separately  for  each  fishing  trip  targeting  specific  species  or  species  assemblages. 
Such quantification is more effective than the use of total effort of a fleet without considering on 
which species or assemblage the effort is exerted. Fishing mortality rates on each single stock is 
in  most  of  the  cases  caused  by  only  a  fraction  of  the  fleet,  for  which  the  specific  resources  are 
vulnerable. A quantification of effort is always necessary, but availability of precise information is 
particularly critical in the case of an effort regime management.    

Catches or landings of each fishing trip must be quantified considering whether it targets a given 
species/species  mix  or  not.  Uncertain  information  on  effort  partitioning  and  their  links  with 
captures  not  accurately  reflect  annual  changes  in  fish  populations  and  can  lead  to  incorrect 
management  advice. Statistical  and  ad-hoc  approaches  can  be  used  for  the  identification  of 
directed  fishing  effort  towards  certain  species.  These  approaches  generally  use  trip  species 
composition to infer the fished grounds exploited during a trip.  Defined minimum proportions of 
a species or group of species in the catch is in general used for defining whether there was a clear 
target  of  a  fishing  operation. However,  the  presence  of  these  species  not  always  represent  an 
important  fraction  even  when  fishers  intended  to  catch  them  based  on  past  experience.  In 
consequence,  classification  of  a  trip  as  directed  to  certain  species  or  species  complex  (and  its 
relative fishing effort) only based on certain arbitrary proportions of target species over the whole 
catch  may  be  misleading.  Without  a  correct  effort  partitioning,  trends  in  the  commercial  catch 
rates  (i.e.CPUEs)  may  not  reflect  the  true  trend  in  the  fisheries  resource  (Walters  2003),  which 
may result in a mismanagement of the fisheries resource and conflicts between stakeholders. 

3.3.1.2  Examples  of  use  of  specific  effort  information  related  to  directed  effort  and  a  more 

precise effort quantification (effective hours fishing). 

To obtain a series of the measure for directiveness of the fishing per year, haul-by-haul data are 
required  that  cover  the  entire  fished  area.  The  information  so  disaggregated  can  be  used  for 
different  analyses  of  the  fleet  (spatial  distribution  of  fishing  effort  by  assemblage,  seasonal 

52 

52 

 
changes,  links  between  fishing  effort  and  fishing  mortality,  use  of  models  that  need  information 
on effort) which result necessary for the assessment of the status of exploitation of mix fisheries 
exploiting groundfish assemblages and for management purposes.  

As shown in ToR 1, a time series of hours fished was not available to EWG 18-13. To illustrate the 
potential benefits of using hours fished, the results of older studies were reported (Abella et al., 
2001, 2006, 2010). They present the analysis of the assemblages that are exploited by the most 
important fleet in the Tyrrhenian Sea (Viareggio fleet) operating in GSA9 (Figure 3.12).  

Figure  3.12.  Studied  area,  grid  used  for  collection  of  information  on  geographic  allocation  of 
fishing effort and trip information sheet  

A  catch  assessment  project  started  in  1990  many  years  before  the  VMS  system  by  installing 
satellite transmitters onboard the fishing vessels have been enforced. The aim of the project was 
to  obtain  more  precise  information  about  fleet  behaviour,  fishing  strategies  and  their  impact  on 
resources,  target  species,  spatial  distribution  of  the  fleets,  seasonal  changes  in  targets, 
identification  of  targeted  species  assemblages,  quantification  of  fishing  effort,  comparison  of 
vessels fishing power, etc. At the moment of the start of the project, fishers were not obliged to 
fill  logbooks  and  hence  no  information  on  the  fishery  on  all  the  fishing  strategies  that  were 
present, effective time fishing by fishing strategy, fishing areas, catch composition by vessel type, 
size structure of the landed species and many other information useful for giving advice.  

Catch  and  effort  survey  was  carried  out  through  interviews  done  to  skippers  during  the  landing 
operations.  The  survey  covered  all  the  fisheries  in  the  port,  for  demersal  and  small  pelagic 
species and includes small-scale boats. Fishermen were requested to furnish precise information 
on fishing area(s) and depths by haul. The operation areas were selected in a fine grid positioned 
on  the  other  side  of  the  sheet,  that  covers  the  fishing  areas  of  the  Viareggio  fleet  selecting  the 
squares where they operated this day. The sheet also included information regarding the fishing 
vessel code in in order to allow the knowledge of with their structural characteristics, date, used 
gear, number of tows, duration of each tow, fishers’ target, time of departure and arrival to the 
port  and  the  landed  composition  as  number  of  boxes  or  fractions  of  each  species  for  each  tow. 
Samples  were  purchased  for reconstruction  of  the  size  composition  of  the  landings  for the main 
species.  Information  of  possible  discard  fractions  is  collected  using  observers  onboard  during 
fishing  operations.  All  the  mentioned  information  was  stored  in  a  special  data  base  in  ACCESS. 
Georeferenced  information  regarding  fishing  effort  by  vessel,  fleet,  fishing  strategy,  species, 
period,  can  be  represented  using  the  specially  designed  software  MLFD  (mapper  of  landed  fish 
data)  (Fortunati  et  al,  2001,  Abella  et  al,  2001).  After  the  departure  of  the  EU  Data  Collection 
program, the survey was not interrupted as sampling intensity (and hence quality and quantity of 
information) was much higher even though restricted to this specific port.   

The  available  data  allowed  to  discriminate  vessels’  fishing  trips  exploiting  the  different  grounds 
where different species assemblages inhabit. This allowed to quantify more precisely the amount 
of  effort  exerted  on  each  species  or  species  assemblage.  Most  of  the  species  live  inside  limited 
depth intervals or grounds. Fishers’ experience is used for determining their targets. The distance 
from the port determines the fraction of the daily trip that they can dedicate to fish capture and 

53 

53 

 
 
 
the rest of the time is navigation or searching of adequate grounds where fishers expect adequate 
yields.  

The detailed information on effort amount directed to each fish assemblage derived from the ad 
hoc  project  allowed  also  to  perform  stock  assessment  in  the  area  using  a  non-equilibrium 
production model  (ASPIC)  for the assessment  of  the  coastal  demersal  assemblage  (Abella  et  al, 
2010).    This  was  useful  for  giving  advice  in  situations  of  limited  data  for  a  fishery  where  prior 
stock assessments have never been done.  Italian official statistics especially are still not able to 
provide    enough  information  on  operation  areas,  nor  on  the  target  species  and  effective  effort 
exerted by a single vessel during each fishing trip. Population projections using ASPIC-P allowed 
evaluating  future  harvest  strategies  for  a  10-year  period  for  the  8  main  species  that  represents 
more  of  the  65%  of  the  landings  and  for  the  whole  assemblage.  Data  of  landings  and  specific 
effort  of  the  Viareggio  fleet  for  the  period  1990-2008  were  analysed.  The  statistical  analysed 
population  was  constituted  by  all  the  vessels  that  operated  along  the  years  from  the  Viareggio 
port using bottom trawl nets and targeting coastal demersal species. The abundance index LPUE 
(landings  per  unit  effort)  was  calculated  for  each  species  as  “total  weight  in  kg  of  the 
landings/fishing  effort”  with  effort  expressed  as  effective  hours  fishing  excluding  time  spent  for 
transfers  to  the  fishing  grounds.  In  the  Mediterranean  few  attempts  of  using  production  models 
based  on  catch  and  effort  data  have  been  done  as  alternative  of  analytic  approaches  when 
information  on  age  structure  of  the  catches  is  not  available.  This  is  probably  due  to  the  lack  of 
adequate information on amounts of directed fishing effort and precise quantification of effective 
fishing effort.  

These  data  sets  allowed  then  to  analyse  the  relationships  between  fishing  effort  f  and  fishing 
mortality  F,  the  information  of  directed  fishing  effort  in  effective  hours  fishing  for  each 
species/species  assemblage  was  plotted  against  corresponding  estimates  of  F  based  on  age 
structure  of  the  catches  for  the  years  1991-2008.  (Abella  et  al  2010).  Reasonable  results  were 
obtained (Figure 3.13). It is considered that the exclusive use of the specific effort directed to a 
stock instead of the overall effort of an entire fleet category (i.e. of all  the bottom trawlers or of 
an entire segment of the fleet targeting different resources) eliminates elements of noise. In fact, 
the  use  of  overall  effort  does  not  consider  the  number  of  vessels  or  number  of  trips  that  really 
have exploited certain species nor the changes in time in fishing pressure that may have occurred 
due  to  changes  in  targets  or  other  causes.  The  next  figure  shows  the  results  obtained  in  the 
above-mentioned study for some species exploited in the coastal area.  

54 

54 

 
Figure  3.13.  Relationship  between  F  and  f  for  single  species  and  for  a  species  pool  of  the 
generalist coastal fishery. Years 1991-2008, Viareggio fleet in GSA 9 

A  second  part  of  the  study  was  to  investigate  differences  in  fishing  power  of  the  single  vessels 
involved.  

Some  vessel’s  characteristics  (size,  power)  may  condition  the  impact  of  a  unit  effort  on  a  fish 
population, and hence standardization of fishing vessels should be necessarily done. When a gear 
is  utilized  by  vessels  with  quite  different  characteristics  or  used  different  kinds  of  gear,  the 
respective efforts must be standardized in order to compute overall effort. 

It is frequently used the product of fishing activity with some measure of fishing capacity (engine 
power, size of the vessel, TSL) if they can in some way standardize the effort information of the 
single  vessels.  However,  the  relationships  between  structural  characteristics  of  the  vessel  with 
fishing power (here expressed in terms in catch per unit of effort, CPUE)  are seldom linear, and 
most of the time relationships show asymptotical shapes. This was also observed in the Viareggio 
fishery in GSA9 for several species, using information on single trips operating in the same depth 
interval,  with  the  same  target  and  in  the  same  period  of  the  year.  Asymptotic  or  almost  flat 
behaviour of the curves were found for the main commercial species in the area. Only in the case 
of very small vessels the differences in fishing power appears more evident (Figure 3.14). 

The  fractions  of  the  fleet  targeting  different  assemblages  were  fairly  uniform  regarding  their 
structural  characteristics  (Kw,  TSL,  size),  and  the  comparison  of  fishing  power  with  such 
characteristics of the vessels suggested an almost similar fishing power in the observed range of 
sizes. In consequence, no standardization based on capacity measures was considered necessary.  

55 

55 

 
 
 
The  assumption  of  a  simple  linear  relationship  expressing  effort  as  kw  x  days  would  have 
produced worse results. Often nominal effort measures as activity x capacity (i.e. KW x days) are 
used  in  management  areas  where  different  fleets  operate  but  with  vessels  characterised  by 
different  size  structure  (as  it  often  occurs  in  the  Mediterranean).  Differences  in  the  amount  of 
effort  (and  potential  fishing  power)  of  such  different  fleets  could  not  be  assessed  correctly  if  a 
more detailed relationship is not used. An example In the case the simplistic measure of effort is 
kw x days fishing is used for comparing fleets fishing capacity, in a fleet a  100 vessels operate 
with  engine  power  of  300kw  and  in  other  fleet  b    300vessels  with  100kw  engines,  it  is  hard  to 
assume that both fleets  have the same fishing power even though a product can be exactly the 
same.   

Figure  3.14.  Relationship  between  engine  power  (HP)  and  catch  performance  (Kg/h)  for  Mullus 
barbatus  (a),  Merluccius  merluccius  (b),  Nephrops  norvegicus(c)  and  in  catch  days  (about  10 
hours effective fishing) for a species pool of the generalist bottom trawl fishery(d) (Abella et al. 
2006) 

Other  problem  regards  the  fishing  mortality  that  a  unit  of  effort  may  produce.  Catchability 
coefficient may be quite different in different areas in the same management unit not only due to 
changes  in  availability/vulnerability  of  the  resources.  The  rate  between  the  swept  area  and  the 
whole  ground  where  the  stock  is  present  and  potentially  vulnerable  can  be  quite  different.  One 
unit of effort removes a fraction of the population and produces mortality. As the area where the 
resource is distributed is much bigger, the negative impact of one unit of effort will be lower.  

This  is  especially  critical  for  instance  for  Nephrops  that  concentrates  in  spots  where  specific 
characteristics  of  the  grounds  make  possible  settlement  and  adequate  food  supply.  The  units  of 
effort  exerted  over  areas  with  different  sizes  must  be  standardized  whenever  we  intend  to 
compare such local efforts with fishing mortality estimates for the same areas. 

An example of the use of standardized measures of effort considering area sizes is shown in the 
next  figure.    It  regards  the  use  of  information  proceeding  from  different  Nephrops  grounds 
exploited with different rates by different fleets operating in GSA9. Data proceed also in this case 
from  the  above-mentioned  program  of  sampling  using  interviews  during  the  landing  operations 
(STECF,  2004)  A  fairly  good  correlation  was  obtained  between  effort  directed  to  fish  Norway 
lobster on four different grounds in GSA9 expressed as standardised effort as number of fishing 
trips targeting this species per unit area and estimates of total mortality Z rates (Figure 3.15). Z 
estimates  for  each  of  these  areas  derive  from  analysis  of  catch  curves.  One  of  the  4  analysed 
grounds is considered very lightly unexploited and others full or overexploited and such contrast 

56 

56 

 
 
is considered quite informative. The intercept with the Y axis (0 effort) can be considered a proxy 
estimate of natural mortality rate. 

Figure  3.15.  Relationship  between  effort  (number  of  fishing  trips  standardised  by  the  size  of 
fishing  grounds)  and  total  mortality  rate  Z  for  a  number  of  Nephrops  fishing  grounds  in  GSA  9, 
years 1998-2000 . Estimates of total mortality rates Z are derived from the size structure 

Catch  per  Unit  Effort  (CPUE)  is  the  most  commonly  used  index  in  fisheries  science  to  monitor 
stock  status  and  the  effect  of  management  measures.  CPUE  are  often  used  as  an  index  of 
Biomass.  However,  for  a  proper  use  of  CPUE  aimed  at  assessing  stock  status  in  multispecies 
fisheries,  also  in  this  case  we  need  to  consider how fishers direct  their effort  to each  species  or 
specie  assemblage.  Trends  in  the  commercial  catch  rate  may  not  reflect  the  true  trend  in  the 
fisheries resource (Walters 2003), which may result in a mismanagement of the fisheries resource 
and conflicts between stakeholders. 

Mutsert  et  al,  (2008)  stated  that  whenever  a  mis-specification  of  any  change  in  the  targets  of 
fractions of the fleet and a lack of detailed knowledge of effort allocation by targets occur, data of 
landings may drive to errors. Many false cases of collapse based simply upon a decline of catch or 
catch rates or in analyses of changes in the trophic level (i.e. in the Pauly et al analysis of Black 
Sea and  Med  landings)  (Pauly  et  al,  2005)  have been  observed.  Mutsert  et  al  (2008)  state  that 
targeting, variability in fishing effort, and market forces should be well known.  

3.3.2  Estimating the swept areas. 

EWG 18-13 attempted to refine the relationships between fishing mortalities (Fbar) and Effort by 
correcting  the  effort  metric  by  accounting  for  the  dimension  of  the  fishing  gears  in  use. 
Accounting  for  the  gear  size  should  make  possible  to  deduce  the  area  swept  during  trawling 
events, which is expected to relate more to different catching power, beyond using hours fished 
only. 

The  refinement  of  the  effort  metric  using  swept  area  estimates  requires  using  individual  vessel 
data because it is required to know the individual specifications of the fishing vessels such as the 
LOA  or  KW,  the  gear  type  in  use  during  the  haul-trip,  and  the  conducted  fisheries  (target 
assemblage).  France  provided  two  types  of  individual  vessels  datasets  for  the  Western  Med. 
These  two  datasets  differ  by  nature;  one  being  trip-by-trip  logbooks  data  covering  all  the  trips 
made  with  the  area  and  the  second  being  the  haul  by  haul  observer  on-board  data  which  is  a 
subset only of the fisheries by nature. Given that observer on-board data are not fully covering all 
the trips at sea, these data do therefore not comprehend the total catches made on stocks by the 
entire  fleet.  Relating  declared  effort  in  the  data  to  swept  area  estimates  and  eventually  to  the 
overall  stock  assessed  F  would  be  only  superficial  at  this  stage  and  has  therefore  not  been 
attempted here. 

Some  knowledge  on  the  dimensions  of  bottom-contact  fishing  gears  has  been recently  collected 
and reviewed at the European scale in a recently published study (Eigaard et al 2016), including 
fisheries  specific  to  the  Med  (e.g.,  OT_MIX_DMF_BEN;  Figure  3.16).  These  estimated  gear 
dimensions are re-used here to deduce the gear widths of trawls and dimension of seines. We use 
these estimates here for assigning a gear width to each record in both the datasets.  

57 

57 

 
 
 
 
Figure  3.16..  Relationship  between  total  gear  width  (door  spread)  and  vessel  size  by  BENTHIS 
métier  for  OT.  The  shaded  (grey)  areas  define  Monte  Carlo  boot-strapped  95%  confidence 
intervals (Eigaard et al., 2016). 

The  haul-by-haul  data  has  the  advantage  to  record  the  start  and  end  positions  of  the  hauls, 
making it possible to compute an estimate of the distance trawled (assuming a linear path) and 
together  with  the  gear  width,  compute  the  swept  area  for  specific  hauls  (Figure  3.17,  right 
panel).  The  trip-by-trip  data  do  not  record  haul  positions  by  the  total  fishing  effort  of  the  trip. 
Assuming an average fishing speed, the swept area per trip can also be deduced from the vessel-
specific  gear  dimension  (i.e.  Figure  3.17)  multiplied  by  the  effort  in  hours  for  that  specific  trip. 
Swept  area  can  further  be  aggregated  per  stock  and  related  to  the  fishing  effort  in  hour  metric 
showing that by construction the main cause explaining the swept area differences is the type of 
the fishing gear in use (Figure 3.17). 

58 

58 

 
 
 
 
Figure 3.17. Relationships between Effort and Swept Area estimates per fishing gear (OTB, OTM, 
OTT,  PS)  from  the  French  individual  vessels  dataset  (left-  French  logbooks  data  in  West  Med., 
right – French haul-by-haul observer data). Each dot is an aggregation per fish stocks and 2012-
2017 year period and, for the logbooks, among "HKE_SA 7"  "MNZ_SA 7"  "BFT_SA 26" "BFT_SA 
5"  "BFT_SA 6"  "BSS_SA 7"  "HKE_SA 12" "BFT_SA 13" "BFT_SA 15" "BFT_SA 16" "SOL_SA 7"  
"MUT_SA 7"  "SBG_SA 7"  "HKE_SA 5"  "HKE_SA 6", for the haul by haul observer data, among 
"BSS_SA  7"  "HKE_SA  7"  "MNZ_SA  7"  "MON_SA  7"  "MUR_SA  9"  "MUT_SA  7"  "SBG_SA  7" 
"SOL_SA 7" "SWO_SA 7" 

Applying the same gear-width relationships from Figure 3.16, this time the STECF effort dataset 
gathering aggregation of nominal effort per country (ESP, FRA and ITA), the spread of the swept 
area  estimates  around  the  nominal  effort  is  captured  (Figure  3.18  and  3.19)  showing  that  to  a 
same level of effort potentially corresponds several levels of actual swept areas. The full spread 
around  a  hypothetical  linear  model  (solid  line  showed  on  Figure  3.19)  is  here  explained  by  the 
different gear types in use (split per gear on Figure 3.18) and by the vessel size. 

59 

59 

 
 
 
 
 
 
 
Figure 3.18. Relationships between French, Spanish and Italian Effort (fishing_days) and proxies 
for  Swept  Area  estimates  per  fishing  gear  (DRB,  OTT,  OTB,  PS)  deduced  from  the  STECF  Effort 
data call dataset. Each point is a record in the dataset. Technological creeping assumed to be 5% 
increment a year. 

60 

60 

 
 
 
 
Figure 3.19. Relationships between French, Spanish and Italian Effort (fishing_days) and proxies 
for Swept Area estimates, all fishing gears (DRB, OTT, OTB, PS) mixed, deduced from the STECF 
Effort data call dataset. Each point is a record in the dataset. Technological creeping assumed to 
be 5% increment a year. 

All  in  all,  the  effort  data  from  the  STECF  data  call  being  aggregated  and  per  large  vessel  size 
classes,  is  currently  not  adequate  to  deduce the  swept  area in  a proper way. There  is  no  doubt 
that  individual  data  are  best  suited  to  deduce  Swept  Area estimates  but the  individual  available 
data to the group were also lacking some crucial information that makes the estimation no more 
than  an academic exercise at this stage. Hence, several points were raised during the estimation 
procedure: 

1-  Not  available  is  the  level  6  DCF  métier  definition  (to  relate  to  the  Eigaard  et  al.  2016 
relationships);  would  be  needed  to  refine  when  different  gear  dimension  are  used 
depending on the target assemblages of species; 

2-  Not available is kW of vessels (to relate to SOME of the Eigaard et al. 2016 relationships); 
3-  Not available is the fishing speed of vessels (to properly compute the swept area); 
4-  Not available is the F for all stocks for all years (?) (to properly relates to F Bar); 
5-  Not  available  is  all  the  total  coverage  of  the  effort  (because  we  used  French  data  here 

only); 

6-  We used rough assumptions to fill the information gaps, especially for seiners; 
7-  We use assumption of 5 % increment to catch technological creeping effect; 
8-  We  did  not  correct  for  potential  various  gear  mouth  openings  depending  on  the 

bathymetry. 

61 

61 

 
 
Ultimately, we want to relate effort-kWdays with change in impact to correct for the technological 
creeping  from  a  year  to  the  next  (the  technological  creeping  is  expected  to  shift  up  all  the 
relationships Swept-Area vs LOA or kW every year). The first priority to investigate would then be 
to  relate  a  change  in  gear  size  to  a  change  in  catch  rate.  Hence,  empirical  studies  or  literature 
review could be done to measure the effect of increasing gear opening or swept area on the catch 
rates.  The  effort-KWdays  can  then  be  multiplied  in  the  time  series  by  this  factor  of  catch  rate 
improvement.  

In  this  regards,  EWG  18-13  recalls  the  information  presented  by  STECF  (2018a)  on  the 
continuous  technical  changes  occurring  in  the  Mediterranean  gears,  with  the  increasing  use  of 
larger trawls and of more efficient trawls (PTB instead of OTB), highlighting that there is potential 
for large technological creep in the near future.  

3.4  Conclusion on ToR 2 

The  analyses  performed  in  ToR  2,  in  addition  to  those  performed  by  EWG  18-09,  provide 
interesting results. For most of the stocks and fleets analysed, the relationships between F and E 
estimated  at  the  aggregated  level  of  the  fishing  fleet,  the  GSA  and  the  year  is  not  very  clearly 
defined. This does not mean that this relationship does not exist (ultimately, F will always reduce 
if effort reduces), but that it is not yet been clearly observed in the recent years where data are 
available.  This is  to  a large  extent  due to  a lack  of  contrasts  in  the  data, with most  of  the  time 
series  having  remained  in  the  areas  of  high  fishing  effort  and  high  fishing  mortality.  But  this  is 
also  linked  to  the  measure  of  effort  used,  where  summing  days  at  sea  for  all  trawlers  together 
cannot  be  considered  a  precise  measure  of  fishing  effort  reflecting  the  true  fishing  pressure  on 
the individual stocks. As described in details in EWG 18-09, it is obvious that fleets can maintain a 
high level of fishing pressure even if nominal effort decreases, by e.g. increasing the number of 
hours  fished  in  a day,  the  engine  power  and  towing  speed,  the  size  and  efficiency  of  the  towed 
gear, the targeting behaviour etc.  

Some non-linear relationships were fitted on the same data sets, providing some rough estimates 
of the Beta parameter and shape of the relationship indicative of the lower decrease rate.  

The  implications  of  these  analyses  at  global  scale  are  that  i)  it  cannot  be  expected  that  fishing 
mortality  will  decrease  by  exactly  the  same  amount  as  fishing  effort  in  the  first  years  of  effort 
reduction in the MAP, but will likely reduce at a lesser rate and ii) it might take a few years before 
the effects of effort reductions can be seen in the stock assessment outcomes.  

In  addition,  a  number  of  exploratory  analyses  were  conducted  on  the  potential  of  using  other 
measures  of  effort  than  fishing  days,  to  refine  the  estimates  of  fishing  effort.  In  particular, 
expressing  fishing  effort  in  terms  of  fishing  hours  and  not  days,  and  taking  into  account  the 
targeting  effect  from  the  overlap  in  the  spatial  distribution  between  the  fishing  effort  and  the 
various stocks is considered would be a much more precise estimate of the actual fishing pressure 
exerted on the stocks. Such data can be compiled using VMS/AIS data. However, these analyses 
remained only preliminary on the basis of what could be achieved during and EWG with the data 
readily available to the EWG. 

More work would be thus needed to collect the data necessary to pursue these analyses further. 
This  would  include  i)  updating  the  time  series  of  fishing  mortality  with  the  most  recent 
assessments from STECF (2018b); ii) collecting a longer time series of hours at sea, similarly to 
those  now  available  in  the  FDI  database  for  the  years  2015-2017  (cf  ToR  1).  These  two  steps 
would  be  necessary  to  re-estimate  the  relationship  at  aggregated  fleet  level.  Additionally, 
alternative time series of fishing effort could be estimated based on individual trip or haul-by-haul 
data, accounting for additional effects of targeting, vessel characteristics and technological creep, 
to improve the precision of the relationship analysis.   

62 

62 

 
 
 
 
 
4  TOR 3 VESSEL PERFORMANCE 

“What are the factors determining vessels’ performance?” 

This  section  presents  the  results  of  quantitative  analysis  to  estimate  factors  that  affect  vessels 
performance and mix-fisheries effects. 

For the quantitative analysis, the EWG used LPUE as an indicator of vessel performance. The data 
available were provided by Italy, Catalonia (Spain), and France. Those from Italy and Spain were 
the  ones  provided  in  June  to  STECF  (2018a),  while  the  data  from  France  were  provided 
specifically for EWG 18-13.  

All these Member States provided logbook-like data which is constituted of records for single days 
of fishing activity by vessel. In detail: 

  Data  for  Italy:  a  single  dataset  (i.e.  the  same  used  for  STECF  2018a)  organized  as 
landings records by day, vessel and species. Each record contains also information about 
species prices at market, coordinates (WGS1984 LAT & LON), fuel cost, and gear; 

  Data for France: two datasets. The first one with the same structure of the other countries 
(landings  records  by  day  and  vessels  for  the  all  the  species,  together  with  related 
information  about  prices  at  market,  coordinates  (WGS1984  LAT  &  LON),  fuel  cost,  and 
gear); The second dataset with non-georeferenced data. To overcome this issue, the AREA 
of  activity  with  respect  to  the  partitioning  in  Figure  4.1  was  provided.  This  allowed  to 
merge the two datasets and perform the GAM modelling with respect to the GSAs; 

  Data for Catalonia (Spain): a single dataset with landings records by day and vessels for 
the 5 species in the MAP (ARA, DPS, MUT, NEP, HKE) and the cumulated landings for the 
other  species,  together  with  related  information  about  prices  at  market,  coordinates 
(WGS1984 LAT & LON), fuel cost, and gear. 

Figure 4.1 Spatial partitioning for the French dataset without LON/LAT coordinates.  The area of 
activity is represented by the GSAs 7 and 8. 

The  analysis  quantified  the  variability  of  LPUE  by  trip,  and  evaluates  the  effect  of  a  set  of 
explanatory  variables  like  vessel  length,  season,  area  and  specialisation.  For  mix-fisheries  the 
analysis looked into non-mix operations, as a proxy for specialization of the fleet.  

4.1  Data by country 

For the analysis presented in this section, vessels performance was defined as the catch per unit 
of  effort  (LPUE).  The  analysis  of  factors  affecting  vessels’  LPUE  was  carried  out  by  fitting  GAM 
models to identify from a set of predictor variables which ones are significantly affecting vessels’ 

63 

63 

 
 
 
performance. The modelling approach was the same for the three country-specific datasets, but 
each  dataset  was  subset  and  the  analysis  limited  to  the  5  species  in  the  MAP:  ARA,  DPS,  MUT, 
NEP, HKE. 
The  model  chosen  was  based  on  experts’  opinion  after  evaluating  a  series  of  modelling  options 
and having in mind the ToR request instead of strictly statistical aspects. As mentioned above the 
model chosen was a generalized additive mixed effects model, with the following variables: 

  Time  (as  Year)  -  fitted  as  smoothed  variable  theoretically  accounting  for  the  “temporal 
inertia” of the trend associated to each species (i.e. this predictor was expected to capture 
the “history” in the exploitation of each species); 

  Depth – fitted as a factor with 2 levels (Shelf, corresponding to the range [0,-100m) and 

Slope, corresponding to the area below 100m); 

  Season - fitted as a factor with 4 levels, assuming each season is independent; 
  LOA  -  fitted  as  a  factor  with  3  levels  in  agreement  with  the  MAP  proposal:  [12m-18m), 

[18m- 24m), and [24-40); 

  Species price at first sale - fitted as a smoothed predictor accounting for economic drivers 

in fleet behaviour; 

  Log of species fraction of the catches (sppfract) - fitted as a smoothed predictor, assuming 
it’s  a  proxy  of  the  targeting  intention  of  fishers  and  then  accounting  for  the  target-
dependent effect on the vessel performance (sensu Marchal et al., 2007); 

  GSA - fitted as a factor with one levels for each, assuming each GSA is independent; 
  Vessel  -  modelled  has  a  random  term,  assuming  each  vessel  as  a  specific  constant 
average  performance  and  all  together  deviate  normally  from  a  cross-vessel  mean 
performance. 

4.1.1  Data for ITALY 

The  Italian  logbook  dataset  represents  a  non-random  sample  of  the  whole  Italian  database. 
Records with information in all the relevant fields (LON, LAT, Species code, Quantity, and Date of 
fishing activity) for the trawling vessels operating in the area of interest were selected, whereas 
records with empty fields or unrealistic values were discarded. A preliminary survey on the quality 
of logbook by LOA suggests that low-quality logbook records are randomly distributed among the 
standard  DCF  LOA  classes  [10-12),  [12-18),  [18-24),  [24-40)  (Figure  4.3).  The  dataset 
comprises  62394 records, related to 27102 days of fishing activity  in the years 2014-2017. The 
corresponding  fleet  is  mainly  represented  by  vessels  with  a  LOA  between  15  and  25  m  (Figure 
4.2). Each logbook record contained the geographic coordinates (WGS 1984 geodetic system) of 
the centroid of the daily area of fishing activity and species-specific values of total daily catch for 
landings above the threshold of 50 Kg/day per species. 

64 

64 

 
 
 
Figure 4.2 The fleet of trawlers corresponding to the records in the logbook dataset analyzed for 
the EWG 18-09. The area of activity is represented by the GSAs 9, 10 and 11 (Tyrrhenian Sea). 

Figure  4.3  The  number  of  Italian  Logbook  records  corresponding  to  the  different  length  classes 
and years, analysed for the EWG 18-09. The area of activity is represented by the GSAs 9, 10 and 
11 (Tyrrhenian Sea). 

The preliminary processing of this dataset allowed enhancing the native information by: 

  Assigning the GSA corresponding to each record by using the centroid’s coordinates; 
  Estimating the sea bottom depth corresponding to each centroid of the fishing activity; 
  Quantifying the relative proportion of the species in the Management Plans (namely: hake, 
deep  water  rose  shrimp,  red  mullet,  Norway  lobster,  giant  red  shrimp,  and  blue  and  red 
shrimp); 

  Computing the daily LPUE as ratio between the total daily catch and the fishing effort; 
  Associating  the  fuel  cost  and  the  species-specific  prices  at  market,  on  a  monthly  scale. 
These  series  of  data  were  kindly  provided  by  experts  participating  to  the  EWG  and 
operative units of the Italian DCF network. 

4.1.2  Data for FRANCE 

France provided two different datasets that were integrated before processing.  

The first dataset, restricted to vessels with geolocalization by tracking devices (VMS), comprises 
records  with  the  same  structure  of  the  Italian  dataset  (daily  logbook  with  information  about 
position in WGS1984, specific composition of catches, vessel characteristics, fuel price and price 
at market of resources). The second dataset comprises records without information about spatial 
origin of catches. These two datasets were merged and harmonized as described above to obtain 
a single dataset with 405352 records, related to 1847 days of fishing activity in the years 2012-
2017. 

The corresponding fleet is mainly represented by vessels with a LOA a 25 m (Figure 4.4). Figure 
4.5  shows  the  number  of  French  Logbook  records  corresponding  to  the  different  length  classes 
and years, evidencing that most of the records are related to the activity of vessels in the length 
classes [18-24) and [24-40).  

65 

65 

 
 
 
 
Figure 4.4 The fleet of trawlers corresponding to the records in the logbook dataset analyzed for 
the EWG 18-09. The area of activity is represented by the GSAs 7 and 8 (Gulf of Lyon). 

Figure  4.5  The  number  of  French  Logbook  records  corresponding  to  the  different  length  classes 
and years, analysed for the EWG 18-09. The area of activity is represented by GSAs 7 and 8 (Gulf 
of Lyon). 

According to the procedure applied for the Italian dataset, the data were integrated as follows:  
  Assigning the GSA corresponding to each record by using the centroid’s coordinates; 
  Estimating the sea bottom depth corresponding to each centroid of the fishing activity; 
  Quantifying the relative proportion of the species in the Management Plans (namely: hake, 
deep  water  rose  shrimp,  red  mullet,  Norway  lobster,  giant  red  shrimp,  and  blue  and  red 
shrimp); 

  Computing the daily LPUE as ratio between the total daily catch and the fishing effort; 

66 

66 

 
 
 
 
 
 
4.1.3  Data for SPAIN (CATALONIA) 

The dataset for Catalonia is, in terms of structure, similar to those for Italy and France but data 
were provided only for the species in the MAP together with cumulated values of catches for the 
other  species.  This  allowed  computing  the  relative  importance  of  each  species  for  each  record. 
The dataset comprises 601860 records, related to 1471 days of fishing activity in the years 2015-
2017. The corresponding fleet is almost uniformly represented by vessels with a LOA between 15 
and  above  27  m  (Figure  4.6).  Most  of  the  records  are  related  to  the  activity  of  vessels  in  the 
length  classes  [12-18)  and  [18-24),  although  the  length  class  [24-40)  is  also  well  represented 
(Figure 4.7). 

Figure 4.6 The fleet of trawlers corresponding to the records in the logbook dataset analyzed for 
the EWG 18-09. The area of activity is represented by the GSAs 6 and 7. 

Figure 4.7 The number of Catalonia Logbook records corresponding to the different length classes 
and years, analysed for the EWG 18-09. The area of activity is represented by the GSAs 6 and 7. 

67 

67 

 
 
 
 
The  preliminary  processing  of  this  dataset  was  the  same  applied  on  the  datasets  for  ITALY  and 
FRANCE. 

68 

68 

 
 
 
 
4.2  GAM modelling results 

The results of this modelling exercise are extensively reported in Annexes 02a-c. Here we shortly 
discuss the main results by species and the general conclusions with respect to the aims of ToR3. 

4.2.1  ITALY 

Hake 
All  the  predictors  with  the  exception  of  price  at  market  have  a  significant  effect  on  the  vessel 
performance (LPUE). Apart from the intercept, the species fraction of the catches and the size of 
the vessel and are the predictors associated to the most important effects (Figure 4.8). 

Figure 4.8 Hake. Top panel: main effects and their relative sizes. Bottom panel: smoother effects 
on LPUE. 

69 

69 

 
 
 
 
Deep water rose shrimp 
All  the  predictors  have  a  significant  effect  on  the  vessel  performance  (LPUE).  Apart  from  the 
intercept,  the  species  fraction  of  the  catches,  the  size  of  the  vessel  and  the  area  are  the 
predictors associated to the most important effects (Figure 4.9). 

Figure  4.9  Deep  water  rose  shrimp.  Top  panel:  main  effects  and  their  relative  sizes.  Bottom 
panel: smoother effects on LPUE. 

70 

70 

 
 
 
 
Red mullet 
All the predictors have a significant effect on the vessel performance (LPUE). The species fraction 
of  the  catches  and  (secondarily)  the  area  are  the  predictors  associated  to  the  most  important 
effects (Figure 4.10). 

Figure 4.10  Red mullet. Top panel: main effects and their relative sizes. Bottom panel: smoother 
effects on LPUE. 

71 

71 

 
 
 
 
Giant red shrimp 
All  the  predictors  have  a  significant  effect  on  the  vessel  performance  (LPUE).  Apart  from  the 
intercept, the area of activity (GSA), the species fraction of the catches and the size of the vessel 
are the predictors associated to the most important effects (Figure 4.11). 

Figure  4.11  Hake.  Top  panel:  main  effects  and  their  relative  sizes.  Bottom  panel:  smoother 
effects on LPUE. 

72 

72 

 
 
 
 
Norway lobster 
All  the  predictors  have  a  significant  effect  on  the  vessel  performance  (LPUE).  Apart  from  the 
intercept, the species fraction of the catches, the area of activity (GSA) and the size of the vessel 
are the predictors associated to the most important effects (Figure 4.12). 

Figure  4.12  Norway  lobster.  Top  panel:  main  effects  and  their  relative  sizes.  Bottom  panel: 
smoother effects on LPUE. 

73 

73 

 
 
 
 
4.2.2  FRANCE 

Hake 
All  the  predictors  with  the  exception  of  price  at  market  have  a  significant  effect  on  the  vessel 
performance  (CPUE).  The  area  of  activity  (GSA)  and  the  species  fraction  in  the  catches  are  the 
predictors associated to the most important effects (Figure 4.13).  

Figure 4.13 Hake. Top panel: main effects and their relative sizes. Bottom panel: smoother 
effects on LPUE. 

74 

74 

 
 
 
Deep water rose shrimp 
All  the  predictors  have  a  significant  effect  on  the  vessel  performance  (CPUE).  Apart  from  the 
intercept, the size of the vessel and the species fraction of catches are the predictors associated 
to the most important effects (Figure 4.14). 

Figure  4.14  Deep  water  rose  shrimp.  Top  panel:  main  effects  and  their  relative  sizes.  Bottom 
panel: smoother effects on LPUE. 

75 

75 

 
 
 
 
 
 
 
Red mullet 
All  the  predictors  have  a  significant  effect  on  the  vessel  performance  (CPUE).  Apart  from  the 
intercept,  the  species  fraction  of  the  catches  and  the  area  of  activity  (GSA)  are  the  predictors 
associated to the most important effects (Figure 4.15). 

Figure 4.15 Norway lobster. Top panel: main effects and their relative sizes. Bottom panel: 
smoother effects on LPUE. 

76 

76 

 
 
 
 
 
Norway lobster 
All  the  predictors  have  a  significant  effect  on  the  vessel  performance  (CPUE).  The  size  of  the 
vessel and the area of activity (GSA) are the predictors associated to the most important effects. 
In particular, the weight of the area of activity resulted more important that the one detected for 
the other species (Figure 4.16). 

Figure  4.16  Norway  lobster.  Top  panel:  main  effects  and  their  relative  sizes.  Bottom  panel: 
smoother effects on LPUE. 

77 

77 

 
 
 
 
4.2.3  CATALONIA (SPAIN) 

Hake 
All  the  predictors  with  the  exception  of  price  at  market  have  a  significant  effect  on  the  vessel 
performance (LPUE). Apart from the intercept, the species fraction of the catches and the size of 
the vessel and are the predictors associated to the most important effects (Figure 4.17). 

Figure  4.17  Hake.  Top  panel:  main  effects  and  their  relative  sizes.  Bottom  panel:  smoother 
effects on LPUE. 

78 

78 

 
 
 
 
 
Deep water rose shrimp 
All  the  predictors  have  a  significant  effect  on  the  vessel  performance  (LPUE).  Apart  from  the 
intercept,  the  species  fraction  of  the  catches  and  the  size  of  the  vessel  are  the  predictors 
associated to the most important effects (Figure 4.18). 

Figure  4.18  Deep  water  rose  shrimp.  Top  panel:  main  effects  and  their  relative  sizes.  Bottom 
panel: smoother effects on LPUE. 

79 

79 

 
 
 
 
Red mullet 
All  the  predictors  have  a  significant  effect  on  the  vessel  performance  (LPUE).  Apart  from  the 
intercept,  the  species  fraction  of  the  catches  and  the  area  are  the  predictors  associated  to  the 
most important effects (Figure 4.19). 

Figure 4.19 Red mullet. Top panel: main effects and their relative sizes. Bottom panel: smoother 
effects on LPUE. 

80 

80 

 
 
 
 
Giant red shrimp 
All  the  predictors  have  a  significant  effect  on  the  vessel  performance  (LPUE).  Apart  from  the 
intercept, the area of activity (GSA), the species fraction of the catches and the size of the vessel 
are the predictors associated to the most important effects (Figure 4.20). 

Figure  4.20  Hake.  Top  panel:  main  effects  and  their  relative  sizes.  Bottom  panel:  smoother 
effects on LPUE. 

81 

81 

 
 
 
 
 
Norway lobster 
All  the  predictors  have  a  significant  effect  on  the  vessel  performance  (LPUE).  Apart  from  the 
intercept, the species fraction of the catches, the area of activity (GSA) and the size of the vessel 
are the predictors associated to the most important effects (Figure 4.21). 

Figure  4.21  Norway  lobster.  Top  panel:  main  effects  and  their  relative  sizes.  Bottom  panel: 
smoother effects on LPUE. 

82 

82 

 
 
 
 
 
4.2.4  General conclusions 

The results of the descriptive analysis and of the modelling exercise suggest that: 

  The factors and effects included in the model were, in general, all significant in statistical 

terms; 

  The  species  fraction  of  the  catches  is  often  a  very  important  predictor.  This  means  that 

vessels actively targeting some species are more efficient that the other ones; 

  Larger trawlers are often more efficient that small ones. This could be interpreted as the 
consequence  of  the  wider  operative  range  of  large  vessels,  that  are  able  to  select  and 
exploit far fishing grounds but also to deploy a non-marginal portion of their daily effort on 
the shelf; 

  The  area  of  activity  is  also  important  to  determine  the  CPUE.  The  most  reliable 
interpretation of this finding is the (sometime) very different status of some stocks in the 
area of study. 

4.3  Mixed fisheries and quantiles analyses for the French fleet 

To  complete  the  analysis  carried  out  in  EWG1809,  LPUE  quantiles  and  mixed  fisheries  analysis 
were carried out for the French fleet. The first were computed to assess the difference between 
average and more efficient vessels. The second to assess the level of mixed fisheries catches in 
the fleet. 

Table 4.1 display the average LPUE per species per day at the 50th quantile (median trip) and at 
the 85th quantile (efficient trip), based on French data in a trip by trip basis. The same values are 
then  standardised  by  dividing  the  85th  quantile  by  the  50th,  to  display  the  range  of  order  of  the 
more efficient trips compared to the median one.  

The results show that efficient trips are about 2 to 7 times more efficient than the median ones. 

Table  4.1.  50th  and  85th  quantile  of  LPUE,  French  trip-by-trip  dataset.  Upper:  absolute  value 
(kg/day). Lower: relative to the 50% quantile. 

Length Percentiles TOTAL 

N. 
norvegicus 

P. 
longirostris 

M. 
merluccius 

M. barbatus 

A. 
antennattus 

X <12 

HR p0,50 

HR p0,85 

12≤ X 
≤ 18 

HR p0,50 

HR p0,85 

18≤ X 
≤ 24 

HR p0,50 

HR p0,85 

X ≥24 

HR p0,50 

HR p0,85 

38.0 

73.6 

19.4 

45.5 

0.5 

1.9 

0.8 

3.0 

30.0 

50.2 

10.0 

42.5 

12.8 

60.8 

25.7 

5.5 

21.0 

1.4 

6.1 

2.0 

13.3 

107.0 

9.0 

19.4 

23.4 

43.0 

3.4 

16.2 

4.8 

17.8 

4.1 

17.1 

Length Percentiles TOTAL 

N. 
norvegicus 

P. 
longirostris 

M. 
merluccius 

M. barbatus 

A. 
antennattus 

X <12 

HR p0,50 

HR p0,85 

1.0 

1.9 

1.0 

1.7 

1.0 

2.2 

83 

83 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
12≤ X 
≤ 18 

HR p0,50 

HR p0,85 

18≤ X 
≤ 24 

HR p0,50 

HR p0,85 

X ≥24 

HR p0,50 

HR p0,85 

1.0 

2.3 

1.0 

3.6 

1.0 

3.8 

1.0 

3.8 

1.0 

4.4 

1.0 

6.7 

1.0 

4.3 

1.0 

4.7 

1.0 

4.2 

1.0 

1.8 

1.0 

4.8 

1.0 

3.7 

1.0 

4.2 

The mix-ﬁsheries analysis was performed to evaluate the level of “non-mix” in the ﬁsheries and 
potential impact of choke species. The rationale is that if a number of hauls are “clean”, it means 
a  certain  level  of  specialization  exists.  Fleet’s  specialization  should  be  explored/fostered  to 
increase the probabilitty of the MAP’s success, since the species targeted by the MAP are not all in 
the  same  level  of  over-exploitation.  On  the  other  hand  if  a haul  is  mostly  made  of  one  species, 
limiting eﬀ ects by other species are less important and can be avoided.  

The analysis presented in Figure 4.22 to Figure 4.26 is based on haul-by-haul data provided by 
the French authorities. The results show that the French fleets are not very specialized, although 
some exemptions exist, e.g. (12,18] meter vessels catching Nephrops.    

Figure 4.22  Cumulative distribution the maximum fraction of the landings belonging to a single 
species by haul for the trawl ﬂeets. 

84 

84 

 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 4.23  Hauls by maximum fraction of the landings belonging to a single species for the 
trawl ﬂeets 

85 

85 

 
 
 
Figure 4.24  Hauls by fraction of the landings belonging to a single species and LOA 

86 

86 

 
 
 
 
Figure 4.25  Hauls by fraction of the landings belonging to a single species, LOA and year 

87 

87 

 
 
 
Figure 4.26 Hauls by fraction of the landings belonging to the species in the MAP by LOA 

88 

88 

 
 
 
 
5  TOR 4. PARTIAL F 

‘Calculate the average partial fishing mortality of trawls exploiting the demersal stocks concerned 
by the MAP, by type of fisheries, effort unit, fleet segment and country, as estimated in the latest 
stock assessments.’ 

ToR  4  could  not  be  completed  during  EWG  18-13.  The  updated  stock  assessments  results  from 
EWG  18-12  were  not  considered  sufficiently  robust  and  finalised  at  the  time  where  EWG  18-13 
met, and the data were not readily available. Additionally, the analysis of ToR 2 did not allow for 
a single robust estimate of transversal data (catch and effort data by fleet and metier), and the 
differences  between  the  different  datasets  remained  unclear  and  poorly  explained.  Decision  is 
therefore  still  to  be  made  on  which  dataset  to  base  the  actual  estimates  of  partial  fishing 
mortality. 

Performing this ToR would otherwise be straightforward once the relevant data are collected, so 
EWG 18-13 decided to postpone the exercise and to include it as part of Step 1 of the RoadMap. 

89 

89 

 
 
 
6  TOR 5. ROAD MAP 

“Create a 2-year roadmap to set-up a mixed fisheries advice for western Mediterranean demersal 
fisheries.  The  plan  should  provide  a  detailed  description  of  all  the  steps  needed  to  achieve  this 
goal.  It  should  outline  the  priorities  in  the  short-  and  medium-term,  any  potential  gaps  in 
knowledge/data/modelling  and  the  actions  that  can  be  taken  to  overcome  it.  It  should  also 
specify  the  skills  and  tools  needed,  including  the  actors  to  be  involved  (e.g.  for  scoping 
decisions). The starting date of the roadmap should be 1 January 2019.” 

STECF  is  requested  to  define  the  steps  that  would  lead  to  providing  a  routine  mixed-fisheries 
advice. In order to address this, it is necessary to identify the requirements for this advice, and 
the gaps in the current state of the art that are necessary to be filled to achieve these.  

This ToR goes thus first through a number of steps. First, the actual concept of “what is a mixed-
fisheries  advice”  is  discussed  by  presenting  in  some  details  the  current  mixed-fisheries  advice 
used  in  the  EU  North-Atlantic  waters  and  published  annually  by  ICES,  assuming  that  EU  is 
requesting an advice of the same vein, although tailored to the Mediterranean context.  

Then, the requirements and the gaps are addressed. The requirements distinguish between three 
types  of  issues:  i)  MODELS:  what  are  the  models  currently  available,  are  they  suitable  for  the 
purpose  in  their  current  form  and  can  they  be  easily  updated  annually?  or  do  they  need  to  be 
further  developed,  or  are  new  models  needed?  ii)  DATA:  what  are  the  data  requirements  for 
these  models?  When  and  where  are  they  made  available?  Are  they  suitable  in  their  current 
definition  or  do  they  need  adaptation?  iii)  PEOPLE:  who  are  the  experts  able  to  perform  the 
work, and what is their availability? 

The section two is thus dedicated to review the state of the art of mixed-fisheries models for the 
demersal  Western  Mediterranean  fisheries,  both  those  presented  and  used  in  EWG  18-09,  and 
other  models  known  to  EWG  18-13  attendees.  Section  three  is  dedicated  to  data  issues,  in 
particular  with  regards  to  the  current  definitions  of  fleets  and  fisheries,  analysing  whether  they 
actually capture enough of the differences in fishing patterns and catchability across the different 
activities.  

In  the  light  of  this  detailed  review  of  the  state  of  the  art,  a  road  map  is  proposed  in  section  4, 
articulated around two STECF EWG to be held in 2019.  

6.1  A summary of the mixed-fisheries advice process in European North-East Atlantic 

waters (ICES area) 

The  wording  “mixed-fisheries  advice”  is  not  entirely  clear,  and  can  mean  different  things  to 
different people. In the EU North-East Atlantic, “mixed-fisheries advice” has now taken a specific 
meaning, and refers to a specific piece of information annually published by ICES as an add-on to 
the usual advice on stock-by-stock fishing opportunities for the following year (TAC advice). Such 
a mixed-fisheries advice is published for the main demersal stocks in the North Sea1, the Bay of 
Biscay2 and the Celtic Sea3 (advice for the Iberian Sea and Irish Sea is in development).  

There  are  numerous  other  scientific  developments  involving  mixed-fisheries  bio-economic 
modelling in the frame of e.g. research projects or other integrated assessments plate-forms, but 
these would not be referred to as “advice”. One of the main difference is that the mixed fisheries 
advice  is,  as  the  single-stock  advice,  focused  on  the  short-term  forecast,  not  medium  or  long-
term projections. This means that the mixed-fisheries model used in the advice must be able to 
reproduce the actual deterministic single-stock forecast, in order to be directly comparable. This 
focus has driven a number of needs and requirements, which can be different from those involved 
in medium term stochastic projections, where less emphasis is put on getting the first year of the 
simulation “exactly right”.  

11 http://ices.dk/sites/pub/Publication%20Reports/Advice/2018/2018/mix-ns.pdf 
2 http://ices.dk/sites/pub/Publication%20Reports/Advice/2018/2018/mix-bbi.pdf 
3 http://ices.dk/sites/pub/Publication%20Reports/Advice/2018/2018/mix-cs.pdf 

90 

90 

 
                                          
This  section  is  thus  dedicated  to  giving  a  brief  overview  on  the  “mixed-fisheries  advice”  in  its 
ICES sense.  

6.1.1  What is the ICES mixed fisheries advice and which information it provides 

Most  fisheries  catch  a  mixture  of  species  and  it  is  not  entirely  possible  to  control  which  species 
and  how  much  of  each  is  caught  (ICES  basis for  advice4)  For  stocks exploited by  mixed-species 
fisheries,  it  may  not  be  possible  to  achieve  the  single-stock  MSY  catch  advice  for  all  the  stocks 
simultaneously. Either the advised catches for some stocks will be exceeded in trying to catch the 
TACs  of  other  stocks,  or  the  TACs  for  some  stocks  will  not  be  caught  in  order  to  prevent 
overshooting  the  TACs  of  other  stocks.  ICES  has  developed  a  mixed-species  fisheries  model  to 
address  this;  ICES  provides  information  on  catch  composition  of  different  fisheries  strategies  to 
illustrate the tradeoffs between the strategies and highlight the risks of potential quota over- or 
undershoot for the following year (advice year) 

The standard mixed-fisheries scenarios are shown in Table 6.1.  

Table 6.1. Mixed fisheries scenarios – as in 2018 (for advice in 2019). North Sea example 

Scenarios 

Max 

Min 

“Maximum”:  For  each  fleet,  fishing  effort  in  2019  stops  when  all  stock  shares*  of 
that  fleet  have  been  caught  up.  This  option  causes  overfishing  of  the  single-stock 
advice possibilities of most stocks. 

“Minimum”:  For  each  fleet,  fishing  effort  in  2019  stops  when  the  most  limiting  of 
the  stock  shares  of  that  fleet  has  been  caught  up.  This  option  is  the  most 
precautionary option, causing underutilization of the single-stock advice possibilities 
of other stocks. This scenario can highlight some potential “choke species” issues. 

Sq_E 

“Status quo effort”: The effort of each fleet in 2018 and 2019 is set equal to the 
effort  in  the  most  recently  recorded  year  for  which  landings  and  discard  data  are 
available (2017). 

Val 

“Value”:  A  simple  scenario  accounting  for  the  economic  importance  of  each  stock 
for  each  fleet.  The  effort  by  fleet  is  equal  to  the  average  of  the  efforts  required  to 
catch the fleet’s stock shares of each of the stocks, weighted by the historical catch 
value  of  that  stock  (see  example  further  below).  This  option  causes  overfishing  of 
some stocks and underutilization of others. 

COD 

“Cod MSY approach”: All fleets set their effort in 2018 and 2019 corresponding to 
their cod stock share, regardless of other catches.  

*the  term  “fleet’s  stock  share”  or  “stock  share”  is  used  to  describe  the  share  of  the  fishing 
opportunities of a stock for each particular fleet in 2018, assuming that the proportion of catches 
by fleet for that stock in 2018 and 2019 is the same as observed in 2017. 

And the standard depiction of these results are shown in Figure 6.1. 

4 

http://www.ices.dk/sites/pub/Publication%20Reports/Advice/2018/2018/Introduction_to_
advice_2018.pdf 

91 

91 

 
 
                                          
Figure 6.1. ICES North Sea Mixed-fisheries projections. Estimates of potential catches (in tonnes) 
by stock and by scenario. Horizontal lines  correspond to the single-stock catch advice for 2019. 
Bars below the value of zero show undershoot (compared to single-stock advice) where catches 
are  predicted  to  be  lower  when  applying  the  scenario.  Hatched  columns  represent  catches  that 
overshoot the single-stock advice.  

In the last two years, a new type of scenario was presented, called “range”, developed to find a 
scenario within the Fmsy ranges defined as:  

“range”: estimates a fishing mortality by stock (using the FMSY ranges) which, if used for setting 
single-stock fishing opportunities, may reduce the gap between the most and the least restrictive 
TACs,  thus  reducing  the  potential  for  quota  over-  and  undershoot.  FMSY  ranges  are  limited  in 
accordance with the MSY approach and the MAP for stocks below MSY Btrigger. 

and shown in Figure 6.2. 

92 

92 

 
 
Figure  6.2.  ICES  Mixed  fisheries  for  the  North  Sea.  North  Sea  mixed-fisheries  2019  “range” 
fishing  mortality  within  the  FMSY  range,  compared  with  FMSY,  current  F  (F  in  2017),  and  F  in  the 
single-stock  advice  for  2019.  The  “range”  F  is  the  one  giving  the  lowest  difference  in  tonnage 
between the “Max” and the “Min” scenario across all stocks and fleets. For cod in the North Sea 
and  sole  in  Division  7.d,  FMSY  ranges  are  limited  in  accordance  with  the  MSY  approach  and  the 
MAP when below MSY Btrigger. 

Central  to  the  mixed-fisheries  advice  is  the  explicit  representation  of  both  fishing  vessels  and 
their  activity.  A  fishing  vessel  belongs  to  one  and  only  one  fleet,  and  fleet  segments  can  be 
defined using any physical characteristic considered relevant: Typically a fleet would be a  group 
of  vessels  with  the  same  length  class  and  predominant  fishing  gear  during  the  year,  but  other 
criteria such as homeport might be considered.  

Vessels in the fleet may have different fishing activities during the year, called métiers.  which is 
a  group  of  fishing  operations  targeting  a  similar  (assemblage  of)  species,  using  similar  gear, 
during the same period of the year and/or within the same area and which are characterized by a 
similar  exploitation  pattern  (DCF,  EC  2008).  As  such,  the  fleet  describes  the  vessels  while  the 
métier(s) describes the fishing activity(ies) in which the fleet engages. The fleets are linked to the 
métiers via the fishing effort, and the metiers are linked to the species/stocks via the catchability 
(Figure 6.3).  

93 

93 

 
 
 
Figure 6.3. Fleets and fisheries. 

6.1.2  Brief summary of historical development of the ICES mixed-fisheries advice since 

inception in 2006 

The ICES development of mixed-fisheries advice started in 2006 (ICES, 2006). At that time, the 
North  Sea  cod  (Gadus  morhua)  stock  was  at  a  very  low  level  whereas  the  stock  of  haddock 
(Melanogrammus  aeglefinus),  which  is  to  a  large  extent  caught  together  with  cod,  was  at  its 
highest biomass in 30 years. In these circumstances, fishers were faced with a dilemma when the 
quota  for  cod  is  exhausted:  stop  fishing  and  underutilize  the  quota  for  haddock,  or  continue 
fishing and discard or illegally land overquota cod. The second option prevailed, and the cod TAC 
did  not  achieve  its  intended  conservation  objective.  Rather,  discards  increased  rapidly,  and 
became  even  larger  than  landings  in  some  years  (Figure  6.4).  Additionally,  the  reliability  of  the 
assessment  of  the  cod  stock  was  jeopardized  because  the  catch  data  on  which  it  was  based 
became more uncertain. 

Figure 6.4. North Sea cod landings and discards moartlity over time  

Some  attempts  were  thus  performed  in  ICES  (ICES,  2006)  to  develop  a  simple  and  robust 
approach  to  model  the  mixed  fisheries  and  evaluate  the  risks  of  not  achieving  the  single-stock 

94 

94 

Fleet (vessel)Métier (trip)Species (catches)EFFORTCATCHABILITYLandings mortalityDiscards mortality 
 
 
management objectives. Several modelling approaches were suggested, and one of them, FCube 
(Ulrich et al., 2011, 2017), got selected at that time and became the standard model used. 

The first actual mixed-fisheries advice was published by ICES in 2009. Since then, the approach 
has  developed  to  include  more  stocks,  and  to  be  applied  to  more  areas.  major  work  was  also 
performed  over  the  time  to  improve  the  definition  of  fleets  and  fisheries.  The  objective  was  to 
find  an  acceptable  balance  between  having  enough  distinct  categories  to  capture  the  main 
differences  in  fishing  patterns  between  different  groups  of  vessels  and  fishing  activities  within 
each  country,  and  still  be  aggregated  enough  to  reduce  the  number  of  units  in  the  models  and 
have adequate  information  (e.g.  discards  by metier).  In  2017,  the  final  data  used  for the North 
Sea contained for example 42 national fleets (plus the OTH fleet). These fleets engage in one to 
five  different  métiers  each,  resulting  in  105  combinations  of  country*fleet*métier*area  catching 
cod, haddock, whiting, saithe, plaice, sole and Nephrops. These fleets and metiers are not exactly 
the  same  as  defined  in  the  DCF  data  call,  but  rather  represent  meaningful  alternative 
aggregations  defined  by  the  modelers,  but  yet  still  compatible  with  the  DCF  metiers  and  the 
datacalls. 

To  achieve  this  advice,  the  greatest  challenge  and  achievement  has  been  to  develop  a 
streamlined  procedure  for  the  collection  of  data,  to  ensure  that  all  the  necessary  data  are 
annually  and  timely  available,  proof-checked  and  consistent,  to  be  able  to  produce  the  mixed-
fisheries  advice  at  the  same  time  as  the  single-stock  advice.  The  various  steps  are  described 
below. 

6.1.3  The current scientific process 

6.1.3.1  Which data are used and how they are updated every year 

The key challenges with regards to data are the following: 

  The most recent stock assessment must be available 
  All  catches  estimated  in  the  single-stock  assessments  shall  be  split  across  fleets  and 
metiers, so that when summing up catches the same amount of landings and discards are 
obtained as in the stock assessment. Eventual differences between fleet-based and stock-
based catches can be pooled into a “Others” fleet. 
If  there  are  great  differences  in  gear  selectivity  among,  this  split  shall  be  best  done  by 
age, but if not possible, total tonnage by fleet and metier can be used. 

 

  Fleets and metiers definitions must be the same across all stocks 

To achieve this, the procedure is as follows: 

  A  joint  single-stock  +  mixed  fisheries  data  call  is  issued  in  March5,  with  data  to  be 
provided a few weeks before the working groups. This data call specify catch (landings + 
discards)  and  effort  data  at  the  disaggregation  level  agreed  and  requested  in  the  mixed-
fisheries model. 

  The  single-stock  assessment  working  groups  meet  around  end  of  April/beginning  of  May 

and perform their assessments 

  The assessment results are transmitted to the MIXFISH group end of May, which combine 
these with the catch and effort data by fleet, run the scenarios and produce its advice 
  Both  single-stock  and  mixed-fisheries  results  are  reviewed  together,  and  the  advice  is 

published jointly at the end of June.  

6.1.3.2  The FCube model 

The standard model used in the ICES mixed-fisheries advice is FCube (Ulrich et al., 2011; 2017). 
The basis of the model is to estimate the potential future levels of effort by fleet corresponding to 

5http://www.ices.dk/marine-

data/Documents/Data_calls/20180320_Revised_Fisheries_data_call_2018.pdf 

95 

95 

 
                                          
the fishing opportunities (TACs by stock and/or effort allocations by fleet) available to that fleet, 
based on how the fleet distributes its effort across its métiers, and on the catchability of each of 
these métiers. This level of effort is in return used to estimate landings and catches by fleet and 
stock, using standard forecasting procedures.  

Effort  distribution  by  metier,  partial  fishing  mortality  and  catchability  by  fleet,  métier  and  stock 
are estimated from observed catches, effort and stocks’ fishing mortality. By default, catchability 
and effort distribution by fleet across metiers are assumed constant in projections, but this can be 
modified in the model settings to run alternative scenarios.  

The target F by stock (e.g. Fmsy) is then split across all fleets and metiers and converted into a 
“stock-dependent  fleet  effort”,  which  is  the  effort  corresponding  to  a  certain  partial  fishing 
mortality  on  a  given  stock.  In  most  cases  though,  the  effort  corresponding  to  each  single-stock 
advice will differ across stocks within a fleet. So the user can therefore explore the outcomes of a 
number of options or rules about fleet behaviour, as explained in the scenario table above.  

Partial  fishing  mortalities  are  then  recalculated  and  summed  by  stock.  These  new  estimates  of 
fishing mortality are used in standard short-term forecast instead of the initial target F, and the 
corresponding catches are estimated and compared with the single-stock projections.  

6.1.3.3  ICES working groups and intersessional work done in the research institutes 

The  development  of  the  mixed-fisheries  models  has  to  some  extent  taken  place  within  research 
projects such as FP6 AFRAME, FP7 EFIMAS and MYFISH, EU Tender DRUMFISH. However, framing 
this into operational and routine advice has primarily taken place within  dedicated ICES working 
groups,  allowing  experts  to  meet  regularly.  Since  its  first  advice  publication  in  2009,  the  ICES 
WGMIXFISH group has met twice a year: 

  A one-week meeting end of May to make the advice itself, based on the most recent stock 

assessment and catch and effort data (WGMIXFISH-ADVICE) 

  A  one  week  meeting  in  October  to  further  develop  the  methods,  the  data  etc,  and  solve 

the potential issues experienced in May (WGMIXFISH-METH). 

This  setup  is  considered  to  have  been  very  useful  for  delivering  an  operational  product,  and  for 
securing the long-term visibility of the work done and the commitment of experts and resources. 
Incidentally, it has also helped securing additional funding for the work to the national institutes 
through the funding of dedicated grants such as the EU tender DRumFISH.  

6.1.3.4  Scenarios and questions asked by managers/recipients of advice 

The  actual  shape  and  content  of  the  mixed-fisheries  advice  has  evolved  over  time,  based  on 
ongoing  discussions  and  feedbacks  between  the  receivers  of  ICES  advice  (EU  DGMare, 
stakeholders)  and  the  ICES  scientists.  So  while  the  model  and  data  sources  have  not  changed 
fundamentally since 2009, the scenarios and the presentation of results has adapted over time to 
address the questions raised by this advice.  

6.2  Available  and  potential  Mixed-fisheries  models  for  the  Western  Mediterranean 

demersal fisheries. 

Which steps would be needed to ensure that West Med mixed-fisheries models could be routinely 
available and updated every year on the basis of most recent assessments_ 

In this section, we reviewed the necessary requirements to provide annual updates for a variety 
of models. The focus here is given on the existing models in their current state of the art (as of 
2018).  Additional  model  developments  are  treated  in  the  next  section  (Chapter  6.3).  Here,  we 
first review the models as presented in STECF (2018a) (MEFISTO, IAM and NIMED). Secondly, we 
discuss  other  potential  models  known  to  the  EWG  18-13  participants.  In  this  second  part,  we 
make  the  distinction  between  mixed-fisheries  models  based  on  groups  (fleets  and  metiers)  at 
aggregated  spatio-temporal  scale  (e.g.  year  and  GSA,  as  is  the  case  of  the  three  models 
presented in 18-09), and mixed-fisheries models based on individuals, or agent-based (modelling 
individual fishermen at fine spatio-temporal scale). Other mixed-fisheries models for the Western 

96 

96 

 
Mediterranean might potentially exist and not be listed here, but are not known to the EWG 18-
13.  

6.2.1  Fleet-based Models presented in EWG 18-09 

6.2.1.1  MEFISTO 

MEFISTO is a multi-species, multi-fleet bio-economic model (Lleonart et al. 2003; Maynou et al. 
2006).  MEFISTO  simulates  alternative  management  strategies  (i.e.  it  is  not  an  optimization 
model). It includes a population dynamics sub-model, that simulates the dynamics of the stock, 
and  an  economic  sub-model.  In  MEFISTO  the  link  between  the  economic  sub-model  and  the 
biological sub-model is made through the fishing mortality vector, which can vary endogenously 
following  certain  behavioral  rules  of  the  fishing  firms.  MEFISTO  does  not  consider  explicitly 
ecological  external  forcing  factors,  such  as  changes  in  fisheries  productivity  due  to  changes  in 
temperature or primary production. Instead, the model does allow considering external economic 
or  policy  factors,  such  as  fuel  price,  changes  in  net  selectivity  or  fishing  effort  limitations, 
including  seasonal  closures  that  have  been  incorporated  in  the  current  version.  MEFISTO 
simulates the internal dynamics of investment / disinvestment and the effort dynamics of fishing 
firms  following  standard  micro-economic  theory  that  generally  assumes  that  the  fishing  firms 
attempt to maximize profits (Prellezo et al. 2012).  

MEFISTO allows regional approach to management since an analysis of the bio-economic effects 
of  different  fleets  competing  for  the  same  resource  or  in  the  same  market  with  different  local 
rules can be carried out (e.g. different fishing days a year or different kind of subsidies by firm).  

Fleet is defined as a group of vessels using the same fishing gear to target the same pool of main 
species. This definition of fleet is close to the definition of fishing tactic or métier. In addition to 
the  main  species  catches,  MEFISTO  considers  also  the  so  called  secondary  species:  In  the  age-
structured  model,  a  secondary  species  (or  pool  of  species)  is  a  part  of  the  catch  of  the  fleet 
whose  dynamics  are  not  known,  but  it  generates  significant  amounts  of  revenues  for  the  fleet. 
The  catches  from  secondary  species  are  determined  in  relation  (positive  or  negative)  to  a  main 
species catches. 

To parametrize MEFISTO it is needed:  

  Biological data and stock assessments 

These data is available from stock assessment reports. In case a full assessment has not 
been carried out, it will be necessary for the user to produce an ad-hoc stock assessment 
using external tools. 
A type of biological information difficult to obtain are the parameters of the stock-
recruitment relationship. The model offers different options, but the assumptions must be 
acknowledged by the user. 

  Economic data 

Can be obtained by means of interviews with fishing firms (labour costs, annual fix costs, 
annual variable costs, opportunity costs,..) ; market parameters. The economic indicators 
include revenues, costs, discounted profit, gross valued added, gross profit and capital by 
fleet. MEFISTO uses the relationship between main and secondary species for economic 
issues. Then, economic indicator expresses the economic value of total catches for each 
fleet. 

 

Information on regulations 
Necessary in order to build alternative management scenarios (changes in the number of 
fishing days or hours; temporal closures; whether imports are likely to have an effect on 
prices,...). 

Data input files 

97 

97 

 
 
 
The input data file, described below, can be the result of some data analysis and pre-processing 
that can be undertaken in any spreadsheet package (not necessarily MS Excel), but the final data 
set that will serve as input to MEFISTO must be saved in *.xls or *.xlsx format. 

The input data file (Excel format) is organised in 7 worksheets comprising the following concepts: 

- stock parameters for the main species (worksheet species) 

- cohorts (or age class) parameters and data for the main species (worksheet cohorts) 

-  type  and  parameters  of  the  stock/recruitment  relationship  for  the  main  species  (worksheet 
recruitment) 

- the interaction matrix between fleets and stocks (worksheet interact) 

- parameters of the fleets (worksheet fleet) 

- parameters and data by individual vessel (worksheet vessels) 

- market parameters (worksheet parameters) 

Current coverage as presented in 18-09: which stocks, which fleets, which time frame 
(historical data, projections) 

MEFISTO was applied to GSA 6 demersal  fisheries.  

The following scenarios were tested (STECF 2018a ToR 5): 

During  the  simulations  the  economic  module  of  MEFISTO  was  disabled.  Capacity  reduction  was 
simulated as fishing days reduction. 

A  total  of  10  species  were  selected,  which  include  the  main  target  species  of  the  demersal 
fisheries  in  GSA  6,  as  well  as  the  most  vulnerable  species  according  to  a  Productivity  and 
Susceptibility  Analysis  (PSA).  The  species  are  the  following:  European  hake    Merluccius 
merluccius (HKE), Norway lobster Nephrops norvegicus (NEP), red mullet Mullus barbatus (MUT), 
striped  red  mullet  Mullus  surmuletus  (MUT),  deep  water  rose  shrimp  Parapenaeus  longirostris 
(DPS),  anglerfish  Lophius  piscatorius  MON),  black-bellied  angler  Lophius  budegassa  (ANK), 
greater  forkbeard  Phycis  blennoides  (GBF),  four-spot  megrim  Lepidorhombus  boscii  (LDB)  and 
blue  and  red  shrimp  Aristeus  antennatus  (ARA).  Most  of  these  species  are  fished  exclusively  by 
bottom trawl, but in some cases the species catch comes from different gears, each one targeting 
a  given  size  (or  ages)  range  (e.g.  Merluccius  merluccius,  Mullus  barbatus,  Mullus  surmuletus, 
Lophius piscatorius, Lophius budegassa). OTB catches are in all cases much higher and sizes (or 
ages) smaller than those of the small-scale gears with which the resource is shared.  

It  is  worth  noting  that  the  five  stocks  in  GSA  6  under  the  proposed  management  plan  are 
included  in  the  MEFISTO  simulations  (Aristeus  anntenatus,  Parapenaeus  longirostris,  Merluccius 
merluccius, Nephrops norvegicus and Mullus barbatus).  

Fleet corresponds to the combination of gear and fleet size segment. A total of seven fleets were 
considered:  3  bottom  otter  trawl  (OTB-VL1218,  OTB-VL1824,  OTB-VL2440),  2  longline  (LL-
VL0612, LL-VL1218) and 2 gillnet and trammel net (GN-VL0612, GN-VL1218).  

The  results  of  the  simulations  under  the  three  proposed  scenarios  included  biological  indicators 
(mean biomass and SSB trends) and fisheries indicators (catches, F, Fbar/F0.1).  

Requirements for the annual update of stock data  

98 

98 

Scenario 15% capacity reduction in yr 1 + 10% fishing days reduction in yr 1,2 and 3210% capacity reduction in yr 1 + 10% fishing days reduction in yr 1,2 and 3310% capacity reduction in yr 1 + 20% fishing days reduction in yr 1,10% in yr 2 and 3 
  
MEFISTO start-up requires the establishment of an initial stock situation. The preparation of the 
input  data  for  the  model  was  done  in  the  frame  of  the  DRuMFISH  research  project6.  DCF  data 
were requested to be used in the project. 

The data used come from the EC Data Collection Framework (DCF) and stock assessment results. 
The  data  taken  from  the  DCF  include  landings,  discards,  length-  frequency  distributions  and 
fishing  fleets  characteristics.  Stock  assessment  results  were  taken  from  the  most  recent 
assessments  performed  by  STECF  (Scientific,  Technical  and  Economic  Committee  for  Fisheries) 
and  GFCM  (General  Fisheries  Commission  for  the  Mediterranean)  Stock  Assessment  Working 
Groups.  Growth  parameters  (length-weight  relationship  and  VBGC  parameters)  and  M  natural 
mortality were the same as used in the assessments.  

The input data required regarding the stock status correspond to the mean value of the last three 
years assessed. In some cases, i.e. when the information in the assessment reports did not allow 
the calculation of this 3-years mean value (results of the assessments not fully presented in the 
reports), or when the species had not been previously assessed, these three assessed years were 
generated  with  pseudo-cohort  analysis.  The  selection  of  the  parameters  (growth,  length-weight, 
M) to be used in those assessments can be rather time-consuming. 

For the update of the stock data with the most recent assessments the preparation of the input 
data for the model should be done after the stocks assessment meetings have been held. For the 
update, DCF data should be made available to the scientists. 

Worksheet species 

Excel name    

description  

a  

b  

Linf  

K  

t0  

a parameter of the length-weight relationship 

b parameter of the length-weight relationship  

L∞ parameter of von Bertalanffy growth function 

k parameter of von Bertalanffy growth 

function 

t0 parameter of von Bertalanffy growth function 

Ncohorts  

number of cohorts of each stock -- 

stockname  

name of the stock (or main species) -- 

Worksheet cohorts 

(some data in this worksheet are estimated from VPA) 

Excel name    

description  

stockname 

name of the stock (or main species) -- 

age  

number  

mat  

M 

age of the cohort  

number of individuals in the cohort  

proportion of mature individuals at age 

natural mortality coefficient at age 

Worksheet recruitment 

Excel name    

description  

stockname  

name of the stock (or main species) -- 

6  (DRuMFISH  Approaches  to  management  for  data-poor  stocks  in  mixed  fisheries,  Contract 

number - EASME/EMFF/2014/1.3.2.4/ SI2.721116) 

99 

99 

 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
                                          
type    

rec1    

rec2    

k  

integer indicating the type of recruitment function. 

parameter N0, 𝛼1, 𝛼2 or 𝛼3,in the stock – recruitment function  

parameter β1, β2 or β3 in the stock – recruitment function  

age of recruitment (in years) k 

Worksheet interact 

(some data in this worksheet are estimated from VPA) 

Excel name    

description  

stockname  

name of the stock (or main species) -- 

age 

F1 to FG  

age of the cohort  

Fishing mortality by each fleet (or ‘fishing gear’) by age class, from fleet 
1 to fleet G 

Requirements for the annual update of fleet/metier catch and effort data 

At the same time as the update of stock data (i.e. after the stock assessment meetings).  

Worksheet fleets 

Excel name    

description  

fleetname 

fleet name -- 

NV  

dismissal  

Part  

NHDmax  

NFDmax  

NHD    

NFD  

ice  

CommCost 

maxcredit  

number of vessels in the fleet -- 

price paid by the fisheries Administration for decommissioning vessels 

share  of  the  total  revenues  belonging  to  the  owner,  after  discounting 
trade and fuel costs (in %) 

Activity: Maximum number of hours a day by law or physically possible 

Activity: Maximum number of day a year by law or physically possible 

Activity: Average number of hours a day  

Activity: Average number of days a year 

daily consumption of ice 

Commercial  or  trade  cost,percentage  paid  to  the  fish  market  for  the 
sale of fish  

Maximum  amount  of  money  lended  by  the  bank,  as  percentage  of  the 
capital  

fuelprice  

price of the fuel, in €/l, paid by each fleet 

oppC    

finC  

varEff   

opportunity costs, i.e. cost of using the capital invested 

financial costs, costs of paying the debt incurred with the bank  

proportion of effort increase when profits are positive  

Requirements for the annual update of economic data 

Part  of  the  update  of  the  economic  data  should  be  done  through  interviews.  These  interviews 
should have been done before the stock assessments meetings, so as to have the full information 
required for the update of the input data ready once the stock assessment meetings have been 
held. Part of the information comes from DCF data. 

Worksheet vessels 

Excel name    

description  

vesselname    

vessel name -- 

100 

100 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
fleetname  

fleet name -- 

K  

gt  

credit   

consfuel  

crewsize  

otherDC 

annualC  

percFC  

percVC 

active  

pEff  

q  

capital of the vessel (€) 

capacity as GT (Gross Tonnage)  

debt to the bank at time = 0 

fuel consumption in l/day  

crew size of the vessel, including the owner if worker  

daily costs other than fuel and ice (e.g. net mending, food for the crew, 
etc.)  

costs  paid  at  an  annual  scale,  disregarding  all  daily  costs.  It  may 
include: engine repair, shipyard, mooring, fishing license, etc.  

percentage of  the  annual  costs  that  are  fixed  or  compulsory  to  remain 
in the fishery (“unavoidable”): mooring, fishing license, etc.  

percentage  of  the  annual  costs  that  are  not  compulsory,  they  are 
usually  not  met  when  the  profits  are  negative  (“avoidable”):  painting, 
repairs, etc. Corresponds to the depreciation of the capital.  

Boolean (0-1) indicating whether the vessel was active at time t= 0 of 
the simulation. 

Effort (in terms of activity: days a year) of the vessel  

relative catchability of each vessel, i.e. relative fishing power, where the 
average vessel has a value of 1 

Worksheet market 

Excel name    

description  

fleetname  

fleet name -- 

stockname  

stock name -- 

g1  

g2  

g3  

g4  

base or average price of main species (in €/kg)  

age-modifier of price, usually positive: larger fish fetch higher prices  

offer-modifier of price related to catch, usually negative: when the offer 
on the market is high, prices diminish 

offer-modifier of price related to imports, usually negative  

funct2sp  

type of function relating main species to secondary species 

mu  

nu  

price 

parameter  μ  in  the  market  relationship  between  main  species  (i)  and 
secondary species (j) 

parameter  n  in  the  market  relationship  between  main  species  (i)  and 
secondary species (j) 

average price of secondary species (in €/kg)  

Availability of expertise to run scenarios– what, when, who and how 

For  the  Spanish  GSAS,  the  update  could  be  done  by  IEO  and  ICM-CSIC  scientists.  For  the 
updating of GSA7 the collaboration of IFREMER would be necessary. 

For the updating around 6 person-month would be necessary.   

6.2.1.2  IAM 

Current coverage as presented in 18-09: which stocks, which fleets, which time frame 
(historical data, projections) 

101 

101 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
A bio-economic analysis based on IAM simulations was sent as a working document for EWG 18-
06  (STECF  2018a).  Bio-economic  results  of  simulation  of  scenarios  of  effort  and  capacity 
reductions were presented.  

Main characteristics of the application are: 

Stocks included: 

  Hake GSA 7 (Merluccius merluccius) – age structured model 
  Sardine  (Sardina  pilchardus)  Anchovy  (Engraulis  encrasicolus)  Octopuses,  Mackerel  and 

Monkfish as static CPUE.  
  Other species as static CPUE 

Fleets  modeled  (based  on  IFREMER  segmentation  which  account  for  the  activity  (i.e.  “metiers” 
used), fishing zone (i.e. “rayon d’action”) and the length class of the vessels) 

-  Demersal trawlers superior to 24m 
-  Demersal trawlers 18-24m 
-  Pelagic and mixed trawlers superior to 24 m 
-  Netters under 3 nautical miles 6-12m 
-  Netters beyond 3 nautical miles 6-12m 
-  Multi-gear netters 6-12 m 
-  Seiners 6-12 m 

Time frame  

Calibration with 2015 hake assessment data, transversal data for 2015 (Ifremer, SIH, DPMA) and 
economic  cost-structure  data  for  2013  (CASD7).  Economic  data  are  based  on  availability  and 
quality  of  data  at  the  moment  of  the  calibration.  Recruitment  is  based  on  2008-2015  historical 
data (Normal Law). 

At  the  time  of  EWG  18-13,  an  updated  version  is  being  developed  calibrated  with  initial 
parameters for the Hake population dynamic based on GFCM assessment on the year 2016 (data 
source:    GFCM,  2017),transversal  data  for  2016  (Ifremer,  SIH,  DPMA)  and  economic  cost-
structure data for 2016.  

Projections 2015-2030 

Requirements for the annual Data Update 

Data sources 

Time 
availability 

of 

Data available  Comment 

Update of stock data 

GFCM 

: 

November 
year 
(GFCM WG) 

n  

Outputs  from 
stock 
assessment 
data 
series 
until year n-1 

indirect 
methods 

Demersal 
species 
hake 
(Merluccius 
merluccius) 
and 
mullet 
(Mullus 
barbatus) 

red 

7  This  work  has  been  partly  supported  by  a  public  grant  overseen  by  the  French  National 
Research Agency (ANR) as part of the « Investissements  d’avenir » program (reference: 
ANR-10-EQPX-17  – Centre d’accès sécurisé aux données – CASD) 

102 

102 

 
 
                                          
PELMED, IFREMER 

4  months 
after 
PELMED 
survey 

Acoustic data, 
year n 

direct 
method 

Small  pelagic 
species, 
anchovy 
(Engraulis 
encrasicolus) 
and 
(Sardina 
pilchardus) 

sardine 

Other species 

IFREMER/SIH/DPMA 

Year n-1 

Statistics  of 
production 

Update of fleet/metier catch and effort data 

IFREMER/SIH/DPMA  November 

year n 

IFREMER/SIH/DPMA  November 

year n 

availability  of 
typology  year 
n-1 

transversal 
data year n-1 

Update of economic data 

IFREMER/SIH/DPMA  September   

year n 

economic 
data n-2 

According  to  schedule  of  availability  of  biological,  transversal  and  economic  data,  bio-economic 
advice provided in year n will rely on n-2 data (and n-3 economic structure if the advice should 
be provided before September of year n) 

Availability of expertise to run scenarios–  

Required expertise in biology, data analysis and bio-economic modelling and thematic knowledge 
on Mediterranean fisheries context and trends. MARBEC and EM availability with STH support. 

6.2.1.3  NIMED (Nisea Model) 

The  model  presented  during  the  EWG-18-09  for  simulating  management  scenarios  for  demersal 
fisheries in GSA 9 was developed within the Horizon 2020 research project “Science, Technology, 
and Society Initiative to minimize Unwanted Catches in European Fisheries (MINOUW)”. NIMED is 
a multi-species and multi-fleet bio-economic model, which main components are derived from the 
BEMTOOL model (see below).  

Current coverage as presented in 18-09: which stocks, which fleets, which time frame 
(historical data, projections) 

The application of the model presented during the EWG-18-09 covered the whole demersal fleet 
operating  in  GSA  9,  which  consists  of  five  fleet  segments:  demersal  trawlers  divided  in  three 
length  classes  –  12-18m,  18-24m  and  24-40m  -  and  passive  polyvalent  divided  in  two  length 
classes, lower and greater than 12m. NIMED uses the same fleet segmentation as adopted within 
the DCF. 

Five stocks were simulated by the model: European hake, Norway lobster, surmullet, red mullet 
and deep-water rose shrimp. These stocks represent around 30% of total landings in weight and 
value  for  the  whole  demersal  fleet.  This  percentage  increases  for  demersal  trawlers  to  around 
40%.  The  selection  of  the  stocks  included  in  the  model  is  based  on  the  availability  of  stock-
assessment outcomes. 

NIMED  uses  historical  data  to  estimate  some  parameters,  like  recruitment  and  landings  prices, 
and  to  compare  real  data  with  the  long-term  yield  curve.  In  the  application  carried  out  during 
EWG-18-09,  recruits  were  estimated  as  a  geometric  mean  of  the  last  three  years  data.  Prices 

103 

103 

 
 
 
 
 
 
 
 
 
dynamic  was  estimated  on  time  series  since  2008  on  landings  and  prices  by  stock  and  fleet 
segment.  

Projections on fisheries, biological and socio-economic indicators were carried out up to 2025. 

Requirements for the annual update of stock data 

NIMED uses the following data for projecting biomass, SSB, catch, landings, discards and fishing 
mortality for each stock: 

  Fishing mortality at age; 
  Natural mortality at age; 
  Stock number at age; 
  Catch number at age by fishing gear; 
  Discards number at age by fishing gear; 
  Maturity at age; 
  Mean weight at age; 
  Selectivity parameters by stock and fishing gear. 

Most of the data reported above are from stock-assessment results. When the outcomes of new 
stock-assessment  are  made  available,  the  model  can  be  updated  in  a  short  time  by  the  model 
developer (NISEA). 

Requirements for the annual update of fleet/metier catch and effort data 

NIMED uses the following data for projecting fleet size, fishing effort in days at sea, landings by 
stock and total, revenues by stock and total: 

  Number of active vessels by fleet segment; 
  Active GT by fleet segment; 
  Active KW by fleet segment; 
  Number of days at sea by fleet segment; 
  Landings in weight and value by stock and fleet segment; 
  Total landings in weight and value by fleet segment. 

All data reported above are collected through DCF in the data call for transversal data. When the 
transversal  data  are  made  available,  the  model  can  be  updated  in  a  short  time  by  the  model 
developer (NISEA). 

Requirements for the annual update of economic data 

NIMED  uses  the  following  data  for  projecting  the  economic  variables  by  fleet  segment  and  to 
estimate socio-economic indicators in the short, medium and long term: 

  Other income by fleet segment; 
  Energy consumption by fleet segment; 
  Energy costs by fleet segment; 
  Other variable costs by fleet segment; 
  Commercial costs by fleet segment; 
  Repair costs by fleet segment; 
  Non-variable costs by fleet segment; 
  Labour costs by fleet segment; 
  Employment by fleet segment; 
  FTE by fleet segment; 
  Depreciation costs by fleet segment; 
  Capital value by fleet segment. 

All data reported above are collected through DCF in the data call for economic  data. When the 
economic  data  are  made  available,  the  model  can  be  updated  in  a  short  time  by  the  model 
developer (NISEA). 

Availability of expertise to run scenarios– what, when, who and how 

104 

104 

 
Experts  from  NISEA  are  available  to  adapt  the  model  and  run  scenarios.  Time  for  model 
adaptations depends on the type of scenarios to be simulated. The model is currently available for 
simulating  effort variations in GSA 9 demersal fisheries.  Data update for the same fisheries is a 
quite fast operation, requiring few days of work. The model setting for other Italian fisheries, like 
the  demersal  fisheries  in  GSA  10  or  GSA  11,  consisting  in  data  collection,  estimation  of 
parameters  and  model  calibration,  would  require  around  a  week  of  work  for  each  additional 
geographical area. The relatively short time for the adaptation of the model to other Italian GSAs 
is  justified  by  the  consistency  of  Italian  data  with  the  model  structure.  The  use  of  the  model  in 
non-Italian  GSAs  would  request  additional  time  for  its  adaptation  to  a  potential  different 
availability and structure of the input data. 

6.2.2  Other existing fleet-based models for the West Med demersal mixed fisheries not 

presented in 18-09  

6.2.2.1  BEMTOOL 

BEMTOOL  is  a  multi-species  multi-gear  bio-economic  simulation  model,  which  resumes  and 
integrates  the  different  bio-economic  models  and  biological  modelling  tools  developed  for 
Mediterranean  fisheries8.  It  consists  of  six  operational  modules  characterized  by  components 
communicating  by  means  of  relationships  and  equations:  Biological,  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  a  number  of 
management  trajectories  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  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  MSE  approach.  The  process  error  is  implemented  besides  that  on  the  recruitment,  on 
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. 

Table 6.2 BEMTOOL applications 

GSA 

Fishery 

Stock 

Available 
Model 

Applied for the following 
management measures 

GSA 9 (Liguria and 
North Tyrrhenian 
Seas)1 

Small pelagic 
fisheries 

Demersal fisheries 

Anchovy 

Sardine 

European hake 

Red mullet 

BEMTOOL 

Effort restrictions 

BEMTOOL 

Effort restrictions, improve 
selectivity 

8 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. 

105 

105 

 
                                          
Deep-water rose 
shrimp 

Norway lobster 

European hake 

GSA 11 (Sardinia)1 

Demersal fisheries 

Red mullet 

BEMTOOL 

Effort restrictions, improve 
selectivity 

GSA 17 and 18 
(Adriatic Sea)1,3,4,5 

Small pelagic 
fisheries 

GSA 17 (Northern 
Adriatic Sea)1,3 

Demersal fisheries 

BEMTOOL 

Effort restrictions, TAC, 
combination of measures 

BEMTOOL 

Effort restrictions, improve 
selectivity, , combination of 
measures 

Giant red shrimp 

Anchovy 

Sardine 

European hake 

Red mullet 

Spottail mantis 
shrimp 

Common sole 

European hake 

Norway lobster 

GSA 18 (Southern 
Adriatic Sea)1,3 

Demersal fisheries 

Red mullet 

BEMTOOL 

Effort restrictions, improve 
selectivity, combination of 
measures 

GSA 10 (Southern 
Tyrrhenian Sea)2 

Demersal fisheries 

GSA 22 (Aegean sea)4  Demersal fisheries 

Deep-water rose 
shrimp 

European hake 

Red mullet 

Deep-water rose 
shrimp 

European hake 

Red mullet 

Deep-water rose 
shrimp 

Horse mackerel 

BEMTOOL 

Landing obligation, improve 
selectivity 

BEMTOOL 

Improve selectivity 

1  European  Commission,  Study  on  the  evaluation  of  specific  management  scenarios  for  the  preparation  of  multiannual  management  plans  in  the 
Mediterranean and the Black Sea, EASME/EMFF/2014/1.3.2.7/SI2.703 193, June 2016. 

2 MIPAAF, Indagine conoscitiva sullo scarto della pesca alle specie demersali nei mari italiani: valutazioni propedeutiche per  l’implementazione 
delle disposizioni comunitarie in tema di obbligo di sbarco (Regolamento UE 1380/2013, art. 15), April 2016. 

3  Tender  DGMARE  2009/05/Lot1.  MAREA  Framework  Contract.  Specific  contract  n°  10  "Improved  knowledge  of  the  main  socio-economic 
aspects  related  to  the  most  important  fisheries  in  the  Adriatic  sea  (SEDAF)"  deliverable  9  Report  and  discussion  of  the  outputs  of  scenario 
modelling obtained using BEMTOOL. 2015 

4 MARE/2012/24 Lot 2 (SI2.672370). Pilot project Catch and discard composition including solutions for limitation and possible elimination of 
unwanted by-catches in trawl net fisheries in the Mediterranean (DISCATCH). 2015- D4.3. Outputs of the BEMTOOL simulations 

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

6.2.2.2  Other MEFISTO applications 

MEFISTO has been applied to different Mediterranean fisheries. Among them we can mention: 

GSA 1. Small scale fishery, fish trap. Sparus aurata, Lithognatus mormyrus (Maynou et al., 2014) 

GSA 4. Purse seine. Sardina pilchardus (Manouel et al., 2014) 

GSA  5.  Small-scale  fishing.  Dentex  dentex,  Coryphaena  hippurus,  Aphia  minuta,  Palinurus 
elephas,  Scorpaena  scrofa,  Mullus usrmuletus, Sepia officinalis,  Loligo  vulgaris.  (Quetglas et  al., 
2016) 

106 

106 

 
GSA  5.  Trammel  net.  Scorpaena  scrofa,  Mullus  surmuletus,  Sepia  officinalis,  Scorpaena  porcus 
(Merino et al., 2008) 

GSA 6 (two ports). Bottom trawl. Aristeus antennatus (Maynou et al., 2006) 

GSA 6. Bottom trawl, longline. Merluccius merluccius (Lleonart et al., 2003) 

GSA5.  Bottom  trawl.  Mullus  surmuletus,  Aristeus  antennatus,  Nephrops  norvegicus,  Merluccius 
merluccius (Merino et al., 2015).  

GSA  17.  Mid-water  pair  trawl.  Sardina  pilchardus,  Engraulis  encrasicolus  (Sivestri  and  Maynou, 
2009)  

GSA 20. Bottom trawl. Aristeus antennatus and Aristaeomorpha foliacea (Guillén et al., 2012). 

GSA  22.  Bottom  trawl,  beach  seiners.  Merluccius  merluccius,  Mullus  barbatus.  (Merino  et  al., 
2007). 

6.2.2.3  Others 

The  Fcube  model  used  in  ICES  has  been  tested  in  a  Mediterranean  context,  for  the  demersal 
mixed-fisheries  in  the  Aegean  Sea  (Hoff  et  al.,  2010,  Maravelias  et  al.  2012).  More  recently,  a 
mixed-fisheries  effort  optimization  model  including  data-poor  stocks  was  developed  for  the 
Aegean  fisheries  as  part  of  the  DrumFish  project  (see  note  above).  While  this  is  not  directly 
applicable  to  the  Western  Mediterranean,  there  are  many  similarities  e.g.  species  and  fleets 
involved and the type of data available, so these models are valuable and potentially applicable to 
the Western Mediterranean fisheries with limited work involved. 

There  are  no  known  applications  of  the  FLBEIA  model  in  the  Mediterranean  Sea.  See  also 
https://stecf.jrc.ec.europa.eu/dd/bioeco for a list of models available for fisheries modeling. 

6.2.3  Individual-based and spatially explicit mixed-fisheries models 

6.2.3.1  SMART 

The  SMART  platform  (Russo  et  al.,  2014)  is  a  R-based  suite  to  model  different  fisheries  taking 
into  account  for  all  the  spatial  and  temporal  dynamics  of  both  fleets  and  resources.  It  was 
primarily developed for demersal fisheries, and the platform has been recently submitted to the 
Comprehensive  R  Archive  Network  (CRAN)  repository.  The  SMART  platform  is  articulated  in 
different submodels that are devised to a) the reconstruction of the origin and the fate of catches 
and  landings  in  terms  of  well-defined  areas  and  times  in  which  resources  are  harvested  and 
harbours  to  which  they  are  delivered  for  sale,  respectively;  b)  explore  the  potential  effects  of 
different  spatial  and/or  temporal  closures  in  terms  of  change  of  the  exploitation  pattern  and 
consequent  effect  on  both  resources  condition  and  economic  performance  of  the  fleets.  The 
SMART platform includes a Bayesian simulator to forecast the potential effects, in terms of fishing 
effort  displacement,  determined  by  some  management  measures  such  as  Fisheries  Restricted 
Areas (FRA).  

From an operational point of view, SMART is an individual-based model and each fishing vessel is 
considered  as  an  independent  agent  that  operates  to  maximize  gains  (as  difference  between 
revenues and costs).  

Moreover,  the  modelling  of  multispecies  fisheries  such  as  trawling  is  carried  out  using  MICE 
models (Models of Intermediate Complexity in Ecosystems; Punt et al. 2016; Punt & Butterworth, 
1995).  The chosen framework  models  a Statistical  Catch At  Age  (SCAA) with a basic  population 
dynamic  which  follows  the  classical  approach  of  Doubleday  (1976)  where  the  catch-at-age 
datasets  are  fitted  for  multiple  cohorts  simultaneously  and  the  fishing  mortality  is  split  into  age 
and year components. 

SMART  processes  and  combines  the  data  derived  from  three  fundamental  entity  of  the  fishery 
system: the environment, the working fleet and the biological resources. 

107 

107 

 
 
Data 

Source 

Application 

Type of 
data 

Fisheries 

costs 

Spatial-related 
(fuel); 
Activity-related 
(crew salary); 
costs 
Other 
(commercialization 
of 
landings,  insurance,  fixed 
costs, etc.) 

costs 

Fisheries 

Vessel-specific  data  about 
landings 

Fisheries 

Vessel-specific  data  about 
fishing effort pattern 

Resources 

Spatial  abundances  and 
age structure 

Data  Collection 
Framework 
– 
Transversal 
variables 

Modelling  costs  with  respect 
to  harbors  of  departure,  area 
of  fishing  activity,  amount  of 
fishing days and landings 

Data  Collection 
Framework 
– 
Biological 
sampling 
catches 

of 

  Modelling spatial and 

temporal LPUE 

  Modelling exploitation 

pattern 

  Modelling spatial age 
structure and growth 
pattern of resources 

Modelling  the  fishing  effort  of 
each vessel 

Tuning 
assessment module 

phase 

of 

the 

Data  Collection 
Framework 
– 
VMS / AIS data 

Data  Collection 
Framework 
– 
(e.g. 
Surveys 
MEDITS, 
GRUND) 

Environmental 

Bathymetry, 
Topology, 
distributions 

Substrates, 
Harbors 

Various 

Definition  of 
domain 

the 

spatial 

The workflow of the SMART approach is as follows: 

1.  Subdivide the case study into functional fishing areas (Fishing Grounds module); 
2.  Detect  the  spatial  origin  of  the  landings/catches  and  estimate  the  Landings/Catches  per 

Unit of Effort (Lander module); 

3.  Determine the growth parameters and subdivide the studied stock(s) into cohorts (Growth 

module);  

4.  Estimate  costs  and  revenues  associated  to  a  given  fishing  effort  pattern  (Performance 

module); 

5.  Simulate different management scenarios (Simulation module); 
6.  Assess the status of the studied stock(s) (Assessment module); 
7.  Forecast  the  middle  and  long-term  effects  of  the  management  measure  (Forecast 

module). 

8.   

108 

108 

 
 
SMART  was  applied  to  the  trawling  in  the  Strait  of  Sicily  (Russo  et  al.,  2014)  and  is  the  main 
approach within the DGMARE project MANTIS, which comprises two case studies:  

1.  the trawl fishing in the Strait of Sicily (GSA12, 13, 14, 15, and 16) with 4 target species: 
red mullet, deep water rose shrimp, giant red shrimp and hake. This case study comprises 
both Italian and Maltese fleets operating in this area; 

2.  the trawl fishing in the Adriatic Sea (GSA17 and 18) with 4 target species: common sole, 
hake, red mullet and Norway lobster. This case study comprises both Italian and Croatian 
fleets operating in this area; 

For each of the case studies, SMART is applied for the following purposes: 

  Evaluate the potential benefits (for the resources) of the progressive implementation of a 

network of Fisheries-restricted areas; 

  Evaluate  the  potential  benefits  of  the  progressive  implementation  of  a  different  effort 
regimes (including the reduction of the fishing capacity and the regulation of the effort); 
  Forecast  the  feedback  of  the  fleets  in  terms  of  displacement  of  the  fishing  effort  and 

bioeconomic performance. 

Given that the application of SMART is linked to the availability of DCF data, this platform is likely 
to be applied in other areas of the Mediterranean Sea, including the Western part. 

6.2.3.2  DISPLACE  

The  DISPLACE  model  framework  (Bastardie  et al.  2014)  is  developing  a  research‐  and  advisory‐
based platform to transform fishermen's detailed knowledge and micro‐decision‐making behavior 
into simulation and management evaluation tools. This involves advanced methods to assess and 
provide  advice  on  the  bio‐economic  consequences  for  the  fisheries  and  fish  stocks  of  different 
fishermen  decisions  and  management  options.  DISPLACE  is  an  agent‐based  simulation  model 
developed  to  support  maritime  spatial  planning  and  management  issues,  especially  from  the 
perspective  of  the  fisheries.  Agent‐based  models  aim  to  consider  the  socioeconomic  and 
ecological  processes  at  the  individual  scale  (e.g.,  the  fishing  vessels)  to  capture  the  effects  of 
human decisions at that level and then go through the individual processes up to the aggregated 
dynamics  (e.g.,  the  fisheries  as  a  whole  or  other  marine  ecosystem  components).  A  particular 
strength  of  the  agent‐based  approach  is  that  it  is  an  adequate  level  to  model  processes  at  the 
spatial (2 × 2 km) and the time scale (hourly time steps) closer to the spatial and time dynamics 
occurring  in  human  decision‐making  and  fish  populations  dynamics.  It  is  also  closer  to  the 
appropriate scale for dealing with management issues such as marine spatial planning. The agent‐
based approach is also keen on integrating process‐based mechanistic relationships that give the 
advantage  of  being  able  to  better  predict  in  novel  conditions.  Accordingly,  DISPLACE  should  be 
able  to  incorporate  the  spatial  and  temporal  details  to  obtain  a  necessary  understanding  of  the 
integrated  fisheries,  behavioral  and  resource  dynamics.  DISPLACE  can  address  fleet/skipper 
behavior  facing  the  experienced  catches  and  the  fisheries  management  in  force  including  Effort 
Regime (overall capacity reduction, limits in days at sea, temporal & spatial closure to fisheries) 
together with multi-annual management plans in a CFP context (i.e. FMSY).  

Most  of  bio-economic  models  for  fisheries  are  working  at  aggregated  level  to  fleet  -segments. 
One  of  the  weaknesses  of  these  aggregated  models  is  that  they  are  not  well-suited  to  easily 
capture individual vessel characteristics that are potentially major drivers for predicting  effect of 
fishing  on  the  harvested  resources,  such  as  the  running  costs  of  fishing  activities  (i.e.,  the 
variable fishing cost depending on vessel specific effort allocation in time and space). Aggregated 
models also tend by nature to ignore important differential aspects among fishers related to their 
various  economic  drivers  or  other  individual  incentives.  An  alternative  approach  is  to  develop 
models that work on a more disaggregated scale to ideally encompass mechanistic processes at 
the  individual  (vessel)  level  and,  ultimately,  let  the  overall  pattern  of  effort  allocation  between 
fisheries,  space  and  time,  and  eventually  the  differential  catchabilities,  emerge  from  all  of  the 
individual fisher’s decisions and varying fishing vessel catching power. 

So far, important progress has been made in a row of applications including the Adriatic Sea, the 
Ionian Sea, the Black Sea, the Baltic Sea and the Irish Celtic Sea. Regional scale applications are 
currently  being  developed  for  the  North  Sea  and  the  Baltic  Sea  fisheries.  On  the  Mediterranean 

109 

109 

 
 
side, DISPLACE has been applied to the north Adriatic (GSA 17) to the Italian demersal fisheries 
(Bastardie et al 2017). We applied the fish and fisheries model to assess the impact of a suite of 
spatial  plans  suggested  by  practitioners  that  could  reduce  the  pressure  on  the  four  demersal 
stocks of high commercial interest in the GSA 17, and that could promote space sharing between 
mutually  exclusive  activities.  The  Adriatic  Sea  application  has  been  recently  updated  with  most 
recent  fish  stock  assessment  data,  extended  to  include  the  Croatian  fisheries  (Figure  6.5),  and 
simulation experimental plans are ongoing. Eventually, it is further intended to expand DISPLACE 
to the Western Mediterranean demersal fisheries (French, Spanish and Italian fishing vessels) as 
soon as the data and resource are made available (data requirement described in Table 6.3). 

Table  6.3:  Restrained  needs  for  developing  a  fisheries-centered  DISPLACE  regional  application 
obtained from different sources. 

Type of data 

DISPLACE parameters 

Data Source 

Fisheries 

Species   specific   catch   rates  per  STECF  Effort  WG/GAM  modelling 

on national logbooks 

vessel or fleet-segments 

from 

ICES 

Landings,  discards  by  gear  by 
stocks 
Intercatch 
database,  STECF  or  National 
stats 
Effort  per  métier  per  vessel  size 
from  MIXFISH  ICES  data  call  or 
STECF Data call 

Selectivity  per  gear  per  species  and 
gear footprint 
Finely  resolved  spatial    distribution 
of the fisheries per fleet segment 
Fisheries 
including  costs 
harbor 
marketable category 
Number  of  vessels  per 
harbor per fleet-segment 

indicators 
fishing  and 
stock  prices  per 

economic 
for 

fishing 

fish 

Biology-related data  Finely  resolved  fish  stock  spatial 

distribution 
Biological 
including 
parameters 
Stock numbers 

size 

stock 

parameters 
trophic 

spectra 

ICES WGs/National data 

ICES WGSFD/International Survey 

STECF  Annual  Economic  Report 
and National Statistics 

and 

register 

national 

EU 
statistics 
Spatial  statistics  from  Academic 
science 
ICES/STECF/GFCM 
assessment  WGs, 
and size spectra modelling 
ICES/STECF/GFCM 
assessment WGs 

Stock 
fishbase.org 

Stock 

Concerning the data, spatial distribution of the fleets and the stocks are required. The simulations 
are more or less accurate depending on the quality on this input. For example for the North Sea, 
ICES  WGSFD  (Spatial  Fisheries  Data  ICES  Working  Group)  is  now  routinely  mapping  the  fishing 
activities  at  sea  on  a  fine  grid  scale  that  can  also  inform  the  spatial  fishing  footprint.  This 
database was used in the simulations performed using the DISPLACE model. In the Adriatic Sea 
application,  VMS  and  AIS  data  have  been  use  to  map  the  initial  fishing  effort  allocation.  The 
population  spatial  distributions  were  obtained  from  data  collected  during  scientific  surveys.  In 
particular,  the  MEDITS  survey  program  (an  international  bottom  trawl  survey  in  the 
Mediterranean). 

110 

110 

 
 
 
 
 
 
 
 
Figure 6.5 Random snapshot of the DISPLACE User Interface for the Demersal Italian & Croatian 
demersal fisheries in the northern Adriatic (GSA17). Movement and catches of individual fishing 
vessels  are  simulated  at  hourly  time  steps  on  a  5y  time  horizon.  DISPLACE  projected  EU  MSFD 
and  AER  bioeconomic  indicators  can  be  aggregated  at  various  levels  (Vessels,  to  Metiers,  to 
Harbours, to Nations) and simultaneously mapped in a unified framework. 

As  one  of  the  full  regional  scale  application,  the  application  of  DISPLACE  to  the  North  Sea 
considered adjustment of spatiotemporal distribution of effort to maximize the profit of the whole 
fishery  under  TAC  constraints.  This  model  is  used  to  model  the  catches  of  the  North  Sea  fleets 
focusing on the Danish fishing vessels that is active in the Baltic Sea and the North Sea in 2015. 
For the finest level, the simulations use hourly time steps and a 6 by 6 km geodesic spatial grid 
(providing 35,309 possible fishing locations). In all, 693 vessels were simulated representing 46% 
of  the  1,505  Danish  vessels  for  which  logbooks  were  available  in  2015.  Each  simulated  vessel 
could only fish in the set of EUNIS habitats and areas where it had previously been fishing. The 
simulated  fishing  vessels  were  allowed  to  use  several  different  gear  types  within  the  same  trip. 
After each trip, the simulated vessels return to port and earn money from selling their landings in 
the harbour. Fish prices are given per stock  and marketable category (small, medium, large) and 
the gross added value is computed from income generated by selling the landings and the actual 
operating  costs  of  the  trip.  For  the  coarser  level  (currently,  other  nations  than  Denmark)  the 
catches are computed from the TACs and the relative stability keys and apply on the distribution 
areas of the harvested stocks. 

6.2.3.3  SWOT Analysis of individual-based models 

The  EWG  discussed  the  possibility  of  applying  several  models  to  the  case  study  of  Western 
Mediterranean.  Leaving  aside  the  issues  related  to  data  availability,  examples  for  the  two  main 
groups  of  modelling  approaches  (i.e.  Fleet-aggregated  models  and  Individual-based  models  - 
IBM) were briefly evaluated as possible options. Extensive discussions on the characteristics and 
the pros and cons of various models for management strategies evaluation can also be found in 
e.g. STECF (2017) and Nielsen et al. (2018) 

Here we summarize,  in the form of SWOT (Strengths, Weaknesses, Opportunities and Threats  - 
Humphrey, 2005) analysis, the aspects related to the application of IBM, which represent the last 
generation of modelling approaches.  

111 

111 

 
 
Strengths 

Weaknesses 

  Avoiding “averaging problems”  
  Avoiding  misclassification 

of 
agents  (e.g.  switching  metiers 
within the same trip); 

  Capturing 

the 

mechanistic 
relationship  between  effort  and 
mortality 
decision-
(fishermen 
making, etc.) 

  Possibility  to  predict  the  individual 
management 
to 

feedback 
strategies 

  Large 

number 

parameters, 
correlation 
parameters.  

of 
potential 
across 

  Large 

number 

of 

assumptions 

  Difficult fitting/validation 
  Computational  complexity, 

time/pc demanding   
  Computer skills needed 
  Difficult  characterization  of 

to 

capture 

uncertainty 

  Better 

suitable 
space/time effects 

  Possibility  to  estimate  parameters 
(including  catchability)  at  a  finer 
scale 

  Tries 

to  capture  human 
behaviour  traits  which  are 
poorly understood. 

Opportunities 

Threats 

  Valorisation  of  datasets  (including 
remote  tracking  devices  such  as 
VMS and AIS) 

  Confidentiality issues 
  Expert knowledge needed 
  Outliers 

effect 

(vessels 

without tracking devices) 
  Fleets coverage in the Med? 
to 
  Biased 
large  number 
and 

results  due 

sometimes 
parameters 
assumptions. 

L
A
N
R
E
T
N
I

L
A
N
R
E
T
X
E

Basically,  the  Strengths  of  IBM  are  related  to  their  ability  of  incorporating  the  different 
characteristics  of  fishers  and  fishing  vessels  in  terms  of  behaviour,  fishing  performance  and 
impact on the resources. This is a very important aspect since it is largely acknowledged that, in 
some  regions  such  as  the  Mediterranean  Sea,  the  grouping  of  fishing  vessels  in  Metiers  is 
sometime too rigid and not efficient. Moreover, the availability of data from tracking devices (i.e. 
VMS  and/or  AIS)  allows  to  evidence  (see  the  Tor  3  of  this  EWG)  that  some  aspects  of  vessel-
specific  strategy  such  as  the  area  of  activity  and  the  species  targeting  (at  a  finer  scale  that 
assemblage) have an important effect on landings and fishing performance. 

The weaknesses come from the fact that IBM tend to be use a large number of parameters and 
sometime  require  a  large  amount  of  data  to  model  human  behaviour  that  make  the  model 
validation challenging with unknown risk for producing biased outcomes. 

The applicability of such IBM model for providing mixed-fisheries advice for the Western demersal 
Mediterranean fisheries should be discussed as part of the Step 1 of the road map below. 

6.2.4  Summary  - availability of mixed-fisheries models and identified gaps 

The  review  above  has  highlighted  that  there  are  many  models  available,  but  with  some  various 
levels of coverage, development, completeness, update, complexity  and user-friendliness. There 
does  not  seem  to  have  one  obvious  choice  of  deciding  upon  one  model  over  the  other  at  this 
stage.  

As a next step for the road map, the EWG 18-13 suggests the following approach: 

112 

112 

 
 
 
 
 
 
 
 
 
 
  Agree on a suite of minimum requirements for what a model for the mixed-fisheries advice 

should be able to do 

  Hold a dedicated STECF EWG in 2019 which the main purpose of testing the operationability 

and suitability of various candidate models and modellers on the most recent data year. 

6.3  Other gaps and issues to consider in order to have an operational mixed-fisheries 

advice 

6.3.1  Improvement of fishing effort definition 

While discussing ToR 2 results in the plenary of EWG 1813, it was observed that the fishing days 
used  in  the  nominal  effort  measurements  were  not  the  same  depending  on  the  country.  In  this 
sense, a fishing day in Spain means that the boat has been out of the port no more than twelve 
hours,  although  there  are  the  exceptions  of  the  fleets  exploiting  the  Ibiza  and  Alboran  Island 
crustacean fisheries in the slope which can be out of the port sixteen hours due to the distance to 
those fishing grounds. On the other hand, in Italy there is no restriction of hours per day and 15 
hours  a  day  could  be  considered  a  normal  fishing  trip,  and  there  is  also  an  exceptionality  that 
allows  some  boats  to  stay  at  sea  for  more  than  one  day  .  For  these  differences  in  the  time 
allowed  at  sea  between  countries,  the  measure  of  the  effort  is  not  standardized  throughout  the 
countries involved in the future MAP. It was also pointed  out that the other variable  included in 
the  effort  measure,  the  KW  of  each  of  the  vessels,  is  an  uncertain  value  and  that  perhaps  the 
effort measure should be more simple and reliable by just using fishing days.  

An option that was proposed during the discussion was to use hours of effective fishing instead of 
fishing  days,  which  would  be  a  standardized  measure  of  effort  throughout  all  the  countries 
involved. It as pointed out that the calculation of the effective fishing hours can be done either by 
using  VMS,  not  currently  requested  in  the  official  data  calls,  or  by  using  the  information  in  the 
log-books which is currently requested not by day but by haul and is requested in the official data 
calls. Both data sources would allow computing the effort as effective fishing hours. Having more 
fine measures of effort could allow improving in the exploration of the relationship between F and 
effort in the core of the proposal of the MAP. VMS have the advantage that could provide a longer 
time series of effort as their implementation started in 2006.  

6.3.2  Definition of fleets and metiers 

There  is  an  ongoing  discussion  whether  the  current  fleets  and  metiers  as  used  in  the  data  calls 
are the best descriptors of the actual differences in fishing activities across vessels and area. In 
all countries, studies have been conducted to investigate the differences in catch composition. In 
this  section  we  summarise  some  of  that  knowledge  first  by  summarising  the  work  performed  in 
previous STECF EWGs, then with additional insights for Spain and Italy. 

6.3.2.1  Previous STECF reports dealing with catch composition in mixed-fisheries 

Most  of  the  work  is  found  in  the  STECF  (2015b,  2015c,  2016b,  2018a).  In  particular,  STECF 
(2015b) focused on analysing the species defining the fisheries, following fisheries definition from 
the Data Collection Framework. For each combination of GSA, metier (DCF Level 6) and Country, 
standard plots of catch composition in tonnage and in value are provided, as for example for the 
metier GSA5_DEMSP_OTB_ESP (Bottom otter trawl targeting demersal species in GSA 5) (Figure 
6.6). 

113 

113 

 
Figure 6.6 Cumulative percentage for GSA05_DEMSP_OTB (source ??) 

Then  a  global  comparison  plot  was  provided  for  each  gear  across  all  GSAs,  target  group  and 
country, as below for the Otter trawl (Figure 6.7). 

114 

114 

 
 
Figure 6.7. Main selected species defining the Mediterranean demersal with a bottom otter trawl 
gears (OTB). The species selected are landed at least by one fishery and have been selected if in 
each fishery they fall within the 75 % cumulative percentage of the incomes. Species in red are 
demersal species subject to minimum sizes as defined in Annex III of the MEDREG. Fisheries in 
green  do  not  have  any  Annex  III’s  species  in  their  landings.  The  bubble  size  represents  the 
percentage of the incomes (value in Euros) of each species on the total incomes, which is given 
by the sum of the incomes considering only these main selected species (From STECF 15-14) 

Finally,  the  analyses  were  supplemented  by  a  literature  review  on  the  references  published  on 
catch composition, with references identified in the following areas (Figure 6.8). 

Figure  6.8  Spatial  distribution  of  the  available  studies  dealing  with  the  characterization  of 
Mediterranean  demersal  fisheries  (trawl  and  small-scale)  found  in  the  literature  search  by 
Geographical Sub-Areas (GSAs) 

During the EWG 18-13, additional information was provided for Spain and Italy on issues with the 
current  definition  of  fleets  and  metiers.  The  analyses  are  described  below,  and  the  detailed 
figures of the results are given in Annex 03. 

115 

115 

 
 
 
6.3.2.2  Spain 

The approach followed so far aiming to determine the fishing effort regime for demersal fisheries 
may not be able to take into consideration the multispecific character and the by-catches of the 
mixed  bottom  trawl  fisheries  in  the  Spanish  Mediterranean.  This  is  in  part  because  some  of  the 
stocks defined in the Regulation as drivers of the mixed fisheries seem not appropriate when the 
particular  landings  of  each  area  are  analyzed.  The  most  clear  example  is  that  of  M.  barbatus  in 
GFCM  sub-area  (GSA)  5,  considered  as  one  of  the  stocks  concerned  by  the  Regulation,  but 
actually  being  of  almost  negligible  importance  in  that  GSA.  In  that  GSA  it  is  M.  surmuletus  the 
important  species  in  terms  of  landings  and  economic  benefits  obtained  from  the  shallow  shelf, 
which is therefore periodically assessed in that area (Quetglas et al. 2016). 

The problem may be more generalized due to the segmentation of the fleet that has been used. 
The  segmentation  requested  in  the  DCF  may  be  too  coarse  to  take  into  consideration  the 
multispecific character and the variety of target species of the mixed bottom trawl fisheries in the 
Spanish Mediterranean. It simply considers the boats length overall and following metiers:  

DEMSP:  Demersal  species.  The  fishing  days  targeting  demersal  fish  species  (either 
fish, cephalopods or crustaceans) from the shelf and upper slope are currently codified with this 
metier. 

DWSP:  Deep  water  species.  The  fishing  days  targeting  deep  crustacean  species 
(Aristeus  anntennatus  and  Aristaeomorpha  foliacea)  in  the  middle  slope  are  currently  codified 
with this metier. 

MDDWSP: Mixed demersal and deep water species. The fishing days in which a boat 
has targeted both demersal fish species (either fish, cephalopods or crustaceans) from the shelf 
and  upper  slope,  and  deep  crustacean  species  (A.  anntennatus  and  A.  foliacea)  in  the  middle 
slope, are currently codified with this metier. 

However,  the  segmentation  of  the  bottom  trawl  fleet  in  Spain  in  2006-2007,  carried  out  within 
the  Programa  Nacional  de  Datos  Básicos  pesqueros,  revealed  a  much  more  complex  level  of 
fishing  strategies  or  metiers.  That  segmentation  was  made  at  port  fleet  level  and  included  the 
most  important  Spanish  Mediterranean  harbours  for  the  bottom  trawl  fleet.  It  was  based  on 
landings from daily sales bills which were analyzed using multi-variate statistical techniques such 
as cluster analysis. 

The analyses in Annex 03 show that there is a high variability in the metiers followed by different 
bottom trawl fleets. Some ports did not exploit at all the middle slope, such as Sant Carles where 
their fishing activity was concentrated on the continental shelf. Others, even within the same GSA 
6, like Llançà, exploited the shelf and slope. The boats from Vélez-Málaga in GSA 1 did not exploit 
the  middle  slope  but  exploited  the  continental  shelf  and  upper  slope,  whereas  in  the  GSA  5  the 
boats  from  Mallorca  exploit  the  shallow  and  deep  continental  shelf  and  the  upper  and  middle 
slope, frequently combining more than one basic fishing strategy in a single day. This variability is 
completely masked if the metiers used to calculate the effort for the multi-annual plan are those 
required in the DCF. 

(Mullus 

barbatus,  Merluccius  merluccius, 

It  is  also  remarkable  that  the  target  species  proposed  as  drivers  of  the  mixed  fisheries  in  the 
western  Mediterranean 
Parapennaeus 
longirostris,Nephrops  norvegicus,  Aristeus  antennatus  and  Aristaeomorpha  foliacea)  may  not  be 
the most indicated to set effort reductions in some areas. The examples  in Annex 03 show that 
although  these  species  are  present  in  most  areas,  they  may  not  be  the  target  ones,  and  that 
most  commonly,  landings  in  the  Spanish  Mediterranean  are  composed  by  a  large  variety  of 
species, especially when they come from the continental shelf. A larger number of species should 
be  included  in  order  to  take  into  account  the  marked  multi-species  character  of  the  Spanish 
bottom trawl fisheries. Some of these species are taken into account in the assessments, such as 
M.  surmuletus  from  GSA  5,  which  is  periodically  assessed  within  the  GFCM,  however,  most  of 
other  important  target  species  at  a  more  regional  scale,  besides  those  stocks  specifically 
mentioned  in  the  Regulation  (Article  1(2)),  are  not  currently  periodically  assessed  (e.g.  O. 
vulgaris, species in the mixed fish categories, S. mantis, Eledone cirrhosa...).  

It must be also taken into account that some of the important by-catch species, such as those in 
the  mixed  fish  category  (Trigloporus  lastoviza,  Trachinus  draco,  Chelidonichthys  cuculus  and 

116 

116 

 
Serranus  cabrilla)  from  the  continental  shelf  of  the  Balearic  Islands,  GSA  5,  have  also  been 
assessed, and showed even a higher overexploitation state than M. surmuletus, one of the main 
target  species  of  the  continental  shelf  in  the  area  (Ordines  et  al.  2014).  Hence,  although  the 
management  of  multi-species  fisheries  based  on  single  species  assessments  should  also  benefit 
the  rest  of  species,  because  by  reducing  the  excessive  fishing  mortalities  of  target  species  to 
sustainable  levels  the  pressure  on  the  rest  of  the  components  of  the  ecosystem  would  also 
decrease (Mace, 2004), it may not suffice species more vulnerable than the ones assessed.  

Another  important  consequence  of  this  variability  of  metiers  and  diversity  of  targeted  species  is 
that the actual effort exerted on the driving stocks proposed could be overestimated. This effort is 
currently calculated based on the metiers described in the DCF and within it, the effort exerted by 
fleets not targeting the driving species also counts in. A clear example of this is M. barbatus from 
GSA 5, which is not targeted in the area but the effort exerted on it should be that exerted by the 
fleet  to  the  DEMSP  metier,  which  includes  in  GSA  5  those  boats  targeting  the  continental  shelf 
and upper slope.  

Although the complex variations, most metiers are quite coincident with the main communities of 
demersal species and resources described in the continental shelf and slope of the Mediterranean 
(e.g.  Massutí  and  Reñones  (2005)  in  the  western  basin,  Biagi  et  al.  (2002)  and  Colloca  et  al. 
(2003)  in  the  central  area,  and  Kallianiotis  et  al.  (2000)  in  the  eastern  basin),  which  usually 
identify up to four main bathymetric assemblages: the shallow shelf, deep shelf, upper slope and 
middle  slope.  The  Article  3(3)  of  the  regulation  specifies  that  ‘The  plan  shall  implement  the 
ecosystem-based approach to fisheries management in order to ensure that negative impacts of 
fishing  activities  on  the  marine  ecosystem  are  minimised’.  Hence,  the  segmentation  used  to 
calculate the effort should also be able to reflect the main communities where it is exerted. 

Taking  everything  into  account,  an  effort  should  be  done  to  improve  segmentation  of  the  fleets 
involved.  A  more  detailed  segmentation  from  the  daily  sales  bills  would  allow  for  1)  better 
definition  of  the  target  species  at  a  GSA  spatial  scale  and  2)  a  better  estimation  of  the  effort 
exerted on them and on the communities affected. 

6.3.2.3  Italy 

At  present,  the  used  fleet  segmentation  is  not  enough  detailed.  It  is  possible  that  the  current 
aggregation  size  structure  and  rough  division  in  only  two  so-called  “métiers”  is  functional  for 
assessing  and  modelize  economic  consequences  of  alternative  choices.  What  is  important  is  to 
understand whether the same aggregation system is also sufficient for biological analyses and for 
the management and forecasting of the demersal mixspecies fisheries of the area.   

It  is  reasonable  to  argue  that  the  targets  of  vessels  belonging  to  different  segments  of  a  fleet 
vary. It is likely that small vessels will prefer coastal resources due to security reasons or due to 
the  longer  time  needed  for  reaching  distant  fishing  grounds.  On  the  other  hand,  bigger  sized 
vessels  are  more  independent  in  their  choices  but  often  choose  the  targeting  to  valuable 
resources  living  far  from  the  coast.  However,  this  is  not  a  rule  and  large  differences  due  to 
tradition, resources availability, oceanographic or geo-morphological reasons may be determinant 
factors for conditioning the fishers’ choices. 

The  proposed  segmentations  in  the  WestMed  Plan  are  only  based  on  vessels’  size  which  obliges 
catch and effort to be pooled without other distinction than size of the vessels or only making a 
rough  separation  in  two  so-called  métiers  (mix  and  deep  water  shrimps).  In  such  high  level  of 
aggregation, effort data will be less informative as it is impossible to link effort with resources on 
which such effort apply. Results will not describe the direct effort exerted by the fractions of the 
fleets  on  the  different  fish  assemblages  or  single  species  that  in  reality  exist  and  are  very  well 
distinct  when  fishers  define  targets  and  operation  areas.  It  is  necessary  to  be  able  to  split  the 
fleet in more “natural” segmentations that considers not only the size or engine power of vessels 
utilizing  the  same  gear  type,  but  also  based  on  vessels    that  have  the  same  group  of  target 
species,  operating  with  the  same  seasonal  frequency,  and  exerting  fishing  effort  with  a  similar 
exploitation  pattern,  which  approach  the  original  idea  which  is  behind  the  métier  concept.  The 
current proposed division of only one or two groups, includes the fisheries operating close to the 
coast, over the shelf border and in the slope (that imply gathering portions of the fleet targeting 
coastal  species  as  Mullus  barbatus  up  to  100m  depth,  those  fishing  on  the  shelf  with  a  species 

117 

117 

 
mix  as  target  (cephalopods,  hake,  etc)  and  those  targeting  Nephrops  norvegicus  at  depths 
between 300 and 600m and where deep sea shrimps are present, also the fleet operating in deep 
waters over 600m..  

In Annex 03 it is shown an example of identification of métiers based on catch composition. The 
dendrogram  separates  clusters  for  the  fleet  of  the  port  of  Viareggio,  inside  the  GSA9.  This fleet 
does not exploit deep-sea areas where the two red shrimp species live as these 2 species are not 
abundant in the grounds facing such port. Multivariate analysis (Hierarchical clustering) based on 
catch composition by vessel shows a clear distinction of métiers, mostly defined by depth interval. 
By  choosing  different  depth  intervals  where  to  go  to  fish,  fishers  decide  in  advance  what  they 
want to catch. Catch composition  is in this area very well stratified,  with only few species living 
over a wide depth range. The analysis describes very well defined (without overlapping) clusters 
that can be associated with fisheries/métiers.  

-a  coastal  fishery  operating  in  the  depth  range  10-100m,  (Sepia  officinalis,  Squilla  mantis,  red 
mullet) 

-a  fishery  operating  over  the  shelf  between  100  and  300m  (Eledone  cirrhosa,  rays,  Zeus  faber, 
Scyliorhinus canicula, Triglidae, Merluccius, pink shrimp, etc) 

-  the  fishery  targeting  Nephrops  norvegicus,  Phycis  blennioides,  Micromesistius  poutassou, 
squids, etc) 

-In this port the fishery targeting deep sea shrimps does not exist as it occurs in other ports of 
the  same  GSA,  as  Santa  Margherita  in  the  North  and  Porto  Santo  Stefano  towards  the  South 
where such fishery is one of the more stable and easy to identify based on catch composition. In 
such  fisheries,  the  two  species  of  Aristaeidae  shrimps  red  shrimps  are  seldom  the  dominant 
species  and  by-catch  is  mainly  composed  by  Phycis  blennioides,  Micromesistius  poutassou, 
Lophius spp, Galeus melastomus.  

Other  clusters  can  be  defined  and  are  shown  in  Annex  03.  They  represent  other  fisheries  for 
instance  pair  midwater  trawls  that  at  this  time  were  targeting  pelagic  resources  or  seasonal  or 
special  fisheries,  where  a  specific  stock  or  group  of  species  predominate  as  concentrated  and 
highgly vulnerable in certain periods of the year. These fisheries are less important, and some of 
them are already disappeared in the area.  

The  division  in  at  least  4  different  species  assemblages  is  clear  for  almost  all  the  western 
Tyrrhenian GSAs.  

The  sampling  plan  and  number  of  métiers  should  be  consistent  in  order  to  obtain  a  sufficient 
number of samples (trips) useful for supporting estimates of key parameters.  Such division is not 
the most detailed possible, but can be a good compromise.  

Other fleets.   

Based  on  the  proposed  stocks  assuming  to  be  representative  of  the  fisheries  in  the  area,  it  is 
noticed that some of them are also caught by other fractions of the fleet using different gears, for 
instance  using  the  set  nets  or  longlines  utilized  by  the  small-scale  vessels.  This  is  the  case 
principally  for  hake,  cuttlefish,  and  red  mullets.  Whenever  in  a  GSA  or  sub-area  removals  from 
small-scale  vessels  are  not  negligible,  without  including  such  removals  it  results  impossible  to 
understand the reasons of the evolution of the stock status, based on observed changes in catch 
rates,  etc  and  in  particular  to  get  reliable  relationships  between  fishing  effort  and  fishing 
mortality. In an effort-based management regime, the knowledge of such relationship is critical.  

The  4  briefly  described  fishing  strategies  are  also  present  in  the  other  Italian  GSAs  facing  the 
Tyrrhenian Sea, but major details are not available. 

6.3.3  Species and stocks issues  

6.3.3.1  Overview 

The Proposal for a Regulation of the European Parliament and of the Council, establishing a multi-
annual plan for the fisheries exploiting demersal stocks in the western Mediterranean Sea, taking 
into account that the exploitation of most demersal stocks in this area ‘exceeds by far the levels 

118 

118 

 
required  to  achieve  MSY’  considers  appropriate  to  establish  a  multi-annual  plan  aiming  for  the 
conservation  and  sustainable  exploitation  of  demersal  stocks.  This  plan  ‘should  take  account  of 
the  mixed  nature  of  the  fisheries  and  the  dynamics  between  the  stocks  driving  them,  i.e.  hake 
(Merluccius  merluccius),  red  mullet  (Mullus  barbatus),  deep-water  rose  shrimp  (Parapenaeus 
longirostris),  Norway  lobster  (Nephrops  norvegicus),  blue  and  red  shrimp  (Aristeus  antennatus) 
and  giant  red  shrimp  (Aristaeomorpha  foliacea).  It  should  also take  account  of by-catch  species 
and demersal stocks for which sufficient data are not available’. Based on this, Article 1(2) of the 
Regulation defines the stocks of the six species (mentioned above), considered to drive the mixed 
fisheries in the western Mediterranean, to which the multi-annual plan should apply. In the same 
article, point 3, it is specified that the Regulation shall also apply to ‘by-catch stocks caught in the 
western  Mediterranean  Sea  when  fishing  for  the  stocks  referred  to  in  paragraph  1.  It  shall  also 
apply  to  any  other  demersal  stocks  caught  in  the  western  Mediterranean  Sea  and  for  which 
sufficient data are not available.’ 

The six species in the plan have a limited importance for some fisheries. Their distributions do not 
overlap in some cases and they make part of different species assemblages exploited by different 
portions  of  the  fleet.  Even  though  the  number  of  métiers  would  increase  and  be  more  precisely 
defined (see above), only one or two species will constitute the effort reference for management 
decisions on the basis of their stock assessment. Such species can however seldom be considered 
as  drivers  of  a  fishery  as  they  often  represent  a  small  fraction  of  the  total  catches  in  a  mixed 
fishery.  Moreover,  depending  on  the  specific  species’  dynamics,  catchability,  etc  of  each  one  of 
them,  the  expectations  regarding  the  evolution  of  the  fishery  after  enforcing  management 
measures  based  only  on  these  selected  species,  the  time  needed  for  reaching  any  fixed 
management  goal,  the  economic  consequences,  etc  could  be  quite  different  to  that  potentially 
obtained  considering  alternative  stocks.  Probably  if  limitation  regards  only  one  species, 
misleading  results  will  be  obtained  for  supporting  decisions  to  be  taken  aimed  at  a  sound 
management  of  the  whole  fishery.  The  EWG  considers  necessary  to  enlarge  the  list,  trying  to 
include  the  species  that  are  in  general  positioned  on  the  top  of  the  landings  by  area/port,  and 
possibly  to  make  assessments  more  precise  as  possible  on  the  status  of  these  stocks  whenever 
such  assessments  have  not  already  been  done.  This  is  especially  important  in  order  to  avoid 
delayed  recover/sub  exploitation  of  these  species  since  they  could  be  more/less  vulnerable  to 
fishing  impacts  than  the  proposed  ones,  and  hence,  be  more/less  impacted  and  require 
larger/smaller  effort  reductions  that  those  dictated  by  the  exploitation  state  of  the  proposed 
stocks.  

6.3.3.2  Species included in the plan 

Six  species  are  proposed  for  being  included  in  the  MP  :Merluccius  merluccius,  Mullus  barbatus, 
Parapenaeus  longirostris,  Nephrops  norvegicus,  Aristaeus  antennatus,  Aristaemorpha  foliacea. 
Such  species  have  been  previously  chosen  during  specific  EWGs  where  they  were  defined  a 
priority list of stocks mainly based on amount of landings combined with commercial value and in 
some cases based on vulnerability and conservation considerations. 

There  are  however  some  issues  with  the  stock  identity  of  these  species  (Figure  6.9).  The 
proposed  MAP  suggest  the  definition  in  the  European  West  Mediterranean  of  two  management 
sub-areas: one including GSAs 1, 5, 6, 7, 8; and  the other covering GSAs 9,10,11. These areas 
are assumed (even though not explicitly mentioned) to encompass single stock units for the six 
selected  species  chosen  as  drivers  in  the  MAP.  STOCKMED  project  has  not  been  able  to  define 
boundaries  for  unit  stocks  for  these  six  species.  While  for  hake,  stock  boundaries  for  one  stock 
identified  in  STOCKMED    (Fiorentino  et  al.,  2015)  in  the  Western  Mediterranean  should  include 
the whole Spanish area and France, for the Italian area should exist another distributed from the 
Gulf of Lyons up to the Ionian Sea, for Mullus barbatus the “best” choice in STOCKMED assumes a 
single  stock  living  along  Spanish,  French  and  Italian  coasts  up  to  the  Adriatic  Sea.  For 
Parapenaeus longirostris a single stock is assumed present along Spanish coasts, France and only 
covering  the  Northern  portion  of  the  Tyrrhenian  Sea.  Other  stock  should  extend  from  central 
Tyrrhenian Sea southward and occupying all the Italian coasts in the Straits of Sicily and part of 
the  Adriatic.  For  Nephrops  three  stock  units  are  hypothesised:  one  in  South  Spain  and  Balearic 
Islands, another in North Spanish coasts. Other stock should cover part of France and all Italian 
coasts  up  to  the  Sicily  straits,  Aristaeus  antennatus  should  be  divided  in  two  stocks:  one  living 
along  Spain  and  France  and  the  second  along  Italy,  Croatia  and  Greece,  Aristaeomorpha  folicea 

119 

119 

 
one single stock unit with boundaries including Spain, France and Sardinia, and a separate stock 
living along the Tyrrhenian Sea, Croatia and Greece. 

Merluccius merluccius                                                          Mullus barbatus 

Parapenaeus longirostris                                                     Nephrops norvegicus 

Aristaeus antennatus                                                            Aristaeomorpha foliacea 

Figure 6.9. Stock identity for the various species (STOCKMED project) 

The  definition  of  wide  areas  where  few  stock  units  are  present  has  practical  advantages.  The 
aggregation  of  data  from  different  areas  imply  lower  costs  to  gather  data  and  possibly  more 
precise  (even  though  less  accurate)  parameter  estimates.  Lower  residuals  obtained  in  certain 
estimates  merging  certain  GSAs  compared  to  those  obtained  in  previous  assessments  using 
separate data for GSA were used in some EWGs for supporting decisions of fusion of GSAs. 

If more than one stock is managed as a single unit, as such stocks may find important biological 
differences  in  the  population  parameters,  but  principally  when  they  are  exploited  with  different 
rates and patterns, many problems should arise. 

The  gathering  of  data  from  different  stocks  over  a  large  area  will  drive  to  the  estimation  of 
average values obtained across the wide spatial scale. If these estimates are used for enforcing 
measures  to  all  the  stocks  within  such  area,  (for  instance  certain  limit  in  catches  or  effort),  for 
some  stocks  such  measures  could  be  too  much  restrictive  while  for  other  limitations  will  not  be 
enough to meet the defined objectives.  

The  management  area  should  be  defined  by  natural  spatial  scales  that  reflect  biologically 
meaningful units exposed to different mortality rates, and having spatial or temporal isolation of 
spawning  groups,  show  microevolution  of  morphological  or  genetic  characteristics,  or  have 
abundances that are affected by local processes; Waldman 2005).  

120 

120 

  
 
 
 
 
 
6.3.3.3  Species not included in the plan but which have a stock assessment/forecast 

In the Western coasts of Italy there were conducted stock assessments for other stocks than the 
six that were preliminarily selected.  

Assessments of Pagellus erythrinus, Mullus surmuletus, Squilla mantis, Micromesistius poutassou 
have been done and approved in STECF EWGs. Some preliminary assessments based on yield per 
recruit  and  life  tables  have  been  also  done  in  EWGs  and  in  GFCM  for  Galeus  melastomus, 
Scyliorhinus  canicula,  Raja  clavata  and  Raja  asterias    for  GSA9.  In  scientific  literature 
assessments  for  some  other  species  for  a  fraction  of  GSA9  are  published  that  regard  Sepia 
officinalis, Squilla mantis, Penaeus kerathurus, Trigla lucerna, Gobius niger. 

6.3.3.4  Other species (not in the plan, no assessment)  

The  examples  of  metiers  carried  out  by  the  bottom  trawl  fleet  in  different  ports  (section  3.2) 
show  the  importance  of  other  species  than  the  target  ones  in  the  MAP  that  are  not  currently 
periodically assessed (e.g. Eledone cirhosa, Squilla mantis in GSA6 port of Sant Carles; Octopus 
vulgaris in Sant Carles, Llançà in GSA 6, Vélez-Málaga in GSA 1, and Mallorca in GSA 5).  

One way to identify these fisheries based on different stocks than the target ones is to perform a 
more detailed segmentation that the one requested in the DCF, by using multivariate techniques 
such  as  cluster  analysis  on  landings  (daily  sale  bills,  that  are  available).  Once  identified,  the 
stocks of these assessments should be performed and/or the collection of data necessary to do it 
should be started. 

6.3.3.5  How these other species are already included in the existing models  

Total revenues by fleet segment is a crucial variable to be projected in bio-economic simulations 
for estimating socio-economic indicators. Revenues by stock and fleet segment, calculated as the 
product of landings and price at the same level of detail, are available only for the stocks included 
in the model. These stocks are generally not enough to calculate total revenues by fleet segment 
because of the great number of stocks exploited in demersal fisheries and the limited number of 
stocks  assessed  and  included  in  the  model  for  simulation.  Therefore,  the  revenues  calculated 
directly from landings and prices in the model relate only to a fraction of the total revenues of the 
fleet segments. 

The approach generally used to overcome this problem (in models like MEFISTO and BEMTOOL) 
consists  in  using  specific  functional  relationships  between  total  revenues  and  the  revenues  from 
assessed stocks. When the fraction of total revenues represented by the revenues from assessed 
stocks  is  quite  stable  over  time,  the  remaining  part  of  revenues  can  be  estimated  by  a  linear 
relationship. Other solutions consist in estimating the landings of non-assessed stocks as a linear 
or non-linear function of the landings of assessed stocks and multiplying by an average price for 
that  group  of  species,  which  is  estimated  on  time  series  data.  Landings  of  non-assessed  stocks 
can be also estimated assuming a linear relationship with fishing effort. 

MEFISTO was run with 10 species, which included the selected species for the WMed Multi-Annual 
plan  

6.3.3.6  Role of other species in the achievement of the other objectives of the MAP 

The  EWG  18-13  discussed  that  the  MAP  has  other  objectives  than  the  achievement  of  the  CFP 
MSY  objective  (Objective  1),  and  considered  whether  these  could  be  achieved  with  the  current 
selection of species to be included.  

Objective  2.  The  plan  shall  contribute  to  the  elimination  of  discards  by  avoiding  and  reducing 
unwanted  catches  as  far  as  possible,  and  to  the  implementation  of  the  landing  obligation 
established  in  Article  15  of  Regulation  (EU)  No  1380/2013  for  the  species  which  are  subject  to 
minimum conservation reference sizes and to which this Regulation applies.  

The  impact  of  an  effort  reduction,  aiming  to  achieve  the  MSY  for  the  selected  species,  was  not 
discussed  in  terms  of  discards  reduction  and  landing  obligation.  This  issue  needs  much  more 

121 

121 

 
information  than  that  treated  so  far.  This  is  a  complex  issue  that  deals  with  spatial/temporal 
distribution of individuals below the minimum landing size. 

Other  demersal  species  with  minimum  sizes,  and  hence  affected  by  the  landing  obligation,  like 
Pagellus  spp,  Mullus  surmuletus,  and  pelagic  species  abundant  in  the  landings  of  demersal 
fisheries such as Trachurus spp have not been considered. 

Objective 3. The plan shall implement the ecosystem-based approach to fisheries management in 
order  to  ensure  that  negative  impacts  of  fishing  activities  on  the  marine  ecosystem  are 
minimized.  It  shall  be  coherent  with  Union  environmental  legislation,  in  particular  with  the 
objective  of  achieving  good  environmental  status  by  2020  as  set  out  in  Article  1(1)  of  Directive 
2008/56/EC and the objectives set out in Articles 4 and 5 of Directive 2009/147/EC and Articles 6 
and 12 of Council Directive 92/43/EEC. 

The extent and the how the effort regime for demersal fisheries in the western Mediterranean Sea 
based  on  the  selected  species  can  contribute  to  improve  the  marine  ecosystems  should  be 
scientifically supported. Demersal vulnerable species such as elasmobranches may need levels of 
effort  reduction  much  higher  than  those  needed  by  the  species  selected.  On  the  other  hand, 
benthic  habitats  protection  need  a  spatial  management  of  the  effort  rather  than  general 
reductions to be accomplished in large areas based on the level of exploitation of selected main 
resources. 

Objective 4. In particular, the plan shall aim to:  

(a)  ensure  that  the  conditions  described  in  descriptor  3  contained  in  Annex  I  to  Directive 
2008/56/EC are fulfilled; and  

(b)  contribute  to  the  fulfillment  of  other  relevant  descriptors  contained  in  Annex  I  to  Directive 
2008/56/EC in proportion to the role played by fisheries in their fulfillment.   

Below are attached the descriptors in Annex 1 of Directive 2008/56/EC in which fisheries can be 
involved: 

(1) Biological diversity is maintained. The quality and occurrence of habitats and the distribution 
and  abundance  of  species  are  in  line  with  prevailing  physiographic,  geographic  and  climatic 
conditions. 

(3)  Populations  of  all  commercially  exploited  fish  and  shellfish  are  within  safe  biological  limits, 
exhibiting a population age and size distribution that is indicative of a healthy stock. 

(4)  All  elements  of  the  marine  food  webs,  to  the  extent  that  they  are  known,  occur  at  normal 
abundance and diversity and levels capable of ensuring the long-term abundance of the species 
and the retention of their full reproductive capacity. 

(6)  Sea-floor  integrity  is  at  a  level  that  ensures  that  the  structure  and  functions  of  the 
ecosystems are safeguarded and benthic ecosystems, in particular, are not adversely affected. 

Descriptor 3 calls for a more general improvement taking into account “safe biological limits” of 
all commercially exploited species, not just the selected species ones. 

The issue of how an effort regime for demersal fisheries in the western Mediterranean Sea based 
only on the selected species can contribute to fulfill descriptors 1, 4 and 6 should be scientifically 
supported. If not supported, then other species and habitats should be taken into account in the 
multi annual plan. 

Objective  5.  Measures  in  the  plan  shall  be  taken  on  the  basis  of  the  best  available  scientific 
advice.  Where  there  is  insufficient  data,  a  comparable  degree  of  conservation  of  the  relevant 
stocks shall be pursued. 

The  selected  species  cannot  be  considered  relevant  stocks  in  some  areas  (see  section  3.2)  and 
so, other species should be included based on a smaller scale definition of the métiers. 

6.3.4  Timing of availability of new data  

Stock assessment results for the Mediterranean stocks are produced by GFCM and STECF and the 
summary results (F, SSB, R) are public. Nevertheless, the age based estimates of population size 

122 

122 

 
and structure, fishing mortality, etc, are not public and must be required to the above mentioned 
groups. 

Most stock assessment groups in the Mediterranean take place in the 4th quarter, which means 
that the assessments are approved late in the year (STECF) or the year after (GFCM).  

Effort  data  is  compiled  by  the  JRC  through  datacalls  and  made  public  after  revision  by  STECF 
EWGs. Effort data follows a similar schedule as stock assessments, being made available  late in 
the year or early next year.  

Economic  data  is  also  compiled  by  the  JRC  through  datacalls  and  made  public  after  revision  by 
STECF EWGs. This dataset has a further one year gap due to the MS’s procedures collecting and 
compiling the data. The results are made available late 2nd quarter referring to two years before.   

Any work needed to support policy decision is, as such, subject to a 2 year gap, if economic data 
is not needed, or 3 years if economic data is required, which makes the advice less precise and 
robust.  

123 

123 

 
 
 
 
 
 
 
6.4  Road Map for the mixed-fisheries advice 

The  review  of  the  state  of  model  and  knowledge  has  highlighted  that  there  is  a  great  level  of 
knowledge  and  data  available  for  the  West  Med,  but  that  not  work  is  still  necessary  to  get  a 
streamlined and operational mixed fisheries advice.  

6.4.1  Summary of the main gaps and issues to be addressed 

6.4.1.1  With regards to models 

  There  are  many  mixed-fisheries  models  available,  but  with  some  various  levels  of 

coverage, development, completeness, update, complexity and user-friendliness. 

  On  the  opposite  the  standard  models  currently  applied  to  mixed-fisheries  advice  in  the 
ICES  area  have  not  yet  been  applied  to  the  Western  Mediterranean  fisheries  (although 
some earlier trials have been performed in the Eastern Med 

  No  models  are  directly  available  for  covering  all  the  MAP  stocks  in  each  of  the  two  sub-

areas 

  Most  models  available  are  developed  towards  medium-term  stochastic  projections,  not 
deterministic short-term forecasts. Their results may thus not be exactly comparable with 
the single-stock advice for next year’s fishing opportunities.  

  Many  species  driving  the  fisheries  are  not  included  in  the  MAP,  and  how  to  include  and 

treat them in the mixed-fisheries advice is unclear 

6.4.1.2  With regards to data 

  The  effort  and  catch  data  (transversal  data)  currently  available  in  the  various  JRC  data 
calls (FDI, AER and MBS) differ, and it is difficult to have a robust quantitative picture of 
the current fisheries 

  The current definition of fleets (vessel groups based on vessel size and gear) and metiers 
(DCF  level  6)  may  not  be  the  most  appropriate  for  describing  the  actual  differences  in 
fishing patterns, targeting behaviour and catchability between fishing vessels. Information 
is available in the national institutes at a more detailed level, but not directly available in 
the JRC databases.  

  Stock definition and stock assessment parameters can differ between STECF and GFCM 
  There are different timings for the availability of the different datasets 
  The important species in terms of revenue are not necessarily the six ones included in the 
plan.  There  are  several  other  important  species,  but  they  are  not  always  assessed  and 
cannot thus be included in a mixed-fisheries model in the same way. 

6.4.1.3  With regards to experts 

  Until  now,  model  development  and  update  has  been  driven  and  financed  by  research 
projects  with  varying  objectives  and  different  timing,  Therefore,  no  structure  is  yet 
established  to  secure  the  routine  update  of  the  models  through  dedicated  and  prioritised 
allocation of financial and human resources in the scientific institutes.  

  The  available  models  are  quite  complex  and  not  easy  to  update  within  a  5  days  working 
group.  Some  additional  work  is  necessary  in  the  institutes,  which  is  less  likely  to  take 
place without dedicated support.  

  The  required  modelling  expertise  is  often  limited  to  a  small  number  of  people  in  the 
institutes,  and  these  people  are  often  committed  to  several  other  tasks  and  their 
availability can be a limiting factor. Early planning is necessary to secure the involvement 
of the relevant persons.  

6.4.2  Suggestion for a roadmap in 2019-2020 

As  highlighted  above,  there  are  quite  a  number  of  steps  to  address.  And  based  on  previous 
experiences  with  regards  to  experts’  participation  both  during  and  between  experts  working 
group, it is recognised that the highest chance to obtain an operational mixed-fisheries advice is 
to  combine  a  suite  of  expert  working  groups  or  workshops  (max  one  per  semester)  with 
additional  funding  dedicated  to  intersessional  developments.  This  funding  would  secure  the 

124 

124 

 
necessary time and commitment of the relevant experts, while the workshops would act as useful 
milestones  reviewing  the  progresses  achieved  and  framing  their  outcomes  into  the  desired 
operational setup.  

The Road map is split into four steps, each of them including a meeting/ workshop: 

Step 1: MODEL SCOPING: Model(s) selection, stock data and agreement on alternative fleet and 
metiers segmentation if relevant 

Step  2:  DRAFT  RUN:  Agreement  on  scenarios,  results,  and  draft  mixed-fisheries  advice  on  data 
available in 2018 (reference year 2017) 

Step  3:  UPDATE  AND  DEVELOPMENT:  Update  to  2019  datasets  (reference  year  2018)  and 
model(s) improvements/extension 

Step  4:  FINAL  OPERATIONAL  SETUP.  Actual  mixed-fisheries  advice  for  2021  (reference  year 
2019) 

NB This roadmap will focus mainly on the Western Med in the first place. But, as it also the case 
in  the  ICES  area,  it  should  not  be  excluded  to  expand  it  to  a  more  global  mixed-fisheries 
approach for the EU Mediterranean demersal fisheries.  

6.4.2.1  Step1 (1st semester 2019): MODEL AND DATA SCOPING, AND FUNDING PLAN 

This step focuses on model(s) requirements and model selection; updated stock data (from EWG 
18-12) and suggestions for alternative fleets and metiers segmentation with corresponding catch 
and effort data 

In  the  first  semester,  some  work  shall  be  dedicated  in  the  institutes  to  address  the  issue  of 
alternative fleets and metiers definition, if it is felt that the current definitions as available in the 
JRC  databases  is  not  fully  appropriate.  If  felt  necessary,  alternative  segmentations  shall  be 
suggested, and the corresponding data provided. Full conversion ability with the DCF definitions 
shall be sought (e.g. for each trip and/or vessel, the allocation to both the DCF definition and the 
alternative  definition  should  be  given,  in  order  to  reach  the  same  summed  totals  of  catch  and 
effort either way). The sum of catches by stock shall also be comparable to the amount used in 
the single stock assessment  

In  parallel,  modellers  in  charge  of  the  models  described  in  section  2  shall  investigate  the 
feasibility of their models to address the gaps listed above and in particular 

  The coverage of several GSAs (models by the two sub-areas of the plan) 
  The inclusion of at least all the stocks of the 6 MAP species within the two sub-areas ( and 

maybe more if already included in the existing models) 

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

2017) 

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

stocks assessed in 2018 on 2017 data) 

EWG 18-13 suggests holding an STECF EWG or scoping workshop in early 2019 to get the work 
started.  [suggested  time:  March  2019,  before  the  STECF  Spring  plenary].  The  Workshop  will 
specifically  require  the  presence  of  both  modellers  and  data  people.  One  outcome  of  this 
workshop  would  be  a  description  of  the  model(s)  and  data  choice  and  of  the  required 
intersessional work (who, what, when, how) with corresponding funding requests.  

This  scoping  could  also include  some  discussion  on  the  possible  scenarios and  results to  display 
which will be needed in Step2. 

On this basis, a discussion should take place between STECF and DG Mare during the 2019 Spring 
Plenary on options to support this intersessional work.  

6.4.2.2  Step2 (2nd semester 2019): DRAFT RUN 

In the second semester, the focus will be on performing an actual draft run of the mixed-fisheries 
advice with the model(s) and data selected in Step 1 and based on 2017 reference year (in order 
not  to  spend  too  much  time  on  data  updates).  An  update  of  the  Step1  discussion  should  take 

125 

125 

 
place during the STECF July Plenary, to ensure that the decisions made in Step1 are followed and 
that the work is engaged as expected.  

The scenarios to run and the selection of results to display should also be agreed with MARE, The 
progresses  achieved  should  be  reviewed  during  a  second  STECF  EWG  or  workshop  before  the 
November STECF Plenary, which main objective will be to deliver an actual draft advice .  

6.4.2.3  Step3 (1st semester 2020): UPDATE AND DEVELOPMENT  

This  step  will  work  towards  making  the  selected  model(s)  and  datasets  more  streamlined  with 
regards  to  easiness  of  model  update  and  data  availability,  including  2018  stock  and  transversal 
data, and considering the possible inclusion of more species than those of the MAP only.  

Progresses with this work should be reviewed during STECF Spring Plenary, and completed before 
the  Summer  Plenary.  The  requirement  for  holding  a  dedicated  EWG  or  workshop  shall  be 
discussed in January 2020 during STECF Bureau. 

6.4.2.4  Step4 (Months 19-24, 2nd semester 2020): FINAL OPERATIONAL SETUP 

The major objective of this step will be to deliver an operational and up-to-date mixed-fisheries 
advice  at  the  end  of  2020  or  early  2021,  including  the  most  recent  stock  and  fleet  data  (data 
2020,  reference  year  2019).  This  should  therefore  take  place  after  the  STECF  November  2020 
plenary (where stock assessment are released) but before the Spring 2021 plenary.  

126 

126 

 
 
 
7  REFERENCES 

Abella, A., Serena, F., Fortunati, L., Mammini, U., Lari, D., Ciardelli, R. 2001. Use of GIS for the 
analysis of spatial data on fishing effort and seasonal changes in fishing strategies for Tyrrhenian 
Sea bottom trawl fisheries.  (in) Nishida, T., Hollingworth, C.E. and Kailola, P. (ed) Proceedings of 
the.  First  International  Symposium  on  GIS  in  Fishery  Science,  Seattle,  WA,  2-4  March  1999. 
Fishery GIS Research Group, Kawagoe, Saitama, Japan.:119-129 

Abella,  A.,  Ria,  M.  Mancusi,  C.  2006  The  coastal  groundfish  assemblage  exploited  by  the 
Viareggio fleet (Southern Ligurian Sea). GFCM SCSA/SCSI/SCESS transversal Workshop on stock 
assessment and Operational Units.  Rome, 26-28 June.   

Abella,  A.,  Ria,  M.,  Mancusi,  C.  2010.  Assessment  of  the  status  of  the  coastal  groundfish 
assemblage exploited by the Viareggio fleet (Southern Ligurian Sea). Scientia Marina, 74 (4):12p. 

Bastardie, F., Nielsen, J.R. and Miethe, T. 2014. DISPLACE: a dynamic, individual based model for 
spatial  fishing  planning  and  effort  displacement  –  integrating  underlying  fish  population  models. 
Canadian Journal of Fisheries and Aquatic Sciences 71:366–386. 

Bastardie,  F.,  Angelini,  S.,  Bolognini,  L.  ,  Fuga,  F.,  Manfredi,  C.,  Martinelli,  M.,  Nielsen,  J.R., 
Santojanni,  A.,  Scarcella,  G.,  and  Grati,  F.  2017.  Spatial  planning  for  fisheries  in  the  Northern 
Adriatic:  working 
fishing.  Ecosphere  8(2):e01696. 
10.1002/ecs2.1696 

toward  viable  and 

sustainable 

Biagi,  F.,  Sartor,  P.,  Ardizzone,  G.D.,  Belcari,  P.,  Belluscio,  A.,  Serena,  F.,  2002.  Analysis  of 
demersal  assemblages  off  Tuscany  and  Latium  coasts  (north-western  Mediterranean).  Scientia 
Marina 66, 233-242. 

Cardinale, M., Osio, G. C. and Scarcella, G. 2017. Mediterranean Sea: A Failure of the European 
Fisheries Management System. Frontiers in Marine Science, 4. doi: 10.3389/fmars.2017.00072. 

Colloca,  F.,  Cardinale,  M.,  Belluscio,  A.,  Ardizzone,  G.D.,  2003.  Structure  and  diversity  of 
demersal assemblages in the central Mediterranean Sea. Estuarine, Coastal and Shelf Science 56, 
469-480. 

Doubleday,  W.  1976.  A  least  squares  approach  to  analyzing  catch  at  age  data.  Int.  Comm. 
Northwest Atl. Fish. Res. Bull, 12, 69–81. 

EC.  2008.  EC  199/2008.  Council  Regulation  (EC)  No  199/2008  of  25  February  2008  concerning 
the establishment of a Community framework for the collection, management and use of data in 
the fisheries sector and support for scientific advice regarding the Common Fisheries Policy. 

Eigaard et al. 2016. Estimating seabed pressure from demersal trawls, seines, and dredges based 
2015;73(suppl_1):i27-i43. 
and 
on 
doi:10.1093/icesjms/fsv099 

J  Mar  Sci. 

dimensions 

design 

ICES 

gear 

Fiorentino  F.,  Massutì,  E.,  Tinti,  F.,  Somarakis,  S.,  Garofalo,  G.,  Russo,  T.,  Facchini,  M.T.,  
Carbonara,  P.,  Kapiris,  K.,  Tugores,  P.,  Cannas,  R.,  Tsigenopoulos,  C.,  Patti,  B.,  Colloca,  F., 
Sbrana,  M.,  Mifsud,  R.,  Valavanis,  V.  and  Spedicato,  M.T.  2015.  Stock  units:  Identification  of 
distinct  biological  units  (stock  units)  for  different  fish  and  shellfish  species  and  among  different 
GFCM-GSA. STOCKMED Deliverable 03: FINAL REPORT. January 2015, 310 p. 

Fortunati, L., Mammini, U., Lari, D., Ciardelli, R., Serena F., Abella, A. 2001. Interactive viewer of 
information  on  catch,  effort,  fleet  and  fishing  grounds  acquired  through  interviews  at  the 
landings.  (In)  Nishida,  T.,  Hollingworth,  C.E.  and  Kailola,  P.  (ed)  Proceedings  of  the.  First 
International  Symposium  on  GIS  in  Fishery  Science,  Seattle,  WA,  2-4  March  1999.  Fishery  GIS 
Research Group, Kawagoe, Saitama, Japan.:90-118 

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 

Guillén J, Maynou F, Floros C, Sampson D, Conides A, Kapiris K. 2012. A bio-economic evaluation 
of the potential for establishing a commercial fishery on two newly developed stocks: the Ionian 
red shrimp fishery. Sci. Mar. 76: 597–605.  

127 

127 

 
Hoff,  A.,  Frost,  H.,  Ulrich,  C.,  Damalas,  D.,  Maravelias,  C.  D.,  Goti,  L.,  &  Santurtun,  M.  2010. 
Economic  effort  management  in  multispecies  fisheries:  the  FcubEcon  model.  ICES  Journal  of 
Marine Science, 67(8), 1802-1810. DOI: 10.1093/icesjms/fsq076 

Humphrey,  A.S.,  2005.  SWOT  Analysis  for  Management  Consulting.  SRI  Alumni  Assoc.  Newsl. 
7,8. 

ICES.  2006.  Report  of  the  Workshop  on  Simple  Mixed  Fisheries  Management  Models 
(WKMIXMAN), 9–13 January 2006, ICES Headquarters. ICES CM 2006/ACFM:14. 47 pp. 

Kallianiotis, A., Sophronidis, K., Virodis, P., Tselepides, A., 2000.  Demersal fish and megafaunal 
assemblages on the Cretan continental shelf and slope (NE Mediterranean): seasonal variation in 
species density, biomass and diversity. Progress in Oceanography 46: 429-455. 

Lleonart  J,  Maynou  F,  Recasens  L,  Franquesa  R.  2003.  A  bioeconomic  model  for  Mediterranean 
fisheries,  the  hake  off  Catalonia  (western  Mediterranean)  as  a  case  study.  Scientia  Marina  67 
(Supp.1): 337-351. 

Mace, P.M. 2004. In defence of fisheries scientists, single-species models and other scapegoats: 
confronting the real problems. In: Browman, H., Stergiou, K.I. (Eds), Perspectives on ecosystem-
based approaches to the management of marine resources. Marine Ecology Progress Series 274, 
269-303 

Manouel D, Maynou F, Bedrani S. 2014. Bioeconomic analysis of small pelagic fishery in Central 
Algeria. Turkish Journal of Fisheries and Aquatic Sciences 14: 897-904. 

Maravelias, CD, Damalas, D, Ulrich, C, Katsanevakis, S  and Hoff, A. 2012, Multispecies fisheries 
management  in  the  Mediterranean  Sea:  application  of  the  Fcube  methodology.  Fisheries 
Management and Ecology, vol 19, no. 3, pp. 189-199. 

Marchal,  P.,  Andersen,  B.,  Caillart,  B.,  Eigaard,  O.,  Guyader,  O.,  Hovgaard,  H.,  Iriondo,  A.,  Le 
Fur,  F.,  Sacchi,  J.,  and  Santurtún,  M.  2007.  Impact  of  technological  creep  on  fishing  effort  and 
fishing mortality, for a selection of European fleets. ICES J. Mar. Sci. 64: 192-209. 

Massutí,  E.,  Reñones,  O.  2005.  Demersal  resource  assemblages  in  the  trawl  fishing  grounds  off 
the Balearic Islands (western Mediterranean). Scientia Marina 69: 167-181. 

Maynou F, Sardá F, Tudela S, Demestre M. 2006. Management strategies for red shrimp (Aristeus 
antennatus)  fisheries  in  the  Catalan  Sea  (NW  Mediterranean)  based  on  bio-economic  simulation 
analysis. Aquat. Living Resour. 19: 161–171. 

Maynou F, Martínez-Baños P, Demestre M, Franquesa R. 2014. Bio-economic analysis of the Mar 
Menor (Murcia, SE Spain) small-scale lagoon fishery. J. Appl. Ichthyol. 30: 978–985. 

Merino  G,  Karlou-Riga  C,  Anastopoulou  I,  Maynou  F,  Lleonart  J.  2007.  Bioeconomic  simulation 
analysis  of  hake  and  red  mullet  fishery  in  the  Gulf  of  Saronikos  (Greece).  Scientia  Marina  71: 
525-535. 

Merino  G,  Morales-Nin  B,  Maynou  F,  Grau  AM.  2008.  Assessment  and  bio-economic  analysis  of 
the Majorca (NW Mediterranean) trammel net fishery. Aquat. Living Resour. 21: 99-107  

Merino  G,  Quetglas  A,  Maynou  F,  Garau  A,  Arrizabalaga  H,  Murúa  H,  Santiago  J,  Barangé  M, 
Prellezo  R,  García  D,  Lleonart  J,  Tserpes  G,  Maravelias  Ch,  Carvalho  N,  Austen  M,  Fernandes  J, 
Oliver P,  Grau AM. 2015. Improving the performance of a Mediterranean demersal fishery toward 
economic objectives beyond MSY. Fish. Res. 161: 131-144  

Mutsert K.,  Cowan J. H.,  Essington T.E., Hilborn  R. 2008  Reanalyses of Gulf of Mexico fisheries 
data:  Landings  can  be  misleading  in  assessments  of  fisheries  and  fisheries  ecosystems 
PNAS February 19, 105 (7) 2740-2744; https://doi.org/10.1073/pnas.0704354105 

Nielsen, J. R., Thunberg, E., Holland, D. S., Schmidt, J. O., Fulton, E. A., Bastardie, F., ... Waldo, 
S. 2018. Integrated ecological-economic fisheries models - evaluation, review and challenges for 
implementation. Fish and Fisheries, 19(1), 1-29. DOI: 10.1111/faf.12232 

Ordines, F., Farriols, M.T., Lleonart, J., Guijarro, B., Quetglas, A., Massutí, E. 2014. Biology and 
population  dynamics  of  by-catch  fish  species  of  the  bottom  trawl  fishery  in  the  western 
Mediterranean. Mediterranean Marine Science 15(3), 613-625.  

128 

128 

 
Pauly,  D.,  Watson,  R.  and  Alder,  J.  2005.  Global  trends  in  world  fisheries:  impacts  on  marine 
ecosystems  and  food  security.  Philosophical  Transactions  of  the  Royal  Society  B-Biological 
Sciences 360, 5–12.  

Prellezo  R,  Accadia  P,  Andersen  J,  Andersen  B,  Buisman  E,  Little  A,  Nielsen  J,  et  al.  2012.  A 
review of EU bio-economic models for fisheries: the value of a diversity of models. Marine Policy, 
36: 423–431. 

Punt  AE,  Butterworth  DS.  1995.  The  effects  of  future  consumption  by  the  Cape  fur  seal  on 
catches and catch rates of the Cape hakes. 4. Modelling the biological interaction between Cape 
fur  seals  Arctocephalus  pusillus  pusillus  and  the  Cape  hakes  Merluccius  capensis  and  M. 
paradoxus.  South  African  Journal  of  Marine  Science,  16(1),  255–285.  ISSN  0257-7615. 
doi:10.2989/025776195784156494.  

Punt  AE,  MacCall  AD,  Essington  TE,  Francis  TB,  Hurtado-Ferro  F,  Johnson  KF,  Kaplan  IC,  Koehn 
LE, Levin PS, Sydeman WJ. 2016. Exploring the implications of the harvest control rule for Pacific 
sardine,  accounting  for  predator  dynamics:  A  MICE  model.  Ecological  Modelling,  337,  79–95. 
ISSN 03043800. doi:10.1016/j.ecolmodel.2016.06.004.  

Quetglas  A,  Merino  G,  Ordines  F,  Guijarro  B,  Garau  A,  Grau  AM,  Oliver  P,  Massutí  E.  2016. 
assessment  and  management  of  western  Mediterranean  small-scale  fisheries.    Ocean  &  Coastal 
Management  133: 95-104. 

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., Parisi,A., Garofalo,G., Gristina,M., Cataudella,S., Fiorentino,F. 2014. SMART: A Spatially 
Explicit Bio-Economic Model for Assessing and Managing Demersal Fisheries, with an Application 
One. 
Trawlers 
to 
https://doi.org/10.1371/journal.pone.0086222 

Italian 

Sicily. 

Strait 

PLOS 

the 

of 

in 

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   

Sivestri, S., Maynou, F. 2009. Application of a bioeconomic model for supporting the management 
process  of  the  small  pelagic  fishery  in  the  Veneto  Region,  northern  Adriatic  Sea,  Italy.  Scientia 
Marina 73: 563-572. 

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

STECF    2004.    European  Union  Mediterranean  Fisheries  and  exploited  resources.  Report  of  the 
subgroup of the Mediterranean Sea (SGMED) of the Scientific, Technical and Economic Committee 
for Fisheries. Commission staff working paper. Brussels, 11.6.2004 SEC (2004):772 

STECF.  2015a.  Mediterranean  assessments  part  1  (STECF-15-18).  Publications  Office  of  the 
European Union, Luxembourg, EUR 27638 EN, JRC 98676, 410 pp. 

STECF.  2015b.  Landing  Obligation  -  Part  6  (Fisheries  targeting  demersal  species  in  the 
Mediterranean Sea) (STECF-15-19). Publications Office of the European Union, Luxembourg, EUR 
27600 EN, JRC 98678, 268 pp. 

STECF.  2015c.  Western  Mediterranean  Multi-annual  Plan  (STECF-15-09).  Publications  Office  of 
the European Union, Luxembourg, EUR 27405EN, JRC 97008, 100 pp. 

STECF.  2016a.  Mediterranean  assessments  2016  -  part  2  (STECF-17-06).  Publications  Office  of 
the European Union, Luxembourg, EUR 28359 EN, JRC106582, 430 pp.  

STECF.  2016b.  Bio-Economic  Methodology  (EWG-17-05).  Publications  Office  of  the  European 
Union, Luxembourg, EUR 28359 EN, JRC106583, 53 pp. 

129 

129 

 
STECF.  2017.  Fisheries  Dependent  -  Information  –  Classic  (STECF-17-09).  Publications  Office  of 
the European Union, Luxembourg 2017, EUR 28359EN, JRC107598, 848 pp.  

STECF.  2018a.  Fishing  effort  regime  for  demersal  fisheries  in  the  western  Mediterranean  Sea 
(STECF-18-09).  Publications  Office  of  the  European  Union,  Luxembourg,  EUR  28359EN, 
JRC112744, 161 pp. 

STECF. 2018b. Stock assessments in the Mediterranean Sea 2018 - Part 2. In press. 

STECF.  2018c.  The  2018  Annual  Economic  Report  on  the  EU  Fishing,  Fleet  (STECF  18-07)  (eds. 
Natacha Carvalho, Michael Keatinge, Jordi Guillen). Office for Official Publications of the European 
Communities, Luxembourg, EUR 28359 EN, JRC112940, 587 pp 

STECF. 2018d. Fisheries Dependent Information – ‘New-FDI” (STECF-18-11). In press. 

Ulrich,  C,  Reeves,  SA,  Vermard,  Y,  Holmes,  S  &  Vanhee,  W.  2011.  Reconciling  single-species 
TACs in the North Sea demersal fisheries using the Fcube mixed-fisheries advice framework. ICES 
JMS, vol 68, no. 7, pp. 1535-1547. 

Ulrich,  C,  Vermard,  Y,  Dolder,  PJ,  Brunel,  T,  Jardim,  E,  Holmes,  SJ,  Kempf,  A,  Mortensen,  LO, 
Poos,  J-J  &  Rindorf,  A.  2017.  Achieving  maximum  sustainable  yield  in  mixed  fisheries:  a 
management  approach  for  the  North  Sea  demersal  fisheries.  ICES  JMS,  vol  74,  no.  2,  pp.  566-
575. 

Waldman, J. R. 2005. Definition of stocks: an evolving concept. (in) Cadrin S. X., Friedland K. D., 
Waldman J. R. eds. Stock identification methods. Elsevier Academic Press, Burlington, MA:555pp 

Walters,  C.  J.  2003.  Folly  and  fantasy  in  the  analysis  of  spatial  catch  rate  data.  Can.  Journal  of 
Fisheries and Aquatic Sciences. 60:1433-1436.  

130 

130 

 
 
 
 
8  CONTACT DETAILS OF EWG-18-13 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 

Telephone 
no. 

Email 

Alvaro ABELLA 

private consultant 

aabellafisheries@gmail.com 

Antonello SALA 

CNR 

a.sala@ismar.cnr.it 

Clara 
(EWG chair) 

ULRICH 

DTU Aqua 

clu@aqua.dtu.dk 

Paloma MARTIN  Consejo  Superior  de 

paloma@icm.csic.es 

Tiit RAID 

Investigaciones 
Científicas-  Instituto  de 
Ciencias del Mar  

Estonian 
Marine 
Institute,  University  of 
Tartu 

tiit.raid@eesti.ee 

Invited experts 

Name 

Address 

Paolo ACCADIA 

Francois 
BASTARDIE 

Fabio 
FIORENTINO 

Telephone 
no. 

Email 

Società 

accadia@nisea.eu 

fba@aqua.dtu.dk 

NISEA 
Cooperativa 

DTU 

CNR - IAMC 

fabio.fiorentino@iamc.cnr.it 

Matteo MURENU  University of Cagliari 

mmurenu@unica.it 

Francesc 
ORDINAS 

Instituto  español  de 
Oceanografía 

Jacopo 
PULCINELLA 

Italian 
Research Council 

National 

xisco.ordinas@ieo.es 

j.pulcinella@an.ismar.cnr.it 

131 

131 

 
 
 
 
  
  
  
  
  
 
  
  
  
  
  
  
Tommaso 
RUSSO 

University  of  Rome  Tor 
Vergata 

Tommaso.Russo@Uniroma2.it 

Mario SBRANA 

Consorzio  per  il  Cemtro 
di 
interuniversitario 
Biologia  Marina 
ed 
Ecologia  Applicata  G. 
Bacci 

msbrana@cibm.it 

JRC experts 

Name 

Address 

Ernesto JARDIM 

European  Commission 
Joint  Research  Centre, 
Ispra, Italy 

Telephone 
no. 

Email 

Ernesto.jardim@ec.europa.eu 

European Commission 

Name 

Address 

Telephone no. 

Email 

Amanda 
PEREZ-
PERERA 

DG  MARE,  D.1, 
Brussels 

Amanda.PerezPerera@ec.
europa.eu  

Christos 
MARAVELIAS 

DG  MARE,  D.1, 
Brussels 

Christos.MARAVELIAS@ec.eur
opa.eu  

Ernesto JARDIM 

JRC, Ispra, Italy 

ernesto.jardim@ec.europa.eu 

Observers 

Name 

Address 

Telephone no. 

Email 

Daniel  ACOSTA 
CAMACHO 

Junta 
Andalucía  

De 

Elisa  CARBAJO 
LLORENTE 

Spanish  Ministry 
Agriculture, 
of 
fisheries and food 

daniel.acosta@juntadeandalucia.es 

ecarbajo@mapama.es 

132 

132 

 
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
 
 
 
 
9  LIST OF ANNEXES  

Electronic annexes are published on the meeting’s web site on:  
http://stecf.jrc.ec.europa.eu/web/stecf/ewg1813 

List of electronic annexes documents: 

EWG-18-13 – Annex 1 - Exploring the relationship between fishing mortality and effort 
using mixed effects models. 10 pp. 
EWG-18-13 – Annex 02a - Mixfisheries analysis and data screaning - French haul by 
haul data. 11 pp. 
EWG-18-13 – Annex 02b - Mixfisheries analysis and data screaning - French trip by 
trip data. 11 pp.  
EWG-18-13 – Annex 02c - Mixfisheries analysis and data screaning – Spanish Catalanian trip by 
trip data update. 11 pp. 
EWG-18-13 – Annex 03 - ALTERNATIVE METIERS DEFINITIONS FOR SPAIN AND ITALY. 10 pp. 

10  LIST OF BACKGROUND DOCUMENTS  

Background documents are published on the meeting’s web site on:  
http://stecf.jrc.ec.europa.eu/web/stecf/ewg0813   

List of background documents: 

EWG-18-13 – Doc 1 - Declarations of invited and JRC experts (see also section 08 of this report – 
List of participants) 

133 

133 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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). 

134 

 
 
 
 
 
K
J
-
A
X
-
1
8
-
0
1
3
-
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. 

135 

doi:10.2760/509604 

ISBN 978-92-79-79396-7