Management strategy evaluation of alternative approaches for the anchovy and sardine stocks in the Adriatic Sea (STECF-21-04).
Date: 2021-12-10
Institutions: Joint Research Centre
Subjects: Adriatic Sea, conservation of fish stocks, evaluation of resources, fishery research, fishery resources, fishing regulations, research report, sea fish

Scientific, Technical and Economic 
Committee for Fisheries (STECF) 
- 
Management Strategy Evaluation 
of alternative approaches for the 
anchovy and sardine stocks in the 
Adriatic Sea  
(STECF-21-04) 

Edited by Christoph Konrad, Henning Winker & Michael Gras 

 EUR 28359 EN 

 
This  publication  is  a  Science  for  Policy  report  by  the  Joint  Research  Centre  (JRC),  the  European 
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Contact information  

Name: STECF secretariat 

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

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

Tel.: +39 0332 789343 

EU Science Hub 

https://ec.europa.eu/jrc 

JRC127695 

EUR 28359 EN 

PDF 

ISBN 978-92-76-45526-4 

ISSN 1831-9424 

doi:10.2760/75012 

STECF 

ISSN 2467-0715 

Luxembourg: Publications Office of the European Union, 2021  

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How to cite this report: Scientific, Technical and Economic Committee for Fisheries (STECF) –  
Management  Strategy  Evaluation  of  alternative  approaches  for  the  anchovy  and  sardine  stocks  in 
the  Adriatic  Sea  (STECF-21-04).  Publications  Office  of  the  European  Union,  Luxembourg,  2021, 
EUR 28359 EN, ISBN 978-92-76-45526-4, doi:10.2760/75012, JRC127695 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Authors: 

STECF advice:  

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

EWG-21-04 report:  

Chair: Christoph Konrad 

Experts: Henning Winker, Michael Gras, Andrés Uriate, Alessandro Orio, Andrea Perucci, Tiit Raid, 
Paulo Accadia, Edo AVDIC MRAVLJE, Vjekoslav Tičina, Vanja Čikeš Keč 

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TABLE OF CONTENTS 

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

SCIENTIFIC, TECHNICAL AND ECONOMIC COMMITTEE FOR FISHERIES 
(STECF) - Management Strategy Evaluation of alternative 
approaches for the anchovy and sardine stocks in the Adriatic Sea 
(STECF-21-04) ....................................................................... 2 

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

STECF observations .......................................................................... 2 

STECF comments .............................................................................. 4 

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

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

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

Executive Summary of the EWG ....................................................... 13 

Expert Working Group EWG-21-04 report .......................................... 17 

1 

Introduction ......................................................................... 18 

1.1 

Terms of Reference for EWG-21-04 ......................................... 19 

2 

MSE Framework .................................................................... 24 

The full feedback approach used for Anchovy (ToR 2.1 a) .................... 24 

The short-cut approach Sardine (ToR 2.1 b) ....................................... 24 

Implementation system (ToR 1.3, 2.2 & 2.3) ..................................... 25 

Harvest Control Rules (ToR 2.4 – methods) ....................................... 26 

Management Objectives .................................................................. 30 

Performance statistics (ToR 4) .......................................................... 30 

1 

MSE ANCHOVY GSA 17 & 18................................................... 33 

Anchovy Operating Model (OM) Conditioning ...................................... 33 

Tuning of harvest control rules (ToR 2.4.2 - results) ........................... 42 

1.1.1  Fixed target fishing mortality (fixedF) ...................................... 42 

1.1.2  Fixed target fishing mortality (fixedF) with annual catch variation 

limits ................................................................................... 45 

1.1.3  Bay of Biscay style (BoB) ....................................................... 48 

1.1.4  Biomass escapement (Besc) ................................................... 53 

1.1.5  Tuning for Bpa, with different Fcaps. ....................................... 53 

1.1.6  Tuning Besc, with fixed Fcap .................................................. 57 

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Robustness Tests of HCRs ................................................................ 61 

Intermediate years’ catch reductions ................................................. 68 

1.1.7  Sensitivity run extending catch reductions into 2024 ................. 72 

Timing of achieving management target (ToR 2.4.1) ........................... 73 

Likelihood to achieve management target by 2025 & 2028 ................... 73 

2 

MSE Sardine GSA 17 & 18 ...................................................... 74 

Introduction: Sardine Operating Model (OM) Conditioning .................... 74 

2.1.1  The Data input for the MSE: ................................................... 75 

2.1.2  Stock assessment ................................................................. 76 

2.1.3  OM Projections ..................................................................... 84 

Results for the Intermediate years 2020-2023 and sensitivity on year 

2024 ................................................................................... 85 

2.1.4  Sardine HCR tuning ............................................................... 89 

2.1.5  Conclusions for the sardine stock ............................................ 89 

3 

Economic performance assessment (ToR 3) ............................. 91 

A crude investigation of risk on gross profits ...................................... 99 

4 

5 

6 

7 

8 

Conclusions ......................................................................... 101 

References .......................................................................... 101 

Contact details of EWG-21-04 participants .............................. 103 

List of Annexes .................................................................... 106 

List of Background Documents ............................................... 106 

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Abstract 

Commission  Decision  of  25  February  2016  setting  up  a  Scientific,  Technical  and  Economic 
Committee  for  Fisheries,  C(2016)  1084,  OJ  C  74,  26.2.2016,  p.  4–10.  The  Commission  may 
consult the group on any matter relating to marine and fisheries biology, fishing gear technology, 
fisheries  economics,  fisheries  governance,  ecosystem  effects  of  fisheries,  aquaculture  or  similar 
disciplines.  Within  a  management  strategy  evaluation  harvest  control  rules  were  tuned  and 
robustness tested for the anchovy and sardine stocks in the Adriatic Sea. This investigation was 
done  in  support  of  GFCM  and  a  continuation  of  the  preliminary  report  (EWG  21-adhoc-02).  For 
the sardine stock no meaningful MSE could be conducted. For the anchovy stock two HCRs were 
identified to be achieving the management targets and being robust. 

1 

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SCIENTIFIC, TECHNICAL AND ECONOMIC COMMITTEE FOR FISHERIES (STECF) - 
Management Strategy Evaluation of alternative approaches for the anchovy and sardine 
stocks in the Adriatic Sea (STECF-21-04) 

Request to the STECF 

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

STECF observations 

The STECF EWG-21-04 was held virtually from 12 to 16 July 2021. The meeting was attended by 
10  experts,  including  fish  dynamic  modelling  and  economic  expertise.  Among  them  there  were 
two STECF members and three JRC experts, but no observers.  

The  EWG-21-04  was  tasked  to  develop  and  test  alternative  harvest  control  rules  (HCR)  for 
anchovy  and  sardine  in  the  Adriatic  Sea  (GSA  17+18)  based  on  the  latest  GFCM  Benchmark 
(ended  in  January  2021),  that  will  ensure  a  low  probability  of  SSB  to  fall  below  Blim  (P<5% 
probability). This should be done through Management Strategy Evaluation framework (MSE). 

The EWG-21-04 had the following ToRs: 

ToR1  

TOR 1) Conditioning / background for simulation testing:   

1.1 Model an intermediate period 2020-23 in line with the work performed for STECF 1802: Using 
anchovy  and  sardine  reported  catches  from  2020,  assuming  status  quo  catches  in  2021  (in  line 
with the Council Fishing Opportunities Regulation 2021) and according to different levels of catch 
reductions  of  Sardine  (of  5-10-15%  per  year)  and  Anchovy  (of  5-10-15%  per  year)  starting  in 
January 2022 and ending in December 2023.  As a sensitivity run, for year 2024, sardine catches 
should be 15% lower and anchovy 5% lower than the level of 2023.   

1.2 Condition operating models for Sardine and Anchovy in the Adriatic Sea (Gsa 17-18) with the 
results  of  Request  for  services  -  1743  -  STECF  Ad  hoc  contract  and  taking  into  account  the 
current GFCM benchmarked stock assessment and adding stock-recruitment models that consider 
multi-annual  cycles  of  recruitment,  which  can  consider  both  biomass  or  non  biomass  related 
drivers for recruitment in the past. Specifically for the sardine stock, due account should be given 
to the uncertainty in the growth in recent years. Additionally model recruitment: In line with S/R 
in  model  conditioning  or  segmented-regression,  S/R  variance  set  in  accordance  with  literature, 
occurrence of Persistent low recruitment or other deemed appropriate  

1.3  Use  an  age  structured  population  model  based  on  a  single  area  and  a  calendar  year  basis 
(January to December)  

TOR 2) In the MSE follow the specifications below:  

2 

2 

 
 
 
 
 
 
 
 
 
 
 
 
 
2.1 Full feedback loop  or shortcut MSE: For Anchovy stock  run MSE  with full feedback loop. For 
Sardine  stock,  owing  to  the  stock  not  being  benchmarked  and  survey  CVs  being  too  wide,  run 
only a shortcut MSE to understand the impact of the transitional period measures on the SSB and 
Rec.  

2.2 Implementation Model: Annual catch limits to be set over for the January – December period. 
Management implementation via catch control  

 2.3 Observation Model: The MSE testing should simulate the provision of scientific advice where 
the data delivery of echo-survey (performed June-Sept) and landings from Year N in April of year 
N+1,  stock  assessment  performed  in  June  (N+1)  and  catch  quota  implementation  from  January 
N+21 .   

 2.4 Management Decisions  

2.4.1.  The  objective  of  the  management  decisions  is  to  reach  the  plan  target  [Fmsy  proxy  or 
SSBtarg] in year : a) 2025 b) 2028  

 2.4.2. From January 2024 annual catch limits/TACs should be set on the basis of HCRs outlined 
below.  Additionally,  as  a  sensitivity  run,  simulate  the  HCR  implementation  starting  in  January 
2025 after catch levels established in point 1.b.   

(HCR1) Test a level of fixed level F proxy (e.g. through catches)   

(HCR2) Test a fixed biomass HCR in line with the Bay of Biscay anchovy or in an ICES like HCR.  

(HCR3) In line with the work of STECF EWG 18-01, update the biomass escapement HCR that will 
ensure a low probability of SSB to fall below Blim (5% probability) for Anchovy and Sardine.   

For the HCR3, define: an optimal level of the  Bescapement, confirm the need of an Fcap and in 
case the appropriate level.   

2.4.3. In combination of the HCR 1-3 test the effect of a TAC stability mechanism that allows TAC 
inter-annual change of 10% downward and 5% upward. Advise if such mechanism is compatible 
with achieving the plan target according to point 2.4.1.b   

TOR 3) Economic Performance.  

If  economic  data  are  available  and  of  adequate  quality,  evaluate  the  maximum  economic 
performance of the HCR.  

TOR 4) Performance Statistics  

1  Note  that  in  the  report  the  notation  of  years  is  not  the  one  in  the  TOR  but  the  following:  The 
assessment and advice  is produced  in year  y (the interim or assessment year), using data  from 
surveys until year y-1, and for the management of year y+1. 

3 

3 

 
 
  
  
                                    
 Evaluate  performance  of  alternate  scenarios  (at  least  250  iterations)  on  10-20  year  time  scale 
using standard MSE diagnostic tools, in line with those developed for STECF EWG 1801, focusing 
in particular on the following in relation to Harvest Rate:  

Probability  of  SSB  falling  below  Blim.    /  Risk  of  SSB  falling  below  Blim  vs  Catch  level  /  Catch 
variability  /  Average  catch    /  Level  of  SSB  /  and  any  other  deemed  useful  and  of  simple 
interpretation  

TOR 5) Consider possible additional scenarios stemming from GFCM SAC discussion.  

The preliminary report (STECF-Adhoc-21-02) was presented to the SAC but no suggestions came 
from the SAC 

STECF comments  

STECF  considers  that  the  EWG  addressed  all  the  ToRs  satisfactorily,  and  acknowledges  the  high 
quantity and quality of  the work performed both before and during the  EWG meeting. The EWG 
was prepared in advance during the first half of 2021 by setting up the management simulations 
framework,  and  running  some  preliminary  explorations  of  the  harvest  control  rules.  The 
preparatory work was  carried out by the JRC team and was  evaluated by STECF (STECF-Adhoc-
21-02).  

ToR1 Conditioning/background for simulation testing 

Intermediate period 2020-2023 

EWG-21-04  conditioned  the  intermediate  period  as  requested  (2020-2023)  conditional  to  the 
prescribed  reduction  of  catches,  assessing  the  expected  biomass  trajectories  and  risks  of  falling 
below  Blim  until  2023,  and,  as  a  sensitivity  test,  also  up  to  2024  subject  to  the  additional 
reduction of catches in 2024 of 15% for sardine and 5% of anchovy (TOR 1.1).  

The  simulation  results  evidenced  that  the  probability  of  the  sardine  biomass  being  below  Blim 
would  be  far  above  5%  in  any  year,  and  would  increase  significantly  from  2020  to  2023.  For 
anchovy  the  situation  would  not  be  as  dramatic,  as  median  biomass  would  tend  to  increase. 
However,  the  risks  of  falling  below  Blim  for  any  of  the  three  prescribed  gradual  reduction  of 
catches until 2023 will remain above 5%. The additional reductions proposed for 2024 were also 
tested  and  the  results  showed  similar  trends  for  both  stocks.  All  these  results  were  shown 
through  graphs  of  trajectories  of  Catch,  SSB,  F  and  recruitments  along  time,  and  by  box  plots 
summarising performance indicators by selected time periods.  

A feature of the prescribed fixed catch reductions over the intermediate period is  the inability to 
adjust catch levels to the stock biomass levels. This increases risks for the biomass if occasional 
major  drops  in  recruitments  would  occur  in  the  coming  years.  The  iterations  that  simulate  such 
sharp decreasing trajectories do end up in fisheries closure in the early stages of the subsequent 
HCR  implementation  in  2024.  This  explains  the  high  risk  levels  throughout  these  intermediate 
years,  given  the  poor  starting  conditions  of  the  two  stocks.  STECF  endorses  the  suggestions 
passed to managers (in the Executive summary) on setting up some extraordinary circumstances 
for  management  actions  if  the  monitoring  system  detects  severe  deteriorations  of  the  stock 
status  of  sardine  or  anchovy  throughout  the  intermediate  period,  in  order  to  prevent  stock 
collapse.  

Operating Models 

The Operating models (OM) to run the MSE of the different HCRs were defined according to GFCM 
benchmarked assessment for anchovy (SAM) and  preferred assessment model for sardine (A4A) 
from latest GFCM Benchmarking process (ended in January 2021, GFCM, 2021) (TOR 1.2). These 

4 

4 

 
  
 
 
 
 
 
were  age  structure  models  with  natural  mortality,  growth  (van  Bertalanffy  model)  and  maturity 
by  ages  and  survey  inputs  taken  as  relative  indexes.  The  best  fitted  Stock  Recruitment  models 
were  adopted  for  the  two  species  (according  to  the  AIC),  i.e.  Beverton-Holt  (BH)  model  for 
anchovy and segmented regression for sardine. The only deviation from the OM’s settings directly 
resulting  from  the  assessments  were  that  the  variance  estimates  around  the  S-R  relationships 
were  judged  to  be  too  small  for  the  two  stocks  in  comparison  with  literature;  therefore  the 
adopted  residual  variance  was  set  at  the  mode  of  the  predictive  distribution  for  σ_R    generated 
using the hierarchical taxonomic life history model FishLife (Thorson 2020). Such deviation is well 
justified in the report. 

STECF notes that the requirements  in TOR 1.2 of considering  multi-annual cycles of  recruitment 
was  discarded  because  recruits  deviations  from  the  fitted  SR  models  were  found  to  be 
uncorrelated.  The  others  requirements  to  be  tested,  such  as  recruitment  failures,  or  any  other 
variant of the OM deemed necessary, were included in the framework of a robustness test of the 
HCRs to alternative OM/Simulations.  

The alternatives considered for this robustness test were:  

•  Natural  Mortality:  Constant  natural  mortality  for  all  age  classes  (=  0.9)  (vs.  the 

base case of changing by age).  

• 

•  Recruitment  failure  (LowRec)  corresponds  to  a  forcing  for  three  consecutive  years 
(from 2028 to 2030) of a 35% reduction of the expected recruitment from the BH-
SR relationship (by application of 0.65 multiplier).  
Increased  recruitment  variability  around  the  SRR  (hiVar),  passing  from  the  modal 
(base case) to the mean variability (higher value) of recruitment (σ_R) according to 
the life history model FishLife around expectations to the BH-SRR, i.e. adopting the 
mean  σ_R=  0.49  –  value  arising  from  the  hierarchical  multivariate  model  FishLife 
(Thorson, 2020). 

•  Autocorrelation within recruitment (ar1Rec); such value was set to 0.456 based on 

the estimate of  ρ = 0.456 by Thorson et al. (2014). 

STECF acknowledges that these alternative configurations of the OM correspond to main variants 
suspected to affect small pelagics in general and give a major support to verify the robustness of 
the harvest controls rules (Siple et al. 2017; 2020; Thorson 2020).  

Biological Reference points Blim and Bpa and Fmsy were adopted from the GCFM benchmark for 
the  two  stocks.  However,  for  anchovy,  the  Fmsy  used  for  the  tuning  of  the  HCRs  was  the  one 
deduced from BH-SRR (0.96), which was slightly higher than the GFCM benchmark defined Fmsy 
(0.81  with  Patterson’s  criteria).  Such  a  deviation  was  required  to  increase  the  inner  consistency 
between the OM while testing the HCR within MSE, so that resulting Fishing Mortalities for every 
HCR can be compared with the Fmsy of the OM.  

ToR2 HCRs definition and tuning & TOR 4 Performance Statistics 

The  setting  up  of  a  management  strategy  evaluation  framework  (MSE)  (TOR  2.1),  required 
developing  ad  hoc  R  scripts  in  particular  for  the  full  MSE  loop  for  anchovy  which  included  the 
yearly  update  of  observed  input  data  (including  catches  and  surveys)  and  of  the  assessment 
(SAM)  in  the  projection  period  of  simulations.  For  sardine,  the  ‘short-cut’  approach  passed  the 
‘true’ age-structured dynamics from the Operating Model (OM) and it was only used to assess the 
impact of the transitional period measures on the SSB and Recruitment. STECF acknowledges the 
extensive work for developing these scripts carried out by the JRC team.  

As requested (TOR 2.2) Management implementation for the two stocks was simulated via catch 
control (TAC) set over for the January – December period.   

Assessment – management time lag 

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STECF notes that the prescribed management  procedure (TOR 2.3)  follows a standard time lag, 
where  the  TACs  for  the  management  year  (Y+1)  are  set  according  to  an  advice  (based  on  the 
HCRs) from an assessment carried out in the interim year (Y)  on data inputs catches and echo-
surveys  (performed  June-Sept)  up  to  the  previous  year  (Y-1).  This  implies  a  two  years  lag 
between the data input (Y-1) and the management (Y+1). This scheduling of the  advice process 
is commonly applied by management bodies worldwide, and has recently been adopted by GFCM 
for these two stocks. STECF acknowledges that this represents  an improvement over the former 
practices which involved a three years time lag.  

Nevertheless,  STECF  emphasises  that  although  this  being  standard  practice,  a  two  years  lag  for 
short  lived  species  still  implies  that  a  large  fraction  of  the  population  used  to  inform  the  HCRs 
could  have  already  died  by  the  management  year,  whereby  TAC  will be  obtained  from  a  mostly 
renewed  population  (SSB  Y+1  being  mostly  composed  of  age  1  entirely  unknown  and  age  2 
poorly  known  from  the  inputs  for  the  advice).  Hence,  robust  improvements  of  the  management 
procedure  could  be  obtained  by  shortening  further  the  lag  between  the  input  data  for  the 
assessment  and  the  management  implementation.  Since  for  short  lived  species  the  shorter  this 
lag is the less uncertainties are in the advice, such reduction of the time lag can ultimately lead to 
some gains in catch opportunities in the long term.  For example, these benefits have been shown 
for the Bay of Biscay anchovy management procedure, which uses the information of the catches 
and  surveys  in  the  interim  year  (Y)  to  provide  the  advice  for  the  management  of  the  following 
year (Y+1) (STECF 2014; Sanchez et al. 2019). 

The  performance  statistics  were  those  defined  in  TOR  4,  plus  a  few  new  ones:  Level  of  SSB 
relative to Bmsy, level of F relative to Fmsy and Prob(Closure). The Performance Statistics were 
evaluated  across  1000  stochastic  iterations  over  three-time  horizons  of  projections  after  the 
intermediate  period:  short  term  (2022-2025),  medium  term  (2022-2028)  and  long  term  (2032-
2044). Time series  of  median values by indicators and box plots for  selected periods of time, in 
addition to tables, were used to present the results.  

The  harvest  control  rules  (HCR)  applicable  to  the  anchovy  stock  were  as  defined  in  TOR  2.4: 
Fixed  target  fishing  mortality  (fixedF),  Biomass  Escapement  (Besc)  and  “Bay  of  Biscay  type” 
(BoB).  The  parameters  of  the  three  HCRs  were  sought  through  a  tuning  procedure,  first  by 
selecting  the  best  combinations  among  a  range  of  potential  parameters,  according  to  their 
performance  through  a  21  years  projection  period  (2024-2044)  starting  just  after  the 
intermediate  period  (2020-2023),  and  complying  with  keeping  risks  for  the  biomass  of  falling 
below  Blim  in  the  long  term  equal  or  less  than  5%.  The  best  performing  rule  (s)  of  every  HCR 
were subsequently confronted to the robustness test defined for TOR 1, comparing their relative 
performance  under  alternative  simulations,  in  order  to  finally  select  an  optimal  HCR  where 
possible.   

STECF notes that the main outcomes of this tuning procedures were:   

 

fixedF:  A  Fixed  target  fishing  mortality  is  applied  every  year.  The  tuning  led  to  select 
Fmultiplier  at  0.7,  i.e.,  fixedF  =  0.7·Fmsy,  as  higher  multipliers  exceeded  the  maximum 
allowable risk. The rule was applied over the survivors of year Y-1 of each iteration. 

  Besc: The Biomass Escapement HCR sets the total allowable catch (TAC) for the next year 
so that the level of SSB that is left at sea at the end of the fishing year is equal or higher 
than a predefined “escapement biomass” (Besc). At the same time the associated fishing 
mortality  (F)  should  be  equal  or  lower  than  a  specified  cap  value  (F_cap),  to  avoid  risky 
situations.  The  rule  was  simulated  on  the  assessment  year  (Y)  and  the  results  passed  to 
the  advice  for  the  management  year.  Little  sensitivity  to  Besc  led  to  fix  it  at  Bpa.  Fcap 
values  ranging  from  0.6*Fmsy  to  Fmsy  were  tested.  Fcap  at  0.8*Fmsy  was  found  to  be 
the maximum Fcap level not exceeding the 5% maximum allowable level of risk of falling 
Blim in the long term. 

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  BoB:  The  Bay  of  Biscay  type  is  an  HCR  setting  directly  TACs  from  the  assessment  of 
spawning  biomass  (projected  at  the  end  of  the  assessment  year),  without  explicit 
definition  of  the  fishing  mortality.  The  TACs  are  bounded  by  a  minimum  and  maximum 
levels (TACmin and TACmax) which will be allocated at biomasses between B1 and B2 and 
at biomass above B3 respectively, while intermediate TAC values will be set proportionally 
to intermediate biomass values between B2 and B3. For the definition of the rule it suffices 
defining  TACmin,  TACmax,  B1  and  B2  and  the  slope  of  increasing  TACs  as  a  function  of 
biomass.  Intuitively  several  rules  with  B1=Blim,  B2=Bpa  and  for  TACmin  at  15000  t  or 
20000t (around Blim) and with TACmax at 35000 t or 40000 t (around MSY) were tuned 
for slopes ranging between 0.6 to 1. Little contrast between the different tuning runs was 
noticed. The preferred formulation was not immediately apparent, as each BoB formulation 
passed the required performance statistics (SSB/Blim, F/Fmsy). As such three HCRs were 
retained: BoB_1535 (i.e. with TACmin and TACmax at 15000 and 35000 t) and BoB_1540 
and  BoB_2040  (with  TACmin  at  15000  and  20000  t  respectively,  and  having  both  the 
TACmax at 40000 t), in all cases with B1=Blim, B2=Bpa and with slopes of 0.8. The higher 
the TACmax the higher is the Catch but also the Interannual variability in catches and the 
probability of closures.  

The  five  retained  HCR  were  considered  to  encompass  most  of  the  potential  production  for  the 
anchovy  stock.  STECF  notes  that  among  them  the  safest  in  terms  of  risks  to  Blim  was  the 
BoB_1535  but  at  the  expense  of  slightly  smaller  median  catches  (around  30000  t,  versus 
maximum around 33000 t of other rules).  

The robustness analysis showed only subtle differences in the performance statistics between the 
different OMs. The HCRs performed slightly better in the OM with a constant Natural Mortality by 
ages than with the reference OM, which indicated that the HCR are able to deal with minor M mis-
specifications. Regarding the remaining robustness tests the only candidate HCR which performed 
adequately across the entire range of runs was BoB_1535, closely followed by BoB_1540. 

STECF  considers  that  the  tuning  process  and  robustness  test  were  comprehensive  and  run  in  a 
high scientific standard manner with associated good diagnostic graphs and tables.  

Finally,  the  management  plan  targets  of  achieving  Fmsy  through  these  HCRs  by  2025  or  2028 
were assessed for anchovy (TOR 2.4). It was found that no rule achieved the objective with 95% 
certainty  by  2025.  However,  by  2028  such  certainty  is  fully  achieved  for  the  BoB_1535  (slope 
0.8) and almost achieved by the FixedF at 0.7Fmsy (which showed a certainty about 0.949). 

Testing the impact of Introducing an inter-annual change limiter (20% down and 10% up) (TOR 
2.4) was only tested for the fixedF rule on anchovy. STECF notes that no major differences were 
found between the performance of the rule with or without such interannual variability restriction 
(with  the  exception  of  smaller  interannual  variability  AAV),  and  that  the  effect  was  not  tested 
further on other HCR. STECF considers that at the current stage of the testing of these HCRs this 
exercise  was  sufficient  to  capture  the  potential  of  this  restriction,  noting  that  it  can  be  further 
tested at a later stage in the process if requested.  

TOR 3 Evaluation of the Economic Performance 

The  EWG  was  requested  to  evaluate  the  maximum  economic  performance  of  the  HCRs  if  the 
economic data were available and of adequate quality. The data from the 2020 Annual Economic 
Report on the EU Fishing Fleet (STECF 20-06), was available but a request to the Italian Ministry 
and to the Croatian authorities was made to get additional detailed data at smaller geographical 
scale.  Both  administrations  made  their  data  available  to  the  EWG.  The  limited  time  available 
during  the  meeting  suggested  the  use  of  short-term  projections  models  (versus  more  complex 
models). Under a short-term approach, the size of the fleets is assumed to be constant along the 
simulation  period  and  variables  associated  with  the  fleet  size,  like  fixed  costs,  repair  and 
maintenance  costs  and  capital  costs,  are  assumed  to  be  constant  over  time.  Therefore,  the 
economic  short-term  effects  of  changes  in  TACs  are  measured  only  in  terms  of  changes  in 
revenues and variable costs as a function of the effort required to get the TACs.  

7 

7 

 
 
 
 
Efforts  corresponding  to  TACs  were  those  corresponding  to  the  inverse  of  Cobb-Douglas 
production  function  fitted  to  the  Italian  anchovy  fleets,  while  the  estimation  of  production 
functions  for  the  Croatian  fleet  segments  was  not  carried  out  because  the  main  target  stock  for 
these fleets is assumed to be sardine, and both landings and SSB of sardine are assumed to be 
constant  over  time  for  the  purposes  of  this  evaluation  of  the  economic  performance.    Such 
constant sardine catches were set at the median catch at Fmsy. 

The  economic  analysis  used  median  values  of  TACs  resulting  from  the  MSE  simulation  of  the 
different  HCRs.  This  implied  that  the  individual  variability  among  iterations  was  not  considered 
and  this  leads  to  overly  optimistic  performances.  As  median  catch  values  do  not  encompass 
catastrophic  events  such  as  fisheries  closures,  a  separate  analysis  was  conducted  based  on  the 
product of the gross profit by the annual probability of shutting down the fisheries.  This analysis 
showed that the inclusion of risks of shutdowns led the fixedF HCR to perform less well than the 
other two HCR types.  

STECF notices that future assessments of economic performance of management strategies would 
need  to  better  couple  (provided  that  adequate  data  and  models  are  available)  the  actual 
variability in the projections iterations obtained from the MSE testing with trajectories of  fishing 
effort  and  costs  associated  to  them,  so  that  the  performance  of  the  rules  including  variability 
could better be assessed. 

STECF conclusions 

STECF  concludes  that  the  EWG  addressed  all  the  ToRs  satisfactorily  and  acknowledges  the 
comprehensive  work  performed  on  developing  ad  hoc  R  scripts  and  running  numerous 
simulations, especially for anchovy.  

STECF  concludes  that  the  conditioning  of  the  Operating  Models  for  both  stocks  (anchovy  and 
sardine) was done correctly according to the TORs, fitted to the current assessment models and 
to  most  appropriate  stock  recruitment  relationships,  including  here  the  justified  decision  of 
changing  of  variability  around  the  SRR  (Sigma  R)  for  the  two  stocks.  Similarly,  the  OM  variants 
considered for the Robustness test are state of the  art and consistent with recent literature, and 
suitable for testing HCRs on short lived species.  

STECF  supports  the  tuning  procedure  used  to  define  the  best  parameters  for  the  three  types  of 
HCRs  tested  by  the  group  for  the  anchovy  stock,  as  well  as  the  Robustness  tests  for  further 
assessing the relative performance of the selected rules.  

For sardine, the projections over the intermediate period evidenced that for the prescribed catch 
reductions in TOR 1.1, the stock will be on average decreasing over time and the risks of falling 
below Blim increasing. Therefore, STECF endorses the conclusion in the report that for sardine the 
next  step  should  be  to  continue  efforts  to  address  the  still  unresolved  issues  in  the  stock 
assessment  and  reach  a  conclusive  benchmark.  STECF  concludes  also  that  precautionary 
management is needed until these issues are resolved, as already prescribed for the intermediate 
period 2020-2023.  

In addition, STECF endorses the suggestions passed to managers (in the Executive summary)  to 
set  up  provisions  for  emergency  management  actions  in  the  Harvest  Control  Rules  if  the 
monitoring  system  detects  more  severe  deteriorations  of  the  stock  status  of  sardine  or  anchovy 
throughout the intermediate period. 

STECF concludes that while the adopted advisory process framework now applies a standard  two 
years  lag  between  the  data  input  (Y-1)  and  the  management  (Y+1),  the  short  life  history  of 
Adriatic  anchovy  and  sardine  still  induces  major  sources  of  uncertainty  on  future  population 
dynamic under this management framework. STECF concludes that shortening further this lag, as 
is currently done for the Bay of Biscay anchovy, would further improve the management system, 
reducing uncertainties and potentially leading to higher yield in the long term.  

8 

8 

 
 
 
 
 
 
 
 
 
STECF  acknowledges  that  the  economic  evaluation  procedure  was  simplified  as  a  result  of  the 
limited  time,  the  data  available  and  the  need  for  assuming  constant  sardine  catches.  STECF 
notices that future assessments of economic performance of management  strategies would need 
to  better  couple  the  actual  variability  in  the  projections  obtained  from  the  MSE  testing  with 
trajectories of fishing effort and costs associated to them, so that the  performance  of the  rules, 
including variability and mixed-fisheries considerations, could be better assessed.  

References  

GFCM  2021.  Benchmark  session  for  the  assessment  of  sardine  and  anchovy  in  GSAs  17-18. 
Working Group on Stock Assessment of Small Pelagic species (WGSASP). FAO headquarters, 
Rome, Italy, 13–16 May 2019 & Online sessions, June–December 2020. 122 pp. 

STECF 2021. Scientific, Technical and Economic Committee for Fisheries (STECF) – PRELIMINARY 
MANAGEMENT  STRATEGY  EVALUATION  (MSE)  FOR  ANCHOVY  AND  SARDINE  IN  THE 
ADRIATIC  SEA  (GSA  17-18)  (STECF-Adhoc-21-02).  Publications  Office  of  the  European 
Union, Luxembourg, 2021, EUR 28359 EN, ISBN XXXXXX, doi:XXXXXXXX, PUBSY No. 

STECF. 2014. Evaluation/scoping of Management plans - Data analysis for support of the impact 
assessment  for  the  management  plan  of  Bay  of  Biscay  anchovy  (COM(2009)399  final). 
(STECF-14-05). Luxembourg. EUR 26611 EN, JRC 89792. 128 pp 

Siple, M. C., Essington, T. E., & E. Plagányi, É. (2019). Forage fish fisheries management requires 

a tailored approach to balance trade‐offs. Fish and Fisheries, 20(1), 110-124. 

Siple,  M.  C.,  Koehn,  L.  E.,  Johnson,  K.  F.,  Punt,  A.  E.,  Canales,  T.  M.,  Carpi,  P.,  ...  & 
Zimmermann,  F.  (2021).  Considerations  for  management  strategy  evaluation  for  small 
pelagic fishes. Fish and Fisheries. 

Sánchez, S., Ibaibarriaga, L., Uriarte, A., Prellezo, R., Andrés, M., Abaunza, P., Jardim, E., et al. 
2019.  Challenges  of  management  strategy  evaluation  for  small  pelagic  fish:  the  Bay  of 
Biscay 
245-263. 
https://doi.org/10.3354/meps12602. 

Progress  Series, 617, 

study. Marine 

anchovy 

Ecology 

case 

Thorson,  J.  T.,  Jensen,  O.  P.,  and  Zipkin,  E.  F.  2014.  How  variable  is  recruitment  for  exploited 
marine  fishes  ?  A  hierarchical  model  for  testing  life  history  theory.  Canadian  Journal  of 
Fisheries and Aquatic Sciences, 71: 973–983. 

Thorson,  J.  T.  2020.  Predicting  recruitment  density  dependence  and  intrinsic  growth  rate  for  all 
fishes  worldwide  using  a  data-integrated  life-history  model.  Fish  and  Fisheries,  21:  237–
251. John Wiley & Sons, Ltd. https://doi.org/10.1111/faf.12427 

Contact details of STECF members 

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

Name 

Affiliation1 

Email 

Abella, J. Alvaro 

Independent consultant 

aabellafisheries@gmail.co
m 

9 

9 

 
 
 
 
Name 

Affiliation1 

Email 

Bastardie, Francois 

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

fba@aqua.dtu.dk  

Borges, Lisa 

FishFix, Lisbon, Portugal 

info@fishfix.eu 

Casey, John 

Independent consultant 

Catchpole, Thomas 

Damalas, Dimitrios 

Daskalov, Georgi 

Döring, Ralf (vice-chair) 

Gascuel, Didier  

Grati, Fabio 

blindlemoncasey@gmail.c
om  

thomas.catchpole@cefas.c
o.uk  

shark@hcmr.gr 

Georgi.m.daskalov@gmail
.com 

ralf.doering@thuenen.de 

CEFAS 
Pakefield 
Suffolk, UK, NR33 0HT 

Lowestoft 
Road, 

Laboratory, 
Lowestoft, 

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

576 

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

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

D-27572 

Sea 

of 

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

Didier.Gascuel@agrocamp
us-ouest.fr 

fabio.grati@cnr.it  

Institute 

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

for 
and 

Ibaibarriaga, Leire  

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

libaibarriaga@azti.es  

Jung, Armelle 

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

armelle.jung@desrequinse
tdeshommes.org  

10 

10 

 
 
Name 

Affiliation1 

Email 

Knittweis, Leyla 

Kraak, Sarah  

Ligas, Alessandro 

Martin, Paloma  

Motova, Arina  

Moutopoulos, Dimitrios 

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

of 

Leyla.knittweis@um.edu.
mt  

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

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

di 

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

sarah.kraak@thuenen.de 

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

paloma@icm.csic.es 

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

arina.motova@seafish.co.
uk 

of 

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

Fisheries 

dmoutopo@teimes.gr 

Nord, Jenny  

Prellezo, Raúl  

O’Neill, Barry 

Raid, Tiit  

Rihan,  Dominic 
chair) 

Sampedro, Paz 

The  Swedish  Agency  for  Marine 
and Water Management (SwAM)  

Jenny.nord@havochvatten
.se 

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

rprellezo@azti.es 

DTU  Aqua,  Willemoesvej  2,  9850 
Hirtshals, Denmark 

barone@aqua.dtu.dk  

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

Tiit.raid@gmail.com  

(vice-

BIM, Ireland 

rihan@bim.ie  

Institute 

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

paz.sampedro@ieo.es  

11 

11 

 
Name 

Affiliation1 

Email 

Somarakis, Stylianos  

Stransky, Christoph  

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

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

of 

somarak@hcmr. gr 

christoph.stransky@thuen
en.de 

Ulrich, Clara (chair) 

IFREMER, France 

Clara.Ulrich@ifremer.fr  

Uriarte, Andres 

Valentinsson, Daniel 

van Hoof, Luc 

Gestión 

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

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

Wageningen  Marine  Research 
Haringkade  1, 
Ijmuiden,  The 
Netherlands 

auriarte@azti.es 

daniel.valentinsson@slu.s
e 

Luc.vanhoof@wur.nl 

Vanhee, Willy  

Independent consultant 

wvanhee@telenet.be 

Villasante, Sebastian 

of 

Santiago 

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

Santiago 

sebastian.villasante@usc.
es 

Vrgoc, Nedo 

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

vrgoc@izor.hr 

12 

12 

 
 
 
 
 
 
 
 
 
 
Executive Summary of the EWG 

  The EWG-20-04 was tasked to develop and test alternative harvest control rules (HCR) for 
anchovy  and  sardine  in  the  Adriatic  Sea  (GSA  17+18)  based  on  the  latest  GFCM 
Benchmark (ended in January 2021), that will ensure a low probability of SSB to fall below 
Blim  (5%  probability).  The  HCRs  should  be  tested  in  a  Management  Strategy  Evaluation 
for  their  robustness  to  different  assumptions  on  several  biological  processes  (such  as 
recruitment, assessment model, misspecification of age 0 maturity, M and age, etc).   
  As  requested  (TOR  1.2)  conditioning  of  the  operating  models  for  Sardine  and  Anchovy  in 
the  Adriatic  Sea  (Gsa  17-18)  were  made  based  on  the  results  of  Request  for  services  - 
1743  -  STECF  Ad  hoc  contract  on  ‘Adriatic  Small  Pelagic  stocks’  and  taking  into  account 
the  current  GFCM  benchmarked  stock  assessment  and  adding  (fitting)  the  most  suitable 
stock-recruitment models and in agreement with literature.  
The  analysis  for  the  conditioning  led  to  adopt  the  following  settings  for  the  operating 
model:  For  both  species  the  OM  was  an  age  structured  model  (TOR  1.3)  (based  on    age 
groups 0-2+ for anchovy and 0-4+ for sardine) and single area and a calendar year basis 
(January to December); Growth following Von Bertalanffy parameters estimated from the 
age-length  keys  (GFCM,  2020),  Natural  mortality  at  age  following  Gislanson’s  modelling 
and maturity knife edge at age 1 for both species. 
For the recruitment, after examination of Beverton & Holt (SRR-BH), Ricker (SRR-Ri) and 
segmented  regressions  (SRR-SR),  the  best  fitted  model  were  retained:  Beverton  &  Holt 
(SRR-BH)  for  anchovy  and  segmented  regressions    (SRR-SR)  conditional  to  Blimfor 
sardine. For the two species, as the variance around the S-R relationship was judged to be 
too small in comparison with literature, adopted residual variance was set at the mode  of 
    generated  using  the  hierarchical  taxonomic  life  history 
the  predictive  distribution  for   

model FishLife (Thorson 2020).  On the base case recruitment deviations were considered 
to uncorrelated in time.  
As  for  robustness  (or  sensitivity  analysis)  alternative  configurations  were  considered  for 
anchovy which included: Higher variability around S-R relationship; Occasional failures  of 
recruitments  for  three  consecutive  years  The  potential  for  multi-annual  cycles  of 
recruitment  was  partially  included  by  consideration  of  autocorrelation  of  recruitment 
deviations from the S-R relationship (up to a value of    ρ = 0.456 -- Thorson et al. 2014). 
In  addition,  Natural  mortality  constant  for  all  fishes  older  than  1  year  old  was  also 
considered.    As  for  sardine  no  MSE  on  HCR  was  run  (see  below)  no  robustness  analysis 
was required. 
Biological  Reference  points  Blim  and  Bpa  and  Fmsy  were  adopted  from  the  GCFM 
benchmark for the two stocks. However for anchovy, the Fmsy used for the tunning of the 
HCRs was the one deduced from BH-SRR (0.96), which was slightly higher than the GFCM 
benchmark defined Fmsy (0.81 with Patterson’s criteria).  

  The EWG was requested to undertake the MSE simulations commencing in January 2024, 
while for intervening period, catches were modelled in accordance with TOR 1.1 This TOR 
prescribed  to  model    an  intermediate  period  to  manage  catches  from  2020  t0  2023, 
assuming status quo catches in 2021 and according to different levels of catch reductions 
of  Sardine  (of  5-10-15%  per  year)  and  Anchovy  (of  5-10-15%  per  year)  starting  in 
January  2022  and  ending  in  December  2023.  In  addition  for  2024,  as  a  sensitivity  run, 
sardine catches should be 15% lower and anchovy 5% lower than the level of 2023. 

  Results for the population and fishery projections over the intermediate period show that: 
a)  For  Anchovy  for  any  of  the  three  prescribed  gradual  reduction  of  catches  until  2023 
median  biomass  would  increase  above  Blim,  Bpa  or  Bmsy  by  year  2025  after 
implementation of any of the tested HCRs in 2024. The same would happen if during 2024 

13 

13 

 
 
 
 
the  complementary  catch  reduction  of  5%  or  15%  would  apply.    Nor  BoB_1535  (slope 
0.8), neither the FixedF at 0.7Fmsy can achieve the objective of setting F at Fmsy by 2025 
with 95% certainty (though the former is very close to such certainty). However by 2028 
such certainty is fully achieved for the BoB_1535 (slope 0.8) and as well (roughly) by the  
FixedF at 0.7Fmsy. It is clear however that over the intermediate years (2022 and 2023) 
risks are higher than 0.05 of falling below Blim.  This is due to the inability to adjust catch 
levels  according  to  the  stock  biomass  level  for  the  prescribed  reductions,  which  leads  to 
occasional  sub-optimal  catch  utilisation  over  that  period  or  to  higher  risks  of  stock 
depletion. In cases where the monitoring system would reveal SSB slipping below Blim or 
F  increasing  dramatically  despite  of  catch  reductions  over  this  intermediate  period,  then 
extraordinary circumstance management actions should be undertaken in order to prevent 
the stock from crashing completely. 
For  sardine  and  the  three  scenarios  of  gradual  catch  reductions  (5%,  10%  and  15%  )  it 
has  been  shown  that  the  probability  of  the  sardine  biomass  being  above  Bmsy  would  be 
low  (below  0.5)  and  it  will  be  actually  diminishing  over  this  intermediate  period  (from 
about  0.42  to  around  0.22  in  all  cases).  Furthermore,  the  probability  of  the  sardine 
biomass  being  below  Blim  would  be  far  higher  than  5%  for  all  the  intermediate  years 
(2020-2023),  and  actually  the  probabilities  of  being  above  Blim  will  be  decreasing  over 
this intermediate period (from about 0.54 to around 0.36 in all cases). The ratio of fishing 
mortality over Fmsy would be increasing. A further reduction of catches by 15% in 2024, 
would  not  revert  the  increasing  risk  for  the  SSB  to  drop  below  Blim.  Given  the  critical 
starting  point  of  the  SSB  in  2020  so  close  to  Blim  and  F  more  than  twice  Fmsy,  the 
simulations show that the proposed reductions in catches would not induce a recovery  of 
the  population  towards  higher  biomass  levels  yet  on  average  but  there  will  be  an 
increasing  risk  of  falling  below  Blim  as  a  result  of  a  still  too  high  fishing  mortality  levels 
which can on average be still increasing.   

  TOR 2 set up the MSE simulation context for the HCRs for anchovy as a Full feedback loop 
(Tor  2.1)  whilst  for  sardine,  owing  to  the  stock  not  being  benchmarked  and  survey  CVs 
being  too  wide,  just  a  shortcut  MSE  to  understand  the  impact  of  the  transitional  period 
measures  on  the  SSB  and  Rec  was  asked.  The  Implementation  Model,  as  requested  TOR 
2.2,  was  made  via  catch  control  based  on  annual  catch  limits  to  be  set  over  for  the 
January  –  December  period.  For  the  observation  model  the  current  inputs  for  the 
assessments  were  simulated  (included  the  two  on  going  surveys  Echoeast  and  Lsurvey) 
(TOR  2.3).  for  the  full  feedback  MSE,  annual  assessment  based  on  FLSAM  was 
implemented.  And  as  requested  the  assessment  and  advice  was  produced  every  year  y 
(the  interim  or  assessment  year),  using  data  from  surveys  until  year  y-1,  and  for  the 
management of the fishery in year y+1. The spawning biomass for anchovy was assessed 
at  mid  year  in  a  consistent  manner  with  the  S-R  relationship  and  the  FLSAM  projection 
procedure.  Actual  management  based  on  the  tested  HCRs  started  after  the  intermediate 
catch  reduction  period  until  2023  or  optionally  2024.  Finally  interannual  catch  variation 
restrictions  were  also  tested  according  to  TOR  2.4.3  (Imposing  an  inter-annual  change 
restriction of  10% downward and 5% upward). 

  There  evaluation  was  based  on  performance  indicators  (TOR  4)  and  the  The  objective  of 
the management decisions was to reach the plan target [Fmsy proxy or SSBtarg] in year : 
a) 2025 or b) 2028 

  The  Harvest  controls  rules  tested  for  anchovy  (according  to  TOR  2.4)  were  defined  after 

some initial tunning as follows:  

o  Fixed target fishing mortality (fixedF), which fishes the population according with a 
constant fishing mortality: This was set at F = 0.7·Fmsy because for 0.8 or higher 
multipliers  the  probability  of  dropping  below  Blim  was  larger  than  5%  over  the 

14 

14 

 
 
 
period 2024-2044. Imposing an inter-annual change restriction of  10% downward 
and 5% upward did not change the former conclusion.  

o  Biomass  Escapment  (Besc):  The  Biomass  Escapement  HCR  (Besc)  sets  the  total 
allowable catch (TAC) for the next year so that the level of SSB that is left at sea at 
the  end  of  the  fishing  year  is  equal  or  higher  than  a  predefined  “escapement 
biomass” (B_esc), while simultaneously keeping estimated fishing mortality F equal 
or  lower  than  a  specified  cap  (F_cap).  The  rule  swas  actually implemented  on  the 
assessment  year  and  the  results  passed  to  the  advice  for  the  management  year. 
B_esc between 1 and 2 Blim was shown to have little impact on the performance of 
the  rule  and  as  such  B_esc  at  Bpa  was  fixed  (i.e.  at  1.6  Blim).    for  Fcap  ranging 
from  0.6*xFmsy  to  1*xFmsy,  it  was  found  that  If  Fcap  was  increased  beyond 
0.8*xFmsy, the HCR started failing the requirement that p(SSB < Blim) < 0.05 and 
thus Fcap at 0.8*xFmsy was retained for subsequent analysis. 

o  Bay  of  Biscay  type  (BoB)  is  a  HCR  setting  directly  TACs  from  the  assessment  of 
spawning  biomass  (projected  at  the  end  of  the  assessment  year),  without  explicit 
definition  of  the  fishing  mortality.  The  TACs  are  bound  by  a  minimum  and 
maximum  levels  (TACmin  and  TACmax)  which  will  be  allocated  at  biomasses 
between B1 and B2 and at biomass above B3 respectively, while intermediate TAC 
values  will  be  set  at  (and  proportionally  to)  intermediate  biomass  values  between 
B2  and  B3.  Actually  B3  is  defined  as  dependent  on  the  B2  and  the  slope  of 
increasing  allowable  catches  as  a  function  of  biomas.  Therefore  the  rules  are 
defined by TACmin, TACmax, B1, B2 and the slope. Several rules were with TACmin 
either  at  15000  t  or  20000t  and  with  TACmax  at  35000  t  or  4000  t,  B1=Blim, 
B2=Bpa  were  tunned  for  slopes  ranging  between  0.6  to  1.  The  TAC  max  respond 
with  the  maximum  expected  catch  for  the  equilibrium  production  curve  for  this 
stock.  The  minimum  TACs  are  around  or  below  the  Blim  values.  Little  contrast 
between  the  different  tuning  runs  was  noticed.    The  preferred  formulation  to  take 
forward  to  robustness  testing  was  not  immediately  apparent,  as  each  BoB 
formulation  passed  the  required  performance  statistics  (SSB/Blim,  F/Fmsy).  As 
such three HCRs were retained BoB_1535 (i.e. with TACmin and TACmax at 15000 
and 35000 t) and  BoB_1540 and BoB_2040, with B1=Blim, B2=Bpa , with slopes 
of  0.8.  Average  catches  are  highest  for  BoB_2040  and  lowest  for  BoB_1535; 
increasing  the  slopes  does  increase  the  catches,  but  to  a  lesser  extend  than 
changing  TACmin/max.  Therefore  retained  rules  were  judged  to  comprise  most  of 
the potential production for this stock.   

In  summary  the  five  retained  rules  all  passed  in  principle  the  criteria  of  keeping  risks  of 
falling  below  Blim  at  or  below  0.05  while  maximizing  catches  as  much  as  possible.  They 
were passed to the robustness analysis.  

  The  robustness  analysis  results  showed:  The  performance  statistics  reveal  just  subtle 
differences between the different  OMs. The differences between the fixedF and Besc  HCR 
were very small, whereas the differences were a little more marked for the BoB HCRs. The 
HCRs  performed  slightly  better  in  the  OM  with  a  constant  Natural  Mortality  by  ages  than 
with  the  reference  OM,  which  indicated  that  the  HCR  are  able  to  deal  with  minor  M  mis-
specifications.  Regarding  the  robustness  to  alternative  dynamics  of  recruitment,  apart 
from  BoB_1535  nearly  all  HCRs  failed  the  robustness  tests.  The  BoB_1540  formulation 
passed  the  lowRec  robustness  test,  but  none  of  the  others  (apart  from  the  reference 
runs).  In  terms  of  risks  of  closures  BoB_2040  is  the  riskiest  one  in  the  short  term 
(reaching a probability of about 0.1).  The aggregated performance statistics clearly show 
that  the  only  candidate  HCR  perform  adequately  across  the  entire  range  of  runs  is 
BoB_1535 (Figure 34), closely followed by BoB_1540. 

15 

15 

 
 
  The  levels  of  intermediate  catch  reductions  did  not  affect  the  long-term  behaviour  of  the 

HCRs for the anchovy stock. 

  Economic analysis of the performance of the different retained HCRs, as required by TOR 
3,  was  based  on  the  median  catches  by  years  resulting  from  the  former  simulations  (so 
individual  variability  among  iterations  were  not  considered).  As  the  different 
scenarios  on  the  HCR  for  small  pelagic in  the Adriatic  are  defined  in terms  of  changes  in 
total catches through the setting of TAC for both sardine and anchovy, only the economic 
variables  directly  affected  by  variations  in  landings  and  fishing  effort  are  supposed  to 
change  over  time.  Under  a  short-term  approach,  the  size  of  the  fleets  is  assumed  to  be 
constant along the simulation period and variables associated with the fleet size, like fixed 
costs,  repair  and  maintenance  costs  and  capital  costs,  are  assumed  to  be  constant  over 
time. Therefore, the economic short-term effects of changes in TACs are measured only in 
terms  of  changes  in  revenues  and  variable  costs.  Efforts  corresponding  to  TACS  were 
those corresponding to the inverse of Cob-Douglas production function fitted to the Italian 
anchovy  fleets,  while  the  estimation  of  production  functions  for  the  Croatian  fleet 
segments was not carried out because the main target stocks for these fleets is assumed 
to be sardine, and both landings and SSB of sardine are assumed to be constant over time 
for  the  purposes  of  this  evaluation  of  the  economic  performance.    Such  constant  sardine 
catches were set at the median catch at Fmsy. 

  An analysis of risk of fisheries closure on the gross profit was carried out. The analysis was 
at fisheries level, all the fleets were amalgamated. This analysis is a simple tool to assess 
the risks to the gross profits if all other aspects remain the same. The shutdown risk was 
only  applied  to  the  anchovy  fisheries,  as  no  data  for  sardines  was  available.  The  risk  of 
shut  down  was  taken  from  section  3.  As  can  be  seen  in  Figure  5.9  the  increased  risk  of 
shutdowns leads the fixedF HCR to perform less well than the other two HCRs. 

16 

16 

 
 
Expert Working Group EWG-21-04 report 

REPORT TO THE STECF 

EXPERT WORKING GROUP ON  
Management Strategy Evaluation of alternative 
approaches for the anchovy and sardine stocks 
in the Adriatic Sea  
(EWG-21-04) 

Ispra, Virtual, 12th – 16th of July 2021 

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

17 

17 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1  INTRODUCTION 

Background 
Natural  resource  management  presents  the  challenge  of  linking  the  interactive  processes  of 
population  dynamics,  management  targets,  decision-making,  strategy  implementation  and 
stakeholder  behavior,  where  each  process  is  associated  with  uncertainties  that  translate  into 
risks.  Because  of  the  inevitable  uncertainties,  scientific  advice  can  be  open  to  challenges  from 
divergent stakeholder interests. The form of scientific advice is therefore changing in many of the 
world; decision rules for resource management cannot be based on expert advice alone but also 
requires stakeholder involvement (Smith et al. 1999). As a result there is an increasing need to 
base management decisions and corresponding strategies on scientific evidence from quantitative 
resource  assessments  (Butterworth  et  al.  2010;  Punt  et  al.  2014;  Sharma  et  al.  2020).    A 
framework  that  has  been  put  forward  to  address  these  challenges  is  known  as  ‘Management 
Strategy Evaluation’ (MSE; Smith et al. 1999), elsewhere referred to as ‘Operating Management 
Procedures’  (OMP;  Butterworth  and  Punt  1999).  This  framework  uses  simulation-testing  to 
evaluate  alternative  management  procedures  (MP)  in  terms  of  risk  to  both  the  stock  under 
assessment and stakeholder interests (Smith et al. 1999; Punt et al. 2014). MSE originates from 
the  management  procedure  approach  of  the  Scientific  Committee  of  the  International  Whaling 
Commission,  which  aimed  to  apply  scientific  principles  in  support  of  the  moratorium  on 
commercial  whaling  in  1982  (Butterworth  et  al.  2010).  To  date,  MSE  is  widely  considered  to  be 
‘best practice’ in fisheries management (Punt et al. 2014). As such, MSE provides powerful tools 
to  evaluate  the  trade-offs  among  management  strategies  and  to  assess  the  consequences  of 
uncertainty for achieving management objectives (Punt et al. 2014). 

The MSE approach: An Overview  
The principle steps of the MSE process can be summarized as follows: 

1. 
Identification  and  specification  of  the  management  objectives.  A  key  aspect  is  to  then 
translate  the  objectives  into  quantifiable  management  targets  against  which  the  management 
procedures  can  be  evaluated.  Here  it  is  considered  critical  that  the  management  objectives  are 
pre-agreed  and  tangible  in  terms  of  target  quantity  and  time  horizon.  Targets  can  include 
combinations  of  maintaining  the  stock  above  a  limit  biomass  with  high  probability  (e.g.  95%), 
attaining a target biomass with a probability of, while minimizing inter-annual variation in quota 
and maximizing long-term yield. 
2. 
Identification of major  sources  of uncertainty (e.g. population parameters,  environmental 
variation,  observation  and  reporting  error  and  compliance)  to  which  the  MP  must  be  robust  to. 
The available data sources and suitable modeling platforms are reviewed and agreed upon. 
3. 
Development of simulation models, referred to as ‘Operating Models’ (OMs), that provide a 
mathematical representation of the population dynamics based on the best knowledge about the 
population  dynamics  and  the  key  processes  of  management  strategy,  including  monitoring, lags 
between data and assessment and the availability of future data. It is common practice that the 
OMs  are  conditioned  fitting  a  range  of  alternatively  parameterised  assessment  models  to  the 
actual data from the fishery under assessment. The OM simulates the response of the population 
to  a  management  strategy  and  tracks  the  desired  metrics  of  ‘true’  populations  (e.g.  numbers, 
age-structured,  sex-ratio  etc.).  It  also  generates  the  ‘observation  data’  that  represent  the 
information  available  from  monitoring  and  reporting,  including  reporting  errors,  observation  and 
process noise. 
The simulated observation data are then passed on to observation model, which can be a 
4. 
full assessment model that updates the stock status based on the new data, a simpler model, or 
an  empirical  rule  (e.g.,  based  on  the  recent  trend  in  the  abundance  index  or  even  length 
indicators).  The  so  called  “short-cut”  MSE  approach  (often  implemented  in  ICES)  omits  the 

18 

18 

 
 
 
 
 
 
 
 
estimation model. Instead, the “true” stock dynamics from the OM are passed through to the next 
step  assuming  perfect  estimation  of  stock  abundance,  albeit  after  admitting  at  least  some 
additional implementation error (e.g. by adding random noise). 
5. 
The outputs of the estimation model are then passed on to the harvest control rule (HCR), 
which translates the outputs into the management action (e.g. setting of a quota or effort limits) 
for the next time step. 
6. 
The  last  step  of  an  MSE  involves  the  performance  evaluation  of  management  strategy 
simulations  against  the  agreed  performance  metrics  and  targets.  This  process  can  assist  to 
identify  a  range  of  suitable  management  strategies  for  a  particular  species,  refine  existing 
strategies  and  identify  management  strategies  that  will  not  work  a  priori    (Butterworth  et  al. 
2010;  Punt  et  al.  2014).  Ideally,  the  performance  for  a  range  of  adequately  performing 
(conditioned) candidate MPs are presented to the decision making body for choosing the MP with 
the desired trade-offs with regards to key performance metrics, such as short-term and medium 
term  yield,  annual  stability in  effort/quota  and  risk  of  not  meeting  biological  and limit  reference 
points. 

1.1 

Terms of Reference for EWG-21-04 

STECF EWG 21-04 

TORs for EXPERT WORKING GROUP on Management Strategy Evaluation of alternative 
approaches for the anchovy and sardine stocks in the Adriatic Sea. 

Venue: Virtual 

Dates: 12-16 July 2021 

DG MARE Focal point: Chato Osio (D1)  

Chair: Konrad Christoph  

Background  

Table 1.1 Advice and reference points from the 2020 GFCM Adriatic small pelagic benchmark 
meeting  

Species   Method  Fbar 

F 
 MSY 

(E =0.4) 

SSB  

SSBlim  SSBpa  Status 

(quantitative) 

2019 

Sardine   a4a  

2.1 

7 

0.4

 198 
600 

178 
200 

 294 
300 

 = 4.43  

F/F 
MSY 

SSB/SSBlim =1.11   

SSB/SSBpa = 0.67  

 Status 2019  

Overexploited 
overexploitation 

and 

in 

19 

19 

 
 
 
 
 
 
 
 
 
 
   
  
 
 
  
Anchovy   SAM  

1.22 

1 

0.8

17089 

16 
200 

 21 
400 

 = 1.51  

F/F 
MSY 

SSB/SSBlim = 1.05  

SSB/SSBpa = 0.80  

Overexploited 
overexploitation 

and 

in 

TORs Biomass ESCAPEMENT STRATEGY  

For anchovy and sardine in the Adriatic Sea (GSA 17+18) based on the latest GFCM Benchmark 
meeting  ended  in  January  2021  (Table  1.1),  develop  and  test  alternative  harvest  control  rules 
(HCR)  that  will  ensure  a  low  probability  of  SSB  to  fall  below  Blim  (5%  probability).  The  HCRs 
should be tested in a Management Strategy Evaluation and the HCR need to be robust to different 
assumptions on recruitment, assessment model, to misspecification of age 0 maturity, M and age.   

The  EWG  is  requested  to  undertake  MSE  simulations  commencing  in  January  2024.  For  the 
intervening period, catches should be modelled in accordance with TOR 1 below.  

TOR 1)  

Conditioning / background for simulation testing  

1.1 Model an intermediate period 2020-23 in line with the work performed for STECF 1802 
(Request for Services 1744-  STECF Ad hoc contract on Adriatic Small Pelagic Stocks) and in 
detail as follow:  

Using anchovy and sardine reported catches from 2020, assuming status quo catches in 2021 (in 
line with the Council Fishing Opportunities Regulation 2021) and according to different levels of 
catch reductions of Sardine (of 5-10-15% per year) and Anchovy (of 5-10-15% per year) starting 
in January 2022 and ending in December 2023.  

As a sensitivity run, for year 2024, sardine catches should be 15% lower and anchovy 5% lower 
than the level of 2023.   

1.2 Condition operating models for Sardine and Anchovy in the Adriatic Sea (Gsa 17-18) with the 
results of Request for services - 1743 - STECF Ad hoc contract on ‘Adriatic Small Pelagic stocks’, 
taking into account the current GFCM benchmarked stock assessment and adding stock-
recruitment models that consider multi-annual cycles of recruitment, which can consider both 
biomass or non biomass related drivers for recruitment in the past. Specifically for the sardine 
stock, due account should be given to the uncertainty in the growth in recent years. Additionally 
model recruitment:  

In line with S/R in model conditioning or segmented-regression, S/R variance set in accordance 
with literature  

20 

20 

 
 
 
 
  
 
 
 
 
 
 
 
 
  
  
Persistent low  

Other deemed appropriate  

1.3 Use an age structured population model based on a single area and a calendar year basis 
(January to December)  

TOR 2)   

In the MSE follow the specifications below:  

2.1 Full feedback loop or shortcut MSE:  

For Anchovy stock run MSE with full feedback loop  

For Sardine stock, owing to the stock not being benchmarked and survey CVs being too wide, run 
only a shortcut MSE to understand the impact of the transitional period measures on the SSB and 
Rec.  

2.2 Implementation Model  

Annual catch limits to be set over for the January – December period  

Management implementation via catch control  

2.3 Observation Model  

The MSE testing should simulate the provision of scientific advice where the data delivery of 
echo-survey (performed June-Sept) and landings from Year N in April of year N+1, stock 
assessment performed in June (N+1) and catch quota implementation from January N+22 .   

2.4 Management Decisions  

2.4.1. The objective of the management decisions is to reach the plan target [Fmsy proxy or 
SSBtarg] in year : a) 2025  

2028  

2  Note  that  in  the  report  the  notation  of  years  is  not  the  one  in  the  TOR  but  the  following:  The 
assessment and advice  is produced  in year  y (the interim or assessment year), using data  from 
surveys until year y-1, and for the management of year y+1. 

21 

21 

 
  
  
  
  
  
                                    
2.4.2. From January 2024 annual catch limits/TACs should be set on the basis of HCRs outlined 
below. Additionally, as a sensitivity run, simulate the HCR implementation starting in January 
2025 after catch levels established in point  

1.b.   

(HCR1) Test a level of fixed level F proxy (e.g. through catches)   

(HCR2) Test a fixed biomass HCR in line with the Bay of Biscay anchovy or in an ICES like HCR 
(Figure 1.1) .  

Figure 1.1 Bay of Biscay Anchovy HCR 2016, based on Sanchez-Maroño et al. 2016 

(HCR3) In line with the work of STECF EWG 18-01, update the biomass escapement HCR that will 
ensure a low probability of SSB to fall below Blim (5% probability) for Anchovy and Sardine.   

For the HCR4, define:  

an optimal level of the Bescapement  

confirm the need of an Fcap and in case the appropriate level.  

2.4.3. In combination of the HCR 1-4 test the effect of a TAC stability mechanism that allows TAC 
inter-annual change of 10% downward and 5% upward. Advise if such mechanism in compatible 
with achieving the plan target according to point 2.4.1.b   

TOR 3) Economic Performance  

If economic data are available and of adequate quality, evaluate the maximum economic 
performance of the HCR.  

22 

22 

 
  
 
 
  
  
  
  
TOR 4) Performance Statistics  

Evaluate performance of alternate scenarios (at least 250 iterations) on 10-20 year time scale 
using standard MSE diagnostic tools, in line with those developed for STECF EWG 1801, focusing 
in particular on the following in relation to Harvest Rate:  

Probability of SSB falling below Blim.   

Risk of SSB falling below Blim vs Catch level  

Catch variability  

Average catch  

Level of SSB  

Any other deemed useful and of simple interpretation  

TOR 5)   

Consider possible additional scenarios stemming from GFCM SAC discussion.  

23 

23 

 
  
  
  
 
 
   
 
2  MSE FRAMEWORK  

The full feedback approach used for Anchovy (ToR 2.1 a)  

The  full  feedback  approach  refers,  here,  to  including  the  observation  model 
(assessment)  into  a    feedback  loop  between  the  operating  model  and  the  HCR 
.The  simulated  observation  data    are  therefore  first  passed  to  the  estimator, 
which  is  in  this  case  the  SAM  model  base-case  as  specified  during  the  GFCM 
benchmark  assessment.  During  each  year  and  iteration,  the  SAM  model  is  re-
fitted  to  simulated  observation  data  in  the  form  of  catch-at-age  and  survey 
indices, both with associated observation error. The SAM model estimates for the 
stock  are  then  passed  to  HCR  and  this  way  translated  via  the  implementation 
system  into  the  Total  Allowable  Catch  (TAC)  advice  for  the  next  fishing  season 
(Figure 2.1). 

Figure  2.1 Schematic illustration of a classical Management Strategy Evaluation system with full-feedback 
control including the Estimator (i.e. Stock Assessment Model). 

The short-cut approach Sardine (ToR 2.1 b) 

In  a  ‘short-cut’  approach  (Section  4.4.3  ICES  2013),  annual  updating  of  the 
estimation model (assessment) is omitted and, instead, the ‘true’ age-structured 
dynamics  from  the  OM  are  passed  directly  to  the  HCR  (Figure  2.2).  Within  the 
FLR mse framework, this approach therefore denoted a ‘perfect observation error 
model’ and elsewhere as also ‘simulated assessment’ approach (Milderberg et al. 
2021).  The  advantages  of  a  short-cut  MSE  lie  in  the  easy  implementation  and 
reduced  calculation  requirements.  However,  the  short-cut  approach  to  MSE 
ignores  uncertainty  resulting  from  imperfect  sampling  of  the  full  age-structure, 
observation  error,  model  estimation  error  and  structural  model  error.  Short-cut 
approaches  are  increasingly  used  to  test  HCR  performances,  while  the 

24 

24 

 
 
 
 
implementation of such an HCR ultimately relies on a well performing assessment 
model as a prerequisite. 

Figure 1.2 Schematic illustration of a ‘short-cut’ approach to Management Strategy Evaluation, in which the 
‘true’  age-structured  dynamics  are  passed  directly  to  the  HCR  by  omitting  the  Estimator  (i.e.  Stock 
Assessment Model). 

Implementation system (ToR 1.3, 2.2 & 2.3) 

The  management  system  is  implemented  via  catch  control  (ToR  2.2),  which  is 
derived through the HCR. This system requires making assumptions about catch 
and population processes during the intermediate assessment year y required to 
perform  a  short-term  forecast  and  predict  the  catch  in  the  management  year 
y+lag (Mildenberg et al., 2021). To do this, it is assumed that total catch in the 
assessment  year  y  equals  to  the  TAC  of  the  previous  year,  while  recruitment  in 
year  y  was  set  geometric  mean  of  the  previous  three  years.  One  of  the  main 
motivations of implementing a tested  and pre-agreed management procedure is 
that the lag between assessment and implementation can be kept to a minimum. 
Therefore,  lags  of  more  than  a  year  are  rarely  considered  in  MSE.  Here,  the 
implementation system of the HCRs was agreed to be based on the assumption 
that  advice  is  given  for  year  y+1  based  on  an  assessment  in  the  year  y  using 
data up until year y-1. The management year was set over the period January – 
December (ToR 2.2). 

Initializing  MSEs  often  requires  a  number  of  intermediate  catch  years  to  bridge 
the  period  between  the  last  assessment  used  to  condition  the  OM  and  the  full 
implementation  of  the  HCR.  In  the  case  of  anchovy  and  sardine  in  GSA  17-18, 
the  last  assessment  year  is  2019,  while  the  implementation  of  a  HCR  rule  is 
considered only feasible after 2024. For the here considered reference case, the 
average  catches  in  2020  and  2021  were  set  to  the  2019  catch  levels  for  all 
scenarios,  while  the  years  2023  and  2024  represent  a  sequential  reduction  in 

25 

25 

 
 
 
 
catches  by  either  5%,  10%  or  15%  (ToR  2.3,  Table  2.1).  In  addition,  the 
scenarios were explored for Anchovy that included extension of the intermediate 
period to 2024 at the same rate of reduction (Table 2.1, Figure 2.3) 

Figure 2.3 Schematic of workflow within our full-feedback MSE including implementation system. 

Table 2.11 Scenarios for intermediate catch reduction levels (by rows) over the period 2020-2023, based 
on the reference catch levels in 2019. Sensitivity runs also include a further fixed catch reduction in 2024. 
The * denotes those catches in the year 2024, which only pertain in the sensitivity runs.    

Reductions 

2019 

2020 

2021 

2022 

2023 

2024* 

y
v
o
h
c
n
A

i

e
n
d
r
a
S

5% 

10% 

15% 

5% 

10% 

15% 

27871 

27871 

27871 

26477 

25254 

23991 

27871 

27871 

27871 

25084 

22576 

21447 

27871 

27871 

27871 

23690 

20137 

19130 

64961 

64961 

64961 

61713 

58627 

49833 

64961 

64961 

64961 

58465 

52618 

44725 

64961 

64961 

64961 

55217 

46934 

39894 

Harvest Control Rules (ToR 2.4 – methods) 

Three Harvest Control Rules (HCRs) were evaluated: 

  Fixed target fishing mortality (fixedF) 
  Biomass Escapment (Besc) 
  Bay of Biscay type (BoB) 

26 

26 

 
 
 
 
 
  
 
 
 
 
These candidate HCRs are specified in details in the following subsections. 

 

fixedF HCR 

The fixedF HCR is the simplest, as it is the only rule which sets the 
next year y+1 corresponding to the fixed Ftrg value (Figure 2.4), such that: 

 in the 

where  g()  denotes  the  Baranov  catch  equation  that  allows  solve  for  catch  C(y+1) 
for any given Ftrg. Note that the fixedF HCR has no mechanism to evoke a closure 
of  the  fishery  (i.e.  F=0),  unless  the  stock  is  extinct.  The  SSB  level  used  to 
calculate the TAC is taken from the forward projection within the implementation 
model,  meaning  it  is  the  SSB  at  the  end  of  the  assessment  year  (SSBy);  in 
contradiction  to  the  following  HCRs,  no  forward  projection  within  the  HCR  is 
performed as F is already known. 

Figure 2.4 Graphical illustration of the fixedF Harvest Control Rule. 

  Besc HCR 

The Biomass Escapement HCR (Besc) sets the total allowable catch (TAC) for the 
next year so that the level of SSB that is left at sea at the end of the fishing year 
is  equal  or  higher  than  a  predefined  “escapement  biomass”  (Besc),  while 

27 

27 

 
 
 
 
 
 
 
 
simultaneously  keeping  estimated  fishing  mortality  F  equal  or  lower  than  a 
specified cap (Fcap). This can be described mathematically in the form of 

where 

 denotes Baranov’s catch equation. In this case, finding 

requires an additional forecast step of the stock dynamics to predict the 
 that 
is  left  at  sea  after  fishing  season  (Figure  2.5)  which  is  essentially  a  function  of 
the  expected  average  surplus  production  minus  the  catch;  this  means  that  the 
TAC is based on the SSB of the forecast at the end of the assessment year 

Figure 2.5  Graphical illustration of the Besc Harvest Control Rule, highlighting the lag between SSBy based 
on the final data year and the forecast of SSBy+1 after applying the Fy+1 HCR response along y-axis. 

  BoB HCR 

The 

  HCR  differs  from  the 

  and 

  in  that 

  (not  )  is  a  direct 

output  of  the  HCR.  To  implement 
  therefore  requires  integrating  the 
implementation system into the HCR. This is because the short-term projection of 

28 

28 

 
 
 
 
 
 
 
 
 
stock dynamics over the assessment year   for 

 is conducted first to derive 

 at the end of assessment year as input for the 

 HCR. 

 candidate HCR is based on a generic HCR parameterisation of the  HCR 
The 
considered  in  the  MSE  for  the  Bay  of  Biscay  sardine  (Sánchez  et  al. 2014).  The 

  rule  introduces  up  to  three  discontinuity  breaks  for  the  output 

,  which 
requires the input of five tuning parameters. These are: (1) the minimum viable 

 that can be caught if 

 falls between the first biomass trigger (2) 

and second biomass trigger (3) 

; and the maximum possible (4) 

, for 

which a third 

 is computed as a function of the (5) tuning parameter  , which 

determines the slope between 

 and 

 (Figure 2.6), such that: 

The 

 rule can then be described mathematically in the form of: 

The  biomass  trigger  point 

,  which  evokes  a  fishery  closure  was  set  to 

; 

and 

,  which  evokes 

  was  set  to 

  (Figure  2.3).  Alternative  settings 

were tested for remaining three tuning parameters 

, 

 and the slope 

.  The  TAC  is  based  on  the  SSB  of  the  forecast  at  the  end  of  the  assessment 

year. 

29 

29 

 
 
 
 
 
 
 
 Figure 2.6 Graphical illustration of the BoB Harvest Control Rule, where three biomass trigger points where 
chosen such that Btri1=Blim⁡  and Btri2 = Bpa, while Btri3 is computing a function of the slope δ, f(δ). 

Management Objectives 

The objectives of  the management plans are twofold: 1) to estimate  the fishing 
pressure  that  will  keep  SSB  above  Blim  and  2)  to  not  exceed  FMSY  with  a 
preference to maintain a biomass above Bpa. The objectives should be achieved in 
either 2024 or 2025 with either date to be tested during the MSE a simple test of 
achieving  FMSY  by  2028  through  gradual  reduction  of  fishing  mortality  is  to  be 
conducted as well. Using adequate performance statistics, the achievement of the 
objectives  were  evaluated.  The  required  reference  points  for  the  performance 
statistics  were  taken  from  the  report  of  the  benchmark  exercise  for  both  stocks 
(GFCM, 2021). 

In  the  process  of  running  the  MSE,  the  tuning  process  is  responsible  to  ensure 
that the management objectives are achieved. To this goal, the parameter space 
of the management plan (intermediate years and HCR) was and will be explored. 
This preliminary report contains some of the required tuning process. 

Performance statistics (ToR 4) 

In MSE, performance statistics are used to represent management objectives and 
to evaluate the ability of MPs to achieve them. 

In  this  report,  the  following  performance  statistics  calculated  for  each  of  the 
HCRs: 

30 

30 

 
 
 
 
 
 
Probability of 

 remaining above 

 for iteration   across years   

Probability of 

 remaining above 

 for iteration   across years   

Average 

 for iteration   across years   

Average catch (in tonnes) for iteration   across years   

 Average 

 for iteration   across years   

Average annual variability in catches (AAV) across years and iterations 

Annual probability of fishing closure for years   across iteration   

 was chosen as criteria to be applicable to all HCRs as a ‘de facto’ 

 where 
closure. Note hwoever that the fixedF does not trigger a fishing closure 
mechanically.  . 

31 

31 

 
 
 
 
 
 
 
 
 
 
 
 
 
The performance statistics were evaluated across 1000 iterations each over three 
time horizons of projections: short term, from years 2022 to 2025, medium term 
(2022-2028) and long term (2032-2044); the results presented in this report are 
over  the  entire  time  period  for  ease  of  reading.  The  ‘hard’  tuning  criteria  was 
agreed to achieve less 5% probability  in  terms of  long  risk that SSB  falls  below 
Blim  in  accordance  with  the  TORs.  Note  that  for  plotting  purpose,  we  chose  to 
illustrate the equivalent risk statistic that of SSB being larger than Blim with 95% 
probability.  Only  if  these  criteria  was  met  in  the  first  instance  additional  short-
term  and  long-term  trade-offs  of  the  above  performance  statistics  were 
evaluated,  including  the  average  catch  performance,  AAV,  the  probability  of 
fishing closures and attaining SSB levels at MSY.  

32 

32 

 
 
 
 
1  MSE ANCHOVY GSA 17 & 18 

Anchovy Operating Model (OM) Conditioning 

For the purpose of this report, the anchovy SAM base-case model, as accepted by 
GFCM as benchmark assessment in 2020 (GFCM, 2021), was used to condition 
the reference OM. This model is based on the SAM model settings that have been 
adopted in previous GFCM assessments for this stock. After extensive 
discussions, the experts evaluated that data prior to 2000 were unreliable due to 
gaps in the series and agreed to short the time series of input data to the period 
2000 – 2019 (GFCM 2021). A SAM model fit to the historical time series 1975-
2019 was kept for reference point estimation purposes. 

The final SAM base-case model specifications were: 

  Three age groups 0-2, where age-2 was treated as plus group 

  Natural Mortality: Gislason estimate of 

 (Table 3.1), derived using von 

Bertalanffy parameters 

  Maturation assumed to knife-edge at age-1 in January (Table 3.1) 

  Commercial catch-at-age data 2000 – 2019 (Figure 3.1) 

  4 survey indices for tuning (Figure3.2) 

1.  EU-MEDIAS (EchoEast) in GSA 17; Age 0 – 2; 2013 – 2019 
2.  GSA 17 West; Age 0 – 2; 2004 – 2014 
3.  GSA 18 West and East; Age 1 – 2; 2004 – 2014 
4.  Lsurvey; Age 1-2; 2015 – 2019 

Table  3.1  Biological  input  data  for  anchovy  SAM  base-case  model.  The  length-at-age  (La)  estimates  are 
based on Von Bertalanffy parameters Linf =18.61, k=0.62 and t0= -0.849 estimated from the age-length key 
(GFCM, 2020). La length at age; Ma Natural mortality at age; P(Mature) fraction of fishes which are mature 
by age.  

Age 

(cm) 

(

)  P(Mature) 

0 

1 

2 

3 

7.6 

2.76 

12.7  1.21 

15.4  0.89 

16.9  0.76 

0 

1 

1 

1 

Reference  points  were  also  updated  during  the  benchmark  process  (Table  2.2). 
The reference point 
), which can be 
 based on Patterson’s criterion (
calculated as 

33 

33 

 
 
 
 
 
 
 
 
where 

  are  the  age  classes  used  to  compute  the  average  fishing  mortality 

mortality 

,  which  were  specified  as  age-1  for  anchovy.  For 

  the  second 

lowest 

  estimate 

  = 

  based  on  the  alternative  SAM  model  run  that 

was fitted to historical time series (1975-2019). The lowest 

 corresponded to 

15,922  tons  in  2016.  That 

  was  not  used  as  there  was  evidence  that 

recruitment  may  not  be  already  impaired  at  this 

  level.  The  precautionary 

biomass  (
equation as ICES: 

)  was  estimated  as  a  function  of 

  using  the  same  emprical 

where 

 = 0.17 is the estimated standard error of 

 from the SAM model. 

Table  3.2  Reference  points provided  by GFCM,  GFCM  biomass reference  points relative to SSB0  =119,780 
(t) derived from the Beverton & Holt SSR (SSR-BH) and the MSY-based reference FMSY and BMSY associated 
with SSR-BH. * Estimate of BMSY via forward projection (100 years) given the GFCM FMSY = 0.81 and the B&H 
SRR. **is the BMSY estimate that is implicit to the B&H SRR 

GFCM 

0.81 

Ratio 
SSB0  SSR-BH 

- 

0.96 

16200 

0.14 

21400 

0.18 

- 

- 

  40125.4*  0.33  33366** 

Total  landings  comprise  reported  catches  from  Albania,  Slovenia,  Croatia, 
Montenegro,  Italy  with  discards  assumed  to  be  negotiable.  Catch-at-length  for 
each  country  and  year  were  transformed  into  catch-at-age  by  applying  the 
corresponding Age-Length Keys (ALKs). The catch-at-age matrices were merged 
into a single catch-at-age matrix and raised by SOP-correction to the total catch. 
Catches-at-age  are  dominated  by  age-1  (Figure  2.1),  while  age-2+  fish  were 
assigned to age-2 plus group as they represented less than 1% in numbers. 

34 

34 

 
 
 
 
 
 
 
 
 
 
 
 
Figure 3.1 Input catch-at-age matrix for the anchovy SAM base-case model. 

Figure 3.2 Time series of survey abundance indices used for tuning of the anchovy SAM base-case model. 

35 

35 

 
 
 
To  propagate  model  estimation  error  into  the  MSE  framework,  a  customized 
bootstrap procedure with 1000 iterations was run for the SAM base-case model. 
The  bootstrap  iterations  were  checked  for  convergence  and  the  first  500 
converged  iterations  were  retained  to  condition  the  anchovy  reference  OM.  The 
median, 10th, 25th, 75th and 90th percentiles from the bootstrap are shown for the 

estimated  trajectories  of  recruitment  (Rec),  SSB,  catch  (t)  and 
  in  Figure  3.2. 
Recruitment  and  SSB  show  a  pronounced  decline  since  2005  to  the  current  low 
levels.  From  2000  to  2006,  catches  increased  from  38,000  t  to  71,000  t.  That 
was followed by a decrease to 28,000 t observed in 2019. Fishing mortality has 
consistently  increased  between  2000  and  2015,  after  which  it  remained  more 
than 50% higher than in the early 2000s (Figure 2.3).  

Figure 3.3 SAM base-case model for anchovy run with 1000 bootrap iterations to depict estimation error 
uncertainty shown as 10th, 25th, 75th and 90th percentiles around the median. 

 As an important component of the OM conditioning, an implicit assumption about 
the stock recruitment relationship (SRR) is required to project the stock dynamics 
into  the  future.  Initially,  alternative  SRRs  were  explored.  These  included  the 
Beverton  &  Holt  (SRR-BH),  Ricker  (SRR-Ri)  and  segmented  regressions  (SRR-

SR),  where  SRR-SR  were  fitted  unconditioned  and  by  fixing  break-point  at 
(Figure 3.4). 

36 

36 

 
 
 
 
 
 
 
The results suggested that the SSR-BH (AIC = -91.1) provided the best fit to the 

SAM  model  estimates  of 
  (Figure  3.5),  followed  by  the 
SRR-Ri (AIC =  -89.3). The unconditioned SSR-SR also  provided a reasonable  fit 
to  the  data  (AIC  =  -77.4)  and,  together  with  the  SRR-Ri,  may  therefore 
represent  an  good  candidate  SRR  for  alternative  OM  conditioning.  The  SSR-SR 

  and  recruitment 

fitted  by  fixing  its  break-point  to 

  (AIC  =  -57.9)  performed  poorest  in 

describing the estimated 

 and recruitment   pattern (Figure 3.4). 

Figure 3.5 Stock-recruitment model fits to the anchovy SAM base-case model estimates of 
recruitment, shown for the Beverton & Holt model (bh), an unconditioned segmented regression (sr) and a 

 and 

segmented regression where the break point was fixed to 

 reference point (dashed vertical line) 

The  recruitment  residuals  were  narrowly  distributed  around  expected  BH-SRR, 

which  results  in  a  very  small  estimate  of  recruitment  variation 
  =  0.08.    This 
low  estimate  is  inconsistent  with  results  from  comprehensive  meta-analysis  for 
small  pelagic  species,  including  anchovy  (Thorson  et  al.  2014),    and  was 
therefore  deemed  to  be  likely  an  artefact  of  using  a  static  age-length  key  and 
smoothing  by  the  SAM  model  resulting  in  imperfect  model  estimation  of 
recruitment  strength.  To  obtain  an  objective  and  biologically  more  plausible  OM 

 (Figure 3.6) was generated using the 
input for  
hierarchical  taxonomic  life  history  model  FishLife  (Thorson  2020).    For  the 

, a predictive distribution for  

anchovy reference OM, the mode of 
 = 0.34 of the predictive distribution was 
selected  to  represent  a  compromise  between  low  SAM  model  based  estimate  of  

 = 0.08 and the expected median from FishLife of 

 = 0.45 (Figure 3.5). For 
this  initial  analysis,  recruitment  deviations  were  assumed  to  be  randomly 

37 

37 

 
 
 
 
distributed,  but  further  should  explore  the  effect  recruit  autocorrelation,  e.g. 

based on the estimate of    = 0.456 by Thorson et al. (2014). 

Figure 3.6 Fit of the Beverton & Holt stock recruitment model to SAM base-case model estimates of SSB 
and recruitment, including basic residual diagnostics. 

38 

38 

 
 
Figure 3.6 Probability density distribution (pdf) of the expected recruitment variation 
hierarchical multivariate model FishLife (Thorson, 2020). The plot also highlights the SSR estimate of 

 predicted from 

 (dotted line), the mode 

 (solid line) and median 

 (dashed line). 

Compared  to  the  GFCM  benchmark  reference  points,  the  BH-SRR  based  estimate  of  FMSY  (0.96) 
and  BMSY  (33366)  were  slightly  higher  and  lower,  respectively  (Figure.  3.7,  Table  3.2).  This 
confirms  that  Patterson  estimator  of  FMSY  is  precautionary  with  respect  to  the  conditioned 
reference  OM  for  anchovy.  The  very  high  F0.1  >  2  indicates  that  the  stock  is  highly  resilient  to 
growth overfishing and the main source of vulnerability is associated with recruitment overfishing.  

39 

39 

 
 
 
 
Figure 3.7 Estimated biological reference points based, indicating the benchmark estimate for FMSY = 0.81. 

The stock status relative to the GFCM benchmarks shows that the current SSB2019 
falls between Blim and Bpa corresponding to 14.3 % of the unfished SSB0, whereas 
current  fishing  mortality  F2019  exceeds  sustainable  levels  at  FMSY  by  more  than 
50%. Blim  corresponds to just 14% of SSB0 and BMSY at 33% of SSB0 (Table 2.2). 
An  emergent  property  of  the  low  Blim  is  a  small  margin  between  Blim  and  Bpa 
relative to an unfished stock (Figure 3.8). 

40 

40 

 
 
 
 
 
 
Figure 3.8Anchovy stock status based on the SAM base-case model relative to estimated benchmarks for 
Blim⁡  (red) , Bpa (orange) and BMSY (green) and FMSY (dotted). 

41 

41 

 
 
Tuning of harvest control rules (ToR 2.4.2 - results) 

For  Anchovy,  tuning  of  the  selected  candidate  HCRs  was  conducted  with  full 
feedback  control  loop  simulations,  which  there  entailed  an  update  of  the  SAM 
estimation  model  at  each  time  step.  The  ‘hard’  tuning  criteria  was  agreed  to 
achieve a 5% probability that SSB falls below Blim over the period 2024-2044. If 
this  criteria  was  met  additional  short-term  and  long-term  trade-offs  were 
evaluated,  including  the  average  catch  performance,  AAV  and  the  probability  of 
fishing closures.  

1.1.1 

Fixed target fishing mortality (fixedF) 

Different  fixed  fishing  mortalities  were  simulated  over  the  period  2024-2044  . 
The  catch  (C)  was  estimated  from  the  fishing  mortality  (F)  using  the  Baranov 
catch  equation  and  the  projected  SSB  for  the  assessment  year  (see  Methods 
section).  FMSY  multipliers  were  used  to  tune  the  target  fishing  mortality,  with 
multipliers  ranging  from  0.6  to  1  in  steps  of  0.1  (i.e.  denoted  as  0.6Fmsy  – 
1Fmsy hereafter). The Fmsy of reference for the tuning of the HCRs was the BH-
SRR based estimate of FMSY shown in the previous section (Figure 3.7), which was 
slightly  higher  than  the  GFCM  benchmark  defined  Fmsy.  The  HCR  initiated  in 
2024,  just  after  initial  scenarios  corresponding  to  the  intermediate  years  catch 
reductions  being:  5%,  10%  or  15%  in  2022  and  2023  (Figure  3.9).  The  long-
term effects of the reductions are minimal. See the section on intermediate year 
catch reductions for more details 

Although  fishing  at  0.8Fmsy  to  1Fmsy    leads  to  the  slightly  larger  catches  on 
average,  such  strategies  would  fail  to  meet  the  risk  criteria  of  SSB  decreasing 
below Blim by the 5% probability and are also associated with increasingly higher 
AAV  and  probabilities  of  closure  of  the  fishery  in  the  short  term  as  well  as  at 
equilibrium (Figure 3.10). Acceptable risk levels could only be achieved by fishing 
at 0.7FMSY or lower. , while F target levels of 0.8Fmsy resulted in probabilities of 
dropping below Blim exceeding 5% (Figure 3.11). As the target fishing mortality 
is  translated  and  implemented  via  a  TAC,  fishing  at  the  limit  of  FMSY  leads  to 
overshooting  the  target  as  the  implementation  of  the  TAC  is  based  on  the 
previous  year’s  SSB  and  thus  F  does  exceed  FMSY  regularly.  This  is  particularly 
evident  when  the  target  fishing  mortality  is  equal  or  greater  than  0.8Fmsy. 
Furthermore, the probability of shutting down the fishery due to the  SSB falling 
below Blim increases once the target F is 0.8 or larger (Figure3.11). 

Due to failure of meeting the Blim risk criterium as the management goal for fixed 
fishing mortality targets greater than 0.7Fmsy, F = 0.7Fmsy was retained for the 
robustness tests. 

42 

42 

 
 
 
 
 
 
Figure  3.9  Summary  of  the  MSE  tuning  runs  for  different  levels  of  fixed  fishing  mortalities  and  the  three 
intermediate period catch reductions (of 5% --int 5%-. 10%-int-10%- and 15%-int 15%). The uncertainty 
envelops are 95%. 

43 

43 

 
 
Figure 3.10 Annual probability of closure for all the tuning runs for the fixedF HCR. It becomes evident that 
the levels of intermediate catch reductions are having an effect in the long term. The shutdown probability is 
affected by the intermediate catch reduction levels in the short to medium term (prior to 2029). However, 
for F levels equal or less than 0.7Fmsy the differences are negligible. 

44 

44 

 
 
 
 
 
 
Figure3.11 Performance statistics for entire (2024-2044) tuning runs for fixedF HCR. 

1.1.2 

Fixed target fishing mortality (fixedF) with annual catch variation 

limits 

This  variation  of  the  fixedF  HCR  implements  the  addition  of  interannual  catch 
variation  limits  (TOR  2.4.3).  These  limits  are  intended  to  achieve  high  catch 
stability for the fishing industry and have the effect of avoiding overshoots when 
good recruitment is incoming. The level of maximum annual catch variation were 
set to 20% reduction and 10% increase.  This dampening effect can be found in 
the reduced average catches and the reduced probability  of closures (Figs  3.12, 
3.13 and 3.14).  

The fixedF HCR with annual catch variation limits effectively reduced the AAV by 
about half. However, similar to the fixedF HCR, target Fs of higher than 0.7Fmsy 
are exceeding the 5% threshold of probability of falling below Blim. As no major 
difference between the two HCRs was found with the exception of smaller AAV, it 
was decided to not continue with this version of the HCR for the robustness runs. 

45 

45 

 
 
 
 
 
 
Figure 3.12 Summary of the MSE tuning runs for different levels of fixed fishing mortalities with limits for 
interannual  catch  variations  (20%  down  and  10% up)  and  the  three  intermediate period  catch  reductions. 
The uncertainty envelops are 95%. 

46 

46 

 
 
Figure 2: Annual probability of closure for all the tuning runs for the fixedF HCR with limits for interannual 
catch  variations  (20%  down  and  10%  up).  It  becomes  evident  that  the  levels  of  intermediate  catch 
reductions are  having  an  effect in the long term.  The shutdown probability is affected by the intermediate 
catch reduction levels in the short to medium turn (prior to 2029). However, for F levels equal or less than 
0.7Fmsy the differences are negligible 

47 

47 

 
 
Figure  3.14  Performance  statistics  for  entire  (2024-2044)  tuning  runs  for  fixedF  HCR  with  limits  for 
interannual catch variations (20% down and 10% up). 

1.1.3 

Bay of Biscay style (BoB) 

The  tuning  runs  for  the  Bay  of  Biscay  (BoB)  s  type  HCR  explored  three 
parameters:  the  two  levels  of  TAC  (TACmin  and  TACmax)  and  the  slope  .  Three 
combinations  for  TACmin  and  TACmax  were  chosen  (Table  3.3),  each  one  being 
tuned  with  slopes  ranging  from  0.6  to  1.  Little  contrast  between  the  different 
tuning  runs  can  be  noticed  (Figure  3.15).  The  lack  of  contrast  continuous 
throughout the annual probabilities of shutting down the fisheries (Figure  3.16). 
Differences  between  the  tuning  runs  become  apparent  when  examining  the 
performance statistics (Figure 3.17).  

Average  catches  are  highest  for  BoB2040  and  lowest  for  BoB_1535;  increasing 
the  slopes  does  increase  the  catches,  but  to  a  lesser  extent  than  changing 
TACmin/max.  The  fishing  mortality  remains  below  FMSY  for  all  tuning  runs. 
Interestingly,  the  probability  of  SSB  falling  below  Blim  is  very  low  for  all  tuning 
runs,  which  is,  in  fact,  reflecting  the  low  probability  of  shutting  down  (Figure 

48 

48 

 
 
 
 
 
 
3.17)  as  this  is  measured  using  SSB<Blim  as  a  proxy.  The  AAV  for  BoB2040  is 
lower than BoB_1540 in Figure 3.17, however this is an artifact, as the AAV plots 
eliminate all values larger than 1. It can be seen in Table 3.4 that BoB_2040 has 
a rather large number of NAs in the AAV statistics. These NAs were generated by 
the  AAV  calculations  when  a  catch  was  set  to  0t  one  year  and  followed  by  a 
reopening of the fisheries. 

The  preferred  formulation  to  take  forward  to  robustness  testing  was  not 
immediately apparent, as each BoB formulation passed the required performance 
statistics  (SSB/Blim,  F/FMSY).  It  was  decided  to  take  all  three  formulations 
forward: BoB_1535 with slope 0.8, BoB_1545 with slope 0.8 and BoB_2040 with 
slope 0.8. 

Table 3.3: Pairs of TACmin and TACmax used for the tuning of the HCR slopes. 

TACmin  
TACmax 

BoB_1535  BoB_1540  BoB_2040 
15000t 
40000t 

20000t 
40000t 

15000t 
35000t 

49 

49 

 
 
  
 
Figure  3.15:  Summary  of  the  MSE  tuning  runs  of  the  BoB  HCRs  for  three  sets  of  TACs.  The  tuning 
parameter  was  the  slope  ranging  from  0.6  to  1.  The  intermediate  years  catch  reduction  was  10%.  The 
uncertainty envelops are 95%. Note the change in scales of the SSB plot for the BoB_2040 HCR 

50 

50 

 
 
 
Figure 3.16 Annual probability of shut down of the fisheries, measured by proxy in the case of SSB > Blim. 
The slope seems to have a large influence, but overall the annual probability remained very low (p <0.05). 
An exception was the starting year (2024), where p >0.1, however, the HCR has little influence on this as 
the  level  of  SSB  used  to  determine  the  TAC  is  set  by  the  deterministic  reduction  of  catches  during  the 
intermediate years.  

51 

51 

 
 
 
 
Figure 3.17 Performance statistics for entire (2024-2044) tuning runs for BoB HCR. It should be noted that 
all HCR tuning runs pass the p(F/Fmsy < 1) > .95 and p(SSB/Blim > 1) > .95 criteria. The averages catch 
levels are driven by the TACmin and TACmax combinations, with 2040 producing the highest catch levels. An 
interesting phenomenon is that for the average annual catch variations (AAV) the 2040 formulation seems to 
have the lowest values, however, this is due  to  the  nature that the  AAV is calculated:  any values above 1 
were set to NA, as they had an overly large influence. These large values were generated when the TAC was 
set to 0 due to the SSB dropping below the Btrig1 with the TAC of following year being over 20kt. As can be 
seen in Table (3.4) this happened quite often for the BoB_2040 HCR.  

Table  3.4:  Total  number  of  NAs  produced  by  the  AAV  performance  statistic 
indicating the number of times the fishery was reopend after a 0 TAC allocation. 
In contradiction to Figure  3.16, the statistics given in this table are calculated 
as a sum across all years and iterations, rather than across iterations per year. 
Thus, the numbers presented can only be compared within this table.  

Slope 

Time period 
24-28  30-35  38-43  24-44 

0.6 
0.7 
0.8 
0.9 

30 
28 
35 
40 

15 
26 
30 
35 

26 
32 
33 
47 

81 
103 
121 
147 

5
3
5
1
_
B
o
B

52 

52 

 
 
 
 
 
 
  
  
 
0
4
5
1
_
B
o
B

0
4
0
2
_
B
o
B

1 
0.6 
0.7 
0.8 
0.9 
1 
0.6 
0.7 
0.8 
0.9 
1 

31 
36 
37 
30 
36 
34 
64 
67 
61 
67 
77 

44 
25 
38 
42 
61 
70 
69 
80 
104 
112 
131 

53 
32 
51 
60 
70 
82 
74 
97 
116 
131 
147 

157 
110 
150 
166 
208 
237 
244 
288 
335 
362 
402 

Table  3.5:  Probability  of  shutting  down  the  fishery  as  measured  by  the  proxy 
SSB  <  Blim.  This  probability  was  calculated  across  the  entire  run  and  all 
iterations. 

Slope 
0.6 
0.7 
0.8 
0.9 
1 

15-35 
0.011 
0.013 
0.015 
0.016 
0.017 

15-40 
0.014 
0.017 
0.020 
0.023 
0.026 

20-40 
0.023 
0.028 
0.032 
0.037 
0.042 

1.1.4 

Biomass escapement (Besc) 

The  biomass  escapement  HCR  has  up  to  two  parameters:  escapement  biomass 
and  the  maximum  fishing  mortality  (Fcap).  We  tuned  both  by  keeping  the 
respective other parameter steady. In the case of tuning the Fcap, the Besc was 
set to Bpa. Whereas for the Besc tuning we used 2 levels for Fcap: Fmsy and 0.7 
Fmsy.  The  choice  of  Fcap  was  driven  by  the  fact  that  fishing  at  Fmsy  leads  to 
unstable stock dynamics as shown during the tuning of the fixedF HCR. Using the 
insights gained during fixedF HCR tuning, we also chose a level of Fcap that we 
knew should pass the management goals.  

The  Besc  HCR  was  tuned  under  all  three  intermediate  years  catch  reduction 
levels (5%,10%, 15%). 

1.1.5 

Tuning for Bpa, with different Fcaps. 

53 

53 

 
 
 
 
 
 
 
 
 
 
 
Tuning runs were performed for Fcap ranging from 0.6Fmsy to 1Fmsy. The effect 
of  the  intermediate  year  catch  reduction  levels  was  minimal.  As  Fcap  was 
increased,  the  levels  of  average  catch  increased  as  well,  although  the  catch 
increasesd after Fcap = 0.8Fmsy were relatively small (Figure 3.18-3.20). If Fcap 
was  increased  beyond  0.8*xFmsy,  the  HCR  started  failing  the  requirement  that 
p(SSB  <  Blim)  <  0.05  and  thus  were  discarded  from  further  analysis  (Figure 
3.20). 

54 

54 

 
 
Figure 3.18 Summary of the MSE tuning runs of the Besc HCRs using different levels of Fcap. The Fcap were 
expressed  as  fractions  of  Fmsy,  ranging  from  0.6  to  1.  The  escapement  biomass  level  was  set  to  Bpa.  The 
titles indicate the different intermediate  year’s catch reductions: 5%, 10% and 15%. Uncertainty envelops 
are 95%. 

55 

55 

 
 
Figure 3.19 Annual probability of closure for all the tuning runs for the Besc HCRs. It becomes evident that 
the  level  of  intermediate  catch  reductions  are  not  having  an  effect  in  the  long  term.  The  shutdown 
probability is affected by the intermediate catch reduction levels in the short to medium turn (prior to 2029). 
The bigger effect on the shutdown probability are the level of Fcap (expressed as fractions of Fmsy). The only 
two Fcaps resulting in probabilities below 5% in the long term are 0.6Fmsy and 0.7Fmsy. 

56 

56 

 
 
 
 
 
Figure  3  Performance  statistics  for  the  Besc  HCR  tuning  runs.  The  fixed  parameter  was  the  escapement 
biomass level (Bpa). Fcap up to 0.8Fmsy were deemed to pass the required performance statistics. 

1.1.6 

Tuning Besc, with fixed Fcap 

The Tuning runs conducted were first performed using the shortcut approach as 
laid  out  in  the  preliminary  report  (STECF  2021).  The  escapement  biomass  was 
tuned  for  two  fixed  Fcap  values  (0.7Fmsy  and  Fmsy)  with  the  escapement 
biomass  ranging  from  1Blim  to  2Blim  in  increments  of  0.2  (notice  that  adopted 
Bpa is ~1.4xBlim). The effect of the different levels of Besc were relatively small 
and are indistinguishable on Figure 3.21. Contrast could be found in the different 
levels  of  Fcap  with  Fcap  =  Fmsy  performing  much  poorer  as  expected.  In 
particular the annual probability of shutting down the fisheries and p(SSB < Blim) 
show that passing or failing depends on Fcap and not on the escapement biomass 
level  (Figure  3.22).  In  view  of  the  absence  of  differences  between  the  biomass 
levels, these tuning runs were not considered any further. It is important to note 
that  the  absence  of  contrast  for  different  levels  of  escapement  biomass  within 
each  Fcap  also  meant  that  no  full-feedback  runs  were  performed.  This  means 
that  the  results  are  quantitatively  not  comparable  to  the  previous  section,  e.g. 
the  average  catch  of  35kt  for  Fcap  cannot  be  compared  to  the  30kt  average 
catches  of  the  Fcap  tuning  runs.  The  quantitative  differences  are  due  to  the 
short-cut  method  rather  a  result  in  itself  -  in  STECF  2021  the  average  catch 

57 

57 

 
 
 
 
levels for Besc tuning runs were around 35kt for Fcap=0.7Fmsy and Besc = Bpa 
(Figure 2.14, p50 STECF 2021).     

Figure 3.21 Tuning runs for different levels of escapement biomass. In contradiction to the previous tuning 
runs  for  the  Besc  HCR,  the  levels  of  Fcap  were  kept  constant  at  2  levels  (0.7xFmsy,  Fmsy)  with  the 
escapement biomass level varying from 1xBlim to 2xBlim, with Bpa ~1.6xBlim. It can be seen that at Fcap = 
Fmsy, the confidence intervals cut across the Blim threshold not only during the initial phase, but also during 

58 

58 

 
 
 
the equilibrium phase late in the runs. This is reflected in  the performance statistics below. The confidence 
intervals are 95%. 

Figure  3.22  Annual  probability  of  shutdown  of  the  biomass  escapement  tuning  runs.  The  probability  of 
shutting  down  was  measured  by  the  proxy  of  SSB  <  Blim.  A  clearly  binary  behaviour  can  be  detected, 
grouping the different levels of Fcap. 

59 

59 

 
 
 
Figure  3.23  Performance  of  the  biomass  escapement  tuning  runs.  The  binary  behviour  noticed  before, 
continues  throughout  the  performance  statistics,  with  the  levels  of  Fcap  dominating  the  behaviour  of  the 
runs. Fcap = Fmsy performs clearly less well, than Fcap = 0.7Fmsy. The level of Besc has a larger effect at 
the higher Fcap, especially in the p(SSB>Blim), p(SSB>Bmsy) and AAV indicators. 

60 

60 

 
 
 
 
 
Figure 3.24 Comparison of the different HCRs taken forward to the robustness test. The intermediate year's 
annual  reduction  is  10%.  As  the  Besc  HCR  with  Fcap  =  0.8Fmsy  passes  and  fails  the  risk  check  for  the 
biomass level (Figure 3.20) depending on the level of intermediate year’s annual reductions, it was decided 
to use 0.8Fmsy rather than 0.7Fmsy as Fcap in the robustness tests. 

Robustness Tests of HCRs 

From the three HCRs we submitted one parameterisation of each fixedF and Besc 
as  well  three  parameterisations  of  the  BoB  HCR.  For  fixedF  and  Besc  the 
parameterisations  chosen  were  the  ones  maximising  catch,  whilst  passing  the 
required checks.  

As all BoB tuning runs passed the checks, we chose formulations that had a good 
trade-off between NAs produced within the AAV calculation, as well as maximised 
catches (keeping in mind that an NA in the AAV calculation means a closure and 
re-opening of the fishery). The following are the parameterisations chosen: 

fixedF with Ftar = 0.7Fmsy 

 
  Besc relative to Bpa with Fcap = 0.8Fmsy 
  BoB_1535 with slope = 0.8 
  BoB_1540 with slope = 0.8 
  BoB_2040 with slope = 0.8 

61 

61 

 
 
 
 
Robustness  tests  are  realisations  of  so  called  “what  if”  scenarios,  e.g.  what  if 
recruitment  failed  for  a  while?  They  are  designed  to  see  if  the  HCRs  are  robust 
against extreme conditions and to see whether stock management would still be 
safe following their catch recommendations.  

We also decided to include our only other tuned operating model in this section, 
as  it  functions  in  our  case  as  a  robustness  test.  This  second  operating  model 
changes  one  assumption  from  the  base  case:  Instead  of  having  a  natural 
mortality at age vector (Table 3.1), it assumes the same natural mortality for all 
age classes (constant at 0.9). As the operating  models are all tested against all 
the  robustness  tests,  also  the  second  operating  model  was  used  to  run  these 
using the same HCR formulations as the base case. 

Besides  this,  there  were  three  robustness  tests:  recruitment  failure  (LowRec), 
increased  recruitment  variation  (hiVar),  and  autocorrelation  within  recruitment 
(ar1Rec);  all  were  run  for  both  OMs.  (Fig  16-17).  These  robustness  tests  were 
set up as follows: 

  recruitment failure (LowRec) corresponds to a forcing for three consecutive 
years (from 2028 to 2030) of a 35% reduction of the expected recruitment 
from the BH-SR relationship (by a application of 0.65 multiplier).  

  Increased recruitment variation (hiVar), passing from the modal to the 

mean variability of recruitment (

around expectations according to the 

BH-SRR, i.e. adopting the mean  = 0.49 – value arising from the 

hierarchical multivariate model FishLife (Thorson, 2020). 

  autocorrelation within recruitment (ar1Rec); such value was set to 0.456 

based on the estimate of  ρ = 0.456 by Thorson et al. (2014). 

The  performance  statistics  reveal  the  subtle  differences  between  the  different 
OMs.  The  differences  between  the  fixedF  and  besc  HCR  are  very  small  (Figure 
3.25  ),  where  as  the  differences  are  a  little  more  marked  for  the  BoB  HCRs 
(Figure  3.25).  The  HCRs  performed  slightly  better  in  the  OM  2  reference  runs, 
which  indicates  that  the  HCR  are  able  to  deal  with  minor  M  mis-specifications 
(Figure 3.26). 

Apart from BoB_1535 nearly all HCRs failed the robustness tests (Fig 3.27,3.28). 
The  BoB_1540  formulation  passed  the  lowRec  robustness  test,  but  none  of  the 
others (apart from the reference runs). In terms of risks of closures BoB_2040 is 
the  riskiest  one  in  the  short  term  (reaching  a  probability  of  about  0.1)  (Figure 
3.30).  The  aggregated  performance  statistics  clearly  show  that  the  only 
candidate  HCR  perform  adequately  across  the  entire  range  of  runs  is  BoB_1535 
(Figure 3.29), closely followed by BoB_1540. 

62 

62 

 
 
Figure  4  Robustness  and  reference  runs  for  the  base  case  operating  model.  In  the  first  row  are  the 
reference runs, second row the low-recruitment test, third row contains the higher autocorrelation runs, and 
in the fourth row the high recruitment variation runs are found. 

63 

63 

 
 
 
 
Figure  3.26  Robustness  and  reference  runs  for  the  constant  natural  mortality  (M=  0.9)  operating  model. 
The tuned operating model assumes a constant natural mortality across all age classes. In the first row are 
the reference runs, second row the low-recruitment test, third row contains the higher autocorrelation runs, 
and in the fourth row the high recruitment variation runs are found. The uncertainty envelopes are 95%. 

64 

64 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 3.27 Performance statistics for fixedF and Besc HCR and OM for robustness tests. 

Figure 3.28 Performance statistics for BoB HCRs and both OMs for the robustness tests. 

65 

65 

 
 
 
 
 
 
 
Figure 3.29 Aggregated performance statistics of the robustness runs. As the robustness runs across both 
OMs  were  balanced,  ie  three  robustness  runs  and  one  reference  run  for  each  OM,  it  was  possible  to 
aggregate the results by HCR. This allows the assessment of the overall performance of each HCR. The only 
HCR  that  passed  the  risk  criteria  of  p(SSB  >  Blim)  >  0.95  was  the  BoB_1535  with  a  slope  0.8,  although 
arguments can be made) that the BoB_1540 is an acceptable candidate albeit with a little greater risk. 

66 

66 

 
 
 
Table  3.6  Median  performance  statistics  of  all  robustness  and  reference  runs. 
Grey areas indicate OM 2 (M = 0.9) 

Run 
besc_ref 
besc_ar1Rec 
besc_hiVar 
besc_lowRec 
besc_refM09 
besc_ar1RecM09 
besc_hiVarM09 
besc_lowRecM09 
fixedF_ref 
fixedF_ar1Rec 
fixedF_hiVar 
fixedF_lowRec 
fixedF_refM09 
fixedF_ar1RecM09 
fixedF_hiVarM09 
fixedF_lowRecM09 
BoB_ref_1535 
BoB_ar1Rec_1535 
BoB_hiVar_1535 
BoB_lowRec_1535 
BoB_refM09_1535 
BoB_ar1RecM09_1535 
BoB_hiVarM09_1535 
BoB_lowRecM09_1535 
BoB_ref_1540 
BoB_ar1Rec_1540 
BoB_hiVar_1540 
BoB_lowRec_1540 
BoB_refM09_1540 
BoB_ar1RecM09_1540 
BoB_hiVarM09_1540 
BoB_lowRecM09_1540 
BoB_ref_2040 
BoB_ar1Rec_2040 
BoB_hiVar_2040 
BoB_lowRec_2040 
BoB_refM09_2040 
BoB_ar1RecM09_2040 
BoB_hiVarM09_2040 
BoB_lowRecM09_2040 

R
C
H
c
s
e
B

R
C
H
F
d
e
x
i
f

R
C
H
5
3
5
1
_
B
o
B

R
C
H
0
4
5
1
_
B
o
B

R
C
H
0
4
0
2
_
B
o
B

Catch 
31738 
28965 
29399 
28497 
31665 
29393 
29636 
28734 
29718 
26878 
27440 
26870 
29666 
27485 
27636 
27189 
29518 
27004 
27166 
27443 
29749 
27431 
27559 
27788 
30675 
28116 
28203 
28136 
31083 
28596 
28867 
28818 
32309 
28995 
29422 
29483 
32649 
29687 
29897 
30008 

67 

F/FMSY 
0.66 
0.72 
0.7 
0.69 
0.65 
0.7 
0.68 
0.68 
0.58 
0.66 
0.64 
0.6 
0.58 
0.63 
0.6 
0.59 
0.54 
0.57 
0.54 
0.58 
0.54 
0.57 
0.55 
0.58 
0.59 
0.61 
0.58 
0.63 
0.6 
0.62 
0.59 
0.63 
0.67 
0.71 
0.69 
0.74 
0.68 
0.72 
0.68 
0.73 

P(SSB>Blim)  P(SSB>Bmsy) 

0.96 
0.88 
0.9 
0.92 
0.97 
0.92 
0.92 
0.94 
0.96 
0.85 
0.88 
0.92 
0.97 
0.9 
0.92 
0.95 
0.98 
0.94 
0.95 
0.96 
0.99 
0.95 
0.96 
0.97 
0.98 
0.93 
0.94 
0.95 
0.98 
0.94 
0.95 
0.96 
0.96 
0.9 
0.91 
0.92 
0.97 
0.92 
0.93 
0.93 

0.76 
0.63 
0.65 
0.69 
0.77 
0.66 
0.67 
0.7 
0.81 
0.63 
0.66 
0.72 
0.82 
0.67 
0.7 
0.75 
0.84 
0.71 
0.73 
0.75 
0.84 
0.72 
0.74 
0.75 
0.81 
0.68 
0.71 
0.72 
0.8 
0.69 
0.71 
0.72 
0.74 
0.62 
0.65 
0.64 
0.74 
0.63 
0.66 
0.65 

AAV 
0.22 
0.24 
0.32 
0.24 
0.24 
0.26 
0.33 
0.26 
0.21 
0.24 
0.29 
0.22 
0.22 
0.24 
0.3 
0.24 
0.2 
0.21 
0.25 
0.23 
0.22 
0.22 
0.26 
0.24 
0.24 
0.24 
0.29 
0.26 
0.26 
0.25 
0.31 
0.28 
0.2 
0.19 
0.23 
0.21 
0.22 
0.2 
0.24 
0.23 

SSB/Bmsy 
1.45 
1.34 
1.42 
1.35 
1.46 
1.37 
1.43 
1.37 
1.57 
1.39 
1.48 
1.43 
1.57 
1.41 
1.5 
1.45 
1.61 
1.48 
1.56 
1.46 
1.59 
1.49 
1.56 
1.45 
1.54 
1.44 
1.51 
1.4 
1.51 
1.43 
1.49 
1.39 
1.43 
1.32 
1.41 
1.28 
1.4 
1.31 
1.39 
1.28 

67 

 
 
 
  
 
 
 
 
 
 
 
 
 
 
Figure 3.29 The median probability of shutting down aggregated by HCR (left) with uncertainty envelopes 
(right). The spike of the starting in 2029 is a result from the low-recruitment run. The uncertainty envelopes 
are 95%. 

Intermediate years’ catch reductions 

The levels of intermediate catch reductions do not affect the long-term behaviour 
of  the  anchovy  stock.  As  can  be  seen  in  the  description  of  the  results  of  the 
tuning runs, the effects of the catch reductions in the long term are negligeable 
(see  for  instance  Figure  3.14  for  the  constant  fishing  mortality  HCR  or  Figure 
3.20  for  the  Besc  HCR)).  No  preference  can  be  given  to  any  of  the  reduction 
levels,  although  some  caution  needs  to  be  taken  as  the  reductions  do  have  an 
effect in the short term, especially in the first two-three years of the HCR kicking 
in (Figure 3.30).  

The main issues arising from the prescribed catch reductions are the inability to 
adjust  catch  levels  according  to  the  stock  biomass  level,  this  does  lead  to 
occasional  sub-optimal  catch  utilisation  over  that  period  or  to  higher  risks  of 
stock depletion. The latter will be the case when the stock is on a trajectory that 
would  require  more  drastic  catch  reductions  than  prescribed  in  the  first  three 
years.  The  iterations  that  are  on  such  unfortunate  trajectory  do  end  up  in 
fisheries  closure  in  the  early  stages  of  the  HCR  implementation  –  regardless  of 
the  HCR  (Figure  3.31).  These  trajectories  need  to  be  covered  by  extra-ordinary 

68 

68 

 
 
 
 
 
circumstance management actions in order to prevent the stock from closing for 
the fisheries or in extremis crashing completely.  

A  further  complication  is  that  the  2  year  lag  of  the  HCR  (1  year  data,  1  year 
management)  means  that  the  HCR  will  initialise  with  data  from  2022  to  fix  the 
TAC  for  2024.  This  means  that  the  iterations  where  the  SSB  is  sub-optimal 
initialise  with  a  fisheries  closure.  It  should  be  recommended  that  such  kind  of 
extra-ordinary  circumstances  should  be  monitored  throughout  the  intermediate 
years.  Signs  that  urgent  management  action  is  required  would  be  SSB  slipping 
below Blim or F increasing dramatically despite of catch reductions. 

69 

69 

 
 
Figure 3.30 Short term effects of intermediate catch reduction levels. It can be seen that in all three cases 
the uncertainty envelopes drop below Blim, resulting in 0t catches. 

70 

70 

 
 
 
Figure  3.31  Median  trajectories  of  iterations  split  into  two  groups  due  to  the  intermediate  years'  catch 
reduction. Success denotes an iteration which never dipped below Blim during the intermediate period 2022-
2023,, failure shows iterations that did. Only catch and SSB are shown as the catch is a direct result of the 
level of SSB, designated via the HCR. The uncertainty envelopes are 95%. 

71 

71 

 
 
 
1.1.7 

Sensitivity run extending catch reductions into 2024 

A  sensitivity  run  was  performed  testing  a  continued  descriptive  catch  reduction 
into the year 2025. This run shows that in 2024 some iterations facing a serious 
(risky) situation appear as some of the iterations struggle to recover. This can be 
seen  in  the  explosion  of  F  and  the  number  of  iterations  where  the  SSB  is 
dropping lower than Blim (Figure 3.32). 

Comparing the performance of the rules for a 5% or 15% continuous decreasing 
of catches over the intermediate period, it can be seen that for the reduction of 
15%  the  Besc  and  BoB  HCRs  keeps  risks  to  Blim  below  0.05  wilst  fixedF  fails. 
However  the  BoB  is  the  only  HCR  that  can  successfully  perform  also  under  the 
5% reduction regime (Figure 3.33).   

Figure 3.32 Levels of proposed catch reduction in 2022 to 2024 with the HCR initialising in 2025. Although 
the catch reductions are deterministic, their effects are no the same for all iterations. This stems from the 
state  of  the  stock  biomass:  if  the  stock  is  too  small  to  support  the  levels  of  catches,  F  will  increase 
dramatically, SSB could dip under. 

72 

72 

 
 
 
 
Figure  3.33  Short  term  performance  statistics  (only  data  between  2025  and  2030  is  considered)  of  the 
sensitivity run which extends the intermediate years catch reduction to 2024 and initialises the HCR in 2025. 
Only two levels of reduction are shown (5% and 15%) as the 10% lies somewhere between the two. Both 
the sensitivity runs are shown (denoted by the appendix “_25”) and the reference runs (denoted by “_ref”). 
The  reduction  of  15%  shows  to  work  throughout  the  Besc  and  BoB  HCRs,  whereas  fixedF  fails  in  both 
reduction levels in the short term. BoB is the only HCR that can successfully perform under both reduction 
regimes. The HCR formulations are the preferred versions resulting from the tuning runs. 

Timing of achieving management target (ToR 2.4.1) 

Likelihood to achieve management target by 2025 & 2028 

The probability to achieve the management target of setting F at Fmsy by 2025 
or 2028 are seen in Figure 40. Nor BoB_1535 (slope 0.8), neither the FixedF at 
0.7Fmsy  can  achieve  such  objective  with  95%  certainty  by  2025  (though  the 
former  is  very  close  to  such  certainty).  However  by  2028  such  certainty  is  fully 
achieved  for  the  BoB_1535  (slope  0.8)  and  as  well  (roughly)  by  the  FixedF  at 
0.7Fmsy. 

Due  to  the  potentially  high  productivity,  the  stock  can  swiftly  rebound  and  fulfil 
the  management  targets.  The  potential  of  recovery  and  achieving  the 

73 

73 

 
 
 
 
 
management targets is increased if the HCRs are implemented earlier rather than 
later  –  this  is  especially  true  in  the  case  of  stock  trajectories  that  are  behaving 
worse than the median in the initial reduction phase. 

Figure 3.34 Performance statistics for the years 2025 and 2028 for the preferred HCR (BoB_1535) and best 
performing  fixedF  (Ftar  =  0.7  Fmsy)  for  contrast.  This  shows  that  BoB_1535  does  not  pass  the 
p(SSB>Blim)>95% check in 2025 but in 2028. Both pass the check to reduce F below Fmsy. 

2  MSE SARDINE GSA 17 & 18 

Introduction: Sardine Operating Model (OM) Conditioning 

During the benchmark session for the assessment of sardines in GSAs 17-18, it 
was agreed that the stock was not benchmarked due to the difficulties in 
achieving a successful convergence of the model, probably linked to the issues in 
the age structure of both the commercial catches and in the surveys. These 
resulted in low cohort signals being identified in the assessment. Finally, a 

74 

74 

 
 
 
 
 
 
 
 
“preferred model” based on A4a was chosen; the reasoning can be found in the 
respective GECM report (GFCM, 2021). This preferred model will be considered as 
the baseline OM. 

The TOR for sardine stock, owing to the stock not being benchmarked and due to 
the high survey CVs (as estimated from the model), makes the simulation of 
management very uncertain, and for that reason only a short-cut MSE was 
carried out just to understand the impact of the transitional period measures on 
the SSB and Recruitment (Rec), and some projection just for feeding the 
economic modelling of the fishery which needs to be taken into account the two 
main species I the area (sardine and anchovy). So, the following sections will 
analyse in the simulation short-cut -MSE approach and the implication on SSB, 
Rec and Fishing mortality (F) of the sardine stock obeying the reduction of 
catches for the period 2022-2024 according to the TOR 1.1, and the 
implementation of the fixed F HCR, the latter as a baseline for the economic 
simulations. 

A set of different levels of catch reductions either of 5%, 10% and 15% per year 
starting in January 2022 and ending in December 2023 was applied. In addition, 
as a sensitivity run, for year 2024, sardine catches should be reduced by 15% 
lower than the level of 2023. 

2.1.1 

The Data input for the MSE:  

The fishery consists of midwater trawlers and purse seine fleets from Albania, 
Croatia, Italy, Slovenia, and Montenegro. For all the countries involved in this 
fishery, the discards were declared to be either inexistent or negligible. Gaps in 
the time series were filled by reconstructed data. After extensive discussion, 
benchmark experts decided to shorten the time series of commercial data and 
start it in 2000. Data prior to 2000 were considered unreliable due to data gaps, 
changes in the catch statistics for some countries and unreliable reconstruction of 
the missing data. Experts considered data prior to 2000 too uncertain to be 
included in the assessment. Over the years 2000-2006 catches decreased from 
25,900 t to 20,400 t (Figure 3.4). That period was followed by a significant 
increase of the catches that reached 83,500 t in 2014. Over the years 2015-2019 
catches decreased to 64,700 t. 

The reference-case a4a model run specifications were: 

•  Five age groups 0-4+, where age-4 is treated as plus group 

•  Natural Mortality: Gislason estimate of 

 (Table 3.1) derived using von 

Bertalanfy parameters (fitted to 2014 data) 

•  Maturation assumed to knife-edge at age-1 in January (Table 3.1) 

•  Commercial catch-at-age data 2000 – 2019 (Figure 3.1) 

•  Weights-at-age in the catch and in the stock were assumed to be identical.  

75 

75 

 
 
 
The  bulk  of  the  stock  is  made  by  age  classes  0-2  in  the  catch-at-age  matrix 
(Figure  3.1).  These  age  classes  followed  first  a  period  of  stagnation  during  the 
first decade of the data. That period was followed by a period of steep increase. 
In  the  last  years,  a  decreasing  trend  was  observed  for  age  2  and  to  a  lesser 
extent for age 0. Age 3 did not exhibit any positive or negative trend while age 4 
slightly  decreased  over  the  years  2000-2019.  There  was  little  internal 
consistency in the catch-at-age matrix  

Five survey indices were used to tune the assessment (Figure 3.2), one of which 
is an amalgamated biomass survey (Echo East Biomass Survey) 

1.  surveys in GSA 17 west: ages 0-4, 2004–2014 
2.  surveys in GSA 18 west: ages 0-3, 2004–2014 
3.  surveys in GSA 17 west and GSA 18 east and west: ages 0-3, 2015–

2019; 

4.  surveys in GSA 17 east: ages 1-4, 2013–2019 
5.  surveys in GSA 17 east: biomass: total, 2003–2010. 

As in the catch-at-age matrix there was little internal cohort consistency.  
The only two surveys continued nowadays are the Italian and Croatian MEDIAS 
(surveys 3 and 4 in the list above). 

2.1.2 

Stock assessment 

Historically, the State space Assessment Model encapsulated in FLR (FLSAM) was 
used to assess Sardines. However, in this benchmark, difficulties were 
encountered to obtain a convergence. Various model parameters were tested but 
none of them could solve the convergence issue.  

As a result, a4a was also considered as a model to assess sardines. The 
parameters of the a4a model were specified as follow: 

  The fishing mortality was dependent on age and year (3 and 10 knots 

respectively) 

  Initial numbers-at-age were modelled using a smoother (3 knots) 

  Recruitment was an annual fixed factor 

  Observation variance of catch-at-age and indices were constant through 

ages 

The goodness of the fit was evaluated by investigating residuals. Although the 
stock was not considered benchmarked, the a4a model was considered good 
enough to provide advice but not to perform short term forecast.  

Over the years 2000-2005,   is decreasing from 0.31 to 0.15 (Figure 3.4). This is 
followed by an increasing trend to 1.79 that is reached in 2019. The F-at-age 
shows that the increase is significantly higher than for younger age groups 
(Figure 3.3). The SSB exhibited two periods of increase (2000-2003 and 2009-
2013) and two periods of decrease (Figure 3.4). Over the modelled period the 
recruitment does not show any significant positive or negative trend (Figure 3.4). 
A segmented regression with a break point at Blim was fitted to the pairs of 
spawning stock and recruitment estimates from the model (Figure 3.5).  

76 

76 

 
In conclusion, based on 2019 data, the stock was assessed to be overexploited 
and in overexploitation, though the uncertainties of both input and output of 
assessment are considered as high. 

TABLE 4.1. BIOLOGICAL INPUT DATA FOR A4A REFERENCE RUN. THE LENGTH-AT-AGE ( ) 
ESTIMATES ARE BASED ON VON BERTALANFFY PARAMETERS 

 AND 

, 

 ESTIMATED FROM A COMBINED AGE-LENGTH KEY FOR 2014 (GFCM, 2020) 

Age 

 (cm) 

 (

)  P(Mature) 

0 

1 

2 

3 

4 

9.8 

12.9 

15.1 

16.6 

17.6 

1.06 

0.83 

0.69 

0.61 

0.48 

0 

1 

1 

1 

1 

The reference points estimated for sardine in GSAs 17-18 (GFCM 2021) were not 
considered benchmarked and therefore not considered for short-term forecasts. 
The reference points in Table 3.2 are therefore treated as preliminary. 

The reference point 
calculated as 

 based on Patterson’s criterion (

), which can be 

where 

 are the age classes used to compute the average fishing mortality  , 

which were specified as age 1 to age 3 for sardine. For 

 = 

, the second 

lowest 

 estimate 

 = 

 was selected instead of the lowest 

, to 

avoid choosing 

 from one of the most recent years where uncertainty is 

higher. The precautionary biomass reference point (

), was estimated using the 

same equation as ICES, as a function of 

, such that: 

where 
assessment year. 

 = 0.3 is the estimated standard error of 

 from the last 

77 

77 

 
 
 
 
 
 
 
TABLE 4.2. PRELIMINARY REFERENCE POINTS ESTIMATED FOR SARDINE BY GFCM AND 
BIOMASS REFERENCE POINTS RELATIVE TO 
REGRESSION SRR (SSR-SR). * ESTIMATE OF 
YEARS) GIVEN THE GFCM 

 VIA FORWARD PROJECTION (100 

 (T) BASED ON THE SEGEMENTED 

 = 0.47. 

GFCM 

Ratio SSB0 

0.47 

178200 

294300 

  213577* 

- 

0.57 

0.94 

0.68 

Figure 4.1 Input catch-at-age matrix for the sardine a4a reference run. 

78 

78 

 
 
 
 
 
 
 
 
 
Figure 4.2 Time series of survey abundance indices used for tuning of the sardine a4a reference model 

The estimated fishing mortality at age (Fa,y) portrays an unusually selectivity 
pattern for a small pelagic target fishery in that only the largest and oldest fishes 
of age-3 and age-4+  would fully vulnerable the fishery (Fig. 4.3) although age 
groups 0-2 represent the bulk of the catch (Fig. 4.1). This also would imply that 
sardine could spawn up two times before becoming fully vulnerable to fishing and 
the yield-per-recruit is vastly underutilized considering the natural mortality rate 
(i.e. fish die faster than they can caught).  However, the fishery mechanics that 
could lead to such selectivity pattern in practice remain unclear. 

79 

79 

 
 
 
 
Figure 4.3 Changes in the fishery exploitation pattern (

) estimated by the a4a sardine reference model 

To propagate model estimation error, a Monte-Carlo Markov Chain (MCMC) 
procedure was run with 1000 iterations for the a4a reference model. The MCMC 
iterations were checked for convergence and the first 500 converged iterations 
were retained to condition the sardine reference OM. The median, 10th, 25th, 75th 
and 90th percentiles from the bootstrap are shown for the estimated trajectories 
of recruitment (Rec), SSB, catch (t) and   in Figure 3.4. Although catches 
showed a steep increase between 2000 and 2013 and fishing mortality ramped 
up throughout the time series from F2000 = 0.32 to F2019 = 2.09, recruitment 
showed SSB showed no clear response to the increase in fishing mortality, with 
SSB 2019 = 198,646 being even slightly higher SSB 2000 = 178,214, while total 
biomass even increased by 180% from B2000 =  to  B2019 = 548,485  despite the 
more than 6 times increase in fishing mortality . The results from the 
a4areference model therefore contrary to expectations from population dynamics 
theory 

80 

80 

 
 
 
Figure 4.4. a4a reference model for sardine run with 1000 Monte-Carlo Markov Chain (MCMC) iterations to 
depict estimation error uncertainty. 

As an important component of the OM conditioning, an implicit assumption about 
the stock recruitment relationship (SRR) is required to project the stock dynamics 
into the future. In the absence of a clear spawner recruitment relationship (as 
also noted in GFCM 2021), a simple segmented regression (SRR-SR) was fitted 
by fixing the breakpoint to the provided Blim reference point (Table 3.2). The 
segmented regression provided a reasonable fit to the available SSB and 
recruitment estimates from the a4a reference model (Figure 3.5) 

The recruitment residuals were approximately randomly distributed around 
expected SRR-SR, which results in a very small estimate of recruitment variation 
 = 0.25.  Similar to anchovy, there were, however, some concerns that this 
relative low estimate could be partially explained by using a static age-length key 
 = 0.34 was adopted for 
and because smoothing by the a4a model. Therefore, 
conditioning the sardine reference OM, which is consistent with the mode of the 
predictive distribution for  
taxonomic life history model FishLife (Thorson 2020; c.f. anchovy in Figure 3.5). 

 for this species as derived from the hierarchical 

81 

81 

 
 
 
 
Figure 4.5. Fit of the segmented regression fitted to a4a reference model estimates of 
including basic residual diagnostics 

 and recruitment, 

The expected stock response to fishing pressure at equilibrium is shown in Figure 
3.6. Yield over F increases steeply until SSB decrease below Blim at about F = 1  
 , which 
and yield decreases to almost zero. Notable is the high estimate of 
suggests that it requires F > 6 to reduce to spawning ratio potential per recruit to 
less 30% .    

82 

82 

 
 
 
 
Figure 4.6. Estimated biological reference points for sardine based on the reference 

The stock status relative to the GFCM reference point estimates shows that the 
current SSB2019  is Blim , which would  correspond to still 63.1% of the unfished 
spawning biomass SSB0.  The reference points Blim and BMSY correspond to 56% 
and 64% of SSB0 (Table 3.2), while Bpa falls above BMSY at 94% SSB0. Fishing 
mortality exceeds the Patterson estimator of FMSY by more than 200% in 2019, 
indicating unsustainable current fishing pressure on the stock (GFCM 2021). 

83 

83 

 
 
 
 
Figure 4.7. Stock status based on sardine reference model relative to esimated benchmarks for 
 (dotted) 

 (orange) and 

 (green) and 

 (red) , 

2.1.3 

OM Projections 

The dynamics were simulated primarily only for the intermediate period 2022-2023 as established 
by the TOR 1.1 (and run for 500 iterations); and next reaching year 2024 with a 15% reduction 
relative to the catch in 2023 (TOR 1.1b). Secondarily, and just as examples of projection under 
two contrasting scenarios the fixed F rule was applied over 20 management years (2024 – 2044) 
and run for 1000 iterations conditioned to two Ftarget: First to F=Fmsy. 

The years 2020-2024 are implemented as the intermediate catch years, with annual catches in 
2020 and 2021 set equal to the 2019 catch levels, followed by a sequential optional 5%, 10% 

and 15% decreases in 
followed by an optional decrease in 2024 equal to 15% as indicated in the TOR 1.1 b. The no 
reduction option in 2024 is left out from simulations because the assessment suggest reduction 
should major and as soon as possible (as latest F/Fmsy is assessed to be 4.43).  

 for 2022 through 2023 (Table 3.3) according to TOR 1.1a, and further 

Table 4.3. Reference scenarios for intermediate catches of sardine over the period 2020-2024, 
based on the 2019 catch (Tons) and on TOR 1.1 a and b 

5% 

2019 

2020 

2021 

2022 

2023 

2024 

Sardine 

64961  64961  64961  61713  58627.3  49833.2 

Yearly 
reduction 

0 

0 

0 

5% 

5% 

15% 

84 

84 

 
 
 
 
10% 

2019 

2020 

2021 

2022 

2023 

2024 

Sardine 

64961  64961  64961  58464.9  52618.4  44725.6 

Yearly 
reduction 

0 

0 

0 

10% 

10% 

15% 

15% 

2019 

2020 

2021 

2022 

2023 

2024 

Sardine 

64961  64961  64961  55216.9  46934.3  39894.2 

Yearly 
reduction 

0 

0 

0 

15% 

15% 

15% 

Uncertainty in the projection and in the intermediate period was introduced 
through (i) model estimation error in the form of bootstrap iteration (Figure 3.4), 
(ii) a lognormal recruitment variation of 
observation error of 
abundance index-at-age (
random lognormal implementation error of the TAC 

 = 0.1 (iv) a lognormal survey observation error for the 

) estimated by the a4a model (Table 3.5) and (v) a 

, (iii) a lognormal catch 

. 

Table 4.4. Age-independent survey index observation error estimates from the sardine reference 
model 

Echo West 17  Echo West 18  Echo L  Echo East  Echo East Biomass 

0.867 

1.148 

1.013 

1.015 

1.015 

Results for the Intermediate years 2020-2023 and sensitivity on year 
2024 

The runs were subject to the fixed catch implementation of Table 3.3 as the 
intermediate years’ catch reduction was prescribed in the TORs. Tables 3.5- 3.7 
below show the performance statistics obtained by year over the intermediate 
period 2020-2023 (plus 2024) for the three levels of annual catch (TAC) 
reduction in 2022 and 2023, i.e., for the 5%, 10% and 15% respectively and the 
further reduction of a 15% of catches in 2024. Additionally, the performance 
indicators over this period are shown in figure 3.8. 

The three scenarios of gradual catch reductions (5%, 10% and 15% ) allow 
concluding: 

a)  the probability of the sardine biomass being below Blim would be far higher 

than 5% for all the intermediate years (2020-2023), and actually the 
probabilities of being above Blim will be decreasing over this intermediate 
period (from about 0.54 to around 0.36 in all cases).  

85 

85 

 
   
 
 
 
 
 
   
 
 
 
 
 
 
 
 
 
 
b)  the probability of the sardine biomass being above Bmsy would be low (below 0.5) and it will be 

actually diminishing over this intermediate period (from about 0.42 to around 0.23 in all cases). The 
mean of the median values of SSB/SSBmsy would be around 0.77 over the entire period.  

c)  The median fishing mortality over Fmsy would be increasing from values 
around 2.21 in 2020 to values of 3.65, 3.01 and 2.41 in 2023 for the 
gradual catch reductions of 5%, 10% and 15%, respectively.  

d) A further reduction of catches by 15% in 2024, would not revert the 

increasing risk for the SSB to drop below Blim for all options of gradual 
reduction of catches in 2022 and 2023.  This possibly means that a more 
substantial reductions than the ones so far foreseen for this intermediate 
period until 2024 should be applied in order to revert this trend. However, 
with respect to the probability of SSB being above BMSY, such a catch 
reduction in 2024 would stabilize for the scenarios of 10 and 15% in years 
2022 and 2023. 

Given the critical starting point of the SSB in 2020 so close to Blim and the 
insufficient reduction of catches and fishing mortality to drive F to Fmsy quickly, 
the overall effect on the population over this intermediate period is summarized 
in Figure 3.9, whereby SSB tend to decrease slightly and hence the recruitment 
as well. Overall, these results show that the proposed reductions in catches 
would not induce a recovery of the population towards higher biomass levels yet 
(see also tables 3.5-7).  

Table 4.5: Performance statistics over the intermediate period for prescribed catches (see table 
3.3) implying reduction on 5% in years 2022 and 2023 (over the cathes in 2021 and 2022 
respectively) and of 15% in year 2024. 

Indicators \ Years 

2020  2021  2022  2023  2024  Mean 

AAV (Interannual 
Variability) 

2020-2024 

0.06 

Catch  

64961  64961  61713  58627  49833  60019 

Median( F/Fmsy ) 

2.25 

2.56 

3.36 

3.98 

4.48 

3.32 

P(SSB>Blim) 

0.54 

0.49 

0.41 

0.36 

0.30 

0.42 

P(SSB>BMSY) 

0.42 

0.37 

0.28 

0.21 

0.16 

0.29 

Median(SSB/BMSY)  0.87 

0.82 

0.71 

0.61 

0.51 

0.70 

Table 4.6: Performance statistics over the intermediate period for prescribed 
catches (see table 3.3) implying reduction on 10% in years 2022 and 2023 (over 
the cathes in 2021 and 2022 respectively) and of 15% in year 2024. 

86 

86 

 
 
 
 
 
 
 
 
 
 
 
Indicators \ Years 

2020  2021  2022  2023  2024  Mean 

AAV (Interannual 
Variability) 

2020-2024 

0.09 

Catch  

64961  64961  58465  52618  44725  57146 

Median( F/Fmsy ) 

2.25 

2.56 

3.11 

3.31 

3.28 

2.90 

P(SSB>Blim) 

0.54 

0.49 

0.41 

0.37 

0.32 

0.42 

P(SSB>BMSY) 

0.42 

0.37 

0.28 

0.22 

0.22 

0.30 

Median(SSB/BMSY)  0.87 

0.82 

0.71 

0.61 

0.55 

0.71 

Table 4.7 Performance statistics over the intermediate period for prescribed catches (see table 3.3) 
implying reduction on 15% in years 2022 and 2023 (over the cathes in 2021 and 2022 respectively) and of 
15% in year 2024. 

Indicators \ Years 

2020  2021  2022  2023  2024  Mean 

AAV (Interannual 
Variability) 

2020-2024 

0.11 

Catch  

64961  64961  55217  46934  39894  54393 

Median( F/Fmsy ) 

2.25 

2.56 

2.86 

2.60 

2.42 

2.54 

P(SSB>Blim) 

0.54 

0.49 

0.41 

0.37 

0.34 

0.43 

P(SSB>BMSY) 

0.42 

0.37 

0.28 

0.23 

0.24 

0.31 

Median(SSB/BMSY)  0.87 

0.82 

0.71 

0.63 

0.58 

0.72 

87 

87 

 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
Figure 4.8: Box plots (with 25 and 75% squares and 5% and 95% lines) of the performance statistics on 
average over the intermediate period 2020-2023 and 2024 for the Sardine (GSA 17-18), according to the 
three scenarios of gradual catch reductions in table 3.3, plus the further 15% reduction of catches in 2024.  

88 

88 

 
 
 
 
 
 
 
Figure 4.9 Median confidence intervals for the Recruitment, SSB, Catch and Fbar (1-3) for the intermediate 
period 2020-2023 and 2024 for the Sardine (GSA 17-18), according to the three scenarios of gradual catch 
reductions in table 3.3, plus the further 15% reduction of catches in 2024.  

2.1.4 

Sardine HCR tuning 

The HCRs which were defined for anchovy, have not been tested in the EWG for 
sardine,  because  of  the  problems  detected  during  the  preliminary  testing  runs 
carried out in  STECF-Adhoc-21-02. In  those  runs it  become clear that given the 
difficulties  in  having  precise  enough  assessments,  owing  to  the  stock  not  being 
benchmarked, and the fact the Bmsy was below Bpa, these harvest control rules 
tested  in  the  short  cut  approach  resulted  in  high  likelihoods  on  closures  or  very 
poor levels of catches. Therefore, and according to TOR 2.1B  the group decided 
to  restrict  the  analysis  to  run  only  a  shortcut  MSE  to  understand  the  impact  of 
the transitional period measures on the evolution of the population (as shown in 
the previous section).   

2.1.5 

Conclusions for the sardine stock 

89 

89 

 
 
 
 
 
 
 
Overall,  these  results  show  that  the  proposed  reductions  in  catches  would  not 
induce a recovery of the population towards higher biomass levels yet (see also 
tables 3.5-7) and the P(SSB<Blim) will still be increasing.  

Therefore  the  next  step  should  be  to  go  for  a  benchmark  ASP  in  order  to 
achieved a safe management of the stock, though a proper MSE for this sardine 
stock. 

The  projection  method  used  was  the  short-cut  method.  The  main  difference 
between this short-cut implementation and the full-feedback method lies in  the 
omission  of  the  simulated  stock  assessment.  The  population  data  is  directly  fed 
into  the  HCR  (no  surveys  were  simulated)  implying  perfect  knowledge  of  the 
stock by the HCR. The decision to only use the short-cut methodology was based 
on  the  infeasibility  of  the  full  feedback  method  due  to  the  shortcomings  of  the 
benchmark model. 

Following the tuning exercise performed by STECF-Adhoc-21-02, it became clear 
that the debate to use or not use a full feedback approach was academical as the 
results  under  perfect  knowledge  already  showed  that  in  their  current  form  the 
HCRs cannot be parameterised in a favourable way. 

90 

90 

 
 
 
3  ECONOMIC PERFORMANCE ASSESSMENT (TOR 3) 

An  MSE  tool  is  used  to  assess  the  biological  implications  of  several  HCRs 
specifically  defined  for  the  Adriatic  small  pelagic  fisheries.  Under  the  ToR  3,  the 
EWG was requested to evaluate the maximum economic performance of the HCR 
if the economic data are available and of adequate quality. The preliminary steps 
to  evaluate  the  economic  performance  of  the  HCRs  consisted  in  verifying  the 
availability  of  economic  data,  collecting  transversal  and  economic  data  for  the 
fisheries  under  investigation,  identifying  the  fleet  segments  involved  in  those 
fisheries, and defining a bio-economic modelling approach to be used. 

Data collection and fleet segments identification 

The  fleets  potentially  involved  in  the  Adriatic  pelagic  fisheries  include  three  EU 
Member  States,  Croatia,  Italy  and  Slovenia,  and  Albania  and  Montenegro. 
However,  most  of  the  landings  of  anchovy  and  sardine  come  from  Italian  and 
Croatian  fleets.  Considering  also  the  availability  of  data,  the  EWG  decided  to 
carry out the economic analysis only for the Italian and Croatian fleets. 

Transversal  and  economic  data  for  the  Italian  and  Croatian  fleet  segments 
involved in the small pelagic fisheries is available at country level from the 2020 
Annual  Economic  Report  on  the  EU  Fishing  Fleet  (STECF  20-06).  However,  data 
published by STECF 20-06 does not include year 2019. Furthermore, as DCF data 
are  collected  at  country  level,  data  on  the  Italian  fleet  segments  does  not 
differentiate  between  Adriatic  and  the  other  small  pelagic  Italian  fisheries.  To 
overcome  these  limitations  and  focus  on  the  Adriatic  fisheries,  it  was  requested 
to the Italian Ministry the authorization to use additional and geographical more 
detailed  data.  A  similar  request  was  sent  to  the  Croatian  authorities  to  have 
available  also  2019  data.  Both  administrations  made  available  their  data  to  the 
EWG.  

Based  on  the  available  data,  five  Italian  fleet  segments  and  four  Croatian  fleet 
segments were identified as active in the Adriatic (GSAs 17 and 18) small pelagic 
fisheries. As reported in the Table 5.1, the Italian fleets include purse seiners and 
pelagic  trawlers,  while  the  Croatian  ones  include  only  purse  seiners.  The  time 
series  available  for  the  Italian  fleet  segments  cover  the  period  2004-2019  (16 
years)  except  for  the  pelagic  trawlers  12-18m,  where  the  time  series  starts  in 
2008  (12  years).  The  data  available  for  the  Croatian  fleets  covers  the  period 
2012-2019 (8 years). The number of available data is relevant for the estimation 
of economic parameters and the quality of the economic projections. 

Table 5.1 – Data availability by fleet segment 

Fleet 
Segment 

200
4 

200
5 

200
6 

200
7 

200
8 

200
9 

201
0 

201
1 

201
2 

201
3 

201
4 

201
5 

201
6 

201
7 

201
8 

201
9 

ITA_PS_VL1
218 

ITA_PS_VL2
440 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

91 

91 

 
 
 
ITA_TM_VL1
218 

ITA_TM_VL1
824 

ITA_TM_VL2
440 

HRV_PS_VL0
012 

HRV_PS_VL1
218 

HRV_PS_VL1
824 

HRV_PS_VL2
440 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

Bio-economic modelling approach 

As  reported  by  STECF-17-05  on  bio-economic  methodology,  the  available 
approaches  to  assess  economic  and  social  impacts  on  fleets  of  TAC  and  quota 
allocations  are  short-term  projections  models,  integrated  bio-economic  models, 
and  economic  general  dynamic  equilibrium  models.  Even  though  the  last  two 
approaches  are  considered  more  feasible  than  short-term  projections  to  provide 
advice on TACs and quotas, also including an evaluation of long-term effects on 
the fleets, their complexity requires the availability of specific software tools and 
computational time to be run. 

The  limited  time  available  during  the  meeting  suggested  the  use  of  short-term 
projections models. For many years, this approach was used to carry out short-
term  projections  for  the  Annual  Economic  Report  (AER)  of  the  EU  fisheries 
through  the  BEMEF  model,  which  is  an  extension  of  the  EIAA  model  (see,  for 
instance, STECF-17-12). Both EIAA and BEMEF simulated the future variations in 
the  economic  variables  by  using  the  changes  in  TAC  or  catch  quotas  under 
specific HCR as main driver.  

Variations in catch quota (TAC) and the size of stock biomass or spawning stock 
biomass  (SSB),  which  are  provided  by  the  MSE  tool,  determine  the  amount  of 
fishing  effort  needed  to  achieve  the  quota  assigned  to  each  fleet  segment.  The 
relationship  of  fishing  effort  with  TAC  and  SSB,  which  allows  to  connect  the 
biological  and  the  economic  model  dimensions,  is  based  on  the  inverse  of  a 
Cobb-Douglas  production  function.  The  fishing  effort  of  each  fleet  segment, 
expressed in terms of days at sea, can be linked to the TAC and the SSB of the 
main target stock or a combination of those stocks. Changes in fishing effort are 
then converted into changes in variable costs. Variations in TACs impact also on 
prices  and  revenues  by  applying  a  price  flexibility  function  per  stock  and  fleet 
segment.  

As the different scenarios on the HCR for small pelagic in the Adriatic are defined 
in terms of changes in total catches through the setting of TAC for both sardine 
and  anchovy,  only  the  economic  variables  directly  affected  by  variations  in 
landings and fishing effort are supposed to change over time. Under a short-term 
approach,  the  size  of  the  fleets  is  assumed  to  be  constant  along  the  simulation 

92 

92 

 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
period  and  variables  associated  with  the  fleet  size,  like  fixed  costs,  repair  and 
maintenance  costs  and  capital  costs,  are  assumed  to  be  constant  over  time. 
Therefore,  the  economic  short-term  effects  of  changes  in  TACs  are  measured 
only  in  terms  of  changes  in  revenues  and  variable  costs.  Even  the  number  of 
people employed on board can be assumed constant over time. 

More details on the economic model adopted are reported in the Annex 1. 

Estimation of the Cobb-Douglas production function coefficients 

The  Cobb-Douglas  is  a  classical  production  function  where  the  production  is  the 
result of a multiplicative function of the production factors, generally capital and 
labour. In the fishing sector, assuming SSB and days at sea (DD) as production 
factors, the function can be reported as follows: 

, 

(eq.1) 

where the production L is expressed in terms of landings of a specific stock. This 
equation,  with  the  appropriate  logarithmic  transformation,  was  estimated  for 
each Italian fleet segment assuming anchovy (ANE) as the main target stock. The 
estimation  of  production  functions  for  the  Croatian  fleet  segments  was  not 
carried  out  because  the  main  target  stocks  for  these  fleets  is  assumed  to  be 
sardine, and both landings and SSB of sardine are assumed to be constant over 
time.  

From the classical Cobb-Douglas in eq.1, it is possible to derive the inverse Codd-
Douglas in eq.2, where DD is the dependent variable and landings becomes one 
of the independent variables: 

, 

which can be rewritten as: 

assuming 

,  

, 

, and 

. 

(eq.2) 

(eq.3) 

, 

  and 

  in  eq.1  are  estimated,  also  the  coefficients  of 
Once  the  coefficients 
eq.3  are  estimated  and  DD  simulated  as  a  function  of  SSB  and  the  landings  L, 
which are proportional to the catch quota established under the HCR. Results of 
the regression models applied  to the  logarithmic transformation of eq.1 and the 
comparison of real and fitted data for DD based on eq.3 are reported for each of 
the Italian fleet segment in Figs. 5.1-5.5. 

The coefficients  ,   and   in eq.3 are reported in Table 5.2. As the level of fishing 
effort (days at sea, DD) allowing to achieve the quota is expected to be positively 
correlated to the TAC (landings) and negatively correlated to the SSB, coefficients 
 and  are expected to be positive and negative, respectively. Results reported in 
Table 5.2 show that coefficients have the correct signs. 

93 

93 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 5.2 – Coefficients of the inverse Cobb-Douglas function 

Fleet 
Segment 

ITA_PS_VL121
8 

ITA_PS_VL244
0 

ITA_TM_VL121
8 

ITA_TM_VL182
4 

ITA_TM_VL244
0 

b 

𝛾 

𝜃 

30.30 

-0.59 

0.79 

0.42 

-0.51 

0.90 

10.07 

-0.64 

0.83 

208.25 

-0.47 

0.50 

0.00 

-0.62 

1.36 

The  regression  models  reported  in  Figs.  5.1-5.5  show  a  good  fitting  with  an 
adjusted R-squared higher than 0.9 for the pelagic trawlers 12-18m and 24-40m, 
and  higher  than  0.8  for  the  purse  seiners  24-40m.  Both  the  coefficients 
associated to the days at sea and those to the SSB are statistically different from 
zero  for  all  fleet  segments,  except  for  the  coefficient  related  to  the  SSB  for  the 
purse seiners 12-18m where the p-value is just higher than 5%. The good fitting 
of  the  regression  models  is  highlighted  also  by  the  graphs  comparing  real  and 
fitted data for the days at sea in the period 2004-2019.  

ITA PS VL1218 

ITA PS VL1218 

Fig.  5.1a  –  Regression  of  ANE  landings  on  SSB  and 
days 

Fig. 5.1b – Days at sea: real vs. fitted 

94 

94 

050010001500200025003000350040002004200520062007200820092010201120122013201420152016201720182018REALESTIMATES 
 
 
 
 
 
ITA PS VL2440 

ITA PS VL2440 

Fig.  5.2a  –  Regression  of  ANE  landings  on  SSB  and 
days 

Fig. 5.2b – Days at sea: real vs. fitted 

ITA TM VL1218 

ITA TM VL1218 

Fig. 5.3a – Regression  of ANE landings on SSB and 
days 

Fig. 5.3b – Days at sea: real vs. fitted 

ITA TM VL1824 

ITA TM VL1824 

Fig. 5.4a – Regression  of ANE landings on SSB and 
days 

Fig. 5.4b – Days at sea: real vs. fitted 

ITA TM VL2440 

ITA TM VL2440 

95 

95 

01000200030004000500060002004200520062007200820092010201120122013201420152016201720182018REALESTIMATES0200040006000800010000120001400016000180002004200520062007200820092010201120122013201420152016201720182018REALESTIMATES010002000300040005000600070002004200520062007200820092010201120122013201420152016201720182018REALESTIMATES 
 
 
 
 
 
 
 
 
 
Fig. 5.5a – Regression  of ANE landings on SSB and 
days 

Fig. 5.5b – Days at sea: real vs. fitted 

Evaluation of the economic performance of the HCRs 

Projections for all economic and social variables included in the logical-conceptual 
pattern of the economic model are carried out until 2044 for each of the 9 fleet 
segments operating in the Adriatic pelagic fisheries (Italian purse seiners 12-18m 
and  24-40m;  Italian  pelagic  trawlers  12-18m,  18-24m  and  24-40m;  Croatian 
purse  seiners  00-12m,  12-18m,  18-24m  and  >24m).  Economic  and  social 
variables  are  then  used  to  estimate  several  indicators  for  each  year  of  the 
simulation  period  and  each  HCR.  Graphs  on  the  projections  of  four  selected 
indicators  –  total  revenues,  variable  costs,  gross  value  added  (GVA)  and  gross 
profit – are reported in Figs. 6-8 for the three HCRs (Fmsy, bobC and besc) and 
for the following three aggregations of fleet segments: 

1.  the Italian purse seiners operating in GSAs 17 and 18 (Figs. 6a-6d); 
2.  the Italian pelagic trawlers operating in GSAs 17 and 18 (Figs. 7a-7d); 
3.  the Croatian purse seiners operating in GSA 17 (Figs. 8a-8d). 

The  total  revenues  are  intended  as  the  revenues  from  landings  of  anchovy  and 
sardine plus revenues from other stocks. 

The  variable  costs  include  both  the  energy  costs  (mainly  fuel  costs)  and  other 
variable  costs  (costs  directly  dependent  on  the  level  of  fishing  activity,  like  ice 
and boxes). 

The GVA, which is calculated as the total revenues minus all expenses (excluding 
labour  and  capital  costs),  is  a  measure  of  the  remuneration  of  the  production 
factors. 

The  gross  profit,  which  is  obtained  by  the  GVA  minus  the  labour  costs,  is  a 
measure of the remuneration of the vessel owner. 

The projections on gross profit show that the best economic performance in the 
long  term  is  expected  under  besc  for  both  Italian  and  Croatian  fleets,  even 
though  besc  and  bobC  are  almost  equivalent  after  2040  for  the  Italian  fleets. 
Fmsy performs better than the other HCRs in the short term for all the fleets. In 
2023 and 2024,  Fmsy is expected to perform better than other HCRs for all the 
fleets.  

Almost identical results in qualitative terms are registered for the projections on 
GVA.  For  the  Italian  fleets,  similarities  are  expected  also  in  quantitative  terms 

96 

96 

02000400060008000100001200014000160002004200520062007200820092010201120122013201420152016201720182018REALESTIMATES 
 
 
with  percentage  variations  in  the  GVA  very  similar  to  those  estimated  for  gross 
profit.  

The economic performance expressed in terms of gross profit and GVA is due to 
the  combined  effects  of  the  HCRs  on  revenues  and  variable  costs.  Revenues 
impacts on GVA and gross profit more than variable costs, which represent only a 
part  of  the  total  costs.  Consequently,  the  trend  expected  on  the  economic 
performance  indicators  reflects  in  a  certain  measure  the  trend  in  revenues.  On 
the  other  hand,  variable  costs  show  values  lower  than  the  current  ones  both  in 
the short and long term under all HCRs. The strongest reduction in variable costs 
is  expected  under  bobC,  followed  by  besc  for  both  Italian  fleets.  As  for  the 
Croatian fleet, variable costs are expected to be constant under all HCRs because 
the assumption of constant landings of sardine and constant sardine SSB. 

Total Revenues 

Variable Costs 

Fig.  5.6a  –  Projections  of  total  revenues  for 
the  Italian  purse  seiners  under  different 
HCRs 

Fig.  5.6b  –  Projections  of  variable  costs  for 
the  Italian  purse  seiners  under  different 
HCRs 

Gross Value Added 

Gross Profit 

Fig.  5.6c  –  Projections  of  gross  value  added 
for  the  Italian  purse  seiners  under  different 
HCRs 

Fig. 5.6d – Projections of gross profit for the 
Italian purse seiners under different HCRs 

Total Revenues 

Variable Costs 

97 

97 

 - 2.00 4.00 6.00 8.00 10.00 12.00 14.002017201820192020202120222023202420252026202720282029203020312032203320342035203620372038203920402041204220432044ITA purse seinersFmsybobCbesc - 0.50 1.00 1.50 2.00 2.50 3.002017201820192020202120222023202420252026202720282029203020312032203320342035203620372038203920402041204220432044ITA purse seinersFmsybobCbesc - 2.00 4.00 6.00 8.00 10.00 12.002017201820192020202120222023202420252026202720282029203020312032203320342035203620372038203920402041204220432044ITA purse seinersFmsybobCbesc - 1.00 2.00 3.00 4.00 5.00 6.002017201820192020202120222023202420252026202720282029203020312032203320342035203620372038203920402041204220432044ITA purse seinersFmsybobCbesc 
 
 
 
 
 
 
 
Fig.  5.7a  –  Projections  of  total  revenues  for 
the  Italian  pelagic  trawlers  under  different 
HCRs 

Fig.  5.7b  –  Projections  of  variable  costs  for 
the  Italian  pelagic  trawlers  under  different 
HCRs 

Gross Value Added 

Gross Profit 

Fig.  5.7c  –  Projections  of  gross  value  added 
for 
trawlers  under 
the 
different HCRs 

Italian  pelagic 

Fig. 5.7d – Projections of gross profit for the 
Italian pelagic trawlers under different HCRs 

Total Revenues 

Variable Costs 

Fig.  5.8a  –  Projections  of  total  revenues  for 
the  Croatian  purse  seiners  under  different 
HCRs 

Fig.  5.8b  –  Projections  of  variable  costs  for 
the  Croatian  purse  seiners  under  different 
HCRs 

98 

98 

 - 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.002017201820192020202120222023202420252026202720282029203020312032203320342035203620372038203920402041204220432044ITA pelagic trawlersFmsybobCbesc - 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.002017201820192020202120222023202420252026202720282029203020312032203320342035203620372038203920402041204220432044ITA pelagic trawlersFmsybobCbesc - 10.00 20.00 30.00 40.00 50.00 60.002017201820192020202120222023202420252026202720282029203020312032203320342035203620372038203920402041204220432044ITA pelagic trawlersFmsybobCbesc - 5.00 10.00 15.00 20.00 25.00 30.00 35.002017201820192020202120222023202420252026202720282029203020312032203320342035203620372038203920402041204220432044ITA pelagic trawlersFmsybobCbesc - 5.00 10.00 15.00 20.00 25.00 30.00 35.002017201820192020202120222023202420252026202720282029203020312032203320342035203620372038203920402041204220432044HRV purse seinersFmsybobCbesc 8.10 8.20 8.30 8.40 8.50 8.60 8.70 8.80 8.90 9.002017201820192020202120222023202420252026202720282029203020312032203320342035203620372038203920402041204220432044HRV purse seinersFmsybobCbesc 
 
 
 
 
 
 
 
 
 
Gross Value Added 

Gross Profit 

Fig.  5.8c  –  Projections  of  gross  value  added 
for the Croatian purse seiners under different 
HCRs 

Fig. 5.8d – Projections of gross profit for the 
Croatian purse seiners under different HCRs 

A crude investigation of risk on gross profits 

The  economic  analysis  used  median  values  resulting  from  the  MSE  simulations 
runs,  which  leads  to  overly  optimistic  performances.  Reasons  for  this  lie  in  the 
fact  that  the  profitability  is  driven  largely  by  the  catch  and  the  relatively  low 
benefits  of  having  a  larger  SSB  left  at  sea.  As  median  catch  values  do  not 
encompass  catastrophic  events  such  as  fisheries  closures,  a  separate  analysis 
was conducted. To this purpose, the gross profit from the previous analysis was 
multiplied with the annual probability of shutting down the fisheries.  

It  is  acknowledged  that  this  method  is  not  refined  as  no  attempt  was  made  to 
control for price increases due to scarcity of landings, nor were other parameters 
adjusted.  The  analysis  was  carried  out  at  fisheries  level,  all  the  fleets  were 
amalgamated.  This  analysis  is  a  simple  tool  to  assess  the  risks  to  the  gross 
profits if all other aspects remain the same. The shutdown risk was only applied 
to the anchovy fisheries, as no data for  sardines was available. The risk of shut 
down was taken from section 3. As can be seen in Figure 5.9 the increased risk 
of shutdowns leads the fixedF HCR to perform less well than the other to HCRs. 

99 

99 

 - 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.002017201820192020202120222023202420252026202720282029203020312032203320342035203620372038203920402041204220432044HRV purse seinersFmsybobCbesc-1.00-0.50 - 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.502017201820192020202120222023202420252026202720282029203020312032203320342035203620372038203920402041204220432044HRV purse seinersFmsybobCbesc 
 
 
 
 
 
Figure  5.9 Effects of risk of shutdown on gross profit.  The economic performance was weighted using the 
gross  profit  estimations  from  the  economic  analysis  and  the  annual  probability  of  shutting  down  the 
fisheries. 

100 

100 

 
 
 
 
 
 
 
  
4  CONCLUSIONS 

The  MSE  simulations  for  anchovy  were  carried  out  successfully  and  meaningful 
results  were  found.  In  contrast,  the  simulations  for  the  sardine  stock  were  not 
successful  as  the  preferred  assessment  was  not  coherent  within  itself  and  the 
reference points were non-sensical, which prevented any relevant conclusions to 
be  drawn  for  that  stock.  The  only  message  that  can  be  stated  with  certitude  is 
that the precautionary principle should be applied to the sardine stocks.  

It  follows  that  the  economic  analysis  is  somewhat  constraint  to  provide  only 
qualitative  indications  for  the  performance  of  the  HCRs  with  regards  to  changes 
in the management procedure of the anchovy stocks. As the economic analysis is 
based  on  deterministic  point  estimate  analysis  of  the  median  results  of  the 
simulation,  it  is  difficult  to  conclusively  state  what  the  effects  will  be.  MSEs  are 
particularly  useful 
framework  (such  as 
p(SSB>Bmsy)  >.95),  thus  ignoring  the  risk  within  the  economic  performance  is 
also detrimental to the assessment of the performance of any given HCR. 

in  a  risk  based  management 

The intermediate catch reductions have no effect on the  long term  performance 
of two HCRs (Besc and BoB) as the HCRs will adjust the level of catches to allow 
the stock to recover. However, in the short term, some simulation runs are close 
to  crashing  the  stock  –  as  no  HCR  is  active  before  2024,  no  automatic  catch 
correction is taking place. In the cases where F increases with catches remaining 
the same, and/or SSB drops below Blim adjustments to the catch levels need to 
be undertaken.  

Tuning the HCRs showed promising candidate formulations for each one; with all 
fishing mortality targets requiring to be below Fmsy as fishing at Fmsy will incur 
a larger than acceptable risk for SSB to drop below Blim. After the tuning runs, it 
was apparent that the following parameterisation of the Besc HCR would fulfil the 
management  targets:  Besc  =  Bpa  and  Fcap  =  0.8Fmsy.  Similarly,  BoB  showed 
promises  with  parameterisations  using  Btrigger1  =  15Kt/20kt  and  Btrigger2  = 
35kt/40kt  with  slopes  of  0.8.  The  fixedF  HCR  was  parameterised  with  F  = 
0.7Fmsy. After the robustness test, including scenarios that were not considered 
to  be  exceptionally  destructive,  it  was  clear  that  the  best  candidate  HCR  was 
BoB1535-slope =0.8 passing nearly all robustness tests; with BoB1540 coming in 
second  by  passing  50%  of  the  robustness  tests.  The  economic  performance 
showed  that  BoB  and  Besc  do  outperform  fixedF  at  Fmsy  if  risk  is  taken  into 
account. 

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Scientific,  Technical  and  Economic  Committee  for  Fisheries  (STECF)  –  Bio-
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Scientific,  Technical  and  Economic  Committee 
for  Fisheries  (STECF)  – 
PRELIMINARY MANAGEMENT STRATEGY EVALUATION (MSE) FOR ANCHOVY AND 
SARDINE IN THE ADRIATIC SEA (GSA 17-18) (STECF-Adhoc-21-02). Publications 
Office of the European Union, Luxembourg, 2021, EUR 28359 EN, ISBN 978-92-
76-40591-7, doi:10.2760/51019, JRC126127. 

Scientific,  Technical  and  Economic  Committee  for  Fisheries  (STECF):  The  2020 
Annual  Economic  Report  on  the  EU  Fishing  Fleet  (STECF  20-06)  Annex.  EUR 
28359  EN,  Publications  Office  of  the  European  Union,  Luxembourg,  2020,  ISBN 
978-92-76-27421-6, doi:10.2760/597156, JRC123089. 

Thorson, J.T., Taylor, I.G., Stewart, I.J. and Punt, A.E., 2014. Rigorous 
meta‐ analysis of life history correlations by simultaneously analyzing multiple 
population dynamics models. Ecological Applications, 24(2), pp.315-326. 

Thorson, J.T., 2020. Predicting recruitment density dependence and intrinsic 
growth rate for all fishes worldwide using a data‐
Fish and Fisheries, 21(2), pp.237-251. 

integrated life‐ history model. 

6  CONTACT DETAILS OF EWG-21-04 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 

TiiT Raid 

Affiliation1 

Email 

Estonian  Marine  Institute, 
University of Tartu 

tiit.raid@gmail.com 

103 

103 

 
 
 
 
 
 
 
 
Andrés Uriate 

AZTI 

auriarte@azti.es 

Invited experts 

Name 

Affiliation1 

Email 

Andrea Prucci 

COISPA 

Andrea.perrucci@hotmail.it 

Alessandro Orio 

University 

Swedish 
Agricultural 
(SLU),  Department 
Aquatic 
Institute 
Research 

of 
Sciences 
of 
Resources, 
Marine 

of 

Alessandro.orio@slu.se 

Paulo Accadia 

NISEA Società Cooperativa  accadia@nisea.eu 

Edo Avdic Mravlje  Fisheries  research  institute 

edoavdic@gmail.com 

of Slovenia 

Vjekoslav Tičina 

Institute  of  Oceanography 
and Fisheries 

ticina@izor.hr 

Vanja Čikeš Keč 

Institute  of  Oceanography 
and Fisheries 

cikes@izor.hr 

JRC experts 

Name 

Affiliation1 

Email 

Christoph Konrad  JRC 

Christoph.konrad@ec.europa.eu 

Henning Winker 

JRC 

henning.winker@ec.europa.eu 

104 

104 

 
 
 
 
 
 
Michael Gras 

JRC 

Michael.gras@ec.europa.eu 

Jordi Guillen 

JRC 

Jordi.guillen@eu.europa.eu 

European Commission 

Name 

Affiliation1 

Email 

Michael Gras 

Commission/STECF 
secretariat 

Jrc-stecf-
secretariat@ec.europa.eu 

Observers 

Name 

Affiliation1 

Email 

Marzia Piron 

Mediterranean 
Council 

Advisory 

marzia_prion@hotmail.it 

Ivana Vuvkov 

of  Agriculture, 
Ministry 
Directorate  of  Fisheries, 
Unit 
for  Data  Collection 
Programme in Fisheries 

Ivana.vukov@mps.hr 

105 

105 

 
 
 
 
 
 
 
 
7  LIST OF ANNEXES  

Electronic annexes are published on the meeting’s web site on:  
http://stecf.jrc.ec.europa.eu/web/stecf/ewg21-04 

List of electronic annexes documents: 

EWG-21-04 – Annex 1 - EQUATIONS OF THE ECONOMIC MODEL 
Annex 2 – Trade off plots for ANE HCRs 

8  LIST OF BACKGROUND DOCUMENTS  

Background documents are published on the meeting’s web site on:  
http://stecf.jrc.ec.europa.eu/web/stecf/ewg21-04 

List of background documents: 

EWG-22-04 – Doc 1 - Declarations of invited and JRC experts (see also section 8 of this report – 
List of participants) 

106 

106 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 
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FINDING INFORMATION ABOUT THE EU 

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EU publications 
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contacting Europe Direct or your local information centre (see https://europa.eu/european-
union/contact_en). 

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STECF 

The Scientific, Technical and 
Economic Committee for 
Fisheries (STECF) has been 
established by the European 
Commission. The STECF is 
being consulted at regular 
intervals on matters pertaining 
to the conservation and 
management of living aquatic 
resources, including biological, 
economic, environmental, social 
and technical considerations. 

108 

doi:10.2760/75012 

ISBN 978-92-76-45526-4