Specific contract for the provision of scientific advice on the development of a multiannual management plan and the evaluation of a management strategy for cod in NAFO Division 3M : final report.
Date: 2016-08-19
Institutions: Directorate-General for Maritime Affairs and Fisheries
Subjects: by-catch, catch by species, conservation of fish stocks, discarded fish, fisheries policy, fishery management, fishery research, sea fish

EUROPEAN COMMISSION 

DIRECTORATE-GENERAL  

Directorate 
Unit 

Specific Contract for the provision of 
scientific advice on the development of a 
multiannual management plan and the 
evaluation of a management strategy for 
cod in NAFO Division 3M. 

Specific Contract No. 2 under Framework Contract No. 
MARE/2012/21 

Final Report 

© European Union, 2015 

Catalogue N°: KL-01-16-675-EN-N 

ISBN: 978-92-79-60017-3 

doi:10.2771/501893 

Page i 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Framework Contract MARE/2012/21 - Specific Contract No 2 (SI2.681887) 

Final Draft v01 

IEO (F. González-Costas, D. González-Troncoso); AZTI (A. Iriondo, A. Urtizberea); IPMA (R. 
Alpoim, A. Avila); IMARES (D. Miller, N. Hintzen); ECOATENEA (J. C. Collado). 

IEO (P. Abaunza) 

Project code: 

Version: 

Prepared by: 

Approved by: 

Page ii 

 
 
 
 
 
Table of Contents 

Table of Contents ............................................................................................................... iii 

Disclaimer .......................................................................................................................... iiii 

Abstract .............................................................................................................................. iv 

1  Executive Summary ..................................................................................................... 6 
Purpose of the specific contract ......................................................................................... 7 
Tasks performed and main results ..................................................................................... 7 

1.1 
1.2 

2 

Introduction ................................................................................................................ 13 
Purpose of the specific contract ....................................................................................... 14 
Tasks to be performed ........................................................................................................ 14 

2.1 
2.2 

2.3 
2.4 

General approach ................................................................................................................ 15 
Structure of the report ........................................................................................................ 16 

3  Results ........................................................................................................................ 18 
3.1  Management objectives and conservation limits: ........................................................... 19 

3.1.1 
Estimate biological reference points such as possible target and limit reference points 
of  fishing  mortality  and  spawning  stock  biomass,  including  MSY  reference  points,  and  provide 
options  for  management  objectives  taking  into  account  the  NAFO  Precautionary  Approach 
Framework  and  the  NAFO  general  framework  for  conservation  plans  and  rebuilding  strategies 
(NAFO SCR 14/19). ...................................................................................................................... 19 
3.1.2 
Consider  ecosystem  interactions  (including  the  relation  with  redfish  and  shrimp  in 
NAFO  division  3M)  when  analysing  possible  management  objectives  and  conservation  limits 
(NAFO SCS 11/22). ...................................................................................................................... 32 
Catch discrepancy: ............................................................................................................. 38 

3.2 

3.2.1 
Provide  options  to  resolve  the  catch  discrepancy  issue  for  the  stock  assessment 
allowing evaluations of management strategies to account for uncertainty in the catch (NAFO SC 
WP). 

38 

3.3  Management Strategy Evaluation: .................................................................................... 49 

3.3.1 
Developing  of  a  3M  cod  MSE  (SCR  14/44).  To  carry  out  quantitative  simulations  to 
evaluate sustainability of management objectives (tasks 3.1), the development of different sets of 
performance  statistics  to  evaluate  the  system  reaching  management  objectives  (task  3.2)  and 
the proposals of Harvest Control Rules for the management of the stock (task 3.3). .................. 49 
3.3.2 
Using qualitative and quantitative analysis compare the different options and evaluate 
whether the options are capable of achieving the biological objectives. In doing so, the study will 
take  into  account  ecosystem  interactions  and  technical  interactions  of  different  fisheries  (SCR 
will be presented in the 2015 NAFO Scientific Council meeting). ................................................. 70 
Socio-economic Analysis. ............................................................................................. 96 
3.3.3 
Overview of the North Atlantic Fishing Fleet ................................................................................... 108 

3.4 

Technical measures for an improved management of the stock:................................ 129 

3.4.1 

Spatial and seasonal fleet activity and cod distribution in Flemish Cap. .................... 129 

3.4.2 
Analyse different technical measures (related to gear, spatial or temporal closures,...) 
that could be adopted to increase Fmsy. ...................................................................................... 155 
4  Recommendations ................................................................................................... 166 

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Disclaimer 

The information and views set out in this report are those of the authors and do not necessarily reflect 
the official opinion of the Commission. The Commission does not guarantee the accuracy of the data 
included  in  this  study.  Neither  the  Commission  nor  any  person  acting  on  the  Commission’s  behalf 
may be held responsible for the use which may be made of the information contained therein.  

Page iv 

 
 
 
Abstract 

The purpose of this study was to provide the European Commission and the NAFO Scientific Council 
with technical and scientific analysis to support the development of a multi-annual management plan 
for the cod stock in NAFO Division 3M. The study objectives were to provide advice for this stock on 
1)  management  objectives  and  conservation  limits;  2)  catch  discrepancy;  3)  Management  Strategy 
Evaluation; and 4) discards elimination and by-catch reduction. To meet these objectives: 1) an Fmsy 
proxy  (F30%SPR)  was  defined;  an  exploratory  three-species  model  (redfish,  shrimp  and  cod)  was 
done; and an analysis of social-economic objectives was performed; 2) comparisons of the available 
catch data were made; 3) an MSE with two Harvest Rules and six Operating Models was carried out 
in a projected period of 20 years, developing five different performance statistics and their targets to 
evaluate  the  management  objectives  and  using  qualitative  and  quantitative  analysis  to  evaluate 
whether  the  options  achieve  social-economic  objectives  and  4)  analysis  of  the  spatial  and  seasonal 
fishing activity  and distribution of 3M cod,  as well as areas with important concentrations of juvenile 
fish and/or spawning aggregations was performed; and finally different technical measures that could 
be adopted to increase Fmsy were analysed. 

Page v 

 
 
 
1  Executive Summary 

6 

 
 
 
7 

1.1  Purpose of the specific contract 

The purpose of this Specific Contract was to provide the European Commission and the NAFO 
Scientific  Council  with  technical  and  scientific  analysis  to  support  the  development  of  a  multi-
annual management plan for the cod stock in Division 3M of NAFO. 

The objectives of the study were to provide advice for the NAFO Division 3M cod stock on: 

1.  Management objectives and conservation limits. 

2.  Catch discrepancy. 

3.  Management Strategy Evaluation. 

4.  Elimination of discards and reduction of by-catch. 

1.2  Tasks performed and main results 

In order to meet these objectives, the tasks performed and their main results are as follows: 

Task 1. Management objectives and conservation limits: 

1.1.  Estimate biological reference points such as possible target and limit reference points 
of fishing mortality and spawning stock biomass, including MSY reference points, and 
the  NAFO 
provide  options 
for 
Precautionary  Approach  Framework  and 
conservation plans and rebuilding strategies (NAFO SCR 14/19). 

for  management  objectives 

the  NAFO  general 

into  account 

framework 

taking 

The  Fmsy  proxy  was  estimated  using  data  from  1972  to  2013  because  trends  in 
biological  parameters  (weights,  maturity  and  partial  recruitment)  have  been  observed 
in the most recent years (2009-2013). 

It was decided not to use Fmax in the Div. 3M cod case as the best Fmsy proxy due to the 
recruitment decline at low spawning stock sizes and that probably Fmax overestimates 
Fmsy.  The  NAFO  Study  Group  on  Limit  Reference  considered  that  when  a  SR 
relationship or a production relationship cannot be determined from the available data, 
consideration  should  be  given  to  SPR  analysis  as  a  means  of  determining  Fmsy.  The 
determination of the appropriate %SPR for use as Fmsy depends on the biology of the 
population. Examination of the data for Div. 3M cod determined that a 30% SPR was 
the most appropriate proxy for Fmsy; therefore the SC decided that F30%SPR (the fishing 
mortality which reduces Spawner Per Recruit (SPR) to 30% of its value at F=0) is the 
best Fmsy proxy at this moment. 

1.2.  Consider  ecosystem  interactions  (including  the  relation  with  redfish  and  shrimp  in 
NAFO Division 3M) when analysing possible management objectives and conservation 
limits.  

An exploratory three-species model (redfish, shrimp and cod) was used to investigate 
the  joint  dynamics  of cod,  redfish  and  shrimp  in  the  Flemish  Cap,  and  to  explore  the 
plausibility  of  producing  a  combined  MSY  for  these  three  species.  Model  results 
suggested  that,  in  unexploited  conditions,  cod  would  be  expected  to  be  a  highly 
dominant  component  of  the  system,  and  high  shrimp  stock  sizes,  like  the  ones 
observed  in  the  mid-late  1990s,  would  not  be  a  stable  feature  in  the  Flemish  Cap. 
Different MSY scenarios were explored, including the maximization of combined yields 
for  the  three  species  (MS),  as  well  as  three  single  species  scenarios  where  fishing 
rates were set to maximize the yield of each one of the individual species (SS Cod, SS 
Redfish and SS Shrimp). Results from these explorations indicated that simultaneously 
achieving the yields produced by single species MSY scenarios is not possible; if such 
“parallel  single-species  MSY”  strategy  is  implement  in  the  model,  significantly  lower 
yields  than  the  ones  from  each  individual  single  species  MSY  scenario  are  obtained. 
Overall, achieving high yields for the fish species implies low levels of shrimp biomass, 

 
 
 
while maximizing shrimp yields would require accepting significantly lower levels of cod 
and  redfish  biomass.  To  a  lesser  degree,  trade-offs  are  also  expected  between  cod 
and redfish. 

This  exploratory  analysis  indicates  that  important  trophodynamic  connections  exist 
among  these  three  species,  hence  maximizing  yields  requires  addressing  the  trade-
offs emerging from these interactions. 

1.3.  Analyse  potential  social  and  economic  objectives  such  as  catch  maximisation  over  a 

pre-identified period. 

To perform the socioeconomic study and to compare the different HCRs, one scenario 
of  the  Model-Based  HCR  (scenario  1)  and  another  one  from  its  equivalent  of  Model-
Free HCR (scenario 2) were chosen. The chosen scenarios were: natural mortality fix 
(Mfix) and Recruitment independent of SSB (SR1) with 35% Ftarget for the Model-Based 
HCR (scenario 1) and natural mortality fix (Mfix) and Recruitment independent of SSB 
(SR1)  for  the  Model-Free  HCR  (scenario  2).  Those  two  scenarios  of  landings  were 
changed into two scenarios of values of landing by using the price series of the Annual 
Fleet economic report of 2014. 

The  economic  impact  of  NAFO  Div.  3M  cod  in  terms  of  total  effect  (sum  of  direct, 
indirect  and  induced  effects)  for  scenario  1  in  revenue  was  99  million  Euros  (this 
implies  a  multiplier  of  2.47)  in  2014  increasing  up  to  170  million  in  2033.  Within  the 
same scenario of landings, the total effect of value added goes from 40 million in 2014 
to 69 million in 2033. Employment starts in 884 in 2014 and reaches 1 514 in 2033. 

By  using the  landings forecasted by scenario 2, the  values  obtained  are much lower. 
Thus, we reach 66 million of revenues as the total effect in 2033, 27 million  Euros for 
the total effect of value added and employment, its total effect, goes down from 914 in 
2014 to 592 in 2033. 

The  scenario  1  is  clearly  preferable  to  scenario  2  when  considering  the  period  2014-
2033.  In  this  case,  revenue  goes  up  by  70  million  Euros  instead  of  only  30  million 
Euros of scenario 2. Employment in scenario 1 goes up by 630 and goes down by 322 
in scenario 2. 

Now, if we consider two time periods, one that goes from 2014 to 2020 and a second 
one going from 2021 to 2033, the results are the following: 

Revenues  in  the  period  2014  to  2020  start  at  99  million  and  go  to  almost  44  million 
Euros for the scenario 1. Value added goes from 40 million Euros to almost 18 million. 
Employment  starts  at  884  in  2014  and  reaches  890  in  2020.  For  the  same  scenario, 
revenues in the year 2021 are 43.5 million Euros and practically 170 million in the year 
2033.  Value  added  goes  from  17.5  in  2021  to  69  million  in  2033.  Employment  goes 
from 889 to 1 514 in years 2021 and 2033 respectively. In the first sub-period there is a 
loss  in  the  value  of  cod  of  about  55  million  Euros  in  revenue  and  22  million  Euros  in 
value  added.  Employment  goes  down  by  almost  500  jobs.  In  the  second  period, 
instead,  the  evolution  is  very  positive.  Revenue  goes  up  by  126  million  Euros,  value 
added by 50 million and employment by 1 125 jobs. 

For the scenario 2, revenues in the period 2014 to 2020 start at 102 million Euros and 
go to 97.5 million. Value added goes from 41 million to 39 million. Employment starts at 
914  in  2014  and  reaches  870  in  2020.  For  the  same  scenario,  revenues  in  the  year 
2021 are 54 million Euros and 66 million in the year 2033. Value added goes from 22 
million in 2021 to 27 million in 2033. Employment goes from 485 to 592 in years 2021 
and 2033 respectively. In this scenario, revenues, value added and employment move 
much less than in the previous scenario. In the first sub-period revenue goes down by 
5 million and in the second sub-period goes up by 12 million. Value added goes down 
2  million  Euros  in  the  first  sub-period  and  goes  up  by  5  million  in  the  second  sub-
period. Employment goes down 44 jobs in the period 2014-2020 and goes up 107 jobs 
in the period 2021-2033.  

Then, for the first period 2014-2020 it is preferable scenario 2 because the losses are 
smaller, about 5 million Euros of revenues and about 44 jobs (the losses in scenario 1 
in  that  period  are  55  million  Euros  of  revenues  and  494  jobs).  For  the  second  period 

8 

 
 
9 

2021-2033 it is much more preferable scenario 1. In this case, revenue gains are 126 
million  Euros  for  scenario  1  and  only  12  million  Euros  for  scenario  2.  Employment 
gains are 1125 for scenario 1 and only 107 for scenario 2. 

Task 2. Catch discrepancy: 

2.1.  Provide  options  to  resolve  the  catch  discrepancy  issue  for  the  stock  assessment 

allowing evaluations of management strategies to account for uncertainty in the catch. 

During  the  June  2014  SC  meeting  the  only  sources  of  recent  catch  information 
available  for  all  stocks  but  one  stock  were  STATLANT  21A  data  and  Daily  Catch 
Records (DCR) for fleets that operated in the NRA. The exception was the availability 
of scientific observer CPUE data for one fleet fishing Div. 3M cod. It was analyzed the 
CPUEs resulting from Daily Catch Reports (DCR) of Div. 3M cod for the period 2011-
2013.  These  CPUEs  were  compared  with  the  available  scientific  data.  The  results  of 
this comparison show significant differences in 2011 and 2012 and a decrease of such 
differences in 2013. Based on these results, Scientific Council decided to use in the 3M 
cod stock assessment the total catches from the DCR in 2013 (13 985 t), maintaining 
the model catch estimation for 2011 and 2012. 

Task 3. Management Strategy Evaluation: 

3.1.  Carry out quantitative simulations to evaluate sustainability of management objectives. 

It  was  decided  that  the  most  appropriate  data  to  implement  the  Div.  3M  cod  MSE 
should  be  the  data  used  in  the  approved  2014  assessment.  It  has  been  defined  six 
different  Operating  Models 
the 
Stock/Recruitment  relationship  and  different  assumptions  about  Natural  mortality  (M) 
as the most appropriated for this case. These OMs are the following: 

(OM)  based  on  different  assumptions 

in 

  M  constant,  estimated  by  the  model  for  all  ages  and  for  all  years  with  the 

followings S/R functions:  

i. Recruitment independent of SSB.  
ii. Segmented Regression fit with Beta=Approved Blim.  
iii. Segmented Regression fit with the assessment results.  

  M  no  constant,  variable  by  time  periods  and  age  ranges  with  the  followings 

S/R functions:  

i. Recruitment independent of SSB.  
ii. Segmented Regression fit with Beta=Approved Blim.  
iii. Segmented Regression fit with the assessment results. 

It  was  agreed  that  the  simulation  period  will  be  20  years  and  that  some  of  the 
Performance  Objectives  proposed  by  the  FC/SC  RBMS  should  be  measured  in  a 
medium (7 years) and long term period (20 years). 

A set of 24 different scenarios resulting from the combination of  the six different OMs 
and the different HCRs to test were examined. 

3.2.  Develop  different  sets  of  performance  statistics  to  evaluate  the  system  reaching 

management objectives. 

The general objective of the 3M Cod MSE is to “maintain the 3M cod Spawning Stock 
Biomass  (SSB)  in  the  safe  zone  as  defined  by  the  NAFO  precautionary  approach 
the  optimum  utilization,  rational  management  and 
framework  and 
conservation of the 3M cod stock”. This general objective should be reached attending 
five different management objectives. 

to  assure 

It was decided to develop 5 different performance statistics (PS) as well as their targets 
to  evaluate  the  management  objectives  agreed  by  the  NAFO  Fisheries  Commission 
and  Scientific  Council  Joint  Working  Group  on  Risk-Based  Management  Strategies 
(FC-SC WG RBMS). The Management objectives, PS and targets are the following: 

 
 
Performance Statistics (PS)   Performance Targets (PT)  

Management 
Objectives 

Very  low  risk  of 
breaching Blim  

SSBy  is  the  Spawning  Stock 
Biomass  in  the  year  y  of  the 
projection period. 

Low 
risk 
overfishing  

of 

For the model-free HCR: 

       y=1,…,20 

       y=1,…,20 

Fy  is  the  Fishing  Mortality  in 
the  year  y  of  the  projection 
period. Fmsy or its proxy.  

Low  risk  of  steep 
decline  

SSBy  is  the  Spawning  Stock 
Biomass  in  the  year  y  of  the 
projection period. 

is 

the 

SSBlowest_y 
lowest 
Spawning  Stock  Biomass 
level  in  the  year  y  of  the 
projection 

Maximum average 
catch  over 
the 
period  

Limited 
catch variation 

annual 

     x=0.1, 0.15, 0.2 

  y=1,…,7 
  y=1,…,20 

3.3.  Define options for Harvest Control Rules for the management of the stock. 

It was decided to use two different Harvest Control Rules (HCRs): A model-based and 
a model-free HCR.  

10 

lim/ySSBBlim/10.1yPSSBBmsy/yFF/10.3ymsyPFF70/SSBSSB200/SSBSSB_70/lowestSSBSSB_200/lowestSSBSSB70/0.250.1PSSBSSB200/0.250.1PSSBSSB_70/0.250.1lowestPSSBSSB_200/0.250.1lowestPSSBSSB71/7iiTAC201/20iiTAC71max/7iiTAC201max/20iiTAC_1_/assessyassessyTACTAC_1_/1assessyassessycountTACTACx_1_/1assessyassessycountTACTACx 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
11 

Option 1 (Model-based HCR): 

A Blim of 14 000 tons was approved by the NAFO Scientific Council for the 3M 
cod stock. Three different levels of F will be considered as Ftarget, corresponding 
to  probabilities  of  20%,  35%  and  50%  of  exceeding  Fmsy.  It  was  presented  by 
the  project  and  approved  by  the  NAFO  SC  that  F30%SPR  (the  fishing  mortality 
which  reduces  Spawner  Per  Recruit  (SPR)  to  30%  of  its  value  at  F=0)  is  the 
best Fmsy proxy at this moment for 3M cod. 

Option 2 (Model-free HCR): 

Where:  slope  =  slope3+  is  the  slope  of  the  log-linear  regression  lines  fit  of  the 
EU Flemish Cap survey B3+ index from y-5 to y-2.  

λ = an adjustment variable to ensure that the relative change in TAC is greater 
than  the  perceived  relative  decline  in  stock  size  (i.e.  λ  >  1,  therefore  allowing 
the strategy to halt the decline of the stock size through positive feedback). 

The index used is the EU Flemish Cap survey 3+ biomass index (1988-2013). 
After  making  some  deterministic  projections  to  understand  how  different  λ 
values perform, it was decided that the best approach for λ is a variable value 
depending  on  the  population  status:  if  the  SSB  is  less  or  more  than  the 
observed SSB in 2013 (by iteration); the 2013 SSB is around  the maximum of 
the historic period (1972-2013). For each of the options, we chose the following 
variable λ approach with different values for slope > 0 and slope < 0 as the best 
options: 

If SSB < maximum observed SSB =>  

         slope3+ < 0 => λ=1.25 

         slope3+ > 0 => λ=1 

         slope3+ < 0 => λ=1 

If SSB > maximum observed SSB =>  

         slope3+ > 0 => λ=1.15 

For  both  options  and  noting  the  desire  for  relative  TAC  stability,  TAC  should  be 
constraint  to  a  fixed  percentage  of  annual  change.  Level  of  constraint  used  in  this 
study is 20%. 

3.4.  Using qualitative and quantitative  analysis compare the different options and evaluate 
whether the options are capable of achieving social and economic objectives. In doing 
so, the study will take into account ecosystem interactions and technical interactions of 
different fisheries. 

None of the tested HCR reached the established performance objectives in the 2014-
2023  period  but  most  of  the  scenarios  met  the  performance  objectives  in  the  period 
2024-2033.  In  the  period  2014-2023,  the  model-based  HCR  is  closer  to  achieve  the 
established  performance  objectives  than  the  model-free  HCR.  The  main  reasons  for 
not achieving these objectives are the high initial F and catch levels and the 20% catch 
constraint.  The  necessary  period  to  achieve  the  performance  objectives  would  be 
longer if we had applied a more restrictive TAC constraint (15% or 10%). 

Differences in the results come mainly from the assumed stock recruitment relationship 
and in a much lesser extent from assumed natural mortality (M) and the different Ftarget 
levels  tested.  The  impact  in  the  results  (but  no  over  the  trends)  of  the  assumed 
variability of the biological parameters for the projection period could be important. 

Two of the 24 scenarios analysed have been chosen to carry out the socio economic 
analysis,  one  for  the  model-based  HCR  and  the  equivalent  for  the  model-free  HCR. 
The main reason for choosing these scenarios are that they are the most similar to SC 
2014 3M Cod approved assessment (See socio-economic results in Task 1.3 results). 

Task 4. Technical measures for an improved management of the stock: 

yytargetylimTAC=TotalBiomassFPSSB>Byy-13+TAC=TAC1+λslope 
 
 
 
 
 
 
 
        
 
  
 
 
 
        
 
 
 
 
 
        
 
  
 
 
 
        
 
4.1.  Analyse the spatial and seasonal activity of the fishing activity and spatial and seasonal 

distribution of cod in the NAFO Division 3M (Flemish Cap). 

In relation to spatial and seasonal fishing activity the results shows that the effort was 
very  low  in  the  period  2008-2009.  In  these  years,  as  the  cod  fishery  was  closed,  the 
main fisheries were shrimp and redfish and were carried out in the Western part of the 
bank: the shrimp fishery in depth more than 300 meters and redfish in depth less than 
300 meters. When cod fishery was re-opened in 2010, a significant increase of fishing 
activity in deeper areas of the East and West of Flemish Cap was observed. 

Regarding  the  spatial  and  seasonal  distribution  of  catches,  in  2012-2013  the  highest 
cod  CPUE  based  on  DCR  data  were  found  in  the  South-West  area  of  Flemish  Cap. 
However,  in  the  same  period,  the  Spanish  fleet  activity  showed  a  displacement  from 
South-West in 2012 to Eastern and shallower waters in 2013 (200-300 m). This change 
in  distribution  was  associated  with  a  change  in  size  distribution  of  catches  to  smaller 
individuals,  from  around  60  cm  in  2012  to  40  cm  in  2013.  However,  the  EU  survey 
showed in these years a constant pattern in distribution of cod in the bank, with fishes 
being distributed evenly around the center of the bank. 

4.2.  Provide  information  on  areas  with  important  concentrations  of  fish  below  a  minimum 
conservation  size  and  areas  where  spawning  aggregations  occur.  Analyse  the 
consistency along the time series of such areas. 

The spatial  distribution  of mature and immature cod female individuals did  not  exhibit 
any  specific  spatial  area  where  spawners  or  juveniles  are  more  abundant.  Results 
show that despite the length 50% maturity changes over time, the sampled population 
maintains the same proportion of mature and immature individuals in the time series. 

The proportion of cod below the MLS (41 cm) has shown a peak in year 2011 due to 
the good recruitments in the period 2009-2012, but the number of bigger individuals is 
stable  in  the  time  series.  Concerning  the  spatial  distribution  of  concentration  of  cod 
below the MLS, as in the case of mature and immature individuals, there is not a clear 
spatial pattern but the cod distribution by depth seems to have a general pattern where 
fish less than 50 cm are at less than 400 m depth and fish with more than 50 cm are at 
depth more than 400 m. 

4.3.  Analyse different technical  measures (related to  gear, spatial or temporal closures,...) 

that could be adopted to increase Fmsy. 

Different  technical  measures  that  could  be  adopted  in  the  NAFO  3M  cod  case  to 
increase the MSY have been analyzed: design characteristics of the gears (mesh size, 
sorting  grids),  spatial  area  closures  and  minimum  fish  size.  There  is  not  so  much 
information  about  selectivity  and  technical  measures  in  the  NAFO  cod  fisheries.  This 
analysis was mainly based on the Norwegian cod fisheries information. 

The  adoption  of  the  studied  technical  measures  seems  to  have  a  biological  support 
because  they  would  allow  to  catch  bigger  fish  minimizing  the  immature  cod  catches 
less than MLS and the redfish by-catches in the NAFO 3M cod fishery.  

These  technical  measures  would  increase  slightly  the  current  equilibrium  yields  but 
with a large increase in the fishing effort. The increase in the equilibrium yield with the 
selectivity change seems not to be very efficient. 

12 

 
 
 
 
13 

2 

Introduction 

 
 
 
 
2.1  Purpose of the specific contract 

The cod stock in NAFO Division 3M (Flemish Cap) experienced very low biomass levels in the 
beginning  of  the  1990s  and  was  under  moratorium  to  direct  fishing  between  1999  and  2009. 
The stock rebuilt and the direct fishery reopened in 2010 with a TAC of 5 000 tons. After that, a 
stepwise increase of the TAC has been decided by NAFO up to 14 521 tons for 2014, but there 
is no management plan and there are no specific management objectives. The spawning stock 
biomass increased substantially since mid-2000s and is now well above the limit reference point 
and  among  the  highest  levels  observed.  The  rebuilding  of  this  cod  stock  was  a  success  for 
NAFO  and  it  is  now  very  important  to  develop  a  management  plan  fully  based  on  scientific 
advice. 

The purpose of this Specific Contract was to provide the European Commission and the NAFO 
Scientific  Council  with  technical  and  scientific  analysis  to  support  the  development  of  a  multi-
annual management plan for the cod stock in Division 3M of NAFO. 

The objectives of the study were to provide advice for the NAFO Division 3M cod stock on: 

1.  Management objectives and conservation limits. 

2.  Catch discrepancy. 

3.  Management Strategy Evaluation. 

4.  Elimination of discards and reduction of by-catch. 

2.2  Tasks to be performed 

In order to meet these objectives the tasks to be performed under this Specific Contract were as 
follows: 

Task 1. Management objectives and conservation limits: 

1.1.  Estimate biological reference points such as possible target and limit reference points 
of fishing mortality and spawning stock biomass, including MSY reference points, and 
the  NAFO 
provide  options 
Precautionary  Approach  Framework  and 
for 
conservation plans and rebuilding strategies (NAFO SCR 14/19). 

for  management  objectives 

the  NAFO  general 

into  account 

framework 

taking 

1.2.  Consider  ecosystem  interactions  (including  the  relation  with  redfish  and  shrimp  in 
NAFO division 3M) when analysing possible management objectives and conservation 
limits. 

1.3.  Analyse  potential  social  and  economic  objectives  such  as  catch  maximisation  over  a 

pre-identified period. 

Task 2. Catch discrepancy: 

2.1.  Provide  options  to  resolve  the  catch  discrepancy  issue  for  the  stock  assessment 

allowing evaluations of management strategies to account for uncertainty in the catch. 

Task 3. Management Strategy Evaluation: 

3.1.  Carry out quantitative simulations to evaluate sustainability of management objectives. 

3.2.  Develop  different  sets  of  performance  statistics  to  evaluate  the  system  reaching 

management objectives. 

3.3.  Define options for Harvest Control Rules for the management of the stock. 

3.4.  Using qualitative and quantitative analysis compare the different options and evaluate 
whether the options are capable of achieving social and economic objectives. In doing 

14 

 
 
15 

so, the study will take into account ecosystem interactions and technical interactions of 
different fisheries. 

Task 4. Technical measures for an improved management of the stock: 

4.1.  Analyse the spatial and seasonal activity of the fishing activity and spatial and seasonal 

distribution of cod in the NAFO Division 3M (Flemish Cap). 

4.2.  Provide  information  on  areas  with  important  concentrations  of  fish  below  a  minimum 
conservation  size  and  areas  where  spawning  aggregations  occur.  Analyse  the 
consistency along the time series of such areas. 

4.3.  Analyse different technical  measures (related to  gear, spatial or temporal closures,...) 

that could be adopted to increase Fmsy. 

2.3  General approach 

Project  tasks  have  been  performed  taking  into  account  the  proposals  and  recommendations 
made by the NAFO Fisheries Commission and Scientific Council Joint Working Group on Risk-
Based Management Strategies (FC-SC WG RBMS) and the comments made by the NAFO SC. 
The  Fisheries  Commission  and  Scientific  Council  Joint  Working  Group  on  Risk-Based 
Management  Strategies  (FC-SC  WG  RBMS)  proposed  in  February  2014  a  plan  for  3M  cod 
based  on  the  General  Framework  on  Risk-based  Management  Strategies.  The  2014  June 
NAFO SC made some comments to reduce the high number of scenarios contemplated in the 
FC-SC WG RBMS proposal. 

MSE  is  a  general  framework  aimed  at  designing  and  testing  MPs  which  specify  previously 
agreed decision rules (HCR) for setting and adjusting TACs or effort levels to achieve a set of 
fishery management objectives. Simulation testing is used to determine the extent to which an 
MP  is  robust  to  uncertainty,  and  MPs  are  usually  selected  so  that  there  is  a  reasonable 
likelihood that the (pre-specified and quantified) management goals can be satisfied. 

The  simulation  algorithm  that  was  used  in  the  3M  cod  to  test  the  MSE  is  an  R  package  to 
conduct  Bio-Economic  Impact  assessments  using  FLR  (FLBEIA)  developed  by  Garcia  et  al. 
(2013). FLBEIA is an R package build on top of FLR libraries. The purpose of the package is to 
provide a flexible and generic simulation model to conduct Bio-Economic Impact Assessments 
of harvest control rule based management strategies under a Management Strategy Evaluation 
(MSE) framework. 

It  was  decided  to  use  the  same  Bayesian  XSA  model  and  input  data  approved  in  2014  SC 
meeting to estimate the abundance at age starting point of the OM. 

All tasks results were based on the work carried out during the project except the task 1.2 that 
was  addressed  based  on  the  results  of  the  December  2011  SC  WG  on  the  Ecosystem 
Approach to Fisheries Management. 

To develop all the tasks, members of the project have attended the following meetings: 

NAFO Scientific Council (SC) June 2014 

SAFEWaters-2 kick-off meeting 2014 

Meetings 

Dates 

30/05-15/06/2014 

16-17/06/2014 

NAFO Scientific Council Working Group Ecosystem Science and Assessment (SC WGESA) 2014 

18-27/11/2014 

Fisheries Commission-Scientific Council Working Group on Catch Reporting (FC-SC WG-CR) 2015 

20-21/04/2015 

Fisheries Commission-Scientific Council Working Group Risk Based Management Strategies (FC-SC 
WG-RBMS) 2015 

Final Report meeting 

22-24/04/2015 

05/2015 

 
 
 
 
The sole responsibility for the content of this report lies with the authors. It does not necessarily 
reflect the opinion of the European Union. Neither DG MARE nor the European Commission are 
responsible for any use that may be made of the information contained therein. 

2.4  Structure of the report 

All tasks were completed according with the work plan presented. The methodology and results 
of the different tasks were or will be presented in NAFO as  Scientific Council Research (SCR) 
documents or Working Papers (WP) except socioeconomic analysis (Task 1.3 and 3.4) because 
there is not an appropriate forum in NAFO to discuss these results. 

Section 3 of this report (Results) is based on the NAFO SCR and WP that have been submitted 
or will be presented to NAFO Scientific Council (SC) and the socioeconomic analysis performed 
report  (Task  1.3  and  3.4).  The  general  structure  of  Results  section  of  this  report  is  to  present 
task  by  task  the  different  results  although  in  some  cases  explained  below  it  was  decided  to 
group some tasks. 

The task 1.2 “Consider ecosystem interactions (including the relation with redfish and shrimp in 
NAFO  division  3M)  when  analysing  possible  management  objectives  and  conservation  limits” 
was addressed with the information of the December 2011 SC WG on the Ecosystem Approach 
to  Fisheries  Management.  Addressing  this  task  in  full  would  require  developing  and  validating 
multispecies  models  but  work  on  this  topic  is  only  on  its  initial  stages.  However,  some 
preliminary  modelling  explorations  made  by  the  SC  WG  on  the  Ecosystem  Approach  to 
Fisheries  Management  in  2011  can  provide  some  useful  insights  in  the  functioning  of  the 
Flemish Cap fish community in general, and the interactions between cod, shrimp and redfish in 
particular.  

It  was decided to group the following tasks in only one section  (3.3.1) called “Developing  of a 
3M  cod  MSE”:  To  carry  out  quantitative  simulations  to  evaluate  sustainability  of  management 
objectives (task 3.1), the development of different sets of performance statistics to evaluate the 
system reaching management objectives (task 3.2) and the proposals of Harvest Control Rules 
for the management of the stock (task 3.3). The main reason to group these tasks was that they 
are  key  elements  in  the  MSE  approach  and  they  have  been  presented  to  NAFO  SC  as 
“Developing of a 3M cod MSE” in a single SCR document. 

The  biological  results  of  the  task  3.4  (Using  qualitative  and  quantitative  analysis  compare  the 
different  options  and  evaluate  whether  the  options  are  capable  of  achieving  social  and 
economic  objectives.  In  doing  so,  the  study  will  take  into  account  ecosystem  interactions  and 
technical interactions of different fisheries) are presented in the section 3.3.2. 

The  results  of  task  1.3  (Analyse  potential  social  and  economic  objectives  such  as  catch 
maximisation  over  a  pre-identified  period)  and  the  socioeconomic  part  of  the  task  3.4  (Using 
qualitative  and  quantitative  analysis  compare  the  different  options  and  evaluate  whether  the 
options are capable of achieving social and economic objectives. In doing so, the study will take 
into  account  ecosystem  interactions  and  technical  interactions  of  different  fisheries)  are 
presented in the section 3.3.3 as Socioeconomic Analysis. 

It was also decided to group the task 4.1 (Analyse the spatial and seasonal activity of the fishing 
activity and spatial and seasonal distribution of cod in the NAFO Division 3M (Flemish Cap) and 
the  task  4.2  (Provide  information  on  areas  with  important  concentrations  of  fish  below  a 
minimum  conservation  size  and  areas  where  spawning  aggregations  occur.  Analyse  the 
consistency along the time series of such areas) under the section 3.4.1 section called “Spatial 
and seasonal fleet activity and cod distribution in Flemish Cap”. 

The SCR documents presented to NAFO with the results of the project funded by the European 
Union  under  the  Framework  Contract  MARE/2012/21  -  "Scientific  advice  for  Fisheries  beyond 
EU Waters" - Specific Contract No 2 (SI2.681887) “Provision of advice on the development of a 
multiannual  management  plan  and  the  evaluation  of  a management  strategy  for  cod  in  NAFO 
Division 3M (SAFEwaters-2)” were the following: 

16 

 
 
17 

Task 1. Management objectives and conservation limits: 

González-Costas, F. and D. González–Troncoso, 2014. Biological Reference Points of 3M cod. 
NAFO SCR Doc. 14/019. Serial No. N6313. 

Task 2. Catch discrepancy: 

González-Costas F. and D. González-Troncoso, 2014. Cod 3M CPUE analyze based on DCR 
and VMS data. NAFO SC Working Paper June 2014. 

Task 3. Management Strategy Evaluation: 

González-Costas F., D. González-Troncoso, D. Miller, A. Urtizberea, A. Iriondo and D. García, 
2014. Biological Developing of a 3M cod MSE. NAFO SCR Doc. 14/044. Serial No. N6341. 

González-Troncoso,  D.  and  F.  González-Costas,  2014.  3M  cod  assessment  for  different 
assumptions over M. NAFO SCR Doc. 14/018. Serial No. N6312. 

González-Troncoso D., A. Urtizberea, F. González-Costas, D. Miller, A. Iriondo and D. García, 
2015. Results of the 3M Cod MSE. To be presented in the 2015 NAFO SC June meeting. 

Task 4. Technical measures for an improved management of the stock: 

Iriondo  A.,  F.  González-Costas,  N.  Hintzen,  M.  Machiels,  D.  González-Troncoso  and  A. 
Urtizberea, 2014. Spatial and seasonal fleet activity and cod distribution in Flemish Cap. NAFO 
SCR Doc. 14-068. Serial No.N6407 

González-Costas  F.,  A.  Iriondo,  D.  González-Troncoso  and  A.  Urtizberea,  2015.  Analyze 
different  technical  measures  that  could  be  adopted  to  increase  Fmsy.  To  be  presented  in  the 
2015 NAFO SC June meeting. 

 
 
 
3  Results 

18 

 
 
 
 
19 

3.1  Management objectives and conservation limits: 

3.1.1  Estimate  biological  reference  points  such  as  possible  target  and  limit 
reference  points  of  fishing  mortality  and  spawning  stock  biomass, 
including  MSY  reference  points,  and  provide  options  for  management 
objectives  taking  into  account  the  NAFO  Precautionary  Approach 
Framework and the NAFO general framework for conservation plans and 
rebuilding strategies (NAFO SCR 14/19). 

This task was addressed based on the results of the NAFO SCR 14/19 presented in the June 2014 NAFO 
SC. 

Introduction 

The assessment of the NAFO 3M cod stock has been performed every year since 2008 using the Bayesian 
model (Fernández et al., 2008). A Blim of 14 000 tons was proposed by the NAFO Scientific Council in 
2000. The appropriateness of this value  given the results from the new method used to assess the stock 
was examined in 2008, concluding that it is still an appropriate reference.  

The NAFO Fisheries Commission formally adopted a Precautionary Approach (PA) framework in 2004 
(NAFO/FC  Doc.  04/17)  as  proposed  by  NAFO  Scientific  Council  (NAFO  SCS  Doc.  03/23).  The  SC 
framework  provides  a  structure  that  included  limits,  buffers,  targets  and  management  strategies  that 
would adjust fishing mortality to keep stocks in the Safe Zone. In 2012, Fisheries Commission requested 
to Scientific Council: to provide Bmsy and Fmsy for cod in Div. 3M. 

In 2013, NAFO Scientific Council discussed the 3M cod reference points based on the results of the study 
presented by Gonzalez-Troncoso et al., 2013. This study used the stock recruitment (S/R) data for 3M cod 
2013  assessment  (Gonzalez-Troncoso  et  al.,  2013).  Three  different  S/R  models  were  fit  to  these  data. 
Results show that no stock recruitment relationship fits appropriately the 3M cod data and the Scientific 
Council  (NAFO,  2013)  noted  that  the  level  of  Bmsy  estimated  from  Yield  per  Recruit  (YPR)  and 
Spawning Per Recruit (SPR) depends on assumptions about the  level of recruitment.  So, more research 
about  the  possibility  of  changes  in  productivity  and  the  level  of  recruitment  that  should  be  used  to 
estimate the MSY is needed. 

A  major  management  objective  in  fisheries  is  to  keep  stocks  at  levels  capable  of  producing  maximum 
sustainable  yields  (MSYs).  Management  objectives  based  on  MSY  or  related  reference  points  (e.g. 
fishing mortality that produces MSY (Fmsy); spawner-per-recruit proxies) are in use for many species and 
stocks  worldwide.  The  various  reference  points  require  different  amounts  and  types  of  information, 
ranging  from  biological  information  (e.g.  natural  mortality,  growth,  and  stock-recruitment  relationship) 
and  fisheries  characteristics  (e.g.  age-specific  selectivity),  to  absolute  estimates  of  biomass  and 
exploitation rates. 

The  Fisheries  Commission  and  Scientific  Council  Joint  Working  Group  on  Risk-Based  Management 
Strategies (FC SC RBMS) proposed for 3M cod two HCRs to be tested under the MSE approach (NAFO, 
2014). The first one is based on fishing mortality assessment model results (model-based HCR) and the 
second one is based on survey indices (model-free HCR). Four different levels of F will be considered as 
Ftarget,  corresponding  to  probabilities  of  20%,  30%,  40%  and  50%  of  exceeding  Fmsy.  If  Fmsy  is  not 
available, an appropriate proxy (e.g. Fmax, current proxy) should be used. 

The aim of this document is to propose the values for Fmsy proxies references points based on the YPR-
SPR analysis taking in account the possibility of changes in productivity and the level of recruitment.  

Material and Methods 

The  3M  cod assessment performed since its reopening  fishery in 2010 uses a Bayesian XSA  model. In 
2014 this assessment was approved during the Scientific Council meeting of June using commercial data 
from 1972 to 2013 and the Canadian survey (1978-1985) and the EU survey (1988-2013) as tuning series. 
Ages used were 1-8+. The number of iterations made for the Bayesian assessment was 5000, so we have 
5000  iterations  of  all  the  parameters  of  the  assessment.  To  see  the  details  about  this  assessment,  see 
González-Troncoso et al. (2014).  

 
 
The mean weights (both in catch and in stock), maturity ogive and partial recruitment at age inputs used 
in  this  study  to  estimate  the  YPR  and  SPR  Reference  Points  (RP)  were  the  same  of  the  3M  Cod  2014 
approved assessment. This was a stochastic assessment having 5000 iterations. Mean-weight-at-age in the 
catch  and  mean-weight-at-age  in  the  stock  are  assumed  with  no  uncertainty,  so  the  value  of  these 
parameters for the 5000 iterations is the same. The natural mortality M was assumed constant for all years 
and  ages  and  was  estimated  for  each  of  the  iterations.  The  median  M  value  estimated  in  the  last 
assessment was 0.156. 

Data Time Series trends 

YPR  can  be  used  to  explore  the  expected  yield  under  equilibrium  conditions,  of  growth,  maturity  and 
natural mortality, for a given or assumed fishery pattern, across a range of exploitation levels. Weights at 
age and maturities change with time and are affected by density dependence. The selection pattern used 
should reflect the contemporary situation to take into account of contemporary trends. 

It was analyzed the YPR and SPR inputs (mean weights, partial recruitment and maturity ogive) to study 
the  possibility  of  changes  in  productivity  in  the  past  and  its  impact  in  the  reference  points  estimated 
values.  

Mean Weights at Age and Abundance: It was chose the mean weight for ages 3 to 5 to observe the stock 
mean weight at age trends. Figure 3.1.1.1 presents the stock mean weights at age (3-5) and the abundance 
for ages 3 to 5. It can be observed different trends: during 1972-1985 the mean weights were variable; in 
1985-1995 the mean weights were more or less stable, in 1996-2007 a clear increasing trend in the mean 
weights and in 2008-2013 a clear decreasing trend in the mean weight were observed. Since the middle of 
the eighties these mean weights trends seem to be negative correlated with the abundance: low abundance 
has a high mean weights and when the abundance increases the mean weight decreases.  

Recruits  per  Spawner:  3.1.1.2  presents  the  recruits  per  spawner  for  the  median  SSB  and  recruitment. 
Trend  in  1972-1991  was  very  variable  with  high  and  low  levels.  The  1992-2003  period  presents  a 
constant very low level. Since 2003 till 2006 the recruits per spawner increased and since then till 2013 
decreased to mean levels. It should be noted that the 2013 point is poorer estimated due to uncertainty in 
recruitment. 

Maturity  at  Age:  Figure  3.1.1.3  shows  the  median  and  the  95%  confidence  interval  of  the  age  of  50% 
maturity. The period 1972-1992 presents a quite constant age of 50% maturity around 5 years old. From 
1993  till  2002  this  age  decreased  till  3  years  old.  Since  then  increased  till  2011  where  the  age  of  50% 
maturity was around 4 years old. In the last two years the 50% maturity age has decreased until 3.4. 

Maturity and mean weights at age seem to be correlated with the abundance: high abundances present low 
mean  weights  and  older  age  of  50%  of  maturity  while  low  abundances  present  the  opposite.  Based  on 
these parameters trends and in the different biomass levels it was decided to divide the time series in three 
different periods depending on if the SSB was below or above the Blim (14 000 t) (Figure 3.1.1.4): 

1972-1995: Mean weights at age and recruits per spawner were variable and maturity at age was high and 
constant till 1990 where started to decline. SSB was variable but normally higher than Blim. The fishery 
was open. 

1996-2008:  SSB  and  abundance  were  very  low,  mean  weights  presented  and  big  increase.  The  age  of 
50% maturity significantly decreased. Recruits per spawner were low till 2003 and high between 2004-
2008. The SSB was during all period below Blim. The fishery was closed from 1999 to 2009. 

2009-2013: Mean weights at age and recruits per spawner decreased. The age of 50% maturity decreased 
to  levels  around  3.5  years  old.  The  SSB  was  during  all  period  above  Blim  and  the  fishery  was  open  in 
2010. 

Partial Recruitment: The median partial recruitment by age was estimated for each year as the median F at 
age divided by the Fbar (3-5). A partial mean recruitment at age was estimated for the different periods. 
The  exploitation  patterns  of  these  different  periods  and  for  the  whole  series  (1972-2013)  are  show  in 
Figure  3.1.1.5.  It  can  be  seen  that  the  1972-1995,  2009-2013  and  1972-2013  partial  recruitments  are 
similar between then and different to the period 1996-2008  where  the  stock  started to  decline and SSB 
was  below  Blim.  Despite  SSB  being  below  Blim  the  fishery  was  open  until  1998.  Fishery  was  closed  in 
1999 until 2009. 

20 

 
21 

YPR and SPR Results 

YPR reference points Fmax and F0.1 and the SPR reference points F20%, F30% and F40% were estimated in the 
periods above described and for the whole series 1972-2013. The reasons to choose these SPR levels were 
that  values  in  the  range  F20%-F30%  have  frequently  been  used  to  characterize  recruitment  overfishing 
thresholds  (Rosenberg  et  al.,  1994),  while  values  in  the  range  F30%  to  F40%  have  been  proposed  as  Fmsy 
proxies  (Gabriel  and  Mace,  1999).  The  percentage  SPR  level  to  use  as  Fmsy  proxy  depends  on  the 
resilience to fishing of the species.  

Figure  3.1.1.6  shows  the  results  for  the  fishing  mortality  YPR  (Fmax  and  F0.1)  and  SPR  (F20%,  F30%  and 
F40%)  reference  points  with  uncertainty  for  the  different  periods  and  Table  3.1.1.1  presents  the  median 
values  estimated.  The  results  shows  that  all  reference  points  values  estimated  in  the  last  period  (2009-
2013) and in the whole period (1972-2013) are very similar and slightly higher than those of the 1972-
1995  period  while  the  values  for  the  period  1996-2008  are  very  different  and  much  higher  than  the 
previous ones. 

Sensitive Analyses 

Natural  mortality  (M)  is  one  of  the  most  influential  quantities  in  fisheries  stock  assessment  and  the 
calculation of management advice. M relates directly to the productivity of the stock, the yields that can 
be  sustained,  and  management  reference  points.  Unfortunately,  M  is  highly  uncertain  for  most  fish 
populations.  M  is  commonly  assumed  to  be  constant  over  age,  time  and  gender,  an  assumption  that  is 
likely to be violated (Vetter, 1988). It is therefore important to evaluate the current assumptions about M 
used in the stock assessment. 

In the 2013 cod 3M approved assessment, the natural mortality M was assumed constant for all years and 
ages and was estimated for each of the iterations. The median M value estimated was 0.156. This value is 
quite different from the  M  value  estimated in other cod stocks. Shelton  et al. (2006) and Swain (2010) 
estimated M values for different cod stocks quite higher than the estimated in the 3M cod assessment. We 
analyzed the sensitive of the RP to different values of M. The median for the whole period data for mean 
weights, partial recruitment, maturity ogive and M at age were used to test the sensibility in deterministic 
way.  Figure  3.1.1.7  shows  the  equilibrium  SSB  results  for  different  M  values  for  Fmax,  F0.1  and  F20%.. 
Results show a high sensitivity to the M value.  

Gonzalez-Troncoso  and  Gonzalez-Costas  (2014)  analyzed  different  M  assumptions  and  their  impact  in 
the  3M  cod  2013  assessment.  The  assumptions  analyzed  were  M  variable  in  time  and  age  and  the  SC 
concluded that it is better to maintain the actual structure of the assessment model about M (M constant 
for all ages and period) because it is  very difficult to the  model estimate  so  much parameters  when  we 
assumed M variable. The median M value constant for all ages (1-8+) and period (1972-2013) estimated 
in the 2014 assessment was 0.156. 

Discussion  

If  a  time  series  of  YPR  inputs  are  available,  the  objective  to  choose  the  inputs  in  the  short  term 
projections should be sufficient year range to smooth out short term noise or measurement error but short 
enough to take account of contemporary trends. But to establish limited reference points with uncertainty 
we  understand that it will better take as long as possible the data to include all the past variation in the 
estimation provided that this long term values are not very different of the contemporary situation. 

In  the  3M  cod  case  we  propose  to  take  the  long  period  (1972-2013)  data  to  estimate  the  Fmsy  proxy 
because the long term period  has a quite similar results in all proxies to the  current period (2009-2013) 
although the long term has incorporated the variability observed in all the inputs time series and the most 
current  period  has  a  very  big  trend  in  all  inputs  (weights,  maturity,  partial  recruitment)  and  we  do  not 
know how these trends will evolutionary in the future. Based on these reasons we discuss only the results 
of the long term (1972-2013) YPR and SPR F references points presented in the Figure 3.1.1.6.  

The NAFO PA Framework (NAFO, 2003) defined Flim as a fishing mortality rate that should only have a 
low probability of being exceeded. Flim cannot be greater than Fmsy. If Fmsy cannot be estimated, then an 
appropriate surrogate may be used instead. 

 
 
 
The  lack  of  fit  of  the  S/R  relationships  is  one  of  the  major  problems  in  3M  cod  to  estimate  Fmsy 
(Gonzalez-Troncoso  et  al.,  2013).  Fmax  was  one  of  the  earliest  measures  used  as  a  proxy  for  Fmsy. 
However, it was often believed to be an overestimate of Fmsy, because it does not account for the fact that 
recruitment must decline at low spawning stock sizes. Computer models have also demonstrated that Fmax 
invariably overestimates Fmsy if a Beverton-Holt stock-recruitment relationship applies, although Fmsy can 
sometimes  exceed  Fmax  with  a  Ricker  curve.  The  ICES  Workshop  on  Implementing  the  ICES  FMSY 
framework (ICES, 2010) recommended that if there is clear peak at low F in the YPR analysis, and there 
is no evidence of recruitment dependence on SSB, then a check should be made that the equilibrium SSB 
implied by this target F is within the observed range of SSB, under this condition Fmax may be appropriate 
proxy of Fmsy.  

In  the  3M  cod  there  are  clear  evidences  of  recruitment  dependence  on  SSB  at  low  SSB  levels,  low 
recruitment have been observed at SSB less than 14 000 tons. The recruitment dependence on biomass is 
less  clear  at  medium  and  high  SSB  levels  although  a  certain  decrease  of  the  recruitment  at  high  SSB 
levels can be observed in the R/SSB plot (Gonzalez-Troncoso et al., 2013). Figure 3.1.1.8 shows the YPR 
and SPR curves for the long term period (1972-2013) periods and it can be observed the curves present a 
clear maximum peak (Fmax). It is not clear for us if the Fmax in the 3M cod case is the best Fmsy proxy due 
to the recruitment decline at low spawning stock sizes and probably Fmax overestimate Fmsy. 

Spawner biomass per recruit analysis should be routinely evaluated in addition to YPR; an advantage of 
SPR based proxies is that they take into account directly the reproductive capacity of the stocks. Several 
studies  have  provided  range  values  for  guidelines  on  percentage  for  spawner  per  recruit  ratios  (in 
reference to unexploited stocks) expected for different life history types of exploited stocks. There is not a 
single  level  of  %  SPR  that  is  optimal  for  all  stocks  and  the  proposal  for  Fmsy  should  include  some 
consideration of life history. Values in the range of F20% to F30% (% SPR relative to SPR at F=0) have been 
characteristic of recruitment overfishing (Rosenberg et al., 1994). Initial studies show that values of F30% 
to F40%  could be used as proxies for Fmsy (Goodyear, 1993; Mace and Sissenwine, 1993). These studies 
suggested  F20%  as  a  minimum  threshold  for  avoiding  recruitment  overfishing  for  stock  with  average 
resilience (Mace and Sissenwine, 1993). Further studies by Clark (1991, 1993) concluded that F35% and 
higher  were  robust  proxies  for  Fmsy,  considering  uncertainty  in  stock‐recruitment  functions  and  or 
recruitment variability. 

The NAFO Study Group on Limit Reference (NAFO, 2004) considered that when a SR relationship or a 
production  relationship  cannot  be  determined  from  the  available  data,  consideration  should  be  given  to 
SPR analysis as a means of determining Flim. The determination of the appropriate %SPR for use as Flim 
depends on the biology of the population. For some stocks 35%SPR may be too low and values of 40% or 
50% may be more appropriate. Although this approach provides a useful way to scale the F to the biology 
of the species, it is not easy to link it to sustainable F levels equivalent to Fmsy. It was noted that %SPR 
would  be  very  sensitive  to  changes  in  stock  productivity  such  as  an  increase  in  M.  Despite  these 
shortcomings,  %SPR  provides  an  approach  that  can  be  applied  to  stocks  when  other  analyses  are  not 
possible. %SPR of 35% should be used as a default Flim for such stocks in the absence of meta analysis 
considerations or other considerations to suggest it should be higher or lower. 

Mace and Sissenwine (1993) found that the Atlantic cod has the smallest mean replacement %SPR of the 
90 stocks analyzed by them, suggesting that it has relatively high resilience to fishing with the degree of 
resilience  being  inversely  related  to  the  magnitude  of  the  replacement  %SPR.  In  the  3M  cod  case  this 
high resilience to fishing could be confirmed by the rapid stock recovery in the last few years. This low 
replacement % SPR could suggest a low F%SPR as Fmsy proxy as was suggested by Mace and Sissenwine 
(1993). We propose a F30% (% SPR relative to SPR at F=0) as a candidate to Fmsy proxy in the case of the 
3M cod. 

Table 3.1.1.2 present the 10, 20, 30, 40, 50, 60, 70, 80 and 90 percentiles of the distribution of F max and 
F30% SPR estimated with the 1972-2013 data. 

The  Fisheries  Commission  and  Scientific  Council  Joint  Working  Group  on  Risk-Based  Management 
Strategies (FC SC RBMS) proposed for 3M Cod two HCRs to be tested under the MSE approach (NAFO, 
2014). The first one is based on fishing mortality assessment model results (model-based HCR) and the 
second one is based on survey indices (model-free HCR). Four different levels of F will be considered as 
Ftarget,  corresponding  to  probabilities  of  20%,  30%,  40%  and  50%  of  exceeding  Fmsy.  If  Fmsy  is  not 
available, an appropriate proxy (e.g. Fmax, current proxy) should be used. Table 3.1.1.3 presents the Yield 
Per Recruit, Spawning Per Recruit, the equilibrium yield and SSB for these probabilities of the F max and 

22 

 
23 

F30%SPR distribution calculated with the 1972-2013 data. To estimate the equilibrium situation by iteration 
we have used the mean of the recruitment of the 1972-2013 periods of each iteration. We decided to use 
all the recruitment series because we are looking for a limited reference points more than a target. Results 
show that even the F values for Fmax and F30%SPR are different, the yield are quite similar for both cases 
while the SSB shows a slight difference being higher in the F30%SPR case.    

Conclusions  

Based on the results we proposed estimate the Fmsy proxies with the long period (1972-2013) data to avoid 
the clear inputs (weights, maturity, partial recruitment) trends in the most recent period. 

We  also  propose  to  chose  F30%SPR  as  the  Fmsy  best  proxy  in  the  3M  Cod  case  to  avoid  the  risk  of 
recruitment overfishing. 

References 

Clark  W.G.  1991  Groundfish  exploitation  rates  based  on  life  history  parameters.  Canadian  Journal  of 
Fisheries and Aquatic Sciences Vol. 48, , pp. 741‐750. 1991. 

Clark  W.G.  1993  The  effect  of  recruitment  variability  on  the  choice  of  target  level  of  spawinig  stock 
biomass  per  recruit.  In  Proceedings  of  the  International  Symposium  on  management  strategies  for 
exploited fish populations Alaska Sea Grant College Programme AK‐SG‐93‐ 02 1993. Pp 233‐246 

Cerviño,  S.  and  A.  Vázquez,  2003.  Re-opening  criteria  for  Flemish  Cap  cod:  a  survey-based  method. 
NAFO SCR Doc. 03/38. Serial Number N4856. 

Fernández,  C.,  S.  Cerviño  and  A.  Vázquez,  2007.  A  Survey-based  assessment  of  cod  in  division  3M. 
NAFO SCR Doc. 07/39. Serial Number N5526. 

Fernández, C., S. Cerviño and A. Vázquez, 2008.  Assessment of the Cod Stock in NAFO Division 3M. 
NAFO SCR Doc. 08/26. Serial Number N5391. 

Gabriel, W. and P. Mace, 1999. A review of biological reference point in the context of the precautionary 
approach. NOAA. Tech. Memo, NMFS-F/SPO, 40: 34-44. 

González-Troncoso, D., C. Hvingel, B. Healey, J. Morgan, F. González-Costas, R. Alpoim, J.C. Mahé and 
A.  Vázquez.  Assessment  of  the  Cod  Stock  in  NAFO  Division  3M.  NAFO  SCR  Doc.  13/41.  Serial 
Number N6196. 

González–Troncoso  D.,  Joanne  Morgan  and  Fernando  González-Costas.  2013.  Biological  Reference 
Points of 3M cod. NAFO SCR Doc. 13/50 Serial No. N6207.  

González–Troncoso D., Fernando González-Costas. 2014. 3M cod assessment for different assumptions 
over M. NAFO SCR Doc. 14/018 Serial No. N6312. 

González–Troncoso  D.  2014.  Assessment  of  the  Cod  Stock  in  NAFO  Division  3M.  NAFO  SCR  Doc. 
14/035 Serial No. N6331. 

Goodyear  C.P.  1993  Spawning  stock  biomass  per  recruit  in  fisheries  management:  Foundation  and 
current use. In S.J. Hunt J.J. Smith, and D. Rivard [Eds] Risk evaluation and biological reference points 
for fisheries management. Canadian Special Publication of Fisheries and Aquatic Sciences. pp 67‐81 

ICES,  2010. Report of the Workshop on Implementing the ICES Fmsy framework, 22-26 March 2010, 
Copenhagen, Denmark. ICES CM 2010/ACOM:54. 83 pp.  

Mace, P.M. and M.P. Sissenwine. 1993. How  much spawning per recruit is enough? In S.J. Smith, J.J. 
Hunt  and  D.  Rivard  [eds.]  Risk  evaluation  and  biological  reference  points  for  fisheries  management. 
Canadian Special Publications in Fisheries and Aquatic Sciences 120:101-118. 

Mesnil,  B. and Rochet, M.J., 2010. A continuous hockey  stick stock-recruit  model for estimating MSY 
reference  points.  ICES  Journal  of  Marine  Science,  67:  1780-1784.  Publisher’s  official  version: 

 
 
 
http://dx.doi.org/10.1093/icesjms/fsq055. 
http://archimer.ifremer.fr/doc/00016/12735/ 8  

Open 

access 

version: 

NAFO, 2003. Proposed NAFO Precautionary Approach Framework from Scientific Council. NAFO SCS 
Doc. 03/23, Serial No. N4900.  

NAFO, 2004. Report of the NAFO Study Group on Limit Reference Points Lorient, France, 15-20 April, 
2004. NAFO SCS Doc. 04/12 Serial No. N4980. 

NAFO,  2004.  Report  of  the  Fisheries  Commission  26th  Annual  Meeting,  September  13-17,  2004. 
NAFO/FC Doc. 04/17, Serial No. N5067. 

NAFO, 2014. Report of the Fisheries Commission and Scientific Council Joint Working Group on Risk-
Based Management Strategies, February 5-7, 2014. NAFO FC/SC Doc. 14/02, Serial No. 6282. 

Rosenberg,  A.,  P.  Mace,  G.  Thompson,  G.  Darcy,  W.  Clark,  J.  Collie,  W.  Gabriel,  A.  MacCall,  R. 
Methot, J. Powers, V. Restrepo, T. Wainwright, L. Botsford, J. Hoenig, and K. Stokes. 1994. Scientific 
review  of  definitions  of  overfishing  in  U.S.  Fishery  Management  Plans.  NOAA  Tech.  Memo.  NMFS-
F/SPO-17. 205 pp. 

Shelton,  P.  A.,  A.  F.  Sinclair,  G.  A.  Chouinard,  R.  Mohn,  and  D.  Duplisea.  2006.  Fishing  under  low 
productivity conditions is further delaying recovery of Northwest Atlantic cod (Gadus morhua) Canadian 
Journal of Fisheries and Aquatic Science 63:235–238. 

Swain D. P., 2010. Life-history evolution and elevated natural mortality in a population of Atlantic cod 
(Gadus morhua). Evolutionary Applications ISSN 1752-4571. 

Sissenwine,  M.P.  and  J.G.  Shepherd,  1987.  An  alternative  perspective  on  recruitment  overfishing  and 
biological. 

Vetter, E.F., 1988. Estimation of natural mortality in fish stocks: a review. Fish. Bull. 86, 25–43. 

24 

 
25 

Table 3.1.1.1.- Median values estimated for Fishing mortality YPR (Fmax and F0.1) and SPR (F20%, F30% 
and F40%) reference points for the different periods and total time series inputs. 

1972-1995 

1996-2008 

2009-2013 

1972-2013 

Fmax 

0.128 

0.315 

0.139 

0.163 

F0.1 

0.074 

0.193 

0.081 

0.094 

F20% 

0.164 

0.300 

0.209 

0.201 

F30% 

0.106 

0.210 

0.134 

0.133 

F40% 

0.074 

0.151 

0.091 

0.091 

Table 3.1.1.2.- Percentiles (10, 20, 30, 40, 50, 60, 70, 80 and 90) of the distribution of F max and F30%SPR 
estimated with the 1972-2013 data. 

Fmax 

0.138 

0.145 

0.151 

0.156 

0.163 

0.168 

0.174 

0.181 

0.194 

F30%SPR 

0.109 

0.116 

0.121 

0.128 

0.133 

0.137 

0.143 

0.149 

0.159 

10% 

20% 

30% 

40% 

50% 

60% 

70% 

80% 

90% 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 3.1.1.3.- Yield Per Recruit, Spawning Per Recruit, the equilibrium yield and SSB for the 10, 20, 
30, 40, 50, 60, 70, 80 and 90 percent probabilities of exceeding Fmax and F30%SPR. 

Fmax 

0.138 

0.145 

0.151 

0.156 

0.163 

0.168 

0.174 

0.181 

0.194 

YPR 

SSBPR 

Yield (t) 

SSB (t) 

0.818 

0.911 

0.980 

1.041 

1.102 

1.164 

1.233 

1.317 

1.438 

3.091 

3.674 

4.158 

4.588 

5.046 

5.517 

6.078 

6.784 

7.832 

20174 

141818 

21000 

128020 

21687 

118414 

22255 

110930 

22812 

104503 

23423 

24101 

24916 

26084 

98330 

92225 

84764 

76585 

F30%SPR 

YPR 

SSBPR 

Yield (t) 

SSB (t) 

0.109 

0.116 

0.121 

0.128 

0.133 

0.137 

0.143 

0.149 

0.159 

0.810 

0.902 

0.971 

1.031 

1.090 

1.152 

1.220 

1.301 

1.419 

3.711 

4.421 

5.014 

5.539 

6.105 

6.704 

7.427 

8.350 

9.761 

19995 

176509 

20810 

157646 

21478 

144446 

22033 

134758 

22570 

126545 

23167 

118453 

23832 

110942 

24610 

101929 

25726 

91931 

10% 

20% 

30% 

40% 

50% 

60% 

70% 

80% 

90% 

10% 

20% 

30% 

40% 

50% 

60% 

70% 

80% 

90% 

26 

 
 
 
 
 
 
 
 
 
 
 
 
27 

Figure 3.1.1.1.- Cod 3M; stock mean weights at age (3-5) and abundance for ages 3 to 5 series 1972-2013 from 
the 2014 assessment. Vertical lines are the time periods suggested in this document to analyze the YPR and SPR 
RP. 

Figure  3.1.1.2.-  Recruits  per  spawner  for  the  median  SSB  and  recruitment  estimated  in  the  2014  3M  cod 
assessment. Vertical lines are the time periods suggested in this document to analyze the YPR and SPR RP. 

 
 
 
 
 
 
 
 
Figure 3.1.1.3.- Median and the 95% confidence interval of the age of 50% maturity. Vertical lines are the time 
periods suggested in this document to analyze the YPR and SPR RP. 

Figure 3.1.1.4.-SSB results for the 3M cod 2014 assessment. The horizontal line is the Blim value (14 000 ton) 
and the vertical lines are the period suggested in this document to analyze the YPR and SPR RP. 

28 

 
 
 
 
 
 
 
 
 
 
29 

Figure 3.1.1.5.- Median partial recruitment by age for the different time periods. 

 
 
 
 
 
 
Figure  3.1.1.6.-  Fishing  mortality  YPR  (Fmax  and  F0.1)  and  SPR  (F20%,  F30%  and  F40%)  reference  points  with 
uncertainty for the different periods and total time series inputs. 

30 

 
 
 
 
 
 
 
 
 
 
31 

Figure  3.1.1.7.-  Equilibrium  SSB  results  for  different  M  values  for  Fmax,  F0.1  and  F20%..These  values  are 
deterministic and were estimated with the median inputs for the weights, maturity at age and partial recruitment 
of the whole series. 

Figure 3.1.1.8.- YPR and SPR curves for the long term period (1972-2013) period. Vertical lines are the YPR 
(Fmax and F0.1 means) and SPR (F20%, F30% and F40% means) references points levels. 

 
 
 
 
 
 
 
 
 
 
3.1.2  Consider  ecosystem  interactions  (including  the  relation  with  redfish  and 
in  NAFO  division  3M)  when  analysing  possible  management 

shrimp 
objectives and conservation limits (NAFO SCS 11/22). 

This task was addressed based on the results of the December 2011 SC WG on the Ecosystem Approach to 
Fisheries Management (NAFO SCS Doc. 11/ 22). 

A  preliminary  exploration  of  the  joint  dynamics  of  cod,  shrimp  and  redfish  in  the  Flemish  Cap  was 
conducted  by  implementing  the  most  simple  trophodynamic  model  possible  that  includes  these  three 
components (Figure 3.1.2.1). 

Figure 3.1.2.1. Graphical depiction of the 3-spp model implemented. This generic representation shows the 
interactions between components. 

The actual model implementation was based on a discrete version of the generalized predator-prey Lotka- 
Volterra formulation of the form: 

where  Bi,  ri,  and  Ki  are  the  stock  biomass,  intrinsic  growth  rate  and  carrying  capacity  for  species  i,  αij 
represents the per capita interaction effect of species j on species i, and Ci is fishing catch. The sub-index t 
indicates time-dependent variables for year t. 

Given the sign conventions used in the above equation, it is expected αij to be negative if the effect of j on 
the biomass of  i is positive (e.g.  i is a predator of  j), and positive if the effect of  j on the  biomass of  i is 
negative (e.g. i is a predator/competitor of j). 

Based on this discrete generalized Lotka-Volterra equation, net biomass production for a given species i in 
year t (Pi,t) can be modelled as: 

32 

 
 
 
 
 
 
 
33 

This  last  production  equation  is  a  linear  one,  so  we  can  estimate  its  parameters  using  standard  multiple 
regressions, as long as we have time series for all stock biomasses and the fisheries catches. In the case of 
the model implemented here, the solution involves a system of three equations, one per species. 

If  we  consider  the  most  common  representation  of  the  observation  equation,  Ii  =  qiBi,  where  Ii  is  the 
observed  index  (e.g.  RV  biomass  index),  and  qi  is  the  scaling  factor  that  relates  actual  biomass  with  the 
observed index for species i, and then we assume that qi=1, we can used the RV biomass indices for these 
species to estimate the parameters in the linearized production equations. The assumption  qi=1 is a major 
one, and hence, any results obtained from its application should only be considered preliminary ones. 

Following these procedures, assuming qi=1, and using EU RV survey indices for cod, shrimp and redfish in 
the  Flemish cap, as  well as NAFO catch statistics for these species in the study area (Figure 3.1.2.2), the 
parameters of this 3-spp model were estimated using linear multiple regressions. 

Figure 3.1.2.2. RV biomass indices and fisheries catches for cod, shrimp and redfish in the Flemish Cap. 

Although the estimated model parameters values are expected to be conditioned to the qi=1 assumption, the 
results  obtained  were  quite  acceptable  for  an  initial  and  preliminary  exploration.  Both  the  order  of 
magnitude  of  most  parameters,  as  well  as  the  signs  from  the  interaction  coefficient  were  in  reasonable 
agreement with prior expectations, with the only exception of a rather large estimate for the intrinsic growth 
rate  of  shrimp  (Table  3.1.2.1).  The  signs  of  the  estimated  interaction  coefficients  were  in  full  agreement 
with available information and expectations; both shrimp and redfish have a positive effect on cod (both are 
known  and  important  prey  for  cod),  while  cod,  as  a  predator,  has  a  negative  effect  on  both  shrimp  and 
redfish; redfish which is also a predator of shrimp, also has a negative effect on shrimp, while shrimp, as a 
prey, has a positive effect on redfish (Table 3.1.2.1). 

Table 3.1.2.1. Estimated parameters for the discrete generalized Lotka-Volterra model. These estimates are 
conditioned  to  the  assumption  of  qi=1.  At  the  present  time  these  parameter  values  must  be  regarded  as 
preliminary. 

In  addition  to  these  reasonably  valued  estimated  parameters,  the  actual  fit  of  the  model  to  the  data  was 
remarkably good given the assumptions and limitations of  this exercise (Figure 3.1.2.3). Expected values 
for cod, shrimp and redfish  were generated using the estimated parameters, and assuming a process error 
structure  (i.e.  model  predictions  are  based  on  the  observed  value  in  the  previous  year;  the  model  only 
predicts the next year). In general, predicted values tracked reasonably well the observations, but in the case 
of shrimp, there were some years were the model predicted negative biomasses. This clearly indicates that 

 
 
 
 
 
 
 
the model fit is not fully satisfactory; this particular biological implausibility may very well be related to the 
qi=1 assumption. Residuals from this model were acceptable but not ideal; normalized residuals were, with 
only  one  exception,  clearly  bounded  between  -3  and  3  standard  deviations,  but  there  seems  to  be  some 
patterns over time, especially in the case of redfish (Figure 3.1.2.4). In any case, these results are promising, 
but warrant caution in the use of any quantitative prediction from this model. 

Figure  3.1.2.3.  Observed  and  model  predicted  RV  biomasses  for  cod,  redfish  and  shrimp  in  the  Flemish 
Cap. 

34 

 
 
 
35 

Figure  3.1.2.4.  Normalized  residuals  for  the  cod,  redfish  and  shrimp  series  in  the  3-spp  Lotka-Volterra 
model for cod-redfish-shrimp system in the Flemish Cap. 

Further explorations with this model provided some interesting observations. In order to investigate alternative 
scenarios of single species and multispecies maximum sustainable yields (SSMSY and MSMSY respectively), 
the  fishing  capture  term  in  the  original  model  formulation  (Ci)  was  replaced  by  FiBi,  where  Fi  represents  a 
fishing  mortality  level  for  the  species  i.  With  fishing  terms  redefined  in  this  way,  and  together  with  the 
estimated  parameters,  the  3  equation  model  was  numerically  solved  for  equilibrium  under  specific  yield 
scenarios. These scenarios were: 

• MS. The fishing mortalities for all species were varied to maximize the overall system yield (i.e. the 
combined yield for cod, redfish, and shrimp). 

• SScod. The fishing mortalities for all species were varied to maximize the cod yield. 

• SSredfish. The fishing mortalities for all species were varied to maximize the redfish yield. 

• SSshrimp. The fishing mortalities for all species were varied to maximize the shrimp yield. 

The results from these MSY explorations indicate that most scenarios render comparable overall catch levels the 
exception being SSshrimp which renders less than the others in terms of total catch. The MS scenario provides 
the most overall catch, but this is achieved with a catch distribution by species where the shrimp catch level is 
quite low in comparison with the yield from the SSshrimp scenario. Even more dramatic, the achievement of the 
goals of SScod and SSredfish require no shrimp catches, and both of these SS scenarios have an overall catch 
which is slightly lower than the MS scenario. Finally, maximizing the yield for shrimp requires catches of cod 
and  redfish  far  lower  that  the  values  in  the  MS,  SScod  or  SSredfish  scenarios.  Figure  3.1.2.5  summarizes  all 
these comparisons among MSY scenarios. 

 
 
 
 
Figure 3.1.2.5. Comparative MSY values for the four scenarios explored. 

Furthermore,  all  these  MSY  scenarios  generate  distributions  of  equilibrium  biomasses  which  are  dramatically 
different from the one of the unexploited system (Figure 3.1.2.6), and in the case of the SScod and SSredfish 
scenarios, the achievement of their respective  MSY  goals require the absence  of shrimp  from the system (i.e. 
collapsing the 3-spp system into a 2-spp system). A similar observation emerges from the equilibrium biomasses 
in  the  unfished  system.  In  this  case,  the  equilibrium  biomass  for  shrimp  is  a  negative  biomass.  This  result  is 
clearly indicating a problem in the model, however, an unfished system with a very large cod population and no 
shrimp, is exactly what we could have expected for this system if we consider the history of this system. This 
system was dominated by cod, and shrimp was not a historically important species in the Flemish Cap; shrimp 
suddenly appears in significant number and increase in biomass in the mid 1990s. This  oddity in the  model is 
not a satisfactory result, and certainly can be the consequence of the many assumptions and restrictions imposed 
by the model structure and the parameter estimation process. Nonetheless, it may also be a hint that the model, 
in  all  its  simplicity,  is  capturing  an  important  feature  of  the  Flemish  Cap  system;  shrimp  may  not  be  an 
structurally important species in this ecosystem, and its large abundances in the past decade are just a transient 
feature of a heavily perturbed system. 

Figure 3.1.2.6. Equilibrium biomasses for cod, redfish, shrimp and all combined (total) under no fishing, and 
the four MSY fishing scenarios described in the text. 

36 

 
 
 
 
 
37 

Finally, and just as an exploration in misguided wishful thinking, we explore what would happen if the fishing 
mortalities for the target species obtained in each one of the SSMSY scenarios were applied simultaneously. We 
compare the actual model predictions under this “parallel SS management scenario”, with the expectations from 
each  one  of  the  original  SSMSY  scenarios.  This  comparison  clearly  shows  that  a  parallel  implementation  of 
SSMSY  strategies  would  render  lower  yields  than  expected  under  the  individual  SSMSY  scenarios  (Figure 
3.1.2.7),  and  for  cod  and  redfish,  also  lower  than  what  would  be  obtained  under  the  MS  scenario  (Figure 
3.1.2.5). This exploration suggests a  potentially important insight,  a true MSMSY  management could provide 
better yields (although not for all species) than independent SSMSY strategies. 

Figure  3.1.2.7.  Comparison  of  the  yields  obtained  in  each  one  of  the  original  SSMSY  scenarios,  with  the 
expected  yields  resulting  from  applying  the  Fmsy  values  estimated  in  each  one  of  the  SSMSY  scenarios 
simultaneously. 

In  summary,  this  modeling  exercise,  although  it  does  not  provide  reliable  quantitative  values  given  all  its 
assumptions and potential shortcomings, still provides important insights in the relationships among cod, redfish 
and  shrimp  in  the  Flemish  Cap.  It  clearly  shows  that  a  simultaneous  maximization  of  the  yields  for  the  three 
species appears unfeasible from a biological perspective, there is a clear trade-off between the yields of cod and 
redfish, and the yield of shrimp; more yield on the shrimp side requires low yields for the fishes. On the other 
side, maximal yields for the fish species can only be realized at very low shrimp population levels. There is also 
a trade-off to be achieved between the two fish species; maximizing the yield of one of them requires assuming 
less  than  maximum  yields  for  the  other.  Attempting  to  provide  fine  details  for  these  trade-offs  exercises  is 
clearly beyond the capacity of this model in its current configuration, but achieving a combined MSY level for 
all  of  them  will  require  not  just  a  better  model  to  address  the  issue  of  biological  plausibility,  it  would  also 
require addressing at the management table the question of how these necessary trade-offs will be discussed and 
agreed upon. 

NAFO  2011.  Report  of  the  4th  Meeting  of  the  NAFO  Scientific  Council  Working  Group  on  Ecosystem 
Approaches to Fisheries Management (WGEAFM). NAFO SCS Doc. 11/ 22. Serial No. N6006. Pag. (68-85).  

References 

 
 
 
 
 
 
 
3.2  Catch discrepancy: 

3.2.1  Provide  options  to  resolve  the  catch  discrepancy  issue  for  the  stock 
assessment  allowing  evaluations  of  management  strategies  to  account  for 
uncertainty in the catch (NAFO SC WP). 

Introduction 

The Fisheries Commission and Scientific Council Ad hoc Working Group on Catch Reporting (FC-SC WG 
CR)  evaluated  the  data  sources  and  discussed  their  individual  limitations  and  potentials  for  utility  to 
validate  catch  data  and/or  generate  catch  estimates  (NAFO  FC-SC  14/01).  It  was  noted  that  these  data 
sources  are  currently  collected  for  fishing  compliance  purposes  and  with  the  exemption  of  daily  catch 
reports (DCR) as part of Vessel Transmitted Information (VTI), are not available for scientific purposes. 

The FC-SC WG CR recognized that in their respective current form, Daily Catch Reports (DCR) data is the 
most useful because of the high level of compliance of the fishing vessels in submitting the DCR and the 
level of detail which they provide – daily catch by species and by Division.  

The  FC-SC  WG  CR  recommended  that  a  process  for  catch  estimation  be  constructed  by  continuing 
dialogue  within  this  working  group,  using  a  suite  of  available  catch  databases  housed  at  the  NAFO 
Secretariat, and any other data, such as scientific observer reports. The process should be fully documented 
and transparent, including documentation of data selection and validation and tools for data synthesis. The 
FC-SC WG CR also recommended that SC, with assistance from the Secretariat, conducts a pilot exercise 
to explore and document the use of all available data, focusing on VMS & DCR for all flag states operating 
in this fishery, for catch estimation of Div. 3M Cod. 

Members  of  the  SAFEwaters-2  proposed  to  the  NAFO  Scientific  Council  to  carry  out  a  pilot  exercise, 
which  principal  objective  was  to  estimate  3M  Cod  catch,  analyzing  the  Daily  Catch  Reports  (DCR)  and 
VMS data. A CPUE was estimated using the catch data from the DCR and the effort data from the VMS 
data;  this  CPUE  was  used  to  estimate  the  total  catch  of  the  3M  Cod  and  was  compared  with  the 
STATLANT  data.  Another  objective  was  to  compare  the  CPUE  of  this  fishery  between  vessels  and 
Contracting Parties or Flag States to study the differences between them. These estimated CPUEs could be 
compared  with  the  scientific  observers  CPUEs.  The  results  of  this  exercise  are  presented  below  in  this 
report. 

Material and Methods 

Currently, in Div 3M at less than 600 meters there are two fisheries: cod and redfish fisheries. It is difficult 
to split the data by fishery because DCR are collected by day and in one day a vessel can be targeting either 
cod or redfish. Daily Catch Reports (DCR) are available by vessel, species and Division since 2011. It was 
decided  to  analyze  the  DCR  data  for  the  available  period  (2011-2013).  To  perform  this  exercise  the 
Scientific Council (SC) asked to the Secretariat to provide the following information for the period 2011-
2013:  

 

the VMS effort, the DCR cod catch and the DCR total catch by year, Flag State, vessel, month and day 
in Div. 3M at depths less than 600 meters. 

  The  STATLANT  21B  catches  of  3M  cod  by  Flag  state  and  month  for  2011-2012  and  for  2013  the 

STATLANT 21A catches of 3M cod by Flag state. 

For Div. 3M  at depths shallower than 600 meters, a database  was created relating the daily DCR catches 
(cod and total) with the corresponding VMS effort by vessel, for the period 2011-2013. To point it out that 
for each record the total catch includes the  cod catch data but the effort is the same for both catches. For 
some vessels the information of their power was also available.  

GLM Analyses 

38 

 
39 

The first objective was to know which of the available variables affect more the cod CPUE. It was decided 
to  study  the  following  variables:  year,  month,  vessel,  flag  and  power.  To  see  the  impact  of  the  different 
variables  a  General  Lineal  Model  (GLM)  was  used  with  the  available  data.  Table  3.2.1.1  presents  the 
ANOVA results for the single factors with the Chi Square Test. By importance order, the most significant 
variables  are:  Flag,  Vessel  and  Month.  Power  data  are  not  available  for  all  countries.  Power  seems  to  be 
significant  but  we  consider  it  redundant  with  the  vessel  factor.  Based  on  these  results  it  was  decided  to 
execute a GLM model to flag, vessel and month factors in this order to study the importance of the different 
factors in the CPUE. Table 3.2.1.2 shows the ANOVA results of this GLM model and all factors are highly 
significant. 

Figure  3.2.1.1 shows  the box plot for cod and total  LogCPUE by flag for the period 2011-2013. We can 
observe  that  the  total  and  cod  LogCPUE  are  quite  different  by  country  but  the  cod  LogCPUE  is  more 
variable than the total. Wide discrepancies between cod and total Cod LogCPUE and total LogCPUE are 
observed for some fleets (B, C, D, E and I) while for other both catch rates are very similar (A, F, G and H). 
Figure  3.2.1.2  presents  the  same  data  by  year.  More  or  less  we  can  observe  the  same  things  each  year, 
similar pattern in the total LogCPUE and more variable in the cod LogCPUE. 

Figure  3.2.1.3  presents  the  total  and  cod  LogCPUE  for  all  vessels  in  the  period  2011-2013.  The  vertical 
lines divide the countries. We can observe similar results to those in the Figure 3.2.1.1, but in this figure the 
total  LogCPUE  are  quite  similar  for  all  vessels  in  this  period  and  the  cod  LogCPUE  are  more  variable. 
Figure 3.2.1.4 presents the same data for the available Scientific Observers LogCPUE, in this case total and 
cod CPUE are quite similar between vessels and have less variability than the DCR data. 

Figure 3.2.1.5 presents the total and cod LogCPUE for all countries by month. The vertical lines divide the 
years.  We  can  observe  that  the  total  LogCPUE  presents  a  quiet  regular  and  constant  pattern  in  the  three 
years  while  the  cod  LogCPUE  has  a  very  much  dispersion  and  variation  between  months.  Figure  3.2.1.6 
shows  the  total  and  cod  LogCPUE  box  plots  by  month  for  the  DCR  data  and  the  Scientific  Observers 
available  data.  The  Scientific  Observer  data  present  less  variability  and  the  trend  is  similar  than  the 
observed in the DCR data. Highest CPUEs are observed in spring season.  

The analysis made above is for all the hauls made less than 600 meters depth where there are two different 
fisheries. It was to try to identify the hauls direct to cod fishery through a histogram of the percentage of 
cod  in  the  total  catches.  Figure  3.2.1.7  presents  the  results  of  this  histogram  and  it  can  be  observed  that 
there  is  a  clear  jump  in  the  distribution  for  the  hauls  where  there  are  more  than  90%  of  cod  in  the  total 
catches. Based on these results the cod LogCPUE was split in two: one using the hauls with more than 90% 
of the cod in the total catches and another using the hauls with less than 90% of the cod in the total catches. 
Figure  3.2.1.8  presents  the  LogCPUE  box  plot  by  vessel  in  the  period  2011-2013  for  both  fisheries.  The 
vertical  lines  divide  the  countries.  The  LogCPUE  by  vessel  for  both  fisheries  presents  clear  different 
between countries. Countries A, G and H have very low information with cod caches less than 90%, most 
of the effort is directed to cod fishery and present the highest CPUE in the Cod Fishery. There are countries 
with both  fisheries (C, D E) and the cod LogCPUE for the  hauls  with  more than 90%  of the cod in total 
catches are very different by vessel and more variable than the observed in the hauls with less than 90% of 
the cod in the total catches.  

Figure  3.2.1.9  presents  the  kilowatts  by  vessel  and  country.  The  vertical  lines  divide  the  countries.  The 
different  cod  LogCPUE,  more  and  less  than  90%  of  the  cod  in  the  total  catches,  seems  to  not  be  much 
related with the power information we have available. 

Figure  3.2.1.10  shows  by  year  and  month  the  Daily  Catch  Reports  (DCR)  and  the  Scientific  Observer 
estimated mean CPUEs, for the hauls more than 90% of the cod in the total catches, for the only country 
with scientific data available. We can observe that normally the Scientific Observers CPUE are higher than 
the DCR. Although in 2011 and 2012 were not much data available, it can be seen that these differences are 
much smaller in 2013. 

Estimate catches 

Table 3.2.1.3 presents the cod 3M TAC and the catches obtained from different data sources for the period 
2011-2013.  The  assessment  column  shows  the  catches  estimated  by  the  model  in  the  2011  and  2012 

 
 
assessment.  It  was  decided  not  to  include  the  STATLANT  21A  2013  information  because  for  the  most 
important  countries  in  the  fishery  this  information  was  not  available  at  the  time  when  this  analysis  was 
made.  The  DCR  Database  was  the  available  data  analyzed  in  this  report.  The  DCR  column  shows  the 
revised DCR data made during the June SC meeting. 

The cod catches estimated in the 2011 and 2012 assessment are significantly higher than the TAC and the 
STATLANT  21A  catches.  The  STATLANT  21A  catches  were  slightly  lower  than  the  TAC.  The  data 
analyzed  in  this  document  (DCR  Database)  were  not  exactly  the  available  DCR  data  (DCR)  due  to 
extraction  problems.  We  can  observe  that  the  2011  DCR  data  are  quite  lower  than  the  TAC  and  the 
STATLANT 21A data. These differences can be due to the DCR first year implementation problems in all 
vessels. For 2012-2013 the DCR system worked pretty well. In 2012 the DCR cod catches were a little bit 
less than the TAC while in 2013 was a little bit more. 

Based on the DCR/VMS Database, five CPUEs were calculated using different ways. These CPUEs were 
used  to  made  different  estimations  of  the  catches  (Table  3.2.1.4  totals  1-5).  The  Total  1  was  made 
calculating  a  CPUE  by  vessel,  year  and  month  (∑catches/∑effort)  and  apply  these  CPUEs  to  the  vessel, 
year and month effort. Results are similar the DCR. Total 2 was made calculating a CPUE by flag and year 
(∑catches/∑effort) and applying these CPUEs to the flag and year effort. Total 2 results are similar to the 
DCR. Total 3 was made calculating a CPUE by flag and year (∑catches/∑effort) and after an all flags mean 
CPUE was estimated by year and applied to the year effort. Results are quite different than the DCR. Total 
4 was equal to Total 2 but it was made calculating a CPUE by flag and year (∑catches/∑effort) separated 
for the data were cod was more and less than 90% of the total catches and applying these CPUEs to the flag 
year vessel effort for  more and less than 90%.  Results are  similar  to  the DCR. Total 5 was equal than  to 
Total 3 but it was made calculating a CPUE by flag year (∑catches/∑effort) separated for the data were cod 
was more and less than 90% of the total catches and thereafter we estimated the all flags mean CPUE by 
year for both cases. After that these CPUEs were applied to the corresponding effort. Results are different 
than the DCR Database.  

These results confirmed what we see in the plots and GLM, the most important variability come from the 
flag. Month seems to be no important. 

NAFO Scientific Council conclusions 

Based on these results the NAFO Scientific Council (NAFO SCS 14/17) noted that: 

As  in  previous  years  STACFIS  conducted  a  general  review  of  catches  in  the  NAFO  SA  0–4  in  2013. 
STACFIS noted that an ad hoc working group had deliberated on catch estimates before the meeting and 
the  conclusions  and  workplan  were  presented  to  SC  and  discussed  during  the  Div.  3LN  Redfish  Webex 
(NAFO SCS Doc. 14/05). NAFO Scientific Council has estimated catch for its stock assessments for many 
years  since  the  1980s  when  large  discrepancies  were  observed  between  various  sources  of  catch 
information. The goal of this exercise was to use the best information available to provide the best possible 
assessments and advice. STACFIS has had available estimates from different sources, but not for all fleets 
or  from  all  Contracting  Parties.  These  various  sources  of  data  have  in  many  years  led  STACFIS  to  the 
conclusion  that  catch  estimates  from  STATLANT  have  been  unreliable  for  a  number  of  stocks.  Lack  of 
catch  estimates  is  hindering  provision  of  advice  for  many  stocks,  and  for  other  cases,  the  accuracy  of 
assessment  results  and  management  advice  rely  on  the  assumption  that  the  STATLANT  data  equals  the 
annual  landings,  an  assumption  which  can  no  longer  be  independently  verified.  It  was  noted  that 
STATLANT  21A  data  was  not available for all  Contracting Parties by the start of the  meeting, therefore 
only data available as of 30 May was considered. 

Key  sources  of  other  data  have  not  been  available  to  evaluate  STATLANT  data  since  2011. Priority  has 
been  given  to  three  stocks  where  discrepancies  between  STATLANT  and  STACFIS,  most  recently  over 
2005-2010,  were  quite  large  and  where  the  absence  of  a  reliable  STACFIS  estimate  would  pose  serious 
problems to the current assessment method: Div. 3M Cod, Greenland halibut in SA2+Div. 3KLMNO and 
American plaice in Div. 3LNO. For some stocks STACFIS is currently assuming that the STATLANT data 
represent nominal landings. 

40 

 
41 

During the June 2014 Scientific Council meeting the only sources of recent catch information available for 
all but one stock were STATLANT 21A data and Daily Catch Records (DCR) for fleets that operated in the 
NRA.  The  exception  was  the  availability  of  scientific  observer  CPUE  data  for  one  fleet  fishing  Div.  3M 
cod. As recommended by the FC-SC Working Group on Catch Reporting anonymized data was examined 
to audit a component of the DCR data. Scientific Council noted that some flag states significant in the Div. 
3M  cod  fishery  did  not  submit  their  2013  STATLANT  21A  data  before  the  start  of  the  meeting,  so 
STATLANT  21A  could  not  be  compared  to  other  catch  estimates  for  2013.  SC  analyzed  the  CPUEs 
resulting from Daily Catch Reports (DCR) of Div. 3M cod for the period 2011-2013. These CPUEs were 
compared with the available scientific data. The results of this comparison show significant differences in 
2011  and  2012  and  a  decrease  of  such  differences  in  2013.  Based  on  these  results,  Scientific  Council 
decided to use total catches from the DCR in 2013 (13 985 t), maintaining the model catch estimation for 
2011 and 2012. 

STACFIS  noted  that  both  the  ad-hoc  WG  on  catch  reporting  and  STACTIC  have  had  encouraging 
discussions about the provision of haul by haul logbook data to the Secretariat. STACFIS considers that the 
provision  of  haul  by  haul  data  is  of  critical  importance  to  the  auditing  process  for  the  reliability  of 
STATLANT data and recommended that such data be submitted to Secretariat in real time if possible for 
use by the Scientific Council for assessment purposes. More generally, this data should be available for all 
fisheries  affecting  NAFO  managed  stocks.  Further,  STACFIS  recommended  that  the  Secretariat  use  the 
information from VMS data to construct measures of effort and compare this information to effort reported 
via  DCR,  as  a  means  to  validate  these  effort  records.  Given  that  DCR  information  is  only  available  for 
fisheries operating in the NRA, priority should be towards Div. 3M cod, Div. 3LNO American plaice and 
SA 2+ Div. 3KLNMO Greenland halibut, followed by any stock having an assessment in 2015. 

Unavailability  of  accurate  catch  data  also  has  implications  on  the  potential  to  provide  quantitative 
assessments  for  stocks  that  are  currently  assessed  qualitatively.  Several  classes  of  population  dynamics 
models  will  have  poor  diagnostics  if  the  removals  data  are  biased  and  are  inconsistent  with  changes  in 
survey  trends.  Consequently,  estimation  of  population  size  and  any  resulting  management  options  using 
biased catch data will be inaccurate. 

References 

NAFO, 2014. Report of the Fisheries Commission and Scientific Council Ad hoc Working Group on Catch 
Reporting, 3-4 February 2014. NAFO/FC-SC Doc. 14/01 Serial No. N6279. 

NAFO,  2014.  AD  HOC  WORKING  GROUP  ON  MANAGEMENT  STRATEGIES  FOR  REDFISH  IN 
DIV. 3LN. NAFO SCS Doc. 14/05 Serial No. N6286 

NAFO,  2014.  SCIENTIFIC  COUNCIL  JUNE  MEETING  –  2014.  NAFO  SCS  Doc.  14/17  Serial  No. 
N6343. 

 
 
 
 
 
 
Table 3.2.1.1.- ANOVA  with the Chi Square Test results  for the  single  factors (flag, vessel,  month,  year 
and power) used in the General Linear Model (GLM). 

Df 

Deviance Resid. 

Df 

Resid. Dev   

Pr(>Chi)     

NULL 

2536      

5540.5               

Cod_Flag   

8 

954.86       

2528      

4585.7 

< 2.2e-16 *** 

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1  

Df 

Deviance Resid. 

Df 

Resid. Dev   

Pr(>Chi)     

NULL 

2536      

5540.5               

Cod_Vessel 

53 

1564.6       

2483 

3975.9 

< 2.2e-16 *** 

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1  

Df 

Deviance Resid. 

Df 

Resid. Dev   

Pr(>Chi)     

NULL 

2536      

5540.5               

Cod_Month 

11 

486.83       

2525 

5053.7 

3.071e-13 *** 

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1  

Df 

Deviance Resid. 

Df 

Resid. Dev   

Pr(>Chi)     

NULL 

2536      

5540.5               

Cod_Year 

2 

51.913 

2534 

5488.6 

0.008944 ** 

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1  

Df 

Deviance Resid. 

Df 

Resid. Dev   

Pr(>Chi)     

NULL 

2383 

5404.8 

Cod_KW 

3 

322.9 

2380 

5081.9 

1.716e-12 *** 

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1  

42 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
43 

Table 3.2.1.2.- ANOVA with the Chi Square Test results for the  more important factors (flag, vessel and 
month) used in the General Linear Model (GLM). 

Df 

Deviance Resid. 

Df 

Resid. Dev   

Pr(>Chi)     

NULL 

2536      

5540.5               

Cod_Flag 

8 

954.86       

2528 

4585.7 

< 2.2e-16 *** 

Cod_Vessel 

45 

609.75       

2483      

3975.9 

5.704e-11 *** 

Month 

11 

293.51       

2472      

3682.4 

4.987e-12 *** 

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1  

Table 3.2.1.3.- Cod 3M TAC and catches (tons) based on different data sources  

TAC 

Assessment 

STATLANT 
21 A 

DCR 
Database 

2011 

10000 

13640 

2012 

9280 

13670 

9794 

9003 

6111 

8087 

DCR 

6777 

9031 

2013 

14113 

12599 

14687 

Table  3.2.1.4.-  Cod  3M  DCR  Database  catches  (tons)  and  different  estimations  made  base  on  the  DCR 
Database estimated  CPUEs. 

DCR 
Database 

Total 
1 

Total 
2 

Total 
3 

Total 
4 

Total 
5 

2011 

6111 

6111 

6111 

21912 

6111 

9444 

2012 

8087 

8087 

8087 

16198 

8087 

10580 

2013 

12599 

12599 

12599 

18454 

12599 

14249 

 
 
 
 
 
 
 
 
 
  
  
 
 
 
 
  
 
Figure 3.2.1.1.- Box plot for cod and total LogCPUE (DCR and VMS data) by flag for the period 2011-
2013. 

Figure 3.2.1.2.- Box plot for cod and total LogCPUE (DCR and VMS data)by flag and year for the period 
2011-2013. 

44 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
45 

Figure 3.2.1.3.- Box plot for cod and total LogCPUE (DCR and VMS data) by vessel for the period 2011-
2013. The vertical lines divided the different countries fishing in Div 3M at less than 600 meters depth. 

Figure 3.2.1.4.- Box plot for cod and total LogCPUE from the Scientific Observers available data by vessel 
for the period 2011-2013. 

 
 
 
 
 
 
 
 
Figure 3.2.1.5.- Box plot for cod and total LogCPUE (DCR and VMS data) by month for the period 2011-
2013. The vertical lines divided the different years. 

Figure  3.2.1.6.-  Box  plot  for  cod  and  total  LogCPUE  for  the  DCR  &  VMS  data  and  for  the  Scientific 
Observers available data by month for the period 2011-2013.  

46 

 
 
 
 
 
47 

Figure  3.2.1.7.-  Histogram  of  the  percentage  of  cod  in  the  total  catches  of  the  DCR  data  for  the  period 
2011-2013. 

Figure  3.2.1.8.-  LogCPUE  (DCR  &  VMS  data)  box  plot  by  vessel  in  the  period  2011-2013  for  the  data 
with more and less than 90% of cod in the total catches. The vertical lines divided the different countries 
fishing in Div 3M at less than 600 meters depth.  

020040060080010001200102030405060708090>90Frequency% Class% Cod in Total Catches Histogram 
 
 
 
 
 
 
Figure  3.2.1.9.-  Kilowatts  by  vessel  fishing  in  Div  3M  at  less  than  600  meters  depth  in  the  2011-2013 
period. The vertical lines divided the different countries.  

Figure  3.2.1.10.-  Mean  cod CPUEs  (Kg/hour)  for  the  only  country  with  scientific  data  available  by  year 
and month from Daily Catch Reports (DCR) and the Scientific Observer data for the hauls with more than 
90% of cod in the total catches. 

48 

0100020003000400050006000123456789101112CPUE (KG/h)MonthYear 2011DCRObs0100020003000400050006000123456789101112CPUE (KG/h)MonthYear 2012DCRObs0100020003000400050006000123456789101112CPUE (KG/h)MonthYear 2013DCRObs 
 
 
 
 
 
 
 
 
49 

3.3  Management Strategy Evaluation: 

The  structure  to  carry  out  quantitative  simulations  to  evaluate  sustainability  of 
management  objectives  (tasks  3.1),  the  development  of  different  sets  of 
performance  statistics  to  evaluate  the  system  reaching  management  objectives 
(task 3.2) and the proposals of Harvest Control Rules for the management of the 
stock  (task  3.3)  are  key  elements  in  the  MSE  approach.  They  have  been 
presented as “Developing of a 3M cod MSE” in a single SCR document. 

3.3.1  Developing  of  a  3M  cod  MSE  (SCR  14/44).  To  carry  out  quantitative 
simulations  to  evaluate  sustainability  of  management  objectives  (tasks  3.1), 
the  development  of  different  sets  of  performance  statistics  to  evaluate  the 
system  reaching  management  objectives  (task  3.2)  and  the  proposals  of 
Harvest Control Rules for the management of the stock (task 3.3). 

Introduction 

The  3M  cod  stock  is  managed  by  NAFO.  In  2007  a  Bayesian  model  was  presented  to  assess  this  stock 
(Fernández  et  al.,  2007)  and  in  2008  this  Bayesian  model  was  further  developed  and  approved  by  the 
NAFO SC (Fernández et al., 2008). In 2014 an assessment based on the Bayesian model was approved by 
NAFO SC (González-Troncoso et al., 2014). The cod stock in Division 3M (Flemish Cap) experienced very 
low biomass levels in the 1990s and was under moratorium to direct fishing between 1999 and 2009. The 
stock rebuilt and the direct fishery reopened in 2010. The spawning stock biomass increased substantially 
since  mid-2000s  and  is  now  well  above  the  limit  reference  point  and  among  the  highest  levels  observed 
since the 1970s. The rebuilding of this cod stock was a success for NAFO.  

The  NAFO  Fisheries  Commission  formally  adopted  a  Precautionary  Approach  (PA)  framework  in  2004 
(NAFO/FC  Doc.  04/17)  as  proposed  by  NAFO  Scientific  Council  (NAFO  SCR  Doc.  03/23).  The  SC 
framework provides a structure that includes limits, buffers,  targets and Harvest Control Rules that adjust 
fishing  mortality  to  keep  stocks  in  the  Safe  Zone.  A  Blim  of  14  000  tons  was  approved  by  the  NAFO 
Scientific Council in 2000 for the 3M cod stock. In 2008 the appropriateness of this value given the results 
from  the  new  method  used  to  assess  the  stock  was  examined,  concluding  that  it  is  still  an  appropriate 
reference  (Fernández  et  al.,  2008).  In  2012,  Fisheries  Commission  requested  to  Scientific  Council:  to 
provide Bmsy and Fmsy for 3M cod. Gonzalez-Troncoso et al. in 2013 estimated the Yield per Recruit (YPR), 
Spawner  per  Recruit  (SPR)  and  Maximum  Sustainable  Yield  (MSY)  reference  points  with  uncertainty  to 
provide candidates for Bmsy and Fmsy for 3M cod (SCR Doc. 13/50). Scientific Council concluded that the 
results of the Bmsy and Fmsy estimated based on different stock-recruitment relationships (Ricker, Beverton-
Holt  and  Segmented  Regression)  were  not  plausible  due  to  the  high  uncertainty  in  the  stock  recruit 
relationship  for  this  stock  and  noted  that  the  level  of  Bmsy  estimated  from  YPR-SPR  depends  on 
assumptions  about  the  level  of  recruitment  and  more  research  about  the  possibility  of  changes  in 
productivity and the level of recruitment that should be used to estimate the MSY is needed (NAFO SCS 
Doc. 13/17).  

NAFO identified the development of a risk-based management strategy for 3M cod as a priority in 2012, 
and  reaffirmed  that  priority  in  2013.  The  Fisheries  Commission  and  Scientific  Council  Joint  Working 
Group on Risk-Based Management Strategies (FC SC RBMS) proposed in February 2014 draft plans  for 
3M  cod  (Annex  1)  and  3LN  redfish  based  on  the  General  Framework  on  Risk-based  Management 
Strategies (NAFO FC/SC Doc. 14/02). It was noted that the draft plans represent a first step and may need 
further elaboration and adjustment once feedback is received from SC and FC. The development of such a 
management plan should be based on scientific advice. 

Management Strategy Evaluation (MSE) is based on a time-proven approach of evaluating models through 
simulation before using them as a basis for decision-making. This approach gained increased prominence 
though  the  evaluation  of  management  procedures  by  the  International  Whaling  Commission  and  is 
described in Kirkwood and Smith (1996) and more recently in ICES by Kell et al. (2007) in the context of a 
new  stock  assessment  environment  in  R-code  called  FLR  (Fisheries  Library  in  R).  The  intention  is  to 
develop for 3M cod a MSE study within the FLR environment.  

 
 
 
The aim of this document is to define the MSE structure for evaluating the proposed FC SC RBMS 3M cod 
Management  Procedure  (MP)  or  Harvest  Control  Rule  (HCR)  and  identify  the  major  inputs  required  to 
simulation-test and the main areas of uncertainty of the method. 

Management Strategy Evaluation 

MSE is a general framework aimed at designing and testing MPs which specify previously agreed decision 
rules  (HCR)  for  setting  and  adjusting  TACs  or  effort  levels  to  achieve  a  set  of  fishery  management 
objectives. Simulation testing is used to determine the extent to which an MP is robust to uncertainty, and 
MPs  are  usually  selected  so  that  there  is  a  reasonable  likelihood  that  the  (pre-specified  and  quantified) 
management  goals  can  be  satisfied  (De  Oliviera  et  al.,  2008).  Butterworth,  Cochrane  and  De  Oliveira 
(1997) defined an MP as a simulation-tested set of rules used to determine management actions, in which 
the  data,  the  methods  for  analyzing  the  data  (including  any  method  of  stock  assessment)  as  well  as  the 
harvest control rule (HCR) are pre-agreed and pre-specified. 

Conceptual framework 

A  prototypical  MSE  incorporates  a  number  of  interlinked  model  structures:  population  dynamics;  data 
collection; data analysis and stock assessment; an HCR that dictates a specific management action (e.g., the 
TAC);  the  harvest  decision  process;  and  implementation  of  that  management  action  (McAllister  et  al., 
1999). An operating model or a reference set of different plausible scenarios of past and future population 
and  fishery  dynamics  are  typically  used  to  generate  “true”  ecosystem  dynamics  including  the  natural 
variations in the system. Holland (2010) described the MSE process as follow: “Data are sampled from the 
operating  model  to  mimic  collection  of  fishery  dependent  data  and  research  surveys  (and  their  inherent 
variability).  These  data  are  passed  to  the  assessment  model.  Based  on  this  assessment  and  the  HCR,  a 
management  action  is  determined  (e.g.,  a  change  in  the  TAC).  Fleet  effort  and  catch  are  then  modeled, 
ideally  accounting  for  potential  error  in  implementation,  and  resulting  catches  are  fed  into  the  operating 
model.  By  repeating  this  cycle  the  full  management  cycle  is  modeled.  It  is  possible  to  test  the  effect  of 
modifying any part of this cycle including changes to the operating model, assumptions about noise,  etc”. 
Alternative MPs can be compared by running many stochastic simulations for several years to identify the 
performance of a rule according to different metrics under the likely range of conditions. The objective is to 
identify MPs that perform “well” under the range of conditions based on the pre-determined objectives and 
constraints. The choice of the MP generally involves a compromise between various objectives. 

In the end, the MP is only as good as the underlying models and assumptions it is based on. The success of 
the  MSE  framework  depends  on  the  extent  to  which  the  true  range  of  uncertainty  can  be  identified  and 
represented in operating models (De Oliveira et al., 2008). 

There are five key elements in the MSE approach (Smith et al., 1999): 

1. Management objectives 

2. Performance measures 

3. Management strategies 

4. Simulation evaluation of alternative management strategy performance 

5. Presenting the results to decision makers 

1. Management objectives 

Clear  management objectives are necessary  before any evaluation of potential  management strategies can 
be  undertaken.  Normally  the  objective  will  be  set  by  the  managers  taking  into  account  the  Precautionary 
Approach  (PA)  principles  and  the  opinion  of  the  stakeholders.  The  Fisheries  Commission  and  Scientific 
Council Joint Working Group on Risk-Based Management Strategies (FC SC RBMS) proposed as general 
objective  for  3M  cod  management  plan  the  following  (Annex  1):  “maintain  the  3M  cod  Spawning  Stock 
Biomass (SSB) in the safe zone as defined by the NAFO precautionary approach framework and to assure 
the  optimum  utilization,  rational  management  and  conservation  of  the  3M  cod  stock”.  This  general 
objective should be reach attending the following performance objectives established by the FC SC RBMS 
(ranked from higher to lower priority): 1) low risk of breaching Blim, 2) low risk of overfishing, 3) low risk 
of steep biomass decline, 4) to maximize average catch and 5) limited annual catch variation. 

50 

 
 
 
 
51 

1.  Very low risk of breaching Blim. The probability of a spawning stock biomass under Blim at 10% 

or lower. 

2.  Low  risk  of  overfishing.  For  the  model  free  HCR  only:  The  probability  of  F  exceeding  Fmsy 

during the evaluation period should be kept at 30% or lower. 

3.  Low  risk  of  steep  decline.  The  probability  of  the  decline  of  25%  or  more  of  spawning  stock 

biomass from year 0 to year 5 is kept at 10% or lower. 

4.  Maximum  averages  catch  over  the  period.  The  average  TAC  over  the  period  should  be 

maximized. 

5.  Limited annual catch variation. 

2. Performance measures 

The  results  of  the  stochastic  MSE  simulations  can  be  divided  into  two  broad  categories:  descriptive 
statistics  and  performance  measures.  The  former  includes  results  that  are  produced  to  develop  an 
understanding of the dynamics of the simulated stock and the fishery. The  latter are measures that can be 
used  to  assess  the  relative  merits  of  candidate  MSs,  either  how  they  perform  with  regards  to  critical 
performance  criteria  or  what  trade-offs  in  performance  they  represent.  Performance  measures  need  to  be 
quantifiable statistics that can be used to directly compare the performance of each candidate management 
strategy in terms of the management objectives. These are needed in addition to descriptive statistics of the 
stock dynamics under a given management strategy. 

In the current MSE, descriptive statistics that shall be presented are annual biomass, SSB, recruitment (age 
1), mean F (ages 3-5) and annual catch. These statistics  will be  presented by OM as time series plots for 
each MS. We also propose a new descriptive statistics to measure the risk of overfishing in the Model based 
HCR  described  below.  This  new  descriptive  statistics  measures  the  probability  that  the  Ftarget  used  in  the 
HCR is higher than the Fmsy or its proxy estimated each year in the assessment process model with the last 
three years means inputs. 

The FC SC RBMS proposed in February 2014 the following Performance Statistics (PS) (Table  3.3.1.1) to 
measure the different performance objectives for 3M cod: 

Noting that this MP proposal shall be in force initially until 2019 and that the last TAC with this proposal 
should  be  made  for  2020;  taking  into  account  that  we  are  going  to  suggest  performing  the  projections  a 
minimum of 20 years; and noting that the NAFO SC approved a 3M cod analytical assessment in 2014 with 
the  data  till  2013,  we  propose  two  different  time  periods  to  measure  the  performance  targets  3,  4  and  5: 
medium term 2015-2020 (projection years 1-7) and long term 2015-2034 (projection years 1-20).  

We  have  written  mathematically  the  FC  SC  RBMS  proposed  Performance  targets.  Table  3.3.1.2 presents 
the FC SC RBMS proposed Management Objectives and the new PS and PT proposed by us: 

3. Management Strategies  

The FC SC RBMS proposed two HCRs to be tested under the MSE approach (Annex 1). The first one is 
based  on  fishing  mortality  assessment  model  results  (model  based  HCR)  and  the  second  one  is  based  on 
survey indices (model free HCR). Both HCRs establish how the TAC is calculated each year when the 3M 
cod SSB is above Blim and the fishery is open. When SSB is below Blim, no directed fishing can be carried 
out and by-catch should be restricted to unavoidable by-catch in fisheries directed to other species. For this 
purpose,  fisheries  managers  will  consider  the  probability  and  establish  risk  tolerance,  noting  that  the 
probability of biomass to be above Blim is an integral part of the HCR proposed in option 1. 

Option 1 (Model based HCR): TAC(y) = Total Biomass(y) x Ftarget x P(SSB(y)>Blim) 

Four different levels of F will be considered as Ftarget, corresponding to probabilities of 20%, 30%, 40% and 
50% of exceeding Fmsy. If Fmsy is  not available, an appropriate proxy (e.g. Fmax, current  proxy) should be 
used.  

yytargetylimTAC=TotalBiomassFPSSB>B 
 
 
 
 
 
 
 
 
Option 2 (Model free HCR): TAC(y)=TAC(y-1) x (1 + λ x slope)     

Where: slope  =  is the  slope of the  log-linear regression lines fit to a recent period of the survey biomass 
indices for an age range.  

λ = an adjustment variable to ensure that the relative change in TAC is greater than the perceived relative 
decline in stock size (i.e. λ > 1, therefore allowing the strategy to halt the decline of the stock size through 
positive feedback). 

For  both  options  and  noting  the  desire  for  relative  TAC  stability,  TAC  should  be  constraint  to  a  fixed 
percentage of annual change. Level of constraint is to be defined by Managers. Different scenarios will be 
tested in the MSE: 10%, 15% and 20% 

The model based HCR (Option 1) is based on the Fmsy reference point or its proxies A Blim of 14 000 tons 
was approved by the NAFO Scientific Council in 2000 for the 3M cod stock. Due to the impossibility of 
estimating Fmsy in the 3M cod stock, the MSE should be carried out based on a proxy. In June 2014, NAFO 
SC decided that F30% (The fishing mortality which reduces Spawner Per Recruit (SPR) to 30% of its value 
at F=0) is the best Fmsy proxy at this moment for 3M cod (NAFO, 2014). 

The  model  free  HCR  (option  2)  is  a  simple  TAC  adjustment  strategy  that  uses  the  change  in  perceived 
status  of  the  stock  from  research  surveys  to  adjust  the  TAC  accordingly.  This  HCR  is  similar  to  the 
approved HCR for Greenland halibut Subarea 2+3KLMNO (Shelton and Miller, 2009 and NAFO, 2010). 

In the 3M cod case we need to decide the survey indices, the age and the period to estimate the slope of the 
survey indices as well as the value for λ. The EU Flemish Cap Survey is the only research survey available 
to implement this HCR in the 3M cod case. Figure 3.3.1.1 presents the EU Flemish Cap survey total and 3+ 
biomass indices for 3M cod. We can observe that in the most recent period both biomass indices have very 
different slopes due to the recruitment variability. Figure 3.3.1.2 presents the accumulative % catch biomass 
by  age  for  the  last  three  years  (2010-2012)  since  the  fishery  has  been  opened.  We  can  see  that  the 
percentage  of  the  3+  biomass  in  catches  is  very  high  in  the  mentioned  period  and  most  of  the  weight  in 
catches is for ages 5+. We propose to use the EU Flemish Cap Survey 3+ biomass index to implement the 
Model free HCR. The reason to choose the 3+ biomass is to take only into account the exploitable biomass 
for the estimation of the TAC in the HCR and to avoid the recruitment variability. The internal consistency 
for the survey indices of age 2 and 3 is not quite good and this is the main reason to propose 3+ biomass 
index  instead  of  2+  index.  To  estimate  the  slope  we  propose  to  use  the  most  recent  4  years  as  the  age 
composition of this stock is not very wide, most of the biomass is distributed in the age range 5+ and it is 
very  difficult  to  find  individuals  with  more  than  9  years  old.  Ranges  bigger  than  4  years  may  be  not 
representative of the stock status at the time when the TAC will be taken. 

Based on the analyses made for the Greenland halibut Subarea 2+3KLMNO HCR (NAFO, 2010) different 
values are required for the λ parameter. It is required a λ > 1 in the case of a perceived decline in stock size 
(slope < 0), but this value of λ could hamper stock recovery in the case of a perceived increase in stock size 
(slope  >  0).  A  variable  λ  approach  with  different  values  for  slope  >  0  and  slope  <  0  could  be  a  good 
solution. We  will  start the MSE process  with a  value  of 1.25 in the case of a declining  stock (allows  for 
adequate  adjustment  of  the  TAC  without  having  excessively  large  fluctuations  from  year  to  year)  and  a 
value of 1 in the case of an increasing stock. The final values of the λ parameter will be chosen after made 
deterministic projections to understand how different λ values perform.   

Based on these, our proposal for option 2 Model free HCR is the following: 

Where  slope3+  is  the  slope  of  the  log-linear  regression  lines  fit  of  the  EU  Flemish  Cap  survey  B3+  index 
from y-5 to y-2.  

λ = 1.25 when slope3+ <0 and λ =1 when slope3+ >0 

The combination of these HCRs with the OMs proposed below generates 90 scenarios to test. This number 
of scenarios makes very difficult to present in a clear way the results and probably will difficult the choice 
of the best HCR. We propose some changes in the proposed HCRs in the point 5 (Presenting the results to 
decision makers) to reduce this number of scenarios. 

52 

yy-13+TAC=TAC1+λslopeyy-1TAC=TAC1+λslope 
 
 
 
 
 
 
53 

4. The simulation algorithm 

The  simulation  algorithm  that  we  are  going  to  use  in  the  3M  cod  MSE  is  a  R  package  to  conduct  Bio-
Economic Impact assessments  using FLR (FLBEIA) developed by Garcia  et al. (2013). FLBEIA is an  R 
package  build  on  top  of  FLR  libraries.  The  purpose  of  the  package  is  to  provide  a  flexible  and  generic 
simulation model to conduct Bio-Economic Impact Assessments of harvest control rule based management 
strategies under a Management Strategy Evaluation (MSE) framework. As such, the model is divided into 
two main blocks, the operating model (OM) that it is the part of the model that simulates the real dynamics 
of the fishery system and the management procedure model (MPM) that simulates the whole management 
process. In turn, these two blocks are  divided in 3 components. The OM is  formed by  the biological, the 
fleet and the covariables components and the MPM by the observation, the assessment and the management 
advice components (Figure 3.3.1.3). 

This simulation algorithm explicitly or implicitly acknowledges different sources of uncertainty in both the 
“real” system and the management strategies (Francis and Shotton, 1997; Kell et al., 2007; Rosenberg and 
Restrepo, 1994). The simulation algorithm in the 3M  cod case takes an initial population, presently from 
1972  to  2013,  and  projects  it  into  the  future.  The  real  biological  population  and  fishery  are  projected,  in 
yearly time steps, using the OM and the MP is applied annually to produce the management advice for the 
next year. The advice obtained within the MP for a certain year constrains the behavior of the fleets in the 
next year.  

4.1. The operating models 

The MP is only as good as the underlying models and assumptions it is based on. The success of the MSE 
framework depends on the extent to which the true range of uncertainty can be identified and represented in 
operating models (De Oliveira et al., 2008). A lot of uncertainty usually exists around the dynamics of fish 
stocks,  so  instead  of  a  single  representation  of  ‘reality”  it  is  advisable  to  consider  many  options 
encompassing  most  of  the  possibilities  to  deal  with  this  uncertainty.  A  group  of  operating  models,  each 
conditioned on different data or based on an alternative realistic hypothesis of the stock dynamics or future 
trends in the fishery, is referred to as a reference set of operating models (Miller et al., 2008). 

The OM consists of an age-structured biological population and a single fishery inducing fishing mortality 
during  the  harvesting  process.  The  operating  model  requires  consideration  of  the  past  system  and  initial 
starting point of the population, biological parameters of the stock, behavior of the fishery/fleet(s) and the 
level of uncertainty/error in the observation of the system. In the 3M case we propose a reference set of 5 
operating models. These operating models are distinguished by: stock-recruit function and M values (Table 
3.3.1.3). 

4.1.1 The initial population 

To assess the ability of a management strategy to achieve the objectives it is necessary to construct the past 
system and the projections starting point to be as close as possible to the real stock at the present time. 

Important elements that are needed to construct the past system/starting point includes: age structure of the 
stock, stock abundance (numbers at age), fishery catch data (age disaggregated), weights at age, maturities 
at age and natural mortality. The age structure of the simulated stock should agree with the best hypotheses 
and data available for the stock.  

The Bayesian XSA model approved by the NAFO SC in 2014 for 3M cod works as a XSA, calculating the 
population  numbers  and  F  from  the  last  year  and  the  last  age,  assuming  a  lognormal  prior  over  the 
survivors. The goal of this assessment model is that it works with years with no catch at age information, as 
in the case of the 3M cod for which there are no catch at age data for years 2002-2005. In these years the 
model assumes a prior over F in all true  ages (1-7) and over the  total catch in order to continue  with the 
calculation  of  the  numbers.  In  the  years  with  catch  at  age  it  works  exactly  as  an  XSA  just  assuming  a 
lognormal prior over the survivors as starting point. Moreover, in 2011 and 2012 there was a lack of total 
catches  in  the  data,  due  to  a  change  in  the  way  of  estimating  the  catch  data  that  makes  the  time  series 
inconsistent. In this case the assessment uses a lognormal prior in order to make an estimation of the total 
catch.  As  tuning,  the  Canadian  survey  in  3M  (1978-1985)  and  the  EU  Flemish  Cap  survey  (1988-2013) 
were  used. 5000 iterations were made via MCMC in order to get uncertainty over the assessment results. 
Detailed descriptions of the Bayesian model approved in 2014 are in Fernandez et al. (2008) and Gonzalez-
Troncoso et al. (2014). 5000 iterations probably is a very large number for carrying out the MSE process 

 
 
 
 
 
and  may  create  technical  computational  problems  when  performing  the  simulations.  The  final  iterations 
numbers  to  perform  the  3M  cod  MSE  will  be  decided  taking  into  account  the  technical  computational 
problems and they will be enough to measure the uncertainty correctly. 

The catch discrepancy is a general problem for all NAFO stocks. NAFO has created this year a Fisheries 
Commission  Ad  hoc  Working  Group  on  Catch  Reporting  to  try  to  solve  the  catch  discrepancies  (NAFO, 
2014).  

NAFO Scientific Council decided to use for 3M cod the total catches from the Daily Catch Reports (DCR) 
in 2013 (13 985 t), maintaining the  model catch estimation  for 2011 and 2012 (NAFO, 2014). The MSE 
starting point will be based on the data and results (age structure of the stock, stock abundance numbers at 
age, fishery catch data age disaggregated, weights at age, maturities at age and natural mortality) of the 3M 
cod approved assessment in 2014 (Gonzalez-Troncoso et al., 2014).  

We  propose  to  use  the  same  Bayesian  XSA  model  and  input  data  approved  in  2014  to  estimate  the 
abundance at age starting point of the OM. 

In  the  approved  2014  assessment,  M  was  estimated  by  the  XSA  Bayesian  model.  M  was  considered 
constant for all ages and years. M is one of the most uncertain parameters to estimate a starting point and 
has a very big influence to estimate proxies of Fmsy. We propose to create two sets of operating models with 
different starting points based on the same XSA Bayesian model structure but with different assumptions of 
M. One options is as in the last approved assessment, with M constant estimated by the model for all ages 
and  for  all  years,  and  other  is  based  on  the  results  of  Gonzalez-Troncoso  and  Gonzalez-Costas  (2014), 
where it has proposed to estimate M by the model for three different ages ranges (1-2, 3-5, 6-8+) and for 
three different time periods (1972-1995, 1996-2008, 2009-last assessment year). 

The catch by year, mean weights at age in catches and stock and maturity ogives (1972-2013) will be the 
same used in the 2014 assessment. The mean  weights in catch and stock are both inputs of the model and 
have no uncertainty. The maturity ogive is calculated from the microscopic reading of the ogives from the 
survey  applying  a  Bayesian  logistic  regression  model  to  proportion  mature  at  age.  They  have  been  fitted 
independently for each year, fitting 5000 iterations in order to get uncertainty in the maturity ogives. 

4.1.2 The projection  

Beginning  at  the  initial  starting  point  of  the  projections,  numbers  at  age  for  ages  2  and  up  are  projected 
using  the  basic  equation  for  updating  population  size.  Natural  mortality  (M),  mean  weights  and  partial 
recruitment (PR) are specified by the operating model, while fishing mortality (F) depends on the harvest 
control rule (HCR) based on the management strategy being evaluated. Recruitment (numbers at age 1) is 
determined by the stock-recruit model applied in the operating model. In subsequent years, given the matrix 
of numbers-at-age of the previous years, the population numbers are carried forward using the exponential 
survival equation and a predefined SR relationship with a lognormal multiplicative random error to generate 
the recruits. As the real TAC for 2014 is known, it is assumed that the fleets will comply exactly with this 
TAC.  The  catchability  of  the  fishery  is  an  input  parameter  and  it  varies  through  iterations.  The  catch  is 
estimated  each  year  by  iteration  using  the  Baranov  catch  equation  (Baranov,  1918),  constraining  it  to 
produce  exactly  the  TAC,  given  the  catchability-,  numbers-,  weights-  and  natural  mortality-at-age  of  the 
real population. In each scenario the biological population and fishery were projected until 2034 for each of 
the  starting  point  iterations.  Thus,  the  last  management  process  is  run  in  2033  and  the  last  ‘perceived’ 
population is obtained for 2032. 

In the projection of the “true” biological population, maturity, weights, Partial Recruitment and M for each 
year  will  be  obtained  from  the  1972-1995  and  2009-2013 periods  based  on  the  analysis  of  the  biological 
parameters  variation  made  by  Gonzalez-Costas  and  Gonzalez-Troncoso  in  2014.  For  each  iteration  we 
select a year randomly from these periods and the maturity, partial recruitment, stock and catch weights are 
taken  together  from  the  selected  year  and  a  period  of  the  next  years.  The  main  reason  to  obtain  the 
biological parameters for the projections in blocks of years is to take in account the possible autocorrelation 
of  the  parameters.  The  variability  of  weights,  maturity  and  partial  recruitment  should  be  related  with  the 
abundance  and  the  particular  environmental  conditions  of  each  period.  In  the  projections  we  do  not  take 
into  account  the  1996-2008  period  due  to  in  this  period  the  abundance  were  very  low,  the  fishery  was 
mainly  close  and  the  mean  weights  and  maturity  values  were  very  different  than  in  the  periods  were  the 
abundance was higher and the fishery was open.   

4.1.3. Stock-recruit relationship 

54 

 
 
 
55 

There  is  a  considerable  uncertainty  regarding  the  appropriate  stock-recruit  function  for  this  stock.  Three 
different  S/R  models  (Ricker,  Beverton-Holt  and  segmented  regression)  were  fit  to  3M  cod  data  in  2013 
(Gonzalez-Troncoso  et  al.,  2013).  Results  showed  that  none  of  these  stock  recruitment  relationship  fits 
appropriately  the  3M  cod  data.  Given  this  large  degree  of  uncertainty,  and  the  importance  of  the  stock-
recruit relationship in MSE simulations, it is necessary to consider a number of possible ways to estimate 
the recruitment to ensure potential management strategies are robust to this uncertainty. 

For  3M  cod  MSE  we  propose  to  test  several  Operating  Models  (OMs)  based  on  three  different 
Stock/Recruitment  (S/R)  relationships:  Recruitment  independent  of  SSB,  Segmented  Regression  with 
Beta=Approved  Blim  and  Segmented  Regression  fit  with  the  assessment  results.  These  three  operating 
models  will  be  applied  to  the  two  different  starting  points  based  on  different  assumptions  of  M  (Table 
3.3.1.1). 

4.1.3.1 Recruitment independent of SSB: Bootstrapping recruitment values from 1972-2011. We propose to 
eliminate the last 3 recruitments of the time series to do the bootstrap due to these recruitments have a high 
uncertainty and they are not well calculated by the model. 

4.1.3.2  Segmented  Regression  with  Beta=Approved  Blim:  Segmented  regression  provides  a  simple 
description of the stock-recruit data with constant recruitment above the breakpoint (Beta) and recruitment 
declining linearly to zero below the breakpoint. We fit a constrained segmented regression model to have a 
beta parameter equal to the approved 3M cod Blim (14 000 tons). 

4.1.3.3 Segmented Regression fit with the assessment results: Although the Segmented Regression fit with 
the assessment results has a no well defined beta parameter in the observed SSB range (Gonzalez-Troncoso 
et al., 2013), we consider this scenario that  is consistent  with a  stock  that has been  severely recruitment-
overfished. 

The stock–recruitment relationship for each scenario will be fitted using the numbers at age 1 and the SSB 
in the initial population. One SR relationship will be fitted in each of the iterations.  

4.1.4. Biological points (RPs) 

A Blim of 14 000 tons was proposed by the NAFO Scientific Council in 2000 for the 3M cod stock. In 2008 
the  appropriateness  of  this  value  given  the  results  from  the  new  method  used  to  assess  the  stock  was 
examined,  concluding  that  it  is  still  an  appropriate  reference  (Fernández  et  al.,  2008).  In  2012,  Fisheries 
Commission  requested  to  Scientific  Council:  to  provide  Bmsy  and  Fmsy  for  cod  in  Div.  3M.  Scientific 
Council  in  2013  estimated  the  Yield  per  Recruit  (YPR),  Spawner  per  Recruit  (SPR)  and  Maximum 
Sustainable Yield (MSY) reference points with uncertainty to provide candidates for B msy and Fmsy for cod 
Div.  3M.  The  results  of  the  Bmsy  and  Fmsy  estimated  based  on  different  stock-recruitment  relationship 
(Ricker,  Beverton-Holt  and  Segmented  Regression)  were  not  plausible  due  to  the  high  uncertainty  in  the 
stock recruit relationship for this stock. 

The  model  based  HCR  (Option  1)  is  based  on  the  Fmsy  reference  point  or  its  proxies.  Due  to  the 
impossibility  of  estimating  Fmsy,  in  this  case  the  MSE  should  be  carried  out  based  on  a  proxy.  In  2014, 
NAFO SC decided that F30%SPR (The fishing mortality which reduces Spawner Per Recruit (SPR) to 30% of 
its value at F=0) is the best Fmsy proxy at this moment for 3M cod. It was also analyzed the YPR and SPR 
inputs  (mean  weights,  partial  recruitment  and  maturity  ogive)  to  study  the  possibility  of  changes  in 
productivity in the past and its impact in the reference points estimated values. The SC proposed to use data 
from 1972 to 2013 to estimate the F30%SPR to avoid the big trends in biological parameters observed in the 
most recent years (2009-2013).  

4.2. The management procedure 

The  MP  model  is  divided  into  three  sub-models:  (i)  the  observation  model  which  simulates  the  data 
collection  and  thereby  links  the  MP  with  the  OM,  (ii)  the  assessment  process  model  that  generates  a 
‘perceived’ population based on the data, including the assessment model and the short term forecast; (iii) 
the management decision model which uses an HCR based on the perceived population and the estimated 
reference points to derive management advice (Figure 3.3.1.4). 

For  3M  cod  the  MP  is  applied  on  2014  and  produces  the  TAC  advice  for  2015  based  on  the  different 
Harvest Control Rules (HCRs) proposed by FC SC RBMS. Each HCR is applied every year up to the end of 
the projections period. FC SC RBMS proposed that the Risk-based Management for cod stock in Subarea 

 
 
 
 
 
3M  shall  be  in  force  initially  until  2019.  We  suggest  performing  the  projections  a  minimum  of  20  years. 
This time period would allow observing the stabilized biomass results of applying the different management 
strategies. The reasons for proposing this period are based on the age composition of this stock (1-8+) and 
the  need  for  the  projections  period  covering  at  least  several  generations  to  have  an  idea  of  the  long  term 
results.  

4.2.1. The observation model.  

Catch-at-age is considered to be known without error in the observation model and the abundance indices 
are  generated  with  a  multiplicative  random  error  assuming  a  linear  relationship  between  catchability  and 
abundance-at-age.  Mean  weights  at  age,  maturity  ogive  and  natural  mortality  assessment  data  in  the 
Management Procedure assessment model are known without error and equal to the real world. 

4.2.2. The assessment process model and the management decision model 

For  the  Model  based  HCR  (
)  we  propose  to  use  as  assessment 
process  model  the  Bayesian  XSA.  The  TAC  for  year  “y”  is  set  based  on  the  assessment  carried  out  in 
previous  year  (“y−1”)  with  assessment  data  available  up  to  year  “y−2”.  The  stock  assessment  therefore 
produces  fishing  mortality  estimates  for  year  “y−2”  and  population  estimates  for  the  beginning  of  year 
“y−1” (except for the recruitment).  

To carry out the short term projections we propose to use the usual method. The recruitment and maturity 
ogive  in  years  “y−1”  and  “y”  are  estimated  as  the  mean  of  the  “y−4”  and  “y−2”  period  from  the 
Management Procedure assessment model. To estimate the fishing mortality in “y−1” it is assumed that the 
fleet catches exactly the TAC set for year “y−1” with the selection pattern and  mean weights equal to the 
average of Management Procedure assessment model between years “y−4” and “y−2”. With the abundance 
and F of year “y−1” we can estimate the “y” abundance and assuming that the “y” mean weights are equal 
to  the  “y−1”  mean  weights,  we  can  estimate  the  Total  and  the  Spawning  biomass  to  apply  the  HCR  and 
know the “y” TAC. 

)  the  TAC  for  year  “y”  is  set  based  on  the  EU 
For  the  Model  free  HCR  (
Flemish Cap survey slope of the log-linear regression line fit to “y−2” till ”y-5” period of the survey indices 
for 3+ biomass. λ is an adjustment variable for the relative change in TAC to the perceived change in stock 
size: λ = 1.25 when slope3+ <0 and λ =1 when slope3+ >0. 

Management  implementation  error,  including  TAC-overruns,  is  a  serious  problem  with  respect  to  many 
NAFO  stocks.  It  may  be  considered  necessary  to  incorporate  some  form  of  implementation  error  in  the 
MSE  simulation  process.  This  could  be  done  by  adding  a  proportional  TAC  overrun.  However,  TAC 
overrun is likely to vary by year depending on the availability of fish to the fishery and the level of TAC. In 
addition to this, there is very little information to apply the  management implementation error in the 3M 
cod  case  because  the  fishery  was  close  between  1999-2009  and  the  fishery  behavior  information  is  very 
scarce. Incorporating implementation error makes it difficult to determine the true success of a management 
strategy – is the failure of a strategy due to inherent shortcomings or to the non-compliance in the fishery? 
Implementation error could be considered to be more a problem for managers to deal with. Based on these 
we propose not to apply the implementation error in the 3M cod MSE. 

5. Presenting the results to decision makers. 

Results of MSE simulations need to be presented in a clear and concise manner to permit that the decision 
makers  can  easily  interpret  the  outcome.  When  there  is  a  large  amount  of  uncertainty  and  HCRs  to  be 
tested, the different scenarios can easily become excessive. It is important that an MSE is well planned so 
that  only  the  most  likely  and  influential  sources  of  uncertainty  and  the  most  appropriated  HCR  are  fully 
explored to keep the number of scenarios manageable.  

In  the  3M  cod  we  have  6  OMs  that  cover  part  of  the  M  and  S/R  uncertainty  but  due  to  the  different 
requirements in the proposed two HCRs we have 90 scenarios to analyze (Table  3.3.1.3). This number of 
scenarios makes very difficult to present in a clear way the results and probably will difficult the choice of 
the best HCR. We propose, in priority order, the following changes to reduce this high number of scenarios: 

The three different TAC constraints (10%, 15% and 20%) probably difficult to know the true behavior of 
the different HCRs tested and its productivity in catch. We propose to test only the 20% TAC constraints in 

56 

yytargetylimTAC=TotalBiomassFPSSB>Byy-13+TAC=TAC1+λslope 
 
 
 
57 

a  first  stage  and  measure  the  importance  of  the  10%  and  15%  constraints  creating  a  new  PS  under  the 
Limited annual catch variation performance objective. This new PS will measure in the medium and long 
term the number of times that TAC(y) > TAC(y-1) + %TAC(y-1) and TAC(y) < TAC(y-1) - %TAC(y-1). 
The  percentage  levels  that  should  be  measured  are  10%  and  15%.  This  PS  would  allow  us  to  know  the 
importance to impose a TAC constraint  of 10% or 15%.. After analyze the results of this new PS we can 
decide the better constraint level to be tested. If this proposal is accepted the number scenarios to analyze 
will be reduced to 30. 

In the Model based HCR, Ftarget is defined as four different levels of Fmsy, corresponding to probabilities of 
20%,  30%,  40%  and  50%  of  exceeding  Fmsy.  Based  on  the  analysis  made  by  Gonzalez-Costas  and 
Gonzalez-Troncoso in 2014 where F30%SPR (the best approved Fmax proxie) was estimated with uncertainty 
(Table  3.3.1.4),  the  difference  between  the  20%  and  50%  probabilities  of  F30%SPR  is  only  0.017  and  this 
difference,  translated  to  the  equilibrium  catches  (Table  3.3.1.5),  is  only  1  760  tons.  Taking  into  account 
these  small  differences  and  to  make  more  manageable  the  number  of  final  scenarios,  we  propose  three 
different probability levels to be tested: 20%, 35% and 50%. With this proposal we reduce 6 scenarios and 
the final scenarios to be tested will be 24.  

We think that these proposals do not change so much the HCR ideas to be tested and greatly facilitate the 
presentation of results and the choice  of one  of the  HCRs. Table  3.3.1.6 shows the final scenarios of our 
proposal. And Table 3.3.1.7 shows the Management Objectives defined by FC SC RBMS and our proposal 
for the Performance Statistics (PS) Performance Targets (PT) to measure these objectives. 

Acknowledgements 

This research was funded by the European Union founds under the Framework Contract MARE/2012/21  - 
"Scientific  advice  for  Fisheries  beyond  EU  Waters"  -  Specific  Contract  No  2  (SI2.681887)  “Provision  of 
advice on the development of a multiannual management plan and the evaluation of a management strategy 
for cod in NAFO Division 3M (SAFEwaters-2)”. 

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967-979. 

58 

 
 
 
 
59 

Table 3.3.1.1.- FC SC RBMS proposed Management Objectives, Performance Statistics and Performance 
Targets for the 3M cod MSE (Annex 1). 

Management Objectives  

Performance Statistics (PS)  

Very  low  risk  of  breaching 
Blim  

SSB / Blim  

Low risk of overfishing  

F/Fmsy  

Low risk of steep decline  

Fmax  is  used  as  a  proxy  for 
Fmsy.  

SSB10/SSB0, where SSB10 = 
spawning  stock  biomass  in 
year 10 and SSB0 = spawning 
stock  biomass 
in  year  0, 
where  year  0  is  the  current 
year  

SSB5/ SSB0  

= 

/  SSB0,  where 
SSBlowest 
SSBlowest 
lowest 
spawning stock biomass level 
during  projected  evaluation 
period  

Maximum average catch over 
the period  

Yearly TAC for the period  

Average TAC over the period  

Limited 
variation  

annual 

catch 

of 

times 

Number 
the 
constraint (at the lower and at 
the  higher  boundaries)  has 
been  applied  on  average 
during the period.  

Performance 
(PT)  

Targets 

The  probability  of  a 
spawning  stock  biomass 
under  Blim  at  10%  or 
lower  

For  the  model  free  HCR 
only:  The  probability  of 
F  exceeding  Fmsy  during 
the 
evaluation  period 
should be kept at 30% or 
lower. 

spawning 

The  probability  of  the 
decline  of  25%  or  more 
of 
stock 
biomass  from  year  0  to 
year  5  is  kept  at  10%  or 
lower.  

The  average  TAC  over 
the  period  should  be 
maximized  

This  will  be  achieved 
through the constraint on 
the TAC variation.  

 
 
 
 
 
 
 
 
Table  3.3.1.2.-  Management  Objectives  and  the  new  Performance  Statistics  and  Performance  Targets 
proposed by the SAFEwaters2 for the 3M cod MSE. 

Management Objectives  

Performance Statistics (PS)  

Performance Targets (PT)  

low 

Very 
breaching Blim  

risk 

of 

is 

the  Spawning 
SSBy 
Stock Biomass in the year y 
of the projection period. 

Low risk of overfishing  

For the model-free HCR: 

       y=1,…,20 

       y=1,…,20 

Fy  is  the  Fishing  Mortality 
in 
the 
the  year  y  of 
projection period.  

Low risk of steep decline  

is 

the  Spawning 
SSBy 
Stock Biomass in the year y 
of the projection period. 

the 

SSBlowest_y 
lowest 
is 
Spawning  Stock  Biomass 
level  in  the  period  1-y  of 
the projection 

Maximum  average  catch 
over the period  

Limited 
variation  

annual 

catch 

TACassess_y 
the  TAC 
is 
given  by  the  assessment  in 
year y before constraints 

     x=0.1, 0.15, 0.2 

      y=1,…,7 

      y=1,…,20 

60 

lim/ySSBBlim/10.1yPSSBBmsy/yFF/10.3ymsyPFF70/SSBSSB200/SSBSSB_70/lowestSSBSSB_200/lowestSSBSSB70/0.250.1PSSBSSB200/0.250.1PSSBSSB_70/0.250.1lowestPSSBSSB_200/0.250.1lowestPSSBSSB71/7iiTAC201/20iiTAC71max/7iiTAC201max/20iiTAC_1_/assessyassessyTACTAC_1_/1assessyassessycountTACTACx_1_/1assessyassessycountTACTACx 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
61 

Table 3.3.1.3.- Operating Models proposed in this document and Management procedures propose by FC 
SC RBMS with their different scenarios.  

OM 

MP 

Scenario 

M value 

S/R Function 

HCR 

Ftarget 

TAC constraint 

Constant 

1 

Model Based 

20% Fmax 

30% Fmax 

40% Fmax 

50% Fmax 

2 

Model Based 

20% Fmax 

30% Fmax 

40% Fmax 

50% Fmax 

3 

Model Based 

20% Fmax 

10% 

15% 

20% 

10% 

15% 

20% 

10% 

15% 

20% 

10% 

15% 

20% 

10% 

15% 

20% 

10% 

15% 

20% 

10% 

15% 

20% 

10% 

15% 

20% 

10% 

15% 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

22 

23 

24 

25 

26 

 
 
 
 
30% Fmax 

40% Fmax 

50% Fmax 

Variable 

1 

Model Based 

20% Fmax 

30% Fmax 

40% Fmax 

50% Fmax 

2 

Model Based 

20% Fmax 

30% Fmax 

40% Fmax 

20% 

10% 

15% 

20% 

10% 

15% 

20% 

10% 

15% 

20% 

10% 

15% 

20% 

10% 

15% 

20% 

10% 

15% 

20% 

10% 

15% 

20% 

10% 

15% 

20% 

10% 

15% 

20% 

10% 

15% 

27 

28 

29 

30 

31 

32 

33 

34 

35 

36 

37 

38 

39 

40 

41 

42 

43 

44 

45 

46 

47 

48 

49 

50 

51 

52 

53 

54 

55 

56 

62 

 
63 

50% Fmax 

3 

Model Based 

20% Fmax 

30% Fmax 

40% Fmax 

50% Fmax 

Constant 

1 

Model Free 

2 

3 

Model Free 

Model Free 

Variable 

1 

Model Free 

2 

Model Free 

20% 

10% 

15% 

20% 

10% 

15% 

20% 

10% 

15% 

20% 

10% 

15% 

20% 

10% 

15% 

20% 

10% 

15% 

20% 

10% 

15% 

20% 

10% 

15% 

20% 

10% 

15% 

20% 

10% 

15% 

57 

58 

59 

60 

61 

62 

63 

64 

65 

66 

67 

68 

69 

70 

71 

72 

73 

74 

75 

76 

77 

78 

79 

80 

81 

82 

83 

84 

85 

86 

 
 
3 

Model Free 

20% 

10% 

15% 

20% 

87 

88 

89 

90 

64 

 
 
 
 
65 

Table 3.3.1.4.- YPR reference point (Fmax) and SPR reference point (F30%,) estimated distribution. 

10% 

20% 

30% 

40% 

50% 

60% 

70% 

80% 

90% 

Fmax 

F30% 

0.138 

0.145 

0.151 

0.156 

0.163 

0.168 

0.174 

0.181 

0.194 

0.109 

0.116 

0.121 

0.128 

0.133 

0.137 

0.143 

0.149 

0.159 

Table  3.3.1.5.-  SPR  F30%  reference  points  and  their  YPR,  SPR  and  equilibrium  yield  and  SSB  in  tons 
estimated distributions. 

F30% 
SPR 

0.109 

0.116 

0.121 

0.128 

0.133 

0.137 

0.143 

0.149 

0.159 

YPR 

0.810 

0.902 

0.971 

1.031 

1.090 

1.152 

1.220 

1.301 

1.419 

SSBPR 

3.711 

4.421 

5.014 

5.539 

6.105 

6.704 

7.427 

8.350 

9.761 

Yield 
(t) 

19995 

20810 

21478 

22033 

22570 

23167 

23832 

24610 

25726 

SSB (t) 

176509 

157646 

144446 

134758 

126545 

118453 

110942 

101929 

91931 

10% 

20% 

30% 

40% 

50% 

60% 

70% 

80% 

90% 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 3.3.1.6.- Operating Models and Management procedures proposed in this study with their different 
scenarios.  

OM 

M value 

Constant 

Variable 

Constant 

Variable 

MP 

Scenario 

S/R Function 

HCR 

Ftarget 

1 

2 

3 

1 

2 

3 

1 

2 

3 

1 

2 

3 

Model Based 

20% Fmax 

35% Fmax 

50% Fmax 

Model Based 

20% Fmax 

35% Fmax 

50% Fmax 

Model Based 

20% Fmax 

35% Fmax 

50% Fmax 

Model Based 

20% Fmax 

35% Fmax 

50% Fmax 

Model Based 

20% Fmax 

35% Fmax 

50% Fmax 

Model Based 

20% Fmax 

35% Fmax 

50% Fmax 

Model Free 

Model Free 

Model Free 

Model Free 

Model Free 

Model Free 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

22 

23 

24 

66 

 
 
 
 
 
 
 
 
 
 
 
67 

Table  3.3.1.7.-  Management  Objectives  defined  by  FC  SC  RBMS  and  our  proposal  for  the  Performance 
Statistics (PS) and Performance Targets (PT) to measure these objectives.  

Management Objectives  

Performance Statistics (PS)  

Performance Targets (PT)  

Very 
breaching Blim  

low 

risk 

of 

is 

SSBy 
the  Spawning 
Stock Biomass in the year y 
of the projection period. 

Low risk of overfishing  

For the model-free HCR: 

       y=1,…,20 

       y=1,…,20 

Fy  is  the  Fishing  Mortality 
in 
the 
the  year  y  of 
projection period.  

Fmsy or its proxy.  

Low risk of steep decline  

is 

SSBy 
the  Spawning 
Stock Biomass in the year y 
of the projection period. 

the 

lowest 
is 
SSBlowest_y 
Spawning  Stock  Biomass 
level  in  the  year  y  of  the 
projection 

Maximum  average  catch 
over the period  

Limited 
variation 

annual 

catch 

     x=0.1, 0.15, 0.2 

      y=1,…,7 

       y=1,…,20 

lim/ySSBBlim/10.1yPSSBBmsy/yFF/10.3ymsyPFF70/SSBSSB200/SSBSSB_70/lowestSSBSSB_200/lowestSSBSSB70/0.250.1PSSBSSB200/0.250.1PSSBSSB_70/0.250.1lowestPSSBSSB_200/0.250.1lowestPSSBSSB71/7iiTAC201/20iiTAC71max/7iiTAC201max/20iiTAC_1_/assessyassessyTACTAC_1_/1assessyassessycountTACTACx_1_/1assessyassessycountTACTACx 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 3.3.1.1.- EU Flemish Cap Survey 3M cod biomass indices (1988-2012).  

Figure 3.3.1.2.- Catch biomass percentage by age in the 3M cod fishery in the 2010-2012 period. 

68 

050000100000150000200000Indexyear3M Cod EU Flemish  Cap biomass Survey IndexTotal 3+0%20%40%60%80%100%12345678+% Biomass CatchagesAccumulative % catch biomass by age201020112012 
 
 
 
 
 
 
 
 
 
 
 
69 

Figure 3.3.1.3.- Conceptual diagram of BEIA from Garcia et al. (2013). 

Figure 3.3.1.4.- Conceptual diagram of the Management Procedure. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
3.3.2  Using  qualitative and quantitative analysis compare the different options and 
evaluate  whether  the  options  are  capable  of  achieving  the  biological 
objectives. In doing so, the study will take into account ecosystem interactions 
and technical  interactions  of  different fisheries  (SCR  will be  presented  in  the 
2015 NAFO Scientific Council meeting). 

Material and Methods 

The 3M Cod MSE is developed in another document (González-Costas et al., 2014) based on the conclusions 
that  the  Fisheries  Commission  and  Scientific  Council  Joint  Working  Group  on  Risk-Based  Management 
Strategies (FC SC RBMS) reached in February 2014 (NAFO, 2014a). 

There are five key elements in the MSE approach (Smith et al., 1999): 

1. Management objectives 
2. Performance measures 
3. Management strategies 
4. Simulation evaluation of alternative management strategy performance 
5. Presenting the results to decision makers 

1. Management objectives 

The general objective of the 3M Cod MSE is to  “maintain the 3M cod Spawning Stock Biomass (SSB) in the 
safe  zone  as  defined  by  the  NAFO  precautionary  approach  framework  and  to  assure  the  optimum  utilization, 
rational management and conservation of the 3M cod stock” (NAFO, 2014a). This general objective should be 
reached attending the following performance objectives (ranked from higher to lower priority): 

1.  Very low risk of breaching Blim. The probability of a spawning stock biomass under Blim at 10% or 

lower. 

2.  Low risk of overfishing. For the model-free HCR only: The probability of F exceeding Fmsy during the 

evaluation period should be kept at 30% or lower. 

3.  Low risk of steep decline. The probability of the decline of 25% or more of spawning stock biomass 

from year 0 to year 5 is kept at 10% or lower. 

4.  Maximum averages catch over the period. The average TAC over the period should be maximized. 
5.  Limited annual catch variation. 

2. Performance measures 

Based on the above objectives a group of Performance Statistics (PS) was developed for 3M cod (Table 3.3.2.1). 
These  PS  were  presented  by  González-Costas  et  al.  (2014)  taking  into  account  the  performance  objectives 
proposed in the FC SC RBMS of 2014 and the NAFO Scientific Council suggestions and comments.  

3. Management Strategies  

Two HCRs are tested under the MSE approach:  

Option 1 (Model-based HCR): 

A  Blim  of  14  000  tons  was  approved  by  the  NAFO  Scientific  Council  in  2000  for  the  3M  cod  stock.  Three 
different  levels  of  F  will  be  considered  as  Ftarget,  corresponding  to  probabilities  of  20%,  35%  and  50%  of 
exceeding Fmsy. In June 2014, NAFO SC decided that F30%SPR (the fishing mortality which reduces Spawner Per 
Recruit (SPR) to 30% of its value at F=0) is the best Fmsy proxy at this moment for 3M cod (NAFO, 2014b). 

Option 2 (Model-free HCR): 

Where: slope = slope3+ is the slope of the log-linear regression lines fit of the EU Flemish Cap survey B3+ index 

from y-5 to y-2.  

70 

yytargetylimTAC=TotalBiomassFPSSB>Byy-13+TAC=TAC1+λslope 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
71 

λ = an adjustment variable to ensure that the relative change in TAC is greater than the perceived relative 
decline  in  stock  size  (i.e.  λ  >  1,  therefore  allowing  the  strategy  to  halt  the  decline  of  the  stock  size 
through positive feedback). 

The  index  used  is  the  EU  Flemish  Cap  survey  (Vazquez  et  al.  2013  and  Mandado  2014)  3+  biomass  index 
(1988-2013). After making some deterministic projections to understand how different λ values perform, it was 
decided that the best approach for λ is a variable value depending on the population status: if the SSB is less or 
more than the observed SSB in 2013 (by iteration); the 2013 SSB is around the maximum of the historic period 
(1972-2013). For each of the options, we chose the following variable λ approach with different values for slope 
> 0 and slope < 0 as the best options:  

If SSB < maximum observed SSB =>  

         slope3+ < 0 => λ=1.25 

         slope3+ > 0 => λ=1 

         slope3+ < 0 => λ=1 

If SSB > maximum observed SSB =>  

         slope3+ > 0 => λ=1.15 

The level of constraint (fix percentage of annual TAC change) is to be defined by Managers. The FC SC RBMS 
(NAFO, 2014a) established three different TAC constraints levels (10%, 15% and 20%). 90 scenarios should be 
analyzed  due  to  the  different  requirements  in  the  FC  SC  RBMS  proposed  HCRs.  This  number  of  scenarios 
makes it very difficult to present the results in a clear  way and it  will probably be difficult to choice  the best 
HCR. To reduce the high number of scenarios the Scientific Council (NAFO, 2014b) proposed to test only the 
20% TAC constraints in a first stage and measure the importance of the 10% and 15% constraints creating a new 
PS under the  Limited annual  catch  variation performance objective. It  was decided to take in account the  SC 
proposal and to use a 20% constraint level in this study. 

4. The simulation algorithm 

The simulation algorithm that was used in the 3M cod MSE is an R package to conduct Bio-Economic Impact 
assessments using FLR (FLBEIA) developed by Garcia et al. (2013).  

4.1. The operating models (OMs) 

In  the  3M  cod  case  we  test  a  set  of  6  operating  models.  These  operating  models  are  distinguished  by:  stock-
recruit function and M values. A set of 24 different scenarios resulting from the combination of six different OM 
and the different HCR to test were examined (Table 3.3.2.2). 

4.1.1 The initial population 
To  assess  the  ability  of  a  management  strategy  to  achieve  the  objectives  it  is  necessary  to  construct  the  past 
system and the projections starting point to be as close as possible to the real stock at the present time. 

Initial values 
The initial values of the MSE are the same as used in the 2014 approved assessment (González-Troncoso et al., 
2014). This includes: total catch, catch-at-age, EU Flemish Cap survey index per age (1988-2013) (Mandado, 
2014), Canadian survey index per age (1978-1985), catch weight-at-age, stock weight-at-age, maturity ogive at 
age and natural mortality. We used the Bayesian XSA model approved by the NAFO SC in 2014 for 3M cod. 
Although in the approved assessment a total of 5000 iterations are made, in our case  and due to the amount of 
scenarios  that  we  have  it  was  very  difficult  to  use  5000  iterations  because  of  computational  time  consuming 
problems. A sensitive analysis with different number of iterations (5000, 2000, 1000, 500, 200 and 100) led to 
the conclusion that with more than 500 iterations the results of the assessment are quite the same, without a big 
loss of uncertainty. So, we used as starting point the results of the approved assessment in June 2014 but with 
500  iterations  instead  of  5000.  All  the  input  indices  are  deterministic  except  maturity  ogive  and  natural 
mortality. The maturity ogive is calculated from the microscopic reading of the ogives from the survey applying 
a Bayesian logistic regression model to proportion mature at age. They have been fitted independently for each 
year, fitting 500 iterations in order to get uncertainty in the maturity ogives. 

 
 
 
 
 
 
 
 
        
 
  
 
 
 
        
 
 
 
 
 
        
 
  
 
 
 
        
 
 
 
 
 
 
 
 
 
In the case of natural mortality (M), we have two sets of operating models with different starting points based on 
the  same  XSA  Bayesian  model  structure  but  with  different  assumptions  of  M.  One  options  is  as  in  the  last 
approved assessment, with M constant estimated by the model for all ages and for all years, and other is based 
on  the  results  of  Gonzalez-Troncoso  and  Gonzalez-Costas  (2014),  with  M  estimated  by  the  model  for  three 
different  ages  ranges  (1-2,  3-5,  6-8+)  and  for  three  different  time  periods  (1972-1995,  1996-2008,  2009-last 
assessment year). 

4.1.2. Projection values 
Beginning at the initial starting point of the projections, numbers at age for ages 2 and up are projected using the 
basic equation for updating population size. Natural mortality (M), mean weights and partial recruitment (PR) 
are  specified  by  the  operating  model,  while  fishing  mortality  (F)  depends  on  the  harvest  control  rule  (HCR) 
based on the management strategy being evaluated. Recruitment (numbers at age 1) is determined by the stock-
recruit  model  applied  in  the  operating  model.  In  subsequent  years,  given  the  matrix  of  numbers-at-age  of  the 
previous  years,  the  population  numbers  are  carried  forward  using  the  exponential  survival  equation  and  a 
predefined  SR  relationship  with  a  lognormal  multiplicative  random  error  to  generate  the  recruits  in  the 
segmented regression cases. As the real TACs for 2014 and 2015 are known, it is assumed that the fleets will 
comply  exactly  with  these  TACs.  The  catchability  of  the  fishery  is  an  input  parameter  and  it  varies  through 
iterations.  The  catch  is  estimated  each  year  by  iteration  using  the  Baranov  catch  equation  (Baranov,  1918), 
constraining it to produce exactly the TAC, given the catchability-, numbers-, weights- and natural mortality-at-
age of the real population. In each scenario the biological population and fishery were projected until 2033 for 
each of the starting point iterations.. 

In  the  projection  (2014-2033,  20  years)  of  the  “true”  biological  population,  maturity,  weights,  Partial 
Recruitment  and  M  for  each  year  will  be  obtained  from  the  1974-1995  and  2009-2013  periods  based  on  the 
analysis of the biological parameters variation made by  Gonzalez-Costas and Gonzalez-Troncoso in 2014. We 
do not take into account the 1996-2008 period due to in this period the abundance  were very low, the fishery 
was  mainly  close  and  the  mean  weights  and  maturity  values  were  very  different  than  in  the  periods  were  the 
abundance was higher and the fishery was open. We obtain the biological parameters in blocks of years to take 
into account the possible autocorrelation of the parameters, choosing randomly four years of the 1974-1995 and 
2009-2013 period and the four next years from that, having 4 blocks of 5 years each one, having a total of 20 
years. All theses indices are taking from the same year as they are all related. 

4.1.3. Stock-recruit relationship 
We  test  several  Operating  Models  (OMs)  based  on  three  different  Stock/Recruitment  (S/R)  relationships,  as 
described in the previous paper (SCR Doc. 14/44):  

4.1.3.1.  Recruitment  independent  of  SSB  (SR1):  Bootstrapping  recruitment  values  from  1972-2010.  We 
eliminated the  last 3 recruitments of the time  series to do the bootstrap due to these recruitments  have a  high 
uncertainty and they are not well calculated by the model. 

The other two S/R relationships are based in the segmented regression. This is a method in regression analysis in 
which  the  independent  variable  (SSB)  is  partitioned  into  intervals  and  a  separate  line  segment  is  fit  to  each 
interval.  The  boundaries  between  the  segments  (Beta  parameter)  are  known  as  breakpoints.  Segmented 
regression  can  be  useful  to  quantify  an  abrupt  change  of  the  response  function  (recruitment)  of  a  varying 
influential factor (SSB). In the S/R relationships, the breakpoint can be interpreted as a critical SSB threshold 
value below which the stock productivity is likely to be seriously impaired (Blim definition). 

4.1.3.2.  Segmented  Regression  with  Beta=Approved  Blim  (SR2):  We  fit  a  constrained  segmented  regression 
model (1972-2010) to have a beta parameter equal to the approved 3M cod Blim (14 000 tons).  

4.1.3.3. Segmented  Regression  fit  with the assessment results (1972-2010) (SR3). The  Segmented  Regression 
was fitted to the observed data and the parameters value of the function were estimating by minimizes the mean 
square error (MSE). 

These three operating models were applied to the two different starting points based on different assumptions of 
M (Table 3.3.2.2). 

The stock–recruitment relationship for each scenario  was fitted using the numbers at age 1 and the SSB in the 
initial population. One SR relationship was fitted in each of the iterations. In Table 3.3.1.3 we present the value 
of the parameters of the two fits of segmented regression (SR2 and SR3), as well as mean of the Recruitment for 

72 

 
 
 
 
 
 
 
 
 
 
73 

the  projected years (2014-2033) for the three SRs. Figures  3.3.2.1 and 3.3.2.2 show the median  fit of the two 
segmented regressions as  well as  the  median recruitment over the  historic  years. We can see in Table 3.3.2.3 
that the level of recruitment is quite different between the three SRs, being the median of the mean Recruitment 
by iteration for years 2014-2033 lowest in the case of the SR1 (19504 for M fix and 21779 for M variable), and 
the highest for SR3 (46780 for M fix and 57891 for M variable). 

4.1.4. Biological Reference points (BRPs) 
A Blim of 14 000 tons was proposed by the NAFO Scientific Council in 2000 for the 3M cod stock. The model-
based HCR (Option 1) is based on the Fmsy reference point or its proxies. Due to the impossibility of estimating 
Fmsy, in this case the MSE should be carried out based on a proxy. In 2014, NAFO SC decided that F30%SPR (the 
fishing mortality which reduces Spawner Per Recruit (SPR) to 30% of its value at F=0) is the best Fmsy proxy at 
this moment for 3M cod. The SC proposed to use data from 1972 to 2013 to estimate the F30%SPR to avoid the big 
trends in biological parameters observed in the most recent years (2009-2013). We used as Ftarget the percentiles 
of the approved Fmsy proxy in June 2014 (based on 5000 iterations): 

4.2. The management procedure (MP) 

For 3M cod the MP is applied on 2014 and produces the TAC advice for 2016 based on the different Harvest 
Control  Rules  (HCRs)  proposed  by  FC  SC  RBMS.  Each  HCR  is  applied  every  year  up  to  the  end  of  the 
projections  period.  We  performed  the  projections  for  20  years.  This  time  period  would  allow  observing  the 
stabilized biomass results of applying the different management strategies. 

4.2.1. The observation model (OM)  
Catch-at-age is considered to be known without error in the observation model and the abundance indices are 
generated  with  a  multiplicative  random  error  assuming  a  log-linear  relationship  between  catchability  and 
abundance-at-age.  Mean  weights  at  age,  maturity  ogive  and  natural  mortality  assessment  data  in  the 
Management Procedure assessment model are known without error and equal to the real world. 

4.2.2. The assessment process model and the management decision model 
For the model-based HCR (

) we used as assessment process model the 

Bayesian  XSA  with  500  iterations.  In  the  case  of  the  Bayesian  model  it  is  necessary  to  choose  not  only  the 
number of  iterations that  we  are going to  use but the  number of iterations that  we are  going to discard at  the 
beginning  of  the  Markov  chain  sample  so  the  effect  of  initial  values  on  the  posterior  inference  is  minimized 
(burn-in). In the case of the initial population, the number of burn-in was 20 000, as in the approved assessment 
of June 2014. But this leads to a large time of running the program, so a sensitive analysis of the impact  of the 
number of iterations of burn-in was made. The conclusion that we reached is that with 10 000 iterations of burn-
in the Markov chains seem to converge, so this value was used in the projection years. 

The TAC for  year  y is  set based on the assessment  with  data  available  up to  year  y−2.  The stock assessment 
therefore produces fishing mortality estimates for year y−2 and population estimates for the beginning of year 
y−1 (except for the recruitment).  

To carry out the short term projections we used the usual method. The recruitment, mean weights and maturity 
ogive in years y−1 and y are estimated as the mean of the y−4 and y−2 period from the Management Procedure 
assessment model. To estimate the fishing mortality in y−1 it is assumed that the fleet catches exactly the TAC 
set  for  year  y−1  with  the  selection  pattern  and  mean  weights  equal  to  the  average  of  Management  Procedure 
assessment  model between  years  y−4 and  y−2. With the abundance and F of  year  y−1 we can estimate  the  y 
abundance and assuming that the y mean weights are equal to the y−1 mean weights, we can estimate the Total 
and the Spawning biomass to apply the HCR and know the y TAC. 

For the model-free HCR (

) the TAC for year y is set based on the EU Flemish Cap 

survey slope of the log-linear regression line fit to y−2 till y-5 period of the survey indices for 3+ biomass.  

yytargetylimTAC=TotalBiomassFPSSB>Byy-13+TAC=TAC1+λslope Percentile 20 0.116 Percentile 35 0.124 Percentile 50 0.133  
 
 
 
 
 
 
 
 
 
 
 
 
 
5. Results 

5.1. Stock indicators (SSB, F and landings) 
In all cases the results presented are the medians of the indicators. The greatest impact on the stock indicators 
results comes from the assumption on the stock recruitment. The assumption of natural mortality (M) has little 
effect  and  the  Ftarget  uncertainty  levels  chosen  have  very  little  influence  on  the  results  of  the  stock  indicators. 
Table 3.3.2.4 and Figures 3.3.2.4 and 3.3.2.5 present the SSB results for the 24 scenarios in the projected period 
(2014-2033).  It  can  be  observed  in  the  results  (Figure  3.3.2.4)  that  the  SSB  trend  in  all  the  scenarios  is  quite 
similar but the level of the SSB is very different. The value of SSB varies in the last year of projection (2033) 
between 160 000 tons for the model-based scenarios with M variable and SR1 (OMs 10-12) and 714 000 tons 
for the  model-free scenarios with M  fix and SR3 (OM  21). At the  end of the projected period there are three 
different main levels depending on the chosen stock recruitment function. The SR1 has around 200 000 tons, the 
SR2  around  350 000  and  the  SR3 650 000  tons  for  M  fix  and  slightly  lower  in  the  cases  of  the  M  variable. 
Figure 3.3.2.5 shows the SSB for the different scenarios of the M (M fix and M variable) operating models. In 
this plot we can observe that the results and trends between the OM are quite similar and that the results for the 
different Ftarget tested in each SR are much the same.  

Table  3.3.2.5  and  Figures  3.3.2.6  and  3.3.2.7  present  the  fishing  mortality  (F)  for  all  the  scenarios  in  the 
projected years. The F decreases in the medium term period (2014-2021) for all the scenarios and after (2022-
2033) remains fairly constant and lower than the Fmsy proxy. The decrease in F is faster in the first years for the 
scenarios with the model-based HCR than the scenarios with the model-free HCR. Figure 3.3.2.7 shows the F 
for the different scenarios of the M (M fix and M variable) operating models. These OMs have a very similar 
trend  and  values  for  F  in  all  the  scenarios  of  each  OM  with  a  decrease  in  the  period  2014-2021  and  a  fairly 
constant F value lower than Ftarget in 2022-2033.  

Table 3.3.2.6 and Figures 3.3.2.8 and 3.3.2.9 present the landings (yield) for all the scenarios in the projected 
years. There are two main trends in yields, one for the scenarios with the model-based HCR and other for the 
scenarios with the model-free HCR. Figure 3.3.2.9 shows the yield for the different scenarios of the M (M fix 
and  M  variable)  operating  models.  In  the  cases  of  the  model-based  HCR  all  the  scenarios  present  a  similar 
decrease  in  landings  until  2020,  when  these  scenarios  reach  a  yield  around  6 500  tons,  and  after  an  increase 
trend till the end of the projected period. This increase trend depends on the SR functions assumed. The yield at 
the end of the period is around 20 500 tons for the SR1 scenarios, 25 500 tons for the SR2 and around 38 500 
tons for the SR3 scenarios for M fix and a bit lower in the cases of the M variable. For the scenarios based on 
the model-free HCR the trend and level are quite different. Till 2020 all the scenarios have a small decrease in 
the yield and after the 2020 level (around 14 000 tons) is quite constant for the SR3 scenario and for the SR1 
and SR2 scenarios continues to decrease until 2022 (around 6 000 tons) and after increase  fairly till 2028 and 
remains constant at levels around 9 000 tons. 

The results of the 24 OMs show us that we can group them by Stock-Recruitment relationship. It is clear that the 
value of Ftarget has not influence in the results, and the choice of M does not vary substantially the results of the 
stock indicators. 

In Annex 1 the Figures  with the median and the 95% confidence interval for 12 scenarios are shown (model-
based scenarios with Ftarget=50%Fmsy and all the model-free scenarios). We present, for the model-based cases, 
only the corresponding results to Ftarget=50%Fmsy as the other Ftarget have very similar results as we said above. 
We can see that the scenarios with SR2 and SR3 have much uncertainty than the ones with SR1. 

5.2. Performance Statistics (PS) 
For  deciding  which  of  the  HCR  that  we  are  analyzing  is  the  best  for  the  purposes  of  the  MSE,  a  series  of 
Performance Statistics were analyzed: 

1. Very low risk of breaching Blim: P(SSB<Blim) ≤ 0.1. The results are in Table 3.3.2.7 and Figure 3.3.2.10. We 
can see that none of the OMs fulfills this PS all the years. In the period 2016-2023, most of the scenarios have a 
value  greater  than  the  limit  or  they  are  very  close  to  the  limit  of  this  PS.  All  scenarios  reach  the  highest 
probability  of  breaching  Blim  in  the  period  2019-2020.  In  this  period,  the  M  variable  OM  cases  have  a  lower 
probability  of  breaching  Blim  than  the  M  fix  cases  and  inside  these  the  probability  of  breaching  Blim  for  OM 
based on different SR in increasing order  are: SR3, SR2 and SR1. The SR1 model-free scenarios (19 and 22) 
have  the  worse  behavior  in  these  middle-years  with  a  maximum  probability  of  0.37  and  0.36  respectively. 
However, from 2024 all of scenarios reach the condition except scenario 6 (model-based, SR2, M fix, F50) that 

74 

 
 
 
 
 
 
 
 
75 

slightly exceeding the limit till 2027. All model-free scenarios and the SR1 model-based scenarios have a lower 
level of probability at the end of the period than the SR2 and SR3 scenarios. 

2.  Low  risk  of  overfishing:  P(F>Fmsy)  ≤  0.3.  The  results  are  in  Table  3.3.2.8  and  Figure  3.3.2.11.  This  PS  is 
achieved  for  all  the  OMs  since  year  2021.  All  the  scenarios  start  the  projections  period  with  a  very  high 
probability (100%) and this probability drops to levels less than 30% in 2021 in all scenarios and remains low 
till the end of the projection period. This decline is faster in the model-based scenarios. In general, the model-
free scenarios have a worse behavior than the model-based scenarios in the period 2014-2021. 

Although this PS was set only for the model-free scenarios, we present the results for the 24 scenarios in order 
to see the differences between them. 

3.  Low risk of steep decline:  The probability of the decline of 25% or more of spawning stock biomass  from 
year 0 to year y is kept at 10% or lower: P(SSBy/SSB2013≤0.75) ≤ 0.1. In this case we check two periods: y=7 
(medium term: year 2020) and y=20 (long term: year 2033). Moreover, we check if the  lowest spawning stock 
biomass  in the periods 2014-2020 and 2021-2033 achieves the condition too. The results are  in Table 3.3.2.9 
and Figure 3.3.2.12.  

So, we have 4 different options in this PS: 

3a. P(SSB2020/SSB2013≤0.75) ≤ 0.1. This condition is not achieved by any of the OMs. The SR1 cases 
have a higher probability than the SR2 and the SR2 more than the SR3 scenarios. The scenarios based 
on the model-free HCR have a higher probability than the model-based scenarios.  

3b. P(SSB2033/SSB2013≤0.75) ≤ 0.1. This condition is achieved for all the OMs.  

3c.  P(min(SSB2014-2020)/SSB2013≤0.75)  )  ≤  0.1.  This  condition  is  never  achieved  for  any  of  the  OMs. 
All  the  scenarios  have  high  probability  that  the  minimum  SSB  in  the  medium  term  period  was  25% 
less than the 2013 SSB although the M fix scenarios have lower probability than the M variable cases. 

3d. P(min(SSB2021-2033)/SSB2013≤0.75) ) ≤ 0.1. This condition is never achieved for any of the OMS. All 
the scenarios have a probability more than 10% that the minimum biomass in this period was 25% less 
than the 2013 biomass. This probability is close to the 10% limit in the model-based scenarios and is 
higher in the model-free scenarios. The SR1 cases have a higher probability than the SR2 and the SR2 
more than the SR3 scenarios. 

One  more  PS  was  calculated  and  is  presented  in  Table  3.3.2.9:  P(min(SSB2014-2033)/SSB2013≤0.75),  but  as  the 
SSB reaches the minimum between 2014 and 2020, this PS is redundant with the 3c PS. 

4. Maximum average catch over the period: max(TAC). The results are presented in Table 3.3.2.10 and Figure 
3.3.2.13. In the Table we present the median of the  mean landings for the periods 2014-2033, 2014-2020 and 
2021-2033,  and  in  the  Figure  we  present  the  median  as  well  as  the  95%  confidence  interval  for  the  periods 
2014-2020 and 2014-2033. In the medium term period (2014-2020), the mean TAC is higher for the model-free 
OMs  than  for  the  model-based  OMs  with  similar  values  within  them  (around  14  300  tons  in  the  model-free 
scenarios  and  around  11  000  tons  in  the  model-based  scenarios).  But  when  we  take  the  complete  projection 
period the model-based scenarios have higher catches than the model-free scenarios with a highest level around 
18 500 tons in the M fix SR3 scenarios (7-9). We achieve similar results for the case of the mean of the years 
2021-2033 but with a highest level around 22 500 tons in the M fix SR3 scenarios (7-9). 

5.  Limited  annual  catch  variation:  Number  of  times  the  constraint  of  20%  (at  the  lower  and  at  the  higher 
boundaries)  has  been  applied  on  average  during  the  period.  We  present  the  number  in  percentage  in  Table 
3.3.2.11 and Figure 3.3.2.14. We can see that in the majority of the cases the constraint is applied in a very high 
percentage of the iterations. In fact, if we look to the mean of the number of times that the constraint of 20% is 
applied  (low  or  up),  in  the  case  of  the  model-based  scenarios  this  percentage  is  around  73%  and  63%  in  the 
model-free cases.  

It  can  be  observed  that  for  the  model-based  scenarios  in  2018  the  low  constraint  (TACy=0.8TACy-1)  was 
applied in the 80% of the cases and this % decreases till 2023 and after that remains very low (less than 20%) 
and quiet constant till 2028 when starts to increase till 2031 (30%). This general trend has small differences for 
M variable and M fix model-based scenarios. In the case of the M variable scenarios, in 2016 the value is high 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
(80%) while in the M fix is very low (less than 5%) and in the period 2031-2033 the scenarios with M variable 
remain stable (30%) whereas that the M fix scenarios slightly decrease.  

The up constraint (TACy=1.2TACy-1) in the model-based scenarios has the opposite trend: starts in very low 
%, this percentage increases till 2023 (80%) and after that slightly decreases till 2031 (60%) and increases a bit 
in the last two years.  

Considering the % that the constraint is applied (up and down) in the  model-based scenarios,  we can observe 
that is very high in all the projected period, being over or around 80% from 2018. For the model-free scenarios 
these trends are quite different. The number of times that the constraint of 20% (at the lower and at the higher 
boundaries) has been applied rather increases in the period 2016-2021 and after that slightly increases till 2033. 
These trends are quite similar for up and down but at different levels.  

6. Discussion 

The  greatest  impact  on  the  stock  indicators  results  comes  from  the  assumption  of  the  stock  recruitment.  The 
assumption  of  natural  mortality  (M)  fix  o  variable  has  a  little  effect  and  the  Ftarget  uncertainty  levels  chosen 
(20%, 35% and 50%) have very little influence on the results of the stock indicators.  

The median SSB in the medium term period (2014-2021) has a small increase trend in the model-based cases 
and is more or less stable in the model-free scenarios (Figure 3.3.2.4). The range of variability increases as we 
move away from the starting point and this range is bigger in the SR2 and SR3 OMs due to the uncertainty in 
the recruitment functions. The relative stability of the SSB  in all the scenarios  with the  increase in  variability 
from the starting point and the PS1 value at the starting point close to the limit (Figure 3.3.2.10) are the reasons 
why most of the scenarios do not meet the PS1 in this period. The model-based cases have PS1 values closer to 
the  limit  than  the  model-free  scenarios.  After  2022-2024  the  SSB  increases  and  the  PS1  values  in  all  the 
scenarios are lower than the proposed limit, except the scenario 6 (model-based, SR2, M fix, F50) that slightly 
exceeding the limit till 2027.  

The great increase of the SSB from 2028 to 2029 for all the OMs is due to the weights-at-age in the stock that 
was randomly chosen for those years, as they are much higher in 2029 than in 2028 (Figure  3.3.2.3). The same 
happens between 2023 and 2024 but in a less order. It seems that between the "jumps" there are three periods of 
stability: until 2023, from 2023 to 2028 and after 2029. If we do not have into account the steps in the SSB, the 
increase  is  more  or  less  lineal.  The  great  variability  of  biological  parameters  (mean  weights,  maturity  ogives, 
etc) for this species are well known (Gonzalez-Costas and Gonzalez-Troncoso, 2014).The impact in the results 
(but  no  over  the  trends)  of  the  biological  parameters  variability  used  for  the  projection  period  could  be 
important. This study include only some variability, it was obtained the biological parameters in blocks of years 
to  take  into  account  the  possible  autocorrelation  of  the  parameters  but  these  blocks  were  the  same  for  all  the 
iterations. It was include only the uncertainty in the time periods but not between iterations. 

The main reason for the stability of the SSB in the medium term period (2014-2020) is the F and catches levels. 
The starting point (2014) has a F and  yield  unsustainable in the long term and  much higher  than  the  F target 
levels (Figures 3.3.2.6 and 3.3.2.8) and due to the 20% TAC restriction  applied down in these cases, the Ftarget 
level and its corresponding yield are reached at the end of this period. The necessary period to achieve the Ftarget 
level  would  be  longer  if  we  had  applied  a  more  restrictive  TAC  constrain  (15%  or  10%).  The  Ftarget  level  is 
reached before for the model-based cases than for the model-free. The Ftarget in the model-free is estimated based 
on the biomass trend and has not into account the high starting point F level, and due to the stability in the SSB 
in this period the yields are higher than in the model-based cases. In the second period (2021-2033) the SSB and 
the yields start to increase in all cases but the increase in yields are lower than the expected Ftarget yields due to 
the 20% TAC restriction applied up in these cases causing that the F levels in all scenarios are lower than the 
proposed Ftarget. In fact, it seems that we are testing an HCR of applying a constraint of 20% (up or low) more 
than a Ftarget model-based HCR or survey indices model-free HCR. 

The SR1 and SR2 yields levels at the end of the projected period are more similar while in cases of SR3 almost 
double  of  the  SR1  cases.  This  is  because  recruitment  levels  assumed  for  each  of  the  cases.  The  level  of 
recruitment is  much  higher  for SR3 cases allowing biomass  increase  more  than the  SR1 and SR2 cases. This 
bigger increase in biomass, mainly in the period after 2021  allows a higher level of catches for the SR3 cases 
than the SR1 and SR2 cases for similar levels of F. 

76 

 
 
 
 
 
 
 
 
 
 
77 

The projection results show two periods with different behavior: 2014-2023 and 2024-2033. In the first period 
none of the tested HCR meets the performance objectives established. The main reasons for not achieving these 
objectives are the high initial F and catch levels and the 20% TAC constraint. The length of time to achieve the 
objectives  is  modulated  by  the  TAC  restriction  that  reduced  gradually  catches.  Lower  percentages  of  TAC 
variation  would  imply  lengthening  the  period  while  higher  percentages  of  TAC  variation  would  imply 
shortening the period. In this period (2014-2023), the performance of the  model-based HCR  is better than the 
model-free HCR based on the established performance objectives. In the period 2024-2033, both HCR meet the 
established performance objectives in most of the scenarios and the 20% TAC constraint is applied reducing the 
increase of the potential Ftarget catch levels. 

In the last two years (2013-2014), the TAC of the 3M Cod was set upon the Fmax catch, which is not sustainable 
over time. It is  necessary to decrease  the  level of catches  of this species to the level of no  more than 50% of 
F30%SPR (proxy Fmsy) in the shorter possible period. That is probably why the PSs are not achieved by 2020 for 
these HCR. For achieving the approved 3M Cod Fmsy proxy by 2020 (as established in the Article 2 of the CFP 
Regulation  (Regulation  (EU)  No  1380/2013  of  the  European  Parliament  and  of  the  Council  of  11  December 
2013)) it should be necessary to eliminate the TAC constraint implemented in the HCRs tested or  the starting 
point (2016) for the MSE must be less that the current catches. To point it out that in the current NAFO PA the 
Fmsy is defined as limit while in the current EU CFP could be interpreted as target. 

7. Conclusion 

None of the tested HCR reached the established performance objectives in the 2014-2023 period but most of the 
scenarios  met the  performance objectives in the period 2024-2033. In the period 2014-2023, the  model-based 
HCR is closer to achieve the established performance objectives than the model-free HCR. The main reasons for 
not achieving these objectives are the high initial F and catch levels and the 20% catch constraint. The necessary 
period  to  achieve  the  performance  objectives  would  be  longer  if  we  had  applied  a  more  restrictive  TAC 
constraint (15% or 10%). 

Differences  in  the  results  come  mainly  from  the  assumed  stock  recruitment  relationship  and  in  a  much  lesser 
extent of assumed natural mortality (M) and the different Ftarget levels tested. The impact in the results (but no 
over  the  trends)  of  the  assumed  variability  of  the  biological  parameters  for  the  projection  period  could  be 
important. 

8. Recommendations 

These  results  were  presented  in  the  2015  April  NAFO  WG  RBMS  meeting.  The  WG  RBMS  recommended 
further  analyses  to  try  to  look  for  a  HCR  that  achieves  the  objectives  established.  The  WG  RBMS 
recommendations based on these results are presented in the Annex 3.3.2.2 

Acknowledgements 

This  research  was  funded  by  the  European  Union  founds  under  the  Framework  Contract  MARE/2012/21  - 
"Scientific advice for Fisheries beyond EU Waters" - Specific Contract No 2 (SI2.681887) “Provision of advice 
on the development of a multiannual management plan and the evaluation of a management strategy for cod in 
NAFO Division 3M (SAFEwaters-2)”. 

References  

Baranov, F.l., 1918. On the question of the biological  basis of fisheries. (in Russian) Nauchnye Issledovaniya 

Ikhriologicheskii Institutu lzvestiya 1, 8 1 - 128. 

Garcia, D., A. Urtizberea, G. Diez, J. Gil and P. Marchal, 2013. Bio-economic management strategy evaluation 

of deepwater stocks using the FLBEIA model. Aquatic Living Resources 26(04): 365-379. 

González-Costas,  F.,  D.  González-Troncoso,  D.  Miller,  A.  Urtizberea,  A.  Iriondo  and  D.  García,  2014. 

Developing of a 3M cod MSE. NAFO SCR Doc. 14-044, Serial No. N6341 

Gonzalez-Costas F. and D. Gonzalez-Troncoso. 2014. Biological Reference Points of 3M cod. NAFO SCR Doc. 

14/19 Serial No. N6313.  

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
González–Troncoso  D.  and  Gonzalez-Costas  F.  2014.  3M  cod  assessment  for  different  assumptions  over  M. 

NAFO SCR Doc. 14/018 Serial No. N6312.  

González–Troncoso  D.,  Gonzalez-Costas  F.,  Healey  B.,  Morgan  J.,  Hvingel  C.  2014.  Assessment  of  the  Cod 

Stock in NAFO Division 3M. NAFO SCR Doc. 14/035 Serial No. N6331. 

Mandado, M. 2014. Results from Bottom Trawl Survey on Flemish Cap of July 2013. NAFO SCR Doc. 14/17, 

Serial No. 6311. 

NAFO, 2014a. Report of the Fisheries Commission and Scientific Council Joint Working Group on Risk-Based 

Management Strategies, February 5-7, 2014. NAFO FC/SC Doc. 14/02, Serial No. 6282. 

NAFO,  2014b.  Report  of  the  NAFO  Scientific  Council  June  Meeting,  30  MAY-12  JUNE  2014.  NAFO  SCS 

Doc. 14/17 Serial No. N6343. 

Smith, A.D.M., K.J. Sainsbury and R.A. Stevens, 1999. Implementing effective fisheries-management systems 
– management strategy evaluation and the Australian partnership approach. ICES J. Mar. Sci. 56: 967-
979. 

Vazquez, A., M. Casas and R. Alpoim, 2013. Protocols of the EU bottom trawl survey of Flemish Cap. NAFO 

SCR Doc. 13/21, Serial No. 6174. 

78 

 
 
 
 
 
79 

Table 3.3.2.1.- Management Objectives and the new Performance Statistics and Performance Targets proposed 

by the SAFEwaters2 for the 3M cod MSE. 

Management Objectives  
Very low risk of breaching 
Blim 

Performance Statistics (PS)  

Performance Targets (PT)  

SSBy  is  the  Spawning  Stock 
Biomass  in  the  year  y  of  the 
projection period. 

       y=1,…,20 

The  probability  of  a  spawning 
stock biomass under Blim at 10% or 
lower. 

Low risk of overfishing 

For the model-free HCR: 

       y=1,…,20 

Fy  is  the  Fishing  Mortality  in 
the  year  y  of  the  projection 
period.  

For the model-free HCR only: The 
probability  of  F  exceeding  Fmsy 
during the evaluation period should 
be kept at 30% or lower. 

Low risk of steep decline 

SSBy  is  the  Spawning  Stock 
Biomass  in  the  year  y  of  the 
projection period. 
SSBlowest_y 
lowest 
Spawning  Stock  Biomass  level 
the 
the  period  1-y  of 
in 
projection. 

the 

is 

The  probability  of  the  decline  of 
25%  or  more  of  spawning  stock 
biomass  from  year  0  to  year  5  is 
kept at 10% or lower. 

Maximum average catch over 
the period 

Limited annual catch variation 

The  average  TAC  over  the  period 
should be maximized. 

TACassess_y is the TAC given by 
the assessment in year y before 
constraints. 

     x=0.1, 0.15, 0.2 
      y=1,…,7 
      y=1,…,20 

This  will  be  achieved  through  the 
constraint on the TAC variation. 

 lim/ySSBB lim/10.1yPSSBB msy/yFF /10.3ymsyPFF 70/SSBSSB 200/SSBSSB _70/lowestSSBSSB _200/lowestSSBSSB 70/0.750.1PSSBSSB 200/0.750.1PSSBSSB _70/0.750.1lowestPSSBSSB _200/0.750.1lowestPSSBSSB 71/7iiTAC 201/20iiTAC 71max/7iiTAC 201max/20iiTAC _1_/assessyassessyTACTAC _1_/1assessyassessycountTACTACx _1_/1assessyassessycountTACTACx 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table  3.3.2.2.-  Operating  Models  and  Management  procedures  proposed  in  this  study  with  their  different 

scenarios.  

Table 3.3.2.3.- Results of the fits for the different stock-recruitment relationships. Median of the parameters. R 

mean is the median of the mean of the Recruitment for all the projected years (2014-2033). 

80 

M fixalfabetaRmeanM varalfabetaRmeanSR119504SR121779SR20.6801400028161SR20.7961400037580SR30.5473226646780SR30.6333325257891OM MP Scenario M value S/R Function HCR Ftarget  Constant 1 Model Based 20% Fmax 1 35% Fmax 2 50% Fmax 3 2 Model Based 20% Fmax 4 35% Fmax 5 50% Fmax 6 3 Model Based 20% Fmax 7 35% Fmax 8 50% Fmax 9 Variable 1 Model Based 20% Fmax 10 35% Fmax 11 50% Fmax 12 2 Model Based 20% Fmax 13 35% Fmax 14 50% Fmax 15 3 Model Based 20% Fmax 16 35% Fmax 17 50% Fmax 18 Constant 1 Model Free  19 2 Model Free  20 3 Model Free  21 Variable 1 Model Free  22 2 Model Free  23 3 Model Free  24   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
81 

Table 3.3.2.4.- Median of the SSB (thousand tons) for the 24 OMs in the projection years: 2014-2033. 

Table 3.3.2.5.- Median of the F for the 24 OMs in the projection years: 2014-2033. 

Table 3.3.2.6.- Median of the Landings (thousand tons) for the 24 OMs in the projection years: 2014-2033. 

123456789101112131415161718192021222324minmax201421.821.821.821.821.821.822.022.022.019.619.619.619.619.619.619.719.719.721.822.021.919.619.719.619.622.0201526.526.526.526.526.526.526.826.826.824.424.424.424.524.524.524.524.524.526.526.726.724.424.524.424.426.8201635.335.335.336.136.136.138.038.038.032.732.732.733.933.933.934.834.834.835.336.137.432.733.834.132.738.0201752.951.849.958.456.854.963.862.160.751.751.751.755.955.855.661.060.760.148.851.555.945.453.854.545.463.8201861.358.154.871.067.864.984.981.876.961.860.360.071.971.069.879.878.977.251.958.368.848.462.166.948.484.9201950.646.742.061.957.753.077.473.368.950.049.247.765.364.062.173.571.968.834.541.454.034.549.053.334.577.4202060.255.149.080.175.870.5110.4104.097.160.859.156.890.986.583.595.992.788.536.844.869.334.558.673.934.5110.4202159.254.349.783.778.873.2128.5120.0114.659.956.754.693.491.088.6114.4112.4107.334.344.878.731.866.089.331.8128.5202268.363.758.097.292.885.7155.5149.5142.165.363.760.4112.8111.0107.7139.9137.0132.742.854.797.940.183.1117.240.1155.5202377.672.668.8107.3100.095.6172.1165.2157.769.667.364.6126.6122.9122.1162.7159.2157.352.962.6116.650.190.4138.350.1172.12024158.3151.6142.1218.9205.4190.4347.0336.4319.6150.4145.1138.3251.9249.3244.3381.2374.6367.1126.5140.8260.4118.0196.9293.7118.0381.22025165.0157.3150.1226.6217.7206.6377.0354.6344.9154.9151.0145.9262.4254.8248.4387.9379.8371.5138.8145.1287.1125.3221.7321.0125.3387.92026125.9121.5117.7180.1174.1164.3309.1293.3284.8122.5118.2112.9208.2201.3195.7322.6319.5316.1108.9121.8240.7102.9180.7258.9102.9322.62027157.0151.5145.8214.7209.0200.2381.5368.0353.6142.4138.3135.3242.7237.6231.3376.0369.7361.6139.4150.8304.6136.8231.4313.3135.3381.52028119.4117.8112.9168.8159.1149.7300.5292.5280.5115.2111.5109.7186.1180.4174.3315.7305.7302.2115.4141.4270.1111.0181.5253.6109.7315.72029319.4310.9303.7452.9437.0405.3801.7784.5754.9288.3280.3272.6506.8497.3468.3889.7877.5862.7319.2434.0740.0313.7528.7755.9272.6889.72030309.5302.0297.5447.2430.0407.6781.8769.1749.6261.8254.5249.4468.9449.9433.0806.9793.2771.7317.8435.2761.7294.9497.5728.4249.4806.92031287.7283.7278.7416.2398.0382.8776.7763.2742.3230.9225.1218.4408.0400.6391.3726.8715.6692.5305.3433.2760.1261.9448.3688.7218.4776.72032238.5234.1229.1353.6343.9315.2635.7620.1608.3170.9168.8163.7314.6303.8296.4563.7554.7542.0264.4377.7675.6204.3369.5555.9163.7675.62033239.2234.3231.0377.1363.4335.6659.8650.2635.8161.9157.6153.3311.6297.8289.7532.1524.6517.1270.5396.2714.7196.2364.6552.9153.3714.7min21.821.821.821.821.821.822.022.022.019.619.619.619.619.619.619.719.719.721.822.021.919.619.719.619.6max319.4310.9303.7452.9437.0407.6801.7784.5754.9288.3280.3272.6506.8497.3468.3889.7877.5862.7319.2435.2761.7313.7528.7755.9889.7123456789101112131415161718192021222324minmax20140.3950.3950.3950.3950.3950.3950.3950.3950.3950.4410.4410.4410.4410.4410.4410.4410.4410.4410.3950.3950.3950.4410.4410.4410.3950.44120150.4440.4440.4440.4350.4350.4350.4210.4210.4210.4950.4950.4950.4700.4700.4700.4610.4610.4610.4440.4380.4300.4950.4680.4570.4210.49520160.2690.2940.3130.2530.2740.2960.2350.2520.2760.2530.2560.2590.2430.2470.2490.2250.2270.2360.3410.3190.3040.3410.3030.3030.2250.34120170.2920.3220.3590.2380.2590.2880.1840.2030.2200.2630.2750.2910.2000.2070.2180.1810.1910.2020.4010.3220.2360.3530.2390.2110.1810.40120180.1810.2040.2360.1490.1690.1890.1250.1410.1570.1720.1820.1880.1360.1430.1510.1190.1250.1340.2920.2680.1910.2750.2070.1790.1190.29220190.0960.1120.1340.0760.0870.0990.0590.0650.0720.0990.1030.1110.0710.0770.0820.0660.0700.0740.1780.1600.0970.1820.1200.1010.0590.18220200.0570.0660.0780.0460.0520.0590.0350.0390.0430.0620.0660.0720.0470.0510.0540.0400.0430.0470.1170.1040.0610.1040.0750.0550.0350.11720210.0460.0510.0570.0360.0390.0450.0270.0290.0320.0480.0510.0550.0350.0370.0390.0300.0320.0350.0420.0410.0280.0470.0260.0280.0260.05720220.0480.0510.0560.0380.0410.0450.0300.0320.0350.0520.0560.0600.0360.0380.0410.0300.0320.0340.0370.0350.0260.0380.0210.0250.0210.06020230.0480.0510.0550.0410.0440.0470.0310.0330.0350.0540.0580.0610.0400.0420.0440.0320.0320.0350.0330.0290.0260.0310.0220.0250.0220.06120240.0370.0390.0410.0320.0330.0360.0240.0250.0270.0410.0430.0450.0320.0340.0350.0230.0250.0250.0260.0230.0200.0250.0180.0180.0180.04520250.0360.0380.0390.0310.0330.0350.0230.0240.0260.0400.0420.0450.0300.0310.0330.0220.0230.0250.0250.0210.0160.0240.0160.0170.0160.04520260.0580.0590.0620.0500.0520.0550.0380.0400.0420.0690.0720.0770.0520.0560.0580.0390.0400.0420.0400.0400.0280.0410.0290.0290.0280.07720270.0590.0600.0620.0530.0550.0580.0390.0420.0440.0670.0700.0730.0520.0560.0590.0390.0400.0420.0350.0330.0260.0360.0290.0250.0250.07320280.0630.0650.0670.0560.0590.0610.0410.0450.0470.0710.0750.0780.0570.0610.0630.0410.0420.0450.0330.0290.0220.0330.0240.0210.0210.07820290.0310.0320.0330.0280.0290.0310.0200.0210.0220.0350.0360.0390.0280.0290.0310.0200.0200.0210.0160.0140.0100.0160.0120.0100.0100.03920300.0500.0510.0530.0460.0470.0500.0340.0360.0370.0580.0610.0630.0420.0440.0450.0320.0330.0350.0230.0170.0130.0260.0170.0130.0130.06320310.0400.0410.0410.0360.0380.0400.0260.0280.0290.0490.0510.0520.0360.0380.0390.0270.0280.0290.0180.0140.0110.0210.0150.0120.0110.05220320.0450.0460.0470.0370.0390.0410.0290.0300.0310.0560.0580.0610.0430.0460.0480.0320.0330.0340.0190.0140.0110.0240.0160.0120.0110.06120330.0610.0630.0660.0520.0550.0600.0390.0410.0420.0780.0800.0830.0590.0620.0640.0460.0460.0480.0220.0160.0130.0310.0190.0150.0130.083min0.0310.0320.0330.0280.0290.0310.0200.0210.0220.0350.0360.0390.0280.0290.0310.0200.0200.0210.0160.0140.0100.0160.0120.0100.010max0.4440.4440.4440.4350.4350.4350.4210.4210.4210.4950.4950.4950.4700.4700.4700.4610.4610.4610.4440.4380.4300.4950.4680.4570.495123456789101112131415161718192021222324minmax201414.514.514.514.514.514.514.514.514.514.514.514.514.514.514.514.514.514.514.514.514.514.514.514.514.514.5201513.813.813.813.813.813.813.813.813.813.813.813.813.813.813.813.813.813.813.813.813.813.813.813.813.813.8201612.513.314.212.513.414.212.613.314.211.011.011.011.011.011.011.011.011.016.616.616.616.616.616.611.016.6201710.511.211.810.811.312.010.711.412.09.710.311.010.010.611.110.210.811.514.914.914.914.914.914.99.714.920188.48.99.48.69.09.58.79.19.67.88.38.88.08.58.98.38.79.314.614.614.614.314.614.67.814.620196.97.37.77.27.58.07.57.78.17.07.47.77.57.88.28.08.48.512.013.713.811.812.212.96.913.820205.76.06.36.36.46.66.87.17.26.16.36.66.76.87.06.87.17.413.014.014.09.314.014.05.714.020215.96.06.06.36.56.77.47.67.85.96.16.37.17.47.67.67.88.17.211.513.35.611.312.75.613.320226.36.26.27.37.67.78.68.99.16.56.76.88.28.68.88.99.29.75.09.412.85.06.813.55.013.520237.47.37.48.89.09.210.210.610.97.88.08.29.710.110.410.510.911.45.06.811.65.06.911.05.011.620248.98.78.910.510.811.012.212.713.09.49.59.711.612.012.412.613.113.66.06.611.95.87.111.55.813.6202510.510.310.412.612.913.014.615.215.611.211.211.413.814.214.715.115.716.36.07.211.26.07.810.56.016.3202612.011.812.014.514.814.917.317.818.412.412.913.316.016.417.017.918.419.06.57.212.26.88.511.56.519.0202712.913.313.515.916.216.420.320.921.613.514.114.217.618.218.720.121.021.37.28.111.57.28.611.67.221.6202815.115.515.217.718.118.223.424.324.915.415.816.119.720.420.822.823.624.27.58.512.07.38.711.37.324.9202916.616.116.120.120.520.727.227.828.916.316.716.522.923.123.326.026.226.98.68.812.98.39.211.98.328.9203017.517.417.221.521.921.729.329.730.016.817.317.422.523.023.927.328.028.28.08.511.87.98.611.57.930.0203117.217.117.221.221.221.529.529.630.515.615.915.822.422.523.329.229.730.57.67.911.67.28.810.57.230.5203219.219.318.623.023.123.532.833.534.116.516.616.623.323.924.431.732.232.38.08.311.78.18.811.48.034.1203320.520.620.425.525.625.437.838.939.516.616.817.023.724.125.132.733.034.07.88.311.77.68.811.07.639.5min5.76.06.06.36.46.66.87.17.25.96.16.36.76.87.06.87.17.45.06.611.25.06.810.55.0max20.520.620.425.525.625.437.838.939.516.817.317.423.724.125.132.733.034.016.616.616.616.616.616.639.5 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 3.3.2.7.- Results for PS1: P(SSBy<Blim). In italics and bold, the values higher than 0.1. 

Table 3.3.2.8.- Results for PS2: P(Fy>Fmsy). In italics and bold, the values higher than 0.3. 

Table 3.3.2.9.- Results for PS3: P(SSBy/SSB2013≤0.75). In italics and bold, the values higher than 0.1. 

Table 3.3.2.10.- Results for PS4: Mean of the TACs of different periods (median).  

82 

PS112345678910111213141516171819202122232420140.080.080.080.080.080.080.080.080.080.130.130.130.120.120.120.120.120.120.080.080.080.130.120.1320150.070.070.070.070.070.070.060.060.060.080.080.080.080.080.080.080.080.080.070.070.070.080.080.0820160.110.110.110.090.090.090.090.090.090.130.130.130.120.120.120.090.090.090.110.100.100.130.100.1020170.120.130.140.090.110.110.100.110.120.130.130.130.100.100.100.080.080.090.140.130.130.160.120.1120180.140.150.170.110.130.140.110.120.130.150.150.150.110.120.120.100.110.110.200.160.150.210.120.1420190.220.250.290.180.210.240.150.160.180.230.250.270.170.180.190.150.160.170.370.290.240.330.250.2020200.220.250.290.170.200.240.130.150.160.210.230.250.170.180.190.140.150.150.370.320.230.360.250.2320210.170.210.230.150.180.210.120.130.140.190.210.220.140.150.160.110.120.120.340.290.190.320.230.1920220.110.130.160.130.160.180.100.110.120.130.140.150.120.130.130.090.100.100.230.210.140.250.190.1320230.090.100.130.110.140.150.090.090.110.110.110.120.100.110.120.080.090.090.180.180.100.180.140.1020240.040.050.060.070.100.120.080.080.090.060.060.060.070.070.080.070.080.080.090.080.040.090.060.0420250.040.040.050.070.090.120.080.080.090.040.040.040.070.070.080.070.080.080.070.060.030.070.050.0320260.030.040.040.080.090.110.080.090.090.040.040.050.070.080.080.070.080.080.070.070.030.070.060.0320270.020.020.030.070.090.110.080.080.090.030.030.040.070.080.080.070.070.080.050.070.020.070.040.0320280.000.010.010.060.080.100.080.090.090.020.030.030.060.070.070.060.060.070.040.060.030.040.060.0320290.000.000.010.050.060.080.060.070.080.010.010.010.050.060.060.050.050.060.010.010.000.000.000.0020300.000.010.010.050.060.080.060.070.080.010.000.000.050.060.060.050.050.060.000.010.000.010.000.0020310.010.010.010.050.060.080.060.070.080.010.010.010.050.060.060.050.050.060.010.010.000.010.000.0020320.010.010.010.050.060.080.060.070.080.000.010.010.050.060.070.050.050.060.010.010.000.020.000.0120330.000.000.000.050.050.070.060.070.080.000.010.010.050.050.060.050.050.050.010.010.000.020.000.00PS212345678910111213141516171819202122232420141.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.001.0020151.001.001.001.001.001.000.990.990.991.001.001.001.001.001.001.001.001.001.000.990.991.001.001.0020160.910.920.940.870.890.920.820.850.870.880.880.880.810.810.820.790.790.800.880.860.830.850.830.8220170.860.880.900.770.790.800.670.690.720.810.820.840.680.700.710.640.650.660.860.790.670.840.710.6320180.650.700.740.570.610.640.460.520.570.600.630.650.510.520.540.440.460.510.740.710.600.710.610.5620190.380.430.510.330.360.390.230.270.300.380.420.440.300.310.320.240.260.290.590.570.420.580.470.4320200.290.320.370.230.270.290.160.180.200.290.310.340.200.220.250.170.180.190.470.440.330.450.390.3320210.170.220.260.160.200.220.120.130.150.180.190.220.150.160.170.120.130.140.280.260.180.290.210.1620220.100.130.170.120.150.180.090.100.110.110.130.150.120.130.150.090.100.100.230.210.120.200.170.1220230.070.100.120.100.130.160.090.090.100.100.120.140.100.120.130.080.090.090.180.180.070.170.120.0720240.040.040.060.070.100.120.080.080.090.050.070.080.070.080.090.070.070.080.100.100.030.100.070.0320250.040.050.050.060.090.110.070.080.090.060.070.080.070.080.090.070.070.080.080.080.020.090.060.0220260.110.120.140.120.140.170.080.090.110.140.160.190.140.170.190.090.090.110.140.170.080.130.140.0720270.090.100.130.110.130.170.070.080.100.150.160.180.130.160.170.080.080.090.110.120.080.100.110.0420280.120.140.140.110.130.180.080.090.110.180.190.220.130.160.190.080.080.100.100.090.060.090.110.0220290.020.020.040.060.060.090.060.070.080.040.050.060.060.070.080.050.050.060.040.040.010.040.020.0120300.050.060.070.070.090.110.060.080.090.090.100.120.100.110.130.050.060.060.040.020.010.040.030.0120310.040.040.050.060.070.100.060.070.080.080.090.100.090.100.120.040.050.060.030.020.010.040.030.0120320.040.050.060.050.070.090.050.060.080.090.100.120.090.110.120.050.060.070.030.020.010.040.030.0120330.110.130.130.110.130.160.080.090.110.200.220.240.160.180.210.090.090.090.040.020.010.060.050.02PS3123456789101112131415161718192021222324P(SSB2020/SSB2013<=0.75)0.290.320.350.230.260.280.170.190.210.300.310.330.220.230.240.180.190.200.440.370.290.430.310.28P(SSB2033/SSB2013<=0.75)0.010.010.020.050.050.080.060.070.080.020.020.020.060.060.070.050.050.060.010.010.000.020.010.00P(min(SSB)2014-2020/SSB2013)<=0.75)0.870.870.870.860.870.870.860.860.870.970.970.970.970.970.970.970.970.970.90.880.880.970.970.97P(min(SSB)2021-2033/SSB2013)<=0.75)0.290.330.370.240.260.280.150.160.180.350.370.390.20.210.240.160.160.170.530.440.270.540.350.27P(min(SSB)2014-2033/SSB2013)<=0.75)0.880.890.890.870.870.870.860.860.870.980.980.980.970.970.970.970.970.970.930.910.890.980.980.97123456789101112131415161718192021222324maxMean TAC_2014-203312.512.612.814.414.814.918.218.719.212.112.412.715.215.616.017.217.518.18.69.513.48.39.412.619.2Mean TAC_2014-202010.410.811.210.711.011.410.811.111.510.110.410.610.410.610.810.610.811.114.314.514.613.814.414.514.6Mean TAC_2021-203313.513.513.716.316.717.021.922.423.213.313.513.717.618.118.820.621.121.76.57.312.36.67.911.623.2 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
83 

Table  3.3.2.11.-  Results  for  PS5:  Number  of  times  the  constraint  of  20%  (at  the  lower  and  at  the  higher 

boundaries) has been applied on average during the period. 

upp2012345678910111213141516171819202122232420140.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.020150.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.020160.00.00.00.00.00.20.00.00.40.00.00.00.00.00.00.00.00.090.690.890.486.886.887.220175.03.44.05.84.44.07.46.05.816.619.424.418.021.827.218.623.628.67.67.47.611.411.411.220180.20.00.00.00.00.00.40.60.61.01.61.62.22.43.42.83.23.82.02.42.03.43.64.020195.04.24.210.810.49.815.815.214.29.810.011.419.619.820.025.026.026.242.443.243.241.441.641.820205.44.64.613.812.812.021.820.821.010.89.210.024.023.224.831.230.829.841.039.841.041.244.843.8202130.827.024.639.437.634.647.848.044.435.834.834.449.649.446.451.452.451.027.831.436.826.833.638.0202251.649.645.258.455.254.672.268.267.655.255.653.864.664.663.870.270.671.235.841.046.638.844.044.6202381.080.675.681.078.275.086.685.683.882.280.881.484.083.683.086.285.684.640.243.047.836.846.647.2202484.281.879.885.281.878.887.285.884.882.282.281.085.683.882.887.087.286.244.238.648.841.438.445.2202583.881.078.284.482.078.485.884.683.680.078.477.682.882.081.286.085.885.050.049.254.050.452.655.0202677.275.674.477.274.473.281.880.879.476.875.874.677.677.676.682.481.682.256.455.257.856.055.859.2202771.472.272.870.070.267.673.873.873.866.268.469.469.069.470.275.674.874.857.853.054.256.053.052.6202877.679.479.473.873.872.278.877.876.270.872.071.872.872.071.476.676.477.047.049.847.445.246.644.2202974.273.273.872.071.070.277.875.672.667.468.468.465.864.463.671.671.870.875.664.869.872.267.069.0203062.864.265.659.659.056.865.863.863.853.452.654.652.051.649.662.261.060.431.833.033.030.433.432.2203151.052.052.649.248.046.450.849.650.444.645.444.845.644.645.250.049.049.041.441.641.438.839.438.4203270.672.071.866.066.263.670.670.070.050.850.649.854.655.856.059.458.658.060.462.661.664.257.859.6203369.469.472.271.271.469.071.270.469.658.259.059.457.456.256.859.459.858.034.640.038.035.639.239.2mean 2014-203345.144.543.945.944.843.349.848.848.143.143.243.446.346.146.149.849.949.839.339.341.138.839.840.6mean 2014-20202.21.71.84.33.93.76.56.16.05.55.76.89.19.648.452.352.452.326.226.226.326.326.926.9mean 2021-203368.167.566.668.366.864.673.171.870.863.463.463.266.365.850.854.955.054.946.446.449.045.646.748.0low2012345678910111213141516171819202122232420140.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.020150.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.020163.00.20.03.20.20.02.40.20.085.878.066.085.677.066.885.879.266.89.49.29.613.213.212.8201745.647.650.240.239.845.840.640.042.838.233.829.035.631.028.234.828.824.23.43.23.24.84.44.8201888.887.489.686.884.086.681.881.281.086.283.884.281.877.876.677.276.876.85.44.04.87.86.46.0201977.478.680.469.669.870.860.662.264.866.865.264.454.253.653.048.447.448.622.221.421.225.625.225.0202067.670.674.657.859.264.246.648.453.863.665.266.449.852.651.447.648.847.227.629.826.630.228.828.2202142.046.448.432.636.438.426.029.231.834.434.635.430.028.427.623.423.224.238.635.630.235.435.230.4202223.827.631.421.824.226.414.816.216.423.825.025.818.619.619.413.614.615.422.820.822.621.621.219.2202310.610.014.010.613.017.29.010.411.810.211.212.212.012.413.09.09.410.225.022.021.424.221.622.620249.210.213.010.412.415.010.210.411.49.610.211.810.610.812.09.09.49.226.228.829.025.429.826.420258.29.010.610.811.815.210.010.612.410.010.412.611.412.412.610.410.211.225.224.225.422.422.424.6202610.211.212.811.814.217.09.010.613.09.49.611.012.213.212.210.810.810.425.223.621.222.022.420.4202712.612.212.414.015.616.013.213.415.214.215.215.616.217.216.212.013.812.824.027.826.423.029.227.620288.89.88.812.213.414.211.414.013.812.412.613.015.215.415.411.411.412.629.032.235.832.036.236.6202913.213.212.415.815.017.611.012.614.815.416.017.019.018.820.213.414.816.612.619.017.414.419.418.0203018.417.016.822.824.826.819.821.422.030.029.031.432.233.834.026.627.028.450.448.247.248.045.046.6203130.833.232.833.633.637.232.433.635.237.838.239.233.638.238.033.234.436.041.642.242.045.043.044.0203217.214.815.019.220.223.814.414.816.431.234.032.629.430.031.227.629.229.823.623.025.222.229.226.6203314.615.814.812.614.814.215.216.416.625.426.227.827.029.030.226.226.026.244.243.444.445.441.444.4mean 2014-203325.125.726.924.325.127.321.422.323.730.229.929.828.728.627.926.025.825.322.822.922.723.123.723.2mean 2014-202040.340.642.136.836.138.233.133.134.648.746.644.343.941.739.442.040.137.79.79.79.311.711.111.0mean 2021-203316.917.718.717.619.221.515.116.417.820.320.922.020.621.521.717.418.018.729.930.129.929.330.529.82012345678910111213141516171819202122232420140.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.020150.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.020163.00.20.03.20.20.22.40.20.485.878.066.085.677.066.885.879.266.8100.0100.0100.0100.0100.0100.0201750.651.054.246.044.249.848.046.048.654.853.253.453.652.855.453.452.452.811.010.610.816.215.816.0201889.087.489.686.884.086.682.281.881.687.285.485.884.080.280.080.080.080.67.46.46.811.210.010.0201982.482.884.680.480.280.676.477.479.076.675.275.873.873.473.073.473.474.864.664.664.467.066.866.8202073.075.279.271.672.076.268.469.274.874.474.476.473.875.876.278.879.677.068.669.667.671.473.672.0202172.873.473.072.074.073.073.877.276.270.269.469.879.677.874.074.875.675.266.467.067.062.268.868.4202275.477.276.680.279.481.087.084.484.079.080.679.683.284.283.283.885.286.658.661.869.260.465.263.8202391.690.689.691.691.292.295.696.095.692.492.093.696.096.096.095.295.094.865.265.069.261.068.269.8202493.492.092.895.694.293.897.496.296.291.892.492.896.294.694.896.096.695.470.467.477.866.868.271.6202592.090.088.895.293.893.695.895.296.090.088.890.294.294.493.896.496.096.275.273.479.472.875.079.6202687.486.887.289.088.690.290.891.492.486.285.485.689.890.888.893.292.492.681.678.879.078.078.279.6202784.084.485.284.085.883.687.087.289.080.483.685.085.286.686.487.688.687.681.880.880.679.082.280.2202886.489.288.286.087.286.490.291.890.083.284.684.888.087.486.888.087.889.676.082.083.277.282.880.8202987.486.486.287.886.087.888.888.287.482.884.485.484.883.283.885.086.687.488.283.887.286.686.487.0203081.281.282.482.483.883.685.685.285.883.481.686.084.285.483.688.888.088.882.281.280.278.478.478.8203181.885.285.482.881.683.683.283.285.682.483.684.079.282.883.283.283.485.083.083.883.483.882.482.4203287.886.886.885.286.487.485.084.886.482.084.682.484.085.887.287.087.887.884.085.686.886.487.086.2203384.085.287.083.886.283.286.486.886.283.685.287.284.485.287.085.685.884.278.883.482.481.080.683.6mean 2014-203370.270.370.870.269.970.671.271.171.873.373.173.275.074.774.075.875.775.262.262.363.862.063.563.8mean 2014-202042.642.443.941.140.141.939.639.240.654.152.351.153.051.350.253.152.150.335.935.935.738.038.037.8mean 2021-203385.085.385.385.886.086.188.288.388.583.684.385.186.887.286.888.088.488.676.376.578.974.977.277.8 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 3.3.2.1.- Median Stock-Recruitment relationships (SR2 and SR3) and median Recruitment for M fix. 

Figure  3.3.2.2.-  Median  Stock-Recruitment  relationships  (SR2  and  SR3)  and  median  recruitment  for  M 

variable. 

Figure 3.3.2.3.- Weight-at-age in stock. Mean of ages 4-8. 

84 

 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
85 

Figure 3.3.2.4.- Values of the median of SSB for all the tested scenarios. 

Figure 3.3.2.5.- Values of the median of SSB by Stock-Recruitment relationship for the different M scenarios. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 3.3.2.6.- Values of the median of F for all the tested scenarios. 

Figure 3.3.2.7.- Values of the median of F by Stock-Recruitment relationship for the different M scenarios. 

86 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
87 

Figure 3.3.2.8.- Values of the median of Landings for all the tested scenarios. 

Figure  3.3.2.9.-  Values  of  the  median  of  Landings  by  Stock-Recruitment  relationship  for  the  different  M 

scenarios. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 3.3.2.10.- Results of the PS1: P(SSB<Blim) for all the tested scenarios. The horizontal line corresponds to 

a 10% of probability. 

Figure 3.3.2.11.- Results of the PS2: P(F>Fmsy) for all the tested scenarios. The horizontal line corresponds to a 

30% of probability. 

88 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
89 

Figure  3.3.2.12.-  Results  of  the  PS3  for  all  the  scenarios:  P(SSB2020/SSB2013≤0.75);  P(SSB2033/SSB2013≤0.75); 
P(min(SSB2014-2020)/SSB2013≤0.75); P(min(SSB2021-2033)/SSB2013≤0.75). The horizontal lines correspond 
to a 10% of probability. 

Figure 3.3.2.13.- Median (solid line) and 95% confidence interval (dot lines) of the mean of the TAC for all the 

cases in the 2014-2020 and 2014-2033 periods.  

 
 
   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 3.3.2.14.- Limited annual catch variation : number of times the constraint is applied (%). 

90 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
91 

ANNEX 3.3.2.1 
. 
Stocks indicators (SSB, F and landings). Median and 95% of confidence interval. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
92 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
93 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
94 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
95 

ANNEX 3.3.2.2. 

Recommendations of the Working Group RBMS 2015 with regards 3M cod MSE. 

Recommendations to forward to the Scientific Council 

The group had some technical discussions on the formulation of the MSE, which raised a number of questions. 
The working group recommends that Scientific Council continue these discussions and perform a full review of 
the MSE. 

SC could consider, inter alia, the following questions for clarification:  

1.  Given the complexity of the analysis, would it be useful to first examine deterministic results from the 
application of the HCRs to the OMs without any uncertainties included to both understand the results 
and to see how different the underlying OM+HCR combinations are? 

2. 

It  is  unclear  how  removing  the  catch  constraint  from  the  model-based  HCR  does  not  result  in  the 
simulated  fishery  reaching  Ftarget  in  a  shorter  period  of  time,  e.g.  by  2016,  the  first  year  for  which  a 
TAC is generated by the HCRs?   

3.  Could the catch constraint be adjusted to allow the model to reach the performance objectives? Perhaps 

the constraint could be tapered so that it is wide to start with but is reduced over time? 

4.  For  the  model-based  HCR,  as  the  SSB  grows,  the  probability  of  being  above  Blim  will  tend  to  1.  In 
which case the F that is applied is one of the three options (0.133, 0.124 or 0.116). Why then do the 
realized F values decrease to well below 0.1 in the long term? 

5.  Are the high levels of realized SSB that occur in the long-term under some scenarios realistic? 

6.  Given  that  each  of  OMs  has  an  underlying  stock-recruit  function  (constant  recruitment  or  hockey 
stick), and given that F30%SPR is only a proxy for the true Fmsy, would it be useful to compare the “true” 
simulated F with the “true” Fmsy derived from the OM? 

Recognizing the scale of the work proposed here, and to ensure that the results of these analyses and alternative 
scenarios  are  examined,  the  WG  requested  that  the  Div.  3M  cod  RBMS  be  retained  on  the  agenda  for  future 
meetings. 

Recommendations to forward to the Fisheries Commission and Scientific Council 

The Working Group recommends: 

1.  Scientific Council perform a review of the Div. 3M cod MSE. 

2.  The working group recommends SC discuss the following HCR options for Div. 3M cod: 

  Starting points  

– Fstatus quo 

– 40% reduction 

  An HCR which meets management objectives 1 and 2 within five, and within ten years. 

- risk calculated for each year in the time series 

- risk calculated for the end of the periods (final year) 

- risk averaged over the periods 

The recommendations will be presented to SC and FC at the 2015 June Scientific Council and Annual Meeting 
for consideration and adoption. 

 
 
 
 
 
 
 
 
 
 
  
 
 
3.3.3  Socio-economic Analysis. 

Introduction. The economic potential of the 3M cod fishery. 

The objective of the specific contract is to provide the European Commission and the NAFO Scientific Council 
with technical and scientific analysis to support the development of a multiannual management plan for the cod 
stock in Division 3M of NAFO. In particular, the study should provide advice on 1) management objectives and 
conservation limits, 2) Catch  discrepancy, 3) Management Strategy Evaluation and 4) Elimination of discards 
and reduction of by-catch. 

The objective of this part of the general study is to assess the social and economic potential of scenarios of catch 
maximisation over a pre-identified period. Specifically, two scenarios of value of landings are analyzed in terms 
of  their  socio-economic  impact.  To  perform  the  socioeconomic  study  and  to  compare  the  different  HCR,  one 
scenario of the Model-Based HCR and another one from its equivalent of Model-Free HCR were chosen. The 
chosen scenarios were natural mortality fix (Mfix) and Recruitment independent of SSB (SR1) with 35% Ftarget 
for the Model-Based HCR (scenario 2 Table 3.3.2.2) named in this section as scenario 1, and natural mortality 
fix  (Mfix)  and  Recruitment  independent  of  SSB  (SR1)  for  the  Model-Free  HCR  (scenario  20  Table  3.3.2.2) 
named  in  this  section  as  scenario  2.  The  main  reason  for  choosing  these  scenarios  is  that  they  are  the  most 
similar to SC 2014 3M Cod approved assessment. To generate the scenarios qualitative and quantitative data is 
necessary.  Once  obtained,  taking  into  account  ecosystem  and  technical  interactions  of  different  fisheries,  a 
methodology of input-output analysis is used to obtain the socio-economic impact. 

The analysis of the fishery’s economic potential is done in three steps:  

1. Development of scenarios to forecast (2014-2033), in particular, landings obtained by the NAFO 3M 
cod fleets (that is considered as a specific economic sector).  

2. Calculation of the value of landings in Euros. Through the previous scenarios of landings and a cod 
price  –through  the  cod  price  series  of  the  last  five  years–  a  series  of  value  of  landings  (for  each 
scenario) is obtained. 

3.  Calculation  of  the  economic  and  social  (employment)  potential  for  the  two  scenarios  of  landings. 
Under  each  scenario,  the  following  is  calculated:  the  direct,  indirect  and  the  induced  effect  for  three 
economic  variables:  revenue  (production),  value  added  and  employment.  This  last  calculation  was 
possible  by  utilizing  the  input-output  table  of  the  27  countries  of  the  European  Union.  This  table  is 
obtained through Eurostat and the last date of its data is 2011. 

Therefore, in this part of the general study (that covers section 1.3 and section 3.4 of the specific contract) we 
present the results for two scenarios of the socio-economic impact of the landings obtained by division 3M of 
NAFO.  The  socio-economic  effects  will  be  referred  to  the  EU27,  i.e.  the  economic  and  social  impact  of  the 
value of landings in two scenarios in the context of an economy represented by an aggregate that comprises the 
27 countries of the European Union and the 70 economic sectors (industries) into which the 27 economies have 
been disaggregated. 

After  this  introduction,  we  will  present  first  a  short  summary  of  the  methodology  to  undertake  the  socio-
economic  impact  of  the  value  of  landings.  Then,  a  framework  of  reference  will  follow  to  present,  first,  the 
economic  relevance  of  the  economy  of  the  seas  or  what  is  called  the  “blue  growth  economy”.  After,  we  will 
approach our target by showing what are the main economic data for the European fleet for the last year, then 
we  will  proceed  to  the  economic  data  for  the  Atlantic  North  fleet  and  for  the  Atlantic  cod.  Then  the 
quantification  for  the  socio-economic  impact  of  the  value  of  landings  for  NAFO  cod  is  presented.  A  short 
section with references will end up this chapter of the general study. 

A short summary of the methodology used in the analysis. 

Input-Output Analysis 

96 

 
 
 
 
97 

Input-Output Tables show the sectoral interdependence in the real world in that each sector offers production for 
final consumption and inputs to the remaining sectors and, at the same time, requires goods from other sectors 
and  primary  inputs  (labor  and  capital).  For  ease  of  construction,  it  considers  a  production  function  of  fixed 
coefficients,  that  is,  of  the  kind  xi  =  Min  {aijxij}  which  implies  constant  scaled  returns  and  perfect 
complementariness. Based on this hypothesis, the industrial structure of a country can be seen in table form as 
follows: 

Part  A  is  referred  to  as  the  inter-industrial  transaction  matrix  and  collects  all  the  intersectoral  national 
transactions,  imports  by  origin  and  destination,  and  the  total  flows.  For  each  sector,  its  column  shows  the 
purchases from other sectors needed to carry out its production activity, and its row shows its sales to the other 
sectors.  Matrix  B  encompasses  the  demand  vectors  or  final  uses,  including  public  and  private  consumption, 
investment, inventory variation, and exports. Finally, Matrix C covers the vectors corresponding to the primary 
inputs:  salaries  and  gross  wages,  deductions  for  social  programs,  gross  surplus,  taxes  on  production,  and 
subsidies. In the case of a three sector economy, the table would be as follows: 

Where  xij  is  the  quantity  of  sector  i  product  consumed  by  j,  Di  is  the  final  demand  vector,  and  Vi  is  the  row 
vector of gross value added. There are three fundamental accounting relationships in the table: 

  The sum of intermediate sales is equal to the sum of intermediate purchases since ÂiÂjxij = ÂjÂixij 

  The sum of final demand is equal to the sum of gross value added plus equivalent imports; that is, the 
sum of the imports of the intermediate consumption matrix plus the imports used for final demand. 

  For  each  sector,  final  demand  plus  intermediate  demand  gives  the  sector  output.  By  column,  sector 

output coincides with intermediate purchases plus primary inputs: Xi = Âjxji + Vi = Âjxij + Di 

In addition to these accounting relationships, the Input-Output Tables make available basic macro measurements 
like GDP using value added or final demand. For this reason, Input-Output Tables are used as a complement and 
control in the National Accounts of many countries. 

The Leontief Model 

The original Input-Output Tables were constructed for the American economy between 1919 and 1929. In the 
first version of his work, Leontief presented a closed model in which the final demand sector received treatment 
identical  to  that  of  any  sector.  Such  a  model  has  attracted  the  attention  of  economists  because  of  its  formal 
mathematical properties and fully theoretical interpretation of the inter-industrial relations, even if this is only 
applicable to the case of a stationary state in which labour remains constant year after year, and there is no net 

ABCSectors123Intermediate OutputFinal DemandTotal Output1x11x12x13∑x1jDlXl2x21x22x23∑x2jD2X23x31x32x33∑x3jD3X3Gross value addedV1V2V3Total inputsXlX2X3Intermediate Inputs∑xi1∑xi2∑xi3 
 
 
 
investment.  Because  of  this,  a  revised,  open  Leontief  system  is  used,  in  which  the  end  demand  is  treated 
independently of the other sectors. Leontief’s demand or open model would be: 

x11 + x12 + ... + x1n + D1 = X1 
x21 + x22 + ... + x2n + D2 = X2 
xn1 + xn2 + ... + xnn + Dn = Xn 

where  the  X1  are  endogenous  variables  and  the  D1  exogenous.  To  resolve  these  equations,  and  given  the 
proposed  production  function,  technical  coefficients  can  be  obtained  as  aij  =  xij/Xj  which  is  the  quantity 
necessary for i to produce a unit of j. The previous equations then become: 

a11X1 + ... + a1nXn + D1 = X1 
a21X1 + ... + a2nXn + D2 = X2 
an1X1 + ... + annXn + Dn = Xn 

which, in matrix terms, is AX + D = X, where A is a square matrix of n order of technical coefficients, X is an 
output vector, and D, the final demand vector. Solving for X gives X = (I-A)-1D. In order for this system to be 
solvable, the inverse of A must exist, and the outputs of each sector be positive. Each element Aij of the Leontief 
inverse matrix represents the production variation of sector i required to satisfy the end demand of sector j in an 
additional unit with the remaining components of the final demand remaining constant. It therefore represents 
both  direct  and  indirect  needs.  (I-A)-
interpreted as one of the successive stages in the production process up to the final product. 

Applications of the Input-Output Tables 

Input-Output Tables have been  key in  studies of economic impact of local,  regional and national investments 
projects or economic projects having to do with economic growth. Given a GDP growth rate objective, Input-
Output  Tables  can  be  used  to  estimate  the  total  outputs  needed  to  supply  the  increase  in  the  projected  final 
demand. Final demand will be estimated through its principal components: private consumption corresponding 
to changes in per capita income, population growth, income elasticity in each sector, changes in employment, 
etc.; public expenditure from the budget projection; exports according to the exchange rate expectation and the 
evolution of external markets; and investment as a function of the growth objectives, and the forecast of interest 
rates.  

Important elements in this process are input-output multipliers. The increase in final demand for a sector implies 
intermediate inputs and for this reason, its effects will reach beyond the specific sector. Input-output multipliers 
show the total effect on revenues, output and employment of increases in the final demand for a sector. The total 
effect is divided into three components: direct, indirect and induced.   

Each effect is explained in greater detail as follows: 

Direct Effect: New investments cause an increase in demand by the sectors that are making the investment and 
must increase production to satisfy the new demand. These increases in production are the direct effect. 

Indirect Effect: The sector directly affected by the investment generates the indirect effects, because in order to 
produce  what  the  investment  requires  it  must  purchase  more  from  the  suppliers,  which  in  turn  also  generates 
new demands on the economy. The final result of these successive rounds of effects on sectoral output is called 
the indirect effect. 

The indirect effect occurs because the investment requires the use of a number of goods and services that must 
be produced by the remainder of the economy. As an example, if the investment sector is construction, it will 
require cement, architectural services, transportation, and other items in order to complete its own production. 
This set of impacts is called the dependent indirect effect. 

In addition to this effect, there is a whole series of indirect effects caused by the production of the goods and 
services just indicated that require, in turn, more raw materials and products from the other sectors. These are 
called  the  independent  indirect  effects,  and  their  impact  on  production  dwindles,  because  in  each  successive 
round of expenditure the indirect effects are less and less until they finally disappear. That is to say, the value of 
the raw materials requirements and other inputs needed to carry out the production of a given good necessarily 
must be less than the value of the good which is produced. 

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Induced  Effect:  The  total  of  the  previously  mentioned  effects  has  its  own  additional  effects.  The  increase  in 
output generates greater employment, which generates an increase in wages, which translates into consumption 
as a function of the marginal propensity for consumption by individuals. The increase in consumption produces 
a whole new chain of effects, as described previously, the sum of which is known as the induced effect. 

General Solution of the Input/Output Model 

The first step for making any analysis of the economic effects of an increase in final demand in any sector is to 
establish a symmetrical input/output table. 

Starting from a symmetrical table, the input/output model uses hypotheses similar to the Leontief formulas that 
facilitate the calculation of the production value associated with a given final demand vector: q = ( I - A )-1 (y - 
m), where q is a production vector for homogeneous products or sectors; y, a vector for product final demand; m 
a  vector for imports by product; I the  identity  matrix;  A, a matrix (product by product) of coefficients a ij that 
represent  participation  by  intermediate  consumption  of  the  product  i  per  monetary  unit  of  production  of  the 
product (or homogeneous sector) j. This matrix A comes from the equation X =  Aq where X is the matrix of 
intermediate  consumption  (product  by  product)  of  the  symmetrical  table.  That  is,  the  elements  of  A  are  the 
technical vertical coefficients of the corresponding production functions.   

If symmetrical tables are not available it is possible to design an approximate multiplier model starting with the 
origin/destination  tables  and  a  hypothesis  based  on  the  construction  of  a  reduced  Input-Output  Table.  For 
example, using the hypothesis of product technology leads us to the expression:  

g = C-1 (I - BC-1)-1 (y – m ) 

where: g is a column vector (k x 1) of the k branches of the economy; B the matrix of the technical coefficients 
(n  x  k)  obtained  from  the  destination  table;  C,  the  matrix  of  the  specialization  coefficients  obtained  from  the 
origin table. The alternative model, under the industrial hypothesis, leads to: 

g = D' (I - B D')-1 ( y – m ) 

where D' is the matrix (transposed) of the market coefficients obtained in the origin table. That is to say, that 
these  equations  are  analogous  to  that  of  the  symmetric  table  and  make  available,  assuming  fixed  or  stable 
coefficients  of  C,  D  and  B,  the  production  of  the  corresponding  branches  and  final  product  demand  (net  of 
imports). 

A practical advantage of the second type of model (under the manufacturing technology) is that it can be arrived 
at directly from the subsystem origin/destination, even though the matrix  is not square. Conversely, the inverse 
matrix C-1 applies to the product technology model, where C must be by definition a square matrix; this model 
would be applicable only in the case where the numbers of products are exactly equal to the number of sectors. 

The input-output methodology and fishing activity. 

Fishing  activity  is  a  complicate  network  of  interdependent  factors.  A  change  in  any  part  of  the 
economy  chain,  leads  to  changes  elsewhere.  Therefore,  the  estimation  of  the  total  impact  on  fish 
harvesting  requires  also  measurements  on  the  changes  elsewhere  in  the  economy.  In  other  words, 
the economic importance of the fishing industry has a direct relationship to the rest of the economy. 
This  is  why  the  application  of  input-output  methodology  appears  appropriate  to  calculate  the 
economic and social (in terms of employment) impact of cod landings. 

The economy of the seas and the blue growth economy. 

The  ocean  covers  71%  of  the  planet’s  surface  and  conditions  95%  of  life  on  earth.  It  also  provides  seafood 
which accounts for at least 15% of the protein eaten by 60% of the planet’s human population, and sustains an 

 
 
 
 
industry worth USD 218 billion a year (Economist, 2013). As far back as 1997, the ocean’s overall value was 
estimated at an annual USD 20 949 trillion. The economic value of the open ocean has been estimated at USD 
8,381 trillion per year; and for the coastal regions at USD 12 568 trillion, respectively1 (Costanza, 1997). 

The  European  Commission  for  Maritime  Affairs  and  Fisheries  acknowledged,  not  long  ago,  the  immense 
economic potential of the seas. 

The seas are simultaneously ways of transport, sources of raw material, and sources of energy, sources of food, 
water reservoirs, the world's climate engine, and a place for leisure. With about 70% of its surface covered by 
water, Earth is the blue planet. 

Europe's coastal regions are home to about 40% of the EU population, while the maritime economy accounts for 
a production value of some 450 billion euro. 

Maritime  economic  activities  are  of  huge  economic  importance  to  Europe  as  a  whole,  and  also  to  individual 
countries. 

To give an example, in France, over 540 000 people are directly employed in the maritime industry, generating 
roughly  75  billion  euro  in  2009.  Island  states  like  Malta  or  Cyprus  have  more  than  10%  of  their  total 
employment in maritime sectors. In Denmark, no part of the country is further away from the sea than 50 km. 

For  the  case  of  Spain,  we  have  more  disaggregated  data  and  we  can  ascertain  that  maritime  activities  have 
evolved  from  2009  to  2013  much  better  than  the  average  economy.  This  can  be  seen  in  the  following  data 
(Table 3.3.3.1): 

Table 3.3.3.1. Gross value added and employment of Maritime Sectors in Spain. 2009-2013. 

These previous figures prompted the previous Commission President Barroso to pinpoint the maritime sector as 
a  prime  source  of  new  growth,  employment  and  social  cohesion.  The  "EUROPE  2020  strategy"  stressed  that 

1  Costanza  at  el.  (1997)  estimated  the  current  economic  value  of  17  ecosystem  services,  based  on  published 
studies  and  a few  original calculations.  Many  of  the  valuation  techniques used  in  the  studies  are  based,  either 
directly or indirectly, on attempts to estimate the willingness-to-pay of individuals for ecosystem services. Marine 
systems  were  further  subdivided  into  open  ocean  and  coastal,  which  itself  includes  estuaries,  sea  grass/algae 
beds, coral reefs, and shelf systems. 

100 

IndustriesGVA (Mio €)Employment (Thousands)GVA (Mio €)Employment (Thousands)GVA (%)Employment (%)Total Maritime industries26.97346229.1474328,1%-6,6%Fishing1.693451.708420,9%-5,8%Shipbuilding844399773615,8%-8,3%Maritime transport1.129131.2941214,6%-11,5%Boats& Yachts1.079161.171158,6%-6,8%Auxiliary manufacture3.099453.5884115,8%-8,3%Military Ships70426764248,6%-6,8%Maritime Research39293578-8,9%-8,7%Maritime Tourism3.303623.586588,6%-6,8%Ports9.3669410.7169214,4%-2,2%Wholesale and distribution2.938523.084485,0%-7,9%Insurance and banking1.734171.25915-27,4%-9,5%Other59343643408,6%-6,8%Source: Innovamar, Madrid, 2014Gross Value Added and Employment of Maritime Industries. Spain. 2009-201320092013Var. 2009-2013 
 
                                                      
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Europe  cannot  continue  to  rely  on  demand-  driven  economic  growth  and  reiterates  the  importance  of  the 
maritime sectors in the particular context of resource efficiency, competitiveness and climate action. 

To bring “Blue growth” and the "Blue Jobs" that go with it will require an approach on three fronts. 

Firstly, we need to analyse sustainable maritime growth scenarios, their drivers and the policy measures required 
to make these scenarios a reality. 

Secondly, we need to establish and satisfy our marine and maritime knowledge and data needs. 

The  third and final  front  involves us agreeing on  what steps are  needed from regulatory  authorities to set the 
appropriate framework conditions to foster growth.  

In  this  potential  Blue  Growth  we  will  encounter  new  activities  but  we  must  not  neglect  the  sustainable 
development  of  well-established  industries  such  as  tourism,  renewable  marine  energy,  shipbuilding,  short-sea 
shipping, fisheries and aquaculture. 

As  for  fisheries,  there  is  a  new  system  in  the  making  that  clearly  focuses  on  sustainability  in  all  its  aspects: 
environmental,  social  and  economic.  By  adopting  an  ecosystem-based  approach,  we  make  sure  we  exploit 
resources sustainably and derive stable catches in the long term – which in turn means stability for the sector.  

Maritime  policy  it  is  called  on  to  play  a  full  part  in  pulling  Europe  out  of  the  ongoing  economic  downturn, 
providing it with the economic and social backbone to help it stand strong in the face of future challenges. 

Sustainability not only enhances skills and promotes advanced technologies. It also implies long-term economic 
opportunities. It is also the only way we know to protect Europe's maritime zones, biodiversity and the marine 
environment.  

Fisheries, therefore, are one of the main sectors within the maritime industries that can contribute to the Blue 
growth and Blue jobs in the following years for the European Union Members.  

Now,  we  will  see  what  are  the  main  economic  and  social  data  for  the  European  fleets.  What  follows  comes 
directly from the 2014 Annual Economic  Report on the EU Fishing Fleet  (see bibliography at the end for the 
exact quotation). 

EU Fleet economic performance results for 2012 

In 2012, the EU fleet generated €6.9 billion in revenue, amounting to €3.3 billion in Gross Value Added (GVA), 
which measures the direct contribution of the fishing fleet to the economy. After deducting for operating costs, 
the fleet generated €1.3 billion in gross profit and €458 million in net profit after accounting for capital costs, 
(Figure 3.3.3.1). 

In  relative  terms,  GVA  as  a  proportion  of  revenue  was  estimated  at  48%,  which  means  that  fishing  fleet 
transforms roughly half of the total revenue into salaries and profits  for the fishing community.  An estimated 
6.6% of the fleet’s revenue was retained as net profit in 2012. 

Figure 3.3.3.1. EU fishing fleet economic performance indicators: comparing 2011 and 2012. 

 
 
 
 
The fleet’s improved economic performance in 2012 was mainly a result of lower overall costs, which declined 
when  compared  to  2011.  More  specifically,  labour  costs  declined  6%  and  capital  costs  (mainly  depreciation) 
13%. 

The data available makes it possible to examine longer time series (2002-2012) but including only 11 MS fleets 
(Figure 3.3.3.2). This time  series analysis shows that the economic performance of the covered national fleets 
improved gradually over the period. This is due to a combination of factors including higher market prices for 
several  commercially  important  species,  improved  situation  of  some  stocks,  implementation  of  long-term 
management plans under the CFP, capacity reduction in some fleet segments, etc. 

Time  series  also  shows  the  impact  of  the  fuel  crises  in  2003  and  2008,  highlighting  the  vulnerability  of  the 
fishing sector to external shocks but also its resilience. 

Figure 3.3.3.2. Trends in fleet economic performance indicators: 2002 to 2012. 

Drivers affecting the economic performance of the EU fleet 

Drivers  that  may  have  contributed  to  improve  economic  performance  include,  but  are  not  limited  to  the 
following (in no specific order): 

  Higher  average  first  sale  prices  for  many  commercially  important  species,  e.g.  European  pilchard, 

anchovy and brown shrimp; 

  Recovery  of  some  stocks,  such  as  the  Baltic  cod,  herring  and  North  Sea  plaice,  leading  to  increased 

TACs (specific quota regimes) and quotas; 

  Research and innovation projects (more selective fishing gears, bio-marine ingredients facility) funded 

by the European Fishing Fund and national support; 

  Capacity reduction (decommissioning with or without public support); 

  Favorable market conditions (internal and export) for several species; 

 

Implementation of certification schemes and the growing demand for certified products; 

  More  fuel  efficient  fishing  techniques  and  changes  of  fishing  behavior  in  order  to  reduce  fuel 

consumption. 

Drivers that may have contributed to deteriorating economic performance include, but are not limited to the 
following (in no specific order): 

 

Increasing fuel prices and other operating costs; 

  Effects of the global economic crisis that continue to affect internal and international markets for some 

species and limit access to credit; 

  Reduced TACs and quotas for several key stocks, such as European sprat and Atlantic herring; 

  Market saturation (e.g. Baltic cod) and poor marketing to place products on new markets; 

102 

 
 
 
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  Low abundance (e.g. anchovy and pilchard in the Mediterranean) and/or low quality of some species 

(e.g. Baltic cod); 

  Severe weather conditions and for a few number of fleets (e.g. Baltic and Celtic Seas) damage caused 

by marine mammals (e.g. seals); 

  Shortage of local crews as young people in fishing communities are less and less attracted to fishing as 

a career choice; 

 

Increase  in  the  number  and  size  of  areas  that  prohibit  or  limit  specific  fishing  access/activity  due  to 
established restrictions for energy production or temporary closures of areas for stock recovery. 

Overview of the EU Fishing Fleet 

The  total  number  of  vessels  in  the  EU  fishing  fleet  on  the  1st  of  January  2012  was  86  283  vessels,  with  a 
combined gross tonnage (GT) of 1.7 million tonnes and engine power of 6.7 million kilowatts (kW). 

According to DCF (data collection framework) data, the EU active fleet generated direct employment for around 
151 000 people in 2012, corresponding to 120 000 FTEs (Full time equivalent jobs). The fleet spent almost 5 
million days at sea, consumed around 2.4 billion liters of fuel and landed 4.3 thousand tonnes of seafood in 2012 
(Table 3.3.3.2). 

The  total amount of income  from landings  was estimated at almost €6.9 billion. The fleet  made an additional 
€92  million  in  other  income  from  non-fishing  activities.  Costs  incurred  by  the  fleet  amounted  to  almost  €6.5 
billion:  €5.6  billion  in  operating  costs  and  €874  million  in  capital  costs.  The  main  costs  items  were  labour 
(almost  €1.8  billion  in  crew  wages  and  €223  million  in  unpaid  labour)  and  energy  (€1.6  billion).  Other  costs 
linked to production amounted to €957 million, while other non-variable and repair costs amounted to another 
€1.1  billion.  Annual  depreciation,  totalling  €804  million,  accounted  for  92%  of  capital  costs,  while  the 
opportunity cost of capital was estimated at €70 million. 

Gross  value  added  and  gross  profit  generated  by  the  fleet  in  2012  amounted  to  €3.3  billion  and  €1.3  billion, 
respectively. After deducting capital costs, net profit was estimated at €458 million, indicating that 6.6% of the 
fleet’s revenue was retained as profit in 2012. 

Table 3.3.3.2. EU fleet employment and average wage by Member State, 2012. Arrows indicate change (∆) in 
relation to 2011: (↗) increase; (↘) decrease and (↔) stable/no change (∆ between -1 and +1%). 

Trends in Employment and average wage: 2008- 2012: Employment in the EU fishing fleet decreased steadily 
over the period. The number of fishers (and FTE), declined on average 2% per year. This decrease of on-board 

 Total employed as % of total ∆ to 2011  FTE as % of total ∆ to 2011 Av. wage per employed ∆ to 2011 Av. Wage per FTE ∆ to 2011 BEL 351 0% ↘ 334 0% ↘ 74,380 ↗ 78,166 ↗ BGR 13,466 - ↗ 7,180 - ↗ - - - - CYP 1,290 1% ↗ 817 1% ↗ 1,297 ↗ 2,047 ↗ DEU 1,752 1% ↗ 1,372 1% ↗ 28,097 ↗ 35,879 ↗ DNK 1,469 1% ↔ 1,556 1% ↘ 71,851 ↘ 67,816 ↘ ESP 34,399 23% ↘ 30,302 25% ↘ 16,775 ↘ 19,043 ↘ EST 2,046 1% ↗ 540 0% ↗ 2,420 ↗ 9,169 ↗ FIN 2,016 1% ↗ 282 0% ↘ 5,133 ↗ 36,694 ↗ FRA 9,678 6% ↘ 7,375 6% ↔ 39,008 ↗ 51,189 ↘ GBR 12,445 8% ↔ 9,868 8% ↗ 18,665 ↗ 23,538 ↗ GRC 27,558 18% - 23,944 20% - 5,968 - 6,869 - HRV 1,125 1% ↘ 163 0% ↘ 8,211 ↘ 56,585 ↘ IRL 3,392 2% ↗ 2,233 2% ↘ 26,679 ↗ 40,528 ↗ ITA 28,292 19% ↘ 20,716 17% ↔ 8,063 ↘ 11,012 ↘ LTU 732 0% ↘ 566 0% ↘ 6,001 ↘ 7,761 ↘ LVA 643 0% ↘ 353 0% ↘ 5,977 ↗ 10,888 ↗ MLT 425 0% ↗ 413 0% ↗ 9,653 ↘ 9,934 ↘ NLD 2,881 2% ↗ 1,769 1% ↘ 32,538 ↗ 52,993 ↗ POL 2,504 2% ↗ 1,737 1% ↗ 6,510 ↗ 9,385 ↗ PRT 16,143 11% ↘ 14,931 12% ↘ 8,813 ↘ 9,529 ↗ ROU 471 0% ↗ 39 0% ↗ 671 ↘ 8,202 ↘ SVN 107 0% ↘ 63 0% ↘ 12,019 ↘ 20,514 ↗ SWE 1,663 1% ↔ 942 1% ↘ 18,244 ↗ 32,192 ↗ Total* 151,383 120,315 14,358 18,066   
 
 
 
employment  can  be  a  result  of  the  reduction  in  the  number  of  EU  vessels.  Decreased  employment  was 
accompanied by a steady increase in average wage (+2% per year over the period) (Figure 3.3.3.3). 

Figure  3.3.3.3.  Trends  in  employment  and  average  wage  indicators:  2008-2012.  Source:  Member  State  data 
submissions under the DCF 2014 Fleet Economic. 

Landings status in 2012: According to DCF data, the EU fleet landed 4 295 thousand tonnes of seafood in 2012, 
corresponding to €6.9 billion in landed value. 

Spain, the UK, France and Denmark together accounted for over half the landings in 2012. The Spanish fleet 
landed the most, accounting for 20% of the landed weight, followed by the UK fleet (14%). 

In terms of landed value, the Spanish fleet generated the highest landed value (28% of the total), followed by the 
French (16%), UK (14%) and then Italian (13%) fleets. 

The SSF (small scale fleet) landed 6% of the landings in weight but accounted for 12% of the landed value in 
2012,  indicating  high-valued  species  composition  and/or  higher  product  quality.  The  LSF  (large  scale  fleet) 
landed 79% in weight and 71% in value while the DWF (distant water fleet) accounted for 15% in weight and 
17% in value. 

Total landings in NAFO Division 3M in 2012 based on the  NAFO STATLANT 21 DATA was around 26000 
tons being the most important species cod (9000), redfish (7000) and Greenland halibut (2000). The EU was the 
contracting  party  with  more  catches  (19000  tons)  in  NAFO  Div  3M  in  2012  being  Spain  (10000  tons)  and 
Portugal (7500 tons) the countries with more catches. 

Trends  in  landings:  2008-2012:  Despite  the  decline  in  landed  weight  since  2009-2010,  landed  value  has 
increased steadily from 2009 to 2011 but declined slightly in 2012. A similar trend is observed if the analysis is 
by effort: an increase in landed value per day at sea since 2009 and a decrease in the weight landed per day at 
sea from 2010 (Figure 3.3.3.4). 

Figure 3.3.3.4. Trend in landings weight and value: 2008-2012. 

Atlantic  herring  has  remained  the  most  landed  species  in  terms  of  weight,  surpassed  only  by  sprat  in  2009, 
reaching a record high in 2012. In terms of landed value, Atlantic mackerel and Norway lobster remained the 

104 

 
 
 
 
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two top species throughout the period, alternating places between 2008 and 2010, after which Atlantic mackerel 
has remained in first place, with a record high in 2011. 

Results  by  main  fishing  activity  followed  the  general  trend  with  landings  in  weight  decreasing  steadily  since 
2009 for all fleet categories. While the average landed price for the LSF and DWF have generally increased over 
recent years, the SSF has suffered declines apart from an increase in 2011. 

Trends in landings in NAFO Div 3M (2008-2012): Catches declined in 2009-2010 to levels around 20000 tones, 
recovering similar levels to those of 2008 (25000 tons) in the period 2011-2012. The main reason of this decline 
in the period 2009-2010 was the decrease and closure of the shrimp fishery in 2011 and the major reason for the 
subsequent recovery was the opening of the cod fishery in 2010. In the period 2008-2012 more than 77% of the 
catches were taken by the EU fleets being the most important countries Portugal, Spain and Estonia. Redfish, 
cod, shrimp and Greenland halibut were the main species caught in this period in NAFO Div. 3M. 

Income and Expenditure, status in 2012: In 2012, the EU fishing fleet generated over €6.9 billion in revenue, 
consisting  of  €6.85  billion  in  seafood  sales  and  €92  million  in  other  income.  The  Spanish  fleet  generated  the 
most amount of revenue (27% of the EU total), almost exclusively from landings income. The UK fleet, which 
was the third most important fleet in terms of revenue, generated the most other income (30% of the EU total), 
equating to almost 3% of its revenue (Table 3.3.3.3). 

Table 3.3.3.3. EU fleet revenue by Member State, 2012 (million  €). Arrows indicate change (∆) in relation to 
2012: (↗) increase; (↘) decrease and (↔) stable/no change (∆ between -1 and +1%). 

Economic Performance Indicators: status in 2012 

The  amount  of  GVA  (Gross  Value  Added)  generated  by  the  EU  fishing  fleet  in  2012  was  estimated  at  €3.3 
billion. Gross profit and net profit were €1.3 billion and €458 million, respectively (Table 3.3.3.4). 

While  GVA  and  gross  profit  suffered  a  marginal  decline  in  2012  compared  to  2011,  mainly  due  to  lower 
revenue, net profit improved, and 6.6% of revenue was retained as net profit, an improvement on 2011 results 
(Table 3.3.3.4). 

Labour  productivity  (GVA/FTE)  was  estimated  at  €35  thousand,  remaining  rather  stable  (+1%)  compared  to 
2011. The  Danish  fishers  were  by  far  the  most  productive  in  2012,  generating  on  average  €156  thousand  per 
FTE, followed by the Belgian (€89 thousand) and Dutch (€74 thousand) fishers. In terms of capital productivity, 

 
 
 
 
i.e. profits in relation to capital invested, the EU fleet generated a 9.5% return on fixed tangible assets (RoFTA) 
in 2012, a significant improvement on 2011 results (5.8%). This indicator is one of the parameters considered in 
the  balance of  fishing capacity and fishing opportunities as a  long-term performance indicators and it  may be 
compared with the interest rate of an alternative investment (normally long term risk-free government bonds). 
In-year  investments  amounted  to  €419  million,  6%  increase  from  the  €396  million  invested  in  2011,  which 
could indicate some optimism in the future of the fishing sector. 

Table  3.3.3.4.  EU  fleet  economic  performance  indicators,  2012  (million  €).  Arrows  indicate  change  (∆)  in 
relation to 2010: (↗) increase; (↘) decrease and (↔) stable/no change (∆ between -1 and +1%). 

Trends in economic performance 2008-2012: Overall, the economic performance of the EU fleet improved in 
2012 compared to 2011, however, showing some signs of stagnation (Figure 3.3.3.5). 

Figure 3.3.3.5. Trends in fleet economic performance indicators: 2008-2012 

Fleet performance in terms of GVA as a percentage of revenue and gross profit margin declined steadily after 
peaking in 2009, mainly reflecting lower fuel prices as revenue declined, levelling off between 2011 and 2012. 
Net  profit  margin has remained rather stable but positive  over the period  with  marked  improvements in 2010 
and 2012, reaching its highest point in 2012. (Figure 3.3.3.5). 

106 

 
 
 
 
 
107 

Labour productivity, defined as gross value added per FTE –full time equivalent jobs-(GVA/FTE), measures the 
amount  of  output  produced  by  the  amount  of  labour  and  gives  an  indication  of  the  economic  growth  in  the 
sector. Labour productivity in the fishing fleet increased over the period analysed, stabilising between 2011 and 
2012,  although  decreasing  steadily  in  the  SSF,  where  productivity  is  on  average  lower.  Fishers  in  the  LSF 
appear  more  efficient,  generating  on  average  more  output  per  employee  while  results  for  the  DWF  shows 
significant variations. 

Table  3.3.3.5  presents  the  trend  of  main  economic  indicators  for  15  selected  EU  fleets  and  Figure  3.3.3.6 
presents  the  trend  in  landings  weight  and  value  of  top  6  species,  including  cod.  Finally,  Figure  3.3.3.7  gives 
trends in average first sales price for key species 2008-2012. 

Table 3.3.3.5. Main economic indicators for 15 selected EU fleets. 

2008 Development 2009 % ∆ 2010 % ∆ 2011 % ∆ 2012 % ∆ trend 2011* 2012* Annex Table 3 Trends on the main DCF variables and estimated indicators for the 15 selected EU fleets Development trend based on the % change in 2012 to the average 2008-2011. Arrows indicate change (∆) in relation to the average 2008-2011: (↗) increase; (↘) decrease and (↔) stable/no change (∆ between -1 and +1%). 2011* and 2012* includes DCF data available for all MS excluding Bulgaria, Croatia, Cyprus, Greece and Malta.   No. Vessels (number) 45,369 44,960 -1% 44,574 -1% 43,747 -2% 42,248 -3% -4% ↘ 61,576 59,545 Vessel tonnage (thousand tonne) 1,088 1,047 -4% 1,015 -3% 990 -3% 941 -5% -7% ↘ 1,576 1,513 Vessel power (thousand kW) 4,179 4,074 -2% 3,841 -6% 3,908 2% 3,759 -4% -5% ↘ 5,758 5,566 Total employed (number) 80,354 77,461 -4% 78,072 1% 75,387 -3% 74,861 -1% -3% ↘ 124,388 120,984 FTE (number) 61,902 59,546 -4% 61,039 3% 58,577 -4% 56,761 -3% -5% ↘ 98,742 94,978 Days at sea (thousand day) 3,202 3,352 5% 3,199 -5% 3,213 0% 3,036 -6% -5% ↘ 3,218 4,684 Fishing days (thousand days) 3,007 3,179 6% 3,031 -5% 3,078 2% 2,884 -6% -5% ↘ 3,082 4,443 Energy consumption (million litre) 1,475 1,480 0% 1,421 -4% 1,303 -8% 1,235 -5% -11% ↘ 2,488 2,228 Energy consumed/landed tonne (litre/tonne) 8,383 8,843 5% 9,160 4% 8,342 -9% 8,107 -3% -5% ↘ 9,131 9,556 Landings weight (thousand tonne) 3,175 3,344 5% 3,320 -1% 3,049 -8% 2,791 -8% -11% ↘ 3,576 4,221 Landings value (million €) 3,779 3,516 -7% 3,665 4% 3,948 8% 3,809 -4% 2% ↗ 5,013 6,821 Landings income (million €) 3,768 3,519 -7% 3,681 5% 3,938 7% 3,863 -2% 3% ↗ 7,036 6,849 Other income (million €) 72 85 18% 115 36% 84 -27% 77 -9% -11% ↘ 126 92.1 Crew wage costs (million €) 879 887 1% 898 1% 906 1% 899 -1% 1% ↔ 1,872 1,772 Unpaid labour (million €) 151 154 2% 150 -3% 145 -3% 136 -6% -8% ↘ 252 223 Energy costs (million €) 885 608 -31% 731 20% 870 19% 895 3% 12% ↗ 1,525 1,550 Repair costs (million €) 322 320 -1% 314 -2% 344 9% 348 1% 6% ↗ 577 556 Other variable costs (million €) 453 441 -3% 448 2% 475 6% 454 -4% 0% ↔ 1,024 957 Other non-variable costs (million €) 301 273 -9% 314 15% 327 4% 293 -10% -3% ↘ 604 551 Annual depreciation (million €) 530 530 0% 521 -2% 577 11% 532 -8% -1% ↘ 886 804 GVA (million €) 1,879 1,962 4% 1,989 1% 2,006 1% 1,949 -3% 0% ↔ 3,430 3,326 GVA to Revenue (%) 48.9% 54.4% 11% 52.4% -4% 49.9% -5% 49.5% -1% -4% ↘ 47.9% 47.9% Gross profit (million €) 849 920 8% 941 2% 955 1% 914 -4% 0% ↔ 1,306 1,332 Gross profit margin (%) 22.1% 25.5% 16% 24.8% -3% 23.7% -4% 23.2% -2% -3% ↘ 18.2% 19.2% Net profit (million €) 269 259 -4% 318 23% 286 -10% 332 16% 13% ↗ 307 458 Net Profit margin (%) 7.0% 7.2% 3% 8.4% 16% 7.1% -15% 8.4% 19% 14% ↗ 4.3% 6.6% GVA per FTE (thousand €) 30,350 32,947 9% 32,591 -1% 34,249 5% 34,332 0% 6% ↗ 34,739 35,023   
 
 
 
 
 
 
Figure 3.3.3.6. Trend in landings weight and value of top 6 species: 2008-2012. 

Figure 3.3.3.7. Trends in average first sales price for key species: 2008-2012. 

Overview of the North Atlantic Fishing Fleet2 

Eleven  Member  State  fleet  operated  in  the  region  in  2012  (data  on  the  Polish  fleet  not  available);  the  most 
important  in  terms  of  active  vessel  number  was  the  Portuguese  fleet,  also  accounting  for  the  most  effort 
deployed. 

In  terms  of  production,  the  UK,  French  and  Irish  fleets  were  collectively  responsible  for  72%  of  the  landed 
weight. In value, the French, UK and Portuguese fleets, together accounted for 80% of the landed value in 2012. 

The  main  species  included  the  small  pelagics  Atlantic  mackerel,  jack  and  horse  mackerels  and  European 
pilchard (sardine) and demersal species, such as Norway lobster and European hake. 

Revenue generated by the North  Atlantic Sea  fleet  was €1.9 billion, 91% distributed amongst four MS  fleets: 
France (€673 million), UK (€509 million), Portugal (€357 million) and Ireland (€238 million). GVA produced 
by the fleet in 2012 is estimated at €951 million and after accounting for operating costs, the fleet made €327 
million in gross profit. These values exclude the Spanish fleet operating in the region. 

The small-scale fleet generated €171 million in GVA and €42 million in gross profits but overall performance 
deteriorated,  with  the  SSF  moving  from  a  profit  making  position  in  2011  to  posting  a  net  loss  in  2012. 
Collectively, the large-scale fleet generated gross profits in 2012, improving compared to 2011, largely a result 
of the improved performance of the UK fleet. 

While overall the North Atlantic fleet generated gross profits, all expect for three MS fleets suffered net loss in 
2012. Profitable fleets were the German (€1.6 million), Dutch (€524 thousand) and UK (€88 million) fleets. 

2 It refers to all fleet operating in international as well as in domestic waters. 

108 

  
 
 
 
 
 
                                                      
Table 3.3.3.6. EU North Atlantic fleet economic performance by MS fleets in 2012 

109 

Region / Member State N Vessels %∆ 2011 Trend 2008-2011 FTE (#) %∆ 2011 Days at Sea (days) %∆ 2011 Landings weight (kg) %∆ 2011 Trend 2008-2011 Value of landings (€) %∆ 2011 Trend 2008-2011 Revenue (€) %∆ 2011 Trend 2008-2011 GVA (€) %∆ 2011 Development Trend Gross Profit (€) %∆ 2011 Development Trend North Atlantic Belgium 40 ↘ Deteriorated 119 ↗ 5,219 ↗ 7,663,571 ↗ Improved 32,632,290 ↗ Improved 34,367,944 ↗ Improved 13,834,013 ↘ Improved 2,971,032 ↘ Improved North Atlantic Denmark 7 ↘ Deteriorated 16 ↗ 342 ↗ 23,909,779 ↘ Deteriorated 7,276,060 ↘ Stable 7,344,998 ↘ Improved 3,776,960 ↘ Deteriorated 2,634,695 ↘ Deteriorated North Atlantic France 2,342 ↘ Improved 4,713 ↗ 352,401 ↘ 300,727,553 ↗ Improved 654,699,053 ↘ Improved 672,799,299 ↘ Improved 339,499,571 ↘ Improved 93,424,294 ↘ Improved North Atlantic Germany 10 ↔ Deteriorated 92 ↘ 1,300 ↘ 10,257,105 ↘ Improved 24,400,788 ↘ Stable 24,418,931 ↘ Stable 13,493,394 ↘ Improved 4,782,264 ↘ Improved North Atlantic Ireland 789 ↗ Improved 1,511 ↔ 52,594 ↗ 235,440,650 ↗ Improved 233,431,628 ↗ Improved 237,557,864 ↗ Improved 118,296,108 ↗ Improved 31,008,344 ↗ Improved North Atlantic Lithuania      683 ↗ 3,276,373 ↗ Deteriorated 5,059,995 ↘ Improved 35,890,915 ↘ Deteriorated 9,903,680 ↔ Stable 6,763,587 ↗ Improved North Atlantic   Netherlands      5,490 ↗ 86,453,731 ↗ Deteriorated 64,314,424 ↗ Improved 65,511,240 ↗ Improved 25,592,124 ↗ Improved 8,710,250 ↗ Improved North Atlantic Portugal 3,314 ↘ Stable 13,476 ↘ 370,512 ↗ 185,852,509 ↘ Deteriorated 358,134,061 ↗ Improved 356,645,970 ↘ Stable 194,048,213 ↘ Deteriorated 67,855,929 ↘ Deteriorated North Atlantic Spain      -  10,465,030   19,936,300   -   -   -   North Atlantic UK 1,668 ↔ Stable 5,294 ↗ 217,414 ↔ 303,503,679 ↔ Improved 492,360,678 ↗ Improved 509,019,438 ↗ Improved 232,847,230 ↗ Improved 109,161,897 ↗ Improved North Atlantic fleet 8,170   25,221  1,005,955  1,167,549,978   1,892,245,277   1,943,556,598   951,291,293   327,312,293     
 
 
 
 
 
The Atlantic Cod, NAFO cod and the economic and social impact of NAFO cod. 

The North Atlantic EU fleet was composed by 8 170 vessels that produced 1,9 billion Euros of revenue, almost 
1 billion of Gross Value added and 25.221 full time equivalent jobs.  

The value of Atlantic landings of cod when considering the EU member States was 307 million Euros and 226 if 
NAFO countries are considered. 

The value of the NAFO 3M cod landings was 38 million Euros and 493 full time equivalent jobs.  

All data above for the last year we have complete data which is 2012. 

The Landings in two scenarios. 

Figures 3.3.3.8 and 3.3.3.9 show the median cod landings in tons from 1972 to 2033. Data from 1972 to 2014 
are the same in both scenarios. They are historical data.  

After 2014 we have landings forecast up to 2033. In this first scenario3, the forecast starts around 14 000 tons, 
then it goes down until 2020 (about 6 000 tons) and after that date it goes up reaching 20 586 tons in 2033. 

Figure 3.3.3.8. Landings for Scenario 1. Data in tonnes. 

In the  second scenario (figure  3.3.3.9), the forecast starts around 14 000 tons, then it  goes up and down  until 
2020 (13 000 toes) and finally it goes down almost reaching 8 000 tons in 2033. 

3  This  first  scenario  corresponds  to  “scenario  2”  in  our  results:  Task  3.4  as  mentioned  above  and  the  second 
scenario in this chapter corresponds to “scenario 20”. 

110 

010000200003000040000500006000019721974197619781980198219841986198819901992199419961998200020022004200620082010201220142016201820202022202420262028203020321972-2033 Landings-Scenario 1. Tonnes 
 
 
                                                      
111 

Figure 3.3.3.9. Landings for Scenario 2. Data in tons. 

In the following graph both scenarios together can be seen: 

The difference between the two landings-scenarios is provided by Table 3.3.3.7, as follows: 

Table 3.3.3.7. Difference in tons between scenario 1 and 2 of cod landings. 

01000020000300004000050000600001972197519781981198419871990199319961999200220052008201120142017202020232026202920321972-2033 Landings. Scenarios 1 and 2. TonnesScenario 1.Scenario 2.010000200003000040000500006000019721974197619781980198219841986198819901992199419961998200020022004200620082010201220142016201820202022202420262028203020321972-2033 Landings_Scenario 2. TonnesTonnes2014-20202021-2033Scenario 174.976              169.434            Scenario 299.353              90.407              Dif. Scenario 2 - Scenario 124.377              79.027 -              
 
 
 
 
 
Up to 2020, the cod landings –the sum of the annual landings from 2014 to 2020– of scenario 2 are 24 377 tons 
larger than those of scenario 1. From 2021 to 2033 the results are the opposite, the cod landings of scenario 1 are 
79 027 tons larger than those belonging to scenario 2. If we consider the time from 2014 to 2033, the scenario 1 
implies about 245 000 tons of cod landings and the scenario 2 about 189 000 tons of cod landings. 

Scientific Council (NAFO, 2014) recommended that the simulations period could be 20 years and that some of 
the Performance Objectives proposed by the FC/SC RBMS could be measure in a medium (5 years) and long 
term period (20 years). The medium term catches projections depend primarily on the current situation of stock 
while  long  term  catch  projections  depend  mainly  on  the  assumptions  of  stock  recruitment  relationship  made. 
This relationship in the case of the 3M cod is one of the most uncertain parameters,  so these long-term results 
should only be taken as indicative of trends rather than quantitative results. 

The value of landings in the two scenarios. 

We  now  multiply  the  series  of  cod  landings  in  tons  by  a  series  of  prices  (see  Figure  3.3.3.7  above  for  the 
evolution of prices last years).  

As can be appreciated in figure 3.3.3.7., the cod prices have varied very little from 2008 to 2012. In fact, they 
don´t change much even the worst years of the last financial crisis. Therefore, our hypothesis here is that prices 
will evolve as the last five years for which we have data. Then the value of landings for both our scenarios is 
given by figures 3.3.3.10 and 3.3.3.11. 

The implication of our hypothesis (very little change) about the price of cod is that the variation of the value of 
cod in both scenarios depends crucially on the evolution of the landings and the hypothesis used there for that 
evolution. 

Figure 3.3.3.10. Value of landings for Scenario 1. Data in Euros. 

112 

Tonnes2014-20202021-2033Scenario 110.711              13.033              Scenario 214.193              6.954                 Average Landings - 10.000.000 20.000.000 30.000.000 40.000.000 50.000.000 60.000.000 70.000.000 80.000.000201420152016201720182019202020212022202320242025202620272028202920302031203220332014-2033 Value of landings.Scenario 1. Euros 
 
 
 
 
113 

Figure 3.3.3.11. Value of landings for Scenario 2. Data in Euros. 

The economy of the European Union 27. 

The original data of this section is obtained directly from Eurostat that publishes the aggregated input-
output table of the 27 European Union members. After the EU27 I-O table, all the calculations are ours. 

Table  3.3.3.8  shows  the  economy  of  the  European  Union  –the  aggregation  of  the  27  union  members– 
disaggregated into 10 economic  sectors. The variables represented are output or production, value added, and 
total  intermediate  purchases.  These  three  economic  variables  represent  the  structure  of  production  of  each 
sector. The output or production of each sector is composed by their intermediate purchases and the value added 
which is composed by the rents of the primary inputs of production (labor and capital) and taxes on production. 
The  other  columns  of  table  3.3.3.8  show  the  final  demand  for  each  sector  composed  by  the  consumption, 
investment  and  exports  of  each  industry  and  the  final  column  shows  the  imports  used  by  each  sector  for  its 
production. 

Table 3.3.3.8. Input Output table of European Union 27. Year 2011. Data in Mill. Euros. 

 - 10.000.000 20.000.000 30.000.000 40.000.000 50.000.000 60.000.000201420152016201720182019202020212022202320242025202620272028202920302031203220332014-2033 Value of landings. Scenario 2. Euros 
 
 
 
In  Table  3.3.3.9,  we  have  the  same  sectors  as  in  Table  3.3.3.7  but  now  the  data  instead  of  being  millions  of 
Euros is presented as percentages of each total. Thus, we can appreciate that agriculture and fishing for the 27 
members of the European Union represent the 1.7% of the total output produced by the EU27 or the 1.6% of 
total value added or the 1% of total consumption or the 1.14% of total investment or the 0.9% of total exports. 
In  Euros,  agriculture  and  fishing  amount  to  391  billion  Euros  of  output  for  the  27  members  of  the  EU,  182 
billion Euros of  value added, 209 billion of intermediated  purchases, 93 billion of consumption, 23 billion of 
investment and 17 billion of exports. 

Table 3.3.3.9. Input Output table of European Union 27. Year 2011. Data in percentages. 

The socio economic impact of the NAFO cod in the economy of the European Union 27. 

According  to  our  methodology,  previously  exposed,  we  now  take  the  value  of  landings  obtained  in  both  our 
scenarios and see what is the value of the inter-relations of the landings –as for any other economic sector in the 
European economy.  

Table  3.3.3.10  presents  the  value  of  these  interrelationships,  of  NAFO  3M  cod  (or  the  value  of  landings  in 
Euros) with the rest of the economic sectors of the 27 members of the European Union. The economic impact is 

114 

Output at basic pricesValue added at basic pricesTotal intermediate consumption/final use at purchasers' pricesFinal consumption expenditureGross capital formationExportsUse of imported productsProducts of agriculture, forestry and fishing391.315         182.155         209.160         93.627           23.641           17.160           14.189           Industrial products (except construction works)7.033.593      2.137.683      4.895.909      1.488.837      522.418         1.193.851      761.465         Constructions and construction works1.699.591      696.530         1.003.060      48.544           1.051.361      5.918             56.438           Wholesale and retail trade; transportation and storage; accommodation and food services4.260.599      2.097.125      2.163.474      1.947.052      138.166         316.037         178.383         Information and communication services1.104.633      542.452         562.181         260.963         176.742         72.762           43.288           Financial and insurance services1.259.462      610.219         649.243         334.285         1.323             110.464         34.490           Real estate services1.852.253      1.300.024      552.229         1.320.662      39.456           3.728             16.466           Professional, scientific and technical services; administrative and support services2.302.668      1.230.521      1.072.147      182.343         100.139         169.749         70.016           Public administration, defence, education, human health and social work services3.056.741      2.117.915      938.826         2.822.336      9.483             5.997             62.468           Arts, entertainment and recreation; other services; services of household and extra-territorial organizations and bodies616.660         393.398         223.262         463.360         8.088             8.665             12.538           Total23.577.515    11.308.024    12.269.491    8.962.009      2.070.817      1.904.333      1.249.740      Output at basic pricesValue added at basic pricesTotal intermediate consumption/final use at purchasers' pricesFinal consumption expenditureGross capital formationExportsUse of imported productsProducts of agriculture, forestry and fishing1,661,611,701,041,140,901,14Industrial products (except construction 29,8318,9039,9016,6125,2362,6960,93Constructions and construction works7,216,168,180,5450,770,314,52Wholesale and retail trade; transportation and storage; accommodation and food services18,0718,5517,6321,736,6716,6014,27Information and communication services4,694,804,582,918,533,823,46Financial and insurance services5,345,405,293,730,065,802,76Real estate services7,8611,504,5014,741,910,201,32Professional, scientific and technical services; administrative and support services9,7710,888,742,034,848,915,60Public administration, defence, education, human health and social work services12,9618,737,6531,490,460,315,00Arts, entertainment and recreation; other services; services of household and extra-territorial organizations and bodies2,623,481,825,170,390,461,00Total100,00100,00100,00100,00100,00100,00100,00 
 
 
 
115 

represented  by  three  effects,  described  above  in  the  methodology,  and  referring  to  three  economic  variables: 
output or revenue or production, gross value added and employment.  

The data of Table 3.3.3.10 corresponds to year 2011 that is the year for which the last EU input-output table has 
produced data. This result, the data of Table 3.3.3.10, will be projected afterwards to the period 2014-2033 and 
the two sub-periods 2014-2020 and 2021-2033. 

Table 3.3.3.10. Economic and social impact of the NAFO cod. Data Mill. € and FTE jobs. 

The direct effect corresponds to the value of landings, the revenue of the NAFO cod, for year 2011, same year 
as the one for the input output table with which the calculations to obtain Table  3.3.3.10 have been done. For 
that  revenue,  about  48%  corresponds  to  value  added  and  52%  to  intermediate  purchases.  The  employment 
generated by that revenue equals 497 full time  equivalent  jobs. The indirect effect represents the  value of the 
other  sectors  to  fulfill  the  demands  of  the  NAFO  cod  to  finally  obtain  its  revenue  and  the  induced  effect 
represents  the  impact  in  other  sectors  of  the  rents  obtained  by  the  primary  inputs  of  the  NAFO  cod.  The 
multiplier for production is 2.47 which means that for every euro put in motion by the landings of NAFO cod 
2.47 Euros are mobilized by  the rest of the economic sectors of EU27. Exactly the same for the  multiplier of 
2.09 of value added and for the 1,69 multiplier of employment. 

The distribution of the economic impact in terms of the sectors affected. 

The  following  table  gives  the  distribution  of  the  economic  effects  of  the  revenue  (production)  for  every 
economic sector of the input output table of EU27 (or every economic sector of the European economy).  

Total effectDirect effectIndirect effectInduced effectMultiplierRevenue (production)*93,9838,1133,7022,172,47Gross Value Added*37,9418,1913,546,212,09Employment**8414972341101,69Notes:*Mill €**Employment (units) 
 
 
Now, the following table gives the same decomposition but for value added. 

116 

Revenue (production). Mill. € Total effectDirect effectIndirect effectInduced effectFish and other fishing products; aquaculture products; support services to fishing39,4538,111,330,02Real estate services (incl imputed rents)3,800,000,683,12Wholesale trade services, except of motor vehicles and motorcycles3,680,002,481,20Food products, beverages and tobacco products3,320,001,811,51Coke and refined petroleum products 3,100,002,680,41Electricity, gas, steam and air-conditioning2,960,002,100,87Financial services, except insurance and pension funding2,500,001,530,97Warehousing and support services for transportation2,060,001,670,39Retail trade services, except of motor vehicles and motorcycles2,020,000,801,22Chemicals and chemical products1,900,001,560,35Legal and accounting services; services of head offices; management consulting services1,780,001,210,57Land transport services and transport services via pipelines1,730,001,070,66Accommodation and food services1,340,000,151,19Constructions and construction works1,330,000,790,54Repair and installation services of machinery and equipment1,100,000,980,12Products of agriculture, hunting and related services1,070,000,550,53Textiles, wearing apparel and leather products1,040,000,750,29Insurance, reinsurance and pension funding services, except compulsory social security1,010,000,540,46Fabricated metal products, except machinery and equipment0,990,000,770,22Telecommunications services0,970,000,470,50Security and investigation services; services to buildings and landscape; office administrative, office support and other business support services0,930,000,580,35Mining and quarrying0,820,000,680,15Wholesale and retail trade and repair services of motor vehicles and motorcycles0,810,000,310,50Rental and leasing services0,760,000,490,26Other transport equipment0,740,000,690,05Rubber and plastics products0,740,000,560,18Motor vehicles, trailers and semi-trailers0,650,000,140,51Computer programming, consultancy and related services; information services0,610,000,380,23Machinery and equipment n.e.c.0,590,000,470,11Basic metals0,560,000,410,15Services auxiliary to financial services and insurance services0,540,000,310,24Water transport services0,540,000,410,12Architectural and engineering services; technical testing and analysis services0,500,000,310,19Advertising and market research services0,470,000,300,18Paper and paper products0,460,000,300,16Sewerage; waste collection, treatment and disposal activities; materials recovery; remediation activities and other waste management services 0,440,000,220,22Employment services0,440,000,300,14Electrical equipment0,420,000,280,14Wood and of products of wood and cork, except furniture; articles of straw and plaiting materials0,390,000,320,07Other personal services0,380,000,130,25Human health services0,370,000,050,31Other non-metallic mineral products0,350,000,240,11Education services0,340,000,120,22Motion picture, video and television programme production services, sound recording and music publishing; programming and broadcasting services0,290,000,110,18Other professional, scientific and technical services; veterinary services0,280,000,160,12Publishing services0,270,000,090,17Computer, electronic and optical products0,270,000,160,11Furniture; other manufactured goods0,240,000,080,16Travel agency, tour operator and other reservation services and related services0,240,000,100,14Creative, arts and entertainment services; library, archive, museum and other cultural services; gambling and betting services0,220,000,030,19Public administration and defence services; compulsory social security services0,220,000,110,11Air transport services0,210,000,080,13Printing and recording services0,210,000,120,09Natural water; water treatment and supply services0,210,000,120,08Postal and courier services0,200,000,110,09Scientific research and development services0,200,000,140,05Basic pharmaceutical products and pharmaceutical preparations0,180,000,100,07Repair services of computers and personal and household goods0,180,000,130,04Social work services0,150,000,000,15Sporting services and amusement and recreation services0,120,000,030,10Services furnished by membership organisations0,120,000,080,04Services of households as employers; undifferentiated goods and services produced by households for own use 0,110,000,000,11Products of forestry, logging and related services0,100,000,070,03Total93,9838,1133,7022,17 
 
 
117 

And finally, the same but for employment: 

GVA. Mill. €Total effectDirect effectIndirect effectInduced effectFish and other fishing products; aquaculture products; support services to fishing18,8318,190,630,01Real estate services (incl imputed rents)2,300,000,471,82Wholesale trade services, except of motor vehicles and motorcycles1,530,001,220,31Retail trade services, except of motor vehicles and motorcycles1,100,000,470,64Financial services, except insurance and pension funding0,990,000,820,16Electricity, gas, steam and air-conditioning0,780,000,660,12Warehousing and support services for transportation0,750,000,730,02Food products, beverages and tobacco products0,730,000,470,26Legal and accounting services; services of head offices; management consulting services0,720,000,690,02Land transport services and transport services via pipelines0,650,000,500,15Accommodation and food services0,640,000,080,57Mining and quarrying0,430,000,430,00Repair and installation services of machinery and equipment0,400,000,400,00Chemicals and chemical products0,400,000,380,02Wholesale and retail trade and repair services of motor vehicles and motorcycles0,380,000,170,21Constructions and construction works0,360,000,320,04Telecommunications services0,350,000,220,13Security and investigation services; services to buildings and landscape; office administrative, office support and other business support services0,330,000,310,02Products of agriculture, hunting and related services0,320,000,250,07Insurance, reinsurance and pension funding services, except compulsory social security0,310,000,190,12Fabricated metal products, except machinery and equipment0,300,000,290,01Rental and leasing services0,300,000,270,03Textiles, wearing apparel and leather products0,290,000,230,06Other personal services0,250,000,100,16Education services0,240,000,100,14Coke and refined petroleum products 0,230,000,220,02Employment services0,230,000,230,00Human health services0,220,000,040,19Computer programming, consultancy and related services; information services0,200,000,200,00Rubber and plastics products0,200,000,190,01Other transport equipment0,170,000,170,01Machinery and equipment n.e.c.0,170,000,160,00Services auxiliary to financial services and insurance services0,160,000,150,01Architectural and engineering services; technical testing and analysis services0,160,000,150,01Sewerage; waste collection, treatment and disposal activities; materials recovery; remediation activities and other waste management services 0,140,000,090,05Advertising and market research services0,120,000,120,00Electrical equipment0,110,000,090,02Motor vehicles, trailers and semi-trailers0,110,000,030,08Creative, arts and entertainment services; library, archive, museum and other cultural services; gambling and betting services0,110,000,010,09Other professional, scientific and technical services; veterinary services0,110,000,090,02Wood and of products of wood and cork, except furniture; articles of straw and plaiting materials0,110,000,100,00Public administration and defence services; compulsory social security services0,110,000,070,03Publishing services0,100,000,050,05Social work services0,100,000,000,10Natural water; water treatment and supply services0,100,000,070,03Repair services of computers and personal and household goods0,100,000,080,02Paper and paper products0,090,000,080,01Other non-metallic mineral products0,090,000,080,01Furniture; other manufactured goods0,090,000,030,05Services of households as employers; undifferentiated goods and services produced by households for own use 0,090,000,000,09Water transport services0,090,000,080,01Motion picture, video and television programme production services, sound recording and music publishing; programming and broadcasting services0,090,000,050,04Basic metals0,080,000,080,00Postal and courier services0,070,000,060,01Scientific research and development services0,070,000,070,00Computer, electronic and optical products0,070,000,050,02Services furnished by membership organisations0,060,000,050,01Basic pharmaceutical products and pharmaceutical preparations0,060,000,040,02Printing and recording services0,050,000,050,00Sporting services and amusement and recreation services0,050,000,010,04Travel agency, tour operator and other reservation services and related services0,050,000,030,02Products of forestry, logging and related services0,050,000,040,01Air transport services0,040,000,020,02Total37,9418,1913,546,21 
 
 
The economic impact of NAFO cod from 2014 to 2033 in two scenarios. 

118 

Employment in thousandsTotal effectDirect effectIndirect effectInduced effectFish and other fishing products; aquaculture products; support services to fishing0,51470,49720,01730,0002Retail trade services, except of motor vehicles and motorcycles0,04520,00000,01920,0260Wholesale trade services, except of motor vehicles and motorcycles0,01950,00000,01560,0039Products of agriculture, hunting and related services0,01850,00000,01430,0043Accommodation and food services0,01780,00000,00220,0156Food products, beverages and tobacco products0,01510,00000,00970,0054Land transport services and transport services via pipelines0,01460,00000,01130,0033Textiles, wearing apparel and leather products0,01080,00000,00860,0022Warehousing and support services for transportation0,01040,00000,01010,0003Security and investigation services; services to buildings and landscape; office administrative, office support and other business support services0,01000,00000,00940,0007Financial services, except insurance and pension funding0,00940,00000,00790,0016Legal and accounting services; services of head offices; management consulting services0,00930,00000,00900,0003Constructions and construction works0,00810,00000,00730,0008Wholesale and retail trade and repair services of motor vehicles and motorcycles0,00750,00000,00330,0042Other personal services0,00700,00000,00260,0044Fabricated metal products, except machinery and equipment0,00670,00000,00650,0002Education services0,00640,00000,00250,0038Electricity, gas, steam and air-conditioning0,00620,00000,00520,0009Repair and installation services of machinery and equipment0,00530,00000,00530,0000Services of households as employers; undifferentiated goods and services produced by households for own use 0,00500,00000,00010,0049Human health services0,00500,00000,00080,0042Chemicals and chemical products0,00450,00000,00430,0002Rubber and plastics products0,00400,00000,00370,0003Insurance, reinsurance and pension funding services, except compulsory social security0,00390,00000,00240,0015Other transport equipment0,00370,00000,00360,0001Mining and quarrying0,00370,00000,00370,0000Social work services0,00360,00000,00010,0035Wood and of products of wood and cork, except furniture; articles of straw and plaiting materials0,00340,00000,00320,0001Real estate services (incl imputed rents)0,00290,00000,00060,0023Machinery and equipment n.e.c.0,00270,00000,00260,0001Architectural and engineering services; technical testing and analysis services0,00270,00000,00260,0001Postal and courier services0,00260,00000,00230,0004Furniture; other manufactured goods0,00260,00000,00100,0016Telecommunications services0,00260,00000,00160,0010Computer programming, consultancy and related services; information services0,00260,00000,00250,0000Repair services of computers and personal and household goods0,00240,00000,00200,0005Other professional, scientific and technical services; veterinary services0,00240,00000,00190,0004Public administration and defence services; compulsory social security services0,00230,00000,00150,0008Motor vehicles, trailers and semi-trailers0,00230,00000,00070,0016Creative, arts and entertainment services; library, archive, museum and other cultural services; gambling and betting services0,00210,00000,00030,0018Employment services0,00210,00000,00210,0000Services auxiliary to financial services and insurance services0,00210,00000,00190,0002Other non-metallic mineral products0,00190,00000,00170,0002Electrical equipment0,00190,00000,00150,0003Sewerage; waste collection, treatment and disposal activities; materials recovery; remediation activities and other waste management services 0,00180,00000,00120,0006Advertising and market research services0,00180,00000,00180,0000Natural water; water treatment and supply services0,00170,00000,00120,0005Coke and refined petroleum products 0,00160,00000,00150,0001Publishing services0,00150,00000,00070,0008Basic metals0,00140,00000,00140,0000Services furnished by membership organisations0,00140,00000,00120,0002Sporting services and amusement and recreation services0,00140,00000,00040,0010Computer, electronic and optical products0,00140,00000,00100,0003Paper and paper products0,00130,00000,00120,0001Printing and recording services0,00130,00000,00120,0001Products of forestry, logging and related services0,00120,00000,00100,0001Motion picture, video and television programme production services, sound recording and music publishing; programming and broadcasting services0,00100,00000,00050,0005Travel agency, tour operator and other reservation services and related services0,00100,00000,00060,0004Scientific research and development services0,00080,00000,00080,0000Water transport services0,00080,00000,00070,0001Rental and leasing services0,00080,00000,00070,0001Air transport services0,00070,00000,00030,0004Basic pharmaceutical products and pharmaceutical preparations0,00060,00000,00040,0002Total0,840,500,230,11 
 
 
119 

The  next  figures  show  the  economic  impact  in  terms  of  revenue,  value  added  and  employment  taking  into 
account the forecasts of landings in the two scenarios. 

Figure 3.3.3.12 represents the total effect, the sum of direct, indirect and induced effect, of NAFO cod in terms 
of  revenue  and  value  added  for  scenario  1  of  landings  forecasts.  Revenues  reach  a  maximum  of  almost  170 
million Euros and value added reaches about 69 million Euros for 2033. 

Figure 3.3.3.12. Scenario 1. Total effect of NAFO cod in revenue and gross value added. Euros. 

Figure 3.3.3.13 shows the total effect, the sum of direct, indirect and induced effect, of NAFO cod in terms of 
revenue and value added for scenario 2 of landings´ forecasts. 

In this case, scenario 2, the forecast of landings implies much less revenue and value added than in the previous 
scenario 1. Now, revenues for 2033 reach about 66 million Euros and value added for the same final year of the 
forecast is about 27 million Euros. 

Figure 3.3.3.13. Scenario 2. Total effect of NAFO cod in revenue and gross value added. Euros. 

 - 20.000.000 40.000.000 60.000.000 80.000.000 100.000.000 120.000.000 140.000.000 160.000.000 180.000.00020142015201620172018201920202021202220232024202520262027202820292030203120322033Scenario 1. Revenue and Gross Value Added. Total effect. EurosRevenueGVA - 20.000.000 40.000.000 60.000.000 80.000.000 100.000.000 120.000.000 140.000.00020142015201620172018201920202021202220232024202520262027202820292030203120322033Scenario 2. Revenue and Gross Value added. Total effect. Euros.RevenueGVA 
 
 
 
 
 
Figure 3.3.3.14 depicts the total effect, the sum of direct, indirect and induced effect, of NAFO cod in terms of 
employment  for  scenario  1  of  landings´  forecasts.  From  a  total  effect  in  employment  of  884  jobs  in  the  year 
2014, we reach 1 514 jobs for year 2033 when the landings are forecast by scenario 1. 

Figure 3.3.3.14. Scenario 1. Total effect of NAFO cod in employment. FTE jobs. 

Figure 3.3.3.15 represents the total effect, the sum of direct, indirect and induced effect, of NAFO cod in terms 
of employment for scenario 2 of landings´ forecasts. From a total effect in employment of 914 jobs in the year 
2014, we only reach 592 jobs for year 2033 when the landings are forecast by scenario 2. 

Figure 3.3.3.15. Scenario 2. Total effect of NAFO cod in employment. FTE jobs. 

A temporal decomposition: The economic impact of NAFO cod in 2014-2020 and 2021-2033. 

Now,  we  present  the  results  decomposing  the  time  period  2014-2033  into  two  sub-periods:  2014-2020  and 
2021-2033.  The  next  figures,  as  before,  represent  the  economic  impact  in  terms  of  revenue,  value  added  and 
employment taking into account the forecasts of landings in the two scenarios and the different time period. 

120 

02004006008001000120014001600Scenario 1. Total effect. Employment (Units of FTE jobs)020040060080010001200Scenario 2. Total effect. Employment (Units of FTE jobs) 
 
 
 
 
121 

Figure 3.3.3.16 represents the total effect that is equivalent to the sum of direct, indirect and induced effect, of 
NAFO cod in terms of revenue  and value  added for scenario 1 of landings´  forecasts and in the period 2014-
2020. Revenue starts in 99 million Euros and decreases to 44 million. Value added goes from 40 million in 2014 
to almost 18 million in 2020. 

Figure 3.3.3.16. Scenario 1. Total effect of NAFO cod in revenue and gross value added. 2014-2020. Euros 

Figure 3.3.3.17 represents the same as figure 3.3.3.16 but now for the period 2021-2033. 

Figure 3.3.3.17. Scenario 1. Total effect of NAFO cod in revenue and gross value added. 2021-2033. Euros. 

Figure 3.3.3.18 represents the total effect which is equivalent to the sum of direct, indirect and induced effect, of 
NAFO cod in terms of revenue and value added for scenario 2 of landings´  forecasts and in the period 2014-

99.038.742   43.701.562   40.001.013   17.650.737    - 20.000.000 40.000.000 60.000.000 80.000.000 100.000.000 120.000.0002014201520162017201820192020Scenario 1. Revenue and Gross Value Added. Total effect. 2014-2020. EurosRevenueGVA169.621.942   68.509.044    - 20.000.000 40.000.000 60.000.000 80.000.000 100.000.000 120.000.000 140.000.000 160.000.000 180.000.0002021202220232024202520262027202820292030203120322033Scenario 1. Revenue and Gross Value Added. Total effect. 2021-2033. EurosRevenueGVA 
 
 
 
 
 
 
 
2020. Revenue  starts in 102 million Euros and decreases to 97  million. Value  added  goes  from 41  million in 
2014 to almost 39.5 million in 2020. 

Figure 3.3.3.18. Scenario 1. Total effect of NAFO cod in revenue and gross value added. 2014-2020. Euros. 

Figure 3.3.3.19 represents the same as figure 3.3.3.18 but now for the period 2021-2033. 

Figure 3.3.3.19. Scenario 2. Total effect of NAFO cod in revenue and gross value added. 2021-2033. Euros. 

Figure 3.3.3.20 represents the total effect, the sum of direct, indirect and induced effect, of NAFO cod in terms 
of employment for scenario 1 of landings´ forecasts. From a total effect in employment of 813 jobs in the year 
2011, year of the input output table for the European Union, we reach 390 jobs for year 2020 when the landings 
are forecast by scenario 1. 

122 

102.445.823   97.508.681   41.377.108   39.383.033    - 20.000.000 40.000.000 60.000.000 80.000.000 100.000.000 120.000.000 140.000.0002014201520162017201820192020Scenario 2. Revenue and Gross Value added. Total effect. 2014-2020. Euros.RevenueGVA66.287.502   26.773.030    - 10.000.000 20.000.000 30.000.000 40.000.000 50.000.000 60.000.000 70.000.000 80.000.0002021202220232024202520262027202820292030203120322033Scenario 2. Revenue and Gross Value added. Total effect. 2021-2033. Euros.RevenueGVA 
 
 
 
123 

Figure 3.3.3.20. Scenario 1. Total effect of NAFO cod in employment. FTE jobs.2014-2020. 

Figure 3.3.3.21 represents the same as figure 3.3.3.20 but now for the period 2021-2033. 

Figure 3.3.3.21. Scenario 1. Total effect of NAFO cod in employment. FTE jobs. 2021-2033. 

In this case, as opposite to the previous period of 2014-2020, now employment is continuously increasing, from 
a value of 389 jobs in 2021 we get 1 514 jobs in 2033. 

Figure 3.3.3.22 represents the total effect, the sum of direct, indirect and induced effect, of NAFO cod in terms 
of employment for scenario 2 of landings´ forecasts. From a total effect in employment of 813 jobs in the year 
2011, year of the  input output table for the European Union,  we only reach 870 jobs for  year 2020  when the 
landings are forecast by scenario 2. 

884   390   010020030040050060070080090010002014201520162017201820192020Scenario 1. Total effect. Employment (Units of FTE jobs). 2014-2020.389   1.514   020040060080010001200140016002021202220232024202520262027202820292030203120322033Scenario 1. Total effect. Employment (Units of FTE jobs). 2021-2033. 
 
 
 
 
Figure 3.3.3.22. Scenario 2. Total effect of NAFO cod in employment. FTE jobs. 2014-2020. 

Figure  3.3.3.23  represents  the  same  as  figure  3.3.3.20  but  now  for  the  period  2021-2033.  In  the  first  period 
represented  before  by  figure  3.3.3.22  employment  stays  almost  constant.  In  the  second  period  of  2021-2033 
employment first goes down reaching a minimum of 341 jobs in 2022. After this date it keeps increasing until 
the end of the period that reaches 592 jobs. 

Figure 3.3.3.23. Scenario 2. Total effect of NAFO cod in employment. FTE jobs. 2021-2033. 

A synthesis of the results for both sub-periods 2014-2020 and 2021-2030 is given by Table 3.3.3.11, as follows: 

Table  3.3.3.11.  Economic  and  social  impact  of  the  NAFO  cod. Two  sub-periods:  2014-2020  and  2021-2033. 
Data € and FTE jobs. 

124 

914   870   0200400600800100012002014201520162017201820192020Scenario 2. Total effect. Employment (Units of FTE jobs).2014-2020.485   592   01002003004005006007002021202220232024202520262027202820292030203120322033Scenario 2. Total effect. Employment (Units of FTE jobs). 2021-2033. 
 
 
 
125 

Conclusions: 

The first thing that should be said before starting with any conclusions is that:  The report lacks first hand data 
provided  by  the  sector.  Because  of  this  lack  of  reliable  data,  with  the  approach  taken  by  the  study    it  is 
impossible to determine processes on the cod 3M stock and the fishery to changing conditions on any of both 
sides of this economic chain. There are available socio-bio-economic models which properly data fed, give good 
approaches on how fish stocks and economic and labour dynamics interact. Unfortunately because of the lack of 
cooperation from the stakeholders a proper analysis could not be carried out.  

Now, we will start with some summary of what has been said in terms of the economic and social impact of 
the NAFO cod. 

The  value  of  all  landings  of  cod  in  the  Atlantic  North  area  was  307  million  Euros  and  the  value  of  the  cod 
landed by NAFO countries was 226 million, for the last year of published data. 

In 2014, the NAFO 3M cod was equivalent in revenue (41,5 million Euros) to the 18% of revenues of cod of 
NAFO countries in the Atlantic North area. 

Two  scenarios  of  landings  are  used  in  this  part  and  obtained  from  previous  parts  of  the  contract.  Those  two 
scenarios  of  landings  were  changed  into  two  scenarios  of  values  of  landing  by  using  the  price  series  of  the 
Annual Fleet economic report of 2014. 

The economic impact of NAFO cod in terms of the total effect (the sum of direct, indirect and induced effects) 
in revenue  was 99  million Euros (this implies a  multiplier  of 2,47) in 2014 and increase  up to 170 million in 
2033. If the values of landings forecasted from 2014 to 2033 are given by the scenario 1 of landings. 

Within the same scenario of landings, the total effect of value added goes from 40 million in 2014 to 69 million 
in 2033. Employment starts in 884 in 2014 and reaches 1 514 in 2033. 

By using the landings forecasted by scenario 2, the values obtained are much lower. Thus, we reach 66 million 
of revenues as the total effect in 2033, 27 million Euros for the total effect of value added and employment, its 
total effect, goes down from 914 in 2014 to 592 in 2033.  

The scenario 1 is clearly preferable to scenario 2 when considering the period 2014-2033. In this case, revenue 
goes up by 70 million Euros instead of only 30 million Euros of scenario 2. Employment, in scenario 1 goes up 
by 630 and goes down 322 in scenario 2. 

Now, if we consider two time periods, one that goes from 2014 to 2020 and a second one going from 2021 to 
2033, the results are the following.  

For  the  cod-scenario  1,  revenues  in  the  period  2014  to  2020  start  at  99  million  and  go  to  almost  44  million 
Euros.  Value  added  goes  from  40  million  Euros  to  almost  18  million.  Employment  starts  at  884  in  2014  and 
reaches  890  in  2020.  For  the  same  cod-scenario  1,  revenues  in  the  year  2021  are  43.5  million  Euros  and 
practically  170  million  in  the  year  2033.  Value  added  goes  from  17.5  in  2021  to  69  million  in  2033. 
Employment goes from 889 to 1 514 in the years 2021 and 2033 respectively. In the first sub-period there is a 

20142020202120332014-20202021-2033Revenue99.038.742      43.701.562    43.540.345    169.624.942   55.337.180 -   126.084.597    Value added40.001.013      17.650.737    17.585.622    68.509.044      22.350.276 -   50.923.422      Employment8843903891514-494112520142020202120332014-20202021-2033Revenue102.445.823    97.508.681    54.296.396    66.287.502      4.937.142 -     11.991.106      Value added41.377.108      39.383.033    21.929.912    26.773.030      1.994.075 -     4.843.118        Employment914870485592-44107Scenario 1Scenario 1Scenario 2Scenario 2Data in tonnes and units of employmentData in tonnes and units of employment 
 
 
 
loss in the value of cod of about 55 million Euros in revenue and 22 million Euros in value added. Employment 
goes down by almost 500 jobs. In the second period, instead, the evolution is very positive. Revenue goes up by 
126 million Euros, value added by 50 million and employment by 1 125 jobs. 

For the cod-scenario 2, revenues in the period 2014 to 2020 start at 102 million Euros and go to 97.5 million. 
Value added goes from 41 million to 39 million. Employment starts at 914 in 2014 and reaches 870 in 2020. For 
the same cod-scenario 2, revenues in the year 2021 are 54 million Euros and 66 million in the year 2033. Value 
added goes from 22 million in 2021 to 27 million in 2033. Employment goes from 485 to 592 in the years 2021 
and 2033 respectively. In this scenario 2, revenues, value added and employment move much less than in the 
previous scenario 1. In the first sub-period revenue goes down by 5 million and in the second sub-period goes 
up by 12 million. Value added goes down 2 million Euros in the first sub-period and goes up by 5 million in the 
second sub-period. Employment goes down 44 jobs in the period 2014-2020 and goes up 107 jobs in the period 
2021-2033.  

Up  to  here  are  the  results  obtained  from  applying  the  input-output  methodology  to  the  data  we  had  from 
landings  and  prices  and  the  EU27  input-output  table.  Given  those  results  we  obtain  the  following 
conclusions: 

The  medium term results (2020) will be  not affecting by the new Common Fisheries Policy (CFP) regulation 
(Regulation (EU) No 1380/2013), mainly the Article 15 about the discard ban, because will be implemented by 
2019. The  long  term  period  (2033)  3M  Cod  results  would  be  affected  in  a  small  degree  by  the  new  discards 
policy. The Specific Contract for the provision of advice on the management of discards in EU fisheries beyond 
EU waters (Specific Contract No. 3 under Framework Contract No. MARE/2012/21) classified the NAFO 3M 
cod  fishery  as  Low  discard  level.  The  most  important  bycatch  species  in  the  cod  fishery  in  Division  3M  is 
redfish  and  it  is  discarded  due  to  the  NAFO  redfish  bycatch  regulations  (Northwest  Atlantic  Fisheries 
Organization Conservation and Enforcement Measures (NCEM) Article 6). The discards of redfish are around 
2% of the  total catch. However, cod is also discarded (3% of total catch) by this fishery due to the  minimum 
length size (NCEM Article 14) for this species. This small percentage of discards in this fishery will not affect 
the general and qualitative conclusions that we can made in the long term period.  

For the first period of the scenario landings: 2014-2020, scenario 2 is preferable. For both scenarios, in that 
period, revenues go down. But in the case of scenario 2 revenues go down less than for scenario 1. Revenues 
decrease 5 million Euros and about 44 jobs in the period 2014-2020 for scenario 2. In the case of scenario 1, in 
the same period, revenues go down by 55 million Euros and jobs decrease by 494.  

For  the  second  period  2021-2033  it  is  much  more  preferable  scenario  1.  In  this  case,  revenue  gains  are  126 
million Euros for scenario 1 and only 12 million Euros for scenario 2. Employment gains are 1 125 for scenario 
1 and only 107 for scenario 2. 

Therefore,  if we now consider the  whole period of 2014-2033, it is preferable scenario 1, because the  larger 
losses  of  scenario  1  in  period  2014-2020  are  more  than  compensated  by  the  larger  gains  of  scenario  1  in  the 
period 2021-2033. Losses and gains in terms of revenues, value added and employment. 

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Lutz,  C.;  Meyer,  B.;  Schnut,  P.  &  Zika,  G.  (2007),  “Economic  Policy  Simulations  with  Global 
Interdependencies: A sensitivity analysis for Germany”. Economic Systems Research 19(1), pp. 37-55. 

Maurer, N. and Yu, C (2006), “What Roosevelt Took: The Economic Impact of the Panama Canal, 1903-37”. 

Miller,  R.E.,  and  P.D.  Blair,  (2010),  Input-Output  Analysis:  Foundations  and  Extensions.  Prentice-Hall, 
Englewood Cliffs. 

Monaco,  R.M.  (1997),  “A  Brief  Review  of  Alternative  Approaches  to  Inter-sectoral  Policy  Analysis  and 
Forecasting”, Inforum w.p. (http://inforumweb.umd.edu/Workpapr.html). 

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Northwest Atlantic Fisheries Organization Conservation and Enforcement Measures (NCEM). 2015. NAFO/FC 
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repealing Council Regulations (EC) No 2371/2002 and (EC) No 639/2004 and Council Decision 2004/585/EC. 
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3.4  Technical measures for an improved management of the stock: 

It was decided to group the task 4.1 (Analyse the spatial and seasonal activity of 
the  fishing  activity  and  spatial  and  seasonal  distribution  of  cod  in  the  NAFO 
Division 3M (Flemish Cap) and 4.2 (Provide information on areas with important 
concentrations  of  fish  below  a  minimum  conservation  size  and  areas  where 
spawning  aggregations  occur.  Analyse  the  consistency  along  the  time  series  of 
such areas) under the Task 4; 4.1 section (Spatial and seasonal fleet activity and 
cod distribution in Flemish Cap). 

3.4.1  Spatial and seasonal fleet activity and cod distribution in Flemish Cap. 

Introduction 

The  Flemish  Cap  (NAFO  Division  3M)  is  a  continental  fragment  that  is  separated  from  the  Grand  Banks  of 
Newfoundland  by  a  rift  zone  known  as  Flemish  Pass  (Figure  3.4.1.1).  It  is  about  200  km  in  radius  with  the 
shallow  parts  being  situated  at  47º  N,  45º W. The  Flemish  Cap  Bank  is  located  about  600  km  to  the  East  of 
Newfoundland within international waters (NAFO Division 3M). Depths range is between 125 m and 700 m. At 
the Southern rim of the bank there is a steep slope. To the West, water depth gradually increases to about 350 m, 
before the 1100 m deep Flemish Pass is reached. Flemish Cap is an oceanic bank area which comprises 4870 
km² within the bathymetric contours of 200 m depth (Stein, 2007). 

The waters of the Flemish Cap have been noted as excellent fishing waters from many decades ago; cod, redfish 
and American plaice have traditionally been fished in this area and, more recently, Greenland halibut, grenadiers 
and  shrimp.  Species  in  Flemish  Cap  underwent  deep  changes  in  abundance  in  the  last  twenty  years  (Pérez-
Rodríguez et al., 2012), as recorded by the EU bottom trawl survey of Flemish Cap and confirmed for the main 
commercial  species  by  the  NAFO  Scientific  Council  (SC).  Vazquez  (2012)  point  it  out  that  the  most  visible 
changes were the collapse of cod and American plaice stocks in 1993, the increase of the shrimp abundance that 
allowed an intense fishery between 1993-2004, the spread of Greenland halibut over the bank to occupy zones 
left by cod when restricted to shallowest part of the bank, and the remarkable increase of redfish. Since 2005, 
most of these species have reverted to their former abundance; only American plaice has remained in the same 
state. In 2010 the cod fishery was reopened and in 2011 the shrimp fishery was closed. 

Northwest  Atlantic  Fisheries  Organization  (NAFO)  regulates  all  marine  fisheries  resources,  except  salmon, 
tunas,  marlins,  whales  and  sedentary  species  (e.g.  certain  species  of  shellfish  except  shrimp)  in  the  NAFO 
Regulatory  Area  (NRA).  The  NRA  is  that  part  of  the  Convention  Area  which  lies  beyond  the  areas  in  which 
Coastal States exercise fisheries jurisdiction (Figure 3.4.1.1). 

Within the NAFO Regulatory Area (NRA) there  are  three main fisheries: the groundfish (GRO  - primarily in 
Div. 3KLMNO), shrimp (PRA - primarily in Div. 3LM) and pelagic redfish fisheries (REB - primarily in Div. 
1F and 2J). The shrimp fishery in Div. 3M has been under moratorium since 2011. The shrimp fishery in Div. 
3L will be under no directed fishery in 2015. And in the last years the pelagic redfish fishery (Div 1F and 2J) 
was closed. The  groundfish  fishery should be  divided into  different  fisheries depending  on the target  species, 
area,  depth  and  gear  (mesh  size).  Based  on  these  aspects  there  are  in  the  NRA  the  following  groundfish 
fisheries: 

Greenland halibut (Reinhardtius hippoglossoides) fishery in Divisions 3LMNO at more than 600 m depth with 
demersal bottom trawl 130 mm mesh size gear. 

Redfish (Sebastes spp) fishery in Divisions 3LMNO with demersal bottom trawl 130 mm mesh size gear in the 
200-600 m depth strata. 

Cod (Gadus morhua) fishery in Division 3M at depths between 150-550 m with 130 mm mesh size bottom trawl 
gear. 

Skates (Raja spp) fishery in Divisions 3NO with demersal bottom trawl 280 mm mesh size gear at less than 200 
m depth. 

At present, three different fisheries are being carried out in Flemish Cap: the Greenland halibut, the redfish and 
the cod fisheries. The first one is carried out at more than 600 m depth while the last two are carrying out at 200-

 
 
 
 
600 m deep. Focusing on cod fishery, in the early sixteenth century, fishermen from England, France, Spain and 
Portugal discovered the Grand Banks waters to fish for cod. The cod fishery on Flemish Cap has traditionally 
been a  directed fishery by Portuguese trawlers and gillnetters, Spanish pair trawlers and Faroese longliners in 
the second half of the twentieth century. Since 1974, when a TAC was established for the first time, estimated 
catches  ranged  from  48000  tons  in  1989  to  a  minimum  value  of  5  tons  in  2004.  Annual  catches  were  about 
30000 tons in the late 1980’s (notwithstanding the fact that the fishery was under moratorium in 1988-1990) and 
diminished since then as a consequence of the stock decline. The fishery was closed in 1999 and this stock had 
been  on  fishing  moratorium  since  1999  to  2009  following  its  collapse,  which  has  been  attributed  to  three 
simultaneous  circumstances:  a  stock  decline  due  to  overfishing,  an  increase  in  catchability  at  low  abundance 
levels and a series of very poor recruitments starting in 1993. The good shape of the stock led to a reopening of 
the  fishery  with  5500  tons  of  catch  in  2010.  The  actual  fishery  is  composed  mainly  by  trawlers  from  Faroe 
Islands  (Denmark),  Portugal,  Spain,  Russia,  Estonia,  United  Kingdom  and  Norway.  TACs  of  10000  tons  in 
2011, 9280 tons in 2012, 14113 tons in 2013 and 14521 tons in 2014 were established (González-Troncoso et 
al., 2014). 

The  NAFO  Conservation  and  Enforcement  Measure  (NAFO,  2014)  collects  all  management  measures  for 
NAFO fisheries. The main measures for managing the cod fishery are a gear mesh size of 130 mm codend and a 
Minimum Landing Size (MLS) for the Atlantic Cod (Gadus Morhua) of 41 cm. 

Cod can be caught at all depths from 1 to 600 m.  Flemish Cap Cod are distributed gradually regarding to age 
and depth. Young individuals prefer shallow  waters and, with ageing, they change gradually its preferences to 
deeper waters. Juveniles prefer shallow waters with complex habitats which provide protection from predators 
including adult cod. Adults are usually found in deeper, colder waters. The spawning season of the Flemish Cap 
cod is known to be short in time and the earliest in the year of all cod stocks in the Northwest Atlantic (Myers et 
al., 1993); it occurs around March while the Flemish Cap survey is carry out normally in July. The period 1972-
1992 presents a quite constant age of 50% maturity around 5 years old. From 1993 till 2002 this age decreased 
till 3 years old. Since then increased till 2011 where the age of 50% maturity was around 4 years old. In the last 
two  years  the  50%  maturity  age  decreased  until  3.4  (González-Costas  and  González-Troncoso,  2014).  These 
rapid changes in the maturity at age in the Flemish Cap cod seem to be correlated with the rapid changes in the 
abundance, high abundances present older age of 50% of maturity while low abundances present the opposite.  

The aim of this analysis is to describe the spatial and seasonal fleet activity and cod distribution in the Flemish 
Cap area in the last years. One of the objectives of this study was to try to know where the spawning areas of 
Cod  3M  stock  are  and  if  there  is  any  spatial  pattern  where  mature  or  immature  individuals  are  located  in 
different spatial areas. 

Material and Methods 

To analyze the spatial and seasonal cod fishing activity and the seasonal distribution of cod in Flemish Cap the 
Daily Catch Reports (DCR), Vessel Monitoring System (VMS), Spanish Scientific Observers and Flemish Cap 
surveys  data  were  analyzed.  All  these  data  sources  didn´t  covered  the  same  time  period,  and  were  used 
differently depending on the pursued goal. The area that embrace the distribution of cod in the Flemish Cap (up 
to 600 m depth) was only fully covered by the EU survey and the Spanish Scientific Observers databases, while 
the  DCR  and  VMS  databases  provided  information  up  to  500  m  depth  due  to  the  600-meter  isobaths  are  not 
completely defined.  

The  VMS  (2008-2013)  and  Spanish  Observers  data  (2007-2008, 2010-2013)  were  used  to  analyze  the  spatial 
and seasonal fishing activity.  The VMS data  represent the  entire effort of all countries in Division 3M,  while 
Spanish observers data only represent a percentage of the effort made by the Spanish fleet in this Division. 

One of the first steps to look for the spawning and recruitment areas is to know the length distribution of cod 
fishes  in  the  Flemish  Cap  surveys  in  different  fishing  grounds  and  depths.  Maturity  at  length  and  age  of  the 
Flemish Cap female cod is determined by a histological method since 1990 at summer, when the EU-survey is 
carried  out  (Saborido,  1997).  Maturity  at  Age  is  an  important  parameter  for  the  stock  assessment  and  it  is 
calculated yearly for this stock. The parameter of length at 50% maturity by year will be used to disaggregate 
each year’s length distribution in “immature” and “mature” individuals and they are plotted to see if there is any 
spatial pattern. 

Next there is a description of each data analyzed and the information provided by each data source. 

Daily  Catch  Reports  (DCR):  The  Fisheries  Commission  and  Scientific  Council  Ad  hoc  Working  Group  on 
Catch  Reporting  (FC-SC  WG-CR)  evaluated  the  data  sources  and  discussed  their  individual  limitations  and 
potentials  for  utility  to  validate  catch  data  and/or  generate  catch  estimates  (NAFO  FC-SC  2014). The  FC-SC 
WG-CR  recognized  that  in  their  respective  current  form,  Daily  Catch  Reports  (DCR)  data  is  the  most  useful 

130 

 
 
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because  of  the  high  level  of  compliance  of  the  fishing  vessels  in  submitting  the  DCR  and  the  level  of  detail 
which they provide – daily catch by species and by Division. 

Since 2011, it has been a requirement for fishing vessels to transmit Daily Catch Report (DCR) by species and 
by  Division.  Except  in  the  first  few  months  of  2011  (when  fishing  vessels  were  still  in  the  learning  curve  in 
fulfilling  the  daily  CAT  requirement),  there  is  a  generally  very  good  compliance  among  the  vessels  in 
transmitting the DCR report. To avoid the 2011 problems, it was decided to analyze in the present study only the 
DCR for 2012 and 2013. 

Vessel  monitoring  system  (VMS):  Vessel  monitoring  system  (VMS)  data  consists  of  positional  and 
navigational (heading and speed) information, transmitted via satellite from fishing vessels to fishery monitoring 
centers. Vessels within the NRA are required to automatically transmit their position at regular intervals since 
2003. In January 2004, the reporting interval changed from every six hours to every two hours and since 2011 
the reporting is hourly. In 2005 there were some transmission problems with the VMS data.  

In  the  present  study  we  have  used  only  the  2008-2013  VMS  data  in  NAFO  Division  3M  at  less  than  500  m 
depth. The main reason to choose this period is to compare distribution of commercial fleet in that period when 
the  3M  cod  fishery  was  closed  (2008-2009)  and  opened  (2010-2013).  In  Division  3M  shallower  than  500  m 
depth, in 2008-2009 opened fisheries were shrimp and redfish; In 2010 shrimp, cod and redfish; and since 2011 
as the shrimp fishery was closed there have been only two, redfish and cod fisheries. 

The  method to analyze the DCR and VMS data  was the same use by Hintzen  et al. (2013). Catches from the 
DCR and effort from the VMS are accounted on grids of 0.250*0.125 minutes. Fishing days were determined 
per VMS ping activity based on speed. Between 0.5 and 5.2 knots means fishing. The time period per ping is 
given in minutes. Divided by (60 * 24) gives days at sea fishing. 

Spanish Scientific Observer data: Scientific Observers on board of the Spanish commercial vessels data from 
Instituto  Español  de  Oceanografía  (IEO)  have  been  used  for  the  analysis.  Besides  recording  catches,  discards 
and  effort,  these  observers  carried  out  biological  sampling  of  the  main  species  taken  in  the  catch.  Most  of 
Spanish  vessels  are  bottom  trawl  freezer  longer  than  40  m  fishing  in  international  waters.  The  periods  2007-
2008 and 2010-2013 was chosen to analyze these data. The analyzed periods for the Scientific Observers (2007-
2008, 2010-2013) and VMS data (2008-2013) are different because the intention was to have at least two years 
of information when the 3M Cod Fishery was closed. As the observers data for 2009 are not available an extra 
back year (2007) was considered to fulfill the two years criteria. A set of hauls in depths lower than 600 m was 
selected and cod length samples by haul and depth were analyzed. 

The Spanish fleet is one of the most important working in NAFO fisheries including the 3M cod fishery. The 
14%  of  the  total  3M  cod  TAC  is  caught  by  the  Spanish  fleet  and  the  Spanish  Scientific  Observers  program 
covers around 20% of the Spanish total effort in the NRA. Spanish Scientific Observers data can give a fairly 
clear  picture  of  the  total  fishing  activity  in  NAFO  Division  3M  and  particularly  in  the  cod  fishery  catch  and 
effort distribution. 

Twelve  vessels  were  monitored;  eleven  of  them  single  bottom  trawlers  and  one  of  them  pair  bottom  trawler. 
Depending on the target species, two different mesh sizes were used in depths lower than 600 m; a mesh size of 
40 mm for shrimp fisheries and a mesh size of 130 mm for cod and redfish fisheries. The hauls made with 130 
mm mesh size were divided in cod and redfish fisheries based on which species was the greater percentage of 
the catch. A summary of the number of trips and hauls by year and fishery is presented in Table 3.4.1.1. 

After  a  revision  of  all  data,  some  very  high  cod  catches  were  detected  in  the  database.  After  checking  this 
information we think that these data may be measurement errors and it was decided to remove them.  

EU Flemish Cap Survey: EU Flemish Cap Survey has been taken place since 1988. This survey is carried out 
by  the  Instituto  Español  de  Oceanografía  (IEO),  Instituto  de  Investigaciones  Marinas  (IIM)  and  Instituto 
Portugues do Mar e da Atmosfera (IPMA) with EU financial support. The results of the Surveys are presented 
annually  in  the  NAFO  Scientific  Council  and  they  are  published  as  research  papers  in  the  SCR  Doc.  series. 
More information in relation to the protocols and the detailed results of the survey are in the reports “Protocols 
of the EU bottom trawl survey of Flemish Cap” (Vazquez et al., 2014) and “Results from Bottom Trawl Survey 
on Flemish Cap of July 2013” (Mandado, 2014). 

A stratified random trawl survey data from 2007 to 2013 were selected in this document to analyze the spatial 
distribution,  total  catch  and  length  distribution  of  cod  and  to  compare  these  results  with  the  commercial 
information (VMS, DCR and Spanish Scientific Observers). For each  year and set, the  following information 
was available: total catches by species, length distribution by sex, spatial position and depth range. A summary 
of  the  Flemish  Cap  Survey  data  about  the  number  of  hauls,  cod  catches  and  cod  measured  in  the  length 
distribution is presented in Table 3.4.1.2. 

 
 
 
Statistical analysis was executed using R software and the main results were plotted using Free and Open Source 
Geographic Information System (QGIS). The Coordinate Reference System (CRS) used for all figures was the 
WGS 84 projection and coordinate system. 

Spatial and Seasonal Fishing Activity 

Results 

Fishing activity based on VMS data 
Figure 3.4.1.2 shows the effort in fishing days in Division 3M at less than 500 m by 270 km2 cells and 
year for the period 2008-2013. It can be observed that in years 2008-2009 the effort was lower than in the 
rest of the years and was carried out mainly in the Western part and close to the 500 m isobath. In the 
period  2010-2013  the  effort  by  year  was  higher  and  more  scattered  all  around  the  bank  except  in  the 
central part where the depth is less. 

Figure  3.4.1.3  presents  the  effort  distribution  by  quarters  and  years.  For  year  2008  there  is  not  data 
available to study the quarter distribution and in 2009 there is information only for the first and second 
quarter. In the period 2010-2013, when the cod fishery was opened, it can be observed a general pattern 
with the effort more concentrated in the Western part of the bank in the first quarter, more scattered all 
around the bank in the second and third quarter and more concentrated in the South Eastern part in the 
last quarter. This pattern is clearer in the years 2012 and 2013.  

Fishing activity based on Spanish Scientific Observers data 

Fishing activity information in relation to mesh size and depth was available from the Spanish Scientific 
Observers data. The effort (fishing hours) distribution by mesh size, year and depth strata was analyzed. 
It was decided to use six depth strata in meters (0-99, 100-199, 200-299, 300-399, 400-499 and 500-599). 
The  results  show  different  fishing  pattern  in  relation  to  fishing  depth  and  mesh  size  (Figure  3.4.1.4). 
From  available  data,  fishing  activity  with  40  mm  mesh  size  was  only  present  in  two  years,  2007  and 
2010, and in both of them the most frequent depth range was the same, from 400 to 499 m, and most of 
the effort was in the 300-499 m depth. The fishing activity using mesh size 40 mm was only until 2010 
because the shrimp fishery in Division 3M has been closed since 2011. In relation to fishing activity with 
130  mm  mesh  size  targeting  cod  and  redfish,  the  most  abundant  fishing  depth  is  different  from  the 
previous one. In  the period 2007-2008, when only the redfish  fishery took place, this fishery  was only 
carried out in two depth strata, with more than 60% of the effort in the 200-299 depth strata with a small 
percentage  in  the  400-499  m  strata.  But  in  the  period  where  there  are  redfish  and  cod  fisheries  (2010-
2013), fishing effort is observed in all the strata. Most of the effort in this period is distributed at similar 
levels in the strata between 200-500 m with a small part of the effort in the strata lower than 100 m and 
greater than 500 m depth. The 2013 pattern was more similar to the 2007-2008 pattern. 

Figure 3.4.1.5 shows the maps with the hauls for the different fisheries (shrimp, cod and redfish) before 
(2007-2008) and after (2010-2013) the cod fishery was reopened. In the first period (2007-2008) only the 
Western part of the bank was exploited at depths between 300 and 500 m. The shrimp fishery was carried 
out in the North West of the bank in waters deeper than redfish fishery, which took place in the South 
West of the bank in shallower waters. In the period 2010-2013 the fishing grounds appear all around the 
bank in depths from 200 to 500 m, with the shrimp fishery in the North (for 2010 only), the redfish in the 
West  and  the  cod  fishery  all  around  the  bank.  The  cod  fishing  grounds  overlapped  with  redfish  in  the 
Western part, and the redfish fishing grounds overlapped the shrimp in the Northern part.  

Catches spatial and seasonal distribution  

The catches distribution was analyzed with the DCR, VMS and Spanish Scientific Observers data. The DCR of 
each vessel was split in fishing cells based on the VMS day effort of each vessel. 

Catches distribution based on DCR data 

The  DCR  were  available  only  for  2012  and  2013.  Figure  3.4.1.6  presents  the  cod  and  redfish  catch 
distribution in Division 3M  at less than 500 m by 270 km2 cells and Figure  3.4.1.7 shows the  cod and 
redfish CPUE distribution for the same period. The higher cod catches and CPUEs were concentrated in 

132 

 
 
 
 
 
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the  South West of the bank. The higher redfish catches and CPUEs distribution were different in 2012 
and 2013. In 2012 took place in the Southern part and in 2013 in the North Western part of the bank. 

Figure 3.4.1.8 presents the cod and redfish CPUE distribution in Division 3M at less than 500 m by 270 
km2 cells for 2012 and 2013 by quarter. The higher cod CPUEs were in the first and second quarter and 
they were concentrated in the South Western part of the bank. The CPUEs were lower in the third and 
fourth  quarter  and  they  were  more  scattered  all  around  the  bank.  The  temporal  redfish  CPUEs 
distribution was less clear in this period. 

Cod distribution based on Spanish Scientific Observers data 

The  Cod  3M  CPUE  (kg/h)  distribution  based  on  the  Spanish  Scientific  Observers  data  for  the  period 
2010-2013 is presented in Figure 3.4.1.9. In addition to the spatial distribution, the centroid value of all 
CPUE data was calculated, to see if there is a change in the spatial CPUE distribution from year to year. 
The centroid calculated with the Scientific Observers is similar for years 2011 and 2012 and it is located 
in the South Western part of the bank. In 2010 and 2013, the centroid was in the central part of the bank. 

In  relation  to  the  spatial  distribution  of  cod  length  distribution  by  depth,  it  was  also  analyzed  in  the 
period 2010-2013, when cod fishery was reopened after the moratorium, based on the Spanish Scientific 
Observers data. Figure 3.4.1.10 shows the length distribution by depth strata. It can be observed a general 
increasing trend of the mean length with depth, with the smallest fish in shallower waters. This general 
trend was different in 2013 when the  mean length in all strata  with information were similar and mean 
length was lower (around 40 cm) compared with previous years. 

Cod distribution based on Flemish Cap Surveys. 

The availability of Flemish Cap Survey data and its spatial distribution allows knowing where the higher 
cod  concentrations  are.  Looking  at  the  surveys  CPUE  from  2007  to  2013,  there  is  a  clear  distribution 
pattern where year by year CPUE was higher (Figure 3.4.1.11). The centroid value of all surveys CPUE 
data was calculated, to see if there is a change in the distribution year to year. We can observe that the 
centroid appears in the central part of the bank in all years. 

The cod length distribution by depth strata from Flemish Cap Survey from 2007 to 2013 are presented in 
Figure 3.4.1.12. A general pattern can be observed in all years; fish with less than 50 cm are at less than 
400 m depth and fish with more than 50 cm are at depth more than 400 m. In the years where the fishery 
was  closed  (2007-2009)  a  general  increase  of  the  mean  length  with  the  depth  in  all  strata  can  be 
observed; this pattern is less clear in the period 2010-2013 where the mean length in the strata less than 
400 m depth are quite similar and smaller than the mean length observed in depth more than 400 m. 

Areas  where  spawning  aggregations  occur  and  areas  with  important  concentrations  of  cod  below  a 
minimum landing size (MLS). 

Based on the fishery information and due to the mesh size used in the cod fishery (130 mm) and that fishery was 
closed  in  the  period  1999-2009,  it  should  be  difficult  to  study  the  spawning  aggregations  and  the  areas  with 
important concentrations of fish below a MLS. The Flemish Cap survey information is the basic information to 
describe in this document the spawning aggregation and recruitment areas. 

Cod spawning aggregations 

Table 3.4.1.3 presents the Age 50% maturity and length 50% maturity for cod 3M stock in the period 
2007-2013.  The  percentage  of  mature  and  immature  females  was  calculated  per  year  in  the  Flemish 
Cap survey samples from 2007 to 2013 to see if there is any pattern or change in the proportion among 
years (Figure 3.4.1.13). Except for 2009, there are no significant changes in the proportion of mature 
and immature female individuals in the samples where the mean of the seven years analysed is around 
13% mature and 87% immature. Figure 3.4.1.14 present the mature fish percentage by depth strata and 
it can be observed a general pattern of increasing the percentage of mature fish with depth. 

Figure  3.4.1.15  shows  the  maps  with  the  numbers  of  mature  and  immature  females  aggregated  for 
years 2007-2009  when cod fishery  was closed and 2010-2013  when it  was reopened.  Looking at  the 
Figure 3.4.1.15 where green dots are for mature and yellow dots for immature, results show that there 

 
 
 
 
 
 
 
 
 
 
is a clear change in the number 3M cod individuals in the two period analysed, with fewer individuals 
in the first period, when the cod fishery was closed. It can be observed in both periods that most of the 
immature individuals are concentrated in the central part of the bank where depths are lower, while the 
mature females are more regularly distributed over the entire bank at depths less than 500 m.  

Minimum Landing Size (MLS) 

The minimum landing size defined for cod will be used to separate Flemish Cap Survey catches in two 
groups:  cod  fish  bigger  than  41  cm  or  smaller  than  41  cm,  to  see  where  the  proportion  of  these 
individuals in the stock is. 

Figure 3.4.1.16 shows that the proportion of cod below the minimum landing size was around 50% in 
2007-2009 and this proportion increased until a peak of 94% in 2011 and a slight decrease in the last 
years.  This  high  increase  of  the  proportion  of  cod  below  the  minimum  size  was  due  to  the  good 
recruitments in the period 2009-2012. 

In Figure 3.4.1.17 it can be observed that whereas bigger cod fish have remained in similar number in 
the  time  series,  the  small  cod  fish  have  had  important  changes  in  the  times  series,  so  the  change  in 
abundance  of  small  fish  is  the  main  responsible  of  the  change  in  the  proportion  of  individuals  by 
minimum landing size. 

Figure 3.4.1.18 presents the proportion of individuals bigger than the MLS by year and depth strata. As 
in the case of the proportion of mature individuals a general pattern of increasing the percentage of fish 
bigger than the MLS with depth can be observed in all years. 

The spatial distribution of individuals based on MLS  gives similar plots as the  mature and immature 
individuals with different distribution in the period with low abundance (2007-2009) than in the period 
with high abundance (2010-2013). In this case, for instance, individuals below 41 cm are less abundant 
than immature individuals, as cod reach maturity at bigger sizes that the established conservation size 
(Figure 3.4.1.19). 

Discussion 

The  use  and  combination  of  four  different  data  sources  to  analyze  spatial  and  seasonal  fleet  activity  and  cod 
distribution  in  Flemish  Cap  has  provided  interesting  results.  Different  data  coverage  in  relation  to  spatial 
resolution, years analyzed or data quality was the first issue to be considered.  

In  relation  to  spatial  and  seasonal  fishing  activity  the  VMS  and  Spanish  Scientific  Observers  onboard 
commercial vessels data were used. VMS data provided a high spatial resolution of the fishing activity that  for 
years  2008-2009  had  a  very  low  effort,  mainly  in  the  Western  part  and  close  to  the  500  m  isobath.  In  these 
years, as the cod fishery was closed, the main target species were shrimp and redfish with a depth ranging from 
300 to 500 m. When cod fishery was opened in 2010, a clear change of fishing activity and target species was 
observed. The fishing activity targeting cod and redfish were the main activity from 2010 onwards. A significant 
increase in the spatial area of fishing activity to the East and West of Flemish Cap was observed and changes in 
the fishing depth were also appreciated. 

Regarding  spatial  and  seasonal  distribution  of  catches,  three  data  sources  were  used:  DCR  data,  Spanish 
Scientific  Observers  onboard  commercial  vessels  and  Flemish  Cap  Survey  data.  As  DCR  data  were  available 
only for years 2012 and 2013, these two years CPUE data were compared with the other data sources. Looking 
at the Figure 3.4.1.7, the highest cod CPUE based on DCR data are in the South Western area of Flemish Cap. 
When  comparing  it  with  Figure  3.4.1.9  cod  CPUE  based  on  Spanish  Scientific  Observers  data  for  the  same 
years, for year 2012 the centroid of CPUE is also in the South Western area of Flemish Cap but however, the 
centroid cod CPUE value for year 2013 is in the Eastern part of Flemish Cap. As Observers data used are for 
Spanish vessels, a possible change in the exploitation pattern of this fleet is detected to Eastern shallower waters 
ranging from 200-300 m. The change in depth to shallower waters is directly linked to a change in cod length 
distribution as the mean length is smaller, around 40 cm for year 2013 in this depth range from 200-300 m.  

Finally a comparison of previous commercial information with survey data should be done. In Figure  3.4.1.11 
for years 2012 and 2013, cod survey CPUE centroid is in the central area of Flemish Cap and this centroid area 
is very constant in the last years of the time series analyzed. Hence, one conclusion that could be extracted from 
this analysis is that till 2012 the commercial fishing activity focused their effort targeting cod in deeper areas 
where larger cod is more abundant but this pattern was different in 2013 at least for the Spanish fleet analyzed in 

134 

 
 
 
 
 
 
 
135 

this document. This change in the fishing pattern has been observed in other fleets as it can be deduced from the 
size  distributions  of  commercial  fleets  in  2012  and  2013  (González-Troncoso  et  al.,  2013  and  González-
Troncoso  et  al.,  2014). The mode  observed  in  the  length  distribution  of  catches  in  the  2013  fishery  was  very 
close to the MLS (41cm) unlike in 2012, when it was around 60 cm. The main reason to this drop in the mode 
size of catches seems to be due to market reasons rather than biological reasons. 

The spawning season of the Flemish Cap cod is known to occur around March while the Flemish Cap survey is 
carry out normally in July thus it is not possible to well evaluate the spawning locations from the survey results 
because  it is conducted in July and cod spawn  in March. The survey season  was chosen in accordance to the 
initial  objectives,  and  cod  recruitment  was  one  of  the  most  important.  Since  cod  spawning  occurs  mainly  in 
March,  sampling  for  survival  of  juveniles  had  been  proposed  to  be  held  between  March  and  September 
(Vazquez et al. 2014).  Therefore what has been discussed in this paper is the distribution of different maturity 
stages  in  July  based  on  the  survey  information  more  than  the  spring  spawning  aggregations.  Mature  and 
immature females were extracted based on Length 50% maturity by year. Figure 3.4.1.13 show that, despite the 
Length  50%  maturity  changes  with  the  years,  the  sampled  population  maintains  similar  proportion  of  mature 
and  immature  individuals  in  the  time  series  except  for  the  year  2009.  The  spatial  distribution  of  mature  and 
immature cod female individuals in two periods (Figure  3.4.1.15) to separate when the fishery was closed and 
open show a general patter in both periods with the immature fish more concentrated in the central part of the 
bank  where  the depths are less and the  mature  fish  more regular distributed all around the  bank at depth  less 
than 500 meters. It can be observed that in the period 2007-2009, when the abundance was lower, the mature 
and immature distribution area was lower than the period 2010-2012, where the abundance was higher and the 
individuals are distributed over the entire bank at depths less than 500 m. When we analyze the proportion of the 
mature fish by year and depth strata it can be observed that the proportion of mature fish increases with  depth 
and  there  are  two  different  patterns:  one  when  abundance  was  lower  and  the  fishery  was  closed  (2007-2009) 
with  a  more  linear  increase  of  the  proportion  of  mature  fish  with  depth  and  a  smaller  depth  range  and  other 
pattern  when abundance  was  higher and  fishery  was opened (2010  -2013) with low proportion of  mature  fish 
less than 400 meters and a higher proportion at less more than 400 meters depth. 

The proportion of cod below the MLS (41 cm) has shown a peak in year 2011 due to the big recruitments in the 
period 2009-2012, but the number of large individuals is stable in the time series. With a length of 41 cm cod 
are  still  mostly  immature,  which  means  that  an  important  portion  of  immature  individuals  are  allowed  to  be 
fished in the stock. As would be expected, the spatial distribution of concentration of cod bellow a MLS (Figure 
3.4.1.19) presents a very  similar pattern to the distribution  of  mature and immature  females  with the  fish less 
than MLS more concentrated in the central part of the bank where the depths are less and the fish bigger than 
MLS  more  regularly  distributed  all  around  the  bank  at  depth  less  than  500  meters.  When  we  analyze  the 
proportion of fish bigger than the MLS by year and depth the same patterns of proportion the mature individuals 
is  also  observed.  This  distribution  by  length  and  depth  was  similar  in  the  cod  commercials  catches  by  depth 
(Figure 3.4.1.10). This different cod depth distribution has been cited in other studies, both in the NAFO area 
(Sinclair,  1992)  and  in  the  North  Sea  area  (Heessen,  1993)  and  it  seems  to  be  related  with  temperature  and 
salinity conditions. 

Recommendations 

To establish the MLS should be taken into account the gear selectivity used in the fishery and the biology of the 
species that are intended to regulate, in our case the Flemish Cap Cod. In 3M cod fishery the MLS is quite well 
established taking into account the biological characteristics.  Length of 50% maturity is around 52 cm in the 
last years and is larger than the MLS (41 cm).  

An  efficient  fishing  gear  in  terms  of  selectivity  should  have  a  50%  retention  probability  (L50%)  that  is  well 
above  the  minimum  landing  size  (MLS)  and  with very low probability of retention of  the  fish lower than the 
MLS  to  avoid  discards.  The  study  results  show  that  both  the  strategy  and  the  fishing  gear  used  in  the  2013 
Flemish Cap cod fishery are not the most suitable because they allow catching large numbers of immature fish 
under the actual MLS. 

In NAFO, it should be studied the possibility of implementing devices to prevent these large catches of small 
individuals observed in 2013. These devices would prevent catches of cod individuals less than MLS to avoid 
discards. 

Acknowledgements 

 
 
 
 
 
This  research  was  funded  by  the  European  Union  founds  under  the  Framework  Contract  MARE/2012/21  - 
"Scientific advice for Fisheries beyond EU Waters" - Specific Contract No 2 (SI2.681887) “Provision of advice 
on the development of a multiannual management plan and the evaluation of a management strategy for cod in 
NAFO Division 3M (SAFEwaters-2)”. 

References 

González-Costas  F.  and  D.  González–Troncoso,  2014.  Biological  Reference  Points  of  3M  cod.  NAFO  SCR 
Doc. 14/19. Serial Number N6313. 

González-Troncoso D., C. Hvingel, B. Healey, J. Morgan, F. González-Costas, R. Alpoim, J. C. Mahé and A. 
Vázquez.  2013.  Assessment  of  the  Cod  Stock  in  NAFO  Division  3M.  NAFO  SCR  Doc.  13/041  Serial  No. 
N6196. 

González-Troncoso  D.,  Fernando  González-Costas,  Brian  Healey,  Joanne  Morgan  and  Carsten  Hvinge.  2014. 
Assessment of the Cod Stock in NAFO Division 3M. NAFO SCR Doc. 14/035 Serial No. N6331. 

Heessen H.J.L. 1993. The Distribution of Cod (Gadus morhua) in the North Sea. NAFO Sci. Coun. Studies, 18: 
59–65. 

Hintzen N., A. Coers and K. Hamon, 2013. A collaborative approach to mapping value of fisheries resources in 
the North Sea (Part 1: Methodology). IMARES Report number C001/13. http://edepot.wur.nl/248628.  

Mandado, M. 2014. Results from Bottom Trawl Survey on Flemish Cap of July 2013. Serial No.N6311 NAFO 
SCR Doc. 14/17. 

Myers, R. A., G. Mertz and C. A. Bishop, 1993. Cod spawning in relation to physical and biological cycles of 
the northern Northwest Atlantic. Fish.Oceanogr. 2: 3/4: 154-165. 

NAFO,  2014.  Report  of  the  Fisheries  Commission  and  Scientific  Council  Ad  hoc  Working  Group  on  Catch 
Reporting, 3-4 February 2014.NAFO/FC-SC Doc. 14/01, Serial No. N6279. 

NAFO, 2014. Northwest  Atlantic  Fisheries  Organization Conservation and Enforcement Measures.  Serial  No. 
N6272 NAFO/FC Doc. 14/1. 

Pérez-Rodríguez,  A.;  Koen-Alonso,  M.;  Saborido-Rey,  F.  2012.  Changes  and  trends  in  the  demersal  fish 
community of the Flemish Cap, Northwest Atlantic, in the period 1988-2008. ICES J. Mar. Sci. 69(5): 902-912. 

Saborido, F., 1997. Sexual Maturity and Spawning Biomass of the Cod Stock on Flemish Cap (Division 3M). 
NAFO SCR Doc. 97/18. Serial Number N2848. 

Sinclair A. 1992. Fish Distribution and Partial Recruitment: The Case of Eastern Scotian Shelf Cod. J. Northw. 
Atl. Fish. Sci., Vol. 13: 15–24. 

Stein, M., 2007. Oceanography of the Flemish Cap and Adjacent Waters.J. Northw. Atl. Fish. Sci., 37: 135–146. 
doi:10.2960/J.v37.m652 

Vazquez, A., 2012.On recruitment of the Flemish Cap cod stock. Serial No. N6066 NAFO SCR Doc. 12/039. 

Vázquez, A., J. Miguel Casas and R. Alpoim, 2014. Protocols of the EU bottom trawl survey of Flemish Cap. 
Scientific Council Studies, 46: 1–42. doi:10.2960/S.v46.m1 

136 

 
 
 
 
 
137 

Table 3.4.1.1.- Summary of Spanish Scientific Observers onboard by year for the period 2007-2013. Number of 
trips, hauls by year and fishery. 

Year  Number of trips  Total Number of hauls  Mean Depth (m)  Shrimp  Redfish 

Cod 

2007  4 

2008  2 

2010  6 

2011  3 

2012  4 

2013  6 

Total  25 

54 

10 

149 

87 

104 

168 

572 

363 

272 

379 

332 

372 

287 

340 

37 

- 

22 

- 

- 

- 

17 

10 

22 

22 

1 

30 

105 

65 

103 

130 

59 (10%)  102(18%)  411(72%) 

Table 3.4.1.2.-Flemish Cap Survey information in relation to cod catches. 

Survey 
year 

Number  of 
hauls 

Total 
catches (kg) 

cod 

Total 
sampled (kg) 

cod 

% 
/total catch 

sampled 

Number 
length sampled 

of 

cod 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

178 

176 

184 

158 

141 

179 

185 

Total 

1201 

3488 

5890 

10833 

7912 

10793 

15295 

10660 

2583 

5159 

9389 

5164 

10474 

10518 

9863 

74% 

88% 

87% 

65% 

97% 

69% 

93% 

2102 

4566 

6341 

9443 

53997 

24199 

16484 

111117 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table  3.4.1.3.-  Age  50%  maturity  and  length  50%  maturity  for  cod  3M  stock  from  the  Flemish  Cap  Survey 
data. 

Year 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

Age 50% maturity (years) 

Length 50% maturity (cm) 

3.31 

3.37 

3.49 

3.52 

4.13 

3.93 

3.39 

57.02 

58.98 

53.65 

53.13 

57.88 

52.43 

46.77 

138 

 
 
 
 
 
139 

Figure 3.4.1.1.- NAFO Convention Area. Limits of Convention Area (black line), and limits of the Regulatory 
Area (inside fish-blue line and black line). 

 
 
 
 
 
 
 
 
Figure 3.4.1.2.- Effort in fishing days in Division 3M at less than 500 m by 270 km2 cells and year for the 
period 2008-2013 based on VMS data. The 500 m isobath is plotted.  

140 

        
 
141 

Figure 3.4.1.3.- Effort in fishing days in Division 3M at less than 500 m by 270 km2 cells, quarter and year for 
the  period  2009-2013  based  on  VMS  data.  The  500  m  isobath  is  plotted.  The  colour  scale  is  the  same  as  in 
Figure 2. 

        
 
 
Figure 3.4.1.4.- Changes of fishing activity of the commercial fleet by mesh size, year and depth based on the 
Spanish Scientific Observers data. 

Figure 3.4.1.5.- Spatial distribution of Spanish Scientific observer hauls by fishery for the periods 2007-2008 
and 2010-2013. 

142 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
143 

Figure 3.4.1.6.- Cod and redfish DCR catches in Division 3M at less than 500 m by 270 km2 cells and year for 
the period 2012-2013. The 500 m. isobaths is plotted.  

 
 
 
 
 
 
 
 
 
 
Figure 3.4.1.7.- Cod and redfish DCR CPUE in Division 3M at less than 500 m by 270 km2 cells and year for 
the period 2012-2013. The 500 m isobath is plotted. 

144 

 
 
 
 
 
 
 
 
145 

Figure 3.4.1.8.- Cod and redfish DCR CPUE in Division 3M at less than 500 m by 270 km2 cells, quarter and 
year for the period 2012-2013. The 500 m isobath is plotted. 

 
 
 
 
 
 
 
 
 
 
 
 
Figure 3.4.1.9.- Spatial distribution of cod CPUE (Kg/hour) of the Spanish Scientific Observers data from 2010 
to 2013. 

146 

 
 
 
 
 
 
 
 
 
 
 
147 

Figure  3.4.1.10.-  Cod  length  distribution  by  depth  (m)  from  Spanish  Scientific  Observers  data  from  2007  to 
2013. 

 
 
 
 
 
 
 
 
 
 
148 

 
 
 
 
 
 
 
149 

Figure 3.4.1.11. Spatial distribution of Flemish Cap Survey cod CPUE from year 2007 to 2013. 

 
 
 
 
 
 
 
 
 
 
 
Figure 3.4.1.12.- Cod length distribution by depth (m) from Flemish Cap survey from 2007 to 2013. 

150 

 
 
 
 
 
 
 
 
 
 
 
 
 
151 

Figure 3.4.1.13.- Percentage of mature and immature cod females from Flemish Cap Survey samples. 

Figure 3.4.1.14.- Percentage of mature cod by year and depth strata from Flemish Cap Survey samples. 

0%10%20%30%40%50%60%70%80%90%100%2007200820092010201120122013percentage (%)years% mature and immature cod femalesimmature femalesmature females0%20%40%60%80%100%100-200200-300300-400400-500500-600Depth  Strata% Mature Individuals2007200820092010201120122013 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 3.4.1.15.- Spatial distribution of immature female cod (left panel) and mature female cod (right panel) 
split in two periods: 2007-2009 and 2010-2013. Data from the Flemish Cap Survey. 

Figure 3.4.1.16.- Flemish Cap Survey number of cod samples based on Minimum Landing Size. 

152 

0%10%20%30%40%50%60%70%80%90%100%2007200820092010201120122013percentage (%) years% cod individuals based on minimum landing size> 41 cm< 41 cm 
 
 
 
 
 
 
 
 
 
 
 
 
 
153 

Figure 3.4.1.17.- Number of cod individuals from Flemish Cap Survey samples separated by Minimum Landing 
Size. 

Figure 3.4.1.18.- Percentage of cod fish bigger than the MLS by year and depth strata from Flemish Cap Survey 
samples. 

01000020000300004000050000600002007200820092010201120122013number of individualsyearsNumber of individuals by minimum landing size< 41 cm> 41 cm0%20%40%60%80%100%100-200200-300300-400400-500500-600Depth  Strata% Individuals bigger than MLS (41 cm)2007200820092010201120122013 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 3.4.1.19.- Spatial distribution of cod below MLS (left panel) and cod above MLS (right panel) split in 
two periods 2007-2009 and 210-2013. Data from the Flemish Cap Survey. 

154 

 
 
 
 
 
 
 
 
 
 
155 

3.4.2  Analyse  different  technical  measures  (related  to  gear,  spatial  or  temporal 

closures,...) that could be adopted to increase Fmsy. 

1. Introduction 

An  integral  part  of  most  fisheries  management  frameworks  has  been  the  regulation  of  technical  aspects  of 
fishing operations, through so-called technical measures. These define where, when and how the fishing fleets 
exploit commercial fish resources and interact with the wider marine ecosystem. Technical measures are part of 
a  suite  of  input  and  output  instruments.  They  are  combined  to  influence  both  the  exploitation  rate  and 
exploitation pattern  with a primary goal of attaining sustainable exploitation of commercially exploited stocks 
and the provision of safeguards for ecosystem considerations. As such, they offer an objective tool for affecting 
several aspects of the interaction between fishing activity on exploited marine organisms and the broader marine 
environment.  These  include:  affecting  the  distribution  of  fishing  pressure;  affecting  the  impact  of  fishing  on 
both  the  physical  and  ecological  environment;  and  providing  an  objective  mechanism  of  defining  fleet 
management units based on their gear type and selectivity patterns.  

Technical  regulations  can  be  grouped  into  those  that  regulate  the  design  characteristics  of  the  gears  that  are 
deployed,  such  as  the  regulation  of  mesh  size;  those  that  regulate  the  operation  of  the  gear  such  as  setting 
maximum limits on how long or what type of gear can be deployed; those that set spatial and temporal controls 
such as closed/limited entry areas and seasonal closures; and those that define minimum sizes of fish and specify 
catch  composition.  Technical  measures  largely  aimed  to  reduce  catches  of  juveniles  of  commercial  and  non-
commercial species, to improve species selectivity, to avoid catches of protected species, to reduce discards and 
minimize the impacts on the environment. 

In  terms  of  fishing  pressure,  technical  measures  can  affect  both  exploitation  pattern  i.e.  the  distribution  of 
fishing pressure across the demographic (age/length) spectrum of a given stock and the overall exploitation rate 
through the deployment of species selective gears which avoid the capture of specific species in a given gear. 
Scott and Sampson (2011) note that the exploitation pattern has a significant influence on the point estimates of 
Fmsy for a given exploitation rate and in general a higher age-at-capture (age at 50% selection) results in a higher 
Fmsy  yield  (MSY).  Exploitation  pattern  is  a  composite  result  of  a  range  of  factors  including  the  selectivity 
characteristics of the gears, the harvest ratios  by different gears, as well as seasonal and spatial distribution of 
fishing  effort  relative  to  the  seasonal  and  spatial  distribution  of  the  resource.  Technical  measures  through 
adjustments in gear selectivity, and measures that set spatial and temporal controls can contribute significantly 
to changes in exploitation pattern and therefore changes in the potential yield that can be removed from a stock 
due to changes in Fmsy exploitation rates. 

Effective  implementation  and  enforcement  of  technical  management  measures  can  be  extremely  difficult. 
Fishers may resist in a variety of ways. Hence, a necessary condition for any successful implementation is the 
industry support.  

The cod fishery on Flemish Cap (NAFO Division 3M) has traditionally been a directed fishery by Portuguese 
trawlers and gillnetters, Spanish pair trawlers and Faroese long-liners in the second half of the twentieth century. 
The  fishery  was  closed  in  1999  and  this  stock  had  been  on  fishing  moratorium  from  year  1999  to  2009. The 
fishery was reopening in 2010 and nowadays is carried out mainly by trawlers from Faroe Islands (Denmark), 
Portugal, Spain,  Russia, Estonia, United Kingdom and Norway at depths between 150-550 meters. The target 
species of this fishery is cod and the most important species in the by-catch is redfish. 

The  NAFO  Conservation  and  Enforcement  Measures  (NCEM)  in  2014  (NAFO/FC  Doc.  14/1,  Serial  No. 
N6272) compile all the NAFO measures adopted by the Fisheries Commission to fishing in NAFO Regulatory 
Area  (NRA).  The  3M  cod  fishery  is  managed  by  total  allowable  catch  (TAC)  and  quotas,  shared  mainly  by 
European  Union  (57%),  Denmark  (22%),  Norway  (9%)  and  Russia  (6%).  Nowadays,  there  is  not  an  effort 
allocation scheme for cod. The actual main technical measures for the 3M cod fishery are the general 130 mm 
trawl mesh size (Article 13) and the Minimum Landing Size (MLS) of 41 cm for cod (Article 17). 

In the following sections possible technical  measures that  could be applied to the cod fishery to increase Fmsy 
will be analysed. 

 
 
 
 
2. Technical measures to increase Fmsy 

2.1. Type of gear 

All  gears  used to catch  fished species are  size/age  selective to some degree. Mesh  sizes, pot entrances, hook-
size, as well as fishing season or location can be set to specifically target or select for different size/age classes 
of fish. Some fisheries can be highly selective, others less so. The actual 3M cod fishery is carried out mainly by 
trawlers.  The  change  of  the  trawl  gear  for  other  with  different  selection  characteristics  would  need  a  great 
economic  investment  to  change  the  gear  and  to  adapt  the  vessels.  It  is  not  clear  that  this  investment  can  be 
economically  recovered  by  improvements  in  the  potential  increase  of  yield.  Therefore,  it  should  be  very  well 
defined  and  proved  the  profitability  of  the  investment,  otherwise  the  industry  would  oppose  to  these  large 
investments without a clear economic return. 

2.2. Design characteristics of the gears (mesh size, sorting grids) 

These  types  of  technical  measures  are  used  to  modify  the  gear  selectivity  and  have  a  direct  impact  on  the 
exploitation pattern of the fisheries. Traditionally, the selective properties of a trawl have been primarily related 
to  the  mesh  size  of  the  net  (particularly  in  the  codend),  but  in  recent  times  have  been  developed  different 
alternative selection measures, such as grids, escape windows, and square-mesh codend.  

In  the  Northeast  Atlantic  trawl  ﬁsheries,  several  technical  measures  have  been  implemented  in  legislation 
(Suuronen and Sardà, 2007; Madsen, 2007). In 1997, the sort-X grid system became mandatory in the Barents 
Sea  to  enhance  sorting  by  size  in  the  demersal  trawl  fisheries  for  Northeast  Arctic  cod.  Kvamme  and  Frøysa 
(2004) showed by simulation that there would be substantial gains, in terms of both stock size and catches, from 
increasing the mean retention length by 5–8 cm relative to the estimated value of 47 cm prevailing before 1997. 

There is not much information about the trawl cod selectivity in NAFO but there is available information about 
the trawl cod selectivity in other areas. Jørgensen  et al. (2006) compared the selectivity of a 135 mm codend 
with that of a trawl with a 135 mm codend and a 55 mm grid; they also estimated the selectivity of a 155 mm 
diamond mesh codend in Barents Sea. The results showed that similar selectivity can be obtained by using a 135 
mm  mesh  codend  in  combination  with  55  mm  Sort-V  grid  and  a  codend  with  a  155  mm  mesh  size.  The 
introduction of the grid in the Barents Sea cod fishery in 1997 in reality meant an increase in L50% (defined as 
the length of the fish that have a 50% probability of being retained by the gear on encounter) corresponding to 
an increase in mesh size from 135 to 155 mm. 

Grimaldo  et  al.  (2007)  presented  the  results  of  the  size  selection  of  cod  (Gadus  morhua)  and  haddock 
(Melanogrammus  aeglefinus)  in  the  Norway  bottom  trawl  fishery  with  three  different  systems:  a  135  mm 
diamond-mesh codend fitted with a 55 mm sorting grid (Sort-V); a 135 mm diamond-mesh codend fitted with 
two lateral exit windows; and a codend built entirely of 155 mm diamond mesh. The selectivity curves showed 
similar Selection Ranges (SR=L75%-L25%) for the three systems. Where L75% is the length of the fish where 75% 
of the fish is retained, and L25% is the length where 25% of the fish is retained. For cod, the mean SR was around 
8 cm. All the estimated L50% values were far above the minimum landing sizes (MLS). For cod, the mean  L50% 
was 56.1, 53.9 and 60.7 cm for the sorting grid, exit windows and diamond-mesh configurations, respectively, 
and was similar to the results obtained by Jørgensen et al. in 2006. From the point of view of fish behaviour, the 
sorting  grid  and  the  exit  window  setups  presented  a  clear  advantage  over  the  diamond-mesh  codend  because 
they  released  fish  at  the  fishing  depth  as  soon  as  the  fish  reached  the  area  of  the  selection  device  (grid  or 
windows). The fish escaping from the diamond-mesh codend were exposed to far more stress, exhaustion, and 
physiological damage than fish that escaped from the other setups. The escape mechanism of the diamond-mesh 
codend raises the question of whether or not escaping fish survive. 

One  of  the  major  problems  in  the  3M  cod  fishery  is  the  decrease  in  the  catches  mean  size  observed  in  2013 
compare with previous years. 3.4.2.1 shows the catches length distributions by country and Flemish Cap survey 
used in the 2013 and 2014 assessments (González-Troncoso et al., 2013 and 2014). The mode observed in the 
length distribution of catches in the 2013 fishery was very close to the MLS (41cm) and it was quite different 
from 2012, when it was around 60 cm. In 2013, a 30% of cod individuals caught was below the MLS while in 
2012  was  10%.  This  change  could  be  also  appreciated  in  the  Task  4.1  and  4.2  results  for  the  Spanish  fleet 
(Iriondo et al., 2014). Results for 2013 shows the concentration of the fishing activity in shallower waters where 
the small fish are more abundant. In previous years the fishing activity was focused on deeper waters catching 
bigger individuals. The main reason to this drop in the mode size of catches in 2013 seems to be due to market 
reasons rather than biological reasons.  

156 

 
157 

Jørgensen et al. (2006) estimated the Barents Sea cod selectivity parameters assuming the logistic curve: 

 𝑟(𝑙) =

𝑒𝑎+𝑏𝑙
1 + 𝑒𝑎+𝑏𝑙 

Where r(l) is the probability that a fish of length l will be retained in the test codend. a and b are the two generic 
selection parameters determining the mean selection length (L50%) and the selection range (SR=L75%-L25%) by the 
relationships: 

𝐿50% =

−𝑎

𝑏

,       𝑆𝑅 =

ln (9)

𝑏

These  parameters  were  estimated  for  135  mm  codend  (L50%=  43.89;  SR=8.72),  135  mm  codend  with  55  mm 
sorting-grid (L50%= 55.02; SR=8.12) and 155 mm codend (L50%= 55.37; SR=14.01). For 135 mm codend  case, 
which is very similar to the NAFO approved 130 mm minimum codend, the L50% would increase from 43.89 cm 
with the 135 mm codend to 55.2 cm with 135 mm codend and sorting-grid and this last was very similar to the 
155  mm  codend  parameters  values.  Figure  3.4.2.2  presents  the  3M  cod  selectivity  age  curves  for  135  mm 
codend  and  135  mm  codend  with  55  mm  sorting-grid  calculated  with  the  Jørgensen  et  al.  (2006)  parameters 
values  as  well  as  the  assessment  Partial  Recruitment  (PR)  estimated  for  2013  for  cod  Div.  3M  (Gonzalez-
Troncoso  et  al.,  2014).  It  can  be  observed  that  the  introduction  of  the  sorting-grids  significantly  decrease  the 
fishing mortalities for ages 3 and 4, avoiding many immature juveniles catches. 

In NAFO, it should be studied the possibility of implementing devices to prevent these large catches of small 
individuals.  These  devices  would  prevent  catches  of  individuals  less  than  MLS  to  avoid  discards  and  will 
improve  the  exploitation  pattern  and  the  MSY.  Good  selection  efficiency  would  reduce  the  pressure  on 
undersized  fish, and thereby increase the  future  size  of exploitable stock (Armstrong  et  al., 1990). The L50%= 
55.2 cm would be a more appropriate value considering the NAFO cod MLS of 41 cm and the 3M cod biology. 
The estimated 50% maturity length and age for 3M cod in the last years were around 52 cm and 4 years (Table 
3.4.2.1).  Grimaldo  et  al.  (2008)  concluded  that  the  sorting  grids  seem  to  have  a  bigger  survival  rate  of  the 
selected fish than the diamond meshes and the exit windows. The implementation of these devices would also 
reduce  the  redfish  by-catch  and  discard  rates  in  the  cod  fishery.  They  would  improve  the  selectivity  and  the 
survival rate of the redfish catch in the cod fishery. 

2.3. Spatial and temporal area closures 

NAFO  has  made  incremental  progress  in  implementing  management  measures  to  protect  vulnerable  marine 
ecosystems. NAFO has identified 19 closure areas, within its Convention Area, as being vulnerable to bottom 
contact  gears  and  subsequently  closed  the  areas  to  bottom  fishing.  The  current  NAFO  closure  areas  due  to 
seamount areas, vulnerable marine ecosystems or high coral and sponge concentrations, do not affect to the cod 
fishery of 3M area as they are out of the 3M cod fishing ground. 

The  exploitation  pattern  depends  not  only  on  the  gear  configuration  but  also  on  the  spatial  and  temporal 
distribution of the fishery and underlying population. One way to improve the exploitation pattern is the spatial 
or temporal closures of areas with high concentrations of young fish.  

There are few data available to study the 3M cod seasonal distribution. The fishery was closed until 2009 and 
there are not much data available to conduct a seasonal distribution study. Survey data do not provide too much 
information for such studies since the survey gives a fixed distribution picture in a particular season. Therefore 
this study will focus on the possibility of spatial closures rather than temporary closures. 

Results  of  the  spatial  3M  cod  length  distribution  based  on  Spanish  Scientific  Observers  and  Flemish  Cap 
Surveys  (Tasks  4.1  and  4.2)  show  a  general  increasing  trend  of  the  mean  length  with  depth,  with  a  bigger 
proportion of juvenile fishes in shallower waters. In Flemish Cap Survey the spatial distribution of hauls covers 
all  the  3M  area,  and  high  abundance  indices  of  juveniles  are  observed  in  shallower  waters  in  the  centre  of 
Flemish Cap. However, before 2013 the commercial fishing activity focused their effort targeting cod in deeper 
areas where larger cod is more abundant but this pattern was different in 2013 when the fishing effort was more 
concentrated  in  the  shallower  waters  of  the  Eastern  part  of  Flemish  Cap  with  smaller  length  distributions  in 
catches. This change in the fishing pattern has been observed in other fleets as it can be deduced from the size 

 
 
 
 
 
distributions of commercial fleets in 2012 and 2013 (González-Troncoso et al., 2013 and González-Troncoso et 
al., 2014). 

One solution to avoid the problem of large catches of juveniles that have occurred in 2013 could be the closure 
of the areas at less than 400 meters depth where these fish are more abundant as it can be concluded from the 
results of the task 4.1 and 4.2. The effect in the exploitation pattern of this technical measure should be similar 
to the implementation of the 135 mm codend with sorting grids: prevent catches of individuals less than MLS to 
avoid cod discards and to improve the exploitation pattern and the MSY. But this measure could increase the by-
catch of beaked redfish (S. mentella and S. fasciatus) as these species are more abundant in depths more than 
400 meters (Avila de Melo et al., 2013). Another problem of implementing these closures would be the effort 
concentration  in  small  areas.  The  individuals  bigger  than  the  MLS  are  regularly  distributed  at  all  depths, 
although the proportion of cod larger than MLS is higher in deeper waters. If this measure is implemented, then 
a part of individuals larger than the MLS could not be fished with the consequent loss of fishing opportunities. 

2.4. Minimum fish size and specify catch composition 

Minimum Landing Size (MLS) for cod in NAFO is established in 41 cm. To establish the MLS should be taken 
into account the gear selectivity used in the fishery and the biology of the species that are intended to regulate, 
in our case the Flemish Cap Cod. The main objective is to establish the technical characteristics of the gear that 
allows catching the maximum biomass considering the growth tax and natural mortality of the species without 
endangering the reproductive potential of the stock. An efficient fishing gear in terms of selectivity should have 
a 50% retention probability (L50%) that is above the minimum landing size (MLS) and with very low probability 
of retention of the fish lower than the MLS to avoid discards.  

In 3M cod fishery the MLS is quite well established taking into account that length of 50% maturity is around 
52  cm  in  the  last  years  and  is  larger  than  the  MLS  (41  cm).  But  considering  the  selectivity  of  the  135  mm 
codend mesh size parameters (L50%= 43.89; SR=8.72) estimated by Jørgensen et al. (2006) the mesh size used in 
the NAFO cod fishery (130 mm) is not the most adequate as it allows catching many individuals smaller than 
the MLS that should be discarded. The use of 130 mm mesh size catches fish of a wide size ranging from 20 to 
120  cm  based  on  the  countries  information.  Considering  that  the  MLS  is  41  cm  and  the  mean  length  of  first 
maturation value for the last seven years is 54.27 cm, a percentage of around 73% in 2013 and 52% in 2012 of 
immature  fish  individuals  were  fished  based  on  the  total  catch  length  distribution  used  in  the  assessment 
(Gonzalez-Troncoso et al., 2014). To avoid this problem the characteristics of the gears should be changed to 
increase the size at first capture (L50%).  

Taking  into account that the  fishery  was re-opened  four  years ago, if the 2013 exploitation pattern  fishing an 
important  percentage  of  immature  fish  remains  in  the  same  line,  some  precautionary  measures  should  be 
considered in the following years in the fishery. One solution could be the implementation of the 55 mm sorting 
grids in the current gear or the increase of the actual codend mesh size around 20 mm as it was discussed in the 
Design characteristics of the gears section. These solutions have the advantage of minimizing the redfish catches 
in the 3M cod fishery and to catch bigger cod than the current catches minimizing the discards in the fishery.  

A biological rank of the technical measures suggested above in terms of preventing catches of small individuals, 
reducing rates of discards, potential cost for the industry, and effect on Fmsy could be the following: 

1.  The actual mesh size, 130 mm diamond-mesh codend, fitted with a 55 mm sorting grid (Sort-V). 
2.  Enlarge the actual mesh size (130 mm codend) till 155 mm mesh codend. 
3.  Closure of the areas at less than 400 meters for the Flemish Cap cod fishery. 
4.  Change the trawl gear by other more selective (longline or gillnets). 
5. 

In  3M  cod  fishery  the  MLS  is  quite  well  established  taking  into  account  the  cod  biological 
characteristics and that the probability of the used gear of retain fish with MLS should be low.  

3. Impact on Fmsy of a change in the exploitation pattern 

It has been estimated a new Fmsy proxy considering the change in the exploitation pattern that could take place if 
the technical measures described above (sorting grids or mesh size change to 155 mm) are implemented in the 
Cod 3M  NAFO  fishery. The method used by González-Costas and González-Troncoso (2014) to estimate the 
approved 3M cod Fmsy proxy  has been used to estimate a new value. The  F30%SPR was chosen as the best Fmsy 
proxy  for  the  3M  cod  case.  It  was  estimated  a  new  F30%SPR  with  the  same  inputs  used  in  2014  but  using  the 

158 

 
 
159 

selection pattern for the 135 mm codend with sorting grids (Figure  3.4.2.2) estimated with the Jørgensen et al. 
(2006) parameters to see the differences between Fmsy proxies, the approved and the new one. 

Table 3.4.2.2 and Figure 3.4.2.3 present the results for both Fmsy values as well as both estimated equilibrium 
yields  and  SSBs.  The  implementation  of  a  selection  pattern  similar  to  the  135  mm  codend  with  sorting  grids 
would  increase  the  current  Fmsy  value  and  the  equilibrium  catches  but  it  should  have  a  small  impact  in  the 
equilibrium SSB. The increment of the equilibrium catches should be around 13% with an increase in the F msy of 
more  than  100%.  If  we  measure  the  efficiency  of  fishing  (ef)  as  yield  per  unit  fishing  mortality  (Y/F),  the 
increase  in  the  equilibrium  yield  with  the  selectivity  change  seems  not  to  be  very  efficient.  The  efficiency  of 
fishing would be reduced by 50% if we change the actual exploitation pattern with an ef = 169 699 to the new of 
the 135mm with sorting grids with an ef = 92 685.  

These  results  are  different  from  the  obtained  by  Scott  and  Sampson  (2011).  They  assumed  similar  selection 
patterns  with a Ricker  function for the  recruitment  whereas in our case  we assume random recruitment.  They 
found  that  as  selectivity  shifted  progressively  to  older  fish  the  MSY  increased  steadily.  Over  the  range  of 
selection curves considered, maximum and minimum values of both MSY and Fmsy varied by about 30%. 

4. Results of the implementation of Technical measures in the 3M model based HCR. 

It was decided to simulate one 3M Cod MSE scenario of the model-based HCR to study what would be the yield 
effects  of  a  new  Fmsy  resulting  of  implementing  a  change  in  the  selection  pattern.  It  was  chosen  the  Task  3.4 
scenario number 2 (M fix SR1 35%) to study the model-based HCR yields. 

The  model-based  HCR  tested  in  this  project  was  based  on  three  different  levels  (20%,  35%  and  50%)  of 
exceeding  the  Fmsy  proxy  as  Ftarget  with  a  TAC  constraint  between  years  of  20%.  The  results  showed  that  the 
projected  catch  and  F  levels  depend  on  the  value  of  this  constraint.  The  M  fix  SR1  35%  scenario  was  run 
without the TAC constraint with the current Fmsy proxy value and with the new Fmsy proxy estimated using the 
selection pattern for the 135 mm codend with sorting grids to see the effects of changing the selection pattern. 

Figure 3.4.2.4 presents the median yield of the scenario 2 (M fix SR1 35%) with the model based HCR tested in 
this project (with the 20%TAC constraint),  with the HCR  without TAC constraint and  with  the  HCR  without 
TAC constraint and the new Ftarget estimated with the change in the exploitation pattern that could take place if 
the technical measures described above (sorting grids or mesh size change to 155 mm) are implemented. It can 
be observed three different periods in the projected time: first period (2014-2020) where the yield decreases in 
all cases in a similar trend and level; second period (2021-2028) where yields increase but this increase is more 
moderate in the case with the TAC constraint and is quite similar in the other two cases till 2024. Between 2024 
and 2029 the case without TAC constraint and the new estimated Ftarget has clearly bigger yields than the other 
two  cases.  In  the  last  period  (2029-2033),  the  case  with  TAC  constraint  presents  an  increase  trend  while  the 
other two cases (without TAC constraint) have a quite similar yield levels and variable trend. 

Figure 3.4.2.5 shows the yearly median of the mean yield for the three scenarios in the medium (2014-2020) and 
long  (2014-2033)  periods.  Results  show  that  the  implementation  of  gear  devices  as  sorting  grids  or  the 
enlargement of the current minimum cod-end mesh size till 155 mm does not lead to a big yield increase in the 
medium  term  period  (2014-2020).  In  the  long  term  period  (2014-2033)  the  implementation  of  these  technical 
measures  leads  to  an  increase  in  the  yearly  mean  yield.  This  increase  is  around  5  000  tons  in  the  scenarios 
without the TAC constraints and around 10 000 tons in the case of the scenario with TAC constraint. 

5. Conclusion 

It  has  analyzed  different  technical  measures  that  could  be  adopted  in  the  NAFO  3M  cod  case  to  increase  the 
MSY. There is not so much information about technical measures in the NAFO cod fisheries. This analysis was 
mainly based on the Norwegian cod fisheries information. 

The  adoption of the studied technical  measures  seems to have a biological  support because  they  would allow 
catching bigger fish minimizing the immature cod catches (length below the MLS) and the redfish by-catches in 
the NAFO 3M cod fishery.  

These technical measures would increase slightly the current equilibrium yields but with a large increase in the 
fishing effort. The increase in the equilibrium yield with the selectivity change seems not to be very efficient. 

One  of  the  major  problems  in  the  3M  cod  fishery  is  the  decrease  in  the  catches  mean  size  observed  in  2013 
compared with previous years. In NAFO, it should be studied the possibility of implementing devices to prevent 

 
 
these large catches of small individuals. These devices would prevent catches of individuals with length below 
MLS to avoid discards and will improve the exploitation pattern and the MSY. 

The selectivity parameters for the 3M cod case were estimated for different characteristics of trawl gear (codend 
mesh sizes 135 mm with and without sorting grids and 155 mm codend mesh sizes). Considering the selectivity 
parameters  of  the  135  mm  codend  mesh  size  and  the  3M  cod  biology,  the  mesh  size  used  in  the  NAFO  cod 
fishery (130 mm) is not the most adequate as it allows catching a lot of immature individuals with lengths below 
MLS  that  should  be  discarded.  One  solution  could  be  the  implementation  of  the  155  mm  sorting  grids  in  the 
actual gear or the increase of the actual codend mesh size around 20 mm. These solutions have the advantage of 
minimizing redfish catches in the 3M cod fishery and to catch bigger cod than in the current catches minimizing 
the discards in the fishery. Sorting grids seem to have a bigger survival rate of the selected fish than the diamond 
meshes and the exit windows.  

Other technical solution to avoid the problem of large catches of juveniles could be the closure of the areas at 
less  than  400  meters  depth  where  these  fish  are  more  abundant.  The  effect  in  the  exploitation  pattern  of  this 
technical  measure should be similar to the  implementation of the 135 mm codend  with  sorting grids. But this 
measure could increase the by-catch of redfish as this species is more abundant in depths more than 400 meters. 
Another problem of implementing these closures would be the effort concentration in small areas.  

The implementation of a selection pattern similar to the 135 mm codend with sorting grids or the enlargement of 
the  actual  minimum  cod-end  mesh  size  till  155  mm  would  increase  the  actual  Fmsy  value  and  the  equilibrium 
catches  but  it  should  have  a  small  impact  in  the  equilibrium  SSB.  The  increment  of  the  equilibrium  catches 
should  be  around  13%  but  with  an  increase  in  the  Fmsy  of  more  than  100%.  This  large  increase  of  F  would 
suppose a greater fishing effort and probably more economical operating expense for a small increase in catches. 
If  we  measure  the  efficiency  of  fishing  (ef)  as  yield  per  unit  fishing  mortality  (Y/F),  the  increase  in  the 
equilibrium yield with the selectivity change seems not to be very efficient. The efficiency of fishing would be 
reduced by 50% if we change the current exploitation pattern to the new. 

The model based HCR applied with the new selection pattern have not a big yield increase in the medium term 
period (2014-2020). In the long term period (2014-2033) the implementation of these technical measures have 
an increase in the yearly mean yield. 

6. Bibliography 

Armstrong, D.W., Ferro, R.S.T., Maclennan, D.N., Reeves, S.A., 1990. Gear selectivity and the conservation of 
fish.  J.  Fish  Biol.  37,  261–262.González-Costas,  F.  and  González–Troncoso,  D.  2014.  Biological  Reference 
Points of 3M cod. NAFO SCR Doc. 14/19. Serial No. N6313. 

Ávila  de  Melo  A.,  R.  Petit  ,  A.  Pérez-Rodríguez,  D.  González  Troncoso,  R.  Alpoim,  F.  Saborido-Rey,  M. 
Pochtar,  F.  González-Costas  and  N.  Brites.  2013.  An  Assessment  of  Beaked  Redfish  (S.  mentella  and  S. 
fasciatus)  in  NAFO  Division  3M  (With  a  Revised  Approach  to  Quantify  the  Increase  on  Redfish  Natural 
Mortality Determined by the Increase on Cod Predation Observed Over Recent Years, 2006-2012). NAFO SCR 
Doc. 13/034. Serial No. N6188. 

González-Troncoso D., C. Hvingel, B. Healey, J. Morgan, F.  González-Costas, R. Alpoim, J. C. Mahé and A. 
Vázquez.  2013.  Assessment  of  the  Cod  Stock  in  NAFO  Division  3M.  NAFO  SCR  Doc.  13/041  Serial  No. 
N6196. 

González-Troncoso  D.,  Fernando  González-Costas,  Brian  Healey,  Joanne  Morgan  and  Carsten  Hvinge.  2014. 
Assessment of the Cod Stock in NAFO Division 3M. NAFO SCR Doc. 14/035 Serial No. N6331. 

Grimaldo, E., M. Sistiaga, and R. B. Larsen. 2008. Evaluation of codends with sorting grids, exit windows and 
271–280. 
fish 
diamond  meshes: 
doi.org/10.1016/j.fishres.2007.12.003 

behavior. 

selection 

Fish. 

Res. 

Size 

and 

97: 

Iriondo  A.,  González-Costas  F.,  Hintzen  N.,  Machiels  M.,  González-Troncoso  D.  and  Urtizberea  A.  2014. 
Spatial  and  seasonal  fleet  activity  and  cod  distribution  in  Flemish  Cap.  NAFO  SCR  Doc.  14-068  Serial 
No.N6407. 

160 

 
 
161 

Jørgensen,  T.,  Ó.  A.  Ingólfsson,  N.  Graham  and  B.  Isaksen,  2006.  Size  selection  of  cod  by  rigid  grids.  Is 
anything gained compared to diamond mesh size only? Fish. Res. 79, 337–348. 

Kvamme,  C.,  and  Frøysa,  K.  G.  2004.  Assessing  the  effects  on  stocks  of  selectivity  changes  in  a  fishery. 
Fisheries Research, 69: 283–293. 

Kvamme, C., Isaksen, B., 2004. Total selectivity of a commercial cod trawl with and without a grid mounted: 
grid and codend selectivity of north-east Artic cod. Fisheries Research 68, 305–318. 

Madsen, N., 2007. Selectivity of fishing gears used in the Baltic Sea cod fishery. Reviews in Fish Biology and 
Fisheries, vol 17, no. 4, pp. 517-544. 10.1007/s11160-007-9053-y. 

NAFO, 2014. Northwest  Atlantic  Fisheries  Organization Conservation and Enforcement Measures. Serial  No. 
N6272 NAFO/FC Doc. 14/1. 

Scott,  R.D.  and  Sampson,  D.B.  2011.  The  sensitivity  of  long-term  yield  targets  to  changes  in  fishery  age-
selectivity. Marine Policy, 35:79-84. 

Suuronen, P., and Sarda, F. 2007. The role of technical measures in European fisheries management and how to 
make them work better. ICES Journal of Marine Science, 64: 751–756. 

 
 
 
 
Table  3.4.2.1.-  Age  50%  maturity  and  length  50%  maturity  for  cod  3M  stock  from  the  Flemish  Cap  Survey 
data. 

Year 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

Mean 

Age 50% maturity (years) 

Length 50% maturity (cm) 

3.31 

3.37 

3.49 

3.52 

4.13 

3.93 

3.39 

3.59 

57.02 

58.98 

53.65 

53.13 

57.88 

52.43 

46.77 

54.27 

Table  3.4.2.2.-  The  Fmsy  proxies  (F30%SPR),  their  equilibrium  catches  and  SSB  estimated  with  the  135  mm 
codend with sorting grids selection curve and the data used by González-Costas and González-Troncoso (2014) 
for 3M cod approved by the 2014 Scientific Council (SC), as well as the efficiency of fishing (ef) 

Values approved by the 2014 SC 

F30%SPR 

Yield (t) 

SSB (t) 

ef 

10% 

0.109 

50% 

0.133 

90% 

0.159 

19995 

22570 

25726 

176509 

183440 

126545 

169699 

91931 

161799 

New values with the 135 mm +SG selection 

F30%SPR 

Yield (t) 

SSB (t) 

ef 

10% 

0.214 

50% 

0.276 

90% 

0.356 

22667 

25604 

29043 

175934 

105921 

126188 

92768 

91731 

81581 

162 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
163 

Figure  3.4.2.1.-  Catches  length  distributions  by  country  for  NAFO  3M  cod  used  in  the  2013  and  2014 
assessment (González-Troncoso et al., 2013 and 2014). 

Figure 3.4.2.2.- The 135 mm codend, the 135 mm codend with sorting grids selection curves for 3M cod based 
on the Jørgensen et al. (2006) parameters and the NAFO 3M cod assessment Partial Recruitment (PR) estimated 
for 2013. 

 
 
 
 
 
 
 
 
 
Figure  3.4.2.3.-  Fmsy  proxies  (F30%SPR)  and  their  equilibrium  catches  and  SSB  estimated  with  the  135  mm 
codend  with  sorting  grids  selection  curve  (X135.SG)  and  the  data  used  by  González-Costas  and  González-
Troncoso (2014) for 3M cod approved by the 2014 Scientific Council (X2014). 

Figure  3.4.2.4.-  Median  yield  by  year  for  scenario  2  (M  fix  SR1  35%)  with:  Circles-  the  model-based  HCR 
(with the 20% TAC constraint); points- the HCR without TAC constraint and stars- with the HCR without TAC 
constraint and the  new Ftarget  estimated  with the change in  the exploitation pattern  that could take place if the 
sorting grids or mesh size change to 155 mm are implemented. 

164 

 
 
 
 
 
 
165 

Figure 3.4.2.5.- Yearly median of the mean yield in the medium (2014-2020) and long term (2014-2033) period 
of the scenario 2 (M fix SR1 35%) with the model based HCR (with the 20% TAC constraint), with the HCR 
without TAC constraint and with the HCR without TAC constraint and the new Ftarget estimated with the change 
in  the  exploitation  pattern  that  could  take  place  if  the  sorting  grids  or  mesh  size  change  to  155  mm  are 
implemented. 

0500010000150002000025000Sc2Sc 2 WithoutNew Ftar whitoutTonScenariosYearly median mean yield in the medium and long term  2014-20202014-2033 
 
 
 
 
4  Recommendations 

The main recommendation is that in the medium term (2016-2020) it will be necessary to reduce the 
current catch level. The level of reduction required depends on the level of risk that managers wish to 
assume to meet the management goals established. The annual reduction of 20% of the catch is not 
enough to achieve the management objectives established in the medium term. 

If a gradual reduction in catches is desired it would be necessary to increase the risk of not achieving 
the  establish  objectives  but  if  the  aim  is  to  achieve  the  objectives  with  the  established  risk  a  more 
pronounced decline in catches would be necessary. 

In this period (2016-2020) the model-based HCRs are closer to achieve the biological objectives than 
the model-free HCRs although the model-based HCRs are socio-economically less favourable. 

In  the  long  term  (2014-2033)  all  tested  HCRs  meet  the  established  biological  objectives  and  the 
model-based HCRs are more convenient socioeconomically. 

The RBMS at its last meeting recommended to revise certain decisions that have been taken on the 
MSE and to study alternative HCRs that meet the established objectives. 

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