Scientific, Technical and Economic Committee for Fisheries (STECF) : monitoring the performance of the common fisheries policy (STECF-Adhoc-18-01).
Date: 2018-06-26
Institutions: Joint Research Centre
Subjects: Community fisheries, common fisheries policy, conservation of fish stocks, environmental economics, evaluation of resources, fishery management, report

Scientific, Technical and Economic 
Committee for Fisheries (STECF) 
- 
Monitoring the performance of the 
Common Fisheries Policy 
(STECF-Adhoc-18-01) 

Edited by Ernesto Jardim, Paris Vasilakopoulos, Alessandro Mannini, Iago 
Mosqueira and John Casey 

EUR 29223 EN 

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

Contact information  

Name: STECF secretariat 

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

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

Tel.: +39 0332 789343 

JRC Science Hub 

https://ec.europa.eu/jrc 

JRC111761 

EUR 29223 EN 

PDF 

ISBN 978-92-79-85802-4 

ISSN 1831-9424 

doi:10.2760/329345 

STECF 

ISSN 2467-0715 

Luxembourg: Publications Office of the European Union, 2018  

© European Union, 2018 

Reuse  is  authorised  provided  the  source  is  acknowledged.  The  reuse  policy  of  European  Commission 
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For any use or reproduction of photos or other material that is not under the EU copyright, permission 
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How  to  cite:  Scientific,  Technical  and  Economic  Committee  for  Fisheries  (STECF)  –  Monitoring  the 
performance of the Common Fisheries Policy (STECF-Adhoc-18-01). Publications Office of the European 
Union, Luxembourg, 2018, ISBN 978-92-79-85802-4, doi:10.2760/329345, JRC111761 

All images © European Union 2018  

Abstract 

Commission  Decision  of  25  February  2016  setting  up  a  Scientific,  Technical  and  Economic  Committee 

for Fisheries, C(2016) 1084, OJ C 74, 26.2.2016, p. 4–10. The Commission may consult the group on 

any  matter  relating  to  marine  and  fisheries  biology,  fishing  gear  technology,  fisheries  economics, 

fisheries governance, ecosystem effects of fisheries, aquaculture or similar disciplines. This report deals 

with monitoring the performance of the Common Fisheries Policy. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Authors: 

STECF advice:  

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

Ad hoc Expert group report:  

E. Jardim, P. Vasilakopoulos, A. Mannini, I. Mosqueira, J. Casey 

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TABLE OF CONTENTS 

SCIENTIFIC, TECHNICAL AND ECONOMIC COMMITTEE FOR FISHERIES (STECF) - 
Monitoring the performance of the Common Fisheries Policy (STECF-
Adhoc-18-01) .......................................................................................................... 6 

Background provided by the Commission ................................................................. 6 

Request to the STECF ........................................................................................................ 6 

STECF observations ............................................................................................................ 6 

General principles for future analysis ........................................................................ 11 

STECF conclusions ............................................................................................................. 12 

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

Expert Group Report ........................................................................................................ 17 

1 

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

1.1 

Terms of Reference to the ad hoc Expert group ..................................... 18 

2 

Data and methods ............................................................................................... 19 

2.1 

Data sources .......................................................................................................... 19 

2.1.1  Stock assessment information ....................................................................... 19 

2.1.2  Management units information ...................................................................... 19 

2.2 

2.3 

2.4 

Methods ................................................................................................................... 19 

Points to note ........................................................................................................ 19 

Differences from the 2017 CFP monitoring report (STECF 17-04) .. 20 

2.4.1  Northeast Atlantic ................................................................................................ 20 

2.4.2  Mediterranean and Black Sea ......................................................................... 20 

3 

3.1 

Northeast Atlantic and adjacent Seas (FAO region 27) ....................... 21 

Number of stock assessments to compute CFP performance indicators
 .................................................................................................................................... 21 

3.2 

Indicators of management performance .................................................... 28 

3.2.1  Number of stocks by year where fishing mortality exceeded FMSY .. 28 

3.2.2  Number of stocks by year for which fishing mortality was equal to, or less 
than FMSY ................................................................................................................. 29 

3.2.3  Number of stocks outside safe biological limits ....................................... 30 

3.2.4  Number of stocks inside safe biological limits ......................................... 31 

3.2.5  Trend in F/FMSY ...................................................................................................... 32 

3.2.6  Trend in SSB (relative to 2003) ..................................................................... 34 

3.3 

Experimental indicators .................................................................................... 36 

4 

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3.3.1  Number of stocks with F above Fmsy or SSB below MSYBtrigger ... 37 

3.3.2  Number of stocks with F below or equal to Fmsy and SSB above or equal 
to MSYBtrigger ...................................................................................................... 38 

3.3.3  Trend in F/FMSY for stocks outside the EU coastal waters .................... 39 

3.3.4  Trend in SSB/Bpa .................................................................................................. 40 

3.3.5  Trend in recruitment (relative to 2003) ..................................................... 41 

3.3.6  Trend in SSB or biomass index for stocks of data category 1-3 (relative to 
2003) ........................................................................................................................ 42 

3.3.7  Trend in SSB or biomass index for stocks of data category 3 (relative to 

2003) ........................................................................................................................ 43 

3.4 

Indicators of advice coverage ......................................................................... 44 

4 

4.1 

Mediterranean and Black Seas (FAO region 37) ..................................... 45 

Indicators of management performance .................................................... 48 

4.1.1  Trend in F/FMSY ................................................................................................... 48 

4.1.2  Trend in SSB (relative to 2003) ..................................................................... 49 

4.2 

Indicators of advice coverage ......................................................................... 50 

5 

6 

7 

8 

9 

Status across all stocks in 2016 .................................................................... 51 

Reports by stock .................................................................................................. 55 

References .............................................................................................................. 58 

Contact details of ad hoc expert group participants .............................. 59 

List of Annexes ..................................................................................................... 61 

Annex I - Protocol .............................................................................................................. 62 

Annex II - Code .................................................................................................................. 63 

Annex III – Quality control of ICES dataset ........................................................... 64 

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SCIENTIFIC, TECHNICAL AND ECONOMIC COMMITTEE FOR FISHERIES (STECF) - 
Monitoring the performance of the Common Fisheries Policy (STECF-Adhoc-18-01) 

Background provided by the Commission 

Article 50 of the Common Fisheries Policy (CFP; Regulation (EU) No 1380/2013 of the European 
Parliament  and  of  the  Council  of  11  December  2013)  stipulates:  “The  Commission  shall  report 
annually to the European Parliament and to the Council on the progress on achieving maximum 
sustainable yield and on the situation of fish stocks, as early as possible following the adoption of 
the  yearly  Council  Regulation  fixing  the  fishing  opportunities  available  in  Union  waters  and,  in 
certain non-Union waters, to Union vessels.” 

Request to the STECF 

The  STECF  is  requested  to  report  on  progress  in  achieving  MSY  objectives  in  line  with  the 
Common Fisheries Policy. 

STECF observations  

STECF  notes  that  to  address  the  above  Terms  of  Reference  a  JRC  Expert  Group  (EG)  was 
convened  to  compile  available  assessment  outputs  and  conduct  the  extensive  analysis.  The  EG 
output  was  presented  in  a  comprehensive  report  accompanied  by  several  detailed  annexes 
providing:  1)  CFP  monitoring  protocols  as  agreed  by  STECF  (STECF,  2017);  2a)  R  code  for 
computing NE Atlantic indicators; 2b) R code for computing Mediterranean indicators and 3) ICES 
data  quality  issues  corrected  prior  to  the  analysis.  The  report  and  Annexes  are  available  at 
https://stecf.jrc.ec.europa.eu/plen18_01 

STECF  notes  that  the  report  is  clear  and  well  laid  out,  transparently  describing  the  analysis 
undertaken,  cataloguing  changes  made  in  approach  since  the  previous  report  (2017)  and 
including  URL  links  to  the  various  reports  and  stock  advice  sheets  underpinning  the  analysis. 
STECF commends the effort employed in updating nomenclature following various changes to the 
ICES database and the careful attention paid to ensuring the correct figures were used.  

The most significant changes in the 2018 approach were: 

i) 
ii) 

iii) 

A revision of the Mediterranean sampling frame used for the analysis  
Where data were unavailable for the most recent year, the data from the previous year 
was rolled forward 
MSYBtrigger was used as a proxy for lower bound of BMSY 

Details of these changes and other points to note can be found in section 2 of the EG report. 

The  EG  report  then  sets  out  results  of  the  analysis  for  the  ICES  area  of  the  NE  Atlantic  and 
Mediterranean & Black Sea separately in Sections 3 and 4 (respectively). Based on these results 
STECF  provides  an  overview  of  what  is  currently  known  regarding  the  achievement  of  the  MSY 
objectives,  drawing  together  the  results  from  the  different  sea  areas  to  provide  a  comparative 
picture.  The  overview  focuses  on  a  limited  number  of  ‘core’  indicators  earlier  agreed  by  STECF 
(2017).  The  EG  report  contains  results  for  a  number  of  ‘experimental’  indicators  which  STECF 
notes  are  still  at  the  development  stage.  It  is  expected  that  these  will  be  further  developed  as 
part  of  another  STECF  EWG  (EWG  18-15)  to  be  held  later  in  2018  (see  conclusions).  In  this 
report, “ICES Area” refers to all stocks in the FAO Area 27 in the Northeast Atlantic assessed by 
ICES,  while  the  denomination  “NE  Atlantic  stocks”  refers  more  specifically  to  the  stocks 
distributed widely, including outside EU Waters 

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Trends towards the MSY objectives in the ICES area and Mediterranean& Black Seas 

The overview below describes the trends observed in the ICES area and the Mediterranean for the 
periods  2003  to  2016  and  2003  to  2015  respectively  and  applies  to  the  stocks  included  in  the 
reference  list  of  stocks  for  these  areas.  The  stocks  are  primarily  those  with  a  full  analytical 
assessment (ICES Category 1).  

Stock status in the ICES area  

The indicators provided by the JRC EG show that stocks status has significantly improved (Figure 
1) but also that many stocks are still overexploited in the ICES area, and that the rate of progress 
has  slowed  in  the  last  few  years.  In  the  ICES  area,  among  the  65  to  71  stocks  which  are  fully 
assessed,  the  proportion  of  overexploited  stocks  (i.e.  F>FMSY,  blue  line)  decreased  from  more 
than 70% to close to 40%, over the last ten years and seems to have stabilised in the last three 
years.  The  proportion  of  stocks  outside  the  safe  biological  limits  (F>Fpa  or  B<Bpa,  orange  line), 
computed  for  the  46  stocks  for  which  both  reference  points  are  available,  follows  the  same 
decreasing trend, from 65% in 2003 to around 30% in 2016.  

Figure  1.  Trends  in  stocks  status,  2003-2016.  Three  indicators  are  presented:  Blue  line:  the 
proportion  of  overexploited  stocks  (F>FMSY)  within  the  sampling  frame  (65  to  71  stocks  fully 
assessed in the ICES area, depending on year); Orange line: the proportion of stocks outside safe 
biological limits (F>Fpa or B< Bpa) (46 stocks); Red line: F>FMSY or SSB <MSYBtrigger 

It  is  important  to  note,  however,  that  some  stocks  now  managed  according  to  FMSY  may  still  be 
outside  safe  biological  limits,  or  conversely  some  stocks  inside  safe  biological  limits  may  still  be 
overfished.  

The red line illustrates changes in the proportion of stocks where F>FMSY or SSB <MSYBtrigger. Here 
the  improvement  in  status  has  been  slower  with  the  indicator  remaining  above  75%  of  stocks 
until  2007  before  declining.    The  decline  then  appears  to  have  stopped  in  2013  and  began  to 
slowly increase again to about 60% of stocks in 2016 where F>FMSY or SSB <MSYBtrigger. 

STECF notes that the number or proportion of stocks above/below BMSY is still unknown, because 
an estimate of BMSY is only provided by ICES for very few stocks.  

STECF observes that the recent slope of the indicators suggests that progress until 2016 has been 
too  slow  to  allow  all  stocks  to  be  maintained  or  restored  to  at  least  the  precautionary  Bpa,  and 
managed according to FMSY by 2020.  

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Stock Status in the Mediterranean & Black Sea 

In the Mediterranean & Black Sea, the variable number of stocks contributing information in the 
early part of the time series renders the calculation of a robust indicator difficult and potentially 
misleading.  STECF  suggests  the  possibility  of  investigating  this  in  the  future  for  a  shorter  time 
period  (e.g.  from  2008  to  2015  when  the  stock  numbers  appear  to  be  more  stable).  For  the 
present STECF has utilised the summary Table 5.1 in the EG report to compute the F status for 
2015  (last  year  in  Mediterranean  stock  assessments).  Out  of  47  stocks,  only  around  13%  (6 
stocks) are not overfished, the majority are overfished. 

Trends in the fishing pressure (Ratio of F/FMSY)  

As  agreed  by  STECF  (2017)  the  Expert  Group  computed  the  trends  in  fishing  pressure  using  a 
robust  statistical  model  (Generalised  Linear  Mixed  Effects  Model,  GLMM)  accounting  for  the 
variability of trends across stocks and including the computation of a confidence interval around 
the  median.  A  large  confidence  interval  means  that  different  stocks  have  different  trends. 
Because  this  is  a  model-based  indicator,  and  because  the  number  of  stocks  is  slightly  different 
from last year, small differences in the resulting outcomes compared to last year’s report should 
not be over interpreted. 

This indicator can be used for regional comparison between the ICES area and Mediterranean & 
Black Seas. In the ICES area, the model-based indicator of the fishing pressure (F/FMSY) shows an 
overall  downward  trend  over  the  period  2003-2015  (Figure  2).  In  the  early  2000s,  the  median 
fishing  mortality  was  more  than  1.5  times  larger  than  FMSY,  but  this  has  reduced  and  has  now 
stabilised  around  1.0.  Reaching  FMSY  for  most  stocks  in  the  analysis  would  require  the  upper 
bound of the confidence interval in figure 3.1 in the EWG report to be around 1. STECF also notes 
that this indicator of fishing pressure has not decreased since 2011. 

The same model-based indicator was computed by the EG for an additional set of 9 stocks located 
in  the  NE  Atlantic,  but  outside  EU  waters.  This  indicator  seems  to  confirm  the  positive  overall 
trend  observed in  EU waters, with  the  median  value  of  the  F/FMSY  indicator closely  tracking  that 
produced for EU waters. STECF notes that the indicator for NE Atlantic stocks outside EU waters is 
based on comparatively few stocks and thus should be considered with care.  

Figure 2. Trends in the fishing pressure. Three model based indicators F/FMSY are presented (all 
referring to the median value of the model): one for 48 EU stocks with appropriate information in 
the ICES area (red line); one for an additional set of 9 stocks also located in the NE Atlantic but 

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outside EU waters (green line), and one for the 47 assessed stocks from the Mediterranean and 
Black Sea region (black line).  

In  contrast,  the  indicator  computed  for  stocks  from  the  Mediterranean  Sea  and  Black  Sea  has 
remained at a very high level during the whole 2003-2015 period, with no decreasing trend. The 
value  of  F/FMSY  varies  around  2.3  indicating  that  the  stocks  are  being  exploited  on  average  at 
rates well above the FMSY CFP objective.  

Trends in Biomass  

The model-based indicator of the trend in biomass shows improvement in the ICES area, but not 
in the Mediterranean and Black Sea (Figure 3). In the ICES area the biomass has been generally 
increasing  since  2006,  and  was  in  2016  on  average  around  39%  higher  than  in  2003.  This 
represents a slight change from the reporting in 2016 reflecting the fact that the modelled trend 
incorporates new information. In the Mediterranean & Black Sea the uncertainty associated with 
this  indicator  (see  Figure  4.4  in  the  EWG  report)  makes  it  difficult  to  conclude  anything  about 
trend and the situation is essentially unchanged since the start of the series in 2003. 

An  improving  trend  is  also  observed  for  data  poor  stocks  (Figure  3.23  in  the  EWG  report), 
according to the indicator computed by the EG for 61 ICES Category 3 stocks. However, in view 
of the fact that this indicator is still regarded as experimental, care in interpretation is required. 

Figure 3. Trends in the indicators of stock biomass (median values of the model-based estimates 
relative  to  2003).  Two  indicators  are  presented:  one  for  the  ICES  area  (54  stocks  considered, 
blue line); one for the Mediterranean region (47 stocks, black line). The EG noticed that a large 
uncertainty is associated to these estimates, coming from the fact that the biomass estimates are 
quite variable from one year to the next. 

Trends per Ecoregion  

For the ICES area, the EG provides some information and figures broken down by Ecoregion. The 
main trends are summarised here. 

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The fishing pressure has decreased and the status of stocks has improved in all ICES Ecoregions. 
In 2016, the proportion of overexploited stocks ranged between to 29 - 50% across the different 
Ecoregions, while the modelled estimate of the F/FMSY ratio for 2016 was between 0.89 and 1.18.  

Some  variations  between  Ecoregions  in  modelled  trends  can  be  seen.  According  to  the  latest 
indicator trends presented in the EG report, the fishing pressure decreased consistently over the 
whole  period  and  the  stock  status  improved  most  markedly  in  the  Celtic  Sea.  Here  the  fishing 
mortality was at a very high level at the beginning of the time series (F/FMSY>1.9) and decreased 
significantly  to  below  1.0.  In  the  remaining  areas,  marked  declines  are  also  evident  in  the  first 
part of the time series but the rate of decline of the indicator falls around 2010 and the indicator 
tends to level out. In the Bay of Biscay and Iberian Ecoregion, and stocks present throughout the 
wider Northeast Atlantic the indicator has fluctuated in the most recent years.  

Coverage of the scientific advice  

Coverage of biological stocks by the CFP monitoring  

As  stated  previously  (STECF  PLEN  16-03),  the  analyses  of  the  progress  in  achieving  MSY 
objectives  in  the  ICES  area  should  consider  all  stocks  with  advice  provided  by  ICES,  on  the 
condition  of  being  distributed  in  EU  waters,  at  least  partially.  Based  on  the  ICES  database 
accessed for the analysis, ICES provides a scientific advice for 257 biological stocks included in EU 
waters  (at  least  in  part).  Of  these,  159  stocks  are  data-poor,  without  an  estimate  of  MSY 
reference  points  (ICES  category  3  and  above).  Details  of  the  numbers  of  ICES  assessments  by 
Category and by area are shown in Table 1. 

Table  1.  Numbers  of  stocks  assessed  by  ICES  for  different  stock  categories  in  different  areas. 
Note that not all of these stocks are managed by TACs and so the numbers are higher than those 
used in the CFP monitoring analysis. 

The present CFP monitoring analysis is focused on stocks with a TAC and for which estimates of 
fishing  mortality,  biomass  and  biological  reference  points  are  available.  As  detailed  in  the  EGs 
technical  reports,  not  all  indicators  can  be  calculated  for  all  stocks  in  all  years,  and  the  EG  was 
able to compute indicators for 46 to 71 stocks of category 1 depending on indicators and years. 
These  stocks  represent  the  vast  majority  of  catches  but  a  large  number  of  biological  stocks 
present in EU waters are still not included in the CFP monitoring. 

STECF  notes  however  that  the  EG  computed  some  additional  indicators  of  trends  in  abundance 
index for 61 data poor stocks of category 3. These indicators are still considered experimental by 

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the EG and are not presented in the current STECF overview. Once this indicator becomes part of 
the  ‘core’  list,  the  total  number  of  stocks  included  in  the  CFP  analysis  will  be  up  to  50%  of  the 
stocks  assessed  by  ICES  (ie  71  Category  1-2  plus  61  Category  3).  STECF  notes  also  that  MSY 
reference  points  are  expected  to  be  computed  by  ICES  for  an  increasing  number  of  data-poor 
stocks over the coming years, which will increase the coverage of the CFP monitoring.  

In  the  Mediterranean  region,  the  EG  selected  230  stocks  (Species/GSA)  in  the  sampling  frame 
(Mannini et.al 2017), of which 47 have been covered by a stock  assessment in recent years. In 
the  Mediterranean  region,  stocks  status  and  trends  can  be  monitored  only  for  a  minority  of 
stocks. 

Coverage of TAC regulation by scientific advice  

According  to  the  EG  report,  STECF  notes  that  156  TACs  (combination  of  species  and  fishing 
management zones) were in place in 2016 in the EU waters of the NE Atlantic. 

STECF  underlines  that  in  many  cases,  the  boundaries  of  the  TAC  management  areas  are  not 
aligned with the biological limits of stocks used in ICES assessments. The EG therefore computed 
an  indicator  of  advice  coverage,  where  a  TAC  is  considered  to  be  “covered”  by  a  stock 
assessment when at least one of its divisions matched the spatial distribution of a stock for which 
reference points have been estimated from an ICES full assessment. Based on this indicator, 56% 
among the 156 TACs are covered, at least partially, by stock assessments that provide estimates 
of FMSY (or a proxy) and 43% by stock assessments that have Bpa (or a proxy). 

Additionally, STECF notes that, using this index, some TACs can be considered as “covered” even 
if they relate to several assessments contributing to a single TAC (e.g. Nephrops functional units 
in the North Sea) or to a scientific advice covering a different (but partially common) area (e.g. 
whiting  in  the  Bay  of  Biscay).  Thus,  such  an  approach  overestimates  the  spatial  coverage  of 
advice (i.e. the proportion of TACs based on a single and aligned assessment). This means that a 
large  number  of  TACs  are  still  imperfectly  covered  by  scientific  advice  based  on  FMSY  or  Bpa 
reference values. 

General principles for future analysis 

Based  on  the  latest  process  of  analysis  and  overview,  STECF  advises  that  the  CFP  monitoring 
process should continue with the following principles:  

  The  three  indicators  of  stock  status  are  useful  and  should  be  regularly  computed  in  the 
coming years (expressed in stock numbers in the detailed report and in proportion in the 
synthesis)  

  As  soon  as  a  representative  number  of  BMSY  estimates  become  available  from  ICES 
assessments,  the  proportion  (and  number)  of  stocks  below  or  above  this  reference  point 
should become part of the ‘core’ indicator set, together with an indicator of trends in the 
B/BMSY ratio.  

  Regarding trends in fishing mortality and biomass, all indicators should be computed in a 
consistent  way.  STECF  considers  that  the  model-based  indicators  should  continue  to  be 
used as the standard method for every time series (including indicators per Ecoregion and 
indicators  for  NE  Atlantic  stocks  outside  EU  waters).  These  model-based  indicators  are 
preferable  to  arithmetic  mean  estimates,  which  although  easy  to  communicate,  are 
generally sensitive to outliers. 

  To maintain ease of visual comparison, indicators of biomass trends should continue to be 

rescaled to the value of the starting year.  

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  As far as possible, according to data availability, the same indicators should be computed 

in the ICES area and in the Mediterranean region.  

Ongoing development 

STECF  notes  that  the  EG  Report  again  includes  sections  providing  preliminary  outputs  from  a 
number  of  experimental  indicators.  STECF  considers  that  these  require  further  development  to 
fully understand their performance and stability before adoption as ‘core’ indicators. STECF draws 
attention to an STECF EWG planned for later in the year (STECF 18-15) which is dedicated to the 
development of CFP monitoring and suggests that further progress on the experimental indicators 
relating  to  fish  stocks  could  be  made.  During  this  meeting  STECF  encourages  exploration  of 
indicators for other aggregations such as stock categories (eg pelagic fish versus demersal fish)  

STECF conclusions 

STECF  acknowledges  that  monitoring  the  performance  of  the  CFP  requires  significant  effort  in 
order  to  provide  a  comprehensive  picture.  The  process  presents  a  number  of  methodological 
challenges  due  to  the  annual  variability  in  the  number  and  categories  of  stocks  assessed 
(especially  in  the  Mediterranean)  and  due  to  the  large  variations  in  trends  across  stocks.  As  a 
result, the choice of indicators and their interpretation is being discussed, expanded and adjusted 
over time, as duly documented in the suite of STECF plenary reports and in the JRC EG technical 
reports. In particular, STECF notes that the CFP monitoring has improved this year thanks to the 
implementation  of  a  revised  protocol  and  ongoing  improvements  in  the  coverage  of  fish  stock 
assessments  and  estimates  of  reference  points.  STECF  is  aware  that  minor  differences  in  the 
indicators  can  occur  compared  to  previous  years.  However  STECF  always  use  the  latest 
assessment and best science available at the time of the report 

Regarding  the  progress  made  in  the  achievement  of  FMSY  in  line  with  the  CFP, STECF notes  that 
the latest results are generally in line with those reported in the 2017 CFP monitoring and confirm 
a  reduction  in  the  overall  exploitation  rate  for  the  ICES  area.  On  average  the  stock  biomass  is 
increasing  and  stock  status  is  improving.  Nevertheless,  based  on  the  set  of  assessed  stocks 
included  in  the  analyses,  STECF  notes  that  many  stocks  remain  overfished  and/or  outside  safe 
biological limits, and that progress achieved until 2016 seems too slow to ensure that all stocks 
will be rebuilt and managed according to FMSY by 2020.  

STECF  also  concludes  that  stocks  from  the  Mediterranean  Sea  and  Black  sea  remain  in  a  very 
poor situation, with no change apparent in terms of fishing pressure or stock biomass.  

STECF  concludes  that  further  progress  has  been  made  on  the  development  of  additional 
indicators  relating  to  fish  stocks  which  would  benefit  from  some  additional  testing  before  being 
adopted  as  core  indicators.  STECF  also  recognises  the  need  to  broaden  the  scope  of  the  CFP 
monitoring  to  cover  additional  aspects  not  so  far  dealt  with.  In  particular,  there  is  a  need  to 
develop the CFP monitoring process to cover wider ecosystem and socio-economic aspects in the 
analysis. STECF notes that the scheduled STECF EWG on CFP monitoring later in the year (STECF 
18-15) will provide an opportunity to progress these requirements. 

Contact details of STECF members 

1  -  Information  on  STECF  members’  affiliations  is  displayed  for  information  only.  In  any  case, 
Members of the STECF shall act independently. In the context of the STECF work, the committee 
members do not represent the institutions/bodies they are affiliated to in their daily jobs. STECF 
members  also  declare  at  each  meeting  of  the  STECF  and  of  its  Expert  Working  Groups  any 
specific interest which might be considered prejudicial to their independence in relation to specific 
items on the agenda. These declarations are displayed on the public meeting’s website if experts 

12 

12 

 
 
 
 
 
explicitly  authorized  the  JRC  to  do  so  in  accordance  with  EU  legislation  on  the  protection  of 
personnel data. For more information: http://stecf.jrc.ec.europa.eu/adm-declarations 

Name 

Address1 

STECF members 

Tel. 

Email 

Abella, 
Alvaro 

J. 

Independent consultant 

Tel. 
3384989821 

0039-

aabellafisheries@gmail.c
om 

Tel.dir.:    +45  35 
33 68 92 

jla@ifro.ku.dk 

Tel.: 
+34667174477 

harri@azti.es 

for  Environment 

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

Marina, 

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

Independent consultant 

Andersen, 
Jesper Levring  

Arrizabalaga, 
Haritz 

Bailey, 
Nicholas  

Bertignac, 
Michel  

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

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

nickbailey2013@btintern
et.com 

michel.bertignac@ifreme
r.fr 

Borges, Lisa 

FishFix, Brussels, Belgium 

info@fishfix.eu 

Cardinale, 
Massimilian 

Catchpole, 
Thomas 

Curtis, Hazel  

Daskalov, 
Georgi 

Föreningsgatan  45,    330 
Lysekil, Sweden 

Tel:  +46 
18750 

523 

massimiliano.cardinale@
slu.se 

Lowestoft 

thomas.catchpole@cefas
.co.uk  

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

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

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

Hazel.curtis@seafish.co.
uk 

Tel.:  +359 
646892 

52 

Georgi.daskalov@gmail.
com 

of 

Marine 

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

Research, 

13 

13 

 
 
 
 
 
 
 
 
 
 
Name 

Address1 

STECF members 

Tel. 

Email 

Döring, 
(vice-chair) 

Ralf 

Thünen 
Bundesforschungsinstitut, 
für  Ländliche  Räume,  Wald 
und  Fischerei,  Institut  für 
Seefischerei 
AG 
- 
Fischereiökonomie, 
Palmaille 
Hamburg, Germany 

D-22767 

9, 

Gascuel, Didier   AGROCAMPUS OUEST 

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

Department of Biology 
University of Malta 
Msida, MSD 2080 
Malta 

Knittweis, 
Leyla 

Malvarosa, 
Loretta  

Tel.:  040  38905-
185 

Fax.:  040  38905-
263 

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

ralf.doering@thuenen.de 

Didier.Gascuel@agroca
mpus-ouest.fr 

Leyla.knittweis@um.edu
.mt  

Fishery 

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

Tel: 
089795775 

+39 

malvarosa@nisea.eu  

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

Tel: 4.93.2309500 
Fax: 
34.93.2309555 

paloma@icm.csic.es 

Motova, Arina 

Murua, Hilario 

Nord, Jenny  

Prellezo, Raúl  

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

Marina, 

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

The  Swedish  Agency  of 
Marine 
Water 
and 
Management (SwAM)  

Tel.:  +44  131  524 
8662 

arina.motova@seafish.c
o.uk 

0034 

Tel: 
667174433 
Fax: 94 6572555 

hmurua@azti.es 

Tel.  0046  76  140 
140 3 

Jenny.nord@havochvatt
en.se 

de 

Tel: 
667174368 

+34 

rprellezo@azti.es 

-Unidad 

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

Ugartea 

Sukarrieta 

Raid, Tiit 

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

of 

Tel.: 
58339340 
Fax: 
6718900 

+372 

Tiit.raid@gmail.com  

+372 

Sabatella, 
Evelina 
Carmen 

Fishery 

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

TEL.: 
089795775 

+39 

e.sabatella@nisea.eu  

14 

14 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Name 

Address1 

STECF members 

Sala, Antonello 
(vice-chair) 

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

Scarcella, 
Giuseppe 

Soldo, Alen 

Somarakis, 
Stylianos 

Stransky, 
Christoph 

Ulrich, 
(chair) 

Clara 

Italian 

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

AP 

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

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

Thünen  Institute  [TI-SF] 
Federal  Research  Institute 
for  Rural  Areas,  Forestry 
and  Fisheries,  Institute  of 
Sea  Fisheries,  Palmaille  9, 
Hamburg, 
D-22767 
Germany  

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

Tel. 

Email 

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

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

071 

a.sala@ismar.cnr.it 

071 

+39 

071 

g.scarcella@ismar.cnr.it 

071 

+357 

gscarcella@apmarine.co
m.cy 

Tel.: 
+385914433906 

soldo@unist.hr  

Tel.:  +30  2810 
337832 

somarak@hcmr. gr 

Fax 
6936566764 

+30 

+49 
Tel. 
38905-228  
Fax: 
+49 
38905-263  

40 

40 

christoph.stransky@thue
nen.de 

clu@aqua.dtu.dk 

van Hoof, Luc 

IMARES,  Haringkade  1, 
Ijmuiden, The Netherlands 

Tel.: 
61061991 

+31 

Luc.vanhoof@wur.nl 

Vanhee, Willy  

Independent consultant 

wvanhee@telenet.be 

Vrgoc, Nedo 

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

Tel.: 
21408002 

+385 

vrgoc@izor.hr 

15 

15 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
16 

16 

 
 
 
 
 
EXPERT GROUP REPORT 

REPORT TO THE STECF 

Report of the ad hoc Expert Group on monitoring 
the performance of the Common 
  Fisheries Policy 

Ispra, Italy, March-April 2018 

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

17 

17 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1 

INTRODUCTION 

Article 50 of the EU Common Fisheries Policy (REGULATION (EU) No 1380/2013) states:  

“The  Commission  shall  report  annually  to  the  European  Parliament  and  to  the  Council  on  the 
progress on achieving maximum sustainable yield and on the situation of fish stocks, as early as 
possible  following  the  adoption  of  the  yearly  Council  Regulation  fixing  the  fishing  opportunities 
available in Union waters and, in certain non-Union waters, to Union vessels.” 

To  fulfil  its  obligations  to  report  to  the  European  Parliament  and  the  Council,  each  year,  the 
European  Commission  requests  the  Scientific,  Technical  and  Economic  Committee  for  Fisheries 
(STECF)  to  compute  a  series  of  performance  indicators  and  advise  on  the  progress  towards  the 
provisions of Article 50. 

In an attempt to make the process of computing each of the indicators consistent and transparent 
and  to  take  account  of  issues  identified  and  documented  in  previous  CFP  monitoring  reports,  a 
revised protocol was adopted by the STECF in 2017 (Annex I). 

An  ad  hoc  Expert  Group  comprising  Experts  from  the  European  Commission’s  Joint  Research 
Centre  (JRC)  was  convened  during  March  and  April  2018  to  compute  the  performance  indicator 
values  according  to  the  agreed  protocol  (Annex  I)  and  to  report  to  the  STECF  plenary  meeting 
scheduled for 09-13 April 2018.  

1.1  Terms of Reference to the ad hoc Expert group 

The Expert group is requested to report on progress in achieving MSY objectives in line with CFP. 

18 

18 

 
 
 
 
 
 
 
2 

DATA AND METHODS 

2.1  Data sources 

The  data  sources  used  referred  to  the  coastal  waters  of  the  EU  in  FAO  areas  27  (Northeast 
Atlantic and adjacent Seas) and 37 (Mediterranean and Black Seas). The Mediterranean included 
GSAs 1, 5, 6, 7, 8, 9, 10, 11, 15, 16, 17, 18, 19, 25 and 29. The NE Atlantic included the ICES 
subareas "III", "IV" (excluding Norwegian waters of division IVa), "VI", "VII", "VIII", "IX" and "X". 

2.1.1  Stock assessment information 

For  the  Mediterranean  region  (FAO  area  37),  the  information  were  extracted  from  the  STECF 
Mediterranean  Expert  Working  Group  repositories  (https://stecf.jrc.ec.europa.eu/reports/medbs) 
and from the GFCM stock assessment forms (http://www.fao.org/gfcm/data/safs/en ). 

For  the  NE  Atlantic  (FAO  area  27),  the  information  was  downloaded  from  the  ICES  website 
(http://standardgraphs.ices.dk)  on  the  19th  March  2018,  comprising  the  most  recent  published 
assessments, carried out up to and including 2017. A thorough process of data quality checks and 
corrections  was  performed  to  ensure  the  information  downloaded  was  in  agreement  with  the 
summary sheets published online (Annex III).  

Table 6.1 shows the URLs for the report or advice summary sheet for each stock.   

2.1.2  Management units information 

For  the  NE  Atlantic,  management  units  are  defined  by  TACs,  annual  fishing  opportunities  for  a 
species or group of species in a Fishing Management Zone (FMZ). The information regarding TACs 
in  2016  was  downloaded  from  the  FIDES  (http://fides3.fish.cec.eu.int/)  reporting  system. 
Subsequently,  such  information  was  cleaned  and  processed,  to  identify  the  FMZ  of  relevance  to 
this work, as well as the ICES rectangles they span to (Gibin, 2017).   

2.2  Methods 

The  methods  applied  and  the  definition  of  the  sampling  frames  followed  the  protocol  (Jardim 
et.al, 2015) agreed by STECF (2016) and updated following the discussion in STECF (2017a). The 
updated protocol is presented in Annex I and the R code used to carry out the analysis in Annex 
II. 

2.3  Points to note 

  Stocks  assessed  with  biomass  dynamics  models  do  not  provide  a  value  for  FPA,  although 
they  may  provide  a  BPA  proxy  (0.5  BMSY).  Consequently,  such  stocks  cannot  be  used  to 
compute the indicators relating to safe biological limits (SBL). 

  The  Generalized  Linear  Mixed  Model  (GLMM)  uses  a  shortened  time  series,  starting  in 
2003, instead of the full time-series of available data. This has the advantage of balancing 
the dataset by removing those years with only a low number of assessment estimates, but 
it has the disadvantage of excluding data that could improve model fit. 

  For all stocks managed with a Bescapement strategy, except Bay of Biscay anchovy (ane.27.8) 
and  Norway  pout  in  the  North  Sea,  Skagerrak  and Kattegat  (nop-27.3a.4),  MSYBescapement 
was set by ICES at BPA instead of BMSY. 

  Norway  pout  in  the  North  Sea,  Skagerrak  and  Kattegat  (nop.27.3a4)  uses  a  probabilistic 
method  to  set  the  catches:  Cy+1=C|(P[SSB<Blim]=0.05).  For  this  stock,  the  lower 
(0.025%) boundary of the SSB confidence interval was compared to Blim. 

19 

19 

 
 
 
 
 
 
  Bay of Biscay anchovy (ane.27.8) uses a HCR with Biomass triggers. ICES does not report 
reference  points  other  than  Blim.  The  HCR’s  upper  biomass  trigger  was  used  as 
MSYBescapement. 
ICES is in the process of shifting MSYBtrigger settings to levels which increase the probability 
of keeping F at FMSY, making it a good proxy for BMSY. Nevertheless, there are still 40 out of 
69 stocks relevant for this exercise, with MSYBtrigger set at BPA. 

 

  The  GLMM  fit  within  the  bootstrap  procedure  does  not  converge  for  all  resamples,  up  to 
20% of the fits fail, with the exception of the trend in SSB or biomass index for stocks of 
data  category  1-3  (relative  to  2003)  which  had  223  over  500  resamples  failing.  Failed 
resamples were excluded when computating model-based indicators. 

  The 2017 ICES update of eco regions’ definition removed the category ‘widely distributed’ 
stocks.  For  compatibility  with  previous  versions  of  this  report,  the  stocks  previously 
included in the category ‘widely distributed’ were kept, and renamed ‘Northeast Atlantic’.    

2.4  Differences from the 2017 CFP monitoring report (STECF 17-04) 

2.4.1  Northeast Atlantic 

  Stocks with less than five years of data were not included in the analysis. 
  The CFP requirements indicator was updated, replacing BPA by MSYBtrigger, making it more 
in  line  with  the  CFP  regulation  and  renamed  to  avoid  misleading  the  readers,  to  ‘Stocks 
with F above/below Fmsy or SSB below/above MSYBtrigger’. 

  Stocks  without  stock  assessment  estimates  for  2015  and/or  2016  were  assigned  values 

equivalent to 2014 and/or 2015 estimates respectively.    

  The  Northern  shrimp  stock  (pra.27.1-2)  was  removed  from  the  computation  of  the 
indicator F/FMSY  outside  the  EU  coastal  waters,  because  the  indicator  values  were  heavily 
influenced by the outlier behaviour of this stock (STECF, 2017a). 

2.4.2  Mediterranean and Black Sea 

  A  new  reference  list  of  stocks  was  adopted  in  accordance  with  the  revised  protocol 
adopted  by  STECF  (2017a).  The  previous  reference  list  (Mannini  et  al.,  2017)  was 
complemented with stock assessment results for selected additional species established by 
the STECF (2017a). 

  Stocks with less than five years of data were not included in the analysis. 
  Stocks  without  stock  assessment  estimates  for  2015  and/or  2016  were  assigned  values 

equivalent to 2014 and/or 2015 estimates respectively.    

Because  of  the  changes  in  data  and  protocol,  the  annual  indicator  values  and  associate  time-
series  trends  for  the  Mediterranean  and  Black  seas  presented  in  the  current  report,  cannot  be 
directly compared to those presented in previous CFP monitoring reports. 

20 

20 

 
 
  
 
 
3 

NORTHEAST ATLANTIC AND ADJACENT SEAS (FAO REGION 27) 

3.1  Number of stock assessments to compute CFP performance indicators 

The  number  of  stock  assessments  with  estimates  of  F/FMSY  for  the  years  2003-2016  for  FAO 
Region 27 are given in Figure 3.1 and by ecoregion in Table 3.1. 

The  time-series  of  data  available  for  each  year  and  stock  (data  categories  1  and  2)  is  shown  in 
Figure 3.2. For stocks without estimates in 2016 the estimates of F and SSB were assumed to be 
the same as 2015. Consequently, the number of stocks included to compute the indicator values 
for 2016 was 71. 

The stocks, including data category 3, used to compute each indicator are shown in Table 3.2.  

Figure 3.1 Number of stocks in the ICES area for which estimates of F/FMSY are available by year. 

Table 3.1 Number of stocks in the ICES area for which estimates of F/FMSY are available by 
ecoregion and year 

EcoRegion 
ALL 
Baltic Sea 
BoBiscay & Iberia 
Celtic Seas 
Greater North Sea 
Northeast Atlantic 

2003  2004  2005  2006  2007  2008  2009  2010  2011  2012  2013  2014  2015  2016 
66 
8 
9 
23 
22 
4 

69 
8 
9 
23 
22 
7 

67 
8 
9 
22 
21 
7 

67 
8 
9 
22 
21 
7 

67 
8 
9 
22 
21 
7 

68 
8 
9 
23 
21 
7 

71 
8 
9 
25 
22 
7 

66 
8 
9 
21 
21 
7 

67 
8 
9 
22 
21 
7 

70 
8 
9 
24 
22 
7 

71 
8 
9 
25 
22 
7 

66 
8 
9 
21 
21 
7 

65 
8 
9 
20 
21 
7 

71 
8 
9 
25 
22 
7 

21 

21 

 
 
 
 
 
 
Figure 3.2 Time series of stock assessment results in the ICES area for which estimates of F/FMSY 
are available by year. Blank records indicate no estimate available for stock and year. 

22 

22 

 
 
Compared to the dataset used for the 2017 analyses (STECF, 2017b), the analyses presented in 
this report include the results from assessments for the following additional stocks of categories 
01 and 02: 

  had-iris (had.27.7a), ple-iris (ple.27.7a), whg-iris (whg.27.7a) and san-ns4 (san.sa.4), 

which were upgraded from category 03 in 2016 to category 01 in 2017.  

  her.27.30.31 which appeared in 2017 for the first time, as a result of merging stocks her-

30 and her-31.  

Meanwhile, there were some stocks included in the 2017 analyses (STECF 2017b) which were 
excluded from the present analyses: 

  her-30 which has now been merged with her-31 into her.27.30.31.   
  nep-2021 (nep.fu.2021) and nep-2324 (nep.fu.2324) due to having less than five years of 

data available. 

ICES  revised  the  eco-region  classification  of  the  stocks.  For  consistency  with  the  2017  report 
(STECF, 2017b), the widely distributed stocks were kept the same as last year and the stocks of 
had.27.46a20, pok.27.3a46 and sol.27.7e were kept in the Greater North Sea eco-region.  

In total, 71 stocks of categories 01 and 02 were included in the present analysis. 

23 

23 

 
 
 
Table 3.2 Indicators computed for each stocks. 

Stock 
ane.27.8 
ane.27.9a 
anf.27.3a46 
ank.27.78ab 
ank.27.8c9a 
aru.27.5b6a 
aru.27.6b7-1012 
bli.27.5b67 
bll.27.3a47de 
boc.27.6-8 
bss.27.4bc7ad-h 
bss.27.8ab 
cod.27.21 
cod.27.22-24 
cod.27.25-32 
cod.27.47d20 
cod.27.6a 
cod.27.7a 
cod.27.7e-k 
dab.27.22-32 
dab.27.3a4 
dgs.27.nea 
fle.27.2223 
fle.27.2425 
fle.27.2628 
fle.27.2729-32 
fle.27.3a4 
gfb.27.nea 
gug.27.3a47d 
had.27.46a20 
had.27.6b 
had.27.7a 
had.27.7b-k 
her.27.1-24a514a 
her.27.20-24 
her.27.25-2932 
her.27.28 
her.27.3031 

Year 
2016 
2016 
2016 
2015 
2016 
2016 
2016 
2015 
2016 
2016 
2016 
2016 
2016 
2016 
2016 
2016 
2016 
2016 
2016 
2016 
2016 
2015 
2016 
2016 
2016 
2016 
2016 
2015 
2016 
2016 
2016 
2016 
2016 
2016 
2016 
2016 
2016 
2016 

above/below 
Fmsy 
X 

in/out 
SBL 

B wrt 
MSYBtrigger 
or 
F wrt FMSY 

F/Fmsy 
trends 

Biomass 
trends 
X 

SSB/Bpa 
trends 

Recruitment 
trends 
X 

X 

X 

X 

X 
X 
X 
X 

X 

X 
X 
X 
X 

X 
X 
X 
X 

X 

X 

X 
X 
X 
X 

X 
X 
X 
X 

X 
X 
X 
X 

X 

X 

X 

X 
X 
X 
X 

X 
X 
X 
X 

X 
X 
X 
X 

X 

X 

X 

X 
X 
X 
X 

X 

X 
X 
X 
X 

X 
X 
X 
X 

24 

24 

X 

X 

X 

X 
X 
X 
X 

X 
X 
X 
X 

X 
X 
X 
X 

X 

X 

X 
X 
X 
X 

X 

X 
X 
X 
X 

X 
X 
X 
X 

X 

X 

X 
X 
X 
X 

X 

X 
X 
X 
X 

X 
X 
X 
X 

Biomass 
data 
category 3 
trends 

X 
X 
X 

X 
X 

X 
X 

X 
X 

X 

X 
X 

X 
X 
X 
X 
X 
X 
X 

Biomass 
data 
category 1-
3 trends 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Stock 
her.27.3a47d 
her.27.6a7bc 
her.27.irls 
her.27.nirs 
hke.27.3a46-8abd 
hke.27.8c9a 
hom.27.2a4a5b6a7a-ce-k8 
hom.27.9a 
jaa.27.10a2 
ldb.27.8c9a 
lem.27.3a47d 
lez.27.4a6a 
lez.27.6b 
lin.27.3a4a6-91214 
lin.27.5b 
mac.27.nea 
meg.27.7b-k8abd 
meg.27.8c9a 
mon.27.78ab 
mon.27.8c9a 
mur.27.3a47d 
nep.fu.11 
nep.fu.12 
nep.fu.13 
nep.fu.14 
nep.fu.15 
nep.fu.16 
nep.fu.17 
nep.fu.19 
nep.fu.22 
nep.fu.25 
nep.fu.2627 
nep.fu.2829 
nep.fu.3-4 
nep.fu.31 
nep.fu.6 
nep.fu.7 
nep.fu.8 
nep.fu.9 
nop.27.3a4 

Year 
2016 
2016 
2016 
2016 
2016 
2016 
2016 
2016 
2015 
2016 
2016 
2016 
2016 
2016 
2016 
2016 
2016 
2016 
2015 
2016 
2016 
2016 
2016 
2016 
2016 
2016 
2016 
2016 
2016 
2016 
2015 
2015 
2016 
2016 
2015 
2016 
2016 
2016 
2016 
2016 

above/below 
Fmsy 
X 
X 
X 
X 
X 
X 
X 
X 

in/out 
SBL 
X 
X 
X 
X 
X 
X 
X 
X 

B wrt 
MSYBtrigger 
or 
F wrt FMSY 
X 
X 
X 
X 
X 
X 
X 
X 

F/Fmsy 
trends 
X 
X 
X 
X 
X 
X 
X 
X 

Biomass 
trends 
X 
X 
X 
X 
X 
X 
X 
X 

SSB/Bpa 
trends 
X 
X 
X 
X 
X 
X 
X 
X 

Recruitment 
trends 
X 
X 
X 
X 
X 
X 
X 
X 

X 

X 

X 
X 
X 

X 

X 
X 
X 
X 
X 
X 
X 
X 
X 

X 

X 
X 
X 
X 
X 

X 

X 

X 
X 
X 

X 

X 

X 
X 
X 

X 

X 

X 

X 
X 
X 

X 

X 

X 
X 
X 

X 

X 

X 
X 
X 

X 

X 

X 

X 
X 
X 

X 

X 
X 
X 
X 
X 

X 
X 
X 

X 
X 
X 
X 

X 

X 

X 

25 

25 

Biomass 
data 
category 3 
trends 

X 

X 

X 
X 
X 

X 

X 

X 
X 
X 

X 

Biomass 
data 
category 1-
3 trends 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 

X 
X 
X 

X 

X 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Stock 
pil.27.8abd 
pil.27.8c9a 
ple.27.21-23 
ple.27.24-32 
ple.27.420 
ple.27.7a 
ple.27.7d 
ple.27.7e 
ple.27.7fg 
ple.27.7h-k 
pok.27.3a46 
pra.27.4a20 
reb.2127.dp 
rjc.27.3a47d 
rjc.27.8 
rjc.27.9a 
rjh.27.9a 
rjm.27.3a47d 
rjm.27.8 
rjm.27.9a 
rjn.27.3a4 
rjn.27.67 
rjn.27.8c 
rjn.27.9a 
rju.27.7de 
rng.27.5b6712b 
san.sa.1r 
san.sa.2r 
san.sa.3r 
san.sa.4 
sbr.27.9 
sdv.27.nea 
sho.27.67 
sho.27.89a 
sol.27.20-24 
sol.27.4 
sol.27.7a 
sol.27.7d 
sol.27.7e 
sol.27.7fg 

Year 
2016 
2016 
2016 
2016 
2016 
2016 
2016 
2016 
2016 
2016 
2016 
2016 
2016 
2016 
2015 
2015 
2015 
2016 
2015 
2015 
2016 
2015 
2015 
2015 
2015 
2015 
2016 
2016 
2016 
2016 
2015 
2016 
2016 
2016 
2016 
2016 
2015 
2016 
2016 
2016 

above/below 
Fmsy 

in/out 
SBL 

B wrt 
MSYBtrigger 
or 
F wrt FMSY 

F/Fmsy 
trends 

Biomass 
trends 

SSB/Bpa 
trends 

Recruitment 
trends 

X 

X 
X 
X 

X 
X 

X 
X 
X 
X 
X 

X 
X 
X 
X 
X 
X 

X 

X 
X 
X 

X 
X 

X 
X 
X 
X 
X 
X 

X 

X 
X 
X 

X 
X 

X 
X 
X 
X 
X 
X 

X 

X 
X 
X 

X 
X 

X 

X 
X 
X 
X 
X 
X 

26 

26 

X 

X 
X 
X 

X 
X 

X 
X 
X 
X 

X 
X 
X 
X 
X 
X 

X 

X 
X 
X 

X 
X 

X 
X 
X 
X 
X 

X 
X 
X 
X 
X 
X 

X 

X 
X 
X 

X 
X 

X 
X 
X 
X 

X 
X 
X 
X 
X 
X 

Biomass 
data 
category 3 
trends 
X 

X 

X 
X 
X 

X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 

X 
X 
X 
X 

Biomass 
data 
category 1-
3 trends 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Stock 
sol.27.7h-k 
sol.27.8ab 
spr.27.22-32 
spr.27.4 
syc.27.3a47d 
syc.27.67a-ce-j 
syc.27.8abd 
syc.27.8c9a 
tur.27.3a 
tur.27.4 
usk.27.3a45b6a7-912b 
whb.27.1-91214 
whg.27.47d 
whg.27.6a 
whg.27.7a 
whg.27.7b-ce-k 
wit.27.3a47d 
Total 

Year 
2016 
2016 
2016 
2016 
2016 
2016 
2016 
2016 
2016 
2016 
2016 
2016 
2016 
2015 
2016 
2016 
2016 

above/below 
Fmsy 

in/out 
SBL 

B wrt 
MSYBtrigger 
or 
F wrt FMSY 

F/Fmsy 
trends 

Biomass 
trends 

SSB/Bpa 
trends 

Recruitment 
trends 

X 
X 
X 

X 
X 
X 
X 
X 

X 
X 

X 
X 
X 
X 
X 

X 
X 

X 
X 
X 
X 
X 

X 
X 

X 
X 
X 
X 
X 

X 
X 
X 

X 
X 
X 
X 
X 

X 
X 
X 

X 
X 
X 
X 
X 

X 
X 
X 

X 
X 
X 
X 
X 

71 

46 

62 

48 

54 

55 

54 

Biomass 
data 
category 1-
3 trends 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
121 

Biomass 
data 
category 3 
trends 
X 

X 
X 
X 
X 
X 
X 
X 

X 
61 

27 

27 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
3.2  Indicators of management performance 

3.2.1  Number of stocks by year where fishing mortality exceeded FMSY 

Figure 3.3 Number of stocks by year for which fishing mortality (F) exceeded FMSY. 

Figure 3.4 Number of stocks by year and ecoregion for which fishing mortality (F) exceeded FMSY. 

Table 3.3 Number of stocks by year and ecoregion for which fishing mortality (F) exceeded FMSY. 

EcoRegion 
ALL 
Baltic Sea 
BoBiscay & Iberia 
Celtic Seas 
Greater North Sea 
Widely distributed 

2003  2004  2005  2006  2007  2008  2009  2010  2011  2012  2013  2014  2015  2016 
29 
4 
5 
9 
9 
2 

49 
6 
7 
14 
18 
4 

46 
7 
6 
13 
13 
7 

37 
5 
5 
12 
13 
2 

28 
3 
6 
8 
9 
2 

32 
2 
6 
8 
13 
3 

48 
6 
6 
16 
16 
4 

45 
6 
6 
12 
16 
5 

51 
6 
8 
16 
17 
4 

32 
4 
5 
10 
10 
3 

29 
4 
5 
8 
10 
2 

50 
6 
7 
14 
18 
5 

39 
6 
5 
12 
12 
4 

39 
6 
5 
13 
12 
3 

28 

28 

 
 
 
 
 
 
3.2.2  Number of stocks by year for which fishing mortality was equal to, or less than FMSY 

Figure 3.5 Number of stocks by year for which fishing mortality (F) did not exceed FMSY. 

Figure 3.6 Number of stocks by year and ecoregion for which fishing mortality (F) did not exceed FMSY. 

Table 3.4 Number of stocks by year and ecoregion for which fishing mortality (F) did not exceed 
FMSY. 

EcoRegion 
ALL 
Baltic Sea 
BoBiscay & Iberia 
Celtic Seas 
Greater North Sea 
Widely distributed 

2003  2004  2005  2006  2007  2008  2009  2010  2011  2012  2013  2014  2015  2016 
42 
4 
4 
16 
13 
5 

18 
2 
2 
8 
3 
3 

20 
1 
3 
8 
8 
0 

33 
3 
4 
12 
9 
5 

29 
2 
4 
10 
9 
4 

39 
6 
3 
17 
9 
4 

20 
2 
3 
8 
5 
2 

37 
4 
4 
13 
12 
4 

16 
2 
1 
6 
4 
3 

28 
2 
4 
10 
9 
3 

19 
2 
3 
6 
5 
3 

42 
4 
4 
17 
12 
5 

43 
5 
3 
17 
13 
5 

16 
2 
2 
7 
3 
2 

29 

29 

 
 
 
 
 
 
3.2.3  Number of stocks outside safe biological limits 

Figure 3.7 Number of stocks outside safe biological limits by year. 

Figure 3.8 Number of stocks outside safe biological limits by ecoregion and year. 

Table 3.5 Number of stocks outside safe biological limits by ecoregion and year. 

EcoRegion 
ALL 
Baltic Sea 
BoBiscay & Iberia 
Celtic Seas 
Greater North Sea 
Widely distributed 

2003  2004  2005  2006  2007  2008  2009  2010  2011  2012  2013  2014  2015  2016 
16 
3 
0 
9 
3 
1 

29 
5 
5 
8 
8 
3 

31 
6 
4 
11 
6 
4 

29 
5 
4 
9 
8 
3 

17 
4 
1 
9 
2 
1 

26 
5 
3 
9 
7 
2 

20 
5 
1 
8 
5 
1 

31 
6 
4 
11 
6 
4 

22 
5 
1 
8 
7 
1 

21 
4 
2 
9 
5 
1 

18 
3 
3 
7 
4 
1 

32 
6 
5 
10 
8 
3 

20 
4 
2 
9 
4 
1 

21 
5 
3 
8 
5 
0 

30 

30 

 
 
 
 
 
 
 
3.2.4  Number of stocks inside safe biological limits 

Figure 3.9 Number of stocks inside safe biological limits by year. 

Figure 3.10 Number of stocks inside safe biological limits by ecoregion and year. 

Table 3.6 Number of stocks inside safe biological limits by ecoregion and year. 

EcoRegion 
ALL 
Baltic Sea 
BoBiscay & Iberia 
Celtic Seas 
Greater North Sea 
Widely distributed 

2003  2004  2005  2006  2007  2008  2009  2010  2011  2012  2013  2014  2015  2016 
30 
5 
7 
6 
8 
4 

17 
3 
2 
7 
3 
2 

15 
2 
3 
4 
5 
1 

15 
2 
3 
4 
5 
1 

14 
2 
2 
5 
3 
2 

26 
3 
6 
7 
6 
4 

20 
3 
4 
6 
4 
3 

28 
5 
4 
8 
7 
4 

24 
3 
6 
7 
4 
4 

25 
4 
5 
6 
6 
4 

25 
3 
4 
7 
6 
5 

26 
4 
5 
6 
7 
4 

17 
3 
3 
6 
3 
2 

29 
4 
6 
6 
9 
4 

31 

31 

 
 
 
 
 
 
3.2.5  Trend in F/FMSY 

Indicators  of  trends  show  the  average  progress  of  the  process  they  represent,  including  its 
uncertainty in terms of 50% and 95% confidence intervals. In the former case corresponding to 
the  range  between  the  25%  and  75%  percentiles,  and  for  the  latter  between  the  2.5%  and 
97.5% percentiles. 

Trends in F/FMSY by ecoregion and year are given in Figure 3.11 and the associated percentiles are 
given  in  Table  3.7.  Figure  3.11  shows  the  indicator  value  in  2016  close  to  1,  which  means  that 
over all stocks, on average, the exploitation levels are close to FMSY. Nevertheless, there are still 
about 40% of the stocks which are being exploited above FMSY (see sections 3.2.1 and 3.2.2).  

Figure 3.11 Trend in F/FMSY. Dark grey zone shows the 50% confidence interval; the light grey zone 
shows the 95% confidence interval. 

Table 3.7 Percentiles for F/FMSY by year. 

2.5% 
25% 
50% 
75% 
97.5% 

2003  2004  2005  2006  2007  2008  2009  2010  2011  2012  2013  2014  2015  2016 
0.84 
1.42 
0.93 
1.55 
0.98 
1.64 
1.03 
1.71 
1.15 
1.87 

0.82 
0.92 
0.97 
1.01 
1.14 

0.90 
1.00 
1.05 
1.10 
1.21 

1.17 
1.27 
1.34 
1.41 
1.52 

0.89 
0.99 
1.04 
1.09 
1.20 

0.89 
1.02 
1.07 
1.13 
1.26 

1.36 
1.48 
1.55 
1.63 
1.77 

1.06 
1.17 
1.23 
1.30 
1.41 

1.00 
1.11 
1.17 
1.23 
1.33 

1.40 
1.53 
1.61 
1.69 
1.85 

1.28 
1.42 
1.48 
1.55 
1.68 

1.32 
1.45 
1.52 
1.58 
1.70 

0.86 
0.98 
1.03 
1.08 
1.20 

32 

32 

 
 
 
 
 
 
 
 
 
 
Trends in F/FMSY by ecoregion are given in Figure 3.13 and Table 3.8. The regional analysis was 
carried out using the same model applied to regional datasets. Due to the small number of stocks 
in each ecoregion it was not possible to compute confidence intervals.  

Figure 3.12 Trend in F/FMSY by ecoregion. 

Table 3.8. Trend in F/FMSY by ecoregion and year 

EcoRegion 
ALL 
Baltic Sea 
BoBiscay & Iberia 
Celtic Seas 
Greater North Sea 
Widely distributed 

2003  2004  2005  2006  2007  2008  2009  2010  2011  2012  2013  2014  2015  2016 
0.98 
1.64 
1.18 
1.60 
0.98 
1.36 
0.89 
1.94 
0.99 
1.47 
0.90 
1.64 

1.52 
1.51 
1.58 
1.63 
1.44 
1.33 

1.07 
1.05 
1.21 
1.08 
1.03 
0.93 

1.05 
1.16 
1.10 
1.10 
1.03 
0.74 

0.97 
1.07 
1.08 
0.90 
0.97 
0.81 

1.48 
1.51 
1.46 
1.66 
1.36 
1.25 

1.34 
1.45 
1.27 
1.52 
1.20 
1.10 

1.03 
1.12 
1.20 
0.95 
1.00 
0.94 

1.61 
1.63 
1.35 
1.93 
1.45 
1.48 

1.17 
1.27 
1.05 
1.38 
1.01 
0.94 

1.55 
1.57 
1.44 
1.78 
1.39 
1.44 

1.23 
1.41 
1.21 
1.38 
1.06 
0.99 

1.04 
1.07 
1.10 
1.14 
0.99 
0.75 

33 

33 

 
 
 
 
 
 
 
3.2.6  Trend in SSB (relative to 2003) 

Indicators  of  trends  show  the  average  progress  of  the  process  they  represent,  including  its 
uncertainty in terms of 50% and 95% confidence intervals. In the former case corresponding to 
the  range  between  the  25%  and  75%  percentiles,  and  for  the  latter  between  the  2.5%  and 
97.5% percentiles. 

Figure 3.13 and Table 3.9 present the evolution of SSB over the period of the study, scaled to the 
initial  (2003)  value  for  presentation  purposes.  Over  the  time  series,  SSB  shows  a  generally 
increasing pattern. 

Figure 3.13 Trend in SSB relative to 2003. Dark grey zone shows the 50% confidence interval; the light 
grey zone shows the 95% confidence interval. 

Table 3.9 Percentiles for SSB by year relative to 2003. 

2.5% 
25% 
50% 
75% 
97.5% 

2003 
0.66 
0.86 
1.00 
1.17 
1.47 

2004 
0.60 
0.79 
0.92 
1.07 
1.37 

2005 
0.57 
0.75 
0.87 
1.03 
1.32 

2006 
0.55 
0.73 
0.84 
1.00 
1.30 

2007 
0.55 
0.73 
0.84 
0.99 
1.31 

2008 
0.59 
0.78 
0.90 
1.06 
1.37 

2009 
0.62 
0.80 
0.92 
1.09 
1.40 

2010 
0.67 
0.86 
1.00 
1.18 
1.54 

2011 
0.80 
1.06 
1.23 
1.42 
1.87 

2012 
0.75 
0.99 
1.14 
1.32 
1.77 

2013 
0.72 
0.94 
1.10 
1.27 
1.70 

2014 
0.76 
1.00 
1.16 
1.34 
1.80 

2015 
0.83 
1.09 
1.26 
1.46 
1.97 

2016 
0.92 
1.19 
1.39 
1.60 
2.12 

34 

34 

 
 
 
 
  
 
 
 
Trends  in  SSB  by  ecoregion  and  year  are  given  in  Figure  3.14  and  Table  3.10.  The  regional 
analysis  was  carried  out  using  the  same  model  applied  to  regional  datasets.  Due  to  the  small 
number of stocks in each ecoregion it wasn’t possible to compute confidence intervals. 

Figure 3.14 Trend in SSB by ecoregion relative to 2003. 

Table 3.10 SSB relative to 2003 by ecoregion. 

EcoRegion 
ALL 
Baltic Sea 
BoBiscay & Iberia 
Celtic Seas 
Greater North Sea 
Widely distributed 

2003  2004  2005  2006  2007  2008  2009  2010  2011  2012  2013  2014  2015  2016 
1.39 
1.00 
1.22 
1.00 
1.80 
1.00 
1.21 
1.00 
1.47 
1.00 
1.54 
1.00 

0.84 
1.17 
1.06 
0.70 
0.72 
1.06 

1.23 
0.98 
1.57 
0.95 
1.45 
1.34 

0.92 
1.07 
1.04 
0.87 
0.83 
1.05 

0.90 
1.00 
1.08 
0.78 
0.89 
1.04 

0.84 
1.11 
1.08 
0.71 
0.75 
1.04 

1.16 
1.21 
1.60 
0.87 
1.20 
1.43 

1.10 
1.08 
1.42 
0.90 
1.10 
1.40 

1.26 
1.20 
1.72 
1.10 
1.24 
1.51 

1.00 
1.01 
1.26 
0.78 
1.08 
1.18 

0.87 
1.17 
1.02 
0.73 
0.80 
1.06 

0.92 
0.99 
1.13 
0.76 
0.95 
1.07 

1.14 
1.03 
1.55 
0.98 
1.14 
1.40 

35 

35 

 
 
 
 
 
 
 
3.3  Experimental indicators 

STECF (2017a) required a list of experimental indicators to be computed, similar to the 2017 exercise 
(STECF, 2017b). The estimates obtained for these indicators are not stable and should be considered 
with care. 

36 

36 

 
 
 
3.3.1  Number of stocks with F above Fmsy or SSB below MSYBtrigger 

Figure 3.15 Number of stocks with F above Fmsy or SSB below MSYBtrigger by year. 

Figure 3.16 Number of stocks with F above Fmsy or SSB below MSYBtrigger by ecoregion and year. 

Table 3.11 Number of stocks with F above Fmsy or SSB below MSYBtrigger by ecoregion and year. 

EcoRegion 
ALL 
Baltic Sea 
BoBiscay & Iberia 
Celtic Seas 
Greater North Sea 
Widely distributed 

2003  2004  2005  2006  2007  2008  2009  2010  2011  2012  2013  2014  2015  2016 
37 
5 
5 
14 
8 
5 

46 
6 
7 
14 
13 
6 

43 
7 
6 
14 
9 
7 

35 
4 
5 
12 
9 
5 

44 
7 
7 
13 
11 
6 

47 
7 
7 
15 
12 
6 

40 
6 
6 
12 
11 
5 

42 
6 
6 
16 
10 
4 

36 
5 
5 
12 
9 
5 

47 
6 
6 
17 
12 
6 

32 
4 
6 
13 
5 
4 

42 
6 
6 
15 
11 
4 

48 
6 
7 
16 
13 
6 

41 
6 
6 
13 
11 
5 

37 

37 

 
 
 
 
 
3.3.2  Number of stocks with F below or equal to Fmsy and SSB above or equal to MSYBtrigger 

Figure 3.17 Number of stocks with F below or equal to Fmsy and SSB above or equal to MSYBtrigger. 

Figure 3.18 Number of stocks with F below or equal to Fmsy and SSB above or equal to MSYBtrigger by 
ecoregion and year. 

Table 3.12 Number of stocks with F below or equal to Fmsy and SSB above or equal to MSYBtrigger by 
ecoregion and year. 

EcoRegion 
ALL 
Baltic Sea 
BoBiscay & Iberia 
Celtic Seas 
Greater North Sea 
Widely distributed 

2003  2004  2005  2006  2007  2008  2009  2010  2011  2012  2013  2014  2015  2016 
25 
3 
3 
10 
7 
2 

14 
2 
1 
8 
2 
1 

30 
4 
2 
11 
10 
3 

19 
2 
2 
9 
4 
2 

14 
1 
1 
7 
4 
1 

16 
1 
2 
7 
6 
0 

13 
2 
2 
5 
3 
1 

27 
4 
3 
12 
6 
2 

19 
2 
2 
8 
4 
3 

21 
2 
2 
11 
4 
2 

19 
2 
2 
7 
5 
3 

12 
2 
1 
6 
2 
1 

12 
1 
1 
6 
3 
1 

26 
3 
3 
12 
6 
2 

38 

38 

 
 
 
 
 
3.3.3  Trend in F/FMSY for stocks outside the EU coastal waters 

Indicators  of  trends  show  the  average  progress  of  the  process  they  represent,  including  its 
uncertainty in terms of 50% and 95% confidence intervals. In the former case corresponding to 
the  range  between  the  25%  and  75%  percentiles,  and  for  the  latter  between  the  2.5%  and 
97.5% percentiles. 

This indicator was based on 9 stocks. The Northern shrimp stock (pra.27.1-2) was removed from 
the  computation  of  the  indicator  F/FMSY  outside  the  EU  coastal  waters,  because  the  indicator 
values were heavily influenced by the outlier behaviour of this stock (STECF, 2017a). 

Figure 3.19 Trend in F/FMSY for stocks outside the EU coastal waters. Dark grey zone shows the 50% 
confidence interval; the light grey zone shows the 95% confidence interval. 

Table 3.13 Percentiles for F/FMSY for stocks outside the EU coastal waters by year. 

2.5% 
25% 
50% 
75% 
97.5% 

2003 
1.24 
1.44 
1.55 
1.67 
1.89 

2004 
1.21 
1.40 
1.52 
1.66 
1.98 

2005 
1.30 
1.50 
1.62 
1.73 
2.02 

2006 
1.16 
1.35 
1.47 
1.58 
1.87 

2007 
1.16 
1.31 
1.41 
1.50 
1.72 

2008 
1.13 
1.31 
1.40 
1.49 
1.75 

2009 
0.91 
1.09 
1.19 
1.32 
1.56 

2010 
0.94 
1.14 
1.25 
1.40 
1.65 

2011 
0.89 
1.05 
1.15 
1.26 
1.47 

2012 
0.92 
1.07 
1.17 
1.27 
1.47 

2013 
0.84 
0.98 
1.06 
1.15 
1.37 

2014 
0.81 
0.94 
1.03 
1.13 
1.40 

2015 
0.84 
0.98 
1.07 
1.17 
1.42 

2016 
0.86 
0.98 
1.06 
1.14 
1.35 

39 

39 

 
 
 
 
  
 
 
 
3.3.4  Trend in SSB/Bpa 

Indicators  of  trends  show  the  average  progress  of  the  process  they  represent,  including  its 
uncertainty in terms of 50% and 95% confidence intervals. In the former case corresponding to 
the range between the 25% and 75% percentiles, and for the last between the 2.5% and 97.5% 
percentiles. 

Figure 3.20 Trend in SSB/Bpa. Dark grey zone shows the 50% confidence interval; the light grey zone 
shows the 95% confidence interval. 

Table 3.14 Percentiles for SSB/Bpa by year. 

2.5% 
25% 
50% 
75% 
97.5% 

2003 
0.87 
0.95 
1.02 
1.07 
1.20 

2004 
0.78 
0.88 
0.94 
1.00 
1.10 

2005 
0.76 
0.84 
0.90 
0.96 
1.09 

2006 
0.73 
0.81 
0.87 
0.92 
1.05 

2007 
0.73 
0.81 
0.87 
0.92 
1.05 

2008 
0.78 
0.87 
0.93 
0.99 
1.12 

2009 
0.79 
0.89 
0.95 
1.01 
1.15 

2010 
0.85 
0.95 
1.02 
1.08 
1.25 

2011 
0.98 
1.12 
1.20 
1.30 
1.50 

2012 
0.95 
1.07 
1.15 
1.22 
1.40 

2013 
0.92 
1.04 
1.12 
1.19 
1.36 

2014 
0.96 
1.10 
1.17 
1.25 
1.43 

2015 
1.05 
1.19 
1.27 
1.36 
1.54 

2016 
1.15 
1.31 
1.40 
1.51 
1.70 

40 

40 

 
 
 
 
 
  
 
 
 
3.3.5  Trend in recruitment (relative to 2003) 

Indicators  of  trends  show  the  average  progress  of  the  process  they  represent,  including  its 
uncertainty in terms of 50% and 95% confidence intervals. In the former case corresponding to 
the  range  between  the  25%  and  75%  percentiles,  and  for  the  latter  between  the  2.5%  and 
97.5% percentiles. 

Figure 3.21 Trend in R/R2003 . Dark grey zone shows the 50% confidence interval; the light grey zone 
shows the 95% confidence interval. 

Table 3.15 Percentiles for R/R2003 by year. 

2.5% 
25% 
50% 
75% 
97.5% 

2003 
0.49 
0.79 
1.00 
1.28 
1.96 

2004 
0.50 
0.75 
0.94 
1.19 
1.76 

2005 
0.48 
0.76 
0.96 
1.23 
1.88 

2006 
0.52 
0.83 
1.06 
1.32 
1.98 

2007 
0.45 
0.70 
0.90 
1.12 
1.69 

2008 
0.43 
0.70 
0.89 
1.12 
1.75 

2009 
0.59 
0.99 
1.30 
1.71 
2.70 

2010 
0.53 
0.80 
1.01 
1.25 
1.88 

2011 
0.48 
0.76 
0.96 
1.20 
1.78 

2012 
0.47 
0.73 
0.94 
1.22 
1.80 

2013 
0.52 
0.82 
1.06 
1.34 
2.00 

2014 
0.62 
0.98 
1.27 
1.62 
2.66 

2015 
0.55 
0.88 
1.11 
1.41 
2.12 

2016 
0.59 
0.94 
1.26 
1.64 
2.67 

41 

41 

 
 
 
 
 
  
 
 
 
3.3.6  Trend in SSB or biomass index for stocks of data category 1-3 (relative to 2003) 

Indicators  of  trends  show  the  average  progress  of  the  process  they  represent,  including  its 
uncertainty in terms of 50% and 95% confidence intervals. In the former  case corresponding to 
the  range  between  the  25%  and  75%  percentiles,  and  for  the  latter  between  the  2.5%  and 
97.5% percentiles. 

Note that the bootstrap procedure failed in 223 over 500 iterations, which is a sign of the poor fit 
of the model to the dataset. It also explains the value of 0.96 in 2003 (Table 3.16), which derives 
from the skewed distribution obtained for this indicator.   

Figure 3.22 Trend in SSB relative to 2003 for category 1-3 stocks. Dark grey zone shows the 50% 
confidence interval; the light grey zone shows the 95% confidence interval. 

Table 3.16 Percentiles for SSB relative to 2003 by year for category 1-3 stocks. 

2.5% 
25% 
50% 
75% 
97.5% 

2003 
0.49 
0.77 
0.96 
1.30 
2.12 

2004 
0.45 
0.73 
0.93 
1.23 
2.00 

2005 
0.45 
0.72 
0.91 
1.23 
1.95 

2006 
0.46 
0.74 
0.95 
1.26 
1.96 

2007 
0.50 
0.76 
0.99 
1.29 
2.01 

2008 
0.53 
0.82 
1.03 
1.37 
2.09 

2009 
0.53 
0.79 
1.01 
1.34 
2.09 

2010 
0.58 
0.83 
1.08 
1.43 
2.22 

2011 
0.63 
0.91 
1.19 
1.60 
2.46 

2012 
0.62 
0.97 
1.25 
1.65 
2.55 

2013 
0.62 
0.94 
1.22 
1.61 
2.47 

2014 
0.64 
0.99 
1.28 
1.69 
2.72 

2015 
0.73 
1.14 
1.48 
1.93 
3.06 

2016 
0.77 
1.20 
1.54 
2.06 
3.35 

42 

42 

 
 
 
 
 
  
 
 
 
3.3.7  Trend in SSB or biomass index for stocks of data category 3 (relative to 2003) 

Indicators  of  trends  show  the  average  progress  of  the  process  they  represent,  including  its 
uncertainty in terms of 50% and 95% confidence intervals. In the former case corresponding to 
the  range  between  the  25%  and  75%  percentiles,  and  for  the  latter  between  the  2.5%  and 
97.5% percentiles. 

Figure 3.23 Trend in SSB relative to 2003 for category 3 stocks. Dark grey zone shows the 50% 
confidence interval; the light grey zone shows the 95% confidence interval. 

Table 3.17 Percentiles for SSB relative to 2003 by year for category 3 stocks. 

2.5% 
25% 
50% 
75% 
97.5% 

2003 
0.59 
0.82 
1.01 
1.21 
1.80 

2004 
0.62 
0.84 
1.02 
1.19 
1.74 

2005 
0.63 
0.84 
1.03 
1.18 
1.74 

2006 
0.66 
0.92 
1.10 
1.29 
1.92 

2007 
0.73 
0.99 
1.18 
1.37 
2.03 

2008 
0.75 
1.04 
1.27 
1.50 
2.13 

2009 
0.71 
0.99 
1.18 
1.40 
2.01 

2010 
0.76 
1.04 
1.25 
1.47 
2.09 

2011 
0.77 
1.09 
1.29 
1.52 
2.18 

2012 
0.90 
1.24 
1.47 
1.74 
2.57 

2013 
0.89 
1.23 
1.47 
1.72 
2.52 

2014 
0.93 
1.29 
1.55 
1.82 
2.64 

2015 
1.08 
1.53 
1.85 
2.15 
3.12 

2016 
1.11 
1.56 
1.87 
2.21 
3.22 

43 

43 

 
 
 
 
 
  
 
 
 
3.4  Indicators of advice coverage 

The indicator of advice coverage computes the number of stocks for which the reference points, 
FMSY, FPA, MSYBtrigger and BPA are available and the number of associated TACs. Note that provided 
part of a given TAC management area overlaps with part of a stock assessment area, the setting 
of  the  TAC  is  considered  as  being  based  on  the  relevant  stock  assessment.  Consequently,  the 
advice  coverage  indicator  is  biased  upwards  if  compared  with  the  full  spatial  coverage  of  TAC 
areas by stock assessments. 

Table 3.18 Coverage of TACs by scientific advice (ICES categories 1+2). 

Fmsy 
MSYBtrigger 
Fpa 
Bpa 

No of 
stocks 
71 
69 
46 
55 

No of 
TACs 
156 
156 
156 
156 

No of TACs based on 
stock assessments 
87 
86 
72 
79 

Fraction of TACs based on 
stock assessments 
0.56 
0.55 
0.46 
0.51 

44 

44 

 
 
 
  
 
4 

MEDITERRANEAN AND BLACK SEAS (FAO REGION 37) 

There was a strong increasing trend in the number of stocks assessed for years 2003-2009, from 
22  up  to  47;  the  number  of  stock  assessments  kept  stable  until  2014  and  decreased  to  39  in 
2015 and 21 in 2016 (Figure 4.1 and Figure 4.2).    

This  situation  renders  the  interpretation  of  the  deterministic  indicators  misleading.  With  such 
differences  in  the  number  of  stocks  assessed  each  year,  the  trends  in  the  indicators  are 
confounded  with  the  number  of  stocks  available  for  their  computation.  Consequently,  only  the 
model-based indicators are shown. 

Nevertheless, the indicator values presented (Figure 4.3 and Figure 4.4) are not very robust due 
to the large changes in the number of stocks available to fit the model, and therefore the results 
should be interpreted with caution. 

Figure 4.1 indicates by year, the number of stocks in the Mediterranean and Black Seas for which 
estimates of F/FMSY are available. The major reduction in 2016 is due to: 

 

 

the  STECF  EWG  part  I  carried  out  analytical  assessments  for  only  8  out  of  11  stocks 
(STECF 2017c). 
the  STECF  EWG  part  II  carried  out  analytical  assessment  for  5  out  of  19  stocks  (STECF, 
2018).  

  GFCM  assessments  performed  in  2017  in  WGSASP  and  WGSADM  have  not  yet  been 
reviewed  and  approved  by  the  GFCM  Scientific  Advisory  Committee.  Consequently,  they 
were not included in the present analysis. 

Table 4.1 shows the stocks added to the current exercise.  

Since  there  are  no  results  for  2016  for  any  of  the  GFCM  stock  assessments  and  the  indicator 
values  for  2016  are  based  on  the  results  of  only  21  stock  assessments,  such  values  are  not 
comparable with those for earlier years of the time-series. Hence in Figure 4.1, the 2016 value is 
represented as stand-alone, and the indicators are plotted up to 2015 only. 

Figure 4.1 Number of stock assessments in the Mediterranean and Black Sea by year. The totals 
include stocks in the following GSAs only: 1, 5-7, 9, 10-19, 22-23, 25 and 29. 

45 

45 

 
  
 
 
 
 
Figure  4.2  Time-series  of  stock  assessments  available  from  both  STECF  and  GFCM  for 
computation of model based CFP monitoring indicators for Mediterranean and Black Seas. The red 
line  indicates  that  only  stock  assessment  results  up  to  and  including  2015  have  been  used  to 
compute the indicator values. 

46 

46 

 
 
 
 
Table 4.1 Stocks added to the current exercise with relation to previous report. 

Description 

Updated  New  Source 

EcoRegion 

Black sea 

Black sea 

Black sea 

Black sea 

Black sea 

Black sea 

Black sea 

Black sea 

Central Med. 

Central Med. 

Central Med. 

Central Med. 

Central Med. 

Central Med. 

Central Med. 

Central Med. 

Central Med. 

Central Med. 

Central Med. 

Central Med. 

Year 

2014 

2014 

2014 

2014 

2016 

2014 

2014 

2016 

2015 

2015 

2015 

2014 

2014 

2014 

2014 

2014 

2014 

2014 

2015 

2016 

Stock 

ane_29 

dgs_29 

hmm_29 

mut_29 

rpw_29 

tur_29 

spr_29 

whg_29 

ane_17_18 

nep_17_18 

pil_17_18 

ars_18_19 

European anchovy in GSA 29 

Picked dogfish in GSA 29 

Mediterranean horse mackerel in GSA 29 

Red mullet in GSA 29 

Rapana whelk in GSA 29 

Turbot in GSA 29 

Sprattus sprattus in GSA 29 

Whiting in GSA 29 

European anchovy in GSA 17, 18 

Nephrops in GSA 17, 18 

European pilchard(=Sardine) in GSA 17, 18 

Giant red shrimp in GSA 18, 19 

dps_17_18_19 

Deep-water rose shrimp in GSA 17, 18, 19 

hke_17_18 

hke_19 

mts_17_18 

mut_17_18 

sol_17 

mut_15_16 

mut_19 

European hake in GSA 17, 18 

European hake in GSA 19 

Spottail mantis squillid in GSA 17, 18 

Red mullet in GSA 17, 18 

Common sole in GSA 17 

Red mullet in GSA 15,16 

Red mullet in GSA 19 

Central Med. 

2014  hke_12_13_14_15_16 

Merluccius merluccius in GSA 12, 13, 14, 15, 16 

Central Med. 

2014  dps_12_13_14_15_16 

Parapenaeus longirostris in GSA 12, 13, 14, 15, 16 

Eastern Med. 

2016 

ane_22_23 

European anchovy in GSA 22, 23 

Eastern Med. 

2016 

pil_22_23 

European pilchard(=Sardine) in GSA 22, 23 

Eastern Med. 

2014 

mut_25 

Mullus barbatus in GSA 25 

Western Med. 

2016 

ane_09_10_11 

European anchovy in GSA 09, 10, 11 

Western Med. 

2015 

Western Med. 

2015 

Western Med. 

2015 

ane_6 

dps_1 

mut_7 

Anchovy in GSA 6 

Deep-water rose shrimp in GSA 1 

Red mullet in GSA 7 

Western Med. 

2015 

dps_09_10_11 

Deep-water rose shrimp in GSA 09, 10, 11 

Western Med. 

2015 

Western Med. 

2015 

Western Med. 

2015 

Western Med. 

2015 

Western Med. 

2015 

mur_9 

ara_9 

ars_9 

nep_9 

nep_6 

Western Med. 

2015 

nep_11 

Western Med. 

2015 

Western Med. 

2015 

Western Med. 

2015 

Western Med. 

2014 

Western Med. 

2014 

Western Med. 

2014 

ara_1 

mur_5 

pil_6 

ara_6 

ars_10 

ars_11 

Surmullet in GSA 9 

Blue and red shrimp in GSA 9 

Giant red shrimp in GSA 9 

Norway lobster in GSA 9 

Norway lobster in GSA 6 

Norway lobster in GSA 11 

Blue and red shrimp in GSA 1 

Striped red mullet in GSA 5 

European pilchard(=Sardine) in GSA 6 

Blue and red shrimp in GSA 6 

Giant red shrimp in GSA 10 

Giant red shrimp in GSA 11 

Western Med. 

2014 

hke_01_05_06_07 

European hake in GSA 01, 05, 06, 07 

Western Med. 

2014 

hke_09_10_11 

European hake in GSA 09, 10, 11 

Western Med. 

2016 

hom_09_10_11 

Atlantic horse mackerel in GSA 09, 10, 11 

Western Med. 

2013 

Western Med. 

2013 

mut_6 

ara_5 

Red mullet in GSA 6 

Aristeus antennatus in GSA 5 

47 

47 

2016 

2016 

2016 

2016 

2016 

2016 

2016 

2016 

2016 

2016 

2016 

2014 

2016 

2014 

2016 

2016 

2014 

2014 

2015 

2016 

2015 

2015 

2016 

2016 

2015 

2016 

2016 

2015 

2015 

2015 

2015 

2015 

2015 

2015 

2015 

2015 

2015 

2015 

2016 

2015 

2014 

2014 

2014 

2014 

2016 

2015 

2015 

N 

N 

N 

N 

Y 

N 

N 

Y 

N 

N 

N 

N 

N 

N 

N 

N 

N 

N 

Y 

Y 

N 

N 

Y 

Y 

N 

Y 

N 

N 

Y 

N 

N 

Y 

Y 

N 

N 

Y 

Y 

Y 

N 

N 

N 

N 

N 

N 

Y 

N 

N 

STECF 

STECF 

STECF 

STECF 

STECF 

STECF 

STECF 

STECF 

STECF 

STECF 

STECF 

STECF 

STECF 

STECF 

STECF 

STECF 

STECF 

STECF 

GFCM 

STECF 

GFCM 

GFCM 

STECF 

STECF 

GFCM 

STECF 

STECF 

STECF 

GFCM 

STECF 

STECF 

GFCM 

GFCM 

STECF 

STECF 

STECF 

GFCM 

GFCM 

STECF 

GFCM 

STECF 

STECF 

STECF 

STECF 

STECF 

GFCM 

GFCM 

 
 
4.1  Indicators of management performance 

4.1.1  Trend in F/FMSY  

Indicators  of  trends  show  the  average  progress  of  the  process  they  represent,  including  its 
uncertainty in terms of 50% and 95% confidence intervals. In the former case corresponding to 
the  range  between  the  25%  and  75%  percentiles,  and  for  the  latter  between  the  2.5%  and 
97.5% percentiles. 

Figure 4.3 Trend in F/FMSY. Dark grey zone shows the 50% confidence interval; the light grey 
zone shows the 95% confidence interval. 

Table 4.2 Percentiles for F/FMSY by year. 

2.50% 
25% 
50% 
75% 
97.50% 

2003 
1.73 
2.00 
2.17 
2.36 
2.72 

2004 
1.82 
2.07 
2.23 
2.37 
2.67 

2005 
1.87 
2.16 
2.34 
2.52 
2.86 

2006 
1.94 
2.22 
2.37 
2.52 
2.81 

2007 
1.80 
2.06 
2.18 
2.32 
2.57 

2008 
1.85 
2.06 
2.19 
2.33 
2.59 

2009 
1.87 
2.08 
2.19 
2.33 
2.58 

2010 
1.88 
2.10 
2.22 
2.36 
2.59 

2011 
2.11 
2.35 
2.49 
2.64 
2.92 

2012 
1.94 
2.14 
2.28 
2.42 
2.68 

2013 
1.95 
2.13 
2.27 
2.38 
2.63 

2014 
1.81 
2.03 
2.15 
2.30 
2.59 

2015 
1.88 
2.10 
2.25 
2.40 
2.72 

The  model  used  is  a  mixed  linear  model,  described  in  the  protocol  (Annex  I).  Values  for  2016 
were removed from the model fit. Bootstrapped quantiles of F/FMSY are displayed (Figure 4.3 and 
Table  4.1).  The  50%  quantile  (black  line),  which  is  equivalent  to  the  median,  shows  a  median 
level  slightly  varying  around  of  F/FMSY  ≈  2.3  for  the  full  time  series.  In  the  Mediterranean  and 
Black  Seas  assessments,  a  more  conservative  proxy  for  FMSY,  such  as  F0.1,  is  commonly  used 
resulting  in  a  higher  ratio  for F/FMSY.  The  lower  quantile  is  above  F/FMSY  =  1,  indicating  that  the 
stocks  are  exploited  well  above  the  CFP  management  objectives.  There  is  no  trend,  to  indicate 
any improvement in exploitation since the implementation of the 2003 reform of the CFP.   

48 

48 

 
 
 
 
 
  
 
 
 
4.1.2  Trend in SSB (relative to 2003) 

Indicators  of  trends  show  the  average  progress  of  the  process  they  represent,  including  its 
uncertainty in terms of 50% and 95% confidence intervals. In the former case corresponding to 
the  range  between  the  25%  and  75%  percentiles,  and  for  the  latter  between  the  2.5%  and 
97.5% percentiles. 

Figure 4.4 Trend in SSB relative to 2003. Dark grey zone shows the 50% confidence interval; the 
light grey zone shows the 95% confidence interval. 

Table 4.3 Percentiles for SSB by year relative to 2003. 

2.50% 
25% 
50% 
75% 
97.5% 

2003 
0.58 
0.84 
1.01 
1.18 
1.73 

2004 
0.53 
0.79 
0.95 
1.10 
1.66 

2005 
0.55 
0.79 
0.94 
1.12 
1.66 

2006 
0.63 
0.91 
1.07 
1.25 
1.87 

2007 
0.57 
0.85 
1.00 
1.15 
1.76 

2008 
0.54 
0.77 
0.91 
1.08 
1.58 

2009 
0.57 
0.82 
0.96 
1.14 
1.66 

2010 
0.58 
0.82 
0.97 
1.12 
1.63 

2011 
0.57 
0.79 
0.93 
1.08 
1.56 

2012 
0.53 
0.75 
0.87 
1.02 
1.44 

2013 
0.53 
0.75 
0.89 
1.03 
1.52 

2014 
0.57 
0.80 
0.96 
1.10 
1.67 

2015 
0.58 
0.81 
0.97 
1.12 
1.75 

The 50% quantile (black line), has varied around B/B2003≈ 0.95 (only in 2006 was the ratio above 
1.0). However, the quantiles are large, representing a high level of uncertainty. 

49 

49 

 
 
 
 
 
  
  
4.2  Indicators of advice coverage 

In the Mediterranean and the Black Seas a total of 241 stocks were considered for the current 
exercise, of which 72 have stock assessments carried out between 2015-2017. The advice 
coverage for the Mediterranean and the Black Sea is 0.30.  

50 

50 

 
 
 
5 

STATUS ACROSS ALL STOCKS IN 2016 

Table 5.1 Stock status for all stocks in the analysis. Columns refer to ecoregion, last year for which the estimated was obtained, stock code and description, 
value  of  F/FMSY  ratio  (F  ind),  if  F  is  lower  than  FMSY  (F  status),  if  the  stock  is  inside  safe  biological  limits  (SBL),  and  if  the  stock  is  inside  the  CFP 
requirements (CFP). Stocks managed under escapement strategies dot not have an estimate of F/FMSY. Symbol ‘o’ stands for ‘YES’, an empty cell stands for 
‘NO’ and ‘-’ unknown due to missing information. 

Region  EcoRegion 
FAO37  Black Sea 
FAO37  Black Sea 
FAO37  Black Sea 
FAO37  Black Sea 
FAO37  Black Sea 
FAO37  Black Sea 
FAO37  Black Sea 
FAO37  Black Sea 
FAO37  Central Med. 
FAO37  Central Med. 
FAO37  Central Med. 
FAO37  Central Med. 
FAO37  Central Med. 
FAO37  Central Med. 
FAO37  Central Med. 
FAO37  Central Med. 
FAO37  Central Med. 
FAO37  Central Med. 
FAO37  Central Med. 
FAO37  Central Med. 
FAO37  Central Med. 
FAO37  Central Med. 
FAO37  Eastern Med. 
FAO37  Eastern Med. 
FAO37  Eastern Med. 
FAO37  Western Med. 
FAO37  Western Med. 
FAO37  Western Med. 
FAO37  Western Med. 
FAO37  Western Med. 
FAO37  Western Med. 
FAO37  Western Med. 
FAO37  Western Med. 
FAO37  Western Med. 
FAO37  Western Med. 
FAO37  Western Med. 

Year  Stock 
2015  ane_29 
2015  dgs_29 
2015  hmm_29 
2015  mut_29 
2015  whg_29 
2015 
tur_29 
2015  spr_29 
2015 
rpw_29 
2014  sol_17 
2015  hke_19 
2015  mut_19 
2015  ane_17_18 
2015  pil_17_18 
2015  nep_17_18 
2014  hke_17_18 
2015  mts_17_18 
2015  dps_17_18_19 
2014  ars_18_19 
2014  mut_17_18 
2015  hke_12_13_14_15_16 
2015  dps_12_13_14_15_16 
2015  mut_15_16 
2015  ane_22_23 
2015  pil_22_23 
2015  mut_25 
2015  dps_1 
2015  dps_09_10_11 
2015  mur_9 
2015  nep_9 
2015  nep_11 
2015  nep_6 
2015  ane_6 
2015  pil_6 
2015  ane_09_10_11 
2015  hom_09_10_11 
2015  ars_9 

Description 
European anchovy in GSA 29 
Piked dogfish in GSA 29 
Horse mackerel in GSA 29 
Red mullet in GSA 29 
Whiting in GSA 29 
Turbot in GSA 29 
European sprat in GSA 29 
Rapana whelk in GSA 29 
Common sole in GSA 17 
European hake in GSA 19 
Red mullet in GSA 19 
European anchovy in GSA 17, 18 
Sardine in GSA 17, 18 
Norway lobster in GSA 17, 18 
European hake in GSA 17, 18 
Spottail mantis shrimp in GSA 17, 18 
Deep-water rose shrimp in GSA 17, 18, 19 
Giant red shrimp in GSA 18, 19 
Red mullet in GSA 17, 18 
European hake in GSA 12, 13, 14, 15, 16 
Deep water rose shrimp in GSA 12, 13, 14, 15, 16 
Red mullet in GSA 15, 16 
European anchovy in GSA 22, 23 
Sardine in GSA 22, 23 
Red mullet in GSA 25 
Deep-water rose shrimp in GSA 1 
Deep-water rose shrimp in GSA 09, 10, 11 
Striped red mullet in GSA 9 
Norway lobster in GSA 9 
Norway lobster in GSA 11 
Norway lobster in GSA 6 
European anchovy in GSA 6 
Sardine in GSA 6 
European anchovy in GSA 09, 10, 11 
Atlantic horse mackerel in GSA 09, 10, 11 
Giant red shrimp in GSA 9 

51 

51 

F ind 
1.46 
19.05 
3.39 
0.88 
2.14 
2.81 
1.82 
1.93 
2.44 
10.43 
2.72 
2.49 
3.18 
1.49 
5.57 
2.20 
2.06 
1.10 
1.32 
6.83 
1.44 
1.71 
1.30 
1.39 
1.03 
0.90 
0.95 
0.95 
1.78 
2.07 
9.49 
1.17 
1.73 
2.04 
3.78 
0.78 

F 
status 

o 

o 
o 
o 

o 

SBL 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Region  EcoRegion 
FAO37  Western Med. 
FAO37  Western Med. 
FAO37  Western Med. 
FAO37  Western Med. 
FAO37  Western Med. 
FAO37  Western Med. 
FAO37  Western Med. 
FAO37  Western Med. 
FAO37  Western Med. 
FAO37  Western Med. 
FAO37  Western Med. 
FAO27  Baltic Sea 

Year  Stock 
2015  ara_5 
2015  ara_6 
2015  ara_9 
2015  mut_6 
2015  mut_7 
2015  mur_5 
2015  ara_1 
2014  ars_10 
2014  ars_11 
2014  hke_01_05_06_07 
2014  hke_09_10_11 
2016  cod.27.22-24 

FAO27  Baltic Sea 

2016  her.27.20-24 

FAO27  Baltic Sea 

2016  her.27.25-2932 

FAO27  Baltic Sea 
FAO27  Baltic Sea 
FAO27  Baltic Sea 
FAO27  Baltic Sea 
FAO27  Baltic Sea 
FAO27  BoBiscay & Iberia 

2016  her.27.28 
2016  her.27.3031 
2016  ple.27.21-23 
2016  sol.27.20-24 
2016  spr.27.22-32 
2016  ane.27.8 

FAO27  BoBiscay & Iberia 

2016  ank.27.8c9a 

FAO27  BoBiscay & Iberia 

2016  hke.27.8c9a 

FAO27  BoBiscay & Iberia 

2016  hom.27.9a 

FAO27  BoBiscay & Iberia 

2016 

ldb.27.8c9a 

FAO27  BoBiscay & Iberia 

2016  meg.27.7b-k8abd 

FAO27  BoBiscay & Iberia 

2016  meg.27.8c9a 

FAO27  BoBiscay & Iberia 

2016  mon.27.8c9a 

FAO27  BoBiscay & Iberia 
FAO27  Celtic Seas 
FAO27  Celtic Seas 
FAO27  Celtic Seas 
FAO27  Celtic Seas 
FAO27  Celtic Seas 

2016  sol.27.8ab 
2016  cod.27.6a 
2016  cod.27.7a 
2016  cod.27.7e-k 
2016  had.27.6b 
2016  had.27.7a 

FAO27  Celtic Seas 
FAO27  Celtic Seas 

2016  had.27.7b-k 
2016  her.27.6a7bc 

FAO27  Celtic Seas 

2016  her.27.irls 

Description 
Blue and red shrimp in GSA 5 
Blue and red shrimp in GSA 6 
Blue and red shrimp in GSA 9 
Red mullet in GSA 6 
Red mullet in GSA 7 
Striped red mullet in GSA 5 
Blue and red shrimp in GSA 1 
Giant red shrimp in GSA 10 
Giant red shrimp in GSA 11 
European hake in GSA 01, 05, 06, 07 
European hake in GSA 09, 10, 11 
Cod (Gadus morhua) in subdivisions 22-24. western Baltic stock (western Baltic Sea) 
Herring (Clupea harengus) in subdivisions 20-24. spring spawners (Skagerrak. Kattegat. and 
western Baltic) 
Herring (Clupea harengus) in subdivisions 25-29 and 32. excluding the Gulf of Riga (central 
Baltic Sea) 
Herring (Clupea harengus) in Subdivision 28.1 (Gulf of Riga) 
Herring (Clupea harengus) in subdivisions 30 and 31 (Gulf of Bothnia) 
Plaice (Pleuronectes platessa) in subdivisions 21-23 (Kattegat. Belt Seas. and the Sound) 
Sole (Solea solea) in subdivisions 20-24 (Skagerrak and Kattegat. western Baltic Sea) 
Sprat (Sprattus sprattus) in subdivisions 22-32 (Baltic Sea) 
Anchovy (Engraulis encrasicolus) in Subarea 8 (Bay of Biscay) 
Black-bellied anglerfish (Lophius budegassa) in divisions 8.c and 9.a (Cantabrian Sea. Atlantic 
Iberian waters) 
Hake (Merluccius merluccius) in divisions 8.c and 9.a. Southern stock (Cantabrian Sea and  
Atlantic Iberian waters) 
Horse mackerel (Trachurus trachurus) in Division 9.a (Atlantic Iberian waters) 
Four-spot megrim (Lepidorhombus boscii) in divisions 8.c and 9.a (southern Bay of Biscay and 
Atlantic Iberian waters East) 
Megrim (Lepidorhombus whiffiagonis) in divisions 7.b-k. 8.a-b. and 8.d (west and southwest of 
Ireland. Bay of Biscay) 
Megrim (Lepidorhombus whiffiagonis) in divisions 8.c and 9.a (Cantabrian Sea and Atlantic 
Iberian waters) 
White anglerfish (Lophius piscatorius) in divisions 8.c and 9.a (Cantabrian Sea and Atlantic 
Iberian waters) 
Sole (Solea solea) in divisions 8.a-b (northern and central Bay of Biscay) 
Cod (Gadus morhua) in Division 6.a (West of Scotland) 
Cod (Gadus morhua) in Division 7.a (Irish Sea) 
Cod (Gadus morhua) in divisions 7.e-k (eastern English Channel and southern Celtic Seas) 
Haddock (Melanogrammus aeglefinus) in Division 6.b (Rockall) 
Haddock (Melanogrammus aeglefinus) in Division 7.a (Irish Sea) 
Haddock (Melanogrammus aeglefinus) in divisions 7.b-k (southern Celtic Seas and English 
Channel) 
Herring (Clupea harengus) in divisions 6.a and 7.b-c (West of Scotland. West of Ireland) 
Herring (Clupea harengus) in divisions 7.a South of 52°30’N. 7.g-h. and 7.j-k (Irish Sea. Celtic 
Sea. and southwest of Ireland) 

F ind 
1.01 
2.43 
0.84 
1.56 
2.26 
3.51 
1.92 
1.40 
1.60 
2.88 
5.26 
3.58 

1.27 

0.92 

1.25 
1.10 
0.76 
0.75 
0.86 
- 

0.45 

2.27 

0.70 

1.14 

1.14 

1.11 

0.68 

1.10 
5.65 
0.09 
1.24 
0.50 
0.39 

1.69 

0.31 

1.56 

F 
status 

o 

o 

o 
o 
o 
o 

o 

o 

o 

o 

o 
o 

o 

SBL 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

o 

o 
o 
o 

o 
- 

- 

o 

o 

o 

o 

o 

o 

o 

o 
o 

o 

52 

52 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
F 
status 
o 
o 

SBL 
o 
- 

Region  EcoRegion 
FAO27  Celtic Seas 
FAO27  Celtic Seas 

Year  Stock 
2016  her.27.nirs 
lez.27.4a6a 
2016 

FAO27  Celtic Seas 

2016  nep.fu.11 

FAO27  Celtic Seas 

2016  nep.fu.12 

FAO27  Celtic Seas 
FAO27  Celtic Seas 
FAO27  Celtic Seas 

2016  nep.fu.13 
2016  nep.fu.14 
2016  nep.fu.15 

FAO27  Celtic Seas 

2016  nep.fu.16 

FAO27  Celtic Seas 

2016  nep.fu.17 

FAO27  Celtic Seas 

2016  nep.fu.19 

FAO27  Celtic Seas 
FAO27  Celtic Seas 
FAO27  Celtic Seas 
FAO27  Celtic Seas 

FAO27  Celtic Seas 
FAO27  Celtic Seas 
FAO27  Celtic Seas 

2016  nep.fu.22 
2016  ple.27.7a 
2016  sol.27.7fg 
2016  whg.27.7a 

2016  whg.27.7b-ce-k 
2015  sol.27.7a 
2015  whg.27.6a 

FAO27  Greater North Sea  2016  cod.27.47d20 

FAO27  Greater North Sea  2016  had.27.46a20 

FAO27  Greater North Sea  2016  her.27.3a47d 

FAO27  Greater North Sea  2016  nep.fu.3-4 

FAO27  Greater North Sea  2016  nep.fu.6 

FAO27  Greater North Sea  2016  nep.fu.7 

FAO27  Greater North Sea  2016  nep.fu.8 

FAO27  Greater North Sea  2016  nep.fu.9 

FAO27  Greater North Sea  2016  nop.27.3a4 
FAO27  Greater North Sea  2016  ple.27.420 
FAO27  Greater North Sea  2016  ple.27.7d 
FAO27  Greater North Sea  2016  pok.27.3a46 

Description 
Herring (Clupea harengus) in Division 7.a North of 52°30’N (Irish Sea) 
Megrim (Lepidorhombus spp.) in divisions 4.a and 6.a (northern North Sea. West of Scotland) 
Norway lobster (Nephrops norvegicus) in Division 6.a. Functional Unit 11 (West of Scotland. 
North Minch) 
Norway lobster (Nephrops norvegicus) in Division 6.a. Functional Unit 12 (West of Scotland. 
South Minch) 
Norway lobster (Nephrops norvegicus) in Division 6.a. Functional Unit 13 (West of Scotland. 
the Firth of Clyde and Sound of Jura) 
Norway lobster (Nephrops norvegicus) in Division 7.a. Functional Unit 14 (Irish Sea. East) 
Norway lobster (Nephrops norvegicus) in Division 7.a. Functional Unit 15 (Irish Sea. West) 
Norway lobster (Nephrops norvegicus) in divisions 7.b-c and 7.j-k. Functional Unit 16 (west 
and southwest of Ireland. Porcupine Bank) 
Norway lobster (Nephrops norvegicus) in Division 7.b. Functional Unit 17 (west of Ireland. Aran 
grounds) 
Norway lobster (Nephrops norvegicus) in divisions 7.a. 7.g. and 7.j. Functional Unit 19 (Irish 
Sea. Celtic Sea. eastern part of southwest of Ireland) 
Norway lobster (Nephrops norvegicus) in divisions 7.g and 7.f. Functional Unit 22 (Celtic Sea. 
Bristol Channel) 
Plaice (Pleuronectes platessa) in Division 7.a (Irish Sea) 
Sole (Solea solea) in divisions 7.f and 7.g (Bristol Channel. Celtic Sea) 
Whiting (Merlangius merlangus) in Division 7.a (Irish Sea) 
Whiting (Merlangius merlangus) in divisions 7.b -c and 7.e-k (southern Celtic Seas and eastern 
English Channel) 
Sole (Solea solea) in Division 7.a (Irish Sea) 
Whiting (Merlangius merlangus) in Division 6.a (West of Scotland) 
Cod (Gadus morhua) in Subarea 4. Division 7.d. and Subdivision 20 (North Sea. eastern 
English Channel. Skagerrak) 
Haddock (Melanogrammus aeglefinus) in Subarea 4. Division 6.a. and Subdivision 20 (North 
Sea. West of Scotland. Skagerrak) 
Herring (Clupea harengus) in Subarea 4 and divisions 3.a and 7.d. autumn spawners (North 
Sea. Skagerrak and Kattegat. eastern English Channel) 
Norway lobster (Nephrops norvegicus) in Division 3.a. Functional units 3 and 4 (Skagerrak and 
Kattegat) 
Norway lobster (Nephrops norvegicus) in Division 4.b. Functional Unit 6 (central North Sea. 
Farn Deeps) 
Norway lobster (Nephrops norvegicus) in Division 4.a. Functional Unit 7 (northern North Sea. 
Fladen Ground) 
Norway lobster (Nephrops norvegicus) in Division 4.b. Functional Unit 8 (central North Sea. 
Firth of Forth) 
Norway lobster (Nephrops norvegicus) in Division 4.b. Functional Unit 9 (central North Sea. 
Moray Firth) 
Norway pout (Trisopterus esmarkii) in Subarea 4 and Division 3.a (North Sea. Skagerrak and 
Kattegat) 
Plaice (Pleuronectes platessa) in Subarea 4 (North Sea) and Subdivision 20 (Skagerrak) 
Plaice (Pleuronectes platessa) in Division 7.d (eastern English Channel) 
Saithe (Pollachius virens) in subareas 4. 6 and Division 3.a (North Sea. Rockall and West of 

53 

53 

F ind 
0.66 
0.35 

0.99 

0.81 

1.16 

0.35 
0.85 

0.90 

1.09 

o 

o 

o 
o 

o 

0.81 

o 

1.78 

0.29 
1.35 
2.59 

0.83 

0.38 
0.32 

1.22 

1.49 

0.78 

0.39 

1.64 

0.19 

0.75 

1.08 

- 

0.96 
0.53 
0.78 

o 

o 

o 
o 

o 

o 

o 

o 

o 
o 
o 

- 

- 

- 

- 
- 

- 

- 

- 

- 

o 

o 

o 

- 

- 

- 

- 

- 

- 

o 
o 
o 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Region  EcoRegion 

Year  Stock 

FAO27  Greater North Sea  2016  pra.27.4a20 

FAO27  Greater North Sea  2016  san.sa.1r 

FAO27  Greater North Sea  2016  san.sa.2r 

FAO27  Greater North Sea  2016  san.sa.3r 

FAO27  Greater North Sea  2016  san.sa.4 
FAO27  Greater North Sea  2016  sol.27.4 
FAO27  Greater North Sea  2016  sol.27.7d 
FAO27  Greater North Sea  2016  sol.27.7e 
FAO27  Greater North Sea  2016  spr.27.4 

FAO27  Greater North Sea  2016  whg.27.47d 

FAO27  Northeast Atlantic  2016  hke.27.3a46-8abd 

FAO27  Northeast Atlantic  2016  hom.27.2a4a5b6a7a-ce-k8 

FAO27  Northeast Atlantic  2016  mac.27.nea 

FAO27  Northeast Atlantic  2016  whb.27.1-91214 

FAO27  Northeast Atlantic  2015  bli.27.5b67 

FAO27  Northeast Atlantic  2015  dgs.27.nea 

FAO27  Northeast Atlantic  2015 

rng.27.5b6712b 

Description 
Scotland. Skagerrak and Kattegat) 
Northern shrimp (Pandalus borealis) in Division 4.a East and Subdivision 20 (northern North 
Sea in the Norwegian Deep and Skagerrak) 
Sandeel (Ammodytes spp.) in divisions 4.b and 4.c. Sandeel Area 1r (central and southern 
North Sea. Dogger Bank) 
Sandeel (Ammodytes spp.) in divisions 4.b and 4.c. and Subdivision 20. Sandeel Area 2r 
(Skagerrak. central and southern North Sea) 
Sandeel (Ammodytes spp.) in divisions 4.a and 4.b. and Subdivision 20. Sandeel Area 3r 
(Skagerrak. northern and central North Sea) 
Sandeel (Ammodytes spp.) in divisions 4.a and 4.b. Sandeel Area 4 (northern and central 
North Sea) 
Sole (Solea solea) in Subarea 4 (North Sea) 
Sole (Solea solea) in Division 7.d (eastern English Channel) 
Sole (Solea solea) in Division 7.e (western English Channel) 
Sprat (Sprattus sprattus) in Subarea 4 (North Sea) 
Whiting (Merlangius merlangus) in Subarea 4 and Division 7.d (North Sea and Eastern English 
Channel) 
Hake (Merluccius merluccius) in subareas 4. 6. and 7. and divisions 3.a. 8.a-b. and 8.d. 
Northern stock (Greater North Sea. Celtic Seas. and the northern Bay of Biscay) 
Horse mackerel (Trachurus trachurus) in Subarea 8 and divisions 2.a. 4.a. 5.b. 6.a. 7.a-c.e-k 
(the Northeast Atlantic) 
Mackerel (Scomber scombrus) in subareas 1-8 and 14 and Division 9.a (the Northeast Atlantic 
and adjacent waters) 
Blue whiting (Micromesistius poutassou) in subareas 1-9. 12. and 14 (Northeast Atlantic and 
adjacent waters) 
Blue ling (Molva dypterygia) in subareas 6-7 and Division 5.b (Celtic Seas, English Channel, 
and Faroes grounds) 
Spurdog (Squalus acanthias) in Subareas 1-10, 12 and 14 (the Northeast Atlantic and adjacent 
waters) 
Roundnose grenadier (Coryphaenoides rupestris) in subareas 6-7, and in Divisions 5.b and 
12.b (Celtic Seas and the English Channel, Faroes grounds, and western Hatton Bank) 

F ind 

F 
status 

SBL 

1.03 

- 

- 

- 

- 

1.08 
0.90 
0.74 
- 

1.63 

0.96 

0.83 

1.53 

1.35 

0.28 

0.40 

0.25 

o 

o 

o 

o 
o 
o 

o 

o 

o 

o 

o 

o 

- 

- 

- 

- 

o 

o 
- 

o 

o 

o 

o 

o 

- 

- 

54 

54 

 
 
 
 
 
 
 
 
 
  
 
6 

 REPORTS BY STOCK 

Table 6.1 - URL links to the source reports by stock. 

Stock 

ars_10 

ars_11 

ars_18_19 
dps_1 
dps_09_10_11 

hke_01_05_06_07 

hke_09_10_11 
mur_9 

mut_17_18 
nep_9 
nep_11 
nep_6 
ane_29 
dgs_29 
hmm_29 
mut_29 
whg_29 
tur_29 
spr_29 
rpw_29 
ane_6 
pil_6 
sol_17 
hke_19 
mut_19 
ane_09_10_11 
hom_09_10_11 
ane_17_18 
pil_17_18 
nep_17_18 
hke_17_18 
mts_17_18 
dps_17_18_19 
ane_22_23 
pil_22_23 
ars_9 

Assessment year 

2015 

2015 

2015 
2016 
2016 

2015 

2015 
2016 

Report 
https://stecf.jrc.ec.europa.eu/documents/43805/1208039/2015-11_STECF+15-18+-
+MED+assessments+part+1_JRC98676.pdf 
https://stecf.jrc.ec.europa.eu/documents/43805/1208039/2015-11_STECF+15-18+-
+MED+assessments+part+1_JRC98676.pdf 
https://stecf.jrc.ec.europa.eu/documents/43805/1291370/2015-05_STECF+16-
08+MED+assessments+part+2_JRC101548.pdf 

https://stecf.jrc.ec.europa.eu/documents/43805/1208039/2015-11_STECF+15-18+-
+MED+assessments+part+1_JRC98676.pdf 
https://stecf.jrc.ec.europa.eu/documents/43805/1208039/2015-11_STECF+15-18+-
+MED+assessments+part+1_JRC98676.pdf 

https://stecf.jrc.ec.europa.eu/documents/43805/1291370/2015-05_STECF+16-
08+MED+assessments+part+2_JRC101548.pdf 

2015 
2016 
2016 
2016 
2017 
2017 
2017 
2017 
2017 
2017 
2017 
2017 
2017  https://stecf.jrc.ec.europa.eu/documents/43805/1674827/STECF+17-15+-+Med+stock+assessments+2017_p1.pdf 
2017  https://stecf.jrc.ec.europa.eu/documents/43805/1674827/STECF+17-15+-+Med+stock+assessments+2017_p1.pdf 
2017 
2017 
2017 
2017  https://stecf.jrc.ec.europa.eu/documents/43805/1674827/STECF+17-15+-+Med+stock+assessments+2017_p1.pdf 
2017  https://stecf.jrc.ec.europa.eu/documents/43805/1674827/STECF+17-15+-+Med+stock+assessments+2017_p1.pdf 
2017  https://stecf.jrc.ec.europa.eu/documents/43805/1674827/STECF+17-15+-+Med+stock+assessments+2017_p1.pdf 
2017  https://stecf.jrc.ec.europa.eu/documents/43805/1674827/STECF+17-15+-+Med+stock+assessments+2017_p1.pdf 
2017 
2017 
2017 
2017 
2017  https://stecf.jrc.ec.europa.eu/documents/43805/1674827/STECF+17-15+-+Med+stock+assessments+2017_p1.pdf 
2017  https://stecf.jrc.ec.europa.eu/documents/43805/1674827/STECF+17-15+-+Med+stock+assessments+2017_p1.pdf 
2016  https://gfcmsitestorage.blob.core.windows.net/documents/SAC/SAF/DemersalSpecies/2016/ARS_GSA_09_2015_ITA.pdf 

55 

55 

Source  Area 

STECF 

FAO37 

STECF 

FAO37 

STECF 
STECF 
STECF 

FAO37 
FAO37 
FAO37 

STECF 

FAO37 

STECF 
STECF 

FAO37 
FAO37 

STECF 
STECF 
STECF 
STECF 
STECF 
STECF 
STECF 
STECF 
STECF 
STECF 
STECF 
STECF 
STECF 
STECF 
STECF 
STECF 
STECF 
STECF 
STECF 
STECF 
STECF 
STECF 
STECF 
STECF 
STECF 
STECF 
STECF 
GFCM 

FAO37 
FAO37 
FAO37 
FAO37 
FAO37 
FAO37 
FAO37 
FAO37 
FAO37 
FAO37 
FAO37 
FAO37 
FAO37 
FAO37 
FAO37 
FAO37 
FAO37 
FAO37 
FAO37 
FAO37 
FAO37 
FAO37 
FAO37 
FAO37 
FAO37 
FAO37 
FAO37 
FAO37 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Stock 
ara_5 
ara_6 
ara_9 

hke_12_13_14_15_16 
mut_6 
mut_7 
mut_25 

Assessment year 

Report 

2016  https://gfcmsitestorage.blob.core.windows.net/documents/SAC/SAF/DemersalSpecies/2016/ARA_GSA_05_2015_ESP.pdf 
2016  https://gfcmsitestorage.blob.core.windows.net/documents/SAC/SAF/DemersalSpecies/2016/ARA_GSA_06_2015_ESP.pdf 
2016  https://gfcmsitestorage.blob.core.windows.net/documents/SAC/SAF/DemersalSpecies/2016/ARA_GSA_09_2015_ITA.pdf 

Source  Area 
GFCM 
GFCM 
GFCM 

FAO37 
FAO37 
FAO37 

https://gfcmsitestorage.blob.core.windows.net/documents/SAC/SAF/DemersalSpecies/2016/HKE_GSA_12-
16_2015_ITA_MLT_TUN.pdf 

GFCM 
2016 
2016  https://gfcmsitestorage.blob.core.windows.net/documents/SAC/SAF/DemersalSpecies/2016/MUT_GSA_06_2015_ESP.pdf 
GFCM 
2016  https://gfcmsitestorage.blob.core.windows.net/documents/SAC/SAF/DemersalSpecies/2016/MUT_GSA_07_2015_ESP_FRA.pdf  GFCM 
GFCM 
2016  https://gfcmsitestorage.blob.core.windows.net/documents/SAC/SAF/DemersalSpecies/2016/MUT_GSA_25_2015_CYP.pdf 

FAO37 
FAO37 
FAO37 
FAO37 

dps_12_13_14_15_16 

2016 

mut_15_16 
mur_5 
ara_1 
ane.27.8 
ank.27.8c9a 
bli.27.5b67 
cod.27.22-24 
cod.27.47d20 
cod.27.6a 
cod.27.7a 
cod.27.7e-k 
dgs.27.nea 
had.27.46a20 
had.27.6b 
had.27.7a 
had.27.7b-k 
her.27.20-24 
her.27.25-2932 
her.27.28 
her.27.3031 
her.27.3a47d 
her.27.6a7bc 
her.27.irls 
her.27.nirs 
hke.27.3a46-8abd 
hke.27.8c9a 
hom.27.2a4a5b6a7a-ce-k8 
hom.27.9a 
ldb.27.8c9a 
lez.27.4a6a 
mac.27.nea 
meg.27.7b-k8abd 
meg.27.8c9a 
mon.27.8c9a 
nep.fu.11 
nep.fu.12 

https://gfcmsitestorage.blob.core.windows.net/documents/SAC/SAF/DemersalSpecies/2016/DPS_GSA_12-
16_2015_TUN_MLT_ITA.pdf 
https://gfcmsitestorage.blob.core.windows.net/documents/SAC/SAF/DemersalSpecies/2016/MUT_GSA_15-
16_2015_MLT_ITA.pdf 

2016 
2016  https://gfcmsitestorage.blob.core.windows.net/documents/SAC/SAF/DemersalSpecies/2016/MUR_GSA_05_2015_ESP.pdf 
2016  https://gfcmsitestorage.blob.core.windows.net/documents/SAC/SAF/DemersalSpecies/2016/ARA_GSA_01_2015_ESP.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/ane.27.8.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/ank.27.8c9a.pdf 
2016  http://www.ices.dk/sites/pub/Publication Reports/Advice/2016/2016/bli-5b67.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/cod.27.22-24.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/cod.27.47d20.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/cod.27.6a.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/cod.27.7a.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/cod.27.7e-k.pdf 
2016  http://www.ices.dk/sites/pub/Publication Reports/Advice/2016/2016/dgs-nea.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/had.27.46a20.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/had.27.6b.pdf 
2017  http://ices.dk/sites/pub/Publication%20Reports/Advice/2017/2017/had.27.7a.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/had.27.7b-k.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/her.27.20-24.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/her.27.25-2932.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/her.27.28.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/her.27.3031.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/her.27.3a47d.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/her.27.6a7bc.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/her.27.irls.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/her.27.nirs.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/hke.27.3a46-8abd.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/hke.27.8c9a.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/hom.27.2a4a5b6a7a-ce-k8.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/hom.27.9a.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/ldb.27.8c9a.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/lez.27.4a6a.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/meg.27.7b-k8abd.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/meg.27.7b-k8abd.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/meg.27.8c9a.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/mon.27.8c9a.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/nep.fu.11.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/nep.fu.12.pdf 

GFCM 

FAO37 

GFCM 
GFCM 
GFCM 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 

FAO37 
FAO37 
FAO37 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 

56 

56 

 
Stock 
nep.fu.13 
nep.fu.15 
nep.fu.17 
nep.fu.6 
nep.fu.7 
nep.fu.8 
nep.fu.9 
nop.27.3a4 
ple.27.21-23 
ple.27.420 
ple.27.7a 
ple.27.7d 
pok.27.3a46 
pra.27.4a20 
rng.27.5b6712b 
san.sa.1r 
san.sa.2r 
san.sa.3r 
san.sa.4 
sol.27.20-24 
sol.27.4 
sol.27.7a 
sol.27.7d 
sol.27.7e 
sol.27.7fg 
sol.27.8ab 
spr.27.22-32 
spr.27.4 
whb.27.1-91214 
whg.27.47d 
whg.27.6a 
whg.27.7a 
whg.27.7b-ce-k 
nep.fu.22 
nep.fu.14 
nep.fu.19 
nep.fu.3-4 
nep.fu.16 

Assessment year 

Report 

2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/nep.fu.13.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/nep.fu.15.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/nep.fu.17.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/nep.fu.6.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/nep.fu.7.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/nep.fu.8.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/nep.fu.9.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/nop.27.3a4.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/ple.27.21-23.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/ple.27.420.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/ple.27.7a.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/ple.27.7d.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/pok.27.3a46.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/pra.27.4a20.pdf 
2016  http://www.ices.dk/sites/pub/Publication Reports/Advice/2016/2016/rng-5b67.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/san.sa.1r.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/san.sa.2r.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/san.sa.3r.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/san.sa.4.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/sol.27.20-24.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/sol.27.4.pdf 
2016  http://www.ices.dk/sites/pub/Publication Reports/Advice/2016/2016/sol-iris.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/sol.27.7d.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/sol.27.7e.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/sol.27.7fg.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/sol.27.8ab.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/spr.27.22-2.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/spr.27.4.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/whb.27.1-91214.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/whg.27.47d.pdf 
2016  http://www.ices.dk/sites/pub/Publication Reports/Advice/2016/2016/whg-scow.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/whg.27.7a.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/whg.27.7b-ce-k.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/nep.fu.22.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/nep.fu.14.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/nep.fu.19.pdf 
2017  http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/nep.fu.3-4.pdf 
 http://www.ices.dk/sites/pub/Publication Reports/Advice/2017/2017/nep.fu.16.pdf 
2017 

Source  Area 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 
ICES 

FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 
FAO27 

57 

57 

 
7 

REFERENCES 

Gibin M. 2017. Integrating Fishing Management Zones, FAO and ICES statistical areas by data 

fusion, JRC Technical Report, JRC105881. 

Jardim, E., Mosqueira, I., Chato Osio, G. Scott. F. 2015. “Common Fisheries Policy Monitoring - 
Protocol for computing indicators.” Publications Office of the European Union, Luxembourg, 
EUR 27566 EN, JRC 98562, 12 pp. 

Mannini, A., Osio G.C., Jardim E., Mosqueira I., Scott F., Vasilakopoulos P., Casey J. – 2017. 

Technical report on: Sampling Frames for Mediterranean and Black Sea CFP Monitoring 
indicators Publications Office of the European Union, Luxembourg; EUR XXXX EN; 
doi:XXXXXXXX 

STECF. 2016. 51st Plenary Meeting Report (PLEN-16-01). Publications Office of the European 

Union, Luxembourg, EUR 27917 EN, JRC 101442, 95 pp. 

STECF. 2017a. 56th Plenary Meeting Report (PLEN-17-03); Publications Office of the European 

Union, Luxembourg; ISBN 978-92-79-77297-9, doi:10.2760/605712, JRC109344 

STECF. 2017b - Monitoring the performance of the Common Fisheries Policy (STECF-17-04). 

Publications Office of the European Union, Luxembourg; EUR 28359 EN; doi:10.2760/491411 

STECF. 2017c. Scientific, Technical and Economic Committee for Fisheries (STECF) – 

Mediterranean Stock Assessments 2017 part I (STECF-17-15). Publications Office of the 
European Union, Luxembourg, 2017, ISBN 978-92-79 67487-7, doi:10.2760/897559, 
JRC109350  

STECF. 2018. Scientific, Technical and Economic Committee for Fisheries (STECF) – 

Mediterranean Stock Assessments 2017 part II (STECF-17-25). Publications Office of the 
European Union, Luxembourg, EUR XXXX EN; doi:XXXXXXXX 

58 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8 

CONTACT DETAILS OF AD HOC EXPERT GROUP PARTICIPANTS 

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

European Commission, Joint research Centre 

Name 

J. Casey 

E. Jardim 

A. Mannini 

I. Mosqueira 

P. 
Vasilakopoulos 

Address 

Telephone no. 

Email 

+39 0332 783936 

john.casey@ec.europa.eu 

+39 0332 785311  ernesto.jardim@ec.europa.eu 

+39 0332 785784  alessandro.mannini@ec.europ

a.eu 

+39 0332 785413 

iago.mosqueira@ec.europa.eu 

+39 0332 785714  paris.vasilakopoulos@ec.europ

a.eu 

European 
Joint 
Commission, 
Research 
Center, 
Unit D.02 Water and 
Marine  Resources, 
Via  Enrico 
fermi 
2749,  21027  Ispra 
(VA), Italy 

European 
Joint 
Commission, 
Research 
Center, 
Unit D.02 Water and 
Marine  Resources, 
fermi 
Via  Enrico 
2749,  21027  Ispra 
(VA), Italy 

European 
Joint 
Commission, 
Research 
Center, 
Unit D.02 Water and 
Marine  Resources, 
Via  Enrico 
fermi 
2749,  21027  Ispra 
(VA), Italy 

European 
Joint 
Commission, 
Center, 
Research 
Unit D.02 Water and 
Marine  Resources, 
Via  Enrico 
fermi 
2749,  21027  Ispra 
(VA), Italy 

European 
Joint 
Commission, 
Research 
Center, 
Unit D.02 Water and 
Marine  Resources, 
Via  Enrico 
fermi 
2749,  21027  Ispra 
(VA), Italy 

59 

 
 
 
 
 
 
 
 
 
60 

 
 
 
 
9 

LIST OF ANNEXES  

Data and code are available in https://stecf.jrc.ec.europa.eu/reports/cfp-monitoring. 

61 

 
 
 
 
 
 
 
 
 
ANNEX I - PROTOCOL 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Protocol for the Monitoring of the Common Fisheries Policy
Version 3.0

April 24, 2018

Ernesto Jardim1 (ernesto.jardim@ec.europa.eu)
Iago Mosqueira1 (iago.mosqueira@ec.europa.eu)
Paris Vasilakopoulos1 (paris.vasilakopoulos@ec.europa.eu)
Alessandro Mannini1 (alessandro.mannini@ec.europa.eu)
John Casey1 (john.casey@ec.europa.eu)

1European Commission, DG Joint Research Centre, Directorate D (cid:22) Sustainable Resources, Unit D.02 Water and Marine
Resources, Via E. Fermi 2749, 21027 Ispra VA, Italy.

1

Contents

1 Introduction

1.1 Scope

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1.2 De(cid:28)nitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2 Data

2.1 Data sources

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2.2 Reference list of stocks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2.3 Selection of stock assessments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3 Indicators of management performance

3.1 Number of stocks where (cid:28)shing mortality exceeds FM SY . . . . . . . . . . . . . . . . . . .

3.2 Number of stocks where (cid:28)shing mortality is equal to or less than FM SY

. . . . . . . . . .

3.3 Number of stocks outside safe biological limits . . . . . . . . . . . . . . . . . . . . . . . . .

3.4 Number of stocks inside safe biological limits

. . . . . . . . . . . . . . . . . . . . . . . . .

3.5 Number of stocks where F is above FM SY or SSB is below BM SY . . . . . . . . . . . . .

3.6 Number of stocks where F is below or equal to FM SY and SSB is above or equal to BM SY

3.7 Trend in F/FM SY

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3.8 Trend in SSB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3.9 Trend in B/BP A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3.10 Trend in recruitment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3.11 Trend in biomass . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3.12 Trend in biomass for data limited stocks . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4 Indicators of changes in advice coverage

4.1 Number of stocks for which estimates of FM SY exist . . . . . . . . . . . . . . . . . . . . .

4.2 Number of stocks for which estimates of BP A exist . . . . . . . . . . . . . . . . . . . . . .

4.3 Number of stocks for which estimates of BM SY exist . . . . . . . . . . . . . . . . . . . . .

4.4 Fraction of TACs covered by stock assessments . . . . . . . . . . . . . . . . . . . . . . . .

5 Transparency

3

3

3

4

4

4

5

5

6

6

6

6

6

6

7

7

7

8

8

8

9

9

9

9

9

9

2

1

Introduction

The monitoring of the Common Fisheries Policy (CFP, Reg (EU) 1380/2013) implementation is of utmost
importance for the European Union (EU), European Commission (EC) and its Directorate-General for
Maritime A(cid:27)airs and Fisheries (DG MARE).

The European Commission Scienti(cid:28)c, Technical and Economic Committee for Fisheries (STECF), as
the major scienti(cid:28)c advisory body on (cid:28)sheries policy to the EC, has the task of reporting on the CFP
implementation through the estimation and publication of a series of indicators.

To make the process as consistent as possible, the following set of rules were developed to be used as a
guiding protocol for computing the required indicators. The rules also contribute to the transparency of
the process.

The protocol covers the three major elements in the process:

• Data issues: data sources, reference list of stocks, selection of stocks, etc;

• Indicators of management performance: description of the indicators, procedures for their compu-

tation and presentation format;

• Indicators of changes in advice coverage: description of the indicators, procedures for their compu-

tation and presentation format.

1.1 Scope

The monitoring of the CFP should cover all areas were (cid:29)eets operate under the (cid:29)ag of any EU member
state. However, due to limitations on data and the mitigated responsibility of the EU on management
decisions on waters outside the EU EEZ (Exclusive Economic Zone), the analysis will mainly focus on
stocks within the EU EEZ in the FAO areas 27 (NEA: Northeast Atlantic and Adjacent Seas) and 37
(MED: Mediterranean and Black Sea).

The analysis will have two perspectives, at the global EU level and a regional overview where the indicators
are computed for the following regions, if enough data is available:

• Baltic Sea (NEA)

• Greater North Sea (NEA)

• Celtic Sea (NEA)

• Bay of Biscay and Iberian Waters (NEA)

• Widely distributed stocks (NEA)

• Western Mediterranean (MED)

• Eastern Mediterranean (MED)

• Central Mediterranean (MED)

• Black Sea (MED)

1.2 De(cid:28)nitions

• f represents (cid:28)shing mortality;

• b represents biomass, either as total stock biomass or spawning stock biomass (SSB);

• k represents a standardized biomass index, which is considered by experts to represent the evolution

of biomass over time;

• r represents recruitment (young individuals entering the (cid:28)shery) in number of individuals;

3

• FM SY represents (cid:28)shing mortality that produces catches at the level of M SY in an equilibrium

situation, or a proxy;

• FP A is the precautionary reference point for (cid:28)shing mortality;

• BM SY is the biomass expected to produce M SY when (cid:28)shed at FM SY in an equilibrium situation,

but also any other relevant proxy considered by the scienti(cid:28)c advice body;

• BP A is the precautionary reference point for spawning stock biomass;

• indices:

(cid:21) j = 1 . . . N indexes stocks, where N is the total number of stocks selected for the analysis;

(cid:21) t = 1 . . . T indexes years, where T is the number of years in the reported time series;

(cid:21) m = 1 . . . M indexes sampling units, where M is the total number of stocks in the reference

list;

(cid:21) s = 1 . . . S indexes bootstrap simulations;

• operations:

(cid:21) ∨ stands for or in Boolean logic;

(cid:21) ∧ stands for and in Boolean logic;

• model parameters:

(cid:21) u is a random e(cid:27)ect;

(cid:21) y is a (cid:28)xed e(cid:27)ect on year.

2 Data

2.1 Data sources

All indicators are computed using results from single species quantitative stock assessments. Time series
of estimates of (cid:28)shing mortality, spawning stock biomass, and the adopted biological reference points for
each stock are to be provided by the International Council for the Exploration of the Sea (ICES), the
General Fisheries Commission for the Mediterranean (GFCM) and STECF.

Results from surplus production models and delay-di(cid:27)erence models, which are mostly reported as ratios
between F and FM SY and/or B over BM SY , are also included in the analysis.

Results from pseudo-cohort analysis and similar methods are not included. These models do not estimate
time series of (cid:28)shing mortality or spawning stock biomass.

Results from methods that directly estimate total abundance and/or harvest rate may be used for the
computation of some indicators.

2.2 Reference list of stocks

The list of stocks to be used for computing indicators, hereafter termed the reference list, is used to
stabilize the basis on which the indicators are computed. It assures that the relevant stocks are considered
and constitutes the base for computing the scienti(cid:28)c coverage of the advise. The reference list must include
at least those stocks that are subject to direct management from the EU, as changes in their status can
be linked more clearly to the implementation of the CFP.

Because of the di(cid:27)erences in the nature and availability of data and information in di(cid:27)erent regions,
region-speci(cid:28)c reference lists were adopted for the EU waters:

• Northeast Atlantic (FAO area 27): The list of stocks comprises all stocks subject to management

by Total Allowable Catch (TAC) limits.

4

• Mediterranean and Black Seas (FAO area 37): the list of stocks1 comprises all stocks of the species

(cid:21) anchovy (Engraulis encrasicolus)

(cid:21) blackbellied angler (Lophius budegassa)

(cid:21) blue and red shrimp (Aristeus antennatus)

(cid:21) giant red shrimp (Aristaeomorpha foliacea)

(cid:21) deep-water rose shrimp (Parapenaeus longirostriss)

(cid:21) hake (Merluccius merluccius)

(cid:21) striped red mullet (Mullus surmuletus)

(cid:21) red mullet (Mullus barbatus)

(cid:21) Norway lobster (Nephrops norvegicus)

(cid:21) sardine (Sardina pilchardus)

(cid:21) common sole (Solea solea)

(cid:21) sprat (Sprattus sprattus)

(cid:21) turbot (Psetta maxima)

(cid:21) blue whiting (Micromesistius poutassou)

(cid:21) whiting (Merlangius merlangus)

plus the stocks ranked in the top ten in either landings or reported economic value over the 2012-
2014 period.

2.3 Selection of stock assessments

• The stock assessments to be selected include all stock assessments carried out in the three years

before the analysis, are listed in the reference list and have at least 5 years of estimates.

• Exploratory assessments or assessments not yet approved by the advisory bodies are not considered;

• When several stocks are merged in a single stock only the aggregated stock is considered, the

reference list must be updated accordingly;

• When a stock is split in two (or more) stocks only the disaggregated stocks are considered, the

reference list must be updated accordingly;

• If two assessments for the same stock exist the most recent one is kept.

• if two assessments in the same year for the same stock exist the one from the relevant RFMO is

kept.

Selected stocks of which the stock assessment results don’t cover the recent period of evaluation, the most
recent estimates available will be expanded up to the last year of the analysis.

3

Indicators of management performance

The indicators employed to monitor the performance of the CFP management regime re(cid:29)ect the evolution
of:

1. exploitation levels

by comparing (cid:28)shing mortality F with the target level FM SY ;

2. conservation status

by comparing (cid:28)shing mortality F and spawning stock biomass SSB with the precautionary levels
of (cid:28)shing mortality and biomass, FP A and BP A, respectively;

1To be discussed and agreed with the Med members

5

3. biomass levels

by comparing spawning stock biomass SSB with the target level BM SY .

A group of indicators, hereafter referred to as model based, are computed with a Generalized Linear Mixed
Model (GLMM), using stock as a random e(cid:27)ect, year as a (cid:28)xed e(cid:27)ect, and a Gamma distribution with
a log link. The indicator is the model prediction of the year e(cid:27)ect, and the indicator’s uncertainty is
computed with a block bootstrap procedure using stock as blocks. This model was tested in a simulation
study2 and in an application to Mediterranean stocks3.

3.1 Number of stocks where (cid:28)shing mortality exceeds FM SY

It =

j=N
(cid:88)

j=1

(fjt > FM SY )

3.2 Number of stocks where (cid:28)shing mortality is equal to or less than FM SY

It =

j=N
(cid:88)

j=1

(fjt ≤ FM SY )

3.3 Number of stocks outside safe biological limits

It =

j=N
(cid:88)

j=1

(fjt > FP A ∨ bjt < BP A)

3.4 Number of stocks inside safe biological limits

It =

j=N
(cid:88)

j=1

(fjt ≤ FP A ∧ bjt ≥ BP A)

3.5 Number of stocks where F is above FM SY or SSB is below BM SY

where in FAO 27

It =

j=N
(cid:88)

j=1

(fjt > FM SY ∨ bjt < BM SY )

BM SY = M SY Btrigger

3.6 Number of stocks where F is below or equal to FM SY and SSB is above

or equal to BM SY

It =

j=N
(cid:88)

j=1

(fjt ≤ FM SY ∧ bjt ≥ BM SY )

where in FAO 27

2Minto, C. 2015. Testing model based indicators for monitoring the CFP performance. Ad-hoc contract report, pp 14.
3Chato-Osio, G., Jardim, E., Minto, C., Scott, F. and Patterson, K. 2015. Model based CFP indicators, F/FM SY and

SSB. Mediterranean region case study. JRC Technical Report No XX, pp 26.

BM SY = M SY Btrigger

6

3.7 Trend in F/FM SY

For these indicators stocks managed under escapement strategies and stocks for which (cid:28)shing mortality
was reported as a harvest rate are not included.

where

and

It = yt

zjt = β0 + yt + uj

zjt = log E[

fjt
FM SY

]

fjt
FM SY

∼ Gamma(α, β)

3.8 Trend in SSB

For this indicator stocks for which biomass was reported as a relative value or total abundance are not
included. This indicator is scaled to the 2003 estimate for presentational purposes.

where

and

3.9 Trend in B/BP A

where

and

It = exp(yts − S−1

s=S
(cid:88)

s=1

y2003,s)

zjt = β0 + yt + uj

zjt = log E[bjt]

bjt ∼ Gamma(α, β)

It = yt

zjt = β0 + yt + uj

zjt = log E[

bjt
BP A

]

bjt
BP A

∼ Gamma(α, β)

7

3.10 Trend in recruitment

For this indicator stocks for which biomass was reported as a relative value or total abundance are not
included. This indicator is scaled to the 2003 estimate for presentational purposes.

where

and

It = exp(yts − S−1

s=S
(cid:88)

s=1

y2003,s)

zjt = β0 + yt + uj

zjt = log E[rjt]

rjt ∼ Gamma(α, β)

3.11 Trend in biomass

This indicator uses biomass trends extracted from SSB estimates for category 1 and 2 stocks, together
with biomass indices published by ICES for stocks of category 3.

where

and

It = yt

zjt = β0 + yt + uj

zjt = log E[kjt]

kjt ∼ Gamma(α, β)

3.12 Trend in biomass for data limited stocks

This indicator uses biomass indices computed from scienti(cid:28)c surveys or CPUE (catch per unit of e(cid:27)ort)
considered by experts to represent the evolution of biomass in time. The data is build from the list of
biomass indices published by ICES for data limited stocks category 3.

The indicator is calculated on a model-based form only,

where

and

It = yt

zjt = β0 + yt + uj

zjt = log E[kjt]

kjt ∼ Gamma(α, β)

8

4

Indicators of changes in advice coverage

These indicators are computed for the last year of the analysis only.

4.1 Number of stocks for which estimates of FM SY exist

I =

j=N
(cid:88)

j=1

(xj = λ)

λ =

(cid:40)

x = 1 FM SY exists
x = 0 otherwise

4.2 Number of stocks for which estimates of BP A exist

I =

j=N
(cid:88)

j=1

(xj = λ)

(cid:40)

λ =

x = 1 BP A exists
x = 0 otherwise

4.3 Number of stocks for which estimates of BM SY exist

I =

j=N
(cid:88)

j=1

(xj = λ)

(cid:40)

λ =

x = 1 BM SY exists
x = 0 otherwise

4.4 Fraction of TACs covered by stock assessments

This indicator considers that a sampling frame unit is covered by a stock assessment if there is at least
a partial overlap between its spatial distribution and the spatial distribution of the stock.

I = M −1

m=M
(cid:88)

m=1

(xm = λ)

(cid:40)

λ =

x = 1 spatial overlap exists
x = 0 otherwise

5 Transparency

Changes or additions to this protocol shall be approved by STECF.

To promote transparency of scienti(cid:28)c advice and allow the public in general, and stakeholders in partic-
ular, to have access to the data and analysis carried out, all code and data part of this analysis must be
published online once approved by the STECF plenary.

9

ANNEX II - CODE 

 
 
 
 
 
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#####################################################################
# EJ(20170302)
# NEA indicators
#####################################################################

library(reshape2)
library(ggplot2)
library(lme4)
library(influence.ME)
library(lattice)
library(parallel)
library(rgdal)
library(reshape2)
library(plyr)
source("funs.R")

#====================================================================
# Setup
#====================================================================

# year when assessments were performed 
assessmentYear <- 2017
# final data year with estimations from stock assessments 
fnlYear <- assessmentYear - 1
# initial data year with estimations from stock assessments 
iniYear <- 2003
# vector of years
dy <- iniYear:fnlYear
# vector of years for valid assessments
vay <- (assessmentYear-2):assessmentYear
# vector of years for stock status projection
vpy <- (fnlYear-2):fnlYear
# options for reading data
options(stringsAsFactors=FALSE)
# number of simulations for mle bootstrap
it <- 500
# number of cores for mle bootstrap parallel
nc <- 6
# quantiles to be computed
qtl <- c(0.025, 0.25, 0.50, 0.75, 0.975)
# to control de seed in mclapply
RNGkind("L'Ecuyer-CMRG")
set.seed(1234)
# to make plots consistent
vp <- dy
vp[c(2,3,5,6,8,9,11,12,13)] <- ""
theme_set(theme_bw())
sc <- scale_x_continuous(breaks=dy, labels=as.character(vp))
th <- theme(axis.text.x  = element_text(angle=90, vjust=0.5), panel.grid.minor = 
element_blank())

#====================================================================
# load & pre-process
#====================================================================

#--------------------------------------------------------------------
# assessments
#--------------------------------------------------------------------
isa <- read.csv("../data/ices/Dataset.csv", stringsAsFactors=FALSE)
# extract the main ecoregion but keep the list
er <- strsplit(isa[,"EcoRegion"], ",")
isa$EcoRegionList <- isa$EcoRegion
isa$EcoRegion <- unlist(lapply(er, function(x) x[1]))
er <- strsplit(isa[,"EcoRegion"], " ")
isa$EcoRegion <- unlist(lapply(er, function(x) paste(x[-length(x)], collapse=" ")))
isa[isa$EcoRegion=="Bay of Biscay and the Iberian Coast", "EcoRegion"] <- "BoBiscay & 
Iberia"

# widely distributed to keep coherent with previous years (taken from 2017's files)
isa[isa$OldFishStock %in% c("arg-rest", "bli-5b67", "boc-nea", "bsf-nea", "dgs-nea", 
"gfb-comb", "her-noss", "hke-nrtn", "hom-west", "lin-oth", "mac-nea", "rng-5b67", "smn-
dp", "trk-nea", "usk-oth", "whb-comb"), "EcoRegion"] <- "Northeast Atlantic"

# fix codes for stock size and fishing mortality
# f
isa[isa$FishingPressureDescription %in% c("Fishing Pressure: F"), 
"FishingPressureDescription"] <- "F"
isa[isa$FishingPressureDescription %in% c("Harvest Rate", "Harvest rate"), 

 
 
 
 
 
 
 
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"FishingPressureDescription"] <- "HR"

# biomass
isa[isa$StockSizeDescription %in% c("TSB/Bmsy"), "StockSizeDescription"] <- "B/Bmsy"

# order by year
isa <- isa[order(isa$Year),]

# reporting stk by data category
stBydc <- unique(subset(isa, Year %in% vpy)[,c("FishStock", "DataCategory", 
"EcoRegion")])
stBydc <- transform(stBydc, cat=as.integer(DataCategory))
write.csv(table(stBydc[,c("EcoRegion","cat")]), file="stBydc.csv")

#--------------------------------------------------------------------
# ICES rectangles data
#--------------------------------------------------------------------

rectangles <- readOGR("../data/ices_areas", layer= 
"ICES_StatRec_map_Areas_Full_20170124")
rectangles <- rectangles@data[,c("Area_27", "AreasList", "ICESNAME")]
colnames(rectangles) <- c("Max_Area","Area_List", "Rectangle")
rectangles <- subset(rectangles, !is.na(Max_Area))
# A new column is added based on Max_Area so that it is comparable across the other 
data sets
rectangles$Area <- paste("27.",toupper(as.character(rectangles$Max_Area)),sep="")
# Check that each rectangle is unique and only appears once in the data
# i.e. each rectangle is uniquely assigned to one area
length(unique(rectangles$Rectangle)) == nrow(rectangles)

#--------------------------------------------------------------------
# sampling frame (TACs)
#--------------------------------------------------------------------

load("../data/ices/sframe.RData")
# fmz is the frame of all TACs
# For consistency
colnames(fmz)[colnames(fmz) == "area"] <- "Area"
colnames(fmz)[colnames(fmz) == "spp"] <- "Species"
colnames(fmz)[colnames(fmz) == "stock_id"] <- "TAC_id"
sframe <- subset(fmz, TAC_id %in% sframe_TAC)

# Each ICES area should only appear once for each FMZ stock (to prevent the appearance 
of duplicate rectangles when merging with the ICES rectangle data later). We check this 
here:

unarea <- daply(sframe, .(TAC_id), function(x){
    return(length(unique(x$Area))==nrow(x))
})
all(unarea)

#====================================================================
# Stocks to retain
# matches sampling frame and ICES assessments through ICES rectangles
#====================================================================

#--------------------------------------------------------------------
# subset assessments and ecoregions, add areas
#--------------------------------------------------------------------

# remove 3+
cols <- c("FishStock","ICES.Areas..splited.with.character....." , "SpeciesName", 
"SGName", "DataCategory", "EcoRegion")
isa12 <- isa[isa$DataCategory<3, cols]
colnames(isa12)[colnames(isa12) == "ICES.Areas..splited.with.character....."] <- "Areas"
# Drop duplicates
isa12 <- unique(isa12)
# Remove white space and any capital letters from assessment name
isa12[,"FishStock"] <- tolower(gsub("\\s", "", isa12[,"FishStock"]))
# Make a species column from the assessment name
spp <- strsplit(isa12[,"FishStock"], "\\.")
isa12$Species <- toupper(unlist(lapply(spp, function(x) x[1])))
# Split ICES area by ~
areas <- strsplit(isa12[,"Areas"], "~")
names(areas) <- isa12[,"FishStock"]
areas <- melt(areas)
colnames(areas) <- c("Area","FishStock")
isa12 <- merge(isa12, areas)

 
 
 
 
 
 
 
 
 
 
 
 
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200

# keep relevant columns only
isa12 <- isa12[,c("FishStock","Area", "Species", "SpeciesName", "SGName", 
"DataCategory", "EcoRegion")]
isa12[,"Area"] <- toupper(gsub("\\s", "", isa12[,"Area"]))
# remove ecoregions outside EU waters
isa12 <- subset(isa12, !(EcoRegion %in% c("Arctic Ocean", "Greenland Sea", "Faroes", 
"Iceland Sea")))
# drop if ecoregion is NA
isa12 <- subset(isa12, !is.na(EcoRegion))
# remove her-noss which is widely distributed but mainly norway
isa12 <- subset(isa12, FishStock!="her.27.1-24a514a")

#--------------------------------------------------------------------
# fix area codes
#--------------------------------------------------------------------

# fix Baltic area codes
rectangles[rectangles$Area == "27.3.A.20","Area"] <- "27.3.A"
rectangles[rectangles$Area == "27.3.A.21","Area"] <- "27.3.A"
rectangles[rectangles$Area == "27.3.B.23","Area"] <- "27.3.B"
rectangles[rectangles$Area == "27.3.C.22","Area"] <- "27.3.C"

isa12[isa12$Area == "27.3.A.20","Area"] <- "27.3.A"
isa12[isa12$Area == "27.3.A.21","Area"] <- "27.3.A"
isa12[isa12$Area == "27.3.B.23","Area"] <- "27.3.B"
isa12[isa12$Area == "27.3.C.22","Area"] <- "27.3.C"

sframe[sframe$Area == "27.3.20","Area"] <- "27.3.A"
sframe[sframe$Area == "27.3.21","Area"] <- "27.3.A"
sframe[sframe$Area == "27.3.23","Area"] <- "27.3.B"
sframe[sframe$Area == "27.3.22","Area"] <- "27.3.C"

# Check: shouldn't have any 24.x.x areas
# Areas in ICES assessment but missing in rectangles
unique(isa12$Area)[!(unique(isa12$Area) %in% unique(rectangles$Area))]
# Areas in FMZ but missing in rectangles
unique(sframe$Area)[!(unique(sframe$Area) %in% unique(rectangles$Area))]

#--------------------------------------------------------------------
# fix species codes
#--------------------------------------------------------------------

# Horse mackerel
isa12[isa12$Species=="HOM","Species"] <- "JAX"
# ANK & MON - Anglerfish - species to genus
isa12[isa12$Species=="ANK","Species"] <- "ANF"
isa12[isa12$Species=="MON","Species"] <- "ANF"
# Megrim - species and genus to genus
isa12[isa12$Species=="MEG","Species"] <- "LEZ"
isa12[isa12$Species=="LDB","Species"] <- "LEZ"
# rays
isa12[isa12$Species=="RNG","Species"] <- "RTX"
# missing species
sort(unique(isa12$Species)[!(unique(isa12$Species) %in% unique(sframe$Species))])

#--------------------------------------------------------------------
# merge assessments,tacs/sf and rectangles
#--------------------------------------------------------------------

# merge assessments with rectangles
isa12r <- merge(isa12, rectangles[,c("Area","Rectangle")], by="Area")

205

# Do we have all the assessments?
all(sort(unique(isa12$FishStock)) == sort(unique(isa12r$FishStock)))

210

215

# Merge sampling frame with rectangles
sfr <- merge(sframe, rectangles[,c("Area","Rectangle")], by="Area")

# Do we have all the TACs?
all(sort(unique(sframe$TAC_id)) == sort(unique(sfr$TAC_id)))

# merge assessments with sampling frame
isa12sf <- merge(sfr, isa12r[,c("Species","Rectangle","FishStock","DataCategory")], by=c
("Species","Rectangle"), all.x = TRUE)

#--------------------------------------------------------------------
# final stock list
#--------------------------------------------------------------------

 
 
 
 
 
 
 
 
 
 
 
 
 
 
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# remove stocks with short time series
sts <- subset(isa, Year %in% dy & !is.na(FishingPressure))$FishStock
# remove short time series
sts <- table(sts)
sts <- names(sts)[sts<5]

# stocks to retain
stkToRetain <- unique(isa12sf$FishStock)[-1]
stkToRetain <- stkToRetain[!(stkToRetain %in% sts)]

#--------------------------------------------------------------------
# subset assessments
#--------------------------------------------------------------------
# filtering
saeu <- subset(isa, FishStock %in% stkToRetain)

# reporting
stkToDrop <- unique(isa[!(isa$FishStock %in% stkToRetain), c("FishStock", "EcoRegion", 
"DataCategory")])
write.csv(stkToDrop, file="stkToDropBySampFrame-nea.csv")
stkToRetain <- unique(isa[isa$FishStock %in% stkToRetain, c("FishStock", "EcoRegion", 
"DataCategory")])
write.csv(stkToRetain, file="stkToRetainBySampFrame-nea.csv")

# check what's available
table(saeu[,c("FishingPressureDescription","StockSizeDescription")])

#====================================================================
# process data for indicators
#====================================================================

#--------------------------------------------------------------------
# fixing BMSYescapment not reported by ICES
#--------------------------------------------------------------------
saeu$MSYBescapement <- NA

# NOP 34 
saeu[saeu$FishStock == "nop.27.3a4", c("StockSize", "MSYBescapement")] <- saeu[saeu
$FishStock == "nop.27.3a4", c("Low_StockSize", "Blim")]

# ANE BISC - need to add value from ss, using upper trigger as proxy for MSYBescapement
saeu[saeu$FishStock == "ane.27.8", "MSYBescapement"] <- 89000 

# acording to the sumsheets SAN and SPR-NSEA use Bpa for MSYBescapement
saeu[saeu$FishStock %in% c
("san.sa.1r","san.sa.2r","san.sa.3r","san.sa.4","spr.27.4"),"MSYBescapement"] <- saeu
[saeu$FishStock %in% c("san.sa.1r","san.sa.2r","san.sa.3r","san.sa.4","spr.27.4"),"Bpa"]

#--------------------------------------------------------------------
# fixing Recruitments of 0
#--------------------------------------------------------------------
saeu[saeu$Recruitment==0 & !is.na(saeu$Recruitment),"Recruitment"] <- NA

#--------------------------------------------------------------------
# Bref
#--------------------------------------------------------------------
saeu$Bref <- saeu$MSYBtrigger
# B escapement as Bref for relevant stocks
saeu$Bref[!is.na(saeu$MSYBescapement)] <- saeu$MSYBescapement[!is.na(saeu
$MSYBescapement)]
saeu$Bref <- as.numeric(saeu$Bref)
# set 0 as NA
saeu$Bref[saeu$Bref==0] <- NA
# if relative Bref = 1
saeu[saeu$StockSizeDescription == "B/Bmsy", "Bref"] <- 1

saeu$Brefpa <- saeu$Bpa
# B escapement as Brefpa for relevant stocks (already in Bpa)
#saeu$Brefpa[!is.na(saeu$MSYBescapement)] <- saeu$MSYBescapement[!is.na(saeu
$MSYBescapement)]
#saeu$Brefpa <- as.numeric(saeu$Brefpa)
# set 0 as NA
saeu$Brefpa[saeu$Brefpa==0] <- NA
# if relative Brefpa = 0.5
saeu[saeu$StockSizeDescription == "B/Bmsy", "Brefpa"] <- 0.5

290

#--------------------------------------------------------------------

 
 
 
 
 
 
 
 
 
 
 
 
 
 
# Fref
#--------------------------------------------------------------------
saeu$Fref <- saeu$FMSY
# no Fref for B escapement 
saeu$Fref[!is.na(saeu$MSYBescapement)] <- NA
saeu$Fref <- as.numeric(saeu$Fref)
# set 0 as NA
saeu$Fref[saeu$Fref==0] <- NA
# if relative Fmsy must be 1
saeu[saeu$FishingPressureDescription %in% c("F/Fmsy", "HR/HRmsy"), "Fref"] <- 1

saeu$Frefpa <- saeu$Fpa
# no Fref for B escapement 
saeu$Frefpa[!is.na(saeu$MSYBescapement)] <- NA
saeu$Frefpa <- as.numeric(saeu$Frefpa)
# set 0 as NA
saeu$Frefpa[saeu$Frefpa==0] <- NA
# if relative Fparef must be NA
saeu[saeu$FishingPressureDescription %in% c("F/Fmsy", "HR/HRmsy"), "Frefpa"] <- NA

#--------------------------------------------------------------------
# COMPUTE F/Fref and B/Bref | year + stock
#--------------------------------------------------------------------
saeu <- transform(saeu, 

indF = FishingPressure/Fref, 
indB=StockSize/Bref, 
indBpa=StockSize/Brefpa, 
indFpa = FishingPressure/Frefpa)

# in case of escapement strategy MSY evaluated by SSB ~ Bref
saeu$indF[!is.na(saeu$MSYBescapement)] <- saeu$Bref[!is.na(saeu$MSYBescapement)]/saeu
$StockSize[!is.na(saeu$MSYBescapement)]
saeu <- transform(saeu, sfFind=!is.na(indF))

#--------------------------------------------------------------------
# COMPUTE SBL | year + FishStock
#--------------------------------------------------------------------
saeu$SBL <- !(saeu$indFpa > 1 | saeu$indBpa < 1)
# if one is NA SBL can't be inferred
saeu$SBL[is.na(saeu$indFpa) | is.na(saeu$indBpa)] <- NA
# no SBL for B escapement 
saeu$SBL[!is.na(saeu$MSYBescapement)] <- NA
saeu <- transform(saeu, sfSBL=!is.na(SBL))

#--------------------------------------------------------------------
# COMPUTE CFP objectives | year + FishStock
#--------------------------------------------------------------------
saeu$CFP <- !(saeu$indF > 1 | saeu$indB < 1)
# if one is NA CFP can't be inferred
saeu$CFP[is.na(saeu$indF) | is.na(saeu$indB)] <- NA
# no CFP for B escapement 
saeu$CFP[!is.na(saeu$MSYBescapement)] <- NA
saeu <- transform(saeu, sfCFP=!is.na(CFP))

#--------------------------------------------------------------------
# final dataset
#--------------------------------------------------------------------
saeu <- subset(saeu, Year>=iniYear & Year <=fnlYear & AssessmentYear %in% vay & sfFind)

#--------------------------------------------------------------------
# project stock status up to last year in cases missing
#--------------------------------------------------------------------

saeu <- projectStkStatus(saeu, vpy)

#====================================================================
# Indicators (design based)
#====================================================================

#--------------------------------------------------------------------
# Number of stocks (remove projected years)
#--------------------------------------------------------------------
df0 <- saeu[!saeu$projected,]
inStks <- getNoStks(df0, "FishStock", length)

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png("figNEAI0a.png", 1800, 1200, res=300)
ggplot(subset(inStks, EcoRegion=="ALL"), aes(x=Year, y=N)) + 

geom_line() + 

 
 
 
 
 
 
 
 
 
 
 
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ylab("No. of stocks") + 
xlab("") + 
ylim(c(0,75)) + 
sc + 
th  

dev.off()

# time series
# NEEDS CHECK, YAXIS IS NOT REVERSED
png("figNEAI0b.png", 3000, 4500, res=300, bg = "transparent")
ggplot(df0, aes(Year, FishStock)) + 

geom_line() + 

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  geom_point(data=aggregate(df0$Year, by=list(FishStock=df0$FishStock), max),
    aes(x, FishStock))+

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geom_line(data=data.frame(Year=2009:2013, FishStock="nep.fu.14"), color="white") +
geom_line(data=data.frame(Year=2007:2009, FishStock="nep.fu.13"), color="white") +
geom_line(data=data.frame(Year=2003:2005, FishStock="nep.fu.13"), color="white") +
geom_point(data=data.frame(Year=2003, FishStock="nep.fu.13"), size=0.3) + 
ylab("Stock") +
xlab("Year") +
sc + 
scale_y_discrete(name="", limits = rev(unique(df0$FishStock))) +
th
dev.off()

# table
write.csv(dcast(inStks, EcoRegion~Year, value.var='N'), file="tabNEAI0.csv", 
row.names=FALSE) 

#--------------------------------------------------------------------
# (I1) Stocks F > Fmsy 
#--------------------------------------------------------------------
fInda <- getNoStks(saeu, "indF", function(x) sum(x>1))

# plot
png("figNEAI1.png", 1800, 1200, res=300)
ggplot(subset(fInda, EcoRegion=='ALL'), aes(x=Year, y=N)) + 

geom_line() +
expand_limits(y=0) + 
geom_point(aes(x=iniYear, y=N[1])) +
geom_point(aes(x=fnlYear, y=N[length(N)]), size=2) + 
ylab("No. of stocks") + 
xlab("") + 
ylim(c(0,75)) + 
sc + 
th 

dev.off()

# plot
png("figNEAI1b.png", 2400, 1200, res=300)
ggplot(subset(fInda, EcoRegion != 'ALL'), aes(x=Year, y=N)) + 

geom_line() + 
facet_grid(.~EcoRegion) +
ylab("No. of stocks") + 
xlab("") + 
sc + 
ylim(0, 20) + 
th
dev.off()

# table
write.csv(dcast(fInda, EcoRegion~Year, value.var='N'), file="tabNEAI1.csv", 
row.names=FALSE)

#--------------------------------------------------------------------
# (I2) Stocks F <= Fmsy
#--------------------------------------------------------------------
fIndb <- getNoStks(saeu, "indF", function(x) sum(x<=1))

# plot
png("figNEAI2.png", 1800, 1200, res=300)
ggplot(subset(fIndb, EcoRegion=='ALL'), aes(x=Year, y=N)) + 

geom_line() + 
expand_limits(y=0) + 
geom_point(aes(x=iniYear, y=N[1])) + 
geom_point(aes(x=fnlYear, y=N[length(N)]), size=2) +
ylab("No. of stocks") + 
xlab("") + 

 
 
 
 
 
 
 
 
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ylim(c(0,75)) + 
sc + 
th
dev.off()

# plot
png("figNEAI2b.png", 2400, 1200, res=300)
ggplot(subset(fIndb, EcoRegion != 'ALL'), aes(x=Year, y=N)) + 

geom_line() + 
facet_grid(.~EcoRegion) +
ylab("No. of stocks") + 
xlab("") + 
sc + 
ylim(0, 20) + 
th
dev.off()

# table
write.csv(dcast(fIndb, EcoRegion~Year, value.var='N'), file="tabNEAI2.csv", 
row.names=FALSE)

#--------------------------------------------------------------------
# (I3) Stocks outside SBL
#--------------------------------------------------------------------
fIndc <- getNoStks(saeu, "SBL", function(x) sum(!x, na.rm=TRUE))

# plot
png("figNEAI3.png", 1800, 1200, res=300)
ggplot(subset(fIndc, EcoRegion=='ALL'), aes(x=Year, y=N)) + 

geom_line() + 
expand_limits(y=0) + 
geom_point(aes(x=iniYear, y=N[1])) +
geom_point(aes(x=fnlYear, y=N[length(N)]), size=2) + 
ylab("No. of stocks") + 
xlab("") + 
ylim(c(0,75)) + 
sc + 
th
dev.off()

# plot
png("figNEAI3b.png", 2400, 1200, res=300)
ggplot(subset(fIndc, EcoRegion != 'ALL'), aes(x=Year, y=N)) + 

geom_line() + 
facet_grid(.~EcoRegion) +
ylab("No. of stocks") + 
xlab("") + 
sc + 
ylim(0, 15) + 
th
dev.off()

# table
write.csv(dcast(fIndc, EcoRegion~Year, value.var='N'), file="tabNEAI3.csv", 
row.names=FALSE)

#--------------------------------------------------------------------
# (I4) Stocks inside SBL
#--------------------------------------------------------------------
fIndd <- getNoStks(saeu, "SBL", function(x) sum(x, na.rm=TRUE))

## plot
png("figNEAI4.png", 1800, 1200, res=300)
ggplot(subset(fIndd, EcoRegion=='ALL'), aes(x=Year, y=N)) + 

geom_line() + 
expand_limits(y=0) + 
geom_point(aes(x=iniYear, y=N[1])) +
geom_point(aes(x=fnlYear, y=N[length(N)]), size=2) + 
ylab("No. of stocks") + 
xlab("") + 
ylim(c(0,75)) + 
sc + 
th
dev.off()

# plot
png("figNEAI4b.png", 2400, 1200, res=300)
ggplot(subset(fIndd, EcoRegion != 'ALL'), aes(x=Year, y=N)) + 

 
 
 
 
 
 
 
 
 
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geom_line() + 
facet_grid(.~EcoRegion) +
ylab("No. of stocks") + 
xlab("") + 
sc + 
ylim(0, 15) + 
th
dev.off()

# table
write.csv(dcast(fIndd, EcoRegion~Year, value.var='N'), file="tabNEAI4.csv", 
row.names=FALSE)

#--------------------------------------------------------------------
# (I5) Stocks outside CFP objectives
#--------------------------------------------------------------------
fIndf <- getNoStks(saeu, "CFP", function(x) sum(!x, na.rm=TRUE))

## plot
png("figNEAI5.png", 1800, 1200, res=300)
ggplot(subset(fIndf, EcoRegion=='ALL'), aes(x=Year, y=N)) + 

geom_line() + 
expand_limits(y=0) + 
geom_point(aes(x=iniYear, y=N[1])) +
geom_point(aes(x=fnlYear, y=N[length(N)]), size=2) + 
ylab("No. of stocks") + 
xlab("") + 
ylim(c(0,75)) + 
sc + 
th
dev.off()

# plot
png("figNEAI5b.png", 2400, 1200, res=300)
ggplot(subset(fIndf, EcoRegion != 'ALL'), aes(x=Year, y=N)) + 

geom_line() + 
facet_grid(.~EcoRegion) +
ylab("No. of stocks") + 
xlab("") + 
sc + 
ylim(0, 20) + 
th
dev.off()

# table
write.csv(dcast(fIndf, EcoRegion~Year, value.var='N'), file="tabNEAI5.csv", 
row.names=FALSE)

#--------------------------------------------------------------------
# (I6) Stocks inside CFP objectives
#--------------------------------------------------------------------
fIndfb <- getNoStks(saeu, "CFP", function(x) sum(x, na.rm=TRUE))

# plot
png("figNEAI6.png", 1800, 1200, res=300)
ggplot(subset(fIndfb, EcoRegion=='ALL'), aes(x=Year, y=N)) + 

geom_line() + 
expand_limits(y=0) + 
geom_point(aes(x=iniYear, y=N[1])) +
geom_point(aes(x=fnlYear, y=N[length(N)]), size=2) + 
ylab("No. of stocks") + 
xlab("") + 
ylim(c(0,75)) + 
sc + 
th
dev.off()

# plot
png("figNEAI6b.png", 2400, 1200, res=300)
ggplot(subset(fIndfb, EcoRegion != 'ALL'), aes(x=Year, y=N)) + 

geom_line() + 
facet_grid(.~EcoRegion) +
ylab("No. of stocks") + 
xlab("") + 
sc + 
ylim(0, 20) + 
th
dev.off()

 
 
 
 
 
 
 
 
# table
write.csv(dcast(fIndfb, EcoRegion~Year, value.var='N'), file="tabNEAI6.csv", 
row.names=FALSE)

#====================================================================
# Indicators (model based)
#====================================================================

#--------------------------------------------------------------------
# (I7) F/Fmsy model
#--------------------------------------------------------------------
idx <- saeu$FishingPressureDescription %in% c("F", "F/Fmsy")
saeu$sfI7 <- idx & is.na(saeu$MSYBescapement)
df0 <- saeu[saeu$sfI7,]
df0$Year <- factor(df0$Year)
yrs <- levels(df0$Year)
nd <- data.frame(Year=factor(yrs))

# fit
ifit <- glmer(indF ~ Year + (1|FishStock), data = df0, family = Gamma("log"), 
control=glmerControl(optimizer="nlminbwrap"))
runDiagsME(ifit, "FishStock", df0, "diagNEAI7.pdf", nc, nd)

# bootstrap
stk <- unique(df0$FishStock)
ifit.bs <- split(1:it, 1:it) 

ifit.bs <- mclapply(ifit.bs, function(x){
stk <- sample(stk, replace=TRUE)
df1 <- df0[0,] 
for(i in stk) df1 <- rbind(df1, subset(df0, FishStock==i))
fit <- glmer(indF ~ Year + (1|FishStock), data = df1, family = Gamma("log"), 

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control=glmerControl(optimizer="nlminbwrap"))

v0 <- predict(fit, re.form=~0, type="response", newdata=nd)
if(length(fit@optinfo$conv$lme4)>0) v0[] <- NA
v0

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}, mc.cores=nc)

ifitm <- do.call("rbind", ifit.bs)
ifitq <- apply(ifitm, 2, quantile, qtl, na.rm=TRUE)
ifitq <- cbind(Year=as.numeric(yrs), as.data.frame(t(ifitq)))

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# plot
png("figNEAI7.png", 1800, 1200, res=300)
ggplot(ifitq, aes(x=Year)) +
  geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`), fill="gray", alpha=0.60) +
  geom_ribbon(aes(ymin = `25%`, ymax = `75%`), fill="gray", alpha=0.95) +
  geom_line(aes(y=`50%`)) + expand_limits(y=0) +
  geom_point(aes(x=Year[1], y=`50%`[1])) +
  geom_point(aes(x=Year[length(Year)], y=`50%`[length(`50%`)]), size=2) +
  geom_hline(yintercept = 1, linetype=2) +
  ylab(expression(F/F[MSY])) + 
  ylim(0, 2.5) + 
  xlab("") +
  theme(legend.position = "none") + 
  sc + 
  th
dev.off()

# table
tb0 <- t(ifitq)[-1,]
colnames(tb0) <- ifitq[,1]
write.csv(tb0, file="tabNEAI7.csv")

#--------------------------------------------------------------------
# (I7b) F/Fmsy model regional
#--------------------------------------------------------------------
df0 <- saeu[saeu$sfI7,]
df0$Year <- factor(df0$Year)
yrs <- levels(df0$Year)
nd <- data.frame(Year=factor(yrs))

ifitRegional <- lapply(split(df0, df0$EcoRegion), function(x){

# fit model
ifit <- glmer(indF ~ Year + (1|FishStock), data = x, family = Gamma("log"), 

control=glmerControl(optimizer="nlminbwrap"))

# no variance with bootstrap due to small number of stocks

 
 
 
 
 
 
 
 
 
 
 
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ifit.pred <- predict(ifit, re.form=~0, type="response", newdata=nd)
# output
list(ifit=ifit, ifit.pred=ifit.pred)

})

lst0 <- lapply(ifitRegional, "[[", "ifit.pred")
fIndfr <- data.frame(EcoRegion=rep(names(lst0), lapply(lst0, length)), N=unlist(lst0), 
Year=as.numeric(as.character(nd[,1])))

# plot
png("figNEAI7b.png", 2400, 1200, res=300)
ggplot(fIndfr, aes(x=Year, y=N)) + 

geom_line() + 
facet_grid(.~EcoRegion) +
ylab(expression(F/F[MSY])) + 
xlab("") + 
sc + 
ylim(0, 2.5) + 
th
dev.off()

# table
write.csv(dcast(fIndfr, EcoRegion~Year, value.var='N'), file="tabNEAI7b.csv", 
row.names=FALSE)

#--------------------------------------------------------------------
# (I7out) F/Fmsy stocks outside EU
#--------------------------------------------------------------------
df0 <- subset(isa, (EcoRegion %in% c("Arctic Ocean", "Greenland Sea", "Faroes", 
"Iceland Sea") | FishStock=="her.27.1-24a514a") & FishStock!="pra.27.1-2" &  
Year>=iniYear & Year<=fnlYear & AssessmentYear %in% vay)
df0$Fref <- df0$FMSY
df0 <- transform(df0, indF = FishingPressure/Fref, sfFind=!is.na(FishingPressure/Fref))
idx <- df0$FishingPressureDescription %in% c("F", "F/Fmsy") & df0$sfFind
df0 <- df0[idx,]

# check data series is complete
table(df0[,c("FishStock","Year")])

# create year variable for prediction
df0$Year <- factor(df0$Year)
yrs <- levels(df0$Year)
nd <- data.frame(Year=factor(yrs))

# fit
ifitout <- glmer(indF ~ Year + (1|FishStock), data = df0, family = Gamma("log"), 
control=glmerControl(optimizer="nlminbwrap")) 
runDiagsME(ifitout, "FishStock", df0, "diagNEAI7out.pdf", nc, nd)

# bootstrap
stk <- unique(df0$FishStock)
ifitout.bs <- split(1:it, 1:it) 
ifitout.bs <- mclapply(ifitout.bs, function(x){

stk <- sample(stk, replace=TRUE)
df1 <- df0[0,] 
for(i in stk) df1 <- rbind(df1, subset(df0, FishStock==i))
fit <- glmer(indF ~ Year + (1|FishStock), data = df1, family = Gamma("log"), 

control=glmerControl(optimizer="nlminbwrap")) 

v0 <- predict(fit, re.form=~0, type="response", newdata=nd)
if(length(fit@optinfo$conv$lme4)>0) v0[] <- NA
v0

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}, mc.cores=nc)

ifitm <- do.call("rbind", ifitout.bs)
ifitq <- apply(ifitm, 2, quantile, qtl, na.rm=TRUE)
ifitq <- cbind(Year=as.numeric(yrs), as.data.frame(t(ifitq)))

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# plot
png("figNEAI7out.png", 1800, 1200, res=300)
ggplot(ifitq, aes(x=Year)) +
  geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`), fill="gray", alpha=0.60) +
  geom_ribbon(aes(ymin = `25%`, ymax = `75%`), fill="gray", alpha=0.95) +
  geom_line(aes(y=`50%`)) + expand_limits(y=0) +
  geom_point(aes(x=Year[1], y=`50%`[1])) +
  geom_point(aes(x=Year[length(Year)], y=`50%`[length(`50%`)]), size=2) +
  ylab(expression(F/F[MSY])) + 
  geom_hline(yintercept = 1, linetype=2) +
  ylim(0, 2.5) + 

 
 
 
 
 
 
 
 
 
 
  xlab("") +
  theme(legend.position = "none") + 
  sc + 
  th
dev.off()

# table
tb0 <- t(ifitq)[-1,]
colnames(tb0) <- ifitq[,1]
write.csv(tb0, file="tabNEAI7out.csv")

#--------------------------------------------------------------------
# (I8) SSB model
#--------------------------------------------------------------------
saeu$sfI8 <- saeu$StockSizeDescription %in% c("SSB", "TSB")
df0 <- saeu[saeu$sfI8,]
df0$Year <- factor(df0$Year)
yrs <- levels(df0$Year)
nd <- data.frame(Year=factor(yrs))

# fit
ifitb <- glmer(StockSize ~ Year + (1|FishStock), data =  df0, family = Gamma("log"), 
control=glmerControl(optimizer="nlminbwrap")) 
runDiagsME(ifitb, "FishStock", df0, "diagNEAI8.pdf", nc, nd)

# bootstrap
stk <- unique(df0$FishStock)
ifitb.bs <- split(1:it, 1:it) 
ifitb.bs <- mclapply(ifitb.bs, function(x){

stk <- sample(stk, replace=TRUE)
df1 <- df0[0,] 
for(i in stk) df1 <- rbind(df1, subset(df0, FishStock==i))
fit <- glmer(StockSize ~ Year + (1|FishStock), data = df1, family = Gamma("log"), 

control=glmerControl(optimizer="nlminbwrap")) 

v0 <- predict(fit, re.form=~0, type="response", newdata=nd)
if(length(fit@optinfo$conv$lme4)>0) v0[] <- NA
v0

}, mc.cores=nc)

ifitm <- do.call("rbind", ifitb.bs)
ifitm <- exp(log(ifitm)-mean(log(ifitm[,1]), na.rm=TRUE))
ifitq <- apply(ifitm, 2, quantile, qtl, na.rm=TRUE)
ifitq <- cbind(Year=as.numeric(yrs), as.data.frame(t(ifitq)))

# plot
png("figNEAI8.png", 1800, 1200, res=300)
ggplot(ifitq, aes(x=Year)) +
  geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`), fill="gray", alpha=0.60) +
  geom_ribbon(aes(ymin = `25%`, ymax = `75%`), fill="gray", alpha=0.95) +
  geom_line(aes(y=`50%`)) + 
  expand_limits(y=0) +
  geom_point(aes(x=Year[1], y=`50%`[1])) +
  geom_point(aes(x=Year[length(Year)], y=`50%`[length(`50%`)]), size=2) +
  geom_hline(yintercept = 1, linetype=2) +
  ylab(expression(B/B[2003])) + 
  xlab("") +
  theme(legend.position = "none") + 
  sc + 
  th
dev.off()

# table
tb0 <- t(ifitq)[-1,]
colnames(tb0) <- ifitq[,1]
write.csv(tb0, file="tabNEAI8.csv")

#--------------------------------------------------------------------
# (I8b) SSB model regional
#--------------------------------------------------------------------
df0 <- saeu[saeu$sfI8,]
df0$Year <- factor(df0$Year)
yrs <- levels(df0$Year)
nd <- data.frame(Year=factor(yrs))

ifitbRegional <- lapply(split(df0, df0$EcoRegion), function(x){

# fit model
ifitb <- glmer(StockSize ~ Year + (1|FishStock), data = x, family = Gamma("log"), 

control=glmerControl(optimizer="nlminbwrap"))

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# no variance with bootstrap due to small number of stocks
ifitb.pred <- predict(ifitb, re.form=~0, type="response", newdata=nd)
# output
list(ifitb=ifitb, ifitb.pred=ifitb.pred/ifitb.pred[nd==iniYear])

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})

lst0 <- lapply(ifitbRegional, "[[", "ifitb.pred")
fIndbr <- data.frame(EcoRegion=rep(names(lst0), lapply(lst0, length)), N=unlist(lst0), 
Year=as.numeric(as.character(nd[,1])))

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# plot
png("figNEAI8b.png", 2400, 1200, res=300)
ggplot(fIndbr, aes(x=Year, y=N)) + 

geom_line() + 
facet_grid(.~EcoRegion) +
geom_hline(yintercept = 1, linetype=2) +
ylab(expression(B/B[2003])) + 
xlab("") +
theme(legend.position = "none") + 
sc + 
th
dev.off()

# table
write.csv(dcast(fIndbr, EcoRegion~Year, value.var='N'), file="tabNEAI8b.csv", 
row.names=FALSE)

#--------------------------------------------------------------------
# (I9) SSB/Bpa model
#--------------------------------------------------------------------
idx <- saeu$StockSizeDescription %in% c("SSB", "TSB", "B/Bmsy")
saeu$sfI9 <- idx & !is.na(saeu$indBpa)
df0 <- saeu[saeu$sfI9,]
df0$Year <- factor(df0$Year)
yrs <- levels(df0$Year)
nd <- data.frame(Year=factor(yrs))

# fit
ifitbpa <- glmer(indBpa ~ Year + (1|FishStock), data =  df0, family = Gamma("log"), 
control=glmerControl(optimizer="nlminbwrap")) 
runDiagsME(ifitbpa, "FishStock", df0, "diagNEAI9.pdf", nc, nd)

# bootstrap
stk <- unique(df0$FishStock)
ifitbpa.bs <- split(1:it, 1:it) 
ifitbpa.bs <- mclapply(ifitbpa.bs, function(x){

stk <- sample(stk, replace=TRUE)
df1 <- df0[0,] 
for(i in stk) df1 <- rbind(df1, subset(df0, FishStock==i))
fit <- glmer(indBpa ~ Year + (1|FishStock), data = df1, family = Gamma("log"), 

control=glmerControl(optimizer="nlminbwrap")) 

v0 <- predict(fit, re.form=~0, type="response", newdata=nd)
if(length(fit@optinfo$conv$lme4)>0) v0[] <- NA
v0

}, mc.cores=nc)

ifitm <- do.call("rbind", ifitbpa.bs)
ifitq <- apply(ifitm, 2, quantile, qtl, na.rm=TRUE)
ifitq <- cbind(Year=as.numeric(yrs), as.data.frame(t(ifitq)))

# plot
png("figNEAI9.png", 1800, 1200, res=300)
ggplot(ifitq, aes(x=Year)) +
  geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`), fill="gray", alpha=0.60) +
  geom_ribbon(aes(ymin = `25%`, ymax = `75%`), fill="gray", alpha=0.95) +
  geom_line(aes(y=`50%`)) + 
  expand_limits(y=0) +
  geom_point(aes(x=Year[1], y=`50%`[1])) +
  geom_point(aes(x=Year[length(Year)], y=`50%`[length(`50%`)]), size=2) +
  geom_hline(yintercept = 1, linetype=2) +
  ylab(expression(B/B[pa])) + 
  xlab("") +
  theme(legend.position = "none") + 
  sc + 
  th
dev.off()

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# table

 
 
 
 
 
 
 
 
 
tb0 <- t(ifitq)[-1,]
colnames(tb0) <- ifitq[,1]
write.csv(tb0, file="tabNEAI9.csv")

#--------------------------------------------------------------------
# (I10) Recruitment model (same data as SSB trends)
#--------------------------------------------------------------------
saeu$sfI10 <- saeu$sfI8 & !is.na(saeu$Recruitment)
df0 <- saeu[saeu$sfI10,]
df0$Year <- factor(df0$Year)
yrs <- levels(df0$Year)
nd <- data.frame(Year=factor(yrs))

# fit
ifitr <- glmer(Recruitment ~ Year + (1|FishStock), data =  df0, family = Gamma("log"), 
control=glmerControl(optimizer="nlminbwrap")) 
runDiagsME(ifitr, "FishStock", df0, "diagNEAI10.pdf", nc, nd)

# bootstrap
stk <- unique(df0$FishStock)
ifitr.bs <- split(1:it, 1:it) 
ifitr.bs <- mclapply(ifitr.bs, function(x){

stk <- sample(stk, replace=TRUE)
df1 <- df0[0,] 
for(i in stk) df1 <- rbind(df1, subset(df0, FishStock==i))
fit <- glmer(Recruitment ~ Year + (1|FishStock), data = df1, family = Gamma("log"), 

control=glmerControl(optimizer="nlminbwrap")) 

v0 <- predict(fit, re.form=~0, type="response", newdata=nd)
if(length(fit@optinfo$conv$lme4)>0) v0[] <- NA
v0

}, mc.cores=nc)

ifitm <- do.call("rbind", ifitr.bs)
ifitm <- exp(log(ifitm)-mean(log(ifitm[,1]), na.rm=TRUE))
ifitq <- apply(ifitm, 2, quantile, qtl, na.rm=TRUE)
ifitq <- cbind(Year=as.numeric(yrs), as.data.frame(t(ifitq)))

# plot
png("figNEAI10.png", 1800, 1200, res=300)
ggplot(ifitq, aes(x=Year)) +
  geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`), fill="gray", alpha=0.60) +
  geom_ribbon(aes(ymin = `25%`, ymax = `75%`), fill="gray", alpha=0.95) +
  geom_line(aes(y=`50%`)) + 
  expand_limits(y=0) +
  geom_point(aes(x=Year[1], y=`50%`[1])) +
  geom_point(aes(x=Year[length(Year)], y=`50%`[length(`50%`)]), size=2) +
  geom_hline(yintercept = 1, linetype=2) +
  ylab(expression(R/R[2003])) + 
  xlab("") +
  theme(legend.position = "none") + 
  sc + 
  th
dev.off()

# table
tb0 <- t(ifitq)[-1,]
colnames(tb0) <- ifitq[,1]
write.csv(tb0, file="tabNEAI10.csv")

#--------------------------------------------------------------------
# (I11) SSB model for cat 1-3
#--------------------------------------------------------------------
df0 <- subset(isa, !(EcoRegion %in% c("Arctic Ocean", "Greenland Sea", "Faroes", 
"Iceland Sea")) & DataCategory<4 & StockSize>0 &  Year>=iniYear & Year <= fnlYear & 
AssessmentYear %in% vay & StockSizeDescription %in% c("Biomass Index", "SSB", "TSB", 
"Relative BI (comb)", "B/Bmsy", "Relative SSB", "standardized CPUE", "Relative BI", 
"Biomass Index (comb)", "LPUE"))

# remove stocks with short time series
sts <- table(df0$FishStock, df0$Year)
sts <- rownames(sts)[apply(sts, 1, sum)<5]
df0 <- subset(df0, !(FishStock %in% sts))

# id
sfI11 <- tapply(df0$Year, df0$FishStock, max)
sfI11 <- data.frame(FishStock=names(sfI11), Year=sfI11, variable="sfI11", value=TRUE)

# project for stocks without 2015, 2016 estimates

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df0 <- projectStkStatus(df0, vpy)

# pre process for model 
df0$Year <- factor(df0$Year)
yrs <- levels(df0$Year)
nd <- data.frame(Year=factor(yrs))

# fit
ifitb123 <- glmer(StockSize ~ Year + (1|FishStock), data =  df0, family = Gamma("log"), 
control=glmerControl(optimizer="nlminbwrap")) 
runDiagsME(ifitb123, "FishStock", df0, "diagNEAI11.pdf", nc, nd)

# bootstrap
stk <- unique(df0$FishStock)
ifitb123.bs <- split(1:it, 1:it) 
ifitb123.bs <- mclapply(ifitb123.bs, function(x){

stk <- sample(stk, replace=TRUE)
df1 <- df0[0,] 
for(i in stk) df1 <- rbind(df1, subset(df0, FishStock==i))
fit <- glmer(StockSize ~ Year + (1|FishStock), data = df1, family = Gamma("log"), 

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control=glmerControl(optimizer="nlminbwrap")) 

v0 <- predict(fit, re.form=~0, type="response", newdata=nd)
if(length(fit@optinfo$conv$lme4)>0) v0[] <- NA
v0

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}, mc.cores=nc)

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ifitm <- do.call("rbind", ifitb123.bs)
ifitm <- exp(log(ifitm)-mean(log(ifitm[,1]), na.rm=TRUE))
ifitq <- apply(ifitm, 2, quantile, qtl, na.rm=TRUE)
ifitq <- cbind(Year=as.numeric(yrs), as.data.frame(t(ifitq)))

# plot
png("figNEAI11.png", 1800, 1200, res=300)
ggplot(ifitq, aes(x=Year)) +
  geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`), fill="gray", alpha=0.60) +
  geom_ribbon(aes(ymin = `25%`, ymax = `75%`), fill="gray", alpha=0.95) +
  geom_line(aes(y=`50%`)) + 
  expand_limits(y=0) +
  geom_point(aes(x=Year[1], y=`50%`[1])) +
  geom_point(aes(x=Year[length(Year)], y=`50%`[length(`50%`)]), size=2) +
  geom_hline(yintercept = 1, linetype=2) +
  ylab(expression(B/B[2003])) + 
  xlab("") +
  theme(legend.position = "none") + 
  sc + 
  th
dev.off()

# table
tb0 <- t(ifitq)[-1,]
colnames(tb0) <- ifitq[,1]
write.csv(tb0, file="tabNEAI11.csv")

#--------------------------------------------------------------------
# (I12) SSB model for cat 3
#--------------------------------------------------------------------
df0 <- subset(isa, !(EcoRegion %in% c("Arctic Ocean", "Greenland Sea", "Faroes", 
"Iceland Sea")) & DataCategory>2 & DataCategory<4 & StockSize>0 &  Year>=iniYear & Year 
<= fnlYear & AssessmentYear %in% vay & StockSizeDescription %in% c("Biomass Index", 
"SSB", "TSB", "Relative BI (comb)", "B/Bmsy", "Relative SSB", "standardized CPUE", 
"Relative BI", "Biomass Index (comb)", "LPUE"))

# remove stocks with short time series
sts <- table(df0$FishStock, df0$Year)
sts <- rownames(sts)[apply(sts, 1, sum)<5]
df0 <- subset(df0, !(FishStock %in% sts))

# id
sfI12 <- tapply(df0$Year, df0$FishStock, max)
sfI12 <- data.frame(FishStock=names(sfI12), Year=sfI12, variable="sfI12", value=TRUE)

# project for stocks without 2015, 2016 estimates
df0 <- projectStkStatus(df0, vpy)

# pre process for model 
df0$Year <- factor(df0$Year)
yrs <- levels(df0$Year)
nd <- data.frame(Year=factor(yrs))

 
 
 
 
 
 
 
 
 
 
 
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# fit
ifitb3 <- glmer(StockSize ~ Year + (1|FishStock), data =  df0, family = Gamma("log"), 
control=glmerControl(optimizer="nlminbwrap")) 
runDiagsME(ifitb3, "FishStock", df0, "diagNEAI12.pdf", nc, nd)

# bootstrap
stk <- unique(df0$FishStock)
ifitb3.bs <- split(1:it, 1:it) 
ifitb3.bs <- mclapply(ifitb3.bs, function(x){

stk <- sample(stk, replace=TRUE)
df1 <- df0[0,] 
for(i in stk) df1 <- rbind(df1, subset(df0, FishStock==i))
fit <- glmer(StockSize ~ Year + (1|FishStock), data = df1, family = Gamma("log"), 

control=glmerControl(optimizer="nlminbwrap")) 

v0 <- predict(fit, re.form=~0, type="response", newdata=nd)
if(length(fit@optinfo$conv$lme4)>0) v0[] <- NA
v0

}, mc.cores=nc)

ifitm <- do.call("rbind", ifitb3.bs)
ifitm <- exp(log(ifitm)-mean(log(ifitm[,1]), na.rm=TRUE))
ifitq <- apply(ifitm, 2, quantile, qtl, na.rm=TRUE)
ifitq <- cbind(Year=as.numeric(yrs), as.data.frame(t(ifitq)))

# plot
png("figNEAI12.png", 1800, 1200, res=300)
ggplot(ifitq, aes(x=Year)) +
  geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`), fill="gray", alpha=0.60) +
  geom_ribbon(aes(ymin = `25%`, ymax = `75%`), fill="gray", alpha=0.95) +
  geom_line(aes(y=`50%`)) + 
  expand_limits(y=0) +
  geom_point(aes(x=Year[1], y=`50%`[1])) +
  geom_point(aes(x=Year[length(Year)], y=`50%`[length(`50%`)]), size=2) +
  geom_hline(yintercept = 1, linetype=2) +
  ylab(expression(B/B[2003])) + 
  xlab("") +
  theme(legend.position = "none") + 
  sc + 
  th
dev.off()

# table
tb0 <- t(ifitq)[-1,]
colnames(tb0) <- ifitq[,1]
write.csv(tb0, file="tabNEAI12.csv")

#====================================================================
# Stocks used in each indicator
#====================================================================

df0 <- melt(saeu[!saeu$projected,], c('FishStock', 'Year'), c('sfFind', 'sfSBL', 
'sfCFP', 'sfI7', 'sfI8', 'sfI9', 'sfI10'))
df0 <- do.call("rbind", lapply(split(df0, df0$FishStock), function(x) subset(x, 
Year==max(x$Year))))
df0 <- rbind(df0, sfI11, sfI12)
levels(df0$variable) <- c('above/below Fmsy', 'in/out SBL', 'in/out CFP', 'F/Fmsy 
trends', 'Biomass trends', 'SSB/Bpa trends', "Recruitment trends", "Biomass data 
category 1-3 trends", "Biomass data category 3 trends")
stkPerIndicator <- dcast(df0, FishStock+Year~variable, value.var='value')

# NOTE: this file must be fixed "by hand" to remove duplications 
# created for the cat 1 stocks which were projected 
# (no time to right code now ...)
write.csv(stkPerIndicator, file="stkPerIndicator.csv")

#====================================================================
# Coverage
#====================================================================

# All stocks of relevance
stocks <- subset(saeu, Year==fnlYear)$FishStock
# All stocks with B indicator
bind_stocks <- subset(saeu, Year==fnlYear & !is.na(indB))$FishStock 
# All stocks with F indicator - Same as stocks
find_stocks <- subset(saeu, Year==fnlYear & !is.na(indF))$FishStock 
# All stocks with Bpa indicator
bpaind_stocks <- subset(saeu, Year==fnlYear & !is.na(indBpa))$FishStock 

 
 
 
 
 
 
 
 
 
 
# All stocks with Fpa indicator - Same as stocks
fpaind_stocks <- subset(saeu, Year==fnlYear & !is.na(indFpa))$FishStock 

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# Current list
all_stocks <- unique(isa12sf$FishStock)
# ignore NA
all_stocks <- all_stocks[!is.na(all_stocks)]

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# Which stocks to drop from all stocks
drop_stock <- all_stocks[!(all_stocks %in% stocks)]

# Which stocks to drop as no f indicator
drop_stock_f <- all_stocks[!(all_stocks %in% find_stocks)]

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# Which stocks to drop as no b indicator
drop_stock_b <- all_stocks[!(all_stocks %in% bind_stocks)]

# Which stocks to drop as no fpa indicator
drop_stock_fpa <- all_stocks[!(all_stocks %in% fpaind_stocks)]

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# Which stocks to drop as no bpa indicator
drop_stock_bpa <- all_stocks[!(all_stocks %in% bpaind_stocks)]

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# Set dropped stocks to NA in FishStock column
isa12sf$FindFishStock <- isa12sf$FishStock
isa12sf[isa12sf$FindFishStock %in% drop_stock_f,"FindFishStock"] <- as.character(NA)
isa12sf$BindFishStock <- isa12sf$FishStock
isa12sf[isa12sf$BindFishStock %in% drop_stock_b ,"BindFishStock"] <- as.character(NA)
isa12sf$FpaindFishStock <- isa12sf$FishStock
isa12sf[isa12sf$FpaindFishStock %in% drop_stock_fpa,"FpaindFishStock"] <- as.character
(NA)
isa12sf$BpaindFishStock <- isa12sf$FishStock
isa12sf[isa12sf$BpaindFishStock %in% drop_stock_bpa,"BpaindFishStock"] <- as.character
(NA)

# Proportion of TACs that have at least one rectangle assessed by FindFishStock and 
BindFishStock
outf <- aggregate(isa12sf$FindFishStock, by=list(isa12sf$TAC_id), function(x) {
          no_rect_ass_find <- sum(!is.na(x))
          assessed_find <- no_rect_ass_find > 1
          return(assessed_find)
})

outb <- aggregate(isa12sf$BindFishStock, by=list(isa12sf$TAC_id), function(x) {
          no_rect_ass_bind <- sum(!is.na(x))
          assessed_bind <- no_rect_ass_bind > 1
          return(assessed_bind)
})

outfpa <- aggregate(isa12sf$FpaindFishStock, by=list(isa12sf$TAC_id), function(x) {
          no_rect_ass_find <- sum(!is.na(x))
          assessed_find <- no_rect_ass_find > 1
          return(assessed_find)
})

outbpa <- aggregate(isa12sf$BpaindFishStock, by=list(isa12sf$TAC_id), function(x) {
          no_rect_ass_bind <- sum(!is.na(x))
          assessed_bind <- no_rect_ass_bind > 1
          return(assessed_bind)
})

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coverage <- data.frame(

No_stocks = c(length(find_stocks), length(bind_stocks), length(fpaind_stocks), 

length(bpaind_stocks)),

No_TACs = length(unique(isa12sf$TAC_id)),
No_TACs_assessed = c(sum(outf$x), sum(outb$x), sum(outfpa$x), sum(outbpa$x)),
Frac_TACs_assessed = c(mean(outf$x),mean(outb$x), mean(outfpa$x), mean(outbpa$x))

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)
rownames(coverage) <- c("F_indicator", "B_indicator", "Fpa_indicator", "Bpa_indicator")

write.csv(coverage, "coverage.csv")

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# number of stocks for which MSYBtrigger==Bpa
#df0 <- transform(saeu, bb=Bpa/MSYBtrigger==1)
#length(unique(subset(df0, bb==TRUE)$FishStock))

#====================================================================
# Exporting and saving

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#====================================================================

write.csv(saeu, file="saeu.csv")
save.image("RData.nea")

 
#####################################################################
# EJ(20170302)
# MED indicators
#####################################################################

library(reshape2)
library(ggplot2)
library(lme4)
library(influence.ME)
library(lattice)
library(parallel)
library(rgdal)
library(reshape2)
library(plyr)
source("funs.R")

#====================================================================
# Setup
#====================================================================

# year when assessments were performed 
assessmentYear <- 2017
# final year with estimations from stock assessments 
fnlYear <- assessmentYear - 1
# initial year with estimations from stock assessments 
iniYear <- 2003
# vector of years
dy <- iniYear:fnlYear
# vector of years for valid assessments
vay <- (assessmentYear-2):assessmentYear
# vector of years for stock status projection
vpy <- (fnlYear-2):fnlYear
# options for reading data
options(stringsAsFactors=FALSE)
# number of simulations for mle bootstrap
it <- 500
# number of cores for mle bootstrap parallel
nc <- 6
# quantiles to be computed
qtl <- c(0.025, 0.25, 0.50, 0.75, 0.975)
# to control de seed in mclapply
RNGkind("L'Ecuyer-CMRG")
set.seed(1234)
# to make plots consistent
vp <- dy
vp[c(2,3,5,6,8,9,11,12,13)] <- ""
theme_set(theme_bw())
sc <- scale_x_continuous(breaks=dy, labels=as.character(vp))
th <- theme(axis.text.x  = element_text(angle=90, vjust=0.5), panel.grid.minor = 
element_blank())

#====================================================================
# load & pre-process
#====================================================================

#--------------------------------------------------------------------
# load and pre-process
#--------------------------------------------------------------------

# assessments
gfcm <- read.csv("../data/med/GFCM_SA.csv")
gfcm$Meeting <- "GFCM"
#gfcm$Fref <- gfcm$Fref_point
stecf <- read.csv("../data/med/STECF_CFP_2018.csv")
msa <- rbind(stecf, gfcm)
msa$Fref <- msa$Fref_point

# keep only one hake 1718 and sol17 assessment, must be adjusted 
# based on plen decision
msa <- subset(msa, !(key %in% c("SOL_17_EWG17_15", "HKE_17_18_EWG17_15") & 
Method=="SS3"))

# keep relevant columns only 
msa <- msa[,c("Stock", "Area", "Year", "R", "SSB", "F", "Fref", "Blim", "Bref", 
"asses_year", "Meeting", "Assessment_URL", "Species", "EcoRegion")]

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# id assessment source 
msa[msa$Meeting!="GFCM","Meeting"] <- "STECF"

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names(msa)[names(msa)=="Meeting"] <- "source"

#--------------------------------------------------------------------
# recode and compute indicators
#--------------------------------------------------------------------
msa$stk <- tolower(paste(msa$Stock, msa$Area, sep="_"))
msa$StockDescription <- paste(msa$Species, "in GSA", gsub("_", ", ",  msa$Area))
msa$Fref <- as.numeric(msa$Fref)
msa <- transform(msa, indF = F/Fref)
msa <- transform(msa, sfFind=!is.na(indF), i1=indF>1, i2=indF<=1)

#--------------------------------------------------------------------
# subset 
# (filtering through the sampling frame done during data harvesting)
#--------------------------------------------------------------------
sam <- msa[!is.na(msa$indF) & msa$Year >=iniYear & msa$Year <= fnlYear & msa$asses_year 
%in% vay,]

#--------------------------------------------------------------------
# project stock status 
# (check fnlYear < assessmentYear-1)
#--------------------------------------------------------------------
sam$projected <- FALSE

# use y-2 for stocks missing in y-1
sy2 <- sam[sam$Year==sort(vpy)[1], "stk"]
sy1 <- sam[sam$Year==sort(vpy)[2], "stk"]
v0 <- sy2[!(sy2 %in% sy1)]
if(length(v0)>0){

df0 <- subset(sam, Year==sort(vpy)[1] & stk %in% v0)
df0$Year <- sort(vpy)[2]
df0$projected <- TRUE
sam <- rbind(sam, df0)

}

# use y-1 for stocks missing in y
sy <- sam[sam$Year==sort(vpy)[3], "stk"]
v0 <- sy1[!(sy1 %in% sy)]
if(length(v0)>0){

df0 <- subset(sam, Year==sort(vpy)[2] & stk %in% v0)
df0$Year <- sort(vpy)[3]
df0$projected <- TRUE
sam <- rbind(sam, df0)

}

#====================================================================
# Indicators
#====================================================================
#--------------------------------------------------------------------
# Number of stocks (remove projected years)
#--------------------------------------------------------------------
df0 <- sam[!sam$projected,]
mnStks <- aggregate(stk~Year, df0, length)
names(mnStks) <- c("Year", "N")

# plot
png("figMedI0.png", 1800, 1200, res=300)
ggplot(subset(mnStks, Year!=fnlYear), aes(x=Year, y=N)) + 

geom_line() + 
ylab("No. of stocks") + 
xlab("") + 
ylim(c(0,50)) + 
sc + 
th +

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    geom_point(aes(x=fnlYear, y=mnStks$N[length(mnStks$N)]), size=2)
dev.off()

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png("figMedI0b.png", 1200, 1600, res=200)
ggplot(sam[!sam$projected,], aes(Year,stk)) + 

geom_line() +
ylab("Stock") +
xlab("Year") +
sc +
th +
geom_vline(xintercept = fnlYear-1, col = "red")

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dev.off()

write.csv(dcast(df0, EcoRegion~Year, value.var='stk', margins=TRUE, 

 
 
 
 
 
 
 
 
 
fun.aggregate=length), file="tabMedI0.csv", row.names=FALSE) 

#--------------------------------------------------------------------
#  drop final assessment year, redo scales for plotting
#--------------------------------------------------------------------
sam <- sam[sam$Year!=fnlYear,]

vp <- iniYear:I(fnlYear-1)
vp[seq(2,13,2)] <- ""
sc <- scale_x_continuous(breaks=iniYear:I(fnlYear-1), labels=as.character(vp))

#--------------------------------------------------------------------
# (I7) F/Fmsy model based indicator
#--------------------------------------------------------------------
df0 <- sam
df0$Year <- factor(df0$Year)
yrs <- levels(df0$Year)
nd <- data.frame(Year=factor(yrs))

# model
mfit <- glmer(indF ~ Year + (1|stk), data = df0, family = Gamma("log"), 
control=glmerControl(optimizer="nlminbwrap"))
runDiagsME(mfit, "stk", df0, "diagMedI7.pdf", nc, nd)

# bootstrap
set.seed(1234)
stk <- unique(df0$stk)
mfit.bs <- split(1:it, 1:it) 
mfit.bs <- mclapply(mfit.bs, function(x){
stk <- sample(stk, replace=TRUE)
df1 <- df0[0,] 
for(i in stk) df1 <- rbind(df1, subset(df0, stk==i))
fit <- glmer(indF ~ Year + (1|stk), data = df1, family = Gamma("log"), 

control=glmerControl(optimizer="nlminbwrap")) 

v0 <- predict(fit, re.form=~0, type="response", newdata=nd)
if(length(fit@optinfo$conv$lme4)>0) v0[] <- NA
v0

}, mc.cores=nc)
# remove failed iters
mfit.bs <- mfit.bs[unlist(lapply(mfit.bs, is.numeric))]

mfitm <- do.call("rbind", mfit.bs)
mfitq <- apply(mfitm, 2, quantile, c(0.025, 0.25, 0.50, 0.75, 0.975), na.rm=TRUE)
mfitq <- cbind(Year=as.numeric(yrs), as.data.frame(t(mfitq)))

# plot
png("figMedI7.png", 1800, 1200, res=300)
ggplot(mfitq, aes(x=Year)) + 

geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`), fill="gray", alpha=0.60) + 
geom_ribbon(aes(ymin = `25%`, ymax = `75%`), fill="gray", alpha=0.95) + 
geom_line(aes(y=`50%`)) + 
expand_limits(y=0) + 
geom_point(aes(x=Year[length(Year)], y=`50%`[length(`50%`)]), size=2) + 
geom_hline(yintercept = 1, linetype=2) + 
ylab(expression(F/F[MSY])) + 
xlab("") + 
theme(legend.position = "none") + 
sc + 
th
dev.off()

# table
tb0 <- t(mfitq)[-1,]
colnames(tb0) <- mfitq[,1]
write.csv(tb0, file="tabMedI7.csv")

#--------------------------------------------------------------------
# (I8) SSB indicator
#--------------------------------------------------------------------
# model
idx <- !is.na(sam$SSB)
df0 <- sam[idx,]
df0$Year <- factor(df0$Year)
yrs <- levels(df0$Year)
nd <- data.frame(Year=factor(yrs))

# model
mfitb <- glmer(SSB ~ factor(Year) + (1|stk), data =  df0, family = Gamma("log"), 

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control=glmerControl(optimizer="nlminbwrap"))
runDiagsME(mfitb, "stk", df0, "diagMedI8.pdf", nc, nd)

# bootstrap
set.seed(1234)
stk <- unique(df0$stk)
mfitb.bs <- split(1:it, 1:it) 
mfitb.bs <- mclapply(mfitb.bs, function(x){

stk <- sample(stk, replace=TRUE)
df1 <- df0[0,] 
for(i in stk) df1 <- rbind(df1, subset(df0, stk==i))
fit <- glmer(SSB ~ Year + (1|stk), data = df1, family = Gamma("log"), 

control=glmerControl(optimizer="nlminbwrap")) 

v0 <- predict(fit, re.form=~0, type="response", newdata=nd)
if(length(fit@optinfo$conv$lme4)>0) v0[] <- NA
v0

}, mc.cores=nc)
# remove failed iters
mfitb.bs <- mfitb.bs[unlist(lapply(mfitb.bs, is.numeric))]

mfitm <- do.call("rbind", mfitb.bs)
mfitm <- exp(log(mfitm)-mean(log(mfitm[,1]), na.rm=TRUE))
mfitq <- apply(mfitm, 2, quantile, c(0.025, 0.25, 0.50, 0.75, 0.975), na.rm=TRUE)
mfitq <- cbind(Year=as.numeric(yrs), as.data.frame(t(mfitq)))

# plot
png("figMedI8.png", 1800, 1200, res=300)
ggplot(mfitq, aes(x=Year)) + 

geom_ribbon(aes(ymin = `2.5%`, ymax = `97.5%`), fill="gray", alpha=0.60) + 
geom_ribbon(aes(ymin = `25%`, ymax = `75%`), fill="gray", alpha=0.95) + 
geom_line(aes(y=`50%`)) + 
expand_limits(y=0) + 
geom_point(aes(x=Year[length(Year)], y=`50%`[length(`50%`)]), size=2) + 
geom_hline(yintercept = 1, linetype=2) +
ylab(expression(B/B[2003])) + 
xlab("") + 
theme(legend.position = "none") + 
sc + 
th
dev.off()

tb0 <- t(mfitq)[-1,]
colnames(tb0) <- mfitq[,1]
write.csv(tb0, file="tabMedI8.csv")

write.csv(sam, file="sam.csv")
save.image("RData.med")

230

235

240

245

250

255

260

265

270

 
 
 
 
 
 
ANNEX III – QUALITY CONTROL OF ICES DATASET 

 
 
 
ICES data quality issues corrected prior to the analysis 

By: Paris Vasilakopoulos 

4 April 2018 

stock 

assessment 

The 
at 
http://standardgraphs.ices.dk/stockList.aspx was used to extract the ICES data needed for the 
CFP indicators analysis. Prior to the analysis, an extensive data quality check was carried out on 
the data relevant to the analysis. During this data quality check the following fields were checked 
and corrected: 

dataset 

graphs 

(SAG) 

found 

  Stock  size,  fishing  pressure  and  reference  points  of  stocks  cat.  1-3  (data  moved  from 

custom columns when needed). 

  Stock size description, stock size units, fishing pressure description, fishing pressure units 

of stocks cat 1-2. 

  Stock size description of stocks cat 3. 

There are still issues to be corrected in the ICES SAG dataset which  were not addressed here 
due to time limitations and because they were not very relevant to our analysis. For example, 
we  noticed  inconsistencies  and  errors  in  stock  units,  fishing  pressure  description  and  fishing 
pressure units of many cat 3 stocks. 

Category 1-2 stocks 

ane.27.8 
Fishing Pressure Units corrected from “NA” to ‘Proportion’ 

ank.27.8c9a 
Stock size description corrected to TSB/Bmsy 
Stock size units corrected to ‘Relative to Bmsy’ 
Fishing Pressure Units corrected to ‘Relative to Fmsy’ 

ghl.27.1-2 
Stock size description corrected to TSB 
Fishing Pressure Units corrected from “NA” to ‘Proportion’ 

ghl.27.561214 
Stock size units corrected to ‘Relative to Bmsy’ 
Fishing Pressure Units corrected to ‘Relative to Fmsy’ 

lin.27.5a 
Fishing Pressure Units corrected from “NA” to ‘Proportion’ 
HRlim, HRmsy, HRpa moved from custom columns to their right place 

usk.27.5a14 
Fishing Pressure Units corrected from “NA” to ‘Proportion’ 
HRlim, HRmsy, HRpa moved from custom columns to their right place 

lez.27.4a6a 
Stock size units corrected to ‘Relative to Bmsy’ 
Fishing Pressure Units corrected to ‘Relative to Fmsy’ 

pil.27.8abd 
Stock size units corrected to ‘Relative to mean’ 
Fishing Pressure Units corrected to ‘Relative to mean’ 

1 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
rng.27.5b6712b 
Stock size units corrected to ‘Relative to Bmsy’ 
Fishing Pressure Units corrected to ‘Relative to HRmsy’ 

bss.27.4bc7ad-h 
Fishing Pressure Units corrected from blank to ‘Year-1’ 

had.27.46a20 
Fishing Pressure Units corrected from blank to ‘Year-1’ 

sol.27.8ab 
Fishing Pressure Description corrected to F 
Fishing Pressure Units corrected from blank to ‘Year-1’ 

whg.27.47d 
Fishing Pressure Units corrected from blank to ‘Year-1’ 

cap.27.1-2 
Fishing Pressure Description corrected to NA 
Fishing Pressure Units corrected to NA 

cap.27.2a514 
Fishing Pressure Description corrected to NA 
Fishing Pressure Units corrected to NA 

pok.27.5a 
Stock size description corrected to SSB 

pra.27.1-2 
Stock size units corrected to ‘Relative to Bmsy’ 
Fishing Pressure Units corrected to ‘Relative to Fmsy’ 

pra.27.4a20 
Stock size description corrected to SSB 
Fishing Pressure Description corrected to F 

reg.27.1-2 
Stock size units corrected to ‘Relative to mean’ 
Fishing Pressure Units corrected to ‘Relative to mean’ 

dgs.27.nea 
Stock size description corrected from ‘ TSB’ to ‘TSB’ 
Fishing Pressure Description corrected to Harvest rate 

hom.27.2a4a5b6a7a-ce-k8 
ICES Areas  splited with character field is blank. Corrected to 27.2.a.1 ~ 27.2.a.2  ~ 27.4.a ~ 
27.5.b.1.a ~ 27.5.b.1.b ~ 27.5.b.2 ~ 27.6.a ~ 27.7.a ~ 27.7.b ~ 27.7.c.1 ~ 27.7.c.2 ~ 27.7.e 
~ 27.7.f ~ 27.7.g ~ 27.7.h ~ 27.7.j.1 ~ 27.7.j.2 ~ 27.7.k.1 ~ 27.7.k.2 ~ 27.8.a ~ 27.8.b ~ 
27.8.c ~ 27.8.d.1 ~ 27.8.d.2 ~ 27.8.e.1 ~ 27.8.e.2 

nep.fu.11 
Stock size description corrected to Abundance 
Stock size units corrected to millions 

nep.fu.12 
Stock size description corrected to Abundance 
Stock size units corrected to millions 

2 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
nep.fu.13 
Stock size description corrected to Abundance 
Stock size units corrected to millions 

nep.fu.14 
Stock size description corrected to Abundance 
Stock size units corrected to millions 
Fishing Pressure Description corrected to Harvest rate 

nep.fu.15 
Stock size description corrected to Abundance 
Stock size units corrected to billions 
Fishing Pressure Units corrected to Percent 

nep.fu.16 
Stock size description corrected to Abundance 
Stock size units corrected to millions 

nep.fu.17 
Stock size description corrected to Abundance 
Stock size units corrected millions 

nep.fu.19 
Stock size description corrected to Abundance 
Stock size units corrected to millions 

nep.fu.2021 
Stock size description corrected to Abundance 
Stock size units corrected to millions 

nep.fu.22 
Stock size description corrected to Abundance 
Stock size units corrected to millions 

nep.fu.2324 
Stock size description corrected to Abundance 
Stock size units corrected to millions 

nep.fu.3-4 
Stock size description corrected to Abundance 
Stock size units corrected to millions 
Fishing Pressure Units corrected to Percent 

nep.fu.6 
Stock size description corrected to Abundance 
Stock size units corrected to millions 
Fishing Pressure Description corrected to Harvest rate 

nep.fu.7 
Stock size units corrected to billions 
Fishing Pressure Description corrected to Harvest rate 

nep.fu.8 
Stock size description corrected to Abundance 
Stock size units corrected to millions 

nep.fu.9 
Stock size description corrected to Abundance 

3 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Stock size units corrected to millions 
Fishing Pressure Units corrected to Percent 

cod.27.1-2 
cod.27.22-24 
cod.27.5a 
cod.27.5b1 
cod.27.6a 
cod.27.7a 
cod.27.7e-k 
had.27.1-2 
had.27.5a 
had.27.5b 
had.27.6b 
had.27.7a 
had.27.7b-k 
her.27.20-24 
her.27.25-2932 
her.27.28 
her.27.3031 
her.27.3a47d 
her.27.5a 
her.27.6a7bc 
her.27.irls 
her.27.nirs 
hke.27.3a46-8abd 
hke.27.8c9a 
hom.27.2a4a5b6a7a-ce-k8 
hom.27.9a 
ldb.27.8c9a 
mac.27.nea 
meg.27.7b-k8abd 
meg.27.8c9a 
ple.27.21-23 
ple.27.7a 
ple.27.7d 
pok.27.1-2 
pok.27.3a46 
pok.27.5a 
pok.27.5b 
pra.27.4a20 
reb.27.1-2 
reg.27.561214 
san.sa.1r 
san.sa.2r 
san.sa.3r 
san.sa.4 
sol.27.20-24 
sol.27.4 
sol.27.7a 
sol.27.7d 
sol.27.7e 
sol.27.7fg 
spr.27.4 
spr.27.22-32 
whb.27.1-91214 
whg.27.7a 
whg.27.7b-ce-k 

4 

 
 
 
 
Fishing Pressure Units corrected from ‘per year’ to ‘Year-1’ 

Category 3 stocks 

old 

sbr-x 

should 

sbr.27.10 
The 
stock 
to http://sd.ices.dk/services/odata4/StockListDWs4 ). 
this http://ices.dk/sites/pub/Publication%20Reports/Advice/2017/Special_requests/EU_sbr-
x_review.pdf its  DataCategory  needs  to  be  updated  to  3  (rather  than  current  5.2),  and  the 
abundance  index  value  and  description  in  the  SAG  dataset  moved  from  Custom1  and 
CustomName1 columns to StockSize and StockSizeDescription, respectively. 
Also, stock size description was corrected to Abundance Index 

(according 
on 

to 
Also, 

sbr.27.10 

updated 

based 

be 

bss.27.8ab 
Stock size moved from custom column 
Stock size description corrected to LPUE 
Stock size unit corrected to kg/day 
Fishing pressure corrected to NA 

ele.2737.nea 
No info in designated columns, everything in custom columns  
Stock size description corrected to ‘NA’ 

wit.27.3a47d 
Stock Size moved from custom column (IBTS Q3-the optimal index according to advice sheet) 
Stock size description corrected to Biomass index 
Stock size unit corrected to kg/h 

ane.27.9a 
Stock size description corrected to Biomass Index (comb) 

rjr.27.23a4 
Stock size description corrected from ‘ Relative AI (comb)’ to ‘Relative AI (comb)’ 

rjn.27.9a 
Stock size description corrected from ‘ Relative BI (comb)’ to ‘Relative BI (comb)’ 

rjh.27.9a 
Stock size description corrected from ‘ Relative BI’ to ‘Relative BI’ 

rjc.27.8 
Stock size description corrected from ‘ Relative BI (comb)’ to ‘Relative BI (comb)’ 

rjc.27.9a 
Stock size description corrected from ‘ Relative BI (comb)’ to ‘Relative BI (comb)’ 

gfb.27.nea 
Stock size description corrected from ‘ Relative BI (comb)’ to ‘Relative BI (comb)’ 

cod.27.21 
Stock size description corrected to Relative SSB 

dab.27.3a4 
Stock size description corrected to Relative SSB 

fle.27.2223 
Stock size description corrected to Biomass Index 

5 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
fle.27.2425 
Stock size description corrected to Biomass Index 

fle.27.2729-32 
Stock size description corrected to Biomass Index 
Stock size unit corrected to kg/fishing station 

ple.27.24-32 
Stock size description corrected to Relative SSB 

sdv.27.nea 
Stock size description corrected to Relative BI (comb) 

spr.27.3a 
Stock size description corrected to Abundance Index 

spr.27.7de 
Stock size description corrected to Relative BI 

syc.27.67a-ce-j 
Stock size description corrected to Relative BI (comb) 
Stock size unit corrected to kg/hour 

tur.27.4 
Stock size description corrected to Relative SSB 

bll.27.22-32 
Stock size description corrected to Abundance Index 

6 

 
 
 
 
 
 
 
 
 
 
 
 
 
K
J
-
A
X
-
1
8
-
0
2
8
-
E
N
N

-

STECF 

JRC Mission 

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

As the science and knowledge 
service of the European 
Commission, the Joint Research 
Centre’s mission is to support EU 
policies with independent,  
evidence throughout the whole  
policy cycle. 

66 

doi:10.2760/329345 

ISBN 978-92-79-85802-4