Scientific, Technical and Economic Committee for Fisheries (STECF) : 2017 Mediterranean stock assessments. Part 2 (STECF-17-15).
Date: 2018-07-12
Institutions: Joint Research Centre
Subjects: Community fisheries, Mediterranean Sea, common fisheries policy, conservation of fish stocks, data collection, evaluation of resources, fishery management, research report, sea fish

Scientific, Technical and Economic 
Committee for Fisheries (STECF) 
- 
2017 Mediterranean Stock 
Assessments - Part 2  
(STECF-17-15) 

Edited by John Simmonds, Alessandro Mannini, Paris Vasilakopoulos 

Report EUR 28359 EN 

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

Contact information  
Name: STECF secretariat 
Address: Unit D.02 Water and Marine Resources, Via Enrico Fermi 2749, 21027 Ispra VA, Italy 
E-mail: stecf-secretariat@jrc.ec.europa.eu 
Tel.: +39 0332 789343 

JRC Science Hub 
https://ec.europa.eu/jrc 

JRC111820 

EUR 28359 EN 

PDF 

ISBN   978-92-79-67494-5 

ISSN 1831-9424 

doi:10.2760/90316 

STECF 

ISSN 2467-0715 

Luxembourg: Publications Office of the European Union, 2018  

© European Union, 2018 

The  reuse  of  the  document  is  authorised,  provided  the  source  is  acknowledged  and  the  original 
meaning  or  message  of  the  texts  are  not  distorted.  The  European  Commission  shall  not  be  held 
liable for any consequences stemming from the reuse. 

How  to  cite:  Scientific,  Technical  and  Economic  Committee  for  Fisheries  (STECF)  -  2017 
Mediterranean  Stock  Assessments  -  Part  2    (STECF-17-15);  Publications  Office  of  the  European 
Union, Luxembourg, ISBN 978-92-79-67494-5, doi:10.2760/90316, JRC111820 

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 the 2017 Mediterranean Stock Assessments - Part 2.   

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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. 

EWG-17-09 report:  

Simmonds,  E.  J.,  Abella,  A.,  Angelini,  S.,  Bitetto,  I.,  Colloca,  F.,  Isajlovic,  I.,  Mannini  A., 
Mantopoulou  Palouka,  D.,  Mantzoyni  E.  Martin,  P.,  Molla  Gazi,  K.,  Murenu,  M.,  Musumeci,  C., 
Ordinas, F., Orio, A., Pengal, P., Recasens, L., Sbrana, M., Sgardeli, V., Tsikliras, A., Tserpes, G., 
Vasilakopoulos, P.  

3 

3 

 
 
 
 
 
 
 
 
 
 
 
 
TABLE OF CONTENTS 

SCIENTIFIC, TECHNICAL AND ECONOMIC COMMITTEE FOR FISHERIES (STECF) - 
Mediterranean Stock Assessments pt I (STECF-17-15) ....................... 16 

Request to the STECF ...................................................................................................... 16 

STECF observations .......................................................................................................... 16 

STECF comments ............................................................................................................... 16 

STECF conclusions ............................................................................................................. 18 

Contact details of STECF members ............................................................................ 22 

Expert Working Group EWG-17-09 report ............................................................... 26 

Executive Summary .......................................................................................................... 27 

1 Introduction ..................................................................................................................... 28 

1.1 Approach to the work ............................................................................................... 28 

1.2 Terms of Reference for EWG-17-09 ................................................................... 28 

2 Findings and Conclusions of the Working Group ............................................... 32 

2.1 Stock-Specific Findings & Conclusions .............................................................. 33 

3 Follow up Items .............................................................................................................. 37 

4 Basis of the report ......................................................................................................... 38 

4.1 Basis of the Advice .................................................................................................... 38 

4.2 Basis of Short Term Forecasts .............................................................................. 39 

4.3 Evaluation of reference points .............................................................................. 40 

5 Summary sheets by stock .......................................................................................... 42 

5.1 SUMMARY SHEET OF HAKE IN GSA 17 and 18 .............................................. 42 

5.1.1.1 Stock development over time (SS3) .......................................................... 43 

5.1.1.2 Stock advice (SS3) ............................................................................................ 44 

5.1.1.3 Basis of the assessment (SS3)..................................................................... 45 

5.1.1.4 Catch options (SS3) .......................................................................................... 45 

5.1.1.5 Reference points (SS3) ................................................................................... 46 

5.1.2.1 Stock development over time (a4a) .......................................................... 47 

5.1.2.2 Stock advice (a4a) ............................................................................................ 48 

5.1.2.3 Basis of the assessment (a4a) ..................................................................... 48 

5.1.2.4 Catch options (a4a) .......................................................................................... 49 

5.1.2.5 Reference points (a4a) .................................................................................... 49 

5.1.3 Data deficiencies .................................................................................................... 50 

5.2 SUMMARY SHEET OF RED MULLET IN GSA 17 and 18 ............................... 51 

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5.2.1 Stock development over time ........................................................................... 51 

5.2.2 Stock advice ............................................................................................................. 52 

5.2.3 Basis of the assessment ...................................................................................... 52 

5.2.4 Catch options ........................................................................................................... 53 

5.2.5 Reference points .................................................................................................... 54 

5.2.6 Data deficiencies .................................................................................................... 54 

5.3 SUMMARY SHEET OF NORWAY LOBSTER IN GSA 17 and 18 ................... 55 

5.3.1 Stock development over time ........................................................................... 55 

5.3.2 Stock advice ............................................................................................................. 57 

5.3.3 Basis of the assessment ...................................................................................... 57 

5.3.4 Catch options ........................................................................................................... 57 

5.3.5 Reference points .................................................................................................... 58 

5.3.6 Data deficiencies .................................................................................................... 58 

5.4 SUMMARY SHEET OF COMMON PANDORA IN GSA 17 and 18 ................. 59 

5.4.1 Stock development over time ........................................................................... 59 

5.4.2 Stock advice ............................................................................................................. 60 

5.4.3 Basis of the assessment ...................................................................................... 60 

5.4.4 Catch options ........................................................................................................... 61 

5.4.5 Reference points .................................................................................................... 61 

5.4.6 Data deficiencies .................................................................................................... 61 

5.5 SUMMARY SHEET OF DEEP-WATER ROSE SHRIMP IN GSA 17, 18 & 1962 

5.5.1 Stock development over time ........................................................................... 62 

5.5.2 Stock advice ............................................................................................................. 63 

5.5.3 Basis of the assessment ...................................................................................... 63 

5.5.4 Catch options ........................................................................................................... 63 

5.5.5 Reference points .................................................................................................... 64 

5.5.6 Data deficiencies .................................................................................................... 65 

5.6 SUMMARY SHEET OF THORNBACK RAY IN GSA 17 ..................................... 66 

5.6.1 Stock development over time ........................................................................... 66 

5.6.2 Stock advice ............................................................................................................. 66 

5.6.3 Basis of the assessment ...................................................................................... 66 

5.6.4 Catch options ........................................................................................................... 66 

5.6.5 Reference points .................................................................................................... 67 

5.6.6 Data deficiencies .................................................................................................... 67 

5.7 SUMMARY SHEET OF COMMON CUTTLEFISH IN GSA 17 and 18 ........... 68 

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5.7.1 Stock development over time ........................................................................... 68 

5.7.2 Stock advice ............................................................................................................. 69 

5.7.3 Basis of the assessment ...................................................................................... 69 

5.7.4 Catch options ........................................................................................................... 69 

5.7.5 Reference points .................................................................................................... 70 

5.7.6 Data deficiencies .................................................................................................... 70 

5.8 SUMMARY SHEET OF SOLE IN GSA 17 ............................................................. 71 

5.8.1 Summary for SS3 assessment - Run 1 ......................................................... 71 

5.8.1.1 Stock development over time (SS3) .......................................................... 71 

5.8.1.2 Stock advice (SS3 Run 1) .............................................................................. 73 

5.8.1.3 Basis of the assessment (SS3 Run 1) ....................................................... 73 

5.8.1.4 Catch options (SS3 Run 1) ............................................................................ 73 

5.8.1.5 Reference points(SS3 Run 1)........................................................................ 74 

5.8.2 Summary for a4a assessment .......................................................................... 74 

5.8.2.1 Stock development over time (a4a assessment) ................................. 74 

5.8.2.2 Stock advice(a4a) .............................................................................................. 76 

5.8.2.3 Basis of the assessment .................................................................................. 76 

5.8.2.4 Catch options (a4a) .......................................................................................... 76 

5.8.2.5 Reference points (a4a) .................................................................................... 77 

5.8.3 Summary for SS3 assessment – Run 7 ........................................................ 77 

5.8.3.1 Stock development over time (SS3 – Run7) .......................................... 77 

5.8.3.2 Stock advice (SS3 – Run 7) .......................................................................... 80 

5.8.3.3 Basis of the assessment (SS3 – Run 7) ................................................... 80 

5.8.3.4 Catch Options (SS3 – Run 7) ........................................................................ 80 

5.8.3.5 Reference points (SS3 – Run 7) .................................................................. 81 

5.8.4 Data deficiencies .................................................................................................... 81 

5.9 SUMMARY SHEET OF SPOTTAIL MANTIS SHRIMP IN GSA 17 and 18 .. 82 

5.9.1 Stock development over time ........................................................................... 82 

5.9.2 Stock advice ............................................................................................................. 83 

5.9.3 Basis of the assessment ...................................................................................... 83 

5.9.4 Catch options ........................................................................................................... 83 

5.9.5 Reference points .................................................................................................... 84 

5.9.6 Data deficiencies .................................................................................................... 85 

5.10 SUMMARY SHEET OF HAKE IN GSA 19 ........................................................... 86 

5.10.1 Stock development over time ........................................................................ 86 

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5.10.2 Stock advice .......................................................................................................... 87 

5.10.3 Basis of the assessment ................................................................................... 88 

5.10.4 Catch options ........................................................................................................ 88 

5.10.5 Reference points .................................................................................................. 89 

5.10.6 Data deficiencies ................................................................................................. 89 

5.11 SUMMARY SHEET OF HAKE IN GSA 20 ........................................................... 90 

5.11.1 Stock development over time ........................................................................ 90 

5.11.2 Stock advice .......................................................................................................... 92 

5.11.3 Basis of the assessment ................................................................................... 92 

5.11.4 Catch options ........................................................................................................ 92 

5.11.5 Reference points .................................................................................................. 92 

5.11.6 Data deficiencies ................................................................................................. 93 

5.12 SUMMARY SHEET OF RED MULLET IN GSA 19 ............................................ 94 

5.12.1 Stock development over time ........................................................................ 94 

5.12.2 Stock advice .......................................................................................................... 95 

5.12.3 Basis of the assessment ................................................................................... 95 

5.12.4 Catch options ........................................................................................................ 95 

5.12.5 Reference points .................................................................................................. 96 

5.12.6 Data deficiencies ................................................................................................. 96 

5.13 SUMMARY SHEET OF RED MULLET IN GSA 20 ............................................ 97 

5.13.1 Stock development over time ........................................................................ 97 

5.13.2 Stock advice .......................................................................................................... 97 

5.13.3 Basis of the assessment ................................................................................... 97 

5.13.4 Catch options ........................................................................................................ 97 

5.13.5 Reference points .................................................................................................. 98 

5.13.6 Data deficiencies ................................................................................................. 98 

5.14 SUMMARY SHEET OF HAKE IN GSA 22 ........................................................... 99 

5.14.1 Stock development over time ........................................................................ 99 

5.14.2 Stock advice ........................................................................................................ 101 

5.14.3 Basis of the assessment ................................................................................. 101 

5.14.4 Catch options ...................................................................................................... 102 

5.14.5 Reference points ................................................................................................ 102 

5.14.6 Data deficiencies ............................................................................................... 102 

5.15 SUMMARY SHEET OF RED MULLET IN GSA 22 .......................................... 103 

5.15.1 Stock development over time ...................................................................... 103 

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5.15.2 Stock advice ........................................................................................................ 106 

5.15.3 Basis of the assessment ................................................................................. 106 

5.15.4 Catch options ...................................................................................................... 107 

5.15.5 Reference points ................................................................................................ 107 

5.15.6 Data deficiencies ............................................................................................... 107 

5.16 SUMMARY SHEET OF DEEP-WATER ROSE SHRIMP IN GSA 22 ........... 108 

5.16.1 Stock development over time ...................................................................... 108 

5.16.2 Stock advice ........................................................................................................ 109 

5.16.3 Basis of the assessment ................................................................................. 109 

5.16.4 Catch options ...................................................................................................... 110 

5.16.5 Reference points ................................................................................................ 110 

5.16.6 Data deficiencies ............................................................................................... 110 

5.17 SUMMARY SHEET OF HAKE IN GSA 23 ......................................................... 111 

5.17.1 Stock development over time ...................................................................... 111 

5.17.2 Stock advice ........................................................................................................ 113 

5.17.3 Basis of the assessment ................................................................................. 113 

5.17.4 Catch options ...................................................................................................... 114 

5.17.5 Reference points ................................................................................................ 114 

5.17.6 Data deficiencies ............................................................................................... 114 

5.18 SUMMARY SHEET OF RED MULLET IN GSA 23 .......................................... 115 

5.18.1 Stock development over time ...................................................................... 115 

5.18.2 Stock advice ........................................................................................................ 116 

5.18.3 Basis of the assessment ................................................................................. 116 

5.18.4 Catch options ...................................................................................................... 117 

5.18.5 Reference points ................................................................................................ 117 

5.18.6 Data deficiencies ............................................................................................... 117 

5.19 SUMMARY SHEET OF DEEP-WATER ROSE SHRIMP IN GSA 23 ........... 118 

5.19.1 Stock development over time ...................................................................... 118 

5.19.2 Stock advice ........................................................................................................ 119 

5.19.3 Basis of the assessment ................................................................................. 119 

5.19.4 Catch options ...................................................................................................... 120 

5.19.5 Reference points ................................................................................................ 120 

5.19.6 Data deficiencies ............................................................................................... 120 

6 ASSESSMENT BY STOCK........................................................................................... 121 

6.1 

HAKE IN GSAs 17 & 18 ................................................................................... 122 

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6.1.1  Biology ................................................................................................................... 122 

6.1.2  Input Data ............................................................................................................ 125 

6.1.2.1  Catch (landings and discards) .................................................................. 125 

6.1.2.2  Effort ................................................................................................................... 128 

6.1.2.3  Survey data ..................................................................................................... 130 

6.1.3  Stock assessment .............................................................................................. 134 

6.1.3.1 Integrated assessment model: SS3 ......................................................... 134 

6.1.3.2 a4a ......................................................................................................................... 150 

6.1.3.3 Comparison between SS3 and a4a stock assessment models ...... 160 

6.1.4  Reference Points ................................................................................................ 161 

6.1.4.1 SS3 model .......................................................................................................... 161 

6.1.4.2 a4a ......................................................................................................................... 161 

6.1.5  Short term Forecast and Catch Options ................................................... 161 

6.1.5.1 SS3 model .......................................................................................................... 161 

6.1.5.2 a4a model ........................................................................................................... 162 

6.1.5.1  Comparison of Results ................................................................................. 163 

6.1.6  Data Deficiencies ............................................................................................... 165 

6.2 

RED MULLET IN GSAs 17 AND 18 ............................................................... 165 

6.2.1  Biology ................................................................................................................... 165 

6.2.2  Input Data ............................................................................................................ 169 

6.2.2.1  Catch (landings and discards) .................................................................. 169 

6.2.2.2  Effort ................................................................................................................... 179 

6.2.2.3  Survey data ..................................................................................................... 181 

6.2.3  Stock assessment .............................................................................................. 183 

6.2.4  Reference Points ................................................................................................ 203 

6.2.5  Short term Forecast and Catch Options ................................................... 203 

6.2.6  Data Deficiencies ............................................................................................... 204 

6.3 

NORWAY LOBSTER IN GSAs 17 & 18 ........................................................ 204 

6.3.1  Biology ................................................................................................................... 204 

6.3.2  Input Data ............................................................................................................ 209 

6.3.2.1  Catch (landings and discards) .................................................................. 209 

6.3.2.2  Effort ................................................................................................................... 213 

6.3.2.3  Survey data ..................................................................................................... 215 

6.3.3  Stock assessment .............................................................................................. 222 

6.3.4  Reference Points ................................................................................................ 237 

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6.3.5  Short term Forecast and Catch Options ................................................... 238 

6.3.6  Data Deficiencies ............................................................................................... 242 

6.4 

COMMON PANDORA IN GSAs 17 & 18 ...................................................... 243 

6.4.1  Biology ................................................................................................................... 243 

6.4.2  Input Data ............................................................................................................ 244 

6.4.2.1  Catch (landings and discards) .................................................................. 244 

6.4.2.2  Effort ................................................................................................................... 250 

6.4.2.3  Survey data ..................................................................................................... 252 

6.4.3  Stock assessment .............................................................................................. 254 

6.4.4  Reference Points ................................................................................................ 263 

6.4.5  Short term Forecast and Catch Options ................................................... 264 

6.4.6  Data Deficiencies ............................................................................................... 264 

6.5 

DEEP-WATER ROSE SHRIMP IN GSAs 17, 18 AND 19 ........................ 264 

6.5.1  Biology ................................................................................................................... 264 

6.5.2  Input Data ............................................................................................................ 265 

6.5.2.1  Catch (landings and discards) .................................................................. 265 

6.5.2.2  Effort ................................................................................................................... 276 

6.5.2.3  Survey data ..................................................................................................... 279 

6.5.3  Stock assessment .............................................................................................. 281 

6.5.4  Reference Points ................................................................................................ 298 

6.5.5  Short term Forecast and Catch Options ................................................... 299 

6.5.6  Data Deficiencies ............................................................................................... 302 

6.6 

THORNBACK RAY IN GSA 17 ........................................................................ 303 

6.6.1  Biology ................................................................................................................... 303 

6.6.2  Input Data ............................................................................................................ 304 

6.6.2.1  Catch (landings and discards) .................................................................. 304 

6.6.2.2  Effort ................................................................................................................... 307 

6.6.2.3  Survey data ..................................................................................................... 310 

6.6.3  Stock assessment .............................................................................................. 314 

6.6.4  Reference Points ................................................................................................ 317 

6.6.5  Short term Forecast and Catch Options ................................................... 320 

6.6.6  Data Deficiencies ............................................................................................... 321 

6.7 

COMMON CUTTLEFISH IN GSAs 17 & 18 ................................................. 322 

6.7.1  Biology ................................................................................................................... 322 

6.7.2  Input Data ............................................................................................................ 323 

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6.7.2.1  Catch (landings and discards) .................................................................. 323 

6.7.2.2  Effort ................................................................................................................... 329 

6.7.2.3  Survey data ..................................................................................................... 330 

6.7.3  Stock assessment .............................................................................................. 334 

6.7.4  Reference Points ................................................................................................ 343 

6.7.5  Short term Forecast and Catch Options ................................................... 344 

6.7.6  Data Deficiencies ............................................................................................... 344 

6.8 

SOLE IN GSA 17 ................................................................................................. 344 

6.8.1  Biology ................................................................................................................... 344 

6.8.2  Input Data ............................................................................................................ 348 

6.8.2.1  Catch (landings and discards) .................................................................. 348 

6.8.2.2  Effort ................................................................................................................... 353 

6.8.2.3  Survey data ..................................................................................................... 355 

6.8.3  Stock assessment .............................................................................................. 359 

6.8.4  Reference Points ................................................................................................ 393 

6.8.5  Short term Forecast and Catch Options ................................................... 393 

6.8.6  Data Deficiencies ............................................................................................... 398 

6.9 

SPOTTAIL MANTIS SHRIMP IN GSAs 17 & 18 ........................................ 399 

6.9.3  Biology ................................................................................................................... 399 

6.9.4  Input Data ............................................................................................................ 401 

6.9.4.1  Catch (landings and discards) .................................................................. 401 

6.9.4.2  Effort ................................................................................................................... 404 

6.9.4.3  Survey data ..................................................................................................... 406 

6.9.3  Stock assessment .............................................................................................. 408 

6.9.4  Reference Points ................................................................................................ 419 

6.9.5  Short term Forecast and Catch Options ................................................... 420 

6.9.6  Data Deficiencies ............................................................................................... 422 

6.10  HAKE IN GSA 19 ................................................................................................ 422 

6.10.1 Biology ................................................................................................................... 422 

6.10.2 Input Data ............................................................................................................ 424 

6.10.2.1  Catch (landings and discards) ............................................................... 425 

6.10.2.2  Effort ................................................................................................................ 432 

6.10.2.3  Survey data ................................................................................................... 438 

6.10.3 Stock assessment .............................................................................................. 440 

6.10.4 Reference Points ................................................................................................ 464 

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6.10.5 Short term Forecast and Catch Options ................................................... 465 

6.10.6 Data Deficiencies ............................................................................................... 469 

6.11  HAKE IN GSA 20 ................................................................................................ 469 

6.11.1 Biology ................................................................................................................... 469 

6.11.2 Input Data ............................................................................................................ 470 

6.11.2.1  Catch (landings and discards) ............................................................... 470 

6.11.2.2  Effort ................................................................................................................ 476 

6.11.2.3  Survey data ................................................................................................... 476 

6.11.3 Stock assessment .............................................................................................. 478 

6.11.4 Reference Points ................................................................................................ 500 

6.11.5 Short term Forecast and Catch Options ................................................... 501 

6.11.6 Data Deficiencies ............................................................................................... 501 

6.12  RED MULLET IN GSA 19 .................................................................................. 501 

6.12.1 Biology ................................................................................................................... 501 

6.12.2 Input Data ............................................................................................................ 503 

6.12.2.1  Catch (landings and discards) ............................................................... 503 

6.12.2.2  Effort ................................................................................................................ 507 

6.12.2.3  Survey data ................................................................................................... 509 

6.12.3 Stock assessment .............................................................................................. 514 

6.12.4 Reference Points ................................................................................................ 525 

6.12.5 Short term Forecast and Catch Options ................................................... 526 

6.12.6 Data Deficiencies ............................................................................................... 528 

6.13  RED MULLET IN GSA 20 .................................................................................. 529 

6.13.1 Biology ................................................................................................................... 529 

6.13.2 Input Data ............................................................................................................ 531 

6.13.2.1  Catch (landings and discards) ............................................................... 531 

6.13.2.2  Effort ................................................................................................................ 536 

6.13.2.3  Survey data ................................................................................................... 536 

6.13.3 Stock assessment .............................................................................................. 538 

6.13.4 Reference Points ................................................................................................ 557 

6.13.5 Short term Forecast and Catch Options ................................................... 557 

6.13.6 Data Deficiencies ............................................................................................... 557 

6.14  HAKE IN GSA 22 ................................................................................................ 557 

6.14.1 Biology ................................................................................................................... 557 

6.14.2 Input Data ............................................................................................................ 558 

12 

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6.14.2.1  Catch (landings and discards) ............................................................... 559 

6.14.2.2  Effort ................................................................................................................ 561 

6.14.2.3  Survey data ................................................................................................... 563 

6.14.3 Stock assessment .............................................................................................. 565 

6.14.4 Reference Points ................................................................................................ 572 

6.14.5 Short term Forecast and Catch Options ................................................... 572 

6.14.6 Data Deficiencies ............................................................................................... 572 

6.15  RED MULLET IN GSA 22 .................................................................................. 573 

6.15.1 Biology ................................................................................................................... 573 

6.15.2 Input Data ............................................................................................................ 574 

6.15.2.1  Catch (landings and discards) ............................................................... 574 

6.15.2.2  Effort ................................................................................................................ 577 

6.15.2.3  Survey data ................................................................................................... 579 

6.15.3 Stock assessment .............................................................................................. 581 

6.15.4 Reference Points ................................................................................................ 596 

6.15.5 Short term Forecast and Catch Options ................................................... 596 

6.15.6 Data Deficiencies ............................................................................................... 596 

6.16  DEEP-WATER ROSE SHRIMP IN GSA 22 .................................................. 597 

6.16.1 Biology ................................................................................................................... 597 

6.16.2 Input Data ............................................................................................................ 597 

6.16.2.1  Catch (landings and discards) ............................................................... 598 

6.16.2.2  Effort ................................................................................................................ 598 

6.16.2.3  Survey data ................................................................................................... 599 

6.16.3 Stock assessment .............................................................................................. 600 

6.16.4 Reference Points ................................................................................................ 609 

6.16.5 Short term Forecast and Catch Options ................................................... 609 

6.16.6 Data Deficiencies ............................................................................................... 609 

6.17  HAKE IN GSA 23 ................................................................................................ 610 

6.17.1 Biology ................................................................................................................... 610 

6.17.2 Input Data ............................................................................................................ 611 

6.17.2.1  Catch (landings and discards) ............................................................... 611 

6.17.2.2  Effort ................................................................................................................ 613 

6.17.2.3  Survey data ................................................................................................... 614 

6.17.3 Stock assessment .............................................................................................. 615 

6.17.4 Reference Points ................................................................................................ 622 

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6.17.5 Short term Forecast and Catch Options ................................................... 622 

6.17.6 Data Deficiencies ............................................................................................... 622 

6.18  RED MULLET IN GSA 23 .................................................................................. 623 

6.18.1 Biology ................................................................................................................... 623 

6.18.2 Input Data ............................................................................................................ 623 

6.18.2.1  Catch (landings and discards) ............................................................... 624 

6.18.2.2  Effort ................................................................................................................ 626 

6.18.2.3  Survey data ................................................................................................... 627 

6.18.3 Stock assessment .............................................................................................. 627 

6.18.4 Reference Points ................................................................................................ 632 

6.18.5 Short term Forecast and Catch Options ................................................... 633 

6.18.6 Data Deficiencies ............................................................................................... 633 

6.19  DEEP-WATER ROSE SHRIMP IN GSA 23 .................................................. 633 

6.19.1 Biology ................................................................................................................... 633 

6.19.2 Input Data ............................................................................................................ 634 

6.19.2.1  Catch (landings and discards) ............................................................... 634 

6.19.2.2  Effort ................................................................................................................ 634 

6.19.2.3  Survey data ................................................................................................... 635 

6.19.3 Stock assessment .............................................................................................. 635 

6.19.4 Reference Points ................................................................................................ 641 

6.19.5 Short term Forecast and Catch Options ................................................... 641 

6.19.6 Data Deficiencies ............................................................................................... 641 

7 Data Quality and deficiencies by stock ............................................................... 642 

7.1 Hake in GSAs 17-18 ................................................................................................. 642 

7.2 Red mullet in GSAs 17-18 .................................................................................... 643 

7.3 Norway lobster in GSAs 17-18 ........................................................................... 643 

7.4 Common Pandora in GSAs 17-18 ...................................................................... 643 

7.5 Deep-water rose shrimp in GSAs 17-18-19 .................................................. 643 

7.6 Thornback ray in GSA 17 ...................................................................................... 644 

7.7 Common cuttlefish in GSAs 17-18 .................................................................... 644 

7.8 Sole in GSA 17 .......................................................................................................... 644 

7.9 Spottail mantis shrimp in GSAs 17-18 ............................................................ 644 

7.10 Hake in GSA 19 ...................................................................................................... 645 

7.11 Hake in GSA 20 ...................................................................................................... 645 

7.12 Red mullet in GSA 19 .......................................................................................... 645 

14 

14 

 
7.13 Red mullet in GSA 20 .......................................................................................... 646 

7.14 Hake in GSA 22 ...................................................................................................... 646 

7.15 Red mullet in GSA 22 .......................................................................................... 646 

7.16 Deep-water rose shrimp in GSA 22 ............................................................... 646 

7.17 Hake in GSA 23 ...................................................................................................... 647 

7.18 Red mullet in GSA 23 .......................................................................................... 647 

7.19 Deep-water rose shrimp in GSA 23 ............................................................... 647 

8 Distribution of juveniles and spawners ............................................................... 647 

9 References ...................................................................................................................... 655 

10 Contact details of EWG-17-15 participants ..................................................... 661 

11 List of Annexes ........................................................................................................... 663 

12 List of Background Documents ............................................................................ 663 

15 

15 

 
 
 
 
 
 
SCIENTIFIC, TECHNICAL AND ECONOMIC COMMITTEE FOR FISHERIES 
(STECF) - Mediterranean Stock Assessments pt I (STECF-17-15) 

The  EWG-17-17  report  was  reviewed  during  the  STECF  plenary  meeting  held  in  
Brussels, XX to XX March 2017. 

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

STECF observations  

The  working  group  was  held  in  Rome,  Italy,  from  27rd  November  to  3rd 
December  2017.  The  meeting  was  attended  by  22  experts  in  total,  including  2 
STECF members and 2 JRC experts with two observers.  

The  objective  of  the  EWG  17-15  was  to  carry  out  demersal  stock  assessments 
defined  in  the  ToRs.  STECF  acknowledges  that  like  the  previous  Mediterranean 
assessment  meeting  (STECF-17-09)  EWG17-15  had  two  additional  days  to 
answer the ToRs. STECF notes that this additional time was of considerable help, 
allowing  a  completion  of  the  assessments  and  a  full  review  of  the  work  and 
agreement on conclusions during the meeting. 

STECF comments 

STECF  considers  that  the  EWG  addressed  thoroughly  all  ToRs.  STECF  notes  that  the  EWG 
carefully  reviewed  the  quality  of  the  assessments  produced.  Some  analyses  were  considered  to 
be suitable for short term forecasts, others were only considered sufficiently reliable to estimate 
F-status, and no forecast was produced. 

For  several  assessments  in  GSAs  20,  22  and  23  it  was  not  possible  to  conclude  stock  status  or 
provide advice. This is considered to be partly because of the absence of Greek demersal survey 
indices  for  several  years.  This  situation  is  only  likely  to  improve  if  the  surveys  are  carried  out 
every year in the future.  

A  total  of  19  area/species  combinations  were  evaluated  (Tables  4.1.1  and  4.1.2).  The  EWG  has 
carried  out  short  term  forecasts  out  of  seven  age-based  or  surplus  production  analytical 
assessments. Catch advice for two stocks was based on  biomass index methods. For ten stocks 
no  catch  advice  has  been  provided,  however,  of  these  five  have  an  indication  of  stock  status  in 
terms  of  fishing  mortality  relative  to  MSY.  The  main  results  are  summarised  in  bullets  below. 
Statements on catch changes are in relation to reaching Fmsy in 2019: 

  Hake in GSA 17-18 is declining and is being overfished. Catches should be reduced by a 

half as a minimum to reach FMSY in 2019.. 

  Red mullet in GSA 17-18 has increased rapidly over the last few years. Fishing mortality is 
uncertain but probably below MSY in the last two years. Catches should be increased by 
no more than 25% in 2019.  

16 

16 

 
 
 
 
 
 
 
 
  Norway lobster in GSA 17-18 is at a low level with biomass close to Blim, F is at 2.3 time 

MSY. Catches should be reduced by around 60% 

  Common Pandora in GSA 17-18 has been increasing over the last five years, fishing 

mortality is uncertain. Catches should be decreased by 4%  

  Deep-water rose shrimp in GSA 17-18-19 is increasing and is being exploited at 2 times 

MSY. Catches should be reduced by around 10% 

  Thornback ray in GSA 17 is depleted, fishing mortality is uncertain but high catches should 

be reduced. 

  Common cuttlefish in GSA 17-18 is likely to be exploited above MSY, but catches are 

uncertain and no catch advice can be provided.  

  Sole in GSA 17 is increasing but is overfished. Catches should be reduced by at a half as a 

minimum to reach FMSY in 2019 

  Spottail mantis shrimp in GSA 17-18 has been increasing over recent years, F is at 2 times 

MSY. Catches should be reduced by around 10%.  

  Hake in GSA 19 is declining and fishing mortality is estimated several times above Fmsy. 

Catches should be reduced by around 80% 

  Hake in GSA 20 data is sparse due to missing DCF data and uncertain, assessments give 

conflicting results, no catch advice is provided. 

  Red mullet in GSA 19 has been increasing over recent years, F has been decreasing and is 
at 1.6 times MSY. There is considerable uncertainty in reported catches from different 
sources, but survey data indicate a required reduction of these of around 10%. 

  Red mullet in GSA 20 data is sparse, due to missing DCF data, and uncertain, assessments 

give conflicting results, no catch advice is provided. 

  Hake in GSA 22 data is sparse, due to missing DCF data and uncertain; the stock is 
considered to be increasing and exploited close to MSY. No catch advice is provided. 

  Red mullet in GSA 22 data is sparse, due to missing DCF data, and uncertain; the stock is 

considered to be increasing and under exploited. No catch advice is provided. 
  Deep-water rose shrimp in GSA 22 data is sparse, due to missing DCF data, and 

uncertain; the stock status is unknown. No catch advice is provided. 

  Hake in GSA 23 data is sparse, due to missing DCF data, and uncertain, the stock is 

considered to be declining and over exploited. No catch advice can be given. 

  Red mullet GSA 23 data is sparse, due to missing DCF data, and uncertain; assessments 

give conflicting results. No catch advice is provided. 

  Deep-water rose shrimp in GSA 22 data is sparse due to missing DCF data and uncertain, 

assessments give conflicting results, no advice is provided. 

STECF  noted  the  difficulties  encountered  by  the  EWG  in  selecting  a  single  assessment  for  both 
hake  in  GSA  17&18  and  sole  in  GSA17  due  marked  differences  particularly  in  biomass  resulting 
from different modelling approaches (SS3 vs a4a) and from shape of the selection curve (dome-
shaped  vs.  logistic).  STECF  agrees  with  the  conclusion  that  both  stocks  were  being  overfished, 
however,  accepts  that  it  is  not  possible  select  a  single  assessment  based  on  the  information 
provided  in  the  EWG  report  (Section  5.1  and  Section  5.8).  STECF  considers  that  more  work  is 
required to identify and confirm a single agreed assessment for each of these stocks. Tables 4.1.2 
and  4.1.3  contain  the  conclusions,  in  terms  of  F  in  2016  and  changes  in  catch  for  2018,  that 
STECF draws from the analyses completed for both these stocks.  

Regarding  hake  in  GSA  17&18,  STECF  is  concerned  that  the  growth  rates  and  selection  that  fit 
best in the SS3 model gives SSB constituted almost exclusively of old animals, with around 90% 
of  SSB  at  ages  greater  than  those  that  contribute  to  the  fishery.  Further  exploration  of  both 
growth and selection is required to establish what is driving these aspects which are not seen to 
the  same  extent  in  the  combined  fleet  model  (a4a).  However,  based  on  results  of  both  models 
STECF is able to conclude that F is high, greater than FMSY and that catches need to be reduced by 
a half as a minimum to achieve FMSY in 2019. STECF is not able to advise on the current state of 
biomass for this stock.  

17 

17 

 
 
For sole in GSA 17 similar issues exist, though differences are less extreme in terms of SSB, and 
there is less uncertainty on growth parameters. An extensive further evaluation was carried out 
after the EWG, concluding that results depend strongly on the choice of selection for the fishery, 
and  even  more  on  the  choice  of  selection  curve  for  the  beam  trawl  survey.  When  strong  dome 
shaped  selectivity  is  used  substantial  biomass,  coming  from  ages  and  areas  outside  the  survey 
and fishery is estimated by the model, similar to the case of hake. Further exploration is required 
to determine why strongly domed selection is better fitted by the SS3 model, whereas evaluation 
of mortality signals in the survey does not support selection in this form. At present STECF is able 
to conclude that for sole in GSA 17, F is greater than FMSY, and catches need to be reduced by a 
half  as  a  minimum  to  achieve  FMSY  in  2019..  STECF  also  advises  that  SSB  has  been  increasing 
over the last three years, although the historical levels of biomass remain uncertain. STECF has 
discussed the various hypotheses and evidences underpinning the various models, and notes that 
this  might  be  further  analysed  by  STECF  18-16.  Although  no  unanimous  conclusion  could  be 
reached by the committee, it is suggested that unless new conclusions are reached by EWG 18-
16,  the  intermediate  SS3  model  (SS3  Run7  section  6.8.3)  with  intermediate  levels  of  cryptic 
biomass (around 15% of adult biomass not accessible to the fishery) is used as the main basis for 
MAP  analyses  in  STECF  18-17.  This  run  is  more  conservative  than  the  run  with  dome-shaped 
selection. 

STECF  notes  that  the  EWG  has  estimated  and  provided  values  of  FMSY  and  MSY  ranges  for  five 
stocks (Table 4.1.3). The values of Flow and FMSY are regarded as reasonable estimates that can be 
expected to be precautionary and thus may be used directly. The values for Fupper are  indicative 
only;  they  have  not  been  evaluated  as  precautionary  and  should  not  be  used  as  such  without 
further evaluation.  

STECF notes that data quality deficiencies and recommendations for further research studies and 
data collection have been comprehensively addressed by the EWG for each stock in section 7 of 
the report, as well as summarized in stock summaries. 

In  response  to  ToR  10,  STECF  notes  that  the  EWG  has  provided  maps  of  persistence  of  several 
species  and  life  stages,  based  on  MEDITS  trawl  survey  data:  Hake  (juveniles  and  adults);  Red 
Mullet  (adults  and  spawners);  Deep-water  rose  shrimp  (juveniles  and  adults).  Juveniles  as  here 
defined by maturation state not fish size. The distribution of adults relates to the distribution at 
the  time  of  the  year  when  the  MEDITS  survey  is  carried  out.  For  red  mullet  this  may  also  be 
suitable as a spawning area as it is possible to identify spawners at the time of the survey. Data 
is unsuitable to draw distribution of persistence of juvenile red mullet.  

The  maps  can  be  used  to  inform  selection  of  suitable  areas  to  protect  juveniles  or  adults.  The 
information  produced  by  the  EWG  provides  the  underlying  information  to  allow  the  selection  of 
areas  with  high  persistence  of  adults  or  juveniles.  If  it  is  intended  to  define  areas  for  use  as  a 
part  of  spatial  management,  such  as  closed  areas,  a  further  step  involving  the  definition  of 
explicit  boundaries  is  required.  This  step  would  apply  GIS  (Geographical  Information  System) 
methodologies  on  the  information  supplied  here  to  define  adequate  areas.  As  a  final  step 
managers  will  be  required  to  make  decisions  on  the  proportion  of  the  area  of  persistence  to  be 
closed for fishing.  

STECF conclusions 

applied  assessment  methodologies  that  allowed  estimation  of  uncertainty  and  conditioning  of 
operating models to be used in future fishery management evaluations (MSEs). 

STECF endorses the assessments and evaluation of stock status produced by the EWG. 

STECF  endorses  the  short  term  forecasts  produced  by  the  EWG.  In  the  cases  of  hake  in  GSA 
17&18  and  sole  in  GSA  17,  where  conflicting  results  were  produced  by  different  assessment 
methods, STECF concludes that as precautionary measure catches should be reduced by a half as 
a minimum to achieve FMSY in 2019.  

STECF  notes  that  the  EWG  was  not  able  to  assess  stock  status  or  provide  advice  for  several 
assessments  in  GSAs  20,  22  and  23,  partly  because  of  the  absence  of  usable  time-series  of 

18 

18 

 
 
 
demersal survey indices. Over the last decade, surveys were performed only in 2008, 2014 and 
2016. Improving the quality of the stock assessments require the surveys to be carried out every 
year in the future 

Table  1.  Summary  of  work  was  attempted  and  basis  for  advice  (given  in  bold).  A4A,  XSA,  and 
SS3 are age based assessment methods; SPiCT and CMSY are surplus production models. STF is 
a  standard  short  term  projection  with  assumptions  of  status  quo  F  in  the  intermediate  year 
(2017)  and  recent  historic  recruitment  for  2017  and  2018.  HR  (Harvest  Rate)  is  a  fraction  of 
biomass  in  the  year  of  the  assessment,  and  assumes  no  population  growth  where  STF  is  not 
suitable. 

Area 

Species 

Previous 

Attempted analyses and 

GSA 17-18 

Hake 

Analysis / year 

basis of advice (in bold) 

XSA 2015 

SS3 2016 

SS3 a4a STF 

GSA 17-18 

Red mullet 

GSA 17 (2016) 
GSA18 (2016) 

SS3 a4a XSA biomass from 
a4a 

GSA 17-18 

Norway lobster 

SPiCT 2016 

SPiCT HR 

GSA 17-18 

Common Pandora 

XSA, a4a biomass index 

GSA 17-18-19 

Deep-water rose 
shrimp 

XSA 2015 

XSA, a4a STF 

GSA 17 

Thornback ray 

GSA 17-18 

Common cuttlefish 

Catch curves 

CSMY 

GSA 17 

Sole 

SS3 2016 

SS3 a4a STF 

GSA 17-18 

Spottail mantis shrimp 

XSA 2015 

XSA a4a STF  

GSA 19 

GSA 20 

Hake 

Hake 

XSA 2015 

XSA a4A STF 

 VIT, 
SURBA 2012 

ASPIC, 

CMSY SPICT a4a  

GSA 19 

Red mullet 

XSA 2015 

xsa a4a STF 

GSA 20 

Red mullet 

ASPIC, SURBA 
and LCA 2012 

SPiCT CMSY a4a  

GSA 22 

Hake 

2010(production) 

SPiCT  

GSA 22 

Red mullet 

2010(production) 

SPiCT a4a  

GSA 22 

Deep-water rose 
shrimp 

GSA 23 

Hake 

GSA 23 

Red mullet 

GSA 23 

Deep-water rose 
shrimp 

19 

19 

SPiCT  

SPiCT  

SPiCT  

SPiCT 

 
 
 
 
 
 
 
 
 
Table  2.  Summary  of advice  from  EWG  17-15  by  area and  species.  F  2016  is  terminal  F  in  the 
assessment. Change in F is the difference as % change between targeted F in 2018 (Fmsy) and 
the  estimated  F  in  2016.  Change  in  catch  is  %  change  from  catch  estimated  2016  to  projected 
catch 2018. Biomass status is given relative to BMSY where available, (only in Nephrops GSA 17-
18)  and  as  an  indication  of  trend  over  the  last  3  years  for  stocks  with  time  series  analytical 
assessments or biomass indices. (L indicated landing only, not catch). 

Area 

Species 

Method/ 
basis 

F 2016 

F 
2018 

Change 
in F 

Catch 
2016 

Catch 
2018 

Change 
in catch 

Biomass 

(status) 

GSA 
17-18 

GSA 
17-18 

Hake 

SS3/a4a 

>FMSY 

0.19 

<-50% 

5200  <2600  <50% 

Declining 

Red mullet 

A4a 
biomass 
index 

6188 

7706 

25% 

Increasing 

GSA 
17-18 

Norway 
lobster 

SPiCT 
STF 

GSA 
17-18 

Common 
pandora 

biomass 
index 

GSA 
17-
18-19 

Deep-water 
rose shrimp 

a4a STF 

0.49 

0.21 

-57% 

1022 

441 

-57% 

0.33 BMSY 

232 

222 

-4% 

Increasing 

1.44 

0.70 

-51% 

3559 

3225 

-9% 

Increasing 

Depleted 

GSA 
17 

Thornback 
ray 

Level 
advice 

Reduce 

catch 

GSA 
17-18 

Common 
cuttlefish 

CMSY  

above FMSY 

No 

advice 

GSA 
17 

GSA 
17-18 

GSA 
19 

GSA 
20 

GSA 
19 

GSA 
20 

Sole 

SS3/a4a 

a4a STF  

Spottail 
mantis 
shrimp 

Hake 

a4a STF 

F>FMSY 

0.25 

<-37% 

2100  <1050  <-50% 

Increasing 

0.65 

0.38 

-42% 

4360 

4028 

-8% 

Increasing 

1.42 

0.16 

-89% 

802 

178 

-78% 

Declining 

SPiCT 
CMSY a4a  

Hake 

Conflicting 

No 

results 

advice 

Red mullet 

a4a STF 

0.56 

0.36 

-36% 

257 

253 

-2% 

Increasing 

Red mullet 

SPiCT 
CMSY a4a  

Conflicting 

no 

results 

advice 

20 

20 

 
 
 
 
 
 
  
 
 
  
 
  
 
 
  
  
 
 
  
 
 
  
 
 
  
 
 
 
 
 
 
GSA 
22 

GSA 
22 

Hake 

SPiCT  

Close to 

no 

MSY  

advice 

Red mullet 

SPiCT 

Under 

no 

a4a  

exploited 

advice 

GSA 
22 

Deep-water 
rose shrimp 

SPiCT  

No 

no 

conclusion 

advice 

GSA 
23 

GSA 
23 

Hake 

SPiCT  

Over 

no 

exploited 

advice 

Red mullet 

SPiCT  

No 

no 

conclusion 

advice 

GSA 
23 

Deep-water 
rose shrimp 

Not 

No 

no 

possible 

conclusion 

advice 

Increasing 

Increasing 

Declining 

Table 3. FMSY ranges (Flow and Fupp) for demersal stocks from the Mediterranean. The values for 
Fupp are indicative only they have not been evaluated as precautionary and should not be used 
as such without further evaluation. 

GSA 

Species 

Fcurr 

F MSY 

Flow 

Fupp 

Fcurr/ 

FMSY 

GSA 17-18 

Norway lobster 

0.49 

0.21 

GSA 17-18-19 

Deep-water rose shrimp 

1.44 

0.7 

GSA 17-18 

Spottail mantis shrimp 

0.65 

0.38 

GSA 19 

GSA 19 

Hake 

1.42 

0.16 

Red mullet 

0.56 

0.36 

0.14 

0.47 

0.25 

0.11 

0.24 

0.29 

2.33 

0.95 

2.06 

0.52 

1.71 

0.22 

8.88 

0.49 

1.56 

21 

21 

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

Name 

Address1 

STECF members 

Tel. 

Email 

Abella, 
Alvaro 

J. 

Independent consultant 

Tel. 
3384989821 

0039-

aabellafisheries@gmail.c
om 

Tel.dir.:    +45  35 
33 68 92 

jla@ifro.ku.dk 

Tel.: 
+34667174477 

harri@azti.es 

for  Environment 

Department  of  Food  and 
Resource 
Economics 
(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, 
Massimiliano  

Föreningsgatan  45,    330 
Lysekil, Sweden 

Tel:  +46 
18750 

523 

massimiliano.cardinale@
slu.se 

Catchpole, 
Thomas 

Curtis, Hazel  

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 

22 

22 

 
 
 
 
 
 
 
 
 
 
Name 

Address1 

STECF members 

Tel. 

Email 

Daskalov, 
Georgi 

Döring, 
(vice-chair) 

Ralf 

of 

Marine 

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

Research, 

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 

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

Fishery 

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

Knittweis, 
Leyla 

Lloret, Josep 

Malvarosa, 
Loretta  

Tel.:  +359 
646892 

52 

Georgi.daskalov@gmail.
com 

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  

josep.lloret@udg.edu 

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 
Water 
and 
Marine 
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 

23 

23 

 
 
 
 
 
 
 
 
 
 
Name 

Address1 

STECF members 

Tel. 

Email 

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  

Sala, Antonello 
(vice-chair) 

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

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

071 

a.sala@ismar.cnr.it 

071 

+39 

Scarcella, 
Giuseppe 

Soldo, Alen 

Somarakis, 
Stylianos 

Stransky, 
Christoph 

Ulrich, 
(chair) 

Clara 

Italian 

National 
1) 
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 
Research 
(HCMR), 
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, 
D-22767 
Hamburg, 
Germany  

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

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

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 

Tel. 
+49 
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 

24 

24 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Name 

Address1 

STECF members 

Tel. 

Email 

van 
Oostenbrugge, 
Hans 

Vrgoc, Nedo 

Fisheries 
Wageningen 
Research, 
Wageningen 
Hague, The Netherlands 

Economics, 
Economic 
LEI 
The 

formerly 

UR, 

Hans.vanOostenbrugge
@wur.nl 

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

Tel.: 
21408002 

+385 

vrgoc@izor.hr 

25 

25 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
EXPERT WORKING GROUP EWG-17-09 REPORT 

REPORT TO THE STECF 

EXPERT WORKING GROUP ON  

Mediterranean Stock Assessments Pt II 

(STECF 17-15) 

Rome, Italy,27 November to 3 December 2017 

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 

26 

26 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
EXECUTIVE SUMMARY  

The  working  group  was  held  in  Rome,  Italy,  from  27  November  -  3  December 
2017.  The  meeting  was  attended  by  22  participants  including  two  JRC  experts 
and one STECF member and one observer.  

A total of 19 area/species combinations were evaluated. The EWG has carried out 
seven age based assessments and one surplus production analytical assessments 
with short term forecasts. Catch advice for two stocks is based on biomass index 
methods.  For  ten  stocks  no  catch  advice  could  be  provided,  however,  of  these 
five  have  an  indication  of  stock  status  in  terms  of  fishing  mortality  relative  to 
MSY. The methods tested and selected along with  a summary of the results are 
provided  in  Tables.  Summary  sheets  by  stock  are  provided  giving  state  of  the 
stock  in  terms  of  spawning  biomass  and  fishery  exploitation.  Where  possible 
catch advice when applying an MSY approach is provided along with other catch 
options.  The  input  data  and  assessment  settings  and  results  are  provided  by 
stock.  

Difficulties  were  encountered  for  hake  in  GSA  17&18  and  Sole  in  GSA19  due  to 
the  differences  in  modelling  approaches  resulting  in  different  estimates  of  stock 
biomass  and  catch.  The  WG  was  able  to  conclude  that  stocks  were  being 
overfished,  no  agreement  was  reach  on  a  single  assessment  and  draft  advice is 
available for multiple options. 

For stocks in GSAs 20, 22 and 23 both the shortage of data due to missing years 
of  data  collection  but  also  due  to  differing  estimates  of  catch  from  different 
sources in Greece (DCF and NSSG) gave rise to uncertainty regarding the basis of 
the assessments. For most stocks in theses area (5 of 8) stock status could not be 
determined.  For  all  these  stocks  no  catch  advice  was  calculated  due  to  the 
uncertainties.  It  would  be  helpful  if  the  different  results  could  be  evaluated  in 
Greece and a single agreed data set created which could then be used to submit 
results to any recipients  

27 

27 

 
 
 
 
 
 
 
1 INTRODUCTION 

1.1 APPROACH TO THE WORK 

The  working  group  was  held  in  Rome,  Italy,  from  27th  November  to  3rd 
December 2017. The meeting was attended by 22 experts in total, including two 
STECF members and two JRC experts. The EWG was attended by one observer. 

The  objective  of  the  Mediterranean  Methodology  EWG  17-15  was  to  carry  out 
assessments and provide draft advice for stocks identified in the ToR supplied by 
STECF. An initial plenary session commenced at 09:00 on the first day. The ToRs 
were  discussed  and  examined  in  detail.  Stocks  were  allocated  to  participants  in 
small groups based on expertise. 

An  ftp  repository  was  created  ad-hoc  to  share  documents,  data  and  scripts  and 
prepare the report. 

The stocks were evaluated by the GSA groups identified in the ToRs, but if data 
were  considered  to  represent  diverse  stocks  within  these  groups  the  data  and 
analyses were maintained separately at GSA level, this was the case for GSA 22 
and 23.   

Plenary sessions were held each day to monitor progress and share results. The 
overall conclusions of each ToR/stock were discussed and finalized in plenary on 
the last day.  

1.2 TERMS OF REFERENCE FOR EWG-17-09 

DG MARE focal persons: Chato Osio  

Chair: John Simmonds  

GENERAL  GUIDELINES:  unless  the  data  used  and  information  provided  comes 
from  the  official  DCF  data  calls,  the  experts  are  requested  to  indicate  the  data 
source  from  where  certain  information  has  been  taken  (e.g.  L-W  relationships, 
prices) or if it is an experts' reasoned guess. 

Data collected outside the DCF shall be used as well and merged with DCF data 
whenever necessary and following quality check. Due account shall also be given 
to  data  used  and  assessments  carried  out  within  the  FAO  regional  projects  co-
funded  by  the  European  Commission  and  EU-Member  States  in  particular  when 
using  data  collected  through  the  DCF/DCR  and  EU  funded  research  projects, 
studies and other types of EU funding. 

28 

28 

 
 
 
 
 
 
 
 
The  raw  data  used  to  generate  the  input  data,  assessment  scripts  as  well  as 
input  files  should  be  made  available  to  the  JRC  for  reproducibility  of  the 
assessments  and  compilation  of  the  STECF  stock  assessment  database 
(https://stecf.jrc.ec.europa.eu/dd/medbs/ram) 

STECF 17-071 defined methodological guidelines to ensure standardized practices 
for the preparation of stock assessment input data. EWG 17-15 should adhere to 
these recommendations referring to the need of: 

 i) coherence of all growth parameters used in the assessments;  

ii) improvement in documenting and defining the growth models and age slicing;  

iii) test where possible age slicing by sex; 

 iv) t0 should be truncated to values between 0 and -0.2; 

 v)  review  the  raw  age  length  data,  where  necessary  refitting  growth  models 
(section 2.2 in the EWG report). . 

For the stocks given in Annex I, the EWG 17-15 is requested: 

ToR 1.  To  compile  and  provide  the  most  updated  information  on  stock 
identification  and  boundaries,  length  and  age  composition,  growth, 
maturity, feeding, essential fish habitats and natural mortality. 

ToR 2.  To  compile  and  provide  complete  sets  of  annual  data  on  landings  and 
discards for the longest time series available up to and including 2016. 
This  should  be  presented  by  fishing  gear  as  well  as  by  size/age 
structure (see Annex II for more details). 

ToR 3.  To  compile  and  provide  complete  sets  of  annual  data  on  fishing  effort 
for  the  longest  time  series  available  up  to  and  including  2016.  This 
should  be  described  in  terms  of  amount  of  vessels,  time  (days  at  sea, 
soaking  time,  or  other  relevant  parameter)  and  fishing  power  (gear 
size,  boat  size  (linear  and/or  GT),  engine  power  kW,  etc.)  by  Member 
State  and  fishing  gear.  Data  shall  be  the  most  detailed  possible  to 
support  the  establishment  of  a  fishing  effort  and/or  capacity  baseline 
(see Annex II for more details). 

1 

https://stecf.jrc.ec.europa.eu/documents/43805/1691180/STECF+17-07+-

+Methods+for+stock+assessments+in+MED_JRCxxx.pdf 

29 

29 

 
 
                                                 
ToR 4.  To compile and provide indices of abundances and biomass by year and 
size/age structure for the longest time series available up to and including 
2016 (see Annex II for more details). 

ToR 5.   To  assess  trends  in  historic  and  recent  stock  parameters  on  fishing 
mortality,  stock  biomass,  spawning  stock  biomass,  and  recruitment. 
Different assessment models should be applied as appropriate, including 
retrospective  analyses.  The  selection  of  the  most  reliable  assessment 
shall be explained. Assumptions and uncertainties shall be specified.  

The stock assessments performed in EWG 17-15 will constitute the 
basis for the preparation of the demersal Adriatic EU MAP. The MAP will 
require  an  extensive  management  strategy  evaluation  (MSE)  in  line 
with  the  work  performed  in  STECF  16-21  and  STECF  15-09.  Since  the 
MSEs, encoded in the Fisheries Libraries in R (FLR), rely on established 
routines where uncertainty and risk play an important role, it is priority 
for the EWG to: 

1.  Give preference to models that allow estimation of uncertainty, in line 

with the recommendations of STECF EWG 17-07.     

2.  To  envision  alternative  stock  assessments 

for 

the  potential 

conditioning of operating models in the context of future MSEs. 

ToR 6.   To  estimate  candidate  MSY  point-value,  MSY  range  values  and 
conservation  reference  points  (precautionary  and  limit)  in  terms  of 
fishing  mortality  and  stock  biomass.  The  proposed  values  shall  be 
related  to  long-term  high  yields  and  low  risk  of  stock/fishery  collapse 
and  ensure  that  the  exploitation  levels  restore  and  maintain  marine 
biological  resources  at  least  at  levels  which  can  produce  the  maximum 
sustainable yield. 

ToR 7.   To  provide  short  and  medium  term  forecasts  of  spawning  stock 
biomass,  stock  biomass  and  catches.  The  forecasts  shall  include 
different  management  scenarios,  inter  alia:  zero  catch,  the  status  quo 
fishing  mortality,  and  target  to  FMSY  (including  the  ranges)  or  other 
appropriate  proxy  by  2020.  In  particular,  on  the  basis  of  the  average 
commercial  catch  rates,  estimate  the  level  of  fishing  effort  exerted  by 
the different fleets which is commensurate with the short- and medium-
term forecasts of the proposed scenarios. 

ToR 8.   To  summarize  and  concisely  describe  all  data  quality  deficiencies, 
including  possible  limitations  with  the  surveys  of  relevance  for  stock 

30 

30 

 
 
assessments and fisheries. Such review and description are to be based 
on  the  data  format  of  the  official  DCF  data  call  for  the  Mediterranean 
Sea launched on the March 2017. Identify further research studies and 
data  collection  which  would  be  required  for  improved  fish  stock 
assessments.  This  review  shall  be  presented  in  a  manner  that  is 
compatible  with  the  online  platform  developed  by  the  JRC  for  data 
issues2. 

ToR 9.   To  provide  a  synoptic  overview  of:  (i)  the  fishery;  (ii)  the  most  recent 
state  of  the  stock  (spawning  stock  biomass,  stock  biomass,  recruits, 
and  exploitation  level  by  fishing  gear);  (iii)  the  source  of  data  and 
methods and; (iv) the management advice, including MSY value, range 
of values and conservation reference points. 

ToR 10.  For  stocks  in  Table  1,    provide  detailed  maps  for  GSA  17-23  at  the 
best  resolution  possible  level  and  related  table  of  correspondence  with 
relevant spatial coordinates, of: 

  The recurrent areas of juveniles' aggregations 

a) 1st-year juveniles;  

b)  juveniles  equal  to  or  smaller  than  the  minimum  conservation 
reference size  

  The recurrent spawning aggregations areas 

2 Castro Ribeiro C. (2015) Fisheries Data Collection Framework - The DCF Reporting and Implementation Cycles and the 

Data End-user Feedback, JRC Technical report. 

31 

31 

 
                                                 
ANNEX I to ToR 

Table I – List of suggested stocks to be assessed by the EWG 17-15. 

Area 

Common name 

Scientific name 

GSA 17-18 

Hake 

Merluccius merluccius 

GSA 17-18 

Red mullet 

Mullus barbatus 

GSA 17-18 

Norway lobster 

Nephrops norvegicus 

GSA 17-18 

Common Pandora 

Pagellus erythrinus 

GSA 17-18-19 

Deep-water rose 
shrimp 

Parapenaeus 
longirostris 

GSA 17 

Thornback ray 

Raja clavata 

GSA 17-18 

Common cuttlefish 

Sepia officinalis 

GSA 17 

Sole 

Solea vulgaris 

GSA 17-18 

Spottail mantis shrimp 

Squilla mantis 

GSA 19 

GSA 20 

GSA 19 

GSA 20 

Hake 

Hake 

Merluccius merluccius 

Merluccius merluccius 

Red mullet 

Mullus barbatus 

Red mullet 

Mullus barbatus 

GSA 22-23 

Hake 

Merluccius merluccius 

GSA 22-23 

GSA 22-23 

Red mullet 

Mullus barbatus 

Deep-water rose 
shrimp 

Parapenaeus 
longirostris 

NOTE:  The  joint  assessments  have  been  proposed  on  the  basis  of  STOCKMED 
and management needs. However, these suggestions can be modified according 
to experts' knowledge and to the most recent scientific information.  

2 FINDINGS AND CONCLUSIONS OF THE WORKING GROUP 

A total of 19 area/species combinations were evaluated. The EWG has carried out 
seven age based  and surplus production analytical assessments with short term 

32 

32 

 
 
 
 
 
 
 
forecasts.  Catch  advice  for  two  stocks  is  based  on  biomass  index  methods.  For 
ten  stocks  no  catch  advice  could  be  provided,  however,  of  these  five  have  an 
indication of stock status in terms of fishing mortality relative to MSY. 

For  two  stocks  the  EWG  was  able  to  fit  assessments,  but  different  models  gave 
different perceptions of biomass and the EWG was not able to agree a final single 
assessment.  

2.1 STOCK-SPECIFIC FINDINGS & CONCLUSIONS 

A range of analyses were considered for all stocks based on data available to the 
meeting  (Table  2.1).  Analytical  age  based  assessments  and  surplus  production 
catch  based  assessments  were  attempted,  and  where  these  were  found  by  the 
EWG to be of sufficient standard they have been used as the basis for advice; see 
Section 5 and the summary values in Table 2.2.  

Length  analyses  were  carried  out  for  all  species/areas  where  sufficient  length 
data  was  available.  Where  relevant  the  results  of  these  length  analyses  are 
included in the stock evaluations in Section 6 and the full set are given in Annex 
I. The length methods applied in EWG 17-15 followed the methods used in EWG 
16-13  and  17-02  which  were  calculated  based  on  Lc  (length  at  first  capture  on 
fitted 25 percentile on catch), which gave results that were much better coupled 
to the observed length distributions. Sensitive of resulting MSY index (LFeM) is still 
known  to  be  sensitive  to  assumptions  on  L  infinity  (Linf)  expert  judgement  was 
used and Linf values were carefully selected for each stock.  

A brief resume of the assessments and any issues are given below by area.  

For hake in GSA 17&18 and sole in GSA 17, the EWG was not able to recommend 
a  single  assessment.  Two  assessments  are  presented,  the  stock status  in terms 
of  F  is  similar,  overfished,  but  in  particular  the  magnitude  of  biomass  at  ages 
greater  than  those  in  the  fishery  was  very  different.  For  both  these  stocks  this 
biomass  is  not  seen  directly  in  the  fishery  or  survey,  but  results  from  the  fitted 
models  particularly  in  the  cases  where  fleets  are  modelled  separately.  The 
individual  issues  are  discussed  in  Section  6.1  and  6.8  respectively.  The  general  
issues  are  discussed  in  more  detail  in  Section  3.  For  hake  in  GSA  17-18  the 
differences arise from different perceptions of both growth and fishery selection. 
For  sole  in  GSA  17  the  differences  come  only  from  selection.    It  is  important  to 
try  to  determine  the  validity  of  the  growth  and  selection  resulting  from  the 
models  and  thus  the  extent  of  the  biomass  coming  from  vary  rarely  observed 
individuals  before  advising  that  management  should  be  based  on  models  with 
such high proportions of SSB at ages greater than those fished.   

For  red  mullet  in  GSA  17&18  three  assessments  were  fitted,  which  all  gave 
similar perceptions of stock up to the last three years. The SS3 assessment was 
very unstable with major retrospective patterns.  The XSA assessment responded 
the  least  to  the  changes  in  biomass  index  from  MEDITS  showing  only  small 

33 

33 

 
 
 
 
 
changes  in  the  last  year.  The  a4a  assessment  provided  greater  stability 
retrospectively,  but  reacted  strongly  to  the  signals  in  the  survey,  which  are  to 
some extent in conflict from year to year and different from the fishery. To give 
advice with the level of conflicting information the most stable assessment (a4a) 
was used as the basis for a biomass index advice, limiting the increase in advised 
catches to 20%. 

Also for common Pandora in GSA 17-18 a biomass index based approach was the 
only approach that was available for catch advice. 

For  common  cuttlefish  the  stock  status  is  uncertain  but  considered  to  be  above 
MSY based on a CMSY assessment.  

For thornback ray only indications of status can be given, current F is considered 
likely three times any F target. In terms of biomass, explicit estimates were not 
available but it is clear that there have been major reductions in abundance since 
the 1950s, and the stock can be considered depleted.  

For  the  other  stocks  in  GSA  17,  18  and  19  the  results  were generally  coherent, 
and stock status has been evaluated and advice provided.  

For  all  the  stocks  evaluated  in  GSA  20,  22  and  23  there  were  considerable 
uncertainty  in  the  Greek  landings  data.  The  differences  between  DCF  and  other 
sources (FAO and Greek statistical services) where substantial. While some of the 
DCF data is missing due to years when it was not collected, for those years that 
should  have been  covered  the  estimated  landing  are  substantially different,  this 
is particularly the case for hake in the three areas, but the issues does also arise 
for red mullet and deep-water rose shrimp. Using the best data available it was 
difficult  to  obtain  coherent  results  for  most  stocks  except  for  red  mullet  where 
assessment  considered  to  be  useful  for  stock  status.  In  no  case  were  the 
assessments considered sufficiently stable to give catch advice. 

34 

34 

 
 
 
 
 
 
  
   
 
 
 
Table 2.1.1 Summary of work was attempted and basis for any advice (given in 
bold). A4A, XSA, and SS3 are age based assessment methods; SPiCT and CMSY 
are  a  surplus  production  model.  STF  is  a  standard  short  term  projection  with 
assumptions of status quo F  in the intermediate year (2017)  and recent historic 
recruitment  for  2017  and  2018.  HR  is  a  fraction  of  biomass  in  the  year  of  the 
assessment, and assumes no population growth where STF is not suitable.  

Area 

Species 

Previous 

Attempted analyses and 

GSA 17-18 

Hake 

Analysis / year 

basis of advice (in bold) 

XSA 2015 

SS3 2016 

SS3 a4a STF 

GSA 17-18 

Red mullet 

GSA 17 (2016) 
GSA18 (2016) 

SS3 a4a XSA biomass from 
a4a 

GSA 17-18 

Norway lobster 

SPiCT 2016 

SPiCT HR 

GSA 17-18 

Common Pandora 

XSA, a4a biomass index 

GSA 17-18-19 

Deep-water rose shrimp 

XSA 2015 

XSA, a4a STF 

GSA 17 

Thornback ray 

GSA 17-18 

Common cuttlefish 

Catch curves 

CSMY 

GSA 17 

Sole 

SS3 2016 

SS3 a4a STF 

GSA 17-18 

Spottail mantis shrimp 

XSA 2015 

XSA a4a STF  

GSA 19 

GSA 20 

Hake 

Hake 

XSA 2015 

XSA a4A STF 

 VIT, 
SURBA 2012 

ASPIC, 

CMSY SPICT  a4a  

GSA 19 

Red mullet 

XSA 2015 

xsa a4a STF 

GSA 20 

Red mullet 

ASPIC, SURBA 
and LCA 2012 

SPiCT CMSY a4a   

GSA 22 

GSA 22 

Hake 

2010(production) 

SPiCT  

Red mullet 

2010(production) 

SPiCT a4a  

GSA 22 

Deep-water rose shrimp 

GSA 23 

GSA 23 

Hake 

Red mullet 

GSA  23 

Deep-water rose shrimp 

SPiCT  

 SPiCT  

 SPiCT  

SPiCT 

35 

35 

 
 
 
 
 
 
 
 
 
 
Table 2.1.2 Summary of advice from EWG 17-15 by area and species. F 2016 is 
terminal F in the assessment. Change in F is the difference as % change between 
target F in 2018 and the estimated F for 2016. Change in catch is % change from 
catch  estimated  2016  to  catch  2018.  Biomass  status  is  given  relative  to  BMSY 
where  available,  (only  Nephrops  GSA  17-18)  and  as  an  indication  of  trend  over 
the  last  3  years  for  stocks  with  time  series  analytical  assessments,  biomass 
indices. (L indicated landing only, not catch).  

Area 

Species 

Method/ 
basis 

F 2016 

F 2018 

Change 
in F 

Catch 
2016 

Catch 
2018 

Change 
in catch 

Biomass 

(status) 

GSA 17-
18 

Hake 

SS3 

a4a 

GSA 17-
18 

GSA 17-
18 

GSA 17-
18 

GSA 17-
18-19 

Red mullet 

A4a 
biomass 
index 

Norway 
lobster 

SPiCT STF 

Common 
pandora 

biomass 
index 

Deep-water 
rose shrimp 

 a4a STF 

0.38 

0.19 

-50%  5144 

2875 

-44% 

Declining 

0.69 

0.18 

-74%  5267 

2042 

-61% 

Declining 

6188 

7706 

25% 

Increasing 

0.49 

0.21 

-57% 

1022 

441 

-57% 

0.33 Bmsy 

232 

222 

-4% 

Increasing 

1.44 

0.70 

-51% 

3559 

3225 

-9% 

Increasing 

GSA 17 

Thornback 
ray 

Level 
advice 

Reduce 

catch 

Depleted 

GSA 17-
18 

Common 
cuttlefish 

GSA 17 

Sole 

GSA 17-
18 

Spottail 
mantis 
shrimp 

CMSY  

above 

No 

SS3 R1 

A4a 

SS3 R7 

a4a STF  

FMSY 

0.41 

advice 

0.26 

-37% 

2093 

1140 

-46% 

Increasing 

0.99 

0.23 

-77% 

2105 

666 

-68% 

Increasing 

0.51 

0.26 

-49% 

2093 

963 

-54% 

Increasing 

0.65 

0.38 

-42% 

4360 

4028 

-8% 

Increasing 

GSA 19 

Hake 

a4a STF 

1.42 

0.16 

-89% 

802 

178 

-78% 

Declining 

GSA 20 

Hake 

SPiCT 
CMSY a4a   

Conflicting 

No 

results 

advice 

GSA 19 

Red mullet 

a4a STF 

0.56 

0.36 

-36% 

257 

253 

-2% 

Increasing 

GSA 20 

Red mullet 

SPiCT 

Conflicting 

no 

36 

36 

 
 
 
 
  
 
 
  
 
  
 
 
  
  
 
 
  
 
 
  
 
 
  
 
 
 
 
 
 
CMSY a4a   

GSA 22 

Hake 

SPiCT  

GSA 22 

Red mullet 

SPiCT a4a  

GSA 22 

Deep-water 
rose shrimp 

SPiCT  

GSA 23 

Hake 

 SPiCT  

GSA 23 

Red mullet 

 SPiCT  

results 

advice 

Close to 

no 

MSY  

advice 

Under 

no 

exploited 

advice 

No 

no 

conclusion 

advice 

Over 

no 

exploited 

advice 

No 

no 

conclusion 

advice 

GSA 23 

Deep-water 
rose shrimp 

Not 

No 

no 

possible 

conclusion 

advice 

Increasing 

Increasing 

Declining 

3 FOLLOW UP ITEMS 

The EWG had considerable difficulties in establishing assessments for two stocks, 
hake  in  GSA  17&18  and  sole  in  GSA  17  in  particular.  Two  different  approaches 
resulted  in  small  but  significant  differences  in  fishing  mortality  and  major 
differences in overall biomass. The differences appear to be linked to both growth 
rates and the way in which catch by fleet is dealt with in the model. For hake in 
GSA17-18 the two approaches were a) fitted growth rates with catches modelled 
separately by fleet, and b) growth rates based on published values and a model 
with  catches  combined.  For  sole  in  17  the  two  approaches  were  a)  catches 
modelled  separately  by  fleet,  and  b)  a  model  with  catches  combined,  in  both 
cases growth rates were based on published values. In both stocks the fisheries 
had  similar  properties,  one  or  more  trawl  fleets  catching  smaller,  younger 
individuals, and gillnet and or longline fisheries in different areas catching larger 
older  individuals.  In  both  cases  the  models  are  set  up  with  a  single  stock  area 
with the fisheries catching from a single population structure through a selection 
function  that  combined  availability  at age  and gear selectivity  at age.  The fitted 
selection  for  the  fleet  based  models  both  show  a  similar  effect,  a  ‘bell  shaped’ 
single  mode  selection  pattern  for  the  main  fishery  that  falls  rapidly  to  zero  at 
older ages, this is the direct consequence of the presence of the second type of 
fishery  that  catches  older  individuals  which  hardly  appear  at  all  in  the  main 
fishery.  It  is  thought  that  in  practice  this  effect  is  partially  due  to  spatial 
separation,  with  the  trawl  fleets  exploiting  areas  which  do  not  have  older 
individuals,  whereas  the  gillnet/longline  fishery  operates  in  areas  less  suited  to 
trawls.  In  modelling  terms  the  selection  pattern  in  the  fleet  based  method 
combines  functions  of  spatial  availability  and  gear  vulnerability  effects  into  fleet 
based  selections.    The  result  is  that  the  trawl  fisheries  therefore  modelled  with 
the implicit assumption that they cannot catch older individuals. When the fleets 
are  combined,  the  overall  selection  pattern  is  not  forced  to  zero,  rather  the 

37 

37 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
relative F at older ages is based on the relative proportions of the magnitude of 
the two types of fishery, the decline in F with age is less sever  and in this case 
the  selection  function  suggests  a  lower  but  still  important  fishery  at  older  ages, 
and  these  ages  are  seen  to  be  reduced  in  the  model.  For  the  fleet  based 
approach,  the  model  allows  some  individuals  to  escape  the  fishery  and  to  be 
subject only to natural mortality. The consequences for the estimates of biomass 
are considerable, in the cased of hake in GSA 17&18 the fleet based model gives 
more than 90% of the biomass from ages that are older than those seen in either 
the fishery or the survey. It was not possible to resolve the issue within the EWG, 
and  the  advice  has  been  developed  accordingly.    In  addition  to  the  selection, 
modelling differences, for hake in GSA 17-18, one model has fitted growth, which 
results  in  much  lower  linf  and  lower  mortality,  resulting  in  higher  numbers 
escaping  the  fishery.  Then  as  these  individuals  are  not  caught  they  continue  to 
grow contributing the higher biomass.  

There  is  a  need  to  understand  how  the  two  modelling  approached  work,  and 
which method is more likely to capture the underlying mortality. The implications 
for the two methods are  rather different, in fleet wise case the SSB is high and 
there  is  a  substantial  proportion  of  the  SSB  unfished  within  the  Adriatic.  In  the 
combined fleet model SSB is substantially lower and there is much less biomass 
at  older  ages.  Further  work  is  required  first  to  further  explore  the  modelling  to 
bring  out  more  clearly  the  most  important  structural  assumptions  that  result  in 
the differences, and secondly to evaluate the ability of the modelling approaches 
to give correct/erroneous results, by simulating the different underlying dynamics 
and  attempting  to  determine  which  methods  are  most  likely  to  give  the  more 
accurate results. 

4 BASIS OF THE REPORT  

The expert working group on Mediterranean stock and fisheries assessment part 
2 STECF EWG 17-15 was held Rome (Italy), 27 November to 3 December 2017. 

4.1 BASIS OF THE ADVICE 

The  summary  sheets  by  stock,  provided  in  Section  5  contain  catch  advice.  The 
basis of this advice depends on the type and quality of information available from 
the analyses and is as follows: 

1)  Full  assessment  and  full  MSY  reference  points  or  with  surplus  production 
model  with  F  and  biomass  relative  to  F  and  BMSY:  Catch  advice  at  MSY 
based on short term forecast. 

2)  Full  assessment  without  full  evaluation  MSY  reference  points  due  to  short 
time  historic  series:  Catch  advice  based  on  MSY  proxy  of  F0.1  based  on 
short term forecast. 

38 

38 

 
 
                     
 
 
 
3)  Assessment providing SSB but not suitable for STF Catch advice under MSY 
approach with Harvest Rate (HR) based estimated SSB in most recent year. 
4)  Assessment  providing  SSB  tend  information  historic  F  evaluation,  not 
suitable  for  STF  Catch  /  Effort  advice  under  precautionary    considerations 
(Patterson 1992) F=FMSY with Harvest Rate (HR) based estimated SSB in 
most recent year.- not used in this report. 

5)  For sparse data with insufficient years for VPA type analysis, but with catch 
at length or age for most of the fishery: advice is based on pseudo cohort 
analysis  at  equilibrium,  with  estimate  of  current  F  relative  to  F0.1  .-  not 
used in this report 

6)  Trend  based  indicator  with  exploitation  and  stock  status  know  to  be  OK: 
Catch  /  Effort  advice  under  precautionary  considerations  based  on  ICES 
smoothed index of trend without precautionary buffer. 

7)  Trend  based 

indictor:  Catch  /  Effort  advice  under  precautionary 
considerations  based  on  ICES  smoothed  index  of  trend  with  precautionary 
buffer (20% reduction).  

8)  Valid  length  analysis:  statement  of  stock  status,  indication  of  direction  of 

change required. .- not used in this report 

9)  No valid analysis: any advice. 

4.2 BASIS OF SHORT TERM FORECASTS 

The objective of the short term forecast is to provide the best estimate of catch 
in year Y+1 based on the assessment with final year y-1. This is then to predict 2 
years  forward  for  a  range  of  catch  options  based  on  range  of  F  options.  The  F 
option  that  corresponded  to  MSY  approach  or  precautionary  approach  (see 
section 4.1) is then presented as advice. The basis of short term forecasts  is as 
follows:- 

–  Biological conditions are assumed to be recent biological conditions 

This  is  mean  Maturity,  Natural  Mortality(M),  Fraction  M  and  F 
before spawning  from the last three years of the assessment. In 
many cases there are constant. 

•  Recruitment  - Most probable recruitment  

–  Trend  ----  Recent  recruitment  …  Arithmetic  Mean  of 

recent years … at least 3 

–  No  trend    expected    value……………….Geometric  mean  of 

series  

–  Fishery is assumed to be the same a recent fishery 

Fishery  selection  is  assumed  to  be  recent  averages  over  the  last 
three years) 

–  F in intermediate year – ---- is assumed to be F status quo 

39 

39 

 
 
 
 
–  If F is fluctuating  ( Fy-2 outside Fy-1 and Fy-3) – mean 

of 3 years 

–  F trend -  (Fy-2 between Fy-1 and Fy-3) – F last year  

4.3 EVALUATION OF REFERENCE POINTS 

FMSY 

Several  stocks  have  been  evaluated  using  surplus  production  models  which 
explicitly set the results in terms of FMSY and BMSY. For these stocks the values 
from the models give the best point estimates, though for some Greek stocks the 
data is considered too poor to give reliable estimates of reference points.  

For several demersal stocks evaluated in this assessment meeting, the number of 
years of S-R data is very limited and it is not possible to carry out full evaluations 
of  MSY,  because  the  stock-recruit  relationships  cannot  be  established.  In  this 
situation  STECF  has  recommended  the  use  of  F0.1  as  a  proxy  for  FMSY.  This 
approach has been followed for most age based assessments. 

In  the  case  of  Nephrops  in  GSA  17  &  18  which  has  a  stable  SpiCT  assessment, 
biomass reference points have been proposed, based on the procedure used for 
sardine and anchovy in GSA 17 & 18 in the STECF Plenary in 2017. Blim = 20% of 
B0 i.e. 40% of BMSY with simple surplus production model. In this case  SSB was 
just  below  Blim  and  the  ICES  MSY  rule  of  F=FMSY*B/MSYBtrigger  was  applied  with 
MSY Btrigger set to Bpa  

MSY Ranges   

The EWG has been requested to provide MSY ranges for the stocks considered by 
the EWG. The usual procedure used by ICES, where MSY ranges were developed 
would be to establish S-R functions and to evaluate the ranges using simulations 
with  recruitment  dependent  on  the  assumed  S-R  relationship,  constraining  the 
upper  range  interval  to  be  precautionary.  As  discussed  above  it  has  not  been 
possible  to  establish  such  relationships  for  most  of  these  stocks,  either  because 
the  data  series  are  too  short  or  because  the  data  series  show  environmental 
effects which mask the SSB dependent aspects.  

To  evaluate  MSY  ranges  for  stocks  in  this  report  the  EWG  uses  the  values  of  F 
associated  with  F01  as  an  FMSY  proxy  which  are  given  in  Table  2.2.  These  are 
the FMSY values from the most updated assessments carried out on Mediterranean 
stocks  assessment.    Those  values  were  then  used  in  the  formulas  provided  by 
STECF  EWG  15-06  (STECF,  2015)  to  derive  FMSY  range  (Flower  and  Fupper).  The 
empirical relationships used to estimate FMSY range are the following: 

40 

40 

 
 
 
 
 
 
 
Flower = 0.00296635 + 0.66021447 x F0.1 

Fupper = 0.007801555 + 1.349401721 x F0.1 

Where F0.1 is a proxy of FMSY. 

For one stocks with a production model, FMSY is estimated  within the model and 
the  values  of  Flower  and  Fupper  can  be  estimated  using  the  explicit  surplus 
production  relationships  Flower=  0.78FMSY,  and  Fupper=1.22  FMSY.  The  derivation  of 
these factors is provided in ICES (2014). 

Neither of these two methods add information on the precautionary nature of the 
FMSY ranges; the values of Fupper and Flower. In the case of stock based on F0.1 this 
is considered to be precautionary, and because Flow is a lower exploitation rate it 
can be safely assumed that this will also be precautionary. As the EWG is unable 
to  parameterise  stock  recruit  models and  does not  currently  have Blim  reference 
values, it has not been possible to evaluate if the Fupper values are precautionary. 
In  previous  evaluations  of  pelagic  stocks  in  then  ICES  region  (ICES  2015)  the 
EWG  notes  that  in  contrast  to  demersal  stocks  most  small  pelagic  stocks 
evaluated  (4  out  of  5)  Fupper  was  not  found  to  be  precautionary.  Given  this 
situation  and  without  explicit  evaluation  the  EWG  considers  the  values  of  Fupper 
should not be used for exploitation. 

Values of Fmsy Fupper and Flower  

The table below (Table 4.3.1) shows the information for the stocks for which FMSY 
values are available. And the estimated values of FMSY range (Flower and Fupper).The 
values  of  Flow  and  FMSY  are  regarded  as  reasonable  estimates  that  can  be 
expected to be precautionary and thus may be used directly. The values for Fupper 
are  indicative  only;  they  have  not  been  evaluated  as  precautionary  and  should 
not be used as such without further evaluation.  

41 

41 

 
 
 
 
 
 
 
Table  4.3.1  FMSY  ranges  (Flow  and  Fupp)  for  demersal  stocks  from  the  Mediterranean. 
The  values  for  Fupp  are  indicative  only  they  have  not  been  evaluated  as  precautionary 
and should not be used as such without further evaluation. 

GSA 

Species 

Fcurr 

F msy 

Flow 

Fupp 

GSA 17-18 

Hake 

0.38 
0.69 

0.19 
0.18 

0.13 
0.12 

0.26 
0.25 

Fcurr/ 

FMSY 

2.00 
3.83 

GSA 17-18 

Norway lobster 

0.49 

0.21 

0.14 

0.29 

2.33 

GSA 17-18-19  Deep-water rose shrimp 

1.44 

0.7 

0.47 

0.95 

2.06 

GSA 17 

Sole 

0.41 

0.26 

0.17 

0.36 

1.58 

0.99 

0.23 

0.51 

0.26 

GSA 17-18 

Spottail mantis shrimp 

0.65 

0.38 

GSA 19 
GSA 19 

Hake 
Red mullet 

1.42 
0.56 

0.16 
0.36 

0.15 

0.17 

0.25 

0.11 
0.24 

0.32 

4.30 

0.36 

1.96 

0.52 

1.71 

0.22 
0.49 

8.88 
1.56 

5 SUMMARY SHEETS BY STOCK 

ToR 9. To provide a synoptic overview of: (i) the fishery; (ii) the most recent state 
of the stock (spawning stock biomass, stock biomass, recruits, and exploitation level 
by  fishing  gear);  (iii)  the  source  of  data  and  methods  and;  (iv)  the  management 
advice, including MSY value, range of values and conservation reference points.  

5.1 SUMMARY SHEET OF HAKE IN GSA 17 AND 18 

Species common name: European hake 

Species scientific name: Merluccius merluccius 

Geographical Sub-area(s) GSA(s): 17 and 18 

The EWG was not able to reach a conclusion regarding the assessment of hake in 17-
GSA 18, two assessments based on different fleet assumptions for the catch have 
been proposed, SS3 treats fleets separately, and a4a combines the fisheries into a 
single fleet.   The two methods give relatively similar perceptions of the fishery in 

42 

42 

 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
terms of stock status F/Fmsy but very different perceptions of biomass and smaller 
but still important differences in terms of catch advice.   

5.1.1.1 STOCK DEVELOPMENT OVER TIME (SS3) 

State of absolute and relative biomass 

The  SSB  showed  a  continuous  and  steep  decrease  over  the  time  series,  the  estimated 
SSB at the beginning of the time series was equal to 218,014 tonnes whereas in 2016 it 
reached the lowest value of 52,957 tonnes. 

State of the juveniles (recruits) 

The  recruitment  shows  a  fluctuating  trend  over  the  time  series  with  a  peak  in  2004 
(332,735  thousands  of  individuals)  and  in  2011  (188,380  thousands).  The  mean  value 
along  the  time  series  is  132,600  thousand  and  the  value  for  the  2016  is  117,588 
thousand. 

State of exploitation 

The  current  Fbar1-6  (0.38)  is  larger  than  the  FMSY  value  (0.19),  which  indicates  that  the 
stock of European hake in GSAs 17 and 18 is being fished above FMSY. 

Table 5.1.1.1.1 European hake in GSAs 17-18. Results from the SS3 model. 

Year 

Total Biomass  SSB 

Recruits  Fbar 

Catch 

1998  227172 

218014  70395 

0.19 

9114 

1999  217416 

209976  114441 

0.16 

6532 

2000  208196 

202073  96969 

0.18 

6124 

2001  197972 

192260  188597 

0.20 

6080 

2002  186979 

180761  73501 

0.21 

5824 

2003  176539 

169353  200876 

0.28 

7296 

2004  164581 

157088  332735 

0.31 

7525 

2005  153211 

145437  173048 

0.37 

8863 

2006  142567 

132426  128524 

0.41 

10943 

2007  130785 

120367  181350 

0.33 

9083 

2008  120771 

112117  61277 

0.32 

8540 

2009  111363 

103996  90901 

0.33 

8126 

43 

43 

 
 
 
 
 
2010  101796 

95616  93954 

0.32 

7022 

2011  92825 

88099  188380 

0.37 

6639 

2012  84137 

79732  98846 

0.41 

6480 

2013  76272 

71176  105489 

0.45 

6334 

2014  68892 

63687  89937 

0.36 

5102 

2015  63058 

58314  112590 

0.38 

5306 

2016  57346 

52957  117588 

0.38 

5144 

Figure  5.1.1.1.1  European  hake  GSAs  17-18.  Results  from  the  SS  model:    total 
biomass and spawning stock biomass in tonnes and recruitment in thousands. 

Figure 5.1.1.1.2 European hake GSAs 17-18. Results from the SS model: total fishing 
mortality (Fbar1-6) on the left and fishing mortality by fleet on the right 

5.1.1.2 STOCK ADVICE (SS3) 

44 

44 

 
 
 
 
 
 
 
STECF  EWG  17-15  advises  that  when  MSY  considerations  are  applied  the  fishing 
mortality in 2018 should be reduced most to FMSY=0.19 this implies catches of no more 
than 2875 tons.   

5.1.1.3 BASIS OF THE ASSESSMENT (SS3) 

Stock Synthesis 3 (SS3; Method and Wetzel, 2013) provides a statistical framework for 
the  calibration  of  a  population  dynamics  model  using  fishery  and  survey  data.  It  is 
designed  to  accommodate  both  population  age  and  size  structure  data  and  multiple 
stock  sub-areas  can  be  analysed.  It  uses  forward  projection  of  population  as  in  the 
“statistical  catch-at-age”  (hereafter  SCAA)  approach.  SCAA  estimates  initial  abundance 
at  age,  recruitments,  fishing  mortality  and  selectivity.  The  overall  model  contains 
subcomponents  which  simulate  the  population  dynamics  of  the  stock  and  fisheries, 
derive the expected values for the various observed data, and quantify the magnitude of 
difference  between  observed  and  expected  data.  Some  SS3  features  include  ageing 
error,  growth  estimation,  spawner-recruitment  relationship,  movement  between  areas. 
The ADMB C++ software in which SS is written searches for the set of parameter values 
that  maximize  the  goodness-of-fit,  then  calculates  the  variance  of  these  parameters 
using  inverse  Hessian  methods.  The  F  at  age  has  been  estimated  from  the  Z  at  age 
estimated  by  the  model  (subtracting  M  at  age  used  in  input);  then,  the  Fbar  has  been 
estimated as average of the selected ages. 

This  assessment  was  carried  out  using  directly  the  length-based  data  thus  the 
abundance at age for each fleet is included by length. Using the growth function, 
which is estimated in the model (in the base case), the model is able to estimate 
abundance  and  biomass  by  length  and  age  classes;  these  are  then  fitted  to  the 
observed  catches  at  length.  Thus,  the  results  are  showed  both  by  age  and  length 
structure. Selectivity by fleet has been generated applied at age but can be expressed as 
length-specific. Fbar was calculated considering age from 1 to 6. The SS3 analyses have 
been carried out considering the following ten fleets: 7 fishing fleets and 3 surveys. 

5.1.1.4 CATCH OPTIONS (SS3) 

Short term prediction for the period 2017 - 2019 was performed using the FLR routines 
provided  by  JRC  and  based  on  the  results  of  the  stock  assessments  performed  during 
EWG 17-15. Table 5.1.1.4.1 shows the resulting short term forecasts obtained using the 
input and the results of the SS3 stock assessment model 

Table  5.1.1.4.1  European  hake  in  GSAs  17-18.  Short  term  forecasts  showing  catch 
options for different fishing mortalities reductions – SS3 model. 

Rationale 

Ffactor 

Fbar 

Catch 
2016 

Catch 
2017 

Catch 
2018 

Catch 
2019 

SSB 2018  SSB 2019  Change 

SSB 
2018-
2019 
(%) 

Change 
Catch 
2016-
2018 
(%) 

Zero catch 

0 

0.000 

5132.71 

5121.27 

0.00 

0.00 

48340.83 

51493.46 

6.52 

-100.00 

F0.1 

0.504093 

0.191 

5132.71 

5121.27 

2874.54 

3546.94 

48340.83 

47983.55 

-0.74 

-44.00 

Status quo 

1 

0.380 

5132.71 

5121.27 

5223.45 

5380.91 

48340.83 

45123.11 

-6.66 

1.77 

45 

45 

 
 
 
 
Different 
Scenarios 

0.1 

0.2 

0.3 

0.4 

0.5 

0.6 

0.7 

0.8 

0.9 

1.1 

1.2 

1.3 

1.4 

1.5 

1.6 

1.7 

1.8 

1.9 

2 

0.038 

5132.71 

5121.27 

614.49 

883.63 

48340.83 

50742.38 

4.97 

-88.03 

0.076 

5132.71 

5121.27 

1206.12 

1669.14 

48340.83 

50019.61 

3.47 

-76.50 

0.114 

5132.71 

5121.27 

1775.87 

2365.86 

48340.83 

49323.95 

2.03 

-65.40 

0.152 

5132.71 

5121.27 

2324.64 

2982.28 

48340.83 

48654.26 

0.65 

-54.71 

0.190 

5132.71 

5121.27 

2853.32 

3526.12 

48340.83 

48009.43 

-0.69 

-44.41 

0.228 

5132.71 

5121.27 

3362.75 

4004.40 

48340.83 

47388.42 

-1.97 

-34.48 

0.266 

5132.71 

5121.27 

3853.72 

4423.48 

48340.83 

46790.21 

-3.21 

-24.92 

0.304 

5132.71 

5121.27 

4327.01 

4789.17 

48340.83 

46213.86 

-4.40 

-15.70 

0.342 

5132.71 

5121.27 

4783.35 

5106.72 

48340.83 

45658.45 

-5.55 

-6.81 

0.418 

5132.71 

5121.27 

5647.97 

5616.09 

48340.83 

44606.98 

-7.72 

10.04 

0.456 

5132.71 

5121.27 

6057.55 

5816.19 

48340.83 

44109.28 

-8.75 

18.02 

0.493 

5132.71 

5121.27 

6452.80 

5984.78 

48340.83 

43629.24 

-9.75 

25.72 

0.531 

5132.71 

5121.27 

6834.33 

6125.12 

48340.83 

43166.13 

-10.70 

33.15 

0.569 

5132.71 

5121.27 

7202.68 

6240.13 

48340.83 

42719.24 

-11.63 

40.33 

0.607 

5132.71 

5121.27 

7558.39 

6332.50 

48340.83 

42287.92 

-12.52 

47.26 

0.645 

5132.71 

5121.27 

7901.98 

6404.64 

48340.83 

41871.51 

-13.38 

53.95 

0.683 

5132.71 

5121.27 

8233.94 

6458.73 

48340.83 

41469.41 

-14.21 

60.42 

0.721 

5132.71 

5121.27 

8554.74 

6496.77 

48340.83 

41081.03 

-15.02 

66.67 

0.759 

5132.71 

5121.27 

8864.82 

6520.54 

48340.83 

40705.82 

-15.79 

72.71 

5.1.1.5 REFERENCE POINTS (SS3) 

Table  5.1.1.5.1  European  hake  in  GSAs  17-18.  Main  reference  points  defined  by  the 
Yield per recruit analysis - SS3 model. 

Framework 

MSY 
approach 

Value 

Technical basis 

Source 

Reference 
point 

MSY Btrigger 

FMSY 

0.19 

F0.1 

Present 
assessment 
(SS3) 

Precautionary  Blim 

46 

46 

 
 
 
 
 
 
 
 
 
 
Approach 

Bpa 

Bpa 

5.1.2.1 STOCK DEVELOPMENT OVER TIME (A4A) 

State of absolute and relative biomass 

The  SSB  showed  a  quite  stable  trend;  the  estimated  SSB  for  year  2009  is  equal  at 
11,604 tonnes, whereas in 2016 accounted for the 10,446 tonnes. The mean value over 
the time series (2009-2016) corresponds to 10,447 tonnes. 

State of the juveniles (recruits) 

The  recruitment  showed  a  slight  decrease  over  the  time  series:  the  number  or  recruits 
estimated in 2009 are 159,179 thousand, whereas in 2016 recruits are equal at 116,974 
thousand.  The  mean  value  over  the  time  series  (2009-2016)  corresponds  to  132,487 
tonnes. 

State of exploitation 

The  current  Fbar1-4  (0.69)  is  larger  than  the  FMSY  value  (0.18),  which  indicates  that  the 
stock of European hake in GSAs 17 and 18 is being fished above FMSY. 

Table  5.1.2.1.2.1  European  hake  in  GSAs  17-18.  a4a  summary  results:  Fbar1-4, 
recruitment, SSB and catch. 

Year 

Fbar1-4 

Recruitment 

SSB 

Catch 

(thousands) 

(tonnes) 

(tonnes) 

2009  0.93244  159179 

11604 

8234.3 

2010  0.8595 

140350 

10392 

6621.9 

2011  0.81288  141252 

10038 

6108 

2012  0.79604  138520 

10359 

6117.7 

2013  0.79315  123544 

10503 

6014.6 

2014  0.77977  118432 

10197 

5691.6 

2015  0.74266  121642 

10040 

5420.8 

2016  0.69048  116974 

10446 

5267.7 

47 

47 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 5.1.2.1.2.1 European hake in GSAs 17-18. Stock summary of the simulated and 
fitted data for the a4a model. 

5.1.2.2 STOCK ADVICE (A4A) 

STECF  EWG  17-15  advises  that  when  MSY  considerations  are  applied  the  fishing 
mortality in 2018 should be reduced most to FMSY=0.18 this implies catches of no more 
than 2042 tons.   

5.1.2.3 BASIS OF THE ASSESSMENT (A4A) 

a4a  is  a  statistical  catch-at-age  method  that  utilizes  catch-at-age  data  to  derive 
estimates of historical population size and fishing mortality (Jardim et al., 2015). Model 
parameters  estimated  using  catch-at-age  analysis  are  done  so  by  working  forward  in 
time  and  analyses  do  not  require  the  assumption  that  removals  from  the  fishery  are 
known  without  error.  Data  typically  used  are:  catch,  statistical  sample  of  age 
composition of catch and abundance index.  

Specifically,  for  the  European  hake  in  GSAs  17  and  18  the  a4a  model  was  carried  out 
considering a time series from 2009 to 2016, since for some fleets no reliable LFDs were 
available  before  2009.  The  same  time  series  was  considered  for  the  three  tuning 
indexes: 1) Italian Medits GSA 17, 2) Croatian Medits GSA 17 and 3) Medits GSA 18. The 
data was organized considering a plus group of age 5+. Catch data (landings + discard if 
available) are included in the model. Fbar was calculated considering age from 1 to 4. 

48 

48 

 
 
 
 
 
 
 
5.1.2.4 CATCH OPTIONS (A4A) 

Short term prediction for the period 2017 - 2019 was performed using the FLR routines 
provided  by  JRC  and  based  on  the  results  of  the  stock  assessments  performed  during 
EWG 17-15. Table 5.1.2.4.1 shows the resulting short term forecasts obtained using the 
input and the results of the a4a stock assessment model 

Table  5.1.2.4.1  European  hake  in  GSAs  17-18.  Short  term  forecasts  showing  catch 
options for different fishing mortalities reductions – a4a model. 

Rationale 

Ffactor 

Fbar 

Catch 2017  Catch 2018  Catch 2019 

SSB 2018 

SSB 2019 

Change SSB 
2018-2019 
(%) 

Change 
Catch 2016-
2018 (%) 

Zero catch 

F0.1 

Status quo 

Different 
Scenarios 

0 

0.1 

1 

0.2 

0.3 

0.4 

0.5 

0.6 

0.7 

0.8 

0.9 

1.1 

1.2 

1.3 

1.4 

1.5 

1.6 

1.7 

1.8 

1.9 

2 

0.000 

5682.29 

0.00 

0.00 

11949.02 

24145.49 

102.10 

-100.00 

0.181 

5682.29 

2041.73 

3371.98 

11949.02 

20191.43 

69.00 

-61.20 

0.690 

5682.29 

6044.75 

6264.99 

11949.02 

12605.26 

5.50 

14.80 

0.069 

5682.29 

829.32 

1534.12 

11949.02 

22534.51 

88.60 

-84.30 

0.138 

5682.29 

1596.99 

2752.83 

11949.02 

21049.12 

76.20 

-69.70 

0.207 

5682.29 

2308.07 

3712.72 

11949.02 

19678.88 

64.70 

-56.20 

0.276 

5682.29 

2967.21 

4460.71 

11949.02 

18414.26 

54.10 

-43.70 

0.345 

5682.29 

3578.62 

5035.68 

11949.02 

17246.56 

44.30 

-32.10 

0.414 

5682.29 

4146.21 

5469.81 

11949.02 

16167.79 

35.30 

-21.30 

0.483 

5682.29 

4673.50 

5789.69 

11949.02 

15170.67 

27.00 

-11.30 

0.552 

5682.29 

5163.75 

6017.30 

11949.02 

14248.53 

19.20 

-2.00 

0.621 

5682.29 

5619.93 

6170.76 

11949.02 

13395.25 

12.10 

6.70 

0.760 

5682.29 

6440.71 

6312.23 

11949.02 

11873.43 

-0.60 

22.30 

0.829 

5682.29 

6810.08 

6322.54 

11949.02 

11195.09 

-6.30 

29.30 

0.898 

5682.29 

7154.96 

6304.14 

11949.02 

10565.94 

-11.60 

35.80 

0.967 

5682.29 

7477.25 

6263.73 

11949.02 

9982.05 

-16.50 

41.90 

1.036 

5682.29 

7778.72 

6206.78 

11949.02 

9439.82 

-21.00 

47.70 

1.105 

5682.29 

8060.97 

6137.68 

11949.02 

8935.95 

-25.20 

53.00 

1.174 

5682.29 

8325.48 

6060.00 

11949.02 

8467.41 

-29.10 

58.00 

1.243 

5682.29 

8573.60 

5976.59 

11949.02 

8031.42 

-32.80 

62.80 

1.312 

5682.29 

8806.57 

5889.72 

11949.02 

7625.45 

-36.20 

67.20 

5.1.2.5 REFERENCE POINTS (A4A) 

49 

49 

 
 
 
 
 
  
 
 
Table  5.1.2.5.1  European  hake  in  GSAs  17-18.  Main  reference  points  defined  by  the 
Yield per recruit analysis – a4a model. 

Framework 

MSY 
approach 

Value 

Technical basis 

Source 

Reference 
point 

MSY Btrigger 

FMSY 

0.18 

F0.1 

Present 
assessment 
(a4a) 

Precautionary 
Approach 

Blim 

Bpa 

5.1.3 DATA DEFICIENCIES   

The  differences  between  the  two  stock  assessment  models,  SS3  and  a4a,  are 
summarised in section 6.1.3.2. 

Regarding  the  data,  thanks  to  the  collaboration  developed  in  the  FAO  framework  (i.e. 
AdriaMed, GFCM) among all the countries and the scientists involved in the sector, it was 
possible  to  use  also  data  from  non-EU  countries  (Montenegro  and  Albania)  and  data 
collected before the accession of some countries to the EU (Croatia). However, Albanian 
catch data for the years 2015 and 2016 are not provided and are assumed equal to the 
value for 2014, since they did not consign information for these years. 

Catch data and their LFDs include landings and discards coming from the DCF samples; 
however, discard is included only in the years and for the fleets in which estimates are 
available. Future elaborations need a harmonization of this point. 

The Medits data for the Italian GSA 17 used in the stock assessment models cover Italy 
and Slovenia; however, for some years only Italian data are included in the assessment. 
This  inaccuracy  has  negligible  effect  since  only  two  hauls  are  foreseen  in  Slovenia 
waters; also hake is not present in this area. However, for the future elaborations it will 
be useful to harmonize also this point. 

50 

50 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5.2 SUMMARY SHEET OF RED MULLET IN GSA 17 AND 18 

Species common name: Red mullet 

Species scientific name: Mullus barbatus 

Geographical Sub-area(s) GSA(s): 17 and 18 

5.2.1 STOCK DEVELOPMENT OVER TIME 

The  change  in  the  estimated  biomass  over  the  last  five  years  was  used  to  provide  an 
index for change (Figure 5.2.1.1). This index is much higher than 1.2 (=4.6). 

Figure 5.2.1.1 Biomass index on the SSB estimated by a4a model. In green the mean 
of  the  last  two  years  compared  to  the  previous  three  years  available  (red,  2056-2016; 
green 2012-2014). 

Figure 5.2.1.2 Landings and discard by year, gear and country.  

51 

51 

050001000015000200002500030000350004000020062007200820092010201120122013201420152016tonsBiomass index method on a4a SSBSSBaverage last 2 yearsaverage previousthree years0500100015002000250030003500tonsLanding by gear and countryItaly 18 OTBItaly 18 GENMontenegro OTBMontenegro NetsAlbaniaCroatia OTBItaly 17 OTB0100200300400500600700800900100020022003200420052006200720082009201020112012tonsDiscardby gear and countryItaly 18 OTBCroatia OTBItaly 17 OTB 
 
 
 
 
 
 
 
 
 
 
Table 5.2.1.1 Red mullet in GSA 17 &18 Landing and discards by fleet. 

Year 

2002 

2003 

2004 

2005 

2006 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

Italy 18 
OTB 

Italy 18 
GEN 

Montenegro 
OTB 

Montenegro 
Nets 

Albania 

Croatia OTB 

Italy 17 
OTB 

Land 

3114 

1750 

1981 

1350 

1803 

1680 

914 

955 

601 

494 

2089 

1203 

1250 

1572 

1398 

Dis 

Land 

Dis 

Land 

Land 

Dis 

Land 

Land 

Dis 

Land 

Dis 

90 

312 

82 

99 

130 

123 

47 

77 

45 

38 

8 

47 

23 

15 

50 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

58 

54 

30 

14 

34 

13 

434 

18 

120 

89 

87 

3.7 

3.6 

3.4 

3.2 

3.5 

3.1 

4 

3.6 

3.6* 

0 

0 

0 

0 

0 

0 

0 

0 

0 

171 

149 

154 

90 

110 

280 

247 

147 

171 

171* 

38 

36 

35 

32 

35 

32 

41 

36 

36 

3101 

3298 

3158 

2433 

1796 

1823 

1464 

1946 

2324 

2143 

2037 

786 

836 

800 

617 

183 

803 

325 

291 

446 

910 

499 

767 

818 

763 

1086 

1248 

1086 

1158 

1127 

951 

207 

220 

206 

293 

336 

162 

453 

300 

158 

State of absolute and relative biomass 

Several methods were examined and all the methods applied detected an increasing 
trend in SSB in the recent years. 

State of the juveniles (recruits) 

All the methods applied detected an increasing trend in recruitment in the recent years, 
in line with the density and MEDITS biomass indices. 

State of exploitation 

The  state  of  exploitation  is  uncertain.  Based  on  size  distribution  in  the  landings  and 
MEDITS  survey,  and  indications  from  the  assessments  carried  out,  the  stock  is  not 
thought to be over exploited. 

5.2.2 STOCK ADVICE 

Based on precautionary considerations, STECF EWG 17-15 advises to not increase the 
total catch more than the 20% of the 2016 catch equivalent to catches of no more than 
7706 tons in each of 2018 and 2019 implemented either through catch restrictions or 
effort reduction for the relevant fleets 

5.2.3 BASIS OF THE ASSESSMENT  

Assessment  was  performed  using  2006-2016  DCF  data  (biomass  landed  and  age 
composition of the catches) and GFCM WGSAD official report data. Three methods were 
applied: SS3, a4a and XSA. All the methods estimated an overall decreasing trend in F 

52 

52 

 
  
  
  
  
  
  
  
  
  
  
 
  
  
  
  
  
  
  
  
  
 
  
  
  
  
  
  
  
  
  
 
  
  
  
  
  
  
  
  
  
 
  
  
  
  
  
  
  
  
  
  
  
  
 
 
and an increasing trend in SSB and recruitment. In particular, in the last three years a4a 
and SS3 estimated a decrease in the F, while the XSA detect a constant trend. The SS3 
assessment  showed  considerable  retrospective  instability  and  upward  revision  in  F  and 
downward revision of SSB.  

The a4a assessment was retained, because the retrospective and the residuals appeared 
the  most  stable  of  the    three  methods.  However,  due  to  the  model  uncertainty  the 
results were applied as an index rather than as and full assessment. 

However, being the last year SSB estimated by a4a about three times the SSB in 2014, 
the  experts  preferred  to  give  the  advice  with  the  biomass  index  method  applied  to  the 
biomass estimated by the assessment, rather than use the F current, that seemed very 
low in the last year. 

5.2.4 CATCH OPTIONS  

Based the ICES approach using a change in a biomass index over the last 5 years using 
the a4a assessment as a relative index of biomass an which shows an increase of 4.6 
times. Based on the mean catch of last three years (6422 t) the catches should not be 
increased by more than 20% equivalent to catches of no more than 7706 tons in each of 
2018 and 2019. As F is estimated to be below F0.1 in both of the last two years a 
precautionary buffer is not applied. 

53 

53 

 
 
 
 
 
 
5.2.5 REFERENCE POINTS  

Reference points were not established for this stock. 

5.2.6 DATA DEFICIENCIES   

The data used for the analyses come from the last DCF official data call (2017). The data 
related  to  non-EU  countries  (Albania  and  Montenegro)  was  taken  by  the  GFCM  WGSAD 
official report.  

Some deficiencies have been detected and the detailed list is reported in section 7 (Data 
quality and deficiencies by stock). 

54 

54 

 
 
 
5.3 SUMMARY SHEET OF NORWAY LOBSTER IN GSA 17 AND 18 

Species common name: Norway lobster 

Species scientific name: Nephrops norvegicus 

Geographical Sub-area(s) GSA(s): 17 and 18 

5.3.1 STOCK DEVELOPMENT OVER TIME 

State of absolute and relative biomass 

The  stock  assessment  shows  that  the  relative  biomass  (B/BMSY)  is  continuously 
decreasing since the 1960s, remaining below Bmsy (6616 t) in the last ten years (B=2119 
t  in  2016;  B2016/BMSY  =  0.33;  Figure  5.3.1.  The  stock  biomass  is  considered  to  be 
depleted (B<<BMSYs). 

State of the juveniles (recruits) 

The recruitment has not been evaluated 

State of exploitation 

The fishing mortality has increasing since mid ‘80s with F estimated to be above FMSYs =0.37 
in  the  last  ten  years  (F2016/FMSY=  1.38).  The  stock  is  considered  to  be  over  exploited 
F>FMSY.  

55 

55 

 
 
 
 
 
 
 
 
Figure 5.3.1.1 Norway lobster in GSAs 17-18. Final SPiCT model Absolute and relative 
Biomass  and  Fishing  mortality  and  catch  from  1960  to  2016.  State  of  the  stock  F/FMSY 
and  SSB/BMSY  and  relative  to  estimated  production.  Short  term  predictions  (B  rel  BMSY) 
based on average productivity are provided for exploitation at 0, .75, 0.95 and 1.0 FMSY    

Table  5.3.1.1  Norway  lobster  in  GSAs  17-18.  Final  SPiCT  model  Biomass  and  Fishing 
mortality  and  catch  from  1960  to  2016.    F/FMSY  and  B/BMSY  are  based  on  reference 
point (Table 5.3.5.1) 

year 

F 

1970 
1971 
1972 
1973 
1974 
1975 
1976 
1977 
1978 
1979 
1980 
1981 
1982 
1983 
1984 
1985 
1986 
1987 
1988 
1989 
1990 
1991 
1992 
1993 
1994 
1995 
1996 
1997 
1998 
1999 
2000 
2001 
2002 
2003 
2004 
2005 
2006 
2007 

Catch 

F/FMSY 

B/BMSY 

1274 
1296 
1351 
1140 
1106 
1216 
1553 
1965 
1507 
1277 
1139 
1202 
1365 
1277 
1305 
2017 
2295 
2186 
2660 
2194 
1964 
2489 
3068 
3144 
3304 
3238 
3454 
3043 
2435 
1831 
1800 
1541 
1363 
2089 
2392 
3285 
3354 
2950 

0.31 
0.31 
0.32 
0.29 
0.29 
0.30 
0.34 
0.41 
0.40 
0.39 
0.37 
0.38 
0.41 
0.39 
0.39 
0.50 
0.57 
0.59 
0.68 
0.66 
0.65 
0.74 
0.86 
0.93 
1.01 
1.08 
1.25 
1.31 
1.29 
1.23 
1.27 
1.14 
0.97 
1.10 
1.16 
1.42 
1.65 
1.69 

1.69 
1.70 
1.74 
1.59 
1.57 
1.65 
1.84 
1.95 
1.54 
1.35 
1.25 
1.29 
1.37 
1.33 
1.35 
1.65 
1.66 
1.51 
1.60 
1.36 
1.24 
1.37 
1.46 
1.38 
1.34 
1.23 
1.14 
0.95 
0.77 
0.61 
0.58 
0.55 
0.57 
0.78 
0.84 
0.95 
0.83 
0.71 

0.11 
0.12 
0.12 
0.11 
0.11 
0.11 
0.13 
0.15 
0.15 
0.14 
0.14 
0.14 
0.15 
0.14 
0.15 
0.18 
0.21 
0.22 
0.25 
0.24 
0.24 
0.27 
0.32 
0.34 
0.37 
0.40 
0.46 
0.49 
0.48 
0.46 
0.47 
0.42 
0.36 
0.41 
0.43 
0.52 
0.61 
0.62 

Biomass 
11169 
11259 
11520 
10506 
10390 
10928 
12182 
12900 
10189 
8903 
8284 
8525 
9073 
8810 
8959 
10937 
10959 
10020 
10553 
8976 
8208 
9063 
9627 
9150 
8853 
8124 
7510 
6271 
5107 
4013 
3844 
3661 
3785 
5133 
5547 
6264 
5503 
4723 

56 

56 

 
 
2008 
2009 
2010 
2011 
2012 
2013 
2014 
2015 
2016 

0.60 
0.68 
0.77 
0.77 
0.70 
0.70 
0.63 
0.55 
0.49 

4412 
4214 
3356 
2524 
2237 
2402 
2164 
2099 
2101 

2657 
2869 
2582 
1945 
1562 
1670 
1355 
1158 
1022 

1.63 
1.84 
2.08 
2.08 
1.89 
1.88 
1.69 
1.49 
1.31 

0.67 
0.64 
0.51 
0.38 
0.34 
0.36 
0.33 
0.32 
0.32 

5.3.2 STOCK ADVICE 

STECF  EWG  17-15  advises  that  when  MSY  considerations  are  applied  the  fishing 
mortality  in  2018  should  be  reduced  most  to  43%  of  fishing  mortality  in  2016  this 
implies catches of no more than 441 tons.   

5.3.3 BASIS OF THE ASSESSMENT  

The assessment is based on SPiCT model (Stock Surplus Production model in Continuous 
Time) using the following data sources: landings in GSA 17-18: 1) Italian landings time 
series from National Institute for Statistics (ISTAT) for the period 1961-2000 for GSA 17 
and 18; 2) GFCM landings for the period 1970-2014 for Croatia, Montenegro and Albania 
extracted  from  FAO  database;  3)  DCF  landings  from  the  2015  DG  MARE  Data  Call, 
covering the period 2002-2016 for GSA 17 and 18 (ITA) and GSA 17 (HRV).  

Four tuning indices were used in the model: 1) CPUE (kg/Fishing day) from Jukic (1975) 
in the East part of Pomo Pit (Blitvenica fishing grounds); 2) Central Adriatic CPUE’s from 
Froglia  &  Gramitto  (1988)  in  the  fishing  grounds  offshore  Ancona  (Western  Central 
Adriatic);  3)  MEDITS  in  GSA  17-18  1995-2001;  4)  MEDITS  in  GSA  17-18  2002-2016, 
details on the CPUEs are in Section 6.11. 

5.3.4 CATCH OPTIONS  

The  short  term  forecast  based  on  the  SPiCT  model  appears  optimistic  (Section 
6.3.5); maintaining status quo F implies annual increases in SSB of 12 and 15% to 
start of 2018. Given that SSB has been relatively stable over the last 5 or 6 years at 
only  slightly  higher  F,  the  assumptions  of  SPiCT  which  assumes  that  average  stock 
growth  will  occur  this  year  does  not  seem  reasonable.  Therefore  the  catch 
predictions are based on a harvest rate relative to estimated F and SSB in 2016. This 
assumes that the stock will not increase at status quo F (0.49) before 2018, which is 
considered  a  reasonable  assumption.  As  the  current  SSB  is  well  below  MSYBtrigger 
=3705t (based on Bpa) the target F should be reduced from FMSY (0.37) in proportion 
to  B/MSYBtrigger an  F  target  of  0.21,  giving  a  catch  of  441t.  The  text  table  below 
provides the basis of the calculation.  

2016 
FMSY 
MYS Rule 

SSB 
2101 

F 
0.49 
0.37 
0.21 

Catch 
1021 
777 
441 

57 

57 

 
 
 
  
 
 
 
 
5.3.5 REFERENCE POINTS  

Table  5.3.5.1  Norway  lobster  in  GSAs  17  and  18.  Reference  points,  values,  and  their 
technical basis. 

Framework 

Reference 
point 

Value 

Technical basis 

Source 

MSY Btrigger 

3705 

Bpa 

This Report 

MSY 
approach 

Precautionary 
Approach 

FMSYd 

BMSYd 

Blim 

Bpa 

0.37 

SPiCT deterministic model 
estimates 

This Report 

6616  

SPiCT deterministic model 
estimates 

This Report 

2646  

40% of BMSY (20% B0) 

This Report 

3705 

1.4*Blim 

This report 

Biomass reference points are estimated or this stock based on Blim=40% of BMSY, 
equivalent to 20% of B0 for the Schaefer model and Bpa = 1.4*Blim. This is in accordance 
with STECF procedure applied to sardine and anchovy in GAS 17-18.  

5.3.6 DATA DEFICIENCIES 

EU DCF landings data prior to 2006 were not available for GSA 17 ITA. Data from Croatia 
(GSA 17) were available for 2013-2016. Discards data in GSA 17 ITA were available only 
for 2011. 

58 

58 

 
  
 
 
 
 
 
 
 
 
 
5.4 SUMMARY SHEET OF COMMON PANDORA IN GSA 17 AND 18 

Species common name: Common pandora 

Species scientific name: Pagellus erythrinus 

Geographical Sub-area(s) GSA(s): 17 and 18 

5.4.1 STOCK DEVELOPMENT OVER TIME 

Following the ICES approach on data limited stocks recent stock trends are inferred 
from a bottom trawl biomass index (Fig 5.4.1.1). 

Figure  5.4.1.1.  Common  pandora  in  GSAs  17  and  18.  Biomass  index  estimated 
from  MEDITS  survey.  In  red  the  mean  of  the  last  two  years  compared  to  the 
previous three years green). 

State of absolute and relative biomass 

Not known. 

State of the juveniles (recruits) 

Not known. 

State of exploitation 

Landings  are  given  in  Figure  5.4.1.2  but  the  exploitation  rates  implied  by  these 
landings are not known. 

59 

59 

 
 
 
 
 
 
 
 
Figure  5.4.1.2.  Common  pandora  in  GSAs  17  and  18  landings  (tonnes;  Italy, 
Slovenia & Croatia). 

Table 5.4.1.1.  Common pandora in GSAs 17 and 18. Total landings (tonnes; Italy, 
Slovenia & Croatia). 

5.4.2 STOCK ADVICE 

STECF  EWG  17-15  advises  that  when  precautionary  considerations  are  applied 
catches should be no more than 222 t in each of 2018 and 2019 implemented either 
through catch restrictions or effort reduction for the relevant fleets. 

5.4.3 BASIS OF THE ASSESSMENT  

Data from MEDITS bottom trawl survey biomass indices for 2002-2016. 

60 

60 

05010015020025030035020062007200820092010201120122013201420152016tonnesCommon pandora GSA17+18GSA17+182006134.4200764.6200834.72009110.02010242.12011180.92012152.52013135.52014216.32015307.72016232.4 
 
 
 
 
 
 
 
5.4.4 CATCH OPTIONS  

Following the ICES procedures for data limited stocks; the change in biomass over 
the five last years was used to provide an index of change (1.1, Figure 5.4.1.1). As 
this index is less than 1.2 and more than 0.8, the value is used to multiply the catch 
to provide an initial advice. The exploitation rate is unknown though indications from 
the length analysis suggest exploitation above MSY, and the state of the stock 
relative to Bmsy is unknown. Therefore a precautionary buffer (0.8) is applied. The 
resulting landings advice taken from the average of the last three years (252 t) is 
222 t. 

5.4.5 REFERENCE POINTS  

Reference points are not defined for this stock. 

5.4.6 DATA DEFICIENCIES   

DCF  data  on  common  pandora  in  GSAs  17  and  18  do  not  include  information  from 
the eastern Adriatic regarding landings, discards and size and age structures. 

Length frequency distributions are reported by Italy for GSA 18 for the period 2009-
2016, but no data for 2015, and for GSA 17, for the period 2011-2016. 

The landings age structure is reported by Italy for GSA 18 for the period 2009- 2016, 
but no data is available for 2015. 

More details can be found in section 6.4.3.4. 

61 

61 

 
 
 
5.5 SUMMARY SHEET OF DEEP-WATER ROSE SHRIMP IN GSA 17, 18 & 19 

Species common name: Deep-water rose shrimp 

Species scientific name: Parapenaeus longirostris 

Geographical Sub-area(s) GSA(s): 17,18 and 19 

5.5.1 STOCK DEVELOPMENT OVER TIME 

State of absolute and relative biomass 

The SSB showed an increase over time and it is estimated at about 3266 t in 2016, 
well above the average along the time series equal to 1171 t. 

State of the juveniles (recruits) 

The  recruitment  showed  a  big  increase  in  the  last  years  of  the  time-series.  The 
estimated  recruitment  for  2016  is  5418527  thousand  individuals,  well  above  the 
average along the time series equal to 1631492 thousand. 

State of exploitation 

The current F (1.44) is larger than FMSY (0.70), which indicates that Deep water rose 
shrimp in GSAs 17-19 is being overfished, fished above FMSY. 

Table  5.5.1.1.    Deep-water  rose  shrimp  GSAs  17-18-19.  F,  Recruitment,  SSB,  TB 
and  Catch  estimates  from  the  assessment  for  Deep-water  rose  shrimp  in  GSA s17-
18-19. Catch refers to the model-estimated values. 

 2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

1.35 

1.54 

1.68 

1.72 

1.69 

1.63 

1.62 

1.57 

1.44 

1.44 

Fbar0-2 

Recruitment 
(thousands) 

SSB (t) 

TB (t) 

1032017 

1110409 

1417453 

1152235 

885830 

886862 

1096130 

1178851 

1188 

1041 

948 

942 

806 

669 

667 

800 

6227 

6567 

6605 

6241 

5247 

4711 

5167 

6176 

Catch 
(t) 

2174 

2224 

2251 

2420 

2057 

1732 

1823 

2062 

2136608 

1380 

10031 

2391 

5418527 

3266 

21086 

3559 

62 

62 

 
 
 
 
 
 
 
 
Figure  5.5.1.1.  Deep-water  rose  shrimp  GSAs  17-18-19.  F  SSB  Recruitment  and 
Catch estimates from the assessment for Deep-water rose shrimp in GSAs 17-18-19. 

5.5.2 STOCK ADVICE 

STECF  EWG  17-15  advises  that,  when  MSY  considerations  are  applied,  the  fishing 
mortality  should  be  reduced  to  no  more  than  F=0.70.    This  implies  catches  of  no 
more than 3225 tons in 2018. 

5.5.3 BASIS OF THE ASSESSMENT  

The  stock  of  Deep  water  rose  shrimp  in  GSAs  17-18-19  was  assessed  applying  an 
a4a method calibrated with fishery independent survey abundance indices (combined 
MEDITS  in  GSAs  17,  18,  and  19).  Input  data  on  landings,  discards  and  length 
frequencies  were  taken  from  EU  DCF  data,  FAO  Official  Statistics  and  national 
statistics for Croatia, Albania and Montenegro, and from the Adriamed pilot project. 
Von  Bertalanffy  growth  parameters  and  length-weight  relationship  were  taken  from 
DCF. The natural mortality vector was obtained using the PRODBIOM method. 

The computation was made using the FLR libraries of the R-project software. 

5.5.4 CATCH OPTIONS  

Short  term  forecast  was  carried  out  by  STECF  EWG.  Short-term  prediction  results 
are shown in the following table (Table 5.5.4.1). 

Table  5.5.4.1.  Deep-water  rose  shrimp  GSAs  17-18-19.  Short  term  forecasts 
showing  catch  options  for  different  fishing  mortalities  reductions.  F2017  is  equal  to 

63 

63 

 
 
 
 
 
F2016, corresponding to a catch2017 of 8810 t. Recruitment 2017 and 2018 is 2389764 
thousands (equal to the mean recruitment of the last three years). 

Rationale 

Ffactor 

Fbar 

Catch 2018 

Catch 
2019 

SSB 2018 

SSB 2019 

Change SSB 2018-
2019 (%) 

Change Catch 
2016-2018 (%) 

Zero 
catch 
High long 
term 
yield 
(F0.1)  

Status 
quo 

Different 
Scenarios 

0 

0.00 

0 

0 

5138 

8436 

64.19 

-100.00 

0.48 

0.70 

3225 

3590 

3532 

3998 

13.21 

-9.37 

1 

1.44 

5027 

4370 

2459 

2267 

0.1 

0.2 

0.3 

0.4 

0.5 

0.6 

0.7 

0.8 

0.9 

1.1 

1.2 

1.3 

1.4 

1.5 

1.6 

1.7 

1.8 

1.9 

2 

0.14 

0.29 

0.43 

0.57 

0.72 

0.86 

1.01 

1.15 

1.29 

1.58 

1.72 

1.87 

2.01 

2.16 

2.30 

2.44 

2.59 

2.73 

2.87 

863 

1606 

2249 

2809 

3299 

3729 

4110 

4449 

4753 

5275 

5500 

5707 

5898 

6075 

6240 

6394 

6538 

6675 

6804 

1296 

2206 

2849 

3306 

3636 

3876 

4053 

4187 

4289 

4434 

4488 

4532 

4571 

4604 

4634 

4662 

4687 

4710 

4731 

4743 

4384 

4058 

3761 

3490 

3244 

3020 

2815 

2629 

2304 

2162 

2032 

1914 

1806 

1706 

1616 

1532 

1456 

1385 

7092 

6023 

5168 

4478 

3917 

3457 

3076 

2759 

2493 

2075 

1910 

1767 

1643 

1535 

1440 

1355 

1281 

1214 

1155 

-7.79 

49.51 

37.38 

27.36 

19.07 

12.22 

6.55 

1.87 

-1.99 

-5.18 

-9.92 

-11.65 

-13.04 

-14.14 

-14.99 

-15.64 

-16.10 

-16.41 

-16.58 

-16.64 

41.25 

-75.75 

-54.87 

-36.79 

-21.07 

-7.31 

4.79 

15.50 

25.03 

33.57 

48.22 

54.57 

60.38 

65.75 

70.71 

75.34 

79.67 

83.73 

87.57 

91.20 

5.5.5 REFERENCE POINTS  

Table  5.5.5.1  Deep-water  rose  shrimp  GSAs  17-18-19.  Reference  points,  values, 
and their technical basis. 

Framework 

MSY approach 

Reference 
point 
MSY Btrigger 

Value 

Technical basis 

Source 

64 

64 

 
 
 
 
 
 
 
 
Precautionary 
Approach 

FMSY 

Blim 
Bpa 

0.70 

F0.1 

Present 
assessment 

5.5.6 DATA DEFICIENCIES 

Data from DCF 2016 as submitted through the Official data call in 2017 were used. 
Discards  for  GSA  17  were  present  just  for  2011  and  from  2013  to  2016  for  the 
Italian fleet and only for 2015 and 2016 for the Croatian fleet. Discards for GSA 18 
were present just from 2009 to 2016 for the Italian fleet. Discards for GSA 19 were 
present just for 2006 and from 2009 to 2016 for the Italian fleet. Landings data for 
GSA  17  were  incomplete.  Italian  landings  were  present  just  for  2006,  2011,  and 
from  2013  to  2016.  Croatian  landings  were  present  just  from  2014  to  2016  in  the 
DCF  database  because  previously  there  was  no  obligation  to  monitor  that  species. 
Landings  data  from  some  gears  in  the  DCF  database  are  reported  inconsistently 
throughout  the  time  series.  For  more  detailed  information  see  section  6.5  of  this 
report. 

65 

65 

 
 
 
 
 
 
 
 
 
 
 
5.6 SUMMARY SHEET OF THORNBACK RAY IN GSA 17 

Species common name: Thornback ray 

Species scientific name: Raja clavata 

Geographical Sub-area(s) GSA(s): 17 

5.6.1 STOCK DEVELOPMENT OVER TIME 

State of absolute and relative biomass 

The assessment shows a fluctuating trend in terms of surveys very low values of 
biomass index expressed in kg/km2, with estimations showing an almost stationary 
situation up to year 2011 followed by an increase in the more recent years up to 
10kg/km2. Regarding density, the evolution in time of numbers per square km have has 
shown similar pattern with a peak in 2015. Given the high historical catches and current low 
catches the stock is considered to be depleted.  

State of the juveniles (recruits) 

It is not possible to evaluate juvenile abundance or its evolution along time with the 
available  data  due  to  the  scarce  number  of  juveniles  that  have  been  caught  in  the 
surveys or sampled on board or at the landings. 

State of exploitation 

Based on survey data Fishing mortality values were estimated to be about 0.45 in the 
former years and more recently of 0.24 which are much higher than the estimated F-based 
reference points F0.1 and Frepl both with estimated values of about 0.08. In conclusion, it is 
considered that the stock is overfished. 

5.6.2 STOCK ADVICE 

STECF  EWG  17-15  is  not  able  to  advice  on  levels  of  neither  F  nor  B  for  the  stock 
compatible with MSY. The observed very low biomass levels and the rough analyses 
related  to  stock  status  assessment  suggest  that  a  drastic  reduction  of  the  fishing 
pressure of a factor of 3 on the stock is needed. 

5.6.3 BASIS OF THE ASSESSMENT  

The  assessment  is  based  on  the  evolution  of  the  mean  size  of  the  catch  in  the 
surveys. Time series of Italian, Croatian and Slovenian DCF landings were available 
for a limited period and at different level of aggregation from 2008 to 2016. Croatian 
data  included  all  the  ray  species  and  with  a  shorter  time  series.  Due  to  the  lack  of 
representative  samples  of  the  commercial  catch,  fishing  mortality  was  estimated 
using a survey method that assumes age structure of the surveys to be similar of the 
age  structure  of  the  commercial  catch.  A  non-equilibrium  size-based  method  was 
used for estimating Z and successively F by subtraction of an assumed M=0.16. 

5.6.4 CATCH OPTIONS  

66 

66 

 
 
 
 
 
No short-term predictions were done as information needed was not available. 

5.6.5 REFERENCE POINTS  

A Leslie matrix that relies primarily on life history parameters (fecundity by age and survival 
rates  by  age)  was  used  for  the  estimation  of  a  value  of  F  that  constitutes  a  limit  reference 
value (Freplacement). Frepl resulted to be about 0.08. 
Also the Reference Point F0.1 was estimated using a B&H yield per recruit model assuming a 
knifedge  selection  pattern  with  t’=30  cm,  L/W  relationship    and  M=0.16.  The  estimated 
reference value (F0.1=0.076) was obtained, which resulted to be very similar to Frepl 

Framework 

MSY 
approach 

Precautionary 
Approach 

Reference 
point 
MSY Btrigger 

FMSY 

Blim 
Bpa 

Value 

Technical basis 

Source 

0.076 

F0.1 

Present 
assessment 

5.6.6 DATA DEFICIENCIES  

Catch data is incomplete. There is no information on discards. The size structure 
of the catches, including these proceeding from trawlers and rapido beam trawls 
that  are  the  gears  responsible  of  the  major  part  of  the  catches  is  lacking. 
Thornback  skate  size  frequencies  in  trawl  surveys  are  limited  due  to  the  low 
abundance of the species in each tow and the high number of tows with 0 catch. 
Biological information is very scarce. 

67 

67 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5.7 SUMMARY SHEET OF COMMON CUTTLEFISH IN GSA 17 AND 18 

Species common name: Common cuttlefish 

Species scientific name:Sepia officinalis 

Geographical Sub-area(s) GSA(s): 17 AND 18 

5.7.1 STOCK DEVELOPMENT OVER TIME 

State of absolute and relative biomass 

The data does not allow for evaluation of absolute abundance over time however, the 
assessment estimates B to be below Bmsy and B/Bmsy in last year is 0.729, 

State of the juveniles (recruits) 

The data does not allow for evaluation of recruitment over time, so current recruitment 
cannot be compared with historic recruitment. 

State of exploitation 

The  assessment  model  estimates  catches  above  MSY;  F/Fmsy  in  the  last  year  is 
estimated to be 1.12.  

Figure 5.7.1.1. Relative biomass and fishing mortality, F/B plot and catch curve 
as given by the CMSY model for common cuttlefish in GSA 17-18. 

68 

68 

 
 
 
 
 
 
 
 
Table  5.7.1.1.  Catch,  relative  biomass  and  fishing  mortality  as  given  by  the 
CMSY model for common cuttlefish in GSA 17-18. 

Year 

Catch 

B/Bmsy 

F/Fmsy 

2002 

1239.17 

2.131889 

0.127229 

2003 

1959.73 

2.330484 

0.179537 

2004 

1711.81 

2.319228 

0.165469 

2005 

3497.54 

2.212134 

0.340426 

2006 

4376.88 

1.856369 

0.517108 

2007 

6095.69 

1.447968 

0.919381 

2008 

6503.59 

0.805145 

1.801873 

2009 

3761.17 

0.329941 

2.54854 

2010 

2817.30 

0.258507 

2.389147 

2011 

3025.99 

0.307734 

2.125252 

2012 

3203.25 

0.417462 

1.681557 

2013 

3829.58 

0.57628 

1.436583 

2014 

3756.29 

0.752465 

1.098467 

2015 

3882.83 

1.029315 

0.818803 

2016 

3632.29 

1.312845 

0.60947 

5.7.2 STOCK ADVICE 

STECF EWG 17-15 was not able to provide catch advice due to the low quality of data, 
high uncertainty of parameter estimates and to the lack of a reliable tuning index. 

5.7.3 BASIS OF THE ASSESSMENT  

The assessment is based on historical landing time series from 1970 to 2016 years and 
MEDITS  data  from  2002  to  2016.  Data  evaluated  using  Surplus  models  (CMSY  and 
SPiCT). 

5.7.4 CATCH OPTIONS  

A short term forecast cannot be carried out, and no specific catch options can be 
provided. 

69 

69 

 
 
 
 
5.7.5 REFERENCE POINTS  

Not estimated due to the high uncertainty of parameter estimate. 

5.7.6 DATA DEFICIENCIES   

The  information  available  on  the  common  cuttlefish  in  GSA  17-18  was  very  limited. 
Inconsistency is notable in historical catch data considering that this species was usually 
reported  together  with  other  species  from  families  Sepiidae  and  Sepiolidae  (e.g.  S. 
elegans,  S.  orbignyana,  Rossia  macrosoma,  etc.)  or  was  not  reported  at  all.  Complete 
data  sets,  regarding  this  specie,  from  Republic  of  Albania  are  currently  not  available. 
Growth parameters of Common cuttlefish in Mediterranean area are scarce. Data on size 
structure was available for bottom trawl, set nets and FPO (pot and traps) fisheries, only 
from  Italian  side  since  2006  (2007  in  GSA  18).  Period  when  the  MEDITS  survey  was 
conducted  during  time  series  could  affect  the  estimation  of  abundance  indices  and 
distribution patterns of common cuttlefish. 

70 

70 

 
 
 
5.8 SUMMARY SHEET OF SOLE IN GSA 17 

Species common name: Sole 

Species scientific name: Solea vulgaris 

Geographical Sub-area(s) GSA(s): 17 

The WG was unable to select a single final assessment and provides the three stock 
assessment  options  using  two  age  based  methods:  Assessment  for  All  (a4a)  and  Stock 
Synthesis  3  (SS3),  the  first  of  these  assumes  dome  shaped  selectivity  for  the  survey, 
the  second  based  on  extensive  further  exploration  after  the  EWG  used  a  logistic 
selectivity for the survey. Both SS3 models use fleet based catch.  

5.8.1 SUMMARY FOR SS3 ASSESSMENT - RUN 1 

5.8.1.1 STOCK DEVELOPMENT OVER TIME (SS3) 

State of the adult abundance and biomass 
The SSB showed a steady decrease from the beginning of the time series, with a slight 
increase  in  the  last  three  years.  The  estimated  SSB  value  for  2016  is  5278  tons  (Fig. 
5.8.1.1.1). 

State of the juveniles (recruits) 
The  recruitment  shows  a  constant  pattern  over  the  time  series  without  any  trend.  The 
value for 2016 is 30260 thousands (Fig. 5.8.1.1.1). 

Figure 5.8.1.1.1 Common sole in GSA 17. Summary results from the SS3 model. 

State of exploitation 

71 

71 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
The  current  Fbar1-4  (0.41)  is  larger  than  the  FMSY  value  (0.26),  which  indicates  that  the 
stock of common sole in GSA 17 is overexploited. 

Table 5.8.1.1.1 Common sole in GSA 17. Results from the SS3 model Run 1. 

Year 

Fbar(1-4) 

Recruitment 
(thousands) 

SSB 
(tons) 

Catch 
(tons) 

Total 
biomass 
(tons) 

1980 

0.34 

33634 

18536  2235 

21368 

1981 

0.20 

33634 

16948  1123 

19506 

1982 

0.21 

33634 

16185  1213 

18842 

1983 

0.29 

33634 

15495  1652 

18164 

1984 

0.26 

33634 

14470  1416 

17060 

1985 

0.29 

33634 

13696  1547 

16301 

1986 

0.31 

33634 

12934  1592 

15516 

1987 

0.62 

33634 

12186  2653 

14750 

1988 

0.58 

33634 

10531  1995 

12860 

1989 

0.62 

33634 

9385 

1890 

11729 

1990 

0.36 

33634 

8459 

1235 

10824 

1991 

0.27 

33634 

8250 

1177 

10789 

1992 

0.39 

33634 

8379 

1900 

11003 

1993 

0.45 

33634 

7994 

2013 

10507 

1994 

0.61 

33634 

7462 

2292 

9906 

1995 

0.61 

33634 

6668 

1971 

9004 

1996 

0.36 

33634 

6098 

1220 

8444 

1997 

0.29 

33634 

6249 

1250 

8783 

1998 

0.23 

33634 

6612 

1183 

9219 

1999 

0.23 

33634 

7150 

1278 

9804 

2000 

0.18 

33634 

7659 

1036 

10315 

2001 

0.18 

33634 

8387 

1104 

11098 

2002 

0.17 

33634 

9048 

1075 

11771 

2003 

0.37 

23942 

9681 

2107 

12189 

2004 

0.45 

23230 

9160 

1822 

11055 

2005 

0.70 

34740 

8245 

1994 

10211 

2006 

0.79 

30920 

7139 

2022 

9327 

2007 

0.83 

19398 

6347 

1588 

8137 

2008 

0.65 

28090 

5692 

1325 

7310 

72 

72 

 
 
 
2009 

1.05 

28982 

5065 

1954 

6975 

2010 

1.01 

31550 

4304 

1614 

6251 

2011 

1.07 

33855 

3927 

1590 

6089 

2012 

0.70 

39742 

3782 

1859 

6286 

2013 

0.40 

47303 

3727 

1253 

6648 

2014 

0.36 

32647 

4570 

2048 

7758 

2015 

0.39 

41327 

5197 

2045 

7995 

2016 

0.41 

30262 

5278 

2093 

7973 

5.8.1.2 STOCK ADVICE (SS3 RUN 1) 

STECF  EWG  17-15  advises  that,  when  MSY  considerations  are  applied,  the  fishing 
mortality  should  be  reduced  to  no  more  than  F=0.26.    This  implies  catches  of  no 
more than 1148 tons in 2018. 

5.8.1.3 BASIS OF THE ASSESSMENT (SS3 RUN 1) 

This assessment was carried out using an SS3 age based model, calibrated with fishery 
independent survey abundance indices coming from the SoleMon framework. Input data 
on  landings  and  age  distributions  were  taken  from  EU  DCF  data  and  FAO  Official 
Statistics. Von Bertalanffy growth parameters and length-weight relationship were taken 
from  the  SoleMon  framework.  The  natural  mortality  vector  was  obtained  using  the 
PRODBIOM method. 
The computation was made using the FLR libraries of the R-project software. 

5.8.1.4 CATCH OPTIONS (SS3 RUN 1) 

Short  term  prediction  for  the  period  2017-2019  was  performed  using  the  FLR  routines 
provided  by  JRC  and  based  on  the  results  of  the  stock  assessments  performed  during 
EWG  17-15.  Table  5.8.4.1  shows  the  resulting  short  term  forecasts  obtained  using  the 
input and the results of the SS3 stock assessment model Run 1,  

Table  5.8.1.4.1  Common  sole  in  GSA  17  SS3  Run  1.  Short  term  forecast  for  the  SS3 
model; catch(2016)=2093 tons, catch(2017)=1713 tons. 

Rationale 

Ffactor  Fbar  Catch_2018  Catch_2019  SSB_2018  SSB_2019 

Change_SSB 

Change_Catch 

2018-2019(%) 

2017-2019(%) 

Zero catch 

0 

0.00 

0 

0 

5366.40 

6916.45 

28.88 

-100 

High long term 
yield (F0.1) 

0.634  0.26 

1147.98 

1266.21 

5366.40 

5836.63 

8.76 

-45.15 

73 

73 

 
 
 
 
 
 
 
 
 
 
Status quo 

1 

0.41 

1663.03 

1643.45 

5366.40 

5369.29 

0.05 

0.1 

0.04 

207.02 

272.49 

5366.40 

6718.58 

0.2 

0.08 

403.49 

513.03 

5366.40 

6531.94 

0.3 

0.12 

590.05 

725.20 

5366.40 

6355.84 

0.4 

0.16 

767.28 

912.18 

5366.40 

6189.64 

0.5 

0.21 

935.72 

1076.80 

5366.40 

6032.74 

0.6 

0.25 

1095.89 

1221.58 

5366.40 

5884.59 

0.7 

0.29 

1248.27 

1348.76 

5366.40 

5744.64 

0.8 

0.33 

1393.30 

1460.31 

5366.40 

5612.41 

0.9 

0.37 

1531.43 

1558.02 

5366.40 

5487.44 

Different Scenarios 

1.1 

0.45 

1788.49 

1718.01 

5366.40 

5257.55 

1.2 

0.49 

1908.16 

1782.93 

5366.40 

5151.84 

1.3 

0.53 

2022.36 

1839.32 

5366.40 

5051.81 

1.4 

0.57 

2131.41 

1888.16 

5366.40 

4957.11 

1.5 

0.62 

2235.59 

1930.33 

5366.40 

4867.43 

25.20 

21.72 

18.44 

15.34 

12.42 

9.66 

7.05 

4.58 

2.26 

-2.03 

-4.00 

-5.86 

-7.63 

-9.30 

1.6 

0.66 

2335.18 

1966.59 

5366.40 

4782.48 

-10.88 

1.7 

0.70 

2430.44 

1997.65 

5366.40 

4701.97 

-12.38 

1.8 

0.74 

2521.60 

2024.11 

5366.40 

4625.66 

-13.80 

1.9 

0.78 

2608.88 

2046.52 

5366.40 

4553.28 

-15.15 

2 

0.82 

2692.52 

2065.35 

5366.40 

4484.62 

-16.43 

-20.54 

-90.11 

-80.72 

-71.81 

-63.34 

-55.29 

-47.64 

-40.36 

-33.43 

-26.83 

-14.55 

-8.83 

-3.37 

1.84 

6.81 

11.57 

16.12 

20.48 

24.65 

28.64 

5.8.1.5 REFERENCE POINTS(SS3 RUN 1) 

Table 5.8.1.5.1 Common sole in GSA 17. Main reference points defined by the Yield per 
recruit analysis - SS3 model Run 1. 

Framework 

MSY approach 

Precautionary 
Approach 

Reference 
point 
MSY Btrigger 

FMSY 

Blim 
Bpa 

Value 

Technical 
basis 

Source 

0.26 

F0.1 

GFCM-WGSAD 
2017  

5.8.2 SUMMARY FOR A4A ASSESSMENT 

5.8.2.1 STOCK DEVELOPMENT OVER TIME (A4A ASSESSMENT) 

State of the adult abundance and biomass 
After  a  slight  decrease  in  the  first  part  of  the  time  series,  the  SSB  slightly  increases, 
reaching a value of  1165 tons in 2016 (Fig. 5.8.2.1.1.). 

74 

74 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
State of the juveniles (recruits) 
The  recruitment  shows  a  constant  pattern  over  the  time  series  without  any  trend.  The 
value for 2016 is 26882 thousands (Fig. 5.8.2.1.1). 

Figure 5.8.2.1.1 Common sole in GSA 17. Summary results from the a4a model. 

State of exploitation 
The  current  Fbar1-4  (0.99)  is  larger  than  the  FMSY  value  (0.23),  which  indicates  that  the 
stock of common sole in GSA 17 is overexploited. 

Table 5.8.2.1.1 Common sole in GSA 17. Results from the a4a model. 

Year 

Fbar(1-
4) 

Recruitment 
(thousands) 

SSB 
(tons) 

Catch 
(tons) 

Total 
biomass 
(tons) 

2006 

1.61 

2007 

1.66 

2008 

1.46 

2009 

1.41 

2010 

1.71 

2011 

1.86 

26073 

20342 

19044 

29121 

40426 

22249 

75 

75 

925 

1961 

2842 

852 

1684 

2374 

678 

1291 

1925 

618 

1291 

2019 

681 

2145 

2695 

766 

2093 

2835 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2012 

1.53 

2013 

1.34 

2014 

1.52 

29738 

42332 

32718 

696 

1186 

2240 

734 

1465 

2806 

970 

2191 

3398 

2015 

1.51 

52225 

1072 

1970 

3539 

2016 

0.99 

26882 

1165 

2105 

3835 

5.8.2.2 STOCK ADVICE(A4A) 

STECF  EWG  17-15  advises  that,  when  MSY  considerations  are  applied,  the  fishing 
mortality  should  be  reduced  to  no  more  than  F=0.23.    This  implies  catches  of  no 
more than 666 tons in 2018. 

5.8.2.3 BASIS OF THE ASSESSMENT 

This assessment was carried out using an a4a age based model, calibrated with 
fishery  independent  survey  abundance  indices  coming  from  the  SoleMon 
framework. Input data on landings and age distributions were taken from EU DCF 
data.  Von  Bertalanffy  growth  parameters  and  length-weight  relationship  were 
taken  from  the  SoleMon  framework.  The  natural  mortality  vector  was  obtained 
using the PRODBIOM method. 

The computation was made using the FLR libraries of the R-project software. 

5.8.2.4 CATCH OPTIONS (A4A) 

Short  term  prediction  for  the  period  2017-2019  was  performed  using  the  FLR  routines 
provided  by  JRC  and  based  on  the  results  of  the  stock  assessments  performed  during 
EWG 17-15 Table 5.8.2.4.1 present the short term forecast for the a4a model. 

Table  5.8.2.4.1  Common  sole  in  GSA  17  a4a  model.  Short  term  forecast  for  the  a4a 
model; catch(2016)=2105 tons, catch(2017)=1599 tons. 

Rationale 

Ffactor  Fbar  Catch_2018  Catch_2019  SSB_2018  SSB_2019 

Change_SSB 

Change_Catch 

2018-2019(%) 

2016-2018(%) 

Zero catch 

0 

0.00 

0 

0 

1789.05 

3581.06 

100.17 

-100 

High long term 
yield (F0.1) 

0.233  0.23 

665.97 

810.28 

1789.05 

2898.63 

62.02 

-68.36 

Status quo 

1 

0.99 

2007.47 

1757.64 

1789.05 

1552.71 

-13.21 

0.1 

0.10 

306.46 

400.64 

1789.05 

3266.28 

Different Scenarios 

0.2 

0.20 

581.56 

719.97 

1789.05 

2984.78 

0.3 

0.30 

828.91 

974.41 

1789.05 

2732.67 

82.57 

66.84 

52.74 

-4.64 

-85.44 

-72.37 

-60.62 

76 

76 

 
 
 
 
 
 
 
 
 
0.4 

0.39 

1051.71 

1176.99 

1789.05 

2506.57 

0.5 

0.49 

1252.75 

1338.06 

1789.05 

2303.51 

0.6 

0.59 

1434.47 

1465.87 

1789.05 

2120.87 

0.7 

0.69 

1599.01 

1566.99 

1789.05 

1956.37 

0.8 

0.79 

1748.29 

1646.67 

1789.05 

1807.98 

40.11 

28.76 

18.55 

9.35 

1.06 

0.9 

0.89 

1883.95 

1709.10 

1789.05 

1673.95 

-6.43 

1.1 

1.09 

2120.14 

1794.99 

1789.05 

1442.87 

-19.35 

1.2 

1.18 

2223.11 

1823.31 

1789.05 

1343.24 

-24.92 

1.3 

1.28 

2317.38 

1844.33 

1789.05 

1252.73 

-29.98 

1.4 

1.38 

2403.85 

1859.48 

1789.05 

1170.39 

-34.58 

1.5 

1.48 

2483.32 

1869.87 

1789.05 

1095.38 

-38.77 

1.6 

1.58 

2556.48 

1876.43 

1789.05 

1026.96 

-42.60 

1.7 

1.68 

2623.97 

1879.90 

1789.05 

964.46 

-46.09 

1.8 

1.78 

2686.34 

1880.89 

1789.05 

907.29 

-49.29 

1.9 

1.88 

2744.08 

1879.89 

1789.05 

854.93 

-52.21 

2 

1.97 

2797.63 

1877.30 

1789.05 

806.91 

-54.90 

-50.04 

-40.49 

-31.86 

-24.04 

-16.95 

-10.51 

0.71 

5.61 

10.08 

14.19 

17.97 

21.44 

24.65 

27.61 

30.35 

32.90 

5.8.2.5 REFERENCE POINTS (A4A) 

Table 5.8.5.1 Common sole in GSA 17. Main reference points defined  by the Yield per 
recruit analysis – a4a model. 

Framework 

MSY approach 

Precautionary 
Approach 

Reference 
point 
MSY Btrigger 

FMSY 

Blim 
Bpa 

Value 

Technical 
basis 

Source 

0.23 

F0.1 

Present assessment 
(a4a) 

5.8.3 SUMMARY FOR SS3 ASSESSMENT – RUN 7 

5.8.3.1 STOCK DEVELOPMENT OVER TIME (SS3 – RUN7) 

State of the adult abundance and biomass 
The SSB showed a steady decrease from the beginning of the time series, with a slight 
increase  in  the  last  six  years.  The  estimated  SSB  value  for  2016  is  3429  tons  (Fig. 
5.8.3.1.1). 

State of the juveniles (recruits) 
The  recruitment  shows  a  constant  pattern  over  the  time  series  without  any  trend.  The 
value for 2016 is 15002 thousands (Fig. 5.8.3.1.1). 

77 

77 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 5.8.3.1.1 Common sole in GSA 17. Summary results from the SS3 Run 7. 

State of exploitation 
The  current  Fbar1-4  (0.51)  is  larger  than  the  FMSY  value  (0.26),  which  indicates  that  the 
stock of common sole in GSA 17 is overexploited. 

Table 5.8.3.1.1 Common sole in GSA 17. Results from the SS3 model. 

Year 

Fbar   Recruitment  SSB  Catch 

Total 

78 

78 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
(1-4) 

0.31 
0.17 
0.18 
0.25 
0.22 
0.25 
0.27 
0.51 
0.44 
0.46 
0.28 
0.25 
0.4 
0.47 
0.63 
0.63 
0.38 
0.34 
0.28 
0.28 
0.21 
0.21 
0.2 
0.41 
0.56 
0.86 
0.98 
0.99 
0.73 
1.18 
1.12 
0.98 
0.68 
0.41 
0.43 
0.47 
0.51 

1980 
1981 
1982 
1983 
1984 
1985 
1986 
1987 
1988 
1989 
1990 
1991 
1992 
1993 
1994 
1995 
1996 
1997 
1998 
1999 
2000 
2001 
2002 
2003 
2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 
2015 
2016 

(thousands)  (tons)  (tons)  biomass 

10604  2235 
1123 
9492 
1213 
9300 
1652 
9123 
1416 
8560 
1547 
8212 
1592 
7815 
2653 
7394 
1995 
6007 
1890 
5201 
1235 
4600 
1177 
4696 
1900 
4983 
2013 
4644 
2292 
4133 
1971 
3352 
1220 
2844 
1250 
3074 
1183 
3420 
1278 
3870 
1036 
4250 
1104 
4837 
1075 
5361 
2107 
5874 
1822 
5214 
1994 
4056 
2022 
2822 
1588 
2073 
1325 
1751 
1954 
1515 
1614 
1103 
1590 
1164 
1859 
1468 
1253 
1784 
2048 
2930 
2045 
3613 
2093 
3429 

(tons) 
12816 
11531 
11421 
11255 
10639 
10303 
9890 
9457 
7925 
7145 
6556 
6748 
7071 
6637 
6076 
5225 
4725 
5077 
5458 
5937 
6316 
6951 
7487 
7671 
6310 
5292 
4465 
3582 
3167 
3268 
3029 
3345 
3983 
4651 
5805 
5836 
5345 

26275 
26275 
26275 
26275 
26275 
26275 
26275 
26275 
26275 
26275 
26275 
26275 
26275 
26275 
26275 
26275 
26275 
26275 
26275 
26275 
26275 
26275 
26275 
11836 
13482 
25093 
26230 
16287 
24648 
27649 
31505 
33251 
39777 
43692 
23925 
32020 
15002 

79 

79 

 
 
5.8.3.2 STOCK ADVICE (SS3 – RUN 7) 

STECF  EWG  17-15  advises  that,  when  MSY  considerations  are  applied,  the  fishing 
mortality  should  be  reduced  to  no  more  than  F=0.26.    This  implies  catches  of  no 
more than 963 tons in 2018. 

5.8.3.3 BASIS OF THE ASSESSMENT (SS3 – RUN 7) 

The  stock  of  common  sole  in  GSA  17  was  assessed  using  two  different  age  based 
models:  a4a  and  SS3.  Both  of  them  were  calibrated  with  fishery  independent  survey 
abundance indices coming from the SoleMon framework. Input data on landings and age 
distributions  were  taken  from  EU  DCF  data  and  FAO  Official  Statistics.  Von  Bertalanffy 
growth  parameters  and  length-weight  relationship  were  taken  from  the  SoleMon 
framework. The natural mortality vector was obtained using the PRODBIOM method. 
The computation was made using the FLR libraries of the R-project software. 

5.8.3.4 CATCH OPTIONS (SS3 – RUN 7) 

Short  term  prediction  for  the  period  2017-2019  was  performed  using  the  FLR  routines 
provided  by  JRC  and  based  on  the  results  of  the  stock  assessments  performed  during 
EWG 17-15. Table 5.8.3.4.1 shows the resulting short term forecasts obtained using the 
input and the results of the SS3 stock assessment model – Run 7. 

Table 5.8.3.4.1 Common sole in GSA 17. Short term forecast for the SS3 model – Run 
7; catch(2016)=2093 tons, catch(2017)=1568 tons. 

Rationale 

Ffactor 

Fbar 

Catch_2018  Catch_2019  SSB_2018 

SSB_2019 

Change_SSB_2018-
2019(%) 

Change_Catch_2017-
2019(%) 

Zero catch 

0 

0.00 

0 

0 

2750.26  4178.32 

51.92 

-100 

High long 
term yield 
(F0.1) 

Status quo 

Different 
Scenarios 

0.513 

0.26 

963.15 

1093.56  2750.26  3279.84 

19.26 

1 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9 
1.1 
1.2 
1.3 
1.4 

0.51 
0.05 
0.10 
0.15 
0.20 
0.25 
0.30 
0.36 
0.41 
0.46 
0.56 
0.61 
0.66 
0.71 

1684.27 
207.25 
404.57 
592.49 
771.54 
942.18 
1104.87 
1260.03 
1408.07 
1549.36 
1813.12 
1936.25 
2053.94 
2166.48 

1646.44  2750.26  2625.68 
2750.26  3983.02 
269.84 
2750.26  3798.02 
509.12 
2750.26  3622.72 
721.06 
2750.26  3456.60 
908.53 
1074.10  2750.26  3299.13 
1220.07  2750.26  3149.83 
1348.53  2750.26  3008.26 
1461.33  2750.26  2873.97 
1560.13  2750.26  2746.57 
1721.59  2750.26  2510.94 
1786.77  2750.26  2402.00 
1843.08  2750.26  2298.56 
1891.47  2750.26  2200.32 

-4.53 
44.82 
38.10 
31.72 
25.68 
19.96 
14.53 
9.38 
4.50 
-0.13 
-8.70 
-12.66 
-16.42 
-20.00 

-53.98 

-19.53 
-90.10 
-80.67 
-71.69 
-63.14 
-54.98 
-47.21 
-39.80 
-32.72 
-25.97 
-13.37 
-7.49 
-1.87 
3.51 

80 

80 

 
 
 
 
 
1.5 
1.6 
1.7 
1.8 
1.9 
2 

0.76 
0.81 
0.86 
0.91 
0.96 
1.01 

2274.14 
2377.18 
2475.82 
2570.30 
2660.83 
2747.60 

1932.81  2750.26  2106.98 
1967.87  2750.26  2018.28 
1997.36  2750.26  1933.98 
2021.90  2750.26  1853.84 
2042.03  2750.26  1777.62 
2058.28  2750.26  1705.13 

-23.39 
-26.61 
-29.68 
-32.59 
-35.37 
-38.00 

8.65 
13.58 
18.29 
22.80 
27.13 
31.28 

5.8.3.5 REFERENCE POINTS (SS3 – RUN 7) 

Table 5.8.3.5.1 Common sole in GSA 17. Main reference points defined by the Yield per 
recruit analysis - SS3 model – Run 7. 

Framework 

MSY approach 

Precautionary 
Approach 

Reference 
point 
MSY Btrigger 

FMSY 

Blim 
Bpa 

Value 

Technical 
basis 

Source 

0.26 

F0.1 

Present assessment 
(SS3)  

5.8.4 DATA DEFICIENCIES   

The  data  used  for  the  a4a  model  come  from  the  last  DCF  official  data  call  (2017).  The 
Croatian  catch  at  age  data  was  lacking  for  years  before  2012  and  they  were 
reconstructed on the base of the total landings suggested by Croatian experts and catch 
at age data composition observed for set netters. 
Regarding  the  SS3  model,  the  data  were  available  thanks  to  the  work  presented  by 
Scarcella et al. at the GFCM- WGSAD 2017; a combination of DCF and FAO Fishstat data 
was used to obtain the long time series utilised. 

81 

81 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5.9 SUMMARY SHEET OF SPOTTAIL MANTIS SHRIMP IN GSA 17 AND 18 

Species common name: Spottail mantis shrimp 

Species scientific name: Squilla mantis 

Geographical Sub-area(s) GSA(s): 17 AND 18 

5.9.1 STOCK DEVELOPMENT OVER TIME 

State of absolute and relative biomass 

According to the assessment results, SSB shows a decreasing trend in the period 2008-
2012  followed  by  a  consistent  increase  to  14071  tons  of  SSB  in  2016  (Table  5.9.1.1.).  
No  precautionary  biomass  reference  points  have  been  agreed  for  the  Spot-tail  mantis 
shrimp stock, therefore, STECF EWG 17-15 is unable to fully evaluate the status of the 
stock spawning biomass with respect to the precautionary approach. 

State of the juveniles (recruits) 

Recruitment showed a decreasing in the first three years of the data series, reaching the 
minimum  value  in  2010  (891  millions).  After  that,  an  increasing  trend  in  recruitment 
until 2016 is observed (1786 millions) (Table 5.9.1.1.). 

State of exploitation 

Recent    F  is  declining  over  last  three  years  to  F  =0.65  in  2016  (Table  5.9.1.1.)  this  is 
greater than F0.1 (0.38), chosen as proxy of FMSY and as the exploitation reference point 
consistent with high long term yields. This indicates that Spot-tail mantis shrimp in GSAs 
17 and 18 is over exploited.  

Table  5.9.1.1.    Spot-tail  mantis  shrimp  in  GSAs  17  and  18.  F  SSB  Recruitment  and 
Catch estimates from the assessment for Spot-tail mantis shrimp.  

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

0.705 

0.886 

0.916 

0.801 

0.728 

0.777 

0.867 

0.826 

0.652 

15270 

14160 

13101 

11559 

10921 

11743 

12225 

12469 

14071 

1196112 

968082 

891400 

936465 

1010338 

1047750 

1107680 

1322943 

1786156 

5588.1 

5723.6 

5394.7 

4292.7 

3824.9 

4309 

4813.4 

4687.7 

4360.4 

Mean  F 
(1-3) 

SSB 
(Tons) 

REC 
(x1000) 

Catch 

(Tons) 

82 

82 

 
 
 
 
 
 
 
 
 
 
 
Figure  5.9.1.1.  Spot-tail  mantis  shrimp  in  GSAs  17  and  18.  XSA  summary 
results. SSB and catch are in tons, recruitment in thousands of individuals. 

5.9.2 STOCK ADVICE 

STECF  EWG  17-09  advises  that  when  MSY  considerations  are  applied  the  fishing 
mortality  in  2018  should  no  more  than  0.38,  this  implies  catches  of  Spot-tail  mantis 
shrimp in 2017 of no more than 6427 tons. 

5.9.3 BASIS OF THE ASSESSMENT  

The  analyses  were  performed  using  2008-2016  DCF  data  (biomass  landed  and  age 
composition of the catches), tuned with fishery independent abundance indices (SoleMon 
survey  in  GSA  17).  A  vector  of  natural  mortality  was  obtained  applying  ProdBiom.  In 
addition,  Yield  per  Recruit  (YPR)  analysis  was  performed  for  the  estimation  of  F0.1  (i.e. 
proxy of FMSY). 
The  analyses  were  performed  by  sex  combined,  as  growth  is  very  similar  between  the 
two sexes. Given that the catches were composed mainly of individuals between 1 and 3 
years, these ages were selected to compute Fbar. 
Two  different  stock  assessment  methods  have  been  tested:  Extended  Survival  Analysis 
(XSA) stock assessment model run through the FLXSA library implemented in R and a4a 
statistical  catch  at  age  framework.  The  best  fits  for  both  methods  gave  very  similar 
between  the  two  methods.  In  general,  the  values  obtained  with  XSA  fall  inside  the 
confidence  intervals  estimated  by  a4a  model.  Compared  to  XSA,  a4a  gives  better 
stability for last year estimates. Due to this, a4a was chosen to evaluate the exploitation 
state of Spot-tail mantis shrimp in GSAs 17 and 18. 

5.9.4 CATCH OPTIONS  

83 

83 

 
 
 
 
Short-term prediction results are shown in the following table (Table 5.9.4.1). 

Table  5.9.4.1.  Spot-tail  mantis  shrimp  in  GSAs  17  and  18.  Short  term  forecasts  of 
status  quo  for  different  fishing  mortalities  reductions.  Fishing  mortality  in  2017  was  F 
status  quo  =0.78  (mean  2014-2016)  giving  catches  for  2017  of  6427.  Recruitment  in 
2017 was assumed to be 1,378,135 (thousand individuals) mean of years 2014 to 2016. 

Rationale 

Ffactor 

Fbar 

Catch 

2017 

Catch 

Catch 

SSB 

SSB 

Change in SSB 

Change in Catch 

2018 

2019 

2018 

2019 

2018-2019 (%) 

2016-2018 (%) 

0 catch 

0 

0 

6427 

0 

0 

18357  24368 

32.7 

-100 

High long 
term yields 
(F0.1) 

0.48 

0.38 

6427 

4028 

4657 

18357 

20125 

9.6 

Status quo 

1 

0.78 

6427 

7034 

6426 

18357  17065 

Scenarios 

0.1 

0.08 

0.2 

0.16 

0.3 

0.23 

0.4 

0.31 

0.5 

0.39 

0.6 

0.47 

0.7 

0.55 

0.8 

0.63 

0.9 

0.70 

1.1 

0.86 

1.2 

0.94 

1.3 

1.02 

1.4 

1.09 

1.5 

1.17 

1.6 

1.25 

1.7 

1.33 

1.8 

1.41 

1.9 

1.49 

2.0 

1.56 

6427 

6427 

6427 

6427 

6427 

6427 

6427 

6427 

6427 

6427 

6427 

6427 

6427 

6427 

6427 

6427 

6427 

6427 

6427 

970 

1393 

18357  23334 

1866 

2521 

18357  22386 

2693 

3433 

18357  21515 

3459 

4171 

18357  20715 

4169 

4769 

18357  19979 

4827 

5253 

18357  19301 

5439 

5645 

18357  18675 

6008 

5962 

18357  18096 

6539 

6219 

18357  17561 

7497 

6594 

18357  16605 

7930 

6729 

18357  16177 

8337 

6839 

18357  15779 

8718 

6927 

18357  15408 

9077 

6998 

18357  15061 

9414 

7056 

18357  14736 

9733 

7102 

18357  14433 

10034 

7140 

18357  14148 

10318 

7171 

18357  13880 

10588 

7195 

18357  13627 

-7.0 

27.1 

21.9 

17.2 

12.8 

8.8 

5.1 

1.7 

-1.4 

-4.3 

-9.5 

-11.9 

-14.0 

-16.1 

-18.0 

-19.7 

-21.4 

-22.9 

-24.4 

-25.8 

-7.6 

61.3 

-77.7 

-57.2 

-38.2 

-20.7 

-4.4 

10.7 

24.7 

37.8 

50.0 

71.9 

81.9 

91.2 

99.9 

108.2 

115.9 

123.2 

130.1 

136.6 

142.8 

5.9.5 REFERENCE POINTS  

Framework 

MSY approach 

Precautionary 
Approach 

Reference 
point 
MSY Btrigger 
FMSY 
Blim 
Bpa 

Value 

Technical basis 

Source 

0.38 

F0.1 

This Report 

84 

84 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5.9.6 DATA DEFICIENCIES 

Although the length frequency distributions of the Italian catches in GSA 17 are available 
since 2007 for landing and since 2010 for discard. The composition of the catch by age is 
not  available  in  the  DCF  database.  VB  growth  parameters  are  not  reported  in  the  DCF 
database.  Biomass  and  density  indices  coming  from  Medits  survey  showed  high 
fluctuations  due  to  the  low  availability  of  the  species,  which  are  considered  completely 
unreliable  due  the  few  numbers  of  specimens  caught.  This  is  issue  results  from  the 
behaviour of the species that is scarcely available to the Medits surveys as the hauls are 
carried out during the daylight hours when most of the specimens are in the borrows. 

The length frequency distributions for the Italian fleet operating in GSA 18 are available 
since  2007  for  landing  and  since  2009  for  discard.  In  these  data  time  series,  the 
coverage  of  the  different  metiers  is  very  high  representing  98%  of  the  total  biomass 
caught in the area. The main data issue for Spot-tail mantis shrimp in GSA 18 is related 
to  the  estimation  of  the  abundance  at  sea  as  the  SoleMon  survey  is  not  carried  out  in 
that area.  

85 

85 

 
 
 
 
 
 
5.10 SUMMARY SHEET OF HAKE IN GSA 19 

Species common name: European hake 

Species scientific name: Merluccius merluccius 

Geographical Sub-area(s) GSA(s): 19 

5.10.1 STOCK DEVELOPMENT OVER TIME 

State of absolute and relative biomass 

The SSB estimated from the assessment showed a decrease over the time series, with a 
peak in 2007 of about 568 tonnes and the lowest values in  2003,2011, 2015 and 2016 
(279, 257, 269 and 255 tonnes respectively). 

State of the juveniles (recruits) 

The recruitment estimated with the a4a model do not showed a clear trend over the time 
series.  Recruitment  peak  in  2006  (104135  thousands  of  individuals)  and  showed  the 
minimum  in  2014  (31820  thousands  of  individuals).  Low  recruitment  were  observed  in 
2003  and  2013  (33910  and  35480  respectively)  also.  In  2016  the  recruitment  is 
estimated as 84 million individuals. 

State of exploitation 

From the assessment the current Fbar 0-3 (1.32) is much larger than the FMSY value (0.16). 
The European hake stock in GSAs 19 considered to be over exploited. 

Table  5.10.1.1.  Hake  in  GSA  19.  F  SSB  Recruitment  and  catch  estimates  from  the 
assessment for Hake in GSA 19. 

year 

Fbar  Recruitment  SSB  TB 

Catch 

2003 

1.14 

33910 

279 

1688 

616 

2004 

1.16 

63930 

349 

2592 

743 

2005 

1.2 

87139 

354 

2952 

1037 

2006 

1.25 

104135 

436 

3318 

1215 

2007 

1.3 

50878 

568 

2943 

1456 

2008 

1.36 

73743 

438 

2989 

1027 

2009 

1.38 

54094 

392 

2492 

1115 

2010 

1.36 

45714 

292 

1811 

707 

86 

86 

 
 
 
 
 
 
 
 
year 

Fbar  Recruitment  SSB  TB 

Catch 

2011 

1.29 

72427 

257 

2157 

744 

2012 

1.23 

71219 

285 

2356 

846 

2013 

1.21 

35480 

389 

1870 

918 

2014 

1.24 

31820 

367 

1525 

613 

2015 

1.31 

57266 

269 

2039 

589 

2016 

1.42 

83799 

255 

2659 

802 

Figure 5.10.1.1. Hake in GSA 19. F SSB Recruitment and Catch simulated and fitted for 
the  a4a  assessment  model.  SSB  (Stock  Spawning  Biomass)  and  catch  are  in  tonnes, 
recruitment in 1000s individuals, harvest refers to fishing mortality for ages 0 to 3. 

5.10.2 STOCK ADVICE 

STECF  EWG  17-09  advises  that  when  MSY  considerations  are  applied  the  fishing 
mortality in 2018 should no more than 0.16, this implies catches of hake in GSA 19 in 
2017 of no more than 178 tons. 

87 

87 

 
 
 
 
 
 
 
5.10.3 BASIS OF THE ASSESSMENT  

Two assessment methods were tested , XSA and an a4a methods, both tuned with 
fishery independent abundance indices (MEDITS in GSA19). The differences between 
methods were minor (see section 6), not changing the perception of the stock. Overall 
the a4a model is considered to best represent the current state of the stock. Input data 
on landings, discards and length frequencies were taken from EU DCF data. Von 
Bertalanffy growth parameters and length-weight relationship as decided from the WG 
were taken from the last assessment (EWG 15-16). The natural mortality vector was 
obtained using the PRODBIOM method. The computations were made in R by applying 
the FLR libraries. 

5.10.4 CATCH OPTIONS  

 Short term prediction for the period 2017 - 2019 was performed using the FLR routines 
provided  by  JRC  and  based  on  the  results  of  the  stock  assessments  performed  during 
EWG 17-15 (Table 5.10.4.2). 

Table  5.10.4.1.  European  hake  in  GSA  19.  Short  term  forecasts  of  status  quo  for 
different fishing mortalities reductions. Fishing mortality in 2017 was F status quo =1.32 
(mean F in 2014-2016) giving catches for 2017 of 1010t. Recruitment in 2017 and 2018 
was assumed to be 58187geometric mean of time series (years 2003 to 2016) 

Rationale 

Ffactor Fbar Catch_2018 Catch_2019 SSB_2019 

Change_SSB 
2018-2019(%) 

Change_Catch 
2016-2018(%) 

zero catch 

0.0 

0.00  0.0 

0.0 

1627.1 

332 

-100 

F01 

0.1 

0.16  178.1 

364.2 

1322.3 

251 

Status quo 

1.0 

1.32  870.27 

830.60 

870.27 

-10 

0.2 

0.26  282.7 

525.4 

1149.9 

205 

0.3 

0.40  395.0 

657.4 

971.5 

158 

0.4 

0.53  492.2 

739.4 

823.8 

119 

0.5 

0.66  576.6 

788.2 

701.5 

86 

Different scenarios 

0.6 

0.79  650.5 

815.5 

600.1 

59 

0.7 

0.92  715.5 

829.0 

515.9 

37 

0.8 

1.06  773.1 

833.9 

445.8 

18 

0.9 

1.19  824.3 

833.7 

387.5 

3 

1.0 

1.32  870.3 

830.6 

338.7 

-10 

1.1 

1.45  911.7 

826.2 

298.0 

-21 

88 

88 

-78 

9 

-65 

-51 

-39 

-28 

-19 

-11 

-4 

3 

9 

14 

 
 
 
 
 
 
1.2 

1.58  949.3 

821.3 

263.8 

-30 

1.3 

1.72  983.6 

816.5 

235.0 

-38 

1.4 

1.85  1015.2 

812.2 

210.7 

-44 

1.5 

1.98  1044.4 

808.5 

190.2 

-50 

1.6 

2.11  1071.4 

805.5 

172.7 

-54 

1.7 

2.24  1096.7 

803.1 

157.8 

-58 

1.8 

2.38  1120.4 

801.4 

145.0 

-62 

1.9 

2.51  1142.8 

800.3 

134.0 

-64 

2.0 

2.64  1163.9 

799.7 

124.5 

-67 

18 

23 

27 

30 

34 

37 

40 

43 

45 

5.10.5 REFERENCE POINTS  

Table  5.10.5.1  European  hake  in  GSA  19.  Main  reference  points  defined  by  the  Yield 
per recruit analysis. 

Framework  Reference point 

Value 

Technical basis 

Source 

MSY approach 

MSY Btrigger  Not defined 

FMSY 

0.16 

F0.1 

This report 

5.10.6 DATA DEFICIENCIES 

Data  from  DCF  2017  were  used.  A  difference  in  the  sum  of  products  compared  to 
landings  was  around  37%  (excluding  2003).  Discards  data  of  2006,  2009-2016  were 
available.  Information  on    number    of    samples    for  landings,  discards  and  catches,  as 
well as the number of measurements by length for landings, discards and catches were 
also  available.  Number  of  otoliths  was  also  available.  MEDITS  raw  data  used  for  this 
assessment  have  been  processed  by  the  expert  using  the  LFD  routine  by  STECF  EWG. 
Biological parameters by length and age and sex ratio were available for the whole time 
series (2003-2016). 

In  2014  the  survey  was  shifted  in  September  as  a  consequence  of  the  administrative 
process undertaken Italian Ministry of Agriculture. 

89 

89 

 
 
 
 
 
 
 
 
5.11 SUMMARY SHEET OF HAKE IN GSA 20 

Species common name: European hake 

Species scientific name: Merluccius merluccius 

Geographical Sub-area(s) GSA(s): 20 

5.11.1 STOCK DEVELOPMENT OVER TIME 

The stock status is uncertain due to gaps in data collection, there are differences 
between different methods, the results given below are considered indicative of 
status but details are likely to change as more data becomes available. 

State of absolute and relative biomass 

Based  on  the  assessment  the  SSB  of  hake  fluctuated  over  the  time  period  examined 
(2003-2016)  from  440  tons  (in  2003)  to  284  tons  in  2016.  A  decline  in  SSB  was 
observed  from  2008  when  the  highest  SSB  was  observed  to  2015  when  the  lowest 
biomass was observed. 

State of the juveniles (recruits) 

The assessment shows a continuous decline in the number of recruits from 2006 to 2012 
and  an  increase thereafter.  The  recruitment  reached  a  maximum number of individuals 
in 2006 and a minimum value in 2012. 

State of exploitation 

Fbar (1-3) shows a continuously increasing trend from 2003 to 2008 and from 2013 to 
2016 reaching the value of 1.42 in 2016. The current F (1.42) is  much higher than F0.1 
(0.504), which indicates that hake in GSA 20 is being fished over fished.  

90 

90 

 
 
 
 
 
 
 
Figure  5.11.1.1  Hake  in  GSA  20.  A4A  assessment  main  outputs  with  confidence 
intervals. 

Table 5.11.1.1 Hake in GSA 20. A4a assessment summary results. 

Year 

Fbar (1-3) 

Recruitment (thousands) 

SSB (t) 

Catch (t) 

2003 

1.211 

2004 

1.426 

2005 

1.641 

2006 

1.804 

2007 

1.860 

2008 

1.796 

2009 

1.646 

2010 

1.469 

2011 

1.319 

2012 

1.224 

2013 

1.193 

2014 

1.223 

2015 

1.304 

27196 

42996 

61932 

73696 

68000 

48790 

29329 

16792 

10606 

8373 

8841 

12406 

21478 

91 

91 

441 

490 

519 

648 

779 

841 

793 

659 

503 

373 

285 

232 

232 

3188 

4235 

5856 

7659 

8471 

7670 

5806 

3895 

2517 

1726 

1389 

1428 

1965 

 
 
 
2016 

1.417 

41155 

284 

3325 

5.11.2 STOCK ADVICE 

The assessment is not considered suitable for specific catch advice due to the divergence 
between  surplus  production  and  analytical  methods  and  the  lack  of  stability  of  the 
analytical  model;  therefore  EWG  17-15  is  not  able  to  provide  catch  advice  for  hake  in 
GSA 20. 

5.11.3 BASIS OF THE ASSESSMENT  

The  stock  of  hake  in  GSA  20  was  assessed  based  on  surplus  production  (SPiCT  and 
CMSY) and analytical (a4a) methods.  

The  surplus  models  SPiCT  (Pedersen  and  Berg,  2016)  and  CMSY  (Froese  et  al.  2017) 
were applied using the official landings from the Ionian Sea (GSA 20) as reported in the 
FAO/GFCM Database (FishStat J) from 1982 to 2016. Data prior to 1982 were excluded 
because  the  landings  of  hake  were  reported  aggregated  with  other  species.  Landings 
records were considered as equal to catch records because according to the Greek DCF 
report the discards of hake in Ionian Sea are negligible. The CPUE from MEDITS bottom 
trawl  surveys  that  were  conducted  in  the  Greek  part  of  the  Ionian  Sea  was  used  as 
tuning index for both models. Tuning index was available from 1994 to 2008 (with gaps 
in 2002 and 2007) and from 2014 to 2016 (with gap in 2015).  
The analytical method applied was the Assessment for All Initiative (a4a) (Jardim et al., 
2014) in FLR environment with the hake landings data of GSA 22 from 2003 to 2016. A 
single  tuning  fleet  was  used  based  on  the  biomass  at  age  estimates  from  summer 
MEDITS  bottom  trawl  surveys  conducted  in  GSA  20  (2003  to  2016  with  gaps  in  2007, 
2009-2013 and 2015). DCF catch data with gaps in the respective missing years for the 
catch at age information were used for the a4a method. Assessment was performed with 
version 1.1.3 of FLa4a, together with version 2.6.5 of the FLR library (FLCore). 

5.11.4 CATCH OPTIONS  

No short-term prediction was carried out for this stock as the STECF EWG-1715 decided 
not to provide scientific advice based on the specific assessments because of the 
divergence among the methods and the lack of stability of the analytical model. 

5.11.5 REFERENCE POINTS  

Table  5.11.5.1  Hake  in  GSA  20.  Reference  points,  values,  and  their  technical  basis  of 
the a4a model. 

Framework 

MSY 
approach 
Precautionary 
Approach 

Reference 
point 
MSY Btrigger 
FMSY 
Blim 
Bpa 

Value 

Technical basis 

Source 

0.504 

F0.1 

This Report 

92 

92 

 
 
 
 
 
 
 
 
 
 
 
 
5.11.6 DATA DEFICIENCIES 

Several  deficiencies  were  found  in  the  DCF  data  provided.  Specifically,  no  DCF  catch  / 
catch-at-length  /  catch-at-age  data  were  provided  for  2007,  2009,  2010,  2011,  and 
2012. Catch-at-age data were provided only for the last trimester for 2013 and 2015. No 
MEDITS  bottom  trawl  surveys  took  place  in  2002,  2007,  2009-2013  and  2015.  The 
landings  as  calculated  from  the  DCF  data  (number  of  individuals  multiplied  by  their 
somatic  weight)  do  not  correspond  to  the  official  landings  reported  for  the  stock.  This 
issue  is  stronger  for  the  years  2003-2006.  The  numbers  and  weights  at  length  are  not 
reported  consistently  (step  size,  initial  value,  unit  of  measurement  vary  among  years). 
Specifically  for  hake  in  GSA  20  there  were  also  some  additional  issues  with  the  2005 
dataset regarding the numbers and format. 

93 

93 

 
 
 
5.12 SUMMARY SHEET OF RED MULLET IN GSA 19 

Species common name: Red mullet 

Species scientific name: Mullus barbatus 

Geographical Sub-area(s) GSA(s): 19 

5.12.1 STOCK DEVELOPMENT OVER TIME 

State of absolute and relative biomass 

The SSB showed a decreasing trend from 2006 to 2014, while in 2015 and 2016 SSB 
is estimated to be increasing.  

State of the juveniles (recruits) 

The recruitment show a peak in 2009 and then decreases until 2013. 2014 and 2015 
recruitment  values  increases,  for  2016  recruitment  is  seen  to  decline  but  is  very 
uncertain. 

State of exploitation 

The F is decreasing along the time series. The current F estimated by the 
assessment (Fstq=0.56) is greater than Fmsy (Fmsy=0.36), which indicates that red 
mullet in GSA 19 is being overfished. 

Table 5.12.1.1.  Red mullet GSA 19 assessment results. F, SSB, Recruitment and Catch 
estimates from the assessment for Red mullet in GSA 19. 

Year  R (thousands)  SSB (t)  Catch (t)  Fbar (0-2) 
1279 
2006 
890 
2007 
555 
2008 
718 
2009 
914 
2010 
734 
2011 
547 
2012 
443 
2013 
426 
2014 
544 
2015 
641 
2016 

95871 
42200 
48865 
66602 
53346 
40768 
35122 
32167 
41147 
47277 
27230 

1112 
645 
400 
420 
594 
522 
384 
298 
278 
295 
257 

3.23 
2.31 
1.77 
1.55 
1.58 
1.78 
1.98 
1.92 
1.52 
0.98 
0.56 

94 

94 

 
 
 
 
  
 
 
 
 
 
 
Figure 5.12.1.1. Red mullet GSA 19. F, SSB, Recruitment and Catch estimates from 
the assessment for Red mullet in GSA 19 estimated from a4a. 

5.12.2 STOCK ADVICE 

STECF  EWG  17-09  advises  that  when  MSY  considerations  are  applied  the  fishing 
mortality in 2018 should no more than 0.36, this implies catches of red mullet in GSA 
19 in 2017 of no more than 272 tons. 

5.12.3 BASIS OF THE ASSESSMENT  

Stock  assessments  were  performed  applying  an  Extended  Survivor  Analysis  (XSA) 
and an Assessment for All (a4a) methods calibrated with fishery independent survey 
abundance  indices  (MEDITS).  The  differences  between  methods  are  minor  and  do 
not  change  the perception  of  the  state of  stock  or  fishery.  A  yield-per-recruit  (Y/R) 
analysis  was  carried  out.  Both  methods  were  based  on  the  size  composition  of 
landings and discards and were taken from DCF. Von Bertalanffy growth parameters, 
length-weight relationship and natural mortality vector, were taken from parameters 
used for red mullet in GSA 19 in the previous assessment (STECF EWG 17-15). 

5.12.4 CATCH OPTIONS  

Table  5.12.4.1.  Red  mullet  in  GSA  19.  Short  term  forecasts  of  status  quo  for 
different  fishing  mortalities  reductions.  Fishing  mortality  in  2017  was  F  status  quo 

95 

95 

 
 
 
 
 
 
=0.56 giving catches for 2017 of 272t. Recruitment in 2017 and 2018 was assumed to 
be 37556 (thousands) mean of years 2014 to 2016 

Basis 

Fbar 

Catch2018  Catch2019  SSB2019 

Change_SSB 
2018-2019(%) 

Change_Catch 
2016-2018(%) 

zero catch 

F0.1 

Different 
scenarios 

0 

0.36 

0.11 

0.23 

0.34 

0.45 

0.56 

0.68 

0.79 

0.90 

1.01 

1.13 

0 

253.4 

91.5 

171.3 

241.0 

302.0 

355.6 

402.8 

444.5 

481.3 

514.1 

543.3 

0 

318.1 

139.5 

238.3 

307.4 

355.1 

387.1 

408.0 

420.9 

428.1 

431.3 

431.8 

1558.0 

1162.6 

1412.9 

1288.4 

1181.5 

1089.4 

1010.0 

941.2 

881.5 

829.6 

784.1 

744.3 

68.3 

25.6 

52.7 

39.2 

27.7 

17.7 

9.1 

1.7 

-4.7 

-10.4 

-15.3 

-19.6 

-100 

-1.2 

-64.3 

-33.3 

-6.1 

17.7 

38.6 

57 

73.2 

87.6 

100.3 

111.7 

5.12.5 REFERENCE POINTS  

Framework 

MSY approach 

Precautionary 
Approach 

Reference 
point 
MSY Btrigger 
FMSY 
Blim 
Bpa 

5.12.6 DATA DEFICIENCIES 

Value 

Technical basis 

Source 

0.359 

F0.1 

This Report 

Survey sampling period (MEDITS) has been done in different periods in different years. 
The  displacement  of  MEDITS  survey  to  August  (2007)  and  September  (2014),  the 
recruitment  period  for  red  mullet,  results  in  difficulties  in  the  tuning  the  assessment 
model. 

It  is  advisable,  that  the  MEDITS  protocol  be  modified  to  require  that  surveys  will  be 
carried out always in the same month of the year every year. 

96 

96 

 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
5.13 SUMMARY SHEET OF RED MULLET IN GSA 20 

Species common name: Red mullet 

Species scientific name: Mullus barbatus 

Geographical Sub-area(s) GSA(s): 20 

5.13.1 STOCK DEVELOPMENT OVER TIME 

The STECF EWG 17 -15 is not able to provide any information about the stock 
development or fishery over time due to the large disagreement between the 
different methods applied for the assessment of the stock. In particular the age 
based method showed a highly overfished stock while the two surplus production 
methods suggested an under exploited stock. 

5.13.2 STOCK ADVICE 

The assessment is not considered suitable for catch advice due to the disagreement 
between  surplus  production  and  analytical  methods.  Therefore  EWG  17-15  is  not 
able to provide catch advice for Red mullet in GSA 20. 

5.13.3 BASIS OF THE ASSESSMENT  

The stock of Red  mullet  in  GSA 20 was  assessed applying three different  methods: 
an age based method calibrated with fishery independent survey abundance indices 
(MEDITS  in  GSA  20)  and  two  surplus  production  models.  Input  data  on  landings 
were taken from FAO Official Statistics and National Statistical Service of Greece and 
length frequencies were taken from EU DCF data. Von Bertalanffy growth parameters 
were taken from the ones reported for GSA 19 and length-weight relationship were 
taken  from  DCF.  The  natural  mortality  vector  was  obtained  using  the  PRODBIOM 
method.  

The  analytical  method  applied  was  the  Assessment  for  All  Initiative  (a4a)  in  FLR 
environment using Red mullet landings data of GSA 20 from 2003 to 2016. A single 
tuning  fleet  was  used  based  on  the  abundance  at  age  estimates  from  summer 
MEDITS bottom trawl  surveys conducted in  GSA20. Catch at age was based on the 
DCF catch data (with gaps in certain years) .  

The  surplus  production  models  SPiCT  and  CMSY  were  applied  using  the  official 
landings from the Ionian Sea (GSA 20) as reported in the FAO/ GFCM Database from 
1994  to  2016  for  SPICT  and  from  1982  for  CMSY,  respectively.  The  CPUE  from 
MEDITS surveys that were conducted in the Greek part of the Ionian Sea was used 
as a tuning index in both models.  
The computation was made using the R-project software and the FLR libraries. 

5.13.4 CATCH OPTIONS  

No short-term prediction was carried out for this stock as the STECF EWG 17- 15 
decided not to provide scientific advice based on the specific assessments because of 
the disagreement among the methods. 

97 

97 

 
 
 
 
5.13.5 REFERENCE POINTS  

There are no reference points for this stock 

5.13.6 DATA DEFICIENCIES  

Large gaps and inconsistencies were detected  both in catch and survey data leading 
to  great  differences  in  the  results  of  the  three  different  methodologies  applied  to 
assess the stock status. In particular: 

  Landings: There were gaps in the landings data for GSA 20, for the years 

2007, 2009 – 2012, while for the years 2013, 2015 landings data were only 
reported for the last quarter of the year. Landings by gear suffered from great 
inconsistencies. The values of the reported landings for the small scale/ coastal 
fisheries in the year 2003 appeared 10 times larger than the ones reported for 
the trawlers while in the other years the ratio was a lot smaller. More 
specifically the ratio of reported landings from trawlers and small scale 
fisheries ranged between 1/3 to 1/5. Moreover by inspecting the 
corresponding length frequency distribution for the year 2003 only three 
length classes seem to be present in the observed data, with values exceeding 
by far the expected ones The size distribution of the landings, due to the 
previous mentioned issues in the dataset, were problematic especially in small 
length classes.  

  Discards: The same gaps observed in the landings data were also apparent in 

the discards data.  

  Effort: Effort data followed the same pattern as landings/discards(significant 

gaps)).  

  Catch at age data: years with no data were more than the years with data. In 
particular: weight at age data were present only for the years 2004-2006, 
2013 and 2014 and concerned only the landed part of the catch. Moreover the 
reported values for mean weight at age diverged significantly from the 
expected ones. The numbers and weights at age suffered from internal 
inconsistencies (step size, initial value, unit of measurement vary among 
years). 

  MEDITS Survey: Gaps were also apparent in the MEDITS trawl survey data 

set. Years 2002, 2007, 2009 -2013 and 2015 were missing. 

98 

98 

 
 
 
5.14 SUMMARY SHEET OF HAKE IN GSA 22 

Species common name: European hake 

Species scientific name: Merluccius merluccius 

Geographical Sub-area(s) GSA(s): 22 

5.14.1 STOCK DEVELOPMENT OVER TIME 

State of absolute and relative biomass 

The stock’s relative and absolute biomass show declining trends in the last 10 years 
and current biomass levels are estimated to be close to Bmsy. However, the 
confidence intervals of the estimates are high. 

State of the juveniles (recruits) 

The  state  of  juveniles  cannot  be  deduced  from  the  modelling  approach  that  was 
used. 

State of exploitation 

Current fishing mortality is estimated to be close to Fmsy, and is at higher levels than 
those  observed  in  the  early  2000’s.  However,  the  confidence  intervals  of  the 
estimates are wide. 

99 

99 

 
 
 
 
 
 
 
 
   
Table 5.14.1.1  Hake GSA 22. Predicted average values of absolute biomass, absolute 
fishing mortality, relative biomass, relative fishing mortality and catch for the period 
1970-2016.  

Year 

B 

1970  11271.30 
1971  13057.65 
1972  14910.92 
1973  16583.87 
1974  17702.62 
1975  18463.10 
1976  19114.53 
1977  19534.52 
1978  19756.53 
1979  19848.58 
1980  19989.13 
1981  20115.74 
1982  20209.70 
1983  20184.74 
1984  20000.94 
1985  19816.12 
1986  19331.52 
1987  18507.05 
1988  17966.74 
1989  17363.94 
1990  16651.12 
1991  16237.98 
1992  16262.91 
1993  16081.43 
1994  15666.89 
1995  14809.24 
1996  14369.87 
1997  14360.72 
1998  14619.63 
1999  15157.89 
2000  15656.96 
2001  16124.76 
2002  16714.30 
2003  17198.62 
2004  17414.64 
2005  17379.31 
2006  17185.78 
2007  16624.57 
2008  15819.01 
2009  14894.67 
2010  14082.84 
2011  13564.02 
2012  13300.85 
2013  13099.50 
2014  12781.01 
2015  12967.52 
2016  13176.73 

F 
0.09 
0.08 
0.07 
0.09 
0.09 
0.09 
0.08 
0.09 
0.09 
0.09 
0.08 
0.08 
0.08 
0.09 
0.10 
0.11 
0.15 
0.16 
0.17 
0.19 
0.20 
0.17 
0.17 
0.19 
0.23 
0.25 
0.22 
0.20 
0.17 
0.15 
0.15 
0.13 
0.13 
0.13 
0.15 
0.16 
0.18 
0.21 
0.24 
0.25 
0.25 
0.24 
0.23 
0.25 
0.23 
0.20 
0.22 

B/Bmsy 
0.82 
0.95 
1.09 
1.21 
1.29 
1.34 
1.39 
1.42 
1.44 
1.45 
1.46 
1.47 
1.47 
1.47 
1.46 
1.44 
1.41 
1.35 
1.31 
1.26 
1.21 
1.18 
1.18 
1.17 
1.14 
1.08 
1.05 
1.05 
1.06 
1.10 
1.14 
1.17 
1.22 
1.25 
1.27 
1.27 
1.25 
1.21 
1.15 
1.08 
1.03 
0.99 
0.97 
0.95 
0.93 
0.94 
0.96 

F/Fmsy 

Cpred 

0.42  1012.97 
0.37  1040.99 
0.34  1096.89 
0.40  1412.11 
0.44  1652.24 
0.40  1584.81 
0.39  1609.43 
0.41  1723.58 
0.43  1798.52 
0.41  1731.15 
0.38  1645.02 
0.38  1626.35 
0.39  1693.37 
0.44  1880.25 
0.46  1972.58 
0.53  2261.49 
0.70  2891.96 
0.75  2972.19 
0.78  2987.55 
0.89  3324.53 
0.92  3265.70 
0.80  2787.28 
0.80  2787.31 
0.90  3094.35 
1.07  3593.10 
1.15  3630.25 
1.02  3130.18 
0.92  2814.95 
0.78  2446.88 
0.71  2305.95 
0.69  2299.04 
0.62  2151.26 
0.59  2099.50 
0.62  2269.22 
0.70  2591.06 
0.76  2840.52 
0.85  3124.17 
0.99  3527.56 
1.11  3768.20 
1.19  3787.95 
1.18  3560.92 
1.12  3253.70 
1.09  3099.03 
1.16  3245.09 
1.06  2910.29 
0.96  2653.58 
1.03  2899.26 

100 

100 

 
 
Figure  5.14.1.1  Hake  GSA  22.  Upper  row:  absolute  and  relative  biomass  prediction 
with 95% CI (solid line) and observed biomass (points), i.e. abundance index scaled by 
catchability, q, and Bmsy (left) and absolute and relative fishing mortality prediction with 
95% CI (right). Bottom row: Observed (points) and predicted catch (solid line) with 95% 
CI  (dotted  lines)  and  average  estimate  of  MSY  (black  solid  line)  with  95%  CI  (grey 
shaded area) (left) and Kobe plot (right). 

5.14.2 STOCK ADVICE 

Given the high uncertainty of the relative biomass and fishing mortality estimates, it 
is provided not possible to provide specific catch advice. 

5.14.3 BASIS OF THE ASSESSMENT 

The assessment was based on a state-space surplus production model implemented 
in SPiCT (Pedersen and Berg, 2017). The data used are the time series of landings in 
the  period  1970-2016  taken  from  FAO  and  a  time-series  of  abundance  indices 
estimated from the MEDITS trawl surveys, when they were accomplished. 

101 

101 

 
 
 
 
5.14.4 CATCH OPTIONS  

Due to the high uncertainty in the relative estimates of biomass and fishing mortality 
no projections were accomplished. 

5.14.5 REFERENCE POINTS  

Due  to  the  high  uncertainty  in  the  parameter  estimates,  no  reference  points  are 
presented. 

5.14.6 DATA DEFICIENCIES  

Catch-at-size  data  were  available  only  for  the  years  that  the  DCF  took  place,  i.e. 
2003-2005,  2008,  2014,  2016  and  partly  for  2013  and  2015.  Abundance  indices 
from the MEDITS surveys have also missing years (2002, 2007, 2009-2013, 2015). 

102 

102 

 
 
 
5.15 SUMMARY SHEET OF RED MULLET IN GSA 22 

Species common name: Red mullet 

Species scientific name: Mullus barbatus 

Geographical Sub-area(s) GSA(s): 22 

5.15.1 STOCK DEVELOPMENT OVER TIME 

State of absolute and relative biomass 

The stock’s relative and absolute biomass show increasing trends since 1996 and 
current biomass levels are above Bmsy, according to the surplus production model. 
However, the confidence intervals of the estimates are very wide and the conclusions 
are sensitive to the last two data points. 

State of the juveniles (recruits) 

The  surplus  production  model  does  not  provide  information  on  recruitment. 
Alternative  recruitment  estimates  based  on  age  structure  model  shows  a  peak  in 
2009  and is  decreasing  afterwards,  but this  is  highly  uncertain  given  that  catch-at-
age data are missing in half of the years used in the age-based assessment, which 
has resulted in poor model fit. 

State of exploitation 

Both  models  used  (surplus  production  with  SPiCT  and  age-structured  model  with 
a4a) predict that the red mullet stock in GSA-22 is currently being fished below FMSY. 
The fishing mortality shows a declining trend since 1996, as predicted by the surplus 
production  model,  and  current  F/Fmsy  is  below  1  but  with  very  wide  confidence 
intervals. The age-structured model indicates that fishing mortality is declining after 
2010 and current F is below F0.1.  

103 

103 

 
 
 
 
 
 
Table 5.15.1.1  Red mullet in GSA 22. Predicted average values of absolute biomass, 
absolute fishing mortality, relative biomass, relative fishing mortality and catch for the 
period 1994-2016 from the surplus production model with SPiCT.  

Year 

1994 

1995 

1996 

1997 

1998 

1999 

2000 

2001 

2002 

2003 

2004 

2005 

2006 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

B 

F 

BBmsy 

FFmsy 

Cpred 

5955.66 

4341.68 

3881.19 

4070.35 

4290.48 

4894.37 

5350.22 

5698.36 

6181.30 

6581.08 

7044.23 

7250.02 

7105.50 

6697.67 

6520.46 

6675.68 

6686.77 

6518.80 

6686.22 

7051.37 

7213.77 

7304.23 

7475.39 

0.88 

0.87 

0.76 

0.68 

0.55 

0.45 

0.43 

0.36 

0.30 

0.23 

0.22 

0.28 

0.35 

0.37 

0.32 

0.32 

0.37 

0.33 

0.27 

0.26 

0.26 

0.24 

0.23 

1.73 

1.26 

1.12 

1.18 

1.24 

1.42 

1.55 

1.65 

1.79 

1.91 

2.04 

2.10 

2.06 

1.94 

1.89 

1.93 

1.94 

1.89 

1.94 

2.04 

2.09 

2.12 

2.17 

1.02 

1.01 

0.89 

0.79 

0.64 

0.53 

0.50 

0.41 

0.35 

0.27 

0.26 

0.32 

0.40 

0.43 

0.37 

0.38 

0.43 

0.38 

0.31 

0.31 

0.30 

0.27 

0.26 

5224.43 

3771.32 

2964.46 

2765.14 

2367.10 

2224.49 

2299.36 

2026.40 

1853.73 

1543.79 

1571.98 

2005.86 

2458.26 

2462.36 

2095.70 

2157.55 

2454.36 

2156.49 

1780.71 

1851.39 

1876.49 

1727.06 

1700.56 

104 

104 

 
 
Figure  5.15.1.1  Red  mullet  in  GSA  22.  Stock  assessment  results  from  a  surplus 
production model using SPiCT. Upper row: absolute and relative biomass prediction with 
95%  CI  (solid  line)  and  observed  biomass  (points),  i.e.  abundance  index  scaled  by 
catchability (q) and Bmsy (left) and absolute and relative fishing mortality prediction with 
95% CI (right). Bottom row: Observed (points) and predicted catch (solid line) with 95% 
CI  (dotted  lines)  and  average  estimate  of  MSY  (black  solid  line)  with  95%  CI  (grey 
shaded area) (left) and Kobe plot (right). 

Table  5.15.1.2    Red  mullet  in  GSA  22.  F,  Recruitment,  SSB  and  predicted  catch 
estimates derived from the a4a assessment.  

Fbar1-3 

Recruitment 
(thousands) 

SSB (t) 

Year 

2003 

2004 

2005 

2006 

2007 

2008 

2009 

2010 

0.46 

0.47 

0.48 

0.50 

0.53 

0.58 

0.61 

0.62 

86154 

92145 

98165 

103650 

107914 

110302 

110336 

5364 

4729 

4622 

4702 

4810 

4852 

4807 

Catch 
(t) 

2448 

1878 

1773 

1836 

1975 

2120 

2209 

107838 

4708 

2192 

105 

105 

 
 
 
 
2011 

2012 

2013 

2014 

2015 

2016 

0.58 

0.49 

0.37 

0.25 

0.16 

0.10 

102981 

96242 

88302 

79887 

71632 

63977 

4612 

4574 

4622 

4760 

4967 

5192 

2051 

1802 

1479 

1131 

809 

550 

Figure  5.15.1.2    Red-mullet  in  GSA  22.  Recruitment,  SSB,  Catch  and  F  estimates 
derived from the a4a assessment. 

5.15.2 STOCK ADVICE 

Given the high uncertainty of the relative biomass and fishing mortality estimates, it  
is not possible to provide specific catch advice. 

5.15.3 BASIS OF THE ASSESSMENT  

The  stocks  status  was  assessed  using  two  different  approaches:  (a)  a  surplus 
production  model  using  the  SPiCT  package  (Pedersen  and  Berg,  2017)  and  (b)  the 
a4a age-structured model (Jardim et al., 2014). Both models were run under the R-
language  environment (R core team 2017). 

106 

106 

 
 
 
 
 
 
Regarding catch information, the data used were the time series of FAO landings for 
the  period  2003-2014  and  landings  from  the  National  Statistical  Service  of  Greece 
(NSSG)  (which  officially  reports  data  to  FAO)  for  years  2015  and  2016.  Abundance 
index  estimates  were  obtained  from  the  MEDITS  trawl  surveys,  when  they  were 
accomplished. Catch-at-age estimates were based on the catch-at-length data from 
the Greek DCF for the years 2003-2006, 2008, 2014 and 2016.  

Discards  were  considered  negligible;  hence,  landings  data  were  considered  as 
representing the total catch. 

Both  methods  lead  to  similar  results  in  terms  of  stock  status  and  fishing  mortality 
but  due  to  the  uncertainties  and  missing  values  in  the  data  the  results  are  not 
considered suitable for short term predictions.    

5.15.4 CATCH OPTIONS  

Due to the high uncertainty in biomass and fishing mortality estimates, no 
projections were carried out. 

5.15.5 REFERENCE POINTS  

Due to the great uncertainty of the model output no reference points are given. 

5.15.6 DATA DEFICIENCIES 

Data  on  length  distribution  by  age  were  available  only  for  the  years  that  the  DCF 
took  place,  i.e.  2003-2005,  2008,  2014,  2016  and  partly  for  2013  and  2015.  The 
data  on  relative  biomass  from  the  MEDITS  survey  have  also  missing  years  (2002, 
2007, 2009-2013, 2015). 

107 

107 

 
 
   
 
 
5.16 SUMMARY SHEET OF DEEP-WATER ROSE SHRIMP IN GSA 22 

Species common name: Deep-water rose shrimp 

Species scientific name: Parapenaeus longirostris 

Geographical Sub-area(s) GSA(s): 22 

5.16.1 STOCK DEVELOPMENT OVER TIME 

State of absolute and relative biomass 

The  stock’s  biomass  levels  appear  to  be  above  Bmsy  since  1996,  but  with  a 
decreasing  trend  in  the  recent  years,  according  to  the  surplus  production  model 
(Figure 5.16.1). However, the confidence intervals of the estimates are very wide. In 
addition, the effect of the two first records of abundance index have a great effect on 
the  assessment  output  both  in  terms  of  historic  trends  and  the  current  levels  of 
biomass and fishing mortality. Thus, the state of the biomass cannot be deduced as 
the assessment result is highly uncertain. 

State of the juveniles (recruits) 

The state of juveniles cannot be deduced from the modelling approach that was used 
(surplus production model). 

State of exploitation 

Fishing  mortality  appears  to  be  below  Fmsy,  with  an  increasing  trend  in  the  recent 
years  (Figure  5.16.1).  However,  the  confidence  intervals  of  the  estimates  are 
relatively  high.  In  addition,  the  effect  of  the  two  first  records  of  abundance  index 
have  a  great  effect  on  the  assessment  output  both  in  terms  of  historic  trends  and 
the  current  levels  of  biomass  and  fishing  mortality.  Thus,  the  estimate  of  fishing 
mortality cannot be deduced as the assessment result is highly uncertain. 

108 

108 

 
 
 
 
No estimates of biomass and fishing mortality are reported as the assessment was 
considered dubious given the extremely high uncertainty of the estimates (Figure 
5.16.1). 

Figure  5.16.1  Deep-water  rose  shrimp  in  GSA  22.  Upper  row:  absolute  and 
relative  biomass  prediction  with  95%  CI  (solid  line)  and  observed  biomass 
(points),  i.e.  abundance  index  scaled  by  catchability,  q,  and  Bmsy  (left)  and 
absolute  and  relative  fishing  mortality  prediction  with  95%  CI  (right).  Bottom 
row:  Observed  (points)  and  predicted  catch  (solid  line)  with  95%  CI  (dotted 
lines) and average estimate of MSY (black solid line) with 95% CI (grey shaded 
area) (left) and Kobe plot (right). 

5.16.2 STOCK ADVICE 

The assessment was considered unreliable due to the very high uncertainty and it is 
not possible to provide specific catch advice. 

5.16.3 BASIS OF THE ASSESSMENT  

The assessment was based on a state-space surplus production model implemented 
in SPiCT (Pedersen and Berg, 2017). The data used are the time series of landings 

109 

109 

 
 
 
 
for the period 1986-2016 taken from FAO and a time-series of abundance indices 
estimated from the MEDITS trawl surveys, in the years when they were carried out. 

5.16.4 CATCH OPTIONS  

Due to the very high uncertainty of the model output, no projections were  

5.16.5 REFERENCE POINTS  

Due to the very high uncertainty in the biomass and fishing parameter estimates, no 
reference points are presented. 

5.16.6 DATA DEFICIENCIES 

Catch-at-size  data  were  available  only  for  the  years  that  the  DCF  took  place,  i.e. 
2003-2005,  2008,  2014,  2016  and  partly  for  2013  and  2015.  Abundance  indexes 
from the MEDITS surveys have also missing years (2002, 2007, 2009-2013, 2015). 

110 

110 

 
 
 
5.17 SUMMARY SHEET OF HAKE IN GSA 23 

Species common name: European hake 

Species scientific name: Merluccius merluccius 

Geographical Sub-area(s) GSA(s): 23 

5.17.1 STOCK DEVELOPMENT OVER TIME 

State of absolute and relative biomass 

The stock’s relative and absolute biomass show declining trends in the last 10 years 
and current biomass levels are below Bmsy. However, this result is quite sensitive to 
the most recent observation of both, abundance index and landings value.  

State of the juveniles (recruits) 

The  state  of  juveniles  cannot  be  deduced  from  the  modelling  approach  that  was 
used. 

State of exploitation 

The fishing mortality is generally increasing in the period 1970-2016. Currently, it is 
well  above  Fmsy,  although  the  uncertainty  of  this  estimate  is  very  high.  The  last 
year’s    (2016)  estimate  is  sensitive  to  the  corresponding  observations  of  both 
abundance index and landings. 

111 

111 

 
 
 
 
 
 
Table 5.17.1.1.  Hake GSA-23. Predicted average values of absolute biomass, absolute 
fishing mortality, relative biomass, relative fishing mortality and catch for the period 
1970-2016.  

Year 
1970 
1971 
1972 
1973 
1974 
1975 
1976 
1977 
1978 
1979 
1980 
1981 
1982 
1983 
1984 
1985 
1986 
1987 
1988 
1989 
1990 
1991 
1992 
1993 
1994 
1995 
1996 
1997 
1998 
1999 
2000 
2001 
2002 
2003 
2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

B 
745.27 
805.04 
846.66 
862.85 
847.02 
836.02 
838.06 
831.28 
823.17 
813.72 
818.10 
820.94 
824.23 
817.96 
801.98 
794.51 
756.99 
694.26 
676.56 
640.47 
592.13 
570.67 
584.68 
564.63 
534.13 
438.89 
378.68 
345.18 
328.15 
354.79 
386.18 
425.45 
483.20 
538.62 
576.17 
583.65 
584.95 
556.31 
504.66 
445.61 
389.77 
354.05 
331.66 
289.69 
212.74 

F 
0.10 
0.09 
0.08 
0.11 
0.14 
0.13 
0.13 
0.14 
0.15 
0.14 
0.13 
0.13 
0.14 
0.16 
0.16 
0.19 
0.28 
0.29 
0.29 
0.37 
0.39 
0.33 
0.34 
0.39 
0.50 
0.65 
0.61 
0.62 
0.52 
0.43 
0.42 
0.35 
0.30 
0.29 
0.31 
0.33 
0.36 
0.45 
0.52 
0.59 
0.62 
0.60 
0.63 
0.82 
0.94 

BBmsy 
1.51 
1.63 
1.72 
1.75 
1.72 
1.69 
1.70 
1.68 
1.67 
1.65 
1.66 
1.66 
1.67 
1.66 
1.62 
1.61 
1.53 
1.41 
1.37 
1.30 
1.20 
1.16 
1.18 
1.14 
1.08 
0.89 
0.77 
0.70 
0.66 
0.72 
0.78 
0.86 
0.98 
1.09 
1.17 
1.18 
1.19 
1.13 
1.02 
0.90 
0.79 
0.72 
0.67 
0.59 
0.43 

FFmsy  Cpred 
75.79 
0.25 
69.34 
0.21 
69.26 
0.20 
98.05 
0.28 
118.49 
0.34 
104.59 
0.30 
107.38 
0.31 
117.65 
0.34 
124.98 
0.37 
117.69 
0.35 
110.34 
0.33 
108.98 
0.32 
112.35 
0.33 
130.67 
0.39 
130.37 
0.39 
147.42 
0.45 
211.47 
0.68 
202.26 
0.71 
195.86 
0.70 
234.86 
0.89 
233.26 
0.95 
187.65 
0.80 
196.54 
0.81 
220.92 
0.95 
269.56 
1.22 
286.66 
1.58 
230.72 
1.48 
213.51 
1.50 
170.05 
1.26 
153.73 
1.05 
161.35 
1.01 
148.53 
0.85 
144.49 
0.72 
154.98 
0.70 
180.63 
0.76 
194.66 
0.81 
212.17 
0.88 
248.42 
1.08 
263.54 
1.27 
263.14 
1.43 
241.37 
1.50 
212.44 
1.45 
209.14 
1.53 
238.79 
2.00 
199.95 
2.28 

112 

112 

 
2015 

170.73 

0.85 

0.35 

2.06 

145.20 

Figure  5.17.1.1.  Hake  GSA-23.  Upper  row:  absolute  and  relative  biomass  prediction 
with 95% CI (solid line) and observed biomass (points), i.e. abundance index scaled by 
catchability, q, and Bmsy (left) and absolute and relative fishing mortality prediction with 
95% CI (right). Bottom row: Observed (points) and predicted catch (solid line) with 95% 
CI  (dotted  lines)  and  average  estimate  of  MSY  (black  solid  line)  with  95%  CI  (grey 
shaded area) (left) and Kobe plot (right). 

5.17.2 STOCK ADVICE 

Given the high uncertainty of the relative biomass and fishing mortality estimates, it 
is not possible to provide specific catch advice. 

5.17.3 BASIS OF THE ASSESSMENT  

The assessment was based on a state-space surplus production model implemented 
in SPiCT (Pedersen and Berg, 2017). The data used are the time series of landings in 
the  period  1970-2016  taken  from  FAO  and  a  time-series  of  abundance  indices 
estimated from the MEDITS trawl surveys, in the years when they were carried out. 

113 

113 

 
 
 
 
5.17.4 CATCH OPTIONS  

Due to the high uncertainty in the relative estimates of biomass and fishing mortality 
no projections were carried out. 

5.17.5 REFERENCE POINTS  

Due  to  the  high  uncertainty  in  the  parameter  estimates,  no  reference  points  are 
presented. 

5.17.6 DATA DEFICIENCIES 

Catch-at-size  data  were  available  only  for  the  years  that  the  DCF  took  place,  i.e. 
2003-2005,  2008,  2014,  2016  and  partly  for  2013  and  2015.  Abundance  indexes 
from the MEDITS surveys have also missing years (2002, 2007, 2009-2013, 2015). 

114 

114 

 
   
 
 
5.18 SUMMARY SHEET OF RED MULLET IN GSA 23 

Species common name: Red mullet 

Species scientific name: Mullus barbatus 

Geographical Sub-area(s) GSA(s): 23 

5.18.1 STOCK DEVELOPMENT OVER TIME 

State of absolute and relative biomass 

The state of the biomass cannot be deduced as the assessment results are highly 
uncertain. 

State of the juveniles (recruits) 

The  state  of  juveniles  cannot  be  deduced  from  the  modelling  approach  that  was 
used. 

State of exploitation 

The state of exploitation cannot be deduced as the assessment result is highly 
uncertain and not trusted 

115 

115 

 
 
 
 
 
 
 
No estimates of biomass and fishing mortality are reported as the assessment was 
considered dubious given the extremely high uncertainty of the estimates (Figure 
5.1.1.1). 

Fig. 5.18.1.1 Red mullet GSA-23. Upper row: absolute and relative biomass prediction 
with 95% CI (solid line) and observed biomass (points), i.e. abundance index scaled by 
catchability, q, and Bmsy (left) and absolute and relative fishing mortality prediction with 
95% CI (right). Bottom row: Observed (points) and predicted catch (solid line) with 95% 
CI  (dotted  lines)  and  average  estimate  of  MSY  (black  solid  line)  with  95%  CI  (grey 
shaded area) (left) and Kobe plot (right). 

5.18.2 STOCK ADVICE 

The assessment  was considered unreliable due to the very high uncertainty  and no 
advice is provided. 

5.18.3 BASIS OF THE ASSESSMENT  

The assessment was based on a state-space surplus production model implemented 
in SPiCT  (Pedersen  and  Berg, 2017). The data used  are the time series of landings 
for  the  period  1994-2016  taken  from  the  National  Statistical  Service  of  Greece 
(NSSG)  and  a  time-series  of  abundance  indices  estimated  from  the  MEDITS  trawl 
surveys, when they were accomplished. 

116 

116 

 
 
5.18.4 CATCH OPTIONS  

Due to the very high uncertainty of the model output no projections were 
accomplished. 

5.18.5 REFERENCE POINTS  

Due to the very high uncertainty in the biomass and fishing parameter estimates, no 
reference points are presented. 

5.18.6 DATA DEFICIENCIES  

Catch-at-size  data  were  available  only  for  the  years  that  the  DCF  took  place,  i.e. 
2003-2005,  2008,  2014,  2016  and  partly  for  2013  and  2015.  Abundance  indexes 
from the MEDITS surveys have also missing years (2002, 2007, 2009-2013, 2015). 

117 

117 

 
 
 
5.19 SUMMARY SHEET OF DEEP-WATER ROSE SHRIMP IN GSA 23 

Species common name: Deep-water rose shrimp 

Species scientific name: Parapenaeus longirostris 

Geographical Sub-area(s) GSA(s): 23 

5.19.1 STOCK DEVELOPMENT OVER TIME 

State of absolute and relative biomass 

The state of the biomass cannot be deduced as the assessment result is highly 
uncertain and not trusted. 

State of the juveniles (recruits) 

The  state  of  juveniles  cannot  be  deduced  from  the  modelling  approach  that  was 
used. 

State of exploitation 

The  state  of  exploitation  cannot  be  deduced  as  the  assessment  result  is  highly 
uncertain and not trusted. 

No estimates of biomass and fishing mortality are reported, as the assessment was 
considered  dubious  given  the  extremely  high  uncertainty  of  the  estimates  (Figure 
5.19.1.1). 

118 

118 

 
 
 
 
 
 
 
Figure  5.19.1.1  Deep-water  rose  shrimp  in  GSA  23.  Upper  row:  absolute  and 
relative  biomass  prediction  with  95%  CI  (solid  line)  and  observed  biomass 
(points),  i.e.  abundance  index  scaled  by  catchability,  q,  and  Bmsy  (left)  and 
absolute  and  relative  fishing  mortality  prediction  with  95%  CI  (right).  Bottom 
row:  Observed  (points)  and  predicted  catch  (solid  line)  with  95%  CI  (dotted 
lines) and average estimate of MSY (black solid line) with 95% CI (grey shaded 
area) (left) and Kobe plot (right). 

5.19.2 STOCK ADVICE 

The assessment  was considered unreliable due to the very high uncertainty  and no 
advice is provided. 

5.19.3 BASIS OF THE ASSESSMENT 

The assessment was based on a state-space surplus production model implemented 
in SPiCT  (Pedersen  and  Berg, 2017). The data used  are the time series of landings 
for  the  period  1994-2016  taken  from  FAO  and  a  time-series  of  abundance  indices 
estimated from the MEDITS trawl surveys, when they were accomplished. 

119 

119 

 
 
 
5.19.4 CATCH OPTIONS 

Due to the very high uncertainty of the model output, no projections were 
accomplished. 

5.19.5 REFERENCE POINTS  

Due to the very high uncertainty in the biomass and fishing parameter estimates, no 
reference points are presented. 

5.19.6 DATA DEFICIENCIES   

Catch-at-size data were available only for the years that the DCF took place, i.e. 
2003-2005, 2008, 2014, 2016 and partly for 2013 and 2015. Abundance indexes 
from the MEDITS surveys have also missing years (2002, 2007, 2009-2013, 2015). 

120 

120 

 
 
6 ASSESSMENT BY STOCK 

ToR 1.  To  compile  and  provide  the  most  updated  information  on  stock 
identification  and  boundaries,  length  and  age  composition,  growth, 
maturity, feeding, essential fish habitats and natural mortality. 

ToR 2.  To  compile  and  provide  complete  sets  of  annual  data  on  landings  and 
discards for the longest time series available up to and including 2016. 
This  should  be  presented  by  fishing  gear  as  well  as  by  size/age 
structure (see Annex II for more details). 

ToR 3.  To  compile  and  provide  complete  sets  of  annual  data  on  fishing  effort 
for  the  longest  time  series  available  up  to  and  including  2016.  This 
should  be  described  in  terms  of  amount  of  vessels,  time  (days  at  sea, 
soaking  time,  or  other  relevant  parameter)  and  fishing  power  (gear 
size,  boat  size  (linear  and/or  GT),  engine  power  kW,  etc.)  by  Member 
State  and  fishing  gear.  Data  shall  be  the  most  detailed  possible  to 
support  the  establishment  of  a  fishing  effort  and/or  capacity  baseline 
(see Annex II for more details). 

ToR 4.  To compile and provide indices of abundances and biomass by year and 
size/age structure for the longest time series available up to and including 
2016 (see Annex II for more details). 

ToR 5.   To  assess  trends  in  historic  and  recent  stock  parameters  on  fishing 
mortality,  stock  biomass,  spawning  stock  biomass,  and  recruitment. 
Different assessment models should be applied as appropriate, including 
retrospective  analyses.  The  selection  of  the  most  reliable  assessment 
shall be explained. Assumptions and uncertainties shall be specified.  

The stock assessments performed in EWG 17-15 will constitute the 
basis for the preparation of the demersal Adriatic EU MAP. The MAP will 
require  an  extensive  management  strategy  evaluation  (MSE)  in  line 
with  the  work  performed  in  STECF  16-21  and  STECF  15-09.  Since  the 
MSEs, encoded in the Fisheries Libraries in R (FLR), rely on established 
routines where uncertainty and risk play an important role, it is priority 
for the EWG to: 

1.  Give preference to models that allow estimation of uncertainty, in line 

with the recommendations of STECF EWG 17-07.     

121 

121 

 
 
 
 
2.  To  envision  alternative  stock  assessments 

for 

the  potential 

conditioning of operating models in the context of future MSEs. 

ToR 6.   To  estimate  candidate  MSY  point-value,  MSY  range  values  and 
conservation  reference  points  (precautionary  and  limit)  in  terms  of 
fishing  mortality  and  stock  biomass.  The  proposed  values  shall  be 
related  to  long-term  high  yields  and  low  risk  of  stock/fishery  collapse 
and  ensure  that  the  exploitation  levels  restore  and  maintain  marine 
biological  resources  at  least  at  levels which  can  produce  the  maximum 
sustainable yield. 

ToR 7.   To  provide  short  and  medium  term  forecasts  of  spawning  stock 
biomass,  stock  biomass  and  catches.  The  forecasts  shall  include 
different  management  scenarios,  inter  alia:  zero  catch,  the  status  quo 
fishing  mortality,  and  target  to  FMSY  (including  the  ranges)  or  other 
appropriate  proxy  by  2020.  In  particular,  on  the  basis  of  the  average 
commercial  catch  rates,  estimate  the  level  of  fishing  effort  exerted  by 
the different fleets which is commensurate with the short- and medium-
term forecasts of the proposed scenarios. 

6.1  HAKE IN GSAS 17 & 18 

6.1.1 BIOLOGY 

The  stock  of  European  hake  was  assumed  in  the  boundaries  of  the  whole  Adriatic  Sea 
(GSA 17-18) (Figure 6.1.1), as suggested by the genetic results of the MAREA StockMed 
project  that  shows  a  common  sub-population  of  hake  throughout  the  Adriatic  Sea. 
However, one project identifies two distinct stock units in the Adriatic Sea, uncorrelated 
with the GSA units (Fiorentino et al., 2014). 

The  northern  Adriatic  Sea  is  characterized  by  generally  shallow  waters,  whereas  the 
central part hosts a three consecutive depressions, called Pomo/Jabuka Pits, that reach 
ca. 270 m in their deepest part. 

The Southern Adriatic Sea is characterized by the presence of a deep central depression 
known  as  the  “South  Adriatic  Pit”  (or  Bari  Pit)  where  the  seabed  reaches  a  depth  of 
1,233 m.  

The  northern  and  southern  portions  of  the  Southern  Adriatic  Sea  feature  substantial 
differences;  the  first  contains  a  wide  continental  shelf  (the  distance  between  the 
coastline and a depth of 200 m is around 45 nautical miles) and a very gradual slope; in 
the second, the isobathic contours are very close, with a depth of 200 m already found 
at around 8 miles from the Cape of Otranto. 

The  continental  shelf  break  is  at  a  depth  of  around  160-200  m  and  is  furrowed  by  the 
heads of canyons running perpendicular to the line of the shelf. 

122 

122 

 
 
The  Adriatic  Sea,  together  with  the  Levant  basin,  is  one  of  three  areas  in  the 
Mediterranean  where  down-welling  processes  produced  by  surface  cooling  lead  to  the 
formation of so-called “dense waters”, rich in oxygen, which supply the lower levels. 

The  species  depth  distribution  (Figure  6.1.2)  ranges  between  several  meters  in  the 
coastal area down to 800 m in the South Adriatic Pit (Kirinčić and Lepetić, 1955; Ungaro 
et al., 1993), though it is most abundant at depths between 100 and 200 m, where the 
catches  are  mainly  composed  of  juveniles  (Bello  et  al.,  1986;  Vrgoč,  2000).  In  the 
northern and central part of the Adriatic Sea adults are mainly caught at depths of 100 
to  150  m  (Vrgoč  et  al.,  2004),  whereas  in  the  south  Adriatic  largest  individuals  are 
caught in waters deeper than 200 m and medium-sized fish appear in waters not deeper 
than 100 m (Ungaro et al., 1993). 

The  geographical  distribution  pattern  of  European  hake  has  been  studied  in  the  area 
using  trawl-survey  data  and  geostatistical  methods.  This  species  presents  the  greatest 
abundance  in  the  central  Adriatic  Sea  in  water  deeper  than  100  meters,  whereas  the 
greatest biomass is found in the eastern part of the Adriatic Sea, where the biggest sizes 
individuals  are  concentrated  (Piccinetti  et  al.,  2012).  Nursery  areas  are  located  in  the 
central Adriatic Sea, off Gargano promontory and in the southern part of Albanian coasts 
(Frattini  and  Paolini,  1995;  Lembo  et  al.,  2000;  Carlucci  et  al.,  2009)  (Figure  6.1.3), 
whereas the spawning grounds are located among the Croatian channels (Figure 6.1.4). 

Figure 6.1.1 Geographical location of GSAs 17-18 

123 

123 

 
 
 
Figure  6.1.2  Distribution  map  of  European  hake  in  the  Adriatic  Sea  from  Medits 
Programme (Sabatella and Piccinetti, 2005) 

Figure 6.1.3 Position of peristent nursery in GSAs 17 and 18 from MEDISEH project 

Figure 6.1.4 Position of persistent areas of potential spawners in GSAs 17 and 18 from 
MEDISEH project 

European  hake  can  grow  to  107  cm  (Grubišić,  1959)  of  total  length.  The  observed 
maximum  lengths  of  European  hake  were  93.5  cm  for  females  and  66.5  cm  for  males 
both  registered  during  Medits  samplings.  In  the  commercial  sampling  also  a  female  of 
93.5 cm length was observed in 2009. However, its usual length in trawl catches is from 
10 to 60 cm. This is a long-lived species, it can live more than 20 years. In the Adriatic, 
however,  the  exploited  stock  is  mainly  composed  in  number  of  0,  1  and  2  year-old 
individuals.  

In the DCF framework the growth has been studied ageing fish by otolith readings using 
the  whole  sagitta  and  thin  sections  for  older  individuals.  However,  the  growth 

124 

124 

 
 
 
 
 
parameters used in  this assessment come  from the  literature and  specifically the  VBGP 
reported in García-Rodríguez and Esteban (2002) were chosen (Table 6.1.1). 

Females  attain  larger  size  than  males,  which  grow  more  slowly  after  maturation  at  the 
age  of  three  or  four  years.  Consequently,  the  proportion  of  males  in  the  population  is 
higher in the lower length classes and proportion of females is higher for greater lengths. 
In the central and northern Adriatic, females already start dominating the population at 
lengths  of  about  30  to  33  cm.  In  trawl  catches  over  38  to  40  cm,  almost  all  the 
specimens are females (Vrgoč, 2000). 

In  the  Adriatic  Sea,  European  hake  spawn  throughout  the  year,  but  with  different 
intensities. The spawning peaks are in the summer and winter periods (Karlovac, 1965; 
Županović,  1968;  Županović  and  Jardas,  1986,  Županović  and  Jardas,  1989;  Jukić  and 
Piccinetti, 1981; Ungaro et al., 1993). Hake is a partial spawner. Females spawn usually 
four  or  five  times  without  ovarian  rests.  In  females  in  the  pre-spawning  stage,  fish  70 
cm long can contain more than 400,000 oocytes (Sarano, 1986). The earliest spawning 
in  the  Pomo/Jabuka  Pit  occurs  in  winter  in  deeper  water  (up  to  200  m).  As  the  season 
progresses into the spring-summer period, spawning occurs in more shallow waters. The 
recruitment of young individuals into the breeding stock has two different maxima. The 
first one is in the spring and the second one in the autumn. The date of spawning for the 
assessment is taken as 1 January. 

Table 6.1.1 European hake GSAs 17-18 - Growth and length model parameters 

Growth parameters  Length weight 

Linf 

k 

t0 

a 

b 

Sex combined  106.8  0.1  -0.994  0.0043  3.2 

Table  6.1.2  European  hake  in  GSAs17-18  -  Proportion  of  mature  specimens  at  age 
(maturity) and natural mortality vector by age 

Age 

0 

Maturity  0 

1 

1 

2 

1 

3 

1 

4 

1 

5+ 

1 

M 

0.2 

0.2 

0.2 

0.2 

0.2 

0.2 

6.1.2 INPUT DATA 

6.1.2.1 

CATCH (LANDINGS AND DISCARDS)  

General description of Fisheries 

European hake is one of the most important demersal fisheries resources in the Adriatic 
Sea, accounting for the highest landing quantity among demersal species (DCF, 2016).  

Fishing  grounds  mostly  correspond  to  the  distribution  of  the  stock.  The  principal  gears 
exploiting  this  stock  are  bottom  trawlers  and  longlines.  Longlines  are  particularly 

125 

125 

 
 
 
 
 
 
 
 
important in Croatia and in the Italian side of the GSA 18 and they target mostly larger 
individuals.  The  landings  come  from  set  gillnet,  trammel  net  and  driftnet  account  for  a 
low quantity. 

This  stock  is  exploited  mainly  by  Italy,  followed  by  Croatia,  Albania  and  Montenegro. 
Slovenia accounts for small quantity of landings of this species. 

Management regulations applicable in 2017 

The management regulations implemented in Italy are determined by the EU regulations 
(mainly EC regulation 1967/2006): 

  Minimum landing sizes: 20 cm TL for European hake 
  Fishing closure for trawling: 30-45 days in summer 
  Codend  mesh  size  of  trawl  nets:  40  mm  (stretched,  diamond  meshes)  till 
30/05/2010. From 1/6/2010 the existing nets have been replaced with a cod end 
with  40  mm  (stretched)  square  meshes  or  a  cod  end  with  50  mm  (stretched) 
diamond meshes 

  Towed  gears  are  not  allowed  within  three  nautical  miles  from  the  coast  or  at 
depths less than 50 m when this depth is reached at a distance less than 3 miles 
from the coast 

  From the 26 of July 2015 to the 26 of July 2016 the Pomo Pit area was closed to 
the  activity  of  the  bottom  trawlers  (MIPAAF,  D.M.  3/07/2015).  Following,  other 
restrictions regarding this area were introduced. In particular, the zone defined as 
“Fondale”  was  closed  to  any  fishing  activities,  also  the  fishing  activity  within  the 
Pomo pit area is allowed only to the vessels sign up in a proper register (MIPAAF, 
D.M. 466 1/06/2017 and D.D. 21/07/2017). 

Since the accession of Croatia to the EU the 1st of July 2013, the same regulations as in 
Italy are implemented by Croatia. Furthermore the following regulations are applied: 

  Bottom trawl fisheries is closed one and half NM from the coast and islands in the 
inner sea, 2 NM around island in the open sea, and 3 NM about several islands in 
the  central  Adriatic.  For  vessel  smaller  than  15  meters,  according  derogation  in 
sea deeper  than  50  meters bottom  trawl  fisheries  is forbidden  within  1NM of the 
coast. Bottom trawl fishery is closed also in the majority of channel area and bays. 
About  1/3  of  the  Croatian  territorial  waters  is  closed  for  bottom  trawl  fisheries 
over  whole  year  and  additionally  10%  is  closed  from  100-300  days  per  years. 
Minimum  mesh  size  on  the  bottom  trawl  net  was  20  mm  (“knot  to  knot”)  in  the 
open  sea,  and  24  mm  (“knot  to  knot”)  in  the  inner  sea.  Recently,  mesh  site 
regulation is according EC 1967/2006 (ie. 40 mm square or 50 mm diamond). 
  In  2015  a  no-take  zone  was  established  in  Jabuka  Pit.  The  establishment  of 
Marine  managed  area  (MMA)  was  based  on  long-time  assessment  of  biological 
resources  and  analysis  carried  out  by  working  group  through  FAO  AdriaMed 
project  that  showed  a  decline  in  biomass  of  these  commercial  species.  The 
proposed MMA covers the waters closed to trawling through a bilateral agreement 
between  Republic  of  Italy  and  Republic  of  Croatia.  The  Pit  was  re-opened  to 
trawling  in  2016.  Recently,  following  the  growing  support  for  a  MMA  in  the 
Jabuka/Pomo  Pit,  Croatia  and  Italy  agreed  to  reintroduce  a  fishing  closure  from 
the 1st of September 2017 to 31st of August 2020 

  Other interventional fisheries regulation measures were introduced in Croatia such 
as  temporal  ban  of  trawl  fisheries  in  open  part  of  central  Adriatic  and  in  channel 
area  of  northern  Adriatic.  The  aim  of  those  measures  were  protection  of 

126 

126 

 
 
 
commercially  important  species  (e.g.  European  hake  and  Norway  lobster)  in 
critical period (spawning or recruitment period). 

A  number  of  regulations  regarding  the  demersal  fishery  are  also  implemented  in  non-
European countries. 

In  Montenegro,  management  regulations  are  based  on  technical  regulations,  such  as 
mesh size (Official Gazette of Montenegro, 8/2011), including the minimum landing sizes 
(Official  Gazette  of  Montenegro,  8/2011),  and  a  regulated  number  of  fishing  licenses. 
There  are  area  limitations;  no–fishing  zone  up  to  3  NM  from  the  coastline  or  8  NM  for 
trawlers  of  24+  m  LOA.  Currently  there  are  no  MPAs  or  fishing  bans  in  Montenegrin 
waters. Mesh size in Montenegro is regulated according to the EC 1967/2006 (ie. 40 mm 
square or 50 mm diamond). 

In Albania, a new law “On fishery” has now been approved, repealing the Law n. 7908. 
The new law is based on the main principles of the CFP, it reflects Reg. 1224/2009 CE; 
Reg.1005/2008 CE; Reg. 2371/2002 CE; Reg. 1198/2006 CE; Reg. 1967/2006 CE; Reg. 
104/2000;  Reg.  1543/2000    as  well  as  the  GFCM  recommendations.  The  legal  regime 
governing  access  to  marine  resources  is  being  regulated  by  a  licensing  system. 
Regarding conservation and management measures, minimum legal sizes and minimum 
mesh  sizes is  those  reflected  in  the  CE  Regulations.  Albania  has already an  operational 
vessel  register  system.  It  is  forbidden  to  trawl  within  3  nautical  miles  (nm)  from  the 
coast  or  inside  the  50m  isobaths  when  this  distance  is  reached  at  a  smaller  distance 
from the shore.   

Landings  

Landings data were reported to STECF EWG 17-15 through the DCF. In GSAs 17 and 18 
most  of  the  landings  come  from  otter  trawls.  Slovenian  landings  are  less  than  1%, 
whereas  the  highest  landings  come  from  the  Italian  side  of  GSA  18  that  represents 
around 63% of the total landings of GSA 17 and 18, followed by Italy GSA 17 (33%) and 
Croatia (4.5%). 

Table 6.1.2.1.1 summarised the landings DCF data for the principal gear by country and 
year.  The  assessment  has  been  carried  out  using  a  wider  time  series,  this  information 
are specified in section XXX. 

Table 6.1.2.1.1 European hake GSAs 17-18: DCF landing data by country 

SVN 

Gear 

HRV 

Gear 

ITA - GSA 17 

ITA - GSA 18 

Gear 

Gear 

Year  GNS  GTR  OTB  GNS  OTB  LLS  OTB  TBB  GNS  GTR  OTB  LLS  TBB 

2002 

2003 

2004 

2005  0 

2006  1 

2007  1 

2008  0 

0 

0 

0 

0 

2 

2 

5 

1 

26 

199 

19 

38 

30 

19 

15 

21 

18 

26 

18 

42 

2006 

2899 

233  2932 

452  3275 

836  4613 

620  3497 

551  3640 

3980 

237 

3435 

3037 

127 

127 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2549 

1863 

1460 

1777 

2192 

43  1013 

774  61 

1789 

769  41 

2011 

58 

54 

39 

12 

15 

30 

62 

585  124  1731 

5 

8 

20 

19 

18 

20 

534  3545 

601  3400 

519  3312 

566  2520 

188  2379 

0 

279  1584 

427  1614 

492  1672 

0 

0 

0 

0 

0 

0 

0 

0 

1 

0 

0 

0 

1 

1 

1 

0 

2009  0 

2010  0 

2011  0 

2012  0 

2013  0 

2014  0 

2015  1 

2016  0 

Discards 

Discards  data  were  reported  to  STECF  EWG  17-15  through  the  DCF.  Total  discard  by 
fleet, year and gear are presented in Table 6.1.2.1.2.  

Discard data are presented only in the most recent years and the gear accounting for the 
highest  discard  is  represent  by  the  bottom  trawl  fishery.  Catch  data  used  in  stock 
assessment models include discard data only in the years in which discard was detected, 
more details are given in section Section 6.1.3. Error! Reference source not found. 

Table  6.1.2.1.2  European  hake  GSAs  17-18  -  Discards  data  in  tonnes  by 
country, year and gear 

ITA - GSA 17 

ITA - GSA 18  HRV 

Gear 

Gear 

Gear 

Year 

OTB 

LLS 

OTB 

LLS  OTB 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

9 

6 

3 

11 

13 

61 

152 

78 

100 

177 

15 

46 

86 

107 

0 

1 

192 

308 

217 

128 

6.1.2.2 

EFFORT 

Hake is a primary specie for the Adriatic fishing fleet, specifically it is a target species for 
the  bottom  trawl  fishery  and  to  a  lesser  extent  for  the  longline  and  gill  net  fisheries. 
Longlines  target  mainly  bigger  individuals,  however  their  activity,  together  with  the  gill 
net activity, are minor compared to the bottom trawl fishery activity. Table 6.1.2.2.1 and 

128 

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Table  6.1.2.2.2  show  the  fishing  effort,  respectively  the  nominal  effort  in  kW  days  and 
the GT days at sea, divided by country and for the main gear. 

Table 6.1.2.2.1 European hake GSAs 1718  - Nominal effort in KW*days of OTB, GNS, 
LLS and TBB divided by county and year and for the total for the period 2004 – 2016. 

SVN 

Gear 

Year 

GNS 

LLS 

OTB 

ITA - GSA 17 

Gear 

GNS 

LLS 

OTB 

TBB 

2004 

4476609 

27823853 

4232537 

2005 

32630.42 

390.44  112663.5 

4980544 

24094431 

3812915 

2006 

37045.87 

1100.9  143525.6 

4315531 

1084 

19896811 

4946237 

2007 

53191.29 

188.52  183977.9 

2538855 

1209 

19409042 

5231834 

2008 

79606.23 

78.74 

198180.5 

2451730 

20038778 

4136346 

2009 

83780.65 

785.78  200880.4 

3280887 

18889991 

4386154 

2010 

103586 

341.15  207861.9 

3396375 

18094570 

3817491 

2011 

93889.39 

455.52  188620.9 

4643321 

16572093 

2584717 

2012 

148011.5 

665.53  153645.5 

5314329 

14020762 

3254187 

2013 

118821 

210.79  113693.6 

2974353 

12614324 

2769675 

2014 

112415.7 

625.19  99847.2 

3864370 

14435027 

3729815 

2015 

124028.1 

219.71  101476.2 

2903140 

13847944 

3448162 

2016 

114633.2 

77.88 

110971.4 

3670471 

14209  14195449 

3307483 

ITA - GSA 18 

Gear 

HRV 

Gear 

Year 

GNS 

LLS 

OTB 

GNS 

LLS 

OTB 

TBB 

Total 

2004 

1457047  596928 

14451460 

2005 

2035861  1054068  13550061 

2006 

1785782  771767 

14744610 

2007 

1280477  633034 

12840209 

2008 

894323 

1260704  11463435 

2009 

1205076  884150 

13878367 

2010 

570405 

1263867  11856268 

2011 

450946 

922942 

11329443 

53038434 

49673564 

46643494 

42172018 

40523181 

42810072 

39310765 

36786428 

2012 

395458 

967941 

9821959 

2555257  810692.7  6878185  272.87 

44321367 

2013 

777758 

452813 

10511626  2314382  906311.4  7151551  187.81 

40705707 

2014 

207752 

297350 

7736320 

2485382  875823.9  7291600  785.07 

41137113 

2015 

1129811  547767 

7013616 

2370247  792914.2  7112694  6865.33  39398884 

2016 

1023952  528362 

7822985 

2283105  648706.5  6795609  1581.9 

40517596 

Table 6.1.2.2.2  European hake GSAs 1718  – Fishing effort in GT days at sea of OTB, 
GNS,  LLS  and  TBB  divided  by  county  and  year  and  for  the  total  for  the  period  2004  – 
2016. 

SVN 

Gear 

ITA -GSA 17 

Gear 

129 

129 

 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
  
  
Year 

GNS 

LLS 

OTB 

GNS 

LLS  OTB 

TBB 

2004 

245401 

5324756 

1003129 

2005 

2740.07  26.68 

9155.06 

262674 

5165331 

785589 

2006 

2844.64  137.82  12290.86  216424  40 

4079669 

1052912 

2007 

4710.41  29.83 

17413.43  156782  60 

4056776 

1096364 

2008 

5003.56  14.03 

18858.18  135113 

4082465 

843741 

2009 

4612.82  66.84 

18191.47  173403 

3830475 

1045203 

2010 

5566.22  42.51 

18235.28  190223 

3837446 

921158 

2011 

5292.89  35.32 

17781.59  236375 

3482614 

665155 

2012 

7710.76  94.06 

15063.24  259488 

3130643 

772706 

2013 

5627.33  31.22 

11960.07  167797 

2645415 

657556 

2014 

6066.21  83.99 

9372.11 

233376 

2836181 

892595 

2015 

5979.54  46.75 

9989.91 

139371 

2872228 

830339 

2016 

5360.48  17.26 

10534.16  178800  1320  3014054 

832100 

HRV 

Gear 

GNS 

ITA -GSA 18 

Gear 

Year 

GNS 

LLS 

OTB 

2004 

67828 

63792 

2510980 

2005 

94644 

77906 

2354637 

2006 

117032  77753 

2662179 

2007 

70224 

69117 

2294240 

2008 

51447 

107911  2039422 

2009 

79662 

64941 

2386555 

2010 

57056 

87474 

2068044 

2011 

44943 

76512 

1900240 

LLS 

OTB 

TBB 

Totale 

9215886 

8752703 

8221282 

7765717 

7283975 

7603110 

7185245 

6428949 

2012 

38287 

73446 

1668749  161600.6  41067.16  1289335  50.12  7458240 

2013 

78862 

32817 

1994855  146237.8  43948.88  1373511  46.95  7158665 

2014 

21679 

38728 

1463644  150426.6  44581.36  1381570  100.44  7078404 

2015 

78693 

56854 

1355193  144366.4  42015.78  1346257  916.56  6882250 

2016 

88202 

54673 

1429243  145113.7  36247.43  1231785  88.02  7027538 

6.1.2.3 

SURVEY DATA 

The  MEDITS  (MEDiterranean  International  Trawl  Survey)  survey  is  an  extensive  trawls 
survey  occurring  in  all  European  countries  and  included  in  the  Data  Collection 
Framework.  According  to  the  MEDITS  protocol  (Bertrand  et  al.,  2002),  it  takes  places 
every year during springtime following a random stratified sampling by depth (5 strata: 
0-50  m,  50-100  m,  100-200  m,  200-500m  and  over  500  m).  The  number  of  hauls  in 
each  stratum  is  proportional  to  the  surface  of  the  stratum  and  their  positions  were 
randomly selected and maintain fixed throughout the time. Same sampling gear (called 
GOC73),  characterized  by  a  20  mm  stretched  mesh  size  cod-end,  is  used  throughout 
GSAs and years. Details on its characteristics and performance are reported in Dremière 
and Fiorentini (1996). 

130 

130 

 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
In  this  assessment,  three  survey  indexes  were  considered:  1)  Italy  and  Slovenia  GSA 
17,  2)  Croatia  GSA  17  and  3)  GSA  18  (including  Italy,  Montenegro  and  Albania).  The 
following  figures  shown  the  abundance  index  (N/Km2)  by  index  and  year  (Figure 
6.1.2.3.1),  as  well  as  the  abundance  by  length  for  each  index  (Figures  6.1.2.3.2-4) 
included in the DCF framework. 

The abundance indexes show a fluctuating trend with a general decrease over the years, 
a peak in 2005 is underlined in each survey (Figure 6.1.2.3.1). 

Figure  6.1.2.3.1    European  hake  in  GSAs  17-18:  Abundance  index  (N/Km2)  by  year 
and  area:  GSA  18  (Italy  and  Montenegro),  Croatia  (HRV)  and  Italy  and  Slovenia  (ITA-
SLO) 

131 

131 

 
 
 
Figure  6.1.2.3.2  European  hake  in  GSAs  17-18:  Length  frequency  distribution  (LFD, 
N/km2) for years 1996, 1997, 2000 and from 2002  to 2016 for the Italian Medits (GSA 
17) 

132 

132 

 
 
Figure  6.1.2.3.3  European  hake  in  GSAs  17-18:  Length  frequency  distribution  (LFD, 
N/km2) from year 2002 to 2016 for the Croatian Medits (GSA 17) 

133 

133 

 
 
Figure  6.1.2.3.4  European  hake  in  GSAs  17-18:  Length  frequency  distribution 
(LFD, N/km2) from year 1994  to 2016 for GSA 18. 

6.1.3 STOCK ASSESSMENT 

6.1.3.1 INTEGRATED ASSESSMENT MODEL: SS3 

Stock Synthesis 3 (SS3; Method and Wetzel, 2013) provides a statistical framework for 
the  calibration  of  a  population  dynamics  model  using  fishery  and  survey  data.  It  is 
designed  to  accommodate  both  population  age  and  size  structure  data  and  multiple 
stock  sub-areas  can  be  analysed.  It  uses  forward  projection  of  population  as  in  the 
“statistical  catch-at-age”  (hereafter  SCAA)  approach.  SCAA  estimates  initial  abundance 
at  age,  recruitments,  fishing  mortality  and  selectivity.  The  overall  model  contains 
subcomponents  which  simulate  the  population  dynamics  of  the  stock  and  fisheries, 
derive the expected values for the various observed data, and quantify the magnitude of 
difference  between  observed  and  expected  data.  Some  SS3  features  include  ageing 
error,  growth  estimation,  spawner-recruitment  relationship,  movement  between  areas. 
The ADMB C++ software in which SS is written searches for the set of parameter values 
that  maximize  the  goodness-of-fit,  then  calculates  the  variance  of  these  parameters 
using  inverse  Hessian  methods.  The  F  at  age  has  been  estimated  from  the  Z  at  age 
estimated  by  the  model  (subtracting  M  at  age  used  in  input);  then,  the  Fbar  has  been 
estimated as average of the selected ages. 

This  assessment  was  carried  out  using  directly  the  length-based  data  thus  the 
abundance at age for each fleet is included by length. Using the growth function, 
which is estimated in the model (in the base case)  the model is able to estimate 

134 

134 

 
 
 
abundance  and  biomass  by  length  and  age  classes,  these  are  then  fitted  to  the 
observed  catches  at  length.  Thus,  the  results  are  showed  both  by  age  and  length 
structure. Selectivity by fleet has been generated applied at age but can be expressed as 
length-specific.  Fbar  was  calculated  considering  age  from  1  to  6.  The  SS3  analyses  has 
been carried out considering the following ten fleets: 7 fishing fleets and 3 surveys. 

Fishing fleet 

1)  Italian bottom trawl GSA 17 (catch and LFDs) 
2)  Croatian bottom trawl (catch and LFDs) 
3)  Croatian longlines (catch and LFDs) 
4)  Italian bottom trawl GSA 18 (catch and LFDs) 
5)  Italian longlines GSA 18 (catch and LFDs 
6)  Montenegrin bottom trawl and nets (catch and LFDs 
7)  Albania bottom trawls (only catch data) 

Survey 

1)  Italian Medits GSA 17 (index N/Km2 and LFDs) 
2)  Croatian Medits (index N/Km2 and LFDs) 
3)  Medits GSA 18 (index N/Km2 and LFDs) 

Input data and fitting of the model 

The  following  figures  show  the  input  data  for  the  SS  model.  Figure  6.1.3.1.1show  the 
time of series of catch (landings, plus discard for the years and fishery in which discard 
estimations are available). 

Figure 6.1.3.1.1 European hake GSAs 17-18: Catch data divided by fleet. 

135 

135 

 
 
 
 
 
Table 6.1.3.1.1 European hake in GSAs 17-18. Catches in tonnes by country and year 
included in the assessment models. 

Year 

ITA_OTB 
17 

HRV_OTB 
17 

LLS_HRV 
17 

ITA_OTB 
18 

LLS_ITA 
18 

OTB_MNE 18  OTB_ALB 18 

1998 

2471 

1999 

2463 

2000 

2050 

2001 

1978 

2002 

2260 

2003 

2998 

2004 

2834 

2005 

3735 

2006 

3980 

2007 

3435 

2008 

3037 

2009 

2561 

2010 

1872 

2011 

1469 

889 

618 

554 

530 

593 

437 

645 

827 

875 

932 

710 

786 

607 

760 

2012 

1783 

1078 

2013 

2195 

1205 

2014 

1800 

1082 

2015 

2024 

2016 

1792 

870 

712 

46 

32 

29 

27 

31 

23 

33 

43 

45 

48 

33 

37 

40 

37 

34 

65 

61 

41 

4468 

2487 

2564 

2542 

2267 

2992 

3025 

3380 

4760 

3609 

3756 

3696 

3478 

3412 

2697 

2395 

1630 

1700 

124 

1779 

697 

388 

400 

397 

354 

233 

233 

452 

836 

620 

551 

534 

601 

519 

566 

188 

279 

427 

492 

67 

67 

65 

74 

39 

75 

93 

52 

55 

54 

63 

56 

49 

40 

42 

43 

44 

38 

39 

476 

477 

462 

532 

280 

538 

662 

374 

392 

385 

390 

456 

375 

402 

280 

243 

206 

206 

206 

Figures  from  Figure  6.1.3.1.2  to  Figure  3.1.3.1.17  shows  the  length  frequency 
distributions (LFDs; grey area) for each fleet and survey considered in the model. 
The  fitting  of  the  model  (green  line  and  Pearson  residual)  seems  satisfying:  the 
LFDs are reconstructed quite well by the model, as well as the residuals are quite 
low, between -2 and 2, and without particular patterns. 

136 

136 

 
 
Figure  6.1.3.1.2  European  hake  GSAs  17-18:  length  frequency  distributions  (LFDs; 
grey  area)  and  fitting  of  the  model  (green  line)  for  the  Italian  bottom  trawl  fishery  in 
GSA 17 

Figure  6.1.3.1.3  European  hake  GSAs  17-18:  Pearson  residuals  for  the  LFDs  coming 
from the Italian bottom trawl fishery in GSA 17 

Figure  6.1.3.1.4  European  hake  GSAs  17-18:  length  frequency  distributions  (LFDs; 
grey area) and fitting of the model (green line) for the Croatian bottom trawl fishery 

137 

137 

 
 
 
 
 
Figure  6.1.3.1.5  European  hake  GSAs  17-18:  Pearson  residuals  for  the  LFDs  coming 
from the Croatian bottom trawl fishery 

Figure  6.1.3.1.6  European  hake  GSAs  17-18:  length  frequency  distributions  (LFDs; 
grey area) and fitting of the model (green line) for the Croatian longlines  fishery 

Figure  6.1.3.1.7  European  hake  GSAs  17-18:  Pearson  residuals  for  the  LFDs  coming 
from the Croatian longlines fishery 

138 

138 

 
 
 
 
Figure  6.1.3.1.8  European  hake  GSAs  17-18:  length  frequency  distributions  (LFDs; 
grey  area)  and  fitting  of  the  model  (green  line)  for  the  Italian  bottom  trawl  fishery  in 
GSA 18 

Figure  6.1.3.1.9  European  hake  GSAs  17-18:  Pearson  residuals  for  the  LFDs  coming 
from the Croatian bottom trawl fishery 

Figure  6.1.3.1.10  European  hake  GSAs  17-18:  length  frequency  distributions  (LFDs; 
grey area) and fitting of the model (green line) for the Italian longlines fishery in GSA 18 

139 

139 

 
 
 
 
Figure  6.1.3.1.11  European  hake  GSAs  17-18:  Pearson  residuals  for  the  LFDs  coming 
from the Croatian longlines fishery 

Figure  6.1.3.1.12  European  hake  GSAs  17-18:  length  frequency  distributions  (LFDs; 
grey area) and fitting of the model (green line) for the Italian Medits survey in GSA 17 

Figure  6.1.3.1.13  European  hake  GSAs  17-18:  Pearson  residuals  for  the  LFDs  coming 
from the Italian Medits survey in GSA 17 

140 

140 

 
 
 
 
 
Figure  6.1.3.1.14  European  hake  GSAs  17-18:  length  frequency  distributions  (LFDs; 
grey area) and fitting of the model (green line) for the Croatian Medits survey in GSA 17 

Figure  6.1.3.1.15  European  hake  GSAs  17-18:  Pearson  residuals  for  the  LFDs  coming 
from the Croatian Medits survey in GSA 17 

Figure  6.1.3.1.16  European  hake  GSAs  17-18:  length  frequency  distributions  (LFDs; 
grey area) and fitting of the model (green line) for the Medits survey in GSA 18 (Italy, 
Montenegro and Albania) 

141 

141 

 
 
 
  
 
Figure  6.1.3.1.17  European  hake  GSAs  17-18:  Pearson  residuals  for  the  LFDs  coming 
from the Medits survey in GSA 18 (Italy, Montenegro and Albania) 

The  following  figures  show  the  indexes  of  abundance  with  the  model  fitting  for 
each survey considered in the model. The best fitting is showed for the Croatian 
Medits  survey  (Figure  6.1.3.1.19),  whereas  for  the  Italian  Medits  in  GSA  17 
(Figure 6.1.3.1.18) and for the Medits survey in GSA 18 (Figure 6.1.3.1.20) the 
fitting  presents  some  inconsistency,  particularly  peaks  and  drops  are  not  very 
well described by the model.  

Figure 6.1.3.1.18 European hake GSAs 17-18: Abundance index (N/Km2) and fitting of 
the model (blue line) for the Italian Medits survey in GSA 17. 

Figure 6.1.3.1.19 European hake GSAs 17-18: Abundance index (N/Km2) and fitting of 
the model (blue line) for the Croatian Medits survey in GSA 17. 

142 

142 

 
 
 
 
 
Figure 6.1.3.1.20 European hake GSAs 17-18: Abundance index (N/Km2) and fitting of 
the model (blue line) for the Medits survey in GSA 18 (Italy, Montenegro and Albania). 

The growth parameters, the natural mortality vector and the vector of proportion 
of  mature  individuals  by  are  used  in  the  model  are  showed  in  Table  6.1.1  and 
Table 6.1.2. 

Assessment results 

The  growth  parameters  estimated  by  the  model  are:  Linf  85.35  cm  and  k  0.08; 
Figure  6.1.3.1.21  shows  the  growth  curve  resulting  by  the  statistical  age  slicing 
of the model. 

Figure 6.1.3.1.21 European hake GSAs 17-18: growth curve estimated by the model. 

The  selectivity  by  fleet  was  model  as  a  dome  shaped  (type  selex  24  in  SS3), 
except for the Medits survey in GSA 18, for which the cubic spline selectivity was 
used. Figure 6.1.3.1.22 summarizes the selectivity curve for each fleet. 

143 

143 

 
 
 
 
 
 
Figure  6.1.3.1.22  European  hake  GSAs  17-18:  selectivity  at  length  for  the  different 
fleet considered in the model. 

Figure 6.1.3.1.23 and Figure 6.1.3.1.24 Figure 6.1.3.1show the results from the 
SS3  model.  The  total  biomass  and  the  spawning  stock  biomass  present  a 
decreasing trend over the years, starting respectively from 227,172 and 218,014 
tonnes to 57,346 and 52,957 tonnes. The recruitment shows a fluctuating trend, 
with  a  general  decrease  over  the  years  and  peak  in  2004  (332,735  thousands) 
and  2011  (188,380  thousands).  Fishing  mortality  shows  an  increasing  trend, 
reaching the maximum value in 2013 (Fbar = 0.45); the terminal Fbar is equal to 
0.38.  The  Italian  bottom  trawl  fishery  (GSA  17  and  18)  account  for  the  highest 
fishing  mortality,  followed  by  the  Croatian  bottom  trawl  fishery  that  increases 
particularly  in  the  last  years;  the  other  fisheries  account  for  very  low  values 
(Figure 6.1.3.1.24, right side). 

Figure  6.1.3.1.23  European  hake  GSAs  17-18.  Results  from  the  SS  model:    total 
biomass and s pawning stock biomass in tonnes and recruitment in thousands. 

144 

144 

 
 
 
 
 
 
Figure 6.1.3.1.24 European hake GSAs 17-18. Results from the SS model: total fishing 
mortality (Fbar1-6) on the left and fishing mortality by fleet on the right 

Table 6.1.3.1.2 European hake in GSAs 17-18. Results from the SS3 model. 

Year 

Total Biomass 

SSB 

Recruits 

Fbar 

1998 

227172 

218014 

70395 

0.19 

1999 

217416 

209976 

114441 

0.16 

2000 

208196 

202073 

96969 

0.18 

2001 

197972 

192260 

188597 

0.20 

2002 

186979 

180761 

73501 

0.21 

2003 

176539 

169353 

200876 

0.28 

2004 

164581 

157088 

332735 

0.31 

2005 

153211 

145437 

173048 

0.37 

2006 

142567 

132426 

128524 

0.41 

2007 

130785 

120367 

181350 

0.33 

2008 

120771 

112117 

61277 

0.32 

2009 

111363 

103996 

90901 

0.33 

2010 

101796 

95616 

93954 

0.32 

2011 

92825 

88099 

188380 

0.37 

2012 

84137 

79732 

98846 

0.41 

2013 

76272 

71176 

105489 

0.45 

2014 

68892 

63687 

89937 

0.36 

2015 

63058 

58314 

112590 

0.38 

2016 

57346 

52957 

117588 

0.38 

145 

145 

 
 
 
Table 6.1.3.1.3  European hake in GSAs 17-18. Results from the  SS3  model: numbers 
at fishing mortality at age and by year. F in the Plus group is unweighted mean F over 
ages 6 and above. 

Numbers at age 

Year 

0 

1 

2 

3 

4 

5 

6+ 

1998 

75878 

76704 

85066 

62264 

47521 

39301 

165420 

1999 

123354 

61964 

55758 

55063 

39361 

30950 

155614 

2000 

104521 

100799 

46350 

37886 

36214 

26399 

144004 

2001 

203286 

85386 

74550 

30781 

24379 

23863 

131389 

2002 

79226 

166018 

62297 

47991 

19094 

15540 

119031 

2003 

216521 

64705 

121045 

39996 

29332 

11941 

103531 

2004 

358650 

176715 

46138 

72043 

22111 

16675 

87931 

2005 

186526 

292552 

122301 

26264 

38264 

12183 

78700 

2006 

138534 

152107 

199516 

66701 

12948 

19475 

67446 

2007 

195474 

112896 

101083 

102949 

31174 

6302 

62210 

2008 

66050 

159422 

77151 

56744 

53725 

16857 

51444 

2009 

97980 

53867 

109467 

43475 

30014 

29533 

50101 

2010 

101271 

79884 

36362 

60561 

22822 

16477 

56005 

2011 

203052 

82560 

53874 

20131 

32226 

12750 

51746 

2012 

106544 

165382 

53057 

27776 

10068 

17166 

45937 

2013 

113705 

86754 

103169 

26381 

13105 

5050 

43663 

2014 

96941 

92576 

53465 

49999 

11794 

6201 

34756 

2015 

121359 

79007 

59567 

28564 

25060 

6192 

30128 

2016 

126746 

98910 

51352 

31520 

13953 

12767 

26406 

F at age 

Year 

0 

1 

2 

3 

4 

5 

6+ 

1998 

0.003 

0.119 

0.235 

0.259 

0.229 

0.176 

0.028 

1999 

0.002 

0.090 

0.186 

0.219 

0.199 

0.154 

0.023 

2000 

0.002 

0.102 

0.209 

0.241 

0.217 

0.167 

0.025 

2001 

0.003 

0.115 

0.240 

0.278 

0.250 

0.193 

0.028 

2002 

0.002 

0.116 

0.243 

0.292 

0.269 

0.209 

0.030 

2003 

0.003 

0.138 

0.319 

0.393 

0.365 

0.284 

0.039 

2004 

0.004 

0.168 

0.363 

0.433 

0.396 

0.306 

0.043 

146 

146 

 
 
 
2005 

0.004 

0.183 

0.406 

0.507 

0.475 

0.372 

0.053 

2006 

0.005 

0.209 

0.462 

0.561 

0.520 

0.407 

0.064 

2007 

0.004 

0.181 

0.377 

0.450 

0.415 

0.324 

0.052 

2008 

0.004 

0.176 

0.374 

0.437 

0.398 

0.311 

0.050 

2009 

0.004 

0.193 

0.392 

0.444 

0.400 

0.310 

0.051 

2010 

0.004 

0.194 

0.391 

0.431 

0.382 

0.296 

0.052 

2011 

0.005 

0.242 

0.462 

0.493 

0.430 

0.330 

0.056 

2012 

0.005 

0.272 

0.499 

0.551 

0.490 

0.378 

0.063 

2013 

0.006 

0.284 

0.524 

0.605 

0.548 

0.423 

0.062 

2014 

0.005 

0.241 

0.427 

0.491 

0.444 

0.344 

0.054 

2015 

0.005 

0.231 

0.436 

0.516 

0.474 

0.371 

0.061 

2016 

0.005 

0.224 

0.435 

0.512 

0.470 

0.370 

0.067 

Retrospective 

The retrospective analysis was applied up to 3 years back. The results show that 
the model is quite stable (Figure 6.1.3.1.25). 

Figure 6.1.3.1.25 European hake in GSAs 17-18. Retrospective analysis: results for the 
spawning stock biomass (SSB; on the left) and the recruitment (on the right). 

6.1.3.1.1  Sensitivity analysis 

Section  6.1.3.1  illustrates  the  selected  SS3  stock  assessment  from  which  the 
reference  points  and  the  short  term  forecasts  has  been  estimated.  This 
assessment was also presented at WGSAD – GFCM 2017, but some adjustments 
were included in the present assessment: 

-  Lmax for the population maximum size was adjusted to 110 cm, instead of 150 cm, 

to better describe the growth curve 

147 

147 

 
 
 
 
 
-  Length  frequency  distributions  for  the  Croatian  Medits  in  2016  were  modifying 

including corrected values. 

During  the  EWG  17-15,  it  was  requested  to  investigate  better  the  effect  of  the 
dome  shaped  selectivity  forced  to  reach  a  selection  of  0  for  the  large  length 
classes  (Figure  6.1.3.1.22)  in  order  to  improve  the  fitting  of  the  LFDs.  The 
reasons  for  this  investigation  was  the  evidence  of  a  quite  high  estimated  stock 
biomass  (Figure  6.1.3.1.23),  mostly  concentrated 
length 
classes/oldest  age  classes  and  particularly  in  the  last  one  (Figure  6.1.3.1.1.1– 
Run  Base).  Thus,  different  setting  of  the  dome  shaped  selectivity  were  tested, 
together with other aspects such as the influence of a different Lmax and the use 
of fixed growth parameters (Table 6.1.3.1.1.1). 

in  the 

larger 

Table 6.1.3.1.1.1 European hake in GSAs 17-18: details of the runs carried out in SS3. 
In red the runs used for the comparison in the next figures. 

Run  

Detail 

Base 

Assessment presented in section 6.1.3.1 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

Base assessment but assuming a fixed value, equal to 0.2 for the bottom trawlers and to 0.5 for the longlines, 
for the descending part of the double selectivity curve 

Base assessment but setting a free value for the descending part of the double normal selectivity curve 

Base assessment but including only four selectivity curves: 1) for all the OTB fishery, 2) for all the LLS fishery, 
3) MEDITS ITA e HRV and 4) MEDITS GSA 18 

Run 2 assessment but including only three selectivities: 1) for all the OTB fishery, 2) for all the LLS fishery, 3) 
one for all the MEDITS surveys 

Run 2 assessment but assuming a logistic selectivity for the MEDITS survey 

Run 2 assessment but assuming a logistic selectivity for the bottom trawlers 

Run 4 assessment assuming also a value of 110 cm for Lmax instead of 150 

Run 7 but assuming also a maximum age of 10 years old, instead of 20 years old 

Run 4 but assuming a fixed value of 0.2 for the descending part of the OTB selectivity curve 

Run 9 but considering Lmax equal to 110 cm 

Run 10 but assuming also a maximum age of 10 years old 

Run 11 but fixing growth parameters 

Run  11  but  fixing  the  growth  parameters  and  age  1  to  4  for  the  calculation  of  Fbar  (to  be  similar  at  the  a4a 
assessment) 

The  use  of  a  more  appropriate  Lmax,  that  is  the  maximum  length  that  the 
population  can  reach,  allowed  to  describe  a  more  reliable  growth  curve.  In  the 
case  of  the  estimation  of  the  growth  parameters,  the  model  supports  the  slow 
growth (Linf ca. 82 cm and k ca. 0.08 depending by the different runs). 

148 

148 

 
 
 
Figure 6.1.3.1.1.1-3 show the results of the selected runs (Base, Run 1, Run 2, 
Run 4, Run 10, Run 12), summarizing the main changes that were tested. All the 
selected  run  presented  an  accumulation  of  biomass  in  the  last  age  class, 
underlining  the  fact  that  changes  in  the  descending  part  of  the  dome  shaped 
curve  do  not  improve  this  point  (Figure  6.1.3.1.1.1);  the  reasons  for  this 
structure have to be searched in the model calculation. Although different shape 
of  the  selectivity  curves  do  not  influence  the  structure  of  the  biomass  at  age, 
they influence the estimated total biomass by year (Figure 6.1.3.1.1.2). Fixing a 
percentage  of  selection  different  from  0  for  the  descending  section  of  the  dome 
shaped  selectivity  (Run  1,  Run  10  and  Run  12)  allowed  to  fish  also  larger 
individuals resulting in lower total biomass. In the case of asking to the model to 
estimate the value of the descending parameters (Run 2), most of the selectivity 
reached value around 0.1 and close to zero, except the Croatian long line fishery 
that was modelled as a logistic curve; thus ranked the estimated total biomass in 
a  mean  position  between  the  other  runs.  Differences  in  the  SSB  and  in  the 
recruitment for the selected runs are showed in Figure 6.1.3.1.1.3. 

The total biomass trend, as well at the SSB trend, are similar among the different 
runs: total biomass and SBB decreased over the year, shower a steeper decrease 
for the base run. Recruitment showed similar trend, with some differences in the 
last years. 

Figure 6.1.3.1.1.1 European hake in GSAs 17 and 18: biomass at age and year for the 
selected SS runs. 

149 

149 

 
 
 
Figure  6.1.3.1.1.2  European  hake  in  GSAs  17-18:  comparison  of  the  total  biomass 
(tonnes) estimated by the selected runs. 

Figure  6.1.3.1.1.3  European  hake  in  GSAs  17-18:  spawning  stock  biomass  (SSB,  left 
panel) and recruitment (right panel) for the selected SS runs. 

Considering  the  analysis  presented  in  this  section,  it  was  decided  to  accept  the 
base  assessment.  More  evidences  are  needed  for  fixing  a  given  value  for  the 
descending  curves,  as  well  as  a  deeper  study  on  the  effect  of  the  shape  of  the 
selectivity curves on the calculation of the total biomass at age by the SS model 
has to be carried out.  

6.1.3.2 A4A 

a4a  is  a  statistical  catch-at-age  method  that  utilizes  catch-at-age  data  to  derive 
estimates of historical population size and fishing mortality (Jardim et al., 2015). Model 
parameters  estimated  using  catch-at-age  analysis  are  done  so  by  working  forward  in 
time  and  analyses  do  not  require  the  assumption  that  removals  from  the  fishery  are 
known  without  error.  Data  typically  used  are:  catch,  statistical  sample  of  age 
composition of catch and abundance index.  

150 

150 

 
 
  
 
 
Specifically,  for  the  European  hake  in  GSAs  17  and  18  the  a4a  model  was  carried  out 
considering a time series from 2009 to 2016, since for some fleets no reliable LFDs were 
available  before  2009.  The  same  time  series  was  considered  for  the  three  tuning 
indexes: 1) Italian Medits GSA 17, 2) Croatian Medits GSA 17 and 3) Medits GSA 18. The 
data was organized considering a plus group of age 5+. Catch data (landings + discard if 
available) are included in the model. Fbar was calculated considering age from 1 to 4. 

Input data 

The Von Bertalanffy growth parameters used in the model are represented in Table 6.1.1 
and these parameters were used to convert the length data into ages, using the a4a age 
slicing tool. The weight at age matrix is showed in Table 6.1.3.2.1. Weight at age comes 
from the DCF data; since there were quite large differences among the weight at age in 
the different years and particular for the last age (5+), the values corresponding to the 
median by age have been selected to include in the model. 

Catch  numbers  at  age  are  showed  in  Table  6.1.3.2.2,  a  SOP  correction  was  applied. 
Table 6.1.3.2.3 shows the abundance at age for the three tuning index. 

Table  6.1.3.2.1  European  hake  in  GSAs  17-18:  natural  mortality  (M),  maturity  (mat), 
weight (Kg), proportion of F (Prop F) and M (Prop M) before spawning by age used in the 
a4a model. 

age 

M 

mat 

0 

0.2 

0 

1 

0.2 

1 

2 

0.2 

1 

3 

0.2 

1 

4 

0.2 

1 

5+ 

0.2 

1 

Weight (Kg) 

0.024 

0.070 

0.151 

0.270 

0.426 

1.230 

Prop F 

Prop M 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

Table  6.1.3.2.2  European  hake  GSAs  17-18:  Sop-corrected  catch  numbers  at  age  (in 
thousands) 

Year/Age 

0 

1 

2 

3 

4 

5+ 

2009 

70737.42 

45929.43  13206.23 

1993.53 

472.34 

389.94 

2010 

61433.47 

38805.74  10090.42 

1480.74 

439.81 

585.68 

2011 

62568.78 

34753.28 

8541.31 

1577.29 

817.41 

520.97 

2012 

106421.18  26735.60 

6443.81 

1656.45 

379.00 

384.30 

2013 

60602.47 

33472.85 

9430.78 

1806.53 

483.10 

340.51 

2014 

73699.96 

21821.84 

6499.17 

1342.94 

387.23 

241.25 

2015 

59342.55 

26281.29 

7608.63 

1264.36 

381.22 

316.59 

2016 

51050.49 

29045.82 

6456.79 

1326.58 

380.39 

317.38 

151 

151 

 
 
 
 
Table 6.1.3.2.3 European hake GSAs 17-18: Abundance index (N/Km2) from the three 
MEDITS survey: ITA - GSA 17, HRV - GSA 17 and ITA - GSA 18 

Medits ITA - GSA 17 

Year/Age 

0 

1 

2 

3 

4 

5+ 

2009 

159.60 

68.45 

23.65 

5.20 

1.42 

0.18 

2010 

194.90 

35.07 

8.86 

2.84 

0.59 

0.48 

2011 

289.05 

35.13 

9.30 

3.37 

0.51 

0.43 

2012 

291.52 

29.86 

15.78 

3.53 

0.88 

0.88 

2013 

247.16 

53.26 

30.22 

6.94 

0.54 

0.51 

2014 

580.18 

134.97 

22.81 

5.00 

1.29 

0.83 

2015 

275.04 

24.58 

9.38 

3.78 

0.81 

0.36 

2016 

303.08 

51.39 

13.58 

4.04 

1.00 

0.41 

Medits HRV - GSA 17 

Year/Age 

0 

1 

2 

3 

4 

5+ 

2009 

342.89 

193.30 

56.53 

13.74 

1.77 

0.68 

2010 

464.69 

101.21 

26.01 

9.11 

2.05 

0.61 

2011 

454.42 

113.94 

28.52 

4.48 

1.48 

1.15 

2012 

1059.92 

115.91 

41.54 

8.15 

2.18 

2.66 

2013 

482.22 

168.15 

60.39 

8.37 

2.70 

1.80 

2014 

370.38 

96.36 

41.19 

9.63 

2.07 

1.55 

2015 

634.38 

115.88 

59.64 

12.88 

6.90 

2.57 

2016 

505.73 

112.07 

32.35 

9.71 

2.77 

0.95 

Medits GSA 18 

Year/Age 

0 

1 

2 

3 

4 

5+ 

2009 

596.19 

133.71 

36.98 

8.01 

4.19 

2.71 

2010 

491.79 

76.70 

22.21 

3.97 

1.10 

3.78 

2011 

332.88 

66.31 

8.20 

2.14 

1.54 

2.52 

2012 

115.76 

35.66 

10.54 

2.91 

0.47 

3.22 

2013 

1352.52 

63.62 

19.22 

3.49 

1.00 

1.80 

2014 

454.33 

64.49 

27.18 

6.09 

1.44 

2.64 

2015 

438.61 

44.82 

13.19 

4.90 

3.00 

3.77 

152 

152 

 
 
2016 

297.10 

37.42 

15.17 

3.78 

0.69 

1.46 

Figure 6.1.3.2.1 European hake GSAs 17-18: age structure of catches. 

Assessment results – a4a 

Different a4a models were tested (combination of different f, q and sr). The best 
model (according to residuals and retrospective) includes the following setting: 

fmod1 <- ~s(age, k=4)+s(year,k=4) q  

qmod <- list(~s(age,k=4),~s(age,k=4),~s(age,k=4)) 

srmod <- ~s(year,k=6) 

153 

153 

 
 
 
 
 
 
Figure  6.1.3.2.2  European  hake  in  GSAs  17-18.  Stock  summary  from  the  a4a  model: 
recruits, SSB (Spawning Stock Biomass), catch and fishing mortality (F - ages 1-4). 

Figure  6.1.3.2.3  European  hake  in  GSAs  17-18:  3D  contour  plot  of  estimated  fishing 
mortality at age and year. 

154 

154 

 
 
 
 
 
Figure  6.1.3.2.4  European  hake  in  GSAs  17-18:  3D  contour  plot  of  estimated 
catchability of MEDITS surveys at age and year. 

Figure  6.1.3.2.5  European  hake  in  GSAs  17-18  Standardized  residuals  for  abundance 
indices  and  for  catch  numbers  (catch.n).  Each  panel  is  coded  by  age  class,  dots 
represent standardized residuals and lines a simple smoother. 

155 

155 

 
 
 
 
Figure 6.1.3.2.6 European hake in GSAs 17-18.  Quantile-quantile plot of standardized 
residuals for abundance indices and for catch numbers (catch.n). Each panel is coded by 
age  class,  dots  represent  standardized  residuals  and  lines  the  normal  distribution 
quantiles. 

Figure 6.1.3.2.7 European hake in GSAs 17-18. Fitted and observed catch at age. 

Figure  6.1.3.2.8  European  hake  in  GSAs  17-18.  Fitted  and  observed  index  at  age  – 
Medits ITA GSA 17. 

156 

156 

 
 
 
 
 
 
Figure  6.1.3.2.9  European  hake  in  GSAs  17-18.  Fitted  and  observed  index  at  age  – 
Medits HRV GSA 17. 

Figure  6.1.3.2.10  European  hake  in  GSAs  17-18.  Fitted  and  observed  index  at  age  – 
Medits GSA 18. 

Retrospective 

Considering the short time series, the retrospective analysis was applied only for 
one year and the model results quite unstable (Figure 6.1.3.2.11). 

157 

157 

 
 
 
 
 
Figure 6.1.3.2.11 European hake in GSAs 17-18. Retrospective analysis output for the 
a4a model. 

Simulations 

Figure 6.1.3.2.12 European hake in GSAs 17-18. Stock summary of the simulated and 
fitted data for the a4a model. 

158 

158 

 
 
 
 
 
 
Table 6.1.3.2.4 European hake in GSAs 17-18. Stock numbers at age in thousands as 
estimated by a4a. 

Year/Age 

0 

1 

2 

3 

4 

5+ 

2009 

159179 

62885 

18607 

4317 

1933 

1954 

2010 

140350 

50804 

13503 

4353 

1669 

2366 

2011 

141252 

48220 

12113 

3484 

1785 

2547 

2012 

138520 

50870 

12293 

3327 

1483 

2774 

2013 

123544 

50742 

13285 

3454 

1436 

2765 

2014 

118432 

45388 

13307 

3748 

1494 

2733 

2015 

121642 

44103 

12134 

3822 

1638 

2757 

2016 

116974 

47028 

12435 

3663 

1721 

2889 

Table  6.1.3.2.5  European  hake  in  GSAs  17-18.  a4a  summary  results:  Fbar1-4, 
recruitment, SSB and catch. 

Year 

Fbar1-4 

Recruitment 

SSB 

Catch 

(thousands) 

(tonnes) 

(tonnes) 

2009 

0.93244 

159179 

11604 

8234.3 

2010 

0.8595 

140350 

10392 

6621.9 

2011 

0.81288 

141252 

10038 

6108 

2012 

0.79604 

138520 

10359 

6117.7 

2013 

0.79315 

123544 

10503 

6014.6 

2014 

0.77977 

118432 

10197 

5691.6 

2015 

0.74266 

121642 

10040 

5420.8 

2016 

0.69048 

116974 

10446 

5267.7 

Table 6.1.3.2.6 European hake in GSAs 17-18. a4a summary results: F at age (y-1). 

Year/Age 

0 

1 

2 

3 

4 

5+ 

2009 

0.942 

1.338 

1.253 

0.750 

0.388 

0.213 

2010 

0.868 

1.234 

1.155 

0.692 

0.358 

0.196 

2011 

0.821 

1.167 

1.092 

0.654 

0.339 

0.186 

2012 

0.804 

1.143 

1.069 

0.640 

0.332 

0.182 

2013 

0.801 

1.138 

1.066 

0.638 

0.330 

0.181 

2014 

0.788 

1.119 

1.048 

0.627 

0.325 

0.178 

159 

159 

 
 
 
2015 

0.750 

1.066 

0.998 

0.598 

0.309 

0.170 

2016 

0.698 

0.991 

0.928 

0.556 

0.288 

0.158 

Based on the a4a results, the stock of European hake in GSAs 17 and 18 shows a 
quite  stable  situation  between  years  2009 and  2016,  this  is  particularly  true  for 
the  SSB,  whereas  recruitment,  catches  and  fishing  mortality  show  a  decreasing 
trend over the years. The highest catches are estimated in 2009 (8234 tonnes), 
whereas the lowest in 2016 (5268 tonnes); the highest fishing mortality (Fbar1-4) 
is recorded in 2009 (0.93), while the lowest in 2016 (0.69). 

6.1.3.3 COMPARISON BETWEEN SS3 AND A4A STOCK ASSESSMENT MODELS 

The  a4a  assessment  was  requested  by  the  EWG  17-15.  The  conclusions  of  the 
two  assessment  (SS3  and  a4a)  are  in  agreement  in  some  aspects  since  both 
models estimated a Fcurrent higher than the reference point (FMSY), suggesting the 
overexploitation of this stock. However, the estimated total biomass and SSB are 
very  different.  Some  of  the  differences  could  be  explained  by  the  different 
assumptions included in the two models and listed hereunder: 

1)  The  stock  assessment  models  considered  two  different  time  series.  The  years 
included  in  the  a4a  model  go  from  2009  to  2016,  whereas  the  SS3  models 
comprise  years  from  1998  to  2016.  Being  an  integrated  model,  SS3  offers  the 
possibility to include  different time series for  the different input data. Thus catch 
data, tuning indexes and LFDs for the Medits surveys were included for the years 
from  1998  to  2016,  whereas  LFDs  of  catches  were  included  from  year  2008  or 
2009, depending by the fleet, to 2016. 

2)  The age slicing applied by the two models is different. The SS3 model was set by 
length, thus the information by age were obtained from the statistical age slicing 
included in  its  framework,  whereas in  the  a4a model information  were  converted 
into  ages  using  the  a4a  slicing  performed  outside  and  before  the  assessment 
model. The a4a slicing used the growth parameters showed in Table 6.1.1, these 
parameters were also used as input in the SS3 model. However, in the SS3 model 
growth parameters were estimated by the model in order to obtain a best fitting, 
thus the age slicing was performed using the estimated growth parameters. 

3)  The weight at age is another source of discrepancy. In the a4a model weight age 
is  derived  from  the  DCF  samples,  whereas  in  the  SS3  the  weight  at  age  was 
reconstructed  within  the  model  considering  the  length-weight  parameters  (Table 
6.1.1).  As  growth  is  different  weight  at  age  differs  between  models.  This  will 
contribute to the lower estimated biomass in the a4a model compared to the SS3 
model,  but  the  different  numbers  at  older  age  in  the  two  models  also  contribute 
importantly to the difference. The SSB represents 30% of the total biomass in the 
a4a model whereas in the SS3 assessment the SSB represents the 80/90% of the 
total biomass. 

The different assumptions included in the models are quite important and result 
in  quite  different  numerical  outputs,  making  difficult  the  comparison  between 
these two assessment.  

160 

160 

 
 
 
6.1.4 REFERENCE POINTS 

The EWG 17-15 decided to accept both the SS3 and the a4a stock assessment models, 
thus reference points and short term forecast are showed for both the models. 

The  reference  points  were  estimated  using  the  FLBRP  package,  that  allows  a  Yield  per 
recruit analysis and an estimate of some F-based Reference Points as Fmax and F0.1. Yield 
per  Recruit  computation  was  performed  using  R  and  the  FLR  libraries.  The  fishing 
mortality rate corresponding to F0.1 in the yield per recruit curve is considered here as a 
proxy of FMSY. 

6.1.4.1 SS3 MODEL 

Reference  points  were  estimated  using  the  input  parameters  and  the  results  of 
the stock assessment model. 

Table 6.1.4.1.1 European hake in GSAs 17-18.  Main reference points defined with the 
Yield per recruit analysis estimated by the input and output of the SS3 model. 

refpt 

harvest 

yield 

rec 

ssb 

biomass 

f0.1 

0.191 

0.044 

1.00 

0.665 

0.665 

6.1.4.2 A4A 

Reference points were estimated using the input parameters and the results of the stock 
assessment model. 

Table 6.1.4.2.1 European hake in GSAs 17-18.  Main reference points defined with the 
Yield per recruit analysis estimated by the input and output of the SS3a4a model. 

refpt 

harvest 

yield 

rec 

ssb 

biomass 

f0.1 

0.181 

0.072 

1 

1.12 

1.15 

6.1.5 SHORT TERM FORECAST AND CATCH OPTIONS 

A deterministic short term prediction for the period 2017 to 2019 was performed 
using the FLR routines provided by JRC and based on the results of the two stock 
assessments performed during EWG 17-15. 

6.1.5.1 SS3 MODEL 

The input parameters were the same used for the SS3 stock assessment and its 
results.  Fstatus  quo  is  equal  to  F2016  (0.38),  corresponding  to  a  catch2017  of  5121 
tonnes; other scenario are showed in Table 6.1.5.1.1. 

161 

161 

 
 
 
 
 
Table  6.1.5.1.1  European  hake  in  GSAs  17-18.  Short  term  forecasts  showing  catch 
options for different fishing mortalities reductions. 

Rationale 

Ffactor 

Fbar 

Catch 
2016 

Catch 
2017 

Catch 
2018 

Catch 
2019 

SSB 
2018 

SSB 
2019 

Change 
SSB 
2018-
2019 
(%) 

Change 
Catch 
2016-
2018 
(%) 

Zero 
catch 

0 

0.000 

5132.71 

5121.27 

0.00 

0.00 

48340.83 

51493.46 

6.52 

-100.00 

F0.1 

0.504093 

0.191 

5132.71 

5121.27 

2874.54 

3546.94 

48340.83 

47983.55 

-0.74 

-44.00 

Status 
quo 

Different 
Scenarios 

1 

0.380 

5132.71 

5121.27 

5223.45 

5380.91 

48340.83 

45123.11 

-6.66 

1.77 

0.1 

0.2 

0.3 

0.4 

0.5 

0.6 

0.7 

0.8 

0.9 

1.1 

1.2 

1.3 

1.4 

1.5 

1.6 

1.7 

1.8 

1.9 

2 

0.038 

5132.71 

5121.27 

614.49 

883.63 

48340.83 

50742.38 

4.97 

-88.03 

0.076 

5132.71 

5121.27 

1206.12 

1669.14 

48340.83 

50019.61 

3.47 

-76.50 

0.114 

5132.71 

5121.27 

1775.87 

2365.86 

48340.83 

49323.95 

2.03 

-65.40 

0.152 

5132.71 

5121.27 

2324.64 

2982.28 

48340.83 

48654.26 

0.65 

-54.71 

0.190 

5132.71 

5121.27 

2853.32 

3526.12 

48340.83 

48009.43 

-0.69 

-44.41 

0.228 

5132.71 

5121.27 

3362.75 

4004.40 

48340.83 

47388.42 

-1.97 

-34.48 

0.266 

5132.71 

5121.27 

3853.72 

4423.48 

48340.83 

46790.21 

-3.21 

-24.92 

0.304 

5132.71 

5121.27 

4327.01 

4789.17 

48340.83 

46213.86 

-4.40 

-15.70 

0.342 

5132.71 

5121.27 

4783.35 

5106.72 

48340.83 

45658.45 

-5.55 

-6.81 

0.418 

5132.71 

5121.27 

5647.97 

5616.09 

48340.83 

44606.98 

-7.72 

10.04 

0.456 

5132.71 

5121.27 

6057.55 

5816.19 

48340.83 

44109.28 

-8.75 

18.02 

0.493 

5132.71 

5121.27 

6452.80 

5984.78 

48340.83 

43629.24 

-9.75 

25.72 

0.531 

5132.71 

5121.27 

6834.33 

6125.12 

48340.83 

43166.13 

-10.70 

33.15 

0.569 

5132.71 

5121.27 

7202.68 

6240.13 

48340.83 

42719.24 

-11.63 

40.33 

0.607 

5132.71 

5121.27 

7558.39 

6332.50 

48340.83 

42287.92 

-12.52 

47.26 

0.645 

5132.71 

5121.27 

7901.98 

6404.64 

48340.83 

41871.51 

-13.38 

53.95 

0.683 

5132.71 

5121.27 

8233.94 

6458.73 

48340.83 

41469.41 

-14.21 

60.42 

0.721 

5132.71 

5121.27 

8554.74 

6496.77 

48340.83 

41081.03 

-15.02 

66.67 

0.759 

5132.71 

5121.27 

8864.82 

6520.54 

48340.83 

40705.82 

-15.79 

72.71 

6.1.5.2 A4A MODEL 

The input parameters were the same used for the a4a stock assessment and its 
results.  Fstatus  quo  is  equal  to  F2016  (0.69),  corresponding  to  a  catch2017  of  5682 
tonnes; other scenario are showed in Table 6.1.5.2.1. 

162 

162 

 
 
 
Table  6.1.5.2.1  European  hake  in  GSAs  17-18.  Short  term  forecasts  showing 
catch options for different fishing mortalities reductions. 

Rationale 

Ffactor 

Fbar 

Catch 2017  Catch 2018  Catch 2019 

SSB 2018 

SSB 2019 

Change SSB 
2018-2019 
(%) 

Change 
Catch 2016-
2018 (%) 

Zero catch 

F0.1 

Status quo 

Different 
Scenarios 

0 

0.1 

1 

0.2 

0.3 

0.4 

0.5 

0.6 

0.7 

0.8 

0.9 

1.1 

1.2 

1.3 

1.4 

1.5 

1.6 

1.7 

1.8 

1.9 

2 

0.000 

5682.29 

0.00 

0.00 

11949.02 

24145.49 

102.10 

-100.00 

0.181 

5682.29 

2041.73 

3371.98 

11949.02 

20191.43 

69.00 

-61.20 

0.690 

5682.29 

6044.75 

6264.99 

11949.02 

12605.26 

5.50 

14.80 

0.069 

5682.29 

829.32 

1534.12 

11949.02 

22534.51 

88.60 

-84.30 

0.138 

5682.29 

1596.99 

2752.83 

11949.02 

21049.12 

76.20 

-69.70 

0.207 

5682.29 

2308.07 

3712.72 

11949.02 

19678.88 

64.70 

-56.20 

0.276 

5682.29 

2967.21 

4460.71 

11949.02 

18414.26 

54.10 

-43.70 

0.345 

5682.29 

3578.62 

5035.68 

11949.02 

17246.56 

44.30 

-32.10 

0.414 

5682.29 

4146.21 

5469.81 

11949.02 

16167.79 

35.30 

-21.30 

0.483 

5682.29 

4673.50 

5789.69 

11949.02 

15170.67 

27.00 

-11.30 

0.552 

5682.29 

5163.75 

6017.30 

11949.02 

14248.53 

19.20 

-2.00 

0.621 

5682.29 

5619.93 

6170.76 

11949.02 

13395.25 

12.10 

6.70 

0.760 

5682.29 

6440.71 

6312.23 

11949.02 

11873.43 

-0.60 

22.30 

0.829 

5682.29 

6810.08 

6322.54 

11949.02 

11195.09 

-6.30 

29.30 

0.898 

5682.29 

7154.96 

6304.14 

11949.02 

10565.94 

-11.60 

35.80 

0.967 

5682.29 

7477.25 

6263.73 

11949.02 

9982.05 

-16.50 

41.90 

1.036 

5682.29 

7778.72 

6206.78 

11949.02 

9439.82 

-21.00 

47.70 

1.105 

5682.29 

8060.97 

6137.68 

11949.02 

8935.95 

-25.20 

53.00 

1.174 

5682.29 

8325.48 

6060.00 

11949.02 

8467.41 

-29.10 

58.00 

1.243 

5682.29 

8573.60 

5976.59 

11949.02 

8031.42 

-32.80 

62.80 

1.312 

5682.29 

8806.57 

5889.72 

11949.02 

7625.45 

-36.20 

67.20 

6.1.5.1 

COMPARISON OF RESULTS  

The differences in state of the stock and advice for selected assessment runs are 
summarised  in  Table  6.1.5.3.  As  noted  above  all  assessments  show  F>F0.1. 
which in all cases supports the conclusion that the stock is overfished. The catch 
advice at FMSY varies from about 1100 to 4000 t which is much less than the SSB 
which  varies  from  around  12000  to  63000  t.  There  appear  to  be  two  main 
aspects  that  give  rise  to  differences  between  models,  growth  parameters  and 
selection at age in the fisheries (Figure 6.1.5.1). The slower growth that results 
from  the  SS3  fit  moves  most  of  the  N  at  age  in  the  fishery  to  one  age  older, 
giving  very  few  hake  at  age  0  in  the  fishery.  This  also  but  also  gives  a  lower 
overall F (Figure 6.1.5.1) and a steeper decline at the oldest ages. F at age 5+ in 
a4a  model  is  low  (0.2),  but  F  at  6  and  older  in  SS4  is  very  much  lower  (0.06), 
contributing  to  maintainance  of  higher  biomass  at  ages  5+.    Some  sensitivity 
tests  using  SS3  exploring  these  differences  have  been  carried  out  during  the 
EWG,  though  these  are  not  exhaustive  they  give  some  indications  of  the 
magnitude of the changes. Run 12 differs from the base case by imposing fixed 
length parameters that have faster growth in line with published growth figures.  
This  results  in  a  substantial  increase  in  F  and  decrease  in  SSB.  Run  12  uses 

163 

163 

 
 
growth parameters that match the priors in the SS3 model and the growth used 
in the a4a run. Run 1 imposes  increased F at ages in the asymptotic parts of the 
selection  curves  (0.2  and  0.5  for  final  values  of  selection  for  trawl  and 
gillnet/longline  fisheries  respectively).  The  increases  are  only  at  ages  above  the 
main ages in the fishery, and these also results in a substantial decreases in SSB. 
The  main  aspect  of  concern  is  the  proportion  of  the  SSB  that  is  at  age  5  and 
above.  These  ages  are  seen  rarely  in  suveys,  and  have  low  selectivity  in  both 
survey and fishery. Even for the lowest SSB estimates the 40% of the SSB is in 
these  rarely  observed  ages  and  for  the  base  run  from  SS3  90%  of  SSB  is  from 
ages 5 and above. It is important to try to determine the validity of the growth 
and  selection  resulting  from  the  models  and  thus  the  extent  of  such  rarely 
observed biomass before advising that management should be based on models 
with such high proportions of SSB at ages greater than those fished.   

Table  6.1.5.3  Comparison  of  management  information  for  selected  assessments 
taken from the SS3 and a4a assessment above and the short term forecasts. 

F0.1 

F2016 

F2016/ 
F0.1 

Frac  

SSB 5+ 

STF 
Catch2018 

SSB2018 

HR 2018 

SSB2018/ 
SSB2016 

a4a 

Base 
Run 

SS3 1 

SS3 2 

SS3 4 

SS3 10 

SS3 12 

0.18 

0.19 

0.17 

0.19 

0.12 

0.12 

0.13 

0.69 

0.38 

0.24 

0.25 

0.22 

0.33 

0.49 

3.83 

2.0 

1.41 

1.32 

1.83 

2.75 

3.77 

0.40 

0.90 

0.91 

0.91 

0.80 

0.90 

0.88 

2042 

2875 

3442 

4005 

3123 

1735 

1104 

11949 

48341 

57400 

62902 

15% 

2% 

-6% 

1.4% 

25531 

-0.5% 

27897 

15510 

-17% 

-28% 

1.14 

0.91 

0.98 

0.98 

1.38 

0.88 

0.78 

Figure 6.1.5.1 Comparison of mean F 2009 to 2016 and mean selection at age in 
the stock for assessments taken from the SS3 and a4a assessment above. (Ages 
5 and 6 are plus groups for a4a and SS3 models respectively) 

SS3

6
1
0
2
-
9
0
0
2
F
e
g
a
r
e
v
A

1.4

1.2

1

0.8

0.6

0.4

0.2

0

SS3

2

1.5

n
o
i
t
c
e
e
S

l

1

0.5

0

0

1

2

3
Age 

4

5

6

0

1

2

3
Age 

4

5

6

164 

164 

 
 
 
 
 
 
 
 
 
 
6.1.6 DATA DEFICIENCIES 

The  differences  between  the  two  stock  assessment  models,  SS3  and  a4a,  are 
summarised in section 6.1.3.2. 

Regarding  the  data,  thanks  to  the  collaboration  developed  in  the  FAO  frameworks  (i.e. 
AdriaMed, GFCM) among all the countries and the scientists involved in the sector, it was 
possible  to  use  also  data  from  non-European  countries  (Montenegro  and  Albania)  and 
data  collected  before  the  annexation  of  some  countries  to  the  EU  (Croatia).  However 
Albanian  catch  data  for  the  years  2015  and  2016  are  assumed  equal  to  the  value  for 
2014, since they did not provide information for these years. 

Catch data and their LFDs include landings and discards coming from the DCF samples; 
however, discards are included only in the years and for the fleets in which estimates are 
available. Future work is needed to harmonization of this aspect across years. 

The Medits data for the Italian GSA 17 used in the stock assessment models cover Italy 
and  Slovenia;  however,  for  some  years  only  Italian  data  are  included  in  the  MEDITS 
index  used  in  the  assessment.  This  aspect  has  a  negligible  effect  since  only  two  hauls 
are found in Slovenia water, also hake is not present in this  part of the area. However, 
for the future elaborations it would be useful to harmonize also this point. 

6.2  RED MULLET IN GSAS 17 AND 18 

6.2.1 BIOLOGY 

STECF  17-15,  after  analysing  the  results  of  the  STOCKMED  project,  concluded  that 
the  region  represented  by  the  GSAs  17  and  18,  corresponding  to  the  Adriatic  Sea 
Sea, is considered inhabited by a unique stock unit. During the GFCM Working Group 
on  Demersal  Species  (WGSAD)  in  2017,  a  first  attempt  of  joint  assessment  with 
Stock  Synthesis  model  was  presented.  This  attempt  was  made  on  the  basis  of  the 
analysis of the survey indices, showing a very similar increasing trend in both areas 
in  the  recent  years,  and  considering  that  the  Western  side  of  both  GSAs  was 
characterized by a decrease in effort from 2004 to 2016.  

The  present  assessment  was  aimed  to  explore  the  status  of  the  stock  with  other 
stock assessment models, to verify the consistency among different models. 

165 

165 

 
 
 
 
 
 
Figure 6.2.1.1 Geographical location of GSAs 17-18. 

Growth 

The  von  Bertalanffy  parameters  from  the  official  Data  call  for  GSA  18  was  used  in 
the  assessment  and  reported  in  table  6.2.1.1;  these  are  the  same  used  during  the 
GFCM WGSAD 2017.  

The  parameters have  been  estimated  in  GSA  18  for  sex  combined,  using  the mean 
lengths  from  Age-  Length  key  from  commercial sampling (discard  and landing) and 
MEDITS survey from 2011 to 2016. A constraint has been included in the VBF fitting 
to  take  into  account  the  exceptional  finding  of  4  cm-sized  metamorphosed 
individuals during MEDITS trawl survey. 

Table  6.2.1.1  Red  mullet  in  GSAs  17  and  18.    Von  Bertalanffy  growth  parameters 
for red mullet used for GSAs 17 and 18. 

GSA 

Linf (cm)  K 

t0 

17-18  30 

0.24  -0.61 

166 

166 

 
 
 
 
 
 
Figure 6.2.1.2 Red mullet in GSAs 17 and 18. Von Bertalanffy growth functions for 
sex combined used for GSA 17-18. 

Maturity 

The  vector  of  proportion of  mature  individuals  by  age  has been  derived  associating 
the  proportion  of  matures  of  the  length  from  DCF  derived  by  von  Bertalanffy 
calculated in the middle of age class. The SS3 assessment was carried out using the 
maturity  at  length  estimated  in  GSA  18  from  DCF  data  in  2016,  while  the  a4a  and 
XSA assessment used the vector by age. 

Table 6.2.1.2 Red mullet in GSAs 17 and 18. Maturity at age of red mullet in GSAs 
17-18. 

Maturity 

Age 

0 

1 

0 

1 

167 

167 

 
 
 
 
 
 
 
 
 
 
2 

3 

4+ 

1 

1 

1 

Figure  6.2.1.3  Red  mullet  in  GSAs  17  and  18.  Maturity  at  length  (sex  combined) 
from DCF 2016. 

Natural mortality 

The natural mortality vector has been estimated as average between the vector used 
in GSA 18 (according to Chen and Watanabe method) and the vector used in GSA 17 
to  be  consistent  with  the  benchmark  assessments  of  GFCM  WGSAD  2016  (where  a 
sensitivity analysis on natural mortality vectors was carried out) and GFCM WGSAD 
2017.  

Table 6.2.1.3 Red mullet in GSAs 17 and 18. Natural mortality at age of red mullet in 
GSAs 17-18. 

M 

0 

Age 

1.32 

168 

168 

 
 
 
 
 
 
 
 
 
1 

2 

3 

4+ 

0.73 

0.55 

0.47 

0.42 

6.2.2 INPUT DATA 

6.2.2.1 

CATCH (LANDINGS AND DISCARDS)  

Landing data in  weight  and the related length  and age distributions are reported  in 
the official Data call for the GSA 17 Italy from 2006 to 2016, for GSA 18 Italy from 
2002 to 2016 and for GSA 17 Croatia from 2013 to 2016. The discard was available 
for GSA 17 Italy from 2010 to 2016, for GSA 17 Croatia from 2013 to 2016 and for 
GSA 18 from 2009 to 2016. In the missing years the discard was estimated on the 
basis of the discard ratio (discard/landing) of the first available years and the landing 
time series. 

For Montenegro and Albania, the report of the last GFCM WGSAD 2016 was used. In 
particular, the Montenegrin LFDs reported in GFCM WGSAD 2015 were used (2008-
2015)  and  for  Albania  the  annual  proportions  in  the  length  classes  of  GSA  18  Italy 
were  applied  to  the  landing  times  series  available  from  the  same  report  (2007-
2015). For 2016 the same value of 2015 was assumed for Albanian and Montenegrin 
fleet.  For  landings  of  Croatia  of  the  previous  years  (2009-2012)  the  GFCM  WGSAD 
2016  report  was  used.  No  discard  data  were  available  for  Albania  and  Montenegro. 
For all the years from 2006 to the first year available, the same landing of the first 
years available was used. 

Table 6.2.2.1.1 Red mullet in GSAs 17 and 18. Landing of red mullet in GSA 17-18 
by year, GSA and country. Weights are in tons. 

Italy 18  Italy 18  Montenegro  Montenegro 

Year 

Albania 

Croatia 
OTB 

OTB 

GEN 

OTB 

Nets 

2002 

3114 

90 

Total 

Italy 
17 

OTB 

3204 

169 

169 

 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
2003 

1750 

312 

2004 

1981 

2005 

1350 

82 

99 

2006 

1803 

130 

2007 

1680 

123 

2008 

914 

2009 

955 

2010 

601 

2011 

494 

2012 

2089 

2013 

1203 

2014 

1250 

2015 

1572 

2016 

1398 

47 

77 

45 

38 

8 

47 

23 

15 

50 

2062 

2063 

1449 

3101  5034 

3298  5272 

767 

3158  5077 

818 

2433  4477 

763 

1796  3333 

171 

149 

154 

90 

110 

1086 

1823  3586 

280 

247 

147 

171 

1248 

1464  5128 

1086 

1946  4564 

1158 

2324  4947 

1127 

2143  5068 

38 

36 

35 

32 

35 

32 

41 

36 

3.7 

3.6 

3.4 

3.2 

3.5 

3.1 

4 

3.6 

36* 

3.6* 

171* 

951 

2037  6188 

*For 2016 the same value of 2015 was assumed.  

Table 6.2.2.1.2 Red mullet in GSAs 17 and 18.  Discard of red mullet in GSA 17-18 
by  year,  GSA  and  country.  Weights  are  in  tons.  In  bold  the  discard  data 
reconstructed on the basis of the first three years available. 

Italy 
18 

Italy 
18 

Year 

Montenegro  Montenegro 

OTB  GEN 

OTB 

Nets 

Albania 

Croatia 
OTB 

Italy 
17 

OTB 

170 

170 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2006 

58 

2007 

54 

2008 

30 

2009 

14 

2010 

34 

2011 

13 

2012  434 

2013 

18 

2014  120 

2015 

89 

2016 

87 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

NA 

NA 

NA 

NA 

NA 

NA 

NA 

NA 

NA 

NA 

NA 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

NA 

NA 

NA 

NA 

NA 

NA 

NA 

NA 

NA 

NA 

NA 

NA 

786 

NA 

836 

207 

800 

220 

617 

206 

183 

293 

803 

336 

325 

162 

291 

453 

446 

300 

910 

158 

499 

The  length  distributions  show  that  the  Italian  landings  in  GSA  17  is  concentrated 
around  14-15  cm,  while  the  discard  is  around  10-11  cm  (Figure  6.2.1.2.1).  The 
Croatian LFDs of GSA 17 show that the landings is targeting individuals around 14-
15  cm,  and  the  discarded  individuals  are  around  12-13  cm  (Figure  6.2.1.2.2).  The 
length  distributions  show  that  the  Italian  landings  in  GSA  18  appears  wider  than 
those of Croatia and Italy in GSA 17. For Italian trawlers of GSA 18 the landing sizes 
ranges between 7 and 20 cm with the mode around 10-12 cm,  while the discarded 
individual  are  generally  10  cm-sized  (Figure  6.2.1.2.3).  For  gill-nets  and  trammel 
nets  in  GSA  18  the  mode  is  around  10  cm  (Figure  6.2.1.2.4).  Montenegro  landing 
shows a mode around 15 cm(Figure 6.2.1.2.5). 

171 

171 

 
 
Figure 6.2.2.1.1 Red mullet in GSAs 17 and 18. Italy GSA 17: landing and discard 
at length for trawlers. 

172 

172 

 
 
 
Figure  6.2.2.1.2  Red  mullet  in  GSAs  17  and  18.  Croatia  GSA  17:  landing  and 
discard at length for trawlers. 

173 

173 

 
 
Figure 6.2.2.1.3 Red mullet in GSAs 17 and 18. Italy GSA 18: landing and discard 
at length for trawlers. 

174 

174 

 
 
 
Figure 6.2.2.1.4 Red mullet in GSAs 17 and 18. Italy GSA 18: landing and discard 
at length for gill-nets and trammel nets. 

Figure  6.2.2.1.5  Red  mullet  in  GSAs  17  and  18  Montenegro  GSA  18:  landing  at 
length for gill-nets + trammel nets + trawlers. 

175 

175 

 
 
 
The Italian landing of GSA 17 is mainly represented by age groups 1 and 2, while the 
discards  are  generally  due  to  smaller  individuals  and/or  probably  damaged  bigger 
individuals. The Croatian landing is mainly due to individuals of age groups 1 and 2, 
but also 3-aged individuals are well represented. The age 0 individuals are practically 
absent both in the Croatian landing and in the discards. 

The Italian landing of GSA 18 is composed by individuals of 1 and 2 years, while the 
discard is due to individuals of 0 and 1 years. The gill-nets and trammel nets of GSA 
18 (Italy) target age group 1, while Montenegrin fleet landing is mainly concentrated 
on age groups 1, 2 and 3 years.  

Figure 6.2.2.1.6 Red mullet in GSAs 17 and 18. Italy GSA 17: landing and discard 
at age for trawlers. 

176 

176 

 
 
 
Figure  6.2.2.1.7  Red  mullet  in  GSAs  17  and  18.  Croatia  GSA  17:  landing  and 
discard at age for trawlers. 

Figure 6.2.2.1.8 Red mullet in GSAs 17 and 18. Italy GSA 18: landing and discard 
at age for trawlers. 

177 

177 

 
 
 
Figure 6.2.2.1.9 Red mullet in GSAs 17 and 18. Italy GSA 18: landing and discard 
at age for gill-nets and trammel nets. 

178 

178 

 
 
Figure  6.2.2.1.10  Red  mullet  in  GSAs  17  and  18.  Montenegro  GSA 18:  landing  at 
age for gill-nets + trammel nets + trawlers. 

6.2.2.2 

EFFORT 

The  effort  data  are  available  for  GSA17  (Italy  and  Croatia)  and  18  (Italy).  In  table 
6.2.1.3.1 and 6.2.1.3.2 is reported the fishing effort for the gear targeting this species in 
terms of GT*fishing days and fishing days. 

Table 6.2.2.2.1 Fishing effort (GT*days at sea)/fishing gear/year in GSA 17-18 
of the gears targeting red mullet in the same area. 

Fleet 

Year 

GNS 

GTR 

OTB 

TOTAL 

HRV GSA17 

2012 

161601  126635  1289335 

1577571 

HRV GSA17 

2013 

146238  130299  1373511 

1650048 

HRV GSA17 

2014 

150427  116713  1381570 

1648710 

HRV GSA17 

2015 

144366  128027  1346257 

1618650 

179 

179 

 
 
 
 
HRV GSA17 

2016 

145114  109194  1231785 

1486093 

ITA GSA 17 

2004 

245401  129028  5324756 

5699185 

ITA GSA 17 

2005 

262674 

80535  5165331 

5508540 

ITA GSA 17 

2006 

216424 

79544  4079669 

4375637 

ITA GSA 17 

2007 

156782  101669  4056776 

4315227 

ITA GSA 17 

2008 

135113 

56788  4082465 

4274366 

ITA GSA 17 

2009 

173403 

65074  3830475 

4068952 

ITA GSA 17 

2010 

190223 

66358  3837446 

4094027 

ITA GSA 17 

2011 

236375 

79984  3482614 

3798973 

ITA GSA 17 

2012 

259488 

78308  3130643 

3468439 

ITA GSA 17 

2013 

167797 

64034  2645415 

2877246 

ITA GSA 17 

2014 

233376 

45568  2836181 

3115125 

ITA GSA 17 

2015 

139371 

55459  2872228 

3067058 

ITA GSA 17 

2016 

178800 

59674  3014054 

3252528 

ITA GSA 18 

ITA GSA 18 

2004 

2005 

67828 

32267  2510980 

2611075 

94644 

69435  2354637 

2518716 

ITA GSA 18 

2006 

117032 

31528  2662179 

2810739 

ITA GSA 18 

ITA GSA 18 

ITA GSA 18 

ITA GSA 18 

ITA GSA 18 

ITA GSA 18 

ITA GSA 18 

ITA GSA 18 

ITA GSA 18 

ITA GSA 18 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

70224 

45292  2294240 

2409756 

51447 

83968  2039422 

2174837 

79662 

80946  2386555 

2547163 

57056 

79765  2068044 

2204865 

44943 

79593  1900240 

2024776 

38287 

60542  1668749 

1767578 

78862 

8196 

1994855 

2081913 

21679 

51077  1463644 

1536400 

78693 

12679  1355193 

1446565 

88202 

5609 

1429243 

1523054 

Table  6.2.2.2.2  Fishing  effort  (Days  at  sea)/fishing  gear/year  in  GSA  17-18  of 
the gear targeting red mullet in the same area.  

Fleet 

Year 

GNS 

GTR 

OTB 

TOTAL 

HRV GSA17 

2016 

56630 

32426 

37201 

126257 

HRV GSA17 

2012 

60504 

34888 

39128 

134520 

HRV GSA17 

2013 

56041 

37239 

39226 

132506 

HRV GSA17 

2014 

57411 

34860 

40553 

132824 

HRV GSA17 

2015 

56695 

36132 

39074 

131901 

ITA GSA 17 

2002 

335599 

124529 

460128 

ITA GSA 17 

2003 

272040 

125106 

397146 

ITA GSA 17 

2004 

487802 

417092 

740495 

1645388 

ITA GSA 17 

2005 

379892 

368466 

594177 

1342535 

ITA GSA 17 

2006 

323310 

244244 

434655 

1002209 

180 

180 

 
 
 
 
 
 
 
 
ITA GSA 17 

2007 

224242 

224242 

382493 

830977 

ITA GSA 17 

2008 

270057 

197633 

354510 

822200 

ITA GSA 17 

2009 

526142 

261673 

345095 

1132911 

ITA GSA 17 

2010 

424178 

231216 

329764 

985157 

ITA GSA 17 

2011 

419686 

344909 

333303 

1097898 

ITA GSA 17 

2012 

514255 

227536 

322785 

1064577 

ITA GSA 17 

2013 

224894 

245016 

292130 

762039 

ITA GSA 17 

2014 

258913 

167612 

259502 

686027 

ITA GSA 17 

2015 

220612 

140111 

265839 

626562 

ITA GSA 17 

2016 

266064 

145210 

258656 

669930 

ITA GSA 18 

2002 

110621 

85424 

196044 

ITA GSA 18 

2003 

63332 

71203 

134536 

ITA GSA 18 

2004 

70899 

128065 

246015 

444978 

ITA GSA 18 

2005 

86016 

103273 

207387 

396676 

ITA GSA 18 

2006 

109714 

80000 

201679 

391394 

ITA GSA 18 

2007 

71797 

71797 

176345 

319940 

ITA GSA 18 

2008 

121136 

81506 

253577 

456218 

ITA GSA 18 

2009 

124844 

87533 

316411 

528788 

ITA GSA 18 

2010 

88940 

88940 

292887 

470766 

ITA GSA 18 

2011 

87234 

87234 

222708 

397177 

ITA GSA 18 

2012 

76470 

76470 

157792 

310731 

ITA GSA 18 

2013 

82110 

23036 

143901 

249047 

ITA GSA 18 

2014 

73021 

73021 

157301 

303344 

ITA GSA 18 

2015 

63400 

60311 

154211 

277922 

ITA GSA 18 

2016 

54072 

47977 

160570 

262619 

6.2.2.3 

SURVEY DATA 

MEDITS survey data are available from the official Data call for GSA 18 from 1994 and 
for GSA 17 from 2002. All the Countries are covered by the survey data. For the present 
assessment the data from 2006 to 2016 were used. In particular, for GSA 18 the survey 
covered also Eastern side from 1996. 

The length and the age distributions show that in the Western side of GSA 17 and in GSA 
18 there is a high concentration of small individuals, while in the Eastern side of GSA 17 
there are generally bigger individuals (Figure 6.4.1.4.1 and Figure 6.4.1.4.2). 

All  the  surveys  explored  reveal  a  strong  increase  in  the  density  and  in  the  biomass 
indices;  this  signal  is  also  present  in  Croatian  MEDITS  survey,  that  has  not  the  shift  in 
the survey time (Figure 6.4.1.4.3). 

181 

181 

 
 
 
 
 
 
 
 
 
Figure  6.2.2.3.1  Red  mullet  in  GSAs  17  and  18.  Age  distributions  from  the  MEDITS 
survey used in the assessment (2006-2016). 

182 

182 

050010001500200025003000350040004500500001234+N/km2MEDITS GSA 18200620072008200920102011201220132014201520160500100015002000250030003500400001234+N/km2MEDITS GSA 17 Italy20062007200820092010201120122013201420152016050010001500200025003000350001234+N/km2MEDITS GSA 17 Croatia2006200720082009201020112012201320142015201605001000150020002500300001234+N/km2MEDITS GSA 17-18 combined20062007200820092010201120122013201420152016050010001500200025001357911131517192123252729N/km2cmMEDITS GSA 18200620072008200920102011201220132014201520160200400600800100012001357911131517192123252729N/km2cmMEDITS GSA 17 Croatia20062007200820092010201120122013201420152016 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
Figure  6.2.2.3.2  Red  mullet  in  GSAs  17  and  18.    Length-frequency  distributions  from 
the MEDITS survey used in the assessment (2006-2016).  

Figure 6.2.2.3.3 Red mullet in GSAs 17 and 18. Density and biomass indices from the 
MEDITS survey (1996-2016 

6.2.3 STOCK ASSESSMENT 

Methods: SS3 (Stock Synthesis) 

The  assessment  presented  during  the  GFCM  WGSAD  2017  taking  into  account  the 
hypothesis  of  a  selectivity  different  among  the  fleets  by  means  of  SS3  model  is  here 
reported, because discussed during the STECF EWG 17-15.  

The  Stock  Synthesis  (SS3,  Methot  and  Wetzel,  2013)  assessment  program  provides  a 
statistical  framework  for  calibration  of  a  population  dynamics  model  using  a  multi-fleet 
approach. It is designed to include different information from fishery and survey data, as 
well as to  consider different subareas within the same stock. The model allows to work 
by  length  or  by  age  and  to  assume  different  selectivity  patterns  for  the  different  fleet 
exploiting  the  stock.  In  the  model  the  selectivity  is  a  combination  of  availability  and 
vulnerability.  

SS3  is  based  on  ADMB  C++  software,  allowing  to  easily  work  with  large  databases,  as 
well  as  to  simultaneously  estimate  a  number  of  parameters.  A  wide  number  of  options 
are  available  for  modelling  the  selectivity  patterns  of  the  different  fishing  gears. 

183 

183 

0200400600800100012001357911131517192123252729N/km2cmMEDITS GSA 17 Italy2006200720082009201020112012201320142015201601002003004005006007008009003579111315171921232528N/km2cmMEDITS GSA 17-18 combined20062007200820092010201120122013201420152016010002000300040005000600070008000199619971998199920002001200220032004200520062007200820092010201120122013201420152016N/km2MEDITS Density index GSA17 ItalyGSA 17 CroatiaGSA 18GSA17-18 combined0102030405060708090100199619971998199920002001200220032004200520062007200820092010201120122013201420152016kg/km2MEDITS Biomassindex GSA17 ItalyGSA 17 CroatiaGSA 18GSA17-18 combined 
 
 
 
 
 
 
 
Moreover,  time  varying  selectivity  can  be  defined  in  order  to  take  into  account  annual 
changes in vulnerability and availability of the stock. 

The model built in SS3 for this stock has the following features: 

  Length based; 
  Discard included in catch data; 
  sex combined; 
  1 area; 
  annual time step; 
  6 commercial fleets (Italian trawlers GSA 18 , Italian gill and trammel netters GSA 
18,  Albanian  trawlers,  Montenegrin  trawlers,  gill  and  trammel  netters,  Italian 
trawlers GSA 17 and Croatian trawlers GSA 17); 

  3 survey fleets (MEDITS whole GSA 18 (ITA, ALB, MON), MEDITS GSA 17 Western 

 

side, MEDITS GSA 17 Eastern side) 
logistic  selectivity  for  the  survey  and  all  commercial  fleet  except  the  Italian  gill 
and  trammel  netters  GSA  18,  Montenegrin  fleet,  Italian  trawlers  GSA  173  and 
MEDITS Western side GSA 171 (double-normal function);(on the basis of spawners 
(East) and recruits (West) distribution); 

  Albanian trawlers with the same selectivity of Italian trawlers; 
  no stock-recruitment relationship (annual scalar recruitment ). 

The number of parameters estimated by the model is 74.  

Time blocks for MEDITS surveys were defined according to the within year survey time, 
in order to inform the model about a different sizes availability (if recruitment is detected 
or not).  

Input data 

The model covers the period 2003- 2016.  

The  proportion  of  the  discards  of  red  mullet  in  the  whole  area  (GSA  17  and  18)  was 
around 15% in 2016. Discard data not available have been estimated on the basis of the 
average discard ratio in the first three available years for all the fleet except Montenegro 
and Albania, where no discard data was available. 

The  LFDs  and  the  landing  of  Italian  trawlers  in  GSA  18  were  available  from  2003  from 
DCF, the LFDs of Italian nets from 2011  and for Montenegrin fleet from  2008. No LFDs 
were provided for Albanian fleet and the same selectivity of Italian fleet was assumed in 
the  model.  The  landing  and  the  LFDs  of  Italian  trawlers  in  GSA  17  were  available  from 
2006 to 2016 from DCF, while for Croatian fleet from 2013 to 2016. 

No reconstruction in LFDs was made to run the model. 

The  MEDITS  data  as  aggregated  indices  (N/km^2)  and  LFDs  were  used  for  the  whole 
period (2003-2016) for the three surveys. 

The  catch  at  length  and  the  catch  in  weight  data  are  the  same  reported  in  paragraph 
6.2.2. The biological parameters are the same reported in paragraph 6.2.1. 

Results 

184 

184 

 
 
 
 
                                                 
 
Figure  6.2.3.1  Red  mullet  in  GSAs  17  and  18.  Length  selectivity  estimated  by  SS3  in 
2016. 

Figure 6.2.3.2 Red mullet in GSAs 17 and 18. Time varying length selectivity estimated 
by SS3 by fleet. 

For MEDITS in GSA 18 the size at first capture estimated by the model ranges between 4 
and  13  cm  along  the  years,  depending  on  the  presence  or  absence  of  the  recruitment 

185 

185 

 
 
 
 
along the years; for MEDITS GSA 17 Eastern side the size at first capture was estimated 
between  12  and  13  cm,  while  for  MEDITS  GSA  17  Western  side  the  size  at  which  the 
selectivity is 1 ranges between 10 and 13 cm. 
Fishing  mortality  (F)  shows  the  minimum  value  of  0.2  (F̅  or  Fbar)  in  2016,  and  a 
maximum of 2.12 in 2003. Average F for the period of last three years (2014-2016) was 
0.38. The total F estimated by SS3 in GSA 18 in 2016 is split in 19 % exerted by Italian 
trawlers  GSA  18,    1%  by  Italian  netters,  1%  by  Montenegrin  fleet,  2%  by  Albanian 
trawler, 55% by Italian trawlers in GSA 17 and 22% by Croatian trawlers. 

The summary of the final run, chosen for the advice is reported below in Fig. 6.2.3.3 and 
Tables 6.2.3.1 and 6.2.3.2. 

Figure 6.2.3.3 Red mullet in GSAs 17 and 18. Results of the final SS3 run. 

Table 6.2.3.1 Red mullet in GSAs 17 and 18. Results of the final SS3 run: Fbar (1-3) by 
fleet and overall. 

Year 

Fbar(1-3) 

2003 
2004 
2005 
2006 
2007 
2008 
2009 
2010 

2.13 
1.28 
0.95 
1.04 
0.93 
1.00 

1.29 
1.03 

GSA18 ITA 
OTB 
0.351 
0.256 
0.150 
0.206 
0.163 
0.116 

0.164 
0.119 

GSA18 
NETS 
0.067 
0.011 
0.012 
0.017 
0.013 
0.007 

0.015 
0.010 

ALB  MNE 

0.037  0.033 
0.019  0.021 
0.011  0.018 
0.010  0.019 
0.010  0.016 
0.009  0.018 

0.014  0.026 
0.016  0.017 

GSA17 ITA 
OTB 
1.183 
0.717 
0.583 
0.617 
0.570 
0.683 

0.801 
0.569 

HRV  
OTB 
0.458 
0.255 
0.178 
0.172 
0.154 
0.165 

0.266 
0.300 

186 

186 

00.511.522.5Fbar(1-3)F0.100.511.522.5Fbar(1-3) by fleetIta_OTB 18Ita_NETS 18MNEALB_OTBITA_OTB_17HRV_OTB_17tota FF0.10500010000150002000020032004200520062007200820092010201120122013201420152016tonsSSBSSBTOT CATCH gsa17-1802000000400000060000008000000100000001200000014000000160000001800000020032004200520062007200820092010201120122013201420152016thousandsRecruitment 
 
 
 
 
   
 
2011 
2012 
2013 
2014 
2015 
2016 

0.99 
1.23 
0.81 
0.63 
0.35 
0.19 

0.071 
0.385 
0.137 
0.106 
0.070 
0.036 

0.006 
0.001 
0.006 
0.002 
0.001 
0.001 

0.013  0.015 
0.014  0.043 
0.011  0.028 
0.008  0.011 
0.004  0.007 
0.002  0.004 

0.545 
0.393 
0.379 
0.294 
0.161 
0.103 

0.340 
0.395 
0.251 
0.208 
0.102 
0.041 

Table 6.2.3.2 Red mullet in GSAs 17 and 18. Results of the final SS3 run: Recruitment, 
SSB and total biomass. 

Year 

2003 
2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 
2015 
2016 

Recruitment 
(thousands) 
3099200 
2384450 
2057700 
2879920 
1177510 
1339760 
1235900 
1877020 
1679370 
2411330 
2713820 
5100450 
6463450 
15488600 

SSB 
(t) 
1935 
3165 
4678 
5362 
5396 
5695 
4023 
2880 
3315 
4179 
3959 
5986 
9639 
18394 

Total 
Biomass (t) 
5721 
7338 
8209 
8837 
9106 
7801 
6028 
4960 
6036 
7042 
7587 
10935 
17712 
31541 

The  retrospective  analysis  shows  considerable  of  instability,  with  successive  upward 
revision in F (F2013 revised from 0.2 to 0.8 over 4 years) and downward revision in SSB 
(SSB2013 revised down from 15000 to 3900 in 4 years). Higher residuals are observed for 
the  Italian  trawlers  (GSA  17  and  18)  and  MEDITS  18  and  17  Western  side  LFDs. 
Generally,    it  happened  when  the  model  was  not  able  to  completely  explain  the 
unusually strong peaks in recruitment. 

187 

187 

 
 
 
 
 
Figure 6.2.3.4 Red mullet in GSAs 17 and 18. Retrospective analysis results. 

188 

188 

00.20.40.60.811.21.41.620032004200520062007200820092010201120122013201420152016Fbar (1-3)2003-20162003-20152003-20142003-2013050001000015000200002500020032004200520062007200820092010201120122013201420152016tonsSSB2003-20162003-20152003-20142003-201302000000400000060000008000000100000001200000014000000160000001800000020032004200520062007200820092010201120122013201420152016tonsRecruitment2003-20162003-20152003-20142003-2013 
 
 
 
 
Figure. 6.2.3.5 Red mullet in GSAs 17 and 18. Pearson residuals by fleet. 

For Italian trawlers in the years of DCR (2003-2007) the model estimates a higher level 
of  uncertainty;  moreover,  the  model  reveals  some  difficulty  in  the  detection  of    the 
peculiar occurrence of recruits in the trawlers GSA 18 catch in 2012-2014. The double-
normal  function  seems  to  fairly  well  represent  the  selectivity  of  Italian  gill-nets, 
Montenegrin fleet, Italian trawlers of GSA 17 and MEDITS GSA 17 Western side. Finally, 
the  trends  in  the  commercial  mean  length  by  year  are  globally  correctly  estimated 
(Figure 6.2.3.9). 

The  MEDITS  abundance  indices  and  the  annual  mean  lengths  are  estimated  in  a 
satisfactory way by the model (Figure 6.2.3.6, 6.2.3.7 and 6.2.3.8). 

189 

189 

 
 
 
Figure  6.2.3.6  Red  mullet  in  GSAs  17  and  18.  Comparison  between  observed  and 
estimated  MEDITS  GSA  18  log  index,  mean  length,  LFDs  and  between  theoretical  and 
empirical quantiles.  

190 

190 

 
 
Figure  6.2.3.7  Red  mullet  in  GSAs  17  and  18.  Comparison  between  observed  and 
estimated  MEDITS  GSA  17  Western  side  log  index,  mean  length,  LFDs  and  between 
theoretical and empirical quantiles.  

191 

191 

 
 
Figure  6.2.3.8  Red  mullet  in  GSAs  17  and  18.  Comparison  between  observed  and 
estimated  MEDITS  GSA  17  Eastern  side  log  index,  mean  length,  LFDs  and  between 
theoretical and empirical quantiles.  

192 

192 

 
 
 
 
 
 
 
 
 
193 

193 

 
 
 
 
 
 
Figure  6.2.3.9  Red  mullet  in  GSAs  17  and  18.  Comparison  between  observed  and 
estimated commercial mean length and LFDs.  

Methods: a4a (Assessment for all) 

A4a  is  a  flexible  statistical  catch  at  age  stock  assessment  model,  based  on  linear 
modelling  techniques,  not  working  by  fleet.  The  method  was  developed  within  FLR 
framework.  

Input data 

The  MEDITS  indices  by  length  were  re-estimated  treating  the  two  GSAs  as  a  unique 
area, starting from the TC files and re-stratifying the single hauls in the TA files.  

The length frequency distributions of all the fleets (available and reconstructed) and the 
MEDITS  LFDs  on  the  whole  area  were  age  sliced  according  to  LFDA  algorithm 
(deterministic slicing) using the von Bertalanffy parameters reported in paragraph 6.2.1. 
The  catch  at  age    matrices  are  reported  in  Table  6.2.3.3  (commercial)  and  6.2.3.4 
(survey). The overall catch in weight by year is reported in Table 6.2.3.5. 

The natural mortality vector and the maturity at age are the same reported in paragraph 
6.2.1. The M and F before spawning were set equal to 0.5. In Table 6.2.3.6 are reported 
the individual weights at age for the stock and for the catch. 

194 

194 

 
 
 
 
 
 
 
Table 6.2.3.3 Red mullet in GSAs 17 and 18. Commercial catch in numbers at age used 
in the a4a assessment (thousands). 

Year/Age 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 
2015 
2016 

0 

1 

2 

3 

28075  122047  89319  8357 
25270  133826  90376  8556 
14407  109944  78230  5202 
14999  115443  58140  5471 
8144  85389  43436  3996 
20393  102012  51375  5433 
94718  131057  66361  6576 
13729  117251  53744  5085 
34514  156233  62342  4513 
36266  132432  76149  7560 
41852  117186  55825  6963 

4+ 
805 
612 
456 
643 
453 
800 
705 
464 
534 
1065 
1246 

Table 6.2.3.4 Red mullet in GSAs 17 and 18. MEDITS catch in numbers at age used in 
the a4a assessment (N/km2). 

Year/Age 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 
2015 
2016 

0 
29 
69 
2 
2 
2 
168 
1356 
637 
2013 
641 
2648 

1 
441 
191 
236 
224 
295 
435 
1110 
1586 
2137 
1141 
2104 

2 
193 
151 
338 
246 
295 
214 
428 
457 
553 
450 
398 

3 
47 
34 
72 
70 
62 
42 
55 
73 
83 
77 
59 

4+ 
11 
9 
15 
13 
13 
8 
8 
9 
14 
16 
12 

Table 6.2.3.5 Red mullet in GSAs 17 and 18. Catch in weight by year (tons). 

Catch 
(tons) 
6282 
6345 
5315 
4636 
3606 
3899 
5928 
4703 
5555 
5446 
4841 

Year 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 
2015 
2016 

195 

195 

 
 
 
 
Table 6.2.3.6 Red mullet in GSAs 17 and 18. Individual weight at age for the stock and 
in the catch (kg). 

Variable 

1 
stock in weight  0.008  0.017  0.037  0.072  0.110 
catch in weight  0.007  0.017  0.042  0.072  0.112 

4+ 

0 

2 

3 

Different combinations of F, q and stock-recruitment sub-models were explored. The list 
of the sub-model is reported below: 

F sub-models: 

~s(age, k = 3) + s(year, k = 5,6,7) 

~s(replace(age, age > 3, 3), k = 3) + s(year, k = 7) 

~s(age, k = 3) + s(year, k = 5) + s(year, k = 5, by = as.numeric(age ==   0)) 

~   s(replace(age,age>3,3),k  =  3, by =  breakpts(year,  c(2014))) +  s(year, k  = 
5)    + s(year, k = 5, by = as.numeric(age==0)) 

q sub-models: 

~s(replace(age, age > 3, 3), k = 3) 

~factor(age) 

~factor(replace(age, age > 3, 3)) 

~ s(replace(age,age>3,3),k = 3, by = breakpts(year, 2012)) 

SR sub-models: 

~factor(year) 

~s(year, k = 6,7) 

The  best  model;  combination  of  the  sub-models;  (in  bold)  was  chosen  on  the  basis  of 
retrospective analysis and residuals. 

In  the  best  model  was  assumed  a  change  in  survey  catchability  from  2012,  due  to  a 
change in the survey period, and a change in the behaviour of the fleet from 2014, due 
to the enforcement of the regulation that does not allow to fishermen to fish within the 3 
nautical miles (where the smaller individuals are generally distributed). A specific term in 
the F sub-model is dedicated to the fitting of the F at age 0. 

Results 

The  F  time  series  estimated  by  a4a  ranges  between  1.65  and  0.17,  with  an  overall 
decrease in time. In the last two years the model estimates a strong increase in SSB and 
recruitment, higher than the SS3 model (Figure 6.2.3.10). 

The fishing mortality at age shows the maximum values in age 2 and 3, with the order of 
magnitude  decreasing  in  time.  The  model  estimated  a  lower  catchability  from  2012  for 
age 2 to 4+ (Figure 6.2.3.11). 

The  fitting  of  the  commercial  catch  at  age  is  quite  satisfactory,  while  the  estimated 
MEDITS  indices  by  age  are  generally  different  from  the  observed  indices,  especially  in 
the last years, when a strong recruitment was detected (Figure 6.2.3.12). 

The  residuals  are  generally  small  (between  -3  and  3)  and  quite  random  distributed  by 
age (Figure 6.2.3.13), but a signal of a conflict is shown by the bubble plot of residuals 

196 

196 

 
 
 
in  the  last  years  of  the  MEDTS  survey(Figure  6.2.3.14).  The  retrospective  analysis 
(Figure  6.2.3.14)  do  not  show  the  strong  revisions  seen  in  the  SS3  model  (Figure 
6.2.3.4), but the larget conflicting residuals by year in thje MEDITS survey data give rise 
to  concern  regaring  the  correct  evaluation  of  the  magnitude  of  the  increase  in 
recruitment (and then SSB) that is seen over the last three years.  

Table  6.2.3.7  Red  mullet  in  GSAs  17  and  18.  Results  of  the  final  a4a  run:  Fbar  (1-3) 
overall, SSB, Recruitment and total biomass. 

Year 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 
2015 
2016 

Fbar(1-
3) 
1.50 
1.65 
1.64 
1.47 
1.32 
1.33 
1.43 
1.37 
0.94 
0.50 
0.17 

Recruitment 
(thousands) 
1657630 
1245196 
1058123 
1062213 
1187140 
1360399 
1593488 
2116420 
3646827 
8517125 
24214589 

SSB 
(tons) 
5375 
4862 
3872 
3419 
3526 
3883 
4233 
4920 
7361 
14733 
38646 

Total biomass 
(tons) 
11985 
9854 
8125 
7689 
8290 
9329 
10606 
13405 
22047 
49244 
136905 

Table 6.2.3.8 Red mullet in GSAs 17 and 18. Fishing mortality  by age and year. 

year 
2006  2007  2008  2009  2010  2011  2012  2013  2014  2015  2016 
0.0 
0.0 
0.0 
0.1 
0.6 
0.6 
0.2 
1.7 
1.9 
0.2 
1.7 
1.9 
0.2 
1.7 
1.9 

0.0 
0.7 
2.1 
2.1 
2.1 

0.0 
0.6 
1.7 
1.7 
1.7 

0.0 
0.6 
1.8 
1.8 
1.8 

0.0 
0.2 
0.7 
0.6 
0.6 

0.0 
0.6 
1.8 
1.8 
1.8 

0.0 
0.4 
1.2 
1.2 
1.2 

0.0 
0.7 
2.1 
2.1 
2.1 

0.0 
0.6 
1.9 
1.9 
1.9 

age 
0 
1 
2 
3 
4+ 

Table 6.2.3.9 Red mullet in GSAs 17 and 18. Stock in numbers  by age and year. 

year 
2011 

2007 

2009 

2008 

2006 

2010 

age 
0  1657600  1245200  1058100  1062200  1187100  1360400  1593500  2116400  3646800  8517100  24215000 
2261200 
1 
380110 
2 
50245 
3 
7356 
4+ 

412210 
91910 
7711 
892 

309140 
75887 
7485 
864 

352530 
84353 
7952 
958 

550130 
110130 
9041 
926 

956380 
177460 
19065 
1885 

426240 
110620 
10512 
1083 

437280 
125870 
10788 
1063 

323620 
101150 
7627 
876 

276410 
77230 
7092 
653 

277560 
71002 
6752 
741 

2013 

2015 

2012 

2014 

2016 

197 

197 

 
 
 
 
 
 
 
Figure 6.2.3.10 Red mullet in GSAs 17 and 18. Summary of the results. 

Figure 6.2.3.11 Red mullet in GSAs 17 and 18. Fishing mortality (left) and catchability 
(right) by age and year. 

198 

198 

 
 
 
 
Figure  6.2.3.12  Red  mullet  in  GSAs  17  and  18.  Comparison  between  observed  and 
fitted catch (left) and index (right) at age. 

Figure 6.2.3.13 Red mullet in GSAs 17 and 18. Log-residuals (left) and qqplot (right) of 
catch and abundance indices by age. 

199 

199 

 
 
 
 
 
Figure 6.2.3.14 Red mullet in GSA 17 and 18. Retrospective analysis and bubble plot of 
residuals. 

Methods: XSA (Extended Survival Analysis) 

FLR libraries were employed in order to carry out an XSA based assessment. The major 
assumption  of  the  method  is  the  flat  selectivity  for  the  oldest  ages  (selectivity  as 
classical ogive). The method performs a tuning by survey index by age and was applied 
using  the  age  data  obtained  by  the  slicing  of  the  length  frequency  distributions  of  the 
catch and survey data.  

Input data 

The same input data and parameters of the a4a assessment was used. 

Results 

Sensitivity analyses were conducted to assess the effect of the main parameters (Figure 
6.2.3.15). Different settings of rage, qage and fse values have been tested. 

The final run was selected according to the retrospective analysis and residuals results. 

The settings of the best run are: 

 

 

fse=2 

rage=1 

  qage=2  

  shk years=3 

  shk ages=2. 

The  results  are  reported  in  Table  6.2.3.8  and  in  Figure  6.2.3.16.  The  F  shows  a 
decreasing trend along the years, less pronounced, especially in the last years, than the 

200 

200 

 
 
 
 
 
 
SS3 and a4a models, being XSA a backward model. The last value of F estimated by XSA 
is  0.86  and  the  values  of  SSB  are  higher  than  the  one  estimated  by  the  two  forward 
methods applied, but does not present the increase in R and SSB in the last two to three 
years. 

The retrospective analysis shows some signal of instability, with upward revision in F and 
downward revision in SSB but not as sever and SS3 model. The residuals are generally 
very low, but presenting some trend in the second part of the time series and conflict in 
the last two years. 

Table  6.2.3.8  Red  mullet  in  GSAs  17  and  18.  Results  of  the  final  XSA  run:  Fbar  (1-3) 
overall, SSB, Recruitment and total biomass. 

Year 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 
2015 
2016 

Fbar(1-
3) 
1.22 
1.39 
1.02 
1.04 
0.85 
0.99 
1.10 
0.85 
0.81 
1.01 
0.86 

Recruitment 
(thousands) 
1748371 
1381848 
1235270 
1184525 
1423307 
1493648 
1684122 
1838420 
1651799 
1608092 
1606499 

SSB 
(tons) 
6433 
5962 
5183 
4435 
4405 
5149 
5208 
5629 
6595 
6069 
6223 

Total biomass 
(tons) 
13428 
11478 
10146 
9190 
10158 
11139 
11670 
13047 
13171 
12461 
12586 

Figure  6.2.3.15  Red  mullet  in  GSAs  17  and  18.  Sensitivity  analysis  on  different  fse 
(XSA). 

201 

201 

 
 
 
 
 
Figure 6.2.3.16 Red mullet in GSAs 17 and 18. Summary of the stock (XSA). 

Figure  6.2.3.17  Red  mullet  in  GSAs  17  and  18.  Bubble  plot  of  residuals  and 
retrospective analysis (XSA). 

Comparison of the models. 

The three models, in an overall basis, are in agreement on the decreasing trend of the F 
and  the  increase  in  SSB,  especially  until  2013.  In  the  last  three  years  a4a  and  SS3 
estimated a decrease in the F, while the XSA detected a constant trend. 

Between  the  three  methods  applied,  the  a4a  was  retained,  because  the  retrospective 
appeared more stable. 

However, being the last year SSB estimated by a4a about three times the SSB in 2014, 
with  high  uncertainty  and  conflicting  residuals  over  trhe  last  3  years,  the  experts 
preferred to give the advice based on the biomass index method applied to the biomass 
estimated by the assessment, rather than use the F current, that seemed very low in the 
last year. 

202 

202 

 
 
 
 
Figure 6.2.3.18 Red mullet in GSAs 17 and 18. Comparison of the three methods used 
for the assessment of red mullet in GSA17-18. 

6.2.4 REFERENCE POINTS 

In  SS3  run  fhe  F0.1  value  was  estimated  on  the  basis  of  the  F  at  age  estimated  by  the 
model  by  means  of  fishmethods  R  package  (yield  per  recruit  model)  and  was  equal  to 
0.55. 

In  a4a  and  XSA  runs  the  F0.1  was  estimated  using  FLBRP  package  and  the  values 
estimated were, respectively, 0.52 and 0.42. 

Considering that the F estimated by the a4a model for 2016 is 0.17, the stock  could be 
classed as fished at less than FMSY in nthe last two years (2015 and 2016) 

6.2.5 SHORT TERM FORECAST AND CATCH OPTIONS 

Following  the  ICES  procedures  the  change  in  the  estimated  biomass  over  the  last  five 
years was used to provide an index for change (Figure 6.4.2.18). 

203 

203 

0.000.501.001.502.002.5020032004200520062007200820092010201120122013201420152016Fbar(1-3)SS3a4aXSA050001000015000200002500030000350004000020062007200820092010201120122013201420152016SSBSS3a4aXSATotal Catch0500000010000000150000002000000025000000RecruitmentSS3a4aXSA 
 
 
 
 
 
 
 
 
Figure 6.2.5.1 Red mullet in GSAs 17 and 18. Biomass index on the SSB estimated by 
a4a model6 

As this index is much higher than 1.2 (=4.6), STECF EWG 17-15 advises to not increase 
the total catch more than the 20% of the average catch for the last three years which  is 
6423  A  the  assessment  indicates  that  F  is  less  than  F0.1  in  both  of  the  last  two  years 
when a precautionary buffer is not required, catches should be no more than 7706 tons 
in  each  of  2018  and  2019,  implemented  either  through  catch  restrictions  or  effort 
reduction for the relevant fleets. 

6.2.6 DATA DEFICIENCIES 

The data used for the analyses come from the last DCF official data call (2017). The data 
related  to  non-EU  countries  (Albania  and  Montenegro)  was  taken  by  the  GFCM  WGSAD 
official report 2015. Landing data before 2006 was lacking for GSA 17 (Italy). 

6.3  NORWAY LOBSTER IN GSAS 17 & 18 

6.3.1 BIOLOGY 

204 

204 

050001000015000200002500030000350004000020062007200820092010201120122013201420152016tonsBiomass index method on a4a SSBSSBaverage last 2 yearsaverage previousthree years 
 
 
 
 
 
 
 
Figure 6.3.1.1 Geographical location of GSAs 17-18. 

The  main  biological  traits  of  the  species  in  the  Adriatic  have  been  revised  during  EWG 
15-16. One of the most relevant features pointed out is the occurrence of a sub-unit of 
individuals living in the Pomo-Jabuka Pit area, and featured by significant differences in 
the  biological  parameters  (e.g.  growth  and  maturity)  in  comparison  with  specimens 
distributed on the continental shelf of the GSA 17 (Froglia and Gramitto, 1988). EWG 15-
16  discussed  the  implications  of  such  spatial  configuration  for  the  assessment  of  the 
stock identifying as a pre-requisite the availability of catch/landings data split by fishing 
grounds (Pomo Pit, continental shelf areas) to properly apply age-based models. 

In GSA 18 the stock is basically distributed on the continental slope, deeper than 200m 
depth, both on the eastern (Montenegro, Albania) and western side (Italy, Puglia) of the 
GSA. 

The  distribution  of  nursery  grounds  and  spawning  areas  has  been  analysed  during  the 
EU project MEDISEH (MAREA tender project). In GSA 17 denser and persistent patches 
of  small  specimens  occur  in  the  Pomo  Pit  area  (MEDISEH  project  report,  2013).  
Aggregations  of  adults  were  identified  in  GSA  17  offshore  the  SW  coasts,  in  the  Pomo 
Pit,  and  in  north  and  south  Croatian  waters  (Figure  6.3.1.1.2).  In  GSA  18  the  more 
persistently  abundant  adult  aggregations  occur  on  the  SE  and  SW  edges  of  the  South 
Adriatic Pit (Figure 6.3.1.1.3). 

205 

205 

 
 
 
 
 
 
 
 
Figure 6.3.1.1.2. Norway lobster in GSA 17 and 18. Position of persistent nursery (left) 
and  spawning  areas  (right)  in  GSA  17  as  identified  by  the  MEDISEH  project 
(Mediterranean Sensitive Habitats, 2013). 

Figure  6.3.1.1.3.  Norway  lobster  in  GSA  17  and  18.  Position  of  persistent  spawning 
areas  in  GSA  18  of  as  identified  by  the  MEDISEH  project  (Mediterranean  Sensitive 
Habitats, 2013). 

Growth 

A  summary  of  the  knowledge  on  growth  and  maturity  pattern  of  Norway  lobster  in 
Adriatic  is  provided  in  the  EWG  15-16  report  (STECF,  2015).    A  comparison  of  the 
growth curves for Norway lobster in GSAs 17 and 18 is showed in Figure 6.3.1.1.4 was 
done  during  EWG  16.17.  Specimens  in  the  Pomo  area  grow  slower  than  the  ones 
distributed on the Adriatic shelf (Ancona area, Froglia and Gramitto, 1988). Their growth 
pattern  in  the  first  2-3  years  appears  similar  to  the  growth  estimated  for  specimens  in 
GSA 18 in the same age range (Table 6.3.1.1.1.). This can be the result of similarity in 
the habitat where the species lives in these two areas, i.e. continental slope below 200m 
depth.  In  the  Pomo  Pit  area  the  proportion  of  specimens  over  40  mm  CL  appears  very 
low  as  probably  determined  by  a  slow  growth  after  the  first  3-4  years.  However,  high 
mortality  rate  of  adults  and  or  dispersion/migration  toward  other  areas  cannot  be 
excluded. In this regard, it would be important to explore the connectivity of the Pomo 
Pit sub-unit with the stock in GSA 18. The Pomo Pit system is in fact well connected with 
the South Adriatic slope through a narrow channel between 100 and 150 m. 

206 

206 

 
 
 
 
 
 
 
 
 
Figure  6.3.1.1.4.  Norway  lobster  in  GSAs  17  and  18.  Growth  curves  of  males  and 
females of Norway lobster in GSAs 17 and 18. 

Table 6.3.1.1.1. Norway lobster in GSAs 17 and 18. Length at age of Norway lobster in 
different Adriatic areas (i.e. Pomo Pit, offshore Ancona, South Adriatic) 

Maturity 

Maturity size of females from available studies in GSAs 17 and 18 are reported in Table 
6.3.1.1.1.2.  

Table  6.3.1.1.1.2.  Norway  lobster  in  GSAs  17  and  18.  Length-at-maturity  information 
on  Norway  lobster  females  from  studies  carried  out  in  Adriatic  Sea  (from  EWG  15-16 
report). 

Maturity ogives provided with the data call and available during EWG16-17 are showed 
in  Figure  6.3.1.1.1.5.  and  Figure  6.11.1.1.1.6.  L50  was  estimated  on  the  proportion  of 
mature specimens using binomial GLM. In GSA 17, L50 of females ranged between 20.2 
and  25.6  mm CL  in  Croatian  waters  in  2013-2016  and  it  was  30.4-30.7  mm  CL  on  the 
Italian  side  in  2015-2016.  Such  differences  might  be  related  to  differences  in  the 
approach  followed  to  select  the  “mature”  individuals  more  than  real  differences  in  the 
maturity process. In GSA 18, L50 was 23.5 mm CL for females and 25.6 mm CL in 2007-
2014 and 24.4 mm CL in 2015 (sex combined, Figure 6.3.1.1.1.5).  

207 

207 

age classGP Off Ancona malesGP Off Ancona femalesGP Pomo pit malesGP Pomo pit femalesGSA 18 malesGSA 18 females0-0.50.14.62.98.16.8122.522.017.016.618.415.5239.236.426.925.628.123.4350.144.934.230.936.229.9457.149.939.434.043.035.3561.752.843.235.848.839.7664.754.646.036.953.743.4766.655.648.037.657.846.4 
 
 
 
 
 
 
According to the different growth curves hypothesized in the region and shown in Figure 
6.3.1.1.3   females would achieve the maturity between 1 and 2 years old in the Italian 
side of the GSA 17, and at age 2 in GSA 18.  

Maturity data for the Pomo Pit sub-unit seems more in line with the pattern observed for 
Norway  lobster  in  GSA  18,  thus  supporting  the  hypothesis  of  similarity  in  biological 
features between the species in these two areas. 

Figure  6.3.1.1.1.5.  Norway  lobster  in  GSA  17.  Maturity  ogives  and  length  at  first 
maturity (L50) of females in Croatian and Italian waters (maturity data from data call).  

208 

208 

 
 
 
 
Figure  6.3.1.1.1.6.  Norway  lobster  in  GSA  18.  Maturity  ogives  and  length  at  first 
maturity (L50) of females, males and sex combined (maturity data from data call).  

6.3.2 INPUT DATA 

6.3.2.1 

CATCH (LANDINGS AND DISCARDS)  

The minimum landing size of Norway lobster is 20 mm carapace length or 70 mm total 
length. Trawl net  cod end mesh size 40 mm (stretched) diamond meshes or a cod end 
with  50  mm  (stretched)  square  meshes.  Towed  gears  are  not  allowed  within  three 
nautical miles from the coast or at depths less than 50 m when this depth is reached at 
a  distance  less  than  3  miles  from  the  coast.  In  GSA  17,  since  26  July  2015  an  area 
corresponding  to  the  Pomo/Jabuka  pit  has  been  closed  to  all  trawling  fisheries  (otter 
trawling,  pair  otter  trawling  and  beam  trawling)  for  a  period  of  one  year,  until  26  July 
2016. This closure was decided among all countries exploiting this area, mainly Italy and 
Croatia. 

Landings 

Data by gear for Croatia were available for the period 2013-2016. Data from 2008-2012 
were obtained from the STECF EWG report 16-08 (Table 6.3.2.1.1). 

Table 6.3.2.1.1 Norway lobster in GSAs 17. Landings data by gear for the period 2006-
2016.  Landings  of  Croatian  fleets  for  the  period  2008-2012  (in  italics)  were  obtained 
from the report of STECF EWG 15-16 (Table 5.2.6.5.3.1.) 

GSA 17 

GSA 18 

Fleet 

HRV  OTB  HRV FPO 

ITA OTB 

TOT 

ITA OTB 

ITA GNS 

ITA NA 

TOT 

2002 

2003 

2004 

2005 

2006 

2007 

1462 

1259 

442.2 

36.3 

478.5 

1039.3 

5.5 

141.8 

1186.5 

1218.4 

1196.4 

2.3 

1218.4 

1198.7 

1436.6 

9.6 

0.5 

1446.6 

1299.9 

14.7 

2008 

324.0 

23.0 

1270 

1617 

1003.0 

9.8 

2009 

342.0 

23.0 

1379 

1744 

1092.9 

2010 

305.0 

19.0 

1216 

1540 

1023.4 

2011 

260.0 

20.0 

937 

1217 

759.2 

2012 

228.0 

17.0 

802 

1047 

458.7 

2013 

278.2 

21.0 

607 

906 

833.8 

209 

209 

1314.6 

1012.8 

1092.9 

1023.4 

759.2 

458.7 

833.8 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2014 

325.2 

14.7 

529 

869 

444.7 

2015 

268.7 

0.2 

450 

719 

442.8 

2016 

202.4 

29.5 

359.5 

591 

395.2 

444.7 

442.8 

395.2 

Annual  landings  of  Italian  trawlers  in  GSA  17  and  18  showed  a  similar  and  steep 
reduction since 2006. The current landings decreased from about 1400 t in 2006 to the 
current 359 t in GSA 17 and 395 t in GSA 18. Croatian landings declined fro, 305-342 t 
in 2008-2009 to the current 202 t (Figure 6.3.2.1.1).   Annual landings of Croatian traps 
was between 14 and 23 t with a very low value (0.25 t) reported for 2015 and 29.5 t in 
2016 (Table 6.11.1.2.1).   

ITA 18 OTB

ITA 17 OTB

HRV  OTB

s
n
o
T

1600.0

1400.0

1200.0

1000.0

800.0

600.0

400.0

200.0

0.0

2
0
0
2

2
0
0
3

2
0
0
4

2
0
0
5

2
0
0
6

2
0
0
7

2
0
0
8

2
0
0
9

2
0
1
0

2
0
1
1

2
0
1
2

2
0
1
3

2
0
1
4

2
0
1
5

2
0
1
6

Figure  6.3.2.1.1.  Norway  lobster  in  GSA  17  and  18.  Landings  of  the  Italian  and 
Croatian trawl fleets.  

Size distributions of the landings 

Length frequency distributions for trawlers in GSA 17 were available for the period 2006-
2017. A peak in the landings was observed in 2006, mostly due to specimens below 30 
mm,  thus  corresponding  to  a  peak  in  recruitment.  In  the  following  years  the  landings 
composition  shift  toward  larger  sizes  (Figure  6.3.1.2.2).  Croatian  data  are  available  for 
two fleets segments, trawlers for the period 2013-2015 and traps (FPO) for 2014-2015 
(Figure 6.3.1.2.3 and Figure 6.3.1.2.4). Length frequency distributions for Croatian OTB 
and  FPO  were  provided  as  total  length  (TL)  and  converted  into  carapace  length  (CL) 
using the following equations (Froglia and Gramitto, 1988): 

210 

210 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
OTB length frequency distributions of GSA 18 are showed in Figure 6.11.1.2.5.  

s
r
e
b
m
u
N

Figure 6.3.2.1.2. Norway lobster in GSA 17. Length frequency distribution of the Italian 
trawlers in the period 2006-2016.  

Figure  6.3.2.1.3.  Norway  lobster  in  GSA  17.  Length  frequency  distribution  of  the 
Croatian trawlers in the period 2013-2016.  

211 

211 

 
 
 
 
 
Figure  6.3.2.1.4.  Norway  lobster  in  GSA  17.  Length  frequency  distribution  of  the 
Croatian traps (FPO) in the period 2013-2015.  

Figure 6.3.3.1.5. Norway lobster in GSA 18. Length frequency distribution of the Italian 
trawlers in the period 2002-2015.  

Discards 

The  amount  of  discards  reported  by  the  trawlers  is  rather  low.  In  GSA  17  the  discards 
reported  for  the  Croatian  trawlers  was  between  7.5  and  17.85  t  in  the  period  2013-
2015.  Data for Italian trawlers in GSA 17 are available only for 2011. In GSA 18 a quite 
high  amount  of  discards  (66.8  t)  is  reported  for  2009  (Table  6.3.1.2.2)  but  even  this 
amount is still small relative to about 2% of 2800 t of total landings in 2009. 

Table  6.3.2.1.2  Norway  lobster  in  GSAs  17-18.  Reported  annual  discards  of  Croatian 
and Italian trawl fleets in GSAs 17 and 18. 

2009  2010  2011  2013  2014  2015  2016 

HRV GSA 17 OTB 

13.74  7.48  17.85  4.2 

ITA GSA 17 OTB 

5 

ITA GSA 18 OTB  66.8  6.2  0.82 

4 

2.27  2.05  0.74 

The  size  distributions  of  discards  of  Croatian  trawlers  and  Italian  trawlers  in  GSAs    17 
and 18 are showed in Fig. 6.3.2.1.6. 

212 

212 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
HRV OTB ITA  

GSA 17 OTB ITA  

GSA 18 OTB   

Figure  6.3.2.1.6  Norway  lobster  in  GSAs  17-18.  Size  distributions  of  discards  of 
Croatian trawlers and Italian trawlers in GSAs  17 and 18 

6.3.2.2 

EFFORT 

Norway lobster in GSAs 17 and GSA 18 is exploited mostly by bottom trawlers. A small 
amount of catch is produced by small-scale vessels using traps in the  northern-eastern 
Adriatic  channels  as  well  as  by  gillnetters  in  GSA  18.  For  this  fleet  Norway  lobster  is  a 
minor  by-catch  of  boats  targeting  hake  on  the  continental  slope.  Effort  data  for  the 
Italian  trawl  fleet  (OTB)  in  GSA  17  and  18  is  available  since  2002,  whereas  nominal 
effort  data  of  Croatian  trawlers  cover  the  period  2012-2015  (Table  6.3.2.2.1,  Figure 
6.3.2.2.1).  The  temporal  trend  shows  a  relevant  reduction  in  the  nominal  effort 
(KW*fishing days) of the Italian trawl fleet both in GSA 17 (-50%) and GSA 18 (-60%). 
The Croatian fleet effort was stable in the last three years. 

Table 6.3.2.2.1. Norway lobster in GSA 17 and 18. Nominal effort in kW days for Italian 
(ITA),  Croatian  (HRV)  OTB  fleets,  Croatian  traps  (FPO)  and  Italian  gillnetters  (GNS)  in 
GSA 18. 

OTB 

FPO* 

GNS 

YEAR 

HRV 
GSA17 

ITA GSA 17 

ITA GSA 18 

HRV 
GSA17 

ITA  GSA 
18 

2002    

2003    

2004    

2005    

2006    

2007    

2008    

2009    

27568094.43  17112021.58    

1722336 

27486392.6  14530792.97    

1002933 

27823853 

14451460    

1457047 

24094431 

13550061    

2035861 

19896811 

14744610    

1785782 

19409042 

12840209    

1280477 

20038778 

11463435    

894323 

18889991 

13878367    

1205076 

213 

213 

 
 
 
 
 
 
 
 
 
 
  
2010    

2011    

18094570 

11856268    

570405 

16572093 

11329443    

450946 

2012 

6878185 

14020762 

9821959  540079  395458 

2013 

7151551 

12614324 

10511626  654040  777758 

2014 

7291600 

14435027 

7736320  678016  207752 

2015 

7112694 

13847944 

7013616  707502  1129811 

2016 

6795609 

14195449 

7822985  740962  1023952 

*FPO idifferent types of traps and fisheries included 

Figure 6.3.2.2.1. Norway lobster in GSA 17 and 18. Trend in nominal effort of 
trawlers in GSA 17 and GSA 18 

CPUE  of  Norway  lobster  of  trawlers  in  GSAs  17-18  were  calculated  as  total  annual 
landings / total annual nominal effort*1000 (Figure 6.3.2.2.2). Trend in GSA 17 for 
the  period  2006-2015  indicate  an  almost  constant  reduction  of  average  CPUE  since 
2006 (-46%). In GSA 18, CPUE increased in the period 2002 to 2007, start declining 
since then (-35%). CPUE of Croatian trawlers in 2014-2015 were slightly higher than 
CPUE of Italian trawlers in GSA 17.  

214 

214 

 
 
 
 
 
cpue 18 ITA

cpue 17 ITA

cpue 17 HRV

0.12

0.1

0.08

0.06

0.04

0.02

0
0
0
1
*
)
t
r
o
f
f
e

l

i

a
n
m
o
n
/
s
g
n
d
n
a
l
(
E
U
P
C

i

0
2000 2002 2004 2006 2008 2010 2012 2014 2016

Figure  6.3.2.2.2.  Norway  lobster  in  GSAs  17  and  18.  CPUEs  of  the  Italian  trawlers  in 
GSA 17 and 18 and Croatian trawlers in GSA 17. 

6.3.2.3 

SURVEY DATA 

Methods 

According to the MEDITS protocol (Bertrand et al., 2002), trawl surveys were carried out 
yearly (May - July), applying a random stratified sampling by depth (5 strata with depth 
limits  at:  50,  100,  200,  500  and  800  m;  each  haul  position  randomly  selected  in  small 
sub-areas  and  maintained  fixed  throughout  the  time  (Figure  6.3.2.3.1).  Haul  allocation 
was  proportional  to  the  stratum  area.  The  same  gear  (GOC  73,  by  P.Y.  Dremière, 
IFREMER-Sète), with a 20 mm stretched mesh size in the cod-end, was used throughout 
the  time  series.  Detailed  data  on  the  gear  characteristics,  operational  parameters  and 
performance are reported in Dremière and Fiorentini (1996). Considering the small mesh 
size  a  complete  retention  was  assumed.  All  the  abundance  data  (number  of  fish  and 
weight  per  surface  unit)  were  standardized  to  square  kilometre,  using  the  swept  area 
method. Abundance and biomass indices were recalculated, based on the DCF data call. 

Data  were  assigned  to  strata  based  upon  the  shooting  position  and  average  depth 
(between shooting and hauling depth). Catches by haul were standardized to 60 minutes 
haul  duration.  Only  hauls  noted  as  valid  were  used,  including  stations  with  no  catches 
(zero catches are included).  

The  abundance  and  biomass  indices  by  GSA  were  calculated  through  stratified  means 
(Cochran,  1953;  Saville,  1977).  This  implies  weighting  of  the  average  values  of  the 
individual  standardized  catches  and  the  variation  of  each  stratum  by  the  respective 
stratum areas in each GSA:  

215 

215 

 
 
 
 
 
 
 
 
 
Where: 

A=total survey area 

Ai=area of the i-th stratum 

si=standard deviation of the i-th stratum 

ni=number of valid hauls of the i-th stratum 

n=number of hauls in the GSA 

Yi=mean of the i-th stratum 

Yst=stratified mean abundance 

V(Yst)=variance of the stratified mean 

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

Figure 6.3.2.3.1. Norway lobster in GSAs 17 and 18. MEDITS trawl survey, distribution 
of the hauls carried out in the area. 

Trends in abundance and biomass 

Abundance  and  biomass  indices  of  MEDITS display  a  decreasing  temporal trend  in  GSA 
17  with  abundance  decreasing  of  about  10  times  since  ‘90s  in  the  Italian  side.  The 
pattern  is  slightly  different  in  Croatian  waters  the  early  decline  is  also  seen  but  where 
the indices show a modest increase since 2012 (Figure 6.3.2.3.2).  

MEDITS  indices  of  GSA  18  appear  to  be  more  stable  with  a  peak  in  2009  and  a 
decreasing since then (Figure 6.3.2.3.3). 

216 

216 

 
 
 
 
 
Italy Gsa 17 

Croatia GSA 17 

Fig. 6.3.2.3.2. Norway lobster in GSAs 17 and 18. Abundance (left) and biomass (right) 
indices from the MEDITS survey in the Italian and Croatian sides of GSA 17 during 1994 
– 2015. 

Gsa 18 

Figure  6.3.2.3.3.  Norway  lobster  in  GSAs  17  and  18.    Abundance  (left)  and  biomass 
(right) indices from the MEDITS survey in GSA 18 in the period 1994 – 2016. 

The  temporal  trend  in  frequency  of  occurrence  (n.  positive  hauls/total  n.  hauls)  of 
Norway lobster in Medits surveys carried out in the Italian side of GSA 17 and in the GSA 
18 is showed in Fig. 6.3.2.3.4. The trend is very similar in the two areas with a reduction 
of about 40% since mid 90s. 

217 

217 

 
 
 
 
 
 
 
 
s
l
u
a
h
e
v
i
t
i
s
o
p
f
o
n
o
i
t
r
o
p
o
r
P

0.6

0.5

0.4

0.3

0.2

0.1

0

Gsa 18 ITA

Gsa 17 ITA

199419961998200020022004200620082010201220142016

Figure  6.3.2.3.4  Norway  lobster  in  GSAs  17  and  18.  Frequency  of  occurrence  (n  of 
positive hauls/total n. hauls) during the Medits. 

Length  frequency  distributions  of  the  Medits  surveys  for  sex  combined  are  showed  in 
Figures  6.3.2.3.5.  In  GSA  17  a  recruitment  peak  appears  in  2006  as  observed  in  the 
catch data. Since then Medits did not register any abundant new year class and this can 
explain the observed decresing trend. 

Length  frequency  distributions  of  the  MEDITS  surveys  sex  combined  are  showed  in 
Figures  6.3.2.3.5  and  6.3.2.3.6.  In  GSA  17  a  recruitment  peak  appears  in  2006  as 
observed in the catch data. 

MEDITS GSA 17  ITA Norway lobster  

Figure  6.3.2.3.5.  Norway  lobster  in  GSAs  17  and  18.  Length  frequency 
distributions of N. lobster (sex combined) of Medits survey in 2002-2016. 

218 

218 

 
 
  
 
 
 
 
 
MEDITS GSA 18  Norway lobster  

Figure  6.3.2.3.6  Norway lobster in GSAs 17  and  18. Length frequency distributions of 
N. lobster sex combined of Medits survey in 1994-2016. 

Spatial distribution  

According  to  Medits  data  the  highest  relative  biomasses  occur  in  the  central  Adriatic 
around the Pomo Pit area. A progressive temporal  reduction seems to have occurred in 
North  Adriatic.  In  GSA  18  the  stock  appears  more  abundant  along  both  the  east  and 
west  slope of the south sector of the GSA (Fig. 6.3.2.3.7). 

219 

219 

 
 
 
 
 
 
 
 
 
 
 
 
GSA 17 

GSA 18 

Figure  6.3.2.3.7  Norway  lobster  in  GSAs  17  and  18.  Spatial  distribution  of  relative 
biomass (kg km-2) during Medits from 2011 to 2016. 

220 

220 

 
 
 
A  visual  inspection  of  the  distribution  of  density  values  observed  during  MEDITS  2006, 
when a high year class has occurred, indicate that the Pomo Pit area plays an important 
role for recruitment. Map in Figure 6.3.2.3.8 shows that the Pomo Pit area is connected 
with the Adriatic Basin in GSA 18 by a narrow channel between 100 and 150 m depth. 

150 – 200 m 

100-150m 

Figure  6.3.2.3.8  Norway  lobster  in  GSAs  17  and  18.  Observed  density  values  during 
MEDITS 2006. (depth >100m in yellow/orange, depth >200m blue) 

Figure  6.3.2.3.9.  clearly  show  a  different  size  distribution  between  the  N.  lobster 
specimens  distributed  inside  and  outside  the  Pomo  Pit  with  this  latter  displaying 
generally a peak of small specimens and the lack of adults over 50 mm CL.  

Figure  6.3.2.3.9  Norway  lobster  in  GSAs  17  and  18.  MEDITS  length  frequency 
distributions (n km-2) of specimens distributed inside and outside the Pomo Pit area 

221 

221 

 
 
 
 
 
 
6.3.3 STOCK ASSESSMENT 

Method 1- Surplus Production model in Continuous Time - SPiCT 

The  Surplus  Production  in  Continuous  time  (SPiCT)  assessment  method  is  briefly 
described here; Pedersen and Berg (2016) contains a comprehensive description of the 
model 

The  SPiCT  assessment  method  is  a  state-space  version  of  the  Pella-Tomlinson  surplus 
production  model  (Pella  and  Tomlinson  1969).  The  dynamics  of  fisheries  (𝐹𝑡)  and 
exploitable biomass (𝐵𝑡) are modelled as latent processes: 

𝑑𝐵𝑡 = 𝑟𝐵𝑡 (1 − (

𝑛−1
)

𝐵𝑡
𝐾

) 𝑑𝑡 − 𝐹𝑡𝐵𝑡𝑑𝑡 + 𝜎𝐵𝐵𝑡𝑑𝑊𝑡 

𝑑log(𝐹𝑡) = 𝑓(𝑡, 𝜎𝐹) 

Where 𝑊𝑡 is Brownian motion and 𝑓 represents a random walk process if yearly data are 
provided and a seasonal model for 𝐹 if subannual data are available. The time series of 
catch and biomass index are used as observations with 𝑒𝑡 and 𝜖𝑡 their corresponding 
error terms: 

log(𝐼𝑡) = log(𝑞𝐵𝑡) + 𝑒𝑡, 𝑒𝑡 ∼ 𝒩(0, [𝛼𝜎𝐵]2) 

log(𝐶𝑡) = log (∫𝑡

𝑡+𝛥

𝐹𝑠𝐵𝑠𝑑𝑠) + 𝜖𝑡, 𝜖𝑡 ∼ 𝒩(0, [𝛽𝜎𝐹]2) 

The following list summarises the model parameters: 

• 

• 

• 

𝐵𝑡: Exploitable biomass 
𝐹𝑡: Fishing mortality 
𝑟: Intrinsic growth rate (growth, recruitment, natural mortality) 

•  𝐾: Carrying capacity 

• 

• 

• 

• 

• 

• 

𝑛: Production curve shape parameter 

𝑞: Catchability 
𝜎𝐵: Standard deviation of 𝐵𝑡 
𝜎𝐹: Standard deviation of 𝐹𝑡 
𝛼: Ratio of standard deviation of 𝐼𝑡 to 𝜎𝐵 
𝛽: Ratio of standard deviation of 𝐶𝑡 to 𝜎𝐹 

SPiCT allows the inclusion of prior distributions for parameters that are difficult to 
estimate. By default, there are wide uninformative priors on 𝑛, 𝛼, and 𝛽; these can be 
removed. 

The continuous time formulation of the model allows for arbitrary and irregular data 
sampling without a need for catch and index observations to match temporally. 

222 

222 

 
 
 
 
 
 
 
Main assumptions 

SPiCT shares many assumptions with other surplus production models: 

1.  No emigration/immigration, changes in biomass occur through growth (𝑟 and 𝐾) and 

fishing. 

2.  No lagged effects in the biomass dynamics 
3.  Constant catchability i.e. no change in technology of fishing technique that changes 

q. 

4.  Gear selectivity is not modelled 
5.  No knowledge of natural mortality is required 

Data requirements - Expected outputs 

SPiCT  requires  a  time  series  of  landings  or  catches  and  one  or  more  time  series  of 
commercial or survey CPUE indices. The expected output include all parameter estimates 
and the most interesting derived quantities are the  𝐹/𝐹𝑚𝑠𝑦 and 𝐵/𝐵𝑚𝑠𝑦 that quantify the 
stock status. The results are presented using SPiCT's extensive plotting capabilites. 

Forecasting and management 

SPiCT  is  able  to  use  the  estimated  underlying  process  model  to  make  forecast  of 
biomass,  fishing  mortality,  catch  and  stock  status  (𝐹/𝐹𝑚𝑠𝑦  and  𝐵/𝐵𝑚𝑠𝑦).  A  forecasting 
period  and  a  fishing  scenario  is  set  before  fitting  the  model.  The  fishing  scenario  is  a 
multiplication factor that is applied to the current fishing mortality. 

Availability 

SPiCT is available as an R (R Core Team 2015) package in the github online repository: 
https://github.com/mawp/spict.  For  fast  and  efficient  estimation,  SPiCT  uses  the 
Template Model Builder package (TMB, Kristensen et al., 2016). 

INPUT Data 

Tuning 
Available  CPUE’s  to  be  used  as  a  tuning  index  in  SPICT  runs  were  recovered  from 
historical literature and from the MED and BS DG MARE Data Call of 2016. 

“HVAR” 
CPUE data for Norway lobster was available in the HVAR demersal survey performed in 
1948-49,  after  8  years  of  no  fishing  in  the  Adriatic  Sea  (Karlovac  1956,  1959). 
Abundance is reported to compare HVAR and MEDITS scaled by swept area and by tow 
duration. Swept area from MEDITS is reported while that of HVAR is reconstructed base 
on the work of G.C Osio (Unpublished) and assumes a constanst wing opening of 0.27 * 
footrope (35 m). In Figure 6.3.3.1.  the lower and upper "hinges" correspond to the first 
and  third  quartiles  (the  25th  and  75th  percentiles),    boxes  are  drawn  with  widths 
proportional to the square-roots of the number of observations in the groups, middle line 
corresponds  to  median.  The  comparison  1948-48  with  the  period  1994-2001  shows  a 
marked  decline  in  both  CPUEs.  This  is  more  pronounced  when  the  net  dimensions  are 
accounted  for.  Given  the  uncertainty  in  reconstructing  HVAR  gear  dimensions  and  the 
distance of the HVAR CPUE from the beginning of landings time series (1970) this CPUE 
was  not  used  in  the  stock  assessment,  but  it  nevertheless  represent  an  important 
background information.  

223 

223 

 
 
 
  
Figure  6.3.3.1.      Norway  lobster  in  GSAs  17  and  18.  Norway  lobster  CPUE  from  the 
HVAR  survey  (1948-49)  and  from  MEDITS  (1994-2001).  Boxplots  with  (upper  panel) 
Norway lobster Abundance/swept area and (lower panel) Abundance/ tow duration. 

Jukic data 
S.  Jukic  reported  the  yearly  performance  of  trawlers  fishing  in  the  commercial  grounds 
off Blitvenica (East Pomo Pit, Central Adriatic, Figure 6.3.3.2.). The CPUE represents on 
average 8 trawlers over 555 fishing days in the period 1960-1970 (Figure 6.3.3.3). 

Figure  6.3.3.2.  Norway  lobster  in  GSAs  17  and  18.  Area  covered  by  the  CPUE  from 
Jukic 1975 

224 

224 

 
 
  
 
Figure  6.3.3.3  Norway  lobster  in  GSAs  17  and  18.  Norway  lobster  CPUE  (kg/Fishing 
day) from Jukic (1975) in the East part of Pomo Pit (Blitvenica fishing grounds), Central 
Adriatic. 

Froglia and Gramitto data 

Froglia  &  Gramitto  (1988)  reported  hourly  CPUE’s  for  Norway  lobster  in  the  fishing 
grounds  offshore  Ancona  (Western  Central  Adriatic).  CPUE’s  are  reported 
for 
night/day/combined fishing and a yearly average was computed to build a tuning index 
(Figures 6.3.3.4 - 5). 

Figure 6.3.3.4  Norway lobster in GSAs 17 and 18. Area were CPUE data was taken by 
Froglia & Gramitto 1988 

225 

225 

 
 
 
 
 
 
Figure 6.3.3.5 Norway lobster in GSAs 17 and 18. CPUE (kg/fishing hour) reconstructed 
from Froglia (1985) in Ancona fishing grounds, Central Adriatic. 

MEDITS 

MEDITS trawl survey data is available from all EU MED GSAs since 1994, however GSA 
17 is the only exception and data is available only since 2002. Given clear indication of a 
declining  trend  in  MEDITS  index  in  GSA  17,  an  effort  was  made  to  recover  the  time 
series since 1994. A source for this data is the Mannini & Sabatella (2015) reporting the 
stratified kg/km2 for Norway lobster on the Italian part of GSA 17 (Figure 6.3.3.6).  

Figure  6.3.3.6  Norway  lobster  in  GSAs  17  and  18.  MEDITS  GSA  17  Italian  part  only 
reported  in  the  MEDITS  ANNUAL  reports.  The  trend  2002-2015  was  replicable  with 
available DCF raw data. 

For GSA 18 two stratified indexes were computed, one only covering the Italian side of 
GSA 18 and one including the tows performed in front of Albania and Montenegro. 

Derivation of combined MEDITS index 

To  build  a  representative  MEDITS  index  covering  the  entire  period  1994-2015  and  the 
whole area of GSA 17-18,  we derived a first index for the period 1995-2001. This was 

226 

226 

 
 
 
 
 
 
 
computed  by  weighting  each  individual  index  by  the  corresponding  surveyed  surface, 
details as follows: 

(𝐴𝐼17 + 𝐴𝐶17) ∗ 𝐼𝑛𝑑𝐼17 + 𝐴18 ∗ 𝐼𝑛𝑑18
𝐴𝐼 + 𝐴𝐶 + 𝐴18

Where 𝐴𝐼17  is the surface (km2) surveyed in the Italian strata of GSA 17, 𝐴𝐶17 
is  the 
surface  (sqkm)  surveyed  in  the  Croatian  strata  of  GSA  17,  𝐴18  is  the  surface  (sqkm) 
surveyd  in  the  all  the  strata  of  GSA  18  (Italy,  Albania  and  Montenegro),  𝐼𝑛𝑑𝐼17  is  the 
stratified  mean  index  of  biomass  (kg/km2)  from  the  Italian  side  of  GSA  17  and  𝐼𝑛𝑑18is 
the stratified mean index of biomass (kg/km2) from the whole GSA 18. 

The second part of the combined index, thanks to the available raw data, was computed 
according  to  the  stratified  means    for  GSA  17-18  from  2002-2005  covering  all  hauls 
performed  by  Italy,  Croatia,  Albania  and  Montenegro  (MEDITS_17_18)  (Table  6.3.3.1, 
Figure 6.3.3.7). 

Table 6.3.3.1 Norway lobster in GSAs 17 and 18.  MEDITS indexes for Norway  lobster 
in GSA 17 and 18 

Year 

MEDITS18_ITA_ALB_MTG  MEDITS17_ITA  MEDITS_17_18 

1994 

1995 

1996 

1997 

1998 

1999 

2000 

2001 

2002 

2003 

2004 

2005 

2006 

2007 

2008 

2009 

2.10 

5.50 

4.90 

2.70 

4.30 

2.80 

1.20 

1.30 

1.20 

1.50 

1.80 

1.50 

2.90 

0.70 

1.70 

1.30 

1.34 

1.93 

2.88 

1.40 

1.40 

1.43 

1.43 

1.79 

0.98 

1.31 

1.65 

1.74 

1.59 

1.04 

3.16 

3.06 

227 

227 

1.92 

4.64 

4.41 

2.38 

3.60 

2.47 

1.25 

1.41 

2.02 

2.73 

3.16 

3.44 

3.50 

1.63 

3.68 

3.28 

 
 
 
 
2010 

2011 

2012 

2013 

2014 

2015 

2016 

1.90 

1.27 

0.81 

0.79 

0.85 

0.50 

0.91 

1.20 

0.70 

0.60 

0.70 

0.80 

0.50 

0.91 

2.02 

1.00 

0.90 

2.08 

1.98 

1.64 

0.87 

Figure 6.3.3.7 Norway lobster in GSAs 17 and 18.   MEDITS trends for Norway lobster 
in  GSA  17  and  18.  MEDITS_18_ITA  covers  only  the  Italian  side  of  GSA  18, 
MEDITS18_ITA_ALB_MTG  covers  the  entire  GSA  18  including  the  East  part,  MEDITS 
17_ITA represents only Italian side of GSA 17, MEDITS 18_17 is the reconstructed index 
covering whole GSA 17 & 18. 

Landings for the assessment 

Stock  assessment  models  and  in  particular  surplus  production  models  need  long  and 
informative time series of catch for unbiased parameter estimation. In the Mediterranean 
these  are  often  not  available,  so  to  provide  a  sufficiently  long  time  series  for  the  stock 
assessment of Norway lobster in GSA 17-18 an effort was made to recover the historical 
landings  for  the  area.  Three  sources  of  landings  were  used  to  reconstruct  catch  time 
series for the whole GSA 17-18: 

 

Italian  landings  time  series  from  National  Institute  for  Statistics  (ISTAT)  for  the 
period 1961-2000 for GSA 17 and 18 (Figures 6.3.3.8 and 6.3.3.9 ). 

  GFCM  landings  for  the  period  1970-2014  for  Croatia,  Montenegro  and  Albania 

extracted from FAO FISAT J. 

  DCF landings from the 2015 DG MARE Data Call, covering the period 2002-2015 

for GSA 17 and 18 (ITA) and GSA 17 (HRV).  

228 

228 

 
 
 
 
 
Figure  6.3.3.8  Norway  lobster  in  GSAs  17  and  18.  Landings  (tons)  from  ISTAT  for 
Norway lobster in Italian waters of GSA 17 and 18. 

The reconstruction of catches was straight forward in GSA 17 with the GFCM series that 
cover  the  whole  area  (FAO  37.2.1),  were  the  trends  and  level  of  landings  is  consistent 
with  the  Italian  ISTAT  for  1970-2000.  In  GSA  18  GFCM  data  for  FAO  area  37.2.2 
includes also GSA 16 and parts of Greece so it was not possible to use these time series 
as  the  Norway  lobster  landings  in  GSA  16  are  large.  For  cross  checking  the  ISTAT 
landings  from  GSA  16  were  subtracted  from  the  GFCM  37.2.2  landings  and  compared 
with  the  ISTAT  trends  in  GSA  18  (1970-2000).  These  were  consistent,  so  the  use  of 
ISTAT GSA 18 was considered representative of Italian removals in GSA 18. 

Figure 6.3.3.9 Norway lobster in GSAs 17 and 18. Landings (tons) from ISTAT and DCF 
for Norway lobster in Italian waters of GSA 18. 

Finally,  for  the  combined  assessment  the  following  time  series  were  used,  in  GSA  17 
GFCM 1970-2014 (ITA & HRV) and DCF 2015 (ITA & HRV), in GSA 18 ISTAT 1970-2000 
(ITA), DCF 2002-2015 (ITA), GFCM 1970-2014 for Albania and Montenegro. The missing 
catch  in  GSA  18  (ITA)  for  year  2001  was  replaced  with  the  mean  of  2000  and  2002. 
Final reconstructed landings are presented in Figure 6.3.3.10 and Table 6.3.3.2 

229 

229 

 
 
 
 
  
 
 
Figure 6.3.3.10. Norway lobster in GSAs 17 and 18. Reconstructed Landings (tons) for 
Norway lobster in GSA 17 and 18 including Albania. 

Table  6.3.3.2  Norway  lobster  in  GSAs  17  and  18.  Reconstructed  Landings  (tons)  for 
Norway lobster in GSA 17 and 18 including Albania. 

year 

gsa17 
(ITA+HRV) 

gsa18 (ITA)  Albania 

1970 

1142 

127.995 

1971 

1175 

108.481 

1972 

1267 

1973 

1974 

1975 

987 

976 

984 

1976 

1247 

1977 

1805 

1978 

1250 

1979 

1980 

1981 

972 

814 

943 

1982 

1113 

1983 

959 

1984 

1027 

1985 

1909 

230 

230 

130 

126 

122 

213 

273 

299 

219 

316 

302 

242 

294 

311 

192 

200 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

 
 
1986 

1986 

1987 

1675 

1988 

2138 

1989 

1767 

1990 

1600 

1991 

2024 

1992 

2531 

1993 

2493 

1994 

2366 

1995 

1811 

1996 

2104 

1997 

1964 

1998 

1341 

1999 

951 

2000 

1051 

2001 

2002 

913 

774 

364 

412 

698 

392 

290 

483 

620 

629 

1000 

1337 

1454 

1094 

1085 

802 

813 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

3 

0 

0 

0 

0 

645.7367 

10 

478.4735 

2003 

1032 

1186.55 

2004 

1061 

1218.43 

2005 

2195 

1198.676 

2006 

1966.1 

1446.647 

2007 

1728.2 

1314.634 

2008 

1530.9 

1012.8 

2009 

1810.5 

1092.894 

2010 

1594.6 

1023.423 

2011 

1223.3 

759.1686 

2012 

1062.6 

458.7038 

2013 

912.1 

833.8332 

231 

231 

5 

2 

2 

0 

4 

0 

1 

0 

0 

0 

0 

0 

 
2014 

886.7 

444.7175 

2015 

718.9296 

442.7535 

2016 

591.0 

395.20 

0 

0 

0 

Stock Assessment  

The  choice  of  stock  assessment  method  to  use  for  this  stock  was  based  on  careful 
consideration  of  a  number  of  issues.  The  different  sources  of  sources  of  data  and  their 
short  comings  discussed  above  were  considered  together.  The  type  of  model  was 
selected  based  on  the  following  arguments:  Ageing  of  Decapoda  like  Nephrops 
norvegicus  is  difficult  and  relies  on  indirect methods.  With  the  specific uncertainties  for 
this stock identified and explained in sections above on growth; the uncertainties on the 
proportion of the stock that lives in and  outside Pomo, the potential mixing of landings 
between Nephrops from GSA 17 and 18 (STECF EWG 16-08), the EWG deemed that the 
only viable approach assessment to provide scientific advice is to use a production model 
on  the  combined  GSA  17-18  as  requested  by  the  TORs.  As  STECF  2016  (PLEN  03) 
recommended the use of SPiCT, this was the model of choice for the surplus production 
assessment.  

Input data described in data section are reported below in the following R list. This forms 
the input data basis for the 3 runs on Nephrops GSA 17-18 combined: 

$obsC (COMBINED LANDINGS GSA 17 + 18) 

 [1] 1269.995 1283.481 1397.000 1113.000 1098.000 1197.000 1520.000 2104.000 14
69.000 1288.000 

[11] 1116.000 1185.000 1407.000 1270.000 1219.000 2109.000 2350.000 2087.000 2
836.000 2159.000 

[21] 1890.000 2507.000 3151.000 3122.000 3366.000 3148.000 3558.000 3058.000 2
426.000 1753.000 

[31] 1864.000 1558.737 1252.473 2218.550 2279.430 3393.676 3412.747 3042.834 2
543.700 2903.394 

[41] 2618.023 1982.469 1521.304 1745.933 1331.417 1161.683 986.00 

$timeC (COMBINED LANDINGS GSA 17 + 18) 

 [1] 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 198
4 1985 1986 1987 

[19] 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 200
2 2003 2004 2005 

[37] 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 

$timeI 

$timeI[[1]] (from Jukic 1975) 

232 

232 

 
 
 
 
 
 
 [1] 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 

$timeI[[2]] (from Froglia 1988) 

 [1] 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1972 

$timeI[[3]] (MEDITS GSA 17 ITA + GSA 18 all) 

[1] 1996 1997 1998 1999 2000 2001 

$timeI[[4]] (MEDITS GSA 17 - 18 ITA+HRV+ALB+MTG)  

[1] 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 

$obsI 

$obsI[[1]] (from Jukic 1975) 

 [1] 5.044500 7.740429 2.766750 1.551000 1.621000 2.169400 1.867563 1.449312 3.
866662 3.348465 

$obsI[[2]] (from Froglia 1988) 

 [1] 68.64132 46.32997 25.28125 16.38208 25.47517 43.61067 67.90581 72.84041 95
.12000 56.87619 

[11] 45.43182  0.00000 

$obsI[[3]] (MEDITS GSA 17 ITA + GSA 18 all) 

[1] 4.408880 2.383859 3.599060 2.467033 1.252567 1.414234 

$obsI[[4]] (MEDITS GSA 17 - 18 ITA+HRV+ALB+MTG) 

 [1] 2.0177569 2.7319675 3.1626022 3.4391495 3.5022395 1.6299287 3.6815984 3.2
841491 2.0248443 

[10] 1.0000061 0.8984729 2.0794126 1.9832374 1.6419650 

To explore the sensitivity of the results to the data different model runs were set up to 
explore  different  combinations  of  areas,  time  span  and  time  series  of  landings  and 
CPUEs. 
Initial  runs  were  performed  on  GSA  17  only  with  FAO  Landings  1970-2015,  all  tuning 
CPUE’s  since  1960,  MEDITS  Italian  side  only  for  1995-2015,  models  had  good 
convergence and residuals (not shown). Similarly runs were performed on GSA 18 only 
with  Landings  1970-2015,  MEDITS  GSA  18  Italian  side  only  for  1995-2015  or  MEDITS 
GSA 18 whole area for 1995-2015 (not shown). 

233 

233 

 
 
 
 
 
 
 
 
 
 
 
Once  these  exploratory  runs  were  made,  in  order  to  directly  address  the  TOR  of  an 
assessment  of  GSA  17-18  combined,  the  data  were  combined  by  sum  for  the  landings 
and a mean index for MEDITS as described above. The three runs carried out are: 
GSA 17-18, Landings 1970-2015, all tuning CPUE’s since 1960 (Juckic and Froglia 1988), 
MEDITS a 1995-2001 and MEDITS b 2002-2016 (run #1). 

Model run #1 
Following the assessment run during EWG 16-17 the final model accepted was the model 
run #1 based on the following input data for GSA 17-18: Landings 1970-2015, all tuning 
CPUE’s since 1960 (Juckic and Froglia), MEDITS a 1996-2001 and MEDITS b 2002-2016. 
MEDITS was explicitly split in two distinct tuning indices to account for the difference of 
the  1996-2001  index  being  derived  only  on  the  Italian  side  in  GSA  17  from  an  index 
covering the whole GSA 17-18 starting in 2002 (Figure 6.3.3.11). 

Figure  6.3.3.11  Norway  lobster  in  GSAs  17  and  18.  Input  Data  from  Norway  lobster 
GSA 17-18 Model run #1. Index 1 = Froglia, Index 2 = Jukic, Index 3 = MEDITS 1995-
2001, Index 4 = MEDITS 2002-2005. 

SPiCT  was  run  with  the  default  prior  settings  and  no  informative  priors  for  initial 
parameter  estimates.  The  model  converged  and  the  diagnostic  results  (Residuals,  Auto 

234 

234 

 
 
 
 
 
 
 
correlation  and  Shapiro  p-values)  are  good  for  both  catches  and  the  4  tuning  indexes 
(Figures 6.3.3.12 and 6.3.3.13 ). 

Figure  6.3.3.12.  Norway  lobster  in  GSAs  17  and  18.  SPiCT  model  fit  for  Run  #1  with 
full time series and 4 CPUE indexes. 

Figure  6.3.2.13.  Norway  lobster  in  GSAs  17  and  18.  Diagnostics  for  Model  Run  #  1 
from Norway lobster GSA 17-18 Index 1 = Froglia, Index 2 = Jukic, Index 3 = MEDITS 
1995-2001, Index 4 = MEDITS 2002-2005 

235 

235 

 
 
 
 
 
A  retrospective  was  run  with  4  retro  years.  For  production  models,  the  most  reliable 
estimates  are  in  terms  of  F/Fmsy  and  B/Bmsy.  The  retrospective  patterns  are  very 
consistent  across  years  in  terms  of  B/Bmsy  with  biomass  estimated  well  below  Bmsy. 
There is have a tendency to higher F in the run without the last 4 years (blue line), this 
is  driven  by  the  MEDITS  index  that  is  showing  an  increase  in  the  last  3  years  so  the 
pattern comes from the data and not a  fitting issue. F/Fmsy is estimated to be greater 
than  1  in  all  runs  for  all  years  after  2005.  The  coherence  of  the  results  indicates  the 
retrospective  performance  is  acceptable  (Figure  6.3.3.14).  The  assessment  is  coherent 
with the assessment reported in last years EWG report (STECF 2017)  

Figure 6.3.3.14 Norway lobster in GSAs 17 and 18. Model Run #1 Retrospective analysi
s for Norway lobster in GSA 17-18 

Model estimates, reference points and summaries are reported below: 
 [1] "Convergence: 0  MSG: both X-convergence and relative convergence (5)" 
 [2] "Objective function at optimum: 33.4501445"                            
 [3] "Euler time step (years):  1/16 or 0.0625"                             
 [4] "Nobs C: 47,  Nobs I1: 10,  Nobs I2: 11,  Nobs I3: 7,  Nobs I4: 15"    
 [5] ""                                                                     
 [6] "Priors"                                                               
 [7] "     logn  ~  dnorm[log(2), 2^2]"                                     
 [8] " logalpha  ~  dnorm[log(1), 2^2]"                                     
 [9] "  logbeta  ~  dnorm[log(1), 2^2]"                                     
[10] ""                                                                     
[11] "Model parameter estimates w 95% CI "                                  
[12] "            estimate        cilow        ciupp    log.est  "          
[13] " alpha1 2.253438e+00    0.8367154 6.068946e+00  0.8124569  "          
[14] " alpha2 2.218750e+00    0.6622779 7.433211e+00  0.7969439  "          
[15] " alpha3 1.395874e+00    0.4525786 4.305253e+00  0.3335211  "          
[16] " alpha4 1.133338e+00    0.4761496 2.697588e+00  0.1251675  "          
[17] " beta   4.196983e-01    0.1085365 1.622926e+00 -0.8682193  "          
[18] " r      7.003530e-01    0.1359925 3.606774e+00 -0.3561707  "          
[19] " rc     7.565253e-01    0.2838255 2.016487e+00 -0.2790193  "          
[20] " rold   8.224939e-01    0.1407888 4.805042e+00 -0.1954142  "          

236 

236 

 
 
 
 
 
[21] " m      2.502788e+03 1849.7498801 3.386375e+03  7.8251605  "          
[22] " K      1.363986e+04 5277.4015458 3.525331e+04  9.5207518  "          
[23] " q1     2.774000e-04    0.0001180 6.521000e-04 -8.1900400  "          
[24] " q2     4.266400e-03    0.0017006 1.070350e-02 -5.4569846  "          
[25] " q3     4.586000e-04    0.0001164 1.806200e-03 -7.6873482  "          
[26] " q4     3.345000e-04    0.0001272 8.798000e-04 -8.0029239  "          
[27] " n      1.851499e+00    0.5150596 6.655637e+00  0.6159958  "          
[28] " sdb    1.981984e-01    0.0948044 4.143543e-01 -1.6184865  "          
[29] " sdf    1.726771e-01    0.0798660 3.733427e-01 -1.7563317  "          
[30] " sdi1   4.466278e-01    0.2661470 7.494971e-01 -0.8060297  "          
[31] " sdi2   4.397528e-01    0.2234921 8.652767e-01 -0.8215426  "          
[32] " sdi3   2.766601e-01    0.1375593 5.564208e-01 -1.2849654  "          
[33] " sdi4   2.246259e-01    0.1311300 3.847846e-01 -1.4933190  "          
[34] " sdc    7.247230e-02    0.0286245 1.834871e-01 -2.6245509  "          
[35] " "                                                                    
[36] "Deterministic reference points (Drp)"                                 
[37] "           estimate        cilow        ciupp    log.est  "           
[38] " Bmsyd 6616.5344772 2741.7169237 15967.559637  8.7973270  "           
[39] " Fmsyd    0.3782626    0.1419128     1.008244 -0.9721665  "           
[40] " MSYd  2502.7878045 1849.7498801  3386.375024  7.8251605  "           
[41] "Stochastic reference points (Srp)"                                    
[42] "           estimate     cilow        ciupp    log.est  "              
[43] " Bmsys 6362.6101355 2687.2918 15064.537589  8.7581940  "              
[44] " Fmsys    0.3703553    0.1357     1.010781 -0.9932925  "              
[45] " MSYs  2354.4184894 1718.2786  3226.069638  7.7640491  "              
[46] "       rel.diff.Drp  "                                                
[47] " Bmsys  -0.03990883  "                                                
[48] " Fmsys  -0.02135069  "                                                
[49] " MSYs   -0.06301739  "                                                
[50] ""                                                                     
[51] "States w 95% CI (inp$msytype: s)"                                     
[52] "                    estimate       cilow        ciupp    log.est  "   
[53] " B_2016.00      2119.6492117 766.7233720 5859.8876006  7.6590059  "   
[54] " F_2016.00         0.5114431   0.1834196    1.4260964 -0.6705190  "   
[55] " B_2016.00/Bmsy    0.3331415   0.1872010    0.5928559 -1.0991881  "   
[56] " F_2016.00/Fmsy    1.3809524   0.7493425    2.5449373  0.3227734  "   
[57] ""                                                                     
[58] "Predictions w 95% CI (inp$msytype: s)"                                
[59] "                  prediction       cilow        ciupp    log.est  "   
[60] " B_2017.00      2187.5524103 761.4118741 6284.8843193  7.6905386  "   
[61] " F_2017.00         0.4719458   0.1663022    1.3393261 -0.7508910  "   
[62] " B_2017.00/Bmsy    0.3438137   0.1469314    0.8045104 -1.0676554  "   
[63] " F_2017.00/Fmsy    1.2743056   0.5936864    2.7352063  0.2424014  "   
[64] " Catch_2017.00  1109.1398101 664.4274337 1851.5056062  7.0113400  "   
[65] " E(B_inf)       4220.9760458          NA           NA  8.3478217  " 

6.3.4 REFERENCE POINTS 

The SPiCT model provides output set directly in the context of MSY, and the results are 
more are estimated by the model, however, these are less precise than the F/Fmsy and 
B/Bmsy  results.  Based  on  model  run  #1  Fmsy  from  deterministic  reference  points  is 

237 

237 

 
 
FMSYd =  0.38 y-1 (0.14 - 1.01) and BMSYd = 6616 t (2741 - 15967) , while the stochastic 
reference points are FMSYs = 0.37 and BMSYs = 6362 t. 

Based  on  these  results  STECF-EWG  17-15  considers  the  stock  has  been  depleted  well 
below Bmsy and is being overexploited (F>Fmsy) for the last 14 years. 

6.3.5 SHORT TERM FORECAST AND CATCH OPTIONS 

The  SPiCT  model  was  used  to  carry  out  a  short  term  forecast    with  the  following 
conditions:- 

Observed interval, index:  1960.00 - 2016.00 

Observed interval, catch:  1970.00 - 2017.00 

Fishing mortality (F) prediction: 2020.00 

Biomass (B) prediction:           2020.00 

Catch (C) prediction interval:    2019.00 - 2020.00 

STF  shows  large  uncertainties  in  the  estimates  as  indicated  by  the  95%  confidence 
boundaries around the predicted mean values. In general the stock under the simulated 
F reductions does not recover to over Bmsy by 2020 except for the no fishing scenario. 
The  the  next  best  scenario  tested  in  term  of  stock  biomass  would  be  obtained  by  a  F 
reducion  of  25%  which  would  bring  B2020/Bmsy  to  0.65.  The  greater  the  reduction  in  F 
the greater the speed of recovery. 

Predictions 

                           C      B     F B/Bmsy F/Fmsy perc.dB perc.dF 

1. Keep current catch  986.8 1793.9 0.547  0.282  1.478   -18.0    16.0 

2. Keep current F     1424.3 3181.3 0.472  0.500  1.274    45.4     0.0 

3. Fish at Fmsy       1366.3 4004.7 0.370  0.629  1.000    83.1   -21.5 

4. No fishing            3.4 8501.3 0.000  1.336  0.001   288.6   -99.9 

5. Reduce F 25%       1348.2 4152.6 0.354  0.653  0.956    89.8   -25.0 

6. Increase F 25%     1401.1 2408.7 0.590  0.379  1.593    10.1    25.0 

95% CIs of absolute predictions 

                       C.lo   C.hi   B.lo     B.hi  F.lo   F.hi 

1. Keep current catch 869.2 1120.4   30.7 104781.1 0.009 33.598 

2. Keep current F     524.2 3870.2  736.9  13733.4 0.143  1.561 

3. Fish at Fmsy       522.3 3574.6 1187.2  13508.9 0.112  1.225 

4. No fishing           1.1   10.6 4804.4  15042.9 0.000  0.002 

238 

238 

 
 
 
 
 
 
 
 
5. Reduce F 25%       516.2 3520.9 1277.9  13493.7 0.107  1.171 

6. Increase F 25%     465.3 4218.8  407.3  14246.0 0.178  1.952 

95% CIs of relative predictions 

                      B/Bmsy.lo B/Bmsy.hi F/Fmsy.lo F/Fmsy.hi 

1. Keep current catch     0.019     4.252     0.188    11.615 

2. Keep current F         0.101     2.486     0.487     3.338 

3. Fish at Fmsy           0.152     2.611     0.382     2.619 

4. No fishing             0.484     3.685     0.000     0.003 

5. Reduce F 25%           0.162     2.637     0.365     2.503 

6. Increase F 25%         0.059     2.409     0.608     4.172 

Full time series of forecasts are outlined in Table 6.3.5.1 and Fig. 6.3.5.1 

Table  6.3.5.1  Norway  lobster  in  GSAs  17-18.  Short  term  forecasts  of  status  quo  and 
different fishing mortalities reductions 

Forecast 
Scenario 

Year 

Fishing 
mortality 
(F) 

Biomass 
(B) 

Catch 

Keep 
catch 

current 

2016 

0.57 

1784.94 

1018.00 

2017 

0.57 

1728.16 

987.33 

2018 

0.57 

1734.44 

989.35 

2019 

0.56 

1767.22 

986.84 

2020 

0.54 

1848.30 

996.55 

Keep current F  2016 

0.49 

2100.99 

1021.53 

2017 

0.47 

2350.14 

1109.14 

2018 

0.47 

2692.47 

1270.70 

2019 

0.47 

3017.87 

1424.27 

2020 

0.47 

3315.35 

1564.67 

Fish at Fmsy 

2016 

0.49 

2100.99 

1021.53 

239 

239 

 
 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
2017 

0.37 

2463.24 

912.27 

2018 

0.37 

3074.54 

1138.67 

2019 

0.37 

3689.21 

1366.32 

2020 

0.37 

4264.72 

1579.46 

No fishing 

2016 

0.49 

2100.99 

1021.53 

2017 

0.00 

2938.63 

1.39 

2018 

0.00 

4937.94 

2.33 

2019 

0.00 

7280.44 

3.44 

2020 

0.00 

9439.91 

4.46 

Reduce F 25%   2016 

0.49 

2100.99 

1021.53 

2017 

0.35 

2482.14 

878.58 

2018 

0.35 

3140.83 

1111.73 

2019 

0.35 

3808.81 

1348.17 

2020 

0.35 

4435.99 

1570.16 

Increase F 25%  2016 

0.49 

2100.99 

1021.53 

2017 

0.59 

2227.14 

1313.86 

2018 

0.59 

2305.58 

1360.14 

2019 

0.59 

2374.99 

1401.08 

2020 

0.59 

2435.87 

1437.00 

F Status quo in 2017 

MSY F rule in 2018 

2016 

2017 

2018 

0.49 

0.47 

0.37 

2100.99 

1021.53 

2350.14 

1109.14 

2692.47 

1048.00 

2016 

0.49 

2100.99 

1021.53 

240 

240 

 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
F Status quo in 2017 

MSY F rule in 2018 

2017 

0.47 

2350.14 

1109.14 

2018 

0.27 

2692.47 

799.00 

Figure 6.3.5.1  Norway lobster in GSAs 17 and 18. Short term forecast for  the period 
2017-2020  according  to  different  scenarios:  1  keep  current  catch,  2,  keep  current  F,  3 
fishing at Fmsy, 4 no fishing, 5 reduce F by 25%, 6 increase F by 25%. 

The short term forecast based on the SPiCT model appears optimistic (Section 
6.3.5), maintaining status quo F implies annual increases in SSB of 12 and 15% to 
start of 2018. Given that SSB has been relatively stable over the last 5 or 6 years at 
only slightly higher F, the assumptions of SPiCT which assumes that average stock 
growth will occur this year does not seem reasonable. Therefore the catch 
predictions are based on a harvest rate relative to estimated F and SSB in 2016. This 
assumes that the stock will not increase at status quo F (0.47) before 2018, which is 
considered a reasonable assumption. As the current SSB is well below MSYBtrigger 
=3705t (based on Bpa) the target F should be reduced from FMSY (0.37) in proportion 
to B/MSYBtrigger an F target of 0.21, giving a catch of 441t. The text table below 
provides the basis of the calculation.  

SSB 

F 

Catch 

2016 

2101 

FMSY 
MYS Rule 

0.49 
0.37 
0.21 

1021 
777 
441 

241 

241 

 
 
 
 
 
 
 
 
 
  
 
 
 
 
  
 
CONCLUSIONS 

The  SPICT  model  accepted  to  assess  Norway  lobster  in  GSA  17-18  is  model  run  #1 
which  uses  the  most  complete  data  set  fitted  to  the  longest  time  series  available 
covering also periods with high biomass and low F, some stock declines and recoveries. 
Model run #1 shows a continous reduction in B/Bmsy since 60s, with valus consistently 
below 1 since mid 90s with the last 3 years being amongst the lowest point of the series. 
In terms of F/Fmsy the model indicate an increasing since early ‘90s with values over 1 
since mid 2000.  

The  status  of  the  stock  in  2016  using  mean  value  by  year,  refered  to  the  stocastic 
reference points (BMSYs FMSYs) is , F2016/FMSYs = 1.38 and B2016/BMSYs = 0.33. When refered 
to the determinsitc reference points, the stock status in 2016 , is F2016/FMSYd= 1.35. and 
B2016/BMSYd = 0.32. 

As this is an update assessment which uses the same basis and data sets as last 
year, giving an assessment in line with last years, no further sensitivity analyses 
in  terms  of  data  used  or  length  of  catch  data  time  series  were  tested.  The 
analyses carried out last year are considered to be still valid. 

6.3.6 DATA DEFICIENCIES 

Being  able  to  split  the  catch  numbers  in  the  Pomo  and  outside  Pomo  area  would  be 
beneficial  for  age/length  based  assessments.  According  to  the  Italian  DCF  workplan 
2011-2014 
(https://datacollection.jrc.ec.europa.eu/np/2014/-
/document_library_display/z9Yv/view/688307/38422?_110_INSTANCE_z9Yv_redirect=h
ttps%3A%2F%2Fdatacollection.jrc.ec.europa.eu%2Fnp%2F2014%3Fp_p_id%3D110_IN
STANCE_z9Yv%26p_p_lifecycle%3D0%26p_p_state%3Dnormal%26p_p_mode%3Dview
%26p_p_col_id%3Dcolumn-2%26p_p_col_count%3D1), the DCF sampling in GSA 17 of 
the OTB gear had a target sampling of 78 fishing trips each year on a quarterly basis in 
the  whole  Italian  side  of  GSA  17  out  of  76525,51  average  total  no.  of  trips  in  the 
reference years. The 78 fishing trips are split in 36 concurrent at sea and 42 concurrent 
at market sampling. This means only 9 separate fishing trips had to be sampled at sea in 
GSA 17 each quarter.   If catches were to be split a posteriori between Pomo area and 
outside  Pomo  on  a  50%  -50%  basis,  at  most  4.5  trips  would  be  sampled  quarterly  in 
Pomo  which  would  arguably  be  insuffiucient  sampling  levels  for  the  reconstruction  of 
catch at length in the different areas. The EWG has no access to primary sampling data, 
but  it  might  also  be  the  case  that  no  fishing  trips  are sampled in  the  Pomo  pit  area.  If 
progress  is  to  be  made  in  resolving  the  population/fishery  distribution  issues,  s 
substantial  increase  in  at  sea  samplking  trips  would  be  required.  This  would  require 
amendment of the national sampling programs for this Nephrops in the Adriatic.  

242 

242 

 
 
 
 
6.4  COMMON PANDORA IN GSAS 17 & 18 

6.4.1  BIOLOGY 

Figure 6.4.1. Geographical location of GSAs 17-18. 

Common  pandora  (Pagellus  erythrinus)  is  distributed  in  the  eastern  Atlantic  and  in  the 
entire  Mediterranean.  It  is  a  gregarious  species  living  on  the  shelf,  more  abundant  in 
rocky and sandy bottoms. Common pandora is omnivorous, but feeds mainly on benthic 
invertebrates and small fishes (Bauchot and Hureau, 1990). 

Common  pandora  is  a  protogynic  hemaphrdyte  species.  Sexual  inversion  occurs  in 
approximately  half  of  the  individuals.  A  5%  of  individuals  remain  males,  while  45% 
would  be  functional  females  and  later  males,  and  50%  of  individuals  would  remain 
females all their life (Larrañeta, 1967). Age at first maturity is 1 year, around 14 cm TL 
(DCF). 

According  to  MEDISEH  project,  spawning  in  the  Adriatic  takes  place  in  spring  and 
beginning of summer, and recruitment occurs in GSA 17 in spring and early autumn, and 
in GSA 18, in summer and autumn. Persistent nursery areas were found in the northern 
GSA  17  and  in  both  the  eastern  and  western  GSA  18.  Persistent  spawning  areas  were 
found in the eastern GSAs 17 and 18. 

No  new  information  was  available  to  the  EWG  17-15  on  stock  identification  and 
boundaries  in  relation  to  that  reported  in  STOCKMED.  According  to  this  project,  four 
common  pandora  stocks  units  were  identified  in  the  Mediterranean,  two  of  them  with 
presence  in  the  Adriatic  sea.  One  stock  unit  would  correspond  to  GSA  17,  while  the 
second one would extend in GSAs 9, 10, 11, 15, 16, 18 (southern Adriatic) and 19. 

Common  pandora  had  been  previously  assessed  in  GSA  9  (STECF  11-14  Med  Stock 
Assessments  part  2_JRC67797)  and  GSAs  15+16  (STECF  12-19  -  MED  stock 
assessements part 1_JRC76735). The species had not been previously assessed in GSAs 
17+18. 

Growth parameters 

243 

243 

 
 
 
The growth parameters  set used in a previous assessment in GSA 9 was selected. This 
set  and  that  from  DCF  define  very  similar  growth  curves.  In  a  marine  reserve  in  the 
north-western  Mediterranean  sizes  up  to  60  cm  TL  have  been  observed  (Martín  et  al. 
2012).  

Length-weight relationship: a= 0.0274 and b=2.9556. 

Natural mortality 

M-at-age was estimated with Prodbiom, fixing M at the older ages at 0.15. 

6.4.2 

INPUT DATA 

6.4.2.1 

CATCH (LANDINGS AND DISCARDS)  

Table 6.4.2.1.1. Common pandora in GSAs 17+18. Landings (t) by GSA as reported in 
the DCF (left). Landings by country (right; data sources: DCF for Italy and Slovenia and 
Eurostat and Fishstat for Croatia). Note the importance of Croatian landings in GSA 17. 

Table  6.4.2.1.2.  Common  pandora  in  GSAs  17+18.  Landings  (t)  by  gear.  These 
values do not include Croatian landings, info not available. Data source: DCF.  

244 

244 

L_INFKT0DCF56.00.105-1.347GSA 954.30.118-1.120GSA 15+1640.00.176-1.000age01234+M0.450.230.190.170.16GSA 17+18GSA 17GSA 18TotalGSA 17+18ITASVNHVRTotal20053.393.420053.43.420064.4130.0134.42006130.04.4134.420075.259.464.6200759.45.264.620086.228.534.7200828.56.234.720094.825.230.0200925.24.880.0110.020105.532.638.1201032.65.5204.0242.1201145.232.777.9201172.35.6103.0180.9201246.725.872.5201256.616.080.0152.5201321.042.563.5201358.05.572.0135.5201430.0117.3147.32014144.13.269.0216.3201545.5195.2240.72015235.84.967.0307.7201632.1142.4174.52016172.12.457.9232.4 
 
 
 
 
 
 
 
 
 
 
 
Table  6.4.2.1.3.  Common  pandora  in  GSAs  17+18.  Landings  length  frequency 
distribution  (TL  in  cm;  thousands).  Only  Italy  reported  data  on  sizes.  Data  source: 
DCF. 

Table  6.4.2.1.4.  Common pandora  in  GSA  17,  by  gear. Landings  length  frequency 
distribution  (TL  in  cm;  thousands).  Only  Italy  reported  data  on  sizes.  Data  source: 
DCF. 

245 

245 

GSA 17+18-1FPOFYKGNDGNSGTRLHPLLDLLSOTBPSPTMTotal20050.0011.2820.2790.0020.1051.6540.0610.0063.39020060.3500.00116.13619.50350.87447.4560.079134.40020070.0120.0050.0269.95911.64913.38429.5510.00164.58820080.0080.0058.34516.8235.5863.9550.00834.72920090.0012.80913.4890.0018.7954.8980.0140.01530.02220102.69815.6130.0227.19412.5550.0070.00138.08920110.0290.00412.03315.0490.0417.01843.5790.15177.90420120.0030.0059.64415.1130.0123.80743.8550.08672.52520130.02040.9081.1600.0040.13121.2420.00763.47220140.0000.01812.05310.1540.01225.66899.3810.042147.32820150.0070.00115.1613.1330.02642.541179.7890.008240.66520160.01511.3350.2940.0254.43645.220113.179174.504ITALY 17+182009-2010-only GSA182009201020112012201320142015201610.00.00.00.00.00.00.00.020.00.00.00.00.00.00.00.030.00.00.00.00.00.00.00.040.00.00.00.00.00.00.00.050.00.00.00.00.00.00.00.060.00.00.00.00.00.00.00.070.00.00.00.00.00.00.00.080.00.00.00.00.00.00.00.090.00.00.00.00.00.00.00.0100.00.00.00.10.00.00.00.0110.90.00.07.91.20.00.04.8120.90.00.045.55.842.31.6100.8131.72.02.066.828.6224.111.1272.91414.57.27.2112.3119.2417.749.8530.91540.044.667.0174.0273.6536.8115.8668.81646.843.788.6170.3341.6515.1264.4452.71744.248.981.2132.1198.3400.3202.8304.31832.336.8166.186.074.3267.188.9254.81910.245.2104.917.837.491.019.876.1208.549.4115.49.222.429.06.424.6210.935.8112.23.611.518.82.53.5222.610.610.60.96.96.60.010.3230.93.33.31.72.35.00.01.6240.02.44.80.92.80.80.00.0250.02.42.40.91.40.80.00.6260.00.00.00.00.00.00.00.0270.90.00.00.00.00.80.00.0280.02.42.40.01.40.00.01.2290.00.00.00.90.00.00.00.0300.00.00.00.00.00.00.00.0310.02.42.40.00.00.00.00.0320.00.00.00.00.00.00.00.0330.00.00.00.00.70.00.00.0340.00.00.00.00.00.00.00.0350.00.00.00.90.00.00.00.0 
 
 
Table  6.4.2.1.5.  Common pandora  in  GSA  18,  by  gear. Landings  length  frequency 
distribution (TL in cm; thousands). Data source: DCF. 

246 

246 

GNSOTBOTBOTBOTBOTBOTB201120112012201320142015201610.00.00.00.00.00.00.020.00.00.00.00.00.00.030.00.00.00.00.00.00.040.00.00.00.00.00.00.050.00.00.00.00.00.00.060.00.00.00.00.00.00.070.00.00.00.00.00.00.080.00.00.00.00.00.00.090.00.00.00.00.00.00.0100.00.00.00.00.00.00.0110.00.01.71.00.00.00.0120.00.06.02.91.61.60.0130.00.011.412.95.111.10.0140.00.049.329.524.249.83.61522.40.0119.873.572.7115.843.91644.90.0146.893.5153.4264.488.41729.92.3126.053.2138.0202.8113.71837.492.084.318.970.488.9133.8197.552.316.35.825.719.830.8207.558.59.22.55.46.43.6210.076.43.41.32.12.52.4220.00.00.90.00.00.06.0230.00.01.70.20.00.00.0240.02.30.90.00.00.00.0250.00.00.90.00.00.00.0260.00.00.00.00.00.00.0270.00.00.00.00.00.00.0280.00.00.00.00.00.01.2290.00.00.90.00.00.00.0300.00.00.00.00.00.00.0310.00.00.00.00.00.00.0320.00.00.00.00.00.00.0330.00.00.00.00.00.00.0340.00.00.00.00.00.00.0350.00.00.90.00.00.00.0 
 
 
 
 
 
 
 
 
 
Table 6.4.2.1.5. cont. 

247 

247 

OTBOTBOTBOTBOTBOTBOTBOTB20092010201120122013201420152016100000000200000000300000000400000000500000000600000000700000000800000000900000000100000.09600001100.85106.1520.196004.81200.851039.5072.15340.6840100.796130.2241.7011.95155.426.711218.9210265.588146.34314.4627.15463.01711.273383.5960479.9791514.99839.98329.91854.22823.747418.4690567.9751621.34146.78926.66623.47419.78290.6030323.1861710.07344.23712.3576.12711.144189.8610166.393184.40232.32712.3571.71310.9154.9880102.395192.16410.20820.8121.5384.5736.81036.798200.6728.50720.16201.2073.875017.599210.4480.85126.0150.1920.4570002202.5523.25200003.22300.8513.25200001.62400000000250000000026000000002700.85100000028000000002900000000300000000031000000003200000000330000000034000000003500000000 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 6.4.2.1.6. Common pandora in GSA 18. Landings age structure (thousands) . 
Data source: DCF. 

Table 6.4.2.1.7. Common pandora in GSAs 17+18. Discards (t). Data source: DCF. 

248 

248 

GTRGTRGNSGNSGNS201120142013201420161000002000003000004000005000006000007000008000009000001000000110000012000.6940013009.02407.321402.37978.4427.5247.2971514.63616.653176.32129.00756.8691617.07523.79228.38447.2741.1031736.58922.997133.97649.41924.2121824.39315.8644.42725.78418.5811924.39316.65327.07311.8188.4462029.2717.9318.74311.8183.378219.75713.4819.7183.2231.126227.3185.5516.9421.0741.1262303.9652.0831.0740242.4390.7932.77700252.4390.7931.38800.56326000002700.793000282.43901.3880029000003000000312.4390000320000033000.6940034000003500000012345620060.000.000.000.000.000.000.0020070.000.000.000.000.000.000.0020080.000.000.000.000.000.000.0020090.0049.1011.560.000.000.000.0020102.12134.7063.953.400.850.000.0020110.3275.1772.8015.620.000.000.00201257.77237.48107.7814.312.443.011.8720134.38651.63158.9215.811.391.390.69201470.241566.52397.5921.511.590.000.0020152016162.081859.54251.177.060.490.000.00 
 
 
 
Table  6.4.2.1.8.  Common  pandora  in  GSAs  17+18.  Discards  length  frequency 
distributions (thousands). Data source: DCF. 

Common  pandora  landings  in  GSAs  17+18  come  from  bottom  trawl  (OTB)  and  small-
scale fishing gears, as gillnet (GNS), trammel net (GTR) and longline (LL). Italy landings 
come  from  GSA  17  and  18,  while  those  from  Croatia  are  obtained  in  GSA  17. 

249 

249 

GNSGTROTBTotal 20050.0010.1630.3680.53220060.0000.1300.6200.75020070.0010.4000.5420.94320080.0010.3260.8841.21120090.0010.6090.4231.03320100.0000.6760.3381.01420110.7840.6832.4983.96420120.0460.6771.3132.03620130.0000.6890.3161.00520140.0010.4980.1980.69720150.0080.8430.3831.23420160.0000.1490.3830.53220112012201320142015201610.000.000.000.000.000.0020.000.000.000.000.000.0030.490.000.000.000.000.0041.580.000.000.000.000.0451.440.001.070.000.000.7069.200.004.730.000.002.88721.700.0011.040.000.004.33828.350.006.440.000.002.74928.240.003.970.000.001.751024.320.004.040.000.001.891113.600.016.760.000.012.40124.510.034.240.010.042.63131.960.125.020.060.162.02142.310.292.680.190.380.99154.200.360.310.410.860.60161.190.310.190.370.660.18170.050.210.020.000.290.04180.000.040.000.000.060.00190.000.020.000.000.000.02200.000.000.000.000.000.00210.000.000.000.000.000.00220.000.000.000.000.000.00235.060.000.000.000.000.00240.000.000.000.000.000.00250.000.000.000.000.000.00260.000.000.000.000.000.00270.000.000.000.000.000.00280.000.000.000.000.000.00290.000.000.000.000.000.00300.000.000.000.000.000.00310.000.000.000.000.000.00320.000.000.000.000.000.00330.000.000.000.000.000.00340.000.000.000.000.000.00350.000.000.000.000.000.00 
 
 
Information  on  the  Croatian  landings  by  gear  is  not  available.  Only  Italy  reports  LFDs 
data, for GSA 17 and 18; and data on ages was reported for GSA 18- Italy.  

According to the information provided, over the period 2005-2016 discards represented 
between  4.8%  in  2011  to  less  than  1%  of  the  total  catch.  In  the  most  recent  years 
2014-2016  discards  represented  ≤0.5  of  the  total  catch,  expressed  in  weight.  Discards 
by size were reported for the period 2011-2016 and discards by age only for 2013 and 
2016 (and units wrong).  

The input for the assessments refers to catches.   

6.4.2.2 

EFFORT 

Table  6.4.2.2.1.  Fishing  effort in  GSAs 17 and  18, expressed as (GT*days at sea) 
and fishing days. 

Table 6.4.2.2.2. Fishing effort in GSAs 17 and 18, expressed as (GT*days at sea), 
by gear. 

250 

250 

GT*days at seaFishing days2002903074200377096120041292219155106532005120362774633749200611818332362497620071169577432960382008105106153231920200910627475383365820101027554533863162011918230432618842012132171643402282201313038612256181020141322321027823822015125757572618492201686295902319019 
 
 
 
 
 
 
 
 
 
 
 
 
Table  6.4.2.2.3.  Fishing  effort  in  GSAs  17  and  18,  expressed  as  fishing  days,  by 
gear. 

251 

251 

-1DRBFPOFYKGNDGNSGTRLHPLLD2004281142849661609572300831322916129568432005215733760190337961952072357318149970273632006153522859016855454781533345611107214863200798470967819777066430322700614696119028200882060846129653774193518656014075669862009939095728497287252442253065146020990220109917957511263930636912472791461232656320111062007468435570850721281318159577177432012109031676263892754306745937626548532850213720131091616178848104031177392897202529395492794920141054937010449214347605405482213358382593356020159512070471313546053174362430196165326701730720167120681313849932762992670026528323053LLSLTLOTBOTMPSPTMSBSVTBBTotal20046379278357361216185746213643710031291292219120057790675199682302227502185904878558912036277200677793674184833152702512066924105291211818332200769177635101637415122037731096364116957742008107911612188722321318440608437411051061520096494162170302911021808140104520310627475201087474590549018725619522909211581027554520117651253828542064531433220665155918230420121145131868608872761289897116342761028498772756132171642013767662712601378113086685167174817271126576031303861220148330925085681395226313859317634116241228926951322321020151002952352557397865762979906146441561993583125612575757201655993444329712002918122588321008629590Fishing days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otal20022099534140235229030742003196309452625649770961200412806598650915195149679824528523193255106532005146243801565132384440099222037209502463374920062444976363348954831906788481237025362497620072100495588383338301134262003203296038200813535760808626618518498817450232319202009157640661505230437180229291981383365820101483516226511353421729192751503386316201114507955601111713215858431348832618842012139285717519705628480314927177720143009340228220131101035884752570202394138252767951324112561810201455979575457356271513371448173572013918527823822015560165734591304208012722413519744746174943261849220161294494192261018531461731410502319019 
 
 
 
Most of common pandora catches come from OTB. OTB represented in the period 2004 
and 2016 between 43% and 62% of fishing effort in GSAs 17+18 expressed in (GT*days 
at  sea).  In  2016  OTB  represented  51%  of  the  total  fishing  effort  in  these  areas.  Since 
2004  the  number  of  fishing  days  has  gradually  decreased  by  60%,  from  5510.7  to 
2319.0 thousand days in 2016.  

Undefined fishing gear (-1) represented 1% of (GT*days at sea) since 2006 and around 
10% of the total fishing days in the most recent years 2013-2016 

6.4.2.3 

SURVEY DATA 

Table  6.4.2.3.1.  Common  pandora  in  GSAs  17  and  18.  MEDITS  survey:  total  density 
(n/km2) and total biomass (kg/ km2) over the period 2002-2016. 

Figure  6.4.2.3.1.  Common  pandora  in  GSAs  17  and  18.  MEDITS  survey:  total  density 
(n/km2) and total biomass (kg/ km2) over the period 2002-2016. 

252 

252 

total_densitytotal_biomass2002123.16.32003139.56.42004117.96.72005130.66.22006136.65.52007138.26.72008156.77.92009141.87.72010210.312.12011129.97.12012157.36.92013158.97.22014253.99.72015204.19.02016227.48.4 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table  6.4.2.3.2.  Common  pandora  in  GSAs  17  and  18.  MEDITS  survey:  LFDs 
(n/km2) over the period 2002-2016.  

253 

253 

TL (mm)200220032004200520062007200820092010201120122013201420152016250.180.170.530.08260.08300.881.042.12350.181.473.100.810.14401.430.430.220.070.090.532.790.27451.790.260.490.220.091.170.34502.130.260.170.781.700.220.090.180.080.220.89551.630.090.261.042.940.220.080.350.440.071.37602.220.440.440.784.830.690.090.090.090.090.070.420.211.86651.330.180.351.823.190.660.170.090.170.210.270.361.52701.590.530.351.654.201.470.340.090.180.170.090.491.620.642.51751.241.210.694.621.200.080.260.170.170.270.281.890.993.08801.532.310.502.004.401.900.510.270.180.180.441.173.690.623.76850.882.710.443.323.181.910.510.350.860.430.710.943.161.113.49900.883.240.323.222.591.401.371.181.530.351.772.814.962.293.01950.562.341.342.972.001.230.941.101.250.772.831.375.593.074.141001.623.121.114.693.292.261.451.831.330.964.783.079.333.316.531052.223.592.673.302.561.532.212.372.631.224.074.298.204.219.391104.914.443.205.315.562.933.403.002.472.465.315.7911.277.3513.991154.576.664.853.674.912.913.153.413.392.465.696.0310.8710.8415.981202.937.156.534.124.704.584.503.904.084.027.637.0813.2512.5319.611252.635.958.015.387.434.145.873.833.823.427.195.3614.8113.0121.771303.698.3712.428.9710.445.168.365.018.297.359.309.8219.3516.0420.881356.308.709.5410.608.803.906.873.717.467.877.968.3414.6711.0616.881404.9111.0310.3810.457.328.8611.718.4113.539.5110.6712.8722.9516.2016.361457.6010.098.608.807.777.089.5710.598.177.679.1711.4820.3814.5410.121505.419.856.657.816.109.0316.8511.8116.3011.5213.6112.9816.2914.1110.761555.997.294.625.145.567.9213.019.2518.037.638.6810.2215.5214.446.851606.396.054.153.922.598.5313.1311.9820.6812.018.1910.7510.2110.816.141656.775.583.382.044.277.787.538.0814.176.915.477.248.627.753.931706.344.373.813.484.578.497.8211.649.659.887.886.768.005.292.381755.524.692.732.051.966.634.915.067.914.674.435.182.665.113.051806.383.513.502.791.987.065.866.9310.664.305.794.523.413.433.361852.793.192.162.181.703.761.604.375.333.063.192.091.893.932.361903.202.023.061.001.775.744.283.358.142.343.583.562.693.913.031952.701.761.971.411.114.111.524.026.363.232.981.762.034.432.202002.272.661.101.250.613.403.694.504.213.482.792.672.013.611.712052.211.050.441.060.982.512.412.987.051.781.362.351.263.231.072101.850.931.190.811.822.944.291.384.122.221.722.341.911.160.512150.180.480.901.170.280.690.610.594.641.300.701.102.231.360.552200.100.470.431.091.061.791.892.237.163.250.500.680.750.880.342251.640.770.490.620.520.680.471.430.180.530.590.900.260.282300.310.671.121.170.360.180.710.801.181.370.420.710.500.240.342350.720.590.810.880.270.340.091.330.550.090.370.300.190.232400.610.200.690.360.610.070.410.721.400.600.220.680.082450.610.090.260.180.090.090.250.070.070.090.300.130.140.082500.610.200.460.180.530.090.240.240.070.080.070.052550.170.530.090.090.160.210.140.090.072600.310.370.180.240.160.210.130.072650.440.540.330.070.210.082700.310.170.370.150.090.070.090.222750.230.080.090.050.072800.090.180.080.180.550.090.080.072850.180.160.072900.290.090.182950.230.270.543000.090.090.093050.090.070.093100.270.233150.140.090.073200.270.080.053403500.093600.273753950.090.274400.094500.09 
 
Figure 6.4.2.3.2. Common pandora in GSAs 17 and 18. MEDITS data:  LFDs (n/km2) by 
country over the period 2002-2016 (see the different scale used in the graphs). 

During  the  meeting,  the  MEDITS  length  frequency  distributions  were  made  available  to 
EWG17-15 by country. According to these data, common pandora is more abundant and 
larger sizes are found in the north-eastern Adriatic (Croatia).  

6.4.3  STOCK ASSESSMENT 

Common pandora in GSAs 17 and 18 was assessed with a4a and XSA using as input the 
DCF data. The main problems with these data are that the LFDs of landings and discards 
are  reported  only  by  Italy  for  GSAs  17  and  18  for  the  period  2011-2016  (i.e.  no 
information  is  available  on  the  LFDs  from  the  eastern  part);  and  the  age  structure  is 
presented  by  Italy  for  GSA  18.  Because  of  this,  all  the  available  information  on  sizes 
from landings and discards was merged and age slicing was done with a4a. Mean weight 
at age was also calculated with a4a. These analyses are summarized below, but due to 
the  incomplete  input  data  and  the  short  period  for  which  information  on  the  length 
structure  of  the  catches  was  available  (2011-2016)  results  were  not  considered  for 
advice. 

Catch advice for common pandora in the Adriatic, GSAs 17 and 18, is based on Biomass 
index. 

Methods 1: Biomass index 

Biomass  Index  refers  to  the  ICES  data  limited  approach  using  a  stock  status  indicator 
(ICES  2012).  In  the  last  years  common  pandora  biomass  has  displayed  a  slightly 
increasing trend. The change in biomass over the last five years was used to provide an 
index for change (1.1). Following the ICES approach, because this index is less than 1.2 
and more than 0.8, the index value is used to multiply the catch (mean catch over 2014-
2016).  Because  the  exploitation  rate  is  thought  to  be  above  MSY  (see  length  analysis) 
and  the  state  of  the  stock  relative  to  Bmsy  is  unknown  a  precautionary  buffer  (catch 
multiplier  of  0.8)  is  applied  giving  a  factor  of  0.88.  Mean  catch  (Italy,  Slovenia  and 

254 

254 

 
 
 
Croatia) for the last three years is 252.1 tonnes. The catch advice which is applicable for 
two years is 222 t.   

Figure 6.4.3.1. Common pandora in GSA17 and 18. Biomass index (kg/km2) estimated 
from  MEDITS  survey.  In  red  the  mean  of  the  last  two  years  (8.7)  compared  to  the 
previous three years (7.9; green). 

Methods 2: a4a 

Input data 

Catches were available for years 2005 to 2016. For the a4a assessment, year 2005 was 
excluded because the number was significantly below the mean of the time series. 

255 

255 

 
 
 
 
Figure  6.4.3.2.Common  pandora  in  GSA  17  and  18.  Age  structure  of  the  catch  data 
used in the a4a assessment. Values of years 2006 to 2010 were replaced as NAs. 

Table  6.4.3.1.  Common  pandora  in  GSA  17  and  18.  Weight-at-age  in  the  catch  data 
used in the a4a assessment constant for all years.  

Age 

0 

1 

2 

3 

4+ 

Weight 
(g) 

0.008 

0.043 

0.113 

0.219  0.355 

Table 6.4.3.2. Common pandora in GSA 17 and 18. Natural Mortality vector used in the 
a4a assessment.  

Age 

0 

1 

2 

3 

4+ 

M 

0.452  0.230  0.186  0.167  0.149 

Table 6.4.3.3. Common pandora in GSA 17 and 18.  Proportion of mature individuals by 
age used in the a4a assessment. 

Age 

0 

1 

2 

3 

4+ 

256 

256 

 
 
 
 
 
 
 
 
 
 
Prop. 
Mature 

0 

1 

1 

1 

1 

Table  6.4.3.4.  Common  pandora  in  GSA  17  and  18.    Catches  used  in  the  a4a 
assessment.  

Year 

2006 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

Catch 

135.15 

65.53 

35.94 

31.06 

39.10 

81.87 

74.56 

64.48 

148.02 

241.90 

175.04 

Assessment results 

Various  combinations  of  the  a4a  models  were  examined.  The  best  model  was  selected 
based on the diagnostics plots and it can be seen below: 

fmod<-~factor(replace(age,age>3,3)) + s(year,k=3) 

qmod<-list(~factor(age)) 

srmod<-~s(year,k=3) 

The EWG 17-15 concluded that due to the missing catch-at-age years from 2006 to 2010 
this  assessment  is  unable  to  provide  forward  projections  and  catch  advice  regarding 
Common Pandora in GSA 17 and 18. 

257 

257 

 
 
 
  
 
Figure 6.4.3.3. Common pandora in GSA 17 and 18. Stock summary sheet for the 
selected model of a4a. From top to bottom: Recruits, Stock Spawning Biomass (SSB), 
Catch and Fishing mortality. 

258 

258 

 
 
Figure 6.4.3.4. Common pandora in GSA 17 and 18. Fitted and observed catch at-age. 

Figure 6.4.3.5. Common pandora in GSA 17 and 18. Fitted and observed index at-age. 

259 

259 

 
 
 
 
Figure  6.4.3.6.  Common  pandora  in  GSA  17  and  18.  Quantile-quantile  plot  of 
standardized  residuals  for  abundance  indices  (acoustic  surveys)  and  for  catch  numbers 
(catch.n).  Each  panel  is  coded  by  age  class,  dots  represent  standardized  residuals  and 
lines the normal distribution quantiles. 

Figure 6.4.3.7. 3D contour plot of estimated fishing mortality at age and year. 

260 

260 

 
 
 
 
 
Figure 6.4.3.8. Common pandora in GSA 17 and 18. Stock summary of the fitted and 
simulated data for the selected a4a model. 

Figure 6.4.3.9. Common pandora in GSA 17 and 18. Retrospective analysis output for 
the selected a4a model. 

261 

261 

 
 
 
 
 
Methods 3: XSA 

The  input  data  were  the  same  used  in  a4a.  The  model  was  run  considering  the  period 
2011-2016 and for age classes 0 to 4+. 

Different  sensitivity  analyses  were  performed  before  selecting  the  final  XSA  run, 
considering different combinations for shrinkage. 

Figure 6.4.3.10.  Common pandora in GSA 17 and 18. Sensitivity analysis considering 
different combinations for shrinkage. 

For  the  final  run  the  following  settings  were  selected  based  on  the  retrospective 
performance  (Figure  6.4.3.12).  In  any  case,  the  results  of  the  retrospective  analysis 
were bad. 

fse=1.5, rage=1, qage=2, shk.n=TRUE, shk.f=TRUE, shk.yrs=1.0, shk.ages=1.0. 

Table 6.4.3.5. Common pandora in GSA17 and 18. Residuals table. 

shrinkage  minimum_MEDITS  maximum_MEDITS  average_MEDITS 

1 

2 

3 

4 

5 

6 

Sh0.5 

Sh1.0 

Sh1.5 

Sh2.0 

Sh2.5 

Sh3.0 

-0.55795 

-0.53836 

-0.49539 

-0.50388 

-0.50827 

-0.51079 

1.74705 

1.72726 

0.57707 

0.57388 

0.57252 

0.57187 

0.47540 

0.45565 

0.23744 

0.22065 

0.21203 

0.20774 

262 

262 

 
 
 
 
 
 
 
Figure 6.4.3.11.  Common pandora in GSA 17 and 18. Residual pattern of the MEDITS 
survey. 

Figure 6.4.3.12.  Common pandora in GSA 17 and 18. XSA retrospective analysis. 

Based in the retrospect analysis it is concluded that the short timeseries of data 2011 to 2016 inclusive 
is not sufficient to support an age based analytic assessment using XSA. 

6.4.4  REFERENCE POINTS 

No reference points were estimated. 

263 

263 

 
 
 
 
 
 
 
 
6.4.5  SHORT TERM FORECAST AND CATCH OPTIONS 

No short term forecast was performed. 

Based on the MEDITS biomass index the increase over 5 years is 1.1 and following the 
ICES approach, because this index is less than 1.2 and more than 0.8, the index value is 
used to multiply the catch (mean catch over 2014-2016). Because the exploitation rate 
is  thought  to  be  above  MSY  and  the  state  of  the  stock  relative  to  Bmsy  is  unknown  a 
precautionary  buffer  (catch  multiplier  of  0.8)  is  applied  giving  a  factor  of  0.88.  Mean 
catch  (Italy,  Slovenia  and  Croatia)  for  the  last  three  years  is  252.1  tonnes.  The  catch 
advice which is applicable for two years, 2018 and 2019 is 222 t. 

6.4.6  DATA DEFICIENCIES 

DCF  data  on  common  pandora  in  GSAs  17  and  18  do  not  include  information  from 
the eastern Adriatic regarding landings, discards and size and age structures. 

Length frequency distributions are reported by Italy for GSA 18 for the period 2009-
2016, but no data for 2015, and for GSA 17, for the period 2011-2016. 

The landings age structure is reported by Italy for GSA 18 for the period 2009- 2016, 
but no data is available for 2015. Units should be checked. 

MEDITS data in the DCF are presented by GSA, not by country, which does not allow 
knowing  whether  differences  exit  regarding  size  structure,  abundance  and  biomass 
between the eastern and western Adriatic. 

6.5  DEEP-WATER ROSE SHRIMP IN GSAS 17, 18 AND 19 

6.5.1  BIOLOGY 

STECF EWG 17-15 was asked to assess the state of Deep-water rose shrimp stocks 
in the Adriatic and Ionian Sea by GSAs combined.  

264 

264 

 
 
 
 
 
Figure 6.5.1.1. Geographical location of GSAs 17-18-19. 

Growth parameters (Linf= 46.0, k= 0.6; to= -0.2, sex combined) and length-weight 
relationship parameters (a=0.0024, b=2.5372) were estimated within the DCF 2017 
for  sexes  combined  and  carapace  length  expressed  in  mm.  These  parameters  were 
used  in  the  assessment.  The  von  Bertalanffy  did  not  change  from  the  previous 
assessment  done  during  STECF  EWG  15-16  while  the  length-weight  relationship 
parameters changed slightly. 

The vector of proportion of mature individuals by age has been derived by slicing the 
maturity ogive by length with the von Bertalanffy coefficients during STECF EWG 15-
16. 

A  vector  of  natural  mortality  was  estimated  by  PRODBIOM  method  (Abella  et  al., 
1997) using growth and length-weight relationship parameters for sex combined. 

Table  6.5.1.1.  Deep-water  rose  shrimp  GSAs  17-18-19.  Proportion  of  mature 
specimens at age and natural mortality at age. 

Age 

0 

1 

2 

3+ 

Maturity 

0.2331 

0.9998 

1.0000 

1.0000 

M 

1.39 

0.79 

0.67 

0.62 

6.5.2 

INPUT DATA 

6.5.2.1 

CATCH (LANDINGS AND DISCARDS)  

General description of Fisheries 

Deep-water rose shrimp is targeted mainly by bottom trawlers in these areas. 

265 

265 

 
 
 
 
 
 
Deep-water rose shrimp is commercially important in the Adriatic Sea: it is targeted by 
trawlers (Italy, Croatia, Albania and Montenegro). The Southern Adriatic Sea makes a 
substantial  contribution  to  the  Italian  Deep-water  rose  shrimp  national  fishery 
production,  with  an  input  comparable  to  that  of  the  Strait  of  Sicily,  accounting  for 
about 13% of total production (Cataudella and Spagnolo, 2011).  

In  the  northwestern  Ionian  Sea,  fishing  occurs  from  coastal  waters  to  700–750  m. 
The  most  important  demersal  resources  in  the  northwestern  Ionian  Sea  are 
represented  by  the  red  mullet  (Mullus  barbatus)  on  the  continental  shelf,  hake 
(Merluccius  merluccius),  deep-water  rose  shrimp  (Parapenaeus  longirostris)  and 
Norway lobster (Nephrops norvegicus) over a wide bathymetric range and the deep-
water red shrimps (Aristeus antennatus and Aristaeomorpha foliacea) on the slope.  

Management regulations applicable in 2017 

In  Italy  management  regulations  are  based  on  technical  measures,  a  restricted 
number of fishing licenses for the fleet and area limitation (distance from the coast 
and  depth).  In  order  to  limit  the  over-capacity  of  fishing  fleet,  the  Italian  fishing 
licenses  have  been  fixed  since  the  late  eighties  and  the  fishing  capacity  has  been 
gradually reduced. Other measures on which the management regulations are based 
regards  technical  measures  (mesh  size),  minimum  landing  sizes  (EC  1967/06)  and 
seasonal  fishing  ban,  that  in  southern  Adriatic  has  been  mandatory  since  the  late 
eighties.  In  the  GSA  19  the  fishing  ban  has  not  been  mandatory  at  all  times,  and 
from one year to the other it was adopted on a voluntary basis by fishers, whilst in 
the  last  years  it  has  been  mandatory.  Regarding  small  scale  fishery  management 
regulations are based on technical measures related to the height and length of the 
gears  as  well  as  the  mesh  size  opening,  minimum  landing  sizes  and  number  of 
fishing licenses for the fleet. 

In  2008  a  management  plan  was  adopted,  that  foresaw  the  reduction  of  fleet 
capacity associated with a reduction of the time at sea. Two biological conservation 
zone (ZTB) were permanently established in 2009 (Decree of Ministry of Agriculture, 
Food  and  Forestry  Policy  of  22.01.2009;  GU  n.  37  of  14.02.2009)  along  the 
mainland, offshore Bari (180 km2, between about 100 and 180 m depth), and in the 
vicinity of Tremiti Islands (115 km2 along the bathymetry of 100 m) on the northern 
border  of  the  GSA  where  a  marine  protected  area  (MPA)  had  been  established  in 
1989.  In  the  former  only  the  professional  small  scale  fishery  using  fixed  nets  and 
long-lines is allowed, from January 1st to June 30th, while in the latter the trawling 
fishery is allowed from November 1st to March 31 and the small scale fishery all year 
round.  A  recreational  fishery  using  no  more  than  5  hooks  is  allowed  in  both  the 
areas.  Since  June  2010  the  rules  implemented  in  the  EU  regulation  (EC  1967/06) 
regarding  the  cod-end  mesh  size  and  the  operative  distance  of  fishing  from  the 
coasts are enforced.  

In Montenegro, management regulations are based on technical regulations, such as 
mesh size (Official Gazette of Montenegro, 8/2011), including the minimum landing 
sizes  (Official  Gazette  of  Montenegro,  8/2011),  and  a  regulated  number  of  fishing 
licenses and area limitation (no–fishing zone up to 3 NM from the coastline or 8 NM 
for  trawlers  of  >24  m  LOA).  Currently  there  are  no  MPAs  or  fishing  bans  in 
Montenegrin waters. 

In  Albania,  a  new  law  “On  fishery”  has  now  been  approved,  repealing  the  Law  n. 
7908.  The  new  law  is  based  on  the  main  principles  of  the  CFP,  it  reflects  Reg. 
1224/2009  CE;  Reg.1005/2008  CE;  Reg.  2371/2002  CE;  Reg.  1198/2006  CE;  Reg. 

266 

266 

 
1967/2006  CE;  Reg.  104/2000;  Reg.  1543/2000  as  well  as 
the  GFCM 
recommendations.  The  legal  regime  governing  access  to  marine  resources  is  being 
regulated  by  a  licensing  system.  Also  concerning  conservation  and  management 
measures, minimum legal sizes and minimum mesh sizes are those proposed by EU 
Regulations.  Albania  has  already  an  operational  vessel  register  system.  It  is 
forbidden  to  trawl  at  less  than  3  nautical  miles  (nm)  from  the  coast  or  inside  the 
50m isobath when this distance is reached at a smaller distance from the shore.   

Since the accession of Croatia to the EU the 1st of July 2013, the same regulations 
as  in  the  Italy  are  implemented.  Furthermore  the  following  regulations  are 
applied:Bottom trawl fisheries is closed one and half NM from the coast and island in 
inner sea, 2 NM around island on the open sea, and 3 NM about several island in the 
central  Adriatic.  For  vessel  smaller  than  15  meters,  according  derogation  in  sea 
deeper  than  50  meters  bottom  trawl  fisheries  is  forbidden  till  1NM  of  the  coast. 
Bottom trawl fishery is closed also in the majority of channel area and bays. About 
1/3 of the territorial waters is closed for bottom trawl fisheries over whole year and 
additionally 10% is closed from 100-300 days per years. Minimum mesh size on the 
bottom trawl net was 20 mm (“knot to knot”) in the open sea, and 24 mm (“knot to 
knot”)  in  the  inner  sea.  Recently,  mesh  site  regulation  is  according  EC  1967/2006 
(ie. 40 mm square or 50 mm diamond). In 2015 the no-take zone was established in 
Jabuka  Pit.  The  establishment  of  Marine  managed  area  (MMA)  was  based  on  long-
time  assessment  of  biological  resources  and  analysis  carried  out  by  working  group 
through FAO AdriaMed project that showed a decline in biomass of these commercial 
species. The proposed MMA covers the waters closed to trawling through a bilateral 
agreement between Republic of Italy and Republic of Croatia. The Pit was re-opened 
to  trawling  in  2016.  Recently,  following  the  growing  support  for  a  MMA  in  the 
Jabuka/Pomo Pit, Croatia and Italy agreed to reintroduce a fishing closure from the 
1st  of  September  2017  to  31st  of  August  2020.  Other  interventional  fisheries 
regulation  measures  were  introduced  in  Croatia  such  as  temporal  ban  of  trawl 
fisheries in open part of central Adriatic and in channel area of northern Adriatic. The 
aim  of  those  measures  were  protection  of  commercially  important  species  (e.g. 
European  hake  and  Norway  lobster)  in  critical  period  (spawning  or  recruitment 
period). 

Landings  

Landings data were reported to STECF EWG 17-15 through the DCF. In GSAs 17, 18, 
and  19,  most  of  the  landings  come  from  otter  trawls.  DCF  data  coming  from  other 
gear were considered inaccurate or sampled inconsistently; therefore they were not 
included in the stock assessment (Table 6.5.2.1.1).  

Table 6.5.2.1.1.  Deep-water rose shrimp GSAs 17-18-19.  Landings data in tonnes 
by fleet. The landings data present in the DCF database are in white. Landings data 
in yellow were obtained by other sources. Landings for 2007 for Croatia, Albania and 
Montenegro were assumed to be equal to 2008. Landings for 2015 and 2016 Albania 
were assumed to be equal to 2016.  

2002 

2003 

2004 

2005 

2006 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

17_HRV_FPO 

17_HRV_OTB 

17_ITA_OTB 

70.4 

70.4 

136.8 

171.8 

149.1 

162.8 

308.2 

362.7 

535.6 

653.7 

54.1 

70.1 

53.9 

43.8 

64.7 

92.5 

52.8 

84.3 

202.3 

278.6 

471.0 

3.1 

267 

267 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
17_MLT_OTB 

0.01 

18_ITA-1 

244.4 

496.3 

8.5 

18_ITA_GNS 

66.7 

7.2 

18_ITA_GTR 

18_ITA_LLS 

1.4 

1.1 

18_ITA_OTB 

902.9 

1253.0 

1847.7 

1181.5 

1464.6 

863.1 

766.2 

939.4 

888.1 

869.6 

522.8 

733.7 

637.7 

651.3 

996.4 

18_ALB_OTB 

18_MNE_OTB 

309.0 

309.0 

275.0 

409.0 

328.0 

335.0 

335.0 

291.0 

291.0 

291.0 

39.0 

39.0 

36.0 

32.0 

27.0 

22.0 

31.0 

28.0 

31.0 

32.0 

19_ITA_-1 

364.8 

744.6 

0.1 

0.04 

19_ITA_FPO 

19_ITA_GNS 

19_ITA_GTR 

3.0 

15.3 

7.0 

19_ITA_LLS 

8.7 

0.08 

1.8 

19_ITA_OTB 

738.5 

646.4 

1170.1 

1243.1 

1244.6 

607.5 

785.0 

767.3 

715.6 

592.8 

487.6 

334.5 

421.5 

622.4 

647.4 

19_ITA_PS 

20.2 

0.8 

19_ITA_PTM 

19_ITA_SB 

19_ITA_SV 

0.05 

0.01 

0.2 

0.01 

0.2 

In the rest of the report, we will refer to and present only data for otter trawl. 

Landings  data  for  GSA  17  were  incomplete.  Italian  landings  were  present  just  for 
2006, 2011, and from 2013 to 2016. Croatian landings were present just from 2014 
to 2016 in the DCF database because previously there was no obligation to monitor 
that species. Landings data for GSA 18 were incomplete for Albania and Montenegro. 
Landings  data  for  Albania  and  Montenegro  were  obtained  from  the  GFCM 
assessment. Landings data for GSA 19 were complete. 

The complete Italian GSA 17 time series of landings was provided by Italian experts. 
The  Croatian  time  series  of  landings  from  2008  was  provided  by  Croatian  experts. 
Landings for 2007 for Croatia, Albania and Montenegro were assumed to be equal to 
2008. Landings for 2015 and 2016 Albania were assumed to be equal to 2016. 

Landings data by year are presented in Table 6.5.2.1.2. Landings by year and fleet 
are presented in figure 6.5.2.1.1. 

Table 6.5.2.1.2.  Deep-water rose  shrimp GSAs 17-18-19.  Landings data in tonnes 
by year. 

2007  2008  2009  2010  2011  2012  2013  2014  2015  2016 

1959 

2024 

2198 

2281 

2059 

1583 

1827 

1943 

2410 

3092 

268 

268 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 6.5.2.1.1. Deep-water rose shrimp GSAs 17-18-19. Landings data in tonnes 
by year and fleet. 

Length  frequency  distribution  of  the  landings  by  year  and  fleet  from  the  DCF 
database are presented in figure 6.5.2.1.2. 

269 

269 

 
 
 
 
 
 
270 

270 

 
 
 
Figure  6.5.2.1.2.  Deep-water  rose  shrimp  GSAs  17-18-19.  Length  frequency 
distribution of the landings by year and fleet. 

For  Croatian  landings  without  length  frequency  distributions,  the  mean  length 
frequency  distribution  of  2014-2016  from  the  Croatian  landings  was  applied.  The 
mean  length  frequency  distribution  of  2006  and  2011  from  the  Italian  landings  in 
GSA  17  was  applied  to  Italian  landings  in  GSA  17  in  2007-2010.  The  mean  length 
frequency  distribution  of  2011  and  2013  from  the  Italian  landings  in  GSA  17  was 
applied  to  Italian  landings  in  GSA  17  in  2012.  The  mean  length  frequency 
distribution  of  2007  and  2009  from  the  Italian  landings  in  GSA  18  was  applied  to 
Italian  landings  in  GSA  18  in  2008.  The  length  frequency  distribution  of  2008  from 
the  Montenegrin  landings  was  applied  to  Montenegrin  landings  in  2007.  The  length 
frequency  distributions  of  Italian  landings  in  GSA  18  were  applied  to  Albanian 
landings. 

Age frequency distribution of the landings by year and fleet from the DCF database 
are presented in figure 6.5.2.1.3. 

271 

271 

 
 
 
 
272 

272 

 
 
 
Figure  6.5.2.1.3.  Deep-water  rose  shrimp  GSAs  17-18-19.  Age  frequency 
distribution of the landings by year and fleet. 

Discards 

Discards data were reported to STECF EWG 17-15 through the DCF. Total discard 
by fleet and year are presented in table 6.5.2.1.3.  

Table  6.5.2.1.3.  Deep-water  rose  shrimp  GSAs  17-18-19.  Discards  data  in  tonnes 
by fleet. The discards data present in the DCF database are in white. Discards data 
in yellow  were  reconstructed  based on the  mean  proportions  between  landings  and 
discards for each fleet. 

2006 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

HRV_OTB_17 

6.0 

6.0 

11.7 

14.7 

12.8 

14.0 

26.4 

31.1 

54.8 

45.2 

ITA_OTB_17 

1.9 

1.4 

1.2 

1.7 

3.2 

1.4 

1.6 

28.1 

36.9 

206.9 

ITA_OTB_18 

16.9 

15.0 

30.8 

17.5 

5.3 

7.2 

12.3 

7.7 

13.9 

20.8 

ITA_OTB_19 

19.0 

26.2 

33.9 

54.6 

36.1 

13.5 

8.0 

20.4 

8.9 

12.0 

25.5 

Total 

51.0 

56.3 

98.3 

70.1 

34.7 

30.5 

60.7 

75.8 

117.6  298.4 

Missing  discards  data  for  Italy  and  Croatia  were  reconstructed  based  on  the  mean 
proportions  between  landings  and  discards  for  each  fleet.  No  information  was 
available for Albania and Montenegro. 

273 

273 

 
 
 
 
 
 
 
 
 
 
 
Considering  the  increasing  trend  in  the  latest  years  they  were  included  in  the 
stock assessment. Therefore, we will refer to catches as landings plus discards in 
the rest of the report. 

Length  frequency  distribution  of  the  discards  by  year  and  fleet  from  the  DCF 
database are presented in figure 6.5.2.1.4. 

274 

274 

 
 
 
Figure  6.5.2.1.4.  Deep-water  rose  shrimp  GSAs  17-18-19.  Length  frequency 
distribution of the discards by year and fleet. 

275 

275 

 
 
 
 
 
 
 
 
For  Croatian  discards  without  length  frequency  distributions,  the  mean  length 
frequency  distribution  of  2015-2016  from  the  Croatian  discards  was  applied.  For 
Italian  discards  without  length  frequency  distributions  in  GSA  17,  the  mean  length 
frequency  distribution  of  2011  and  2013  from  the  Italian  discards  in  GSA  17  was 
applied.  For  Italian  discards  without  length  frequency  distributions  in  GSA  18,  the 
mean length frequency distribution of 2009-2011 from the Italian discards in GSA 18 
was  applied.  For  Italian  discards  without  length  frequency  distributions  in  GSA  19, 
the mean length frequency distribution of 2006 and 2009 from the Italian discards in 
GSA 19 was applied. 

Age frequency distribution of the discards was not available in the DCF database. 

6.5.2.2 

EFFORT 

Fishing effort data were reported to STECF EWG 17-15 through DCF. 

Table 6.5.2.2.1. Deep-water rose shrimp GSAs 17-18-19. Fishing effort in GT*Days 
at sea by year and fishing gear. 

HRV_OTB_17  ITA_OTB_17  ITA_OTB_18  ITA_OTB_19 

Total 

2004 

2005 

2006 

2007 

2008 

2009 

2010 

2011 

5324756 

2510980 

761067 

8596803 

5165331 

2354637 

430253 

7950221 

4079669 

2662179 

672536 

7414384 

4056776 

2294240 

491942 

6842958 

4082465 

2039422 

574366 

6696253 

3830475 

2386555 

711619 

6928649 

3837446 

2068044 

759137 

6664627 

3482614 

1900240 

805415 

6188269 

2012 

1289335 

3130643 

1668749 

785235 

6873962 

2013 

1373511 

2645415 

1994855 

621952 

6635733 

2014 

1381570 

2836181 

1463644 

615493 

6296888 

2015 

1346257 

2872228 

1355193 

696946 

6270624 

2016 

1231785 

3014054 

1429243 

823366 

6498448 

276 

276 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 6.5.2.2.2. Deep-water rose shrimp GSAs 17-18-19.  Fishing effort in Days at 
sea by year and fishing gear. 

HRV_OTB_17  ITA_OTB_17  ITA_OTB_18  ITA_OTB_19 

Total 

2002 

2003 

2004 

2005 

2006 

2007 

2008 

2009 

2010 

2011 

124529 

85424 

31381 

241334 

125106 

71203 

31586 

227895 

740495 

246015 

159279 

1145788 

594177 

207387 

106138 

907703 

434655 

201679 

148969 

785303 

382493 

176345 

99351 

658189 

354510 

253577 

220628 

828715 

345095 

316411 

231642 

893148 

329764 

292887 

238933 

861584 

333303 

222708 

235344 

791355 

2012 

39128 

322785 

157792 

329280 

848985 

2013 

39226 

292130 

143901 

339803 

815060 

2014 

40553 

259502 

157301 

197050 

654406 

2015 

39074 

265839 

154211 

192136 

651260 

2016 

37201 

258656 

160570 

297039 

753466 

277 

277 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure  6.5.2.2.1.  Deep-water  rose  shrimp  GSAs  17-18-19.  Fishing  effort  in 
GT*Days at sea by year and fishing gear. 

Figure 6.5.2.2.2. Deep-water rose shrimp GSAs 17-18-19. Fishing effort in Days at 
sea by year and fishing gear. 

278 

278 

 
 
 
 
6.5.2.3 

SURVEY DATA 

Since  1994,  MEDITS  trawl  surveys  has  been  regularly  carried  out  each  year  during 
the  spring  season.  In  the  current  assessment  combined  MEDITS  data  for  GSAs  17-
18-19 from 2007 onwards were used. 

The sampling design of MEDITS is random stratified with number of haul by stratum 
proportional  to  stratum  surface.  Data  were  assigned  to  strata  based  upon  the 
shooting  position  and  average  depth  (between  shooting  and  hauling  depth).  Hauls 
noted  as valid  were  used only,  including  stations with  no catches (zero  catches are 
included). Based on the DCF data call, abundance and biomass indices for combined 
GSAs were re-calculated.  

Observed abundance and biomass indices of Deep-water rose shrimp and the length 
frequency  distributions  are  given  on  the  figures below  (Figures  6.5.2.3.1-6.5.2.3.2-
6.5.2.3.3). Both estimated abundance and biomass indices show similar trends, with 
a sharp increase in the last year. 

Figure  6.5.2.3.1.  Deep-water  rose  shrimp  GSAs  17-18-19.  Estimated  biomass 
indices (kg/km2). 

279 

279 

 
 
 
 
 
Figure  6.5.2.3.2.  Deep-water  rose  shrimp  GSAs  17-18-19.  Estimated  abundance 
indices (N/km2). 

Figure  6.5.2.3.3.  Deep-water  rose  shrimp  GSAs  17-18-19.  Length  frequency 
distribution by year of MEDITS. 

280 

280 

 
 
 
 
 
 
6.5.3  STOCK ASSESSMENT 

Age based methods : a4a and XSA 

Two  age  based  methods  were  used  for  this  stock.  a4a  is  a  statistical  catch-at-age 
method  that  utilize  catch-at-age  data  to  derive  estimates  of  historical  population  size 
and fishing mortality. However, unlike XSA, model parameters estimated using catch-at-
age  analysis  are  done  so  by  working  forward  in  time  and  analyses  do  not  require  the 
assumption that removals from the fishery are known without error. Data typically used 
are:  catch,  statistical  sample  of  age  composition  of  catch  and  abundance  index. 
Specifically,  for  Deep-water  rose  shrimp  GSAs  17-18-19  we  used  a)  the  Assessment 
for All Initiative (a4a) (Jardim et al., 2015) and b) the Extended Survivor Analysis (XSA) 
in FLR environment. Both models were carried out using as input data the period 2007-
2016  for  the  catch  data  (landings  +  discards)  and  2007-2016  for  the  tuning  file.  Both 
catch numbers at length and index number at length were sliced using the l2a routine in 
FLR. 

A single tuning fleet was used in both methods based on the biomass at age estimates 
from MEDITS GSAs 17-18-19 as reported in the DCF. 

The analyses were carried out for the ages 0 to 3+. Concerning the Fbar, the age range 
used was 0-2 age groups for both methods. 

Input data 
The growth parameters used for VBGF were Linf= 45 mm CL; K = 0.6 yr-1; t0= -0.2 yr. 
The length-to-weight coefficients used were a= 0.0024, b= 2.5372.  

Total catches and catch numbers at age from the single GSAs were used as input data. 
SOP  correction  was  applied  to  catch  numbers  at  age.  Table  6.5.3.1  present  the  SOP 
correction vector applied. 

Table 6.5.3.1. Deep-water rose shrimp GSAs 17-18-19. SOP correction vector. 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

SOP 

1.03 

1.01 

1.08 

1.10 

1.11 

1.12 

1.11 

0.98 

1.01 

1.10 

Table  6.5.3.2  lists  the  input  parameters  to  the  XSA,  namely  catches,  catch  number  at 
age,  weight  at  age,  maturity  at  age,  natural  mortality  at  age,  Proportion  of  M  and  F 
before spawning, and the tuning series at age. 

Table 6.5.3.2. Deep-water rose shrimp GSAs 17-18-19. Input data for the a4a and 
XSA models. 

Catches (t) 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

2010 

2080 

2297 

2351 

2094 

1614 

1887 

2019 

2528 

3409 

281 

281 

 
 
 
 
 
 
Catch numbers-at-age matrix (thousands) 

Age 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

0 

1 

2 

166217  183829  230274  188088  149712  153348  189982  188225  257611  420655 

121549  122799  146812  164510  143955  101178  119023  128418  152574  213687 

6868 

5826 

6186 

9462 

9321 

5730 

5251 

6438 

4805 

6631 

3+ 

347 

306 

321 

549 

570 

335 

299 

166 

267 

382 

Weights-at-age (kg) 

Age 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

0 

1 

2 

0.0034  0.0038  0.0031  0.0032  0.0034  0.0034  0.0032  0.0036  0.0037  0.0031 

0.0106  0.0102  0.0099  0.0095  0.0097  0.0097  0.0099  0.0094  0.0097  0.0092 

0.0198  0.0195  0.0189  0.0189  0.0185  0.0188  0.0189  0.0189  0.0188  0.0182 

3+ 

0.0281  0.0295  0.0267  0.0273  0.0250  0.0267  0.0261  0.0253  0.0261  0.0251 

Maturity, natural mortality, proportion of M and F before spawning vectors. 

Age 

0 

1 

2 

3+ 

Maturity  0.2331 

0.9998 

1.0000 

1.0000 

M 

1.39 

0.79 

0.67 

0.62 

Prop M 

Prop F 

0.5 

0.5 

0.5 

0.5 

0.5 

0.5 

0.5 

0.5 

Deep-water rose shrimp GSAs 17-18-19. MEDITS number (n/km2) at age for GSAs 17-18-19.  

Age 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

0 

1 

2 

185.07  288.97  445.85  361.07  275.81  311.79  301.89  405.27  385.89  1784.90 

160.89  237.19  212.40  239.86  141.99  197.39  105.49  157.34  276.09 

444.04 

27.84 

35.51 

18.48 

14.00 

7.60 

7.38 

11.24 

5.62 

9.70 

10.35 

3+ 

7.30 

7.73 

2.07 

1.73 

0.46 

0.56 

0.67 

0.70 

0.54 

0.40 

282 

282 

 
 
 
 
 
Figures 6.5.3.1-6.5.3.2-6.5.3.3 show the age structure of the catches, of the index and 
the weight at age matrix. 

Figure  6.5.3.1.  Deep-water  rose  shrimp  GSAs  17-18-19.  Age  structure  of  the 
catches. 

Figure 6.5.3.2. Deep-water rose shrimp GSAs 17-18-19. Age structure of the index. 

283 

283 

 
 
 
 
Figure 6.5.3.3. Deep-water rose shrimp GSAs 17-18-19. weight at age matrix. 

Assessment results 

Method a4a 

Different a4a models were performed (combination of different f, q and sr). The 
best model (according to residuals and retrospective) included:  

f ~ te(age, year, k = c(3,5)) + s(year, k = 5, by = as.numeric(age==0)) 

q ~ list(~ factor(age)) 

sr ~ factor(year) 

Results are shown in Figures 6.5.3.4-6.5.3.10. 

284 

284 

 
 
 
 
 
 
Figure 6.5.3.4. Deep-water rose shrimp GSAs 17-18-19. Stock summary from the 
a4a  model  for  Deep-water  rose  shrimp  in  GSAs  17-18-19,  recruits,  SSB  (Stock 
Spawning Biomass), catch and harvest (fishing mortality for ages 0 to 2). 

Figure  6.5.3.5.  Deep-water  rose  shrimp  GSAs  17-18-19.  3D  contour  plot  of 
estimated fishing mortality at age and year. 

285 

285 

 
 
 
 
Figure  6.5.3.6.  Deep-water  rose  shrimp  GSAs  17-18-19.  3D  contour  plot  of 
estimated catchability at age and year. 

286 

286 

 
 
 
Figure  6.5.3.7.  Deep-water  rose  shrimp  GSAs  17-18-19.  Standardized  residuals 
for  abundance  indices  and  for  catch  numbers  (catch.n).  Each  panel  is  coded  by 
age class, dots represent standardized residuals and lines a simple smoother. 

287 

287 

 
 
 
Figure 6.5.3.8. Deep-water rose shrimp GSAs 17-18-19. Quantile-quantile plot of 
standardized  residuals  for  abundance  indices  and  for  catch  numbers  (catch.n). 
Each panel is coded by age class, dots represent standardized residuals and lines 
the normal distribution quantiles. 

288 

288 

 
 
Figure  6.5.3.9.  Deep-water  rose  shrimp  GSAs  17-18-19.  Fitted  and  observed 
catch at age. 

Figure  6.5.3.10.  Deep-water  rose  shrimp  GSAs  17-18-19.  Fitted  and  observed 
index at age. 

289 

289 

 
 
 
 
 
 
Retrospective 

The retrospective analysis was applied up to 2 years back.  Models results were 
quite stable (Figure 6.5.3.11). 

Figure 6.5.3.11. Deep-water rose shrimp GSAs 17-18-19. Retrospective analysis 
output for the a4a model. 

Simulations 

290 

290 

 
 
 
 
Figure 6.5.3.12. Deep-water rose shrimp GSAs 17-18-19. Stock summary of the 
simulated and fitted data for the a4a model. 

In  the  tables  6.5.3.3  and  4  the  population  estimates  of  Deep-water  rose  shrimp 
obtained by a4aare provided. 

Table  6.5.3.3.  Deep-water  rose  shrimp  GSAs  17-18-19.  Stock  numbers  at  age 
(thousands) as estimated by a4a. 

Age 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

0 

1 

2 

1032017  1110409  1417453  1152235  885830 

886862  1096130  1178851  2136608  5418527 

198645 

183237 

195038 

250830  203515 

154293 

153010 

188925 

210005 

416595 

21893 

18299 

11653 

9394 

12188 

11058 

7383 

5317 

6875 

14242 

3+ 

4667 

3601 

2669 

1704 

899 

578 

636 

718 

685 

591 

Table 6.5.3.4. Deep-water rose shrimp GSAs 17-18-19. a4a summary results and F 
at age. 

Fbar0-2 

Recruitment 
(thousands) 

SSB (t) 

TB (t) 

  2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

1.35 

1.54 

1.68 

1.72 

1.69 

1.63 

1.62 

1.57 

1.44 

1.44 

1032017 

1110409 

1417453 

1152235 

885830 

886862 

1096130 

1178851 

1188 

1041 

948 

942 

806 

669 

667 

800 

6227 

6567 

6605 

6241 

5247 

4711 

5167 

6176 

Catch 
(t) 

2174 

2224 

2251 

2420 

2057 

1732 

1823 

2062 

2136608 

1380 

10031 

2391 

5418527 

3266 

21086 

3559 

291 

291 

 
 
 
 
 
 
 
 
 
 
 
F at age 

0 

1 

2 

3+ 

0.34 

1.59 

2.12 

0.11 

0.35 

1.97 

2.29 

0.12 

0.34 

2.24 

2.46 

0.18 

0.34 

2.23 

2.60 

0.53 

0.36 

2.12 

2.58 

1.52 

0.37 

2.25 

2.29 

1.64 

0.37 

2.57 

1.92 

0.73 

0.34 

2.52 

1.84 

0.42 

0.24 

1.90 

2.18 

0.64 

0.15 

1.24 

2.92 

1.63 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

Based on the a4a results, the deep-water rose shrimp SSB fluctuated over 2007-
2015  around  1000  tons  and  in  2016  shows  an  increase  up  to  3266.  The 
assessment shows an increasing trend in the number of recruits in the last years. 
The  recruitment  (age  0)  reached  a  maximum  of  5418527  thousands  individuals 
in 2016. Fbar (0-2) shows a fluctuating trend around 1.5 with a value at 1.44 in 
2016.   

The EWG 17-15 concluded that the output of this model was suitable to provide 
the basis of the current status of the stock. 

Method XSA 

The  same  input  data  used  fropr  the  a4a  assessment  were  used  for  XSA. 
Sensitivity analyses were conducted to assess the effect of the main parameters. 
Values  ranging  from  0.5  to  3  (0.5  increasing)  for  the  shrinkage,  values  ranging 
from 1 to 3 for shrinkage years and a combination of values between 1 to 3 for 
the qage parameter and from  -1 to 1 for the rage parameter have been tested. 
Comparison  of  trends  between  the  settings  has  been  done.  Different 
combinations between the settings that looked more stable were tested. 

292 

292 

 
 
 
 
 
 
Figure 6.5.3.13. Deep-water rose shrimp GSAs 17-18-19. Sensitivity on shrinkage 
weight. 

Figure 6.5.3.14. Deep-water rose shrimp GSAs 17-18-19. Sensitivity on shrinkage 
age. 

293 

293 

 
 
 
 
 
 
Figure 6.5.3.15. Deep-water rose shrimp GSAs 17-18-19. Sensitivity on qage and 
rage. 

As  a  result,  the  settings  that  minimized  the  residuals  and  showed  the  best 
diagnostics output were used for the final assessment, and are the following: 

Fbar 

0-2 

fse 

2 

rage 

qage 

shk.yrs 

shk.age 

-1 

1 

3 

1 

The residuals pattern of the MEDITS trawl survey is shown in Figure 6.5.3.16. 

7 

294 

294 

 
 
 
 
 
 
Figure  6.5.3.16.  Deep-water  rose  shrimp  GSAs  17-18-19.  XSA  residuals  for  the 
MEDITS survey from 2007 to 2016.  

The results of the retrospective analysis are shown in Figure 6.5.3.17. 

Figure  6.5.3.17.  Deep-water  rose  shrimp  GSAs  17-18-19.  XSA  retrospective 
analysis. 

295 

295 

 
 
 
 
 
The results of the XSA are shown in the following figure. 

Figure  6.5.3.18.  Deep-water  rose  shrimp  GSAs  17-18-19.  XSA  summary  results. 
SSB and catch are in tonnes, recruitment in 1000s individuals. 

In  the  Tables  6.5.3.5  and  6.5.3.6  the  population  estimates  of  Deep  water  rose 
shrimp obtained by XSA are provided. 

Table  6.5.3.5.  Deep-water  rose  shrimp  GSAs  17-18-19.  Stock  numbers  at  age 
(thousands) as estimated by XSA. 

Age 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

0 

1 

2 

1164265  1373708  1563916  1312847  980799  1100972  1215488  1379688  1967038  6251824 

211351 

207035 

250413 

274609  233128 

169575 

197693 

207933 

249708 

361374 

13961 

14036 

11234 

14744 

13803 

8824 

8799 

9538 

7857 

10542 

3+ 

651 

690 

531 

750 

723 

450 

449 

211 

388 

536 

296 

296 

 
 
 
 
 
 
 
 
 
 
Table 6.5.3.6. Deep-water rose shrimp GSAs 17-18-19. XSA summary results and F 
at age. 

Fbar 0-
2 

Recruitment 
(thousands) 

SSB (t)  TB (t) 

  2007 

1.14 

1164265 

1091 

6546 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

1.10 

1373708 

1151 

7644 

1.29 

1563916 

1154 

7543 

1.60 

1312847 

1061 

7049 

1.91 

980799 

811 

5884 

1.63 

1100972 

780 

5537 

1.47 

1215488 

854 

6011 

1.89 

1379688 

909 

7151 

1.54 

1967038 

1264 

9806 

1.45 

6251824 

2953 

23124 

F at age 

0 

1 

2 

3+ 

0.34 

1.92 

1.16 

1.16 

0.31 

2.12 

0.87 

0.87 

0.35 

2.04 

1.47 

1.47 

0.34 

2.20 

2.27 

2.27 

0.37 

2.48 

2.88 

2.88 

0.33 

2.17 

2.38 

2.38 

0.38 

2.24 

1.80 

1.80 

0.32 

2.49 

2.87 

2.87 

0.30 

2.37 

1.93 

1.93 

0.14 

2.10 

2.11 

2.11 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

297 

297 

 
 
 
 
 
 
The  XSA  results,  summarized  in  Table  6.5.3.6  and  in  Figure  6.5.3.18,  show  an 
increasing trend in the catches, recruitment, SSB and an estimated Fcurr of 1.53. 

The XSA assessment is in very good agreement with the a4a assessment, as the a4a 
assessment  is  based  on  a  explicit  fitting  method  and  also  provides  estimates  of 
uncertainty this is preferred. 

6.5.4  REFERENCE POINTS 

The FLBRP package allowed a Yield per recruit analysis and an estimate of some F-
based  Reference  Points  as  Fmax  and  F0.1.  Yield  per  Recruit  computation  was  made 
using  R  project  software  and  the  FLR  libraries.  The  fishing  mortality  rate 
corresponding to  F0.1  in the  yield  per recruit  curve is  considered here as a proxy of 
FMSY. 

The  input  parameters  were  the  same  used  for  the  a4a  stock  assessment  and  its 
results. 

Table  6.5.4.1.  Deep-water  rose  shrimp  GSAs  17-18-19.  Main  reference  points 
defined with the Yield per recruit analysis. 

refpt 

harvest 

yield 

rec 

ssb 

biomass 

f0.1 

0.70 

0.002 

1.00 

0.002 

0.003 

Figure 6.5.4.1. Deep-water rose shrimp GSAs 17-18-19. Yield per recruit curve. 

298 

298 

 
 
 
 
 
 
6.5.5  SHORT TERM FORECAST AND CATCH OPTIONS 

A  deterministic  short  term  prediction  for  the  period  2017  to  2019  was  performed 
using  the  FLR  routines  provided  by  JRC  and  based  on  the  results  of  the  a4a  stock 
assessments performed during EWG 17-15. 

The  input  parameters  were  the  same  used  for  the  a4a  stock  assessment  and  its 
results.  F  status  quo  is  equal  to  F2016,  corresponding  to  a  catch2017  of  8810  t. 
Recruitment  2017  and  2018  is  2389764  thousands  (equal  to  the  mean  recruitment 
of the last three years).  

Table  6.5.5.1.  Deep-water  rose  shrimp  GSAs  17-18-19.  Short  term  forecasts 
showing  catch  options  for  different  fishing  mortalities  reductions.  F2017  is  equal  to 
F2016, corresponding to a catch2017 of 8810 t. Recruitment 2017 and 2018 is 2389764 
thousands (equal to the mean recruitment of the last three years). 

Rationale 

Ffactor 

Fbar 

Catch 2018 

Catch 
2019 

SSB 2018 

SSB 2019 

Change SSB 2018-
2019 (%) 

Change Catch 
2016-2018 (%) 

Zero 
catch 
High long 
term 
yield 
(F0.1)  

Status 
quo 

Different 
Scenarios 

0 

0.00 

0 

0 

5138 

8436 

64.19 

-100.00 

0.48 

0.70 

3225 

3590 

3532 

3998 

13.21 

-9.37 

1 

1.44 

5027 

4370 

2459 

2267 

0.1 

0.2 

0.3 

0.4 

0.5 

0.6 

0.7 

0.8 

0.9 

1.1 

1.2 

1.3 

1.4 

1.5 

1.6 

1.7 

1.8 

0.14 

0.29 

0.43 

0.57 

0.72 

0.86 

1.01 

1.15 

1.29 

1.58 

1.72 

1.87 

2.01 

2.16 

2.30 

2.44 

2.59 

863 

1606 

2249 

2809 

3299 

3729 

4110 

4449 

4753 

5275 

5500 

5707 

5898 

6075 

6240 

6394 

6538 

1296 

2206 

2849 

3306 

3636 

3876 

4053 

4187 

4289 

4434 

4488 

4532 

4571 

4604 

4634 

4662 

4687 

4743 

4384 

4058 

3761 

3490 

3244 

3020 

2815 

2629 

2304 

2162 

2032 

1914 

1806 

1706 

1616 

1532 

299 

299 

7092 

6023 

5168 

4478 

3917 

3457 

3076 

2759 

2493 

2075 

1910 

1767 

1643 

1535 

1440 

1355 

1281 

-7.79 

49.51 

37.38 

27.36 

19.07 

12.22 

6.55 

1.87 

-1.99 

-5.18 

-9.92 

-11.65 

-13.04 

-14.14 

-14.99 

-15.64 

-16.10 

-16.41 

41.25 

-75.75 

-54.87 

-36.79 

-21.07 

-7.31 

4.79 

15.50 

25.03 

33.57 

48.22 

54.57 

60.38 

65.75 

70.71 

75.34 

79.67 

83.73 

 
 
 
 
1.9 

2 

2.73 

2.87 

6675 

6804 

4710 

4731 

1456 

1385 

1214 

1155 

-16.58 

-16.64 

87.57 

91.20 

Short term predictions 2017-2019 by fleet 

A  deterministic  short  term  prediction  by  fleet  for  the  period  2017  to  2019  was 
performed  using  the  FLR  routines  provided  by  JRC  and  based  on  the  results  of  the 
a4a stock assessments performed during EWG 17-15.  

The same parameters used in the short term by single fleet were used. 

Short term predictions by fleet assume recent historic fleet allocations. These values 
do not respresent recommended allocations to fleet, the allocations between fleets is 
not a scientific issue, but are provided only as an illustration of catch if each all fleets 
made the same relative changes to catch. 

Table 6.5.5.2. Deep-water rose shrimp GSAs 17-18-19. Short term forecast by fleet 
and GSA. 

Fleet 

Year 

Catches  Scenario  Partial_F 

ita17 

2017 

1019 

fsq * 1 

0.19 

ita17 

2017 

1019 

f0.1 

0.19 

hvr17 

2017 

2182 

fsq * 1 

0.39 

hvr17 

2017 

2182 

f0.1 

0.39 

ita18 

2017 

2830 

fsq * 1 

0.42 

ita18 

2017 

2830 

f0.1 

0.42 

alb18 

2017 

1208 

fsq * 1 

0.19 

alb18 

2017 

1208 

f0.1 

0.19 

ita19 

2017 

1736 

fsq * 1 

0.22 

ita19 

2017 

1736 

f0.1 

0.22 

mon18  2017 

129 

fsq * 1 

0.02 

mon18  2017 

129 

f0.1 

0.02 

ita17 

2018 

573 

fsq * 1 

0.19 

ita17 

2018 

376 

f0.1 

0.09 

hvr17 

2018 

1174 

fsq * 1 

0.39 

300 

300 

 
 
 
hvr17 

2018 

767 

f0.1 

0.19 

ita18 

2018 

1520 

fsq * 1 

0.42 

ita18 

2018 

970 

f0.1 

0.20 

alb18 

2018 

646 

fsq * 1 

0.19 

alb18 

2018 

414 

f0.1 

0.09 

ita19 

2018 

1027 

fsq * 1 

0.22 

ita19 

2018 

619 

f0.1 

0.11 

mon18  2018 

68 

fsq * 1 

0.02 

mon18  2018 

44 

f0.1 

0.01 

ita17 

2019 

470 

fsq * 1 

0.19 

ita17 

2019 

434 

f0.1 

0.09 

hvr17 

2019 

997 

fsq * 1 

0.39 

hvr17 

2019 

880 

f0.1 

0.19 

ita18 

2019 

1350 

fsq * 1 

0.42 

ita18 

2019 

1093 

f0.1 

0.20 

alb18 

2019 

569 

fsq * 1 

0.19 

alb18 

2019 

467 

f0.1 

0.09 

ita19 

2019 

961 

fsq * 1 

0.22 

ita19 

2019 

676 

f0.1 

0.11 

mon18  2019 

58 

fsq * 1 

0.02 

mon18  2019 

50 

f0.1 

0.01 

301 

301 

 
Figure  6.5.4.1.  Deep-water  rose  shrimp  GSAs  17-18-19.  Short  term  forecast  by 
fleet and GSA. 

6.5.6  DATA DEFICIENCIES 

Data from DCF 2016 as submitted through the Official data call in 2017 were used. 
Discards  for  GSA  17  were  present  just  for  2011  and  from  2013  to  2016  for  the 
Italian fleet and only for 2015 and 2016 for the Croatian fleet. Discards for GSA 18 
were present just from 2009 to 2016 for the Italian fleet. Discards for GSA 19 were 
present just for 2006 and from 2009 to 2016 for the Italian fleet. Landings data for 
GSA  17  were  incomplete.  Italian  landings  were  present  just  for  2006,  2011,  and 
from  2013  to  2016.  Croatian  landings  were  present  just  from  2014  to  2016  in  the 
DCF  database  because  previously  there  was  no  obligation  to  monitor  that  species. 
Landings  data  from  some  gears  in  the  DCF  database  are  reported  inconsistently 
throughout the time series. 

For more detailed information on missing length and age frequency distributions see 
section 6.5.2.1. 

302 

302 

 
 
 
 
 
6.6  THORNBACK RAY IN GSA 17 

6.6.1  BIOLOGY 

The  stock  unit  geographic  boundaries  for  thornback  ray  are  not  known  and  this 
aspect  leaves  in  doubt  that  GSA17  contains  a  unique  stock  unit  or  that  the  stock 
distribution extends beyond the limits of GSA17.  

Figure 6.6.1 Geographical location of GSA 17 

Chondrichtyan  species  show  in  the  area  a  limited  commercial  interest  and  this  fact 
has conditioned the quality of the information regarding their catches and biological 
features.  The  species’  landings  often  occur  in  boxes  containing  species  mixes. 
Moreover, there are identification difficulties for sampling operators at species level. 
Priorities  in  the  sampling  have  been  historically  given  to  bonyfish  stocks.  Even 
though  in  recent  years  a  major  attention  was  dedicated  to  these  species  enough 
accurate and long time series are still lacking. 

Even though bottom trawl surveys have been performed in the area since the mid of the past 
century,  only  since  2002  there  exist  a  scientific  survey  conducted  every  year  with  a 
standardized methodology that provides annual comparable information on stock distribution 
by abundance, size, and other biological variables. MEDITS project started in 1994 
and covers the whole GSA17 area and is made with a standard protocol. 

303 

303 

 
 
 
 
 
 
 
 
 
 
The GSA17 is characterized by relatively shallow waters, seldom deeper than 50 
m towards the western part while deeper in the Eastern fraction. Raja clavata is a 
species  that  lives  over  the  continental  shelf  and  upper  slope,  but  is  found  more 
frequently  between  50  and  700m.  In  consequence,  only  in  a  limited  number  of 
tows  and  mostly  towards  east  is  the  species  caught.  This  spatial  limitation  may 
also be due to the historical lower fishing pressure on which stocks are exposed 
in this part of the GSA17. 

Trawl surveys tows carried out in recent years indicate the areas where the stock 
is more frequent caught, though catch rates are very low, and catches composed 
of  few  individuals.  Earlier  surveys  conducted  in  the  middle  of  the  last  century 
have shown a quite different picture, with much higher catch rates for R. clavata  

Catch statistics proceeding from the DCR refers to a relatively short period as for 
this stock it was paid minor attention in the past. Previous information on catches 
and  landings  of  Raja  clavata  are  considered  unreliable,  if  some  few  specific 
studies  in  restricted  areas  are  excluded.  The  better  information  available  on 
thornback  skate  landings  regards  the  reconstruction  which  is  present  in  the 
STECF  data  base  in  the  Economic  Report  of  2017  on  transversal  data  (2008-
2016). In any case, also in this document there are some errors and gaps. In the 
case  of  Croatia,  in  the  time  series  available  all  ray  species  are  gathered  in  a 
mixed commercial category.  

Some historical information, especially in the Croatian area, shows in the 1950’s 
and  1960’s  landings  of  the  species  were  very  important,  and  the  species  was 
among the top regarding the trawlers’ landings. 

6.6.2 

INPUT DATA 

6.6.2.1 

CATCH (LANDINGS AND DISCARDS)  

Catch information proceeds from the STECF data base in the Economic Report of 
2017 on transversal data that regards only the 2008-2016 period, but only since 
2011  for  Croatia.  In  the  case  of  Croatia,  landings  regard  a  mix  of  landed  ray 
species,  as  fishers  use  to  mix  species  in  the  same  box  as  they  are  of  similar 
commercial value. In such circumstances, it is difficult for samplers to distinguish 
and record them at species level.  Considering this issue and the different size of 
the time series, it is not possible to merge all the information aimed at showing a 
comprehensive  trend  for  thornback  ray  landings  in  GSA17.    No  information  on 
discards is available. 

The  Italian  landings  proceeds  from  different  fisheries  using  different  gears, 
mainly from otter trawlers and by rapido beam trawls. There are some doubts on 
the quality of data as young thornback skates can be confused with other species 
as Raja asterias or R.miraletus that are quite common and are among the main 
exploited stocks with the rapido beam trawl. 

304 

304 

 
 
 
Table 6.6.2.1.1 Thornback ray in GSA 17. Landings by gear in Italy (kg) 

Raja clavata ITALY kg 

25000.00

20000.00

15000.00

10000.00

5000.00

0.00

2008

2009

2010

2011

2012

2013

2014

2015

GNS

GTR

OTB

TBB

PTM

total

Figure 6.6.2.1.1 Thornback ray in GSA 17. Landings by gear in Italy  

Italian  landings  appear  quite  fluctuating,  with  a  peak  in  2011  but  not  showing 
any evident trend. 

In  the  case  of  Croatia,  there  is  not  distinction  among  ray  species  and  the 
landings  of  the  different  species  may  proceed  from  more  fisheries  as  they  may 
show different species-specific behaviour and spatial distribution. In consequence 
it  can  be  possible  that  rays  may  proceed  from  catches  utilizing  other  gears.  In 
any case, otter trawlers are again those contributing in a major proportion of the 
landings.  The  time  series  is  very  short  for  deriving  any  consideration  regarding 
the landings evolution. The availability of a very limited number of years is due to 
the recent accession of Croatia to the EU. It appears that in some cases there are 
some mistakes in gear utilization. 

305 

305 

   GNS GTR OTB TBB PTM total 2008.0 38.4 38.7 8288.6 2607.0 0.0 10972.7 2009.0 27.9 460.7 12449.9 2271.9 0.0 15210.4 2010.0 20.5 2571.7 14628.8 4636.3 0.0 21857.2 2011.0 96.6 1289.8 19606.5 2131.2 0.0 23124.1 2012.0 111.9 654.6 16737.8 2217.2 0.0 19721.5 2013.0 703.0 938.2 4951.0 7712.9 142.6 14447.7 2014.0 25.1 201.0 8377.8 2193.7 86.3 10883.8 2015.0 70.7 233.1 11079.2 3173.9 0.0 14556.9  
 
 
 
 
 
 
 Table  6.6.2.1.2  Thornback  ray  in  GSA  17.  Landings  of  Raja  spp  in  Croatia  by 
gear (kg). 

Croatia Raja spp landings in kg per gear 

120000

100000

80000

60000

40000

20000

0

2012

2013

2014

2015

2016

GNS

GTR

LLS

OTB

others

total

Figure 6.6.2.1.2 Thornback ray in GSA 17. Raja spp catches in Croatia 

The Slovenia landings are modest if compared to those of Italy and Croatia. The 
main Raja clavata catches proceed from trawlers and trammel nets. An increase 
in the overall landings is suggested using the available information. 

 Table 6.6.2.1.3 Thornback ray in GSA 17. Landings of Raja clavata in Slovenia (kg) 

306 

306 

DRBFPOFYKGNSGTNGTRLHPLLDLLSLTLNKOTBOTMPSSBtotal2012158.934.07.04725.442.52737.4357.210.05992.039.348161.113.536.062314.2201388.1205.03.04594.281.62733.375.26.011689.950.765287.833.04.076.084927.82014175.367.14.08913.2219.73662.378.512.012767.255.281058.771.557.5107142.22015354.4118.51.58146.8190.56532.0276.413062.13.426.070683.11.75.0115.199516.52016482.232.66991.9106.84988.9113.511346.343.558333.432.0167.282638.1GNSGTNGTROTBPStotal20083.627.555.30.586.9200925.875.676.8178.2201011.767.970.5150.120112.542.670.1115.220128.450.050.2108.6201327.6121.154.3203.0201434.10.6195.2167.1397.02015104.0106.9112.0322.9201633.5143.6177.4354.5 
 
 
 
 
 
 
Figure 6.6.2.1.3 Thornback ray in GSA 17. Trend of Raja clavata landings by gear 

It is not possible to show the evolution over the whole GSA17 as the information 
in  the  different  countries  differ  regarding  time  series  completeness  and  level  of 
detail regarding species identification. 

No information on discards is available. 

6.6.2.2 

EFFORT 

The  table  includes  the  nominal  effort  of  the  fleets  in  GSA17.  The  major  effort 
exerted  on  Raja  clavata  is  produced  principally  by  the  otter  trawl  fleet  (OTB), 
followed by trammel nets and beam trawls. 

Nominal  effort  for  the  gears  the  species  is  exposed  to  show  a  light  decreasing 
trend. There is no information of specific effort dedicated to the species as data is 
not  sufficiently  disaggregated  and  Raja  clavata  is  not  the  target  species  in  any 
country or fishery. 

307 

307 

 
 
 
 
 
 
 
 
 
 
 
 
 
Table 6.6.2.2.1 Thornback ray in GSA 17. Nominal effort by gear in GSA17 

The  effort  mainly  related  to  Raja  clavata  catches  almost  regard  4  gears:  otter  trawls, 
trammel  nets,  gillnets  and  rapido  beam  trawls.  The  evolution  of  the  numbers  of  these 
gears  is  shown  in  figure  6.1.1.1.2.1.    The  otter  trawlers  nominal  effort  is  the  more 
important and it appears as lightly decreasing along the analysed years.   Trammel nets 
nominal  effort  did  increase  in  recent  years  while  rapido  and  gillnets  appear  as  fairly 
stable. 

Figure 6.6.2.2.1 Thornback ray in GSA 17.  Nominal effort in kw/days of the fleets 
operating in GSA17.  

308 

308 

yearGEAR200220032004200520062007200820092010201120122013201420152016-1798913470399026356178550629255062922680149265573729432872811114313598525407642285308236176223342261587402DRB638124175178607313137588197458819746609865598116342143964324692540794754804955253283606582056376795953774GNS929724476460034476609498054449805442538855245173032808873396375464332178695865288735634975252733873670471OTB275680942748639327823853240944312409443119409042200387781888999118094570165720932089894719765875217266272096108614195449PTM784134776360507821818745525474552546385581504938353305745508572384999057368226242360604492647849396880012FPO169944797888918570882031589184246616019312092116179866816350371185355GTR179005512755581275558146336089328010795911261497150892136763673996909277009835253381293819FYK668999795328795328143578078160298950712327349223331517699910603114942614804171763099LLD9574113209013209016354669897684364301239241.5416109522420117632723395OTM121601818718187208.06849231130PS707139763898763898140474575225897414445415146503511497398121721071247513011579317191159TBB4232537381291538129155231834413634643861543817491258471732544602769863373060034550273307483GND786786FPO97888920002721769844LLS1209810692.790631187582480425714209LHP797177.710511581018190888009LTL45176.48639246525163091SB125.12448388SV373711.7364023319142261615 
 
 
 
 
 
There  exists  some  information  on  spatial  distribution  of  fishing  effort  that  derive  from 
the  analysis  of  VMS.  The  study  mainly  regards  the  bigger  trawlers  that  are  obliged  to 
have on board a satellite transmitter (blue box) that track their geographic position. 

The  following  picture  shows  the  effort  distribution  with  different  colours  indicating  the 
areas more frequented by the fleet.  The white arrow highlights an area positioned closer 
to  the  Italian  coasts  that  fishers  often  avoid  for  fishing  operations  probably  due  to  the 
unsuitable  bottom  characteristics  or  to  the  composition  of  the  demersal  species 
assemblages. Raja clavata is every year found in this area in the tows carried out during 
the scientific cruises and its relative increased persistence could be explained by a lower 
fishing pressure exerted there by the commercial fleets.  

Figure 6.6.2.2.2 Thornback ray in GSA 17. Fishing effort distribution of trawlers. 
The  arrow  shows  an  area  where  effort  is  limited.  (from  Mapping  EU  fishing 
activities 
alii) 
https://doi.org/10.1080/17445647.2016.1195299 

tracking 

(Vespe 

using 

data 

ship 

et 

309 

309 

 
 
 
 
 
 
 
 
 
6.6.2.3 

SURVEY DATA 

MEDITS surveys data regards the period 2002-2016. 

The  analyses  of  data  allowed  the  estimation  of  biomass  and  density  indexes  and  the 
representation  of  the  distribution  by  abundance  of  the  total  stock,  recruits  and  adults 
and their evolution along time. 

Biomass index as kg/Km2 has shown since 2002 a fairly stable level up to 2011 of about 
5kg/Km2  and  successively  an  increase  up  to  levels  fluctuating  around  10  kg/km2.  (Fig. 
6.6.2.3.1) 

Figure 6.6.2.3.1 Thornback ray in GSA 17. Evolution of surveys biomass index 

Regarding  density,  the  numbers  per  Km2  show  similar  pattern  with  a  peak  in 
2015 (Fig 6.6.2.3.2). 

Figure 6.6.2.3.2 Thornback ray in GSA 17. Evolution of surveys density index 

310 

310 

 
 
 
 
 
The  estimated  mean  size  of  the  sampled  individuals  during  the  survey  is  not 
much  different  along  the  analysed  period.  Fluctuations  are  observed  (mainly 
between 40 and 50cm). There is an increasing trend, even though interrupted by 
the  drastic  decline  in  the  last  year  due  to  a  major  presence  of  small-sized 
individuals in the surveys tows.  

Figure 6.6.2.3.3  Thornback ray in GSA 17.distribution of size frequencies in mm 
(sex combined) by survey 

311 

311 

 
 
 
 
 
 
Figure 6.6.2.3.4 Thornback ray in GSA 17. Evolution of surveys individual mean 
length in mm. 

Figure 6.6.2.3.5  Thornback ray in GSA 17. Evolution of surveys individual mean 
weight in grams 

The  major  concentrations  of  recruits  were  observed  located  in  different  areas 
along  the  years  between  2002-2016  period,  but  in  the  major  number  of  cases 
were mainly concentrated closer to the Eastern coasts. In any case, the density 
was  always  very  limited  (generally  show  concentrations  with  maximum  around 
250 individuals per Km2.) and hence is not possible to define them as proper and 
spatially persistent nursery areas for this stock.  

312 

312 

 
 
 
 
Figure  6.6.2.3.6  Thornback  ray  in  GSA  17.  Example  of  different  spatial 
distribution of recruits observed in 2002 and 2011 MEDIT surveys 

Even  though  fluctuations  in  mean  size  and  abundance  can  be  explained  by 
possible differences in recruitment strength along years,  due to climate changes 
or  other  causes,  it  is  likely  that  the  results  are  uncertain  due  to  the  limited 
number of sampled individuals due to the extremely low catch rates and the very 
low number of stations where the species is caught.  

It  is  worth  noting  that  the  species  is  mostly  concentrated  towards  the  eastern 
coasts. In Italian waters, only an area in the northern portion in almost in all the 
years show some concentrations. In such area, indicated with an arrow in figure 
6.6.2.3.7 (right), fishing activity of the Italian fleet is quite reduced  

Figure  6.6.2.3.7  Thornback  ray  in  GSA  17.  Abundance  indices  in  survey 
2016(left). The cross marks represent tows with 0 catch. On the right it is shown 
the  spatial  distribution  of  fishing  effort  (in  dark  blue  are  represented  the  areas 
with major effort). 

313 

313 

 
 
 
 
 
Other  surveys  data  that  have  provided  important  information  are  those  carried 
out  at  the  middle  of  the  20th  century.  It  is  important  to  especially  quote  the 
surveys  made  in  the  Croatian  waters  namely  the  Hvar  trawl  survey  cruises 
(1948-1949).    In  these  surveys  tows  were  conducted  every  month  over  fixed 
stations,  for  a  total  of  120  hauls.  Such  historical  information  provides  CPUE 
indices  expressed  in  (numbers  per  hour  fishing  recorded  for  104  demersal 
species  including  Raja  clavata.  It  is  difficult  to  directly  compare  information 
obtained with two gears and different sampling protocols, but results are showing 
such  dramatic  differences  that  cannot  be  explained  only  by  differences  in 
sampling period, gear, or other variables.    

In  the  Jukic  et  al.  (2001)  comparisons  of  the  catches  obtained  during  the  Hvar 
survey  in  1948  and  in  MEDITS  surveys    in  1998,  it  is  shown  that  in  1948,  Raja 
clavata  was  the  second  species  in  a  ranking  of  the  catches  of  the  123  hauls  on 
the shelf representing about 15% of the total catch and was in 4th position over 
the  slope  in  the  15  conducted  tow  sharing  the  13.9%  while  in  the  MEDITS 
surveys, covering almost the same area,  the species does not appear among the 
5 top species on the slope in the 111 analysed tows and is in the 4th position on 
the slope with 4.5% of the overall catch of 16 tows. 

6.6.3  STOCK ASSESSMENT 

Available data does not allow for a formal assessment of the stock status of Raja 
clavata.  This  situation  applies  over  the  whole  Mediterranean  not  only  for 
thornback skate but neither for any other ray or demersal shark.... 

The  objective  difficulties  for  attempting  an  assessment  of  the  stock  status  are 
due to a general lack of the suitable and reliable information of age structure of 
the  commercial  catches,  on  the  amounts  of  the  catches  per  fishing  gear  and 
country combined with a limited knowledge on life history features of the species. 
Unsustainable  exploitation  status  of  R.  clavata  was  in  any  case  hypothesised  in 
many  Mediterranean  areas  i.e.  in  the  Gulf  of  Lions  (Aldebert,  1997,  in  Tunisian 
waters Bradai, 2010, in Italy Abella et al., 2011, in Adriatic Sea Jukic-Peladic et 
al.,  2001).  Such  perceptions  are  mainly  based  on  analyses  of  trends  of  surveys 
or  landings  or  on  historical  or  anecdotic  information.  In  these  studies,  no 
reference  points  were  in  general  defined  nor  produced  estimates  of  the  current 
mortality  rates.  An  attempt  to  define  such  reference  points  as  F0.1  and 
reproduction-based  reference  points  using  Leslie  matrices  was  made  by  Abella 
and Serena (2012) for some rays and small sharks in GSA9. 

As regards Raja clavata in GSA17, with the available data set, it is not possible to 
carry  out  either  formal  age  based  stock  assessments,  or  to  use  simpler 
approaches  based  on  surveys  abundance  or  total  catches  used  in  other  areas 
(i.e. CMSY, DCAC; AIM). Also analysis of size structure of the commercial catches 
is uncertain due to the limited number of sampled individuals and the absence of 
such information in the data bases.  

The higher catch rates that were observed in the 1950s could be at least in part 
explained  by  an  extremely  higher  biomass  due  to  the  limited  fishing  pressure 

314 

314 

 
 
 
exerted  at  these  times.  By  comparing  Hvar  and  MEDITS  surveys  and  other 
sources of information Ferretti et al ,(2008, 2013) observed that elasmobranchs 
at  sea  declined  by  94.5%  over  these  57  years.    They  show  that  sharks  have 
declined more than rays and the thornback ray, that was in the 1940s the most 
abundant  ray  species,  has  shown  an  extremely  strong  decline  of  about  97.2%. 
The  authors  stressed  that  the  differences  in  the  intrinsic  vulnerabilities  among 
species (reflected by their rebound potential r) were not sufficient to explain the 
observed  species-specific  rates  of  change.  It  is  probably  that  the  real  level  of 
reduction  had  been  less  dramatic  as  these  extreme  differences  could  be  in  part 
explained  by  the  different  catch  efficiency  of  the  fishing  gears  used  in  the  two 
series of catch rates derived from the scientific cruises. MEDITS trawl net is much 
less  efficient  for  the  capture  of  flat  fish.  In  conclusion,  important  long  term 
reduction of the species biomass is in any case evident, and this phenomenon is 
supported by evidence derived from commercial landings.  

Jukic-Peladic  et  al  (2001)  analysed  historical  commercial  landings  of  thornback 
skate  and stressed  that  the  species  that  in the past  was on  the top  positions  in 
importance in weight or number, moved to a much lower level in recent years.  

Even though it is not possible to estimate the current level of F by reconstructing 
the commercial age structure of the stock, an attempt aimed at providing some 
rough estimation not of the exact values but on the possible changes in this rate 
was  done.  There  were  used  size  frequencies  distributions  of  the  trawl  surveys 
MEDITS  performed  between  2002  and  2016.  It  is  here  assumed  that  the  size 
structure  reconstructed  for  each  year  is  similar  to  the  structure  that  potentially 
could be obtained using commercial catch data. The mean size for each year was 
estimated  and  it  was  applied  a  variant  of  the  Beverton  &  Holt  (1957)  equation 
that  allows  estimating  Z based  on  growth parameters,  an  estimate  of  the  mean 
length  in  the  commercial  catch  and  the  knowledge  of  the  size  completely 
recruited  to  the  fishery  L’.  The  Gedamke  and  Hoenig  (2006)  variant, 
implemented  in  the  SEINE  software  included  in  the  NOAA  Stock  Assessment 
Toolbox  was  used.  This  procedure  allows  a  series  of  mortality  rates  to  be 
estimated  from  mean  length  data  representing  non-equilibrium  conditions  in 
multiple  years.  The  estimation  of  Z  is  done  using  information  of  mean  size  of 
catch,  size  of  full  capture  and  growth  parameters.  With  this  approach,  the 
transitional  behaviour  of  the  mean  length  statistic  is  derived  for  use  in  non-
equilibrium conditions. 

The  results  (Figure  6.6.2.1  and  6.6.2.2)  suggest  a  current  F  that  moved  from 
about  0.61  at  the  beginnings  of  the  time  series  to  0.41  at  the  end,  with  an 
estimated shift around 2007. 

These values, after M=0.16 was subtracted, provided the F values 0.45 and 0.25 
corresponding  to  the  two  identified  phases.  Both  values  of  F  are  much  higher 
than  the  estimated  F-based  reference  points  F0.1  and  Frepl  both  with  estimated 
values of about 0.08 (see section below). 

315 

315 

 
 
 
 
Figure 6.6.3.1 Thornback ray in GSA 17. (left) Reconstruction of the evolution of 
the  mean  size  (green  line)  based  on  the  observed  yearly  values  (blue  line). 
(right) Residuals. 

Figure  6.6.3.2  Thornback  ray  in  GSA  17.  Results  of  the  Gedamke  and  Hoenig 
with the estimation of Z, in the top the observed shift in total mortality estimated 
with  SEINE  (blue  line)  compared  to  those  estimated  for  each  year  with  the 
equilibrium Beverton & Holt model (green line). In the lower panel are the main 
results and statistics. 

316 

316 

 
 
 
 
 
 
 
 
 
6.6.4  REFERENCE POINTS 

A Leslie matrix based on biological information on fecundity by age and survival 
rates  was  used  for  the  estimation  of  a  reproduction-based  reference  Point.  It 
consists  of  a  value  of  F  that  constitutes  a  limit  reference  point  (Freplacement).  The 
demographic  analysis  rely  primarily  on  life  history  parameters  and  is  here  used 
for the definition of the status of the stock regarding its capacity of self-renewal 
(Caswell,1989;  Cortés,1998,  2000,  2011;  Frisk  et  alii,  2002;  Mollet  and  Cailliet, 
2002; Simpfendorfer et al.,2008. Age specific survival values are needed as input 
as well as a fecundity by age vector. The matrices were analysed using Poptools, 
a versatile add-in for PC versions of Microsoft Excel that facilitates the analysis of 
stochastic  processes. 
matrix  population  models 
(http://www.cse.csiro.au/poptools/).  The  Leslie  Matrix  has  been  adapted  to 
include  information  on  fishing  mortality  at  specific  ages  allowing  introducing 
changes in survival rates at age.  

simulation  of 

and 

A lifespan of 20 years was assumed based on literature and age readings. 

Maturity  is  reached  at  5  years  in  females.  Fecundity  at  age  increases  with  age 
and  weight.  The  input  values  used  here  were  based  on  several  studies  found  in 
literature  that  describe  eggs  yearly  production  from  52  up  to  170  eggs  per 
female,  as  well  as  from  data  collected  in  GSA9.  It  is  assumed  a  20%  loss  for 
mortality  due  to  predation  or  impact  of  fishing.  As  the  analysis  regards  only 
females,  considering  a  sex  ratio  of  1:1,  the  total  number  of  eggs  produced  for 
each age was divided by 2. 

Table 6.6.4.1 Thornback ray in GSA 17. Input values of fecundity by age 

Natural  mortality  was  estimated  with  PRODBIOM  (Abella  et  al,  1997)  following 
the  Caddy  (1991)  equation  Mt=A+B/t  assuming  constant  M,  by  setting  B 
(curvature parameter) equal to 0. Growth parameters are taken from Cannizzaro 
et alii (1995) for thornback rays in the Sicilian channel. 

Table  6.6.4.2  Thornback  ray  in  GSA  17.  Main  parameters  used  in  the 
computations for the estimation of Z and reference points. 

317 

317 

AGEFEC001020304052263173684294610491152125513551456155716571757185819582058 
 
 
 
Sex 

L∞ (growth) 

K (growth) 

t0 (growth) 

length-weight 
relationship 

Female 

126.5 

0.098 

Male 

116.7 

0.106 

-0.512 

-0.412 

a (length-weight) 

0.00177 

0.0036 

b (length-weight) 

3.3076 

3.1243 

sex ratio 

0.5 

0.5 

M 

0.1568 

0.1696 

Assuming  the  species  as  fully  vulnerable  at  one  year  old,  the  analysis  was 
performed by scanning over F for the value at which the population growth rate 
rm is null and the expected number of replacements Ro is 1. The model provided 
a value of F=0.08 for the Frepl limit reference point. 

The  Leslie  matrix  gives  also  information  on  other  demographic/ecological 
parameters  as  the  maximum  population  growth  rate  rm  without  fishing  that 
resulted equal to 0.197, and the values correspondent to different levels of F of 
the  expected  number  of  replacements,  generation  time,  average  age  of  parents 
of the offspring. 

Table  6.6.4.3  Thornback  ray  in  GSA  17.  Main  parameters  estimated  with  the 
Leslie matrix. 

At  F=0 

318 

318 

EigenvaluesEigenvectors (R&L)RealImaginaryAge/stage structReprod val1.217939552040.5%0.1%0.654433091-0.96808070328.3%0.2%0.6544330910.96808070316.9%0.3%0.077908952-0.2165650668.6%0.6%0.0779089520.2165650663.7%1.5%0.032940078-0.066767581.4%4.0%0.0329400780.066767580.4%4.9%0.01209249-0.1030670540.1%5.4%0.012092490.1030670540.0%5.8%000.0%6.1%-0.017798152-0.1737020730.0%6.4%-0.0177981520.1737020730.0%6.6%-0.048967226-0.0916777160.0%6.7%-0.0489672260.0916777160.0%6.6%-0.093629827-0.034830310.0%6.6%-0.0936298270.034830310.0%6.5%-0.124464559-0.1251012220.0%6.4%-0.1244645590.1251012220.0%6.4%-0.21672333600.0%6.4%-0.474145001-0.9677896450.0%6.3%-0.4741450010.9677896450.0%6.1%-1.03795590600.0%0.0%r0.197160539(rate of increase)Ro3.675230278(expected number of replacements)T6.601806808(generation time - time for increase of Ro)mu16.676137326(mean age of parents of offspring of a cohort)N (fundamental matrix) 
 
 
 
 
 
 
 
 
 
Figure  6.6.4.4  Thornback  ray  in  GSA  17.Number  of  replacements  at  different  F 
values  for  the  assumed  current  exploitation  pattern  (top)  and  of  population 
growth  rate  rm  at  different  combinations  of  age  of  first  capture  and  F  (bottom). 
The  isoline  between  green  and  yellow  represent  combinations  giving  values  of 
rm=0. 

The  Reference  Point    F0.1  was  estimated  by  using  a  yield  per  recruit  model 
assuming a knife-edge selection pattern with L’=30 cm, the L/W relationship  of 
table  6.1.1.3.2  and  assuming  M=0.16.  A  reference  value  F0.1=0.076  was 
estimated, which resulted to be very similar to Frep. 

Table 6.6.4.5 Thornback ray in GSA 17.Main results of the Yield/Recruit analysis 

319 

319 

 
 
 
 
 
 
 
 
Other  Reference  Points  based  on  the  minimum  %SSB  definition  based  on  S/R 
curve  steepness  information  have  been  frequently  used  for  data  poor  stocks, 
especially  for  sharks  and  rays,    but  were  not  explored  here  as  information  of 
steepness  in  S/R  relationship  for  this  species  or  for  close  species  were  not 
available.  In  the  case  of  the  Y/R  obtained  results  shown  in  table  6.1.1.3.4,  a 
fishing mortality that reduces the spawning stock biomass to 40% of the pristine 
biomass correspond to F=0.08 that is coincident with the estimated  F0.1 and Frep 
values. A 40% of survival has been in many cases recommended in literature for 
granting sustainability for elasmobranchs (i.e. Mace & Sissenwine, 1993). 

6.6.5  SHORT TERM FORECAST AND CATCH OPTIONS 

This  exercise  as  conducted  traditionally  was  not  carried  out  due  to  the  lack  of 
data.  

Some  simple  proposals  as  those  included  in  the  ICES  special  report  on 
assessment  and  advice  in  data  poor  stocks  (ICES,  2012)  were  not  feasible.  For 
example,  a  comparison  of  abundance  estimates  of  the  more  recent  years  with 
those  of  previous  years  that  is  an  approach  that  in  the  ICES  area  is  used  for 
regulating future amounts of catches needs of a longer time series in the case of 
rays and sharks as they are characterized by longer lifespans. The available time 
series for Raja clavata in GSA17 is not enough long. 

Several general conclusions regarding advice can be drawn. 

Recent  F  is  uncertain  but  has  been  observed  to  be  between  3  and  6  times  F 
reference points (F=0.45 and F=0.24, F0.1=0.08). This implies a need to reduce 
fishing mortality by at least a factor of 3.  

General  reductions  in  fishing  effort  would  be  expected  to  benefit  this  species, 
though  reductions  would  need  to  be  sever  if  F  is  to  be  reduced  to  below  the  F 
reference  points  of  0.08.  Effort  reductions  alone  are  unlikely  to  be  sufficient  to 
enable population recovery.   

Permenently  closed  areas  to  form  refuges  may  be  helpful.  It  is  likely  that  the 
areas  where  fishing  pressure  is  currently  less  important  do  already  represent 
some  sort  of  refuge  for  those  species  as  rays  that  are  particularly  sensitive  to 
fishing. Whenever such hypothesis is confirmed, these spatial aspects should be 
taken  in  consideration  when  an  area  management  plan  is  discussed.  The 
management plan should consider not only the status of the more commercially 
important  stocks,  but  also  to  those  that  need  of  measures  aimed  at  their 
conservation  as  many  elasmobranchs  that  is  known  have  shown  in  the  area 
dramatic long term declines in abundance. 

320 

320 

 
 
 
 
 
6.6.6  DATA DEFICIENCIES 

The reduced commercial importance of elasmobranchs results in a low priority for 
data collection and research in the Mediterranean Sea, even though the collection 
of  more  complete  data  (on  catches,  sizes,  etc)  became  compulsory  in  recent 
years.    In  essence,  elasmobranchs  information  for  the  different  fisheries  is  data 
deficient  and  makes  stock  assessments  a  difficult  task.  Impact  assessments 
suggest  that  many  elasmobranch  stocks  urgently  require  of  specific  scientific 
studies  aimed  at  the  definition  of  appropriate  management  actions.  Despite  the 
general agreement on the need to define their stock status and to identify proper 
measures  for  stock  recovery,  this  will  not  be  possible  if  a  minimum  amount  of 
data is not available. 

There  were  noticed  in  the  area  frequent  problems  of  species  misidentification. 
Hence, there is the need of efforts for a proper identification of the species that 
are  present  in  the  area.  There  is  also  the  need  of  a  better  knowledge  of  some 
biological features used for stock assessment purposes. Considering that most of 
the  elasmobranchs  has  no  high  commercial  importance  and  that  most  of  the 
species are caught in a very reduced number, the possibility of giving priority in 
their collection through some ad hoc plan included in the National plans appears 
essential.  

A  detailed  catch  information  is  vital  for  a  successful  management  of  these 
species.  It  was  observed  that  the  data  bases  still  present  errors  and  bugs  that 
need to be solved. Even though rays are in principle compulsively measured and 
weighted  during  the  conduction  of  trawl  surveys  data  still  show  several 
deficiencies.  Commercial  catch  size/age  composition  is  unknown  as  size 
frequencies on this species is in the area almost completely lacking.  

Catch  and  landings  reporting  require  that  any  single  elasmobranch  be  correctly 
identified and recorded at species level. 

Even though some improvements have been done and more attention is paid to 
chondrichtyans, the limited number of individuals that are sampled do not allow 
to  get  the  representative  size  frequencies  needed  for  a  proper  reconstruction  of 
both the commercial structure of the catch and of the population at sea. The lack 
of enough long and reliable time series of abundance of overall catches does not 
allow  neither  using  of  some  simple  approaches  for  stock  assessment  usually 
utilized for data poor stocks. 

321 

321 

 
 
 
 
 
6.7  COMMON CUTTLEFISH IN GSAS 17 & 18 

6.7.1  BIOLOGY 

Figure 6.7.1.1 Geographical location of GSAs 17-18. 

There is somewhat information on stock units of Sepia officinalis in the Adriatic Sea, as 
well as in Mediterranean Sea. This species is found throughout the Mediterranean basin 
and  the  eastern  Atlantic  Ocean,  from  the  Baltic  Sea  to  about  17°  N.  It  is  a  demersal 
species,  more  abundant  in  coastal  waters  on  muddy  and  sandy  bottoms  covered  with 
seaweed and phanerogams, but its distribution can be extended to a depth of about 200 
m (Relini et al., 1999). In the Adriatic Sea it inhabits the entire coastal part, but usually 
makes seasonal migrations. During the winter period, common cuttlefish resides mostly 
in  circalitoral  zone  where  it  matures  sexually.  In  spring,  it  migrates  to  the  shallower 
infralitoral region to spawn (Mandić, 1984). In the central and northern Adriatic it occurs 
predominantly  on  sandy  and  muddy  bottoms  up  to  100-150  m  deep  (Županović  and 
Jardas, 1989). In the southern Adriatic, in the colder part of  the year is most dense at 
depths  from  50  to  60  m.  During  the  warmer  part  of  the  year,  it  migrates  closer  to  the 
coast for spawning and forms dense settlements at 10 to 30 m depth (Mandić, 1984). 

This  species  can  grow  to  a  maximum  of  35  cm  (mantle  length),  but  the  usual  length 
ranges  between  15  to  20  cm.  The  length-weight  relationship  shows  negative  allometry 
(cited in Vrgoč et al., 2004) (Table 6.a.a.a.a). 

Table  6.7.1.1  Common  cuttlefish  in  GSAs  17  and  18.  The  Mantle  Length  (ML,  cm)  – 
weight (g) relationship of Sepia officinalis in the Adriatic. 

Author 

Sex 

a 

b 

Manfrin Piccinetti and Giovanardi, 1984 

M+F 

0.22041 

2.773 

Longevity  is  18  to  30  months  (Fisher  et  al.,  1987).  The  length  of  the  mantle  at  first 
sexual maturity is about 10 cm. The spawning period of this species extends throughout 
the  year,  with  peaks  in  spring  and  summer.  In  the  northern  and  central  Adriatic,  it 
reproduces  in  April  and  May,  but  females  with  mature  eggs  can  be  found  even  in  June 
and  July  (Manfrin  Piccinetti  and  Giovanardi,  1984).  In  the  southern  Adriatic,  it  spawns 

322 

322 

 
 
 
 
from  February  to  September,  but  with  a  peak  from  April  to  June.  The  diameter  of  the 
eggs is from 6 to 8 mm (Mandić, 1984). 

Growth parameters of Common cuttlefish in Mediterranean area are scarce.  

Table 6.7.1.2 Growth parameters of Sepia officinalis from Mediterranean area. 

L∞ 

(cm) 

Length 

K 

Type 

(1/y) 

to 

Sex 

M 

(1/y) 

Ø' 

Country 

Locality 

24.28* 

ML 

0.630 

0.73 

2.57 

Egypt 

Port Said 

24.35* 

ML 

0.610 

0.73 

2.56 

Egypt 

Port Said 

25.20* 

ML 

0.580 

0.73 

2.57 

Egypt 

Port Said 

27.06* 

ML 

0.831 

-0.05 

F 

2.78 

Tunisia 

Tunisia 

28.58* 

ML 

0.739 

-0.07 

2.78 

Tunisia 

Tunisia 

29.51* 

ML 

0.723 

-0.06  M 

2.80 

Tunisia 

Tunisia 

16.6** 

ML 

C 

Slovenia 

GSA 17 

29** 

ML 

0.75 

-0.07  C 

17.7** 

ML 

0.64 

-0.75  C 

Italy 

Italy 

GSA 11 

GSA 16 

* Source: Sealifebase.org ** Source: DCF 

The common cuttlefish is an active predator. It feeds mostly on crustaceans, especially 
decapods, and fish. In the absence of this food, it can become cannibalistic (Fabi, 2001). 

6.7.2 

INPUT DATA 

6.7.2.1 

CATCH (LANDINGS AND DISCARDS)  

The  information  available  on  the  common  cuttlefish  in  GSA  17-18  was  very  limited. 
Inconsistency is notable in historical catch data considering that this species was usually 
reported  together  with  other  species  from  families  Sepiidae  and  Sepiolidae  (e.g.  S. 
elegans,  S.  orbignyana,  Rossia  macrosoma,  etc.)  or  was  not  reported  at  all.  Data 
regarding  this  species  from  Republic  of  Albania  are  currently  not  available,  while  from 
Croatia only information of total landings could be found in fisheries statistic databases 
(EUROSTAT  and  FishStat).  Data  regarding  the  common  cuttlefish,  collected  under 
framework  of  Data  Collection  Framework  program,  were  assumed  reliable,  but  stock 
related  variables  were  not  provided  by  Croatia,  since  exemption  rules  were  applied  for 
this species. Data on size structure were  available only from Italian side  of the Adriatic 
and only since 2006. 

323 

323 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Taking  in  consideration  that  data  collected  through  DCF  are  assumed  reliable,  average 
ratio  between  catches  of  different  Sepiidae,  Sepiolidae  species  were  calculated 
separately  for  each  country  based  on  existing  data.  This  information  was  used  for 
reconstruction  of  historical  catch  data  of  common  cuttlefish  from  1970  to  presence 
according  to  available  information  from  fisheries  statistic  databases  (EUROSTAT, 
FishStat, DCF tables and national statistics bureaus).  

The total landings of common cuttlefish in the Adriatic Sea (GSA 17 and 18) ranged from 
1239.17 to 7548.18 t with average value of 4174.6 t. Majority of landings were achieved 
by  Italian  fleet  with  average  value  of  4015.54  t  per  year  (Figure  6.7.2.1.1;  Table 
6.7.2.1.1). 

Figure  6.7.2.1.1  Common  cuttlefish  in  GSAs  17  and  18.  Landings  of  common 
cuttlefish in GSA 17 and 18 

Table  6.7.2.1.1  Common  cuttlefish  in  GSAs  17  and  18.  Landings  of  common 
cuttlefish in the Adriatic Sea by country. 

Year 

Italy 

Yugoslavia SFR 

Croatia  Montenegro  Slovenia 

TOTAL 

1970 

5205.87 

140.20 

1971 

5082.70 

145.77 

1972 

6499.92 

172.70 

1973 

4705.02 

158.77 

1974 

5567.23 

191.27 

1975 

4907.59 

216.33 

1976 

3738.94 

242.33 

1977 

3930.37 

193.12 

1978 

2769.89 

168.98 

1979 

3141.63 

139.27 

324 

324 

5346.07 

5228.47 

6672.62 

4863.79 

5758.49 

5123.92 

3981.27 

4123.50 

2938.87 

3280.90 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1980 

6509.57 

197.76 

1981 

4847.49 

157.84 

1982 

5360.21 

144.84 

1983 

6226.86 

174.55 

1984 

4941.72 

152.27 

1985 

6246.90 

147.63 

1986 

5541.26 

142.98 

1987 

4364.44 

176.41 

1988 

4502.46 

218.19 

1989 

3143.85 

198.69 

1990 

3108.98 

274.83 

1991 

4288.76 

156.91 

6707.33 

5005.33 

5505.05 

6401.42 

5093.99 

6394.53 

5684.24 

4540.85 

4720.65 

3342.55 

3383.81 

4445.67 

1992 

2831.47 

153.97 

2.00 

12.00 

2999.45 

1993 

3051.10 

187.17 

6.00 

21.00 

3265.27 

1994 

7430.39 

108.80 

5.00 

4.00 

7548.18 

1995 

4108.45 

108.80 

9.00 

10.00 

4236.25 

1996 

2915.32 

94.04 

10.00 

6.00 

3025.36 

1997 

2896.77 

139.22 

9.00 

5.00 

3049.99 

1998 

2615.55 

198.23 

10.00 

18.00 

2841.78 

1999 

2433.76 

133.69 

10.00 

18.00 

2595.45 

2000 

2270.52 

127.24 

10.00 

11.00 

2418.76 

2001 

2228.97 

78.37 

10.00 

72.00 

2389.34 

2002 

1165.68 

41.49 

10.00 

22.00 

1239.17 

2003 

1860.19 

64.54 

10.00 

25.00 

1959.73 

2004 

1636.85 

35.96 

10.00 

29.00 

1711.81 

2005 

3382.78 

73.76 

8.00 

33.00 

3497.54 

2006 

4272.42 

65.46 

15.00 

24.00 

4376.88 

2007 

5952.79 

83.90 

18.00 

41.00 

6095.69 

2008 

6400.29 

73.30 

15.00 

15.00 

6503.59 

2009 

3672.17 

68.00 

7.00 

14.00 

3761.17 

2010 

2715.30 

86.00 

9.00 

7.00 

2817.30 

2011 

2901.99 

105.00 

11.00 

8.00 

3025.99 

325 

325 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2012 

3012.25 

169.00 

12.00 

10.00 

3203.25 

2013 

3625.58 

189.00 

11.00 

4.00 

3829.58 

2014 

3530.29 

207.00 

13.00 

6.00 

3756.29 

2015 

3672.85 

192.00 

14.00 

3.98 

3882.83 

2016 

3515.06 

112.00 

5.23 

3632.29 

Information on annual landings data by country and fishing gear are incomplete 
and  were  available  only  through  DCF  tables  (Italy  and  Slovenia)  and  national 
fishery  statistics  (Croatia).  Time  series  are  limited  to  the  last  several  years. 
Majority of catches were achieved by bottom trawl (54%), pot and traps (21%) 
and  beam  trawl  (16%)  fisheries.  Again,  Italian  fisheries  fleet  significantly 
dominated in landings of this species by almost all métiers (Table 6.7.2.2.2). 

Table  6.7.2.2.2  Common  cuttlefish  in  GSAs  17  and  18.  Landings  of  common 
cuttlefish in the Adriatic Sea by country and by fishing gear. 

Only  Slovenian  fleet  reported  information  on  discards  by  country  and  fisheries 
fleet.  The  amount  of  unwanted  (discarded)  catch  is  very  low,  practically 
negligible in compare to the total landings of this species.  

Data on size structure were available for bottom trawl, set nets and FPO (pot and 
traps) fisheries, from Italian side since 2006 (2007 in GSA 18). 

Size  distribution  of  common  cuttlefish  caught  by  Italian  bottom  trawlers  in  GSA 
17 ranged from 1 to 26 cm (ML), while in GSA18 the range was from 2 to 24 cm 
(Figure  6.7.2.2.3).  Average  mantle  length  of  landed  specimens  in  GSA  17 
between  2006  and  2016  varied  from  7.8  to  9.8  cm  with  overall  average  of  8.6 
cm. In GSA 18 average length varied between 8.2 to 10.7 cm from 206 to 2016 
with overall average of 9.5 cm (Figure 6.7.2.2.4). 

326 

326 

FYKSVNITA17ITA18HRVSVNITA 17ITA 18HRVSVNITA 17ITA 18HRVSVNHRVITA 18SVNITA 17HRVITA 1818.941.380.0121.931921.001007.481.92443.970.026.780.01890.2036.792339.00686.003.32409.220.042518.000.0012.122183.00642.333.712.50317.2415.780.012250.000.610.070.151007.740.8310.061782.00795.3227.903.46447.4415.090.010.650.000.130.17647.606.904.561200.00593.2528.032.14546.9521.340.060.540.020.250.000.26374.7910.516.66822.00505.1744.481.48239.00360.3835.570.03697.000.420.040.230.000.18277.6211.689.231043.00473.74107.950.73244.00189.7034.380.02692.000.280.020.220.09370.4414.191.841259.00458.3896.680.99527.6148.530.03767.000.360.040.020.21582.0930.77115.993.151389.00468.3399.371.68101.00225.8448.860.01641.0032.430.330.020.010.13587.7244.75104.341.941285.00548.7170.701.97102.00225.9753.500.02725.000.410.800.040.020.10681.8652.4393.733.271363.08510.1030.731.8165.37366.3131.820.01644.000.300.140.020.04472.5836.56OTBSETNETSFPOLHPLLSTBB 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure  6.7.2.2.3  Common  cuttlefish  in  GSAs  17  and  18.Size  distribution  of 
common cuttlefish landed by bottom trawl fisheries 

Figure 6.7.2.2.4 Common cuttlefish in GSAs 17 and 18. Average mantle length 
of common cuttlefish landed by bottom trawl fisheries 

Data on size distribution of common cuttlefish caught by Italian set net fisheries 
were scarce and available only for last several years. In GSA 17 it ranged from 7 
to 25 cm (ML), while in GSA18 the range was from 3 to 23 cm (Figure 6.7.2.2.5). 
Average  mantle  length  of  landed  specimens  in  GSA  17 between  2011  and  2016 
varied from 11.7 to 15.2 cm with overall average of 13.6 cm. In GSA 18 average 
length varied between 9.3 to 13.7 cm from 2010 to 2016 with overall average of 
11.3 cm (Figure 6.7.2.2.6). 

327 

327 

 
 
 
 
 
 
 
 
 
 
 
Figure  6.7.2.2.5  Common  cuttlefish  in  GSAs  17  and  18.Size  distribution  of 
common cuttlefish landed by set net fisheries 

Figure  6.7.2.2.6  Common  cuttlefish  in  GSAs  17  and  18.Average  mantle  length 
of common cuttlefish landed by set net fisheries 

Size  distribution  of  common  cuttlefish  caught  by  Italian  pot  and  traps  (FPO) 
fisheries in GSA 17 ranged from 6 to 30 cm (ML), while in GSA18 catches of S. 
officinalis  from  this  fishery  are  not  reported  in  DCF  tables  (Figure  6.7.2.2.7  a). 
Average mantle length of landed specimens in GSA 17 between 2006 and 2016 varied 
from 9.7 to 12.1 cm with overall average of 11.3 cm. (Figure 6.7.2.2.7 b). 

Figure 6.7.2.2.7 Common cuttlefish in GSAs 17 and 18.Size structure (a) and average 
mantle length (b) of common cuttlefish landed by FPO fishery in GSA 17 

328 

328 

 
 
 
 
 
 
 
 
 
 
 
 
6.7.2.2 

EFFORT 

Effort by fleet based on GT and Days at sea are given in tables 6.7.2.2.1 and 6.7.2.2.2. 

Table  6.7.2.2.1.  Fishing  effort  in  GT  days  at  sea  by  Italian  fleet  for  the  main  gears 
targeting S. officinalis in GSA 17 and 18 for the period 2002-2016. 

Table 6.7.2.2.2. Days at sea by Italian fleet for the main gears targeting S. officinalis in 
GSA 17 and 18 for the period 2002-2016. 

329 

329 

TBBSA 17SA 18SA 17SA 18SA 17SA 18SA 17SA 18SA 17SA 18SA 172002-1-1-1-12003-1-1-1-1200460957230083744291000956379253247562510980100312920053379619520343281164079779065165331235463778558920068554547815295968148560407775340796692662179105291220077770664303258451115516606911740567762294240109636420086537741935191901135415107911408246520394228437412009728725244223847716060864941383047523865551045203201063930636912565811368218747438374462068044921158201155708507213163591245367651234826141900240665155201259088429593377969882973446313064316687497727062013428864121305665152318318705832817264541519948556575562014512205123387368743278944727563872828361811463644892595201596113314848157488319483091372568542872228135519383033920164527646566671895812384749381113205467330140541429243832100SETNETSFPOFYKLLSOTB 
 
 
 
 
 
 
 
 
 
 
 
 
 
6.7.2.3 

SURVEY DATA 

The MEDITS surveys are carried out annually during spring-summer period by all Adriatic 
countries.  In GSA 17 data were available from 2002 to 2016 (for  Slovenia from 1996). 
In GSA 18 Italian data were available from 1994, in Albanian first survey has been held 
in 1996, while in Montenegro MEDITS survey start from 2008. 

Trends and geographical distribution of abundance and biomass  

The common cuttlefish in GSA 17-18 shows oscillating trend in their mean standardized 
abundance/biomass  indices  during  the  time  series  analyzed,  but  in  generally,  negative 
trend  is  visible  from  2002  to  2011.  Starting  from  2012,  positive  trend  appears  with 
significantly  high  values  in  2014  and  2016  (Figure  6.7.2.3.1).  However,  these  values 
should  be  taken  with  caution  considering  that  in  these  years’  surveys  from  Italian  side 
were  performed  in  later  period  (late  November  in  2014  and  late  September  in  2016). 
The  noted  high  values  could  be  affected  by  behavioral  characteristics  of  common 
cuttlefish like seasonal migration and grouping of individuals. 

Figure  6.7.2.3.1  Common  cuttlefish  in  GSAs  17  and  18.  Index  of  abundance  and 
biomass of common cuttlefish in GSA 17 and 18 

330 

330 

TBBSA 17SA 18SA 17SA 18SA 17SA 18SA 17SA 18SA 17SA 18SA 172002335598.8110621124529.1485423.82003272039.563332125106.0871203.32004343613.8375912.4904893.7198963128065740494.532460152319322005320796.5279640.2796432.7189288146243594177.442073872095022006252198.3244243.6567553.918971483732160765434654.92016792370252007257449.4224241.9448483.714359581085128964382492.91763452003202008197633.3288043.6467690.5202641135357354509.562535771745022009319336.3391870.6787815.7212378157640345094.973164112919812010254786.2362584.5655393.3177879148351329764.022928872751502011274348.7306093.1764595.31744691450793333032227083134882012211319.7310364.4741791.3152939126505322785.171577921429812013130942.227764173212.511283.03469909.210514696856.5292129.761439011323832014214106.941388263079.438696.7426524.814604343026.9259501.91573011391752015210024.719654260774.932758.91360723.612371143457.4265838.881542111748882016145210.524485225976.429587.27411274.31020498086748581.8258655.79160570141050FPOFYKSETNETSLLSOTB 
 
 
 
 
 
  
 
 
 
 
 
 
 
Geomorphological  characteristic  in  the  northern  and  central  part  of  Adriatic  sea  (GSA 
17),  like  type  of  sediment  and  area  of  depth  strata,  are  favored  for  distribution  of  this 
species.  In  GSA  17  the  shallower  area  covered  with  sandy  sediments  predominates  in 
compare  to  southern  part  of  Adriatic  (GSA  18).  Southern  part  is  characterized  with 
narrow  costal  platform  covered  mostly  by  muddy  sediments  which  limits  distribution  of 
common cuttlefish. Its occurrence fluctuates during the MEDITS surveys time series, but 
in generally is almost always significantly higher in GSA 17 showing that Sepia officinalis 
is  more  abundant  and  widespread  in  GSA  17  than  in  GSA  18.  (Figures  6.7.2.3.2; 
6.7.2.3.3).  

Figure  6.7.2.3.2  Common  cuttlefish  in  GSAs  17  and  18.  Occurrence  of  common 
cuttlefish in the Adriatic Sea during MEDITS surveys 

331 

331 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure  6.7.2.3.3  Common  cuttlefish  in  GSAs  17  and  18.  Distribution  of  common 
cuttlefish by depth and sediment type in the Adriatic Sea 

Abundance and biomass indices in GSA 17 ranged from 0.9 Nkm-2/0.08 kgkm-2 (2012) to 
53.2  Nkm-2/4.8  kgkm-2  (2014)  with  overall average  of  14.56  Nkm-2/1.24kgkm-2.  Higher 
values in  some years  should be  taken with  caution  considering  the  period when  survey 
has  been  conducted  (in  2002  and  2016  in  late  September,  while  in  2014  it  was  late 
November). Since occurrence of common cuttlefish in GSA 18 is sporadic, fluctuation of 
the indices are more pronounced. The overall average was 4.1Nkm-2 and 0.21 kgkm-2 for 
GSA  17  and  18  respectively  The  higher  values  noted  in  2007  should  be  taken  with 
caution  due  time  of  survey  which  has  been  in  October.  Trends  of  indices  by  GSA  and 
countries are showed on Figure 6.7.2.3.4. 

332 

332 

 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
Figure  6.7.2.3.4  Common  cuttlefish  in  GSAs  17  and  18.  Trends  of  density  indices  in 
GSA 17 and 18 by countries during MEDITS surveys 

Length distributions and size trends 

The  overall  size  distribution  of  common  cuttlefish  in  GSA  17  and  18  from  the  MEDITS 
surveys ranged from 1 to 21.5 cm of mantle length with average of 6.45 cm in GSA 17 
and  5.71  cm  in  GSA  18  (Figure  6.7.2.3.5).  T-test  confirmed  statistically  significant 
difference of mean values  of length  between populations in  GSA 17 and GSA 18  (Table 
6.7.2.3.1). 

Figure 6.7.2.3.5 Common cuttlefish in GSAs 17 and 18. Length frequencies distribution 
of common cuttlefish in the GSA 17 and GSA 18 during the MEDITS surveys 

Table  6.7.2.3.1  Common  cuttlefish  in  GSAs  17  and  18.  Results  of  T  -  test  between 
mean value of size distribution between common cuttlefish in GSA 17 and GSA18 

The mean value of size distribution showed fluctuation between MEDITS surveys during 
the time series which was more pronounced in GSA 18. Values of average size of mantle 

333 

333 

t-Test: Two-Sample Assuming Unequal VariancesVariable 1Variable 2Mean64.5408157.71053Variance308.1283601.7412Observations8515190Hypothesized Mean Difference0df193t Stat3.81631P(T<=t) one-tail9.12E-05t Critical one-tail1.652787P(T<=t) two-tail0.000182t Critical two-tail1.972332 
 
 
 
 
 
 
 
 
 
 
 
length ranged from 4.1 to 9.8 cm in GSA 17 while in GSA 18 it ranged from 3.3 to 13.5 
cm (Figure 6.7.2.3.6). 

Figure  6.7.2.3.6  Common  cuttlefish  in  GSAs  17  and  18.  Trends  of  average  mantle 
length of common cuttlefish in GSA 17 (a) and GSA 18 (b) during the MEDITS surveys 

6.7.3  STOCK ASSESSMENT 

After  comprehensive  analysis  of  the  data  provided  throughout  the  DCF  data  call  and 
fisheries  statistical  databases  for  this  area  EWG  noticed  some  inconsistency.  The  main 
inconsistency  were  partial  availability  of  size  data  from  commercial  fisheries  and 
insufficiency  of  growth  parameters  for  this  species.  This  data  limited  situation  prevents 
possibility  to  use  advanced  age/size  based  assessment  models  and  therefor  surplus 
models were used in order to conduct stock assessment of common cuttlefish in GSA 17-
18. 

Method 1: CMSY     

CMSY  is  a  Monte-Carlo  method  that  estimates  fisheries  reference  points  (MSY,  Fmsy, 
Bmsy) as well as relative stock size (B/Bmsy) and exploitation (F/Fmsy) from catch data 
and  broad  priors  for  resilience  or  productivity  (r)  and  for  stock  status  (B/k)  at  the 
beginning  and  the  end  of  the  time  series.  Part  of  the  CMSY  package  is  an  advanced 
Bayesian  state-space  implementation  of  the  Schaefer  surplus  production  model  (BSM). 
The  main  advantage  of  BSM  compared  to  other  implementations  of  surplus  production 
models is the focus on informative priors and the acceptance of short and incomplete (= 
fragmented) abundance data.  

required 

The 
files 
(O_Stocks_Catch_14_Med.csv  and  O_Stocks_ID_17_Med.csv)  can  be  downloaded  from 
https://github.com/SISTA16/cmsy 

(CMSY_O_7p.R) 

example 

R-code 

some 

input 

and 

Input data 

Landings 

The  official  reported  landings  from  DCF  data  call  and  fisheries  statistical  databases 
(EUROSTAT, GFCM Database FishStat J) for the Adriatic Sea (GSA 17 and 18) were used. 
The  available  landings  data  ranged  from  1970  to  2016;  however,  historical  time  series 
were  adjusted  considering  the  catch  of  other  species  from  order  Sepiida  which  were 
reported  together  with  catches  of  common  cuttlefish.  According  to  the  DCF  tables,  the 
discards of common cuttlefish in commercial fisheries are negligible. 

Biomass 

334 

334 

 
 
 
 
 
 
 
 
 
 
 
The biomass from MEDITS surveys in GSA 17 and 18 was used as tuning index. Survey 
data  for  complete  area  were  available  by  DCF  from  2002  onwards.  Considering  the 
extreme  values  of  biomass  index  in  2014,  which  is  most  likely  consequence  of 
conducting the survey in late summer (autumn) period, data were extrapolated as mean 
value between previous and next survey.   

Settings 

Considering biology of this species that is described as fast growing, short living species 
with high reproductivity (Vrgoč et al. 2004), resilience or productivity (r) prior was set at 
upper  medium level. The selected  r - value is in accordance with methodology used by 
the  authors  of  model  stated  in  Froese  et  al.  2016.  The  other  priors  have  been  set  as 
medium  depletion  (0.3  -  0.7)  at  the  begin  of  the  series  taking  into  account  the  high 
value  of  catches  observed  in  the  seventies  and  eighties  in  the  central  and  northern 
Adriatic.  

Results of CMSY model 

Figure  6.7.3.1  Common  cuttlefish  in  GSAs  17  and  18.    Summary  of  the  final  CMSY 
model  fit  and  output.  Catch  curve,  viable  r-K  paris  and  their  analysis,  relative  biomass 
and fishing mortality, production curve of common cuttlefish in GSA 17-18. 

335 

335 

 
 
Figure  6.7.3.2  Common  cuttlefish  in  GSAs  17  and  18.Relative  biomass  and  fishing 
mortality, F/B plot and catch curve as given by the CMSY model for common cuttlefish in 
GSA 17-18. 

The  output  of  the  model  (Model  estimates,  reference  points  and  summaries)  are 
reported below. 

Species: Sepia officinalis, stock: SEPIOFF Cuttlefish in Adriatic Sea  

Catch data used from years 1970 - 2016 , abundance = CPUE  

Prior initial relative biomass = 0.3 - 0.7 expert   

Prior intermediate rel. biomass= 0.01 - 0.4 in year 2003 default   

Prior final relative biomass   = 0.2 - 0.6 expert  

 Prior range for r = 0.3 - 0.9 expert, , prior range for k  = 6.63 - 79.5 

Results from Bayesian Schaefer model using catch & CPUE  

 r = 0.492 , 95% CL = 0.341 - 0.709 , k = 36.1 , 95% CL = 26 - 50.1  

MSY = 4.44 , 95% CL = 3.87 - 5.09  

Relative biomass in last y = 0.364 k, 2.5th perc = 0.19 , 97.5th perc = 0.681  

Exploitation F/(r/2) in last year = 1.12 

q = 3.79e-05 , lcl = 2.88e-05 , ucl = 4.99e-05 

336 

336 

 
 
 
 
Results  of  CMSY  analysis  with  altogether  1822  viable  trajectories  for  1377  r-k 
pairs  

 r = 0.499 , 95% CL = 0.376 - 0.663 , k = 36.9 , 95% CL = 29.7 - 45.9  

 MSY = 4.61 , 95% CL = 4.13 - 5.14   

Relative biomass last year = 0.539 k, 2.5th = 0.289 , 97.5th = 0.597  

Exploitation F/(r/2) in last year = 0.731  

Results for Management (based on BSM analysis)  

 Fmsy = 0.246 , 95% CL = 0.171 - 0.354 (if B > 1/2 Bmsy then Fmsy = 0.5 r) 

 Fmsy  =  0.246  ,  95%  CL  =  0.171  -  0.354  (r  and  Fmsy  are  linearly  reduced  if  B  <  1/2 
Bmsy) 

 MSY  = 4.44 ,  95% CL = 3.87 - 5.09  

 Bmsy = 18.1 ,  95% CL = 13 - 25.1  

 Biomass in last year  = 13.2 , 2.5th perc = 6.84 , 97.5 perc = 24.6  

 B/Bmsy in last year   = 0.729 , 2.5th perc = 0.379 , 97.5 perc = 1.36  

 Fishing mortality in last year = 0.276 , 2.5th perc = 0.148 , 97.5 perc = 0.531  

 F/Fmsy  = 1.12 , 2.5th perc = 0.601 , 97.5 perc = 2.16 

Results for Management (based on CMSY analysis)  

 Fmsy = 0.264 , 95% CL = 0.199 - 0.349 (if B > 1/2 Bmsy then Fmsy = 0.5 r) 

 Fmsy  =  0.264  ,  95%  CL  =  0.199  -  0.349  (r  and  Fmsy  are  linearly  reduced  if  B  <  1/2 
Bmsy) 

 MSY  = 4.63 ,  95% CL = 4.22 - 5.08  

 Bmsy = 17.6 ,  95% CL = 14 - 22.1  

 Biomass in last year  = 19 , 2.5th perc = 9.06 , 97.5 perc = 20.9  

 B/Bmsy in last year   = 1.08 , 2.5th perc = 0.516 , 97.5 perc = 1.19  

 Fishing mortality in last year = 0.191 , 2.5th perc = 0.174 , 97.5 perc = 0.401  

 F/Fmsy  = 0.724 , 2.5th perc = 0.658 , 97.5 perc = 1.52 

Conclusions to CMSY model 
The CMSY model indicating the recovery of common cuttlefish stock with negative trends 
in  exploitation  rate  and  fisheries  mortality  with  lower  value  of  biomass  in  compare  to 
BMSY. However, the estimated confidence intervals were quite large concerning both the 
estimates of exploitation rate and relative biomass. Considering these results, EWG were 
not able to determine current stock status or biomass; thus, this assessment will not be 
used for specific advice. 

337 

337 

 
 
 
 
 
 
 
Method 2: SPiCT 

The  stochastic  surplus  production  model  in  continuous-time  (SPiCT)  incorporates 
dynamics  in  both  biomass  and  fisheries  and  observation  error  of  both  catches  and 
biomass indices. The model has a general state-space form that as special cases contain 
process and observation-error models as well as state-space models that assume error-
free  catches.  More  information  on  the  SPiCT  assessment  method  is  described  in 
Pedersen and Berg (2016). 

Input data 

Landings 

The  official  reported  landings  from  DCF  data  call  and  fisheries  statistical  databases 
(EUROSTAT, GFCM Database FishStat J) for the Adriatic Sea (GSA 17 and 18) were used. 
However,  the  longest  available  historical  time  series  from  1970  could  not  be  used 
because  of  model  failure  to  converge.  Only  setting  the  landing  time  series  that  are 
compatible  with  survey  data  from  2002  enabled  model  to  converge.  Historical  time 
series  were  adjusted  considering  the  catch  of  other  species  from  order  Sepiida. 
According  to  the  DCF  tables,  the  discards  of  common  cuttlefish  in  commercial  fisheries 
are negligible. 

Biomass 

The biomass from MEDITS surveys in GSA 17 and 18 was used as tuning index. Survey 
data  for  complete  area  were  available  by  DCF  from  2002  onwards.  Considering  the 
extreme  values  of  biomass  index  in  2014,  which  is  most  likely  consequence  of 
conducting the survey in late summer (autumn) period, data were extrapolated as mean 
value between previous and next survey.   

Settings 

No  priors  on  any  of  the  model  parameters  or  variables  were  required  for  the  model  to 
converge. The Schaefer production model was selected. 

Results 

338 

338 

 
 
 
 
 
 
 
Figure  6.7.3.3  Common  cuttlefish  in  GSAs  17  and  18.Input  data  and  explorative 
analysis for stock assessment of common cuttlefish in GSA 17-18 

The  assessment  results  show  that  for  the  period  2010-2015,  the  common  cuttlefish 
stock was not fished in a sustainable manner. The current biomass and fishing mortality 
are above Bmsy and below Fmsy estimates, but the uncertainty around those estimates 
is high. (Figure 6.7.3.4)    

339 

339 

 
 
 
Figure  6.7.3.4  Common  cuttlefish  in  GSAs  17  and  18.  Summary  of  the  final  SPiCT 
model  fit  and  output.  Absolute  and  relative  Biomass  and  Fishing  mortality,  state  of  the 
stock in F/B space and relative to estimated production. 

The  output  of  the  model  (Model  estimates,  reference  points  and  summaries)  are 
reported below. 

[1] "Convergence: 0  MSG: relative convergence (4)"                        

 [2] "Objective function at optimum: 30.0812496"                            

 [3] "Euler time step (years):  1/16 or 0.0625"                             

 [4] "Nobs C: 15,  Nobs I1: 15"                                             

 [5] ""                                                                     

 [6] "Priors"                                                               

 [7] "     logn  ~  dnorm[log(2), 2^2]"                                     

 [8] " logalpha  ~  dnorm[log(1), 2^2]"                                     

340 

340 

 
 
 
 
 
 [9] "  logbeta  ~  dnorm[log(1), 2^2]"                                     

[10] ""                                                                     

[11] "Model parameter estimates w 95% CI "                                  

[12] "            estimate        cilow        ciupp    log.est  "          

[13] " alpha     5.3197940    0.1314385 2.153114e+02  1.6714346  "          

[14] " beta      0.1295725    0.0088475 1.897596e+00 -2.0435146  "          

[15] " r         1.5020928    0.4171156 5.409250e+00  0.4068593  "          

[16] " rc        3.2792521    1.5466760 6.952648e+00  1.1876154  "          

[17] " rold     17.9074577    0.0000856 3.744297e+06  2.8852173  "          

[18] " m      4637.6301491 3739.2848019 5.751799e+03  8.4419588  "          

[19] " K      8037.9916080 3727.4508570 1.733338e+04  8.9919345  "          

[20] " q         0.0002333    0.0001088 5.002000e-04 -8.3632950  "          

[21] " n         0.9161191    0.3288522 2.552132e+00 -0.0876088  "          

[22] " sdb       0.1051668    0.0030552 3.620067e+00 -2.2522079  "          

[23] " sdf       0.4444605    0.2870732 6.881351e-01 -0.8108942  "          

[24] " sdi       0.5594656    0.3466292 9.029872e-01 -0.5807733  "          

[25] " sdc       0.0575899    0.0044419 7.466524e-01 -2.8544088  "          

[26] " "                                                                    

[27] "Deterministic reference points (Drp)"                                 

[28] "          estimate       cilow       ciupp   log.est  "               

[29] " Bmsyd 2828.468221 1443.687085 5541.528050 7.9474906  "               

[30] " Fmsyd    1.639626    0.773338    3.476324 0.4944682  "               

[31] " MSYd  4637.630149 3739.284802 5751.798683 8.4419588  "               

[32] "Stochastic reference points (Srp)"                                    

[33] "          estimate        cilow       ciupp   log.est rel.diff.Drp  " 

[34] " Bmsys 2812.099250 1404.2333135 5631.473145 7.9416865 -0.005820908  " 

[35] " Fmsys    1.635057    0.7968264    3.355073 0.4916776 -0.002794484  " 

[36] " MSYs  4597.867349 3721.9880286 5679.863557 8.4333479 -0.008648096  " 

[37] ""                                                                     

[38] "States w 95% CI (inp$msytype: s)"                                     

[39] "                    estimate        cilow       ciupp    log.est  "   

[40] " B_2016.00      3328.8929606 1639.1023097 6760.730113  8.1103951  "   

[41] " F_2016.00         1.1256245    0.5071057    2.498553  0.1183380  "   

[42] " B_2016.00/Bmsy    1.1837751    0.5684208    2.465292  0.1687085  "   

[43] " F_2016.00/Fmsy    0.6884314    0.2795636    1.695277 -0.3733396  "   

[44] ""                                                                     

[45] "Predictions w 95% CI (inp$msytype: s)"                                

341 

341 

 
[46] "                  prediction        cilow       ciupp    log.est  "   

[47] " B_2017.00      4063.8712469 2158.8286574 7650.004763  8.3098913  "   

[48] " F_2017.00         0.9231190    0.4054161    2.101911 -0.0799971  "   

[49] " B_2017.00/Bmsy    1.4451379    0.8022143    2.603324  0.3682048  "   

[50] " F_2017.00/Fmsy    0.5645792    0.2271132    1.403483 -0.5716747  "   

[51] " Catch_2017.00  3900.0801252 2324.0908383 6544.763540  8.2687524  "   

[52] " E(B_inf)       4393.3215660           NA          NA  8.3878408  "   

Figure 6.7.3.5 Common cuttlefish in GSAs 17 and 18. Diagnostics from SPiCT model for 
common cuttlefish in GSA 17-18. 

Retrospective analysis 

A  retrospective  analysis  was first run  with  5 retro  years,  but  the  retrospective patterns 
showed  week  consistency.  Then  retrospective  analysis  was  run  with  3  retro  years  and 

342 

342 

 
 
 
 
patterns  were  more  consistent  across  years  in  terms  of  B/Bmsy  but  not  so  in  terms  of 
F/Fmsy. This could lead that the current state of the stock is uncertain. 

Figure  6.7.3.6  Common  cuttlefish  in  GSAs  17  and  18.Retrospective  analysis  for  the 
SPiCT  model  for  common  cuttlefish  in  GSA  17-18  (a  -analysis  with  5  retro  years;  b- 
analysis with 3 retro years) 

Conclusions to SPiCT model 
EWG were not able to determine current stock status or biomass based on results of this 
model  due  to  very  short  time  series  used  and  high  uncertainties  of  estimated 
parameters. Thus, this assessment will not be used for specific advice. 

6.7.4  REFERENCE POINTS 

Not estimated due to the high uncertainty of parameter estimates. 

343 

343 

 
 
 
 
 
 
6.7.5  SHORT TERM FORECAST AND CATCH OPTIONS 

EWG could not produce any short term forecast for this stock due to the high uncertainty 
of parameter estimates and to the lack of a reliable tuning index. 

6.7.6  DATA DEFICIENCIES 

The  information  available  on  the  common  cuttlefish  in  GSA  17-18  was  very  limited. 
Inconsistency is notable in historical catch data considering that this species was usually 
reported  together  with  other  species  from  families  Sepiidae  and  Sepiolidae  (e.g.  S. 
elegans,  S.  orbignyana,  Rossia  macrosoma,  etc.)  or  was  not  reported  at  all.  Complete 
data  sets  regarding  this  species  from  Republic  of  Albania  are  currently  not  available. 
Growth parameters of Common cuttlefish in Mediterranean area are scarce. Data on size 
structure  were  available  for  bottom  trawl,  set  nets  and  FPO  (pot  and  traps)  fisheries, 
only from Italian side since 2006 (2007 in GSA 18). Period when the MEDITS survey was 
conducted  during  time  series  could  affect  the  estimation  of  abundance  indices  and 
distribution patterns of common cuttlefish. 

6.8  SOLE IN GSA 17 

6.8.1  BIOLOGY 

The  assessment  on  common  sole  carried  out  during  the  STECF  EWG  17-15  considered 
the stock confined within the boundaries of GSA 17 (Fig. 6.8.1).  

Figure 6.8.1 Geographical location of GSA 17. 

S.  solea  is  a  demersal  and  sedentary  species  (Figure  5.2.5.1.1),  living  on  sandy  and 
muddy  bottoms  (Tortonese,  1975,  Fisher  et  al.,  1987,  Jardas,  1996).  Although  Jardas 
(1996) stated that the species is distributed from coastal waters to 250 m depth, it was 

344 

344 

 
 
 
 
 
 
 
 
exclusively  caught  up  to  100  m  during  the  MEDITS  expedition  in  1996-1998  (Vrgoč, 
2000). 
Tagging experiments carried out on common sole in the northern Adriatic Sea, using the 
traditional  mark-and-recapture  procedure,  showed  that  all  individuals  were  re-captured 
within  the  sub-basin  (Pagotto  et  al.,  1979).  Local  currents,  eddies  and  marked 
differences of oceanographic features of this sub-basin with respect to those of southern 
Adriatic and Ionian Sea (Artegiani et al., 1997) may prevent a high rate of exchange of 
adult spawners and the mixing of planktonic larval stages from nursery areas of adjacent 
basins (Magoulas et al., 1996). Guarnieri et al. (2002), taking into account differences of 
sole  specimens  from  five  different  central  Mediterranean  areas  in  the  control  region 
sequence marker, suggested that two near-panmictic populations of common sole could 
exist  in  the  Adriatic  Sea:  one  would  inhabit  the  entire  GSA  17,  the  other  seems  to  be 
spread  along  the  Albanian  coasts  (eastern  part  of  GSA  18).  The  hydrogeographical 
features of  this semi-enclosed  basin might  support  the  overall  pattern of  differentiation 
of the Adriatic common soles. 
The northern Adriatic Sea has a high geographical homogeneity, with a wide continental 
shelf and eutrophic shallow-waters. The southern Adriatic in contrast, is characterized by 
narrow  continental  shelves  and  a  marked,  steep  continental  slope  (1200  m  deep; 
Adriamed, 2000). This deep canyon could represent a significant geographical barrier for 
S. solea. 
On  these  bases,  different  actions  for  fishery  management  should  be  proposed  for  the 
Adriatic  common  sole  stocks  in  GSA  17  and  GSA  18.  In  the  former  area  the  stock  is 
shared  among  Italy,  Slovenia  and  Croatia,  while  in  the  latter  one  seems  to  be  shared 
only between Montenegro and Albania. 
A  study  supported  by  ADRIAMED-FAO  (SoleDiff),  about  the  population  structure  of 
common  sole  in  the  Adriatic  Sea,  confirmed  the  previous  evidences  about  the  genetic 
differentiation  between  the  stocks  in  GSA  17  and  GSA  18.  Capitalizing  on  an  available 
dataset of 353 S. solea individuals sequenced in previous projects, additional sequences 
for  62  individuals  of  S.  solea  that  were  collected  during  the  SoleMon  survey  in  2007  in 
the  eastern  side  of  GSA18  (Albania  and  Montenegro)  and  9  from  GSA17  have  been 
generated.  The  analyses  of  the  Adriatic  populations  showed  a  low  but  significant 
differentiation between GSA 17 and GSA 18 populations, with a stronger gene flow from 
the GSA 18 to the GSA 17. 

Growth 
In  the  Adriatic  Sea,  growth  analyses  on  this  species  have  been  made  using  otoliths, 
scales and tagging experiments. A great variability in the growth rate was noted: some 
specimens had grown 2 cm in one month, while others, of the same age group, needed 
a  whole  year  (Piccinetti  and  Giovanardi,  1984)  (Tab.  6.8.1.1).  Von  Bertalanffy  growth 
equation parameters have been calculated using various methods. Within the framework 
of  SoleMon  project,  growth  parameters  of  sole  were  estimated  through  the  length-
frequency distributions obtained from surveys (Tab. 6.8.1.2 and Figure 6.8.1.1). Growth 
parameters were available from Italy and Croatia from the 2017 DCF official data call. 

Table 6.8.1 Common sole in GSA 17. Growth rates from different studies. (TL, cm; age, 
yr). 

345 

345 

 
 
 
Author 

Ghirardelli (1959) 
Piccinetti   and   Giovanardi 

(1984) 

Sex 

M+F 

Age 

1 
16.8 

2 
21.4 

3 

  4 

5 

23.9    25.6  33.1 

M+F  18-20  21-30 

- 

- 

- 

6 
- 
- 

Vallisneri et al. (2000) 

F 

20 

25 

29 

  32 

34  37 

Table  6.8.1.1.2  Common  sole  in  GSA  17.  Von  Bertalanffy  parameters  estimated  in 
different studies.*( k, yr--1; t0, yr). 

Author 

Sex 

(g) 

(cm) 

(month-1) 

(month) 

W∞ 

L∞ 

k 

t0 

Piccinetti   and   Giovanardi 

(1984) 

Froglia and Giannetti (1985) 

Froglia and Giannetti (1986) 

Fabi et al. (2009) 
DCF 2015 data call Italy 
DCF 2015 data call Croatia 

M+F 

- 

40.10 

0.68* 

- 

M+F 
M 
F 

- 
323 
562 
M+F  576 
M+F 
M+F 
M+F 

- 

38.25 
23.20 
37.87 
38.25 
39.60 
32.028 
38.25 

0.041 
0.069 
0.042 
0.041 
0.44* 
0.785 
0.041 

-3.57 
-1.66 
-5.36 
-3.57 
-0.46* 
-0.714 
-3.57 

346 

346 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 6.8.1.2 Common sole in GSA 17. Von Bertalanffy growth functions estimated for 
the GSA 17, based on SoleMon length frequency distributions (2005-2012). 

Maturity 
In  the  Mediterranean  Sea,  the  reproduction  of  common  sole  occurs  from  December  to 
May  (Bini,  1968-70;  Tortonese,  1975;  Fisher  et  al.,  1987).  Within  the  framework  of 
SoleMon project, it has been observed that in the central and northern Adriatic Sea the 
reproduction  takes  place  from  November  to  March.  Data  on  the  spatial  distribution  of 
spawners  provided  by  the  project  show  a  higher  concentration  of  reproducers  off  the 
western coast of Istria (Fabi et al., 2009). 
Length  at  first  maturity  is  25  cm  (Fisher  et  al.,  1987;  Jardas,  1996;  Vallisneri  et  al., 
2000);  this  value  has  been  estimated  at  25.8  using  data  from  SoleMon  project.  The 
proportion of mature by age estimated by SoleMon data is presented in Table 6.8.1.1.3. 
Females having a weight of 300 g have about 150,000 eggs, while those weighting 400 
g  have  about  250,000  eggs  (Piccinetti  and  Giovanardi,  1984);  eggs  are  pelagic.  The 
male-female  ratio  is  approximately  1:1  (Piccinetti  and  Giovanardi,  1984;  Fabi  et  al., 
2009). 
Hatching  occurs  after  eight  days  and  the  larva  measures  3  to  4  mm  TL  (Tortonese, 
1975).  Eye migration  starts  at  7  mm  TL  and  ends  at 10-11  mm TL.  Benthic  life  begins 
after seven or eight weeks (15 mm) in coastal and brackish waters (Bini (1968-70); Fabi 
et al., 2009). 

Table 6.8.1.3 Common sole in GSA 17. Maturity at age of common sole in  GSA 17. 

Maturity 

Age 

0 
0.16 
0.76 
0.96 
0.99 
1.00 

0 
1 
2 
3 
4 
5+ 

347 

347 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Natural mortality 
A  vector  of  natural  mortality  rate  at  age  was  estimated  using  the  PRODBIOM 
spreadsheet (Abella et al., 1997) and it is reported in Table 6.8.1.1.4. 

Table 6.8.1.4 Common sole in GSA 17. Natural mortality at age of common sole in GSA 
17. 

M 

Age 

0 
1 
2 
3 
4 
5+ 

0.70 
0.35 
0.28 
0.25 
0.23 
0.22 

6.8.2 

INPUT DATA 

6.8.2.1 

CATCH (LANDINGS AND DISCARDS)  

The  common  sole  is  a  very  important  commercial  species  in  the  central  and  northern 
Adriatic  Sea  (Ghirardelli,  1959;  Piccinetti,  1967;  Jardas,  1996;  Vallisneri  et  al.,  2000; 
Fabi  et  al.,  2009).  Italian  rapido  trawlers  exploit  this  resource,  usually  providing  about 
50% of landings. Sole is also a target species of the Italian and Croatian set netters, and 
it represents an accessory species for otter trawlers. 
Landings data in weight are reported in the official Data call for the GSA  17 from 2006 
(Tab.  6.8.2.1.1).  The  Italian  fleet  provides  the  bulk  of  the  landings,  while  the  eastern 
part of the basin contributes for about the 10% of the total landings, with on average 10 
tons from Slovenia and 200 tons from Croatia (Fig. 6.8.2.1.1). 

Table 6.8.2.1.1 Common sole in GSA 17. Landings of common sole in GSA17 by year. 
Weights are in tons. 

Landings 
GSA 17 
2022 
1588 
1325 
1954 
1614 
1589 
1859 

Years 

2006 
2007 
2008 
2009 
2010 
2011 
2012 

348 

348 

 
 
 
 
 
 
 
 
2013 
2014 
2015 
2016 

1253 
2048 
2045 
2093 

Total landings by country/year 

)
s
n
o
t
(

i

s
g
n
d
n
a
L

2500

2000

1500

1000

500

0

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

Italy

Croatia

Slovenia

Figure  6.8.2.1.1  Common  sole  in  GSA  17.  Total  landings  by  country  from  2006  to 
2016. 

Regarding  the  Italian  fleet,  the  rapido  trawl,  providing  about  the  50%  of  the  total 
landings, gives the highest proportion (Fig. 6.8.2.1.2). 

Total landings by gear - Italian fleet 

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

TBB

OTB

GNS

Figure 6.8.2.1.2 Common sole in GSA 17. Percentage of Italian landings (by gears) 
in the GSA 17, from 2006 to 2016. 

349 

349 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Small  sized  specimens  traditionally  dominate  the  rapido  trawl  landings;  they  are 
basically  composed  by  1  and  2  years  old  individuals.  Set  net  fishery  lands  mostly  the 
same  portion  of  the  population,  while  the  otter  trawl  fishery,  exploiting  wider  fishing 
grounds,  shows  a  different  size  distribution  of  the  landings.  In  the  eastern  part  of  the 
basin,  common  sole  is  exploited  mainly  by  set  netters  (using  trammel  net),  and  the 
catch  composition,  as  suggested  by  the  Croatian  data  collection  carried  out  in  2010  in 
the framework of Primo Project, is dominated by adults (Fig. 6.8.2.1.3). 

Figure  6.8.2.1.3  Common  sole  in  GSA  17.  Size  structure  of  the  landings  provided  in 
2005-2006  by  rapido  trawl,  otter  trawl  and  set  nets  in  the  GSA  17  (SoleMon  project 
data; left). Size structure of the landings in 2010 by set nets in the eastern part of GSA 
17. 

The length and the age structures of landings are also reported for the Italian OTB, GNS 
and TBB in GSA 17.  The length frequency distributions are shown in Figures 6.8.2.1.4 – 
6; the age distributions in Figures 6.8.2.1.7 – 9 and Table 6.8.2.1.2.  

Figure 6.8.2.1.4 Common sole in GSA 17. Size structure of the landings in 2006-2016 
provided by the 2017 Italian DCF data call; OTB. 

350 

350 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 6.8.2.1.5 Common sole in GSA 17. Size structure of the landings in 2006-2016 
provided by the 2017 Italian DCF data call; GNS. 

Figure 6.8.2.1.6 Common sole in GSA 17. Size structure of the landings in 2006-2016 
provided by the 2017 Italian DCF data call; TBB. 

351 

351 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 6.8.2.1.7 Common sole in GSA 17. Age structure of the landings in 2006-2016 
provided by the 2017 Italian DCF data call; OTB. 

Figure 6.8.2.1.8 Common sole in GSA 17. Age structure of the landings in 2006-2016 
provided by the 2017 Italian DCF data call; GNS. 

Figure 6.8.2.1.9 Common sole in GSA 17. Age structure of the landings in 2006-2016 
provided by the 2017 Italian DCF data call; TBB. 

Table  6.8.2.1.2  Common  sole  in  GSA  17.  Age  structure  of  the  landings  in  2006-2016 
provided by the 2017 DCF data call by gear and country. 

GTR_Croatia+Slovenia 

Year 
2006 
2007 

Age0 
0 
0 

Age1 
0 
0 

Age2 
134 
155 

Age3 
518 
601 

Age4 
27 
31 

Age5+ 
8 
9 

352 

352 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
2008 
2009 
2010 
2011 
2012 
2013 
2014 
2015 
2016 

2006 
2007 
2008 
2009 
2010 
2011 
2012 

2013 

2014 
2015 
2016 

2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 
2015 
2016 

0 
0 
0 
0 
0 
0 
0 
0 
0 

1937 
340 
572 
5112 
4443 
4358 
4053 

961 

421 
698 
406 

1017 
90 
298 
855 
873 
815 
4081 
454 
225 
193 
344 

0 
0 
0 
0 
0 
0 
0 
0 
0 

6215 
5528 
4603 
4532 
2985 
3436 
4152 

4935 

10462 
9744 
11703 

4294 
2943 
2835 
3239 
3213 
3830 
4906 
1618 
5336 
2829 
3620 

96 
244 
140 
173 
116 
162 
136 
283 
130 

373 
948 
544 
671 
449 
629 
526 
414 
344 

TBB+OTB_Italy 

958 
802 
474 
407 
248 
414 
642 

22 

1324 
1554 
894 

119 
288 
63 
49 
37 
27 
24 

513 

15 
59 
2 

GNS_Italy 

541 
442 
32 
276 
266 
602 
272 
1 
345 
356 
78 

67 
60 
0 
33 
15 
24 
2 
31 
2 
13 
1 

19 
49 
28 
35 
23 
32 
27 
81 
56 

0 
1 
0 
1 
1 
4 
0 

0 

0 
0 
0 

0 
0 
0 
0 
0 
2 
0 
0 
0 
0 
0 

5 
14 
8 
10 
6 
9 
8 
32 
0 

0 
1 
1 
2 
1 
6 
0 

0 

0 
0 
0 

0 
0 
0 
2 
2 
14 
0 
0 
0 
0 
0 

Several projects carried out in a portion of GSA 17 highlighted that discards of sole both 
by rapido  trawl and  set net fisheries is negligible (Fabi et al., 2002a; 2002b) since the 
damaged specimens are also commercialized, even though at a lower price.  

6.8.2.2 

EFFORT 

The effort data are available for GSA17. In Tables 6.8.2.2.1 and 6.8.2.2.2 is reported the 
fishing  effort  for  the  main  gears  targeting  this  species  in  terms  of  GT*fishing  days  and 
number of days at sea. 

353 

353 

 
  
 
 
 
Table  6.8.2.2.1  Common  sole  in  GSA  17.  Fishing  effort  in  GT  days  at  sea  by  fleet  for 
the main gears targeting S. solea in GSA 17 for the period 2004-2016. 

GT days at sea 
ITA 
GNS 
245185 
262674 
215431 
156782 
134853 
172839 
190127 
236241 
258525 
167797 
233376 
139371 
178772 

OTB 
3543021 
4205417 
3759299 
3779272 
4031883 
3804025 
3795874 
3447262 
3060578 
2642061 
2711270 
2822198 
3010644 

TBB 
1003129 
785589 
1052912 
1096364 
843741 
1045203 
921158 
665155 
772706 
657556 
892595 
830339 
832100 

2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 
2015 
2016 

HRV 

SLO 
OTB - SVN  other - SVN  GNS - HRV 
-  
9155 
12291 
17413 
18858 
18191 
18235 
17782 
15063 
11960 
9372 
9990 
10534 

 - 
74113 
65429 
77090 
63715 
82011 
75770 
122922 
20003 
23063 
20244 
19397 
15808 

- 
- 
- 
- 
- 
- 
- 
- 
199209 
182962 
171666 
165894 
151156 

Table  6.8.2.2.2  Common  sole  in  GSA  17.  Days  at  sea  by  fleet  for  the  main  gears 
targeting S. solea in GSA 17 for the period 2004-2016. 

Days at sea 
ITA 
GNS 
417092 
379893 
272846 
224242 
197633 
347780 
337240 
344909 
318779 
224894 
258913 
220612 
237760 

OTB 
538910 
489142 
412614 
362390 
324803 
323131 
314090 
313488 
304712 
269752 
242857 
231025 
248877 

TBB 
231932 
209502 
237025 
200320 
174502 
291981 
275150 
313488 
142981 
132383 
139175 
174888 
141050 

2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 
2015 
2016 

HRV 

SLO 
OTB - SVN  other - SVN  GNS - HRV 
-  
831 
963 
1202 
1254 
1205 
1263 
1178 
917 
766 
680 
696 
812 

- 
- 
- 
- 
- 
- 
- 
- 
57590 
53251 
51157 
48459 
45264 

 - 
3855 
3712 
4614 
5101 
5228 
5441 
6173 
6497 
6687 
7534 
7191 
6316 

Figure  6.8.2.2.1  shows  the  fall  in  rapido-trawl  effort  of  Italian  vessels  over  the  years 
2006–2011 in GSA 17. The first zone of effort concentration is inshore between 3 and 9 
nautical  miles  from  the  Italian  coast,  between  43°  and  44°  latitude,  and  is  mainly 
exploited  by  vessels  belonging  to  Ancona  and  Rimini  harbours.  The  second  zone  is 
between  Po  river  mouth  and  Venice  lagoon  and  is  concentrated  at  the  same  distance 
from the coast as the first region. This region is mainly exploited by the Chioggia rapido 
trawl fleet. The third area of effort concentration is offshore, near Istria peninsula and is 
exploited  by  both  Chioggia  and  Rimini  rapido  trawl  fleets.  As  expected,  the  area  is 
characterised  by  a  low  abundance  of  sole,  as  suggested  by  survey  data  in  Grati  et  al. 

354 

354 

 
 
 
 
  
 
 
 
 
 
  
 
(2013), and has a relatively low fishing effort. The area southward of this last region is 
not  exploited  by  rapido  trawlers  mainly  due  to  the  high  concentrations  of  debris  and 
benthic communities that are dominated by holothurians. 
(Despalatović  et  al.,  2009).  The  data  presented  in  the  Figure  6.8.2.2.1  are  quite 
important in order to explain the population selectivity curves used in the SS3 model in 
order to carry out the Statistical Catch at Age analysis (see discussion below). 

Figure  6.8.2.2.1  Common  sole  in  GSA  17.  Maps  of  spatial  distribution  of  rapido  trawl 
fishing effort estimated in mean fishing hours in each 5 x 5 km rectangle. The 6 and 9 
nautical  miles  from  the  Italian  coast  are  shown  respectively  by  broken  and  continuous 
black lines (Scarcella et al., 2014). 

6.8.2.3 

SURVEY DATA 

With reference to the SoleMon project, different rapido trawl fishing surveys were carried 
out in GSA 17 during 2005 to 2016: two systematic “pre-surveys” (spring and fall 2005), 
these were followed by random haul location surveys in spring and fall 2006, and then a 
sequence of fall surveys 2007-2016). The random surveys were stratified on the basis of 
depth  (0-30  m,  30-50  m,  50-100m).  Hauls  were  carried  out  by  day  using  2-4  rapido 
trawls  simultaneously  (stretched  codend  mesh  size  =  40.2  ±  0.83).  The  following 
number of hauls was reported per depth stratum (Tab. 6.8.2.3.1). 

Table 6.8.2.3.1 Common sole in GSA 17. Number of hauls per year and depth stratum 
in GSA 17, 2005-2016. 

355 

355 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Depth strata Spring 
2005 

Fall 
2005 

Spring 
2006 

Fall 
2006 

Fall 
2007 

0-30 
30-50 
50-100 
HR islands 
TOTAL 

30 
14 
24 
0 
68 

30 
12 
15 
5 
62 

20 
10 
8 
4 
42 

35 
20 
8 
4 
67 

32 
19 
11 
0 
62 

Fall 
2008- 

2016 
39 
17 
11 
0 
67 

Abundance and biomass indexes from rapido trawl  surveys were computed using ATrIS 
software  (Gramolini  et  al.,  2005)  which  also  allowed  drawing  GIS  maps  of  the  spatial 
distribution of the stock, spawning females and juveniles. 
The abundance and biomass indices by GSA 17 were calculated through stratified means 
(Cochran,  1953;  Saville,  1977).  This  implies  weighting  of  the  average  values  of  the 
individual  standardized  catches  and  the  variation  of  each  stratum  by  the  respective 
stratum area in the GSA 17: 

Yst = Σ (Yi*Ai) / A 
V(Yst) = Σ (Ai² * si ² / ni) / A² 

Where: 
A=total survey area 
Ai=area of the i-th stratum 
si=standard deviation of the i-th stratum 
ni=number of valid hauls of the i-th stratum 
n=number of hauls in the GSA 
Yi=mean of the i-th stratum 
Yst=stratified mean abundance 
V(Yst)=variance of the stratified mean 

The variation of the stratified mean is then expressed as standard deviation. 
Length  distributions  represented  an  aggregation  (sum)  of  all  standardized  length 
frequencies over the stations of each stratum. Aggregated length frequencies were then 
raised to stratum abundance and finally aggregated (sum) over the strata to the GSA.  

According to data collected during SoleMon surveys (Scarcella et al., 2014), age class 0+ 
aggregates  inshore  along  the  Italian  coast,  mostly  in  the  area  close  to  the  Po  river 
mouth (Fig. 6.8.2.3.1). Age class 1+ gradually migrates off-shore and adults concentrate 
in the deepest waters located at South West from Istria peninsula. 

356 

356 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 6.8.2.3.1 Common sole in GSA 17. Maps of hotspots calculated for the different 
age classes.  

The 6 and 9 nautical miles from the Italian coast are shown respectively by broken and 
continuous black lines (Scarcella et al., 2014). 
The SoleMon trawl surveys provided  data both on sole total abundance and biomass as 
well  as  on  important  biological  events  (recruitment,  spawning).  Figure  6.8.1.4.2  shows 
the  abundance  and  biomass  indices  of  sole  obtained  from  2005  to  2016;  increasing 
trends occurred from the beginning of the period observed, with a peak in 2014 followed 
by a decrease in the last two years. 

357 

357 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 6.8.2.3.2 Common sole in GSA 17. Abundance index obtained from the SoleMon 
survey. 

Figure  6.8.2.3.3  Common  sole  in  GSA  17.  Biomass  index  obtained  from  the  SoleMon 
survey. 

358 

358 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
In Figure 6.8.2.3.4 the age composition obtained from the SoleMon survey is reported. 

Figure 6.8.2.3.4 Common sole in GSA 17. Age distributions obtained from the SoleMon 
survey. 

6.8.3  STOCK ASSESSMENT 

The  common  sole  stock  in  GSA  17  was  assessed  during  the  EWG  16-08  applying  an 
Extended Survivor Analysis (XSA) and a Statistical catch at age using Stock Synthesis 3 
(SS3).  
In  the  EWG  17-15  the  stock  assessment  has  been  conducted  using  two  age  based 
methods: Assessment for All (a4a) and Stock Synthesis 3 (SS3). 

359 

359 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Methods: a4a (Assessment for All) 
A4a  is  a  flexible  statistical  catch  at  age  stock  assessment  method,  implemented  in 
R/FLR/ADMB (Jardim et al., 2015). The model structure is defined by sub-models, which 
are based on linear modelling techniques. 

Input data 
Landing  at  age  data  series  of  the  period  2006-2016  were  provided  by  official  statistics 
from the 2017 DCF data call. Croatian landing at age data were reconstructed in 2006-
2012 on the base of the total landings suggested by Croatian experts and landing at age 
data composition observed for set netters (mainly using trammel nets) (Tab. 6.8.3.1). 
The  total  landing  numbers  at  age  were  rescaled  based  on  the  SOP  correction  observed 
between the reconstructed total landing and the total landing provided by 2017 Italian, 
Croatian and Slovenia DCF official statistics (Tab. 6.8.3.2).  
Tuning  data  were  provided  by  SoleMon  surveys,  carried  out  in  fall  for  the  years  2006-
2016  (Tab.  6.8.3.3).  The  individual  weights  at  age  for  the  catch  are  reported  in  Table 
6.8.3.4. 
Maturity  at  age,  Length-Weight  relationships,  growth  parameters  were  provided  in  the 
framework  of  SoleMon  project.  A  vector  of  natural  mortality  rate  at  age  was  estimated 
using  the  PRODBIOM  spreadsheet  (Abella  et  al.,  1997)  (Tab.  6.8.3.5).  The  M  and  F 
before spawning were set equal to 0. A plus group was set at age 5. The Fbar considered 
was for ages 1-4. 

Table 6.8.3.1 Common sole in GSA 17. Total landings, weight in tons. 

2006  2007  2008  2009  2010  2011  2012  2013  2014  2015  2016 
Landings (tons)  2022  1588  1325  1954  1614  1589  1859  1253  2048  2045  2093 

Table 6.8.3.2 Common sole in GSA 17. Landings at age, numbers in thousands. 

2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 
2015 
2016 

Age 0 
2954 
430 
870 
5967 
5316 
5173 
8134 
1415 
646 
827 
750 

Age 1 
10508 
8471 
7438 
7771 
6198 
7266 
9058 
6553 
15798 
11671 
15323 

Age 2 
1633 
1399 
602 
927 
654 
1189 
1030 
186 
1805 
2035 
1103 

Age 3 
704 
949 
436 
1030 
596 
721 
475 
1173 
544 
451 
347 

Table 6.8.3.3 Common sole in GSA 17. Survey index (N/km2). 

2006 
2007 
2008 
2009 
2010 
2011 

Age 0 
56.8 
74.8 
24.0 
72.7 
15.7 
68.1 

Age 1 
171.3 
195.4 
109.9 
107.0 
200.0 
246.5 

Age 2 
82.3 
75.0 
72.4 
60.4 
41.2 
45.0 

Age 3 
8.3 
27.8 
14.9 
7.7 
9.1 
7.7 

Age 4 
27 
32 
19 
50 
29 
41 
23 
32 
27 
75 
56 

Age 4 
0.8 
3.1 
5.3 
2.9 
1.3 
1.4 

Age 5+ 
5 
7 
4 
10 
6 
12 
4 
6 
5 
30 
1 

Age 5+ 
0.2 
0.6 
1.4 
0.2 
2.5 
0.9 

360 

360 

 
 
 
  
 
 
 
 
2012 
2013 
2014 
2015 
2016 

52.1 
181.6 
75.7 
227.0 
72.687 

254.5 
421.4 
608.2 
242.7 
394.534 

107.0 
90.6 
213.4 
123.0 
95.479 

10.6 
14.9 
15.0 
12.8 
33.548 

2.6 
3.2 
4.6 
1.3 
1.712 

0.1 
0.1 
0.3 
3.1 
2.76 

Table 6.8.3.4 Common sole in GSA 17. Individual weight at age for the catch (kg). 

2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 
2015 
2016 

Age 0 
0.07 
0.07 
0.08 
0.08 
0.08 
0.07 
0.07 
0.07 
0.05 
0.05 
0.05 

Age 1 
0.13 
0.13 
0.13 
0.14 
0.16 
0.12 
0.11 
0.12 
0.13 
0.13 
0.13 

Age 2 
0.19 
0.19 
0.21 
0.22 
0.25 
0.20 
0.18 
0.19 
0.15 
0.15 
0.15 

Age 3 
0.36 
0.36 
0.36 
0.36 
0.36 
0.36 
0.27 
0.22 
0.34 
0.34 
0.34 

Age 4 
0.45 
0.45 
0.45 
0.45 
0.45 
0.45 
0.45 
0.45 
0.45 
0.45 
0.45 

Age 5+ 
0.52 
0.52 
0.52 
0.52 
0.52 
0.52 
0.52 
0.52 
0.52 
0.52 
0.52 

Table 6.8.3.5 Common sole in GSA 17. Individual weight at age for the stock, maturity 
and natural mortality vectors. 

Individual weight at age for the stock (kg) 
Age 0 
0.02 

Age 2 
0.21 

Age 1 
0.10 

Age 3 
0.30 

Age 4 
0.38 

Age 5+ 
0.52 

Maturity at age 
Age 0 
0.00 

Age 1 
0.16 

Natural Mortality 
Age 0 
0.70 

Age 1 
0.35 

Age 2 
0.76 

Age 3 
0.96 

Age 4 
0.99 

Age 5+ 
1.00 

Age 2 
0.28 

Age 3 
0.25 

Age 4 
0.23 

Age 5+ 
0.22 

Different combinations of F and q sub-models were explored. The list of the sub-model is 
reported below: 

F sub-models: 

~ factor(age)+factor(year) 

~ factor(age)+s(year, k=5) 

~ s(age, k=3)+s(year, k=5) 

~ factor(replace(age, age>4,4))+s(year,k=7) 

q sub-models: 

list(~ factor(age)) 

361 

361 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
list(~ s(age, k=3)) 

list(~ s(age, k=3)+ s(year, k=5) 

The  best  model  (reported  below)  was  chosen  on  the  basis  of  the  residuals  and 
retrospective analysis. 

f ~ factor(replace(age, age>4,4))+s(year,k=7) 

q ~ list(~ factor(age)) 

Results 

The  SSB  estimated  by  the  a4a  final  model  shows  an  increasing  trend,  especially  in  the 
last  four  years  of  the  time  series,  reaching  a  value  of  1165  tons  in  2016.  The  F  time 
series  shows  high  values,  ranging  between  1.86  and  0.99,  with  a  slightly  decrease  in 
time.  The  recruitment  is  fluctuating  and  does  not  show  any  evident  trend  (Fig.  6.8.3.1 
and  Table  6.8.3.5).  The  fishing  mortality  at  age  peaks  at  ages  1  and  3  (Fig.  6.8.3.2). 
Numbers and F at age are given in Tables 6.8,3.6 and 6.8.3.7 

Figure  6.8.3.1  Common  sole  in  GSA  17.  Stock  summary  from  the  results  of  the  a4a 
model. 

362 

362 

 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table  6.8.3.5  Common  sole  in  GSA  17.  Results  of  the  final  a4a  run:  Fbar  (1-4),  SSB, 
recruitment and total biomass. 

Year 

2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 
2015 
2016 

Fbar(1-
4) 

Recruitment 
(thousands) 

SSB 
(tons) 

1.61 
1.66 
1.46 
1.41 
1.71 
1.86 
1.53 
1.34 
1.52 
1.51 
0.99 

26073 
20342 
19044 
29121 
40426 
22249 
29738 
42332 
32718 
52225 
26882 

925 
852 
678 
618 
681 
766 
696 
734 
970 
1072 
1165 

Total 
biomass 
(tons) 
2842 
2374 
1925 
2019 
2695 
2835 
2240 
2806 
3398 
3539 
3835 

Figure 6.8.3.2 Common sole in GSA 17. Fishing mortality (left) and survey catchability 
(right) by age and year. 

363 

363 

 
 
 
 
 
 
 
 
Table 6.8.3.6 Common sole in GSA 17. Results of the final a4a run: F at age. 

0 

1 

2 

3 

4 

5 

2006  0.097  1.507  0.780  2.510  1.656  1.656 

2007  0.100  1.550  0.802  2.582  1.703  1.703 

2008  0.088  1.368  0.707  2.278  1.502  1.502 

2009  0.085  1.320  0.683  2.198  1.450  1.450 

2010  0.103  1.594  0.825  2.655  1.752  1.752 

2011  0.112  1.738  0.899  2.895  1.910  1.910 

2012  0.092  1.427  0.738  2.376  1.567  1.567 

2013  0.081  1.249  0.646  2.080  1.372  1.372 

2014  0.092  1.423  0.736  2.370  1.564  1.564 

2015  0.091  1.409  0.729  2.347  1.548  1.548 

2016  0.060  0.922  0.477  1.536  1.013  1.013 

Table 6.8.3.7 Common sole in GSA 17. Results of the final a4a run: numbers at age. 

0 

1 

2 

2006  26073 

14862 

2712 

2007  20342 

11745 

2320 

2008  19044 

9138 

1756 

2009  29121 

8657 

1640 

2010  40426 

13278 

1630 

2011  22249 

18109 

1900 

2012  29738 

9874 

2243 

2013  42332 

13466 

1671 

2014  32718 

19391 

2721 

2015  52225 

14819 

3292 

3 

828 

940 

786 

654 

626 

540 

584 

811 

662 

985 

2016  26882 

23676 

2552 

1201 

4 

36 

52 

55 

63 

57 

34 

23 

42 

79 

48 

73 

5 

4 

6 

8 

11 

14 

10 

5 

5 

9 

15 

11 

The fitting of the commercial catch at age and the estimated indices are generally good, 
except  for  a  slight  underestimation  of  the  catches  in  the  years  2012-2014  (Figure 
6.8.3.3).  The  residuals are generally  low  (between  -3 and  3)  and  don’t  show  particular 
patterns (Figures 6.8.3.4-6). 

364 

364 

 
 
 
 
 
 
 
 
 
Figure 6.8.3.3 Common sole in GSA 17. Comparison between fitted and observed index 
(left) and catch (right)  

Figure 6.8.3.4 Common sole in GSA 17.  Log-residuals of catch and abundance indices 
by age. 

365 

365 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 6.8.3.5 Common sole in GSA 17. Quantile-quantile plot of catch and abundance 
indices. 

366 

366 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 6.8.3.6 Common sole in GSA 17. Bubble plot of the residuals. 

The  retrospective  analysis,  applied  up  to  3  years  back,  shows  some  instability  (Fig. 
6.8.3.7), the timeseries is short so some instability might be expected. 

Figure 6.8.3.7 Common sole in GSA 17. Retrospective analysis. 

Methods: SS3 (Stock Synthesis 3) 

Stock  Synthesis  3  provides  a  statistical  framework  for  the  calibration  of  a  population 
dynamics  model  using  fishery  and  survey  data.  It  is  designed  to  accommodate  both 
population age and size structure data and multiple stock sub-areas can be analysed. It 
uses  forward  projection  of  population  in  the  “statistical  catch-at-age”  approach.  SCAA 
estimates  initial  abundance  at  age,  recruitments,  fishing  mortality  and  selectivity. 
Differently  from  VPA  based  approaches  (e.g.  by  XSA)  SCAA  calculates  abundance 
forward in time and allows for errors in the catch at age matrices. Selectivity has been 
generated  as  age-specific  by  fleet,  with  the  ability  to  capture  the  major  effect  of  age-
specific  survivorship.  The  overall  model  contains  subcomponents  which  simulate  the 
population  dynamics  of  the  stock  and  fisheries,  derive  the  expected  values  for  the 
various observed data, and quantify the magnitude of difference between observed and 
expected  data.  Some  SS  features  include  ageing  error,  growth  estimation,  spawner-
recruitment  relationship,  movement  between  areas;  in  the  present  assessment  such 
features  are  not  summarized  in  the  results.  The  ADMB  C++  software  in  which  SS  is 

367 

367 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
written searches for the set of parameter values that maximize the goodness-of-fit, then 
calculates  the  variance  of  these  parameters  using  inverse  Hessian  methods.  In  the 
present assessment the variance is not shown for fishing mortality results, because the 
model outputs provide F values (called continuous F) within a year as standardized into 
selection  coefficients  by  dividing  each  F  value  by  the  maximum  value  observed  for  any 
age class in the year (e.g., Derio et al., 1985; Sampson and Scott, 2011). For a better 
comparison  with  the  results  of  the  assessment  carried  out  with  the  a4a  method,  the  F 
values  are  standardized  by  dividing  by  the  average  of  the  F  values  observed  over  a 
defined  range  of  age  classes  (Fbar)  (e.g.,  Darby  and  Flatman,  1994;  Sampson  and 
Scott, 2011). 

Input data 
SS3  model  allowed  to  specify  the  different  source  of  data,  providing  different 
uncertainties  estimates  for  each  data  set.  Moreover  also  the  total  landings  presented 
from 1970 to 2005 (FAO-Fishstat source) has been used in the model, together with the 
DCF  and  Croatian  data  for  the  period  2006-2016  (Tab.  6.8.3.9).  Also  in  this  case  the 
model considered the different sources of the data sets and treated the error separately 
for each period. In order to facilitate the convergence of the model a higher number of 
ages has been employed for natural mortality, fecundity and weight at age, and no plus 
group was considered.  

Table 6.8.3.9 Common sole in GSA 17. Landings by fishing fleet and gear from 1970 to 
2016 from differect sources. 

Total landings/year 

year 

GNS Italy 

TBB+OTB 
Italy 

GTR 
HRV+SVN 

Total 
GSA17 

1980 
1981 
1982 
1983 
1984 
1985 
1986 
1987 
1988 
1989 
1990 
1991 
1992 
1993 
1994 
1995 
1996 
1997 
1998 
1999 
2000 
2001 

694 
348 
377 
513 
440 
480 
494 
823 
619 
586 
383 
365 
590 
625 
711 
612 
379 
388 
367 
397 
309 
319 

1233 
620 
669 
911 
781 
854 
878 
1464 
1101 
1043 
682 
650 
1048 
1110 
1265 
1087 
673 
690 
653 
705 
559 
579 

368 

368 

308 
155 
167 
228 
195 
213 
220 
366 
275 
261 
170 
162 
262 
278 
316 
272 
168 
172 
163 
176 
168 
206 

2235 
1123 
1213 
1652 
1416 
1547 
1592 
2653 
1995 
1890 
1235 
1177 
1900 
2013 
2292 
1971 
1220 
1250 
1183 
1278 
1036 
1104 

 
 
 
2002 
2003 
2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 
2015 
2016 

298 
633 
561 
594 
717 
466 
410 
509 
520 
625 
781 
207 
562 
388 
388 

539 
1147 
1015 
1075 
1106 
913 
775 
1134 
901 
706 
906 
793 
1350 
1469 
1588 

238 
327 
246 
325 
199 
209 
140 
311 
193 
258 
172 
253 
136 
188 
117 

1075 
2107 
1822 
1994 
2022 
1588 
1325 
1954 
1614 
1589 
1859 
1253 
2048 
2045 
2093 

DCF 
FAO Fishstat 
Combined  DCF  (for  SVN)/FAO  Fishstat-Primo  Project  (for 
HRV) 

Italian gill netters 
Italian rapido and otter trawler 
Croatian and Slovenian set netters. 

The SS3 analysis was carried out considering the following three fleets: 
1. 
2. 
3. 
The  catch  at  age  for  the  three  fleets  are  summarized  in  Table  6.8.3.10  and  Figure 
6.8.3.8.  

Table  6.8.3.10  Common  sole  in  GSA  17.  Age  structure  of  the  landings  in  2006-2016 
provided by the 2017 DCF data call for GSA 17 by gear and country. 

GTR_Croatia+Slovenia 

Year 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 
2015 
2016 

Age0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 

Age1 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 

2006 
2007 

TBB+OTB_Italy 

1937 
340 

6215 
5528 

Age2 
134 
155 
96 
244 
140 
173 
116 
162 
136 
283 
130 

958 
802 

369 

369 

Age3 
518 
601 
373 
948 
544 
671 
449 
629 
526 
414 
344 

119 
288 

Age4 
27 
31 
19 
49 
28 
35 
23 
32 
27 
81 
56 

0 
1 

Age5+ 
8 
9 
5 
14 
8 
10 
6 
9 
8 
32 
0 

0 
1 

 
  
 
 
  
  
2008 
2009 
2010 
2011 
2012 

2013 

2014 
2015 
2016 

2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 
2015 
2016 

572 
5112 
4443 
4358 
4053 

961 

421 
698 
406 

GNS_Italy 

1017 
90 
298 
855 
873 
815 
4081 
454 
225 
193 
344 

4603 
4532 
2985 
3436 
4152 

4935 

10462 
9744 
11703 

4294 
2943 
2835 
3239 
3213 
3830 
4906 
1618 
5336 
2829 
3620 

474 
407 
248 
414 
642 

22 

1324 
1554 
894 

541 
442 
32 
276 
266 
602 
272 
1 
345 
356 
78 

63 
49 
37 
27 
24 

513 

15 
59 
2 

67 
60 
0 
33 
15 
24 
2 
31 
2 
13 
1 

GTR HRV+SVN 

1000

800

600

400

200

0

6000

5000

4000

3000

2000

1000

0

Age0

Age1

Age2

Age4

Age3
GNS ITA 

Age5

Age0

Age1

Age2

Age3

Age4

Age5

370 

370 

1 
2 
1 
6 
0 

0 

0 
0 
0 

0 
0 
0 
2 
2 
14 
0 
0 
0 
0 
0 

0 
1 
1 
4 
0 

0 

0 
0 
0 

0 
0 
0 
0 
0 
2 
0 
0 
0 
0 
0 

2006

2007

2008

2009

2010

2011

2012

2006

2007

2008

2009

2010

2011

2012

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
14000

12000

10000

8000

6000

4000

2000

0

TBB + OTB ITA 

2006

2007

2008

2009

2010

2011

2012

Age0

Age1

Age2

Age3

Age4

Age5

Figure  6.8.3.8  Common  sole  in  GSA  17.  Age  structure  of  the  landings  in  2006-2016 
provided by the 2017 DCF data call for GSA 17 by gear and country. 

SS3 – Run 1 
The  following  assessment  was  presented  and  endorsed  during  the  GFCM  WGSAD  2017 
as it is reported below. 

The selectivity patterns of the fleets and the survey  were rescaled as in Figure 6.8.3.9, 
assuming  a  dome  shaped  selectivity  for  each  fleet  and  the  survey.  The  selectivity  was 
set as a double normal, forcing the final values to go to 0 for all the fleets, however, a 
model option with F at oldest ages estimated still resulted in low/zero F at oldest ages. 

Figure 6.8.3.9 Common sole in GSA 17. Selectivity by age used in the SS3 model Run 
1. 

371 

371 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Results SS3 Run1 

The SSB estimated by the SS3 Run 1 model shows a generally decreasing trend, with a 
quite evident increase from 2013 to 2016, when the SSB reaches a value of 5278 tons. 
The  F  time  series  shows  values  ranging  between  1.07  and  0.17,  with  a  peak  in  2009-
2011  followed  by  a  strong  decrease.  The  recruitment  does  not  show  any  evident  trend 
(Fig. 6.8.3.10 and Table. 6.8.3.11). The fishing mortality at age peaks at ages 1 and 3, 
as highlighted also by the a4a results (Fig. 6.8.3.11). Numbers and F at age are given in 
Tables 6.8.3.12 and 6.8.3.13  

Figure  6.8.3.10  Common  sole  in  GSA  17.  Stock  summary  of  the  SS3  model  Run  1. 
Long time series used for the SS3 model (left) and short time series comparable with the 
a4a results (right). 

Table  6.8.3.11  Common  sole  in  GSA  17.  Results  of  the  SS3  Run  1:  Fbar(1-4),  SSB, 
recruitment and total biomass.  

Year 

1980 
1981 
1982 
1983 
1984 
1985 
1986 
1987 
1988 
1989 
1990 

Fbar  
(1-4) 

Recruitment 
(thousands) 

0.34 
0.20 
0.21 
0.29 
0.26 
0.29 
0.31 
0.62 
0.58 
0.62 
0.36 

33634 
33634 
33634 
33634 
33634 
33634 
33634 
33634 
33634 
33634 
33634 

372 

372 

SSB 
(tons) 

Total 
biomass 
(tons) 
18536  21368 
16948  19506 
16185  18842 
15495  18164 
14470  17060 
13696  16301 
12934  15516 
12186  14750 
10531  12860 
11729 
9385 
10824 
8459 

 
 
 
 
 
 
1991 
1992 
1993 
1994 
1995 
1996 
1997 
1998 
1999 
2000 
2001 
2002 
2003 
2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 
2015 
2016 

0.27 
0.39 
0.45 
0.61 
0.61 
0.36 
0.29 
0.23 
0.23 
0.18 
0.18 
0.17 
0.37 
0.45 
0.70 
0.79 
0.83 
0.65 
1.05 
1.01 
1.07 
0.70 
0.40 
0.36 
0.39 
0.41 

33634 
33634 
33634 
33634 
33634 
33634 
33634 
33634 
33634 
33634 
33634 
33634 
23942 
23230 
34740 
30920 
19398 
28090 
28982 
31550 
33855 
39742 
47303 
32647 
41327 
30262 

8250 
8379 
7994 
7462 
6668 
6098 
6249 
6612 
7150 
7659 
8387 
9048 
9681 
9160 
8245 
7139 
6347 
5692 
5065 
4304 
3927 
3782 
3727 
4570 
5197 
5278 

10789 
11003 
10507 
9906 
9004 
8444 
8783 
9219 
9804 
10315 
11098 
11771 
12189 
11055 
10211 
9327 
8137 
7310 
6975 
6251 
6089 
6286 
6648 
7758 
7995 
7973 

Figure  6.8.3.11  Common  sole  in  GSA  17  SS3  model  Run  1  .  Fishing  mortality  by  age 
and year. 

373 

373 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
374 

374 

 
 
 
Table 6.8.3.12 Common sole in GSA 17. Results of the SS3 Run 1: F at age. 

0 

1 

2 

3 

4 

5 

2006  0.150  1.190  0.941  0.808  0.226  0.009 

2007  0.120  0.926  0.792  1.207  0.378  0.011 

2008  0.121  0.941  0.751  0.706  0.202  0.007 

2009  0.168  1.286  1.071  1.410  0.432  0.014 

2010  0.136  1.068  0.929  1.539  0.488  0.014 

2011  0.100  0.828  0.806  2.002  0.661  0.018 

2012  0.108  0.890  0.734  0.888  0.268  0.009 

2013  0.052  0.377  0.340  0.668  0.216  0.006 

2014  0.076  0.576  0.447  0.320  0.085  0.004 

2015  0.090  0.655  0.502  0.311  0.078  0.004 

2016  0.103  0.742  0.560  0.278  0.062  0.003 

6 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

7 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

8 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

9 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

10 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

Table 6.8.3.13 Common sole in GSA 17. Results of the SS3 run: numbers at age. 

0 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

2006  30920  14959  2420  1141  1074  1531  1751  1598  1245  984  3062 

2007  19398  13220  3207  714 

396 

681  1218  1419  1309  1029  3405 

2008  28090  8544  3690  1098  166 

216 

540 

987  1162  1082  3732 

2009  28982  12357  2348  1317  422 

108 

172 

438 

808 

961  4053 

2010  31550  12161  2406  608 

250 

218 

85 

139 

358 

668  4222 

2011  33855  13675  2944  718 

102 

122 

172 

69 

114 

296  4120 

2012  39742  15211  4209  994 

75 

2013  47303  17716  4403  1527  318 

42 

46 

2014  32647  22301  8562  2369  610 

204 

96 

33 

37 

2015  41327  15020  8833  4138  1339  445 

163 

78 

27 

30 

140 

57 

94 

47 

94 

53 

18 

3723 

3220 

2756 

2404 

2072 

114 

64 

22 

24 

2016  30262  18752  5499  4042  2361  984 

356 

132 

375 

375 

 
 
 
 
 
 
The  SoleMon  abundance  indices  and  the  annual  mean  ages  for  the  survey  and  the 
commercial  fleets  are  well  estimated  by  the  model  in  SS3  Run  1(Fig.  6.8.3.12  and 
6.8.3.13). 

Figure  6.8.3.12  Common  sole  in  GSA  17  SS3  Run  1.  Comparison  between  observed 
and estimated SoleMon age composition, mean age and log index. 

376 

376 

 
 
 
 
Figure 6.8.2.13 Common sole in GSA 17SS3 Run 1. Comparison between observed and 
estimated commercial mean ages and age compositions. 

The  retrospective  analysis  confirmed  the  stability  of  the  model,  as  shown  in  Figure 
6.8.3.14; the residuals are very low, between -1 and 1, and don’t show any pattern (Fig. 
6.8.3.15). 

377 

377 

 
 
 
 
 
 
 
 
Figure 6.8.3.14 Common sole in GSA 17 SS3 Run 1. Retrospective analysis on rescaled 
data (SoP corrected data) SSB, Recruits and Fbar ages 1-4. 

378 

378 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 6.8.3.15 Common sole in GSA 17. Pearson residuals. 

SS3 – Run 7 
The  model  described  below  assumed  a  logistic  selectivity  pattern  for  the  survey  and  a 
double  normal  selectivity  pattern  for  commercial  fleets,  with  the  only  constraint  to  be 
constant from age 4 (Figure 6.8.3.16).  

379 

379 

 
 
Figure  6.8.3.16  Common  sole  in  GSA  17.  Selectivity  by  age  used  in  the  SS3  model  – 
Run 7. 

Results 

The SSB shows a generally decreasing trend, with a peak in 2003 followed by a strong 
decrease  until  2010.  From  2010  to  2016,  the  SSB  increased  from  1103  to  3429  tons. 
The  F  time  series  shows  values  ranging  between  1.18  and  0.17.  After  a  peak  in  2009-
2010  followed  by  a  strong  decrease,  the  F  reaches  a  value  of  0.51  in  2016.  The 
recruitment  does  not  show  any  evident  trend  (Fig.  6.8.3.17  and  Tab.  6.8.3.14).  The 
fishing  mortality  at  age  peaks  at  ages  1  and  3,  as  highlighted  also  by  the  other  two 
models (Fig. 6.8.3.18 and Table 6.8.3.15). 

380 

380 

 
 
 
 
 
 
 
Figure  6.8.3.17  Common  sole  in  GSA  17.  Stock  summary  of  the  SS3  -  Run  7.  Long 
time series used for the SS3 model (left) and short time series comparable with the a4a 
results (right). 

Table  6.8.3.14  Common  sole  in  GSA  17.  Results  of  the  SS3  -  Run  7:  Fbar(1-4),  SSB, 
recruitment and total biomass.  

Year 

1980 
1981 
1982 
1983 
1984 
1985 
1986 
1987 
1988 
1989 
1990 
1991 
1992 
1993 
1994 
1995 
1996 
1997 
1998 
1999 
2000 
2001 
2002 
2003 
2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 
2015 

Fbar  

(1-4) 

Recruitment 
(thousands) 

SSB 
(tons) 

0.31 
0.17 
0.18 
0.25 
0.22 
0.25 
0.27 
0.51 
0.44 
0.46 
0.28 
0.25 
0.40 
0.47 
0.63 
0.63 
0.38 
0.34 
0.28 
0.28 
0.21 
0.21 
0.20 
0.41 
0.56 
0.86 
0.98 
0.99 
0.73 
1.18 
1.12 
0.98 
0.68 
0.41 
0.43 
0.47 

26275 
26275 
26275 
26275 
26275 
26275 
26275 
26275 
26275 
26275 
26275 
26275 
26275 
26275 
26275 
26275 
26275 
26275 
26275 
26275 
26275 
26275 
26275 
11836 
13482 
25093 
26230 
16287 
24648 
27649 
31505 
33251 
39777 
43692 
23925 
32020 

10604 
9492 
9300 
9123 
8560 
8212 
7815 
7394 
6007 
5201 
4600 
4696 
4983 
4644 
4133 
3352 
2844 
3074 
3420 
3870 
4250 
4837 
5361 
5874 
5214 
4056 
2822 
2073 
1751 
1515 
1103 
1164 
1468 
1784 
2930 
3613 

381 

381 

Total 
biomass 
(tons) 
12816 
11531 
11421 
11255 
10639 
10303 
9890 
9457 
7925 
7145 
6556 
6748 
7071 
6637 
6076 
5225 
4725 
5077 
5458 
5937 
6316 
6951 
7487 
7671 
6310 
5292 
4465 
3582 
3167 
3268 
3029 
3345 
3983 
4651 
5805 
5836 

 
 
 
2016 

0.51 

15002 

3429 

5345 

Figure 6.8.3.18 Common sole in GSA 17. Fishing mortality by age and year. SS3 - Run 
7. 

Table 6.8.3.15 Common sole in GSA 17. Results of the SS3 - Run 7: F at age. 

2 

1 

8 

6 

4 

0 

9 

5 

3 

7 

10 
2006  0.13  1.25  0.81  1.48  0.38  0.38  0.38  0.38  0.38  0.39  0.39 
2007  0.10  0.85  0.70  2.01  0.40  0.40  0.40  0.40  0.40  0.40  0.40 
2008  0.10  0.91  0.64  1.01  0.34  0.34  0.34  0.34  0.34  0.34  0.34 
2009  0.14  1.18  0.95  2.01  0.56  0.56  0.56  0.56  0.56  0.56  0.56 
2010  0.12  0.99  0.83  2.18  0.48  0.48  0.48  0.48  0.48  0.48  0.48 
2011  0.09  0.79  0.65  2.12  0.36  0.36  0.36  0.36  0.36  0.36  0.36 
2012  0.09  0.87  0.59  0.95  0.31  0.31  0.31  0.31  0.31  0.31  0.31 
2013  0.04  0.33  0.31  0.79  0.21  0.21  0.21  0.21  0.21  0.21  0.21 
2014  0.07  0.54  0.41  0.50  0.24  0.24  0.24  0.24  0.24  0.24  0.24 
2015  0.07  0.62  0.46  0.52  0.28  0.28  0.28  0.28  0.28  0.28  0.28 
2016  0.08  0.67  0.51  0.53  0.32  0.32  0.32  0.32  0.32  0.32  0.32 

Table 6.8.3.16 Common sole in GSA 17. Results of the SS3 – Run 7: numbers at age. 

4 

0 

2 

3 

5 
1 
664  1033  822 
2006  26230  10940  1006  477 
565 
360 
84 
2007  16287  11493  2199  338 
194 
45 
2008  24648  7349  3462  825 
35 
26 
20 
2009  27649  11081  2085  1384  233 
9 
106 
144 
2010  31505  11937  2392  609 

6 

7 
656 
454 
307 
112 
12 

8 
441 
366 
249 
179 
52 

9 
319 
248 
203 
147 
84 

10 
567 
507 
426 
376 
251 

382 

382 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
  
70 
2011  33251  13940  3118  791 
54 
30 
74 
2012  39777  15160  4447  1230 
43 
2013  43692  17998  4464  1856  372 
2014  23925  20768  9129  2466  658 
240 
2015  32020  11126  8501  4579  1159  409 
2016  15002  14752  4238  4040  2123  697 

52 
40 
18 
28 
151 
248 

5 
30 
24 
12 
18 
93 

6 
3 
18 
16 
7 
11 

27 
3 
2 
12 
10 
5 

174 
118 
76 
53 
43 
34 

The  SoleMon  abundance  indices  and  the  annual  mean  ages  for  the  survey  and  the 
commercial fleets are generally well estimated by the model, even if in some cases there 
is an overestimation of individuals at older ages (Fig. 6.8.3.19 and 6.8.3.20). 

Figure 6.8.3.19 Common sole in GSA 17. Comparison between observed and estimated 
SoleMon age composition, mean age and log index. SS3 – Run 7 

383 

383 

 
 
 
 
 
 
 
Figure 6.8.3.20 Common sole in GSA 17. Comparison between observed and estimated 
commercial mean ages and age compositions. SS3 – Run 7 

384 

384 

 
 
 
 
 
 
The  retrospective  analysis  confirmed  the  stability  of  the  model,  as  shown  in  Figure 
6.8.3.21; the residuals are very low, between -2 and 2, and don’t show any pattern (Fig. 
6.8.3.22). 

Figure 6.8.3.21 Common sole in GSA 17. Retrospective analysis on rescaled data (SoP 
corrected data) SSB, Recruits and Fbar ages 1-4. 

385 

385 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 6.8.3.22 Common sole in GSA 17. Pearson residuals. SS3 – Run 7 

Comparison of the models 

During the EWG it was not possible to explore these assessments sufficiently and further 
evaluation and models were tested. The results given by the two initial models (a4a and 
SS3 run 1) show similar trends for recruitment, fishing mortality and SSB, especially for 
the  last  years  of  the  time  series.  However,  there  is  an  evident  difference  in  terms  of 
scale  regarding  the  SSB  and  the  F:  the  SS3  model  estimates  about  four  times  higher 
values  for  the  SSB  and  considerably  lower  values  for  the  fishing  mortality  (Fig. 
6.8.3.23). Such differences can be explained considering that the SS3 model allows the 
assumption  of  a  dome-shaped  population  selection  curve,  while  the  a4a  model  uses  a 
constant catchability at the older ages. On the other hand, the SS3 model results in high 
biomass from ages greater than those caught in the fishery (cryptic biomass), while this 
issue is not present in the a4a model results. Consequently, the SS3 model gives about 
40%  of  SSB  from  ages  older  than  age  4  and  the  a4a  model  just  about  0.5%  (Tab. 
6.8.3.17).  

The  main  reason  for  these  differences  appears  to  derive  from  the  fitted  selection 
functions. The underlying mortality signals in the data were further explored through log 
ratio  of  catch  at  age  a  in  year  y  and  catch  at  age  a+1  in  year  y+1  for  the  both  the 
Solemon  survey  and  commercial catch,  both show  a  generally rising  mortality with  age 
and  a  decline  to  the  plus  group  (Figure  6.8.3.24).  There  are  some  differences;  the 

386 

386 

 
 
 
 
 
 
 
 
survey  shows  generally  consistent  mortality  results  over  years  with  noise  in  the  plus 
group  and  a  slight decline at the  older ages.  The  catch has  two  peaks  at ages  1  and  3 
corresponding to the two different fisheries. These mortality functions can be compared 
with the selection functions for SS3 catch (Figure 6.8.3.11) and survey (Figure 6.8.3.9)  
and for a4a both catch and survey (Figure 6.8.3.2). 

The  a4a  model  generally  follows  the  mortality  signal  in  the  catch  data,  with  the  same 
two peaks and a modest decline to age 4 and the plus group (5+) is made equal to age 
4.  However,  the  survey  it  fitted  as  a  rising  selection.  The  SS3  model  (Figure  6.8.3.11) 
gives a fall in F from age 1 and very low F at ages 4 and 5+. The assumption of double 
normal selection in both catch and survey models is the main reason for the difference 
at older ages. For the multifleet SS3 model the reduction in F at older ages in selection 
on the Italian trawl and GNS fisheries would be consistent with the hypothesis that older 
fish move from this area and become available to the GTR fisery of Slovenia and Croatia. 
Thus the selection is spatially related not just gear related. In the a4a assessment with 
the  catches  combined,  the  set  net  and  line  fisheries  are  fitted  with  a  slightly  higher  or 
similar mortality to the trawl fishery (Figure 6.8.17), in the SS3 assessment where this 
fishery is deal with independently it is assigned a lower F. This conclusion for SS3 is the 
same  irrespective  of  whether  the  fleets  are  combined  or  not  as  long  as  the  survey 
selection  is  still  assumed  to  be  double  normal.  Overall,  the  survey  gives  a  plausible 
largely  flat  topped  mortality  signal,  with  not  many  fish  escaping  into  the  plus  group  at 
age  5+.  The  a4a  model  fits  a  continually  rising  mortality  function  to  this  data,  which 
results  in  higher  mortality  at  these  older  ages.  Residuals  on  the  a4a  survey  (Figure 
6.8.3.5) suggest that the assumption of F in the plus group (age 5+) equals F at age 4 is 
a reasonable assumption. SS3 fits a double normal selection to the survey (and catch), 
and assumes that the catches represent a smaller proportion of the population at these 
older ages. There are some arguments to support this, as the survey does not cover the 
whole  area,  and  some  older  sole  are  thought  to  escape  the  area  into  more  inshore 
waters  along  the  Croatian  coast.  However,  is  this  happening  to  such  an  extent?  Figure 
6.8.2.3.1 shows that older fish are not found in the north and west of the area, but the 
survey  also  misses  an  area  that  may  plausibly  have  younger  fish  in  the  North  East  as 
well  as  the  older  fish  to  the  East,  so  the  conclusion  that  it  misses  more  older  sole  is 
debatable. Has this region outside the survey supported between 35 to 80% of the SSB 
at ages that are not observed by survey or fishery?  

387 

387 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 6.8.3.23 Common sole in GSA 17. Stock summary of the two models: a4a (red 
line) and ss3 (blue line). 

Table 6.8.3.17 Common sole in GSA 17. Comparison between the SSB percentages at 
ages 0-4 and 5+ for the two models. 

a4a 

Ages 
0-4 

SS3 

Ages 
5+ 

Ages 
0-4 

Ages 
5+ 

2006  99.8 

0.2 

19.1 

80.9 

2007  99.6 

0.4 

17.0 

83.0 

2008  99.3 

0.7 

19.4 

80.6 

2009  99.0 

1.0 

22.1 

77.9 

2010  98.9 

1.1 

19.8 

80.2 

2011  99.3 

0.7 

23.9 

76.1 

2012  99.6 

0.4 

32.6 

67.4 

2013  99.7 

0.3 

41.7 

58.3 

2014  99.5 

0.5 

57.7 

42.3 

2015  99.3 

0.7 

64.5 

35.5 

2016  99.5 

0.5 

61.5 

38.5 

Catch ln(Na,y/Na+1,y+1) 

Survey ln(Na,y/Na+1,y+1) 

)
1
+
y
,
1
+
a
N
/
y
,
a
N
N
L

(

4

2

0

-2

-4

0

1

2

3

4

)
1
+
y
,
1
+
a
N
/
y
,
a
N
N
L

(

4

3

2

1

0

-1

-2

-3

Mean

-SD

+SD

388 

388 

0

1

2

3

4

Series1

Series2

Series3

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 6.8.3.24 Mortality signal in the combined catch and survey data mean over all years (+- 1 SD). 
Ln(N at age and year / N at age+1 and year+1).   

The EWG was not able to resolve the issue and has presented both options, and drafted 
advice options from each in Section 5.8. After the EWG the sole GAS 17 assessment was 
further  evaluated  as  neither  model  gave  appeared  to  represent  the  signals  in  the  data 
well,  and  the  structural  assumptions  on  selection  appeared  to  be  the  cause  of  the 
differences.  The  SS3  framework  was  used  to  explore  further  options,  as  this  provided 
the  flexibility  to  evaluate  the  different  options  in  both  multi  and  single  fleet  catch 
options. A further 6 runs (runs 2 to 7) were evaluated with the following assumptions for 
catches.   

Table 6.8.3.18 Common sole GSA 17.Summary of the runs carried out. 

FLEETS 

commercial fleets 

survey 

Double normal constant 
from age 4 -5+ 

SELECTIVITY 

a4a  Original combined run 

Combined   

Ind. at age 5+= Sel4 

Ind. at age 

SS3 Runs 

combined 

separated 

logistic 

double normal 

logistic 

double normal 

_1 

_2 

_3 

_4 

_5 

_6 

_7 

Original_double normal 

Separated_Logistic 

Separated_Logistic_Survey 

Combined_double normal 

Combined_Logistic_Survey 

Combined_Logistic 

Separated_Logistic_Survey_Sel4 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

The results in terms of SSB, Fbar (Figure 6.8.3.25) and F at age (Figure 6.8.3.26) depend 
strongly on these selectivity assumptions.  

389 

389 

 
 
 
 
   
 
 
 
 
 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
SSB 

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

a4a

Run_1

Run_2

Run_3

Run_4

Run_5

Run_6

Run_7

Fbar(1-4) 

8000

7000

6000

5000

4000

3000

2000

1000

0

2.00

1.80

1.60

1.40

1.20

1.00

0.80

0.60

0.40

0.20

0.00

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

a4a

Run_1

Run_2

Run_3

Run_4

Run_5

Run_6

Run_7

Figure  6.8.3.25.  Assessment  results in  terms of SSB  and  Fbar  (ages  1-4)  for  a4a and  
runs 1 to 7 for SS3. 

390 

390 

 
 
 
 
 
Figure  6.8.3.26  F  at  age  for  a4a  and  SS£  runs  1  to  7,  resulting  from  the  different 
structural assumptions for catch and selectivity (see table 6.8.3.18). 

391 

391 

 
 
 
 
 
 
 
 
 
 
 
All  models  are  the  best  fits  based  on  the  structural  assumptions  on  selectivity.  In  SS3 
Run  1  fits  the  data  best  in  terms  of  residuals  but  the  model  is  the  most  flexible  with 
higher  parameter  count.  Both  this  run  and  run  4,  catch  combined,  both  with  double 
normal assumption for the survey, result in high proportions of biomass in the 5+ group 
(Table  6.8.3.17  for  run  1).  The  selectivity  on  the  survey  is  identified  as  a  key 
parameterisation  choice,  affecting  particularly  historic  SSB  (  through  5+  biomass),  this 
can be seen in the SSB for runs 1 and 4 in Figure 6.8.3.25. The results of catch analysis 
(Figure  6.8.3.24)  show  no  support  for  this  selection  function.  At the  other extreme lies 
the  a4a  assessment  which  fits  a  strongly  rising  selection  at  age  to  the  survey  and  a 
greater increase in F at age than seen in the catch analysis (Figure 6.8.3.2). Again there 
is  no  strong  evidence  for  such  a  scale  of  rise  selection  at  age  in  both  survey  and 
commercial catch. A further option is explored through the choice of logistic selection in 
SS3 runs 2,3,5,6&7. These runs differ in their parameterisation of catch. The difference 
amongst these runs are mostly in terms of F at age, and have only minor influences on 
SSB.    Runs  2,  3  &  7  treat  the  commercial  fleets  as  separate,  run  5  &  6  use  combined 
catch.  The  runs  with  combined  catch  fit  more  closely  to  the  survey  (not  shown  here), 
than  those  with  catch  by  fleet,  however,  this  is  likely  the  result  of  the  considerable 
increase in parameters used to model catch in a multi fleet model (runs 2,3 & 7), down 
weighting  the  influence  of  the  survey.  These  runs  (5  &  6)  Figure  (6.8.3.26)  do  not 
capture the variation in catch at age that is clearly apparent in then catch data analysis 
(Figure 6.8.3.24). Thus the use of separate fleets appears to be worthwhile. Run 2 with 
logistic selection throughout does not deal well with the different catch at older ages in 
the  Italian  and  Croation/Slovenian  fisheries,  and  fits  poorly.  Run  3  &  7  differ  in  their 
treatment of Italian GNS and OTB fleets. Run 7 fits better and appears to be consistent 
with  the  perceptions  of  spatial  distribution  of  the  stock.    The  run  7  does  exhibit  some 
biomass in the plus group, this can be compared with other runs in Table 6.8.3.19. For 
a4a the biomass is negligible with a maximum of 1.1%. For SS3 run 1 the 5+ biomass is 
important,  reducing  from  a  high  of  83%  in  2007  to  39%  in  2016.  For  Run  7  the  initial 
values in 2006-208, are high ~62% but fall rapidly to less than 10% 2012 to 2015 rising 
to  16%  in  2016.  These  later  values  are  not  considered  an  issue,  but  do  point  to  some 
potential for sensitivity to the use of strong double normal selection in the fishery.  

In  conclusion  the  further  exploration  carried  out  after  the  EWG  raise  issues  with  the 
choice  of  selection  function  for  the  Solemon  survey.  The  data  from  the  survey  appears 
relatively  well  behaved  with  close  to  asymptotic  selection  at  older  ages.  Overall  this 
support  the  use  of  logistic  selection.  The  complexity  of  the  catch  appears  to  be  best 
represented by Run 7. 

The report provides catch options and advice (Section 5.8) based on the two assessment 
concluded at the EWG and additionally based in Run 7 which appears to be a reasonable 
compromise interpretation of the data.        

Table 6.8.3.19   Percentage of biomass by year in 5+ ages in differing assessments runs tested. 

Year 

a4a 

2006 

0.2 

SS3 Run 1 

80.9 

SS3 Run 2 

47.7 

SS3 Run 3 

69.9 

SS3 Run 4 

85.1 

SS3 Run 5 

69.8 

SS3 Run 6 

45.2 

SS3 Run 7 

74.2 

2007 

2008 

2009 

2010 

2011 

2012 

0.4 

83.0 

36.4 

64.4 

86.2 

45.7 

28.2 

67.8 

0.7 

80.6 

14.4 

50.5 

85.4 

30.9 

15.2 

47.4 

1.0 

77.9 

6.4 

41.1 

83.1 

24.0 

10.1 

33.7 

1.1 

80.2 

2.5 

36.8 

83.7 

22.7 

13.1 

26.9 

0.7 

76.1 

0.8 

23.7 

81.0 

17.6 

10.7 

16.4 

0.4 

67.4 

0.1 

12.6 

73.4 

11.6 

7.0 

8.8 

2013 

0.3 

58.3 

0.3 

6.9 

67.7 

11.5 

7.6 

5.7 

2014 

0.5 

42.3 

1.1 

5.0 

53.8 

10.2 

9.0 

6.2 

2015 

0.7 

35.5 

3.4 

7.5 

49.9 

17.1 

15.0 

8.8 

2016 

0.5 

38.5 

8.6 

15.5 

52.2 

28.1 

20.4 

15.7 

392 

392 

 
 
 
 
6.8.4  REFERENCE POINTS 

Reference points and short term forecasts are reported for the SS3 and the a4a 
models  (Tables  6.8.4.1  and  6.8.4.2  respectively),  as  the  EWG  17-15  accepted 
both  of  them.  In  addiction,  reference  points  and  short  term  forecasts  are 
reported also for the SS3 run 7, carried out after the EWG (Table 6.8.4.3). 

The reference points were estimated using the FLBRP package, applying the Yield 
per Recruits approach, where F0.1 is considered a proxy of FMSY. 

Table 6.8.4.1 Common sole in GSA 17. Main reference points defined with the Yield 
per recruit analysis (SS3 model). 

refpt 

harvest 

yield 

rec 

ssb 

biomass 

f0.1 

0.26 

0.045 

1.00 

0.16 

0.21 

Table 6.8.4.2 Common sole in GSA 17. Main reference points defined with the Yield 
per recruit analysis (a4a model). 

refpt 

harvest 

yield 

rec 

ssb 

biomass 

f0.1 

0.23 

0.058 

1.00 

0.24 

0.32 

Table 6.8.4.3 Common sole in GSA 17. Main reference points defined with the Yield 
per recruit analysis (SS3 model – Run 7). 

refpt 

harvest 

yield 

rec 

ssb 

biomass 

f0.1 

0.26 

0.060 

1.00 

0.22 

0.30 

6.8.5  SHORT TERM FORECAST AND CATCH OPTIONS 

Short term forecast was carried out for all three models using the routine made available 
by JRC.  
Regarding  the  SS3  model  Run  1,  the  reference  point  used  was  F0.1=0.26.  The 
recruitment from 2017 to 2019 was assumed equal to the geometric mean of the whole 
time  series.  22  different  F  scenarios  were  simulated  in  order  to  evaluate  the  change  in 
SSB and in the catch in the short term (Table 6.8.5.1 and Figure 6.8.5.1). 

Table  6.8.5.1  Common  sole  in  GSA  17  SS3  Model  Run  1.  Short  term  forecast  for  the 
SS3 model; catch(2016)=2093 tons, catch(2017)=1713 tons. 

393 

393 

 
 
 
 
 
 
 
 
 
Rationale 

Ffactor 

Fbar  Catch_2018  Catch_2019  SSB_2018  SSB_2019 

Change_SSB 
2018-2019(%) 

Change_Catch 
2017-2019(%) 

Zero catch 

0 

0.00 

0 

0 

5366.40 

6916.45 

28.88 

-100 

High long term 
yield (F0.1) 

0.634 

0.26 

1147.98 

1266.21 

5366.40 

5836.63 

8.76 

-45.15 

Status quo 

1 

0.41 

1663.03 

1643.45 

5366.40 

5369.29 

0.05 

0.1 

0.2 

0.3 

0.4 

0.5 

0.6 

0.7 

0.8 

0.9 

0.04 

207.02 

272.49 

5366.40 

6718.58 

0.08 

403.49 

513.03 

5366.40 

6531.94 

0.12 

590.05 

725.20 

5366.40 

6355.84 

0.16 

767.28 

912.18 

5366.40 

6189.64 

0.21 

935.72 

1076.80 

5366.40 

6032.74 

0.25 

1095.89 

1221.58 

5366.40 

5884.59 

0.29 

1248.27 

1348.76 

5366.40 

5744.64 

0.33 

1393.30 

1460.31 

5366.40 

5612.41 

0.37 

1531.43 

1558.02 

5366.40 

5487.44 

Different Scenarios 

1.1 

0.45 

1788.49 

1718.01 

5366.40 

5257.55 

25.20 

21.72 

18.44 

15.34 

12.42 

9.66 

7.05 

4.58 

2.26 

-2.03 

-4.00 

-5.86 

-7.63 

-9.30 

-20.54 

-90.11 

-80.72 

-71.81 

-63.34 

-55.29 

-47.64 

-40.36 

-33.43 

-26.83 

-14.55 

-8.83 

-3.37 

1.84 

6.81 

11.57 

16.12 

20.48 

24.65 

28.64 

1.2 

1.3 

1.4 

1.5 

1.6 

1.7 

1.8 

1.9 

0.49 

1908.16 

1782.93 

5366.40 

5151.84 

0.53 

2022.36 

1839.32 

5366.40 

5051.81 

0.57 

2131.41 

1888.16 

5366.40 

4957.11 

0.62 

2235.59 

1930.33 

5366.40 

4867.43 

0.66 

2335.18 

1966.59 

5366.40 

4782.48 

-10.88 

0.70 

2430.44 

1997.65 

5366.40 

4701.97 

-12.38 

0.74 

2521.60 

2024.11 

5366.40 

4625.66 

-13.80 

0.78 

2608.88 

2046.52 

5366.40 

4553.28 

-15.15 

2 

0.82 

2692.52 

2065.35 

5366.40 

4484.62 

-16.43 

394 

394 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 6.8.5.1 Common sole in GSA 17. Short term forecast for the SS3 model Run 1. 

The  SS3  Run  1  analysis  showed  that  fishing  at  F=0.26  would  increase  the  SSB  (from 
2018  to  2019) of about the  9%,  and  decrease the  catch  (from 2016  to  2018) of  about 
the 45%, while fishing at the status quo level would increase the SSB of the 0.05% and 
decrease the catch of about the 21%.  

Regarding the a4a model, the reference point used was F0.1=0.23. The recruitment from 
2017 to 2019 was assumed equal to the geometric mean of the whole time series. Also 
in this case, 22 different F scenarios were simulated in order to evaluate the change in 
SSB and in the catch in the short term (Table 6.8.5.2 and Figure 6.8.5.2). 

Table  6.8.5.2  Common  sole  in  GSA  17.  Short  term  forecast  for  the  a4a  model; 
catch(2016)=2105 tons, catch(2017)=1599 tons. 

Rationale 

Ffactor  Fbar  Catch_2018  Catch_2019  SSB_2018  SSB_2019 

Change_SSB 
2018-2019(%) 

Change_Catch 
2016-2018(%) 

Zero catch 

0 

0.00 

0 

0 

1789.05 

3581.06 

100.17 

-100 

High long term 
yield (F0.1) 

0.233  0.23 

665.97 

810.28 

1789.05 

2898.63 

62.02 

-68.36 

Status quo 

1 

0.99 

2007.47 

1757.64 

1789.05 

1552.71 

-13.21 

0.1 

0.10 

306.46 

400.64 

1789.05 

3266.28 

0.2 

0.20 

581.56 

719.97 

1789.05 

2984.78 

0.3 

0.30 

828.91 

974.41 

1789.05 

2732.67 

0.4 

0.39 

1051.71 

1176.99 

1789.05 

2506.57 

0.5 

0.49 

1252.75 

1338.06 

1789.05 

2303.51 

0.6 

0.59 

1434.47 

1465.87 

1789.05 

2120.87 

0.7 

0.69 

1599.01 

1566.99 

1789.05 

1956.37 

0.8 

0.79 

1748.29 

1646.67 

1789.05 

1807.98 

82.57 

66.84 

52.74 

40.11 

28.76 

18.55 

9.35 

1.06 

0.9 

0.89 

1883.95 

1709.10 

1789.05 

1673.95 

-6.43 

Different Scenarios 

1.1 

1.09 

2120.14 

1794.99 

1789.05 

1442.87 

-19.35 

1.2 

1.18 

2223.11 

1823.31 

1789.05 

1343.24 

-24.92 

1.3 

1.28 

2317.38 

1844.33 

1789.05 

1252.73 

-29.98 

1.4 

1.38 

2403.85 

1859.48 

1789.05 

1170.39 

-34.58 

1.5 

1.48 

2483.32 

1869.87 

1789.05 

1095.38 

-38.77 

1.6 

1.58 

2556.48 

1876.43 

1789.05 

1026.96 

-42.60 

1.7 

1.68 

2623.97 

1879.90 

1789.05 

964.46 

1.8 

1.78 

2686.34 

1880.89 

1789.05 

907.29 

-46.09 

-49.29 

1.9 

1.88 

2744.08 

1879.89 

1789.05 

854.93 

-52.21 

2 

1.97 

2797.63 

1877.30 

1789.05 

806.91 

-54.90 

-4.64 

-85.44 

-72.37 

-60.62 

-50.04 

-40.49 

-31.86 

-24.04 

-16.95 

-10.51 

0.71 

5.61 

10.08 

14.19 

17.97 

21.44 

24.65 

27.61 

30.35 

32.90 

The  a4a  analysis  showed  that  fishing  at  F=0.23  would  increase  the  SSB  (from  2018  to 
2019) of about the 60%, and decrease the catch (from 2016 to 2018) of about the 70%, 

395 

395 

 
 
 
 
 
 
 
 
 
 
while fishing at the status quo level would decrease the SSB of the 13% and the catch of 
the 5%.  

Figure 6.8.5.2 Common sole in GSA 17. Short term forecast for the a4a model. 

The  reference  point  used  for  the  SS3  model  –  Run  7  was  F0.1=0.26.  The  recruitment 
from 2017 to 2019 was assumed to be equal to the geometric mean of the whole time 
series.  22  different  F  scenarios  were  simulated  in  order  to  evaluate  the  change  in  SSB 
and in the catch in the short term (Table 6.8.5.3 and Figure 6.8.5.3). 

Table 6.8.5.3 Common sole in GSA 17. Short term forecast for the SS3 model – Run 7; 
catch(2016)=2093 tons, catch(2017)=1568 tons. 

Rationale 

Ffactor 

Fbar 

Catch_2018  Catch_2019  SSB_2018 

SSB_2019 

Change_SSB_2018-
2019(%) 

Change_Catch_2017-
2019(%) 

Zero catch 

0 

0.00 

0 

0 

2750.26  4178.32 

51.92 

-100 

High long 
term yield 
(F0.1) 

0.513 

0.26 

963.15 

1093.56  2750.26  3279.84 

19.26 

-53.98 

396 

396 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Status quo 

Different 
Scenarios 

1 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9 
1.1 
1.2 
1.3 
1.4 
1.5 
1.6 
1.7 
1.8 
1.9 
2 

0.51 
0.05 
0.10 
0.15 
0.20 
0.25 
0.30 
0.36 
0.41 
0.46 
0.56 
0.61 
0.66 
0.71 
0.76 
0.81 
0.86 
0.91 
0.96 
1.01 

1684.27 
207.25 
404.57 
592.49 
771.54 
942.18 
1104.87 
1260.03 
1408.07 
1549.36 
1813.12 
1936.25 
2053.94 
2166.48 
2274.14 
2377.18 
2475.82 
2570.30 
2660.83 
2747.60 

1646.44  2750.26  2625.68 
2750.26  3983.02 
269.84 
2750.26  3798.02 
509.12 
721.06 
2750.26  3622.72 
2750.26  3456.60 
908.53 
1074.10  2750.26  3299.13 
1220.07  2750.26  3149.83 
1348.53  2750.26  3008.26 
1461.33  2750.26  2873.97 
1560.13  2750.26  2746.57 
1721.59  2750.26  2510.94 
1786.77  2750.26  2402.00 
1843.08  2750.26  2298.56 
1891.47  2750.26  2200.32 
1932.81  2750.26  2106.98 
1967.87  2750.26  2018.28 
1997.36  2750.26  1933.98 
2021.90  2750.26  1853.84 
2042.03  2750.26  1777.62 
2058.28  2750.26  1705.13 

-4.53 
44.82 
38.10 
31.72 
25.68 
19.96 
14.53 
9.38 
4.50 
-0.13 
-8.70 
-12.66 
-16.42 
-20.00 
-23.39 
-26.61 
-29.68 
-32.59 
-35.37 
-38.00 

-19.53 
-90.10 
-80.67 
-71.69 
-63.14 
-54.98 
-47.21 
-39.80 
-32.72 
-25.97 
-13.37 
-7.49 
-1.87 
3.51 
8.65 
13.58 
18.29 
22.80 
27.13 
31.28 

397 

397 

 
 
Figure 6.8.5.3 Common sole in GSA 17. Short term forecast for the SS3 Run7. 

The  SS3  Run  7  analysis  showed  that  fishing  at  F=0.26  would  increase  the  SSB  (from 
2018 to 2019) of about the 19%, and decrease the catch (from 2016 to 2018) of about 
the 54%, while fishing at the status quo level would decrease the SSB of the 4.5% and 
the catch of about the 20%.  

6.8.6  DATA DEFICIENCIES 

The  data  used  for  the  a4a  model  come  from  the  last  DCF  official  data  call  (2017).  The 
Croatian  catch  at  age  data  was  lacking  for  years  before  2012  and  they  were 
reconstructed on the base of the total landings suggested by Croatian experts and catch 
at age data composition observed for set netters. 
Regarding  the  SS3  model,  the  data  were  available  thanks  to  the  work  presented  by 
Scarcella et al. at the GFCM working group; a combination of DCF and FAO Fishstat data 
was used to obtain the long time series utilised. 

398 

398 

 
 
 
6.9  SPOTTAIL MANTIS SHRIMP IN GSAS 17 & 18 

6.9.3  BIOLOGY 

The  spot-tail  mantis  shrimp  is  found  in  the  Mediterranean  and  in  the  adjacent  eastern 
Atlantic  ocean,  from  the  Gulf  of  Cadiz  to  Angola.  It  is  found  from  sublittoral  depths  on 
sandy and muddy bottoms to around 150 m depth (Abelló  et al., 2002). There is not a 
clear  distribution  pattern  by  size  and  depth;  however,  juveniles  are  generally  more 
abundant in waters shallower than 30 m depth (Abelló and Martín, 1993). In the Italian 
waters, it is found along the coasts of the whole peninsula, and is particularly abundant 
in  the  northern  and  central  Adriatic  Sea,  where  it  ranks  amongst  the  most  relevant 
species exploited by commercial fisheries (Froglia, 2010). 
The  spot-tail  mantis  shrimp  digs  U-shaped  burrows  in  which  it  hides  during  the  day.  It 
has threfore a preference for areas with suitable burrowing substrate, such as fine sand 
and  sandy-  muddy  bottoms,  especially  where  the  influence  of  river  sediment  intakes  is 
important  (Froglia,  1996;  Atkinson  et  al.,  1997).  In  fact,  it  is  very  abundant  on  the 
continental shelves at the mouths of Ebro, Rhone, Po, and Nile rivers, as a matter of fact 
the species is very abundant in the western side of the Adriatic basin, while it is almost 
absent  in  the  eastern  side,  where  the  sediment  features  are  not  as  suitable  for  their 
borrowing behaviour. It is a strongly sedentary species and seasonal trends appearing in 
catch  data  are  due  more  to  its  reproductive  and  burrowing  behaviour,  and  recruitment 
pattern, than to temporal changes in its distribution (Maynou et al., 2004).  
In the present assessment a combination of data coming from the two Adriatic GSAs (17 
and 18) has been carried out. 

Figure 6.9.1.1. Geographical location of GSAs 17-18. 

Growth 

Froglia  et  al.  (1996)  used  an  indirect  method  to  study  the  growth  of  Spot-tail  mantis 
shrimp  in  GSA  17.  The  length  frequency  distributions  for  males  and  females  recorded 
during  experimental  trawls  carried  out  in  the  central  area  of  the  GSA  17  in  1994  and 
1995  (Froglia  et  al.,  1996)  showed  similar  size  ranges  for  both  sexes.  The  largest 
specimens were collected in September 1994 (39 mm CL for males and females) and the 
smallest specimens were observed in November 1994 (5 mm CL for males and females). 
The last probably represent the new generation of Spot-tail mantis shrimps whose larvae 
settled on the bottom in late summer and early autumn of the same year. The results of 

399 

399 

 
 
 
the  study  indicate  that  the  growth  rate  is  similar  for  males  and  females,  both  sexes 
reaching around 18 mm CL at the end of the first year of life and around 32 mm CL at 
the end of the third year of life. It seems that mantis shrimp individuals live up to five or 
six years of age.  

The  Von  Bertalannfy  (VBGF)  parameters  were  computed  using  the  above  data  and  are 
presented in Table 6.9.1.1. 

Table  6.9.1.1.  Spottail  mantis  shrimp  in  GSAs  17  and  18.  Von  Bertalanffy  growth 
parameters. 

Linf 
41.53 

K 
0.49 

t0 
-0.0105 

Maturity 
The  life  cycle  of  this  species  is  well  known:  the  spawning  period  is  concentrated  from 
winter to spring and planktonic larvae are found in summer, with the settlement of post-
larvae  occurring  from  the  end  of  summer  to  mid-autumn.  Recruitment  to  the  fishery 
starts  in  late  autumn,  with  full  recruitment  being  reached  between  January  and  May 
(Maynou  et  al.,  2004).  In  the  central  Adriatic  (GSA  17),  the  peak  of  ovarian  maturity 
was  reported  in  February  and  March,  when  up  to  80%  of  the  females  had  ripe  ovaries 
(Froglia, 1996). Spent females were mainly observed from April to September, when the 
sex  ratio  (M/F)  is  strongly  in  favour  of  males  (Piccinetti  and  Piccinetti  Manfrin,  1971; 
Froglia  et  al.,  1996).  According  to  Abelló  and  Martín  (1993)  and  Froglia  (1996), 
settlement of post-larvae takes place at the end of summer and the beginning of autumn 
at  17-20  mm  Total  Length  (TL),  or  3-4  mm  Carapace  Length  (CL).  In  GSA  18  the 
monthly  percentage  of  female  maturity  stages  shows  that  the  reproductive  period 
extends  from  October  to  June  with  a  peak  during  the  coldest  months  (winter-early 
spring). L50 (±s.e.) for GSA 18 is 21.1 mm (Carbonara et al., 2013).  
Combined  maturity  at  age  were  calculated  as  a  weighted  average  using  the  stock 
numbers.  

Tab. 6.9.1.2. Spottail mantis shrimp in GSAs 17 and 18. Maturity by age. 

0+ 

1 

0.003  0.809 

2 
1 

3 
1 

4 
1 

5 
1 

6 
1 

7 
1 

Natural Mortality 
The vector of natural mortality as obtained from PRODBIOM model (Abella et al., 1998) 
is shown in Table 6.9.1.2. 

Tab. 6.9.1.3. Spottail mantis shrimp in GSAs 17 and 18. Mortality by age. 

0+ 
1.20 

1 
0.70 

2 
0.60 

3 
0.52 

4 
0.48 

5 
0.48 

6 
0.48 

7 
0.48 

Fishery 
Catches  show  marked  dial  periodicity  with  significantly  more  animals  caught  at  night 
(Froglia and Giannini, 1989; Froglia and Gramitto, 1989). The burrowing behaviour of S. 
mantis  makes  it  vulnerable  only  when  individuals  are  out  of  their  burrows  and  this 

400 

400 

 
 
 
 
 
 
 
 
 
 
 
occurs  mainly  at  night,  between  sunset  and  sunrise.  Seasonal  variations  in  catchability 
result  from  reduced  out-of-burrow  activity,  because  females  rarely  exit  their  burrow 
when  they  are  incubating  their  egg  mass  in  spring  and  early  summer.  Conversely, 
catches  increases  in  winter,  when  mating  takes  place.  Catches  increase  further  in  late 
autumn with the arrival of new recruits. The reproductive behaviour of the species also 
influences  the  relative  proportion  of  males  and  females  in  the  catches  by  season: 
females  outnumber  males  only  in  winter  (mating  season),  while  the  sex-ratio  is  biased 
towards males in spring and summer. Additionally, weather and sea conditions represent 
an  important  influence  on  the  catchability  of  this  species  as  catches  increase  after 
prolonged  bad  weather  conditions  probably  because  of  disturbance  of  the  burrow 
systems as a result of the high turbidity (Froglia et al., 1996). 
Although S. mantis ranks first among the crustaceans landed in the Adriatic ports of GSA 
17, it is not the target of a specialized fishery, but it is an important component of local 
multispecies trawl and gillnet fisheries. It is caught by 4 fisheries, namely DEMF, DEMSP, 
MDPSP and SPF within which 10 different fishing gears are being used. The main species 
caught  in  GSA  17  associated  with  mantis  shrimp  are  Sepia  officinalis,  Trigla  lucerna, 
Merluccius merluccius, Mullus barbatus and Eledone spp. As concerns artisanal fisheries, 
S. mantis is a by catch (only in few cases it also targeted) of gillnetters targeting  Solea 
solea,  especially  during  spring-summer  seasons in  the  coastal area.  Only in  the  Gulf of 
Trieste  it  is  the  target  of  a  small  artisanal  fishery  with  creels  (Froglia  and  Giannini, 
1989). The Italian annual landing for 2016 comes for 81% from the bottom otter trawls 
(2,531  tons),  13%  from  the  gillnett  (408  tons)  and  for  6%  from  the  rapido  trawl  (195 
tons).  
The  species  is  absent  from  the  landings  statistic  of  Croatia  (FAO-FISHSTAT  J  –  GFCM 
Database)  and  it  accounted  only  for  1.8  tons  in  the  Slovenian  catches  of  2016  (2017 
DCF data). The species is not present in the list of shared stock of GFCM.  
Like in GSA 17, mantis shrimp in GSA 18 is mainly a by-catch of trawlers and to a much 
lesser  extent  by  small  scale  fisheries  using  gillnets  and  trammel  nets.  Fishing  grounds 
are  located  along  the  coasts  of  the  whole  GSA  18.  The  species  is  landed  with  other 
important  commercial  species  such  as  Mullus  spp.,  Pagellus  sp.,  Eledone  moschata, 
Octopus  vulgaris.,  M.  merluccius,  etc.  The  exploitation  of  mantis  shrimp  is  mainly 
exerted by the bottom trawlers, both on the western and the eastern sides. In 2016 the 
Italian annual landing of mantis shrimp in GSA 18 comes for 94% from the bottom otter 
trawls (877 tons), 4% from the trammel net (36 tons) and for 2% from the gillnet (16 
tons). 

6.9.4 

INPUT DATA 

6.9.4.1 

CATCH (LANDINGS AND DISCARDS)  

Landings 
In  GSA  17  landing  data  are  available  for  Italy  since  2007  and  for  Slovenia  starting  in 
2005. No fishery is reported for Croatia. Landing from Slovenia are negligible compared 
to the ones from Italy. No size structure of the landing was available for Slovenia. 
Total landings available in GSA 18 covered the period 2006-2016. 

Table  6.9.2.1.1.  Spot-tail  mantis  shrimp  in  GSA  17-18.  Landings  by  fishing  fleet  and 
gear from 2005 to 2016. 

401 

401 

 
 
 
 
 
The landing of the combined GSAs 17 and 18 shows a slight increase in the first part of 
the time series, followed by an evident decrease between 2010 and 2012. This reduction 
is  mainly  due  to  the  GSA  17.  Starting  from  2013  the  landing  remained  quite  stable 
around 4,000 tons. The trend is shown in Figure 6.9.2.1.1. 

Figure  6.9.2.1.1  Spot-tail  mantis  shrimp  in  GSAs  17  and  18.  Trend  in  landing  (in 
tonnes) from 2007 to 2016. 

Discards 
Discard in GSA 17 is available for Slovenia starting in 2005, and for Italy from 2010 to 
2016.  Slovenian  discard  is  less  than  1%  of  the  total.  The  discard  before  2010  for  Italy 
has  been  reconstructed  assuming  an  average  percentage  of  10%  over  the  landings 
(average contribution of discard from 2010 to 2014, Table 6.9.2.1.2.). Size structure of 
discard is available for Italy only from 2010 to 2014, therefore the size and age structure 
before that were reconstructed using an average proportion from 2010 to 2014. 

402 

402 

GSAYearFPOFYKGNDGNSGTRLLSOTBOTMPSGNSGTRLLSOTBTBB1720050.6650.1970.5363.1641720060.4440.1620.2750.0131.5291720070.3170.0030.3520.5036.0690.001936.0522969.0461720080.4460.8671.1933.7170.002831.1342858.627308.8011720090.2840.4930.6172.2100.025872.4813167.340489.5101720100.4160.0030.3390.9953.241961.0973163.351440.2551720110.7750.0020.1840.4172.2101136.3202399.060250.8491720120.0500.0010.0920.2140.0100.3611140.5571681.103283.2381720130.0480.0450.0940.0040.111205.3791681.854240.3601720140.0270.0210.1200.310296.1612325.688183.7281720150.0240.0030.0320.1480.554324.9272476.783261.5831720160.0180.0370.0831.657408.3509.1592531.259194.587182006160.88025.8428.1891075.95218200787.92112.5951157.94018200851.92731.004833.89118200954.14118.129820.09918201019.08019.169415.80618201144.27119.423288.57718201216.94219.897594.84318201344.9852150.9521820140.4714.255999.1671820155.79611.570993.38618201616.22436.085876.848SLOVENIAITALY010002000300040005000600020062007200820092010201120122013201420152016TonsGSA17GSA18Total 
 
 
 
Discard  data  in  GSA  18  were  available  for  the  period  2009-2016  (Table  6.9.2.1.2.). 
Discard for 2008 was reconstructed based on the average discard in 2009-2011. 

Table  6.9.2.1.2.  Spottail  mantis  shrimp  in  GSAs  17-18.  Discard  by  fishing  fleet  and 
gear from 2005 to 2016. 

Total catch (landing + discard) 
The  catch  data  used  for  the  stock  assessment  goes  from  2008  to  2016  (Table 
6.9.2.1.3.).  Total  catch  showed  a  fluctuating  trend  from  a  minimum  of  4109.7  tons  in 
2012 to a maximum of 5491.9 tons in 2010. 
No  age  structure  of  catches  for  GSA  17  was  available  in  the  DCF  database,  therefore 
catch at age data have been reconstructed using the VB growth parameters described in 
the previous section and the LFDA5 software. For GSA 18, landing and discard numbers 
at age data were available in the DCF database. Figure 6.9.2.1.1. shows the catch at age 
by year. 

Table  6.9.2.1.3.  Spot-tail  mantis  shrimp  in  GSAs  17-18.  Landing,  Discard  and  total 
catch from 2008 to 2016. 

403 

403 

GSAYEARGNSGTROTBGNSOTBTBB1720050.4101720060.1251720070.0060.8801720080.0330.5061720090.0050.2941720100.0010.438374.5321720110.2600.945704.83116.1021720120.0030.014103.0641720130.001258.0351720140.0010.009394.4064.2791720150.050324.21010.9361720160.1021041.89918200990.91018201093.1701820111.19060.7701820120.640268.6701820132.860423.55018201478.710182015119.457182016144.418SLOVENIAITALY 
 
 
 
 
 
Figure 6.9.2.1.2 Spottail mantis shrimp in GSAs 17 and 18. Catch at age by year from 
2008 to 2016. 

6.9.4.2 

EFFORT 

Fishing effort data were reported to STECF EWG 17-15 through DCF. The nominal fishing 
effort  in  the  whole  area  (Figure  6.9.2.1.3)  shows  an  evident  decreasing  trend  in  the 
period 2004-2013. In the last three years the value resulted more stable. The reduction 
is mainly due to the decreasing in fishing effort for OTB, both in GSA 17 and 18, and it is 
the direct  consequence of the  gradually reduction in fishing  capacity adopted by Italian 
administration since the late eighties.  

404 

404 

LandingDiscardCatch20084921.6488.0*4921.620095425.3549.9**5425.320105023.8468.15491.920114142.1784.14926.220123737.3372.44109.720134323.8684.45008.320143809.9477.44287.420154074.8454.74529.520164074.31186.45260.7* reconstructed** reconstructed only for GSA 1702000040000600008000010000012000001234567+N (in thousands)200820092010201120122013201420152016 
 
 
 
 
Figure  6.9.2.1.3.  Fishing  effort  in  GSA  18.  Trends  in  annual  bottom  otter  trawler 
nominal fishing effort (kw*days) in GSA 18 from 2002 to 2014. 

Table  6.9.2.1.4.  Spot-tail  mantis  shrimp  in  GSAs  17  and  18.  Nominal  fishing  effort 
(kW*days  in  thousands)  for  the  mixed  fishery  fleet  catching  S.  mantis  for  Italy  and 
Slovenia for the period 2004 – 2016 as reported through the DCF official data call. 

Table 6.9.2.1.5. Spot-tail mantis shrimp in GSAs 17 and 18. Fishing effort (GT*days in 
thousands) for  the  mixed  fishery  fleet catching  S.  mantis  for  Italy and  Slovenia  for the 
period 2004 – 2016 as reported through the DCF official data call. 

405 

405 

0100002000030000400005000060000700002004200520062007200820092010201120122013201420152016Nominal effort ( in thousands)GSA 17  OTBGSA 17  TBBGSA 17  Other gearsGSA 18  OTBGSA 18  Other gearsTotalYearOTBOthersOTBTBBGNSGTROTBGNSGTR200427823.94232.54476.61790.114451.51457.0433.82005112.7389.224094.43812.94980.51275.613550.12035.9515.22006143.5358.019896.84946.24315.51157.314744.61785.868.52007184.0463.919409.05231.82538.91463.412840.21280.5324.52008198.2441.720038.84136.32451.7893.311463.4894.31021.62009200.9530.418890.04386.23280.91079.613878.41205.1837.32010207.9523.718094.63817.53396.41261.511856.3570.4885.32011188.6731.016572.12584.74643.31508.911329.4450.9777.72012153.6362.114020.83254.25314.31505.99822.0395.5541.12013113.7394.912614.32769.72974.41654.310511.6777.860.2201499.8358.514435.03729.83864.4685.47736.3207.8427.42015101.5351.213847.93448.22903.11212.67013.61129.887.72016111.0296.414195.43307.53670.51293.87823.01024.045.5ITALYGSA 17SLOVENIAGSA 18ITALY 
 
 
 
 
 
6.9.4.3 

SURVEY DATA 

1#  SoleMon survey 

Fourteen  rapido  trawl  fishing  surveys  were  carried  out  in  GSA  17  from  2005  to  2016: 
two systematic “pre- suveys” (spring and fall 2005) and twelve random surveys (spring 
and  fall  2006,  fall  2007-2016)  stratified  on  the  basis  of  depth  (0-30  m,  30-50  m,  50-
100m). Hauls were carried out by day using 2- 4 rapido trawls simultaneously (stretched 
codend mesh size = 40.2 ± 0.83). The following number of hauls was reported per depth 
stratum (Tab. 6.9.2.3.1.). 

Table 6.9.2.3.1 Number of hauls per year and depth stratum in GSA 17, 2005-2016. 

Depth 
strata 
0-30 
30-50 
50-100 
HR islands 
Total 

Spring 
2005 
30 
14 
24 
0 
68 

Fall 
2005 
30 
12 
15 
5 
62 

Spring 
2006 
20 
10 
8 
4 
42 

Fall 2006 

35 
20 
8 
4 
67 

Fall 
2007 
32 
19 
11 
0 
62 

Fall 2008-
2016 
39 
17 
11 
0 
67 

Abundance and biomass indexes from rapido trawl surveys were computed using ATrIS 
software  (Gramolini  et  al.,  2005)  which  also  allowed  drawing  GIS  maps  of  the  spatial 
distribution  of  the  stock,  spawing  females  and  juveniles.  Underestimation  of  small 
specimens  in  catches  due  to  gear  selectivity  was  corrected  using  the  selective 
parameters given by Ferretti and Froglia (1975). 

406 

406 

YearOTBOthersOTBTBBGNSGTROTBGNSGTR20045324.81003.1245.4129.02511.067.832.320059.210.45165.3785.6262.780.52354.694.669.4200612.311.44079.71052.9216.479.52662.2117.031.5200717.416.24056.81096.4156.8101.72294.270.245.3200818.917.24082.5843.7135.156.82039.451.484.0200918.219.83830.51045.2173.465.12386.679.780.9201018.220.23837.4921.2190.266.42068.057.179.8201117.820.83482.6665.2236.480.01900.244.979.6201215.120.03130.6772.7259.578.31668.738.360.5201312.023.12645.4657.6167.864.01994.978.98.220149.420.22836.2892.6233.445.61463.621.751.1201510.018.52872.2830.3139.455.51355.278.712.7201610.514.93014.1832.1178.859.71429.288.25.6SLOVENIAITALYITALYGSA 17GSA 18 
 
 
 
 
 
 
 
 
 
The abundance and biomass indices by GSA 17 were calculated through stratified means 
(Cochran,  1953;  Saville,  1977).  This  implies  weighting  of  the  average  values  of  the 
individual  standardized  catches  and  the  variation  of  each  stratum  by  the  respective 
stratum area in the GSA 17: 

Yst = Σ (Yi*Ai) / A 

V(Yst) = Σ (Ai² * si ² / ni) / A² 

Where: 

A=total survey area 

Ai=area of the i-th stratum 

si=standard deviation of the i-th stratum 

ni=number of valid hauls of the i-th stratum 

n=number of hauls in the GSA 

Yi=mean of the i-th stratum 

Yst=stratified mean abundance 

V(Yst)=variance of the stratified mean 

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

It was noted that while this is a standard approach, the calculation may be biased due to a number of 
different  factors  including  the  change  in  the  number  of  hauls  over  time,  and  change  of  the  survey 
time  over  the  years.    Precision  may  also  be  affected  by  the  choice  of  parametric  distribution,  a 
normal distribution is often assumed, whereas data may be better described by a delta-distribution, 
quasi-Poisson. Indeed, data may be better modelled using the idea of conditionality and the negative 
binomial (e.g. O’Brien et al. 2004). 
Length  distributions  represented  an  aggregation  (sum)  of  all  standardized  length 
frequencies over the stations of each stratum. Aggregated length frequencies were then 
raised to stratum abundance and finally aggregated (sum) over the strata to the GSA. 
Given the sheer number of plots generated, these distributions are not presented in this 
report. 

The SoleMon trawl surveys provided trend in abundance for S. mantis. Figure 6.9.2.3.1. 
displays the stratified abundance indices by size obtained in GSA 17 from 2011 to 2016 
during fall survey. The trends in biomass and abundance indices show a clear decrease 
of  the  stock  in  2007  followed  by  an  increase  in  the  rest  of  the  time  series.  In  2016  a 
decrease in respect to the previous year. 

407 

407 

0200400600800100012001400200520062007200820092010201120122013201420152016Kg/km205101520253035200520062007200820092010201120122013201420152016N/km2 
 
 
 
 
Figure 6.9.2.3.1. Spot-tail mantis shrimp in GSA 17. SoleMon biomass and abundance 
indices. 

Figure 6.9.2.3.2. Spot-tail mantis shrimp in GSA 17. Number at age per km2 estimated 
from SoleMon for the period 2011-2016. 

2#  Medits survey 

Medits survey was carried out in GSAs 17 and 18 since 1994. Although the target of the 
survey  are  demersal  species,  Spot-tail  mantis  shrimp  is  scarcely  caught.  This  is  due  to 
the behaviour of the species that spends most of the time borrowed during the daylight 
hours.  In  GSA  17  the  number  of  specimens  measured  in  2009,  2010,  2011  and  2013 
was really low mainly due to the paucity of individuals in the catches.  
However,  based  on  the  DCF  data  call,  abundance  and  biomass  indices  were  calculated 
for GSAs 17 and 18 using the ad hoc script.  

MEDITS survey was deemed inappropriate to be used as tuning index of Spot-tail mantis 
shrimp  in  GSA17.  Moreover  the  use  of  MEDITS  survey  indexes  from  GSA  18  were  not 
used  in  the  present  assessment  because  of  the  high  residuals  observed  in  the  XSA 
diagnostics. 

6.9.3 

STOCK ASSESSMENT 

During EWG 17-15 the stock assessment was performed over the period 2008-2016. The 
age classes considered from the catches range from 0 to 8; plus group was set at age 7. 
Discards  were  included  in  the  analyses.  Since  no  discard  data  were  available  for  2008-
2009  in  GSA  17  and  for  2008  in  GSA  18,  an  estimate  based  on  the  average  discard 
ratios  and  discard  age  structures  of  the  available  nearest  years  was  performed.  The 
SoleMon  trawl  survey  was  used  as  tuning  index  of  the  assessment  and  the  age  range 
used goes from 0 to 6. Age data from SoleMon were available for the period 2011-2016. 

408 

408 

0501001502002503003504000+123456N/km2Age class201120122013201420152016 
 
 
 
 
 
Two  different  methods  have  been  applied:  Extended  Survival  Analysis  (XSA)  stock 
assessment  model  run  through  the  FLXSA  library  implemented  in  R  and  a4a  statistical 
catch at age framework developed by the Joint Research Centre (Jardim et al., 2015). 
Data  used  are  reported  in  Table  6.9.3.1..  A  natural  mortality  vector  computed  using 
ProdBiom  (Abella  et  al.,  1998)  was  used.  The  analyses  were  performed  by  sex 
combined, as growth is very similar between the two sexes. Given that the catches were 
composed mainly of individuals between 1 and 3 years, these ages were selected as the 
Fbar. 

Table  6.9.3.1.  Spottail  mantis  shrimp  in  GSAs  17  and  18.  Input  parameters  for  the 
stock assessment models. 

Catches at age 

Mean weight at age (Catches) 

Mean weight at age (Stock) 

409 

409 

200820092010201120122013201420152016011397921240441028311417139774702591110049135235752414327167040482058183345301458791098392468747223571698578264713950833527804549250253318223174471363811831129991275412696173749042482782736524610022432201729914137117854021352208710757530395980227262383141108039169149245554757+21070446011110127615993420082009201020112012201320142015201600.0110.0070.0080.0080.0080.0100.0070.0070.00710.0260.0230.0260.0260.0230.0230.0230.0230.02320.0410.0400.0390.0400.0400.0400.0400.0400.04030.0600.0600.0600.0590.0590.0590.0590.0590.05940.0750.0750.0750.0750.0740.0740.0740.0740.07450.0850.0850.0850.0870.0840.0840.0840.0840.08460.0930.0930.0930.0930.0930.0930.0930.0930.0937+0.1100.1060.1050.1060.1070.1000.1020.1020.102 
 
 
 
 
 
 
 
 
 
 
Proportion of mature 

Natural mortality 

Tuning (SoleMon survey, kg/km2) 

Method: XSA 

410 

410 

20082009201020112012201320142015201600.0110.0070.0080.0080.0080.0100.0070.0070.00710.0260.0230.0260.0260.0230.0230.0230.0230.02320.0410.0400.0390.0400.0400.0400.0400.0400.04030.0600.0600.0600.0590.0590.0590.0590.0590.05940.0750.0750.0750.0750.0740.0740.0740.0740.07450.0850.0850.0850.0870.0840.0840.0840.0840.08460.0930.0930.0930.0930.0930.0930.0930.0930.0937+0.1100.1060.1050.1060.1070.1000.1020.1020.10220082009201020112012201320142015201600.0030.0030.0030.0030.0030.0030.0030.0030.00310.8090.8090.8090.8090.8090.8090.8090.8090.80921.0001.0001.0001.0001.0001.0001.0001.0001.00031.0001.0001.0001.0001.0001.0001.0001.0001.00041.0001.0001.0001.0001.0001.0001.0001.0001.00051.0001.0001.0001.0001.0001.0001.0001.0001.00061.0001.0001.0001.0001.0001.0001.0001.0001.0007+1.0001.0001.0001.0001.0001.0001.0001.0001.00020082009201020112012201320142015201601.201.201.201.201.201.201.201.201.2010.700.700.700.700.700.700.700.700.7020.600.600.600.600.600.600.600.600.6030.520.520.520.520.520.520.520.520.5240.500.500.500.500.500.500.500.500.5050.480.480.480.480.480.480.480.480.4860.480.480.480.480.480.480.480.480.487+0.480.480.480.480.480.480.480.480.48201120122013201420152016027121320911121252501972651802285188255291371281310780697091444151314812951321136011101 
 
 
 
 
 
 
 
 
 
 
 
XSA  was  run  setting  shrinkage  at  1.0,  1.5,  2.0,  2.5  and  3.0.  As  showed  by  Figure 
6.9.3.1, the five different settings produced similar estimates of recruitment, SSB and F. 

Figure  6.9.3.1  Spottail  mantis  shrimp  in  GSAs  17  and  18.  XSA  outputs  of  different 
shrinkage scenarios. 

Model  with  2.0  shrinkage  was  adopted  as  final  model  based  on  the  analysis  of  residual 
distributions (Figure 6.9.3.2.). Residuals from tuning fleets (SoleMon) per age and year 
were relatively low, ranging from 1 to -1, and did not show any trend with time. 
Moreover,  a  retrospective  analysis  was  conducted  on  recruitment,  mean  F  and  SSB 
(Figure 6.9.3.3.) to ensure the robustness of the final estimates. The retrospective series 
indicate  good  agreement  between  years  in  the  assessment  results,  with  no  systematic 
bias. 

411 

411 

 
 
 
 
 
 
 
Figure  6.9.3.2  Spottail  mantis  shrimp  in  GSAs  17  and  18.  Residuals  at  age  obtained 
with shrinkage set at 2.0. 

Figure  6.9.3.3  Spottail  mantis  shrimp  in  GSAs  17  and  18.  Retrospective  analysis  with 
shrinkage set at 2.0. 

Based on these simulation analyses, the inputs reported in Table 6.9.3.1. were selected 
to run the final XSA: 

412 

412 

 
 
 
 
 
FLXSA.control.aa2  <-  FLXSA.control(x=NULL,  tol=1e-09,  maxit=30,  min.nse=0.3, 
rage=1,  qage=2,  shk.n=TRUE,  shk.f=TRUE,  shk.yrs=3,  shk.ages=2, 
fse=2.0, 
window=100, tsrange=20, tspower=3, vpa=FALSE) 

XSA  main  outputs  are  displayed  in  Figure  6.9.3.4.  and  in  Table  6.9.3.2..    Recruitment 
showed  a  slightly  fluctuating  trend  from  a  minimum  of  1045  millions  in  2006  to  1347 
millions  in  2015.  After  a  decreasing  trend  in  the  period  2008-2012,  SSB  shows  a 
recovering  passing  from  12201  tons in  2012 to 15717 tons in  2016.  After  reaching  the 
highest value in 2010 (0.948), fishing mortality showed a constant decreasing trend with 
minimum value in 2016 (0.513).  

Figure  6.9.3.4  Spottail  mantis  shrimp  in  GSAs  17  and  18.  XSA  summary  results.  SSB 
and catch are in tons, recruitment in thousands of individuals. 

413 

413 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 6.9.3.2 Spottail mantis shrimp in GSAs 17 and 18. XSA summary results. 

 Table 6.9.3.2 Spottail mantis shrimp in GSAs 17 and 18. XSA summary results. 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

16624 

15618 

14356 

13325 

12201 

13587 

13144 

14077 

15717 

1296851 

1049188 

1067224 

1045430 

1225407 

1088327 

1155401 

1374115 

1157518 

SSB 

(Tons) 

REC 

(x1000) 

F age 0 

0.016 

0.016 

0.007 

0.018 

0.017 

0.024 

0.007 

0.008 

0.016 

F age 1 

0.163 

0.325 

0.220 

0.354 

0.250 

0.386 

0.224 

0.209 

0.477 

F age 2 

0.689 

1.191 

1.211 

0.994 

0.848 

0.853 

0.871 

0.660 

0.668 

F age 3 

0.971 

1.039 

1.414 

1.142 

1.108 

1.001 

0.888 

1.614 

0.394 

F age 4 

2.194 

0.528 

2.463 

0.479 

1.312 

0.743 

1.115 

1.497 

0.606 

F age 5 

1.638 

0.806 

2.008 

0.442 

0.859 

0.800 

1.926 

2.284 

0.466 

F age 6+ 

1.638 

0.806 

2.008 

0.442 

0.859 

0.800 

1.926 

2.284 

0.466 

Mean F 

(1-3) 

0.608 

0.852 

0.948 

0.830 

0.735 

0.747 

0.661 

0.828 

0.513 

Method: a4a 

Different  a4a  models  were  performed  (combination  of  different  f,  q).  The  best  model 
included:   

qmod<- list(˜ factor(age)) 
fmod<- ˜s(replace(age,age>5,5), k=4) + s(year, k=5) 
srmod<- ˜s(year,k=5) 

The diagnostics and the outputs of the a4a model for Spot-tail mantis shrimp in GSAs 17 
and 18 are shown in Figures 6.9.3.5-6.9.3.9. 
The  retrospective  analysis  was  applied  up  to  2  years  back.    Models  results  were  quite 
stable (Figure 6.9.3.10). 
a4a results are displayed in Figure 6.9.3.11. and Table 6.9.3.3.. 
Recruitment showed a decreasing in the first three years of the data series, reaching the 
minimum  value  in  2010  (891  millions).  After  that  an  increasing  trend  until  2016  is 
observed (1786 millions).   
SSB  shows  a  similar  trend  but  the  recovery  starts  since  2013.  The  model  estimates 
14071 tons of SSB in 2016.  
Fishing  mortality  shows  a  fluctuating  trend  with  maximum  values  in  2010  (0.916)  and 
2014 (0.867). In 2016 F was 0.652. 

414 

414 

 
 
 
 
 
 
 
 
Figure 6.9.3.5 Spottail mantis shrimp in GSAs 17 and 18. Log residuals for catch- and 
SoleMon indices- at-age from the a4a basic model. 

Figure 6.9.3.6  Spottail mantis shrimp  in  GSAs 17  and  18.  Bubble  plot of log residuals 
for catch- and SoleMon indices-at-age from the a4a basic model. 

415 

415 

 
 
 
 
 
Figure  6.9.3.7  Spottail  mantis  shrimp  in  GSAs  17  and  18.  Quantile-quantile  plot  of 
standardized  residuals  for  abundance  indices  (SoleMon)  and  for  catch  numbers 
(catch.n). 

Figure  6.9.3.8  Spottail  mantis  shrimp  in  GSAs  17  and  18.  Observed  vs  fitted  catch 
indices-at-age. 

416 

416 

 
 
 
 
 
 
Figure 6.9.3.9  Spottail mantis shrimp in GSAs 17 and 18.  Observed  vs fitted SoleMon 
indices-at-age. 

Figure 6.9.3.10 Spottail mantis shrimp in GSAs 17 and 18. Retrospective analysis with 
a4a basic model. 

417 

417 

 
 
 
 
 
Figure 6.9.3.11 Spottail mantis shrimp in GSAs 17 and 18. a4a summary results. SSB 
and catch are in tons, recruitment in thousands of individuals. 

Table 6.9.3.3 Spottail mantis shrimp in GSAs 17 and 18. a4a summary results. 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

15270 

14160 

13101 

11559 

10921 

11743 

12225 

12469 

14071 

1196112 

968082 

891400 

936465 

1010338 

1047750 

1107680 

1322943 

1786156 

SSB 

(Tons) 

REC 

(x1000) 

F age 0 

0.014 

0.017 

0.018 

0.015 

0.014 

0.015 

0.017 

0.016 

0.013 

F age 1 

0.219 

0.275 

0.284 

0.249 

0.226 

0.241 

0.269 

0.257 

0.202 

F age 2 

0.896 

1.125 

1.164 

1.018 

0.925 

0.987 

1.102 

1.050 

0.828 

F age 3 

1.001 

1.257 

1.300 

1.137 

1.034 

1.103 

1.231 

1.173 

0.925 

F age 4 

1.022 

1.284 

1.327 

1.161 

1.056 

1.126 

1.257 

1.197 

0.945 

F age 5 

1.774 

2.228 

2.305 

2.016 

1.833 

1.955 

2.182 

2.079 

1.640 

F age 6+ 

1.774 

2.228 

2.305 

2.016 

1.833 

1.955 

2.182 

2.079 

1.640 

Mean F 

(1-3) 

0.705 

0.886 

0.916 

0.801 

0.728 

0.777 

0.867 

0.826 

0.652 

Comparison of the XSA and a4a results 

The  results  of  the  best  fits  obtained  with  XSA  and  a4a  have  been  compared  (Figure 
6.9.3.12.).  The  trends  estimated  for  recruitment,  SSB,  Catch  and  fishing  mortality 
resulted very similar between the two models. In general, the values obtained with XSA 
fall inside the confidence intervals estimated by a4a model. Reterospective performance 
is similar, but with a4a giving more stable and smoother F at the expense of instability in 
recruitment. 

418 

418 

 
 
 
 
 
 
 
Compared  to  XSA,  a4a  runs  forward  and  allows  to  reach  a  better  stability  for  last  year 
estimates. Due to this fact, a4a was chosen to evaluate the exploitation state of Spot-tail 
mantis shrimp in GSAs 17 and 18. 

Figure 6.9.3.12 Spottail mantis shrimp in GSAs 17 and 18. Comparison of the XSA and 
a4a results. SSB and catch are in tons, recruitment in thousands of individuals. 

6.9.4 

REFERENCE POINTS 

The time series of SSB and R values is not sufficient to allow evaluation of S-R elements 
of MSY, so the WG has applied the STECF recommended method of F0.1.  
Using  the  FLR  libraries  a  yield  per  recruit  (YpR)  analysis  was  conducted  assuming 
equilibrium  conditions  and  having  as  a  base  the  exploitation  pattern  resulted  from  the 
a4a  analysis  of  Spot-tail  mantis  shrimp  in  GSAs  17  and  18.  YpR  was  used  for  the 
estimation of F0.1.  
As a result of the analysis the Fcurr (0.65) (F2016) is larger than F0.1 (0.375), chosen as 
proxy  of  FMSY  and  as  the  exploitation  reference  point  consistent  with  high  long  term 
yields, which indicates that Spot-tail mantis shrimp in GSAs 17 and 18 is exploited above 
FMSY. 

419 

419 

 
 
 
 
 
Figure  6.9.3.1.1.  Spot-tail  mantis  shrimp  in  GSAs  17  and  18.  Yield  per  Recruitment, 
a4a model 

6.9.5 

SHORT TERM FORECAST AND CATCH OPTIONS 

A  deterministic  short-term prediction  for  the  period  2017  to  2018  was  performed  using 
the FLR routines and based on the results of the a4a stock assessment.  
The input parameters for the deterministic short-term predictions for the period 2017 to 
2019  were  the  same  used  for  the  a4a  stock  assessment  and  its  results.  An  average  of 
the  last  three  years  has  been  used  for  weight  at  age,  maturity  at  age  and  F  at  age. 
Recruitment  (age  0)  has  been  estimated  from  the  population  results  as  the  geometric 
mean of the last 3 years (1,378,135 thousand individuals).  
A  short-term  projection  of  the  fleet  (Table  6.9.5.1.)  fishing  at  the  status  quo  (F=0.78) 
generates an increase of the catch of 61.3% from 2016 to 2018, with a reduction of 7% 
of the spawning stock biomass from 2018 to 2019. Fishing at F0.1 (0.38) generates a loss 
of the catch of 7.6% from 2016 to 2018 and an increase of the spawning stock biomass 
of 9.6% from 2018 to 2019.  

Table  6.9.5.1.  Spot-tail  mantis  shrimp  in  GSAs  17  and  18.  Short  term  forecast  in 
different  F  scenarios.  The  input  parameters  from  a4a  stock  assessment  weight  at  age, 
maturity at age and F at age, averages 2014-16. Recruitment (age 0) geomean 2014-16 
(1,378,135 thousand individuals). 

High long 
term yields 
(F0.1) 

Rationale 

Ffactor 

Fbar 

0 catch 

0 

0 

Catch 

2017 

Catch 

Catch 

SSB 

SSB 

Change in SSB 

Change in Catch 

2018 

2019 

2018 

2019 

2018-2019 (%) 

2016-2018 (%) 

6427 

0 

0 

18357  24368 

32.7 

-100 

Catch 

2016 

4360 
4360 

0.48 

0.38 

6427 

4028 

4657 

18357 

20125 

9.6 

-7.6 

420 

420 

 
 
 
 
 
 
 
 
 
 
 
 
Status quo 

1 

0.78 

Scenarios 

0.1 

0.08 

0.2 

0.16 

0.3 

0.23 

0.4 

0.31 

0.5 

0.39 

0.6 

0.47 

0.7 

0.55 

0.8 

0.63 

0.9 

0.70 

1.1 

0.86 

1.2 

0.94 

1.3 

1.02 

1.4 

1.09 

1.5 

1.17 

1.6 

1.25 

1.7 

1.33 

1.8 

1.41 

1.9 

1.49 

2.0 

1.56 

4360 

4360 

4360 

4360 

4360 

4360 

4360 

4360 

4360 

4360 

4360 

4360 

4360 

4360 

4360 

4360 

4360 

4360 

4360 

4360 

6427 

7034 

6426 

18357  17065 

6427 

6427 

6427 

6427 

6427 

6427 

6427 

6427 

6427 

6427 

6427 

6427 

6427 

6427 

6427 

6427 

6427 

6427 

6427 

970 

1393 

18357  23334 

1866 

2521 

18357  22386 

2693 

3433 

18357  21515 

3459 

4171 

18357  20715 

4169 

4769 

18357  19979 

4827 

5253 

18357  19301 

5439 

5645 

18357  18675 

6008 

5962 

18357  18096 

6539 

6219 

18357  17561 

7497 

6594 

18357  16605 

7930 

6729 

18357  16177 

8337 

6839 

18357  15779 

8718 

6927 

18357  15408 

9077 

6998 

18357  15061 

9414 

7056 

18357  14736 

9733 

7102 

18357  14433 

10034 

7140 

18357  14148 

10318 

7171 

18357  13880 

10588 

7195 

18357  13627 

-7.0 

27.1 

21.9 

17.2 

12.8 

8.8 

5.1 

1.7 

-1.4 

-4.3 

-9.5 

-11.9 

-14.0 

-16.1 

-18.0 

-19.7 

-21.4 

-22.9 

-24.4 

-25.8 

61.3 

-77.7 

-57.2 

-38.2 

-20.7 

-4.4 

10.7 

24.7 

37.8 

50.0 

71.9 

81.9 

91.2 

99.9 

108.2 

115.9 

123.2 

130.1 

136.6 

142.8 

421 

421 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure  6.9.5.1.  Spottail  mantis  shrimp  in  GSAs  17  and  18.  Short-term  forecast  in 
different F scenarios. 

6.9.6 

DATA DEFICIENCIES 

The  length  frequency  distributions  of  the  Italian  catches  are  available  since  2007  for 
landing and since 2010 for discard. No data are available for Croatia; catches from the 
eastern coast of the Adriatic Sea seem negligible because the type of bottoms does not 
represent  the  ideal  habitat  of  the  species.  Composition  of  the  catches  by  age  are  not 
available  in  the  DCF  database.  VB  growth  parameters  are  not  reported  in  the  DCF 
database. Medits data for this species are considered completely unreliable due the few 
numbers of specimens caught. However, this is an issue related to the behaviour of the 
species  that  is  scarcely  available  to  the  Medits  surveys  as  the  hauls  are  carried  out 
during the daylight hours when mostly of the specimens are in the borrows. 

The length frequency distributions for the Italian fleet operating in GSA 18 are available 
since  2007  for  landing  and  since  2009  for  discard.  In  these  data  time  series,  the 
coverage  of  the  different  metiers  is  very  high  representing  98%  of  the  total  biomass 
caught  in  the  area.  The  main  issue  for  Spot-tail  mantis  shrimp  in  GSA  18  is  related  to 
the estimation of the abundance at sea as SoleMon survey is not carried out in that area. 
Biomass and density indices coming from Medits survey showed high fluctuations due to 
the low availability of the species. 

6.10  HAKE IN GSA 19 

6.10.1  BIOLOGY 

Hake is one of the most important commercial resources in the GSA 19, being with red 
mullet and deep-water deep-water rose  shrimp a key species of fishing assemblages in 
the Ionian Sea (GSA 19). 

According  to  the  main  outcomes  of  the  EU  StockMed  project  carried  out  in  MAREA 
framework, hake in the GSA 19 seems to belong to a wider stock unit distributed on the 
Central Mediterranean Sea. However, for the purposes of this assessment it is assumed 
a single, homogeneous stock confined in GSA 19 (Figure 6.10.1.1.). 

422 

422 

 
 
 
Figure 6.10.1.1. Geographical location of GSAs 19. 

The GSA 19 covers a surface of about 16500 km2 in the depth range between 10-800 m 
along  a  coast  line  of  about  1000  km  (Italian  regions  of  Apulia,  east  Lucania,  east 
Calabria and east Sicily). The Northern Ionian Sea is geo-morphologically divided in two 
sectors by the Taranto Valley, which is exceeding 2200 m in depth. The former is located 
between  the  Taranto  Valley  and  the  Apulia  region  and  is  represented  by  a  broad 
continental  shelf.  Along  Calabria  and  Sicily  instead,  the  shelf    is  generally  very  limited 
with the shelf break located at a depth varying between 30 and 100 m. 

According  to  Medits  and  Grund  surveys  data  M.  merluccius  has  been  caught  at  depth 
ranging from 14 to 800 m in the GSA 19. Adult specimens of European hake are mainly 
found on the slope, while recruits and pre-adult are mainly distributed on the shelf and 
shelf-break upper slope. 

European  hake  is  considered  fully  recruited  to  the  bottom  at  10  cm  TL  (from  SAMED, 
2002).  The  length  structures  from  trawl  surveys  are  generally  dominated  by  juveniles, 
while  large  size  individuals  are  rare.  This  pattern  might  be  also  due  to  the  different 
vulnerability of older fish (Abella and Serena, 1998) beside the effect of high exploitation 
rates. Shelter for adults of this species can be represented by many submarine canyons 
located along the coasts of this GSA. The few large European hake caught during trawl 
surveys are generally females and inhabit deeper waters. 

Growth 

In the DCF framework the growth has been studied ageing fish by otolith readings using 
the  whole  sagitta  and  thin  sections  for  older  individuals.  Length  frequency  distributions 
were  also  analyzed  using  techniques  as  Batthacharya  for  separation  of  modal 
components. 

DCF  von  Bertalanffy  growth  parameters  for  each  sex  were  estimated  from  average 
length  at  age  using  an  iterative  non-liner  procedure  that  minimizes  the  sum  of  the 
square differences between observed and expected values. 

The table 6.10.1.1. summarizes the estimates obtained by the DCF Data Call for the von 
Bertalanffy growth parameters. 

423 

423 

 
 
 
 
 
Table 6.10.1.1. Hake in GSA 19. Summary of the estimated obtained by the DCF Data 
Call for the von Bertalanffy growth parameters. 

PERIOD 

SEX  VB_LINF  VB_K  VB_T0  VB_SIZE_ RANGE 

2003 - 2016  C 

111 

0.1024  -0.60 

5-82 cm 

2003 - 2016 

F 

111 

0.1024  -0.60 

5-82 cm 

2003 - 2016  M 

73.0 

0.1453  -0.73 

5-65 cm 

Maturity 

Spawning  season  extends  over  all  year  round  with  maturity  peak  around  December-
March.  

As reported by EWG-15-16  report the estimated size at first maturity in GSA19 for Hake 
is about 33.6 cm (maturity range 2.4 cm) for females and 17.5 cm (maturity range 1.1 
cm) for males. 

6.10.2  INPUT DATA 

For the purposes of this assessment the following growth parameters have been 
used  for  sex  combined,  according  to  the  choice  of  setting  a  fast  growth  pattern 
for European hake in the EWG-15-16 STECF working group for this species in this 
area as in other ones of the Mediterranean: 

L∞ =104 cm; K=0.2 (year); t0=-0.01 

However  further  studies  based  on  different  techniques  that  allow  validation  are 
recommended to give insight in this growth pattern, given that these parameters 
might  not  be  suitable  for  all  the  components  of  the  population  (e.g.,  sexes,  life 
stage). 

For the purposes of this assessment the following vector of maturity at age (table 
6.10.2.1.)  has  been  used,  in  line  with  the  growth  parameters  selected  for  the 
assessment.  The  vector  of  proportion  of  mature  individuals  by  age  has  been 
derived  by  slicing  the  maturity  ogive  by  length  with  the  von  Bertalanffy 
coefficients during STECF EWG 15-16. 

Table  6.10.2.1.  Hake  in  GSA  19.  Maturity  proportion  at  age  adopted  in  the 
present assessment. 

Age 

0  1 

2 

3  4  5  6+ 

Proportion of matures  0  0.19  0.86  1  1  1  1 

424 

424 

 
 
 
 
 
 
 
A vector of natural mortality at age was estimated according to Prodbiom method 
(Abella et al., 1997), using growth and length-weight relationship parameters for 
sex combined selected for the assessment  (Table 6.10.2.2.). 

Table  6.10.2.2.  Hake  in  GSA  19.  Vector  of  natural  mortality  adopted  in  the  present 
assessment. 

Age 

0 

1 

2 

3 

4 

5 

6+ 

Natural mortality  1.48  0.45  0.28  0.22  0.19  0.17  0.16 

6.10.2.1  CATCH (LANDINGS AND DISCARDS)  

Catch  data  from  DCF  were  analysed  from  STECF  EWG  17-15.  The  overall  catches,  as 
landings  and  discards  are  listed  in  table  6.10.2.1.1  and  figure  6.10.2.1.1.  While  the 
landings  are  reported  for  all  years,  discards  are  missing  at  the  beginning  of  the  time 
series (2002-2005) and in two other years (2007-2008) as collection of discard data was 
not foreseen by DCF. 

As shown in figure 6.10.2.1.1. landings after a peak in 2006 drammatically decrease to 
minimum  values  in  the  last  five  years.  Current  level  of  landing  is  around  700  tons 
compared  with  1630  tons  in  2006.  Also  discards  drop  down  to  minimum  values  of 
around 4 tons (2014) after the peak shown in 2009 (53 tons). 

Table 6.10.2.1.1. Hake in GSA 19. Catches as landings + discards from DCF. 

year gsa  landings  discards 
2002  19 1356.8194        NA 
2003  19 1513.8160        NA 
2004  19 1298.7318        NA 
2005  19 1271.1942        NA 
2006  19 1629.1298 34.170370 
2007  19  882.3411        NA 
2008  19  932.1399        NA 
2009  19  998.6430 53.127207 
2010  19  838.7930 10.957570 
2011  19  810.2298  8.563903 
2012  19  674.8485 11.478274 
2013  19  760.3392 11.454187 
2014  19  739.9736  3.852499 
2015  19  806.5847  5.364918 
2016  19  706.8578 17.826430 

425 

425 

 
 
 
 
 
 
 
Figure 6.10.2.1.1. Hake in GSA 19. Trend of landings and discards by year. 

As  regards  the  catch  composition  by  gear  (table  6.10.2.1.2  and  figure  6.10.2.1.2)  the 
bulk of catches are represented by Bottom otter trawls (OTB) and longlines (LLS) for the 
landing  fraction,  and  by  OTB  for  the  discard  component.  Discard  varied  from  year  to 
year and was about 1.5-6% of landings. 

Table  6.10.2.1.2.  Hake  in  GSA  19.  Catches  as  landings  (a)  +  discards  (b)  from  DCF. 
The fishing technique targeting hake are highlighted. 

a) 
      -1 GND GNS GTR LLD LLS  OTB PS PTM SB SV 
2002 390   0   0 263   0   0  688 16   0  0  0 
2003 478   0   0 367   0   0  668  1   0  0  0 
2004   0   0  35   7   0 204 1053  0   0  0  0 
2005   0   0  20   9   0 147 1078 17   0  0  0 
2006   0   0   8  92  64 136 1330  0   0  0  0 
2007   0   0   0  25  12 275  572  0   0  0  0 
2008   0   0  37  16   0 196  682  0   0  0  0 
2009   0   0  25  25   0 296  652  0   0  0  0 
2010   0   0  17  18   0 240  564  0   0  0  0 
2011   0   0  21  18   0 237  534  0   0  0  0 
2012   2   1  34  57  10 166  406  0   0  0  0 
2013   0   0 153 135   0 236  237  0   0  0  0 
2014   0   0 120  90   0 320  210  0   0  0  0 
2015   0   0 135 105   1 214  304 49   0  0  0 
2016   0   0 110  91   1 197  285 23   0  0  0 

b) 
     OTB 
2006  34 
2009  53 

426 

426 

 
 
 
 
 
2010  11 
2011   9 
2012  11 
2013  11 
2014   4 
2015   5 
2016  18 

 a 

 b 

Figure 6.10.2.1.2. Hake in GSA 19. Trend of landings (a) and discards (b) by gear. 

Taking in to account the fleet targeting hake, the decrease of landings in bottom trawlers 
is contrasted by the increasing of landings in longlines and nets (figure 6.10.2.1.3) 

427 

427 

 
 
 
Figure 6.10.2.1.3. Hake in GSA 19. Trend of landings of fleets targeting haker. 

The  figure  6.10.2.1.4.  reports  the  length  frequency  distributions  of  the  catches 
(landings+discards).  Generally these distributions are dominated by individuals up to 30 
cm total length. 

a 

428 

428 

 
 
 
Figure  6.10.2.1.4.  Hake  in  GSA19.  Length  frequency  distribution  of  catches 
(a=landings, b=discards). 

As shown in figure 6.10.2.1.5. different gears accounts for a different selectivity. 

b 

a 

429 

429 

 
 
Figure  6.10.2.1.5.  Hake  in  GSA19.  Length  frequency  distribution  of  catches 
(a=landings, b=discards) by gear. 

b 

Since  in  some  years  discard  data  were  not  found  by  DCF,  the  size  structure  was 
reconstructed, and then total catches and total catches at length were reconstructed. To 
derive discards quantities in missing years the mean proportion of catch discarded  was 
calculate  and  then  it  was  applied  to  the  landing  of  the  missing  year  (table  6.10.2.1.3, 
figure 6.10.2.1.6). 

Table  6.10.2.1.3.  Hake  in  GSA  19.  Catches  as  landings  (a)  +  discards  (b)  after  the 
reconstruction for missing years. 

year  discards  landings   catches 
2002 17.539155  950.4086  967.9478 
2003 19.102421 1035.1186 1054.2210 
2004 23.965549 1298.6409 1322.6064 
2005 23.130613 1253.3975 1276.5281 
2006 34.170370 1565.3105 1599.4808 
2007 16.068614  870.7232  886.7918 
2008 17.196228  931.8261  949.0224 
2009 53.127207  998.6430 1051.7702 
2010 10.957570  838.7930  849.7506 
2011  8.563903  810.1490  818.7129 
2012 11.478274  662.7373  674.2155 
2013 11.454187  760.3392  771.7934 
2014  3.852499  739.9736  743.8261 
2015  5.364918  756.7236  762.0885 
2016 17.826430  682.4565  700.2830 

430 

430 

 
 
 
 
Figure 6.10.2.1.6. Hake in GSA19. Catch data as basis for the assessment. 

The landings and discards at length were then split into ages by applying the L2a routine 
as  implemented  in  a4a  package.  For  landings  the  zero  values  in  older  ages  (>4)  were 
replaced with ½ lowest value in the data series. 

For  years  without  discard  ages  values  (2003-2008)  the  mean  age  structure  of  discards 
over all years was applied raised by the average discard rate by age class as following: 

Xij=(X/Y)*mean(Xi) 

where:Xij=discard of the i-th age class by j-year 

Xi= mean of discard of the i-th age 

X=mean of total discards 

Y=mean of total landings  

and where Xi and X/Y are calculateds from years with discard ages values. 

The  catch  at  age  matrix  was  finally  obtained  as  sum  of  landings  and  discards  at  ageas 
shown in Table. 6.10.2.1.4 and Figure 6.10.2.1.7. 

Table 6.10.2.1.4. Hake in GSA 19. Catches at age after the reconstruction for missing 
years. 

Year/age      0         1        2       3      4      5      6 
2003   968.1948  4834.850 329.0240   0.000  0.000  0.000  0.000 
2004  1901.0640  7848.930 442.3920 127.791 32.993 11.877 17.157 
2005 12769.2816 22460.228 175.9601  10.387  0.000  0.000  0.000 
2006 11676.7892 20160.572 414.6100  41.430 18.949  8.186  1.776 
2007  2773.2280  7530.856 268.4510  96.854 32.650  5.155  5.155 
2008 10469.8286 15386.560 242.9190  50.894 25.187  6.788  2.642 
2009  5814.0470  6553.978 548.9920  89.296 22.606  5.833  0.813 
2010  5297.3820  7329.445 320.1230 160.716 26.855  3.082  3.595 
2011 11577.2840 14202.551 246.0990  56.498 25.539  9.485  0.000 

431 

431 

 
 
 
 
2012  6675.3310  9225.924 235.7180  44.649 10.421  0.000  0.000 
2013  2718.2630  5410.361 631.2630  21.449 13.575  0.639  0.000 
2014  1771.8960  4061.235 514.0290  78.532 29.622  4.232 14.106 
2015  6339.0560  9571.952 290.2030  34.143 29.885  5.847  0.000 
2016  6864.2450  7256.511 236.7620  55.395 10.295  5.594  2.214 

Figure 6.10.2.1.7. Hake in GSA19. Catch at age data as basis for the assessment. 

6.10.2.2  EFFORT 

Fishing effort data were reported to STECF EWG  17-15 through DCF. The data analysis 
were done by country and gear type. 

The trend in fishing effort in GSA 19 by year and vessels are listed in tables 6.10.2.2.1-
3. (effort in kw*days, GT*days and days at sea) and showed in figures 6.10.2.2.1-3, and 
highlight  the  effort  by  vessels  of  other  country  for  some  years.  For  Italian  vessels  the 
nominal effort show a decreasing ternd in the last years. 

Table 6.10.2.2.1. Hake GSA 19. Fishing effort in nominal effort kw*days at sea by year 
and fishing gear (time series 2003-2016) from DCF. 

kw*days      CYP    FRA      ITA 
2004    0      0 20017379 
2005    0      0 17901210 
2006    0      0 17965471 
2007    0      0 16186864 
2008    0      0 17066927 
2009    0      0 17904605 
2010 2031      0 20854737 
2011    0      0 17199131 
2012    0      0 17526966 

432 

432 

 
 
 
 
 
 
2013    0      0 19083607 
2014    0      0 17220771 
2015    0  77430 16105506 
2016    0 108380 15119953 

Table  6.10.2.2.2.  Hake  GSA  19.  Fishing  effort  in  GT*Days  at  sea  by  year  and  fishing 
gear (time series 2003-2016) from DCF. 

gt*days     CYP   FRA     ITA 
2004   0     0 2607989 
2005   0     0 2248654 
2006   0     0 2161172 
2007   0     0 1921091 
2008   0     0 2075339 
2009   0     0 2087117 
2010 860     0 2361968 
2011   0     0 1983230 
2012   0     0 2012809 
2013   0     0 2025150 
2014   0     0 1781341 
2015   0 22981 1833639 
2016   0 30536 1857262 

Table 6.10.2.2.3. Hake GSA 19. Fishing effort in Days at sea by year and fishing gear 
(time series 2003-2016) from DCF. 

days@sea     CYP FRA     ITA 
2004   0   0 3228264 
2005   0   0 2852781 
2006   0   0 2655807 
2007   0   0 2094102 
2008   0   0 2331766 
2009   0   0 2541250 
2010   8   0 2640467 
2011   0   0 2851116 
2012   0   0 2583462 
2013   0   0 2281654 
2014   0   0 1912825 
2015   0  82 2332700 
2016   0  99 2352972 

433 

433 

 
 
 
 
 
Figure  6.10.2.2.1.  Hake  GSA  19.  Fishing  effort  in  nominal  effort  kw*days  at  sea  by 
year and fishing gear (time series 2003-2016) from DCF. 

Figure 6.10.2.2.2. Hake GSA 19. Fishing effort in GT*Days at sea by year and fishing 
gear (time series 2003-2016) from DCF. 

434 

434 

 
 
 
 
 
 
 
Figure 6.10.2.2.3. Hake GSA 19. Fishing effort in Days at sea by year and fishing gear 
(time series 2003-2016) from DCF. 

Looking to the effort for Italian vessels and fleets targheting hake (tables 6.10.2.2.4-6, 
figures  6.10.2.2.4-6),  the  trawlers  showed  the  highest  value  in  terms  of  nominal  effort 
but a lower value of days at sea.  

Table 6.10.2.2.4. Hake GSA 19. Fishing effort in nominal effort kw*days at sea by year 
and fleets targheting hake. 

         GNS     GTR     LLS     OTB 

2004  797996  925004  852696 6128857 
2005 2742293 1062369 6642497 2065333 
2006 1143710 5312380 1705748 2358945 
2007 5875474 1275650 1777574 1763634 
2008 1197159 1131865 1056634 6027003 
2009 2115507  620865 6832229 1683536 
2010  861956 5350926 1627697 2315821 
2011 4181999 1441596 1590170 1804600 
2012 1402176 1653130 1307624 5922925 
2013 1106682  679391 6382671 1773208 
2014  870853 6361017 2394257 2512945 
2015 6770477 1813781 3379761 1192364 
2016 1473754 1896850 2054032 5986840 

Table  6.10.2.2.5.  Hake  GSA  19.  Fishing  effort  in  GT*Days  at  sea  by  year  and  fleets 
targheting hake. 

        GNS    GTR    LLS    OTB 
2004  78308  88113  71070 621952 
2005 233891  89442 759137 161938 
2006 110883 491942 117849 182299 
2007 761067  98544 140997 151206 
2008 101868 102936 101916 615493 
2009 197023  64130 805415 133892 
2010  69009 574366 114717 191360 
2011 430253 107494 130340 150612 
2012 123299 141967 128798 696946 

435 

435 

 
 
 
 
2013 104406  68039 785235 145665 
2014  68640 711619 183557 202803 
2015 672536 134114 243041  91811 
2016 123789 149802 159044 823366 

Table  6.10.2.2.6.  Hake  GSA  19.  Fishing  effort  in  Days  at  sea  by  year  and  fleets 
targheting hake. 

        GNS    GTR    LLS    OTB 
2004 267188 218595 255379 339803 
2005 311118 218595 238933 272108 
2006 319623  99351 339810 234446 
2007 159279 288863 258306 234446 
2008 252642 225973 258306 197050 
2009 266071 225973 235344 405445 
2010 266071 220628 332321 240394 
2011 106138 313541 226466 242308 
2012 219769 251055 226466 192136 
2013 277689 251055 329280 401247 
2014 277689 231642 417884 230151 
2015 148969 374136 274646 230151 
2016 216347 255379 274646 297039 

Figure  6.10.2.2.4.  Hake  GSA  19.  Fishing  effort  in  nominal  effort  kw*days  at  sea  by 
year and fleets targheting hake. 

436 

436 

 
 
 
 
 
 
 
Figure  6.10.2.2.5.  Hake  GSA  19.  Fishing  effort  in  GT*Days  at  sea  by  year  and  fleets 
targheting hake. 

437 

437 

 
 
 
 
 
 
Figure  6.10.2.2.6.  Hake  GSA  19.  Fishing  effort  in  Days  at  sea  by  year  and  fleets 
targheting hake. 

6.10.2.3  SURVEY DATA 

Since  1994,  MEDITS  trawl  surveys  has  been  regularly  carried  out  yearly  during  the 
spring season (May-July). In 2014 the survey was shifted to September. 

According  to  MEDITS  protocol  (Bertrand  et  al.,  2002)  a  random  stratified  sampling 
by  depth  (5  strata  with  depth  limits  at:  50,  100,  200,  500  and  800  m) was applyied. 
Each  haul  position  was  randomly  selected  in  small  sub-areas  and  maintained  fixed 
throughout  the  time.  Haul  allocation  was  proportional  to  the  stratum  area.  The  same 
gear (GOC 73, by P.Y. Dremière, IFREMER-Sète), with a 20  mm  stretched  mesh  size  in 
the  cod-end,  was  utilized  along  the  time  series.  Considering the  small  mesh  size  a 
complete  retention  was  assumed.  All  the  abundance  data  (number  of  fish  per  surface 
unit) were standardized to square  kilometer, using the  swept area method. 

In  GSA  19  the  following  number  of  hauls  was  reported  per  depth  stratum  (Table 
6.10.2.3.1).  

Table  6.10.2.3.1.  Hake  in  GSA  19.  Number  of  hauls  per  year  and  depth  stratum 
(1994-  2016). 

Depth Stratum 

0-50  50-100  100-200  200-500  500-800  10-800 

year  1994 -2001  9 

year  2002-2016  9 

8 

8 

10 

10 

15 

14* 

32 

32 

74 

74 

* 15 hauls in 2005. 

Based  on the DCF data call input, abundance and biomass indices were recalculated. 

Observed abundance and biomass indices of Hake and the length frequency distributions 
are given on the figures below (Figures 6.10.2.3.1). 

438 

438 

 
 
 
 
 
 
 
 
 
 
Figure 6.10.2.3.1. Hake in GSA 19. Standardized length frequency distributions (N/km2) 
from the survey. 

Both  estimated  abundance  and  biomass  indices  show  similar  trends,  with  a  sharp 
increase in the last year (Figures 6.10.2.3.2). 

439 

439 

 
 
Figure 6.10.2.3.2. Hake in GSA 19. Estimated biomass (kg/km2) and abundance (N/km2) 
indices from the survey. 

6.10.3  STOCK ASSESSMENT 

Methods 

The  Extended  Survivors  Analysis  (XSA-Darby  and  Flatman,  1994)  and  the  the 
Assessment  for  All  Initiative  (a4a-Jardim  et  al.,  2015)  have  been  applyied  to  derive 
estimates  of  historical  population  size  and  fishing  mortality.  Both  models  were  carried 
out using as input data catch data and catch data at age (landings + discards) and one 
tuning  fleet  (medits  survey).  Since  no  discard  data  were  available  before  2006  and  for 
2007 and 2008, an estimate of total values and catch at age were based on the length 
structures of the other years as described before (section 6.10.2). 

Taking  in  to  account  the  selected  growth  parameters  by  STECF  EWG  15-16  (L∞  =104 
cm; K=0.2; t0=-0.01), catch numbers and indices at age were derived by applying the 
FLR “l2a” procedure for slicing landings, discards and indices at length. 

The  same  time  period  (2003-2016),  age  range  (0-6+)  and  Fbar  age  range  (0-3)  were 
used as setting parameters for both XSA and a4a methods. 

Input data 

The  same  growth  parameters  used  in  the  previous  assessement  for  VBGF  (STECF  EWG 
15-16) were utilized Linf= 104 cm CL; K = 0.2 yr-1; t0= -0.01 yr. 

Total catches and catch numbers at age from the single GSAs were used as input data. 
Since total catches derived from biological sampling (catch at age data) differ from total 

440 

440 

 
 
 
 
 
catches reported from official landings (figure 6.10.3.1) a SOP correction was applied to 
catch numbers at age. Table 6.10.3.1 present the SOP correction vector applied. 

Figure 6.10.3.1. Hake in GSA 19. SOP correction needs. 

Table 6.10.3.1. Hake in GSA 19. SOP correction vector. 

Year  2003  2004  2005  2006  2007  2008  2009  2010  2011  2012  2013  2014  2015  2016 
2.51  1.79  1.20  1.70  1.59  1.31  1.55  1.84  1.46  1.55  1.65  1.83  1.53  1.72 
SOP 

Table 6.10.3.2 lists the input parameters to the XSA, namely catches, catch number at 
age,  tuning  number  at  age,  weight  at  age,  maturity  and  natural  mortality  at  age, 
proportion of M and F before spawning. 

Table 6.10.3.2. Hake in GSA 19. Input data for the a4a and XSA models. 

### catch in weight (ton) by year 

age  2003 

2004 

2005 

2006 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

all  1054.22  1322.61  1276.53  1599.48  886.79  949.02  1051.77  849.75  818.71  674.22  771.79  743.83  762.09  700.28 

### Catch at age matrix (numbers in thousands) 

age 

2003  2004  2005  2006  2007  2008  2009  2010  2011  2012  2013  2014  2015  2016 

0 

1 

2 

3 

4 

5 

6 

968.2  1901.1  12769.3 14559  2773.2  10469.8 5814  5297.4  11577.3 6675.3  2718.3  1771.9  6339.1  6864.2 

3902.6  5993  9734.5  8549.6  4787.9  4950 

4602.4  3396.2  3350.3  3929.3  3766.1  2679.7  3885.7  2940.1 

329 

442.4  176 

414.6  268.5  242.9 

549 

320.1  246.1 

235.7  631.3  514 

290.2  236.8 

0.3 

127.8  10.4 

41.4 

96.9 

50.9 

89.3 

160.7  56.5 

44.6 

21.4 

78.5 

34.1 

55.4 

0.3 

33 

0.3 

18.9 

32.6 

25.2 

22.6 

26.9 

25.5 

10.4 

13.6 

29.6 

29.9 

10.3 

0.3 

11.9 

0.3 

8.2 

5.2 

6.8 

5.8 

3.1 

9.5 

0.3 

0.6 

4.2 

5.8 

5.6 

0.3 

17.2 

0.3 

1.8 

5.2 

2.6 

0.8 

3.6 

0.3 

0.3 

0.3 

14.1 

0.3 

2.2 

441 

441 

 
 
 
 
 
 
 
### TUNING .- Medits  

age 

2003 

2004 

2005 

2006 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

0 

1 

2 

3 

4 

5 

6 

150.1 

1515.8 

1124.3 

468.1 

405.7 

1219 

285.3 

66.9 

616.1 

1250.5 

433.8 

428.1 

460.6 

544.8 

81.3 

95.1 

109.7 

154.3 

116.6 

158 

117 

56.7 

68.4 

32.5 

147.1 

46.7 

30.5 

65.1 

16.9 

16 

20.2 

24.8 

17.9 

24.2 

20.2 

22.6 

12.9 

7.8 

36.1 

15.5 

9.6 

14.4 

4 

3.2 

6.5 

8.1 

8.8 

5.2 

9.6 

3.8 

1.6 

1.5 

7.5 

4.5 

3.2 

1.9 

0.6 

1.2 

2.3 

2.9 

0.5 

2.5 

2.6 

1.3 

1.1 

1.1 

2.3 

4.1 

1.5 

2.4 

0.4 

0.1 

1.4 

0.4 

0.4 

0.7 

0.9 

0.3 

0.9 

0.3 

0.5 

2 

0.2 

0.2 

0 

0.1 

0.4 

0 

0.3 

0.5 

0.3 

1.3 

0.1 

0.3 

0.1 

1.3 

0.5 

0.3 

### Mean Weight@age (kg) in stock, catch, landings 

age 

2003 

2004 

2005 

2006 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

0 

1 

2 

3 

4 

5 

6 

0.026 

0.028 

0.021 

0.020 

0.025 

0.028 

0.024 

0.024 

0.020 

0.021 

0.021 

0.026 

0.026 

0.023 

0.086 

0.080 

0.079 

0.063 

0.077 

0.069 

0.084 

0.058 

0.074 

0.059 

0.075 

0.066 

0.062 

0.059 

0.179 

0.214 

0.155 

0.177 

0.197 

0.192 

0.186 

0.173 

0.164 

0.167 

0.179 

0.221 

0.191 

0.172 

0.376 

0.417 

0.347 

0.337 

0.334 

0.376 

0.358 

0.367 

0.326 

0.367 

0.349 

0.362 

0.381 

0.368 

0.663 

0.701 

0.614 

0.633 

0.670 

0.594 

0.633 

0.620 

0.590 

0.633 

0.619 

0.633 

0.661 

0.667 

0.969 

0.969 

0.748 

0.944 

1.024 

0.977 

0.956 

0.990 

0.888 

0.943 

0.887 

0.937 

1.080 

0.848 

1.486 

1.486 

1.281 

1.444 

1.432 

1.584 

1.405 

1.374 

1.355 

1.517 

1.470 

1.399 

1.685 

1.431 

### Mortality and Maturity vectors@age 

age 

0 

1 

2 

3 

maturity  0 

0.19  0.86  1 

4 

1 

5 

1 

6 

1 

mortality  1.48  0.45  0.28  0.22  0.19  0.17  0.16 

### other xsa settings 

min  max  plusgroup  minyear  maxyear  minfbar  maxfbar 

0 

6 

6 

2003 

2016 

0 

3 

Proportion of M and F before spawning 

2003-2016  0 

442 

442 

 
 
 
 
 
 
 
Figures reported below (figure 6.10.3.2) show the age structure of the tuning (a) of the 
catches (b) and the weight at age matrix (c). 

a 

443 

443 

 
 
b 

c 

Figure 6.10.3.2. Hake in GSA 19. the age structure of the tuning (a) of the catches (b) 
and the weight at age matrix (c). 

444 

444 

 
 Method 1 – XSA 

Results 

The STECF EWG 17-15 applied the Extended Survivors Analysis (XSA, Shepherd, 1992) 
and the technique “shrinkage to the mean” for assessing the GSA19’ s stock of hake in 
2003-2016. 

Multiple  XSA  runs  were  done  using  different  combination  of  setting  parameters.  The 
minimum standard error for population estimates derived from each fleet was set to 0.3, 
the shk.n=TRUE, shk.f=TRUE, shk.yrs=3. Natural and Fishing mortality before spawning 
was  set  to  0.  Shrinkage  was  set  varying  from  1  to  4  (shk.ages  =  1  to  4),  catchability 
was  set  dependent  on  stock  size  varying  ages  from  0  to  1  (rage=  0  to  1)  and 
independent  of  ages  for  ages  between  2  and  5  (qage=2  to  5).  For  all  runs  S.E.  of  the 
mean to which the estimates are shrunk (fse) were set varying from 0.5 to 3 (by a step 
of 0.5). 

Overall  192  models  were  run  and  tested.  Among  these  models  only  those  runs  with 
values  lower  than  the  first  quartile  of  abs  mean  were  selected  (n=48)  for  a  more 
detailed sensitivity and diagnostic check. The used settings and the minimum, maximum 
and  average  residuals by shrinkage options for  the  main runs  are reported in  the  table 
6.10.3.3. 

Table 6.10.3.3. Hake in GSA 19. Run settings for the sensitivity analysis and statistics 
on residuals (in bold the final selected xsa model). 

run_n setsens 

shkage fse 

rage qage minres  maxres 

absmean  absmax 

180  sh2se3r1q5 

sh2 

se3 

r1  q5 

-1.48232  1.005837  0.287701  1.482321 

186  sh3se3r1q5 

sh3 

se3 

r1  q5 

-1.48264  0.959927  0.288602  1.482643 

192  sh4se3r1q5 

sh4 

se3 

r1  q5 

-1.48226  0.91517  0.288778  1.482263 

168  sh4se3r1q4 

sh4 

se3 

r1  q4 

-1.48234  0.903765  0.289019  1.482341 

156  sh2se3r1q4 

sh2 

se3 

r1  q4 

-1.47954  0.907363  0.289723  1.479541 

162  sh3se3r1q4 

sh3 

se3 

r1  q4 

-1.4821  0.905878  0.290012  1.482096 

174  sh1se3r1q5 

sh1 

se3 

r1  q5 

-1.48299  1.048427  0.290017  1.482992 

179  sh2se2.5r1q5  sh2 

se2.5  r1  q5 

-1.48706  0.982217  0.290937  1.487065 

150  sh1se3r1q4 

sh1 

se3 

r1  q4 

-1.47695  0.914877  0.291706  1.476953 

173  sh1se2.5r1q5  sh1 

se2.5  r1  q5 

-1.48655  1.02801  0.293384  1.486554 

185  sh3se2.5r1q5  sh3 

se2.5  r1  q5 

-1.48893  0.925492  0.293395  1.488928 

167  sh4se2.5r1q4  sh4 

se2.5  r1  q4 

-1.4893  0.889419  0.293478  1.489296 

155  sh2se2.5r1q4  sh2 

se2.5  r1  q4 

-1.48535  0.894675  0.293887  1.485346 

191  sh4se2.5r1q5  sh4 

se2.5  r1  q5 

-1.49006  0.871117  0.294365  1.490063 

445 

445 

 
 
 
 
run_n setsens 

shkage fse 

rage qage minres  maxres 

absmean  absmax 

161  sh3se2.5r1q4  sh3 

se2.5  r1  q4 

-1.48899  0.89245  0.294403  1.488988 

120  sh4se3r1q2 

sh4 

se3 

r1  q2 

-1.3121  1.160113  0.295936  1.312102 

149  sh1se2.5r1q4  sh1 

se2.5  r1  q4 

-1.48172  0.904592  0.296353  1.481717 

108  sh2se3r1q2 

sh2 

se3 

r1  q2 

-1.31473  1.156015  0.296478  1.314733 

114  sh3se3r1q2 

sh3 

se3 

r1  q2 

-1.31525  1.157358  0.296888  1.315249 

102  sh1se3r1q2 

sh1 

se3 

r1  q2 

-1.31496  1.161017  0.298224  1.314957 

178  sh2se2r1q5 

sh2 

se2 

r1  q5 

-1.4962  0.925954  0.298693  1.4962 

144  sh4se3r1q3 

sh4 

se3 

r1  q3 

-1.2514  1.260668  0.298872  1.260668 

132  sh2se3r1q3 

sh2 

se3 

r1  q3 

-1.25313  1.258257  0.299136  1.258257 

138  sh3se3r1q3 

sh3 

se3 

r1  q3 

-1.25398  1.258939  0.299629  1.258939 

119  sh4se2.5r1q2  sh4 

se2.5  r1  q2 

-1.31029  1.15776  0.299719  1.310287 

107  sh2se2.5r1q2  sh2 

se2.5  r1  q2 

-1.314 

1.152174  0.299937  1.314004 

113  sh3se2.5r1q2  sh3 

se2.5  r1  q2 

-1.31476  1.15392  0.300672  1.314765 

154  sh2se2r1q4 

sh2 

se2 

r1  q4 

-1.49626  0.872065  0.300865  1.496262 

126  sh1se3r1q3 

sh1 

se3 

r1  q3 

-1.25327  1.264347  0.30108  1.264347 

101  sh1se2.5r1q2  sh1 

se2.5  r1  q2 

-1.31424  1.158895  0.302153  1.314238 

131  sh2se2.5r1q3  sh2 

se2.5  r1  q3 

-1.25043  1.257398  0.302325  1.257398 

143  sh4se2.5r1q3 sh4 

se2.5 r1  q3 

-1.24797 1.260644 0.302327 1.260644 

137  sh3se2.5r1q3  sh3 

se2.5  r1  q3 

-1.25166  1.258218  0.303184  1.258218 

125  sh1se2.5r1q3  sh1 

se2.5  r1  q3 

-1.25058  1.265676  0.304621  1.265676 

106  sh2se2r1q2 

sh2 

se2 

r1  q2 

-1.31283  1.145445  0.305568  1.312829 

130  sh2se2r1q3 

sh2 

se2 

r1  q3 

-1.24676  1.254101  0.307307  1.254101 

100  sh1se2r1q2 

sh1 

se2 

r1  q2 

-1.31295  1.154774  0.308297  1.312951 

124  sh1se2r1q3 

sh1 

se2 

r1  q3 

-1.24684  1.265784  0.310411  1.265784 

84 

sh2se3r0q5 

sh2 

se3 

r0  q5 

-1.48269  1.005467  0.32699  1.482694 

90 

sh3se3r0q5 

sh3 

se3 

r0  q5 

-1.48299  0.959618  0.327866  1.482993 

96 

sh4se3r0q5 

sh4 

se3 

r0  q5 

-1.4826  0.914892  0.327947  1.482595 

72 

sh4se3r0q4 

sh4 

se3 

r0  q4 

-1.48267  0.903404  0.328188  1.482674 

60 

sh2se3r0q4 

sh2 

se3 

r0  q4 

-1.47988  0.90701  0.328892  1.479877 

66 

sh3se3r0q4 

sh3 

se3 

r0  q4 

-1.48243  0.905531  0.329183  1.482427 

446 

446 

 
run_n setsens 

shkage fse 

rage qage minres  maxres 

absmean  absmax 

78 

sh1se3r0q5 

sh1 

se3 

r0  q5 

-1.48339  1.048079  0.329311  1.483387 

83 

sh2se2.5r0q5  sh2 

se2.5  r0  q5 

-1.48743  0.981864  0.330145  1.487429 

54 

sh1se3r0q4 

sh1 

se3 

r0  q4 

-1.47729  0.914512  0.330856  1.47729 

89 

sh3se2.5r0q5  sh3 

se2.5  r0  q5 

-1.48926  0.925224  0.332535  1.489261 

The  different  settings  gives  minimal  different  estimates  of  recruitment  as  well  as  very 
small different estimates of SSB and F (Figure 6.10.3.3). 

Figure  6.10.3.3.  Hake in GSA 19. Sensitivity analysis and  stock overview of spawning 
stock biomass, recruitment and F (0-3) for different levels of shrinkage and catchability. 

The  best  model  fit  was  chosen  in  order  to  give  reduced  trends  in  catchability  residuals 
and  on  the  basis  of  the  retrospective  behavior  of  the  fishing  mortality  (average  over 
ages 0-3), SSB and recruitment for different assessment runs. 

Overall the best model in terms of residual plots and retrospective patters is the model 
143 and this basis XSA run is the retained one. 

The  residuals  of  log  transformed  catchability  are  plotted  for  each  tuning  index  and 
shrinkage  level  in  figures.The  bubbles  plot  neither  indicate  particular  pattern  nor  high 
values (Fig. 6.10.3.4). 

447 

447 

 
 
 
 
 
 
 
 
Figure  6.10.3.4.  Hake  in  GSA  19.  Residuals  of  log  transformed  catchability  for  the 
selected run. 

The  retrospective  series  indicate  a  quite  good  agreement  between  years  in  the 
assessment results, with no large systematic bias (Figure 6.10.3.5). 

448 

448 

 
 
 
 
 
Figure 6.10.3.5.  Hake in GSA 19. XSA retrospective patterns for last 3 years with  the 
selected run. 

Figure  6.10.3.6.  Hake  in  GSA  19.  XSA  stock  overview.  SSB  and  catch  are  in  tonnes, 
recruitment in 1000s individuals. 

XSA outputs and diagnostics are listed below respectively in table 6.10.3.4 and 6.10.3.5. 

Table 6.10.3.4. Hake in GSA 19. XSA results. 

ssb 

fbar 

rec 

catch 

landings 

2003 

514.9 

0.5 

67306 

1054.2 

1054.2 

2004 

663.9 

2.08 

79075 

1322.6 

1322.6 

2005 

408.8 

0.49 

116724 

1276.5 

1276.5 

2006 

530.6 

1.27 

98797 

1599.5 

1599.5 

2007 

453.4 

1.07 

55127 

886.8 

886.8 

2008 

385.7 

0.76 

73782 

949 

949 

2009 

563 

0.94 

57619 

1051.8 

1051.8 

2010 

453.3 

1.22 

51432 

849.8 

849.8 

2011 

289.3 

1.14 

79484 

818.7 

818.7 

2012 

298.3 

0.64 

64458 

674.2 

674.2 

449 

449 

 
 
 
 
 
 
2013 

477 

0.62 

41105 

771.8 

771.8 

2014 

708.9 

1.02 

44018 

743.8 

743.8 

2015 

318.3 

1.28 

54173 

762.1 

762.1 

2016 

288.8 

1.22 

68680 

700.3 

700.3 

Table 6.10.3.5. Hake in GSA 19. XSA diagnostic. 

CPUE data from indices 

Catch data for 14 years 2003 to 2016. Ages 0 to 6. 

   fleet  

first age   last age   first year  last year  alpha  

beta 

Medits   0  

5   

 2003 

2016 

  <NA> 

 <NA> 

 Time series weights : 

    Tapered time weighting applied 

   Power =   3 over  20 years 

 Catchability analysis : 

     Catchability independent of size for ages >   1  

     Catchability independent of age for ages >   3  

 Terminal population estimation : 

     Survivor estimates shrunk towards the mean F 

    of the final   3 years or the  4 oldest ages. 

    S.E. of the mean to which the estimates are shrunk =   2.5  

    Minimum standard error for population 

    estimates derived from each fleet =  0.3  

    prior weighting not applied 

Regression weights 

     year 

age  2007  2008 

2009  2010 

2011 

2012 

2013 

2014  2015 

2016 

all 

0.751  0.82 

0.877  0.921  0.954  0.976  0.99 

0.997  1  1 

 Fishing mortalities 

age  2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

0 

1 

2 

3 

4 

5 

6 

0.183  0.492  0.397  0.505  0.591  0.411  0.259  0.168  0.47 

0.447 

2.244  1.494  2.04 

2.166  2.022  1.438  1.601  1.899  2.106  1.725 

1.297  0.707  1.063  1.579  1.028  1.223  1.552  2.001  1.349  1.246 

1.089  0.758  0.856  1.856  1.186  0.587  0.353  1.08 

0.609  1.547 

1.13 

0.728  1.323  0.895  1.826  0.792  0.38 

1.592  1.424  0.429 

0.788  0.56 

0.434  0.76 

0.668  0.003  0.099  0.213  1.484  1.584 

0.788  0.56 

0.434  0.76 

0.668  0.003  0.099  0.213  1.484  1.584 

 XSA population number (Thousand) 

450 

450 

 
 
 
 
 
age 

year 

0 

1 

2 

3 

4 

5 

6 

2007  55127  10663  676 

259  84  16  16 

2008  73782  10445  721 

140  70  23  9 

2009  57619  10268  1495  269  53  28  4 

2010  51432  8817 

851 

390  92  12  13 

2011  79484  7069 

644 

133  49  31  0 

2012  64458  10019  596 

174  33  7 

7 

2013  41105  9728 

1517  133  78  12  0 

2014  44018  7221 

1251  243  75  44  146 

2015  54173  8473 

689 

128  66  13  0 

2016  68680  7706 

658 

135  56  13  5 

 Estimated population abundance at 1st Jan  2017 

                                                age 

year 

0 

1 

2 

3 

2017 

23 

10001 

875 

143 

4 

23 

5 

30 

6 

2 

 Fleet:  Medits  

 Log catchability residuals. 

year 

age  2003 

2004 

2005 

2006 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

0 

1 

2 

3 

4 

5 

-0.669  0.289 

-0.048 

-0.218 

-0.047  0.348 

-0.178 

-0.738  0.012 

0.429 

0.204 

0.126 

0.104 

0.014 

-0.148  0.097 

-0.12 

0.093 

0.193 

0.141  0.166 

-0.004  0.18 

-0.503  0.184 

-0.024 

-0.251  0.028 

-0.471  0.343 

0.369 

0.031 

0.291 

0.164 

-0.526  0.467 

-0.155 

-0.46 

0.341 

-0.035 

-0.319  0.07 

-0.209  0.045 

0.767 

0.423 

0.49 

0.378  0.397 

-0.286 

-0.485 

-1.193  0.543 

-0.121 

-0.112 

-0.109 

-0.714 

-0.523  0.748 

0.868 

-1.248  0.299  0.993 

-0.521  0.514 

0.284 

-0.106  1.261 

0.274 

0.299 

0.225 

-0.478  0.229 

-0.071 

-0.057  0.043  0 

-0.013  0.043 

0.089 

0.034 

0.208 

-0.057 

-0.041 

Regression statistics 

Ages with q dependent on year class strength 

0.677971 

0.644474 

6.076362 

5.433252 

Terminal year survivor and F summaries: 

 ,Age 0 Year class =2016  

source  

       scaledWts survivors yrcls 

Medits 

0.156 

10208 

2016 

451 

451 

 
 
 
 
 
 
 
 
 
fshk 

0.011 

14270 

2016 

nshk 

0.833 

9915 

2016 

 ,Age 1 Year class =2015  

source  

       scaledWts survivors yrcls 

Medits 

0.893 

914 

2015 

fshk 

0.107 

685 

2015 

 ,Age 2 Year class =2014  

source  

       scaledWts survivors yrcls 

Medits 

0.935 

153 

2014 

fshk 

0.065 

83 

2014 

 ,Age 3 Year class =2013  

source  

       scaledWts survivors yrcls 

Medits 

0.813 

fshk 

0.187 

21 

86 

2013 

2013 

 ,Age 4 Year class =2012  

source  

       scaledWts survivors yrcls 

Medits 

0.874 

fshk 

0.126 

41 

8 

2012 

2012 

 ,Age 5 Year class =2011  

source  

       scaledWts survivors yrcls 

Medits 

0.934 

fshk 

0.066 

2 

4 

2011 

2011 

Method 2 –a4a 

Results 

Different  a4a  models  were  carried  out  (combination  of  different  f,  q  and  sr)  (figure 
6.10.3.7). 

452 

452 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
453 

453 

 
 
 
Figure 6.10.3.7. Hake in GSA 19. XSA stock overview of several a4a models. SSB and 
catch are in tonnes, recruitment in 1000s individuals. 

According to the diagnostic, the restrospective analysis, the AIC and the BIC indices the 
best  model  was  selected  for  the  final  assessment.  The  settings  of  the  best  model  a4a 
were the following: 

fmodel = ~ te(age, year, k = c(3,5))+s(age,k=5) 

qmodel4 =  list(~s(age, k=4)) 

srmodel1 =~factor(year) 

The  stock  summary  overview  is  shown  in  Figure  6.10.3.8.  Fishing  mortality  and 
catchability  are  shown  in  Figures  6.10.3.9-10,  and  diagnostics  and  restrospective 
analysis in Figures 6.10.3.11-15. 

Based  on  the  a4a assessment  method  the SSB of  hake fluctuated  over  the  time  period 
examined (2003-2016) from 568 tons (in 2007) to 255 tons in 2016. A drop in SSB was 
observed  in  the  years  2007  to  2011  (lowest  biomass)  followed  by  an  increase  in  2014 
and  a  new  decrease  in  the  last  years.  As  regards  the  number  of  recruits  the  minimum 
and  maximum  values  were  respectively  observed  in  2014  and  2006,  but  slightly 
fluctuated  in  the  last  period  (2009-2016)..  Fbar (0-3)  shows  an  increasing  trend  in  the 
last 4 years reaching the value of 1.42 in 2016 (Table 6.10.3.2.4). 

454 

454 

 
 
 
 
 
 
Figure 6.10.3.8. Hake in GSA 19. a4a stock overview. SSB (Stock Spawning Biomass) 
and  catch  (landings+discards)  are  in  tonnes,  recruitment  in  1000s  individuals,  harvest 
refers to fishing mortality for ages 0 to 3. 

Figure 6.10.3.9. Hake in GSA 19. 3D contour plot of estimated fishing mortality at age 
by year. 

455 

455 

 
 
 
 
Figure 6.10.3.10. Hake in GSA 19. 3D contour plot of estimated catchability at age by 
year. 

456 

456 

 
 
 
 
 
Diagnostics 

Figure 6.10.3.11. Hake in GSA 19. Standardized residuals by year. 

457 

457 

 
 
 
 
 
Figure 6.10.3.12. Hake in GSA 19. Standardized residuals and smoother for abundance 
indices and for catch by age and year. 

458 

458 

 
 
 
Figure  6.10.3.13.  Hake  in  GSA  19.  QQ  plot  of  standardized  residuals  for  abundance 
indices and for catch by age and year. 

459 

459 

 
 
 
 
 
a 

b 

Figure  6.10.3.14.  Hake in  GSA  19.  Fitted values  versus  observed  catch  (a) and  index 
(b). 

460 

460 

 
 
Retrospective 

The  retrospective  analysis  was  applied  up  to  2  years  back,  the  short  dat  series  did  not 
allow more restrospective analysis. Models results were stable (Figure 6.10.3.15). 

Figure 6.10.3.15. Hake in GSA 19. Retrospective analysis. 

461 

461 

 
 
 
 
 
 
 
Simulations 

Figure  6.10.3.16.  Hake in  GSA  19.  Stock  summary  of  the  simulated  and  fitted  values 
for the a4a model. SSB (Stock Spawning Biomass) and catch are in tonnes, recruitment 
in 1000s individuals, harvest refers to fishing mortality for ages 0 to 3 

Results  and  population  estimates  for  the  fitted  a4a  model  are  reported  in  tables 
6.10.3.5-6. 

Table 6.10.3.4. Hake in GSA 19. a4a results. Stock numbers at age (in thousands). 

age  2003 

2004 

2005 

2006 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

0 

1 

2 

3 

4 

5 

6 

33910  63930  87139  104135  50878  73743  54094  45714  72427  71219  35480  31820  57266  83799 

7299 

6541 

11737  15119 

17141  8102 

11628  8581 

7339 

11772  11720  5908 

5358 

9745 

751 

996 

745 

1113 

1222 

1242 

558 

802 

617 

563 

961 

996 

506 

453 

72 

104 

143 

109 

161 

169 

162 

70 

103 

85 

84 

146 

143 

64 

9 

9 

2 

18 

29 

43 

34 

48 

46 

41 

18 

30 

27 

28 

44 

36 

3 

5 

7 

5 

13 

7 

19 

10 

14 

17 

15 

14 

7 

13 

11 

9 

8 

10 

14 

11 

13 

12 

15 

16 

15 

13 

462 

462 

 
 
 
 
 
 
Table  6.10.3.5.  Hake  in  GSA  19.  a4a  summary  results.  Fishing  mortality  (ages  0-3), 
recruitment in thousands individuals, Stock Spawning Biomass (SSB), total biomass (TB) 
and catch are in tonnes. 

Year 

Fbar  Recruitment  SSB 

TB 

Catch 

2003 

1.14 

33910 

279 

1688 

616 

2004 

1.16 

63930 

349 

2592 

743 

2005 

1.2 

87139 

354 

2952 

1037 

2006 

1.25 

104135 

436 

3318 

1215 

2007 

1.3 

50878 

568 

2943 

1456 

2008 

1.36 

73743 

438 

2989 

1027 

2009 

1.38 

54094 

392 

2492 

1115 

2010 

1.36 

45714 

292 

1811 

707 

2011 

1.29 

72427 

257 

2157 

744 

2012 

1.23 

71219 

285 

2356 

846 

2013 

1.21 

35480 

389 

1870 

918 

2014 

1.24 

31820 

367 

1525 

613 

2015 

1.31 

57266 

269 

2039 

589 

2016 

1.42 

83799 

255 

2659 

802 

Table 6.10.3.5. Hake GSAs 17-18-19. F at age. 

age  2003  2004  2005  2006  2007  2008  2009  2010  2011  2012  2013  2014  2015  2016 
0.28 
0.36 
2.05 
2.22 
1.94 
1.8 
1.42 
1.14 
1.33 
0.96 
2.14 
1.39 
0.95 
0.56 

0  0.17 
1  1.54 
2  1.69 
3  1.18 
4  0.81 
5  0.77 
6  0.18 

0.32 
2.06 
1.63 
0.91 
0.59 
0.6 
0.16 

0.22 
1.72 
1.66 
1.06 
0.7 
0.68 
0.17 

0.37 
2.23 
1.76 
1.08 
0.85 
1.15 
0.43 

0.27 
1.91 
1.64 
0.97 
0.64 
0.63 
0.16 

0.32 
2.07 
1.65 
0.95 
0.64 
0.67 
0.19 

0.36 
2.17 
1.7 
0.99 
0.72 
0.85 
0.28 

0.29 
2.02 
1.78 
1.15 
0.89 
1.12 
0.38 

0.3 
2.01 
1.66 
0.97 
0.65 
0.67 
0.19 

0.35 
2.18 
1.78 
1.12 
0.91 
1.25 
0.48 

0.34 
2.12 
1.7 
1.01 
0.73 
0.87 
0.28 

0.31 
2.02 
1.61 
0.89 
0.55 
0.53 
0.13 

Comparison of assessments 

463 

463 

 
 
 
 
 
 
 
The  two  assessment  model  results  were  compared  (Figure  6.10.3.17).  Both  the  model 
showed  good  retrospective  results  and  suggests  overexploitation  for  hake  stock  in  GSA 
19. 

Both model give comparable results and patters for the estimation of recruitment and F. 

Overall the a4a model is considered to best represent the current state of the stock. 

Figure  6.10.3.17.  Hake  in  GSA  19.  Comparison  of  stock  summary  outputs  (XSA  and 
a4a).  SSB  (Stock  Spawning  Biomass)  and  catch  are  in  tonnes,  recruitment  in  1000s 
individuals, harvest refers to fishing mortality for ages 0 to 3. 

6.10.4  REFERENCE POINTS 

Methods 

To predict the effect of changes in fishing mortality on future yields and to define 
reference points  F0.1 (as a proxy for FMSY) and Fmax a Yield per Recruit analysis (YPR) 
was carried out in R using the FLBRP package. 

Input data 

464 

464 

 
 
 
 
 
 
 
 
 
As input the same population parameters used for the XSA and a4a and their outputs of 
the exploitation pattern  were used. 

Results 

The reference points obtained are shown in table 6.10.4.1. The curve of the  yield per 
recruit is plotted in figure 6.10.4.1. 

Table  6.1.4.1.1  Hake  in  GSA  19.  Main  reference  points  defined  with  the  Yield 
per recruit analysis estimated by the input and output of the assessment models. 

f0.1  harvest  yield  rec 

ssb  biomass 

xsa  0.11 

0.03  1.00  0.34  0.38 

a4a  0.16 

0.03  1.00  0.30  0.34 

Figure 6.10.4.1. Hake in GSA 19. Yield per recruit curve estimated by the input 
and output of the assessment models (XSA and a4a). 

6.10.5  SHORT TERM FORECAST AND CATCH OPTIONS 

 xsa 

465 

465 

 
 
 
 
 
 
 
 
The input parameters were the same used for the XSA stock assessment and its results. 

Fstatus  quo  is  equal  to  F2016  (1.17),  corresponding  to  a  catch2017  of  861  tonnes;  other 
scenario are showed in Table 6.10.5.1.1. 

Table 6.10.5.1.1. European hake in GSAs 19. Short term forecasts based on XSA input 
and outputs showing catch options for different fishing mortalities reductions. 

Rationale 

Ffactor Fbar Catch_2017 Catch_2018 Catch_2019 SSB_2019 

Change_SSB 
2018-2019(%) 

Change_Catch 
2016-2018(%) 

zero catch 

0.0 

0.00 

861 

0.0 

0.0 

1466.7 

F01 

0.1 

0.11 

861 

124.3 

256.0 

1264.6 

306 

250 

Status quo 

1.0 

1.17 

861 

810.29 

793.91 

810.29 

-5 

0.2 

0.23 

861 

249.7 

458.9 

1068.4 

0.3 

0.35 

861 

351.8 

585.0 

915.5 

0.4 

0.47 

861 

441.8 

668.8 

786.9 

0.5 

0.58 

861 

521.5 

723.1 

678.4 

0.6 

0.70 

861 

592.3 

757.1 

586.9 

0.7 

0.82 

861 

655.6 

777.3 

509.6 

0.8 

0.94 

861 

712.5 

788.2 

444.1 

0.9 

1.05 

861 

763.8 

793.0 

388.5 

1.0 

1.17 

861 

810.3 

793.9 

341.3 

Different 
scenarios 

1.1 

1.29 

861 

852.7 

792.5 

301.2 

1.2 

1.40 

861 

891.5 

789.9 

266.9 

1.3 

1.52 

861 

927.3 

786.7 

237.6 

1.4 

1.64 

861 

960.3 

783.4 

212.4 

1.5 

1.75 

861 

991.0 

780.2 

190.8 

1.6 

1.87 

861 

1019.5 

777.5 

172.1 

1.7 

1.99 

861 

1046.3 

775.1 

156.0 

1.8 

2.10 

861 

1071.4 

773.3 

142.0 

1.9 

2.22 

861 

1095.0 

772.0 

129.8 

2.0 

2.34 

861 

1117.4 

771.2 

119.1 

196 

154 

118 

88 

63 

41 

23 

8 

-5 

-17 

-26 

-34 

-41 

-47 

-52 

-57 

-61 

-64 

-67 

-100 

-82 

16 

-64 

-50 

-37 

-26 

-15 

-6 

2 

9 

16 

22 

27 

32 

37 

42 

46 

49 

53 

56 

60 

466 

466 

 
 
 
 
Figure 6.10.5.1. Hake in GSA 19. Short term forecast of the xsa assessment model. 

a4a 

The input parameters were the same used for the a4a stock assessment and its results. 

Fstatus  quo  is  equal  to  F2016  (1.32),  corresponding  to  a  catch2017  of  994  tonnes;  other 
scenario are showed in Table 6.10.5.1.2. 

Table 6.10.5.1.2. European hake in GSAs 19. Short term forecasts based on a4a input 
and outputs showing catch options for different fishing mortalities reductions. 

Rationale 

Ffactor Fbar Catch_2017 Catch_2018 Catch_2019 SSB_2019 

Change_SSB 
2018-
2019(%) 

Change_Catch 
2016-
2018(%) 

zero catch 

0.0 

0.00 

1010 

0.0 

0.0 

1627.1 

F01 

0.1 

0.16 

1010 

178.1 

364.2 

1322.3 

Status quo 

1.0 

1.32 

1010 

870.27 

830.60 

870.27 

Different 
scenarios 

0.2 

0.26 

1010 

282.7 

525.4 

1149.9 

0.3 

0.40 

1010 

395.0 

657.4 

971.5 

0.4 

0.53 

1010 

492.2 

739.4 

823.8 

0.5 

0.66 

1010 

576.6 

788.2 

701.5 

0.6 

0.79 

1010 

650.5 

815.5 

600.1 

0.7 

0.92 

1010 

715.5 

829.0 

515.9 

0.8 

1.06 

1010 

773.1 

833.9 

445.8 

0.9 

1.19 

1010 

824.3 

833.7 

387.5 

332 

251 

-10 

205 

158 

119 

86 

59 

37 

18 

3 

-100 

-78 

9 

-65 

-51 

-39 

-28 

-19 

-11 

-4 

3 

467 

467 

 
 
 
 
 
1.0 

1.32 

1010 

870.3 

830.6 

338.7 

1.1 

1.45 

1010 

911.7 

826.2 

298.0 

1.2 

1.58 

1010 

949.3 

821.3 

263.8 

1.3 

1.72 

1010 

983.6 

816.5 

235.0 

1.4 

1.85 

1010 

1015.2 

812.2 

210.7 

1.5 

1.98 

1010 

1044.4 

808.5 

190.2 

1.6 

2.11 

1010 

1071.4 

805.5 

172.7 

1.7 

2.24 

1010 

1096.7 

803.1 

157.8 

1.8 

2.38 

1010 

1120.4 

801.4 

145.0 

1.9 

2.51 

1010 

1142.8 

800.3 

134.0 

2.0 

2.64 

1010 

1163.9 

799.7 

124.5 

-10 

-21 

-30 

-38 

-44 

-50 

-54 

-58 

-62 

-64 

-67 

9 

14 

18 

23 

27 

30 

34 

37 

40 

43 

45 

Figure 6.10.5.2. Hake in GSA 19. Short term forecast of the a4a assessment model. 

468 

468 

 
 
 
 
 
 
6.10.6  DATA DEFICIENCIES 

Data from DCF 2017 were used. A difference in the sum of products compared to 
landings  was  around  37%  (excluding  2003).  Discards  data  of  2006,  2009-2016 
were available. Information on  number  of  samples  for  landings, discards and 
catches, as well as the number of measurements by length for landings, discards 
and  catches  were  also  available.  Number  of  otoliths  was  also  available.  MEDITS 
raw data used for this assessment have been processed by the expert using the 
LFD  routine  by  STECF  EWG.  Biological  parameters  by  length  and  age  and  sex 
ratio were available for the whole time series (2003-2016). 

In  2014  the  survey  was  shifted  in  September  as  a  consequence  of  the 
administrative process undertaken Italian Ministry of Agriculture. 

6.11  HAKE IN GSA 20 

6.11.1  BIOLOGY 

STECF EWG 17-15 was asked to assess the state of hake stock in the Ionian Sea (GSA 
20).  The  previous  assessment  of  this  stock  was  in  2012  using  VIT  ASPIC  and  SURBER 
(STECF 12-21).  

Hake  is  one  of  the  most  important  fish  stock  in  GSA  20  for  bottom  trawlers,  nets  and 
longlines. The stock is distributed in depth between 50-600 m, with a peak in abundance 
in  depths  between  200  and  300  m.  The  stock  is  exploited  almost  exclusively  by  the 
Greek fishing fleet.  

Figure 6.11.1.1 Geographical location of GSA 20. 

469 

469 

 
 
 
 
 
 
 
 
Growth  parameters  (Linf=  104.0  cm,  k=  0.12  y-1;  t0=  -0.01  y,  sexes  combined)  and 
length-weight  relationship  parameters  (a=0.0033,  b=3.23),  as  taken  from  the  DCF 
estimates of hake in GSA 19, were used in the assessment.  

The vector of proportion of mature individuals by age was also constructed according to 
the size at maturity of hake in GSA 19, sexes combined (Table 6.11.1.1). 

A vector of natural mortality was estimated by PRODBIOM method (Abella et al., 1997) 
using  growth  and  length-weight  relationship  parameters  for  sexes  combined  (Table 
6.11.1.1). 

Table  6.11.1.1.  Hake  in  GSA  20.  Proportion  of  mature  specimens  (Pmat)  at  age  and 
natural mortality (M) at age. 

Age 

Pmat 

M 

0 

1 

2 

3 

4 

5 

6 

7 

0 

0.19 

0.86 

1 

1 

1 

1 

1 

1.24 

0.73 

0.48 

0.39 

0.35 

0.32 

0.31 

0.29 

6.11.2  INPUT DATA 

6.11.2.1  CATCH (LANDINGS AND DISCARDS)  

General description of Fisheries 

Hake  mainly  lives  on  muddy  substrates  in  depths  between  50  and  600  m  and,  in  the 
Greek part of the Ionian Sea (GSA 20), is mainly targeted by the bottom trawl fishery, 
nets (gill- and trammel) and longlines (Table 6.11.2.1, Figure 6.11.2.1).  
The  bottom  trawl  fishery  in  Greece  is  a  mixed  fishery,  operating  24hr  per  day.  Bottom 
trawl fishing targeting hake, is taking place mainly during the day in muddy bottoms in 
depths ranging from 80 to 400 m. Since the October 2001 the mesh size of the cod-end 
net  of  bottom  trawls  has  been  increased  according  to  European  Regulation  to  40  mm 
and since 2006 to 50 mm diamond or 40 mm square. Apart from hake, important target 
species  for  bottom  trawler  are  shrimps,  anglerfish,  blue  whiting,  megrims,  picarel  and 
red mullet. The gill nets are set in depths ranging from 80 to 300 m. The mesh size used 
is usually  48  to  64  mm.  The  fishery  is  carried  out  mainly during  summer when  bottom 
trawl fishery is prohibited. Longline fishery for hake operates in deeper waters, down to 
500  m,  mainly  during  the  summer.  The  main  landing  port  in  GSA  20  is  the  port  of 
Patras. 
After  an  increase  from  2000  to  2008,  the  official  landings  of  hake  are  continuously 
declining since 2008 (Figure 6.11.2.2, Table 6.11.1.2.2). 

470 

470 

 
 
 
 
 
 
Figure  6.11.1.2.1  Hake  in  GSA  20.  Hake  DCF  landings  by  the  Greek  fleet  in  GSA  20 
from  different  gears  is  available  intermittently  since  2002.  Years  2007  and  2009-2012 
are  missing,  while  data  from  2013  and  2015  come  only  from  the  fourth  quarter.  Total 
refers to the official landings as they appear in FAO/GFCM Database (FishStat J) for the 
same period of time. 

Table  6.11.1.2.1  Hake  in  GSA  20.  Hake  DCF  landings  in  tonnes  by  the  Greek  fleet  in 
GSA  20  from  different  gears.  Years  2007  and  2009-2012  are  missing,  while  data  from 
2013 and 2015 come only from the fourth quarter. 

Year 

2003 
2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 
2015 
2016 

GNS 
Landings (t) 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
128 
241 
141 
596 

GTR 
Landings (t) 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
38 
23 
- 
- 

LLS 
Landings (t) 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
23 
21 
14 
70 

OTB 
Landings (t) 
308 
404 
516 
754 
- 
459 
- 
- 
- 
- 
203 
300 
64 
157 

Other/ 
unspecified 
(t) 

3094 
3404 
2768 
- 
2821 
- 
- 
- 
- 
- 
- 
- 
- 

471 

471 

 
 
 
 
 
 
 
 
 
 
Figure  6.11.1.2.2  Hake  in  GSA  20.  Hake  landings  in  GSA  20  according  to  the  official 
statistics  as  they  appear  in  FAO/GFCM  Database  (FishStat  J)  since  1982.  Because  the 
FAO/GFCM  Database  was  not  updated,  years  2015  and  2016  were  completed  with  the 
data from the Hellenic Statistical Authority which forms the basis of FAO/GFCM data. 

Individuals  of  ages  1,  2  and  3  make  up  the  majority  of  landings  (Figure  6.11.1.2.3), 
while  most  individuals  that  are  caught  (landed  and  discarded)  are  less  than  20  cm  of 
total length (Figure 6.11.1.2.4). 

472 

472 

 
 
 
 
 
 
 
 
Table  6.11.1.2.2  Hake  in  GSA  20.  Hake  landings  in  GSA  20  according  to  the  official 
statistics  as  they  appear  in  FAO/GFCM  Database  (FishStat  J).  Because  the  FAO/GFCM 
Database was not updated, years 2015 and 2016 were completed with the data from the 
Hellenic Statistical Authority which forms the basis of FAO/GFCM data. 

Year 
1982 
1983 
1984 
1985 
1986 
1987 
1988 
1989 
1990 
1991 
1992 
1993 
1994 
1995 
1996 
1997 
1998 
1999 
2000 
2001 
2002 
2003 
2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 
2015 
2016 

Hake FAO/GFCM_Landings (t) 
264 
202 
606 
291 
195 
176 
279 
431 
382 
499 
662 
1560 
1523 
1450 
1144 
1091 
661 
602 
487 
510 
591 
604 
683 
798 
1124 
1101 
1336 
1173 
1035 
876 
933 
908 
526 
430 
528 

473 

473 

 
 
 
Figure 6.11.1.2.3 Hake in GSA 20. Hake landings at age by the Greek fleet in GSA 20. 
Years  2007,  2009-2012  and  2015-2016  are  missing,  while  data  from  2013  come  only 
from the fourth quarter. 

Figure  6.11.1.2.4  Hake  in  GSA  20.  Hake  length  frequency  distribution  of  catch 
(landings  and  discards  combined)  by  the  Greek  fleet  in  GSA  20  from  OTB.  Years  2007 
and 2009-2012 are missing, while data from 2013 and 2015 come only from the fourth 
quarter. 

According to the Greek DCF, the discards of haκe in GSA 20 were over 750 t in the mid 
2000s and have been declined to negligible values (<20t) since 2013 with the exception 
of  GNS  discards  in  2016  (30  t)  (Figure  6.11.1.2.5,  Table  6.11.1.2.3).  The  highest 
proportion of discards is attributed to OTB.  

474 

474 

 
 
 
 
 
 
 
Figure 6.11.1.2.5 Hake in GSA 20. Hake discards by the Greek fleet in GSA 20.  

Table 6.11.1.2.3 Hake in GSA 20. Hake discards in tonnes by fishing gear in GSA 20 as 
reported by the DCF. 

Year 

2003 
2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 
2015 
2016 

OTB_Discards 
(t) 
33 
19 
70 
50 
- 
25 
- 
- 
- 
- 
16 
10 
2 
5 

GNS_Discards 
(t) 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
1 
1 
31 

Unspecified gear 
Discards (t) 
- 
- 
761 
774 
- 
581 
- 
- 
- 
- 
- 
- 
- 
- 

475 

475 

 
 
 
 
 
 
 
 
 
 
 
6.11.2.2 

EFFORT 

Figure 6.11.1.3.1 Hake in GSA 20. Nominal effort (days at sea) of OTB, GTR, GNS and 
LLS in GSA 20 as reported by DCF. 

The high effort values that appear in the DCF for coastal gears in 2016 are probably the 
result  of  miscalculation  or  a  biased  extrapolation  to  the  total  coastal  fleet  since  the 
number  of  vessels  and  their  days  at  sea  has  not  changed  dramatically  from  2014  to 
2016. In fact the number of vessels declined. 

6.11.2.3 

SURVEY DATA 

The  MEDITS  bottom  trawl  survey  was  used  for  the  estimation  of  abundance  index  of 
hake in GSA 20. The survey is carried out in June/July each year since 1994. No survey 
was carried out in 2002, 2007, 2009-2013 and 2015. A decline in the abundance of hake 
was  observed  from  2005  (highest  value)  to  2014  and  a  slight  increase  in  2016  (Figure 
6.11.1.4.1, Table 6.11.1.4.1). 

476 

476 

 
 
 
 
 
 
 
Figure 6.11.1.4.1 Hake in GSA 20. MEDITS survey abundance index (kg/km2) of hake 
in GSA 20 as reported by DCF. No survey was carried out in 2002, 2007, 2009-2013 and 
2015. The survey is carried out in June/July. 

Table 6.11.1.4.1 Hake in GSA 20. MEDITS survey abundance index of hake in GSA 20 
as reported by DCF. No survey was carried out in 2002, 2007, 2009-2013 and 2015. The 
survey is carried out in June/July. 

Year 
1994 
1995 
1996 
1997 
1998 
1999 
2000 
2001 
2002 
2003 
2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 
2015 
2016 

Hake abundance (kg/km2) 
24.96 
73.85 
33.26 
25.71 
16.99 
15.55 
32.61 
31.66 
- 
42.18 
53.69 
76.75 
61.51 
- 
52.84 
- 
- 
- 
- 
- 
40.19 
- 
46.44 

Ages 0, 1 and 2 make up the majority of individuals caught during the MEDITS bottom 
trawl survey (Figure 6.11.1.4.2) while the total length of individuals is lower than 30 cm, 
with a peak in 12 cm (Figure 6.11.1.4.3). 

477 

477 

 
 
 
 
 
 
Figure  6.11.1.4.2  Hake  in  GSA  20.  Age  frequency  distribution  of  the  MEDITS  survey 
abundance  index  of  hake  in  GSA  20  as  reported  by  DCF.  No  survey  was  carried  out  in 
2007, 2009-2013 and 2015. The survey is carried out in June/July except. 

Figure 6.11.1.4.3 Hake in GSA 20. Length frequency distribution of the MEDITS survey 
abundance  index  of  hake  in  GSA  20  as  reported  by  DCF.  No  survey  was  carried  out  in 
2007, 2009-2013 and 2015. The survey is carried out in June/July. 

6.11.3  STOCK ASSESSMENT 

Analytical method: a4a  
The Assessment for All Initiative (a4a) (Jardim et al., 2014), a4a, a statistical catch-at-
age  analysis  method  were  used  for  this  stock  that  utilize  catch-at-age  data  to  derive 
estimates of historical population size and fishing mortality. However, unlike VPA, model 
parameters  using  catch-at-age  analysis  are  estimated  by  working  forward  in  time  and 
the  methods  do  not  require  the  assumption  that  removals  from  the  fishery  are  known 
without  error.  Data  that  are  typically  used  are:  catch,  abundance  index,  statistical 
sample  of  age  composition  of  catch  and  abundance  index.  Assessment  was  performed 
with  version  1.1.3  of  FLa4a,  together  with  version  2.6.5  of  the  FLR  library  (FLCore)  in 
FLR environment. 
A  single  tuning  fleet  was  used  in  both  methods  based  on  the  CPUE  and  weight  at  age 
estimates from summer bottom trawl surveys (MEDITS) conducted in the Greek part of 
Ionian  Sea  (GSA  20)  from  2003  to  2016  (with  gaps  in  2007,  2009-2013  and  2015)  as 
reported in the DCF. 
The  analysis  was  carried  out  for  the  ages  0  to  4+  class  for  the  a4a  but  several  trials 
were  conducted  with  plus  groups  5  that  did  not  converge  or  the  fit  was  very  low. 
Concerning the Fbar, the age range used was 1-3 age groups. 

Input data 

478 

478 

 
 
 
 
 
 
 
Hake (Merluccius merluccius) is the most important target species  for the bottom trawl 
fishery  in  Ionian  Sea  (GSA  20).  Hake  is  being  exploited  mainly  by  the  bottom  trawl 
(OTB)  fishery  and  coastal  fleet  using  trammel  and  gill-nets.  The  age  data  from  OTB 
landings  were  used  for  the  stock  assessment.  According  to  the  Greek  DCF  report  (DCF 
2014)  the  percentage  of  hake  discards  is  less  than  5%  of  their  total  catch  since  2013. 
Therefore, discards were excluded from the analysis. 

Growth 
Natural mortality (M) was estimated using PRODBIOM (Abella et al. 1997) and is shown 
in Table 6.11.2.1. The input parameters used were Linf = 104 cm, k = 0.12, t0 =-0.01. 
The values of M vector were the used in the last approved assessment for hake in GSA 
19. 

Table  6.11.3.1.  Hake  in  GSA  20.  Natural  mortality  estimates  per  age  for  hake  in  GSA 
20. 

Age 

0 

1 

2 

3 

4 

5 

6 

7 

M 

1.24 

0.73 

0.48 

0.39 

0.35  0.32  0.31  0.29 

Maturity 
The  following  maturity  at  age  ogive  was  used  for  hake  assessment  in  GSA  20  as 
estimated  from  biological  sampling  based  on  length  at  first  maturity.  Hake  spawns 
throughout  the  year  in  many  areas  of  the  Mediterranean  with  a  peak  of  spawning 
occurring during the summer. 

Table 6.11.3.2 Hake in GSA 20. Proportion of mature fish by age. 

Age 

0 

1 

2 

3 

4 

5 

6 

7 

Pmat 

0 

0.19 

0.86 

1 

1 

1 

1 

1 

Catch Data 

The  time  series  of  official  landings  for  the  Greek  part  of  Ionian  Sea  (GSA  20),  as  they 
appear in the FAO/GFCM FishStat J database was used for the period 2003-2014 and the 
landings  of  the  Hellenic  Statistical  Authority,  that  form  the  basis  for  the  FAO/GFCM 
FishStat J database, were used for 2015-2016, because the FishStat J was not updated 
(Figure 6.11.3.3.1). The DCF reported landings were considered unreliable for the early 
years  of  the  dataset  and  were  excluded.  Based  on  the  DCF  report,  hake  discards  were 
considered  negligible  in  GSA  20.  The  total  landings  data  used  for  assessment  are 
reported in Table 6.11.2.3. Catch was considered equivalent to landings. 

Landings at age data for the period 2003-2016 were those reported by the DCF. No DCF 
was carried out in 2007, 2009-2012 and DCF covered only the last trimester in 2013 and 
2015. Thus, in the a4a method, NA (non available) was used for the catch at age data in 
the  years  that  no  DCF  was  carried  out.  Age  structure  of  the  landings  data  used  for 
assessment is the DCF reported age readings (Figure 6.11.2.1).  

Table 6.11.3.3 Hake in GSA 20. Greek catch data in tones used for assessment.  

Year  Catches (t) 
604 
2003 
683 
2004 
798 
2005 

479 

479 

 
 
 
 
 
 
 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 
2015 
2016 

1124 
1101 
1336 
1173 
1035 
876 
933 
908 
526 
430 
528 

Figure 6.11.3.1 Hake in GSA 20. Age structure of the landings data.  

480 

480 

 
 
 
 
Figure 6.11.3.2 Hake in GSA 20. Number at age in the landings.  

Figure 6.11.3.3 Hake in GSA 20. Weight at age in the landings.  

481 

481 

 
 
 
 
 
 
Figure 6.11.3.4 Hake in GSA 20. Weight at age in the stock by cohort. 

Discards  

Discards  data  were  reported  to  STECF  EWG  17-15  through  the  DCF.  Discards  were 
considered negligible because they are less than 5% of the reported landings. This does 
not necessarily mean that undersized individuals are not caught but that they are caught 
and sold instead of being discarded.  

Survey Indices of abundance and biomass by year and size/age  

MEDITS bottom trawl survey 

We used reported data to STECF EWG 17-15 through the DCF concerning the evaluations 
of the MEDITS bottom trawl survey for 2003 to 2016 (with gaps in 2007, 2009-2013 and 
2015) of total biomass, abundance, length and age composition for hake in GSA 20.  

MEDITS bottom trawl survey methodology 
Tables TA, TB, TC were provided according to the MEDITS protocol. The MEDITS survey 
was  carried  out  in  GSA  20  every  summer  from  1994  to  2016,  except  in  2002,  2007, 
2009-2013  and  2015  because  of  administrative  problems.  During  1994  and  1995  the 
survey in GSA 20 was carried out in a small number of stations (12 and 15). The number 
of stations kept increasing and in 1998 was more than doubled (32 stations). The survey 
vessel  changed  in  1998.  Due  to  these  changes  in  the  survey  design,  caution  is  needed 
when investigating the trends of relevant indicators in the MEDITS time series.  
Data  were  assigned  to  strata  based  upon  the  shooting  position  and  average  depth 
(between shooting and hauling depth). Few obvious data errors were corrected. Catches 
by  haul  were  standardized  to  60  minutes  hauling  duration.  Hauls  noted  as  valid  were 

482 

482 

 
 
 
 
 
 
 
used  only,  including  stations  with  no  catches  of  hake,  red  mullet  or  pink  shrimp  (zero 
catches are included). 
The  abundance  and  biomass  indices  by  GSA  were  calculated  through  stratified  means 
(Cochran,  1953;  Saville,  1977).  This  implies  weighting  of  the  average  values  of  the 
individual  standardized  catches  and  the  variation  of  each  stratum  by  the  respective 
stratum areas in each GSA. 

Trends in abundance by age 
Abundance indexes were reported to STECF EWG 17-15 through DCF. The time series of 
abundance and biomass indices of hake from MEDITS bottom trawl survey in GSA 20 are 
shown and described in the following figures. 

Figure 6.11.3.5 Hake in GSA 20. Weight at age in the landings.  

483 

483 

 
 
 
 
Figure  6.11.3.6  Hake  in  GSA  20.  MEDITS  survey  abundance  index  of  hake  in  GSA  20 
by age, as reported by the DCF and used for assessment. No survey was carried out in 
2007, 2009-2013 and 2015. The survey was carried out in June/July. 

Assessment results 

ANALYTICAL METHOD A4A 
Different a4a models were performed (combination of different f, q, sr). The best model 
(according to a combination of AIC, BIC and residuals) included:  

f: factor(age) + s(year,k=4)+s(year,k=3,by=as.numeric(age==0))  
q: list(~ te(age, year, k = c(3,4))) 
sr: ~s(year,k=4) 

The  input  data  to  the  model,  the  main  results  and  the  model  diagnostics  are  shown  in 
Figures 6.11.3.7-6.11.3.13 

Based  on  the  a4a assessment  method  the SSB of  hake fluctuated  over  the  time  period 
examined (2003-2016) from 441 tons (in 2003) to 284 tons in 2016. A drop in SSB was 
observed in the years 2009 to 2015 (historically lowest biomass) followed by an increase 
in  2016.  The  assessment  shows  an  increasing  trend  in  the  number  of  recruits  since 
2012. The recruitment (age 0) reached its maximum value in 2006 and its minimum in 
2011.  Fbar  (1-3)  shows  an  increasing  trend  since  2013  reaching  the  value  of  1.42  in 
2016.   

484 

484 

 
 
 
 
 
 
 
 
 
 
 
 
Figure  6.11.3.7  Hake  in  GSA  20.  Stock  summary  for  hake  in  GSA  20,  recruits,  SSB 
(Stock Spawning Biomass), catch (landings) and harvest (fishing mortality). 

Figure 6.11.3.8 Hake in GSA 20. 3D contour plot of estimated fishing mortality at age 
and year. 

485 

485 

 
 
 
 
Figure  6.11.3.9  Hake  in  GSA  20.  Standardized  residuals  for  abundance  indices 
(MEDITS)  and  for  catch  numbers  (catch.n).  Each  panel  is  coded  by  age  class,  dots 
represent standardized residuals and lines a simple smoother. 

Figure  6.11.3.10  Hake  in  GSA  20.  Quantile-quantile  plot  of  standardized  residuals  for 
abundance  indices  (MEDITS)  and  for  catch  numbers  (catch.n).  Each  panel  is  coded  by 
age  class,  dots  represent  standardized  residuals  and  lines  the  normal  distribution 
quantiles. 

486 

486 

 
 
 
 
Figure 6.11.3.11 Hake in GSA 20. Fitted and observed catch at age. 

Figure 6.11.3.12 Hake in GSA 20. Fitted and observed index at age. 

Retrospective 
The  retrospective  analysis  was  applied  up  to  3  years  back.  Models  results  were  quite 
unstable because of the missing data years (Figure 6.11.3.12). 

487 

487 

 
 
 
 
 
Figure 6.11.3.13 Hake in GSA 20.  Restrospective analysis output. 

Simulations 

Figure 6.11.3.14 Hake in GSA 20. Stock summary of the simulated and fitted data. 

488 

488 

 
 
 
 
 
 
 
 
 
 
The EWG 17-15 concluded that the output of this model  was not suitable to provide an 
indication of the current status of the stock and due to the lack of surveys and catch-at-
age data and agreed not to provide forward projections and catch advice based on this 
assessment. Overall this modelling approach where missing data are dealt with correctly 
in the model is preferred to the other models described below. 

489 

489 

 
 
 
 
 
Surplus production method 1: SPICT 
The Surplus Production in Continuous time (SPiCT) assessment method is fully described 
in Pedersen and Berg (2016). SPiCT is available as an R (R Core Team 2015) package in 
the github online repository: https://github.com/mawp/spict.  
SPiCT  requires  a  time  series  of  catches  and  one  (or  more)  time  series  of  tuning  index 
(CPUE  or  biomass;  in  this  case  MEDITS  index).  The  expected  output  includes 
management  reference  points  F/Fmsy  and  B/Bmsy  that  quantify  the  exploitation  rate 
and stock status. A forecasting period and a fishing management scenario can be tested 
by changing the multiplication factor that is applied to the current fishing mortality and 
projecting to the future. Main advantages of SPiCT are: 
1. 
2. 
evaluation. 
3. 
index observations, and process error is included in fishing and stock dynamics. 
4. 
arbitrarily sampled data. 

All estimated reference points (MSY, Fmsy, Bmsy) are reported with uncertainties. 
The  model  can  be  used  for  short-term  forecasting  and  management  strategy 

The  model  is  fully  stochastic  in  that  observation  error  is  included  in  catch  and 

The  model  is  formulated  in  continuous-time  and  can  therefore  incorporate 

Input data 

Landings 

The official reported landings from the Ionian Sea (GSA 20) were used as reported in the 
FishStat  J  from  the  FAO/GFCM  Database  (FishStat  J).  The  landings  data  from  1982  to 
2016  was  used,  with  the  landings  of  the  two  latest  years  derived  from  the  Statistical 
Authority  of  Greece  (they  fully  correspond  to  the  FAO/GFCM  ones)  because  the 
FAO/GFCM  database  was  not  updated  after  2014.  Data  prior  to  1982  were  excluded 
because the landings of hake were reported aggregated with other species. According to 
the DCF report, the discards of hake by weight in GSA 20 are negligible; thus they were 
excluded from the analysis. 

Biomass 

The  CPUE  from  MEDITS  bottom  trawl  surveys  that  were  conducted  in  Ionian  Sea  was 
used as tuning index. Survey data were available by DCF from 1994 onwards (with gaps 
in 2002, 2007, 2009-2013 and 2015).  

Settings 

No  priors  on  any  of  the  model  parameters  or  variables  were  required  for  the  model  to 
converge. The Schaefer production model was selected. 

Table  6.11.3.4.  Hake  in  GSA  20.  Official  landings  (tons)  for  hake  in  GSA  20  (FishStat 
J). 

Year 
1982 
1983 
1984 
1985 
1986 
1987 

Greek landings (t) 
264 
202 
606 
291 
195 
176 

490 

490 

 
 
 
 
 
1988 
1989 
1990 
1991 
1992 
1993 
1994 
1995 
1996 
1997 
1998 
1999 
2000 
2001 
2002 
2003 
2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 
2015 
2016 

279 
431 
382 
499 
662 
1560 
1523 
1450 
1144 
1091 
661 
602 
487 
510 
591 
604 
683 
798 
1124 
1101 
1336 
1173 
1035 
876 
933 
908 
526 
430 
528 

Figure 6.11.3.15 Hake in GSA 20. Input data for hake in GSA 20. 

491 

491 

 
 
 
Figure 6.11.3.16 Hake in GSA 20. MEDITS survey index as CPUE (kg/km2) for hake in 
GSA 20.  

Assessment results 

Stock summary 

Figure  6.11.3.17  Hake  in  GSA  20.  Summary  of  the  final  SPiCT  model  fit  and  output. 
Absolute and relative Biomass and Fishing mortality, state of the stock in F/B space and 
relative to estimated production.  

The  output  of  the  model  (Model  estimates,  reference  points  and  summaries)  are 
reported below. 

492 

492 

 
 
 
 
 
 
 
 
[1] "Convergence: 0  MSG: relative convergence (4)"                           

[2] "Objective function at optimum: 31.7049548"                               

[3] "Euler time step (years):  1/16 or 0.0625"                                

[4] "Nobs C: 35,  Nobs I1: 15"                                                

[5] "Catch/biomass unit: tones "                                              

[6] ""                                                                        

[7] "Priors"                                                                  

[8] "     logn  ~  dnorm[log(2), 2^2]"                                        

[9] " logalpha  ~  dnorm[log(1), 2^2]"                                        

[10] "  logbeta  ~  dnorm[log(1), 2^2]"                                        

[11] ""                                                                        

[12] "Fixed parameters"                                                        

[13] "   fixed.value  "                                                        

[14] " n           2  "                                                        

[15] ""                                                                        

[16] "Model parameter estimates w 95% CI "                                     

[17] "            estimate        cilow        ciupp    log.est  "             

[18] " alpha     4.9676411    0.5607400   44.0087320  1.6029451  "             

[19] " beta      0.3377959    0.0943200    1.2097758 -1.0853135  "             

[20] " r         0.8830753    0.4913479    1.5871076 -0.1243448  "             

[21] " rc        0.8830753    0.4913479    1.5871076 -0.1243448  "             

[22] " rold      0.8830753    0.4913479    1.5871076 -0.1243448  "             

[23] " m       999.8785285  887.2946990 1126.7474864  6.9076338  "             

[24] " K      4529.0747529 2577.4420777 7958.4787938  8.4182729  "             

[25] " q         0.0186637    0.0105971    0.0328704 -3.9811772  "             

[26] " sdb       0.0585259    0.0068393    0.5008247 -2.8382852  "             

[27] " sdf       0.3855468    0.2435531    0.6103240 -0.9530927  "             

[28] " sdi       0.2907359    0.1987026    0.4253963 -1.2353401  "             

[29] " sdc       0.1302361    0.0517535    0.3277349 -2.0384063  "             

[30] " "                                                                       

[31] "Deterministic reference points (Drp)"                                    

[32] "           estimate       cilow        ciupp   log.est  "                

[33] " Bmsyd 2264.5373765 1288.721039 3979.2393969  7.725126  "                

[34] " Fmsyd    0.4415377    0.245674    0.7935538 -0.817492  "                

[35] " MSYd   999.8785285  887.294699 1126.7474864  6.907634  "                

[36] "Stochastic reference points (Srp)"                                       

493 

493 

 
 
[37] "           estimate        cilow        ciupp    log.est rel.diff.Drp  " 

[38] " Bmsys 2257.3044183 1283.3670391 3970.3553867  7.7219266 -0.003204246  " 

[39] " Fmsys    0.4407501    0.2452791    0.7919982 -0.8192773 -0.001786857  " 

[40] " MSYs   994.9013867  888.7676609 1113.7092547  6.9026436 -0.005002648  " 

[41] ""                                                                        

[42] "States w 95% CI (inp$msytype: s)"                                        

[43] "                    estimate        cilow        ciupp    log.est  "     

[44] " B_2016.00      3168.6882362 1844.0715908 5444.7914000  8.0610730  "     

[45] " F_2016.00         0.1516503    0.0785978    0.2926013 -1.8861778  "     

[46] " B_2016.00/Bmsy    1.4037487    0.9572789    2.0584498  0.3391463  "     

[47] " F_2016.00/Fmsy    0.3440733    0.1906142    0.6210789 -1.0669005  "     

[48] ""                                                                        

[49] "Predictions w 95% CI (inp$msytype: s)"                                   

[50] "                  prediction        cilow        ciupp    log.est  "     

[51] " B_2017.00      3433.0574765 2081.2116689 5662.9913300  8.1412065  "     

[52] " F_2017.00         0.1605306    0.0772291    0.3336834 -1.8292708  "     

[53] " B_2017.00/Bmsy    1.5208660    1.1486517    2.0136942  0.4192799  "     

[54] " F_2017.00/Fmsy    0.3642213    0.1901247    0.6977379 -1.0099935  "     

[55] " Catch_2017.00   562.2535840  299.5115647 1055.4820914  6.3319530  "     

[56] " E(B_inf)       3686.6822479           NA           NA  8.2124822  " 

494 

494 

 
 
 
 
Figure 6.11.3.18  Hake in GSA 20. Relative biomass and fishing mortality, F/B plot and 
production curve as given by the SPiCT model for hake in GSA 20. 

Figure 6.11.3.19 Hake in GSA 20. Diagnostics from SPiCT model for hake in GSA 20.  

Retrospective analysis 
A  retrospective  analysis  was  run  with  4  retro  years.  The  retrospective  patterns  are 
consistent across in terms of B/Bmsy but results in poorer performance when F/Fmsy is 
concerned. 

Figure 6.11.3.20 Hake in GSA 20. Retrospective analysis for the SPiCT model for hake 
in GSA 20. 

495 

495 

 
 
 
 
 
 
Conclusions to SPiCT model 
The SPICT model estimates  B_2016/Bmsy=1.40 and F_2016/Fmsy=0.34. However, the 
estimated  confidence  intervals  were  quite  large  concerning  the  F  estimates  and  the 
contrasting  reference  points  with  the  a4a  age  based  analytical  model,  which  indicated 
F>1,  lead  the  EWG  17-15  to  decide  that  the  model  results  were  not  able  to  determine 
current  stock  status  or  biomass;  thus,  this  assessment  will  not  be  used  for  specific 
advice. 

Surplus production method 2: CMSY 

Hake is GSA 20 was also assessed with the surplus production model that is incorporated 
in  the  CMSY  method  (Froese  et  al.  2017),  using  catch  data  and  priors  for  productivity 
and stock status as inputs. Probable ranges for the maximum intrinsic rate of population 
increase (r) and for unexploited population size or carrying capacity (k) are filtered with 
a  Monte  Carlo  approach  to  detect  ‘viable’  r-k  pairs.  Stocks,  for  which  abundance  data 
(biomass,  catch  per  unit  of  effort  or  abundance  indices)  are  available,  can  also  be 
assessed with an advanced Bayesian state-space implementation of the Schaefer (1954) 
surplus  production  model  (BSM),  where  r,  k  and  MSY  are  predicted  from  catch  and 
biomass data.  
The main advantages of CMSY are: 
1. 
2. 
index observations, and process error is included in fishing and stock dynamics. 

All estimated reference points (MSY, Fmsy, Bmsy) are reported with uncertainties. 
The  model  is  fully  stochastic  in  that  observation  error  is  included  in  catch  and 

The  model  is  formulated  in  continuous-time  and  can  therefore  incorporate 

3. 
arbitrarily sampled data or missing data points as is the case for Greek data. 

Input data 

Landings 

The official reported landings from the Ionian Sea (GSA 20) were used as reported in the 
FishStat  J  from  the  FAO/GFCM  Database  (FishStat  J).  The  landings  data  from  1982  to 
2016  was  used,  with  the  landings  of  the  two  latest  years  derived  from  the  Statistical 
Authority  of  Greece  (they  fully  correspond  to  the  FAO/GFCM  ones)  because  the 
FAO/GFCM  database  was  not  updated  after  2014.  Data  prior  to  1982  were  excluded 
because the landings of hake were reported aggregated with other species. According to 
the  DCF  report  (DCF  2014),  the  discards  of  hake  by  weight  in  GSA  20  are  negligible; 
thus they were excluded from the analysis. 

Biomass 

The  CPUE  from  MEDITS  bottom  trawl  surveys  that  were  conducted  in  Ionian  Sea  was 
used as tuning index. Survey data were available by DCF from 1994 onwards (with gaps 
in 2002, 2007, 2009-2013 and 2015).  

Settings 

No external priors on any of the model parameters or variables were used. 

496 

496 

 
 
 
 
 
 
 
 
 
Table  6.11.3.5.  Hake  in  GSA  20.  Official  landings  (tons)  for  hake  in  GSA  20  (FishStat 
J). 

Year 
1982 
1983 
1984 
1985 
1986 
1987 
1988 
1989 
1990 
1991 
1992 
1993 
1994 
1995 
1996 
1997 
1998 
1999 
2000 
2001 
2002 
2003 
2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 
2015 
2016 

Hake landings (t) 
264 
202 
606 
291 
195 
176 
279 
431 
382 
499 
662 
1560 
1523 
1450 
1144 
1091 
661 
602 
487 
510 
591 
604 
683 
798 
1124 
1101 
1336 
1173 
1035 
876 
933 
908 
526 
430 
528 

497 

497 

 
 
 
 
 
 
Figure  6.11.3.21  Hake  in  GSA  20.  Summary  of  the  final  CMSY  model  fit  and  output. 
Catch  curve,  viable  r-K  pairs  and  their  analysis,  relative  biomass  and  fishing  mortality, 
production curve of hake in GSA 20.  

Figure 6.11.3.22  Hake in GSA 20. Relative biomass and fishing mortality, F/B plot and 
catch curve as given by the CMSY model for hake in GSA 20. 

The  output  of  the  model  (Model  estimates,  reference  points  and  summaries)  are 
reported below. 

498 

498 

 
 
 
 
 
 
Species: Merluccius merluccius , stock: MERLMER_IS  

 Hake in Ionian Sea  

Source: NA  

Region: Mediterranean , Ionian Sea  

Catch data used from years 1982 - 2016 , abundance = CPUE  

Prior initial relative biomass = 0.2 - 0.6 default  

Prior intermediate rel. biomass= 0.5 - 0.9 in year 1994 default  

Prior final relative biomass   = 0.01 - 0.3 , default  

Prior range for r = 0.22 - 0.95 expert, , prior range for k  = 1.59 - 27.5 

Prior range of q = 0.0105 - 0.0438 

Results of CMSY analysis with altogether 2245 viable trajectories for 581 r-k pairs  

r = 0.66 , 95% CL = 0.467 - 0.934 , k = 5.23 , 95% CL = 3.47 - 7.88  

MSY = 0.864 , 95% CL = 0.764 - 0.976  

Relative biomass last year = 0.193 k, 2.5th = 0.0228 , 97.5th = 0.297  

Exploitation F/(r/2) in last year = 1.5  

Results from Bayesian Schaefer model using catch & CPUE  

r = 0.705 , 95% CL = 0.519 - 0.957 , k = 5.11 , 95% CL = 3.98 - 6.55  

MSY = 0.9 , 95% CL = 0.824 - 0.983  

Relative biomass in last year = 0.32 k, 2.5th perc = 0.216 , 97.5th perc = 0.395  

Exploitation F/(r/2) in last year = 0.868 

q = 0.0176 , lcl = 0.0135 , ucl = 0.0229 

Results for Management (based on BSM analysis)  

Fmsy = 0.352 , 95% CL = 0.259 - 0.478 (if B > 1/2 Bmsy then Fmsy = 0.5 r) 

Fmsy  =  0.352  ,  95%  CL  =  0.259  -  0.478  (r  and  Fmsy  are  linearly  reduced  if  B  <  1/2 
Bmsy) 

MSY  = 0.9 ,  95% CL = 0.824 - 0.983  

Bmsy = 2.55 ,  95% CL = 1.99 - 3.27  

Biomass in last year  = 1.63 , 2.5th perc = 1.1 , 97.5 perc = 2.02  

B/Bmsy in last year   = 0.64 , 2.5th perc = 0.432 , 97.5 perc = 0.79  

Fishing mortality in last year = 0.306 , 2.5th perc = 0.248 , 97.5 perc = 0.454  

F/Fmsy  = 0.868 , 2.5th perc = 0.704 , 97.5 perc = 1.29  

Comment: Catch=landings from FishStat (Greece  

 ---------------------------------------------------------- 

499 

499 

 
 
 
 
 
 
Conclusions to CMSY model 
The  CMSY  model  estimates  B_2016/Bmsy=0.64  and  F_2016/Fmsy=0.87  indicating  a 
recovering  stock.  However,  the  estimated  confidence  intervals  were  quite  large 
concerning  the  F  estimates  and  the  contrasting  reference  points  with  the  analytical 
models lead the EWG 17-15 to decide that the model results were not able to determine 
current  stock  status  or  biomass;  thus,  this  assessment  will  not  be  used  for  specific 
advice. 

Comparison of assessments 

The three assessment model results can be compared (Fig 6.11.2.25). The SPiCT model 
has  wide  confidence  intervals  and  good  retrospective  results  but  implies  that  the  stock 
status is constantly improving since 1982, whereas previous assessments report that the 
stock of hake in GSA 20 was suffering from overexploitation. The CMSY model trends are 
similar  to  the  SPiCT.  But  the  resulting  stock  status  in  terms  of  F  relative  to  FMSY  is  not 
consistent  for the three models. The age based model a4a suggests overexploitation of 
the  hake  stock  but  the  retrospective  results  are  poor  and  indicate  instability  of  the 
model. The divergence among all models in the last year is of concern. Overall the a4a 
model  is  considered  to  best  represent  the  current  state  of  the  stock  in  terms  of  F  in 
recent  years  as  this  uses  the  length/age  data  which  is  not  used  by  the  surplus 
production  models.  However,  due  to  the  considerable  uncertainty  in  the  model  due  to 
missing data, the model is not considered suitable for catch advice.  

Figure 6.11.3.23 Hake in GSA 20. Comparative assessment main outputs.  

6.11.4 REFERENCE POINTS 

Given the short time series in the age based assessments and the disagreement among 
the models, no specific MSY analysis was carried out and MSY reference points for hake 
in GSA 20 were not estimated in EWG 1715. 

500 

500 

 
 
 
 
 
 
6.11.5  SHORT TERM FORECAST AND CATCH OPTIONS 

No  short  term  forecast  and  catch  options  were  carried  out  for  hake  stock  in  GSA  20 
within STECF EWG 17-15 

6.11.6  DATA DEFICIENCIES 

Particular deficiencies were found in the DCF data provided. Specifically, no DCF catch / 
catch-at-length  /  catch-at-age  data  were  provided  for  2007,  2009,  2010,  2011,  and 
2012. Catch-at-age data were provided only for the last trimester for 2013 and 2015. No 
MEDITS  surveys  took  place  in  2002,  2007,  2009-2013  and  2015.  The  landings  as 
calculated from the DCF data (number of individuals multiplied by their somatic weight) 
do  not  correspond  to  the  official  landings  reported.  This  issue  is  stronger  for  the  years 
2003-2006. The numbers and weights at length are not reported consistently (step size, 
initial  value,  unit  of  measurement  vary  among  years).  Specifically,  for  hake  in  GSA  20 
there were also some issues with the 2005 dataset. 

6.12  RED MULLET IN GSA 19 

6.12.1  BIOLOGY 

Stock of red mullet (Mullus barbatus) was assumed in the boundaries of the whole GSA 
19. Red mullet is with hake, deep-water deep-water rose shrimp, anchovy and sardine a 
key species of fishing assemblages in the Ionian Sea (GSA 19) (Figure 6.12.1.1). 

Figure 6.12.1. Geographical location of GSA 19. 

Growth 

In the DCF framework the growth has been studied ageing fis by otolith readings using 
the  whole  sagitta  and  thin  sections  for  older  individuals.  DCF  Von  Bertalanffy  growth 
parameters  for  each  sex  were  estimated  from  average  length  at  age  using  an  iterative 

501 

501 

 
 
 
 
 
 
non-liner procedure that minimizes the sum of the square differences between observed 
and expected values. The observed maximum length of red mullet was 27 cm registered 
in the OTB landings. 

For the present assessments, in line with the previous ones (STECF EWG 12-19, STECF 
EWG  15-16)  there  are  used  fast  growth  parameters  and  corresponding  length  weight 
relationship (table 6.12.1.1). 

Table  6.12.1.1  Red  mullet  in  GSA  19.  Growth  parameters  used  in  the  present 
assessments. 

SEX  VB_LINF  VB_K  VB_T0 

C 

30 

0.4 

A 
-0.3  0.0072  3.168 

B 

Maturity 

Size at first maturity was estimated from commercial samples using a generalized linear 
model  (GLM)  with  logistic  link  to  describe  the  proportion  of  adult  individuals  on  the 
length as independent variable (ICES, 2008). Specimens were considered adults at stage 
2b (recovering), 2c (maturing), 3 (mature), 4a (spent) and 4b (resting), and immature 
as  stage  1  (immature)  and  2a  (virgin  developing).  The  estimated  size  at  first  maturity 
was about 12 cm for females and 11 cm for males.  

For  the  present  assessments,  in  line  with  the  previous  assessments,  the  fast  growth 
parameters  have  been  used  to  estimate  maturity  at  age  as  reported  in  the  table 
6.12.1.2. 

Table 6.12.1.2 Red mullet in GSA 19. Maturity proportion at age adopted in the present 
assessments. 

Age 
0 
1 
2 
3+ 

Proportion of 
matures 
0.46 
0.98 
1 
1 

Natural mortality 

For  the  present  assessments,  in  line  with  the  previous  ones,  the  vector  of  natural 
mortality  estimated  according  to  PRODBIOM  (Abella  et  al.,  1997)  and  reported  in  the 
table 6.12.1.3. has been adopted. It is based on fast growth parameters. 

Table  6.12.1.3  Red  mullet  in  GSA  19.  Vector  of  natural  mortality  used  in  the  present 
assessments. 

Natural 
mortality 
1.03 
0.71 
0.65 

Age 
0 
1 
2 

502 

502 

 
 
 
 
  
 
3+ 

0.62 

6.12.2  INPUT DATA 

6.12.2.1  CATCH (LANDINGS AND DISCARDS)  

Fisheries 

Red mullet is mostly targeted by trawlers (about 50% of the total catch), but also with 
small scale fisheries using mostly gillnet and trammel net. The sum of these three gears 
(OTB+GNS+GTR) represents more than 90% of the total catch. 

Catches 

Total catch by year is reported in table 6.12.2.1.1 (in term of landing and discard) and 
figure  6.12.2.1.1.  Catches  include  the  discards  of  OTB  gear,  given  that  discard  is  not 
present in GNS and GTR nets. 

Table 6.12.2.1.1 Red mullet in GSA 19. Catches in terms of landings and discards. 

Landings 
951.0 
1113.7 
887.4 
541.1 
447.8 
529.5 
538.1 
588.4 
483.3 
274.4 
251.0 
303.6 
277.9 

2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 
2015 
2016 

Discards 
(only 
OTB) 

9.96 

0.06 
3.29 
0.02 
1.45 
0.08 
0.12 

Total 
catch 
951.0 
1113.7 
887.4 
541.1 
447.8 
539.5 
538.1 
588.4 
486.6 
274.5 
252.5 
303.7 
278.0 

503 

503 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 6.12.2.1.1 Red mullet in GSA 19. Total catch all gears included (tons). 

Landings 

Available  landing  data  are  from  DCF  regulations.  EWG  15-17  received  Italian  landings 
data  for  GSA  19  by  fishing  gears,  which  are  listed  in  table  6.12.2.1.2  and  figure 
6.12.2.1.2. 

Landings  fluctuate  around  250  and  1100  tons  with  the  maximum  in  2005  and  the 
minimum in 2014. An important part of the landings of red mullet is distributed between 
trawlers  (between  40-75%)  and  the  other  part  mainly  between  nets  (GNS  and  GTR). 
Landings  of  gears  other  than  OTB,  GNS  and  GTR  can  be  considered  negligible  or 
misreporting (on average less than 2%). 

Table 6.12.2.1.2 Red mullet in GSA 19. Annual landings (t) by gear type, 2004-2016. 

LLD  LLS  PS  PTM  SB  SV 

0.1 

0.7 

0.0  0.0  951.0 
6.2  6.2  1113.7 

OTB  GNS  GTR 
-1 
2004  363.8  52.0  535.0  0.1 
2005  297.5  42.8  760.3  0.0 
2006  566.0  64.7  240.9 
2007  287.8  54.7  189.5  1.0 
2008  348.3  68.5  29.3 
2009  389.8  114.1  16.1 
2010  283.5  220.0  13.1 
2011  371.5  172.9  25.0 
2012  309.3  145.9  20.8 
2013  110.5  119.2  41.3 
2014  102.7  122.8  23.7 
2015  189.4  65.0  28.9 
2016  165.5  95.2  17.1 

0.5  0.4  0.6 
1.4 
0.1 
5.6 
3.0 
0.1 
0.7 
0.4 

0.5 

19.7 

0.4  7.7  7.7  887.4 
4.1  4.1  541.1 
0.1  0.1  447.8 
4.0  4.0  529.5 
7.9  7.9  538.1 
8.0  8.0  588.4 
3.6  3.6  483.3 
1.4  1.4  274.4 
0.7  0.7  251.0 
303.6 
277.9 

504 

504 

0200400600800100012002004200520062007200820092010201120122013201420152016tonsMUT GSA 19, Total catch 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 6.12.2.1.2 Red mullet in GSA 19. Landings by gear and total landings. 

Length  distribution  of  landings  is  reported  for  the  three  main  gears  in  DCF  from  2006-
2016 and it is shown in figure 6.12.2.1.3 

Figure 6.12.2.1.3 Red mullet in GSA 19. Landing length distribution from 2006-2016. 

Discards 

Discards  of  red  mullet  in  the  GSA  19  are  reported  for  2009,  2011-2016.  In  2006-2008 
and  2010  the  discards  was  not  available.  The  volume  of  discards  is  rather  variable 
among years, but anyway discards no greater than 1% of the total catch except in 2007 
(1.8%).  Volume  of  discard  is  reported  in  table  6.12.2.1.3  and  in  figure  6.12.2.1.4  and 
length frequencies are reported in figure 6.12.2.1.5. 

Table 6.12.2.1.3 Red mullet in GSA 19. Annual discard (t) reported in the period 2006-
2016. 

OTB 

2006 
2007 
2008 
2009  9.96 
2010 
2011  0.06 

505 

505 

0200400600800100012002004200520062007200820092010201120122013201420152016tonsMUT GSA19, Landings/fishing gearOTBGNSGTRTotalminor gears0100020003000400050006000700080009000024681012141618202224262830ThousandsTL (cm)MUT GSA 19, OTB+GNS+GTR Landings20062007200820092010201120122013201420152016 
 
 
 
 
 
 
 
 
 
 
 
2012  3.29 
2013  0.02 
2014  1.45 
2015  0.08 
2016  0.12 

MUT GSA 16 Discards 

s
n
o
t

10

8

6

4

2

0

6
0
0
2

7
0
0
2

8
0
0
2

9
0
0
2

0
1
0
2

1
1
0
2

2
1
0
2

3
1
0
2

4
1
0
2

5
1
0
2

6
1
0
2

Figure 6.12.2.1.4 Red mullet in GSA 19. Discards by gear (only OTB). 

Figure 6.12.2.1.5 Red mullet in GSA 19. Discards length frequencies (gear OTB). 

Catch at age 

For the present assessments, in order to make the present results comparable with the 
previous ones, age distribution of red mullet (catches) in GSA 19 has been obtained as 
sum  of  landing  and  discard  age  distribution  re-estimated  using  the  knife-edge  slicing 
method  (LFDA  algorithm)  with  the  fast  growth  parameters  used  also  in  the  previous  
STECF-EWG assessments.  

Age data from DCF obtained with a different set of parameters have not been used. 

Age distribution by year of the red mullet in GSA 19 is reported in table 6.12.2.1.4 and 
in figure 6.12.2.1.6. 

506 

506 

050100150200250300350400450500024681012141618202224262830thousandsTL (cm)MUT GSA 19, Discards  frequencies2009201120122013201420152016 
 
 
 
 
 
 
 
Table 6.12.2.1.4 Red mullet in GSA 19. Catch at age (thousands) by year. 

0 

1 

2 

2006  11239  21579  491 
14426  55 
2007  6176 
221 
2008  8478 
9077 
180 
2009  23511  8939 
2010  6423 
12370  376 
2011  14425  10074  696 
365 
2012  14752  8843 
172 
6462 
2013  6000 
146 
6035 
2014  5703 
232 
5821 
2015  5857 
495 
6563 
2016  1173 

3+ 
4 
0 
11 
4 
22 
65 
21 
19 
7 
4 
34 

Figure 6.12.2.1.6 Red mullet in GSA 19. Catch at age (thousands) by year. 

6.12.2.2  EFFORT 

The trends in fishing effort by fleet level and major gear type targeting red mullet in GSA 
19  (OTB,  GNS  and  GTR)  are  listed  in  tables  6.12.2.2.1  and  6.12.2.2.2  and  shown  in 
figure 6.12.2.2.1 and 6.12.2.2.2.  

Table  6.12.2.2.1  Red  mullet  in  GSA  19.  Trend  in  nominal  fishing  effort  (kW*days)  by 
fleet level from 2002-2016, DCF data. 

OTB 

GNS 

GTR 

-1 

LLD 

LLS 

PS 

PTM 

2002  5125805 
2003  5002396 

4669872  13116917 
9192253  9143877 

978456 
1629676 

507 

507 

05000100001500020000250000123+thousandsMUT GSA 19 Catch at age20062007200820092010201120122013201420152016 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2004  5875474  797996  2742293  1418952  5367540  1143710  1564124  0 
2005  4181999  1197159  2115507  1081525  6420870  861956  1652286 
2006  6770477  1402176  1106682  1776585  4414699  870853  896924  11424 
2007  5312380  1473754  925004  1747956  4431347  1062369  897398 
2008  5350926  1275650  1131865  1126093  5603064  620865  1452553 
2009  6361017  1441596  1653130  2427917  3987741  679391  791024 
2010  6642497  1813781  1896850  3744421  4245026  852696  765213 
2011  6832229  1705748  1777574  2058250  2359140  1056634  692104  13898 
2012  6382671  1627697  1590170  540335 
2013  6128857  2394257  3379761  420069 
2014  6027003  2065333  2358945  410146 
2015  5922925  1683536  2315821  531128 
190392 
2016  5986840 

3916244  1307624  1014674 
3885256  2054032  615055 
3835483  1763634  511171 
2921397  1804600  524963 
2203858  1192364  908220 

Figure 6.12.2.2.1 Red mullet in GSA 19. Trend in nominal fishing effort for the pulled 
fleet, from 2002 to 2016. 

Table  6.12.2.2.2  Red  mullet  in  GSA  19.  Trend  in  GT*days  at  sea  by  fleet  level  from 
2002 to 2016, DCF data. 

OTB 

GNS 

GTR 

-1 

LLD 

LLS 

PS 

PTM 

-1 
-1 

-1 
-1 

-1 
-1 

-1 
-1 

2002 
2003 
2004  761067  78308  233891  171584  992388  110883  208336  820 
2005  430253  101868  197023  133509  1086458  69009  190975 
2006  672536  123299  104406  188462  806070  68640  132197  1478 
2007  491942  123789  88113  181487  804784  89442  109924 
2008  574366  98544  102936  127281  892144  64130  184237 
2009  711619  107494  141967  356898  595411  68039  81658 
2010  759137  134114  149802  540555  583783  71070  82491 
2011  805415  117849  140997  292749  392865  101916  93319  3012 
2012  785235  114717  130340  68756  555414  128798  139663 
2013  621952  183557  243041  33284  684044  159044  83819 
2014  615493  161938  182299  39523  532175  151206  75839 

508 

508 

010000002000000300000040000005000000600000070000008000000900000010000000200220032004200520062007200820092010201120122013201420152016kw*daysNominal effortOTBGNSGTR 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2015  696946  133892  191360  98644  444088  150612  71124 
2016  823366  145665  202803  24605  405494  91811  128675 

Figure 6.12.2.2.2 Red mullet in GSA 19. Trend in GT*days at sea for the pulled fleet, 
from 2002 to 2016. 

6.12.2.3  SURVEY DATA 

Methods 

According  to  the  MEDITS  protocol  (Bertrand  et  al.  2002),  trawl  surveys  were  yearly 
carried out, the majority of them in May-July, applying a random stratified sampling by 
depth  (5  strata  with  depth  limits  at:  50,  100,  200,  500  and  800  m;  each  haul  position 
randomly  selected  in  small  sub-areas  and  maintained  fixed  throughout  the  time).  Haul 
allocation  was  proportional  to  the  stratum  area.  The  same  gear  (GOC  73,  by  P.Y. 
Dremière,  IFREMER-Sète),  with  a  20  mm  stretched  mesh  size  in  the  cod-end,  was 
employed  throughout  the  years.  Detailed  data  on  the  gear  characteristics,  operational 
parameters  and  performance  are  reported  in  Dremière  and  Fiorentini  (1996). 
Considering  the  small  mesh  size  a  complete retention  was assumed.  All  the  abundance 
data (number of fish per surface unit) were standardized to square kilometer, using the 
swept  area  method.  The  period  when  MEDITS  survey  has  been  done  in  GSA  19  is 
reported in figure 6.12.2.3.1 

509 

509 

0100000200000300000400000500000600000700000800000900000GT*daysGT*days at sea GSA 19OTBGNSGTR 
 
 
 
 
 
 
Figure 6.12.2.3.1 MEDITS sampling period in GSA 19. 

Table  6.12.2.3.1.  Number  of  hauls  per  depth  stratum  in  MEDITS  trawl  survey  (1994-
2016). 

Year 
1994 
1995 
1996 
1997 

1998 
1999 
2000 
2001 
2002 
2003 

2004 
2005 
2006 
2007 
2008 
2009 

2010 
2011 
2012 
2013 
2014 
2015 

2016 

10-50 
9 
9 
9 
9 

50-100 
8 
8 
8 
8 

100-200 
10 
10 
10 
10 

200-500 
16 
15 
15 
15 

500-800 
30 
32 
32 
32 

Total 
73 
74 
74 
74 

9 
9 
9 
9 
9 
9 

9 
9 
9 
9 
9 
9 

9 
9 
9 
9 
9 
9 

9 

8 
8 
8 
8 
8 
8 

8 
8 
8 
8 
8 
8 

8 
8 
8 
8 
8 
8 

8 

10 
10 
10 
10 
10 
10 

10 
10 
10 
10 
10 
10 

10 
10 
10 
10 
10 
10 

10 

15 
15 
15 
15 
14 
14 

14 
14 
14 
14 
14 
14 

14 
14 
14 
14 
14 
14 

14 

32 
32 
32 
32 
29 
29 

29 
29 
29 
29 
29 
29 

29 
29 
29 
29 
29 
29 

29 

74 
74 
74 
74 
70 
70 

70 
70 
70 
70 
70 
70 

70 
70 
70 
70 
70 
70 

70 

510 

510 

 
 
Data  were  assigned  to  strata  based  upon  the  shooting  position  and  average  depth 
(between shooting and hauling depth). The density and biomass indices of red mullet in 
GSA 19 were estimated on the depth strata to 10-800 m and standardized to km2. 

The  abundance  and  biomass  indices  by  GSA  were  calculated  through  stratified  means 
(Cochran,  1953;  Saville,  1977).  This  implies  weighting  of  the  average  values  of  the 
individual  standardized  catches  and  the  variation  of  each  stratum  by  the  respective 
stratum areas in the GSA: 

Yst = Σ (Yi*Ai) / A  
V(Yst) = Σ (Ai² * si ² / ni) / A²  
Where:  
A=total survey area  
Ai=area of the i-th stratum  
si=standard deviation of the i-th stratum  
ni=number of valid hauls of the i-th stratum  
n=number of hauls in the GSA  
Yi=mean of the i-th stratum  
Yst=stratified mean abundance  
V(Yst)=variance of the stratified mean  

The variation of the stratified mean is then expressed as +/- standard deviation. 

It was noted that while this is a standard approach, the calculation may be biased due to a number of 
different  factors  including  the  change  in  the  number  of  hauls  over  time,  and  change  of  the  survey 
time  over  the  years.    Precision  may  also  be  affected  by  the  choice  of  parametric  distribution,  a 
normal distribution is often assumed, whereas data may be better described by a delta-distribution, 
quasi-Poisson. Indeed, data may be better modelled using the idea of conditionality and the negative 
binomial (e.g. O’Brien et al. 2004). 

Length  distributions  represented  an  aggregation  (sum)  of  standardized 
length 
frequencies  distribution  raise  to  standardized  haul  abundance  per  square  km  over  the 
stations of each stratum.  

Geographical distribution 

The  geographical  distribution  pattern  of  red  mullet  has  been  studied  in  the  area  using 
trawl-survey data and applying geostatistical methods. 

In  the  STOCKMED  project  (MAREA  Framework;  Fiorentino  et  al.,  2015)  biomass  trends 
(average of the last 10 years) have been estimated (Figure 6.12.2.3.2). 

511 

511 

 
 
 
 
Figure  6.12.2.3.2  Geographical  distribution  of  red  mullet  in  the  Mediterranean  basin 
(kg/km2), STOCKMED Project. 

If spawners are considered, the higher concentration in the GSA 19 was localised in the 
southern side. Recent estimations (MEDISEH Project, MAREA Framework; Giannoulaki et 
al., 2013) have confirmed the presence of spawning areas with persistence along time in 
the southern part of the GSA (figure 6.12.2.3.3). 

Figure  6.12.2.3.3  Red  mullet  in  GSA  19.  Spawning  areas  with  the  persistence  along 
time, MEDISEH Project. 

Trends in abundance and biomass 

Fishery  independent  information  regarding  the  state  of  the  red  mullet  in  GSA  19  was 
derived  from  the  international  survey  MEDITS.  Figure  6.12.2.3.3.1  displays  the 
estimated  trend  of  red  mullet  abundance  and  biomass  indices  standardized  to  the 
surface  unit  in  the  GSA  19.  Indices  from  MEDITS  trawl-surveys  show  two  important 
recruitment  peaks  in  2007  and  2014  due  to  the  displacement  of  MEDITS  survey  to 
August and September, that it is the recruitment period for red mullet. 

512 

512 

 
 
 
 
 
 
Figure 6.12.2.3.4 Red mullet in GSA 19. Abundance and biomass time series of derived 
from MEDITS (dotted lines indicated standard deviation). 

Table  6.12.2.3.2  Red  mullet  in  GSA  19.  Stratified  abundance  indices  (N/km2  and 
kg/km2) by year, 1994-2016. 

Year  N/km2 
1994 
1995 
1996 
1997 
1998 
1999 
2000 
2001 
2002 
2003 
2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 
2015 
2016 

265 
695 
281 
107 
260 
61 
148 
303 
432 
262 
610 
428 
460 
4876 
2679 
341 
987 
484 
531 
1511 
5808 
1634 
564 

St. 
Dev 
89 
464 
133 
32 
83 
19 
86 
91 
170 
123 
175 
174 
208 
1529 
2408 
140 
467 
178 
126 
528 
1209 
768 
166 

CV 
(%)  Kg/km2 

34 
67 
47 
30 
32 
32 
58 
30 
39 
47 
29 
41 
45 
31 
90 
41 
47 
37 
24 
35 
21 
47 
30 

7.93 
17.89 
7.87 
3.61 
7.01 
2.47 
5.96 
11.59 
14.22 
8.23 
14.64 
13.59 
14.07 
32.46 
75.72 
9.97 
22.34 
14.31 
13.99 
32.99 
54.66 
36.25 
19.45 

St. 
Dev 
2.52 
12.01 
3.15 
1.13 
1.85 
0.83 
3.15 
3.35 
4.87 
2.76 
3.57 
5.07 
5.02 
8.75 
66.51 
3.50 
9.32 
4.87 
3.03 
10.68 
13.74 
17.66 
5.82 

CV 
(%) 
32 
67 
40 
31 
26 
34 
53 
29 
34 
34 
24 
37 
36 
27 
88 
35 
42 
34 
22 
32 
25 
49 
30 

Trends in abundance by length 

The  following  figure  display  the  stratified  abundance  indices  of  red  mullet  in  GSA  19  in 
1994-2016. 

513 

513 

 
 
 
 
 
 
 
Figure  6.12.2.3.5  Red  mullet  in  GSA  19.  Stratified  abundance  indices  by  size,  1994-
2016. 

6.12.3  STOCK ASSESSMENT 

XSA  

An assessment has been conducted using XSA method 

The Extended Survivors Analysis (XSA – Darby and Flatman, 1994) has been performed 
using  the  same  parameters  than  have  been  used  in  the  previous  assessments  (STECF 
EWG 15-11, STECF EWG 12-19) in order to compare reference points obtained. XSA has 
been used with an age range from 0 to 3+ and an Fbar 0-2. Discard was included in the 
analysis. 

Input data 

For  the  assessment  of  red  mullet  in  GSA  19  the  DCF  data  on  the  length  structure  has 
been  used: no  SOP  correction  has been  applied  as  differences were far  less  than  10%. 
The  age  distribution  has  been  estimated  using  the  knife-edge  slicing  method  (LFDA 
algorithm)  with  the  fast  growth  parameters  used  in  the  previous  assessments.  A  sex-
combined analysis was carried out. 

The survey indices from MEDITS data from 2006 to 2016 have been used for the tuning.  

514 

514 

 
 
 
 
 
Figure  6.12.3.1  Red  mullet  in  GSA  19.  Catch  (including  discard)  in  numbers 
(thousands) by age and year used in the XSA. 

Table 6.12.3.1 Red mullet in GSA 19. Catch (including discard) in numbers (thousands) 
by age and year used in the XSA. 

Year 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 
2015 
2016 

0 

1 

11239  21579 
6176  14426 
9077 
8478 
23511  8939 
6423  12370 
14425  10074 
14752  8843 
6462 
6000 
6035 
5703 
5821 
5857 
6563 
1173 

2 
491 
55 
221 
180 
376 
696 
365 
172 
146 
232 
495 

3+ 
4 
0 
11 
4 
22 
65 
21 
19 
7 
4 
34 

Table 6.12.3.2 Red mullet in GSA 19. Abundance indices (N/km2) by age and year from 
MEDITS survey used in the XSA. 

Year 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 
2015 
2016 

0 
109 
4586 
543 
69 
470 
130 
211 
785 
5068 
803 
68 

1 
326 
248 
2110 
255 
497 
324 
305 
693 
708 
801 
438 

2 
23 
36 
22 
14 
18 
29 
14 
31 
27 
28 
53 

3+ 
2 
6 
4 
3 
2 
1 
1 
2 
6 
2 
5 

515 

515 

05000100001500020000250000123+thousandsAge classMUT GSA 19 Catch at age20062007200820092010201120122013201420152016 
 
 
 
 
Figure  6.12.3.2  Red  mullet  in  GSA  19.  Abundance  indices  (N/km2)  by  age  and  year 
from MEDITS survey used in the XSA. 

Table  6.12.3.3  Red  mullet  in  GSA  19.  Weights  at  age  (kg)  used  in  the  XSA  (used  for 
the catch) 

2 

1 

0 

Year 
2006  0.015  0.031  0.089 
2007  0.017  0.031  0.079 
2008  0.013  0.036  0.078 
2009  0.009  0.033  0.083 
2010  0.014  0.033  0.085 
2011  0.013  0.034  0.089 
2012  0.011  0.033  0.086 
2013  0.012  0.030  0.090 
2014  0.011  0.033  0.082 
2015  0.005  0.037  0.093 
2016  0.005  0.033  0.088 

3+ 
0.138 
0.148 
0.164 
0.138 
0.144 
0.152 
0.153 
0.140 
0.155 
0.175 
0.175 

Table  6.12.3.4  Red  mullet  in  GSA  19.  Weights  at  age  (kg)  used  in  the  XSA  (used  for 
the stock). 

2 

1 

0 

Year 
2006  0.0054  0.0423  0.1035 
2007  0.0054  0.0423  0.1035 
2008  0.0054  0.0423  0.1035 
2009  0.0055  0.0419  0.1012 
2010  0.0060  0.0407  0.0933 
2011  0.0056  0.0397  0.0926 
2012  0.0058  0.0396  0.0911 
2013  0.0056  0.0394  0.0914 
2014  0.0055  0.0383  0.0890 
2015  0.0047  0.0372  0.0927 
2016  0.0055  0.0331  0.0879 

3+ 
0.1683 
0.1683 
0.1683 
0.1634 
0.1464 
0.1467 
0.1434 
0.1446 
0.1408 
0.1752 
0.1752 

516 

516 

01000200030004000500060000123+N/km2Age classMUT GSA 19, MEDITS abundance 20062007200820092010201120122013201420152016 
 
 
 
 
Results 

Several runs of XSA have been performed with the following settings: 

-  Proportion of F before spawning = 0 
-  Proportion of M before spawning = 0 
-  Minimum standard error (mse) for population estimates derived from each fleet = 

0.3 

-  Shk.n= TRUE, shk.f=TRUE, shk.yrs=3, shk.ages=2, rage=0, qage=2 

Sensitivity analyses have been performed varying the following settings: 

-  Shrinkage of the mean (fse) = 0.5, 1, 1.5, 2 and 2.5 

Figure  6.12.3.3  Red  mullet  in  GSA  19.  Plot  of  the  stock  parameters  estimated  in  the 
sensitivity analyses. 

Table 6.12.3.5 Red mullet in GSA 19. Residual values for the best 6 runs. 

run 
1 
2 
3 
4 

shrinkage  minimum  maximum  average 

Sh0.5 
Sh1.0 
Sh1.5 
Sh2.0 

-3.933 
-0.822 
-0.745 
-0.739 

3.675 
1.687 
1.755 
1.777 

0.955 
0.322 
0.253 
0.240 

517 

517 

 
 
 
 
5 
6 

Sh2.5 
Sh3.0 

-0.735 
-0.733 

1.791 
1.800 

0.232 
0.228 

The  run  with  catchability  (rage)  independent  on  stock  size  for  all  ages  =  0,  the 
catchability (qage) independent of age for ages > 2 and shrinkage of the mean (fse) = 
1.5 has been chosen on the basis of the residuals and of the retrospective analysis. 

Figure 6.12.3.4 Red mullet in GSA 19. Retrospective analysis (2013-2016) 

518 

518 

 
 
 
 
Figure  6.12.3.5  Red  mullet  in  GSA  19.  XSA  results  in  terms  of  recruitment,  SSB, 
Catches and fishing mortality. 

The Fbar along the time series is on average 1.39, with a minimum of 0.93 in 2015 and 
a  maximum  of  2.02  in  2006  (Table  6.12.1.2.6).  The  recruitment  show  a  peak  in  2009 
equal  to  95093  thousands  individuals  and  after  that  year  slightly  decreases  until  2013 
(37179  thousands),  after  that  it  increases  again.  The  SSB  showed  a  decreasing  trend 
until 2014 (403 t), and then remain stable, reaching 455 t in 2016. 

Table  6.12.3.6  Red  mullet  in  GSA  19.  Fishing  mortality  at  age  by  year,  Fbar(0-2), 
spawning stock biomass (SSB, t) and Recruitment (R, thousands) estimated with XSA. 

Year 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 
2015 
2016 

F age 
0 
0.27 
0.24 
0.31 
0.53 
0.22 
0.49 
0.63 
0.31 
0.28 
0.28 
0.03 

F age 
1 
3.72 
3.20 
3.01 
2.40 
2.18 
2.59 
2.66 
2.49 
2.19 
1.59 
1.99 

F age 
2 
2.07 
0.23 
1.23 
1.42 
1.78 
2.02 
2.12 
0.71 
0.67 
0.93 
1.08 

F age 
3+ 
2.07 
0.23 
1.23 
1.42 
1.78 
2.02 
2.12 
0.71 
0.67 
0.93 
1.08 

Fbar 
(0-2) 
2.020 
1.221 
1.519 
1.450 
1.394 
1.700 
1.802 
1.170 
1.050 
0.934 
1.032 

SSB 
(t) 
1389 
928 
612 
610 
857 
721 
583 
438 
403 
431 
455 

R 
(thousands) 
78898 
48471 
53462 
95093 
54265 
62136 
52850 
37179 
38721 
40191 
59488 

519 

519 

 
 
 
 
 
Figure 6.12.3.6 Red mullet in GSA 19. Fishing mortality at age by year estimated with 
XSA. 

Method: a4a 

A second assessment has been conducted using a4a method, based on linear modelling 
techniques,  not  working  by  fleet,  using  the  same  input  data  as  the  XSA  model.  The 
method was developed within FLR framework. 

Input data 

The  catch  at  age  matrices,  survey  MEDITS  data  and  individual  weights  at  age  for  the 
stock and for the catch were the same than used on the previous XSA assessment and 
reported in paragraph 6.12.1.2. The natural mortality vector and the maturity at age are 
the same reported in paragraph 6.12.1.  

The final model chosen for the analysis was the same than used on XSA. 

Results 

The  F  time  series  estimated  by  a4a  ranges  between  3.23  and  0.56,  with  an  overall 
decrease  in  time.  The  Recruitment  series  also  decrease  in  time  from  95871  to  a 
minimum of 27230 in the last year. The SSB show a decrease along the time but the last 
four years values stabilise between 426 and 547 tons.   

Table 6.12.3.7 Red mullet in GSA 19. Results of the final a4a run: F by age, Fbar (0-2), 
SSB and Recruitment. 

Year 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

Fage 
0 
0.60 
0.43 
0.33 
0.29 
0.29 
0.33 
0.37 
0.36 
0.28 

Fage 
1 
4.99 
3.56 
2.73 
2.39 
2.43 
2.74 
3.05 
2.97 
2.34 

Fage2 
4.11 
2.93 
2.24 
1.96 
2.00 
2.26 
2.51 
2.44 
1.93 

Fage 
3+ 
4.11 
2.93 
2.24 
1.96 
2.00 
2.26 
2.51 
2.44 
1.93 

Fbar(0-
2) 
3.23 
2.31 
1.77 
1.55 
1.58 
1.78 
1.98 
1.92 
1.52 

SSB 
(tons) 
1279 
890 
555 
718 
914 
734 
547 
443 
426 

Recruitment 
(thousands) 
95871 
42200 
48865 
66602 
53346 
40768 
35122 
32167 
41147 

520 

520 

00.511.522.533.540123+MUT GSA 19, F at age20062007200820092010201120122013201420152016 
 
 
 
 
2015 
2016 

0.18 
0.11 

1.51 
0.87 

1.24 
0.71 

1.24 
0.71 

0.98 
0.56 

544 
641 

47277 
27230 

Figure 6.12.3.7 Red mullet in GSA 19. Fishing mortality. 

Figure 6.12.3.8 Red mullet in GSA 19. Comparison between observed and fitted catch 
at age. 

521 

521 

 
 
 
 
 
 
 
Figure 6.12.3.9 Red mullet in GSA 19. Comparison between observed and fitted index 
at age. 

Figure 6.12.3.10 Red mullet in GSA 19. Log-residuals of catch and abundance indices 
by age. 

522 

522 

 
 
 
 
 
 
Figure 6.12.3.11 Red mullet in GSA 19. Qqplot of catch and abundance indices by age. 

523 

523 

 
 
 
Figure  6.12.3.12  Red  mullet  in  GSA  19.  Retrospective  analysis  and  bubble  plot  of 
residuals. 

524 

524 

 
 
 
 
 
 
 
 
 
Comparison of the models 

The  two  models  conducted  in  EWG  16-17,  XSA  and  a4a,  are  in  agreement  on  the  SSB 
and  catch  temporal  series.  Concerning  recruitment,  XSA  show  an  increase  in  the  last 
year while a4a show a decreasing trend, 2015 and 2016 have a confidence interval wider 
than in the global series. Fbar show a decreasing trend in both methods more sharply in 
a4a than XSA. 

6.12.4  REFERENCE POINTS 

To predict the effect of changes in fishing effort of future yields and to define reference 
points F01 (as a proxy for FMSY) and Fmax a Yield per Recruit analysis (YPR) was carried out 
in R. 

Input data 

As  input  the  same  population  parameters  used  for  the  XSA  and  its  output  of  the 
exploitation pattern were used. 

Results 

The reference points calculated with FLBRP package are shown in table 6.12.4.1 

Table 6.12.4.1 Red mullet in GSA 19. Reference points estimated on the Fbar(0-2) using 
XSA. 

F0.1 
0.42 

Total 
Yield 
442 

Recruitment  SSB 
1353 
54228 

Biomass 
1650 

525 

525 

 
 
 
 
 
 
 
 
Figure 6.12.4.1 Red mullet in GSA 19. Yield per Recruitment, XSA. 

Comparison of the models 

The reference point F0.1 calculated by XSA is F0.1 = 0.42 

The reference point F0.1 calculated by a4a is F0.1 = 0.359 

The  reference  point  F0.1  calculated  for  red  mullet  in  GSA  19  in  the  STECF  EWG  15-16 
was:   

F0.1 = 0.45 

In the table below are reported the following reference points calculated from Fmsy=0.42 
(XSA), F0.1 = 0.359 (a4a) and F0.1 = 0.45 (XSA, EWG 17-15). 

Table 6.12.4.2 Red mullet in GSA 19. F values obtained in different assessments. Flower 
and Fupper calculated from Fmsy=0.42 (XSA) and Fmsy=0.36 (a4a). Fcurrent (Fstq). 

F0.1 
0.42 (XSA) 
0.36 (a4a) 
0.45 (EWG 17-15) 

Flower 
0.28 
0.24 
0.30 

Fupper 
0.57 
0.49 
0.62 

Fstq 
1.004 
0.94 
0.99 

6.12.5  SHORT TERM FORECAST AND CATCH OPTIONS 

XSA 

The  same  input  parameters  used  in  the  XSA  analysis  were  used.  Different  scenarios  of 
constant harvest strategy with Fbar calculated as the average of ages 0 to 2 and F status 
quo (Fstq=1.004; geometric mean of the last three years) were performed. Recruitment 

526 

526 

 
 
 
 
 
 
 
 
(class 0) has been estimated from the population results from the geometric mean of the 
last three years 2014-2016 (45238 thousands individuals) estimated using XSA. 

Results  

The results of the short term forecasts related to the whole fleet are summarised in the 
table 6.12.5.1 

Table  6.12.5.1  Red  mullet  in  GSA  19.  Short  term  forecast  in  different  F  scenarios 
computed  for  red  mullet  in  GSA  19.  Basis:  F(2017)  =  mean  Fbar0-2  (2014-2016)  = 
1.004;  R  (2017)  =  geometric  mean  of  the  recruitment  of  the  last  3  years  =  45238 
(thousands); SSB (2017) = 827 t, Catch (2017) = 559 t. 

Rationale 

Ffactor  Fbar  Catch_2018  SSB_2019 

zero catch 

0 

0 

F0.1 

0.417  0.419 

Status quo 

1 

1.004 

Different scenarios 

0.1 

0.2 

0.3 

0.4 

0.5 

0.6 

0.7 

0.8 

0.9 

1.1 

1.2 

1.3 

1.4 

1.5 

1.6 

1.7 

1.8 

1.9 

2 

0.10 

0.20 

0.30 

0.40 

0.50 

0.60 

0.70 

0.80 

0.90 

1.10 

1.20 

1.31 

1.41 

1.51 

1.61 

1.71 

1.81 

1.91 

2.01 

0 

241.9 

418.7 

71.8 

133.8 

187.6 

234.5 

275.5 

311.4 

343.2 

371.3 

396.3 

438.8 

456.9 

473.3 

488.3 

501.9 

514.5 

526.1 

536.8 

546.7 

556.0 

1302.0 

889.0 

614.5 

1176.1 

1069.5 

978.7 

901.2 

834.8 

777.6 

728.1 

685.1 

647.5 

585.2 

559.2 

535.9 

515.0 

496.0 

478.7 

462.8 

448.2 

434.6 

422.0 

Change_SSB 
2018-2019(%) 
57.5 

Change_Catch 
2016-2018(%) 
-100 

7.5 

-25.7 

42.3 

29.4 

18.4 

9.0 

1.0 

-5.9 

-11.9 

-17.1 

-21.7 

-29.2 

-32.4 

-35.2 

-37.7 

-40.0 

-42.1 

-44.0 

-45.8 

-47.4 

-49.0 

-11.0 

54.0 

-73.6 

-50.8 

-31.0 

-13.8 

1.3 

14.5 

26.2 

36.5 

45.7 

61.3 

68.0 

74.0 

79.5 

84.6 

89.2 

93.4 

97.4 

101.0 

104.5 

A short term  projection of the whole fleet (table 6.12.5.1) assuming an Fstq of 1.004 in 
2017  and  a  recruitment  of  45238  thousands  individuals  (geometric  mean  on  last  3 
years) show that:  

-  Fishing at the Fstq (1.004) generates an increase of the catch of 54% from 2016-
2018 along with as decrease of spawning stock biomass (-26%) from 2017-2019. 
-  Fishing  at  F0.1  (0.42)  generates  a  decrease  of  the  catch  of  11%  from  2016-2018 

and an increase of the spawning stock biomass of 7.5% from 2018-2019.  

a4a 

The same input parameters used in the a4a analysis were used. 

527 

527 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table  6.12.5.2  Red  mullet  in  GSA  19.  Short  term  forecast  in  different  F  scenarios 
computed  for  red  mullet  in  GSA  19.  Basis:  Fstq(2017)  =  mean  Fbar0-2  (2014-2016)  = 
0.56;  R  (2017)  =  geometric  mean  of  the  recruitment  of  the  last  3  years  =  37556 
(thousands); SSB (2018) = 926 t, Catch (2017) = 272 t. 

Rationales 

Fbar  Catch2018  Catch2019  SSB2019 

SSB_change 
2018-2019(%) 

Catch_change 
2016-2018(%) 

zero catch 

0 

0 

F0.1 

0.359 

253.4 

Different scenarios 

0.11 

0.23 

0.34 

0.45 

0.56 

0.68 

0.79 

0.90 

1.01 

1.13 

91.5 

171.3 

241.0 

302.0 

355.6 

402.8 

444.5 

481.3 

514.1 

543.3 

0 

318.1 

139.5 

238.3 

307.4 

355.1 

387.1 

408.0 

420.9 

428.1 

431.3 

431.8 

1558.0 

1162.6 

1412.9 

1288.4 

1181.5 

1089.4 

1010.0 

941.2 

881.5 

829.6 

784.1 

744.3 

68.3 

25.6 

52.7 

39.2 

27.7 

17.7 

9.1 

1.7 

-4.7 

-10.4 

-15.3 

-19.6 

-100 

-1.2 

-64.3 

-33.3 

-6.1 

17.7 

38.6 

57 

73.2 

87.6 

100.3 

111.7 

A  short  term  projection  of  the  whole  fleet  (table  6.12.5.2)  assuming  an  Fstq  of  0.94  in 
2017  and  a  recruitment  of  37556  thousands  individuals  (geometric  mean  on  last  3 
years) show that:  

-  Fishing at F0.1 (0.359) generates a decrease of the catch of 1.2% from 2016-2018 

and an increase of the spawning stock biomass of 25.6% from 2018-2019.  

The  catch  advice  is  very  similar  for  both  assessments  with  a  difference  of  about  4% 
between advised catch 2018 based on the different models; the model assumptions are 
very  similar.  The  EWG  has chosen to use  the  a4a assessment  as this is consided  to be 
more explicitly consistent in its treatment of the data.  

6.12.6  DATA DEFICIENCIES 

Survey  sampling  period  (MEDITS)  has  been  done  in  different  year  periods.  The 
displacement  of  MEDITS  survey  to  August  (2007)  and  September  (2014),  that  it  is  the 
recruitment period for red mullet, difficult the tuning of the VPA. 

It  is  advisable,  as  it  is  write  in  the  MEDITS  protocol,  that  surveys  will  be  carried  out 
always in the same month of the year (Fig. 6.12.2.3.1). 

528 

528 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6.13  RED MULLET IN GSA 20 

6.13.1  BIOLOGY 

STECF  EWG  17-15  was  asked  to  assess  the  state  of  Red  Mullet  stock  in  the  Southern 
Ionian  Sea  (GSA  20).  The  previous  assessment  of  this  stock  was  in  2012  (STECF  EWG 
12 – 21). 

Figure 6.13.1.1 Geographical location of GSA 20. 

Red  mullet  is  one  of  the  main  targeted  species  in  demersal  fisheries.  In  GSA  20  it  is 
mainly  exploited  by  bottom  trawls.  In  eastern  Ionian  Sea  operate  about  35  bottom 
trawlers. 

Red mullet is found  on gravel, sand and  mud bottoms of the  continental shelf. It feeds 
on  small  benthic  crustaceans,  worms  and  molluscs.  Red  mullet  is  distributed  along  the 
shelf of all the Mediterranean countries. The species can be found at depths over 200m, 
but is mainly concentrated in the depth range 0-100m. All the year classes and nursery 
and  spawning  areas  are  well  distributed  along  the  narrow  Mediterranean  shelves.  The 
STOCKMED report gives 2 different stock units in eastern (GSA20) and western (GSA19) 
Ionian Sea; red mullet from eastern Ionian Sea (GSA20) belong to the same stock unit 
distributed  in  the  Adriatic  and  Aegean  Sea.  The  analysis  assumed  that  red  mullet  in 
GSA20 is a single stock unit. 

Red mullet is a fast growing species. The Von Bertallanfy growth parameters estimated 
within the DCF 2016 (Linf = 27.7, k = 0.23, t0 = -1.16) considered to have a very low t0, 
(STECF  EWG  12  –  02)  and  thus,  the  STECF  EWG  17-15  decided  to  borrow  the  ones 
corresponding to GSA 19 (Linf = 30.0, k = 0.04, t0 = -0.3). Length – weight parameters 
(a  =  0.0083,  b  =  3.1134)  were  derived  from  STECF  EWG  12  –  02  for  sexes  combined 
and total length expressed in cm. These parameters were used in the statistical catch at 
age assessment (a4a). 

A vector of natural mortality was estimated by PRODBIOM method (Abella et al., 1997) 
using growth and length-weight relationship parameters for sex combined. 

529 

529 

 
 
 
 
 
 
 
The species reaches massively the sexual maturity at one year old the following sizes at 
age maturity by sex were derived from the report of STECF 12-21. 

Table 6.13.1 Red mullet GSA 20. Maturity and natural mortality. 

Age 

0 

1 

2 

3+ 

Maturity  0.16 

0.92 

1.0000  1.0000 

M 

1.00 

0.61 

0.54 

0.50 

Red  mullet  is  among  the  most  commercially  valuable  species  in  the  area  and  is  an 
important component of a species assemblage that is the target of the bottom trawling 
fleets  and  small  scale  fisheries  operating  near  shore.  The  species  is  mostly  caught  by 
trawlers    and  small-scale  fisheries  using  set  nets.  Red  mullet  catch  rates  are  higher 
during  the  post-recruitment  period  (from  September  to  November).  The  trawlers,  the 
trammel  nets  and  the  gillnetsare  the  main  categories  that  exploit  the  species  in  the 
studied area. 

As  with  all  demersal  fisheries  in  GSA  20,  those  catching  red  mullet  are  managed 
according  to  EU  regulation  1967/2006  which  include  spatial  fishery  closures  for  the 
bottom trawlers, gear configuration specifications and minimum landing sizes. Additional 
national measures include a temporal (4.5 months) closure of the bottom trawl fisheries 
accompanied by certain localized spatio-temporal closures. 

The  applied  technical  measures  include  minimum  landing  size  (MLS)  regulations  for 
several  commercial  species,  specifically  for  Red  mullet  at  11cm,  and  control  of  gear 
characteristics (mainly codend mesh-size). The specific management measures imposed 
by  the  Community  legislation  through  the  Common  Fisheries  Policy  (CFP)  include:  (a) 
prohibition of fishing in depths less than 50m or at a distance less than 3 miles from the 
coast (whatever it comes first), (b) prohibition of fishing at a distance less than 1.5 mile 
from the coast independently of depth and, (c) MCS regulations and gear specifications. 
Apart  from  the  Community  legislation,  according  to  pre-existing  Greek  legislation,  the 
bottom trawl fishery in the national waters is closed from June to September (4 months) 
and it is not allowed at a distance less than one mile from the coast. The latter measure 
has exceeded by the 1.5 mile trawl ban established through the EC legislation. 

530 

530 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6.13.2  INPUT DATA 

6.13.2.1  CATCH (LANDINGS AND DISCARDS)  

Landings  

Table 6.13.2.1.1 Red mullet GSA 20. Landings data in tonnes by year. Years 2007 and 
2009 – 2012 are missing, while data from 2013 and 2015 are only reported for the last 
quarter. 

Year 

2003  2004  2005  2006  2008  2013  2014  2015  2016 

Landings  2269  903 

740 

586  1034 

139 

303 

89 

507 

Due  to  inconsistences  in  reporting  the  landings  of  the  DCF  (see  section  6.1.1.5  –  Data 
Deficiencies)  the  STECF  EWG  decided  to  use  the  landings  reported  by  NSSG  (National 
Statistical Service of Greece) to FAO GFCM for the years from 2003 up to 2014 and the 
landings  data  coming  from  NSSG  (not  yet  reported  to  FAO),  for  the  years  2015  and 
2016. 

Table  6.13.2.1.2  Red  mullet  GSA  20.  FAO  data  in  tonnes  by  year.  Years  2007 
and 2009 – 2012 are missing, while data from 2013 and 2015 are only reported 
for the last quarter. 

Year 

2003 

2004 

2005 

2006 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

Landings 
(FAO) 

355 

350 

297 

223 

280 

270 

264.9 

173 

348.2 

319.9 

205.8 

189.1 

214 

287 

531 

531 

 
 
 
 
 
 
 
 
 
 
Figure  6.13.2.1.1  Red  mullet  in  GSA  20.  Landings  data  in  tonnes  by  year  and  fleet. 
Years  2007  and  2009  –  2012  are  missing,  while  data  from  2013  and  2015  come  only 
from the last quarter. 

Figure  6.13.2.1.2  Red  mullet  in  GSA  20.  Landings  data  in  tonnes  by  year  and  fleet. 
Years  2007  and  2009  –  2012  are  missing,  while  data  from  2013  and  2015  come  only 
from the last quarter. 

532 

532 

 
 
 
Figure  6.13.2.1.3  Red  mullet  in  GSA  20.  Landings  data  in  tonnes  by  year  and  fleet. 
Years  2007  and  2009  –  2012  are  missing,  while  data  from  2013  and  2015  come  only 
from the last quarter. 

DISCARDS 

Figure  6.13.2.1.4  Red  mullet  in  GSA  20.  Discards  data  in  tonnes  by  year  and  fleet. 
Years  2007  and  2009  –  2012  are  missing,  while  data  from  2013  and  2015  come  only 
from the last quarter. 

533 

533 

 
 
 
 
 
Figure  6.13.2.1.5  Red  mullet  in  GSA  20.  Landings  data  in  tonnes  by  year  and  fleet. 
Years  2007  and  2009  –  2012  are  missing,  while  data  from  2013  and  2015  come  only 
from the last quarter. 

Figure  6.13.2.1.6  Red  mullet  in  GSA  20.  Landings  data  in  tonnes  by  year  and  fleet. 
Years  2007  and  2009  –  2012  are  missing,  while  data  from  2013  and  2015  come  only 
from the last quarter. 

534 

534 

 
 
 
 
CATCH at AGE 

Many gaps also appeared in the catch at age data reported in the DCF Data Call of 2017. 
Weight  at  age  existed  only  for  a  few  years  and  only  for  the  landings  part  of  the  catch. 
Quality control of the dataset reveal many inconsistencies and incorrect reporting both in 
weight at age and mean weight at age. 

Figure  6.13.2.1.7  Red  mullet  in  GSA  20.  Landings  data  in  tonnes  by  year  and  fleet. 
Years  2007  and  2009  –  2012  are  missing,  while  data  from  2013  and  2015  come  only 
from the last quarter. 

There were no discards at age reported for Red mullet in GSA 20. 

535 

535 

 
 
 
 
 
 
 
 
 
 
 
 
6.13.2.2  EFFORT 

Table 6.13.2.2.1 Red mullet GSA 20. FAO data in tonnes by year. Years 2007 and 
2009 – 2012 are missing, while data from 2013 and 2015 are only reported for the 
last trimester. 

Year 

2003 

2004 

2005 

2006 

2008 

2013 

2014 

2015 

2016 

Days * GT 

4110555 

3760074 

3700244 

3402598 

3648212 

397952.4 

1789211 

924160 

4141910 

Days 

-9 

-8 

-8 

-8 

-9 

-17 

459445 

172472 

554350 

6.13.2.3  SURVEY DATA 

Since  1994,  MEDITS  trawl  surveys  has been  carried  out during  the  summer  season,  as 
part  of  the  DCF  National  Program.  In  the  current  assessment,  for  the  a4a  method, 
MEDITS  data  from  2003  onwards  were  used.  MEDITS  survey  did  not  took  place  during  
the years 2002, 2007, 2009 – 2013 and 2015. 

The  sampling  design  of  MEDITS  is  random  stratified  sampling  with  number  of  hauls  by 
stratum  proportional  to  stratum  surface.  Data  were  assigned  to  strata  based  upon  the 
shooting position and average depth (between shooting and hauling depth). Hauls noted 
as valid were used only, including stations with no catches (zero catches are included). 
Based on the DCF data call, abundance and biomass indices were calculated.  

Observed  abundance  and  biomass  indices  of  Red  mullet  and  the  length  frequency 
distributions  are  given  on  the  figures  below  (Figures  6.13.2.3.1-3).  Both  estimated 
abundance and biomass indices show similar increasing trends throughout the years. 

536 

536 

 
 
 
 
 
 
Figure 6.12.2.3.1 Red mullet in GSA 20. Estimated biomass index. Years 2002, 2007, 
2009 – 2013 and 2015 are missing. 

Figure 6.12.2.3.2 Red mullet in GSA 20. Estimated biomass index. Years 2002, 2007, 
2009 – 2013 and 2015 are missing. 

537 

537 

 
 
 
 
 
Figure  6.12.2.3.3  Red  mullet  in  GSA  20.  Length  frequency  distribution.  Years  2002, 
2007, 2009 – 2013 and 2015 are missing. 

6.13.3  STOCK ASSESSMENT 

Three different methodologies were applied for assessing the stock of red mullet in GSA 
20.  One  age  based  method,  a4a  (Jardim  et  al.,  2015)  and  two  surplus  production 
models. Specifically, Surplus Production In Continuous Time (SPiCT) and CMSY. 

A4a 

A4a  is  a  statistical  catch  –  at  –  age  method  that  utilize  catch  at  age  data  to  derive 
estimated  of  historical  population  size  and  fishing  mortality.  Model  parameters  are 
estimated  by working  forward  in  time and  analyses  do not require the  assumption  that 
removals  from  the  fishery  are  known  without  error.  A4a  is  implemented  as  a  package 
(Fla4a) of the FLR library.  

SPiCT 

SPiCT  is  a  surplus  production  model  implemented  as  state-space  model  that  separates 
random  variability  of  stock  dynamics  from  error  in  observed  indices  of  biomass.  In 
addition to stock dynamics, SPiCT also models the dynamics of the fisheries. 

538 

538 

 
 
 
 
 
 
CMSY 

CMSY  is  a  Monte  -  Carlo  method  that  estimates  fisheries  reference  points  (MSY,  Fmsy, 
Bmsy) as well as relative stock size (B/Bmsy) and exploitation (F/Fmsy) from catch data 
and  broad  priors  for  resilience  or  productivity  (r)  and  for  stock  status  (B/k)  at  the 
beginning  and  the  end  of  the  time  series.  Part  of  the    CMSY  package  is  an  advanced 
Bayesian  state-space  implementation  of    the    Schaefer    surplus    production    model 
(BSM).    The    main    advantage    of    BSM    compared    to    other    implementations    of  
surplus production models is the focus on informative priors and the acceptance of short 
and incomplete (= fragmented) abundance data 

In  the  figure  below  observed  landings  data  for  Red  mullet  in  GSA  20  are  presented  as 
they are reported from NSSG to FAO/GFCM database and as they are reported in 2017 
Data  Call  for  the  DCF.  Great  inconsistencies  appear  especially  in  year  2003  where  DCF 
landings  from  small  scale  fisheries,  mainly  gillnets  and  trammel  nets,  are  ten  times 
larger  than  the  ones  reported  for  the  bottom  trawl  fisheries,    which  is  in  contrast  with 
what  is  reported  in  bibliography  for  the  Red  mullet  fishery.  Due  to  the  previous 
mentioned  discrepancies  between  reported  gears,  the  STECF  EWG  17  -15  decided  to 
choose FAO/GFCM reported landings as the base of all the assessments applied for this 
species.  

Figure  6.13.3.1  Red  mullet  in  GSA  20.  Landings  data  in  tonnes  by  year  for  a)  FAO  – 
GFCM  landings,  b)DCF  landings  all  gears  combined,  c)DCF  landings  from  gillnets  and 
trammel  nets  and  d)DCF  landings  from  bottom  trawl  fisheries.  For  the  DCF  landings 
years  2007  and  2009  –  2012  are  missing,  while  data  from  2013  and  2015  come  only 
from the last quarter. 

Discards were considered negligible and regarding to the Management Plan for the Greek 
Bottom  Trawlers  (Anonymous,  2013)  account  less  than  2%  of  the  total  catch.  So  from 
now we will refer to landings as total catch. 

539 

539 

 
 
 
 
A4a  

input data 

Data typically used in the a4a model are: catch, statistical sample of age composition of 
catch, mean weight at age and an abundance index. 

In the case of Red mullet FAO/GFCM catch data were used for the period 2003  - 2016. 
The statistical sample of age composition as well as the mean weight at age, provided by 
DCF  data  call  2017  included  many  gaps  and  inconsistencies,  so  STECF  EWG  17-15 
decided  to  calculate  mean  weight  at  age  by  the  provided  growth  and  length  weight 
relationship  parameters;  the  mean  weight  at  age  considered  constant  throughout  the 
years. 

Catch  in  numbers  at  age  were  derived  from  deterministic  age  slicing  the  numbers  at 
length  provided  from  the  DCF.  Age  slicing  performed  by  using  the  l2a  function  of  FLR 
and growth parameters the ones reported in the section 6.13.1 

SoP  correction  was  applied  to  catch  numbers  at  age.  Table  6.13.3.1  present  the  Sop 
correction  vector  applied.  The  empty  years  correspond  to  the  absence  of  catch  at  age 
data for these years. 

A  single  tuning  fleet  was  used  based  on  the  abundance  at  age  estimates  from  MEDITS 
GSA 20 as reported in the DCF. 

The analyses were carried out for the ages 0 to 4+ for the catch data and 0 to 3+ for the 
abundance index. Concerning the Fbar, the age range used was 1-3 age groups. 

Table 6.13.3.1 Red mullet GSA 20. Sum of Products correction vector. 

2003 

2004 

2005 

2006 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

SoP 

4.31 

4.07 

2.46 

2.79 

NA 

1.39 

NA 

NA 

NA 

NA 

2.32 

1.19 

3.68 

2.05 

The  following  tables  lists  the  input  parameters  to  the  a4a,  namely  catches,  catch 
numbers  at  age,  mean  weight  at  age,  natural  mortality  at  age,  maturity  at  age  and 
proportion of F and M before spawning. 

Table 6.13.3.2 Red mullet GSA 20. Catch input data for a4a model. 

Year 

2003  2004  2005  2006  2007  2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015  2016 

Catch 
(t) 

355 

350 

297 

223 

280 

270 

264.9 

173 

348.2  319.9  205.8  189.1 

214 

287 

Table 6.13.3.3 Red mullet GSA 20. Catch in thousands at age input data for a4a 
model. 

age 

2003 

2004 

2005 

2006 

2007 

2008 

2009 

0 

1 

2 

3 

1888.2621  4460.1259 

577.147 

710.8827 

10329.149  12214.501  7081.1266  7351.6962 

2050.8256  1452.8108  2028.3308  1062.5314 

62.5254 

61.7426 

88.7924 

35.4152 

NA 

NA 

NA 

NA 

549.3419 

8506.7218 

1421.8258 

24.2359 

NA 

NA 

NA 

NA 

540 

540 

 
 
 
 
 
 
 
 
4 

12.5042 

1 

33.2999 

2.5285 

NA 

8.0786 

NA 

age 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

0 

1 

2 

3 

4 

NA 

NA 

NA 

NA 

NA 

NA 

NA 

NA 

NA 

NA 

NA 

NA 

NA 

NA 

NA 

183.6691 

641.6763 

396.2998 

744.8878 

5173.9486  4987.0296  3812.875  7533.2491 

1314.8721  1144.4101  1538.6649  1782.5251 

93.308 

83.3484 

272.0199 

89.8373 

10.878 

8.3412 

1 

23.207 

Table  6.13.3.4  Red  mullet  GSA  20.  Mean  weight  at  age  input  data  for  a4a 
model. 

age 

0 

1 

2 

3 

4 

all years  0.00037218  0.0198334  0.0676982  0.1252312  0.2374901 

Table  6.13.3.5  Red  mullet  GSA  20.  Maturity,  natural  mortality,  proportion  of  F 
and M before spawning. 

age 
maturity 
M 
Prop M 
Prop F 

1 
0.92 
0.61 

0 
0.16 
1 

2 
1 
0.54 
0.375  0.375  0.375  0.375  0.375 
0.375  0.375  0.375  0.375  0.375 

3 
1 
0.5 

4 
1 
0.5 

For  the  tuning  index  of  the  a4a  method  the  STECF  EWG  decided  to  use  the  MEDITS 
abundance index for the period 2003 – 2016 in order to correspond to the existing data 
for  the  distribution  of  catches  at  age.  Age  slicing  was  also  performed  to  the  length 
frequency  distribution  of  abundance  index.  The  following  table  presents  the  estimated 
numbers at age for the MEDITS tuning index. 

Table 6.13.3.6 Red mullet GSA 20. MEDITS number (n / km2) at age. 

age 

2003 

2004 

2005 

2006 

2007 

2008 

2009 

0 

1 

2 

3 

260.2817 

327.353 

27.9972 

69.0745 

799.0955  1192.4159  627.0231  780.1763 

57.2141 

68.0251 

72.4357 

106.5916 

2.0936 

4.0495 

1.1221 

4.9136 

NA 

NA 

NA 

NA 

114.4136 

1140.3343 

98.8329 

6.588 

NA 

NA 

NA 

NA 

age 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

0 

1 

2 

3 

NA 

NA 

NA 

NA 

NA 

NA 

NA 

NA 

NA 

NA 

NA 

NA 

NA 

NA 

NA 

NA 

84.9669 

726.0487 

50.3795 

3.0842 

NA 

NA 

NA 

NA 

123.2075 

611.5017 

39.5947 

1.8896 

541 

541 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
The following figures show the age structure of the catches and of the index. 

Figure 6.13.3.2 Red mullet in GSA 20. Number of catches in thousands at age. 

Figure 6.13.3.3 Red mullet in GSA 20. Number of catches in thousands at age. 

542 

542 

 
 
 
 
 
 
 
Assessment Results 

Different  a4a  models  were  investigated;  however  the  existent  data  gaps  did  not  allow 
the model to converge. 

Smoothing  splines  were  essential  in  fitting  a  model  to  the  missing  data.  Whenever 
smoothing splines were applied, the degrees of freedom for the years under study had to 
be set to a level equal or less to the number of years in the existing dataset.  

The  following  model  was  selected  on  the  basis  of  best  fit,  both  for  residuals  as  well  as 
fitted  vs  observed  data;  this  model  also  coincides  with  the  general  perception  of  the 
STECF  EWG  on  fishing  mortality  allocation  throughout  age  groups,  as  well  as  on  the 
catchability of the index. 

fmod <-  ~ s(replace(age, age>2, 2), k = 3) + s(year, k = 5) 

qmod <- list(~ factor(age)) 

srmod <- ~ s(year,k=7) 

The following figure presents the summary of the stock object after the it of the model. 
The recruitment, spawning stock biomass catch and fishing mortality. 

Figure  6.13.3.4  Red  mullet  in  GSA  20.  Stock  summary  from  the  a4a  model  for  Red 
mullet  in  GSA  20,  recruits,  SSB  (Stock  Spawning  Biomass),  catch  and  harvest  (fishing 
mortality for ages 1 to 3). 

543 

543 

 
 
 
 
 
 
 
 
The  following  plots  present  estimated  fishing  mortality  by  age  and  year  and  estimated 
catchability by age and year. 

Figure 6.13.3.5 Red mullet in GSA 20. 3D contour plot of estimated fishing mortality by 
age and year. 

Figure 6.13.3.6 Red mullet in GSA 20. 3D contour plot of estimated catchability by age 
and year. 

Diagnostics 

544 

544 

 
 
 
 
 
 
Several diagnostic plots presented below for the goodness of fit of the selected model for 
the assessment of Red mullet stock. 

Figure  6.13.3.7  Red  mullet  in  GSA  20.  Standardized  residuals  for  catch,  abundance 
indices and for catch numbers. 

545 

545 

 
 
 
 
 
Figure 6.13.3.8 Red mullet in GSA 20. Quantile-quantile plot of standardized residuals 
for catch, abundance indices and for catch numbers. 

Figure 6.13.3.9 Red mullet in GSA 20. Fitted and observed catch at age. 

Figure 6.13.3.10 Red mullet in GSA 20. Fitted and observed index at age 

RETROSPECTIVE 

546 

546 

 
 
 
 
 
 
The retrospective analysis was applied up to 3 years back. Models results were stable. 

Figure 6.13.3.11 Red mullet in GSA 20. Retrospective analysis for the a4a model. 

SIMULATIONS 

547 

547 

 
 
 
 
 
Figure  6.13.3.12  Red  mullet  in  GSA  20.  Stock  summary  of  the  simulated  and  fitted 
data for the a4a model. 

Table 6.13.3.7. Red mullet GSA 20. F at age 

age 
0 
1 
2 
3 
4+ 

age 
0 
1 
2 
3 
4+ 

2003 

2004 

2005 

2006 
0.040165  0.039835  0.038784  0.036549  0.033305  0.029777  0.026773 
1.36496  1.24383  1.11205  0.999867 
1.50002  1.48767  1.44842 
2.62653  2.39344  2.13987 
2.88641  2.86265  2.78713 
2.62653  2.39344  2.13987 
2.88641  2.86265  2.78713 
2.62653  2.39344  2.13987 
2.88641  2.86265  2.78713 

1.924 
1.924 
1.924 

2007 

2009 

2008 

2010 

2012 

2011 

2013 
0.024834  0.024184  0.024838  0.026696  0.02958  0.033274  0.037604 
1.40438 
0.927467  0.903195  0.927622  0.997004  1.10472  1.24267 
2.70239 
1.91849  2.12576  2.39121 
1.78468  1.73798  1.78498 
2.70239 
1.91849  2.12576  2.39121 
1.78468  1.73798  1.78498 
2.70239 
1.91849  2.12576  2.39121 
1.78468  1.73798  1.78498 

2016 

2014 

2015 

The EWG 17 - 15 concluded that the output of this model was not suitable to provide an 
indication of the current status of the stock, due to the lack of survey and catch at age 
data  and  agreed  not  to  provide  forward  projections  and  catch  advice  based  on  this 
assessment.  

SPiCT – Surplus Production model in Continuous Time 

Input data 

The  assessment  based  on  the  biomass  production  model  using  SPiCT,  facilitate  the 
timeseries  of  FAO  landings  for  the  period  1994  -  2016  and  the  time  series  of  biomass 
indices  from  the  MEDITS  trawl  survey.  SPiCT  model  with  default  parameters  did  not 
converge.  To  achieve  convergence  a  prior  for  the  growth  rate  was  used.  This  was 
estimated  from  independent  data  on  life  history  parameters  (i.e.,  feccundity  by  age, 
mortality  by  age,  natural  mortality  and  growth)  using  Krebs  demographic  method 
(McAllister et al., 2001). Fecundity estimates were not available for the Ionian stock, and 
these were borrowed from red mullet stocks from the Aegean sea reported in (Carbonara 
et al. 2015) 

548 

548 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure  6.13.3.13.  Red  mullet  in  GSA  20.  Stock  summary  of  the  simulated  and  fitted 
data for the a4a model. 

Table 6.13.3.8. Red mullet GSA 20. Spict input data, catch and survey index 

42.43 

Year  Catch  Index 
223 
2006 
29.03 
280 
2007 
270 
2008 
264.9 
2009 
173 
2010 
348.2 
2011 
319.9 
2012 
205.8 
2013 
2014 
189.1  20.91 
2015 
2016 

214 
287 

48.93 

Year  Catch  Index 
19.26 
357 
1994 
10.55 
304 
1995 
8.34 
312 
1996 
22.44 
356 
1997 
33.37 
356 
1998 
33.66 
353 
1999 
17.32 
337 
2000 
43.13 
318 
2001 
299 
2002 
355 
2003 
350 
2004 

31.43 
37.22 

2005 

297 

19.22 

549 

549 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 6.13.3.9. Red mullet GSA 20. Spict input data, prior for the intrinsic growth rate 

r 

Distribution  lognormal 

Μ 
Σ 

-0.249 
0.443 

The above parameters of lognormal distribution correspond to a mean value of 0.86 and 
variance 0.34. 

Assessment Results 

Figures below (Figure 6.13.3.14-15)present the output of the assessment with the prior 
for the growth rate. The results show that for the period 1994  -2016 mostly fished in a 
sustainable  way  with  a  decreasing  fishing  mortality.  The  current  biomass  is  well  above 
BMSY  while  fishing  mortality  is  a  bit  lower  than  0.5  of  FMSY.  Although  95%  CI  for  the 
absolute biomass and fishing mortality are wide the 95% CI for the relative biomass and 
relative  fishing  mortality  are  narrower,  being  just  above  and  just  below  for  Bmsy  and 
Fmsy respectively.  

Figure  6.13.3.14.  Red  mullet  in  GSA  20.  Summary  of  results  of  SPiCT.  Blue  line: 
estimated  absolute  biomass  and  fishing  mortality,  dotted  blue  lines:  95%  CI’s  of 
absolute values, shaded blue regions: 95% CI’s of relative biomass and fishing mortality, 
black  lines:  estimated  reference  points,  grey  region:  95%  CI’s  of  the  estimated 
reference points. 

550 

550 

 
 
 
 
 
 
Figure  6.13.3.15.  Upper  left:  The  plot  shows  the  development  of  biomass  and  fishing 
mortality since the initial year (here 1994) indicated with a circle until the terminal year 
(here 2016) indicated with a square. Lower left: plot shows estimated catches (blue line) 
versus observed catches (points) with the estimate of MSY plotted as a horizontal black 
line with its 95% CI given by the grey region. Upper right: Production over B/K. Lower 
right: Time to Bmsy under different scenarios about F. 

SUMMARY OF RESULTS 

Table  6.13.3.10.  Red  mullet  GSA  20.  Spict  summary  results.  Model  parameters  and 
Dynamic  components.  r:  growth  rate,  K:  biomass  carrying  capacity,  BMSY:  biomass  at 
MSY,  FMSY:  fishing  mortality  at  MSY,  b/BMSY:  relative  biomass  in  2016,  F/FMSY: 
relative fishing mortality in 2016. 

low 
95% CL 
0.462 

upper 
95% CL 
2.706 

503.311  4162.752 
251.656  2081.376 

0.231 
1.235 
0.207 

1.353 
2.347 
0.699 

r 
K 
Bmsy 
Fmsy 
B/Bmsy 
F/Fmsy 

estimate 

1.118 
1447.467 
723.733 
0.559 
1.702 
0.381 

551 

551 

 
 
 
 
 
 
 
 
 
DIAGNOSTICS 

Figure 6.13.3.16. Red mullet GSA 20. Diagnostic test of the fit for the residuals of the 
catch  and  abundance  index  series.  First  row:  log  of  input  data  series.  Second  row: 
residuals  plot.  Third  row:  autocorrelation  of  residuals  and  fourth  row:  normality  of 
residuals.  

Figure 6.13.3.17. Red mullet GSA 20. Comparison of prior and posterior distribution of 
parameters. ratios of observation to process error (σC/σF), growth rate (r). 

552 

552 

 
 
 
 
 
 
 
 
Figure  6.13.3.18.  Red  mullet  GSA  20.  Retrospective  plots  of  relative    and  absolute 
biomass  and  fishing  mortality,  produced  by  repeating  the  stock  assessment  after 
excluding  1  (black  line)  to  5  (cyan  line)  final  year  observations  of  the  catch  and 
abundance index time series. The dotted black lines are the estimates when the full time 
series is considered.      

CMSY 

Red  mullet  in  GSA  20  was  also  assessed  with  the  advanced  Bayesian  state-space 
implementation of the Schaefer (1954) surplus production model (BSM), where r, k and 
MSY are predicted from catch and biomass data.  

Input data 

Landings 
The official reported landings from the Ionian Sea (GSA 20) were used as reported in the 
FishStat  J  from  the  GFCM  Database  (FishStat  J).  The  landings  data  from  1982  to  2016 
was used, with the landings of the two latest years derived from the Statistical Authority 
of Greece (they fully correspond to the GFCM ones) because the GFCΜ database was not 
updated  after  2014.  Data  prior  to  1982  were  excluded  because  the  landings  of  Red 
mullet were reported aggregated with Striped red mullet. According to the Management 
Plan for the Greek bottom trawl fisheries (Anonymous, 2013), the discards of Red mullet 
by weight in GSA 20 are negligible; thus they were excluded from the analysis. 

Biomass 
The  biomass  from  MEDITS  surveys  that  were  conducted  in  Ionian  Sea  was  used  as 
tuning index. Survey data were available by DCF from 2003 onwards (with gaps in 2007, 
2009-2013 and 2015).  

553 

553 

 
 
 
 
 
 
Settings 
Default priors of the model where used as they are presented in the table 6.13.3.11 

Table 6.13.3.11. Red mullet GSA 20. Priors used in the CMSY model. 

Prior 

range 

initial relative biomass 

0.2 - 0.6 

intermediate rel. 
biomass in year 1990 

0.5 - 0.9 

final relative biomass 

0.01 - 0.4 

R 
K 
Q 

0.22 - 1.2 
0.372 - 8.46 
0.0248 - 0.118 

SUMMARY OF THE MODEL 

The output of the model is presented below 

 Species: Mullus barbatus , stock: MULLBAR_IS  

 Red mullet in Ionian Sea  

 Source: NA  

 Region: Mediterranean , Ionian Sea  

 Catch data used from years 1982 - 2016 , abundance = CPUE  

Results of CMSY analysis with altogether 3235 viable trajectories for 1519      r-k pairs  

r = 0.733 , 95% CL = 0.445 - 1.21 ,  

k = 1.72 , 95% CL = 1.13 - 2.61  

MSY = 0.315 , 95% CL = 0.292 - 0.341  

Relative biomass last year = 0.299 k, 2.5th = 0.0276 , 97.5th = 0.396  

Exploitation F/(r/2) in last year = 1.06  

Results from Bayesian Schaefer model using catch & CPUE  

r = 0.714 , 95% CL = 0.504 - 1.01 ,  

k = 1.8 , 95% CL = 1.31 - 2.49  

MSY = 0.322 , 95% CL = 0.297 - 0.35  

Relative biomass in last year = 0.404 k, 2.5th perc = 0.249 , 97.5th perc = 0.508  

Exploitation F/(r/2) in last year = 0.768 

q = 0.0411 , lcl = 0.0296 , ucl = 0.0571 

554 

554 

 
 
 
 
 
 
 
 
Results for Management (based on BSM analysis)  

Fmsy = 0.357 , 95% CL = 0.252 - 0.506  

(if B > 1/2 Bmsy then Fmsy = 0.5 r) 

Fmsy = 0.357 , 95% CL = 0.252 - 0.506  

(r and Fmsy are linearly reduced if B < 1/2 Bmsy) 

MSY  = 0.322 ,  95% CL = 0.297 - 0.35  

Bmsy = 0.902 ,  95% CL = 0.655 - 1.24  

Biomass in last year  = 0.73 , 2.5th perc = 0.449 , 97.5 perc = 0.917  

B/Bmsy in last year   = 0.809 , 2.5th perc = 0.497 , 97.5 perc = 1.02  

Fishing mortality in last year = 0.274 , 2.5th perc = 0.218 , 97.5 perc = 0.446  

F/Fmsy  = 0.768 , 2.5th perc = 0.611 , 97.5 perc = 1.25 

Figure 6.13.3.19. Observed catch (solid line) and MSY estimate with 95%CI, predicted 
relative  biomass  with  95%CI,  predicted  relative  fishing  mortality  with  95%CI  and  Kobe 
plot  

555 

555 

 
 
  
 
 
 
 
  
Figure  6.13.3.20.  From  left  to  right  and  top  to  bottom.  Observed  landings,  viable  r-K 
pairs resulting from MCMC runs, most probable r-K pair from CMSY (blue) and average 
estimate  of  r-K  pair  from  BSM,  predicted  relative  biomass  with  95%CI  (blue)  and 
abundance  index  scaled  by  catchability  with  95%CI  (red),  predicted  relative  fishing 
mortality  (catch  over  predicted  biomass)  (blue)  and  catch  over  abundance  index  (red), 
and  Schaefer  production  curve  with  the  right  side  of  the  parabola  indented  because 
below  0.25  k,  a  linear  decline  of  surplus  production  due  to  reduced  recruitment  is 
assumed. Predicted relative catch by CMSY (blue dots) and by BSM (red dots).   

CONCLUSION 

Three  completely  different  perceptions  of  the  stock  status  between  the  applied 
methodologies.  The  statistical  catch  at  age  method  revealed  a  highly  overfished  stock 
while  the  surplus  production  models  assessed  the  stock  as  healthy.  The  surplus 
production model starting in 1994 (SPiCT) concludes the stock has been underexploited, 
the inclusion of earlier years, some with higher catch, in CMSY suggests the population 
scaling  is  different  and  that  the  stock  has  been  below  Bmsy  since  1994.  Though  the 
estimates of F from CSMY still largely support an under exploited stock. F from the age 
based assessment is very high, though the data is sparse, the conclusions depend on the 
selection patterns for survey and fishery. Of particular importance is the selectivity in the 
survey  rises  to  level  and  remains  flat  at  the  older  ages.  Thus  the  age  structure  in  the 
survey gives directly as the age structure in the population.  Overal the disparity anong 
these  different  sources  and  the  lack  of  dynamis  in  the  catch  data  make  it  difficult  to 
draw  conclusions  on  stock  status.It  is  hoped  that  more  data  from  the  fishery  and 
surveys over the coming years will help resolve these issues. 

556 

556 

 
 
 
 
6.13.4  REFERENCE POINTS 

Due  to  conflict  between  the  three  assessment  methods  no  reference  points  were 
given from the STECF EWG 17-15 for the Red mullet in GSA 20. 

6.13.5  SHORT TERM FORECAST AND CATCH OPTIONS 

No short term forecast or catch options were given for the Red mullet stock in GSA 
20 due to lack of persistence in the results of the three assessment methods. 

6.13.6  DATA DEFICIENCIES 

The STECF EWG 17-15 was not possible to give any reference points or catch advice 
for  Red  mullet  in  GSA  20  for  the  following  reasons:  Large  gaps  and  inconsistencies 
were detected  both in catch and survey data lead to great differences in the results 
of the three different methodologies applied to assess the stock status. In particular: 

  Landings:  There  were  gaps  in  the  landings  data  for  GSA  20,  for  the  years 
2007,  2009  –  2012,  while  for  the  years  2013,  2015  landings  data  were  only 
reported for the last quarter of the year. Landings by gear suffered from great 
inconsistencies. In particular the values of the reported landings for the small 
scale/  coastal  fisheries  in  the  year  2003  appeared  10  times  larger  than  the 
ones reported for the trawlers. The size distribution of the landings, due to the 
previous mentioned problems in the dataset, suffered from the same problems 
especially in small length classes.  

  Discards: The same gaps observed in the landings data were also apparent in 

the discards data.  

  Effort: Effort data seemed also had the same gaps as the previous data sets of 

landings and discards.  

  Catch at age data: More gaps than data appeared in the catch at age data set, 
in particular: weight at age data appeared only for the years 2004-2006, 2013 
and  2014  and  only  for  the  landing  part  of  the  catch,  moreover  the  reported 
values  for  mean  weight  at  age  diverged  a  lot  from  the  expected  ones.  The 
numbers  and  weights  at  age  were  not  reported  consistently  (step  size,  initial 
value, unit of measurement vary among years). 

  MEDITS  Survey:  Gaps  were  also  apparent  in  the  MEDITS  trawl  survey  data 

set. Years 2002, 2007, 2009 -2013 and 2015 were missing. 

6.14  HAKE IN GSA 22 

6.14.1  BIOLOGY 

Figure  6.14.1  shows  the  location  of  GSAs  22  and  23  in  the  Mediterenean.  GSA-22 
has been considered  as a unique area for management purposes due to its  specific 
geo-physical  characteristics  and  its  separation  from  nearby  areas,  such  as  GSA-23 

557 

557 

 
 
 
 
(Crete),  as  the  Cretan  Sea  is  a  deep  (2500m)  and  large  in  volume  generally 
oligotrophic  basin  (Psarra  et  al.,  1996;  Lykousis  et  al.,  2002).  In  addition,  fishery 
exploitation patterns differ between the two nearby areas, with the trawling activities 
being much less intense in GSA-23 (Anonymous, 2013).  

Figure 6.14.1 Geographical location of GSAs 22 and 23. The assessment was done for 
GSA-22. 

Growth parameters for the Aegean hake stock are found in 2016 DCF report (Linf= 
106.5,  k=  0.143;  to=  -0.54,  sex  combined).  These  parameters,  together  with 
literature information were used to estimate the intrinsic growth rate, r of the hake 
stock, using Kreb’s demographic method (McAllister et al. 2001).  

6.14.2  INPUT DATA 

The vast majority of European hake landings come from the Greek fleets. The stock 
is exploited by bottom trawlers and various artisanal fisheries using gillnets and 
demersal longilnes. It is mainly exploited by four fleet segments but the majority of 
landings (~60%) is coming from bottom trawlers (Anonymous, 2013). The Greek 
bottom trawl fishery has multi-species characteristics and similarly to most 
Mediterranean demersal trawl fisheries, captures more than 100 commercial species. 
However, hake together with red mullets and shrimps compose the main bulk of 
landings.  

As with all demersal fisheries in GSA-22, those catching hake are managed according 
to  EU  regulation  1967/2006,  which  implies  spatial  fishery  closures  for  the  bottom 
trawlers,  gear  configuration  specifications  and  minimum  catching  sizes.  Additional 
national  measures  include  a  temporal  (4.5  months)  closure  of  the  bottom  trawl 
fisheries  accompanied  by  certain  localized  spatio-temporal  closures  and  an  one-
month (February) closure of the artisanal fisheries targeting hake. 

558 

558 

 
 
 
 
 
6.14.2.1  CATCH (LANDINGS AND DISCARDS)  

Landings  and  discards  data  were  reported  to  STECF  EWG  17-15  through  the  DCF 
(Tables  6.14.2.1.1-2  and  Figures  6.14.2.1.1-2).  These  are  available  only  for  the 
years  the  DCF  took  place  in  Greece  (2003-2006,  2008,  2014,  2016  and  partly  for 
2013  and  2015).  Due  to  the  limited  data,  a  full  age-based  assessment  was  not 
attempted and the assessment was based on a surplus production model facilitating 
landings time series from FAO.  

Table 6.14.2.1.1 Hake GSA-22. Landings and discards per year provided to STECF EWG 
17-15 through the DCF. Data for 2013 and 2015 correspond to the last trimester of the 
year only. 

Year 

Landings 
(tonnes) 

Discards 
(tonnes) 

2003 
2004 
2005 
2006 
2008 
2013 
2014 
2015 
2016 

4950.64 
7614.02 
8505.93 
9062.09 
7251.94 
675.73 
1789.26 
1399.37 
2930.91 

223.80 
610.48 
636.65 
655.24 
461.12 
24.66 
87.21 
57.52 
25.28 

Table  6.14.2.1.2  Hake  GSA-22.  Landings  and  discards  per  year  and  fishing  gear 
provided to STECF EWG 17-15 through the DCF. Data for 2013 and 2015 correspond to 
the last trimester of the year only. 

Year 

Fishing gear 

2003 
2003 
2003 
2004 
2004 
2004 
2005 
2005 
2006 
2006 
2008 
2008 
2013 
2013 
2013 

NA 
OTB 
PS 
NA 
OTB 
PS 
NA 
OTB 
NA 
OTB 
NA 
OTB 
GNS 
GTR 
OTB 

559 

559 

Landings 
(tonnes) 
2506.89 
2443.70 
0.05 
4038.72 
3571.93 
3.37 
4649.43 
3856.50 
5227.38 
3834.72 
3459.22 
3792.72 
148.37 
5.68 
521.68 

Discards 
(tonnes) 
- 
223.8 
- 
255.05 
355.43 
- 
274.25 
362.4 
103.42 
551.83 
- 
461.12 
4.07 
1 
19.59 

 
 
 
 
 
 
 
2014 
2014 
2014 
2014 
2015 
2015 
2015 
2015 
2016 
2016 
2016 
2016 
2016 

GNS 
GTR 
LLS 
OTB 
GNS 
GTR 
LLS 
OTB 
GNS 
GTR 
LLS 
OTB 
PS 

362.34 
39.36 
155.63 
1231.94 
186.47 
10.44 
287.14 
915.32 
707.64 
79.81 
609.78 
1533.55 
0.13 

11.63 
6.22 
- 
69.36 
5.99 
- 
- 
51.53 
25.21 
0.06 
- 
- 
0.01 

Figure 6.14.2.1.1 Hake GSA-22. Landings (in numbers) by year, gear and length class 
provided to STECF EWG 17-15 through the DCF. “-1” denotes unidentified coastal boats 
mainly  GNS  GTR  LLS.  Data  for  2013  and  2015  correspond  to  the  last  trimester  of  the 
year only. 

560 

560 

 
 
 
 
 
Figure  6.14.2.1.2  GSA-22.  Discards  (in  numbers)  by  year,  gear  and  length  class 
provided to STECF EWG 17-15 through the DCF. “-1” denotes unidentified coastal boats 
mainly GNS GTR LLS. 

The main bulk of discards concerns undersized individuals. Discards vary, depending on 
the  gear,  area  and  season  and  according  to  a  recent  report  of  the  Mediterranean 
Advisory  Committee  (MEDAC,  Ref  190/2016)  they  are,  around  5-8%,  in  terms  of 
biomass, for the bottom trawlers.  

6.14.2.2  EFFORT 

Effort data were reported to STECF EWG 17-15 through the DCF (Tables 6.14.2.2.1-
2). Data for 2013 and 2015 correspond to the last trimester of the year only. 

Table 6.14.2.2.1 Effort in GT*days_at_sea and days at sea, per year in GSA-22.  

“-1”  denotes  unidentified  coastal  boats  mainly  GNS,  GTR  and  LLS.  .  Data  for  2013  and 
2015 correspond to the last trimester of the year only. 

561 

561 

 
 
 
 
 
 
 
 
 
 
 
year 

GT*days_at_sea  days_at_sea 

2003 
2004 
2005 
2006 
2008 
2010 
2011 
2012 
2013 
2014 
2015 
2016 

16119646 
15841695 
16668789 
16272934 
15564468 
2800 
2400 
2480 
2362008 
8819129 
6965432 
18877477 

- 
- 
- 
- 
- 
35 
30 
31 

1392228 
460174 
1412992 

Table  6.14.2.2.2  Effort  in  GT*days_at_sea  and  days  at  sea,  per  year  and  gear  in 
GSA-22.  “-1”  denotes  unidentified  coastal  boats  mainly  GNS,  GTR  and  LLS.  Data  for 
2013 and 2015 correspond to the last trimester of the year only. 

Year 

2003 
2003 
2003 
2003 
2003 
2004 
2004 
2004 
2004 
2004 
2005 
2005 
2005 
2005 
2005 
2006 
2006 
2006 
2006 
2006 
2008 
2008 
2008 
2008 
2008 
2010 
2011 
2012 

Gear 
-1 
LLS 
OTB 
PS 
SV 
-1 
LLS 
OTB 
PS 
SV 
-1 
LLS 
OTB 
PS 
SV 
-1 
LLS 
OTB 
PS 
SV 
-1 
LLS 
OTB 
PS 
SV 
LLD 
LLD 
LLD 

GT*days_at_sea  Days at sea 

8567144 
332005.2 
4927349 
1998253 
294895.6 
8034837 
577572.4 
4972085 
1987556 
269645.1 
7939836 
603419.1 
5553804 
2295466 
276264.9 
7571041 
780137.7 
5556446 
2108039 
257271.2 
6916918 
1146528 
5355704 
1930333 
214984.8 
2800 
2400 
2480 

562 

562 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
35 
30 
31 

 
 
 
 
 
2013 
2013 
2013 
2013 
2013 
2013 
2013 
2014 
2014 
2014 
2014 
2014 
2014 
2014 
2015 
2015 
2015 
2015 
2015 
2015 
2015 
2016 
2016 
2016 
2016 
2016 
2016 
2016 

FPO 
GNS 
GTR 
LLD 
LLS 
OTB 
PS 
FPO 
GNS 
GTR 
LLD 
LLS 
OTB 
PS 
FPO 
GNS 
GTR 
LLD 
LLS 
OTB 
PS 
FPO 
GNS 
GTR 
LLD 
LLS 
OTB 
PS 

29522.42 
122828.6 
353708.9 
640 
84741.67 
1530062 
240504.2 
320224 
1119222 
1450864 
3680 
555213 
4200152 
1169774 
28402 
365054 
456691 
2640 
404912 
4102239 
1605494 
119187 
3193652 
4692168 
8400 
4926529 
4279654 
1657887 

- 
- 
- 
8 
- 
- 
- 
84455 
385442 
601502 
46 
259992 
39153 
21638 
13637 
115020 
160781 
33 
99771 
37762 
33170 
79962 
415065 
523530 
105 
319625 
39565 
35140 

6.14.2.3  SURVEY DATA 

The  “MEDITS”  bottom  trawl  surveys  are  accomplished  in  GSA-22  since 
1994  on  an  annual  basis.  However,  no  surveys  were  accomplished  in 
2003,  2009-2013  and  in  2015.  Sampling  includes  sampling  in  129  pre-
defined  stations  (Figure  6.14.2.3.1)  following  a  standardized  protocol. 
Trawling was made by means of a standard net GOC 73 having a cod-end 
mesh opening of 20 mm and selection of stations was based on a depth-
stratified  sampling  scheme  that  included  five  depth  zones:  10-50,  50-
100, 100-200, 200-500 and 500-800 m. Collected data included number, 
weight,  gonad  maturation  stage  and  total  length  measurements  for  a 
wide  range  of  fish,  cephalopod  and  crustacean  species  (MEDITS 
Handbook  v9,  2017).  From  the  collected  data,  standardized  abundance 
indexes  by  year,  expressed  in  terms  of kg  per  square  km  of  swept area 
(kg/km2), were calculated for hake (Table 6.14.2.3.1).  

563 

563 

 
 
 
 
 
 
Figure 6.14.2.3.1 Hake in GSA 22. MEDITS stations in GSA-22 

Table 6.14.2.3.1 Hake in GSA 22. Survey data from the MEDITS trawl survey  

Year 

1994 
1995 

1996 
1997 

1998 

1999 
2000 

2001 

2003 
2004 

2005 

2006 
2008 

2014 

2016 

Abundance 
index (kg/km2) 
35.283 
29.761 

31.529 
47.049 

45.074 

51.227 
44.243 

34.462 

44.274 
32.573 

46.544 

67.553 
54.229 

28.717 

37.120 

564 

564 

 
 
 
 
 
 
 
 
6.14.3  STOCK ASSESSMENT 

Method 

The  stock  was  assessed  in  2012  in  STECF  12-21  (JRC  scientific  and  policy  reports, 
2012)  using  VIT,  surplus  production  with  APSIC  and  SURBA  but  with  data  up  to 
2005.  The  conclusion  was  that  the  stock’s  status  cannot  be  deducted  although  the 
SSB  of  the  stock  showed  increasing  trends  since  1994.  The  last  assessment  with 
data up to 2008 was conducted in 2013 within the management plan for the Greek 
bottom  trawlers  (Anonymous,  2013).  The  assessment  was  based  on  a  non 
equilibrium  Schaefer  surplus  production  model  and  predicted  that  the  stock  was 
healthy  in  terms  of  biomass  but  overfishing  was  taking  place.  The  current 
assessment  is  based  on  a  state-space  surplus  production  model  implemented  with 
the SPiCT package (Pedersen and Berg, 2017) under the R-language environment (R 
core team 2017). 

Input data and parameters 

The assessment was based on FAO landings for the period 1970-2016 and the time 
series  of  abundance  indices  from  the  MEDITS  trawl  surveys  (Table  6.14.3.1  and 
Figure 6.14.3.1). The abundance index time series has a number of missing values, 
i.e. years when the sampling did not take place. The FAO records combine GSAs 22 
and  23  for  the  period  1970-2014.  These  were  disaggregated  by  GSA  using  the 
average ratio of landings between GSAs 22 and 23 that are available from the Greek 
Statistical 
1990-2016 
(http://www.statistics.gr/en/home/).  Landings  by  GSA  are  collected  by  the  NSSG, 
which then provides the data to FAO. The last two years missing from the FAO data, 
i.e.  2015  and  2016,  were  taken  from  the  NSSG  time  series.  Discards  for  hake  are 
considered  negligible  and  only  landings  data  are  used  for  the  biomass  production 
assessment.  

of  Greece 

(NSSG) 

Service 

period 

the 

for 

Initially  SPiCT  was  run  with  default  parameters  but  convergence  was  not  achieved. 
To  facilitate  convergence  a  prior  for  the  intrinsic  growth  rate  was  used.  This  was 
estimated from  data on life history parameters (i.e., fecundity by age, mortality by 
age,  natural  mortality  and  growth)  using  Kreb’s  demographic  method  (McAllister  et 
al., 2001). Still, the model with prior distribution for r did not converge. Convergence 
was achieved using a prior on the initial relative biomass (B/Bmsy) in 1970. To assess 
the sensitivity  of the  model  output  to  this  prior, the  assessment  was  repeated  with 
three  different  prior  distributions  for  B/Bmsy  in  1970  with  means  0.5,  1,  1.5  and  2 
respectively  and  coefficient  of  variation  0.33  in  all  cases.  The  outcome  in  terms  of 
relative fishing mortality and biomass in 2017 (prediction year) is not very sensitive 
to  this  prior  (16%  and  12%  maximum  deviation  respectively).  Table  6.1.1.2.2.2 
gives the informative prior distributions used in the final assessment. 

565 

565 

 
 
 
 
  
 
 
Table 6.14.3.1 Landings and survey index for hake in GSA-22 in the period 1970-2016. 
Landings data taken from FAO for  the Aegean sea (GSA-22  and 23) and  disaggregated 
by GSA using the ratio of landings in the two GSAs available from the Greek Statistical 
Service  of  Greece  (NSSG)  (http://www.statistics.gr/en/home/).  Survey  index  from 
MEDITS surveys. 

Year 

Landings 
(tonnes) 

1970 

1971 
1972 

1973 
1974 
1975 
1976 

1977 
1978 

1979 

1980 
1981 

1982 

1983 
1984 

1985 

1986 
1987 

1988 

1989 
1990 

1991 

1992 
1993 

1994 
1995 
1996 
1997 

1998 
1999 

2000 

2001 
2002 

2003 

2004 
2005 

2006 

2007 
2008 
2009 
2010 

1121.8 

951.3 
1160.1 

1711.7 
1665.8 
1480.4 
1696.7 

1751.9 
1875.5 

1615.2 

1646.1 
1577.8 

1763.2 

2012.2 
1881.1 

2594.7 

3386.8 
2667.7 

3216.4 

3534.8 
3148.5 

2386.8 

3080.9 
3036.2 

4336 
3286.3 
3040.1 
2694.1 

2208.4 
2333.6 

2268.3 

2006.2 
2101.1 

2373.4 

2855 
2873.7 

3385.5 

3761.4 
3849.2 
3789.7 
3382.1 

566 

566 

Abundance 
index 

(kg/km2) 
- 
- 

- 

- 
- 

- 

- 
- 

- 

- 
- 

- 

- 
- 

- 
- 
- 

- 
- 
- 
- 

- 
- 

- 

35.283 
29.761 
31.529 
47.049 

45.074 
51.227 

44.243 

34.462 
- 

44.274 

32.573 
46.544 

67.553 
- 
54.229 
- 
- 

 
2011 

2012 
2013 

2014 
2015 
2016 

3093 

3008 
3579.5 

2427.9 
2741.8 
3085.5 

- 

- 
- 

28.717 
- 
37.120 

Figure  6.14.3.1  Times  series  of  FAO  landings  and  MEDITS  survey  index  used  in  the 
SPiCT assessment of hake in GSA-22.  

Table  6.14.3.2  Parameters  of  prior  distributions  for  the  intrinsic  growth  rate,  r,  and 
initial biomass (B/Bmsy in 1970) used in the assessment of hake in GSA-22.    

Parameter priors 

r 

B/Bmsy (1970) 

Distribution 

lognormal 

lognormal 

μ (mean of the log) 

-0.380 

σ (std of the log) 

0.489 

0.35 

0.32 

567 

567 

 
 
 
 
 
 
 
 
 
 
Results 

Figures  6.14.3.2-5  show  the  output  of  the  assessment  with  a  prior  for  the  intrinsic 
growth rate and an assumption for the initial biomass in 1970 (prior with mean 1.5).  

The  assessment  results  show  that  for  the  period  1970-2015,  the  hake  stock  was 
mostly  fished  in  a  sustainable  way  but  with  increasing  fishing  mortality.  (Figure 
6.1.1.2.3.1).  The  current  biomass  and  fishing  mortality  are  very  close  to  Bmsy  and 
Fmsy  estimates,  although  the  uncertainty  around  those  estimates  is  high  (Figure 
6.1.1.2.3.1 and Table 6.1.1.2.3.1)    

Retrospective  analysis  shows  consistent  long  term  trends  in  biomass  and  fishing 
mortality,  with  33%  and  17%  deviation  respectively  between  the  lower  and  upper 
estimates,  with  the  estimate  corresponding  to  the  full  time  series  being 
intermediate.  All restrospective estimates lie within the confidence intervals.   

Figure  6.1.1.2.3.3  compares  prior  and  posterior  distribution  for  the  parameters  for 
which priors were set.  

568 

568 

 
 
Figure 6.14. 3.2 Stock assessment results for hake in GSA-22. First row: median (blue 
solid  line)  of  relative  biomass  and  relative  fishing  mortality  with  95%  CI  (blue  shaded 
area).  Second  row:  Observed  (points)  and  estimated  catch  (solid  line)  with  95%  CIs 
(left)  and  Kobe  plot  of  relative  fishing  mortality  versus  relative  biomass  (right).  Third 
row: Production curve (black line) with predicted values of surplus production (blue line 
and points) (left) and comparison of prior and posterior distributions of the n parameter, 
which  determines  the  shape  of  the  production  curve  in  the  Pella-Tomlinson  model 
(right). 

569 

569 

 
 
Fig.  6.14.3.3  Diagnostic  test  of  the  fit  for  the  residuals  of  the  catch  and  abundance 
index  series.  First  row:  log  of  input  data  series.  Second  row:  residuals  plot.  Third  row: 
autocorrelation of residuals and fourth row: normality of residuals. If the header is green 
the test is not significantly different from parametric assumptions, otherwise the header 
is red.  

Table  6.14.3.3  Mean  and  standard  deviation  of  model  parameters  and  dynamical 
components  for  the  assessment  of  hake  in  GSA-22.  K:  biomass  carrying  capacity,  r: 
intrinsic  growth  rate,  MSY:  Maximum  Sustainable  Yield,  Bmsy:  biomass  at  MSY,  Fmsy: 
fishing  mortality  at  MSY,  B/Bmsy:  relative  biomass  in  2016,  F/Fmsy:  relative  fishing 
mortality in 2016   

570 

570 

 
 
 
 
 
Parameter  mean 

Sd 

K 

r 

MSY 

Bmsy 
Fmsy 
B/Bmsy(2016) 
F/Fmsy(2016) 

20632 

14450 

0.649 

3009 

0.38 

1204 

10958 

122529 

0.27 

1.02 

0.95 

133 

5.5 

1.04 

Figure  6.14.3.4  Comparison  of  prior  and  posterior  distribution  of  parameters  in  the 
assessment  of  hake  in  GSA-22.  First  row:  parameter  of  the  Pella-Tomlinson  model 
determining  the  skewness  of  the  production  curve,  n,  ratios  of  observation  to  process 
error,    alpha=σΙ/σΒ  and  beta=σC/σF  respectively.  Second  row:  intrinsic  growth  rate,  r, 
and initial relative biomass (B/Bmsy in 1970). 

571 

571 

 
 
Figure 6.14.3.5 Retrospective plots of relative biomass and relative fishing mortality for 
the  stock  assessment  of  hake  in  GSA-22,  produced  by  repeating  the  stock  assessment 
after  excluding  1  (black  line)  to  5  (cyan  line)  final  year  observations  of  the  catch  and 
abundance index time series. The dotted black lines are the estimates when the full time 
series is used.      

6.14.4  REFERENCE POINTS 

The SPiCT model provides estimates of B and F relative to MSY allowing estimates to 
be  considered  relative  to  reference  values,  however,  the  values  of  FMSY  and  BMSY 
themselves  are  uncertain,  and  given  the  retrospective  variation  are  not  considered 
sufficiently reliable to be taken as fixed refence points. 

6.14.5  SHORT TERM FORECAST AND CATCH OPTIONS 

Given the instability and unceretainty of the assessment, no short term forecast 
and catch options are presented. 

6.14.6  DATA DEFICIENCIES 

Data  on  catch  at  size  are  available  only  for  the  years  that  the  DCF  took  place,  i.e. 
2003-2005, 2008, 2014, 2016  and partly (only for  the last  trimester) for  2013 and 
2015. Due to the limited amount of data and the large gaps between 2008 and 2014 
an analytical age-based assessment was not attempted. Landings data from DCF are 
also  limited  to  the  above  mentioned  years  and  are  not  sufficient  to  parameterize  a 
biomass  production  model;  hence  data  from  FAO  were  used  as  input  to  the 
assessment.  For  the  years  that  the  DCF  landings  are  reported,  these  are 
considerably different from FAO landings, particularly for the period 2003-2008. For 
2014  and  2016  the  two  estimates  are  quite  close.  Note  that  FAO  landing  rates  are 
not necessarily similar to those obtained from DCF, as they are based on estimates 
of  the  National  Statistical  Service  of  Greece  deriving  from  a  different  sampling 
scheme than the one followed in DCF. For DCF data to be useful in assessments (age 
based or biomass production), longer time series with full sampling within each year 

572 

572 

 
 
are  needed.  The  data  on  relative  biomass  from  the  MEDITS  survey  also  have  gaps 
for the years the survey didn’t take place, i.e. 2002, 2007, 2009-2013, 2015. 

6.15  RED MULLET IN GSA 22 

6.15.1  BIOLOGY 

Figure 6.1.1 shows the location of GSAs 22 and 23 in the Mediterenean. GSA-22 has 
been considered as a unique area for management purposes due to its specific geo-
physical  characteristics  and  its  separation  from  nearby  areas,  such  as  GSA  23 
(Crete),  as  the  Cretan  Sea  which  is  a  deep  (2500m)  and  large  in  volume  generally 
oligotrophic  basin  (Psarra  et  al.,  1996;  Lykousis  et  al.,  2002).  In  addition,  fishery 
exploitation patterns differ between the two nearby areas, with the trawling activities 
being much less intense in GSA 23 (Anonymous, 2013).  

Figure 6.15.1 Geographical location of GSAs 22 and 23. The current assessment refers 
to GSA-22. 

Given the lack of a VG growth function with reasonable value of t0 (between -0.2 
and 0) for the examined stock, conversion of length at ages for the needs of the 
a4a  assessment  were  based  on  a  generic  age-length  key  proposed  by  Bianchini 
and Ragonese (2011). The proportion of mature individuals at-age was based on 
Tserpes  et  al.  (2016),  while  the  natural  mortality  at  age  estimates  were  taken 
from  the  STECF  EWG  21-12  report  (JRC  scientific  and  policy  reports,  2012) 
(Table 6.1.1.1). 

573 

573 

 
 
 
 
 
 
 
 
 
Table  6.15.1.1  Red  mullet  in  GSA-22.  Maturity  (proportion  of  mature)  and 
natural mortality (M) by age. 

Age 

1 

2 

3 

4 

0.72 

0.89 

0.98 

1 

5 

1 

Maturi
ty 

M 

0.61 

0.54 

0.50 

0.50 

0.50 

For  the  production  model  needs,  the  above  parameters,  together  with  literature 
information  on  fecundity  were  used  to  estimate  the  prior  of  the  intrinsic  growth 
rate, r, using Kreb’s demographic method (McAllister et al. 2001).  

6.15.2  INPUT DATA 

Red  mullet  is  exploited  by  bottom  trawlers  and  various  artisanal  fisheries  using 
gillnets. It is exploited by several fleet segments but the main bulk of catches is 
coming  from  bottom  trawlers  over  18m  length.  The  Greek  bottom  trawl  fishery 
has  multi-species  characteristics  and  similarly  to  most  Mediterranean  demersal 
trawl  fisheries,  captures  more  than  100  commercial  species.  However,  few 
species,  such  as  red  mullets,  hake  and  shrimps  compose  the  main  bulk  of 
landings, with red mullet being one of the most important targets. 

As with all demersal fisheries in GSA-22, those catching red mullet are managed 
according  to  EU  regulation  1967/2006  which  include  spatial  fishery  closures  for 
the  bottom  trawlers,  gear  configuration  specifications  and  minimum  landing 
sizes.  Additional  national  measures  include  a  temporal  (4.5  months)  closure  of 
the  bottom  trawl  fisheries  accompanied  by  certain  localized  spatio-temporal 
closures. 

6.15.2.1  CATCH (LANDINGS AND DISCARDS)  

Landings and discards data were reported to STECF EWG 17-15 through the DCF 
(Tables 6.15.2.1.1-2 and Figures 6.15.2.1.1-2). These are available only for the 
years the DCF took place in Greece (2003-2006, 2008, 2014, 2016 and partly for 
2013 and 2015).  

574 

574 

 
 
 
 
 
 
 
 
 
 
 
Table  6.15.2.1.1  Red  mullet  in  GSA-22.  Landings  and  discards  per  year 
provided  to  STECF  EWG  17-15  through  the  DCF.  Data  for  2013  and  2015 
correspond to the last trimester of the year only. 

Year 

Landings 
(tonnes) 

Discards 
(tonnes) 

2003 

4136.01 

2004 

3271.79 

123.67 

100.73 

2005 

3269.76 

2006 

2877.35 

2008 

2429.66 

2013 

434.17 

2014 

1470.86 

2015 

527.20 

2016 

1842.42 

46.81 

6.22 

18.05 

2.51 

15.93 

3.93 

17.55 

Table  6.15.2.1.2  Red  mullet  in  GSA-22.  Landings  and  discards  per  year  and  fishing 
gear  provided  to  STECF  EWG  17-15  through  the  DCF.  Data  for  2013  and  2015 
correspond to the last trimester of the year only. 

Year 

Fishing gear 

2003 

2003 
2004 
2004 

2004 

2005 
2005 

2006 

2006 
2008 

2008 

2013 
2013 

2013 
2014 
2014 
2014 
2015 

NA 

OTB 
NA 
OTB 

PS 

NA 
OTB 

NA 

OTB 
NA 

OTB 

GNS 
GTR 

OTB 
GNS 
GTR 
OTB 
GNS 

575 

575 

Landings 
(tonnes) 
2365.77 

Discards 
(tonnes) 
- 

1770.24 
1126.84 
2144.65 

0.30 

1588.93 
1680.83 

1686.63 

1190.72 
1053.77 

1375.89 

74.32 
83.24 

276.61 
676.19 
133.60 
661.07 
162.99 

123.67 
82.99 
17.73 

- 

28.63 
18.18 

- 

6.22 
16.81 

1.24 

0.34 
0.39 

1.78 
10.09 
2.26 
3.59 
2.43 

 
 
 
2015 

2015 

2016 
2016 

2016 

2016 

GTR 

OTB 

GNS 
GTR 

OTB 

PS 

88.18 

276.03 

820.48 
225.52 

795.70 

0.72 

- 

1.50 

9.04 
3.06 

5.13 

0.32 

Figure  6.15.2.1.1  Red  mullet  in  GSA-22.  Landings  by  year,  gear  and  length  class 
provided to STECF EWG 17-15 through the DCF. “-1” denotes unidentified coastal boats 
mainly GNS,  GTR  and  LLS.  Data  for  2013  and  2015  correspond  to the last trimester  of 
the year only. 

576 

576 

 
 
 
 
 
 
Figure  6.15.2.1.2  Red  mullet  in  GSA-22.  Discards  by  year,  gear  and  length  class 
provided to STECF EWG 17-15 through the DCF. “-1” denotes unidentified coastal boats 
mainly GNS,  GTR  and  LLS.  Data  for  2013  and  2015  correspond  to the last trimester  of 
the year only. 

The main bulk of discards for red mullet are undersized individuals. The discard ratio 
in terms of biomass is in most cases <2%  (Anonymous 2013). Discards in terms of 
numbers of individuals are also a small proportion of landings (Figures 6.15.2.1.1-2). 
For both models, i.e. the biomass production and the age-structured model, discards 
were considered negligible and were not included in the assessment. 

6.15.2.2  EFFORT 

Effort data were reported to STECF EWG 17-15 through the DCF (Tables 6.15.2.2.1-
2). 

Table 6.15.2.2.1 Effort in GT*days_at_sea and days at sea, per year in GSA-22. Data 
for 2013 and 2015 correspond to the last trimester of the year only.  

year 

GT*days_at_sea  days_at_sea 

2003 
2004 
2005 
2006 

16119646 
15841695 
16668789 
16272934 

- 
- 
- 
- 

577 

577 

 
 
 
 
 
2008 
2010 
2011 
2012 
2013 
2014 
2015 
2016 

15564468 
2800 
2400 
2480 
2362008 
8819129 
6965432 
18877477 

- 
35 
30 
31 

1392228 
460174 
1412992 

Table  6.15.2.2.2  Effort  in  GT*days_at_sea  and  days  at  sea,  per  year  and  gear  in 
GSA-22.  “-1”  denotes  unidentified  coastal  boats  mainly  GNS,  GTR  and  LLS.  Data  for 
2013 and 2015 correspond to the last trimester of the year only. 

Year 

2003 
2003 
2003 
2003 
2003 
2004 
2004 
2004 
2004 
2004 
2005 
2005 
2005 
2005 
2005 
2006 
2006 
2006 
2006 
2006 
2008 
2008 
2008 
2008 
2008 
2010 
2011 
2012 
2013 
2013 
2013 
2013 
2013 
2013 
2013 

Gear 
-1 
LLS 
OTB 
PS 
SV 
-1 
LLS 
OTB 
PS 
SV 
-1 
LLS 
OTB 
PS 
SV 
-1 
LLS 
OTB 
PS 
SV 
-1 
LLS 
OTB 
PS 
SV 
LLD 
LLD 
LLD 
FPO 
GNS 
GTR 
LLD 
LLS 
OTB 
PS 

GT*days_at_sea  Days at sea 

8567144 
332005.2 
4927349 
1998253 
294895.6 
8034837 
577572.4 
4972085 
1987556 
269645.1 
7939836 
603419.1 
5553804 
2295466 
276264.9 
7571041 
780137.7 
5556446 
2108039 
257271.2 
6916918 
1146528 
5355704 
1930333 
214984.8 
2800 
2400 
2480 
29522.42 
122828.6 
353708.9 
640 
84741.67 
1530062 
240504.2 

578 

578 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
35 
30 
31 
- 
- 
- 
8 
- 
- 
- 

 
 
 
 
2014 
2014 
2014 
2014 
2014 
2014 
2014 
2015 
2015 
2015 
2015 
2015 
2015 
2015 
2016 
2016 
2016 
2016 
2016 
2016 
2016 

FPO 
GNS 
GTR 
LLD 
LLS 
OTB 
PS 
FPO 
GNS 
GTR 
LLD 
LLS 
OTB 
PS 
FPO 
GNS 
GTR 
LLD 
LLS 
OTB 
PS 

320224 
1119222 
1450864 
3680 
555213 
4200152 
1169774 
28402 
365054 
456691 
2640 
404912 
4102239 
1605494 
119187 
3193652 
4692168 
8400 
4926529 
4279654 
1657887 

84455 
385442 
601502 
46 
259992 
39153 
21638 
13637 
115020 
160781 
33 
99771 
37762 
33170 
79962 
415065 
523530 
105 
319625 
39565 
35140 

6.15.2.3  SURVEY DATA 

The  “MEDITS”  bottom  trawl  surveys  are  accomplished  in  GSA-22  since 
1994  on  an  annual  basis.  However,  no  surveys  were  accomplished  in 
2003,  2009-2013  and  in  2015.  Sampling  includes  sampling  in  129  pre-
defined  stations  following  a  standardized  protocol  (Figure  6.15.2.3.1). 
Trawling was made by means of a standard net GOC 73 having a cod-end 
mesh opening of 20 mm and selection of stations was based on a depth-
stratified  sampling  scheme  that  included  five  depth  zones:  10-50,  50-
100, 100-200, 200-500 and 500-800 m. Collected data included number, 
weight,  gonad  maturation  stage  and  total  length  measurements  for  a 
wide  range  of  fish,  cephalopod  and  crustacean  species  (MEDITS 
Handbook  v9,  2017).  From  the  collected  data,  standardized  abundance 
indices  by  year,  expressed  in  terms  of  kg  per  square  km  of  swept  area 
(kg/km2), were calculated for red mullet (Table 6.15.2.3.1).  

579 

579 

 
 
 
 
Figure 6.15.2.3.1 MEDITS stations in GSA-22 

Table 6.15.2.3.1 Red mullet in GSA-22. Survey data from the MEDITS trawl survey  

Year 

1994 
1995 
1996 
1997 
1998 
1999 
2000 
2001 
2003 
2004 
2005 
2006 
2008 
2014 
2016 

Abundance 
index 
(kg/km2) 
7.31 
7.84 
7.43 
11.64 
15.46 
8.96 
24.69 
7.13 
29.08 
17.53 
14.00 
15.14 
13.36 
36.77 
44.59 

580 

580 

 
 
 
 
 
Figure  6.15.2.3.2  Red  mullet  in  GSA-22.  MEDITS  index  of  abundance  (N/km2)  by  age 
and year. 

6.15.3  STOCK ASSESSMENT 

Previous assessments 

The  stock  was  assessed  in  2012  within  STECF  12-21  (JRC  scientific  and  policy 
reports, 2012) using VIT, surplus production with ASPIC and SURBA with data up to 
2008.  Based  on  the  results  of  ASPIC,  the  stock  was  considered  over-exploited  with 
biomass below Bmsy.  

Model 1: surplus production using SPiCT 

Method 

The  assessment  is  based  on  a  state-space  surplus  production  model  implemented 
with  the  SPiCT  package  (Pedersen  and  Berg,  2017)  under  the  R-language  
environment (R core team 2017). 

Input data and parameters 

The assessment was based on a time series of landings from the Greek and Turkish 
fleets in GSA-22 and an abundance index from the MEDITS trawl survey (Figure and 
Table 6.15.3.1). The Greek landings were taken from the National Statistical Service 
of  Greece  for  the  period  1994-2016  and  the  Turkish  landings  were taken  from  FAO 
for  the  same  period.    Landings  data  are  also  available  from  the  DCF  for  the  years 

581 

581 

 
 
 
 
 
 
  
 
this  took  place  (i.e.  2003-2005,  2008,  2014,  2016  and  partly  for  2013  and  2015). 
These  were  not  used  as  they  are  insufficient  years  to  parameterize  a  surplus 
production  model.  Apart  from  this,  the  DCF  data  are  considerably  different  (i.e. 
reporting  higher  landings)  from  FAO  data  for  2003-2005.  Note,  however,  that  FAO 
landing  rates  are  not  necessarily  similar  to  those  obtained  from  DCF,  as  they  are 
based  on  estimates  of  the  National  Statistical  Service  of  Greece  deriving  from  a 
different sampling scheme than the one followed in DCF. The abundance index time 
series  has  a  number  of  missing  values,  i.e.  years  when  the  sampling  did  not  take 
place. Discards for red mullet are negligible in terms of biomass (<2%) (Anonymous, 
2013) and only landings data were used in the assessment.  

Initially  SPiCT  was  run  with  default  parameters  but  convergence  was  not  achieved. 
To  facilitate  convergence  a  prior  for  the  intrinsic  growth  rate  was  used.  This  was 
estimated from data on life history parameters (i.e., feccundity by age, mortality by 
age,  natural  mortality  and  growth)  using  Kreb’s  demographic  method  (McAllister  et 
al.,  2001).  Table  6.15.3.2  gives  the  informative  prior  distributions  used  in  the  final 
assessment. 

Figure  6.15.3.1  Red  mullet  in  GSA-22.  Landings  from  Greek  and  Turkish  fleets  in  the 
period  1994-2016.  Data  taken  the  National  Statistical  Service  of  Greece  (NSSG)  and 
FAO.  

Table  6.15.3.1  Red  mullet  in  GSA-22.  Landings  from  Greek  and  Turkish  fleets  in  the 
period  1994-2016.  Data  taken  the  National  Statistical  Service  of  Greece  (NSSG)  and 
FAO.  

582 

582 

 
 
 
 
 
Year 

Landings 
(tonnes) 

1994 

1995 
1996 

1997 

1998 
1999 

2000 
2001 
2002 
2003 

2004 
2005 

2006 

2007 
2008 

2009 

2010 
2011 

2012 

2013 
2014 

2015 

2016 

4422.2 
3414.3 
2769.6 
2705.3 
2163.7 
2338.8 
2166.6 
1933.3 
1740.5 
1463.8 
1781 
2211.5 
2564.7 
2280.2 
2052.1 
2320.7 
2410.6 
1932.8 
1764.2 
1920.1 
1790.1 
1705.119 
1700.134 

Survey index 

(kg/km2) 
7.31 
7.84 
7.43 
11.64 
15.46 
8.96 
24.69 
7.13 
- 
29.08 
17.53 
14.00 
15.14 
- 
13.36 
- 
- 
- 
- 
- 
36.77 
- 
44.59 

Table 6.15.3.2 Parameters of prior distributions for the intrinsic growth rate, r, used in 
the assessment of red mullet in GSA-22.    

Parameter priors 

r 

Distribution 

lognormal 

μ (mean of the log) 

-0.249 

σ (std of the log) 

0.443 

Results 

Figures 6.15.3.2-5 show the output of the assessment. The assessment result shows 
that  the  stock  is  sustainably  exploited  since  1996,  with  increasing  biomass  and 
decreasing  fishing  mortality.  The  uncertainty,  however,  on  both  the  biomass  and 

583 

583 

 
 
 
 
 
 
 
 
fishing mortality estimates is very high (Figure 6.15.3.2). The peak of the production 
curve is not well captured, as it is not included within the catch data.  

Retrospective  analysis  shows  consistent  trends  in  biomass  and  fishing  mortality 
similar,  with  20%  and  30%  maximum  deviation  between  the  different  runs.  In  all 
cases, the biomass is above Bmsy and fishing mortality below Fmsy.    

Figure  6.15.3.3  compares  prior  and  posterior  distribution  for  the  parameters  for 
which priors were set.  

Figure. 6.15.3.2 Red mullet in GSA 22. Stock assessment results for red mullet in GSA-
22.  First  row: median  (blue solid  line) of  relative  biomass  and  relative fishing  mortality 

584 

584 

 
 
 
with  95%  CI  (blue  shaded  area).  Second  row:  Observed  (points)  and  estimated  catch 
(solid line) with 95% CIs (left) and Kobe plot of relative fishing mortality versus relative 
biomass (right). Third row: Production curve (left) and comparison of prior and posterior 
distributions of the n parameter, which determines the shape of the production curve in 
the Pella-Tomlinson model (right). 

Fig.  6.15.3.3  Diagnostic  test  of  the  fit  for  the  residuals  of  the  catch  and  abundance 
index  series  for  the  stock  assessment  of  red  mullet  in  GSA-22.  First  row:  log  of  input 
data  series.  Second  row:  residuals  plot.  Third  row:  autocorrelation  of  residuals  and 
fourth  row:  normality  of  residuals.  If  the  header  is  green  the  test  is  not  significantly 
different from parametric assumptions, otherwise the header is red.  

585 

585 

 
 
 
 
 
Table  6.15.3.3  Red  mullet  in  GSA  22.  Mean  and  and  standard  deviation  of  model 
parameters  and  dynamical  components  for  the  stock  assessment  of  red  mullet  in  GSA-
22.  K:  biomass  carrying  capacity,  r:  intrinsic  growth  rate,  MSY:  Maximum  Sustainable 
Yield,  Bmsy:  biomass  at  MSY,  Fmsy:  fishing  mortality  at  MSY,  B/Bmsy:  relative  biomass  in 
2016, F/Fmsy: relative fishing mortality in 2016   

Parameter   mean 

Sd 

K 

r 

MSY 

10384 

0.709 

2969 

3450 

Bmsy 
Fmsy 
B/Bmsy(2016)  2.17 
F/Fmsy(2016)  0.26 

0.86 

6540 

0.370 

706 

8164 

3.2 

1.44 

0.30 

586 

586 

 
 
 
 
 
 
Figure  6.15.3.4    Red mullet in  GSA 22.  Comparison  of prior  and  posterior distribution 
of parameters for the stock assessment of red mullet in GSA-22. First row: parameter of 
the  Pella-Tomlinson  model  determining  the  skewness  of  the  production  curve,  n,  ratios 
of  observation  to  process  error,  σΙ/σΒ,  and  σC/σF,  respectively.  Second  row:  intrinsic 
growth rate, r. 

587 

587 

 
Figure  6.15.3.5  Red  mullet  in  GSA  22.  Retrospective  plots  of  relative  biomass  and 
relative fishing mortality for the stock assessment of red mullet in GSA-22, produced by 
repeating the stock assessment after excluding 1 (black line) to 5 (cyan line) final year 
observations  of  the  catch  and  abundance  index  time  series.  The  dotted  black  lines  are 
the estimates when the full time series is used.      

Model 2: age-structured model with a4a 

Method 

The Assessment for All Initiative (a4a) (Jardim et al., 2014) was used to assess the 
status of red mullet in GSA-22. 

Input data and parameters 

Catch-at-age estimates were based on the catch-at-length data from the Greek DCF 
for  the  years  2003-2006,  2008,  2014  and  2016.  These  were  raised  to  the 
corresponding  FAO  landings  data  for  GSA-22  for  the  period  2003-2016.  Discards 
were  considered  negligible;  hence,  landings  data  were  considered  as  representing 
the total catch. The MEDITS abundance index by age, expressed in terms of N/km2 
was  used  for  tuning  purposes.  Given  the  lack  of  a  VG  growth  function  with 
reasonable t0 value (i.e. between -0.2 and 0) for the examined stock, conversion of 
length  at  ages  were  based  on  a  generic  age-length  key  proposed  by  Bianchini  and 
Ragonese (2011).  

The  proportion  of  mature  individuals  at-age  was  based  on  Tserpes  et  al.  (2016), 
while the natural mortality at age estimates were taken from the STECF EWG 21-12 
report (JRC scientific and policy reports, 2012) (Table 6.15.3.6). 

588 

588 

 
 
 
 
 
 
 
Table 6.15.3.4 Red mullet  in  GSA-22.  FAO landings for the period 2003-2016 used in 
the a4a assessment. 

year 

Landings 
(tonnes) 

2003 
2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 
2015 
2016 

1463.8 
1781.0 
2211.5 
2564.7 
2280.2 
2052.1 
2320.7 
2410.6 
1932.8 
1764.2 
1920.1 
1790.1 
1705.1 
1700.1 

Table 6.15.3.5 Red mullet in GSA-22. Catch numbers at-age by year. 

Age 

2003 

2004 

2005 

2006 

2008 

2014 

2016 

1 

2 

3 

4 

5649.2 

2115.5 

6070.3 

2937.7 

6043.9 

318.1 

40.7 

12456.8 

5860.7  15211.2  23639.7  25716.2 

7422.1 

363.5 

6158.0 

6589.2  11079.4  10083.3 

4588.6 

7123.7 

6739.0 

1383.4 

3001.4 

2584.4 

3214.8 

1432.8 

3271.4 

2320.6 

5+ 

414.7 

3882.2 

1069.9 

1290.6 

582.2 

3363.3 

5851.0 

Table  6.15.3.6  Red  mullet  GSA  in  22.  Maturity  (proportion  of  mature)  and  natural 
mortality (M) by age. 

Age 

Maturity 

M 

1 

0.72 
0.61 

2 

0.89 
0.54 

3 

0.98 
0.50 

4 

1 
0.50 

5+ 

1 
0.50 

Table 6.15.3.7 Red mullet in GSA-22. MEDITS number (n/km2) at age.  

Age 

2003 

2004 

2005 

2006 

2008 

2014 

2016 

1 

2 

3 

4 

5+ 

169.5 

622.8 

461.8 

142.5 

66.4 

63.5 

182.1 

88.1 

181.3 

293.0 

494.2 

309.1 

621.2 

760.6 

1182.0  1285.8 

831.0 

266.2 

171.2 

756.3 

331.1 

206.5 

344.1 

147.6 

93.2 

9.5 

371.1 

534.3 

337.0 

538.1 

1.2 

18.2 

505.4 

239.0 

936.2 

589 

589 

 
 
 
 
 
 
 
 
Figure 6.15.3.6 Red mullet in GSA-22. Catch at age numbers by year. 

Figure 6.15.3.7 Red mullet in GSA-22. MEDITS index of abundance (N/km2) by age and 
year. 

Results 

Preliminary runs with different assumptions for the f, q and r sub-models were made 
and based on the residual plots, the final model included the following:  

590 

590 

 
 
 
 
 
 
 
 
f <- ~s(replace(age, age>4, 4), k=3) + s(year, k=4) 

q <- list(~factor(age))  

r <- ~s(year, k=3)  

The  assessment  shows  that  fishing  mortality  follows  a  declining  trend  after  2010, 
while  the  spawning  stock  biomass  is  increasing  (Fig  6.15.3.8).  Fishing  mortality  is 
generally increasing with age (Figure 6.15.3.9). 

Figures  6.15.3.10-11  illustrate  the  estimated  catch  and  stock  numbers  at-age 
respectively.  Retrospective  analysis  did  not  show  any  particular  pattern  (Figure 
6.15.3.17),  but  there  are  downward  revisions  in  F  and  upward  revisions  in  SSB. 
Terminal values support the view that F  is low, but cannot be well estimated, most 
likely due to the sparse data in recent years. 

Figure 6.15.3.8 Red mullet in  GSA-22.  From top  to bottom: Recruitment,  SSB  (Stock 
Spawning Biomass), catch and fishing mortality estimates from the final a4a run. 

591 

591 

 
 
 
 
 
 
 
 
Figure 6.15.3.9 Red mullet in GSA-22. Estimated fishing mortality by age and year. 

Figure 6.15.3.10 Red mullet in GSA-22. Estimated catch by age and year. 

592 

592 

 
 
 
 
Figure 6.15.3.11 Red mullet in GSA-22. Estimated numbers of individuals by age and 
year. 

Residual plots of the catch and abundance indices are shown in Figures 6.15.3.12-13 
and  .  The  observed  patterns,  particularly  regarding  the  total  catch,  indicate  poor 
model fit.  

593 

593 

 
 
 
 
 
 
Figure  6.15.3.12  Red  mullet  in  GSA-22.  Log  residuals  for  abundance  indices  and  for 
catch numbers. Each panel is coded by age class, dots represent standardized residuals 
and lines a simple smoother. 

Figure  6.15.3.13  Red  mullet  in  GSA-22.  Quantile-quantile  plot  of  log  residuals  for 
abundance  indices  and  for  catch  numbers.  Each  panel  is  coded  by  age  class,  dots 
represent standardized residuals and lines the normal distribution quantiles. 

Figure  6.15.3.14  indicate  large  discrepancies  between  fitted  and  observed  catch 
numbers  at  age  by  year.  Such  discrepancies  are  smaller  in  the  case  of  the  tuning 
index (Figure 6.15.3.15). 

594 

594 

 
 
 
 
 
  
 
Figure 6.15.3.14 Red mullet in GSA-22. Fitted and observed catch at age. 

Figure 6.15.3.15 Red mullet in GSA-22. Fitted and observed index at age. 

595 

595 

 
 
 
 
 
Figure  6.15.3.16  Red  mullet  in  GSA-22.  Retrospective  analysis  showing  the  model 
output when removing 0 to 3 last years of data 

6.15.4  REFERENCE POINTS 

The SPiCT model provides estimates of B and F relative to MSY allowing estimates to 
be  considered  relative  to  reference  values,  however,  the  values  of  FMSY  and  BMSY 
themselves  are  uncertain,  and  given  the  retrospective  variation  are  not  considered 
sufficiently reliable to be taken as fixed refence points. 

6.15.5  SHORT TERM FORECAST AND CATCH OPTIONS 

Given the poor model fit no forecast runs are presented. 

6.15.6  DATA DEFICIENCIES 

Data on length distribution by age are available only for the years that the DCF took 
place, i.e. 2003-2005, 2008, 2014, 2016 and partly for 2013 and 2015. The data on 
relative  biomass  from  the  MEDITS  survey  also  have  gaps  for  the  years  the  survey 
didn’t  take  place,  i.e.  2002,  2007,  2009-2013,  2015.  As  a  consequence,  the  years 
with missing data were even more than those with full data-sets. This has resulted in 
poor model fit, which is particularly reflected in the case of the total catch residuals 
and  when  comparing  predicted  with  actual  catches.  For  the  surplus  production 
model, FAO data were used because DCF landings data are missing for most of the 
years (i.e. in 1994-2003, 2006-2007, 2009-2012).  For the years DCF landings data 
were  available,  these  where  considerably  different  from  FAO  data,  especially  in  the 
period  2003-2005.  For  the  rest  of  the  years  (2006,  2008,  2014  and  2016)  the 
deviation is less than 20%. Note that FAO landing rates are not necessarily similar to 
those obtained from DCF, as they are based on estimates of the National Statistical 
Service of Greece deriving from a different sampling scheme than the one followed in 

596 

596 

 
 
 
  
DCF. For DCF data to be useful in assessments (age based or biomass production), 
longer time series with full sampling within each year are needed. 

6.16  DEEP-WATER ROSE SHRIMP IN GSA 22 

6.16.1  BIOLOGY 

Figure 6.16.1 shows the location of GSAs 22 and 23 in the Mediterranean. GSAs 22 
and  23  have  been  considered  as  distinct  areas  for  management  purposes  due  to 
their specific geo-physical characteristics; the Cretan Sea is deep (2500m) and large 
in volume, generally an oligotrophic basin (Psarra et al., 1996; Lykousis et al., 2002). 
In  addition,  fishery  exploitation  patterns  differ  between  the  two  nearby  areas,  with 
the trawling activities being much less intense in GSA-23 (Anonymous, 2013).  

Figure  6.16.1.  Geographical  location  of  GSAs  22  and  23.  The  assessment  was 
performed for GSA-22. 

For  the  production  model  needs,  life-history  parameters  found  in  literature  were 
used to estimate  the  prior of the intrinsic growth rate, r, using  Kreb’s demographic 
method (McAllister et al. 2001) 

6.16.2  INPUT DATA 

Deep-water  rose  shrimp  is  exploited  almost  exclusively  by  bottom  trawlers.  The 
Greek  bottom  trawl  fishery  has  multi-species  characteristics  and  similarly  to  most 
Mediterranean demersal trawl fisheries, captures more than 100 commercial species. 
However, few species, such as red mullets, hake and shrimps compose the main bulk 
of landings. 

All  demersal  fisheries  in  GSA-22  are  managed  according  to  the  EU  regulation 
1967/2006  which  foresees  spatial  fishery  closures  for  the  bottom  trawlers,  gear 
configuration  specifications  and  minimum  catching  sizes.  Additional  national 

597 

597 

 
 
 
 
 
measures  include  a  temporal  (4.5  months)  closure  of  the  bottom  trawl  fisheries 
accompanied by certain localized spatio-temporal closures 

6.16.2.1  CATCH (LANDINGS AND DISCARDS)  

Landings  and  discards  data  were  reported  to  STECF  EWG  17-15  through  the  DCF 
(Tables  6.16.2.1.1-2).  These  are  available  only  for  the  years  the  DCF  took  place  in 
Greece (2003-2006, 2008, 2014, 2016 and partly for 2013 and 2015).  

Table  6.16.2.1.1  Deep-water  rose  shrimp  GSA-22.  Landings  and  discards  per  year 
provided to STECF EWG 17-15 through the DCF.  

Year  Landings (tonnes)  Discards (tonnes) 

2003 
2004 
2005 
2006 
2008 
2013 
2014 
2015 
2016 

866.69 
3258.09 
3925.94 
4052.56 
3745.47 
544.23 
2221.03 
947.53 
2945.95 

53.36 
664.96 
163.58 
350.00 
762.80 
67.26 
143.32 
61.37 
0.07 

Table 6.16.2.1.2 Deep-water rose shrimp GSA-22. Landings and discards per year and 
fishing gear provided to STECF EWG 17-15 through the DCF.  

Year 

2003 
2004 
2005 
2006 
2008 
2013 
2014 
2014 
2014 
2015 
2015 
2016 
2016 

Fishing gear  Landings (tonnes)  Discards (tonnes) 

OTB 
OTB 
OTB 
OTB 
OTB 
OTB 
GNS 
GTR 
OTB 
GNS 
OTB 
GNS 
OTB 

866.688 
3258.08702 
3925.93799 
4052.55901 
3745.47397 
544.2297 
5.03945 
6.8253 
2209.16909 
1.16422 
946.36199 
1.85 
2944.1 

53.36295 
664.95576 
163.58074 
350.00379 
762.79994 
67.26434 
0.05294 
- 
143.26312 
- 
61.37095 
0.069 
- 

6.16.2.2  EFFORT 

Effort data reported to STECF EWG 17-15 through the DCF (Tables 6.16.2.2.1-2). 

598 

598 

 
 
 
 
 
Table 6.16.2.2.1 Effort in GT*days_at_sea and days_at_sea, per year in GSA-22. 

year  GT*days_at_sea  days_at_sea 

2003 
2004 
2005 
2006 
2008 
2013 
2014 
2015 
2016 

4927349 
4972085 
5553804 
5556446 
5355704 
2006599.5 
6770238 
4923984 
12165474 

- 
- 
- 
- 
- 
- 
1026097 
313563 
978160 

Table  6.16.2.2.2  Effort  in  GT*days_at_sea  and  days_at_sea,  per  year  and  gear  in 
GSA-22. 

Year 

2003 
2004 
2005 
2006 
2008 
2013 
2013 
2013 
2014 
2014 
2014 
2015 
2015 
2015 
2016 
2016 
2016 

Gear 
OTB 
OTB 
OTB 
OTB 
OTB 
GNS 
GTR 
OTB 
GNS 
GTR 
OTB 
GNS 
GTR 
OTB 
GNS 
GTR 
OTB 

GT*days_at_sea 
4927349 
4972085 
5553804 
5556446 
5355704 
122828.6 
353708.9 
1530062 
1119222 
1450864 
4200152 
365054 
456691 
4102239 
3193652 
4692168 
4279654 

Days at sea 
- 
- 
- 
- 
- 
- 
- 
- 
385442 
601502 
39153 
115020 
160781 
37762 
415065 
523530 
39565 

6.16.2.3  SURVEY DATA 

The  “MEDITS”  bottom  trawl  surveys  are  accomplished  in  GSA-22  since  1994  on  an 
annual  basis.  However,  no  surveys  were  accomplished  in  2002,  2007,  2009-2013 
and  in  2015.  Sampling  includes  sampling  in  129  pre-defined  stations  following  a 

599 

599 

 
 
 
 
 
 
 
standardized  protocol.  Trawling  was  made  by  means  of  a  standard  net  GOC  73 
having a cod-end mesh opening of 20 mm and selection of stations was based on a 
depth-stratified  sampling  scheme  that  included  five  depth  zones:  10-50,  50-100, 
100-200,  200-500  and  500-800  m.  Collected  data  included  number,  weight,  gonad 
maturation  stage  and  total  length  measurements  for  a  wide  range  of  fish, 
cephalopod  and  crustacean  species  (MEDITS  Handbook  v9,  2017).  From  the 
collected  data,  standardized  abundance  indexes  by  year,  expressed  in  terms  of  kg 
per square km of swept area (kg/km2), were calculated for deep-water rose shrimp 
(Table 6.16.2.3.1).  

Table  6.16.2.3.1  Deep-water  rose  shrimp  in  GSA-22.  Abundance  index  from  the 
MEDITS trawl survey  

Year  Abundance index (kg/km2) 
1994 
1995 

1.55 
2.99 

1996 
1997 

1998 

1999 
2000 

2001 

2003 
2004 

2005 

2006 
2008 

2014 

2016 

13.87 
16.08 

10.60 

15.60 
13.18 

12.19 

12.74 
14.75 

13.52 

13.02 
9.11 

10.51 

18.32 

6.16.3  STOCK ASSESSMENT 

Method 

The  assessment  is  based  on  a  state-space  surplus  production  model  implemented 
with  the SPiCT  package (Pedersen and  Berg,  2017) in R  environment (R  core team 
2017). 

Input data and parameters 

The  assessment  is  based  on  the  time-series  of  FAO  landings  for  the  period  1986-
2016  and  the  time  series  of  abundance  indices  from  the  MEDITS  trawl  surveys 
(Table 6.16.3. 1, Figure 6.16.3.1).  

Regarding  landings  time-series,  there  are  significant  gaps  in  the  DCF  data, 
particularly,  in  the  recent  years,  due  to  non-implementation  of  the  project.  More 
specifically,  the  values  are  missing  for  the  years  2007,  2009  –  2012,  while  for  the 
years 2013, 2015 landings data were only reported for the last quarter of the year, 
and  thus  could  not  be  included  in  the  assessment.  The  same  gaps  exist  in  the 
datasets concerning discards, effort, catch-at-length and MEDITS trawl surveys.  

600 

600 

 
 
 
 
On  the  other  hand,  FAO  landings  data  for  the  Aegean  Sea  (GSAs  22  and  23 
combined) are available for a longer time period. Landings by GSA are collected by 
the  National  Statistical  Service  of  Greece  (NSSG),  which  then  provides  the  data  to 
FAO. These were disaggregated by GSA using the average ratio of landings between 
GSA  22  and  23  that  are  available  from  the  NSSG.  The  two  years  missing  from  the 
FAO data, i.e. 2015 and 2016, were taken from the NSSG time series. Landings rates 
appearing in FAO are not necessarily similar to those obtained from DCF, as they are 
based  on  estimates  of  the  NSSG,  which  are  derived  from  a  different  sampling 
scheme than the one followed in DCF.  

Discards for deep-water rose shrimp are considered negligible and only landings data 
are used for the biomass production assessment.  

The  abundance  index  time  series  derived  from  the  MEDITS  surveys  also  has  a 
number of missing values, similar to the ones described above for the landings data.  

The numbers and weights –at- age suffered from internal inconsistencies (step size, 
initial value, unit of measurement varying among years). 

Discards  for  deep-water  rose  shrimp  are  considered  negligible,  given  that  the  main 
bulk  of  deep-water  rose  shrimp  discards  refer  to  undersized    individuals  and  the 
discard  ratio  in  terms  of  biomass  is  in  most  cases  <10%.  Thus,  only  landings  data 
are used for the biomass production assessment.  

Initially  SPiCT  was  run  with  default  parameters  but  convergence  was  not  achieved. 
To facilitate convergence a prior for the growth rate was used and the parameter n 
of  the  Pella-Tomlinson  surplus  production  was  fixed  to  2  (Schaefer  production 
curve).  The  growth  rate  was  estimated  from  independent  data  on  life  history 
parameters  (i.e.,  fecundity  by  age,  mortality  by  age,  natural  mortality  and  growth) 
using Kreb’s demographic method (McAllister et al., 2001). To assess the sensitivity 
of the model to the first years of the survey index which are considerably lower than 
the  rest  of  the  records  and  their  reliability  was  doubted,  the  assessment  was 
repeated after removing these two records and using the same parameterization as 
before.  Table  6.16.3.2  gives  the  informative  prior  distributions  used  in  the  final 
assessment. 

601 

601 

 
Figure  6.16.3.1  Deep-water  rose  shrimp  in  GSA  22.  Times  series  of  landings  and 
MEDITS survey index used in the SPiCT assessment of deep-water rose shrimp in GSA-
23.  

Table 6.16.3.1 Deep-water rose shrimp in GSA 22. Landings and abundance index for 
Deep-water rose Shrimp in GSA 22 in the period 1986-2016. Landings data taken from 
FAO for the Aegean  Sea (GSA 22 and 23) and disaggregated by GSA using the ratio of 
landings in the two GSAs available from the Greek Statistical Service of Greece (NSSG).  

Year 

1986 

1987 

1988 
1989 

1990 

1991 
1992 

1993 
1994 
1995 
1996 

Landings 
(tonnes) 

966.8 
1261.1 

1131.7 

1025.9 
1084.0 

952.1 

1524.9 
1156.9 
1071.9 
765.5 
1028.5 

602 

602 

Abundance 
index 

(kg/km2) 
- 

- 

- 
- 

- 

- 
- 
- 

1.55 
2.99 
13.87 

 
 
 
 
 
1997 

1998 
1999 

2000 
2001 
2002 

2003 
2004 
2005 
2006 

2007 
2008 
2009 

2010 
2011 

2012 

2013 
2014 

2015 

2016 

1394.7 

1015.4 

1017.2 
887.0 

674.4 

763.8 
679.6 

883.5 

1046.7 
780.2 

815.8 

775.0 
574.3 

599.0 
720.3 
772.9 
836.0 

696.5 
746.4 

1778.6 

16.08 

10.60 

15.60 
13.18 

12.19 
- 

12.74 

14.75 

13.52 
13.02 
- 
9.11 

- 
- 
- 

- 
- 

10.51 

- 
18.32 

Table 6.16.3.2 Deep-water rose shrimp in GSA 22. Parameters of prior distribution for 
the growth rate (r) used in the assessment of deep-water rose shrimp in GSA 22.    

Parameter priors 

r 

Distribution 

lognormal 

μ (mean of the log) 

0.56 

σ (std of the log) 

0.40 

Results 

Figures  6.16.3.2-5  show  the  output  of  the  assessment  with  a  prior  for  the  growth 
rate and a Schaefer production model. Figures 6.16.3.6-9 show the results when the 
survey index records for 1994-95 are excluded from the assessment. In both cases 
catch resilduals  do not  conform  to  error distribution assumptions in the model, and 
in the case where the full survey timeseries is included nor to the survey residuals.  

Retrospective analysis shows that the exclusion of the end year of survey index and 
landings  has  a  great  effect  on  the output.  After  removing  the  end  year,  the  rest  of 
the retrospective results are consistent and have very similar trends.    

Figure 6.16.1.2.3.4 compares prior and posterior distribution for the parameters for 
which priors were set.  

The  assessment  result  with  all  data  included  shows  that  for  the  period  1986-1995 
the  shrimp  stock  was  overfished.  Currently  the  stock  is  fished  below  Fmsy  and  the 
biomass is above  Bmsy  (Figure 6.16.3.2).  By removing the survey index from 1994-
95  the  assessment  result  shows  that  the  stock  has  been  underfished  and  at  a 
healthy  biomass  state  throughout  the  period  1986-1995.  Retrospective  plots  show 

603 

603 

 
 
 
 
consistent trends and small deviation, except for the relative biomass evolution when 
the  full  time  series  is  considered,  which  shows  great  fluctuations  and  an  increasing 
trend towards the recent years. This last assessment is considered unreliable as both 
the landings and survey index time series lack sufficient contrast. 

In  conclusion,  the  effect  of  the  two  first  records  of  abundance  index  have  a  great 
effect  on  the  assessment  output  both  in  terms  of  historic  trends  and  the  current 
levels  of  biomass  and  fishing  mortality,  although  in  both  cases  relative  biomass  is 
above  Bmsy  and  fishing  mortality  is  below  Fmsy.  The  assumptions  of  the  model  are 
violated  for  both  catch  and  survey  residuals.  For  these  reasons  it  was  decided  that 
the current situation this stock is uncertain and no status, reference points or advice 
are given.         

Figure  6.16.3.2  Deep-water  rose  shrimp  in  GSA  22.  Stock  assessment  results  for 
P.longirostris  in  GSA-22.  Upper  row:  Median  (blue  solid  line)  of  relative  biomass  and 
relative  fishing  mortality  with  95%  CI  (blue  shaded  area).  Middle  row:  Observed  (blue 
points)  and  estimated  catch  with  95%  CIs  (left)  and  Kobe  plot  of  relative  fishing 
mortality versus relative biomass (right). Bottom row: Schaefer production curve (black 
line) and predicted surplus production (blue line with dots). 

604 

604 

 
 
 
Figure 6.16.13.3 Deep-water rose shrimp in GSA 22. Diagnostic test of the fit for the 
residuals  of  the  catch  and  abundance  index  series.  Log  of  input  data  series  (first  row), 
residuals  plot  (second  row),  autocorrelation  of  residuals  (third  row)  and  normality  of 
residuals  (fourth  row).  If  the  header  is  green  the  test  is  not  significantly  different  from 
parametric  assumptions,  otherwise  where  the  distribution  is  significantly  different  the 
header is red (ie. for both survey and catch residuals).  

605 

605 

 
 
 
 
 
 
Figure 6.16.3.4 Deep-water rose shrimp in GSA 22. Comparison of prior and posterior 
distribution  of  parameters.  Ratios  of  observation  to  process  error  (σΙ/σΒ),  (σC/σF),  and 
intrinsic growth rate (r)  

Figure  6.16.3.5  Deep-water  rose  shrimp  in  GSA  22.  Retrospective  plots  of  relative 
biomass and relative fishing mortality for P.longirostris, produced by repeating the stock 
assessment after excluding 1 (black line) to 5 (cyan line) final year observations of the 
catch  and  abundance  index  time  series.  The  dotted  black  lines  are  the  estimates  when 
the full time series is considered.     

606 

606 

 
 
 
 
 
 
 
 
 
 
 
Assessment results after excluding 1994-95 survey index  

Figure  6.16.3.6  Deep-water  rose  shrimp  in  GSA  22.  Stock  assessment  results  for 
P.longirostris  in  GSA-22  after  excluding  survey  index  records  in  1994-95.  Upper  row: 
Median  (blue  solid  line)  of  relative  biomass  and  relative  fishing  mortality  with  95%  CI 
(blue  shaded  area).  Middle  row:  Observed  (blue  points)  and  estimated  catch  with  95% 
CIs  (left)  and  Kobe  plot  of  relative  fishing  mortality  versus  relative  biomass  (right). 
Bottom  row:  Production  curve  (left)  and  comparison  of  prior  and  posterior  distributions 
of  the  n  parameter,  which  determines  the  shape  of  the  production  curve  in  the  Pella-
Tomlinson model (right). 

607 

607 

 
 
 
Figure  6.16.3.7  Deep-water  rose  shrimp  in  GSA  22.  Diagnostic  test  of  the  fit  for  the 
residuals  of  the  catch  and  abundance  index  series.  Log  of  input  data  series  (first  row), 
residuals  plot  (second  row),  autocorrelation  of  residuals  (third  row)  and  normality  of 
residuals  (fourth  row).  If  the  header  is  green  the  test  is  not  significantly  different  from 
parametric assumptions, otherwise the header is red.  

608 

608 

 
 
 
Figure 6.16.3.8 Deep-water rose shrimp in GSA 22. Comparison of prior and posterior 
distribution  of  parameters.  Ratios  of  observation  to  process  error  (alpha=σΙ/σΒ), 
(beta=σC/σF) and  intrinsic growth rate (r)  

Figure  6.16.3.9  Deep-water  rose  shrimp  in  GSA  22.  Retrospective  plots  of  relative 
biomass and relative fishing mortality for P.longirostris, produced by repeating the stock 
assessment after excluding 1 (black line) to 5 (cyan line) final year observations of the 
catch  and  abundance  index  time  series.  The  dotted  black  lines  are  the  estimates  when 
the full time series is considered.     

6.16.4  REFERENCE POINTS 

Not estimated due to the very high uncertainty of parameter estimates 

6.16.5  SHORT TERM FORECAST AND CATCH OPTIONS 

No analysis were carried out during the meeting 

6.16.6  DATA DEFICIENCIES 

Data  on  landings  and  on  size  composition  of  the  catch  are  available  only  for  the  years 
when the DCF took place, i.e. 2003-2005, 2008, 2014, 2016 and partly (referring only to 

609 

609 

 
 
 
the last quarter of the year) for 2013 and 2015. Also, the available catch-at- length data 
suffered  from  internal  inconsistencies  (step  size,  initial  value,  unit  of  measurement 
varying across years). 

The data on relative biomass from the MEDITS survey also have gaps for the years the 
survey didn’t take place, i.e. 2002, 2007, 2009-2013, 2015.  For the above reasons, an 
analytical age-based assessment was not attempted.  

Due to the significant gaps in the DCF landings data, especially in the recent years, due 
to the non-implementation of the project, as described above, the data provided by FAO 
were  used  in  the  assessment.  In  addition,  the  FAO  landings  are  available  for  a  longer 
time  period  than  DCF.  Landings  rates  appearing  in  FAO  are  not  necessarily  similar  to 
those obtained from DCF, as they are based on estimates of the Greek Statistical service 
of  Greece  (NSSG),  which  are  based  on  a  different  sampling  scheme  than  the  one 
followed in DCF. 

6.17  HAKE IN GSA 23 

6.17.1  BIOLOGY 

Figure  6.1.1  shows  the  location  of  GSAs  22  and  23  in  the  Mediterenean.  GSAs  22 
and  23  have  been  considered  as  distinct  areas  for  management  purposes  due  to 
their  specific  geo-physical  characteristics;  the  Cretan  Sea  is  a  deep  (2500m)  and 
large in volume and generally oligotrophic basin (Psarra et al., 1996; Lykousis et al., 
2002). In addition, fishery exploitation patterns differ between the two nearby areas, 
with the trawling activities being much less intense in GSA-23 (Anonymous, 2013).  

Figure 6.17.1. Geographical location of GSAs 22 and 23. The assessment was done for 
GSA-23. 

Growth parameters for the Aegean hake stock are found in 2016 DCF report (Linf= 
106.5, k= 0.143; to= -0.54, sex combined). These parameters, together with 

610 

610 

 
 
 
 
 
literature information were used to estimate the intrinsic growth rate, r, of the hake 
stock, using Krebs demographic method (McAllister et al. 2001).  

6.17.2  INPUT DATA 

The vast majority of European hake landings come from the Greek fleets. The stock 
is exploited by bottom trawlers and various artisanal fisheries using gillnets and 
demersal longilnes. It is mainly exploited by four fleet segments but the majority of 
landings (~60%) is coming from bottom trawlers (Anonymous, 2013). The Greek 
bottom trawl fishery has multi-species characteristics and similarly to most 
Mediterranean demersal trawl fisheries, captures more than 100 commercial species. 
However, hake together with red mullets, and shrimps compose the main bulk of 
landings.  

As with all demersal fisheries in GSA-23, those catching hake are managed according 
to  EU  regulation  1967/2006,  which  foresees  spatial  fishery  closures  for  the  bottom 
trawlers,  gear  configuration  specifications  and  minimum  catching  sizes.  Additional 
national  measures  include  a  temporal  (4.5  months)  closure  of  the  bottom  trawl 
fisheries  accompanied  by  certain  localized  spatio-temporal  closures  and  an  one-
month (February) closure of the artisanal fisheries targeting hake. 

6.17.2.1  CATCH (LANDINGS AND DISCARDS)  

Landings  and  discards  data  were  reported  to  STECF  EWG  17-15  through  the  DCF 
(Tables  6.17.2.1.1-2  and  Figures  6.17.2.1.1-2).  These  are  available  only  for  the 
years  (2014  and  2016  and  partly  for  2013  and  2015).  Due  to  the  limited  data,  an 
age-based  assessment  was  not  attempted  and  the  assessment  was  based  on  a 
surplus production model based on landings time series from FAO.  

Table 6.17.2.1.1 Hake GSA-23. Landings and discards per year provided to STECF EWG 
17-15 through the DCF. Data for 2013 and 2015 correspond to last trimester of the year 
only. Data for 2013 and 2015 correspond to last trimester of the year only. 

Year 

Landings 
(tonnes) 

Discards 
(tonnes) 

2013 
2014 
2015 
2016 

14.07 
56.80 
26.50 
51.19 

2.82 
12.90 
5.49 
4.55 

Table  6.17.2.1.2  Hake  GSA-23.  Landings  and  discards  per  year  and  fishing  gear 
provided to STECF EWG 17-15 through the DCF. Data for 2013 and 2015 correspond to 
last trimester of the year only. 

611 

611 

 
 
 
 
 
 
 
 
Year 

Fishing gear 

2013 
2014 
2014 
2014 
2015 
2015 
2016 
2016 
2016 

OTB 
GTR 
LLS 
OTB 
LLS 
OTB 
GNS 
LLS 
OTB 

Landings 
(tonnes) 
14.07 
2.66 
0.57 
53.57 
3.68 
22.82 
3.62 
0.39 
47.18 

Discards 
(tonnes) 
2.82 
- 
0.02 
12.89 
- 
5.49 
0.32 
- 
4.23 

Figure 6.17.2.1.1 Hake GSA-23. Landings (in numbers) by year, gear and length class 
provided to STECF EWG 17-15 through the DCF. Data for 2013 and 2015 correspond to 
last trimester of the year only.  

612 

612 

 
 
 
 
Figure 6.17.2.1.2 Hake GSA-23. Discards (in numbers) by year, gear and length class 
provided to STECF EWG 17-15 through the DCF. Data for 2013 and 2015 correspond to 
last trimester of the year only. 

The main bulk of discards concerns undersized individuals. Discards vary, depending on 
the  gear,  area  and  season  and  according  to  a  recent  report  of  the  Mediterranean 
Advisory  Committee  (MEDAC,  Ref  190/2016)  they  are,  around  5-8%,  in  terms  of 
biomass, for the bottom trawlers. 

6.17.2.2  EFFORT 

Effort data reported to STECF EWG 17-15 through the DCF (Tables 6.1.2.2.1-2). 

Table 6.17.2.2.1 Effort in GT*days_at_sea and days at sea, per year in GSA-23. 

year  GT*days_at_sea  days_at_sea 

2013 
2014 
2015 
2016 

81734.78 
409313 
355699 
1429794 

613 

613 

- 
70212 
25391 
81740 

 
 
 
 
 
 
 
 
Table  6.17.2.2.2  Effort  in  GT*days_at_sea  and  days  at  sea,  per  year  and  gear  in 
GSA-23. 

Year 

2013 
2013 
2013 
2013 
2013 
2013 
2014 
2014 
2014 
2014 
2014 
2015 
2015 
2015 
2015 
2015 
2016 
2016 
2016 
2016 
2016 

Gear 
FPO 
GNS 
GTR 
LLS 
OTB 
PS 
GNS 
GTR 
LLS 
OTB 
PS 
GNS 
GTR 
LLS 
OTB 
PS 
GNS 
GTR 
LLS 
OTB 
PS 

GT*days_at_sea  Days at sea 

0 
206.9 
11033.59 
6565.35 
57827.58 
6101.36 
33095 
132772 
31702 
177264 
34480 
1349 
47304 
61564 
193976 
51506 
159724 
591774 
422533 
201177 
54586 

- 
- 
- 
- 
- 
- 
11922 
42120 
14176 
1498 
496 
863 
11527 
10363 
1581 
1057 
5426 
42731 
30804 
1637 
1142 

6.17.2.3  SURVEY DATA 

The  “MEDITS”  bottom  trawl  surveys  are  accomplished  in  GSA-23  since 
1994  on  an  annual  basis.  However,  no  surveys  were  accomplished  in 
2003,  2009-2013  and  in  2015.  Sampling  includes  sampling  in  20  pre-
defined stations following a standardized protocol. Trawling was made by 
means  of  a  standard  net  GOC  73  having  a  cod-end  mesh  opening  of  20 
mm  and  selection  of  stations  was  based  on  a  depth-stratified  sampling 
scheme  that  included  five  depth  zones:  10-50,  50-100,  100-200,  200-
500  and  500-800  m.  Collected  data  included  number,  weight,  gonad 
maturation stage and total length measurements for a wide range of fish, 
cephalopod  and  crustacean  species  (MEDITS  Handbook  v9,  2017).  From 
the collected data, standardized abundance indexes by year, expressed in 
terms  of  kg  per  square  km  of  swept  area  (kg/km2),  were  calculated  for 
hake (Table 6.17.2.3.1).  

614 

614 

 
 
 
 
 
 
 
 
Table 6.17.2.3.1 Hake in  GSA 23. Survey data from the MEDITS trawl survey  

Year 

1994 
1995 

1996 

1997 
1998 

1999 

2000 
2001 

2003 
2004 
2005 
2006 

2008 
2014 
2016 

Abundance 
index 
(kg/km2) 
56.74 
25.11 
21.04 
3.79 
11.08 
7.10 
9.55 
3.76 
49.53 
33.10 
11.88 
37.60 
65.18 
3.60 
6.19 

6.17.3  STOCK ASSESSMENT 

Method 

Hake  in  GSA-23  has  never  been  assessed  before.  The  current  assessment  is  based 
on  a  state-space  surplus  production  model  implemented  with  the  SPiCT  package 
(Pedersen and Berg, 2017) under the R-language environment (R core team 2017). 

Input data and parameters 

The  assessment  was  based  on  landings  data  taken  from  FAO  for  the  period  1970-
1989  and  from  the  National  Statistical  Service  of  Greece  (NSSG)  for  the  period 
1990-2016.  Note  that  the  NNSG  collects  and  reports  data  to  FAO.  A  time  series  of 
abundance indices were taken from the MEDITS trawl surveys. The abundance index 
time  series  has  a  number  of  missing  values,  i.e.  years  when  the  sampling  did  not 
take  place  (Table  6.17.3.1  and  Figure  6.17.3.1).  The  NSSG  data  (1990-2016)  are 
available  separately  for  GSAs  22  and  23.  The  FAO  records  (1970-1989),however, 
combine GSAs 22 and 23. For these years the FAO landings were disaggregated by 
GSA using the average ratio of landings between GSAs 22 and 23 that are available 
from  NSSG  for  the  period  1990-2016  (http://www.statistics.gr/en/home/).  The 
average  ratio  (landings_gsa23/landings_gsa22)  was  estimated  to  0.06  with  a 
standard  deviation  of  0.03.  By  comparing  the  actual  records  with  those  estimated 
using the average ratio (for data between 1990 and 2016), the average deviation is 
58%. This means that the estimated landings based on this split for GSA 23 before 
1990  are  quite  uncertain;  however  this  is  the  only  available  information  that  could 
be  used  to  disaggregate  the  data  by  GSA  before  1990.    Discards  for  hake  are 

615 

615 

 
 
 
 
 
considered  negligible  and  only  landings  data  are  used  for  the  biomass  production 
assessment.  

Initially  SPiCT  was  run  with  default  parameters  but  convergence  was  not  achieved. 
To  facilitate  convergence  a  prior  for  the  intrinsic  growth  rate  was  used.  This  was 
estimated from data on life history parameters (i.e., feccundity by age, mortality by 
age,  natural  mortality  and  growth)  using  Krebs  demographic  method  (McAllister  et 
al., 2001). Still, the model with  prior distribution for r did not converge. Covergence 
was achieved using a prior on the initial relative biomass (B/Bmsy) in 1970 and fixing 
the  surplus  production  function  to  the  Schaefer  symmetric  form  (i.e.  n=2  in  the 
Pella-Tomlinson  model).  To  asses  the  sensitivity  of  the  model  output  to  this  prior, 
the  assessment  was  repeated  with  three  different  prior  distributions  for  B/Bmsy  in 
1970  with  means  1,  1.5  and  2  respectively  and  coefficient  of  variation  0.33  in  all 
cases. The outcome in terms of relative fishing mortality and biomass in the last year 
is not sensitive to this prior (0.5% and 12% maximum deviation respectively). Table 
6.17.3.2 gives the informative prior distributions used in the final assessment. 

Table 6.17.3.1 Hake in  GSA 23. Landings and survey index for hake in GSA-23 in the 
period  1970-2016.  Landings  data  taken  from  the  NSSG  for  the  period  1990-2016  and 
from  FAO  for  the  Aegean  sea  (GSA  22  and  23)  for  the  period  1970-1989  and 
disaggregated by GSA using the average ratio of landings in the two GSAs available from 
the  Greek  Statistical  Service  of  Greece  (NSSG)  (http://www.statistics.gr/en/home/). 
Survey index from MEDITS surveys. 

Year 

Landings 
(tonnes) 

1970 

1971 
1972 

1973 

1974 
1975 

1976 
1977 
1978 
1979 

1980 
1981 

1982 

1983 
1984 

1985 

1986 
1987 

1988 

1989 
1990 
1991 
1992 

76.24 
64.66 
78.85 
116.34 

113.22 
100.62 

115.32 

119.07 
127.47 

109.78 

111.88 
107.23 

119.84 

136.76 
127.85 

176.35 

230.19 
181.31 

218.61 

240.24 
190.30 
222.40 
243.40 

616 

616 

Abundance 
index 

(kg/km2) 
- 
- 
- 

- 
- 

- 

- 
- 

- 

- 
- 

- 

- 
- 

- 

- 
- 

- 

- 
- 
- 
- 
- 

 
 
1993 

1994 
1995 

1996 
1997 
1998 

1999 
2000 
2001 
2002 

2003 
2004 
2005 

2006 
2007 

2008 

2009 
2010 

2011 

2012 
2013 

2014 

2015 
2016 

519.40 

334.40 

313.80 
336.70 

380.50 

276.70 
126.90 

91.90 

134.40 
68.70 

95.20 

115.50 
120.70 

174.10 
201.40 
206.70 
193.50 

152.00 
95.90 

80.70 

88.60 
69.10 

87.20 

100.90 

- 

56.74 

25.11 
21.04 

3.79 

11.08 
7.10 

9.55 

3.76 
- 

49.53 

33.10 
11.88 

37.60 
- 
65.18 
- 

- 
- 

- 

- 
3.60 

- 

6.19 

617 

617 

 
 
 
Figure 6.17.3.1 Hake in  GSA 23. Times series of landings from FAO and NSSG (upper 
panel) and MEDITS survey index (lower panel) used in the SPiCT assessment of hake in 
GSA-23.  

Table  6.17.3.2  Hake  in    GSA  23.  Parameters  of  prior  distributions  for  the  intrinsic 
growth  rate,  r,  and  initial  biomass  (B/Bmsy  in  1970)  used  in  the  assessment  of  hake  in 
GSA-23.    

Parameter priors 

R 

B/Bmsy (1970) 

Distribution 

lognormal 

lognormal 

μ (mean of the log) 

-0.380 

σ (std of the log) 

0.489 

0.35 

0.32 

Results 

Figures  6.17.3.2-5  show  the  output  of  the  assessment  with  a  prior  for  the  intrinsic 
growth rate and an assumption for the initial biomass in 1970 (prior with mean 1.5).  

The  assessment  results  show  that  for  the  period  1970-1995,  the  hake  stock  was 
fished  in  a  sustainable  way  but  with  increasing  fishing  mortality.  (Figure  6.17.3.1). 
The  current  biomass  and  fishing  mortality  are  below  Bmsy  and  above  Fmsy  estimates 
respectively,  although  the  uncertainty  around  those  estimates  is  quite  high, 
especially for the relative fishing mortality (Figure 6.17.3.1 and Table 6.17.3.1). The 
survey index is largely uninformative, the residuals are close to the magnitude of the 
survey  values,  suggesting  either  very  noisy  survey  or  one  that  is  unrelated  to  the 
magnitude of the catch values.   

Retrospective  analysis  shows  that  the  inclusion  of  the  last  observation  of  the 
landings  and  survey  index  time  series  considerably  affects  the  model  estimates  for 
the  most  recent  years,  suggesting  that  the  current  state  of  the  stock  is  quite 
uncertain. 

Figure  6.17.3.4  compares  prior  and  posterior  distribution  for  the  parameters  for 
which priors were set.  

618 

618 

 
 
 
 
 
 
 
Figure  6.17.3.2  Hake  in  GSA  23.  Stock  assessment  results  for  hake  in  GSA-23.  First 
row: median (blue solid line) of relative biomass and relative fishing mortality with 95% 
CI  (blue  shaded  area).  Second  row:  Observed  (points)  and  estimated  catch  (solid  line) 
with  95%  CIs  (left)  and  Kobe  plot  of  relative  fishing  mortality  versus  relative  biomass 
(right).  Third  row:  Schaefer  surplus  production  curve  (black  line)  with  predicted  values 
of surplus production (blue line and points) (left).  

619 

619 

 
 
 
Figure 6.17.3.3 Hake in GSA 23. Diagnostic test of the fit for the residuals of the catch 
and abundance index series for the stock assessment of hake in GSA-23. First row: log 
of input data  series.  Second  row: residuals plot.  Third row:  autocorrelation  of residuals 
and fourth row: normality of residuals. If the header is green the test is not significantly 
different from parametric assumptions, otherwise the header is red.  

620 

620 

 
 
 
 
 
 
 
 
 
Table 6.17.3.3 Hake in GSA 23. Mean and and standard deviation of model parameters 
and  dynamical  components  for  the  stock  assessment  of  hake  in  GSA-23.  K:  biomass 
carrying  capacity,  r:  intrinsic  growth  rate,  MSY:  Maximum  Sustainable  Yield,  Bmsy: 
biomass at MSY, Fmsy: fishing mortality at MSY, B/Bmsy: relative biomass in 2016, F/Fmsy: 
relative fishing mortality in 2016   

Parameter   mean 

Sd 

K 

r 

MSY 

988 

0.83 

204 

494 

Bmsy 
Fmsy 
B/Bmsy(2016)  0.30 
F/Fmsy(2016)  2.05 

0.41 

362 

0.35 

9 

180 

0.17 

0.23 

0.82 

Figure  6.17.3.4  Hake  in  GSA  23.  Comparison  of  prior  and  posterior  distribution  of 
parameters for the stock assessment of hake in GSA-23. First row: ratios of observation 
to  process  error  alpha=σΙ/σΒ  and  beta=σC/σF  respectively.  Second  row:  intrinsic  growth 
rate, r, and initial relative biomass (B/Bmsy in 1970). 

621 

621 

 
 
 
 
Figure  6.17.3.5  Hake  in  GSA  23.  Retrospective  plots  of  relative  biomass  and  relative 
fishing  mortality  for  stock  assessment  of  hake  in  GSA-23,  produced  by  repeating  the 
stock assessment after excluding 1 (black line) to 5 (cyan line) final year observations of 
the  catch  and  abundance  index  time  series.  The  dotted  black  lines  are  the  estimates 
when the full time series is considered.      

6.17.4  REFERENCE POINTS 

Not estimated due to the high uncertainty of parameter estimates. 

6.17.5  SHORT TERM FORECAST AND CATCH OPTIONS 

No analysis were carried out during the meeting 

6.17.6  DATA DEFICIENCIES 

Data on  length  distribution  by  age  are  available  only  for  2014  and  2016  and partly 
for 2013 and 2015 (only for the last trimester). Due to the limited amount of data an 
analytical  age-based  assessment  was  not  attempted.  Landings  data  from  DCF  are 
also  limited  to  the  above  mentioned  years  and  are  not  sufficient  to  parameterize  a 
biomass production model; hence data from FAO and the National Statistical Service 
of  Greece  (NSSG)  were  used  as  input  to  the  assessment.  Data  between  1990  and 
2016 are available for GSA 23 by the NSSG. Before 1990 data are only available for 
GSAs  22 and  23 combined, from  FAO. These were disaggregated by GSA using the 
average  ratio  of  landings  between  GSA  23  and  22  that  are  available  for  the  period 
1990-2016. This induces an average error of 58% to the disaggregated data in GSA 
23.The  DCF  landings  data  for  years  2014  and  2016  do  not  coincide  with  the  data 
from FAO, with the reported values in DCF being 20% lower and 97% lower than the 
corresponding values from FAO in 2014 and 2016 respectively. Note, however, that 
FAO  landing  rates  are  not  necessarily  similar  to  those  obtained  from  DCF,  as  they 
are based on estimates of the National Statistical Service of Greece deriving from a 

622 

622 

 
 
 
 
different sampling scheme than the one followed in DCF. For DCF data to be useful in 
assessments  (age-based  or  biomass  production),  longer  time  series  with  full 
sampling  within  each  year  are  needed.  The  data  on  relative  biomass  from  the 
MEDITS survey also have gaps for the years the survey didn’t take place, i.e. 2002, 
2007, 2009-2013, 2015. 

6.18  RED MULLET IN GSA 23 

6.18.1  BIOLOGY 

Figure  6.18.1  shows  the  location  of  GSAs  22  and  23  in  the  Mediterenean.  GSAs  22 
and  23  have  been  considered  as  distinct  areas  for  management  purposes  due  to 
their  specific  geo-physical  characteristics;  the  Cretan  Sea  is  a  deep  (2500m)  and 
large in volume generally an oligotrophic basin (Psarra et al., 1996; Lykousis et al., 
2002). In addition, fishery exploitation patterns differ between the two nearby areas, 
with the trawling activities being much less intense in GSA-23 (Anonymous, 2013).  

Figure 6.18.1. Geographical location of GSAs 22 and 23. The assessment was done for 
GSA-23. 

For the red mullet stock in GSAs 22 and 23 the VG growth function parameters from 
DCF give ureasonable value of t0 (i.e. <-0.2) and were not used. For the production 
model  needs,  life-history  parameters  found  in  literature  were  used  to  estimate  the 
prior of the intrinsic growth rate, r, using Kreb’s demographic method (McAllister et 
al. 2001).  

6.18.2  INPUT DATA 

Red mullet is exploited by bottom trawlers and various artisanal fisheries using 

623 

623 

 
 
 
 
 
 
 
gillnets. It is exploited by five fleet segments  but the main bulk of catches is coming 
from bottom trawlers over 18m length. The Greek bottom trawl fishery has multi-
species characteristics and similarly to most Mediterranean demersal trawl fisheries, 
captures more than 100 commercial species. However, few species, such as red 
mullets, hake and shrimps compose the main bulk of landings, with red mullet being 
one of the most important targets. 

As  with  all  demersal  fisheries  in  GSA-23,  those  catching  red  mullet  are  managed 
according  to  EU  regulation  1967/2006  which  include  spatial  fishery  closures  for  the 
bottom  trawlers,  gear  configuration  specifications  and  minimum  landing  sizes. 
Additional national measures include a temporal (4.5 months) closure of the bottom 
trawl fisheries accompanied by certain localized spatio-temporal closures. 

6.18.2.1  CATCH (LANDINGS AND DISCARDS)  

Landings  and  discards  data  were  reported  to  STECF  EWG  17-15  through  the  DCF 
(Tables  6.18.2.1.1-2  and  Figures  6.18.2.1.1-2).  These  are  available  only  for  the 
years 2014 and 2016 and partly for 2013 and 2015 (only for the last trimester). Due 
to  the  limited  data,  no  age-based  assessment  was  attempted  and  the  assessment 
was based on a surplus production model using the time series of landings obtained 
from the NSSG records.  

Table  6.18.2.1.1  Red  mullet  GSA-23.  Landings  and  discards  per  year  provided  to 
STECF  EWG  17-15  through  the  DCF.    Data  for  2013  and  2015  correspond  to  the  last 
trimester of the year only. 

Year 

Landings 
(tonnes) 

Discards 
(tonnes) 

2013 
2014 
2015 
2016 

84.93 
133.08 
48.64 
116.88 

0.11 
20.78 
7.26 
4.60 

Table 6.18.2.1.2  Red mullet GSA-23. Landings and discards per year and fishing gear 
provided to STECF EWG 17-15 through the DCF. Data for 2013 and 2015 correspond to 
the last trimester of the year only.  

Year 

Fishing gear 

2013 
2013 
2014 
2014 
2014 
2015 
2015 
2016 

GTR 
OTB 
GNS 
GTR 
OTB 
GTR 
OTB 
GNS 

624 

624 

Landings 
(tonnes) 
1.52 
83.41 
0.54 
25.42 
107.11 
8.26 
40.38 
0.08 

Discards 
(tonnes) 
0.11 
4.80 
0.06 
10.55 
10.17 
3.43 
3.83 
0.05 

 
 
 
 
 
2016 
2016 

GTR 
OTB 

38.34 
78.46 

3.76 
0.79 

Figure 6.18.2.1.1 Red mullet GSA-23. Landings (in numbers) by year, gear and length 
class  provided  to  STECF  EWG  17-15  through  the  DCF.  Data  for  2013  and  2015 
correspond to the last trimester of the year only. 

625 

625 

 
 
 
 
 
Figure 6.18.2.1.2 Red mullet GSA-23. Discards (in numbers) by year, gear and length 
class  provided  to  STECF  EWG  17-15  through  the  DCF.  Data  for  2013  and  2015 
correspond to the last trimester of the year only. 

The  main  bulk  of  red  mullet  discards  refers  to  undersized    individuals.  The  discard 
ratio in terms of biomass is in most cases <2% (Anonymous 2013) 

6.18.2.2  EFFORT 

Effort data reported to STECF EWG 17-15 through the DCF (Tables 6.18.2.2.1-2). 

Table 6.18.2.2.1 Effort in GT*days_at_sea and days_at_sea, per year in GSA-23. Data 
for 2013 and 2015 correspond to the last trimester of the year only. 

year  GT*days_at_sea  days_at_sea 

2013 
2014 
2015 
2016 

81734.78 
409313 
355699 
1429794 

- 
70212 
25391 
81740 

Table  6.18.2.2.2  Effort  in  GT*days_at_sea  and  days_at_sea,  per  year  and  gear  in 
GSA-23. Data for 2013 and 2015 correspond to the last trimester of the year only. 

Year 

2013 
2013 
2013 
2013 
2013 
2013 
2014 
2014 
2014 
2014 
2014 
2015 
2015 
2015 
2015 
2015 
2016 
2016 
2016 
2016 
2016 

Gear 
FPO 
GNS 
GTR 
LLS 
OTB 
PS 
GNS 
GTR 
LLS 
OTB 
PS 
GNS 
GTR 
LLS 
OTB 
PS 
GNS 
GTR 
LLS 
OTB 
PS 

GT*days_at_sea  Days_at_sea 

- 
- 
- 
- 
- 
- 
11922 
42120 
14176 
1498 
496 
863 
11527 
10363 
1581 
1057 
5426 
42731 
30804 
1637 
1142 

0 
206.9 
11033.59 
6565.35 
57827.58 
6101.36 
33095 
132772 
31702 
177264 
34480 
1349 
47304 
61564 
193976 
51506 
159724 
591774 
422533 
201177 
54586 

626 

626 

 
 
 
 
 
6.18.2.3  SURVEY DATA 

The  “MEDITS”  bottom  trawl  surveys  are  accomplished  in  GSA-23  since 
1994  on  an  annual  basis.  However,  no  surveys  were  accomplished  in 
2003,  2009-2013  and  in  2015.  Sampling  includes  sampling  in  20  pre-
defined stations following a standardized protocol. Trawling was made by 
means  of  a  standard  net  GOC  73  having  a  cod-end  mesh  opening  of  20 
mm  and  selection  of  stations  was  based  on  a  depth-stratified  sampling 
scheme  that  included  five  depth  zones:  10-50,  50-100,  100-200,  200-
500  and  500-800  m.  Collected  data  included  number,  weight,  gonad 
maturation stage and total length measurements for a wide range of fish, 
cephalopod  and  crustacean  species  (MEDITS  Handbook  v9,  2017).  From 
the collected data, standardized abundance indexes by year, expressed in 
terms  of  kg  per  square  km  of  swept  area  (kg/km2),  were  calculated  for 
red mullet (Table 6.1.2.3.1).  

Table 6.18.2.3.1 Red mullet GSA-23. Abundance index from the MEDITS trawl survey  

Year 

Abundance 
index 
(kg/km2) 

1994 

1995 
1996 

1998 

1999 
2000 

2001 
2003 
2004 
2005 

2006 
2008 

2014 

2016 

19.47 

12.91 
20.78 

54.60 

87.61 
64.10 

217.29 

82.32 
40.26 

23.63 

13.39 
35.22 

41.00 

115.20 

6.18.3  STOCK ASSESSMENT 

Method 

Red  mullet  in  GSA-23  has  never  been  assessed  before.  The  current  assessment  is 
based  on  a  state-space  surplus  production  model  implemented  with  the  SPiCT 
package  (Pedersen  and  Berg,  2017)  under  the  R-language    environment  (R  core 
team 2017). 

Input data and parameters 

627 

627 

 
 
 
 
 
 
  
The  assessment  was  based  on  landings  from  the  National  Statistical  Service  of 
Greece  (NSSG)  for  the  period  1994-2016  and  the  time  series  of  abundance  indices 
from the MEDITS trawl surveys (Table 6.18.3.1 and Figure 6.18.3.1). The abundance 
index time series has a number of missing values, i.e. years when the sampling did 
not  take  place.  Discards  for  red  mullet  are  considered  negligible  and  only  landings 
data are used for the biomass production assessment.  

Initially  SPiCT  was  run  with  default  parameters  but  convergence  was  not  achieved. 
To facilitate convergence a prior for the intrinsic growth rate, r, was used. The prior 
for r was estimated from life-history parameters (i.e., fecundity by age, mortality by 
age,  natural  mortality  and  growth)  using  Kreb’s  demographic  method  (McAllister  et 
al.,  2001).  Still,  this  model  did  not  converge.  Convergence  was  achieved  by  using 
the  Schaefer  surplus  production  curve  (i.e.  fixing  n=2  in  Pella-Tomlinson  model) 
Table  6.18.3.2  gives  the  parameters  of  the  informative  prior  distribution  for  r  that 
were used in the assessment. 

Table  6.18.3.1  Red  mullet  in  GSA  23.  Landings  and  survey  index  for  red  mullet  in 
GSA-23  in  the  period  1994-2016.  Landings  data  taken  from  The  National  Statistical 
Service  of  Greece  (NSSG)  (http://www.statistics.gr/en/home/).  Annual  abundance 
indexes were estimated from the MEDITS surveys. 

Abundance 
index 

(kg/km2) 
19.47 
12.91 

20.78 
- 

54.60 

87.61 
64.10 

217.29 

- 
82.32 

40.26 

23.63 
13.39 

- 

35.22 
- 

- 

- 
- 

- 

41.00 
- 
115.20 

Year 

Landings 
(tonnes) 

1994 

1995 
1996 

1997 

1998 
1999 

2000 
2001 
2002 
2003 

2004 
2005 

2006 

2007 
2008 

2009 

2010 
2011 

2012 

2013 
2014 
2015 
2016 

123.4 
103.6 

221.4 
180.7 

181.3 

147 
100.9 

121.9 

65.7 
47.9 

75.6 

73.4 
119.5 

73.2 

84.6 
66.7 

71.6 

96.7 
100.6 

124.6 

109.8 
181.2 
140.9 

628 

628 

 
 
 
 
Figure  6.18.3.1  Red  mullet  in  GSA  23.Times  series  of  landings  and  MEDITS  survey 
index used in the SPiCT assessment of red mullet in GSA-23.  

Table 6.18.3.2 Red mullet in GSA 23. Parameters of prior distributions for the intrinsic 
growth rate, r, used in the assessment of red mullet in GSA-23.    

Parameter priors 

R 

Distribution 

lognormal 

μ (mean of the log) 

-0.249 

σ (std of the log) 

0.442 

Results 

Figures  6.18.3.2-5  show  the  output  of  the  assessment  with  a  prior  for  the  intrinsic 
growth rate and a Schaefer surplus production curve.  

The  assessment  suggests  that  the  red  mullet  stock  in  GSA-23  is  under-exploited. 
Overall results, however, are dubious due to the very high uncertainty of the output 
estimates (Figure 6.18.3.2). Retrospective analysis shows consistent trends for both 

629 

629 

 
 
 
 
 
 
 
 
 
fishing mortality and biomass (Figure 6.18.3.5). Figure 6.18.3.4 compares prior and 
posterior distribution for the parameters for which priors were set. Only one year of 
data (1994) is on the left side of the production curve, suggesting that the model is 
poorly parameterized. The survey values fit poorly to the model with the magnitude 
of  the  residuals  similar  to  the  variation  in  the  data.  Confidence  intervals  are  very 
wide. 

In conclusion, the  stock status  remains unknown and reference point estimates are 
doubtful. 

Figure  6.18.3.2  Red  mullet  in  GSA  23.  Stock  assessment  results  for  red  mullet  in 
GSA-23.  First  row:  median  (blue  solid  line)  of  relative  biomass  and  relative  fishing 

630 

630 

 
 
 
mortality with 95% CI (blue shaded area). Second row: Observed (points) and estimated 
catch  (solid  line)  with  95%  CIs  (left)  and  Kobe  plot  of  relative  fishing  mortality  versus 
relative  biomass  (right).  Third  row:  Schaefer  surplus  production  curve  (black  line)  with 
predicted values of surplus production (blue line and points) (left)  

Figure 6.18.3.3 Red mullet in GSA 23. Diagnostic test of the fit for the residuals of the 
catch  and  abundance  index  series  for  the  stock  assessments  of  red  mullet  in  GSA-23. 
First row: log of input data series. Second row: residuals plot. Third row: autocorrelation 
of residuals and fourth row: normality of residuals. If the header is green the test is not 
significantly different from parametric assumptions, otherwise the header is red.  

631 

631 

 
 
 
 
Figure 6.18.3.4 Red mullet in GSA 23. Comparison of prior and posterior distribution 
of  parameters  for  the  stock  assessments  of  red  mullet  in  GSA-23.  From  left  to  right: 
ratios  of  observation  to  process  error,  alpha  =  σΙ/σΒ,  beta  =  σC/σF  and  intrinsic  growth 
rate, r. 

Figure 6.18.3.5 Red mullet in GSA 23. Retrospective plots of relative biomass and 
relative fishing mortality for the stock assessment red mullet in GSA-23, produced by 
repeating the stock assessment after excluding 1 (black line) to 5 (cyan line) final year 
observations of the catch and abundance index time series. The dotted black lines are 
the estimates when the full time series is considered. 

6.18.4  REFERENCE POINTS 

Not estimated due to the very high uncertainty of parameter estimates. 

632 

632 

 
 
 
 
 
 
6.18.5  SHORT TERM FORECAST AND CATCH OPTIONS 

No analysis were caeeried out during the meeting 

6.18.6  DATA DEFICIENCIES 

Data on  length  distribution  by  age  are  available  only  for  2014  and 2016  and partly 
for 2013 and 2015 (only for the last trimester). Due to the limited amount of data an 
analytical  age-based  assessment  was  not  attempted.  DCF  data  for  years  2014  and 
2016 do not coincide with the data from FAO, with the reported values in DCF being 
17%  higher  and  20%  lower  than  the  corresponding  values  from  FAO  in  2014  and 
2016  respectively.  This  deviation  is  not  very  large  and  could  definitely  be  expected 
from  the  entirely  different  sampling  scheme  followed  from  NSSG  (which  provides 
data  to  FAO)  and  DCF.  For  DCF  data  to  be  useful  in  assessments  (age  based  or 
biomass  production),  longer  time  series  with  full  sampling  within  each  year  are 
needed.  The  data  on  relative  biomass  from  the  MEDITS  survey  also  have  gaps  for 
the years the survey didn’t take place, i.e. 2002, 2007, 2009-2013, 2015.  

6.19  DEEP-WATER ROSE SHRIMP IN GSA 23 

6.19.1  BIOLOGY 

Figure 6.19.1 shows the location of GSAs 22 and 23 in the Mediterranean. GSAs 22 
and  23  have  been  considered  as  distinct  areas  for  management  purposes  due  to 
their  specific  geo-physical  characteristics;  the  Cretan  Sea  is  a  deep  (2500m)  and 
large in volume generally an oligotrophic basin (Psarra et al., 1996; Lykousis et al., 
2002). In addition, fishery exploitation patterns differ between the two nearby areas, 
with the trawling activities being much less intense in GSA-23 (Anonymous, 2013).  

Figure  6.19.1.  Geographical  location  of  GSAs  22  and  23.  The  assessment  was 
performed for GSA-23. 

Life-history  parameters  found  in  literature  were  used  to  estimate  the  prior  of  the 
intrinsic growth rate, r, using Kreb’s demographic method (McAllister et al. 2001). 

633 

633 

 
 
 
 
6.19.2  INPUT DATA 

Deep-water  rose  shrimp  is  exploited  almost  exclusively  by  bottom  trawlers.  The 
Greek  bottom  trawl  fishery  has  multi-species  characteristics  and  similarly  to  most 
Mediterranean demersal trawl fisheries, captures more than 100 commercial species. 
However, few species, such as red mullets, hake and shrimps compose the main bulk 
of landings. 

All  demersal  fisheries  in  GSA-23  are  managed  according  to  the  EU  regulation 
1967/2006  which  foresees  spatial  fishery  closures  for  the  bottom  trawlers,  gear 
configuration  specifications  and  minimum  catching  sizes.  Additional  national 
measures  include  a  temporal  (4.5  months)  closure  of  the  bottom  trawl  fisheries 
accompanied by certain localized spatio-temporal closures. 

6.19.2.1  CATCH (LANDINGS AND DISCARDS)  

Landings  and  discards  data  were  reported  to  STECF  EWG  17-15  through  the  DCF 
(Tables  6.19.2.1.1-2  and  Figures  6.19.2.1.1-2).  These  are  available  only  for  the 
years 2013-2016.  

Table  6.19.2.1.2  Deep-water  rose  shrimp  in  GSA  23.  Landings  and  discards  per  year 
and fishing gear provided to STECF EWG 17-15 through the DCF. 

Year 

2013 
2014 
2015 
2015 
2016 

Fishing gear  Landings (tonnes)  Discards (tonnes) 

OTB 
OTB 
GTR 
OTB 
OTB 

12.9599 
37.11179 
0.04035 
0.815 
1.94 

0.64915 
1.13597 
- 
0.02494 
0.047 

6.19.2.2  EFFORT 

Effort data reported to STECF EWG 17-15 through the DCF for 2013-2016 (Table 
6.19.2.2.1). 

Table 6.19.2.2.1 Effort in GT*days_at_sea and days_at_sea, per year in GSA-23.  

year  GT*days_at_sea  days_at_sea 

2013 
2014 
2015 
2016 

- 
1498 
1581 
1637 

57827.58 
177264 
193976 
201177 

634 

634 

 
 
 
 
  
 
 
 
6.19.2.3  SURVEY DATA 

The  “MEDITS”  bottom  trawl  surveys  are  accomplished  in  GSA-23  since  1994  on  an 
annual  basis.  However,  no  surveys  were  accomplished  in  2002,  2007,  2009-2013 
and  in  2015.  Sampling  includes  sampling  in  20  pre-defined  stations  following  a 
standardized  protocol.  Trawling  was  made  by  means  of  a  standard  net  GOC  73 
having a cod-end mesh opening of 20 mm and selection of stations was based on a 
depth-stratified  sampling  scheme  that  included  five  depth  zones:  10-50,  50-100, 
100-200,  200-500  and  500-800  m.  Collected  data  included  number,  weight,  gonad 
maturation  stage  and  total  length  measurements  for  a  wide  range  of  fish, 
cephalopod  and  crustacean  species  (MEDITS  Handbook  v9,  2017).  From  the 
collected  data,  standardized  abundance  indexes  by  year,  expressed  in  terms  of  kg 
per square km of swept area (kg/km2), were calculated for deep-water rose shrimp 
(Table 6.19.2.3.1). 

Table  6.19.2.3.1  Deep-water  rose  shrimp  in  GSA  23.  Abundance  index  from  the 
MEDITS trawl survey  

Year 

Abundance 
index 
(kg/km2) 

1994 
1995 
1996 
1997 
1998 
1999 
2000 
2001 
2003 
2004 
2005 
2006 
2008 
2014 
2016 

2.08 
22.63 
5.91 
0.90 
15.27 
13.76 
27.73 
4.38 
21.98 
26.59 
22.75 
27.06 
26.39 
5.01 
4.20 

6.19.3  STOCK ASSESSMENT 

Method 

The  assessment  is  based  on  a  state-space  surplus  production  model  implemented 
with  the SPiCT  package (Pedersen and  Berg,  2017) in R  environment (R  core team 
2017). 

Input data and parameters 

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The  assessment  is  based  on  the  time-series  of  FAO  landings  for  the  period  1994-
2016  and  the  time  series  of  abundance  indices  from  the  MEDITS  trawl  surveys 
(Table 6.19.1.2.2.1).  

Regarding  landings  time-series,  there  are  significant  gaps  in  the  DCF  data, 
particularly,  in  the  recent  years,  due  to  non-implementation  of  the  project.  More 
specifically,  the  values  are  missing  for  the  years  2007,  2009  –  2012,  while  for  the 
years 2013, 2015 landings data were only reported for the last quarter of the year, 
and  thus  could  not  be  included  in  the  assessment.  The  same  gaps  exist  in  the 
datasets concerning discards, effort, catch-at-length and MEDITS trawl surveys.  

On  the  other  hand,  FAO  landings  data  for  the  Aegean  Sea  (GSAs  22  and  23 
combined) are available for a longer time period. Landings by GSA are collected by 
the  National  Statistical  Service  of  Greece  (NSSG),  which  then  provides  the  data  to 
FAO. These were disaggregated by GSA using the average ratio of landings between 
GSA  22  and  23  that  are  available  from  the  NSSG.  The  two  years  missing  from  the 
FAO data, i.e. 2015 and 2016, were taken from the NSSG time series. Landings rates 
appearing in FAO are not necessarily similar to those obtained from DCF, as they are 
based  on  estimates  of  the  NSSG,  which  are  derived  from  a  different  sampling 
scheme than the one followed in DCF.  

Discards for deep-water rose shrimp are considered negligible and only landings 
data are used for the biomass production assessment.  

The  abundance  index  time  series  derived  from  the  MEDITS  surveys  also  has  a 
number of missing values, similar to the ones described above for the landings data.  

The numbers and weights –at- age suffered from internal inconsistencies (step size, 
initial value, unit of measurement varying among years). 

Discards  for  deep-water  rose  shrimp  are  considered  negligible,  given  that  the  main 
bulk  of  deep-water  rose  shrimp  discards  refer  to  undersized    individuals  and  the 
discard  ratio  in  terms  of  biomass  is  in  most  cases  <10%.  Thus,  only  landings  data 
are used for the biomass production assessment. Initially SPiCT was run with default 
parameters but convergence was not achieved. To facilitate convergence, a prior for 
the  growth  rate  was  used  and  the  parameter  n  of  the  Pella-Tomlinson  surplus 
production  was  fixed  to  2  (Schaefer  production  curve).  The  growth  rate  was 
estimated from  independent data on life history parameters (i.e., fecundity by age, 
mortality  by  age,  natural  mortality  and  growth)  using  Kreb’s  demographic  method 
(McAllister et al., 2001). Table 6.19.1.2.2.2 gives the informative prior distributions 
used in the final assessment. 

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Figure  6.19.3.1  Deep-water  rose  shrimp  in  GSA  23.  Times  series  of  landings  and 
MEDITS survey index used in the SPiCT assessment of deep-water rose shrimp in GSA-
23.  

Table 6.19.1.2.2.1 Deep-water rose shrimp in GSA 23. Landings and survey index for 
Deep-water rose Shrimp in GSA 23 in the period 1994-2016. Landings data taken from 
the Greek Statistical Service of Greece (NSSG).  

Year 

1994 
1995 
1996 
1997 
1998 
1999 
2000 
2001 
2002 
2003 
2004 

Landings (tonnes)  Survey  index 

(kg/km2) 

191.2 
124.4 
171.7 
250.8 
120.7 
83.6 
91.3 
91.2 
62.6  - 
79.8 
132.9 

2.077415 
22.62836 
5.908267 
0.90176 
15.26606 
13.76134 
27.73389 
4.383737 

21.98133 
26.58736 

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2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 
2015 
2016 

86 
95.5 
84.2  - 

22.74963 
27.05802 

101.1 

26.39019 

64.5  - 
54.3  - 
89.8  - 
75.5  - 
49  - 

42.9 
109.1  - 
74 

5.010499 

4.2 

Table 6.19.1.2.2.2 Deep-water rose shrimp in GSA 23. Parameters of prior distribution 
for the growth rate (r) used in the assessment of deep-water rose shrimp in GSA 23.    

Parameter priors 

R 

Distribution 

Lognormal 

μ (mean of the log) 

0.56 

σ (std of the log) 

0.40 

Results 

Figures  6.19.3.2-5  show  the  output  of  the  assessment  with  a  prior  for  the  growth 
rate and a Schaefer production model.  

Retrospective  analysis  (Figure  6.19.3.5)  shows  relatively  consistent  trends  with 
greater uncertainty towards the beginning of the time series.      

Figure  6.19.3.4  compares  prior  and  posterior  distribution  for  the  parameters  for 
which priors were set.  

The  assessment  results  show  that  the  stock  has  been  fished  below  Fmsy  and  the 
biomass  is  above  Bmsy  (Figure  6.19.3.2).  However,  the  confidence  intervals  for  the 
biomass  and  fishing  mortality  are  very  wide.  The  survey  fits  very  poorly  to  the 
model,  the  magniture  of  the  residuals  is  similar  to  the  values  in  the  survey 
timeseries.  For  these  reasons,  the  result  of  the  assessment  is  not  trusted  and  the 
state of the stock cannot be deduced.    

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Figure  6.19.3.2  Deep-water  rose  shrimp  in  GSA  23.  Stock  assessment  results  for 
P.longirostris  in  GSA-23.  Upper  row:  Median  (blue  solid  line)  of  relative  biomass  and 
relative  fishing  mortality  with  95%  CI  (blue  shaded  area).  Middle  row:  Observed  (blue 
points)  and  estimated  catch  with  95%  CIs  (left)  and  Kobe  plot  of  relative  fishing 
mortality versus relative biomass (right). Bottom row: Schaefer production curve (black 
line) and predicted surplus production (blue line with dots)  

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Figure  6.19.3.3  Deep-water  rose  shrimp  in  GSA  23.  Diagnostic  test  of  the  fit  for  the 
residuals  of  the  catch  and  abundance  index  series.  Log  of  input  data  series  (first  row), 
residuals  plot  (second  row),  autocorrelation  of  residuals  (third  row)  and  normality  of 
residuals  (fourth  row).  If  the  header  is  green  the  test  is  not  significantly  differnet  from 
parametric assumptions, otherwise the header is red.  

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Figure 6.19.3.4  Deep-water rose shrimp in GSA 23. Comparison of prior and posterior 
distribution  of  parameters.  Ratios  of  observation  to  process  error  (σΙ/σΒ),  (σC/σF),  and 
intrinsic growth rate (r)  

Figure  6.19.3.5  Deep-water  rose  shrimp  in  GSA  23.  Retrospective  plots  of  relative 
biomass and relative fishing mortality for P.longirostris, produced by repeating the stock 
assessment after excluding 1 (black line) to 5 (cyan line) final year observations of the 
catch  and  abundance  index  time  series.  The  dotted  black  lines  are  the  estimates  when 
the full time series is considered.     

6.19.4  REFERENCE POINTS 

Not estimated due to the very high uncertainty of parameter estimates. 

6.19.5  SHORT TERM FORECAST AND CATCH OPTIONS 

No analysis were carried out during the meeting 

6.19.6  DATA DEFICIENCIES 

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Data  on  landings  and  on  size  composition  of  the  catch  are  available  only  for  the 
years  when  the  DCF  took  place,  i.e.  2003-2005,  2008,  2014,  2016  and  partly 
(referring only to the last quarter of the year) for 2013 and 2015. Also, the available 
catch-at-  length  data  suffered  from  internal  inconsistencies  (step  size,  initial  value, 
unit of measurement varying across years). 

The data on relative biomass from the MEDITS survey also have gaps for the years 
the  survey  didn’t  take  place,  i.e.  2002,  2007,  2009-2013,  2015.  For  the  above 
reasons, an analytical age-based assessment was not attempted.  

Due to the significant gaps in the DCF landings data, especially in the recent years, 
due to the non-implementation of the project, as described above, the data provided 
by FAO were used in the assessment. In addition, the FAO landings are available for 
a longer time period. Landings rates appearing in FAO are not necessarily similar to 
those  obtained  from  DCF,  as  they  are  based  on  estimates  of  the  Greek  Statistical 
service of Greece (NSSG), which are based on a different sampling scheme than the 
one followed in DCF. 

7 DATA QUALITY AND DEFICIENCIES BY STOCK 

ToR  8.  To  summarize  and  concisely  describe  all  data  quality  deficiencies,  including 
possible  limitations  with  the  surveys  of  relevance  for  stock  assessments  and 
fisheries.  Such  review  and  description  are  to  be  based  on  the  data  format  of  the 
official DCF data call for the Mediterranean Sea launched on the March 2017. Identify 
further  research  studies  and  data  collection  which  would  be  required  for  improved 
fish  stock  assessments.  This  review  shall  be  presented  in  a  manner  that  is 
compatible with the online platform developed by the JRC for data issues. 

7.1 HAKE IN GSAS 17-18 

The  differences  between  the  two  stock  assessment  models,  SS3  and  a4a,  are 
summarised in section 0. 

Regarding the data, thanks to the collaboration developed in the FAO frameworks 
(i.e. AdriaMed, GFCM) among all the countries and the scientists involved in the 
sector,  it  was  possible  to  use  also  data  from  non-European  countries 
(Montenegro  and  Albania)  and  data  collected  before  the  annexation  of  some 
countries  to  the  EU  (Croatia).  However  Albanian  catch  data  for  the  years  2015 
and  2016  are  assumed  equal  to  the  value  for  2014,  since  they  did  not  provide 
information for these years. 

Catch  data  and  their  LFDs  include  landings  and  discards  coming  from  the  DCF 
samples;  however,  discard  is  included  only  in  the  years  and  for  the  fleets  in 
which  estimates  are  available.  Future  elaborations  need  a  harmonization  of  this 
point. 

The  Medits  data  for  the  Italian  GSA  17  used  in  the  stock  assessment  models 
cover Italy and Slovenia; however, for some years only Italian data are included 
in  the  assessment.  This  inaccuracy  has  a  negligible  effect  since  only  two  hauls 
are  foreseen  in  Slovenia  water,  also  hake  is  not  present  in  this  area.  However, 
for the future elaborations it will be useful to harmonize also this point. 

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7.2 RED MULLET IN GSAS 17-18 

The data used for the analyses come from the last DCF official data call (2017). The data 
related  to  non-EU  countries  (Albania  and  Montenegro)  was  taken  by  the  GFCM  WGSAD 
official report. Landing data before 2006 was lacking for GSA 17 (Italy) 

7.3 NORWAY LOBSTER IN GSAS 17-18 

Being  able  to  split  the  catch  numbers  in  the  Pomo  and  outside  Pomo  area  would  be 
beneficial  for  age/length  based  assessments.  According  to  the  Italian  DCF  workplan 
2011-2014 
(https://datacollection.jrc.ec.europa.eu/np/2014/-
/document_library_display/z9Yv/view/688307/38422?_110_INSTANCE_z9Yv_redirect=h
ttps%3A%2F%2Fdatacollection.jrc.ec.europa.eu%2Fnp%2F2014%3Fp_p_id%3D110_IN
STANCE_z9Yv%26p_p_lifecycle%3D0%26p_p_state%3Dnormal%26p_p_mode%3Dview
%26p_p_col_id%3Dcolumn-2%26p_p_col_count%3D1), the DCF sampling in GSA 17 of 
the OTB gear had a target sampling of 78 fishing trips each year on a quarterly basis in 
the  whole  Italian  side  of  GSA  17  out  of  76525,51  average  total  no.  of  trips  in  the 
reference years. The 78 fishing trips are split in 36 concurrent at sea and 48 concurrent 
at market sampling. This means 9 non unique fishing trips had to be sampled at sea in 
GSA 17 each quarter.   If catches were to be split a posteriori between Pomo area and 
outside  Pomo  on  a  50%  -50%  basis,  at  most  4.5  trips  would  be  sampled  quarterly  in 
Pomo  which  would  arguably  carry  no  significant  sampling  levels  for  a  reconstruction  of 
catch at length in the different areas. The EWG has no access to primary sampling data, 
but it might also be the case that no fishing trips are sampled in the Pomo pit area 

7.4 COMMON PANDORA IN GSAS 17-18 

DCF  data  on  common  pandora  in  GSAs  17  and  18  do  not  include  information  from  the 
eastern Adriatic regarding landings, discards and size and age structures. 

Length  frequency  distributions  are  reported  by  Italy  for  GSA  18  for  the  period  2009-
2016, but no data for 2015, and for GSA 17, for the period 2011-2016. 

The  landings  age  structure  is  reported  by  Italy  for  GSA  18  for  the  period  2009-  2016, 
but no data is available for 2015. Units should be checked. 

MEDITS  data  in  the  DCF  are  presented  by  GSA,  not  by  country,  which  does  not  allow 
knowing  whether  differences  exit  regarding  size  structure,  abundance  and  biomass 
between the eastern and western Adriatic. 

7.5 DEEP-WATER ROSE SHRIMP IN GSAS 17-18-19 

Data  from  DCF  2016  as  submitted  through  the  Official  data  call  in  2017  were  used. 
Discards  for  GSA  17  were  present  just  for  2011  and  from  2013  to  2016  for  the  Italian 
fleet  and  only  for  2015  and  2016  for  the  Croatian  fleet.  Discards  for  GSA  18  were 
present  just  from  2009  to  2016  for  the  Italian  fleet.  Discards  for  GSA  19  were  present 
just for 2006 and from 2009 to 2016 for the Italian fleet. Landings data for GSA 17 were 
incomplete.  Italian landings were present  just for  2006,  2011,  and  from  2013 to 2016. 
Croatian  landings  were  present  just  from  2014  to  2016  in  the  DCF  database  because 
previously  there  was  no  obligation  to  monitor  that  species.  Landings  data  from  some 
gears  in  the  DCF  database  are  reported  inconsistently  throughout  the  time  series.  For 
more detailed information on missing length and age frequency distributions see section 
6.5.2.1. 

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7.6 THORNBACK RAY IN GSA 17 

The  reduced  commercial  importance  of  elasmobranchs  results  in  a  low  priority  for  data 
collection  and  research  in  the  Mediterranean  Sea,  even  though  the  collection  of  more 
complete data (on catches, sizes, etc) became compulsory in recent years.  In essence, 
elasmobranchs  information  for  the  different  fisheries  is  data  deficient  and  makes  stock 
assessments  a  difficult  task.  Impact  assessments  suggest  that  many  elasmobranch 
stocks urgently require of specific scientific studies aimed at the definition of appropriate 
management  actions.  Despite  the  general  agreement  on  the  need  to  define  their  stock 
status  and  to  identify  proper  measures  for  stock  recovery,  this  will  not  be  possible  if  a 
minimum amount of data is not available. 

There  were  noticed  in  the  area  frequent  problems  of  species  misidentification.  Hence, 
there is the need of efforts for a proper identification of the species that are present in 
the area. There is also the need of a better knowledge of some biological features used 
for stock assessment purposes. Considering that most of the elasmobranchs has no high 
commercial  importance  and  that  most  of  the  species  are  caught  in  a  very  reduced 
number,  the  possibility  of  giving  priority  in  their  collection  through  some  ad  hoc  plan 
included in the National plans appears essential.  

A  detailed  catch  information  is  vital  for  a  successful  management  of  these  species.  It 
was observed  that  the  data bases  still  present errors and  bugs that need to be  solved. 
Even  though  rays  are  in  principle  compulsively  measured  and  weighted  during  the 
conduction  of  trawl  surveys  data  still  show  several  deficiencies.  Commercial  catch 
size/age composition is unknown as size frequencies on this species is in the area almost 
completely lacking.  

Catch and landings reporting require that any single elasmobranch be correctly identified 
and recorded at species level. 

Even  though  some  improvements  have  been  done  and  more  attention  is  paid  to 
chondrichtyans,  the  limited  number  of  individuals  that  are  sampled  do  not  allow  to  get 
the  representative  size  frequencies  needed  for  a  proper  reconstruction  of  both  the 
commercial structure of the catch and of the population at sea. The lack of enough long 
and reliable time series of abundance of overall catches does not allow neither using of 
some simple approaches for stock assessment usually utilized for data poor stocks. 

7.7 COMMON CUTTLEFISH IN GSAS 17-18 

EU DCF landings data prior to 2006 were not available for GSA 17 ITA. Data from Croatia 
(GSA  17)  were  available  for  2013-2015  only.  MEDITS  hauls  carried  in  Albanian  and 
Montenegrin waters in GSA 18 are designate with “ITA” country code. 

7.8 SOLE IN GSA 17 

The  data  used  for  the  a4a  model  come  from  the  last  DCF  official  data  call  (2017).  The 
Croatian  catch  at  age  data  was  lacking  for  years  before  2012  and  they  were 
reconstructed on the base of the total landings suggested by Croatian experts and catch 
at age data composition observed for set netters. 
Regarding  the  SS3  model,  the  data  were  available  thanks  to  the  work  presented  by 
Scarcella et al. at the GFCM working group; a combination of DCF and FAO Fishstat data 
was used to obtain the long time series utilised. 

7.9 SPOTTAIL MANTIS SHRIMP IN GSAS 17-18 

Some issues have been identified in the data for S. mantis in GSA 17. 

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Length  frequency  distributions  of  the  catches  are  not  available  for  Slovenia.  However, 
this is a minor problem for the assessment as the Italian catches represent about 99.9% 
of the total catches of the species in the area. No data are available for Croatia; catches 
from the eastern coast of the Adriatic Sea seem negligible because the type of bottoms 
does not represent the ideal habitat of the species.  
The Italian length frequency distributions are available since 2007 for landing and since 
2010 for discard. 
Composition of the catches by age are not available in the DCF database.  
VB growth parameters are not reported in the DCF database. 
Medits data for this species are considered completely unreliable due the few numbers of 
specimens caught. However, this is an issue related to the behaviour of the species that 
is  scarcely  available  to  the  Medits  surveys  as  the  hauls  are  carried  out  during  the 
daylight hours when mostly of the specimens are in the borrows. 
The length frequency distributions for the Italian fleet operating in GSA 18 are available 
since  2007  for  landing  and  since  2009  for  discard.  In  these  data  time  series,  the 
coverage  of  the  different  metiers  is  very  high  representing  98%  of  the  total  biomass 
caught  in  the  area.  The  main  issue  for  Spot-tail  mantis  shrimp  in  GSA  17  is  related  to 
the estimation of the abundance at sea as SoleMon survey is not carried out in that area. 
Biomass and density indices coming from Medits survey showed high fluctuations due to 
the low availability of the species. 

7.10 HAKE IN GSA 19 

Data  from  DCF  2017  were  used.  A  difference  in  the  sum  of  products  compared  to 
landings  was  around  37%  (excluding  2003).  Discards  data  of  2006,  2009-2016  were 
available. Information on  number  of  samples  for  landings, discards and catches, as 
well as the number of measurements by length for landings, discards and catches were 
also  available.  Number  of  otoliths  was  also  available.  MEDITS  raw  data  used  for  this 
assessment  have  been  processed  by  the  expert  using  the  LFD  routine  by  STECF  EWG. 
Biological parameters by length and age and sex ratio were available for the whole time 
series (2003-2016).  In 2014 the survey was shifted in  September as a consequence of 
the administrative process undertaken Italian Ministry of Agriculture. 

7.11 HAKE IN GSA 20 

Particular deficiencies were found in the DCF data provided. Specifically, no DCF catch / 
catch-at-length  /  catch-at-age  data  were  provided  for  2007,  2009,  2010,  2011,  and 
2012. Catch-at-age data were provided only for the last trimester for 2013 and 2015. No 
MEDITS  surveys  took  place  in  2002,  2007,  2009-2013  and  2015.  The  landings  as 
calculated from the DCF data (number of individuals multiplied by their somatic weight) 
do  not  correspond  to  the  official  landings  reported.  This  issue  is  stronger  for  the  years 
2003-2006. The numbers and weights at length are not reported consistently (step size, 
initial  value,  unit  of  measurement  vary  among  years).  Specifically,  for  hake  in  GSA  20 
there were also some issues with the 2005 dataset. 

7.12 RED MULLET IN GSA 19 

Survey  sampling  period  (MEDITS)  has  been  done  in  different  year  periods.  The 
displacement  of  MEDITS  survey  to  August  (2007)  and  September  (2014),  that  it  is  the 
recruitment period for red mullet, difficult the tuning of the VPA. 

It  is  advisable,  as  it  is  write  in  the  MEDITS  protocol,  that  surveys  will  be  carried  out 
always in the same month of the year 

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7.13 RED MULLET IN GSA 20 

The STECF EWG 17-15 was not possible to give any reference points or catch advice for 
Red  mullet  in  GSA  20  for  the  following  reasons:  Large  gaps  and  inconsistencies  were 
detected    both  in  catch  and  survey  data  lead  to  great  differences  in  the  results  of  the 
three different methodologies applied to assess the stock status. In particular: 

Landings: There were gaps in the landings data for GSA 20, for the years 2007, 2009 – 
2012,  while  for  the  years  2013,  2015  landings  data  were  only  reported  for  the  last 
quarter  of  the  year.  Landings  by  gear  suffered  from  great  inconsistencies.  In  particular 
the values of the reported landings for the small scale/ coastal fisheries in the year 2003 
appeared 10 times larger than the ones reported for the trawlers. The size distribution of 
the landings, due to the previous mentioned problems in the dataset, suffered from the 
same problems especially in small length classes.  

Discards:  The  same  gaps  observed  in  the  landings  data  were  also  apparent  in  the 
discards data.  

Effort: Effort data seemed also had the same gaps as the previous data sets of landings 
and discards.  

Catch  at  age  data:  More  gaps  than  data  appeared  in  the  catch  at  age  data  set,  in 
particular:  weight  at  age  data  appeared  only  for  the  years  2004-2006,  2013  and  2014 
and only for the landing part of the catch, moreover the reported values for mean weight 
at age diverged a lot from the expected ones. The numbers and weights at age were not 
reported consistently (step size, initial value, unit of measurement vary among years). 

MEDITS  Survey:  Gaps  were  also  apparent  in  the  MEDITS  trawl  survey  data  set.  Years 
2002, 2007, 2009 -2013 and 2015 were missing. 

7.14 HAKE IN GSA 22 

Data on catch at size are available only for the years that the DCF took place, i.e. 2003-
2005,  2008,  2014,  2016  and  partly  for  2013  and  2015.  Due  to  the  limited  amount  of 
data  and  the  large  gaps  between  2008  and  2014  an  analytical  age-based  assessment 
was  not  attempted.  The  data  on  relative  biomass  from  the  MEDITS  survey  also  have 
gaps for the years the survey didn’t take place, i.e. 2002, 2007, 2009-2013, 2015 

7.15 RED MULLET IN GSA 22 

Data  on  length  distribution  by  age  are  available  only  for  the  years  that  the  DCF  took 
place,  i.e.  2003-2005,  2008,  2014,  2016  and  partly  for  2013  and  2015.  The  data  on 
relative biomass from the MEDITS survey also have gaps for the years the survey didn’t 
take place, i.e. 2002, 2007, 2009-2013, 2015. As a consequence, the years with missing 
data were even more than those with full data-sets. This has resulted in poor model fit, 
which  is  particularly  reflected  in  the  case  of  the  total  catch  residuals  and  when 
comparing predicted with actual catches. 

7.16 DEEP-WATER ROSE SHRIMP IN GSA 22 

Data on size composition of the catch are available only for the years when the DCF took 
place,  i.e.  2003-2005,  2008,  2014,  2016  and  partly  for  2013  and  2015.  The  data  on 
relative biomass from the MEDITS survey also have gaps for the years the survey didn’t 
take  place,  i.e.  2002,  2007,  2009-2013,  2015.  Thus,  an  analytical  age-based 
assessment was not attempted. 

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7.17 HAKE IN GSA 23 

Data  on  length  distribution  by  age  are  available  only  for  the  years  that  the  DCF  took 
place,  i.e.  2003-2005,  2008,  2014,  2016  and  partly  for  2013  and  2015.  Due  to  the 
limited  amount  of  data  and  the  large  gaps  between  2008  and  2014  an  analytical  age-
based  assessment  was  not  attempted.  The  data  on  relative  biomass  from  the  MEDITS 
survey also have gaps for the years the survey didn’t take place, i.e. 2002, 2007, 2009-
2013, 2015. 

7.18 RED MULLET IN GSA 23 

Data  on  length  distribution  by  age  are  available  only  for  the  years  that  the  DCF  took 
place,  i.e.  2003-2005,  2008,  2014,  2016  and  partly  for  2013  and  2015.  Due  to  the 
limited  amount  of  data  and  the  large  gaps  between  2008  and  2014  an  analytical  age-
based  assessment  was  not  attempted.  The  data  on  relative  biomass  from  the  MEDITS 
survey also have gaps for the years the survey didn’t take place, i.e. 2002, 2007, 2009-
2013, 2015. 

7.19 DEEP-WATER ROSE SHRIMP IN GSA 23 

Data on size composition of the catch are available only for the years when the DCF took 
place,  i.e.  2003-2005,  2008,  2014,  2016  and  partly  for  2013  and  2015.  The  data  on 
relative biomass from the MEDITS survey also have gaps for the years the survey didn’t 
take  place,  i.e.  2002,  2007,  2009-2013,  2015.  Thus,  an  analytical  age-based 
assessment was not attempted. 

8 DISTRIBUTION OF JUVENILES AND SPAWNERS 

ToR 10.   For  stocks  in  Table  1,    provide  detailed  maps  for  GSA  17-23  at  the  best  resolution 

possible level and related table of correspondence with relevant spatial coordinates, of: 

  The recurrent areas of juveniles' aggregations 

a) 1st-year juveniles;  

b) juveniles equal to or smaller than the minimum conservation reference size  

  The recurrent spawning aggregations areas 

DG MARE requested the STECF 17-15 EWG to provide detailed maps for GSAs 17-23 at 
the  best  resolution  possible  level  and  related  table  of  correspondence  with  relevant 
spatial coordinates of recurrent areas of juveniles' and spawning aggregation areas. 

According  to  Colloca  et  al.  (2015),  the  identification  of  nursery  grounds  and  other 
essential  fish  habitats  of  exploited  stocks  is  a  key  requirement  for  the  development  of 
spatial  conservation  planning  aimed  at  reducing  the  adverse  impact  of  fishing  on  the 
exploited populations and ecosystems. The identification of these grounds is in the core 
of designing management plans aiming at reducing fishing mortality in immature fish, a 
fundamental prerequisite for sustainable fisheries in the direction of the “spawn at least 
once” rule (Beverton and Holt, 1957). In this sense, the MEDISEH project (2013) located 
the nursery areas and spawning grounds of several important commercial species in the 

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Mediterranean for the first time several years ago. The data used included MEDITS data 
from  1994  to  2010  and  persistence  was  calculated  based  on  the  output  of  different 
models in different areas. 

It is believed that the MEDISEH project results already provide the information that was 
requested from this EWG, so an attempt was made to update the MEDISEH results with 
additional data acquired since the project conclusion. However, it was impossible for the 
EWG  17-15  to  follow  the  modelling  approach  adopted  by  MEDISEH  due  to  time 
constraints.  Therefore,  a  different  approach  was  sought,  but  which  would  still  yield 
persistency  as  the  result.  The  methodology  developed  and  adopted  by  the  EWG  is 
described in detail below. 

Since  the  results  of  this  analysis  are  mostly  in  line  with  the  MEDISEH  project  and  the 
knowledge of the EWG experts, it is believed that the deviations of this results from that 
of  the  MEDISEH  project  arise  from  the  additional  6  years  of  data  that  were  included  in 
this  analysis.  Moreover,  different  models  were  applied  to  individual  GSAs  in  the 
MEDISEH  project,  whereas  GSAs  were  grouped  according  to  the  stock  assessment  unit 
for  the  current  analysis  and  the  same  methodology  was  applied  to  all  of  these  groups. 
The detailed description of the biology and persistence areas is available in the MEDISEH 
project report (2013), so only the deviations of our results from those  of the MEDISEH 
project are presented in this report. 

Methodology 

Geospatial analysis to identify hauls with sampling stations 

An R-script was used in order to identify samples as belonging to a particular sampling 
station  (Annex  II).  The  input  file  for  the  script  is  a  data  frame  containing  the 
identification of each sample (the DCF TAc table). There were 9348 hauls from 1994 to 
2016 performed in GSAs 17, 18, 19, 20, 22 and 23. 

Cluster  analysis  was  carried  out  on  MEDITS  samples  by  strata  (strata  A  and  B  were 
treated  as  one  stratum).  First,  a  triangular  matrix  was  computed  based  on  Euclidean 
distances  in  degrees  using  the  Latitude  and  Longitude at  the  middle point  of  each  haul 
as  the  haul  location  variable.  In  this  matrix,  the  minimum  distances  between  all 
individual  hauls  were  calculated.  Based  on  these  distances,  samples  were  grouped  into 
clusters  using  the  average  linkage  procedure.  After  that,  a  threshold  (maximum 
Euclidean distance) was applied to separate the haul clusters. In concept, these clusters 
should be comprised of hauls, performed at the same station in consecutive years. 

Several thresholds were tested, the magnitude chosen based on the distance covered by 
a  MEDITS  haul  (between  1.5  and  3  miles  for  half  an  hour  and  one  hour  hauls, 
respectively),  and  minimum  distance  expected  between  different  hauls  at  the  same 
sampling  stations.  The  following  thresholds  were  tested:  0.015,  0.02,  0.025,  0.03, 
0.035, 0.04 and 0.045 degrees. 

It is expected that the haul depths of the nearby hauls are similar, so the coefficient of 
variation of the haul depth within the groups was calculated. In addition, the number of 
unclustered  hauls  was  calculated  to  indicate  the  proportion  of  hauls  that  would  be 
eliminated  when  using  the  different  thresholds.  Both  variables  were  used  to  find  the 
most suitable clustering threshold, looking for the lowest CV by losing the least amount 
of  data.  The  0.03  threshold  was  set  as  the  best  compromise  between  the  number  of 
unclustered  hauls  and  within-group  coefficient  of  variation  in  depth.  The  analyses 
implicitly assumes that hauls belonging to the same station are carried out in the same 
trawl track or very close to it, but with a variation in its middle point that should not be 
larger than 0.03º. Stations located further than this distance were considered a different 

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station. Hence, the analysis yielded clusters (stations) for which the central position was 
at least 0.03º away from the central position of the next closest group.  

Finally,  a  manual  revision  of  the  clusters,  now  presumed  to  be  the  stations,  was 
undertaken.  This  revision  was  done  in  order  to  check  that  clusters  did  not  include  two 
samples in the same year. This happened due to two different situations: 1) a particular 
station  had  been  repeated  in  a  given  year  and  both  were  finally  considered  valid  (102 
cases in the whole time series and all GSAs); 2) twenty-two clusters out of 853 showed 
two different stations clustered together, leading to two time series in the same cluster. 
In  this  case,  the  stations  were  separated  according  to  their  different  depths  and/or 
number of haul if it had been consistently the same for the whole time series for at least 
one of the stations. 

Clusters  having  less  than  four  hauls  were  excluded  (which  involved  a  total  of  1546 
hauls), since persistence, analysis with small numbers of hauls would not yield relevant 
results.  Finally,  a  total  of  82.4%  of  the  hauls  (7702  hauls)  in  MEDITS  database  were 
selected and used to perform the persistence analyses. 

High density persistence analysis at sampling stations 

The  EWG-17-15  decided  to  use  maturity  stages  for  determining  the  juvenile,  adult  and 
spawning fractions of the populations, since it gives a more direct and hence less biased 
information  than  either  the  age  or  minimum  conservation  length.  Therefore,  MEDITS 
maturity  stages  were  used  to  define:  juveniles  (undetermined  and  immature:  stages  0 
and 1, respectively), adults (developing, spawning, post-spawning and resting: stages 2-
4, respectively) or spawners (stage 3; Annex 2). An R script was developed to calculate 
the  persistence  of  “high”  values  of  standardized  abundance  for  each  fraction  of  the 
population:  those  above  the  50th  percentile  threshold  when  only  non-zero  values  are 
included.  The  50th  percentile  threshold  was calculated on  an  annual  basis  and  following 
the GSA aggregation adopted in the stock assessment carried out for each stock during 
the  STECF-17-15.  Once  the  standardized  abundance  was  calculated  for  each  haul,  the 
persistence was calculated as the number of years (hauls) within each sampling  cluster 
with values above the 50th percentile threshold divided by the total number of years that 
each particular sampling cluster had been sampled, obtaining a persistence (probability) 
value ranging from 0 to 1 for each stock. 

The  persistence  values  were  plotted  on  a  final  map  including  all  GSAs.  Tables  are 
provided  in  .csv  format  containing  information  of  the  geographical  midpoint  of  each 
sampling station and the value of persistence. 

Results 

The number of hauls detected at each cluster is plotted in Figure 8.1. The map shows a 
consistent  repetition  of  hauls  year  after  year  in  the  GSAs  18,  19,  20,  22  and  23.  The 
GSA17  showed  more  variability  in  the  position  of  hauls,  leading  the  cluster  analysis  to 
detect a larger number of groups separated more than 0.03º, so in that area the more 
common values of number of hauls detected in each station mainly ranged from 4 to 13 
(Figure 8.1). 
The  results  obtained  for  the  distribution  of  the  persistent  hotspots  of  juveniles  (above 
the 0.6 persistence value) agree with those of Colloca et al. (2015) in terms of location 
of most important areas. As an example, the approach presented here was also able to 
detect  the  Pomo  Pit  in  the  Adriatic,  and  the  west  and  the  east  of  the  Peloponnese 
important persistent areas for juveniles of hake. 

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Figure 8.1 Number of hauls detected at MEDITS clusters/stations for GSAs 17-23 in the 
period 1994-2016. 

Hake (HKE) 

The MEDITS data allowed detection of the main areas of distribution of juveniles of hake 
(Figure  8.2),  but  not  spawners  since  the  surveys  are  carried  out  in  late  spring  and/or 
beginning  of  summer.  Instead  of  spawners,  the  distribution  of  adults  was  mapped 
(Figure 8.3). 

The distribution of juvenile persistent areas detected with this method is in line with the 
results  of  the  MEDISEH  project  (2013).  In  the  Adriatic,  a  high  persistency  area  was 
detected  in  the  middle  Adriatic,  in  the  Pomo  Pit  area,  extending  southwards  to  south 
Italy  and  north  of  Greece.  Adults’  persistent  areas  presented  a  different  pattern  of 
distribution,  with  the  eastern  part  of  the  Adriatic  being  more  important  than  the  west 
part,  also  in  coincidence  with  MEDISEH  project  (2013).  In  the  rest  of  GSAs,  there  was 
more coincidence between the distribution of persistent juvenile and adult areas. 

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Figure 8.2 Persistence of hake juveniles at MEDITS stations for GSAs 17-23. 

Figure 8.3 Persistence of hake adults at MEDITS stations for GSAs 17-23. 

Red mullet (MUT) 

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The  MEDITS  data  allowed  detection  of  the  spawning  areas  (Figure  8.4)  of  red  mullet, 
since  the  survey  is  performed  during  the  spawning  season  and  the  catch  is  mostly 
composed of spawners. The adult distribution (Figure 8.5Error! Reference source not 
ound.) was also mapped for comparison, but the juveniles distribution mapping was not 
considered  reliable  since  red  mullet  already  spawn  at  1  year  of  age,  so  the  immature 
individuals are not detected with the MEDITS survey. 

The  distribution  of  red  mullet  spawning  areas  detected  with  this  method  is  in  line  with 
the  results  of  the  MEDISEH  project  (2013).  In  the  Adriatic,  the  high  persistency  area 
starts  close  to  the  midline  in  the  Northern  Adriatic  and  extends  towards  SE  Croatian 
coast. Since the fishing pressure in the central part of the Northern Adriatic is lower than 
the surrounding areas,  it would be advisable to check whether this area might  function 
as a refugee for red mullet. Several smaller spawning areas identified around the Greek 
coast also confirm the MEDISEH results. 

Figure 8.4 Persistence of red mullet spawners at MEDITS stations for GSAs 17-23. 

Since  the  MEDITS  survey  is  performed  during  the  spawning  season,  the  distribution  of 
adults  (Figure  8.5)  is  similar  to  that  of  spawners,  so  it  yields  minimal  additional 
information.  It  can  be  seen  from  Figure  8.5  that  the  areas  of  adult  persistence  only 
slightly extend from the spawning areas. 

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Figure 8.5 Persistence of red mullet adults at MEDITS stations for GSAs 17-23. 

Deep-water rose shrimp (DPS) 

The MEDITS survey does not coincide with the spawning season of the deep-water rose 
shrimp,  so  it  was  possible  to  detect  juveniles  and  adults’  distribution,  but  not  the 
spawning areas of this species. The results obtained are similar to those of the MEDISEH 
project with minor differences of detected persistence in some areas, which are believed 
to arise from the additional data available for this analysis. 

The persistent areas of juvenile deep-water rose shrimp distribution ( Figure 8.6) in the 
Middle  Adriatic  in  this  analysis  was  detected,  but  their  intensity  was  relatively  lower 
compared to results of the MEDISEH project (2013). This difference may be due to both 
the additional data used in the present analysis, and the calculation of the 50th percentile 
threshold  taking  into  account  GSAs  17,  18  and  19,  whereas  in  the  MEDISEH  approach 
the thresholds used where calculated on a GSA basis.  The MEDISEH project found these 
areas to be persistent as well, but here they are relatively less important than the Middle 
Adriatic  ones.  MEDISEH  also  found  the  South-eastern  nursery  area  to  coincide  with 
spawning grounds of this species. 

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Figure 8.6 Persistence of deep-water rose shrimp juveniles at MEDITS stations for GSAs 
17-23. 

The adults distribution areas (  Figure 8.7) determined in this analysis coincide with the 
spawning areas identified by the MEDISEH project. 

Figure  8.7  Persistence  of  deep-water  rose  shrimp  adults  at  MEDITS  stations  for  GSAs 
17-23. 

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Conclusions 

The  results  presented  here  relate  to  juveniles  as  defined  by  maturation  state  not  fish 
size. The distribution of adults relates to the distribution at the time of the year that the 
MEDITS survey is carried out, though for red mullet this may also be very suitable as a 
spawning area.  

The  results  from  these  analyses  provide  the  spatially  located  observations  for 
persistence  and  the  underlying  data  set.  If  this  is  to  be  used  to  inform  selection  of 
suitable  areas  to  protect  juveniles  or  adults,  then  these  can  be  obtained  by  using  the 
areas of persistence. To obtain area boundary information, these persistence values can 
be evaluated within a GIS framework to give explicit areas. 

Recommendations 

The  results  of  the  cluster  analysis  applied  to  Euclidean  distances  between  hauls 
calculated from mean latitude and longitude showed it is a useful automatic method for 
the classification of hauls into sampling stations. However, the results obtained require a 
“manual” checking which is time consuming. 

Therefore,  it  would  be  advisable  to  update  the  MEDITS  protocol  to  require  the  unique 
identification of each sampling station, which should be reported for each individual haul. 
This  information  would  enable  reliable  spatial  analysis  needed  to  better  identify 
persistence areas. 

It  may  be  advisable  to  analyse  the  percentage  of  overlapping  of  juveniles  and  adults 
persistent areas to explore the viability of the fisheries if managed trying to avoid either 
juveniles or spawners. 

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10 CONTACT DETAILS OF EWG-17-15 PARTICIPANTS 

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

STECF members 

Name 

Address1 

Telephone no. 

Email 

ABELLA, Alvaro 

MARTIN, Paloma 

Independent consutant 

aabellafisheries@gmail.com 

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 

Invited experts 

Name 

Address 

Telephone 
no. 

Email 

SIMMONDS, 
(Chair) 

John 

Private Consultant, 

Netherby 
Kirkbymoorside, 

-  West  End 

- 

YO62 6AD North Yorkshire, 

United  Kingdom  of  Great 
Britain and Northern Ireland 

e.j.simmonds1@gmail.com 

ANGELINI, Silvia 

CNR ISMAR Ancona 

silvia.angelini@an.ismar.cnr.it 

BITETTO, Isabella 

COISPA Tecnologia & Ricerca 

bitetto@coispa.it 

COLLOCA, Francesco 

CONSIGLIO  NAZIONALE  DELLE 
RICERCHE 
ISTITUTO 
- 
AMBIENTE MARINO COSTIERO 

ISAJLOVIC, Igor 

Institute  of  Oceanography  and 
Fisheries 

MANTOPOULOU 
PALOUKA, Danai 

Hellenic  Centre 
Research 

for  Marine 

  francesco.colloca@iamc.cnr.it 

  igor@izor.hr 

danaim@hcmr.gr 

MANTZOYNI, Eirini 

Fisheries Research Institute (FRI)- 
Kavala 

  emantzo@inale.gr 

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MOLLA 
Karolina 

GAZI, 

DTU Aqua 

kmollagazi@gmail.com 

MURENU, Matteo 

Università di Cagliari - DBAE 

mmurenu@unica.it 

MUSUMECI, Claudia 

CIBM 

clamusu@gmail.com 

ORDINAS, Francesc 

Instituto Español de Oceanografía 

ORIO, Alessandro 

Swedish University of Agricultural 
Sciences- Department of Aquatic 
Resources- Institute of Marine 
Research 

PENGAL, Polona 

REVIVO- 
ichthyological 
research 

Institute 
and 

for 
ecological 

  xisco.ordinas@ieo.es 

  alessandro.orio@slu.se 

  polona.pengal@ozivimo.si 

RECASENS, Laura 

Institut  Ciències  Mar  Barcelona 
(ICM-CSIC) 

  laura@icm.csic.es 

SBRANA, Mario 

Centro  Interuniversitario  Biologia 
Marina (CIBM) 

  msbrana@cibm.it 

SGARDELI, Vasiliki 

Hellenic Centre for Marine 
Research (HCMR) 

  vsgard@hcmr.gr 

TSIKLIRAS, 
Athanassios 

School of Biology- Aristotle 
University of Thessaloniki 

TSERPES, George 

Hellenic Centre for Marine 
Research (HCMR) 

atsik@bio.auth.gr 

  gtserpes@hcmr.gr 

JRC experts 

Name 

Address  Telephone no. 

Email 

MANNINI,  Alessandro 

JRC 

Tel.+390332785784 

Fax+390332789658 

alessandro.mannini@ec.europa.eu 

VASILAKOPOULOUS,  Paris  

JRC 

Tel. +390332785714  paris.vasilakopoulos@ec.europa.eu 

European Commission 

Name 

Address 

Telephone no. 

Email 

OSIO, Giacomo Chato 

MARE.D.1 

+32 229-93939 

Giacomo-Chato.OSIO@ec.europa.eu 

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VASILAKOPOULOUS,  
Paris  

STECF 
secretariat 

JRC-stecf-secretariat@ec.europa.eu 

Observers 

Name 

BERNAL, Miguel 

Address 

GFCM 

PIRON, Marzia 

Mediterranean Advisory Council (MEDAC) 

Telephone no. 

Email 

Miguel.Bernal@fao.org 

marzia_piron@hotmail.it 

11 LIST OF ANNEXES  

Electronic annexes are published on the meeting’s web site on:  
https://stecf.jrc.ec.europa.eu/ewg1715 

List of electronic annexes documents: 

EWG-17-15 – Annex I - Length Based Indicators 

EWG-17-15 – Annex II – Rcode to produce ToR10 maps 

12 LIST OF BACKGROUND DOCUMENTS  

Background documents are published on the meeting’s web site on:  
https://stecf.jrc.ec.europa.eu/ewg17015 

List of background documents: 

EWG-17-15 – Declarations of invited and JRC experts (see also section XX of this 
report – List of participants) 

ToRs_STECF_EWG17-15.pdf 

663 

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B
-
A
X
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1
7
-
0
2
2
-
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. 

665 

doi:10.2760/90316 

ISBN 978-92-79-67494-5