Scientific, Technical and Economic Committee for Fisheries (STECF) : Mediterranean assessments part 1 (STECF-15-18).
Date: 2016-01-12
Institutions: Institute for the Protection and Security of the Citizen
Subjects: Mediterranean Sea, catch quota, common fisheries policy, data collection, evaluation of resources, fishery management, research report, sustainable development

Scientific, Technical and Economic 
Committee for Fisheries (STECF) 

Mediterranean assessments part 1  
(STECF 15-18)  

Edited by Massimiliano Cardinale, Dimitrios Damalas and Giacomo Chato Osio  

This report was reviewed by the STECF during its 50TH plenary meeting  
held from 9 to 13 November 2015 in Brussels 

Report EUR 27638  EN 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
European Commission 

Joint Research Centre (JRC) 

Institute for the Protection and Security of the Citizen (IPSC) 

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JRC 98676 
EUR 27638 EN 
ISBN 978-92-79-54141-4 
ISSN 1831-9424 
doi:10.2788/406771 
Luxembourg: Publications Office of the European Union, 2015 
© European Union, 2014 
Reproduction is authorised provided the source is acknowledged 

How to cite this report: 
Scientific, Technical and Economic Committee for Fisheries (STECF) – Mediterranean assessments part 1 (STECF-15-18). 2015. Publications 
Office of the European Union, Luxembourg, EUR 27638 EN, JRC 98676, 410 pp. 

Abstract 
The Expert Working Group meeting of the Scientific, Technical and Economic Committee for Fisheries EWG 15-11 was held 
from  31  Aug-04  Sep  2015  in  Palma  de  Mallorca,  Spain  to  assess  the  status  of  demersal  and  small  pelagic  stocks  in  the 
Mediterranean Sea against the proposed FMSY reference points. The report was reviewed by the STECF plenary in November 
2015. 

 
 
  
 
 
 
 
 
 
 
 
 
TABLE OF CONTENTS 

TABLE OF CONTENTS .................................................................................................................. 3 
Request to the STECF ............................................................................................................... 21 
Observations of the STECF ....................................................................................................... 21 
Conclusions of the STECF ......................................................................................................... 22 
Executive summary .................................................................................................... 26 
1. 
Findings And Conclusions Of The Working Group ..................................................... 29 
2. 
Follow Up Items ......................................................................................................... 30 
3. 
Introduction ............................................................................................................... 31 
4. 
TERMS OF REFERENCE FOR EWG-15-11 ............................................................................. 31 
4.1 
ASSESS TRENDS IN HISTORIC AND RECENT STOCK PARAMETERS ............................. 34 
5 
SUMMARY SHEETS ..................................................................................................... 34 
5.1 
5.1.1 
SUMMARY SHEET OF HAKE IN GSA 1 ......................................................................... 34 
5.1.1.1  Stock development over time .................................................................................... 34 
5.1.1.2  Stock advice ................................................................................................................ 34 
5.1.1.3  Basis of the assessment ............................................................................................. 35 
5.1.1.4  Catch options ............................................................................................................. 35 
5.1.1.5  Reference points ........................................................................................................ 36 
5.1.1.6  Quality of the assessment .......................................................................................... 36 
5.1.2 
SUMMARY SHEET OF HAKE IN GSA 5 ......................................................................... 37 
5.1.2.1  Stock development over time .................................................................................... 37 
5.1.2.2  Stock advice ................................................................................................................ 37 
5.1.2.3  Basis of the assessment ............................................................................................. 38 
5.1.2.4  Catch options ............................................................................................................. 38 
5.1.2.5  Reference points ........................................................................................................ 39 
5.1.2.6  Quality of the assessment .......................................................................................... 39 
5.1.3 
SUMMARY SHEET OF HAKE IN GSA 6 ......................................................................... 40 
5.1.3.1  Stock development over time .................................................................................... 40 
5.1.3.2  Stock advice ................................................................................................................ 40 
5.1.3.3  Basis of the assessment ............................................................................................. 41 
5.1.3.4  Catch options ............................................................................................................. 41 
5.1.3.5  Reference points ........................................................................................................ 41 
5.1.3.6  Quality of the assessment .......................................................................................... 42 
5.1.4 
SUMMARY SHEET OF HAKE IN GSA 7 ......................................................................... 43 
5.1.4.1  Stock development over time .................................................................................... 43 
5.1.4.2  Stock advice ................................................................................................................ 43 
5.1.4.3  Basis of the assessment ............................................................................................. 44 
5.1.4.4  Catch options ............................................................................................................. 44 
5.1.4.5  Reference points ........................................................................................................ 45 
5.1.4.6  Quality of the assessment .......................................................................................... 45 

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5.1.5 
SUMMARY SHEET OF HAKE IN GSA 8 ......................................................................... 46 
5.1.5.1  Stock development over time .................................................................................... 46 
5.1.5.2  Stock advice ................................................................................................................ 46 
5.1.5.3  Basis of the assessment ............................................................................................. 46 
5.1.5.4  Catch options ............................................................................................................. 46 
5.1.5.5  Reference points ........................................................................................................ 46 
5.1.5.6  Quality of the assessment .......................................................................................... 46 
SUMMARY SHEET OF HAKE IN GSA 9 ......................................................................... 47 
5.1.6 
5.1.6.1  Stock development over time .................................................................................... 47 
5.1.6.2  Stock advice ................................................................................................................ 48 
5.1.6.3  Basis of the assessment ............................................................................................. 48 
5.1.6.4  Catch options ............................................................................................................. 48 
5.1.6.5  Reference points ........................................................................................................ 49 
5.1.6.6  Quality of the assessment .......................................................................................... 49 
5.1.7 
SUMMARY SHEET OF HAKE IN GSA 10 ....................................................................... 50 
5.1.7.1  Stock development over time .................................................................................... 50 
5.1.7.2  Stock advice ................................................................................................................ 51 
5.1.7.3  Basis of the assessment ............................................................................................. 51 
5.1.7.4  Catch options ............................................................................................................. 51 
5.1.7.5  Reference points ........................................................................................................ 52 
5.1.7.6  Quality of the assessment .......................................................................................... 52 
SUMMARY SHEET OF HAKE IN GSA 11 ....................................................................... 53 
5.1.8 
5.1.8.1  Stock development over time .................................................................................... 53 
5.1.8.2  Stock advice ................................................................................................................ 53 
5.1.8.3  Basis of the assessment ............................................................................................. 54 
5.1.8.4  Catch options ............................................................................................................. 54 
5.1.8.5  Reference points ........................................................................................................ 54 
5.1.8.6  Quality of the assessment .......................................................................................... 55 
5.1.9 
SUMMARY SHEET OF HAKE IN GSA 1-7 ...................................................................... 56 
5.1.9.1  Stock development over time .................................................................................... 56 
5.1.9.2  Stock advice ................................................................................................................ 56 
5.1.9.3  Basis of the assessment ............................................................................................. 57 
5.1.9.4  Catch options ............................................................................................................. 57 
5.1.9.5  Reference points ........................................................................................................ 57 
5.1.9.6  Quality of the assessment .......................................................................................... 58 
5.1.10  SUMMARY SHEET OF HAKE IN GSA 9-11 .................................................................... 59 
5.1.10.1 Stock development over time .................................................................................... 59 
5.1.10.2 Stock advice ................................................................................................................ 60 
5.1.10.3 Basis of the assessment ............................................................................................. 60 
5.1.10.4 Catch options ............................................................................................................. 60 
5.1.10.5 Reference points ........................................................................................................ 61 

4 

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5.1.10.6 Quality of the assessment .......................................................................................... 61 
5.1.11  SUMMARY SHEET OF GIANT RED SHRIMP IN GSA 9 .................................................. 62 
5.1.11.1 Stock development over time .................................................................................... 62 
5.1.11.2 Stock advice ................................................................................................................ 62 
5.1.11.3 Basis of the assessment ............................................................................................. 63 
5.1.11.4 Catch options ............................................................................................................. 63 
5.1.11.5 Reference points ........................................................................................................ 63 
5.1.11.6 Quality of the assessment .......................................................................................... 64 
5.1.12  SUMMARY SHEET OF GIANT RED SHRIMP IN GSA 10 ................................................ 65 
5.1.12.1 Stock development over time .................................................................................... 65 
5.1.12.2 Stock advice ................................................................................................................ 65 
5.1.12.3 Basis of the assessment ............................................................................................. 66 
5.1.12.4 Catch options ............................................................................................................. 66 
5.1.12.5 Reference points ........................................................................................................ 66 
5.1.12.6 Quality of the assessment .......................................................................................... 67 
5.1.13  SUMMARY SHEET OF GIANT RED SHRIMP IN GSA 11 ................................................ 68 
5.1.13.1 Stock development over time .................................................................................... 68 
5.1.13.2 Stock advice ................................................................................................................ 68 
5.1.13.3 Basis of the assessment ............................................................................................. 69 
5.1.13.4 Catch options ............................................................................................................. 69 
5.1.13.5 Reference points ........................................................................................................ 69 
5.1.13.6 Quality of the assessment .......................................................................................... 70 
5.1.14  SUMMARY SHEET OF BLUE AND RED SHRIMP IN GSA 1 ............................................ 71 
5.1.14.1 Stock development over time .................................................................................... 71 
5.1.14.2 Stock advice ................................................................................................................ 72 
5.1.14.3 Basis of the assessment ............................................................................................. 72 
5.1.14.4 Catch options ............................................................................................................. 72 
5.1.14.5 Reference points ........................................................................................................ 73 
5.1.14.6 Quality of the assessment .......................................................................................... 73 
5.1.15  SUMMARY SHEET OF BLUE AND RED SHRIMP IN GSA 6 ............................................ 74 
5.1.15.1 Stock development over time .................................................................................... 74 
5.1.15.2 Stock advice ................................................................................................................ 75 
5.1.15.3 Basis of the assessment ............................................................................................. 75 
5.1.15.4 Catch options ............................................................................................................. 75 
5.1.15.5 Reference points ........................................................................................................ 76 
5.1.15.6 Quality of the assessment .......................................................................................... 76 
STOCK ASSESSMENT ................................................................................................... 77 
5.2 
5.2.1 
STOCK ASSESSMENT OF HAKE IN GSA 1 ..................................................................... 77 
5.2.1.1  Stock Identification .................................................................................................... 77 
5.2.1.2  Growth ....................................................................................................................... 77 
5.2.1.3  Maturity ...................................................................................................................... 77 

5 

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5.2.1.4  Natural mortality ........................................................................................................ 77 
5.2.1.5  Fisheries ...................................................................................................................... 77 
5.2.1.5.1  General description of the fisheries ........................................................................ 78 
5.2.1.5.2  Management regulations applicable in 2015 ........................................................ 78 
5.2.1.5.3  Catches ................................................................................................................... 78 
5.2.1.5.4  Landings (by fleet if posible) ................................................................................... 78 
5.2.1.5.5  Discards (by fleet if posible) .................................................................................... 79 
5.2.1.5.6  Fishing effort (by fleet if possible) .......................................................................... 80 
5.2.1.6  Scientific surveys ........................................................................................................ 81 
5.2.1.6.1  Survey #1 ( MEDITS) ................................................................................................ 81 
5.2.1.6.1.1  Methods ............................................................................................................... 81 
5.2.1.6.1.2  Geographical distribution .................................................................................... 82 
5.2.1.6.1.3  Trends in abundance and biomass ...................................................................... 82 
5.2.1.6.1.4  Trends in abundance by length or age ................................................................ 83 
5.2.1.7  Stock Assessment ....................................................................................................... 84 
5.2.1.7.1  Methods: XSA .......................................................................................................... 84 
5.2.1.7.2 
Input data ............................................................................................................... 84 
5.2.1.7.3  Results ..................................................................................................................... 86 
5.2.1.8  Reference points ........................................................................................................ 89 
5.2.1.8.1  Methods .................................................................................................................. 89 
5.2.1.8.2 
Input data ............................................................................................................... 89 
5.2.1.8.3  Results ..................................................................................................................... 89 
5.2.1.9  Data quality ................................................................................................................ 90 
5.2.1.10 Short term predictions 2016-2018 ............................................................................. 90 
5.2.1.10.1  Method ................................................................................................................. 90 
5.2.1.10.2 
Input parameters .................................................................................................. 90 
5.2.1.10.3  Results ................................................................................................................... 90 
5.2.1.11 Short term predictions 2015-2017 by fleet ................................................................ 91 
5.2.1.11.1  Method ................................................................................................................. 91 
Input parameters .................................................................................................. 91 
5.2.1.11.2 
5.2.1.11.3  Results ................................................................................................................... 91 
5.2.1.12 Medium term predictions .......................................................................................... 92 
5.2.1.12.1  Method ................................................................................................................. 92 
5.2.1.13 Stock advice ................................................................................................................ 92 
5.2.1.14 Management strategy evaluation .............................................................................. 92 
STOCK ASSESSMENT OF HAKE IN GSA 5 ..................................................................... 94 
5.2.2 
5.2.2.1  Stock Identification .................................................................................................... 94 
5.2.2.2  Growth ....................................................................................................................... 94 
5.2.2.3  Maturity ...................................................................................................................... 95 
5.2.2.4  Natural mortality ........................................................................................................ 95 
5.2.2.5  Fisheries ...................................................................................................................... 95 

6 

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5.2.2.5.1  General description of the fisheries ........................................................................ 95 
5.2.2.5.2  Management regulations applicable in 2015 ........................................................ 95 
5.2.2.5.3  Catches ................................................................................................................... 95 
5.2.2.5.4  Discards .................................................................................................................. 96 
5.2.2.5.5  Fishing effort ........................................................................................................... 96 
5.2.2.6  Scientific surveys ........................................................................................................ 97 
5.2.2.6.1  Survey #1 (BALAR- MEDITS) .................................................................................... 97 
5.2.2.6.1.1  Methods ............................................................................................................... 97 
5.2.2.6.1.2  Geographical distribution .................................................................................... 97 
5.2.2.6.1.3  Trends in abundance and biomass ...................................................................... 98 
5.2.2.6.1.4  Trends in abundance by length or age ................................................................ 98 
5.2.2.7  Stock Assessment ....................................................................................................... 98 
5.2.2.7.1  Methods .................................................................................................................. 98 
5.2.2.7.2 
Input data ............................................................................................................... 99 
5.2.2.7.3  Results ................................................................................................................... 100 
5.2.2.8  Reference points ...................................................................................................... 105 
5.2.2.8.1  Methods ................................................................................................................ 105 
5.2.2.8.2 
Input data ............................................................................................................. 105 
5.2.2.8.3  Results ................................................................................................................... 105 
5.2.2.9  Data quality .............................................................................................................. 106 
5.2.2.10 Short term predictions 2016-2018 ........................................................................... 106 
5.2.2.10.1  Method ............................................................................................................... 106 
5.2.2.10.2 
Input parameters ................................................................................................ 106 
5.2.2.10.3  Results ................................................................................................................. 106 
5.2.2.11 Medium term predictions ........................................................................................ 107 
5.2.2.11.1  Method ............................................................................................................... 107 
5.2.2.12 Stock advice .............................................................................................................. 107 
5.2.2.13 Management strategy evaluations .......................................................................... 108 
5.2.3 
STOCK ASSESSMENT OF HAKE IN GSA 6 ................................................................... 109 
5.2.3.1  Stock Identification .................................................................................................. 109 
5.2.3.2  Growth ..................................................................................................................... 109 
5.2.3.3  Maturity .................................................................................................................... 109 
5.2.3.4  Natural mortality ...................................................................................................... 109 
5.2.3.5  Fisheries .................................................................................................................... 109 
5.2.3.5.1  General description of the fisheries ...................................................................... 109 
5.2.3.5.2  Management regulations applicable in 2015 ...................................................... 109 
5.2.3.5.3  Landings ................................................................................................................ 110 
5.2.3.5.4  Discards ................................................................................................................ 110 
5.2.3.5.5  Fishing effort ......................................................................................................... 110 
5.2.3.6  Scientific surveys ...................................................................................................... 111 
5.2.3.6.1  Survey #1 (MEDITS) ............................................................................................... 111 

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5.2.3.6.1.1  Methods ............................................................................................................. 111 
5.2.3.6.1.2  Trends in abundance and biomass .................................................................... 111 
5.2.3.6.1.3  Trends in abundance by length or age .............................................................. 111 
5.2.3.7  Stock Assessment ..................................................................................................... 112 
5.2.3.7.1  Methods ................................................................................................................ 112 
5.2.3.7.2 
Input data ............................................................................................................. 112 
5.2.3.7.3  Results ................................................................................................................... 115 
5.2.3.8  Reference points ...................................................................................................... 119 
5.2.3.8.1  Methods ................................................................................................................ 119 
Input data ............................................................................................................. 119 
5.2.3.8.2 
5.2.3.8.3  Results ................................................................................................................... 119 
5.2.3.9  Data quality .............................................................................................................. 119 
5.2.3.10 Short term predictions 2015-2017 ........................................................................... 119 
5.2.3.10.1  Method ............................................................................................................... 119 
5.2.3.10.2 
Input parameters ................................................................................................ 119 
5.2.3.10.3  Results ................................................................................................................. 119 
5.2.3.11 Short term predictions 2015-2017 by fleet .............................................................. 121 
5.2.3.11.1  Method ............................................................................................................... 121 
Input parameters ................................................................................................ 121 
5.2.3.11.2 
5.2.3.11.3  Results ................................................................................................................. 121 
5.2.3.12 Medium term predictions ........................................................................................ 122 
5.2.3.13 Stock advice .............................................................................................................. 122 
5.2.3.14 Management strategy evaluation ............................................................................ 122 
5.2.4 
STOCK ASSESSMENT OF HAKE IN GSA 7 ................................................................... 123 
5.2.4.1  Stock Identification .................................................................................................. 123 
5.2.4.2  Growth ..................................................................................................................... 123 
5.2.4.3  Maturity .................................................................................................................... 123 
5.2.4.4  Natural mortality ...................................................................................................... 124 
5.2.4.5  Fisheries .................................................................................................................... 125 
5.2.4.5.1  General description of the fisheries ...................................................................... 125 
5.2.4.5.2  Management regulations applicable in 2015 ...................................................... 125 
5.2.4.5.3  Catches ................................................................................................................. 126 
5.2.4.5.4  Landing ................................................................................................................. 127 
5.2.4.5.5  Discards ................................................................................................................ 127 
5.2.4.5.6  Fishing effort ......................................................................................................... 128 
5.2.4.6  Scientific surveys ...................................................................................................... 128 
5.2.4.6.1  Survey #1 ( MEDITS) .............................................................................................. 128 
5.2.4.6.1.1  Methods ............................................................................................................. 128 
5.2.4.6.1.2  Trends in abundance and biomass .................................................................... 129 
5.2.4.6.1.3  Trends in abundance by length or age .............................................................. 129 
5.2.4.7  Stock Assessment ..................................................................................................... 130 

8 

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5.2.4.7.1  Methods ................................................................................................................ 130 
Input data ............................................................................................................. 130 
5.2.4.7.2 
5.2.4.7.3  Results ................................................................................................................... 132 
5.2.4.8  Reference points ...................................................................................................... 138 
5.2.4.8.1  Methods ................................................................................................................ 138 
5.2.4.8.2 
Input data ............................................................................................................. 138 
5.2.4.8.3  Results ................................................................................................................... 138 
5.2.4.9  Data quality .............................................................................................................. 139 
5.2.4.10 Short term predictions 2015-2017 ........................................................................... 139 
5.2.4.10.1  Method ............................................................................................................... 139 
5.2.4.10.2 
Input parameters ................................................................................................ 139 
5.2.4.10.3  Results ................................................................................................................. 140 
5.2.4.11 Short term predictions 2015-2017 by fleet .............................................................. 140 
5.2.4.11.1  Method ............................................................................................................... 140 
5.2.4.11.2 
Input parameters ................................................................................................ 140 
5.2.4.11.3  Results ................................................................................................................. 140 
5.2.4.12 Medium term predictions 2015-2017 by fleet ......................................................... 141 
5.2.4.12.1  Method ............................................................................................................... 141 
5.2.4.13 Stock advice .............................................................................................................. 141 
5.2.4.14 Management strategy evaluation ............................................................................ 142 
5.2.5 
STOCK ASSESSMENT OF HAKE IN GSA 8 ................................................................... 143 
5.2.5.1  Stock Identification .................................................................................................. 143 
5.2.5.2  Growth ..................................................................................................................... 143 
5.2.5.3  Maturity .................................................................................................................... 143 
5.2.5.4  Fisheries .................................................................................................................... 143 
5.2.5.4.1  General description of the fisheries ...................................................................... 143 
5.2.5.4.2  Management regulations applicable in 2015 ...................................................... 144 
5.2.5.4.3  Landings ................................................................................................................ 144 
5.2.5.4.4  Discards ................................................................................................................ 144 
5.2.5.4.5  Fishing effort ......................................................................................................... 144 
5.2.5.5  Scientific surveys ...................................................................................................... 144 
5.2.5.5.1  Survey #1 (MEDITS) ............................................................................................... 144 
5.2.5.5.1.1  Methods ............................................................................................................. 144 
5.2.5.6  Stock Assessment ..................................................................................................... 144 
5.2.5.6.1  Methods ................................................................................................................ 144 
Input data ............................................................................................................. 145 
5.2.5.6.2 
5.2.5.6.3  Results ................................................................................................................... 148 
5.2.5.7  Reference points ...................................................................................................... 152 
5.2.5.7.1  Methods ................................................................................................................ 152 
5.2.5.8  Data quality .............................................................................................................. 152 
5.2.5.9  Short term predictions 2016-2018 ........................................................................... 152 

9 

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5.2.5.10 Medium term predictions ........................................................................................ 152 
5.2.5.11 Stock advice .............................................................................................................. 153 
5.2.6 
STOCK ASSESSMENT OF HAKE IN GSA 9 ................................................................... 154 
5.2.6.1  Stock Identification .................................................................................................. 154 
5.2.6.2  Growth ..................................................................................................................... 155 
5.2.6.3  Maturity .................................................................................................................... 155 
5.2.6.4  Natural mortality ...................................................................................................... 156 
5.2.6.5  Fisheries .................................................................................................................... 156 
5.2.6.5.1  General description of the fisheries ...................................................................... 156 
5.2.6.5.2  Management regulations applicable in 2015 ...................................................... 156 
5.2.6.5.3  Landings ................................................................................................................ 157 
5.2.6.5.4  Discards (by fleet if posible) .................................................................................. 161 
5.2.6.5.5  Fishing effort ......................................................................................................... 162 
5.2.6.6  Scientific surveys ...................................................................................................... 163 
5.2.6.6.1  Survey #1 (MEDITS) ............................................................................................... 163 
5.2.6.6.1.1  Methods ............................................................................................................. 163 
5.2.6.6.1.2  Geographical distribution .................................................................................. 164 
5.2.6.6.1.3  Trends in abundance and biomass .................................................................... 165 
5.2.6.6.1.4  Trends in abundance by length or age .............................................................. 166 
5.2.6.7  Stock Assessment ..................................................................................................... 167 
5.2.6.7.1  Method: XSA ......................................................................................................... 167 
Input data ............................................................................................................. 167 
5.2.6.7.2 
5.2.6.7.3  Results ................................................................................................................... 168 
5.2.6.8  Reference points ...................................................................................................... 175 
5.2.6.8.1  Methods ................................................................................................................ 175 
5.2.6.8.2 
Input data ............................................................................................................. 175 
5.2.6.8.3  Results ................................................................................................................... 175 
5.2.6.9  Data quality .............................................................................................................. 176 
5.2.6.10 Short term predictions 2015-2017 ........................................................................... 177 
5.2.6.10.1  Method ............................................................................................................... 177 
5.2.6.10.2 
Input parameters ................................................................................................ 177 
5.2.6.10.3  Results ................................................................................................................. 177 
5.2.6.11 Short term predictions 2015-2017 by fleet .............................................................. 178 
5.2.6.11.1  Method ............................................................................................................... 178 
5.2.6.11.2 
Input parameters ................................................................................................ 178 
5.2.6.11.3  Results ................................................................................................................. 178 
5.2.6.12 Medium term predictions ........................................................................................ 179 
5.2.6.12.1  Method ............................................................................................................... 179 
5.2.6.13 Stock advice .............................................................................................................. 179 
5.2.6.14 Management strategy evaluation ............................................................................ 179 
STOCK ASSESSMENT OF HAKE IN GSA 10 ................................................................. 181 
5.2.7 

10 

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5.2.7.1  Stock Identification .................................................................................................. 181 
5.2.7.2  Growth ..................................................................................................................... 181 
5.2.7.3  Maturity .................................................................................................................... 183 
5.2.7.4  Natural mortality ...................................................................................................... 183 
5.2.7.5  Fisheries .................................................................................................................... 184 
5.2.7.5.1  General description of the fisheries ...................................................................... 184 
5.2.7.5.2  Management regulations applicable in 2015 ...................................................... 184 
5.2.7.5.3  Catches ................................................................................................................. 184 
5.2.7.5.4  Landings ................................................................................................................ 185 
5.2.7.5.5  Discards ................................................................................................................ 186 
5.2.7.5.6  Fishing effort (by fleet if possible) ........................................................................ 186 
5.2.7.6  Scientific surveys ...................................................................................................... 187 
5.2.7.6.1  Survey #1 (MEDITS) ............................................................................................... 187 
5.2.7.6.1.1  Methods ............................................................................................................. 187 
5.2.7.6.1.2  Geographical distribution .................................................................................. 189 
5.2.7.6.1.3  Trends in abundance and biomass .................................................................... 189 
5.2.7.6.1.4  Trends in abundance by length or age .............................................................. 190 
5.2.7.6.2  Survey #2 (GRUND - historical information) ......................................................... 193 
5.2.7.6.2.1  Methods ............................................................................................................. 193 
5.2.7.6.2.2  Geographical distribution .................................................................................. 193 
5.2.7.6.2.3  Trends in abundance and biomass .................................................................... 193 
5.2.7.6.2.4  Trends in abundance by length or age .............................................................. 193 
5.2.7.7  Stock Assessment ..................................................................................................... 194 
5.2.7.7.1  Methods ................................................................................................................ 194 
Input data ............................................................................................................. 194 
5.2.7.7.2 
5.2.7.7.3  Results ................................................................................................................... 197 
5.2.7.8  Reference points ...................................................................................................... 208 
5.2.7.8.1  Methods ................................................................................................................ 208 
5.2.7.8.2 
Input data ............................................................................................................. 208 
5.2.7.8.3  Results ................................................................................................................... 208 
5.2.7.9  Data quality .............................................................................................................. 208 
5.2.7.10 Short term predictions 2016-2018 ........................................................................... 209 
5.2.7.10.1  Method ............................................................................................................... 209 
5.2.7.10.2 
Input parameters ................................................................................................ 209 
5.2.7.10.3  Results ................................................................................................................. 209 
5.2.7.11 Medium term predictions ........................................................................................ 211 
5.2.7.11.1  Method ............................................................................................................... 211 
5.2.7.12 Stock advice .............................................................................................................. 211 
5.2.7.13 Management strategy evaluation ............................................................................ 211 
5.2.8 
STOCK ASSESSMENT OF HAKE IN GSA 11 ................................................................. 213 
5.2.8.1  Stock Identification .................................................................................................. 213 

11 

11 

 
5.2.8.2  Growth ..................................................................................................................... 213 
5.2.8.3  Maturity .................................................................................................................... 213 
5.2.8.4  Natural mortality ...................................................................................................... 214 
5.2.8.5  Fisheries .................................................................................................................... 214 
5.2.8.5.1  General description of the fisheries ...................................................................... 214 
5.2.8.5.2  Management regulations applicable in 2015 ...................................................... 214 
5.2.8.5.3  Landings ................................................................................................................ 215 
5.2.8.5.4  Discards ................................................................................................................ 216 
5.2.8.5.5  Fishing effort ......................................................................................................... 218 
5.2.8.6  Scientific surveys ...................................................................................................... 219 
5.2.8.6.1  Survey #1 (MEDITS) ............................................................................................... 219 
5.2.8.6.1.1  Methods ............................................................................................................. 219 
5.2.8.6.1.2  Geographical distribution .................................................................................. 220 
5.2.8.6.1.3  Trends in abundance and biomass .................................................................... 220 
5.2.8.6.1.4  Trends in abundance by length or age .............................................................. 221 
5.2.8.7  Stock Assessment ..................................................................................................... 223 
5.2.8.7.1  Method: XSA ......................................................................................................... 223 
5.2.8.7.2 
Input data ............................................................................................................. 223 
5.2.8.7.3  Results ................................................................................................................... 225 
5.2.8.8  Reference points ...................................................................................................... 229 
5.2.8.8.1  Methods ................................................................................................................ 229 
Input data ............................................................................................................. 229 
5.2.8.8.2 
5.2.8.8.3  Results ................................................................................................................... 229 
5.2.8.9  Data quality .............................................................................................................. 229 
5.2.8.10 Short term predictions 2015-2017 ........................................................................... 233 
5.2.8.10.1  Method ............................................................................................................... 233 
Input parameters ................................................................................................ 233 
5.2.8.10.2 
5.2.8.10.3  Results ................................................................................................................. 233 
5.2.8.11 Medium term predictions ........................................................................................ 234 
5.2.8.11.1  Method ............................................................................................................... 234 
5.2.8.12 Stock advice .............................................................................................................. 234 
5.2.8.13 Management strategy evaluation ............................................................................ 234 
5.2.9 STOCK ASSESSMENT OF HAKE IN GSA 1-7 .................................................................... 236 
5.2.9.1  Stock Identification .................................................................................................. 236 
5.2.9.2  Growth ..................................................................................................................... 236 
5.2.9.3  Maturity .................................................................................................................... 236 
5.2.9.4  Natural mortality ...................................................................................................... 236 
5.2.9.5  Fisheries .................................................................................................................... 236 
5.2.9.5.1  General description of the fisheries ...................................................................... 236 
5.2.9.5.2  Management regulations applicable in 2015 ...................................................... 236 
5.2.9.5.3  Catches ................................................................................................................. 237 

12 

12 

 
5.2.9.5.4  Landings ................................................................................................................ 237 
5.2.9.5.5  Discards ................................................................................................................ 237 
5.2.9.5.6  Fishing effort ......................................................................................................... 237 
5.2.9.6  Scientific surveys ...................................................................................................... 237 
5.2.9.6.1  Survey #1 (MEDITS) ............................................................................................... 237 
5.2.9.6.1.1  Methods ............................................................................................................. 237 
5.2.9.6.1.2  Geographical distribution .................................................................................. 239 
5.2.9.6.1.3  Trends in abundance and biomass .................................................................... 240 
5.2.9.6.1.4  Trends in abundance by length or age .............................................................. 240 
5.2.9.7  Stock Assessment: XSA ............................................................................................. 240 
5.2.9.7.1  Method: XSA ......................................................................................................... 240 
5.2.9.7.2 
Input data ............................................................................................................. 240 
5.2.9.7.3  Results ................................................................................................................... 241 
5.2.9.8  Stock Assessment: a4a ............................................................................................. 245 
5.2.9.8.1  Method: a4a ......................................................................................................... 245 
Input data ............................................................................................................. 245 
5.2.9.8.2 
5.2.9.8.3  Results ................................................................................................................... 247 
5.2.9.9  Reference points ...................................................................................................... 257 
5.2.9.9.1  Methods ................................................................................................................ 257 
5.2.9.9.2 
Input data ............................................................................................................. 257 
5.2.9.9.3  Results ................................................................................................................... 257 
5.2.9.10 Data quality .............................................................................................................. 258 
5.2.9.11 Short term predictions 2015-2017 ........................................................................... 258 
5.2.9.11.1  Method ............................................................................................................... 258 
Input parameters ................................................................................................ 258 
5.2.9.11.2 
5.2.9.11.3  Results ................................................................................................................. 258 
5.2.9.12 Short term predictions 2015-2017 by fleet .............................................................. 259 
5.2.9.12.1  Method ............................................................................................................... 259 
5.2.9.12.2 
Input parameters ................................................................................................ 259 
5.2.9.12.3  Results ................................................................................................................. 259 
5.2.9.13 Stock advice .............................................................................................................. 261 
5.2.9.14 Management strategy evaluation ............................................................................ 261 
5.2.10  STOCK ASSESSMENT OF HAKE IN GSA 9-11 ............................................................. 263 
5.2.10.1 Stock Identification .................................................................................................. 263 
5.2.10.2 Growth ..................................................................................................................... 263 
5.2.10.3 Maturity .................................................................................................................... 263 
5.2.10.4 Natural mortality ...................................................................................................... 263 
5.2.10.5 Fisheries .................................................................................................................... 263 
5.2.10.5.1  General description of the fisheries .................................................................... 263 
5.2.10.5.2  Management regulations applicable in 2015 .................................................... 264 
5.2.10.5.3  Catches ............................................................................................................... 264 

13 

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5.2.10.5.4  Landings .............................................................................................................. 264 
5.2.10.5.5  Discards .............................................................................................................. 264 
5.2.10.5.6  Fishing effort ....................................................................................................... 264 
5.2.10.6 Scientific surveys ...................................................................................................... 264 
5.2.10.6.1  Survey #1 (MEDITS) ............................................................................................. 264 
5.2.10.6.1.1  Methods ........................................................................................................... 264 
5.2.10.6.1.2  Geographical distribution ................................................................................ 264 
5.2.10.6.1.3  Trends in abundance and biomass .................................................................. 265 
5.2.10.6.1.4  Trends in abundance by length or age ............................................................ 265 
5.2.10.7 Stock Assessment ..................................................................................................... 265 
5.2.10.7.1  Method: XSA ....................................................................................................... 265 
5.2.10.7.2 
Input data ........................................................................................................... 265 
5.2.10.7.3  Results ................................................................................................................. 268 
5.2.10.8 Reference points ...................................................................................................... 274 
5.2.10.8.1  Methods .............................................................................................................. 274 
Input data ........................................................................................................... 274 
5.2.10.8.2 
5.2.10.8.3  Results ................................................................................................................. 274 
5.2.10.9 Data quality .............................................................................................................. 275 
5.2.10.10  Short term predictions 2015-2017 ...................................................................... 275 
5.2.10.10.1  Method ............................................................................................................. 275 
5.2.10.10.2 
Input parameters .............................................................................................. 275 
5.2.10.10.3  Results ............................................................................................................... 276 
5.2.10.11  Short term predictions 2015-2017 by fleet ......................................................... 276 
5.2.10.11.1  Method ............................................................................................................. 276 
Input parameters .............................................................................................. 276 
5.2.10.11.2 
5.2.10.11.3  Results ............................................................................................................... 277 
5.2.10.12  Medium term predictions ................................................................................... 278 
5.2.10.12.1  Method ............................................................................................................. 278 
5.2.10.13  Stock advice ......................................................................................................... 278 
5.2.10.14  Management strategy evaluation ....................................................................... 278 
5.2.11  STOCK ASSESSMENT OF GIANT RED SHRIMP IN GSA 9 ............................................ 279 
5.2.11.1 Stock Identification .................................................................................................. 279 
5.2.11.2 Growth ..................................................................................................................... 279 
5.2.11.3 Maturity .................................................................................................................... 280 
5.2.11.4 Natural mortality ...................................................................................................... 281 
5.2.11.5 Fisheries .................................................................................................................... 281 
5.2.11.5.1  General description of the fisheries .................................................................... 281 
5.2.11.5.2  Management regulations applicable in 2015 .................................................... 282 
5.2.11.5.3  Landings .............................................................................................................. 282 
5.2.11.5.4  Discards .............................................................................................................. 283 
5.2.11.5.5  Fishing effort ....................................................................................................... 284 

14 

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5.2.11.6 Scientific surveys ...................................................................................................... 285 
5.2.11.6.1  Survey #1 (MEDITS) ............................................................................................. 285 
5.2.11.6.1.1  Methods ........................................................................................................... 285 
5.2.11.6.1.2  Geographical distribution ................................................................................ 286 
5.2.11.6.1.3  Trends in abundance and biomass .................................................................. 287 
5.2.11.6.1.4  Trends in abundance by length or age ............................................................ 287 
5.2.11.7 Stock Assessment ..................................................................................................... 291 
5.2.11.7.1  Methods .............................................................................................................. 291 
5.2.11.7.2 
Input data ........................................................................................................... 291 
5.2.11.7.3  Results ................................................................................................................. 294 
5.2.11.8 Reference points ...................................................................................................... 302 
5.2.11.8.1  Methods .............................................................................................................. 302 
Input data ........................................................................................................... 302 
5.2.11.8.2 
5.2.11.8.3  Results ................................................................................................................. 303 
5.2.11.9 Data quality .............................................................................................................. 304 
5.2.11.10  Short term predictions 2015-2017 ...................................................................... 305 
5.2.11.10.1  Method ............................................................................................................. 305 
5.2.11.10.2 
Input parameters .............................................................................................. 305 
5.2.11.10.3  Results ............................................................................................................... 305 
5.2.11.11  Medium term predictions ................................................................................... 306 
5.2.11.11.1  Method ............................................................................................................. 306 
5.2.11.12  Stock advice ......................................................................................................... 306 
5.2.11.13  Management strategy evaluation ....................................................................... 306 
5.2.12  STOCK ASSESSMENT OF GIANT RED SHRIMP IN GSA 10 .......................................... 308 
5.2.12.1 Stock Identification .................................................................................................. 308 
5.2.12.2 Growth ..................................................................................................................... 309 
5.2.12.3 Maturity .................................................................................................................... 311 
5.2.12.4 Natural mortality ...................................................................................................... 311 
5.2.12.5 Fisheries .................................................................................................................... 312 
5.2.12.5.1  General description of the fisheries .................................................................... 312 
5.2.12.5.2  Management regulations applicable in 2015 .................................................... 312 
5.2.12.5.3  Catches (by fleet if possible) ............................................................................... 312 
5.2.12.5.4  Landings (by fleet if possible) ............................................................................. 312 
5.2.12.5.5  Discards (by fleet if posible) ................................................................................ 314 
5.2.12.5.6  Fishing effort (by fleet if possible) ...................................................................... 314 
5.2.12.6 Scientific surveys ...................................................................................................... 315 
5.2.12.6.1  Survey #1 (MEDITS) ............................................................................................. 315 
5.2.12.6.1.1  Methods ........................................................................................................... 315 
5.2.12.6.1.2  Geographical distribution ................................................................................ 317 
5.2.12.6.1.3  Trends in abundance and biomass .................................................................. 317 
5.2.12.6.1.4  Trends in abundance by length or age ............................................................ 318 

15 

15 

 
5.2.12.7 Stock Assessment ..................................................................................................... 322 
5.2.12.7.1  Methods .............................................................................................................. 322 
5.2.12.7.2 
Input data ........................................................................................................... 322 
5.2.12.7.3  Results ................................................................................................................. 324 
5.2.12.8 Reference points ...................................................................................................... 329 
5.2.12.8.1  Methods .............................................................................................................. 329 
5.2.12.8.2 
Input data ........................................................................................................... 329 
5.2.12.8.3  Results ................................................................................................................. 329 
5.2.12.9 Data quality .............................................................................................................. 330 
5.2.12.10  Short term predictions 2016-2018 ...................................................................... 330 
5.2.12.10.1  Method ............................................................................................................. 330 
5.2.12.10.2 
Input parameters .............................................................................................. 330 
5.2.12.10.3  Results ............................................................................................................... 330 
5.2.12.11  Short term predictions 2015-2017 by fleet ......................................................... 331 
5.2.12.11.1  Method ............................................................................................................. 331 
Input parameters .............................................................................................. 331 
5.2.12.11.2 
5.2.12.11.3  Results ............................................................................................................... 331 
5.2.12.12  Medium term predictions ................................................................................... 332 
5.2.12.12.1  Method ............................................................................................................. 332 
5.2.12.13  Stock advice ......................................................................................................... 332 
5.2.12.14  Management strategy evaluation ....................................................................... 333 
5.2.13  STOCK ASSESSMENT OF GIANT RED SHRIMP IN GSA 11 .......................................... 334 
5.2.13.1 Stock Identification .................................................................................................. 334 
5.2.13.2 Growth ..................................................................................................................... 335 
5.2.13.3 Maturity .................................................................................................................... 335 
5.2.13.4 Natural mortality ...................................................................................................... 336 
5.2.13.5 Fisheries .................................................................................................................... 336 
5.2.13.5.1  General description of the fisheries .................................................................... 336 
5.2.13.5.2  Management regulations applicable in 2015 .................................................... 337 
5.2.13.5.3  Landings .............................................................................................................. 337 
5.2.13.5.4  Discards .............................................................................................................. 340 
5.2.13.5.5  Fishing effort ....................................................................................................... 340 
5.2.13.6 Scientific surveys ...................................................................................................... 341 
5.2.13.6.1  Survey #1 (MEDITS) ............................................................................................. 341 
5.2.13.6.1.1  Methods ........................................................................................................... 341 
5.2.13.6.1.2  Geographical distribution ................................................................................ 342 
5.2.13.6.1.3  Trends in abundance and biomass .................................................................. 342 
5.2.13.6.1.4  Trends in abundance by length or age ............................................................ 343 
5.2.13.7 Stock Assessment ..................................................................................................... 345 
5.2.13.7.1  Methods .............................................................................................................. 345 
Input data ........................................................................................................... 345 
5.2.13.7.2 

16 

16 

 
5.2.13.7.3  Results ................................................................................................................. 346 
5.2.13.8 Reference points ...................................................................................................... 350 
5.2.13.8.1  Methods .............................................................................................................. 350 
5.2.13.8.2 
Input data ........................................................................................................... 350 
5.2.13.8.3  Results ................................................................................................................. 350 
5.2.13.9 Data quality .............................................................................................................. 351 
5.2.13.10  Short term predictions 2015-2017 ...................................................................... 352 
5.2.13.10.1  Method ............................................................................................................. 352 
5.2.13.10.2 
Input parameters .............................................................................................. 352 
5.2.13.10.3  Results ............................................................................................................... 352 
5.2.13.11  Medium term predictions ................................................................................... 353 
5.2.13.12  Stock advice ......................................................................................................... 353 
5.2.13.13  Management strategy evaluation ....................................................................... 353 
5.2.14  STOCK ASSESSMENT OF BLUE AND RED SHRIMP IN GSA 1 ..................................... 354 
5.2.14.1 Stock Identification .................................................................................................. 354 
5.2.14.2 Growth ..................................................................................................................... 354 
5.2.14.3 Maturity .................................................................................................................... 355 
5.2.14.4 Natural mortality ...................................................................................................... 355 
5.2.14.5 Fisheries .................................................................................................................... 355 
5.2.14.5.1  General description of the fisheries .................................................................... 355 
5.2.14.5.2  Management regulations applicable in 2015 .................................................... 356 
5.2.14.5.3  Catches ............................................................................................................... 356 
5.2.14.5.4  Landings .............................................................................................................. 357 
5.2.14.5.5  Discards .............................................................................................................. 357 
5.2.14.5.6  Fishing effort ....................................................................................................... 357 
5.2.14.6 Scientific surveys ...................................................................................................... 358 
5.2.14.6.1  Survey #1 (MEDITS) ............................................................................................. 358 
5.2.14.6.1.1  Methods ........................................................................................................... 358 
5.2.14.6.1.2  Geographical distribution ................................................................................ 358 
5.2.14.6.1.3  Trends in abundance and biomass .................................................................. 359 
5.2.14.6.1.4  Trends in abundance by length or age ............................................................ 359 
5.2.14.7 Stock Assessment ..................................................................................................... 360 
5.2.14.7.1  Methods .............................................................................................................. 360 
5.2.14.7.2 
Input data ........................................................................................................... 360 
5.2.14.7.3  Results ................................................................................................................. 362 
5.2.14.8 Reference points ...................................................................................................... 365 
5.2.14.8.1  Methods .............................................................................................................. 365 
5.2.14.8.2 
Input data ........................................................................................................... 365 
5.2.14.8.3  Results ................................................................................................................. 365 
5.2.14.9 Data quality .............................................................................................................. 366 
5.2.14.10  Short term predictions 2016-2018 ...................................................................... 366 

17 

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5.2.14.10.1  Method ............................................................................................................. 366 
Input parameters .............................................................................................. 366 
5.2.14.10.2 
5.2.14.10.3  Results ............................................................................................................... 366 
5.2.14.11  Medium term predictions ................................................................................... 367 
5.2.14.12  Stock advice ......................................................................................................... 367 
5.2.14.13  Management strategy evaluation ....................................................................... 367 
5.2.15  STOCK ASSESSMENT OF BLUE AND RED SHRIMP IN GSA 6 ..................................... 370 
5.2.15.1 Stock Identification .................................................................................................. 370 
5.2.15.2 Growth ..................................................................................................................... 370 
5.2.15.3 Maturity .................................................................................................................... 370 
5.2.15.4 Natural mortality ...................................................................................................... 370 
5.2.15.5 Fisheries .................................................................................................................... 370 
5.2.15.5.1  General description of the fisheries .................................................................... 370 
5.2.15.5.2  Management regulations applicable in 2015 .................................................... 371 
5.2.15.5.3  Catches (by fleet if posible) ................................................................................. 371 
5.2.15.5.4  Landings .............................................................................................................. 372 
5.2.15.5.5  Discards .............................................................................................................. 372 
5.2.15.5.6  Fishing effort (by fleet if possible) ...................................................................... 372 
5.2.15.6 Scientific surveys ...................................................................................................... 373 
5.2.15.6.1  Survey #1 (MEDITS) ............................................................................................. 373 
5.2.15.6.1.1  Methods ........................................................................................................... 373 
5.2.15.6.1.2  Geographical distribution ................................................................................ 374 
5.2.15.6.1.3  Trends in abundance and biomass .................................................................. 374 
5.2.15.6.1.4  Trends in abundance by length or age ............................................................ 375 
5.2.15.7 Stock Assessment ..................................................................................................... 376 
5.2.15.7.1  Methods .............................................................................................................. 376 
Input data ........................................................................................................... 376 
5.2.15.7.2 
5.2.15.7.3  Results ................................................................................................................. 378 
5.2.15.8 Reference points ...................................................................................................... 384 
5.2.15.8.1  Methods .............................................................................................................. 384 
5.2.15.8.2 
Input data ........................................................................................................... 384 
5.2.15.8.3  Results ................................................................................................................. 384 
5.2.15.9 Data quality .............................................................................................................. 385 
5.2.15.10  Short term predictions 2015-2017 ...................................................................... 385 
5.2.15.10.1  Method ............................................................................................................. 385 
Input parameters .............................................................................................. 385 
5.2.15.10.2 
5.2.15.10.3  Results ............................................................................................................... 385 
5.2.15.11  Medium term predictions ................................................................................... 386 
5.2.15.12  Stock advice ......................................................................................................... 386 
5.2.15.13  Management strategy evaluation ....................................................................... 386 

18 

18 

 
6 

SPATIO-TEMPORAL MAPS OF HIGH OCCURRENCE OF JUVENILES AND/OR SPAWNERS OF 
HAKE, GIANT RED SHRIMP AND BLUE AND RED SHRIMP ........................................ 388 
7 
DATA QUALITY AND COMPLETENESS ....................................................................... 396 
7.1 Data Overview .................................................................................................................. 396 
7.2 Stock Specific Data Issues ................................................................................................. 396 
Hake in GSA 1 ......................................................................................................................... 396 
Hake in GSA 5 ......................................................................................................................... 397 
Hake in GSA 6 ......................................................................................................................... 397 
Hake in GSA 7 ......................................................................................................................... 397 
Hake in GSA 8 ......................................................................................................................... 397 
Hake in GSA 9 ......................................................................................................................... 397 
Hake in GSA 10 ....................................................................................................................... 397 
Hake in GSA 11 ....................................................................................................................... 397 
Hake in GSA 9-11 .................................................................................................................... 398 
Giant Red Shrimp in GSA 9 ..................................................................................................... 398 
Giant Red Shrimp in GSA 10 ................................................................................................... 398 
Giant Red Shrimp in GSA 11 ................................................................................................... 398 
Blue and Red Shrimp in GSA 1 ................................................................................................ 398 
Blue and Red Shrimp in GSA 6 ................................................................................................ 398 
REFERENCES ............................................................................................................. 399 
8 
CONTACT DETAILS OF STECF MEMBERS AND EWG-15-11 List of Participants ........ 404 
9 
List of Background Documents ................................................................................ 409 
10 

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SCIENTIFIC, TECHNICAL AND ECONOMIC COMMITTEE FOR FISHERIES 
(STECF) 

Mediterranean assessments part 1 (STECF-15-18) 

THIS REPORT WAS REVIEWED BY THE STECF DURING ITS 50TH PLENARY 
MEETING HELD FROM 9 TO 13 NOVEMBER 2015 IN BRUSSELS 

20 

20 

 
 
 
 
 
 
 
 
 
 
Request to the STECF 

STECF is requested to review the report of  the STECF Expert Working Group meeting 15-11, 

evaluate the findings and make any appropriate comments and recommendations. 

Observations of the STECF 
The  meeting  was  held  in  Palma  de  Mallorca,  Spain,  from  31  Aug  -  4  Sep  2015  and  hosted  by  the 
Centro Oceanográfico de Baleares - Instituto Español de Oceanografía. It was the first of the STECF 
expert meetings, within STECF’s 2015 work programme, planned to undertake stock assessments in 
the  Mediterranean  Sea.  The  meeting  was  chaired  by  Massimiliano  Cardinale  and  attended  by  22 
experts, including 4 STECF members. Furthermore, two JRC experts and one DG MARE representative 
were  also  present.  Data  of  historical  fisheries  and  scientific  surveys  derived  from  the  official 
Mediterranean DCF data call issued to Member States on April 2015 with deadline on 2nd of July 2015 
and ‘operational deadline’ on 17th of August.  

The terms of reference for EWG-15-11of the meeting were: 

For the 15 stocks given in Table 4.1.1, the STECF-EWG 15-11 is requested to: 
ToR  1  –  Assess  trends  in  historic  and  recent  stock  parameters  for  the  longest  time  series  possible 
available up to and including 2014, for the stocks proposed in the Table below. This shall cover the 
evaluation  of  the  level  of  fishing  mortality  at  age,  spawning  stock  biomass,  stock  biomass,  and 
recruits  at  age.  Data  on  fishing  effort  shall  be  provided  by  fleet  segments  and  shall  be  the  most 
detailed  possible  to  support  the  establishment  of  a  fishing  effort  or  capacity  baseline.  Different 
assessment models should be applied as appropriate, including analyses of retrospective effects. 

List of proposed stocks 

Nb 

1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
11 
12 
13 
14 
15 

Geographical 
Sub-Areas 
GSA 1 
GSA 5 
GSA 6 
GSA 7 
GSA 81 
GSA 9 
GSA 10 
GSA 11 
GSAs 1-7 
GSAs 8-11 
GSA 9 
GSA 10 
GSA 11 
GSA 6 
GSA 1 

Common name 

Scientific name 

Priority 

Hake 
Hake 
Hake 
Hake 
Hake 
Hake 
Hake 
Hake 
Hake 
Hake 
Giant red shrimp 
Giant red shrimp 
Giant red shrimp 
Blue and red shrimp 
Blue and red shrimp 

High 
High 
High 
High 
High 
High 
High 
High 
High 
High 

Merluccius merluccius 
Merluccius merluccius 
Merluccius merluccius 
Merluccius merluccius 
Merluccius merluccius 
Merluccius merluccius 
Merluccius merluccius 
Merluccius merluccius 
Merluccius merluccius 
Merluccius merluccius 
Aristaeomorpha foliacea  Medium 
Aristaeomorpha foliacea  Medium 
Aristaeomorpha foliacea  Medium 
Aristeus antennatus 
Aristeus antennatus 

High 
High 

1 Although a full analytical assessment may not be possible to perform for hake in GSA 8, the EWG is requested to provide a preliminary 
analysis  with  some  elements  such  as  the  level  of  fishing  mortality,  fishing  effort,  CPUE  or  survey  indexes,  even  if  the  time  series  are 
limited. 

21 

21 

 
 
 
 
 
 
 
 
                                                       
In case it is not possible to carry out an evaluation of those stocks listed in table 4.1.1, is provided a 
reserve list of stocks  

ToR 2 – Propose and evaluate candidate MSY value or range of values and safeguard points 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  3  –  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 or other appropriate proxy by 2018 and 2020. In particular, 
predict: 

i)  The  level  of  fishing  mortality  which  minimize  the  risk  of  SSB  falling  below  Blim  with  a    5% 

probability and provide MSY or maximize the total yield from the stock in the long term; and 
   ii)  The  level  of  fishing  effort  exerted  by  different  fleet  segments  which  is  commensurate  to  the 
sustainable short-term and medium-term forecasts of the proposed changes. 

ToR  4  –  On  the  basis  of  the  existing  information,  prepare  and/or  up-date  maps  showing  areas  and 
periods  with  high  occurrence  of  juveniles  and/or  spawners  of  Merluccius  merluccius,  Aristeus 
antennatus and Aristaeomorpha foliacea. 

ToR  5  –  Provide  a  synoptic  overview  of:  (i)  the  fishery;  (ii)  the  most  recent  state  of  the  stock 
(spawning  stock  biomass,  stock  biomass,  recruits,  and,  if  possible,  exploitation  level  by  fleet 
segment); (iii) the source of data and methods and; (iv) the management advice, including MSY value 
or range of values and safeguard points. 

ToR 6 - Summarize and concisely describe all data quality deficiencies, including possible limitations 
with the surveys, of relevance for the assessment of stocks and fisheries. Such review and description 
are to be based on the data format of the official DCF data calls for the Mediterranean Sea issued on 
April 2015. 

Comments of the STECF 
Based  on  the  findings  in  the  EWG-14-19  report,  STECF  observes  that  the  EWG  15-11  undertook  the  stock 
assessment of 15 stocks. Mediterranean hake was assessed in the individual GFCM GSAs 1, 5, 6, 7, 8, 9, 10, 11 
and jointly for GSA 1, 5, 6, 7 and 9, 10, 11. Giant red shrimp was assessed in GSA 9, 10, 11 and Blue and red 
shrimp in GSA 1 and 6. 

For 1 stock (Hake in GSA 8), the assessment was conducted but not accepted due to insufficient length data 
being available. STECF notes that hake only constitute ~2% of total demersal landings in GSA8.  

A total of 13 out of 14 stocks for which assessment was accepted were classified as exploited unsustainably 
with the exception of Giant red shrimp in GSA 9 (see Table 0-1 for details). 

STECF notes that partial fishing mortality by fleet is presented for the main fisheries that exploit each single 
stock in the area. There were also estimated ranges for FMSY based on empirical relationship for F0.1 based on 
information of stocks of ICES area. 

22 

22 

 
 
 
 
 
 
 
 
 
 
 
Table  0-1  Synoptic  table  of  the  stock  assessed  during  EWG  15-11.  In  red  are  stocks  for  which  current  F  is 
larger than FMSY. 

STECF  notes  that  for  hake  in  GSA7  and  GSA  11,  very  high  F/FMSY  ratios  were  estimated  (F/FMSY  >>  5~15).  No 
explanations  as  to  why  the  ratios  are  so  high  (besides  assuming  that  these  are  correctly  estimated  by  the 
assessment model) are given in the report but it is possible that the high ratios are due to inappropriate stock 
boundary definitions. Current GSAs boundaries may be not necessarily encompass the entire stock, which may 
in fact be spread across more than one GSA. The results of the assessments conducted over wider areas (i.e. 
GSAs  1,  5,  6,  and  7  combined  and  GSAs  9,  10  and  11  combined  for  hake)  have  shown  lower  F/FMSY  ratios 
compared to the single GSAs (Table 0-1), and may partially explain the very high ratios observed in some of the 
single  GSA  assessments  e.g.  Hake  in  GSA7.  While  the  high  F/FMSY  rations  could  also  be  influenced  by  other 
factors  such  as  data  quality  or  assumptions  in  the  assessment  models,  (i.e.  constrained  selection  pattern, 
growth  parameters,  mortality  at  age,  etc.),  STECF  notes  that  the  ratios  of  F/FMSY  for  the  GSA  combined 
assessments for hake are still very high, 3.59 and 5.5 for GSAs 1_7 and GSAa 9_11, respectively meaning that 
these stocks are heavily overexploited irrespective of stock boundary assumptions.   

STECF notes that EWG 15-11 prepared or up-dated maps showing areas and periods with high occurrence of 
juveniles  and/or  spawners  of  Merluccius  merluccius,  Aristeus  antennatus  and  Aristaeomorpha  foliacea.  The 
TOR  was  addressed  by  creating  new  maps  using  MEDITS  data  showing  the  main  concentrations  of  juveniles 
and  adults.  STECF  notes  the  intrinsic  limitations  of  the  distribution  maps  when  trying  to  infer  spatial 
distribution  of  these  species.  MEDITS  surveys  are  conducted  only  in  late  spring-summer  and  are  therefore 
unlikely to be representative of the spatial distributions at other times of the year. 

STECF also notes that in fulfilment of TOR (6), stock specific evaluations of the data quality were conducted for 
all stocks requested under ToR (1-5) by the experts. Deficient DCF data were observed for Hake for GSA 8 (i.e. 
Corsica), and no MEDITS data for Italian GSA 17 prior to 2002 were available. However, STECF acknowledges 
that hake catches in GSA 7 are typically only 2% of total demersal catches.  

STECF notes that stock-specific evaluations of the data quality were conducted for all stocks requested under 
ToR (1-5) by the experts and endorses the main findings. STECF notes that some unresolved issues remain, in 
particular relating to data quality and delays in data submission. 

23 

23 

 Stock areaCommon nameAssessmentF*F trawlers**F trawlers**F gillnets**F trammel**F longlinesFMSYFMSY rangeF/FMSYBlimBcurrB/BlimShort termMSEGSA 1HakeXSA1.200.910.050.110.210.14-0.295.712202201.00Yes0GSA 5HakeXSA1.120.150.10-0.217.4731752.41Yes0GSA 6HakeXSA1.391.620.10¤0.260.17-0.365.35153315991.04Yes0GSA 7HakeXSA1.641.40?0.16??0.170.030.110.08-0.1614.9176911151.45Yes0GSA 8HakeSurbanot acceptedGSA 9HakeXSA1.030.770.150.030.230.16-0.324.48156921971.40Yes0GSA 10HakeXSA1.100.260.440.210.200.13-0.275.5696716351.69Yes0GSA 11HakeXSA1.600.170.11-0.249.4173731.00Yes0GSAs 1_7HakeXSA1.401.030.070.05¦0.390.26-0.533.59518681331.57Yes0GSAs 9_11HakeXSA1.100.500.100.240.120.200.14-0.285.50235529121.24Yes0GSA 9Giant red shrimpXSA0.130.510.34-0.690.2580941.18Yes0GSA 10Giant red shrimpXSA0.910.500.010.650.43-0.881.402652651.00Yes0GSA 11Giant red shrimpXSA0.500.310.21-0.431.6126461.77Yes0GSA 1Blue and red shrimpXSA1.400.410.27-0.563.412243221.44Yes0GSA 6Blue and red shrimpXSA0.750.360.24-0.492.08128738482.99Yes0*Last year**Average of the last 3 years? French trawlers?? Spanish trawlers¤Gillnet and longliners¦ Longliners also included other gears***Probability of SSB to fall below Blim 
 
 
 
 
  
 
Conclusions of the STECF 
STECF concludes that the EWG-14-19 adequately addressed the Terms of Reference. 

24 

24 

 
 
 
 
 
Expert Working Group EWG-15-11 report 

Report to the STECF 

EXPERT WORKING GROUP ON 
Mediterranean assessments part 1 
(EWG-15-11) 

Palma de Mallorca, Spain, 31 Aug-4 Sep 2015 

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. 

25 

25 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1. 

Executive summary 

The  meeting  was  the  first  of  two  STECF  expert  meetings,  within  STECF’s  2015  work  programme, 
planned to undertake stock assessments of demersal/small pelagic species in the Mediterranean Sea. 
The meeting was organized by JRC in Palma de Mallorca from 31st of August to 4th of Septmber 2015 
and was kindly hosted by the Centro Oceanográfico de Baleares - Instituto Español de Oceanografía. 
The meeting was chaired by Massimiliano Cardinale and attended by 22 experts in total, including 4 
STECF members. Furthermore, two JRC experts and one DG MARE representative were present (see 
Chapter 13). 

Historical fisheries and scientific survey data were obtained from the official Mediterranean DCF data 
call  issued  to  Member  States  on  April  2015  with  deadline  on  2nd  of  July  2015  and  ‘operational 
deadline’ on 17th of August.  

In  fulfilment  of  TORs  1  and  5  the  EWG  15-11  undertook  the  stock  assessment  of  15  stocks. 
Mediterranean hake was assessed in the individual GFCM GSAs 1, 5, 6, 7, 8, 9, 10, 11 and jointly for 
GSA 1, 5, 6, 7 and 9, 10, 11. Giant red shrimp was assessed in GSA 9, 10, 11 and Blue and red shrimp 
in GSA 1 and 6. 
For  1  stocks  (Hake  in  GSA  8),  the  assessment  was  conducted  but  not  accepted  due  to  data  issues, 
while  a  total  of  13  out  of  14  stocks  with  an  accepted  assessment  were  classified  as  exploited 
unsustainably with the exception of Giant red shrimp in GSA 9 (see Table 1 for details). 

Table 1 *. Synoptic table of the stock assessed during EWG 15-11. In red are stocks for which current F is larger 
than FMSY. 

*ToRs requested GSA 8-11, however lack of analytical data for GSA 8 limited the assessment to GSA 9 to 11. 

TOR 2 requested to propose and  evaluate candidate MSY values  or  ranges and safeguard  points in 
terms of fishing mortality and stock biomass. This was requested for the first time to the EWG MED 
assessment working group and was addressed by using Management Strategy Evaluation to evaluate 
if the MSY ranges are precautionary or not. The MSE functions were run using R scripts developed for 
and  tested  during  STECF  15-09.  The  management  strategy  evaluation  included  uncertainty  in:  a) 

26 

26 

Stock areaCommon nameAssessmentF*F trawlers**F trawlers**F gillnets**F trammel**F longlinesFMSYFMSY rangeF/FMSYBlimBcurrB/BlimShort termMSEGSA 1HakeXSA1.200.910.050.110.210.14-0.295.712202201.00Yes0GSA 5HakeXSA1.120.150.10-0.217.4731752.41Yes0GSA 6HakeXSA1.391.620.10¤0.260.17-0.365.35153315991.04Yes0GSA 7HakeXSA1.641.40ǂ0.16ǂǂ0.170.030.110.08-0.1614.9176911151.45Yes0GSA 8HakeSurbanot acceptedGSA 9HakeXSA1.030.770.150.030.230.16-0.324.48156921971.40Yes0GSA 10HakeXSA1.100.260.440.210.200.13-0.275.5696716351.69Yes0GSA 11HakeXSA1.600.170.11-0.249.4173731.00Yes0GSAs 1_7HakeXSA1.401.030.070.05¦0.390.26-0.533.59518681331.57Yes0GSAs 9_11HakeXSA1.100.500.100.240.120.200.14-0.285.50235529121.24Yes0GSA 9Giant red shrimpXSA0.130.510.34-0.690.2580941.18Yes0GSA 10Giant red shrimpXSA0.910.500.010.650.43-0.881.402652651.00Yes0GSA 11Giant red shrimpXSA0.500.310.21-0.431.6126461.77Yes0GSA 1Blue and red shrimpXSA1.400.410.27-0.563.412243221.44Yes0GSA 6Blue and red shrimpXSA0.750.360.24-0.492.08128738482.99Yes0*Last year**Average of the last 3 yearsǂ French trawlersǂǂ Spanish trawlers¤Gillnet and longliners¦ Longliners also included other gears***Probability of SSB to fall below Blim 
 
 
 
 
 
 
 
recruitment around a mean level resulting from the geometric mean of the last 3 years of data, b) 
uncertainty in the MEDITS tuning fleet indices, and c) uncertainty in the perceived stock status.  
Fmsy ranges were proposed and tested for robustness of the higher F (Fupper) for all assessed stocks. 
Fupper was considered safe if the probability of SSB to fall below Blim at F = Fupper was equal to 0, which 
was the case for all stocks. FMSY ranges are summarized in Table 1. 

Following TOR 3 the EWG 15-11 also conducted short term forecasts of stock size and catches for 14 
stocks. For the first time the forecasts were also produced by fleet. No medium term forecasts were 
carried out for any of the stocks assessed at the meeting because no meaningful stock-recruitment 
relationship was estimated for any of the stock assessed. However the MSE where Fupper  would be 
reached  in  2020  is  a  long  term  forecast  under  the  assumption  of  mean  recruitment  which  is 
effectively a conservative projection of stock trends at the upper range of FMSY.  

TOR 4 requested to prepare and/or up-date maps showing areas and periods with high occurrence 
of  juveniles  and/or  spawners  of  Merluccius  merluccius,  Aristeus  antennatus  and  Aristaeomorpha 
foliacea.  The  TOR  was  addressed  by  creating  new  maps  using  MEDITS  data  (1994-2014)  and 
calculating  yearly  maps  of  high  occurrence  of  juveniles  and  spawners,  defined  base  on  a  length 
threshold.  An  intrinsic  limitation  of  the  perceived  spatial  distribution  of  these  species  is  due  to  the 
limited seasonal span of the MEDITS survey (May-September). For Mediterranean hake the identified 
areas of high juvenile occurrence include parts of the Catalan coast (GSA 6), the gulf of Lions (GSA 7), 
and the Ligurian sea (GSA 9), while there is high variability in occurrence around the Balearic Islands 
(GSA  5)  and  Sardinia  (GSA  11).  These  results  are  consistent  with  Colloca  (2013)  and  Druon  (2015). 
Sardinia  (GSA  11)  and  the  gulf  of  Lions  (GSA  7)  exhibited  the  highest  concentrations  of  hake 
spawners. 
Aristaeomorpha. foliacea occurrence in MEDITS showed relatively high concentrations of juveniles in 
every year in Sardinia (GSA 11) and the Tyrrhenian sea (GSA 10). GSAs 10 and 11 appear to be the 
main areas of occurrence of A. foliacea spawners, while there have been a few years when spawning 
aggregations  occurred  in  GSA  9  as  well,  especially  after  2004.  These  findings  for  A.  foliacea  are  in 
general agreement with previous studies (Colloca et al. 2013; 2015). 
For Aristeus. antennatus juveniles, the timing of the MEDITS survey is not considered suitable. Also, 
recruitment for this species takes place mostly at depths beyond 900m, which are not accessed by 
MEDITS  (Sarda  and  Company,  2012).  Therefore,  the  maps  produced  are  not  considered  truly 
representative  of  the  actual  nursery  areas.  Annual  maps  of  spatial  occurrence  of  A.  antennatus 
juveniles  exhibit  great  variability  from  year  to  year,  with  Sardinia  (GSA  11)  exhibiting  the  most 
persistent occurrence of juveniles.  
High  occurrence  of  individuals  compatible  with  spawning  occurred  in  almost  every  year  in  Sardinia 
(GSA 11), gulf of Lions (GSA 7) and Ligurian Sea (GSA 9), while in the Spanish GSAs (1, 5, 6) there was 
a greater interannual variability. 

TOR 6, the data call was issued on April 2015. The 'legal' deadline for submissions was the 2nd of July 
2015.  Upon  communication  with  the  member  states  some  data  tables  were  corrected  and  re-
uploaded  in  relation  to  the  'operational'  deadline  of  the  17th  August  2015.  Data  was  uploaded  by 
each country according to the following table: 

Timeline of data upload from Mediterranean Member States, data call 'legal' deadline of the 2h of July 2015; 
'operational' deadline 17 August 2015. 

COUNTRY 
ITA 

First Upload 
29 June 2015 

Last Upload 
12 August 2015 

27 

27 

 
 
 
 
 
 
ESP 
FRA 
SVN 
MLT 
CYP 
GRC 
HRV 

01 July 2015 
19 June 2015 
05 June 2015 
02 July 2015 
01 July 2015 
02 July 2015 
27 June 2015 

05 August 2015 
02 July 2015 
23 July 2015 
02 July 2015 
06 August 2015 
31 Aug 2015 
31 July 2015* 

*: additional submissions on 4 Sep 2015 upon a request by the EWG 

The overall 2015 Data Call performance of data coverage, timeliness and progress of submissions by 
member state and main  table/variable  will be  made available  by the  end of the year and after the 
completion  of  the  EWG  15-16  Mediterranean  stock  assessments  part  2,  on  the  dedicated  weblink: 
http://datacollection.jrc.ec.europa.eu/coverage 

In fulfilment of TOR (6), stock specific evaluations of the data quality were conducted for all stocks 
requested  under  ToR  (1-5)  by  the  EWG  15-11  experts.  Moreover,  JRC  team  examined  the  data 
coverage and quality of the fisheries and survey data. Results of the evaluations are reported under 
Chapter 7 and at the end of the assessment section of each stock. The main issues found by EWG 15-
11 were: deficient DCF data for Hake  for GSA 8 (i.e. Corsica), although for the first time some data 
was reported for this area; moreover effort data for all French GSA's are absent prior to 2009. There 
were some missing information for hake in GSA 9, 10 and 11 (see details in Chapter 7) and no MEDITS 
data for Italian GSA 17 prior to 2002. More detailed issues identified in the data are described at the 
end of each stock assessment sections.  

28 

28 

 
 
 
 
 
 
 
 
2. 

Findings And Conclusions Of The Working Group 

Stock-Specific Findings & Conclusions 

See the stock specific summary sheets.  

29 

29 

 
 
 
 
 
3.  Follow Up Items 

The  text  below  highlights  some  issues  that  arose  during  the  EWG  15-11  meeting,  which  created 
difficulties for the meeting or for the process of completing the report.  The EWG offers the following 
suggestions  for  next  year  to  improve  the  process  for  preparing  assessments  of  the  Mediterranean 
Sea stocks: 

(1) 

(2) 

increasing  demand 

for  Management  Strategy  Evaluations 

(MSE)  on 
The 
Mediterranean  stocks  makes  it  absolutely  essential  that  experts  receive  specialized 
training  on  the  principles  behind  MSE  and  its  specific  application  to  Mediterranean 
fisheries.  To  this  end,  JRC  experts  could  provide  dedicated  training  courses  for 
selected experts.  

DCF  data  calls  need  to  reconsider  the  length  of  the  time  series  called  to  avoid  that 
the  historical  survey  and  fisheries  data  are  continuously  changed,  with  no  clear 
indication of the reasons, creating an extra work burden for Memebr States and JRC 
during the data calls and the experts at the start of each meeting. In principle, data 
calls in the future should only include the last year of data (both survey and fisheries 
data). However given the amount of errors still detected in many DCF data sources, it 
should  be  still  possible  to  upload  corrections.  To  prevent  the  abuse  of  the  system, 
any changes to the historical survey and fisheries data should be clearly justified by 
the MS and changes to the database should be made by JRC.   

30 

30 

 
 
 
 
4. 

Introduction 

The expert working group on Mediterranean stock and fisheries assessment part 1 STECF EWG 15-11 
held its first meeting planned for 2015 in Palma de Mallorca (Spain), 31 Aug-04 Sep 2015. 

The chairman opened the meeting at 09:00 on Monday, 31 Aug 2015, and adjourned the meeting by 
16:00  on  Friday,  04  Sep  2015.  The  meeting  was  attended  by  22  experts  in  total,  including  4  STECF 
members and an additional 2 JRC experts.  

The structure of the present report is in accordance with the terms of reference to STECF, as defined 
in the following chapter. 

4.1  TERMS OF REFERENCE FOR EWG-15-11 

For the 15 stocks given in Table 4.1.1, the STECF-EWG 15-11 is requested to: 

ToR 1  – Assess trends in historic  and recent stock parameters for the  longest time  series possible 
available  up  to  and  including  2014,  for  the  stocks  proposed  in  Table  4.1.1.  This  shall  cover  the 
evaluation  of  the  level  of  fishing  mortality  at  age,  spawning  stock  biomass,  stock  biomass,  and 
recruits  at  age.  Data  on  fishing  effort  shall  be  provided  by  fleet  segments  and  shall  be  the  most 
detailed  possible  to  support  the  establishment  of  a  fishing  effort  or  capacity  baseline.  Different 
assessment models should be applied as appropriate, including analyses of retrospective effects. 

Table 4.1.1 – List of proposed stocks 

Nb 

1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
11 
12 
13 
14 
15 

Geographical 
Sub-Areas 
GSA 1 
GSA 5 
GSA 6 
GSA 7 
GSA 82 
GSA 9 
GSA 10 
GSA 11 
GSAs 1-7 
GSAs 8-11 
GSA 9 
GSA 10 
GSA 11 
GSA 6 
GSA 1 

Common name 

Scientific name 

Hake 
Hake 
Hake 
Hake 
Hake 
Hake 
Hake 
Hake 
Hake 
Hake 
Giant red shrimp 
Giant red shrimp 
Giant red shrimp 
Blue and red shrimp 
Blue and red shrimp 

Merluccius merluccius 
Merluccius merluccius 
Merluccius merluccius 
Merluccius merluccius 
Merluccius merluccius 
Merluccius merluccius 
Merluccius merluccius 
Merluccius merluccius 
Merluccius merluccius 
Merluccius merluccius 
Aristaeomorpha foliacea 
Aristaeomorpha foliacea 
Aristaeomorpha foliacea 
Aristeus antennatus 
Aristeus antennatus 

Priority 

High 
High 
High 
High 
High 
High 
High 
High 
High 
High 
Medium 
Medium 
Medium 
High 
High 

2 Although a full analytical assessment may not be possible to perform for hake in GSA 8, the EWG is requested to provide a preliminary 
analysis  with  some  elements  such  as  the  level  of  fishing  mortality,  fishing  effort,  CPUE  or  survey  indexes,  even  if  the  time  series  are 
limited. 

31 

31 

 
 
 
 
 
 
 
 
 
 
 
                                                       
In  case  it  is  not  possible  to  carry  out  an  evaluation  of  those  stocks  listed  in  table  4.1.1,  below  is 
provided a reserve list of stocks (Table 4.1.2.). 

Table 4.1.2. – Reserve stock list 

Geographical 
Sub-Areas 

GSA 7 

GSA 1 

GSA 6 

GSA 10 

GSA 1 

GSA 5 

GSA 6 

Common name 

Scientific name 

Priority 

Sole 

Solea solea 

Deep water pink shrimp 

Parapenaeus longirostris 

Deep water pink shrimp 

Parapenaeus longirostris 

Deep water pink shrimp 

Parapenaeus longirostris 

Anglerfish 

Anglerfish 

Anglerfish 

Lophius budegassa 

Lophius budegassa 

Lophius budegassa 

High 

High 

High 

High 

Medium 

Medium 

Medium 

ToR  2  –  Propose  and  evaluate  candidate  MSY  value  or  range  of  values  and  safeguard  points  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 3 – Provide short and medium3  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 or other appropriate proxy by 2018 and 2020. In particular, 
predict: 

i)  The  level  of  fishing  mortality  which  minimize  the  risk  of  SSB  falling  below  Blim  with  a  5% 
probability and provide MSY or maximize the total yield from the stock in the long term; and 
ii) The level of fishing effort exerted by different fleet segments which is commensurate to the 
sustainable short-term and medium-term forecasts of the proposed changes. 

ToR 4 – On the basis of the existing information, prepare and/or up-date maps showing areas and 
periods  with  high  occurrence  of  juveniles  and/or  spawners  of  Merluccius  merluccius,  Aristeus 
antennatus and Aristaeomorpha foliacea. 

ToR  5  –  Provide  a  synoptic  overview  of:  (i)  the  fishery;  (ii)  the  most  recent  state  of  the  stock 
(spawning  stock  biomass,  stock  biomass,  recruits,  and,  if  possible,  exploitation  level  by  fleet 
segment); (iii) the source of data and methods and; (iv) the management advice, including MSY value 
or range of values and safeguard points. 

ToR 6 - Summarize and concisely describe all data quality deficiencies, including possible limitations 
with the surveys, of relevance for the assessment of stocks and fisheries. Such review and description 

3 Medium term forecast only when an acceptable stock-recruitment relationship is identifiable. 

32 

32 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                       
are to be based on the data format of the official DCF data calls for the Mediterranean Sea issued on 
April 2015. 

33 

33 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5  ASSESS TRENDS IN HISTORIC AND RECENT STOCK PARAMETERS 

5.1  SUMMARY SHEETS 

5.1.1  SUMMARY SHEET OF HAKE IN GSA 1 

Species common name: European Hake 
Species scientific name: Merluccius merluccius 
Geographical Sub-area(s) GSA(s): 1 

5.1.1.1   Stock development over time 

State of the adult abundance and biomass 
SSB  is  decreasing  in  the  last  years,  from  a  maximum  of  480  tonnes  in  2010  to  a  minimum  of  220 
tonnes in 2014. 

State of the juveniles (recruits) 
Recruitment  has  a  fluctuating  trend  with  a  mean  recruitment  of  15177  thousand  individuals.  The 
recruitment of the last year (2014) is the maximum of the series (28673 thousand individuals). 

State of exploitation 
The current F (1.20) is larger than FMSY (0.21), which indicates that European hake in GSA 1 is being 
fished above FMSY.  

Hake in GSA 1. XSA summary results. SSB and catch are in tonnes, recruitment in 1000s individuals. 

5.1.1.2   Stock advice 
STECF  EWG  15-11  advises  the  relevant  fleets’  catches  and/or  effort  to  be  reduced  until  fishing 
mortality  is  below  or  at  the  proposed  FMSY  level  (0.21),  in  order  to  avoid  future  loss  in  stock 
productivity  and  landings.  This  should  be  achieved  by  means  of  a  multi-annual  management  plan 

34 

34 

 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
taking  into  account  mixed-fisheries  considerations.  Catches  of  European  hake  in  GSA  1  in  2016 
consistent with FMSY should not exceed 160 t. 

5.1.1.3   Basis of the assessment 
The  state  of  exploitation  was  assessed  for  the  period  2003-2014  applying  the  Extended  Survivor 
Analysis  (XSA)  method  calibrated  with  fishery  independent  survey  abundance  indices  (MEDITS).  In 
addition, a yield-per-recruit (Y/R) analysis was carried out. Both methods were performed from the 
size composition of trawl, gillnet and longline catches, transforming length data into ages by slicing 
(L2AGE program).  
Input data on landings, discards and size structure by gear were taken from DCF.  Natural mortality 
(vector) was estimated using PRODBIOM. Von Bertalanffy growth parameters used in the assessment 
correspond to fast growth (Linf= 110.0 cm; k= 0.178). 

5.1.1.4   Catch options 
Catch options are summarized in the following table 5.1.1.4.1. 

Table  5.1.1.4.1.  Short  term  forecast  in  different  F  scenarios  computed  for  hake  in  GSA  1.  Basis:  F(2015)  = 
mean(Fbar 0-2 2012-2014)= 1.20; R(2015) = geometric mean of the recruitment of the last 3 years; R = 13364 
thousands; SSB(2014) = 220 t, Catch (2014)= 313 t. 

Rationale 

Ffactor 

Fbar 

Catch 
2015 

Catch 
2016 

Catch 
2017 

SSB 
2016 

SSB 
2017 

Zero catch 

0 

0 

0.18 

0.21 

726 

726 

0 

0 

160 

281 

367 

367 

1158 

883 

Change 
SSB_2016-
2017(%) 
215.56 

Change 
Catch_2014-
2016(%) 
-100.00 

140.49 

-48.93 

High long 
term yield 
(FMSY) 
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 

1.20 

0.12 

0.24 

0.36 

0.48 

0.60 

0.72 

0.84 

0.96 

1.08 

1.32 

1.44 

1.56 

1.68 

1.80 

1.92 

2.04 

2.16 

2.28 

2.40 

726 

726 

726 

726 

726 

726 

726 

726 

726 

726 

726 

726 

726 

726 

726 

726 

726 

726 

726 

726 

550 

96 

180 

251 

314 

367 

414 

455 

491 

522 

574 

596 

615 

632 

647 

660 

673 

684 

694 

703 

459 

185 

307 

385 

433 

459 

472 

476 

474 

468 

450 

440 

430 

420 

411 

402 

394 

387 

380 

373 

367 

367 

367 

367 

367 

367 

367 

367 

367 

367 

367 

367 

367 

367 

367 

367 

367 

367 

367 

367 

273 

991 

850 

730 

628 

542 

470 

408 

355 

311 

240 

213 

189 

169 

152 

137 

124 

113 

104 

96 

-25.72 

170.01 

131.47 

98.85 

71.21 

47.80 

27.94 

11.09 

-3.22 

-15.38 

-34.53 

-42.04 

-48.45 

-53.93 

-58.62 

-62.64 

-66.10 

-69.08 

-71.64 

-73.87 

75.47 

-69.21 

-42.68 

-19.76 

0.08 

17.29 

32.25 

45.29 

56.69 

66.68 

83.22 

90.07 

96.16 

101.59 

106.44 

110.80 

114.73 

118.28 

121.51 

124.46 

35 

35 

 
 
 
 
 
 
 
5.1.1.5   Reference points 

Table 5.1.1.5.1. Hake in GSA 1. Reference points, values and their technical basis. 

Framework 

Reference point 

Value 

Technical basis 

Source 

MSY approach 

MSY Btrigger 

Precautionary 
approach 

EU-GFCM management 
strategy 

FMSY 
Blim 
Bpa 
Flim 
Fpa 
SSBlower 
SSBupper 
Flower 
Fupper 

0.21 
220 t 

F0.1 estimated with YPR 
Bloss 

Present assessment 
Present assessment 

0.14 
0.29 

Empirical relationship 
Empirical relationship 

Present assessment 
Present assessment 

5.1.1.6  Quality of the assessment  

The detailed assessment can be found in section 5.2.1. 

36 

36 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5.1.2  SUMMARY SHEET OF HAKE IN GSA 5 

Species common name: European hake 
Species scientific name: Merluccius merluccius 
Geographical Sub-area(s) GSA(s): 5  

5.1.2.1   Stock development over time 

State of the adult abundance and biomass 
SSB oscillated without trend between 30 and 115 tons.  

State of the juveniles (recruits)  
Recruitment varied between 2 and 11 millions during the time series with a slight declining trend. In 
the  last  20  years,  recruitment  never  showed  the  high  values  found  in  the  middle  1980s  and  early 
1990s, where catches also reach maximum values. 

State of exploitation 
F has declined from around 2 in the beginning of the time series to around 1 in the latest years. The 
current  F  (1.12)  is  larger  than  FMSY (0.15),  which  indicates that  hake in  GSA  5  is  being  fished above 
FMSY. 

Hake in GSA 5. XSA summary results. SSB and catch are in tonnes, recruitment in 1000s individuals. 

5.1.2.2   Stock advice 
STECF EWG 15-11 recommends the relevant fleets’ catches and/or effort to be reduced until fishing 
mortality is below or at the proposed FMSY level, in order to avoid future loss in stock productivity and 
landings. This should be achieved by means of a multi-annual management plan taking into account 

37 

37 

 
 
 
 
 
 
 
 
 
 
 
mixed-fisheries  considerations.  Catches  of  European  hake  in  GSA  5  in  2016  consistent  with  FMSY 
should not exceed 31.9 t. 

5.1.2.3   Basis of the assessment 
The  data  used  in  the  assessment  were:  (i)  Catches  time  series  1980-2014  from  OTB;  (ii)  Age 
distributions  obtained  from  slicing  of  length  distributions  1980-2014  (Figure  5.2.2.7.1);  (iii)  BALAR-
MEDITS survey used as tuning fleet. 

The assessment has been performed with an Extended Survivor Analysis (XSA) using the FLR library in 
R. 

5.1.2.4   Catch options 
Catch options are summarized in the following table 5.1.2.4.1. 

Table  5.1.2.4.1.  Short  term  forecast  in  different  F  scenarios  computed  for  hake  in  GSA  5.  Basis:  F(2015)  = 
mean(Fbar0-3  2012-2014)=  1.06;  R(2015)  =  geometric  mean  of  the  recruitment  of  the  last  3  years;  R  =  4600 
thousands; SSB(2014) = 67 t, Catch (2014)= 124 t. 

Rationale 

Ffactor 

Fbar 

Catch 
2016 

Catch 
2017 

SSB 
2017 

Change SSB 
2016-2017 
(%) 

Change Catch 
2014-2016 (%) 

Zero catch 
High long-
term yield 
(FMSY) 
Status quo 
Different 
scenarios 

0.0 

0.00 

0.0 

0.0 

341.18 

331.84 

-100.00 

0.1 

0.16 

31.9 

63.9 

273.43 

246.08 

-74.21 

1.0 

0.1 

0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9 
1.0 
1.1 
1.2 
1.3 
1.4 
1.5 
1.6 
1.7 
1.8 
1.9 
2.0 

1.06 

131.6 

135.0 

84.19 

6.55 

0.11 

22.6 

47.3 

293.07 

270.94 

0.21 
0.32 
0.43 
0.53 
0.64 
0.75 
0.85 
0.96 
1.06 
1.17 
1.28 
1.38 
1.49 
1.60 
1.70 
1.81 
1.92 
2.02 
2.13 

42.1 
59.2 
74.0 
86.9 
98.3 
108.3 
117.0 
124.8 
131.6 
137.8 
143.2 
148.1 
152.4 
156.4 
159.9 
163.1 
166.1 
168.8 
171.2 

80.0 
102.3 
116.9 
126.3 
131.8 
134.8 
135.9 
135.9 
135.0 
133.7 
132.2 
130.5 
128.9 
127.2 
125.7 
124.2 
122.9 
121.7 
120.6 

252.27 
217.64 
188.24 
163.25 
142.00 
123.92 
108.52 
95.39 
84.19 
74.62 
66.44 
59.44 
53.44 
48.30 
43.88 
40.08 
36.81 
33.98 
31.54 

219.30 
175.47 
138.25 
106.63 
79.73 
56.85 
37.35 
20.73 
6.55 
-5.55 
-15.91 
-24.76 
-32.35 
-38.87 
-44.46 
-49.27 
-53.41 
-56.99 
-60.08 

6.34 

-81.78 

-65.97 
-52.22 
-40.24 
-29.77 
-20.60 
-12.55 
-5.47 
0.79 
6.34 
11.27 
15.66 
19.59 
23.12 
26.30 
29.18 
31.78 
34.16 
36.33 
38.33 

38 

38 

 
 
 
 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
5.1.2.5   Reference points 

Table 5.1.1.5.1 Hake in GSA 5. Reference points, values and their technical basis. 

Framework 

Reference point 

Value 

Technical basis 

Source 

MSY approach 

MSY Btrigger 

Precautionary 
approach 

EU-GFCM 
management 
strategy 

FMSY 
Blim 
Bpa 
Flim 
Fpa 
SSBlower 
SSBupper 
Flower 
Fupper 

0.15 
31 t 

0.10 
0.21 

F0.1 estimated with YPR. 
Bloss 

Present assessment 
Present assessment 

Empirical relationship 
Empirical relationship 

Present assessment 
Present assessment 

5.1.2.6  Quality of the assessment  

The detailed assessment can be found in section 5.2.2. 

39 

39 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5.1.3  SUMMARY SHEET OF HAKE IN GSA 6 

Species common name: European hake 
Species scientific name Merluccius merluccius 
Geographical Sub-area(s) GSA(s): 6 

5.1.3.1   Stock development over time 

State of the adult abundance and biomass 
SSB increased from 2003 to 2006 but then decreased progressively down to a minimum in 2012. 

State of the juveniles (recruits) 
Recruitment showed an important decrease from 2003 to 2013 with a peak in 2008. 

State of exploitation 
F increased from 1.29 in 2004 to 2.0 in 2012 and then decreased to 1.39 in 2014. The current F (1.72) 
is larger than FMSY (0.260), which indicates that hake from GSA 6 is is being fished above FMSY. 

Hake in GSA 6. XSA summary results. SSB and catch are in tonnes, recruitment in 1000s individuals. 

5.1.3.2   Stock advice 
STECF EWG 15-11 recommends the relevant fleets’ catches and/or effort to be reduced until fishing 
mortality is below or at the proposed FMSY level, in order to avoid future loss in stock productivity and 
landings. This should be achieved by means of a multi-annual management plan taking into account 
mixed-fisheries  considerations.  Catches  of  European  hake  in  GSA  6  in  2016  consistent  with  FMSY 
should not exceed 785 t. 

40 

40 

 
 
 
 
 
 
 
 
 
 
 
 
5.1.3.3   Basis of the assessment 
The assessment includes landings from all main fleets (trawlers, longliners, gillnetters). However, as 
size  distributions  from  longliners  are  only  available  for  the  most  recent  years  (2009-2014),  the 
assessment  was  done  considering  two  fleets:  trawlers  and  others  (longliners  and  gillnetters 
combined). 
Growth  parameters  were  from  Mellon  et  al  (2010).  Maturity  and  length-weight  parameters  were 
from  the  Spanish  DCF.  Natural  mortality  was  obtained  from  PRODBIOM.  XSA,  Y/R  and  projections 
were carried out using R.  

5.1.3.4   Catch options 
Catch options are summarized in the following table 5.1.3.4.1. 

Table  5.1.3.4.1.  Short  term  forecast  in  different  F  scenarios  computed  for  hake  in  GSA  6.  Basis:  F(2015)  = 
mean(Fbar1-3 2012-2014)= 1.74; R(2015) = geometric mean of the recruitment of the last 3 years; R = 100806 
thousands; SSB(2014) = 1600 t, Catch (2014)= 2230 t. 

Rationale 

Ffactor 

fbar 

Catch 
2016 

Catch 
2017 

SSB 2017 

Zero catch 
High long-term 
yield (FMSY) 
Status quo 
Different 
scenarios 

0.0 
0.15 

1.0 
0.10 

0.20 
0.30 
0.40 
0.50 
0.60 
0.70 
0.80 
0.90 
1.00 
1.10 
1.20 
1.30 
1.40 
1.50 
1.60 
1.70 
1.80 
1.90 
2.00 

0.0 
0.260 

1.721 
0.172 

0.344 
0.516 
0.688 
0.860 
1.032 
1.204 
1.376 
1.548 
1.721 
1.893 
2.065 
2.237 
2.409 
2.581 
2.753 
2.925 
3.097 
3.269 
3.441 

0.0 
785 

3101 
538 

1003 
1406 
1755 
2060 
2325 
2558 
2762. 
2942 
3101 
3242 
3368 
3480 
3581 
3672 
3754 
3829 
3898 
3960 
4018 

0.0 
1718 

3047 
1255 

2070 
2583 
2891 
3061 
3139 
3158 
3139 
3099 
3047 
2989 
2930 
2873 
2819 
2768 
2722 
2680 
2642 
2608 
2577 

7276 
5763 

1727 
6234 

5350 
4601 
3966 
3426 
2968 
2579 
2248 
1967 
1727 
1523 
1349 
1201 
1074 
965 
872 
793 
724 
665 
614 

Change SSB 
2016-2017 
(%) 
305.4 
221.0 

Change Catch 
2014-2016 
(%) 
-100.0 
-64.8 

-3.8 
247.3 

198.1 
156.3 
120.9 
90.9 
65.3 
43.7 
25.2 
9.6 
-3.8 
-15.1 
-24.8 
-33.2 
-40.2 
-46.2 
-51.4 
-55.8 
-59.7 
-62.9 
-65.8 

39.1 
-75.8 

-55.0 
-37.0 
-21.3 
-7.6 
4.3 
14.7 
23.9 
31.9 
39.1 
45.4 
51.0 
56.1 
60.6 
64.7 
68.4 
71.7 
74.8 
77.6 
80.2 

5.1.3.5   Reference points 

Table 5.1.3.5.1 Hake in GSA 6. Reference points, values and their technical basis. 

41 

41 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Framework 

Reference point 

Value 

Technical basis 

Source 

MSY approach 

MSY Btrigger 

Precautionary 
approach 

EU-GFCM 
management 
strategy 

FMSY  
Blim 
Bpa 
Flim 
Fpa 
SSBlower 
SSBupper 
Flower 
Fupper 

0.26 
1133 t 

F0.1 estimated with YPR. 
Bloss 

Present assessment 
Present assessment 

0.17 
0.36 

Empirical relationship 
Empirical relationship 

Present assessment 
Present assessment 

5.1.3.6  Quality of the assessment  

The detailed assessment can be found in section 5.2.3. 

42 

42 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5.1.4  SUMMARY SHEET OF HAKE IN GSA 7 

Species common name: Hake 
Species scientific name: Merluccius merluccius 
Geographical Sub-area(s) GSA(s): 7 

5.1.4.1   Stock development over time 

State of the adult abundance and biomass 
The stock spawning biomass (SSB) displays a decreasing trend over the analysed period, with a slight 
increase in 2014. 

State of the juvenile (recruits) 
The  highest  recruitment  values  observed  over  the  period  are  in  1998,  2002-2003  and  2007.  Since 
2007,  the  recruitment  follows  a  decreasing  trend  and  is  currently  at  a  low  level.  The  recruitment 
estimated  for  2014  is  40913  thousands  individuals,  which  is  below  the  average  of  the  time  series 
(47449 thousands).  

State of exploitation 
F  increased  from  around  1  to  2  over  the  time  period  analysed,  with  a  peak  in  2013.  The  current  F 
(1.64) is larger than FMSY (0.11), which indicates that hake from GSA 7 is is being fished above FMSY.  

Figure  5.2.4.1.1.  Hake  in  GS  7.  XSA  summary  results.  SSB  and  catch  (tons),  recruitment  (numbers  in 
thousands). 

5.1.4.2   Stock advice 
STECF  EWG  15-11  advise  the  relevant  fleets’  cacthes  and/or  effort  to  be  reduced  until  fishing 
mortality is below or at the proposed FMSY level in order to avoid future loss in stock productivity and 

43 

43 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
landings. This should be achieved by means of a multi-annual management plan taking into account 
mixed-fisheries  considerations.  Catches  of  European  hake  in  GSA  7  in  2016  consistent  with  FMSY 
should not exceed 209 t. 

5.1.4.3   Basis of the assessment 
The stock of European hake in GSA 7 was assessed using data coming from DCF (catch at age from 
French and Spanish trawlers, French gillnetters and Spanish longliners) for the period 1998-2014 and 
applying an XSA analysis calibrated with fishery independent survey abundance indices (MEDITS). In 
addition,  a  yield-per-recruit  (Y/R)  analysis  was  carried  out.  Discards  were  included  in  the  catches. 
Growth  parameters  were  derived  from  tagging  experiments  (Mellon  et  al,  2010)  conducted  in  GSA 
07, length-weight relationship and maturity ogive from the Data Collection Framework (DCF). Natural 
mortality was estimated using PROBIOM (Abella 1997).  

Growth parameters (Mellon et al., 2010) 

Linf 
K 
t0 

72.8 (males); 100.7 (females) 
0.233 (males); 0.236 (females) 
0 

Length-Weight- parameters (from DCF data, 2002-2014) 

A 
B 
Natural mortality (PROBIOM; Abella, 1997) 

0.0085 
2.97 

Age 0 
Age 1 
Age 2 
Age 3 
Age 4 
Age 5+ 

0.88 
0.43 
0.33 
0.25 
0.22 
0.20 

Maturity ogive (from DCF data, 2002-2014) 

Age 0 
Age 1 
Age 2 
Age 3 
Age 4 
Age 5+ 

0,066 
0,308 
0,685 
0,907 
0,986 
0,996 

5.1.4.4   Catch options 
Catch options are summarized in the following table 5.1.4.4.1. 

Table  5.1.4.4.1.  Short  term  forecast  in  different  F  scenarios  computed  for  hake  in  GSA  7.  Basis:  F(2015)  = 
mean(Fbar0-2 2012-2014)= 1.75; R(2015) = geometric mean of the recruitment of the last 3 years; R = 44364 
thousands; SSB(2014) = 1115 t, Catch (2014)= 1983 t. 

Rationale 

Ffactor  Fbar 

Catch 
2014 

Catch 
2015 

Catch 
2016 

Catch 
2017 

SSB 
2016 

SSB 
2017 

Change SSB 
2016-2017(%) 

Change Catch 
2014-2016(%) 

Zero catch 
High long 
term yield 
(FMSY) 
Status quo 

Different 
Scenarios 

0,00 

0.00 

1981 

1871 

0 

0 

740 

2798 

0.06 

0.11 

1981 

1871 

209 

598 

740 

2499 

0.10 
0.20 
0.30 

0.18 
0.35 
0.53 

1981 
1981 
1981 

1871 
1871 
1871 

320 
586 
809 

858 
1329 
1578 

740 
740 
740 

2343 
1980 
1689 

278 

238 

217 
168 
128 

-100.00 

-89.47 

-83.87 
-70.43 
-59.16 

44 

44 

 
 
 
 
 
 
0.40 
0.50 
0.60 
0.70 
0.80 
0.90 
1.00 
1.10 
1.20 
1.30 
1.40 
1.50 
1.60 
1.70 
1.80 
1.90 
2.00 

0.70 
0.88 
1.05 
1.23 
1.40 
1.58 
1.75 
1.93 
2.10 
2.28 
2.45 
2.63 
2.81 
2.98 
3.16 
3.33 
3.51 

1981 
1981 
1981 
1981 
1981 
1981 
1981 
1981 
1981 
1981 
1981 
1981 
1981 
1981 
1981 
1981 
1981 

1871 
1871 
1871 
1871 
1871 
1871 
1871 
1871 
1871 
1871 
1871 
1871 
1871 
1871 
1871 
1871 
1871 

998 
1159 
1297 
1417 
1521 
1612 
1693 
1766 
1830 
1889 
1942 
1991 
2035 
2077 
2115 
2151 
2184 

1699 
1749 
1758 
1746 
1723 
1695 
1666 
1637 
1610 
1584 
1560 
1538 
1518 
1499 
1482 
1466 
1451 

740 
740 
740 
740 
740 
740 
740 
740 
740 
740 
740 
740 
740 
740 
740 
740 
740 

1455 
1265 
1109 
982 
877 
790 
716 
655 
602 
558 
519 
486 
456 
431 
408 
388 
369 

97 
71 
50 
33 
18 
7 
-3 
-12 
-19 
-25 
-30 
-34 
-38 
-42 
-45 
-48 
-50 

-49.63 
-41.51 
-34.53 
-28.50 
-23.24 
-18.62 
-14.53 
-10.89 
-7.62 
-4.67 
-1.98 
0.47 
2.72 
4.81 
6.74 
8.54 
10.23 

5.1.4.5   Reference points 

Table 5.1.1.5.1 Hake in GSA 7. Reference points, values and their technical basis. 

Framework 

Reference point 

Value 

Technical basis 

Source 

MSY approach 

MSY Btrigger 

Precautionary 
approach 

EU-GFCM 
management strategy 

FMSY 
Blim 
Bpa 
Flim 
Fpa 
SSBlower 
SSBupper 
Flower 
Fupper 

0.11 
769 

0.08 
0.16 

F0.1 estimated with YPR. 
Bloss 

Present assesment 
Present assesment 

Empirical relationship 
Empirical relationship 

Present assesment 
Present assesment 

5.1.4.6  Quality of the assessment  

The detailed assessment can be found in section 5.2.4. 

45 

45 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5.1.5  SUMMARY SHEET OF HAKE IN GSA 8 

Species common name: European hake 
Species scientific name: Merluccius merluccius 
Geographical Sub-area(s) GSA(s): 8 

5.1.5.1   Stock development over time 
No assessment was conducted due to data limitations. 

5.1.5.2   Stock advice 
No assessment was conducted due to data limitations. 

5.1.5.3   Basis of the assessment 
No assessment was conducted due to data limitations. 

5.1.5.4   Catch options 
No assessment was conducted due to data limitations. 

5.1.5.5   Reference points 
No assessment was conducted due to data limitations. 

Table 5.1.1.5.1 Hake in GSA 8. Reference points, values and their technical basis. 

Value 

Technical basis 

Source 

Framework 

MSY approach 

Reference 
point 

MSY Btrigger 

Precautionary 
approach 

EU-GFCM 
management 
strategy 

FMSY 
Blim 
Bpa 
Flim 
Fpa 
SSBlower 
SSBupper 
Flower 
Fupper 

5.1.5.6  Quality of the assessment  

The detailed assessment can be found in section 5.2.5. 

46 

46 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5.1.6  SUMMARY SHEET OF HAKE IN GSA 9 

Species common name: 
Species scientific name: 
Geographical Sub-area(s) GSA(s): 

European hake 
Merluccius merluccius 
9 

5.1.6.1   Stock development over time 

State of the adult abundance and biomass 
The SSB is fluctuating along the series with an average of 2278 t.  

State of the juveniles (recruits)  
The  recruitment  estimated  for  2014  is  88907  thousand  individuals,  slightly  higher  compared  to  the 
series average (86615 thousand).  

State of exploitation 
The current F (1.03) is larger than FMSY (0.23), , which indicates that European hake in GSA 9 is is being 
fished above FMSY.  

European hake in GSA 9. XSA summary results. SSB and catch are in tonnes, recruitment in 1000s individuals. 

47 

47 

 
 
 
 
 
 
 
 
 
 
 
 
 
5.1.6.2   Stock advice 
STECF  EWG  15-11  advises  the  relevant  fleets’  catches  and/or  effort  to  be  reduced  until  fishing 
mortality is below or at the proposed FMSY level in order to avoid future loss in stock productivity and 
landings.  Catches  of  European  hake  in  2016  in  GSA  9  consistent  with  FMSY  should  not  exceed  635 
tonnes. 

5.1.6.3   Basis of the assessment 
The  stock  of  European  hake  in  GSA  9  was  assessed  applying  an  Extended  Survivor  Analysis  (XSA) 
method calibrated with fishery independent survey abundance indices (MEDITS). In addition, a yield-
per-recruit (Y/R) analysis was carried out. Both methods were performed from the size composition 
of landings and discards, transforming length data to ages using the LFDA 5.0 slicing software. 
Input  data  landings,  discards  and  length  frequencies  were  taken  from  DCF.  Von  Bertalanffy  growth 
parameters  and  length-weight  relationship  were  taken  from  parameters  estimated  for  European 
hake in GSA 9. Natural mortality (vector) was estimated using PROBIOM. 

5.1.6.4   Catch options 
Catch options are summarized in the following table 5.1.6.4.1. 

Table  5.1.6.4.1.  Short  term  forecast  in  different  F  scenarios  computed  for  hake  in  GSA  9.  Basis:  F(2015)  = 
mean(Fbar 0-2 2012-2014)= 0.95; R(2015) = geometric mean of the recruitment of the last 3 years; R = 68172 
thousands; SSB(2014) = 2197 t, Catch (2014)= 1553 t. 

Rationale 

Ffactor 

Fbar 

Catch 
2015 

Catch 
2016 

Catch 
2017 

SSB 
2016 

SSB 
2017 

Change 
SSB 2016-
2017(%) 

Change 
Catch 
2014-
2016(%) 
-100.00 

Zero catch 

High long 
term yield 
(F0.1) 
Status quo 

Different 
Scenarios 

0 

0.00 

1821 

0 

0 

2567 

6175 

140.52 

0.24 

0.23 

1821 

635 

1136 

2567 

4891 

90.52 

-59.10 

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 

0.95 

0.10 

0.19 

0.29 

0.38 

0.48 

0.57 

0.67 

0.76 

0.86 

1.05 

1.14 

1.24 

1.33 

1.43 

1.53 

1.62 

1.72 

1.81 

1821 

1821 

1821 

1821 

1821 

1821 

1821 

1821 

1821 

1821 

1821 

1821 

1821 

1821 

1821 

1821 

1821 

1821 

1821 

1867 

1911 

282 

536 

763 

969 

1156 

1325 

1479 

1620 

1749 

1977 

2077 

2171 

2258 

2339 

2414 

2485 

2551 

2614 

566 

991 

1305 

1532 

1692 

1800 

1867 

1903 

1916 

1894 

1866 

1832 

1793 

1751 

1708 

1663 

1619 

1575 

48 

48 

2567 

2567 

2567 

2567 

2567 

2567 

2567 

2567 

2567 

2567 

2567 

2567 

2567 

2567 

2567 

2567 

2567 

2567 

2567 

2649 

5596 

5088 

4640 

4245 

3896 

3586 

3310 

3065 

2845 

2472 

2312 

2168 

2038 

1919 

1811 

1712 

1622 

1538 

3.16 

117.98 

98.18 

80.74 

65.35 

51.74 

39.67 

28.94 

19.37 

10.82 

-3.72 

-9.93 

-15.54 

-20.62 

-25.24 

-29.45 

-33.30 

-36.83 

-40.08 

20.28 

-81.82 

-65.51 

-50.82 

-37.56 

-25.56 

-14.66 

-4.73 

4.34 

12.64 

27.31 

33.81 

39.84 

45.43 

50.64 

55.51 

60.06 

64.33 

68.35 

 
 
 
 
 
2 

1.91 

1821 

2672 

1532 

2567 

1462 

-43.07 

72.13 

5.1.6.5   Reference points 

Table 5.1.6.5.1 Hake in GSA 9. Reference points, values and their technical basis. 

Framework 

MSY approach 

Reference 
point 

MSY Btrigger 

Precautionary 
approach 

EU-GFCM 
management 
strategy 

FMSY 
Blim 
Bpa 
Flim 
Fpa 
SSBlower 
SSBupper 
Flower 
Fupper 

Value 

0.23 
1569 

0.16 
0.32 

Technical basis 

Source 

F0.1 estimated with YPR. 
Bloss 

Present assesment 
Present assessment 

Empirical relationship 
Empirical relationship 

Present assesment 
Present assessment 

5.1.6.6  Quality of the assessment  

The detailed assessment can be found in section 5.2.6. 

49 

49 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5.1.7  SUMMARY SHEET OF HAKE IN GSA 10 

Species common name: European hake 
Species scientific name: Merluccius merluccius 
Geographical Sub-area(s) GSA(s): 10 

5.1.7.1   Stock development over time 

State of the adult abundance and biomass 
The  SSB  showed  a  slight  increase  over  tme  and  it  estimated  at  about  1,635  t  in  2014,  being  the 
average along the time series equal to 1261. 

State of the juveniles (recruits) 
The recruitment has a slightly decreasing trend, even if in 2012 it increased again to a value equal to 
51,400. The maximum recruitment is reached in 2009 and it is equal to 76,500 thousands individuals, 
while in 2014 it is 35919. 

State of exploitation 
The average F along the time series is 0.98, with a minimum of 0.73 in 2013 and a maximum of 1.19 
in 2008 and no particular trend. The current F (1.10) is larger than FMSY (0.20) , which indicates that 
European hake in GSA 10 is is being fished above FMSY.  

50 

50 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
5.1.7.1.1. Hake in GSA 10. XSA summary results. SSB and catch are in tonnes, recruitment in 1000s individuals, 
harvest is F1-4. 

5.1.7.2   Stock advice 
STECF EWG 15-11 recommends the relevant fleets’ catches and/or effort to be reduced until fishing 
mortality is below or at the proposed FMSY level, in order to avoid future loss in stock productivity and 
landings. This should be achieved by means of a multi-annual management plan taking into account 
mixed-fisheries  considerations.  Catches  of  European  hake  in  GSA  10  in  2016  consistent  with  FMSY 
should not exceed 404 t. 

5.1.7.3   Basis of the assessment 
The  stock  assessment  was  performed  applying  an  Extended  Survivor  Analysis  (XSA)  method 
calibrated  with  fishery  independent  survey  abundance  indices  (MEDITS)  and  CPUE  of  longlines.  In 
addition, a yield-per-recruit (Y/R) analysis was carried out. Both methods were performed from the 
size composition of landings and discards, transforming length data to ages using slicing technique. 
Input data of length frequencies of landings and discards and were taken from DCF. Von Bertalanffy 
growth parameters and length-weight relationship were taken from parameters estimated for hake 
in GSA 10. Natural mortality (vector) was estimated using PROBIOM 

5.1.7.4   Catch options 
Catch options are summarized in the following table 5.1.7.4.1. 

Table  5.1.7.4.1.  Short  term  forecast  in  different  F  scenarios  computed  for  M.  merluccius  in  GSA  10.  Basis: 
F(2015) = mean(Fbar1-4 2012-2014)= 0.906; R(2015) = geometric mean of the recruitment of the last 3years; R 
= 43764 (thousands); SSB(2014) = 1382 t, Catch (2014)= 1635 t. 

Rationale 
Zero catch 
High long-term 
yield (F0.1) 
Status quo 
Different  scenarios 

Ffactor 
0 

fbar 
0.000 

Catch 2016 
0 

0.218 
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 

0.198 
0.906 
0.091 
0.181 
0.272 
0.362 
0.453 
0.543 
0.634 
0.725 
0.815 
0.996 
1.087 
1.178 
1.268 
1.359 
1.449 
1.540 

404 
1289 
197 
374 
532 
675 
804 
920 
1026 
1122 
1209 
1362 
1430 
1491 
1549 
1601 
1650 
1696 

51 

51 

Catch 2017  SSB 2017 

0 

769 
1297 
419 
724 
941 
1092 
1193 
1257 
1293 
1308 
1308 
1279 
1256 
1229 
1201 
1171 
1141 
1112 

3770 

2927 
1296 
3352 
2988 
2669 
2389 
2144 
1929 
1740 
1573 
1426 
1180 
1078 
987 
905 
833 
768 
710 

Change SSB 2016-
2017 (%) 
192.42 

Change Catch 
2014-2016 (%) 
-100.00 

127.03 
0.50 
160.03 
131.74 
107.00 
85.34 
66.34 
49.65 
34.96 
22.02 
10.60 
-8.45 
-16.39 
-23.46 
-29.76 
-35.39 
-40.43 
-44.96 

-70.80 
-6.74 
-85.74 
-72.97 
-61.50 
-51.17 
-41.86 
-33.43 
-25.79 
-18.85 
-12.52 
-1.44 
3.42 
7.90 
12.03 
15.86 
19.40 
22.69 

 
 
 
 
 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
1.8 
1.9 
2 

1.630 
1.721 
1.812 

1738 
1778 
1815 

1083 
1055 
1028 

657 
610 
567 

-49.03 
-52.69 
-56.01 

25.76 
28.62 
31.30 

5.1.7.5   Reference points 
Insert the reference point table 

Table 5.1.1.5.1 Hake in GSA 10. Reference points, values and their technical basis. 

Framework 

Reference point 

Value 

Technical basis 

Source 

MSY approach 

MSY Btrigger 

Precautionary 
approach 

EU-GFCM 
management 
strategy 

FMSY 
Blim 
Bpa 
Flim 
Fpa 
SSBlower 
SSBupper 
Flower 
Fupper 

0.20 
967 

F0.1 estimated with Yield-per-Recruit analyses 
Bloss 

Present assessment 
Present assessment 

0.13 
0.27 

Empirical relationship 
Empirical relationship 

Present assessment 
Present assessment 

5.1.7.6  Quality of the assessment  

The detailed assessment can be found in section 5.2.7. 

52 

52 

 
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5.1.8  SUMMARY SHEET OF HAKE IN GSA 11 

Species common name: 
Species scientific name: 
Geographical Sub-area(s) GSA(s): 

European hake 
Merluccius merluccius 
11 

5.1.8.1   Stock development over time 

State of the adult abundance and biomass 
SSB  estimates  showed  a  decreasing  pattern,  with  a  minimum  in  2014.  MEDITS  indices  show 
fluctuations, with lower values in the last period.  

State of the juveniles (recruits) 
Recruitment declined over the time series, reaching a minimum value in 2013. 

State of exploitation 
F oscillates between 2 and 1.5 over the entire time series, without any particular trend. The current F 
(1.61) is larger than FMSY (0.17), , which indicates that European hake in GSA 11 is being fished above 
FMSY.  

European hake in GSA 11. XSA results. SSB and catch are in tonnes, recruitment in 1000s individuals. 

5.1.8.2   Stock advice 
STECF EWG 15-11 recommends the relevant fleets’ catches and/or effort to be reduced until fishing 
mortality is below or at the proposed FMSY level, in order to avoid future loss in stock productivity and 
landings. This should be achieved by means of a multi-annual management plan taking into account 

53 

53 

 
 
 
 
 
 
 
 
 
  
 
 
mixed-fisheries  considerations.  Catches  of  European  hake  in  GSA  11  in  2016  consistent  with  FMSY 
should not exceed 41 t. 

5.1.8.3   Basis of the assessment 
Stock assessment has been performed applying Extended Survivors Analysis (XSA) to the DCF data of 
size landings age-sliced for the period 2006-2014. DCF data adjusted to avoid misreporting and errors 
(biomass  landed  and  size  composition  of  the  catches  sliced  according  to  the  growth  parameters), 
tuned  with  fishery  independent  abundance  indices  (MEDITS  survey).  A  vector  of  natural  mortality 
was  obtained  applying  PRODBIOM.  In  addition,  Yield-per-Recruit  (YPR)  analysis  was  performed  for 
the estimation of F0.1 (proxy of FMSY). 

5.1.8.4   Catch options 
Catch options are summarized in the following table (Table 5.1.8.4.1). 

Table  5.1.8.4.1.  Short  term  forecast  in  different  F  scenarios  computed  for  hake  in  GSA  11.  Basis:  F(2015)  = 
mean (Fbar 0-3 2012-2014)= 1.49; R(2015) = geometric mean of the recruitment of the last 3 years; R = 8720 
thousands; SSB(2014) = 73 t, Catch (2014)= 140 t. 

Rationale 

Ffactor 

Fbar 

Zero catch 

0 

0 

Catch 
2015 
229 

Catch 
2016 
0 

High long term 
yield (FMSY) 
Status quo 

Different 
Scenarios 

0.11 

0.17 

229 

41 

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 

1.49 

0.15 

0.30 

0.45 

0.60 

0.75 

0.90 

1.05 

1.20 

1.34 

1.64 

1.79 

1.94 

2.09 

2.24 

2.39 

2.54 

2.69 

2.84 

2.99 

229 

229 

229 

229 

229 

229 

229 

229 

229 

229 

229 

229 

229 

229 

229 

229 

229 

229 

229 

229 

190 

37 

68 

94 

115 

134 

149 

162 

173 

182 

198 

204 

209 

214 

218 

222 

225 

228 

231 

233 

Catch 
2017 
0 

107 

182 

99 

158 

192 

208 

214 

213 

208 

200 

191 

172 

163 

154 

146 

138 

131 

124 

118 

113 

107 

SSB 
2016 
95 

95 

95 

95 

95 

95 

95 

95 

95 

95 

95 

95 

95 

95 

95 

95 

95 

95 

95 

95 

95 

95 

SSB 
2017 
470 

381 

86 

389 

323 

269 

226 

190 

160 

136 

116 

100 

74 

65 

57 

50 

44 

39 

35 

31 

28 

25 

Change SSB 
2016-2017(%) 
395.49 

Change Catch 
2014-2016(%) 
-100 

301.21 

-9.45 

309.93 

240.42 

183.84 

137.68 

99.92 

68.95 

43.47 

22.45 

5.03 

-21.54 

-31.67 

-40.21 

-47.43 

-53.57 

-58.81 

-63.32 

-67.20 

-70.57 

-73.51 

-70.66 

36.08 

-73.41 

-51.32 

-32.92 

-17.52 

-4.58 

6.35 

15.61 

23.50 

30.26 

41.13 

45.52 

49.36 

52.75 

55.75 

58.42 

60.81 

62.96 

64.90 

66.67 

5.1.8.5   Reference points 

Table 5.1.8.5.1 Hake in GSA 11. Reference points, values and their technical basis. 

54 

54 

 
 
 
 
 
 
 
 
Framework 

Reference point 

Value 

Technical basis 

Source 

MSY approach 

MSY Btrigger 

Precautionary 
approach 

EU-GFCM 
management 
strategy 

FMSY 
Blim 
Bpa 
Flim 
Fpa 
SSBlower 
SSBupper 
Flower 
Fupper 

- 

0.17 
73 t 

0.11 
0.24 

5.1.8.6  Quality of the assessment  

F0.1 estimated with Yield-per-Recruit analyses 
Bloss 

Present assessment 
Present assessment 

Empirical relationship 
Empirical relationship 

Present assessment 
Present assessment 

The detailed assessment can be found in section 5.2.8. 

55 

55 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5.1.9  SUMMARY SHEET OF HAKE IN GSA 1-7 

Species common name: European hake 
Species scientific name Merluccius merluccius 
Geographical Sub-area(s) GSA(s): 1, 5, 6 and 7. 

5.1.9.1   Stock development over time 
State of the adult abundance and biomass 
SSB  fluctuated  over  2003-2014  with  no  clear  trend.  In  the  most  recent  years,  SSB  increased  and  F 
decreased. 

State of the juveniles (recruits) 
Recruits  fluctuated  over  2003-2014  with  no  clear  trend.  In  the  most  recent  years,  the  number  of 
recruits has increased. 

State of exploitation 
The current F (1.40) is larger than FMSY (0.39), which indicates that European hake in GSAs 1-7 is being 
fished above FMSY. 

Hake in GSAs 1-7. XSA summary results. SSB and catch are in tonnes, recruitment in 1000s individuals. 

5.1.9.2   Stock advice 
STECF  EWG  15-11  advises  the  relevant  fleets'  effort  and/  or  catches  to  be  reduced  until  fishing 
mortality is below or at the proposed FMSY level in other to avoid future loss in stock productivity and 
landings. This should be achieved by means of a multi-annual management plan taking into account 
mixed-fisheries considerations. Catches of European hake in 2016 consistent with F0.1 (0.39) should 
not exceed 2416 tonnes. 

56 

56 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5.1.9.3   Basis of the assessment 
The  state  of  exploitation  was  assessed  for  the  period  2003-2014  applying  the  Extended  Survivor 
Analysis (XSA) method calibrated with fishery independent survey abundance indices (MEDITS). Catch 
and catch numbers at age input data were the merging of the XSA input data used in the assessments 
performed  by  GSA.  Four  tuning  MEDITS  files  were  used,  one  per  GSA,  the  same  as  used  in  the 
assessments by GSA. In addition, a yield-per-recruit (Y/R) analysis was carried out. 

5.1.9.4   Catch options 
The catch options for the European hake stock in GSAs 1-7 are summarized in Table 5.1.9.4.1. 

Table  5.1.9.4.1.  Short  term  forecast  in  different  F  scenarios  computed  for  hake  in  GSA  1-7.  Basis:  F(2015)  = 
mean (Fbar 1-3 2012-2014)= 1.40; R(2015) = geometric mean of the recruitment of the last 3 years; R = 459070 
thousands; SSB(2014) = 8133 t, Catch (2014)= 4650 t. 

Rationale 

Ffactor  Fbar 

Catch 
2015 

Catch 
2016 

Catch 
2017 

SSB 
2016 

SSB 
2017 

Change  
SSB 2016- 
2017(%) 

Change 
Catch 
2014-2016(%) 

Zero catch 
High long 
term yield 
(FMSY) 
Status quo 
Different 
scenarios 

0 

0.00 

6221 

0 

0 

7843  21566 

174.99 

-100.00 

0.28 

0.39 

6221 

2416 

3901 

7843  15746 

100.78 

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 

1.40 

6221 

6192 

6162 

7843 

7758 

0.14 

6221 

953 

1807 

7843  19225 

0.28 

6221 

1805 

3123 

7843  17179 

0.42 

6221 

2569 

4076 

7843  15391 

0.56 

6221 

3256 

4760 

7843  13827 

0.70 

6221 

3874 

5247 

7843  12458 

0.84 

6221 

4432 

5589 

7843  11258 

0.98 

6221 

4937 

5826 

7843  10206 

1.12 

6221 

5395 

5987 

7843 

1.26 

6221 

5812 

6094 

7843 

1.54 

6221 

6539 

6203 

7843 

1.68 

6221 

6857 

6225 

7843 

1.82 

6221 

7149 

6236 

7843 

1.96 

6221 

7419 

6239 

7843 

2.10 

6221 

7667 

6237 

7843 

2.24 

6221 

7898 

6233 

7843 

2.38 

6221 

8112 

6228 

7843 

2.52 

6221 

8312 

6223 

7843 

2.66 

6221 

8498 

6219 

7843 

2.79 

6221 

8672 

6216 

7843 

9282 

8471 

7130 

6577 

6089 

5659 

5279 

4942 

4644 

4380 

4145 

3936 

-1.08 

145.13 

119.05 

96.25 

76.31 

58.85 

43.55 

30.13 

18.36 

8.01 

-9.09 

-16.14 

-22.36 

-27.84 

-32.69 

-36.98 

-40.79 

-44.16 

-47.15 

-49.81 

-48.04 

33.15 

-79.51 

-61.18 

-44.74 

-29.98 

-16.68 

-4.68 

6.18 

16.03 

24.99 

40.62 

47.46 

53.75 

59.54 

64.89 

69.85 

74.46 

78.75 

82.75 

86.50 

5.1.9.5   Reference points 

Table 5.1.1.5.1 Hake in GSA 1-7. Reference points, values and their technical basis. 

Framework 

Reference point 

Value 

Technical basis 

Source 

MSY approach 

MSY Btrigger 

57 

57 

 
 
 
 
 
 
 
 
 
 
Precautionary 
approach 

EU-GFCM 
management 
strategy 

FMSY 
Blim 
Bpa 
Flim 
Fpa 
SSBlower 
SSBupper 
Flower 
Fupper 

0.39 
5186 

0.26 
0.53 

F0.1 estimated with YPR 
Bloss 

Present assessment 
Present assessment 

Empirical relationship 
Empirical relationship 

Present assessment 
Present assessment 

5.1.9.6  Quality of the assessment  

The detailed assessment can be found in section 5.2.9. 

58 

58 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5.1.10  SUMMARY SHEET OF HAKE IN GSA 9-11 

Species common name: 
Species scientific name: 
Geographical Sub-area(s) GSA(s): 

European hake 
Merluccius merluccius 
9, 10, and 11 

5.1.10.1  Stock development over time 
State of the adult abundance and biomass 
The SSB showed a slight decline over the time series, with an average of 2900 t.  

State of the juveniles (recruits)  
The recruitment estimated for 2014 is 140913 thousand individuals, slightly lower compared to the 
series average (166055 thousand individuals, period 2006-2014). Recruitment has generally declined 
over the time series. 

State of exploitation 
The current F (1.10) is larger than FMSY (0.20), which indicates that European hake stock in GSAs 9, 10 
and 11 is being fished above FMSY.  

Hake in GSAs 9-11. XSA summary results. SSB and catch are in tonnes, recruitment in 1000s individuals. 

59 

59 

 
 
 
 
 
 
 
 
 
 
 
5.1.10.2  Stock advice 
STECF  EWG  15-11  advises  the  relevant  fleets’  effort  and/or  catches  to  be  reduced  until  fishing 
mortality is below or at the proposed FMSY level in order to avoid future loss in stock productivity and 
landings. This should be achieved by means of a multi-annual management plan taking into account 
mixed-fisheries  considerations.  Catches  of  European  hake  in  2016  consistent  with  FMSY  should  not 
exceed 1029 tonnes in GSAs 9, 10, and 11. 

5.1.10.3  Basis of the assessment 
The  stock  of  European  hake  in  GSAs  9,  10,  and  11  was  assessed  applying  an  Extended  Survivor 
Analysis  (XSA)  method  calibrated  with  fishery  independent  survey  abundance  indices  (MEDITS  in 
GSAs 9, 10, and 11), and CPUE by long-liners in GSA 10. Input data on landings, discards and length 
frequencies were taken from DCF. Von Bertalanffy growth parameters and length-weight relationship 
were taken from parameters agreed and used in previous EWGs. 

5.1.10.4  Catch options 
The  catch  options  for  the  European  hake  stock  in  GSAs  9,  10,  and  11  are  summarized  in  Table 
5.1.10.4.1. 

Table 5.1.10.4.1. Short term forecast in different F scenarios computed for hake in GSA 9-11. Basis: F(2015) = 
mean (Fbar 1-4 2012-2014)= 0.96; R(2015) = geometric mean of the recruitment of the last 3 years; R = 120861 
thousands; SSB(2014) = 2911 t, Catch (2014)= 3075 t. 

Rationale 

Ffactor 

Fbar 

Catch 
2015 

Catch 
2016 

Catch 
2017 

SSB 
2016 

SSB 
2017 

Change 
SSB 2016-
2017(%) 

Change 
Catch 
2014-
2016(%) 

Zero catch 

High long 
term yield 
(FMSY) 
Status quo 

Different 
Scenarios 

0.00 

0.00 

3185 

0 

0 

2739 

8656 

216.01 

-100.00 

0.21 

1.00 

0.10 

0.20 

0.30 

0.40 

0.50 

0.60 

0.70 

0.80 

0.90 

1.10 

1.20 

1.30 

1.40 

1.50 

1.60 

1.70 

1.80 

0.20 

0.96 

0.10 

0.19 

0.29 

0.38 

0.48 

0.57 

0.67 

0.76 

0.86 

1.05 

1.15 

1.24 

1.34 

1.43 

1.53 

1.62 

1.72 

3185 

3185 

3185 

3185 

3185 

3185 

3185 

3185 

3185 

3185 

3185 

3185 

3185 

3185 

3185 

3185 

3185 

3185 

3185 

1029 

3218 

522 

981 

1386 

1744 

2063 

2347 

2601 

2828 

3033 

3385 

3538 

3676 

3803 

3919 

4026 

4125 

4216 

2040 

3233 

1151 

1964 

2526 

2899 

3133 

3265 

3323 

3327 

3293 

3156 

3068 

2974 

2877 

2780 

2684 

2590 

2500 

60 

60 

2739 

2739 

2739 

2739 

2739 

2739 

2739 

2739 

2739 

2739 

2739 

2739 

2739 

2739 

2739 

2739 

2739 

2739 

2739 

6622 

2755 

7613 

6716 

5943 

5275 

4697 

4195 

3759 

3379 

3046 

2498 

2273 

2073 

1896 

1739 

1598 

1473 

1360 

141.76 

-66.52 

0.57 

177.94 

145.20 

116.97 

92.59 

71.48 

53.17 

37.24 

23.35 

11.21 

-8.79 

-17.03 

-24.32 

-30.78 

-36.53 

-41.65 

-46.23 

-50.33 

4.66 

-83.04 

-68.11 

-54.93 

-43.26 

-32.90 

-23.67 

-15.41 

-8.01 

-1.35 

10.11 

15.06 

19.57 

23.69 

27.47 

30.95 

34.15 

37.12 

 
 
 
 
 
 
1.90 

2.00 

1.82 

1.91 

3185 

3185 

4300 

4379 

2414 

2332 

2739 

2739 

1259 

1168 

-54.03 

-57.36 

39.87 

42.42 

5.1.10.5  Reference points 

Table 5.1.10.5.1 Hake in GSA 9_11. Reference points, values and their technical basis. 

Framework 

Reference point 

Value 

Technical basis 

Source 

MSY approach 

MSY Btrigger 

Precautionary 
approach 

EU-GFCM 
management 
strategy 

FMSY 
Blim 
Bpa 
Flim 
Fpa 
SSBlower 
SSBupper 
Flower 
Fupper 

0.20 
2355 

0.14 
0.28 

5.1.10.6 Quality of the assessment  

F0.1 estimated with YPR. 
Bloss 

Present assesment 
Present assessment 

Empirical relationship 
Empirical relationship 

Present assesment 
Present assessment 

The detailed assessment can be found in section 5.2.10. 

61 

61 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5.1.11  SUMMARY SHEET OF GIANT RED SHRIMP IN GSA 9 

Species common name: Giant red shrimp 
Species scientific name: Aristaeomorpha foliacea 
Geographical Sub-area(s) GSA(s): 9 

5.1.11.1  Stock development over time 

State of the adult abundance and biomass 
SSB estimates showed an increasing pattern between 2008 and 2011 followed by a decline. In the last 
two years the values were quite stable.  

Giant red shrimp in GSA 9. XSA summary results. SSB and catch are in tonnes, recruitment in 1000s individuals. 

State of the juveniles (recruits)  
Recruitment does not show any particular trend over the times series, with a peck between 2008 and 
2010.  

State of exploitation 
F  has  largely  declined  from  2012  to  2104.  The  current  F  (0.13)  is  lower  than  FMSY  (0.51),  which 
indicates that Giant red shrimp in GSA 9 is being fished below FMSY.  

5.1.11.2  Stock advice 
STECF  EWG  15-11  advises  the  relevant  fleets’  effort  and/or  catches  to  be  reduced  until  fishing 
mortality is below or at the proposed FMSY level in order to avoid future loss in stock productivity and 
landings.  Catches  of  Giant  red  shrimp  in  2016  consistent  with  FMSY  should  not  exceed  44  tonnes  in 
GSAs 9. 

62 

62 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
5.1.11.3  Basis of the assessment 
An XSA analysis was performed using 2006-2014 DCF data using catch at age data provided and tuned 
with  fishery  independent  abundance  indices  (MEDITS  survey).  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). 

5.1.11.4  Catch options 
Catch options are summarized in the following table (Table 5.1.11.4.1). 

Table 5.1.11.4.1. Short term forecast in different F scenarios computed for Giant red shrimp in GSA 9. Basis: 
F(2015) = mean (Fbar 1-3 2012-2014)= 0.51; R(2015) = geometric mean of the recruitment of the last 3 years; R 
= 10018 thousands; SSB(2014) = 94 t, Catch (2014)= 17 t. 

Rationale 

Ffactor 

Fbar 

0.000 

0.000 

Catch 
2016 
0.00 

Catch 
2017 
0.00 

SSB 
2017 
142.79 

Change SSB 2016-
2017(%) 
25.96 

Change Catch 2014-
2016(%) 
-100.00 

Zero catch 
High long term 
yield FMSY 
Status quo 
Different 
scenarios 

1.744 

0.514 

43.89 

38.07 

96.42 

1.000 

0.295 

27.53 

27.47 

113.36 

0.100 

0.029 

3.09 

3.71 

139.45 

0.200 
0.300 
0.400 
0.500 
0.600 
0.700 
0.800 
0.900 
1.100 
1.200 
1.300 
1.400 
1.500 
1.600 
1.700 
1.800 
1.900 
2.000 

0.059 
0.088 
0.118 
0.147 
0.177 
0.206 
0.236 
0.265 
0.324 
0.354 
0.383 
0.412 
0.442 
0.471 
0.501 
0.530 
0.560 
0.589 

6.10 
9.02 
11.88 
14.66 
17.37 
20.01 
22.58 
25.09 
29.91 
32.23 
34.50 
36.71 
38.86 
40.96 
43.01 
45.01 
46.96 
48.86 

7.16 
10.38 
13.38 
16.18 
18.78 
21.19 
23.44 
25.53 
29.27 
30.94 
32.49 
33.92 
35.25 
36.47 
37.61 
38.65 
39.62 
40.50 

136.19 
133.04 
129.97 
127.00 
124.11 
121.30 
118.58 
115.93 
110.87 
108.44 
106.09 
103.80 
101.58 
99.42 
97.32 
95.28 
93.30 
91.38 

-14.94 

0.00 

23.01 

20.14 
17.35 
14.65 
12.03 
9.48 
7.00 
4.60 
2.27 
-2.20 
-4.34 
-6.42 
-8.44 
-10.40 
-12.30 
-14.15 
-15.95 
-17.70 
-19.40 

160.94 

63.68 

-81.64 

-63.76 
-46.34 
-29.38 
-12.85 
3.26 
18.95 
34.25 
49.15 
77.85 
91.66 
105.12 
118.25 
131.05 
143.54 
155.73 
167.61 
179.20 
190.52 

5.1.11.5  Reference points 

Table 5.1.11.5.1 Giant red shrimp in GSA 9. Reference points, values and their technical basis. 

Framework 

Reference point 

Value 

Technical basis 

Source 

MSY approach 

MSY Btrigger 

Precautionary 
approach 

EU-GFCM 
management 

FMSY 
Blim 
Bpa 
Flim 
Fpa 
SSBlower 
SSBupper 

0.51 
80 

F0.1 estimated with Yield per Recruit analyses 
Bloss 

Present assessment 
Present assessment 

63 

63 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
strategy 

Flower 
Fupper 

0.34 

0.69 

Empirical relationship 
Empirical relationship 

Present assessment 

Present assessment 

5.1.11.6 Quality of the assessment  

The detailed assessment can be found in section 5.2.11. 

64 

64 

 
 
 
 
 
 
 
 
5.1.12  SUMMARY SHEET OF GIANT RED SHRIMP IN GSA 10 

Species common name: 
Species scientific name: 
Geographical Sub-area(s) GSA(s): 

Giant red shrimp 
Aristaeomorpha foliacea 
10 

5.1.12.1  Stock development over time 
State of the adult abundance and biomass 
SSB showed an increasing trend up to 2011 followed by a constant decrease up to the minimum in 
2014 (265.3 t).   

State of the juveniles (recruits)  
Recruitment is characterised by a fluctuating trend, varying from a minimum of 113 millions in 2006 
to 209 millions in 2011.  

State of exploitation 
Fishing mortality showed an evident increasing trend in the last three years. The current F (0.91) is 
larger than FMSY (0.65), which indicates that Giant red shrimp stock in GSAs 10 is being fished above 
FMSY.  

Giant red shrimp in GSA 10. XSA summary results. SSB and catch are in tons, recruitment in 1000s individuals. 

5.1.12.2  Stock advice 
STECF  EWG  15-11  advises  the  relevant  fleets’  effort  and/or  catches  to  be  reduced  until  fishing 
mortality is below or at the proposed FMSY level in order to avoid future loss in stock productivity and 
landings. Catches of European Giant red shrimp in 2016 consistent with FMSY should not exceed 314 
tonnes in GSAs 10. 

65 

65 

 
 
 
 
 
 
 
 
 
 
 
 
5.1.12.3  Basis of the assessment 
The  stock  assessment  of  giant  red  shrimp  in  GSA  10  was  performed applying  an  Extended Survivor 
Analysis  (XSA)  method  calibrated  with  fishery  independent  survey  abundance  indices  (MEDITS).  In 
addition, a yield-per-recruit (Y/R) analysis was carried out. Both methods were performed from the 
size composition of landings and discards, transforming length data to ages using slicing technique. 
Input  data  landings,  discards  and  length  frequencies  were  taken  from  DCF.  Von  Bertalanffy  growth 
parameters  and  length-weight  relationship  were  taken  from  parameters  estimated  for  giant  red 
shrimp in GSA 10. Natural mortality (vector) was estimated using PROBIOM. 

5.1.12.4  Catch options 
The catch options for giant red shrimp stock in GSA 10 are summarised in Table 5.1.12.4.1. 

Table 5.1.12.4.1. Short term forecast in different F scenarios computed for giant red shrimp stock in GSA 10. 
Basis: F(2015) = mean (Fbar 0-3 2012-2014)= 0.65; R(2015) = geometric mean of the recruitment of the last 3 
years; R = 156034 thousands; SSB(2014) = 265 t, Catch (2014)= 465 t. 

Rationale 

Ffactor  Fbar  Catch 2016  Catch 2017  SSB 2017 

Zero catch 
High long term 
yield F(0.1) 
Status quo 
Different 
scenarios 

0.0 

0.00 

0.00 

0.72 
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 

0.65 
0.91 

0.09 

0.18 
0.27 
0.36 
0.45 
0.54 
0.64 
0.73 
0.82 
1.00 
1.09 
1.18 
1.27 
1.36 
1.45 
1.54 
1.63 
1.72 
1.82 

315 
396 

57.5 

110 
157 
200 
240 
276 
310 
341 
369 
420 
443 
465 
485 
503 
521 
538 
554 
569 
583 

0.0 

333 
376 

84 

151 
24 
246 
280 
307 
330 
348 
363 
387 
396 
404 
411 
418 
423 
428 
432 
437 
440 

580 

201 
139 

494 

423 
363 
313 
271 
235 
205 
179 
158 
123 
110 
99 
88 
80 
72 
66 
60 
55 
50 

Change SSB 
2016-2017(%) 
99.3 

Change Catch 
2014-2016(%) 
-100 

10.0 
-8.7 

81.7 

66.1 
52.3 
40.1 
29.3 
19.7 
11.2 
3.7 
-2.9 
-13.9 
-18.4 
-22.4 
-26.0 
-29.1 
-318 
-34.3 
-36.4 
-38.3 
-39.9 

-30.6 
-12.9 

-87.3 

-75.8 
-65.4 
-55.9 
-47.1 
-39.1 
-31.8 
-25.0 
-18.7 
-7.4 
-2.4 
2.3 
6.8 
10.9 
14.8 
18.5 
22.0 
25.2 
28.4 

5.1.12.5  Reference points 

Table 5.1.12.5.1 Giant red shrimp in GSA 10. Reference points, values and their technical basis. 

Framework 

Reference point 

Value 

Technical basis 

Source 

MSY approach 

Precautionary 
approach 

MSY Btrigger 

FMSY 
Blim 
Bpa 

0.65 
265 

F0.1 estimated with YpR 
Bloss 

Present assessment 
Present assessment 

66 

66 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
EU-GFCM 
management 
strategy 

Flim 
Fpa 
SSBlower 
SSBupper 
Flower 
Fupper 

0.43 
0.88 

Empirical relationship 
Empirical relationship 

Present assessment 
Present assessment 

5.1.12.6 Quality of the assessment  

The detailed assessment can be found in section 5.2.12. 

67 

67 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5.1.13  SUMMARY SHEET OF GIANT RED SHRIMP IN GSA 11 

Species common name: Giant red shrimp 
Species scientific name: Aristeomorpha foliacea 
Geographical Sub-area GSA: 11 

5.1.13.1  Stock development over time 
SSB estimates showed an increasing pattern since 2007, with a peak in 2013.  

Giant red shrimp GSA 11. XSA summary results. SSB and catch are in tonnes, recruitment in 1000s individuals. 

State of the juveniles (recruits)  
Recruitment has increased until 2009, followed by a decreasing trend.  

State of exploitation 
The current F (0.50) is larger than FMSY (0.31), which indicates that Giant red shrimp stock in GSAs 11, 
is being fished above FMSY.  

5.1.13.2  Stock advice 
STECF  EWG  15-11  advises  the  relevant  fleets’  effort  and/or  catches  to  be  reduced  until  fishing 
mortality is below or at the proposed FMSY level in order to avoid future loss in stock productivity and 
landings. Catches of Giant red shrimp stock in 2016 consistent with FMSY should not exceed 36 tonnes 
in GSAs 11. 

68 

68 

01000020000020406002550751000.000.250.500.751.00RecSSBCatchHarvest20062008201020122014 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5.1.13.3  Basis of the assessment 
An XSA analysis was performed using 2006-2013 DCF data (biomass landed and size composition of 
the  catches sliced  according  to  the  growth  parameters  and the  sex-ratio  reported  in  the  data call), 
tuned  with  fishery  independent  abundance  indices  (MEDITS  survey).  A  vector  of  natural  mortality 
was  obtained  applying  PRODBIOM.  In  addition,  Yield-per-Recruit  (YPR)  analysis  was  performed  for 
the estimation of F0.1 (proxy of FMSY). 

5.1.13.4  Catch options 
Catch options are summarized in the following table (Table 5.1.13.4.1). 

Table 5.1.13.4.1 Short term forecast in different F scenarios computed for Giant red shrimp in GSA 11. Basis: 
F(2015) = mean (Fbar 0-3 2012-2014)= 0.53; R(2015) = geometric mean of the recruitment of the last 3 years; R 
= 17374 thousands; SSB(2014) = 46 t, Catch (2014)= 49 t. 

Scenarios  Ffactor  Fbar 

Catch 

2016 

2015 

SSB 

% change 
in SSB 

% change 
in Catch 

2017 

2016 

2017 

2016-2017  2014-2016 

No 
fishery 
F status 
quo 
FMSY 
Different 
scenarios 

0 

0 

53.49 

0 

0 

75.47  128.05 

69.66 

-100 

1 
0.58 
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 

0.53 
0.31 
0.05 
0.11 
0.16 
0.21 
0.27 
0.32 
0.37 
0.42 
0.48 
0.58 
0.64 
0.69 
0.74 
0.80 
0.85 
0.90 
0.95 
1.01 
1.06 

53.49 
53.49 
53.49 
53.49 
53.49 
53.49 
53.49 
53.49 
53.49 
53.49 
53.49 
53.49 
53.49 
53.49 
53.49 
53.49 
53.49 
53.49 
53.49 
53.49 
53.49 

55.31 
35.83 
6.98 
13.58 
19.82 
25.73 
31.33 
36.64 
41.67 
46.45 
50.99 
59.41 
63.32 
67.05 
70.60 
73.99 
77.22 
80.32 
83.28 
86.11 
88.82 

55.15 
41.51 
9.89 
18.41 
25.76 
32.08 
37.51 
42.19 
46.21 
49.65 
52.61 
57.32 
59.18 
60.77 
62.14 
63.31 
64.31 
65.17 
65.91 
66.54 
67.09 

54.20 
54.28 
62.22 
76.83 
73.01  117.14 
70.63  107.23 
98.23 
68.34 
90.04 
66.12 
82.59 
63.97 
75.81 
61.90 
69.64 
59.90 
64.01 
57.96 
58.88 
56.09 
49.93 
52.53 
46.02 
50.85 
42.45 
49.21 
39.19 
47.64 
36.20 
46.11 
33.47 
44.64 
30.96 
43.21 
28.66 
41.83 
26.55 
40.50 
24.61 
39.21 

-0.15 
23.48 
60.44 
51.81 
43.74 
36.18 
29.11 
22.48 
16.26 
10.44 
4.98 
-4.97 
-9.49 
-13.74 
-17.73 
-21.49 
-25.02 
-28.35 
-31.49 
-34.45 
-37.24 

12.00 
-27.44 
-85.87 
-72.50 
-59.86 
-47.89 
-36.55 
-25.81 
-15.61 
-5.93 
3.26 
20.32 
28.23 
35.78 
42.97 
49.83 
56.39 
62.65 
68.65 
74.38 
79.88 

5.1.13.5  Reference points 

Table 5.1.13.5.1 Giant red shrimp in GSA 11. Reference points, values and their technical basis. 

Framework 

Reference point 

Value 

Technical basis 

Source 

69 

69 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
MSY approach 

MSY Btrigger 

- 

Precautionary 
approach 

EU-GFCM 
management 
strategy 

FMSY 
Blim 
Bpa 
Flim 
Fpa 
SSBlower 
SSBupper 
Flower 
Fupper 

0.31 
26 t 

F0.1 estimated with Yield-per-Recruit analyses 
Bloss 

Present assessment 
Present assessment 

0.21 
0.43 

Empirical relationship 
Empirical relationship 

Present assessment 
Present assessment 

5.1.13.6 Quality of the assessment  

The detailed assessment can be found in section 5.2.13. 

70 

70 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5.1.14  SUMMARY SHEET OF BLUE AND RED SHRIMP IN GSA 1 

Species common name: 
Species scientific name 
Geographical Sub-area(s)   GSA(s): 1 

Blue and red shrimp   
Aristeus antennatus 

5.1.14.1  Stock development over time 

State of the adult abundance and biomass 
The SSB is fluctuating along the series between 200 and 400 tons, with an average of 313 tonnes and 
a final value (2014) of 322 t.  

State of the juveniles (recruits)  
The recruitment estimated for 2014 is around 35000 individuals, slightly higher than the time series 
average (32000). 

State of exploitation 
The current F (1.40) is larger than FMSY (0.41), which indicates that Blue and red shrimp in GSAs 1 is 
being fished above FMSY.  

Blue  and  red  shrimp  in  GSA  1.  XSA  summary  results.  SSB  and  catch  are  in  tonnes,  recruitment  in  1000s 
individuals. 

71 

71 

 
 
 
 
 
 
 
 
 
 
 
 
 
5.1.14.2  Stock advice 
STECF  EWG  15-11  advises  the  relevant  fleets’  effort  and/or  catches  to  be  reduced  until  fishing 
mortality is below or at the proposed FMSY level in order to avoid future loss in stock productivity and 
landings. Catches of European hake in 2016 consistent with FMSY should not exceed 96 tonnes in GSA 
1. 

5.1.14.3  Basis of the assessment 

  Number of ages: 5 (0-4+)  
  Number of years: 13 (2002-2014) 
  One single gear: bottom otter trawl 
  No discards, all catches are landed 
  M= 0.46 year-1 for all ages 
  Growth parameters: Linf=80 mm, K=0.37 year-1, t0=0.032 year  
  Length to age by slicing 
  Length-weight relationship: a=0.002038 gr b=2.506 (DCF 2015, Spain) 
  Maturity ogive: (0) 0.22, (1) 0.95, (2) 1.0, (3) 1.0, (4+) 1.0 (L50=23.5mm) 
  Tuning MEDITS numb/km2 

Assessment method: XSA using FLR. 
Analysis of YPR using FLR andYield per Recruit v 3.3 NOAA 
MSE (Management Strategies Evaluation) input from XSA, and using a4a inside iterations loop. 

5.1.14.4  Catch options 
Catch options are summarized in the following table (Table 5.1.14.4.1).  

Table 5.1.9.4.1. Short term forecast in different F scenarios computed for hake in GSA 1. Basis: F(2015) = mean 
(Fbar  1-2  2012-2014)=  1.40;  R(2015)  =  geometric  mean  of  the  recruitment  of  the  last  3  years;  R  =  2838 
thousands; SSB(2014) = 322 t, Catch (2014)= 184 t. 

Rationale 

F scenario 

F factor 

Catch 
2016 

Catch 
2017 

SSB 2017  Change SSB 
2016-2017 
(%) 

Change 
catch 2014-
2016 (%) 

Zero catch 

High long-term 
yield (FMSY) 
Status quo 
Different 
scenarios 

0.0 

0.40 
1.40 

0.15 
0.31 
0.62 
0.77 
0.93 
1.08 
1.24 
1.54 
1.7 
1.85 
2.01 
2.16 

0.0 

0.3 
0.9 

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

0.0 

96 
204 

36 
68 
120 
141 
160 
176 
191 
215 
226 
235 
243 
250 

0.0 

146 
203 

66 
113 
168 
182 
192 
198 
201.15 
202.96 
202.48 
201.51 
200.26 
198.88 

674 

517 
349 

615 
563 
479 
445 
416 
390 
368 
332 
317 
304 
293 
283 

72 

72 

93.6 

48.4 
0.2 

76.5 
61.7 
37.6 
27.9 
19.5 
12.1 
5.7 
-4.7 
-9.0 
-12.7 
-16.0 
-18.8 

-100 

-47.7 
10.8 

-80.4 
-63.2 
-34.9 
-23.4 
-13.2 
-4.2 
3.8 
17.0 
22.6 
27.5 
31.9 
35.8 

 
 
 
 
 
 
 
 
  
  
  
  
  
  
  
  
  
  
  
2.32 
2.47 
2.63 
2.78 
2.93 
3.09 

1.5 
1.6 
1.7 
1.8 
1.9 
2 

256 
262 
268 
272 
277 
281 

197.45 
196.06 
194.73 
193.5 
192.36 
191.34 

274 
267 
259 
253 
248 
243 

-21.3 
-23.6 
-25.5 
-27.2 
-28.8 
-30.1 

39.4 
42.6 
45.4 
48.0 
50.4 
52.5 

5.1.14.5  Reference points 

Table 5.1.14.5.1 Blue and red shrimp in GSA 1. Reference points, values and their technical basis. 

Framework 

Reference point 

Value 

Technical basis 

Source 

MSY approach 

MSY Btrigger 

Precautionary 
approach 

EU-GFCM 
management 
strategy 

FMSY 
Blim 
Bpa 
Flim 
Fpa 
SSBlower 
SSBupper 
Flower 
Fupper 

0.41 
224 

F0.1 estimated with Yield-per-Recruit analyses 
Bloss 

Present assessment 
Present assessment 

0.27 
0.56 

Empirical relationship 
Empirical relationship 

Present assessment 
Present assessment 

5.1.14.6 Quality of the assessment  

The detailed assessment can be found in section 5.2.14. 

73 

73 

 
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5.1.15  SUMMARY SHEET OF BLUE AND RED SHRIMP IN GSA 6 

Species common name: Blue and red shrimp 
Species scientific name: Aristeus antennatus 
Geographical Sub-area(s) GSA(s): 06 

5.1.15.1  Stock development over time 

State of the adult abundance and biomass 
SSB  increased  since  2006  from  919  t  in  2005  to  3848  t  in  2014,  the  highest  value  over  the  whole 
period (2002-2014).  
State of the juveniles (recruits)  
Recruitment (age 0 individuals) has been steadily increasing over the entire period 2002 – 2014, with 
a low value of 103 million individuals in 2004 and a high of 304 million in 2012.  

State of exploitation 
Fishing  mortality  oscillated  between  0.52  and  1.5,  with  the  lowest  values  observed  in  the  2  most 
recent  years  (2013,  2014).  The  current  F  (0.78)  is  larger  than  FMSY  (0.36),  which  indicates  that  Blue 
and red shrimp stock in GSAs 6 is being fished above FMSY.  

74 

74 

 
 
 
 
 
 
 
 
 
 
Blue  and  red  shrimp  in  GSA  6.  XSA  summary  results.  SSB  and  catch  are  in  tonnes,  recruitment  in  1000s 
individuals. 

5.1.15.2  Stock advice 
STECF  EWG  15-11  advises  the  relevant  fleets’  effort  and/or  catches  to  be  reduced  until  fishing 
mortality is below or at the proposed FMSY level in order to avoid future loss in stock productivity and 
landings. This should be achieved by means of a multi-annual management plan taking into account 
mixed-fisheries considerations. Catches of  Blue  and red shrimp in 2016  consistent with FMSY should 
not exceed 525 tonnes in GSAs 6. 

5.1.15.3  Basis of the assessment 
The  state  of  exploitation  was  assessed  for  the  period  2002-2014  applying  the  Extended  Survivor 
Analysis (XSA) tuned with fishery independent abundance indices (MEDITS survey). In addition, Yield 
per Recruit (YPR) analysis was carried out. Both methods were performed from the size composition 
of bottom trawl landings, transforming length data to ages using knife edge slicing. Input fishery data 
were  taken  from  DCF  2015  Data  Call,  complemented  with  specific  data  from  other  sources.  In 
particular, total catches have been reconstructed for the period 2002-2009 from data sources of local 
Fisheries  Directorates  (Catalonia  and  Valencia).  Discards  are  very  low  or  nil  because  of  the  high 
economic value of the species and were considered 0 in the assessment. 

5.1.15.4 Catch options 
Catch options are summarized in the following table (Table 5.1.15.4.1). 

Table  5.1.15.4.1.  Short  term  forecast  in  different  F  scenarios  computed  for  Blue  and  red  shrimp  in  GSA  6. 
Basis: F(2015) = mean (Fbar 1-3 2012-2014)= 0.52; R(2015) = geometric mean of the recruitment of the last 3 
years; R = 278633 thousands; SSB(2014) = 3848 t, Catch (2014)= 547 t. 

Ffactor 

Fbar 

0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9 
1 

1.1 
1.2 
1.3 
1.4 
1.5 
1.6 
1.7 

0.00 
0.08 
0.15 
0.23 
0.30 
0.38 
0.45 
0.53 
0.60 
0.68 
0.78 

0.83 
0.90 
0.98 
1.05 
1.13 
1.20 
1.28 

Catch_2
015 
0 
93 
182 
266 
346 
422 
494 
563 
628 
690 

749 
805 
858 
909 
957 
1003 
1047 
1089 

Catch_
2016 
0 
124 
240 
349 
450 
544 
633 
715 
792 
864 

932 
995 
1054 
1109 
1161 
1210 
1255 
1298 

Catch_
2017 
0 
190 
347 
477 
584 
671 
741 
797 
842 
877 

903 
923 
938 
947 
953 
956 
956 
954 

75 

75 

status 
quo 

SSB_2016 
4375 
4375 
4375 
4375 
4375 
4375 
4375 
4375 
4375 
4375 

SSB_2017 
7172 
6764 
6385 
6034 
5709 
5407 
5127 
4868 
4627 
4404 

Change_SSB_
2016-2017(%) 
63.91 
54.58 
45.93 
37.91 
30.48 
23.58 
17.18 
11.25 
5.75 
0.65 

Change_Catch_
2014-2016(%) 
-100.00 
-77.25 
-56.02 
-36.20 
-17.69 
-0.40 
15.75 
30.85 
44.96 
58.15 

4375 
4375 
4375 
4375 
4375 
4375 
4375 
4375 

4197 
4004 
3826 
3661 
3508 
3365 
3233 
3111 

-4.09 
-8.48 
-12.55 
-16.33 
-19.84 
-23.09 
-26.10 
-28.90 

70.49 
82.03 
92.84 
102.95 
112.43 
121.30 
129.62 
137.42 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1.8 
1.9 
2 
0.48 

1.35 
1.43 
1.50 
0.36 

1130 
1168 
1204 
407 

1338 
1375 
1410 
525 

951 
946 
941 
654 

4375 
4375 
4375 
4375 

2997 
2892 
2794 
5468 

FMSY 

-31.50 
-33.91 
-36.14 
24.97 

144.73 
151.59 
158.03 
-3.90 

5.1.15.5  Reference points 

Table 5.1.15.5.1 Blue and red shrimp in GSA 6. Reference points, values and their technical basis. 

Framework 

Reference point 

Value 

Technical basis 

Source 

MSY 
approach 

Precautionary 
approach 

EU-GFCM 
management 
strategy 

MSY Btrigger 

FMSY 
Blim 
Bpa 
Flim 
Fpa 
SSBlower 
SSBupper 
Flower 
Fupper 

0.36 
1287 

F0.1 estimated with Yield-per-Recruit analyses 
Bloss 

Present assessment 
Present assessment 

0.24 
0.49 

Empirical relationship 
Empirical relationship 

Present assessment 
Present assessment 

5.1.15.6 Quality of the assessment  

The detailed assessment can be found in section 5.2.15. 

76 

76 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5.2  STOCK ASSESSMENT 

5.2.1  STOCK ASSESSMENT OF HAKE IN GSA 1 
5.2.1.1  Stock Identification 

The  delimitation  of  the  hake  stock  in  GSA  1  is  considered  largely  unknown  (Fig.5.2.1.1.1).  Likely 
connections with hake in GSA 6 may exist, because of the continuity of shelf. Large exchanges with 
the south Alboran Sea (GSA 3) are instead believed to be insignificant. 

Figure.5.2.1.1.1. Geographical location of GSA 1. 

5.2.1.2  Growth 
Growth parameters (Linf=110; k=0.178; males and females combined) were taken from Mellon-Duval 
et  al.  (2010).  These  growth  parameters  were  estimated  through  tagging  in  the  Gulf  of  Lions  and 
correspond  to  fast  growth  for  the  species.  The  length-weight  relationship  parameters  used  are 
a=0.00677 and b=3.035097 (DCF 2011).  

5.2.1.3  Maturity 
Maturity ogive was taken from García-Rodríguez and Esteban (1995), with size at first maturity (50 %) 
at 33 cm TL. 

Age 
% mature 

0 
0 

1 
0.15 

2 
0.82 

3 
0.98 

4          5+ 
1 
1 

5.2.1.4  Natural mortality 
Natural mortality was estimated using PRODBIOM (Abella et al. 1997). M at the mid-point of the year 
was selected as M representative for that annual class.  

Age 

5.2.1.5  Fisheries 

Natural Mortality (M) at age 
3 
0 
0.40 
1.24 

1 
0.58 

2 
0.45 

4 
0.37 

5+ 
0.35 

77 

77 

GSA01 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
General description of the fisheries 

5.2.1.5.1 
European hake is one of most important demersal target species of the Mediterranean fishing fleets, 
exploited  in  GSA  1  mainly  by  trawlers  (87%  landings)  on  the  shelf  and  slope,  and  by  small-scale 
fisheries  using  small  scale  nets  (gillnet  and  trammel  nets;  8%)  and  long  lines  (3%)  on  the  shelf 
(average 2002-2014). 

Management regulations applicable in 2015  

5.2.1.5.2 
In addition to the regulations specified in (CE) regulation nº 1967/2006, trawl fisheries in GSA01 are 
regulated  by  “Orden  AAA/2808/2012”  published  in  the  Spanish  Official  Bulletin  (BOE  nº  313  29 
December 2012), that establishes an Integral Management Plan for Mediterranean fishery resources. 
Regulations  include  trawling  fishing  license  linked  fishing  area,  engine  power  limited  to  316  KW  or 
500 HP, codend mesh size (40 mm square or 50 mm rhomboidal), fishing forbidden within upper 50 
m depth, time at sea (12 hours per day and 5 days per week) and minimum legal size for hake (20 cm 
TL).  This  Management  Plan  proposes  a  reduction  of  fishing  effort  by  at  least  20%  over  the  period 
2013-2017, based on the number of vessels active on 1 January 2013. Fishing effort reduction will be 
measured in terms of number of vessels, engine power and tonnage. 

5.2.1.5.3 

 Catches  

Hake annual catches (in tons) by gear in GSA 1 from DCF are shown in Table 5.2.1.5.3.1. 

Table 5.2.1.5.3.1. Hake catches (t) by gear: artisanal nets (GNS+GTR), longlines (LLS) and otter trawls (OTB) in 
GSA 1. 

Year 
2002 
2003 
2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

GNS+GTR 
40 
37 
31 
35 
48 
39 
37 
50 
26 
19 
15 
26 
25 

LLS 
44 
14 
2 
6 
12 
6 
7 
6 
21 
16 
9 
11 
13 

OTB 
451 
416 
516 
313 
283 
275 
295 
584 
545 
654 
458 
347 
275 

5.2.1.5.4  Landings (by fleet if posible) 

78 

78 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 5.2.1.5.4.1. Hake landings (t) by gear: artisanal nets (GNS+GTR), longlines (LLS) and otter trawls (OTB) in 
GSA 1. 

Year 
2002 
2003 
2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

GNS+GTR 
40 
37 
31 
35 
48 
39 
37 
50 
26 
19 
15 
26 
25 

LLS 
44 
14 
2 
6 
12 
6 
7 
6 
21 
16 
9 
11 
13 

OTB 
451 
416 
516 
296 
283 
275 
282 
564 
530 
648 
437 
337 
245 

Figure 5.2.1.5.4.1. European hake in GSA 1. Annual landings by gear for the period 2002-2014. 

5.2.1.5.5  Discards (by fleet if posible) 
OTB data on discards are available for 2005 and 2008 to 2014. Discards represented around ≤5% of 
the OTB catch, in weight, except on 2014 when discards represented 11% of the total catch.  No data 
was provided on the discards sizes. Data on discards for small scale nets were available for 2011 and 
2014 and represented 0.2% of the total catch. 

Table 5.2.1.5.5.1. European hake in GSA 1. Discards by gear for the period 2005- 2014. 

OTB 
17.4 

Discards  GNS+GTR 

2005 
2006 
2007 

79 

79 

01002003004005006007002002200320042005200620072008200920102011201220132014Landings (t)HKE, GSA 1, LandingsGNS+GTRLLSOTB 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

0.2 

0.2 

12.5 
20.7 
14.9 
5.8 
20.8 
10.4 
30.5 

Figure  5.2.1.5.5.1  Hake  in  GSA  1.  Size  structure  of  the  landings  over  the  period  2003-  2014  (see  section 
5.2.1.7.2). 

Figure  5.2.1.5.5.2.  Hake  in  GSA  1.  Size  structure  of  the  discards  over  the  period  2003-  2014  (see  section 
5.2.1.7.2). 

5.2.1.5.6  Fishing effort (by fleet if possible) 
Data on fishing effort in GSA 1 by fleet are available from 2009 to 2014. No details for species were 
provided. 

80 

80 

0200400600800100012001400610141822263034384246505458626670747882thousandsTotal length (cm)HKE01 CATCH2003200420052006200720082009201020112012201320140100200300400500600610141822263034384246505458626670747882thousandsTotal length (cm)HKE01 DISCARDS200320042005200620072008200920102011201220132014 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 5.2.1.5.6.1. Annual fishing effort (GT*days at sea) in GSA 1 over 2009- 2014. 

OTB 

GTR+GNS 
LLS 
12365.55  5468.06  363674.9 
14064.84  6209.44  441135.9 
10267.43  7070.66  355930.3 
10065.19  1494.42  383345 
11223.85 
315009.9 
951.2 
15934.76  1780.63  320447.5 

2009 
2010 
2011 
2012 
2013 
2014 

Figure 5.2.1.5.6.1. Annual fishing effort (GT*days at sea) for OTB (left axis) and GTR+GNS and LLS  (right axis) in 
GSA 1 over 2009-2014. 

5.2.1.6  Scientific surveys 
5.2.1.6.1    Survey #1 ( MEDITS) 
5.2.1.6.1.1  Methods 

Based on the DCF data call, abundance and biomass indices were recalculated. In GSA 1 the following 
number of hauls was reported per depth stratum: 

Table 5.2.1.6.1.1. Number of hauls per year and depth stratum in GSA 1, 1994-2012. 

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  hauling  duration.  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:  

Yst = Σ (Yi*Ai) / A  
V(Yst) = Σ (Ai² * si ² / ni) / A²  

81 

81 

0200040006000800010000120001400016000180000100000200000300000400000500000200920102011201220132014GSA 1, EFFORT (gt_days_at_sea)OTBGTR+GNSLLSSTRATUM1994199519961997199819992000200120022003200420052006200720082009201020112012GSA01_010-0502122222444424442333GSA01_050-10055566966812888878668GSA01_100-2003335555586566777444GSA01_200-50089111071113101111131113131313688GSA01_500-8008912101212121313141311191399678 
 
 
 
 
 
 
  
 
 
 
 
 
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  
Length  distributions  represented  an  aggregation  (sum)  of  all  standardized  length  frequencies 
(subsamples  raised  to  standardized  haul  abundance  per  km2)  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.  

5.2.1.6.1.2   Geographical distribution 

Figure  5.2.1.6.1.2.1.  Hake  in  GSA  1.  spatial  distribution  of  estimated  abundances  indices  (N/Km2)  for  the 
period 2007-2011. MEDITS_ES trawl surveys. (GSA 1, Northern Alboran Sea). 

5.2.1.6.1.3   Trends in abundance and biomass 

Fishery independent information regarding the state of the European hake in GSA 1 was derived from 
the international survey MEDITS. Figure 5.2.1.6.1.3.1 displays the estimated trend in hake abundance 
and biomass in GSA 1 over 1994- 2014. 

82 

82 

 
 
 
 
 
 
 
 
Figure  5.2.1.6.1.3.1  Hake  in  GSA  1.  Abundance  and  biomass  trend  during  1994-2014  as  estimated  from  the 
MEDITS survey data. 

5.2.1.6.1.4 Trends in abundance by length or age 

83 

83 

 0 10 20 30 40 50 60 70 80 5 10 15 20 25 30 35 40 45 50 55 60 65 N/Km 2 Total length (cm) GSA01 2003 0 10 20 30 40 50 60 70 80 5 10 15 20 25 30 35 40 45 50 55 60 65 N/Km 2 Total length (cm) GSA01 2004  0 10 20 30 40 50 60 70 80 5 10 15 20 25 30 35 40 45 50 55 60 65 N/Km 2 Total length (cm) GSA01 2005 0 10 20 30 40 50 60 70 80 5 10 15 20 25 30 35 40 45 50 55 60 65 N/Km 2 Total length (cm) GSA01 2006  0 10 20 30 40 50 60 70 80 5 10 15 20 25 30 35 40 45 50 55 60 65 N/Km 2 Total length (cm) GSA01 2007 0 10 20 30 40 50 60 70 80 5 10 15 20 25 30 35 40 45 50 55 60 65 N/Km 2 Total length (cm) GSA01 2008  
 
 
 
 
 
 
 
Figure  5.2.1.6.1.4.1.  Hake  in  GSA  1.  Trends  in  abundance  (n/km2)  during  2003-2014  (data  source:  MEDITS 
survey). 

5.2.1.7  Stock Assessment 
5.2.1.7.1   Methods: XSA 

This  stock  was  assessed  through  XSA  by  EWG15-11,  using  an  ad-hoc  R-script.  SOP  correction  was 
made before running the analysis. XSA was run considering age classes 0 to 5+, the same as in the 
assessment performed in 2013 (EWG13-09), and input data over the period 2003-2014.  

5.2.1.7.2  Input data 
Hake in GSA 1 is exploited by OTB and small scale gears (GNS+GTR; LL). As explained above, discards 
are relevant for OTB and negligible for the small- scale gears. Data on size distributions were available 
for OTB landings over 2003-2014, and (GNS+GTR) in 2009, 2010 and 2014. So as to include all catch 
data in the assessment, when missing, the size distributions of the small scale gears and OTB discards 
were built taking as reference the size structure of (GNS+TRB) and LL in GSA 6 (the closest area to 
GSA  1)  and  of  OTB  discards  in  GSA7  (the  only  GSA  where  OTB  discards  sizes  were  available).  Size 
frequencies distributions were transformed into age by slicing using L2A routine.  

84 

84 

 0 10 20 30 40 50 60 70 80 5 10 15 20 25 30 35 40 45 50 55 60 65 N/Km 2 Total length (cm) GSA01 2009 0 10 20 30 40 50 60 70 80 5 10 15 20 25 30 35 40 45 50 55 60 65 N/Km 2 Total length (cm) GSA01 2010  0 10 20 30 40 50 60 70 80 5 10 15 20 25 30 35 40 45 50 55 60 65 N/Km 2 Total length (cm) GSA01 2011 0 10 20 30 40 50 60 70 80 5 10 15 20 25 30 35 40 45 50 55 60 65 N/Km 2 Total length (cm) GSA01 2012  
 
 
 
 
 
 
Figure 5.2.1.7.2.1. Hake in GSA 1. Catch at age.  

Natural mortality was estimated using PROBIOM. M at the mid-point of the year was selected as M 
representative for that annual class.  

Table 5.2.1.7.2.1. Hake in GSA 1. XSA input parameters: catch; catch numbers at age; weight at age; natural 
mortality at age; and tuning parameters (MEDITS survey 2003- 2012).  

Catch (t) 

Catch numbers at age (thousands) 

Weight at age (kg) 

Natural Mortality (M) at age and Maturity vectors 
2 
0.45 
0.82 

1 
0.58 
0.15 

3 
0.40 
0.98 

4 
0.37 
1 

0 
1.24 
0 

Age 
M 
Maturity 

5+ 
0.35 
1 

MEDITS number at age (2003-2014) 

85 

85 

200320042005200620072008200920102011201220132014466.4549354.6343.8320338.8639.9591.2689483.6384.9313.3200320042005200620072008200920102011201220132014013012414131828579099617871058525127963518681188236261212764137410583067257336312334130910462430448371361263315432101551832433720539053605146468412743253826457526861442215+11122411100020032004200520062007200820092010201120122013201400.0290.0270.0190.020.0330.0190.020.0180.0210.0160.0160.01610.110.0980.1040.120.1250.1240.1160.150.1280.1290.1390.13520.4230.3870.430.4250.4160.4230.4170.4050.3950.3710.3940.39731.0110.9750.9340.9660.910.9510.9170.9460.9110.9160.9240.9341.6051.6311.5181.5911.6851.5981.6331.571.6041.5871.5141.5195+2.363.7412.7812.6552.2482.7882.582.6442.4132.2972.5433.6 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5.2.1.7.3  Results 
Different  sensitivity  analyses  were  performed  before  running  the  final  XSA,  considering  different 
shrinkage  weight  from  0.5  to  2.5  (0.5  increasing),  shrinkage  ages  (1,2,3),  rage  (-1,0,1)  and  qage 
(2,3,4). Comparison of trends between settings has been done. Different combinations between the 
settings that looked more stable were tested. 

Figure 5.2.1.7.3.1. Hake in GSA 1. Sensitivity on shrinkage weight. 

Figure 5.2.1.7.3.2. Hake in GSA 1. Sensitivity on shrinkage age. 

Figure 5.2.1.7.3.3. Hake in GSA 1. Sensitivity on rage and qage. 

The  following  settings  that  minimized  the  residuals  and  showed  the  best  diagnostics  outpout  were 
used for the final XSA final run: 

Fbar 
0-2 

fse 
2 

rage 
-1 

qage 
4 

Shk.n 
TRUE 

Shk.f 
TRUE 

Shk.yrs 
3 

shrk ages 
2 

86 

86 

20032004200520062007200820092010201120122013201401(cid:9)238.51(cid:9)184.71(cid:9)166.31(cid:9)348.71(cid:9)355.21(cid:9)303.91(cid:9)311.71(cid:9)130.21(cid:9)113.962.336.6382.9135.827.618.634.726.836.681.4113.949.717.422.62324.10.83.82.84.16.25.419.7131.6NA2.83001.921.51.21.630.70.50.12.84+000.60.60.30.30.2000.501 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
The residuals pattern of the MEDITS trawl survey is shown in Fig. 5.2.1.7.3.4. 

Figure 5.2.1.7.3.4. Hake in GSA 1. XSA residuals for MEDITS survey from 2003 to 2014. 

The results of the retrospective analysis are shown in Figure 5.2.1.7.3.5. 

Figure 5.2.1.7.3.5. Hake in GSA 1. XSA retrospective analysis. 

The results of the XSA are shown in the following figure. 

87 

87 

 
 
 
 
 
 
Figure 5.2.1.7.3.6. Hake in GSA 1. XSA results. SSB and catch are in tonnes, recruitment in 1000s individuals. 

In  the  tables  5.2.1.7.3.1  and  2  the  population  estimates  of  hake  in  GSA  1  obtained  by  XSA  are 
provided. 

Table 5.2.1.7.3.1. European hake in GSA 1. Stock numbers at age (thousands) as estimated by XSA. 

Table 5.2.1.7.3.2. Hake in GSA 1. XSA summary results 

Fbar 0-2  Recruitment 
(thousands) 
19461 
12318 
8218 
14506 
9092 
20929 
14704 
20343 
15341 
10895 
7641 
28673 

1.41 
1.59 
1.27 
1.23 
1.20 
0.94 
1.36 
1.62 
2.11 
1.54 
1.62 
1.97 

SSB (t) 

TB (t) 

378.0 
345.8 
327.7 
323.4 
271.9 
333.9 
449.6 
479.8 
387.5 
287.7 
290.0 
220.1 

1295.8 
1138.8 
747.3 
830.9 
894.1 
1047.9 
1343.4 
1335.7 
1361.0 
955.2 
739.9 
915.7 

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

F at age 

88 

88 

 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 0 19461.00 12318.00 8218.10 14506.00 9092.30 20929.00 14704.00 20343.00 15341.00 10895.00 7640.70 28673.00 1 3281.60 5014.10 2483.40 1712.60 2729.50 2585.70 5539.70 3324.00 5536.00 4171.70 2442.30 1849.40 2 583.57 594.69 547.99 539.15 412.78 560.29 684.26 878.44 674.22 524.00 533.00 329.92 3 109.18 68.94 81.24 71.65 68.20 65.35 115.03 102.47 60.59 37.53 66.87 55.32 4 6.40 8.24 10.29 8.20 8.07 10.57 7.25 10.88 4.53 7.00 3.69 12.23 5+ 1.27 1.08 2.07 8.98 2.45 4.88 1.57 0.45 0.61 1.22 0.30 1.73  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2003 
2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

0 
0.12 
0.36 
0.33 
0.43 
0.02 
0.09 
0.25 
0.06 
0.06 
0.26 
0.18 
0.15 

1 
1.13 
1.63 
0.95 
0.84 
1.00 
0.75 
1.26 
1.02 
1.78 
1.48 
1.42 
1.89 

2 
1.69 
1.54 
1.58 
1.62 
1.39 
1.13 
1.45 
2.22 
2.44 
1.61 
1.82 
2.05 

3 
2.18 
1.50 
1.89 
1.78 
1.46 
1.80 
1.96 
2.72 
1.76 
1.92 
1.30 
1.02 

4 
1.99 
1.56 
0.87 
0.29 
1.76 
2.20 
2.31 
2.54 
2.12 
0.36 
1.58 
0.17 

5+ 
1.99 
1.56 
0.87 
0.29 
1.76 
2.20 
2.31 
2.54 
2.12 
0.36 
1.58 
0.17 

The XSA results summarized in Tables 5.2.1.7.3.1 and 2 and in Figure 5.2.1.7.3.6. show a decreasing 
trend in SSB since 2010, a fluctuation of recruitment with 2014 as the highest recruitment over 2003-
2014, as well as decreasing landings in the most recent years and an estimated Fcurr of 1.20. 

5.2.1.8  Reference points 
5.2.1.8.1  Methods 
The XSA package used 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  by  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. 

5.2.1.8.2  Input data  
The input parameters were the same used for the XSA stock assessment and its results. 

5.2.1.8.3  Results 

Table 5.2.1.8.3.1. Hake in GSA 1. Main reference points defined with the yield per recruit analysis. 

refpt 
virgin 
msy 
crash 
f0.1 
fmax 
spr.30 

harvest 
0.00 
0.32 
41.93 
0.21 
0.32 
0.26 

yield 
0.00 
0.05 
0.01 
0.04 
0.05 
0.05 

rec 
1.00 
1.00 
1.00 
1.00 
1.00 
1.00 

ssb 
0.71 
0.17 
0.00 
0.26 
0.17 
0.21 

biomass 
0.78 
0.23 
0.02 
0.32 
0.23 
0.27 

89 

89 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 5.2.1.8.3.1. Hake in GSA 1. Yield per recruit curve. 

5.2.1.9  Data quality 
Data from DCF 2014 as submitted through the Official data call in 2015 were used. 
A number of errors were detected in the MEDITS database (e.g. an error in the 2013 size frequencies 
abundances  in  length  class  38  cm/age  class  3,  not  considered  in  the  analysis;  2013-2014  data 
submitted twice). Because of this, Medits data used in the assessment were provided by EWG15-11 
invited experts. No data on OTB discarded sizes of European hake in GSA 1 available. No data on LL 
landings sizes available. Concerning GNS+GTR, size data were available for 2009, 2010 and 2014. For 
more details see section 5.2.1.7.2. 

5.2.1.10 Short term predictions 2016-2018 

5.2.1.10.1  Method 
A  deterministic  short  term  prediction  for  the  period  2015  to  2017  was  performed  using  the  FLR 
routines  provided  by  JRC  and  based  on  the results  of  the XSA  stock assessments performed during 
EWG 15-11. 

5.2.1.10.2  Input parameters  
Input parameters were the same used for the XSA 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 (13364.21 thousand individuals). 

5.2.1.10.3  Results 

Table 5.2.1.10.3.1. Hake in GSA 1. Short term forecast in different F scenarios Basis: F(2015) = mean (Fbar 0-2 
2012-2014)=  1.20;  R(2015)  =  geometric  mean  of  the  recruitment  of  the  last  3  years;  R  =  13364    thousands; 
SSB(2014) = 220 t, Catch (2014)= 313 t. 

90 

90 

 
 
 
 
 
 
 
 
 
 
Rationale 

F 
factor 

Fbar 

Catch 
2015 

Catch 
2016 

Catch 
2017 

SSB 
2016 

SSB 
2017 

0 

0 

0.18 

0.21 

726 

726 

0 

0 

160 

281 

367 

367 

1158 

883 

Change 
SSB_2016-
2017(%) 
215.56 

Change 
Catch_2014-
2016(%) 
-100.00 

140.49 

-48.93 

Zero catch 

High long 
term yield 
(F0.1) 
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 

1.20 

0.12 

0.24 

0.36 

0.48 

0.60 

0.72 

0.84 

0.96 

1.08 

1.32 

1.44 

1.56 

1.68 

1.80 

1.92 

2.04 

2.16 

2.28 

2.40 

726 

726 

726 

726 

726 

726 

726 

726 

726 

726 

726 

726 

726 

726 

726 

726 

726 

726 

726 

726 

550 

96 

180 

251 

314 

367 

414 

455 

491 

522 

574 

596 

615 

632 

647 

660 

673 

684 

694 

703 

459 

185 

307 

385 

433 

459 

472 

476 

474 

468 

450 

440 

430 

420 

411 

402 

394 

387 

380 

373 

367 

367 

367 

367 

367 

367 

367 

367 

367 

367 

367 

367 

367 

367 

367 

367 

367 

367 

367 

367 

273 

991 

850 

730 

628 

542 

470 

408 

355 

311 

240 

213 

189 

169 

152 

137 

124 

113 

104 

96 

-25.72 

170.01 

131.47 

98.85 

71.21 

47.80 

27.94 

11.09 

-3.22 

-15.38 

-34.53 

-42.04 

-48.45 

-53.93 

-58.62 

-62.64 

-66.10 

-69.08 

-71.64 

-73.87 

75.47 

-69.21 

-42.68 

-19.76 

0.08 

17.29 

32.25 

45.29 

56.69 

66.68 

83.22 

90.07 

96.16 

101.59 

106.44 

110.80 

114.73 

118.28 

121.51 

124.46 

5.2.1.11 Short term predictions 2015-2017 by fleet 
5.2.1.11.1  Method 
A deterministic short term prediction by fleet for the period 2015 to 2017 was performed using the 
FLR  routines  provided  by  JRC  and  based  on  the  results  of  the  XSA  stock  assessments  performed 
during EWG 15-11. 

5.2.1.11.2  Input parameters  
The same parameters used in the short term by single fleet were used. 

5.2.1.11.3  Results 

Table 5.2.1.11.3.1. Hake in GSA 1. Short term forecast by fleet. 

fleet 
OTB 
GNS 
LL 

year 
2015 
2015 
2015 

91 

91 

catches 
699.8 
7.5 
9.9 

Partial_F 
1.13 
0.05 
0.02 

 
 
 
 
 
 
 
 
 
 
OTB 
GNS 
LL 
OTB 
GNS 
LL 

2016 
2016 
2016 
2017 
2017 
2017 

112.6 
4.5 
2.1 
219.3 
12.7 
7.4 

0.13 
0.01 
0.002 
0.13 
0.01 
0.002 

Figure 5.2.1.11.3.1. Hake in GSA 1. Short term forecast by fleet. 

5.2.1.12 Medium term predictions 
5.2.1.12.1  Method 
Medium  term  was  not  conducted  because  no  meaningful  stock-recruitment  relationship  was 
estimated. 

5.2.1.13 Stock advice 
The current F (1.20) is larger than F0.1 (0.21), chosen as proxy of FMSY and as the exploitation reference 
point consistent with long term yields (FMSY), which indicates that European hake in GSA 1 is is being 
fished  above  FMSY.  Catches  of  European  hake  in  2016  consistent  with  FMSY  should  not  exceed  160 
tonnes. 

5.2.1.14 Management strategy evaluation 

A  Management  Strategy  Evaluation  (MSE)  was  conducted  to  evaluate  if  the  MSY  ranges  were 
precautionary.  The  FMSY  ranges  were  derived  using  the  formula  provided  by  STECF  15-09.  F  ranges 

92 

92 

 
 
 
 
 
 
 
 
 
results were Fupper = 0.29 and Flower = 0.14. Blim was estimated as Bloss = 220 (t). The following figure 
shows the results of the MSE of the Fupper. 

Figure 5.2.1.14.1. European hake in GSA 1. Marine Strategy Evaluation. 

The probability of SSB to fall below Blim at F = Fupper is equal to 0. 

93 

93 

 
 
 
 
 
 
 
 
5.2.2  STOCK ASSESSMENT OF HAKE IN GSA 5 

5.2.2.1  Stock Identification 

GSA 5 (Figure 5.2.2.1.1) has been pointed as an individualized area for assessment and management 
purposes  in  the  western  Mediterranean  (Quetglas  et  al.,  2012)  due  to  its  main  specificities.  These 
include:  1)  Geomorphologically,  the  Balearic  Islands  (GSA  5)  are  clearly  separated  from  the  Iberian 
Peninsula (GSA 6) by depths between 800 and 2000 m, which would constitute a natural barrier to 
interchange  of  adult  stages  of  demersal  resources;  2)  Physical  geographically-related 
the 
characteristics,  such  as  the  lack  of  terrigenous  inputs  from  rivers  and  submarine  canyons  in  GSA  5 
compared to GSA 6, give rise to differences in the structure and composition of the trawling grounds 
and  hence  in  the  benthic  assemblages;  3)  Owing  to  these  physical  differences,  the  faunistic 
assemblages  exploited  by  trawl  fisheries  differ  between  GSA  5  and  GSA  6,  resulting  in  large 
differences in the relative importance of the main commercial species; 4) There are no important or 
general  interactions  between  the  demersal  fishing  fleets  in  the  two  areas,  with  only  local  cases  of 
vessels targeting red shrimp in GSA 5 but landing their catches in GSA 6; 5) Trawl fishing exploitation 
in GSA 5 is much lower than in GSA 6; the density of trawlers around the Balearic Islands is one order 
of  magnitude  lower  than  in  adjacent  waters;  and  6)  Due  to  this  lower  fishing  exploitation,  the 
demersal resources and ecosystems in GSA 5 are in a healthier state than in GSA 6, which is reflected 
in  the  population  structure  of  the  main  commercial  species  (populations  from  the  Balearic  Islands 
have  larger  modal  sizes  and  lower  percentages  of  small-sized  individuals),  and  in  the  higher 
abundance and diversity of elasmobranch assemblages. 

Figure 5.2.2.1.1. Geographical localization of GSA 5. 

5.2.2.2  Growth 
The  growth  parameters  used  during  the  EWG  15-11  were  those  estimated  by  Mellon-Duval  et  al. 
(2010)  from  tagging  experiments  in  the  Gulf  of  Lions,  Linf=  110  cm,  k=  0.178.  Length-weight 

94 

94 

 
 
 
 
 
 
relationship parameters were those estimated in the Spanish Data Collection Framework: a=0.00677 
and b=3.035097. 

5.2.2.3  Maturity 
Maturity ogive was estimated in the Spanish Data Collection Framework: 

Age 
Prop. Matures 

Maturity oogive 
2 
1 
0 
0.15  0.82  0.98 
0 

3 

4 
1 

5+ 
1 

5.2.2.4  Natural mortality 
Natural mortality was estimated using PRODBIOM: 

Age 
Prop. Matures 

5.2.2.5  Fisheries 

Natural mortality 
1 
0 

2 
1.24  0.58  0.45  0.40  0.37 

4 

3 

5+ 
0.35 

5.2.2.5.1  General description of the fisheries 
 In  the  Balearic  Islands  (western  Mediterranean),  commercial  trawlers  develop  up  to  four  different 
fishing tactics, which are associated with the shallow shelf (SS), deep shelf (DS), upper slope (US) and 
middle  slope  (MS)  (Guijarro  and  Massutí  2006;  Ordines  et  al.  2006),  mainly  targeted  to:  (i)  Spicara 
smaris, Mullus surmuletus, Octopus vulgaris and a mixed fish category on the shallow shelf (50-80 m); 
(ii) Merluccius merluccius, Mullus spp., Zeus faber and a mixed fish category on the deep shelf (80-250 
m);  (iii)  Nephrops  norvegicus,  but  with  an  important  by-catch  of  big  M.  merluccius,  Lepidorhombus 
spp.,  Lophius  spp.  and  Micromesistius  poutassou  on  the  upper  slope  (350-600  m)  and  (iv)  Aristeus 
antennatus  on  the  middle  slope  (600-750  m).  The  MS  fishing  tactics  coincides  with  the  metier 
OTB_DWSP; OTB_DEMSP corresponds to those days in one of the other fishing tactics is present (SS, 
DS and/or  US) and OTB_MDDWSP corresponds to those days in which one haul in MS and at least 
one of the other fishing tactics is performed. 

Fishing license: number of licenses observed 
Engine power limited to 316 KW or 500 HP: not fully observed. 

5.2.2.5.2  Management regulations applicable in 2015  
- 
- 
-  Mesh size in the codend (40 mm square or 50 mm diamond -by derogation-): fully observed. 
- 
-  Minimum landing size (EC regulation 1967/2006, 20 mm CL): mostly fully observed. 

Time at sea (12 hours per day and 5 days per week): fully observed. 

5.2.2.5.3   Catches  
Hake  catches  came  exclusively  from  bottom  trawlers  (OTB)  in  GSA  5.  They  show  important 
oscillations along the data series, between 50 and 250 tons. These oscillations seem to be related to 
environmental  conditions,  as  hake  recruitment  seems  to  be  benefitted  by  a  certain  scenario  of 
enhanced  productivity  resulted  from  particularly  cold  years,  which  determines  the  regional 
circulation  around  the  Balearic  Islands  and  from  certain  climatic  conditions  on  the  areas  where 
Western  Intermediate  Waters  (the  main  water  mass  where  hake  population  is  found)  are  formed 

95 

95 

 
 
 
 
 
 
 
 
 
 
 
(Massutí  et  al.,  2008).  By  métier,  landings  in  OTB_DEMSP  represent  94%,  OTB_DWSP  3%  and 
OTB_MDDWSP 3%. 

Figure 5.2.2.5.3.1. Hake in GSA 5. Historical catches. 

5.2.2.5.4  Discards  
Discards  represent  around  3-5%  of  the  catches  of  hake  for  the  metiers  OTB_DEMSP  and 
OTB_MDDWSP and are almost null for OTB_DWSP. 

5.2.2.5.5  Fishing effort  
Fishing  effort  available  from  the  Data  Call  included  years  2009-2014.  Table  5.2.2.5.6.1  summarizes 
the effort data for the gear OTB according to the DCF Data Call in terms of nominal effort and GT days 
at sea. Number of boats cannot be calculated from the information available in the Data Call as it is 
disaggregated  by  quarter  and  my  métier  (OTB_DEMSP,  OTB_MDDWSP  and  OTB_DWSP)  and  so  it 
cannot be accumulated, as the same boat may be included in different quarters and/or in different 
métiers. 

Table 5.2.2.5.6.1. Effort data for OTB in GSA 5 according to the DCF Data Call. 

Nominal effort 
GT days at sea 

2009 

2010 
2784175  2927650  2694399 
616593 
672070 
648577 

2011 

2012 
2675591 
630595 

2013 

2014 

2745967  2828550 
670025 
641523 

96 

96 

 
 
 
 
 
 
 
 
 
 
 
 
 
5.2.2.6  Scientific surveys 
5.2.2.6.1    Survey #1 (BALAR- MEDITS) 
5.2.2.6.1.1  Methods 

Although MEDITS survey started in Spain in 1994, it did not cover the Balearic Islands, except for a 
small number of hauls (2-4) carried out some of the years in the Ibiza channel, in waters deeper than 
200 m. From 2001, the Spanish Institute of Oceanography (IEO) has performed annual bottom trawl 
surveys following the same methodology and sampling gear described in the MEDITS protocol (BALAR 
surveys,  Massutí  and  Reñones,  2005).  Since  2007,  this  survey  has  been  included  in  the  MEDITS 
program (Bertrand et al., 2002). The abundance indices used here has been calculated from IEO data 
bases, using data collected in the Balearic Islands during BALAR surveys in the first years and MEDITS 
in the lasts, in order to have the most complete series of abundance indices available. 
Mean  stratified  abundances  and  biomasses  by  km2  has  been  computed  using  the  methodology 
described by Grosslein and Laurec (1982), with the following formula: 

-  Mean catch by stratum: 

-  Variance by stratum: 

-  Mean total catch: 

-  Total variance: 

-  SE (standard error): 

Nh:  number  of  hauls  in  each  sub-stratum;  Yh:  mean  catch  by  haul  in  each  sub-stratum;  A:  total 

stratum area; Ah: sub-estratum area; 

 variance in each sub-stratum. 

Abundance indices were available during the meeting from the information in the Data Call and both 
sources of information have been compared for years 2007-2014, when it is expected to have a high 
agreement.  For  these  years,  the  only  differences  between  both  data  bases  should  be  the 
incorporation  in  the  Data  Call  of  the  above-mentioned  hauls  carried  out  some  years  in  the  Ibiza 
channel. The results of this comparison are described in the data quality section. 

5.2.2.6.1.2   Geographical distribution 
Hake is mainly distributed in the fishing grounds sited in the Menorca channel (NE Mallorca) and in 
the south of Mallorca (Figure 5.2.2.6.2.1). 

97 

97 

hhstYNY1212)(1)(sthhstYYNYS)(1hsttAYAYhhsttNAYSAYS2222)(1)()(2stYSSE)(2stYS22.533.544.53939.540 BALAR'01- MEDITS'14M. merlucciusN/km202505001000150020002500 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 5.2.2.6.2.1. Hake in GSA 5. Geographical distribution based on bottom trawl surveys (2001-2014). 

5.2.2.6.1.3   Trends in abundance and biomass 
Both abundance indices from the scientific surveys and landings per unit of effort obtained from the 
fishing fleet in Alcúdia (which mainly targets hake) showed similar results, especially for the smaller 
size category (as a proxy of recruitment). Indices showed oscillations along the data series, with the 
highest values in 2006 and 2013. For 2014, survey indices showed a drop to practically half values of 
the  previous  year,  which  can  also  seem  (but  to  a  lesser  extent)  in  the  small  commercial  category 
(Figure 5.2.2.6.1.3.2). 

Figure  5.2.2.6.1.3.2.  Hake  in  GSA  5.  Standardized  abundance  indices  (n/km2)  from  scientific  surveys  and 
landings  per  unit  of  effort  (kg/day·boat)  for  the  total  catch  and  for  the  small  category  (as  a  proxy  of 
recruitment) in the Alcúdia port (NE Mallorca) 

5.2.2.6.1.4  Trends in abundance by length or age 
No analysis were conducted during EWG 15-11. 

5.2.2.7  Stock Assessment 
5.2.2.7.1   Methods 
The assessment has been performed with an Extended Survivor Analysis (XSA) using the FLR library in 
R. This stock is an update of the one presented in the GFCM Working Group of Demersal Species in 
2014 (WGSAD, 2014) and was assessed for the last time by the STECF in 2010 (STECF SGMED 10-02, 
2010). 

98 

98 

010203040506070809005001000150020002500200020012002200320042005200620072008200920102011201220132014kg hake/day n/km2Total CPUE: surveys vs. commercial fleet  Surveys (50-800 m)Commercial catch (Alcúdia)Commercial catch-small (Alcúdia) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5.2.2.7.2  Input data 
The  data  used  in  the  assessment  were:  (i)  Catches  time  series  1980-2014  from  OTB;  (ii)  Age 
distributions obtained from slicing of length distributions 1980-2014 (Figure 5.2.2.7.2.1); (iii) BALAR-
MEDITS survey used as tuning fleet (abundances by age in n/km2, Figure 5.2.2.7.2.1). 

Figure 5.2.2.7.2.1 Hake. GSA 5. Age distribution by year for the commercial and survey data. 

Mean weight in catch 

0 

1 

2 

3 

4 

5+ 

0.028  0.101  0.406  0.943  1.642  2.474 

Growth parameters 
t0 
k 
L∞ 
- 
0.178 
110 

Length-weight relationship 

a 
0.00677 

b 
3.035097 

Maturity oogive 

Age 
Prop. Matures 

0 
0 

1 

2 
0.15  0.82  0.98 

3 

4 
1 

5+ 
1 

Natural mortality (PROBIOM; Abella et al., 1997) 

Age 
M 

0 

1 
1.24  0.58  0.45 

2 

3 
0.4 

4 
0.37 

5+ 
0.35 

The number of individuals by age was SOP corrected [SOP = Landings / a (total catch numbers at age 
a x catch weight-at-age a)] before performing any analysis. 

Different  sensitivity  analyses  were  performed  before  running  the  final  XSA,  considering  different 
weights and ages for shrinkage and different ages for catchability. In all the cases (weight shrinkage: 
Figure 5.2.2.7.2.2; age shrinkage: Figure 5.2.2.7.2.3; catchability: Figure 5.2.2.7.2.4) the results were 
very robust. 

99 

99 

0500100015002000012345+·1000Commercial020040060080010001200012345+n/km2Surveys 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure  5.2.2.7.2.2.  Hake  in  GSA  5.  Sensitivity  analysis  for  F,  R  and  SSB  considering  different  weights  for 
shrinkage. 

Figure 5.2.2.7.2.3. Hake. GSA 5. Sensitivity analysis for F, R and SSB considering different ages for shrinkage. 

Figure 5.2.2.7.2.4. Hake. GSA 5. Sensitivity analysis for F, R and SSB considering different ages for catchability. 

For the final XSA run, the following settings were used: 

fse 
0.5 

rage 
-1 

qage 
2 

shk.n 
TRUE 

shk.f 
TRUE 

shk.yrs 
3 

shk.ages 

2 

5.2.2.7.3  Results 
Recruitment showed important oscillations between 2 and 11 millions for all the data series and SSB 
between 30 and 115 tons. In the last 20 years, recruitment never showed the high values found in the 
middle 1980s and early 1990s, with not-so-marked oscillations. Recruitment show very similar values 
for last two years and SSB showed an increasing trend for the last 2 years (Figure 5.2.2.7.3.1, Table 
5.2.2.7.3.1). F has oscillated between 0.8 and 2. 

100 

100 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 5.2.2.7.3.1. Hake. GSA 5. XSA results. 

Table 5.2.2.7.3.1. Hake GSA 5. XSA results. 

Population in 
number 
(thousands) 
5052.1 
3422.1 
4282.0 
9664.9 
10736.9 
9978.8 
4849.5 
4263.2 
5745.9 
5670.5 
4721.2 
9883.2 
9157.5 
6690.9 
3536.5 
2589.0 
2755.5 
4085.7 
3657.3 

1980 
1981 
1982 
1983 
1984 
1985 
1986 
1987 
1988 
1989 
1990 
1991 
1992 
1993 
1994 
1995 
1996 
1997 
1998 

Population in 
weight (tons) 

367.5 
190.9 
191.5 
374.7 
505.3 
479.6 
321.0 
230.1 
282.2 
293.8 
279.7 
531.2 
516.5 
378.4 
276.4 
182.1 
176.1 
203.2 
220.9 

101 

101 

Recruitment 
number (age 0, 
thousands) 
3256.8 
2661.3 
3691.5 
8636.6 
8241.5 
7987.0 
3040.3 
3380.3 
4705.1 
4315.9 
3488.4 
8724.2 
6475.8 
5020.8 
1973.8 
1945.2 
2226.0 
3385.3 
2614.1 

SSB 

F0-3 

1.58 
98.29 
1.71 
57.83 
1.91 
31.17 
1.34 
32.16 
1.65 
66.27 
1.51 
99.11 
1.73 
74.70 
1.69 
39.51 
1.46 
45.47 
1.16 
44.28 
0.80 
58.75 
89.77 
0.95 
111.47  1.58 
1.11 
74.75 
1.03 
85.51 
0.89 
83.84 
0.76 
72.44 
1.30 
65.63 
1.55 
70.37 

 
 
 
 
 
 
1999 
2001 
2002 
2003 
2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

3140.3 
4482.1 
4307.1 
2652.7 
3815.2 
6692.7 
7537.5 
5988.2 
3896.6 
5523.1 
5661.6 
5473.4 
3291.4 
5584.9 
5961.8 

155.2 
213.5 
250.5 
218.3 
164.3 
253.2 
286.5 
315.1 
281.9 
252.4 
310.6 
308.5 
226.6 
229.1 
319.9 

2451.9 
3763.1 
3201.9 
1672.6 
3379.6 
5724.2 
6164.0 
4334.2 
2781.7 
4683.7 
4406.1 
4059.1 
2131.2 
4942.8 
4509.1 

42.06 
64.53 
76.61 
89.62 
48.85 
35.52 
57.51 
75.11 
92.03 
68.41 
56.16 
85.72 
66.94 
47.92 
66.78 

1.29 
0.84 
1.00 
1.62 
1.52 
1.22 
1.18 
0.97 
1.14 
1.20 
0.99 
1.31 
1.27 
0.93 
1.12 

Residuals  from  the  scientific  survey  tuning  fleet  showed  low  values  for  all  the  ages  and  years 
considered  (Figure  5.2.2.7.3.2).  Ages  0-4  from  the  MEDITS  survey  were  considered  for  the 
assessment. 

Figure 5.2.2.7.3.2. Hake. GSA 5. Log catchability residual plots (XSA) for scientific surveys. 

The diagnostics of the stock were as follows: 

FLR XSA Diagnostics 2015-09-02 11:57:27 

CPUE data from indices 

102 

102 

 
 
 
 
 
 
 
 
Catch data for 35 years 1980 to 2014. Ages 0 to 5. 

    fleet first age last age first year last year alpha beta 
1 FLEET 1         0        4       2001      2014  <NA> <NA> 

 Time series weights : 

    Tapered time weighting not applied 

 Catchability analysis : 

     Catchability independent of size for all ages 

     Catchability independent of age for ages >   2  

 Terminal population estimation : 

     Survivor estimates shrunk towards the mean F 
    of the final   3 years or the  2 oldest ages. 

    S.E. of the mean to which the estimates are shrunk =   0.5  

    Minimum standard error for population 
    estimates derived from each fleet =  0.3  

    prior weighting not applied 

Regression weights 
     year 
age   2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 
  all    1    1    1    1    1    1    1    1    1    1 

 Fishing mortalities 
   year 
age  2005  2006  2007  2008  2009  2010  2011  2012  2013  2014 
  0 0.155 0.277 0.094 0.143 0.029 0.118 0.092 0.045 0.112 0.088 
  1 1.844 1.856 1.898 1.842 1.388 1.929 2.243 1.526 1.869 1.965 
  2 1.658 1.025 1.414 1.512 1.646 2.039 1.474 1.273 1.183 1.379 
  3 1.047 0.725 1.172 1.315 0.900 1.145 1.274 0.892 1.299 1.200 
  4 1.376 0.891 1.313 1.463 1.310 1.652 1.410 1.108 1.302 1.308 
  5 1.376 0.891 1.313 1.463 1.310 1.652 1.410 1.108 1.302 1.308 

 XSA population number (Thousand) 
      age 
year      0    1   2  3 4 5 
  2005 6164 1273  92  7 1 0 
  2006 4334 1528 113 11 2 0 
  2007 2782  951 134 26 4 1 
  2008 4684  733  80 21 5 1 
  2009 4406 1175  65 11 4 0 
  2010 4059 1239 164  8 3 0 
  2011 2131 1044 101 14 2 0 
  2012 4943  562  62 15 3 0 
  2013 4509 1367  68 11 4 2 
  2014 4366 1167 118 13 2 0 

 Estimated population abundance at 1st Jan  2015  
      age 
year   0    1  2  3 4 5 
  2015 0 1157 92 19 3 0 

 Fleet:  FLEET 1  

 Log catchability residuals. 

   year 

103 

103 

 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
0 
1 
2 
3 
4 
year 

0 
1 
2 
3 
4 

2002 

2004 

2006 
2001 
2005 
2003 
-0.603  -0.155  -1.273  -0.485  -0.4 
0.782  0.494 
-0.393  0.247  0.383  0.041  0.169  -0.133  0.506 
0.633  0.076  0.469  0.529  0.446  0.146  -0.255 
0.701  -0.515  -0.709  -2.064 
0.365  0.438  0 
1.817  0 
0.062  1.242  1.83 

0.629 

2007 

0 

2009 

2010 

2008 
2011 
-1.134  0.573  0.218  0.581  0.557  0.829  0.016 
-0.241  -0.604  -0.243  -0.055  0.457  0.042  -0.176 
-0.174  -0.039  0.413  -0.441  -1.092  -0.505  -0.205 
-0.117  -1.335  0 
0 
0 
0.3 

-0.54  -0.917  -1.576  -0.01 
0.212  -0.173  1.502 
0 

2013 

2012 

2014 

 Mean log catchability and standard error of ages with catchability  
 independent of year class strength and constant w.r.t. time  

                0       1       2       3       4 
Mean_Logq -1.6626 -1.0725 -1.7850 -1.7850 -1.7850 
S.E_Logq   0.6913  0.6913  0.6913  0.6913  0.6913 

 Terminal year survivor and F summaries:  

 ,Age 0 Year class =2014  

source  
        scaledWts survivors yrcls 
FLEET 1     0.307      1175  2014 
fshk        0.693      1148  2014 

 ,Age 1 Year class =2013  

source  
        scaledWts survivors yrcls 
FLEET 1     0.234        77  2013 
fshk        0.766        92  2013 

 ,Age 2 Year class =2012  

source  
        scaledWts survivors yrcls 
FLEET 1     0.199        15  2012 
fshk        0.801        20  2012 

 ,Age 3 Year class =2011  

source  
        scaledWts survivors yrcls 
FLEET 1     0.064         3  2011 
fshk        0.936         3  2011 

 ,Age 4 Year class =2010  

source  
        scaledWts survivors yrcls 
FLEET 1     0.042         2  2010 
fshk        0.958         0  2010 

Retrospective analysis was performed and showed robust results for all the parameters considered 
(Figure 5.2.2.7.3.3). 

104 

104 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 5.2.2.7.3.3. Hake. GSA 5. Restrospective analysis for SSB, recruitment and F. 

5.2.2.8  Reference points 
5.2.2.8.1  Methods 
Yield per recruit was calculated using FLR. As the last year that the reference point F0.1 was calculated 
in the framework of STECF, it was recalculated again. 

5.2.2.8.2  Input data  
The input parameters were the same used for the XSA stock assessment and its results. 

5.2.2.8.3  Results 
Table 5.2.2.8.3.1 shows the reference F (Fref) as well as the reference point F0.1 (as a proxy of FMSY). 
Figure 5.2.2.8.3.1 shows the yield per recruit graph. 

Table 5.2.2.8.3.1. Hake. GSA 5. Reference F and reference point (F0.1). 

Fref (0-3) 
F0.1 

0.15 
1.12 

Figure 5.2.2.8.3.1. Hake. GSA 5. Yield per recruit. 

105 

105 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5.2.2.9  Data quality 
Information  about  catches,  discards,  and  length  and  age  frequency  distributions  was  available 
through the Official Data Call for all the years. However, discarded biomass for 2014 showed values 
unusually low and should be further checked. Effort information was available for 2009-2014. MEDITS 
data was also available. A comparison of the abundance indices by size from the surveys covering the 
period  2007-2014  between  the  Data  Call  and  the  national  database  was  performed.  They  showed 
high  agreement  for  the  last  years,  but  inconsistent  values  for  2007-2008,  which  should  also  be 
checked. 

5.2.2.10 Short term predictions 2016-2018 
5.2.2.10.1  Method 
A  deterministic  short  term  prediction  for  the  period  2016  to  2018  was  performed  using  the  FLR 
routines, which takes into account the catch and landings in numbers and weight and the discards, 
and based on the results of the XSA stock assessment performed. 

5.2.2.10.2  Input parameters  
The  input  parameters  were  the  same  used  for  the  XSA  stock  assessment  and  its  results.  Different 
scenarios of constant harvest strategy with Fbar calculated as the average of ages 0 to 2 (Fbar ages 0-3) 
and  F  status  quo  (Fstq  =0.15)  were  performed.  Recruitment  (class  0)  has  been  estimated  from  the 
population results from the geometric mean of the last three years 2012-2014 estimated with FLR. 

5.2.2.10.3  Results 
A short term projection (Table 5.2.2.10.3.1), assuming an Fstq of 1.06 (as a geometric average 2012-
2014) in 2015 and a recruitment of 4600 thousands individuals shows that: 

  Fishing at the Fstq (1.06) generates an increase of the catch of 6% from 2014 to 2016 along with an 

increase of the spawning stock biomass of 6% from 2016 to 2017. 

  Fishing at F0.1 (0.16) generates a decrease of the catch of 74% from 2014 to 2016 and an increase 

of the spawning stock biomass of 246% from 2016 to 2017. 

Table 5.2.2.10.3.1. Hake. GSA 5. Short term forecast in different F scenarios. 
Basis:  F(2015) = mean(Fbar  0-3  2012-2014)= 1.06;  R(2015) = geometric mean  of  the recruitment of  the  last 3 
years; R = 4600 (thousands); SSB(2014) = 67 t; Catch (2014)= 124 t. 

Rationale 

Ffactor 

Fbar 

Catch 
2016 

Catch 
2017 

SSB 
2017 

Change SSB 
2016-2017 
(%) 

Change Catch 
2014-2016 (%) 

Zero catch 
High long-
term yield 
(FMSY) 
Status quo 
Different 
scenarios 

0.0 

0.00 

0.0 

0.0 

341.18 

331.84 

-100.00 

0.1 

0.16 

31.9 

63.9 

273.43 

246.08 

-74.21 

1.0 

0.1 

0.2 
0.3 
0.4 
0.5 
0.6 

1.06 

131.6 

135.0 

84.19 

6.55 

0.11 

22.6 

47.3 

293.07 

270.94 

0.21 
0.32 
0.43 
0.53 
0.64 

42.1 
59.2 
74.0 
86.9 
98.3 

80.0 
102.3 
116.9 
126.3 
131.8 

252.27 
217.64 
188.24 
163.25 
142.00 

219.30 
175.47 
138.25 
106.63 
79.73 

6.34 

-81.78 

-65.97 
-52.22 
-40.24 
-29.77 
-20.60 

106 

106 

 
 
 
 
 
 
 
  
  
  
  
  
0.7 
0.8 
0.9 
1.0 
1.1 
1.2 
1.3 
1.4 
1.5 
1.6 
1.7 
1.8 
1.9 
2.0 

0.75 
0.85 
0.96 
1.06 
1.17 
1.28 
1.38 
1.49 
1.60 
1.70 
1.81 
1.92 
2.02 
2.13 

108.3 
117.0 
124.8 
131.6 
137.8 
143.2 
148.1 
152.4 
156.4 
159.9 
163.1 
166.1 
168.8 
171.2 

134.8 
135.9 
135.9 
135.0 
133.7 
132.2 
130.5 
128.9 
127.2 
125.7 
124.2 
122.9 
121.7 
120.6 

123.92 
108.52 
95.39 
84.19 
74.62 
66.44 
59.44 
53.44 
48.30 
43.88 
40.08 
36.81 
33.98 
31.54 

56.85 
37.35 
20.73 
6.55 
-5.55 
-15.91 
-24.76 
-32.35 
-38.87 
-44.46 
-49.27 
-53.41 
-56.99 
-60.08 

-12.55 
-5.47 
0.79 
6.34 
11.27 
15.66 
19.59 
23.12 
26.30 
29.18 
31.78 
34.16 
36.33 
38.33 

5.2.2.11 Medium term predictions 
5.2.2.11.1  Method 
Following the agreement reached during the discussions of the EWG-12-19, medium term prediction 
would only be performed  if there is a reliably fit of a stock-recruitment relationship. In the case of 
hake in GSA 5, no medium term predictions were made as such relationship was not adequate to fit a 
model (Figure 5.2.2.11.1). 

Figure 5.2.2.11.1. Hake. GSA 5. Spawning stock biomass (SSB) and recruitment (R) relationship. 

5.2.2.12 Stock advice 
The current F (1.12) is larger than FMSY(0.15), which indicates that hake in GSA 5 is being fished above 
FMSY. STECF EWG 15-11 recommends the relevant fleets’ effort to be reduced until fishing mortality is 
below or at the proposed FMSY level, in order to avoid future loss in stock productivity and landings. 
This  should  be  achieved  by  means  of  a  multi-annual  management  plan  taking  into  account  mixed-
fisheries considerations. Catches of European hake in GSA 5 in 2016 consistent with FMSY should not 
exceed 32 t. 

107 

107 

 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5.2.2.13 Management strategy evaluations 
The Management Strategy Evaluation to evaluate if the MSY ranges are precautionary were run using 
R script provided during by STECF 15-09. F ranges results were Fupper= 0.10 and Flower= 0.21. Blim was 
estimated in 31.2 t. Figure 5.2.2.13.1 show the results of the MSE. 

Figure 5.2.2.13.1. Hake. GSA 5. Marine Strategy Evaluation. 

The probability of SSB to fall below Blim at F = Fupper is equal to 0. 

108 

108 

 
 
 
 
 
 
 
 
 
 
 
 
5.2.3  STOCK ASSESSMENT OF HAKE IN GSA 6 
5.2.3.1  Stock Identification 
Due to the lack of information about the structure of hake (Merluccius merluccius) population in the western 
Mediterranean, this stock was assumed to be confined within the GSA 6 boundaries (Figure 5.2.3.1.1). 

Figure 5.2.3.1.1. Geographical localization of GSA 6. 

5.2.3.2  Growth 
Growth parameters were taken from Mellon et al. (2010) and the length-weight parameters from the 
Spanish DCF (see tables below). 

5.2.3.3  Maturity 
Maturity parameters were also taken from the Spanish DCF (see tables below). 

5.2.3.4  Natural mortality 
Natural mortality was obtained from PRODBIOM (see tables below). 

5.2.3.5  Fisheries 
5.2.3.5.1  General description of the fisheries 
European hake is largely exploited in GSA 6, mainly by trawlers on the shelf and slope (91% landings), 
but  also  by  small-scale  fisheries  using  long  lines  (6%)  and  gill  nets  and  trammel  nets  (3%)  (average 
percentages estimated between 2009 and 2013). According to official statistics, around 1000  boats 
are  involved  in  this  fishery,  with  total  annual  landings  oscillating  around  an  average  value  of  3667 
tons  for  the  period  2003-2014.  The  trawl  fleet  is  the  largest  in  number  of  boats  and  landings  (472 
trawlers and 2966 tons in 2013). 

5.2.3.5.2  Management regulations applicable in 2015  
Trawl fisheries in GSA 6 are regulated by “Orden AAA/2808/2012” published in the Spanish Official 
Bulletin (BOE nº 313 29 December 2012) containing an Integral Management Plan for Mediterranean 
fishery resources. To the traditional fisheries regulations already in place (e.g. the daily and weekly 
fishing effort limited to 12 hours per day five days a week; trawl cod end 40 mm square mesh or 50 
mm diamond stretched mesh; engine power of maximum 373 kW; license system; minimum landing 
size of 20 cm TL), this plan adds that fishing mortality for Merluccius merlucicus in GSA 6 should be 
kept at or below the reference value F01 = 0.15 and that fishing effort be reduced by  20% or more 

109 

109 

  
 
 
 
 
 
 
 
 
 
over  the  period  2013-2017  (based  on  the  effort  established  on  1  January  2013).  This  fishing  effort 
reduction will be measured in terms of number of vessels, engine power and tonnage. 

5.2.3.5.3  Landings  
During  2003  and  2014,  the  annual  landings  of  hake  in  GSA  6  showed  a  general  decreasing  trend 
punctuated  by  important  peaks  in  2006  and  2009  (Fig.  5.2.3.5.4.1A).  The  size  structure  of  the 
population taken by the fishery shows a modal size of 13 cm (Fig. 5.2.3.5.4.1 B). 

A 

B 

Fig. 5.2.3.5.4.1. Hake in GSA 6. Total annual landings (A) and mean size distribution (B) during 2003-2014. 

5.2.3.5.4  Discards  
Discards were included in the analysis. Reported discards of hake from trawlers in GSA 06 were 141.6 
t  in  2011,  194.3  in  2012  and  156.6  t  in  2013.  These  amounts  represented  4.7%,  7.3%  and  5.5% 
respectively of the trawl fleet annual catch. 

5.2.3.5.5  Fishing effort  
The  fishing  effort  (number  of  days)  shows  a  marked  decreasing  trend  during  2003-2014  (Fig. 
5.2.3.5.6.1). 

Fig. 5.2.3.5.6.1. Hake in GSA 6. Fishing effort in days during 2003-2014. 

110 

110 

0100020003000400050006000200320042005200620072008200920102011201220132014Landings (tons)01234560102030405060708090N individuals (·106)050100150200250200320042005200620072008200920102011201220132014Fishing effort (Ndays x1000) 
 
 
 
 
 
 
 
 
5.2.3.6  Scientific surveys 
5.2.3.6.1    Survey #1 (MEDITS) 
5.2.3.6.1.1  Methods 
Since  1994  standard  bottom  trawl  surveys  have  been  conducted  in  GSA  6  in  spring,  following  the 
general methodology of the MEDITS protocol described in Bertrand et al. (2002). 

5.2.3.6.1.2   Trends in abundance and biomass 
Abundance and biomass indices from MEDITS showed a general decreasing trend (Fig. 5.2.3.6.1.3.1) 
punctuated by a maximum in 2006. 

Fig. 5.2.3.6.1.3.1. Hake in GSA 6: Abundance and biomass indices from MEDITS surveys during 2003-2014. 

5.2.3.6.1.3  Trends in abundance by length or age 
Important  changes  were  observed  in  the  size  structure  during  the  study  period  since  modal  size 
during 2012-2014 (about 20 cm) were markedly higher than the previous years (about 13 cm)  (Fig. 
5.2.3.6.1.4.1). 

111 

111 

0500100015002000250030003500200320042005200620072008200920102011201220132014Abundance (n/km2) 05101520253035200320042005200620072008200920102011201220132014Biomass (kg/km2)  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Fig. 5.2.3.6.1.4.1. Hake in GSA 6. Size-structure of catches during 2003-2014. 

5.2.3.7  Stock Assessment 
5.2.3.7.1   Methods 
An XSA was applied using the R libraries developed in the framework of the EWG. 

5.2.3.7.2  Input data 
The length of the available data series (12 years, from 2003 to 2014) allowed the use of a VPA tuned 
with MEDITS data. Although there exist catch and MEDITS data from previous years, size-frequency 
distributions are only available from 2003. 

The assessment includes landings from all main fleets (trawlers, longliners, gillnetters). However, as 
size  distributions  from  longliners  are  only  available  for  the  most  recent  years  (2009-2014),  the 
assessment  was  done  considering  two  fleets:  trawlers  and  others  (longliners  and  gillnetters 
combined; see below). 

Landing time series: 2003-2014. 

Size-distributions were sliced to age-distributions using the L2AGE4 software. 

Group plus was set at age 5. 

The number of individuals by age was SOP corrected [SOP = Landings / a (total catch numbers at age 
a x catch weight-at-age a)]. 

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

SOP 

0.935  0.932  0.929  0.935  0.943  0.929  0.933  0.938  0.948  0.941  0.941  0.947 

Growth parameters (from Mellon et al 2010) 

LWR (from Spanish DCF) 

Linf 
110 

K 
0.178 

t0 

a 
0.00677 

b 
3.035097 

Natural mortality (from PRODBIOM) 

0 
1.24 

1 
0.58 

2 
0.45 

3 
0.4 

4 
0.37 

5+ 
0.35 

112 

112 

0246810120102030405060708090N individuals (·103)200320042005200620072008200920102011201220132014 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
input  data  are 

The 
below: 

0 
0.00 

Maturity (from DCF 2003-2012) 
1 
4 
1.00 
0.15 

2 
0.82 

3 
0.98 

shown 

in 

the 

table 

5+ 
1.00 

CATCH 

2003 

2004 

2005 

2006 

2007 

2008 

2009 

2010 

2011 

2012 

4176 

3750 

4035 

4635 

3391 

4021 

5082 

3278 

3254 

2900 

2013 

2014 
3256  2230 

CATNUM 
0 
1 
2 
3 
4 
5+ 

CATWT 
0 
1 
2 
3 
4 
5+ 

2003 

2004 

73465  77534 
22025  18211 
1497 
211 
16 
13 

1835 
225 
68 
5 

2005 
58311 
19610 
2333 
303 
29 
10 

2010 

2011 

2007 

2009 

2014 
2006 
2008 
70282  47735  70579  60702  34250 
7245  6589 
20704  11695  18501  29946  16868  18868  19564  20993  10610 
2126  1997 
124 
9 
3 

2643 
424 
30 
9 

2160 
191 
56 
6 

2113 
240 
50 
3 

2482 
371 
58 
14 

2085 
233 
101 
79 

2481 
236 
20 
6 

1867 
180 
27 
1 

172 
11 
2 

2012 

2013 

8862 

9913 

2003 
0.017 
0.096 
0.414 
0.966 
1.603 
2.814 

2004 
0.017 
0.101 
0.409 
0.91 
1.645 
2.8 

2005 
0.018 
0.101 
0.399 
0.936 
1.634 
2.702 

2006 
0.019 
0.099 
0.415 
0.924 
1.581 
2.904 

2007 
0.018 
0.103 
0.42 
0.957 
1.62 
2.499 

2008 
0.018 
0.102 
0.401 
0.989 
1.559 
2.772 

2009 
0.021 
0.091 
0.399 
0.97 
1.693 
2.508 

2010 
0.015 
0.107 
0.412 
0.93 
1.684 
2.544 

2011 
0.026 
0.102 
0.409 
0.904 
1.699 
2.585 

2012 
0.026 
0.095 
0.403 
0.903 
1.665 
2.239 

2013 
2014 
0.028  0.022 
0.107 
0.12 
0.389  0.401 
0.933  0.911 
1.599  1.622 
2.456  2.814 

XSA  

Tuning index was obtained using abundance indices from MEDITS (N/km2) carried out in GSA 6 during 
2003–2014.  

Based on the log catch curves results (Fig. 5.2.3.7.2.1), ages 1 to 3 were selected as the Fbar. 

Fig. 5.2.3.7.2.1. Hake GSA 6. Log catch curves. 

Different  sensitivity  analyses  were  performed  before  running  the  final  XSA.  The  first  sensitivity 
analysis  tested  different  qages  and  the  best  fit  was  obtained  using  qage=2.  The  second  sensitivity 
analysis tested different shrinkage weights (0.5, 1.0, 1.5, 2.0 and 2.5); the two first values (0.5, 1.0) 

113 

113 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
showed  slightly  higher  residuals  (range:  3  to  -3)  than  the  remaining  values  (range:  2  to  -2)  (Fig. 
5.2.3.7.2.2A) and gave a rather different trend in the Fbar (Fig. 5.2.3.7.2.2A). As no differences were 
observed  using  1.5,  2.0  and  2.5  values,  the  middle  value  (2.0)  was  chosen.  The  third  sensitivity 
analysis tested different shrinkage ages (1, 2 and 3) using shrinkage weight of 2.0; again, as the first 
option showed slightly higher residuals (range: 3 to -3) than the remaining values (range: 2 to -2) (Fig. 
5.2.3.7.2.2B), the option of 2 ages shrinkage was selected. Based on these  simulation analyses, the 
following inputs were selected to run the final XSA: 

fse 
2.0 

rage 
1 

qage 
2 

shk.n 
TRUE 

shk.f 
TRUE 

shk.yrs 
3 

shk.ages 
2 

Log residuals of the sensitivity analyses of a set of trials for the shrinkage weights (0.5, 1.5 and 2.5) 
and the three shrinkage ages (1, 2 and 3) are shown in Figure 5.2.3.7.2.3. 

Fig.  5.2.3.7.2.2.  Hake  in  GSA  6.  Sensitivity analyses  using  different shrinkage  weights  (A)  and  shrinkage  ages 
(B). Shrinkage weights modeled were 0.5, 1.0, 1.5, 2.0 and 2.5 (Sh05 to Sh25) and shrinkage ages were 1, 2 and 
3 (Sh1, Sh2 and Sh3). 

114 

114 

A 

B 

 
 
 
 
 
Fig. 5.2.3.7.2.3. Hake in GSA 6. Log residuals of the sensitivity analyses of a set of trials for the shrinkage 
weights (0.5, 1.5 and 2.5) and the three shrinkage ages (1, 2 and 3). 

5.2.3.7.3  Results 
The residuals per age and year of the tuning fleet were relatively low, ranging from 2 to -2, and did 
not show any tendency with time (Fig. 5.2.3.7.3.1). 

Fig. 5.2.3.7.3.1. Hake in GSA 6. Log residuals for the tuning fleets. 

Results  of  XSA  (Fig.  5.2.3.7.3.2)  revealed  that  all  main  population  parameters  (recruitment,  SSB 
and catch) showed a decreasing trend punctuated by some important peak. The fishing mortality 
increased from 1.29 in 2004 to 2.0 in 2012 and then decreased progressively down to 1.39 in 2014. 

115 

115 

 
 
 
 
 
 
Fig.  5.2.3.7.3.2.  Hake  in  GSA  6.  XSA  summary  results.  SSB  and  catch  are  in  tonnes,  recruitment  in  1000s 
individuals. 
. 

The XSA dignostics are reported below: 

FLR XSA Diagnostics 2015-09-02 14:34:18 

CPUE data from indices 

Catch data for 12 years 2003 to 2014. Ages 0 to 5. 

            fleet first age last age first year last year alpha beta 
1 Surveys (N/km2)         0        4       2003      2014  <NA> <NA> 

 Time series weights : 

    Tapered time weighting not applied 

 Catchability analysis : 

     Catchability independent of size for ages >   1  

     Catchability independent of age for ages >   2  

 Terminal population estimation : 

     Survivor estimates shrunk towards the mean F 
    of the final   3 years or the  2 oldest ages. 

    S.E. of the mean to which the estimates are shrunk =   2  

    Minimum standard error for population 
    estimates derived from each fleet =  0.3  

    prior weighting not applied 

Regression weights 
     year 
age   2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 
  all    1    1    1    1    1    1    1    1    1    1 

116 

116 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 Fishing mortalities 
   year 
age  2005  2006  2007  2008  2009  2010  2011  2012  2013  2014 
  0 0.640 1.012 0.623 0.601 0.754 0.473 0.142 0.147 0.177 0.119 
  1 1.496 1.594 1.311 1.709 2.052 1.522 1.815 1.775 1.790 1.227 
  2 1.355 1.546 1.582 1.647 1.944 1.732 2.219 1.868 2.223 1.605 
  3 0.929 1.641 1.608 0.826 1.233 1.857 1.547 2.363 1.474 1.340 
  4 0.746 0.258 1.845 1.500 1.611 1.960 1.862 0.976 1.826 0.283 
  5 0.746 0.258 1.845 1.500 1.611 1.960 1.862 0.976 1.826 0.283 

 XSA population number (Thousand) 
      age 
year        0     1    2   3   4   5 
  2005 212879 31368 3656 569  62  21 
  2006 191923 32468 3934 601 151  42 
  2007 180636 20183 3691 535  78  17 
  2008 269904 28048 3045 484  72   5 
  2009 198692 42831 2842 374 142 105 
  2010 158367 27041 3081 259  73   8 
  2011 118018 28551 3304 348  27   8 
  2012 126909 29634 2603 229  50   2 
  2013  78292 31705 2811 256  14   3 
  2014 103099 18987 2962 194  39  12 

 Estimated population abundance at 1st Jan  2015  
      age 
year    0     1    2   3  4  5 
  2015 22 26480 3116 379 34 20 

 Fleet:  Surveys (N/km2)  

 Log catchability residuals. 

   year 
age   2003   2004   2005   2006   2007   2008   2009   2010   2011   2012   
2013   2014 
  0 -0.153  0.184  0.146  0.600 -0.165 -0.392 -0.027  0.213 -0.245 -0.094  
0.240 -0.307 
  1  0.187 -0.041 -0.239  0.245 -0.073 -0.074 -0.086 -0.186  0.248 -0.274  
0.190  0.103 
  2  0.431  1.017 -0.593  0.924 -0.453 -1.136  0.117 -0.349 -0.168 -0.370  
0.378  0.202 
  3  0.369  0.553 -1.216 -1.001  0.084 -0.113 -0.170  0.431 -0.895  0.745 -
0.617  0.303 
  4 -0.035  0.000  0.000  0.132  0.007 -0.040  0.013  0.000  0.000  0.131  
0.000  0.097 

 Regression statistics  
 Ages with q dependent on year class strength  
[1] "0.735167328470271" "0.747942826143583" "6.44771215187117"  
"6.76939357385684"  

 Terminal year survivor and F summaries:  

 ,Age 0 Year class =2014  

source  
                scaledWts survivors yrcls 
Surveys (N/km2)     0.157     17432  2014 
fshk                0.010     18468  2014 
nshk                0.834     28761  2014 

 ,Age 1 Year class =2013  

source  
                scaledWts survivors yrcls 

117 

117 

 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
Surveys (N/km2)     0.929      3576  2013 
fshk                0.071      1365  2013 

 ,Age 2 Year class =2012  

source  
                scaledWts survivors yrcls 
Surveys (N/km2)     0.649       464  2012 
fshk                0.351       194  2012 

 ,Age 3 Year class =2011  

source  
                scaledWts survivors yrcls 
Surveys (N/km2)     0.681        46  2011 
fshk                0.319        18  2011 

 ,Age 4 Year class =2010  

source  
                scaledWts survivors yrcls 
Surveys (N/km2)     0.971        23  2010 
fshk                0.029         2  2010 

Year 

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

Stock numbers 
(·103) 
263987 
274957 
248555 
229119 
205140 
301556 
244986 
188830 
150255 
159427 
113082 
125294 

Stock biomass 

8533.6 
8559.2 
9199.2 
9454.3 
7568.7 
9541.9 
10099.7 
6922.1 
7712 
7459.4 
6946.4 
6021.9 

Recruitment 
numbers (·103) 
228682 
242726 
212879 
191923 
180636 
269904 
198692 
158367 
118018 
126909 
78292 
103099 

SSB (t) 

F1-3 

1810.2 
1724.6 
2401.2 
2770.6 
2261 
2022.6 
2402.8 
1853.8 
1918.7 
1573.9 
1669 
1599.7 

1.8659 
1.2885 
1.2602 
1.5937 
1.5003 
1.3942 
1.7431 
1.7037 
1.8604 
2.0022 
1.8292 
1.3906 

Finally, retrospective analyses showed rather consistent results except for the mean F during the 
first years (Fig. 5.2.3.7.3.3). 

Fig. 5.2.3.7.3.3. Hake in GSA 6. XSA retrospective analyses. 

118 

118 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
5.2.3.8  Reference points 
5.2.3.8.1  Methods 
Yield  per  recruit  analysis  was  used  to  calculate  the  reference  point  F0.1  and  the  estimated 
reference fishing mortality (Fref). 

5.2.3.8.2  Input data  
Reference  F  was  estimated  using  the  R  script  provided  by  STECF  EWG,  which  used  the  default 
assumptions  agreed  in  the  meeting,  e.g.,  weights  are  means  of  the  last  3  years  and  future 
recruitment are obtained as the geometric mean of the last 3 years. 

5.2.3.8.3  Results 
The  yield  per  recruit  graph,  together  with  the  reference  point  F0.1  and  the  estimated  reference 
fishing mortality (Fref), revealed a highly overexploited stock (Fig. 5.2.3.8.3.1). 

F0.1 

0.260 

Fref (2014; ages 1-3)  1.391 

Fig. 5.2.3.8.3.1. Hake in GSA 6. yield per recruit. 

5.2.3.9  Data quality 
Data from DCF 2014 were used. The data available are of sufficient quality to perform XSA.  The 
data submitted to the EWG 15-11 are in general of good quality. 

5.2.3.10 Short term predictions 2015-2017 
5.2.3.10.1  Method 
A  deterministic  short  term  prediction  for  the  period  2015  to  2017  was  performed using  the  FLR 
routines provided by JRC, which takes into account the catch and landings in numbers and weight 
and the discards. 

5.2.3.10.2  Input parameters  
The same input parameters used in the XSA analysis shown above were used. Different scenarios 
of constant harvest strategy with Fbar calculated as the average of ages 1 to 3 and F status quo (Fstq 
= 1.72) were performed. 

5.2.3.10.3  Results 
A short term projection (Table 5.2.3.10.3.1), assuming an Fstq of 1.720 in 2014 and a recruitment of 
100806.2 thousand individuals shows that: 

119 

119 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  Fishing at the Fstq (1.720) generates an increase of the catch of 39.06% from 2014 to 2016 along 

with a decrease of the spawning stock biomass of 3.77% from 2016 to 2017. 

  Fishing at F0.1 (0.260) generates a decrease of the catch of 64.80% from 2014 to 2016 and an 

increase of the spawning stock biomass of 221.04% from 2016 to 2017. 

Table 5.2.3.10.3.1. Hake in GSA 6. Short term forecast in different F scenarios. Basis: F(2015) = mean(Fbar1-3 
2012-2014)=  1.740;  R(2015)  =  geometric  mean  of  the  recruitment  of  the  last  3  years;  R  =  100806 
thousand; SSB(2014) = 1600 t, Catch (2014)= 2230 t. 

Rationale 

Ffactor 

fbar 

Catch 
2016 

Catch 
2017 

SSB 2017 

0.000 
0.151 

0.000 

7276.159 
0.000 
0.260  784.862  1718.155  5762.505 

0.000 

Change SSB 
2016-2017 
(%) 
305.363 
221.036 

Change Catch 
2014-2016 
(%) 
-100.000 
-64.804 

Zero catch 
High long-term 
yield (FMSY) 
Status quo 
Different 
scenarios 

1.000 
0.100 

1.721  3100.981  3046.672  1727.233 
0.172  538.021  1255.012  6233.791 

-3.774 
247.291 

0.200 
0.300 
0.400 
0.500 
0.600 
0.700 
0.800 
0.900 
1.000 
1.100 
1.200 
1.300 
1.400 
1.500 
1.600 
1.700 
1.800 
1.900 
2.000 

0.344  1002.983  2070.175  5350.261 
0.516  1405.701  2583.372  4601.065 
0.688  1755.343  2891.092  3965.507 
0.860  2059.683  3060.623  3426.103 
1.032  2325.320  3138.687  2968.079 
1.204  2557.854  3157.544  2578.946 
1.376  2762.046  3139.310  2248.149 
1.548  2941.941  3098.997  1966.761 
1.721  3100.981  3046.672  1727.233 
1.893  3242.096  2988.966  1523.179 
2.065  3367.783  2930.140  1349.195 
2.237  3480.169  2872.835  1200.708 
2.409  3581.071  2818.594  1073.847 
2.581  3672.041  2768.224  965.335 
2.753  3754.402  2722.053  872.396 
2.925  3829.290  2680.100  792.677 
3.097  3897.677  2642.197  724.188 
3.269  3960.394  2608.069  665.240 
3.441  4018.156  2577.388  614.402 

198.069 
156.330 
120.923 
90.872 
65.355 
43.676 
25.247 
9.570 
-3.774 
-15.142 
-24.835 
-33.107 
-40.175 
-46.220 
-51.398 
-55.839 
-59.655 
-62.939 
-65.771 

39.057 
-75.873 

-55.023 
-36.964 
-21.285 
-7.638 
4.274 
14.702 
23.859 
31.926 
39.057 
45.385 
51.022 
56.061 
60.586 
64.666 
68.359 
71.717 
74.784 
77.596 
80.186 

120 

120 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5.2.3.11 Short term predictions 2015-2017 by fleet 
5.2.3.11.1  Method 
A deterministic short term prediction by fleet for the  period 2015 to 2017 was  performed  using 
the  FLR  routines  provided  by  JRC  and  based  on  the  results  of  the  XSA  stock  assessments 
performed during EWG 15-11. 

5.2.3.11.2  Input parameters  
The  same  parameters  used  in  the  short  term  by  single  fleet  were  used.  As  reported  above,  two 
fleets were analysed: trawlers and others (longliners and gillnetters combined). 

5.2.3.11.3  Results 

Table 5.2.3.11.3.1. Hake in GSA 6. Short term forecast by fleet: trawl (OTB) and others (including longliners 
(LLS) and gillnetters (GNS) combined). 

Fleet 
Others (LLS+GNS) 
Trawl (OTB) 
Others (LLS+GNS) 
Trawl (OTB) 
Others (LLS+GNS) 

Year 
2015 
2015 
2016 
2016 
2017 

Catches 
98.074 
3180.659 
23.756 
733.512 
91.187 

Partial F 
0.099 
1.621 
0.014 
0.234 
0.014 

Trawl (OTB) 

2017 

1584.807 

0.234 

Figure 5.2.3.11.3.1. Hake in GSA 6. Short term forecast by fleet. 

121 

121 

 
 
 
 
 
 
 
 
 
5.2.3.12 Medium term predictions 
The  medium  term  projection  was  not  conducted  because  no  meaningful  stock-recruitment 
relationship was found. 

5.2.3.13 Stock advice 
The current F (1.72) is larger than FMSY (0.260), which indicates that hake in GSA 6 is being fished 
above FMSY. STECF EWG 15-11 recommends the relevant fleets’ effort to be reduced until fishing 
mortality is below or at the proposed FMSY level, in order to avoid future loss in stock productivity 
and landings. This  should be achieved by means of a multi-annual  management plan taking into 
account  mixed-fisheries  considerations.  Catches  of  European  hake  in  2016  consistent  with  FMSY 
should not exceed 785 tonnes in GSAs 6. 

5.2.3.14 Management strategy evaluation 
if  the  MSY  ranges  were 
Management  Strategy  Evaluation  was  carried  out  to  evaluate 
precautionary. The FMSY ranges were derived using the formula provided by STECF 15-09. F ranges 
results  were  Fupper=0.358  and  Flower=0.175.  Blim  was  estimated  as  Bloss=1532.7  (t).The  following 
figure shows the results of the MSE. 

Figure 5.2.3.14.1. Hake in GSA 6. Marine Strategy Evaluation. 

The probability of SSB to fall below Blim at F = Fupper is equal to 0. 

122 

122 

 
 
 
 
 
 
 
 
 
 
5.2.4  STOCK ASSESSMENT OF HAKE IN GSA 7 
5.2.4.1  Stock Identification 

Due  to  the  lack  of  information  about  the  structure  of  hake  population  in  the  western 
Mediterranean,  this  stock  was  assumed  to  be  confined  within  the  GSA  7  boundaries.  (Figure 
5.2.4.1.1). 

Figure 5.2.4.1.1. Geographical location of GSA 7. 

5.2.4.2  Growth 
The growth of European Hake (Merluccius merluccius) in the Gulf of Lions was reestimated from 
tagging experiments carried out by IFREMER (Mellon-Duval et al., 2010). The new parameters have 
not been yet compared to a re-analysis of otoliths readings, because of the uncertainty on otoliths 
readings. Therefore, the data sent to the data call were in length and were converted in age using 
the  length-to-age  slicing  functions  available  in  the  R  package  a4a.  The  growth  parameters  used 
during the EWG 15-11 are indicated in the following table. 

Females   Males 
72.8 

100.7 

0.236 
- 

0.233 
- 

Linf 

K 
t0 

5.2.4.3  Maturity 
The maturity was calculated using data collected within the DCF (2002-2014). 

123 

123 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 5.2.4.3.1. Hake in GSA 7. Maturity at age. 

1998 
1999 
2000 
2001 
2002 
2003 
2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

0 
0.06 
0.06 
0.06 
0.06 
0.05 
0.08 
0.06 
0.06 
0.07 
0.08 
0.09 
0.08 
0.08 
0.09 
0.11 
0.03 
0.01 

1 
0.23 
0.33 
0.34 
0.33 
0.25 
0.34 
0.32 
0.32 
0.37 
0.32 
0.22 
0.38 
0.29 
0.33 
0.27 
0.25 
0.34 

2 
0.72 
0.69 
0.74 
0.70 
0.67 
0.67 
0.70 
0.71 
0.78 
0.70 
0.65 
0.69 
0.65 
0.64 
0.64 
0.61 
0.68 

3 
0.92 
0.91 
0.92 
0.90 
0.91 
0.90 
0.90 
0.90 
0.91 
0.92 
0.91 
0.89 
0.89 
0.88 
0.89 
0.94 
0.92 

5.2.4.4  Natural mortality 

Table 5.2.4.4.1. Hake in GSA 7. Natural Mortality (M) at age (PRODBIOM). 

1998 
1999 
2000 
2001 
2002 
2003 
2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

0 
0.88 
0.88 
0.88 
0.88 
0.88 
0.88 
0.88 
0.88 
0.88 
0.88 
0.88 
0.88 
0.88 
0.88 
0.88 
0.88 
0.88 

1 
0.43 
0.43 
0.43 
0.43 
0.43 
0.43 
0.43 
0.43 
0.43 
0.43 
0.43 
0.43 
0.43 
0.43 
0.43 
0.43 
0.43 

2 
0.33 
0.33 
0.33 
0.33 
0.33 
0.33 
0.33 
0.33 
0.33 
0.33 
0.33 
0.33 
0.33 
0.33 
0.33 
0.33 
0.33 

3 
0.25 
0.25 
0.25 
0.25 
0.25 
0.25 
0.25 
0.25 
0.25 
0.25 
0.25 
0.25 
0.25 
0.25 
0.25 
0.25 
0.25 

4 
0.99 
0.99 
0.99 
0.99 
0.99 
0.99 
0.98 
0.98 
0.98 
0.98 
0.98 
0.98 
0.98 
0.98 
0.98 
1.00 
1.00 

4 
0.22 
0.22 
0.22 
0.22 
0.22 
0.22 
0.22 
0.22 
0.22 
0.22 
0.22 
0.22 
0.22 
0.22 
0.22 
0.22 
0.22 

5+ 
1.00 
1.00 
1.00 
1.00 
1.00 
1.00 
0.99 
0.99 
0.99 
0.99 
1.00 
0.99 
0.99 
0.99 
0.99 
1.00 
1.00 

5+ 
0.20 
0.20 
0.20 
0.20 
0.20 
0.20 
0.20 
0.20 
0.20 
0.20 
0.20 
0.20 
0.20 
0.20 
0.20 
0.20 
0.20 

124 

124 

 
 
 
 
 
 
 
 
 
 
 
 
5.2.4.5  Fisheries 
5.2.4.5.1  General description of the fisheries 
Hake is one of the most important demersal target species for the commercial fisheries in the Gulf 
of  Lions  (GSA  7).  In  this  area,  hake  is  exploited  by  French  trawlers,  French  gillnetters,  Spanish 
trawlers and  Spanish  longliners.  Since  1998,  an  average  of  243  boats  are  involved in  this  fishery 
and,  according  to  official  statistics,  the  total  annual  catches  for  the  period  1998-2014  have 
oscillated around an average value of 2012 tons (1983 tons in 2014). In 2009, because of the large 
decline  of  small  pelagic  fish  species  in  the  area,  the  trawlers  fishing  small  pelagic  have  diverted 
their  effort  on  demersal  species.  Between  1998  and  2014,  the  number  of  French  trawlers 
operating  in  the  GSA  7  has  decreased  by  39%,  while  it  decreased  by  20%  between  2010  and 
2013.The French trawler fleet is the largest considering catches realized, the proportion of boats 
and  catches  are  respectively  (27%  and  73%).  The  length  of  hake  in  the  trawler  catches  ranges 
between 3 and 92 cm total length (TL), with an average size of 21 cm TL. The second largest fleet is 
the  French  gillnetters  (41  and  16%  respectively,  range  13-86  cm  TL  and  average  size  39  cm  TL), 
followed by the Spanish trawlers (9 and 10%, respectively, range 5-88 cm TL, and average size 24 
cm TL), and the Spanish longliners (4 and 1%, respectively, range 22-96 cm TL and average size 52 
cm  TL).  The  hake  trawlers  exploit  a  highly  diversified  species  assemblage:  Striped  red  mullet 
(Mullus surmuletus), red mullet (M. barbatus), angler fish (L. piscatorius), blackbellied angler fish 
(L.  budegassa),  european  conger  (Conger  conger),  poor-cod  (Trisopterus  minutus  capelanus), 
fourspotted megrim (Lepidorhombus boscii), soles (Solea spp.), horned octopus (Eledone cirrhosa), 
squids  (Illex  coindetii),  gilthead  seabream  (Sparus  aurata),  European  seabass  (Dicentrarchus 
labrax),  seabreams  (Pagellus  spp.),  blue  whiting  (Micromesistius  poutassou),  tub  gurnard 
(Chelidonichtys lucerna). 

5.2.4.5.2  Management regulations applicable in 2015  
French Trawlers: 
Fishing license: fully observed 
Engine power limited to 316 KW or 500 CV: Not full compliance 
Cod-end mesh size (bottom trawl: square 40 mm or 50 mm diamond, by derogation): not fully 
observed 
Fishing forbidden within 3 miles (France): not fully observed 
Time at sea: fully observed 
Temporal bans depending on years (2011 and 2012, 1 month/year): fully observed 

French gillnetters: 
Fishing license: fully observed 
Maximum length of net: not fully observed 

Spanish trawlers: 
Fishing license: fully observed 
Engine power limited to 316 KW or 500 CV: not observed 
Mesh size in the codend (before Jun 1st 2010: 40 mm diamond: after Jun 1st 2010: 40 mm square 
or 50 mm diamond, by derogation): fully observed 
Fishing forbidden <50 m depth: fully observed 
Time at sea: fully observed 
Temporal bans depending on years (2014, 1 month): fully observed 

125 

125 

 
 
 
 
 
 
Spanish longliners: 
Fishing license: fully observed 
Number of hook per boat: not fully observed 

In  2009,  GFCM  proposed  the  creation  of  a  High  Sea  Fishery  Restricted  Area  (FRA,  GFCM/33/2009/1)  in 
which the fishing effort for demersal stocks of vessels using towed nets, bottom and mid-water longlines, 
bottom-set nets shall not exceed the level of fishing effort applied in 2008 in the fisheries restricted area of 
the eastern Gulf of Lions as bounded by lines joining the following geographic coordinates: 42°40'N, 4°20' E; 
42°40'N, 5°00' E; 43°00'N, 4°20' E; 43°00'N, 5°00' E. In the article 4 from the EU Regulation No. 1343/2011 
of  the  European  Parliament  and  of  the  Council  of  13  December  2011,  this  fisheries  restricted  area  was 
established  and  in  2012  both  French  (Arrêté  du  28  décembre  2012,  NOR:  TRAM1240493A)  and  Spanish 
(Orden AAA/1857/2012 de 22 de agosto) governments published their own laws regulating this FRA. 

5.2.4.5.3   Catches 

Figure 5.2.4.5.3.1. Hake in GSA 7. Catch by gear in tons (1978-2014). 

Table 5.2.4.5.3.1. Hake in GSA 7. Annual catches (t) by gear (DCF data). 

Gears/Years 
1998 
1999 
2000 
2001 
2002 
2003 
2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 

OTB-French 
1688 
1525 
1347 
1835 
2168 
2024 
1023 
1002 
1014 
1282 
2071 
1642 
1527 
970 
768 
1337 

OTB-Spanish  GNS-French 

500 
500 
500 
500 
182 
248 
99 
255 
299 
168 
111 
286 
247 
245 
175 
161 

140 
279 
166 
196 
231 
206 
101 
126 
116 
107 
227 
258 
156 
116 
163 
198 

126 

126 

GTR-French 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
5 
- 
21 

LLS-Spanish 
101 
109 
285 
163 
146 
112 
78 
101 
170 
143 
97 
84 
54 
29 
18 
18 

 
 
 
 
 
 
 
 
2014 

1441 

202 

284 

32 

24 

5.2.4.5.4  Landing 

Table 5.2.4.5.4.1. Hake in GSA 7. Annual landings (t) by gear (DCF data). 

Gears/Years  OTB-French  OTB-Spanish 
1688 
1525 
1347 
1835 
2168 
2024 
1023 
1002 
1014 
1282 
1898 
1633 
1527 
970 
759 
1292 
1392 

1998 
1999 
2000 
2001 
2002 
2003 
2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

140 
279 
166 
196 
231 
206 
101 
125 
116 
107 
192 
258 
156 
113 
162 
198 
200 

GNS-French 
500 
500 
500 
500 
182 
248 
99 
255 
299 
168 
111 
286 
247 
245 
175 
161 
284 

GTR-French 

- 
- 
- 
- 
- 
- 
- 
- 
- 
5 
- 
21 
32 

LLS-Spanish 
101 
109 
285 
163 
146 
112 
78 
101 
170 
143 
97 
83 
53 
29 
18 
18 
24 

5.2.4.5.5  Discards  
The French discards were not included before 2008 as as they represented a negligible amount. 

Table 5.2.4.5.5.1. Hake in GSA 7. Annual discards (t) by gear (DCF data). 

Gears/Years  OTB-French  OTB-Spanish 

GNS-French 

GTR-French 

LLS-Spanish 

1998 
1999 
2000 
2001 
2002 
2003 
2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

0 
- 
0 
0 
0 
173 
9 
- 
- 
9 
46 
49 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

- 
- 
1 
- 
- 
35 
0 
0 
3 
1 
0 
2 

127 

127 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

- 
- 
- 
- 
- 
- 
1 
1 
- 
- 
- 
- 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5.2.4.5.6  Fishing effort  

Table 5.2.4.5.6.1. Hake in GSA 7. Fishing effort (kW·days) by gear for France and Spain, 2009-2014. 

GNS 
2009 
2010 
2011 
2012 
2013 
2014 

OTB-
French 
- 
- 
- 
- 
3121214 
2819032 

OTB-Spanish  GNS-French  GTR-French 
- 
- 
- 
3081607 
30200 
40683 

- 
- 
- 
2908493 
30507 
39284 

1623651 
1456054 
1630298 
1339565 
1302803 
1386059 

LLS-Spanish 
52941 
175962 
137453 
115316 
126165 
144669 

5.2.4.6  Scientific surveys 
5.2.4.6.1    Survey #1 ( MEDITS) 
5.2.4.6.1.1  Methods 
Fishery independent information regarding the state of the hake in GSA 07 was derived from the 
international survey MEDITS. 

The  data  was  assigned  to  strata  based  upon  the  shooting  position  and  average  depth  (between 
shooting and hauling depth). Catches by haul were standardized to 60 minutes hauling duration. 
The  abundance  and  biomass  indices  by  GSA  were  calculated  through  stratified  means  (Cochran, 
1953;  Saville,  1977).  This  involves  weighting  the  average  values  of  the  individual  standardized 
catches and the variation of each stratum by the respective stratum areas in each 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 the 95 % confidence interval:   
Confidence interval = Yst ± t(student distribution) * V(Yst) / n  
Length distributions were obtained by the sum of all standardized length frequencies (subsamples 
raised  to  standardized  haul  abundance  per  hour)  over  the  stations  of  each  stratum.  Aggregated 
length frequencies were then raised to stratum abundance * 100 (because of low numbers in most 
strata) and finally aggregated (sum) over the GSA strata. 

128 

128 

 
 
 
 
 
 
 
  
 
 
 
5.2.4.6.1.2  Trends in abundance and biomass 
Fishery independent information regarding the state of the hake in GSA 7 was derived from the 
international survey MEDITS. Figure 5.2.4.6.1.3.1. displays the time series of abundance in GSA 7. 
No clear trend can be detected over the total period, but since 2012, lowest value observed in the 
time  series,  the  index  shows  some  slight  increase.  The  age  structure  (fig.  5.2.4.6.1.4.1.)  did  not 
exhibit any substantial change in 2014 compared to the other years. 

Figure 5.2.4.6.1.3.1. Hake in GSA 7. Medits abundance index (N/hour) for hake. 

5.2.4.6.1.3  Trends in abundance by length or age 

Figure 5.2.4.6.1.4.1. Hake in GSA 7. Age structure of the MEDITS abundance index (n/hour). 

129 

129 

 
 
 
 
 
 
5.2.4.7  Stock Assessment 
5.2.4.7.1   Methods 
During EWG 15-11, the stock assessment was performed over the period 1998-2014 using an XSA 
model over age classes ranging from 0 to 5+ and with MEDITS index, as tuning fleet (ages 0-2). An 
attempt was made to use the a4a model, developed by the Joint Research Center, instead of XSA 
for assessing the stock. a4a is a statistical catch at age model, which flexibility allows to fit a wide 
range of models to the data. A comparison between the 2 methods of the results can be find in 
the section 5.2.4.7.3 (Results). The final diagnosis is based upon XSA analysis. 

5.2.4.7.2  Input data 

4500

4000

3500

3000

2500

2000

1500

1000

500

0

0
0
0
1
*
s
r
e
b
m
u
N

-10

10

30

Lengths 

50

70

90

Figure 5.2.4.7.2.1. Hake in GSA 7. Length distribution of total catch. 

Table 5.2.4.7.2.1. Hake in GSA 7. Catch at age in numbers (thousands). 

1998 
1999 
2000 
2001 
2002 
2003 
2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

0 
21010 
6571 
7575 
12526 
24183 
6190 
6225 
5826 
2816 
3211 
12079 
3841 
7289 
2679 
2912 
6287 
6476 

1 
13203 
8996 
6992 
9850 
14310 
10323 
5269 
5691 
4452 
6097 
16923 
7804 
9621 
6188 
6558 
10374 
10591 

2 
1554 
2644 
2080 
2561 
2066 
2561 
1284 
1565 
1616 
1821 
1595 
2371 
1924 
1403 
915 
1440 
1953 

130 

130 

3 
228 
281 
330 
344 
231 
347 
162 
177 
240 
232 
148 
375 
210 
163 
101 
13 
24 

4 
39 
34 
60 
39 
25 
23 
12 
15 
28 
21 
13 
15 
12 
5 
4 
3 
1 

1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014

5+ 
12 
8 
24 
21 
13 
16 
3 
3 
6 
7 
5 
4 
2 
1 
1 
0 
0 

 
 
 
 
 
 
 
 
 
Table 5.2.4.7.2.2. Hake in GSA 7. Weight at age (kg) in the catch and stock (kg). 

1998 
1999 
2000 
2001 
2002 
2003 
2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

0 
0.0236 
0.0263 
0.0242 
0.0225 
0.0216 
0.0316 
0.0228 
0.0248 
0.0304 
0.0351 
0.0380 
0.0323 
0.0317 
0.0394 
0.0434 
0.0358 
0.0259 

1 
0.0858 
0.1257 
0.1304 
0.1264 
0.0940 
0.1286 
0.1197 
0.1211 
0.1441 
0.1237 
0.0846 
0.1505 
0.1122 
0.1285 
0.1036 
0.1060 
0.1216 

2 
0.3509 
0.3283 
0.3788 
0.3300 
0.3088 
0.3024 
0.3234 
0.3397 
0.4206 
0.3492 
0.3047 
0.3170 
0.2850 
0.2694 
0.2793 
0.2705 
0.2571 

3 
0.6866 
0.6165 
0.7348 
0.5755 
0.6580 
0.5954 
0.5858 
0.5625 
0.6452 
0.7019 
0.6905 
0.5286 
0.5196 
0.4846 
0.5615 
1.0979 
0.8088 

4 
1.7755 
1.5267 
1.8415 
1.7442 
1.6604 
1.6092 
1.1613 
0.9783 
1.0535 
1.1964 
1.3747 
1.0419 
1.2359 
1.2260 
1.1225 
1.2002 
1.2002 

5+ 
2.5426 
2.0843 
2.5966 
2.6060 
2.1780 
2.4015 
1.6772 
1.3058 
1.3081 
1.2715 
1.8235 
1.4363 
1.2238 
1.1589 
1.2012 
1.3687 
1.3687 

Table 5.2.4.7.2.3. Hake in GSA 7. MEDITS index at age (1998-2014). 

1 
860 
346 
127 
181 
563 
365 
140 
150 
94 
330 
2115 
595 
247 
164 
336 
877 
460 

2 
19 
51 
39 
42 
41 
74 
24 
22 
30 
55 
43 
104 
40 
35 
15 
52 
84 

1998 
1999 
2000 
2001 
2002 
2003 
2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

0 
7678 
2622 
7493 
6317 
11549 
952 
5681 
2428 
3331 
3414 
13518 
5460 
5188 
1951 
1425 
1902 
3295 

131 

131 

 
 
 
 
 
 
 
 
 
 
 
 
5.2.4.7.3  Results 

Model: XSA 

Same settings as last year (Shrinkage on the last 4 years, Shrinkage on the last 3 ages, weight 
of  shrinkage  fse=1.5,  Constant  catchability  for  all  ages)  after  performing  sensitivity  analysis 
(figure 5.2.4.7.3.1). 

(a)  ShkYears  

(b) ShkAge 

(b) fse 

(d) Qage 

Figure 5.2.4.7.3.1. Hake in GSA 7. Sensitivity analysis on shrinkage on the last years (a), last ages (b), weight 
of the shrinkage (c), catchability at age (d).  

132 

132 

 
  
 
 
 
 
 
 
 
 
Figure 5.2.4.7.3.2. Hake in GSA 7. Log-residuals of the MEDITS survey. 

Figure 5.2.4.7.3.3. Hake in GSA 7. Retrospective analysis performed with XSA. 

Table 5.2.4.7.3.1. Hake in GSA 7. Fishing mortality at age estimated by XSA. 

1998 
1999 
2000 
2001 
2002 
2003 
2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

0 
0.617224 
0.264897 
0.254349 
0.302475 
0.690336 
0.33476 
0.320939 
0.341007 
0.147654 
0.077083 
0.453101 
0.155278 
0.406231 
0.14819 
0.106754 
0.228469 
0.281063 

1 
1.349275 
1.216579 
0.9712 
1.29112 
1.499851 
1.699181 
1.054164 
1.096407 
0.910785 
1.088087 
1.676924 
1.24791 
1.660899 
1.709571 
1.397943 
1.481432 
1.789523 

2 
1.407172 
1.808165 
1.624713 
2.111476 
1.65378 
2.419613 
1.676628 
1.672051 
1.758312 
2.198621 
1.399298 
2.217939 
2.254655 
2.402282 
3.855902 
3.817109 
2.842383 

3 
1.625985 
1.36651 
1.900198 
2.326604 
2.093304 
2.966352 
2.003908 
1.620607 
2.164633 
2.520462 
2.055486 
3.179168 
3.432401 
3.402128 
3.34125 
2.290177 
3.165804 

4 
1.482131 
1.485128 
1.582999 
1.942937 
1.782153 
2.404276 
1.605981 
1.479304 
1.646954 
1.982981 
1.741229 
2.249941 
2.489042 
2.543168 
2.909915 
2.569422 
2.640126 

5+ 
1.482131 
1.485128 
1.582999 
1.942937 
1.782153 
2.404276 
1.605981 
1.479304 
1.646954 
1.982981 
1.741229 
2.249941 
2.489042 
2.543168 
2.909915 
2.569422 
2.640126 

133 

133 

 
 
 
 
 
 
 
 
 
 
 
 
 
Table 5.2.4.7.3.2. Hake in GSA 7. Stock number at age estimated by XSA. 

1998 
1999 
2000 
2001 
2002 
2003 
2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

0 
70831 
43839 
52373 
74512 
75309 
33783 
35210 
31309 
31855 
67203 
51477 
41466 
33902 
30197 
44654 
47795 
40913 

1 
22104 
15848 
13952 
16845 
22839 
15662 
10026 
10595 
9234 
11399 
25807 
13572 
14726 
9367 
10800 
16646 
15775 

2 
2427 
3730 
3054 
3436 
3013 
3316 
1863 
2273 
2303 
2416 
2498 
3138 
2535 
1820 
1103 
1736 
2461 

3 
321 
427 
440 
432 
299 
414 
212 
250 
307 
285 
193 
443 
246 
191 
118 
17 
27 

4 
57 
49 
85 
51 
33 
29 
17 
22 
39 
27 
18 
19 
14 
6 
5 
3 
1 

5+ 
16 
11 
32 
27 
17 
18 
4 
4 
8 
9 
7 
4 
2 
1 
1 
0 
0 

Table 5.2.4.7.3.3. Hake in GSA 7. Summary of the XSA analysis. 

1998 
1999 
2000 
2001 
2002 
2003 
2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

SSB (tons) 
1491 
1920 
2079 
1980 
1529 
1741 
983 
1158 
1545 
1473 
1305 
1809 
1173 
915 
769 
816 
1115 

Fbar(0-2) 
1,12 
1,10 
0,95 
1,24 
1,28 
1,48 
1,02 
1,04 
0,94 
1,12 
1,18 
1,21 
1,44 
1,42 
1,79 
1,84 
1,64 

Rec. (thousands) 

70831 
43839 
52373 
74512 
75309 
33783 
35210 
31309 
31855 
67203 
51477 
41466 
33902 
30197 
44654 
47795 
40913 

134 

134 

 
 
 
 
 
 
 
 
Figure 5.2.4.7.3.4. Hake in GSA 7. Time series of the estimated parameters from XSA. SSB and catch (tons), 
recruitment (numbers in thousands). 

Figure 5.2.4.7.3.5. Hake in GSA 7. Residuals for MEDITS data from XSA. 

Model: a4a 
During EWG 15-11 the stock assessment was also performed over the period 1998-2014, over age 
classes  ranging  from  0  to  5+,  using  a4a  model  and  the  MEDITS  index,  as  tuning  fleet.  The  a4a 
model, developed by the Joint Research Center is a statistical catch at age model, which flexibility 
allows to fit a wide range of models to the data. Compared to XSA, a4a runs forward and allows to 
reach a better stability for last years estimates. The results were compared to XSA run. The general 
specification of the model, in R language, was the following: 

index <- hke.idx 
qmod <- list(~s(age, k = 3, by=breakpts(year, 2011)) + s(year, k=3, by=as.numeric(age==2))) 
fmod  <-  ~  s(year,  k=10,  by=breakpts(age,  0.5))+  s(age,  k=4,  by=breakpts(year,  2011))  +  te(year, 
age, k = c(3, 3)) 

fit <- a4aSCA(stock = hke, indices = index, fmodel = fmod, qmodel = qmod) 

135 

135 

 
 
 
 
 
 
 
 
This model allowed for an effect of age for the catchability of the MEDITS index (submodel qmod). 
The  model  also  allowed  for  an  effect  of  time  and  age  and  a  combined  effect  of  both  these 
variables  on  the  fishing  mortality  estimates  (submodel  fmod).  The  flexibility  parameters  for  the 
smoother  effects  (k)  for  the  qmod  and  vmod  were  set  to  constant  values  to  ensure  the  fit  of  a 
reasonable model. We assessed the quality of the model fits using model residuals, MEDITS index 
and catches, figure 5.2.4.7.3.7. Time series of the estimated parameters from the a4a analysis are 
presented in figure 5.2.4.7.3.6. 

Figure 5.2.4.7.3.6. Hake in GSA 7. Time series of the estimated parameters from the a4a analysis. 

Figure 5.2.4.7.3.7. Hake in GSA 7. Residuals for the catch and MEDITS data from the a4a analysis. 

136 

136 

 
 
 
 
 
 
 
 
 
Figure 5.2.4.7.3.8. Hake in GSA 7. Predicted and observed catch by age class. 

Figure 5.2.4.7.3.9. Hake in GSA 7. Predicted and observed MEDITS index by age class. 

Comparison with XSA 
The 'best' a4a model has similar results to XSA in terms of catch, fishing mortality and spawning 
stock  biomass  but  gives  higher  estimates  of  recruitment,  especially  in  the  last  year  (figure 
5.2.4.7.3.10.).  Residuals  patterns  of  this  model  was  generally  good  with  no  extreme  values.  XSA 
was finally kept as the base-case model for the hake stock assessment this year. 

137 

137 

 
 
 
 
 
 
 
 
Figure 5.2.4.7.3.10. Hake in GSA 7. Comparison of the XSA and a4a run. 

5.2.4.8  Reference points 
5.2.4.8.1  Methods 
Yield per recruit analysis was used (FLBRP) to calculate the reference point (F0.1) and the estimated 
reference fishing mortality (Fcurrent). Last year the final diagnosis was based upon a4a analysis. 
For that reason F0.1, was re-estimated this year using the input parameters of XSA model. 

5.2.4.8.2  Input data  
The same population parameters used for the XSA model and exploitation pattern derived from 
the final model were used as input for the yield per recruit analysis. 

5.2.4.8.3  Results 

Table 5.2.4.8.3.1. Hake in GSA 7. Reference points. 

Year of assessement 
2015 
2014 
2013 

Model 
XSA 
a4a 
XSA 

F0.1 
0.11 
0.17 
0.11 

Fcur 
1.64 
1.67 
1.83 

Ratio 
14.9 
9.8 
16.6 

138 

138 

 
 
 
 
 
 
 
 
 
  
 
Figure 5.2.4.8.3.1. Hake in GSA 7. Yield per recruit curve. 

With the estimated value for F0.1 of 0.11, the current level of F of 1.64 is higher, and hence, the 
stok is being fished above FMSY.  

5.2.4.9  Data quality 
All  the  length  data  was  available  in  the  database.  Effort  data  were  missing  before  2009.  The 
growth  of  European  Hake  (Merluccius  merluccius)  in  the  Gulf  of  Lions  were  reestimated  from 
tagging experiments carried out by IFREMER (Mellon-Duval et al., 2010). The new parameters have 
not been yet compared to a re-analysis of otoliths readings, because of the uncertainty on otoliths 
readings. Therefore, the data sent to the data call were in length and were converted in age using 
the length-to-age slicing functions available in the R package a4a and parameters from Mellon et 
al.  The  other  biological  parameters  (sex-ratio,  length-weight  and  maturity)  are  coming  from  the 
DCF and were present in the data call. 

5.2.4.10 Short term predictions 2015-2017 
5.2.4.10.1  Method 
A  deterministic  short  term  prediction  for  the  period  2015  to  2017  was  performed using  the  FLR 
routines provided by JRC and based on the results of the XSA stock assessments performed during 
EWG 15-11. 

5.2.4.10.2  Input parameters  
The  input  parameters  were  the  same  used  for  the  XSA  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 (44364.16 thousands individuals). 

139 

139 

 
 
 
 
 
 
 
 
5.2.4.10.3  Results 
Table 5.2.4.10.3.1. European hake in GSA 7. Short term forecast in different F scenarios. Basis: F(2015) = 
mean  (Fbar  0-2  2012-2014)=  1.75;  R(2015)  =  geometric  mean  of  the  recruitment  of  the  last  3  years;  R  = 
44364 thousands; SSB(2014) = 1115 t, Catch (2014)= 1983 t. 

Rationale 

Ffactor  Fbar 

Catch 
2015 

Catch 
2016 

Catch 
2017 

SSB 
2016 

SSB 
2017 

Change SSB 
2016-2017(%) 

Change Catch 
2014-2016(%) 

Zero catch 
High long 
term yield 
(F0.1) 

Different 
Scenarios 

0,00 

0,00 

1871 

0 

0 

740 

2798 

278 

-100,00 

0,06 
0,10 
0,20 
0,30 
0,40 
0,50 
0,60 
0,70 
0,80 
0,90 
1,00 
1,10 
1,20 
1,30 
1,40 
1,50 
1,60 
1,70 
1,80 
1,90 
2,00 

0,11 
0,18 
0,35 
0,53 
0,70 
0,88 
1,05 
1,23 
1,40 
1,58 
1,75 
1,93 
2,10 
2,28 
2,45 
2,63 
2,81 
2,98 
3,16 
3,33 
3,51 

1871 
1871 
1871 
1871 
1871 
1871 
1871 
1871 
1871 
1871 
1871 
1871 
1871 
1871 
1871 
1871 
1871 
1871 
1871 
1871 
1871 

209 
320 
586 
809 
998 
1159 
1297 
1417 
1521 
1612 
1693 
1766 
1830 
1889 
1942 
1991 
2035 
2077 
2115 
2151 
2184 

598 
858 
1329 
1578 
1699 
1749 
1758 
1746 
1723 
1695 
1666 
1637 
1610 
1584 
1560 
1538 
1518 
1499 
1482 
1466 
1451 

740 
740 
740 
740 
740 
740 
740 
740 
740 
740 
740 
740 
740 
740 
740 
740 
740 
740 
740 
740 
740 

2499 
2343 
1980 
1689 
1455 
1265 
1109 
982 
877 
790 
716 
655 
602 
558 
519 
486 
456 
431 
408 
388 
369 

238 
217 
168 
128 
97 
71 
50 
33 
18 
7 
-3 
-12 
-19 
-25 
-30 
-34 
-38 
-42 
-45 
-48 
-50 

-89,47 
-83,87 
-70,43 
-59,16 
-49,63 
-41,51 
-34,53 
-28,50 
-23,24 
-18,62 
-14,53 
-10,89 
-7,62 
-4,67 
-1,98 
0,47 
2,72 
4,81 
6,74 
8,54 
10,23 

5.2.4.11 Short term predictions 2015-2017 by fleet 
5.2.4.11.1  Method 
A deterministic short term prediction by fleet for the period  2015 to 2017 was  performed using 
the  FLR  routines  provided  by  JRC  and  based  on  the  results  of  the  XSA  stock  assessments 
performed during EWG 15-11. 

5.2.4.11.2  Input parameters  
The same parameters used in the short term by single fleet were used. 

5.2.4.11.3  Results 
Table 5.2.4.11.3.1. European hake in GSA 7. Short term forecast by fleet. 

Fleet 
FR_OTB 
FR_GN 

Year 
2015 
2015 

Catches 
1538 
125 

Partial_f 
1.399 
0.170 

140 

140 

 
 
 
 
 
 
 
SP_OTB 
SP_LL 
FR_OTB 
FR_GN 
SP_OTB 
SP_LL 
FR_OTB 
FR_GN 
SP_OTB 
SP_LL 

2015 
2015 
2016 
2016 
2016 
2016 
2017 
2017 
2017 
2017 

195 
12 
169 
15 
22 
1 
423 
89 
73 
13 

0.160 
0.025 
0.088 
0.011 
0.010 
0.002 
0.088 
0.011 
0.010 
0.002 

Figure 5.2.4.11.3.1. European hake in GSA 7. Short term forecast by fleet. 

5.2.4.12 Medium term predictions 2015-2017 by fleet 
5.2.4.12.1  Method 
Medium  term  was  not  conducted  because  no  meaningful  stock-recruitment  relationship  was 
estimated. 

5.2.4.13 Stock advice 
The  current  F  (1.64)  is  larger  than  F0.1  (0.11),  chosen  as  proxy  of  FMSY  and  as  the  exploitation 
reference point consistent with high long term yields, which indicates that European hake in GSA 7 
is  being  fished  above  FMSY.  Catches  of  European  hake  in  2016  consistent  with  FMSY  would  not 
exceed 209 tons. 

141 

141 

 
 
 
 
 
5.2.4.14 Management strategy evaluation 
We ran the Management Strategy Evaluation to evaluate if the MSY ranges were precautionary. 
The FMSY ranges were derived using the formula provided by STECF 15-09. F ranges results were 
Fupper=0.16  and  Flower=0.08.  Blim  was  estimated  as  Bloss=769  (t).  The  following  figure  shows  the 
results of the MSE.  

Figure 5.2.4.13.1. European hake in GSA 7. Short term forecast by fleet. 

The probability of SSB to fall below Blim at F = Fupper is equal to 0. 

142 

142 

 
 
 
 
 
 
 
 
5.2.5  STOCK ASSESSMENT OF HAKE IN GSA 8 
5.2.5.1  Stock Identification 
Hake is distributed along the narrow Mediterranean shelves and slope at depths up to 1000m, but 
is  mainly  concentrated  in  the  depth  range  0-400m.  There  is  not  any  evidence  that  inside  GSA  8 
boundaries  inhabits  a  single,  homogeneous  hake  stock  that  behaves  as  a  single  well-mixed  and 
self-perpetuating population. The GSA boundaries are, as those for other areas, arbitrary and do 
not  consider  neither  the  existence  of  local  biological  features  nor  differences  in  the  spatial 
allocation in fishing pressure within it. It is likely some connectivity as larval drifts, movements of 
individuals and sharing of spawning areas in particular with GSA 9.  

5.2.5.2  Growth 
Since  growth  parameters  are  not  estimated  for  this  GSA,  it  was  decided  to  use  parameters 
estimated  for  the  GSA  7,  which  assume  a  fast  growing  performance.  They  were  estimated  from 
tagging experiments carried out by IFREMER (Mellon-Duval et al., 2010). 

Females 
Males 

L∞=100.7 
L∞=72.8 

K=0.236 
K=0.233 

t0=0 
t0=0 

5.2.5.3  Maturity 
The estimates of the proportion mature-at-age were derived from GSA 7. With the assumption of 
fast-growing, hake mature massively at 3 years old. 

age 
Maturity 
at age 

0 
0.06 

1 
0.23 

2 
0.72 

3 
0.92 

4 
0.99 

5+ 
1 

Natural mortality 
A  vector  of  natural  mortality  rates  is  not  available  for  hake  in  GSA  8.  For  natural  mortality,  the 
vector  of  natural  morality  declining  at  age  used  for  GSA7  derived  from  PRODBIOM  (Abella  et 
al.,1997) was used. 

age 
M value 

0 
0.88 

1 
0.43 

2 
0.33 

3 
0.25 

4 
0.22 

5+ 
0.20 

5.2.5.4  Fisheries 
5.2.5.4.1  General description of the fisheries 
The semi-industrial fisheries are not well developed in Corsica (GSA8) area and very few trawlers 
(about 7 ) operate targeting demersal species (Norway lobster, striped red mullet)  including some 
very few catches of hake (around 2% of the catches for the period 2012_2014). 

Even  though  small-scale  fisheries  are  quite  important  along  the  coasts,  fishers  targets  are  other 
resources (lobster, finfish living on hard bottoms) and the choice  regarding targets conditions the 
gears to be utilised and the operation areas.  There are no available data for the size structure of 
the landings of hake, likely as a consequence of their negligible amount of landings, since it is not a 
target species of trawlers and it is absent from other gears catches.  It is possible that the species 
is not included among the main landed commercial species that needs to be reported in the DCF. 
Moreover, it is important to notice that trawlers can only work on the eastern part of Corsica since 
the western part almost doesn’t have a continental shelf. 

143 

143 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
5.2.5.4.2  Management regulations applicable in 2015  
Minimum landing sizes: EC regulation 1967/2006 defined 20 cm TL as minimum legal landed size. 
Cod end mesh size of trawl nets: the 50 mm (stretched, diamond meshes) or alternatively a 40 mm 
codend with square mesh geometry.  
Trawling  is  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. 

5.2.5.4.3  Landings  
Landings  information  is  available  for  hake  only  for  the  more  recent  years  in  the  area.  Total 
amounts are extremely small and only concentrated on OTB. 

5.2.5.4.4  Discards  
No discards information is available for hake in GSA 8. 

5.2.5.4.5  Fishing effort  
Noinformation is available for hake in GSA 8. 

5.2.5.5  Scientific surveys 
5.2.5.5.1    Survey #1 (MEDITS) 
5.2.5.5.1.1  Methods 
French  contribution  to  the  internationally  coordinated  MEDITS  (International  bottom  Trawl 
Surveys in the Mediterranean) survey in Corsica area (GSA9), is constituted by about one week of 
operations occurring in the second quarter every year since 1994 excluding in 2002. A GOV bottom 
trawl with short wings is utilized. On average 20 hauls are carried out; haul duration is half an hour 
above 200 m depth which corresponds to about 0.05 km² and one hour for bottom depths greater 
than 200 m (about 0.1 km²). MEDITS provides a representative picture of the 4 562 km² of Eastern 
Corsican island plateau. See 5.2.5.6.2 for details. 

5.2.5.6  Stock Assessment 
5.2.5.6.1   Methods 
Several problems were found in order to perform a complete and sound stock assessment of hake 
in  GSA8  as  the  species  is  not  a  target  species  in  Corsica.  Only  few  trawlers  are  operating  in  the 
area without targeting hake. Moreover, small scale fisheries are the only well developed fisheries 

144 

144 

landings (tons)Gear20102011201220132014GNS0.0189230.0213660.0415910.0193720.010131GTR0.0245180.0283420.2576260.0320250.271475LLD0.0130.003118LLS0.0179360.00830.098814OTB9.81348312.9854811.575960.8498286.13496OTM0.10.1907960.051299Total9.9748613.2389911.883470.9012256.569797  
 
 
 
 
 
 
 
 
 
 
targeting  other  species  than  hake.  Hence,  hake  is  not  included  among  the  stock  for  which 
collection of sizes is compulsory, because of the extremely limited quantities landed. Neither long 
time series of such landings is available (2010-2014). 
It was suggested in the WG to facilitate survey-based analyses of the fishery and in particular to 
made an attempt to use trawl surveys data and to run SURBA. SURBA is a VPA-based model that 
assumes  fishery  mortality  separable  into  an  age  (s)  and  a  year  effect  (f)  (Needle,  2003;  Beare, 
2005).  The  method  can  be  considered  a  useful  technique  for  investigating  the  dynamics  of  the 
fishery  independently  of  the  commercial  catch  and  CPUE  data.  The  model  estimates  the  mean-
standardised survey abundance indices by age and year, the trend in mean F, the trend in F by age 
group, the trend in relative SSB and the trend in model parameters (F, SSB and Recruitment) 
MEDITS time series data is in principle suitable for performing such analysis. Even though MEDITS 
started in 1994, the time series of abundance indices from MEDITS shows an interruption in year 
2002, because of a technical problem with the boat that made impossible the conduction of the 
cruise  such  year.  This  fact  precludes  the  use  of  the  complete  series.  In  consequence,  data  used 
here regards  only  the  period  2003  to  2014.  The  period  1994-2001  is  shorter  and  the  number of 
individuals  caught  more  limited.  Moreover,  efficiency  of  the  used  gear  was  lower  in  the  first 
surveys and hence the reconstruction of numbers at size per square kilometers  less comparable 
with  those  obtained  in  successive  cruises  and  more  difficult  to  follow  the  cohorts  decline  with 
time.  
The number of hauls performed every year in GSA 8 is relatively modest (generally 20-23 tows). 
Moreover,  the  MEDITS  gear  in  use  is  not  suitable  for  the  catch  of  all  year  classes  in  the  same 
proportion as are actually present in the swept area and such limitation may produce a distorted 
image of the size structure which imply consequently important errors in the Z estimates from one 
age  class  to  the  successive.  A  corrective  procedure  is  hence  necessary  and  the  software  allows 
such  action  by  changing  in  input  the  catchabilities  at  age.  A  vector  of  catchability  at  age  was 
constructed based on the information of selection capability and availability of the different age 
classes observed in neighbouring areas. There were made several trials of alternative changes in 
q(age) trying to avoid  negative F estimates and reducing size and trends in residuals.  

5.2.5.6.2  Input data 
Input data consists in capture by age for each survey. Data was standardized in numbers per Km2. 
Such numbers were derived from the database, which included separate data for males, females 
and  undetermined  sex.  Hake  shows  an  important  sex  dimorphism  in  maximum  size  sexes  may 
reach and hence, the use of a single growth model for transforming sizes in age is not considered 
optimal. Age splitting was performed by sex and successively the two age compositions combined. 
Undetermined individuals were assigned to both sexes based in sex ratios by size.  

Table 5.2.5.7.2.1 and Figure 5.2.5.7.2.2 shows the age structure in each year. 

Table 5.2.5.7.2.1. Hake in GSA 8. Age structure of MEDITS surveys data. 

145 

145 

 
 
 
 
 
Figure 5.2.5.7.2.2. Hake in GSA 8. Numbers by age per km2 of MEDITS surveys data. 

Figure 5.2.5.7.2.3. shows the trend of overall numbers per Km2. 

146 

146 

   Numbers per Km2 at age020406080100120199419951996199719981999200020012002200320042005200620072008200920102011201220132014age 0age 1age 2age 3age 4age 5+1994199519961997199819992000200120022003age 058.035.018.2710.248.9735.5823.2752.512.81age 169.1327.7521.319.635.6921.0346.4128.3611.69age 22.834.171.060.750.681.733.424.564.82age 30.000.890.790.800.241.273.011.242.41age 40.000.520.000.590.080.000.720.530.04age 50.000.000.000.000.000.000.000.000.68age 60.000.000.000.000.000.000.000.000.00age 70.000.000.000.110.000.000.000.000.00age 80.000.000.000.000.000.000.000.000.00age 90.000.000.000.000.000.000.000.000.0020042005200620072008200920102011201220132014age 011.9131.7549.5888.9946.4113.9113.1927.322.568.4212.22age 110.2915.02104.6331.9235.5724.1014.7720.6414.1317.2718.82age 24.741.856.6910.231.917.371.552.111.450.894.64age 31.100.890.862.551.820.201.351.360.960.192.06age 40.460.610.082.261.090.100.000.000.000.000.06age 50.000.000.000.000.000.030.000.580.000.000.88age 60.000.000.000.000.000.000.000.000.000.000.00age 70.000.000.000.000.000.000.000.000.000.000.00age 80.000.030.000.000.000.000.000.000.000.000.00age 90.000.060.000.000.000.000.000.000.000.000.00 
  
 
 
 
 
 
Figure 5.2.5.7.2.3. Overall total numbers per km2 of MEDITS surveys data. 

SURBA needs of information on maturity at age, estimates of natural mortality rates and of mean 
weight  at  age.  Such  information  was  not  available  for  the  area  and  hence,  in  order  to  allow 
computations, vectors estimated for GSA 7 were used here. The vectors of maturity at age, M and 
stock weight at age were as follows: 

147 

147 

 020406080100120140160180199419951996199719981999200020012002200320042005200620072008200920102011201220132014kg(km2  MATURITY AT AGE012345+20030.060.230.720.920.99120040.060.230.720.920.99120050.060.230.720.920.99120060.060.230.720.920.99120070.060.230.720.920.99120080.060.230.720.920.99120090.060.230.720.920.99120100.060.230.720.920.99120110.060.230.720.920.99120120.060.230.720.920.99120130.060.230.720.920.99120140.060.230.720.920.991NATURAL MORTALITY20030.880.430.330.250.220.220040.880.430.330.250.220.220050.880.430.330.250.220.220060.880.430.330.250.220.220070.880.430.330.250.220.220080.880.430.330.250.220.220090.880.430.330.250.220.220100.880.430.330.250.220.220110.880.430.330.250.220.220120.880.430.330.250.220.220130.880.430.330.250.220.220140.880.430.330.250.220.2WEIGHT AT AGE20030.0240.0860.3510.6871.7762.60320040.0240.0860.3510.6871.7762.60320050.0240.0860.3510.6871.7762.60320060.0240.0860.3510.6871.7762.60320070.0240.0860.3510.6871.7762.60320080.0240.0860.3510.6871.7762.60320090.0240.0860.3510.6871.7762.60320100.0240.0860.3510.6871.7762.60320110.0240.0860.3510.6871.7762.60320120.0240.0860.3510.6871.7762.60320130.0240.0860.3510.6871.7762.60320140.0240.0860.3510.6871.7762.603  
 
 
Different hypothesis of catchability at age were tested. Values set as (q(0) = 0.5, 1.0 for age classes 
1  and  2  and  0.8,  0,55,  and  0,4  respectively  for the  successive  age  classes  constituted  the  vector 
producing  the  better  fitting.  For  the  age  classes  not  well  represented  in  the  samples,  a  lower 
weight (influence) was assigned for the computations. 

Fig. 5.2.5.7.3.1. Hake in GSA 8. Hypotheses for catchability and  weight of the different age classes in the 
computations used in the SURBA analysis. 

5.2.5.6.3  Results 
Due  to  the  limited  number  of  hauls  and  overall  number  of  caught  individuals,  the  decline  in 
numbers  did  not  always  follow  the  expected  exponential  decline  nor  linear  after  the  Log 
transformation shape. 

148 

148 

Age0.1.2.3.4.5.6.7.8.911.1Age-weightinghakegsa8meditsQ3: Q and Wt0.1.2.3.4.5.6.7.8.911.1Catchability123456Age weightingCatchability  
 
 
 
 
 
 
Fig. 5.2.5.7.3.1. Hake in GSA 8. Log cohort abundances at age from the SURBA analysis. 

There were analysed the residuals by age. In figure 5.2.5.7.3.2  it is observed that age 0 is rather 
noisy, and that there are large residuals for ages 4 and 5, but that there is no overall pattern in 
residuals. These high residuals persisted even after imposed alternative selection patterns by age. 

Fig. 5.2.5.7.3.2. Hake in GSA 8.  Residuals by age of the SURBA analysis. 

149 

149 

Year-101234562002200420062008201020122014Agehakegsa8meditsQ3: log index residuals-ve+ve Year2002200420062008201020122014-6-5-4-3-2-1012Log index (smoothed)hakegsa8meditsQ3: smoothed log cohort abundance  
 
 
 
 
 
 
Figure 5.2.5.7.3.3. Hake in GSA 8. Fitting of the different cohorts from the SURBA analysis. 

Fig. 5.2.5.7.3.3. Hake in GSA 8. Scatter plots of log indices at consecutive ages from the SURBA analysis. 

Figure 5.2.5.7.3.4 illustrates comparative scatterplots of Log indices between different ages. 

Fig. 5.2.5.7.3.4. Hake in GSA 8. Cohorts comparison from the SURBA analysis. 

150 

150 

 -1.5-1-.50.511.522.50.511.522.5Log index at age  0Log index at age  1-1.5-1-.50.511.522.5-2.5-2-1.5-1-.50Log index at age  0Log index at age  2-1.5-1-.50.511.522.5-4-3.5-3-2.5-2-1.5Log index at age  0Log index at age  3-1.5-1-.50.511.522.5-5.5-5-4.5-4-3.5-3-2.5-2-1.5Log index at age  0Log index at age  40.511.522.5-2.5-2-1.5-1-.50Log index at age  1Log index at age  20.511.522.5-4-3.5-3-2.5-2-1.5Log index at age  1Log index at age  30.511.522.5-5.5-5-4.5-4-3.5-3-2.5-2-1.5Log index at age  1Log index at age  4-2.5-2-1.5-1-.50-4-3.5-3-2.5-2-1.5Log index at age  2Log index at age  3-2.5-2-1.5-1-.50-5.5-5-4.5-4-3.5-3-2.5-2-1.5Log index at age  2Log index at age  4-4-3.5-3-2.5-2-1.5-5.5-5-4.5-4-3.5-3-2.5-2-1.5Log index at age  3Log index at age  4hakegsa8meditsQ3: Comparative scatterplots at age   -4-3-2-101234012345Year class 1998-4-3-2-101234012345Year class 1999-4-3-2-101234012345Year class 2000-4-3-2-101234012345Year class 2001-4-3-2-101234012345Year class 2002-4-3-2-101234012345Year class 2003-4-3-2-101234012345Year class 2004-4-3-2-101234012345Year class 2005-4-3-2-101234012345Year class 2006-4-3-2-101234012345Year class 2007-4-3-2-101234012345Year class 2008-4-3-2-101234012345Year class 2009-4-3-2-101234012345Year class 2010-4-3-2-101234012345Year class 2011-4-3-2-101234012345Year class 2012-4-3-2-101234012345Year class 2013-4-3-2-101234012345Year class 2014hakegsa8meditsQ3: Observed (points) v. Fitted (lines)AgeLog index  
 
 
 
 
 
 
The retrospective analysis results are shown in the next figure.  

Fig. 5.2.5.7.3.5. Hake in GSA 8.  Retrospective analysis from the SURBA analysis. 

Figure 5.2.5.7.3.6 shows the residuals for each age. We can notice clear trends in some ages which 
indicates a poor fitting of the model.   

Fig. 5.2.5.7.3.6. Hake in GSA 8. Residuals by age from the SURBA analysis. 

Despite the bad fitting of the model, results (even though uncertain) suggest for SSB a relative higher initial 
value and a drastic drop in the successive year (2004) followed by a period of fairly stable low levels. F in 
the study period (2003-2014) varied between a minimum of about 0.4 in 2005 to a maximum of 1.2 in years 
2007-2008. There are observed two extremely low values for years 2010 and 2014 (Fig . 5.2.5.7.3.7). 

151 

151 

   -1.5-1-.50.511.520052010Age 0-1.5-1-.50.511.520052010Age 1-1.5-1-.50.511.520052010Age 2-1.5-1-.50.511.520052010Age 3-1.5-1-.50.511.520052010Age 4-1.5-1-.50.511.520052010Age 5hakegsa8meditsQ3: ResidualsYearLog index residual    Year200320062009201220150.2.4.6.811.21.41.61.82Temporal trendAge0.2.4.6.811.21.4Age effect012345Year class19972001200520092013-2-1012345Cohort effectYear200320062009201220150.2.4.6.811.2Mean F (1-3)Year200320062009201220150.2.4.6.811.21.4SSB at survey timeYear20032006200920122015050100150200250Recruitment at age 0hakegsa8meditsQ3  
 
 
 
 
Fig. 5.2.5.7.3.7. Hake in GSA 8.  Main outputs of the SURBA analysis. 

5.2.5.7  Reference points 
5.2.5.7.1  Methods 
No reference points were defined because the model was not accepted. In any case, estimation of 
such RPs was unfeasible as F vectors for defining the exploitation pattern are not available for the 
stock.    

5.2.5.8  Data quality 
DCF  data  quality  is  deficient  for  this  particular  species.  Catch  data,  proceeding  from  the  limited 
number of trawlers cover only the period 2010-2014. Landings are too low in all the years where 
data are available. Age structure of the catch is not available due to the scarce commercial interest 
of  this  species  in  the  area.  Surveys  data  were  used  for  performing  a  rough  assessment  of  the 
exploitation  status  of  the  stock  and  evolution  of  some  parameters,  but  results  cannot  be 
considered  reliable  due  to  the  bad  fitting  of  data  to  the  model.  Such  uncertain  results  can  be 
mainly  due  to  the  limited  number  of  individuals  caught  in  the  scarce  number  of  tows  (23)  that 
each year are carried out in this area. Moreover, the presence of a gap in the time series, due to a 
technical  problem  that  made  impossible  the  utilization  of  the  research  vessel  to  carry  out  the 
cruise in 2002, likely had a negative effect in the quality of the analysis.  

5.2.5.9  Short term predictions 2016-2018 
Not conducted due to the fact that the assessment was not accepted. 

5.2.5.10 Medium term predictions 
Not conducted due to the fact that the assessment was not accepted. 

152 

152 

   
 
 
 
 
 
 
 
 
 
5.2.5.11 Stock advice 
No advice is given  for this stock because the assessment was not accepted.  

153 

153 

 
 
 
5.2.6  STOCK ASSESSMENT OF HAKE IN GSA 9 
5.2.6.1  Stock Identification 
Due to a lack of information about the structure of hake population in the western Mediterranean, 
this stock was assumed to be confined within the GSA 9 boundaries (Figure 5.2.6.1.1). 

Figure 5.2.6.1.1. Geographical location of GSA 9. 

Hake is distributed in the whole area between 10 and 800 m depth (Biagi et al., 2002; Colloca et 
al., 2003). Recruits peak in abundance between 150 and 250 m depth over the continental shelf-
break  and  appear  to  move  slightly  deeper  when  they  reach  10  cm  total  length.  Crinoid 
(Leptometra phalangium) bottoms over the shelf-break are the main settlement habitat for hake 
in  the  area  (Colloca  et  al.,  2004,  2006;  Reale  et  al.,  2005).  Migration  from  nurseries  takes  place 
when  juveniles  attained  a  critical  size  between  13  and  15.5  cm  TL  (Bartolino  et  al.,  2008a). 
Maturing hakes (15-35 cm TL) persist on the continental shelf with a preference for water of 70-
100 m depth, while larger hakes can be found in a larger depth range from the shelf to the upper 
slope. Juveniles show a patchy distribution with some main density hot spots (i.e. nurseries areas) 
showing  a  high  spatio-temporal  persistence  (Abella  et  al.,  2005;  Colloca  et  al.,  2006;  2009;  Jona 
Lasinio et al., 2007) as also highlighted by the MEDISEH project in areas with frontal systems and 
other  oceanographic  structures  that  can  enhance  larval  transport  and  retention  (Abella  et  al., 
2008). 
Although  hakes  are  demersal  fish  feeding  typically  upon  fast-moving  pelagic  preys  while 
ambushed  in  the  water  column  (Alheit  and  Pitcher,  1995),  there  is  evidence  that  hakes  feed  in 
mid-water or at the surface during night-time, undertaking daily vertical migrations (Orsi-Relini et 
al., 1989, Carpentieri et al., 2008) which are more intense for juveniles. In GSA 9 many different 
studies are available on hake diet. Results from stomach data collected in the 1996-2001 period 
can be found in Sartor et al. (2003) and Carpentieri et al. (2005). Hake diet shifts from euphausids 
and mysiids consumed by smaller hake (<16 cm TL), to fishes consumed by larger hake. 
Before  the  transition  to  the  complete  ichthyophagous  phase  (TL>  36  cm)  hake  shows  more 
generalized  feeding  habits  where  decapods,  benthic  (Gobiidae,  Callionymus  spp.,)  and  necktonic 

154 

154 

 
 
 
 
 
fish  (S.  pilchardus,  E.  encrasicolus)  dominated  the  diet,  whereas  cephalopods  had  a  lower 
incidence. 
Estimation  of  cannibalism  rate  has  been  provided  for  the  southern  part  of  the  GSA  (Latium,  EU 
Because  project).  Cannibalism  increased  with  size  and  can  be  considered  significant  for  hakes 
between  30  and  40  cm  TL  (up  to  20%  by  weight  in  diet)  and  seems  to  relate  closely  to  hake 
recruitment density and level of spatial overlapping. 
Consumption rate has been estimated for juveniles and piscivorous hakes. Daily consumption of 
juveniles, calculated in proportion of body weight (%BW), varied between 5 (July) and 5.9 % BW 
(Carpentieri et al., 2008). The estimated relative daily consumption for hake between 14 and 40 
cm TL, using a bioenergetic approach (EU Because project), was between 2.9 and 2.3 BW%. 

5.2.6.2  Growth 
Juvenile growth rate was estimated to be about 1.5 cm per month using daily growth increments 
on otoliths (Belcari et al., 2006). According to this growth rate, hake reaches an average length of 
about 18 cm TL at the end of the first year. According to these observations, the growth of hake in 
the GSA 9 seems to follow the pattern estimated in the NW Mediterranean (Garcia-Rodriguez and 
Esteban,  2002)  adopting  the  hypothesis  that  two  rings  are  laid  down  on  otoliths  each  year. This 
new interpretation of otolith ring patterns returns a growth rate (Linf = 103.9, k = 0.212, t0 =0.031) 
almost double than that assumed in the past. 

5.2.6.3  Maturity 
The  catchability  of  hake  spawners  to  the  Mediterranean  trawl  nets  is  rather  limited.  The 
distribution of adults which are more abundant on deeper or untrawlable grounds, or the ability of 
larger fish to avoid capture have been claimed as causes of the observed extremely reduced catch 
of  adult  hake  by  trawlers  in  the  Mediterranean  (Abella  et  al.,  1997).  Also  during  trawl  surveys 
(MEDITS and GRUND) the catch rate of mature specimens was very low, reducing the possibility of 
use  trawl  survey  data  to  explore  patterns  in  gonad  development  as  well  as  the  relationships 
between growth rate and maturation processes.  
Large size hake  are targets of a specifically targeted gillnet fishery carried out by several vessels 
working  in  the  southern  part  (northern  and  central  Tyrrhenian  Sea)  of  the  GSA  9  (Sartor  et  al., 
2001a).  
Reproductive biology and fecundity of hake have been studied in northern Tyrrhenian Sea (Biagi et 
al.,  1995;  Nannini  et  al.,  2001;  Recasens  et  al.,  2008)  by  monthly  samplings  of  adults  caught  by 
trawling and gillnets.  
Females  in  advanced  maturity  stages,  spawning  and  partial  post-spawning  are  present  all  year 
round, but reproductive activity is concentrated from January to May, with two peaks of spawning 
in  February  and  May.  The  presence  of  hake  spawners  seems  to  be  more  concentrated  in  the 
southern part of GSA 9. 
Female length at first maturity was estimated at 35 cm TL in northern Tyrrhenian Sea (Recasens et 
al.,  2008).  This  value  is  consistent  with  the  observations  obtained  from  trawl  surveys  over  the 
Latium (Colloca, pers. comm.) reporting first maturity from 31 to 37 cm TL for females and from 21 
to 25 cm TL for males. 
Batch  fecundity  was  about  200  eggs  per  gonad-free  female  gram,  with  asynchronous  oocyte 
development (Recasens et al., 2008). 

155 

155 

 
 
 
 
5.2.6.4  Natural mortality 
Natural  mortality  was  estimated  using  PRODBIOM  (Abella  et  al.,  1998)  and  is  shown  in  Table 
5.2.6.4.1. The input parameters used were Linf = 103.9, k = 0.212, t0 =0.031, a = 0.006657 and b = 
3.028. 

Table 5.2.6.4.1. Hake in GSA 9. Natural mortality. 

Age 
0 
1 
2 
3 
4 
5 
6+ 

M 
1.2 
0.62 
0.44 
0.37 
0.33 
0.31 
0.29 

5.2.6.5  Fisheries 
5.2.6.5.1  General description of the fisheries 
Hake  is  one  of  the  main  target  species  of  bottom  trawlers  in  the  GSA  9  in  terms  of  landings, 
incomes  and  vessels  involved.  The  analysis  of  available  information  suggests  that  about  50%  of 
landings of hake are obtained by bottom trawl vessels, the remaining fraction being provided by 
artisanal vessels using set nets, in particular gillnets.  
The  trawl  fleet  of  GSA  9  accounted  for  197  vessels  in  2014  based  in  several  ports:  Viareggio, 
Livorno,  Porto  Santo  Stefano,  Civitavecchia,  Fiumicino,  Anzio,  Terracina,  Gaeta,  Formia.  They 
accomplish  daily  fishing  trips  exploiting  both  continental  shelf  and  slope  areas.  Hake  fishing 
grounds  comprise  all  the  soft  bottoms  of  continental  shelves  and  the  upper  part  of  continental 
slope. Fishing pressure shows a spatial pattern inside the GSA 9 according to the consistency of the 
fleets and the distance of the fishing grounds from the main ports. 
The  artisanal  fleets,  according  to  the  last  official  data  (2014),  accounted  for  1006  vessels  that 
operate in several harbours along the continental and insular coasts.  

5.2.6.5.2  Management regulations applicable in 2015  

  Fishing closure for trawling: 45 days in late summer (not every year have been enforced). 
  Minimum landing sizes: EC regulation 1967/2006: 20 cm TL for hake. 
  Cod  end  mesh  size  of  trawl  nets:  40  mm  square  meshes  or  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.  

  Two small No Take Zones (“Zone di Tutela Biologica”, ZTB) are present inside the GSA 9; 
one off the Giglio Island (50 km2, northern Tyrrhenian Sea) another off Gaeta, (125 km2, 
central  Tyrrhenian  Sea).  Bottom  fishing  was  not  allowed  in  the  two  ZTB.  A  recent 
regulation of the Italian Ministry of Agricultural, Food and Forestry Policies has established 
that fishing activity can be carried out in these two areas from July 1st to December 31st. 

156 

156 

 
 
 
 
 
 
 
5.2.6.5.3  Landings  
Landings data were reported to STECF EWG 15-11 through the DCF. In GSA 09 the bulk of catches 
(64% in weight) are from otter trawl, while artisanal fisheries represents the rest of the catches. 
The largest individuals are caught by gillnets. 

Table 5.2.6.5.4.1. Hake in GSA 9. Annual landings (t) by gear in GSA 9 from the DCF data. 

OTB 
508.16 
1147.56 

GNS 
154.32 
658.51 

GTR 
236.15 
258.39 

PS 
7.21 
15.40 

1179.96 
1024.96 
914.77 
853.24 
834.14 
795.36 
653.57 
1044.30 
1010.37 

592.57 
576.22 
345.23 
401.26 
576.26 
502.08 
309.33 
199.21 
177.73 

403.96 
131.85 
61.12 
53.98 
56.71 
54.30 
48.62 
98.12 
76.85 

2002 
2003 
2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

Before  2006  the  data  are  incomplete  and  will  not  be  used  for  the  stock  assessment.  The  time 
series  of  landings  data  (tons)  by  gear  for  the  period  2006-2014  is  shown  in  Figure  5.2.6.5.4.1. 
Maximum landings values are observed in 2006 and minimum values in 2012. 

HKE, GSA 9, Landings 

)
t
(

i

s
g
n
d
n
a
L

1400,00

1200,00

1000,00

800,00

600,00

400,00

200,00

0,00

OTB

GNS

GTR

Figure 5.2.6.5.4.1. Hake in GSA 9. Total annual landings by gear for the period 2006-2014. 

2006 2007 2008 2009 2010 2011 2012 2013 2014

Figure 5.2.6.5.4.2 shows the size structure of landings from 2006 to 2014. The landing composition 
of fisheries exploiting hake is showed in Figure 5.2.6.5.4.3. 

157 

157 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 5.2.6.5.4.2. Hake in GSA 9. Size structure of the landings from 2006 to 2014. 

158 

158 

 
 
 
159 

159 

 
Figure 5.2.6.5.4.3. Hake in GSA 9. Size composition of the landings by year and fishery. 

DCF data on age structure of European hake landings in GSA 9 were available for the period 2006-
2014, and are shown in Figure 5.2.6.5.4.4.  

Figure 5.2.6.5.4.4. Hake in GSA 9. Age frequency distribution of the landings from 2006 to 2014 as obtained 
from the DCF. 

160 

160 

 
 
 
 
 
 
5.2.6.5.4  Discards (by fleet if posible) 
Discards data were reported to STECF EWG 15-11 through the DCF. Information on OTB discards 
was available for 2006 and from 2009 to 2014. 
Several  EU  and  national  projects  carried  out  in  GSA  9  highlighted  the  problem  of  hake  trawl 
discards. High quantities of hake are routinely discarded, especially in summer and on the fishing 
grounds located near the main nursery areas (Table 5.2.6.5.5.1). 
The size at which 50% of the specimens caught is discarded is progressively increased  in the last 
years from about 11 cm TL in 1995 (Sartor  et al., 2001b) to about 17 cm TL in 2006 (De Ranieri, 
2007), due to the introduction of the EU Regulations on minimum sizes. This phenomenon might 
be also explained by a reduction of the fishing pressure on the nursery areas.  

Table 5.2.6.5.5.1. Hake in GSA 9. Annual OTB landings and discards in tons. 

OTB 
1179.96 
1024.96 
914.77 
853.24 
834.14 
795.36 
653.57 
1044.30 
1010.37 

2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

OTB Discards 
105.20 

697.27 
116.41 
527.79 
174.23 
242.43 
285.84 

Data  on  the  length  frequency  of  discards  is  available  for  the  same  years  and  is  shown  in  Figure 
5.2.6.5.5.1. 

161 

161 

 
 
 
 
 
 
 
 
Figure 5.2.6.5.5.1. Hake in GSA 9. Size composition of the OTB discards by year. 

5.2.6.5.5  Fishing effort  
The fishing capacity of the GSA 9 has shown in these last 20 years a progressive decrease. Fishing 
effort  (kW*fishing  days)  performed  by  the  GSA  09  trawlers  decreased  of  26%  since  2004,  from 
about 15,000,000 to 11,000,000 in 2014. The effort displayed by the artisanal fleet exploiting hake 
remained  constant  for  vessels  using  trammel  nets  (GTR)  whereas  the  effort  of  gillnetters 
decreased abruptly (-61%) from 2011 (Figure 5.2.6.5.6.1). 

s
y
a
D
*
W
K

16000000

14000000

12000000

10000000

8000000

6000000

4000000

2000000

0

OTB

GNS

GTR

Year 

Figure 5.2.6.5.6.1. Effort trends (days and kW*days) by major fleets, 2004-2014. 

162 

162 

 
 
 
 
 
 
 
 
5.2.6.6  Scientific surveys 
5.2.6.6.1    Survey #1 (MEDITS) 
5.2.6.6.1.1  Methods 
Based  on  the  DCF  data  call,  abundance  and  biomass  indices  were  recalculated.  In  GSA  9  the 
following numbers of hauls were reported per depth stratum (Table 5.2.6.6.1.1.1). 

Table 5.2.6.6.1.1.1. Numbers of hauls per year and depth stratum in GSA 9, 1994-2014. 

Row Labels 
GSA09_010-050 
GSA09_050-100 
GSA09_100-200 
GSA09_200-500 
GSA09_500-800 

1994 
21 
21 
38 
40 
33 

Row Labels 
GSA09_010-050 
GSA09_050-100 
GSA09_100-200 
GSA09_200-500 
GSA09_500-800 

2005 
16 
16 
31 
34 
23 

1995  1996  1997  1998  1999  2000  2001  2002  2003  2004 
15 
20 
17 
21 
30 
39 
34 
40 
24 
33 

20 
21 
39 
41 
32 

21 
20 
39 
40 
33 

20 
22 
38 
42 
31 

15 
17 
30 
31 
27 

20 
20 
40 
40 
33 

20 
22 
38 
42 
31 

15 
17 
30 
33 
25 

20 
22 
38 
41 
32 

2006  2007  2008  2009  2010  2011  2012  2013  2014 
15 
15 
15 
19 
19 
18 
29 
29 
29 
36 
34 
35 
21 
23 
23 

16 
17 
30 
35 
22 

15 
18 
30 
33 
24 

15 
17 
31 
35 
22 

15 
18 
29 
35 
23 

16 
16 
31 
34 
23 

16 
16 
31 
34 
23 

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 hauling duration. 
Hauls noted as valid were 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: 

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. 

163 

163 

 
 
 
 
 
 
 
 
 
 
It  was  noted  that  while  this  is  a  standard  approach,  the  calculation  may  be  biased  due  to  the 
assumptions  over  zero  catch  stations,  and  hence  assumptions  over  the  distribution  of  data.  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 
(subsamples raised to standardized haul abundance per hour) over the stations of each stratum. 
Aggregated  length  frequencies  were  then  raised  to  stratum  abundance  *  100  (because  of  low 
numbers in most strata) 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. 

5.2.6.6.1.2   Geographical distribution 
According to recent studies (Orsi Relini et al., 2002), the density of hake recruits concentrations in 
nursery areas in GSA 9 is by far higher than that of the other GSAs of the western Mediterranean 
and, probably, also of the other Mediterranean GSAs (Figure 5.2.6.6.1.2.1). 

Figure  5.2.6.6.1.2.1.  Hake  in  GSA  9.  MEDITS  density  indices  of  the  hake  recruits  (<12  cm  TL)  obtained  in 
different Mediterranean GSAs (from Orsi-Relini et al., 2002, modified). 

Generalized  additive  models  were  developed  to  investigate  hake  recruitment  dynamics  in  the 
Tyrrhenian  Sea  in  relation  to  spawner  abundance  and  selected  key  oceanographic  variables. 
Thermal  anomalies  in  summer,  characterized  by  high  peaks  in  water  temperature,  revealed  a 
negative effect on the abundance of recruits in autumn, probably due to a reduction in hake egg 
and larval survival rate. Recruitment was reduced when elevated sea-surface temperatures were 
coupled with lower levels of water circulation. Enhanced spring primary production, related to late 
winter  low  temperatures  could  affect  water  mass  productivity  in  the  following  months,  thus 
influencing  spring  recruitment.  In  the  central  Tyrrhenian  a  dome-shaped  relationship  between 
wind  mixing  in  early  spring  and  recruitment  could  be  interpreted  as  an  “optimal  environmental 
window”  in  which  intermediate  water  mixing  level  played  a  positive  role  in  phytoplankton 
displacement,  larval  feeding  rate  and  appropriate  larval  drift  (Bartolino  et  al.,  2008b)  (Figure 
5.2.6.6.1.2.2). 

164 

164 

020004000600080001000012000AlboranAlicanteCatalanGulf of LionsLigurian-TyrrhenianSardiniaS. TyrrhenianDensity index n/Km2 
 
 
 
 
 
 
Figure 5.2.6.6.1.2.2. Hake in GSA 9. Effects of: (a) sstm.w, (b) sstmax8 and (c) wmix4 on hake recruitment in 
the central Tyrrhenian (from Bartolino et al., 2008b). 

The  temporal  trend  in  spatial  distribution  of  hake  >  26  cm  TL  showed  a  clear  reduction  of 
distribution area, particularly in the Tyrrhenian part of the GSA (GRUND data, Figure 5.2.6.6.1.2.3). 

Figure  5.2.6.6.1.2.3.  Hake  in  GSA 9.  Distribution  of  individuals  larger  than  26  cm  TL  in  1985-87,  1996-98, 
2000-01, 2002-03. 

5.2.6.6.1.3   Trends in abundance and biomass 
Figure  5.2.6.6.1.3.2  displays  the  re-estimated  trend  in  hake  abundance  and  biomass  in  GSA  9 
(kg/km2) based on the MEDITS DCR data call. Both biomass and density showed large fluctuations 
without temporal trends. 

165 

165 

 
 
 
 
 
 
 
 
Biomass index (kg/km2)  

Density index (n/km2) 

80

60

40

20

0

10000

8000

6000

4000

2000

0

4
9
9
1

6
9
9
1

8
9
9
1

0
0
0
2

2
0
0
2

4
0
0
2

6
0
0
2

8
0
0
2

0
1
0
2

2
1
0
2

4
1
0
2

4
9
9
1

6
9
9
1

8
9
9
1

0
0
0
2

2
0
0
2

4
0
0
2

6
0
0
2

8
0
0
2

0
1
0
2

2
1
0
2

4
1
0
2

Figure  5.2.6.6.1.3.2.  Hake  in  GSA 9.  MEDITS  time  series  of  survey  biomass  and  density  indices  (mean  +/- 
standard deviation). 

5.2.6.6.1.4  Trends in abundance by length or age 
The  following  Figure  5.2.6.6.1.4.1  displays  the  stratified  abundance  indices  of  European  hake  in 
GSA 9 from 1994 to 2014. 

Figure 5.2.6.6.1.4.1. Hake in GSA 9. Stratified abundance indices by size, 1994-2014. 

166 

166 

 
  
 
 
 
 
 
 
 
5.2.6.7  Stock Assessment 
5.2.6.7.1   Method: XSA 
FLR libraries were employed in order to carry out an XSA based assessment (Darby and Flatman, 
1994). This stock was assessed for the last time during in EWG 14-09: XSA was performed using as 
input data the period 2005-2013. XSA has been carried out this time using as input data the period 
2006-2014 for the catch data and 2006-2014 for the tuning file because the data in the database 
have been changed. 

5.2.6.7.2  Input data 
The  growth  parameters  used  for  VBGF  were  Linf=  103.9  cm  TL;  K  =  0.212  yr-1;  t0=  0.031  yr.  The 
length-to-weight coefficients used were a= 0.006657, b= 3.028. 
Catch numbers have been raised taking into account the LFD that were missing for some years and 
gears. For GNS and GTR in 2007 the LFD of GNS 2006 was used to raise the landings, for GTR of the 
other  missing  years  the  LFD  of  GNS  of  the  same  years  were  used.  Discards  for  OTB  in  2007  and 
2008 were estimated as the mean discard % of the entire time-series (35.59%, 2007=364 tons and 
2008=325 tons). The LFD of OTB discards of 2009 were used to raise the discards. 
LFDA 5.0 slicing software has been used to transform the annual size distribution of the landings 
and MEDITS LFDs in age distributions in order to apply XSA model. 
Zero values in the catch at age have been substituted with the lowest value in the time series. 
Table  5.2.6.7.2.1  lists  the  input  parameters  to  the  XSA,  namely  landings,  catch  number  at  age, 
weight  at  age,  maturity  at  age,  natural  mortality  at  age  and  the  tuning  series  at  age  (MEDITS). 
Natural mortality values (vector) were computed with the PROBIOM routine.  

Table 5.2.6.7.2.1. Hake in GSA 9. Input data to the XSA model. 

Catch (t) 

2006 
2281.69 

2007 
2097.81 

2008 
1646.69 

2009 
2005.74 

2010 
1583.52 

2011 
1879.53 

2012 
1185.75 

2013 
1584.06 

2014 
1550.79 

Catch number at age matrix (thousands) 

2006 

2007 

2008 

2009 

2010 

2011 

23197.434  32439.190  35592.882  60804.151  11959.687  41216.305 
5913.918 
5961.151 
529.379 
1351.802 
96.099 
170.594 
52.529 
59.429 
13.009 
1.704 
2.465 
1.101 

6327.486 
403.203 
105.076 
39.848 
9.220 
1.935 

7990.058 
691.831 
73.124 
10.484 
1.704 
1.101 

5038.450 
514.133 
132.456 
53.847 
25.831 
5.523 

5751.982 
383.198 
92.520 
15.488 
11.567 
3.590 

Age 
0 
1 
2 
3 
4 
5 
6+ 

Age 
0 
1 
2 
3 
4 

2014 

2012 

2013 
12689.716  13083.162  30613.053 
5584.203 
7206.196 
4275.239 
439.276 
326.750 
319.563 
77.032 
40.263 
82.431 
11.562 
18.262 
34.292 

167 

167 

 
 
 
 
 
 
 
 
5 
6+ 

7.632 
0.888 

3.101 
0.533 

2.793 
0.670 

Weight at age (kg) 

Age 
0 
1 
2 
3 
4 
5 
6+ 

2006 
0.008 
0.166 
0.578 
1.200 
1.949 
2.745 
3.529 

2007 
0.008 
0.166 
0.578 
1.200 
1.949 
2.745 
3.529 

2008 
0.008 
0.166 
0.578 
1.200 
1.949 
2.745 
3.529 

2009 
0.008 
0.166 
0.578 
1.200 
1.949 
2.745 
3.529 

2010 
0.008 
0.166 
0.578 
1.200 
1.949 
2.745 
3.529 

2011 
0.008 
0.166 
0.578 
1.200 
1.949 
2.745 
3.529 

Age 
0 
1 
2 
3 
4 
5 
6+ 

2012 
0.008 
0.166 
0.578 
1.200 
1.949 
2.745 
3.529 

2013 
0.008 
0.166 
0.578 
1.200 
1.949 
2.745 
3.529 

2014 
0.008 
0.166 
0.578 
1.200 
1.949 
2.745 
3.529 

Maturity and natural mortality vectors. 

Age 
Maturity 
M 

0 
0 
1.2 

1 
0.25 
0.62 

2 
0.9 
0.44 

3 
1 
0.37 

4 
1 
0.33 

5 
1 
0.31 

6+ 
1 
0.29 

Age 
0 
1 
2 
3+ 

2008 

2006 

2007 
1686.571  2514.259  5871.627 
57.216 
38.88 
58.583 
1.241 
2.24 
2.502 
0.766 
1.635 
0.442 

MEDITS number at age. 

2009 
6573.9 
52.838 
1.085 
0.533 

2010 

2011 

2469.127  769.899 
29.391 
37.298 
1.29 
2.573 
0.429 
0.178 

2014 

2012 

2013 
1464.35  1743.236  1564.17 
27.137 
35.288 
21.931 
1.901 
1.001 
0.991 
0.512 
0.429 
0.796 

5.2.6.7.3  Results 
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  2  to  4  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. 

168 

168 

 
 
 
 
 
 
 
 
 
 
 
Figure 5.2.6.7.3.1. Hake in GSA 9. Sensitivity on shrinkage weight. 

169 

169 

 
 
 
Figure 5.2.6.7.3.2. Hake in GSA 9. Sensitivity on shrinkage age. 

170 

170 

 
 
 
Figure 5.2.6.7.3.3. Hake in GSA 9. 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 
3 

rage 
1 

qage 
2 

shk.yrs 
3 

shk.age 
2 

The residuals pattern of the MEDITS trawl survey is shown in Figure 5.2.6.7.3.4. 

171 

171 

 
 
 
 
 
 
 
Figure 5.2.6.7.3.4. Hake in GSA 9. XSA residuals for the MEDITS survey from 2006 to 2014. 

The results of the retrospective analysis are shown in Figure 5.2.6.7.3.5. 

172 

172 

 
 
 
 
 
 
 
Figure 5.2.6.7.3.5. Hake in GSA 9. XSA retrospective analysis. 

The results of the XSA are shown in the following figure. 

173 

173 

 
 
 
 
Figure 5.2.6.7.3.6. Hake in GSA 9. XSA summary results. SSB and catch are in tonnes, recruitment in 1000s 
individuals. 

In the tables 5.2.6.7.3.1 and 2 the population estimates of hake obtained by XSA are provided. 

Table 5.2.6.7.3.1. Hake in GSA 9. Stock numbers at age (thousands) as estimated by XSA. 

Age 
0 
1 
2 
3 
4 
5 
6+ 

2006 

2007 

2008 

2009 

2010 

2011 

84450.000  90885.000  100830.000  141620.000  53382.000  99813.000 
9514.900 
11107.000  12705.000 
1299.200 
1602.800 
2902.000 
352.580 
784.160 
360.770 
80.091 
107.410 
94.190 
50.397 
17.326 
2.944 
9.425 
11.118 
1.842 

10836.000 
929.920 
319.860 
252.630 
332.580 
69.464 

9571.000 
974.140 
477.070 
480.870 
68.333 
21.003 

9284.600 
1188.100 
275.320 
133.610 
147.840 
31.298 

Age 
0 
1 
2 
3 
4 
5 

2012 

2014 

2013 
63723.000  55923.000  88907.000 
7443.100  12229.000  9663.600 
1293.000 
868.340 
780.930 
297.020 
246.490 
411.880 
136.800 
215.990 
163.670 
139.800 
88.592 
13.040 

174 

174 

 
 
 
 
 
 
6+ 

1.468 

15.161 

33.381 

Table 5.2.6.7.3.2. Hake in GSA 9. XSA summary results. 

Fbar0-2 

0.96 
1.26 
1.14 
1.30 
0.88 
1.33 
0.90 
0.94 
1.03 

Recruitment 
(thousands) 
84450 
90885 
100830 
141618 
53382 
99813 
63723 
55923 
88907 

SSB (t) 

TB (t) 

2599.8 
2596.9 
2674.6 
2966.8 
2109.4 
1820.4 
1568.7 
1971.6 
2197.2 

4833.0 
5005.9 
4738.4 
5516.1 
3765.1 
3887.8 
3055.9 
3995.3 
4194.2 

 2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

0 
0.69 
1.05 
1.03 
1.52 
0.52 
1.40 
0.45 
0.56 
0.99 

1 
1.32 
1.95 
1.71 
1.59 
1.35 
1.88 
1.53 
1.63 
1.55 

2 
0.87 
0.77 
0.67 
0.78 
0.77 
0.71 
0.71 
0.63 
0.55 

F at age 

3 
0.84 
0.12 
0.27 
0.50 
0.86 
0.40 
0.28 
0.22 
0.37 

4 
1.36 
0.12 
0.04 
0.21 
0.64 
1.49 
0.28 
0.11 
0.11 

5 
1.13 
0.12 
0.22 
0.03 
0.23 
0.36 
1.15 
0.04 
0.02 

6+ 
1.13 
0.12 
0.22 
0.03 
0.23 
0.36 
1.15 
0.04 
0.02 

The XSA results summarized in Table 5.2.6.7.3.2 and in Figure 5.2.6.7.3.6 show a decreasing trend 
in the catches, a fluctuation on recruitment, SSB and an estimated Fcurr of 1.03. 

5.2.6.8  Reference points 
5.2.6.8.1  Methods 
The  XSA  package  used  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 by 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. 

5.2.6.8.2  Input data  
 The input parameters were the same used for the XSA stock assessment and its results. 

5.2.6.8.3  Results 
Table 5.2.6.8.3.1. Hake in GSA 9. Main reference points defined with the Yield per recruit analysis. 

refpt 

harvest 

yield 

rec 

ssb 

biomass 

175 

175 

 
 
 
 
 
 
 
 
 
 
 
 
 
virgin 
msy 
crash 
f0.1 
fmax 
spr.30 

0.00 
0.32 
11.77 
0.23 
0.32 
0.31 

0.00 
0.04 
0.01 
0.04 
0.04 
0.04 

1.00 
1.00 
1.00 
1.00 
1.00 
1.00 

1.04 
0.30 
0.00 
0.42 
0.30 
0.31 

1.09 
0.34 
0.01 
0.46 
0.34 
0.35 

Figure 5.2.6.8.3.1. Hake in GSA 9. Yield per recruit curve. 

5.2.6.9  Data quality 
Data  from  DCF  2014  as  submitted  through  the  Official  data  call  in  2015  were  used.  Length 
frequencies distributions that were missing are presented in the following table. 

Table 5.2.6.9.1. Hake in GSA 9. Missing LFD in the landings. 

Year 
2008 
2010 
2011 
2012 
2013 

Gear 
OTB 
OTB 
OTB 
OTB 
OTB 

Fishery 
DWSP 
DWSP 
DWSP 
DWSP 
DWSP 

Area 
GSA 9 
GSA 9 
GSA 9 
GSA 9 
GSA 9 

Species 
HKE 
HKE 
HKE 
HKE 
HKE 

Landings 
4.11279 
3.21425 
3.97375 
3.00489 
5.35848 

176 

176 

 
 
 
 
 
 
 
 
2014 
2007 
2006 
2007 
2009 
2011 
2012 
2013 
2014 

OTB 
GNS 
GTR 
GTR 
GTR 
GTR 
GTR 
GTR 
GTR 

DWSP 
DEMF 
DEMSP 
DEMSP 
DEMSP 
DEMSP 
DEMSP 
DEMSP 
DEMSP 

GSA 9 
GSA 9 
GSA 9 
GSA 9 
GSA 9 
GSA 9 
GSA 9 
GSA 9 
GSA 9 

HKE 
HKE 
HKE 
HKE 
HKE 
HKE 
HKE 
HKE 
HKE 

3.10412 
576.223 
403.9606 
131.8538 
53.97693 
54.30141 
48.61781 
98.11776 
76.84722 

Discard data were missing for 2007 and 2008. 

5.2.6.10 Short term predictions 2015-2017 
5.2.6.10.1  Method 
A  deterministic  short  term  prediction  for  the  period  2015  to  2017  was  performed using  the  FLR 
routines provided by JRC and based on the results of the XSA stock assessments performed during 
EWG 15-11. 

5.2.6.10.2  Input parameters  
The  input  parameters  were  the  same  used  for  the  XSA  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 (68172 thousand individuals). 

5.2.6.10.3  Results 

Table 5.2.6.10.3.1. Hake in GSA 9. Short term forecast in different F scenarios. Basis: F(2015) = mean (Fbar 0-
2 2012-2014)= 0.95; R(2015) = geometric mean of the recruitment of the last 3 years; R = 68172 thousands; 
SSB(2014) = 2197 t, Catch (2014)= 1553 t. 

Zero catch 

High long 
term yield 
(F0.1) 
Status quo 

Different 
Scenarios 

Rationale 

Ffactor 

Fbar 

Catch 
2015 

Catch 
2016 

Catch 
2017 

SSB 
2016 

SSB 
2017 

Change 
SSB 2016-
2017(%) 

Change 
Catch 
2014-
2016(%) 
-100.00 

0 

0.00 

1821 

0 

0 

2567 

6175 

140.52 

0.24 

0.23 

1821 

635 

1136 

2567 

4891 

90.52 

-59.10 

1 

0.1 

0.2 

0.3 

0.4 

0.5 

0.6 

0.7 

0.8 

0.95 

0.10 

0.19 

0.29 

0.38 

0.48 

0.57 

0.67 

0.76 

1821 

1821 

1821 

1821 

1821 

1821 

1821 

1821 

1821 

1867 

1911 

282 

536 

763 

969 

1156 

1325 

1479 

1620 

566 

991 

1305 

1532 

1692 

1800 

1867 

1903 

2567 

2567 

2567 

2567 

2567 

2567 

2567 

2567 

2567 

2649 

5596 

5088 

4640 

4245 

3896 

3586 

3310 

3065 

3.16 

117.98 

98.18 

80.74 

65.35 

51.74 

39.67 

28.94 

19.37 

20.28 

-81.82 

-65.51 

-50.82 

-37.56 

-25.56 

-14.66 

-4.73 

4.34 

177 

177 

 
 
 
 
 
 
 
 
 
0.9 

1.1 

1.2 

1.3 

1.4 

1.5 

1.6 

1.7 

1.8 

1.9 

2 

0.86 

1.05 

1.14 

1.24 

1.33 

1.43 

1.53 

1.62 

1.72 

1.81 

1.91 

1821 

1821 

1821 

1821 

1821 

1821 

1821 

1821 

1821 

1821 

1821 

1749 

1977 

2077 

2171 

2258 

2339 

2414 

2485 

2551 

2614 

2672 

1916 

1894 

1866 

1832 

1793 

1751 

1708 

1663 

1619 

1575 

1532 

2567 

2567 

2567 

2567 

2567 

2567 

2567 

2567 

2567 

2567 

2567 

2845 

2472 

2312 

2168 

2038 

1919 

1811 

1712 

1622 

1538 

1462 

10.82 

-3.72 

-9.93 

-15.54 

-20.62 

-25.24 

-29.45 

-33.30 

-36.83 

-40.08 

-43.07 

12.64 

27.31 

33.81 

39.84 

45.43 

50.64 

55.51 

60.06 

64.33 

68.35 

72.13 

5.2.6.11 Short term predictions 2015-2017 by fleet 
5.2.6.11.1  Method 
A deterministic short term prediction by fleet for the period 2015 to 2017 was  performed using 
the  FLR  routines  provided  by  JRC  and  based  on  the  results  of  the  XSA  stock  assessments 
performed during EWG 15-11. 

5.2.6.11.2  Input parameters  
The same parameters used in the short term by single fleet were used. 

5.2.6.11.3  Results 

Table 5.2.6.11.3.1. European hake in GSA 9. Short term forecast by fleet. 

Fleet 
GNS 
OTB 
GTR 
GNS 
OTB 
GTR 
GNS 
OTB 
GTR 

Year 
2015 
2015 
2015 
2016 
2016 
2016 
2017 
2017 
2017 

Catches 
466 
1257 
75 
142 
405 
24 
269 
724 
47 

Partial F 
0.15 
0.77 
0.03 
0.04 
0.19 
0.01 
0.04 
0.19 
0.01 

178 

178 

 
 
 
 
 
 
 
 
Figure 5.2.6.11.3.1. Hake in GSA 9. Short term forecast by fleet. 

5.2.6.12 Medium term predictions 
5.2.6.12.1  Method 
Medium  term  was  not  conducted  because  no  meaningful  stock-recruitment  relationship  was 
estimated. 

5.2.6.13 Stock advice 
The  current  F  (1.03)  is  larger  than  F0.1  (0.23),  chosen  as  proxy  of  FMSY  and  as  the  exploitation 
reference point consistent with high long term yields, which indicates that European hake in GSA 9 
is being fished above FMSY. Catches of European hake in 2016 consistent with FMSY (0.23) would not 
exceed 635 tonnes. 

5.2.6.14 Management strategy evaluation 
Management  Strategy  Evaluation  was  conducted  to  evaluate 
if  the  MSY  ranges  were 
precautionary. The FMSY ranges were derived using the formula provided by STECF 15-09. F ranges 
results were Fupper=0.32 and Flower=0.16. Blim was estimated as Bloss=1569 (t). The following figure 
shows the results of the MSE.  

179 

179 

 
 
 
 
 
 
 
Figure 5.2.6.14.1. Hake in GSA 9. Marine Strategy Evaluation. 

The probability of SSB to fall below Blim at F = Fupper is equal to 0. 

180 

180 

 
 
 
  
 
5.2.7  STOCK ASSESSMENT OF HAKE IN GSA 10 
5.2.7.1  Stock Identification 
The stock of European hake was assumed in the boundaries of the whole GSA 10. M. merluccius is 
with red mullet and deep-water pink shrimp a key species of fishing assemblages in the central-
southern Tyrrhenian Sea (GSA 10) (Figure 5.2.7.1.1).  

Figure 5.2.7.1.1. Geographical location of GSA 10. 

European hake is generally also ranked among species with higher abundance indices in the trawl 
surveys  (e.g.  Spedicato  and  Lembo,  2011).  It  is  a  long  lived  fish  mainly  exploited  by  trawlers, 
especially  on  the  continental  shelves  of  the  Gulfs  (e.g.  Gaeta,  Salerno,  Palermo)  but  also  by 
artisanal fishers using fixed gears (gillnets, bottom long-line).  

Trawl-survey data have evidenced highest biomass indices on the continental shelf of the GSA 10 
(100-200 m; Spedicato and Lembo, 2011), where juveniles (less than 12 cm total length) are mainly 
concentrated. During autumn trawl surveys, one of the main recruitment pulses of this species is 
observed. Two main recruitment events (in spring and autumn; Spedicato and Lembo, 2011) are 
reported in GSA 10 as for other Mediterranean areas. 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. The few large European hake caught during trawl surveys are generally females 
and inhabit deeper waters. The overall sex ratio (~0.41-0.47) estimated from trawl survey data is 
slightly skewed towards males. 

5.2.7.2  Growth 
Estimates of growth parameters were achieved during the SAMED project (SAMED, 2002) by the 
analysis of length frequency distributions.  
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.  

181 

181 

 
 
 
 
 
 
 
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  5.2.7.2.1  summarizes  the  estimated  obtained  by  the  DCF  Data  Call  for  the  von 
Bertalanffy growth parameters and the length-weight relationship. 

Table  5.2.7.2.1.  Hake  in  GSA  10.  Summary  of  the  estimated  obtained  by  the  DCF  Data  Call  for  the  von 
Bertalanffy growth parameters and the length-weight relationships. 

START
_YEAR 
2003 
2003 
2003 
2006 
2006 
2006 
2007 
2007 
2007 
2008 
2008 
2008 
2009 
2009 
2009 
2010 
2010 
2010 
2011 
2011 
2011 
2012 
2012 
2012 
2013 
2013 
2013 
2014 
2014 
2014 

SEX 

END_
YEAR 
2005 
F 
2005  M 
C 
2005 
C 
2006 
2006 
F 
2006  M 
C 
2007 
2007 
F 
2007  M 
C 
2008 
2008 
F 
2008  M 
C 
2009 
2009 
F 
2009  M 
C 
2010 
F 
2010 
2010  M 
C 
2011 
2011 
F 
2011  M 
C 
2012 
2012 
F 
2012  M 
C 
2013 
2013 
F 
2013  M 
C 
2014 
F 
2014 
2014  M 

VB_LINF 

VB_K 

VB_T0 

97.9 
50.8 
97.9 
98 
98 
71.5 
98 
98 
71.5 
98 
98 
71.5 
98 
98 
71.5 
98 
98 
71.5 
98 
98 
71.5 
98 
98 
71.5 
98 
98 
71.5 
98 
98 
71.5 

0.134 
0.25 
0.13 
0.109 
0.118 
0.134 
0.109 
0.118 
0.134 
0.109 
0.118 
0.134 
0.109 
0.118 
0.134 
0.109 
0.118 
0.134 
0.109 
0.118 
0.134 
0.109 
0.118 
0.134 
0.109 
0.118 
0.134 
0.109 
0.118 
0.134 

-0.39 
-0.39 
-0.39 
-0.78 
-0.69 
-1.04 
-0.78 
-0.69 
-1.04 
-0.78 
-0.69 
-1.04 
-0.78 
-0.69 
-1.04 
-0.78 
-0.69 
-1.04 
-0.78 
-0.69 
-1.04 
-0.78 
-0.69 
-1.04 
-0.78 
-0.69 
-1.04 
-0.78 
-0.69 
-1.04 

VB_SIZE_
RANGE 
13-83 cm 
13-45 cm 
8-83 cm 
5-88 cm 
5-88 cm 
5-65 cm 
5-88 cm 
5-88 cm 
5-65 cm 
5-88 cm 
5-88 cm 
5-65 cm 
5-88 cm 
5-88 cm 
5-65 cm 
5-88 cm 
5-88 cm 
5-65 cm 
5-88 cm 
5-88 cm 
5-65 cm 
5-88 cm 
5-88 cm 
5-65 cm 
5-88 cm 
5-88 cm 
5-65 cm 
5-88 cm 
5-88 cm 
5-65 cm 

A 

B 

0.0035 
0.0036 
0.0038 
0.0036 
0.0038 
0.0034 
0.0036 
0.0038 
0.0034 
0.0036 
0.0038 
0.0034 
0.0038 
0.0040 
0.0043 
0.0038 
0.0040 
0.0045 
0.0047 
0.0037 
0.0045 
0.0045 
0.0044 
0.0047 
0.0040 
0.0040 
0.0038 
0.0038 
0.0037 
0.0028 

3.2100 
3.2150 
3.1890 
3.1998 
3.1911 
3.2269 
3.1998 
3.1911 
3.2269 
3.1998 
3.1911 
3.2269 
3.1866 
3.1770 
3.1493 
3.1765 
3.1698 
3.1240 
3.1220 
3.1976 
3.1296 
3.1297 
3.1325 
3.1129 
3.1696 
3.1703 
3.1909 
3.1952 
3.1931 
3.1093 

L_W_SIZE_RAN
GE (g) 
8-3820 
12-665 
4-3820 
2-2900 
12-2900 
12-463 
4-3828 
9-3828 
12-449 
3-3787 
9-3787 
9-766 
1-4950 
12-4950 
13-991 
2-5494 
13-5494 
13-2049 
1-5399 
13-5399 
13-1192 
1-3977 
14-3977 
13-999 
1-3523 
14-3523 
14-476 
2-4420 
15-4420 
14-392 

The observed maximum length of European hake was 88 cm for females and 58 cm for males both 
registered in the landings (bottom long-lines). 

For the present assessment, in line with the previous ones, the fast growth parameters have been 
used and the length weight relationship parameters as reported in the table 5.2.7.2.2. 

Table 5.2.7.2.2. Hake in GSA 10. Growth parameters used in the present assessment. 

SEX 

VB_LINF 

VB_K 

VB_T0 

A 

B 

C 

104 

0.2 

-0.01 

0.00437 

3.154 

182 

182 

 
 
 
 
 
  
 
5.2.7.3  Maturity 
A  proxy  of  size  at  first  maturity  was  estimated  in  the  SAMED  project  (SAMED,  2002)  using  the 
average  length  at  stage  2  (females  with  gonads  at  developing  stage)  that  indicates  an  average 
length of about 30 cm. According to the data obtained in the DCF maturity at age is reported in 
figure 5.2.7.3.1. 

For the present assessment, in line with the previous ones, the fast growth parameters have been 
used to estimate maturity at age as reported in the table 5.2.7.3.1 

Table 5.2.7.3.1. Hake in GSA 10. Maturity proportion at age used in the present assessment. 

Age 

0 
1 
2 
3 
4 
5 
6+ 

Proportion of 
matures 
0 
0.19 
0.86 
1 
1 
1 
1 

5.2.7.4  Natural mortality 
For  the  present  assessment,  in  line  with  the  previous  ones,  the  vector  of  natural  mortality 
estimated according to prodbiom and reported in the table 5.2.7.4.1 has been adopted. It is based 
on fast growth parameters. 

Table 5.2.7.4.1. Hake in GSA 10. Vector of natural mortality used in the present assessment. 

183 

183 

00.10.20.30.40.50.60.70.80.9112345678910111213mature proportionageFemale maturity (years-2002-2014)00.10.20.30.40.50.60.70.80.9112345678mature proportionageMale maturity (years-2002-2014) 
 
 
 
 
 
 
 
 
 
 
Age 
0 
1 
2 
3 
4 
5 
6+ 

Natural mortality 
1.16 
0.53 
0.4 
0.35 
0.32 
0.3 
0.3 

5.2.7.5  Fisheries 
5.2.7.5.1  General description of the fisheries 
European  hake  is  mostly  targeted  by  trawlers,  but  also  by  small  scale  fisheries  using  nets  and 
bottom long-lines. Fishing grounds are located on the soft bottoms of continental shelves and the 
upper part of continental slope along the coasts of the whole GSA. Catches from trawlers are from 
a  depth  range  between  50-60  and  500  m  and  hake  occurs  with  other  important  commercial 
species  as  Illex  coindetii,  M.  barbatus,  P.  longirostris,  Eledone  spp.,  Todaropsis  eblanae,  Lophius 
spp., Pagellus spp., P. blennoides, N. norvegicus. 

5.2.7.5.2  Management regulations applicable in 2015  
Management regulations are based on technical measures, closed 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. Other 
measures on which the management regulations are based regard technical measures (mesh size) 
and minimum landing sizes (EC 1967/06).  
After 2000, in agreement with the European Common Policy of Fisheries, a gradual decreasing of 
the  fleet  capacity  was  implemented.  Along  northern  Sicily  coasts  two  main  Gulfs  (Patti  and 
Castellammare) have been closed to the trawl fishery up 200 m depth, since 1990. In the GSA 10 
the  fishing  ban  has  not  been  mandatory  along  the  time,  and  from  one  year  to  the  other  it  was 
adopted on a voluntary basis by fishers, whilst in the last three years it was mandatory. Regarding 
long-lines the management regulations are based on technical measures related to the number of 
hooks  and  the  minimum  landing  sizes  (EC  1967/06),  besides  the  regulated  number  of  fishing 
licences. 
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).  One  is  located  along  the  mainland,  in  front  of  Sorrento  peninsula  in  the 
vicinity of the MPA of Punta Campanella (Napoli Gulf, 60 km2, within 200 m depth) and a second 
one is along the coasts of Amantea (Calabrian coasts, 75 km2 up to 250 m depth). In these areas 
trawling  is  forbidden  and  other  fishing  activities  are  allowed  under  permission.  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. 

5.2.7.5.3   Catches 
Catches and landing are reported in Figure 5.2.7.5.3.1. Catches include the discards of OTB gear,  
given that discard is not present in the nets and LLS gear. 

184 

184 

 
 
 
 
 
Figure 5.2.7.5.3.1 Hake in GSA 10. Catches and total landings of OTB gear. 

5.2.7.5.4  Landings  
Available landing data are from DCF regulations. EWG 15-11 received Italian landings data for GSA 
10 by fishing gears, which are listed in Table 5.2.7.5.4.1.  
The landings fluctuates around 1,100 and 1,500 tons with the maximum in 2006 and the minimum 
in 2013. Most part of the landings of hake is distributed almost homogenously between trawlers, 
nets (GNS and GTR) and longlines (LLS) (Figure 5.2.7.5.4.1). Landings of gears other than OTB, LLS, 
GNS and GTR can be considered negligible or misreporting. 

Table 5.2.7.5.4.1. Hake in GSA 10. Annual landings (t) by major gear type, 2004-2014. 

Landings 

tons 

SPECIES  GEAR 

2004 

2005 

2006 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

HKE 

-1 

GND 

GNS 

GTR 

LLD 

LLS 

OTB 

PS 

PTM 

SB 

SV 

6.6 

177.2 

202.2 

266.4 

485.9 

1.3 

0.7 

0.7 

8.3 

12.0 

8.0 

10.6 

8.1 

9.2 

1.6 

0.2 

293.8 

322.7 

219.8 

311.3 

283.2 

431.0 

290.8 

317.8 

237.8 

486.1 

124.2 

152.1 

157.3 

67.6 

107.5 

202.0 

152.8 

138.2 

354.7 

158.9 

0.5 

1.5 

2.9 

36.1 

72.6 

14.3 

269.2 

287.7 

240.2 

232.3 

246.6 

183.6 

318.0 

214.4 

145.1 

277.7 

611.9 

759.3 

640.7 

500.6 

441.2 

475.1 

442.7 

418.9 

314.4 

346.2 

2.0 

1.5 

0.7 

0.7 

0.2 

1.5 

0.3 

0.8 

0.8 

1.1 

1.1 

1140.8  1307.7  1544.1  1268.7  1122.8  1090.5  1329.5  1278.5  1107.2  1052.2  1271 

185 

185 

020040060080010001200140016001800200620072008200920102011201220132014tonsM. merluccius  GSA 10Catch and landing dataCatchesLandings 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 Figure 5.2.7.5.4.1. Hake in GSA 10. Landings by gear and total landings.  

5.2.7.5.5  Discards 
The discards of hake in the GSA 10 are reported for 2006, 2009-2014, as in 2007 and 2008 DCF did 
not foresee collection of discard data. The volume of discards is rather variable among years. 

Figure 5.2.7.5.5.1. Hake in GSA 10. Discards by year (gear OTB). 

5.2.7.5.6  Fishing effort (by fleet if possible) 
The trends in fishing effort by year and major gear type is listed in Table 5.2.7.5.6.1 and shown in 
Figure 5.2.7.5.5.1 The total fishing effort in kW*days from 2004 to 2009 is decreasing. From this 
year onward is variable around 20*106 kw*days.  

186 

186 

02004006008001000120014001600180020042005200620072008200920102011201220132014landings tonsGNSGTRLLSOTBtotalOTB+LLS+GNS+GTR0.020.040.060.080.0100.0120.0140.0200620072008200920102011201220132014tonsM. merluccius  GSA 10Discard data 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table  5.2.7.5.6.1.  Hake  in  GSA  10.  Trend  in  fishing  effort  (kW*days)  for  the  GSA  10  by  fleet  level,  2002-
2014.  

Figure 5.2.7.5.6.1. Hake in GSA 10.  Trend in nominal fishing effort for the pulled fleet, from 2002 to 2014. 

5.2.7.6  Scientific surveys 
5.2.7.6.1    Survey #1 (MEDITS) 
5.2.7.6.1.1  Methods 
According  to  the  MEDITS  protocol  (Bertrand  et  al.,  2002),  trawl  surveys  were  yearly  (May-July) 
carried out, 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. 
Based  on  the  DCF  data  call,  abundance  and  biomass  indices  were  recalculated.  In  GSA  10  the 
following number of hauls was reported per depth stratum (Table 5.2.7.6.1.1.1). 

187 

187 

Nominal effortyearareagear2002200320042005200620072008200920102011201220132014SA 10-11E+078E+066E+064E+064E+063E+063E+063E+063E+062E+061E+06600716447521DRB9466329540865052944243121801441862381221889092095741966922411455950888658FPO031450814966915671997438492130683GND2820861273456235984540154966804359131126324462153742766738343GNS4E+065E+063E+062E+063E+063E+063E+063E+063E+062E+062E+06GTR6E+067E+063E+062E+064E+064E+063E+062E+063E+063E+063E+063E+063E+06LLD1E+061E+067935633637313877681E+062E+062E+062E+061E+061E+06LLS5E+062E+061E+061E+061E+061E+061E+062E+061E+061E+063E+06LTL0632489312334OTB7E+067E+068E+068E+068E+067E+066E+066E+066E+065E+066E+066E+069E+06OTM383607PS3E+063E+064E+063E+062E+062E+061E+062E+062E+066E+062E+062E+062E+06PTM6173902Total3E+073E+073E+073E+072E+072E+072E+072E+072E+072E+072E+072E+072E+07050000001000000015000000200000002500000030000000350000002002200420062008201020122014TotalOTBGTRLLSGNS 
 
 
 
 
 
 
 
Table 5.2.7.6.1.1.1. Hake in GSA 10. Number of hauls per year and depth stratum in GSA 10, 1994-2014. 

STRATUM 
GSA10_010-050 
GSA10_050-100 
GSA10_100-200 
GSA10_200-500 
GSA10_500-800 
STRATUM 
GSA10_010-050 
GSA10_050-100 
GSA10_100-200 
GSA10_200-500 
GSA10_500-800 

1994  1995  1996  1997  1998  1999  2000  2001  2002  2003  2004  2005  2006  2007 

8 
10 
17 
24 
26 

7 
8 
14 
18 
23 

7 
8 
14 
18 
23 

7 
8 
14 
18 
23 

7 
8 
14 
18 
23 

7 
8 
14 
18 
23 

7 
8 
14 
18 
23 

8 
10 
17 
22 
28 

8 
10 
17 
22 
27 

8 
10 
17 
22 
28 

7 
10 
17 
22 
28 

8 
10 
17 
22 
28 

8 
10 
17 
23 
27 

8 
10 
17 
22 
28 
2008  2009  2010  2011  2012  2013  2014 
7 
8 
14 
18 
23 

7 
8 
14 
18 
23 

7 
8 
14 
18 
23 

7 
8 
14 
18 
23 

7 
8 
14 
19 
22 

7 
7 
14 
18 
23 

7 
8 
14 
18 
23 

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 hauling duration. 
Hauls noted as valid were 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 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  the 
assumptions  over  zero  catch  stations,  and  hence  assumptions  over  the  distribution  of  data.  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  raised  to  standardized  haul  abundance  per  square  km  over  the  stations  of  each 
stratum.  

188 

188 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5.2.7.6.1.2   Geographical distribution 
The geographical distribution pattern of European hake has been studied in the area using trawl-
survey data and applying geostatistical methods. In these studies both the total abundance indices  
and the abundance indices of recruits were analysed (Lembo et al., 2000).  
Recently  in  the  STOCKMED  project  (MAREA  Framework;  Fiorentino  et  al.,  2015)  biomass  trends 
(average of the last 10 years) have been estimated  (Figure 5.2.7.6.1.2.1). 
If  recruits  are  considered,  the  higher  concentration  in  the  GSA  10  was  localised  in  the  northern 
side  (Gulfs  of  Napoli  and  Gaeta).  Recent  estimations  (MEDISEH  Project,  MAREA  Framework; 
Giannoulaki  et  al.,  2013)  have  confirmed  the  presence  of  important  zone  for  recruits  in  the 
northernmost  part  of  the  GSA,  although  sites  with  a  high  probability  of  locating  a  nursery 
appeared also along the coasts of southern part of the mainland and North Sicily. 
From GRUND data (autumn survey) the higher abundance of recruits were instead localised in the 
central part of the GSA, along the mainland coasts. Persistence of the nursery areas along the time 
was estimated from the indicator kriging (Figure 5.2.7.6.1.2.2). 

Figure 5.2.7.6.1.2.1. Hake in GSA 10.  Geographical distribution of hake in the Mediterranean basin. 

Figure 5.2.7.6.1.2.2. Hake in GSA 10. Nursery areas of hake in GSA 10 with the persistence along time. 

5.2.7.6.1.3   Trends in abundance and biomass 
Fishery independent information regarding the state of the hake in GSA 10 was derived from the 
international survey MEDITS. Figure 5.2.7.6.1.3.1 displays the estimated trend of hake abundance 
and  biomass  indices  standardized  to  the  surface  unit  in  the  GSA10.  Indices  from  MEDITS  trawl-

189 

189 

Medits 2001 Reclute (N/kmq)0 - 66 - 2828 - 5656 - 281281 - 563563 - 11251125 - 16881688 - 22502250 - 2813No DataContour of Persistence Index0.6 - 1M. merluccius (0-600 m)Grund 2005 Reclute (N/kmq)0 - 6464 - 129129 - 644644 - 12881288 - 25772577 - 38653865 - 64426442 - 1192011920 - 16653No DataContour of Persistence Index0.6 - 1M. merluccius (0-600 m) 
 
 
 
 
 
 
surveys  show  an  increasing  pattern  up  to  2009,  although  variability  is  high,  and  a  decrease  in 
2010-2011. The value of 2014 are low (Figure 5.2.7.6.1.3.1). 

Fig.  5.2.7.6.1.3.1.  Hake  GSA  10.  Abundance  and  biomass  time  series  of  hake  in  GSA  10  derived  from 
MEDITS (dotted lines indicated standard deviation). 

5.2.7.6.1.4  Trends in abundance by length or age 
The following figure display the stratified abundance indices of GSA 10 in 1994-2014. 

190 

190 

0500100015002000250030003500199419951996199719981999200020012002200320042005200620072008200920102011201220132014M. merluccius GSA 10N/km^2N/Km2St. Dev (low)St. Dev (up)0102030405060199419951996199719981999200020012002200320042005200620072008200920102011201220132014M. merluccius GSA 10kg/km^2kg/km2St. Dev (low)St. Dev (up) 
 
 
 
 
 
 
 
191 

191 

 
 
 
 
Fig. 5.2.7.6.1.4.1. Hake in GSA 10. Stratified abundance indices by size, 1994-2014. 

192 

05010015020002.557.51012.51517.52022.52527.53032.53537.54042.54547.550Total length (cm)GSA10 200605010015020002.557.51012.51517.52022.52527.53032.53537.54042.54547.550Total length (cm)GSA10 200705010015020002.557.51012.51517.52022.52527.53032.53537.54042.54547.550Total length (cm)GSA10 200805010015020002.557.51012.51517.52022.52527.53032.53537.54042.54547.550Total length (cm)GSA10 200905010015020002.557.51012.51517.52022.52527.53032.53537.54042.54547.550Total length (cm)GSA10 201005010015020002.557.51012.51517.52022.52527.53032.53537.54042.54547.550Total length (cm)GSA10 201105010015020002.557.51012.51517.52022.52527.53032.53537.54042.54547.550Total length (cm)GSA10 201205010015020002.557.51012.51517.52022.52527.53032.53537.54042.54547.550Total length (cm)GSA10 201305010015020002.557.51012.51517.52022.52527.53032.53537.54042.54547.550Total length (cm)GSA10 2014 
 
 
 
 
 
 
 
 
 
5.2.7.6.2  Survey #2 (GRUND - historical information) 
5.2.7.6.2.1  Methods 
Under DCF Grund surveys was conducted since 2003 to 2008 using the same vessel and gear in the 
whole  GSA.  Sampling  scheme,  stratification  and  protocols  were  similar  as  in  MEDITS.  All  the 
abundance data (number of fish and weight per surface unit) were standardised to square kilometre, 
using the swept area method.   

5.2.7.6.2.2   Geographical distribution 
Mapping of the hake recruits obtained applying the indicator kriging technique with contouring that 
represents probability (in percentage) is reported in the figure  5.2.7.6.1.2.2. 

5.2.7.6.2.3   Trends in abundance and biomass 

Trends  derived  from  the  GRUND  surveys  are  shown  in  Figure  5.2.7.6.2.3.1.  Abundance  indices 
increased significantly (p<0.05 on ln-transformed data), as well as recruitment indices, while biomass 
indices were almost stationary. 

Figure 5.2.7.6.2.3.1. Hake in GSA 10.  Abundance and biomass indices of hake in GSA 10 derived from GRUND 
surveys. Recruitment indices (N/km2) with standard deviation are also reported. 

5.2.7.6.2.4  Trends in abundance by length or age 
No trend in the mean length was observed in GRUND survey (Figure 6.7.9.), nor at the third quantile 
lengths  as  obtained  from  the  length  structures  of  GRUND  time  series  from  1994  to  2006  (Figure 
5.2.7.6.2.4.1.). 

193 

Abundance indices - M. merluccius - GSA10- Grund surveyy = 43.923x - 86906R2 = 0.347705001000150020002500199319941995199619971998199920002001200220032004200520062007yearsn/km2Biomass indices - M. merluccius - GSA10- Grund surveyy = -0.0537x + 128.78R2 = 0.00290.05.010.015.020.025.030.035.040.045.0199319941995199619971998199920002001200220032004200520062007yearskg/km2M. merluccius - Grund survey GSA10020040060080010001200140016001800199419961998200120032005yearsindex R (N/kmq) 
 
 
 
 
 
 
 
 
 
 
Figure 5.2.7.6.2.4.1. Hake in GSA 10.  Quantile derived from the GRUND length structures in 1994-2006.  

5.2.7.7  Stock Assessment 
5.2.7.7.1   Methods 
Stock assessment has been conducted using 2 methods XSA and a4a. 

Method: XSA 
The Extended Survivors Analysis (XSA – Darby and Flatman, 1994) has been used with an age range 
from 0 to 6+. Discard was included in the analysis. Since no discard data were available for 2007 and 
2008, an estimate based on the length structures of the previous and following years has been done. 

Method: a4a 
An  attempt  was  made  to  use  the  a4a  framework  developed  by  the  Joint  Research  Centre  to  fit  an 
assessment model for this stock. a4a is a framework that allows to compute statistical catch at age 
models.  Its  flexibility  allows  to  fit  a  wide  range  of  models  to  the  data.  Compared  to  XSA,  a4a  runs 
forward  and  allows  to  reach  a  better  stability  for  last  years  estimates.  As  it  is  the  first  year  this 
method was used, the results were compared to an XSA run. 

5.2.7.7.2  Input data 
For the assessment of hake in GSA 10 the DCF official 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 growth parameters used in 
the past assessment. A sex-combined analysis was carried out.  
The survey indices from MEDITS data from 2006 to 2014 have been used for the tuning and LLS CPUE 
The  age  distribution  of  catches  is  showed  in  Figure  5.2.7.7.2.1  and  Table  5.2.7.7.2.1.  The  age 
distribution of the tuning indices (MEDITS and LLS CPUE) are reported in the Figures 5.2.7.7.2.2. and 
5.2.7.7.2.3, as well as in the tables 5.2.7.7.2.4 and 5.2.7.7.2.5. 

194 

III quantile length - hake - GSA10 - GRUND y = -0.1223x + 256.72R2 = 0.15860.02.04.06.08.010.012.014.0199219941996199820002002200420062008yearsTL (cm) 
 
 
 
 
 
 
 
 
 
 
 
Figure 5.2.7.7.2.1. Hake in GSA 10. Catch (including discard) in numbers (thousands) by age and year used 
in the XSA.  

Figure 5.2.7.7.2.2. Hake in GSA 10. MEDITS in numbers (thousands) by age and year used in the XSA.  

Figure 5.2.7.7.2.3. Hake in GSA 10. LLS CPUES by age and year used in the XSA.  

Table 5.2.7.7.2.1. Hake in GSA 10. Catch in numbers (thousands, including discards) by age and year used in 
the XSA.  

0 

3 
Year 
2006  15744.1  6355.5  561.9 
89.1 
2007  20385.0  4805.3  450.8  121.9 

2 

1 

4 
34.8 
41.1 

5 
19.0 
9.3 

6+ 
0.0 
1.5 

195 

0500010000150002000025000300000123456+number in thousandsCATCH at age200620072008200920102011201220132014050010001500200025000123456+MEDITS at age20062007200820092010201120122013201400.0050.010.0150.020.0250.030.0350.04123456+CPUE LLS at age200620072008200920102011201220132014 
 
 
 
 
 
 
 
 
 
 
 
2008  13856.9  3864.8  367.6  138.0 
57.6 
2009  24960.6  4205.5  317.0 
2010  13062.0  6267.7  723.7 
65.8 
2011  10180.3  3711.8  506.6  175.4 
2012  15987.9  4895.6  448.7  117.4 
2013  10749.6  4711.3  326.1 
77.5 
6604.1  4488.5  847.2  105.7 
2014 

54.3 
34.4 
6.7 
46.2 
17.6 
28.0 
25.5 

22.1 
10.4 
8.9 
23.2 
5.0 
3.3 
19.1 

4.2 
7.3 
7.6 
5.9 
1.5 
3.9 
19.1 

Table 5.2.7.7.2.2. Hake in GSA 10. Weights at age (kg) in the catche used in the XSA.  

Year/age 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

0 
0.020 
0.018 
0.018 
0.018 
0.016 
0.016 
0.016 
0.016 
0.016 

1 
0.115 
0.118 
0.118 
0.122 
0.108 
0.129 
0.120 
0.121 
0.143 

2 
0.430 
0.471 
0.469 
0.439 
0.481 
0.443 
0.458 
0.539 
0.406 

3 
1.200 
1.195 
1.115 
1.112 
1.101 
1.164 
1.106 
1.165 
1.115 

4 
1.935 
1.813 
1.918 
1.881 
2.007 
1.860 
1.920 
1.818 
1.827 

5 
2.760 
3.003 
2.723 
2.721 
2.935 
2.684 
2.991 
2.964 
2.718 

6+ 
2.760 
5.921 
3.730 
3.763 
4.379 
4.262 
4.058 
4.520 
3.877 

Table 5.2.7.7.2.3. Hake in GSA 10. Weights at age (kg) in the stock used in the XSA.  

Year/age 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

0 
0.006 
0.006 
0.006 
0.006 
0.006 
0.006 
0.006 
0.006 
0.006 

1 
0.137 
0.137 
0.137 
0.137 
0.137 
0.137 
0.137 
0.137 
0.137 

2 
0.519 
0.519 
0.519 
0.519 
0.519 
0.519 
0.519 
0.519 
0.519 

3 
1.141 
1.141 
1.141 
1.141 
1.141 
1.141 
1.141 
1.141 
1.141 

4 
1.931 
1.931 
1.931 
1.931 
1.931 
1.931 
1.931 
1.931 
1.931 

5 
2.808 
2.808 
2.808 
2.808 
2.808 
2.808 
2.808 
2.808 
2.808 

6+ 
3.704 
3.704 
3.704 
3.704 
3.704 
3.704 
3.704 
3.704 
3.704 

Table 5.2.7.7.2.4. Hake in GSA 10. Indices from MEDITS survey used in the XSA. 

Year/age 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

0 
1250.42 
1907.19 
1544.78 
1890.43 
813.51 
639.35 
907.4 
1252.29 
610.5 

2 
1 
2.32 
99.67 
0.95 
51.52 
2.97 
92.69 
78.11 
0.38 
131.46  1.46 
2.45 
67.18 
2.37 
56.44 
4.37 
67.21 
4.00 
64.50 

3 
0.49 
0.97 
1.52 
0.32 
0.3 
1.2 
0.29 
0.29 
0.20 

4 
0.01 
0.14 
0.01 
0.01 
0.17 
0.01 
0.01 
0.01 
0.30 

5 

6+ 
0.01  0.01 
0.14  0.01 
0.01 
0.4 
0.32  0.01 
0.15  0.24 
0.01  0.01 
0.16  0.01 
0.22  0.01 
0.01  0.01 

196 

 
 
 
 
 
 
 
 
 
 
 
Table 5.2.7.7.2.5. Hake in GSA 10. Indices from LLS (CPUE). 

Year 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

1 

3 
0.011819  0.012778  0.005738  0.002327  0.000583 

2 

4 

5 

6+ 
0 

0 

0.004451  0.012014  0.003051  0.001027  0.000171 
0.009911  0.003799  0.002676  0.00165  0.000871  0.000236 
0.035813  0.010841  0.00422  0.002638  0.000486  0.000574 
0.003372  0.027127  0.003106  0.000952  0.001266  0.000806 
0.004089  0.007395  0.007279  0.003293  0.00049  0.000427 
0.013372  0.010703  0.007996  0.001233  0.000428  0.000107 
0.018687  0.024985  0.012861  0.004887  0.001097  0.001097 
0.001902  0.003283  0.007114  0.002094  0.001818  0.001818 

For this assessment, as in that carried out in EWG 13_09 the fast growth parameters have been 
used.  These  as  well  as  maturity  and  natural  mortality  vectors  are  those  reported  in  the  tables 
5.2.7.2.2,  5.2.7.3.1 and 5.2.7.4.1. The stock object resulting from the XSA run with shrinkage 2 and 
Fbar 1-4 was used as input file for the a4a approach. 

5.2.7.7.3  Results 

Method: XSA 

The XSA run with the following settings has been performed: 
- Catchability (rage) independent on stock size for all ages =0. 
- Catchability (qage) independent of age for ages >= 5. 
- Minimum standard error for population estimates derived from each fleet = 0.300. 
- shk.n=TRUE, shk.f=TRUE, shk.yrs=3, shk.ages=2 

Sensitivity  analysis  have  been  performed  with  S.E.  of  the  mean  to  which  the  estimates  are  shrunk 
equal to 0.5,  1,  1.5 and 2.  

Shrinkage  Minimum  Maximux  Average 

0.5 
1 
1.5 
2 

-2.128 
-2.438 
-2.473 
-2.491 

2.588 
2.238 
2.222 
2.225 

0.721 
0.702 
0.699 
0.697 

The  run  with  shrinkage  2  has  been  chosen  on  the  basis  of  the  residuals  and  of  the  retrospective 
analysis. 

- Shrinkage of the mean (fse): 2. 

The  log-catchability  residuals  at  age  and  the  retrospective  analysis  results  are  shown  in  Figure 
5.2.7.7.3.1 and Figure 5.2.7.7.3.2. 

197 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 5.2.7.7.3.1. Hake in GSA 10. Log-catchability residuals at age for the tuning index, XSA of hake in GSA 
10.  

The  residuals  do  not  show  any  trend  and  overall  the  absolute  values  are  low.  As  expected  some 
relatively larger values were observed in the older ages of MEDITS and in the younger age (age 1) of 
LLS. The retrospective analysis shows also a consistent pattern. Fbar was set both between 0-5 ages 
and 1-4. The second setting gave more stable value and thus it was used in the final XSA model. 

198 

 
 
 
 
 
 
 
 
 
 
 
Figure 5.2.7.7.3.2. Hake in GSA 10. XSA Retrospective analysis (2010-2013). 

199 

 
 
 
 
 
 
 
Figure 5.2.7.7.3.3. Hake in GSA 10. XSA summary results. SSB and catch are  in tonnes, recruitment in  1000s 
individuals. 

Both  the  Fbar(1-4)  and  the  SSB  are  varying  along  the  time  with  F,  catch  and  recruitment  slightly 
decreasing and SSB slightly increasing. The Fbar along the time series is 0.98, with a minimum of 0.73 
in 2013 and a maximum of 1.19 in 2008 (Table 5.2.7.7.3.1). The SSB is about 1,635 t in 2014, being 
the  average  along  the  time  series  equal  to  1261.  The  recruitment  has  a  slightly  decreasing  trend, 
even if in 2012 it increased again to a value equal to 51,400. The maximum recruitment is reached in 
2009 and it is equal to 76,500 thousands individuals, while in 2014 it is 35919 (Table 5.2.7.7.3.1). 

Table 5.2.7.7.3.1. Hake in GSA 10. Fishing mortality at age by year, Fbar(1-4), spawning stock biomass (SSB, t) and 
Recruitment (R, thousands) estimated with XSA. 

Age0  Age1  Age2  Age3  Age4  Age5  Age6+ 

fbar  (1-
4) 

SSB 

R 

2006  0.786  1.047  0.694  0.874  0.766  0.554  0.733  0.974 
2007  1.771  1.885  1.692  1.231  1.695  1.432  1.769  1.011 
2008  0.973  0.800  1.141  0.864  1.092  0.850  0.951  1.186 
2009  0.543  0.733  0.790  0.666  0.535  1.198  0.601  0.826 
2010  0.610  0.627  1.121  0.543  0.166  1.188  0.392  0.872 
2011  1.237  0.363  1.016  0.781  0.293  1.954  0.411  1.167 
2012  1.237  0.363  1.016  0.781  0.293  1.954  0.411  0.928 

1301 
1278 
1122 
967 
1389 
1257 
1138 

51675 
56104 
49445 
76498 
43602 
42722 
54977 

200 

 
 
 
 
 
 
 
 
 
  
2013  0.786  1.047  0.694  0.874  0.766  0.554  0.733  0.727 
2014  1.771  1.885  1.692  1.231  1.695  1.432  1.769  1.101 

1258 
1635 

42445 
35919 

Model: a4a 

In  order  to  achieve  the  best  results,  different  models  were  fitted  to  the  data  until  reaching 
results  that were  both  statistically  sound  and  biologically  interpretable.  Five  of  the  models  run 
are presented here, and the general specifications of the models in R were the following: 

qmod1 <- list(~ factor(age),~ factor(age) )  
qmod2 <- list(~ factor(age),~ s(age, k=5))  
qmod3 <- list(~ factor( replace(age, age>3,3) ),~ factor(replace(age, age>3,3))) 
qmod4 <- list(~ factor(age)+year,~ factor(replace(age, age>3,3) )) 

fmod1 <- ~ factor(age) + factor(year) 
fmod2 <- ~ factor(age) + s(year, k=4)  

srmod1 <- ~ factor(year) 
srmod2 <- ~ s(year, k=4) 

fit1 <- a4aSCA(stock=spe.stk, indices=spe.idx, fmodel=fmod1, qmodel=qmod1, srmodel=srmod1) 
fit2  <- a4aSCA(stock=spe.stk, indices=spe.idx,fmodel=fmod2, qmodel=qmod1, srmodel=srmod2) 
fit3<-a4aSCA(stock=spe.stk, indices=spe.idx,fmodel=fmod2,qmodel=qmod2,srmodel=srmod2) 
fit4<-a4aSCA(stock=spe.stk, indices=spe.idx,fmodel=fmod2,qmodel=qmod3,srmodel=srmod2) 
fit5<-a4aSCA(stock=spe.stk, indices=spe.idx,fmodel=fmod2,qmodel=qmod4,srmodel=srmod2) 

The  best  model  of  the  five  models  was  selected  in  terms  of  residuals  pattern,  retrospective 
analysis, consistency with the XSA outputs, AIC, and BIC (Table 5.2.7.7.3.3). Although the model 5 
not  resulted  as  the  best  model  according  to  AIC  and  BIC,  it  was  the  fitting  with  best  results  in 
terms  of  residuals  pattern,  retrospective  analysis  and  consistency  with  the  results  obtained  by 
XSA. Therefore, it was selected as the best a4a model for the hake stock in GSA 10. 

This model allows the catchability of the MEDITS survey to be estimated by age and year, while 
fixing the catchability of the older age classes in the long-lines CPUE. Fishing mortality can vary 
by  age  and  is  modelled  by  a  smoothing  function  on  year.  Recruitment  is  also  modelled  by  a 
smoother on year. 

Table 5.2.7.7.3.2. Hake in GSA 10. Summary table of the a4a models fitted. 

Model 

Problem with 
residuals 

F 

SSB 

Fit1 

Catch 0 and 1  0.4-0.6 

Fit2 

Medits 

Around 
1 

3000-
4000 t 
900-
1000 t 

Retrospective 
analysis 

Not reliable 

Not reliable 

Notes 

AIC 

BIC 

Different 
from XSA 
Similar to 
XSA 

471.7 

624.9 

488.7 

610.0 

201 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Fit3 

Medits 

Around 
1 

Fit4 

Medits 

0.8-1.2 

Fit5 

Medits (age 2 
and 3 only) 

0.6-1.0 

900-
1000 t 

1000-
1500 t 

1000-
1700 t 

Not reliable 

Consistent 

Consistent 

Similar to 
XSA 
Very 
similar to 
XSA 
Very 
similar to 
XSA 

486.7 

604.8 

494.9 

597.2 

483.2 

598.1 

Figure 5.2.7.7.3.4. Hake in GSA 10. Plot of the stock parameters estimated by the five a4a models fitted. 

The diagnostics and the outputs of the best a4a model for hake in GSA 10 are shown in Figure 
5.2.7.7.3.5-5.2.7.7.3.12. 

202 

 
 
 
 
 
 
 
 
 
 
Figure 5.2.7.7.3.5. Hake in GSA 10.  Log residuals for catch-,  Medits indices-,  and long-lines CPUE-at-age 
from the a4a best model. 

Figure 5.2.7.7.3.6. Hake in GSA 10. Bubble plot of log residuals for catch-, Medits indices-, and long-lines 
CPUE-at-age from the a4a best model. 

203 

 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 5.2.7.7.3.7. Hake in GSA 10. Observed vs fitted MEDITS indices-at-age. 

204 

 
 
 
 
 
 
 
 
 
Figure 5.2.7.7.3.8. Hake in GSA 10. Observed vs fitted long-lines CPUE-at-age. 

205 

 
 
 
 
 
 
 
Figure 5.2.7.7.3.9. Hake in GSA 10. Observed catch-at-age vs catch-at-age fitted by the a4a best model. 

Figure 5.2.7.7.3.10. Hake in GSA 10. Time series of estimated parameters by means of a4a analysis. 

206 

 
 
 
 
 
 
 
 
 
Figure 5.2.7.7.3.11. Hake in GSA 10. Retrospective analysis with a4a best model. 

Figure 5.2.7.7.3.12. Hake in GSA 10. F-at-age estimated by the a4a best model. 

Comparison with XSA 
An XSA run was performed following the approach classically used for this stock, involving sensitivity 
analyses on parameters to select the best run. The comparison of the a4a results with those from the 
XSA  run  displayed  a  good  consistency  as  the  trends  for  the  various  variables  were found  to  be  the 
same. 

207 

 
 
 
 
 
 
 
 
 
 
 
However, due to the presence of some patterns in the residuals in the best of the a4a models fitted, 
it was decided to base the assessment on the XSA results. 
Because  of  the  still  short  time  series  of  data  used  in  the  assessment  (and  the  associated  limited 
number  of  degrees  of  freedom)  it  was  not  possible  to  use  complex  smoother  functions  to  model 
catchability and F-at-age in the a4a framework in order to improve the residuals. 

5.2.7.8  Reference points 
5.2.7.8.1  Methods 
Yield Per Recruit in XSA 
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.  

5.2.7.8.2  Input data  
As input the same population parameters used for the XSA and its output of the exploitation pattern 
were used. 

5.2.7.8.3  Results 
The  reference  points  are  shown  in  table  5.2.7.8.3.1.  The  reference  points  computed  from  the  a4a 
model are also shown, although the assessment and advice for hake in GSA 10 are based on XSA. 

Table 5.2.7.8.3.1. Hake in GSA 10. Reference Points estimated on the Fbar 1-4 using XSA. 

shrinkage 
0.5 
1 
1.5 
2 

f0.1 
0.177 
0.196 
0.198 
0.198 

Total.Yield  Recruitment 

2886 
2388 
2344 
2347 

49011 
49316 
49306 
49274 

SSB 
25451 
23873 
23701 
23682 

Biomass 
27737 
26088 
25910 
25892 

Table 5.2.7.8.3.2. Hake in GSA 10.  Reference Points estimated on the Fbar 1-4 using a4a. 

virgin 
msy 
crash 
f0.1 
fmax 
spr.30 

F 
0 
0.229 
25.030 
0.164 
0.229 
0.236 

Total Yield  Recruitment 

0 
2812 
701 
2703 
2812 
2811 

46628 
46628 
46628 
46628 
46628 
46628 

SSB 
58180 
18075 
0 
24605 
18075 
17454 

Biomass 
60707 
20096 
280 
26751 
20095 
19461 

5.2.7.9  Data quality 
Data from DCF 2015 were used. Raw upload data with success were used, because those stored in 
the databases supplied by JRC showed some inconsistency (fishery data).  A difference in the sum of 
products  compared  to  landings  was  always  far  less  than  10%.  Discards  data  of  2006,  2009,  2010, 
2011, 2012, 2013 and 2014 were available. Information on number of samples for landings, discards 

208 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  software  FishTrawl  (used  to  validate  the  routine  for 
estimating indices by MEDITS files). Growth, maturity by length and age and sex ratio were available 
for the whole time series (2002-2014). 

5.2.7.10 Short term predictions 2016-2018 
5.2.7.10.1  Method 
A  deterministic  short  term  prediction  for  the  period  2015  to  2017  was  performed  using  the  FLR 
routines provided by JRC, which takes into account the catch and landings in numbers and weight and 
the discards. This routine performs short terms for the whole fleet.  

A  generic  approximate  multifleet  projections  with  FLR  provided  by  JRC  was  also  used  to  split  the 
fishing mortality by fleet using proportion of catch in number by age and fleet.   

5.2.7.10.2  Input parameters  
The same input parameters used in the XSA analysis shown above were used. Different scenarios of 
constant harvest strategy with Fbar calculated as the average of ages 1 to 4 and F status quo (Fstq = 
0.906;  geometric  mean  of  the  last  three  years)  were  performed.  Recruitment  (class  0)  has  been 
estimated  from  the  population  results  from  the  geometric mean  of  the  last  three years  2012-2014 
(43764 thousands individuals) estimated using XSA. 

5.2.7.10.3  Results 
The results of the short term forecasts related to the whole fleet are summarised in the table Table 
5.2.7.10.3.1 and in the figure 5.2.7.10.3.1. 

Table 5.2.7.10.3.1. Hake in GSA 10. Short term forecast in different F scenarios computed for M. merluccius in 
GSA 10. Basis: F(2015) = mean(Fbar1-4 2012-2014)= 0.906; R(2015) = geometric mean of the recruitment of the 
last 3 years; R = 43764 (thousands); SSB(2014) = 1382 t, Catch (2014)= 1635 t. 

Rationale 
Zero catch 
High long-
term yield 
(F0.1) 
Status quo 
Different 
scenarios 

Ffactor 
0 

fbar 
0.000 

Catch 
2016 
0 

Catch 
2017 
0 

SSB 
2017 
3770 

Change SSB 
2016-2017 
(%) 
192.42 

Change Catch 
2014-2016 
(%) 
-100.00 

0.218 
1 

0.198 
0.906 

0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 

0.091 
0.181 
0.272 
0.362 
0.453 
0.543 
0.634 
0.725 

404 
1289 

197 
374 
532 
675 
804 
920 
1026 
1122 

769 
1297 

419 
724 
941 
1092 
1193 
1257 
1293 
1308 

209 

2927 
1296 

3352 
2988 
2669 
2389 
2144 
1929 
1740 
1573 

127.03 
0.50 

160.03 
131.74 
107.00 
85.34 
66.34 
49.65 
34.96 
22.02 

-70.80 
-6.74 

-85.74 
-72.97 
-61.50 
-51.17 
-41.86 
-33.43 
-25.79 
-18.85 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0.9 
1.1 
1.2 
1.3 
1.4 
1.5 
1.6 
1.7 
1.8 
1.9 
2 

0.815 
0.996 
1.087 
1.178 
1.268 
1.359 
1.449 
1.540 
1.630 
1.721 
1.812 

1209 
1362 
1430 
1491 
1549 
1601 
1650 
1696 
1738 
1778 
1815 

1308 
1279 
1256 
1229 
1201 
1171 
1141 
1112 
1083 
1055 
1028 

1426 
1180 
1078 
987 
905 
833 
768 
710 
657 
610 
567 

10.60 
-8.45 
-16.39 
-23.46 
-29.76 
-35.39 
-40.43 
-44.96 
-49.03 
-52.69 
-56.01 

-12.52 
-1.44 
3.42 
7.90 
12.03 
15.86 
19.40 
22.69 
25.76 
28.62 
31.30 

A short term projection of the whole fleet (table 5.2.7.10.3.1), assuming an Fstq of 0.91 in 2014 and a 
recruitment of 43764 thousands individuals shows that: 

- 

- 

Fishing  at  the  Fstq  (0.906)  generates  a  decrease  of  the  catch  of  6.74%  from  2014  to  2016 
along  with  an  approximately  stable  spawning  stock  biomass  (change  +0.5%)  from  2016  to 
2017. 
Fishing  at  F0.1  (0.2)  generates  a  decrease  of  the  catch  of  70.8%  from  2014  to  2016  and  an 
increase of the spawning stock biomass of 127% from 2016 to 2017. 

Results  of  the  short  term  multifleet  projections  are  reported  in  the  table  5.2.7.10.3.2  and  Figure 
5.2.7.10.3.2. 

Table 5.2.7.10.3.2. Hake GSA 10. Short term forecast by fleet. 

Fbar 1-
4 

2006  2007  2008  2009  2010  2011  2012  2013  2014  Mean of 
last three 
years 
0.26 
0.44 
0.21 
0.92 

0.32  0.40  0.48  0.22  0.26  0.27  0.30  0.17  0.29 
0.31  0.32  0.41  0.29  0.45  0.38  0.37  0.40  0.55 
0.34  0.28  0.29  0.31  0.16  0.50  0.25  0.15  0.22 
overall  0.97  1.01  1.19  0.83  0.87  1.17  0.93  0.73  1.10 

trawl 
nets 
lls 

210 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 5.2.7.10.3.2. Hake in GSA 10. Short term forecast by fleet. 

5.2.7.11 Medium term predictions 
5.2.7.11.1  Method 
Medium  term  was  not  conducted  because  no  meaningful  stock-recruitment  relationship  was 
estimated. 

5.2.7.12 Stock advice 
STECF-EWG 15-11 proposes F0.1=0.20 as limit management reference point consistent with high long 
term yield and lower risk of stock collapse.  
SSB  showed  an  increasing  trend  in  the  last  years  while  recruitment  fluctuated  and  was  slightly 
decreasing.  Also  F  was  decreasing  in  the  recent  years  except  the  last  one.  According  to  the  F 
estimates obtained using landing, discard data and survey indices in XSA, in the last year of the time 
series (2014) F (1.10) was above the estimated reference value of F0.1=0.20.  
STECF-EWG 15-11 considers the stock in overfishing situation and advises to reduce the current level 
of effort of the relevant fleets in order to avoid future loss in stock productivity.  Catches of hake in 
2016 consistent with FMSY should not exceed 404 tonnes. 

5.2.7.13 Management strategy evaluation 
F ranges results were Fupper=0.27 and Flower=0.13. Blim (967 t) was estimated as was estimated as the 
minimum SSB estimated in XSA assessment. 
A  management  strategy  was  conducted  with  an  FLR  script  distributed  during  the  meeting.  The 
Management Strategy Evaluation was ran to evaluate if the MSY ranges were precautionary. The FMSY 
ranges were derived using the formula provided by STECF 15-09. 
The  management  strategy  evaluation  included  uncertainty  in  the  recruitment  around  a  mean  level 
resulting  from  the  geometric  mean  of  the  last  3  years  of  data  and  uncertainty  in  the  MEDITS  and 
longlines  CPUE  tuning  fleet  indices.  The  stock  was  assessed  by  XSA,  with  the  same  settings  of  the 
assessment  at  each  iteration.  The  number  of  iterations  was  250.  The  following  figure  5.2.3.13.1 

211 

00.10.20.30.40.50.60.70.80.911.11.21.31.41.51.61.71.81.92f0.102505007500250500750025050075002505007500250500750200620082010201220142016200620082010201220142016yeardatafleettrawlnetslls 
 
 
 
 
 
 
 
 
 
shows  the  evolution  of  the  main  four  stock  indicators.  The  probability  of  SSB  going  below  Blim  was 
estimated at 0. 

Figure 5.2.3.13.1. Hake GSA 10. Management Strategy Evaluation. 

212 

 
 
 
 
 
 
 
 
 
5.2.8  STOCK ASSESSMENT OF HAKE IN GSA 11 
5.2.8.1  Stock Identification 
Due to a lack of information about the structure of hake population in the western Mediterranean, 
this stock was assumed to be confined within the GSA 11 boundaries (Figure 5.2.8.1.1). 

Figure 5.2.8.1.1. Geographical location of GSA 11. 

Hake is distributed in the whole area between 10 and 800 m depth. Recruits peak in abundance over 
the  continental  shelf-break  (between  150  and  250  m  depth).  The  stock  is  mainly  exploited  by  the 
local fishing fleet, although seasonally and occasionally some other Italian  fleet use to fish in some 
areas of the GSA 11. Spawning is taking place almost all year round, with a peak during winter–spring. 
Juveniles showed a patchy distribution with some main density hot spots (nurseries) showing a high 
spatio-temporal persistence (Murenu et al., 2010a) in western areas. 

5.2.8.2  Growth 
There are no specific studies on the growth pattern of the species in Sardinian waters. The same fast 
growth of the previous SGMED meetings have been used in this assessment (Linf=100.7 cm, K=0.2, 
t0= -0.01). 

5.2.8.3  Maturity 
Due to the low catchability of large hake in the trawl, the catch rate of mature specimens during the 
MEDITS  trawl  survey  is  usually  very  low,  influencing  the  identification  of  gonad  development  and 
growth  rate  for  large  individuals.  Female  length  at  first  maturity  is  estimated  at  around  36  cm. 
Although  spawning  around  Sardinian  coasts  (GSA  11)  occurs  nearly  all  over  the  year  (January  to 
September), a maturity peak is usually observed in winter and spring (February-May). 

213 

 
 
 
 
 
 
 
 
 
 
 
5.2.8.4  Natural mortality 
Natural  mortality  was  estimated  using  PRODBIOM  (Abella  et  al.,  1998)  and  is  shown  in  Table 
5.2.8.4.1. The input parameters used were Linf = 100.7, k = 0.2, t0 =-0.01, a = 0.004 and b = 3.1672. 

Table 5.2.8.4.1. European hake in GSA 11. Natural mortality. 

Age 
M 

0 

5+ 
1.15  0.57  0.46  0.41  0.38  0.37 

4 

2 

1 

3 

5.2.8.5  Fisheries 
5.2.8.5.1  General description of the fisheries 
The fleet of GSA 11 is composed of about 1311 boats. Trawlers (n=155) account for about 11 % of the 
fleet. Most of them (n=79) are based on the main southern fishery ports (Cagliari and Sant’Antioco). 
From 1994 to 2004, the trawl fleet showed remarkable changes in GSA 11. Those mostly consisted of 
a general increase in the number of vessels and by the replacement of the old, low tonnage wooden 
boats by larger steel boats. For the entire GSA an increase of 85% for boats >70 tons class occurred. A 
decrease of 20% for the smaller boats (<30 GRT) was also observed. 
In GSA 11 most of the trawlers utilize nets similar to the original commercial “Italian trawl net”. The 
main differences lie in overall size, mesh dimensions and some hanging details. The dimensions of the 
commercial trawl net can change in relation to the trawlers engine power and bottom characteristic 
also.  Generally  the  Italian  trawl  nets  have  a  maximum  vertical  opening  of  about  2  m  while  the 
horizontal net opening is more variable (around 25 m). 
Detailed maps of the fishing-grounds of trawlers are reported in Murenu et al. (2010b). Most of the 
effort  is  concentrated  within  a  relative  short  distance  around  the  major  fishing  ports  (Cagliari, 
Alghero,  Porto  Torres,  La  Caletta,  Sant’antioco,  Oristano,  Alghero).  Moreover,  some  large  trawlers 
move seasonally in different fishing grounds far from the usual ports. 
Although hake is not a target of a specific fishery in GSA 11, it is the third species in terms of biomass 
landed  (Murenu  M.,  pers.  com.)  and  it  is  caught  exclusively  by  a  mixed  bottom  trawl  fishery  that 
operates at depth between 50 and 800 m. No gillnet or longline fleets target this species while it can 
be find as a by catch of gillnet fleets targeting other species (ex. Palinurus spp.). 

5.2.8.5.2  Management regulations applicable in 2015  
As  in  other  areas  of  the  Mediterranean,  management  is  based  on  the  control  of  fishing  capacity 
(licenses),  fishing  effort  (fishing  activity),  technical  measures  (mesh  size  and  area  closures),  and 
minimum landing sizes (EC 1967/06). 
By  the  actual  regulation,  cod  end  mesh  size  of  trawl  nets  are  40  mm  square  meshes  or  50  mm 
(stretched) diamond meshes. The minimum landing size for hake is 20 cm TL. 
In  the  GSA  11  there  are  five  coastal  Marine  Protected  Areas  (Asinara,  Capo  Caccia-Isola  Piana, 
Penisola  del  Sinis-Maldiventre,  Capo  Carbonara,  Tavolara)  and  two  small  offshore  closed  areas 
established  to  protect  Norway  lobster.  Moreover  the  use  of  trawl  nets  and  towed  gears  is  not 
allowed within 1.5 nautical miles of the coast (EU council regulation No 1967/2006) or at depths less 
than 50 m when this depth is reached within the distance above mentioned. 
Since 1991, a fishing closure for trawlers had been enforced almost every year. From 1991 to 2004 
and  in  2006  and  2011  the  fishing  closure  was  for  45  trawling  days.  From  2008  to  2015  (2011 

214 

 
 
 
 
 
 
 
 
 
 
excluded) the fishing closure was for 30 trawling days. In 2005 and 2007 the fishing closures had not 
been enforced. 

5.2.8.5.3  Landings  
Landings data were reported to STECF EWG 15-11 through the DCF. In GSA 11 the bulk of catches are 
from otter trawl, while artisanal fisheries represents the smallest part of the catches. Further, catch 
data for artisanal fisheries are discontinuous and variable in time (table 5.2.8.5.4.1). The time series 
of landings data (tons) by gear for the period 2005-2014 is shown in Figure 5.2.8.5.4.1. For the OTB, 
maximum landings values are observed in 2005 and minimum values (45t) in 2014. 

Figure 5.2.8.5.4.1. European hake in GSA 11. Total annual landings by gear for the period 2005-2014. 

Table 5.2.8.5.4.1. European hake in GSA 11. Annual landings (t) by gear in GSA 11 from the DCF data. 

Gear/Year  2005 
GTR 
LLS 
OTB 
total 

2006  2007  2008  2009  2010  2011  2012  2013  2014 
42.5 

28.6 

100.6  206.0 

765.4  421.7  176.7  278.7  260.5  175.9  277.4  175.9  195.8  45.0 
866.0  627.7  176.7  307.3  267.5  218.4  277.4  175.9  195.8  45.0 

7.0 

The available information of the size structure of landings is shown in Figure 5.2.8.5.4.2. 

215 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 5.2.8.5.4.2. European hake in GSA 11. Size structure of the landings from 2006 to 2014 from DCF. 

Comparing the information of total landings with those on lengths of landings it is clear that length 
data  are  missing  for  OTB  in  2005  and  for  GTR  in  2005,  2006  and  2008.  To  overcome  this  problem 
EWG 15-11 decides to exclude the 2005 from the analysis and to reconstruct the length information 
for  landings  only  for  the  years  where  total  values  have  been  reported.  Furthermore,  since  the  size 
distribution of GTR landings in 2010 seems to be unrealistic, EWG 15-11 decided to use the OTB size 
distribution  of  landing  to  reconstruct  and  fill  the  gap  of  the  missing  data  (see  the  data  quality 
paragraph). 

5.2.8.5.4  Discards  
Discards data were reported to STECF EWG 15-11 through the DCF. Information on OTB discards was 
available  for  2006  and  from  2009  to  2014,  and  on  GTR  only  for  2005  (Figure  5.2.8.5.5.1,  Table 
5.2.8.5.5.1). Furthermore in 2006 the reported value for discard in OTB seems to be overestimated. 

216 

 
 
 
 
 
 
 
 
Table 5.2.8.5.5.1. European hake in GSA 11. Annual discards in tons by gear from DCF. 

2006  2007  2008  2009  2010  2011  2012  2013  2014 

Gear/Year  2005 
386.9 
GTR 
LLS 
OTB 
total 

1036.7 
386.9  1036.7 

106.8  208.7  353.8 
106.8  208.7  353.8 

47.0 
47.0 

32.3  95.0 
32.3  95.0 

Data on the length frequency of discards by gear is available for the same time period of data on total 
values for OTB while for the GTR the LFD of discards of 2010 is not coherent with total values (Figure 
5.2.8.5.5.2). 

217 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 5.2.8.5.5.2. European hake in GSA 11. Size composition of the OTB discards by year from DCF. 

Looking  at  the  discard  information  by  length  for  GTR  EWG  15-11  considers  it  as  misreported  and 
decided to exclude it for the assessment. Further, to fill the gap of missing years for the discard of 
OTB was decided to apply a raising procedure (see next paragraphs, input data for stock assessment 
and data quality). 

5.2.8.5.5  Fishing effort  
The fishing capacity of the GSA 11 has shown in these last 10 years a progressive decrease. Fishing 
effort (kW*fishing days) performed by the GSA 11 trawlers decreased of 43% since 2006, from about 
6 milion to 3 milion in 2014. The effort displayed by the artisanal fleet showed an increase in the last 
two  years  for  vessels  using  trammel  nets  (GTR)  whereas  the  effort  of  gillnetters  (GNS)  in  2014 
increase  3  times  of  the  values  registered  in  2006  but  shows  an  anomalous  drop  in  2013  (Figure 
5.2.8.5.6.1). 

218 

 
 
 
 
 
 
 
 
 
Figure 5.2.8.5.6.1 Effort trends (days and kW*days) by major fleets, 2006-2014. 

5.2.8.6  Scientific surveys 
5.2.8.6.1    Survey #1 (MEDITS) 
5.2.8.6.1.1  Methods 
Based  on  the  DCF  data  call,  abundance  and  biomass  indices  were  recalculated.  In  GSA  11  the 
following numbers of hauls were reported per depth stratum (Table 5.2.8.6.1.1.1). 

Table 5.2.8.6.1.1.1. Numbers of hauls per year and depth stratum in GSA 11, 1994-2014. 

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 hauling duration. Hauls noted 
as valid were 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: 

Yst = Σ (Yi*Ai) / A 

V(Yst) = Σ (Ai² * si ² / ni) / A² 

Where: 

219 

Strata199419951996199719981999200020012002200320042005200620072008200920102011201220132014A161820212019191720181517192017181920192021B252023232222222519192022191919201918201919C202431313130312924242423242422242425232424D262224242323212220201820202121192020212121E292327272726302919181815161616161718181717 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  the 
assumptions over zero catch stations, and hence assumptions over the distribution of data. 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 
(subsamples  raised  to  standardized  haul  abundance  per  hour)  over  the  stations  of  each  stratum. 
Aggregated  length  frequencies  were  then  raised  to  stratum  abundance  *  100  (because  of  low 
numbers  in  most  strata)  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. 

5.2.8.6.1.2   Geographical distribution 
The  spatial  distribution  of  European  hake  has  been  described  by  modeling  the  spatial  correlation 
structure  of  the  abundance  indices  using  geostatistical  techniques  (i.e.  kriging).  In  different  studies 
either  total  abundance  index  or  abundances  of  recruits  and  adults  were  analysed  (Murenu  et  al., 
2007). 
On average, considering the analyzed yearly distributions (1994-2005), the recruits were considered 
individuals  smaller  than  12.3  cm  (±1.41).  These  individual  are  belonging  to  the  age  0  group. 
Persistence of the nursery areas along the years was studied by applying indicator kriging technique 
(Journel 1983, Goovaerts, 1997) to abundance estimations of recruits (Murenu et al., 2010a).  Main 
results and maps are reported in the “nursery section” of the SGMED 09-02 report. 

5.2.8.6.1.3   Trends in abundance and biomass 
Fishery  independent  information  regarding  the  state  of  hake  in  GSA  11  was  derived  from  the 
international survey MEDITS. Figure 5.2.8.6.1.3.1 displays the estimated trend in hake abundance and 
biomass in GSA 11. As shown below both for biomass and abundance in some years a high level of 
variability is evident. The estimated abundance and biomass indices since 1999 show high variation 
without any trend. 

220 

 
 
 
 
 
 
  
 
Figure  5.2.8.6.1.3.1.  Hake  in  GSA  11.  MEDITS  time  series  of  survey  biomass  and  density  indices  (mean  +/- 
standard deviation). 

5.2.8.6.1.4  Trends in abundance by length or age 
Boxplots  and  histograms  of  the  MEDITS  standardized  length  frequencies  distributions  (LFD)  are 
shown  in  Figure  5.2.8.6.1.4.1.  All  distributions  are  characterized  by  a  various  numbers  of  superior 
outliers. The median show a small variability, as well as a small variation of the degree of dispersion 
along  the  time  series.  The  greater  variability  is  to  account  to  the  total  abundances  (box  sizes  are 
proportional to numbers). 

221 

0200040006000800010000120001400016000180001994199619982000200220042006200820102012Density (N/km2)0501001502002503003504001994199619982000200220042006200820102012Biomass (kg/km2) 
 
 
 
 
 
 
Figure 5.2.8.6.1.4.1. Hake in GSA 11. Boxplot of the stratified length frequency distributions (MEDITS). 

The following Figure 5.2.8.6.1.4.2 displays the stratified abundance indices of European hake in GSA 
11 from 1994 to 2014. 

222 

 
 
 
 
 
 
 
Figure 5.2.8.6.1.4.1. Hake in GSA 11. Stratified abundance indices by size, 1994-2014. 

5.2.8.7  Stock Assessment 
5.2.8.7.1   Method: XSA 
The  hake  stock  was  assessed  for  the  last  time  during  in  EWG  13-09  using  as  input  data  the  period 
2005-2012, but due to the poor quality of data the assessment was not accepted. 
During  in  EWG  15-11  the  quality  of  landings  and  discard  information  was  checked  again,  and 
unfortunately the problems were not solved (see the paragraphs on catches and data quality). 
To overcome this situation and use as much as possible the available information EWG 15-11 decided 
to  operate  some  changes  in  the  discard  database  and  to  use  as  a  proxy  the  information  of  others 
years and gears to reconstruct the missing pieces of information needed for the assessment. 
Once the data have been corrected the FLR libraries were used to carry out an XSA based assessment 
(Darby and Flatman, 1994). XSA has been carried out using as input data the period 2006-2014 both 
for the catch data and for the tuning file. 

5.2.8.7.2  Input data 
The growth parameters used for VBGF were Linf= 100.7 cm TL; K = 0.2 yr-1; t0= -0.01 yr. The length-to-
weight coefficients used were a= 0.0044, b= 3.1457. 
Catch numbers have been raised taking into account the LFD that were missing for some years and 
gears (see data quality for details). 

223 

 
 
 
 
 
 
 
 
 
 
LFDA 5.0 slicing software has been used to transform the annual size distribution of the landings and 
MEDITS  LFDs  in  age  distributions  in  order  to  apply  XSA  model.  Since  the  catches  in  numbers  and 
weight  at  age  were  not  consistent  with  total  landings  at  age,  a  rescaling  procedure  using  Sum  Of 
Product correction (SOP) was carried out. 

A vector of natural mortality value by age was obtained using ProdBiom (Abella et al., 1997). Finally, 
zero values in the catch at age have been substituted with the lowest value in the time series. Table 
5.2.8.7.2.1  lists  the  input  parameters  to  the  XSA,  namely  landings,  catch  number  at  age,  weight  at 
age, maturity at age, natural mortality at age and the tuning series at age (MEDITS). 

Table 5.2.8.7.2.1. Hake in GSA 11. Input data to the XSA model. 

xsa initial settings 
max 
5 

min 
0 

5 

plusgroup  minyear  maxyear  minfbar  maxfbar 

2006 

2014 

0 

3 

Maturity and natural mortality vectors 
1 
0.57 
0.25 

2 
0.46 
0.9 

0 
1.15 
0 

age 
M 
Maturity 

3 
0.41 
1 

4 
0.38 
1 

5 
0.37 
1 

Catch (t) 

2006 
860 

2007 
327.33 

2008 
544.89 

2009 
367.33 

2010 
427.09 

2011 
631.27 

2014 
2013 
2012 
222.85  228.06  139.96 

Catch number at age matrix (thousands) 

age 
0 
1 
2 
3 
4 
5+ 

age 
0 
1 
2 
3 
4 
5 

2006 
43482 
2286 
386 
39 
9 
9 

2007 
9196 
1181 
171 
14 
3 
3 

2008 
15672 
2506 
120 
21 
11 
4 

Mean weight at age (kg) 

2006 
0.007 
0.133 
0.466 
0.98 
1.61 
2.292 

2007 
0.007 
0.133 
0.466 
0.98 
1.61 
2.292 

2008 
0.007 
0.133 
0.466 
0.98 
1.61 
2.292 

2009 
5896 
1827 
92 
9 
12 
6 

2009 
0.007 
0.133 
0.466 
0.98 
1.61 
2.292 

2010 
17473 
1216 
214 
27 
8 
2 

2010 
0.007 
0.133 
0.466 
0.98 
1.61 
2.292 

MEDITS number at age 
2007 
2006 
age 
41.5 
670.5 
0 
894.6 
2937.1 
1 
52.1 
318.9 
2 
9.4 
9.7 
3 

2008 
15.6 
1789.6 
331.4 
86.1 

2009 
169.9 
1096.9 
41 
2.4 

2010 
425.3 
5498.6 
325.5 
11.1 

2011 
15351 
3195 
168 
14 
2 
2 

2011 
0.007 
0.133 
0.466 
0.98 
1.61 
2.292 

2011 
131.2 
1448 
108.3 
10.7 

2012 
1291 
1101 
112 
9 
2 
1 

2012 
0.007 
0.133 
0.466 
0.98 
1.61 
2.292 

2013 
2222 
982 
91 
16 
7 
5 

2014 
5346 
516 
45 
4 
3 
2 

2013 
0.007 
0.133 
0.466 
0.98 
1.61 
2.292 

2014 
0.007 
0.133 
0.466 
0.98 
1.61 
2.292 

2014 
2013 
2012 
177.1 
33.7 
3.6 
932.3  1588.5  531.8 
48.6 
125.8 
44.4 
5 
9.9 
2.4 

224 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4 
5+ 

8.3 
0.3 

1.2 
0.6 

5 
0.1 

1 
0.1 

0.2 
0.2 

2.6 
0.1 

0.5 
0.3 

0.9 
0.4 

0.7 
0.2 

5.2.8.7.3  Results 
Sensitivity analyses were conducted to assess the effect of the main parameters on the assessment. 
Values  ranging  from  0.5  to  3  (0.5  increasing)  for  the  shrinkage  (Figure  5.2.8.7.3.1),  values  ranging 
from 1 to 3 for shrinkage years (Figure 5.2.8.7.3.2) and a combination of values between 2 to 4 for 
the qage parameter and from -1 to 1 for the rage parameter (Figure 5.2.8.7.3.3) have been compared 
and tested. Different combinations between the settings that looked more stable were tested. 

Figure 5.2.8.7.3.1. Hake in GSA 11. Sensitivity on shrinkage weight. 

225 

 
 
 
 
 
 
 
 
 
Figure 5.2.8.7.3.2. Hake in GSA 11. Sensitivity on shrinkage age. 

Figure 5.2.8.7.3.3. Hake in GSA 11. 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 these were the following: 

226 

 
 
 
 
 
 
 
 
 
 
Fbar 
0-3 

fse 
1 

rage 
0 

qage 
4 

Shk.n 
true 

Shk.f 
true 

shk.yrs 
3 

shk.age 
3 

The residuals pattern of the MEDITS trawl survey is shown in Figure 5.2.8.7.3.4. 

Figure 5.2.8.7.3.4. Hake in GSA 11. XSA residuals for the MEDITS survey from 2006 to 2014. 

The results of the retrospective analysis are shown in Figure 5.2.8.7.3.5. 

Figure 5.2.8.7.3.5. Hake in GSA 11. XSA retrospective analysis. 

The results of the XSA are shown in the figure 5.2.8.7.3.6. 

227 

 
 
 
 
 
 
 
 
 
 
 
 
Figure 5.2.8.7.3.6. Hake in GSA 11.  XSA summary results. SSB and catch are  in tonnes, recruitment in  1000s 
individuals. 

In the tables 5.2.8.7.3.1, 5.2.8.7.3.2 and 5.2.8.7.3.3 the population estimates of hake obtained by XSA 
are provided. 

Table 5.2.8.7.3.1. Hake in GSA 11. Stock numbers at age (thousands) as estimated by XSA. 

Age 

2006 

2007 

2008 

2009 

0 
1 
2 
3 
4 
5+ 

83307.6  27844.2  37213.8  16992.3 
2964.8 
3576.2 
174.9 
551.4 
26.7 
56 
19.9 
12.3 
9.1 
11.6 

3641.9 
192.6 
55.2 
16.3 
5.6 

1910.6 
303.1 
41.8 
5.5 
5.3 

2010 
45482 
2062.9 
302.5 
37.3 
10.5 
2.6 

2011 
32613.7 
4569.3 
251.9 
21.1 
2.9 
2.2 

2012 
6787 
1688.7 
181.6 
25.6 
2.8 
2.2 

2013 
6315.2 
1422.6 
127.3 
25.6 
9.4 
6.6 

2014 
15474.6 
749.5 
66.1 
7.7 
3.7 
2.9 

Table 5.2.8.7.3.2. Hake in GSA 11. XSA summary results. 

Age 
Fbar(0-3) 
rec 
SSB (t) 

2006 
2.14 

2007 
1.10 

2008 
1.49 

2009 
1.07 

2010 
1.76 

2011 
1.98 

2012 
1.13 

2013 
1.84 

2014 
1.61 

83307.6  27844.2  37213.8  16992.3  45482  32613.7  6787  6315.2  15474.6 

451.4 

252.5 

295.1 

251.0 

254.8 

288.0 

166.9  156.1 

72.7 

Table 5.2.8.7.3.1. Hake in GSA 11. F at age as estimated by XSA. 
2010 
1.15 
1.53 
2.20 
2.16 
2.20 

2006 
2.63 
1.90 
2.12 
1.91 
1.94 

2008 
1.38 
2.47 
1.52 
0.61 
1.57 

2007 
0.88 
1.72 
1.24 
0.53 
1.20 

2009 
0.96 
1.71 
1.08 
0.52 
1.23 

Age 
0 
1 
2 
3 
4 

2011 
1.81 
2.66 
1.83 
1.62 
1.96 

2012 
0.41 
2.02 
1.50 
0.59 
1.38 

2013 
0.98 
2.50 
2.35 
1.53 
2.26 

2014 
0.95 
2.47 
1.88 
1.12 
1.86 

228 

 
 
 
 
 
 
 
 
 
 
 
5 

1.94 

1.20 

1.57 

1.23 

2.20 

1.96 

1.38 

2.26 

1.86 

The  XSA  results  summarized  in  Table  5.2.8.7.3.2  show  a  decreasing  trend  in  the  SSB,  a  great 
fluctuation on recruitment and an estimated Fcurr of 1.61. 

5.2.8.8  Reference points 
5.2.8.8.1  Methods 
The XSA package used 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  by 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. 

5.2.8.8.2  Input data  

The input parameters were the same used for the XSA stock assessment and its results. 

5.2.8.8.3  Results 

Table 5.2.8.8.3.1. Hake in GSA 11. Main reference points defined with the Yield per recruit analysis. 

refpt 
virgin 
msy 
crash 
f0.1 
fmax 
spr.30 

harvest 
0 
0.25 
15.64 
0.17 
0.25 
0.25 

yield 
0 
0.05 
0.01 
0.05 
0.05 
0.05 

rec 
1 
1 
1 
1 
1 
1 

ssb 
0.7 
0.22 
0 
0.3 
0.22 
0.21 

biomass 
0.74 
0.26 
0.01 
0.34 
0.26 
0.25 

The  current  level  of  F  is  about  1.61,  which  is  higher  than  the  estimated  value  for  FMSY  (0.17)  and 
hence the stock of hake in GSA 11 is being fished above FMSY. 

5.2.8.9  Data quality 
Data  from DCF  2014  as  submitted  through  the  Official  data  call  in  2015  were  checked  for  hake.  As 
already highlighted in the text before, catches information for the artisanal fleets (GTR and LSS) are 
represented only in some years (Figure 5.2.8.9.1) and sometimes there is no relation in time with the 
data on lengths of catches. 

229 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 5.2.8.9.1. Hake in GSA 11. Total annual catches by gear for the period 2005-2014 from the DCF (original 
values not modified). 

In particular, although the DCR database has values for total landings of hake in GSA 11, data at length 
are missing for some years and gears (OTB in 2005 and for GTR in 2005, 2006 and 2008). Similarly a 
gap for total values of discards (GTR 2010) was detected while some data of discard at length were 
present.  It  was  also  noted  that  the  size  distribution  of  both  GTR  landings  (2010)  and  GTR  discard 
(2005, 2010) seems to be unrealistic for this species. 
The last problem  identified were some  unusual value for total discards and numbers of discards at 
age in some years (OTB, 2006; GTR, 2005). 
To overcome these data quality problems of GSA 11, a deep check of information was made in the 
first  days  of  the  meeting  and  it  was  decided  to  fill  gaps  and  correct  records  in  order  to  be  able  to 
successfully perform the assessment. 
In the catch table some records (OTB 2006, id 224701 and GTR 2005, id: 2266154) were identified as 
errors and were modified (Table 5.2.8.9.1). 

Table 5.2.8.9.1. Hake in GSA 11. Revision of catch data information. 

After these changes, a raising procedure was applied to fill the gaps in total values of discard when 
was not compulsory to collect discards (2007 and 2008). Using the mean discard % calculated for the 
time  series  2006  and  2009-2014,  a  rising  factor  of  85%  was  applied  to  landing  values  for  deriving 

230 

 
 
 
 
 
 
 
 
 
 
 
 
 
discard  total  quantities  in  the  missing  years  (Table  5.2.8.9.2,  Figure  5.2.8.5.4.2).  The  same  rising 
factor was also applied to 2005 because total discard was missing. 

Table 5.2.8.9.2. Hake in GSA 11. Revision of catch data information (*new values derived from the raising; # 
new correct value). 

Catches  Gear/Year 
landing 
landing 
discard 

GTR 
OTB 
OTB 
total 

2009 

2007 

2006 
206.0 
421.7 

2005 
100.6 
765.4 
652.6*  232.6#  150.6*  237.6*  106.8  208.7  353.8 
1519 

2008 
28.6 
278.7 

2010 
42.5 

260.5  175.9  277.4  175.9  195.8 
32.3 

45.0 
95.0 
367.3  427.1  631.3  222.9  228.1  140.0 

2012 

2013 

2014 

544.9 

327.3 

860.3 

176.7 

2011 

47.0 

Figure 5.2.8.5.4.2. Hake in GSA 11. Total annual landings by gear corrected for the period 2006-2014 (values 
corrected by EWG 15-11). 

Once all the gaps of total catches were filled the LFD of OTB discards of 2009 were used to raise the 
LFD of discards for OTB in 2007 and 2008 (Figure 5.2.8.5.4.3). 

231 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 5.2.8.5.4.3. Hake in GSA 11. Corrected size composition of discards for OTB (period 2006-2014). 

Finally to derive the LFD of landings for GTR (years 2006, 2008 and 2010) the ratio of total landings 
between  GTR  and  OTB  was  used  by  year  as  a  rising  factor  for  each  size  class  of  the  LFD  of  OTB 
landings (Figure 5.2.8.5.4.4). 

Figure 5.2.8.5.4.4. Hake in GSA 11. Corrected size composition of landings for GTR (period 2006-2014). 

232 

 
 
 
 
 
 
 
 
 
 
 
5.2.8.10 Short term predictions 2015-2017 
5.2.8.10.1  Method 
A  deterministic  short  term  prediction  for  the  period  2015  to  2017  was  performed  using  the  FLR 
routines  provided  by  JRC  and  based  on  the results  of  the XSA  stock assessments performed  during 
EWG 15-11. 

5.2.8.10.2  Input parameters  
The input parameters were the same used for the XSA 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 (8720.879 thousand individuals). 

5.2.8.10.3  Results 

Table 5.2.8.10.3.1. Hake in GSA 11. Short term forecast in different F scenarios. Basis: F(2015) = mean(Fbar0-3 
2012-2014)=  0.294;  R(2015)  =  geometric  mean  of  the  recruitment  of  the  last  3years;  R  =  8720  (thousands); 
SSB(2015) = 73 t, Catch (2014)= 140 t. 

Rationale 

Ffactor 

Fbar 

Zero catch 

0 

0 

Catch 
2015 
229 

Catch 
2016 
0 

Catch 
2017 
0 

SSB 
2016 
95 

SSB 
2017 
470 

Change SSB 
2016-2017(%) 
395.49 

Change Catch 
2014-2016(%) 
-100 

High long 
term yield 
(F0.1) 
Status quo 

Different 
Scenarios 

0.11 

0.166 

229 

41 

107 

95 

381 

301.21 

-70.66 

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 

1.49 

0.15 

0.30 

0.45 

0.60 

0.75 

0.90 

1.05 

1.20 

1.34 

1.64 

1.79 

1.94 

2.09 

2.24 

2.39 

2.54 

2.69 

2.84 

2.99 

229 

229 

229 

229 

229 

229 

229 

229 

229 

229 

229 

229 

229 

229 

229 

229 

229 

229 

229 

229 

190 

37 

68 

94 

115 

134 

149 

162 

173 

182 

198 

204 

209 

214 

218 

222 

225 

228 

231 

233 

-9.45 

309.93 

240.42 

183.84 

137.68 

99.92 

68.95 

43.47 

22.45 

5.03 

-21.54 

-31.67 

-40.21 

-47.43 

-53.57 

-58.81 

-63.32 

-67.20 

-70.57 

-73.51 

86 

389 

323 

269 

226 

190 

160 

136 

116 

100 

74 

65 

57 

50 

44 

39 

35 

31 

28 

25 

95 

95 

95 

95 

95 

95 

95 

95 

95 

95 

95 

95 

95 

95 

95 

95 

95 

95 

95 

95 

182 

99 

158 

192 

208 

214 

213 

208 

200 

191 

172 

163 

154 

146 

138 

131 

124 

118 

113 

107 

233 

36.08 

-73.41 

-51.32 

-32.92 

-17.52 

-4.58 

6.35 

15.61 

23.50 

30.26 

41.13 

45.52 

49.36 

52.75 

55.75 

58.42 

60.81 

62.96 

64.90 

66.67 

 
 
 
 
 
 
 
 
 
 
A short term projection (Table 5.2.8.10.3.1), assuming an Fstq of 0.166 in 2015 and a recruitment of 
8720 thousands individuals show that: 

  Fishing at the Fstq (1.49) generates an increase of the catch of about 36% from 2014 to 2016 

along with a small decrease of the spawning stock biomass from 2016 to 2017. 

  Fishing at F0.1 (0.166) generates a decrease of the catch of about 70% from 2014 to 2016 and 

an increase of the spawning stock biomass of about 300% from 2016 to 2017. 

5.2.8.11 Medium term predictions 
5.2.8.11.1  Method 
Medium  term  was  not  conducted  because  no  meaningful  stock-recruitment  relationship  was 
estimated. 

5.2.8.12 Stock advice 
The current F (1.6) is larger than F0.1 (0.17), chosen as proxy of FMSY and as the exploitation reference 
point  consistent  with  high  long  term  yields,  which  indicates  that European  hake  in  GSA  11  is  being 
fished  above  FMSY.  Catches  of  European  hake  in  2016  consistent  with  FMSY  should  not  exceed  41 
tonnes. 

5.2.8.13 Management strategy evaluation 
Management Strategy Evaluation was conducted to evaluate if the MSY ranges were precautionary. 
The  FMSY  ranges  were  derived  using  the  formula  provided  by  STECF  15-09.  F  ranges  results  were 
Fupper=0.24 and Flower=0.12. Blim was estimated as Bloss=73 (t). The following figure shows the results of 
the MSE.  

Figure 5.2.8.14.1. Hake in GSA 11. Marine Strategy Evaluation. 

234 

 
 
 
 
 
 
 
 
 
 
 
The probability of SSB to fall below Blim at F > Fupper is equal to 0, even if the F never reaches the F 
upper estimated by the empirical relationship. The dynamics observed for this stock are the result of 
the stock assessment model (i.e. XSA) settings used inside the MSE becoming less appropriate as the 
stock status changes in time (i.e. stock assessment settings are often specific to a particular range of 
stock status). This leads to an increasing difference between the perceived stock and the operating 
model (i.e. the 'true' stock). To avoid this behaviour in the future, for some of the stocks as it is the 
case  here,  a  more  general  stock  assessment  method  should  be  used  in  the  MSE  loop  that  is  less 
sensitive to the stock status. 

235 

 
 
 
 
 
5.2.9 STOCK ASSESSMENT OF HAKE IN GSA 1-7 

5.2.9.1  Stock Identification 
The  delimitation  of  the  hake  stock  in  GSAs  1-7  (GSA  1,  GSA  5,  GSA  6  and  GSA  7)  is  considered 
unknown.  A  parallel  study  (STOCKMED:  Fiorentino  et  al.,  2015)  on  the  European  hake  (and  other 
commercial species) stock potential distribution in the Mediterranean Sea has been funded by the EU 
and undertaken under the framework of the MAREA Project. This study suggested that there are two 
stocks of European hake in the Western Mediterranean Sea: one distributed from the Alboráan Sea 
to the Gulf of Lions and another one from the Gulf of Lions to the Strait of Sicily and beyond. In the 
view of those findings, STECF  EWG 15-11 was asked to assess the state of European hake stocks in 
the Western Mediterranean Sea following two approaches: by single GSAs and GSAs combined. The 
present assessment will investigate the state of the hake stock in GSAs 1, 5, 6 and 7. 

5.2.9.2  Growth 
Growth  parameters  are  those  used  in  each  GSA  (see  sections  of  GSA1,  GSA5,  GSA6  and  GSA7 
assessments). 

5.2.9.3  Maturity 
Maturity  ogives  were  taken  from  each  GSA  (see  sections  of  GSA1,  GSA5,  GSA6  and  GSA7 
assessments).  Combined  maturity  at  age  were  calculated  as  a  weighted  average  using  the  stock 
numbers. 

5.2.9.4  Natural mortality 
Natural  mortality  was  taken  from  each  GSA  (see  sections  of  GSA1,  GSA5,  GSA6  and  GSA7 
assessments).  Combined  natural  mortality  at  age  were  calculated  as  a  weighted  average  using  the 
stock numbers. 

5.2.9.5  Fisheries 
5.2.9.5.1  General description of the fisheries 
See sections of GSA 1, GSA 5, GSA 6 and GSA 7 assessments. 

5.2.9.5.2  Management regulations applicable in 2015  
See sections of GSA 1, GSA 5, GSA 6 and GSA 7 assessments. 

236 

 
 
 
 
 
 
 
 
 
 
 
5.2.9.5.3   Catches  
Hake annual catches (t) by fleet over the period 2003 to 2014 are the sum of those in each  
(see sections of GSA 1, GSA 5, GSA 6 and GSA 7 assessments). 

GSA 

5.2.9.5.4  Landings  
Hake annual landings (t) by fleet over the period 2003 to 2014 are the sum of those in each   GSA 
(see sections of GSA 1, GSA 5, GSA 6 and GSA 7 assessments). 
5.2.9.5.5  Discards  
Hake annual discards (t) by fleet over the period 2003 to 2014 are the sum of those in each GSA (see 
sections of GSA 1, GSA 5, GSA 6 and GSA 7 assessments). 

5.2.9.5.6  Fishing effort  
See fishing effort by fleet and GSA in the corresponding sections of GSA 1, GSA 5, GSA 6 and GSA 7 
assessments. 

5.2.9.6  Scientific surveys 
5.2.9.6.1    Survey #1 (MEDITS) 
5.2.9.6.1.1  Methods 
See sections of GSA 1, GSA 5, GSA 6 and GSA 7 assessments for data description. Individual MEDITS 
tuning indexes from the different GSAs were used in the assessment model separately so that timing 
of the sureys could be accommodated and catchability could vary across surveys. Numbers at age by 
survey (Figure 5.2.9.6.1.1) show poor tracking of yearly cohorts within and across surveys, with the 
exception of a strong cohort in 2008 in GSA 7. There is a general increase in the last two years of age 
class  2  in  GSA  7  and  age  class  3-4  in  GSA  1  and  5,  which  is  difficult  to  observe  in  younger  ages  in 
previous years.  

237 

 
 
 
 
 
 
 
 
 
 
Fig. 5.2.9.6.1.1. Hake in GSA 1-7. MEDITS tuning indices (numbers at age(1-5+)) for different GSAs, h1= GSA 1, 
h5= GSA 5, h6 = GSA 6, h7 = GSA 7. 

Overall the internal consistency of the surveys is not high. MEDITS GSA 1, is not able of  tracking the 
cohorts except for a weak signal between age 0-1 and 1-2 (Figure 5.2.9.6.1.2). 

Figure 5.2.9.6.1.2. Hake in GSA 1-7. Internal consistency of MEDITS in GSA 1 for Mediterranean hake. 

MEDITS GSA 5 is not able of tracking the cohorts except for a weak signal in age 1-2 and 2-3 (Figure 
5.2.9.6.1.3).   

Figure 5.2.9.6.1.3. Hake in GSA 1-7. Internal consistency of MEDITS in GSA 5 for Mediterranean hake. 

In MEDITS GSA 6 is not able of tracking the cohorts except for a weak signal in age 1-2 and 2-3 (Figure 
5.2.9.6.1.4).   

238 

 
 
 
 
 
 
 
 
 
 
Figure 5.2.9.6.1.4. Hake in GSA 1-7. Internal consistency of MEDITS in GSA 6 for Mediterranean hake. 

MEDITS  GSA  7  is  not  able  of  tracking  the  cohorts  except  for  a  weak  signal  in  age  1-2  (Figure 
5.2.9.6.1.5)   

Figure 5.2.9.6.1.5. Hake in GSA 1-7. Internal consistency of MEDITS in GSA 7 for Mediterranean hake. 

5.2.9.6.1.2   Geographical distribution 
See sections of GSA1, GSA5, GSA6 and GSA7 assessments. 

239 

 
 
 
 
 
 
 
 
 
  
 
5.2.9.6.1.3   Trends in abundance and biomass 
See sections of GSA1, GSA5, GSA6 and GSA7 assessments. 

5.2.9.6.1.4  Trends in abundance by length or age 
See sections of GSA1, GSA5, GSA6 and GSA7 assessments. 

5.2.9.7  Stock Assessment: XSA 
5.2.9.7.1   Method: XSA 
This stock was assessed through XSA, using an ad hoc R-script and input data over the period 2003-
2014. 

5.2.9.7.2  Input data 
Catch and catch numbers at age input data were generated merging the XSA input data used in the 
assessments  performed  by  single  GSA.  Four  tuning  MEDITS  files  were  used,  one  per  each  GSA,  the 
same  as  used  in  the  assessments  by  single  GSA  (see  sections  of  GSA1,  GSA5,  GSA6  and  GSA7 
assessments).  

HKE 1_7 Catch 

s
d
n
a
s
u
o
h
t

90000
80000
70000
60000
50000
40000
30000
20000
10000
0

0

1

2

3

4

5+

ages 

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

Figure 5.2.9.7.2.1. Hake in GSAs 1-7. Catch at age.  

Table  5.2.9.7.2.1.  Hake  in  GSA  1-7.    Lists  the  input  parameters  to  the  XSA,  namely  landings,  catch 
numbers at age and weight at age. 

Table 5.2.9.7.2.1. Hake in GSA 1-7.  Input data to the XSA. 

2004 

2005 

2006 

2007 

2008 

Catch (t) 
2009 

2010 

2011 

2012 

2013 

2014 

5683.1 

5989.2 

6716.3 

5534.2 

6961.9 

8085.9 

5999.6 

5419.2 

4567.6 

5497.6 

4649.9 

2003 

7293 

Age 

2003 

2004 

2005 

2006 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

Catch number at age matrix (thousands) 

0 

76179.8 

81210.0 

61699.8 

71834.6 

48463.3 

78948.7 

62256.3 

40304.5 

11676.7 

13682.3 

14037.6 

15006.9 

240 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1 

2 

3 

4 

5+ 

32757.6 

25817.2 

25844.2 

25508.0 

19028.6 

35593.2 

39365.6 

27817.6 

28213.3 

27713.6 

32390.5 

22558.6 

4726.8 

3086.3 

4139.5 

4490.5 

4491.3 

3911.3 

4776.3 

4688.7 

4308.3 

3042.8 

3835.7 

4143.0 

644.2 

406.1 

519.2 

690.1 

639.1 

428.2 

678.0 

471.6 

436.2 

304.0 

221.5 

177.5 

92.8 

21.7 

32.1 

15.6 

48.0 

13.6 

58.9 

16.4 

83.6 

22.2 

70.8 

12.6 

117.6 

74.3 

28.5 

33.1 

17.7 

11.9 

79.1 

7.9 

7.4 

2.8 

3.6 

3.2 

Age 

0 

1 

2 

3 

4 

5+ 

2003 

0.018 

0.107 

0.354 

0.772 

1.605 

2.976 

2004 

0.018 

0.104 

0.371 

0.788 

1.462 

2.654 

2005 

0.019 

0.105 

0.379 

0.808 

1.411 

4.027 

2006 

0.020 

0.107 

0.418 

0.830 

1.332 

2.794 

Weight at age (kg) 

2007 

0.019 

0.111 

0.391 

0.861 

1.517 

2.312 

2008 

0.021 

0.094 

0.363 

0.881 

1.531 

2.305 

2009 

0.022 

0.105 

0.360 

0.720 

1.604 

2.708 

2010 

0.018 

0.111 

0.358 

0.751 

1.602 

2.268 

2011 

0.029 

0.111 

0.362 

0.749 

1.607 

2.375 

2012 

0.029 

0.100 

0.362 

0.792 

1.592 

2.052 

2013 

0.031 

0.108 

0.345 

0.942 

1.691 

2.751 

2014 

0.023 

0.121 

0.333 

0.908 

1.711 

3.636 

5.2.9.7.3  Results 
Different  sensitivity  analyses  were  performed  before  running  the  final  XSA,  considering  different 
shrinkage  weight  (0.5-2.5),  shrinkage  ages  (1,2,3),  rage  (-1,0,1)  and  qage  (2,3,4).  Comparison  of 
trends  between  settings  has  been  done.  Different  combinations  between  the  settings  that  looked 
more stable were further tested. 

Figure 5.2.9.7.3.1. Hake in GSAs 1-7. Sensitivity on shrinkage weight. 

Figure 5.2.9.7.3.2. Hake in GSAs 1-7. Sensitivity on shrinkage age. 

Figure 5.2.9.7.3.3. Hake in GSAs 1-7. Sensitivity on rage and qage. 

241 

 
 
 
 
 
 
 
 
 
 
 
 
 
The  following  settings  that  minimized  the  residuals  and  showed  the  best  diagnostics  outpout  were 
used for the final XSA final run: 

Fbar 
1-3 

fse 
1.5 

rage 
1 

qage 
4 

Shk.n 
TRUE 

Shk.f 
TRUE 

Shk.yrs 
3 

Shk ages 
2 

The residuals pattern of the MEDITS trawl survey in each GSA are shown in Figure 5.2.9.7.3.4. 

242 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 5.2.9.7.3.4. Hake in GSA 1-7. XSA residuals for MEDITS survey in each GSA. 

The results of the retrospective analysis are shown in Figure 5.2.9.7.3.5. 

Figure 5.2.9.7.3.5. Hake in GSA 1-7. XSA retrospective analysis. 

The results of the XSA are shown in the following figure. 

243 

 
 
 
 
 
 
 
 
 
 
 
 
Figure 5.2.9.7.3.6. Hake in GSA 1-7. XSA summary results. SSB and catch are in tonnes, recruitment in 1000s 
individuals. 

In tables 5.2.9.7.3.1 and 2 the population estimates of hake in GSAs 1-7 obtained by XSA are given. 

Table 5.2.9.7.3.1 European hake in GSAs 1-7. Stock numbers at age (thousands) as estimated by XSA. 
2008 

2007 

2004 

2005 

2006 

2009 

2010 

2012 

2011 

2013 

2003 

2014 

423850  418100  363900  389290  565160  533400  424010  370190  323600  410250  457670  515270 
58620 
66007 
7770 
9578 
485 
1147 
80 
138 
21 
30 

52388 
8393 
1166 
147 
37 

55252 
8819 
1187 
156 
42 

59062 
6730 
711 
53 
23 

87408 
8738 
920 
165 
28 

79199 
9648 
1265 
198 
126 

61851 
9039 
799 
103 
10 

57104 
5151 
472 
79 
7 

57976 
8447 
968 
78 
21 

55365 
7215 
743 
42 
10 

68812 
6609 
427 
31 
6 

age 
0 
1 
2 
3 
4 
5+ 

Table 5.2.9.7.3.2. Hake in GSAs 1-7. XSA summary results. 
TB (t) 

SSB (t) 

2003 
2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 

Fbar 1-3  Recruitment 
(thousands) 
423850 
418100 
363900 
389290 
565160 
533400 
424010 
370190 
323600 
410250 

1.74 
1.27 
1.19 
1.31 
1.23 
1.08 
1.53 
1.67 
1.71 
1.75 

244 

11736.3 
6837.2 
7716.3 
8888.7 
9159.3 
9919.1 
11750.2 
9316 
6650.9 
5186.1 

19445 
16772 
17086 
18532 
21278 
23809 
22538 
17622 
18750 
19882 

 
 
 
 
 
 
 
 
 
 
 
2013 
2014 

1.43 
1.09 

457670 
515270 

6318.6 
8113 

24335 
22128 

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

0 
0.48 
0.52 
0.43 
0.50 
0.22 
0.38 
0.38 
0.28 
0.09 
0.08 
0.09 
0.08 

1 
1.43 
1.10 
1.09 
1.12 
0.86 
1.16 
1.36 
1.24 
1.52 
1.33 
1.31 
1.03 

F at age 

2 
1.63 
1.15 
1.23 
1.31 
1.48 
1.12 
1.56 
1.62 
1.99 
1.80 
1.86 
1.57 

3 
2.17 
1.56 
1.25 
1.51 
1.35 
0.96 
1.67 
2.15 
1.62 
2.11 
1.14 
0.67 

4 
1.92 
1.81 
1.59 
0.64 
1.32 
0.80 
1.33 
2.50 
2.14 
0.75 
1.52 
0.21 

5+ 
1.92 
1.81 
1.59 
0.64 
1.32 
0.80 
1.33 
2.50 
2.14 
0.75 
1.52 
0.21 

The XSA results summarized in Table 5.2.9.7.3.2 and in Figure 5.2.9.7.3.6. show a decreasing trend in 
landings since the peak in 2009, a fluctuation of recruitment and SSB, increasing in the last years, and 
an estimated Fsq of 1.40. 

5.2.9.8  Stock Assessment: a4a 
5.2.9.8.1   Method: a4a 
The assessment  was run with the FLR a4a model on the same input data as in the XSA run for the 
combined Mediterranean Hake assessment (GSA 1-7).  

5.2.9.8.2  Input data 
Different  settings  in  the  model  runs  were  specified  to  get  to  the  best  fitting  model.  The  stock 
recruitment model (srmod) was kept fixed across models and allowed to change yearly. The fishing 
mortality  model  (fmod)  was  specified  with  different  splines  and  or  breakpoints.  The  catchability 
model (qmod) was mainly allowing catchability to vary by age class or year. 

# Mod 1 
fmod <- ~factor(year) + factor(age) 
srmod <- ~factor(year) 
fit1 <- sca(stock = stk, indices = flq.idx, fmodel = fmod,  fit = "assessment") 

# Mod 2 
qmod <- list(~factor(age), ~factor(age), ~factor(age), ~factor(age)) 
fit2 <- sca(stock = stk, indices = flq.idx, fmodel = fmod, qmodel = qmod,  fit 
= "assessment") 

# Mod 3 
qmod <- list(~s(age, k=3), ~s(age, k=3),  ~s(age, k=3),  ~s(age, k=3)) 
fit3 <- sca(stock = stk, indices = flq.idx, fmodel = fmod, qmodel = qmod,  fit 
= "assessment") 

# Mod 4 

245 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
fmod <- ~s(year, k=5) + s(age, k=3) 
qmod <- list(~s(age, k=3), ~s(age, k=3), ~s(age, k=3), ~s(age, k=3)) 
fit4 <- sca(stock = stk, indices = flq.idx, fmodel = fmod, qmodel = qmod,  fit 
= "assessment") 

# Mod 41 
fmod <- ~s(year, k=10) + s(age, by=breakpts(year, 2009), k=6) 
qmod <- list(~s(age, k=3), ~s(age, k=5), ~s(age, k=5), ~s(age, k=3)) 
fit41 <- sca(stock = stk, indices = flq.idx, fmodel = fmod, qmodel = qmod,  fit 
= "assessment") 

# Mod 5 
fmod <- ~ s(year, k=10) + s(age, k=5) 
fit5 <- sca(stock = stk, indices = flq.idx, fmodel = fmod, qmodel = qmod,  fit 
= "assessment") 

# Mod 6 
fmod <- ~ s(year, k=10) + s(age, k=5) 
qmodel <- list(~ te(age, year, k = c(5,10)), ~ te(age, year, k = c(5,10)), 
               ~ te(age, year, k = c(5,10)),  ~ te(age, year, k = c(5,10))) 
fit6 <- sca(stock = stk, indices = flq.idx, fmodel = fmod, qmodel = qmod,  fit 
= "assessment") 

# Mod 7 
fmod <- ~s(year, k=10) + s(age,  k=5)  
qmod <- list(~s(age, k=3), ~s(age, k=3),~s(age, k=3),~s(age, k=3)) 
fit7 <- sca(stock = stk, indices = flq.idx, fmodel = fmod, qmodel = qmod,  fit 
= "assessment") 

# Mod 8 
fmod <- ~s(year, k=10) + s(age,  k=5)   
qmod <- list(~s(age, k=3), ~s(age, k=3),~s(age, k=3),~s(age, k=3)) 
fit8  <-  sca(stock  =  stk,  indices  =  flq.idx,  fmodel  =  fmod,  qmodel  =  qmod, 
srmodel=srmod,  fit = "assessment") 

# Mod 10  
fmod <- ~s(year, by=breakpts(age, 2), k=10) + s(age,  k=4)  
#qmod <- list(~s(age, k=3), ~s(age, k=3)) 
qmod  <-  list(~  s(age,  k=5)  +  s(year,  k=10),  ~  s(age,  k=5),  ~  s(age,  k=5),  ~ 
s(age, k=3)) 
fit10  <-  sca(stock  =  stk,  indices  =  flq.idx,  fmodel  =  fmod,  qmodel  =  qmod, 
srmodel=srmod,  fit = "assessment") 

# Mod 12  
fmod <- ~s(year, by=breakpts(age, 2), k=10) + s(age,  k=4)  
qmod  <-  list(~  s(age,  k=5)  +  s(year,  k=10),  ~  s(age,  k=5),  ~  s(age,  k=5),  ~ 
s(age, k=3)) 
fit12  <-  sca(stock  =  stk,  indices  =  flq.idx,  fmodel  =  fmod,  qmodel  =  qmod, 
srmodel=srmod,  fit = "assessment") 

Based on model fitting and residual patterns, Model 41, were F is modelled as a function of smooth 
of year and smooth of age with a breakpoint in 2009 and catchability is modelled as a spline of age, 
with variable degrees of freedom (k), depending from survey. 

246 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5.2.9.8.3  Results 
The log residuals of the abundance indices (Figure 5.2.9.8.3.1) don’t show any clear trends, except in 
the age 0 in from the Alboran Sea survey (M1), which was not possible to improve. The residuals of 
the catch are acceptable. 

Figure 5.2.9.8.3.1. Hake in GSA 1-7.  Log Catch residual of catch and abundance indices (H7 = Gulf of Lions 
(GSA 7), H6 = Catalan Sea (GSA 6), H5 = Balearic Islands (GSA 5), H1 = Alboran Sea (GSA 1). 

Model fit 41 is also able of predicting the catch numbers reasonably well in all years except 204 and 
2010 (Figure 5.2.9.8.3.2). 

247 

 
 
 
 
 
 
 
 
Figure 5.2.9.8.3.2. Hake in GSA 1-7. Predicted vs observed catch numbers for all areas combined. 

The a4a model prediction of the catches in the MEDITS survey does not fit well the observed catches 
in 2013, 2008 and 2003 in the GSA 5 survey (Figure 5.2.9.8.3.3). 

248 

 
 
 
 
 
 
 
Figure 5.2.9.8.3.3. Hake in GSA 1-7. Fitted vs observed caches in MEDITS survey in the GSA 5 MEDITS. 

The a4a model prediction of the catches in the MEDITS survey in GSA 6 does not fit well the observed 
catches except in 2003 and 2009 (Figure 5.2.9.8.3.4). 

249 

 
 
 
 
 
 
 
 
Figure 5.2.9.8.3.4. Hake in GSA 1-7.  Observed and predicted catch in numbers in MEDITS performed in the 
Catalan Sea (GSA 6). 

The a4a model prediction of the catches in the MEDITS survey in GSA 1 does not fit well the observed 
catches except in 2004-2005 and 2008 (Figure 5.2.9.8.3.5). 

250 

 
 
 
 
 
 
 
 
Figure 5.2.9.8.3.5. Hake in GSA 1-7. Observed vs predicted catch numbers in MEDITS survey performed in 
Alboran Sea (GSA 1). 

The a4a model prediction of the catches in the MEDITS survey in GSA 7 does not fit well the observed 
catches except in 2005-2007 and 2014 (Figure 5.2.9.8.3.6). 

251 

 
 
 
 
 
 
 
 
Figure 5.2.9.8.3.6. Hake in GSA 1-7. Observed vs predicted catch numbers in MEDITS survey performed in Gulf 
of Lions (GSA 7). 

The estimated fishing mortality at age (Figure 5.2.9.8.3.7) shows an increase in mortality in age 2-3 in 
recent years before a general decrease in F. 

252 

 
 
 
 
 
 
 
Figure 5.2.9.8.3.7. Hake in GSA 1-7. Fishing mortality at age estimate from the a4a model 41. 

The perception of the stock from model 41 (Figure 5.2.9.8.3.8) shows a variable recruitment, with an 
upward trend in the recent years. SSB varies around 7000 tons and after the lowest point in 2011-
2013 is increasing. Catches are steadily declining since 2009. F (harvest) is high throughout the time 
series but is declining in the most recent years.   

253 

 
 
 
 
 
 
 
Figure 5.2.9.8.3.8. Hake in GSA 1-7. Summary table of hake stock estimates from Mod 41. 

Fbar (age 1-3) is 0.76 in 2014, the lowest since 2003 (Table 5.2.9.8.3.1). 

Table  5.2.9.8.3.1.  Hake  in  GSA  1-7.  Estimated  fishing  mortality  at  age  and  Fbar  (1-3)  from  the  a4a 
model run.  

Year 

F @ Age 

2003 

2004 

2005 

2006 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

0.81 

1.50 

1.08 

1.28 

1.13 

1.13 

0.64 

1.50 

1.36 

0.93 

0.75 

0.75 

1.01 

1.59 

1.55 

1.64 

0.26 

0.26 

0.62 

1.31 

1.58 

1.61 

1.85 

1.85 

0.60 

1.71 

1.65 

0.83 

1.50 

1.50 

0.75 

2.05 

1.94 

1.23 

1.61 

1.61 

0.47 

1.52 

1.73 

1.86 

1.96 

1.96 

0.14 

1.82 

2.22 

1.55 

1.86 

1.86 

0.15 

1.78 

1.87 

2.36 

0.98 

0.98 

0.18 

1.79 

2.22 

1.47 

1.83 

1.83 

0.12 

1.23 

1.60 

1.34 

0.28 

0.28 

0 

1 

2 

3 

4 

5 

0.82 

1.92 

1.63 

2.04 

2.06 

2.06 

Year 

Age 

Fbar (1-3) 

2003 

2004 

2005 

2006 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

1.56 

1.20 

1.17 

1.31 

1.14 

1.17 

1.38 

1.74 

1.49 

1.72 

1.29 

0.76 

The a4a assessment was compared with the XSA best model run and the  results are comparable in 
overall stock perception. The main difference is the F in the most recent year, the a4a run giving an F 
= 0.76 while XSA F= 1.09. All a4a model runs are plotted, along the XSA run in Figure 5.2.9.8.3.10. 

254 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 5.2.9.8.3.9. Hake in GSA 1-7. Comparison of XSA and best model fit in a4a (mod 41). 

255 

 
 
 
 
 
 
Figure 5.2.9.8.3.10. Hake in GSA 1-7. Comparative parameters estimates from all model runs. 

Reference points were derived similarly to the XSA reference points as described in Section 5.2.9.9. 
The proxy for Fmys is F0.1 which is estimated at 0.48 by a4a final model (Table 5.2.9.8.3.2 and Figure 
5.2.9.8.3.10. The XSA estimate is instead F0.1 = 0.39. 

Table 5.2.9.8.3.2. Hake in GSA 1-7. Main reference  points defined  by the yield per recruit  analysis  using the 
a4a model. 

refpt 
virgin 
msy 
crash 
f0.1 
fmax 
spr.30 

ssb 

yield 

biomass 
rec 
harvest 
0.00E+00  0.00E+00  3.80E+05  8.24E+04  6.81E+04 
8.86E-01  5.16E+03  3.80E+05  1.18E+04  2.36E+04 
3.80E+05 
4.85E-01  4.82E+03  3.80E+05  2.43E+04  3.18E+04 
8.86E-01  5.16E+03  3.80E+05  1.18E+04  2.36E+04 
4.77E-01  4.80E+03  3.80E+05  2.47E+04  3.20E+04 

NaN 

NaN 

NaN 

NaN 

256 

 
 
 
 
 
 
 
 
 
 
Figure 5.2.9.8.3.11. Hake in GSA 1-7. Yield per recruit reference points derived by the a4a assessment. 

Since  the  outputs  of  the  XSA  and  a4a  were  consistent,  although  slightly  different  in  the  stock 
perception, it was decided to use the XSA run for the short term predictions, the reference points and 
the MSE since the assessments ran for the individual GSA level were performed with XSA as well. 

5.2.9.9  Reference points 
5.2.9.9.1  Methods 
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  by  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. 

5.2.9.9.2  Input data  
The input parameters were the same used in the XSA stock assessment and its results. 

5.2.9.9.3  Results 
Table 5.2.9.8.3.1. European hake in GSA 1-7. Main reference points defined with the yield per recruit analysis 
by the XSA assessment. 

257 

 
 
 
 
 
 
 
 
 
 
 
 
refpt 

virgin 
msy 
f0.1 
fmax 
spr.30 

harvest 
0.00 
0.70 
0.39 
0.70 
0.45 

yield 
0.00 
0.01 
0.01 
0.01 
0.01 

rec 
1.00 
1.00 
1.00 
1.00 
1.00 

ssb 
0.22 
0.04 
0.08 
0.04 
0.07 

biomass 
0.18 
0.07 
0.09 
0.07 
0.08 

Figure 5.2.9.9.3.1. Hake in GSA 1-7. Yield per recruit curve from the XSA assessment; F01=0.39 and Fsq(1-3)= 1.40. 

5.2.9.10 Data quality 
For details in data quality, see the sections corresponding to the assessments by GSA. 

5.2.9.11 Short term predictions 2015-2017 
5.2.9.11.1  Method 
A  deterministic  short  term  prediction  for  the  period  2015  to  2017  was  performed  using  the  FLR 
routines  provided  by  JRC  and  based  on  the results  of  the XSA  stock assessments performed during 
EWG 15-11. 

5.2.9.11.2  Input parameters  
Input parameters were the same used for the XSA 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 (459070 thousand individuals). 

5.2.9.11.3  Results 

Table  5.2.9.11.3.1.  Hake  in  GSAs  1-7.  Short  term  forecast  in  different  F  scenarios.  Basis:  F(2015)  = 
mean (Fbar 1-3 2012-2014)= 1.40; R(2015) = geometric mean of the recruitment of the last 3 years; R = 
459070 thousands; SSB(2014) = 8133 t, Catch (2014)= 4650 t. 

Rationale 

Ffact
or 

Fbar 

Catch 
2014 

Catch 
2015 

Catch 
2016 

Catch 
2017 

SSB 
2016 

SSB 
2017 

Change  
SSB 
2016- 

Change 
Catch 
2014-2016(%) 

258 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Zero catch 
High long 
 term 
yield  
(FMSY) 
Status 
quo 
Different 
scenarios 

0 

0.00 

6221 

0 

0 

7843 

21566 

174.99 

-100.00 

2017(%) 

0.28 

0.39 

6221 

2416 

3901 

7843 

15746 

100.78 

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 

1.40 

0.14 

0.28 

0.42 

0.56 

0.70 

0.84 

0.98 

1.12 

1.26 

1.54 

1.68 

1.82 

1.96 

2.10 

2.24 

2.38 

2.52 

2.66 

2.79 

6221 

6221 

6221 

6221 

6221 

6221 

6221 

6221 

6221 

6221 

6221 

6221 

6221 

6221 

6221 

6221 

6221 

6221 

6221 

6221 

6192 

953 

1805 

2569 

3256 

3874 

4432 

4937 

5395 

5812 

6539 

6857 

7149 

7419 

7667 

7898 

8112 

8312 

8498 

8672 

6162 

1807 

3123 

4076 

4760 

5247 

5589 

5826 

5987 

6094 

6203 

6225 

6236 

6239 

6237 

6233 

6228 

6223 

6219 

6216 

7843 

7843 

7843 

7843 

7843 

7843 

7843 

7843 

7843 

7843 

7843 

7843 

7843 

7843 

7843 

7843 

7843 

7843 

7843 

7843 

7758 

19225 

17179 

15391 

13827 

12458 

11258 

10206 

9282 

8471 

7130 

6577 

6089 

5659 

5279 

4942 

4644 

4380 

4145 

3936 

-1.08 

145.13 

119.05 

96.25 

76.31 

58.85 

43.55 

30.13 

18.36 

8.01 

-9.09 

-16.14 

-22.36 

-27.84 

-32.69 

-36.98 

-40.79 

-44.16 

-47.15 

-49.81 

-48.04 

33.15 

-79.51 

-61.18 

-44.74 

-29.98 

-16.68 

-4.68 

6.18 

16.03 

24.99 

40.62 

47.46 

53.75 

59.54 

64.89 

69.85 

74.46 

78.75 

82.75 

86.50 

5.2.9.12 Short term predictions 2015-2017 by fleet 

5.2.9.12.1  Method 
A deterministic short term prediction by fleet for the period 2015 to 2017 was performed using the 
FLR  routines  provided  by  JRC  and  based  on  the  results  of  the  XSA  stock  assessments  performed 
during EWG 15-11. 

5.2.9.12.2  Input parameters  
The same parameters used in the short term by single fleet were used. 

5.2.9.12.3  Results 

259 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 5.2.9.12.3.1. Hake in GSA 1-7. Short term forecast by fleet. 

Fleet 
GSA 1 OTB 
GSA1 GNS 
GSA 1 LL 
GSA5 OTB 
GSA6 OTB 
GSA 6 OTHER 
GSA 7 GNS-FR 
GSA 7 OTB-FR 
GSA7 LL- SP 
GSA7 OTB-SP 
GSA 1 OTB 
GSA1 GNS 
GSA 1 LL 
GSA5 OTB 
GSA6 OTB 
GSA 6 OTHER 
GSA 7 GNS-FR 
GSA 7 OTB-FR 
GSA7 LL- SP 
GSA7 OTB-SP 
GSA 1 OTB 
GSA1 GNS 
GSA 1 LL 
GSA5 OTB 
GSA6 OTB 
GSA 6 OTHER 
GSA 7 GNS-FR 
GSA 7 OTB-FR 
GSA7 LL- SP 
GSA7 OTB-SP 

Catches 
534.5 
19.2 
17.8 
130.8 
4022.9 
135.8 
173.3 
1616.5 
41.8 
200.0 
207.8 
8.3 
6.2 
48.9 
1484.6 
58.9 
71.8 
632.4 
15.8 
79.6 
330.5 
17.3 
12.5 
71.9 
2061.9 
119.3 
143.2 
934.4 
32.3 
123.5 

Partial_f 
0.11 
0.01 
0.00 
0.02 
0.55 
0.04 
0.06 
0.28 
0.01 
0.04 
0.03 
0.00 
0.00 
0.01 
0.17 
0.01 
0.02 
0.08 
0.00 
0.01 
0.03 
0.00 
0.00 
0.01 
0.17 
0.01 
0.02 
0.08 
0.00 
0.01 

Year 
2015 
2015 
2015 
2015 
2015 
2015 
2015 
2015 
2015 
2015 
2016 
2016 
2016 
2016 
2016 
2016 
2016 
2016 
2016 
2016 
2017 
2017 
2017 
2017 
2017 
2017 
2017 
2017 
2017 
2017 

260 

 
 
 
 
 
 
 
Figure 5.2.9.12.3.1. Hake in GSAs 1-7. Short term forecast by fleet. 

5.2.9.13 Stock advice 
The current F (1.40) is larger than F0.1 (0.39), chosen as proxy of FMSY and as the exploitation reference 
point consistent with long term yields, which indicates that European hake in GSAs 1-7 is being fished 
above  FMSY.  Catches  of  European  hake  in  2016  consistent  with  F0.1  (0.23)  should  not  exceed  2416 
tonnes. 

5.2.9.14 Management strategy evaluation 
We  ran  the  Management  Strategy  Evaluation  (MSE)  to  evaluate  if  the  MSY  ranges  were 
precautionary.  The  FMSY  ranges  were  derived  using  the  formula  provided  by  STECF  15-09.  F  ranges 
results were Fupper = 0.53 and Flower = 0.26. Blim was estimated as Bloss = 5186 (t). The following figure 
shows the results of the MSE. 

261 

 
 
 
 
 
 
 
 
 
 
Figure 5.2.9.14.1. Hake in GSAs 1-7. Marine Strategy Evaluation. 

The probability of SSB to fall below Blim at F = Fupper is equal to 0. 

262 

 
 
 
 
 
 
 
 
 
 
5.2.10  STOCK ASSESSMENT OF HAKE IN GSA 9-11 
5.2.10.1 Stock Identification 
A study (STOCKMED; Fiorentino et al., 2015) on the European hake (and other commercial species) 
stock  potential  distribution  in  the  Mediterranean  Sea  has  been  funded  by  the  EU  and  undertaken 
under  the  framework  of  the  MAREA  Project.  This  study  suggested  that  there  are  two  stocks  of 
European hake in the Western Mediterranean Sea: one distributed from the Alboran Sea to the Gulf 
of Lion and another one from the Gulf of Lion to the Strait of Sicily and beyond. In the view of those 
findings,  STECF  EWG  15-11  was  asked  to  assess  the  state  of  European  hake  stocks  in  the  Western 
Mediterranean  Sea  following  two  approaches:  by  single  GSAs  and  GSAs  combined.  The  present 
assessment will investigate the state of the hake stock in GSAs 9, 10, and 11. 

Figure 5.2.10.1.1. Geographical location of GSAs 9, 10, and 11. 

5.2.10.2 Growth 
Growth  parameters  are  those  used  in  each  GSA  (see  sections  of  GSA  9,  GSA  10  and  GSA  11 
assessments). 

5.2.10.3 Maturity 
Maturity ogives were taken from each GSA (see sections of GSA 9, GSA 10 and GSA 11 assessments). 
Combined maturity at age were calculated as a weighted average using the stock numbers. 

5.2.10.4 Natural mortality 
Natural mortality was taken from each GSA (see sections of GSA 9, GSA 10 and GSA 11 assessments). 
Combined natural mortality at age were calculated as a weighted average using the stock numbers. 

5.2.10.5 Fisheries 
5.2.10.5.1 General description of the fisheries 
Hake is one of the main target species of bottom trawlers in terms of landings, incomes and vessels 
involved.  The  analysis  of  available  information  suggests  that  about  60%  of  landings  of  hake  are 
obtained by bottom trawl vessels, the remaining fraction being provided by artisanal vessels using set 

263 

 
 
 
 
 
  
 
 
 
 
 
 
nets,  i.e.  gillnets  and  trammel  net,  and  long-lines.  See  Chapters  5.2.6-8  in  the  Report  for  further 
details on hake fisheries in GSAs 9, 10, and 11. 

5.2.10.5.2 Management regulations applicable in 2015  
See Chapters 5.2.6-5.2.8 in the Report for management regulations on hake fisheries in GSAs   9,  10, 
and 11. 

5.2.10.5.3  Catches  
Landing and discards by fleet are described in the following sections 5.2.10.5.4 and 5.2.10.5.5. 

5.2.10.5.4  Landings  
Landings data were reported to STECF EWG 15-11 through the DCF. In GSAs 9, 10, and 11, the bulk of 
catches is from otter trawl, while artisanal fisheries (trammel net, gill net, and long-lines) represent 
the rest of the catches. DCF data on age structure of European hake landings in GSAs 9, 10, and 11 
are available for the period 2006-2014. DCF data prior to 2006 were considered inaccurate, therefore 
they  were  not  included in  the  stock  assessment.  For  more  details  on  landings  and  age-structure  of 
landings, please see sections 5.2.6-5.2.8 in this report.  

5.2.10.5.5  Discards  
Discards data were reported to STECF EWG 15-11 through the DCF. Information on OTB discards was 
available  for  2006  and  from  2009  to  2014.  The  size  at  which  50%  of  the  specimens  caught  are 
discarded is progressively increased in the last years due to the introduction of the EU Regulations on 
minimum sizes. This phenomenon might be also explained by a reduction of the fishing pressure on 
the nursery areas. Data and information on length-frequency distributions of discards of hake in GSAs 
9, 10, and 11 are available in sections 5.2.6, 5.2.7, and 5.2.8, respectively, of this report. 

5.2.10.5.6  Fishing effort  
The  nominal  fishing  capacity  in  the  three  areas  involved  in  the  present  assessment  has  shown  a 
progressive  decrease  in  the  last  20  years.  Fishing  effort  (kW*fishing  days)  performed  by  trawlers 
decreased  of  25%  since  2004.  The  effort  displayed  by  the  artisanal  fleet  exploiting  hake  remained 
constant. For more details on fishing effort exerted on hake in the three GSAs involved in the present 
assessment, please see sections 5.2.6-5.2.8 in this report.  

5.2.10.6 Scientific surveys 
5.2.10.6.1   Survey #1 (MEDITS) 
5.2.10.6.1.1  Methods 
Based  on  the  DCF  data  call,  abundance  and  biomass  indices  were  re-calculated.  The  data  coming 
from MEDITS surveys carried out in GSAs 9, 10, and 11 from 1994 to 2014 are presented in sections 
5.2.6-5.2.8 of this report. 

 Geographical distribution 

5.2.10.6.1.2 
According to recent studies (Orsi Relini  et al., 2002; Colloca et al., 2004, 2006), the density of hake 
recruits  in  nursery  areas  in  GSA  9  is  by  far  higher  than  that  in  the  other  GSAs  of  the  western 
Mediterranean and, probably, also of the other Mediterranean GSAs (Figure 5.2.10.6.1.2.1). 

264 

 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 5.2.10.6.1.2.1. Hake in GSAs 9-11. MEDITS density indices of the hake recruits (<12 cm TL) obtained in 
different Mediterranean GSAs (from Orsi-Relini et al., 2002, modified). 

Further information on the spatial and temporal distribution of hake recruits as well as of adults in 
GSAs 9, 10, and 11 is presented in sections 5.2.6-5.2.8 of this report. 

 Trends in abundance and biomass 

5.2.10.6.1.3 
European  hake  time  series  of  abundance  and  biomass  indices  from  MEDITS  surveys  carried  out  in 
GSAs 9, 10, and 11 (1994-2014) are shown and described in sections 5.2.6-5.2.8 of this report. 

5.2.10.6.1.4  Trends in abundance by length or age 
The stratified abundance  indices of  European hake  in GSAs  9, 10, and  11 are presented in sections 
5.2.6, 5.2.7, and 5.2.8, respectively, of this report. 

5.2.10.7 Stock Assessment 
5.2.10.7.1  Method: XSA 
FLR libraries were employed in order to carry out an XSA based assessment. The European hake stock 
in GSAs 9-11 was assessed for the first time. XSA was carried out using as input data the period 2006-
2014 for the catch data and 2006-2014 for the tuning file. 
The a4a framework was also used to fit assessment models on the hake stock in GSAs 9-11. However, 
the  results  in  terms  of  residuals  and  retrospective  analysis  were  not  satisfactory;  therefore,  the 
results of this approach were not presented in the report. The lack of a sufficient time series of data 
on this stock may have hampered the use of a4a. However, it is recommended to test the use of a4a 
as a suitable assessment tool on this stock in future years, once a sufficient time series of data will be 
available. 

5.2.10.7.2  Input data 
The growth parameters used for VBGF were Linf= 103.9 cm TL; K = 0.212 yr-1; t0= 0.031 yr. The length-
to-weight coefficients used were a= 0.006657, b= 3.028. 
Total catches and catch numbers at age from the single GSAs were used as input data. The R script 
prepared by JRC was used to create a combined stock object to be used in the assessment. Natural 
mortality  and  maturity  were  estimated  as  weighed  mean  from  the  parameters  used  in  the 
assessments of the single GSAs. 
Table  5.2.10.7.2.1  lists  the  input  parameters  to  the  XSA,  namely  landings,  catch  number  at  age, 
weight at age, maturity at age, natural mortality at age and the tuning series at age.  

265 

020004000600080001000012000AlboranAlicanteCatalanGulf of LionsLigurian-TyrrhenianSardiniaS. TyrrhenianDensity index n/Km2 
 
 
 
 
 
 
 
 
 
 
 
Table 5.2.10.7.2.1. Hake in GSAs 9-11. Input data to the XSA model. 

Catches (t) 

2006 
4657 

2007 
3830 

2008 
3406 

2009 
3664 

2010 
3384 

2011 
3757 

2012 
2641 

2013 
2895 

2014 
3075 

Catch numbers-at-age matrix (thousands) 

Age 
0 
1 
2 
3 
4 
5 
6+ 

2006 

2007 

2008 

2009 

2010 

2011 

82424.000  62020.000  65121.000  91660.000  42494.000  66748.000 
14603.000  13976.000  12123.000  12360.000  12523.000  12820.000 
1203.900 
2299.300 
285.330 
298.580 
100.770 
102.970 
37.943 
29.440 
8.372 
0.001 

1314.100 
208.990 
54.785 
12.424 
2.645 

1451.500 
225.100 
68.254 
36.735 
11.873 

870.330 
251.030 
80.515 
37.507 
7.758 

812.160 
171.530 
85.904 
25.232 
9.252 

Age 
0 
1 
2 
3 
4 
5 
6+ 

2014 

2012 

2013 
29969.000  26054.000  42564.000 
10271.000  12899.000  10589.000 
1331.000 
744.270 
880.350 
186.980 
134.050 
209.180 
39.646 
53.242 
53.596 
24.068 
11.699 
14.063 
19.806 
3.865 
2.020 

Weights-at-age (kg) 

Age 
0 
1 
2 
3 
4 
5 
6+ 

2006 
0.010 
0.139 
0.523 
1.172 
1.916 
2.621 
3.001 

2007 
0.011 
0.147 
0.526 
1.183 
1.827 
2.822 
4.924 

2008 
0.010 
0.144 
0.516 
1.135 
1.883 
2.685 
3.637 

2009 
0.011 
0.146 
0.511 
1.159 
1.876 
2.635 
3.714 

2010 
0.010 
0.134 
0.513 
1.145 
1.917 
2.766 
3.983 

2011 
0.009 
0.147 
0.505 
1.167 
1.902 
2.687 
4.046 

Age 
0 
1 
2 
3 

2012 
0.012 
0.140 
0.502 
1.138 

2013 
0.011 
0.147 
0.547 
1.153 

2014 
0.009 
0.154 
0.465 
1.147 

266 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
4 
5 
6+ 

1.929 
2.787 
3.825 

1.836 
2.603 
4.383 

1.849 
2.683 
3.865 

Age 
Maturity 
M 

0 
0.00 
1.18 

Maturity and natural mortality vectors. 
1 
0.23 
0.58 

2 
0.88 
0.43 

3 
1.00 
0.36 

4 
1.00 
0.33 

5 
1.00 
0.32 

6+ 
1.00 
0.30 

Hake in GSA 9-11. MEDITS number (n/km2) at age for GSA 9 only. Age 4+ was used in this assessment, 
although a Age 3+ was used in the assessment of hake in GSA 9. 

Year/age 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

1 

0 

2 

1686.57  58.583  2.502 
2.24 
2514.259 
1.24 
5871.627 
1.09 
6573.9 
2.57 
2469.127 
1.29 
769.899 
0.99 
1464.35 
1743.236 
1.00 
1564.17 

4+ 
0.182 
0.10 
38.88 
0.45 
57.22 
0.08 
52.84 
0.08 
37.30 
0.10 
29.39 
0.31 
21.93 
35.29 
0.33 
27.137  1.901  0.218  0.294 

3 
0.26 
1.54 
0.32 
0.46 
0.10 
0.33 
0.48 
0.10 

Hake in GSA 9-11. MEDITS number (n/km2) at age for GSA 10. 

Year/age 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

0 
1250.42 
1907.19 
1544.78 
1890.43 
813.51 
639.35 
907.4 
1252.29 
610.5 

1 
99.67 
51.52 
92.69 
78.11 
131.46 
67.18 
56.44 
67.21 
64.50 

2 
2.32 
0.95 
2.97 
0.38 
1.46 
2.45 
2.37 
4.37 
4.00 

3 
0.49 
0.97 
1.52 
0.32 
0.3 
1.2 
0.29 
0.29 
0.20 

4 
0.01 
0.14 
0.01 
0.01 
0.17 
0.01 
0.01 
0.01 
0.30 

5 
0.01 
0.14 
0.01 
0.32 
0.15 
0.01 
0.16 
0.22 
0.01 

6+ 
0.01 
0.01 
0.4 
0.01 
0.24 
0.01 
0.01 
0.01 
0.01 

Hake in GSA 9-11. Indices from long-line fishery (CPUE at age) from GSA 10. Although Age 1 was used 
for the assessment of hake in GSA 10, it was removed in the combined assessment of hake in GSAs 9-
11. 

Year 
2006 
2007 
2008 
2009 

2 

3 

4 

5 

0.012778  0.005738  0.002327  0.000583 
0.004451  0.012014  0.003051  0.001027  0.000171 
0.003799  0.002676  0.00165  0.000871  0.000236 
0.010841  0.00422  0.002638  0.000486  0.000574 

6+ 
0 

267 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
2010 
2011 
2012 
2013 
2014 

0.027127  0.003106  0.000952  0.001266  0.000806 
0.007395  0.007279  0.003293  0.00049  0.000427 
0.010703  0.007996  0.001233  0.000428  0.000107 
0.024985  0.012861  0.004887  0.001097  0.001097 
0.003283  0.007114  0.002094  0.001818  0.001818 

Hake in GSA 9-11. MEDITS number (n/km2) at age for the GSA 11. 

Year/age 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

0 
670.54 
41.52 
15.63 
169.90 
425.29 
131.21 
177.07 
3.55 
33.66 

2 

1 

3 
9.71 
2937.10  318.85 
894.59 
9.41 
52.06 
1789.55  331.40  86.14 
1096.91  41.02 
2.35 
5498.63  325.50  11.09 
1448.03  108.27  10.66 
2.42 
44.40 
932.29 
9.89 
1588.47  125.84 
4.96 
48.62 
531.75 

4 
8.32 
1.21 
4.99 
1.04 
0.21 
2.59 
0.55 
0.92 
0.71 

5 
0.26 
0.59 
0.11 
0.10 
0.21 
0.10 
0.33 
0.41 
0.23 

5.2.10.7.3  Results 
Sensitivity analyses were conducted to assess the effect of the main parameters. Values ranging from 
0.5 to 3 (with a 0.5 step increase) for the shrinkage have been tested. Comparison of trends between 
the settings has been done. 

268 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 5.2.10.7.3.1. Hake in GSAs 9-11. Sensitivity on shrinkage weight. 

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 
1-4 

fse 
2 

rage 
0 

qage 
5 

shk.yrs 
3 

shk.age 
2 

The residuals patterns of the MEDITS trawl surveys in GSAs 9, 10, and 11, and long-line fishery CPUE 
in GSA 10 are shown in Figure 5.2.10.7.3.2. 

269 

 
 
 
 
 
 
 
 
 
 
 
Figure 5.2.10.7.3.2. Hake in GSAs 9-11. XSA residuals for the MEDITS surveys from 2006 to 2014 and long-line 
fishery CPUE in GSA 10. 

The results of the retrospective analysis are shown in Figure 5.2.10.7.3.3. 

270 

 
 
 
 
 
 
 
 
 
 
 
 
Figure 5.2.10.7.3.3. Hake in GSAs 9-11. XSA retrospective analysis. 

The  results  of  the  XSA  are  shown  in  Fig.  5.2.10.7.3.4.  Recruitment,  SSB,  and  catches  are  showing  a 
slight decreasing trend, with a slight increasing pattern in the last few years. F remains at high levels. 

271 

 
 
 
 
 
 
 
 
Figure  5.2.10.7.3.4.  Hake  in  GSAs  9-11.  XSA  summary  results.  SSB  and  catch  are  in  tonnes,  recruitment  in 
1000s individuals. 

The  stock  parameters  estimates  of  hake  obtained  by  XSA  are  provided  in  Tables  5.2.10.7.3.1-
5.2.10.7.3.3. 

272 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 5.2.10.7.3.1. Hake in GSAs 9-11. Stock numbers at age (thousands) as estimated by XSA. 

Age 
0 
1 
2 
3 
4 
5 
6+ 

2006 
217730 
24028 
3612 
592 
233 
58 
0 

2007 
175120 
21838 
2583 
490 
161 
80 
17 

2008 
186880 
19451 
1736 
621 
167 
70 
14 

2009 
233820 
21346 
1813 
430 
223 
52 
18 

2010 
141050 
20767 
2688 
527 
156 
87 
28 

2011 
174000 
20196 
2326 
586 
178 
54 
11 

Age 
0 
1 
2 
3 
4 
5 
6+ 

2012 
123680 
16383 
1705 
547 
171 
43 
6 

2013 
101300 
21497 
1531 
405 
206 
78 
25 

2014 
140910 
16701 
2360 
399 
170 
103 
84 

Table 5.2.10.7.3.2. Hake in GSAs 9-11. XSA summary results. 

Fbar1-4 

1.22 
1.04 
1.07 
0.89 
1.04 
1.22 
0.97 
0.85 
1.05 

Recruitment 
(thousands) 
217732 
175119 
186877 
233825 
141055 
174001 
123684 
101295 
140914 

SSB (t) 

TB (t) 

3717 
3121 
2696 
2657 
3078 
2941 
2355 
2618 
2911 

8640 
7703 
6810 
7675 
6830 
6931 
5768 
6296 
6346 

 2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

273 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 5.2.10.7.3.3. Hake in GSAs 9-11. F-at-age matrix obtained from XSA. 

2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

0 
1.13 
1.02 
0.99 
1.24 
0.78 
1.18 
0.57 
0.62 
0.79 

1 
1.66 
1.95 
1.79 
1.49 
1.62 
1.89 
1.80 
1.63 
1.88 

2 
1.56 
1.00 
0.97 
0.81 
1.10 
1.02 
1.02 
0.92 
1.18 

F at age 

3 
0.93 
0.72 
0.66 
0.65 
0.72 
0.87 
0.61 
0.51 
0.82 

4 
0.74 
0.51 
0.84 
0.61 
0.73 
1.10 
0.46 
0.36 
0.32 

5 
0.91 
0.20 
0.99 
0.85 
0.68 
1.74 
0.49 
0.20 
0.32 

6+ 
0.91 
0.20 
0.99 
0.85 
0.68 
1.74 
0.49 
0.20 
0.32 

5.2.10.8 Reference points 
5.2.10.8.1  Methods 
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. 

5.2.10.8.2  Input data  
The input parameters were the same used for the XSA stock assessment and its results. 

5.2.10.8.3  Results 
Table  5.2.10.8.3.1.  Hake  in  GSAs  9-11.  Main  reference  points  defined  with  the  Yield  per  recruit 
analysis. 

refpt 

virgin 
msy 
crash 
f0.1 
fmax 
spr.30 

harvest 
0.00 
0.28 
28.63 
0.20 
0.28 
0.25 

Yield (t) 
0 
7407 
1657 
7124 
7407 
7368 

Recruitment 
(thousands) 
160880 
160880 
160880 
160880 
160880 
160880 

SSB (t) 
186670 
48506 
0 
69683 
48506 
56002 

Biomass 
(t) 
195760 
55736 
1756 
77356 
55736 
63404 

274 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 5.2.10.8.3.1. Hake in GSAs 9-11. Yield per recruit curve. 

With  the  estimated  value  for  F0.1  of  about  0.20,  the  current  level  of  F  of  about  1.05  is  higher,  and 
hence, a status of overexploitation can be assumed. 

5.2.10.9 Data quality 
Data from DCF 2014 as submitted through the Official data call in 2015 were used. Problems in the 
data were due to the lack of size structure information for some of the fisheries in GSA 9 and GSA 11 
(e.g. trammel net). 

Discard data were missing for 2007 and 2008. 

 Method 

Short term predictions 2015-2017 

5.2.10.10 
5.2.10.10.1 
A  deterministic  short  term  prediction  for  the  period  2015  to  2017  was  performed  using  the  FLR 
routines  provided  by  JRC  and  based  on  the results  of  the XSA  stock assessments performed during 
EWG 15-11. 

 Input parameters  

5.2.10.10.2 
The input parameters were the same used for the XSA 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 (120861 thousand individuals). 

275 

 
 
 
 
 
 
 
 
 
 
 
 
 
5.2.10.10.3 

 Results 

Table 5.2.10.10.3.1. Hake in GSAs 9-11. Short term forecast in different F scenarios. Basis: F(2015) = mean (Fbar 
1-4 2012-2014)= 0.96; R(2015) = geometric mean of the recruitment of the last 3 years; R = 120861 thousands; 
SSB(2014) = 2911 t, Catch (2014)= 3075 t. 
. 

Rationale 

Ffactor 

Fbar 

Catch 
2015 

Catch 
2016 

Catch 
2017 

SSB 
2016 

SSB 
2017 

Change 
SSB 2016-
2017(%) 

Change 
Catch 
2014-
2016(%) 

Zero catch 

High long 
term yield 
(F0.1) 
Status quo 

Different 
Scenarios 

0.00 

0.00 

3185 

0 

0 

2739 

8656 

216.01 

-100.00 

0.21 

1.00 

0.10 

0.20 

0.30 

0.40 

0.50 

0.60 

0.70 

0.80 

0.90 

1.10 

1.20 

1.30 

1.40 

1.50 

1.60 

1.70 

1.80 

1.90 

2.00 

0.20 

0.96 

0.10 

0.19 

0.29 

0.38 

0.48 

0.57 

0.67 

0.76 

0.86 

1.05 

1.15 

1.24 

1.34 

1.43 

1.53 

1.62 

1.72 

1.82 

1.91 

3185 

3185 

3185 

3185 

3185 

3185 

3185 

3185 

3185 

3185 

3185 

3185 

3185 

3185 

3185 

3185 

3185 

3185 

3185 

3185 

3185 

1029 

3218 

522 

981 

1386 

1744 

2063 

2347 

2601 

2828 

3033 

3385 

3538 

3676 

3803 

3919 

4026 

4125 

4216 

4300 

4379 

2040 

3233 

1151 

1964 

2526 

2899 

3133 

3265 

3323 

3327 

3293 

3156 

3068 

2974 

2877 

2780 

2684 

2590 

2500 

2414 

2332 

2739 

2739 

2739 

2739 

2739 

2739 

2739 

2739 

2739 

2739 

2739 

2739 

2739 

2739 

2739 

2739 

2739 

2739 

2739 

2739 

2739 

6622 

2755 

7613 

6716 

5943 

5275 

4697 

4195 

3759 

3379 

3046 

2498 

2273 

2073 

1896 

1739 

1598 

1473 

1360 

1259 

1168 

141.76 

-66.52 

0.57 

177.94 

145.20 

116.97 

92.59 

71.48 

53.17 

37.24 

23.35 

11.21 

-8.79 

-17.03 

-24.32 

-30.78 

-36.53 

-41.65 

-46.23 

-50.33 

-54.03 

-57.36 

4.66 

-83.04 

-68.11 

-54.93 

-43.26 

-32.90 

-23.67 

-15.41 

-8.01 

-1.35 

10.11 

15.06 

19.57 

23.69 

27.47 

30.95 

34.15 

37.12 

39.87 

42.42 

 Method 

Short term predictions 2015-2017 by fleet 

5.2.10.11 
5.2.10.11.1 
A deterministic short term prediction by fleet for the period 2015 to 2017 was performed using the 
FLR  routines  provided  by  JRC  and  based  on  the  results  of  the  XSA  stock  assessments  performed 
during EWG 15-11. 

5.2.10.11.2 
The same parameters used in the short term by single fleet were used. 

 Input parameters  

276 

 
 
 
 
 
 
  
 
 
 
 
5.2.10.11.3 

 Results 

Table 5.2.10.11.3.1. Hake in GSAs 9-11. Short term forecast by fleet and GSA. 

fleet 
trawl9 
trawl9 
trawl9 
trawl10 
trawl10 
trawl10 
trawl11 
trawl11 
trawl11 
gtr9 
gtr9 
gtr9 

year 
2015 
2016 
2017 
2015 
2016 
2017 
2015 
2016 
2017 
2015 
2016 
2017 

catches  partial_f 
1089.5 
356.0 
620.0 
552.7 
179.8 
309.3 
483.5 
156.0 
309.2 
79.7 
26.4 
67.6 

0.31 
0.07 
0.07 
0.12 
0.03 
0.03 
0.07 
0.01 
0.01 
0.03 
0.01 
0.01 

fleet 
gtr10 
gtr10 
gtr10 
gtr11 
gtr11 
gtr11 
gns9 
gns9 
gns9 
lls10 
lls10 
lls10 

year 
2015 
2016 
2017 
2015 
2016 
2017 
2015 
2016 
2017 
2015 
2016 
2017 

catches  partial_f 
436.8 
150.7 
344.4 
28.4 
9.5 
22.9 
297.5 
93.4 
240.3 
217.3 
55.4 
122.9 

0.21 
0.04 
0.04 
0.00 
0.00 
0.00 
0.10 
0.02 
0.02 
0.11 
0.02 
0.02 

Figure 5.2.10.11.3.1. Hake in GSAs 9-11. Short term forecast by fleet and GSA. 

277 

 
 
 
 
 
 
 
 
 
 
 
 
 
Medium term predictions 

5.2.10.12 
5.2.10.12.1 
Medium  term  forecasts  were  not  conducted  because  no  meaningful  stock-recruitment  relationship 
was estimated. 

 Method 

Stock advice 

5.2.10.13 
The current F (1.05) is larger than F0.1 (0.20), chosen as proxy of FMSY and as the exploitation reference 
point consistent with high long term yields, which indicates that European hake in GSAs 9-11 is being 
fished  above  FMSY.  Catches  of  European  hake  in  2016  consistent  with  FMSY  should  not  exceed  1029 
tonnes. 

Management strategy evaluation 

5.2.10.14 
A Management Strategy Evaluation was run to evaluate if the MSY ranges were precautionary. The 
FMSY  ranges  were  derived  using  the  formula  provided  by  STECF  EWG  15-09.  F  ranges  results  were 
Fupper=0.28 and Flower=0.14. Blim was estimated as Bloss=2355 (t). The following figure shows the results 
of the MSE.  

Figure 5.2.10.14.1. Hake in GSAs 9-11. Management Strategy Evaluation. 

The probability of SSB to fall below Blim at F = Fupper is equal to 0. 

278 

 
 
 
 
 
 
 
 
 
 
 
 
 
5.2.11  STOCK ASSESSMENT OF GIANT RED SHRIMP IN GSA 9 

5.2.11.1 Stock Identification 
Due  to  a  lack  of  enough  information  about  the  structure  of  giant  red  shrimp  (Aristaeomorpha 
foliacea)  in  the  western  Mediterranean,  this  stock  was  assumed  to  be  confined  within  the  GSA  9 
boundaries. 
The giant red shrimp is mainly to be found in the epibathyal and mesobathyal waters of the western 
Mediterranean 
In  the  GSA  9,  A.  foliacea  is  more  abundant  in  the  Central  Tyrrhenian  (Ardizzone  et  al.,  1994)  while 
lower concentrations are present in the Northern Tyrrhenian (Anonymous, 2008) and in the Ligurian 
Sea, where this species considerably decrease over time (Orsi Relini and Relini, 1985). 

Figure 5.2.11.1.1 Limit of Geographical Sub-Areas (GSAs). 

5.2.11.2 Growth 
In  general  the  length-frequency  distributions  have  a  polymodal  pattern,  with  4-5  components  for 
females  (adult  modes  of  are  less  defined)  and  2  components  for  males  (Leonardi  and  Ardizzone, 
1994). 
Analysis  on  the  size  structure  histograms  relating  to  the  central-southern  Tyrrhenian  shown, 
particularly  in  spring,  a  highly  differentiated  structure.  Both  males  and  females  are  present  in  the 
young  classes,  with  a  certain  prevalence  of  the  latter.  In  the  range  from  32  to  38  mm  a  mode 
composed solely of males appears, and over 42 mm distribution is composed solely of females. This 
characteristic highlights a different mode of growth of the two sexes. 
In the last decade different set of growth parameters were estimated for A. foliacea in the Tyrrhenian 
sea (Leonardi and Ardizzone, 1994) but in this analysis were used the set of parameters obtained in 
the  REDS  project  (FISH/2004/03-32)  for  the  male  and  from  the  analysis  of  size  distributions  data 
gathered during GRUND surveys carried out in the GSA9 for female. 
The feeding of red shrimps (A. foliacea, A. antennatus), studied by Brian (1931) in the Ligurian sea, 
indicated  the  euryphagous  feeding  behaviour  of  the  two  species  which  alternate  phases  of  active 
hunting with phases in which they consume small benthonic prey (Lagardere, 1972). 
Red shrimps obtain food from an area of the sea which extends vertically for several hundred metres 
(Orsi  Relini,  1984).  Their  diet  includes  both  organisms  from  the  muddy  bed  and  herbivorous 

279 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
organisms which use surface plankton. The former include Ophiocten abyssicolum, which is probably 
useful to the shrimps as a source of calcium with which to build their exoskeleton. The latter include 
the shrimps of the genuses Pasiphaea, Sergestes and the Eufasiacean Meganyctiphanes norvegica. In 
the  night  these  prey  move  up  to  the  surface  waters  for  feeding  needs,  while  during  the  day  they 
remain near the sea bed (Orsi Relini and Wurtz, 1977). A. foliacea is quite voracious, possibly due to 
needs  imposed  by  the  rapid  maturing  of  the  eggs,  and  is  also  capable  of  attacking  shrimps  of  the 
Plesionika genus which can even measure up to 2/3 the size of the aggressor. Food characteristics of 
this  type  could  entail  a  greater  vulnerability  of  this  species  in  an  altered  marine  ecosystem  (Orsi 
Relini, 1984). 

5.2.11.3 Maturity 
The reproduction period of A. foliacea lasts from May to September, with a peak in the summer (July-
August). Four stages of ovary maturity were described by using a macroscopic colorimetric scale (Levi 
and Vacchi, 1989) and the mature ovaries can be recognised because initially they are grey coloured, 
with increasingly dark shades until they become black, due to the presence of carotenoproteins (Orsi 
Relini and Semeria, 1983).  
Mature females are concentrated in the mesobathyal bottoms from spring to autumn. The fertility of 
A. foliacea has been estimated as being equal approximately to 1/3 of the fertility of A. antennatus 
(Orsi Relini and Semeria, 1983). Analyses of the ultrastructure of the ovary indicated cells arranged in 
a line. A. foliacea has a dome-shaped thelycum and characteristics which can be compared to those 
of decapod crustaceans with a closed thelycum, with coupling coinciding with the moult phases (Orsi 
Relini  L.,  in  Anonymous,  1997).  In  males  the  spermatophore  originates  by  passing  through  the 
deferent duct, and the spermatic mass is contained in a chamber with “wings” at the edge that serve 
a protective purpose. 
In the Northern Tyrrhenian (Righini and Abella, 1994) the smallest female with spermatophore had a 
carapace length (CL) of 40 mm. In the Central Tyrrhenian (southern Tuscan Archipelago), the smallest 
mature female measured 28 mm (CL), and the smallest mature male 29 mm (CL) (Mori et al., 1994). 
Mature  males  were  observed  all  year  round.  In  the  Central  Tyrrhenian  (Latium),  the  size  at  first 
maturity  is  30-31  mm  for  males  and  the  smallest  female  with  spermatophore  measured  33  mm 
(Leonardi and Ardizzone, 1994). 
Female  maturity  ogive  (Fig.  5.2.11.3.1)  was  obtained  using commercial data  gathered  during  in  the 
2011  DCF  grouping  as  mature,  individuals  belonging  to  the  maturity  stage  2b  (according  to  the 
MEDITS maturity scale) onwards. The estimated size at first maturity resulted about 34mm CL. 

Figure 5.2.11.3.1 Giant red shrimp in the GSA 9. Maturity ogive and proportion of mature female.  

280 

 
 
 
 
 
 
 
 
Biological data gathered during MEDITS surveys (1994-2012) was used to estimate a sex ratio vector 
(Fig. 5.2.11.3.2). Smaller sizes were more represented by females, instead between 33 to 39mm CL 
males  become  predominant  and  from  40mm  carapace  length  (CL)  the  proportion  was  totally  to 
advantage of female.  

Figure 5.2.11.3.2. Giant red shrimp in the GSA 9. Sex ratio by length.  

5.2.11.4 Natural mortality 
Natural  mortality  vector  was  the  same  used  in  the  previous  assessment  and  estimated  using 
PRODBIOM (Abella et al., 1997) and it is shown in Table 5.2.11.4.1.  

Table 5.2.11.4.1. Giant red shrimp in the GSA 9. Natural mortality. 

Age 
0 
1 
2 
3 
4+ 

M 
1.28 
0.58 
0.44 
0.38 
0.34 

5.2.11.5 Fisheries 
5.2.11.5.1 General description of the fisheries 
In  the  GSA  9  the  giant  red  shrimp,  Aristaeomorpha  foliacea,  is  one  of  the  most  important  target 
species of the otter bottom trawl fishery carried out on the muddy bottoms of the upper and middle 
slope.  The  main  fishing  grounds  are  located  in  the  central  and  southern  part  of  the  GSA9  (eastern 
Ligurian Sea, northern and central Tyrrhenian Sea). The species is mainly exploited by the trawl fleets 
of Porto S. Stefano and Porto Ercole, in Tuscany, and Fiumicino, Anzio, and Terracina, in Latium. 
As  an  example,  Fig.  5.2.11.5.1.1  shows  the  landings  per  unit  of  effort  (LPUE,  kg/vessel/day)  by  the 
Porto S. Stefano trawl fleet, which is one of the fleets historically targeting the giant red shrimp in the 
GSA 09. Seasonality fluctuations are a proper characteristic of the landings of this species, as shown 
by  the  LPUE  produced  by  the  fleet  of  Porto  S.  Stefano  in  the  period  1991-2010.  The  highest  catch 
rates are observed in late spring-summer; even though peaks due to recruitment and other biological 
aspects  do  exist,  the  main  factor  affecting  this  seasonal  pattern  is  the  spatial  distribution  of  the 

281 

 
 
 
 
 
 
 
 
 
 
 
 
 
fishing effort. In fact, the fishing grounds where the giant red shrimp is targeted are distant from the 
coast, thus this fishery is strongly influenced by the weather conditions (Sartor et al., 2003; Sbrana et 
al., 2003). 

Figure 5.2.11.5.1. Giant red shrimp in the GSA 9. LPUE of Porto Santo Stefano from January 1991 to May 2010. 

5.2.11.5.2 Management regulations applicable in 2015  
EC regulation 1967/2006 don’t provide for a minimum length size for this species. Italian national law 
provided in the last years a fishing ban of a month which, for the Ligurian fleet, is enforced after the 
summer fishing season. 

5.2.11.5.3  Landings  
Total landings of giant red shrimps decreased from about 60 tons in 2006 to 24 tons in 2007, in 2008 
and  2009  landings  remain  quite  stable  (around  30-40  tons)  and  then  an  increasing  up  to  about  70 
tons was observed in 2011 followed by a new decrease in the 2012 (Fig. 6.6.2.3.1.1; Tab. 6.6.2.3.1.1). 
The landings are entirely taken by OTB fleets. Landings data were observed also in 2008 for Gillnet 
(about  700kg)  and  in  2012  for  trammel  (about  1.2  tons).  Seasonality  fluctuations  are  a  proper 
characteristic  of  the  landings  of  this  species,  as shown  by  the  LPUE  produced  by  the  fleet  of  Santa 
Stefano in the period 1991-2010 (Fig. 6.6.2.1.1). 

282 

0246810121416Jan-91Sep-91May-92Jan-93Sep-93May-94Jan-95Sep-95May-96Jan-97Sep-97May-98Jan-99Sep-99May-00Jan-01Sep-01May-02Jan-03Sep-03May-04Jan-05Sep-05May-06Jan-07Sep-07May-08Jan-09Sep-09May-10 
 
 
 
 
 
 
 
 
 
 
Figure 5.2.11.5.4.1. Giant red shrimp in the GSA 9. Total landings (tons) 2006-2014.  

Table  5.2.11.5.4.1.  Giant  red  shrimp  in  the  GSA  9.  Annual  landings  (tons)  by  fishing  technique  as  provided 
through the official DCF data call 2015. 

YEAR 

2006 

2007 

2008 

2008 

2008 

2009 

2010 

2010 

2011 

2011 

2012 

2012 

2012 

2013 

2013 

2014 

2014 

GEAR 

OTB 

OTB 

OTB 

OTB 

GNS 

OTB 

OTB 

OTB 

OTB 

OTB 

GTR 

OTB 

OTB 

OTB 

OTB 

OTB 

OTB 

FISHERY 

MDDWSP 

MDDWSP 

DWSP 

MDDWSP 

DEMF 

MDDWSP 

DWSP 

MDDWSP 

DWSP 

MDDWSP 

DEMSP 

DWSP 

MDDWSP 

DWSP 

MDDWSP 

DWSP 

MDDWSP 

LANDINGS 

62.60995 

36.65032 

8.73874 

24.38813 

0.69851 

34.29335 

17.70095 

36.85313 

17.62392 

50.80815 

1.24131 

8.34909 

52.37722 

2.5635 

20.51493 

0.6136 

16.20556 

5.2.11.5.4  Discards  
Discards data were available only 2012 and resulted almost nil (0.45kg). 

283 

 
 
 
 
 
 
 
 
 
5.2.11.5.5  Fishing effort  
The trends in fishing effort by fishing technique are listed in Tab.5.2.11.5.6.1 From 2004 until now the 
effort slightly decreased. (Fig. 5.2.11.5.6.1).  

OTB 

i

)
s
y
a
d
g
n
h
s
i
f
*
w
k
(

t
r
o
f
f
e

16000000

14000000

12000000

10000000

8000000

6000000

4000000

2000000

0

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Figure 5.2.11.5.6.1. Giant red shrimp in the GSA 9. Trends in annual trawlers fishing effort as nominal effort 
(kw*days) deployed in GSA 9 from 2004 to 2014. 

284 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 5.2.11.5.6.2. Giant red shrimp in the GSA 9. Trends in annual deeper trawlers fishing effort as nominal 
effort (kw*days) deployed in GSA 9 from 2004 to 2014. 

Table 5.2.11.5.6.1. Giant red shrimp in the GSA 9. Trends in annual fishing effort as nominal effort (kW*days) 
deployed in GSA0 9 from 2004 to 2014 as reported through the DCF official data call. 

Country 

Area 

Gear 

Year 

DEMSP  DWSP  MDDWSP 

OTB 
Totale 

FISHERY 

ITA 
ITA 
ITA 
ITA 
ITA 
ITA 
ITA 
ITA 
ITA 
ITA 
ITA 

9 
9 
9 
9 
9 
9 
9 
9 
9 
9 
9 

OTB 
OTB 
OTB 
OTB 
OTB 
OTB 
OTB 
OTB 
OTB 
OTB 
OTB 

2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

 - 
 - 
 - 

6498842  40824 
8280673  14820339 
1990472 
12710127  14700599 
2972712 
9432075  12404787 
8404088  12782144 
4378056 
8533729  208500  2033653  10775882 
9585297  504214  2083240  12172751 
7751226  712502  2764273  11228001 
8223517  626629  1846020  10696166 
6956565  725731  2315611  9997907 
7910486  1320396  1493999  10724881 
9088034  658396  1229266  10975696 

5.2.11.6 Scientific surveys 
5.2.11.6.1   Survey #1 (MEDITS) 
5.2.11.6.1.1  Methods 
MEDITS  surveys  were  carried  out  from  late  spring  to  mid  summer  and  the  sampling  design  was 
always  random  depth-stratified  in  respect  on  five  depth  strata:  10–50,  50–100,  100–200,  200–500 
and 500–800 m. GOC 73 trawl net was used during the surveys. The cod-end mesh size was of 20 mm 
in  MEDITS  surveys.  Hauls  duration  was  of  0.5  h  for  the  hauls  carried  out  on  the  shelf  (10–200m 
depth) and 1 h for the hauls carried out on the slope (200–800m depth) fishing grounds. Details of 
sampling protocol can be found in Bertrand et al. (2002). 
Based on the DCR data call, abundance and biomass indices were recalculated. In GSA9 the following 
number of hauls was reported per depth stratum (Tab. 5.2.11.6.1.1.1).  

Table 5.2.11.6.1.1.1 Number of hauls per year and depth stratum in GSA9, 1994-2014. 

Stratum 

1994 
1995 
1996 
1997 
1998 
1999 
2000 
2001 
2002 

GSA09_010-
050 
21 
20 
20 
20 
21 
20 
20 
20 
15 

GSA09_050-
100 
21 
21 
20 
22 
20 
21 
22 
22 
17 

GSA09_200-
500 
40 
40 
40 
41 
40 
41 
42 
42 
33 

GSA09_500-
800 
33 
33 
33 
32 
33 
32 
31 
31 
25 

Total 

153 
153 
153 
153 
153 
153 
153 
153 
120 

GSA09_100-
200 
38 
39 
40 
38 
39 
39 
38 
38 
30 

285 

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

15 
15 
16 
15 
15 
16 
16 
15 
15 
15 
16 
15 

17 
17 
16 
18 
18 
16 
16 
19 
18 
17 
17 
19 

30 
30 
31 
29 
29 
31 
31 
29 
30 
31 
30 
29 

31 
34 
34 
35 
35 
34 
34 
34 
33 
35 
35 
36 

27 
24 
23 
23 
23 
23 
23 
23 
24 
22 
22 
21 

120 
120 
120 
120 
120 
120 
120 
120 
120 
120 
120 
120 

Data were assigned to strata based upon the shooting position and average depth (between shooting 
and hauling depth). Catches by haul were standardized to swept area. 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:  

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:   
Confidence interval = Yst ± V(Yst)   

Length  distributions  represented  an  aggregation  (sum)  of  all  standardized  length  frequencies 
(subsamples raised to standardized haul abundance per square kilometers) over the stations of each 
stratum.  

 Geographical distribution 

5.2.11.6.1.2 
The  stock  is  more  abundant  in  the  southern  part  of  the  GSA  (Tyrrhenian  Sea)  as  showed  in  Figure 
5.2.11.6.1.2.1. 

286 

 
 
 
 
 
 
 
 
 
  
Figure 5.2.11.6.1.2.1. Giant red shrimp in the GSA 9. Abundance by haul obtained in two different years during 
MEDITS survey. 

 Trends in abundance and biomass 

5.2.11.6.1.3 
Fishery  independent  information  regarding  the  state  of  the  giant  red  shrimp  in  GSA  9  was  derived 
from the international survey MEDITS. The estimated abundance and biomass indices do not reveal a 
clear trend. In the period analyzed (2006-2014) indices showed a remarkable increase in 2010 both in 
terms of biomass and abundance indices (Fig. 5.2.11.6.1.3).  

Figure 5.2.11.6.1.3. Giant red shrimp in the GSA 9. MEDITS trends in biomass and density from 1994 to 2014. 

5.2.11.6.1.4  Trends in abundance by length or age 
The following Figures 5.2.11.6.1.4.1-2 display the stratified abundance indices of GSA 9 in 1994-2014 
while the Figures 5.2.11.6.1.4.3-4 the related boxplot. 

287 

 
 
 
 
 
 
 
 
 
 
 
Figure 5.2.11.6.1.4.1. Giant red shrimp in GSA 9. Female stratified abundance indices , 1994-2014. 

288 

 
 
 
 
 
 
 
Figure 5.2.11.6.1.4.2. Giant red shrimp in GSA 9. Male stratified abundance indices , 1994-2014. 

289 

 
 
 
 
 
 
 
 
 
 
Figure  5.2.11.6.1.4.3.  Giant  red  shrimp  in  GSA  9.  Boxplot  of  the  female  stratified  abundance  indices,  1994-
2014. 

290 

 
 
 
 
 
 
 
Figure 5.2.11.6.1.4.4. Giant red shrimp in GSA 9. Boxplot of the male stratified abundance indices, 1994-2014. 

5.2.11.7 Stock Assessment 
5.2.11.7.1  Methods 
The  assessment  of  giant  red  shrimp  in  the  GSA9  has  been  performed  during  EWG  11-15  using  XSA 
approach. 

5.2.11.7.2  Input data 
Data from DCF provided at EWG-11-15 contained information on giant red shrimp landings and the 
respective age structure for 2006-2014 were used. Since in the 2008 and 2012 were observed landing 
for GNS and GTR fisheries respectively and since there  were no catch at age data, the catch at age 
data for OTB were raised to the total amount of landings for those years. 
A  vector  of  natural  mortality  value  by  age  was  obtained  using  PRODBIOM  (Abella  et  al.,  1997).  
MEDITS survey indices used for tuning were obtained by sex and then summed up.  
Catches in numbers and weight were consistent with total landings and so, no rescaling using Sum Of 
Product correction (SOP) was carried out. 
In figure 5.2.11.7.2.1-2 are showed catches in numbers by age from commercial and survey data. 

291 

 
 
 
 
 
 
 
 
Figure 5.2.11.7.2.1. Giant red shrimp in GSA 9. Catch in numbers by age and year used in the XSA. 

Figure  5.2.11.7.2.2.  Giant  red  shrimp  in  GSA  9.  Catch  in  numbers  by  age  and  year  obtained  in  the  Medits 
survey and used in the XSA as tuning data. 

The other inputs are reported in the tables below: 

Table 5.2.11.7.2.1. Giant red shrimp in GSA 9. Catch in numbers by age and year used in XSA. 

Catch in numbers (thousands) 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

292 

0 
49 
0 
15 
8 

1 
235 
88 
507 
275 
185  1186 
858 
909 
464 
326 

1 
23 
7 
397 

2 
1128 
646 
213 
373 
741 
1360 
783 
400 
102 

4+ 
3 
405  342 
313 
70 
278  228 
237  219 
183  102 
190  107 
295  355 
28 
18 

64 
32 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 5.2.11.7.2.2. Giant red shrimp in GSA 9. Mean weights at age used in the XSA (both in catch and stock). 

Weight at age (kg) 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

3 

0 

1 

4+ 

2 
0.006  0.020  0.026  0.036  0.039 
0.008  0.020  0.029  0.040  0.055 
0.010  0.014  0.030  0.042  0.038 
0.008  0.014  0.031  0.043  0.039 
0.008  0.014  0.031  0.046  0.046 
0.007  0.017  0.030  0.044  0.042 
0.009  0.016  0.027  0.040  0.040 
0.009  0.016  0.028  0.039  0.053 
0.015  0.016  0.028  0.047  0.062 

Table 5.2.11.7.2.3. Giant red shrimp in GSA 9. Indices from MEDITS survey used in XSA. 

Survey indices (n/km2) 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

2 

1 

3 

4+ 
0 
14.51  22.23  9.51  2.76 
1.84 
21.10  16.12  9.72  3.97 
1.57 
27.73  14.05  3.76  1.06 
8.89 
2.42  1.55 
80.73 
12.64 
109.15  113.75  42.55  4.84  1.53 
40.19  44.07  5.99  1.00 
20.27  22.90  9.79  0.92 
45.42  25.77  11.19  5.28 
15.80  24.32  8.90  5.70 

2.78 
1.40 
2.39 
0.36 

8.15 

Table 5.2.11.7.2.4. Giant red shrimp in GSA 9. Proportion of matures ate age used in XSA. 

Maturity 
Age0  Age1  Age2  Age3  Age4+ 
1 

0.6 

0 

1 

1 

Table 5.2.11.7.2.5. Giant red shrimp in GSA 9. Natural mortality at age used in XSA. 

Natural mortality 
Age0  Age1  Age2  Age3  Age4+ 
0.34 
1.28  0.58  0.44  0.38 

Table  5.2.11.7.2.6.  Giant  red  shrimp  in  GSA  9.  Growth  and  length  weight  relationships  parameters  used  in 
PRODBIOM. 

Female  Male 
42.7 
0.77 

72 
0.4 

Linf 
K 

293 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
t0 
a 
b 

0 
0.004 
2.357 

-0.27 
0.003 
2.434 

Figure 5.2.11.7.2.3. Giant red shrimp in GSA 9.Growth function for female and male. 

Figure 5.2.11.7.2.4. Giant red shrimp in GSA 9. Length weight relationship for female and male. 

5.2.11.7.3  Results 
XSA  was  run  using  different  shrinkage  values  (Sh1.0,  Sh1.5,  Sh2.0)  and  two  different  qage  values 
(2,3).  As  showed  by  Figure  5.2.11.7.3.1,  the  different  settings  produced  similar  estimates  of 
recruitment and SSB.  

Comparison of different shrinkage values and relative residuals diagnostics. 

294 

 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 5.2.11.7.3.1. Giant red shrimp in GSA 9. XSA outputs for different shrinkage scenario. 

12 

295 

 
 
 
 
 
 
 
 
 
 
 
Figure 5.2.11.7.3.2. Giant red shrimp in GSA 9. Log residuals for the tuning fleet. 

Model with 1.5 shrinkage was adopted and a  new run was made with two different values of qage 
(2,3). 

Figure 5.2.11.7.3.3. Giant red shrimp in GSA 9. XSA outputs for different qage scenario. 

296 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 5.2.11.7.3.4. Giant red shrimp in GSA 9. Log residuals for the tuning fleet with different qage scenario. 

Model  with  1.5  shrinkage  and  qage2  was  adopted  as  final  model  based  on  the  analysis  of  residual 
distributions.  Residuals  from  tuning  fleets  (MEDITS)  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 
5.2.11.7.3.5) to ensure the robustness of the final estimates. The retrospective series indicate good 
agreement between years in the assessment results, with no systematic bias. 

Figure 5.2.11.7.3.5. Retrospective analysis with shrinkage set at 1.5 and qage2. 

297 

 
 
 
 
 
 
 
 
 
 
Figure 5.2.11.7.3.6. Retrospective analysis with shrinkage set at 1.5 and qage3. 

Based  on  these  simulation  analyses,  the  inputs  reported  in  Table  5.2.11.6.4.1  were  selected  to  run 
the final XSA. 

Table 5.2.11.7.3.1. Giant red shrimp in GSA  9. Inputs selected to run the final XSA. 

fse 
1.5 

rage 
1.0 

qage 
2.0 

Shk.n 
true 

Shk.f 
true 

Shk.yrs 
5.0 

Shk.ages 
2.0 

XSA main outputs (Fig. 5.2.11.7.3.7) showed that F values changing around 0.60 from 2006 to 2012 
and then largely decreased. Recruitment varied from a minimum of 6 millions in 2006 to 23 millions 
in  2009.  In  the  last  two  years  analyzed  (2013-2014)  the  estimated  number  of  recruits  was  quite 
stable, around 10 millions of individuals. SSB showed stable values in the last two years around 94 
tons. XSA stock summary results are reported in the Tab. 5.2.11.7.3.2. 

298 

 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 5.2.11.7.3.7. Giant red shrimp in GSA 9. XSA summary results. SSB and catch are in tons, recruitment in 
thousands of individuals. 

Table 5.2.11.7.3.2. Giant red shrimp in GSA 9. XSA stock summary results. 

SSB 
Tons 

Rec 
(x1000) 

Stock number 
0 
1 
2 
3 
4+ 

F by age 
0 
1 
2 
3 
4+ 

Fbar 
(1-3) 

2006 

2014 
2010 
161.176  101.347  80.511  92.16  137.04  166.207  145.643  93.356  94.35 

2008 

2012 

2013 

2007 

2009 

2011 

2006 
6141 

2006 
6141 
3041 
2528 
851 
696 

2006 
0.02 
0.11 
0.81 
0.86 
0.86 

2006 
0.59 

2007 
9116 

2008 
2010 
2009 
12526  23960  16247 

2011 
11969 

2012 
10055 

2013 
9273 

2014 
10785 

2007 
9116 
1682 
1527 
723 
156 

2007 
0.00 
0.07 
0.75 
0.74 
0.74 

2007 
0.52 

2008 
2010 
2009 
12526  23960  16247 
6658 
3475 
2534 
1740 
1040 
876 
370 
393 
465 
200 
347 
365 

2011 
11969 
4420 
2840 
526 
289 

2008 
0.00 
0.31 
0.36 
1.29 
1.29 

2009 
0.00 
0.11 
0.59 
1.31 
1.31 

2010 
0.02 
0.27 
0.76 
0.91 
0.91 

2008 
0.65 

2009 
0.67 

2010 
0.65 

2011 
0.00 
0.30 
0.91 
0.57 
0.57 

2011 
0.59 

2012 
10055 
3327 
1832 
738 
863 

2012 
0.00 
0.45 
0.76 
0.66 
0.66 

2012 
0.63 

2013 
9273 
2784 
1183 
551 
238 

2013 
0.00 
0.25 
0.55 
0.15 
0.15 

2014 
10785 
2574 
1211 
441 
242 

2014 
0.07 
0.19 
0.11 
0.09 
0.09 

2013 
0.32 

2014 
0.13 

299 

 
 
 
 
 
 
 
 
 
 
 
 
 
The XSA diagnostics are reported below: 

FLR XSA Diagnostics 2015-09-03 10:47:08 

CPUE data from indices 

Catch data for 9 years 2006 to 2014. Ages 0 to 4. 

fleet first age last age first year last year alpha beta 
1 Medits         0        3       2006      2014  <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   5 years or the  3 oldest ages. 

S.E. of the mean to which the estimates are shrunk =   1.5 

Minimum standard error for population 
estimates derived from each fleet =  0.3 

prior weighting not applied 

Regression weights 
year 
age   2006  2007  2008  2009  2010 2011  2012 2013 2014 
all 0.82 0.877 0.921 0.954 0.976 0.99 0.997    1    1 

Fishing mortalities 
year 
age  2006  2007  2008  2009  2010  2011  2012  2013  2014 
0 0.010 0.000 0.001 0.000 0.014 0.000 0.003 0.001 0.050 
1 0.084 0.047 0.218 0.062 0.179 0.182 0.256 0.145 0.122 
2 0.478 0.510 0.219 0.353 0.341 0.469 0.361 0.241 0.059 
3 0.323 0.296 0.566 0.527 0.376 0.171 0.218 0.055 0.033 
4 0.323 0.296 0.566 0.527 0.376 0.171 0.218 0.055 0.033 

300 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
XSA population number (Thousand) 
age 
year       0    1    2    3    4 
2006  9200 3909 3701 1775 1475 
2007 12469 2532 2013 1478  323 
2008 21863 3467 1352  778  623 
2009 34778 6071 1562  700  631 
2010 25186 9665 3193  706  388 
2011 19351 6905 4524 1462  814 
2012 16587 5380 3224 1823 2161 
2013 13688 4600 2332 1448  626 
2014 15492 3802 2228 1181  651 

Estimated population abundance at 1st Jan  2015 
age 
year   0    1    2    3   4 
2015 0 4146 1907 1376 796 

Fleet:  Medits 

Log catchability residuals. 

year 
age   2006  2007  2008   2009  2010   2011   2012  2013   2014 
0  0.144 0.015 0.036 -0.085 0.307 -0.070 -0.102 0.035 -0.246 
1 -0.294 0.196 0.165  0.261 0.141 -0.149 -0.297 0.192 -0.218 
2 -0.291 0.017 0.096 -0.509 0.421  0.187 -0.196 0.172  0.046 
3 -0.015 0.174 0.033 -0.324 0.264 -0.378 -0.077 0.185  0.147 

Regression statistics 
Ages with q dependent on year class strength 
[1] "0.357803574798871" "0.714375289122282" "9.02527295097246"  
"5.65372377497984" 

Terminal year survivor and F summaries: 

,Age 0 Year class =2014 

source 
scaledWts survivors yrcls 
Medits     0.311      2058  2014 
fshk       0.046     53430  2014 
nshk       0.642      4843  2014 

,Age 1 Year class =2013 

source 

301 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
scaledWts survivors yrcls 
Medits     0.928      1388  2013 
fshk       0.072      1331  2013 

,Age 2 Year class =2012 

source 
scaledWts survivors yrcls 
Medits     0.959      1417  2012 
fshk       0.041       189  2012 

,Age 3 Year class =2011 

source 
scaledWts survivors yrcls 
Medits      0.96       905  2011 
fshk        0.04       327  2011 

5.2.11.8 Reference points 
5.2.11.8.1  Methods 
The  yield  per  recruit  (YpR)  analysis  was  run  using  NOAA  software.  The  analysis  was  performed  to 
estimate F0.1 as target equilibrium YPR reference point for the stock. 

5.2.11.8.2  Input data  
In the following table are reported the setting inputs for the YpR analysis. The data were the same 
used in the XSA. 

Table 5.2.11.8.2.1. Giant red shrimp in GSA 9. Input data for YpR analysis. 

302 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5.2.11.8.3  Results 
YpR  output  curve  is  illustrated  in  the  Figure  5.2.11.8.3.1.  while  the  main  reference  points  defined 
with the Yield per recruit analysis are reported in the table 5.2.11.8.3.1. F0.1 estimated by the model 
was 0.51. 

303 

 
 
 
 
 
 
 
 
 
 
Figure 5.2.11.8.3.1. Giant red shrimp in GSA 9. Yield per Recruit curve. 

Table  5.2.11.8.3.1.  Giant  red  shrimp  in  GSA  9.  Main  reference  points  defined  with  the  Yield  per  recruit 
analysis. 

Since  F0.1  estimated  by  XSA  (0.51)  was  coeherent  with  YpR  NoAA  estimation,  the  value  was 
considered as reference point and also used in the short term forecast. 

5.2.11.9 Data quality 
MEDITS  abundance  indexes  were  computed  directly  by  the  experts.  Although  landings  data  were 
observed in 2008 for gillnet and in 2012 for trammel any length distribution was available. It is also 
true that landing values for these two fisheries and years were very low (about 700kg and about 1.2 
tons respectively) compare to those of the trawlers. 

304 

 
 
 
 
 
 
 
 
 
 
 
 
 
Short term predictions 2015-2017 
 Method 

5.2.11.10 
5.2.11.10.1 
A  deterministic  short  term  prediction  for  the  period  2015  to  2017  was  performed  using  the  FLR 
routines  provided  by  JRC  and  based  on  the results  of  the XSA  stock assessments performed during 
EWG 15-11 for the years 2006–2014. 

 Input parameters  

5.2.11.10.2 
The  input  parameters  were  the  same  used  for  the  XSA  stock  assessment  and  its  results.  Different 
scenarios, zero catch, harvest at reference point, Fstatusquo and a series of  multiplier of Fstq were 
performed. Fstq=0.294 has been estimated as the geometric mean of the last three years 2012-2014 
of Fbar values estimated with FLR. 
Recruitment  (class  0)  has  been  estimated  from  the  population  results  from  the  geometric  mean 
(10018 thousands individuals). 

5.2.11.10.3 

 Results 

Table  5.2.11.10.3.1.  Giant  red  shrimp  in  GSA  9.  Short  term  forecast  in  different  F  scenarios. 
Basis:F(2015) = mean(Fbar 1-3 2012-2014)= 0.294; R(2015) = geometric mean of the recuitment of the 
last 3 years; R = 10018 (thousands); SSB(2015) = 94 t, Catch (2014)= 17 t. 

Rationale 

Ffactor 

Fbar 

Zero catch 
High long term yield 
F(0.1) 
Status quo 
Different scenarios 

0.000 

0.000 

1.744 
1.000 
0.100 
0.200 
0.300 
0.400 
0.500 
0.600 
0.700 
0.800 
0.900 
1.100 
1.200 
1.300 
1.400 
1.500 
1.600 
1.700 
1.800 
1.900 
2.000 

0.514 
0.295 
0.029 
0.059 
0.088 
0.118 
0.147 
0.177 
0.206 
0.236 
0.265 
0.324 
0.354 
0.383 
0.412 
0.442 
0.471 
0.501 
0.530 
0.560 
0.589 

Catch 
2016 
0.00 

43.89 
27.53 
3.09 
6.10 
9.02 
11.88 
14.66 
17.37 
20.01 
22.58 
25.09 
29.91 
32.23 
34.50 
36.71 
38.86 
40.96 
43.01 
45.01 
46.96 
48.86 

Catch 
2017 
0.00 

38.07 
27.47 
3.71 
7.16 
10.38 
13.38 
16.18 
18.78 
21.19 
23.44 
25.53 
29.27 
30.94 
32.49 
33.92 
35.25 
36.47 
37.61 
38.65 
39.62 
40.50 

SSB 
2017 
142.79 

96.42 
113.36 
139.45 
136.19 
133.04 
129.97 
127.00 
124.11 
121.30 
118.58 
115.93 
110.87 
108.44 
106.09 
103.80 
101.58 
99.42 
97.32 
95.28 
93.30 
91.38 

Change SSB 2016-
2017(%) 
25.96 

Change Catch 
2014-2016(%) 
-100.00 

-14.94 
0.00 
23.01 
20.14 
17.35 
14.65 
12.03 
9.48 
7.00 
4.60 
2.27 
-2.20 
-4.34 
-6.42 
-8.44 
-10.40 
-12.30 
-14.15 
-15.95 
-17.70 
-19.40 

160.94 
63.68 
-81.64 
-63.76 
-46.34 
-29.38 
-12.85 
3.26 
18.95 
34.25 
49.15 
77.85 
91.66 
105.12 
118.25 
131.05 
143.54 
155.73 
167.61 
179.20 
190.52 

A short term projection (Table 5.2.11.10.3.1), assuming an Fstq of 0.294 in 2015 and a recruitment of 
10018 thousands individuals show that: 

305 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  Fishing at the Fstq (0.294) generates an increase of the catch of about 60% from 2014 to 

2016 along with no increase of the spawning stock biomass 2016 to 2017. 

  Fishing at F0.1 (0.51) generates an increase of the catch of about 161% from 2014 to 2016 

and a decrease of the spawning stock biomass of about 15% from 2016 to 2017. 

Medium term predictions 
 Method 

5.2.11.11 
5.2.11.11.1 
Medium  term  prediction  would  only  be  performed  if  there  is  a  reliable  fit  of  a  stock-recruitment 
relationship  obtained  from  a  quite  long  time  series.  Since  the  fit  for  the  giant  red  shrimp  was  not 
considered  reliable  and  the  time  series  cover  only  nine  years,  medium  term  predictions  were  not 
carried out. 

Stock advice 

5.2.11.12 
On  the  basis  of  the  estimated  limit  management  reference  point  for  sustainable  exploitation 
(FMSY=0.51)  and  considering  that  this  value  is  higher  compare  to  currently  fishing  mortality  value 
(0.13) the stock is in underfishing conditions. It is important underling that recently figures obtained 
in ad hoc project promoted by EU (STOCKMED) put in evidence as this stock should be analysed at 
wider geographical level (e.g. GSA 9,10 and 11 together). 

Management strategy evaluation 

5.2.11.13 
We ran the Management Strategy Evaluation to evaluate if the MSY ranges were precautionary. The 
FMSY  ranges  were  derived  using  the  formula  provided  by  STECF  15-09.  F  ranges  results  were 
Fupper=0.69 and Flower=0.34. Blim was estimated as Bloss=80.51 (t). The following figure shows the results 
of the MSE. 

Figure 5.2.11.13.1. Giant red shrimp in GSA 9. Marine Strategy Evaluation. 

The probability of SSB to fall below Blim at F = Fupper is equal to 0. The dynamics observed for this stock 
are the result of the  stock assessment model (i.e. XSA) settings used inside the MSE becoming less 
appropriate as the stock status changes in time (i.e. stock assessment settings are often specific to a 
particular range of stock status). This leads to an increasing difference between the perceived stock 

306 

 
 
 
 
 
 
 
 
 
 
and the operating model (i.e. the 'true' stock). To avoid this behaviour in the future, for some of the 
stocks as it is the case here, a more general stock assessment method should be used in the MSE loop 
that is less sensitive to the stock status. 

307 

 
 
 
 
 
 
 
5.2.12  STOCK ASSESSMENT OF GIANT RED SHRIMP IN GSA 10 
5.2.12.1 Stock Identification 
The stock of giant red shrimp, Aristaeomorpha foliacea, was assumed in the boundaries of the whole 
GSA10, lacking specific information on stock identity (Figure 5.2.12.1.1). This species and the blue-red 
shrimp,  Aristeus  antennatus,  are  deep-water  decapods  characterised  by  seasonal  variability  and 
annual  fluctuations  of  abundance  (Spedicato  et  al.,  1994),  as  reported  for  different  geographical 
areas (e.g. Relini and Orsi Relini, 1987). The giant red shrimp A. foliacea is distributed beyond 350 m 
depth, but mainly in water deeper than 500 m. Generally mean length estimated using trawl survey 
data varies remarkably with depth, for the whole population and the two sexes, increasing at deeper 
waters. 

Fig. 5.2.12.1.1. Geographical location of GSA 10. 

In the recent years, A. foliacea was ranked among the most abundant species (in number and weight) 
in the trawl survey catches. Higher biomass indices occur particularly southwards the Gulf of Naples 
(Spedicato et al., 1994). 
This  species  has  a  discrete  recruitment  pattern  and  during  spring  trawl  surveys  (MEDITS)  the 
recruitment pulse is observed. Since the reproduction takes place in the late spring-summer, recruits 
could be attributed to the spawning events of the previous year (Spedicato et al., 1999). A. foliacea is 
considered fully recruited to grounds at ~24 mm CL (from Samed, AAVV, 2002). Recently a study at 
Mediterranean scale, using Medits data from 1994 to 2004, has evidenced that the higher abundance 
indices of recruits were observed in the central-southern Tyrrhenian Sea (AAVV, 2008).  
In general, the length frequency distributions of the giant red shrimp have a polymodal pattern, with 
4-5  components  for  females  (the  modes  of  adults  are  less  defined)  and  2-3  components  for  the 
males. For the females a life span of 6-8 years was estimated. The structure of the sizes of A. foliacea 
is  characterised  by  marked  differences  in  growth  between  the  sexes.  The  larger  individuals  are 
females and inhabit deeper waters.  
Sex ratio values of ~0.5 show that males and females are not segregated into different bathymetric 
ranges  (Spedicato  et  al.,  1994).  The  reproduction  period  extends  from  May  to  September,  with  a 

308 

 
 
 
 
 
 
 
 
peak in the summer (July-August) (Spedicato et al., 1999). Mature males have been observed all year 
round. 
According to the benthic bionomic classification of Pérès and Picard, P. longirostris, N. norvegicus and 
red-shrimps  typify  the  populations  of  slope  and  bathyal  bottoms  in  the  GSA10.  Depending  on  the 
depth  and  zone,  this  fauna 
is  accompanied  by  characteristic  bentic  species  as  Funiculina 
quadrangularis, Geryon longipes, Polycheles typhlops, Isidella elongata, Griphus vitreus.  
In  the  central-southern  Tyrrhenian  Sea,  the  giant  red  shrimp  represents  a  specific  target  of  deep-
waters trawling fishery given its high economic value (Spedicato et al., 1994). 

5.2.12.2 Growth 
Estimates of the growth pattern of the giant red shrimp in the GSA 10 were previously obtained using 
GRUND length frequency distributions from 1991 to 1995 and methods as Elefan and Batthacharya 
for the analysis of LFDs. Parameters of females were as follows: CL∞=73.24 mm; K=0.483; t0= -0.435 
(Spedicato et al., 1998). In the Samed project (AAVV, 2002) and using the Medits data from 1994 to 
1999  a  new  set  of  parameters  was  estimated  for  the  Tyrrhenian  sea  down  the  Strait  of  Messina 
(females:  L∞=73  mm;  K=0.44;  t0=  -0.05;  males:  L∞=48  mm;  K=0.59;  t0=  -0.2).  The  observed 
maximum carapace length of females and males were 72 and 46 mm respectively. 
Growth  has  been  also  studied  in  the  DCF  framework  and  in  the  Red  Shrimps  project  (AAVV,  2008) 
through  the  analysis  of  the  LFDs  and  the  separation  of  modal  components.  These  estimates  have 
been  done  using  both  MEDITS  and  GRUND  average  length  at  putative  age,  where  age  was  set 
according to the date of each survey with a birthday on 1st July.  
Table 5.2.12.2.1 reports putative ages, mean carapace lengths with relative standard deviations for 
females.  

Table 5.2.12.2.1.  Giant red shrimp in GSA 10. Putative age, mean length of modal components of the LFD of 
MEDITS and GRUND survey and relative standard deviations. 

309 

putative agemean CLst. dev.putative agemean CLst. dev.putative agemean CLst. dev.0.821.92.292.045.52.583.154.31.010.822.52.362.047.52.053.254.52.110.923.03.382.044.91.83.253.51.330.924.62.782.046.73.063.255.31.520.923.03.752.045.93.763.257.01.531.026.62.962.146.21.853.257.22.11.025.03.162.245.12.593.254.32.231.026.01.952.246.61.553.253.51.711.024.82.262.249.22.233.252.91.971.029.12.792.245.62.983.356.01.471.128.23.822.249.13.313.353.61.251.231.02.582.245.82.33.860.32.461.233.32.682.245.92.623.857.92.141.232.82.372.246.61.983.960.02.381.233.42.652.346.11.83.957.62.151.233.73.052.346.22.394.063.12.541.231.12.662.854.72.384.060.31.551.232.13.552.852.61.844.063.81.31.232.02.812.955.03.164.061.12.351.332.93.072.954.02.054.160.54.561.333.53.162.950.91.814.261.32.351.842.62.773.054.83.054.262.01.141.843.82.423.054.92.744.260.43.371.944.42.383.055.72.94.258.82.051.945.22.533.054.83.534.259.61.031.943.83.63.055.63.184.357.81.37 
 
 
 
 
 
 
 
The  following  estimates  of  von  Bertalanffy  growth  parameters  for  each  sex  were  obtained  from 
average  length  at  age  using  an  iterative  non-liner  procedure  that  minimises  the  sum  of  the  square 
differences between observed and expected values and fixing the asymptotic length on the basis of 
the observed maximum values: females CL∞=72.5 mm, K=0.438, t0= -0.1; males: CL∞=44 cm, K=0. 5, 
t0=  -0.1  (Figure  5.2.12.2.1).  These  estimates  are  more  accurate,  although  very  close,  to  those 
previously obtained.  
Average  parameters  of  the  length-weight  relationship  were  a=0.0014,  b=2.622  for  females  and 
a=0.000848, b=2.78 for males, for length expressed in mm. 

CL  (mm)= 

k/year= 
t0(year)= 

females  males 

72.5 
0.438 
-0.10 

44 
0.5 
-0.1 

Fig. 5.2.12.2.1. Giant red shrimp in GSA 10. Von Bertalanffy growth functions and parameters for females. 

310 

putative agemean CLst. dev.putative agemean CLst. dev.putative agemean CLst. dev.0.821.92.292.045.52.583.154.31.010.822.52.362.047.52.053.254.52.110.923.03.382.044.91.83.253.51.330.924.62.782.046.73.063.255.31.520.923.03.752.045.93.763.257.01.531.026.62.962.146.21.853.257.22.11.025.03.162.245.12.593.254.32.231.026.01.952.246.61.553.253.51.711.024.82.262.249.22.233.252.91.971.029.12.792.245.62.983.356.01.471.128.23.822.249.13.313.353.61.251.231.02.582.245.82.33.860.32.461.233.32.682.245.92.623.857.92.141.232.82.372.246.61.983.960.02.381.233.42.652.346.11.83.957.62.151.233.73.052.346.22.394.063.12.541.231.12.662.854.72.384.060.31.551.232.13.552.852.61.844.063.81.31.232.02.812.955.03.164.061.12.351.332.93.072.954.02.054.160.54.561.333.53.162.950.91.814.261.32.351.842.62.773.054.83.054.262.01.141.843.82.423.054.92.744.260.43.371.944.42.383.055.72.94.258.82.051.945.22.533.054.83.534.259.61.031.943.83.63.055.63.184.357.81.37GSA 10 - Aristaeomorpha foliacea - females 010203040506070800.01.02.03.04.05.06.07.0Age (years)Mean CL (mm)Exp.Obs. 
 
 
 
 
 
 
 
 
 
 
        
 
 
 
5.2.12.3 Maturity 
The  maturity  ogive  (Figure  5.2.12.3.1)  was  obtained  from  a  maximum  likelihood  procedure  applied 
grouping as mature individuals belonging to the maturity stage 2b (according to the MEDITS maturity 
scale)  onwards.  The  fitting  of  the  curve  was  fairly  good,  however  the  estimates  of  the  size  at  first 
maturity Lm50%  (3.5 cm ±0.023 cm) and of the maturity range (0.36 cm ±0.020 cm), reported in the 
figure below, seem slightly lower if compared with literature values (average of the smallest females 
in the GSA ~34 mm CL; 39.6 mm carapace length according to Ragonese & Bianchini, 1995). 

Fig. 5.2.12.3.1.  Giant red shrimp in GSA 10. Maturity ogive and proportions of mature female (MR indicates 
the difference Lm75%-Lm25%). 

The sex ratio from DCR evidenced the prevalence of males in the size class from 3.4 to 3.8 cm while 
from 4 cm onwards the proportion of females was dominant (Figure 5.2.12.3.2). 

Fig. 5.2.12.3.2. Giant red shrimp in GSA10.   Sex ratio. 

5.2.12.4 Natural mortality 
A vector of natural mortality by age was estimated using PRODBIOM (Abella et al., 1998). The results 
are shown in Table 5.2.12.4.1. The input parameters used for M estimation were Linf = 73, k = 0.438, 
t0 =-0.1, a = 0.0014 and b = 2.62. 

311 

0.00.30.50.81.000.511.522.533.544.555.566.57carapace length (cm)p    Lm50% = 3.5 ± 0.023 cm    MR    = 0.36 ± 0.020 cm                           00.20.40.60.8122.63.23.84.455.66.2total length (cm)proportion by sexSR FSR M 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 5.2.12.4.1. Giant red shrimp in GSA 10. Natural mortality by age. 

age 0  age 1  age 2  age 3  Age 4+ 
0.53 
0.61 
1.33 

0.73 

0.56 

5.2.12.5 Fisheries 
5.2.12.5.1  General description of the fisheries 
The  Giant  red  shrimp  is  only  targeted  by  trawlers  and  fishing  grounds  are  located  offshore  200  m 
depth, mainly southward Salerno Gulf. Catches from trawlers are from a depth range between 400 
and  700  m  depth  and  giant  the  red  shrimp  occurs  with  A.  antennaus,  P.  longirostris  and  N. 
norvegicus, P. blennoides, M. merluccius, depending on operative depth and area. 

5.2.12.5.2  Management regulations applicable in 2015  
Management regulations are based on technical measures, closed 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.  Other  measures  on 
which the management regulations are based regard technical measures (mesh size) and minimum 
landing sizes (EC 1967/06).  
After 2000, in agreement with the European Common Policy of Fisheries, a gradual decreasing of the 
fleet capacity is implemented. Along northern Sicily coasts two main Gulfs (Patti and Castellammare) 
have been closed to the trawl fishery up 200 m depth, since 1990. In the GSA10 the fishing ban has 
not been mandatory along the time, and from one year to the other it was adopted on a voluntary 
basis by fishers, whilst in the last years it was mandatory. 
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). One is located along the mainland, in front of Sorrento peninsula in the vicinity of 
the MPA of Punta Campanella (Napoli Gulf, 60 km2, within 200 m depth) and a second one is along 
the  coasts  of  Amantea  (Calabrian  coasts,  75  km2  up  to  250  m  depth).  In  these  areas  trawling  is 
forbidden  and  other  fishing  activities  are  allowed  under  permission.  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. 

5.2.12.5.3  Catches (by fleet if possible) 
The catch is composed almost esclusively by marketed individuals. 

5.2.12.5.4  Landings (by fleet if possible) 
Available landing data are from DCF regulations. EWG 15-11 received Italian landings data for GSA10 
by fisheries which are listed in Table 5.2.12.5.4.1. 
In general, demersal trawlers account for the majority of the total landing. Small amounts are due to 
some artisanal vessels fishing with gillnet (Figure 5.2.12.5.4.1). This fishery contributes from 0 to 5.8% 
to the total landing according to the different years. Landings are decreasing from 2006 to 2008 and 
then slightly increasing from 2008 to 2010. High values are observed in 2013 and 2014. 
The size frequency distributions of the trawl landing are comprised between 10 and 67 mm CL and 
show different modal classes. Gillnetters mainly catch big specimens (Figure 5.2.12.5.4.2). 

312 

 
 
 
 
 
 
 
 
 
 
Table 5.2.12.5.4.1. Giant red shrimp in GSA 10. Annual landings (tons) by fishery, from 2004 to 2014. 

YEAR 
2004 
2004 
2005 
2005 
2006 
2006 
2007 
2007 
2008 
2008 
2009 
2009 
2009 
2010 
2010 
2010 
2011 
2011 
2012 
2012 
2013 
2014 
2014 
2014 

GEAR 
GNS 
OTB 
GNS 
OTB 
GNS 
OTB 
GNS 
OTB 
GNS 
OTB 
GNS 
OTB 
OTB 
GNS 
OTB 
OTB 
GNS 
OTB 
GNS 
OTB 
OTB 
GNS 
OTB 
OTB 

DEMF 

DEMF 

DEMF 

DEMF 

FISHERY  LANDINGS 
4 
DEMF 
203 
7 
498 
8 
412 
9 
291 
7 
113 
5 
59 
148 
1 
62 
127 
6 
135 
8 
152 
399 
5 
279 
171 

DEMF 
DWSP 
MDDWSP 
DEMF 
DWSP 
MDDWSP 
DEMF 

DEMF 
DWSP 
MDDWSP 

DEMF 

Fig. 5.2.12.5.4.1.  Giant red shrimp in GSA 10. Annual landings (tons) from 2004 to 2014. 

313 

010020030040050060020042005200620072008200920102011201220132014Landing (tons)OTBOTB_DWSOTB_MDDGNS 
 
 
 
 
    
 
 
 
 
 
 
 
 
 
 
 
Fig.  5.2.12.5.4.2.      Giant  red  shrimp  in  GSA  10.  Demographic  structure  of  the  landing  (trawling  above  and 
gillnet below) from 2006 to 2014. 

5.2.12.5.5  Discards (by fleet if posible) 
Discards data from 2009 to 2014 were available. The amount of discards of giant red shrimp in the 
GSA 10 was generally negligible. 

5.2.12.5.6  Fishing effort (by fleet if possible) 
The  trends  in  fishing  effort  by  year  and  major  gear  type  in  terms  of  kW*days  are  listed  in  Table 
5.2.12.5.6.1 and in Figure 5.2.12.5.6.1.  

Table 5.2.12.5.6.1.  Effort (kW*days) for GSA10 by gear type, 2004-2014 as reported through the DCF 
official data call. 

Gear 

2004 

2005 

2006 

2007 

2008 

2009 

2010 

2011 

DRB 

FPO 

GND 

86505 

294424 

312180 

144186 

238122 

188909 

209574 

196692 

314508 

153589 

156 

369729 

128153 

676640 

443277 

496680 

435913 

112632 

44621 

2012 

2013 

2014 

241145 
71997 

53742 

59508 
438492 

88658 
130683 

7667 

38343 

314 

0500100015002000250030000510152025303540455055606570Number of individuals (thousands)CL(mm)200620072008200920102011201220132014OTB0510152025303540450510152025303540455055606570Number of individuals (thousands)200920112012GNS 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
GNS 

GTR 

LLD 

LLS 

LTL 

none 

OTB 

OTM 

PS 

PTM 

4362276 

5038906 

3024622 

2226520 

2506323 

2525668 

2782604 

2963679 

3671219 

1745574 

4394209 

3883167 

3208597 

2450304 

2689599 

2611624 

1823662 

1138482 

1013389 

361358 

387768 

1471790 

2469932 

2130245 

7079323 

1811552 

1493720 

1185423 

1399622 

1010226 

1272999 

1695680 

2536182 

1904962 

2476523 

2697356 

2919718 

2995387 

1643421 

1136408 

1036683 

1051670 

1339212 

2676577 

6324 

893 

12334 

7799360 

4540824 

3986171 

3370493 

2539043 

3487970 

2681538 

2106037 

6970928 

8028733 

7156787 

7112581 

5724631 

5997764 

5603044 

5234759 

5807234 

2502000 

1781508 

1783526 

1188917 

1903718 

1652686 

1567061 

6995 

1336435 

600716 

447521 

6051158 

6154030 

8797448 

383607 

1548326 

1721519 

1601791 

912 

Fig. 5.2.12.5.6.1.  Nominal fishing effort of OTB and GNS (KW*days). 

The fishing effort of trawlers, that is the major component of fishing in the area, shows a decreasing 
trend  from  2004  to  2011.  In  the  last  three  years  a  reversal  tendency  has  been  observed,  with  a 
particularly high value in 2014. After a decline between 2004 and 2007, gillnet fishery shows a quite 
constant trend.   

5.2.12.6 Scientific surveys 
5.2.12.6.1   Survey #1 (MEDITS) 
5.2.12.6.1.1  Methods 
According  to  the  MEDITS  protocol  (Bertrand  et  al.,  2002),  trawl  surveys  were  yearly  (May-July) 
carried  out,  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  and  weight  per  surface 
unit) were standardised to square kilometre, using the swept area method. 

315 

01000200030004000500060007000800090001000020042005200620072008200920102011201220132014kW*days (x1000)OTBGNS 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
Based on the DCF data call, abundance and biomass indices were recalculated with a standardization 
to  the  hour.  In  GSA  10  the  following  number  of  hauls  was  reported  per  depth  stratum  (Table 
5.2.12.6.1.1.1). 

Table 5.2.12.6.1.1.1. Number of hauls per year and depth stratum in GSA10, 1994-2014. 

m
u
t
a
r
t
S

10-
50 
50-
100 
100-
200 
200-
500 
500-
800 

4
9
9
1

7 

10 

17 

22 

28 

5
9
9
1

8 

10 

17 

23 

27 

6
9
9
1

8 

10 

17 

22 

28 

7
9
9
1

8 

10 

17 

22 

28 

8
9
9
1

8 

10 

17 

22 

28 

9
9
9
1

8 

10 

17 

22 

27 

0
0
0
2

8 

10 

17 

22 

28 

1
0
0
2

8 

10 

17 

24 

26 

2
0
0
2

7 

8 

14 

18 

23 

3
0
0
2

7 

8 

14 

18 

23 

4
0
0
2

7 

8 

14 

18 

23 

5
0
0
2

7 

8 

14 

18 

23 

6
0
0
2

7 

8 

14 

18 

23 

7
0
0
2

7 

8 

14 

18 

23 

8
0
0
2

7 

8 

14 

19 

22 

9
0
0
2

7 

8 

14 

18 

23 

0
1
0
2

7 

8 

14 

18 

23 

1
1
0
2

7 

8 

14 

18 

23 

2
1
0
2

7 

8 

14 

18 

23 

3
1
0
2

7 

8 

14 

18 

23 

4
1
0
2

7 

8 

14 

18 

23 

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 hauling duration. Hauls noted 
as valid were used only, 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: 

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  the 
assumptions over zero catch stations, and hence assumptions over the distribution of data. A normal 
distribution is often assumed, whereas data may be better described by a delta-distribution and/or 
quasi-poisson. Indeed, data may be better modeled using the idea of conditionality and the negative 
binomial (e.g. O’Brien et al. (2004)). 
Length distributions represent the number of individual per km2 (Cochran, 1977). 

316 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 Geographical distribution 

5.2.12.6.1.2 
The  geographical  distribution  pattern  of  the  giant  red  shrimp  has  been  studied  in  the  area  using 
trawl-survey  data, 
length  frequency  distribution  analyses  via  modal  component  separation 
techniques  and  geostatistical  methods.  The  abundance  of  the  whole  population,  as  derived  from 
Medits survey, was higher in the southern part of the GSA 10 along the Calabrian coasts as well as the 
abundance of recruits (Figure 5.2.12.6.1.2.1). The probability of find a nursery area was the highest in 
the same zone with a high temporal continuity. 

Fig.  5.2.12.6.1.2.1.  Giant  red  shrimp  in  GSA  10.  Maps  of  abundance  (left)  and  of  the  probability  of  nursery 
localization  (right)  from  MEDITS  survey  of  1997  and  2003  respectively.  The  contour  of  persistence  is  also 
evidenced in the map of abundance. 

 Trends in abundance and biomass 

5.2.12.6.1.3 
Fishery independent information regarding the state of the giant red shrimp in GSA 10 was obtained 
from  the  international  survey  MEDITS.  Figure  5.2.12.6.1.3.1  displays  the  estimated  trend  of  A. 
foliacea  abundance  and  biomass  standardized  to  the  surface  unit  in  GSA  10.  Indices  from  MEDITS 
trawl-surveys  show  a  fluctuating  pattern  with  two  peaks  in  1997,  2005  and  2010,  but  without  any 
trend. The last year shows the lowest value of the whole data series both in density and biomass. 

317 

050010001500200025003000199419951996199719981999200020012002200320042005200620072008200920102011201220132014N/km2Arismr970 - 7575 - 600600 - 15001500 - 45004500 - 76007600 - 1100011000 - 1500015000 - 2000020000 - 35144No DataContours of Persistence Index_Arisfol_Medits0.6 - 1NEWSArismir1944030 - 2020 - 4040 - 6060 - 8080 - 100No DataNEWS 
 
 
 
 
 
 
 
 
 
 
Fig. 5.2.12.6.1.3.1. Giant red shrimp in GSA 10. MEDITS abundance and biomass indices (±95% conf. int.). 

5.2.12.6.1.4  Trends in abundance by length or age 
The  following  figures  display  the  stratified  abundance  indices  by  length  of  GSA  10  for  the  period 
1994-2014 (MEDITS data). 

Fig. 5.2.12.6.1.4.1.  Giant red shrimp in GSA 10. Stratified abundance indices by size, 1994-2014. 

318 

0510152025199419951996199719981999200020012002200320042005200620072008200920102011201220132014kg/km2 
 
 
 
 
 
 
 
Fig. 5.2.12.6.1.4.2. Giant red shrimp in GSA 10. Stratified abundance indices by size, 1994-2014 (Females). 

Fig. 5.2.12.6.1.4.3. Giant red shrimp in GSA 10. Stratified abundance indices by size, 1994-2014 (Males). 

319 

 
 
 
 
 
 
 
 
Fig. 5.2.12.6.1.4.4. Giant red shrimp in GSA 10. Demographic characteristics of the population for the period 
1994-2014. 

320 

 
 
 
 
 
 
 
 
 
Fig.  5.2.12.6.1.4.5.  Giant  red  shrimp  in  GSA  10.  Demographic  characteristics  of  the  females  for  the  period 
1994-2014. 

Fig. 5.2.12.6.1.4.6. Giant red shrimp in GSA 10. Demographic characteristics of the males for the period 1994-
2014. 

321 

 
 
 
 
 
 
 
 
 
5.2.12.7 Stock Assessment 
5.2.12.7.1  Methods 
The last assessment of giant red shrimp in GSA 10 has been performed during the EWG-12-19. In the 
last 2015 data call, data from 2006 to 2014 have been provided for the EWG-15-11; the time series 
from 2006 to 2014 has been considered covering the mean life span of the species, allowing to assess 
the stock using XSA method. The age distributions from age class 0 to 4+ have been used. 

5.2.12.7.2  Input data 
For the assessment of giant red shrimp stock in GSA 10 the DCF official data on the length structure 
has  been  divided  in  males  and  females  length  structures  by  means  of  sex  ratio  by  length;  the  age 
distributions  by  sex  have  been  estimated  using  the  age  slicing  method  and  then  the  resulting 
distributions were summed up. The DCF official landing data of commercial catch have been used. A 
sex combined analysis was carried out. The maturity at age has been estimated using the maturity at 
length  transformed  to  ages  by  slicing  procedure.  The  natural  mortality  has  been  calculated  using 
PRODBIOM (Abella, 1998). The survey indices from MEDITS data from 2006 to 2014 have been used 
for the tuning. The age distribution is showed in the graph and in the figures and tables below: 

Fig. 5.2.12.7.2.1. Giant red shrimp in GSA 10. Commercial catch in numbers by age and year used in the XSA. 

The other inputs are reported in the tables below: 

Table 5.2.12.7.2.1. Giant red shrimp in GSA 10. Catch in numbers by age and year used in the XSA.  

322 

02000400060008000100001200014000200620072008200920102011201220132014Number of individuals (thousands)01234+0.002000.004000.006000.008000.0010000.0012000.0014000.0001234+Catch at age -commercial data2006200720082009201020112012201320140.0100.0200.0300.0400.0500.0600.001234+Catch at age -MEDITS200620072008200920102011201220132014 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Catch in numbers    (thousands) 

2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

age 1 

age 3 

age 0 

age 4+ 
age 2 
6191.53  9249.14  9308.32  2739.20  167.68 
83.67  4937.09  8137.44  1775.92  230.49 
679.19  107.73 
581.82  2652.89  2498.44 
601.81  120.38 
1410.77  6174.48  5445.77 
731.71  66.77 
1983.98  5147.38  4245.29 
281.51  46.74 
209.76  2164.70  4336.70 
771.52  36.41 
3581.45  4743.26  3274.08 
5317.65  12511.43  9634.34 
750.89  102.17 
933.20  10360.65  9572.44  2923.43  494.99 

Table 5.2.12.7.2.2.  Giant  red shrimp in  GSA  10.  Weights  at  age  used  in  the  XSA  (used  for  the stock and  the 
catch).  

Weight at age (kg) 

2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

age 1 

age 0 
age 2  age 3 
0.006  0.013  0.021  0.022 
0.009  0.013  0.021  0.029 
0.006  0.012  0.026  0.023 
0.007  0.013  0.018  0.031 
0.006  0.013  0.021  0.026 
0.006  0.021  0.020  0.022 
0.006  0.011  0.020  0.025 
0.006  0.013  0.019  0.024 
0.008  0.015  0.020  0.028 

age 4+ 
0.041 
0.055 
0.038 
0.043 
0.045 
0.029 
0.043 
0.033 
0.036 

Table 5.2.12.7.2.3. Giant red shrimp in GSA 10.  Indices from MEDITS survey used in the XSA. 

Survey indices (n/km2) 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

97.7 

84.2 

age 0  age 1  age 2  age 3  age 4+ 
22.2 
313.8  402.9  357.7  108.3 
21.8 
59.5 
43.3 
13.1 
123.0  493.9  161.6  43.0 
17.9 
142.5  360.1  310.6  60.7 
7.6 
501.4  523.6  351.1  88.3 
12.7 
61.9  112.1  367.4  134.7 
14.6 
79.1  128.2  193.2  105.9 
16.9 
119.6  223.8  290.2  100.2 
16.9 
62.9 
1.2 

19.2 

94.5 

Table 5.2.12.7.2.4. Giant red shrimp in GSA 10. Proportion of matures at age used in the XSA. 

Maturity 
Year 
2006 
2007 
2008 
2009 
2010 
2011 

age 0 
0.0035 
0.0035 
0.0035 
0.0025 
0.0025 
0.0025 

age 1 
0.424 
0.424 
0.424 
0.382 
0.382 
0.382 

Age 3 
0.9990 
0.9990 
0.9990 
0.9995 
0.9995 
0.9995 

Age 4+ 
1 
1 
1 
1 
1 
1 

age 2 
0.961 
0.961 
0.961 
0.937 
0.937 
0.937 

323 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2012 
2013 
2014 

0.0030 
0.0030 
0.0030 

0.3065 
0.3065 
0.3065 

0.936 
0.936 
0.936 

0.9995 
0.9995 
0.9995 

1 
1 
1 

Table 5.2.12.7.2.5. Giant red shrimp in GSA 10. Natural mortality at age used in the XSA. 

Natural mortality 
age 1 
0.73 

age 0 
1.33 

age 2 
0.61 

age 3 
0.56 

Age 4+ 
0.53 

Table 5.2.12.7.2.6. Giant red shrimp in GSA 10. Growth parameters and length-weight relationship coefficient 
used in PRODBIOM. 

Growth parameters 
CLinf 
K 
t0 
a 
b 

73 
0.438 
-0.1 
0.0014 
2.62 

5.2.12.7.3 Results 
XSA  was  run  setting  shrinkage  at  1.0,  1.5,  2.0  and  2.5.  As  showed  by  Fig.  5.2.12.7.3.1,  the  four 
different settings produced similar estimates of recruitment and SSB. 

Fig. 5.2.12.7.3.1. Giant red shrimp in GSA 10. XSA outputs for different shrinkage scenario and log residuals for 
the tuning fleet. 

324 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Model with 1.5 shrinkage was adopted as final model based on the analysis of residual distributions 
(Fig.  5.2.12.7.3.2).  Residuals  from  tuning  fleets  (MEDITS)  per  age  and  year  were  relatively  low, 
ranging from 2 to - 2, and did not show any trend with time. 
Moreover  a  retrospective  analysis  was  conducted  on  recruitment,  mean  F  and  SSB  (Figure 
5.2.12.7.3.3) to ensure the robustness of the final estimates. The retrospective series indicate good 
agreement between years in the assessment results for F, with no systematic bias. More differences 
are observed for SSB and recruitment. 

Fig. 5.2.12.7.3.2. Giant red shrimp in GSA 10. Residuals at age obtained with shrinkage set at 1.5. 

Fig. 5.2.12.7.3.3. Retrospective analysis with shrinkage set at 1.5. 

325 

 
 
 
 
 
 
 
 
Based on these simulation analyses, the inputs reported in Table 5.2.12.7.3.1. were selected to run 
the final XSA. 

Tab. 5.2.12.7.3.1. Inputs selected to run the final XSA. 

fse 
1.0 

rage 
0.0 

qage 
2.0 

Shk.n 
true 

Shk.f 
true 

Shk.yrs 
5.0 

Shk.ages 
2.0 

XSA main outputs (Fig. 5.2.12.7.3.4) showed a decrease of fishing mortality in the period 2007-2011; 
then, an evident increase was oserved, reaching a very high value in the last year of the time series. 
SSB showed an increasing trend up to 2011 followed by a constant decrease up to the minimum in 
2014. Recruitment is characterized by a fluctuating trend, varying from a minimum of 113 millions in 
2006 to 209 millions in 2011. XSA stock summary results are reported in Table 5.2.12.7.3.2. 

Fig. 5.2.12.7.3.4. Giant red shrimp in GSA 10. XSA summary results. SSB and catch are in tons, recruitment in 
thousands of individuals. 

Tab. 5.2.12.7.3.2. Giant red shrimp in GSA 10. XSA stock summary results. 

SSB 
Tons 

2006 
554.91 

2007 
346.77 

2008 
294.27 

2009 
469.81 

2010 
552.13 

2011 
579.46 

2012 
481.92 

2013 
501.88 

2014 
265.32 

REC 
(x1000) 

2006 
112642 

2007 
149443 

2008 
174966 

2009 
167735 

2010 
115566 

2011 
208897 

2012 
202465 

2013 
111398 

2014 
168437 

F by age 
0 
1 
2 
3 

2006 

0.113 
0.348 
0.905 
0.664 

2007 
0.001 
0.311 
1.258 
0.702 

2008 
0.006 
0.102 
0.448 
0.474 

2009 
0.016 
0.215 
0.562 
0.280 

2010 
0.034 
0.186 
0.389 
0.201 

2011 
0.002 
0.112 
0.411 
0.058 

2012 
0.035 
0.132 
0.429 
0.177 

2013 
0.097 
0.429 
0.824 
0.249 

2014 
0.011 
0.817 
1.610 
1.195 

326 

 
 
 
 
 
 
 
 
 
 
 
 
4+ 

0.664 

0.702 

0.474 

0.280 

0.201 

0.058 

0.177 

0.249 

1.195 

Fbar 
(0-3) 

2006 
0.508 

2007 
0.568 

2008 
0.257 

2009 
0.268 

2010 
0.202 

2011 
0.146 

2012 
0.193 

2013 
0.400 

2014 
0.908 

The XSA diagnostics are reported below: 

XSA Diagnostics  2015-09-03 11:07:14 

CPUE data from indices 

Catch data for 9 years 2006 to 2014 Ages 0 to 4+ 

fleet 
Medits                    0            3              2006             2014         <NA> 

         first age   last age      first year 

last year      alpha 

     beta 
    <NA> 

Time series weights: 

Tapered time weighting applied 
Power = 3 over 20 years 

Catchability analysis: 

Catchability independent of size for ages > 0 

Catchability independent of size for ages > 2 

Terminal population estimation: 

Survivor estimates shrunk towards the mean F 
of the final 5 years of the 2 oldest ages. 

S.E. of the mean to which the estimates shrunk = 1.5 

Minimum standard error for population 
estimates derived from each fleet = 0.3 

prior weighing not applied 

weights 
year 
age 
all 

2006 
0.82 

2007 
0.877 

2008 
0.921 

2009 
0.954 

2010 
2011 
0.976       0.99 

2012 
0.997          1 

2013       2014 

  1 

2007 

Fishing mortalities 
year 
2010 
2006 
age 
0.113      0.001       0.006       0.016       0.034       0.002      0.035       0.097       0.011 
0 
0.348      0.311       0.102       0.215       0.186       0.112      0.132       0.429       0.817 
1 
0.905      1.258       0.448       0.562       0.389       0.411      0.429       0.824       1.610 
2 
3 
0.664      0.702       0.474       0.280       0.201       0.058      0.177       0.249       1.195 
4+           0.664      0.702        0.474       0.280       0.201       0.058      0.177       0.249       1.195 

2008 

2011 

2009 

2012 

2013        2014 

XSA population number (Thousand) 
Age 
year 

      0 

     1 

    2 

   3            4+ 

327 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014         168437  26727 

438 
7473 
 112642  45326 
578 
4658 
149443  26607 
365 
2381 
 174966  39481 
636 
3263 
 167735  45975 
5323 
475 
 115566  43637 
6580      1076 
 208897  29544 
291 
6288 
 202465  55141 
 110826   52637      23651     4523        597 
879 

21200 
15422 
9395 
17185 
17870 
17456 
12735 

16232 

5548 

Estimated population abundance at 1st Jan 2015 
age 
year 
2015 

0 
0             44069 

   3           4+ 
1764 

   2 
5688 

    1 

959 

Fleet: Medits 

Log catchability residuals. 

Year 
  2006 
age 
0 
 0.066 
1               0.701 
2 
 0.329 
3              -0.003 

 2007 
-0.071 
-0.206 
-0.582 
-0.106 

  2008 
0.101 
0.89 
0.065 
0.099 

 2010 
  2009 
0.122 
0.104 
0.900 
0.492 
0.186 
0.162 
0.009     -0.155 

 2012 
 2011 
 0.082 
0.102 
-0.297    -0.774 
-0.072 
 0.244 
-0.155 
-0.033 

 2013       2014 
-0.048    -0.437 
0.031      -1.525 
-0.024    -0.302 
0.079 
0.168 

Regression statistics 
Ages with q dependent on year class strength 
-0.336697942 

13.67359336 

Terminal year survivor and F summaries: 

,Age 0 Year class 2014 
               scaledWts  survivors    yrcls 
Medits       0.109  161263      2014 
  2014 
fshk            0.031 
  2014 
nshk           0.860 

  11805 
  39185 

,Age 1 Year class 2013 
               scaledWts  survivors    yrcls 
   1238        2013   
Medits       0.563 
   2013 
 28994 
fshk            0.437 

,Age 2 Year class 2012 
              scaledWts  survivors    yrcls 
  2012  
Medits      0.833 
  2012 
fshk           0.167 

1304 
9851 

,Age 3 Year class 2011 
              scaledWts  survivors    yrcls 
  2011 
Medits      0.883 
  2011 
fshk           0.117 

1038 
  873 

328 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5.2.12.8 Reference points 
5.2.12.8.1  Methods 
The  yield  per  recruit  (YpR)  analysis  was  run  using  XSA  method.  The  analysis  was  performed  to 
estimate F0.1 as target equilibrium YPR reference point for the stock. 

5.2.12.8.2  Input data  
The input parameters were the same used for the XSA stock assessment and its results. 

5.2.12.8.3  Results 
YpR output  curve is illustrated in the Figure 5.2.12.8.3.1,  while  F0.1 and Fbar are compared in Figure 
5.2.12.8.3.2. F0.1 estimated by the model was 0.65. 

Fig. 5.2.12.8.3.1. Giant red shrimp in GSA 10. Yield per Recruit curve. 

329 

 
 
 
 
 
 
 
 
 
 
 
Fig. 5.2.12.8.3.2. Giant red shrimp in GSA 10. Trend of Fbar obtained by means of XSA and comparison with F0.1. 

5.2.12.9 Data quality 
Landing  and  discard  data  were  available  for  the  period  2006-2014.  Demographic  structures  of  the 
gillnet landing were available for three years only. However, this fishery contributes minimally to the 
total landing of A. foliacea in the GSA 10, representing on average 2.4% of the biomass landed per 
year. 

 Method 

Short term predictions 2016-2018 

5.2.12.10 
5.2.12.10.1 
A  deterministic  short  term  prediction  for  the  period  2015  to  2017  was  performed  using  the  FLR 
routines  provided  by  JRC  and  based  on  the results  of  the XSA  stock assessments performed during 
EWG14-19 for the years 2006–2014. 

 Input parameters  

5.2.12.10.2 
The  input  parameters  were  the  same  used  for  the  XSA  stock  assessment  and  its  results.Different 
scenarios, zero catch, harvest at reference point, Fstatus quo and a series of multiplier of Fstq were 
performed. Fstq=0.908 has been estimated as the fishing mortality of last year (2014) estimated with 
FLR. 

 Results 

5.2.12.10.3 
A short term projection (Table 5.2.12.10.3.1), assuming an Fstq of 0.908 in 2015 and a recruitment of 
168437 thousands individuals show that: 

•  Fishing at the Fstq (0.908) generates a decrease of the catch of about 13% from 2014 to 2016 
and a decrease of about 9% of the spawning stock biomass 2016 to 2017. 

•  Fishing at F0.1 (0.65) generates a decrease of the catch of about 31% from 2014 to 2016 and an 
increase of the spawning stock biomass of about 10% from 2016 to 2017. 

330 

0.00.10.20.30.40.50.60.70.80.91.0200620072008200920102011201220132014FFbar (0-3)F0.1 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table  5.2.12.10.3.1.  Giant  red  shrimp  GSA  10.  Short  term  forecast  in  different  F  scenarios.  Basis: 
F(2015) = mean (Fbar 0-3 2012-2014)= 1.40; R(2015) = geometric mean of the recruitment of the last 3 
years; R = 156034 thousands; SSB(2014) = 265 t, Catch (2014)= 465 t. 

Rationale 

Ffactor 

Fbar 

0.0 

0.000 

Catch 
2016 
0.000 

Catch 
2017 
0.000 

SSB 
2017 
579.807 

Change SSB 
2016-2017(%) 
99.265 

Change Catch 
2014-2016(%) 
-100 

Zero catch 
High long term 
yield F(0.1) 
Status quo 
Different 
scenarios 

0.716 
1 

0.650 
0.908 

314.867 
395.637 

332.890 
375.903 

200.574 
139.228 

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 

0.091 

57.523 

84.372 

494.482 

0.182 
0.272 
0.363 
0.454 
0.545 
0.636 
0.726 
0.817 
0.999 
1.090 
1.180 
1.271 
1.362 
1.453 
1.544 
1.634 
1.725 
1.816 

109.693 
157.148 
200.438 
240.043 
276.380 
309.810 
340.651 
369.179 
420.237 
443.166 
464.590 
484.654 
503.487 
521.204 
537.906 
553.685 
568.621 
582.786 

151.054 
203.984 
246.209 
280.092 
307.465 
329.747 
348.042 
363.205 
386.653 
395.859 
403.834 
410.830 
417.034 
422.598 
427.642 
432.259 
436.522 
440.490 

423.088 
363.244 
312.989 
270.702 
235.044 
204.909 
179.378 
157.695 
123.455 
109.942 
98.329 
88.313 
79.646 
72.117 
65.553 
59.807 
54.757 
50.301 

9.981 
-8.707 

81.661 

66.075 
52.277 
40.0635 
29.254 
19.690 
11.223 
3.749 
-2.864 
-13.867 
-18.422 
-22.440 
-25.982 
-29.103 
-31.849 
-34.266 
-36.389 
-38.253 
-39.889 

-30.646 
-12.855 

-87.330 

-75.838 
-65.386 
-55.851 
-47.127 
-39.123 
-31.760 
-24.967 
-18.683 
-7.437 
-2.386 
2.333 
6.752 
10.900 
14.803 
18.482 
21.957 
25.247 
28.367 

 Method 

Short term predictions 2015-2017 by fleet 

5.2.12.11 
5.2.12.11.1 
A deterministic short term prediction by fleet for the period 2015 to 2017 was performed using the 
FLR  routines  provided  by  JRC  and  based  on  the  results  of  the  XSA  stock  assessments  performed 
during EWG 15-11. 

5.2.12.11.2 
The same parameters used in the short term by single fleet were used. 

 Input parameters  

5.2.12.11.3 

 Results 

Table 5.2.12.11.3.1. Giant red shrimp in GSA 10. Short term forecast by fleet. 

Fleet 
GNS 
OTB 
GNS 

Year 
2015 
2015 
2016 

Catches 
2 
223 
5 

Partial F 
0.004 
0.408 
0.006 

331 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
OTB 
GNS 
OTB 

2016 
2017 
2017 

376 
4 
340 

0.606 
0.006 
0.606 

Figure 5.2.12.11.3.1. Giant red shrimp in GSA 10. Short term forecast by fleet. 

Medium term predictions 

5.2.12.12 
5.2.12.12.1 
The  medium  term  projections  were  not  conducted  because  no  meaningful  stock-recruitment 
relationship was found. 

 Method 

Stock advice 

5.2.12.13 
EWG 15-11 proposes F0.1=0.65 as limit management reference point consistent with high long term 
yield and lower risk of stock collapse (proxy of FMSY).  
SSB  showed  a  decresing  trend  in  the  last  years  while  recruitment  fluctuated.  As  concerns  F,  an 
evident  increasing  trend  is  observed  in  the  last  three  years.  According  to  the  F  estimates  obtained 
using landing and discard data with XSA, in  the last year of the time series (2014) F was above the 
estimated reference value of FMSY=0.65.  
STECF-EWG 15-11 advises to reduce the current level of effort and/or catches of the relevant fleets in 
order to avoid future loss in stock productivity. Catches of giant red shrimp in 2016 consistent with 
FMSY should not exceed 315 tonnes. 

332 

 
 
 
 
 
 
 
 
 
 
 
 
 
Management strategy evaluation 

5.2.12.14 
The Management Strategy Evaluation was ran to evaluate if the FMSY ranges were precautionary. The 
FMSY  ranges  were  derived  using  the  formula  provided  by  STECF  15-09.  F  ranges  results  were 
Fupper=0.88 and Flower=0.43. Blim was estimated as Bloss=265 t. The following figure shows the results of 
the MSE. The probability of SSB to fall below Blim is equal to 0. 

Figure 5.2.3.14.1. Giant red shrimp GSA 10. Marine Strategy Evaluation. 

The dynamics observed for this stock are the result of the stock assessment model (i.e. XSA) settings 
used  inside  the  MSE  becoming  less  appropriate  as  the  stock  status  changes  in  time  (i.e.  stock 
assessment settings are often specific to a particular range of stock status). This leads to an increasing 
difference between the perceived stock and the operating model (i.e. the 'true' stock). To avoid this 
behaviour in the future, for some of the stocks as it is the case here, a more general stock assessment 
method should be used in the MSE loop that is less sensitive to the stock status. 

333 

 
 
 
 
 
 
 
 
 
5.2.13  STOCK ASSESSMENT OF GIANT RED SHRIMP IN GSA 11 
5.2.13.1 Stock Identification 
According  to  StockMed  project  (Fiorentino  et  al.,  2014)  the  stock  configuration  with  2  clusters 
represents  the  best  hypothesis  of  stock  structure  in  the  western  Mediterranean.  In  particular  the 
stock inhabiting GSA 11 seems to be the same in GSA 1, 5, 7, 8 and the northern portion of GSA 9. 
However  due  to  the  lack  of  more  detailed  information  and  analysis,  for  the  present  report  the 
assessment have been  carried out only in GSA 11 (Fig.  5.2.13.1.1), as it was carried out in the past 
during STECF EWG 14-19 and STECF EWG 11-14 . 

Fig. 5.2.12.1.1. Geographical localization of GSA 11. 

Aristaeomorpha foliacea (Risso, 1827) is a dominant species of bathyal megafaunal assemblages and 
it is sympatric with Aristeus antennatus in all the GSA 11. Both species have considerable interest for 
fisheries.  
The giant red shrimp is considered midbathyal occupying mainly the middle slope, between 450 and 
600 m of depth, although the range of occurrence is wider (250 and 1300 m) and includes also the 
epibathyal grounds.  
Regarding its trophic ecology, Cartes et al. (2014) found a significant correlation with environmental 
variables,  such  us  temperature  and  salinity  of  intermediate  waters,  feeding  intensity  (gut  fullness) 
and prey diversity and stated that the GSA 11 is one of the optimal ecological habitats of A. foliacea 
in  the  Mediterranean  sea.  In  their  preferred  (core)  habitats,  species  may  reach  their  greatest 
densities and best biological condition in terms of size, survivorship and fecundity. In the case of  A. 
foliacea, the best trophic conditions coincide with areas with the highest densities, where the species 
has more structured populations, with peaks of small recruits and larger females.  

334 

 
 
 
 
 
 
 
 
The giant red shrimp shows high densities and well-structured populations with a clear multimodal 
size pattern in the GSA 11. Seasonal changes have been reported from southern Sardinia in both the 
vertical  distribution  and  size-related  spatial  abundance  of  A.  foliacea,  with 
large  females 
(preferentially) tending to move gradually deeper (to 650-740 m) from spring to summer (Mura et al., 
1997). 

5.2.13.2 Growth 
The von Bertalanffy Growth Function parameters of A. foliacea by sex in the Sardinian seas found in 
the  scientific  literature,  are reported  in  Table  5.2.13.2.1.  The  species  shows  a  marked  difference  in 
growth  between  sexes,  with  females  reaching  bigger  length  than  males:  the  observed  maximum 
length in the landings was CL = 69 mm for females and CL = 48 mm for males. 

Tab. 5.2.13.2.1. Giant red shrimp in GSA 11. Von Bertalanffy Growth function parameters for Aristaeomorpha 
foliacea in GSA 11. Values marked in bold were used in the current assessment. 

Linf. 
75.40 
70.70 
72.21 

Females 
K 
0.46 
0.54 
0.50 

t0 
0.58 
0.27 
0 

70.70 

0.58 

-0.27 

Males 

Reference 

Linf. 
49-53 

K 
0.6-0.67 

t0 
0.001-0.3 

42.71 

0.77 

-0.27 

Cau et al. 1994 
Cau et al. 2002 
AAVV  2008;  Red’s 
Project 
DCF 2015 

5.2.13.3 Maturity 
In the western Mediterranean, the spawning season occurs between the end of July and September, 
with  a  peak  in  the  summer  (July-August)  (Mura  et  al.,  1992;  Cau  et  al.,  1994;  Mori  et  al.,  1994; 
Spedicato et al., 1994; Ragonese and Bianchini, 1995, Perdichizzi et al., 2012). Before spawning, large 
females gradually move deeper, to 650–740 m for reproduction (Mura et al., 1997). The size at onset 
of sexual maturity occurs at about 32.6 mm CL for females (AAVV, 2008). 

The  maturity  vectors  for  males  and  females  came  from  the  DCF  data  of  year  2014.  A  weighted 
maturity vector for males and females combined was produced and used in the current assessment 
(Table 5.2.13.3.1). 

Table 5.2.13.3.1. Giant red shrimp in GSA 11. Sex ratio and maturity vectors for males, females and both sexes 
combined. Values marked in bold were used in the current assessment. 

Sex Ratio (F/F+M) 
Females 
Males 
Combined 

Age 0 
0.46 
0 
0 
0 

Age 1 
0.77 
0.59 
0.81 
0.64 

Age 2 
1 
1 
1 
1 

Age 3 
1 
1 
1 
1 

Age 4+ 
1 
1 
1 
1 

335 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5.2.13.4 Natural mortality 
The natural mortality vector was calculated using PRODBIOM (Abella et al. 1997) separately for males 
and females (Table 5.2.13.4.1). A weighted vector for both sexes combined was constructed for the 
assessment. 

Table  5.2.13.4.1.  Giant  red  shrimp  in  GSA  11.  Natural  mortality  vectors  for  males,  females  and  both  sexes. 
Values marked in bold were used in the current assessment. 

Females 
Males 
Combined 

Age 0 
1.12 
1.22 
1.17 

Age 1 
0.57 
0.61 
0.58 

Age 2 
0.46 
0.49 
0.46 

Age 3 
0.41 
0.44 
0.41 

Age 4+ 
0.36 
0.38 
0.36 

5.2.13.5 Fisheries 
5.2.13.5.1 General description of the fisheries 
As a consequence of government incentives aimed at the fleet modernization, since 1994 up to 2004 
the trawl sector showed gradually but remarkable changes, with a general increase in the number of 
vessels and the replacement of the older ones, low tonnage wooden boats by larger steel boats. 
Currently,  in  GSA  11  operate  a total  of  about 1300  boats,  150  of  which  are  small,  medium  and  big 
trawlers. Administratively they all belong to the major fishing ports (“compamare”), namely Cagliari, 
La  Maddalena,  Olbia,  Oristano  and  Porto  Torres  (Fig.  5.2.13.5.1.1).  Other  important  ports  are 
Alghero, La Caletta and Sant’Antioco. 

Fig. 5.2.13.5.1.1. Number of trawlers operating in GSA 11 grouped by the main ports. 

The  giant  red  shrimp  is  a  high-value  species,  being  a  target  of  a  specific  deep  trawl  fishery  in  the 
whole GSA 11. The large trawlers of GSA 11 operate all the week from Monday to Friday doing daily 
or bi-daily fishing trips and delivering products to local markets. Moreover, due to the distance of the 
fishing grounds to the main harbours of the western cost and the dominant weather conditions, the 
fleet  targeting  A.  foliacea  shows  some  seasonal  variations,  with  more  time  spent  at  sea  from  mid 
spring to mid-autumn (Murenu et al., 2011). Some large trawlers move seasonally to different fishing 
grounds far from the usual ports. When the weather permits, small trawlers also perform daily fishing 
trips to target giant red shrimp. 

336 

 
 
 
 
 
 
 
 
 
 
 
 
 
5.2.13.5.2  Management regulations applicable in 2015  
As in other areas of the Mediterranean, management of the stock is based on the control of fishing 
capacity (licenses), fishing effort (fishing activity) and technical measures (mesh size and area/season 
closures). EC regulation 1967/2006 does not provide for a minimum length size for this species. 
Since 2012, a reduction of the fishing ban period that generally was enforced for 45 days has occured. 
In 2012 and 2013 the fishing ban was established by the autonomous region of Sardinia from 1st to 
30th  of  September,  while  in  2014  it  was  established  from  the  15th  of  September  until  the  15th  of 
October. 

5.2.13.5.3  Landings  
Giant  red  shrimp  fishery  are  targeted  only  by  trawlers.  According  to  DCF  data  uploaded  for  the 
purposes  of  STECF  EWG  15-11  the  landings  of  giant  red  shrimp  were  at  a  maximum  of  170  tons  in 
2005 followed by a gradual decline in the successive years (Fig. 5.2.13.5.3.1). The lowest value (38.6 
tonnes) was obtained in 2008. 

Figure 5.2.13.5.3.1. Giant red shrimp in GSA 11. Annual landings according to DCF data. 

The  age  structure  of  the  landings,  according  to  the  DCF  data,  showed  that  most  of  the  catch  is 
composed  by  the  age  groups  1  and  2,  corresponding  to  a  length  range  between  22  and  37  mm  CL 
(Figs. 5.2.13.5.3.2 and 5.2.13.5.3.3). 

337 

0204060801001201401601802005200620072008200920102011201220132014Landings (tonnes) 
 
 
 
 
 
 
 
 
 
 
 
 
Fig. 5.2.13.5.3.2. Giant red shrimp in GSA 11. Catch composition by age from 2006 to 2013 according to DCF 
data. 

338 

01000200030004000500060000123456789Landings (tonnes)Age classes (y)200620072008200920102011201220132014 
 
 
 
 
 
 
 
 
Fig. 5.2.13.5.3.3. Giant red shrimp in GSA 11. Catch composition by length from 2006 to 2013 according to DCF data. 

339 

010020030040050060070080090013579111315171921232527293133353739414345474951535557596163656769N. ind. (x 1000)CL mm2006010020030040050060070080090013579111315171921232527293133353739414345474951535557596163656769N. ind. (x 1000)CL mm2007010020030040050060070080090013579111315171921232527293133353739414345474951535557596163656769N. ind. (x 1000)CL mm2008010020030040050060070080090013579111315171921232527293133353739414345474951535557596163656769N. ind. (x 1000)CL mm2009010020030040050060070080090013579111315171921232527293133353739414345474951535557596163656769N. ind. (x 1000)CL mm2010010020030040050060070080090013579111315171921232527293133353739414345474951535557596163656769N. ind. (x 1000)CL mm2011010020030040050060070080090013579111315171921232527293133353739414345474951535557596163656769N. ind. (x 1000)CL mm2012010020030040050060070080090013579111315171921232527293133353739414345474951535557596163656769N. ind. (x 1000)CL mm2013010020030040050060070080090013579111315171921232527293133353739414345474951535557596163656769N. ind. (x 1000)CL mm2014 
 
 
 
 
 
 
In  the  DCF  data  provided  to  the  group  also  2005  catch  at  age  data  were  present.  However,  the 
reported  catch  composition  by  age  was  inconsistent  with  that  of  years  2006-2014  as  it  was 
represented  only  by  ages  0  and  1.  Therefore,  2005  catch  at  age  data  are  not  presented  in  figure 
5.2.13.5.3.2.  
In figure 5.2.13.5.3.4, the LFDs age-sliced on the basis of the VBGF (Table 5.2.13.2.1) are presented. 
The age-slicing has been carried out using LFDA (Kirkwood et al., 2001). 

Fig.  5.2.13.5.3.4.  Giant  red  shrimp  in  GSA  11.  Catch  composition  by  age  from  2006  to  2013  calculated  from 
age-slicing of LFD data reported in the DCF. 

5.2.13.5.4  Discards  
No data on discards were present in the 2015 DCF data call for this species in GSA 11.  Discards are 
considered to be neglegible. 

5.2.13.5.5  Fishing effort  
Fishing  effort  has  been  decreasing  since  2004  with  the  lowest  values  reached  in  2013  (Tab. 
5.2.13.5.5.1; Fig. 5.2.13.5.5.1). 

Table 5.2.13.5.5.1. Fishing effort of the trawl fleet targeting giant red shrimp in GSA 11. 

Country 
Italy 
Italy 
Italy 
Italy 
Italy 
Italy 
Italy 
Italy 
Italy 
Italy 
Italy 

Area 
GSA 11 
GSA 11 
GSA 11 
GSA 11 
GSA 11 
GSA 11 
GSA 11 
GSA 11 
GSA 11 
GSA 11 
GSA 11 

Gear 
OTB 
OTB 
OTB 
OTB 
OTB 
OTB 
OTB 
OTB 
OTB 
OTB 
OTB 

Year 
2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

Nominal Effort 
7706431 
7324728 
5752588 
5867826 
4326313 
4370758 
4036734 
3788057 
3824269 
3139044 
3298194 

340 

Gt * Days at sea 
1721988 
1785484 
1358732 
1414387 
1095797 
1045255 
943795 
939676 
922717 
695331 
848000 

No Vessels 
167 
146 
194 
241 
146 
149 
124 
90 
78 
89 
102 

010002000300040005000600001234567Landings (tonnes)Age classes (y)200620072008200920102011201220132014 
 
 
 
 
 
 
 
 
 
 
 
 
Fig. 5.2.13.5.5.1. Trends in fishing effort (kW*days) for trawl fleet targeting giant red in GSA 11 in the period 
2004-2014. 

5.2.13.6 Scientific surveys 
5.2.13.6.1   Survey #1 (MEDITS) 
5.2.13.6.1.1  Methods 
Since 1994 the MEDITS trawl surveys have been carried out annually between May and July (except 
in 2007). 
According  to  the  MEDITS  protocol  (Relini,  2000;  Bertand  et  al.,  2002)  a  stratified  random  sampling 
design with allocation of hauls proportional to depth strata extension (depth strata: A: 10–50 m, B: 
51–100 m, C: 101–200 m, D: 201–500 m, E: 501–800 m) was adopted. A specific gear (GOC 73, with a 
20 mm stretched mesh size in the cod-end) was always used following the specifications reported in 
Dremière and Fiorentini (1996). 
Based  on  the  DCR  data  call,  abundance  and  biomass  indices  were  recalculated.  In  GSA  11  the 
following number of hauls was reported per depth stratum (Tab. 5.2.13.6.1.1). 

Tab. 5.2.13.6.1.1 Giant red shrimp in GSA 11. Number of hauls per year and depth stratum in GSA 11, 
1994-2013. 

Data were assigned to strata based upon the shooting position and average depth (between shooting 
and  hauling  depth).  Catches  by  haul  were  standardized  to  square  kilometre.  Hauls  noted  as  valid 
were  used  only,  including  stations  with  no  catches  of  hake,  red  mullet  or  giant  red  shrimp  (zero 
catches are included). 

341 

010000002000000300000040000005000000600000070000008000000900000020042005200620072008200920102011201220132014Nominal effort (Kw*Days) of OTB in GSA 11Strata199419951996199719981999200020012002200320042005200620072008200920102011201220132014A161820212019191720181517192017181920192021B252023232222222519192022191919201918201919C202431313130312924242423242422242425232424D262224242323212220201820202121192020212121E292327272726302919181815161616161718181717 
 
 
 
 
 
  
 
 
 
 
 
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: 
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 

Length  distributions  represented  an  aggregation  (sum)  of  all  standardized  length  frequencies 
(subsamples  raised  to  standardized  haul  abundance  per  hour)  over  the  stations  of  each  stratum. 
Aggregated  length  frequencies  were  then  raised  to  stratum  abundance  *  100  (because  of  low 
numbers  in  most  strata)  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.  

 Geographical distribution 

5.2.13.6.1.2 
The  spatial  distribution  of  Aristaeomorpha  foliacea  has  been  described  by  modelling  the  spatial 
correlation  structure  of  the  abundance  indices  using  geostatistical  techniques.  The  stock  is  more 
abundant in the southern part of the GSA (Sardinian Sea) as shown in Figure 5.2.13.6.1.2.1. 
The species shows a wide depth distribution over muddy and sandy-muddy bottoms from 450 to 700 
m depth. The highest densities are found around the shelf break and deep slope of the south-western 
coast where are located the most persistent nursery and spawning areas (Fig. 5.2.13.6.1.2.1). 

Fig.  5.2.13.6.1.2.1.  Giant  red  shrimp  in  GSA  11.  Temporal  persistence  of  nursery  areas  (left)  and  spawning 
areas (right) based on MEDITS data 1994-2010 (maps from the EU Mediseh-marea project). 

 Trends in abundance and biomass 

5.2.13.6.1.3 
Fishery independent information regarding the state of the giant red shrimp in GSA 11 was derived 
from the international survey MEDITS. Figure 5.2.13.6.1.3.1 displays the estimated trends of giant red 

342 

 
 
 
 
 
 
 
 
 
 
 
 
shrimp  abundance  and  biomass  in  GSA  11  by  sex  and  for  both  sexes  combined.  The  estimated 
abundance and  biomass  indices  since  the  beginning  of  the  time-series  show  high  variation  without 
any trend until 2007, when a significant reduction was observed. In the period from 2008 to 2014 the 
trend  was  fluctuating  again  but  showing  in  general  lower  values  that  the  previous  period.  The 
females are in general more abundant and exhibit bigger fluctuations than males. 
From  1994  to  2005  two  trawl  surveys  were regularly carried  out  each  year:  MEDITS,  in  spring,  and 
GRUND, in autumn, although the MEDITS data only are available to the STECF. 

Fig. 5.2.13.6.1.3.1. Giant red shrimp in GSA 11. MEDITS abundance and biomass indices. 

5.2.13.6.1.4  Trends in abundance by length or age 
Figs 5.2.13.6.1.4.1 and 5.2.13.6.1.4.2 show the standardized length frequency distribution (n/Km2) of 
A. foliacea females and males in GSA 11 for the period 1994-2013. 

343 

0100200300400500600199419951996199719981999200020012002200320042005200620072008200920102011201220132014MEDITS Abundance Index (N/km2)MalesFemalesTotal01234567199419951996199719981999200020012002200320042005200620072008200920102011201220132014MEDITS Biomass Index (Kg/km2)MalesFemalesTotal 
 
 
 
 
 
 
 
 
 
Fig. 5.2.13.6.1.4.1. Giant red shrimp in GSA 11. Stratified abundance indices (n/km2) of females by size, 1994-
2014. 

Fig. 5.2.13.6.1.4.1. Giant red shrimp in  GSA 11. Stratified abundance  indices (n/km2) of males by size, 1994-
2013. 

344 

 
 
 
 
 
 
 
 
5.2.13.7 Stock Assessment 
This  stock  was  assessed  for  the  last  time  by  EWG  15-06.  A  survey-based  (SURBA)  model  was  used 
back then based on MEDITS data, but the results were not used to draft management advices. The 
stock  was  also  assessed  by  STECF  EWG  11-14  using  a  Length  Cohort  Analysis  carried  out  with  ViT 
software for the years 2006, 2007, 2009 and 2010. 

5.2.13.7.1  Methods 
The  assessment  of  giant  red  shrimp  in  GSA  11  has  been  performed  here  using  the  XSA  method 
because the time series covered more than the life span of the species. FLR libraries were employed 
in order to carry out the XSA assessment (Darby and Flatman 1994).  

5.2.13.7.2  Input data 
The catch at age matrix employed in the XSA was calculated from the age-slicing carried out on the 
size  frequency  distributions  (LFDs)  provided  by  the  2015  DCF  data  call  (Fig.  5.2.13.5.3.4  and  Tab. 
5.2.1.7.2.1). The original catch at age matrix provides in the framework of the 2015 DCF data call was 
not  used  due  the  inconsistencies  found  in  the  age  distributions  and  the  VBGF  growth  parameters 
reported for the species.  

Tab. 5.2.13.7.2.1. Giant red shrimp in GSA 11. Input parameters for XSA, catch at age data. 

Catch at age 
(thousands) 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

Age 0 

Age 1 

Age 2 

Age 3 

Age 4+ 

262.5 
135.0 
1398.9 
3334.5 
874.7 
3060.5 
1443.1 
445.1 
1205.1 

2723.2 
1124.7 
1447.6 
3569.8 
2949.1 
3452.4 
1862.2 
1614.0 
2084.9 

543.1 
286.5 
336.3 
979.0 
764.6 
770.7 
593.8 
546.3 
558.8 

190.0 
68.3 
7.0 
122.0 
152.6 
101.3 
178.3 
65.4 
54.9 

80.0 
28.9 
0.1 
50.0 
56.2 
16.1 
38.2 
39.9 
12.7 

The mean weight at ages employed in the XSA both for the catch weight at age and stock weight at 
age matrices were assumed to be constant over the whole period and equal to the series reported 
for 2014 in the 2015 official data call due to the higher number of samples measured in 2014 (Tab. 
5.2.13.7.2.2). 

Tab. 5.2.13.7.2.2. Giant red shrimp in GSA 11. Input parameters for XSA, catch and stock weight at age data. 

Catch and stock 
weight (Kg) 

2006 - 2014 

Age 0 

0.009 

Age 1 

0.010 

Age 2 

0.022 

Age 3 

Age 4+ 

0.044 

0.070 

345 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
The natural mortality (M) and the proportion of mature specimens are reported in tables 5.2.13.3.1. 
and  5.2.13.4.1. The proportion of F and M before spawning was assumed  equal to 0.5. 

The  tuning  fleet  matrix  was  estimated  from  the  age-slicing  of  MEDITS  LFDs    and  are  presented  in 
table 5.2.13.7.2.3. 

Tab. 5.2.13.7.2.3. Giant red shrimp in GSA 11. Input parameters for XSA, tuning fleet matrix. 

N/km2 

Age 0 

Age 1 

Age 2 

Age 3 

Age 4+ 

2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

38.1 
10.6 
116.9 
145.5 
121.3 
13.8 
135.9 
34.8 
42.2 

73.5 
24.4 
36.5 
73.1 
113.9 
59.1 
71.2 
106.7 
46.7 

7.5 
7.4 
8.7 
8.1 
11.4 
6.9 
6.9 
13.2 
13.9 

0.5 
0.5 
0.7 
0.2 
1.0 
1.2 
0.6 
2.3 
0.8 

0.4 
0.001 
0.1 
0.001 
0.1 
0.7 
0.4 
0.2 
0.4 

The main settings used in the final run of the XSA are reported in Table 5.2.13.7.2.4. 

Tab. 5.2.13.7.2.4. Giant red shrimp in GSA 11. Setting parameters for XSA. 

r_age 

q_age 

shrinkage  N_years 

N_ages 

Fbar 

0 

4 

1 

4 

4 

0 - 3 

Final 
run 

5.2.13.7.3  Results 
The  final  XSA  results  and  diagnostics  are  reported  in  figures  5.2.13.7.3.1-3  and  table  5.2.13.7.3.1. 
Several  XSA  runs  were  carried  out  during  the  meeting  using  different  combinations  of  setting 
parameters.  Once  the  r_age,  q_age,  N_years  and  N_ages    were  selected  considering  the  model 
outputs and diagnostics (not presented in the report) three values of shrinkage (0.5, 1 and 2) were 
tested (Figs. 5.2.13.7.3.4-6). The model with 1.0 shrinkage was adopted as final model based on the 
analysis of residual distributions (Fig. 5.2.13.7.3.2). Residuals from tuning fleets (MEDITS) per age and 
year  were  relatively  low,  ranging  from  1  to  -  1,  and  did  not  show  any  trend  with  time.  Also  the 
retrospective analysis did not show any particular inconsistencies (Fig. 5.2.13.7.3.3). 
The  results  of  the  assessment  (Figure  5.2.13.7.3.1)  show  an  oscillating  trend  of  recruits  and  an 
increasing pattern of spawning stock biomass (SSB). The fishing mortality showed a minimum value in 
2008  followed  by  fluctuations  in  the  period  2009-2014.  The  current  Fbar  0-3  is  equal  to  0.50.  The  F 
values by age are shown in table  5.2.13.7.3.1 and are in general higher for ages 1 and 2. 

346 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Fig. 5.2.13.7.3.1. Giant red shrimp in GSA 11. XSA summary results. SSB and catch are in tons, 
recruitment in thousands of individuals. 

Table 5.2.13.7.3.1. Giant red shrimp in GSA 11. XSA summary results. 

F at age 
0 

1 

2 

3 

4+ 

F bar 
Ages 0-3 

SSB 

Tonnes 

Recruitment 

Thousands 

2006 
0.05 

1.47 

1.18 

1.23 

1.23 

2006 
0.98 

2006 
32.08 

2006 
9567 

2007 
0.01 

0.76 

0.90 

0.58 

0.58 

2007 
0.56 

2007 
25.52 

2007 
16918 

2008 
0.10 

0.47 

0.85 

0.06 

0.06 

2008 
0.37 

2008 
35.41 

2008 
26749 

2009 
0.25 

1.01 

1.14 

1.39 

1.39 

2009 
0.95 

2009 
47.76 

2009 
27432 

2010 
0.06 

0.90 

0.98 

0.71 

0.71 

2010 
0.66 

2010 
53.50 

2010 
25188 

2011 
0.33 

0.99 

1.02 

0.42 

0.42 

2011 
0.69 

2011 
52.82 

2011 
19699 

2012 
0.15 

0.83 

0.68 

0.99 

0.99 

2012 
0.66 

2012 
44.40 

2012 
18775 

2013 
0.05 

0.56 

1.02 

0.18 

0.18 

2013 
0.45 

2013 
62.97 

2013 
15348 

2014 
0.13 

0.96 

0.58 

0.32 

0.32 

2014 
0.50 

2014 
45.97 

2014 
18201 

347 

01000020000020406002550751000.000.250.500.751.00RecSSBCatchHarvest20062008201020122014 
 
 
 
 
 
 
 
 
Fig. 5.2.13.7.3.2. Giant red shrimp in GSA 11. Residuals at age obtained with shrinkage set at 1.0. 

Fig. 5.2.13.7.3.3. Giant red shrimp in GSA 11. Retrospective analysis with shrinkage set at 1.0. 

348 

Proportion at age by year Sh1.0yearage0.00.51.01.52.02.53.020062008201020122014v1Scale 1.00 0.75 0.50 0.25 0.00-0.25-0.50-0.75-1.00010000200003000002040608002550751000.000.250.500.751.00RecSSBCatchHarvest2006200820102012201412345 
 
 
 
 
 
 
 
 
 
Fig. 5.2.13.7.3.4. Giant red shrimp in GSA 11. Model comparison (SSB). 

Fig. 5.2.13.7.3.5. Giant red shrimp in GSA 11. Model comparison (Recruitment). 

Fig. 5.2.13.7.3.6. Giant red shrimp in GSA 11. Model comparison (Fishing mortality). 

349 

Stock spawning biomassYearTonnes20304050607020062008201020122014Sh0.5Sh1.0Sh2.0RecruitmentYearThousands1000015000200002500020062008201020122014Sh0.5Sh1.0Sh2.0FYearFbar0.51.020062008201020122014Shr0.5Shr1.0Shr2.0 
 
 
 
 
 
 
 
 
 
 
 
 
 
Fig. 5.2.13.7.3.7. Giant red shrimp in GSA 11. Residuals at age obtained with shrinkage set at 0.5. 

Fig. 5.2.13.7.3.8. Giant red shrimp in GSA 11. Residuals at age obtained with shrinkage set at 2.0. 

5.2.13.8 Reference points 
5.2.13.8.1  Methods 
The  reference  point  has  been  estimated  with  yield-per-recruit  (YpR)  analysis.  The  analysis  was  run 
using  FLBRP  package  in  FLR  software.  The  analysis  was  performed  to  estimate  F0.1  as  target 
equilibrium YPR reference point for the stock. 

5.2.13.8.2  Input data  
The same input data used for the XSA have been employed for the YpR analysis. 
5.2.13.8.3  Results 
YpR output outputs are illustrated in the Figure 5.2.13.8.3.1. F0.1 estimated by the model was 0.31. 

350 

Proportion at age by year Sh0.5yearage0.00.51.01.52.02.53.020062008201020122014v1Scale 2.0 1.5 1.0 0.5 0.0-0.5-1.0-1.5-2.0Proportion at age by year Sh2.0yearage0.00.51.01.52.02.53.020062008201020122014v1Scale 1.00 0.75 0.50 0.25 0.00-0.25-0.50-0.75-1.00 
 
 
 
 
 
 
 
 
 
 
Fig. 5.2.13.8.3.1. Giant red shrimp in GSA 11. Yield per Recruit outputs. 

5.2.13.9 Data quality 
Data  from  DCF  2015  official  data  call  were  used.  An  improvement  in  the  data  quality  of  giant  red 
shrimp GSA 11 data has been observed in comparison to the data provided during STECF EWG 14-19.  
The sum of products of landings was in general consistent with landing submitted in the 2015 official 
data call (difference less than 2%).  
Catch at age data were available for the period 2005-2014. However due the inconsistencies in age 
distributions,  the  experts  decided  to  age-slice  the  LFDs  provided  in  the  framework  of  2015  official 
data call.  
Moreover,  due  to  the  general  low  amount  of  samples  analysed  in  the  period  2005-2013  (Fig. 
5.2.13.9.1), some of the input data used for the analyses (sex ratio by length, proportion of mature, 
mean catch weight at age) were selected from 2014 only. 

Fig. 5.2.13.9.1. Giant red shrimp in GSA 11. Number of samples measured. 

351 

Equilibrium SSB v. FEquilibrium Recruitment v. SSBEquilibrium Yield v. FEquilibrium Yield v. SSB0.0000.0050.0100.0150.0200.500.751.001.251.500.0000.0010.0020.0030.0040.0000.0010.0020.0030.004012340.0000.0050.0100.0150.020012340.0000.0050.0100.0150.020xyrefptf0.10500100015002000250030002005200620072008200920102011201220132014NumbersNumber of length measurements in the landings 
 
 
 
 
 
 
  
 
 
 
 
Short term predictions 2015-2017 

5.2.13.10 
5.2.13.10.1 
A  deterministic  short  term  prediction  for  the  period  2015  to  2017  was  performed  using  the  FLR 
routines, which takes into account the catch and landings in numbers and weight and the discards. 

 Method 

 Input parameters  

5.2.13.10.2 
The same input parameters used in the XSA analysis shown above were used. Different scenarios of 
constant harvest strategy with F status quo as an average of Fbar from 2012 to 2014 (Fstq = 0.53) were 
performed. Recruitment (class 0) has been estimated from the population results from the geometric 
mean of the last three years 2012-2014 (17374 thousands individuals) estimated with FLR. 

 Results 

5.2.13.10.3 
Short  term  projection  (Table  5.2.13.10.3),  assuming  an  Fstq  of  0.53  in  2015  and  a  recruitment  of 
17,374  thousands individuals shows that: 

Fishing at the Fstq (0.53) generates an increase of the catch of 12% from 2014 to 2016 along with an 
decrease of the spawning stock biomass of 0.15% from 2016 to 2017. 
Fishing at F0.1 (0.31) generates a decrease of the catch of 27% from 2014 to 2016 and an increase of 
the spawning stock biomass of 24% from 2016 to 2017. 
Catches of giant red shrimp in 2016 consistent with FMSY should not exceed 55.31 tons. 

Table  5.2.13.10.3  Giant  red  shrimp  GSA  11.  Short  term  forecast  in  different  F  scenarios  computed  for 
Aristeomorpha foliacea in GSA 11. Basis: F(2014) = mean(Fbar0-3 2012-2014)= 0.53; R(2015) = geometric mean 
of the recruitment of the last 3 years; R = 17,374 (thousands). SSB 2014 = 46 t; Catch 2014 = 49 t. 

Scenarios 

Ffactor 

Fbar 

Catch 

SSB 

% change 
in SSB 

% change 
in Catch 

No fishery 
F status 
quo 
FMSY 
Different 
scenarios 

0 

0 

2014 
53.49 

2015 
0 

2016 
0 

2016 

2017 
75.47  128.05 

1 
0.58 
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 

9.89 

54.20 
0.53  53.49  55.31  55.15  54.28 
76.83 
0.31  53.49  35.83  41.51  62.22 
0.05  53.49 
73.01  117.14 
6.98 
0.11  53.49  13.58  18.41  70.63  107.23 
98.23 
0.16  53.49  19.82  25.76  68.34 
90.04 
0.21  53.49  25.73  32.08  66.12 
82.59 
0.27  53.49  31.33  37.51  63.97 
75.81 
0.32  53.49  36.64  42.19  61.90 
69.64 
0.37  53.49  41.67  46.21  59.90 
64.01 
0.42  53.49  46.45  49.65  57.96 
58.88 
0.48  53.49  50.99  52.61  56.09 
49.93 
0.58  53.49  59.41  57.32  52.53 
46.02 
0.64  53.49  63.32  59.18  50.85 
42.45 
0.69  53.49  67.05  60.77  49.21 
39.19 
0.74  53.49  70.60  62.14  47.64 
36.20 
0.80  53.49  73.99  63.31  46.11 
33.47 
0.85  53.49  77.22  64.31  44.64 
30.96 
0.90  53.49  80.32  65.17  43.21 
28.66 
0.95  53.49  83.28  65.91  41.83 
26.55 
1.01  53.49  86.11  66.54  40.50 

352 

2017 

2016-2017  2014-2016 

69.66 

-0.15 
23.48 
60.44 
51.81 
43.74 
36.18 
29.11 
22.48 
16.26 
10.44 
4.98 
-4.97 
-9.49 
-13.74 
-17.73 
-21.49 
-25.02 
-28.35 
-31.49 
-34.45 

-100 

12.00 
-27.44 
-85.87 
-72.50 
-59.86 
-47.89 
-36.55 
-25.81 
-15.61 
-5.93 
3.26 
20.32 
28.23 
35.78 
42.97 
49.83 
56.39 
62.65 
68.65 
74.38 

 
 
 
 
 
 
 
 
 
  
2 

1.06  53.49  88.82  67.09  39.21 

24.61 

-37.24 

79.88 

Medium term predictions 

5.2.13.11 
The  medium  term  projections  were  not  conducted  because  no  meaningful  stock-recruitment 
relationship was identified. 

Stock advice 

5.2.13.12 
Current  F  (0.5)  is  larger than  F0.1  (0.31),  which  was  chosen  as  proxy  of  FMSY  and  as  the  exploitation 
reference point consistent with high long term yields. This indicates that giant red shrimp in GSA 11 is 
being fished above FMSY.  
STECF  EWG  15-11  advises  the  relevant  fleets’  effort  and/or  catches  to  be  reduced  until  fishing 
mortality is below or at the proposed FMSY level, in order to avoid future loss in stock productivity and 
landings. Catches of giant red shrimp in 2016 consistent with FMSY  should not exceed 55 tonnes. 

Management strategy evaluation 

5.2.13.13 
A Management Strategy Evaluation (MSE) has been conducted in order to evaluate if the FMSY ranges 
were  precautionary.  The  FMSY  ranges  were  derived  using  the  formula  provided  by  STECF  15-09.  F 
ranges results were Fupper=0.43 and Flower=0.21. Blim was estimated as Bloss=25.51 t (Bpa= 35.7 t). 

The graphs in the figure 5.2.13.13.1 show the results of the MSE.  

Figure 5.2.6.13.13.1. Giant red shrimp GSA 11. Management Strategy Evaluation. 

The probability of SSB to fall below Blim fishing at F equal to FMSY upper level is equal to 0. 

353 

 
 
 
 
 
 
 
 
 
 
 
 
 
5.2.14  STOCK ASSESSMENT OF BLUE AND RED SHRIMP IN GSA 1 
5.2.14.1 Stock Identification 
No information was documented during regarding stock delimitation of blue and red shrimp, Aristeus 
antennatus (Risso, 1816). It is assumed that the stock geographical distribution corresponds to GSA 1. 
(Figure 5.2.14.1.1). 

Figure 5.2.14.1.1. Geographical localization of GSA 1 

5.2.14.2 Growth 
Von  Bertalanffy  growth  parameters  (Linf  =  80  mm  (carapace  length),  K = 0.37  year-1,  t0 = 0.032  year) 
were  calculated  following  the  modal  progression  approach,  based  on  monthly  length  frequency 
distribution obtained from Data Collection Framework (DCF), 2014. 

Length-weight  relationshipfor  2014  calculated  from  DCF  2014  data:  a = 0.002038  gr  and  b = 2.506 
(weight = a · lengthb).  

The weight at age was calculated annually from 2002 and results are presented in Table 5.2.14.2.1 

Table 5.2.14.2.1. Blue and red shirmp in GSA 1. Individual weight (kg) by age and year. 

0 
0.005 
0.005 
0.005 
0.005 
0.005 
0.005 

2002 
2003 
2004 
2005 
2006 
2007 

1 
0.01 
0.011 
0.01 
0.01 
0.011 
0.011 

2 
0.028 
0.03 
0.03 
0.03 
0.028 
0.03 

3 
0.047 
0.047 
0.047 
0.047 
0.049 
0.05 

4+ 
0.069 
0.069 
0.069 
0.069 
0.065 
0.069 

354 

 
 
 
 
 
 
 
 
 
 
 
 
 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

0.005 
0.004 
0.005 
0.005 
0.005 
0.005 
0.005 

0.012 
0.012 
0.012 
0.012 
0.012 
0.012 
0.012 

0.031 
0.031 
0.03 
0.03 
0.03 
0.03 
0.03 

0.049 
0.05 
0.048 
0.049 
0.049 
0.047 
0.047 

0.065 
0.066 
0.069 
0.064 
0.066 
0.066 
0.069 

5.2.14.3 Maturity 
Maturity ogive, calculated from DCF 2014, is presented in Table 5.2.14.3.1.  

Table 5.2.14.3.1. Blue and red shirmp in GSA 1. Proportion of matures. 

Age 

2 
0 
Prop mat  0.22  0.95  1.0 

1 

3 
1.0 

4+ 
1.0 

The value of L50 is 23.5 mm (carapace length). 

5.2.14.4 Natural mortality 
Two models for natural mortality M where tested. 

a)  The same value (M=0.46 year-1) for all ages. This value has been obtained from the Djabaliet 

al. (1994) empirical approach calculated for the Vera Gulf (GSA 1). 

b)  M vector according to PRODBIOM (Abella et al., 1997) is presented in Table 5.2.14.4.1.  

Table 5.2.14.4.1. Blue and red shirmp in GSA 1.  Natural mortality by age estimated by PRODBIOM.    

Age 
M 

1 

0 

2 
1.58  0.91  0.158  0.147  0.141 

4+ 

3 

However,  after  several  assessment  trials,  the  option  (a)  resulted  more  robust  than  (b),  so  the 
constant M value was adopted for the assessment.  

5.2.14.5 Fisheries 
5.2.14.5.1 General description of the fisheries 
Aristeus antennatus, is present in the eastern part of GSA 1 at depths comprised between 400 and 
800 m. It is particularly abundant in front of Cape of Gata. 

This resource is caught only by depth bottom otter trawl and only by the fleet segment composed by 
the  largest  trawlers.  The  fleet  segment  is  E-trawl  (12-24  m)  according  to  GFCM  Operational  Units 
code. Around 50 vessels targeting A. antennatus (average 2009-2014). 

355 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
This  segment  fleet  catches  about  147  tonnes  of  blue  and  red  shrimp  (average  2011-2014).  This 
fishery can be considered as monospecific with no significant discards, due to the very high price of 
the species. 

5.2.14.5.2 Management regulations applicable in 2015  

-  Fishing license: fully observed 

-  Engine power limited to 316 KW or 500 HP: not fully observed 

-  Mesh size in the codend (40 mm square or 50 mm rhomboidal): fully observed (In force since 
June 2010) 

-  Fishing forbidden within upper 50 m depth: fully observed 

-  Time at sea (12 hours per day and 5 days per week): fully observed 

-  Minimum landing size (20 mm CL), (EC regulation 1967/2006): mostly fully observed 

5.2.14.5.3  Catches  
Due to its high economic value, and because is the only target of the fishery, no significant discards 
are produced. Hence catch and landings are the same figures and are presented in figure 5.2.14.5.3.1. 

catch 

400

350

300

250

200

150

100

50

s
e
n
n
o
T

0
2000

2002

2004

2006

2010

2012

2014

2016

2008

year 

Figure 5.2.14.5.3.1.  Blue and red shirmp in GSA 1.  Catches by year. 

Figure 5.2.14.5.3.2 shows the size structure of landings from 2002 to 2014.  

356 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 5.2.14.5.3.2. Blue and red shirmp in GSA 1. Length frequency distributions by year from DCF.  

5.2.14.5.4  Landings  
See chapter 5.2.14.5.3. 

5.2.14.5.5  Discards  
Discards are considered negligible. 

5.2.14.5.6  Fishing effort  
The fishing effort expressed as fishing days by year is presented in Figure 5.2.14.5.6.1. 

effort 

7000

6000

5000

4000

3000

2000

1000

s
y
a
d

0
2000

2002

2004

2006

2008

2010

2012

2014

2016

year 

Figure 5.2.14.5.6.1. Effort of the fleet targeting Blue and red shirmp in GSA 1, expressed in days at sea. Source: 
Spanish Institute of Oceanography (IEO) for the period 2002-2014. 

357 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
5.2.14.6 Scientific surveys 
5.2.14.6.1   Survey #1 (MEDITS) 
5.2.14.6.1.1  Methods 
The  Spanish  Institute  of  Oceanography  carries  out  two  scientific  surveys  under  the  Data  Collection 
Regulation: MEDITS and MEDIAS. Both are international coordinated surveys. 

The  IEO  is  involved  in  the  international  bottom  trawl  survey  MEDITS  since  1994.  The  survey  takes 
place in all European Mediterranean countries and the main target species are demersal species. The 
Spanish  MEDITS  survey  carries  out  about  170  –  180  hauls  in  spring.  It  samples  4  GSAs,  including 
Balearic Islands, and the sampling procedure is based on the common methodology included in the 
MEDITS instruction manual. The GSAs sampled are: GSA 1, GSA 2, GSA 5 and GSA 6. 

Table 5.2.14.6.1.1.1. Number of hauls by year from MEDITS in GSA 1. 

year 
Hauls 

2002  2003  2004  2005  2006  2007  2008  2009  2010  2011  2012  2013  2014 
55 
43 
46 

38 

51 

39 

31 

39 

43 

47 

28 

45 

26 

 Geographical distribution 

5.2.14.6.1.2 
Aristeus antennatus, is present in the eastern part of GSA 1 at depths comprised between 400 and 
800 m. It is particularly abundant in front of Cape of Gata. 

Figure  5.2.14.6.1.2.1.  Blue  and  red  shirmp  in  GSA  1.  Geographical  distribution  according  to  MEDITS  (2009-
2014). 

358 

 
 
 
 
 
 
 
 
 
 
 
 
5.2.14.6.1.3 

 Trends in abundance and biomass 

Figure 5.2.14.6.1.3.1. Blue and red shirmp in GSA 1. Series of abundance indices in biomass (kg) and density 
(number of individuals per km2) obtained from MEDITS. 

Figure 5.2.14.6.1.3.2. Blue and red shirmp in GSA 1. Series of abundance indices in number of individuals per 
km2 compared with the catch in tonnes. 

5.2.14.6.1.4  Trends in abundance by length or age 
In the table 5.2.14.6.1.4.1 and figure 5.2.14.6.1.4.1 the trends in abundance by age are presented. 

Table 5.2.14.6.1.4.1 Number of individuals per km2 by age and year according to MEDITS surveys.  

359 

0123456781994199519961997199819992000200120022003200420052006200720082009201020112012201320142015K/Km201002003004005006007001994199519961997199819992000200120022003200420052006200720082009201020112012201320142015N/Km2 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2002 
2003 
2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

70

60

50

40

30

20

10

0

m
k
e
r
a
u
q
s
y
b
s
l
a
u
d
i
v
i
d
n

i

f
o
r
e
b
m
u
N

0 
17.2 
4.7 
7.9 
8.9 
6.5 
8.8 
10.1 
3 
5 
9.6 
6.4 
7 
14 

1 
38.1 
68.6 
46.7 
36.5 
56.2 
19.6 
29.6 
32.7 
18.9 
34.2 
49 
27.4 
46.1 

2 
4.8 
16 
5.2 
5.3 
22.3 
4.1 
2.8 
8.1 
5.8 
7.8 
12.1 
7.1 
12.2 

3 
0.8 
3.8 
1.7 
0.9 
1.5 
0.9 
0.8 
1.3 
2.6 
1 
0.8 
1.4 
0.5 

0

1

2

age 

3

4+

4+ 
0.8 
2.6 
0.5 
0.8 
1.3 
0.5 
0.5 
1 
2.1 
0.6 
0.6 
0.7 
0.3 

2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014

Figure 5.2.14.6.1.4.1. Blue and red shirmp in GSA 1. Age frequency distributions by year according to MEDITS 
surveys. 

5.2.14.7 Stock Assessment 
5.2.14.7.1  Methods 
The Extended Survivor Analysis (XSA, Darby and Flatman, 1994) using FLR library was used. This stock 
was assessed for the last time during 2011 (STECF EWG 1105) using LCA with VIT software (Lleonart 
and Salat, 1997). 

5.2.14.7.2  Input data 
Weight at age per year in table 5.2.14.2.1 
Proportion of matures in table 5.2.14.3.1. 
Natural mortality: M = 0.46 year-1 for all ages.  
Indices from table 5.2.14.6.1.4.1 

360 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Catches (in tonnes) in table 5.2.14.7.2.1 
Catch at age (in numbers) table 5.2.14.7.2.2 

Table 5.2.14.7.2.1. Blue and red shirmp in GSA 1. Catches in tonnes.  

2002  2003  2004  2005  2006  2007  2008  2009  2010  2011  2012 

2013  2014 

244.5  355.4  275.5  212.3  282.1  177.4  125.3  144.6  152.1  131.4  148.6  124.9  184.0 

Table 5.2.14.7.2.2. Blue and red shirmp in GSA 1. Catch at age. 

Year 

2002  2003  2004  2005  2006  2007  2008  2009  2010  2011  2012  2013  2014 

Age 0  5192  5085  4629  3484  5165  1246  888  1364  1079  984  878  1040  946 

1 

2 

3 
4+ 

18014  21063  18976  15345  21773  11781  6581  6717  8309  8013  8422  7878  9975 

1553  3444  2280  1353  1332  1501  1105  1510  1373  1114  1289  994  2014 

11 
0 

49 
0 

90 
0 

141 
0 

12 
4 

49 
0 

212  294  179  116  131 
0 

14 

9 

4 

3 

68 
0 

109 
0 

XSA settings 

-  Catchability  dependent on stock size for ages <    1 
- 
 Catchability independent of age for ages >=    2 
-  Survivor estimates shrunk towards the mean F   of the final   5 years or the   3 oldest ages. 
-  S.E. of the mean to which the estimates  are shrunk =    0.5 
- 
 Minimum standard error for population estimates =    0.3 

The XSA parameters are:  
xsa_control<- FLXSA.control(x=NULL, tol=1e-09, maxit=150, min.nse=0.3, fse=0.5, 
                             rage=1, qage=2, shk.n=TRUE, shk.f=TRUE, shk.yrs=3, shk.ages=3, 
                             window=100, tsrange=20, tspower=0, vpa=FALSE) 

Catchability analysis : 

Catchability independent of size for all ages 
Catchability independent of age for ages >   1  

Sensitivity analysis 

Figure 5.2.14.7.2.1. Blue and red shirmp in GSA 1. Sensitivity on shrinkage weight. 

361 

 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
Figure  5.2.14.7.2.2.  Blue  and  red  shirmp  in  GSA  1.  Sensitivity  for  different  shrinkage  ages  with  shrinkage 
weight 0.5. 

Figure 5.2.14.7.2.3. Blue and red shirmp in GSA 1. Sensitivity for different r age and q age. 

5.2.14.7.3  Results 
The results of the different XSA runs are presented in the following figures and tables. 

362 

 
 
 
 
 
 
 
 
 
 
Table 5.2.14.6.1.4.1. Blue and red shirmp in GSA 1.  F by age and year. 

1 

2002  2003  2004  2005  2006  2007  2008  2009  2010  2011  2012  2013  2014 
Year 
Age 0  0.12  0.14  0.15  0.08  0.2  0.08  0.06  0.08  0.06  0.06  0.05  0.05  0.05 
1.42  1.83  2.27  2.04  2.01  1.63  1.15  1.27  1.56  1.49  1.66  1.12  1.39 
2.86  3.01  2.3  4.08  2.47  1.24  0.94  1.54  1.78  1.6  2.1  1.59  1.8 
1.49  1.69  1.61  2.14  1.56  0.98  0.79  1.07  1.16  1.08  1.32  0.91  1.11 
1.49  1.69  1.61  2.14  1.56  0.98  0.79  1.07  1.16  1.08  1.32  0.91  1.11 

4+ 

2 

3 

Table 5.2.14.6.1.4.2. Blue and red shirmp in GSA 1.  Stock  number at age. 

Year 

Age 
0 

1 

2 

3 

4+ 

2005 
2101
05 
2908
8 

2004 
1514
18 
3448
9 

2003 
1786
45 
4168
4 

2002 
2138
13 
4048
6 

8061
2 
1695
2 
2215  4867  3416  1843  1973  2969  2551  2648  2260  1914  2000  1716  3286 
217 
0 

2007  2008  2009  2010  2011 
8731
8966
8504
6 
6 
0 
1759
1620
2490
7 
1 
5 

108  539  601  353  238 
26 

2006 
1322
90 
4169
5 

2014 
1050
16 
2256
9 

2013 
1118
65 
2058
1 

2012 
1018
53 
1753
7 

8780
8 
1785
0 

238 
4 

148 
0 

206 
0 

155 
0 

18 
0 

20 
7 

78 
0 

22 

0 

0 

7 

363 

 
 
 
 
 
 
 
 
 
 
Figure 5.2.14.7.3.4. Blue and red shirmp in GSA 1. XSA residuals for the MEDITS survey. 

Table 5.2.14.7.3.1. Blue and red shirmp in GSA 1. XSA summary results (Recruitment, B, SSB, and F). 

SSB 
396.07 
511.43 
386.71 
320.40 
396.22 
292.12 
250.56 
253.64 
247.40 
225.31 
237.95 
241.36 
322.58 

Fbar1-2 
2.14 
2.44 
2.28 
3.10 
2.18 
1.42 
1.04 
1.39 
1.68 
1.50 
1.88 
1.32 
1.57 

Year 
2002 
2003 
2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

R 
55259 
46997 
39706 
51130 
34399 
20265 
19255 
22123 
20869 
21478 
23301 
27063 
34803 

B 
626.20 
711.69 
554.42 
530.57 
546.65 
381.00 
332.76 
329.55 
336.53 
316.48 
336.52 
355.32 
468.10 

364 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 5.2.14.7.3.5. Blue and red shirmp in GSA 1. XSA retrospective analysis results.  

5.2.14.8 Reference points 
5.2.14.8.1  Methods 
Yield per Recruit computation was made using the NOAA software (results very similar with those of 
XSA  with  FLR).  The  fishing  mortality  rate  corresponding  to  F0.1  in  the  yield  per  recruit  curve  is 
considered here as a proxy of FMSY. 

5.2.14.8.2  Input data  
The input parameters were the same used for the XSA stock assessment and its results.  

5.2.14.8.3  Results 
The results are presented in the figure 5.2.14.8.3.1. and the table 5.2.14.8.3.1.   

365 

 
 
 
 
 
 
 
 
 
 
 
Figure 5.2.14.8.3.1.  Blue and red shirmp in GSA 1. Yield per recruit analysis using the software from NOAA and 
as input parameters the same used for the XSA stock assessment and its results.  

Table 5.2.14.8.3.1. Blue and red shirmp in GSA 1. Reference points from the yield per recruit analysis. 

FMAX 
F0.1 (proxy of FMSY) 
Fcurrent 

0.78 
0.41 
1.57 

NOAA software 
NOAA software 

5.2.14.9 Data quality 
Data from DCF 2014 as submitted through the Official data call in 2015 were used. Data quality for 
this  stock  is  appropriate,  except  for  the  catch  series  in  the  2002-2008  period,  which  needed  to  be 
reconstructed. For this period, catches  for GSA 1 also included GSA 2. Threfore, data time series from 
regional government sources were used to estimate catches for GSA 1 only during 2002-2008. 

 Method 

Short term predictions 2016-2018 

5.2.14.10 
5.2.14.10.1 
A  deterministic  short  term  prediction  for  the  period  2015  to  2017  was  performed  using  the  FLR 
routines  provided  by  JRC  and  based  on  the results  of  the XSA  stock assessments performed during 
EWG 15-11. 

5.2.14.10.2 
The same input parameters of the XSA were used for running the short term forecast. 

 Input parameters  

 Results 

5.2.14.10.3 
Table 5.2.14.10.3.1. Blue and red shirmp in GSA 1. Short term forecast in different F scenarios. Basis: F(2015) = 
mean (Fbar 1-2 2012-2014)= 1.40; R(2015) = geometric mean of the recruitment of the last 3 years; R =  2838 
thousands; SSB(2014) = 322 t, Catch (2014)= 184 t. 

366 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Rationale 

F scenario 

F factor 

Catch 
2016 

Catch 
2017 

SSB 
2017 

Change 
SSB 
2016-
2017 (%) 

Change 
catch 
2014-
2016 (%) 

zero catch 

High long-term 
yield (F0.1) 
Status quo 
Different 
scenarios 

0 

0.4 
1.4 

0.15 
0.31 
0.62 
0.77 
0.93 
1.08 
1.24 
1.54 
1.7 
1.85 
2.01 
2.16 
2.32 
2.47 
2.63 
2.78 
2.93 
3.09 

0 

0.3 
0.9 

0.1 
0.2 
0.4 
0.5 
0.6 
0.7 
0.8 
1 
1.1 
1.2 
1.3 
1.4 
1.5 
1.6 
1.7 
1.8 
1.9 
2 

0 

0 

674.26 

93.63 

-100 

96.28 
203.87 

146.14 
202.66 

516.79 
348.75 

48.41 
0.15 

36.14 
67.75 
119.7 
141.03 
159.79 
176.34 
190.94 
215.34 
225.52 
234.59 
242.69 
249.94 
256.44 
262.28 
267.56 
272.32 
276.65 
280.58 

66.15 
112.79 
167.5 
182.36 
191.97 
197.86 
201.15 
202.96 
202.48 
201.51 
200.26 
198.88 
197.45 
196.06 
194.73 
193.5 
192.36 
191.34 

614.66 
562.99 
479.25 
445.44 
416.05 
390.46 
368.18 
331.79 
316.97 
304.00 
292.64 
282.67 
273.90 
266.19 
259.38 
253.36 
248.02 
243.28 

76.51 
61.67 
37.63 
27.92 
19.47 
12.13 
5.73 
-4.72 
-8.98 
-12.7 
-15.96 
-18.83 
-21.34 
-23.56 
-25.52 
-27.24 
-28.78 
-30.14 

-47.67 
10.80 

-80.36 
-63.18 
-34.94 
-23.35 
-13.16 
-4.17 
3.77 
17.03 
22.57 
27.5 
31.9 
35.84 
39.37 
42.55 
45.41 
48 
50.35 
52.49 

Medium term predictions 

5.2.14.11 
Medium term predictions were not carried out as no meaningful stock-recruitment relationship was 
identified. 

Stock advice 

5.2.14.12 
The current F (1.57) is larger than F0.1 (0.41), chosen as proxy of FMSY and as the exploitation reference 
point consistent with high long term yields, which indicates that Blue and red shirmp in GSA 1 is being 
fished above FMSY.  

STECF  EWG  15-11  advises  the  effort  and/or  cacthes  of  Blue  and  red  shirmp  in  GSA  1  should  be 
reduced until fishing mortality is below or at the proposed FMSY level (0.41), in order to avoid future 
loss in stock productivity and landings. Catches of Blue and red shirmp in GSA 1  in 2016 consistent 
with FMSY should not exceed 96 tonnes. 

Management strategy evaluation 

5.2.14.13 
The Management Strategies Evaluation was performed with the R-script provided by JRC. The input 
data was the output of XSA analysis. After several simulations the final result was run under the a4a 
option  in  the  loop  of  250  iterations.  The  FMSY  ranges  were  derived  using  the  formula  provided  by 
STECF  15-09,  being  Flower=0.27  and  Fupper=0.56  and  Blim  was  estimated  as  Bloss=224  tonnes.  The 
probability of SSB falling below Blim fishing at Fupper is 0. The dynamics observed for this stock are the 
result  of  the  stock  assessment  model  (i.e.  XSA)  settings  used  inside  the  MSE  becoming  less 

367 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
appropriate as the stock status changes in time (i.e. stock assessment settings are often specific to a 
particular range of stock status). This leads to an increasing difference between the perceived stock 
and the operating model (i.e. the 'true' stock). To avoid this behaviour in the future, for some of the 
stocks as it is the case here, a more general stock assessment method should be used in the MSE loop 
that is less sensitive to the stock status. 

Figure 5.2.14.13.1. Blue and red shirmp in GSA 1. Management Strategy Evaluation (MSE). Predictions of YMSY 
strategy with 250 iterations. Using XSA as input and a4a in the iterations loop. 

368 

 
 
 
 
 
 
 
 
 
 
 
Figure  5.2.14.13.2.  Blue  and  red  shirmp  in  GSA  1  Frequency  distribution  of  SSB  in  2037  applying  the  FMSY 
strategy (250 iterations) and using XSA as input and a4a in the iterations loop. 

369 

 
 
 
 
 
 
 
 
5.2.15  STOCK ASSESSMENT OF BLUE AND RED SHRIMP IN GSA 6 
5.2.15.1 Stock Identification 
Due  to  insufficient  information  about  the  stock  structure  of  blue  and  red  shrimp  (Aristeus 
antennatus) in the western Mediterranenan Sea, this stock was assumed to be confined within the 
boundaries of the GSA 6. 

Fig. 5.2.15.1.1. Geographical location of GSA 6. 

5.2.15.2 Growth 
The  growth  parameters  used  were  taken  from  Garcia-Rodriguez  (2003),  estimated  from  length 
frequency distributions analysis (Linf = 77.0; K = 0.38; t0= -0.065), and coincide with the parameters 
in the Data Collection Framework (DCF) official data call 2015. The parameters of the length-weight 
relationship were taken from DCF data call 2015 (a= 0.0020; b= 2.5120). 

5.2.15.3 Maturity 
The  maturity  ogive  was  taken  from  García  Rodriguez  (2003),  with  size  at  first  maturity  (50  %) 
estimated at 23.5 mm CL. 

Table 5.2.15.3.1. Blue and red shrimp in GSA 6. Maturity at age. 

Age class 
Prop. mature 

0 
0.07863 

1 
0.7669 

2 
0.998 

3 
1.0 

4+ 
1.0 

5.2.15.4 Natural mortality 
A constant value of natural mortality M=0.46 yr-1 was used, based on Pauly’s (1980) equation and a 
mean annual temperature of 13 ºC.  

5.2.15.5 Fisheries 
5.2.15.5.1 General description of the fisheries 
Blue  and  red  shrimp  is  the  most  important  crustacean  species  in  catches  and  value  of  GSA  06 
(Northern Spain) fisheries. It is a deepwater species caught exclusively by bottom trawl. The blue and 
red shrimp has a wide bathymetric distribution, between 80 and 3300 m depth (Sardà et al., 2004), 

370 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
although commercial fishing grounds are located between 450 and 900 m depth. Deeper areas may 
act as a refuge for the stock, specially for the juvenile fraction, as they are located far from the main 
fishing ports and below 1000 m of depth where the trawl fishing is banned (GFCM resolution 2005/1). 
Females predominate in the landings, representing nearly 80% of the total landings. Discards of the 
blue  and  red  shrimp  are  practically  nil  because  of  the  high  commercial  value  of  the  species.  Other 
accompanying  species  of  commercial  value  in  the  catches  are  large  individuals  of  hake,  greater 
forkbeard, Nephrops and blue whiting. In GSA 06, the number of harbours with vessels fishing blue 
and red shrimp is 14. Exploitation is based on young age classes, mainly 1 and 2 year old individuals. 

5.2.15.5.2  Management regulations applicable in 2015  
Trawl fisheries in GSA 6 are regulated by “Orden AAA/2808/2012” published in the Spanish Official 
Bulletin (BOE no 313 29 December 2012) containing an Integral Management Plan for Mediterranean 
fishery resources. To the traditional fisheries regulations already in place (e.g. the daily and weekly 
fishing effort limited to 12 hours per day five days a week; trawl cod end 40 mm square mesh or 50 
mm  diamond  stretched  mesh;  engine  power  of  maximum  373  kW  [not  observed];  license  system; 
minimum landing size of 20 mm CL), this plan adds that fishing mortality for  Aristeus antennatus in 
GSA 6 should be kept at or below the reference value F01 = 0.24, and that fishing effort be reduced by 
20%  or  more  over  the  period  2013-2017  (based  on  the  effort  established  on  1  January  2013).  This 
fishing effort reduction will be measured in terms of number of vessels, engine power and tonnage. 

5.2.15.5.3  Catches (by fleet if posible) 
The  catches  by  the  bottom  trawl  fleet  are  reported  in  the  following  table  and  figure.  Note  that 
catches in the official data call before 2011 are incomplete and correspond only to catches reported 
in logbooks. This problem was reported in the previous assessment (2012, EWG 12-19). The catches 
for the period 2002 – 2010 have been corrected using the official data of local Fisheries Directorates 
of the Autonomous Communities of Catalonia and Valencia, while for 2011 – 2014 the data from the 
local Fisheries Directorates and the Data Call 2015 coincide. 

Table 5.2.15.5.3.1. Blue and red shrimp in GSA 6. Annual catches (t). 

2002 
723 
2009 
743 

2003 
583 
2010 
647 

2004 
577 
2011 
669.5 

2005 
308 
2012 
703.5 

2006 
354 
2013 
678.9 

2007 
579 
2014 
545.6 

2008 
730 

371 

 
 
 
 
 
 
 
 
 
 
 
 
Figure. 5.2.15.5.3.1. Blue and red shrimp in GSA 6. Annual catches (t).  

5.2.15.5.4  Landings  
Landings are assumed to be equal to catches because discards are negligible (Table 5.2.15.5.3.1). 

5.2.15.5.5  Discards  
Discards are negligible due to the high commercial value of the species. 

5.2.15.5.6  Fishing effort (by fleet if possible) 
All indicators of fishing effort have been decreasing over the last 6 years, as well as capacity (number 
of vessels). The fleet segments involved in the deepwater trawl fishery are in length classes VL1224 
and VL2440. 

Table 5.2.15.5.6.1. Effort of the bottom trawl fishing fleet (OTB) in GSA 6. 

Effort (kW * days) 
28 339 356 
26 306 047 
24 805 884 
23 553 925 
22 821 990 
23 422 870 

Effort (GT * days) 
6 063 794.54 
5 673 235.42 
5 343 285.49 
5 109 806.37 
5 021 556.13 
5 216 516.97 

2009 
2010 
2011 
2012 
2013 
2014 

Table 5.2.15.5.6.2. Number of vessels of the bottom trawl fishing fleet (OTB) in GSA 6 active in the first quarter 
of each year. 

372 

01002003004005006007008002002200320042005200620072008200920102011201220132014catch (t) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2009 
2010 
2011 
2012 
2013 
2014 

843 
827 
756 
729 
713 
700 

Figure 5.2.15.5.6.1. OTB fishing fleet in GSA 6. Number of vessels, nominal effort (000s of kW*days at sea) 
and nominal capacity (000s GT*days at sea). 

5.2.15.6 Scientific surveys 
5.2.15.6.1   Survey #1 (MEDITS) 
5.2.15.6.1.1  Methods 
The  abundance  (N/km2)  and  biomass  (kg/km2)  indices  obtained  by  means  of  the  MEDITS  surveys 
were computed, based on the DCF data call 2015. Blue and red shrimp is present only in the deepest 
stratum (500 – 800 m) of the MEDITS survey. The number of hauls in that stratum along the 21 year 
time series is shown in the following table: 

stratum 
500 - 800 m 
stratum 
500 - 800 m 

7 

11 

1994  1995  1996  1997  1998  1999  2000  2001  2002  2003  2004 
12 
10 

11 
2005  2006  2007  2008  2009  2010  2011  2012  2013  2014 
10 

10 

12 

9 

7 

8 

9 

4 

8 

8 

7 

8 

8 

7 

9 

7 

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  hauling  duration.  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: 

Yst = Σ (Yi*Ai) / A 
V(Yst) = Σ (Ai2 * si 2 / ni) / A2 
Where: 
A=total survey area 

373 

0500010000150002000025000300000100200300400500600700800900200920102011201220132014000sNVkW * day010002000300040005000600070000100200300400500600700800900200920102011201220132014000sNVGT * day 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 
Length  distributions  represented  an  aggregation  (sum)  of  all  standardized  length  frequencies 
(subsamples  raised  to  standardized  haul  abundance  per  hour)  over  the  stations  of  each  stratum. 
Aggregated  length  frequencies  were  then  raised  to  stratum  abundance  *  100  (because  of  low 
numbers in most strata) and finally aggregated (sum) over the strata to the GSA.  

5.2.15.6.1.2 

 Geographical distribution 

Fig. 5.2.15.6.1.2. Blue and red shrimp in GSA 6.  Average density (N/km2) in MEDITS surveys over the period 
1994-2014 (circle diameter proportional to abundance). 

 Trends in abundance and biomass 

5.2.15.6.1.3 
The  abundance  indices  derived  from  the  MEDITS  surveys  show  an  increasing  trend  over  time, 
although with high fluctuations, from approximately 75 individuals/km2 on average in the late 1990s 
to approximately 100 individuals/km2 in the last 5 years. In terms of biomass, the increase is from 1 
kg/km2 to 1.5 kg/km2 approximately over the same period. 

374 

 
 
 
 
 
 
 
 
 
 
 
Fig. 5.2.15.6.1.3.  Blue and red shrimp in GSA 6. Trends in abundance (left) and biomass (right) indices from 
1994 to 2014. 

5.2.15.6.1.4  Trends in abundance by length or age 
The  size  distribution  of  Aristeus  antennatus  sampled  during  the  MEDITS  surveys  is  shown  in  the 
following two figures. The average size has fluctuated over the 21 year period of samples, with mean 
size in the last 5 years being relatively small (25 to 28 mm CL). 

Figure. 5.2.15.6.1.4.1. Blue and red shrimp in GSA 6. Length frequency distributions. 

375 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure.  5.2.15.6.1.4.2.  Blue  and  red  shrimp  in  GSA  6.  Box  plots  of  size  frequency  histograms,  showing  the 
median  and  0.25  -  0.75  quantiles  (box)  and  0.1  –  0.9%  quantiles  of  the  distribution  (lines).  Dots  represent 
outlyers. 

5.2.15.7 Stock Assessment 
5.2.15.7.1  Methods 
The available DCF Data Call 2015 was deemed adequate for the application of an Extended Survivors 
Analysis  (XSA)  tuned  with  fishery  independent  data  (MEDITS  abundance  indices).  A  catch-at-age 
matrix  for  the  period  2002-2014  was  constructed,  assuming  no  discarding,  and  the  analysis  was 
carried out in the FLR framework. 

5.2.15.7.2  Input data 

Table 5.2.15.7.2.1. Blue and red shrimp in GSA 6. Catch-at-age matrix (number of individuals in the commercial 
catches, in thousands). 

Year 
2002 
2003 
2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 

age0 
834 
1553 
323 
102 
292 
123 
254 
203 
352 
197 
882 
127 

age1 
65048 
59192 
48567 
28086 
28548 
44272 
62949 
63245 
50158 
37928 
56854 
69790 

age2 
13327 
10281 
10180 
4918 
8077 
12577 
11536 
13851 
14562 
15208 
17049 
11527 

376 

age3 
1806 
1154 
1334 
773 
633 
1528 
2259 
1866 
1908 
2519 
2556 
2057 

age4 
541 
318 
545 
266 
83 
203 
1027 
669 
345 
577 
414 
319 

age5+ 
45 
38 
54 
63 
38 
32 
107 
69 
34 
74 
44 
39 

 
 
 
 
 
 
 
 
 
 
 
 
2014 

345 

65001 

12345 

2130 

288 

31 

Fig. 5.2.15.7.2.1. Blue and red shrimp in GSA 6. Log-catch curves. 

Table 5.2.15.7.2.2. Blue and red shrimp in GSA 6. Catch and stock weights at age (kg). 

Age group 
2002 
2003 
2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

age3 

age1 

age2 

age4 

age0 
age5+ 
0.0050  0.0120  0.0290  0.0460  0.0600  0.0770 
0.0040  0.0120  0.0290  0.0470  0.0600  0.0774 
0.0050  0.0100  0.0290  0.0480  0.0620  0.0773 
0.0050  0.0100  0.0290  0.0480  0.0630  0.0768 
0.0050  0.0110  0.0280  0.0480  0.0000  0.0761 
0.0050  0.0110  0.0290  0.0460  0.0600  0.0772 
0.0050  0.0100  0.0290  0.0480  0.0640  0.0750 
0.0050  0.0110  0.0290  0.0480  0.0630  0.0780 
0.0050  0.0110  0.0290  0.0480  0.0640  0.0790 
0.0050  0.0130  0.0280  0.0470  0.0630  0.0790 
0.0040  0.0110  0.0290  0.0470  0.0640  0.0740 
0.0050  0.0100  0.0290  0.0470  0.0600  0.0773 
0.0050  0.0110  0.0290  0.0470  0.0640  0.0770 

Table. 5.2.15.7.2.3. Blue and red shrimp in GSA 6. Maturity vector and natural mortality. 

Age 
group 

age0 

age1 

age2 

age3 

age4 

age5+ 

maturity  0.07863  0.7669 
0.46 

0.46 

M 

0.998 
0.46 

1 
0.46 

1 
0.46 

1 
0.46 

Table. 5.2.15.7.2.2. Blue and red shrimp in GSA 6. Tuning index (MEDITS), estimated number of individuals per 
km2.  

377 

Log catch curves for Aristeus antennatus in GSA 6agelog ratio-6-4-20201234Log catch curves for Aristeus antennatus in GSA 6yearlog ratio-6-4-202200420062008201020122014 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Age 0 
34.0 
27.4 
18.5 
8.6 
14.6 
17.5 
50.3 
23.2 
16.4 
26.5 
49.4 
41.1 
36.0 

Age 1 
15.4 
15.5 
10.8 
4.0 
24.6 
6.3 
16.1 
16.8 
10.0 
17.7 
23.6 
27.8 
28.0 

Age 2 
1.0 
5.0 
1.6 
1.0 
4.3 
2.4 
2.1 
3.3 
1.0 
3.0 
2.1 
14.6 
15.0 

Age 3 
0.0 
2.3 
0.6 
0.2 
0.2 
0.0 
0.5 
0.7 
0.5 
0.2 
0.0 
0.5 
0.6 

Age 4+ 
0.0 
1.1 
0.5 
0.1 
0.1 
0.0 
0.3 
0.5 
0.4 
0.1 
0.0 
0.4 
0.3 

2002 
2003 
2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 

5.2.15.7.3  Results 
The selection of the control parameters for the final XSA run was performed by running sequential 
sensitivity  analysis,  testing  for  a  range  of  suitable  parameters  in  shrinkage  weight assumptions  (fse 
range: 0.5 to 2.5), shrinkage on the last ages (shk.age range: 0 to 4), catchability dependent on stock 
size (r.age range: 0 to 4), age after which catchability is no longer estimated (q.age range: 0 to 4), and 
shrinkage  on  the  last  years  (range:  1  to  5).  The  following  figure  reproduces  the  results  of  the  last 
sensitivity analysis (shrinkage on the last years), for fse= 2.0, shk.age=3, r.age=1, and q.age=2. 

378 

Spawning stock biomassYearTonnes10001500200025002002200420062008201020122014Sh1Sh2Sh3Sh4Sh5RecruitmentYearThousands1000001500002000002500002002200420062008201020122014Sh1Sh2Sh3Sh4Sh5 
 
 
 
 
 
 
 
 
 
 
Figure. 5.2.15.7.3.1. Blue and red shrimp in GSA 6. Sensitivity to different shrinkage. 

All these analyses were repeated with a natural mortality vector with varying by each class, derived 
from  the  application  of  the  PRODBIOM  model,  but  a  constant  mortality  value  of  M=0.46  produced 
consistently better results and thus it was used in the final XSA model. 

Table 5.2.15.7.3.1. Blue and red shrimp in GSA 6. Natural mortality (M) vector computed with PRODBIOM.  

Age group 
M 

age0 
1.25 

age1 
0.58 

age2 
0.44 

age3 
0.39 

age4 
0.35 

age5+ 
0.31 

Settings of XSA final run 
Period: 2002-2014 
Age 5+ group was used as input. 
Catchability analysis: 
Catchability dependent on stock size for ages < 1 
Catchability independent of age for ages >= 2 
M constant at 0.46 yr-1. 
Survivor estimates shrunk towards the mean F of the final 3 years or the 3 oldest ages. S.E. of the 
mean to which the estimates are shrunk = 2.0  

Table. 5.2.15.7.3.2. Blue and red shrimp in GSA 6. Log-catchability of the residuals for the tuning fleet MEDITS. 

age0 
0.419 
0.482 
0.239 
-0.708 
-0.350 
-0.403 
0.629 
-0.059 

age1 
0.172 
0.277 
0.308 
-0.876 
0.689 
-0.72 
0.062 
0.068 

age2 
-1.249 
0.892 
-0.03 
-0.052 
0.447 
-0.439 
-0.403 
0.151 

age3 
-0.023 
0.779 
0.456 
-0.26 
-0.118 
0.067 
-0.776 
-0.209 

age4 
0.006 
0.128 
0.003 
-0.023 
0.074 
-0.023 
0.006 
0.188 

2002 
2003 
2004 
2005 
2006 
2007 
2008 
2009 

379 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2010 
2011 
2012 
2013 
2014 

-0.256 
0.086 
0.137 
0.032 
0.004 

-0.457 
0.203 
0.499 
-0.093 
-0.007 

-1.112 
-0.263 
-0.528 
1.527 
0.219 

-0.070 
-0.919 
-0.023 
-0.405 
-0.337 

0.135 
-0.051 
0.006 
0.067 
0.091 

Fig. 5.2.15.7.3.2. Blue and red shrimp in GSA 6. Log-catchability of the residuals for the tuning fleet MEDITS. 

The  annual  vectors  of  fishing  mortality  estimated  by  the  selected  XSA  model  are  shown  in  table 
5.2.15.7.3.3.  Fishing  mortality  has  fluctuated  around  1.0  yr-1  in  the  study  period,  with  the  notable 
exception of the 2 most recent years, when it decreased to 0.52 yr-1 in 2014. The average F over the 
period 2012-2014 is 0.78 yr-1. 

Table. 5.2.15.7.3.3. Blue and red shrimp in GSA 6. Fishing mortality estimates. 

4 

3 

0 

1 

5+ 

age 
2 
2002  0.0067  1.3846  1.0421  0.2601  0.9208  0.9208 
2003  0.0164  1.3901  1.3837  0.2900  0.0865  0.0865 
2004  0.0040  1.7060  1.7228  0.9532  0.2899  0.2899 
2005  0.0010  0.7898  1.2834  0.8059  0.7002  0.7002 
2006  0.0025  0.6206  0.7940  0.7592  0.2366  0.2366 
2007  0.0008  0.9286  0.9168  0.4515  0.8612  0.8612 
2008  0.0017  1.1385  0.9996  0.5572  0.9352  0.9352 
2009  0.0015  1.1013  1.3457  0.5798  0.4305  0.4305 
2010  0.0030  0.8602  1.3182  0.9673  0.2615  0.2615 
2011  0.0014  0.7073  1.0640  1.3859  1.5196  1.5196 
2012  0.0037  1.0838  1.3182  0.7038  1.5168  1.5168 

Fbar(1-3) 
0.8956 
1.0213 
1.4607 
0.9597 
0.7246 
0.7656 
0.8984 
1.0089 
1.0485 
1.0524 
1.0352 

380 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2013  0.0006  0.6144  0.9893  0.7403  0.2255  0.2255 
2014  0.0017  0.6191  0.2715  0.6822  0.2787  0.2787 

0.7813 
0.5243 

Table. 5.2.15.7.3.4. Blue and red shrimp in GSA 6. XSA summary table.  

RECRUITS 
Age 0 
158201 
120269 
102952 
123222 
146284 
184800 
189207 
173593 
149580 
171553 
304224 
280926 
253111 

TOTALBIO  TOTSPBIO  LANDINGS  YIELD/SSB  FBAR 1-3 

3377.6 
2733.3 
2014.1 
1638.1 
2177.5 
2972.1 
3249.5 
3289.9 
2998.4 
3166.1 
3603.0 
4339.9 
5472.6 

2341.8 
2011.6 
1364.8 
919.0 
1303.3 
1883.1 
2104.8 
2183.0 
2027.3 
2088.9 
2202.8 
2598.3 
3848.3 

723 
583 
577 
308 
354 
579 
730 
743 
647 
670 
704 
679 
546 

0.31 
0.29 
0.42 
0.34 
0.27 
0.31 
0.35 
0.34 
0.32 
0.32 
0.32 
0.26 
0.14 

0.8956 
1.0213 
1.4607 
0.9597 
0.7246 
0.7656 
0.8984 
1.0089 
1.0485 
1.0524 
1.0353 
0.7813 
0.5243 

181378.6 
(Thousands) 

3156.32 
(tonnes) 

2067.46 
(tonnes) 

603.19 
(tonnes) 

0.31 

0.9366 

2002 
2003 
2004 
2005 
2006 
2007 
2008 
2009 
2010 
2011 
2012 
2013 
2014 
Arith. 
mean 
units 

381 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure  5.2.15.7.3.3.  Blue  and  red  shrimp  in  GSA  6.  SSB  and  catch  are  in  tonnes,  recruitment  in  1000s 
individuals. 

A retrospective analysis conducted on SSB, F and recruitment shows that the results of the final 
XSA estimates are rather robust (Fig. 5.2.15.7.3.4). 

382 

 
 
 
 
 
 
 
 
 
Fig. 5.2.15.7.3.4. Blue and red shrimp in GSA 6. Results of the retrospective analysis. 

Figure  5.2.15.7.3.5  shows  that  the  trends  estimated  between  total  biomass  and  recruits  closely 
match (top left panel), but a meaningful SSB/R relationship could not  be established. The top right 
panel shows that landings are not strongly correlated with F, but with the exception of years 2009-
2010, landings follow the same trend of abundance detected for age 1 shrimp by the MEDITS indices 
(bottom left panel). 

383 

SSB retrospectiveYearSSB1000150020002500300035002002200420062008201020122014mean F retrospectiveYearF0.60.81.01.21.42002200420062008201020122014Recruitment retrospectiveYearRecruits100000150000200000250000300000350000200220042006200820102012201401000200030004000500060000500001000001500002000002500003000003500002002200320042005200620072008200920102011201220132014RECRUITSTOTALBIO0.00000.20000.40000.60000.80001.00001.20001.40001.600001002003004005006007008002002200320042005200620072008200920102011201220132014LANDINGSFBAR 1-3 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Fig.  5.2.15.7.3.5.  Blue  and  red  shrimp  in  GSA  6.  Relationships  between  selected  pairs  of  indicators:  recruits, 
total biomass, landings, Fbar and Age 1 individuals from MEDITS surveys. 

5.2.15.8 Reference points 
5.2.15.8.1  Methods 
Yield  per  recruit  analysis  (YPR)  was  conducted  assuming  equilibrium  conditions,  based  on  the 
exploitation pattern resulting from the XSA analysis. YPR was used for the estimation of F0.1 (i.e. proxy 
of FMSY) and Fmax. 

5.2.15.8.2  Input data  

Table 5.2.15.8.2.1.  Blue and red shrimp in GSA 6. Input parameters used in the YPR analysis. 

Age group 

0 
1 
2 
3 
4 
5+ 

Stock 
weight (kg) 
0.005 
0.011 
0.029 
0.047 
0.064 
0.077 

Catch 
weight (kg) 
0.005 
0.011 
0.029 
0.047 
0.064 
0.077 

maturity 

F 2014 

M 

0.079 
0.777 
0.998 
1 
1 
1 

0.0024 
1.0553 
0.3972 
0.6931 
0.2707 
0.2707 

0.46 
0.46 
0.46 
0.46 
0.46 
0.46 

5.2.15.8.3  Results 

Table 5.2.15.8.3.1. Blue and red shrimp in GSA 6. Results of the YPR analysis. 

F 
0 
0.36 
0.66 

Y/R (kg) 
0 
0.0067 
0.0073 

SSB/R 
(kg) 
0.0503 
0.0188 
0.0107 

Bio/R 
(kg) 
0.0664 
0.0325 
0.0233 

mean 
age 
1.7121 
0.9470 
0.7174 

0.46 

0.0071 

0.0153 

0.0285 

0.8509 

F0.1 
Fmax 
F at SSB 
0.3 

384 

0.010.020.030.040.050.060.001002003004005006007008002002200320042005200620072008200920102011201220132014LANDINGSage1 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 5.2.15.8.3.1. Blue and red shrimp in GSA 6. Results of the YPR analysis. 

5.2.15.9 Data quality 
Data  quality  for  this  stock  is  adequate,  except  for  the  catch  series  in  the  2002-2010  period,  which 
needed to be reconstructed from local fisheries statistical data. This problem had been noted earlier 
in the previous assessment carried out in STECF 12-19. 

Short term predictions 2015-2017 

5.2.15.10 
5.2.15.10.1 
A short term forecats was produced using the FLR script provided by JRC. 

 Method 

 Input parameters  

5.2.15.10.2 
Input parameters are the output of the XSA stock assessment, with FMSY set as 0.36 from the yield-per 
recruit analysis in section 5.2.15.8. 

5.2.15.10.3 

 Results 

Table 5.2.15.10.3.1. Blue and red shrimp in GSA 6. Short term forecast in different F scenarios. Basis: F(2015) = 
mean (Fbar 1-3 2012-2014)= 0.78; R(2015) = geometric mean of the recruitment of the last 3 years; R = 278633 
thousands; SSB(2014) = 3848 t, Catch (2014)= 547 t. 

Ffactor 

Fbar 

Catch_201
5 

0.00 

0.10 

0.20 

0.30 

0.40 

0.00 

0.08 

0.15 

0.23 

0.30 

0 

93 

182 

266 

346 

Catch_2016  Catch_20

0 

124 

240 

349 

450 

17 

0 

190 

347 

477 

584 

385 

SSB_201
6 

SSB_2
017 

4375 

4375 

4375 

4375 

4375 

7172 

6764 

6385 

6034 

5709 

Change_SS
B_2016-
2017(%) 
63.91 

Change_Catc
h_2014-
2016(%) 
-100.00 

54.58 

45.93 

37.91 

30.48 

-77.25 

-56.02 

-36.20 

-17.69 

-0.0014E-180.0010.0020.0030.0040.0050.0060.0070.00800.20.40.60.811.21.41.61.82Y/R (kg)FY/R ARA GSA06Y/R (kg)Current FF01 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0.50 

0.60 

0.70 

0.80 

0.90 

1.00 

1.10 

1.20 

1.30 

1.40 

1.50 

1.60 

1.70 

1.80 

1.90 

2.00 

0.48 

0.38 

0.45 

0.53 

0.60 

0.68 

0.78 

0.83 

0.90 

0.98 

1.05 

1.13 

1.20 

1.28 

1.35 

1.43 

1.50 

0.36 

422 

494 

563 

628 

690 

749 

805 

858 

909 

957 

1003 

1047 

1089 

1130 

1168 

1204 

407 

status 
quo 

FMSY 

544 

633 

715 

792 

864 

932 

995 

1054 

1109 

1161 

1210 

1255 

1298 

1338 

1375 

1410 

525 

671 

741 

797 

842 

877 

903 

923 

938 

947 

953 

956 

956 

954 

951 

946 

941 

654 

4375 

4375 

4375 

4375 

4375 

4375 

4375 

4375 

4375 

4375 

4375 

4375 

4375 

4375 

4375 

4375 

4375 

5407 

5127 

4868 

4627 

4404 

4197 

4004 

3826 

3661 

3508 

3365 

3233 

3111 

2997 

2892 

2794 

5468 

23.58 

17.18 

11.25 

5.75 

0.65 

-4.09 

-8.48 

-12.55 

-16.33 

-19.84 

-23.09 

-26.10 

-28.90 

-31.50 

-33.91 

-36.14 

24.97 

-0.40 

15.75 

30.85 

44.96 

58.15 

70.49 

82.03 

92.84 

102.95 

112.43 

121.30 

129.62 

137.42 

144.73 

151.59 

158.03 

-3.90 

5.2.15.11 
Not conducted as a meanigful stock recruitment relationship was not identified. 

Medium term predictions 

Stock advice 

5.2.15.12 
EWG 15-11 proposed F0.1 = 0.36 as proxy of FMSY and as the exploitation reference point consistent 
with  high  long  term  yields.  Taking  into  account  the  results  obtained  by  the  XSA  analysis  presented 
here (current F is estimated at 0.52), the stock is considered to be being fished above FMSY. 

Management strategy evaluation 

5.2.15.13 
The  application  of  the  empirical  formula  derived  in  the  EWG  15-06  meeting  (Ispra,  June  2015) 
produced a range of FMSY from 0.24 to 0.49. 
A management strategy for FMSY at the upper range achieved by 2020 was evaluated using FLR script 
provided by JRC. The management strategy evaluation included uncertainty in: a) recruitment around 
a  mean  level  resulting  from  the  geometric  mean  of  the  last  3  years  of  data,  b)  uncertainty  in  the 
MEDITS tuning fleet indices, and c) uncertainty in the perceived stock status. The stock was assessed 
by a statistical catch at age (SCA in a4a library) at each iteration, with a total of 250 iterations. The 
following figure shows the evolution of the main four stock indicators. The probability of SSB falling 
below Blim fishing at Fupper was estimated at 0. The dynamics observed for this stock are the result of 
the stock assessment model (i.e. XSA) settings used inside the MSE becoming less appropriate as the 
stock status changes in time (i.e. stock assessment settings are often specific to a particular range of 
stock status). This leads to an increasing difference between the perceived stock and the operating 
model (i.e. the 'true' stock). To avoid this behaviour in the future, for some of the stocks as it is the 
case  here,  a  more  general  stock  assessment  method  should  be  used  in  the  MSE  loop  that  is  less 
sensitive to the stock status. 

386 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Fig.  5.2.15.13.1.  Blue  and  red  shrimp  in  GSA  6.  Projection  of  recruitment,  spawning  stock  biomass,  landings 
and fishing mortality for the period 2015 – 2037 based on a management strategy achieving FMSY upper (0.49) 
by 2020.  

387 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
6 

SPATIO-TEMPORAL MAPS OF HIGH OCCURRENCE OF JUVENILES AND/OR SPAWNERS OF HAKE, 
GIANT RED SHRIMP AND BLUE AND RED SHRIMP  

The  spatial  distribution  of  juveniles  and  spawners  of  three  important  commercial  species:  hake 
(Merluccius  merluccius),  giant  red  shrimp  (Aristaeomorpha  foliacea),  and  blue  and  red  shrimp 
(Aristeus antennatus) was examined in the western Mediterranean (GSAs 1 and 5-11). Stratified CPUE 
data (n/km2) coming from the MEDITS survey were analysed for this purpose. For years 1994-2014, 
when  MEDITS  data  were  available,  yearly  CPUEs  were  standardised  by  dividing  with  the  maximum 
CPUE value of the year and bubble plots were created (Figs. 6.1 - 6.12). 

The size thresholds applied to identify juveniles and spawners of each species were extracted from 
the final report of the MEDISEH project (Colloca et al. 2013). Juveniles are generally considered to be 
the  0-age  recruits,  while  spawners  are  considered  to  be  adult  specimens  that  have  reached  a  size 
suggesting that they can reproduce. Within each species, often different threshold sizes for juveniles 
and/or  spawners  were  reported  in  the  MEDISEH  report  in  different  GSAs.  Therefore,  the  threshold 
sizes  for  each  species  used  here  were  calculated  by  averaging  the  available  threshold  sizes  in  the 
MEDISEH  report  across  the  GSAs  (Table  6.1).  This  approach  allows  for  a  uniform  representation  of 
nurseries  and  spawner  concentrations  across  the  whole  western  Mediterranean  basin,  but  it  does 
not account for the different growth and maturation rates that are known to occur in different GSAs. 

Table  6.1  Size  thresholds  used  to  group  juveniles  and  spawners  of  Merluccius  merluccius,  Aristaeomorpha 
foliacea and Aristeus antennatus. 

Species 

Upper 
threshold 
juveniles (mm) 

size 
for 

Lower size threshold 
for spawners (mm) 

Merluccius merluccius 
Aristaeomorpha 
foliacea 
Aristeus antennatus 

114 
26 

23 

336 
36 

26 

It should be noted that the construction of the maps of high occurrence of juveniles and spawners of 
the three species was based on data from a single seasonal survey, the MEDITS, which is carried out 
in May-July. Therefore, identified areas of high occurrence correspond to nurseries and aggregation 
areas of spawners/adults during this specific period of the year. Supplementary nurseries/spawning 
grounds could be used in other seasons by these species (Colloca et al. 2013).  

For  M.  merluccius,  the  timing  of  the  MEDITS  survey  allowed  for  a  good  depiction  of  the  juvenile 
occurrence. Areas with persistently high concentrations of hake juveniles include parts of the Catalan 
coast  (GSA  6),  the  gulf  of  Lions  (GSA  7),  and  the  Ligurian  sea  (GSA  9)  (Fig.  6.1,  6.2).  Around  the 
Balearic Islands (GSA 5) and Sardinia (GSA 11), occurrence of hake juveniles showed high variability 
from  year  to  year.  Interestingly,  the  Balearic  Islands  exhibited  substantial  occurrence  of  hake 
juveniles only from 2007 onwards. The nursery areas identified here are in general agreement with 
previous findings (Colloca et al. 2013; Colloca et al. 2015; Druon et al. 2015). Regarding M. merluccius 
spawners, there was a mismatch between the MEDITS survey period (late spring-early summer) and 
the  main  spawning  period  (winter-spring)  of  the  species,  which  resulted  into  low  catch  of  big hake 
during  MEDITS  in  most  GSAs.  Sardinia  (GSA  11)  and  the  gulf  of  Lions  (GSA  7)  exhibited  the  highest 

388 

 
 
 
 
 
 
 
 
 
 
 
concentrations  of  spawners  (Fig.  6.2,  6.3).  The  persistent  occurrence  of  hake  spawners  around 
Sardinia is probably due to the fact that unlike most other areas in the western Mediterranean, the 
reproductive period of M. merluccius in Sardinia is long, with two peaks of activity that fall in winter 
and summer (Colloca et al. 2013), the latter coinciding with the timing of the MEDITS survey.  

A. foliacea was rarely caught during MEDITS in the N-W Mediterranean (GSAs 1, 5, 6 7), while in GSA 
9 the smallest specimens caught were age 1+ (Colloca et al. 2013). That explains why the only areas 
with relatively high concentrations of juveniles in every year were found to be Sardinia (GSA 11) and 
the  Tyrrhenian  sea  (GSA  10)  (Fig.  6.5,  6.6).  The  spawning  period  of  A.  foliacea  in  the  western 
Mediterranean (summer months) coincides with the MEDITS survey period, meaning that the survey 
data provide good approximations of the spawning aggregations (Colloca et al. 2013). GSAs 10 and 11 
appear to be the main areas of occurrence of A. foliacea spawners, while there have been a few years 
when  spawning  aggregations  occurred  in  GSA  9  as  well,  especially  after  2004  (Fig.  6.7,  6.8).  These 
findings for A. foliacea are in general agreement with previous studies (Colloca et al. 2013; 2015). 

For  A.  antennatus  juveniles,  the  timing  of  the  MEDITS  survey  is  not  considered  suitable;  a  more 
accurate  depiction  of  nursery  areas  would  require  sampling  in  late  autumn-winter  (Guijarro  et  al., 
2008). Also, recruitment for this species takes place mostly at depths beyond 900m, which are not 
accessed by MEDITS (Sarda and Company, 2012). Therefore, the maps produced here (Fig. 6.9, 6.10) 
are  not  considered  truly  representative  of  the  actual  nursery  areas  and  reflect  the  occurrence  of 
bigger juveniles. Annual maps of spatial occurrence of A. antennatus juveniles exhibit great variability 
from year to year, with Sardinia (GSA 11) exhibiting the most persistent occurrence of juveniles (Fig. 
6.9).  The  peak  of  the  spawning  period  of  A.  antennatus  in  the  western  Mediterranean  (summer 
months) coincides with the MEDITS survey period; hence, the occurrence of spawning aggregations 
can  be  identified  (Colloca  et  al.  2013)  (Fig.  6.11,  6.12).  Spawning  aggregations  occurred  in  almost 
every year in Sardinia (GSA 11), gulf of Lions (GSA 7) and Ligurian Sea (GSA 9), while in the Spanish 
GSAs (1, 5, 6) there was a greater interannual variability (Fig. 6.11). 

It should be noted that the MEDITS data that were made available to STECF 15-11 contained some 
obvious  errors  regarding  the  coordinates  of  some  hauls  in  GSA  6  in  years  2010  and  2013.  In  these 
years, some coordinates in GSA 6 corresponded to areas in continental Spain (Fig. 6.1, 6.3, 6.9, 6.11) 
Therefore,  years  2010  and  2013  were  excluded  for  the  construction  of  the  figures  with  the  pooled 
data (Fig. 6.2, 6.4, 6.10, 6.12). 

389 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure  6.1.  Annual  spatial  occurrence  of  Merluccius  merluccius  juveniles  in  the  western  Mediterranean  in 
1994-2014, based on data from the MEDITS survey.  

Figure 6.2. Pooled spatial occurrence of Merluccius merluccius juveniles in the western Mediterranean in 1994-
2014, based on data from the MEDITS survey.  

390 

 
 
 
 
 
 
 
 
 
 
Figure  6.3.  Annual  spatial  occurrence  of  Merluccius  merluccius  spawners  in  the  western  Mediterranean  in 
1994-2014, based on data from the MEDITS survey.  

Figure  6.4.  Pooled  spatial  occurrence  of  Merluccius  merluccius  spawners  in  the  western  Mediterranean  in 
1994-2014, based on data from the MEDITS survey.  

391 

 
 
 
 
 
 
 
 
 
 
Figure  6.5.  Annual  spatial  occurrence  of  Aristaeomorpha  foliacea  juveniles  in  the  western  Mediterranean  in 
1994-2014, based on data from the MEDITS survey. 

Figure  6.6.  Pooled  spatial  occurrence  of  Aristaeomorpha  foliacea  juveniles  in  the  western  Mediterranean  in 
1994-2014, based on data from the MEDITS survey. 

392 

 
 
 
 
 
 
 
 
 
 
Figure 6.7. Annual spatial occurrence of  Aristaeomorpha foliacea spawners in the western Mediterranean in 
1994-2014, based on data from the MEDITS survey. 

Figure 6.8. Pooled spatial occurrence of  Aristaeomorpha foliacea spawners in the western Mediterranean in 
1994-2014, based on data from the MEDITS survey. 

393 

 
 
 
 
 
 
 
 
 
 
 
Figure 6.9. Annual spatial occurrence of Aristeus antennatus juveniles in the western Mediterranean in 1994-
2014, based on data from the MEDITS survey. 

Figure 6.10. Pooled spatial occurrence of Aristeus antennatus juveniles in the western Mediterranean in 1994-
2014, based on data from the MEDITS survey. 

394 

 
 
 
 
 
 
 
 
 
Figure  6.11.  Annual  spatial  occurrence  of  Aristeus  antennatus  spawners  in  the  western  Mediterranean  in 
1994-2014, based on data from the MEDITS survey. 

Figure 6.12. Pooled spatial occurrence of Aristeus antennatus spawners in the western Mediterranean in 1994-
2014, based on data from the MEDITS survey. 

395 

 
 
 
 
 
 
 
 
 
 
 
7  DATA QUALITY AND COMPLETENESS 

7.1 Data Overview 

The data call was issued on April 2015. The 'legal' deadline for submissions was the 2nd of July 2015. 
Upon communication with the member states some data tables were corrected and re-uploaded in 
relation to the 'operational' deadline of the 17th August 2015.  
Data was uploaded by each country according to the following table: 

Table 7.1.1. Timeline of data upload from Mediterranean Member States, data call 'legal' deadline of the 2h of 
July 2015; 'operational' deadline 17 August 2015. 

COUNTRY 
ITA 
ESP 
FRA 
SVN 
MLT 
CYP 
GRC 
HRV 

First Upload 
29 June 2015 
01 July 2015 
19 June 2015 
05 June 2015 
02 July 2015 
01 July 2015 
02 July 2015 
27 June 2015 

Last Upload 
12 August 2015 
05 August 2015 
02 July 2015 
23 July 2015 
02 July 2015 
06 August 2015 
31 Aug 2015 
31 July 2015* 

*: additional submissions on 4 Sep 2015 upon a request by the EWG 

The overall 2015 Data Call performance of data coverage, timeliness and progress of submissions by 
member state and main  table/variable  will be  made available  by the  end of the year and after the 
completion  of  the  EWG  15-16  Mediterranean  stock  assessments  part  2,  on  the  dedicated  weblink: 
http://datacollection.jrc.ec.europa.eu/coverage 

MEDITS Specific data problems 

It should be noted that the MEDITS data that were made available to STECF 15-11 contained some 
obvious errors regarding the majority of hauls coordinates in GSA 6 in years 2010 and 2013 and the 
entire years can‘t be used in the context of any spatial analysis. The error clearly is related with the 
incorrect specification of the Hauling Quadrant and should be fixed.  

7.2 Stock Specific Data Issues  

Hake in GSA 1 
A number of errors were detected in the MEDITS database (e.g. an error in the 2013 size frequencies 
abundances  in  length  class  38  cm/age  class  3,  not  considered  in  the  analysis;  2013-2014  data 
submitted twice). Because of this, MEDITS data used in the assessment were provided by EWG15-11 
invited experts. 
No data on OTB discarded sizes of European hake in GSA 1 available. 
No data on LLS landings sizes available.  

396 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
   
Concerning GNS+GTR, size data were available for 2009, 2010 and 2014. 

Hake in GSA 5 
Discarded biomass for 2014 showed values unusually low and should be further checked.  
Effort information was available for 2009-2014.  
A  comparison  of  the  abundance  indices  by  size  from  the  surveys  covering  the  period  2007-2014 
between the Data Call and the national database was performed. They showed high agreement for 
the last years, but inconsistent values for 2007-2008, which should also be checked. 

Hake in GSA 6 
No apparent issues. 

Hake in GSA 7 
Effort data were missing before 2009. 
Stock structure data submitted through the data call were in length and were converted in age using 
the length-to-age slicing functions. The growth of European Hake (Merluccius merluccius) in the Gulf 
of  Lions  was  recently  re-estimated  through  tagging  experiments  carried  out  by  IFREMER  (Mellon-
Duval  et  al.,  2010).  The  new  parameters  have  not  been  yet  compared  to  a  re-analysis  of  otoliths 
readings,  because  of  the  uncertainty  on  otoliths  readings.  Therefore  the  age  -growth  parameters 
submitted  through  the  Data  call  were  not  used  since  they  were  not  derived  from  the  recent 
estimates. 

Hake in GSA 8 
DCF data quality was deficient for this particular species.  
Catch  data,  proceeding  from  the  limited  number  of  trawlers  cover  only  the  period  2010-2014. 
Landings are too low in all the years where data are available.  
Age structure of the catch is not available and probably not collected due to the scarce commercial 
interest of this species in the area.  
Survey data suffered a gap in the time series, due to a technical problem that made impossible the 
utilization of the research vessel to carry out the cruise in 2002, and likely had a negative effect in the 
quality of the analysis.  

Hake in GSA 9 
Length frequencies distributions for several years were missing. 
Discard data were absent for years 2007-2008. 

Hake in GSA 10 
Raw  upload  data  were  used,  because  those  stored  in  the  databases  supplied  by  JRC  showed  some 
inconsistency (fishery data).   
Discards data of 2007, 2008 were absent. 

Hake in GSA 11 
Catch  information  for  the  artisanal  fleets  (GTR  and  LSS)  are  represented  only  in  some  years  and 
sometimes there is no relation in time with the data on lengths of catches. In particular, although the 
DCR/DCF database has values for total landings of hake in GSA11, data at length are missing for some 
years and gears (OTB in 2005 and for GTR in 2005, 2006 and 2008). Similarly a gap of information for 
total values of discards (GTR 2010) was detected while some data of discard at length were present. 

397 

 
 
 
 
 
 
 
 
 
 
 
 
It  is  also  true  that  the  size  distribution  of  both  GTR  landings  (2010)  and  GTR  discard  (2005,  2010) 
seems to be unrealistic for this species. 
The last problem  identified were some  unusual value for total discards and numbers of discards at 
age  in  some  years  (OTB,  2006;  GTR,  2005).  To  overcome  these  data  quality  problems  of  GSA  11,  a 
deep check of information was made in the first days of the meeting and it was decided to fill gaps 
and correct records in order to be able to successfully perform the assessment. 

Hake in GSA 9-11 
Lack of size structure information for some of the fisheries in GSA9 and GSA11 (e.g. trammel-net) 

Giant Red Shrimp in GSA 9 
Although  landings  data  were  observed  in  2008  for  Gillnet  and  in  2012  for  trammel,  no  length 
distribution was available.  
It is also true that landing values for these two fisheries and years  were very low (about 700kg and 
about 1.2 tons respectively) compared to the trawlers ones. 

Giant Red Shrimp in GSA 10 
Demographic structures of the gillnet landings were available for only three years.  

Giant Red Shrimp in GSA 11 
An  improvement  in  the  data  quality  of  giant  red  shrimp  GSA  11  data  has  been  observed  in 
comparison to the data provided during STECF EWG 14-19.  
Due the inconsistencies in age distributions, the experts decided to age-slice the LFDs provided in the 
framework of 2015 official data call.  
Due to the general low amount of samples analysed in the period 2005-2013 some of the input data 
used  for  the  analyses  (sex  ratio  by  length,  proportion  of  mature,  mean  catch  weight  at  age)  were 
selected from 2014 only. 

Blue and Red Shrimp in GSA 1 
No issues identified. 

Blue and Red Shrimp in GSA 6 
Catch  series  for  the  2002-2010  period,  needed  to  be  reconstructed  from  local  fisheries  statistical 
data. This problem had been noted earlier in the previous assessment carried out in STECF 12-19. 

398 

 
 
 
 
 
 
 
 
 
 
 
 
 
8  REFERENCES  

AA.VV. 2002. Stock Assessment in the Mediterranean-SAMED. Final Report EU Project n° 99/047.  
AA.VV. (2008). Status of deep-sea Red Shrimps in the Central and Eastern Mediterranean Sea, Final Report. Project Ref 

FISH/2004/03-32. 

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Spedicato  M.T.,  Lembo  G.,  Carbonara  P.,  Silecchia  T.  (1994)  -  A  first  attempt  to  estimate  biological  parameters  and 
dynamics of Aristaeomorpha foliacea in the Southern Tyrrhenian Sea. In "Life cycles and fisheries of deep water 

402 

 
 
 
 
 
 
red shrimps Aristaeomorpha foliacea and Aristeus antennatus". Proceedings of the International Workshop held 
in the Istituto di Tecnologia della Pesca e del Pescato (ITPP  - CNR), Mazara del Vallo (Italy), 28-30 Aprile 1994. 
N.T.R. - I.T.P.P. Special Publication, n. 3: 35-36.  

Spedicato M.T., Silecchia T., Carbonara P. (1999) – Aristeus antennatus. In: Sintesi delle conoscenze sue risorse da pesca 
dei fondi del Mediterraneo Centrale (Italia e Corsica). Relini G., Bertrand J., Zamboni A (eds.). Biol. Mar. Medit., 6 
(suppl. 1): 517-530. 

Spedicato M.T., Lembo G. 2011. South and Central Tyrrhenian Sea. In: Cataudella S. & Spagnolo M. (eds), 2011 - The state 
of  Italian  marine  fisheries  and  aquaculture.Ministero  delle  Politiche  Agricole,  Alimentari  e  Forestali  (MiPAAF), 
Rome (Italy), 620 p. 

WGSAD  (2014).  Report  of  the  Working  Group  on  Stock  Assessment  of  Demersal  Species  (WGSAD).  Subcommittee  on 
Stock Assessment (SCSA). Scientific Advisory Committee (SAC). Rome, Italy, 24-27 November 2014. 58 pp. 

403 

 
 
 
 
 
 
 
 
 
9  CONTACT DETAILS OF STECF MEMBERS AND EWG-15-11 List of Participants 

Information  on  STECF  members  and  invited  experts’  affiliations  is  displayed  for  information  only.  In  some 
instances  the  details  given  below  for  STECF  members  may  differ  from  that  provided  in  Commission 
COMMISSION DECISION of 27 October 2010 on the appointment of members of the STECF (2010/C 292/04) as 
some  members’  employment  details  may  have  changed  or  have  been  subject  to  organisational  changes  in 
their  main  place  of  employment.  In  any  case,  as  outlined  in  Article  13  of  the  Commission  Decision 
(2005/629/EU and  2010/74/EU)  on STECF,  Members  of  the  STECF,  invited experts,  and  JRC  experts  shall  act 
independently of Member States or stakeholders. 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  invited  experts  make  declarations  of  commitment  (yearly  for  STECF  members)  to  act 
independently in the public interest of the European Union. 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 

Tel. 

Email 

STECF members 

Abella,  J.  Alvaro  (vice-
chair) 

Private consultant 

aabellafisheries@gmail.com  

Andersen, 
Levring (vice-chair) 

Jesper 

Department  of  Food  and  Resource 
Economics (IFRO) 

Tel.dir.:  +45 35 28 68 92 

jla@ifro.ku.dk 

Section  for  Environment  and  Natural 
Resources 

University of Copenhagen 

Rolighedsvej 25 

1958 Frederiksberg 

Denmark 

Bailey, Nicholas  

Fisheries Research Services  

Tel: +44 (0)1224 876544  

baileyn@marlab.ac.uk   

Marine Laboratory, P.O Box 101  

Direct: +44 (0)1224 295398  

n.bailey@marlab.ac.uk 

375 Victoria Road, Torry 

Aberdeen AB11 9DB  

UK 

Fax: +44 (0)1224 295511 

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  

michel.bertignac@ifremer.fr 

Cardinale,  Massimiliano

Föreningsgatan 
Sweden 

45, 

  330  Lysekil, 

Tel: +46 523 18750 

massimiliano.cardinale@slu.se 

404 

 
 
 
 
 
 
 
  
 
 
 
 
Name 

Address1 

Tel. 

Email 

STECF members 

Curtis, Hazel  

Sea Fish Industry Authority 

Tel: +44 (0)131 558 3331 

H_Curtis@seafish.co.uk 

18 Logie Mill 

Logie Green Road 

Edinburgh 

EH7 4HS 

Fax: +44 (0)131 558 1442 

Delaney, Alyne 

Innovative Fisheries Management, 

Tel.: +45 9940 3694 

ad@ifm.aau.dk 

-an Aalborg University Research 

Centre, Postboks 104, 9850 

Hirtshals, Denmark 

Daskalov, Georgi 

Laboratory of Marine Ecology, 

Tel.: +359 52 646892 

gmdaskalov@yahoo.co.uk 

Institute of Biodiversity and 

Ecosystem Research, Bulgarian 

Academy of Sciences 

Döring, Ralf  

Thünen  Bundesforschungsinstitut, 
für 
Ländliche  Räume,  Wald  und  Fischerei, 
Institut 
AG 
Fischereiökonomie,  Palmaille  9,  D-22767 
Hamburg, Germany 

Seefischerei 

für 

- 

Tel.: 040 38905-185 

ralf.doering@ti.bund.de 

Fax.: 040 38905-263 

Gascuel, Didier  

AGROCAMPUS OUEST 

Tel:+33(0)2.23.48.55.34 

Didier.Gascuel@agrocampus-ouest.fr 

65 Route de Saint Brieuc, bat.4 

Fax: +33(0)2.23.48.55.35 

Graham, 
(chair) 

Garcia Rodriguez, 
Mariano 

CS 84215, 

F-35042 RENNES Cedex 

France 

Norman 

Marine Institute, Fisheries 

Tel: + 353(0) 91 87200 

norman.graham@marine.ie 

Science Services (FSS), Rinville, 

Oranmore, Co. Galway, Ireland 

Instituto Español de 

Oceanografía, Servicios 

Centrales, Corazón de María 8, 

28002, Madrid, Spain 

Mariano.Garcia@md.ieo.es 

Gustavsson,  Tore  Karl-
Erik  

Independent 
Sweden 

Consultant, 

Göteborg, 

tore.gustavsson@hotmail.com 

Jennings, Simon  

CEFAS Lowestoft Laboratory, 

Tel.: +44 1502562244 

simon.jennings@cefas.co.uk 

Pakefield Road, 

Lowestoft 

Suffolk, UK 

NR33 0HT 

Fax: +44 1502513865 

Kenny, Andrew 

CEFAS Lowestoft Laboratory, 

Tel.: +44 1502562244 

andrew.kenny@cefas.co.uk 

Pakefield Road, 

Lowestoft 

Suffolk, UK 

NR33 0HT 

Fax: +44 1502513865 

Kraak, Sarah  

Thünen-Institut  für  Ostseefischerei,  Alter 
Hafen Süd 2, 18069 Rostock Germany 

Tel. +49 3818116113 

sarah.kraak@ti.bund.de 

405 

 
 
 
 
 
 
 
 
 
 
 
Name 

Address1 

Tel. 

Email 

STECF members 

Kuikka, Sakari 

University of Helsinki, 

Tel.: +358 50 3309233 

skuikka@mappi.helsinki.fi 

Department of Environmental 

Fax. +358-9-191 58754 

Sciences, P.O. Box 65 

(Viikinkaari 1), FI-00014 

University of Helsinki, FINLAND 

Martin, Paloma   

CSIC Instituto de Ciencias del Mar   

PasseigMarítim, 37-49 

08003 Barcelona 

Spain 

Tel: 
direct line : 34.93.2309552 

34.93.2309500          

paloma@icm.csic.es 

Fax: 34.93.2309555 

Malvarosa, Loretta  

NISEA S.c.a.r.l.  

malvarosa@nisea.eu 

Murua, Hilario 

AZTI - Tecnalia / Unidad de 

Tel: 0034 667174433 

hmurua@azti.es 

Investigación Marina, Herrera 

Fax: 94 6572555 

Nord, Jenny  

Nowakowski, Piotr  

Prelezzo, Raul  

kaia portualdea z/g 20110 Pasaia 

(Gipuzkoa), Spain 

Southeast  Asian  Fisheries  Development 
Centre SEAFDEC 

Food 

Maritime University of Szczecin. – Faculty 
Fisheries, 
of 
Department 
Technique, 
Szczecin 

Science 
of 

Fishing 

and 

jenny@seafdec.org 

npfgd@poczta.onet.pl    

AZTI  -  Tecnalia  /  Unidad  de  Investigación 
Marina 

Txatxarramendi Ugartea z/g 

48395 Sukarrieta (Bizkaia), Spain 

Tel: 94 6029400 Ext: 406-  

rprellezo@suk.azti.es 

Fax: 94 6870006 

Sala, Antonello 

Fishing Technology Unit 

Tel: +39 071 2078841 

a.sala@ismar.cnr.it 

National Research Council (CNR) 

Fax: +39 071 55313 

Institute  of  Marine  Sciences  (ISMAR)  - 
Fisheries Section 

Largo Fiera della Pesca, 1 

60125 Ancona - Italy 

Scarcella, Giuseppe 

Environmental Management Unit 

Tel: +39 071 2078846 

g.scarcella@ismar.cnr.it 

National Research Council (CNR) 

Fax: +39 071 55313 

Institute  of  Marine  Sciences  (ISMAR)  - 
Fisheries Section 

Largo Fiera della Pesca, 1 

60125 Ancona - ITaly 

Somarakis, Stylianos 

Department of Biology 

University of Crete 

VassilikaVouton 

P.O. Box 2208 

71409 Heraklion 

Crete 

Greece 

Tel.:  +30  2610  394065,  +30 
6936566764 

somarak@biology.uoc.gr 

Stransky, Christoph 

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

Tel. +49 40 38905-228  

christoph.stransky@ti.bund.de 

Fax: +49 40 38905-263  

406 

 
 
 
 
 
 
 
 
 
 
 
 
Name 

Address1 

Tel. 

Email 

STECF members 

Theret, Francois  

Scapêche 

Ulrich, Clara  

17 Bd Abbé Le Cam 

56100 Lorient 

France 

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

Charlottenlund 

ftheret@comata.com 

cu@aqua.dtu.dk 

Vanhee, Willy  

ILVO  -  Institute  for  Agricultural  and  Fisheries 
Research 

Tel 

00-32-59-34-22-55

willy.vanhee@ilvo.vlaanderen.be 

Unit Animal Sciences - Fisheries 

Fax 

00-32-59-33-06-29

Ankerstraat 1, B-8400 Oostende, Belgium   

van Oostenbrugge, 

LandbouwEconomishInstituut- 

Tel:+31 (0)70 3358239 

Hans.vanOostenbrugge@wur. 

Hans  

LEI, Fisheries Section, Burg. 

Patijnlaan 19 

P.O.Box 29703 

2502 LS The Hague 

The Netherlands 

EWG-15-11  participants: 

Fax: +31 (0)70 

3615624 

Nl 

Address 

Tel. 

Email 

Swedish University 
Föreningsgatan, 45, 330 
Lysekil, Sweden 

Tel. +46 523 18750 

massimiliano.cardinale@slu.se 

STECF members 

Name 

Cardinale, 
Massimiliano 
(Chair) 

Abella, J. Alvaro 

per 

la 
della 
Funzionale 

ARPAT  –  AREA  MARE,  Agenzia 
Protezione 
Regionale 
Toscana 
Ambientale 
Articolazione 
RIBM 
Risorse 
e  Biodiversità 
Marina  Via  Marradi  114,  57126 
Livorno – Italia 

Ittiche 

Martin, Paloma   

CSIC Instituto de Ciencias del Mar   

PasseigMarítim, 37-49 

08003 Barcelona 

Spain 

Tel. 0039-0555-3206956 

alvarojuan.abella@arpat.tosca
na.it 

Tel: 
direct line : 34.93.2309552 

34.93.2309500          

paloma@icm.csic.es 

Fax: 34.93.2309555 

407 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
STECF members 

Name 

Address 

Tel. 

Email 

Scarcella, Giuseppe  Environmental Management Unit 

Tel: +39 071 2078846 

g.scarcella@ismar.cnr.it 

National Research Council (CNR) 

Fax: +39 071 55313 

Institute 
(ISMAR) - Fisheries Section 

of  Marine 

Sciences 

Largo Fiera della Pesca, 1 

60125 Ancona - ITaly 

Invited experts 
Name 

Address 

Guijarro, 
Beatriz 

Jadaud, 
Angélique 

Ligas, 
Alessandro 

Spanish  Institute of oceanography 
Apt. 291 
7015 Palma de Mallorca 
Spain 
IFREMER  
1, rue Jean Monnet 
34200 Sète, France 
Centro Interuniversitario di Biologia Marina ed Ecologia 
Applicata, Viale N. Sauro 4  Livorno 

Passeig  Mari tim  de  la  Barceloneta- 
BARCELONA 

LLeonart, 
Jordi 
Mannini, 
Alessandro 
Maynou, 
Francesc 
Minto, Coilin  Galway-Mayo Institute of Technology 

ICM/CSIC 
37-49  8003 
Universita` di Genoa  DIP.TE.RIS., Viale Benedetto XV, 3  
16132 Genova, Italy 
Institut de Ciències del Mar CSIC,   Psg  Marítim  de  la 
Barceloneta 37-49, 8003, Barcelona, Spain 

Tel. 

Email 

Tel. +34971133739 
Fax +34971404945 

beatriz@ba.ieo.es 

Tel. +33499573243 
Fax +33499573295 

ajadaud@ifremer.fr 

Tel.39  
3382919904 

ligas@cibm.it 

Tel.  
00 34 630422463 
Tel.+390103533015 
Fax +39010357888 
34932309500 

lleonart@icm.csic.es 

biolmar@unige.it 

maynouf@icm.csic.es   

Dublin 

35391742514 

coilin.minto@gmit.ie 

Murenu, 
Matteo 

Gil, 

Orio, 
Alessandro 
Pengal, 
Polona 
Pérez 
José Luis 
Quetglas, 
Antoni 
Ramirez, 
John 
Recasens, 
Laura 

Sbrana, 
Mario 

Scott, Finlay 

Road,  GALWAY, Ireland 
University of Cagliari (DBAE) 
Viale Poetto,1  
09126 Cagliari, Italy 

  5,  

Institute  of  Slovenia,  Spodnje 

Swedish  University  of  Agricultural  Sciences, 
Turistgatan, 45330, Lysekil, Sweden 
Fisheries  Research 
Gameljne 61a, 1211, Ljubljana-Šmartno, Slovenia 
IEO  -  Spanish 
pesquero S/N, 29640, Fuengirola (Málaga), Spain 
Spanish  Institute  of  Oceanography,  Apt.  291,  7015, 
Palma de Mallorca, Spain 
ICM/CSIC  Passeig  Marítim  de  la  Barceloneta-  37-49

Institute  of  Oceanography,  Puerto 

8003 

BARCELONA 

Spain 

Institut Ciències Mar Barcelona (ICM-CSIC) 
Passeig Marítim 37-49 
8191 Barcelona 
Spain 
Centro Intruniversitario di Biologia Marina 
Viale Nazario Sauro 4 
57128 Livorno, Italy 
Self-employed  Via Salita San Gabriele 21 ISPRA Italy 

408 

Tel. 
+390706758017 
Fax +390706758022 

mmurenu@unica.it 

46104784067 

aleorio@yahoo.it 

+386 31 885953 

polona.pengal@zzrs.si 

+034 952197124 

34-971401561 

joseluis.perez@ma.ieo.e
s 
toni.quetglas@ba.ieo.es 

685411405 

j.gabriel@icm.csic.es 

Tel.  
+3493 2309563 
Fax 
+3493 2309555 
Tel.+390586260723 
Fax+390586260723 

laura@icm.csic.es 

msbrana@cibm.it 

drfinlayscott@gmail.co

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Spedicato, 
Maria Teresa 
Vasilakopoul
os, 
Paraskevas 

JRC experts 
Name 

Damalas, 
Dimitrios 

Osio, 
Giacomo 
Chato 

COISPA  via Dei Trulli 18  70126  BARI 

Italy 

+39 335 7321505 

spedicato@coispa.it 

m 

Hellenic Centre for Marine Research 
Athens-Sounio ave. 

19013  ANAVYSSOS;  ATTIKI

46.7km 

Greece 

Address 
Joint Research Centre (IPSC) 
Maritime Affairs Unit 
Via E. Fermi, 2749 
21027 Ispra (Varese), Italy 

Joint Research Centre (IPSC) 
Maritime Affairs Unit 
Via E. Fermi, 2749 
21027 Ispra (Varese), Italy 

+30 210 9856703 

pvasilakopoulos@hcmr.
gr 

Telephone no. 

Email 

Tel.+390332789677 
Fax+390332789658 

dimitrios.damalas@jrc.e
c.europa.eu 

Tel.+390332785948 
Fax+390332789658 

giacomo-
chato.osio@jrc.ec.europ
a.eu 

European Commission 
Name 

Address 

Telephone no. 

Email 

Perez  Perera, 
Amanda 

DG MARE, D2, Rue Joseph II 99/Josef II-straat 99  
1000 Brussels 

+32 229-59785 

Amanda.PEREZ-
PERERA@ec.europa.eu 

Observers:  
None 

10  List of Background Documents  

Background documents are published on the meeting’s web site on:  
http://stecf.jrc.ec.europa.eu/web/stecf/ewg1511 

List of background documents: 

1.  EWG-15-11 – Doc 1 - Declarations of invited and JRC experts (see also section 10 of this report – List of 

participants) 

409 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Europe Direct is a service to help you find answers to your questions about the European Union 

Freephone number (*): 00 800 6 7 8 9 10 11 

(*) Certain mobile telephone operators do not allow access to 00 800 numbers or these calls may be billed. 

A great deal of additional information on the European Union is available on the Internet. 

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The Publications Office has a worldwide network of sales agents. 

You can obtain their contact details by sending a fax to (352) 29 29-42758. 

European Commission 

EUR 27638 EN – Joint Research Centre – Institute for the Protection and Security of the Citizen 
Title: Scientific, Technical and Economic Committee for Mediterranean Fisheries Assessments Part 1 (STECF-15-11). 

Authors: 

STECF members:  
Graham, N., Abella, J. A., Andersen, J., Bailey, N., Bertignac, M., Cardinale, M., Curtis, H., Daskalov, G., Delaney, A., Döring, R., Garcia 
Rodriguez, M., Gascuel, D., Gustavsson, T., Jennings, S., Kenny, A., Kraak, S., Kuikka, S., Malvarosa, L., Martin, P., Murua, H., Nord, J., 
Nowakowski, P., Prellezo, R., Sala, A., Scarcella, G., Somarakis, S., Stransky, C., Theret, F., Ulrich, C., Vanhee, W. & Van Oostenbrugge, H.  

EWG-15-11 members:  
Cardinale M. (Chair), Abella J.A., Damalas, D., Martin P., Scarcella G., Guijarro B., Jadaud, A., Ligas, A., Mannini A., Maynou F., Minto, C., 
Murenu M., Orio A., Pengal P., Pérez Gil J.L., Quetglas A., Ramirez, J., Recasens L., Sbrana M., Scott, F., Spedicato, M.T., Vasilakopoulos, 
P.  

Luxembourg: Publications Office of the European Union  
2015 – 410 pp. – 21 x 29.7 cm 
EUR – Scientific and Technical Research series – ISSN 1831-9424 (online), ISSN 1018-5593 (print)  
ISBN 978-92-79-54141-4 
doi:10.2788/406771 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
STECF 

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

L
B
-
N
A
-
2
7
6
3
8
-
E
N
N

-

JRC Mission 

As the Commission’s  
in-house science service,  
the Joint Research Centre’s  
mission is to provide EU  
policies with independent,  
evidence-based scientific  
and technical support  
throughout the whole  
policy cycle. 

Working in close  
cooperation with policy  
Directorates-General,  
the JRC addresses key  
societal challenges while  
stimulating innovation  
through developing  
new methods, tools  
and standards, and sharing  
its know-how with  
the Member States,  
the scientific community  
and international partners. 

Serving society  
Stimulating innovation  
Supporting legislation 

411 

doi:10.2788/406771 

ISBN 978-92-79-54141-4