Economic effects of the cod recovery plan on the mixed fisheries in the North Sea · 2016. 9....

Economic effects of the cod recovery plan on the mixed fisheries in the North Sea.

(EcoCoRP)

Final Report February 2008 for The European Commission Directorate-General for Fisheries

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Centre for the Economics and Management of Aquatic Resources (CEMARE) University of Portsmouth 1-8 Burnaby Terrace Burnaby Road Portsmouth Hants PO1 3AE UK The report was prepared by: Simon Mardle, CEMARE, John Pinnegar, CEFAS, and Andy Hill, Ventana Systems UK.

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Table of Contents Executive Summary..................................................................................... 1 Introduction .................................................................................................. 2

1.1 The North Sea fisheries ........................................................................ 2 1.1.1 The decline of the North Sea cod stocks ............................................ 2 1.1.2 State of the cod stocks and stock recovery programmes...................... 3 1.1.3 Management response.................................................................... 5 1.1.4 Economic implications .................................................................... 7

1.2 Objectives of this study........................................................................ 8 2 North Sea cod before and after 2003 ........................................................... 8

2.1 Description of cod fishery in the North Sea before and after 2003............... 9 2.1.1 Beam trawl ≥80mm. ...................................................................... 9 2.1.2 Demersal Trawls ≥100mm ............................................................ 10 2.1.3 Demersal Trawls 70-99 mm. ......................................................... 11 2.1.4 Demersal/industrial Trawls 16-31 mm. ........................................... 12 2.1.5 Demersal longline ........................................................................ 13 2.1.6 Static gears ................................................................................ 13 2.1.7 ‘Other’ Gears............................................................................... 14 2.1.8 Conclusions................................................................................. 14

2.2 Landings by Country.......................................................................... 14 2.3 Trends in fleet specific nominal effort by regulated gears and countries..... 15

2.3.1 Conclusions................................................................................. 19 2.4 Changes in revenue: English otter trawl and Danish gillnet fleets ............. 20 2.5 Profitability of the North Sea demersal fleets ......................................... 21

3 Review of previous models of North Sea fisheries ........................................ 21 3.1 Biological models of the fishery incorporating multi-species interactions .... 21 3.2 Previous bioeconomic models of the North Sea ...................................... 22

4 Dynamic bioeconomic model of the North Sea fisheries ................................ 24 4.1 Overview ......................................................................................... 24 4.2 Basic description of the EcoCorp bioeconomic model............................... 25 4.3 Biological component ......................................................................... 26

4.3.1 Assessment process ..................................................................... 26 4.3.2 Prediction process........................................................................ 28

4.4 Economic component ......................................................................... 29 4.4.1 Management controls ................................................................... 31 4.4.2 Cost information.......................................................................... 31

4.5 Vensim EcoCoRP bioeconomic model .................................................... 32 4.5.1 Key features ............................................................................... 33 4.5.1i Transparency ................................................................................ 34 4.5.1ii Flexibility ..................................................................................... 35 4.5.2 Model structure and preliminary outputs ......................................... 36

5 Initial Model Setup.................................................................................. 39 5.1 Biological Inputs ............................................................................... 40

5.1.1 Initial Stock Levels....................................................................... 40 5.1.2 Recruitment ................................................................................ 41 5.1.3 Taking Account of Environmental Variability..................................... 43

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5.1.4 Natural Mortality.......................................................................... 45 5.1.5 Predator Mortality ........................................................................ 45 5.1.6 Aging ......................................................................................... 47 5.1.7 Maturity Proportions..................................................................... 47

5.2 Financial Inputs ................................................................................ 48 5.2.1 Vessels....................................................................................... 48 5.2.2 Revenue..................................................................................... 50 5.2.3 Other Income.............................................................................. 50 5.2.4 Costs ......................................................................................... 51

6 Model Assumptions ................................................................................. 53 7 Baseline Scenario ................................................................................... 54

7.1 Assumptions..................................................................................... 54 7.2 Stock Levels ..................................................................................... 54 7.3 Age Distribution ................................................................................ 55 7.4 SSB................................................................................................. 56 7.5 Recruitment ..................................................................................... 57 7.6 Predation ......................................................................................... 58 7.7 Fleet Profit (M Euro) .......................................................................... 58 7.8 Other Species Stock .......................................................................... 60 7.9 Catch by Species............................................................................... 61

8 Baseline Sensitivity ................................................................................. 62 8.1 Technology Creep.............................................................................. 71 8.2 Validating the Biological Model ............................................................ 72 8.3 Comparison of Baseline Model Sensitivity Results................................... 73 8.4 Age Distribution ................................................................................ 76

9 Scenario 1: Effort Reduction..................................................................... 80 9.1 Assumptions..................................................................................... 80 9.2 Results ............................................................................................ 82

10 Scenario 2: Harvest Control Rules......................................................... 90 10.1 Assumptions .................................................................................. 90 10.2 Results ......................................................................................... 91

11 Scenario 3: Decommissioning .............................................................. 96 11.1 Assumptions .................................................................................. 96 11.2 Results ......................................................................................... 97

12 Scenario 4: No Cod Fishing .................................................................103 12.1 Assumptions .................................................................................103 12.2 Results ........................................................................................103

13 Comparison Across Scenarios..............................................................106 13.1 Cod Stock Level ............................................................................106

14 User Interface...................................................................................111 14.1 Title screen: .................................................................................111 14.2 Background: .................................................................................112 14.3 Model structure:............................................................................113 14.4 Model initialisation:........................................................................114 14.5 Create scenarios: ..........................................................................115 14.6 Simulate: .....................................................................................116 14.7 Outputs: ......................................................................................116 14.8 Sensitivity testing:.........................................................................118

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15 Summary.........................................................................................119 16 References .......................................................................................120 Appendix 1: Review of cod-recovery legislation (2001-2004)................................ 1 1 Cod ‘closure area’ ..................................................................................... 9 2 Days Absent from Port............................................................................. 10 3 Interim Fishing Limitation ........................................................................ 11 4 Derogations from days present within the area ........................................... 12 Appendix 2: Analysis of contributions to changes in the revenue of vessels targeting cod in the North Sea: ..................................................................................... 1 1 English otter trawl and Danish gillnet fleets .................................................. 1

1.1 Methodology....................................................................................... 1 1.2 Data and fleet descriptions ................................................................... 2

1.2.1 English otter trawl ......................................................................... 2 1.2.2 Danish gillnet ................................................................................ 8

1.3 Results ............................................................................................ 12 1.3.1 Changes in capacity, production per unit of capital and price.............. 12 1.3.2 Changes in capacity, production per unit of capital and price including stock abundance..................................................................................... 15 1.3.3 Changes in apparent fishing production per unit of effort ................... 21

1.4 Conclusions ...................................................................................... 22 Appendix 3: Impact of input controls on the profitability of the North Sea demersal fleet: a restricted profit function approach ......................................................... 1

1.1 Methodology....................................................................................... 1 1.2 Data.................................................................................................. 2 1.3 Model estimation and results................................................................. 4 1.4 Technical change............................................................................... 10 1.5 Impact of management changes.......................................................... 12 1.6 Discussion and Conclusions................................................................. 13

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Table of Figures

Figure 1: The North Sea.................................................................................. 2 Figure 2: Cod spawning stock biomass, 1963-2004 ............................................. 3 Figure 3: Maximum days at sea per month by fishing gear for vessels catching cod,

2003....................................................................................................... 4 Figure 4: ICES advice(a) and final TACs for North Sea cod. a) ICES advice for 1991-95

imputed as effort rather than catch limits were proposed. The advice since 2001 has been for zero catch. Source: ICES (2004).............................................. 5

Figure 5: Minimum and target biomass of cod Source: Commission of the European Communities (2003b)................................................................................ 6

Figure 6: Effect of returns to effort on share of effort reduction............................. 8 Figure 7: International beam trawl (≥80mm mesh) landings and discards (t) in the

North Sea and Skagerrak by species, in 2004 ............................................. 10 Figure 8: International demersal trawl (≥100mm mesh) landings and discards (t) in

the North Sea and Skagerrak by species, in 2004........................................ 11 Figure 9: International demersal trawl (70-99mm mesh) landings and discards (t) in

the North Sea and Skagerrak by species, in 2004........................................ 12 Figure 10: International demersal/industrial trawl (16-31mm mesh) landings and

discards (t) in the North Sea and Skagerrak by species, in 2004.................... 12 Figure 11:International demersal longline landings and discards (t) in the North Sea

and Skagerrak by species, in 2004 ............................................................ 13 Figure 12: International static gear (including gill nets, trammel nets and tangle

nets) landings and discards (t) in the North Sea and Skagerrak by species, in 2004..................................................................................................... 13

Figure 13: International ‘other’ gear landings and discards (t) in the North Sea and Skagerrak by species, in 2004 .................................................................. 14

Figure 14: Proportion of cod, Nephrops, sole, haddock, saithe, plaice and whiting landings from the North Sea (ICES area IV) by country in 2003.................... 15

Figure 15: Trends in nominal fishing effort (kw*days) by effort-regulated gear types in the North Sea and Skagerrak, 2000-2004. .............................................. 16

Figure 16: Trend in the nominal effort (KW*days at sea) by country for beam trawl ≤80mm, 2000-2004 (from STECF 2005). ................................................... 16

Figure 17: Trend in the nominal effort (KW*days at sea) by country for demersal trawl ≥100mm, 2000-2004 (from STECF 2005). ......................................... 17

Figure 18: Trend in the nominal effort (KW*days at sea) by country for demersal/industrial trawl 16-31mm, 2000-2004 (from STECF 2005). ............. 17

Figure 19: Trend in the nominal effort (KW*days at sea) by country for demersal trawl 70-99mm, 2000-2004 (from STECF 2005).......................................... 18

Figure 20: Trend in the nominal effort (KW*days at sea) by country for longline gears, 2000-2004 (from STECF 2005). ...................................................... 18

Figure 21: Trend in the nominal effort (KW*days at sea) by country for static gears, 2000-2004 (from STECF 2005). ................................................................ 18

Figure 23: Evolution of landings, gillnet Fleet .................................................. 20 Figure 24: The biological process modelled by 4Mbiological process modelled by 4M.

............................................................................................................ 26 Figure 25: Country and gear economic costs data matrix ................................... 32

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Figure 26: Example Causal Loop Diagram (CLD) ............................................... 33 Figure 27: Causal tree; example tracing model structure ................................... 34 Figure 28: Causal graph; example tracing model behaviour................................ 35 Figure 29: Species Modelled .......................................................................... 36 Figure 30: Species Age Ranges ...................................................................... 36 Figure 31: Estimated Fish Stock ..................................................................... 37 Figure 32: Surviving Fish............................................................................... 38 Figure 33: Costs by Fleet............................................................................... 39 Figure 34: Revenue by Fleet .......................................................................... 39 Figure 35: Initial Stock Levels ........................................................................ 40 Figure 36: Ricker Recruitment Coefficients....................................................... 42 Figure 37: Ricker Recruitment Curve............................................................... 42 Figure 38: Natural Mortality (M1) Coefficients................................................... 45 Figure 39: Non VPA Species Predator Population............................................... 46 Figure 40: Non VPA Species Predator Consumption ........................................... 46 Figure 41: Average VPA Species Weight by Age ................................................ 47 Figure 42: VPA Species Age Range ................................................................. 47 Figure 43: VPA Species Maturity Proportions .................................................... 48 Figure 44: Fleet Proportions in ICES Area IV ................................................... 48 Figure 45: Fleet Sizes Modelled ..................................................................... 49 Figure 46: Average Thousand Days as Sea Per Vessel in 2003 ........................... 49 Figure 47: Price per kg in 2003 ..................................................................... 50 Figure 48: Estimated Other Income 2003 ....................................................... 50 Figure 49: Average Crew Cost 2003............................................................... 51 Figure 50: Average Vessels Costs 2003 .......................................................... 51 Figure 51: Average Fuel Usage...................................................................... 51 Figure 52: Average Running Costs ................................................................. 52 Figure 53: Depreciation & Interest Costs as Proportion of Variable Costs ............. 52

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List of Charts Chart 1: Cod Stock Level............................................................................... 54 Chart 2: Cod Age Distribution ........................................................................ 55 Chart 3: Cod Spawning Stock Biomass ............................................................ 56 Chart 4: Cod SSB by Age Comparison ............................................................. 56 Chart 5: Cod Year 1 Recruitment .................................................................... 57 Chart 6: Ricker Recruitment Fit ...................................................................... 57 Chart 7: Cod Predation Source ....................................................................... 58 Chart 8: Profit by Fleet.................................................................................. 59 Chart 9: VPA Species Stock Levels.................................................................. 60 Chart 10: Catch by Species ........................................................................... 61 Chart 11: Cod Stock Level Sensitivity.............................................................. 62 Chart 12: Code Recruitment Sensitivity ........................................................... 63 Chart 13: UK Fleet Sensitivity ........................................................................ 64 Chart 14: Denmark Fleet Sensitivity................................................................ 65 Chart 15: Holland Fleet Sensitivity.................................................................. 66 Chart 16: Belgium Fleet Sensitivity ................................................................. 67 Chart 17: France Fleet Sensitivity ................................................................... 68 Chart 18: Germany Fleet Sensitivity................................................................ 69 Chart 19: Norway Fleet Sensitivity.................................................................. 70 Chart 20: Effect of Technology Creep on Cod Stock........................................... 71 Chart 21: Total Cod Biomass (Tonnes) ............................................................ 74 Chart 22: Cod Spawning Stock Biomass (Tonnes) ............................................. 74 Chart 23: Cod Recruitment Aged 1 (thousands)................................................ 75 Chart 24: Cod Annual Catch (kg/Year) ............................................................ 75 Chart 25: Cod Age Distribution 2007 (%) ....................................................... 76 Chart 26: Profit (M Euro) – UK BEAM > 24M Fleet ............................................. 78 Chart 27: Profit (M Euro) – UK Demersal Trawl /Seine 12-24M Fleet .................... 78 Chart 28: Profit (M Euro) – Denmark Demersal Trawl <24M Fleet....................... 79 Chart 29: Profit (M Euro) – Denmark Pelagic Fleet ............................................ 79 Chart 30: Scenario 1 - Cod Stocks .................................................................. 82 Chart 31: Scenario 1 - Cod Predation Source.................................................... 83 Chart 32: Age Distribution............................................................................. 83 Chart 33: Scenario 1a - Fleet Profits ............................................................... 84 Chart 34: Scenario 1b - Fleet Profit................................................................. 85 Chart 35: Scenario 1c - Fleet Profit ................................................................. 86 Chart 36: Scenario 1d - Fleet Profit................................................................. 87 Chart 37: Scenario 1e - Fleet Profit................................................................. 88 Chart 38: Scenario 1f - Fleet Profit ................................................................. 89 Chart 39: Scenario 2 - Cod Stocks .................................................................. 91 Chart 40: Aggregated Cod F-Value.................................................................. 92 Chart 41: Days at Sea .................................................................................. 92 Chart 42: Scenario 2 - Cod Predation Source.................................................... 93 Chart 43: Annual Catch 103 ........................................................................... 93 Chart 44: Scenario 2a - Fleet Profit ....................................................................... 94 Chart 45: Scenario 2b - Fleet Profit ....................................................................... 95

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Chart 46: Scenario 3 - Cod Stock Level ........................................................... 97 Chart 47: Applied Decommissioning %............................................................ 98 Chart 48: Scenario 3 - Cod Predation Source.................................................... 99 Chart 49: Scenario 3a - Fleet Profit................................................................100 Chart 50: Scenario 3b - Fleet Profit................................................................101 Chart 51: Scenario 3c - Fleet Profit ......................................................................102 Chart 52: Scenario 4 - Cod Stock Level ..........................................................103 Chart 53: Ricker Recruitment Curve...............................................................104 Chart 54: Cod Stock v SSB...........................................................................104 Chart 55: Scenario 4 - Fleet Profit .................................................................105 Chart 56: Cod Stock Comparison across Scenarios ...........................................106 Chart 57: Cod Stock Comparison...................................................................107 Chart 58: UK Fleet Profit Comparison (2020)...................................................107 Chart 59: UK Bottom Trawlers >24M Revenue.................................................108 Chart 60: Cod Stock – Baseline v Scenario 1c .................................................109 Chart 61: UK Fleet Profit Comparison (2020)...................................................110

Acronyms

4M MULTISPECIES, MULTI-FLEET, MULTI-AREA MODEL AER ANUAL ECONOMIC REPORT

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AFCM ADVISORY COMMITTEE FOR FISHERIES MANAGEMENT BEL BELGIUM CEFAS CENTRE FOR ENVIRONMENT, FISHERIES & AQUACULTURE SCIENCE

CEMARE CENTRE FOR THE ECONOMICS AND MANAGEMENT OF AQUATIC RESOURCES

CPUE CATCH PER UNIT EFFORT DEN DENMARK

ECOCORP ECONOMIC EFFECTS OF THE COD RECOVERY PLAN ON THE MIXED FISHERIES IN THE NORTH SEA

EFIMAS OPERATIONAL EVALUATION TOOLS FOR FISHERIES MANAGEMENT OPTIONS

EIAA ECONOMIC INTERPRETATION OF THE ACFM ADVICE ENG ENGLAND EU EUROPEAN UNION FRA FRANCE FSP FISHERY-SCIENCE PARTNERSHIP GER GERMANY GRT GROSS REGISTERED TONNAGE GT GROSS TONNAGE HCR HARVEST CONTROL RULES ICA INTEGRATED CATCH-AT-AGE ANALYSIS ICES INTERNATIONAL COUNCIL FOR THE EXPPLORATION OF THE SEA KFISH THOUSAND FISH KT KILO TONNES KW KILOWATTS M EURO MILLION EURO MAGP MULTI-ANNUAL GUIDANCE PROGRAMME MAWG MULTI-SPECIES ASSESSMENT WORKING GROUP MSFOR MULTI-SPECIES FORWARD PROJECTION MODE MSVPA MULTI SPECIES VIRTUAL POPULATION ANALYSIS NED NETHERLANDS NOR NORWAY NPV NET PRESENT VALUE RAC REGIONAL ADVISORY COUNCIL SCO SCOTLAND SGECA SUB-GROUP ON ECONOMIC ASSESMENT SGMSNS STUDY GROUP ON MULTISPECIES ASSESSMENTS IN THE NORTH SEA SSB SPAWNING STOCK BIOMASS STECF SCIENTIFIC, TECHNICAL AND ECONOMIC COMMITTEE FOR FISHERIES SXSA EXTENDED SURVIVOR ANALYSIS TAC TOTAL ALLOWABLE CATCH

TECTAC TECHNICAL DEVELOPMENTS AND TACTICAL ADAPTATIONS OF IMPORTANT EU FLEETS.

VPA VIRTUAL POPULATION ANALYSIS XSA SEASONAL EXTENDED SURVIVORS ANALYSIS

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Parameters and Variables

a age class

i species

j country

k gear type

t time (year)

Economic

capj,k average capital value of the vessel

csj,k crew share for the fleet segment

ocpdj,k other variable costs per day

CCOSTj,k,t crew costs

COSTSj,k,t total costs

d depreciation rate

δ discount rate

fcj,k average fixed cost per boat

fpricej fuel price

FCOSTj,k,t fixed costs

KCOSTj,k,t capital costs

NPV net present value of total fishery profits over time

pj,i is the average price of species i in country j

x

PROFj,k,t level of economics profits of boats using gear type k from country j

REVj,k is the total revenue of the fleet segment in time t

vbyj,k the average value of other (bycatch) species landed each day

VCOSTj,k,t variable (trip/day) costs

Technical and biological

α, β Ricker recruitment function parameters

qj,k,I,a catchability coefficient

B Average biomass

Bi,a,t biomass in each age class of the stock in year t

boatsj,k current number of boats in each fleet segment

BOATSj,k,t number of boats operating in time period t

BSCALEt scaling factors that alter the number of days and boats in each fleet segment respectively

CATCHj,k,I,y catch of each species in each year by each fleet segment

Cia catch at age

CONVEFF food conversion efficiency (somatic growth)

DAYSj,k,t total number of days expended by the fleet segment in time period t

ESCALEt scaling factors that alter the number of days and boats in each fleet segment respectively

Fia fishing mortality rate

Fj,k,I,,a,t partial fishing mortality

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FoodAvai available food

fpdj,k fuel-use per day

GCONVEFF food conversion efficiency (spawning products)

k constant expressing the log of the biomass of other food when predation is zero

L constant expressing the amount of change in biomass of other food per unit of consumption

LANDj,k,i,t total landings of species i by each fleet segment

M1 natural mortality

M2 predation mortality rates

MET_A and MET_B are constant to assess intake for unchanged weight (basal metabolism)

MSVPA multispecies virtual population analysis

Niat abundance of species i at age a at time t

Nia(t=1) abundance at end of year

R food intake

SSB spawning stock biomass

sea_daysj,k (unconstrained) days at sea of a boat in each fleet segment

SPAWN factor of body weight lost due to spawning

VPA virtual population analysis

W Weight at age

Z total mortality rate

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Executive Summary The North Sea contains a number of interacting multi-species fisheries of great importance to many countries. This study addresses the requirements of the European Commission for evaluating the 'Economic effects of the cod recovery plan on the mixed fisheries in the North Sea'. The primary aim of the study is to determine the likely economic impacts on the different fishing fleet segments currently operating in the North Sea resulting from the implementation of effort reductions imposed by cod recovery measures. Current effort reduction measures for the recovery of the North Sea cod stock were imposed in 2003. As a result, changes in gear types, targeting behaviour and spatial allocation of effort have been observed. The model developed as part of the project is based on the biological multi-species interactions of the 4M model. The 4M model is based upon the ICES multi-species VPA programme MSVPA, and the corresponding prediction programme MSFOR. The only relationship in the 4M model between fleets and the biological species production is through catch. The bioeconomic model developed for EcoCoRP consists of a biological component and an economic component, linked by effort controls. In accordance with the structure of the model, management controls within the model are identified. The baseline scenario provides the model forecasts of cod stocks if the fleet sizes and effort levels are maintained at those for 2003. It is a purely deterministic simulation run. Stock recruitment is the main driver of stock levels in the model. The model forecasts that the cod stock will start to recover, and by 2020 be back around the level from the early 1993. Results from a number of scenarios are presented. In general, in each scenario one variable changes compared with the baseline. This will facilitate comparisons with the baseline as well as among scenarios. Scenarios include effort reduction, harvest control rules, fleet decommissioning and no cod fishing. Generally speaking, all scenarios imply a larger stock level in 2020 than the baseline. Moreover, in all but a few cases, annual profits rise during the period 2003-2020.

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Introduction This study addresses the requirements of the European Commission for evaluating the 'Economic effects of the cod recovery plan on the mixed fisheries in the North Sea'. The primary aim of the study is to determine the likely economic impacts on the different fishing fleet segments currently operating in the North Sea (ICES Division IV) resulting from the implementation of effort reductions imposed by cod recovery measures. This approach uses a state-of-the-art “dynamic bio-economic model” of the North Sea fisheries, developed in a systems dynamics framework using multispecies and economic modelling.

1.1 The North Sea fisheries The North Sea (ICES Divisions IVa,b and c - Figure 1) contains a number of interacting multi-species fisheries of great importance to many countries. Commercial activity in the region is mostly undertaken by fishers from: UK, Denmark, the Netherlands, France, Germany, Belgium and Norway. Although Norway is not a member of the European Union, it imposes complementary management measures.

Figure 1: The North Sea

Despite the recent declines in stocks of many of the key species, the North Sea is still the major fishing area in European Community waters. In 2005, around half (48 per cent) of the combined total allowable catches of all species in all EU waters were to be taken from the North Sea. In total, 23 species are subject to quota controls in the North Sea, accounting for around 50 per cent of the total value of landings from the area. The remaining non-quota species include inshore crustaceans and finfish, many of which are high value but low quantity species.

1.1.1 The decline of the North Sea cod stocks Fisheries scientists apply several reference points as indicators of the health (or otherwise) of fish stocks. Two key reference points are the spawning stock biomass (SSB) relative to a precautionary target (Bpa) based on the population of mature fish

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required for sound recruitment; and the limit biomass (Blim), the minimum spawning stock biomass below which recruitment will be impaired and the probability of stock collapse is high (Mace and Sissenwine, 1993). The cod SSB in the North Sea has been estimated to have been below Bpa since the early 1980s (Figure 2), despite reductions in total allowable catches and the overall reduction in fleet size and capacity following a number of decommission programmes. Further, the SSB has fallen below Blim since 2000 (ICES, 2004a). Research surveys and the results from models fitted to the commercial catch-at-age data indicate that the SSB is at about 20-25 per cent of the level it was in the 1980s (ICES, 2003a). The fishery is also now thought to be heavily dependent on the annual recruitment to the fishery, with the catch being dominated by small, young fish, with only about 5 per cent of individuals at age 1 surviving to age 5 (ICES, 2003a).

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Figure 2: Cod spawning stock biomass, 1963-2004

Despite the decline in the stock to below the precautionary level since the early 1980s, TACs have generally remained above the levels recommended by ICES (Figure 4). The stock assessment process contains considerable uncertainty, and policy makers have been reluctant to greatly reduce the quotas given this uncertainty due to the more certain financial impact this would have on the fishing industry and regional economies dependent on fishing in the short term.

1.1.2 State of the cod stocks and stock recovery programmes The continuing decline in stocks of cod and other key species in the North Sea have prompted the call for more urgent measures to be applied to the fishery. As

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mentioned, cod stocks have been below the critical Bpa level since the late 1980s

(Figure 2) despite reductions in total allowable catches and the overall reduction in fleet size and capacity following the fourth Multi-Annual Guidance Programme. Survey indices and results from models fitted to the commercial catch at age data indicate that the spawning stock biomass is at about 20-25 per cent of the level it was in the 1980s, with only about 5 percent of individuals at age 1 surviving to age 5 (ICES, 2003a). In response, a multi-faceted stock recovery programme has been proposed that includes a range of management measures. In 2001, a cod closure area was introduced as part of the stock recovery programme (Council Regulation (EC) No 259/2001). The area was closed to any fishing activity during this period, with the exception of purse seining and trawling for sandeels and pelagics. This temporary closed area was designed to cover the main spawning period of cod in the North Sea, and was in force throughout the period 14 February to 30 April 2001. Also, TAC reductions in 2001 and 2002 were aimed at reducing fishing mortality by more than 50 per cent (ICES, 2003a). Fishing effort restrictions were also implemented from 1 February 2003 for vessels of overall length greater than or equal to 10m (Figure 3). This restricted the number of days per month different types of vessels (i.e., using different gear types) could employ in different parts of ICES areas IV and IIIa (Council Regulation (EC) No 671/2003, amending Council Regulation (EC) 2341/2002). Gear type Maximum days at sea per

month Demersal trawl, seines or similar towed gear of mesh size

• ≥ 100mm 10 • ≥ 70mm and < 100mm 22 • ≥ 16mm and < 31mm 20

Beam trawls of mesh size ≥ 80mm 14 Static demersal nets 14 Demersal long line 17

Figure 3: Maximum days at sea per month by fishing gear for vessels catching cod, 2003

In December 2003, the Agriculture and Fisheries Council agreed a longer-term cod recovery plan, founded on Annex V of Council Regulation (EC) No. 2287/2003. The management plan focuses on cod stocks in the Kattegat; North Sea, Skagerrak and eastern Channel; West of Scotland; and the Irish Sea (Council Regulation (EC) No. 423/2004). The plan specifies target minimum biomass levels of mature cod in each area. In order to achieve these targets, both upper (based on expected fishing mortality) and lower limits on TACs are imposed. Further, restrictions on the degree to which TACs can change from one year to the next are also prescribed. Effort limits are also continued to complement the reduced TACs. While the biological impact of these changes have been considered (ICES, 2003b), the economic impacts of these measures have not yet been fully assessed. This study addresses some of these issues.

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1.1.3 Management response The existence of excessive fishing capacity in the industry as a whole has been widely recognised, and measures were implemented in an attempt to redress this problem. A series of decommissioning programmes under the multi-annual guidance programme (MAGP) have been implemented since the early 1980s in an attempt to reduce the fleet harvesting capacity to that which is comparable with the reproductive potential of the stock. While these programmes have achieved their objectives in terms of reduction in physical capacity (measured in terms of engine power and gross tonnage), excess capacity still remains a problem in the North Sea (Tingley and Pascoe, 2003). Additional measures were introduced in a bid to prevent the cod stock from further deteriorating. A review of these measures is presented in Appendix 1.

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(kt)

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TAC

Figure 4: ICES advice(a) and final TACs for North Sea cod. a) ICES advice for 1991-95 imputed as effort rather than catch limits were proposed. The advice since 2001 has been for zero catch. Source: ICES (2004)

In December 2003, the Agriculture and Fisheries Council agreed a longer-term cod recovery plan, founded on Annex V of Council Regulation (EC) No. 2287/2003. The stock recovery plan specifies the criteria for setting both TACs and the number of days that can be fished. The general principle of the recovery plans is to set TACs and complementary effort levels that will result in a 30 percent annual increase in the stock. Increases in TACs between years are restricted to be no more than 15 percent, even if greater increases may be compatible with the stock increase objective. Similarly, decreases in TACs are limited to a maximum of 15 percent from year to year. The plan specifies target levels of biomass as well as minimum biomass levels of mature cod in each area (Figure 5). For the North Sea cod, these have been set at

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150 kt and 70 kt respectively, representing the Bpa and Blim levels noted earlier. Non-zero TACs can only be set if the stock is above the minimum level. If the stock exceeds the target level for two consecutive years then the above restrictions on TAC setting and fishing mortality rates are removed. Stock Minimum biomass

of mature fish (kt) Target biomass of mature fish (kt)

Kattegat 6.4 10.5 North Sea, Skagerrak and Eastern Channel

70.0 150.0

West of Scotland 14.0 22.0 Irish Sea 6.0 10.0

Figure 5: Minimum and target biomass of cod Source: Commission of the European Communities (2003b)

Effort restrictions are also imposed at an aggregate level to complement the TACs with the aim of reducing the potential for over quota catch. Controls are placed on the total allowable kilowatt-days on each Member State in line with their share of the TAC as well as on individual vessels. The reduction in total kilowatt-days is determined relative to the fishing mortality in a given three-year reference period (Fref) and the target fishing mortality (Ftac). The total reduction in kilowatt-days for each stock is given by (Commission of the European Communities, 2003)

∑

−=

msmsrefmsrefref

reftac

DPFFF

K)/(

1)(

,2

,

where K is the total reduction* in kilowatt-days fished, Dref,ms is the average kilowatt-days fished by each Member State (i.e., the subscript ms) over the reference period, and Pref,ms is the average share of total catch of each Member State over the reference period. The reduction required by each Member State is based on the share of total catch over the reference period and the total reduction in kilowatt-days. The resultant maximum possible kilowatt-days of each Member States (Dmax,ms) is given by

)0(,,max, <+= KwhereKPDD msrefmsrefms At the individual vessel level, the days at sea limits introduced in 2003 were also continued. The number of days that a vessel can operate each month in the North Sea is limited depending on gear type used. Vessels can only operate for a maximum of 11 months. Baseline limits were established for all fleets in the North Sea that effectively halved the number of days that trawlers could operate each month. Member States that decommissioned some of the fleet could re-allocate the associated days to the vessels remaining in the fishery. For example, for the UK fleet, additional decommissioning programmes introduced in 2003 enabled these baselines to be increased by 5 days for the demersal trawlers and 2 days for beam

* As Ftac < Fref, K < 0.

7

trawlers (Fisheries Departments of the UK, 2004), effectively increasing the number of days these vessels could fish each month by 50 per cent and 15 per cent respectively. In the longer term cod recovery programme, the days-at-sea are transferable. That is, a vessel may transfer some or all of its days to another vessel in a given month, such that the total number of days fished is not exceeded. Transfers between different sized vessels are controlled by ensuring that the total kilowatt-days fished are not exceeded. For example, a vessel with a 100kW engine would need to transfer 10 days to a vessel with a 1000kW engine in order to allow it to fish for one extra day (Fisheries Departments of the UK, 2004).

1.1.4 Economic implications A detailed economic assessment of the cod recovery plan has yet to be undertaken (other than in this project). A simple analysis of the impact of the reduction in quotas and days fished on fleet profitability suggested that most UK whitefish trawlers would experience losses on average equivalent to between 10 and 21 per cent of earnings (SGECA, 2004). The total reduction in kilowatt-days (K) and the relative reduction in kilowatt-days experienced by each Member State are affected by the returns to effort (the relationship between the increase in kilowatt-days and output). If constant returns are present, such that the effect of a kilowatt-day is the same irrespective of the total level, then each Member State receives a proportional decrease in allowable kilowatt-days. If increasing returns exist, such that output increases by more than the level of fishing effort, then the total number of kilowatt-days to be reduced (K) is less than the case of constant returns, and the Member States employing the most effort are subject to a greater than proportional decrease in kilowatt-days. Conversely, if decreasing returns exist, such that output increases by less than the level of fishing effort, then K is again reduced, but the Member States employing the least effort are subject to the greatest proportional reduction. This can be illustrated using a simple hypothetical example (Figure 6) assuming five fleets employing differing levels of effort, and assuming that Ftac is 20 percent lower than Fref. With constant returns, the catch share is directly proportional to the effort share, and the percentage decrease in effort required (as defined by the EU methodology) is the same for all Member States. The total reduction in kilowatt-days (K) in this example is 100 kilowatt-days, and this is shared equally among the fleets (i.e., 20 percent each). With both decreasing and increasing returns, K= 93 kilowatt days. However, with decreasing returns, the fleets employing the lower levels of effort experience the greatest proportional decrease. The converse is the case with increasing returns, with the boats employing the most effort experiencing the greater than proportional decrease.

8

Member State

Kilowatt-Days

Catch Share % Decrease

Constant Decreasing Increasing Constant Decreasing Increasing

1 50 0.1 0.15 0.05 -20% -28% -9% 2 100 0.2 0.20 0.20 -20% -19% -19% 3 200 0.4 0.30 0.50 -20% -14% -23% 4 100 0.2 0.20 0.20 -20% -19% -19% 5 50 0.1 0.15 0.05 -20% -28% -9%

Figure 6: Effect of returns to effort on share of effort reduction

1.2 Objectives of this study The objectives of the final report are to report on the key components of the study:

• assessment of the short term impact of the introduction of the effort controls in 2003 (a before and after analysis);

• development of a dynamic bioeconomic model of the fisheries including multi-species interactions;

• development of a range of simulations of alternative scenarios (agreed with the Commission), taking into account the uncertainty in the system by undertaking Monte Carlo simulations; and

• development of a user-friendly interactive interface to enable increased stakeholder interaction with the model.

2 North Sea cod before and after 2003 Current effort reduction measures for the recovery of the North Sea cod stock were imposed in 2003. As a result, changes in gear types, targeting behaviour and spatial allocation of effort have been observed. Therefore, it is of value to consider the state of the cod fishery before and after 1 January 2003. The first stage of the before/after analysis is largely descriptive. In addition to the description of changes in the fishery, a preliminary economic assessment of the impact of the management change has been undertaken. In order to achieve this, changes in economic performance have been disaggregated into changes due to economic conditions (e.g. input and output prices), changes due to stock availability and changes due to fishing effort (i.e., the result of the regulation). Two multivariate statistics approaches have been used to evaluate these issues. Firstly, an index number approach has been implemented to decompose revenue changes into the component factors (i.e., prices, stock abundance and effort levels). Secondly, the impact of the management change directly on the profitability of the vessels has been implemented. In this case, a profit function is estimated from annual economic information, taking into account the effects of changes in input and output prices on profits, the level of stocks of the main species as well as technological progress. Further, the impacts of management change are estimated though the use of dummy variables.

9

2.1 Description of cod fishery in the North Sea before and after 2003 In June 2005 a special sub-group of STECF was convened in order to revue and evaluate the nominal success of the 2003 cod recovery measures. One important activity carried out by this group was to collate and assemble data from member states concerning landings and discards by fleet segments regulated under the cod-recovery measures (STECF 2005). Six fleets are regulated under the various cod recovery measures outlined in Annex 1: (1) demersal trawls 70-99mm mesh, (2) demersal trawls ≥100mm mesh, (3) demersal/industrial trawls 16-31mm mesh, (4) beam trawls ≥80mm mesh (5) demersal longline gears, and (6) static gears – including gill nets, trammel nets and tangle nets. STECF did not consider pelagic trawls since these are not covered by the cod-recovery legislation. Member states provided data to STECF from observer and discard monitoring programmes. Sampling of catches at sea is expensive and difficult, consequently coverage tends to be somewhat limited and estimates of discards are subject to high uncertainty. Where the coverage was considered adequate to estimate the overall catch composition of specific fleets these were reported by STECF in June 2005 (and are reiterated here). Despite the low sampling levels, estimated compositions of the gears including discards appear fairly consistent over the years 2003 and 2004. These estimates indicate that a substantial proportion of the catch of some species, e.g. plaice, haddock and whiting, is discarded.

2.1.1 Beam trawl ≥80mm. This fleet segment mainly targets flatfish (sole and plaice), but is also known to catch cod, whiting and dab. The fleet operates over known nursery grounds for cod, whiting, plaice, and sole in the southern North Sea, and this results in problematically high by-catch and discard rates of non-target species (Figure 7). Since 1989, the fleet has been somewhat regulated through the so called North Sea ‘plaice box’, which is accessible only to small beamers ≤221 Kw engine power. Large by-catches of undersized plaice are caught in the 80 mm beam-trawl fisheries, and the effort deployed is substantially higher than that needed to take the highest sustainable yield of plaice. According to the available sampling data, the catch of this category is mainly composed of plaice, whiting, sole and cod (Figure 7). Discard rates in weight are highest for whiting (96%) but also significant for cod (47%) and plaice (51%). The discard rates for plaice are of the same order as the annual TAC of about 50,000 tonnes, and these mainly comprise fish at age 3 and younger (~90% in numbers are discarded). Discards of sole are estimated in the order of 10% of the catch weight and are mainly fish at ages 2 and 3.

10

Species Landings Discards Catch Discard Rate COD 3754 3309 7063 0.47 HAD 502 14 516 0.03 NEP 44 44 PLE 46118 47393 93512 0.51 POK 40 0 40 0 SOL 16881 2524 19405 0.13 WHG 1190 32780 33970 0.96

Figure 7: International beam trawl (≥80mm mesh) landings and discards (t) in the North Sea and Skagerrak by species, in 2004

2.1.2 Demersal Trawls ≥100mm This fleet segment covers a wide range of fisheries targeting roundfish and flatfish. It is within this segment that we find vessels that have the highest catch of cod. The other demersal stocks exploited by this fleet segment are all, with the exception of saithe and haddock, fully utilised or over fished. Derogations based on track records are effective for vessels with less than 5% each of cod, sole and plaice in their landings in 2002. This derogation seems in practice only to affect vessels having targeted saithe. This derogation adopted in December 2004, giving more days to vessels fishing with mesh sizes above 120mm, has most likely not had a positive effect on the cod stock. Depending on the various fishing strategies, the catch composition is found to be more diverse than the beam ≥80mm and is mainly composed of roundfish species haddock, saithe, cod and whiting. Plaice, whiting and Nephrops constitute minor components of the catch. Discard rates by weight are highest for whiting (44%), haddock (23%) and plaice (40%). Cod (14%) and saithe (10%) discard rates are relatively low, but indicate total annual discards of around 2000 and 8000 t respectively (Figure 8).

11

Species Landings Discards Catch Discard Rate COD 1264 2024 114289 0.14 HAD 44243 13380 57623 0.23 NEP 1772 1772 PLE 9963 6534 16497 0.4 POK 84931 8227 93158 0.09 SOL 193 15 207 0.07 WHG 4967 3979 8947 0.44

Figure 8: International demersal trawl (≥100mm mesh) landings and discards (t) in the North Sea and Skagerrak by species, in 2004

2.1.3 Demersal Trawls 70-99 mm. The main target species for this fleet segment is Nephrops. The Nephrops fishery can operate with only 30% Nephrops on board, up to 20% cod, and the remaining catch made up of whiting, anglerfish, sole etc. As such it is effectively a mixed Nephrops/fish fishery, though individual fishing operations can target particular species quite effectively. The Nephrops trawl has to be equipped with certain escapement devices. In the North Sea operators are obliged to use nets equipped with an 80mm square-mesh panel. In addition to the Nephrops vessels the fleet segment also includes vessels fitted with a mesh size of 80mm or more, fish for plaice and or roundfish like cod, haddock, whiting and red mullet in the southern part of the North Sea, often using multi-net rigs or seines. Saithe is a minor bycatch (being found mainly in the North); the target species must account for at least 70% of the landings. The 20% cod limit also applies to these vessels and unofficial information indicates substantial landings in excess of those officially reported in recent years. The national sampling programmes reveal that these small meshed trawl fisheries have the most diverse catch composition, with almost equal shares of Nephrops, haddock, whiting and plaice (Figure 9). Substantial discard rates by weight are indicated for whiting (70%), plaice (56%), haddock (40%) and cod (34%). It should be noted that Nephrops discards have not been reported to the database. The great majority of fish discarded of all species are juveniles. Numbers of discarded cod at ages 1 and 2 by the 70-99mm gear category are lower than for the demersal trawl ≥100 mm, but of the same order of magnitude.

12

Species Landings Discards Catch Discard Rate COD 3408 1721 5129 0.34 HAD 5163 3423 8586 0.4 NEP 23765 23765 PLE 6296 8153 14449 0.56 POK 3154 763 3917 0.19 SOL 139 139 278 0.5 WHG 3607 8468 12075 0.7

Figure 9: International demersal trawl (70-99mm mesh) landings and discards (t) in the North Sea and Skagerrak by species, in 2004

2.1.4 Demersal/industrial Trawls 16-31 mm. In 2004, Denmark deployed 90% of the international effort for this gear category. The catch composition is dominated by Norway-pout. The target species of the 16-31mm fleet segment are Norway-pout, blue-whiting and sprat, while sandeel fisheries often use a mesh <16mm with catch retained on board of not more than 10% other species. The Norway-pout fishery was closed during the whole of year 2005. The sandeel fishery was closed in July 2005. As the great majority of the catch is reduced to meal and oil, discarding is not an issue for these fleets, however, the information on catch composition of this gear category are sparse. It is thought that exploitation using small mesh nets results in a substantial by-catch of young whitefish. Currently, industrial fishing for Norway Pout is not allowed in an area known as the ‘Norway-pout box’ defined in EC Regulation No 3094/86, 850/98. A CEFAS fishery-science partnership (FSP) project in December 2003, which fished with a small-meshed net on Norway pout grounds, found that haddock was the most prevalent by-catch species (38% by weight on average), the majority of which was above the legal minimum landing size. Whiting and herring were also caught in relatively large numbers. The combined by-catch level of haddock, whiting, herring and other minor species was estimated to be on average 75% by weight (43% by number) of the total catch, although there was large between-haul variation. A ‘sand eel box’ was introduced in 2000 restricting fishing operations within an area of 18,000 km2 in the Firth of Forth. Species Landings Discards Catch Discard Rate COD 2 0 2 0 HAD 6 1 7 0.13 NEP PLE 1 0 1 0 POK 28 0 28 0 SOL WHG 2 2 4 044

Figure 10: International demersal/industrial trawl (16-31mm mesh) landings and discards (t) in the North Sea and Skagerrak by species, in 2004

13

2.1.5 Demersal longline This gear could target almost any species in a highly selective pattern, but is used mainly to catch roundfish (cod, haddock, saithe etc.). Professional fishermen deploy this gear with very low effort, but in local recreational fisheries the catch could raise to significant levels. Landings are generally low compared to other gear categories and no discard information was available to STECF in 2005. Species Landings Discards Catch Discard Rate COD 740 0 740 0 HAD 422 422 NEP 1 1 PLE 4 4 POK 430 0 430 0 SOL 0 0 WHG 4 0 4 0

Figure 11:International demersal longline landings and discards (t) in the North Sea and Skagerrak by species, in 2004

2.1.6 Static gears This group covers a wide diversity of fisheries, including cod-directed gill net fisheries, large-mesh static nets directed at turbot or anglerfish, and smaller-meshed trammel nets directed at sole. A derogation is available permitting vessels in the eastern channel to fish with trammel nets of mesh size equal to or less than 110mm and absent from port for no more than 24h per trip, to be absent from port for 19 days per month. In the North Sea, gear of this type is used by Denmark to target sole, by Denmark and the UK to catch both sole and cod, and also by France to target cod specifically. The compilation of national landings and discard data reveals that static gears catch cod, saithe, sole, plaice and monkfish with very low discard rates (Figure 12). Species Landings Discards Catch Discard Rate COD 5862 0 5862 0 HAD 437 437 NEP 0 0 PLE 3671 9 3680 0 POK 4522 0 4522 0 SOL 1167 5 1172 0 WHG 40 0 40 0

Figure 12: International static gear (including gill nets, trammel nets and tangle nets) landings and discards (t) in the North Sea and Skagerrak by species, in 2004.

14

2.1.7 ‘Other’ Gears This gear category of ‘other’ represents gears which are not effort regulated and landings for which gear types have been sufficiently defined, including small-meshed beam trawls. Pelagic trawls are not included and the main demersal target species cod, haddock, whiting, saithe, plaice, sole and Nephrops constitute significant portions in the landings and discards. However, overall the landings and discards appear to be relatively low (Figure 13). Species Landings Discards Catch Discard Rate COD 753 4 757 0 HAD 256 27 283 0.09 NEP 332 332 PLE 327 0 327 0 POK 972 11 984 0.01 SOL 108 0 108 0 WHG 63 10 73 0.13

Figure 13: International ‘other’ gear landings and discards (t) in the North Sea and Skagerrak by species, in 2004

2.1.8 Conclusions In the North Sea and Skagerrak cod are mainly caught by demersal trawls ≥100mm (42%). Cod catches of beam ≥80mm, small-mesh demersal trawls (70-99mm) and static gears are lower (15-20%). In the North Sea and Skagerrak, discard rates in relation to estimated total catch of cod was 10% for beam trawls ≥80mm, 6% for large-mesh demersal trawls (≥100mm) and 5% for small-mesh demersal trawls (70-99mm). Estimated discard rates were highest for whiting and plaice. Beam trawls (≥80mm) contributed most (64%) to total discards of cod, haddock, whiting, saithe, place and sole in 2004 (>150,000t), while demersal trawls ≥100mm and demersal trawls 70-99mm contributed 22 and 15% respectively.

2.2 Landings by Country Unfortunately it is extremely difficult gain access to commercial catch data by fishing fleet from each member state, however ICES collate aggregate data on total landings by each nation in the North Sea Different nations clearly target different species (figure 14). with the UK (Scotland and England) taking the largest share of cod, Nephrops, haddock and whiting in 2003, Norway and France taking the largest share of saithe and Netherlands and Belgium taking the largest share of sole and plaice.

15

Figure 14: Proportion of cod, Nephrops, sole, haddock, saithe, plaice and whiting landings from the North Sea (ICES area IV) by country in 2003.

2.3 Trends in fleet specific nominal effort by regulated gears and countries Overall, the total nominal effort (in KW*days at sea) employed by all demersal fishing gear types in the North Sea has decreased steadily since 2000 (STECF 2005). Between 2000 and 2004 effort decreased by 21%; 15% between 2002 and 2004 (Figure 15). The roundfish trawl fleet ≥100mm has shown the steepest decline of 43% since 2000 and 35% since 2002, while demersal trawls 70-99mm show significant increases of 54% and 12% respectively. During the periods 2000 to 2004 and 2002 to 2004, beam trawls ≤80mm exhibited modest declines of 25% and 14%. Such continuous trends in nominal effort appear only partly connected with days-at-sea regulations since 2003 (see conclusions).

Norway lobsterAtlantic Cod

BelgiumDenmarkFranceGermanyGreenlandNetherlandsNorwaySwedenUK - Eng+Wales+N.Irl.UK – Scotland

Common sole Haddock

Saithe Plaice Whiting

Norway lobsterAtlantic Cod



Common sole Haddock

Saithe Plaice Whiting

16

Gear 2000 2001 2002 2003 2004 Beam ≤80mm

71613627 68955178 62810456 55318313 54001358

Demersal Trawl ≥100mm

57646663 53227168 50440783 38105983 32773830

Demersal/Industrial Trawl 16-31mm

245638 269251 146763 169729 100509

Demersal Trawl 70-99mm

16268495 17359576 22249439 25618005 25014902

Longline 203275 146005 173568 137190 62635 ‘Other’ 27023031 27951600 25471036 26131462 24729156 Static Gears 5124366 4748754 4327918 3267396 3377810 TOTAL 178125095 172657532 165619963 148748078 10060200 Change (relative to 2000) -0.03 -0.07 -016 -0.21 Change (relative to 2002) -0.1 -0.15

Figure 15: Trends in nominal fishing effort (kw*days) by effort-regulated gear types in the North Sea and Skagerrak, 2000-2004.

For beam trawl ≤80mm a decrease in the nominal effort by 25% when compared to 2000 and 14% compared to 2002 was observed (Figure 16). Since 2000 the greatest decrease in absolute terms was met by the Dutch fleets, while French and UK-England fleets show the highest relative decline since 2000 of around 50%. Country 2000 2001 2002 2003 2004 BEL 5381338 4947690 4508762 3779583 3578123 DEN 1074157 1230444 1393934 1349965 1290806 ENG 8316622 8283711 6072252 4809522 4723454 FRA 111520 75680 112752 57246 54338 GER 2850547 2357885 2155098 1891552 2377306 NED 4789070 45040459 41491705 37816572 35220714 NOR 1251569 1126981 1600285 997447 1421391 SCO 5338804 5892328 5475668 4616426 5335226 Total 71613627 68955178 62810456 55318313 54001358 Change (relative to 2000) -0.04 -0.12 -0.23 -0.25 Change (relative to 2002) -0.12 -0.14

Figure 16: Trend in the nominal effort (KW*days at sea) by country for beam trawl ≤80mm, 2000-2004 (from STECF 2005).

17

During the period 2000-2004 the nominal effort of demersal trawls ≥100mm, the gear type distinguished by the highest cod catches, decreased strongly by 43% (Figure 17). Scotland contributed most to this decrease in absolute terms and almost all other countries reported significant declines in this sector. Only the main holders of the saithe quota (Norway, France and Germany) increased their relatively low effort due to derogation for directed fishing. Country 2000 2001 2002 2003 2004 DEN 9402721 10069985 9971411 7094101 6111104 ENG 4424917 4204662 3163570 1958711 1295282 FRA 1967547 1855316 2610036 2548157 2782250 GER 2472476 2082833 2833663 2763762 3478294 NED 1449902 966601 1048343 468983 408732 NOR 2554237 2733642 5429344 6313703 5043732 SCO 34885864 30847606 24870201 16786930 13559088 SWE 488999 466523 514215 171636 95348 Total 57646663 53227168 50440783 38105983 32773830 Change (relative to 2000) -0.08 -0.13 -0.34 -0.43 Change (relative to 2002) -0.24 -0.35

Figure 17: Trend in the nominal effort (KW*days at sea) by country for demersal trawl ≥100mm, 2000-2004 (from STECF 2005).

The target species of the gear group of demersal/industrial trawl 16-31mm are Norway-pout, blue-whiting, sprat and sandeel. Danish and Swedish effort decreased significantly in 2002 leading to an overall reduction by about 60% since 2000 (Figure 17). Country 2000 2001 2002 2003 2004 DEN 223951 225245 133891 145366 84048 ENG 4486 231 2189 4369 FRA 6804 3240 6156 365 GER 1967 4940 570 1088 NED 1372 4248 5283 10439 3544 SCO 4470 10647 4853 SWE 7058 27108 632 3330 Total 245638 269251 146763 169729 100509 Change (relative to 2000) +0.1 -0.4 -0.31 -0.59 Change (relative to 2002) 0.16 -0.32

Figure 18: Trend in the nominal effort (KW*days at sea) by country for demersal/industrial trawl 16-31mm, 2000-2004 (from STECF 2005).

The nominal effort of the various fleets aggregated under the category of demersal trawl 70-99mm increased by 54% from 2000 until 2004. While all countries increased their efforts (Figure 18) in this fleet sector significantly, the main contributors were Scotland and Denmark in absolute terms.

18

Country 2000 2001 2002 2003 2004 DEN 4714929 4290768 5822251 7016629 7386546 ENG 1148516 1179118 981560 2030896 1832405 FRA 989056 1849271 1473167 1207857 1512267 GER 280033 292311 323114 969416 829333 NED 412305 574437 779432 1411082 1064050 SCO 6232892 6718610 10399358 10864642 10437309 SWE 2490764 2455061 2470557 2117483 1952992 Total 16268495 17359576 22249439 25618005 25014902 Change (relative to 2000) +0.07 +0.37 +0.57 +0.54 Change (relative to 2002) +0.15 +0.12

Figure 19: Trend in the nominal effort (KW*days at sea) by country for demersal trawl 70-99mm, 2000-2004 (from STECF 2005)

Scotland was the only country that reported (to STECF) significant nominal effort using longlines. During 2002 to 2004, the effort decreased strongly by 64% and by 69% since 2000 (Figure 20). Country 2000 2001 2002 2003 2004 FRA 2080 327 NED 964 2399 356 SCO 203275 146014 170524 134791 61952 Total 203275 146014 173568 137190 62635 Change (relative to 2000) -0.28 -0.15 -0.33 -0.69 Change (relative to 2002) -0.21 -0.64

Figure 20: Trend in the nominal effort (KW*days at sea) by country for longline gears, 2000-2004 (from STECF 2005).

Static gear fisheries are dominated by Danish vessels which exhibited an overall decline of 22% between 2002 and 2004. Scotland, the Netherlands and Sweden are distinguished by strong increases of their low efforts since 2000 (Figure 21). Country 2000 2001 2002 2003 2004 DEN 3456283 3368330 2943832 2065065 2171621ENG 472282 465253 267184 176270 216268 FRA 645511 553006 666174 447862 459167 GER 341031 117740 164420 184958 116779 NED 60170 84163 96687 95024 113532 SCO 70429 77229 95591 195184 171723 SWE 78660 83033 94030 103033 128720 Total 5124366 4748754 4327918 3267396 3377810Change (relative to 2000) -0.07 -0.16 -0.36 -0.34 Change (relative to 2002) -0.25 -0.22

Figure 21: Trend in the nominal effort (KW*days at sea) by country for static gears, 2000-2004 (from STECF 2005).

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2.3.1 Conclusions Trends in nominal effort of the different gear categories appear to be only partly connected to the introduction of days-at-sea regulations in 2003. The increase in effort by vessels using demersal trawls with small mesh size 70-99mm and the simultaneous decrease in the demersal trawl ≥100mm have resulted in an apparent overall reduction of the mesh size used in demersal fisheries. STECF (2005) notes that the achieved reductions in fishing effort do coincide with a reduction in estimated fishing mortality, but that the reduction in fishing mortality is insufficient to be considered consistent with the cod recovery plan. With the introduction of the days-at-sea scheme, vessel operators were further encouraged to reduce mesh-size and shift to other fisheries in order to benefit from more days at sea. While mesh-size changes and effort limitations may have had some beneficial effect on cod stocks, the impact on haddock and whiting may be undesirable.

Concerning static gears, STECF stated that it is unlikely that any stock benefits could be achieved by manipulation of days-at-sea allocations, since this may cause increased ‘soak-time’ and increased number of gears deployed, in turn resulting in increased discarding due to quality reduction in the catch. Derogates for large-mesh (≥100mm) demersal trawlers based on track records were introduced (EC671-2003) for vessels with less than 5% each of cod, sole and plaice in their landings in 2002. This derogation seems in practice only to affect vessels having targeted saithe (in the northern North Sea). The derogation adopted in December 2004, giving more days to vessels fishing with mesh sizes above 120mm, has most likely not had a significant positive effect on the cod stock (STECF 2005).

020000000400000006000000080000000

100000000120000000140000000160000000180000000200000000

2000 2001 2002 2003 2004

Year

KW*d

ays

at s

ea

Static Gears‘Other’LonglineTrawl 70-99mmIndustrial Trawl 16-31mmTrawl >100mmBeam <80mm

Figure 22: trends in nominal effort of the different gear categories

20

2.4 Changes in revenue: English otter trawl and Danish gillnet fleets† The analysis uses data on the prices and/or quantities of inputs and outputs to identify the contribution of the growth rates of input prices and quantities on the growth of the total production of a selected fishing fleet (Herrick and Squires, 1989; Squires, 1992 and 1994). The English otter trawl and Danish gillnet fleets are two of the most important with respect to North Sea cod catches. In both fleets, cod has historically formed a significant proportion of their catch. For the otter trawl fleet this reached its highest in 1998 at 54% of the total revenues achieved, and for the gillnet fleet this reached its peak in 1999 at 62% of the total fleet revenues. By 2003, revenues from cod for each of these two fleets had reduced to 20% and 26% respectively. As such, they were selected as one of several species, for analysis of factors affecting changing revenues for these fleets. Species selected for inclusion in the analysis were anglerfish, cod, haddock, lemon sole, nephrops, plaice, saithe, sole, whiting and ‘other’. Trends of both fleets appear similar. Price indices indicate that prices have increased through the period studied and were at their highest levels in 2003. This was particularly the case for cod in both countries. For both fleets plaice and sole became proportionally more important to the revenues of the fleets over the period. Even though, similar to cod, stocks of plaice and sole declined during the period. In the otter trawl case, more importance was placed on haddock and nephrops catches in recent years. In the case of nephrops, catches for this fleet were at proportionally similar levels to those seen in the early 1990s (Figure 23). It is also a pertinent fact that revenues from catches of ‘other’ species were higher in 2003 than in other years studied. This indicates a growing importance of less traditionally targeted species to vessel revenues.

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Figure 23: Evolution of landings, gillnet Fleet

The full results of the index analyses and details on the methodology applied is presented in Appendix 2.

† This methodology was developed under the EC 5th Framework Project Q5RS-2002-01291 “Technological developments and tactical adaptations of important EU fleets (TECTAC)”, to which this section relates closely.

21

2.5 Profitability of the North Sea demersal fleets A restricted profit function was estimated including the key fleet segments that catch cod either as a targeted fishery or incidentally in the North Sea. The function was used to estimate the impact of area closures and effort controls imposed on the fleets in 2001 and 2003 respectively on their level of profitability. The results suggest that, the effort control measures implemented in 2003 had a significant negative impact on profitability, as might be expected. The full results of the profitability analysis and details on the methodology applied are presented in Appendix 3.

3 Review of previous models of North Sea fisheries

3.1 Biological models of the fishery incorporating multi-species interactions

The idea of incorporating a model of predation mortality into single species fish stock assessment models was initially put forward in two independent papers presented at the statutory meeting of ICES in 1979 (Helgason and Gislason, 1979; Pope, 1979). The presentations generated enough interest for ICES to convene an Ad Hoc Working Group on Multi-species Assessment Model Testing (ICES, 1980). The ICES Ad Hoc Working Group on Multi-species Assessment met in 1984 to perform the first quarterly North Sea MSVPA‡ (ICES, 1984). The ICES Ad Hoc Working Group on Multi-species Assessment used numerous meetings to refine the model, test a predictive version, the MSFOR model, and add additional food composition data. Over the period from 1984 to 1997, the ICES Multi-species Assessment Working Group (MAWG) performed sensitivity analyses of MSVPA and MSFOR. The major conclusion of the work was that natural mortality was much larger for the younger ages of species exploited for human consumption than previously assumed. It was found that the long-term predictions of the MSFOR model differed significantly from single species predictions. The MAWG was mostly concerned with biological interactions. A new version of the MSVPA/MSFOR programmes – the 4M model (Multi-species, Multi-fleet, Multi-area Model package) (Vinther et al., 2002) – was subsequently developed that also allowed the evaluation of the impact of technical interactions. Apart from including technical interactions, the 4M model had much better features for data handling than the old MSVPA/MSFOR programs. It was possible to tune the terminal fishing mortalities to survey CPUE (catch-per-unit-effort) and effort time series, as used by the single species working groups applying the tuning packages XSA, SXSA and ICA. Furthermore, the model provided possibilities for studying the effect of area closures. In 1997, the ICES Multi-species Assessment Working Group (MAWG) was disbanded, but reconvened in 2002 as the ICES Study Group on Multi-species Assessments in the North Sea (SGMSNS). In 2003, SGMSNS (co-chaired by Dr John K. Pinnegar of ‡ A comprehensive account of the mathematical aspects of MSVPA and the underlying assumptions of the model can be found in Magnusson (1995).

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CEFAS and Dr Morten Vinther of RIVO) were tasked with evaluating the single-species recovery plan for North Sea cod by taking into account biological interactions. The 4M package (Vinther et al. 2002) was used to run MSVPA and MSFOR at this study group. The 4M single- or multi-species key-run was used as the starting point for all simulations. Status quo F or Fpa were used for year 2002 and the Commission’s HCR (harvest control rules) were applied from 2003 onwards. This was at least one year too early, as the EC regulation had not yet become official, but the results from the multi-species and single species scenarios were made for comparison and not to predict the actual year for cod recovery. In total, 17 different HCR scenarios were tested and cod recovery examined using both single and multi-species formulations. Both single and multi species models predicted cod SSB to continue to decline, when fished at the current F level. When the proposed HCR for cod were applied, both single and multi species models predicted cod SSB recovery. The predicted recovery of cod SSB was slower when taking multi-species interactions into account, and Bpa was reached approximately one year later (2008 instead of 2006 / 2007 in the single species predictions). In terms of the impact of a cod recovery plan on other species in the North Sea, haddock SSB was predicted to decline to beyond the established Blim under all multi-species scenario simulations, but in the single-species simulations, it was predicted that haddock SSB and yield would increase after an initial drawback under HCRs for cod. Multi species scenario simulations predicted that whiting SSB and yield would initially remain constant but decrease thereafter, and from 2006 onwards whiting SSB was predicted to decline below Bpa. Single species scenario simulations predicted that whiting SSB and yield would increase under HCRs for cod. Multi species simulations predicted that Norway pout SSB would fall below Bpa after approximately 5 years of the application of the HCRs for cod, when continued to be fished at Fpa. Single-species simulations predicted that SSB would remain stable above Bpa. Finally, sandeel was predicted to stay above Blim in the longer term when using single species models. However, under multi-species considerations sandeel SSB was predicted to fall below Bpa in 2004 and gradually decrease towards Blim thereafter. The divergence in the predicted outcomes between the single-species and multi-species interactions models indicates that greater consideration needs to be given to multi-species interactions in the fisheries.

3.2 Previous bioeconomic models of the North Sea Compared with fisheries elsewhere in the world, considerable attention has been devoted to the development of bioeconomic models of the North Sea fisheries, reflecting the relative importance of the area to the EU. A number of models have been developed for a single or limited range of species in the region. Bjørndal and Conrad (1987) and Bjørndal (1988, 1990) developed a model of the North Sea herring fishery that included a fleet dynamics function. This allowed for changes in fleet size but not in fleet structure. Dol (1996) developed a

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simulation model of the flatfish (sole and plaice) fishery in the North Sea, primarily for the Dutch beam trawl fleet. A number of multi-species models have also been constructed. Kim (1983) developed a surplus production multi-species model of the demersal fishery to estimate the potential economic rent that could be achieved. Frost et al. (1993) developed two bioeconomic models of the North Sea fishery; a linear programming model and a larger simulation model to estimate levels of effort and catches. Mardle et al. (2000) developed a multi-objective long run equilibrium model of the fishery which was used to estimate the optimal level of catch taking into consideration the multiple objectives of the Common Fisheries Policy. The analysis looked at trade-offs between sustainable levels of employment, discarding and fishery profitability in a long run equilibrium setting. The model was further developed by Mardle and Pascoe (2002), who incorporated a short run component to the model, and estimated the trade-off between long and short run objectives in the fishery. In addition, Pascoe et al. (1999) also further developed the original model used by Mardle et al. (2000) to incorporate market interactions between the North Sea demersal species and farmed salmon. The model was used to examine how market interactions between farmed and wild caught species can affect the development of the fishery. The dynamic model developed by Pascoe et al. (1999) was updated with more recent biological and economic information (e.g. costs, prices, fleet information, stock biomass etc) and used to estimate the long-run economic impact on the UK fish catching industry of a “days at sea” regime for cod stocks implemented from January 2004, embodying tradable effort entitlements (Mardle and Pascoe, 2002). The model included the predator-prey relationships of the earlier versions of the model. The model was used to estimate the ‘optimal’ level of effort reduction in terms of both fleet size and utilisation required to maximise fisheries profits over time. The ‘optimal’ solution was found to roughly halve the fleet size from the 2001 level and also introduce up to a 60 per cent reductions on days at sea for the first 4-5 years. Ulrich et al. (2002) also developed a dynamic bioeconomic model of the North Sea flatfish fishery. Included in this model was an allowance for increased productivity through changes in average technical efficiency as the fleet changed over time. A similar approach was also adopted by the Prime Minister’s Strategy Unit (2004) in their conceptual bioeconomic model of the North Sea cod fishery. The model was designed to explore the capital investment decision (entry-exit) and to consider policy issues such as decommissioning and tie-up schemes. A further model has been developed to provide an indication of the economic impact of changes in quotas on key fleet segments – the EIAA model (Economic Interpretation of the ACFM Advice) (Salz and Frost, 2000). The current version of this model has been used to assess the impact of quotas on the key fleet segments operating in the North Sea. The model is not a true bioeconomic model as it does not have an explicit biological component, but works backwards from information on quotas and stock sizes to estimate how costs and revenues may change given a change in quota. The model is designed to be applicable in the short run (i.e., to evaluate the consequences of proposed TACs in the forthcoming year). As a consequence, neither fleet dynamics nor stock dynamics are included that would be required for a medium to long-term analysis. Furthermore, it is assumed that the

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species are caught separately and that fishers can target individual species (i.e. there is no joint production). While a useful guide to provide rough estimates of changes in economic performance of particular fleet segments, the model has been heavily criticised with regard to the simplistic assumptions about effort allocation and catches. As the model is also reactive (i.e., effort is determined by allowable catch rather than catch being determined by effort), it is not appropriate to apply to the type of analysis required in this study. The model also contains fairly simplistic (and uniform) assumptions as to how prices change with changes in landings. The magnitude of these ‘elasticity’ parameters relevant in the short run§ are not necessarily valid for the medium and long run. A more detailed critical review of this model was recently undertaken by STECF (2004). Several other economic studies have been conducted in the North Sea that have relevance to this study. These include studies of efficiency (Pascoe et al. 2001, 2003, Hutton et al. 2003), capacity (Tingley and Pascoe 2003, Pascoe 2004) and effort allocation (Mardle and Pascoe 2004, Coglan et al. 2004). The efficiency and capacity studies confirmed the need to reduce fishing capacity in the North Sea. The effort allocation studies suggest that fishers’ location choice are heavily influenced by habit as well as economic incentives. Differences in risk attitudes also seem to affect their decisions as to where to fish. This study was further used by Pascoe and Mardle (2005) to examine the economic impact of closed areas on the UK fishing fleets operating in the North Sea.

4 Dynamic bioeconomic model of the North Sea fisheries

4.1 Overview The bioeconomic model developed for this project is based on the previous models identified above, and incorporates the most recent information on the fishery arising from the EFIMAS and TECTAC projects, as well as data provided by various STECF groups. The model differs from those being developed in the other studies as it explicitly considers multi-species predator-prey and technical interactions (based on the 4M model). The biological component of the bioeconomic model was primarily developed by CEFAS, based on the ICES multispecies study group assumptions. The economic and fleet dynamics components were developed by CEMARE. The economic component includes the most recent cost information from the Annual Economic Report, as well as incorporating models of price changes based on published price flexibilities.** Assumptions about changes in costs also need to be made, particularly fuel costs. The model also allows for discarding behaviour if quotas are not compatible.

§ However, even in the short run, with large changes in proposed quota, the subsequent effects may be invalid. ** The EIAA model (SGECA, 2004) has a similar price function, but assumes that all species face the same price flexibility. There are numerous published empirical studies of fish price formation in Europe to indicate that this is not the case. The species specific flexibilities will be used instead of a single common price flexibility.

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Analyses of seafood markets with the purpose of estimating species specific demand functions have not been undertaken, as this would go far beyond the scope of this project. Moreover, when looking at future developments, numerous changes are likely to take place, e.g. income and population growth, changes in consumer preferences as well as changes in quantity. All these variables will have an impact on price. It would therefore not be appropriate to only consider the impact of changes in quantity on price. The approach taken in this report is standard for this kind of research. Fleet dynamics largely focus on likely participation in the fishery and effort levels, taking into account the restrictions but not necessarily forcing the vessels to operate at these levels. Work was undertaken to consider entry/exit (participation) in the TECTAC project, and these results were available to the project team. The methods for incorporating efficiency change in the model used by Ulrich et al. (2002) and the Prime Minister’s Strategy Unit (2004) were adapted to allow for efficiency improvements in the fishery over time. The model has been developed in the VENSIM package. The main advantage of the package is in its presentational features, and the possibility of developing a user-friendly simulation interface that will allow the simulations to be run easily by the Commission. The software was used by the UK Prime Minister’s Strategy Unit (2004) in order to present the model results to a wide variety of stakeholders, most of whom had little technical knowledge of modelling.

4.2 Basic description of the EcoCorp bioeconomic model As noted above, the model developed as part of the EcoCorp project is based on the biological multi-species interactions of the 4M model. The 4M model manual has been the key document used (“Specification and documentation of the 4M package containing multi-Species, multi-Fleet and multi-Area models”, Vinther, Lewy and Thomsen, 2002). The 4M model is based upon the ICES multi-species VPA programme MSVPA, the corresponding prediction programme MSFOR (Gislason and Sparre, 1987) and the STCF prediction programme ABC (Lewy et al., 1992). As a result, the 4M model provides for the primary operations: multispecies VPA and multispecies catch prediction. According to Vinther et al. (2002), the multi-area facet of the 4M model is not fully operational. The only relationship in the 4M model between fleets and the biological species production is through catch. That is, fishing mortality (F) is estimated from the total catch within MSVPA. Since catch per fleet is known (or given), then partial F per fleet can be computed:

t

f

CCFF =′ (1)

where F’ is partial F, Cf is catch of the fleet under consideration and Ct is total catch. Partial F per fleet is further (modified/scaled) by the exploitation pattern of a sub-fleet where the sub-fleet is a fleet with a particular mesh size. The exploitation pattern is the equivalent of a selection pattern/curve which is length dependent.

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In the 4M model, effort (e.g. days fished) is stored in a normalised form and the assumption is that a change in fishing mortality results in a proportional change in effort. In effect, the catchability coefficient (q) is equal to 1 ignoring units. In order to extend this relationship to include economic relationships, effort must be explicitly incorporated. For example, the evaluation of the economic activity of a fleet is highly dependent on the description of that fleet’s effort. Variable costs (including fuel cost) cannot be realistically incorporated otherwise. The bioeconomic model developed for EcoCorp consists of a biological component and an economic component, linked by effort controls (in this case the dynamics of effort related to fishing mortality). In accordance with the structure of the model, management controls within the model are identified.

4.3 Biological component There are two parts to the biological component: the multispecies VPA and the so-called ‘prediction’ element. In the model, they are maintained as separate processes, but are closely linked

Figure 24: The biological process modelled by 4Mbiological process modelled by 4M.

4.3.1 Assessment process The MSVPA process is an extension of single species VPA. In fact, the general process uses a series of single-species VPA models that are linked by a feeding model to calculate natural mortality rates. The system of linked single species models is run iteratively until the predation mortality rates (i.e., M2) converge. The basic model is performed in two iteration loops:

1. all single species VPAs are run to calculate population sizes at all ages for predators and prey, then predation mortality rates are calculated for all age classes of each species based upon the feeding model; and

2. the single species VPAs are run again using the calculated M2 rates, and this iteration is repeated until convergence.

The prediction process (generates population

catch)

The assessment process (generates the

‘perceived’ stock)

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The single species VPA solves for each species, i, the following basic equation iteratively given a natural rate, Mia, and either population size or total mortality rate in the oldest age class:

)1)(exp()1( tMFNN iaiatiaiat −+= = (2)

where for age class, a, and time within a year (0< t < 1), Niat is abundance of species i at age a at time t, Nia(t=1) is abundance at the end of the year and, Fia is the fishing mortality rate. The standard equation for catch at age, Cia, is then:

[ ])exp(1)1( iaia

iaia

iatiaia MF

MFFNC −−−+

= =

(3) The MSVPA process then resolves natural mortality into predation mortality, M2ia, and other mortality, M1ia. Hence, total mortality rate is:

iaiaiaia MMFZ 21 ++= (4) The 4M model contains several extensions to MSVPA (source: Vinther et al., 2002). Where information exists for predator and prey species, they are modelled using VPA. Stock numbers for other predators are given as input and for other prey species using a production function. Other food biomass is also given as an input. The growth of VPA predators is modelled as a function of the amount of available food. Weight at age (W) at time t is defined as the weight at age in the preceding year plus a growth term that is based on average growth observed and the amount of available food (FoodAvail) relative to the average amount of food:

1

111

*−

−−− +=

t

tttt F oodA vail

G row thF oodA vailWW (5)

The food intake (R) at time t is defined as “a ‘bioenergetic’ model taking basal metabolism, somatic growth and spawning into account” (see Vinther et al., 2002):

⎟⎟⎠

⎞⎜⎜⎝

⎛+

−+= + 0,***_max 1_

t

ttt

t

ttBMETttt GCONVEFF

SPAWNWPROPMATCONVEFF

WWWAMETR t

(6) where MET_A and MET_B are constants to assess intake for unchanged weight (basal metabolism), CONVEFF is food conversion efficiency (somatic growth), GCONVEFF is food conversion efficiency (spawning products), SPAWN is factor of body weight lost due to spawning, and PROPMAT is proportion mature.

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The estimated weight (W) and food intake (R) are estimated by iteration until convergence of the values of estimated weight. W is then used in the calculation of consumption (and total and spawning biomasses). Average biomass ( B ) of other food p in year y is assumed to be:

nconsumptioLKpy

ppeB **= (7)

where K is a constant expressing the log of the biomass of other food when predation is zero and L is a constant expressing the amount of change in biomass of other food per unit of consumption. At this stage in the assessment process, numbers at age (including plus groups) in the species population can be calculated. This is an equilibrium state. Multispecies tuning can then be undertaken.

4.3.2 Prediction process The prediction process is built upon the basic age-structured population model of the form:

( )[ ]21exp1 MMFNN tt ++−=+ (8) Population biomass can be calculated as:

ttt wNB = (9) Catch is estimated as:

[ ])exp(1 ia

ia

iaiaia Z

ZFNC −−=

(10) For EcoCoRP, output data were obtained from the 2005 SGMSNS ‘key-run’ of the MSVPA model, and projected forward in time using modelled predator-prey ‘suitabilites’ and Ricker stock recruitment relationships (see sections 5.1.2 and 5.1.3).

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4.4 Economic component The economic component of the North Sea dynamic bioeconomic model is based on earlier models developed by Pascoe et al. (1999), Mardle et al. (2000) and Mardle and Pascoe (2002). The mathematical description of the model is presented below. Endogenous variables are presented in upper case and exogenous variables (and parameters) in lower case. Number of vessels and days fished as well as activity of vessels are direct inputs into the system as shown in the equations below. The model is developed as a simulation model. While several criteria will be of interest, the key criterion will be the economic implications of different effort control scenarios. Foremost of these will be the net present value of profits. The calculation order is reversed in this section for simplification of discussion. The model is driven from effort to catch rather than the other way around. NPV is expressed as: ( ) 1/(,,∑∑∑ +=

t j k

ttkjPROFNPV δ (11)

where NPV is the net present value of total fishery profits over time, while PROFj,k,t is the level of economic profits of boats using gear type k from country j (i.e., a given fleet segment in the fishery) in time t and δ is the discount rate. The profit of each fleet segment in each time period is given by the revenue less the costs. The revenue of each fleet segment is given by:

( ) kjDAYSvbyLANDpREV

itkjkjtikjijtkj ,, ,,,,,,,,, ∀+= ∑ (12)

where REVj,k is the total revenue of the fleet segment in time t, and pj,i is the average price of species i in country j (assumed constant over time), LANDj,k,i,t is the total landings of species i by each fleet segment, and vbyj,k is the average value of other (bycatch) species landed each day††. The costs of each fleet segment are broken down into four components: Fixed costs (FCOSTj,k,t), variable (trip/day) costs (VCOSTj,k,t), crew costs (CCOSTj,k,t) and capital costs (KCOSTj,k,t). The total costs (COSTSj,k,t), and individual cost components, of each fleet segment are given by:

tkjtkjtkjtkjtkj KCOSTCCOSTVCOSTFCOSTCOST ,,,,,,,,,, +++= (13)

tkjkjtkj BOATSfcFCOST ,,,,, *= (14)

tkjkjjkjtkj DAYSocpdfpricefpdVCOST ,,,,,, *)*( += (15)

tkjkjtkj REVcsCCOST ,,,,, *= (16)

†† In the previous models, this was assumed constant over time. The bycatch includes all species not otherwise explicitly modelled. The parameter value is estimated from logbook data.

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tkjkjtkj BOATScapdKCOST ,,,,, **)( δ+= (17)

where csj,k is the crew share for the fleet segment; the average running cost per day of boats in each fleet segment is given by fuel-use per day fpdj,k multiplied by fuel price (e.g. fpricej is say €0.5 per litre) plus other variable costs per day ocpdj,k; fcj,k is the average fixed cost per boat; d is the depreciation rate; δ is the discount rate (equivalent to the opportunity cost of capital), capj,k is the average capital value of the vessel; BOATSj,k,t are the number of boats operating in time period t and DAYSj,k,t is the total number of days expended by the fleet segment in time period t. An indication of average Kw*Days by fleet can also be indicated. Data for these will be obtained from the Annual Economic Reports. The economic forecast that the model provides can be viewed at different levels. First, the NPV indicator gives a long term view of the effects of a modelled management option. Second, as the model is run in the form of a dynamic simulation over time, a yearly (i.e., non-discounted) indication of economic profits as well as costs is provided. The link between the biological and economic components of the models is fishing mortality. The partial fishing mortality of each species by each fleet is given by:

tikjDAYSqF tkjaikjtaikj ,,,, ,,,,,,,,, ∀= (18) where qj,k,I,a is the catchability coefficient, and Fj,k,I,,a,t is the partial fishing mortality such that:

∑∑=j k

taikjtai FF ,,,,,,

The catch of each species in each year by each fleet segment (CATCHj,k,I,y) is given by:

tikjBFCATCHa

taitaitikj ,,,, ,,,,,,, ∀= ∑ (19)

where Bi,a,t is the biomass in each age class of the stock in year t, and

∑∑=j k

aikjaikjai BDAYSqC ,,,,,, (ignoring subscripts t for convenience). Landings are

given by:

tikjCATCHLAND tikjtikj ,,,, ,,,,,, ∀≤ (20)

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where landings are also affected by the TACs. For the purposes of simplicity, it will be assumed that:

C,

TAC

C

C, ,,,a ai,

i

aai,

,,,

a ai,,,,

∑∑=

>∑

≤

itikj

itikj

TACCATCH

TACCATCHtikjLAND (21)

That is, any overquota catch is assumed to occur in equal proportion by each fleet segment. In order to calibrate catchability, catch-at age, by fleet and country was obtained from STECF and used (in a proportional way) to scale the overall landings by fleets. This data also included some discarding data, which was used (in a proportional way) to estimate discards at age for fleets and countries. The data was normalised, relative to the numbers or biomass at age in the population, thus resulting in a selection ogive where most of the remaining old fish could be selected, but where only a few of the younger ages could be selected.

4.4.1 Management controls In the baseline model, fishing effort follows levels of fishing mortality as estimated by the biological component (in the base year 2003). The key ‘control’ variables in the model are the number of days fished and the number of boats each year. To impose a common change in these variables on all fleet segments, the existing days/boat numbers in each fleet segment were multiplied by a common scaling factor. That is,

j,k ,*BOATS*ESCALEsea_daysDAYS tj,ktj,ktkj ∀= ,,,, (25)

j,k , BSCALEboatsBOATS tj,ktkj ∀= *,, (26)

where sea_daysj,k is the (unconstrained) days at sea of a boat in each fleet segment, and boatsj,k is the current number of boats in each fleet segment. ESCALEt and BSCALEt are scaling factors that alter the number of days and boats in each fleet segment respectively.

4.4.2 Cost information The available cost information determines the fleet structure in the model. Based on an evaluation of the fleets detailed in the annual economic report (AER, 2005) with the recent data regulation (Commission Regulation (EC) No 1639/2001), the fleet structure for the EcoCorp model has been described.

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Bea

m

<22

1kw

(<

24m

) Bea

m

>22

1kW

(>

24m

)

Bea

m s

hrim

p

Bot

tom

tr

awl

(<24

m)

Bot

tom

tr

awl

(>24

m)

Dan

ish/

Sco

ttis

h Sei

ne

Nep

hrop

s tr

awl

Pela

gic

traw

l/se

ine

Long

line

Fixe

d ne

ts

Belgium X X X Denmark X X X X X France1 X X X X X Germany X X X Netherlands X X X X X Norway1

X X X UK2

X X X X X X

1 These fleets mostly operate outside the North Sea, proxiesare used for NS activity 2 Data for English bottom trawlers is not available in theAER

Figure 25: Country and gear economic costs data matrix

Economic data are available for key European fleets from countries bordering the North Sea, i.e. Belgium, Denmark, France, Germany, the Netherlands, Norway and the United Kingdom (Figure 25). In addition, economic data was also included for English beam trawlers >24m, Scottish pelagic vessels using seine and trawl (from SEAFISH report). The effort, profit and costs information contained within the AER was only available for the whole fleets of particular countries irrespective of the geographic area they fished. Thus it was necessary to re-scale this information, with reference to the effort applied ‘inside’ and outside’ of the North Sea using appropriate data provided by various STECF groups. This re-scaling particularly affected the fleets of Belgium, France and the UK (which also fish in the Channel and to the west of the UK) and Denmark (which also fishes in the Kattegat, Skagerrak and Baltic). The information provided by STECF did not contain data for Norway, and hence the effort deployed by bottom trawlers was scaled by the relative ‘inside’ and outside’ catches of cod (only ~2% of cod taken by Norway is caught in the North Sea) as collated by ICES, and the pelagic fleet data was scaled by the relative ‘inside’ and outside’ catches of herring (~21% of all herring caught by Norway derive from the North Sea).

4.5 Vensim EcoCoRP bioeconomic model The project team have developed the EcoCorp bioeconomic model using the System Dynamics software, Vensim, and a well-refined systematic approach to model building. Vensim is a state-of-the-art business simulation tool developed by Ventana Systems, Inc.

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The biological and economic models described above have been developed based on the relationships derived for the 4M model and captured in the form of causal loop diagrams (CLD); a simple example (not from the 4M model) is included in Figure 26. Arrows show the cause and effect influences between any two variables in the system with closed loops denoting feedback. In the Figure 26 example, changes to the Prey Population have an “influence” on the Rate Prey Fished in Area. When stocks are abundant, the catch will tend to increase. If the stock levels decline, so will the catch rate. The positive sign on the arrow head denotes the same type of change (increase or decrease) will happen at the arrow head from changes at the arrow tail. A negative sign on the arrow head infers the “opposite” type of change is likely to occur. For example, an increase in the Predator Population will increase the rate the prey is killed and consequently reduce the prey stock.

Prey Populationrate prey populationincreases

rate prey die ofnatural causes

average prey l ifeexpectancy

prey population ferti li ty

prey spawning rate

rate prey fishedfrom area

Rate Prey Caught

-

-

rate prey kil led bypreditors

+

-

<Preditor Population>

PREDITOR RATION

+

+

+

+

+ +-

prey catch by fleet

+

+

prey kil led per preditor

+

-

Figure 26: Example Causal Loop Diagram (CLD)

4.5.1 Key features This section examines a number of key features of the Vensim modelling approach to Strategic Modelling.

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4.5.1i Transparency The Vensim software provides a number of tools for examining the model structure and the results generated, enabling the behaviour of key outputs to be traced back through the model structure to the underlying assumptions.

total vpa species stockEstimated Fish Stock By Age

fish stock recruitment rate

fish stock to age

initial n

ONE FISH

ONE YEAR

overall mortality rate

rate fish age

Figure 27: Causal tree; example tracing model structure

Figure 27 shows an example of Causal Tracing™; the automatic output presented to the model user when interrogating the structure of the model. In this way, it is explicitly obvious what is included in the model. In Figure 28, a further example of Causal Tracing™, this time an automatic output provided when tracing model behaviour. Following graphical output of the variable of interest, the tool automatically presents a display of the behaviour of all influences (inputs) on that variable, for rapid assessment of the “why”. In this output, the estimated number of two year old cod is plotted, and illustrated below, the direct influences on that profile (ageing into the group, ageing out of the group and the overall mortality within the age group).

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DT125cEstimated Fish Stock By Age[COD,"2"]200,000150,000100,00050,000

0fish stock to age[COD,"2"]60,00045,00030,00015,000

0overall mortality rate[COD,"2"]80,00060,00040,00020,000

0rate fish age[COD,"2"]80,00060,00040,00020,000

02003 2006 2009 2012 2015

Time (Year) Figure 28: Causal graph; example tracing model behaviour

4.5.1ii Flexibility Ventana models are designed to allow for the greatest level of flexibility possible. The purpose is to enable new scenarios to be setup, run and analysed within the timeframe normally available to senior decision makers. Through the use of a bespoke user-interface combined with sophisticated analysis tools, the model user will be able to manipulate assumptions, run scenario experiments and analyse results in a matter of minutes.

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4.5.2 Model structure and preliminary outputs The EcoCoRP model developed in Vensim simulates the predator - prey interactions between the species shown in Figure 29. The model has been validated with previous 4M model implementations by ICES multi-species working groups. Predators Predators & Prey Prey SEA BIRDS COD HERRING GREY GURNARDS WHITING N POUT HORSE MACKEREL HADDOCK PLAICE NORTH SEA MACKEREL SAITHE RAJA RADIATA SANDEEL GREY SEALS SOLE WESTERN MACKEREL SPRAT

Figure 29: Species Modelled

The EcoCoRP model then forecasts forward from 2003 to 2015, though this can be easily extended. For each prey species, the population is modelled in yearly age groups, to a maximum age as shown in Figure 30. Prey Species Maximum Age COD 11 HADDOCK 10 HERRING 9 N POUT 3 PLAICE 11 SAITHE 10 SANDEEL 4 SOLE 11 SPRAT 4 WHITING 8

Figure 30: Species Age Ranges

The core of the ‘biological’ model is illustrated by the influence diagram in Figure 31.

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Fish Stock

EstimatedFish Stock

By Age

estimated stockmortality

total fish stockestimate

fish stock toage

initial n

max fish age

overallmortality rate

fish stockrecruitment rate

rate fish age

<survivng fish>

total vpa speciesstock

IMPORTINITIAL N

<liveweight>

ricker estimatedrecruitment

total mortality rate byspecies

<recruitment inqtr1> <quarter>

<average ssb>

Annual Mortality Rate

total annual mortality

AnnualRecruitmentRate

Figure 31: Estimated Fish Stock

The influence diagram illustrates the structure within the model controlling the population of each of the prey species within one year age groups. The stock levels for each of the prey species are increased by a quarterly recruitment rate into the first age group. The initial recruitment rates are based on the latest 2003 estimates from the 4M model simulations. The annual recruitment rates thereafter are based on Ricker type forecasts based on the spawning stock biomass of each species in the previous year, and a stochastic element of randomness. Ricker α and β parameters were obtained from Kempf et al. (2005), and Goodwin et al. [sole and saithe]. As the EcoCoRP model is projected forward, the cohort from each age group is automatically aged until they reach the maximum age for that species. Within any of the age groups there is an overall mortality rate, calculated on a quarterly basis. This overall mortality rate is a combination of:

• Natural Mortality (M1) • Predator Mortality (M2) • Fishing Mortality (F)

The natural mortality (‘residual mortality’) is an input to the model and has individual values for each age group within each species. The predator mortality is the sum across all predators and predator age groups likely to eat the prey within that prey age group. The fishing mortality has initial values set to those values estimated by

38

the most recent 4M model runs, though the values are ‘user defined’ and can be changed between forecast years. Each simulated year, the process of surviving fish moving up the ageing chain is shown in Figure 32.

survivng fish

Estimated Fish Stock By Age

fish stock recruitment rate

fish stock to age

initial n

ONE FISH

ONE YEAR

overall mortality rate

rate fish age

derived ff to use

m natural mortality fractionm1

m2

Figure 32: Surviving Fish

The predator – prey interactions leading to the M2 calculation take into account:

• Predator Consumption Rates • Suitability Index of ‘Predator by Age’ to ‘Prey by Age’ in each quarter of the

year • Prey weight at age

The Suitability Index has been derived from analysis of stomach contents and input directly into the model. The predator consumption rates vary within the model depending on the simulated predator populations. The economic aspects of the model follow standard accounting procedure.

Profit by Fleet = Revenue by Fleet – Costs by Fleet The costs modelled are shown in the causal tree diagram in Figure 33.

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cost by fleet

crew costsVESSELS

average crew costs per vessel

depreciation & interest costsDepreciation and interest costs percentage

(variable costs)

fixed costs(VESSELS)

average vessel cost

variable coststotal fuel costs

total other running costs

Figure 33: Costs by Fleet

Similarly, the revenue generated by each fleet is given in Figure 34.

revenue by fleet

VESSELS

Fleet - number of vessels

(inside iv prop)

NET CHANGE IN VESSELS

average other income per vessel

(VESSELS)

inside iv prop

OTHER INCOME

revenue by fleet and species

catch by fleet

DISCARD PROP

kg per tonne

m euro to euro

price euro

Figure 34: Revenue by Fleet

5 Initial Model Setup The ECOCORP model has been developed in the Vensim simulation package. It is based on the System Dynamics methodology which represents elements within the systems in terms of “Stocks” and “Flows”. In system dynamics modelling, the dynamic behaviour in the system occurs when flows accumulate in stocks.

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An example of a stock within the model would be the population estimate of 3 year old cod. Over time, this will change due to four flows:

• Rate the fish are caught • Rate the fish die • Rate fish age from 3 year old to 4 year old • Rate fish age from 2 year old to 3 year old

At the start of the simulation however, estimates are required for the initial value of the 3 year old cod stock. The majority of the biological stock initialisation values in the ECOCORP model are derived from the output estimates from the MSVPA model runs for 2003. The stock initialisations for the financial aspects of the model such as fleet sizes are drawn from the Annual Economic Report 2004 on ‘Economic performance of selected European fishing fleets’. The second type of input required for the initial model setup is the input constants which control the flows between the stocks. These are varied in nature, covering aspects such natural mortality, predator suitability to prey, price received for catch, fuel costs and many more. All of the key inputs for the baseline scenario are provided in the remainder of this section.

5.1 Biological Inputs

5.1.1 Initial Stock Levels The initial stock levels for the starting point of the simulation model were set to match the 2003 estimates from the MSVPA model.

103 Age (Years) VPA Species 1 2 3 4 5 6 7 8 9 10 11

Cod 13003 60677 12843 9127 1708 213 142 41 20 9 2

Haddock 526450 62144 380832 1101409 9311 3857 1689 141 643 118 0

Herring 7033877 10952950 3092033 4012059 1129448 519596 575138 172971 38727 0 0 Norway Pout 31874170 3904707 586029 0 0 0 0 0 0 0 0

Plaice 166840 591707 149458 131657 75063 48978 48969 5179 3595 4896 0

Sathe 150999 154000 139000 96326 84868 19942 21837 4645 5130 2696 0

Sandeel 69355460 62654710 13706450 3818087 0 0 0 0 0 0 0

Sole 133247 208560 44225 48318 14271 14072 12550 1110 446 1527 0

Sprat 52261470 7931364 928202 343318 0 0 0 0 0 0 0

Whiting 800828 656464 442592 308621 116308 19376 6990 4852 0 0 0

Figure 35: Initial Stock Levels

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5.1.2 Recruitment The term ‘recruitment’ refers to the number of “new recruits” or juveniles entering a fish population in a given year. Attempts to relate the number of new recruits (juveniles) to the size of the parental spawning stock has for many stocks proved exceedingly difficult, as witnessed by many graphs giving observations on these two variables. Intuitively one would expect a reasonable relationship between numbers of recruits and parental stock size; that is, the larger the number of reproductive adults in the stock, the larger the number of resulting offspring. However, variation in recruitment is often considerable, suggesting that recruitment is related to factors other than simply the parental stock (see section 5.1.3). Interest in this stock-recruitment relationship is due to its potential to provide insight into what happens in the long run when a fish stock is reduced by fishing as well as when fishing is curtailed to allow the stock to regenerate. Assumptions about the stock-recruitment relationship can drive the future dynamics of a fisheries model and heavily influence model outcomes. Data have been used to fit numerous stock-recruitment functions. The most widely applied are those proposed by Beverton & Holt in 1957 and that of Ricker (1954, 1975). However, more often than not, the observed data appear as a ‘cloud’ of scattered points and any fit to these data seems somewhat arbitrary. Clearly, there would be no recruitment of juveniles if the adult stock is reduced to zero, thus the relationship can be anchored through the origin. In the present model, recruitment is based on the Ricker relationship: Recruitment = α . SSB e-βSSB Recruitment is measured in number of individual fish, while spawning stock biomass (SSB) is measured in kilograms. The α and β coefficients were taken for each VPA species‡‡ from: REPORT OF THE STUDY GROUP ON MULTISPECIES ASSESSMENT IN THE NORTH SEA (SGMSNS). This particular stock-recruitment relationship assumes the presence of density-dependent mechanisms at high stock abundance; for example adults might compete more successfully for the same resources as juveniles, or adults might prey on the young of the same species. The Ricker stock-recruitment relationship was chosen because it is thought to provide a more realistic long-term prediction of future recruitment (particularly in hitherto unobserved conditions when stocks are allowed to fully recover to high densities) in comparison with the geometric mean used by many ICES stock assessment groups to provide short-term predictions for quota setting.

‡‡ Ricker Coefficients for Plaice & Saithe were sourced separately from the paper “Life history correlates of density-dependent recruitment in marine fishes“Nicholas B. Goodwin, Alastair Grant, Allison L. Perry, Nicholas K. Dulvy, and John D. Reynolds

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VPA Species α coefficients β coefficientsCod 3.66 1.8e-009 Haddock 26.8 1.2e-009 Herring 20.89 5.6e-010 Norway Pout 234.9 3.1e-010 Plaice 4.06 3.5e-009 Saithe 2.51 3.2e-009 Sandeel 438.86 7e-010 Sole 5.110 1.68e-008 Sprat 189.75 2.1e-010 Whiting 18.45 1.6e-009

Figure 36: Ricker Recruitment Coefficients

For Cod, this relationship between SSB and year 1 recruit can be seen below in Figure 37.

Ricker Recruitment Curve (Cod)800 M

600 M

400 M

200 M

00 500000 1e+006 1.5e+006 2e+006

SSB Tonnes

fish

Recruits

Figure 37: Ricker Recruitment Curve

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5.1.3 Taking Account of Environmental Variability From time to time, most fish populations show strong year classes, which persist in catches over several years. Strong recruitments occur periodically when favourable aspects of the environment coincide. One confounding factor is that recruitment is often separated from the spawning event by a long period, in some cases several years. This period usually includes a planktonic phase, during which eggs and larvae are exposed to highly variable environmental and hydrological conditions, as well as high juvenile mortality rates (due to predation and starvation). This often means that apparent relationships between the number of juveniles recruiting into a population and the concurrent spawning adult population are greatly obscured or obliterated altogether, and thus future recruitment patterns are very difficult to predict. Fishermen and scientists have known for over 100 years that the status of fish stocks can be greatly influenced by prevailing climatic conditions (Cushing 1982; Hjort 1914). In the case of cod, there is a well-established relationship between recruitment and sea temperature (O’Brien et al., 2000; Clarke et al., 2003; Beaugrand et al., 2003). In the North Sea, close to the southern limits of the species’ distribution range, warm conditions lead to weaker than expected year classes, and vice-versa. During the late 1960s and early 1970s, cold temperature conditions were correlated with a sequence of positive recruitment deviations in cod, haddock and whiting (Brander and Mohn, 2004), a phenomenon which has come to be known as the ‘gadoid outburst’ (Heath and Brander, 2001). However, in more recent years, a warming climate has prevailed and year class strength has been weaker than average. This in turn has led to a decline in the level of fishing mortality that can be sustained by the stock. For cod, plaice and sole in the North Sea, where there are extensive data and many published analyses, we can say with high confidence that climate change has likely compromised the ability of stocks to withstand fishing mortality. However, at present we are not sure of the precise mechanisms by which climate change affects recruitment, beyond that the effects are correlated with sea temperature and to some extent with zooplankton, abundance and composition. For other commercially important species (herring, mackerel, saithe, whiting, sandeel etc.) we have very little knowledge of the likely effects of future climate change (see supporting document for ‘fish & fisheries’ at www.mccip.org.uk). It is highly unlikely that responses will be the same for all 15 fish species included in the MSVPA model. Some species may benefit from warmer conditions whereas others will suffer.

Kell et al. (2005) modelled the effect of introducing a ‘cod recovery plan’ in the North Sea, assuming various climate change scenarios proposed by the Intergovernmental Panel on Climate Change (IPCC). These broadly corresponded with ‘good’, ‘average’ and ‘bad’ environmental conditions, and impacts on both recruitment and growth were considered. The length of time taken for the cod stock to recover in this single-species model was not greatly affected by the choice of climate scenario (generally around 5-6 years). However, long-term productivity was impacted, and stock biomass (SSB) was predicted to be considerably less than would have been the case assuming no temperature increase (251,035 tonnes compared to 286,689 tonnes in

44

2015). The overall message from this study was that in the short term (<20 years), climate change has little effect on stock recovery, which depends instead upon reducing fishing effort to allow existing year classes to survive to maturity. In the longer term (>20 years), climate change may have a greater effect on stock status, but higher yields and biomass might equally be expected (perhaps more so) if fishing mortality is reduced. Kell et al. (2005) concluded that “Incorporating environmental covariates in stock assessment predictions will not achieve sustainable resource use”. The EcoCoRP model has only been used for scenarios covering the 17-year period 2003-2020. Thus it was not considered necessary to try to accommodate predicted long-term (decadal) climate trends in scenarios of future recruitment. Short-term (weekly or monthly) weather patterns, which impact individual year classes of cohorts, are exceedingly difficult to predict into the future. However, the user of the EcoCoRP model is able to manipulate the level of inter-annual stochasticity (variability) in recruitment, and can run the model either in ‘deterministic’ mode (where no stochasticity in the Ricker recruitment parameters is assumed), or stochastic’ mode (assuming a specified level of stochasticity on Ricker parameters). Some fish species, notably haddock, are known to exhibit highly stochastic recruitment in the North Sea. Much has been written about the possible inclusion of environmental information in future stock predictions (for example see the recent cooperative research report of ICES PRISM working group www.ices.dk/pubs/crr/crr282/CRR282.pdf). However, climate variables themselves can be extremely difficult to project into the future, and scientists have been struggling, with little success, to address this issue. No ICES working group is currently using environmental data to predict future recruitment patterns, although several groups have tried.

45

5.1.4 Natural Mortality Natural mortality coefficients were sourced from the MSVPA model. The values used are shown in Figure 38.

Age

cod

hadd

ock

HER

RIN

G

N P

OU

T

plai

ce

saithe

SAN

DEE

L

sole

SPR

AT

whi

ting

"1" 0.2 0.2 0.1 0.2 0.1 0.2 0.2 0.1 0.2 0.2 "2" 0.2 0.2 0.1 0.2 0.1 0.2 0.2 0.1 0.2 0.2 "3" 0.2 0.2 0.1 0.2 0.1 0.2 0.2 0.1 0.2 0.2 "4" 0.2 0.2 0.1 0.2 0.1 0.2 0.2 0.1 0.2 0.2 "5" 0.2 0.2 0.1 0.2 0.1 0.2 0.2 0.1 0.2 0.2 "6" 0.2 0.2 0.1 0.2 0.1 0.2 0.2 0.1 0.2 0.2 "7" 0.2 0.2 0.1 0.2 0.1 0.2 0.2 0.1 0.2 0.2 "8" 0.2 0.2 0.1 0.2 0.1 0.2 0.2 0.1 0.2 0.2 "9" 0.2 0.2 0.1 0.2 0.1 0.2 0.2 0.1 0.2 0.2 "10" 0.2 0.2 0.1 0.2 0.1 0.2 0.2 0.1 0.2 0.2 "11" 0.2 0.2 0.1 0.2 0.1 0.2 0.2 0.1 0.2 0.2

Figure 38: Natural Mortality (M1) Coefficients

These values represent the M1 values in the estimation of the total mortality rate (Z) for each species at each age:

iaiaiaia MMFZ 21 ++=

5.1.5 Predator Mortality Predation mortality within the model is based upon:

• Predator Populations • Predator Consumption • Suitability Coefficients of prey to predator • Average prey weight

In the model, the populations of non VPA predators in these scenarios are assumed to be constant, although future scenarios may be run to apply an annual net change percentage. The default non VPA predator populations are shown in Figure 39.

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Individuals Age

PREDATOR "0" "1" "2" "3" "4" "5" "6"

SEA BIRDS 1876 0 0 0 0 0 0

GREY GUNARDS 348840 609271 514471 477744 0 0 0

HORSE MACKEREL 883999 599638 172011 0 0 0 0

NORTH SEA MACKEREL 0 31646 66677 39648 30848 26718 52684

RAJA RADIATA 99517 105975 91706 0 0 0 0

GREY SEALS 68 0 0 0 0 0 0

WESTERN MACKEREL 955649 1905364 0 0 0 0 0

Figure 39: Non VPA Species Predator Population

For each of the predator populations (both VPA and non VPA species), the consumption rates in kilograms by age group used by the model are shown in Figure 39. For non fish predator species, e.g. Sea Birds and Seals, all are assigned into age 0.

Kg/predator Age (Years)

PREDATOR 0 1 2 3 4 5 6 7 8 9 10 11

COD 0.09 1.46 4.65 13.64 21.61 28.76 33.50 40.96 41.82 45.53 47.86 51.02

HADDOCK 0.07 0.39 0.82 1.31 1.69 2.04 2.39 2.81 3.28 3.79 4.53 0.00

SAITHE 0.00 0.00 0.00 0.00 4.57 6.76 9.21 12.30 15.45 19.15 29.65 0.00

WHITING 0.06 0.34 0.59 0.91 1.03 1.14 1.18 1.31 1.52 0.00 0.00 0.00

SEA BIRDS 160.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

GREY GUNARDS 0.11 0.24 0.49 1.17 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

HORSE MACKEREL 0.49 1.03 1.66 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 NORTH SEA MACKEREL 0.00 0.64 0.88 0.99 1.00 1.00 1.07 0.00 0.00 0.00 0.00 0.00

RAJA RADIATA 0.00 0.17 0.35 0.78 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

GREY SEALS 2,325 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

WESTERN MACKEREL 0.55 1.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Figure 40: Non VPA Species Predator Consumption

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For each of the VPA prey species, the average weight at age values are taken from the MSVPA model output for 2003.

Kg Age (Years) 1 2 3 4 5 6 7 8 9 10 11

Cod 0.55 0.94 2.06 3.74 5.91 8.06 9.62 11.17 12.41 13.12 14.68

Haddock 0.14 0.26 0.34 0.42 0.52 0.75 1.02 1.38 1.76 2.19 0.00

Herring 0.04 0.09 0.15 0.16 0.18 0.20 0.21 0.23 0.24 0.00 0.00

Norway Pout 0.02 0.03 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Plaice 0.22 0.24 0.28 0.35 0.39 0.45 0.51 0.72 1.00 0.93 1.21

Sathe 0.63 0.65 0.87 1.04 1.34 1.91 2.91 3.96 5.21 7.54 0.00

Sandeel 0.01 0.01 0.01 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Sole 0.13 0.17 0.23 0.26 0.28 0.31 0.34 0.36 0.54 0.51 0.38

Sprat 0.01 0.01 0.01 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Whiting 0.11 0.18 0.23 0.28 0.30 0.31 0.33 0.34 0.00 0.00 0.00

Figure 41: Average VPA Species Weight by Age

5.1.6 Aging The model calculates stock populations every quarter of a year. At the end of the year period, any fish remaining in the age group after catch, natural mortality and predator mortality are automatically moved to the next age cohort. This process is repeated until the species reaches their maximum age input. Figure 42 displays the maximum ages modelled of the VPA species.

VPA Species Maximum Age (years)Cod 11 Haddock 10 Herring 9 Norway Pout 3 Plaice 11 Sathe 10 Sandeel 4 Sole 11 Sprat 4 Whiting 8

Figure 42: VPA Species Age Range

5.1.7 Maturity Proportions For use in the calculation of spawning stock biomass, the following inclusion proportions were applied to each age groups biomass.

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Proportion "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11"Cod 0.01 0.05 0.23 0.62 0.86 1 1 1 1 1 1Haddock 0.01 0.32 0.71 0.87 0.95 1 1 1 1 1 1Herring 0 0.43 0.93 1 1 1 1 1 1 1 1Norway Pout 0.1 1 1 1 1 1 1 1 1 1 1Plaice 0 0.51 0.5 0.7 0.94 0.99 1 1 1 1 1Sathe 0 0 0 0.15 0.7 0.9 1 1 1 1 1Sandeel 0 1 1 1 1 1 1 1 1 1 1Sole 0 0 1 1 1 1 1 1 1 1 1Sprat 0.5 1 1 1 1 1 1 1 1 1 1Whiting 0.11 0.92 1 1 1 1 1 1 1 1 1

Figure 43: VPA Species Maturity Proportions

5.2 Financial Inputs

5.2.1 Vessels Vessel numbers are based on the data from the Annual Economic Report for 2003 (AER). These figures are then adjusted using known catch data for each fleet which operate in multiple ICES areas. The adjustment proportions used are shown in Figure 44 and the fleet sizes modelled in to represent those active in ICES Area IV Figure 45. Proportion in Area IV UK Denmark Holland Belgium France Germany Norway

Beam <221kw (<24m), 1 0.52

Beam >221kW (>24m), 1 1 0.52

Beam shrimp, 1 1 1

Bottom trawl (<24m), 0.67 1 0.76 0.02

Bottom trawl (>24m), 0.67 1 0.99 0.76 0.95 0.02

Danish/Scottish Seine, 0.93 1

Nephrops trawl, 0.49 1

Pelagic trawl/seine, 0.64 1 0.57 0.59 0.52 0.21

Longline, 1

FIXED NETS 1 0.83

Figure 44: Fleet Proportions in ICES Area IV

49

Vessels

"Bea

m <

221k

w

(<24

m)"

"Bea

m >

221k

W

(>24

m)"

Bea

m s

hrim

p

"Bot

tom

tra

wl

(<24

m)"

"Bot

tom

tra

wl

(>24

m)"

"Dan

ish/

Sco

ttis

h Sei

ne"

Nep

hrop

s tr

awl

"Pel

agic

tra

wl/

sein

e"

Long

line

FIXED

NET

S

UK 0.0 29.0 0.0 110.6 50.9 33.5 145.0 19.8 0.0 0.0

DENMARK 0.0 0.0 0.0 202.5 67.0 34.5 0.0 21.8 0.0 353.4

HOLLAND 173.0 128.0 54.0 0.0 15.8 0.0 0.0 9.7 0.0 0.0

BELGIUM 16.6 30.2 25.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

FRANCE 0.0 0.0 0.0 303.2 421.8 0.0 0.0 6.9 174.0 340.3

GERMANY 0.0 0.0 289.0 0.0 17.1 0.0 0.0 13.6 0.0 0.0

NORWAY 0.0 0.0 0.0 22.1 0.7 0.0 0.0 17.4 0.0 0.0 Figure 45: Fleet Sizes Modelled For each of the adjusted fleets, the average number of days at sea per year at the start of the simulation (2003) used are displayed in Figure 46. These values are modified during some of the alternative scenarios tested.

103 Days

"Bea

m <

221k

w

(<24

m)"

"Bea

m >

221k

W

(>24

m)"

Bea

m s

hrim

p

"Bot

tom

tra

wl

(<24

m)"

"Bot

tom

tra

wl

(>24

m)"

"Dan

ish/

Sco

ttis

h Sei

ne"

Nep

hrop

s tr

awl

"Pel

agic

tra

wl/

sein

e"

Long

line

FIXED

NET

S

UK 0 0.25 0 0.2655 0.2145 0.1833 0.2 0.2613 0 0

DENMARK 0 0 0 0.1691 0.2403 0.1754 0 0.2524 0 0.1276

HOLLAND 0.1301 0.1953 0.1167 0 0.075 0 0 0.2765 0 0

BELGIUM 0.1875 0.2586 0.16 0 0 0 0 0 0 0

FRANCE 0 0 0 0.1955 0.2342 0 0 0.2613 0.1609 0.1878

GERMANY 0 0 0.143 0 0.0322 0 0 0.2613 0 0

NORWAY 0 0 0 0.1677 0.1647 0 0 0.2322 0 0

Figure 46: Average Thousand Days as Sea Per Vessel in 2003

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5.2.2 Revenue The price received for each species catch is based on the age of the fish. Initially these prices were provided in GBP, but have been converted into Euros using an exchange rate of 0.61 Euros to the GBP.

Euro/KG "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11"Cod 0.61 0.61 0.81 1.16 1.56 1.56 1.97 1.97 1.97 1.97 1.97 Haddock 0.42 0.67 1.04 1.34 1.34 1.34 1.34 1.34 1.34 1.34 1.34 Herring 0.17 0.32 0.32 0.32 0.32 0.32 0.32 0.32 0.32 0.32 0.32 Norway Pout 0.61 0.61 0.61 0.61 0.61 0.61 0.61 0.61 0.61 0.61 0.61 Plaice 0.61 0.61 0.61 0.92 1.57 1.57 1.95 1.95 1.95 1.95 1.95 Sathe 0.71 0.77 0.77 1.04 1.04 1.29 1.29 1.29 1.29 1.29 1.29 Sandeel 0.61 0.61 0.61 0.61 0.61 0.61 0.61 0.61 0.61 0.61 0.61 Sole 3.31 5.01 5.01 6.38 6.38 6.38 6.38 6.38 6.38 6.38 5.45 Sprat 0.61 0.61 0.61 0.61 0.61 0.61 0.61 0.61 0.61 0.61 0.61 Cod 0.28 0.30 0.30 0.53 0.84 0.84 0.84 0.84 0.84 0.84 0.84

Figure 47: Price per kg in 2003

5.2.3 Other Income As only a proportion of some fleets fish Area IV, estimates of the income from outside Area IV were made on the 2003 data from the Annual Economic Report. These estimates are shown below and remain constant throughout each scenario, that is, in scenarios where effort in Area IV is reduced, no increase in fishing outside the area is assumed.

M Euro / Year UK DENMARK HOLLAND BELGIUM FRANCE GERMANY NORWAY"Beam <221kw (<24m)" 0 0 0.2127 0.1531 0 0 0 "Beam >221kW (>24m)" 1.297 0 0.2468 0.3741 0 0 0 Beam shrimp 0 0 0.1555 0.08 0 0.1657 0 "Bottom trawl (<24m)" 0.2829 0.1098 0 0 0 0 0.0254 "Bottom trawl (>24m)" 1.022 0.2459 0.275 0 0.1729 0.15 0.3882 "Danish/Scottish Seine" 0.4444 0.0927 0 0 0 0 0 Nephrops trawl 0.0970 0 0 0 0 0 0 Pelagic trawl/seine" 0 0.4476 7.005 0 0 0 2.595 Longline 0 0 0 0 0.0804 0 0 FIXED NETS 0 0.03 0 0 0.1990 0 0

Figure 48: Estimated Other Income 2003

51

5.2.4 Costs Fleet costs are modelled as the sum of Fixed Costs and Variable Costs. The costs modelled are: Crew Costs, Depreciation & Interest Costs, Fixed Vessel Costs, Fuel Costs, & Other Running Costs. The inputs values for each of the above are based on the values in the AER. A summary of the cost data inputs are shown Figure 49 to Figure 53. M Euro/VESSEL UK DENMARK HOLLAND BELGIUM FRANCE GERMANY NORWAYBeam <221kw (<24m) 0.1271 0.1437 Beam >221kW (>24m) 0.2282 0.3687 0.3448 Beam shrimp 0.0592 0.076 0.0505 0 Bottom trawl (<24m) 0.1533 0.1216 0.1052 0 0.0496 Bottom trawl (>24m) 0.2828 0.2451 0.1437 0.2036 0.0388 0.8941 Danish/Scottish Seine 0.1638 0.1550 Nephrops trawl 0.0820 Pelagic trawl/seine 0.3741 0.4833 2.170 0.3741 0.3741 1.096 Longline 0.0402 FIXED NETS 0.0821 0.1146

Figure 49: Average Crew Cost 2003

M euro per vessel UK DENMARK HOLLAND BELGIUM FRANCE GERMANY NORWAY"Beam <221kw (<24m)" 0.0421 0.05 "Beam >221kW (>24m)" 0.2468 0.1664 0.0931 Beam shrimp 0.0259 0.036 0.0487 "Bottom trawl (<24m)" 0.1587 0.0477 0.0476 0.0171 "Bottom trawl (>24m)" 0.275 0.1596 0.0437 0.1117 0.05 0.5823 "Danish/Scottish Seine" 0.1750 0.0579 Nephrops trawl 0.0574 "Pelagic trawl/seine" 0.2451 0.3761 1.264 0.2451 0.2451 0.6931 Longline 0.0114 FIXED NETS 0.0231 0.0634

Figure 50: Average Vessels Costs 2003

Litres per 1000 Days UK DENMARK HOLLAND BELGIUM FRANCE GERMANY NORWAY"Beam <221kw (<24m)"

0 0 877,777 1.041 M 0 0 0

"Beam >221kW (>24m)"

7.106 M 0 4.97 M 2.466 M 0 0 0

Beam shrimp 0 0 396,825 812,499 0 308,529 0 "Bottom trawl (<24m)" 667,808 473,186 0 0 320,512 0 57,951 "Bottom trawl (>24m)" 2.116 M 1.258 M 1.458 M 0 961,538 1.293 M 4.732 M "Danish/Scottish Seine" 946,969 206,611 0 0 0 0 0 Nephrops trawl 426,520 0 0 0 0 0 0 "Pelagic trawl/seine" 1.697 M 2.075 M 10.90 M 0 1.697 M 1.697 M 3.923 M Longline 0 0 0 0 89,285 0 0 FIXED NETS 0 139,175 0 0 194,805 0 0

Figure 51: Average Fuel Usage

52

M Euro per 1000 days UK DENMARK HOLLAND BELGIUM FRANCE GERMANY NORWAY"Beam <221kw (<24m)" 0 0 0.4755 0.5166 0 0 0 "Beam >221kW (>24m)" 0.7227 0 1.204 0.9466 0 0 0 Beam shrimp 0 0 0.3015 0.325 0 0.0024 0 "Bottom trawl (<24m)" 0.4132 0.1719 0 0 0.0897 0 0.0700 "Bottom trawl (>24m)" 0.9447 0.4161 1 0 0.2769 0.3448 1.785 "Danish/Scottish Seine" 0.6969 0.1900 0 0 0 0 0 Nephrops trawl 0.1858 0 0 0 0 0 0 "Pelagic trawl/seine" 0.4938 0.7547 7.319 0 0.4938 0.4938 1.881 Longline 0 0 0 0 0.0357 0 0 FIXED NETS 0 0.1257 0 0 0.1298 0 0

Figure 52: Average Running Costs

UK DENMARK HOLLAND BELGIUM FRANCE GERMANY NORWAY"Beam <221kw (<24m)" 0 0 0.5670 0.6671 0 0 0 "Beam >221kW (>24m)" 0.3133 0 0.4396 0.6867 0 0 0 Beam shrimp 0 0 0.8571 0.2024 0 0.9381 0 "Bottom trawl (<24m)" 0.4974 1.401 0 0 0.8006 0 13.96 "Bottom trawl (>24m)" 0.2759 1.482 0.3858 0 0.6369 0.6606 21.16 "Danish/Scottish Seine" 0.3869 1.187 0 0 0 0 0 Nephrops trawl 0.4963 0 0 0 0 0 0 "Pelagic trawl/seine" 1.116 2.001 0.8846 0 0.8715 0.8715 3.218 Longline 0 0 0 0 0.5789 0 0 FIXED NETS 0 1.125 0 0 0.8654 0 0

Figure 53: Depreciation & Interest Costs as Proportion of Variable Costs

The price of fuel remains constant during all simulations and included no inflationary aspects. The estimated price of fuel used based on 2003 data is 0.4 Euros per litre.

53

6 Model Assumptions All simulation models are a simplification of reality, and as such, certain details are excluded from it. This model is no different. The main assumptions, simplifications and limitations of the model used in the scenarios are:

• Fixed predator populations – the population sizes of non VPA species predators, for example grey seals, remain constant throughout each simulation.

• As the age distribution changes, so do F values proportionally. As the balance of the

age distribution changes within a species stock, the model also adjusts the F –Values proportionally.

• Although “technology creep” can be modelled as an annual percentage increase in

catch for the same amount of effort and stock levels, this configuration was not used in the scenarios tested.

• No increase in fuel prices though out each simulation • No change in price per fish received for catches • Area IV effort proportions have been estimated based on known catch data. However,

some subjective inputs have been required. • Costs fixed. Only fleet variable costs will change based on changes in days at sea or

number of vessels • Revenue from outside area/other species has been estimated from catch data,

however, subjective reviewing was required. • Fixed average weight at age. The average weight of species by age does not change

in the model simulations • Fixed discard proportions. Discard proportions were estimated from 2003 landing and

catch data. These are applied for each year simulated by species and gear type. There is no separation between countries.

• Ricker noise. Stochastic variation on the recruitment forecasts was based on +/-

100% of the Ricker estimates. • Financial data for some fleets in the Annual Economic Report were not provided. In

these cases, estimates were used based on calculations from similar national fleet data.

• No “starvation effect” is modelled. Thus a lack of suitable prey will not cause a decline

in predator species. • Environmental factors such as sea temperature are not modelled. • Natural mortality factors (M1) are the same across all age groups within a species.

54

7 Baseline Scenario The Baseline scenario provides the model forecasts of cod stocks if the fleet sizes and effort levels are maintained at those for 2003. The baseline scenario is a purely deterministic simulation run, with no stochastic variation in recruitment or any of the other model inputs. This assumes results based on “average” expectations. However, stock recruitment is the main driver of stock levels in the model. Although this is calculated from the Ricker recruitment function using the spawning stock biomass of each species, historical records show that there is significant variation around these predications. For an assessment of possible outcomes where stochastic variation is included, see Section 8.

7.1 Assumptions

• Fleets – maintained at 2003 values • Effort – maintained at 2003 values • Technology creep - none • Costs – no rise in fuel costs • Revenue – price per kg maintained at 2003 value • Predator populations – maintained at 2003 estimates

7.2 Stock Levels

In the baseline scenario, Chart 1 shows that the model forecasts that cod stock will start to recover, and by 2020 they should be back around the level from the early 1990s.

Stock Level Estimates (Thousand Fish)4 M

3 M

2 M

1 M

01963 1969 1975 1981 1987 1993 1999 2005 2011 2017

Time (Year)

KFi

sh

Baseline Scenario Historic Estimates

Chart 1: Cod Stock Level

55

7.3 Age Distribution

Age Distribution - Cod

0

0.2

0.4

0.6

0.8

1

1.2

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

2014

2017

2020

Year

Prop

ortio

n

11

10

9

8

7

6

5

4

3

2

1

Chart 2: Cod Age Distribution

Chart 2 displays the historical age distribution for Cod from MSVPA from 1963 to 2003. From 2003 onwards, the breakdown shown is that from the Ecocorp model. As the baseline scenario is purely deterministic, the age breakdown is much smoother that the historical estimates, with around:

• 53% 1 year old cod • 24% 2 year old cod • 14% 3 year old cod • 5% 4 year old cod • 4% older than 4 years

The model age distribution appears to be under-estimating the younger cod, and consequently over-estimating the older fish. There are several possible reasons for this:

1. The natural mortality coefficient M1 is constant in the model across all ages. This is probably unrealistic.

2. The adjustment of F Values based on shifts in age distribution should be based on more complex relationship than a purely linear one.

3. Food sources are not modelled as a limiting factor on growth & aging in the model.

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7.4 SSB

Spawning Stock Biomas Estimates (Tonnes)400,000

300,000

200,000

100,000

0

4

3

22

2

22

1

1

1963 1969 1975 1981 1987 1993 1999 2005 2011 2017Time (Year)

Tonn

es

Baseline Scenario 1 1Historic Estimates 2

B Lim : bob 3 3B pa : bob 4 4 4

Chart 3: Cod Spawning Stock Biomass

Cod SSB rises to B Lim by 2010 and reaches B pa by 2015. It continues to rise reaching its highest level in 2020. The SSB projections appear much greater than has been estimated in the historical period, and rise much faster than total cod stock estimates. This is mainly due to the shift in the age distribution structure forecast by the model, with proportionally more older and heavier fish in the 4 to 7 year age groups.

SSB by Age Comparison to Historical Average

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

1 2 3 4 5 6 7 8 9 10 11

Cod Age (Years)

%

Historic Average (1963-2003)

Model 2005

Model 2010

Model 2020

Chart 4: Cod SSB by Age Comparison

57

7.5 Recruitment

Stock Recruimtent into Year 12 M

1.5 M

1 M

500,000

01963 1969 1975 1981 1987 1993 1999 2005 2011 2017

Time (Year)

KFi

sh/Y

ear

Baseline Scenario Historic Estimates

Chart 5: Cod Year 1 Recruitment

Annual recruitment of year one cod is calculated using the Ricker Recruitment curve, with the input to this function being cod SSB from the previous year. Chart 5 displays a growth trend in annual cod recruitment as would be expected given the model calculations for cod SSB shown in Chart 3.

Ricker Recruitment Function Fit

0

100000

200000

300000

400000

500000

600000

700000

800000

900000

1000000

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

Year

103 R

ecru

its

Historic Estimates

Ricker Fitted Recruitment

Chart 6: Ricker Recruitment Fit

58

Historical estimates have shown that recruitment are highly volatile, resulting in large prediction errors when based on the Ricker Recruitment function. Such errors can be seen plotting the Ricker Recruitment values against the historical recruitment estimate from 1980-2003 (Chart 6). Most likely, recruitment will be influenced by more than just the species SSB, factors such as sea temperature, pollution and predation of year 0 fish, none of which are covered in this model.

7.6 Predation The model shows a shift in the source of predation to cod in Chart 7. There is a growth in cannibalism of cod and a reduced proportion eaten by Grey Seals.

Cod Predation Mortality Proportion

0

20

40

60

80

100

120

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Year

Prop

ortio

n

HORSE MACKERELGREY SEALSRAJA RADIATASEA BIRDSWESTERN MACKERELNORTH SEA MACKERELGREY GUNARDSWHITINGSAITHEHADDOCKCOD

Chart 7: Cod Predation Source

The cannibalism aspect is derived from a greater proportion of older cod, who target small cod. However, the proportions may be distorted by the lack of population change in non-vpa species populations as these populations are fixed through the entire simulation to their 2003 estimates.

7.7 Fleet Profit (M Euro) Chart 8 displays the predictions for profit by fleet across each country and gear category modelled. Values are only tabulated for 2003, 2005, 2010 and 2020 for clarity. In all but a few cases, overall profit levels rise during the period 2003 – 2020. The large declines in profitability are seen with the Pelagic Trawlers. In the model, the vast majority of the catch from the pelagic trawlers is herring. In Chart 9, herring is one of the species shown in decline, thus reducing the annual catch estimated for the pelagic trawler fleets and subsequently their profitability.

59

The largest gainers in profitability are the Bottom trawl (>24m). In all countries, these fleets improve their revenue streams with larger annual catches. These bottom trawlers are all catching species forecasted to grow in stock, such as haddock, saithe and whiting.

2003 2005 2010 2015 2020 Beam >221kW (>24m) -2.2 7.4 7.5 10.3 11.6 Bottom trawl (<24m) -6.2 -12.9 4.2 54.1 102.2 Bottom trawl (>24m) -2.8 -15.2 20.0 120.6 217.3 Danish/Scottish Seine -1.5 -29.1 -13.6 24.7 61.3 Nephrops trawl 1.7 12.5 16.2 32.1 44.9

UK

Pelagic trawl/seine 0.0 -2.3 -9.6 -11.5 -12.1 Bottom trawl (<24m) -17.0 -11.1 -13.5 5.1 38.5 Bottom trawl (>24m) -16.7 -17.0 -38.3 -10.2 31.8 Danish/Scottish Seine 0.0 -0.7 1.2 5.8 11.4 Pelagic trawl/seine -1.8 10.2 -35.5 -35.7 -14.0 D

ENM

AR

K

Fixed Nets -8.7 2.4 7.3 24.9 56.4 Beam <221kw (<24m) -1.3 9.3 7.7 15.5 24.3 Beam >221kW (>24m) -1.7 62.6 41.9 47.6 62.2 Beam shrimp -1.5 -1.4 -1.2 -1.2 -1.2 Bottom trawl (>24m) 0.4 0.5 1.4 3.7 7.4

HO

LLAN

D

Pelagic trawl/seine 0.9 0.1 -1.5 -3.4 -3.8 Beam <221kw (<24m) 1.3 3.3 3.5 5.0 8.4 Beam >221kW (>24m) 1.7 9.3 11.0 19.4 34.0

BE

LGIU

M

Beam shrimp 0.2 0.4 0.5 0.5 0.5 Bottom trawl (<24m) 15.0 16.6 18.8 24.3 28.4 Bottom trawl (>24m) 38.0 40.1 52.5 62.6 70.5 Pelagic trawl/seine 0.0 -0.8 -3.4 -4.1 -4.3 Longline 1.9 2.0 2.2 2.2 2.2 FR

ANC

E

Fixed Nets 19.0 20.4 22.1 21.7 21.5 Beam shrimp 16.8 17.7 17.9 17.7 17.6 Bottom trawl (>24m) 0.5 5.2 17.0 44.2 73.2

GE

RM

ANY

Pelagic trawl/seine 0.0 -1.6 -6.7 -8.1 -8.4 Bottom trawl (<24m) 0.9 13.7 27.1 63.4 102.3

Bottom trawl (>24m) 4.3 33.9 59.7 139.7 208.9

NO

RW

AY

Pelagic trawl/seine 35.4 40.1 33.8 47.9 75.3

Chart 8: Profit by Fleet

60

7.8 Other Species Stock

baseline 1 1 1 1 1 1 1 1 1 1 1 1 1 1total vpa species stock[haddock]

20 M15 M10 M5 M

01 1 1 1 1 1 1 1 1 1 1 1 1 1

total vpa species stock[HERRING]20 M

17.5 M15 M

12.5 M10 M

1 1 11 1 1 1

1 1 1 1 1 1 1total vpa species stock[N POUT]200 M150 M100 M50 M

01 1 1 1 1 1 1 1 1 1 1 1 1 1

total vpa species stock[plaice]2 M

1.7 M1.4 M1.1 M

800,000 1 1 1 1 1 1 1 1 1 1 1 1 1 1

total vpa species stock[saithe]2 M

1.5 M1 M

500,0000

1 1 1 1 1 1 1 1 1 1 1 1 1 1

total vpa species stock[SANDEEL]200 M150 M100 M50 M

0

1 11

1 1 1 1 1 1 1 1 1 1 1

total vpa species stock[sole]400,000300,000200,000100,000

0

1 1 1 1 1 1 1 1 1 1 1 1 1 1

total vpa species stock[SPRAT]40 M30 M20 M10 M

0

1 1 1 1 1 1 1 1 1 1 1 1 1 1

total vpa species stock[whiting]10 M7.5 M

5 M2.5 M

0

1 1 1 1 1 1 1 1 1 1 1 1 1 1

2004 2008 2012 2016 2020Time (Year)

Chart 9: VPA Species Stock Levels

The forecast stock levels other species modelled are shown in Chart 9. With the exception of sole, sprat and herring, these all increase in size over the period modelled. Herring, although initially showing population growth, starts to decline in

61

2006, and continues until 2020 when there are 2.5 million less fish than estimated in 2003. Sole shows a decline in stock numbers year on year, and by 2020 the stock size is only 50% of the 2003 estimates. Sprat stock level has a similar behaviour to herring. Following an increase rise to 2006, there is then a steady decline. By 2020, sprat stock is reduced to a bit more than one-third of the 2006 level.

7.9 Catch by Species

Species Catch (Tonnes)

0

500000

1000000

1500000

2000000

2500000

3000000

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Year

Tonn

es

[whiting][SPRAT][sole][SANDEEL][saithe][plaice][N POUT][HERRING][haddock][cod]Time (Year)

Chart 10: Catch by Species

Chart 10 displays the annual catch for each of the vpa species over the period modelled. Significant increases in catch can be seen for cod, haddock, Norway pout, saithe and whiting. The total catch across vpa species increases from 150 thousand tonnes in 2003 to 275 thousand tonnes in 2020.

62

8 Baseline Sensitivity In the initial baseline scenario discussed, the model was run in a purely deterministic simulation. To test the sensitivity of the key outputs the baseline scenario was re-run in a stochastic mode. The parameters selected for sensitivity impact were

• Recruitment (by vpa species) • Natural mortality coefficient M1 (by vpa species and age) • Predator suitability coefficients (by vpa species, vpa age group 7 predator) • Predator consumption (by predator) • Prey mean weight (by vpa species) • Initial F-values (by vpa species and age)

For each of the above, the model randomly selected a new value +/- 25% of the original baseline value. In the case of recruitment, a +/- 100% range was used. The baseline scenario was then rerun 1000 times, each with a different random selection of parameter values.

sensitivityhistoric stock50% 75% 95% 100%total vpa species stock[cod]

4 M

3 M

2 M

1 M

01963 1977 1992 2006 2020

Time (Year)

Chart 11: Cod Stock Level Sensitivity

Chart 11 displays the confidence ranges of the cod stock over the 100 simulations. The average over all these still shows a recovery in the cod stock, and the original deterministic baseline scenario falls within the 50% confidence bounds. Although simulations were found that continued the cod stock level decline, the sensitivity chart shows that it is more likely the cod stocks will recover.

63

sensitivityhistoric stock50% 75% 95% 100%annual recruitment no of fish[cod]

2 M

1.5 M

1 M

500,000

01963 1977 1992 2006 2020

Time (Year) Chart 12: Code Recruitment Sensitivity

The main driver within the model is stock recruitment. As would be expected, the confidence patterns display for cod stock level closely match those seen in Chart 12 which display the randomly selected recruitment rates. The impact of these sensitivity runs on fleet profitability can be seen in Chart 13 to Chart 19.

64

UK Fleet sensitivity50% 75% 95% 100%proft by fleet[UK,"Beam >221kW (>24m)"]

60

30

0

-30

-60proft by fleet[UK,"Bottom trawl (<24m)"]

400

280

160

40

-80proft by fleet[UK,"Bottom trawl (>24m)"]

600

400

200

0

-200proft by fleet[UK,"Danish/Scottish Seine"]

200

100

0

-100

-200proft by fleet[UK,Nephrops trawl]

100

74.8

49.6

24.4

-0.8proft by fleet[UK,"Pelagic trawl/seine"]

40

20

0

-20

-402003 2007 2012 2016 2020

Time (Year) Chart 13: UK Fleet Sensitivity

65

sensitivity50% 75% 95% 100%proft by fleet[DENMARK,"Bottom trawl (<24m)"]

400

280

160

40

-80proft by fleet[DENMARK,"Bottom trawl (>24m)"]2,000

1,450

900

350

-200proft by fleet[DENMARK,"Danish/Scottish Seine"]

40

20

0

-20

-40proft by fleet[DENMARK,"Pelagic trawl/seine"]2,000

1,450

900

350

-200proft by fleet[DENMARK,FIXED NETS]

200

145

90

35

-202003 2007 2012 2016 2020

Time (Year) Chart 14: Denmark Fleet Sensitivity

66

sensitivity50% 75% 95% 100%proft by fleet[HOLLAND,"Beam <221kw (<24m)"]

60

40

20

0

-20proft by fleet[HOLLAND,"Beam >221kW (>24m)"]

200

100

0

-100

-200proft by fleet[HOLLAND,"Bottom trawl (>24m)"]

20

14.5

9

3.5

-2proft by fleet[HOLLAND,"Pelagic trawl/seine"]

40

20

0

-20

-402003 2007 2012 2016 2020

Time (Year) Chart 15: Holland Fleet Sensitivity

67

sensitivity50% 75% 95% 100%proft by fleet[BELGIUM,"Beam <221kw (<24m)"]

40

29.5

19

8.5

-2proft by fleet[BELGIUM,"Beam >221kW (>24m)"]

100

72.5

45

17.5

-102003 2007 2012 2016 2020

Time (Year) Chart 16: Belgium Fleet Sensitivity

68

sensitivity50% 75% 95% 100%proft by fleet[FRANCE,"Bottom trawl (>24m)"]

80

60

40

20

0proft by fleet[FRANCE,"Pelagic trawl/seine"]

20

10

0

-10

-20proft by fleet[FRANCE,FIXED NETS]

40

32.5

25

17.5

102003 2007 2012 2016 2020

Time (Year) Chart 17: France Fleet Sensitivity

69

sensitivity50% 75% 95% 100%proft by fleet[GERMANY,"Bottom trawl (>24m)"]

200

145

90

35

-20proft by fleet[GERMANY,"Pelagic trawl/seine"]

40

20

0

-20

-402003 2007 2012 2016 2020

Time (Year) Chart 18: Germany Fleet Sensitivity

70

sensitivity50% 75% 95% 100%proft by fleet[NORWAY,"Bottom trawl (<24m)"]

400

290

180

70

-40proft by fleet[NORWAY,"Bottom trawl (>24m)"]2,000

1,485

970

455

-60proft by fleet[NORWAY,"Pelagic trawl/seine"]2,000

1,495

990

485

-202003 2007 2012 2016 2020

Time (Year) Chart 19: Norway Fleet Sensitivity

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8.1 Technology Creep The baseline scenario assumed that for each fleet, if stock levels remained constant as did the number of days at sea, the catch for each fleet would remain constant. This does not allow for any improvement in techniques, efficiency or equipment of any national fleet. Technology creep is the term used to represent the improvement in efficiency of each fleet. Five variations on the baseline scenario were simulated, in the first, all fleet were assumed to increase in catch efficiency by 1% annually. The subsequent simulations increased this figure by a further 1% each time. Chart 20 displays the effect on cod stocks if assumptions about technology creep were applied. In all cases where technology creep was applied, a rate of decline in cod stocks was seen by 2020. However, in all but the 5% creep scenario, the cod stock recovered for a period then started to decline.

total vpa species stock800,000

600,000

400,000

200,000

02003 2005 2007 2009 2011 2013 2015 2017 2019

Time (Year)

OO

Os

total vpa species stock[cod] : Tech Creep 5%total vpa species stock[cod] : Tech Creep 4%total vpa species stock[cod] : Tech Creep 3%total vpa species stock[cod] : Tech Creep 2%total vpa species stock[cod] : Tech Creep 1%total vpa species stock[cod] : baseline

Chart 20: Effect of Technology Creep on Cod Stock

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8.2 Validating the Biological Model The model is based on the best information available. Many, if not most, of the parameters are based on past observations, e.g. data series from ICES covering several decades. The biological model (MSVPA) on which the EcoCoRP bioeconomic framework is founded, has been “tuned” to fisheries and survey catch data from 1963 to 2003. Consequently, the starting parameters of the model, actually reflect a comprehensive ‘fitting’ and validation exercise involving over 40 years of detailed catch-at age information for the 10 main species. Most single-species fishery stock assessments, including all of those on which MSVPA are based, routinely perform ‘retrospective’ analyses to check whether predictions reflect what has actually been ‘observed’ in recent years (for a detailed discussion of this issue and an assessment of model performance see Reeves & Pastoors 2007). In addition, the feeding functions included in the MSVPA model (not a feature of the single-species models) have been validated using several independent sets of stomach content data. Central to the MSVPA model is the concept of ‘suitability’ coefficients. ‘Suitability’ expresses how much a predator of a certain age would eat of a certain prey, relative to the total prey biomass available, of all possible ages, of all prey species. There has been much discussion about the stability (inter-annual) of ‘suitability’ coefficients (e.g. Rice et al. 1991; Jurado-Molina et al. 2005; Kempf et al. 2006). If parameters are stable, models can be used in the development of mid to long-term management advice. If parameter estimates vary greatly from year to year, such models may have limited value for managers. To test the stability of ‘suitability’ coefficients additional stomach sampling was carried out in the North Sea beyond the original 1981 ‘Year of the Stomach’, in 1985, 1986 and 1987. However, the partial nature of these samplings made the tests (reported in Rice et al. 1991) incomplete. Hence it was decided to repeat a full sampling in 1991 when a further 77,000 fish stomachs were collected. Analyses (ICES 1994) suggested considerable shifts in some suitability coefficient estimates. However, those shifts did not appear to result in large changes in partial M2 values, i.e., the proportion of ‘predation mortality’ attributable to each predator-age combination. Thus it was argued that the incorporation of the new data would most likely have little impact on stock-assessment estimates. Kempf et al. (2006) have recently revisited some of the earlier work and discovered large discrepancies between future stock recruitment and yield predictions depending on the particular set of stomach data used (1981 vs. 1991). This appears to be related to large absolute differences in cumulative M2s (rather than ‘partial M2s’) especially for young age groups. The biological part of the EcoCoRP model was tested (prior to implementing the economic component) to ensure that it gave the same overall responses to fishing, in comparison with the stand-alone MSVPA model on which it is based. Particular attention was given to the implementation of the stock-recruitment functions. A further point of validation for the model is that the predicted slight recovery of cod stocks in the ‘baseline’ scenario by 2006 is now being realised in the real

73

environment, with a slight turn-around being observed in the most recent (December 2007) ICES stock assessment.

8.3 Comparison of Baseline Model Sensitivity Results As part of the confidence building process in the model, results were compared to other predictions and observations within the same fisheries environment. For the biological comparison, key model variables for cod were compared against values contained in the ICES Working Group report on the Assessment of Demersal Stocks in the North Sea and Skagerrak§§. The report contained median stock and management estimation metrics for years 1963-2006. The model values were compared in the overlap short-term forecast period of 2003-2006. The metrics compared were:

• Total cod biomass • Cod spawning stock biomass • Cod recruitment to age 1 • Cod annual catch • Cod age distribution 2007

For the fleet Economies, net profit predictions were compared to estimates from the Scientific, Technical and Economic Committee for Fisheries (STECF)***. Comparisons were made for the largest fleets from Denmark & United Kingdom, where data was most populated. This again allowed for an overlap comparison period 2003-2006.

• Profit by Fleet o UK beam trawlers > 24m o UK demersal trawl & seine 12-24m o Danish demersal trawl <24m o Danish pelagic fishing fleet

The numbers against which the model is compared for 2006, 2007 and 2008 are not strictly observed values; they are the computed EIAA predictions assuming the agreed TACs that were set. Consequently any management measures (such as days-at-sea restrictions) that are additional may result in different estimates. Further, if the net profits are very low, vessels may exit the fleet also resulting in different estimates than we have used as “observed” to compare with.

§§ WGNSSK Draft report for 2007 *** Commission Staff Working Paper -Scientific, Technical and Economic Committee of Fisheries Subgroup on Economic Affairs (SGECA). Meeting on Economic Assessment of EU Fleets 23 – 27 October 2006.

74

Total Cod Biomass sensitivity sbSTECF50% 75% 95% 100%fish biomass tonnes[cod]400,000

300,000

200,000

100,000

02003 2004 2005 2006 2007

Time (Year) Chart 21: Total Cod Biomass (Tonnes)

Chart 21 compares the total cod stock biomass. The red line displays the ICES estimates, which falls within the 50% confidence bounds of the sensitivity run of the model. This shows a reasonably good fit of observed versus predicted. Total Cod Spawning Stock Biomass sensitivity sbSTECF50% 75% 95% 100%ssb[cod]80,000

60,000

40,000

20,000

02003 2004 2005 2006 2007

Time (Year) Chart 22: Cod Spawning Stock Biomass (Tonnes)

Chart 22 compares the total cod spawning stock biomass. Again, the red line displays the ICES estimates, which falls outside the 100% confidence bounds from the model. The ICES predictions appear as a linear extrapolation, declining through to 2006, although the 2007 predicted SSB of 32677 (not shown) is clearly back within the confidence limits. Apart from differences in assumed F the factor resulting in the difference is the recruitment dynamics, and the 3-4 year lag between the

75

recruits and these fish eventually contributing to SSB. The assessment values are based on low recruitment estimates (see next chart). Cod Recruitment Age 1 sensitivity sbSTECF50% 75% 95% 100%annual recruitment no of fish[cod]600,000

450,000

300,000

150,000

02003 2004 2005 2006 2007

Time (Year) Chart 23: Cod Recruitment Aged 1 (thousands)

The model starting values for recruitment are lower than estimated in 2003. In 2004 to 2005 the STECF estimates fall within the 50% confidence bounds from the model sensitivity runs and within the 75% bounds for 2006. Recruitment estimates for the EcoCoRP model are based on a stock recruitment relationship whereas the assessment values are based on the age-based assessment model (B-Adapt). Note, variation in recruitment is often considerable, as recruitment is related to many factors other than simply the parental stock (see section 5.1.3). Cod Annual Catch sensitivity sbSTECF50% 75% 95% 100%ACTUAL TOTAL CATCH RATE[cod]100 M

75 M

50 M

25 M

02003 2004 2005 2006 2007

Time (Year) Chart 24: Cod Annual Catch (kg/Year)

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The annual catch of cod is under estimated by the model at the start of the simulation period. Though the period 2004 to 2006, a much closer alignment is displayed, with all years falling with the confidence bounds of the model.

8.4 Age Distribution

2007 - Age Distribution Comparison

0%

10%

20%

30%

40%

50%

60%

70%

1 2 3 4 5 6 6+

Age Group (yrs)

STECF

Model

Chart 25: Cod Age Distribution 2007 (%)

The model predicted age distribution of cod in 2007 was compared with estimates from ICES. The model estimated slightly higher age 1 cod (58% v 52%), and age 3 cod (15% v 4%), under estimated year 2 cod (25% v 41%). However, unlike the previous comparisons, this is comparing the deterministic baseline scenario. The individual distribution by age in the sensitivity runs are shown below, with the red star indicting the STECF estimated percentage in 2007. Age distributions in the early age groups will be highly correlated to the cod recruitment and in the EcoCoRp model a stock recruitment relationship is used to compute recruits whereas the assessment values are based on the age-based assessment (B-Adapt). Furthermore, as already discussed, year on year variations in recruitment appear to be determined by more than just the size of the parental stock. The following charts display the distribution of percentages within each age group (1yr old – 3yr old) then the model is run in stochastic sensitivity mode. The red star in each chart shows the positions of the ICES estimate in 2007.

77

Cod Aged 1

sensitivity sbstock age pc[cod,"1"] @ 2007 sensivity histogram

80

60

40

20

0-10-0

0-1010-20

20-3030-40

40-50

50-6060-70

70-80

80-9090-100

100-110

Cod Aged 2


60

45

30

15

0-7.5-0

0-7.57.5-15

15-22.522.5-30

30-37.5

37.5-4545-52.5

52.5-60

60-67.567.5-75

75-82.5

Cod Aged 3


80

60

40

20

00-5

5-1010-15

15-2020-25

25-30

30-3535-40

40-45

45-5050-55

55-60

78

Profit - UK Beam > 24M

UK Beam >24-40m

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

av. 2003-2005 2006 2007 2008

M odelObserved

Chart 26: Profit (M Euro) – UK BEAM > 24M Fleet

Chart 26 displays the model-estimated profit for the UK Beam trawlers >24 m. The model appears to over estimate the observed values as sole are contributing to high revenues. These differences are due to the so-called “observed” values (which are actually predicted EIAA model estimates based on lower agreed sole TAC) possibly underestimating what was truly observed. In all these fleets one has to reserve judgment on goodness of fit between “observed” and predicted until the actual observed values have been collated (and take account of additional factors such as the effects of days-at-sea restrictions and other factors that impacted on the fleet in reality, but were not included in the baseline).

Uk Demersal traw l and seine 12-24m

-100.0

-80.0

-60.0

-40.0

-20.0

0.0

20.0

av. 2003-2005

2006 2007 2008 Model

Observed

Chart 27: Profit (M Euro) – UK Demersal Trawl /Seine 12-24M Fleet

Chart 27 displays the model-estimated profit for the UK Bottom Trawls <24m. The so-called observed values show significantly greater losses than the model predicts.

79

Potential reasons for this include:

• the observed average for 2003-2005 is due to massive constraints on the real fishery

• large quota cuts and days at sea restrictions that carry though to EIAA predicted years

It is more likely that in reality the net profits were lower due to vessels exiting the fishery thus the observed would fall with in range indicated in the chart as bars which are less negative.

DENMARK (<24) demersal traw l

-30.0

-25.0

-20.0

-15.0

-10.0

-5.00.0

5.0

10.0

15.0

20.0

av. 2003-2005

2006 2007 2008 Model

Observed

Chart 28: Profit (M Euro) – Denmark Demersal Trawl <24M Fleet

For this fleet the model tends to over-estimate the negative net profits. As noted, one has to reserve judgment on goodness of fit between observed and predicted until the actual observed values have been collated (to account for additional factors such as days-at-sea restrictions and other factors not included in the baseline).

Denmark (Pelagic)

-40.0

-30.0

-20.0

-10.0

0.0

10.0

20.0

av. 2003-2005 2006 2007 2008

M odel

Obser ved

Chart 29: Profit (M Euro) – Denmark Pelagic Fleet

80

The STECF and model estimates display closer fit between observed and model predicted.

9 Scenario 1: Effort Reduction

9.1 Assumptions • Fleets – Maintained at 2003 values • Effort –

o Scenarios 1abc - Global reduction in days at sea across all fleets: 10%, 20% & 30% reductions in the 2003 values and applied from 2006 onwards.

1a - global reduction by 10% to days at sea 1b - global reduction by 20% to days at sea 1c - global reduction by 30% to days at sea

o Scenarios 1def - Proportional reduction in kw/days. Establish the

'track-record' for 3 year 'reference period' between 2000 and 2003 in terms of kw/days per fleet, per country. Then reduce the total kw/days proportionally across fleets in accordance with the track record by 10%, 20% and 30% (in 2006) and convert back to 'days at sea'.

1d - KwDays reduction by 10% to days at sea 1e - KwDays reduction by 20% to days at sea 1f - KwDays reduction by 30% to days at sea

In cases where fleets had already reduced their number of days at sea in 2003 below the reduced values in scenarios 1def, the 2003 value was maintained.

• Technology Creep - None • Costs – No rise in fuel costs • Revenue – price per kg maintained at 2003 value • Predator populations – maintained at 2003 estimates

81

For example, scenarios 1abc for French Bottom trawl (<24m):

FRANCE 2003 Days at Sea (K) 56.2 Prop Time in IV 0.76 Adj Days at Sea (K) 42.7 @10% Reduction 38.5 @20% Reduction 34.2 @30% Reduction 29.9

And for scenarios 1def: • Track Record FRANCE Units 2000200120022003 Bottom trawl (<24m) Days at Sea (K)68 73 77 74 Bottom trawl (<24m) KiloWatts 75 77 80 78 • Proportion of time in Area IV: FRANCE Units 2000200120022003 Bottom trawl (<24m), Days at Sea (K)0.76 0.76 0.76 0.76 • Calculate average KWD: FRANCE 2000 2001 2002 2003 AverageDays at Sea 51.7 55.5 58.5 56.2 55.5 KW 57.0 58.5 60.8 59.3 58.9 KWD 2945.8 3246.73558.03333.93271.1 • Apply % reduction in average KWD to 2003 values:

10% 20% 30% KWD 2944.0 2616.92289.82003 KWD 3333.9 3333.93333.9Applied % 12% 22% 31%

82

9.2 Results

Cod Stock (000s)

0

200000

400000

600000

800000

1000000

1200000

Baseline : scenario 1a : scenario 1b : scenario 1c : scenario 1d : scenario 1e : scenario 1f

Scenario

Fish

103

2003

2005

2010

2015

2020

Chart 30: Scenario 1 - Cod Stocks

In scenarios 1abc, cod stocks recover to greater levels in 2020 as would be expected. A 10% reduction in days at sea increased the size of the stock to just over 800 million fish, compared to the baseline scenario value of around 700 million. A 20% reduction results in 950 million cod and the 30% reduction in 1100 million. However, the improvements in stock levels are not significantly different until after 2010.

83

Cod - Predation Source in 2020

0

20

40

60

80

100

120

1a 1b 1c 1d 1e 1f baseine

Scenario

%

WHITINGNORTH SEA MACKERELGREY SEALSGREY GUNARDSCOD

Chart 31: Scenario 1 - Cod Predation Source In all cases where cod stocks recover, cod cannibalism rises proportionally. This appears to be at the expense of those cod taken by grey seals.

baselinescenario 1ascenario 1bscenario 1cscenario 1dscenario 1escenario 1f

current age distribution[cod,PREDITOR AGE GROUP] @ 20200.6

0.45

0.3

0.15

0"1"

"2""3"

"4""5"

"6"

"7"

Chart 32: Age Distribution

In all variants of scenario 1, there was no significant difference between the age distributions of cod in 2020. Chart 33 to chart 38 display the estimated fleet profits by country and gear types. Results are tabulated for 2003 and then at 5 year intervals from 2005 onwards.

84

Chart 33: Scenario 1a - Fleet Profits

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Beam >221kW (>24m) -2.2 2.7 7.4 8.0 5.8 5.1 8.9 10.0 10.7 12.2 13.1 13.8 14.4 15.0 15.5 15.8 16.1 16.4

Bottom trawl (<24m) -6.2 -

11.4 -

12.9 -

38.2 -

36.3 -

38.3 -9.9 6.9 10.6 19.9 32.7 49.4 61.2 70.7 81.1 92.6 104.3 115.6

Bottom trawl (>24m) -2.8 -

12.6 -

15.2 -

68.4 -

67.9 -

72.6 -

12.9 22.6 30.5 50.1 76.1 109.7 133.1 151.6 172.6 195.8 219.6 242.5 Danish/Scottish Seine -1.5

-19.9

-29.1

-48.1

-50.5

-46.0

-23.4

-12.7

-11.3 -4.4 7.8 20.3 28.7 35.7 43.8 52.5 61.4 69.9

Nephrops trawl 1.7 4.0 12.5 8.7 17.1 7.4 11.2 17.6 22.1 26.9 26.7 31.4 35.1 38.1 40.9 43.7 46.7 49.5

UK

Pelagic trawl/seine 0.0 -3.4 -2.3 -7.2 -7.2 -7.7 -8.8 -9.2 -

10.3 -

10.4 -10.1 -10.5 -11.0 -11.3 -11.4 -11.5 -11.6 -11.6

Bottom trawl (<24m) -

17.0 -

19.3 -

11.1 -

20.3 -

22.0 -

23.4 -

17.1 -

13.3 -

11.8 -8.6 -5.0 1.1 7.6 12.9 19.0 27.2 36.2 45.7

Bottom trawl (>24m) -

16.7 -

39.7 -

17.0 -

44.7 -

47.8 -

45.7 -

42.8 -

43.4 -

38.0 -

32.4 -30.0 -24.0 -16.1 -10.3 -3.3 6.4 15.4 24.3 Danish/Scottish Seine 0.0 -0.6 -0.7 -2.5 -3.3 -3.0 0.1 1.6 1.7 2.8 4.2 5.7 6.9 8.0 9.1 10.5 12.0 13.5

Pelagic trawl/seine -1.8 -

11.2 10.2 -

14.9 -

23.5 -

31.4 -

36.2 -

43.1 -

44.4 -

44.7 -46.6 -46.4 -43.5 -41.5 -38.4 -33.1 -28.1 -23.0

DE

NM

AR

K

Fixed Nets -8.7 -4.3 2.4 4.8 3.4 -0.8 3.9 9.5 12.2 16.8 20.2 24.9 31.4 37.7 44.2 51.8 60.9 71.3 Beam <221kw (<24m) -1.3 4.8 9.3 11.9 9.3 7.4 8.4 10.1 11.4 12.9 14.2 16.7 19.3 21.3 23.2 25.3 27.6 30.0 Beam >221kW (>24m) -1.7 35.9 62.6 74.1 50.1 39.0 46.7 50.8 50.8 52.9 55.3 59.5 63.7 67.1 70.6 74.9 79.9 85.1

Beam shrimp -1.5 -1.5 -1.4 -1.2 -0.7 -0.7 -0.7 -0.7 -0.7 -0.7 -0.7 -0.7 -0.7 -0.7 -0.7 -0.7 -0.7 -0.7

Bottom trawl (>24m) 0.4 0.4 0.5 -0.2 -0.1 -0.1 0.9 1.7 1.8 2.1 2.7 3.5 4.3 4.9 5.7 6.6 7.6 8.8

HO

LLA

ND

Pelagic trawl/seine 0.9 -2.3 0.1 0.7 4.6 4.1 3.1 2.8 1.7 1.6 1.9 1.5 1.0 0.7 0.7 0.6 0.5 0.5 Beam <221kw (<24m) 1.3 2.7 3.3 3.6 3.0 2.7 3.3 3.9 4.0 4.1 4.5 5.1 5.8 6.4 7.1 7.9 9.0 10.1 Beam >221kW (>24m) 1.7 6.4 9.3 11.1 8.9 8.4 10.4 13.0 13.9 15.2 17.0 19.9 23.1 25.8 28.7 32.3 36.6 41.2

BE

LGIU

M

Beam shrimp 0.2 0.3 0.4 0.6 0.9 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8

Bottom trawl (<24m) 15.0 15.5 16.6 17.3 18.9 19.0 19.6 20.7 21.6 22.5 23.8 25.1 26.4 27.4 28.3 29.1 30.0 30.9

Bottom trawl (>24m) 38.0 37.7 40.1 45.8 54.4 54.1 58.4 61.3 62.4 64.2 66.8 69.7 72.0 73.8 75.5 77.3 79.1 80.7

Pelagic trawl/seine 0.0 -1.2 -0.8 -2.6 -2.6 -2.8 -3.2 -3.3 -3.7 -3.8 -3.6 -3.8 -4.0 -4.1 -4.1 -4.1 -4.2 -4.2

Longline 1.9 1.9 2.0 2.2 2.4 2.4 2.4 2.4 2.4 2.4 2.4 2.4 2.4 2.4 2.4 2.4 2.4 2.4

FRA

NC

E

Fixed Nets 19.0 19.6 20.4 22.8 24.7 24.4 24.3 24.3 24.2 24.1 24.1 24.0 24.0 23.9 23.9 23.9 23.8 23.8

Beam shrimp 16.8 17.2 17.7 18.4 19.0 18.9 18.9 18.8 18.8 18.7 18.7 18.6 18.6 18.6 18.6 18.5 18.5 18.5

Bottom trawl (>24m) 0.5 2.0 5.2 -0.1 -1.9 -2.8 7.6 16.6 20.9 26.4 32.9 40.7 47.3 52.7 58.6 65.5 73.3 81.2

GE

RM

AN

Y


Bottom trawl (<24m) 0.9 5.8 13.7 9.6 5.6 4.8 16.0 26.3 33.3 41.5 49.2 58.1 66.5 73.4 80.9 90.0 99.9 109.9

Bottom trawl (>24m) 4.3 9.1 33.9 27.0 17.2 17.3 36.2 55.0 74.4 94.6 109.5 124.9 140.3 152.4 165.5 180.9 195.9 210.8

NO

RW

AY

Pelagic trawl/seine 35.4 30.9 40.1 35.9 34.8 35.4 36.3 35.8 35.9 39.5 42.0 44.3 48.2 51.6 56.0 62.0 67.5 73.3

85

Chart 34: Scenario 1b - Fleet Profit

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Beam >221kW (>24m) -2.2 2.7 7.4 8.0 8.5 6.3 10.7 12.5 13.5 15.5 16.8 17.7 18.6 19.4 20.1 20.6 21.0 21.3


11.4 -

12.9 -

38.2 -

33.8 -

37.0 -9.5 9.7 14.1 23.8 37.4 55.7 69.2 79.9 91.6 104.5 118.1 131.2


12.6 -

15.2 -

68.4 -

65.8 -

73.5 -

15.6 25.0 34.8 55.5 83.4 120.5 147.3 168.6 192.1 218.2 245.6 272.1

Danish/Scottish Seine -1.5 -

19.9 -

29.1 -

48.1 -

49.7 -

46.6 -

24.6 -

11.9 -

10.1 -3.0 9.8 23.6 33.2 41.1 50.0 59.8 70.0 79.9

Nephrops trawl 1.7 4.0 12.5 8.7 18.5 8.4 12.1 18.9 23.9 29.3 29.1 34.3 38.5 41.9 45.1 48.3 51.7 54.9

UK

Pelagic trawl/seine 0.0 -3.4 -2.3 -7.2 -6.2 -7.5 -8.5 -8.8 -9.9 -

10.0 -9.6 -10.0 -10.5 -10.8 -10.9 -11.0 -11.1 -11.1


17.0 -

19.3 -

11.1 -

20.3 -

19.5 -

24.5 -

18.1 -

13.4 -

11.5 -8.0 -3.9 2.9 10.3 16.5 23.5 32.8 43.3 54.3


16.7 -

39.7 -

17.0 -

44.7 -

44.8 -

52.1 -

48.8 -

48.8 -

43.8 -

38.3 -35.7 -29.8 -22.1 -16.4 -9.7 -0.4 8.3 16.8

Danish/Scottish Seine 0.0 -0.6 -0.7 -2.5 -3.0 -3.0 0.1 1.9 2.2 3.4 5.0 6.7 8.1 9.4 10.7 12.3 14.1 16.0


11.2 10.2 -

14.9 -

21.4 -

40.5 -

44.6 -

51.1 -

52.5 -

52.8 -54.5 -54.3 -51.7 -49.7 -46.9 -41.8 -37.1 -32.4

DEN

MA

RK

Fixed Nets -8.7 -4.3 2.4 4.8 5.0 -0.5 4.9 11.6 15.2 20.8 25.0 30.7 38.8 46.8 55.1 64.6 76.2 89.4

Beam <221kw (<24m) -1.3 4.8 9.3 11.9 11.9 9.0 10.4 12.4 13.9 15.7 17.2 20.1 23.1 25.5 27.7 30.3 33.2 36.2

Beam >221kW (>24m) -1.7 35.9 62.6 74.1 59.7 41.9 52.7 59.3 60.8 64.6 68.6 74.3 80.3 85.2 90.1 96.0 102.6 109.5

Beam shrimp -1.5 -1.5 -1.4 -1.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2

Bottom trawl (>24m) 0.4 0.4 0.5 -0.2 0.1 0.0 1.1 1.9 2.1 2.5 3.1 4.0 4.9 5.7 6.6 7.6 8.9 10.3 HO

LLAN

D

Pelagic trawl/seine 0.9 -2.3 0.1 0.7 9.4 8.2 7.2 7.0 5.9 5.8 6.2 5.8 5.3 5.0 5.0 4.9 4.8 4.8

Beam <221kw (<24m) 1.3 2.7 3.3 3.6 3.4 2.9 3.6 4.3 4.4 4.7 5.1 5.8 6.7 7.5 8.3 9.3 10.7 12.1

Beam >221kW (>24m) 1.7 6.4 9.3 11.1 11.0 9.5 11.9 14.9 16.1 17.7 19.7 23.1 27.0 30.4 33.9 38.2 43.5 49.1

BE

LGIU

M

Beam shrimp 0.2 0.3 0.4 0.6 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1

Bottom trawl (<24m) 15.0 15.5 16.6 17.3 20.9 20.8 21.5 22.5 23.5 24.4 25.6 27.0 28.4 29.5 30.5 31.4 32.5 33.5

Bottom trawl (>24m) 38.0 37.7 40.1 45.8 63.2 62.7 66.8 70.0 71.3 73.1 75.8 79.0 81.4 83.4 85.3 87.3 89.3 91.2


Longline 1.9 1.9 2.0 2.2 2.7 2.7 2.7 2.7 2.7 2.7 2.7 2.7 2.7 2.7 2.7 2.7 2.7 2.7 FRAN

CE

Fixed Nets 19.0 19.6 20.4 22.8 26.9 26.5 26.5 26.5 26.5 26.4 26.3 26.3 26.3 26.3 26.2 26.2 26.2 26.2

Beam shrimp 16.8 17.2 17.7 18.4 19.9 19.6 19.7 19.6 19.6 19.6 19.6 19.5 19.5 19.5 19.5 19.5 19.5 19.5

Bottom trawl (>24m) 0.5 2.0 5.2 -0.1 -1.8 -4.4 6.0 16.1 21.0 27.1 34.2 43.0 50.8 57.1 63.9 72.0 81.1 90.6

GE

RM

ANY


Bottom trawl (<24m) 0.9 5.8 13.7 9.6 6.1 2.1 13.8 25.2 33.0 41.9 50.5 60.4 69.9 77.8 86.1 96.2 107.4 118.9

Bottom trawl (>24m) 4.3 9.1 33.9 27.0 17.9 8.8 29.0 49.5 70.1 91.6 108.1 124.8 141.2 153.8 167.2 182.6 198.0 213.2

NO

RW

AY


86

Chart 35: Scenario 1c - Fleet Profit

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Beam >221kW (>24m) -2.2 2.7 7.4 8.0 11.2 7.3 12.3 14.8 16.2 18.7 20.4 21.7 22.8 23.9 24.8 25.5 26.0 26.4


11.4 -

12.9 -

38.2 -

31.4 -

35.7 -9.6 12.3 17.7 27.9 42.5 62.7 78.3 90.7 104.0 118.6 134.2 149.5


12.6 -

15.2 -

68.4 -

63.7 -

74.6 -

19.2 27.3 39.3 61.4 91.6 132.7 163.9 188.7 215.4 245.0 276.5 307.3


19.9 -

29.1 -

48.1 -

49.0 -

47.3 -

26.1 -

11.1 -8.9 -1.6 12.0 27.1 38.3 47.4 57.4 68.4 80.1 91.6 Nephrops trawl 1.7 4.0 12.5 8.7 19.8 9.5 13.0 20.2 25.8 31.8 31.8 37.6 42.5 46.4 50.2 53.9 57.7 61.5

UK



17.0 -

19.3 -

11.1 -

20.3 -

16.9 -

25.9 -

19.3 -

13.7 -

11.3 -7.5 -2.8 4.8 13.4 20.8 28.9 39.4 51.7 64.6


16.7 -

39.7 -

17.0 -

44.7 -

41.8 -

59.0 -

55.4 -

54.7 -

49.9 -

44.5 -41.7 -35.8 -28.3 -22.7 -16.2 -7.3 1.0 9.2 Danish/Scottish Seine 0.0 -0.6 -0.7 -2.5 -2.6 -3.2 0.0 2.2 2.6 4.0 5.7 7.7 9.4 10.9 12.5 14.4 16.5 18.7


11.2 10.2 -

14.9 -

19.2 -

50.2 -

53.6 -

59.6 -

61.0 -

61.4 -62.8 -62.7 -60.3 -58.4 -55.7 -51.0 -46.6 -42.2

DEN

MA

RK

Fixed Nets -8.7 -4.3 2.4 4.8 6.5 -0.3 5.7 13.6 18.3 25.1 30.3 37.1 47.2 57.6 68.1 80.2 94.8 111.7 Beam <221kw (<24m) -1.3 4.8 9.3 11.9 14.4 10.4 12.3 14.6 16.4 18.4 20.2 23.4 27.0 29.8 32.5 35.6 39.1 42.9 Beam >221kW (>24m) -1.7 35.9 62.6 74.1 69.4 43.9 57.7 67.0 70.4 76.0 81.8 89.2 97.2 104.0 110.6 118.1 126.6 135.5 Beam shrimp -1.5 -1.5 -1.4 -1.2 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 Bottom trawl (>24m) 0.4 0.4 0.5 -0.2 0.4 0.2 1.2 2.2 2.4 2.8 3.5 4.6 5.6 6.6 7.6 8.9 10.4 12.1 H

OLL

AND


BE

LGIU

M

Beam shrimp 0.2 0.3 0.4 0.6 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1.4 Bottom trawl (<24m) 15.0 15.5 16.6 17.3 22.8 22.6 23.3 24.3 25.3 26.2 27.5 28.9 30.4 31.6 32.6 33.7 34.9 36.2 Bottom trawl (>24m) 38.0 37.7 40.1 45.8 72.0 71.2 75.2 78.7 80.1 82.1 84.8 88.3 91.0 93.2 95.4 97.6 99.9 102.0 Pelagic trawl/seine 0.0 -1.2 -0.8 -2.6 -2.0 -2.8 -3.1 -3.1 -3.5 -3.6 -3.4 -3.5 -3.7 -3.8 -3.9 -3.9 -3.9 -3.9 Longline 1.9 1.9 2.0 2.2 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 FR

ANC

E

Fixed Nets 19.0 19.6 20.4 22.8 29.0 28.5 28.6 28.7 28.7 28.7 28.6 28.6 28.6 28.6 28.6 28.6 28.6 28.6 Beam shrimp 16.8 17.2 17.7 18.4 20.7 20.4 20.5 20.5 20.5 20.5 20.5 20.5 20.5 20.5 20.5 20.5 20.5 20.4 Bottom trawl (>24m) 0.5 2.0 5.2 -0.1 -1.8 -6.2 4.2 15.4 21.0 27.7 35.6 45.5 54.7 62.2 70.1 79.4 90.3 101.5

GE

RM

ANY

Pelagic trawl/seine 0.0 -2.4 -1.6 -5.1 -3.9 -5.4 -6.0 -6.2 -6.9 -7.0 -6.7 -6.9 -7.3 -7.5 -7.6 -7.6 -7.7 -7.7 Bottom trawl (<24m) 0.9 5.8 13.7 9.6 6.5 -0.8 11.1 23.8 32.4 42.1 51.7 62.8 73.8 82.9 92.3 103.5 116.2 129.4 Bottom trawl (>24m) 4.3 9.1 33.9 27.0 18.7 -0.3 20.7 43.1 65.0 87.9 106.4 124.6 142.3 155.8 169.4 184.9 200.5 216.1

NO

RW

AY


87

Chart 36: Scenario 1d - Fleet Profit

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Beam >221kW (>24m) -2.2 2.7 7.4 8.0 4.1 4.3 7.5 8.3 8.7 9.9 10.6 11.0 11.5 11.9 12.2 12.5 12.7 12.9


11.4 -

12.9 -

38.2 -

38.5 -

39.6 -

10.2 5.0 8.2 17.2 29.6 45.3 56.2 64.9 74.7 85.4 96.2 106.6


12.6 -

15.2 -

68.4 -

68.5 -

71.4 -9.6 22.5 29.5 48.6 73.8 105.4 126.8 143.9 163.5 185.1 207.0 228.0


19.9 -

29.1 -

48.1 -

50.8 -

45.3 -

22.0 -

12.6 -

11.4 -4.4 7.4 19.2 27.0 33.5 41.2 49.4 57.6 65.4 Nephrops trawl 1.7 4.0 12.5 8.7 16.0 6.4 10.3 16.3 20.4 24.9 24.6 29.0 32.3 35.0 37.5 40.1 42.8 45.4

UK

Pelagic trawl/seine 0.0 -3.4 -2.3 -7.2 -7.3 -7.7 -8.9 -9.2 -

10.3 -

10.4 -10.0 -10.5 -11.0 -11.3 -11.3 -11.4 -11.5 -11.5


17.0 -

19.3 -

11.1 -

20.3 -

22.8 -

21.9 -

15.7 -

12.4 -

11.0 -7.9 -4.5 1.3 7.4 12.3 18.1 25.8 34.2 42.9


16.7 -

39.7 -

17.0 -

44.7 -

47.9 -

40.0 -

37.4 -

38.4 -

32.8 -

26.9 -24.6 -18.5 -10.1 -4.2 3.2 13.4 22.9 32.2 Danish/Scottish Seine 0.0 -0.6 -0.7 -2.5 -3.4 -2.8 0.3 1.6 1.6 2.7 4.0 5.4 6.5 7.4 8.5 9.7 11.1 12.5


11.2 10.2 -

14.9 -

25.6 -

26.8 -

32.0 -

39.1 -

40.3 -

40.6 -42.6 -42.3 -39.2 -37.2 -33.9 -28.4 -23.2 -17.9

DE

NM

AR

K

Fixed Nets -8.7 -4.3 2.4 4.8 2.8 -0.8 3.6 8.6 10.9 15.2 18.2 22.4 28.2 33.6 39.3 45.9 53.8 62.7 Beam <221kw (<24m) -1.3 4.8 9.3 11.9 8.0 6.9 7.6 8.9 10.0 11.2 12.2 14.3 16.3 17.9 19.4 21.0 22.8 24.7 Beam >221kW (>24m) -1.7 35.9 62.6 74.1 44.5 37.6 43.7 46.6 45.6 46.8 48.3 51.6 54.9 57.4 60.1 63.7 67.8 72.1 Beam shrimp -1.5 -1.5 -1.4 -1.2 -1.2 -1.2 -1.2 -1.2 -1.2 -1.2 -1.2 -1.2 -1.2 -1.2 -1.2 -1.2 -1.2 -1.2 Bottom trawl (>24m) 0.4 0.4 0.5 -0.2 -0.3 -0.3 0.8 1.5 1.6 1.9 2.4 3.2 3.9 4.5 5.1 6.0 6.9 7.9 H

OLL

AN

D


BE

LGIU

M


AN

CE


GE

RM

AN

Y

Pelagic trawl/seine 0.0 -2.4 -1.6 -5.1 -5.2 -5.5 -6.3 -6.5 -7.3 -7.4 -7.1 -7.4 -7.8 -8.0 -8.0 -8.1 -8.1 -8.1 Bottom trawl (<24m) 0.9 5.8 13.7 9.6 4.1 4.2 14.6 23.9 30.3 38.0 45.0 52.8 60.0 65.9 72.5 80.3 88.7 97.1 Bottom trawl (>24m) 4.3 9.1 33.9 27.0 13.3 16.1 34.0 51.4 69.9 89.1 102.9 117.1 131.3 142.4 154.6 168.8 182.5 195.9

NO

RW

AY


88

Chart 37: Scenario 1e - Fleet Profit

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Beam >221kW (>24m) -2.2 2.7 7.4 8.0 5.2 4.9 8.1 9.1 9.6 10.9 11.6 12.2 12.7 13.1 13.5 13.8 14.1 14.3


11.4 -

12.9 -

38.2 -

37.7 -

39.3 -9.5 6.6 10.1 19.5 32.3 48.8 60.4 69.7 80.1 91.5 103.3 114.6


12.6 -

15.2 -

68.4 -

65.7 -

70.4 -7.3 26.9 35.1 55.4 81.6 115.0 138.0 156.3 177.2 200.3 224.0 246.9


19.9 -

29.1 -

48.1 -

50.1 -

45.0 -

21.2 -

11.1 -9.5 -2.2 10.1 22.6 31.0 37.9 46.0 54.8 63.7 72.2 Nephrops trawl 1.7 4.0 12.5 8.7 16.2 7.2 10.7 16.3 20.1 24.4 24.1 28.1 31.3 33.8 36.3 38.7 41.2 43.7

UK



17.0 -

19.3 -

11.1 -

20.3 -

18.3 -

20.7 -

14.1 -

10.4 -8.7 -5.4 -1.6 4.6 11.3 16.7 23.0 31.4 40.7 50.3


16.7 -

39.7 -

17.0 -

44.7 -

46.4 -

47.2 -

44.4 -

45.3 -

40.0 -

34.4 -

32.1 -26.5 -18.6 -13.0 -6.2 3.3 12.2 20.9 Danish/Scottish Seine 0.0 -0.6 -0.7 -2.5 -3.1 -2.7 0.6 2.0 2.1 3.2 4.7 6.2 7.4 8.4 9.6 11.0 12.5 14.0


11.2 10.2 -

14.9 -

25.4 -

36.4 -

40.8 -

47.3 -

48.5 -

48.7 -

50.3 -50.0 -47.2 -45.2 -42.1 -36.9 -32.0 -27.0

DE

NM

AR

K


OLL

AN

D


BE

LGIU

M


AN

CE


GE

RM

AN

Y


NO

RW

AY


89

Chart 38: Scenario 1f - Fleet Profit

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Beam >221kW (>24m) -2.2 2.7 7.4 8.0 7.5 6.0 9.7 11.2 12.0 13.6 14.6 15.4 16.1 16.7 17.3 17.7 18.0 18.3


11.4 -

12.9 -

38.2 -

35.9 -

38.1 -

10.7 5.6 9.6 19.0 31.5 47.9 60.0 69.7 80.3 92.1 104.4 116.4


12.6 -

15.2 -

68.4 -

61.1 -

68.7 -2.4 37.2 48.0 71.2 100.7 138.8 166.0 187.9 212.2 239.2 267.3 294.6


19.9 -

29.1 -

48.1 -

48.8 -

44.5 -

19.4 -7.3 -5.0 3.2 16.8 31.1 41.0 49.3 58.6 68.8 79.4 89.6 Nephrops trawl 1.7 4.0 12.5 8.7 16.7 8.1 11.3 16.7 20.5 24.8 24.6 28.6 31.9 34.5 37.0 39.5 42.1 44.7

UK



17.0 -

19.3 -

11.1 -

20.3 -

15.3 -

22.1 -

15.5 -

11.3 -9.4 -5.9 -1.8 4.7 11.7 17.6 24.3 33.1 43.0 53.3


16.7 -

39.7 -

17.0 -

44.7 -

43.5 -

54.5 -

51.4 -

52.1 -

47.2 -

41.8 -39.4 -34.1 -26.8 -21.4 -15.0 -6.1 2.2 10.3 Danish/Scottish Seine 0.0 -0.6 -0.7 -2.5 -2.9 -2.9 0.3 1.9 2.2 3.4 4.9 6.5 7.9 9.0 10.3 11.7 13.4 15.0


11.2 10.2 -

14.9 -

23.4 -

46.3 -

49.9 -

56.0 -

57.2 -

57.3 -58.6 -58.4 -55.7 -53.8 -51.0 -46.1 -41.5 -36.9

DE

NM

AR

K

Fixed Nets -8.7 -4.3 2.4 4.8 7.3 1.4 7.6 15.0 18.8 25.1 29.6 35.7 44.0 52.1 60.3 69.9 81.4 94.6 Beam <221kw (<24m) -1.3 4.8 9.3 11.9 11.5 9.4 10.5 12.1 13.2 14.5 15.6 17.7 19.9 21.6 23.2 25.1 27.1 29.2 Beam >221kW (>24m) -1.7 35.9 62.6 74.1 59.6 43.2 54.2 60.6 61.8 65.2 69.0 74.3 79.7 84.0 88.4 93.6 99.4 105.5 Beam shrimp -1.5 -1.5 -1.4 -1.2 -0.7 -0.7 -0.7 -0.7 -0.7 -0.7 -0.7 -0.7 -0.7 -0.7 -0.7 -0.7 -0.7 -0.7 Bottom trawl (>24m) 0.4 0.4 0.5 -0.2 -0.2 -0.3 0.9 1.8 2.0 2.4 3.0 4.0 4.9 5.7 6.6 7.7 9.0 10.4 H

OLL

AN

D


BE

LGIU

M


AN

CE


GE

RM

AN

Y

Pelagic trawl/seine 0.0 -2.4 -1.6 -5.1 -4.0 -5.4 -6.0 -6.2 -6.9 -7.0 -6.6 -6.8 -7.2 -7.4 -7.4 -7.5 -7.5 -7.5 Bottom trawl (<24m) 0.9 5.8 13.7 9.6 2.9 -1.9 8.4 18.1 25.3 33.5 41.0 49.1 56.7 62.8 69.2 76.7 84.9 93.2 Bottom trawl (>24m) 4.3 9.1 33.9 27.0 11.0 -2.1 16.7 35.4 54.9 75.4 90.8 105.6 119.7 130.4 141.4 153.8 165.7 177.3

NO

RW

AY


90

10 Scenario 2: Harvest Control Rules

10.1 Assumptions • Fleets – Maintained at 2003 values • Effort - Running forward from 2003 to 2006 assuming status quo F, then

implement restrictions on kw/days (proportionally, using the reference period). kw/days are increased or decreased by 15% if F < or > F0.4. Thus, vessels will be allowed to fish more, or less in the future depending on the state of the stock.

o F = 0.4 is the precautionary reference limit for this stock o 2a - TAC restriction applied to Cod F Value only o 2b - TAC restrictions applied to "days at sea" to all Fleets

• Technology creep - None • Costs – no rise in fuel costs • Revenue – price per kg maintained at 2003 value • Predator populations – maintained at 2003 estimates Example scenario method: For scenario 2a

• Reference F Value for Cod : 0.4 • Calculate aggregated F Value for Cod (Age 3,4,5) • If Aggregated Cod F > Reference F, Cod F(t) = Cod F(t-1) x (85%) • If Aggregated Cod F < Reference F, Cod F(t) = Cod F(t-1) x (115%)

For Scenario 2b

• Reference F Value for Cod : 0.4 • Aggregated F Value for Cod (Age 3,4,5) • If Aggregated Cod F > Reference F, Effort(t) = Effort(t-1) x (85%) • If Aggregated Cod F < Reference F, Effort(t) = Effort(t-1) x (115%)

91

10.2 Results

Cod Stocks

0

200000

400000

600000

800000

1000000

1200000

1400000

1600000

baseline : scenario 2a : scenario 2b

Year

103

20032005201020152020

Chart 39: Scenario 2 - Cod Stocks

Chart 39 displays a greater recovery in cod stocks in both scenario 2a and 2b. Following scenario 2b, there are almost twice as many cod in 2020 compared to the baseline scenario. Improvement in cod stock levels Scenario 2a take longer to appear, with little difference by 2015 to the baseline scenario. By 2020 however, scenario 2a adds around 600 million extra fish. In scenario 2b, an extra 1000 million cod are achieved by 2015, and by 2020, the cod stocks are double those shown for the baseline scenario. Scenario 2a modified the cod F-Value directly, either increasing or reducing the previous year’s values by 15%, depending on the aggregated cod reference value in that year. In scenario 2b, the aggregated cod F-value was modified indirectly by changes implemented to days at sea effort. Chart 40 displays the profile of the aggregated value for both scenarios and compared to the baseline scenario. Chart 41 displays the reduction in the UK fleets days at sea that were applied to achieve the aggregated F-values for scenario 2b.

92

aggregated f for cod4

3

2

1

02003 2005 2007 2009 2011 2013 2015 2017 2019

Time (Year)

dmnl

aggregated f for cod : baselineaggregated f for cod : scenario 2aaggregated f for cod : scenario 2b

Chart 40: Aggregated Cod F-Value

CURRENT Effort40

20

02003 2005 2007 2009 2011 2013 2015 2017 2019

Time (Year)

thou

day

s

CURRENT Effort[UK,"Beam >221kW (>24m)"] : scenario 2bCURRENT Effort[UK,"Bottom trawl (>24m)"] : scenario 2bCURRENT Effort[UK,"Danish/Scottish Seine"] : scenario 2bCURRENT Effort[UK,Nephrops trawl] : scenario 2bCURRENT Effort[UK,"Pelagic trawl/seine"] : scenario 2b

Chart 41: Days at Sea

By 2020, Chart 42 displays both scenario 2a & 2b show an increase in cod cannibalism, with scenario 2a almost double the percentage in the baseline scenario. As in the scenario 1 suite, this appears to be at the expense of grey seals.

93


0

20

40

60

80

100

120

Baseline 2a 2b

Scenario

%

WHITINGNORTH SEA MACKERELGREY SEALSGREY GUNARDSCOD

Chart 42: Scenario 2 - Cod Predation Source

Chart 42 display the annual catch of cod in each scenario compared to the baseline. Applying these harvest control rules, the annual catch is reduced until 2018, where scenario 2b then provides a greater catch estimate due to the increased stock level available. By 2019, scenario 2a also matches the catch from the baseline scenario.

baselinescenario 2ascenario 2b

Annual Catch[cod,year] @ 2020200,000

150,000

100,000

50,000

0"2003"

"2004""2005"

"2006""2007"

"2008"

"2009""2010"

"2011"

"2012""2013"

"2014"

"2015""2016"

"2017"

"2018""2019"

Chart 43: Annual Catch 103

Chart 44 and Chart 45 display the estimated fleet profits by country and gear types. Results are tabulated for 2003 and then at 5 year intervals from 2005 onwards.

94

Chart 44: Scenario 2a - Fleet Profit

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Beam >221kW (>24m) -2.2 2.7 7.4 5.5 0.8 2.6 4.4 5.3 6.5 7.6 8.7 9.7 10.7 11.5 12.1 12.6 13.0 13.4


11.4 -

12.9 -

40.2 -

24.8 -8.7 -1.9 1.6 9.8 18.6 30.5 43.5 56.9 72.7 86.9 95.8 105.6 114.0


12.6 -

15.2 -

73.5 -

42.3 -7.5 10.2 19.7 36.9 54.7 79.5 107.7 137.6 175.4 210.2 231.0 256.2 278.7


19.9 -

29.1 -

50.6 -

35.7 -

11.4 -2.7 -2.0 6.8 14.5 25.1 38.8 48.2 61.4 82.9 100.0 115.5 127.1 Nephrops trawl 1.7 4.0 12.5 8.2 17.8 15.1 10.9 10.5 13.2 17.4 23.8 30.9 37.7 44.4 50.5 55.3 60.0 64.3

UK

Pelagic trawl/seine 0.0 -3.4 -2.3 -8.8 -6.6 -5.4 -7.4 -8.9 -9.6 -9.8 -

10.0 -10.5 -10.9 -11.2 -11.5 -11.7 -11.8 -12.0


17.0 -

19.3 -

11.1 -

23.7 -

18.0 -

16.5 -

17.7 -

15.5 -

14.2 -

14.0 -

12.0 -8.8 -4.8 2.2 9.7 14.0 20.5 27.3


16.7 -

39.7 -

17.0 -

44.8 -

22.2 -

29.2 -

40.7 -

34.8 -

32.6 -

33.5 -

30.8 -26.1 -22.1 -18.5 -13.2 -7.5 -2.8 2.6 Danish/Scottish Seine 0.0 -0.6 -0.7 -3.3 -2.7 -0.2 0.9 1.2 2.0 2.6 3.5 4.6 5.9 7.6 9.1 9.9 11.0 12.0


11.2 10.2 -

22.6 -

10.8 -

19.1 -

33.3 -

32.1 -

34.4 -

39.1 -

40.6 -41.0 -41.6 -42.2 -41.0 -38.8 -37.0 -34.4

DE

NM

AR

K

Fixed Nets -8.7 -4.3 2.4 0.0 -0.4 -0.3 -0.1 1.8 4.1 6.6 9.5 12.8 17.2 26.8 36.9 41.6 50.8 62.1 Beam <221kw (<24m) -1.3 4.8 9.3 8.1 4.1 3.2 3.8 4.7 5.7 6.5 7.0 7.8 8.7 10.3 11.9 12.7 13.9 15.1 Beam >221kW (>24m) -1.7 35.9 62.6 54.3 24.2 20.6 25.0 28.1 31.1 33.5 35.5 38.1 41.3 46.4 51.5 54.2 58.3 62.4 Beam shrimp -1.5 -1.5 -1.4 -1.2 -1.2 -1.2 -1.2 -1.2 -1.2 -1.2 -1.2 -1.2 -1.2 -1.2 -1.2 -1.2 -1.2 -1.2 Bottom trawl (>24m) 0.4 0.4 0.5 -0.4 0.0 0.7 1.2 1.6 1.9 2.1 2.5 3.0 3.8 5.3 6.8 7.6 8.9 10.2 H

OLL

AN

D

Pelagic trawl/seine 0.9 -2.3 0.1 -0.8 1.3 2.5 0.6 -0.9 -1.5 -1.7 -1.9 -2.3 -2.7 -3.0 -3.3 -3.5 -3.6 -3.8 Beam <221kw (<24m) 1.3 2.7 3.3 2.9 2.0 2.1 2.3 2.5 2.6 2.7 2.8 2.9 3.2 3.9 4.5 4.9 5.4 6.1 Beam >221kW (>24m) 1.7 6.4 9.3 7.7 6.9 9.1 7.4 8.3 9.7 10.5 11.3 12.4 14.0 16.9 19.9 21.3 23.7 26.2

BE

LGIU

M


AN

CE

Fixed Nets 19.0 19.6 20.4 22.4 22.4 22.2 22.1 22.0 22.0 22.1 22.1 22.0 22.0 22.0 22.0 22.0 22.0 22.0 Beam shrimp 16.8 17.2 17.7 18.1 18.1 17.8 17.7 17.8 17.9 17.9 17.9 17.9 17.8 17.8 17.8 17.8 17.8 17.8 Bottom trawl (>24m) 0.5 2.0 5.2 -4.1 -0.4 6.3 11.0 12.8 15.8 18.7 22.9 27.9 33.4 40.9 47.9 51.5 56.7 61.8

GE

RM

AN

Y


NO

RW

AY


95

Chart 45: Scenario 2b - Fleet Profit

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Beam >221kW (>24m) -2.2 2.7 7.4 8.1 4.6 7.0 11.4 13.6 15.3 17.9 20.3 22.1 24.1 26.1 26.2 32.9 38.1 43.5


11.4 -

12.9 -

38.2 -

38.1 -

37.7 -

11.4 0.9 4.6 11.8 20.6 32.8 45.7 56.8 67.6 98.2 133.3 151.2


12.6 -

15.2 -

68.4 -

67.2 -

66.5 4.5 50.1 57.7 76.9 100.9 135.3 171.9 203.4 238.3 337.7 449.0 500.5


19.9 -

29.1 -

48.1 -

50.5 -

43.6 -

16.8 -0.6 10.1 30.0 64.0 108.4 135.9 155.1 176.0 241.3 266.6 360.5 Nephrops trawl 1.7 4.0 12.5 8.7 16.1 8.1 11.9 16.9 20.8 25.3 25.4 29.5 34.3 38.6 42.7 50.9 58.7 63.4

UK



17.0 -

19.3 -

11.1 -

20.3 -

20.7 -

16.6 -

13.1 -

12.5 -

12.9 -11.8 -10.1 -6.6 -1.1 4.1 8.2 29.1 53.9 76.8


16.7 -

39.7 -

17.0 -

44.7 -

47.1 -

40.6 -

47.4 -

57.2 -

60.8 -63.9 -68.8 -71.4 -72.3 -74.0 -77.3 -64.2 -48.9 -31.4 Danish/Scottish Seine 0.0 -0.6 -0.7 -2.5 -3.3 -2.5 0.3 1.2 1.4 2.3 3.7 5.0 6.5 8.0 9.3 14.6 20.3 24.1


11.2 10.2 -

14.9 -

25.5 -

29.9 -

45.8 -

62.8 -

73.6 -82.5 -91.1 -97.8 -

102.6 -

107.3 -

112.7 -

105.6 -96.6 -83.0

DE

NM

AR

K

Fixed Nets -8.7 -4.3 2.4 4.9 3.3 2.3 8.8 15.2 19.4 26.7 33.7 41.9 55.0 70.3 86.3 127.2 170.8 206.8 Beam <221kw (<24m) -1.3 4.8 9.3 12.0 8.5 9.9 12.3 14.8 16.9 18.8 20.5 22.7 25.4 27.6 28.1 34.1 40.7 47.5 Beam >221kW (>24m) -1.7 35.9 62.6 74.6 46.5 49.7 61.2 68.7 73.3 79.7 86.9 95.2 105.3 114.9 116.9 153.9 188.6 223.9 Beam shrimp -1.5 -1.5 -1.4 -1.2 -1.2 -0.8 -0.1 0.4 0.9 1.3 1.7 2.0 2.3 2.5 2.3 2.0 1.7 2.0 Bottom trawl (>24m) 0.4 0.4 0.5 -0.2 -0.3 -0.2 1.1 2.3 2.8 3.5 4.3 5.6 7.1 8.5 9.8 13.5 18.2 22.1 H

OLL

AN

D


BE

LGIU

M


AN

CE


GE

RM

AN

Y


NO

RW

AY


96

11 Scenario 3: Decommissioning

11.1 Assumptions

• Fleets - From 2006, a one off reduction in the number of vessels across all

fleets (in proportion to their 'track record' 2000-2003) by 10%, 20%, 30% • Effort – Maintained at 2003 values • Technology Creep - None • Costs – No rise in fuel costs • Revenue – Price per kg maintained at 2003 value • Predator Populations – maintained at 2003 estimates

In cases where fleets had already reduced their number of vessels in 2003 below the reduced values in scenarios 3abc, the 2003 value was maintained.

Example: French: Bottom trawl (<24m)

• Vessel numbers

FRANCE 2000 2001 20022003Average

Vessels 362 386 403 399 387.5

• Decommissioning - reduced numbers FRANCE 10% 20% 30% % Av Vessels 348.8 310.0 271.32003 Vessels 399.0 399.0 399.0Applied % 13% 22% 32%

97

11.2 Results

Cod Stocks

0

100000

200000

300000

400000

500000

600000

700000

800000

900000

1000000

baseline : scenario 3a : scenario 3b : scenario 3c

Year

103

20032005201020152020

Chart 46: Scenario 3 - Cod Stock Level

As would be expected, reducing the size of the fleets improves cod stocks. A 10% reduction however, only marginally increased the stock size in 2020. A 20% reduction adds an extra 100 million by 2020 and a 30% reduction adds around an extra 200 million cod. When interpreting the results it must be remembered that not all fleets may have been reduced by the 10%, 20% or 30% value. For example, the UK Bottom trawl (>24m) fleet is not changed in size under the 10% and 20% decommissioning scenarios, as the fleet size in 2003 was already below the reduced 2000-2003 averages. Not until the 30% decommissioning case was decommissioning applied to this fleet. The actual decommissioning percentages applied for all fleets are shown in Chart 47.

98

Country Fleet 10% 20% 30%

UK Beam >221kW (>24m) 9% 19% 29%

UK Bottom trawl (<24m) n/a 7% 19%

UK Bottom trawl (>24m) n/a n/a 1%

UK Danish/Scottish Seine n/a n/a n/a

UK Nephrops trawl 6% 16% 27%

UK Pelagic trawl/seine 10% 20% 30%

Denmark Bottom trawl (<24m) n/a 5% 17%

Denmark Bottom trawl (>24m) 3% 14% 24%

Denmark Danish/Scottish Seine 6% 16% 27%

Denmark Pelagic trawl/seine 6% 16% 27%

Denmark FIXED NETS n/a n/a 8%

Holland Beam <221kw (<24m) 13% 23% 32%

Holland Beam >221kW (>24m) n/a 10% 21%

Holland Beam shrimp n/a 6% 18%

Holland Bottom trawl (>24m) n/a 9% 20%

Holland Pelagic trawl/seine 10% 20% 30%

Belgium Beam <221kw (<24m) 11% 21% 31%

Belgium Beam >221kW (>24m) 7% 17% 28%

Belgium Beam shrimp 3% 14% 24%

France Bottom trawl (<24m) 13% 22% 32%

France Bottom trawl (>24m) 3% 14% 24%

France Pelagic trawl/seine 10% 20% 30%

France Longline 7% 17% 27%

France FIXED NETS 9% 19% 29%

Germany Beam shrimp 9% 19% 29%

Germany Bottom trawl (>24m) n/a 9% 20%

Germany Pelagic trawl/seine 10% 20% 30%

Norway Bottom trawl (<24m) 7% 18% 28%

Norway Bottom trawl (>24m) 11% 21% 31%

Norway Pelagic trawl/seine 7% 17% 28% n/a – no reduction applied to 2003 fleet size Chart 47: Applied Decommissioning %

99


0

20

40

60

80

100

120

3a 3b 3c Baseline

Scenario

%

GREY SEALSNORTH SEA MACKERELGREY GUNARDSWHITINGCOD

Chart 48: Scenario 3 - Cod Predation Source

Chart 48 displays that there were only marginal changes to the predator source to cod amongst the 3 decommissioning scenarios compared to the baseline. Chart 49 to Chart 51 display the estimated fleet profits by country and gear types. Results are tabulated for 2003 and then at 5 year intervals from 2005 onwards.

100

Chart 49: Scenario 3a - Fleet Profit

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Beam >221kW (>24m) -2.2 2.7 7.4 8.0 2.0 2.2 5.5 6.3 6.7 7.9 8.6 9.1 9.5 9.9 10.3 10.5 10.8 10.9


11.4 -

12.9 -

38.2 -

38.3 -

39.5 -

10.2 5.1 8.4 17.4 29.9 45.7 56.6 65.3 75.1 85.8 96.7 107.0


12.6 -

15.2 -

68.4 -

68.3 -

71.3 -9.5 22.7 29.8 49.1 74.3 106.0 127.4 144.6 164.3 185.8 207.8 228.8


19.9 -

29.1 -

48.1 -

50.8 -

45.3 -

22.0 -

12.6 -

11.3 -4.3 7.5 19.4 27.2 33.7 41.4 49.6 57.8 65.6 Nephrops trawl 1.7 4.0 12.5 8.7 16.3 7.1 10.8 16.6 20.5 24.9 24.6 28.7 32.0 34.5 37.0 39.4 42.0 44.5

UK



17.0 -

19.3 -

11.1 -

20.3 -

22.4 -

21.8 -

15.6 -

12.3 -

10.9 -7.8 -4.4 1.4 7.4 12.3 18.1 25.9 34.3 43.0


16.7 -

39.7 -

17.0 -

44.7 -

47.6 -

40.5 -

37.9 -

38.9 -

33.3 -

27.5 -

25.2 -19.1 -10.9 -5.0 2.3 12.4 21.8 31.1 Danish/Scottish Seine 0.0 -0.6 -0.7 -2.5 -3.3 -2.8 0.2 1.4 1.4 2.4 3.7 5.0 6.0 6.9 7.9 9.1 10.4 11.6


11.2 10.2 -

14.9 -

25.4 -

27.5 -

32.6 -

39.6 -

40.8 -

41.2 -

43.1 -42.8 -39.8 -37.7 -34.6 -29.0 -23.9 -18.7

DE

NM

AR

K


OLL

AN

D

Pelagic trawl/seine 0.9 -2.3 0.1 0.7 0.9 0.6 -0.5 -0.8 -1.8 -1.9 -1.6 -2.0 -2.5 -2.7 -2.8 -2.9 -2.9 -2.9 Beam <221kw (<24m) 1.3 2.7 3.3 3.6 2.7 2.7 3.2 3.7 3.6 3.8 4.1 4.6 5.1 5.5 6.1 6.7 7.6 8.5 Beam >221kW (>24m) 1.7 6.4 9.3 11.1 7.7 7.9 9.6 11.7 12.4 13.5 15.1 17.6 20.2 22.5 24.8 27.8 31.3 35.1

BE

LGIU

M


AN

CE


GE

RM

AN

Y


NO

RW

AY


101

Chart 50: Scenario 3b - Fleet Profit 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

UK Beam >221kW (>24m) -2.2 2.7 7.4 8.0 1.8 0.9 4.6 5.8 6.5 8.0 8.9 9.6 10.3 10.8 11.3 11.7 12.0 12.3 Bottom trawl (<24m) -6.2 -11.4 -12.9 -38.2 -36.7 -38.5 -10.3 5.4 9.1 18.3 30.7 46.8 58.2 67.4 77.6 88.8 100.3 111.3 Bottom trawl (>24m) -2.8 -12.6 -15.2 -68.4 -64.8 -70.0 -5.8 30.1 39.0 60.3 87.6 122.5 146.9 166.3 188.3 212.6 237.6 261.6 Danish/Scottish Seine -1.5 -19.9 -29.1 -48.1 -49.9 -44.9 -20.6 -9.9 -8.1 -0.5 12.2 25.3 34.2 41.6 50.1 59.3 68.6 77.6 Nephrops trawl 1.7 4.0 12.5 8.7 17.4 8.5 12.0 17.5 21.3 25.6 25.4 29.4 32.6 35.2 37.7 40.1 42.7 45.2 Pelagic trawl/seine 0.0 -3.4 -2.3 -7.2 -3.9 -5.1 -6.1 -6.3 -7.4 -7.5 -7.1 -7.5 -8.0 -8.3 -8.4 -8.4 -8.5 -8.5

DENMAR

K

Bottom trawl (<24m) -17.0 -19.3 -11.1 -20.3 -18.1 -21.1 -14.6 -10.8 -9.1 -5.8 -2.0 4.2 10.8 16.2 22.5 30.8 40.0 49.6

Bottom trawl (>24m) -16.7 -39.7 -17.0 -44.7 -44.2 -46.3 -43.5 -44.3 -39.0 -33.4 -31.1 -25.4 -17.7 -12.0 -5.2 4.3 13.1 21.8 Danish/Scottish Seine 0.0 -0.6 -0.7 -2.5 -2.7 -2.5 0.4 1.7 1.9 2.9 4.2 5.5 6.6 7.5 8.6 9.8 11.1 12.5 Pelagic trawl/seine -1.8 -11.2 10.2 -14.9 -23.3 -36.0 -40.3 -46.8 -48.0 -48.3 -49.9 -49.7 -46.9 -44.9 -41.9 -36.7 -31.9 -27.0 Fixed Nets -8.7 -4.3 2.4 4.8 5.3 0.5 5.9 12.1 15.2 20.4 24.2 29.3 36.3 43.0 49.9 57.9 67.5 78.5

HOLLAND

Beam <221kw (<24m) -1.3 4.8 9.3 11.9 8.4 6.7 7.7 9.2 10.3 11.6 12.7 14.8 17.0 18.6 20.2 22.0 23.9 25.9

Beam >221kW (>24m) -1.7 35.9 62.6 74.1 57.0 44.5 53.4 58.2 58.4 60.8 63.6 68.0 72.3 75.7 79.3 83.6 88.5 93.6 Beam shrimp -1.5 -1.5 -1.4 -1.2 -1.1 -1.1 -1.1 -1.1 -1.1 -1.1 -1.1 -1.1 -1.1 -1.1 -1.1 -1.1 -1.1 -1.1 Bottom trawl (>24m) 0.4 0.4 0.5 -0.2 -0.3 -0.3 0.8 1.5 1.6 1.9 2.5 3.3 4.0 4.6 5.3 6.2 7.2 8.3 Pelagic trawl/seine 0.9 -2.3 0.1 0.7 2.3 1.2 0.3 0.0 -1.0 -1.1 -0.7 -1.1 -1.5 -1.8 -1.9 -1.9 -2.0 -2.0

BELGIUM

Beam <221kw (<24m) 1.3 2.7 3.3 3.6 3.1 2.9 3.5 4.0 4.0 4.2 4.5 5.0 5.6 6.1 6.7 7.4 8.3 9.3

Beam >221kW (>24m) 1.7 6.4 9.3 11.1 9.6 9.0 11.0 13.4 14.2 15.4 17.1 19.7 22.5 25.0 27.5 30.7 34.5 38.5 Beam shrimp 0.2 0.3 0.4 0.6 1.1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

FRANCE

Bottom trawl (<24m) 15.0 15.5 16.6 17.3 31.5 31.5 32.1 33.0 33.8 34.5 35.6 36.7 37.7 38.6 39.4 40.1 40.8 41.5

Bottom trawl (>24m) 38.0 37.7 40.1 45.8 65.6 65.4 69.3 71.8 72.9 74.6 76.9 79.6 81.6 83.3 84.9 86.5 88.1 89.6 Pelagic trawl/seine 0.0 -1.2 -0.8 -2.6 -1.5 -1.9 -2.3 -2.4 -2.8 -2.8 -2.6 -2.8 -3.0 -3.1 -3.1 -3.1 -3.1 -3.1 Longline 1.9 1.9 2.0 2.2 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.8 Fixed Nets 19.0 19.6 20.4 22.8 25.1 24.8 24.7 24.7 24.7 24.6 24.5 24.5 24.4 24.4 24.4 24.3 24.3 24.3

GERMAN

Y

Beam shrimp 16.8 17.2 17.7 18.4 16.0 15.9 15.9 15.8 15.8 15.7 15.7 15.7 15.7 15.6 15.6 15.6 15.6 15.6

Bottom trawl (>24m) 0.5 2.0 5.2 -0.1 -0.5 -2.2 8.3 17.5 22.1 28.1 34.7 42.5 49.0 54.2 60.0 66.6 74.0 81.6 Pelagic trawl/seine 0.0 -2.4 -1.6 -5.1 -3.0 -3.8 -4.5 -4.7 -5.4 -5.5 -5.2 -5.5 -5.8 -6.0 -6.1 -6.1 -6.2 -6.2

NORWAY

Bottom trawl (<24m) 0.9 5.8 13.7 9.6 4.7 2.4 13.1 23.0 29.9 38.1 45.5 53.8 61.4 67.7 74.4 82.4 91.0 99.8

Bottom trawl (>24m) 4.3 9.1 33.9 27.0 10.8 6.0 24.1 41.8 60.5 79.9 94.2 108.4 122.4 133.1 144.6 157.9 170.7 183.2 Pelagic trawl/seine 35.4 30.9 40.1 35.9 36.1 34.5 35.4 35.0 34.9 38.2 40.7 42.9 46.5 49.6 53.7 59.4 64.7 70.1

102

Chart 51: Scenario 3c - Fleet Profit

scenario 3c 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

[UK,"Beam >221kW (>24m)"] -2.2 2.7 8.9 0.3 -7.4 -8.3 -4.2 -1.5 -1.9 -0.4 3.1 4.8 5.7 6.6 7.5 8.3 8.9 9.4

[UK,"Bottom trawl (<24m)"] -6.2 -11.5 -7.8 -43.4 -

39.1 -

44.1 -

41.1 -

13.2 -6.1 5.9 18.7 33.8 44.8 53.7 63.4 73.8 84.7 95.1

[UK,"Bottom trawl (>24m)"] -2.8 -12.7 -3.8 -73.5 -

68.0 -

83.6 -

76.6 -7.7 11.2 41.5 75.4 112.9 140.0 160.5 183.5 208.4 234.3 259.0

[UK,"Danish/Scottish Seine"] -1.5 -20.0 -

26.2 -50.4 -

51.3 -

49.5 -

47.4 -

21.3 -

15.6 -7.3 7.2 21.0 30.5 38.2 47.2 56.8 66.7 76.1

[UK,Nephrops trawl] 1.7 3.3 13.9 5.4 16.5 7.9 7.6 10.6 14.5 20.6 21.8 25.9 29.2 31.6 33.8 36.0 38.3 40.6

[UK,"Pelagic trawl/seine"] 0.0 -6.1 -1.6 -11.7 -5.3 -6.0 -7.3 -7.4 -8.5 -9.4 -8.8 -8.2 -8.9 -9.2 -9.2 -9.3 -9.4 -9.5

[DENMARK,"Bottom trawl (<24m)"] -

17.0 -38.8 -

30.6 -45.7 -

35.5 -

37.0 -

34.0 -

28.4 -

25.1 -

22.2 -

17.9 -11.3 -5.4 -1.3 4.3 11.4 18.9 27.4

[DENMARK,"Bottom trawl (>24m)"] -

16.7 -87.3 -

68.3 -

107.8 -

85.6 -

82.3 -

82.0 -

82.5 -

71.2 -

66.6 -

66.2 -58.4 -52.0 -49.6 -43.0 -35.2 -29.0 -20.8

[DENMARK,"Danish/Scottish Seine"] 0.0 -0.6 -0.3 -3.3 -3.3 -3.6 -2.6 0.0 0.1 0.8 2.8 4.3 5.4 6.2 7.2 8.4 9.6 10.8

[DENMARK,"Pelagic trawl/seine"] -1.8 -

102.6 -

81.4 -

107.9 -

83.5 -

83.3 -

84.4 -

90.4 -

86.0 -

86.1 -

89.1 -86.2 -83.9 -84.0 -80.8 -76.4 -73.2 -68.2

[DENMARK,FIXED NETS] -8.7 -5.1 2.6 1.0 4.2 -4.5 -1.1 7.0 6.7 10.3 18.8 25.3 31.9 38.0 44.4 51.7 60.4 70.2

[HOLLAND,"Beam <221kw (<24m)"] -1.3 4.8 11.0 1.4 1.2 1.7 4.3 5.1 5.2 7.0 8.5 10.4 12.3 13.8 15.6 17.4 19.3 21.2

[HOLLAND,"Beam >221kW (>24m)"] -1.7 35.3 73.1 13.7 6.7 10.6 32.4 37.5 31.8 38.2 49.2 55.8 60.0 64.4 69.9 75.3 80.6 86.2

[HOLLAND,Beam shrimp] -1.5 -1.5 -1.5 -1.5 -1.3 -1.3 -1.3 -1.3 -1.3 -1.3 -1.3 -1.3 -1.3 -1.3 -1.3 -1.3 -1.3 -1.3

[HOLLAND,"Bottom trawl (>24m)"] 0.4 0.4 0.5 -0.5 -0.4 -0.5 -0.2 0.7 0.9 1.2 1.7 2.4 3.0 3.5 4.1 4.8 5.6 6.4

[HOLLAND,"Pelagic trawl/seine"] 0.9 -4.9 -0.6 -10.2 -4.4 -5.1 -6.3 -6.4 -7.4 -8.3 -7.7 -7.2 -7.8 -8.1 -8.1 -8.2 -8.3 -8.4

[BELGIUM,"Beam <221kw (<24m)"] 1.3 2.7 3.6 1.9 1.9 2.0 2.8 3.3 3.2 3.4 3.9 4.5 5.0 5.5 6.0 6.6 7.4 8.3

[BELGIUM,"Beam >221kW (>24m)"] 1.7 6.3 10.5 2.7 3.7 4.8 7.7 10.0 10.2 11.7 14.1 16.8 19.4 21.7 24.3 27.3 30.9 34.6

[BELGIUM,Beam shrimp] 0.2 0.3 0.3 0.3 1.2 1.1 1.2 1.2 1.1 1.1 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2

[FRANCE,"Bottom trawl (<24m)"] 15.0 15.5 16.7 14.7 35.9 35.8 36.2 36.8 37.5 38.0 39.0 39.9 40.8 41.7 42.5 43.3 44.1 44.8

[FRANCE,"Bottom trawl (>24m)"] 38.0 37.5 38.9 32.6 71.3 70.6 71.1 75.0 76.4 78.3 80.5 83.0 84.9 86.5 88.1 89.8 91.4 92.8

[FRANCE,"Pelagic trawl/seine"] 0.0 -2.1 -0.6 -4.1 -2.0 -2.2 -2.7 -2.8 -3.1 -3.4 -3.2 -3.0 -3.3 -3.4 -3.4 -3.4 -3.4 -3.5

[FRANCE,Longline] 1.9 1.9 1.9 1.9 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1.4

[FRANCE,FIXED NETS] 19.0 19.5 20.1 20.0 24.2 23.5 23.7 24.1 23.9 23.8 23.9 23.9 23.9 23.9 23.9 23.9 23.9 23.9

[GERMANY,Beam shrimp] 16.8 17.1 17.6 17.1 13.9 13.6 13.8 13.9 13.7 13.8 13.8 13.9 13.8 13.8 13.8 13.8 13.8 13.8

[GERMANY,"Bottom trawl (>24m)"] 0.5 2.0 8.0 -2.1 -5.2 -

10.0 -7.7 1.3 6.7 11.8 20.7 28.4 35.1 39.4 44.6 50.4 56.7 63.0

[GERMANY,"Pelagic trawl/seine"] 0.0 -4.2 -1.1 -8.1 -3.9 -4.4 -5.3 -5.4 -6.1 -6.8 -6.3 -6.0 -6.4 -6.6 -6.6 -6.7 -6.8 -6.8

[NORWAY,"Bottom trawl (<24m)"] 0.9 4.8 16.9 6.1 -0.7 -7.9 -5.1 4.0 12.0 18.2 30.5 40.4 49.1 54.0 60.8 68.3 75.9 84.0

[NORWAY,"Bottom trawl (>24m)"] 4.3 1.2 34.2 8.6 -7.9 -

23.0 -

20.6 -7.8 14.7 28.4 54.0 73.6 90.4 97.6 110.3 123.0 134.2 146.3

[NORWAY,"Pelagic trawl/seine"] 35.4 22.1 35.1 12.0 25.2 24.7 21.2 21.7 22.4 21.2 23.1 27.4 28.4 29.2 33.1 37.0 39.8 44.6

103

12 Scenario 4: No Cod Fishing

12.1 Assumptions

• All Cod F-Values set to zero from 2003 • Fleets – Maintained at 2003 values • Effort – Maintained at 2003 values • Technology Creep - None • Costs – No rise in fuel costs • Revenue – Price per kg maintained at 2003 value • Predator Populations – maintained at 2003 estimates

12.2 Results

0

200000

400000

600000

800000

1000000

1200000

1400000

1600000

1800000

2000000

Fish

103

2003 2005 2007 2009 2011 2013 2015 2017 2019

Year

Cod Stock Level

No Cod FishingBaseline

Chart 52: Scenario 4 - Cod Stock Level

Stopping all cod fishing results in a dramatic rise in cod stocks. Chart 52 however, displays that this rise reaches a plateau in 2012, after which the stock starts to decline. By 2020, the stock level is below 50% of its peak value. The reason for the decline is a function of the Ricker recruitment curve. Ricker recruitment estimates are based on the SSB of cod from the previous year. The relationship between SSB and recruitment is not linear however. The recruitment function applied has the profile displayed in Chart 53. Once the cod SSB goes beyond 750,000 tonnes, recruitment numbers start to fall. Since all cod are not fully mature until age 6, this reduced number of recruits does not decline the cod SSB, there is at least a 6 year delay. Thus, although the cod stock level will start to fall, the cod SSB continues to rise for a number of years after peak recruitment. The relationship between the cod stock and the SSB is shown in chart 45.

104

Ricker Recruitment Curve (Cod)800 M

600 M

400 M

200 M

00 1.5e+006 3e+006 4.5e+006 6e+006

SSB Tonnes

fish

Recruits

Chart 53: Ricker Recruitment Curve

Cod Stock v SSB2 M K Fish6 M Tonnes

1 M K Fish3 M Tonnes

0 K Fish0 Tonnes

2003 2005 2007 2009 2011 2013 2015 2017 2019Time (Year)

Stock K FishSSB Tonnes

Chart 54: Cod Stock v SSB

Chart 46 displays the estimated fleet profits by country and gear types. Results are tabulated for 2003 and then at 5 year intervals from 2005 onwards.

105

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020UK Beam >221kW (>24m) -2.2 2.7 7.4 8.0 3.1 3.7 6.9 7.5 7.8 8.9 9.5 9.9 10.3 10.7 11.0 11.2 11.4 11.6

Bottom trawl (<24m) -6.2 -12.6 -14.4 -37.4 -37.1 -38.5 -11.2 0.2 1.6 7.3 13.1 18.8 18.2 15.0 11.9 9.7 9.2 10.8 Bottom trawl (>24m) -2.8 -15.1 -18.3 -67.3 -67.0 -69.9 -12.5 11.5 14.7 27.2 39.6 51.8 51.1 45.2 39.6 35.9 35.4 38.9 Danish/Scottish Seine -1.5 -21.1 -30.1 -47.7 -49.6 -44.5 -23.2 -17.1 -17.1 -12.7 -6.3 -2.5 -3.7 -6.3 -8.5 -9.8 -10.0 -8.6 Nephrops trawl 1.7 4.0 12.2 8.3 15.5 6.0 10.0 15.7 19.2 22.8 21.5 24.1 25.1 25.1 24.5 23.7 23.3 23.5 Pelagic trawl/seine 0.0 -3.5 -2.3 -7.3 -8.5 -8.3 -9.7 -10.2 -11.6 -11.8 -11.7 -12.3 -12.9 -13.2 -13.3 -13.4 -13.4 -13.3

DENMAR

K

Bottom trawl (<24m) -17.0 -20.6 -12.9 -20.2 -23.9 -22.4 -19.0 -18.8 -17.8 -15.7 -14.5 -12.5 -10.4 -9.1 -7.4 -4.9 -2.5 -0.1

Bottom trawl (>24m) -16.7 -39.8 -17.3 -44.8 -51.0 -40.1 -38.1 -39.4 -33.7 -27.7 -25.3 -19.1 -10.7 -4.6 3.1 13.5 23.1 32.8 Danish/Scottish Seine 0.0 -0.9 -0.9 -2.5 -3.3 -2.8 -0.2 0.4 0.4 1.1 1.8 2.2 2.2 2.1 2.1 2.1 2.2 2.4 Pelagic trawl/seine -1.8 -11.3 9.8 -15.8 -27.6 -25.6 -31.5 -38.5 -39.1 -38.4 -38.7 -35.9 -30.4 -25.9 -20.4 -12.8 -6.0 0.4 Fixed Nets -8.7 -6.1 -1.4 1.3 -1.6 -3.2 -0.7 -0.8 -0.4 1.6 2.3 2.6 3.0 3.3 3.6 3.8 4.0 4.1

HOLLAND

Beam <221kw (<24m) -1.3 4.0 8.8 11.6 6.8 5.7 5.5 5.9 7.0 7.7 7.6 7.8 7.8 7.3 6.7 6.0 5.5 5.1

Beam >221kW (>24m) -1.7 33.9 61.3 73.1 40.7 34.9 37.9 37.5 35.8 35.6 34.4 33.9 32.8 31.5 30.0 28.7 27.5 26.4 Beam shrimp -1.5 -1.5 -1.4 -1.2 -1.2 -1.2 -1.2 -1.2 -1.2 -1.2 -1.2 -1.2 -1.2 -1.2 -1.2 -1.2 -1.2 -1.2 Bottom trawl (>24m) 0.4 0.3 0.2 -0.2 -0.2 -0.3 0.5 0.7 0.7 0.9 1.0 1.2 1.2 1.1 1.0 1.0 1.0 1.0 Pelagic trawl/seine 0.9 -2.3 0.0 0.6 -0.4 -0.3 -1.6 -2.1 -3.4 -3.6 -3.5 -4.1 -4.6 -4.9 -5.0 -5.1 -5.1 -5.0

BELGIUM

Beam <221kw (<24m) 1.3 2.3 3.0 3.5 2.6 2.4 2.6 2.6 2.5 2.5 2.5 2.4 2.4 2.4 2.4 2.4 2.4 2.3

Beam >221kW (>24m) 1.7 4.7 8.2 10.4 6.9 7.0 7.2 7.4 7.7 8.2 8.3 8.4 8.4 8.2 8.1 7.9 7.7 7.6 Beam shrimp 0.2 0.3 0.4 0.6 0.6 0.6 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

FRANCE

Bottom trawl (<24m) 15.0 15.4 16.4 17.4 17.2 17.3 17.7 18.5 19.3 19.8 20.2 20.4 20.4 20.2 19.9 19.6 19.4 19.4

Bottom trawl (>24m) 38.0 37.5 40.0 46.0 45.9 45.8 49.9 52.0 52.8 53.9 55.0 56.0 55.8 55.2 54.6 54.0 53.8 54.0 Pelagic trawl/seine 0.0 -1.2 -0.8 -2.6 -3.0 -3.0 -3.5 -3.7 -4.1 -4.2 -4.2 -4.4 -4.6 -4.7 -4.7 -4.8 -4.8 -4.7 Longline 1.9 1.9 2.0 2.2 2.2 2.2 2.2 2.2 2.2 2.2 2.2 2.2 2.2 2.2 2.2 2.2 2.2 2.2 Fixed Nets 19.0 19.6 20.4 22.8 22.6 22.4 22.2 22.1 22.0 21.9 21.8 21.8 21.7 21.7 21.6 21.6 21.5 21.5

GERMAN

Y

Beam shrimp 16.8 17.2 17.7 18.4 18.2 18.1 18.0 18.0 17.9 17.8 17.8 17.7 17.7 17.7 17.6 17.6 17.6 17.6

Bottom trawl (>24m) 0.5 0.9 3.6 -0.1 -1.2 -1.0 6.9 12.1 15.2 19.0 21.9 24.2 24.8 24.7 24.5 24.4 24.5 25.0 Pelagic trawl/seine 0.0 -2.4 -1.6 -5.2 -6.0 -5.9 -6.8 -7.2 -8.1 -8.3 -8.2 -8.6 -9.0 -9.2 -9.3 -9.4 -9.4 -9.3

NORWAY

Bottom trawl (<24m) 0.9 4.4 11.9 9.9 6.2 7.7 15.8 22.0 27.7 34.3 38.7 42.5 45.5 47.8 50.2 53.0 55.6 58.2

Bottom trawl (>24m) 4.3 7.9 32.3 27.3 17.5 25.6 41.0 55.2 73.1 91.4 102.7 113.7 124.9 133.6 143.5 154.9 165.0 174.9 Pelagic trawl/seine 35.4 30.8 39.9 35.7 29.4 32.4 33.1 32.3 32.4 36.1 38.7 41.5 46.3 50.7 56.5 64.0 71.1 78.4

Chart 55: Scenario 4 - Fleet Profit

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13 Comparison Across Scenarios

13.1 Cod Stock Level Charts 56 & 57 compare the cod stocks from 2003 to 2020 across all the scenarios simulated.

total vpa species stock2 M

1.5 M

1 M

500,000

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2003 2005 2007 2009 2011 2013 2015 2017 2019Time (Year)

OO

Os

total vpa species stock[cod] : baseline 1 1 1total vpa species stock[cod] : scenario 1a 2 2 2total vpa species stock[cod] : scenario 1c 3 3 3total vpa species stock[cod] : scenario 1d 4 4 4total vpa species stock[cod] : scenario 1f 5 5 5total vpa species stock[cod] : scenario 2a 6 6 6total vpa species stock[cod] : scenario 2b 7 7 7total vpa species stock[cod] : scenario 3a 8 8 8 8total vpa species stock[cod] : scenario 3c 9 9 9 9total vpa species stock[cod] : scenario 5 0 0 0 0

Chart 56: Cod Stock Comparison across Scenarios

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Cod Stock Level Comparison

0

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2005 2010 2015 2020

Year

103

Baseline : scenario 1a : scenario 1b : scenario 1c : scenario 1d : scenario 1e : scenario 1f : scenario 2a : scenario 2b : scenario 3a : scenario 3b : scenario 3c : scenario 4

Chart 57: Cod Stock Comparison

Apart from the ‘no cod fishing’ scenario, the scenario giving rise to the most cod in 2020 was scenario 2b. This scenario was based on the harvest control rule, where effort was reduced by 15% if the aggregated f-value for cod was above the reference value of 0.4. The second most successful scenario for replenishing cod stocks was the other harvest control rule, where cod F-values were reduced by 15% if the previous year’s F-value was above the reference value.

UK- Average Fleet Profit Comparison

-40

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Scenario

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uro

Beam >221kW (>24m)

Bottom trawl (<24m)

Bottom trawl (>24m)

Danish/Scottish Seine

Nephrops trawl

Pelagic trawl/seine

Chart 58: UK Fleet Profit Comparison (2020)

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The results initial seem counter intuitive. For example, Scenario 1c which modelled a 30% reduction in days at sea, resulted in a greater average annual profit than the baseline scenario. The reason for this is two-fold. Firstly, there is a reduction in variable costs, predominantly fuel costs, since fewer days are spent at sea. Secondly, since the cod stocks recover faster in Scenario 1c, more cod stock can is caught for the same amount of effort. This is reflected in Chart 59.

revenue by fleet and species200

150

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50

0 2 2 2 22 2

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2003 2005 2007 2009 2011 2013 2015 2017 2019Time (Year)

m e

uro

revenue by fleet and species[UK,"Bottom trawl (>24m)",cod] : baseline 1 1revenue by fleet and species[UK,"Bottom trawl (>24m)",cod] : scenario 1c 2

Chart 59: UK Bottom Trawlers >24M Revenue

In the Baseline scenario (from 2006 to 2010) revenue in marginally greater than in the case of Scenario 1c, where a 30% reduction in effort was enforced in 2006. However, from 2010 onwards, the revenue from scenario 1c grows much faster in scenario 1c than in the base line scenario. By 2020, the annual revenue from Scenario 1c is almost double than that in the baseline scenario. This is in direct correlation to the cod stock sizes in each scenario, shown in Chart 60.

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total vpa species stock2 M

1.5 M

1 M

500,000

0 2 2 2 22

2 22

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2003 2005 2007 2009 2011 2013 2015 2017 2019Time (Year)

OO

Os

total vpa species stock[cod] : baseline 1 1 1 1 1 1total vpa species stock[cod] : scenario 1c 2 2 2 2 2

Chart 60: Cod Stock – Baseline v Scenario 1c

Comparisons of profit by fleet for all countries in 2020 is shown in Chart 61.

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Base 1a 1b 1c 1d 1e 1f 2a 2b 3a 3b 4 Beam >221kW (>24m) 11.6 16.4 21.3 26.4 12.9 14.3 18.3 13.4 43.5 10.9 12.3 11.6 Bottom trawl (<24m) 102.2 115.6 131.2 149.5 106.6 114.6 116.4 114.0 151.2 107.0 111.3 10.8 Bottom trawl (>24m) 217.3 242.5 272.1 307.3 228.0 246.9 294.6 278.7 500.5 228.8 261.6 38.9 Danish/Scottish Seine 61.3 69.9 79.9 91.6 65.4 72.2 89.6 127.1 360.5 65.6 77.6 -8.6 Nephrops trawl 44.9 49.5 54.9 61.5 45.4 43.7 44.7 64.3 63.4 44.5 45.2 23.5

UK

Pelagic trawl/seine -12.1 -11.6 -11.1 -10.6 -11.5 -10.9 -10.4 -12.0 -8.3 -10.3 -8.5 -13.3 Bottom trawl (<24m) 38.5 45.7 54.3 64.6 42.9 50.3 53.3 27.3 76.8 43.0 49.6 -0.1 Bottom trawl (>24m) 31.8 24.3 16.8 9.2 32.2 20.9 10.3 2.6 -31.4 31.1 21.8 32.8 Danish/Scottish Seine 11.4 13.5 16.0 18.7 12.5 14.0 15.0 12.0 24.1 11.6 12.5 2.4 Pelagic trawl/seine -14.0 -23.0 -32.4 -42.2 -17.9 -27.0 -36.9 -34.4 -83.0 -18.7 -27.0 0.4 D

EN

MA

RK

Fixed Nets 56.4 71.3 89.4 111.7 62.7 72.0 94.6 62.1 206.8 62.6 78.5 4.1 Beam <221kw (<24m) 24.3 30.0 36.2 42.9 24.7 25.6 29.2 15.1 47.5 23.6 25.9 5.1 Beam >221kW (>24m) 62.2 85.1 109.5 135.5 72.1 83.9 105.5 62.4 223.9 72.0 93.6 26.4 Beam shrimp -1.2 -0.7 -0.2 0.3 -1.2 -1.2 -0.7 -1.2 2.0 -1.2 -1.1 -1.2 Bottom trawl (>24m) 7.4 8.8 10.3 12.1 7.9 8.6 10.4 10.2 22.1 7.9 8.3 1.0 H

OLL

AN

D

Pelagic trawl/seine -3.8 0.5 4.8 9.1 1.6 5.9 10.1 -3.8 28.0 -2.9 -2.0 -5.0 Beam <221kw (<24m) 8.4 10.1 12.1 14.3 8.4 8.5 9.5 6.1 15.1 8.5 9.3 2.3 Beam >221kW (>24m) 34.0 41.2 49.1 58.1 35.3 35.8 39.9 26.2 63.6 35.1 38.5 7.6

BE

LGIU

M

Beam shrimp 0.5 0.8 1.1 1.4 0.5 0.5 0.6 0.5 2.4 0.6 1.0 0.5 Bottom trawl (<24m) 28.4 30.9 33.5 36.2 29.6 30.7 32.4 24.0 40.4 35.5 41.5 19.4 Bottom trawl (>24m) 70.5 80.7 91.2 102.0 71.2 77.6 87.0 69.9 131.0 74.7 89.6 54.0 Pelagic trawl/seine -4.3 -4.2 -4.0 -3.9 -4.1 -4.0 -3.8 -4.3 -3.3 -3.7 -3.1 -4.7 Longline 2.2 2.4 2.7 3.0 2.2 2.4 2.7 2.2 4.1 2.0 1.8 2.2 FR

AN

CE

Fixed Nets 21.5 23.8 26.2 28.6 23.1 25.2 27.6 22.0 38.6 22.7 24.3 21.5 Beam shrimp 17.6 18.5 19.5 20.4 18.4 19.2 20.1 17.8 24.6 16.6 15.6 17.6 Bottom trawl (>24m) 73.2 81.2 90.6 101.5 76.6 73.2 74.1 61.8 95.5 79.4 81.6 25.0

GE

RM

AN

Y

Pelagic trawl/seine -8.4 -8.2 -8.0 -7.7 -8.1 -7.8 -7.5 -8.4 -6.5 -7.3 -6.2 -9.3 Bottom trawl (<24m) 102.3 109.9 118.9 129.4 97.1 93.1 93.2 74.5 114.6 100.7 99.8 58.2 Bottom trawl (>24m) 208.9 210.8 213.2 216.1 195.9 185.4 177.3 144.5 177.6 191.4 183.2 174.9

NO

RW

AY

Pelagic trawl/seine 75.3 73.3 71.3 69.3 73.4 71.9 70.1 47.7 63.2 73.2 70.1 78.4

Chart 61: UK Fleet Profit Comparison (2020)

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14 User Interface Effectively communicating the results of the model analyses to stakeholders is as important, if not more important, than developing the most scientifically robust model possible. A model has limited value if the results cannot be interpreted by the main users. A key feature of the proposed model is that it has been designed for maximum usability by non-experts. The development of such user-friendly interfaces is becoming increasingly important as stakeholders are playing an increasing role in fisheries policy advice. The recently formed North Sea RAC is an obvious potential user of the model, but it is unlikely that members of this Council will have the technical skills to use a “traditional” bioeconomic model unless a user-friendly interface is developed. The main components of the user interface are introduced below.

14.1 Title screen:

The opening page of the packaged simulation model. This screen provides the main navigation menu for accessing the simulation and analysis tools.

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14.2 Background: By selecting “Background” from the main menu, the user may read an overview description of the scope of the model developed. To return to the main menu, simply select the return arrow in the lower left corner.

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14.3 Model structure: View the structure of model by each functional area. The user can navigate to each of these functional areas by clicking on the view names listed on the left-hand side of the screen. Views may be printed or copied to the clipboard by selecting FILE>PRINT. To return to the main menu, simply select the return arrow in the lower left corner.

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14.4 Model initialisation: View/amend model inputs based on 2003 data from MSVPA and Annual Economic Report. The default data used for 2003 is held in two spreadsheets:

• ECONDATA.XLS • ECOCORP DATA.XLS

The ECONDATA.XLS holds the majority of the econometric data used by the model and ECOCORP DATA>XLS stores the biological inputs. Both of these spreadsheets are located in the same directory as the model. The data inputs in the spreadsheet are automatically loaded each time the model is simulated. Changed made on this screen are only applied to the next simulation run, with the input data then reverting to the default values in the spreadsheets.

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14.5 Create scenarios: The fleet data can be viewed by county. For each country, the fleet size and number of days at sea in 2003 is displayed. For each individual country scenarios can be modified by:

• Changes in days at sea • Changes to fleet size

In addition to fleet specific data, scenarios can be modified by:

• Global increases in all fleet efficiencies • Stochastic variation in recruitment estimates • Changes to non VPA predator populations • price of fuel

From this screen, the user can also loads the pre-defined data sets which were used to generate the scenarios outlined in this report.

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14.6 Simulate: Execute the model with the desired 2003 inputs and scenario configurations. Simulations take only seconds to run. As the simulation executes, the forecast stocks sizes update under the picture of each VPA species modelled. The chart displays the variations in these stock sizes over the duration of the simulation

14.7 Outputs: View model variables by: time series; bar charts; tabular form; and causal tree diagrams. View model variables by configurations: country; fleet; species; and age groups.

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Key model variables are accessed by clicking on the parameter names on the left hand side of the screen. Two lists are available, the user being able to toggle between Biological Output and Economic Outputs. In the lower part of the screen are options to only select sub groups to display in the charts. These subgroups include:

• VPA species • Age groups • Country • Fleet gear type • Predator • Year

Note, these sub groups are only pertinent to certain modelled parameters. The Compare Scenario option allows the user to load previously generated scenarios for comparison purposes. Scenario outputs are selected for display by entering them into the right-hand side window shown below.

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14.8 Sensitivity testing: Multiple runs of model to test result sensitivity to:

• 2003 F Values • Live weight estimates • Natural mortalities • Predator consumption • 2003 recruitment rates • Prey/predator suitability coefficients.

The user selects one or more input item to test the model sensitivity by selecting the check box. For each input selected, the user can then define the amount of variability to be applied in terms of a percentage figure.

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15 Summary The primary aim of the study is to determine the likely economic impacts on the different fishing fleet segments currently operating in the North Sea (ICES Division IV) resulting from the implementation of effort reductions imposed by cod recovery measures. This has been achieved using a state-of-the-art “dynamic bioeconomic model” of the North Sea fisheries, developed in a systems dynamics framework using multispecies and economic modelling. The likely economic effects/consequences on the different fishing fleet segments which are fishing in ICES division IV resulting from effort reductions imposed by cod recovery measures have been evaluated, as well as their impact on the profitability of these fishing fleets. The results presented above show clearly that the economic consequences on the current fleet are worsening into the future. The ICES advice for the past 6 years has been to stop cod fishing in the North Sea. The effects of doing so are uncertain for the stock. However, as indicated by ICES, it is likely that some increase in stock could be seen over time (see Figure 54). sensitivity 5historic stock50% 75% 95% 100%total vpa species stock[cod]

4 M

3 M

2 M

1 M

01963 1977 1992 2006 2020

Time (Year)

Figure 54: Stop All Cod Fishing - Estimate on Cod Stock

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16 References

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recruitment in the North Sea. Nature, 661-664.

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mailto:[email protected]

Appendix 1 - 1

Appendix 1: Review of cod-recovery legislation (2001-2004) EC Reference Number

Primary directive or Amendment

Topic/implications Date entering force

259-2001 �

P Throughout the period 14 February to 30 April 2001 it was prohibited to conduct any fishing activity within the ‘closure area’ detailed in APPENDIX 1 Section 1 (below).in order “to allow as many cod as possible to spawn”. However, fishing with gears appropriate for the capture of pelagic fish and sandeels were perceived as representing no threat to the cod stock. Fisheries for these species were permitted to operate in the ‘closed area’ provided observers be placed on board Community vessels for at least 50 voyages. To further ensure compliance by Community fishing vessels operating within or passing through the closed area, additional measures were required, notably on at least 100 occasions, the landings of vessels which had operated within the ‘closed area’ but with no observer on board must be sampled immediately after landing and the results transmitted to the Commission.

10/02/2001

714-2001 �

A (259-2001) Amending Regulation (EC) No 259/2001. Confirms that fishing for pelagic fish and sandeels creates no peril for cod, and that vessels which fish with purse seines and sandeel gear may operate in the closed area, provided that Member States ensure that, observers shall be on board for at least 50 voyages. The amendment stipulates that observers should not only be placed on Community fishing vessels but also on third country vessels which fish for these species within the closed area in Community fishing waters.

12/04/2001

2056-2001

P Establishing additional technical-measures for the recovery of the stocks of cod in the North Sea and to the west of Scotland. — a general increase in the mesh size of towed nets and static nets used to catch cod. — additional conditions to ensure that capture of juvenile cod by towed nets of mesh size less than 120 mm is reduced.

01/01/2002

Appendix 1 - 2

2341-2002 P Comprehensive update of fisheries legislation (109 pages). Effort limitations and associated conditions for the management of cod stocks in the Kattegat, Skagerrak, North Sea, and to the West of Scotland introduced. ANNEX XVII - FISHING EFFORT AND ADDITIONAL CONDITIONS FOR MONITORING, INSPECTION AND SURVEILLANCE IN THE CONTEXT OF RECOVERY OF CERTAIN COD STOCKS Each Member State shall ensure that within each specified geographic area, individual fishing vessels, when carrying on board any of the fishing gears defined in APPENDIX 1 Section 1 (below)., shall be absent from port for no more than the number of days specified. (b) Additional days to compensate for steaming time between home ports and fishing grounds and to compensate for adjustment to the newly installed effort management scheme may be allocated to the Member States by the Commission. (c) An additional number of days on which a vessel may be absent from port while carrying on board any of the gears defined in paragraph 4a may be provisionally allocated to Member States by the Commission on the basis of the achieved results or the expected results of decommissioning programmes in 2002 and 2003. During a month or during a two-month period determined under the conditions laid down, a vessel may deploy only two of the gears. These gears may be deployed only on different days and the total number of days available to such vessels may be no more than half the sum of the days allocated to each gear. (a) A Member State may permit any of its fishing vessels to transfer a maximum of 20 % of the days to which it is eligible from a month to the next month or from an alternative time period. (b) A Member State may permit any of its fishing vessels to transfer days to which it is eligible for a given month or an alternative time period to another of its vessels only when the vessel receiving days has an installed engine power equal to or less than that of the vessel contributing the days. A Member State may permit any of its vessels to aggregate days absence from port: within any period of no more than two consecutive months; and within any period of no more than four consecutive month when it has been decided that the vessels of that Member State will remain in port during any part of that period to avoid the capture of spawning fish. The master of a fishing vessel, or his representative, who wishes to land more than 1 tonne of cod in any Member State shall inform, at least four hours in advance of landing, the competent authorities of that Member State of: the landing location, the estimated time of arrival at that location, the quantities of cod retained on board, the quantities of cod to be landed. Whenever more than 2 tonnes of cod are to be landed from a fishing vessel, the master of the vessel shall ensure that such landings are made only at designated ports. Each Member State shall designate ports into which any landing of cod in excess of 2 tonnes shall take place. It shall be prohibited to retain on board a fishing vessel in any individual box or other container any quantity of cod mixed with any other species of marine organism.

01/01/2003

Appendix 1 - 3

671-2003 A (2341-2002) Amending Regulation (EC) No 2341/20032. The implementation of that Annex XVII has shown that certain of its provisions need to be either clarified or made more flexible, in order to improve its applicability and effectiveness. It is necessary to ensure that any change to the scheme does not result in a lesser conservation value for the measures in question. Notwithstanding the conditions laid down in paragraph (a), a Member State may permit any of its vessels fishing with demersal trawls, seines or similar towed gears of mesh size equal to or greater than 120mm to spend no more than 16 days absent from port provided that: (i) the Member State has previously notified the Commission of its intention to do so; and (ii) the Member State has verified from the track record of that vessel that less than 5 % of the landings in live weight by that vessel during 2002 was comprised of cod. A Member State may permit any of its fishing vessels to transfer days to which it is eligible for a given month or an alternative time period determined under the conditions of paragraph 11 to another of its vessels: (i) when the vessel receiving days has an installed engine power equal to or less than that of the vessel contributing days; or (ii) when the installed engine power of the vessel receiving days is greater than that of the vessel contributing days, provided that the product of the days received by a vessel multiplied by the installed engine power in kilowatts of that vessel is equal to or less than the product of the days transferred by the smaller vessel multiplied by the installed engine power in kilowatts of that vessel. The kilowatts of the larger vessel and the smaller vessel shall be those recorded for each vessel in the register of Community fishing vessels. A Member State shall not count against the days allocated to any of its vessels any days when the vessel has been absent from port but has been unable to fish due to exceptional circumstances including mechanical breakdown or adverse weather conditions. The Member State concerned shall provide justification to the Commission of any decisions taken on this basis.

16/04/2003

1845-2003 A (2341-2002) Amending Regulation (EC) No 2341/20032. Annex XVII to Regulation (EC) No 2341/2002 provides the possibility for the Commission to allocate additional days of absence from port to Member States for these vessels to compensate for steaming time between home ports and fishing grounds and for adjustment to the newly-installed effort management scheme. Vessels using gears defined in paragraph 4(a) (demersal trawls seines or similar towed gears of mesh size equal to or greater than 100mm except beam trawls) traditionally carry on board different kinds of gear, and according to Annex XVII, this practice is not allowed for fishing vessels subject to that Annex. This rule results in a particular need for those vessels to receive additional days for adjustment to the new scheme, in order for to go back to port to change gear when necessary. Two additional days is considered as appropriate for this purpose. Annex XVII to Regulation (EC) No 2341/2002 provides the possibility for the Commission to allocate additional days of absence from port in recognition of the achieved results or expected results of decommissioning of fishing vessels in 2002 and 2003. Denmark and United Kingdom have submitted data on decommissioning of vessels carrying on board gear defined in paragraph 4(a), and a description of their intentions to further decommission such fishing vessels in 2003. A Commission Decision is necessary to allocate additional days at sea for fishing vessels. In accordance, the additional days which may be allocated in each calendar month shall be as follows: (a) Denmark: two days, (b) United Kingdom: four days.

15/03/2003

Appendix 1 - 4

2287-2003

P Comprehensive update of fisheries legislation (119 pages). Effort limitations and associated conditions for the management of stocks

i. For the period 1 January 2004 to 31 January 2004, fishing effort limitations and associated conditions laid down in Annex XVII of Regulation (EC) No 2341/2002 (1) shall apply.

i. For the period 1 February 2004 to 31 December 2004, the fishing effort limitations and associated conditions laid down in ANNEX V shall apply.

ANNEX V INTERIM FISHING EFFORT LIMITATION AND ADDITIONAL CONDITIONS FOR MONITORING, INSPECTION AND SURVEILLANCE IN THE CONTEXT OF CERTAIN FISH STOCKS RECOVERY Each Member State shall ensure that, fishing vessels shall be present within the area and absent from port for no more than the number of days specified. The maximum number of days in any calendar month for which a vessel may be present within the area and absent from port having carried on board any one of the fishing gears referred is shown in APPENDIX 1 Section 3 (below). A Member State may aggregate the days present within the area and absent from port in Table I within management periods of up to eleven calendar months. Member States shall notify the Commission of their intention to aggregate management periods before the beginning of any aggregated period. An additional number of days on which a vessel may be present within the area and absent from port when carrying on board any of the gears referred to in point 4 may be allocated to Member States by the Commission on the basis of the achieved results of decommissioning programmes that have taken place since 1 January 2002. Derogations from the number of days present within the area and absent from port shown in Table I may be allocated to vessels by Member States under the conditions shown in APPENDIX 1 Section 4 (below). Member States wishing to allocate these additional days shall notify the Commission with details of the vessels that will benefit and with details of their track records at least two weeks before the additional days are to be granted. Any vessel allocated extra days under this provision may not at any time retain on board more than 5 % of each of the following species: cod, sole and plaice. Inspection and surveillance at sea and in port by the competent authorities shall be undertaken for verification of compliance with the above requirement. Any vessel found to be not complying with the requirement shall with immediate effect no longer be entitled to the additional days.

01/01/2004

Appendix 1 - 5

Special Conditions for fishing for Haddock in the North Sea For the purposes of this point, ‘cod protection area' means that part of ICES Divisions IV included in the following ICES rectangles that lies further than 12 nautical miles from coastal baselines: 49E6, 48E6, 47E6, 46E6, 50E7, 49E7, 48E7, 50E8, 49E8, 51E9, 50E9, 49E9, 48E9, 47E9, 50F0, 49F0, 48F0, 47F0, 51F1, 50F1, 49F1, 50F2, 49F2, 46F3, 45F3, 45F4, 44F4, 43F5, 43F6, 43F7, 42F7, 38E9, 37E9, 37F0, 46E8, 45E8, 47E9, 46E9, 45E9, 44E9, 47F0, 46F0, 45F0, 44F0, 47F1, 46F1, 45F1, 44F1. Vessels to which a Member State has issued a special fishing permit for directed fishing for haddock shall comply with the following conditions: (i) notify national authorities of the place and time at which any landing of fish will be made; (ii) make such landings exclusively at ports that shall be designated by the flag Member State; (iii) submit the relevant logbook sheet(s) to the national authorities prior to the commencement of the discharge of the catches retained on board; (iv) not discharge any fish retained on board until permission to do so has been given by the relevant national authorities; (v) retain on board no more than 5 % of cod as a proportion of the live weight of marine organisms on the vessel; (vi) not trans-ship any fish at sea; (vii) fish exclusively outside the cod protection area; (viii) not transit within the cod protection area unless the fishing gear on board is securely lashed and stowed; (ix) not to carry on board or deploy trawl gear of less than 100 mm mesh size. No special fishing permit shall be if any of the following events have occurred in the period of validity of the permit: (i) upon inspection by the national fisheries inspection service, the vessel is found to have more than 5 % of cod on board, measured in live weight as a percentage of all fish on board the vessel; (ii) the vessel fails to provide a VMS report or, in the event of a failure of the VMS system, a manual position report or provides a false position report; (iii) upon inspection of a landing by the national fisheries inspection service, the vessel is found to have landed or to have retained on board more than 10 % more fish of any species (in live weight) than the quantity of that species declared in the logbook-sheet(s).

Appendix 1 - 6

682-2004 A (2287-2003) On the allocation of additional days absent from port to Denmark and the United Kingdom in accordance with Annex V to Council Regulation (EC) No 2287/2003 Annex V enables the Commission to allocate an additional number of days on which a vessel may be absent from port on the basis of the achieved results of decommissioning programmes since 1 January 2002 Denmark and United Kingdom have submitted data on the decommissioning in 2002 and 2003 of fishing vessels carrying on board demersal trawls, seines or similar towed gears of mesh size equal to or greater than 100mm except beam trawls. In the view of the data submitted, an additional number of days should be allocated to Denmark and the United Kingdom for fishing vessels carrying on board such fishing gears defined in paragraph 4(a) and (b) of Annex V. The following additional days shall be allocated in each calendar month for vessels carrying on board demersal trawls, seines or similar owed gears of mesh size equal to or greater than 100mm except beam trawls as follows: (a) Denmark: three days, (b) United Kingdom: five days. Two additional days, shall be allocated to the United Kingdom in each calendar for vessels carrying on board beam trawls of mesh size equal or greater than 80 mm.

08/10/2004

Appendix 1 - 7

867-2004 A (2287-2003) Amending Regulation (EC) No 2287/2003

New information on the distribution of catches of cod and haddock indicates that some areas of high abundance of haddock but relatively low abundance of cod were inappropriately included in the “cod protection area” defined in Annex IV. Also, some areas of relatively high cod abundance had been inappropriately excluded. An amendment to the geographical extent of the cod protection area is therefore required. Section (a) shall be replaced with the following: “(a) For the purposes of this point, “cod protection area” means that part of ICES Division IV included in the following ICES rectangles that lies further than 12 miles from coastal baselines: 49E6, 48E6, 47E6, 50E7, 49E7, 48E7, 50E8, 51E9, 50E9, 49E9, 50F0, 49F0, 48F0, 47F0, 46F0, 45F0, 51F1, 50F1, 49F1, 48F1, 47F1, 46F1, 45F1, 44F1, 50F2, 49F2, 48F2, 47F2, 46F2, 45F2, 44F2, 46F3, 45F3, 44F3, 45F4, 44F4, 43F5, 43F6, 43F7, 42F7, 38E9, 37E9, 37F0.”; “A Member State may record haddock catches caught in the period 1 January 2004 to 21 April 2004 on the basis of whether they were taken inside or outside the area defined in sub-point (a).”

03/05/2004

1811-2004

A (2287-2003) Amending Regulation (EC) No 2287/2003 as concerns the number of days at sea for vessels fishing for haddock in the North Sea New scientific information indicates that the catches of cod taken in fisheries carried out under the conditions specified in point 17 of Annex IV to Council Regulation (EC) No 2287/2003 are likely to be low, and consequently these fisheries pose little additional risk to cod recovery. An increase in the number of days fishing for haddock is therefore justified. In Annex V the following is added: ‘By way of derogation from the number of days referred to in point (a), Table I, “Grouping of fishing gears referred to in point 4a”; Member States may increase the maximum days present within the area and absent from port to 12 for vessels fitted with VMS and holding special fishing permits.

23/10/2004

1928-2004

A (2287-2003) Amending Regulation (EC) No 2287/2003 Annex V to Regulation (EC) No 2287/2003 (2) lays down an interim fishing effort limitation and additional conditions for monitoring, inspection and surveillance in the context of the recovery of certain fish stocks. Implementation of Annex V has shown that certain of its provisions need to be either clarified or made more flexible, in order to improve its applicability and effectiveness.

09/11/2004

38-2005 A (2287-2003) On the allocation of additional days absent from port to Netherlands in accordance with Annex V to Council Regulation (EC) No 2287/2003 Annex V enables the Commission to allocate an additional number of days on which a vessel may be absent from port on the basis of the achieved results of decommissioning programmes since 1 January 2002 The Netherlands have submitted data on the decommissioning in 2002 and 2003 of fishing vessels carrying on board beam trawls of mesh size equal to or greater than 80 mm. In the view of the data submitted, an additional number of days should be allocated to the Netherlands for fishing vessels carrying on board such fishing gears defined in paragraph 4(b) of Annex V. Two additional days, in relation to those set out in paragraph 6(a) of Annex V to Regulation (EC) No 2287/2003, shall be allocated to the Netherlands in each calendar month for vessels carrying on board beam trawls of mesh size equal to or greater than 80 mm.

21/01/2005

Appendix 1 - 8

423-2004 �

P Measures taken to establish multi-annual plans (harvest-control-rules) for the recovery of cod stocks. TACs shall not exceed a level of catches which a scientific evaluation, carried out by the STECF in the light of the most recent report of the ICES, has indicated will result in an increase of 30 % in the quantities of mature fish in the sea at the end of the year of their application, compared to the quantities estimated to have been in the sea at the start of that year. The Council shall not adopt a TAC whose capture is predicted by the STECF, in the light of the most recent report of the ICES, to generate in its year of application a fishing mortality rate greater than the following values: • Cod in the Kattegat 0,60 • Cod in the North Sea, Skagerrak and eastern Channel 0,65 • Cod to the west of Scotland 0,60 • Cod in the Irish Sea 0,72 Plans considered to be achieved when, for two consecutive years, the quantity of mature cod has been greater than that decided upon by managers as being within ‘safe biological limits’. It is expected that recovery of these stocks under the conditions of this Regulation will take between five and 10 years.

16/03/2004

� denotes a directive which is no longer in force. � directive introduces ‘harvest-control-rules’, as modelled by ICES multispecies study-group (SGMSNS) in 2003.

Appendix 1 - 9

1 Cod ‘closure area’ Cod ‘closure area’ consisted of the following ICES statistical rectangles or parts thereof:

50 E7 (3), 50 E8 (3), 50 E9, 50 F0, 50 F1, 50 F2 (4) 49 E6 (3), 49 E7 (3), 49 F1, 49 F2 48 E6, 48 F1, 48 F2 47 F1, 47 F2, 47 F3 (5) 46 F3 (6) 45 F3 (7), 45 F4 (7) 44 F3, 44 F4 (7), 44 F5 (7) 43 F4, 43 F5, 43 F6, 43 F7 (8) 42 F5, 42 F6, 42 F7 (9) 41 F5, 41 F6, 41 F7 (9) 40 F4, 40 F5, 40 F6, 40 F7 (9) 39 F4, 39 F5, 39 F6, 39 F7 (9) 38 F4, 38 F5, 38 F6 34 F3, 34 F4 33 F2, 33 F3, 33 F4 32 F1, 32 F2, 32 F3.

Appendix 1 - 10

2 Days Absent from Port The number of days in each calendar month on which a vessel may be absent from port while carrying on board any of the fishing gears defined in paragraph 4 are: Gear defined in paragraph:

Area defined in paragraph:

4a) demersal trawls, seines or similar towed gears of mesh size equal to or greater than 100 mm except beam trawls;

4b) beam trawls of mesh size equal to or greater than 80 mm;

4c) static demersal nets including gill nets, trammel nets and tangle nets;

4d) demersal longlines;

4e) demersal trawls, seines or similar towed gears of mesh size between 70 mm and 99 mm except beam trawls;

4f) demersal trawls, seines or similar towed gears of mesh size between 16 mm and 31 mm except beam trawls.

2a 9 0 16 19 25 23 2b 9 15 16 19 25 23 2c 9 15 16 19 25 23 For the purposes of this Annex, the following definitions of geographical areas shall apply: (a) That part of ICES Division IIIa bounded on the north by a line drawn from the Skagen lighthouse to the Tistlarna lighthouse and from this point to the nearest point on the Swedish coast and on the south by a line drawn from Hasenøre to Gnibens Spids, from Korshage to Spodsbjerg and from Gilbjerg Hoved to Kullen. (b) That part of Division IIIa not covered by the area specified in subparagraph (a) and ICES sub-area IV excluding the following ICES statistical rectangles: 52E6, 52E7, 52E8, 52E9, 52F0, 52F1, 52F2, 51E6, 51E7, 51E8, 51E9, 51F0, 51F1, 51F2, 50E6, 50E7 (1), 50E8 (1), 50F2 (2), 49E6 (1), 49E7 (1), 49F3, 48F3, 47F3 (3) 6F3, 46F4, 46F5, 45F3, 45F4, 45F5, 45F6, 44F5, 44F6. (c) ICES Division VIa excluding that part which lies to the west of a line drawn by sequentially joining with straight lines the following geographical coordinates:

60°00¡N, 04°00¡W 59°45¡N, 05°00¡W 59°30¡N, 06°00¡W 59°00¡N, 07°00¡W 58°30¡N, 08°00¡W 58°00¡N, 08°00¡W 58°00¡N, 08°30¡W 56°00¡N, 08°30¡W 56°00¡N, 09°00¡W 55°00¡N, 10°00¡W 55°00¡N, 09°00¡W 54°30¡N, 10°00¡W

.

Appendix 1 - 11

3 Interim Fishing Limitation INTERIM FISHING EFFORT LIMITATION AND ADDITIONAL CONDITIONS FOR MONITORING, INSPECTION AND SURVEILLANCE IN THE CONTEXT OF CERTAIN FISH STOCKS RECOVERY For the purpose of this Annex, the following groupings of fishing gears shall apply: (a) demersal trawls, seines or similar towed gears of mesh size equal to or greater than 100 mm except beam trawls; (b) beam trawls of mesh size equal to or greater than 80 mm; (c) static demersal nets including gill nets, trammel nets and tangle nets; (d) demersal longlines; (e) demersal trawls, seines or similar towed gears of mesh size between 70 mm and 99 mm except beam trawls with mesh size between 80 mm and 99 mm; (f) demersal trawls, seines or similar towed gears of mesh size between 16 mm and 31 mm except beam trawls. The number of days in each calendar month on which a vessel may be absent from port while carrying on board any of the fishing gears defined in paragraph 4 are: Gear defined in paragraph:

Area defined in paragraph:

4a) demersal trawls, seines or similar towed gears of mesh size equal to or greater than 100 mm except beam trawls;

4b) beam trawls of mesh size equal to or greater than 80 mm;

4c) static demersal nets including gill nets, trammel nets and tangle nets;

4d) demersal longlines;;

4e) demersal trawls, seines or similar towed gears of mesh size between 70 mm and 99 mm except beam trawls with mesh size between 80 mm and 99 mm;

4f) demersal trawls, seines or similar towed gears of mesh size between 16 mm and 31 mm except beam trawls.

Kattegat, North Sea and Skagerrak, West of Scotland, Eastern Channel, Irish Sea.

10 14 14 17 22 20

Appendix 1 - 12

4 Derogations from days present within the area Derogations from days present within the area and absent from port in Annex C and associated conditions Area Defined in point 2)

Gear defined in point 4 2002 vessel track record (* Days

2(a)

4(a) and 4(e) Less than 5 % of each of cod, sole and plaice

no days restriction

2(a)

4(a) Less than 5 % cod 100 to 120 mm up to 14 over 120 mm up to 15

2(a) Kattegat (ICES Division IIIa south), North Sea

4(c) gear of mesh size equal to or greater than 220 mm

Less than 5 % cod and more than 5 % of turbot and lumpfish

Up to 16 days

2(a) Eastern channel ICES Division VIId

4(c) gear of mesh size equal to or less than 110 mm

Vessels of less than 15 m in length with landings of over 35 % unregulated species and absent from port for no more than 24 hours (**)

Up to 20 days

(*) As verified by the EC logbook — average annual landing in live weight. (**) Notwithstanding this provision, the derogation shall also apply to a maximum of six vessels flying the flag of France and registered in the Community of length overall equal to or greater than 15 metres. A list of such vessels shall be submitted to the Commission before 1 February 2004.

Appendix 2 - 1

Appendix 2: Analysis of contributions to changes in the revenue of vessels targeting cod in the North Sea:

1 English otter trawl and Danish gillnet fleets

1.1 Methodology The economic theory of index numbers has a significant history of application. For instance, indices due to Fisher, Laspeyre and Paasche are very well known for the evaluation of changes in production, due to price and/or quantity. These indices form the basis of the revenue analysis presented here.††† The analysis uses data on the prices and/or quantities of inputs and outputs to identify the contribution of the growth rates of input prices and quantities on the growth of the total production of a selected fishing fleet (Herrick and Squires, 1989; Squires, 1992 and 1994). The methodology may be applied to different sets of data for selected fleets. In this report, it is applied separately to two specific fleets targeting North Sea cod where the time step of analysis is the year. Three main components are required for analysis: quantities (i.e. volume of catches by species); prices by species; and an indication of capital. Capital inputs may be modelled using number of vessels, total kW, total GT or another available physical or monetary measure of capital inputs. In European fisheries, typically number of vessels, total kW, total GT are available where more detailed economic data on capital is not. Fishing activity measured in total days may also be incorporated into the analysis. Also, stock abundance via SSB or TAC can also be included. Models of contributions to change in the total revenue of a fishing fleet are based on the following specifications:

• Two factor model, ∑=i

iiqpTR

• Three factor model, ∑=i

ii K

qpKTR

• Four factor model, ii i

ii A

KAqpKTR ∑=

††† This methodology was developed under the EC 5th Framework Project Q5RS-2002-01291 “Technological developments and tactical adaptations of important EU fleets (TECTAC)”, to which this section relates closely.

Appendix 2 - 2

where TR is the total revenue per time period, pi is the average price of species i, qi is the quantity landed of species i with qi/K the apparent productivity of capital inputs measured in terms of output of species i per unit of K (e.g. tons/GT), and Ai is an abundance index for species i. Fishing activity can be taken into account if a relevant time series of effort for the selected fleet is available. In this case the effects of changes in fishing activity (i.e. intensity and allocation of fishing activity) can be included in the contribution of apparent productivity of capital units (Q/K), which also includes effects due to changes in the technical efficiency of fishing vessels. Hence, in the four factor model K can be replaced with KE which may be viewed as the apparent fishing production per unit of effort measured in terms of days at sea per time period.

1.2 Data and fleet descriptions

1.2.1 English otter trawl Logbook data for the English otter trawl fleet operating in the North Sea between 1990 and 2003 was collated for analysis. Trips of vessels greater than 10 metres in length that logged use of otter trawl gear during this period were included. Activity of vessels using such gear is detailed in Figure A.1 and Table A.1. As would be expected the number of days fished closely follows the number of vessels operating during the period.

Appendix 2 - 3

0

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25000

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35000

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45000

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2003

Tota

l day

s fis

hed

0

50

100

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250

300

350

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Ave

rage

ves

sels

per

mon

th

Figure A.1. Summary of activity, vessels using otter trawl gear

(bar=days fished; line=vessels)

A summary of catch and effort logbook data for the period 1990-2003 was available that included vessel characteristics such as engine power, size of vessel and home port, and a summary of trip activity attributes including area fished with effort and catch (weight and value) by species (Source: CEFAS). Vessels and associated trip records were collated based on a vessel fishing in the North Sea and using otter trawl during the period of study. Vessels less than ten metres in length‡‡‡ were excluded from the analysis. The most important species to this fleet according to catch value are: the roundfish (cod, haddock, whiting and saithe) that in 2003 accounted for 39% of total value; flatfish (plaice, sole and lemon sole) that in 2003 accounted for 31% of total value; and Nephrops that in 2003 accounted for 9% of total value. An indication of the activity of the fleet analysed is summarised in Figure 6. Note that average days fished is based on all vessels using otter trawl and does not take into account polyvalent vessels and activity using other gears during the year. Hence, days fished should be interpreted with caution in this instance. Summary vessel characteristics for 1990-2003 are presented in Table A.1. Based on physical characteristics, the fleet has reduced in size significantly over this period. However, average engine power of each vessel in the otter trawl fleet has increased. The evolution of key characteristics of the fleet is presented in Figure A.2. It is evident that after a small increase of vessels using otter trawl in 1993, there has been a steady decrease on average of 12% in the number of vessels operating in

‡‡‡ Vessels less than ten metres in length are not required to submit logbooks, hence this information could not be validated as part of this study.

Appendix 2 - 4

the fleet. The decline in the number of vessels could be associated with the decline in the catch rate (observed at the same time) of North Sea cod as this fleet historically has predominantly targeted cod in the North Sea. Decommissioning programmes were operated in years 1993-1996, 1997, 2001/02 and 2003. Year Average number of

active vessels per month

Average total engine power of active

vessels per month (kW)

Average engine power per vessel

(kW)

1990 325 6804 1431991 313 6296 1431992 285 5829 1311993 357 7545 1301994 239 4794 1291995 213 4513 1331996 189 4414 1381997 181 4052 1371998 171 3539 1451999 137 3121 1472000 125 3524 1582001 109 3045 1722002 84 2544 1742003 75 2221 166Table A.1. English otter trawl fleet characteristics

Appendix 2 - 5

0

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are)

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rage

eng

ine

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er (k

W) p

er v

esse

l (tri

angl

e)

Figure A.2. – Evolution of the English otter trawl fleet, 1990-2003

Total landings of the otter trawl vessels have been decreasing throughout the period of analysis, both in volume and in value (Table A.2, Figure A.3). This is largely a consequence of the state of the resources, particularly of cod. However, due to the significant reduction in number of vessels, the vessels that remain are on average catching more per vessel at the end of the period than at the beginning. In the case of value per vessel, there has been a steady increase in value per vessel. This is attributed to both increasing prices and a shift in species composition of the catch (e.g. higher value species such as lemon sole and nephrops). These points being highlighted by the fact that average annual landings by weight has not seen an increase per vessel (or per kW) over the period. During the mid to late 1990s, otter trawl vessels were increasing their productivity by volume and value consistently. From 1993-1998 the fleet almost doubled its catch. Potential reasons for this, particularly with respect to changing catch composition are explored below. It appears to be attributed more to increased landings in general than to increasing prices. Since this time, the productivity of the fleet has dropped significantly, to levels that are slightly above those seen in 1993. Together with the developments in the fleet, the changes in landings per vessel have resulted in an increase in volume of landings by 20% of their 1990 level, and a total increase in value of landings by 27% of their 1990 level.

Appendix 2 - 6

Year Total landings

(million t)

Average annual

landings per vessel (t 1000s)

Average annual

landings per kW (t)

Total landings

(£m)

Average annual

landings per vessel (£ 1000s)

Average annual

landings per kW (£)

1990 22.41 68.92 3.29 24.36 74.92 3.581991 21.31 68 3.38 23.06 73.6 3.661992 21.85 76.59 3.75 21.22 74.39 3.641993 24.61 68.98 3.26 22.71 63.64 3.011994 20.21 84.55 4.22 16.99 71.08 3.541995 21.04 98.69 4.66 16.77 78.66 3.721996 20.16 106.65 4.57 17.16 90.78 3.891997 19.93 110.39 4.92 16.67 92.37 4.111998 20.28 118.77 5.73 19.45 113.89 5.491999 14.13 103.08 4.53 15.38 112.22 4.932000 10.8 86.15 3.07 12.71 101.33 3.612001 9.47 86.99 3.11 10.94 100.54 3.592002 10.33 122.8 4.06 9.84 116.99 3.872003 6.18 82.46 2.79 7.14 95.2 3.22Table A.2. Landings, English otter trawl Fleet

0.00

20.00

40.00

60.00

80.00

100.00

120.00

140.00

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1991

1992

1993

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2003

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ual l

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ngs

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l (to

nnes

'000

s)

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2.00

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onne

s '0

00s)

0.00

20.00

40.00

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120.00

140.00

1990

1991

1992

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1994

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1997

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Ave

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ual l

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'000

s)

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2.00

3.00

4.00

5.00

6.00

Ave

rage

ann

ual l

andi

ngs

per k

W (£

'000

s)

Figure A.3. Evolution of landings, otter trawl fleet

(diamond=vessel; square=kW)

Appendix 2 - 7

At first glance, the structure of the landings has changed little during the study period (Figures A.4a and A.4b). Otter trawlers by their design target demersal species that includes roundfish, flatfish, nephrops and anglerfish. For English otter trawl vessels in the UK, historically cod has comprised the largest component of the catch. Furthermore, TACs, which are in force for the majority of North Sea targeted species and are based on historic catch, have meant that for the most part catch composition has remained similar. However, with the decreasing stock size of cod in the North Sea, total catches of cod have declined significantly.

0

5000000

10000000

15000000

20000000

25000000

30000000

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

Volu

me

of la

ndin

gs (k

g)

otheranglerfishsoleplaicepollacklemon_solenephropswhitinghaddockcod

0%

20%

40%

60%

80%

100%

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

Perc

enta

ge v

olum

e of

land

ings other

anglerfishsoleplaicepollacklemon_solenephropswhitinghaddockcod

Figure A.4a. Structure of landings by volume, otter trawl fleet

0

5000000

10000000

15000000

20000000

25000000

30000000

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

Valu

e of

land

ings

(£s)

otheranglerfishsoleplaicepollacklemon_solenephropswhitinghaddockcod

0%

20%

40%

60%

80%

100%

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

Perc

enta

ge v

alue

of l

andi

ngs other

anglerfishsoleplaicepollacklemon_solenephropswhitinghaddockcod

Figure A.4b – Structure of landings by value, otter trawl fleet

However, there have been some minor modifications to catching strategy of the fleet over the period. In the early part of the 1990s, cod and nephrops were more important to the value of catch than in the mid to late 1990s. In the later period, catch of all key species has become more equal, particularly in terms of value. Cod still makes up 20% of the catch value in 2003, but this is low compared to its highest level in 1998 of about 55% of the catch value. The value of all other species have increased

Appendix 2 - 8

in recent years as the importance of cod has declined (Figure 7b). It is noticeable that ‘other’ species are proportionally higher in 2003 than at any other time during the period of study. Skates and turbot make up a significant part of this.

1.2.2 Danish gillnet Data for the Danish gillnet fleet operating in the North Sea between 1996 and 2003 was collated for analysis. The number of vessels active during this period is given in Figure A.5.§§§ A summary of catch data for the period 1996-2003 was available that included vessel characteristics such as engine power, tonnage of vessel a summary of trip activity relating to catch (weight and value) by species (Source: FOI). Vessels and associated data were collated based on the National categorisations of vessels using gillnets and fishing in the North Sea during the period of study. The most important species to this fleet according to catch value are cod, plaice and sole. In 2003, cod accounted for 26% of total value; plaice accounted for 29% of total value and sole accounted for 21% of total value. A large ‘other’ species category (not including anglerfish, haddock, lemon sole, saithe and whiting) accounted for 21% of total value also.

0

200

400

600

800

1,000

1,200

1,400

1,600

1996 1997 1998 1999 2000 2001 2002 2003

Tota

l ves

sels

per

yea

r

Figure A.5. Summary of activity, vessels using gillnets (line=vessels)

§§§ Number of days fished was not available for this fleet.

Appendix 2 - 9

Summary vessel characteristics for 1996-2003 are presented in Table A.4. Based on physical characteristics, the fleet has reduced in size significantly over this period. Total fleet gross tonnage in 2003 was almost two-thirds the level in 1996. However, average GT per has been more variable, between 1996 and 2001 it remained consistent however since it has dropped. The evolution of key characteristics of the fleet is presented in Figure A.6. The steady decrease in the number of vessels operating in the fleet has on average fallen by 4% per year. As with the English otter trawl fleet, the decline in the number of vessels could be associated with the decline in the catch rate (observed at the same time) of North Sea cod and plaice as this fleet historically has predominantly targeted these species in the North Sea.

Year Total number of

active vessels

Total gross tonnage of active vessels

(GT)Total gross tonnage

per vessel (GT)1996 1409 12180 8.641997 1294 11032 8.531998 1240 10660 8.61999 1181 10146 8.592000 1184 10004 8.452001 1166 10104 8.672002 1130 9205 8.152003 1064 8417 7.91Table A.4. Gillnet fleet characteristics Total landings of the gillnet vessels have been decreasing throughout the period of analysis, both in volume and in value (Table A.4). This is largely a consequence of the state of the resources, particularly of cod and plaice as noted previously. It is noticeable that even with number of vessels decreasing, weight of landings by vessels and GT is also decreasing. However, value of landings by vessels and GT has shown a different trend. In this case, value per either unit of production showed an increase from 1996 to 1999 but has since declined. Value per GT in 2003 was only 11% less than in 1996, compared to the 41% decrease in volume per GT. Increasing prices can be a factor, but a shift in species composition of the catch (e.g. higher value species) is also likely.

Appendix 2 - 10

0

2000

4000

6000

8000

10000

12000

14000

1996 1997 1998 1999 2000 2001 2002 2003

Tota

l fle

et G

T (d

iam

ond)

7.40

7.60

7.80

8.00

8.20

8.40

8.60

8.80

Ave

rage

ves

sel G

T (s

quar

e)

Figure A.6. Evolution of the gillnet fleet, 1996-2003

Together with the developments in the fleet, the changes in landings per vessel have resulted in a significant decrease in volume of landings by almost 50% of their 1996 level (Figure A.7). However, this has been offset by only a decrease in value of landings by 19% of their 1996 level. Factors contributing to this decrease are considered below. Year Total

landings (1000 t)

Mean annuallandings per vessel (1000

t)

Mean annual

landings per GT

(1000 t)

Total landings

(DKK million)

Mean annual

landings per vessel

(DKK million)

Mean annual

landings per GT

(DKK million)

1996 22164 15.73 1.82 300254 213 251997 20355 15.73 1.85 300372 232 271998 18104 14.6 1.7 290996 235 271999 17049 14.44 1.68 314058 266 312000 13693 11.57 1.37 272706 230 272001 11802 10.12 1.17 241774 207 242002 10485 9.28 1.14 212813 188 232003 9063 8.52 1.08 184629 174 22Table A.5. Landings, gillnet fleet

Appendix 2 - 11

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

1996 1997 1998 1999 2000 2001 2002 2003

Tota

l vol

ume

per v

esse

l (10

00 t)

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

2.00

Tota

l vol

ume

per G

T (1

000

t)

0

50

100

150

200

250

300

1996 1997 1998 1999 2000 2001 2002 2003

Tota

l val

ue p

er v

esse

l (D

KK

mill

ion)

0

5

10

15

20

25

30

35

Tota

l val

ue p

er G

T (D

KK

mill

ion)

Figure A.7 – Evolution of landings, gillnet Fleet

(diamond=vessel; square=GT)

At first glance, the structure of the landings has changed little during the study period (Figures A.8a and A.8b). Danish gillnetters target predominantly cod and plaice. As shown in the figures below, cod has formed a significant proportion of the catch (over 70% in weight and over 60% in value in 1998). Since 1998, cod stocks have decreased and as a result catch has seen a significant drop. In value this amount to a drop of almost 75% in cod catch value. Furthermore, TACs which are in force for the majority of North Sea targeted species and which are by definition based on historic catch, have meant that for the most part catch composition has remained similar, but the importance of those species has changed dramatically with plaice and sole making up over 50% of the catch value to the fleet. ‘Other’ species also add considerably to revenues, which in 2003 was higher than at any other year during the period of study.

0

5,000

10,000

15,000

20,000

25,000

1996 1997 1998 1999 2000 2001 2002 2003

Vol

ume

(100

0 to

nnes

) OtherWhitingSoleSaithePlaiceLemon soleHaddockCodAnglerfish

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1996 1997 1998 1999 2000 2001 2002 2003

Perc

enta

ge v

olum

e

OtherWhitingSoleSaithePlaiceLemon soleHaddockCodAnglerfish

Figure A.8a – Structure of landings by volume, gillnet fleet

Appendix 2 - 12

0

50,000,000

100,000,000

150,000,000

200,000,000

250,000,000

300,000,000

350,000,000

1996 1997 1998 1999 2000 2001 2002 2003

Valu

e (D

KK

)


0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1996 1997 1998 1999 2000 2001 2002 2003

Perc

enta

ge v

alue


Figure A.8b – Structure of landings by value, gillnet fleet

1.3 Results

1.3.1 Changes in capacity, production per unit of capital and price

1.3.1.1 Indices the evolution of the indices for prices, production per unit of capital, capital and revenue (total and per vessel) of the English otter trawl and Danish gillnet fleets are shown in Figures A.9a and A.9b respectively. In this analysis, the last year (i.e. 2003) has been used as the reference year. In the case of English otter trawlers, production per unit of capital (F(Q/K)) over the research period increased in the mid to late 1990s but has since fallen back to levels in 1990. Fluctuations in prices have followed contrasting trends and to some degree in the later part of the period as landings have decreased prices have increased. The price index shows a similar position in 1990 to that of 2003. Even so as shown in the trends of this fleet in Table 3, the average vessel has actually increased volume and value of catches during this period. The evolution in the TR (total revenue) index for the English otter trawl fleet clearly shows the effects of both the total revenue per unit of capital and the total capital. Total revenue has shown a steady decline since 1990, except in 1993 and 1998 where increases were evident. In 1993 this can be explained by an increase in capital of the fleet, but in 1998 it is due to increased production. Revenue per unit of capital increased up to 1998 and has since fallen to similar levels as those in 1990.

Appendix 2 - 13

-

1.00

2.00

3.00

4.00

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Inde

x va

lue

for P

and

Q/K

-

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

Inde

x va

lue

for K

FP index F(Q/K) index K index -

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

TR in

dex

-

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

5.00

TR/K

inde

x

TR index TR/K index Figure A.9a. Evolution of index values of prices, production per unit of capital, capital and revenue of the English otter trawl

fleet

-

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

1996 1997 1998 1999 2000 2001 2002 2003

Inde

x va

lue

for P

and

Q/K

-

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

Inde

x va

lue

for K

FP index F(Q/K) index K index -

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

1996 1997 1998 1999 2000 2001 2002 2003

TR in

dex

-

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

TR/K

inde

x

TR index TR/K index Figure A.9b – Evolution of index values of prices, production per unit of capital, capital and revenue of the Danish gillnet fleet

In the case of Danish gillnetters, production per unit of capital (F(Q/K)) between 1996 and 1999 remained relatively stable, but since has fallen considerably (Figure A.9b). Price trends increased steadily from 1996 to 2001, but have since remained stable. Capital of the fleet (measured by total GT) has decreased steadily throughout the period as number of vessels has reduced. The evolution in the TR (total revenue) index for the Danish gillnet fleet clearly shows the effects of both the total revenue per unit of capital and the total capital. Total revenue has shown a decline since 1999, as total fleet size has decreased in line with catch reductions. This is evident as revenue per unit of capital in 2003 showed similar levels to those in 1996. In fact, there was an increase in revenue per unit of capital to 1999 and has since fallen.

1.3.1.2 Contributions to total revenue change Contributions to total revenue change of the English otter trawl fleet is shown in Figure A.10a. The decrease in the total revenue since 1993 has come about due to the decrease in vessel numbers as the fleet has adjusted in size to meet available

Appendix 2 - 14

stock sizes. However, variation in revenue can be explained more by changes in production per unit of capital and price. Throughout the period, it is estimated that price has had a negative effect on the total revenue in the fleet relative to 2003 (Figure 13a), indicating that in 2003 prices had a greater positive impact on revenue than in the rest of the period. However, since 1999, revenue variation due to price has been small compared to 2003. In contrast, production per unit of capital has been seen to have a positive impact on revenues relative to 2003, indicating that in 2003 that production per unit capital was at its lowest. This is confirmed by the F(Q/K) index in Figure 13a. Variation in total revenue due to an overall change in capacity (i.e. engine power) of this fleet is negligible throughout the period.

-40

-20

0

20

40

60

80

100

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Tota

l rev

enue

var

iatio

n du

e to

P, K

and

Q/K

cha

nges

0

5

10

15

20

25

30

Tota

l Rev

enue

(£m

illio

ns, 2

003)

Total revenue variation due to p change Total revenue variation due to K changeTotal revenue variation due to Q/K change Total revenue

-40

-20

0

20

40

60

80

1996 1997 1998 1999 2000 2001 2002 2003

Tota

l rev

enue

var

iatio

n du

e to

P, K

and

Q/K

cha

nges

0

50

100

150

200

250

300

350

Tota

l Rev

enue

(DK

K m

illio

ns, 2

003)

Total revenue variation due to p change Total revenue variation due to K changeTotal revenue variation due to Q/K change Total revenue

Figure A.10. Evolution of total revenue and of the contribution to changes in total revenue of Q/K, p, and K (fixed reference year 2003), a) English otter trawl fleet; b) Danish gillnet fleet

Contributions to total revenue change of the Danish gillnet fleet is shown in Figure 13b. Similarly to the otter trawl fleet, the decrease in the total revenue since 1993 has come about due to the decrease in vessel numbers as the fleet has adjusted in size to meet available stock sizes. However, in the same way, variation in revenue can be explained more by changes in production per unit of capital and price. Trends in price and production per unit capital are the same for the Danish gillnetters and the English otter trawlers through the comparative period. For the most part, this indicates similar species targeting strategies of the fleets (i.e. cod and plaice) and similar market structures. As above, variation in total revenue due to an overall change in capacity (i.e. engine power) of this fleet is negligible throughout the period.

Appendix 2 - 15

1.3.2 Changes in capacity, production per unit of capital and price including stock abundance

1.3.2.1 Biological information In order to see the extent landings per unit of capital are affected by fluctuations in the stocks, information on the status of stocks (measured by spawning stock biomass, SSB) has been included in the analysis. Where stock size is not readily available, UK TAC for the stock is used as a proxy for stock size and the associated catch opportunities. The evolution of the stock indices for the main species incorporated are given in Figure A.11.

0

0.5

1

1.5

2

2.5

3

3.5

4

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Spaw

ning

Sto

ck B

iom

ass

inde

x

Anglerfish Cod Haddock Lemon Sole NephropsPlaice Saithe Sole Whiting Turbot

Figure A.11. – Evolution of TAC indices for key species

For the stocks modelled using TACs (i.e. turbot, lemon sole and anglerfish), TACs were not in fact set for most of the 1990s. In these cases, the TAC has been held constant at the level of first introduction for the years before that. For all species, except haddock, during 1990-2003 the stock size has reduced. Particularly the stocks of plaice, sole and cod have seen significant declines since the early 1990s. Since the introduction of TACs on turbot & brill and lemon sole in 2000, the stocks (as measured through levels of UK TAC) have declined.

Appendix 2 - 16

1.3.2.2 Results of the analysis The results of the four factor model (i.e. Abundance = SSB or TAC, landings per unit of capital and abundance, price, capital) shows clearly that over the study period, the higher stock sizes and therefore TACs have resulted in higher revenues (Figures 15a and 15b). This is particularly evident in the early to mid-1990s for the UK analysis and to some degree in the mid-1990s for the Danish analysis. For the English otter trawl fleet (Figure 15a), the higher stock levels in the early 1990s of cod, plaice and sole (as well as anglerfish, lemon sole and whiting) increased the total revenue by a significant margin until 1995. Hereafter, the effect has dropped. In contrast, species that in 2003 are indicated to have more of a positive effect on revenue are haddock, saithe and nephrops. Overall, cod and haddock are indicated to have most effect on the variation of total revenues of this fleet with respect to changes in abundance. Variation in production (in volume) for the otter trawl fleet per unit of stock and capital over the period are indicated to show a distinct change in strategy as the stock sizes have changed. The landings per unit of stock and capital have on balance had a falling effect on the total revenue for most of the period. Stocks which contribute to positive variation in production through the period are in particular cod, haddock and nephrops. Stocks which contribute to positive variation in production are sole, plaice and lemon sole. This indicates that, even though the stock of plaice in particular may be declining, the importance of plaice to the revenues of this fleet is increasing. Nephrops is not shown to explain revenue variations in a positive way in 2003 to previously with regards to production per unit capital. As reported previously, prices have largely had a negative effect on variations in total revenues relative to 2003. Cod, nephrops, plaice and sole contribute significantly to this. It is noticeable that since1998, the prices of ‘other’ species have contributed less revenue variation than before. This indicates a definite change in price composition in the total revenue of the targeted species, also shown in Figures 7a and 7b. The decline to total revenue due to change in fleet capital alone has declined dramatically since 1990 (except 1993). The contrasting patterns in the effects of the stock size and the landings per unit of stock and capital may be subject to two different reasons. First, differences in effort are not taken into account in the measure of capital, which assumes that fishing effort per unit of capital has been stable over the research period. This question is investigated further later. Second, the unit of capital (i.e. the engine power) is not a totally accurate measure for capital investment and more importantly for fishing capacity. Measuring effective fishing capacity through physical vessel characteristics is a major problem in fisheries. The efficiency of engines and other technologies has increased over the last decades, which again is assumed to have remained constant in this analysis.****

**** Effects of misreporting are also assumed to have been constant throughout this analysis.

Appendix 2 - 17

-4

-3

-2

-1

0

1

2

3

4

5

6

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

TR v

aria

tion

due

to A

cha

nges

(£m

200

3) Turbot

Whiting

Sole

Saithe

Plaice

Other

Nephrops

LemonSoleHaddock

Cod

Anglerfish

-4

-2

0

2

4

6

8

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

TR v

aria

tion

due

to Q

/KA

cha

nges

(£m

200

3)

TurbotWhitingSoleSaithePlaiceOtherNephropsLemon SoleHaddockCodAnglerfish

-5

-4

-3

-2

-1

0

1

2

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

TR v

aria

tion

due

to P

cha

nges

(£m

200

3)


0.000

2.000

4.000

6.000

8.000

10.000

12.000

14.000

16.000

18.000

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

TR v

aria

tion

due

to K

cha

nges

(£m

200

3)

Figure A.12a – Evolution of the contribution to changes in total revenue of A, Q/KA, p, and K (fixed reference year 2003),

English otter trawl Fleet

Appendix 2 - 18

-40

-20

0

20

40

60

80

100

1996 1997 1998 1999 2000 2001 2002 2003

TR v

aria

tion

due

to A

cha

nges

(DK

Km

200

3)

Whiting

Sole

Saithe

Plaice

Other

LemonSoleHaddock

Cod

Anglerfish

-60

-40

-20

0

20

40

60

80

100

120

1996 1997 1998 1999 2000 2001 2002 2003

TR v

aria

tion

due

to Q

/KA

cha

nges

(DK

K m

200

3)

Whiting

Sole

Saithe

Plaice

Other

Lemon Sole

Haddock

Cod

Anglerfish

-100

-80

-60

-40

-20

0

20

1996 1997 1998 1999 2000 2001 2002 2003

TR v

aria

tion

due

to P

cha

nges

(DK

Km

200

3)

Whiting

Sole

Saithe

Plaice

Other

Lemon Sole

Haddock

Cod

Anglerfish

0.000

10.000

20.000

30.000

40.000

50.000

60.000

70.000

80.000

90.000

100.000

1996 1997 1998 1999 2000 2001 2002 2003

TR v

aria

tion

due

to K

cha

nges

(DK

K m

200

3)

Figure A.12b – Evolution of the contribution to changes in total revenue of A, Q/KA, p, and K (fixed reference year 2003),

Danish gillnet Fleet

For the Danish gillnet fleet (Figure A.12b), the higher stock levels in the mid-1990s of cod predominantly increased the total revenue by a significant margin until 1999. With respect to stock effects, cod explains the majority of the variation. Plaice on the other hand is shown to contribute negative variation in total revenue relative to 2003. This trend is shown even more conclusively in the explanation of total revenue variation due to changes in production per unit of stock and capital for the gillnet fleet. This indicates a distinct change in strategy as the stock sizes have changed. Between 1996 and 2003, cod has a positive effect on total revenue variation relative to 2003. Plaice, sole and ‘other’ species also show negative variation relative to 2003, which indicates the changing importance of these species to the catch. As reported previously, prices have largely had a negative effect on variations in total revenues relative to 2003. Cod contributes significantly to explaining variation in total revenue due to price changes. In contrast to the English fleet, it is noticeable that since1998 the price of ‘other’ species have contributed more revenue variation than before. This indicates a definite change in price composition in the total revenue of the targeted species, also shown in figures 9a and 9b. The decline to total revenue due to change in fleet capital alone has declined dramatically since 1990.

Appendix 2 - 19

1.3.2.3 Key species effects – Cod Cod is historically the most important species for the UK otter trawl and Danish gillnet fleets. This is the case throughout the study period, even though the catches of cod have clearly been declining in importance to the fleets over the past 10 years. The TAC for cod has therefore been the principal factor behind total revenue of the fleet. The highest point of cod catch by value was reported in 1998 for the English otter trawl and Danish gillnet fleets (Figures A.13a and A.13b).

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

10.00

11.00

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Inde

x va

lue

Q index Price index TR index

0.00

1.00

2.00

3.00

4.00

5.00

6.00

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Inde

x va

lue

Abundance index Q/K index TR/K index

-2.00

-1.00

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

TR v

aria

tions

due

to p

, A a

nd Q

/KA

cha

nges

(£m

, 200

3)

Total revenue variationdue to p change

Total revenue variationdue to A change

Total revenue variationdue to Q/KA change

Appendix 2 - 20

b) Danish gillnet fleet

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

1996 1997 1998 1999 2000 2001 2002 2003

Inde

x va

lue

Q index Price index TR index

0.00

1.00

2.00

3.00

4.00

5.00

6.00

1996 1997 1998 1999 2000 2001 2002 2003

Inde

x va

lue

Abundance index Q/K index TR/K index

-100.00

-50.00

0.00

50.00

100.00

150.00

200.00

1996 1997 1998 1999 2000 2001 2002 2003

TR v

aria

tions

due

to p

, A a

nd Q

/KA

cha

nges

(DK

K m

, 200

3)

Total revenue variationdue to p change

Total revenue variationdue to A change

Total revenue variationdue to Q/KA change

Figure A.13. Contributions to changes in total revenue from cod

a) English otter trawl fleet

In the case of the English otter trawl fleet (Figure A.13a), this is shown clearly in the quantity index and total revenue index. It is even more apparent in the quantity and revenue per unit capital for the fleet. A smaller fleet in 1998 than previously is shown to be considerably more productive per unit capital than previously. Since this particularly productive year, there has been a sharp declining trend. Prices of cod are shown to be at their highest level in 1999-2003 than previously, a reaction of the market to the sudden changing supply of cod. In the case of the Danish gillnet fleet (Figure A.13b), as with the English otter trawl fleet, the highest point of cod production is shown in the quantity index and total revenue index. It is also evident in the quantity and revenue per unit capital for the fleet. A smaller fleet existed at this time than previously due to fleet capacity reduction measures, and vessels are shown to be considerably more productive per unit capital than previously. As with the English case, since this particularly productive year, there has been a sharp declining trend. Prices of cod are shown to be at their highest level in 2001-2003 than previously, as above, a reaction of the market to the sudden changing supply of cod. There is more revenue variation due to production per

Appendix 2 - 21

unit of capital and stock change than due to stock abundance change for the gillnet fleet over the otter trawl fleet. This is perhaps due to the technical differences of the different gears.

1.3.3 Changes in apparent fishing production per unit of effort For the English otter trawlers, information on total days at sea was available. As a result, changes in apparent fishing production per unit of effort was incorporated. Hence, the evolution of the contribution to changes in total revenue of production per unit effort and unit stock was developed. At first glance the trends are almost identical between production per unit effort and per unit capital. This could be expected due to the measure of capital (i.e. engine power) and days fished following similar trends as number of vessels decreased. However, it is shown that there is a higher level of negative variation (e.g. plaice and sole) and reduced level of positive variation (e.g. cod and haddock) in revenue per unit effort than per unit capital. It could be surmised that this is an indication that production (in value) per unit effort in 2003 was greater than previously and much of this change relates to a changing strategy towards other species than cod.††††

-4

-2

0

2

4

6

8

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

TR v

aria

tion

due

to Q

/EA

cha

nges

(£m

200

3)


-4

-2

0

2

4

6

8

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

TR v

aria

tion

due

to Q

/KA

cha

nges

(£m

200

3)


Figure A.14 – Evolution of the contribution to changes in total revenue of Q/AE (left) and Q/AK (right) (fixed reference year

2003), English otter trawl fleet

†††† Species specific effort is not available to investigate this further.

Appendix 2 - 22

1.4 Conclusions The English otter trawl and Danish gillnet fleets are two of the most important with respect to North Sea cod catches. In both fleets, cod has historically formed a significant proportion of their catch. For the otter trawl fleet this reached its highest in 1998 at 54% of the total revenues achieved, and for the gillnet fleet this reached its peak in 1999 at 62% of the total fleet revenues. In 2003, revenues from cod for each of these two fleets had reduced to 20% and 26% respectively. As such, they were selected for analysis of factors affecting changing revenues for these fleets. Species selected for inclusion in the analysis were anglerfish, cod, haddock, lemon sole, nephrops, plaice, saithe, sole, whiting and ‘other’. Trends of both fleets appear similar. Price indices indicate that prices have increased through the period studied and are at their highest levels in 2003. This is particularly the case for cod in both countries. For both fleets plaice and sole has become proportionally more important to the revenues of the fleets over the period. Even though, similarly to cod, stocks of plaice and sole have declined during the period. In the otter trawl case, more importance has also been placed on haddock and nephrops catches in recent years. In the case of nephrops, catches for this fleet are at proportionally similar levels to those seen in the early 1990s (Figure 7a). It is also a pertinent fact that revenues from catches of ‘other’ species is higher in 2003 than in other year studied. This indicates a growing importance of less traditionally targeted species to vessel revenues.

Appendix 3 - 1

Appendix 3: Impact of input controls on the profitability of the North Sea demersal fleet: a restricted profit function approach

1.1 Methodology Fisheries management can impinge on the profitability of a vessel through restricting either its level of inputs or its level of outputs. Several authors have suggested that when outputs are restricted, such as under quota conditions, then dual approaches (i.e. cost and profit functions) are more appropriate than primal approaches (i.e. production functions) to analyse fisher behaviour and performance (see Jensen 2002 for a review of both the theory and applications of such functions). Underlying this is the assumption that fishers will either attempt to minimise costs for the given level of output, or, if quotas are transferable, aim to maximise profits. In contrast, when effort controls are imposed, the underlying assumption is that fishers attempt to maximise revenue given the constraints on their levels of input. As a consequence, primal approaches may be more appropriate. An advantage of using a profit function is that it allows for variation in both inputs and outputs, with both assumed to be endogenous with respect to their relative prices. When both inputs and outputs are constrained, then profits are not likely to be maximised given the prevailing set of prices. The impact of these constraints can be measured through the use of dummy variables when estimating profit functions. A range of functional forms of the profit function are available. The most frequently used functional form is the translog functional form of the profit frontier. This is a relatively flexible functional form, as it does not impose assumptions about constant price elasticities‡‡‡‡ nor elasticities of substitution§§§§ between inputs and outputs. The translog profit function is given by:

tPtt

DPD

ZZZZ

PPPP

n

iiittt

n

i

m

ddiid

m

ddd

r

kkkk

r

lk

r

lklkkl

r

kkk

n

iiii

n

ji

n

ijjiij

n

iii

∑++

+∑∑∑ +

+∑+∑ ∑∑ +

+∑+∑ ∑∑ ++=

≠ ≠

≠ ≠

ln

ln

lnlnlnln

lnlnlnlnln

2

2

20

γγγ

δδ

βββ

ααααπ

(1)

‡‡‡‡ This represents the percentage change in profit from a one percent change in the level of the price. §§§§ This represents the degree to which one input/output is able to substitute for another as a result of relative input/output price changes while still holding profits constant.

Appendix 3 - 2

Where ∏ is the observed level of short-run profit, Pi are the prices of the inputs and outputs, Zk are the fixed input quantities and t is a time trend used to estimate the effects of technical progress. A set of dummy variables (D) are also included to capture the regulatory changes. From Hotelling’s lemma, the partial derivative of the profit function with respect to the input and output prices (lnPi) yields a set of profit share equations, given by:

tDZPPS id

m

didk

r

kikj

n

ijijiiiii γδβααα +∑+∑+∑++=

≠lnlnln2 (2)

where π/iii YPS = is the profit share of the ith input or output, and Yi is the quantity of the input/output used or produced. These share equations also represent the input demand and output supply equations. The profit function in equation 1 and the associated set of share equations given by equation 2 need to be estimated simultaneously. As the profit shares sum to 1 (one), one of the share equations needs to be excluded in order to avoid problems of singularity. A number of restrictions also need to be imposed on the system to ensure consistency with economic

theory. Homogeneity in input prices and output requires 0,0,1 =∑=∑=∑ ni ik

ni ij

ni i and βαα , while symmetry in input and

output prices requires jiij αα = . The system of equations is estimate using Zelner’s seemingly unrelated regression. Restrictions are imposed across the system to ensure that the conditions identified above hold, as well as ensuring that the estimated coefficients in each equation are equivalent.

1.2 Data Data on vessels operating in the North Sea were obtained from the Annual Economic Reports over the years 1999 to 2004. Although earlier editions of the report were available, fleet segment definitions changed substantially prior to this period, preventing the use of earlier data. These data are fleet level rather than individual vessel data, so the fleet was assumed to be the production unit. Sufficient data were available on nine fleet segments (Table 9), with a total number of observations over the period of 76. While information on other fleet segments were available in the Annual Economic Report, these were incomplete, preventing their use in the analysis.

Appendix 3 - 3

Average Input/output “prices” (€/kg)

Time period

Average Profit Cod

"Other" species Effort Crew Vessel kW

Belgian Beam <24

1996-2003 3.26 2.14 3.58 0.76 0.04 0.18 7.0

Belgian Beam >24

1996-2003 13.70 2.14 3.58 1.72 0.04 0.10 49.3

Dutch Beam <24 1994-2003 9.97 2.21 3.01 0.75 0.04 0.22 32.3

Dutch Beam >24 1994-2003 45.19 2.03 2.88 2.62 0.04 0.09 265.6

Danish Seiners 1994-2003 4.37 1.87 1.83 0.36 0.04 0.34 17.1

Danish Trawl <24

1994-2003 14.26 1.43 0.44 0.36 0.04 0.20 108.2

Scottish Seiners 1998-2003 7.37 1.39 2.02 0.91 0.04 0.45 28.3

Scottish Trawl <24

1997-2003 11.13 1.39 2.23 0.54 0.03 0.41 66.8

Scottish Trawl >24

1997-2003 18.40 1.39 1.86 1.41 0.05 0.36 74.6

Table A2.1. Summary of data used in the analysis Profits were estimated as short run profits, derived by subtracting, fuel, running costs, crew costs and annual vessel costs from revenue. The price of cod was derived by dividing the total value of landings by the total quantity. The price of “other” species was similarly derived by dividing the total value of landings of all species (excluding cod) by the quantity landed. The price of effort represents the average cost per day, and was derived by dividing total fuel and running costs by the number of days at sea. The crew “price” was the average payment per crew member. For the annual vessel costs, the price was to derive as the total annual vessel costs divided by the total number of kWs, thereby allowing for differences in size of vessels.***** The fixed inputs used in the analysis included kW (representing the amount of capital invested in the fleet),††††† and two stock indexes, the first representing the cod stock and the second representing the other species. The “other” species stock index was derived as a weighted average of the stock indexes of the other key species in the fishery. A separate stock index was estimated for each fleet segment based on their revenue shares of the other species.

***** Information on gross tonnage (GT) and gross registered tonnage (GRT) were also available. However, a consistent time series across the period of the data were not available, with the earlier years reporting GRT only and the latter time periods only GT. ††††† Information on capital values was available in the Annual Economic Report. However, these were not consistently measured (in some cases replacement values, in other cases insured values) so were not considered reliable for the purposes of the analysis.

Appendix 3 - 4

Dummy variables were included in the model to represent the area closure introduced in 2001, and the effort reduction programme introduced in 2003. A third dummy variable was included to represent beam trawlers, as it is likely that these might be affected by prices and regulations differently to the demersal trawlers (particularly given beam trawlers do not target cod as a main species, but catch it as bycatch). Ideally, the model would be run separately for the beam and demersal trawl fleet, but insufficient data points required their pooling into a common data set. The data were normalised such that the mean of the logged variables were zero. This is necessary to ensure that the production technology is appropriately estimated, as the translog specification of the profit function is valid only under such conditions. However, it also has the advantage of allowing the key elasticities to be easily estimated from the regression results.

1.3 Model estimation and results The limited degrees of freedom in the data set required additional modifications to the data. The lnkW and related variables were removed from the system, and the profit was re-defined as profit per kW. This imposes an assumption of constant returns to scale on the fleet segments, as well as input-output separability. Given that aggregated data were used, an assumption of constant returns to scale is appropriate (Reziti and Ozanne, 1999). Similarly, input-output separability is a necessary assumption in order to aggregate the “other” species into a composite output measure. It was not possible to test these assumptions empirically as the number of variables in the system when including kW was too high, preventing the model to converge to a solution. The revised system of equations was estimated using the restricted iterative Zelner’s seemingly unrelated regression (SUR) technique. As is often the case in large translog models, many of the variables appeared to be not significantly different from zero. This is often a consequence of correlation between the variables (i.e. between the levels, the squared terms and the cross products) rather than an indicator that the variables are not having an impact on the explanatory power of the model. A series of tests can be conducted to test the specification of the models to determine whether or not variables may be excluded. These are tested through imposing restrictions on the model and using the generalized likelihood ratio statistic to determine the significance of the restriction. The generalized likelihood ratio statistic is given by:

[ ])}(ln{)}(ln{2 10 HLHL −−=λ (3)

where ln{L(Ho)} and ln{L(H1)} are the values of the log-likelihood function under the null (Ho) and alternative (H1) hypotheses. The values of L(H0) and L(H1) are derived from the estimation of the restricted and unrestricted (e.g. general base model) respectively. The value of λ has achi-square distribution with the number of degrees of freedom given by the number of restrictions imposed.

Appendix 3 - 5

The significance of removing the technological change variables, as well as the variables relating to the 2001 and 2003 dummy variables (representing policy changes) was tested using the ratio likelihood test. The test results (Table 10) confirmed that all three components had a significant impact on the model, and hence should be retained.

H0 H1 λNumber of restrictions Significance Decision

No technical change -521.8 -544.3 45.0 8 >0.1% RejectNo 2001 dummy -528.6 -544.3 31.4 11 >0.1% RejectNo 2003 dummy -516.0 -544.3 56.6 11 >0.1% RejectNote: Base model (H1) includes technical change and both sets of dummy variables Table A2.2. Specification tests on technical change and dummy variables The estimated coefficients for the profit function and related output supply and input demand equations are given in Tables 11-13 respectively. In order to impose the appropriate restrictions, a “constant” variable was included in each model and the system estimated assuming no constant. This distorts the normal indicators of model performance, particularly the goodness of fit measure, R2. For the share equations, an alternative measure (the raw moment R2) was available. This was not available for the profit function or the system as a whole. However, based on the goodness of fit measures for the share equations, the model appears to capture most of the variability in the dependent variables. Further, the signs on the coefficients are as expected, further indicating that the model is performing reasonably well. For example, from Table 11, the coefficients on the price variables represent the percentage change in profit as a result of a one percent change in price (i.e. elasticities) at the mean input and output levels.‡‡‡‡‡ From these, profit increases as the price of cod and other species increases, and decreases as the price of effort, crew and other short run vessel costs increases, all other things being equal. Similarly, from the share equations, the supply of cod increases as cod price increases and decreases as the price of other species increases, all other things being equal. This suggests a potential substitution relationship between cod and other species. However, this may be an artefact of incorporating both beam and demersal trawlers into the same model. Higher cod prices favour demersal cod trawlers, resulting in more cod, where as higher “other” species prices favours beam trawlers.

‡‡‡‡‡ The price elasticities are given by ∂ln�/∂lnP for each price. These can be read directly from the regression results as the data have been normalised such that the mean value of each variable is zero.

Appendix 3 - 6

Variable Coefficient t-value Variable Coefficient t-value Constant 4.447 1.689* Beam dummy -2.736 -0.865 Cod price (Pcod) 1.446 1.702* Beam*Pcod -1.011 -1.930 * Other price (Pother) 5.908 5.380*** Beam*Pother 0.022 0.023 Effort price (Peffort) -1.328 -1.616 Beam*Peffort -0.490 -1.014 Crew price (Pcrew) -2.656 -2.194** Beam*Pcrew 0.488 0.636 Vessel price (Pvessel) -2.370 -2.464** Beam*Pvessel 4.885 2.776 *** Cod stock 3.879 0.768 2001 dummy 5.841 2.017 ** Other stocks -1.216 -0.506 2001*Pcod -0.475 -0.943 Cod price2 0.574 2.882*** 2001*Pother -2.263 -1.781 * Other price2 0.527 2.698*** 2001*Peffort 0.864 1.379 Effort price2 -0.058 -1.173 2001*Pcrew 1.273 1.502 Crew price2 -0.133 -0.613 2001*Pvessel 1.526 0.525 Vessel price2 0.061 0.169 2001*Beam 4.925 1.180 Cod stock2 -9.415 -1.550 2003 dummy -5.608 -1.622 Other stocks2 11.821 2.990*** 2003*Pcod 0.014 0.029 Pcod*Pother -0.578 -2.300** 2003*Pother 2.192 1.845 * Pcod*Peffort 0.005 0.052 2003*Peffort -0.387 -0.653 Pcod*Pcrew -0.113 -0.505 2003*Pcrew -0.823 -1.018 Pcod*Pvessel 0.111 0.446 2003*Pvessel -2.407 -0.803 Pcod*cod stock -0.378 -0.391 2003*Beam -4.456 -1.023 Pcod*other stock -0.200 -0.309 Time -0.588 -0.555 Pother*Pcrew 0.211 0.610 Time2 0.126 0.864 Pother*Peffort -0.120 -0.679 Time*beam -0.370 -0.677 Pother*Pvessel -0.040 -0.191 Time*Pcod -0.002 -0.013 Pother*cod stock -6.471 -4.188*** Time *Pother -0.147 -0.777 Pother*other stock 3.781 2.141** Time *Peffort -0.028 -0.214 Pcrew*Peffort 0.144 0.968 Time *Pcrew 0.052 0.277 Pcrew*Pvessel -0.109 -0.370 Time *Pvessel -0.744 -2.541 ** Pcrew*cod stock 2.723 1.903* Pcrew*other stock -0.249 -0.222 Peffort*Pvessel -0.023 -0.167 Peffort*cod stock 2.343 2.418** Peffort*other stock -1.214 -1.445

Appendix 3 - 7

Pvessel*cod stock 1.783 1.711* Pvessel*other stock -2.118 -2.689*** Cod stock*other stock -6.888 -1.183 *** significant at the 1% level; ** significant at the 5% level; * significant at the 10% level Table A2.3. Profit function results

Appendix 3 - 8

Cod supply Other species supply Variable Coefficient t-statistic Coefficient t-statistic

Constant 1.446 1.702* 5.908 5.380 ***

Cod price (Pcod) 1.148 2.882*** -0.578 -2.300 **

Other price (Pother) -0.578 -2.300** 1.054 2.698 ***

Effort price (Peffort) 0.005 0.052 -0.120 -0.679 Crew price (Pcrew) -0.113 -0.505 0.211 0.610 Vessel price (Pvessel) 0.111 0.446 -0.040 -0.191

Cod stock -0.378 -0.391 -6.471 -4.188 ***

Other stocks -0.200 -0.309 3.781 2.141 **Beam -1.011 -1.930* 0.022 0.023 2001 dummy -0.475 -0.943 -2.263 -1.781 * 2003 dummy 0.014 0.029 2.192 1.845 * Time -0.002 -0.013 -0.147 -0.777 Raw Moment R2 0.605 0.746 *** significant at the 1% level; ** significant at the 5% level; * significant at the 10% level Table A2.4. Share equations: Output Supply

Appendix 3 - 9

Effort Demand Crew Demand Vessel demand

(kW)a

Variable Coefficie

nt t-

statistic Coefficie

ntt-

statistic Coefficie

ntt-

statistic

Constant -1.328 -1.616 -2.656 -2.194** -2.370 -2.464 **

Cod price (Pcod) 0.005 0.052 -0.113 -0.505 0.111 0.446 Other price (Pother) -0.120 -0.679 0.211 0.610 -0.040 -0.191 Effort price (Peffort) -0.117 -1.173 0.144 0.968 -0.023 -0.167 Crew price (Pcrew) 0.144 0.968 -0.265 -0.613 0.111 0.446 Vessel price (Pvessel) -0.023 -0.167 -0.109 -0.370 -18.830 -3.100

***

Cod stock 2.343 2.418** 2.723 1.903* 1.783 1.711 *

Other stocks -1.214 -1.445 -0.249 -0.222 -2.118 -2.689 ***

Beam -0.490 -1.014 0.488 0.636 4.885 2.776 ***

2001 dummy 0.864 1.379 1.273 1.502 1.526 0.525 2003 dummy -0.387 -0.653 -0.823 -1.018 -2.407 -0.803 Time -0.028 -0.214 0.052 0.277 -0.744 -2.541 **Raw Moment R2 0.696 0.615 n.a. *** significant at the 1% level; ** significant at the 5% level; * significant at the 10% level

a. Not directly estimated by derived from the imposed homogeneity conditions and profit function results Table A2.5. Share equations: Input Demand

Appendix 3 - 10

1.4 Technical change The contribution of technical change to profitability in the fishery can be determined by differentiating profit with respect to time. Unlike the price elasticities, technical change is evaluated under the price conditions prevailing each year (rather than at the mean price levels). The estimated average annual rates of technical change for demersal and beam trawlers derived from the model are illustrated in Figure 18.

Appendix 3 - 11

Demersal trawl

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004Year

Ave

rage

rate

of t

echn

ical

pro

gres

s (%

)

Beam trawl

-80%

-60%

-40%

-20%

0%

20%

40%

60%

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Year

Ave

rage

rate

of t

echn

ical

pro

gres

s (%

)

Figure A2.1. Average annual technical progress with 95% confidence intervals

Appendix 3 - 12

From Figure A2.1, demersal trawlers were estimated to experience and annual improvement in profit efficiency as a result of technical change. In each year, however, this was not significantly different from zero. In contrast, the average profit efficiency of beam trawlers declined annually prior to 1998, with this decline being significantly different to zero. Since 1998, there has been an apparent, but not statistically significant, increase in profit efficiency in the beam trawl fleets. This apparent significant increase in efficiency (from negative to at least zero change) for the beam trawl fleet may be related to the reduction in overall vessel numbers operating in the fishery (across all fleets) under the Multi-Annual Guidance Programme. This would have had a two-pronged effect. First, the reduction in vessel numbers would have reduced the effects of crowding, which would otherwise have been increased as a result of the reduced size of the resource (and may have contributed to the declining efficiency in the beam trawl fleet prior to 1998). Second, the removal of the less efficient vessels through decommissioning would have resulted in the average efficiency increasing. For the demersal fleet, it would be expected that similar benefits from fleet reduction would have been realised. However, cod stocks declined at a greater rate than demersal vessel numbers, so the benefits of reduced crowding would not have been apparent. Instead, it most likely prevented a decline in profit efficiency rather than improve efficiency.

1.5 Impact of management changes The impact of the management interventions in 2001 and 2003 can be assessed through considering the related dummy variable terms in the model. Again, the impact can be determined by partially differentiating the profit function with respect to each dummy variable, and evaluating the final elasticity at the prevailing prices in the relevant time periods. The estimated elasticities are presented in Table 14. Fleets/year Elasticity Standard error Significance2001 • Demersal

trawlers 6.880 3.401 0.043

• Beam trawlers

9.447 4.302 0.028

2003 • Demersal

trawlers -7.499 3.715 0.044

• Beam trawlers

-8.360 4.673 0.074

Table A2.6. Impact of management changes: elasticities

Appendix 3 - 13

From Table A2.6, there is an apparent substantial and statistically significant increase in profitability in 2001 for both vessel groups that is not explained by input or output prices, nor stock conditions. The purpose in including the dummy variable for 2001 was in order to capture the impact of the area closure on the profitability of the vessels. Other studies (e.g. Pascoe and Mardle 2005) have suggested that the 2001 closure would have had negligible impact on profitability as vessels were able to relocate their effort and achieve similar catches. Hence, an a priori expectation was that the 2001 dummy variable would indicate no significant impact.§§§§§ The fact that a substantial significant impact was observed does not necessarily mean that the area closure was responsible for the increase in profitability. The dummy variable only indicates that, in 2001, profits were greater than should be expected. This may arise for other reasons already mentioned. In particular, many fleets were reduced in 2001 in order to achieve the MAGP targets. In some fleets (e.g. the Scottish fleets), this reduction in 2001 was substantial. In the case of the Scottish fleets, many vessels were also permitted to continue fishing during 2001 before final scrapping. During this period, maintenance work would not have been undertaken, resulting in higher profits. From the 2002 Annual Economic Report, improvements in performance were experienced by more than half of all fleet segments examined in 2001. In contrast, the effort restrictions in 2003 are likely to be the major factor explaining the substantial decline in profits in this year. From the estimated elasticities in Table 6, average profits were effectively reduced to zero****** as a result of management change. While the impact on the beam trawl fleet was not significant at the 5% level, it was significant at the 10% level. This suggests that the impact may not have been as substantial for the beam trawl fleet as the demersal fleet despite the higher absolute value of the elasticity.

1.6 Discussion and Conclusions The estimated profit function and associated input demand and output supply equations appears to conform with a priori expectations about the influence of prices and stocks on profitability, output levels, effort levels and the use of other inputs. The model results suggest that productivity in the fishery was generally increasing as a result of technological change. However, individual estimates of technological change in each year were highly uncertain, with each year being not significant from zero. Exclusion of technological change from the model could not be accepted when tested using the likelihood ratio test. From this, technological change does exist, but its impact on profits is highly uncertain.

§§§§§ If prices are deflated by the harmonised consumer price index, then these elasticities become not significant at the 5% level, but remain significant at the 10% level. The magnitude of the impact is roughly unaffected by the use of real prices rather than nominal prices. As all prices (input and output) are included in the analysis, there is no necessity to use real rather than nominal prices in the analysis. It is likely that this change in significance level reflects the limited data available for the analysis. ****** The actual impact on profits is given by the exponential of the elasticities in Table 6.

Appendix 3 - 14

The results of the analysis suggest that the effort controls imposed in 2003 had a substantial negative impact on the profitability of the fleet, all other things being equal. While actual profits were still positive, but small, on average, this was a result of higher cod and “other” prices. Without the effort controls, these profits would have been substantially greater. The dummy variable used to examine the impact of the area closures in 2001 resulted in an unanticipated impact. Based on other studies, the expectation was that the area closures would have either had a zero or slight negative impact on profitability. From the model, profits in 2001 where substantially higher than expected given the set of prices and stock conditions facing the fleets. It is likely that other factors are driving this result, such as the impact of decommissioning. There is insufficient information, however, to determine what is causing this impact with any certainty. The analysis is limited in its conclusions by the available data. Although 77 observations were available, these were only achieved by pooling quite different fishing activities (beam and demersal trawl). Ideally, these fleet segments should have been analysed separately. However, this provided too few observations for the model to converge. The use of the dummy variable to separate the effects of the beam trawlers from the demersal trawlers helped alleviate the problem, although the model imposes common price elasticities (both input and output prices) on both gear types. The implicit substitutability of cod and other species from the output supply equations is an artefact of this pooled data. Other studies of substitution possibilities within the separate fleet segments have indicated that substitutability is very limited (see Bjorndal et al., 2003). Additional data should be available from the 2005 Annual Economic Report, which should be available shortly. This should increase the number of observations by over 10%, which may help reduce some of the uncertainty in the system.

Economic effects of the cod recovery plan on the mixed fisheries in the North Sea · 2016. 9....

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