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The English Channel : subtitle a mixed fishery, but which mix is best? Stanford, Richard James 2002

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The English Channel: a mixed fishery, b u t which mix is best? by Richard James Stanford B.Sc, The University of Southampton, 2000  A THESIS SUBMITTED IN P A R T I A L F U L F I L L M E N T OF THE REQUIREMENTS FOR THE D E G R E E OF  M A S T E R OF SCIENCE in THE F A C U L T Y OF G R A D U A T E STUDIES (Resource Management and Environmental Studies - Fisheries)  We accept this thesis as conforming to the required standard  THE UNIVERSITY OF BRITISH C O L U M B I A September 2002  © Richard James Stanford, 2002  In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head of my department  or by his  or  her  representatives.  It  is  understood  that  copying or  publication of this thesis for financial gain shall not be allowed without my written permission.  Department of The University of British Columbia Vancouver, Canada Date  DE-6 (2/88)  Abstract Ecopath with Ecosim ecosystem models were built for the English Channel (ICES areas V l l d and Vile) in 1973 and 1995. The 1973 model was run forwards in time with a timeseries of fishing mortality data to assess how realistically the model predicted the changes in biomass that had occurred. The parameters for both models were modified so that the biomass trends reflected stock assessment data. This "tuning" required slight changes to some of the basic input parameters, and the addition of 5 juvenile groups and 5 functions that forced 8 groups to react to annual mean water temperature. The final 1995 Ecosim model consisted of 50 groups, with nine different fisheries exploiting 31 of these groups.  The market price, fleet profitability and jobs/catch value ratio were entered into Ecosim to run policy optimisations. To set extreme boundaries of possible gains from the Channel, initially the computer searched for the economic, social, ecological and rebuilding for recreational species optima. Netting and lining were the most profitable fleets and also had a high jobs/catch value ratio so were significantly increased for the economic and social optima. The ecological and rebuilding optima reduced the fleet to nearly zero.  Trade-off frontiers were created by weighting each of the objective  functions differently and these, along with the results of RAPFISH, a rapid appraisal technique that determined the sustainability of the fisheries, were used to generate three robust management alternatives that were assumed to be most beneficial to the special interest groups (stakeholders). The effect of climate change was incorporated by running the model for two scenarios where the average sea temperature increased by 0.15 °C and 0.3 °C per decade. Some of the inherent uncertainty of the data was accounted for by varying vulnerabilities, sea temperatures and the discount rate and by using the 'closed loop' optimisation analysis. A discussion of the future management of the Channel followed, suggesting that changes to both the fishing fleet and the European management structure were required for sustainable management of the English Channel.  n  Table of contents Abstract  ii  Table of contents  iii  List of tables  vii  List of  figures  viii  Acknowledgements  ix  1. Introduction  1  1.1 Objective 1.2 The English Channel : 1.2.1 Physical and biological characteristics 1.2.2 Fisheries 1.3 Previous Research 2. Ecopath - creating the basic model  1 1 1 4 9 11  2.1 The basic parameters 2.1.1 Biomass 2.1.2 Production per unit of biomass (P/B) 2.1.3 Consumption per unit of biomass (Q/B) 2.1.4 Diet composition 2.1.5 Ecotrophic efficiency 2.2 Functional group descriptions 1) Primary production 2) Zooplankton 3) Carnivorous zooplankton Benthos 4) Deposit feeders 5) Sessile suspension feeders 6) Shrimp and prawns 7) Whelk 8) Echinoderms 9) Bivalves 10) Scallops 11) Crab 12) Commercial crab 13) Lobsters 14) Small demersals 15) Small gadoids 16) Mullet 17) Sole 18) Plaice 19) Dab 20) Other flatfish ;  11 12 14 16 17 17 17 17 19 20 21 23 23 24 25 25 27 27 28 29 30 31 31 31 32 32 33 34 iii  21) Gurnards 22) Whiting 23) Cod 24) Hake 25) Rays and dogfish 26) Pollack 27) Large bottom fish 28) Seabreams 29) John Dory 30) Sandeel 31) Herring 32) Sprat 33) Pilchards 34) Mackerel 35) Scad (horse mackerel) 36) Bass 37) Sharks 38) Basking sharks 39) Cephalopods 40) Seabirds 41) Toothed cetaceans 42) Seals 43) & 44) Detritus groups 2.3 Catch Data 2.4 Discards 2.4.1 Otter trawling, beam trawling and dredging discards 2.4.2 Midwater trawling discards 2.4.3 Net discards , 2.4.4 Discard summary 2.5 Balancing 2.5.1 Further adjustments 2.6 Further economic Ecosim data requirements 2.6.1 Market price 2.6.2 Fleet profitability 2.6.3 Relative employment 2.7 Final 1995 input parameters 3. Back to the Future -1973 3.1 Reconstructing the past 3.1.1 Modifying P/B and Q/B 3.1.2 Catch data 3.1.3 1973 Functional group descriptions 1) Primary production 2) Zooplankton and 3) carnivorous zooplankton 4) Deposit feeders, 5) suspension feeders and 6) shrimps and prawns 7) Whelk 8) Echinoderms  35 35 36 37 38 39 39 40 40 40 41 42 43 44 45 45 46 47 48 50 52 54 55 58 60 60 61 63 63 64 66 68 68 68 69 70 75 75 75 76 77 77 78 78 78 78 iv  9) Bivalves 10) Scallops 11) Crab 12) Commercial crab 13) Lobster 14) Small demersals, 15) small gadoids and 16) mullet 17) Sole 18) Plaice 19) Dab 20) Other Flatfish 21) Gurnards 22) Whiting 23) Cod 24) Hake 25) Rays and Dogfish 26) Pollack .' 27) Large bottom fish 28) Seabreams 29) John Dory 30) Sandeels 31) Herring 32) Sprat 33) Pilchard 34) and 35) Mackerel/Over-wintering mackerel 36) Scad 37) Bass 38) Sharks 39) Basking shark 40) Cephalopods 41) Seabirds 42) Toothed cetaceans 43) Seals 44) Discarded catch 3.1.4 Balancing the 1973 model 3.1.5 Electivity 4. Ecosim - tuning and simulating 4.1 Tuning the model 4.1.1 Time-series data 4.1.2 Vulnerabilities 4.1.3 Tuning individual groups 4.2 Simulating - single objective results 4.2.1 Economic 4.2.2 Social 4.2.3 Ecological 4.2.4 Mandated rebuilding 4.2.5 The impact of varying vulnerabilities  78 78 79 79 79 79 80 80 81 82 82 82 83 84 85 85 86 86 86 86 86 87 87 88 90 90 90 91 91 91 92 92 92 93 93 96 96 96 98 98 109 112 114 116 118 120 v  4.2.6 The impact of temperature 4.2.7 Discount rate 4.3 Eat it or leave it? 4.3.1 Employment and profit 4.3.2 Profit and ecosystem health 4.3.3 Employment and ecosystem health 4.4 RAPFISH 4.4.1 Individual RAPFISH fields 4.4.2 Conclusions  121 124 125 126 129 129 130 131 135  :  5. Making tough decisions 5.1 Towards a solution 5.1.1 Option A 5.1.2 Option B 5.1.3 Option C 5.1.4 Changes in temperature and vulnerability 5.1.5 Closed loop analysis 5.2 Discussion 5.2.1 The greatest benefit 5.2.2 Management suggestions 5.2.3 Limitations and future research 5.2.4 Summary  136  ;  136 139 142 144 146 147 150 150 151 155 158  References  160  Appendix  172  vi  List of tables Table 1.1: The significance of each gear in the Channel 7 Table 2.1: Methodology for calculating the biomass of finfish groups in the model 14 Table 2.2: P/B estimation for the fin-fish groups 15 Table 2.3: Q/B of the finfish groups 16 Table 2.4: Biomass of macroalgae off the northern coast of Brittany 19 Table 2.5: Total biomass and average P/B of primary producers in the Channel 19 Table 2.6: Relative constituents of the benthos from a scoop sampler. 22 Table 2.7: % of benthos in the Channel based on trawl surveys 22 Table 2.8: Origin and type of diet composition data used for small demersals 31 Table 2.9: Estimated biomass of rays and dogfish in the Channel from beam trawls 38 Table 2.10: Numbers of seabirds in the Channel 51 Table 2.11: Total biomass estimates for toothed cetaceans in the model 53 Table 2.12: Diet composition of the three species of cetacean in the model 54 Table 2.13: Estimates of grey and harbour seals in the Channel 55 Table 2.14: Pre-balancing diet composition 56 Table 2.15: Annual Channel catch by gear type 59 Table 2.16: Metiers where discarding was measured by Sea Fish 60 Table 2.17: Midwater/pelagic trawling metier discarding 61 Table 2.18: Discarding of the main species by midwater trawlers in the Channel 62 Table 2.19: Channel discards as entered into the Ecopath model 64 Table 2.20: Groups that prevented the model balancing 64 Table 2.21: Groups with an unacceptably low P/Q ratio 66 Table 2.22: Basic input parameters for the balanced model 67 Table 2.23: Market price of the commercially exploited species in the Channel 68 Table 2.24: A n economic breakdown of the English fishing industry by gear type...;.... 69 Table 2.25: Ratio of jobs to catch value throughout the Channel metiers 70 Table 2.26: Final input basic input parameters for the 1995 model 71 Table 2.27: Final version of the 1995 diet composition 72 Table 3.1: Changes in P/B for groups that were fished less heavily in 1973 75 Table 3.2: Functional groups in the 1973 model with an E E greater than 1 93 Table 3.3: Basic input parameters for the 1973 balanced model 95 Table 4.1: Origin of time-series estimates of F and biomass for tuning the model 97 Table 4.2: Optimal fleets for single objective optimizations Ill Table 4.3: Optimal economic fleet configuration with the forcing functions 122 Table 4.4: Optimal social fleet configuration with the forcing functions 122 Table 4.5: Relative performance of each gear type 135 Table 5.1: Chosen optimal fleet configurations 138 Table A l : C S V file used when tuning the model 172 Table A2: Forcing functions used when optimizing for temperature change 176 Table A3: Mixed trophic impact from the final model 177 Table A4: RAPFISH data used in the analysis and its origin 183  List of figures Figure 1.1: The English Channel 2 Figure 1.2: Effort of trawlers in the English Channel from 1919-1990 4 Figure 3.1: % difference between the two landings data sets in the years 1993-95 77 Figure 3.2: ICES biomass estimates for the two sole stocks 80 Figure 3.3: ICES biomass estimates for the three plaice stocks 81 Figure 3.4: Change in biomass of the whole whiting stocks 83 Figure 3.5: ICES biomass estimates for the entire hake stock in Vlle-k 84 Figure 3.6: 5-year running annual average of sea surface temperature in the Channel... 88 Figure 3.7: Mackerel landings from the Channel. 89 Figure 4.1: Impact of the forcing function on adult sole biomass 100 Figure 4.2: Predicted biomass of hake 103 Figure 4.3: Response of North Sea cod to temperature 105 Figure 4.4: Biomass of bass predicted by the model 108 Figure 4.5: Potential sea temperature changes in the Channel Ill Figure 4.6: Change in biomass and value of the groups that showed a difference of more than 5 % from the optimal economic fleet configuration 113 Figure 4.7: Change in biomass and value of the groups from the social optimum 115 Figure 4.8: Change in biomass for the ecologically optimal fleet configuration 117 Figure 4.9: Change in biomass for the mandated rebuilding optimal fleet 119 Figure 4.10: Effect of vulnerabilities on profit and ecosystem structure 120 Figure 4.11: Maxima for a temperature increase of 0.6 °C (a) and of 1.2 °C (b) 121 Figure 4.12: Effect of temperature on five recreationally important stocks 123 Figure 4.13: Significance of cephalopods in the Channel 124 Figure 4.14: Impact of changing the discount rate when optimising for economics 125 Figure 4.15: Trade-off between profit and employment as estimated by the model 126 Figure 4.16: Trade-off between profit and employment in the Channel (CFSG) 127 Figure 4.17: Trade-off between profit and ecosystem health 129 Figure 4.18: Trade-off between employment and ecosystem health 130 Figure 4.19: M D S RAPFISH output for the 5 fields 133 Figure 5.1: A comparison of the three 'best' fleet configurations 139 Figure 5.2: Major biomass changes resulting from option A 141 Figure 5.3: Major biomass changes that result from option B 143 Figure 5.4: Major biomass changes resulting from option C 145 Figure 5.5: Effect of increasing temperature and varying the vulnerabilities 146 Figure 5.6: Difference between the open loop and closed loop simulations 149 Figure A l : Leveraging analysis for each R A P F I S H field 184 Figure A2: Model pedigree 185  Acknowledgements There are a number of people who I would like to warmly thank for helping me:  Firstly I would like to thank Tony Pitcher for his direction, support and 'big-picture' view throughout the duration of the work. Villy Christensen permanently had an open door to questions concerning Ecopath and was particularly valuable for focusing the modelling. The other members of my committee, Les Lavkulich and Rashid Sumaila, helped to shape the work and also had an open door for questions. M y research group as a whole, and particularly Cameron Ainsworth, Sheila Heymans and Eny Buchary, were very helpful as we worked through the details of the software together. Also from the Fisheries Centre, Dawit Tesfamichael and Steve Martell, were very helpful with RAPFISFf and Ecosim respectively.  From CEFAS, I have really appreciated Matthew Dunn and Mike Pawson who were a wealth of knowledge and extremely helpful too. Clara Ulrich (Danish Institute of Fisheries Research) kindly provided her Channel thesis and data, and Sean Pascoe (CEMARE) assisted me with economic data. The Sussex Sea Fisheries Committee and Brixham Sea Fisheries Inspectorate agreed to be interviewed for RAPFISH and Martin Genner and Alan Southward (Marine Biological Association) provided a great deal of data from benthic surveys that was very useful.  I would also like to thank Adam Lukasiewicz for running many simulations and along with Benson Hsu and Arman Sarfehnia for teaching me much. Hearty thanks go to Mum, Dad, Joy Steve, Sally and Matt for posting nice things and coming to visit me in Vancouver. I would like to especially thank Zoe Byrne for proof reading but more for her amazing support and making me laugh an awful lot over this last 2 years.  Finally, I would like to thank my L O R D Jesus Christ for joy and hope for the future.  ix  1. Introduction 1.1 Objective The objective of this research is to describe the ecosystem of the English Channel ("La Manche") using the computer program Ecopath with Ecosim and, using the same model, to peer into the future to generate the ecological, economic and social responses to possible management alternatives.  A 1997 paper by historian Harry Scheiber outlines the changes that have occurred in fisheries management since the 1890s (Scheiber, 1997). There has been a shift from single species stock assessment to an ecosystem management perspective, the seeds of which have grown from scientific vision into policy, while the management tools to implement this have proved somewhat elusive. Repeated failures of fish stocks around the world, such as the Peruvian anchovy (Walsh, 1981) and Newfoundland cod (Hutchings and Myers, 1994), forced scientists to evaluate the methods that they use and pushed them to look at the relationships between organisms. Because of ecosystem complexity, this holistic approach was extremely problematic in reality. However, a rapid development in computing power has meant that some of this difficulty can be coped with more adequately than in the past.  1.2 The English Channel 1.2.1 Physical and biological characteristics The English Channel (hereafter called 'the Channel') is a shallow area of continental shelf ranging from 40 m depth in the Dover Straits to 100 m in the Western Approaches (Figure 1.1). It is characterized by strong tides, up to 6-8 knots off Cape de la Hague, with a general range of 6-10 m, although in the Channel Islands it can be as high as 15 m and on the coast of Dorset as low as 1-3 m (Pingree and Maddock, 1977). The strong tides are caused by a propagation of flow from west to east so that when the west is at high tide the east is at low tide (Larsonneur et al, 1982). There is a general current flow from west to east creating a 'river' that connects the northeast Atlantic and the North Sea. 1  The Channel has a range of freshwater inputs, although the Seine estuary accounts for two thirds of the drainage area into the Channel (Pawson, 1995).  Figure 1.1: The Channel, showing depth contours, ICES areas boundaries indicated by parallel dashed lines and the names of the significant places referred to in the text. Modified from Pawson (1995).  For the purposes of the model, the Channel was assumed to be the entirety of ICES areas V l l d and Vile. The western Channel accounts for 63 %, and the eastern Channel the remainder of the combined surface area of 89,607 km (R. Watson, Fisheries Centre, 2  U B C , pers. comm.). Although in the model the Channel has been taken as a whole, there would certainly be a rationale for making two models, separating the western from the eastern Channel, because of their distinctiveness. The western Channel is deeper, stratifying in the summer to form a thermocline west of the 100 m isobath. In the eastern Channel the shallower water and constriction of the Channel ensure homogenous conditions throughout the year. The western Channel sediment is finer, consisting primarily of bioclastic material and the eastern Channel is mostly composed of lithoclastic larger gravels. A large pebbly zone running from Cotentin to the Isle of Wight 2  separates the two sections (Larsonneur et al, 1982). There are exceptions to these general sedimentary patterns and both the French and English coasts have many estuaries and bays (e.g. Fowey Estuary, Lyme Bay and Baie de Seine) where the low energy conditions cause the deposition of fine sands and muds (Larsonneur et al, 1982). There are at least 5 different species assemblages in the Channel based on sediment type (Ellis, 2001) and a general decrease in diversity of the benthos from west to east as a number of species are limited in their distribution to the west (Pawson, 1995).  The distribution of species in the Channel has provoked much interest, and a lot of effort has been invested in describing and understanding the situation. Although current systems and substrate type will certainly influence both demersal and pelagic organisms, climate also seems to have an effect. It appears that the south-west Channel is close to a marine biogeographic boundary that separates cold-water species to the north and warm water species to the south (Southward et al, 1988a). Climatic fluctuations appear to modify the distribution of indicator species in the 'Russell cycle' (Russell, 1935). Between 1930 and 1936 a plankton community characterized by the chaetognath Sagitta elegans was replaced by one characterised by S. setosa. Simultaneously, the south-west herring (Clupea harengus) fishery crashed and pilchard (Sardina pilchardus) eggs recovered in surveys increased by 2 or 3 orders of magnitude (Cushing, 1961). There were similar changes in intertidal barnacles with the cooler water Semibalanus balanoides being replaced by warmer water Chthalamus spp. (Southward et al, 1995). The ecosystem appeared to remain in this warmer state until the mid 1960s when S. elegans and herring returned. More recently there has been an increase in temperature and the reversal has been occurring once more. With the effect of global warming expected to increase sea temperature, there is a predicted 200-400 mile latitudinal shift in the distribution of fish, plankton and benthos (Southward et al, 1995).  The effects of the North Atlantic drift mean that the western Channel is usually warmer during the winter, with a lowest mean monthly temperature of 8.9 °C at Newlyn and 5.9 °C at Dover, averaged from 1980 to 1996 (Dunn, 1999b). The shallower eastern Channel is more susceptible to seasonal temperature change and is generally warmer in the summer; the Dover mean was 16.7 °C and Newlyn 15.8 °C. 3  1.2.2 Fisheries Reports on the changes in the herring and pilchard fisheries since the 16  th  Century  (Southward et al, 1988a), and on the history of Brixham (Morton, 2002), outline how important fishing has been to coastal communities along the Channel. Fishing effort in the Channel has been high with the two major English ports of Brixham and Newlyn having a particularly strong part in the history of the fishing industry. Even before the advent of the motorized trawler there had been considerable fishing effort (Figure 1.2) with gadoids, rays (Raja spp.), flatfish, and pilchards as target species.  Figure 1.2: Effort of trawlers in the English Channel from 1919-1990. Modified from Marine Biological Association data (Anon., 2001b).  - S a i l # Motor A Steam  A AAA'  A A A  • •  ™ A -  1910  1920  1930  •  •  •  A * - AAAAAA 1940  A  1950  A  1960  1970  1980  1990  Year  The work of the Channel Fisheries Study Group (CFSG), a group of scientists from the U K , France and Belgium, has identified fishing practices according to various metiers. A metier is defined as "a fishing activity that is characterized by one catching gear and a group of target species, operating in a given area and during a given season, within which the catches taken by any unit of fishing effort account for the same pattern of exploitation by species and size group" (Tetard et al, 1995). There are approximately seventy metiers in the Channel that can be broadly separated into one of eight gear types (Gray, 1995): 4  •  Otter trawl - the mouth of the net is held open by the floated headline, weighted ground rope and otter boards, which push outwards as the net is towed along. Rubber discs or steel bobbins attached to the gear allow it to be towed over rough ground, and 'tickler' chains disturb flatfish in the substrate. This gear catches a wide range of species and has high levels of discarding. The commonly used stretched mesh size is 80 mm.  •  Beam trawl - the net is kept open by a beam, with modern trawlers having one beam on either side to increase stability. Tickler chains are often used and a chain matrix may be attached to the base of the net when used over rocky ground. There has been a large increase in beam trawl fishing since the 1970s although as Table 1.1 shows they still contribute a low percentage of the landings compared to otter trawling. There are minimum mesh size regulations of 80 mm and there are power limitations within the 12 nautical mile limit of the coast.  •  Midwater trawl - the nets can be towed by one or a pair of boats and two main mesh sizes exist, 80-90 mm for bass (Dicentrarchus labrax) and black bream (Spondyliosoma cantharus), and 50 mm when targeting mackerel (Scomber scombrus). The catch generally has a low value per kilo, but huge landings of mackerel and scad (Trachurus trachurus), which have necessitated a closed area called the 'mackerel box' in the western Channel, compensate for the low price.  •  Dredge - these are dragged along the bottom and rake molluscs using metal teeth that dig into the substrate. The catch is then collected in a reinforced net or bag. Scallop (Pecten maximus) is the main target species and there is a European minimum landing size of 100 mm shell length.  •  Net- there are three main types of nets used in the Channel. Gillnets can be fixed or drifting and are set taut to enmesh fish in the gill covers. Tangle nets are fixed loosely from the seabed and are designed to catch fish in the gills or spines and shellfish by the legs. Trammel nets consist of three layers, outer layers with a wider mesh and an inner layer with a smaller mesh that is responsible for trapping the fish. Nets are often set around wrecks in places inaccessible for trawlers, and in some locations they can only be used during neap tides because of the powerful currents.  5  •  Pot - many small boats throughout the Channel use pots to catch shellfish. Inkwell pots are dome shaped and Crustacea enter the baited pot from the top. Parlour pots are rectangular and have two chambers, trapping the shellfish in the second chamber whilst they try to escape. There are minimum landing sizes for each of the crustacean species.  •  Line - longlines can be set on the bottom or floated, and 6,000 to 50,000 hooks can be automatically hauled per-day, depending on the size of the boat. Smaller numbers of hooks are used when fishing for conger eels (Conger conger), dogfish (Squalus acanthias and Scyliorhinus spp.), and other sharks. Handlining is the traditional method of catching mackerel, bass and pollack (Pollachius pollachius) and feathers or alternative lures are used to attract the fish. Handlining is permitted in the mackerel box. This grouping does not include recreational rod and line fishing.  •  Seaweed - a vast quantity of seaweed is caught (Table 1.1) by a gear called a 'scoubidou', which acts like a corkscrew turning in the weed. This fishery is exclusively French.  The metier grouping does not account for the diving or aquaculture metiers; however, these were not significant in the Channel. The Channel fisheries are extremely mixed, with a single boat having the capability to change gear depending on market prices and whether the quota has been reached, making the fishery very fluid and opportunistic. There are approximately 4000 boats operating in the fishery, ranging in length from 3 m to > 30 m, and the total direct employment in the fishery industry is about 4,300 people in the U K and 4,800 in France (Pascoe and Mardle, 2001). The U K fleet was estimated to have made losses over the period 1995-1996 and 1996-1997 (Coglan and Pascoe, 2000). The French fleet was estimated to have been profitable during 1997 and accounted for 60% of the Channel landings value, which was € 500 million in total (Boncoeur and Le Gallic, 1998). About 50 species are fished commercially in the Channel, but only herring, mackerel, sole (Solea soled), plaice (Pleuronectes platessa), cod (Gadus morhua), whiting  (Merlangius  merlangus),  anglerfish  (Lophius  piscatorius),  megrim  (Lepidorhombus whiffiagoni), hake (Merluccius merluccius), and pollack are subject to quota restrictions (Dunn, 1999b). 6  Table 1.1: The significance of each gear in the Channel. Data provided by Tetard et al. (1995). Gear type Eastern Channel Western Channel Total Channel BM BM % BM % % Otter trawl (*) 6,400 14.5 12.4 4,860 11,270 26.8 Beam trawl (*) 320 2.8 1,060 2.9 1,220 5.4 Midwater trawl 275 3.9 280 4.7 530 8.4 Dredge 1,300 4.2 6.2 3,000 4,300 10.4 Nets (*) 9,750 3.2 6,060 3.5 14,000 6.1 0 Pots 0 10,200 10.1 10,200 10.1 Lines 900 0.1 4,600 1.6 5,500 1.7 Seaweed 0 0 360 72.6 360 31.1 B M = boat months, % = % of landings (by weight) for that area. Boat months are an indication of the intensity of effort and are the aggregated months of vessels at sea. It is worth noting that a boat only has to be active for one day in that month for it to constitute 1 boat month. (*) Includes metiers common to the whole channel that could be included in both the eastern and western Channel, hence the total number of Channel boat months is less than the sum of both. As a common pool resource, fisheries are under pressure and face the potential of overexploitation (Hardin, 1968). The Common Fisheries Policy (CFP) is the European Union's method of managing a common resource, not just between individual fishers but also between all countries with a fishing fleet. The first measures date from 1970 when it was agreed in principle that all member states should have access to other countries fisheries resources. In 1977 member states extended their rights to marine resources out to the 200 nautical miles Exclusive Economic Zone (EEZ). By 1983 years of difficult negotiations finally produced the CFP, which to a degree has been argued about ever since. Nation states still control the territorial sea (12 nautical miles), with the U K having the further subdivision of the Sea Fisheries Committees (SFCs) controlling 0-6 nautical miles. The legal framework in the territorial sea cannot undercut the CFP; countries still cannot exceed their quota. SFCs have the power to make local byelaws such as temporary closures or increasing minimum landing sizes above those legislated from Europe. Because many countries at this time gained control of the 200 nautical mile zone much of the European distant water fleet, which was concentrated around Canada, Iceland and Norway, was forced to return to European waters and further depleted the local fragile stocks.  The CFP seeks to consider the biological, economic and social aspects of the European fishing industry and does so through 4 main areas: 7  Conservation — Based on stock assessments the Council of Ministers decides what the total allowable catch will be for a stock and this then is divided up between member states. It is then the responsibility of the member state to enforce that this quota is not exceeded and in theory a European Community Inspectorate ensures that the national enforcement agencies apply the same standards of quality and fairness. Technical measures that seek to conserve stocks include minimum mesh and fish sizes, bans on some gears, recording of catches in special log-books and closed areas and seasons.  Structures The E U seeks to restructure the fishing fleet so that funding is available to get rid of excess fishing capacity, for market and development research, and for the modernization of fishing fleets.  Common organization of the market This was one of the first measures that sought to unify the market and match landings with demand.  Fisheries agreements with other countries With the extension of the EEZ, the E U lost access to many of its traditional fishing grounds. As a result, the E U negotiates access to resources with other countries in return for some kind of compensation.  In 1994 there were 126 different license types and quotas in the Channel (Dunn, 1999b), but from the 1980s to the present, licenses have become successively more restrictive. Fisheries management in the Channel is complex and for a more comprehensive summary  of  the  Common  Fisheries  Policy  see  Dunn  (1999b)  or  http://europa.eu.int/comm/fisheries/policy en.htm.  8  1.3 Previous Research There are many research institutions on the coast of the Channel and the area has been studied for many decades. A good source of material has been the Marine Biological Association (UK), which has compiled semi-quantitative data on the changes to demersal fish species and benthic invertebrates since 1913 (Anon., 2001b), as well as publishing the Journal of the Marine Biological Association, which contains many studies on the Channel. The Channel Fisheries Study Group (CFSG) is coordinated by C E F A S (The Centre for Environment, Fisheries and Aquacultural Science) Lowestoft and includes members from IFREMER (Institut Francais de Recherche pour L'Exploitation de la Mer) laboratories at Boulogne-sur-Mer, Brest and Port-en-Bessin, the Fisheries Research Station at Ostende, and the Sea Fisheries Committees of Jersey and Guernsey. Publications by the CFSG have included the distribution, reproduction and migrations of 25 of the most commercially significant finfish and shellfish species in the Channel (Pawson, 1995). Progress has also been made in understanding the exploitation of selected non-quota species (Dunn, 1999b). Regarding the fisheries, comprehensive outlines of the respective metiers and their interactions in the Channel are particularly informative (Tetard et al, 1995). There has also been a simulation model of the Channel (Bio-Economic Channel Model) (Ulrich et al, 1999) and an optimisation model for the fisheries of the Channel (Pascoe and Mardle, 2001). These have incorporated both biological and economic data with the intention of producing an optimal fishing fleet for the Channel. While the aim of this ecosystem-modelling work is very similar, the method by which it is attained is different. The biological component of these bioeconomic models is based on production-effort relationships. Therefore, it only accounts for those species that are currently caught in the Channel and does not include non-commercial species. Furthermore, the models do not account for the predator-prey relationships between species and the possible effects of climate change. The strengths of the Ecopath model are the way that the entire ecosystem is analysed and that the effects of fishing can be seen throughout the food web from primary production to marine mammals.  If justification for this thesis was sought then it is no more clearly spelled out than as follows: "the [European] Commission will encourage research not only on technical aspects, but also on the development of an ecosystem approach" (Anonymous, 1999). 9  Furthermore, the mandate for the ecosystem approach in the U K comes from its signature to the 1992 OSPAR Convention. This was further extended by the UK's ratification of the additional Annex V 'On the Protection and Conservation of the Ecosystems and Biological Diversity of the Maritime Area' to the Convention that was signed in 1998 coming into force on 30 August 2000.  A n Ecosystem model of the North Sea has already been built (Christensen, 1995), but the species assemblage  and the environment itself is different in the Channel, so  extrapolating this would not be reliable (Rogers et al, 1998). Hence there was good reasons to embark on this project, as well as a considerable amount of information available from the CFSG that would prove vital in building the models.  10  2. Ecopath: creating the basic model 2.1 The basic parameters Ecopath with Ecosim (EwE) is a mass-balance and dynamic trophic ecosystem-modelling tool . The first step to building a model is to define a purpose and an area. In this thesis 1  the purpose was to create an ecosystem model that could be used to predict into the future with a range of management alternatives. The area of this 'ecosystem' was the Channel, specifically ICES areas V l l d and Vile. It was important to clarify the purpose at an early stage because the aim of the model will shape all other decisions and provides a framework so that the modeller does not get caught in 'interesting' distractions. The next step was to define the functional groups that will enable the aim to be attained. A functional group is a user-defined cluster of similar organisms, an individual species or a particular life stage of one species that warrants being distinguished as a group for the purpose of the model. The core of the Ecopath model is two 'master' equations:  1) Production = catch + predation mortality + biomass accumulation + net migration + other mortality.  This is an accounting system of production that breaks down the actual matter of a functional group. If primary production increases as a result of temperature then zooplankton is likely to increase. This equation documents what happens to the potential increase in zooplankton (or any other functional group) biomass.  2) Consumption = production + unassimilated food + respiration.  This second equation ensures that in the model the energy coming into a group is balanced by the energy leaving it. As with equation 1, it is an accounting system but this time for energy rather than production.  More comprehensive descriptions of EwE can be found in Christensen et al, (2000), or at www.ecopath.orR. 11  There are five basic sets of parameters per functional group for an Ecopath model. The modeller enters three: biomass, production/biomass and consumption/biomass and the model calculates the ecotrophic efficiency based on mass-balance. In the absence of biomass data, the modeller can estimate ecotrophic efficiency. The fifth parameter, diet composition, must be entered because this provides the essential linkages between functional groups.  While the static Ecopath model is a valuable snapshot of the ecosystem at a particular time, Ecosim allows the modeller to look at how the ecosystem has changed through time. Different life stages of a single group can be incorporated and this can be very useful when temperature influences recruitment or when fishing gear selects for particular sizes. Predator-prey behavioural relationships can also be included in the model through the setting of vulnerabilities. A very useful aspect of Ecosim for the purposes of this Channel work is the optimisation routine. This allows the modeller to search for a fleet configuration that will provide the greatest profit, employment, ecological health or biomass of certain species. Alternatively a combination of these objectives can be searched for. Ecosim differs from other more economically focused models by using the diet composition matrix to incorporate predator-prey relationships to the level of detail that the modeller wishes to incorporate into the model. Trophic mediation functions allow a third organism to impact the feeding rate of one group on another and forcing functions enable environmental effects on the ecosystem to be included. Whereas Ecopath allows the structure of the ecosystem to be replicated, Ecosim enables the function of groups within the ecosystem to be simulated. Because Ecosim can track the dynamic changes in the Ecosystem and be modified to reflect past data, it provides an ecosystem model that can be used to predict into the future having shown that it is trustworthy with regard to the past. For a more detailed explanation of Ecosim see Christensen et al. (2000).  2.1.1 Biomass This is the amount of a functional group in units of tonnes per km , and in the Channel. 2  model was calculated in one of two main ways:  12  Using ICES data: Virtual Population Analysis (VPA) uses a historical time-series of catch-at-age data to estimate fishing mortality and biomass during each year. The principle behind this is that the total landings for a cohort combined with some estimate of natural mortality each year, over the course of its lifetime, will equal initial recruitment of that cohort. The fishing mortality co-efficient F may be calculated because the numbers of a cohort caught and the numbers that were alive are both known. The main difficulty with this method is estimating the terminal fishing mortality. When moving back towards the youngest ages, the errors in the numbers alive or F decrease irrespective of the terminal F estimate (Pope, 1972), hence a time-series of catch data that captures a large fraction of the cohort is required. ICES working groups improve the estimate of the terminal F using tuning methods such as Extended Survivor Analysis (XSA) (Shepherd, 1999). X S A requires a large amount of additional data such as survey vessel catchability and commercial fleet CPUE, and was only available for species in the Channel that were subject to quotas (Table 2.1). For large stocks, such as whiting that primarily existed outside of the Channel, it was necessary to estimate the biomass in the Channel by the proportion of catch in the Channel compared to that caught in the whole stock. This has a considerable error attached to it but was the best way to deal with the problem ( M . Pawson, pers. comm.).  Using CFSG data: The CFSG had amassed a considerable amount of data in order to calculate the biomass of non-quota species. Clara Ulrich (DIFRES, pers. comm.) and Matthew Dunn (CEFAS, pers. comm.) provided me with pseudo-equilibrium analysis data for the stocks shown in Table 2.1. This method was used when age-structured data were available only for a short period of time (Pascoe, 2000). Cohort analysis deals with a single year-class throughout its lifetime but pseudo-cohort is where each age group in the catch belongs to a distinct cohort. This method relies largely on the assumption that recruitment and F do not show a significant trend from year to year. For the 1990s, the stocks in Table 2.1 that used this method could reasonably be assumed to be at this equilibrium (Pascoe, 2000).  13  Table 2.1: Methodology for calculating the biomass of each of the finfish groups in the model. ICES reports refer to the annual working group reports for stocks that are subject to quotas.  Reports Sole Plaice Whiting Cod Hake Herring Mackerel Scad  Pseudo-equilibrium analysis Other flatfish Gurnards (Chelidonichthys spp.) Pollack Large bottom fish Black bream Bass  ICES  Other method Small demersals Small gadoids Mullet (Mullus spp.) Dab (Limanda limandd) Rays and dogfish John Dory (Zeus faber) Sandeel (Ammodytes spp.) Sprat (Sprattus sprattus) Pilchard Sharks Basking sharks (Cetorhinus maximus)  2.1.2 Production per unit of biomass (P/B) P/B is equal to total instantaneous mortality, Z (Allen, 1971). Consequently it was calculated as fishing mortality (F) plus natural mortality (M) for commercial exploited stocks, and was set equal to natural mortality for non-commercial stocks. There were two main methods of calculating P/B for the groups (Table 2.2). Firstly, using the same recruitment, weight at age and fishing mortality at age data provided by C. Ulrich and M . Dunn (pers. comm.) to calculate the biomass, production was calculated by: Production = total mortality * biomass at age  This was then divided by the total biomass to estimate P/B.  Secondly, for non-commercially fished groups or for those where data were not available from the CFSG, natural mortality (M) was calculated using the following empirical equation (Pauly, 1980): M = K°' *L, -°6 5  *T -  2 7 9  0  n f  4 6 3  Where K is the von Bertalanffy growth constant, Lj f is the asymptotic length in cm and T n  is the average water temperature in °C. In the Channel the average temperature was taken to be 12.71 °C from climate data provided by the Hadley Centre (Anon., 2001 d). Fishing mortality was then estimated individually as shown in Table 2.2. 14  Table 2.2: P/B (year ') estimation for the fin-fish groups. Group P/B Source (year" ) Small demersals 1.32 Based on an M calculated from Pauly (1980) for sand goby and hooknose. Small gadoids 1.02 Based on an M of 0.82 from Pauly (1980) for pouting and in the absence of data an assumed F of 0.2. Mullet 0.50 Based on an M of 0.4 from Pauly (1980) and in the absence of data an assumed F of 0.1. Sole 0.43 Calculated on the basis of C F S G data. Plaice 0.65 Calculated on the basis of C F S G data. Dab 0.75 Calculated on the basis of C F S G data. Other flatfish 0.35 Calculated on the basis of C F S G data. Gurnards 0.43 Calculated on the basis of C F S G data. Whiting 1.07 Calculated on the basis of C F S G data. Cod Calculated on the basis of C F S G data. 1.13 Hake 0.47 Calculated on the basis of C F S G data. Rays and Dogfish 0.44 Assumed to be the same as in the North Sea (Christensen, 1995) as rays are very heavily exploited in the Channel (Southward and Boalch, 1992). Pollack 0.62 Calculated on the basis of C F S G data. Large bottom fish 0.40 Weighted for ling and anglerfish and calculated on the basis of CFSG data. Seabream 0.58 Calculated on the basis of C F S G data for black bream only. John Dory 0.46 Based entirely on an M of 0.46 calculated from Pauly (1980). Sandeels 1.14 Based on M from Pauly (1980). Herring 0.62 Calculated on the basis of C F S G data. Sprats 1.21 Sprat in the North Sea have a P/B of 1.21 according to (Christensen, 1995) and this was used for the Channel. Pilchard 0.66 Based on a value of M of 0.33 from Dias et al., (1983) and of 0.3 for F from Anon. (2000d). Both these values were for area 8 c, the Bay of Biscay. Mackerel 0.74 Based on an M of 0.49 calculated from Pauly (1980) and an F of 0.25 (Anon., 1999c). Scad 0.50 Based on an M of 0.34 calculated from Pauly (1980) and an F of 0.16 (Anon., 1999c). Bass 0.60 Based on an M of 0.20 calculated from Pauly (1980) and Mike Pawson (pers. comm.) estimating an F of 0.40. Sharks 0.19 Based on an M calculated from Pauly (1980) and averaged for blue shark, porbeagle and tope. Basking shark 0.07 Based on an M calculated from Pauly (1980). 1  15  2.1.3 Consumption per unit of biomass (Q/B) The intake of food by a group over a specified time period (consumption) divided by the biomass equals the Q/B value for the model. A l l finfish Q/Bs were calculated using the following empirical equation (Christensen and Pauly, 1992): Q/B = 10  6 7 3  * 0.0313 ^ * W  0 1 6 8 i n f  *1.38  P f  *1.89  H d  Where Tk is 1000/average temperature in Kelvin, Wj f is the asymptotic weight in grams n  and was converted from the asymptotic length using the a and b parameters from the length weight relationship for the species from Fishbase (Froese and Pauly, 2000), P f is equal to one for carnivores and zero for herbivores and detritivores, Hd is equal to zero for carnivores and one for herbivores and detritivores. The results for the finfish groups and the species they were calculated from are shown in Table 2.3. Table 2.3: Q/B (year "') of the finfish groups. Group Q/B ( y e a r ) Comments Small demersals 8.98 Averaged from hooknose and dragonet. Small gadoids 5.93 Averaged from pouting and poor cod. Mullet 7.10 Based on all four mullets. Sole 5.06 Plaice 4.11 Dab 6.41 Other flatfish 5.46 Weighted by the biomass of megrim, turbot and brill. Gurnards 5.74 Weighted by the biomass of grey and red gurnards. Whiting 5.47 Cod 3.03 Hake 3.76 Rays and Dogfish 4.19 Weighted by the biomass of cuckoo ray, spotted ray, thornback ray, spurdog, lesser spotted dogfish and blue skate, Pollack 3.23 Large bottom fish 2.90 Weighted by the biomass of anglerfish, ling, and conger eel. Seabream 4.72 Blackspot, gilthead and black bream. John Dory 4.21 Sandeel 10.82 Herring 6.39 Sprat 11.07 Pilchard 8.58 Mackerel 6.78 Scad 5.31 Bass 3.45 Sharks 2.37 Based on blue shark, porbeagle and tope. Basking sharks 3.70 1  16  2.1.4 Diet composition Diet composition data must be entered because they provide the trophic links between organisms. They can be entered as percentage weight or volume but not as frequency of occurrence. When data were not specifically available for the Channel, they were taken for the same species from the closest proximity to the Channel.  2.1.5 Ecotrophic efficiency This is the fraction of production that has been accounted for by the model. 1-EE is the fraction of biological production of the functional group that has not been explained by the model. In situations where the biomass was not available this had to be estimated to allow the model to calculate the biomass. Once these data had been entered into the model, it was necessary to enter catch and discard data before the Ecopath model could be balanced.  2.2 Functional group descriptions What follows is a description of the functional groups for the 1995 Ecopath model. The input parameters estimated by Ecopath are shown in Table 2.22.  1) Primary production The biomass of primary production was composed of three parts:  Phytoplankton Using data derived by chlorophyll determinations (Harvey, 1950) and a conversion factor of 2 from kilocalories (kcal) to wet weight flesh (Crisp, 1975), the biomass of phytoplankton was calculated as 40 t/km (t = tonnes) off the coast of Plymouth. The P/B 2  using these sources was calculated as 67.5 year' . Alternatively, SeaWiFS data (Reg 1  Watson, pers. comm.) indicated that the average productivity for the Channel was 633 g C m . Using a Channel biomass estimate of 4.15 g C m between 1993-1995 from 2  2  Plymouth (Roger Harris, Plymouth Marine Laboratory, pers. comm.) and SeaWiFS (Seaviewing Wide Field-of-view Sensor) data a P/B of 152.5 year  _1  was calculated. The 17  difference between these estimates may reflect the former point biomass estimate being from Plymouth and the latter being satellite data that included the more productive eastern Channel. Because the SeaWiFS data covered the whole Channel and were more recent, a P/B of 152.5 year  _ 1  was used.  Benthic micro-flora Using an estimate of 140 mg Chi a m" (Sagan and Thouzeau, 1998) and a conversion 2  ratio C/Chl a = 40 (De Jonge, 1980) the micro-phytobenthos biomass in the western Channel was estimated to be 5.6 g m" C. This was converted to wet weight using a 2  multiplier of 20 (Crisp, 1975). Because primary production occurred off the coast of Devon to a depth of 25 m (Southward and Boalch, 1992), the calculated value of 112 t/km was used for areas shallower than 25 m. Hence when averaged over the Channel this part of the primary production contributed 3.92 t/km to the biomass. This was based 2  on average depth values provided by R. Watson (pers. comm.).  Benthic macro-algae The biomass of macroalgae off the northern coast of Brittany in summer was estimated as 3 million t (Table 2.4) (Kerambrun, 1984); (P. Arzel, IFREMER, pers. comm.). This study was for the summer biomass only for an area from St. Guenole, south of Brest, to Le Mont Saint Michel, near Granville both on the north Coast of Brittany. In the winter there was only 13.6 % of the biomass in summer (P. Arzel, pers. comm.). This corresponded to a winter biomass of 402,000 t and an average of 1,700,000 t was used to represent the annual average biomass. In the absence of data for the entire Channel the annual average biomass was multiplied by four because the study area covered approximately a quarter of the Channel. Hence, macroalgae contributed 6.7 million t (75 t/km ) to primary production. O f the 58,228 t of benthic macroalgae that were harvested 2  off the French coast approximately 74 % were Laminaria digitata, 3 % were L. hyperborean, 10 % were Ascophyllum spp., 10 % were Fucus spp. and 3 % were Chondrus spp. The market price of macroalgae was 0.04 €/kg. On the English coast there was no commercial harvesting of macroalgae, but some washed up algae was collected for use as fertilizer (Southward and Boalch, 1992). This was deemed to have a negligible impact in the ecosystem. 18  Species Pelvetia canaliculata Fucus spiralis Ascophyllum nodosum Fucus vesiculosus Fucus serratus Bifurcaria rotunda Himanthalia elongata Laminaria digitata Saccorhiza polyschides Laminaria ochroleuca Laminaria saccharina Laminaria hyperborean Total  Biomass (tonnes) 4,005 12,015 123,354 24,030 64,080 10,680 10,680 320,400 9,621,200 142,400 71,200 1,214,850 2,958,894  Table 2.4: Biomass of macroalgae off the northern coast of Brittany. Data from Kerambrun (1984) and P.Arzel, (IFREMER, pers. comm.).  Table 2.5: Biomass and P/B of primary producers in the Channel. P/B for macrolagae came from the Channel (Kerambrun, 1984) and for microphytobenthos from the Elbe Estuary (Kies, 1997). Group Production Biomass t/km P/B (year ) (t/km /year ) Phytoplankton 7304 47.8 152.8. Macroalgae 74.3 75 0.99 Micro-phytobenthos 169.7 3.9 43.3 Total 7548 126.7 Average = 59.6 2  2  _1  _I  The total biomass of primary producers in the Channel was estimated to be 127 t/km and 2  the P/B 60 y e a r ( T a b l e 2.5).  2) Zooplankton Splitting zooplankton into just 'zooplankton' and 'carnivorous zooplankton' was a broad approach to modelling this aspect of the ecosystem but there seemed little value in further segregating them because the aim of the model was to look at fishing policy in the Channel.  According to Dauvin et al. (1998) "The [Channel] mesozooplankton community, defined as a euryhaline marine assemblage, was dominated by the calanoid copepods Acartia spp., Temora longicornis and Centropages hamatus, the cladoceran Evadne nordmanni and the appendicularian Oikopleura dioica ". Consequently, when estimating the biomass of zooplankton a conversion ratio from dry weight to wet weight of 13.95 % for copepods was used (Beers, 1966). Dry weight data was used from the eastern and mid English 19  Channel to represent the entire channel (Le Fevre-Lehoeerff et al, 1993). It was averaged from 4 stations over 12 months to be estimated as 21.6 mg/m" . Then it was averaged by 3  depth to equal 7.4 t/km - of this 12.9 % were allocated to the carnivorous zooplankton 2  group, so a value of 6.45 t/km was calculated for zooplankton. A degree of caution must 2  be used with this data because different zooplankters have varying degrees of water content so the conversion from dry to wet weight may not be correct (Harvey, 1950).  Trawl surveys in 1934 and 1949 (Harvey, 1950) indicated that there was, on average, 2 g dry weight of plankton below a square meter. Analysis of mixed plankton communities showed that they contained approximately 83 % water. Hence, using a mean value of dry weight biomass of 1.742 g (it was 2 g but 12.9 % had been allocated to carnivorous zooplankton), there would be 8.5 t/km  2  of zooplankton. This was close to the  contemporary data of Dauvin et al. (1998) and because the conversion ratio was specific to the Channel, this was the value that was used in the model.  A P/B of 18 year  _ 1  and a Q/B of 60 year ~~ were used, taken from an Ecopath model of ]  the North Sea (Christensen, 1995).  The diet of zooplankton was assumed to be 90% phytoplankton, 3 % zooplankton and 7 % detritus based on zooplankton in the North Sea (Christensen, 1995) and the assumption that although the majority of the zooplankton would be herbivorous there would have been a small element of predation too.  3) Carnivorous zooplankton This group was comprised of the Hydromedusae, the Scyphomedusae  and the  chaetognaths and they were separated from the rest of the zooplankton because they prey primarily on copepods (Nicholas and Frid, 1999). The "jelly" nature of the two medusae groups has made sampling difficult and there were few studies specifically on the Channel. Biomass data were taken from Harvey (1950), which indicated that 7.1 % of the total biomass of zooplankton were medusae and 5.8 % were chaetognaths - hence the reason 20  that 12.9 % were moved from 'zooplankton'. This would correspond to the group having a biomass of 1.1.  A P/B value of 7 year  _ 1  was used for medusae based on medusae data off the coast of  British Columbia (Larson, 1987).  A Q/B value of 23.33 y e a r  -1  was used from the carnivorous jellies group of the southern  B.C. shelf model (Pauly and Christensen, 1996). The diet composition was assumed to be entirely zooplankton.  Benthos The five benthic groups proved problematic because there were species differences depending on habitat within the Channel (Gray, 1974), and consequently different authors had different estimates of the abundance of different groups (Mare, 1942; Holme, 1953; Gros and Hamon, 1990; Ellis et al., 2000). Personal communication with Jim Ellis (CEFAS), who has been responsible for a great deal of trawl surveys in the Channel, was invaluable in making choices about which data to base the model on. Part of the problem was that different authors used different techniques to measure the benthos and that while trawling may have caught large mobile animals it missed out on worms and smaller bivalves. Using a scoop sampler, Holme (1953) calculated an average total biomass for different substrates around Plymouth as 49.1 t/km taking into account the weight of sand 2  in guts and those organisms that had been missed by the instrument or been lost through the mesh of the 1.0 mm sieve. These data are shown in Table 2.6. This data was used as a benchmark value for the total benthic biomass in the channel, but there were further considerations that needed to be highlighted: •  These data were from the 1950s and may not represent the current situation.  •  These data were from the area around Plymouth and, although they encompassed a range of substrates, they did not go as far as the eastern Channel. Positively, because the data were from the Plymouth area the likelihood is that it would have been heavily trawled and so would reflect the present situation.  •  The contemporary trawl data indicated much lower biomasses from C P U E data. 21  Table 2.6: Relative constituents of the benthos from a scoop sampler (Holme, 1953). Group % biomass Weight in g based on 49.1 total Suspension feeders 10.34 5.07 Polychaeta and Nemertinea 25.79 12.66 Crab 10.51 5.16 Gastropoda 0.02 0 Bivalves 35.46 17.41 Echinodermata 17.88 8.78 For the purposes of the model, the biomass of crabs were not used from Table 2.6 because their sampling was not as effective as later trawl surveys (Table 2.7) (Ellis et al., 2000; Ellis, 2001).  Table 2.7: % of benthos in the Channel based on beam trawl surveys. In the absence of specific western Channel data, this area was based on the Bristol Channel on the advice of J. Ellis (pers. comm.). The weights (Wt) were calculated on the basis that there was 49.1 t/km total biomass. Group East channel (Ellis, West Channel (Ellis Total Channel 2001) et al., 2000) % Wt Wt % Wt % Suspension feeders 64.04 33.94 30.31 14.88 42.78 21.00 Bivalves 2.58 1.37 0 0 0.95 0.47 Deposit feeders 0 0 0 0 0 0 Crab 17.29 9.16 34.53 16.95 28.16 13.83 Echinoderms 16.09 8.53 35.16 17.26 28.11 13.80 2  The main differences between Tables 2.6 and 2.7 is the absence of small animals in the trawl surveys, which seems to misleadingly inflate the importance of crabs, echinoderms and suspension feeders. It is noteworthy that data from Holme (1953) does not include representatives from shrimps or prawns and in the absence of data Ecopath calculated the biomass of this group.  Coull (1972) records an expected benthic biomass on continental shelves as 50-100 t/km . The highest biomass calculated from the CPUE data was 9.54 t/km from the 2  2  eastern English Channel and most of the assemblages were considerably lower than this. Furthermore a macrobenthic biomass of 75 t/km , which did not include 31.5 t/km of 2  2  polychaetes, was measured in a muddy deposit off Plymouth, although this was earlier in the twentieth Century (Mare, 1942). Another example of a higher biomass is found in Le Guellec and Bodin (1992) where macrobenthos biomass in the Bay of St. Brieuc is 9.3 g/m Ash Free Dry Weight. Converting this to wet weight using a ratio of 15:1 (as used 22  by Christensen (1995)) the expected biomass would be 139.5 t/km . The last two 2  examples were close to the coast and one would expect the biomass to be higher in this vicinity, but these data did give legitimacy to base the model mainly from the higher estimates of Holme (1953), while being aware that these may have to be further increased. Consequently, the Holme (1953) estimates of biomass were used for suspension feeders, deposit feeders, bivalves and echinoderms, and the eastern Channel trawl surveys of Ellis (2001) were used for crab.  4) Deposit feeders This group was mainly composed of worms but also includes small invertebrates such as amphipods that feed on detritus. The P/B was variable, between 1.9 year  and 3 year  depending on the substrate, for a  generalized group called "deposit feeders" in the Bay of Morlaix, western Channel (Ameziane et al, 1995). A value of 2.5 year  was used because this was the P/B of  deposit feeders inhabiting coarse gravel, which dominates the Channel (Larsonneur et al, 1982).  Because there were no Q/B estimates available, a value of 0.15 for gross food conversion efficiency was used based on the North Sea model (Christensen, 1995).  Although some worms, such as the Nereidae, are carnivorous, because they were in such small quantities in the Channel according to Warwick and Price (1975), cannibalism was deemed to be negligible and it was assumed that this group fed entirely on detritus in accordance with the North Sea and Newfoundland models (Christensen, 1995; Bundy et al, 2000).  5) Sessile suspension feeders Benthic cnidarians, sponges, bryozoans and ascidians seemed to be significant in the Channel both from the work of Holme (1953) and the trawl surveys (Ellis et al, 2000; Ellis, 2001); and merited a group of their own, even though data were scarce, because 23  they are distinct from the filter feeding bivalves. The P/B of sessile suspension feeders was taken to be 0.1 year  _ 1  on the advice of Dr Roland Pitcher from CSIRO  (Commonwealth Scientific and Industrial Research Organisation) (pers. comm.). This was a personal global approximation in the absence of data and needs to be treated with caution.  A gross food conversion efficiency of 0.15 was used in the absence of other estimates for Q/B.  A general qualitative diet for sea anemones comprised of zooplankton, isopods, amphipods and polychaetes (Van-Praet, 1985). Hunt (1925) found that the sponges, Desmacidon fructicosa, Ficulina ficus and Cliona celata were eating fine detritus and minute diatoms in the area around Plymouth. The diet of the hydroid Campanularia everta was recorded as 54 % zooplankton and 46 % detritus by weight in the western Mediterranean (Coma et al, 1995). Consequently, the diet of this group was entered as 45% zooplankton 35% detritus, 10% deposit feeders and 10% primary production.  6) Shrimp and prawns Very little information could be located for the basic parameters of this group and there was some consideration given to whether it could be combined with crabs to form a broad decapod group. It was decided to leave it separate and base this group on data from other areas and other Ecopath models. 152 t of pink shrimp (Palemon serratus) and 340 t of brown shrimp were caught in the Channel with an average value of 10.42 €/kg. Hopkins (1988) calculated 1.7 year  _ 1  for the P/B of the deep-water prawn Pandalus  boreali in Northern Norway and in lieu of Channel or species data this had to be used.  In accordance with Mackinson (2000) shrimps and prawns were assigned a gross food efficiency of 15 % and an ecotrophic efficiency of 0.95.  The diet of shrimps and prawns was based entirely on Northern shrimp off the coast of Newfoundland (Bundy et al, 2000). 24  7) Whelk The biomass of whelk (Buccinum undatum) (0.247 t/km ) was calculated in the same way 2  as many finfish species by using age structured data provided by the CFSG. A P/B of 0.586 year was calculated from the same data. -1  Q/B was not available so a gross food conversion efficiency of 0.15 was used.  Himmelman and Hamel (1993) examined the stomachs of 200 whelks off the eastern coast of Canada but found only 30 of them contained identifiable prey items. Similarly, Taylor (1978) found that many stomachs were empty. Both sources indicate the high significance of polychaetes in the diet of whelks (50-85%) with molluscs, crustaceans and echinoderms forming the rest of the diet in different quantities depending on the location. The diet composition that was entered into Ecopath is as follows: 70% deposit feeders, 10% bivalves, 10 % crustaceans, 5 % shrimps and prawns, and 5 % echinoderms.  8) Echinoderms This group represented all echinoderms in the Channel whether they were mostly carnivorous (Asterias rubens), or mainly detritivorous (Ophiothrix fragUis). The main species that occur in the Channel are Asterias rubens, Astropecten irregularis, Spatangus purpureus, Psammechinus miliaris, Echinus esculentus, Solaster endeca, Ophiura ophiura, Crossaster papposus, Echinocarsium cordatum and Ophiothrix fragilis (Ellis, 2001).  A range of estimates were available for echinoderm P/B: 1) A general value of 2 year  _ 1  for benthos in the North Sea from Christensen (1995) and 2) a calculated value of 0.26 year  using an average depth of 55m, a bottom water temperature of 11 °C and a mean  individual body mass of 23.7 K J (Brey, 1999). The body mass in K J was calculated from an average ash free dry weight of 0.25 g of Asterias rubens in the Baltic (Anger et al, 1977). This was converted to 5.925 K J using the conversion factor of l g ash free dry weight = 23.7 K J (Brey, 1999). 3) A value of 0.6 year  from the Southern B C shelf  model had also been calculated for echinoderms using data from Brey (1999). Because 25  0.26 year  indicated that echinoderms were being very wasteful and there was no  evidence to back this up, the higher value of 0.6 year  was used.  Anger et al. (1977) calculated the daily consumption of Asterias rubens to range from 1.9 to 11.4 % of body weight. This translated to a Q/B of between 6.935 year ~ and 41.61 ]  year  . Because Asterias rubens is an active motile carnivore the Q/B will be larger than  other more sedentary echinoderms. The lowest value in the range was consequently used. The gross efficiency of 0.087 obtained from a P/B of 0.6 year 1  was comparable with a value of 0.09 year  _ 1  _ 1  and a Q/B of 6.935 year"  assumed by Jarre-Teichmann and Guenette  (1996). This is a value that needs further research in the Channel. Having entered these values, the model calculated a respiration/assimilation ratio of 0.892. This was comparable with values of 0.78 - 0.82 for the sea urchin Parechinus angulosus off the coast of South Africa (Greenwood, 1980).  Relative frequency diet data were available for 3 species, Echinus esculentus, Crossaster papposus and Solaster endeca from the north-west Atlantic (Himmelman and Dutil, 1991), and initially, in absence of weight or volume data, these were converted to % weight. By using this approximate method cannibalism was excessive. The species investigated by Himmelman and Dutil (1991) were not completely representative of all echinoderms because many feed on detritus (J. Ellis, pers comm.; Hunt, 1925). The trawl data of (Ellis et al., 2000) indicated that Asterias rubens is an abundant echinoderm in the Channel. A study on the diet of Asterias rubens in the western Baltic Sea (Anger et al., 1977) indicated that gastropods, bivalves, deposit feeders and detritus were important components of the diet but also that the diet of Asterias rubens corresponds to the species diversity found in the environment. Using this information on echinoderm diets, previous models Christensen (1995); Bundy et al. (2000) and also the recommendations of Jim Ellis (pers. comm.), the following diet was entered into Ecopath:  5 % Primary producers, 11 % deposit feeders, 1 % suspension feeders, 5 % bivalves, 6 % Echinoderms, 72 % detritus.  26  9) Bivalves This group includes bivalves other than scallops such as cockles (Cerastoderma edule), soft-shelled clam (Mya arenaria), blue mussels (Mytilus edulis), and oysters (Ostrea edulis and Crassostrea gigas)  P/B ratios for bivalves ranged from 0.2 year  _ 1  (Cerastoderma edule) to 0.5 year  _ 1  (Mya  arenaria) in an estuarine mud-flat off Plymouth (Warwick and Price, 1975), although these bivalves were unexploited. From this functional group, blue mussels, clams, oysters and cockles are caught from the Channel. The P/B of scallops as estimated from data provided by Ulrich (2000) ranged between 0.35 year area. A value of 0.6 year  _ 1  _ 1  and 1.25 year  _ 1  depending on the  was used for this group because although fishing mortality  was very high in certain areas, when averaged over the Channel and all of the bivalve species its impact will be reduced. Catch data was used from ICES and indicated that 13,557 t of non-scallop bivalves were caught on average between 1993 and 1995. This comprised blue mussels, clams, common cockles and oysters. No market price was available for this group so the same value as scallops of 2.63 €/kg was entered.  In the absence of other data a gross food conversion efficiency of 0.09 was used for bivalves from the B C shelf Ecopath model (Guenette, 1996).  I found little quantitative information on the dietary composition of bivalves but it does seem that a combination of phytoplankton (Thouzeau et al., 1996) and detritus (Guenette, 1996) comprises the diet. Hence I allocated 50% of the diet to each.  10) Scallops As a sub-group of bivalves, scallops (this included common scallops (Pecten maximus) and queen scallops (Chlamys opercularis)) were separated because they seemed to be a particularly significant fishery in the Channel.  The P/B of scallops as estimated from data provided by Ulrich (2000) ranged between 0.35 year  _ 1  and 1.25 year  depending on the area, and the midpoint of 0.8 year ~ was l  27  used in the model. According to Ulrich (2000), 26,259 t of scallops were landed annually from the Channel, and so the biomass was calculated using catch divided by an average F of 0.6 year  (Ulrich, 2000) to be 43,765 t or 0.488 t/km .  _ 1  2  In absence of other data a gross food conversion efficiency of 0.09 was used from bivalves in the B C shelf model (Guenette, 1996).  As with 'bivalves' above, the diet was allocated 50 % to phytoplankton and 50 % to detritus.  11) Crab According to both Ellis (2001) and Holme (1953) there were a high proportion of crabs in the Channel, some of which support a large potting fishery. Commercial crabs were separated from the remainder, which formed this group and included the shore crab (Carcinus maenas), the common hermit crab (Pagurus bernhardus), the hairy crab (Pilumnus hirtellus) and the velvet swimming crab (Necora puber). Ellis (2001) indicated that the relative importance of crabs was much higher than Holme (1974) suggested in the Channel, so initially the biomass was entered as 8.67 t/km , the midpoint between the 2  crab biomass in Tables 2.6 and 2.7.  There were no P/B values specifically for this group in the Channel, so an average value of 1.05 year  _ 1  was used based on the mid-point between 1.8 year  B C shelf (Jarre-Teichmann, 1996) and 0.3 year  - 1  _ 1  for 'crabs' from the  for edible crabs in Norway (Gundersen,  1976). The rationale being that many of the crabs in this group would be smaller and faster growing than the edible crab.  There were no Q/B estimates so a gross food conversion efficiency of 0.15 was used from Christensen (1995).  The diet of this group was based entirely on a generic benthic crab (Brey, 1995), 62 % detritus, 20 % bivalves, 15 % shrimps and prawns and 3 % cannibalism. There is a lot of  28  uncertainty with the diet composition for this group, mainly because it is difficult to know the proportion of the diet that is already dead, i.e. detritus.  12) Commercial crab This group was composed of the edible crab, Cancer pagurus and the spider crab, Maja squinado.  The trawl surveys of Ellis et al. (2000) were used to calculate the biomass of crabs in the Channel. J. Ellis (pers. comm.) had stated that the Bristol Channel had the most similar assemblage to the western Channel and spider crab in this had a C P U E of 10.8 kg/hr. In the eastern Channel CPUE for spider crab was 2.6 kg/hr and for edible crab it was 2.3 kg/hr. The greater biomass in crabs from the western Channel seemed to be reflected by the concentration of effort from both the English and French fishers (Brown and Bennett, 1980); (Tetard et al., 1995). Because there were no edible crab recorded in the Bristol Channel and catches were approximately equal to spider crab in the English Channel, 10.8kg/hr was used to represent the western Channel edible crab C P U E and 2.3 kg/hr for the eastern Channel from Ellis et al. (2000). This translated into a total commercial crab biomass in the Channel of 0.514 t/km . 2  A P/B of 0.46 year  _ 1  was used based on a natural mortality of 0.3 year  _ 1  for edible crabs  in Norway (Gundersen, 1976) and a fishing mortality of 0.16 from a production model of edible crabs in the Channel (Anon., 1999a).  Q/B data was unavailable, so a value of 0.15 for gross food conversion efficiency was used (Christensen, 1995).  The diet of this group was based entirely on a generic benthic crab (Brey, 1995 cited in (Jarre-Teichmann and Guenette, 1996)), 62 % detritus, 20 % bivalves, 15 % shrimps and prawns and 3 % cannibalism.  29  13) Lobsters This group was comprised entirely of European lobsters Homarus gammarus and although the crawfish (Palinurus elephas) catch of 24 t was included in this group, crawfish data were not used to estimate any of the other parameters in the model because the lobster catch was so much larger at 473 t.  Although there were no extensive data on lobsters, because they are such a lucrative fishery (19.17 €/kg - Table 2.23), they should be separated from crab. No lobsters were recorded in any of the trawl data suggesting that they were not common enough to be represented, they evaded the trawl, or the rocky areas they inhabit could not be accessed by the gear.  Lobster biomass was estimated on the basis of catch/F = biomass. 473 t of lobster were caught with a fishing mortality of 0.4 year  _ 1  (Bannister and Addison, 1984) = total  biomass of 1183 which is 0.013 t/km . 2  A P/B of 0.5 year  - 1  and 0.4 year  for F from the south-west stock (Bannister and Addison, 1984).  _ 1  was used based on the conventional 0.1 year  for M (Anon., 1979)  Southward and Boalch (1992) report that lobsters on the south coast of Devon are very seriously overfished.  Q/B data was used from the Newfoundland Ecopath model (Bundy et al, 2000) for the American lobster as 5.85 year . _ 1  Crawfish from the Channel consumed bivalves and echinoderms in the laboratory (Hunter et al, 1996), and Vasserot (1965) referred to the preference crawfish show for echinoderms. The diet was based entirely on Homarus americanus from Bonavista Bay, Newfoundland (Ennis, 1973) with the unidentified food being allocated to the detritus because although lobsters are primarily predators scavenging behaviour is well developed (Herrick, 1991). This too showed that echinoderms were an important part of the diet.  30  14) Small demersals Many of the demersal feeding fish groups consumed small gobies and similar species. This was a group designed to incorporate these small fish into the model and the species used were sand goby (Pomatoschistus minutus), hooknose (Agonus cataphractus) and dragonet (Callionymus maculates). There were many species that would have been applicable in this category but these seemed representative of other species. There were no biomass estimates for this group, so an ecotrophic efficiency of 0.95 was entered and Ecopath calculated the biomass. Table 2.8: Origin and type of diet composition data used for small demersals from W.Scotland. Species Data type Source Sand goby % weight (Gibson and Ezzi, 1987) Hooknose % weight (Gibson and Robb, 1996) Dragonet % weight (Gibson and Ezzi, 1987) Diet composition was based on the average of the diets in Table 2.8.  15) Small gadoids The parameters for this group were based entirely on pouting (Trisopterus luscus), Norway pout (Trisopterus esmarkii) and poor cod (Trisopterus minutus). The biomass was estimated by the model based on an assumed ecotrophic efficiency of 0.95.  Diet composition for the model was based on an average of pouting and poor cod from the Irish Sea (Armstrong, 1982).  16) Mullet This group comprised four species of mullet, thinlip mullet (Liza ramada), golden grey mullet (Liza aurata), thicklip grey mullet (Chelon labrosus) and striped red mullet (Mullus surmuletus).  No biomass data was available for the group so ecotrophic efficiency was set to 0.95 and the model estimated the biomass.  31  Only red mullet were landed commercially and 90% of the 1,005 t was caught by otter trawling.  Diet composition for this group was based on striped red mullet from a study in the Bay of Biscay (Olaso and Rodriguez-Marin, 1995).  17) Sole Sole (Solea soled) are a very lucrative species (9.9 €/kg) in the Channel and are landed in large quantities by beam and otter trawls as well as by dredging and trammel netting. Sole do not migrate extensively out of the Channel (Pawson, 1995). Using ICES data averaged between 1990 and 1999 (Anon., 2000c), a value of the biomass for area VTId was 17,038 t. For Vile it was 3,200. This meant a total biomass of 20,238 t (0.226 t/km ). 2  Diet composition for the group was taken from Beyst et al. (1999) for juvenile sole off the coast of Belgium. This was very similar to the diet composition data for sole in the whole of the North Sea (Christensen, 1995) and off the North coast of Spain (Molinero and Flos, 1992), with deposit feeders forming approximately 80 % of the diet.  A juvenile sole group was added during tuning to incorporate the effect of temperature on recruitment (section 4.1.3).  18) Plaice Like sole, plaice (Pleuronectes platessa) are considered a very important species in the Channel and are mainly caught by otter and beam trawls, dredging and trammel netting.  Biomass calculations were complicated by the migration of plaice into the North Sea. ICES area VUe had a local stock meaning the biomass could be calculated as 4,102 t, averaged between 1990 and 1999. Many plaice were tagged and recaptured during 1971 and 1972 with the results indicating that 20-30 % of the plaice caught in the eastern Channel contained migratory North Sea fish (Pawson, 1995). The estimated plaice biomass in area Vfld was averaged over the years 1990-1999 (Anon., 2000c). This was 32  calculated as 15,572 t. Taking into consideration 25 % of this would only be in the Channel for 4 months during winter (Pawson, 1995), the total permanent biomass in the Channel, including area Vile, was estimated as 13,892 t, with an additional 3,893 t being present for 4 months only. 21.9 % of the total biomass was migrating and in an attempt to incorporate this into the model the whole biomass was included in the basic input and 21.9 % of the diet composition was allocated to import, with the assumption being that the migratory plaice consumption while in the Channel would be minimal. So the total biomass entered was 17,785 t or 0.199 t/km . 2  The diet composition was based on plaice from the west coast of Scotland (Gibson and Robb, 1996). It included a 13.1 % component of unidentified fish that were allocated entirely to small demersals.  A juvenile plaice group was added during tuning to incorporate the effect of temperature on recruitment (section 4.1.3) and this necessitated a change in the initial biomass.  19) Dab Initially dab (Limanda limandd) had been included with the group other flatfish (see below) because the biological parameters in Ecopath are similar but they were separated because dab is much cheaper than the other species of flatfish (Table 2.23). A biomass value of 3,168 t for the entire channel was calculated with data originating from the CFSG but this seemed very low. There is a very high discarding of dab because they are a cheap fish so when this was added on to the catch the estimated biomass from catch divided by a fishing mortality of 0.35, estimated from CFSG data, was much higher. This value of 0.103t/km was used in the model.  Diet composition data was based on juvenile dab and came from the west coast of Scotland (Gibson and Ezzi, 1987).  33  20) Other flatfish This  group  included lemon sole  (Microstomus  kitt), megrim (Lepidorhombus  whiffiagonis), turbot (Psetta maxima) and brill (Scophthalmus rhombus). Initially it had been anticipated to include witch (Glyptocephalus cynoglossus), sand sole (Pegusa lascaris), and flounder (Platichthys flesus) in this group but their absence in the catch data and in a report on the catches of each metier (Tetard et al, 1995) provided no reason to include them at this stage. Lemon sole are targeted by otter trawls, megrim by beam trawls and turbot with nets, with the remainder of the catch being by-catch from trawling, dredging and netting. The total catch of other flatfish according to Ulrich (2000) was 2,717 t. 380 t were brill, 425 t were turbot, 1,466 were lemon sole and 446 t were megrim. The market price for this group was averaged on the basis of the current catches. This was not ideal because brill and turbot were more expensive (6.9 and 9.6 €/kg respectively) than lemon sole (3.8 €/kg) and megrim (3.7 €/kg) but the value of 5.1 €/kg that was entered into Ecopath does reflect that this is a lucrative fishery (Table 2.23). Megrim were assessed over a large area, ICES areas VII b, c, e-k and VIE a, b and d. In this situation and for all stocks that had a proportion of the population migrating into the channel, personal communication with Mike Pawson and Matthew Dunn suggested that the best way to calculate biomass was to estimate the entire stock and then base the proportion of the stock in the Channel on landings into Channel ports. Catch data were used from Ulrich (2000) because in these data there had been an attempt to weed out vessels landing fish caught outside of ICES areas VUd and Vile. The average biomass of megrim in ICES area VUb, c, e-k and Villa, b and d, from 1990-99 was calculated as 81,414 t (Anon., 2000e). The 446 t caught in the Channel contributed 2.5% of the total catch. 2.5 % of the entire stock biomass was 2,003 t, which was the estimate of the biomass of megrim in the Channel. The biomasses of lemon sole (5,172 t), turbot (1,377 t) and brill (1,239 t) were calculated using data provided by the CFSG. The total biomass for this group was 9,791 1 (0.109 t/km ). 2  Diet composition data were available for adult lemon sole off the west coast of Scotland (Rae, 1965) and for juvenile brill and turbot off the Belgian coast (Beyst et al, 1999). Although all data were in the useable form of % weight, brill and turbot contained a high 34  proportion (> 80 %) of unidentified fish. For this reason, and because lemon sole contributed the majority of the biomass, the data from lemon sole were used to represent the whole group.  21) Gurnards There are three species of gurnard found in the Channel, red gurnard (Chelidonichthys cuculus), tub gurnard (Chelidonichthys lucernd) and grey gurnard (Chelidonichthys gurnardus). A l l species were landed primarily by otter trawling and the landings of red gurnard (3,417 t) were considerably greater than the sum of the others (1,826 t).  The biomass of red gurnard was available from data provided by the C F S G but the others were not, so were calculated proportionally on the basis that i f a catch of 3,127 t of red gurnard equated to a biomass of 11,414 t (Ulrich, 2000) then a catch of 1,826 t of other gurnards would mean a biomass of 6,617 t. The total biomass was estimated to be 18,031 t (0.201 t/km ). 2  Diet composition data were available for grey gurnard from the Northern Mediterranean (Moreno-Amich, 1994) and red gurnard from the Bay of Biscay (Velasco et al, 1996). Information on the presence/absence in the diet of tub gurnard was available and as this indicated a similarity with grey gurnard, shrimps and prawns and small demersals comprising the diet, grey gurnard diet was used to represent "other gurnards". Red gurnard and grey gurnard diet data were weighted on the basis of biomass.  22) Whiting There is a large catch of whiting (Merlangius merlangus) in the Channel (7,591 t) and it has traditionally been a "bread and butter" fish for the otter trawlers. In terms of migration, whiting are fairly stationary in the Channel but because abundances are high in the west of the Channel and in the North Sea they are assessed by ICES in two sections. V l l d is part of the North Sea assessment in area IV (Anon., 2000c) and VUe was part of the southern stock assessment in areas Vlle-k (Anon., 2000e). The approach used for the megrim stock (see above) was used for whiting with the assumption that the catch to 35  biomass ratio was the same in the Channel as in the entire stock. The averaged total biomass between 1990-99 in area IV and VII d was 350,784 t with a total catch (averaged between 1990-99) in this area of 93,672 t. The catch in the eastern channel according to (Ulrich, 2000) was 5,484 t, which was 5.85 %. 5.85 % of the total biomass of 350,784 t equalled 20,537 t (0.229 t/km ) 2  The total biomass in area Vlle-k was 62,940 t and the catch was 17,641 t. The channel catch was 2,107 t, which was 11.9 % of total. The biomass in the western channel was therefore calculated as 7,517 t. A combined biomass of 28,054 t (0.313 t/km ) was 2  averaged over the area of the Channel and entered into Ecopath.  Diet composition data came from Daan (1989) for the North Sea. Some adjustment was necessary for the Channel: 3.1 % haddock was allocated to small gadoids. 18.7 % other prey fish was allocated 9 % to small demersals, 9 % to small gadoids and 0.7 % to dab. 3.1 % other invertebrates went entirely to deposit feeders. 3.1 % other macrobenthos was split between deposit feeders and bivalves and the 9.9 % of other crustaceans was allocated 4.9 % to shrimps and prawns, 4.9 % to crabs and 0.2 % to commercial crab.  A juvenile whiting group was added during tuning to incorporate the effect of predation from other gadoid groups on whiting (section 4.1.3).  23) Cod North Atlantic cod (Gadus morhud) are mainly caught by otter trawlers and gillnets. It is a species that migrates and this complicates the biomass estimates for the Channel. As with whiting, the assessment for cod in areas V l l d and Vile were part of a much larger assessment. V l l d was assessed as part of area IV, V l l d and Ilia while area Vile was part ofVIIe-k.  The North Sea had a total stock biomass of 370,405 t and total catches from that of 222,921 t, both averaged from 1990-99 (Anon., 2000c). The VHd catch according to Ulrich (2000) was 2,375 t, which was 1.07 % of the total catch. 1.07 % of the entire North Sea biomass was 3,946 t. 36  The southern stock had a total biomass of 20,246 t and catches of 10,897 t, both averaged over the years 1990-99 (Anon., 2000e). The catch of cod in the western Channel was 814 t, which was 7.47 % of the total catch in ICES areas VUe-k. 7.47 % of the total biomass was 1,512 t. The combined biomass without considering migrations was 5,449 t (0.061 t/km ). Immature cod in the eastern Channel are generally thought to have been spawned 2  there and approximately 40 % of tagged cod, 30-49 cm in length, moved from the eastern Channel to the North Sea during April to November. The impact that this had on the biomass calculations is uncertain because many fish may have been caught before migrating meaning that the biomass had been overestimated for the entire year. On the assumption that fishing pressure and predation were constant throughout the year and that half of this 40 % had migrated, midway though the year it was possible to assume that the current biomass calculation estimated from the landings data correctly estimated the actual biomass, including migrating immature fish.  The diet was based on North Sea cod (Daan, 1989).  A juvenile cod group was added during tuning to incorporate the effect of temperature on recruitment (section 4.1.3).  24) Hake Hake (Merluccius merluccius) are mainly a deep-water fish confined to the western approaches of the Channel ( M . Dunn, pers. comm.); (Pawson, 1995). The total stock biomass for ICES areas Vllb-k and Villa, b and d was 194,411 t averaged from 1990-99 (Anon., 2000e). The catch of this was 51,248 t averaged from 1990-99. The catch in the channel from Ulrich (2000) was 435 t, which was 0.85 % of the total stock catch. Consequently the biomass was calculated as 1,650 t (0.018 t/km ). 2  Diet data were used from the Bay of Biscay (Guichet, 1995). The 5.19 % of blue whiting (Micromestius poutassou), which did not feature in the model, was allocated to 'whiting' (4.19 %) and to John Dory (1 %) on the advice of M . Dunn {pers. comm.).  37  25) Rays and dogfish This group included the cuckoo ray (Raja naevus), spurdog (Squalus acanthias), lesserspotted dogfish (Scyliorhinus canicula), small-eyed ray (Raja microocellata) greaterspotted dogfish (Scyliorhinus stellaris), blonde ray (Raja brachyura), longnosed skate (Dipturis oxyrinchus), blue skate (Dipturis batis) spotted ray (Raja montagui) and thornback ray (Raja clavata). 3,196 t of 'dogfishes' were caught in the Channel, mainly by otter trawling and long/handlining. Rays and skates are also mainly caught by otter trawling and an average of 3,112 t were landed between 1993-95.  The biomass was calculated on the basis of 30, 000 m being covered per hour by the 2  beam trawl (Ellis et al, 2000). Personal communication with Jim Ellis had ascertained that in the absence of western channel trawl data the Bristol Channel would be the most accurate substitute for Table 2.9. For the eastern Channel, data from the North Sea was used (Sparholt, 1990).  Table 2.9: Estimated biomass of rays and dogfish in the Channel from beam trawls. Species E . Channel t/km W. Channel t/km Average biomass Cuckoo ray No data 0.042 0.042 Spurdog No data 0.0807 0.081 Lesser-spotted dogfish 0.21 0.007 0.135 Small-eyed ray 0.07 No data 0.07 Greater-spotted dogfish No data No data 0 Blonde ray No data No data 0 Longnosed skate No data No data 0 Blue skate No data 0.005 0.0053 Spotted ray No data 0.0088 0.0088 Blonde ray No data No data 0 Thornback ray 0.12 0.014 0.0808 Where, there were no data in one area then the other area was extrapolated to include the unknown. Where there was no data at all the species was deemed too rare to warrant inclusion. 2  2  The total biomass entered into Ecopath was 0.42 t/km . This seemed reasonable 2  compared with a value of 0.53 t/km in the North Sea (Christensen, 1995). 2  Diet composition data came from the Bristol Channel for thornback ray (Ajayi, 1982), spurdog (Ellis et al, 1996), lesser-spotted dogfish (Ellis et al, 1996), cuckoo ray (Ellis et al, 1996) and spotted ray (Ajayi, 1982) and were weighted according to biomass. 38  26) Pollack The range of haddock (Melanogrammus aeglefinus) and saithe (Pollachius virens) extends into the Channel according to Fishbase (Froese and Pauly, 2000) and initially this group included both these species. But the catch data (Ulrich, 2000) and biogeographical work of Pawson (1995) does not refer to them and as there were no biomass estimations, this group was entirely pollack (Pollachius pollachius). The biomass of pollack was estimated as 3,308 t (0.037 t/km ) using data provided by the CFSG. 2  The diet composition of pollack was taken from work in the Norwegian deep (Bergstad, 1991). 7.3 % of the diet were unidentified teleosts and these were assigned to small demersals.  27) Large bottom fish This group was an aggregation of three large bottom dwelling fish and included anglerfish (Lophius piscatorius), ling (Molva molva), and conger eel (Conger conger). 978 t of conger eel were caught in the channel worth 2.2 €/kg, 1,338 t of ling were caught worth 2.1 €/kg and 2,011 t of anglerfish were caught worth 5.5 €/kg.  Anglerfish biomass was calculated using an ICES stock assessment for areas Vllb-k and Villa, b and d (Anon., 2000e) and it was assumed that all of the 2,011 t caught were from this stock. The total stock biomass averaged between 1990-98 was 87,622 t and the total catch was 17,945 t. 11.2 % of this was caught in the channel and this equated to a biomass in the channel of 9,8141. Ling biomass was calculated as 6,172 t using data from the CFSG. Conger eel biomass was unavailable but was based on the catch to biomass proportion of ling because the same types of gear caught these, mainly fixed net and longlining. The catch of ling was 21.7 % of the biomass and assuming the ratio was the same for conger eel, a catch of 978 t means a biomass of 4,5 l i t . The total biomass for this group was calculated as 20,498 t (0.229 t/km ). 2  Diet composition for the group was based on data for anglerfish from the Irish Sea (Crozier, 1985) and conger eel from the Bay of Biscay (Olaso and Rodriguez-Marin, 39  1995) and weighted according to their biomass. Presence/absence information for ling (Froese and Pauly, 2000) indicated that the diet of ling included benthic invertebrates, whiting and other finfish and hence appeared similar to the weighted diets of anglerfish and conger eel.  28) Seabreams This group includes blackspot seabream (Pagellus bogaraveo), gilthead seabream (Sparus auratus) and black bream (Spondyliosoma cantharus). O f the three breams only black bream were caught commercially. Black bream were primarily caught by otter trawling but were also caught by midwater trawls. The biomass of black bream was calculated as 10,582 t (0.118 t/km ) using data from the CSFG. 2  The diet composition came from black bream off the coast of Portugal (Goncalves and Erzini, 1998).  29) John Dory This group is based entirely on John Dory (Zeus faber). A trawl survey of the western Channel gave a C P U E of 1.2 individuals/hr and in 1 hour the trawl covered 0.059 k m  2  (Symonds and Vince, 1992). The mean average size of John Dory caught in the Channel was 29 cm (Dunn, 2000), which equated to a weight of 0.479 kg (a=0.02 and b = 2.91 (Froese and Pauly, 2000)). The number of individuals multiplied by the average weight equalled 0.575 kg in 0.059 km . Multiplying this by the area of the Channel equalled a 2  biomass in the Channel of 869 t (0.0lt/km ). 2  The diet composition for John Dory came from Greece (Stergiou and Fourtouni, 1991). 39 % of the diet was unallocated bony fish. On the advice of M . Dunn (pers. comm.) this was equally split between sprats and small demersals.  30) Sandeel Sandeels (Ammodytes tobianus and Ammodytes marinus) proved something of an unknown quantity. In the North Sea there is a sandeel fishery because of the sandy habitat 40  in areas like the Dogger Bank (M. Pawson, pers comm.). The North Sea Ecopath model records a sandeels biomass of 2.58 t/km (Christensen, 1995) and diet information for 2  seabirds and whiting from the North Sea indicate that sandeels are a very important prey species (Furness, 1994; Daan, 1989). In the absence of diet data specific to the Channel for seabirds and whiting, the model indicates that sandeels are important in the Channel and discussion with scientists from CEFAS could not ascertain whether this was correct. It seems on the basis of sediments (Larsonneur et al, 1982) that sandeels may be locally important in bays along the English coast but this has not been confirmed. Sandeels are not landed commercially in the Channel, but are used for bait, although the quantity of this catch is unknown. Consequently, the model calculated the biomass of sandeels from an ecotrophic efficiency of 0.95.  According to Meyer et al. (1979) zooplankton composed 100 % of the diet of the American sandlance (Ammodytes americanus) and this was split 60 % to zooplankton and 40 % to gelatinous zooplankton. This American sandlance diet was used to represent sandeel in the model.  31) Herring Both the herring (Clupea harengus) and pilchard (Sardina pilchardus) fisheries have fluctuated in the English Channel according to sea surface temperature (Southward et al, 1988a). The fishery for herring in the western Channel is no longer prosecuted regularly (Pawson, 1995) but small resident stocks still remain. The biomass of these in the western Channel was calculated as 2,134 t using data from Ulrich (2000). In the eastern Channel the migration of the Downs stock makes the biomass calculations complicated. Personal communication with M . Pawson and Beatriz Roel, both of C E F A S , ascertained that approximately half of the herring in the Downs stock would be in the Channel during spawning time. They will remain in the eastern Channel from November through to February before returning to the North Sea. Whilst in the Channel these adult herring do not feed. ICES working group data (Anon., 2001c) estimated that on average between 1990-1999 the Downs stock made up 22.8 % of the total North Sea Stock. The biomass of the total North Sea herring stock averaged over 1990-99 is 2,443,985 t and the total catch was 523,011 t (Anon., 2000a). So 22.8 % of this is 557,473 t. Half of 557,473 t = 41  278,737 t present in the Channel for only 4 months and not feeding. The resident population was only 1 % of the total so in the diet composition import was set as 99 % and the resident diet squeezed into the remaining 1 %. The total biomass was 3.134 t/km . 2  The diet composition of herring came from a study in the Irish Sea (Rice, 1963).  32) Sprat The ICES herring assessment (Anon., 2000a) included a short section on sprat (Sprattus sprattus) in areas V l l d and Vile. It commented that "the state of the stock is unknown" and so locating information on their biomass has proved difficult. The method of estimating the sprat stock from egg abundances as with pilchards (see below) proved problematic because a) the mesh used did not sample all of the eggs as sprat are smaller than mackerel or pilchard, b) the early larvae are not sampled well either and c) the commercial fishery for sprats is well east of Plymouth meaning that they were not consistently sampled by the Marine Biological Association (A. Southward, M B A , pers. comm.). Christensen (1995) gave a value of 0.55 t/km for the North Sea but this was 2  when the catch rates were 0.34 t/km . In the Channel 2,159 t of sprat were caught and this 2  meant a catch rate of 0.024 t/km (Ulrich, 2000). Using the same catch/biomass 2  proportion of Christensen (1995) would result in a biomass of 0.039 t/km - 3,479 t in the 2  whole Channel. This seemed low and there was too much predation on sprats for this to be a valid figure. A n acoustic survey in the Bay of Biscay estimated 193,000 t pilchards, 18,000 sprats, 105,000 mackerel and 37,000 scad (Anon., 1999c). So the ratios were sprat = 1, scad = 2.06, mackerel = 5.83 and pilchards =10.72. In the 1995 Channel model there was 1.515 t/km of mackerel so dividing this by 5.83 equalled 0.260 t/km of sprat. 2  2  Dividing the scad value of 0.852 t/km by 2.06 equalled 0.416 t/km . There are many 2  2  problems with this method, notably that when the same technique is applied to pilchards a biomass of between 2.7 and 3 t/km is calculated, which is much more than is estimated 2  below. According to Keith Bower (Brixham Sea Fisheries Inspectorate, pers. comm.) the fishery is opportunistic so that when the shoals of sprat are present, local boats will change over from their normal mode of fishing to catch them whilst the market can support their supply. This may help to explain why consistent biomass estimates were 42  impossible to locate. Further biomass data were available from (Milligan, 1986). A n estimate of 497,932 t (5.56 t/km ) for the Channel was calculated based on 3 plankton 2  cruises carried out in 1981 and a fecundity/length relationship from the west coast of Scotland (De Silva, 1973). The biomass estimate seemed extremely high and because sprat egg counts were not separated by A . Southward (pers. comm.), it was very difficult to determine i f this was an extraordinary egg production year or if there were errors in the estimation. Steve Milligan (CEFAS, pers. comm.) highlighted that there were potential errors in the conversion of eggs to biomass, particularly because sprat are serial spawners, which makes an individual's seasonal fecundity difficult to estimate, but the reason that this method was used was that the landings catch data could a) not be trusted and b) was not reflective of the sprat population. Clearly there is a great deal of uncertainty with widely ranging estimates for the biomass of sprat 0.024 -5.56 t/km . 2  Very little is known of sprat and this was a situation where it was necessary to allow the model was left to calculate biomass using an estimated E E of 0.95.  Diet composition data came from immature sprat in the North Sea (Last, 1987).  33) Pilchards The biomass of Pilchards in ICES area VIIIc and FXa, averaged between 1990-99 was 557,850 t. The catch in this area was 125,219 t and using the same catch to biomass ratio for the Channel as the bay of Biscay, and a catch of 5588 t the biomass in ICES area V l l d and Vile would be 24,895 t or 0.278 t/km . 2  There was some scope to check this calculation for pilchard. Working from the number of eggs and the fecundity of females, Cushing (1957) and Southward (1963) calculated that there were approximately 10,000 mature pilchards in each k m of the western 2  Channel with a mean size of 20.5 cm. Using the length-weight relationship of a = 0.0059 and b = 3.077 from the Bay of Biscay (Froese and Pauly, 2000) this corresponded to an average weight of 68.8 g. The total biomass of pilchards was calculated as 0.688t/km . It 2  is noteworthy that pilchards were much more abundant in the western Channel than in the east channel so this value could be an over estimate. If pilchards were just in the west Channel then the average biomass for the entire Channel would be 0.434 t/km . The data 2  43  of Cushing (1957) was from the 1950s but was useful because it corresponded to a 'warm phase' (Figure 3.6) so it is more likely to represent the present than data from the 1970s and 1980s, hence 0.434 t/km was entered into Ecopath. 2  Pilchard feed on plankton and their gill rakers are small enough to eat phytoplankton and at some stages this can form up to 50 % of the gut contents but zooplankton are the preferred food (Southward et al, 1988a). Using this and a frequency occurrence study from northern Spain (Varela, 1988) the diet of pilchards was estimated as 70 % zooplankton and 30 % phytoplankton.  34) Mackerel The North-east Atlantic mackerel (Scomber scombrus) stock is huge and has been the target of such intense fishing effort that the "mackerel box" was created. This is an area off the south-west coast of England where fishing activity is limited to the traditional handlining. In the 1960s and 1970s the majority of the stock over-wintered in the Channel but now after spawning they migrate to northern Scotland (Pawson, 1995). There was a dramatic fall in mackerel landings after 1979 that was officially attributed to a northwest shift of the mackerel stock (Saville, 1985) although local opinion suggests that purse seiners were responsible (Southward and Boalch, 1988b).  The biomass of mackerel in the Channel was calculated on the basis that the catches only came from the Western stock and not the North Sea stock (M. Pawson, pers. comm.). The biomass of this stock, which covered areas II, m, IV, V , VI, VII, and VUIa and b, was 3,397,576 t and the total landings were 657,076 t (Anon., 2000d). Both of these values were averaged from 1990-99. The catch in the Channel was 26,260 t, which was 4 % of total catch, which equates to a biomass of 135,784 t (1.515 t/km ) of mackerel in the 2  channel. Because of a large biomass of over-wintering mackerel a second group of mackerel was added to the model (section 4.1.3).  The diet of mackerel came from the mid north-east Atlantic (Warzocha, 1988). 3.105 % of the diet was attributed to 'Clupeidae'. This was split equally between herring, pilchards and sprats in the model. 44  35) Scad (horse mackerel) As recommended by the mackerel and scad working group (Anon., 2000d) and M . Pawson (pers. comm.) scad (Trachurus trachurus) in the Channel were attributed to the Western stock and were assumed to behave in a similar way to the Western mackerel. There have been only 7 strong year classes in the last 50 years with the most recent of these being 1982. Scad are a long-lived fish, and it is not uncommon to find specimens of 30 years and more (Pawson, 1995).  The biomass of the entire stock was estimated to be 2,534,770 t and the total catch 378,595 t (Anon., 2000d). The Channel catch of scad was 11,407 t, which was 3.01% of the total catch of this stock. 3.01% of the total stock biomass equalled 76,373 t (0.852 t/km ) of scad in the Channel. 2  The diet of scad came from a study in the Bay of Biscay (Olaso et al, 1999).  36) Bass Between 1993-95 the average annual catch of bass (Dicentrarchus labrax) in the Channel was 1,097 t (Ulrich, 2000). These were caught by lining (both longlining and handlining), netting, midwater trawling and otter trawling. It is noteworthy that there was also a recreational fishery for bass that caught 415 t in 1987 and 412 t in 1993 ( M . Pawson, pers. comm.). It was therefore assumed that 412 t were caught by the recreational fishery each year from 1993 to 1995.  Although there are multiple stocks of bass in the Channel and these do migrate into the southern North Sea and Western Approaches (Pawson, 1995), M . Pawson (pers. comm.) suggested that for the purposes of the model it was fair to assume that emigration was equal to immigration. The biomass was calculated as 8,135 t (0.091 t/km ) in the entire 2  Channel using data from the CFSG.  45  Diet composition for bass came from the Channel (Kelley, 1953). The data were initially in units of frequency of occurrence in 250 fish and this was converted directly into % weight.  A juvenile bass group was added during tuning to incorporate the effect of temperature on recruitment (section 4.1.3).  37) Sharks Sharks have proved a difficult group to gain information on because there have been no continuously operating commercial fisheries for them in the Channel. In 1991 a limited longline fishery for blue sharks developed off Newlyn, Cornwall, but the majority of shark catches seem to have been made by recreational fisheries (Vas, 1995). The three species, tope (Galeorhinus galeus), porbeagle (Lamna nasus) and blue shark (Prionace glauca) seem to be the most significant, although starry smooth-hound (Mustelus asterias), smooth-hound (Mustelus mustelus) and thintail thresher (Alopias vulpinus) were also occasionally present. The biomass of sharks in the Channel is difficult to estimate because not all catches of sharks are reported and many shark fisheries are opportunistic, they only exist when there is a high biomass in a certain area (Vas, 1995). Exploitation of the Channel blue shark population by a sport fishery began in 1952 and there have been significant declines in catches since the 1960s (Vas, 1990).  The current catch of sharks per year is approximately 500 (Vas, 1995). Assuming a catch rate of 10 % there would be 5000 sharks in the Channel. The average weight of blue sharks caught and tagged off the coast of Ireland was 22kg (www.shark.ie). Assuming that the average weight caught is representative of that in the Channel, 5000 * 22 = 170,000 kg or 170 t in the whole channel (0.0012 t km ). 2  The biomass of tope was assumed to be the same as in the North Sea, which was 0.0035 t km (Sparholt, 1990). Approximately 5,000 tope were caught around the U K per year but the 'majority' were released (Vas, 1995). It was assumed that 10 % were kept and half of these came from the Channel, this meant that 250 tope were caught from the Channel.  46  Based on data from Leonard Nevell (UK Shark Trust, pers. comm.) the estimated weight offish landed from the Channel was 20 kg. So the biomass caught would be 5 t.  The biomass of porbeagle was also difficult to determine. Off the coast of Cornwall and west Wales there were an estimated 25 sharks landed per year (Vas, 1995) with a mean weight of 35-40 kg. Assuming a catch rate of 10 %, a mean weight of 37.5 kg and that half of these were in the Channel the biomass could be approximately estimated as 4.68 t in the entire Channel. The total biomass of sharks was estimated as 0.005 t/km . 2  The recreational shark fishery only started in the 1960s and seems to have had a significant effect on biomass (Vas, 1990). Using data from Vas (1990) it was assumed that 30 % of the catch of blue shark was landed. At 34 kg per fish (the minimum specimen weight) this meant that the catch in the whole channel was 5.1 t. Combined with tope (5 t) and porbeagle (0.47t) the total catch was estimated as 10.57 t or 0.00012 t/km . 2  Diet composition was estimated from the average of blue shark off the coast of France (Clarke and Stevens, 1974) and porbeagle in the N W Atlantic (Bowman et al, 1900), showing that cephalopods constituted 100 % of the diet. (Stevens, 1973) showed qualitatively that clupeids and mackerel were important in the Channel so in the model 70 % of the diet was allocated to cephalopods, 20 % to mackerel, 3.3 % to sprat, 3.3 % to herring and 3.3 % to pilchard.  38) Basking sharks High concentrations of zooplankton off the south west coast of England attract basking sharks (Cetorhinus maximus), which remain in the area from May to July. The range of basking sharks extends throughout the western Channel to the Isle of Wight in the North and the Channel Islands in the South (D. Sims, M B A , pers. comm.), depending on the location of fronts and the abundance of zooplankton (Sims and Quayle, 1998). A boat survey identified 58 individual sharks from May to July in an area of 350 km (Sims et 2  al, 1997). The mean length of these sharks was 4.06 m, which corresponds to a weight of 328 kg (a= 0.0049 and b = 3 (Froese and Pauly, 2000)). Assuming that there were 58 47  2  sharks per 350 km throughout the western Channel, for the entire 3 months, means that in the entire western Channel there would be 9,360 sharks. This is most likely an over estimate because of the patchy distribution of zooplankton. Assuming that the mean weight of these was 328 kg, this means a biomass of 3,0701 (0.0341 km ) for 3 months of 2  the year. February 2001 saw the start of a 3-year project to investigate the abundance and migration of basking sharks in European waters. It seems that the biomass present in the Channel changes from year to year (Speedie, 1999) and the ecosystem impact of basking sharks is not fully understood. The consumption of basking sharks is only recently being understood. The traditional opinion was that they migrate to deeper water during November to March (Matthews and Parker, 1950) but current research suggests that they do continue to search for food all year (D. Sims, pers. comm.). For the model it was estimated that 30 % of their consumption came from the Channel and 70 % of their diet was indicated as an import. This is to represent the fact that a high proportion of their feeding may be outside the boundaries of this study.  Basking sharks feed entirely on zooplankton (Sims et al, 1997) and copepods seem to dominate although they also consume teleost eggs, chaetognaths, larval Crustacea and at least one species of deepwater shrimp (Sergestes similes) (Martin, 2002).  39) Cephalopods This group included squid (Loligo forbesi and Loligo vulgaris) and cuttlefish (Sepia officinalis). The squid species have the same economic value (3.4 €/kg) and are not distinguished by the fishing industry. Between 1993-1995 an annual average of 4,065 t of squid were landed with the more common species being Loligo forbesi (Holme, 1974). Squid are almost entirely caught by otter trawling. Squid do migrate westwards from the eastern Channel during the autumn and winter as the temperature decreases but they do remain in the deeper waters of the western Channel and so no immigration/emigration was included in the biomass calculations. (Robin et al, 1998) used landings per unit effort to estimate abundance of squid in area V l l d . This was averaged from 1993-1995 and extrapolated for the entire Channel so that 0.181 t/km was calculated for the entire 2  squid catch, which composed Loligo vulgaris and L. forbesi.  48  Between 1993-1995 the annual average catch of cuttlefish in the Channel was 10,568 t. The catch of cuttlefish has shown a dramatic increase since the 1980s when market prices were less than 30 % of what they were in 1993-1995 (Dunn, 1999a). Trawl surveys in the eastern Channel indicated that the biomass of cuttlefish, when it was present at all, was between 0.08 and 0.37 t/km (Ellis, 2001). A mid point of 0.23 t/km was taken for the 2  2  biomass of cuttlefish and this corresponded to a total Channel biomass of 20,162 t. Combining this with the squid biomass resulted in a total biomass of 0.406 t/km . 2  Using an empirical relationship where natural mortality increased with increasing growth rate, water temperature and decreasing body size, natural mortality was calculated as 2.0 year  _ 1  (Pierce et al, 1996) for Loligo forbesi. For Sepia aculeata natural mortality was  calculated as between 1.33 year M was 1.59 year  _ 1  - 1  and 2.75 year  _ 1  (Rao et al, 1993) and for S. elliptica  (Kasim, 1993). Both of these are Indian species and given that the  water in the Channel is cooler, a lower natural mortality would be expected. Hence it was assumed that cuttlefish have a natural mortality of 1.5 year . Weighting cephalopod _ 1  mortalities on the basis of biomass generated a natural mortality of 1.72 year mortality was estimated to be 0.75 year  _ 1  Fishing  from a personal communication with M . Dunn.  These mortalities were combined to represent the P/B of the entire group as 2.47 year  Consumption/biomass was taken to be 15 year  _ 1  based on a value used for squid from a  model of the Alaska gyre (Pauly and Christensen, 1996)  There were no % weight diet composition studies available for either squid or cuttlefish. Pinczon du Sel et al. (2000) gave an account of the % frequency in the diet of cuttlefish in the northern Bay of Biscay. This indicated that benthic crustaceans, scad and small demersal fish were most abundant in the diet. For squid, frequency of occurrence data were used for Loligo forbesi from Scotland (Pierce et al, 1994) and for Loligo vulgaris from Portugal (Pierce et al, 1994). Frequency of occurrence data were converted to percentages and then weighted according to biomass with 80 % of the squid biomass being attributed to Loligo forbesi.  49  40) Seabirds This group includes fulmar (Fulmarus glacialis), manx shearwater (Puffinus puffinus), storm petrel (Hydrobates pelagicus), gannet (Sula bassana), cormorant (Phalacrocorax carbo), shag (Phalacrocorax aristotelis),  arctic skua (Stercorarius  parasiticus),  Mediterranean gull (Larus melanocephalus), black-headed gull (Larus ridibundus), common gull (Larus canus), lesser black-backed gull (Larus fuscus), herring gull (Larus argentatus), great black-backed gull (Larus marinus), kittiwake (Rissa tridactyla), sandwich tern (Sterna sandvicensis), roseate tern (Sterna dougalli), common tern (Sterna hirundo), arctic tern (Sterna paradisaea), little tern (Sterna albifrons), guillemot (Uria aalge), razor bill (Alca torda) and puffin (Fratercula arctica).  The biomass of this group was calculated by multiplying the number of pairs of seabirds in the Channel (Webb et al, 1995) by their body mass and by the length of time they spent in the Channel (Table 2.10). Added to this, there will be colonies in the Channel attended by non-breeding and pre-breeding seabirds and because the information on this proportion in the Channel was not available, studies on the North Sea populations were used (Table 2.10). The total biomass entered for this group was 105.8 t (0.0012 t/km ) 2  The daily ration of seabirds was calculated from the equation:  LogR = -0.293 + 0.85 logW Where R is the daily ration in g and W is the body weight in g (Nilsson and Nilsson, 1976). This value was divided by the mass of the bird and then multiplied by 365 to estimate Q/B. This was then weighted on the total biomass of the bird to calculate a Q/B for the group of 72.11 year . _ 1  The P/B for the group was set as 0.4 year  _ 1  based on the North Sea model (Mackinson,  2000), which used data on the total mortality of adult kittiwakes from the north Pacific (Hatch etal, 1993).  50  Table 2.10: Numbers of seabirds in the Channel. Species Breeding pairs Norirbreeders in the Channel Northern fulmar  Sii  in the area  810  MCBS  365 ft  f  Manx shearwater . £  550*  0:6fall>months  British stormpetrel''..  550*  Great  Number of days j  as a proportion,, mass (g)  3,100  Arctic skua  Body  450  240 #„  , 0.5-all months j  26  f80 '#  ;  .1  10  0.2-all months '  465  150'# ''  black-backed i  1,700  0.2-.(2<months), ?  1600  365 #  0.1 (I month) ] ! Common tern  4,077  Arctic tern , Sandwich tern • „,kGuillemot  ;  Puffin  900  0.1  Too  0 . 1 - 2 months  '235  9,400  Cormorant  2,600 i w0 . 2  Black-headed gull .  100"#  *365.,#''  390  365 #  3 months  3000  180 -  all months  2200  365 -  0.2  —  365'4:  r  2,400 I-.-0.2 - all months 27,600  ___  980 0.3 • 3 m o n t h s  Gannct  *  125  ,,0.1---2 months";  4,200 700  Shag  2 months  ;  0.2 (2-months),  ". 180-—  ^ :0.r;(l month) 0.2 (2 months),  C o m m o n gull  380  180 ~-  800  180-.;  900  180 ~;  390 !  180 ~  0.1 (I month) 1 esser  blackbacked .  - ^:^4Herring gull - ;.•''-]  3,000  0.2 (2 months),  •;\6..1*(1 month)]  g u l 1  :  18,500  ; 0.2 (2 months); ' 0 . 1 ( 1 month)  Kitliwake  1,480  '- 0:2 - 2 months  * Refers to an estimate taken, which recorded there were between 101 and 1000 pairs. Number of breeding pairs information from Webb et al. (1995). Proportion of non-breeders from Tasker and Furness (1996) for all species except British stormpetrel, manx shearwater and Arctic skua where the data is from Furness (1994). Body mass information from Furness (1994) and Tasker and Furness (1996). Number of days information was for 'North East Atlantic' from Furness (1994) for all species marked # and from the 'North Sea' for all species marked ~ from Tasker and Furness (1996).  51  |  In the absence of Channel data the diet composition of seabirds was based on summary information for fulmar, herring gull, guillemot, shag, great black-backed gull, kittiwake, gannet, puffin and razorbill in Tasker and Furness (1996), which documented the diets of seabirds in the North Sea and adjacent areas. The most recent data were used from the multiple options on pages 21-24 of their report. The 30 % invertebrates section for herring gulls was allocated 15 % to deposit feeders and 15 % to crabs. Guillemot diet was allocated 50 % sandeel and 16.7 % each to pilchard, herring and sprat. Great blackbacked gull diet was assigned 30 % to sandeel and 70 % to discards. It was assumed that these birds would represent the entire group and these data were weighted on the basis of biomass. It is noteworthy that this data indicates that sandeel are very significant and it is uncertain how important they really are in the Channel.  41) Toothed cetaceans Although occasionally a minke whale (Balaenoptera acutorostrata) will be present in the Channel, these occurrences were too rare to justify a group in this model (Pollack et al, 1997); (Cresswell and Walker, 2001). Likewise, when attempting to estimate the population of odontocetes in the Channel those species such as the orca (Orcinus orca) and the bottlenose dolphin (Tursiops truncates) that were rarely seen were not included. The common species in the Channel according to Cresswell and Walker (2001) and (Pollack,  1997) were harbour porpoise (Phocoena phocoena), common dolphin  (Delphinus delphis) and long-finned pilot whale (Globicephala melas).  A survey of the cetaceans and seabirds around Ireland including the western Channel was conducted from 1980 to 1997 and this estimated the numbers of each species using ships and aerial surveys (Pollack, 1997). Distribution maps were generated and these indicated the approximate abundance of cetaceans by coloured squares. In the absence of other data this was used to represent the eastern Channel too. The squares referred to a range of abundances i.e. 0.01-0.09 individuals per km and to produce single estimates the midpoint was used. Where seasonal distribution varied the annual average was calculated. One problem with the data was that it referred to sighting per km travelled  52  2  rather than per km . In the absence of better data these values were simply converted to number of individuals per km squared. Table 2.11: Total biomass estimates for toothed cetaceans in the model. Mass came from (Trites and Pauly, 1998). Species Numbers per km Individual mass (kg) Biomass t/km Common dolphin 0.05 80.15 0.004 Harbour porpoise 0.01 31.05 0.0003 Long-finned pilot whales 0.002 850.5 0.0017 Total 0.006 In order to verify that the data in Table 1 were approximately correct they were checked with (Cresswell and Walker, 2001), who recorded 0.05 common dolphins per 10 km transect and 0.28 harbour porpoises per 10 km transect.  The P/B of this group was estimated as 0.4 year  _ 1  based on the B C shelf model (Pauly  and Christensen, 1996) for toothed cetaceans.  The mean mass of the three species in this group is shown in Table 2.11 and this was used in the following equation to calculate individual ration ' R ' in body weight per day (Innes et al., 1987):  R=0.1W  0 8  Where W equals the individual weight in kg. This value was multiplied by 365 and then divided by the individual mass to get Q/B. Q/B was weighted according to biomass and calculated as 13.727 year . -1  Diet composition was calculated from standardized diets in (Pauly et al., 1998a)(Table 2.12) and split to suit the respective groups in the Channel. These values were weighted by biomass and entered into the model.  53  Table 2.12: Diet composition of the three species of cetacean in the model. Species Diet from Pauly et al, (1998a) Diet for the model Common dolphin 0.15 small squid 0.3 cephalopods 0.15 large squid 0.1 herring 0.1 small pelagics 0.1 sprats 0.1 pilchards 0.1 mackerel 0.4 mesopelagics 0.1 scad 0.05 seabreams 0.05 whiting 0.2 miscellaneous fish 0.05 cod 0.05 pollack Long-finned pilot 0.4 small squid Cephalopods 0.75 whale 0.35 large squid 0.0625 seabreams 0.25 miscellaneous fish  Harbour porpoise  0.05 benthic invertebrates 0.1 small squid 0.1 large squid  0.3 small pelagics  0.45 miscellaneous fish  0.0625 whiting 0.0625 cod 0.0625 pollack 0.025 bivalves 0.025 crabs 0.2 cephalopods 0.06 herring 0.06 sprat 0.06 pilchard 0.06 mackerel 0.06 scad 0.1125 seabreams 0.1125 whiting 0.1125 cod 0.1125 pollack  42) Seals Population estimates for seals in the Channel were very difficult to locate as much of the British research is based at the Sea Mammal Research Unit in Scotland and has been conducted off the Scottish coast. Personal communications from Callan Duck (Sea Mammal Research Unit, University of St. Andrews) and Vincent Ridoux (Centre de Recherche sur les Mammiferes Marins) provided the estimates displayed in Table 2.13. Based on Table 2.13, there are 1,110 Grey seals (Halichoerus grypus) and 100 harbour seals (Phoca vitulind) in the Channel and it was assumed that they remained there for the 54  entire year. Based on a mean mass of 63.6 kg for harbour seals and 160 kg for grey seals (Trites and Pauly, 1998) the biomass was then estimated as 184 t (0.0021 t/km ). 2  The P/B ratio for this group was assumed to be 0.04 year  1  based on a general pinnipeds  group in the B C shelf model (Pauly and Christensen, 1996).  The Q/B ratio was calculated in the same way as toothed cetaceans using the equation, R=0.1- W  0 8  and a value of 14.567 year  Table 2.13: Estimates of grey and harbour V. Ridoux and C. Duck (pers. comm.). Location Sand banks off Dunkirk Bay of Somme Bay des Veys Channel Islands Baie du Mont Saint Michel St Malo to Brest Western tip of Brittany Isolated individuals off Brittany Isles of Scilly Cornwall Poole  1  was entered into Ecopath.  seals in the Channel. * refers to guesstimates made by ' Seal species and abundance 5 Grey, 5 Harbour 60 Harbour 20 Harbour 50 Grey * 5 Grey, 15 Harbour 20 Grey 50 Grey 10 Grey 750 Grey * 200 Grey * 20 Grey *  Diet composition for harbour seals came from the south-west North Sea (Hall et al., 1998) and from the Inner and Outer Hebrides for grey seal (Hammond, 1994). Both of these were based on faeces. The grey seal data was averaged throughout the year and for both the Inner and Outer Hebrides. These estimated were weighted according to biomass and entered into the model.  43) & 44) Detritus groups The detritus group was further split into a discarded catch group. A l l discards went to the latter group, which then fed seabirds. The biomass of discarded catch was 0.046.  The complete pre-balancing diet composition for the 1995 model is shown in table 2.14.  55  Table 2.14: Pre-balancing diet composition. For predators read vertically and for prey horizontally. Lobsters, rays and dogfish, bass, sharks, basking sharks, seabirds, toothed cetaceans and seals were not included as prey in this table because they were not consumed by anything. • • I (•roup  S  t  s  ?  .  *  a-  Prim" prod .900 f z ^ p i i l t o n .030 1.00 Cam. Zp. 'Dep.. feeders . ~;,. Sus. feeders |_sln imps Whelk  /  _  = y:  "Eu.  - >\ -y.  .100 .085 .45o . j T  o• .=  4, ..^  O  _  _  " ?  _  _  S  2  _ ,  .050 .500 .500 qET"" r  5  -  =  7-  2  « o*| ' H  .016 .001  ' ' ' ' T ^ l J S H W i F ' " - . r - p T M  ! ; ; !025f871fl76|.820l832f675 |.844j.640|329|071j :.. IU ... .0481 456 .005 .200 . I50 1 5 0 • i V d " " |.045 || > :,|1?0|049| ••• • .. . ii , s.i-S.032 -i."*. s :  |fioolp;3pf70q|nof' (l()s  •)  :  1  1  Echiriodei ins . Bivalves ^Scallops ( uh [cTornm. crab i. Sm. dem. Sin. gads Mullet |Sole' Plaice  m ^  1  100 050  joo  •  [f68]l0M.J ;',;i!| ;;i ,,  ;  •  .030 .030 .500 .005 .278 .028 .005 .075  Willi  O. flatfish Gurnards Whiting  i .,Jp32jj >'' §003 j .040.128. 015 :  .001  .066.049  .102 .066  .101 .090  " " "  ii 11 r: >T. , h--- "f . • • • J.  '  I  .200 .200 .107 .060 .002  . •I-  t  * ., J  !• Hake  .018 . i  .  Lg. bottom John Dory .Herring  ?.  "  , •  .028  Pilchard Scad i Discards T31ntGi T  Import  ' •• " ,,.070  I 00 .350ir525][_ Jj25';.500 ;:  ••), • • .500 .620 .(,20 .104  • J012* _ ;  .222  56  Table 2.14 continued.  sr Group  _  I  =*  ^  .vco' =  =  S  n j .a  « i|- 1  bbii 2 | -Si ©  Prim, prod .300? |l^pll5E^ Cam. Zp. '.399 " " . * *< • D e p . itt6^Vi^7T"^i^\^'fy^WfprS^\\.040' ~(00T3543ire06u Sus. feeders .015.303 .038 .069  .048 <  L  5  r  .„  lrr  Whelk  .007.  Bivalves  .004  Crab  .161.020.367  .016.001  . ~* ' i.003P>2  :  .007  .. ,.007  .001 ,  *•  '  . 0 0 1 '  .017.542  .552.032  .091.034'  .149;  .037  .010  i5=iEK3£SHB£ " T " : 1 L 3 E 3 E ! ] L W : IZIZIOEDL71 Sm. dem.  j Sm. gacls -  .; ,410 :287;iM.004 .060 .081 ,  v  "Mullet Plaice  .161  .)ab.  "  .001  ?  /  .037:  7H».  or  .008  .042  .030.053.029  .123  _r  ; _ ._  M i l  .•  mm i  :  .042 •  .006  "  *  : Lg. bottom  •  , John Dory SandW .Herring  .010 i"M.'" .023  Pilchard Mackerel Scad Cephalopods ;  ,  ".":« i .104 , r , |  .057 .075 aB7p26'  pJJIilfcMM ; Hake  ! . " .042  .183.011 :«>85 01"  /  "- "  001  * ;  ;  •  .283  = W W " " ^ ' .  / I _ "/2<r " .010 .033  .078  .010.019 IMP;  .  .033  .017.070  £  :^poilTp;u)3dj .005 .070.013  ,o 00" " 0 0 . ,03.,  .K.|,.»o "  . -  _  ,_  j ,M4],0ii2 "00  . Discards i i M l a S l S S I P S i  Import  07  ;  .003 _  a_flatfish Whiting  ,025  CXjl' % ^IlZfe^""'* " ^J^El^ ;  Tj-T3rj.635 083  .991  700  :  57  2.3 Catch Data The catch data were based on an average of 1993-1995 from the B A H A M A S (Base Halieutique pour Manche Stratifiee) database (Dintheer, 1995). This is primarily an aggregation of national statistics from France, the U K and Belgium, but also includes fish caught in the Channel and landed elsewhere. There were a number of possible sources of error with the data (M. Dunn, pers. comm.):  •  Non-quota species do not have to be recorded in log-books. It is a false assumption that statistics will be better because there is no reason to mis-report as some fishers simply do not record catches if they do not have to.  •  Mis-reporting of landings to avoid management restrictions  •  Illegally landed "black" fish that would have exceeded quotas.  •  Fabrication of landings in order to maintain track records.  •  Data from merchants may not be complete because the catch may be sold directly to hotels and restaurants or used as bait.  •  Recreational landings are not recorded.  •  Discard data is scarce due to the expense of observers needing to be onboard vessels.  Because of these errors, some data were modified by Ulrich (2000) so that the data I have used in my model are the best available data. The catch data were broken down into the 8 gear types and are shown in Table 2.15. It also includes a recreational fishery that incorporated the catch of sharks (Vas, 1995) and bass (M. Pawson, pers. comm.).  58  Table 2.15: Annual Channel catch by gear type (t/km ), averaged between 1993-1995 using data provided by Ulrich (2000). Midw refers to midwater trawling, Drge refers to dredging, S.weed refers to seaweed harvesting and Rec. refers to recreational angling. .Group Otter Beam Midw Drge Net Pot Line S.weed Rec. Total Prim. prod. 0.003 0.647 0.650 2  1  Shrimps 0.004 7 J7;;77JJJ7 J . . 0.002 ' Whelk 0.114 Bivalves. ' "•;'!•„;. . • 0:151 *X--:.[\. Scallops 0.011 0.002 0.297 Comm. crab 0.002 -i 0.032 0.132 Lobster 0.005 Sm. gadoids 0.044 0.004 0.001 ' 7 J " 7 '~' ' Mullet 0.010 0.001 0.001 Sole 0.018 0.011 0.007 0.022 • 0.002 , Plaice 0.036 0.018 0.001 0.009 0.010 Dab .0,008 0.001 0.001 0.001 \ ~\ O. flatfish 0.017 0.008 0.002 0.003 r  J;:;J7|G:": '  0.006 0.114 .' r.\V):.r^. . .' 0.151 0.310 0.166 0.005 0.050 0.012 " ' f"7 , 0.060' 0.074 ;_ • 7.Q.011 0.030  ;  s:  ;  Gumards77,7; 0.055 Whiting 0.066 cod/' J J JJ7Q:Qi9 Hake 0.002 Rays/dogfish 0.055 Pollack 0.011 Lg. bottom , 0.020 Seabream 0.017  '0.002 0.001 -.0.001  7  '  1  ;  !!l:;.l^yr^ir.'  . 7 '.7lW$59 0.002 ' 0.073 0.013 ' - J J J J ^ ^ J J 7 Q;p34 0.002 0.004 0.002 0.001 / ' 0.008} J ~ 0 . 0 0 9 . " ! tV '' 0.075; 0.009 0.001 0.021 0.005 '•" ' •j ! : 0.001 0.011 0.011 / v ,'=;;•:.. , ; J . ' 0.048 0.007 0.024  1  John Dory Herring  ;  0.004 o.ooi  '  :  !  :  ;:  L  :  ;  , .0.004" J ^ J..7,..., - ||. 0.004 0.076 ;  • -;i/ji;;0.024••-  ' ' ' ' " ' " ^ l ! ' 177. i>\--.  ; i  rj-. •; •  "1  _.., . .. :t; ~T ";_ r  LP-PP4" 0.080  .;  0.024'  Pilchard . 0.001 0.061 0.062 Mackerel "'' \J).040j' J7 • 7 0.239,', ' '" J " " ' ' •'" 0.014 7'Z7l:£7 ' 0.293.' Scad 0.011 0.116 0.127 •%ss.7 J ' . 7 • 0.004 | 0.001""' " ~ " ' ~ 0 . 0 0 2 ^ , J.J! "$QQ4' """"''V '•'• '1 [;0:p05 .:. 0.016 Sharks 0.0001 0.0001 Cephalopods 0.129 0.018 0.002 0.002 0.002 0.010 0.163 ?  ;  r  ]  -  :  59  2.4 Discards  2.4.1 Otter trawling, beam trawling and dredging discards The U K fishing industry authority, Sea Fish, commissioned a number of surveys of discarding in the Channel during 1995 (Course et al, 1996) and 1997/8 (Searle et al, 1999). Onboard discard officers measured the size and quantity of discards in the following U K metiers (Table 2.16):  Table 2.16: Metiers where discarding was measured by Sea Fish. Year Metier code Name Gear and Area of Activity 1995 Ul.l U K TR West Otter trawl west 1995 U1.2 U K TR East Otter trawl east 1995 U2.1 U K Beam Off. East Beam offshore east 1995 U2.2 U K Beam Off. West Beam offshore west 1995 U2.3 U K Beam In. West Beam inshore west 1995 U4.1 U K Dredge West Scallop dredge west 1997/8 U4.1 U K Dredge West Scallop dredge west 1997/8 U4.2 U K Dredge West Scallop dredge west These surveys were used to estimate an average discard rate for each gear as a proportion of the catch. When catch data were split into the eastern and western Channel, as for cod and whiting, the quantity of discards could be calculated for both sides of the Channel independently. For some groups, there were discards when there were no actual catches, which made it impossible for discards to be estimated as a proportion of the catch of that group. Hence the following decisions were made: for spider crabs, discards were set on the basis that for every 130 kg of plaice landed in the western Channel there were 8 kg of spider crab discarded. In the eastern channel the ratio was 41 kg of plaice to 88 kg of spider crab. For beam trawls in the western channel the ratio was 536 kg of plaice to 78 kg of spider crab and in the eastern Channel, 252 kg of plaice to 37 kg of spider crab. For scallop dredging in the western channel it was 6,556 kg of scallop landings and 23 kg of spider crab, in the eastern Channel it was 168 kg of discarded spider crab and 6027 kg of scallop. The percentage of scallop discards was fairly high but Grant Course (CEFAS, pers. comm.) maintained that while there would be displacement, scallops were returned alive. As a result discards for scallops and bivalves were set as 0. For rays, the % weight 60  of discards was averaged for spotted ray, blond ray, thornback ray and cuckoo ray. Occasionally in the discard data there would be dragonets and other small demersals but these were in tiny quantities and were not included in the model. Although there were only a few whelks officially landed from trawlers, the mortality caused by beam trawlers in the North Sea was high (Mensink et al, 2000). Consequently the discards were set as 0.01t/km . 2  2.4.2 Midwater trawling discards EFREMER had commissioned a study of discarding from the midwater/pelagic trawling sector (Morizur et al, 1996) and the results of this are shown in Table 2.17.  Table 2.17: Midwater/pelagic trawling metier discarding for vessels that were relevant to the catch data. Modified from (Morizur et al, 1996). Fisheries Metier Total discards per Main Discards per code landed ton of the discards landed ton of the target species (t) target species (t) French black F3.1 0.34 Black bream 0.10 bream trawling Pilchard 0.11 (VH e) Mackerel 0.10 Scad 0.03 French sea bass F3.1 0.021 Mackerel 0.006 trawling (VII e, Pilchard 0.006 VII b) Lumpsucker 0.005 Garfish 0.003 Herring 0.001 UK mackerel U3.1 0.13 Mackerel 0.1 trawling (VII e) Pilchard 0.03 UK pilchard U3.1 0.14 Pilchard 0.07 trawling (VII e) Mackerel 0.07  Because these metiers were only active in the western Channel, it was assumed that they were representative of the whole Channel. Using Table 2.17 the following discards were estimated for the entire Channel (Table 2.18):  61  Table 2.18: Discarding of the main species caught by midwater trawlers in the Channel. Species French % discard U K % discard Mackerel 5.3 8.5 Bass 0 0 Black bream 10 Not caught Pilchard 5.8 5 Scad 3 3 (Assumed) Herring 0.1 Unknown r  Herring seemed to have discards that were too low and a value of 6.4 % was used based on the total herring discards in ICES areas IVc and V l l d (Anon., 2000a). Scad data was compared with Western stock discard data from Anon. (2000d). The range of this from 1990-1997 was 0.5-4.4 % with the mean being 1.7. The value of 3 % from (Morizur et al, 1996) is hence plausible. For cephalopods it was estimated that there was 1 % discards based on Anon. (2000b), which wrote that "the proportion of all commercial cephalopod species discarded can be considered to be negligible compared with landings".  In the absence of data it was necessary to estimate the discards for some species. For the French pilchard fisheries in the Bay of Biscay there were sprat discards of 0.05 t for every ton of pilchard landed (Morizur et al, 1996). 5 % of the Channel pilchard catch was 269 t. This was used as the quantity of sprats discarded. The other species caught by midwater trawling were all non-commercial species and were caught in low quantities. In the absence of data the discard level was assumed to be 0 for these.  It is noteworthy that in the JFREMER study (Morizur et al, 1996), the French bass fishery caught a single common dolphin and trawling in the Bay of Biscay caught both bottlenose and common dolphins. The direct impact that fishing has on the cetacean population has been investigated but is still unknown (Cresswell and Walker, 2001). The Cornwall dolphin report highlighted that the midwater trawl fisheries do catch dolphins (Anon., 2001a) although it was unknown exactly how many. This report showed that 100 dolphins were washed up on the coast of Cornwall alone between January and March in 1992 and 30 and 20 respectively during the same periods in 1993 and 1994. Because this was just the coast of Cornwall and was for only a quarter of the year, a value of 200 dolphins per year was estimated to be the bycatch of the pelagic trawl fishery, equating to 62  2  a discard of 0.00018 t/km . There was also a bycatch of porpoises from the hake gillnet fishery in the Celtic Sea which was estimated to be 6.2 % of the population per year (Cresswell and Walker, 2001). The majority of the hake stock exists outside of the Channel and the porpoise bycatch in the Channel is likely to be a lot less. In lieu of data it was estimated that 2 % of the porpoise biomass was discarded - i.e. 6 x 10" t/km . 6  2  2.4.3 Net discards It was possible to estimate the netting discards based on Smith et al. (1995). This report outlined the discard rates of the English static net fisheries. For all species, except for whiting and plaice, there was only a single value available for the level of discarding, hence this was used to represent the discard rate for all metiers. Whiting and plaice were caught by a number of metiers and there were different discard percentages depending on the metier. For plaice it was either 30 % discards (in the sole net metier) or 0% in the plaice net metier. When 290 t of sole were caught, there were 5 t of plaice and this ratio was used to calculate the plaice discards. Whiting discards were low in all metiers so were set at 0 %.  2.4.4 Discard summary In the absence of data to the contrary it was assumed that all of the fish that were discarded died except for the flatfish. For all flatfish groups in the model (sole, plaice, dab and other flatfish) the percentage of discards surviving was 50 % from otter trawls (Millner et al, 1993) and 10 % from beam trawls (Van Beek et al, 1989). There was no data available for discards from potting or lining and this was left as 0 %. The final discards that were entered into Ecopath are shown in Table 2.19.  63  Group Otter tr. Beam tr. Midw tr. Dredge Net Total Whelk 0.010 0.010 Commercial crab 0.065 0.002 0.003 0.070 Small gadoids 0.046 0.006 0.052 Mullet 0.001 0.001 Dab 0.017 0.003 0.005 0.025 Sole 0.001 0.001 0.001 Plaice 0.010 0.001 0.002 0.013 Other flatfish 0.002 0.009 0.011 Gurnards 0.049 0.001 0.050 Cod 0.001 0.001 Whiting 0.014 0.002 0.016 Rays and dogfish 0.012 0.001 0.014 Pollack 0.018 0.018 Large bottom fish 0.003 0.001 0.004 Seabream 0.018 0.001 0.019 Herring 0.005 0.005 Sprat 0.003 0.003 Pilchard 0.003 0.003 Mackerel 0.011 0.019 0.030 Scad 0.007 0.003 0.010 Bass 0.0001 0.0001 Cephalopods 0.001 0.001 Toothed cetaceans 0.0002 0.0002 Table 2.19: Channel discards as entered into the Ecopath model. The absence of the potting, lining, seaweed and recreationalfisherieswas due to there being an assumed zero discarding from these gears.  2.5 Balancing Group Deposit feeders Suspension feeders Bivalves Crab Commercial crab Other Flatfish Gurnards Hake Pollack John Dory Pilchard  Ecotrophic efficiency (EE) 1.52 2.821 1.868 1-3 1.217 1.812 1.256 1.281 1.73 1.4 1.068  Groups that prevented the model balancing by having an ecotrophic efficiency greater than 1.  T a b l e  2  2  0  :  64  As Table 2.20 indicates, there were a number of groups that had greater mortality than production. The input data were indicating that more of them were being eaten or caught than actually existed. Balancing is a manual iterative process of varying the data that was least certain although at the time of writing an autobalancing routine is in development at the Fisheries Centre, U B C , which would be a useful timesaving device for future models.  •  Hake cannibalism was reduced from 0.042 to 0.02 with Ecopath splitting the remainder and the P/B was increased to 0.6 year  _ 1  (P/B was changed and not  biomass because biomass by both data from the C F S G and ICES were similar). •  The biomass of pollack was increased from 0.037 t/km to 0.11 t/km because 2  2  fishing mortality was too high. •  The biomass of commercial crab was increased from 0.514 t/km to 0.65 t/km 2  2  because fishing mortality was too high. •  Predation on bivalves by crabs was reduced from 0.2 to 0.08 with the rest going to detritus; still with the increased biomass of crabs there was a high pressure on bivalves that necessitated an increase in their biomass from 17.401 t/km to 20 2  t/km . 2  •  The biomass of deposit feeders was increased from 12.660 t/km to 19 t/km 2  2  because of high predation pressure from many groups. •  The biomass of crab was increased from 9.16 t/km to 10.5 t/km .  •  Suspension feeders P/B was too low to accommodate echinoderm predation  2  pressure, so it was increased from 0.1 year •  _ 1  2  to 0.3 year . - 1  The calculated fishing mortality of sole, other flatfish, mackerel and gurnards was very high compared to predation mortality and this was a function of either too low of a P/B or too low of a biomass. The P/B of 0.347 year  _ 1  for other flatfish  was low compared to the other flatfish groups and ICES W G reports indicated an average fishing mortality of 0.32 year ~ for this group. The average natural 1  mortality from these species was calculated as 0.28 year  _ 1  (Pauly, 1980). Total  mortality calculated from a combination of these equalled 0.6 year  - 1  , which  seemed to be likely when compared to the other flatfish groups in the model, and so the P/B was raised to this. The biomass was also increased from 0.109 t/km to 2  0.155 t/km because of high fishing pressure. The biomasses of gurnards was 65  increased from 0.201 t/km to 0.275 t/km and of John Dory from 0.01 t/km to 2  2  2  0.0125 t/km because of high fishing mortality. These changes brought both the EEs close to 0.95. •  The 3 % of the mackerel diet that had been attributed to 'Clupeidae' was allocated 1.8% to herring, 1.1 % to sprat and 0.1 % to pilchard as predation on pilchards from the large mackerel group was too high for the biomass to sustain.  •  Although it was not an EE greater than 1, the bass E E seemed too low at 0.37 and as the fishing mort was only 0.19 rather than 0.4 (M. Pawson, pers. comm.) the biomass was reduced to 0.043.  2.5.1 Further adjustments After conversation with Villy Christensen there were adjustments that needed to be made in the gross efficiency ratio (production/consumption - P/Q). A value of less than 10% was unacceptable for marine fish species (Table 2.21 shows the problem groups). It is noteworthy that although the herring group was only marginally below 10 %, it comes from the same stock as the North Sea and was changed to concur with Christensen (1995). Table 2.21: Groups with an unacceptably low P/Q ratio and the necessary changes that were made.  Group Mullet Sole Gurnards Herring  P/B 0.496 0.437 0.432 0.620  Q/B 7.097 5.063 5.740 6.388  P/Q 0.070 0.086 0.075 0.097  Changes made to rectify P/Q Q/B reduced to 4.96 year" to make P/Q 0.1 P/B increased to 0.65 year P/B increased to 0.574 year P/B increased to 1.04 year ~ and Q/B lowered to 4.6 year . Q/B reduced to 6.6 year ]  _ 1  _ 1  x  - 1  Pilchard  0.66  8.58  0.077  _ 1  After these changes had been entered, the model balanced (Table 2.22).  66  Table 2.22^ Basic input parameters for the balanced model. Biomass P/B Q/B Group mm  Primary production pZooplanklon Carnivorous zooplankton Deposit feeders Suspension feeders fy'Shf imps and prawns Whelk f.ehiiuKlerms  Crab Commercial crab : Lobster .Small. demersaK Small gadoids •Mullet Sole [Plaice"...' \. . . l : , Dab . " " .Other flatfish Gurnards  12" _ ' X 500 1.100 I9.( 5.070  60 18.000. 7.000  r  1700 •  11.031  A  A  _ 2.000*  r  0 24" S.7K0 20.000 0.488 10.500  0.381  A  o7«)j8!  0 150 _  M.333  A  0.553'  0 1  0.950 A A  A  A  A  A  A  A  A  A  A  A  A  2.632 1.304 0.852  1 319 1.022  A A  A  0.496  0,147  0.950  0.172  0.950 0.950  A  5.928 "4.960 5.063  A  roTioo<  0.128 0.583 4 .0.158 , !|. 0.778 :X0:650~"^r4O09 6.408 0.118' 0.733 0.753 0.110' 0.929 Tolls'' J 0-600 fr-5.464 0.275 0.574 5.740 0.100 0.712 1.068 if 5.466 0 3M 0.195 l 0.922 ' 0.061 1.134 3.031 0.374 0.666 0.018 ' 0.600 3.764 0.159 II 0.968' 0.423 _0.440 _ 4.191 0.490 0.110 T).890 J< o . 6 i ¥ T T 3 ! 2 3 b T - . ' 0.229 . 0.396 2.900 0.136 0.540 0.1 IS 0.575t 4.72.7'•.£^f.0.122 i.0,968 0.013 0.457 0.879 4.206 0.109 0.681 1.137 ',-0.95,0 ii' 10,8f6l7]t0,105 3.134 1.040 0.298 4.600 0.226 0.217 1 210 11.072 5.109? 0.950 0.434 0.660 0.716 6.600 0.100 1.515 0 736 6 778 _ 0.109 ().338 0.650  0.226 0.199 0.103  A  A  A  A  A  A  Cod Rays and dogfish • Pollack Large bottom fish [jSeabieam John Dory Sandeels Herring  JLiL736_ _  • 0.150  A  Hake,'  A  A  0.586 3.907 • 0.150 j 0.964 0.783 0.600 6.935 : 0.087. 0.600 0.936 6.667 0.090 0.800 0.090 0.902 10.000 0.831 1.050 0.150 7.600 0.815 0.460 • ]':3.067 : T1 0.728 0.500^ 0.085 5.850  0.013  lilt  16.667 "  EE'. 0.072  A  II»I5()  t  2.500 " '.  0.300  P/Q  jf 60.000 ft O J O C F 23.330 0.300A  A  !  A  A  mi  A  A  A  A  r  A  A  A  A  A  A  A  A  A  A  A  Pilchard Mackerel Scad " Bass ; Sharks  fiMsklnFsharks;  0.852 0 043 0.005 0.034  0.497 „04Q0. vfl  0.190 0 070 2.470 0.400 0.400 0.400'  5.307  A  A  A  A  0.094  A  3.448 ' 10.145* 2.370 0.080' A 3.700. _."_i0.019 _~ 15.000' 0.165' 72.420" 0.006' 13.727 0.029'  0.510  A  0.653 " 0.124 0 0.521 A  A  A Cephalopods 0.406 (.Dill I Seabirds 0A Toothed cetaceans 0.006 0.078^ A 74 "Seals 7f ^ • 0,002 0:027 uel.iA.. A Discarded catch 0.36" 0.049 'Detritus 0.079A _ j and a bold type refers to values estimated by the model. The diet composition table remained the same as Table 2.14 except for the changes that were made during balancing to crab predation on bivalves, hake cannibalism and mackerel feeding on pilchard, herring and sprat. A  1.000  67  2.6 Further economic Ecosim data requirements Aside from the ecological balancing of the Ecopath model, there were economic data that needed to be included so that the model could be run using Ecosim.  2.6.1 Market price The average market price for the commercially exploited species was entered into the model (Ulrich, 2000). For lobsters and crabs the French price was considerably higher than the U K price and to reflect the high degree of exporting to the French market, the French market price was used in the analysis. For functional groups that represented more than one species, the average for the group was based on the biomass (Table 2.23). In the model, prices were assumed to be fixed regardless of the quantity landed and preliminary results suggest that this is a fair assumption, particularly for U K landings (Pascoe, 2000). It is noteworthy that in France, the prices of sole, scallops, spider crab and brill, seem to be more responsive to the quantity landed (Pascoe, 2000).  Table 2.23: Market price of the commercially exploited species in the Channel. Group Price (€/kg) Group Price (€/kg) Seaweed 0.04 Cod 2.50 Herring 0.30 Bivalves 2.63 Small gadoids 0.41 Scallops 2.63 Pilchard 0.50 Seabream 2.75 Mackerel 0.60 Pollack 3.10 Whelk 0.90 Large bottom fish 3.72 Whiting Hake 1.10 4.70 Dab Other flatfish 1.12 5.12 Gurnards 1.17 Mullet 6.10 Plaice 1.30 John Dory 6.70 Scad 1.40 Bass 9.74 Rays and dogfish Sole 1.81 9.90 Sprat 1.96 Shrimps and prawns 10.42 Commercial crab 2.07 Lobster 19.17 Cephalopods 2.24  2.6.2 Fleet profitability In order to run relevant policy simulations, data on the relative profitability of each fishery was needed. A survey by Cattermoul and Pascoe (2000) separated the percentage of the revenue that was composed by fixed costs and running costs into gear type for the 68  U K English Channel fleet during 1994-1995 and this work was ideally suited to an Ecopath model (Table 2.24). Fixed costs are those that do not vary within a year regardless of the effort, and they include repairs and maintenance, harbour dues, interest payments, insurance costs, equipment hire and administration costs. Running costs vary according to the level of activity and include fuel, food, ice and crew costs, which are related to the number of days fished and levies, which are determined as a percentage of the catch. In the absence of available French data, the U K data was used to represent the entire Channel fleet although this was not an entirely accurate assumption because the French fleet appeared to be more profitable (Pascoe, 2000).  Fleet Otter trawl Beam trawl Midwater trawl Dredge Net Pot Line  Fixed cost 43.1 35.3 39.9 31.7 48.0 31.9 21.0  Running cost 44.0 58.7 40.0 52.4 37.7 46.2 11.8  Profit 12.9 6.0 20.1 15.9 14.3 21.9 52.8  Table 2.24: An economic breakdown of the English fishing industry by gear type during 1994-1995 (Cattermoul and Pascoe, 2000).  2.6.3 Relative employment Part of the policy optimisation routine requires data to be entered for the number of jobs per value of catch. S. Pascoe ( C E M A R E , pers. comm.) provided data on the number of jobs in both France and England for the 8 different gear types. The mean size of each gear was calculated (Tetard et al, 1995) and this was used to estimate the average number of jobs per vessel (Table 2.25). Then the number of boat units was multiplied by the number of jobs to calculate a job unit index for the entire Channel catch.  69  Table 2.25: Ratio of jobs to catch value throughout the Channel metiers. Boat units refer to the number of months that a metier was practiced in the Channel. Gear  Otter trawl Beam trawl Midwater trawl Dredge Nets Pots Lines Seaweed  Mean length (m) 13 32.9 19.8 11 8.7 8.3 7.1 9  Crew number 3.17 5.61 5 2.65 2.14 1.8 2.1 2.1  Number of Catch value Jobs/catch boat units * €/kg value no of jobs. 35734 1.324 0.301 6844 0.255 0.299 2650 0.463 0.064 11403 1.000 0.127 29979 0.473 0.707 18360 0.387 0.529 11550 0.117 1.102 756 0.026 0.326  2.7 Final 1995 input parameters The final versions of the basic input parameters for the 1995 model are shown in Tables 2.26 and 2.27. These data were used to run the optimisations of chapter 4 and differ from Tables 2.14 and 2.22 because they include modifications made during tuning (section 4.1.3.).  70  Table 2.26: Final input basic input parameters for the 1995 model, ^and bold = parameters estimated by Ecopath. Group liiomass Primary production ••'"{ii ; Zooplankton _ Carnivorous zooplankton JL100_ j Deposit feeders ft.. \ 19.000-' Suspension feeders 5X)70 , Shi l.mpsand prawns 11.I2() Whelk 0.24" Kchjnoderms 8. X() Bivalves 20.000 Scallops' " ' 6.488 ' Crab' ' . • , • : 10.500 S Commercial crab 0,650:1 Lobster ; . . 0i)13_ rSmall'demersals' * m 1.304^ Small gadoids \ Mullet.-'^TS '. J " ^ ' 0.852 Sole JU84 Plaice . .<>.150 A  7  A  1  _ Other flatfish Gurnards Whiting Cod Hake Rays and dogfish "PollacF Large bottom fish Scjhicjm John Dory Saiuieels Herring D a b  JLlfCL 0.229 TO.11.8H £.013 _ .. 0.681 3.134^ 0.217 . 0.434 1.515 A  Pilchard Mackerel Over-wintering mackerel Scad Bass •• Shaiks  Basking sharks •. Cephalopjods Seabirds ["Toothed cetaceans Seals Juvenile bass Juvenile sole Juvenile plaice Juvenile cod Juvenile^whiti in; Discarded catch Detritus  ro -  0 852 0 043 "''"0 005""' 0 034 i n .406 0 001 ') 006 0,002 < . >:032 . o 042 o 150 _ 103 oi 115 0, 360 i; 000  iv i i  Q/B  A  A  ;  JD.440 it 0.6JL8. 0.496 0.575 0.457 1 137 _ .. 1-210. 0.660 1  ;  0  4  0  0.736 • 0.736 0.497 0.600 0.070 0.400_ •0 400 J).400_ 1.300 1.300. 2.268 i n 2.136  EE  ".' P/Q  60 j 1X000" .G0UOO 7.000 23.330^ 2.505" 0.300 2.000^ 1.700 0.650j .4.333 S>.600. 6.935 0.600 6.667' ' 0.900 IO.QOOJ: 1.050 7.000 A ~0.460 '1[.:3..067 . 5.850_ , 0.550 l i n o "j J5.928 _ 1.022 "4.960";". 1,0 4% 0.650_ 5.063_ .:..0.o5() ... 4 109 .j 6.408 5.464  0.072 0.300 lfO.150". 0. 1.50. _ 0. 150 0 150_ 0 087 0 090 0 090 0. 150 lift 150 0 094^ 147A A  A  o  0 i7r 100' i28r 158 _ A  A  0.381 (0:944 -A  A  0553  A  .0.450 .. 0.869^ 0.783 0.936 0.„8l«> 0.831 0.815 Q.728 . 0.950 0.950_ 0.950 • A  A  r  A  A  A  a583 51829 ... A  A  0.733  A  0 3.647. 4.206 . , 10.816 4.600_ 11 072 ' £600_  J3JT£ .• 6.778 5.307 3.448 2 .370 3.700  • 0.136' 0.540 0.122; 0.968£L. 0.109 _ 0.879 .105 1R)'.950 >.226 0.298 109 0 950 0 100 0.717 0;109?" ]cA338 0 ,109 0.000 094 0.510 174 0.653 W 080J1 0.124 . 0 019 A  A  JEft o  A  A  A  ja  A  A  A  A  A  A  A  o o o  ^pXuooE 30?  A  A  A  A  A  A  1.65A  o •;o.(  72.120 _1 029 • 13.727 14.567 •JL027 ..6 896. ' . J U L 145 . 0.128_ 10.126_ 158 8.218 374 (. ()(>4 0.195 I0.9s"4 006  ,S o.  •HE  A  A  o  A  •  if 0.078  A  A  0.003 _ J  • pj _ 471  0.1ft«» 0.039 0.598 0.049" 0.080'  71  Table 2.27: Final version of the 1995 diet composition. For predators read vertically and for prey horizontally. F s  •a. S <N  (•roup  a yd. • U •- 5  ^  =  If.. .*.100 «  Prim, prod .900 pbopKmktoi) . uv] _;| 00 Cam. Zp. 7r  -  =  4j  7r.  |  s  SOSIIS  .050  .500  .500  .016 ' .001  .  iT7bT?lPSTT7TM^71' .  r7?lP25;-:|[|7T^p76 .-.82ff71  T  :  Whelk l-.eliinnderms • Bivalves ...  ! - a.  •=  L  ,085 >{'_'_"_ "-4S0 3(,0 ,  l T^yY\^p0"7.030  gepTlfeSdeTs ';. • Sus. Feeders  =3  VS -  050  .005 "  .050 .100  .000 .050  ,  .048  •  .150  .150  .456 .041  '.032" '  :  llSISISilBIIlf .100  ( uh  tComm.Oab . Sm. dem. .  ,  .080  .200  -.107  .060  .002  .030  .030  .500  .005  .278  !  .005  .075  .028  . ~ _ . . . '__. . ,i  •  •  • Mullet I'SOIC" Plaice ' Dab O. flatfish (luriuuds Whiting  _ _ _ _ _  f  " -W1177  iiSiilillllll ^^^^^^^^P ^^^^^^^^^  "Cod Hake Lg. bottom FSeabream John Dory [Sandeels Herring Sprat Pilchard Scad  '  :,  . ,. T —  -  .  •!  :  j  g l U i —  .  ___e_jhalopiids Juv bass .Juvcsole Juv plaice .  -- • •  " • .  , •;: .' " . 1  j  •iisti  -it 1  Juv whiting J Discards Detritus  .070  1.00  .350  .525  .725  .500  .500  .740  .620  lllllltll  .104  .012 72  Table 2.27 continued:  . / I .'•  - ' -f.  a..  ? v « -; B '  ...  Group  Cub  .001  Sm. dem. j Sm. gads Mullet Sole Plaice Dab " O. flatfish  .314  32')  190  s  .099 ;  50  071  112  017  '.002  . H O  .049  01*  303  |f,2  049  "3*;:  I G i- B  •4>'j  u  B>  •B © —  .066'  :E,-I  w  jf.600 .|006 - j •399 .069  .817 038  0*0  B B S  066  003  .009  ' .007  .015  ,  SB  .053  ".016  /.004  ^ 0 0 IN S I  .049  .162  .020  .367  ;  ,007  ^ ^ i ^ ^ B ^ l o l i i g g a ^ > ilOl . . 0 9 0 . 0 7 3 . 2 3 6 : 1 7 5 5001 - : 1 9 5 . . . 2_39_ 132 043 " 0 8 ^ 1 4 l o , 287 _ .004 .060 .081  :  •S-i  .007  .066  .102  1-  ja ... si  "PH"-"  Prim, prod ["Zooplankton . 0 0 1 Cam. Zp: Dep. foedeis . 8 3 2 6 7 5 . S 4 4. 6 4 0 Sus. Feeders .005 . 2 0 0 [Shrirnp.s .130 04* Whelk feEchiiiodeiiiis .032 Bivalves . 0 4 0 .128' rScIjlop^ 037  E  o ©  .001 .001  ;  .008  .003  •III .008  .042  .007  .031  fGunuids  Whiting  .053  H  Hake •Pollack . V , Lg. bottom jiSeabieani John Dory  .021 • . 0 4 0 "  :•.•:}  ,:•  Herring Pilchard 'Mackerel Scad iCephalopods"? Juv bass Juv" sole  : Import  B  -  IBB! f  - j , t i  L  .010  .252  .080  .028  .023  .077  .022  1  .  .006 .002  .  '  .00-  •' .'-"T-" ••'  ~WXV<  -020 .042  .011  .465  . .007  . .  1  1il"95;I"T^:i7~l  .  .[01-  '_o(Fr__li30  1,'L ' . J L . ; . i ¥ 0  .078  HP _ _ « T . .187  :  "  .000 .  "  ,  001  090  .""ClIFZ  •> -d L.ni""~i~«-. ••Z—jJt  .222  73  Table 2.27 continued: Diet composition for juvenile sole was not included as this was identical to adult sole.  .•a u on  u  a  Prim, prod .300 Zopplal55oT]!.960 '"71700 j[%0 Cam. Zp. rDepTfeedeni p 4 0 ' f ~7%M)l Sus. Feeders  cet.  SC  B  u  S  Ml  so  © ©  U  > -'  >  >  Ju  Group  3j  <u . .©. !s u ' . 3 1-9-  .' 3  , —  • .5 •* 'iS " >  .048 ' I 9N  '  .543 .069  .OO'i.  OOS  .117  Bivalves  .007  : '  Crab (omm.Cnib Sin dcm  .017 .091  .542 .002 034  Mullet  .037  R  Whelk  Plaice Dab O. flatfish (nunauK Whiting  .001 .552  .013  .032  .050  .149  •  .025  .010  :'~  ^020  .010  ISliill.350  .220  """^  104  JL  llBllBiBilttyE  i  .057  ''075  ^•b'57--  Hake [Pollack"., • I.g. bottom  |126  i:  L . '- *ZJl!_7 (  .•  S e a b , o a m - ; " 7 7  "  00*  ()S7  .283 'J'."~|p~  John Dory Herring _Sput Pilchard •Mackerel • Scad (ephak^xU Juv bass JMfts&i&&®>^^  L  Juv plaice Juv cod Juv whiting  .018  .033  (off  001  1019  :  .033  . "  *$WMMmM3bM MmSS&2imMM  014  .002  ,0-  .V>0  .040  .017  .030  .141 ..017  0-0 '.070  ' •  .230  ]__"_"~ II"j 5 ; : I  ]-oM^rio8 7][rio7ilo3() .0054  .700  1009  .025  .070  .013  :  • I  ; .  _<-^'~"^ •  -P04 17" J ; IJL7777a[J271  S^^M^mmMM^3siS£s.s.^-;,-i £^-MS^S  :  mi  Detritus .700  74  ; "Zl  3. Back to the Future - 1973 3.1 Reconstructing the past Building a past model, and running this to a current model of the same system, allows the modeller to monitor how biomasses have changed through time. This enables model predicted biomasses to be compared with stock assessment estimated biomasses and where discrepancies between these occur, it may be necessary to modify the input parameters so that the model more accurately reflects the reality of the ecosystem. Originally it had been intended to build a past model of the 1950s but the earliest ICES data that were accessible went back only to 1960. Furthermore, fishing mortality data for a number of groups only began in the early 1970s so it was decided to build the past model from 1973. This would mean that time series data only stretched over a 22-year period but that these data were more accurate and would provide a better anchor to the model than going back further.  3.1.1 Modifying P/B and Q/B Fishing mortality on several stocks was much lower in 1973 than in 1995 so the total mortality estimates from 1995 were too high and needed to be reduced (Table 3.1). Because the P/Q ratio for a species remains more similar throughout its life than the P/B or Q/B (V. Christensen, Fisheries Centre, U B C , pers. comm.), changes to Q/B were also necessary (Table 3.1).  Group Whelk Scallops Commercial crab Lobster Whiting Cod Pollack Large bottom fish Scad Bass  1995 P/B 0.586 0.900 0.46 0.500 1.068 1.134 0.618 0.396 0.497 0.60  1973 P/B 0.25 0.800 0.36 0.4 0.868 0.834 0.318 0.296 0.397 0.25  Q/B changes Changed by Ecopath. Changed by Ecopath. Changed by Ecopath. To 4.68 To 4.451 To 2.2299 To 1.665 To 2.161 To 4.223 To 1.724  Table 3.1: Changes in P/B for groups that were fished less heavily in 1973. The changes in Q/B that were necessary to maintain a constant P/Q are also shown.  75  3.1.2 Catch data The 1990s model was based on catch data for the Channel that had been collated and analysed by IFREMER and CEFAS experts. The author consulted with Matthew Dunn and Clara Ulrich about the possibility of getting data back until 1973 but there were a number of barriers to this: •  The type of catch data used for the 1990s model were only available in the 1990s.  •  Dunn (1999b) published U K data for 8 non-quota species and said in a personal communication that there may be other scattered data, however it would be difficult to assemble a comprehensive dataset from these.  •  The author attempted to access raw data from the U K and France in order to get another view of the catch situation, but the cost of retrievals proved prohibitive.  The B A H A M A S (Dintheer, 1995) database was originally developed by the C F S G to store international bioeconomic fisheries data in a common format and because there were concerns about catches being allocated to the Channel from ICES data that were not actually caught there. After exhausting all the other possibilities it seemed that to use ICES data for the 1973 model was the only option. It is worth mentioning that although the ICES data were deemed less trustworthy, they still gave some useful trends. Moreover, the ICES data would not be used in the crucial policy optimisation routine as this would be run from the 1995 model where the B A H A M A S data had been used. One further problem was that the ICES data were sometimes grouped together so that, for example, there would be a catch of "fin-fishes" that could have been composed of a number of different species. Because the proportion of species in these groups was unknown from year to year it was decided to only include data that could easily be allocated to one of the Ecopath functional groups.  76  Figure 3.1: % difference between the two landings data sets in the years 1993-95. Positive values are groups where ICES data were higher and negative values where BAHAMAS data (Ulrich, 2000) were greater. The middle groups, close to zero, indicate that both data sets were similar. 350  m  250  0) o c  I  150 50 |Pi|^l|P3|W*|M»«|gWjlw«|».».|i  -50  'l-l-l  1—I—Ii  roc  E ° o o o  I  I hit I M  I W I  M I  -150 -250 -350 rofc;.£. 2 — o ro co o B 2: O D  »ll»l  -o  w  w  °- a>  i l l l - l ^ i5f i l l l p l l i s i l E E o s  jc a ro x. O O -£= TO -j o. a> a) CO O  -O  Q_  O T3  - ro  x: CO  1  The catch data for 60 % of the groups were less than 25 % different (Figure 3.1) and the contemporary ICES catch data can be used for these groups with some confidence. For the other groups, ICES data still had to be used but for the purposes of tuning, these data were less trustworthy. Moving further back in time the catch data are likely to be less reliable because there may be changes in the catch data that reflect the U K joining the European Union's common fishing policy, which increased the policing of the industry and, according to M . Dunn {pers. comm.), resulted in more catches being declared. These increases were likely to have taken place in the late 1970s and early 1980s.  3.1.3 1973 Functional group descriptions 1) Primary production Although variations in species assemblages have been occurring, inter-annual variation of primary productivity has been low (Tappin and Reid, 2000) and it has been suggested that any changes in primary productivity have had little effect on the long-term state of the Channel (Southward, 1980, 1983). It is possible that in warmer periods the blooms occur earlier but with the same intensity (Southward and Boalch, 1988b), resulting in the assumption that primary production in the Channel had remained constant from 1973 to 1995. 77  2) Zooplankton and 3) carnivorous zooplankton There have been changes in the species assemblage (Southward and Boalch, 1988b), but the absolute biomass of both the zooplankton and carnivorous zooplankton groups show no long-term variations and were assumed to have remained constant.  4) Deposit feeders, 5) suspension feeders and 6) shrimps and prawns In lieu of other data, deposit feeders, suspension feeders and shrimp and prawns were assumed to be the same in 1973 as in 1995.  7) Whelk Whelk biomass in the North Sea was reported to have declined over the last 20-25 years and this may well have been a result of increased beam trawl fishing and the associated mortality (Ten Hallers-Tjabbes et al., 1996; Mensink et al, 2000). There was no serious whelk fishery prior to 1978 so it was assumed that the biomass had decreased in the English Channel and it was set it to be 1.5 times as large in 1973 as in 1995. The mortality resulting from discards was estimated as 0.008 t/km based on the assumption 2  that there was less beam trawling in 1973 but that there would still be significant mortality.  8) Echinoderms In lieu of other data, Echinoderms were assumed to be the same as in 1995.  9) Bivalves The biomass of bivalves was increased from 20 t/km in 1995 to 22 t/km in 1973 2  2  because catches were greater and the increase was necessary to prevent the E E exceeding 1.  10) Scallops The biomass is very likely to be larger in the past because there has been a great deal of exploitation since 1973 and based on catch data it was initially estimated as 1.5 times 78  bigger. There has been a scallop fishery off the coast of Devon since the 1960s but it collapsed in the 1970s according to Southward and Boalch (1992). This decline was not in evidence in the catch data of the whole channel stock but it seems likely that the biomass of scallops was higher in 1973.  11) Crab In lieu of other data, crabs were assumed to remain constant.  12) Commercial crab Off the coast of Devon there was a large increase in crab fishing effort during the 1970s and it seemed that the level of exploitation could be sustained. In reality, however, during the 1980s, boats needed to travel further offshore to maintain the catches (Southward and Boalch, 1992) and it therefore seemed prudent to increase the biomass in the 1973 system and, in the absence of data, it was assumed to be 1.5 times higher, which was a reasonable estimate according to M . Dunn (pers. comm.).  13) Lobster The south-west lobster fishery appears to have been seriously overfished with catches showing a decline (Southward and Boalch, 1992) and consequently very few lobsters now reach their maximum size. The biomass was set to be 1.5 times larger in 1973, which was a reasonable estimate according to M . Dunn (pers. comm.).  14) Small demersals, 15) small gadoids and 16) mullet No changes were made to these groups but as their biomass was calculated from an EE of 0.95 and predation on them was different to the 1995 model, their biomasses were calculated to be slightly different (Table 3.3).  79  17) Sole ICES data for the biomass of sole was available for area Vile back before 1969 (Anon., 2000e) and for VHd from 1982 (Anon., 2000c). The two areas showed different trends in biomass with area Vile peaking in the early 1980s but remaining fairly flat throughout the time series and area VHd showing a decline back in time (Figure 3.2). Tracing this decline back gave an estimate of the biomass in 1973 as 7,500 t, which combined with Vile equated to 9,873 t (0.110 t/km ). This indicated that the biomass of sole had 2  approximately doubled even though fishing mortality had increased between 1973 and 1995. This trend in part seems to be substantiated by M B A data (M. Genner, MBA., pers. comm.), which showed that the 2001 survey abundance was more than twice that in 1985. A strong pulse of recruits in the early 1990s appear to have caused this increase.  18) Plaice Plaice fishing mortality and biomass data were available back until 1980 for area V l l d (Anon., 2000c) and 1976 for area Vile (Anon., 2000e). The biomass trends were plotted in order to fill in the gaps back until 1973 (Figure 3.3). The biomass for both areas increased in the mid 1980s with V l l d showing a sharp peak resulting from a particularly strong year class in 1986. Vile was estimated to decline to 1,900 t in 1973. V l l d followed a similar pattern to data for the North Sea and these data reached back until 1957 (Anon., 80  2000c). Consequently, the fairly constant profile of biomass back from 1980-1973 meant that a constant value of 16,512 t was used from 1973 to 1980 for area V l l d . The total biomass was therefore 18,412 t or 0.205 t/km . 2  70 -•—Vlld -©—Vile  60  - a- - - IV *-  V)  50  -a a a  o o  p  a  Figure 3.3: ICES biomass estimates for the three plaice stocks. IV is the ICES area code for the North Sea.  a  TJ  40  (A w  -in  E re o 30 b3 20 10  1973  1976  1979  1982  1985  1988  1991  1994  19) Dab Dab are a species that live close inshore and are rarely caught below 40 m. It can therefore be assumed that even before 1973 they would have endured a considerable mortality from small boats that fished close inshore. There had been a decrease in dab landings since the late 1980s possibly suggesting a reduction in biomass, although discards of this species are so high that it is very difficult to ascertain trends from landings data. Furthermore, the biomass of dab in the North Sea had been increasing even when fishing mortality had been increasing (Heessen and Daan, 1996). M . Dunn {pers. comm.) said that there are still large quantities of dab and he suggested keeping the biomass of this group constant. As a result, the parameters of this group remained unchanged in the 1973 model.  81  20) Other Flatfish One of the reasons that this group was created was to account for the significant flatfish species in the Channel that were not covered by ICES stock assessments. Because of this, finding time series data for this group was difficult, as ICES stock assessment data for megrim only went back to 1984 and this showed little significant change. Moreover, megrim are more of a deep water fish that are caught mainly outside of the channel, so even i f there was a longer data series this may not reflect changes in turbot, brill and lemon sole. M B A data ( M . Genner, pers. comm.) showed large fluctuations although there did seem to be a slight reduction in biomass from the 1970s to the present data. With nothing firm to base changes on, the biomass was kept the same in 1973 as in 1995.  21) Gurnards There were no data for fishing mortality or biomass trends of gurnards in the Channel, but there were in the North Sea (Heessen and Daan, 1996). This showed that there was a very fluctuating profile of abundance until the late 1980s when the biomass showed a steady increase until 1993. These data must be taken with caution because there are large errors associated with trawl surveying for gurnards as they occasionally form dense shoals (Heessen and Daan, 1996). Half the biomass of gurnards was estimated to have been there in 1973 compared to the present. M . Dunn (pers. comm.) suggested that a doubling of gurnard biomass since 1973 was not unreasonable because 1) they seem to have increased because of the disturbance on the seabed caused by trawling, and 2) in his fishing experience a lot of warmer water species such as the streaked gurnard had become more abundant in the Channel.  22) Whiting Whiting proved an interesting species because the western and eastern sides of the Channel had reverse population changes (Figure 3.4). Area V l l d was part of the North Sea assessment and this showed a total stock biomass decline so that the stock is currently a fifth of the biomass that it was in 1973. In the western Channel, data only went back until 1982 but this showed that the biomass of whiting had increased by 3.6 times since then. Extrapolating back to 1973 from 1982 suggested that the stock in Vfle-k 82  had increased by about 5 times since 1973. Consequently it seemed reasonable to base the biomass of whiting on the proportion of catches currently made in the Channel, i.e. 73.2 % of the catch was from Area V l l d so this would have increased by 5 times i.e. 0.313 * 0.722 *5 = 1.130 t/km . Vile was the opposite 0.313 *0.278 / 5 = 0.017 t/km . 2  2  The estimated total biomass was 1.147 t/km . 2  Figure 3.4: Change in biomass of both whiting stocks. Only a small proportion of these biomasses reside in the Channel. The North Sea stock corresponds with Vlld axis and the Western stock with the Vile axis. 120  <A O  o p  100  ~ (A  g  (A (A  (0 E o  m T3 1973  1976  1979  1982  1985  1988  1991  1994  1997  23) Cod The ICES catch data for the Channel indicated that 2,077 t of cod were caught. In the North Sea section 239,051 t were caught from a total stock biomass of 655,931 t (36.4 %). In the Vlle-k section 4,015 t were caught from a total biomass of 9,816 t (41 %). It was assumed that the same proportion of catches were made from the east and west Channel as in the 1995 model, i.e. 25.5 % west and 74.5 % east Channel. Hence there would be 1,547 t caught from the east and 530 t caught from the west. So i f 1,547 t were 36.4 % of the biomass, then the biomass would be 4,246 t in the east Channel and i f 530 were 41 % of the biomass, the biomass in the west Channel would be 1,295 t. So the total biomass would be 5,541 t or 0.062 t/km . 2  83  24) Hake According to ICES data the catch of hake in 1973 was 787 t (M. Zarecki, ICES, pers. comm.). The ICES report gave biomass for hake in area VTIe-k back until 1978 (Anon., 2000e). These data were plotted and the biomass showed a steady decline through time (Figure 3.5). It was then possible to extrapolate back and estimate that the biomass in 1973 was 327,500 t for the area Vlle-k. The ratio of biomass to catch was plotted in order to estimate the entire catch for the area in 1973. There was a trend from 1978 until 1984 of higher biomasses to catch ratio and so a trend line was set using this data and a value of 46,786 t was estimated for 1973. Hence a catch in the channel of 787 t would mean a biomass of 5,509 or 0.0614 t/km . Because the 1995 biomass was only 30 % of 0.0614 2  t/km , this seemed too high based on the gradient of Figure 3.5. Consequently, it was assumed that the Channel hake remained a constant proportion of the larger stock and the biomass in 1973 was estimated as 3,179 t (0.036 t/km ). 2  350  Figure 3.5: ICES biomass estimates for the entire hake stock in Vne4c. ro 150 E o  5  100 50 \ 0  1973 1976  1979 1982  1985 1988  1991  1994  1997  It is worth noting that there is one problem with using a biomass estimate of 0.036 t/km  2  because in 1979 hake landings were higher than the expected biomass. When checking the ICES catches, this particular year was 3 times larger than the years either side of it. This was the result of France reporting 2,326 t from ICES area V l l d , which seems unusual as very little hake were reportedly caught from this area. It was not possible to get confirmation that this datum was incorrect but it was assumed that it refers to hake caught outside of the Channel and it was not included in the model. 84  25) Rays and Dogfish From the M B A data ( M . Genner, pers. comm.) it seems that rays and dogfish have remained fairly constant since 1973. M . Pawson (pers. comm.) said that little is known about changes in dogfishes and rays. Theoretically their longevity and low fecundity should make them vulnerable to overfishing, but certainly nothing catastrophic has happened since 1973. M . Dunn (pers. comm.) maintained that in certain areas dogfishes would be the dominant species by biomass in the trawl, and that although there may be less rays they are still often caught in trawls. He also mentioned the presence of two stingrays in the channel, which are typically a warm water species, and so there may be shifts in the abundance of different species of rays caused by climate (Southward and Boalch, 1992). To maintain the entire group as constant seems a valid assumption.  26) Pollack There was no specific stock assessments or time series data for pollack so it was necessary to base the 1973 model on similar species. There were two similar gadoid groups, whiting and cod, and both of these were caught in approximately the same way as pollack, primarily by otter trawls and nets. The problem was that whiting and cod had very different profiles since 1973. Whiting showed a decline in catches, F and biomass (Figure 3.4), while cod showed an increase in F and a peaked profile of catches. Furthermore, there were differences between the different stocks of both cod and whiting with a decreasing biomass of the North Sea stock and an increasing biomass of the Celtic Sea stock. Because the catch profile was more similar to cod than to whiting, the values of F for cod were scaled for pollack and this was used to calculate the biomass of 322 t (0.004 t/km ). But this was extremely low and the E E of pollack exceeded 1. 2  Consequently, in lieu of other data it was assumed that the biomass of pollack was twice as large in 1973 as in 1995 and 0.22 t/km was entered into the model. It is noteworthy 2  that pollack are more abundant in the western Channel and "relatively scarce" in the southern North Sea (Pawson, 1995). Hence the biomass of pollack may actually have increased with time as conditi ons in the western Channel have spread eastwards with the increase in temperature.  85  27) Large bottom fish ICES data for anglerfish from 1986 indicated that there was little change in the biomass. In the absence of other indications the biomass of large bottom fish was assumed to be the same.  28) Seabream These are a warm water fish and M . Dunn (pers. comm.) suggested that the biomass would have been less in 1973 than currently, so it was reduced to half of the 1995 biomass and 0.059 t/km was entered into the model. It is noteworthy that although the biomass should be lower in cooler water, the catch data does hint that the stock may have been heavily exploited.  29) John Dory Dunn (2000) wrote "the current status of the John Dory population in U K waters is unknown and investigations of the biology of John Dory in the northeast Atlantic have been limited". He indicated (pers. comm.) that John Dory have increased in the Channel since 1973 and in the absence of data he estimated that they had doubled. The reason behind this increase is a large stock of John Dory in the Western Approaches that migrates according to sea surface temperature. Recent warm years have resulted in an increased biomass entering the Channel, which meant that the estimated 1973 biomass was 435 t or 0.005 t/km . 2  30) Sandeels None of the input parameters were changed, but as the biomass was estimated from an EE of 0.95, this changed with predator/prey relationships.  31) Herring In the late 1990s, 22.8 % of the North Sea herring stock was composed of the Downs stock. It was assumed that this percentage remained the same and that half of this stock entered the Channel to spawn. Consequently half of the 22.8 % of the North Sea stock 86  (1,155,651) was 131,744 t. The fishery for herring in area Vile was more significant in the past than it is at present, indicating that the biomass was probably higher (Pawson, 1995). The 70s and 80s corresponded with a cooling down that favours herring, and so a biomass of 4,000 t was used for Vile. As in the 1995 model, this was dwarfed by the Downs stock. The total biomass entered was 135,744 t (1.515 t/km ). 2  32) Sprat The difficulties in obtaining contemporary sprat data were the same for the earlier years as in 1995. Sprat egg counts had not been performed except for a preliminary survey in 1970 off the coast of Plymouth (Demir and Southward, 1974). The total annual egg counts were 10,842 for sprat and 74,430 pilchard i.e. the abundance of sprats was 14.6 % of that of pilchard. In the 1995 model the biomass of sprats was 55 % of the biomass of pilchards, and this gives an indication that basing the biomass data on egg counts may lack accuracy. Temperature does seem a significant factor in the spawning of sprat and it has been considered in Russell Cycle investigations (A. Southward, pers. comm.). Because separating the eggs from other clupeids was time consuming there was no sprat time-series data. Sprats have a long spawning season and, following a cold winter, spawning is retarded, meaning that a proportion of the larvae will not metamorphose before the following winter. It was therefore proposed that colder winters may cause a reduction in biomass (De Silva, 1973). Fage (1920) gives the temperature range for spawning as 8-11°C but the Channel monthly mean temperature has only once dropped below 8 °C (to 7.93 °C in 1986) since 1973. Hence temperature was assumed to have had no effect in the model and in absence of other data the biomass was started with an E E of 0.95 as in 1995.  33) Pilchard Time series data on the biomass of pilchards were not available and as it had proved difficult to get contemporary data on absolute biomass the past was no different. Much has been written on the trends associated with the Russell Cycle so it was possible at least to have an idea of the climatic effects on the biomass. A warm phase during the 1950s ended, leading to a decline from 1960 to 1985, and then to the rapid increase of 87  temperature that is presently continuing (Figure 3.6). The early 1980s signalled the return of the pilchard fishery indicating that the biomass had increased. Initially, using egg count data (Southward et al, 1988a), the biomass in 1973 was estimated as 0.066 t. It is noteworthy that there may be a degree of error in this caused by overlap between pilchard and sprat eggs in May and June (Demir and Southward, 1974), although this would appear to be negligible. This estimate was far too small for the amount of fishing mortality as catches were approximately the same in 1973 as in 1995. Hence Ecopath estimated the biomass based on an EE of 0.95. Figure 3.6: 5-year running annual average of sea surface temperature in the Channel. The grey dashed line is the mean temperature between 1950 and 2000. Data courtesy of Anon. (200Id). 13.5  =_  1950  1954  1958  1962  1966  1970  .  1974  1978  1982  1986  1990  1994  1998  34) and 35) MackereUOver-wintering mackerel The biomass and fishing mortality for 1973 was taken from the ICES working group report (Anon., 2000d). The total biomass of the Western stock was estimated as 4,039,602 t and the landings were 219,445 t. The channel landings were reportedly 46,296 t so the stock in the Channel was estimated as 852,229 t or 9.511 t/km . This 2  made it 6 times larger than the stock in 1995 and could not be sustained by the model estimates of Clupeidae prey. Pawson (1995) refers to the Western mackerel stock overwintering in the Channel during the 1960s and throughout the 1970s, but this changed in the 1980s and they over-wintered off northern Scotland. This seems to be reflected in the catch data, which shows a big decline during the 1980s (Figure 3.7). Southward et al, (1988a) also refers to a decline in mackerel stocks after 1979 and he commented that there were local suspicions that this was not merely a migratory change but the result of intensive purse seining. The large biomass of mackerel was probably there only in winter 88  so would not be feeding but would be available for capture. Consequently, it was necessary to create a new group, over-wintering mackerel, which would have identical parameters to mackerel except that diet composition would be 100 % import. It was necessary to split both the mackerel biomass and catch into mackerel or over-wintering mackerel. There was a gradual decreasing trend in the total stock biomass so that in 1995 it was 84.1 % of the 1973 biomass and the same trend was assumed for the Channel mackerel biomass meaning that in the 1973 model, the mackerel biomass was 1.802 t/km . The over-wintering biomass was hence estimated as 9.511 - 1.802 = 7.709 t/km . 250 H Over-w inter • Permanent 200  (/)  o o o  150  £  100  re  Figure  3.7:  Mackerel  landings from the Channel. Data courtesy of ICES (M.  Zarecki, pers. comm.).  O  50  J 1973  II U m u i U l l l M 1976  1979  1982  1985  1988  1991  1994  I had to split the catch component of the Channel into the permanent stock and overwintering stock. This was done on the basis of F from ICES stock assessments (Anon., 2000d) and the biomass of the permanent stock in the Channel divided by F equalled the catch for the permanent stock. The remainder of ICES recorded catches were allocated to the over-wintering component. Using this method, the time-series data indicated by 1986 the biomass of the over-wintering stock had disappeared completely.  Finally, I returned to the 1995 model and inserted a new group, over-wintering mackerel. This was just a dummy group, only there because during the tuning process it was necessary to run the over-wintering group's biomass down to zero. It was included in the model to make future modellers aware that there is a 'second' mackerel group that may need to be included. 89  36) Scad ICES catch data (M. Zarecki, pers. comm.) for the Channel indicated that 62,157 t of scad were landed in 1973 and 62,376 t in 1995. Catch data from the CFSG (Ulrich, 2000) showed that the annual average catch in the Channel between 1993 and 1995 was 11,407 t. Hence there was a great deal of inconsistency between these data sets (Figure 3.1).  The scad stock assessment data only went back as far as 1982 so it was necessary to extrapolate this to 1973. This was complicated by the stock being characterized by large infrequent recruitment pulses (Pawson, 1995); (Anon., 2000d). Fishing mortality was assumed to remain constant at 0.15 back until 1973. The biomass was then estimated by the equation Channel catch/F = biomass. There still remained the question of whether to use the entire ICES catch data allocation for the Channel to estimate the 1973 biomass. If the catch was 62,157 t then the biomass was estimated to be 4.6 t/km , which seemed 2  very high, and with such a high degree of difference between the 1995 catch data it was assumed that the catch data were not wholly correct. The USSR caught a vast quantity of scad and when this was removed, the catch was just 3,364 t. This meant a biomass of 0.902 t/km , which seemed much more reasonable. 2  37) Bass Bass is a long-lived slow growing species and because, in the early 1980s, anglers complained about the small size of bass (M. Dunn, pers. comm.), there was probably a much larger biomass of older fish in 1973. M . Pawson (pers. comm.) said that it would not be unreasonable to have 20-25 % of the 1973 biomass in 1995. The more conservative of these estimates was chosen and this meant a biomass of 0.172 t/km . 2  38) Sharks Vas (1990) outlined the change in C P U E of blue shark since 1973, and this was used to estimate the biomass of the entire shark group by assuming a constant catchability. The value that was used for 1973 was a CPUE of 2.0, which was 2.29 times the C P U E of the mid 1990s. Hence the biomass of the shark group in 1973 was entered as (0.005 * 2.29) =  90  0.012 t/km . Using data from Vas (1990), the recreational fishery was estimated to have 2  landed twice as many sharks than in 1995 (0.00024 t/km ).  39) Basking shark In lieu of other data, all the parameters were assumed to be the same.  40) Cephalopods Although the catch of cephalopods rapidly increased in response to a higher market price in the 1980s, it is uncertain i f the fishery has had a significant effect on the biomass. There is a strong suggestion that this group responds positively to warmer temperatures (Robin and Denis, 1999); (Pierce, 1995) but in the absence of data to the contrary the parameters for this group were kept the same in 1973.  41) Seabirds At the time of writing, the data for seabird 2000 is being analysed and will be available shortly. This is a venture by the U K ' s Joint Nature Conservation Committee (JNCC) and will help to understand how seabird populations have changed. The first survey of this kind, 'Operation Seafarer', was conducted in 1969-1970 and the second was conducted between 1985 and 1988. A summary of the changes that have occurred between these throughout Britain and Ireland was provided by Furness (1989). Herring gulls decreased from 300,000 to 182,000 pairs, great black backed gulls have declined slightly but the majority of seabird species have increased. Guillemot abundance doubled from 1969-70 to 1985-88 and possible reasons for this were the increase in sandeels, as their predators had been depleted by fishing, and increases of discards. The population of Arctic terns showed a positive relationship (r = 0.68) with the sandeel recruitment estimates. It was interesting to note that most seabirds showed a peak between the surveys and then a decline prior to the 1985-88 study. While one would expect an increase in the Channel as smaller fish increased in biomass, it is unknown how important sandeels were. Furthermore, until the results of the 2000 survey become available it will be unknown i f the decline prior the 1985-88 study continued. It was decided to reduce the biomass in 1973 by 10 % to 0.0011 tkm , mainly because of the reduction in discards. 2  91  42) Toothed cetaceans Groups of thousands of dolphins have become rare since the 1950s and Cornish fishermen report a notable decline in recent years (Anon., 2001a). The exact magnitude of this decline, or the reasons for it are unknown. It is possible that the reduction in overwintering mackerel may have caused dolphins, which are very migratory, to go elsewhere. The discarding of toothed cetaceans from pelagic fisheries in the Channel may have had an effect but because the majority of the toothed cetaceans live outside of the Channel the effects on the stock may primarily be because of the tuna drift net fishery (Anon., 2001a), rather than any Channel metiers. Whatever the cause, there does seem to have been a decline, and in the absence of other data the biomass was set to be 50 % larger than the 1995 biomass and 0.009 t/km was entered. 2  43) Seals In lieu of other data, the input parameters for seals were kept the same.  44) Discarded catch The biomass of this was reduced to 0.325 t/km as the fishery was much smaller than in 2  1995.  92  3.1.4 Balancing the 1973 model Group Zooplankton Carnivorous zooplankton Deposit feeders Bivalves Crab Gurnards Dab Seabream  EE 1.031 1.746 1.146 1.031 1.248 2.144 1.414 2.539  Cause Whiting Whiting Whiting Whiting Whiting Fishing Mortality Fishing Mortality Fishing Mortality  Table 3.2: Functional groups in the 1973 model with an EE greater than 1 and the main cause of this excessive EE.  One of the main problems with the unbalanced model was whiting (Table 3.2). Their increased predation on sandeels forced the model to increase the sandeel biomass, which consequently made the E E of zooplankton and carnivorous zooplankton greater than 1. Because there was a great deal of uncertainty about the whiting biomass estimate, the biomass in 1973 was estimated to be twice that in 1995 and a biomass of 0.626 t/km was 2  used. This immediately lowered the zooplankton and carnivorous zooplankton EEs below 1. The biomass of deposit feeders was increased from 19 to 22 t/km and bivalves was 2  increased from 20 to 22 tkm in order to meet the increased predation mortality from 2  small demersals and small gadoids.  Ecopath identified fishing mortality to be too high for gurnards, dab and seabream. M . Dunn (pers. comm.) had indicated that the catch data for gurnards may be incorrect because they were often used as general group to allocate the demersals catch to. In the absence of alternative catch data the E E was set to be the same as in 1995, 0.712. For dab the biomass was increased from 0.103 t/km to 0.2 t/km . For seabream the biomass was 2  2  increased to 0.155 t/km . 2  3.1.5 Electivity The diet composition of each group was kept the same in both models but it was important to compare the electivities of both. Electivity describes a predator's preference for a prey on a scale of-1 to +1, with -1 being total avoidance of the prey, +1 being total preference for a prey and 0 meaning that the prey is eaten proportionally to its abundance (Ivlev, 1961). The electivity equation used in Ecopath is the standardized forage ratio (Sj) 93  by Chesson (1983) and it has been transformed so that it can be interpreted as the Ivlev index (Christensen et al., 2000). n  Si = ( ^ 0 / ( 1 ^ ) n=l  Where r\ is the relative abundance of a prey in a predator's diet, Pj is the prey's relative abundance in the ecosystem and n is the number of groups in the system. If the values for electivity were very different between the models it may indicate that the diet composition entered into the contemporary model was incorrect. Because the biomasses of herring and sole were twice as large in 1995, rays and dogfish, large bottom fish and seals, which prey on sole and herring, showed a greater preference for them when they were scarcer in the 1973. Conversely, the biomass of dab was half as large in 1995 and so there was a much weaker preference for them shown by whiting, rays and dogfish and large bottom fish in 1973. These changes were in the region of 0.2 to 0.3 and although they generated questions concerning how much preference predators would show for prey, they were not large enough to warrant a change in the initial diet matrix. The final balanced basic input data for the 1973 model is shown in Table 3.3.  94  Table 3.3: Basic input parameters for the 1973 balanced model. and a bold type refers to values estimated by the model. A  iGrojup Primary production  Biomass  mm  P/B  126.720  Iflooplankton .....  . S.500  Carnivorous zooplankton jEteposit feeders;- - *j Suspension feeders fSclihodci ms  Bivalves  22.000 0.733 10.500  I. 050  Commuciul crab  0.975  II. Vl.ll  A  Lobster ti  i l l  gSmau demersals gSmall l u l l c l gadoids  John Dory Sandeels ; Herring Pilchard  1.310  A  6.1 lo  Basking sharks  ElhakfeodsZ,  .'o.OO'O  0.313'  0.574  " (1.626 0.062 _  Seabirds pjoftthefl. cetaceans Seals - • jRJIarded catch" Detritus  II 0.834 0,600_  0.423 _  0.440  0.220  03IS  0.229  0.296  0.155  0.575 ,  0.015 1.515  0.384  A  0.330 _____ " 7.709 __0.172_  •  .JS>MF:  0.314 1.000  0.090 0.150_  A  fi INI 0.085 0.147 0.172  5.928  0.050  A  0.950  2E3EOIOO .  . ~i'5.464_._" ^P~j_l~0 _ A  0.100 Hi 4l4S-t;V : . f 0 i l 9 5 2.230 0.374 TI3fX764 0.125' 4.191 0.126 1 665'. . 6.191 2.160 0.137  A  5.740  A  A  A  A A  JJJ22  4.206 10.816  1.040  4.600  1.210  11.072.  0.660  6.600  0.736  ; 6 77S._.  0.736  6.778 T4l23  9,397. 6.250  :  1.724  A A  A  A  A A  A  A  A  o..i goTT^FXmT^r^i^ 0.070  2.4/0,:. • 0 400 0.400  •i 0.019 lHT5lOOO^T_|ro.|fi5 72.120 0.006 OSE"! 0.029 14.567 0.027 0.050 0.087 3.700  0.950  0.429 0.315' 0.728'  A  6.604  A  0.718 0.148 0.696 JL.0i92.5Jl. 0.269 0.352 0.334 0.967 0.693 0.950 0._575_ 0.950 A  A  A  A  A  A  A  JU50 0.105 0.226 . 0.109 (U00 . . . 0.109 0.109' . . 0.094 . •f 0.145 _  A  A  A  5.063  A A  A  ?1MM<>  \3T  0.400 0.002  A  A  4.680  0.457  1.216*  0 009  0.090  A  ;  0.036  0 406  6.667 8.889 7.000 2.400-  A  ' n . 155'""  0.034  0.087  A  A  0.151' ' F^O-'rvPl 411097,VI-.0.187' 0.753 6.408 0.118  ' 1.S02  Over-wintering mackerel 'Scad Bass  6.935  0.230 0.777 0.863 0.600 0.895 0.527 0.547 _  A  0.650  0 205 0.200  _____  •  1.022 0:496 „  A  Sole Plaice.  Rays and dogfish jlllick Large bottom fish  A  0.950  0.150  A  A  00  2.438 ^L750 O.730  i  Dab Other natfish Gurnards JlWhitinK Cod  °A „  0 2 0  0.150  A  T  ,,"076"! i 0.600 II Mm  Scallops,  0.150  16.667 'XS1k0.150  0.300_ 1.700  Crab  2.000 11.333 1.667  0.681' 0.768' 0.525^_  A  0.25V  0.371 8.780  0.300'  23.330  2.500-,  1I.I61  ___  0.073'  7.000  A  Whelk  EE  59.560  5.070_  jjllplfips" and pra \\ ns  P/Q  JJLQ.QQ.  1.100 22.000  1  Q/B  A  A A  A  A A  A  A  A  0  iLl  6A  0.266 0.053 :• Q.439 0.115 0.540' 0 . 0.423. . A  A  A  A  A  A  QA  J[oT652  A  0 0.050 0.087 A  A  A  95  4 Ecosim - tuning and simulating Because Ecopath represents the ecosystem at one particular reference point it gives no indication of the changes that may occur with differences in fishing mortality. Ecosim provides the opportunity to dynamically simulate the ecosystem and go into the realms of fisheries management and policy rather than just being an ecologically descriptive tool. Consequently, there is scope to run an Ecosim model with time-series stock assessment data to see if the model reflects the changes that are known to have occurred.  4.1 Tuning the model 4.1.1 Time-series data Continuous time-series estimates from V P A for fishing mortality and biomasses were available for some groups from stock assessments. For all exploited groups there were time-series data for catches from ICES ( M . Zarecki, pers. comm.). For each exploited group it was necessary to enter values of fishing mortality from 1973 to 1995 (read as a CSV file) to see how the biomasses of each group would change in the Ecosim model (Appendix, Table A l ) . The fishing mortalities (Table 4.1) drove changes in simulated catch and biomass in the model, while the original biomass and catch estimates from V P A were used for comparison. 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Vulnerabilities range from 0 to 1, and when V is high it means that a high proportion of the biomass is vulnerable to predation, which results in fluctuating predator-prey curves. Conversely, i f prey are able to hide from predators, V will be lower. In the model, Vs were based on both tuning to biomass data and the trophic level of the prey with the principle behind this being that higher trophic level organisms have been most heavily depleted in ecosystems (Cheung et al., 2002). If fishing was significantly reduced, these stocks may be expected to recover towards their un-fished state and hence should be allocated a higher V . Initially, using time-series biomass data it was possible to understand how each group had changed since 1973. Then V s were allocated for each group from 0.2 to 0.8 based on trophic level, but the result of this was an unrealistic increase in the biomass of some groups such as seals. Consequently, V s ranging from 0.2 to 0.7, based on trophic level, were used in the Channel model. Each group was then checked against the time-series biomass data during tuning and it was found that using this method of estimating Vs produced realistic responses to changes in fishing.  2  4.1.3 Tuning individual groups As well as running the model with the time-series F data, F was also reduced to zero and increased to 4 times the 1995 value. These 'extreme' runs of the model helped to show up problems and suggest any changes that were necessary to each of the functional groups are described below. Whelk - When running the model from 1973 to 1995 the biomass of whelk nearly disappeared and, as an important whelk fishery still remains in the Channel, this change was too severe. When fishing was shut off in the 1995 model the whelk biomass increased dramatically over 4 times, so it was decided to increase the P/B value from  2  Further consideration to the effect of varying vulnerability is given in section 4.2.5.  98  0.586 year  _1  to 0.650 year ~\ This also seemed logical because the E E in the 1995 model  was very high, 0.964. The rapid decline of whelk biomass also indicated that the past biomass was significantly more than 1.5 times larger. With a P/B of 0.65 year  _1  the  biomass of whelks increased by 2.5 times when F was 0.  Scallops - When F was 0 in the current model biomass more than trebled. This seemed too large and was caused by fishing mortality comprising almost all of the mortality. Because there was a range of initial estimates for P/B, this was increased to 0.9 year '  l  and when the model was run the biomass of scallops increased by twice as much when F was 0.  Lobster - When F was 0 the biomass of lobster increased rapidly to 4 times their current level so P/B was increased from 0.5 year  to 0.55 year  _1  to dull this increase to a more  reasonable level of 3 times their current level.  Sole - F was a high component of total mortality and ICES data indicated that it had increased in the period 1973 to 1995. The same ICES data also indicated that the catch and biomass of sole in the Channel had been increasing (Anon., 2000e); (Anon., 2000c), which seemed to indicate that some other factor, aside from fishing, was affecting sole. The index of recruitment showed a general increase and so it was assumed that temperature was an important driving force behind the biomass increase, based on studies by Rijnsdorp et al, (1992), Henderson and Seaby (1994) and Philippart et al. (1996). There was some debate about the precise time of year that temperature most significantly affected recruitment. Philippart et al, (1996) showed that severe winters had a positive effect on the recruitment of sole in the Wadden Sea and while Rijnsdorp et al, (1992) agreed about this effect in the North Sea, the same was not true in the Bristol Channel, English Channel or Irish Sea. Henderson and Seaby (1994) found significant correlations between both warmer average annual temperatures and year class strength, and warmer spring temperatures and year class strength. With the ICES and Hadley Centre (Anon., 200Id) data there was a stronger correlation between recruits and the annual average temperature (Pearson correlation = 0.62, p<0.01) and this was used to create a forcing  99  function in the model. A trendline was fitted to the temperature (in °C) and recruits data and the following equation was calculated:  Number of recruits = 14507 * average annual temperature - 155414  The values from this equation were divided by the 100-year temperature mean and then 'stretched-out' by multiplying the positive values by thirty. Thirty was iteratively chosen as the value which caused the model to have the lowest sum of squares difference between the predicted biomass and the ICES time-series data. Because temperature affected recruitment, a juvenile sole group was added to the model. Based on ICES working group data it was assumed that sole recruit to the fishery at age 2, that K from the V B G F was 0.3, that their average adult weight/the transition weight was 1.75, that their P/B and Q/B were twice as large as the adult group and that 18.7 % of the total biomass was juveniles. Hence, of the initial 0.226 t/km that was entered into Ecopath, 2  0.042 t/km was allocated to juvenile sole. The forcing function was applied only to the juvenile group. Sole predation by cephalopods was also moved from adult sole to juvenile sole. The diet composition of juvenile sole was assumed to be the same as adults. Figure 4.1: Impact of the forcing function on adult sole biomass. The blue line (2) shows the predicted sole biomass without the forcing function, the pink line (1) shows the predicted sole biomass with the forcing function and the blue circles shows the absolute adult biomass from ICES data. The time-series of fishing mortality is shown by the red bars beneath the line graph.  1973  1978  100  It is noteworthy that even with the addition of the forcing function, the biomass trend predicted from the model significantly differs from the ICES data (Figure 4.1). Part of the reason for this was that the juveniles recruit to the fishery at age two and, because in the temperature time-series there were rarely two consecutively warm years, the variation in total juvenile numbers proved to be a lot less than from year-to-year. The changes in adult sole predicted by the model headed in the right direction but were not as large as the ICES data suggested. The model did accurately predict catches and M . Dunn (pers. comm.) suggested that the biomass results could be biased to a certain extent, as catch rates were higher in the 1990s due to technology creep. For sole, and all groups that were split into adults and juveniles, all of the discards were allocated to the juvenile group. Plaice - There was a large increase in plaice biomass in the mid 1980s that seemed to be caused by recruitment responses to temperature. Research on the recruitment of plaice suggested that in the North Sea the temperature between February and June was strongly correlated, but the same report did not indicate such a strong recruitment for the Channel (Fox et al, 2000). In the ICES data there was a significant correlation of 0.50 (p= 0.015) between temperature and recruitment, but when the large 1986 recruitment was removed, the correlation was merely 0.18, which was non-significant. Therefore to build a relationship into the model based only on temperature was difficult as the regression line had huge residuals. Because there is a high degree of plaice migration from the North Sea, possibly one option for the sharp increase in biomass in 1986 would be more fish coming from the North Sea, with temperature seeming to have some effect on migration (Ewan Hunter, C E F A S , pers. comm.), although the exact mechanism of this is unknown. Nevertheless it does seem that very cold years are advantageous for plaice (Philippart et al, 1996, Van der Veer and Witte, 1999, Fox et al, 2000) and it is important that this is incorporated into the model in some form of a forcing function. For the purposes of this model a forcing function was entered so that when the temperature drops below 9.6 °C the biomass of the juvenile group goes twice as large. Clearly this would not be adequate when predicting into the future with cooler scenarios but purely for tuning this was useful.  101  Half of the total plaice biomass was composed of juveniles and they become adults at 2 years. The average adult weight/average juvenile weight was 2.94 and K was 0.08. Hence half of the plaice biomass was moved and this meant the E E of adult plaice was above 1. Consequently, 95 % of the cod predation was allocated to juvenile plaice. Still the E E remained too high because of fishing mortality and it was necessary to increase the adult biomass from 0.1 to 0.145, which brought the E E below 1 and made the F approximately 0.5 year  which is what the ICES data suggested. The same biomass value was used for  juvenile plaice. Diet composition for juvenile plaice came from the Belgian coast (Beyst  et al, 1999) and as 38 % of the eastern Channel juveniles came from the North Sea (Pawson, 1995), 38 % of the diet was allocated to import.  Dab - From the catch data and Southward and Boalch (1992), there was some suggestion of adding a forcing function that would lower the biomass when the temperature increased. In the North Sea, even when the temperature has been warm the biomass of dab has been increasing (Heessen and Daan, 1996), and consequently there was not significant evidence to justify a forcing function for this group.  Hake - F changed from 0.22 to 0.37 year  but F in the 1973 model was too high  meaning that the biomass was increasing even though fishing mortality was gradually increasing. This was one example of many of the inconsistencies between F estimates from catch/biomass and those from ICES data. To overcome this problem the 1973 hake biomass was increased to 0.045 t/km and this lowered the F meaning that hake in the 2  model accurately reflected the trends in ICES data (Figure 4.2).  102  Figure 4.2: Predicted biomass of hake. The model biomass estimates are shown as the blue line and the ICES absolute biomass data are the blue circles. Fishing mortality estimates are the red bars beneath the graph. At the left of the graph the 'absolute' circles start off lower than the model estimated biomass because this had to be increased in the model to ensure F was correct. 1.0  0.0 1.23 0.92  0.61 0.31 o.oo;  pi  1£;_-  ,1978'  1983  198B  1993  Years  Whiting - This group has proved extremely problematic because of inconsistent data and a high degree of uncertainty (Pope and Macer, 1996; Anon., 2000c). In 1997 the ICES W G reported that the spawning stock biomass (SSB) had remained stable since 1984 in the North Sea and that the recent fishing mortality, i f maintained, should lead to increases in biomass (Anon., 1997). By 1999 this view had been revised and the W G showed that both SSB and recruitment had steadily reduced since 1980 (Anon., 1999b). The relationship between SSB and recruitment is complex (J. Keable, C E F A S , pers. comm.) and scientists are still uncertain as to what caused the 'gadoid outburst' and what has caused this most recent decline (Hislop, 1996; Serchuk et al, 1996). At times, whiting has replaced itself "comfortably higher" than the highest F while at other times it has had very low replacement rates (Pope and Macer, 1996).  One of the main problems when looking at changes in whiting through time using Ecosim was that there was a large difference between F estimated by ICES stock assessments and F estimated from catch/biomass by the model. In 1973, ICES estimated F to be 0.98 year but when using catch/biomass for the entire stock, F was estimated as 0.27 year ~\ Because the model calculated F from catch/biomass, the same method was used to enter F for the time-series data. Running the model through time showed that an initial biomass  103  of 0.626 t/km was too high because the starting F was so low. Consequently two aspects 2  of the model were changed. Firstly, the 1973 biomass was lowered to 0.4 t/km for 2  whiting in the Channel. Secondly, because F was still too low with a biomass of 0.4 t/km  2  and because there was predation from other gadoids, a juvenile whiting group was created. For the juvenile group P/B and Q/B were set to twice that of the adult, diet composition came from Hamerlynck and Hostens (1993) for 0-group whiting in the southern North Sea and the biomass of juveniles was estimated from CFSG data as 50 % of the total biomass, based on an age of transition to the adult group of 1. K was estimated as 0.25 and the average adult weight/average juvenile weight was 6.27. A l l predation from whiting, cod and hake on 'whiting' was allocated to juvenile whiting. 75 % of predation from rays and dogfish and large bottom fish were allocated to juvenile whiting. Juveniles comprised 16.4 % of the catch composition according to ICES (Anon., 2000c), and so this was taken from the adult group and allocated to juvenile whiting. Although there was some evidence that whiting recruitment is positively affected by cooler temperatures (Philippart et al, 1996), with such uncertainty in the whiting data, there was not a clear enough relationship to warrant a forcing function in the model. Finally, when running the model with these changes to whiting it seemed that the F in the current model was too low. As a result the biomass in the current model of the both the adult and juvenile groups was lowered from 0.156 t/km to 0.115 t/km . 2  2  Cod - Like whiting the estimates of F from ICES were very different from that calculated by catch/biomass for the model. As a result time-series catch/biomass estimates of F were used. Cod does seem to be influenced by temperature (Figure 4.3) and (Planque and Fox, 1998) and (O'Brien et al, 2000) indicate that cooler water is advantageous for recruitment. Consequently a juvenile cod group was added to the model. 70 % of the 1995 stock biomass and 7 % of the catch were juveniles and the age of transition into the adult group was 1 (Anon., 2000c). P/B and Q/B were set at twice that of adult cod, K was 0.2, and the average adult weight/average juvenile weight was 5.8. Diet composition for this group was based on 12-16 cm cod from the Baltic Sea (Hussy et al, 1997).  104  Figure 4.3: Response of North Sea cod to temperature. The grey bars show total stock biomass as estimated from XSA and the black line the average annual temperature. 1200  w o  g  r  1000  13.5 13  12.5  800  12  11.5 11  0) l_ 3  re  0) a.  E O 1-  10.5 1973  1976  1979  1982  1985  1988  1991  1994  1997  Predation from whiting and cod on 'cod' was allocated wholly to juvenile cod and 50% of the large bottom fish predation went to juvenile cod. Because adults only composed 30 % of the total biomass, but the majority of the catch was adults, fishing mortality was 1.92 and the E E was 2.275 in the 1995 model. To combat this, F was reduced to 0.8 year by using a biomass of 0.044 t/km , which meant that the juvenile biomass was 0.103 t/km . 2  Planque and Fox (1998) and O'Brien et al, (2000) showed that there was a negative correlation between the temperature during February - May and recruitment of cod in the Irish Sea. This appeared to be the case in the North Sea too (Pearson value - 0.46, p<0.01) and to a lesser degree in the Celtic Sea (Pearson value - 0.37, p<0.05). Because the Channel seems to have a greater affinity with the North Sea stock of cod (Pawson, 1995), the relationship between February-May temperature (in °C) and North Sea recruitment was used to create the equation:  Recruits = -225195 * temperature + 3,000, 000.  The forcing function entered into Ecosim was the difference between the number of recruits from the equation, and the estimated value from the 100-year February - May mean temperature between 1900 an 2000. The forcing function was used to drive both adult and juvenile groups as there was a suggestion from the data that cooler years may permit more North Sea cod to reside in the Channel. 105  Large bottom fish - The estimated biomass was assumed to be constant but when the model was run with the time-series Fs, the predicted biomass halved. It was assumed that P/B needed to be higher, so it was increased in both models by 0.1 year "', which in the 1995 model meant a P/B of 0.496 year . This seemed reasonable as M was estimated to _1  be between 0.15 and 0.2 year "'using the empirical equation of Pauly (1980), and combined to an F of 0.3 year , this was close to 0.5 year _1  Even with this change large  bottom fish still decreased but the decline was much more gradual. M . Dunn (pers. comm.) had indicated that there was concern about the anglerfish stock so to assume that this had actually been decreasing as the model predicted was not unreasonable.  Seabream/John Dory - There were few data available for seabream but both the catch data and M . Dunn (pers. comm.) strongly suggested that the stock was much larger, or that there was a second stock in the western Channel. The stock seemed to be under very high pressure in the late 1970s as the modal length changed from 37-38 cm in 1977 to 28 -30 cm in 1979 (Pawson, 1995). Because black bream all mature as males and between 30 -40 cm turn into females, a change in size of 7-8 cm can have great importance for the stock. Hence there is sufficient evidence to believe that the stock did in deed crash after 1980. Furthermore, M . Dunn (pers. comm.) referred to increases in both seabream and John Dory as the Channel warmed. Fishing mortality was increased in the 1970s to cause the model to make the stock crash and a forcing function was used in an attempt to replicate its recovery. It is important to note that this group was tuned only for trends as, in the absence of point estimates of biomass or fishing mortality, all that could be hoped for was that the group would behave in a manner that was reasonable. For both seabream and John Dory the same forcing function was used. This was calculated by: 1 + (difference in temperature from the 100-year mean * 20) where temperature is expressed in °C.  Pilchard - There were 3 data sets available to understand changes in pilchard biomass over time, catch, temperature and egg counts, but there was no correlation between them. Part of the difficulty with these data was that the egg counts stopped before the very warm years of the 1980s, which, i f pilchards were affected by temperature, would have  106  been very important years. A further problem was that there was a large difference between the pilchard catch data from the C F S G and that from ICES. Between 1993 and 1995 the CFSG estimated that there were 5,588 t of pilchards landed on average but during the same period ICES showed that it was 20,000 t. Direct biomass time series data were not available and as the egg count data had proved unreliable in 1973 this could not be used as a surrogate. Consequently, although both Southward et al. (1988a) and Haynes and Nichols (1994) argue that there is a relationship with temperature, the magnitude of the impact was unknown and consequently the same forcing function as seabream and John Dory was used.  Scad - Inconsistencies with the catch data and the fact that the ICES stock assessment data only went back to 1982 meant that there was a great deal of uncertainty about scad. Consequently, the model conservatively predicted a biomass that changed little even though it is known that large infrequent recruitment pulses can have a strong effect on the biomass (Pawson, 1995).  Bass - The biomass of bass significantly decreased as F increased but it simply reduced in steps as the fishing mortality increased in steps (Figure 4.4). According to M . Pawson (pers. comm.) there was the beginning of a recovery of bass stocks in the 1990s following a warm year in 1989 and it was important to include the effect of temperature on bass into the model. A juvenile bass group was added to the model because there was a relationship between temperature and recruitment. Using data from Henderson and Corps (1997) and Pawson (1992), the following simple linear relationship was calculated connecting temperature (in °C) between July and October and relative year class abundance; recruitment = 23.059 * temperature - 336.04. The number of recruits from the equation was divided by the estimated number of recruits from the 100-year mean temperature to get the actual forcing function.  Using data from the CFSG juvenile bass were estimated to be 20% of the adult biomass. They recruited to the fishery at age 4, had a K of 0.2 and a value of 2.48 for the average adult weight/weight at transition. P/B and Q/B were doubled. The diet composition of  107  juvenile bass was estimated based on frequency occurrence studies by Kennedy and Fitzmaurice (1972) in Ireland and Kelley (1953) in the Channel. Shrimps and deposit feeders seemed to dominate with some copepods and crab so 45 % were allocated to shrimps and prawns, 45 % to deposit feeders, 5 % to copepods and 5 % to crab.  When fishing mortality was zero the biomass of bass increased but only doubled: a greater change was expected. P/B was lowered from 0.6 year  _ 1  to 0.5 year  A  in the 1995  model because the unaccounted mortality was causing the sluggish response.  F i g u r e 4.4: Biomass of bass predicted by the model. The green line (1) shows the adult biomass with no forcing function, the blue line (3) shows the adult biomass with the forcing function included and the black line (2) shows the juvenile bass biomass with the forcing function. The red blocks beneath the graph show the increase in fishing mortality.  1973  1978  1983  1988  1993  Years  Sharks - The 1995 shark F entered into the model could be multiplied by 10 times from 0.01 to 0.1 year  1  and it barely affected the biomass. Consequently, in the absence of  data, the biomass in the 1995 model was lowered to one fifth of the estimated value. This meant that sharks were much more sensitive to changes in fishing mortality, which historically seems to have been the case (Vas, 1990, 1995).  Cephalopods - Research into the effect of temperature on Scottish squid landings (Pierce, 1995) and in the English Channel (Robin and Denis, 1999) provided evidence that temperature and landings were positively related, although more research was required to ascertain the mechanisms of this and the precise changes in biomass. As with  108  pilchard, a forcing function of unknown magnitude was required. Part of the problem was that fishing effort on cuttlefish had simultaneously increased with temperature, so it would be hard to separate the effects of temperature from increased effort. Hence the same forcing function as seabream, John Dory and pilchard was employed, and this produced slight changes in biomass with a maximum change of only 20 % even in the warm year of 1989.  Toothed cetaceans - Changes in the herring population in the early 1980s allowed the simulated biomass of toothed cetaceans to increase. This increase was unrealistic because the majority of toothed cetaceans came from the western Channel and the herring stock primarily came from the eastern Channel. Ideally there would be one model for each side of the Channel or an Ecospace model for the whole Channel that could incorporate this effect, but in the absence of these, the herring component of the diet was reduced from 0.07 to 0.03 and the remainder was added to mackerel.  4.2 Simulating - single objective results The aim of the Channel Ecosim model was exploration of the optimal fishing fleet structure. Open loop optimisation allows the modeller to specify weightings on 1 or more of 4 objective functions in Ecosim, according to management priorities. The computer then searches for the optimal fleet structure that will bring the most benefit. The four different objectives are:  1. Economic - this is purely profit and will tend to trim down over-capacity and focus effort on the most lucrative species. Operating and fixed costs are included in the calculation as is discounting over the simulated period. 2.  Social - the number of jobs per catch value had been specified in the model and this was used to focus effort on the most labour intensive gears. The aim of this function is to provide the maximum number of jobs possible.  3. Mandated rebuilding - this alters fishing effort to increase the biomass of specified groups that receive a weighting.  109  4. Ecosystem structure - because ecosystems that contain large quantities of longlived species are deemed to be more healthy (Odum, 1971), in this objective the weightings of each group are the inverse of the P/B ratios as a surrogate for charismatic species. A number of aims were investigated using the open loop search routine: •  Purely economic  •  Purely social  •  Purely ecosystem  For these three, the specified objective function was weighted with 10 and the other groups with 0. •  Mandated rebuilding for recreational fishery - cod, rays, large bottom fish, bass and sharks were judged to be the most significant recreational fisheries. Each group was weighted with 10 and the mandated rebuilding objective function was weighted with 10 while the other objective functions were set at 0.  •  M y choice - Using all of the available information the author attempted to produce the 'best' combination of objectives to optimise.  Climate change modelling has a high degree of uncertainty and many variables (Figure 4.5). Using data from D. Viner (University of East Anglia, pers. comm.), two climate change scenarios were created using a minimum sea temperature change of 0.15 °C per decade and a maximum change of 0.3 °C per decade over the next 40 years. These temperatures were entered into the forcing function equations to produce a time-series for the affected groups. For the forcing functions, an average temperature of 12.57 °C between 1993-1995 was used as the starting point. Hence in extreme circumstances the model was predicting that after 40 years the temperature would increase to 13.77 °C. The forcing functions necessary to tune the model were modified to predict into the future warmer sea temperatures, these are shown in the appendix (Table A2). As well as seeking for the optimal fleet when the Channel increases in temperature, the effect that temperature had on the initial optimal fleets was also investigated.  110  F i g u r e 4.5: Potential sea temperature changes in the Channel. Part a) shows qualitatively how 7 factors influence 6 climate prediction scenarios. Part b) quantifies these differences. Figures taken from Watson et al. (2001)  a)  b)  To ensure that the search routine did not stop at a local optimum, each optimisation was run 3 times from base Fs and 20 times from random Fs, or until an obvious optimum had been found. A l l searches used the Adams-Basforth optimisation routine.  T a b l e 4.2: Optimal fleets for single objective optimisations. The values are multipliers of the current level of fishing. Two results for social are shown because social (1) was far too costly economically and social (2) was a useful local optimum. Levels of profit, jobs and ecosystem health are shown relative to the current level as 1.  Fishery  Economic  Social (1)  Social (2)  Ecosystem  Rebuilding  Otter trawl  1.02  7.91  0.59  0.02  0.02  Beam trawl  0.06  0.24  0.86  0.11  0.01  Midwater trawl  1.86  6.40  0.31  0.10  0.09  Dredge  0.81  0.45  0.79  0.10  0.33  Net  1.68  3.17  3.91  0.07  0.02  Pot  0.71  0.65  0.46  0.03  2.83  Line  2.07  20.09  8.9  0.21  0.07  Profit Jobs Ecosystem health  1.34 1.19 0.99  -3.54 5.51 0.89  0.43 2.27 1.03  -1.68 0.07 1.49  -2.09 0.51 1.30  111  4.2.1 Optimising purely for the economic objective When only the economic objective function was weighted, the fishing industry was able to generate 34 % more profits than in 1995 (Table 4.2) and much of this was due to an increase in highly profitable lining and midwater trawling (Table 2.22). B y reducing fishing mortality from potting on crabs and lobsters, and on scallops by dredging, their stocks were allowed to recover. This resulted in catches of commercial crab and lobster that were higher and of scallops that were nearly equal to the current level (Figure 4.6) but with costs that were much reduced. The increase of midwater trawling meant that the bycatch of toothed cetaceans was large enough to deplete their biomass. The biomass of seals also decreased because large bottom fish, which constitute an important part of their diet, were being depleted by the lining fishery. The increase of all of the finfish fisheries caused the higher trophic level piscivorous species such as large bottom fish and rays and dogfish to be reduced in biomass. This allowed an increase in whiting, small gadoids, small demersals and other flatfish.  When only the economic objective was optimised, some of the higher trophic level sources of natural mortality were removed through fishing. The groups hit hardest by this were large bottom fish, rays and dogfish, toothed cetaceans and seals. There was less change in the biomasses of cod, pollack, whiting and cephalopods than expected from the increased fishing effort but because of the reduction of toothed cetaceans, seals, large bottom fish and rays and dogfish, predation on them was much less. The biomass of sprat did not dramatically increase yet the value of this species nearly doubled. This again was the result of predators such as hake, rays and dogfish being depleted and the potential biomass increase being removed by midwater trawling. Although both hake (4.7 €/kg) and seabream (2.75 €/kg) are valuable species and when abundant will form a significant localized part of fishers' income, because their biomass is small compared to mackerel and the flatfish, they did not largely contribute to the value of the fishery. When optimising for profit, groups such as seabreams will tend to be caught as a by-product when pursuing other species because in their own right seabreams do not carry enough economic weight. Both conservationists and recreational anglers would find this option unacceptable because toothed cetaceans, seals, bass, cod, rays and large bottom are all  112  depleted. Furthermore, the replacement of natural predator mortality with fishing mortality is a major manipulation of the ecosystem. It is in these types of manipulations where unexpected ecosystem repercussions are likely to be seen that would not be identified by the model. Figure 4.6: Change in biomass and value of the groups that showed a difference of more than 5 % from the optimal economic fleet configuration. The change in E/S refers to the difference from the start to the end of the biomass over the simulation period. In this figure, +1 means the biomass has doubled and -1 if the group became locally extinct. Whelk Lobster Other Flatfish John Dory Scallops Whiting  KSSSXWSN  Plaice  SNXVVSXN  Mullet Commercial crab Cephalopods Sprat Sole Pilchard  Hvalue  Herring  H biomass  Seals Rays and Dogfish Seabirds  t\wwv  Cod  LV\V\Vs>J  Pollack Large bottom Mackerel  KNNVCSXsSl  Bass Seabreams Scad Toothed cetaceans Hake  -0.60  I  I  -0.40  -0.20  0.00  0.20  0.40  0.60  0.80  1.00  Change in E/S  113  4.2.2 Optimising purely for the social objective The jobs/catch value ratio of lining was much greater than any other gear (Table 2.25) and as a result the optimisation to maximize the social objective function calls for a 20fold increase in the effort employed by this sector (Table 4.2). Netting also had a high jobs/catch value ratio and consequently this was significantly increased too. Although the jobs/catch value ratio of midwater trawling was very low, pelagic species such as sprat and herring increase in abundance as their predators were removed, enabling this large increase of effort to be sustained. Although in one sense the effort was sustained, as there were still fish being landed, the effect of social optimisation (1) was over-exploitation. From an ecological standpoint the results were a disaster as the only fisheries that remain are those for lobster, small gadoids and mullet, sole, herring, sprat, and cephalopods. Two positive outcomes for conservationists would be that the biomass of seabirds increases by 9 times because of high discarding, and that the reduction in potting allows the lobster biomass to increase. In reality, using this fleet configuration, the ecosystem has been significantly changed with most of the finfish species becoming locally extinct. Furthermore, there was tremendous over-capacity of fishing with costs increasing by two and a half times meaning that subsidies would be required to make this a feasible option (Table 4.2). Although in many fisheries there are subsidies to make fishing economically viable when in theory it is not, in this model the extreme social optimisation is going too far away from a plausible management scheme.  When optimising, the computer often settled on one local optimum, which is a more useful reference point for the purposes of this model. Like social (1), social (2) increased lining and netting and enabled 2.3 times as many jobs (Table 4.2). The main difference between this optimisation and social (1) was that only large bottom fish were wiped out completely (Figure 4.7). The extinction of large bottom fish allows there to be an increase in whiting, which in turn, combined with the decrease in midwater trawling, allows toothed cetaceans to increase. Because fish caught by netting and lining will yield greater employment, competing fisheries were be lowered. Consequently otter trawling fishing effort is reduced to prevent the extinction of rays and dogfish or bass. Potting is reduced to prevent the extinction of commercial crab, midwater trawling is reduced so that  114  mackerel can be caught by netting and beam trawling/dredging are decreased to enable netting to have the maximum catch of sole, cephalopods and plaice. Figure 4.7: The change in biomass and value of the groups that showed a difference of more than 5 % from the optimal social fleet configuration. Whiting Other Flatfish Whelk  K\\\\\\\\\\\\\\\\\\\N  Toothed cetaceans  ,\\\\\\\\\\\^SS  Lobster John Dory Seabreams Gurnards Scad Small gadoids Mullet Hake Pilchard Mackerel  [lvalue  Bivalves  H biomass  Cephalopods Shrimp and Prawns Sandeels  Ed  Sprat  I. -a,.  Herring  ISS5  Discarded catch  LV.WW  Seals Sole  k\\\\\M  Seabirds  NWSWN  Rays and Dogfish Cod  \  *  *  . ...  Pollack Bass Large bottom  ||=™^g  -1.00  -0.50  0.00  0.50  1.00  1.50  2.00  Change in E/S  115  4.2.3 Optimising purely for the ecological objective In the purely ecological optimum, all of the fleets were reduced, the aim of this being to allow the ecosystem to return towards its un-fished state of large, long-lived species. In the Channel, bass is a classic example of the decline in long-lived species as in recent years recreational anglers have been arguing that they catch many more small bass than in previous times ( M . Pawson, pers. comm.). The model predicts that with a large decrease in fishing effort, bass and heavily exploited shellfish species such as crabs, lobsters and scallops "bounce back" (Figure 4.8). There are similar resurgences in rays and dogfish, large bottom and cod, although the increase of some finfish species, such as whiting, is tempered by the rise in seals and toothed cetaceans. This gives an indication of how fisheries may have displaced marine mammals at the peak of the food chain in the Channel. The lower trophic level schoolers ("bait fish", or "forage fish"), small gadoids, small demersals, mullet and sprats, all decreased as the ecosystem shifted to favour longer lived, high trophic level organisms, which prey on them. One notable exception to this is seabirds. The decrease in discards from a reduction in fishing fleet lowers their biomass by more than half.  This optimisation produces a "healthy" ecosystem, with large numbers of long-lived species. The reason for some fishing rather than a complete absence appears to be that some fishing effort has positive effects on a group. The obvious example is the way that discards feed seabirds but sharks are also positively affected through lining because competition for the prey of sharks is reduced by lining depleting rays and dogfish and large bottom fish. The impact of this fleet configuration on levels of profit and employment was drastic (Table 4.2) with the heavy reduction in fleet reducing employment to less than 10 % of current levels. Large losses resulted from the fleet because of the high percentage of fixed costs (Table 2.24) that were not lowered even when the fleet was reduced close to zero.  116  Figure 4.8: Change in biomass for the ecologically optimal fleet configuration.  Seals Commercial crab Lobster John Dory Whelk Plaice Seabreams Toothed cetaceans Gurnards  ^S\SSS\S\S\SS\\SSSS\S\SN  Cod Scallops Hake Bass Rays and Dogfish Sole Large bottom Dab Pollack Scad Mackerel  S3  Whiting Other Flatfish Pilchard Cephalopods  EE  Small demersals Sprat  ES  Small gadoids  EES  Mullet  ESS  Seabirds Discarded catch  -1.00  -0.50  0.00  0.50  1.00  1.50  2.00  2.50  3.00  Change in E/S  117  4.2.4 Optimising purely for mandated rebuilding for the recreational fishery In general the fleet structure in this optimisation was similar to the ecosystem health optimisation but the increase of potting was a major difference (Table 4.2). Potting catches commercial crabs and cephalopods, which have a diet composition overlap with cod, rays and dogfish, large bottom fish and bass. Although the mixed trophic impact (appendix Table A3) indicates that the individual negative effect of commercial crabs and cephalopods on each group is small, there is a cumulative justification to reduce the biomass of these competitors. Cephalopods form a large proportion of the diet of sharks and this limits the increase of potting. Using this fleet configuration, the biomass of cod more than doubles because fishing mortality is almost entirely reduced to 0 and because juvenile plaice and pollack, two important prey species, increase in biomass (Figure 4.9). Predators of cod do increase but, as there are no fisheries for seals or toothed cetaceans and large bottom fish increase when the lining fishery is reduced for bass, there is no way to prevent this. Rays and dogfish are caught primarily by otter trawling and netting and these gears are reduced to near 0 (Table 4.2). With no natural predators in the model and with little change in the biomass of their prey, a reduction in fishing mortality is the only reason for the increase in rays and dogfish. Fishing mortality on large bottom fish is significantly reduced and although the increase of seals limits the growth of their biomass there is still an increase of 1.6 times. The bass biomass nearly doubles as a result of the reduction in fishing mortality. This increase would have been greater still i f the recreational fishery had been shut down. The same is true with sharks. Because there is no commercial fishery for sharks a greater increase in biomass could be achieved by shutting off the recreational fishery. The increase in shark biomass of 7 % was caused by the increase in mackerel biomass that came from the reduction in midwater trawling.  118  Figure 4.9: Change in biomass for the mandated rebuilding optimal fleet configuration.  Seals John Dory Plaice Seabreams Toothed cetaceans Gurnards Cod Hake Bass Large bottom  s\\\\\\\\\\\N  Scallops  s\\\\\\\\\\\N  Rays and Dogfish Juvenile plaice  S^\\\\\SNI  Sole Pollack Dab Scad Mackerel Whiting Other Flatfish Sharks  s  Pilchard Bivalves  3  Sprat  IS  Small gadoids  ES  Mullet  ESS  Seabirds Commercial crab Discarded catch Lobster Whelk 1  -0.5  ()  0.5  1  1.5  2  2.5  3  Change in E/S  119  4.2.5 The impact of varying vulnerabilities The effect of changing the vulnerability (V) between 0.2 and 0.8 on the four optimal fleet configurations in Table 4.2 was considered and the results of these are shown in Figure 4.10. These indicate that although a range of 0.2-0.7 was used according to biomass data and trophic level, these were fairly conservative values and resulted in an ecosystem that was not very different from a default setting of 0.2. The reason for this is that much of the diet of the higher trophic level organisms was composed of low trophic level organisms that had a low V . Using a default V of 0.8 produced massive variation in both profit and ecosystem health. When specifically optimising for these characteristics, a higher V meant that the rewards were much greater. Testing Vs was a valuable exercise because it showed that higher Vs accentuated what the modeller would hope to achieve. Hence, it was prudent to use conservative Vs, as the actual outcome of a specific fleet configuration may be better than expected, rather than using high Vs and building false hopes. It is noteworthy that when the social optimum has a high V the system is healthy. The reason for this is that higher discarding and sandeels benefits seabirds, which have a high B/P ratio and seals and toothed cetaceans increase with the whiting and other flatfish made available by the absence of rays and dogfish and large bottom fish. In reality many of the finfish species have been made extinct by the intensive fishing and the system cannot be labelled as healthy. Figure 4.10: Effect of high and low default vulnerabilities on profit (a) and ecosystem structure (b) for the optimal fleet configurations.  Current  Bcon  Soc  Bcol  Rec  Current  Bcon  Soc  fcol  Rec  120  4.2.6 The impact of temperature Similarly to changes in V , the effect of increasing temperature was positive, enabling higher profits and a healthier ecosystem. Both adult and juvenile sole and cod groups, seabreams, John Dory, pilchard, cephalopods and juvenile bass had a forcing function acting directly on them but because of the complex predator-prey relationships these were not the only groups influenced by temperature. As much as the model was capable of predicting the ecosystem changes that would occur if the Channel warmed, it showed that using the current fleet structure led to more profits because of the increase in lucrative sole and bass fisheries. The increase in a number of finfish species and cephalopods also allowed higher trophic predators to increase in biomass, meaning that the ecosystem was healthier.  Going one stage further, optimisations were run with temperature increases of both 0.6 °C and 1.2 °C for each of the single objective functions (Figure 4.11). Figure 4.11: Maximum potential discounted profits, employment, ecosystem health and mandated rebuilding for a temperature increase of 0.6 °C (a) and of 1.2 °C (b). Optimisations were run with the forcing functions invoked to get these results.  121  Table 4.3: Optimal economic fleet configuration with the forcing functions. Gear Current economic Economic optimum - Economic optimum optimum 0.6 °C increase 1.2 °C increase Otter trawl 1.02 1.24 1.13 Beam trawl 0.06 0.24 0.05 Midwater trawl 1.86 1.97 1.91 Dredge 0.81 0.77 0.77 Net 1.68 2.70 2.70 Pot 0.71 0.50 0.48 Line 2.07 1.26 2.01 When optimising with an assumed increase of 0.6 °C, the maximum potential profit was 86 %, and with an increase of 1.2 °C it was 89 %, greater than the 1995 current maximum profit. The actual fleet structure that generates these profits changes little (Table 4.3) with only netting significantly increasing because of the larger sole biomass. Unlike trawling, netting catches only a few species so it is able to more effectively respond to temperature by selectivity.  When optimising for employment with an increase in temperature, the search routine did not stop at a local optimum and so it led to an ecosystem that was extremely depleted. Table 4.4 shows that the effort is even higher than social (1) and that the growth of the sole biomass permitted an increase in netting effort. Table 4.4: Optimal social fleet configuration with the forcing functions. Fishery Otter trawl Beam trawl Midwater trawl Dredge Net Pot Line  Current social optimum Social (1) 7.91 0.24 6.40 0.45 3.17 0.65 20.09  Social (2)  Social optimum 0.6 °C increase  Social optimum 1.2 °C increase  0.59 0.86 0.31 0.79 3.91 0.46 8.9  10.52 0.24 4.55 0.23 10.40 0.32 3.04  10.75 0.309 4.72 0.22 8.78 0.37 3.13  The ecological optimisation showed little change in fleet configuration from the initial optimisation.  122  Figure 4.12 indicates that temperature would be very important for recreational fishers. An increase of 1.2  C would mean that the possible cod biomass increase would be  limited to just 1.5 times the current level, rays and dogfish to 1.35 times the current level and large bottom fish to only 1.23 times the current level. Cod is directly affected by temperature but rays and dogfish and large bottom fish decrease as there is increased competition for small gadoids from cephalopods. Bass increase purely through the recruitment from the forcing function on juveniles and sharks increase due to their prey, cephalopods, being directly affected by temperature.  • reb. opt.  Figure 4.12: Effect of temperature on the potential rebuilding of five recreationally important stocks. All values are relative to the current biomass of 1.  cod  rays and  large  dogfish  bottom  bass  sharks  Further temperature effects can be seen throughout the ecosystem. The model is not sensitive enough to pick up the kind of zooplankton changes that Southward (1963, 1983) records but it is clear that the increase in cephalopods depletes sandeels, which allows the carnivorous zooplankton to multiply. Interestingly, there are important changes at the top of the food chain in response to cephalopods (Figure 4.13). With the reduction in large bottom fish and sandeels, the biomass of seals drops to less than half of its current biomass. Similarly, sandeel depletion causes a drop in seabird biomass by half. Conversely the increase in cephalopods provided additional prey for sharks and toothed cetaceans causing their biomass to increase.  123  Significance of cephalopods in the Channel. These are screen captures from two Ecosim runs both with fishing effort kept at current rates. Part b) shows how the addition of the cephalopod forcing function to the model had a considerable effect on many groups. Section 4.1.3 gives a description of the cephalopod forcing function. F i g u r e 4.13:  Biomass/original biomass  John Dory  a) Without cephalopod forcing function.  8iomass/ofiginal biomass  b) With cephalopod forcing function.  juv sole  Ijuv sole John Doty  Bass  Ray? and Dogfish Poteck wtnbng juv whiting  4.2.7 Discount rate For all of the optimisations documented up to this point the discount rate had been left at the default setting of 0.04 but because the discount rate is unpredictable a sensitivity analysis was carried out to determine the effect that this important parameter had on the optimisation routine (Figure 4.14). Essentially, the higher the discount rate, the more susceptible the fishery was to have a 'fishing down the food web' scenario (Pauly et al, 1998b; Sumaila, 2001). When the discount rate was high, there was an increase in lower trophic level 'schoolers' such as sprats and small gadoids. Otter trawling was markedly increased because although it was not the most profitable gear, it did remove the greatest range of species out of the water quickly, so that the money can be invested elsewhere.  124  It is worth noting that there were not significant changes until the discount rate increased above 0.2. According to R. Sumaila (Fisheries Centre, U B C , pers. comm.) the discount rate is likely to vary from slightly greater than 0 to 0.1 in the UK/France and for the purposes of this model is not a significant issue.  (default)  4.3 Eat it or leave it? Up to this point only the influence of each individual optimum has been considered but in the background there has constantly been the issue of trade-offs. A n old phrase spells out this truth as "you cannot have your cake and eat it!". Quite simply, with respect to the management of the Channel, the optimum ecological fleet configuration is not the same as the optimal social or economic fleet structure. Because these multiple objectives are incompatible there are a number of trade-offs and prices that need to be paid when managing the Channel. The aim of this section is to elucidate these in order that the task of selecting " my choice" might become clearer.  Although there are micro-scale trade-offs in predator prey relationships, such that one cannot expect to have more cephalopods i f there are many more sharks, the three tradeoffs that are most significant to the model are those between employment and profit, profit and ecosystem health, and jobs and ecosystem health.  125  4.3.1 Employment and profit By weighting employment and profit differently, yet keeping ecosystem health at a constant of 1, it was possible to create Figure 4.15. This shows the economic optimum and also the social optimum but only for runs when there was a positive profit. The most significant aspect of this is that it suggests that employment can be increased and there would still be an increase in profit up until 1.4 times as many jobs. After this point any increase in employment will have negative implications for profits. Figure 4.15: Trade-off between profit and employment as estimated by the model. The point estimates refer to specific optimisations run with different objective function weightings. 1.2 r -  :  ,  1  Employment  Sean Pascoe and Simon Mardle (2001) investigated the same profit/employment frontier using a bio-economic approach and came up with a very different result (Figure 4.16). Their work suggested that there was currently overcapacity in the Channel and that higher profits could be obtained by reducing jobs. Their optimum economic fleet configuration was entered into the Ecosim model and this generated profits of only 65 % of the current situation. Clearly the two models are producing very different results even though much of the data for them comes from similar sources.  126  F i g u r e 4.16: Trade-off between profit and employment in the Channel according to the C F S G bio-economic model. Figure provided by Sean Pascoe from Pascoe (2000).  100  10000 Crew employment  Intuitively one would think that with the Channel having had such a high intensity of fishing for decades, many stocks would be in a similar state of depletion to shellfish and that the most profitable option would be to allow a recovery of stocks, which is what the bio-economic model indicates. But the EwE model indicates that for maximum profit, slightly higher effort for some gears is required. This is an important difference between the models and the cause of this difference needs further research.  The two models draw on the same sources of data for prices and fleet profitability. Some of the biomasses that were entered into the EwE Channel model came from the same CFSG data that the bio-economic model used. One important difference in the data sources was that in the bio-economic model the number of jobs for each fleet was more precise than the semi-quantitative boat months used in the EwE model , but because the 3  EwE model was only optimising for profit this should not have had an effect. Both models had certain weaknesses that may have contributed to the difference between  3  It was only during writing up that this data became available so it was unable to be used in these analyses.  127  them. The EwE model had aggregated all of the metiers into 8 gears while the bioeconomic model used 13 gears and split these into France and the U K , but it seems unlikely that this alone would have made the results so different. The bio-economic model had production-effort relationships that did not consider how fishing might affect predator-prey relationships. This is likely to be the cause of much difference between the models because when dissecting what the EwE search routine was doing to produce the optimal fleet, it became clear that groups would be suppressed by a gear mainly to prevent their predation on other species. These effects would not be seen in the bioeconomic model. Furthermore, a comparison of the production-effort relationships and the 5 basic input parameters in the EwE model would be extremely useful to highlight how the models respond differently. The reduction of dredgers and potters when optimising for profit in EwE were similar to the changes required in the bio-economic model. In EwE the important point about these groups was that they had responded positively when fishing was lowered. This may mean that the P/B, biomass or vulnerabilities of other groups was too low and that changing fishing was not having a strong effect. Low vulnerabilities mean that groups become resistant to increases in fishing, potentially allowing fishing effort to be increased without wiping them out. Finally, regarding the initial input parameters, no negative biomass accumulations were entered for the model. This means that as a starting point, the current level of fishing was sustainable. Some of the gadoid biomasses such as whiting do seem to be decreasing at the current level of fishing. If this is indeed the case, and it could be entered into Ecosim, the policy routine may opt to lower whiting F in order to allow this group to recover.  Further research is required to ascertain what is causing the differences between the models but this kind of research is the ideal situation. Different models are being used to simulate the Channel and optimise the fleets and because they are producing different results, scientists are pressed to confer and look more deeply at the strengths and weaknesses of their models, hopefully leading to a better simulation of the Channel in the future.  128  4.3.2 Profit and ecosystem health Figure 4.17 indicates that as a general trend, a decrease in profits was required to increase ecosystem health. There is one point on the graph that is important though. When economic was weighted with 2 and ecological and social with 1 there were 16 % more profits, 62 % more jobs and a reduction of only 0.1 % for ecosystem health. In theory this seems an ideal solution but in reality there are decreases in bass and marine mammals that would be unacceptable to conservationists and recreational anglers. This serves to show that although these trade-off graphs have value, a detailed look at the ecosystem response to fishing is required.  Figure 4.17: Trade-off between profit and ecosystem health as estimated by the model. 160  T  140 £ 120 ro « 100 §  1 in  8  80 60-  Current situation  4020 0 -• -2.5  -2  -1.5  -1  -0.5  0  0.5  1  1.5  Profit  4.3.3 Employment and ecosystem health When weight was only placed on ecosystem health, the amount of jobs decreased to nearly zero (Figure 4.18). As the social weighting was increased, ecosystem health decreased until the current situation was reached where both jobs and ecosystem health were much lower than they could be. One would expect that as fishing pressure is increased to accommodate more employees the downward trend of ecosystem health would occur, as it did in Figure 4.17. The reverse occurs, because as the social weighting was increased, trawling and dredging, with their destructive ecosystem practices, remain low and lining and netting were rapidly increased. This caused large bottom fish and bass to be wiped out entirely, and rays and dogfish were greatly reduced. The loss of rays and dogfish and large bottom fish allowed the biomass of whiting to increase. Whiting are an  129  important component of the diet of toothed cetaceans and seals so their biomass consequently increased. Hence, the ecosystem is perceived to be healthier because these heavily weighted mammals increase. The reality was that there has been a significant decline in many finfish species and the ecosystem was not as 'healthy' as the model indicated  Figure 4.18: Trade-off between employment and ecosystem health as estimated by the model. 160 -. 140 120 • E 100 0) w 80 >. in  o o UJ  60 -  Current situation  40 20 00.5  1.5  2.5  Employment  4.4 RAPFISH RAPFISH, A new rapid appraisal technique for the sustainability of fisheries has been developed at the Fisheries Centre, U B C (Pitcher and Preikshot, 2001). Different categories of fishing can be compared and contrasted for five fields, ecological sustainability, economic sustainability, technological sustainability, social sustainability and ethical sustainability. For each field there are 8-10 attributes that require a score on a scale from good to bad: 'good' referring to a fishery that is sustainable and 'bad' for a fishery that is unsustainable. Using a visual basic program inside of a Microsoft Excel spreadsheet, ordinations can be performed using multi-dimensional scaling (MDS). For M D S to be acceptable, goodness of fit stress values must be below 0.25 (Clarke and Warwick, 1997). Leverage analyses can be used to indicate the relative importance of each attribute to the position on the M D S 'map' and would be expected to be less than 10 % (T. Pitcher, Fisheries Centre, U B C , pers. comm.) A Monte Carlo routine is also available to estimate errors. For a more detailed description of the RAPFISH methods please see (Pitcher and Preikshot, 2001).  130  For the purposes of the thesis, RAPFISH was a useful tool that would help to bring sustainability issues out of the Channel that were not incorporated into the EwE model. For example, in EwE the social objective function is based entirely on jobs where as RAPFISH has much more sensitivity, including the importance of the fishery to the local economy and to families containing a fisher. In conjunction with the results from the model it was hoped that RAPFISH would enable a more holistic optimal fleet configuration.  RAPFISH data for the Channel came from three people who were interviewed, the head of the Sea Fisheries Inspectorate in Brixham, Devon, the fisheries officer of the Sussex 4  Sea Fisheries Committee and a C E F A S scientist. For some attributes, such as primary production, direct estimates were available from other sources but for the majority of attributes the interview data were used. Where there was disagreement on the scoring for an attribute, the value was based on the author's judgement. The data used in the analysis are shown in the appendix (Table A4).  4.4.1 Individual RAPFISHfields Ecological •  Highest leverage was less than 5 %.  •  Stress = 0.14 (see appendix, Figure A l ) .  The gears ranged from 74 (pot) to 55 (net) (Figure 4.19). There were not large differences between gears because 3 of the attributes were scored equally across the gears. Potting scored well because there were few discards, with the majority of these being returned alive, and because crabs and lobsters do not extensively migrate. Netting, like trawlers, caught many species and had a high discard and bycatch. Netting also caught migrating species such as hake and according to the interview data the netting catch had also decreased in size. It is noteworthy that migrating and catch size may actually be  4  This is the UK governmental fisheries enforcement agency.  131  correlated. Because some species migrate further, they may be susceptible to other types of gears that are depleting their biomass. Hence the average size could be reduced making netting seem more ecologically perilous than it is. Certainly it is worth considering how attributes may be related.  Economic •  Highest leverage was 7 %.  •  Stress = 0.14 (see appendix, Figure A l ) .  For this field, the gears were even more bunched than ecological with a range of just 64 (line) to 52 (dredge) (Figure 4.19). Lining was slightly higher than the rest because sector employment was less than 10 % and because it was highly profitable, but aside from this there was little separating the economic sustainability of the fisheries.  Social •  Highest leverage was less than 5 %.  •  Stress = 0.16 (see appendix, Figure A l ) .  The range of social sustainability of the gears was 49 (net) to 61 (midwater trawl). Pots, lines and otter trawls had an identical score of 53.96 that was similar to dredging (53.29) and beam trawling (56.01). Nets scored lowest purely because a single attribute, conflict status, was higher then the trio of otter trawl, lines and pots. This is certainly legitimate because passive netting prevents other commercial and recreational fisheries from accessing the fishing grounds over a wide area for a long period of time. Midwater trawling scored highest because of the low conflict with other resource users, because they work as part of a fishing cooperative and because the majority of household income comes from fishing.  132  4 2 0  15  Bm Trwl jj_OttTrwl BAD  40  20  -2  60  Net J  A  i  -6  80  -10  100  I  20  Net  -15  Pot i Line  -20 Economic  0  Bm Trwl J _T MidWTrwl  -2  1  -6  Net t Otter trawl, line OttTrwl and pot have an Line |j Pot identical score  j;"™" 0 1  BmTrwl  ST  20  6 | Line  Dredge  ]j  GOOD 80  100  jl Net  -8 -10  BAD  -12  GOOD  60  BAD  •A  Dredge  40  100  OttTrwl  2  20  80 I  Ecological  20 18 16 14 12 10 8 6 4 2 0  GOOD  40  -10  Line J  -14  Bm Trwl £  BAD  -5  Pot  j MdWTrwl  -12  5 0  Dredge  -8  .MdWTrwl  10  GOOD  80  100  Social  J MdWTrwl  -14 Technological  30 Pot  25 Dredge Jj  20 -I  Figure 4.19: The M D S RAPFISH output for the 5 fields showing how sustainable the 7 Channel fisheries were on the horizontal access and how different the scoring had been to get these results on the vertical access. 95 % confidence intervals from the median are also shown.  I Net  Bm Trwl {  T Line  15 OttTrwl 10 -I  i MdWTrwl BAD  GOOD  20  40  60  80  100  Ethical  133  Technological •  Highest leverage was less than 6 %.  •  Stress = 0.14 (see appendix, Figure A l ) .  Scores for this field were broad compared to the first 3 fields, ranging from 37 (beam trawl) to 63 (net). The gears could roughly be separated into a lower active gear group (the three trawlers and dredging, 37-45) and an upper passive gear group (lines, pots and nets, 57-63). Whether the gear was passive or active had the most significant leveraging effect, which helped to explain the difference between these groups. Furthermore, the active group tended to be much larger and go for longer trips than the smaller inshore activities of the passive gear, landing their catch at dispersed sites. A focus of much recent research (Collie et al., 2000; Kaiser et al, 2000) has been the effects of towed gear on bottom structure and these are incorporated into this field by higher scores for destructive fishing practices.  Ethical •  Highest leverage was less than 5 %.  •  Stress = 0.17 (see appendix, Figure A l ) .  Scores ranged from 33 (beam trawl) to 56 (pot). There is less segregation between active and passive gears than in the technological field but lining, potting and netting are still the most sustainable gears. The active gears score poorly because there has been significant damage to the ecosystem and less mitigation. Beam trawling scores particularly badly because of high discarding and a considerable amount of illegal fishing. Of the passive gears netting scores lowest because a number of net fisheries have no minimum mesh sizes specified.  134  4.3.2 Conclusions T a b l e 4.5: Relative performance of each gear type analysed by ranks - 1 is the top score, the most sustainable. 'Range' refers to the range of scores for all gears for that field.  Gear  Ecological  Economic  Social  Tech.  Ethical  Average  Pot  1  6  4  3  1  3  Line  6  1  4  2  2  3  Midw trawl  5  2  1  6  4  3.6  Otter trawl  2  5  4  4  5  4  Beam trawl  3  3  2  7  7  4.4  Net  7  4  7  1  3  4.4  Dredge  4  7  6  5  6  5.6  Range  55-74  52-64  49-61  37-63  33-56  Of the five fields technological and ethical seem to be the most significant and have the greatest range (Table 4.5). Ecological, economic and social all have a lower range of scores which are almost entirely greater than 50. Generally there is very little to segregate different fisheries from one other and on many occasions a number of attributes received the same score. This is because the Channel is a multi-species, multi-gear fishery. There is a great deal of overlap of the species that each gear catches and because boats will change from one gear type to another, it proved difficult to see distinctions between the gear for the three fields. However, technological and ethical had a much wider range of results, both with scores in the 30s. They more clearly distinguished the active from the passive gears and outlined the negative effects that beam trawling and dredging can have on the ecosystem.  135  5. Making tough decisions 5.1 Towards a solution Although there will be many opinions that are important in choosing the "best" fleet configuration, some, such as the sport diving industry, will be less important than others. Consequently, the following 4 priorities were identified as the most important. The order does not reflect their importance:  1. Profit making objectives of the industry. Both fishers and government are intent that. the industry will be as profitable as possible. This means a reduction in overcapacity, a streamlining of the fleet to favour the most profitable gear types, and the removal of species that may inhibit profits.  2. A continuation of recreational angling and larger specimens for anglers. While the recreational anglers themselves are well organized with relatively powerful members' organizations, there are also a number of industries that are linked to angling. For commercial fishers, summer may be the period during which their vessels are used exclusively by anglers. Countless bait and tackle shops are dependent upon anglers and to a lesser extent so is the tourism industry of local areas, which provide anglers with accommodation, food, etc. These non-market values for the fish have not been included in the model but nevertheless require consideration when selecting a "best" fleet.  3. Regional dependency on fishing. Whereas once the heartbeat of whole towns would be fishing, an increase in fishing efficiency and effort, combined with decreasing stocks have meant that the fishing sector has lost significance. However in places such as Newlyn and Brixham, fishing is still extremely important and there is strong pressure from fishers for their jobs to survive. Historically there would be an expectation that boys would join their fathers fishing with "9 out of 10 young men of Brixham going to sea" in the early twentieth century (Dickinson, 1987), and there  136  were also strong kin relationships in the processing and marketing aspects of the industry. The social RAPFISH field indicated that these bonds are weaker today with many fishers having left to pursue alternative careers or to become part-time (Dunn, 1999b). With the formation of fishing cooperatives the voice of those fishers that remain has become louder. Furthermore, the areas that are most dependent on fishing also have the highest unemployment rates (Slaymaker, 1989), so any increase in employment would be well received. Hence, while the social considerations may not be as large as in previous years, they are still very important.  4. Conservation priority. For the general public, marine mammals and seabirds are the most charismatic species in the ecosystem and receive a high profile. The Royal Society for the Protection of Birds has a huge British membership and it is birds caked in oil that receive the greatest publicity following an oil spillage. NGOs such as the Whale Conservation Society and the World Wildlife Fund ensure that any harm caused to marine mammals is publicly understood. Unlike the social voice above, conservation has become a hotter potato in recent years and the effect of any fleet configuration on marine mammals and birds, as well as sharks and other elements of the ecosystem, requires careful thought.  Although these 4 voices are loud and have lobbying power, it is important to remember that they do not manage the Channel fishery. At its core, that responsibility lies in the hands of the European Commission. European Commissioner Franz Fischler outlines that the problem is over-capacity and the resulting stock depletion causes increased fishing effort to catch what remains. He argues that:  " We cannot put up with this situation any longer. It is our collective responsibility to end this vicious circle for the sake of todays sector as well as for future generations. I am fully committed to proposing and defending difficult measures that will impose tough times on all concerned but which will represent the best guarantee for a sustainable fisheries sector" (Fischler, 2002).  137  This was extremely encouraging and relaxed the boundaries to the subsequent analysis, enabling a range of options to be considered with confidence, because the decision makers were willing to make sacrifices. It is very important that scientists and policy makers are on the same wavelength. Historically, there has been a reductionistic approach, in which economists have provided information about how the fishery can become economically efficient, local communities have argued about the level of employment that is necessary for sustenance, and scientists have provided biologically acceptable limits for stocks. The failure of this type of management is in evidence throughout the world (see in particular Pauly et al., 2002) . Hence there is a responsibility 5  on both the scientist and the decision maker to overlap (Scheiber, 1997). Consequently, using the scientific information that was collected, Franz Fischler's guidelines and the 4 priorities from the different groups above, it was possible to generate three "best" fleet configurations (Table 5.1).  Gear Otter trawl Beam trawl Midwater trawl Dredge Net Pot Line Fishing industry  Option A 0.48 0 0.68 0.85 1.90 0.79 0.23  Option B 0.5 0 1.91 0.83 2.37 0.70 2.04  Option C 0.69 0 1.03 0.84 1.7 0.77 0.44  ££  ©©  ©  ©©  8£  ©  ££  ©©  ©©©  &&©  Sport anglers Fishing villages Conservationists  Table 5.1: Chosen optimal fleet configurations and how the four special interest groups will react to the results. Fleet values are multipliers of the current fleet. An ideal outcome for the special interest group will warrant 3 smiley faces, while a disaster will mean 3 skull and crossbones.  ©©  Finally, there are three choices, and which one is the 'best' depends on the relative strengths of the 4 groups (Table 5.1). After the RAPFISH analysis it was decided that as This was recently highlighted in the 2 0 0 2 World Summit on Sustainable Development in Johannesburg as the global restoration of depleted fish resources was of high importance on the agenda. 5  138  much as possible the destructive benthic dragging gear should be reduced. Beam trawling and dredging seemed to be the most destructive. Scallop dredging is an extremely valuable fishery in the Channel but has been over exploiting stocks for a long time, so this was reduced to allow stocks to recover. This, as the economic optimisation (Figure 4.6) showed, did not result in a major reduction of catch. The toughest decisions concerned otter trawling and beam trawling, particularly where beam trawling had been very destructive to benthic flora and fauna and, because it composed less than 6 % of the workforce and was half as profitable as otter trawling, it was removed completely. Because both trawlers were indiscriminate catchers of fish and had severely depressed many stocks, otter trawling was reduced in two of the three 'best' fleet optimisations by weighting mandated rebuilding of bass and toothed cetaceans and fixed at half the current level in option B .  Figure 5.1: A comparison of the three 'best' fleet configurations with the 1995 situation. The dashed line at 1 indicates the level of profits, jobs and biomasses during 1995.  5.1.1 Option A The fleet configuration for option A was created by fixing beam trawling at 0, and then allowing the search routine to locate an optimum using a weighting of 1 in economic and 139  0.5 in mandated rebuilding. Only toothed cetaceans and bass had a weighting of 2 for rebuilding because these were the most critical to conservationists and anglers. The result was a fleet that was markedly reduced in effort with only netting being increased. Despite this reduction there still remained 88 % of the employment available and 87 % of the profits (Figure 5.1). From his comments this would be the kind of fleet reduction that Franz Fischler envisaged (Fischler, 2002).  The major species changes were predominantly positive (Figure 5.2). There were large increases in seals and toothed cetaceans, which the conservationists would find appealing and there were also increases in bass, large bottom fish, rays and dogfish, which the recreational anglers would benefit from. Sharks showed no change in biomass and cod showed a slight decrease because of the increase in netting. Seabirds also significantly decreased but this was inevitable because of their dependence on discarded fish. As the biomass of seabirds seems to be "unnaturally high" from the discarding of fish (Anon., 1999d), any moderate decrease in their biomass should not be cause for concern for conservationists. Although to obtain this ecosystem there would have to be some fairly large changes in beam and otter trawling, there would certainly be benefits for the industry at a later stage. Figure 5.2 shows that, aside from hake and cod, it is generally the inexpensive and lower trophic level groups such as mullet, small demersals, small gadoids and sprat that are decreasing. The lucrative flatfish species, as well as shellfish, all increase in biomass, which would be available for capture when safe sustainable limits had been ascertained.  140  Figure 5.2: Major changes in biomass and value that result from option A.  John Dory Seals Seabreams  EH  Gurnards Plaice Toothed c e t a c e a n s R a y s and dogfish  NWSWWSSN  Whelk Lobster I  C o m m e r c i a l crab Large b o t t o m  \\\\\N  Scallops Other  flatfish  r&7%  Bass Whiting Scad Dab  pa  Mackerel Pollack Herring Small d e m e r s a l s Cephalopods small gadoids  -K3  Sprat  TS3  Cod Mullet  D value  Hake  H biomass  Seabirds Discarded c a t c h  -0.8  -0.4  0  0.4  Change from E/S  0.8  1.2  5.1.2 Option B To achieve this fleet structure in the optimisation routine, otter trawling was fixed at 0.5, beam trawling fixed at 0, and the maximum profit was sought from the ecosystem. Because the extreme economic fleet configuration (section 4.2.1) had been too ecologically damaging, the aim of this option was to head towards a highly profitable Channel while tempering the ecological harm by fixing otter trawlers at half their current rate.  Table 5.1 shows that dredging and potting were lowered below 1 because this reduced costs and kept the catch nearly the same as the shellfish biomass recovered. Midwater trawling was increased to 1.9 times the current level because higher netting had reduced hake predation on scad and with the reduction in otter trawling and the removal of beam trawling there were additional whiting and seabream to be caught. Furthermore, as the mackerel stock was fished down, their prey, sprat, were allowed to recover and be caught in higher quantities by the midwater trawl. Netting and lining were increased to make the most of the available sole, large bottom fish, rays and dogfish and bass that the reduction in trawling allowed and netting also reduced hake allowing higher John Dory and scad catches. There were increases in profits (26 %) and jobs (20 %) (Figure 5.1) that would please the fishing industry and governments although the cost of this would be for conservationists and recreational anglers. O f the crucial charismatic and recreational species only seals and rays and dogfish showed a positive response, with bass, toothed cetaceans, sharks, cod, large bottom fish and seabirds all showing declines (Figure 5.3). Other lucrative species such as sole and hake showed declines but with such a high increase in netting and lining this was to be expected. This option would be much more amenable to conservationists i f the toothed cetaceans bycatch from, midwater trawling could be minimized, as it is this that causes their biomass decline.  142  Figure 5.3: Major changes in biomass and value that result from option B. John Dory Whiting  v\\\\\\.\.\.XXNX\\l i  Gurnards Whelk  i  O t h e r Flatfish  Lobster Seabreams Scallops Seals  \\\\\N 1  C o m m e r c i a l crab R a y s a n d Dogfish  i  Mullet  kvvsa  Dab Pollack  a — '  Sprat S m a l l gadoids  E  1  Sole  S  SI  Sandeels Sharks Pilchard Herring Toothed c e t a c e a n s Cod  KVNW1  Large bottom  LV.VXV1  Mackerel 1  Bass Scad  k\\\\\\S\  •-mi  Discarded c a t c h Seabirds Hake -1  1  1  -0.6  -0.2  0.2  Change from E/S  0.6  1  5.1.3 Option C This fleet was generated when beam trawling was fixed at zero, bass and toothed cetaceans were allocated a mandated rebuilding weight of 2 and the search routine optimised for profit, weighted with 1.2, and mandated rebuilding, weighted with 0.5. As Table 5.1 clearly shows, options A and B had split the 4 interest groups. Option A was beneficial for recreational anglers and conservationists but not so good for the industry or communities and option B was the reverse. Consequently, this final 'best' option sought to compromise and keep more of the interest groups satisfied.  With otter trawling not being fixed as in option B it was allowed to increase (Table 5.1) and it was this that made the greatest change from option A . As with both of the other options, dredging and potting were decreased to allow stocks to recover. Because there were toothed cetacean discards from midwater trawling there was a clear effect from the different weighting schemes between options A - C (Table 5.1). With a higher economic emphasis (option B) the highly profitable midwater trawling increased to capitalize on the scad and sprat stocks, depleting toothed cetaceans through discarding as well as through reducing important diet components such as mackerel and scad. A weighting on toothed cetacean biomass in option A caused midwater trawling to be reduced. Because option C was seeking for a compromise, midwater trawling was only marginally increased from the current level and the biomass of toothed cetaceans rose following an increase of their diet, seabreams, whiting, mackerel and cod. Because otter trawling was slightly higher than options A and B , there was less catch available for the more profitable netting and this increased to only 1.7 times its current level. Lining decreased because mackerel had been caught by increased midwater trawling, large bottom fish and rays and dogfish had been caught by increased netting and bass, the other main group caught by lining, had been 'protected' by weighting mandated rebuilding.  While option C would pacify the fishing industry it would still require huge structural changes because the fleet configuration is so different to the current situation. If those painful changes were going to be made, it seems prudent to make them for the bigger long-term gains of option A . Possibly an attempted compromise to please all interest  144  groups has been the standard unsuccessful way of managing fisheries and a bolder stance such as option A needs to be advocated.  Figure 5.4: Major changes in biomass and value that result from option C. John Dory Seals  P]  Plaice Whelk  pis  Gurnards Lobster  1  Seabreams R a y s and Dogfish C o m m e r c i a l crab Scallops 1  O t h e r Flatfish Large b o t t o m Toothed c e t a c e a n s Bass  1-'>•..,< VA'.,  Whiting  I  Dab Sprat  1  S m a l l gadoids  i  Mullet Cod Seabirds Discarded catch Hake I  -0.8  1  1  1  1  1  -0.4  0  0.4  0.8  1.2  Change in E/S  145  5.1.4 Changes in temperature and vulnerability (V) With increasing temperature all options became more profitable and increased in bass biomass relative to the base (Figure 5.5). When V was 0.2 for all options, the response of profits and of the important biomasses was dulled. Everything responded in the same direction as when V s were set to trophic level, but the change was not as dramatic. When temperature was increased, the biomass of toothed cetaceans rose in option A , the most favourable option for conservationists and recreational anglers. Seals significantly decreased relative to the baseline, although even with an increase of 1.2 °C their biomass was still 2 % higher than the current level. The reason for this was that there was a decrease in large bottom fish, their prey, with increasing temperature, as cephalopods competed with large bottom fish for small gadoids. When V was 0.6, profit and the biomass of toothed cetaceans and seals increased tremendously. Option C had a very similar profile to option A (Figure 5.5), although it looks like option A with a lower V , i.e. there were not such large changes from the baseline.  0>  £  c i_ 3  o  3  o  O  >  >  0  base  t=0.6  t=1.2  v=0.2 v=0.6  base  3 ]Option C  c  • profit  o  bass  3  E toothed cetac.  O  or  III  a:  OH  >  Option B  0)  i—  =  3  iMli base t=0.6  • seals  t=0.6  t=1.2 v=0.2 v=0.6  Figure 5.5: Effect of increasing temperature and varying the default vulnerabilities on the profits and key biomasses for options A-C. A l l results have been scaled so that 1 on the y-axis is the 1995 situation.  t=1.2 v=0.2 v=0.6  146  In option B , increasing temperature lowered seal biomass until it became 73 % of 1995 levels. Toothed cetaceans increased so that using this option with a 1.2 C increase would mean that the biomass decline was only 8 % from 1995 levels. A V of 0.6 caused a large increase in profits and seal biomass, but a large decline in bass and toothed cetacean biomass.  In summary, option B seems to be the most robust to changes in temperature and V with the outcome changing the least of the three options. The positive part about option A is that increases in temperature and V served to emphasize the bass and toothed cetacean biomass increases, which would maintain anglers and conservationists confidence in this policy. The negative aspect of this is that although these specific groups increased there were large changes in other groups meaning that although the industry in theory would make higher profits (Figure 5.5) exactly how much and from which group would depend on the V or the temperature. Although the Channel is a mixed gear fishery and smaller boats are able to adapt quickly to changes in the ecosystem, for the larger boats the unpredictability of option A may create more of a problem.  In many respects option C is a poorer version of option A , although it has the bonuses of not having to shrink the trawling fleet so dramatically, being marginally more profitable and of responding less to changing temperature and V . Changes in temperature would mean that the industry and recreational anglers will be more pleased with option B , and conservationists feel about the same because while seals decrease, toothed cetaceans increase.  5.1.5 Closed loop analysis Having run the optimal search routine using an open loop optimisation routine it was decided to attempt to assess how imperfect knowledge would affect the results. The open loop finds the system that is the best in a perfect world, assuming perfect knowledge of the stocks and the ability to catch exact quotas. The reality is that things are not that simple and it is uncertainty that makes fisheries science so difficult. Legislatively, uncertainty has necessitated the adoption of the precautionary principle in policy. In the  147  optimisation routine, uncertainty is dealt with by searching for a fleet configuration where the total objective performance in the closed loop is close to the open loop. It was also possible to investigate the impact that the coefficient of variation (CV) in estimates of F and catchability had on the potential errors of implementing management policies.  The difference between the open loop (perfect information) and closed loop (imperfect information) constituted the errors, and Figure 5.6 examines the changes in these errors when altering C V for 2 assessment methods of fishing mortality, catch/biomass and direct assessment. The errors are also shown for annual increases in catchability for each option. When calculating fishing mortality for options A - C the trends are essentially the same (Figure 5.6). Whether F is estimated by C/B or by direct assessment, as the C V increased (i.e. as greater mistakes are made in fisheries management), the social and economic optima became lower and the errors increased (Figure 5.6). Conversely, the ecosystem health and mandated rebuilding objectives were actually improved by an increase in C V . Looking more closely, economically, option A seems the most robust to the effect of changing C V for both methods of estimating F with errors of about 100 less than option B and 50 less than option C. Options A and C show very similar error profiles for mandated rebuilding.  A n increase in annual catchability causes increases in errors for each option that are almost identical. Economically and socially the greatest increase in errors comes when catchability changes from 0 to 0.2 and after 0.2, increases in gear efficiency of up to 4 times has little effect compared to the initial rise. For mandated rebuilding and ecosystem structure there were more continuous increases with a large jump between an increase of 1 and 2 per year. Option A was economically more robust to increases in catchability, having low errors than the other options regardless of the rate of increase of catchability.  The closed loop analysis gives an interesting insight into how implementing these three options may affect the four objectives. It is clear from this analysis that no single option is significantly better or worse when it comes to implementation but that economically option A is slightly more favourable.  148  j o j j a qay/sAsoog  CO  JOJJO  o o  CO  o o  CM  CM  T-  oos/uoog  j o j j a qay/sAsoo3 o o o T-  JOJJ3  oos/uoog  j o j j a qey/sAsoog  j o j j a -sAsoog  o o  CD  o o o o o o o o o o o o m co O J J 0 J J 3 00S/U003 j o j j a sAsoog  o  o o  CD  o o o o o o o o o o o in  CO  CM  o  T-  o  j o j j a oos/uocg j o j j a qay/sAsoog  <D  O  H eg  S 5  ^ bo O  o c g  8 g  I  5.2 Discussion  5.2.1 The greatest benefit "Fisheries management is concerned with the utilization of the natural fish resource for the greatest benefit to society" (Pascoe et al, 1997). This statement begs the question, what is the greatest benefit to society? In Devon and Cornwall there is a strong reliance on the fishing industry and fewer alternatives to fishing (Slaymaker, 1989). The fishing industry has strong linkages with other industries meaning there are positive and negative multiplier effects depending on the success of fishing (Slaymaker, 1989). For those societies to have a thriving fishing industry is much more significant than for Berkshire, a county in England with no coastline and only recreational angling in lakes and rivers. The greatest benefit for the residents of Berkshire is to have a Channel free from pollution where they can spend their holidays, although people in Berkshire would also be concerned i f their taxes were being used to supply unemployment benefit for Devon and Cornwall. Hence, there is inequality between areas. The need of certain regions for fishing is acute yet on a national and international stage those needs are dwarfed. The greatest benefit to society will depend on how society is defined.  The possible options that have been provided for the Channel are the options in a perfect world. Even though imperfections have been included using the closed loop analysis, the costs of changing the fleet structure to the optimum have not been included, nor have the costs of training redundant fishers or providing them with early retirement. Conversely, the costs to future generations by further depleting natural resources have not been included and the issue of whether fish have an intrinsic value has not been considered. Do we as humans have a right to manipulate the ecosystem to suit our needs? If by removing marine mammals we were able to sustainably catch significantly greater values of fish, would it be right to do so? Could it be argued that the Channel is not a significant place for marine mammals and that the European commission could sacrifice them for the sake of the fisheries? Similarly, prior to trawling, the bottom structure of the Channel would have been very different to how it is now. The cost of returning to the un-fished state  150  would be substantial, as trawling and dredging would need to cease. Is society at large prepared to accept the burden of this cost for some corals that nobody will see? Could marine parks be created instead to allow recreational divers to view the sea floor while the majority of the Channel is aimed towards maximum economic efficiency?  The purpose of this thesis was not to provide answers to these questions. It is extremely worthwhile to raise them though because they serve to highlight that even i f this was a perfect ecosystem model, even with the ability to perfectly understand stocks and to catch exact quotas, the initial question of what is "best" for society still remains. The government still has to make decisions, weighting social, economic and ecological factors. This difficult process still exists and will only become a greater issue as our abilities to manipulate ecosystems increase.  Nevertheless, some objectives are clearer than others, and the fact that there has been depletion of many of the stocks through short-term decision-making is known to be a problem. In the next section some suggestions are proposed about how these problems may be tackled.  5.2.2 Management suggestions The primary issue that was raised by Fischler (2002), Slaymaker (1992) and Anon. (1999d) is that the excess capacity of the fleet needs to be reduced. The fleet needs to be streamlined in the manner of option A above. Franz Fischler clearly stated that this will mean job losses, but in order to safeguard the industry in the long-term this is inevitable. The problem that this brings is that many of the areas where job cuts will occur are the places, such as Devon and Cornwall, where they are most needy. The European Union has some aid available for retraining and early retirement schemes for fishers but as Dunn (1999b) indicates, some fishers simply do not want to do anything else. Even when there are low profits being made they still hold on to their boats and licenses. Frankly, there is little room for this sentimentality. The collapse of the coalmining industry in the U K has caused tremendous upheaval for many regions and the same will occur in fishing unless pruning occurs now. Possibly the increase in bass and mammals may allow some  151  skippers to use their boats purely for recreational fishing or eco-tourism, which may soothe this issue a little, although the E U must be tough, and it appears that they are prepared to be so (Fischler, 2002).  For the fishers that remain after the initial job cuts, producers organisations (POs) should be encouraged. POs are good for three main reasons (Goodlad, 1992):  1) Decisions are made at the level of the fisher meaning that the PO is more able to respond to market demand. 2) They remove the pressure from the government to allocate quotas. 3) Each vessel is allowed to plan its annual fishing in order to maximize potential profitability.  But their development in the Channel needs to be monitored. Because the POs apply for an allocation of the national quota based on track records, there is a tendency for PO members to jeopardize future catching opportunities to ensure they have a positive track record. This can lead to competition between vessels in the PO and independent vessels. The only way to overcome this is to reduce overcapacity so that in essence there are enough fish to go around for the remaining vessels (Slaymaker, 1992). The POs would then help to magnify potential profits through reasons 1) and 2) above.  Enforcement is a crucial issue that is emphasized by both Fischler (2002) and Dunn (1999b). This relates to both the ethical and social field of RAPFISH in the sense that certain fisheries are more prone to illegal fishing than others. There is one positive example in South Devon where the social interaction between different fishers mean that cheating is not an option (Hart, 1996), but generally, illegal fishing does occur and better policing of fisheries is imperative. The problem that exists in the E U is the unequal policing between different countries that exploit the same stocks. This causes resentment among fishers and even less incentive to abide by the regulations. If a more homogenous and equitable method of enforcement can be used in managing the E U , compliance would be greatly encouraged. Similar inequality exists between countries scientific programs.  152  Traditionally, the intention of fisheries scientists was to aid the industry by enabling it to make the most out of what was available. Hence the financial support could come from the industry itself. The problems came when the fishers of one country were paying for monitoring while another country that accessed the same stock did not have such stringent monitoring. Although ICES, an international body, does provide much of the stock advice, this is provided by scientists who have been working on the fisheries of their own country. Inequality would be greatly reduced if more financial support could be provided for each country that must be used for stock assessment.  The issues of the involvement of ICES and the relationships between the industry, scientists and policy makers were neatly brought together by Corten (1996). This paper identified that many of the problems are structural in nature. The European Union has jurisdiction over the entire area, so in theory, after the establishment of the CFP, intercountry squabbling over the resources should be minimal. ICES has existed since 1902 and has prided itself on the best scientific advice. Just reading a working group report outlines the sophistication of the methods that are used to assess stocks. Countless institutions across Europe produce highly educated scientists who push the boundaries of fisheries science further and further. Yet in the midst of all of these scientific achievements the stocks have still declined. Understandably, the fishing industry, taxpayers and environmentalists are not happy with what has been going on.  Corten (1996) writes "modern fisheries management requires advisory and decision making bodies that can react quickly to specific local problems". This is exactly what the European Commission and ICES are not. While the management of the majority of sectors have been de-centralized, decisions about the fishing industry across Europe are still made by politicians in Brussels. They are too far from the situation and, as a result, decisions are made at too high a level. ICES is a scientific organization where speed is not required and i f the E U wants specific advice on a problem it has to go through a bureaucratic chain to get the answer. The total procedure takes a minimum of 1 year and is not an incentive for communication (Corten, 1996).  153  When conducting RAPFISH interviews the author found that the Sussex Sea Fisheries Committee were repositories of a wealth of knowledge. They were aware of each of the boats in their jurisdiction, had a working relationship with many of the fishers, and were intimately aware of the issues that were specific to that coastline. They, like all SFCs, are capable of making byelaws that apply to their local region. They can rapidly respond to disasters such as pollution or to problems with recruitment. They are not perfect but they are an example of the direction that the current E U management system needs to move in. Of course the SFCs cannot deal adequately with migratory stocks but the E U could delegate its management to regional departments that were large enough to include the majority of most stocks. The North Sea would be an example of the appropriate size. Regional management would also enable the fishers, decision makers, and scientists to have a closer relationship. Funding could be provided by the industry for the regional stock assessments and this would increase the accountability from scientists.  In concluding this section there is a certain degree of discouragement that many of these points have remained as merely rhetoric. Three years after the paper of Corten (1996) a report from the European Commission (EC) outlined many of the same points (Anon. 1999d). The E C wanted to reduce overcapacity to equate to the resources available, which agrees with what Slaymaker (1992) had been saying. They wanted to increase financial support for scientific research, a greater contribution from scientists into fisheries management, and improved selectivity of gears. One can see this as a positive step with the European Commission responding to the advice of scientists. But the most important part of Corten (1996) was the issue of regional management, and this was not mentioned. Possibly most discouraging of all was that 3 years after the E C Report, the website of Franz Fischler is saying the same, meaning that in the 6 years between Corten (1996) and Franz Fischler very little has happened. The year 2002 signals a reform to the CFP and what Franz Fischler wants, everybody wants, "environmental, economic and social sustainability". He gives ideas of what is necessary to achieve this, i.e. increasing accountability and scientific advice, which Corten (1996) would agree with, but the question of exactly how these aims will be achieved needs to be asked. Fischler (2002) says that "there is no time to lose" and the author would heartily agree, encouraging the  154  European commission to get some practical proposals on the table. Hopefully the CFP reform will see this.  5.2.3 Limitations and future research Clearly the model will only be as good as the data that is used to create it. Hence, although building the model does act as an ecological accounting system and unreasonable data can be weeded out at an early stage, there are different levels of accuracy in the data. Given a wish list of which data to improve, the first choice would be the diet composition data. Much of this comes from areas outside of the Channel where the species compositions may be different. The classic example of this was with the high importance that sandeels had in the diet of seabirds. Although a number of pictures of puffins eating sandeels originate from the south coast of England, the lack of a sandeel fishery suggests they are not as abundant as in the North Sea. Hence, the diet of seabirds may have comprised more sprats in the Channel than in the North Sea. This may have had implications for the sprat biomass data. The ecological relationships hinge on the diet composition data, so a stomach-sampling program would be invaluable in tightening up this aspect of the model.  The second data that would be very useful is better stock assessment of shellfish. Tappin and Reid (2000) refer to the absence of scientific studies for management purposes of shellfish, and for such an important part of the Channel fisheries these studies need to be done. As well as for the commercially exploited shellfish, the benthic groups were certainly the worst in the pedigree (appendix, Figure A2) and better information, particularly on P/B as well as on biomass, would be essential for further work.  Boat months were only a semi-quantitative method of evaluating effort. Tetard et al., (1995) referred to boat-months as a month in which a gear type had been a 'significant activity,' but in reality even i f a gear was used for only 1 day in that month, it would constitute 1 boat month (M. Dunn, pers. comm.). Consequently the jobs/catch value data were not as precise as one would hope and in future research a more quantitative method of estimating effort should be used. 155  Some conclusions were made from increases in temperature, but it is important to make 3 points about these.  1) The actual increase in temperature that is likely to occur is unknown. 2) Because during the 1990s the Channel is experiencing a very warm period, the effect of increasing the temperature even further has unpredictable effects. One suspects that the influx of warm water species such as John Dory and cuttlefish will be greater, but the magnitude of this is unknown. 3) Aside from cod there may be other negative effects on cooler water species such as lemon sole. These have not been included in the model at this stage and consequently bias the model to be economically more successful as lucrative warmer water species are increasing.  The RAPFISH analysis fulfilled its purpose because it identified additional aspects of sustainability, which helped to choose optimum fleets with broader perspectives. The sampling size should be increased to include France, and should incorporate the opinions of fishers so that comparisons can be made between the different groups of people to see how the fisheries are performing.  One of the most frustrating aspects of the project was conflicting data. Catch data were the classic example where the ICES time-series data were different in the 1990s to that provided by the CFSG. It also proved difficult to harmonise catch, biomass and fishing mortality data when tuning the model. There were many groups that did not have timeseries F or biomass data, and although basing time-series estimates of F on catches, similar species, and personal communications was better than nothing, it was far from ideal. There may be other untapped sources of information for non-quota species available, or in the absence of this, information on the fishing effort of gears could be used to drive the model.  EwE does have the Ecoranger option, which allows the user to enter a range and mean/mode values for all the basic parameters. Hence with the uncertainty in seabird diet  156  discussed above, it would be possible to allocate a range of both sprats and sandeels in the diet. For a more in-depth discussion of Ecoranger please see Christensen et al. (2000). The Ecoranger routine allows the modeller to have a number of models and can help the user to select one that fits a given set of constraints. Because Ecoranger was not connected to the optimisation routine it was not used in this analysis, but the long-term aim would be to have a more fluid starting model so that each point estimate becomes a range of values based on the confidence of that data. Then, when running this in the optimisation routine, one could more adequately consider uncertainty. To some degree, the closed loop analysis, testing with a range of vulnerabilities and different temperatures, have considered some aspects of uncertainty but this could be more focused on the initial input parameters themselves. Moreover, the EwE model could be run varying market prices, discount.rates and recruitment in order to further test i f the optimisation is robust to these unpredictable variables. To pre-program the model to run a number of optimisations varying these parameters overnight would be a useful time-saving addition to EwE. It would also be very useful i f the modeller could pre-program various weightings for the objective functions so that the model could locate the optima that are necessary to produce trade-off graph figures such as those in section 4.3.  Although the next procedural step is to have this current model further compared and contrasted with the work of the Channel Fisheries Study Group's bio-economic model, after this has taken place, it would be very useful to use Ecospace to add a spatial dimension to the model. One of the biggest problems that faces the Ecopath user is defining the area over which the ecosystem model will be built. While the English Channel does contain stocks that only exist in ICES areas V l l d and Vile, many of the important commercial stocks overlap into the North Sea or the Western Approaches. In an ideal world there would be three separate but linked Ecospace models, the North Sea, the English Channel and the Western Approaches. This would mean that stocks such as herring could be traced from the North Sea into the English Channel and back out again. The effect of increasing temperature would be clearly seen by the eastward movement of species towards the North Sea. A n Ecospace model of the Channel would also enable the investigation of marine protected areas. Already the mackerel box exists to the south-  157  west of England and an Ecospace model could look at the effectiveness of this. A large amount of biogeographical work on the Channel already exists (Pawson, 1995), and because this even includes the movements of juveniles and locates where most fish are caught, there is much detail available that could be incorporated into future models.  The model could also be made sufficiently more detailed by separating the gear types into a lower level such as inshore and offshore, large and smaller boats. This would mean that with more social data the optimisation routine could become less "ruthless" (Shepherd and Garrod, 1981). Having had communication with the policy makers, the model could be used to run any number of optimisations. For example, i f they decided that certain sectors of the industry could not possibly be reduced because of social implications, these could immediately be tested in the model.  5.2.4 Summary At the conclusion of this project it is clear that the model needs further testing. Throughout the whole process the best data available have been retrieved and applied. There has been considerable contact with the experts, particularly from the English side, and where possible they have provided constructive criticism. But as they have made extremely clear since the first communications, because many of the boats in the Channel are less than 10 m, catch data can be dubious. Similarly, because there were data suitable for stock assessment for only 7 species (Tappin and Reid, 2000), one must be cautious about the inferences that were made for the non-quota species.  Nevertheless, as long as the attitude persists that no model is perfect then the model is useful. This EwE model has pieced together much scattered information on the Channel, it has identified linkages in the ecosystem that are not seen in the bio-economic work of the CFSG, it clarifies the need for further research in specific areas, and it has provided possible options for the management of the Channel which can be included in future discussions.  158  In closing, one stock in the Channel has provoked much interest. There have only been 7 strong year classes of scad in the last 50 years (Pawson, 1995). What causes an exceptionally strong class is unknown and until it is known it cannot be modelled. Hence, all of the uncertainty with scad and other groups must lead decision-makers towards the precautionary principle. Ecosystems are extremely complex and models will continue to improve in their ability to simulate them, but they will never fully capture this complexity. 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VO {00 ;os oc 00 oc 50 ,00 00 00 00 00 ,00 OS •OS, O N , O S os •Os OS os •Os ,o>' 1-< ,T-( T-H 1-H 1-H i—i 1-H xr H, f ,1-H  T-H  P-; CN ro  T-H  ro  00 CN  OO  ©  oo  in ©  CN  OS CN  IB  VO  VO ro  CN  CN  in  oo  •  l-H CN V. Os Os os Os- ;0\" OS Os OS OS 'OS "Os' 'Os  © —  1-H --H  1-H -T-H "1-H  ,4.'/if 4*'  T a b l e A 2 : Forcing functions used when optimising with a predicted temperature change of 0.6 °C and 1.2 °C. F F 0.6 °C F F 0 . 6 ° C S e a b r e a m .lu\ hav. Pool code Type 1995 1996 1997 1998 1999 2000 .'2001 2002 2003 2004 2005 2006 20117 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 "21122 2023 2024 2025 2026 202" 202S 2029 7  2030 2031 2032 2033 2034  F F 0.6 °C J u v sole  F F 0.6 C cod  2  4  5  2 1.486 1.511 1.535 1.560 1.584 1.609 1.633 1.658 1.682 1.706 1.731 1.755 1.780 1.804 1.829 1.853 1.878 1.902 1.926 1.951 1.975 2.000 2.024 2.049 2.073 2.098 2.122 2.146 2.171 2.195 2.220 2.244 2.269 2.293 2.318 2.342 2.366 2.391 2.415 2.440  2 5.745 6.034 6.322 6.611 6.900 7.189 7.477 7.766 8.055 8.343 8.632 8.921 9.210 9.498 9.787 10.076 10.365 10.653 10.942 11.231 11.520 11.808 12.097 12.386 12.675 12.963 13.252 13.541 13.829 14.118 14.407 14.696 14.984 15.273 15.562 15.851 16.139 16.428 16.717 17.006  1 534 1 555 1.576 1.597 1.618 1.639 1.660 1.681  I  1 H)2 1 723 1.744 1 "65 1.786 1.807 1.828 1.849 1 870 1 S'll 1.912 1 933 1 954 1 9' 5 1 996 2.017 2 038 2 059 2.0X0 2 101 2.122 2 143 2 164 2 18<" 2 206 2 227 2.248 2 269 2 290 7  2.332 2 ^  :  F F 1.2 °C F F 1.2 " C S e a b r e a m .ln\ liass  " : 0.991 0.987 0.9M 0 97S 0.974 0.970 0.965 0 901 0 <;S7  .: I -'0,953 0.948 0 <)44 0 940 ()9sS 0 9.1 0.927 0 922 0.918 *' 0.914 . 0.910- "0.905 0.901 0 897 0.892 0.888 0.884 0 880 0 875 0.871 • 0 86 0 .S62 0 S.-8 0.854 0 850 0 845 0 841 0.837 0 832 : "0.828 pi 0.824 , 7  2 2 1.486 1.535 1.584 1.633 1.682 1.731 1.780 1.829 1.878 1.926 1.975 2.024 2.073 2.122 2.171 2.220 2.269 2.318 2.366 2.415 2.464 2.513 2.562 2.611 2.660 2.709 2.758 2.807 2.855 2.904 2.953 3.002 3.051 3.100 3.149 3.198 3.247 3.295 3.344 3.393  F F 1.2 °C J u v sole  F F 1.2 " C .lu\ r o d  4 •\  <:2' '1.534' J  I otst: 1.660 1.702 1 744 1.786 1 828 I 8-0 1.912; 1 9M 1 096 2 0 18 2 080 2 164 2.206 2 248 2 290 ,-,..,> 2.332 2 374 2 416 2.4>8 2 500 2 542 2.583 2 62-s 2 667 2 709 27S1 2 "93  • i i i m  2.877 2 919 2 961 3.003 3 045 3 087 ,-.3.129.. 3 171  i  - '"5;;  2 2 5.745 " ' ,0.991--. 6.322 - - 0:983i 6.900 0 974 ..««.« 7.477 8.055 0 957 8.632 0 948 9.210 0.940 9.787 0.931 10.365 0 922 10.942 ,, 0.914'. 11.520 ,0.905/ 12.097 0 8<>7 12.675 0.888 13.252 13.829 0.871 14.407 0.862 14.984 . 0 854 15.562 0 S45 16.139 . ,',0.83.7.: 16.717 0.828 17.294 0 820 17.872 0.811 18.449 0 802 i) - . . 4 19.027~1 19.604 0.785 0 777 20.182 0 708 20.759 21.337 0.700" 21.914 0 75 r 22.492 0 "42 23.069 0 734 0.725 23.647 24.224 0.717 24.801 25.379 25.956 0.691 26.534 0.082 27.111 0.074 27.689 0 005 28.266 PJ657;*,  176  Table A3: Mixed trophic impact from the final model. Red ("bold) values show positive impacts. ' Impacted ^  . tf C -f X I 1 liii|i;i(-Mii<: J3 > ' •u V. 'C/5 y. ft. SJ C C CO WMaSBmrnlSM 0 1 -0 051 0 217 0.1 -O009 0.006 0.298 0.1 Prim, prod -0.362 0.3 0.028 •"_•0.038 0.031 /.oophiiikton -0 374 -0 533 0.157 -0 015 0 177 0.1. i --0:014'"-0.034 111 Cam. Zp. •0.019 0.002 0.003 0.032 0.018 •0.667^6.083" •0.293 "6.001: 0.005 -0.017 0 6".oo3"-o.o3i • f Depj,.fcotlci s •0:054j"0gl2T 6".089' 0.062hafl8"-0 158,- O.063"•0.138, 0.37"4[ 6.053 -olooirp.,gi3' -0 03: -ff.473tQ.l58.;- 0*005' -0.01 Sus. Feeders o!oJ3-o.oiV •0.009 aoo3 0.005 O.605" 0^603"o'ooT 0.05-0.013-0.001 -0.014^0.663'• 0.163^0.025; Shrimps 0.014'" 0.q45QO;pT3i0 0i5 0.068; 0028 -0 106p0J0j 0.126^0.175* 0 046, Whelk •O.OOT _£0 07 0 051 0.003^.222 •0.004 •6: "134"•6.216-0.0031-5.001: b7o.6oi 0.003-0.007" 0.006' 0 01 0013•0.457 O.OO? 0 002 -0.002 • I ihiiiuili'i iris •0.1342J0.03 -0 004-0.013" 0.076 -0.035-0.0CI6 0 : 0 0 7 0 . 0 0 7 •0.009 -0 052 -0 17 -0.06 0.006' 0 007 Bivalves 0.105" 0.043 0.015-0.028 •0.086""-0.197 0.017' -0.078j-0.037 •0.001 -0 056 -0 055 0, 0.001 0:001" ScaMnRs. . •0 006lT0T3"37-0.001 -0T0"06."67001 " "j O[0j003i -0 001 0.001 1 -0.00,1 -0.001 '. Crab 0.145 0.043 0.042 0.361 0.036* •O.OI1" •0355 0.061 -6.326-0.104 0.321 "-6:03"-610J8 0.012^6723T"0.002 0T026r0-00"5R).001 "0J99 -0.287 "01004E0.011| j Comm. C rah "67006 __ ' : Lobster -0.0041 0.001 -0.023 0 002 0.001 -0.008 " * ~6 0 0 -0.003 •bjooj • 13,005'"0:002"0.001 0Sm. I)cni. 0 054 0 068 0 035 0"5'68~rj"0~35-67251 0.0T5 0.047 -0204 _ 0 003 -0 129 -aqi6 0.008 0.001 0 025 0 011 0.062 OJ014"0.007 0 083 -0 022 -0 156 •0.019 -6.065 -0 101 0.129 Sm. Gads '0002j0".004'- 0 003: -0 041 0.004 0.011 -0 026 0.007 liMiillit 0.007 -0 027 0.013 6.oi7jo7qoo 0003 -o^oot "6.001 O.001 • Sole -0T0O8" 0.005 o;oo6"6.oo6" 0.002 0-0005 0.004 0.002 -0 065 0 003 O.OI2; 0.004~-0 015 O.OOg 0 0 0'005 -0:001'.0r006 0.001:1-0:002" I-Plaice 0.001[l-0.007t 0.002 •6.0*17 0-002.-0.012' 6.002 -0.004""""" 0 -0.001: •bb67 0 • Dab .001 6.002 -6:001" o.ooi"0.002 6*6630.Q03 " *p^b6T"o:ooiiigjgpj 0.001 •0003 -0 178 0.0O2.1-0.OO7/-0.00,2,00.004 ; O. flatfish Gurnards "-6*6"6i""6.o6i"•O.OI3" 0.003 "0.004--o7oi 1 "0.011" 0^05-0.003 -0.004"•6:019 *b"6ot-6.632 0.01, -0 007 0 004 0.02 0.012 "0.011 0.015l-0:088; \\ Idling " 0;01 -0.01 10.129 0.002 -0.003 0.003 0.006 -b7001 0.013 0:0^9 , Cod .. 6qo3"-o.oo4 0.007 0.004 -0 001 0.001 0:6040 0010 0.006 0 002 0.04 0.016 "6.0020 003 T).00i;0.0l'2"l-0".005 o:ooi " o.oo yb.001 . .-'-'-»«• 0/ 00 .0_1-6.018'0.002E01J01; -0:602 0.002 •0.02'f -i 0.009 0.017" 6ro3i""-b*:64i ' R y i dog 0.006 0.005 0*.017-0.054" 0 . 0 0 3 0.004 0 004 -0.009! 0.002 0.018 "''' • OS "0.011:1)7008 -0.066F0.09l OToW 0.007" f Pollack 0.0lV """60.02? -0.011 0 0.001 0.006 6.633-0.087 o.ofi O001 Lg. Bottom ~070"04"0.'006 0.025 -0.008] 0 0 0.016 0."004'J).009 0.008, 0.02"5 ESeabream 0 ° l i 0* 0 0.001" 0 001 0 039 -0 01 00 0.001 __ 0-0:002"6.001: John Dory 0 008 0 016 0 645 -0.04670:057 M 6 0.001 o"-0.007 0.006^0005 -0 007 -0.02 Sandeels 0. 0 01 0 0.017" -0.007": Oj-aobi 0.005 2 0 002 •6.005 -0_.003-0.019-0.022 0 024 -0 001 Herring :poo8r^6!oi' 0 010jp01 0.003! •0^002^.001 0.002 HHSEM 0 0"0""4.i-0" '008 6.007'' 0.02 0^005_b.003"" , Pilchard Sprat" _ "0.003 ' 0.004^0.007 -1o.ooi"b.002;0."008 -0 002-0 001 0.001 O00J 0^05-6.004 y0.026;-0.032 0.014 0.008 -0.002 0:003[ 'Mackerel : -0.01 0.015O.623" 0 6.014: Scad 07002; O;FI8;O;OO4: 0.067 o.6i?•owe 0 002^0.004 0.032, • 0 . 0 6 2 J Bass b7003 ""'o.of-0.077 0.028 0-0 006 "~0" "6*001 -0.001 0.0010.004 Sharks •0. 0 021E0B ' 6 0.002 0 0 0 0 0 Basking sk 0 0 •0:003 •0.088 0.001 0.003 0.029 -0.051 0.24 Cephalopods TfpgMf-(5"06lf, 0.022 "77J5^q.ob2*^oTooT "7 0 b •-.Seabirds 0 "0_ " " 0-0.146 '' 0 •0' 0 0 ob"o3777770 " -0.003 0.004"-l •0.019 0.008:-0.001 " 6 Toothed Cet. 0 0.009 0.0020 003"-0 001 0 0.005 "6.001' 6.006'""07002-0.601 •0.095 -0.215 0 003 •0.001' ' Seals -6iboi'i"o.ooi 0 •0.001 "6.001 Juv bass 0.004 -0.001 0 0".002 0 -0 003 "6 ~ _ ° 0.001 0 004 0 0010.002 -0 002 0001 ~°" 1 J? "°- b;0pJ X001-O.OOSJ-OJPJ Juv sole 0.006 0.022 " -b.004 aoos O.OOi 6.062 "b.002•0 025 0.002' 0.001 0 001 -0.029 0.001 Juv plaice 0b'.bo5 "6.601-0:00i*-0.007 0 005' 0.018 • 0 . 0 0 2 0 . 0 0 2 0.001' " 6 -0.032f-0.002 •0.003. { Jiiv cod 0.006-0.001 •oTpo^ippg? 0.00110.002J-0.002 " , 0-'0.002 b.001"-0.6867Q.001 -0.003 0JJ03 0.013 0.026 -0.007 •0.017: 0.017' Juv whiting _ P 0:604 -0.0080.001 •0.014"* 0 0.005 0 0 0 0 Discards 0 0' 0 0" o 0 6; •6.003 6.054 ¥.452 0.037 0.095 0U 69 0.286" 0.352 Detritus 0.275 0.273 6.156 0 . 0 0 1 0.028 0.188 0.033 0 082 -0.008 ,0.012 0 009 0.039 •0.011 0.083 0.029 Otter trawl """""" 0 0.005' 0?6_01_0.029 •0.603"7o.65 • 7 f f : 6.01JW3.006 0.004 0 158 0.0*19 0.05 0.J13 Beam trawl 0.009*: i-Midwi trawl g'So'i 0.007 0.007 -0 01,[ oojii 0.033 '0T004 0.134o.oigpo7667l 0:062" 6^09 0Tpp5' 0'.025j-0.b35 0 . 0 6 1 0.06T 0.002* 0.002 Dredge 0.003 0.002 "°l i" 0Si02 0 001 -0.001 0 011: 0 • O 7 6 9 2 T 0 "0T004:^et" " 0.007, " 0^6-003 •RJiOM ML:o* f 0.036: 0.007-0.537" ' "0 •0.321 6 •OiQIIf •-0"."005 -."573-o:o6,9io;p"28 Pot -0.001 •0.001" 6 0.003-0.003 0-0.491 oTooi" 6.002-6.001 I me -0 001 0.002, 0.002 0 -0 008 0 004 0 005 0 0.605ro:bl9-0.004 "0.007O.0010- -0,002 -0.00"9ro:b22 0 0.005 6""0.001 0.001 0 0 " o.bT-o.oo"} Recreational 0 -0.001 0 bTobr-0:001 -0:004 *""7"o"" 6*-o.ooi -N;  XI  >-  ;  r  "oToo4rpToo2;  0  :  r  J  0.016":  i  il  1  :  a  0.001  sa n (  " 0.001  r  o.qo2"o."ooi'  0.022:  0 2  ' • bra!  001  •0.001^0.002-  "^^niwb"  0" J3.002  :  o:6o"z  105  :ab"oi  r  00  177  Table A3 continued. Impact cd  ^  Impacting  o y.  c  a. • a  a  s  =  w  a.  r°z  a  00  J  C/5,  Prim, prod 0p27 "0.026 -0.076 0.2 "joTi""_-0.05 0.042 0.087 0.101 0.052 0.090.061" ,O.OO31@I023|-0T637i o.oo2[zrQllF^i?T 07062 0.072 -0:043 0.275 0.309 Zooplankton 0.023 0.21 -obi 1 •b.ooi 0.001 6~002~ Cam. Zp. O-0.025 0.039" 0.002 -0.013 0.004" aoi3' •0,006 0.003 -O056 0.4 0.024 0.043 "6.3ir\ 0.369r0.328' 0!398' '07213,rp.13l£0!032'' -0T01""0.63'9f 0.002 0.07 0.102 Dep. feedei s -0.035 -0.017 -0.016 -0.009 0.174 -0.013 -a008 •0.005 0.014 " 0.01 •0.006"•0.003 0.026 -0.018-0.008 Sus. Feeders^ , p.017^0.092.-0.072" 0*095ro.0"26r"' ' 0"0.156-0.075 0.005 0.293 0 089 0 081 -0.173^0.129 ' Shrimps ^O006_^-0'01 ~6\011" 0.003^0.014 0.006 0.003 O01 0X05." 0.0J1 0.007 0.001 0.001 0.003 0.001 Whelk .Echinoderms,. -0.023'-07028 ^0.026 0.007*"*0.0057-0.004"" 0'.004' 0.009' -0 016 0.006! 0.005 -0.042 -0.076-0.048 -0.011 O07 -0.03-0.004--0.023~b.017 Bivalves b •O023 ¥.006" -O042 -0.014-0.029 0.00ir0T008f^006" ro.0037 OL022 "0.003-0 006 _ 6 ;fScallops!.,, Crab -0J)_16 -0.033 0.008 -0.005 -0J326 -0.007 -O04J 0.004 0.036 0.209 -0/16" •6.072 -0.031 0.03l' •0.063 1 Comm. Crab.' •. P.0T8T^30^p0T63l7^024;•0.007ir 0.04r-0.009"'"W^^^^ 0.032" 0.035, -0.02 -0 028 jo.qi 0 0.001 0 0 •_ ° " 0" -0.001 0^001 0 Lobster Sm.dcm. -0.,1W0,.164^0.057,-0.144^07Ji8rO.OO"2"[FOTOO¥^ 033"}" "0.02" 07134 "0.083 -0.188 0.146 •0.045 0.009 -0.016-0.043-0.063 -0.05-0.099 0.12 0.018-0.054-0 058 0.259 0.169 -0.015 -0.074 0.008 Sm. Gads ^-0.057, 0 . 0 3 7 ^ 0 . 0 3 2 ^ 4 9 , ^0.04,7^021^026^ -001 r - 0 016 0 044-0 017 0 054 -0 035 r"0.008[ l Mullet "aoi""- 6Toi8 -o:bi7-o.oi9' -0.03 -O.O1V-b"6"09* 0.003 Sole 0.003 -6.06T-0.041 -0.023-0.012-0.013 6' o.oi if-o:"046" -0J353-O.OI9; 0022i 0.029.i-0.0,07. -0;P2 -O 006j-0;p26:0.017-0.032 -0.014 -0 03JO005 Plaice 0.002-0.021' -0.021 -0.016* ~-b.oi'-o"b2i-oTbo3"•0.01 cT-0 012;-0.016 -0.014 . "\*p -0.013 -6'017-~0".003 Dab 0.004' P27;-0.039j-0.6T8" '0.'P73fbT0T4p^i09'" 0 018'-0"012 -0 01 -0 01" 0.009 -0 016 -0.0f2' "0.005 nbTfTatnsh 0.023 007 -0.035 -O.O2V O?00t-0.b87~bT037" 0.03r-O^OOa-0".065 -0.037 -0.018 -0.042 -0.074 0.014 Gurnards -0 004V-0.PT3ycrp83 0.002'-0.038f'-'-0 09 -0 045"-0"024.-0.026 -0.0 0^048 0.015 -0:024 -0.067" -07T4 • 0.001 -0.023 -0.024 -0.007 •0.001 -0.003 -0.004 •0.12 -0.01 •0.009 •6*086 6~018 "-0.009 -0.006 -0.011 J0".041^0 "oM^O'.ppgillOg:"0T0O4f~" OfoToPT 0 007 -0 15G 0 001 0.002 0.005 -0 001 -0.119"--07664; Hake -6.214^0 ,f36lT057*^b.067 -0^147-0.6^-6.108 0.008 -0.017" -0 06 0.054 •0.076 0.01 -0.005 0.023 _ Rays and dog "0.013f-0.PT4"-0.01 OJ002 JD.012, -0 01 -0 022 67001 "1PP02;•0.016 0.058 0.048 -0 01 -0.012 0.005 PiilUk 0 0^013 -0.15 -0.266-0:001-0.154 ¥*092 -0.302 •0.019 0.086 -0.14 0.611 0.078 0.022 -0.129: : l.g. Bottom 0 -0.006 0.005 •0.013 "0.004 0.001 -0 013 -0.014; 0.031 'p7009' QP9Z9T°T1! 07016 -0 011 -0 025 l_Seabi e.nn 0^0.003-0.004 '2 o -o.ooT O00T -0002" 0.005 •0.003 o'oor 0 001 -0.001 -0.012 0.001 John Dory O006' 8§02 -0 01 "0.0T6 707OO2^'O.007,f 0.169 0.039 0.008 0.001 0.019 0.025 -0 017 0 0"17'-0"298 Sandeels 0.004" b .003 -0.005 -o.oVb •bb2i*o.oii*-o'od2 0.014" 0.047 0.029 0.021 0.07 -0.008 -0.002 -0.018 Herring "OP69 i 7003-0002 -0.007, 0-0 006 0 063 0.011 0.078 0.005 0.008 Sprat *" 0 -0.008, 0.172' -0.02 0.013 •0.003-o.bor-o.¥oi 0 0.004 -0.007 -0.019 -0.007 o ib:ob'Co.bb5 5:001 "014*""b.bl2 Pilchard "0".067 'XL P02;>070120.,0p4 p.0"24^P:041;^.P47 P^OO?, 0.039, -0.043 0.007 0.052 -0.037 -0.12-0.019 Mackerel Scad -0.328 0 "014*0.004 pp09 ^-b\6r-o.ooi" "0.021 0.018 0.298"-0.022 6.001 0.025 0.005 -0.045 0.038 Bass 0.005 -0 *002 ^02~OTOp3 0Tpp65070p3£0.p0"6 -0 005 0.002 -0 012 0.001 0.014 -0.002 -0 004; -0.036 0 0.002 0.001 0.002 0.002 0002 0.005 Sharks 6""" o • ^ 0 -0.001-b.ooro.ooi 0 002 0.001 0 0 o 0 0 0 0 0 0 0 0 0 0 Basking sk 0 o Cephalopods -0.034 0 .031 -0.02 0.042 0.044-0.039 -0.147 -0.079 -0.01 -0.087 0.093 •0.079 -0.133 -0.08 -0.29 0 0.001 -0.007 -0.002" 0.00"3f' 0I 0.001 0.002 0.002 -0.005I -0.023 -0.002 <• " T j o.ooi] | | | i § ! Seabirds o".007 0. 002 0.006*^0.002 -"b.002' 0.008-0.027-0.082. -oor 0.011 0 055 0.013 -0.058 0.011 0.023 Toothed Cet. "0.617"~67"004 -oroit^gj *O003| 0.003; 07005 -0.057" 0.043 0 006 0 033 0.114 0.002 -0.007 -0.003 Seals 0-0.001 0 0.001 0.001 Juv bass 0 0 0.002 •0.001 -o'.oot b-6 .001 0-0.001 Juv sole -0.002 004 -0.001 W0. o^ooi|[-b.ooi [^o7ooi' 0.002 -0.001' 0 001 0.002 0.001 -0.003 Juv plaice 0.005 .007 -0.006 -O002""Q.002;-b.0b9-'0.004 0.127 J 0 •0.008 _ 0.026 •0:009 -0.003 -0.003 0.004 0.006" 0.003 0 67002 p;p04f-p.p03f-O6pi] O_013 IIIV 1(1(1 0-0.003 0 012 •0.001 0.005 -0.006 0.003 0.014 0..013 -0.00Z 0.001 -brob2"-obi"9 -0.009 0.006""o".035t"6.b09 •0p37 0.038 0.018 -0.033 0.016 Juv whiting 0 0 0 0 0 -0.001 -0.005 0 0-0 002 0 -0.001 0 0 Discards 0.226 0.244 OJ94 0.296 0.2 0.094 0.001 0035 0^14' 0.127 6.072 0.053 0.294 -0.054-0:057 Detritus 06T"0.265^0 227 " -0.02j-0.714F67302r 0 248-0 064 -0 52 0 227 0.137 -0.415 -0.579 "0.094 f Otter trawl 124*¥j 91 ^0*031 -0J53 -0^03 "O016"0661^0.021"-0.014 0^02 0 055 0.013 -0.035,0.006 Beam trawl 0"04-0.p02,-b7pp"5 TP.Oi 3[J3-.01 2(J0JOJ3[U"p65p^:»f8lf0t0\2| 0?053 07628 I I S 0:07"9r-O'0"22 [ Midwjrjiwr 069 -0057 -0.001 -0.003 'om^O.O^" Dredge *6.'6o7""6.bi6"b."bb4 b.01'1"¥.029 0.006 0.006 -0.001 256 -0:089~0l055 "*07036r "070210.035" b-i^":0^i'p7[-qLp64 0 072 0 121 0 012 0.025i 0.001 I'NeF"' .016 0.027 0.015 0.008 0.018 0.006 op25 j o o n o.oip 6.01'3 0.018 _0.012 0.015*-0.001 Pot [63iWo¥4ra7p3T 0.058T0T004F0.042 M i l l 0-P57f-0~p83"' o . M f 5'. 145 -Pp04 -07008^67004 Recreational -0.001 0.001 "If'-o.ooi" 0.062' •b'ooT 6.002 -0.002 ""0**"0.005 0 -0.001 0-0.001 r  :  r  ;  r  r  r  r  -  ;  1  L  r  ;  k  r  r  r  r  :  ;  l  r  1  178  Table A3 continued: Impacted i w > . 1  Impacting*  £  Prim nrod  -0 086  L^PiMKlon S  0.1 : q  q  g  =  0.4  _ _ 53  »  «  0.261 -0.034 _  Q  Q  q  .  q  »i PQ f U  0.03 0.062 0.114 0.033 0.1 Q 5  - _  _  Q  Q  Q  Q 1 3  Q  S  H  If-35'  -  Q 4 9  P9  .-. J5  0  ~.  fttMJ.024 Q 2 6  '! C/J  Q-  .q  |,  -  ,,  0JM9  .0.008-0.013-0.009  i -oW "0.03'-0.128 " -0T02 W ^ m ^ M ^ ^ m ^ P W A ^ 6.088 ° °S u ^ S e " ^ *"0007 -0007 -0.03-0.017 0.049 0,0.009-0.005 -0.008 -0.006 -0.004 0.013 -0.009-0.005-0.002 t  1 4 5  5 0 5  s  wEF  O O O t W 0. 0'^:003-0.66i-0.006 . ' 0-0.005 0 ^ 0 J . 0 0 1 0.008 0.001 -0.004 ^ . "0* " 0 0.025 0.009-0 0411 0 . 0 0 2 f O . O O ' r o T ^ 2 ^ m [ o M ^ ~" 0008 -0 022 -0.032 -0.023 -0.023 " "" 0 :6"66l"-0.011 0.007 -0.015 0.01 -0 006 -0 044 -0 072 -0 014  BlShS  r»>" " -0.001 -o.ooi o 0-0.00-1. o:mr^n^mmrror7:x-aooa-0.005 0.017^.021, T^h ' lodi-ToOSS 0.027 0.033 -6.012" 0.226 -0.031 0.025 0.258 0.011 0.091 -0.042 -0.128,,^,2^0177 F « m f ? i r " ' 0 " 0.01 0.006 0.002 0.006 -0.022 "0.017 W P ^ ^ i e i - O Z I -0.05 0.003 0.015 loh^'r 0 0 0 0 0-0.001 0 0-0.001 0 0 0 0.001 0.001 0.001 r ^ g ^ . ^ , . Q . 0 8 9 - 0 012 0.027 0.001 -0.042. .0:018>5ftS2&OW^ S m G a d T ^ " 0038-0.007 0.003-0.003 0.009-0.6*97 6.07 0.003-0.034 0_ 0 0.086-0.101 0.043-0.064 m " o W 0 : 0 0 2 20:"OO9^^ 0.003 -0.018 -0-034- 0.006 ..iJMiei. , , _ - ^ - - _ _ - 0 J ) J 7 ^ 6 0 J 1 3 00^5 -0-002-0 005 0.003 0-O.OOT-O.OO^OTOOST^J^S^-OjOOJ;0.0191-0.028 0.001J-0.002 "60630663O.OO3 .0-0.002.-0.005~6.b04~* 0-0.002 0.001 ^.005 -0.005-Q.022^102- 0.003 •WoroWo.ooa 0.003-"o.oiiko7ooi£MS^ -pToW.0:094-0.013-0.002 0.003 Crnar.ls * 0 Ol" 'aOlTToOT^O^f^.bo^ ."-0.011 0.001 0-0.012 0.006-0 014-0.017-0.094 0.013-0.012 F^hithiP 0"007:0T74"" 0 -0.009 0 . 0 0 5 1 ) 7 5 ^ 0 1 2 ^ ^ oop8 o,oia bMUg -"^'ooTToori "-6*00^0 003 """"6:6^08 0.006-0.001*"-0.001 -0.005 0.042 0.093-0.001 -0.002-0.539 pBite"j 0.005.-0.018.-0.001, , 0 -Ol/ijl-OMir0*007* 0' "ToTpooOS' 0.017'-0.00"i -0."001 -OOff 0.005 Rays and dog 0.024"6.005-"6.062-6.647 0-028 -0.036-0.032 0-0^811 0.002 -0.0*26 -0.053 "-6.014i0.08t 6.009 flPbilaek . - ~ T n ? b T o ^ 07p.p02.r0-00"2r 0"05 0.'094~-0.013" 0.'003J-0.064 Lg. Bottom " 0.049* 0.045 "6.001 -0.017"0:d43Tb.032 0.036 _._ 0 _67009 07011 -6.019 6.187 6.03-0.009 0.069 IsTa^eanT""'"";; 0 015 0 005 07001 -0.001 -0.005; 0.008f-07051 "I).002'-0 ¥63^07017 0.015,"r"0b01 ,-0.034,, 07052,7-07009 John Dory ^_ 0-0039 0 0.001 0~ _0-0.001 _ __0_ _ 0 -0.004 -0.002 -0.001 -0.003 0.001 " " " 0 iSliaeels ' ./^r-pT018"l).p09 ,07019, "o:036"-0"'.003-';p.156[-O.Tl9-, 07017; 07032,707212, "07012 0.102 -0.003 -0.028f-0.017 Herring^ • -0.061 0.014 -0.024"-O04V-0.047,-0.018 0.054" : 0 0.024*" 6.005 "-0.067 0.021 -0.002-0.016-0.008 ESpraJ,\.;7777" J " ' . -0-03 -0-007 -0.022 -0.031'-0.005; 0.032"-000377"" 7pJp.l"05: p7037,i:o7002:-0.004 -0.002-0.008 'Pilchard ' 0.005 0.009 -0.01.6 -0.056 -0.046-0.604 0.028 ^o7o02 0.004 0.008 -6.014-0.002 0.001 -0.004 0.001 [7MackeTeT7 ^^~-"07l 8"ll):427"-0.068 -0.213 -07143'"-070777072197^701 "^OTo'gro.Oll~ -0722 0.003 -0.036 -0.021 -0.008 Scad •>'•"** 6.026" 0.02 -0.4-0.053-0 135 0.008 -0 679**0.004 -0 071 6 005 -Q112"6*003" 0.003" 0.077 -0.014* [ BassTZ-'. "•"777:0-001" 0.001 0.001 -0.002 -0.002i-07234r 0.?25^676gi70,p05;-O.OTl0"-0.009 -0.002 -0.004 0.003 Sharks " 0.003 0.001 -0.002 -0.003 "0.002 -0.003 -0.029"" 6 -0.01 * 0.001 -0.003 0.001 0 0.009 -0.001 Basking sk 0 0 0 0 0, 0 0. 0 0 0 0 0 0 0 0 Cephalopods -0 145 0.023 0.02 -0.061 -0.056-0.111 .0.548-0.015-0.331 -0.095 0.303-0.068 0.013-0.593^.088 [Se^iiiirTTTT 0.002-0.004 "0.001 0.002 0.001 Toothed Cet. 0009-6.008 -0.017-0.0097o.014K 6.01 t^Q38~67o0176.038^0.005 -0^023-0.014 0.005 0.034 0.05 [.SealsA .;. 7-0.006-0*002 " 0*0-001 -0.007iroT006^.007^rT'0^.00^ -07001 0.003 0.034 Juv bass " " 0" 0____ 0 0.001 0 *" 6 **" 0 0 0 0 b7 " " o"-0.002^0.001t-O.Ooi Tim sole?*.'7j -0 001 - "0" 0.001" " 0"-0".002l Ql 0.003r* *~0.'0.0.Q3j-'7 ~CF07001 -0.001 -0.002;-b.0Q-7|['0.001 Juv plaice 0.002 0.003 0.002 O004 0.001 7o_,003 0.003^ 0.002-0.001 0.002" b'.OoT 0.011 -6.0*18 -0.001 -6.092 | Juv cod- 7 Y-X •'/}>_ 0.004' 0.001 ~, • "76 "0.001^ " 7 0,76.ppT^07001777,01-0.003; 0:001 -67002~ ~6"6.002 0.007.C67612 Juv whiting 7Q726J"6.003 '6."o"65 0.013*^67617^-0.004 -0r01'8"0.001 -0.024 0.003 "6.009."b.'6o'4-0.013 0.04l7"q.bf5 | Discards' "TTTj o.opi-0ob6,-6oo'i-0003 6.poi-oooi| ^OEZTTJ"...Vt.CQ-lgC"" p'b7oof7^ "o qF7;Q Detritus -"O04*4"'0.6'31 -0".12*1 *-o7o*49 0242 0.103-0.039-0.001 0.107*1x001 6.05""o7063 0.138 0.286 ¥.03 |SQle^trawl^^ 0.032 O.i[38 0.062 -0.029 -q.q05"-qp49J7o7021 s.;0.004 -0.04lj{.-'0.044'/-0 06-0.119"^0.801" 0.03570.076 Beam trawl 676o37o76o3-0".66"2 0.004 0.004 0.009: -0b2 6-0.018 0.002-0.605"-0.035 0.015 0.012-0.026 ||Midw. trawl77^77 0.009 7 07158"-0.'026-074'27"-TD7355p7028f07Qp6 OL003; 0.tf67j,^07q277q.7447J.p07 0.024 -0 065'-0.028 Dredge *-0".663-0.003 0 0.002 * ' 6 6.6*05 7o766y_~0 -0.001 'o"o.0O2 -0.008" OOI -0.031 -6.033 tEetllTTi'-llllTr. "-"07012-0.003 0.003 0.008 0.019j-0.p85i'o".q867 .y'Q-Wp5J7070bl7"O 0.017"'-0 026' Q'115 Pot^ Z.'" 0-001 -0.004-0.003 0 0.001 0;"609"-0.011"""" 0"-O00476766ro.003"o.012'*"b.02" 0.008-0.008 |7Line7_',7Z73." -Oq05 0.005 0.008-0.018 0 -0.19[7b7f8707ppj"j"M4|77; "0jj)7012 -0.03-0.002-0.004-0.013 Recreational ""-6.002 -0.001 0.001 0.004 -6.001 -6.206 -o7773~ 0 0.009" 0.o62"l)".o63 0.001 0-0.008 6 !  &  r  t  o  q  o Q 4  q  ! q  Q q i  4  Q  Q Q 0 1  Q 0 Q 1  Q 0 Q 9  ;  L  r  :  !  i  0  0 0 3  ?  T  L  T  f  7  t  :  T  :  L  l  r  l  179  Table A3 continued: j, Impacted ^ (impacting  ^  .,, ^.., , Q  Q  Q  |  M  ^  s  ^ j j£ j ,j  Q  - g  2  Prim, prod " ~ ""b1)45 ~"*"" - "" 6.044'*6.003 "TT " 0.184 0.00T~0.014~0.081 0.031 .Zooplankton •., ~ 6.038 -0.157,;,-0.013 "0.043roTo02j 0.1 '"' - "O.OOlI ',~^ 0.1 j.036 Carn. Zp. __ "-0.009-0.049 0.002-0.003"" 0-0.031 0.021 0.001 0.003 -0.01& "6.051 Dep.feiders~~ 0.2 0.065-6.023j-0.247 0.061 0.i6l£q.*P45' -0.08 0.075 0.023; 076'18 -0.075 Sus. Feeders - -0.008 0.666 0.002 0 0.02"""'""'0 0.005 -0.001 -O.OoT-0005 -0.001 Shrimps". ?7' 0.1 0.144,(6:666-0.051 0.047 O.M^OT677 0.023 0.053 J3.028-0.002 0.039 Whelk - -0.003 0*"o.004 -0.01 0.062 "o 0.001 -0.033 6.264 0003 0.003 , Echinoderms „'.". - -0.004 -QpQI 0.002 -0.008, -0.02^0.662,-0 025 -0 014|-6 0Tl[ 0 0.002 Bivalves *~ -"""-676l 0.015-0.064 -0.009 "-0008 -0.019'""o".165 6.005 ' 6.655"""-0".bl 0 Scallops ;JO:007 O009~V .0>0'.0bT""O.Op5;-0.0"OlT-g.0O1 6.407 -6P09J-0.003 -0.007~0.p0i Crab ~ - -6.141 -0.011--6.009~ 0.022 0.029 0.013 -0.075 -0.028 -0.023 0.057"~ 0,22 Comm. Crab ^"O016 OTpQ.OpS.. "." "6," 0.046;;PP"l7pTOO"3"~Op01 0.17j'6.293'p~022>0.021 Lobster bbbi 0.001 0 "T ~ 0-0.002 0 "b O001 0.007 _ _0.-0.00t " Sm. d e n j . r 7 ' 0 . 0 3 9 0.198. '0.02; 0.112 0.008„-0.04"2~P.021 0.022 -0.012i70.0J8 j)Ji34" 0.019 Sm.Gads '" '-"7-0.123 ~ 0-0.002 0.077 0.061 0.008 ~6.012 0.02-0.006~0.034-0.093 pMullet'^-^:'^ 0.004;~6.0T4jgpOir 6P08 -0.0M[|0^pj3~P2^.pp3 Sole ' "6.011 0.0i2""-0T00l'"'6'.005 0.01 "6.079 O.661' -6.028"0.143 -0.004~0.6l4 -0.013 fPlaice" r0~00"4 001 -0 0,02 0 6.028 0~143[j).opi70."P12 O.OS^.pp^OTplS^O.OOTj "Dab "- ;-0."003~-6.00i: 0"002 0.02 O021 "" "6 "6.002" 0.036 -0.004 -"6.005 -6.005 f 6 " . T l a ~ ~ ~ ~ ~ ~ ~ r ~ J ~ " -0.p02.,-0.003i p~03~p.O13f0.134j~~~~j", 6.002 0.P~07f 0.PP5^ 0 -0 001' Gurnards • .. ™ "O-OOI-6.006-oToOt" 6.091 0.009 0.002-0.001 -0.022•-6.66T-b.b23 -0.009 i Whiting" - -0.055 6767p"~O001 0.042 -0.009{~O006"" 0 -0.009""6.6l~4,-07p16-0.026 Cod ^ ' - -0.005 o".obif_ •'_ 0 0.006 -0.004 -07602 0 6.07 -6.003-pTbob-0.007 . Hake . . 0.602-0.018" 6"C~"3_07001 ' " 6"o7001p67623 07601 0.013.-0.661 • 0.002 -0.001 Rays and dog _0.6~3 -0.19 -6.002"6b02 0.022-0.048 -0.P3~6.002 0.614-0.031 o7i 5~"-6'.036 I Pollack 7ip0l0iT-QI022U0.002:-07062. 0.015j7-0:0if . P;-0pQ2^P7052Fb70b3; 0.016-0.003 Lg. Bottom " "- -0.299-0.011;-0.0b"t-"6.025-0.022 0.009 0.002 0.04~ 0.002 0.22 -6.03 "Seabream, ~ ~ ~"-" 0.003" -676170.005 0.0~27|-6:oi3~"0.0i4767003-db^^ 0.007 ^John Dory ~~ 0 . 0 0.004 Tb.004 0.002-0.001 b-0.001 0 -0.0010 Sandee|s""r77~"_ , - -0.001 -"072T270.003"™0.01607op2.ro.016-0.014 0.002-0.00270022.0.149 Herring " ~ ^ -0.173-0.005-0.004 0.007 0.004 0.098 0.001 0.007 "^b"0.007-0.016 SpraF TZ'.'Z"'"'-0.008-0,ip5,;0.001 Q:002!-0.001j 07029 0.003 0.002r ,. 0~-P7607 -0.004 Pilchard" ~ "" 0 -0.006-0.008 0-0.002' " 0 6To77""OPJ)2 P.P04 0-0.0JI5-0.603 ' MackeFeTT, , 0~OliroT60"9"^ 0.03 0.602nr2"7"^:oT8~"7oTOlij J 0"7"0T2327 -P7P7 Scad """" - -6.015-0.005-0.006'"6."0"02'"-"0.015 0^17-6J~7 0~J005_ o'"-0.q29 0.006 !' Bass . ;@:QOliOi008ro:01lF^"7p7ir Sharks - ~ 6-0.001. 0-0.001 -0.001 -0.001 -O.66V 0 0-0.001 0.021 Basking sk 0 0 0 0 0 0 0 0 0 0 0 0 Cephalopods 0075 0.023 0.095-0^022 0^04 0.088^0.058 0.031 -0.021 0.025 -0.071 -0.095 Seabirds.:- 7- 7Z~~0 ' ~0-0~8"~T"T" 0-07001' 0,"6."057 0 0C.~ 6"-0065-0.004 Toothed Cet. 0^018 " 0~-o7o05" P.001 "-0.009-6.b03-0.009 -6.002 -0.009 -6.00V 0.001 0.009 , Seals 0.057 ~07041~0.p0l7~'~~0~ 6 -0.603r67002 ^ "0 -07015r~~ 0"^b703"0.605 juvbass^ - -o.ob_^ 0 o.ojoT_ _0 6 0 6 6__ 0~ 0 0 r j i i v sole - - "77""-" "-"0".001t •O'^7602~0.00iy"" Of" 0^0 6f6 0.004| • -'0 , o_ _o Juv plaice - -0.006-0.002 0.002 0.008" 0.006 "o.002~o!o01 0.009 -6.002^-0.o6"4-0.003 Fluv"codT7TTT~ 7! >-.^,'.i6.612 -O,OO.I,!TQ.oo2^o76oiXo.oo2[ 67^or^o.ooi• -"77- QI ... p;-o.ooi-o oot Juv whiting - -0.111 -0.003 -0.012 oToi'f 0.016^0026 -0.004 O.OOjl 0 0.011 ^0.005 ['Jbjsc"ards'.__^7 ~ "Q-—^^T ~ "" ~o"" ~ q;-0"jp2 " 0 "0f7~H~-0.00T-0.001 Detritus 0.2 'o.04Tbb6l"" 6 6"i26 0.164* 0]6l7 0.137 0.175 0.218"6.043 0.099 " Otter trawl ~7! .7- 0.118 "~34> 0.005-0.207 -0.095~J.p087-0.019 -0.178;-0.p45767l79"76.047 Beam trawl "0.OO8 0.016 -0002-0.001 -0.015-0.077 O.o6f-O002 -0.043"-6.018 -Pbi6~o'.ob9 i Mi'dwtrawi . """ - 0.00,3r7o.p277"" ' 6 -0.018J7^rti~^'O2g'l' ~ 0 0 0p8rp7002'-0.i33"-6.028 Dredge_ 0.013 0.011 0 0.6oi -o7b08 -0.025 6"-6"336 ~6".Q18'-O.OO'l -0.006" 0.004 . Net ,J~0"698 0.067,0.0017O001 -0 018;-6 037;? 0.00676.007-0.118"~7032::0.0577-0.081, Pot_ " - -o.oo9"o.ooi"5.6b3-o.oi8-6"64T 0" "0-6.069"-0.381" d7di"6"o6a » line •j^tf^^Jn0.047 0.072^ 0^J^P""" 0, 0 008J-P'008-6 001 -0 013}^0.0"^~8T -0.18 Recreational "•' " - -0.002-O.OO^""" 6 " o'o.OOt "6.001 0-0.0036"-0.02 -0.22 ;  y  :  ;  :  T  ^  •  r  :  j  lr  r  r  L  t  !  ;  r  7  >  T  m  7  Tr  ,  ?;  :  0  Q  y  T  r  :  B  180  Table A4: RAPFISH data used in the analysis and its origin. The scientist's scores were generally used when there were discrepancies between scores because the scientist had worked on the whole Channel fisheries, including the French, and had a holistic perspective.  "•> I!con: L\ploiiution status "2 "•.col: Recruitment vunubilil) l-col: ("haniie in 1 level  LeoI: Migratory range Hail: Range collapse Lcol: Si/e of fish caught Ecol: Catch < maturity Lcol: Discarded'hvculcli Ecol: Species caught  1  1  J\  Leon: ('DP' peison ilOOO's) Econ: Average wage Leon: Limited entry  1 """">" i  -1  0  0  (1 2 . 0  0  0  0, .0 1 1*4  1  0  2 2  0  0  0  1  1  2 - 1  1 3  J)  2  "->  i__ f) 0  5  0  1  0  1  2' - 1 *0.5 _() _1_ I) -2>r 2" 1  1 0.8  () n  n'  11  n  n n  2  3  3  2  2  4  .  ...  —.  ....  —  Scoies based puiek on scientists values. Scores ranged from 0 to 1.5 and I agreed with the scientist's values. Scores ranucd from 0 to 1.5 and I .selected the entered • 1 andi»Two scorers\allies. allocated the other 0.1 opted f o r i . Scores ^eie;ba.edon.lhed lscaiil datal entered into Lcopath (section 2.4). All scorers agreed for all gears.  2"~* -2  1 1.5 0.8  3  Scores based purely on scientist's values. Scores based purely on scientist's values. Two scorers allocated 0 and the other. I opted for 0.  1_  0 m  0  2  Scores ranged from 0 to 2 and I selected the entered values. opted, lor 0, Scores ranged from 1 to 3 and I selected the entered values. _ _ All scorers agreed lor all geais evcept  flHHflHMMH  Econ: Marketable right 2  2  2  1 0  2  1  .Leon: Oilier income Econ: Sector employment  1 I "con: Ownership-11 anslei •(>>. I *-2-v<- I Econ: Market 1 1 2 .Jkon:.S^>sLdi Soc: Socialization of fishing  2  Hi 0  1  J  0  0  y }'  WM 2  0  1  J• col:JTimarv productipn Econ: Profitability  .  1 ine  E.  Origin of score Pot  •~~> V  Dredge  Field/attribute  Bin trw  Score  1  11  11  11  i)  0  2  2  2  Scores were the same for the trawlers but different for the other gears so I selected these scores. 'All scoieis a'gteed lbi-alUgcdi.a.-, One SFC scored 1 for all gears while the 1 1 1 0 other scored 0.1 opted to use the scientist's values. _ MSI \ l l scorers agieed for otter trawling, nets, wis arid hnevbut for the otheiluears there -D^0 vas disagreement so I choose the scores. | All scorers agreed for otter and beam 0 0 0 trawling and for the other gears I opted to use the scientist's values. 11 11 0 A l l scorers agreed for all gears. No gear was the same from all scorers so 0 0 0 I selected the scores for each gear. 1  1  1  181  Table A4 continued: Score 7 1  Soc: Fisher influence Soc: Fishing income  "b  Bl  1.3 1.3 1 3 1.3  i  l  i  1  1  t  Origin of score  B  &p HB B B 2  i  1  9M  Soc: Km participation  I  1  2  i  1  1  2  2  §1  0  1  i  1 ech: '1 rip length  IIP 2 Tech: Landing sites  0  1  H 2  11181 0  1  jjj •  Tech I're-sale pioccssing Tech: On-board handling I ech: Gear Tech: Selective gear  j  All scorers agreed for beam and midwater trawling, pots and lines and for the other gcais I used the scientist's scores. 0 0 All scorers agreed for all gears. Iiach senior allocated the same score I'm 1.3 1 3 all gears but these were different for each scorer (1. 1.5 and 2). I used a value of 1.3. No gear was the same from all scorers so 1 1 I opted to use the scientist's scores. No gear was the same from all scorers so 1 opted to use the scientist's scores. Two scorers allocated 1 and the other 2.1 1 i opted for 1. No gear was the same from all scorers so 1 opted to use the scientist's scores. No gear was the same from all scorers so 1 i I opted to use the scientist's scores.. Mthoimh the vallies for each near weie \ cry similar tor each gear they were differently about;0:5 for all gears. I opte'd to use the scientist's scores. No gear was the same from all scorers so 0 0 I opted to use the scientist's scores. No gear had the same value from all ' r" scorers so I selected the entered values Scores were the same for potting and 0 0 lining. Values were the same from each scoiei. 0 Only dredging received the same value 1 i from each scorer so the scientist's scores were used for the other gears. 0.5 0.5 Values weie the same from each scorer Values were based on averaged data from 1 i [Tetarde/a/., 1995 #33]. 1 _' Values were the same from each scorer. Values from each scorer were similar but only beam trawling was identical. The 0 0 other scores were based on the scientist's values. P i t -Values were the same trom each scoici. 0  ()"  vr  ,> j  Line  Dredg  11B Wm  Soc: Lducation level Soc: Conflict status  Bm tr  Ott II Soc: Fishing sector Soc: F,nv ironmenlal knowledge  o  C  Soc: New entianls into  i'  Pot  Field/attribute  i  0  "f  i  (1  0  0  0  0  0  II  "  0  2  4  4  3  1  1  1  1  1  1  1  2  1  2  0  t  1  T  3  0  717J 0  lech: ( atchine power Tech: Gear side effects  Lthic: Adjacencv <& reliance  SB  ii B  1  0  kui 1 \ H , Tech: Vessel size  0  fet  in  3  2  182  Table A4 continued: Score Field/attribute  Origin of score DC  •a  •a v  —  2  1  2  2  2  2  2  2  2  Ethic: Alternatives 2  1  IPaEuuvin  enirs Ethic: Just management  1 1 1 : 1 1 1 1 I-.lhic: Mitigation of habitat i destruction 0 5'0  Ethic: Mitigation of ecosystem depletion I .line illegal lishine  0.5  1  0  0 M  2  2  0  2  2  1  1  0  1  1  Ethic: discards and wastes 1.5  2  1  2  1  One SFC scored 1 for all gears while the other scored 2.1 opted to use the scientist's values. \ allies were the same from each seoiei. One SFC scored 2 for all gears while the other scored 3.1 opted to use the scientist's values. One SI ( ' scored 4 for all geais. I he other scorers allocated \allies between 0 and 2 I here were identical Values for otrer tiaw ling, nets and pots and I entered the scoies lor the other gears. All scores were different so I selected the entries. One SI (" scored 0.5,for all gears while the other scoicd I. 1 opted to use the scientist's values Only dredging was scored the same by each person and I selected the scores for the other gears.  183  Ecological  Economic  primary production  subsidy  species caught  market  discarded bycatch  ownership/ transfer  catch < maturity  sector employment  •g size of fish caught JO  ~  range collapse  a  other incorre  ~j  marketable right  rrigratory range  limited entry  change in T level  average wage  recruitment variability  GDRperson (1000s)  exploitation status  profitability -F  o  Social  Technological  kin participation  gear side effects  fishing incorre  catching power Vessel Size  fisher influence  a) "3  1  FADS conflict status  ±j  JO  selective gear  JO  education level  3f  = C  environ, knowledge  gear on-board handling  fishing sector  pre-sale processing  new entrants to fishery  landing sites  socialisation of fishing  trip length  0  0 Ethical  1  Figure A l : The leveraging analysis for each RAPFISH field. The horizontal axis shows the root mean square change in ordination when the selected attribute was removed (on the sustainability scale of 0 to 100).  1  discards & wastes illegal fishing rrft. of ecos. dep. rrit. of habitat des. influences in ethics just rranagerrEnt  •g JQ  V <  equity in entry alternatives adjacency & reliance  mm  184  Figure A2: Model pedigree. The different colours refer to confidence limits (+/- %) as follows: |=10 2=20 |=30 5=40 |=50 6=60 g=70 |=80 Hence blue (6,7,8) indicate data that are less trustworthy. Blank rectangles refer to places where there were no data.  185  


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