1. CS 5.1 Barcelona small-scale fishery CSIC F. Maynou

2. Barcelona SSF MV analysis to describe métiers identification of target species (17 sp. make up 85% in econ. value) assess the state of exploitation of these species: –the Port Authority claim that fishery resources are overexploited –the Fishers’ Association claim that harbour development destroyed fishery resources Depending on the results of our assessment, fishers will bring the case to Court and sue the Port Authority

3. Tools Data available: daily landings and prices of the entire fleet for the period 1992-2005 1992-2001 as base line data set Harbour development took place more intensely in 2002-2003 – diminishing catch rates, lower fishery production in general. No previous assessment of the state of the resources Given the nature of the data, we approach the CS using a surplus production modelling framework (bio. and, later on, bio-economic) Complication: given the time span of the data series (6-10 years), very difficult to estimate biological parameters of a surplus production model using max. likelihood (CEDA). Used the Bayesian framework (implementation of Meyer and Millar 1999 model in WinBUGS)

4. Identification of métiers and target species (D3as/D3bs) Following the multivariate classification from D3as (Case study 5.1), we defined the following 7 groups of vessels (métiers): We selected the 2 most representative métiers (#2 dredges and #3 trammelnet group # nb of boats main speciesfishing gear 19swordfish, bonito, hake, Triglidae, Scorpenidae, Sparidae surface longline, palangró 28bivalves, snails (L. littorea and B. brandaris); tuna, bonito and melva dredges (gàbies and rastell), aros; some boats use surface longline 346almost all species are taken, this is the classical trammelnet fishery. Bonito, bullet tuna, Mugilidae, cuttlefish and octopus (2 species) are important trammel net; some may use longline, dredges, jonquiller and traps (cadups) 45similar to (3) but lower cpue; Mugilidae are dominant trammel net; some may use longline, dredges, jonquiller and traps (cadups) 55similar to (3) and (4) but low cpue; Sparidae are important and bivalves are absent trammel net 63hake, pandora, swordfish and bonito; low species diversity surface longline, palangró 73low cpue, mainly Sparidae, low species diversity. Bivalves absent trammel net

5. Identification of métiers and target species (D3as/D3bs) The final table of species / métiers selected for implementing the Bayesian model are : Local nameEnglish nameDredges (métier 2)Trammelnet (métier 3) Acanthocardia tuberculataEscopinya verrucosaTuberculate cocklex Donax trunculusTellerinaTruncate donaxx Chamelea gallinaRossellonaStriped venusx Callista chionePetxina lluentSmooth callistax Dicentrarchus labraxLlobarroSeabassx Mullus surmuletusMollsStripes red mulletx Solea vulgarisLlenguadoSolex Scophthalmus rhombusRèmolBrillx Octopus vulgarisPopOctopusx Sepia officinalisSípiaCuttlefishx Palinurus elephasLlagostaSpiny lobsterx

6. catches of the main species of bivalves by métier 2 over the period 1992-2005. The broken line spans the period of impact (2002-2003). catch per unit effort (CPUE) of the main species of bivalves by métier 2 over the period 1992- 2005. The broken line spans the period of impact (2002- 2003)

7. catches of the main species caught by métier 3 over the period 1992-2005. The broken line spans the period of impact (2002-2003) catch per unit effort (CPUE) of the main species caught by métier 3 over the period 1992-2005. The broken line spans the period of impact (2002- 2003).

8. Socio-economic data The results of questionnaires circulated among fisheries managers (General Directorate of Fisheries, DARP, of the Autonomous Government of Catalonia) and representatives of the Fishermen’s Association’s showed that the biggest concern for stakeholders in the fishery is to maintain the livelihoods of fishermen. Both the managers and the fishermen showed little concern about the conservation state of the resources. The management policy adopted for this, and other small scale fisheries in Catalonia, is to control fishing effort by limiting the number of licenses and fishing gear permitted. This policy serves to ensure the long-term sustainability of the fishery as long as there are no external threats, such as the enlargement of the commercial harbour facilities. Cost structure of métiers 2 (bivalve dredges) and 3 (trammel nets) obtained through questionnaires. The values shown are average values for an individual boat in 2006. [1][1] In the fleets studied, the vessel owner is the skipper of the vessel and also a worker, counted as crew. Bivalve dredgesTrammelnets Trading costs as % of revenues 30.00 Share to the vessel owner (%)[1][1] 50.00 Crew (including vessel skipper) 1.002.00 Daily costs of ice (€/day) 6.005.00 Other daily costs (food, net mending) (€/day) 17.0014.00 Capital (sale value) of the vessel (€) 96,619.3696,161.93 Daily fuel consumption (€/day) 15.7219.83 Fuel price in 2006 (€/l) 0.26 Fixed annual costs (mooring, fishing license) (€/year) 400.00 Other annual costs (major repairs, shipyard) (€/year) 1,503.001,442.00

9. Establishing priors Following Meyer and Millar (1999): –Model reparameterisation: –Lognormal priors for K and r –Hyperprior  for P stochastic (process error mean around 1: 0.86-1.15; gamma distr.) –Hyperprior  for I stochastic (sampling error ~misreporting around 12% (8-18%); gamma distr.) –Inverse gamma for q (non-informative) Id t = log( qKP t ) Main difficulty: establishing priors for K. This resulted in “bad” parameter estimates or lack of convergence in about 50% of cases

10. Progress in parameter estimation Métier 2 (dredges): biological parameters successfully estimated for 3 of 4 species Métier 3 (trammel net): biological parameters successfully estimated for 4 of 7 species Need to refine priors for K (or put informative prior to q; they may be related)

11. after having estimated biol. parameters of a surplus model... forecast catch of target species for 2002- 2003 compare forecast with actual production Estimate losses in weight and money (simple bioeconomic model)