Case study 8: Hake fisheries in the Mediterranean

The main objective of the “Hake in the Mediterranean” case study under the framework of the EFIMAs project is utilising the FLR packages to implement and evaluate different management scenarios. The Mediterranean Hake Fishery is managed through technical measures and control effort regulations thus scenarios to be evaluated included fishing effort and specific technical measures restrictions.

Hake is one of the main target species of several multi-species Mediterranean fish-eries and especially of the bottom trawlers exploiting the continental slope. Different types of nets and set-longlines are also exploiting hake stocks. Genetic studies using allozymes indicate that there are no clear stock delimitation in the western Mediterranean. Application of the same methods showed that a clear genetic difference exists between the Atlantic and Mediterranean hake whereby the Strait of Gibraltar is the geographic barrier between them. These findings confirm the hypothesis based on morphometric studies that the Mediterranean hake is a subspecies distinct from that in the Atlantic. For management purposes the General Fisheries Commission for the Mediterranean (GFCM) has divided Mediterranean in 28 management units and hake is exploited virtually in all units (Fig. 1).

Figure 1. GFCM Geographic subareas (Athens 2001).

On the basis of the available data most of the hake stocks in the Mediterranean are either fully exploited or overexploited. In most of the cases a decreasing trend in individual lengths of the hake caught and in the catches per unit of effort of the trawlers can be observed. In general, juveniles undergo a very high fishing pressure. This results essentially from the fact that the sizes at first catch are very often almost similar to those at which fishes appear in the fisheries (recruitment). Artisanal fleets affect more the adult population, even though there is some degree of overlap. Fisheries management in the Mediterranean, in generally, is at a relatively early stage of development, judging by the criteria of North Atlantic fisheries. In the case of the demersal fisheries, their multi-species and multi-gear nature poses difficulties in applying specific management measures for a given species. Things are worsen by the lack of sufficient data to provide concrete quantitative conclusions on the state of the stock. Consequently, none explicit management objective exist for any of the management units and all management actions aim towards universal objectives, such as sustainability. As it happens with all demersal stocks, management is exclusively based on technical measures and control effort regimes. Within the EU countries, there is a series of general international and national regulations aiming to the conservation of not only hake, but all demersal stocks. International regulations concern only the EU waters and they include:

• a) a minimum landing size of 20cm,
• b) prohibition of bottom trawling within three miles of the coast or in depths less than 50m (whatever comes first),
• c) a 40mm minimum mesh-size of bottom trawls cod-end and
• d) control capacity regimes, i.e. limitation on national fleets’ horse-power and gross tonnage.

At the national level, the fisheries legislation of the different Mediterranean countries participated in the EFIMAS project, contains a great variety of technical and control effort management measures aiming to the conservation of all demersal stocks; thus covering also hake. They include temporal closures of certain fisheries in order to protect sensitive biological processes, such as spawning and re-cruitment, establishment of marine protected areas, and effort restrictions. The present study originally aimed in analysing five Mediterranean areas. Data from Gulf of Lions, Ligurian Sea and Aegean Sea were successfully acquired. Data from the Alboran Sea and the South Tyrhennian Sea were not possible to be collated in spite of the repeated efforts made by the partners. Characteristics of the hake fisheries in the Gulf of Lions and the Aegean Sea are summarized below. In the Gulf of Lions, hake is the most important, in economic terms, target species in the shared multi-species fishery of for the Spanish and French fleets. Hake is ex-ploited in a more diversified way than in other Mediterranean areas, as four fleets are targeting this species with different gears, two demersal trawler fleets (Spanish and French), a Spanish long-line fleet and a French gill-net fleet (Oliver & Massutí, 1995; Cheret et al., 2002). Because of these different types of exploitation, the whole population becomes accessible, from individuals of very small size to the large females that cannot be caught easily in areas where only trawl fishing exists. This pattern of exploitation can easily turn out in an overexploitation situation (Aldebert et al., 1993). In the Aegean Sea, hake is one of the main target species of the demersal trawl fishery and around to 300 vessels are currently involved to this fishery having an annual hake production of about 3000 t. As very minor catches are obtained from other gears, such as nets and longlines, the present study will focus on trawlers. Demersal trawlers operate from October 1 to May 31 exploiting the continental shelf and the upper part of the continental slope (from 30 to 400m).

Description of the base case

For the base case, we consider that a medium-term projection of the current situation should be done. The biological operating model was based: a) for the stocks where regular assessments have been applied within the Scientific Advisory Committee (SAC) of the GFCM, as for example in the Gulf of Lions since 2000 (“French-Spanish Working Group”, Cheret et al., 2002), the results can be adopted, without excluding several previous existing assessments, conducted outside the GFCM framework (Aldebert et al., 1993; Aldebert and Recasens, 1996, etc.) and b) in the absence of previous assessment results for the Aegean hake stock, a preliminary stock assessment was necessary for EFIMAS project.

The management measures and corresponding strategies will be evaluated in the light of potential future increase of hake biomass by using either effort reduction schemes or more specific technical measures (e.g. selection pattern). For the current study the various base case evaluations have been named as “BC”, while the scenario managements have been named according to the following approaches:

• Approach A: Evaluation of effort reduction schemes
• Approach B: Evaluation of selection restrictions (reflecting possible application of closing areas and/or seasons)
• Approach C: Evaluation of capacity restrictions
• Approach D: Evaluation of combined rules (e.g. effort reduction along with selection restrictions)

Case study 8, Base case BC1: Evaluation of the current exploitation pattern to different fleet segments assuming stock independent recruitment. (Aegean Sea: bio-economic)

Case study 8, Base case BC2: Evaluation of the current exploitation pattern to different fleets of different coutries harvesting the same stock by assumming recruitment independent of stock size. (Gulf of Lions: biological)

Case study 8, Approach A1 : Implication of a reduction in fishing Effort to different fleet segments (Aegean Sea: bio-economic scenarios A1.1 & A1.2 with 10% & 20% Effort reduction).

Case study 8, Approach A2 : Implication of a reduction in fishing Effort to different fleets (Gulf of Lions: biological scenarios A2.1 & A2.2 with 10% and 20% Effort reduction).

Case study 8, Approach B1: Implication of selection restrictions (Aegean Sea: bio-economic scenarios B1.1 to B1.4 with changes of selection pattern in ages 0-1).

Case study 8, Approach B2: Implication of engine power reduction measures to the fleet operating in Northern Alboran Sea. Bio-economic scenarios assuming engine power compliance with the law.

Case study 8, Approach A3: Implication of an effort reduction measure to the fleet operating in Northern Alboran Sea. Bio-economic scenarios assuming 10 and 20% reduction in effort

Case study 8, Approach D1: Implication of Combined effort and selection restrictions (Aegean Sea: bio-economic scenarios D1.1 to D1.4).

Case study 8, Approach D2: Implication of effort and engine power reduction measures to the fleet operating in Northern Alboran Sea. Bio-economic scenarios with combinations of effort reduction and engine power compliance with the law.

Case study 8, Ligurian Sea Hake Fishery. Approaches A, C and D.

• All different scenarios evaluated resulted to an increase in landings (and revenues) compared to the base case scenario.
• In all scenarios landings initially decreased in relation to the base case due to the reduction of either fishing effort or selection pattern, resulting from the applied management measures. Later, however, as the stock responded to the effort/selection reduction an overall increase was observed.

Table 1. Scenarios used for the hake trawl fishery in the Aegean Sea showing the reduction percentages in either effort or selection pattern.

Figure 2. Mean rate of change of hake landings relative to first year (2004) for the total trawl fleet (all segments) in the Aegean Sea by scenario approaches.(For the names of scenarios see above Table 1).

The present projections revealed that with regard to the:

Effort control

• the 10% effort reduction scheme applied on the two fleet segments in the Aegean eventually stabilised the catches (and thus landings and revenues) at 6% higher level in terms of weight.
• the 20% effort reduction scheme was even more beneficial for the hake fishery as it resulted in an approx 13.5 % higher values of catches (in weight) in the Aegean Sea.

Selection control

• The B1.1. scenario, i.e. 50% selection restriction at age 0 resulted in a 10% higher level in terms of landing weight after a period of 8 years and remained in that plateau thereafter.

• The B1.2. scenario, i.e. 50% selection restriction at age 0 and 50% selection restriction at age 1 resulted in a 68% higher landings after a period of 10 years and remained in that plateau thereafter.

• The B1.3. scenario, i.e. 100% selection restriction at age 0 and 50% selection restriction at age 1 resulted in a 68% higher level in terms of landing weight after a period of 10 years and remained in that plateau thereafter.

• The B1.4. scenario, i.e. 100% selection restriction at age 0 and age 1 resulted in a 68% higher level in terms of landing weight after a period of 7 years and remained in that plateau thereafter.

Combined rules

• The D1.1. scenario, i.e. 50% selection restriction at age 0 and a simultaneous 10% effort reduction scheme resulted in a 18% higher level in terms of landing weight after a period of 10 years and stabilised thereafter.

• The D1.2. scenario, i.e. 100% selection restriction at age 0 and a simultaneous 10% effort reduction scheme resulted in an earlier 18% increase in landing weights after a period of 7 years and stabilised thereafter.

• The D1.3. scenario, i.e. 100% selection restriction at age 0 alongside a 50% selection at age 1 and a simultaneous 10% effort reduction scheme resulted in a substantial increase in landing weights that level up to 70% higher than the initial landings after a period of 10 years.

• The D1.4. scenario, i.e. 100% selection restriction at age 0 and a simultaneous 20% effort reduction scheme resulted in a 23% increase in landing weights after a period of 7 years and stabilised thereafter.

Figure 3. Mean rate of change of hake net revenues relative to first year (2004) for the total trawl fleet and the two fleet segments in the Aegean Sea by scenario approaches.(For the names of scenarios see Table 1).

The present projections revealed that:

Effort control

• the 10% effort reduction scheme applied on the two fleet segments in the Aegean eventually stabilise the revenues at 20% higher level in terms of net revenues after 6 years.
• the 20% effort reduction scheme was even more beneficial for the hake fishery as it resulted in an approx 50 % higher revenues in the Aegean Sea.

In both effort reduction scenarios the 24-40 m fleet was benefitting the most compared to the 12-24 m fleet segment.

Selection control

• The B1.1. scenario, i.e. 50% selection restriction at age 0 resulted in a 10% higher level in terms of net revenues after a period of 8 years and remained in that level thereafter.

• The B1.2. scenario, i.e. 50% selection restriction at age 0 and 50% selection restriction at age 1 resulted in a substantial rapid increase after year 3 and level at 200% higher net revenues after a period of 10 years and remained in that plateau thereafter.

• The B1.3. scenario, i.e. 100% selection restriction at age 0 and 50% selection restriction at age 1 resulted in a substantial rapid increase after year 3 and level at 200% higher net revenues after a period of 10 years and remained in that plateau thereafter.

• The B1.4. scenario, i.e. 100% selection restriction at age 0 and age 1 resulted in an even more abrupt increase in net revenues reaching a 200% higher level in just 6 years.

The effects of all the above selection control scenaria were more evident in the 24-40 m fleet segment.

Combined rules

• The D1.1. scenario, i.e. 50% selection restriction at age 0 and a simultaneous 10% effort reduction scheme resulted in a 50% higher level in terms of net revenues after a period of 7 years and stabilised thereafter.

• The D1.2. scenario, i.e. 100% selection restriction at age 0 and a simultaneous 10% effort reduction scheme resulted in a 50% higher level in terms of net revenues after a period of 7 years and stabilised thereafter.

• The D1.3. scenario, i.e. 100% selection restriction at age 0 alongside a 50% selection at age 1 and a simultaneous 10% effort reduction scheme resulted in a substantial rapid increase after year 3 and level at 200% higher net revenues after a period of 9 years and remained in that plateau thereafter.

• The D1.4. scenario, i.e. 100% selection restriction at age 0 and a simultaneous 20% effort reduction scheme resulted in a 55% higher level in terms of net revenues after a period of 7 years and stabilised thereafter.

The effects of all of the above combined effort+selection control scenaria were more evident in the 24-40 m fleet segment.

In this case all the scenarios considered (effort reduction by 10%, effort reduction by 20% and engine power compliance) resulted in a steady decrease of the ship-owner profits or fishermen salaries at least for a period of 15 years which would be less sharp than when considering constant effort. Only in the case of the combined reduction in effort (by 20%) and reduction in engine power (or simply engine power compliance), the fishermen's salary and the ship-owner benefits after a sharp decline will steadily keep increasing.

The following paper has been produced from CS work carried out so far. This paper will also be presented in the 38th COMMISSION INTERNATIONALE POUR L’EXPLORATION CIENTIFIQUE DE LA MER MÉDITERRANÉE (CIESM) Congress to be held in Istanbul Turkey in April 9-13th :

J. Haralabous, CD Maravelias, G. Tserpes & C. Papaconstantinou. 2007. Developing a FLR operational model for evaluation of fisheries management strategies: an application to Mediterranean hake fishery. 38th CIESM Congress, 9-13 April 2007, Istanbul, Turkey.

• Delivery Matrix by April 2006 (Note: See text from Meeting Minutes WP4 from EFIMAS Maastricht Meetings, September 2006).Case Study 8 Delivery Matrix

This work has been performed under the EFIMAS Project.

Coordinator: Costas Papaconstantinou

Participants: Costas Papaconstantinou, John Haralabous, Christos Maravelias, George Tserpes

A potential economic model to simulate fleet dynamics

The proposed long term fleet based behavioural model to simulate the entry-exit from the fishery depends on three related decisions: a) investment; b) decommissioning; c) selling in the second hand market. These decisions can be simulated at each time t during the simulation period and each of them determines the change in the operating number of vessels. Decisions are based on the average value of vessels. Four average values are considered: 1) The profit value estimated by the sum of actualized profits expected along the lifespan of a vessel (V_Π); 2) The value of a decommissioned vessel which, may be estimated as the decommissioning rate (€/GT) times the average GT per vessel (V_DG); 3) The market value of an existing vessel in the second hand market, estimable as the average price per GT paid by the market times the average GT per vessel (V_SH); 4) the average investment value of a new vessel estimated as the average price of a new GT times the average GT per vessel (V^IN).

a) Investment: Investment (I⁺) is simulated only if the profit value is positive, VΠ>0. Thus,(I⁺) is an increasing function of the profit value. The number of new vessels can approximated by equation (1).

ΔN´_t+1= I_t⁺ Π_t/V_t^IN (1)

b) Decommissioning: Since obviously the decommissioning value cannot be negative, the maximum number of vessels to be decommissioned (ΔN_DG) is calculated as the ratio of the decommissioning grant (DG_t) for the fleet and the decommissioning value (V_DG,t) per vessel. The number of vessels abandoning the fleet (ΔN´´_t+1) (2) is estimated as the difference between the profit value (V_DG,t) and the decommissioning value.

ΔN´´_t+1= β-γ(V_Π,t - V_DG,t) (2)

c) Selling: The number of vessels potentially sold on the market (ΔN´´´_t+1)(3) is a function of the difference between the profit value (V_Π) and the market value (V_SH). The higher this difference, the lower the number of vessels sold on the market.

ΔN´´´_t+1= β'-γ'(V_Π,t - V_SH,t) (3)

Based on a), b) and c) the simulated number of vessels at time t+1 is defined by equation 4.

ΔN_t+1= ΔN´_t+1 - ΔN´´_t+1 - ΔN´´´_t+1 (4)

The link between fleet and effort dynamics

In many fisheries the fishing effort represents the main management control variable. Thus fishing effort, estimated in terms of days at sea for each fleet, allows measuring the impact of the simulated variation in the number of vessels on the biological and economic indicators. It is defined by equation (5), where ds_t is days at sea and ds_t N_t is the number of vessels at time t.

E_t = ds_t/N_t (5)

R code: R-code

— Christos Maravelias 2007/02/7 14:54

— John Haralabous 2007/02/7 14:54