Ruditapes Decussatus) Exploitation in the Plentzia Estuary (Basque Country, Northern Spain)

Modelling the management of clam (Ruditapes decussatus) exploitation in the Plentzia estuary (Basque Country, Northern Spain) Juan Bald & Angel Borja AZTI Foundation, Department of Oceanography and Marine Environment Herrera Kaia Portualdea z/g, 20110 PASAIA (Gipuzkoa) SPAIN Tel: +34 943 00 48 00 Fax: +34 943 00 48 01 www.azti.es [email protected] Abstract Some of the estuaries of the Basque Country (Northern Spain) have been areas of exploitation of clam populations, both by professional and illegal fishermen. There is a real possibility of overfishing and, therefore, the Department of Fisheries of the Basque Government needs to: (a) understand the situation relating to these populations; and (b) provide a tool to establish the most adequate management for the exploitation of clams. In order to simulate different alternatives for the exploitation, based upon scientific data on the population, the best tool available is a system dynamics model. Here the VENSIM® model was employed, utilising data on clam populations: summer stock and biomass in the Plentzia estuary, in 1998, 2000; winter stock in 1999, 2000, and 2001; the area occupied by the species; the length and weight class distribution; the number of fishermen; mean of biomass, captured by the fishermen; natural mortality, by length class; maturation; fertility rate; etc. This study improves previous experiences in modelling clam exploitation in the Plentzia and Mundaka estuaries. Following validation of the model, after running it for 1 and 10 years, some cases were simulated. This analysis was undertaken in order to establish the effect of modifying the number of fishermen, the aperture-close season of captures, the minimum sustainable biomass, the exploitation area and the minimum legal length for shellfishing. This approach tries to establish the sustainable exploitation of the clam populations in the Basque Country. Key words: modelling, shellfishing, clams, management model, marine resources, Vensim Software, Basque Country, Spain. Introduction Some of the estuaries of the Basque Country (Northern Spain), such as Plentzia and Mundaka (Figure 1), have exploited clam populations (Ruditapes decussatus), both by professional and illegal fishermen. There exists a very real possibility of overfishing of this scarce resource. Therefore, the Department of Fisheries of the Basque Government needs to know the real situation regarding these populations, together with a tool to establish the most adequate management for the exploitation of clams. FRANCE Bay of Biscay SPAIN CANTABRIAN SEA PLENTZIA MUNDAKA 43º 20’N N 0 10 20 SAN SEBASTIÁN Scale (km) 3º W BILBAO 2º W Figure 1. Location of the Basque Country in the Northern Spain, showing the Plentzia and Mundaka estuaries. In order to simulate different alternatives for the exploitation, based upon scientific data on the population, the best tool available is a model with a clear purpose (Sterman, 1988). From the system dynamics perspective, a model is developed to address a specific set of questions and its purpose helps guide its formulation (Richardson & Pugh, 1981). In this particular case, the purpose of the model is to answer such questions as, for example: how many fishermen could support this exploitation?; what happens if number of fishermen increase strongly?; is it possible to modify the capture season, or the minimum legal length of capture?; what happens after and during the establishment of a closed-season?; can we modify the establishment of it?; and what are the implications in maintaining a minimum sustainable biomass?. A model is simply an ordered set of assumptions about a complex system (Meadows et al., 1972). It is an attempt to understand some aspects of the infinitely varied world, by selecting from perceptions and past experience a set of general observations applicable to the problem. This paper tries to solve this particular problem, by means of a system dynamics model sensu (Forrester, 1973); this approach allows for a “whole system” analysis. System dynamics can be employed for non-linear and dynamically complex problems, involving disequilibrium conditions, bottlenecks, delays, stocks and flow relationships, and realistic decision-making (Sterman, 1987). The VENSIM® software has been used previously in urban dynamics, demography, sustainable development, Alfred & Graham., 1976; Barney et al., 1995) and clam exploitation (Borja & Bald, 2000a, 2000b) and Bald y Borja (2001). Methodology The modelling of the exploitation of the clam in the Plentzia estuary is based upon the methodology described in Borja & Bald (2000a, 2000b). The model was constructed graphically; first variables and levels were defined, afterwards the relationships between them. Six levels, corresponding to six length and weight class distribution of clams, were established. These classes were defined from biological data of the species (Perez- Camacho, 1979) (Figure 2). Hence, the class (0-20 mm) includes immature clams and the other five classes are mature individuals. Taking into account that minimum legal length for fishing clams in Spain is 40 mm, these five classes are divided further into two non-exploited mature classes (21-30 mm and 31-40 mm) and three exploited matures classes (41-50 mm, 51-60 mm and >60 mm). Figure 2. Stock and length class structure of the model. The weight in kilograms of each level, which contributes to the final stock, is the result of the incorporation of new individuals from a previous class, less those that mature and pass to the next class and those who are dead. The death of clams is due both to natural and fishing mortality. The fishing mortality is equal to zero, in classes under legal length for fishing (Table 1); however it could be modified in order to simulate illegal captures. On the other hand, the “births” in clam population are the result of the sum of births on each class bias a fertility rate (Table 1). The contribution of each size class to the total weight of births was calculated taking in count the weight of each individual in each size class according to Borja and Bald (2000) and Bald and Borja (2001) (Figure 3). Table 1. Clam population data inputs to the model, obtained from field experimentation and bibliography. LENGTH CLASSES (mm) VARIABLES 0-20 21-30 31-40 41-50 51-60 >60 Initial Population (kg)(1) 1504 502,3 333,95 333,95 41,4 0 Natural Mortality Rate(2) 0,060 0,044 0,031 0,052 0,071 0,070 Fishing Mortality Rate(1) 0 0 0 0,65 0,30 0,05 Fertility Rate(2) 0 0,90 0,85 0,80 0,75 0,7 (1) Bald y Borja, 2001. (2) Pérez-Camacho, 1979. Figure 3. Births structure of the model. The clam population biomass is calculated on the basis of dividing the total stock, by the shellfishing area (Figure 4). A minimum sustainable biomass is established, in order to calculate the overfishing. Therefore, if real biomass is below established minimum sustainable biomass, there is a deficit that corresponds to an overfishing effort. According to relationships established in the corresponding tables (tables 3 and 3bis), this biomass deficit correspond to a determinate number of fishermen. These Tables of double entry relate biomass with shellfishers number in such a way that increasing the availability of the resource (more biomass) supposes an increasing number of shellfishers. Then, the adequate number of shellfishers is equal to the number of shellfishers without minimum biomass control less the remaining shellfishers according to objectives established by sustainable minimum biomass. The total captures are the result of shellfishers capture sums. Shellfisher’s captures depend upon three factors: biomass of clam population; aperture and close season; and shellfishers capture day rate (Figure 5). The clam biomass modifies captures by mean of different Tables function (Table 1 in Figure 5). These Tables of double entry relate biomass with shellfishers captures, in such a way that increasing the availability of the resource (more biomass) supposes an increasing number of captures. Figure 4. Biomass and number of shellfishers structure in the model. Figure 5. Captures structure in the model. Furthermore, some improvements described in Bald and Borja (2001) have been incorporated to the model, including: · new capabilities of modelling and analysis; and · an easily used approach based upon different “windows”, with each one, being capable acceding to a different part of the model. In order to undertake these objectives, we have used a new version of VENSIM® (version 4.1). Relating to the new capabilities of modelling and analysis, the main improvements, as outlined below, are: a) The capacity to establish a close season every 2 or 5 years, or the establishment of a closed season only one selected year. This is made by means of two variables (“Periodic close season” and “Within a year close season” variables) that equal to cero, multiplies the adequate number of shellfishers the year or the period selected (every 2 or 5 years) (Figure 6). b) Beginning within a particular year, the capacity to modify the capture season annually throughout the time of the modelling. c) Beginning within a particular year, the capacity to increase or decrease the number of fishermen annually throughout the time of the modelling, or in only a selected year. This is made by means of two variables that multiplies the adequate number of fishermen increasing or decreasing in a determinate percentage the number of fishermen. (Figure 7). d) Beginning in a particular year, the capacity to modify the fishing effort throughout the

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