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 E&S Home > Vol. 16, No. 3 > Art. 26

Copyright © 2011 by the author(s). Published here under license by The Resilience Alliance.
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The following is the established format for referencing this article:
Melaku Canu, D., P. Campostrini, S. Dalla Riva, R. Pastres, L. Pizzo, L. Rossetto, and C. Solidoro. 2011. Addressing sustainability of clam farming in the Venice lagoon. Ecology and Society 16(3): 26.
http://dx.doi.org/10.5751/ES-04263-160326


Research, part of Special Feature on A Systems Approach for Sustainable Development in Coastal Zones

Addressing Sustainability of Clam Farming in the Venice Lagoon

Donata Melaku Canu 1, Pierpaolo Campostrini 2, Simona Dalla Riva 2, Roberto Pastres 3, Lara Pizzo 4, Luca Rossetto 4 and Cosimo Solidoro 1


1National Institute of Oceanography and Experimental Geophysics OGS, 2CORILA Consorzio per la Gestione del Centro di Coordinamento delle AttivitÓ di Ricerca inerenti il Sistema Lagunare di Venezia, 3Ca' Foscari University of Venice, 4University of Padova TeSAF Dept



ABSTRACT


The clam fishing and aquaculture system in the Venice Lagoon still appears insufficiently resilient to buffer external and internal perturbations, such as productivity fluctuations, unregulated fishing, and market related dynamics, despite the efforts of regional and local authorities to achieve the sustainable development. According to the System Approach Framework (SAF), based on previous studies and stakeholder interactions, we developed a model integrating ecological, social, and economic (ESE) aspects. We chose the aspects necessary to represent the essential dynamics of major ecological, social, and economic clam farming system components to project the consequences of implementing alternative management policies and to address the ecological and social carrying capacity. Results of the simulations suggest that a properly managed farming system can sustain an acceptable income and support the local community, while reducing negative environmental impacts, social conflicts, and consumer health risks and improving system resilience. The results highlight the importance of an interdisciplinary, participatory, and adaptive approach in planning the management of this important renewable resource.


Key words: clam farming; model integration; social carrying capacity; System Approach Framework; Tapes philippinarum; Venice Lagoon



INTRODUCTION


Multiple-use conflict is a common issue in European coastal zones. Without proper management, the unregulated superposition of drivers may lead to a chronic conflict among hardened stakeholders or to the selective survival of the few more relevant activities, with a drastic reduction of the system complexity and the consequent loss of its adaptive capability. In particular, socioeconomic activities that have a local/marginal impact on the global economy are at risk of being badly managed because they might require efforts that are apparently too large in comparison to the benefits they provide. In these cases, unplanned dynamics can emerge and drive a system toward unwanted configurations. Exploitation of the clam (Ruditapes philippinarum) resource in the Venice Lagoon is a case in point (Solidoro et al. 2010). In fact, when it started, this activity was considered little more than another fishing activity and gathered little attention. Once it became clear that its ecological-social-economical (ESE) dimensions were not negligible, the system had already developed along an undesirable path, difficult to correct. In a few years the system overshot its ecological and social carrying capacities, generating environmental and social concerns. Catches increased during the 1990s, reaching a peak at the end of the decade, and subsequently declined (Fig. 1). Employment greatly fluctuated too, whereas environmental impact has remained high. Different stakeholders have shown different attitudes toward clam farming in the Venice Lagoon, sometimes conflicting with each other.

Actually, the clam business can be roughly estimated at about €85-100 million/year. According to MAV-CVN 2008, around 1300 people work as clam harvesters, mainly residents of the lagoon islands and the town of Chioggia. The clam licensed fishing fleet is formed of 400 small fishing boats and 80 fishing vessels that employ vibrating dredgers (Zentilin et al. 2008, Torricelli et al. 2009). It has also been estimated that around 200 people are fishing illegally, with about 500 tons, 2% of total production, confiscated each year (www.gral.venezia.it).

Open-access clam fishing started in the Venice Lagoon at the end of the 1980s; it expanded traditional fisheries to include clam fishing, which was more profitable, and stimulated a large increase in fishermen including those without previous specific experience. Clam fishing soon grew out of control and also expanded into prohibited and polluted areas, therefore production quality and safety standards could not be ensured. The dredging tools themselves have negative impacts on the environment, i.e., biodiversity, sea grass beds, and sediment loss (Badino et al. 2004, Pranovi et al. 2004, Boscolo et al. 2009), requiring spatial limitation of harvesting activity to mediate between clam farmer requirements and other Lagoon uses. Social tensions and conflicts arose among the fishermen and between the fishermen and local authorities.

Since the 1990s local institutions made several attempts to promote a transition from an open-access system to extensive aquaculture (Torricelli et al. 2009), to limit impacts and to preserve other natural services of the lagoon. Local institutions also made efforts to manage clam recruits (seed size of 10-14 mm) that are taken from natural nursery areas in the lagoon and, to a lesser extent, that are imported from other sites. However, a rational integrated management of this resource has not been achieved. Social issues persist including natural clam seed provisions and conflicts continue among fishermen to obtain more productive areas. Illegal fishing still occurs. The presence of rule-breakers has worsened the problem by encouraging other individuals to behave illegally and renewing open-access fishing to all areas, including prohibited ones. The persistence of this conflict is locally well known; at least once a month local newspapers report news related to illegal clam fishing. Videos on clam seizing can be seen on YouTube (April 2011; www.youtube.com/watch?v=kbuwWGW4zCM). Fishing outside the designated area continues because of economic pressure to achieve a higher profitability. Local authorities believe instead that the economic problem is mainly due to improper management of the concessions.

Many technical and research studies addressing different aspects of the clam issue in the Venice Lagoon have provided support to local authorities in their management of clam producing areas. Research addresses clam biology (Pellizzato and Da Ros 2005, Pellizzato et al. 2005), habitat suitability (Pastres et al. 2001, GRAL 2006, 2009, MAV-CVN 2008, Torricelli et al. 2009), clam growth (Solidoro et al. 2000), production carrying capacity (Pastres et al. 2001, Melaku Canu et al. 2010), economics (Boatto et al. 2005, Nunes et al. 2004) and governance (Nunes et al. 2008), but the integration of the ecological and socioeconomic dimensions has only been approached in a simplified way (Pastres 2001, Solidoro et al. 2003, Melaku Canu et al. 2010).

We believe that a fuller integration of ESE components, made with the involvement of local stakeholders and their estimation of sustainable productivity and employment, is needed for proper clam resource planning and will eventually improve system resilience. In this context, integrated modeling tools can be helpful because they can be used for vision sharing, demonstrative scenario analysis, understanding the system complexity, and thus in making choices through increased awareness. This goal will likely support the development of local governance, which is recognized to be the key for local resilience (Kajer 2004, Kaufmann et al. 2009).

In this paper, we address the ESE sustainability of the exploitation of the clam in the Venice Lagoon. In particular we explore ecological and social carrying capacity of clam farming (sensu McKindsey et al. 2006) in the lagoon. This implies the need to consider not only the level of clam production, as opposed to lagoon productivity, but also the associated externalities. Clam management needs to respect the trade-offs between the need to protect lagoon ecosystem quality and related services and the socioeconomic demands.

The methodology follows the System Approach Framework (SAF; T. S. Hopkins, D. Bailly, and J. G. Støttrup, unpublished manuscript). Alternative management scenarios were identified, with stakeholder involvement, considering spatial and technical constraints, the annual seed availability, and evaluating alternative options for seed provision in terms of provenience (natural or from hatchery), cost, and quantity. To explore the dynamics occurring in the Venice Lagoon clam system under those scenarios, we integrated an ESE model using a biogeochemical, a clam bioenergetic and population dynamic, and an economic model. We therefore explore the productivity of alternative exploitation strategies in terms of production, externalities, income, and number of farmers sustained, i.e., maximum employment. We also address uncertainty in model output giving final results by using a precautionary approach.



METHODS


Policy-stakeholders involvement

To promote vision sharing and participation, we contacted local stakeholder groups (Table 1) with an interest in clam farming issues in the Venice Lagoon, gathering different feedbacks and comparing ideas and expectations. Surprisingly, fishermen and management institutions were less motivated to participate. We interpreted this reluctance to be the result of frustration, political conflicts, and perceived risks in taking an open position. Moreover, fishermen were not motivated to participate because they did not recognize political/institutional leadership in our work and they did not see an immediate payback. Only one consortium representative participated, sharing knowledge and exploring scenarios. We had four meetings with the director of GRAL, a mid-level institution with management tasks regarding Tapes philippinarum. The Venice Municipality (Environment Sector) was interested in meeting and sharing results. Local interest groups, Osservatorio Laguna and Vela al Terzo, a recreational traditional nautical association that expressed concerns about the environmental impact of illegal fishing, also participated. Consumer Association members (Confconsumatori) confirmed those worries (Appendix 1).

The SWOT analysis

A summary of key elements in terms of strengths, weaknesses, opportunities, and threats (SWOT) characterizing the Venice clam system has been derived from stakeholder interactions and literature review and is presented in Table 2. It has been used to focus the choice of processes to be included in the model and of model output, as well as to guide result interpretation. In this framework we explored the opportunity of addressing an overall sustainable clam system management following an integrated approach.

The DPSIR analysis

According to the drivers, pressures, states, impacts, and pressures (DPSIR) analysis (Fig. 2), clam growth depends on the local trophic status whereas the quality of clam production is influenced by toxic contamination of water and sediment. The findings highlight the importance of a proper description of nutrient and pollutant cycles and trophodynamics, which are in turn influenced by transport processes. The scheme also emphasizes that clam dredging causes sediment resuspension and increased water turbidity, removal of organic biomass, toxic mobilization, and an impact on sediment that significantly impedes other activities in the fished area. In aquaculture, impacts are reduced because the surface is limited and harvesting is done only at the end of the growth season.

The integrated ESE model

Using results of stakeholder discussions, SWOT and DPSIR analysis, and previous knowledge, we constructed a simulation model by integrating major biogeochemical and ecological components with the most relevant economic processes of clam farming (Fig. 3), using the ExtendSim 7 platform. A 3D biogeochemical model driven by nutrient loads and meteorological conditions simulates space-time distributions of biogeochemical variables, which constrain, as a boundary condition, the simulated dynamics of biogeochemical properties within an aquaculture concession. A bioenergetic model for clam growth and a population dynamic model for clam density describe the time course of clam biomass within aquaculture concession as a function of biogeochemical properties, water temperature, and aquaculture management strategy. A bioaccumulation module defines toxic concentrations within market size mollusks and helps to define their selling price. Starting from simulated productivity, the economic module computes prices, costs, externalities, and other economic parameters that concur with the evaluation of profitability and sustainability of the exploitation strategy analyzed. To reduce computation time to a level that permits interactive sessions, the 3D biogeochemical model is coupled off-line and run in advance. This model implies acceptance of the concept that seston depletion within aquaculture concessions has no impact on seston concentration in the remaining, much larger, lagoon area, an approximation that was found to be valid by Melaku Canu et al. (2010).

The biogeochemistry model

We used 3-D tropho-dynamic model results as biogeochemistry inputs to better resolve the spatial variability. The 3D Trophodynamic Diffusive Model (TDM; Solidoro et al. 2005) is a coupled physical-biogeochemical model specifically developed for the Venice Lagoon. Transport processes are described by a simplified version of the advection-diffusion equation, which is suitable for simulating processes that occur at time scales longer than tidal cycles (Dejak et al. 1998) and reduces the computational cost to acceptable values, even for multidecadal runs (Cossarini et al. 2008). The set of biogeochemical state variables includes phytoplankton, zooplankton, nitrate, ammonia, phosphate, nutrient content in detritus and upper sediments, and dissolved oxygen. The microbial loop is implicitly included in the parameterization of recycling processes.

The bioaccumulation model

The bioaccumulation model allows the estimation of the lipophilic contaminants concentration, such as PCBs and dioxins, in clam tissues on the basis of site specific data concerning the contamination of water and sediment, the physico-chemical properties of the toxicants and the physiology and ecology of the target organisms. Model equations are based on the more general food-web bioaccumulation models thoroughly described elsewhere (Arnot and Gobas 2004, Micheletti et al. 2007, Ciavatta et al. 2009) and are described in the Appendix 2. The model uses a steady state assumption, likely to be satisfied in shallow water environments. It was tested against clam contamination data concerning three PCB congeners (PCB 105, PCB 118, and PCB 180). Sediment and water contamination input data were estimated on the basis of literature (Ciavatta et al. 2009) and site-specific measurements (Table 3; MAV-CVN 2003).

Clam growth and population dynamic model

The bioenergetic model simulates the growth of an individual clam as a function of water temperature, seston concentration, and seston energy content (Solidoro et al. 2000). According to the model, if there is no food limitation, the growth rate is limited by the metabolic processes within the clam and varies with clam size and temperature, regardless of food availability. Conversely, when the ingested food provides less energy than potentially needed, there is a food limitation. Temperature influences the rate of metabolic processes, which increase exponentially as temperature increases from lower values and approaches an optimum and then decreases down to zero when the temperature reaches an upper limit. Moreover, physiological thermal limits of anabolic and catabolic processes vary with individual clam size. The model simulates stock depletion due to natural mortality and, when present, harvesting. It simulates also the age-structured population dynamic, and the gonadal development and spawning events as function of clam size and temperature regimes (Solidoro et al. 2003).

New clams are added after each spawning event (twice/year). The number of newly born clams depends on the number of adults and is modulated by a random function between 0 and one. The simulation of seed recruitment allows us to demonstrate the benefits derived from harvesting larger sized clams and thereby preserve the adult stock until the new clams are born (Appendix 3). With seed we refer here to newly born clams at the juvenile stage, settled in the sediment bottom. When we refer to hatchery or natural recruited seed we refer to juvenile clams of various sizes.

The economic model

The social and economic submodel (Fig. 3) converts the growth model results to economic data while taking into account social aspects, such as ensuring fishermen’s employment, reducing health risks for clam consumers, and mitigating environmental damage. Employment is measured as the number of fishermen working on clam aquaculture. Consumer and environmental aspects have been assessed by introducing the willingness of consumers to pay for reducing the health risks linked to clam consumption (Castellini et al. 2011) whereas environmental aspects are estimated as costs for restoring the damage produced by the intensive clam harvesting tools, i.e., dredging, on the morphology of the lagoon (Orel et al. 2000). These are nonmarket values, not actually internalized as market price, but they are included in the model as public or social effects, which are helpful in policy maker decisions.

The socioeconomic variables are calculated with a daily time step and overall revenues and costs have been discounted along the whole simulation time length. The fleet size is estimated according to the number of fishing days or harvesting time (Pellizzato and Da Ros 2005). Specifically, at each time step the model calculates catches, revenues, costs, and number of harvesting vessels, whereas overall profits and fleet size are evaluated at the end of the simulation time (see details on the economic model formulations in the Appendix 4).

Endogenous prices could not be estimated because most of clam production is not exchanged on the market but goes directly from producers to retailers through cleaning clam centers (Torricelli et al 2009). In the model, prices are exogenously defined; they were calculated using a five-year time series (2005-2009) of the clam prices fixed in the wholesale market of Chioggia. We introduced in the model the average price, removing trend and cycle effects, while retaining seasonality effects. Furthermore, the model computes a premium price for selling bigger clams. The cost for cleaning clams is subtracted from the price. Because this cost is around €0.25-0.30/kg (Boatto et al. 2005), we assume a value of €0.40/kg to be conservative. The cost associated with clam aquaculture includes several components: (i) variable fishing costs, i.e., fuel and oil expenses; (ii) seeding costs; (iii) monitoring costs; (iv) license costs; and (v) fixed costs, mainly depreciation. The model calculates fixed costs at the end of the simulation according to the number of vessels and the fishing period length. Labor costs are excluded because they are difficult to define. The salary of a fisherman depends not only on fixed components but also on the gross profit, i.e., revenue minus variable costs, coming from harvesting and selling clams. The latter is variable depending on fishing and market conditions.

The model takes into account the social benefits through two components: (i) the willingness to pay for the reduction of health risk, which is included as a price change, and (ii) the environmental damage due to clam harvesting activity that may alter lagoon morphology.



RESULTS


Identification and selection of management strategy scenarios

We selected scenarios to be compared based on stakeholder meetings, technical documents produced by local institutions (see Table 1), and GIS maps of lagoon habitats, uses, and conflicts provided by Osservatorio Naturalistico della Laguna (Fig. 4). At the two extremes of the exploitation level arrow are the “conservation scenario,” supported by some environmental groups, and the “full exploitation scenario,” suggested by the behavior of the illegal fishermen (fishers group 2). Other stakeholders suggested more intermediate scenarios in which most players were eventually able to compromise.

Based on stakeholder interactions, we assumed that the actual lagoon area devoted to clam farming (~30 km²) is acceptable under a multiuser perspective, and we made the scenarios assuming this spatial configuration (Fig. 5). Other criticisms raised in the stakeholder meetings were the availability and cost of clam seed and, indeed, the building of a local hatchery system has been proposed by a new project lead by the Venice Municipality. Our scenarios explore this possibility via consideration of the options of natural recruitment from the lagoon versus the use of locally hatched seed. We also included the costs of these alternatives. Other parameters considered in the definition of the scenarios included seeding size, seeding density, seeding month, and harvesting size, which are all related to aquaculture practice, and the type of lagoon area, which in turn is related to natural variability. Parameters that could not be controlled inside the analyzed system, such as climate or nutrient loads from the drainage basin, were excluded a priori. Variation related to level of harvesting technology allowed were also not considered.

However, not all possible combinations of considered parameters defined realistic, feasible, or equally important situations. Therefore, we limited the range of variation of our parameters, considering environmental and technical constraints. As an example, we fixed the seeding density to 400 ind/m², for the 11 mm seeding size (or to the equivalent density for the 14 mm seeding size) and we conditioned the seeding time with respect to seeding area and natural seed availability. These choices reduced the initial number (~1700) of possible scenarios to 60 combinations of seed type, seed size, harvesting size, and area type (Table 4). We considered four area types, which have been represented by using site specific biogeochemical forcing for concession areas 1, 3, 6, and 9 (Fig. 5). These areas were selected based upon the results of a previous modeling study, to introduce spatial trophic variability in the analysis (Melaku Canu et al. 2010). Scenario definition was completed by introducing the selling price, given by the market time series (www.chioggia.org/ittico/index.php), and the gross profit without labor cost, which was fixed to a starting value of €50,000/y. These parameters were common to all 60 scenarios (Table 5).

We therefore compared the results by simulating 10 years of harvesting in one hectare and exploring harvest, productivity, relative impacts on the lagoon (externalities), and payback (profits before paying employees). Extending the results to the whole clam farming surface, we estimated the total Venice Lagoon clam productivity and employment possibilities.

Simulation results

The set of simulations indicates that profit, productivity, and employment vary significantly in response to different exploitation strategies. The bioaccumulation model confirms that the production of the selected area meets quality standards, and therefore, the premium price has been introduced in our results to take into account consumer preference. The average sensitivity of profit to the change of parameters has been evaluated by comparing the dispersion index around the median value of profits (Table 6). The sensitivity to seeding source is very high, 72%, followed by the sensitivity to seeding month, 47%, and to harvest size, 31%. Sensitivities to seed size and area type are less relevant, with a value of, respectively, 9% and 10%.

Comparison of the results of final scenarios selection (Table 7) confirms that the highest profit values are reached when harvesting larger individuals and using natural seed. The set of simulations indicates that the amount of seed needed for the whole clam farming area varies between 2300 and 7800 tons/y. However the natural seed availability is estimated to be lower; GRAL 2009 reports values as low as 720 and 1200 tons for years 2006 and 2008. Therefore, even assuming that these values are underestimated, we excluded from our results those scenarios requiring more than 3000 tons/year of natural seed that are considered to be unsustainable. Similarly, we excluded scenarios requiring hatchery seeding exceeding 4000 tons/y. A posteriori, we observed that, by doing this, we also excluded cases presenting productivity higher than 1.6 kg/m²/y, which was the production carrying capacity computed by Melaku Canu et al. (2010). In fact, the productivity of the remaining 16 scenarios ranged between 1.16 and 1.36 kg/m²/y. It can also be observed that the 14 mm seed option is also filtered out, along with strategies based on a local hatchery. The computed average employment is of 1297 farmers, with a minimum of 569 in the worst case (11 mm hatched seeds grown in area type 1 and harvested at 25 mm) and of 1873 in the best case (11 mm natural seeds grown in area type 2 and harvested at 27 mm).

However, to suggest a resilient social carrying capacity, we should also take into account some uncertainty related to variations that cannot be controlled by policy makers, such as fluctuations in prices or in nature. We therefore performed an additional set of simulations by reducing the average clam selling price by €1/kg and by €2/kg; we also increased the natural mortality by 20% as a proxy of a natural condition in a ‘bad’ year. Sensitivity to price is very high, with an average decrease over the 60 original scenarios of about 59% (€-1/kg scenario) and 117% (€-2/kg scenario) in profit. Incidentally, this finding alone indicates that management strategies and economic constraints can be more relevant than trophic variability. Increasing the clam mortality model parameter of 20% and 50% caused a reduction in productivity of 10% and 22%, respectively. The worst-case scenario, in which the selling price is around €1.50/kg and mortality is increased, presents a substantial decrease of profits. Under these very unfavorable conditions, employment is no higher than 470 people (Table 8).

Externalities are lowest when harvesting the 30 mm clams and highest, about 40% higher, when harvesting at 25 mm. This is because of the amount of harvesting that occurs in the field over the 10 years of our simulation, which is lower when the seed is smaller and/or when the harvesting size is larger.

Finally, we simulated the local natural seed produced inside the clam field, varying harvesting size among 25, 27, or 30 mm. This estimate is approximate; the parameter is difficult to model because of random events such as predation and hydrodynamics that influence the actual recruitment (Hunt 2004, Ripley and Caswell 2006, Dang et al. 2010). We therefore combined deterministic formulation, which is based on biological observations related to temperature and gonadal development (Paesanti and Pellizzato 2000, Solidoro et al. 2000), and a random function, aiming to include the natural randomness. This value was not directly included in the profit analysis, but it is nevertheless a factor that should be considered in management choices. Profits related to natural recruitment are 60% higher when harvesting at 30 mm in comparison to 25 mm.

Upscaling at the lagoon level: confronting extreme and realistic scenarios

In the previous section, we explored the implications of a number of feasible strategies of clam exploitation in one hectare of lagoon area for 10 years. In this section we combine the simulated profits, productivities, and social effects of these different management strategies for the assessment of consequences of implementation of alternative management scenarios at the whole lagoon level.

Table 9 summarizes the comparisons among five alternative scenarios (see also Fig 4): the full exploitation scenario, in which figures are inferred from data and estimates from 1999, a total conservation scenario, and three alternative management scenarios.

According to Orel et al. (2000), fishermen harvested a surface area of 40,500 ha in 1999 that, considering sediment resuspension and loss, induced externalities estimated as €12.15 million/year. Assuming an estimated production in 1998 of 40,000 tons, corresponding to €64 million when assuming a price of €2/kg, the share of environmental damage is equal to 19% of production value. ScenMix1 is a scenario constructed with the assumption that the seed comes from natural nurseries (800 tons) and from foreign hatcheries (1600 tons). Over 10 years of simulations, six yields are produced, seeded at the 11 mm size and harvested at the 30 mm size. The externalities of ScenMix1 are computed by summing the externalities generated by clam harvesting in the 3000 ha of lagoon surface plus the externalities generated by seed dredging in 600 ha of lagoon surface nursery area (6 harvests in 10 years). Under this scenario, the total area devoted exclusively to clam aquaculture is almost the value that most of the stakeholders agree upon and the externalities are around 1.5% of revenue, lower than in the ‘full exploitation scenario.’ ScenNat is based only on natural seed: 1600 tons of seed are assumed to be collected from 1800 ha of natural nursery area, whereas ScenMix2 simulates both the increase of the natural nursery area up to 1200 ha and the seeding of 1600 tons of hatchery seed. For comparison, the model computes profits and externalities using the same unit value but with a higher selling price for the ScenMix1, ScenNat, and ScenMix2 scenarios to take into account consumer quality preference. Assuming an individual annual revenue of €50,000, we compute the maximum sustainable number of fishermen for the three scenarios ScenMix1, ScenNat, and ScenMix2 as 935, 1288, and 1111 individuals, respectively. To take into account uncertainties due to natural and price variability, this value can be conservatively reduced to ~800, which is lower than the MAV-CVN estimate of total number of clam workers in 2008, but not far from the current figure of 742 regular clam farmers, according to GRAL (www.gral.venezia.it).



DISCUSSION


Our purpose was to explore the ESE system of clam farming in the Venice Lagoon, following the SAF, demonstrating the feasibility and advantages of sustainable management. Clearly, feedbacks exist between socioeconomic and environmental components. The model describes in a quantitative way the relationships and feedbacks between the natural environment, the clam stocks, and the choices of humans, enabling the stakeholders to better understand and to compare consequences of alternative scenarios, therefore supporting management planning. Consumer preferences, evaluated by the questionnaire (Appendix 1), have been taken into account in the scenario analysis by simulating changes in prices to reflect consumers’ willingness to pay for a healthier product. Results are highly sensitive to price changes thus indicating the implementation of a quality certification system as a viable support toward market resilience.

Specific modules making up the integrated model provide, in general, only a simplified description of reality. However, this simplification is unavoidable and possibly even useful when dealing with a high level of complexity, such as that of the governance of an ecological-social-economical system. Our approach is, in fact, not based on the maximization of production alone; it considers a balance among different uses, demands, and natural properties and vocations. Therefore, a multidisciplinary, holistic, systemic view is required, whose coupling with very accurate descriptions of selected processes is difficult, possibly useless, and potentially distracting.

Our model suggests that a properly managed aquaculture system that uses about 3000 ha of lagoon and employs about 800 full-time people leads to a sustainable situation that is accepted by most stakeholders and, from a socioeconomic viewpoint, is not much different from the current status. Uncertainty analysis suggests that this configuration is sustainable even when adopting a conservative, precautionary approach. We can therefore speculate that the current problems are at least partially due to insufficient implementation of appropriate management policy and persistence of illegal fishing.

Environmental externalities, even though not explicitly internalized in the economic model, have been quantified to show the relative impacts among the selected harvesting scenarios (Fig. 6). Results indicate that the most sustainable management strategy is also the one with the longest harvesting cycles and the highest uncertainty. The best scenarios could be operated by farmers only if an agreement on practices can be achieved. This requires the promotion of education, vision sharing, self-enforcement, and tools for risk reduction, such as building a local hatchery. In addition, as also emerged from stakeholder interactions, we suggest the integration of other activities, such as fishing-tourism, and the promotion of marketing solutions, such as traceability and transformation (Nunes et al. 2008). In summary, the promotion of fishermen participation in a diversification system will achieve a higher level of resilience and reduce some of the environmental impacts.

However, our experience also shows that although it is relatively easy to share information, data, and ideas among stakeholders, it is more difficult to really influence the management process. The level of stakeholder involvement was not very high because major clam fishing governance structure was going through an internal reorganization process, and fishermen did not see an immediate payback in participation. Furthermore, because of its dimension, complexity, and history, the present day Venice Lagoon clam system shows a substantial inertia against adaptations (D. Melaku Canu and C. Solidoro, unpublished manuscript). It was possible to engage a group of stakeholders in the identification of model structures and management scenarios. This favored stakeholder interactions, enabling different groups to consider different perspectives, therefore promoting common language and holistic views. Possibly this will initiate an iterative process with engagement of other stakeholders that will lead to the support of sustainable management. On the other hand it is increasingly recognized that scientific knowledge alone, although a prerequisite for informed management and a crucial component of decision support systems, is not sufficient to prompt efficient implementation of any policy (Daw and Gray 2005, Ostrom 2009) especially in large and complex systems. Finally, by providing concrete basis for discussion, and indicating the existence of viable solutions, our results also elucidate and emphasize the need to address issues related to the institutional structure and its role.



CONCLUSION


In this work, we attempted to integrate the existing knowledge, scientific findings, social experiences, and awareness to address the social-ecological carrying capacity of the clam exploitation system in the Venice Lagoon. We developed and applied an integrated model that was also based on repeated interactions with different stakeholders, and used it to compare effects on the ecological, social, and economical components of implementation of alternative management strategies.

In closing, we would like to reiterate our major conclusions:
  1. The integrated model suggests the sustainability of a properly managed aquaculture system that uses about 3000 ha of lagoon and employs about 800 full-time people. From an economic perspective, this situation would not be very different from the current one, but would have less impact from an ecological perspective, and socially, would be more stable.
  2. The most sustainable management strategy is also the one with the longest harvesting cycles and the highest uncertainty. This further stresses the need to promote clam farmers’ cohesion and self-enforcement, along with their inclusion in the decision making process.
  3. Model results suggest that productivity inefficiencies are more related to management choices and seed scarcity than to environmental constraints.
  4. Models show a high range of uncertainty that depends in part on the model parameterization but also on the randomness of natural/biological processes, mainly predation and mortality, and to economic factors such as price and consumer preferences. This suggests using a precautionary approach when addressing the social carrying capacity.
  5. Based on stakeholder meetings and other studies, we underline the need for diversification, i.e. transformation, fishing-tourism, and hatchering, as well as quality control, such as traceability, as strategies to reduce risk.
  6. Efficient implementation of scientifically sound management policies cannot be prompted by scientific knowledge alone but requires proper governance actions that need proper time to be implemented. Stakeholder engagement, in model development and scenario analysis, favors interaction and vision sharing, thus promoting better management.



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ACKNOWLEDGMENTS

This work was carried out within the EU FP6 SPICOSA project. The authors thank: The stakeholders for their engagement: G. Chiaia and Erminio Di Nora (GRAL directors), Venice Municipality, Enzo Fornaro (FederCooPesca), Lorenzo Miozzi (Coopconsumatori), Massimo Gin (Vela al Terzo), Michele Pellizzato (local expert), and Marco Favaro (Lagoon Natural Observatory). Gianpiero Cossarini for TDM model simulations. Giacomo Marchi and Daniele Brigolin for ExtendSim biogeochemical and bio-accumulation implementation. Valentina Mosetti for graphical assistance. Thomas Sawyer Hopkins, Josianne St°ttrup, and Audun Sandberg for providing helpful feedbacks to an earlier version of the manuscript.




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Address of Correspondent:
Donata Melaku Canu
OGS Borgo Grotta Gigante 42 c 34010 Sgonico
Trieste, Italy
dcanu@inogs.it

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