Biol. Mar. Mediterr. (2016), 23 (1): 136-137
D. Brigolin, A.A. Forchino, E.M.D. Porporato, R. Pastres Department of Environmental Sciences, Informatics and Statistics, Università Ca’ Foscari di Venezia, Via Torino, 155 - 30170 Mestre (VE), Italy.
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COMBINING MODELS AND SATELLITE DATA FOR THE ALLOCATION OF AREAS TO SHELLFISH FARMING ALONG THE ADRIATIC COAST INTEGRAZIONE DI MODELLI E DATI SATELLITARI PER L’INDIVIDUAZIONE DI AREE IDONEE ALLA MOLLUSCHICOLTURA LUNGO LA COSTA ADRIATICA Abstract - The selection of Allocated Zone for Aquaculture (AZA) represents a key factor for the sustainable development of this sector. The present study combines satellite data and mechanistic models in order to designate the most suitable areas for shellfish culture. Optimal shellfish conditions combined with environmental and socio-economic indicators were employed for a Spatial Multi-Criteria Evaluation (SMCE). Two different scenarios were carried out in order to highlight most suitable areas for Mytilus galloprovincialis and Crassostrea gigas culture. The presented results underlined that the integration of multiple instruments can represent an effective strategy to identify optimal sites where mussels farming activities can be located. Key-words: GIS, mussel culture, mathematical models, remote sensing.
Introduction - The selection of sites/areas allocated to aquaculture plays a key role in supporting the sustainable development of this industry (EATIP, 2012), in compliance with EU Directives and policies, which aim, on one side, at promoting the growth of this sector, [Blue Growth Strategy COM (2012) 494] and, on the other, at preventing for further deterioration and enhancing the status of marine ecosystems (Marine Strategy Framework Directive 2008/56/CE). In this context, the allocation of areas to aquaculture should take into account both the production, ecological and social carrying capacity of a given area and the conflicting uses of a maritime space. The synergistic integration of GIS software and Multi-Criteria Evaluation (MCE) techniques in a Spatial Multi-Criteria Evaluation (SMCE) represent a valid tool able to help decision-makers in solving complex spatial decision problems (Malczewski, 2000). In this paper, we present a methodology for optimizing the selection of areas to be allocated to shellfish culture, taking into consideration both the presence of interfering activities and the biomass yield. The methodology was applied to the Italian coastal area of the Adriatic Sea and, in particular to the Emilia-Romagna coastline in the northern Adriatic, where mussel farming is a consolidated activity. Two suitability scenarios were considered: A) Mytilus galloprovincialis Lamarck, 1819 and Crassostrea gigas (Thunberg, 1793) have been considered to have the same importance; B) the M. galloprovincialis farms have a weight three times higher respect the C. gigas ones. Materials and methods - The best areas for allocating shellfish farms were identified by integrating information concerning the trophic potential of areas and the constraints due to the presence of other uses. We achieved this goal by implementing in a GIS environment a multi-criteria analysis (Radiarta et al., 2008) based on the following criteria: time to reach the market size; dry weight at the harvest; depth of the area; constraints due to the presence of other activities. The multi-criteria analysis required two steps: i) the normalization of criteria and the assignment of a weight for each criterion employing an Analytical Hierarchy Process (AHP);
Combining models and satellite data for the allocation of areas to shellfish farming along the Adriatic coast
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ii) the calculation of a Suitability Index through a Weighted Linear Combination (WLC) technique. Subsequently, the constraints were superimposed. Criteria related to mussel and oyster growth performance were estimated using bioenergetic models (Pouvreau et al., 2006; Brigolin et al., 2009). Chlorophyll a concentration (CHLa) was taken as a proxy of food resources. Time series of monthly Sea Surface Temperature and concentration of CHLa, estimated from satellite data, were extracted from the EMIS (http://emis.jrc.ec.europa.eu/) database for the years 2003-2012. We assumed that shellfish is stocked in June and harvested after 11 months and estimated for the whole study area the above criteria for both M. galloprovincialis and C. gigas. Subsequently, we calculated, at each grid point, the average value and the standard deviation of each criterion.
Results - The main results highlighted that the total suitable area (Suitability Index>0.45) resulted of 999 km2 and 1494 km2 for A and B scenarios, respectively. However, considering constrains, these values showed a reduction down to 531 km 2 (scenario A) and 783 km2 (scenario B). In both scenarios, the whole coastal area comprised within 3 NM was found to be suitable for farming of both mussel and oyster. This is not the case for the area comprised between 3 and 12 NM: the northern part is very suitable, whereas the southern one is less suitable concerning the scenario A. In the B scenario, new farming sites should be located in the northern part of the investigated area, even though the constraints markedly reduce the available surface. For both scenarios, beyond 12 NM, the northern part is still classified as the most suitable one, for setting up a shellfish farm. Conclusions - The presented results showed that the integration of multiple instruments (such as satellite data and mechanistic models) can represent an effective strategy to identify optimal sites where mussels farming activities can be located, assisting the decision makers in the designation of AZA (Allocated Zone for Aquaculture). In this study, satellite data have not been used as indicators of marine productive capacity, as commonly, but have been provided as input variable to the mathematical models for estimating the potential growth of M. galloprovincialis and C. gigas. The possibility to include environmental and socio-economic indicators in SMCE analysis represents a valid methodological framework to implement MSP based on an Ecosystem Approach to Aquaculture (EAA). References BRIGOLIN D., DAL MASCHIO G., RAMPAZZO F., GIANI M., PASTRES R. (2009) - An individual-based population dynamic model for estimating biomass yield and nutrient fluxes through an off-shore mussel (Mytilus galloprovincialis) farm. Estuar. Coast. Shelf S., 82: 365-376. EATIP (2012) - The future of European Aquaculture. European Aquaculture Technology and Innovation Platform (EATIP), Liege: 41 pp. MALCZEWSKI J. (2000) - On the use of weighted linear combination method in GIS: common and best practice approaches. Trans. GIS, 4 (1): 5-22. POUVREAU S., BOURLES Y., LEFEBVRE S., GANGNERY A., ALUNNO-BRUSCIA M. (2006) Application of a dynamic energy budget model to the Pacific oyster, Crassostrea gigas, reared under various environmental conditions. J. Sea Res., 56: 156-167. RADIARTA I.N., SAITOH S.I., MIYAZONO A. (2008) - GIS-based multi-criteria evaluation models for identifying suitable sites for Japanese scallop (Mizuhopecten yessoensis) aquaculture in Funka Bay, southwestern Hokkaido, Japan. Aquaculture, 284: 127-135.
