Use of GIS-Based Models for Integrating and Developing Marine Fish Cages within the Tourism Industry in Tenerife (Canary Islands)

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1 Coastal Management, 31: , 2003 Copyright Taylor & Francis Inc. ISSN: print / online DOI: / Use of GIS-Based Models for Integrating and Developing Marine Fish Cages within the Tourism Industry in Tenerife (Canary Islands) O. M. PÉREZ T. C. TELFER L. G. ROSS Institute of Aquaculture University of Stirling Stirling, Scotland, United Kingdom Site selection is a key factor in any aquaculture operation, affecting both success and sustainability. Moreover, it can solve conflicts between different coastal activities, making a rational use of the coastal space. Geographical information systems (GIS) have become of increased significance for environmental planning and assessment mainly because of the need to compare a great number of spatially related data, and because it can be used to couple these spatial data with their attributes and overlay them. This study used GIS and related technology to build a spatial database using those criteria which were considered to have any influence in integrating marine fish-cage culture within the tourism industry in Tenerife. Criteria were grouped in three submodels (distance to beaches, nautical sports, and viewshed), which were combined to generate a final output showing the most suitable areas for cage culture development in coexistence with tourism. Most areas of the coastline of Tenerife were identified as being suitable (56%) or very suitable (46%), suggesting that marine cage aquaculture could be developed on the island in coexistence with the well-established tourism industry. Keywords aquaculture, cage culture, GIS, site selection, Tenerife, tourism Introduction Aquaculture, as with any other economic activity which uses natural resources, depends upon inputs (e.g., water, seed, feed) and attendant processes (e.g., capacity of the environment to degrade wastes) to produce a final product (e.g., fish, mussels, shrimp) for consumers. This interaction with the environment may have social, economic, and environmental benefits, such as provision of food, employment, increase of income, improved nutrition and health, decreased pressure on natural stocks, etc. (Beveridge, 1996). Received 12 March 2002; accepted 1 June The authors are grateful to Tenerife Council for the valuable use of their CD-MAP information. This work was carried out as part of a training project financed by the EC under the FAIR program (GT ). Address correspondence to O. M. Pérez, Investigaciones y Servicios Marinos S.L. (INSEMAR), San Clemente 14, 5, S/C de Tenerife, Canary Islands, Spain. 355

2 356 O. M. Pérez et al. On the other hand, interactions with the environment can also generate negative impacts. Wastes, which are generated by the farming activity and released into the environment, may have a negative effect on natural resources, decreasing their quality and quantity. Furthermore, an indirect consequence of waste impacts in the environment is the rise of a negative perception of aquaculture. The increasing number of marine farms threatens to bring competition between fish farmers and other actual and potential users of coastal space, such as the tourism industry. Therefore, to ensure a sustainable development of the aquaculture industry, there is a great need to allocate aquaculture to suitable locations (site selection) to resolve competing demands for coastal space, avoid undesirable impact on the environment, as well as ensure the profitability of the operation (Kapetsky, 1987; GESAMP, 1991, 1997). While development and implementation of aquaculture site selection (zonation) strategies is now an established concept, the tools and methodologies for achieving such goals are still under development (Clark, 1996; GESAMP, 1996). It is clear, however, that for any management of the shore to be effective, it is necessary for the strategies to be based on informed decision making. This in turn requires ready access to appropriate, reliable, and timely data and information, in suitable form for the task at hand (Urbanski, 1999). Since much of this information and data are likely to have a spatial component, Wright and Bartlett (2000) pointed out that geographical information systems (GIS) have relevance to this task. GIS is a system specifically designed to work with data referenced by spatial or geographic coordinates. In other words, a GIS is both a database system with specific capabilities for spatially referenced data, as well as a platform for analytical operations for working with the data (DeMers, 1997). GIS can allow for the analysis of both qualitative and quantitative data types, identify associations between components, and therefore, build a living database with exploratory data analysis, interpretative and mapping capabilities (Booth, 1998). GIS has several advantages for aquaculture development programs. It not only provides a visual inventory of the physical, biological, and economical characteristics of the environment, it also allows rational management without complex and time-consuming manipulations (Krieger & Sandor, 1990). Despite this, the use of GIS in aquaculture has been very limited and only few studies are reported in the literature. Nath et al. (2000) concluded that the limited range of aquaculture applications using GIS is due to (1) a lack of appreciation of the benefits of such systems for this sector, (2) limited understanding about GIS principles and associated methodology, (3) inadequate administrative support to ensure GIS continuity among organizations, and (4) poor levels of interaction among GIS analyst, subject matter specialists, and end users of the technology. The first applications of GIS in aquaculture date from the late 1980s (Kapetsky, 1985, 1987). Since then, the use of GIS has been quite limited. Despite this, GIS applications in aquaculture are surprisingly quite diverse, targeting a broad range of species (fish, crustacean, and mollusc) as well as geographical scales, ranging from local areas (i.e., small bays; Ross, Mendoza, & Beveridge, 1993; and big bays; Scott & Ross 1999), to subnational regions (i.e., individual states/provinces; Aguilar-Manjarrez & Ross, 1995), to national (Salam & Ross, 2000) and continental (Aguilar-Manjarrez & Nath, 1998) expanses. They also vary with regard to the degree to which GIS outcomes have been used for practical decision making (Nath et al., 2000). At the present time, the extent of GIS applications in aquaculture include site selection for target species such as fish (Grita, 1998; Alarcon & Villanueva, 2001; Benetti et al., 2001), oysters (Chenon et al., 1992), mussels (Krieger & Sandor, 1990; Scott, Cansado, & Ross, 1998), clams (Arnold, Norris, & Berrigan, 1996; Arnold & Norris, 1998; Arnold et al., 2000; scallop (Halvorson, 1997), shrimp (Salgado & Blanco, 2000; Alarcon & Villanueva, 2001), and seaweed (Brown et al., 1999); environmental impact assessment

3 Fish Cages and Tourism in Tenerife 357 (Thriscutt, Lobsiger, & Levesque, 1997; Fuchs, Martin, & Populace, 1998; Gupta, 1998); conflicts and trade-offs among alternate uses of natural resources (Angell, 1997; Biradar & Abidi, 2000); and consideration of the potential for aquaculture from the perspectives of technical assistance and alleviation of food security problems (Meaden & Kapetsky, 1991; Kapetsky, 1994; Kapetsky & Nath, 1997). However, there have been no studies on the integration of aquaculture with the tourism industry. The objective of this study was to evaluate the integration and coexistence of marine fish cages within the tourism industry in Tenerife (Canary Islands) based on the use of GIS-based models and related technology to support the decision-making process. This preliminary assessment could indicate whether marine aquaculture in Tenerife has the potential to coexist with the well-established, and main economical sector, the tourism industry, before carrying out detailed and costly studies on water quality, currents, or waves. Methodology Study Area The Canary Archipelago comprises seven main islands and several minor ones and is located in the Northeast Atlantic Ocean between latitude 27.6 and 29.5 N and longitude 18.2 and W. It is only 100 km from the northwestern edge of the African continent (Figure 1). The islands are the emerged parts of an important volcanic formation Figure 1. The study area, Tenerife, Canary Islands.

4 358 O. M. Pérez et al. on the oceanic-continental transit of the Afro-Atlantic plate, forming an independent set of islands with a water depth of 2000 m between them. Tenerife is the largest island, with an area of 2,036 km 2, and has the longest coastline, at 398 km. Its volcanic nature is responsible for the coastal and seabed topography, which generally can be described as abrupt and highly irregular. The coastal shelf is reduced to only 200 m. Tourism is the island s most important economic subsector, and the activity which most influences the rest of the island economy. The hostelry and tourism service sectors account for 60% of the GDP (ISTAC, 2001). Aquaculture has considerable potential in Tenerife and could diversify the island s economic activities. Also, it may provide a constant fish supply and hence decrease some pressure on the natural resources. Tenerife has very favorable conditions for culture of marine fish because of its clean and well-oxygenated waters, favorable temperatures for growth (17 25ºC), and stable oceanic salinity (36 37%). In Tenerife, the growth cycle of some temperate species, such as seabream (Sparus aurata) has been reduced to months, compared with the months needed in the Mediterranean (J. P. Cejas, personal communication, 1999). On the other hand, due to the small size of the island, overpopulation, and competition with tourism and agriculture, land availability in Tenerife is low. This pressure on the land is increasing with the growing local population and the developing tourism and agriculture industries. There are no lagoons available for aquaculture and the scarce bays are heavily used by tourism. Therefore, at present, strategically placed offshore cage culture seems like the most viable system for ongrowing fish farming in Tenerife. General Methodology There are numerous alternative ways to organize the sequence of activities in the decision-making process (Raiffa, 1968; Brown et al., 1974; Rietveld, 1980; Burrough, 1990; Goodwin & Wright, 1992; Kleindorfer et al., 1993), and the quality of the decision can depend on the sequence in which the activities are undertaken. This study is based on an approach built up by the Institute of Aquaculture, University of Stirling, which has been researching GIS for aquaculture support for some years. 1 It is based on the analytical hierarchy process and the pairwise comparison method developed by Saaty (1977), which has also been recommended by other authors (Pereira & Duckstein, 1993; Lowry et al., 1995; Barredo, 1996; Malczewski, 1999). The spatial multicriteria analysis focused on specifying and creating a comprehensive set of evaluation criteria (also known as production functions) that reflected all the concerns relevant to the decision problem. These evaluation criteria were of two types, factors and constraints. A factor is defined as a criterion which enhances or detracts from the suitability of a specific alternative for the activity under consideration (Eastman, 1993). On the other hand, a constraint is a criterion which serves to limit the alternatives under consideration (Eastman, 1993). All data integrated into the database needed some manipulation and reclassification to create the thematic layers, as well as registering each layer to a common coordinate system. Given the variety of scales on which all criteria were measured, multicriteria decision analysis requires that the values contained in the various layers be transformed to comparable units. A scoring system of 1 to 8 was chosen, 8 being the most suitable and 1 the least for developing marine fish aquaculture in Tenerife. At this stage, the decision maker s preferences with respect to the evaluation criteria were incorporated into the decision model. These being expressed in terms of the relative importance (weights) assigned to the evaluation under consideration, express the importance of each criterion relative to other criteria. Eventually, the criteria layers and their weights were integrated to provide an overall assessment of the

5 Fish Cages and Tourism in Tenerife 359 alternatives. This step is known as multicriteria evaluation (MCE) and was accomplished by appropriate decision rules, which are formal mathematical expressions that combine the weights and scores of each of the layers used. The end results of the decisionmaking process are recommendations for future action, which should be based on the ranking of alternatives. It may include a description of the best alternative or group of alternatives considered as candidates for implementation. If the current result is found to be unsatisfactory, information about the output may be used to return to the problem formulation step, as model tuning (GESAMP, 1991). Hardware and Software The GIS software, IDRISI 32.11, was operated on a twin 400MHz PII PC with 512 Mb RAM and 45 Gb harddisk. Display was via a DELL 21 color monitor. Digitizing and scanning was done using a CalComp Drawingboard III table arid a Hewlett-Packard flatbed scanner model ScanJet 3c, respectively. Identification of Variables and Database Generation The model structure for selecting the best sites for marine fish-cage aquaculture within the tourist industry in Tenerife was built based on hierarchical structures. Hierarchical structures break down all criteria into smaller groups (or submodels). To break down a hierarchy into clusters, first it was decided which elements to group together in each cluster. This was done accordingly to the similarity of the elements with respect to the function they perform or property they share (Saaty, 1988). Figure 2 represents the Figure 2. Conceptual structure of the suitability analysis for integration of marine fish cages within the tourism industry in Tenerife.

6 360 O. M. Pérez et al. suitability analysis for marine fish cage site selection in Tenerife as a hierarchical structure. The top or first level in the hierarchy represents the ultimate goal of the multicriteria decision-making analysis process. The intermediate or second hierarchy level lists the relevant evaluation criteria that were compared pairwise to assess their relative weights. Each of these clusters was considered as a submodel. The lowest level in the hierarchy contains the evaluation objects. These are all the criteria identified as influencing the goal of the study and may represent primary data or be the result of some preliminary data manipulation or model. Beach Submodel. Beaches in Tenerife are a rare and fragile resource. The 600,000 local inhabitants of the island together with a constant tourist presence are increasing the pressure on the beaches. Cages occupy space which can affect local amenities, boat traffic, local currents, and sedimentation dynamics and decrease the water quality of nearby beaches. Therefore, cage siting should be planned to avoid interference and conflict for space and water. A preliminary screening, of 79 from a total of 137 beaches, was made to identify the important beaches on the island, based on composition and size. All sand beaches were first selected regardless of their size. Second, beaches with gravel or a mix of gravel, cobble, and sand, longer than 100 m in length and 20 m in width were also selected. Subsequent classification focused on grouping these 79 beaches into four groups of importance, by using MCE. This grouping was based on how important each beach was, and therefore how appropriate it will be to develop cage aquaculture in its proximity. For each beach, five criteria were selected to assess its importance; beach length, beach width, beach composition, rate of occupation (how much the beach is used by people) and rate of urbanization (degree of infrastructure around the beach site). Table 1 shows the threshold values used for assigning the standardized criterion scores. Basically, beaches that were short in length and width, with a low rate of occupation and urbanization, and composition other than sand, scored as least important, and therefore, more suitable for aquaculture development in their proximities. When the five criteria were scored, weights of importance for each were calculated using a pairwise matrix and later combined in a MCE. The output from this operation was a layer with beaches classified from 1 (very important) to 4 (least important). A further manipulation calculated a buffer zone for each of the four groups of beaches, and later reclassified according to the distance threshold values shown in Table 2a. Buffer zones for beaches tagged as very important are greater than for those tagged as least important. The beach submodel suitability map was created by overlaying these four images (Figure 3a), where areas close to beaches were scored low (less suitable) and areas away from beaches scored high (most suitable). Nautical Sports Submodel. The geographic location and conditions of Tenerife provide its waters with a varied wealth of fauna and flora, with a mix of species from the Table 1 Threshold-values used for each beach group Beach 4 Beach 3 Beach 2 Beach 1 Length (m) < >900 Width (m) < >45 Composition Others Sand + gravel + cobble Sand+ gravel Sand Rate of occupation Low Medium High Rate of urbanization Virgin Rustic Semi-urban Urban

7 Fish Cages and Tourism in Tenerife 361 Table 2 Distance threshold values (m) used for (a) each beach group, (b) four diving criteria, (c) windsurfing sites and near-shore sailing areas, and (d) visibility distance values (m) used for each beach group Score Criteria (a) Distance threshold values (m) used for each beach group Distance to > <500 Beach Distance to > <500 Beach Distance to > <300 Beach Distance to > <300 Beach (b) Distance threshold values (m) used for the four diving criteria Scuba diving > sites Particular > >0 0 habitats Shipwrecked > boats Spearfishing > > (c) Distance threshold values used for windsurfing sites and near-shore areas Wind- Outside Inside surfing windsurfing windsurfing areas areas Near-shore Outside Inside sailing sailing sailing areas areas (d) Visibility distance values (m) used for each beach group Distance to > Beach 1 Distance to > Beach 2 Distance to > Beach 3 Distance to > Beach 4

8 362 O. M. Pérez et al. Figure 3. (a) Beach submodel suitability map. (b) Nautical sport submodel suitability map. Note the black color in the map (zero value). This area corresponds to the windsurfing spot in El Medano and was set as a constraint because of its importance for this sport. (c) Tourism resources in Tenerife. (d) Viewshed model (tourist resources + beaches) suitability map. (e) Overall site selection suitability map masked to depths exceeding 50 m (for each score the suitable area is shown in km 2 ).

9 Fish Cages and Tourism in Tenerife 363 Mediterranean and the Atlantic and with other of distinctly tropical character. This characteristic along with the variety of sea beads floors makes Tenerife an ideal place for the practice of scuba diving and underwater fishing. To avoid conflict with aquaculture development in these sites, a buffer zone was calculated around each diving site and later reclassified using the threshold values shown in Table 2b. The weather and the geographical location of Tenerife are ideal for sailing. Sailing is a popular sport in Tenerife, practiced by both locals and tourists, and it is important in the tourism industry. Many tourists come to the island for its ideal sailing conditions, the principal activities being windsurflng and near-shore sailing. Table 2c shows the buffer zone threshold values used for windsurfing and near-shore sailing sites. El Medano is the most outstanding spot for windsurfing in Tenerife, and so it was classified as a constraint area. Weights of importance for each of the six criteria forming this submodel, scuba diving, scuba diving in particular marine habitats, shipwrecked boats, spearfishing, windsurfing and near-shore sailing, were calculated using a pairwise matrix. A MCE was used to combine the six criteria, and the output from this operation was the nautical-sport suitability map shown in Figure 3b. Viewshed Submodel. Scenic landscapes and other natural attractions provide the basis for tourism in Tenerife, and their value in the region to the tourism industry must be fully appreciated. Unlike less obvious impacts such as changes in water quality, visual impacts have a direct and immediate effect upon people s surroundings, and therefore may arouse strong feelings (Institute of Environmental Assessment and The Landscape Institute, 1995). They may also be used by the public as a focus for a variety of other concerns about the impact of marine fish cage farming development (Beveridge, 1996). This author referred to visual impacts as one of the most important causes of public concern about cage farm developments in Scotland, Ireland, Chile, and North America. Therefore, it is essential that assessment of the visual impacts of proposed fish cage sites are carried out in as measured and controlled way as possible. The assessment of the visual impacts is commonly called viewshed analysis. This is based on intervisibility on a digital elevation model (DEM) and is a GIS function that identifies all areas on a terrain surface that are visible from a pre-defined observation point (Lee, 1998). The result of a viewshed analysis within a raster GIS is a Boolean visibility map; a cell is either classified as visible or invisible (Nackaerts et al., 1999). The two criteria used for estimating visibility of potential fish farm sites, and hence their suitability, were (1) visibility from important tourist resources, and (2) visibility from beaches. The visibility from tourist resources (buildings) was calculated as follows. First, the most important tourist resources were identified (Figure 3c). For these 15 sites, building distribution was obtained. The module VIEWSHED was then used in combination with the DEM for Tenerife to calculate the visibility of potential farm sites in the vicinity of these tourist resources. The DEM used in this study came from the digitized contour lines of 209 1:5000 maps provided by CD-Map (1999). The contour lines, imported into IDRISI32, were interpolated (linear interpolation) to produce a faceted model. The visibility distance from buildings was set to 2 km because cages are flat, small, and not very visible structures. Suitability scores assigned were a value of 1 (totally unsuitable) for visible areas and 8 (very suitable) for not visible areas. Visibility from beaches was calculated in the same way, however this time the visibility distance value was different for beaches classified as important and for those classified as not important. The beach importance classification followed is as proposed earlier in the Beach Submodel section. Table 2d shows the visibility distance values assigned to

10 364 O. M. Pérez et al. each beach group. Visibility distance values were bigger for beaches catalogued as important to minimize the visual impact. On the other hand, visibility distance values for less important beaches were smaller, as visual impact was of lesser concern. The two criteria forming this submodel, building and beach viewshed suitability layers, were overlaid. Figure 3d shows the viewshed suitability map, where areas visible from beaches and tourist resources were scored low (totally unsuitable) and areas not noticeable scored high (very suitable). GIS-Based Models The three submodels were combined using MCE and the calculated weights of importance shown in Table 3. To focus on the areas of maximum interest for development of cage systems, these images were then masked to exclude water depths in excess of 50 m. This is because mooring systems become very complex to install and manage in deep water (T. Meyer, personal communication, 2001). Figure 3e shows the final model output generated and the suitable area (km 2 ) in each category. The total available area was km 2. Discussion and Conclusions Although a larger number of variables could be used for marine cage siting (Beveridge, 1996), this study focused only on those controlling their integration and coexistence with the tourism industry in Tenerife. Is it important to have knowledge of the future development potential of the island if aquaculture is to be developed without directly competing for the same coastal space and resources as the tourism industry. From the km 2 of available coastal area (using only that area above the 50 m isobaths), 46% was identified as being very suitable (score 8). The total percentage of all suitable scores (scores 5, 6, 7, and 8) was 56%. The remaining 44% of coastal area was considered unsuitable, and therefore aquaculture development in these sites would not easily coexist with the tourism industry. These results are very encouraging in terms of marine cage culture development in Tenerife and suggested that the very important and well-established tourism industry in the island could coexist with the development of marine aquaculture. At present, almost half of the coastal area in Tenerife shows a good potential. Once the potential for developing marine aquaculture in Tenerife has been determined, further logical steps would be to investigate site selection in terms of water quality requirements (such as temperature, suspended solids, sewage discharges, etc.) marine environment (such as waves, currents, bathymetry, etc.), infrastructure to support the industry (such as roads, airports, ports, etc.), and other uses of the coastal space, such as fisheries. The model could also be modified by expert panels to fine-tune the output. GIS are powerful tools that can be used to organize and present spatial data in a way that allows effective environmental management planning. GIS technology in aquaculture Table 3 Ranks and calculated weights (based on a pairwise comparisons matrix) Weight Beach 0.44 Nautical sports 0.17 Viewshed 0.39

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