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Aquacultural Engineering 23 (2000) 233 – 278 www.elsevier.nl/locate/aqua-online

Applications of geographical information systems (GIS) for spatial decision support in aquaculture Shree S. Nath a,*, John P. Bolte b, Lindsay G. Ross c, Jose Aguilar-Manjarrez d a Skillings-Connolly, Inc., 5016 Lacey Boule6ard S.E., Lacey, WA 98503, USA Department of Bioresource Engineering, Oregon State Uni6ersity, Cor6allis, OR 97331, USA c Institute of Aquaculture, Uni6ersity of Stirling, Stirling FK9 4LA, UK d Food and Agricultural Organisation of the U.N, Viale delle Termi di Caracalla, 00100 Rome, Italy b

Received 1 September 1999; accepted 2 October 1999

Abstract Geographical information systems (GIS) are becoming an increasingly integral component of natural resource management activities worldwide. However, despite some indication that these tools are receiving attention within the aquaculture community, their deployment for spatial decision support in this domain continues to be very slow. This situation is attributable to a number of constraints including a lack of appreciation of the technology, limited understanding of GIS principles and associated methodology, and inadequate organizational commitment to ensure continuity of these spatial decision support tools. This paper analyzes these constraints in depth, and includes reviews of basic GIS terminology, methodology, case studies in aquaculture and future trends. The section on GIS terminology addresses the two fundamental types of GIS (raster and vector), and discusses aspects related to the visualization of outcomes. With regard to GIS methodology, the argument is made for close involvement of end users, subject matter specialists and analysts in all projects. A user-driven framework, which involves seven phases, to support this process is presented together with details of the degree of involvement of each category of personnel, associated activities and analytical procedures. The section on case studies reviews in considerable detail four aquaculture applications which are demonstrative of the extent to which GIS can be deployed, indicate the range in complexity of analytical methods used, provide insight into

* Corresponding author. Tel.: + 1-360-4913399; fax: + 1-360-4913857. E-mail address: [email protected] (S.S. Nath) 0144-8609/00/$ - see front matter © 2000 Elsevier Science B.V. All rights reserved. PII: S 0 1 4 4 - 8 6 0 9 ( 0 0 ) 0 0 0 5 1 - 0

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issues associated with data procurement and handling, and demonstrate the diversity of GIS packages that are available. Finally, the section on the future of GIS examines the direction in which the technology is moving, emerging trends with regard to analytical methods, and challenges that need to be addressed if GIS is to realize its full potential as a spatial decision support tool for aquaculture. © 223 Elsevier Science B.V. All rights reserved. Keywords: GIS; Aquaculture; Spacial decision support

1. Introduction The rapid growth of aquaculture worldwide has stimulated considerable interest among international technical assistance organizations and national-level governmental agencies in countries where fish culture is still in its infancy, and has resulted in increased concerns about its sustainability in countries where the industry is well established. Planning activities to promote and monitor the growth of aquaculture in individual countries (or larger regions) inherently have a spatial component because of the differences among biophysical and socio-economic characteristics from location to location. Biophysical characteristics may include criteria pertinent to water quality (e.g. temperature, dissolved oxygen, alkalinity/salinity, turbidity, and pollutant concentrations), water quantity (e.g. volume and seasonal profiles of availability), soil type (e.g. slope, structural suitability, water retention capacity and chemical nature) and climate (e.g. rainfall distribution, air temperature, wind speed and relative humidity). Socio-economic characteristics that may be considered in aquaculture development include administrative regulations, competing resource uses, market conditions (e.g. demand for fishery products and accessibility to markets), infrastucture support, and availability of technical expertise. The spatial information needs for decision-makers who evaluate such biophysical and socioeconomic characteristics as part of aquaculture planning efforts can be well served by geographical information systems (GIS; Kapetsky and Travaglia, 1995). Moreover, it is often the case that governmental agencies involved with issuing new aquaculture permits need to perform spatial analysis on a proposed site to assess its potential environmental, economic and social impacts on other locations. This situation is analogous to the need for monitoring existing operations in terms of environmental and/or other impacts. As noted by Osleeb and Kahn (1998), these decision support needs cannot be effectively addressed without the use of GIS. Finally, Kapetsky and Travaglia (1995) also point out that the individual investor interested in aquaculture development also requires spatial information particularly at the time of site selection from among a range of alternative locations with different biophysical and socio-economic characteristics. GIS is potentially a powerful tool for assisting this class of decision-makers, and is already being effectively used for such purposes in some places (Carswell, 1998; LUCO, 1998; Arnold et al., 2000) where advanced capabilities in terms of infrastructure and trained personnel exists. In general, however, increased deployment of GIS for practical decisionmaking in aquaculture is hampered by several constraints including: (1) a lack of


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appreciation of the benefits of such systems on the part of key decision-makers; (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 analysts, subject matter specialists and end users of the technology (see also Kapetsky and Travaglia, 1995). The primary goals of this overview are to examine the above constraints in depth, and to supplement previous reviews of GIS applications in aquaculture (Meaden and Kapetsky, 1991; Kapetsky and Travaglia, 1995; Aguilar-Manjarrez, 1996; Ross, 1998). Our overview includes an introduction to GIS terminology and methodology, topics that are relatively well documented in the literature (Burrough, 1986), but to which personnel in aquaculture have had limited exposure, especially from the perspective of GIS as a tool for practical decision-making. A summary of selected cases where such tools have been applied in aquaculture is also provided. Finally, a section of the paper focuses on the future of GIS in aquaculture, and recommendations for its continued use.

2. GIS terminology GIS is an integrated assembly of computer hardware, software, geographic data and personnel designed to efficiently acquire, store, manipulate, retrieve, analyze, display and report all forms of geographically referenced information geared towards a particular set of purposes (Burrough, 1986; Kapetsky and Travaglia, 1995). An excellent introduction to GIS terminology, maintained by the Association of Geographic Information, is available on the World Wide Web (WWW) at http://www.geo.ed.ac.uk/root/agidict/html/welcome.html. There are essentially two types of GIS — vector and raster systems. These systems differ in the manner by which spatial data are represented and stored. Burrough (1986) and Meaden and Kapetsky (1991) provide useful comparisons of vector and raster systems in summary form. In both systems, a ‘geographic coordinate system’ is used to represent space. Many coordinate systems have been defined, ranging from simple Cartesian X-Y grids to spatial representations that correspond to the real world such as latitude/longitude pairings, State Plane coordinates or the Universal Transverse Mercator system coordinate system (Burrough, 1986; Thompson, 1998).

2.1. Vector GIS In vector GIS, spatial data are defined and represented as ‘points’, ‘lines’ or ‘polygons’ (Fig. 1). A point is defined by a single set of coordinates. Examples of point data for aquaculture applications may include features of a farm such as pump locations, supply wells and small buildings like storage sheds. Features such as roads, rivers and canals are conveniently represented as lines (Fig. 1). Lines have starting and ending points referred to as ‘nodes’, which may include a large number of ‘vertices’ (Fig. 1). The segment of a line between two vertices is referred to as an

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‘arc’. Polygons are used in GIS to represent enclosed areas (Fig. 1). A polygon consists of a number of lines, but is distinguished by the characteristic that its starting and ending nodes are the same. For aquaculture applications, examples of polygons include a reservoir, a certain soil class, and a distinct type of land classification (e.g. a mangrove forest). Based on the coordinate system used, a vector GIS knows where the spatial feature (point, line or polygon) exists (i.e. its absolute location), and its relationship to other features (topology or relative location). As an example, a GIS would thus be aware that a reservoir supplying water to a fish farm is located north of the farm ponds. After spatial features are represented in vector GIS, their associated properties can be specified in a separate database. As an example, properties of a soil polygon such as pH, bulk density, cation exchange capacity, and infiltration rate can be archived in a database. These properties are often referred to as ‘themes’, which can be presented in so called ‘thematic maps’. Vector GIS lend themselves well to the use of relational databases because once the spatial features are specified (once), any amount of related data can be associated with them. The term ‘coverage’ is used in the GIS literature to refer to the combination of a geographically referenced feature and its associated data. Thus, a ‘stream coverage’ would refer to the line in a vector GIS that represents its course, and the associated data such as flow rates, temperature, water quality characteristics, and withdrawal rates (and possibly spatial locations) for different uses.

2.2. Raster GIS In a raster GIS, space is represented by a uniform grid, each cell of which is assigned a unique descriptor depending on the coordinate system used (Fig. 2).

Fig. 1. Representation of spatial features (points, lines and polygons) in a vector GIS (adapted from Thompson, 1998).


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Fig. 2. Representation of spatial features (points, lines and polygons) in a raster GIS (adapted from Thompson, 1998).

Thus, a cell in a grid that uses the latitude/longitude system would have a pair of coordinates. Numerical data pertaining to spatial features that are represented in the grid are assigned to the appropriate cell. Raster GIS can be conveniently thought of as being a spreadsheet, and one of the earliest GIS applications in aquaculture was essentially a spreadsheet-based system to assess carp culture possibilities in Pakistan (Ali et al., 1991). In raster systems, georeferenced data stored in a grid constitute a ‘layer’, as opposed to a coverage in vector systems. Each grid contains a unique set of information. Thus, each of the soil properties described above for vector GIS would be stored in a separate layer if a raster system is used. In other words, there would be as many layers as there are properties of the spatial feature. Consequently, data storage requirements for raster GIS are generally higher than comparable vector systems. Further, if there is a need to integrate different layers that describe a certain feature, the process may also be more costly in terms of access time. These disadvantages are perhaps less important in present day computers because large hard disks are not only relatively inexpensive, but also allow rapid data retrieval. A more important disadvantage of raster GIS is that the spatial resolution of analyses is limited to the cell size of the coarsest layer. When the cell size is large relative to the scale of analysis, maps created from raster data tend to be somewhat blotchy. Nevertheless, raster systems are particularly useful when it comes to numerical manipulations because of the uniform grid structure. For instance, they lend themselves well to spatial modeling applications. As an example, a water temperature simulation model can be executed with weather datasets (e.g. air temperature, solar radiation, cloud cover, relative humidity, and wind speed) in the form of raster inputs. Model output can be exported to a gridded file, which in turn can be

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imported into raster systems for further classification and display (Kapetsky and Nath, 1997). Similarly, raster GIS can be used to model effluents discharged by aquaculture operations into natural water bodies by applying appropriate equations to cells in the spatial grid (Perez-Martinez, 1997). Another advantage of raster GIS is that remote sensed data (which are usually stored in raster format) can often be directly imported into the software and immediately become available for use (Burrough, 1986).

2.3. Analytical scope, reporting and 6isualization One of the powerful features of both vector and raster GIS packages is that statistical summaries of layers/coverages, model stages or outcomes can easily be obtained. Statistical data can include area, perimeter and other quantitative estimates, including reports of variance and comparison among images. A further powerful analytical tool that aids understanding of outcomes is visualization of outcomes through graphical representation in the form of 2D and 3D maps. For example, entire landscapes and watersheds can be viewed in three dimensions, which is very valuable in terms of evaluating spatial impacts of alternative decisions. Techniques have also been developed to integrate GIS with additional tools such as group support systems, that allow interactive scenario development and evaluation, and support communication among stakeholders via a local area network (LAN; Faber et al., 1997). There is also currently rapid development and deployment of Internet-enabled GIS tools, which allow a wider community of decision makers to have instant access to spatial data. All of these tools are constantly being added to GIS packages and are of great value if appropriately used. Presently available tools include Arc/Explorer and Internet Map Server (from ESRI, Inc) and MapXtreme (from MapInfo Corporation). Because GIS technology is evolving at a very rapid rate, we have chosen not to evaluate specific products. Information about GIS and related tools are, however, available from various Web sites including: http://www.utexas.edu/depts/grg/virtdept/resources/vendors/vendors.htm and http://www.gislinx.com/. Guidelines for selecting GIS tools are available in: Burrough (1986), Meaden and Kapetsky (1991) and Burrough and McDonnell (1998).

3. GIS methodology Ideally, any GIS study will consist of seven phases: identifying project requirements, formulating specifications, developing the analytical framework, locating data sources, organizing and manipulating data for input, analyzing data and verifying outcomes, and evaluating outputs (Fig. 3). In practice, it may be necessary to iterate within the overall process, particularly among the first four phases. The figure also indicates the degree to which different types of project personnel namely end users, subject matter experts, and GIS analysts can be expected to be involved. The degree of personnel involvement within each phase will vary according to the


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organization(s) and/or types of personnel, as well as according to the requirements of specific projects. With regard to the first phase of any GIS project (basically a conceptualization and planning step that precedes actual implementation), our opinion is that it has traditionally been somewhat neglected both in the GIS literature as well as in practice. This is true despite the fact that it will likely determine the extent to which information generated by the use of GIS is used in real world decision making. The latter criterion, of course, is the ultimate yardstick by which success of spatial methods and technology will be measured over the long-term. The phases involved in any GIS study (Fig. 3; described in detail below) occur iteratively in the sense that project personnel may often conduct a pilot-scale study

Fig. 3. Schematic representation of the phases in a GIS project. In practice, most of the iteration within the overall process is to be found within the first four phases. Involvement of end users (), subject matter specialists ( ), and GIS analysts ( ) within each of the phases is also indicated, with symbol sizes reflecting the importance of their respective roles.

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with available information, and then successively enhance and/or refine the analysis until a satisfactory end point is reached. Alternately, subsequent phases in the project cycle may result in new information that needs to be incorporated in the preceding phase(s). For example, it may become evident during the development of the analytical framework that requirements documented during the first iteration of the project cycle (Fig. 3) may need to be revised. Recognizing the iterative nature of a GIS study allows the entire project team to develop an improved understanding of the issue on hand, and how spatial methods and technology can be best be used to address it. Once project personnel explicitly recognize that GIS work is iterative in nature and begin to document what is learnt during each phase, it becomes easier to track how user requirements are explicitly addressed during implementation (often referred to as traceability in the software literature; Ambler, 1998), and to deliver timely as well as meaningful project updates (e.g. GIS outputs and reports).

3.1. Identifying project requirements The process of identifying requirements for a GIS is essentially a multiple stakeholder decision-making situation. This is because such work is invariably executed by a group of subject matter experts and analysts, and because results of the analyses are potentially useful to a range of decision-makers. Individuals in these three broad groups of stakeholders are likely to have different perspectives and expectations with regard to the capabilities and limitations of GIS. It is important that these perspectives and expectations are allowed to surface during the early stages of GIS work so that an enabling environment can be created within which decision support needs of end users are discussed and project goals formulated. Once project personnel, particularly end users, have had an opportunity to present their spatial decision support needs, discussions can begin to examine how GIS tools can address these needs, and clarify the limitations of such tools (e.g. spatial data availability and quality, software/hardware resources that may be needed, cost and time constraints, etc.). At this stage, it is usually not necessary or even productive to invest an inordinate amount of time in discussing these issues. Instead, the intent is to develop common understanding about decision support needs, specify project goals, clarify GIS functionality and develop a listing of general requirements. More importantly, such discussions can facilitate the development of a creative and productive project team, wherein each participant has an opportunity to identify their own roles and responsibilities, and those of their partners. The above observations are consistent with the work by Campbell (1994) (see also Campbell and Masser, 1995), who suggests that the following factors are important in successful GIS implementation and its subsequent use in decisionmaking: an information management strategy in which the needs of users are clarified, and resources and values of the organization(s) are accounted for;


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identification of simple applications that generate information fundamental to the work of potential users; implementation that is user-directed, and which has the commitment and participation of staff throughout the organization(s) involved; and the ability for the organization(s) to create sufficient stability or to be able to cope with change, both in terms of evolving specifications and rapidly changing technology. In our own experience, there has been a tendency among some of the technical assistance organizations with which we collaborate to invest a considerable amount of time, effort and financial resources in developing GIS capabilities and acquiring expensive spatial datasets. However, it is often the case that this investment is made without a clear understanding about what they wish to accomplish with such capabilities, and the stakeholder groups (both within and outside the organization) whose decision support needs they are attempting to fulfil. More often than not, GIS capabilities of such organizations are primarily used as a tool for generating and displaying maps. The current state of spatial methods and technology, on the other hand, clearly indicates that GIS capabilities go beyond data management and visualization alone. For instance, process-based models and GIS (especially raster systems) are very complementary tools in that the former can be used to generate new output layers from multiple base maps already in GIS format. The derived layers often contain information that is more useful to decision-makers. Moreover, it is now possible to directly integrate within GIS models that have distributed (i.e. spatially explicit) parameters, and to seamlessly run alternate scenarios within a single software package. Prior to the availability of such techniques, it was necessary to execute multiple model runs outside of a GIS environment, and to import the output into GIS for visualization. Such indirect coupling often requires additional time and technical expertise to handle model management and data transfer requirements, besides limiting the range of scenarios that end users may wish to analyze. Direct integration of models within GIS, however, has the potential to enable such users in the aquaculture domain to explore alternate scenarios in a truly interactive way, simultaneously reducing the need for the continued presence of analysts or at least using their expertise in a manner that is more cost effective. The trend towards interactive GIS use is already evident in both in the business world and in some natural resource management domains such as conservation efforts. Another observation to be made in the current context is that if the target decision support audience is not adequately consulted to assess their needs, the possibility exists that considerable effort may be expended by organizations to generate information that is rarely used by the intended audience. The fundamental message here is that if end users are involved early on in the planning process for GIS projects and their needs better understood, experts and analysts are more likely to generate strategies that better address these needs. Moreover, they are also more likely to make optimal use of spatial methods and technology within any organizational constraints that may exist. It should be noted that our observations about adopting a stakeholder-driven approach to project design and management, and

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specific techniques to manage the process are not unique to GIS applications, but are increasingly recognized within a number of domains including organizational development (Adams, 1986; Senge, 1990), concurrent engineering design (Voland, 1992), and information technology (Ambler, 1998).

3.2. Formulating specifications The goals and requirements of the project articulated in the first phase above are by necessity of a general nature. This is largely because it is rarely productive to embark upon an exercise to specify project components without a broad understanding of decision support needs. Furthermore, if team members begin to focus too much on project specifics early on in the overall process, they run the risk of not being able to adequately accommodate additional needs of end users (given that such needs usually tend to evolve over time), and of already confining themselves to a certain mindset about implementation strategies. However, once an overall understanding of project requirements has been developed among team members, it is helpful to develop a listing of more functional specifications corresponding to each of the requirements that have been identified. For instance, if the project requires that the final GIS be interactive (implying that the end user can explore alternate scenarios on their own), one or more of the following are representative of possible functional specifications: capability of generating and editing different thematic maps; features for querying attributes in spatial databases; support for adding new themes and/or updating current information in the GIS over time; enabling application of the analytical approach to different geographical regions; and capability to modify existing models and/or link new models to the GIS. The process of identifying functional specifications involves an in-depth analysis of each of the project requirements. In practice, it is usually beneficial for the subject matter experts and GIS analysts to first formulate project specifications jointly, and to then share these with the end users. This approach tends to result in time (and therefore cost) savings. Moreover, the need for communicating (potentially) difficult technical concepts is minimized because end users are likely to be more interested in the actual specifications, rather than in the process by which they were determined. The benefits of formulating specifications for a GIS project far outweigh the initial investment in terms of resource and time commitments. Some of the benefits include an increased probability of implementing a system that does indeed meet end user needs, laying the foundation for a work breakdown structure with associated timelines and responsibilities (for different project personnel), and an improved likelihood of building a GIS with the appropriate types of hardware and software tools. Experience from the software industry also suggests that when project specifications are formally identified, there is an improved probability of minimizing cost over-runs and time delays. On the other hand, as noted by the


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same author, it is safe to assume that no project can ever be fully specified (particularly during the initial phases of development) and a judicious end point for this phase must be identified to ensure that timely progress towards the overall goals is maintained. It is also often the case that when functional specifications are identified, project personnel may identify new requirements, which need to be appropriately documented with existing information from the first phase of the overall project (i.e. identifying project requirements; Fig. 3).

3.3. De6eloping the analytical framework The previous two phases deal primarily with aspects of what is to be accomplished (i.e. project needs). Development of the analytical framework for a GIS project, on the other hand, addresses issues of how these needs will be addressed. This phase largely involves subject matter specialists and GIS analysts (Fig. 3). However, consultations with end users are recommended to ensure that the project will indeed address their needs, to accommodate new needs that may arise, and to foster an improved understanding of analytical methods that may be used. An understanding of the methods used, as well as their limitations, is critical in terms of appropriate application of GIS outcomes for decision support. Several methods have been used, either singly or in combination, by GIS practitioners to integrate spatial information into a useful format for analysis and decision making. The following represent analytical methods that have already been used in aquaculture GIS or have potential for use in the future. Their inclusion here is intended to provide readers with an overview of the range of analytical methods that may come up for discussion during this phase of a GIS project.

3.3.1. Arithmetic operators A large number of arithmetic operators can be used in GIS (Burrough, 1986). For instance, a useful precursor is the scalar operation in which a constant term is to be applied to spatial data (e.g. to estimate the potential annual consumption of fish in a region, the population size of towns can be multiplied by a constant representing the annual fish consumption per capita). Another example would be the subtraction of monthly evapotranspiration and seepage layers from a precipitation layer to estimate mean monthly water requirements for ponds (Aguilar-Manjarrez and Nath, 1998). 3.3.2. Classification It is almost always the case that the source data, whether in real or integer format, will need to be further classified before further use. Classification is an essential part of any data reduction process, whereby complex sets of observations are made understandable. Although any classification process involves some loss of information, a good scheme not only aims to minimize this loss, but by identifying natural groups that have common properties, provides a convenient means of information handling and transfer (Burrough, 1986). Further, in any classification process, care must be taken to preserve the appropriate level of detail needed for

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sensible decision making at a later stage (Burrough, 1986; Aguilar-Manjarrez, 1996; Ross, 1998). Aguilar-Manjarrez (1996) provides an exhaustive review of five methods that have been explored to classify data on land types for various uses: 1. The FAO land evaluation methodology which assesses land suitability in terms of an attribute set corresponding to different activities. 2. The limitation method in which each land characteristic is evaluated on a relative scale of limitations. 3. The parametric method in which limitation levels for each characteristic are rated on a scale of 0 to 1, from which a land index (%) is calculated as the product of the individual rating values of all characteristics. 4. The Boolean method which assumes that all questions related to land use suitability can be answered in a binary fashion, and that all important changes occur at a defined class boundary. 5. The fuzzy set method in which an explicit weight is used to assess the impact of each land characteristic. Fuzzy techniques are then used to combine the evaluation of each land characteristic into a final suitability index. Apart from a dominant suitability class, the fuzzy set method equally provides information on the extent to which a certain land unit belongs to each of the suitability classes discerned. For GIS applications, any of the above methods can be used to classify source data into a four- or five-point scale of suitability (with one being the least suitable). However, the choice among classification methods is dependent on the type of data and intended uses of the output information. Classification allows normalization of all data layers, an essential pre-requisite for further modeling. An example is the grouping of soil data into four classes based on a number of properties important for pond construction (Kapetsky and Nath, 1997).

3.3.3. Simple o6erlay The most common technique in geographic information processing is that of topological overlaying in which multiple data layers are overlain in a vertical manner. Topological overlays may be broadly classified into simple and weighted (see below) methods. After reclassification of thematic maps in terms of suitability for an activity has been accomplished, simple overlay can be accomplished by applying mathematical or logical operators to the layers. This can often generate very valuable outcomes. Simple overlays operations assume that all layers have equal importance. Outcomes of the operation can be further refined if they are overlain with constraint layers by subtraction. A more common practice, however, is to represent constraint layers in Boolean format, with disallowed and allowed areas defined as 0 and 1, respectively. In this way, when the constraint layer is multiplied by one containing suitability data, the former is automatically excluded from the outcome. A series of such simple reclassifications and overlays can be assembled as a tree of sequential choices (i.e. a decision tree) to provide a rational outcome to a spatial problem. Examples of constraint layers are large water bodies and forested lands that are unavailable for aquaculture (Kapetsky and Nath, 1997).


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3.3.4. Weighted o6erlay In decision making, it is usually the case that different attributes under consideration do not have the same level of importance. This calls for a weighted overlay approach, in which each layer is assigned a weight that is proportional to its importance. To accomplish this, each source layer is first reclassified onto a common scale and then multiplied by a weighting factor. The resulting values are used in further overlay operations to obtain an outcome (applying Boolean constraints in the final stage if needed). The derivation of weightings for source attributes is often assumed to be an objective scientific matter. However, it is clear from a range of studies that even expert opinions on ranking of attributes may differ substantially. Aguilar-Manjarrez (1996) showed that, from a list of attributes, a group of experts with similar backgrounds generally agree upon which are the most relevant to use for any given decision process. However, the ranking assigned to the attributes by these experts can differ. In general, Aguilar-Manjarrez (1996) showed that from a list of ten selected attributes, different experts would be in agreement on the priority of the first three to four and the last three. However, attributes with mid-level priority are often dealt with quite differently. Moreover, subject matter experts with different backgrounds (e.g. aquaculturist, coastal zone planner, conservationist, etc.) bring differing agendas to the same problem, resulting in a range of outcomes. At worst, it is clear that without guidance, a range of prioritizations can be obtained which are cumulatively meaningless. GIS analysts therefore need to ensure that weightings applied personally are as objective as possible and that where expert group input is used, the basis for this input is fully explained and carefully analyzed before its use for decision making. Summary tables that transparently document factors used in the evaluation and the weights assigned to them by different experts are a useful aid in this regard (Aguilar-Manjarrez, 1996; Aguilar-Manjarrez and Nath, 1998). As the number of layers in a model increases, the logic of processing them can become confusing. Consequently, special routines to assist with this process have been provided in certain GIS packages. For example, the decision support module in IDRISI (Eastman, 1993) supports multi-criteria evaluation (MCE), which is based on the analytical hierarchy process (AHP) described by Saaty (1980). This module enables a structured, logical approach to weighted overlay with built-in crosschecks at some stages of the weighting process to ensure consistency of logic by the operator. AHP falls into the broader category of pairwise comparison techniques in which attributes are ranked against each other to assess their relative importance. Although AHP or other pairwise comparison variants do provide opportunity for increased objectivity in the ranking of factors, they do not adequately address the issue of different weightings that may be assigned by diverse subject matter experts. For instance, different weightings were assigned to factors important for pond aquaculture by different experts in continental-scale GIS studies (Kapetsky and Nath, 1997; Aguilar-Manjarrez and Nath, 1998). The AHP process was useful in these studies in terms of providing a logical framework for comparing the factors. However, in both instances, the authors ultimately relied on their own ranking schemes (which had similar patterns to those of the experts) to

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assign weights to the factors. In general, it is unlikely that technical methods such as AHP or other multi-criteria decision making tools (see review by Merkhofer, 1998) can be a complete replacement to a well-facilitated exercise in which stakeholders (i.e. subject matter specialists, GIS analysts and end users in the current context) are encouraged to seek consensus on the relative importance of attributes for use in GIS. Instead, such tools should be viewed as providing additional support to the consensus building process.

3.3.5. Neighborhood analysis A key function provided by GIS, and one which cannot be easily addressed by the use of any other decision support tool, is its capability to allow evaluation of the characteristics of an area that surrounds a specific location, which is referred to as neighborhood analysis. GIS software provides a range of neighborhood functions including point interpolation techniques in which unknown values are estimated from known values of neighboring locations. These techniques are typically used to convert point coverages into grids or to generate elevation data either in triangular irregular network (TIN) format for vector GIS or as a digital elevation model (DEM) for raster GIS. DEMs have not as yet found widespread use in aquaculture, but are often at the heart of environmental GIS that are targeted towards analyses of entire landscapes. For instance, the DEM for a watershed of interest is a key layer in many spatially explicit hydrological models. In the future, such tools are likely to find applications in aquaculture from the perspectives of assessing water availability and modeling environmental impacts of existing and/or proposed aquaculture operations. A common type of neighborhood operation is buffering, which allows the creation of distance buffers (or buffer zones) around selected features. An example where this operation may be useful in aquaculture is when there is a need to determine how many streams are available (or to be avoided) within a specified distance of a proposed facility.

3.3.6. Connecti6ity analysis This type of analysis is characterized by an accumulation of values over the area that is being traversed. Support for connectivity analysis in commercially available GIS software is not as standardized in comparison to the operators described above. Network analysis is one type of connectivity operation, which is characterized by the use of feature networks such as hydrographic hierarchies (i.e. stream networks) and transportation networks. As an example of the use of network analysis in aquaculture, Kapetsky and Nath (1997) estimated the shortest transportation path from a given site to the nearest market. The most sophisticated type of connectivity operation is three dimensional analysis which is helpful in terms of visualizing impacts of alternate decisions on entire landscapes (e.g. watersheds). Such advanced features of GIS are likely to have applications in aquaculture but have apparently yet to be used in the systems that have been developed in this domain.


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3.3.7. Hierarchical models In practice, comprehensive representation of the real world in a GIS often involves the use of a large number of source variables, which when processed into GIS using one or more of the analytical methods described above, may result in considerable complexity. Experience suggests that when the number of layers exceeds about 10, MCE becomes difficult, even to the experienced modeler. Such situations warrant the development of a hierarchical modeling scheme. In this approach, naturally grouped variables are first considered together to produce ‘sub-model’ outcomes such as water needs, soil suitability, input availability, farm gate sales, and markets (Kapetsky and Nath, 1997). It is often the case that a source variable or processed layer will be used in more than one sub-model and that the layer may need to be transformed depending on the intended purpose. Each of these sub-models may, in turn, be derived from lower-level models which pre-process variable data into useful factors. Another example from aquaculture is the estimation of wave height from wind velocity and fetch (Ross et al., 1993). It is, of course, not necessary to embed all of the models and/or sub-models within a single system per se. This is because commercial GIS can export and import information quite seamlessly with external modules. For instance, growth estimates derived from an external bioenergetic model (linked to spatial weather and water temperature datasets) have been used to predict fish yields in continentalscale GIS (Kapetsky and Nath, 1997; Aguilar-Manjarrez and Nath, 1998). In a different context, external rule-based systems are being used in conjunction with GIS to generate future agricultural land use patterns for assessing policy alternatives in the Pacific Northwest region of the United States (Bolte, unpublished information).

3.3.8. Multi-objecti6e land allocation Once an activity has been modeled and quantified, it will invariably have some potential to conflict with other uses of the space or resources. For example, practices such as agriculture, forestry, and aquaculture may compete for available land and associated resources. This calls for trade-off decisions to be made so that activities can coexist. These decisions typically require consideration of economic, environmental and social ramifications of alternative land use practices. Decision support tools are available in some GIS packages to facilitate this process. The multi-objective land allocation (MOLA) and multi-dimensional decision space (MDCHOICE) tools in IDRISI are good examples. MOLA was used by AguilarManjarrez and Ross (1995) to identify areas suitable for agriculture and aquaculture across the Sinaloa state of Mexico (see section on case studies below). Inputs for the MOLA technique originate from the results of weighted overlays. The approach allows GIS analysts to set limits on areas required for different land uses and assign requirements for these uses. An iterative process is then used to successively reassign ranked cells to alternate activities depending upon how closely they match the associated requirements.

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3.4. Identifying data sources Once the analytical framework has been developed, it is necessary to identify data sources to be used in the project. This phase is largely restricted to GIS analysts (Fig. 3), although subject matter specialists often provide helpful advice (e.g. identifying non-spatial datasets). Information for spatial decision-making and analysis is varied, and will usually consist of data describing the biophysical, economic, social and infrastructural environments. These data can come from a variety of sources ranging from primary data gathered in the field or satellite scenes to all forms of secondary data, including textual databases and reports. It is generally both costly and time consuming to collect field (primary) data first hand. Therefore, all GIS practitioners attempt to locate the data they need from existing secondary sources, either in paper or digital form. The initial consideration is identifying what data are needed for the overall analysis. This is followed by attempts to source the data, and to assess their age, scale, quality and relative cost. Different data types are often developed in different geographic coordinate systems, and must be re-projected onto a common coordinate system. Other common issues are ensuring that features common across multiple layers are spatially coincident, and understanding the resolution, constraints and uncertainty associated with the data. For these reasons, data collection and preprocessing are typically the most expensive and time-consuming component of an analysis. Digital database availability is increasing at a rapid rate throughout the world. These databases contain information ranging from natural resources (e.g. maps of soils, water resources and temperature distributions), to population census and cadastral (i.e. property ownership and associated boundaries) data. In many cases, such data may only be available in hard copy reports, although information is also available on CD-ROMs, from which databases for GIS use can be extracted. A further source of data is the WWW, from which a wide range of mapped and other spatial data can be found by carefully executed searches. Spatial information at low resolution can often be obtained in this way, good examples being the Global 30 arc second elevation (http://edcwww.cr.usgs.gov/landdaac/gtopo30/ gtopo30.html), and the 1-km Global Land Cover datasets. Other online datasets include information on drainage basins, and weather, which can be downloaded from Web sites at some universities and international agencies. In this regard, a useful site that provides metadata search capabilities and access to a range of data resources is the one maintained by the Center for International Earth Science Information Network (CIESIN) at http://www.ciesin.org. In some countries such as the USA, GIS clearinghouses have been established on the Web and offer access to consolidated datasets from multiple agencies (see http://www.usgs.gov). Many of these free spatial databases are of immense value and should not be overlooked. The ‘lingua franca’ of GIS is the thematic map in which particular attributes of the geographical region are represented digitally or on a specially prepared hard copy map. Clearly, where such maps are available in digital form, they are directly usable in a GIS. Hard copy maps can also be digitized, and the information imported into GIS. The digitizing process, however, is very time consuming and requires many hours of data editing to ensure superior quality.


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Thematic data can be in the form of a choropleth (areas of equal value separated by boundaries), isopleth (lines which connect points of equal value) or point maps (Burrough, 1986), all of which are often valuable, a good example in terms of aquaculture being the hydrographic chart. The more common topographic map frequently contains many thematic data (including elevations, water bodies, roads, cities, and woodlands) which are of value to the GIS analyst. These themes can be extracted at the stage of digitization, and established as separate layers in the spatial database.

3.5. Organizing and manipulating data After datasets have been collected, it is necessary to organize and manipulate them for use in the target GIS. This phase is also largely restricted to GIS analysts (Fig. 3), although depending on the type of application, occasional interaction with subject matter specialists may be warranted. Some of the key activities that occur in this phase include verification of data quality, data consolidation and reformatting, creation of proxy data and database construction. Proxy data refer to information that is derived from another data source, for which established relationships may exist. Examples include estimation of water temperature from air temperature (Kapetsky, 1994), extraction of semi-quantitative texture from FAO soil distribution maps (Kapetsky and Nath, 1997) or calculation of maximum wave heights from wind speed and fetch (Ross et al., 1993). In terms of verification of data quality, the reliability of thematic maps worldwide is variable, as is the currency of their content. These aspects must be considered where critical decisions are based upon such material. Although digital maps are often quite up to date, the spatial accuracy of some printed material can be suspect. Critical assessment and verification of source data quality is very important. The value of such verification for all input data cannot be over-stressed, before digitizing as well as after the fact. It is usually the case that at least some of the layers required in a GIS database will not be of a high enough standard, and verification on paper may very well need to become verification by survey, where warranted. A detailed overview of technical methods to address data quality issues is available in Burrough (1986). Certain data sources such as satellite data are already in digital form, but all others may require some work in order to consolidate them for spatial analysis. Satellite images provide a rich source of data in a form suitable for use in a spatial database. The information collected by the scanners on LANDSAT and SPOT are aimed specifically at natural resource work and the source data can be reprocessed in a variety of ways to reveal details of the environment that may not be apparent in the raw state. Most GIS packages have tools to assist with reprocessing, including the ability to filter and clean up the image, make corrections for atmospheric variations and allow georeferencing of the image to known reference points (Burrough, 1986). Images can then be classified based on spectral signatures of different features of the environment such as forest, grassland, and water bodies so as to reveal different

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land uses, often in some detail. A number of widely used vegetation indices, such as normalized difference vegetation index (NDVI), can also be calculated and the resultant thematic data extracted for immediate use in GIS. A potential example in aquaculture would be the development of an algal bloom index to assess the risk of fish kills due to dissolved oxygen depletion in pond systems. The digital nature of the product means that the data are easy to incorporate and their relative costs low compared to primary data collection surveys. Remotely sensed images are therefore a common starting point for GIS work. Reformatting of data for use in a particular GIS may also include classification of the information (as discussed in the preceding section on analytical methods) and conversion of available data from vector to raster format (or vice versa). Finally, spatial data are often available at different resolutions, and it is necessary to convert the datasets to the desired scale for the analysis to ensure appropriate data processing. This may very well involve seeking expert opinion on the validity of reconciling scale differences. Database construction is another set of activities that is typically undertaken in this phase. Designing an appropriate database is important both in terms of ensuring that the information can be readily accessed for the target application, and is available for re-use at a later time. It has been the case in the past that spatial data were usually stored in formats suited to the type of GIS software being used. This often resulted in spatial databases that were not relatively easy to extract either for use in other types of applications or software. However, many organizations are taking advantage of recent advances both in GIS and database technology by storing both raw and processed information in relational databases. Such data can seamlessly be imported for use in stand-alone GIS applications, but are in principle available for alternate uses (e.g. data publishing across the WWW).

3.6. Analyzing data and 6erifying outcomes This phase represents the culmination of effort that has been expended, particularly on the part of GIS analysts, to develop the analytical framework, locate data sources and organize data for the analysis. As can be expected, the GIS analysts play the most important role in this phase but are likely to interact with subject matter specialists and end users in terms of verifying preliminary results (Fig. 3). Activities that may be encountered in this phase include executing analytical methods (i.e. overlays, model runs and/or other querying knowledge based systems, etc.), importing and exporting data as needed (e.g. intermediate GIS outputs which are required by other components within the overall analytical framework), computation of relevant statistics (e.g. means, standard deviations, ranges, classes, etc.), generation of output information (e.g. maps, tables, graphs, and reports), and verification of outcomes. Field verification as part of any GIS work is absolutely essential, both for quality control of certain data sources (as previously discussed) and for testing the outcomes of models (or other analytical tools). Although environments and activities can be modeled in total isolation as an academic exercise, it is only through careful verification that the general applicability of results can be ensured.


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Fieldwork as part of a verification exercise is frequently referred to as groundtruthing. The general approach to such work is similar to any field survey, and standard techniques for survey, and environmental measurements can be used. The main difference is in the sampling plan and a verification exercise will typically be based on a series of sample points designed to cover the area. Such coverages would normally be well distributed over the ground or water surface, but are often more efficient if a random stratified sampling pattern is used. This allows effort to be concentrated on ensuring that differences between different features in the landscape are assessed, rather than using a simple randomized pattern, which may repeatedly cover a large uniform area. Global positioning systems (GPS) have greatly aided the spatial accuracy of ground-truthing and field verification, in most cases removing the need to use surveying techniques completely. These systems provide three-dimensional position locations from satellites to within about 100 m under normal operation, although actual accuracy is frequently as good as 20 m or even less. By operating in differential mode, two GPS units can give real-time locations accurate to less than a meter and with post-processing to within a few millimeters (Aguilar-Manjarrez, 1996). The value of these systems has been rapidly recognized by GIS practitioners. Some GIS data acquisition systems will accept direct input from GPS so that the location can be displayed over a real satellite image of the study area. Apart verifying data and outcomes of models, field verification can provide feedback into the analytical process itself by allowing the GIS analysts and subject matter specialists to understand, quantify and document errors of the assumptions used. Such documentation should be an integral part of the overall project report.

3.7. E6aluating outputs In this final phase of a GIS project, outputs generated are jointly evaluated by the overall team (i.e. end users, subject matter specialists and analysts; Fig. 3). Several activities are likely to be encountered during this phase, including a summary review of key findings, more detailed examination of individual components of the project together with their underlying assumptions, limitations (if any) of the findings, and an evaluation of the degree to which each of the original requirements of the project have been met. The results of the latter activity provide a useful means of assessing the success of the project. However, it is often the case that outputs from a GIS project are not put to immediate use, but form a component of a larger decision making process (e.g. development of new policies and/or development plans pertaining to). In this regard, it may be difficult to properly assess the value of the information generated by a GIS project, and therefore its contribution towards decision support. If careful attention is given to this aspect at the beginning of a GIS project (i.e. during the phase of identifying project requirements), it should be possible to develop a set of indicators which can be measured over time and used to track how GIS information is used in aquaculture decision making by end users. Feedback from such indicators is likely to be helpful in improving further GIS development and application.

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Little work has apparently been done in the area of indicator monitoring by personnel involved in aquaculture GIS, perhaps because many of the efforts to date have been undertaken primarily as academic exercises. In other contexts where GIS have been developed by technical assistance organizations, it would appear that an implicit assumption by the implementing personnel is that if the information is produced, the target audience of decision makers would actually use it. However, because end users have often not been consulted in aquaculture GIS projects (as previously indicated), this assumption is somewhat questionable. Clearly, there is need for typical end users, subject matter specialists and spatial analysts to collaborate more actively in the development and application of GIS for aquaculture.

4. Case studies in aquaculture The use of GIS in aquaculture, together with selected cases, has previously been documented (Meaden and Kapetsky, 1991; Kapetsky and Travaglia, 1995; AguilarManjarrez, 1996; Ross, 1998). In this section, we build on the foundation that has been set forth by the above authors, specifically examining selected cases (in some detail) from the perspective of their applications for spatial decision support in aquaculture. A more comprehensive listing of GIS applications for aquaculture is presented in Table 1 (see Meaden and Kapetsky, 1991 and Aguilar-Manjarrez, 1996 for other examples). The selected cases represent a broad sampling across geographic scales ranging from local areas (i.e. a small bay), to sub-national regions (i.e. individual states/ provinces), to national and continental expanses. They also vary with regard to the degree to which GIS outcomes have been used for practical decision making. Further, the case studies demonstrate the large extent of GIS applications that are possible in aquaculture including: site selection for targeted species, environmental impact assessment, conflicts and trade-offs among alternate uses of natural resources, and consideration of the potential for aquaculture from the perspectives of technical assistance and alleviation of food security. The cases also vary significantly with regard to complexity of the analytical methods used (i.e. ranging from simple overlays to weighted combinations to use of relatively sophisticated models). Finally, the case studies are indicative of issues associated with data procurement and manipulation, and the diversity of GIS packages that are available. Each of the case studies is presented in the following format: source of the work; objectives; target decision support audience; geographic area and scale of analysis; analytical methods and results; and comments (e.g. limitations of the approaches used, possible enhancements, and actual use for decision making).


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Table 1 GIS applications in aquaculture according to the kind of assessment and scale of study (adapted and updated from Kapetsky and Travaglia, 1995) Purpose

GIS Software

Author(s)

Large area assessments (low resolution) Warm water aquaculture Continental Africa and Madagascar Inland aquaculture Latin America

ERDAS and ARC/INFO ARC/INFO

Kapetsky (1994)

Inland aquaculture

Continental Africa

ARC/INFO

Medium area assessments (medium Trout farms Carp culture (ponds) Tilapia and Clarias culture in ponds Small reservoir fisheries

resolution) England and Wales Pakistan Ghana Zimbabwe

GIMMS Spreadsheet ARC/INFO and ERDAS ARC/INFO

Shrimp culture in ponds; fish culture in cages Fish and crayfish farming in ponds Pond and cage culture

Johor (State) Malaysia

ERDAS

Louisiana (State), USA

ELAS

Tabasco (State), Mexico Tunisia

IDRISI

Sinaloa (State), Mexico

IDRISI

Fish, shrimp and mollusc culture Land aquaculture

Geographical region

Shellfish and salmon aquaculture British Columbia (Province), Canada Small reservoir fisheries Southern Africa Small area assessments (high resolution) Catfish farming Franklin County, Louisiana, USA Shrimp and fish farming in Gulf of Nicoya, Costa ponds Rica Brackishwater aquaculture Lingayen Gulf, Philippines Shellfish culture Prince Edward Island, Canada Salmonid cage culture Camas Bruaich Ruaidhe Bay, Scotland Shellfish culture Sepetiba Bay, Brazil Shellfish culture Indian River Lagoon, Florida, USA

ARC/INFO

ARC/INFO

Kapetsky and Nath (1997) Aguilar-Manjarrez and Nath (1998) Meaden (1987) Ali et al. (1991) Kapetsky et al. (1990a) Chimowa and Nugent (1993) Kapetsky (1989) Kapetsky et al. (1990b) Aguilar-Manjarrez (1992) Ben Mustafa (1994) Aguilar-Manjarrez and Ross (1995) LUCO (1998)

MapInfo, Windisp

ALCOM (1998)

ELAS

SPANS

Kapetsky et al. (1988) Kapetsky et al. (1987) Paw et al. (1994)

CARIS

Legault (1992)

OSU-MAP for-the-PC IDRISI ArcView

Ross et al. (1993)

ELAS

Scott et al. (1998) Arnold et al. (2000)

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4.1. Site selection for salmonid aquaculture, Scotland (source: Ross et al., 1993) 4.1.1. Objecti6es The primary objective of this work was to examine GIS as a tool for assessing the potential of salmonid cage aquaculture in a small bay. A secondary objective was to develop a general methodology for spatial analysis of coastal cage aquaculture potential. 4.1.2. Target decision support audience A specific audience for this work was not identified by the authors perhaps because the work was primarily a research effort to investigate the feasibility of using GIS for assessing cage culture potential at a local scale (i.e. fine resolution). Nevertheless, the outcomes of the study and the GIS per se would certainly be of interest to governmental agencies responsible for promoting and/or monitoring aquaculture development in the bay, and to individual investors wishing to identify suitable sites for cage culture. 4.1.3. Geographic area and scale of analysis The study area comprised the Camas Bruaich Ruaidhe Bay located along the West Coast of Scotland. The bay is only 19.8 ha in size. Two scales of resolution (25×25 m, and 10 × 10 m) were pursued in the study, of which the coarser scale was found to be unsatisfactory for processing of most data. As noted by the authors, however, the issue of scale is typically a trade-off between data needs/ availability and objectives of the GIS work. In this case, the finer resolution was more appropriate. 4.1.4. Analytical methods and results This study used a successive screening process for different criteria identified to be of importance in evaluating aquaculture sites, and application of simple overlay techniques within a very low cost raster GIS (OSU-MAP for the PC). The criteria included depth, current velocity, salinity, dissolved oxygen, and temperature (considered to be key factors in salmonid cage culture), as well as other factors (i.e. local infrastructure, topography and exposure). With the exception of local infrastructure (judged to be quite suitable for aquaculture based on a qualitative assessment of accessibility to markets, and availability of labor, services and supplies), all of the other criteria were analyzed spatially for the entire bay. Water quality sampling in the bay suggested that dissolved oxygen and temperature were not likely to limit salmonid culture. Consequently, these two criteria were excluded from further analysis. Topographical information was used primarily to outline the bay and its main features (i.e. it did not have any direct relevance to the issue of suitability for salmonid cage culture, but provided the geographical base over which the remaining criteria were added). From a procedural perspective, following initial examination and screening of the digitized data, the criteria ultimately analyzed within the GIS were: depth (determined on the basis of a bathymetric contour map), exposure (expressed in terms of a proxy variable, namely wave height), current intensity, and salinity, in that order.


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More specifically, the GIS analysis involved overlaying the base topographic map with each of the above layers to obtain independent maps depicting their spatial characteristics. Following this step, a sequential procedure (in which suitability scores were assigned to each attribute; Table 2) was used in successive overlays to arrive at a final assessment of salmonid cage culture potential. In terms of criteria used for each attribute, depth and salinity information (Table 2) essentially serve as constraints in that a score of zero implies that the site is not suitable from the perspective of adequate depth to support cage culture or salinity fluctuations exceeding the tolerance of the target species. The combination of exposure and current intensity, on the other hand, allow alternative sites to be classified into a wider range of suitability categories (1= Most suitable and 4=Least suitable). Example output from such a GIS likely to be of most interest to the target audience of decision-makers would be the final map in which the results of a set of spatial operations similar to those described above (and in Table 2) are added to the base topographical layer so as to show the potential for salmonid cage culture.

4.1.5. Comments According to Ross et al. (1993), only about 6% of the areal expanse of the bay would be suitable for cage culture. Their analysis essentially corresponded to a Table 2 Source layers, associated attributes (and classes), and scores used in the GIS to evaluate salmonid cage culture potential (adapted from Ross et al., 1993) Source layer

Attribute classes

Scores

Bathymetry

Depth B6 m ]6 m

0 1

Wa6e height B60 cm ]60 cm

1 2

Exposurea

Current intensityb

Salinityc

a

Velocity IF Wa6e HeightB60 cm AND VelocityB50 cm s−1 Velocity]50 cm s−1 IF Wa6e Height]60 cm AND VelocityB50 cm s−1 Velocity]50 cm s−1 Salinity 6ariation B8 ppt ]8 ppt

1 2 3 4 1 0

Data layer added to the output resulting from the bathymetric scoring, and exposure scoring criteria applied. b Data layer added to the output from the previous set of operations, and current velocity criteria applied. c Data layer added to the output from the previous set of operations, and salinity constraints applied.

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situation of ‘worst-case modelling’ in that wave heights, for instance, were computed based on the longest recorded fetches and highest wind speed combinations. In other words, the possibility exists that a larger area of the bay would be suitable for cage culture. Apart from the data used in GIS analyses of this nature, the ‘sequence’ in which spatial operators are used will influence the output (Burrough, 1986; Ross et al., 1993). Thus, if the original decision sequence (i.e. depth, exposure, current intensity, and salinity) is changed, the ensuing output will be different because of Boolean algebraic operations. It is therefore important that analysts involved with GIS work closely with subject matter experts to establish the priority of different criteria, which in turn establishes the sequence of spatial operations. This case study is indicative of the potential advantages associated with GIS use for site selection over manual evaluation — the authors in fact made a comparison of time and resource requirements between GIS use and physical assessment by aquaculturists. They concluded that in terms of time, the GIS was more efficient. However, it was more costly to conduct the GIS work, as opposed to a manual assessment — on the other hand, these costs are expected to decline substantially as the tools and techniques are used for additional projects. Moreover, if the GIS were to be refined for use in routine monitoring (e.g. pollution assessment of the bay due to cage culture), its benefits as a management tool would clearly outweigh the investments over the long term. Finally, Ross et al. (1993) proposed a set of guidelines whereby a generic GIS logic sequence could be used for site selection in coastal waters. The guidelines involve screening out locations where depths are unsuitable, grading areas that do have adequate depth on the basis of wave heights (simultaneously addressing engineering specifications for the possible types of cages), grading the resulting areas according to the range of current velocities, and finally examining water quality parameters in terms of their suitability for the target culture species (in the geographic location where their work was conducted, oxygen and temperature were not limiting factors but may very well be in other places). It should, however, be pointed out that the guidelines proposed by Ross et al. (1993) relate only to the biophysical characteristics to be considered in site selection for coastal areas. A more robust site selection strategy would also include detailed analysis of economic (e.g. infrastructure support, availability of and distance to markets, etc.) and social factors (e.g. impacts on coastal communities). Their implementation of a GIS for the Camas Bruaich Ruaidhe Bay did qualitatively addressed some of these factors but not in a rigorous manner. The case studies that follow demonstrate, to different extents, how GIS can be used to address biophysical, economic and social factors although it should be noted that they were designed to generate information for decision support at a more strategic level. At the fine resolution used by Ross et al. (1993), consideration of economic and social factors would, by necessity, have to include information and data types that are very site specific.


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4.2. Assessment of land suitability for aquaculture and agriculture in Sinaloa State, Mexico (sources: Aguilar-Manjarrez and Ross, 1995; Aguilar-Manjarrez, 1996) 4.2.1. Objecti6es The main objective of this work was to develop a detailed GIS that could serve as an analytical and predictive tool to guide (shrimp) aquaculture development at a state-level in Mexico. The GIS was intended to provide planners and managers with a tool to assess land suitability for aquaculture and agriculture in the state of Sinaloa, and to provide guidance for exploring the consequence of land use decisions before they are committed to action. Aguilar-Manjarrez (1996) also extended the GIS to allow evaluation of shrimp aquaculture opportunities at an individual ‘site’ (the Huizache Caimanero Lagoon in Sinaloa). This work is not discussed here due to space constraints. 4.2.2. Target decision support audience The GIS developed by the authors (as well as outcomes generated) would be of interest to governmental agencies responsible for promoting and/or monitoring shrimp aquaculture development in the Sinaloa State of Mexico. More likely than not, information generated would be useful for decision making at the state, regional and national levels. Investors keen on large-scale operations would also be interested in identifying opportunities for shrimp culture where the potential for production is high, and conflicts with agricultural use of the land likely to be limited. 4.2.3. Geographic area and scale of analysis The study area comprised the zone between 22° 12% –27° 13% N and 105° 19% –109° 33% W ensuring coverage of the entire state of Sinaloa (located along Mexico’s northwest coastline), areas of neighboring states (Sonora, Chihuahua, Durango, and Nayarit) and the Gulf of California. The authors report the area of the Sinaloa state to be roughly 58 480 km2. Spatial analysis was conducted at a resolution of 250 m. 4.2.4. Analytical methods and results In this study, models addressing themes pertaining to land use/environmental characteristics as well as water resources were first constructed from source data. The water resource model was developed in the form of a water balance, whereas general environmental and land use models were developed based on a combination of various weighted criteria. Model outputs were then successively integrated by the use of MCE and MOLA techniques to ultimately assess site suitability and land use conflicts relevant to aquaculture and agriculture activities (Fig. 4). The scheme demonstrates how a diverse range of information can be successively integrated within GIS to generate outcomes that are potentially useful to decision makers involved in policy development and technical assessment. The authors used IDRISI as the primary GIS software.

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Fig. 4. A hierarchical modeling scheme with MCE and MOLA to evaluate suitability of locations for aquaculture and agriculture and resolve associated conflicts, in the Sinaloa state of Mexico (adapted from Aguilar-Manjarrez and Ross, 1995).

Thirty base layers (thematic maps) were used in the study and included information ranging from pollution sources, population density, general environmental characteristics, land use practices, infrastructure, and water resources (Fig. 4). These layers were organized into 14 criteria, represented either as factors (a measure of the suitability of the criterion relative to the activity under consideration) or constraints (which limits the alternatives under consideration in a binary manner). Two broad categories of factors were identified: physical and environmental characteristics (e.g. water resources, climate, temperature, soils, topography, etc.) and land use type and infrastructure (e.g. agriculture, livestock rearing, population centers, industries, roads, etc.). Constraints used for agriculture and aquaculture were assumed to be identical (e.g. both activities would not be possible in protected land and polluted areas). The identified factors were then grouped into suitability classes. In this study (Aguilar-Manjarrez, 1996), it was found that the FAO, Boolean and fuzzy methods were needed to evaluate land suitability. The FAO classification method was appropriate for factors that needed a limited threshold (such as water bodies) and Boolean classification was used when a constraint was incorporated in the evaluation (e.g. protecting existing conservation areas like mangroves). Finally, fuzzy classification proved most appropriate for factors whose boundaries were difficult to define, such as soils and slopes.


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After suitability classes were assigned, the factors were weighted by the use of the AHP technique. This was followed by generation of factor maps, which in turn were multiplied by the various constraints to mask out unsuitable areas. The process of classifying factors, their weighting, generation of maps, and application of constraints involved the use of an automated MCE procedure in IDRISI. The final step in the analysis was to apply the MOLA technique in order to maximize the land allocation area for aquaculture and agriculture. The rationale behind the use of this technique was that aquaculture and agriculture are often in conflict because they require similar types of infrastructure, share a number of common biophysical requirements, and have parallel economic and social impacts. Use of the MOLA technique requires assignment of weights for each of the alternative activities under consideration — for this study, equal weights were assigned for aquaculture and agriculture. Outcomes of the various steps listed above (see also Fig. 4) included suitability maps for both aquaculture and agriculture, and a single map depicting areas found by the MOLA technique to be most suitable for these two land use activities (Fig. 5). The GIS analysis and predictions in this study were entirely dependent on a variety of information sources (which themselves are likely to have some level of inaccuracy) and even different scales. Hence, it was considered to be of paramount importance to carry out field verification. Moreover, it was also considered to be very important to locate other data (e.g. pollution sources or other relevant information) which were either not identified or required updating during the creation of the original database. This involved the use of GPS (see Aguilar-Manjarrez, 1996 for full details) to locate points on the ground, and comparison of GIS output to results of a manual survey conducted by two Mexican consultancy firms. The verification exercise suggested that although the areal expanses suitable for aquaculture were comparable between the GIS and manual survey results (roughly 2090 km2 or about 3.5% of the area of Sinaloa), suitable locations that were identified differed among the two methods. This was presumably because of differences in the logic and analytical techniques used. However, it would appear that outcomes from the GIS were more indicative of the true potential for aquaculture because of the range of criteria considered and their integration into suitability models. Moreover, the GIS was able to identify and resolve areas of potential conflict between agriculture and aquaculture. In principle, such information can be helpful to decision makers in terms of exploring alternative land use practices ‘prior’ to committing them to the landscape.

4.2.5. Comments This case study is truly indicative of the potential for use of GIS output to evaluate aquaculture potential and guide its development at a regional scale, particularly in terms of comprehensiveness and sophistication of the analytical methods used. The outcomes resulting from the work are clearly valuable, notwithstanding some limitations in the quality/extent of data available (e.g. weather information), and the analytical methods used (e.g. consideration of only aquaculture and agriculture as alternate land use types, sensitivity of results to the sequence of GIS procedures and criteria weighting, etc.).

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In terms of routine use for decision making, some of the drawbacks of this GIS include limitations of the IDRISI software from the perspective of allowing users to truly interact with the decision models (e.g. easily formulating and executing ‘what-if’ scenarios) and generating reports of analyses undertaken. We discuss these issues in more depth within the case study from British Columbia (see below). This case study could be even further enhanced by the use of analytical methods that have since been developed and applied to aquaculture. For example, in addition to the criteria used, comprehensive analysis of site suitability should include consideration of the potential impacts of land-based aquaculture on surface and ground water resources (e.g. by the use of more recent techniques to model waste dispersion from aquaculture farms as in Perez-Martinez, 1997). The GIS would also likely benefit from integration of its output with economic analysis and marketing tools which can allow a more comprehensive examination of the costs as well as benefits associated with aquaculture and agriculture. Finally, as noted by the authors, the GIS should also be expanded to address land use options besides aquaculture and agriculture (e.g. urban development, forestry, and livestock rearing) if it is to serve as a comprehensive tool for sustainable resource use planning in the state of Sinaloa.

4.3. Shellfish and finfish aquaculture management in British Columbia, Canada (sources: Carswell, 1998; EAO, 1998; LUCO, 1998) 4.3.1. Objecti6es The primary objective of the work represented by this case study was to develop a fully integrated information system (British Columbia Aquaculture System (BCAS); Carswell, 1998), within which GIS tools play a key role, to provide guidance for assessment of site capability for shellfish and finfish aquaculture. Although BCAS also includes inventories of marine plants (e.g. Nori, kelp) which can be accessed for use by personnel interested in farming them, we do not address this aspect in the case study. 4.3.2. Target decision support audience BCAS represents one effort within an overall set of governmental initiatives in Canada to compile information required for land use planning, and related resource management. Within the province of British Columbia, these initiatives include efforts by local governments, federal and provincial governmental agencies, and the First Nations. Development and management of BCAS is an activity of the Ministry of Agriculture, Fisheries and Food (MAFF). At the time it was conceived, BCAS was intended to generate marine aquaculture information that would be of use for the development of policies at the provincial level (e.g. as part of land and resource management plans). Decision-makers at this level continue to be a target Fig. 5. GIS maps indicating suitability classes of land areas for agriculture and aquaculture in the Sinaloa of Mexico (Source: Aguilar-Manjarrez and Ross, 1995). Fig. 7. Suitability for commercial farming and potential yield (crops/year) of Nile tilapia as estimated by the Africa GIS developed by Aguilar-Manjarrez and Nath (1998).

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audience, but use of BCAS has evolved in a hierarchical manner to include local governments in British Columbia as well as individual farmers (or entrepreneurs). For instance, decision-makers in various governmental positions routinely consult with GIS analysts not only to evaluate aquaculture potential, but to combine such analyses with information about unemployment rates and per capita income in order to set policies that potentially facilitate aquaculture development in disadvantaged zones (Carswell, MAFF, personal communication). Carswell also indicates that BCAS is being used by private consultants to advise farmers with regard to identifying suitable sites for shellfish aquaculture. However, because of a current moratorium on further development of finfish aquaculture, this module is not being as extensively used at the current time.

4.3.3. Geographic area and scale of analysis BCAS includes marine resource inventories for the province of British Columbia, which has a coastline of about 29 489 km. Spatial assessment of sites for both shellfish and finfish operations are typically conducted at a scale of 1:40 000 (LUCO, 1998). 4.3.4. Analytical methods and results BCAS includes a range of inventories (e.g. all marine plants, existing and proposed sites for aquaculture, etc) compiled by the aquaculture and commercial fisheries branch of MAFF. In addition, the system interfaces with digital data on a range of spatial biophysical and land use variables, which have been compiled by British Columbia’s Land Use and Coordination Office (LUCO). The biophysical variables are used within BCAS to estimate capability indices of sites to support shellfish and/or finfish aquaculture. The digital databases in which the biophysical variables are archived (and updated) are also used for a range of other applications (e.g. analysis of oil spillage impacts and associated remedial measures, ecological classification of British Columbia’s marine resources, etc.), and represent an excellent example of how spatial databases can be effectively employed by multiple organizations for sustainable natural resource management. In terms of the technology used, BCAS is a Windows-based, menu driven GIS/database application that automates analysis, integration and display of site capability indices for shellfish and finfish aquaculture. The system uses Borland’s Delphi programming language to link the Borland Database Engine and Report Smith with the ArcView GIS software. The database engine is used to access and manipulate data, and to calculate site capability indices on demand. The latter functionality is accomplished by accessing specific modules that have been separately developed to assess the potential for shellfish and finfish aquaculture (see below). The Report Smith tool allows creation of standard tables following any user-specified analyses. Finally, the ArcView component displays the site capability for the chosen aquaculture species, again in a standardized (mapping) format. 4.3.4.1. Site capability index (SCI) for shellfish. The shellfish module in BCAS uses models based on 14 biophysical criteria (Cross and Kingzett, 1992; Cross, 1993) to


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evaluate the capability of a potential site to support the culture of Pacific Oyster, Japanese Scallop and Manila Clam. The criteria are organized into the following subgroups (Cross and Kingzett, 1992): direct impact on growth: water temperature, chlorophyll A (a measure of food availability), fetch, and exposure; direct impact on survival: suspended sediments, tidal flow, fouling/disease/predators, substrate and beach slope (the latter two variables are used only for bottom culture); and indirect impact of water chemistry on growth/survival: salinity, dissolved oxygen, and pH. In terms of specific suitability ranges, the biophysical criteria differ among the three species, and their use to evaluate the suitability of a location for culture involves three steps as follows (Cross and Kingzett, 1992): 1. SCIs for each of the three subgroups above: this involves computations to evaluate each of the criteria for a target shellfish species, and identification of the criterion within each group that is most limiting. In essence, the expected response of any of the shellfish species to levels of the biophysical criteria determines the ‘weight’ of that variable (i.e. the degree to which performance is impacted). Once each of the criteria are evaluated, the one that has the largest impact (i.e. most limiting) within each subgroup is assumed to be the most important; 2. Overall SCI: this value, for any given species and culture type, is calculated as the geometric mean of the SCI for growth and survival, provided the SCI for the water chemistry group is above 0.70 (assumed to be non-limiting). If the water chemistry SCI is less than 0.70, the geometric mean of SCIs for growth and water chemistry are first estimated, with the geometric mean of the resulting value and that for the SCI estimated for survival assumed to be the overall SCI; and 3. Assignment of capability classes: each potential location is assigned one of four capability classes based on the final SCI. The classes are: good (\ 0.75), medium (0.51 – 0.75), poor (0.26 –0.50) and not advisable (B 0.26). Sites classified as good are recommended for the target species and require little mitigation, those classified as medium will likely benefit from mitigation, and those classified as poor are not recommended. Output from the third step outlined above is used within BCAS to generate site capability maps for the target shellfish species. However, it is important to note that the SCI for any of the three species is based solely on data for the biophysical criteria and to a limited extent, on the type of culture system. The SCI does not address social or economic constraints to shellfish culture.

4.3.4.2. Assessment of biophysical capability for finfish. The finfish module in BCAS uses models based on 12 key biophysical criteria (Caine, 1987) to evaluate coastal waterways in terms of their capability to support salmonid farming in cages. The criteria are applied in the following order as per their priority: first order (factors directly affecting fish growth and survival): water temperature, dissolved oxygen, salinity, and phytoplankton;

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second order (factors that may have long term detrimental effects on fish survival): pollution, currents, depth, site physiography, and hydrology (freshwater flow); and third order (risk factors that impact the physical and financial condition of operations, but which can be mitigated by suitable culture techniques): predators, marine plants/fouling organisms, wind and wave action. In BCAS, factors within each of these groups are evaluated and categorized into suitability classes (Table 3), following the strategy outlined by Caine (1987). Clearly, the site capability model in BCAS is more comprehensive than the one documented in the first case study (Ross et al., 1993) and provides improved support for assessing finfish aquaculture potential with the caveat that much more data are needed (EAO, 1998). As with shellfish, social and economic factors are not considered in evaluation of sites for finfish aquaculture.

4.3.5. Comments Among the GIS applications we are aware of in aquaculture, this case study is unique in that: (i) it demonstrates the value of collaborative implementation of such tools and associated spatial databases among multiple governmental organizations, a conclusion previously arrived to by Kapetsky and Travaglia (1995); (ii) it is used to both analyze site potential and for routine management (i.e. information on all aquaculture leases is archived in the databases; see also Arnold et al. (2000), for similar applications of GIS in Florida); and (iii) it is actively used to meet decision support needs of a range of clients. Increase in the use of BCAS and related information systems in British Columbia has generated debates among policy makers in the province with regard to data ownership/pricing issues (particularly when the tools are being used by private consultants). Such debates are indeed a welcome sign because they demonstrate that use of GIS has progressed beyond the domain of analysts per se, and that these decision support tools are being actively used by decision-makers. A continuing concern among those responsible for collecting and archiving information resources, however, is the degree to which end users are aware of limitations in the databases and analysis tools. For instance, the resolution of the data is such that the analyses are not very useful in evaluating specific sites but allow screening of a wide range of alternatives into a few promising ones, which would then require more specific examination to determine their suitability for the target species and associated culture techniques. Moreover, potential farmers would need to examine the economics of proposed operations. Another limitation is that all of the biophysical data needed are not available for all sites, and BCAS generates SCI values based on the information available. End users must pay special attention to the recommendations that are generated to ensure their validity. Despite these limitations (which are being addressed by the responsible governmental agencies), BCAS remains an outstanding example of successful GIS deployment in aquaculture.

Close to sea lion haulouts with many avian/mammal predators

None

Low levels of fouling organisms; no kelp Site not exposed to polar outflows; wave height B0.6 m

Marine plants and fouling organisms Winds and waves/snowfall and freeze over

Nearby sea lion rookeries and haulouts; bird colonies nearby and many mammal predators High levels of fouling organisms; kelp onsite Complete exposure to polar outflows; wave height \1.2 m

\4

B15° Mud or organic ooze

10–19

B2 100–200

B15 Frequent and lethal blooms Within high pollution areas

57

\21 B4

Poor

a In addition to the rating classes shown, sites that fall outside the range of values presented are classified as not acceptable for finfish culture (table adapted from Caine, 1987).

Moderate levels of fouling organisms; kelp nearby Partial exposure to polar outflows; wave height 0.6–1.0 m

1–4

15–30° Sand or mixed rock

B1

\30° Rock, sand or gravel

Site Physiography Slope Substrate

20–49

Hydrology (freshwater lens depth in m) Predators

\50

2–10 50–100

15–24 Infrequent harmful blooms Nearby, low level sources

\24 No record of harmful blooms No sources nearby 10–15 10–50

79

16–21 5–7

Medium

100%

10–15 \7

Good

Rating

Low tide depth (m)

Currents (cm s−1) Slack water Peak flows

Dissolved oxygen (% saturation) Salinity (ppt) Plankton Pollution

Temperature (°C) Summer Winter

Source layer

Table 3 Source layers for biophysical criteria, and ratings used in BCAS to evaluate the capability of waterways to support salmonid cage culturea

S.S. Nath et al. / Aquacultural Engineering 23 (2000) 233–278 265

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4.4. Inland aquaculture potential in Africa (source: Aguilar-Manjarrez and Nath, 1998) 4. 4. 1. Objecti6es The motivation for the current study was the pressing need for up-to-date information that can be used to guide aquaculture development in Africa (World Bank, 1996). Recently compiled, more comprehensive spatial databases for the African continent as well as advances in spatial methods to assess fish farming potential provided an additional incentive to conduct the study.

4.4.2. Target decision support audience The primary audience for continental-scale and country-level estimates of inland aquaculture potential across Africa potentially include national/international technical assistance agencies, large aquaculture corporations, and the international donor community. At this time, however, we are unaware of any instances where the outcomes from the study have actually been used for project assessment or to meet other decision support needs. 4.4.3. Geographic area and scale of analysis GIS analyses were conducted for the entire African continent, with the exception of Madagascar for which some essential weather datasets were not available. The study examines the extent to which ‘sites’ (essentially areas 5 km× 5 km in size corresponding to 3¦ grid cells) satisfy criteria for small-scale and commercial fish farming, and assesses the performance of three index fish species (Nile tilapia, African catfish, and Common carp) under such farming systems. 4.4.4. Analytical methods and results The basis of the present study is conceptually similar to traditional studies for assessing aquaculture potential (Muir and Kapetsky, 1988; Born et al., 1994). However, use of a GIS greatly enhanced the evaluation, especially with regard to the application of objective decision-making methods, quantifying limitations imposed by different production factors, providing estimates of the predicted fish farming potential, and visualizing outcomes. GIS analyses were primarily accomplished using the ArcInfo system, although other software tools (IDRISI, ERDAS and IDA) were also used. Spatial analysis was limited to assessment of land-based inland aquaculture potential, which for practical purposes implies pond systems. The potential for aquaculture in large inland water bodies (e.g. using cage culture) and in ocean waters was not assessed. This case study provides a good example of data consolidation from multiple sources (Table 4). Analytical procedures in the study were largely derived from previous efforts (Aguilar-Manjarrez, 1996; Kapetsky and Nath, 1997), and involved three phases: 1. criteria identification, classification, and standardization; 2. integration of primary criteria; and

Data source

Agricultural by-products Land cover for Africa (crops)

Land uses and infrastructure Livestock wastes Cattle, goats, sheep and pigs (animal/km2) (manure) (animal/km2)

Rome, Italy

Sioux falls, USA.

FAO

USGS-EROS Data Center

1996

1995

1997

1995

1992

1992

1997

Date

Sioux falls USA

1996

US Army CERL and 1996 CRSSA, Cook College, Rutgers University. USA

Austria

IIASA

GlobalArc™ GIS Database. National Center for Atmospheric Research USGS-EROS Data Center

Australia

USA

USA

United Kingdom

Location

CRES

Protected areas of Africa WCMC (conservation, wildlife reserves and forests) Perennial inland water DCW bodies Major cities ARCWORLD-ESRI (Beta version)

Name

Farming system models Physical and en6ironmental resources Precipitation (mm) Mean monthly precipitation Potential Monthly potential evapotranspiration evapotranspiration (mm) Soils Digital Soil Map of the World (DSMW) CD-ROM (version 3.5) Slope (%) GTOPO 30

Major cities

Water bodies

Constraints Protected areas

Criteria category and associated factors

Vector

Vector

30 s Raster

Lambert Azimuthal Equal Area

30 s Raster

Lat/Long Africa 4%48¦ or finer Raster

Lat/Long

Lat/Long World 5%×5% Vector and Raster

Lat/Long World 30%×30% Raster

Lat/Long Africa 3%×3% ASCII

Lat/Long World Vector

Lat/Long

Lat/Long

Projection/covera Cell size ge (minutes) and format

Table 4 Sources of spatial data used to assess inland aquaculture potential across Africa (Aguilar-Manjarrez and Nath, 1998; see also Fig. 4)

S.S. Nath et al. / Aquacultural Engineering 23 (2000) 233–278 267

Wind velocity (m/s)

Fish growth Air temperature (°C)

Roads Mean monthly minimum and maximum values. Mean annual wind velocity.

Australia

FAO/ESRI/UNEP/GRID Geneva

CRES

1986

1995

1992

1992

Date

Major cities with ARCWORLD-ESRI (Beta USA accompanying population version) classification. Road types ARCWORLD-ESRI USA

NCGIA-ESRI

Location

1992

Population density

Farm-gate sales (persons/km2) Major cities

Data source

USA

Name

Criteria category and associated factors

Table 4 (Continued)

Lat/Long Africa 2%×2% Raster

Lat/Long Africa 3%×3% ASCII



Lat/Long World

Projection/covera Cell size ge (minutes) and format

268 S.S. Nath et al. / Aquacultural Engineering 23 (2000) 233–278


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3. development of models that manipulate and integrate selected criteria. Terminology used in the study is essentially the same as that described in the second case study (Aguilar-Manjarrez and Ross, 1995).

4.5. Criteria identification, classification, and standardization Criteria used in this study reflect their importance to fish farming, as well as practical considerations of data availability for African countries. The criteria (organized into factors and constraints as in Aguilar-Manjarrez and Ross, 1995) include general environmental characteristics, land use practices, infrastructure, and population distribution data (Fig. 6). Criteria classification involved revising the primary spatial datasets for Africa whereby each factor was scored on a scale from one to four. This classification scheme kept the analysis manageable, and ensured that the results were more easily comprehensible and comparable. Additionally, this scheme enabled normalization of the data in the base layers. The scoring levels (value in parentheses) correspond to the following classes: very suitable or VS (4); suitable or S (3); moderately suitable or MS (3); and unsuitable or US (1). This interpretation of the classification scores proved to be appropriate because most raw data were already classified within a range of four values, this scheme matched the FAO classification in terms of suitability of land for defined

Fig. 6. A hierarchical modeling scheme with a MCE approach to assess the suitability of locations, and associated yield potentials for inland aquaculture in Africa (adapted from Aguilar-Manjarrez and Nath, 1998).

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uses, and the same methodology had been successfully used in aquaculture (Kapetsky, 1994; Aguilar-Manjarrez, 1996; Kapetsky and Nath, 1997). The interpretation of suitability classes is as follows (Kapetsky and Nath, 1997): ‘The VS level provides a situation in which minimum time or investment is required in order to develop fish farming. For a level classified as S, modest time and investment are required, while if MS, significant interventions may be required before fish farming operations can be conducted. If the suitability level is US, the time or cost, or both, are too great to be worthwhile for fish farming.’ In accordance with the suitability classification scheme, ranges of data (or thresholds) that pertain to a desired level of suitability for each of the criteria had to be chosen. Selection of such thresholds involved interpretation of the data selected, a process that was guided with literature research (e.g. for soil types) and opinions from expert staff at FAO.

4.6. Integration of primary criteria In this phase, selected and scored criteria were grouped into a series of sub-models (Kapetsky, 1994; Aguilar-Manjarrez, 1996; Kapetsky and Nath, 1997). Submodels for commercial fish farming assessment included water requirement, soils/terrain suitability, pond input availability, farm-gate sales, and urban market potential (Fig. 6). All of these sub-models (with the exception of urban market potential) were used to assess small-scale farming opportunities. The rationale for use of these sub-models was based on previous work (Kapetsky and Nath, 1997). As opposed to Aguilar-Manjarrez and Ross (1995) who integrated constraints only after individual suitability maps were generated (Fig. 4), Aguilar-Manjarrez and Nath (1998) included constraints within each sub-model outcomes (Fig. 6). This was because when suitability classes were developed, it was found that some of them were located within constraint regions and thus affected the suitability ranges. In the Sinaloa case study, however, this was not the situation and it was convenient to deal with constraints following application of the MCE technique.

4.7. De6elopment of the models The third major analytical procedure involved the development and evaluation of models to estimate the potential for small-scale and commercial fish farming in ponds. Two groups of models were used: farming systems models to assess land suitability for aquaculture (involving use of MCE to integrate sub-models developed in the previous phase), and simulation models to estimate fish yield potential (in crops/year). Use of the MCE approach involved consultations among one of the authors (J. Aguilar-Manjarrez) and four aquaculture experts with the objective of arriving at weights for the farming systems sub-models as a measure of their importance to small-scale and commercial farming systems. For small-scale farming, final weights used for the water requirement, pond input availability, soils/terrain suitability, and farm-gate sales sub-models were 0.56, 0.26, 0.12 and 0.06, respec-


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tively. Corresponding weights for commercial farming were 0.30, 0.04, 0.13, and 0.07, respectively, with the urban market potential sub-model receiving a weight of 0.46. Thus, water requirement and pond input availability were identified as being most important for small-scale farming, whereas urban market potential and water requirement were most important for commercial fish farming. Weights assigned to all of the sub-models for each fish farming category were applied to generate integrated suitability maps. Two linked simulation models were also used in this study. The first of this was used to generate mean monthly water temperature profiles across the African continent. This output was then used, among other input parameters, in a bioenergetics model (Nath, 1996) to estimate fish yield potential (in crops/year) for the Nile tilapia, African catfish, and Common carp under small-scale and commercial farming conditions. The resulting output was exported to GIS for further analysis and manipulation. For simplicity, all annual production results were reported by dividing the ranges estimated under small-scale and commercial farming scenarios into four quarters (Q). Suitability maps from the farming system models were overlaid with those from the bioenergetics model to reach a combined evaluation that indicated the coincidence of each land quality suitability class with a range of yield potential. Finally, existing fish farm locations (obtained by the use of GPS with additional field work to determine on-site production conditions) from four countries (Kenya, Malawi, Uganda and Zimbabwe) were used to verify the accuracy of the results.

4.8. Results A very small subset of the results discussed by Aguilar-Manjarrez and Nath (1998) are presented here due to space restrictions. From the perspective of a continental overview, despite multiple constraints imposed by the need to meet land quality requirements for both fish farming systems and favorable fish yields, over 15% of Africa has land areas scored as VS-1stQ for the three fish species and for both types of culture systems (Table 5). The largest surface area scored in the VS-1stQ combinations was found for common carp in the small-scale model. Suitability ranges for Nile tilapia were markedly more restrictive than the other two species in the VS-1stQ, in large part because of its more restricted range of required water temperatures. The major difference between the two culture systems was that the commercial farming results showed patchy distributions due to the urban market potential sub-model (Fig. 7), a feature that was not present in the small-scale model results. Very suitable sites with 1stQ yield ranges for both commercial and small-scale farming of the three fish species are located across Africa between 14°–20° S and 17°–40° E. Again, however, the importance of the urban market potential submodel in the commercial farming scenario does identify areas not identified in the small-scale model as having higher or lower potential such as the Democratic Republic of the Congo and South Africa, respectively.

15.5 18.5 21.2 15.4 19.7 19.0

Small-scale Tilapia Catfish Carp Commercial Tilapia Catfish Carp

VS-1stQ

4.4 1.7 2.5

3.5 3.2 1.0

VS-2ndQ

2.0 1.4 1.1

2.9 0.8 0.4

VS-3rdQ

10.4 13.9 13.3

11.9 13.1 15.4

S-1stQ

5.7 6.5 7.3

2.5 3.8 3.9

S-2ndQ

5.8 2.6 2.3

4.8 3.4 1.1

S-3rdQ

11.4 13.5 13.1

8.3 8.8 12.1

MS-1stQ

3.5 3.5 4.0

3.5 5.5 3.8

MS-2ndQ

2.5 1.0 0.8

3.5 2.5 1.0

MS-3rdQ

Table 5 Suitability of small-scale and commercial farming system models combined with fish yields (crops/year) expressed as a percentage of the surface area in continental Africa (source: Aguilar-Manjarrez and Nath, 1998)

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From the perspective of a country-level overview, potential areas for small-scale farming of Nile tilapia are also smaller when compared to the other two species. Only 11 African countries were scored as VS-1stQ in 50% or more of their surface area; however, the ranks of these countries are quite similar when compared to the other two fish species. For African catfish, 14 countries have 50% or more of their land area scored as VS-1stQ; an additional seven countries possess this score in more than 25% of their area. These results are similar to those for Common carp; however, a few more countries which offer VS-1stQ and S-1stQ possibilities are favored for this species. At the country-level, commercial farming results are similar to those for small-scale farming, except that the spatial distribution of the potential sites for commercial farming is greater for the VS-1stQ, VS-2ndQ and S-2ndQ ranges resulting in a larger number of countries where these combinations coincide for all three fish species. In all, 11 countries that meet or exceed S-1stQ requirements for 25% or more of their national areas for African catfish, nine for common carp and eight for Nile tilapia. Liberia, Sierra Leone, Ivory Coast, and Equatorial Guinea were found to be the most favorable countries for the three fish species and for both culture systems. In summary, results from this work confirm Kapetsky’s (Kapetsky, 1994) findings that the potential for inland aquaculture in Africa is high. The final fish farming potential estimates for the three species together show that about 37 and 43% of the African surface contains areas with at least some potential for small-scale and commercial farming, respectively. The most significant finding of the study was that 15% of the same areas have the highest suitability score. This implies that for small-scale fish farming, from 1.3 to 1.7 crops/year of Nile tilapia, 1.9–2.4 crops/year of African catfish and 1.6 – 2.2 crops/year of Common carp can be achieved in these areas. Similar ranges for commercial farming are: 1.6–2.0 crops/year of Nile tilapia, 1.3–1.7 crops/year of African catfish and 1.2–1.5 crops/year of Common carp. In general, the verification exercises conducted for four African countries indicated that outcomes generated by the GIS were consistent with on-the-ground evaluations of fish farming suitability.

4.8.1. Comments Estimates of area with potential (or lack of it) for fish farming development in this study were influenced by many factors, some of which originated from data inaccuracy, their spatial and temporal availability, the analytical approach and the underlying assumptions adopted. However, the increasing availability of data and greater capabilities of computer technology suggest an expanded role of GIS for large-scale assessments of aquaculture potential, and many of the problems affecting results of this study will likely be minimized or eliminated as more data becomes available, and more experience is gained with aquaculture-oriented GIS. One of the limitations of this study was the use of a single set of models (e.g. threshold selection and grouping of various criteria into commercial and small-scale models) to evaluate inland aquaculture potential. This limitation was necessary because of the lack of adequate information comparable across all countries that could be selectively used in the analyses, and because of the strategic nature (i.e.

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continental-scale focus) of the study. For example, the use of a global threshold of 300 individuals/km2 for population density at which land was assumed to become too expensive for small-scale fish farming may not apply to all situations as indicated by the verification exercise conducted for Kenya. Nevertheless, it should be possible to use the GIS together with additional country-specific information, to generate assessments that are more suitable for national-level planning activities. The verification exercise provided an important insight into the value of such strategic studies of fish farming opportunities ‘prior’ to encouraging its development. For instance, Zimudzi (1997) reported that after an initial enthusiasm for aquaculture in the 1980s, the majority of Zimbabwean farmers have stopped fish farming due to a variety of reasons including water shortage, poor yields of Nile tilapia due to low water temperatures, and unpredictable survival rates. These same areas were classified as being only marginally suitable in the GIS. As previously noted, despite the apparent value of the GIS outcomes for policy planning efforts in Africa, it is not clear if the results are being used by decisionmakers. This may be due to a number of factors including: (i) a lack of awareness among such key personnel that GIS work to assess aquaculture potential has been conducted; (ii) a lack of appreciation of the methods used and outcomes generated; (iii) low priority of aquaculture development efforts among international donors; and (iv) constraints imposed by the poor state of most African governmental agencies involved in aquaculture development. These issues must be investigated further if progress is to be made with regard to actual use of GIS output for development and planning efforts.

5. Future trends in GIS Geographic information systems science and technology, like many other areas of computing and information management, continues to evolve at a rapid pace. Advances are being made with regard to ease of use, manipulation of large (\ 100 MB) datasets, interoperability of databases among different systems, and in the collection and preprocessing of datasets. Perhaps the most significant development is an increasing trend towards the use of GIS as a component of a larger decision support system. This has been facilitated by the development of industry-wide component standards and object-oriented program designs. For example, the current release of ArcInfo (Version 8; ESRI, Inc.) has replaced the monolithic application model with an object-based component model that facilitates embedding GIS technology in broader application frameworks. Thus, application developers can deploy GIS-enabled applications by incorporating software components supporting geographic manipulation and visualization functions that are needed for their specific application, in conjunction with any other component-based functionality (e.g. reporting modules, database support, etc.) required for the application. This allows developers to produce customized applications utilizing spatial datasets, and has particular usefulness in allowing more robust interaction between models and GIS datasets, and in connecting relational database systems to map displays.


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Further, the need to purchase a single, large software application (most functionality of which is rarely used) is somewhat obviated. Another area in which GIS is playing an increasingly important role is in landscape visualization and ‘futuring’. Combined systems which model change in a watershed or landscape resulting from different land management strategies, and allow visualization of those changes in three dimensions, are becoming valuable tools for resource managers and stakeholders to better understand the implications of these strategies. This often takes the form of ‘virtual photographs’ where GIS datasets are mapped onto elevation grids, with appropriate visual ‘textures’ mapped into each polygon displayed. Results of such operations can be very realistic photo simulations of what a particular landscape might actually look like under a particular set of assumptions. In many respects, this trend mirrors the increasing use of watersheds as the basic unit of analysis in natural resource management domains. Kapetsky (1998, 1999) is of the opinion that this trend will become increasingly used in both inland fisheries assessments, and aquaculture as well. Technological progress continues to be made in the acquisition of spatially explicit datasets. Increasingly, remote sensed data are becoming available from a rich set of satellite sources and from lower-altitude aerial fly-overs. A substantial commercial industry focused on developing and supplying a broad range of datasets has recently arisen. As previously indicated, the internet has greatly facilitated the distribution of datasets of all types, and most government agencies are beginning to distribute many datasets online. The cost of electronic storage (often less than $20/GB) and increasing capabilities of personal computers is allowing sophisticated GIS analyses to be readily accomplished by inexpensive workstations. Finally, a host of mobile data collection devices, ranging from pen-driven handheld computers to GPS units and laser range finders are facilitating more efficient spatial data collection; these will only increase in their sophistication and ease of use. Given information technology trends, there can be little doubt that future GIS tools will provide a range of functions embedded in various components that can be tailored for specific uses. However, barriers with regard to actually using these tools for real world decision making merit special attention (that has not been forthcoming) and will need to be overcome if GIS is to play an integral role in aquaculture development and management. More specifically, there is the continuing need for appreciation workshops targeted towards decision-makers (i.e. end users) and domain experts to expand knowledge about GIS applications, and the need for relevant coursework in aquaculture/fisheries curricula at the university level (Kapetsky and Travaglia, 1995). Such training and education efforts can go a long way towards making GIS a routine analytical tool in the aquaculture domain. Symposiums such as the First International Symposium on Fishery Sciences recently held in the US (Seattle; March 2 – 4, 1999) are also valuable in this regard. Finally, organizations and individuals must facilitate the migration of GIS tools from the realm of the academic towards an environment within which analysts, subject matter experts and end users can fruitfully collaborate to address issues relevant to the sustainable growth of aquaculture. Work conducted by various governmental

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agencies in the province of British Columbia (Canada) provides a useful model in this regard, and demonstrates the value of inter- and intra-organizational collaboration with regard to strategic natural resource management initiatives, of which aquaculture is only one component.

Acknowledgements We acknowledge the assistance and information provided by Barron Carswell (MAFF, BC) and Mark Zacharias (LUCO, BC) with regard to the use of GIS for aquaculture management in British Columbia. Dr James M. Kapetsky (FAO), a pioneer in the use of GIS for aquaculture and fisheries, has provided much advice over the years and furnished information that was used in the preparation of this paper. The senior author (S.S. Nath) acknowledges support provided by the Department of Biological and Agricultural Engineering at the University of Georgia, Athens, GA during the preparation of this manuscript.

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