Spatial modelling for freshwater cage location

in the Presa Adolfo Mateos Lopez (El Infiernillo),

Michoacan, Mexico

Lindsay G Ross1, L. Lynne Falconer1, Antonio Campos Mendoza2 & Carlos A Martinez Palacios3

1Institute of Aquaculture, University of Stirling, Stirling, UK2Fac. Biol. Universidad Michoacana de San NicolaŁ s de Hidalgo, MichoacaŁ n, Me¤ xico3Instituto de Investigaciones Agropecuarias y Forestales, Universidad Michoacana de San NicolaŁ s de Hidalgo, Morelia,

MichoacaŁ n, Me¤ xico

Correspondence: L G Ross, Institute of Aquaculture, University of Stirling, Stirling FK94LA, UK. E-mail: l.g.ross@stir.ac.uk

Abstract

The tilapia ¢shery in a large Mexican reservoir hasdeclined in recent years, promoting e¡orts to enhancestock quality in conjunction with feasibility studiesfor cage aquaculture. Spatial models to optimize cagelocation have been developed, incorporating aspectsof topography, climate, hydrography, water qualityand quantity, land use, infrastructure and socioeco-nomics. Three cage sizes were modelled, 5, 10 and15m diameter, for both high- and low-water levels.When full the, reservoir has a total area of 312 km2

but a reduction in water level of 13m was recordedbetween wet and dry seasons, signi¢cantly reducingthe surface area available for cages to 265 km2. Theresults indicate a signi¢cant di¡erence in the avail-ability of sites between seasons. For the small, med-ium and large cages at high water level, the areasclassi¢ed as highly suitable or suitable were 151, 184and 146 km2, respectively, while at low-water levelthese areas reduced to113,88 and 62 km2. Modellingthis change in water level and the subsequent loss ofavailable sites provide qualitative and quantitativeguidelines for the development of aquaculture sites,their seasonal management and future monitoring,outcomes that can only be achieved using Geographi-cal Information Systems and associated technologies.

Keywords: GIS, modelling, cage location, El In¢er-nillo, tilapia

Introduction

Throughout the world, catch from inland ¢sheriesis declining, the reasons for which vary but include

eutrophication, pollution, habitat destruction andmodi¢cation, loss of natural spawning locations,poor ¢sheries management, over¢shing of the natur-al ¢sh populations and introductions of alien species(Welcomme & Bartley 1998). In recent years, therehave been e¡orts to mitigate this problem throughimproved management schemes and expansion ofaquaculture projects withinwater bodies both for re-stocking and food production. One of the fundamen-tal prerequisites for any aquaculture project is siteselection as this can determine the overall successor failure of any development (Lawson 1995). Poorsite selection can be detrimental to the environment,result in stressed ¢sh, decreased growth and overalldecreased production (Costa-Pierce & Bridger 2002).In order to select suitable sites for aquaculture,

many factors must be considered, including hydro-graphic conditions (e.g. depth, currents), water qual-ity (e.g. dissolved oxygen, pH, temperature), climate(e.g. precipitation, temperature), land use, soil condi-tions (e.g. slope, water retention) and socioeconomicfactors (e.g. infrastructure) (Lawson 1995; Nath,Bolte, Ross & Anguilar-Manjarrez 2000; Pillay &Kutty 2005). Analysis and evaluation of all of thesefactors can be complex and time consuming, espe-cially as some are more important than others. Geo-graphical Information Systems (GIS) enable multiplefactors to be considered simultaneously leading tothe development of complex spatial models (Bern-hardsen 1999; Heywood, Cornelius & Carver 2002).The use of GIS is becoming increasingly importantin terms of site selection and GIS models have beenused in several previous studies concerning site se-lection for aquaculture where it has the advantage

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of allowing the identi¢cationof many sites dependingon selected criteria (Valavanis 2002). Awide range ofexamples of GIS-based decision support for aquacul-ture is outlined by Ross, Handisyde and Hunter(2009). Ross, Mendoza and Beveridge (1993) usedGIS models for site selection of salmon cages inScotland and Pe¤ rez, Telfer and Ross (2005) used GISmodels for locating o¡shore marine ¢sh cages inTenerife. However, to date, there have been few exam-ples of use of GIS for freshwater cage systems.A number of dams were built throughout Mexico

in the 1960s to create reservoirs (Presas) for electri-city generation, irrigation and ¢sheries enhance-ment (Fitzsimmons 2000). One of the largest ofthese, the Presa Adolfo Mateos Lopez (El In¢ernillo)was seeded with tilapia in 1970 (JuaŁ rez 1995;Jime¤ nez-Badillo 2004) in order to establish and im-prove the ¢sheries of the reservoir.The reservoir soonbecame the main Mexican tilapia ¢shery, principallybased on the Blue Tilapia, Oreochromis aureus (Stein-dachner 1864), which remains the most abundantspecies within the reservoir (Jime¤ nez-Badillo 2004).In recent years, both the quality and quantity of tila-pia taken from the reservoir have declined markedly,from a peak of 20000 tonnes per annum in 1985 toonly 4700 tonnes per annum in recent years (Men-doza Alfaro, Escalera Gallardo, Contreras Balderas,Kole¡ Osorio, Ram|¤ rez Mart|¤ nez, AŁ lvarez Torres,Arroyo DamiaŁ n & Orbe-Mendoza 2009). Research iscurrently underway to improve production and thepreferred strategy is to use improved stocks of tilapiawith rigorous hatchery management and on-grow-ing in cage systems within the reservoir. Good siteselection and location of the cages are important, asthe improved stocks should be on-grown in areaswith optimal conditions for intensive aquaculture.As the reservoir is very large (312 km2), it will beimportant to use all of the available information andspatial tools to make an informed choice when decid-ing where to locate the cages.Although the reservoir has been used as a source

of food and income by local people for almost 50years, there has been no attempt at commercial cageculture within the reservoir. There have been pre-vious attempts at small-scale cage culture in a fewareas within the reservoir using small (4 � 4 �2m) cages, which were intended only for consump-tion by the operators and their families. Commercialcage culture of Tilapia is attractive as the cage sys-tems require lower investment both in terms of capi-tal and production costs than large-scale tanks andraceways, theyalso have increased £exibility in terms

of management as they can be moved to other loca-tions if required (Rojas &Wadsworth 2007). An addi-tional bene¢t of growing tilapia in cages is that mixedsex populations can be growntogether, with any eggsbeing lost from the cage, thus preventing overcrowd-ing with smaller ¢sh, as can happen in tank systemswhere it can result in growth reduction in the mainstock (Lorenzen 2000). The development of a cage-based aquaculture industry within the reservoirwould provide an income stream as well as bene¢tsto the local economy in the form of employment forconstruction, maintenance and husbandry of thecage systems.The aim of this study was to develop GIS models to

identify optimal locations for freshwater cages for ti-lapia or native species within El In¢ernillo reservoirusing a range of available spatial data supplementedby data collection in the ¢eld. This is combined withveri¢cation of data and models leading to identi¢ca-tion and quanti¢cation of potential locations for cageculture under a range of scenarios.

Materials and methods

Study area

El In¢ernillo reservoir is located at 18116 03000 N and101153 0 4000 Won the boundary between the statesof MichoacaŁ n and Guerrero in Mexico. At approxi-mately 120 km in length and with a surface area ofabout 312 km2 when full, the reservoir is one of thelargest in Mexico. It occupies a £ooded valley, thesouthern section being a deep, narrow canyon withsteep sides while the northern sections are shallowerwith less steep sides, especially in the north east.Two¢eld campaigns were conducted for ground truthing,bathymetric survey and water quality data collectionin November 2007 and April 2008.

Satellite imagery

The baseline imagery was developed from a pre-SLC-o¡ Landsat-7 Enhanced Thematic Mapper Plus(ETM1) scene, Path 28, Row 47 from 28 November1999. All data bands were pansharpened using databand 8 so that each image and layer developed had aspatial resolution of 14.25m. A RGB354 compositeimage was developed as the base layer because thiscombination has clear land/water boundaries. Aland use image was developed by isoclusteringwithin IDRISI Andes (Clark Labs), from which ahigh-water-level reservoir mask was extracted by

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reclassi¢cation. These, and all other data layers, weregeoreferenced to the UTM13N projection, which isdecimal based and the coordinates translate directlyto distances on the ground. UTM had been used inprevious data collection on the reservoir and thisfacilitated the easy combination of all available data.

Photo layers and veri¢cation

Digital photographs were captured as part of the in-itial ground truthing and veri¢cation process, allphotographs being georeferenced using a GarminGPS76 mapper (Olathe, KS, USA). One photo layerwas created along the route of the motorway focusedupon identi¢cation of potential access points and ageneral overview of the surrounding geography. Anadditional photo layer was created from the surfaceof the reservoir using images, which identi¢ed bays,potential aquaculture locations and other notablelandscape features. These were then used for photo-veri¢cation of the data and subsequent model out-comes.

Model development

Models were developed using IDRISI Andes for twoclimatic scenarios: low water level (during the dry

season) and highwater level (during the wet season).For each scenario, three cage sizes appropriate for theenvisaged scale of the development were used: small(5m diameter), medium (10m diameter) and large(15m diameter). Numerous data layers were usedwithin eachmodel and after each layer had been pre-pared they were reclassi¢ed to a scoring systemwhere1is the least suitable and 5 is the most suitablefor the planned activity. Pe¤ rez et al. (2005) note thatthis method has been used previously with variousnumber ranges; however, for this study a ¢ve-pointscale gave an adequate combination of simplicityand sensitivity.Four sub-models were used to generate the ¢nal

models: hydrography, access, water quality and po-tential pollution. Finally, the outcomes of these mod-els were modi¢ed using ¢xed and variable constraintsub-models. The overall model structure is shown inFig.1.

Hydrography

Initial siting assessments for location of £oatingcages can be based upon physical environmentalfactors related to manufacturers’ speci¢c cage de-signs (Ross et al.1993; Pe¤ rez et al. 2005; Hunter,Telfer

Figure1 Structural representation of the overall model processes for cage aquaculture in the Presa Adolfo Mateos Lopez(El In¢ernillo), Mexico.

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& Ross 2009). The principal variables considered inthis sub-model were signi¢cant wave height and cur-rent velocity. Bathymetry is also very signi¢cant butis incorporated into the model at a later stage.

Wave height

Signi¢cant wave height depends principally uponwater depth, wind velocity and fetch. A fetch layer,simply de¢ned as ‘the extent of open water acrosswhich the wind blows’ (Bascom 1964), was createdin IDRISI using themacromethod developed by Scott(2004) and Hunter (2009). A longer fetch has a great-er wave generating potential as there is more oppor-tunity to absorb energy from the wind (US ArmyCorps of Engineers 1984).Wind velocity at In¢ernilloincreases during the day from a near-zero minimumin the morning to maximum in the late afternoon,and this was measured in the ¢eld using a hand an-emometer. These variables were used, with bathy-metric data, in a macro to implement the signi¢cantwave height equation used by US Army Corps ofEngineers (1984, 2008). The macro was run twice todevelop a signi¢cant wave height for high-and low-water-level conditions. Finally, signi¢cant waveheights were classi¢ed for further use following thescheme shown inTable1.

Current velocity

Currents are an important parameter when decidingwhere to locate a cage, as it is essential to have good

water exchange through the cages to prevent the ac-cumulation of wastes and to ensure anadequate sup-ply of oxygen (Beveridge 2004). Two 40 � 40 cmdrogues set at 2m depth were used to measure cur-rents at various locations within the reservoir. Thetime and location that the drogues were releasedand collected were recorded using a Garmin GPS76mapper and the current velocity was subsequentlycalculated from these data. The current velocitieswere ¢rst digitized in IDRISI in vector point formatand then vector polygons were developed from thesedata to represent current zones. Alpaslanu and Pula-tˇn (2008) found that 4 cm s�1 was su⁄cient forwaste dispersion for Rainbow trout (Oncorhynchusmykiss) in a Turkish reservoir; however, there werestill some localized impacts. The polygons were re-classi¢ed using the scoring system inTable1.

Hydrography sub-model

The classi¢ed signi¢cant wave heights and currentvelocity layers were combined in a multicriteria eva-luation (MCE) with equal weighting, developing out-comes for high and low water levels.

Access

Good access to sites is essential in terms of generaldaily operations, longer travel distances being less ef-¢cient both in terms of travel time and cost. Easy ac-cess by water from embarkationpoints is essential fordaily operations on cage systems, and access to these

Table 1 Classi¢cation scheme for layers within sub-models developed for cage siting in the Presa In¢ernillo, MichoacaŁ n,Mexico

Sub-model Layer

Classification

1 (highlyunsuitable)

2(unsuitable) 3 (moderate) 4 (suitable)

5 (highlysuitable)

Hydrography Significant wave height (m) 1.0–0.6 0.6–0.5 0.5–0.35 0.35–0.2 0.2–0

Current velocity (cm s�1) 0–2 2–3 3–5 5–8 8–12

Access By water (km) 0–6.7 (20 min) 6.7–10 (30 min) 10–13.3 (40 min) 13.3–16.7 (50 min) 16.71 (1 h1)

Water quality Water temperature ( 1C) 0–15 15–20 20–25 30–33 26–30

33–36

Dissolved oxygen (mg L� 1) 0–3 3–5 5–6 6–7 71

Secchi depth (m) 0–5 5–10 10–20 20–30 301

Suspended solids (mg L� 1) 50–60 40–50 35–40 30–35 0–30

Pollution River point sources (high, km) 0–0.25 0.25–0.5 0.5–1 1–2 21

River point sources (low, km) 0–0.2 0.2–0.4 0.4–0.8 0.8–1.6 1.61

Human settlements (km) 0–0.25 0.25–0.5 0.5–1 1–2 21

Fishing camps and access

points

0–0.25 0.25–0.5 0.5–1 1–2 21

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locations for goods and services and other less fre-quent operations such as stocking from a hatcheryand harvesting from the cages is also important.Although In¢ernillo is a large reservoir, there arecurrently few access points that would be suitable asmany areas are remote and without access roads.

Access by water

Daily operations are likely to be carried out by peopleliving on site or nearby and thus the distance tra-velled is principally from these embarkation pointsto the cages. Currently, there are only seven suitableaccess points for embarkation. However, these weresupplemented with other locations which had cleardevelopment potential and which were identi¢ed by¢eld surveyand careful scrutinyof Google Earth ima-gery. The radial distance was calculated from each ofthe access points to create a layer, whichwas then re-classi¢ed based on the time taken to travel (Table 1).Assuming the use of a boat with an average speed of20 kmh�1, areas within 20min travel time wereclassi¢ed as most suitable and each additional10minwas less suitable until areas that were furtherthan an hour away were classi¢ed as least suitable.This produced a layer indicating the ease of accessfor daily operations.

Access for goods and services

Access to the sites for supply of goods and servicesoriginating from cities in the north is most importantas this will bring in feed, equipment, spares and fuelto the site andwill also be the route to themarkets forthe ¢sh. The current road system was mapped di-rectly into IDRISI using a Garmin GPSmap76.The re-lative cost of travelling to each site from thenorthernmost point of the road system was calcu-lated. Each road type was classi¢ed based on howeasy or e⁄cient it would be to use, with motorwaybeing given a score of 1 and dirt tracks a score of 5as they would be most di⁄cult to use, especially dur-ing the rainy season. The cumulative relative travelcost to each embarkation point was then modelled.

Access from the hatchery

The classi¢ed road systemwas alsoused to determinerelative cost of transportation of juveniles fromthe hatchery in the south of the reservoir to the

embarkation points. This access route from thehatchery would be used less frequently, perhapsthree or four times a year.

Access sub-model

The three classi¢ed access variables were combinedin a MCE in which the relative weightings wereaccess by water50.7, access for goods and ser-vices50.2 and access from the hatchery50.1.

Water quality

Goodwater quality is essential for cage culture (Pe¤ rez,Ross, Telfer & del Campo Barquin 2003). Four layerswere developed for use within the water quality sub-model; water temperature, dissolved oxygen, trans-parency (Secchi depth) and suspended solidslevels. These variables were recorded at a number ofstations during the ¢eld campaigns and were supple-mented by data provided by D. Pardo (pers. comm.).

Temperature

The lowest surface water temperature recorded was26.8 1C in December 2007 in the north east, and thehighest temperature recorded was 33.6 1C in thenorth west in February 2007, with an average meantemperature of 29.2 1C throughout the reservoir overthe year. Tilapia have an optimal temperature rangebetween 26 and 30 1C (Muir,Van Rijn & Hargreaves2000; Pillay & Kutty 2005) and consequently thewater temperature layer was reclassi¢ed using thesedata (Table1).

Dissolved oxygen

Water temperature and altitude data were used tomodel potential maximum and minimum dissolvedoxygen levels using the equation from APHA (2005).The highest modelled dissolved oxygen level was7.6mg L�1 in the north east during December 2007and the lowest dissolved oxygen levelwas 5.9mg L�1

in the northwest in February 2007. However, the ¢elddata indicated lower values of dissolved oxygen in thesouthern areas of the reservoir and higher values inthe north of the reservoir. The ideal value for dis-solved oxygen in cage culture of tilapia is over6mg L�1 (Muir et al. 2000) and hence the area to

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the north was classi¢ed as highly suitable while thearea to the southwas classi¢ed as suitable (Table1).

Transparency (Secchi depth)

Plankton and organic suspended solids can producesubstantial dissolved oxygen depletion (Beveridge2004). Secchi depths were greater in the south thanin the north, and this was found to be the generalcase throughout the year, although when the waterlevel is low the measurements were less than at highwater level.The Secchi measurements were classi¢edand then used in the water quality sub-model(Table1).

Suspended solids

Suspended solids levels ranged from 3 to 40mg L�1

andwere highest just after the highwater season. Le-vels were generally lower in the central basin andhigher in the north and south. The suspended solidslevels were classi¢ed for use in the water quality sub-model (Table1).

Water quality sub-model

The four classi¢ed water quality layers were com-bined in an MCE in which the relative weightingswere dissolved oxygen 50.35, temperature50.35,Secchi depth50.15 and suspended solids50.15.

Potential pollution

It is essential to avoid the location of cages within thein£uence of pollution sources. Four potential pollu-tion sources were considered in this sub-model, riv-ers as point sources, human settlements, ¢shingcamps and embarkation points.

Rivers as point sources

Rivers are a point source of pollution, dischargingsuspended solids, and allochthonous materials intolakes and reservoirs. Satellite imagery, maps, GoogleEarthTM (Google) and photographs from the ¢eldcampaigns were used to identify all rivers dischar-ging into the reservoir. River inputs vary seasonallyand in the dry season some rivers are dry and the lar-ger rivers considerably reduce in size. The potential

detrimental e¡ect of discharge of materials into thereservoir from these point sources was modelled forhigh water level by developing a bu¡er zone fromeach point of discharge. At low water level, the riverinputs will not disperse as far; hence, a more conser-vative dispersion ranking was used. The two result-ing layers were reclassi¢ed in term of pollution riskto facilities based onTable1.

Pollution from human settlements

There are a number of small villages scatteredaround the reservoir, which will generate waste dis-charging into the reservoir. As there is currently noinformation available on discharge levels from thesesources, bu¡er zones were developed from each po-tential point of discharge and the resulting layer wasreclassi¢ed as shown inTable1.

Pollution from ¢shing camps

There are speci¢c points where ¢shermen land theircatches and where the majority of initial processing,gutting and cleaning are completed. Large quantitiesof ¢sh wastes are released into the reservoir daily atthese points and clearly, cages should be located wellaway from such points. Bu¡er zones were developedfrom each ¢shing camp and the resulting layer wasreclassi¢ed as shown inTable1.

Pollution from access points

The current and potential access points for embarka-tion are also potential sources of pollution fromvehi-cles and other human activity. Bu¡er zones weredeveloped from each embarkation point and the re-sulting layer was reclassi¢ed as shown inTable1.

Potential pollution sub-model

The four classi¢ed potential pollution layers werecombined in anMCE inwhich the relative weightingswere river point sources50.3, human settlements5

0.3, ¢shing camps50.3 and access points50.1. Sub-model output was developed separately for high andlow water levels using the two river point sourcelayers.

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Constraints

Variable constraints sub-model: bathymetry

Although bathymetry is a hydrographic parameter, itis most useful when considered as a variable con-straint, which takes into account minimum depthfor a particular cage design and maximum depth forpractical mooring systems. Cages need to be locatedin areas where thewater is deep enough to avoid con-tact with the benthos, and allow good water ex-change.While the size of cage and depth of net canvary greatly it is recommended that the nets shouldbe at least 4^5m above the sediments (Beveridge2004).With an increasing water depth, there are alsoproblems and costs associated with mooring systems(Beveridge 2004; Pe¤ rez et al. 2005).Bathymetry was surveyed during ¢eld campaigns

in 2007 and 2008 using a Garmin Fish¢nder 90 echosounder and a Garmin GPS III to directly input datainto IDRISI. IDRISI was then used to develop thedepth layers by adding the bathymetric data pointsto the reservoir mask layer and using a combinationof ¢ltering, updating and interpolation methods todevelop a full bathymetric surface. The di¡erence inwater level between November and April, surveyedusing a laser level, was13m. Two bathymetric layerswere created, one representing high water level andanother representing low water level.The suitable depth ranges used for the three

selected cage types were: 5m cages (minimum10m,maximum 30m), 10m cages (minimum 15m, maxi-mum 40m) and 15m cages (minimum 20m, maxi-mum 50m). Six Boolean layers were developedshowing the location of the appropriate depth rangefor each of the three cage types at high and lowwaterlevels. As the reservoir is manmade, there are otherobjects, such as trees especially in the shallowerareas that could damage nets. Depth reclassi¢cationtook this into account by allowing additional clear-ance below the nets.

Fixed constraint sub-model

There are currently few ¢xed constraints to cage aqua-culture in the reservoir. A1kmbu¡er zonewas createdpreventing development in the proximity of the damand the water discharge systems. An additional layerallowing for future protected ecological areas was alsocreated, although there are currently no such restric-tions inplace.These areaswere combined ina Booleanlayer for removal from the ¢nal models.

Final models

In order to produce the set of ¢nal models, the out-puts of the hydrography, access, water quality andpotential pollution sub-models were processed inthree stages. In the ¢rst stage, the four sub-model out-puts were given equalweight andwere seriallymulti-plied and then rescaled to a 25-point scale ofsuitability. Two iterations of a macro-developed out-puts for high and low water levels. In the secondstage, the ¢xed constraints were removed by Booleanoverlay. In the ¢nal stage, bathymetry was applied asa variable constraint using the six Boolean layersdeveloped for the bathymetric data.

Results and discussion

The model outcomes (Figs 2, 3, 4) show that thenorth^west sector of the reservoir contains substan-tial highly suitable areas although these can besomewhat exposed to the prevailing winds from thesouth. The north^east sector is generally less favour-able because of limited water depth and poorer waterquality parameters. There are numerous highly sui-table areas in the southern sector, many being wellprotected from the prevailing winds and waves be-cause the valley is a steep-sided canyon with manysmall sheltered bays.Waves can damage the cages and ¢sh stocks and

may even lead to escapes (Pillay & Kutty 2005;Lekang 2007); therefore, it is an important considera-tion when selecting what cage type to use. Largercages would be able to withstand waves better thanthe smaller cages; however, they have higher capitaland mooring costs and would require more mainte-nance (Lekang 2007). There are areas where thewaves are not as strong, particularly in the shelteredbay areas of the south where smaller cages could belocated. Consequently, depending upon the cage typeused, but particularly the 5 and10m cages, it may bemore suitable to locate the cages in the southern sec-tion of the reservoir, where it is more sheltered andless exposed.The models show a reduction in lake surface area

from 312 km2 at high water to 265 km2 at low waterlevel, with a concomitant considerable reduction insuitable areas for the three di¡erent sizes of cage (Figs2,3,4).This seasonal change has a major e¡ect on po-tential area for cage location for all three cage types(Table 2) and most of the highly suitable areas at lowwater level are located in the north^west and inthe south of the reservoir. The reduction in area is

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an important issue when selecting sites and cagesizes.While the small cages require less water depthand su¡er a lower reduction of available area, theyare less productive and many more small cageswould be required, covering a larger area, to producethe same tonnage of ¢sh as the larger cages coveringa smaller area. However capital costs will be higherwith larger cages; therefore, it is important to ac-knowledge all of the issues associated with each cagetype (Pillay & Kutty 2005).It can be seen that, with careful management and

site selection, the reservoir could be used for year-round ¢sh production. Floating cages can be movedrelatively easily depending on thewater level or waterquality and this would allow producers to make opti-mal use of the reservoir in each season (McLarney1998). Cages for tilapia or native species could be lo-cated in highly suitable areas for part of the seasonand in suitable areas for the remainder of the year.As the reservoir is a common pool resource, it is pos-sible that other developments outwith the control ofthe cage operators could become established on thereservoir, and these mayadversely impact the qualityof the sites (Beveridge 2004). Again, cage relocationwould help to alleviate these e¡ects and the advan-tage of GIS models is that they can be easily adapted

to new scenarios when needed (Longley, Goodchild,Maguire & Rhind 2005).The use of georeferenced digital photographs for

ground truthing was important both for assessingdata input and model outputs. Ground truthing isone of the most important steps in a GIS as it allowsthe user to knowhow representative the GIS model is(Steinberg & Steinberg 2005). GIS model output canrequire some interpretation, but by combining digitalphotography with the GIS models the information ismade more accessible and understandable, and thiscan be an essential factor in communicating withstakeholders (Meaden & Kapetsky 1995; Quan, Oud-water, Pender & Martin 2001; Sui & Goodchild 2001).While cage positioning is partly dependent on the

currents, waves and bathymetry, (McLarney 1998) afurther consideration is spacing of farms relative toeach other as this a¡ects the spread of disease andthe maintenance of good water quality within thecages and the external reservoir environment(Schmittou 2006). The GIS model could be furtherdeveloped to optimize farm spacing to minimizethese e¡ects. It is also important to acknowledgethe additional social and potential economic impactsthat the installation of cages may have on thesurrounding area (Pillay 1992), and hence further

Figure 2 Potential locations for 5 m cages at high and lowwater level. Expanded area indicates further detail of the areato the south of Pinzandaran in the southern sector of the lake.

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socioeconomic studies would be valuable before thedevelopment of any systems within the reservoir.This would have the additional bene¢t of introducing

the new activity to the communities and engagingthem in the process, thus gaining their support.Furthermore, there is an increasing domestic market

Figure 4 Potential locations for15mcages at highand lowwater level. Expanded area indicates further detail of the areato the south of Pinzandaran in the southern sector of the lake.

Figure 3 Potential locations for10m cages at highand lowwater level. Expanded area indicates further detail of the areato the south of Pinzandaran in the southern sector of the lake.

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for tilapia within Mexico (Rojas & Wadsworth 2007)as well as a potential export market to the north anda market study to assess product demand before thedevelopment of any systems would be valuable (Law-son 1995). There is also a good opportunity to use‘green marketing’ within the region to market theproduct as an environmentally friendly local producewith decreased foodmiles (Pillay1994;Midlen & Red-ding1998). The data from all such studies can be ea-sily subsumed into the GIS.GIS has been successfully used to develop complex

models for site selection for the culture of scallops(Radiarta, Saitoh & Miyazono 2008), mussels (Long-dill, Healy & Black 2008), crab and shrimp (Salam,Ross & Beveridge 2003), marine ¢sh (Pe¤ rez et al.2005), carp (Salam, Khatun & Ali 2005), and furtherexamples may be accessed from the FAO Gis¢sh siteat http://www.fao.org/¢shery/gis¢sh/index.php. Theaim of the present study was to use GIS models as adecision support tool for optimal site selection forfreshwater cage aquaculture systems in a large waterbody. The models developed have shown that thereare multiple locations within the reservoir that areboth suitable and highly suitable for cage cultureand provide a basis for evaluating the reservoir forthe purpose of tilapia cage aquaculture at a morecommercial level. The reservoir is a large area andthe use of robust models to provide decision supportis clearly advantageous, especially as GIS models areeasily updated and modi¢ed as requirements changeand new information becomes available.

Acknowledgments

The authors wish to thank CONACyT, Me¤ xico for ¢-nancial support through grant number 53437, FondoMixto MichoacaŁ n-CONACYT and Dr Edmundo DiazPardo for provision of some water quality data. LFwishes to thank the Scottish Academic AwardsAgency for ¢nancial support.

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