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Ocean & Coastal Management 82 (2013) 95e103

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Ocean & Coastal Management

journal homepage: www.elsevier .com/locate/ocecoaman

Benthic habitat b-diversity modeling and landscape metrics for theselection of priority conservation areas using a systematic approach:Magdalena Bay, Mexico, as a case study

Rodolfo Rioja-Nieto a,*, Erick Barrera-Falcón a, Gustavo Hinojosa-Arango b,Rafael Riosmena-Rodríguez c

aUnidad Académica-Sisal, Facultad de Ciencias, Universidad Nacional Autónoma de México, Puerto de Abrigo s/n, Sisal, Yucatán, México CP. 97355, MexicobCentro para la Biodiversidad Marina y la Conservación, A.C., Calle del Pirata 420, La Paz, BCS, México C.P. 23050, MexicocUniversidad Autónoma de Baja California Sur, Carretera al Sur km 5.5, La Paz, BCS, México C.P. 23080, Mexico

a r t i c l e i n f o

Article history:Available online 2 July 2013

* Corresponding author. Tel.: þ52 988 9120147x713E-mail addresses: rrioja@ciencias.unam.mx (R. Rio

gocmarineprogram.org (G. Hinojosa-Arango), riosmeRodríguez).

0964-5691/$ e see front matter � 2013 Elsevier Ltd.http://dx.doi.org/10.1016/j.ocecoaman.2013.06.002

a b s t r a c t

Albeit the need and importance of protected areas worldwide, there is no best practice methodology fortheir placement and design as this depends on several factors such as resource availability, datadisponibility, and technical capacity. In this study, we show a novel approach on the use of b-diversitymodeling and landscapemetrics for the selection of priority conservation areas to assist themanagement ofMagdalena Bay, one of the most important bays of Mexico. Quantitative estimates on the percentage ofcover of benthic substrates from 60 ground truthed sites were used to perform a cluster analysis using theBrayeCurtis similarity index. Seven habitats were recognized, with habitats characterized by an inter-mixof sand, seagrass, macroalgae and rock substrates. A thematic map of benthic habitats was producedutilizing a supervised classification with the maximum likelihood rule on a natural color mosaic of highresolution satellite scenes (overall accuracy 80%, Kappa ¼ 0.69). Matlab was used to calculate b-diversityvalues based on the nature and distribution of habitats. A weighted overlay analysis considering habitattype, patch area/perimeter2 ratio and b-diversity was performed to prioritize areas for conservation. Threeareas, which constitute w20% of the shallow benthic landscape, are proposed to be considered withina Marine Protected Area scheme. By selecting sites with these landscape characteristics we developed acost-effective systematic approach to protect the natural processes responsible for maintaining diversityand the livelihoods of the local communities.

� 2013 Elsevier Ltd. All rights reserved.

1. Introduction

Marine Protected Areas (MPAs) are one of the main strategiesimplemented at a global scale to counteract anthropogenic degra-dation of coastal and marine ecosystems. Several studies suggestthat no-take MPAs increase the catch and size of commerciallyimportant species (Aburto-Oropeza et al., 2011; Gell and Roberts,2003; Roberts et al., 2001; Sale et al., 2006), increase species rich-ness and density (Lester et al., 2009) and may ensure ecosystemswill remain within desired states under changing environmentalconditions (Aronson and Precht, 2006; Folke et al., 2004; Micheliet al., 2012).

4.ja-Nieto), gustavo.hinojosa@na@uabcs.mx (R. Riosmena-

All rights reserved.

In addition to the inherent biological andecological complexityofmarine ecosystems, the social, political and economic needs,particularly in developing countries,make the establishment, designand management of MPAs difficult. For their design, it is funda-mental to place and define zones that regulate different activitiesthat permit the achievement of their biodiversity objectives andwincommunity support (Teh et al., 2012). MPAs have recently benefitedfrom spatially explicit tools (Klein et al., 2009; Malcolm et al., 2012;Teh et al., 2012; Turner and Klaus, 2005), which can allow a sys-tematic process to identify conservation targets for ecosystemfunction. Systematic planning before the placement and design of aMarine Protected Area (MPA), increase efficiency of conservationefforts (Malcolm et al., 2012; Pressey and Bottrill, 2009).

Several spatially explicit tools have been developed for sys-tematic reserve placement and design. Tools range from those thatuse Geographic Information Systems (GIS) interfaces and GISmulti-criteria analysis (Merrifield et al., 2013; Villa et al., 2002), heuristic

1 Pixels classified as habitats 1, 2 and 7 located >100 m from the coast and pixelsclassified as habitats 3 and 4 distributed in regions with >6 m depth were re-codedto the type of habitat that dominated in an 8 � 8 neighborhood.

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algorithms for the design of MPAs networks (Klein et al., 2009; Tehet al., 2012; Ward et al., 1999; Watts et al., 2009), whole ecosystemsimulations (Fouzai et al., 2012), to those that use a combination oftwo ormore of thesemethods (Freudenberger et al., 2013; Xiaopinget al., 2012). Best practice spatial planning is still changing(Malcolm et al., 2012), and b-diversity modeling combined withlandscape metrics for systematic MPA placement and design hasnot been previously explored.

Different metrics at a landscape scale can be used for theidentification and design of MPAs. In regions where taxonomic datais scarce or expensive to obtain, habitats can be used as surrogatesto estimate biodiversity (Dalleau et al., 2010; Ward et al., 1999).Results on habitat efficiency to represent biodiversity are mixedand largely depend on taxa, information availability, site selection,and spatial scale among other parameters (Brooks et al., 2004;Shokri and Gladstone, 2012; Van Wynsberge et al., 2012).Detailed habitat maps are relatively fast to produce, and seem toimprove species representation (Dalleau et al., 2010). Thereforethey can be used as a cost-effective tool for conservation planning(Shokri and Gladstone, 2012; Wabnitz et al., 2010). Habitats can bedominated by species that have a large effect on communitystructure. These foundation species generate habitat that supportsa high diversity of associated species and determine compositionand abundance of other species within a community (Crowder,2005; Shelton, 2010). Foundation species can provide refuge frompredation, enhance reproductive success and/or increase foodsupply (Bruno and Berness, 2001). Considering the distribution androle of these species is important for maintaining diverse ecologicalcommunities (Shelton, 2010).

Habitat patch size and shape are important for landscape con-servation as anthropogenic habitat fragmentation accelerates(Carrol et al., 2012). Although there is confounding evidence of the“edge effect” on marine species diversity and ecological processes(see review by Boström et al., 2011), edges (low area:perimeterratio) are currently considered inadequate landscape characteris-tics as these are associated with increased predation and decreasedhabitat quality (Carrol et al., 2012). Furthermore, the more elon-gated a patch is, the larger the contact, and hence the area that canbe affected by a disturbance (Bogaert et al., 2000).

High b-diversity generally implies strong environmental gradi-ents that control and maintain ecosystem function and the pro-cesses that sustain biodiversity (Harborne et al., 2006; Reyers et al.,2002). Incorporating b-diversity patterns into protected areas se-lection and zonation benefits conservation as it captures habitatcomplexity (Harborne et al., 2006; Rioja-Nieto and Sheppard,2008), species assemblage variation (McKnight et al., 2007), andunderlying environmental heterogeneity (Margules and Pressey,2000) into management strategies and planning.

In this study, we propose a novel approach on the use of b-di-versity modeling, habitat type and compactness for the systematicidentification of priority conservation areas (PCOA), using Magda-lena Bay, Mexico, as a case study. Considering these characteristicson an MPA design is fundamental, in particular if long-term persis-tence of habitats and their ecological services are to be considered.

2. Methods

2.1. Study-area

Magdalena Bay (Fig. 1), located on the Pacific side of the BajaCalifornia Peninsula is one of the most important bays of Mexicodue to the high value of fisheries (w21 million USD for 2006) thatoperate in the region. It is considered a marine priority area forconservation by the Mexican Council for the Use and Conservationof the Biodiversity (CONABIO) and the RAMSAR convention, given

the bay’s high biodiversity, degree of disturbance to the benthiclandscape caused by small boat traffic and fisheries, and lack ofregulation and enforcement for fishing and tourism activities(Arriaga Cabrera et al., 1998). Given the ecological characteristics ofthe bay and the need for resource management in the area, theMexican government has considered the establishment of an MPAsince 2005 (SEMARNAT, 2005). The bay comprises a mixture ofshallow channels and wetlands connected to the Pacific Ocean by a5.6 km wide mouth. The central area has a depth between 21 and41 m, and gradually ascends toward the coast. The main town islocated to the North East of the bay and most of the economic ac-tivity depends on the capture, processing and commerce of fisheryproducts (INEGI, 2005). This study provides the first comprehen-sive description of the benthic landscape in the area.

2.2. Image pre-processing

Amosaic of eight high resolution scenes (RGB, 2009e2011), fromthe Geoeye-1 platform was constructed using a weighted seamlinefor overlap areas and histogrammatching to reduce the difference ofbrightness and color between scenes. Deep (>10 m) areas and land,identified fromnavigation charts and theGeneral Bathymetric Chartof the Oceans (GEBCO 08), were removed from the mosaic andmasked fromanalysis. The limited transparency of thewater columnmade it difficult to obtain information at greater depths. Atmo-spheric (Chavez,1996),water columncorrections (Green et al., 2000;Lyzenga, 1981), and a 7 � 7 low pass filter were used to enhancecontrast and eliminate noise from themosaic (Schowengerdt, 2007).

2.3. Habitat characterization and thematic map

Quantitative estimates on the percentage of coverage of benthicsubstrates (macroalgae, hard coral, soft coral, sponges, seagrass,pebbles, rock, sand) obtained from 60 ground-truthed sites wereused to perform a hierarchical classification analysis, using theBrayeCurtis similarity index with a group average linkage method,to obtain main habitat classes (Mumby and Harborne, 1999;Sheppard et al., 1995). Ground-truthed sites (training sites) were“seeded” on the mosaic (Green et al., 2000) to construct a signaturefile to perform a supervised classification with the maximum like-lihoodalgorithm.Using80% level of theBrayeCurtis similarity indexbetween ground-truthed sites, contextual editing,1 and Euclidianspectral distance (>2.6) between habitat classes, a classificationwith7 classes (Table 1)was obtained toproduce the thematicmapofthe shallow habitats of Magdalena Bay. Accuracy assessment wasestimated using an error matrix (Congalton, 1991; Story andCongalton, 1986) to compare habitat predicted types to trainingsites. An overall accuracy of 80.1% and a kappa coefficient of 0.69were obtained. Image processing, classification and accuracyassessment was performed using ERDAS Imagine 2011.

2.4. Habitat, b-diversity and compactness

Using the habitat thematic map, scripts written in Matlab wereutilized to calculate b-diversity values for each pixel by consideringthe dissimilarity between habitats in a “window” of 0.23 km2

(Harborne et al., 2006; Rioja-Nieto and Sheppard, 2008). Afterapplying this methodology in a marine area in the Caribbean usingseveral window sizes (see Rioja-Nieto and Sheppard, 2008), aw0.2 km2 window was deemed to be most suitable for taking into

Fig. 1. Location of Magdalena Bay in the Pacific side of the Baja California Sur Peninsula, Mexico.

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consideration ecological interaction between habitats. The windowarea for pixels located less than 480 m from the edges of the image,diminished as the location of the pixel of interest moved closer tothe border. The size of the image produced (>1.6 GB) forced toresample the pixel spatial resolution of the thematic map to 4 m2 inorder to substantially reduce computer processing time.

A compactness raster estimated as the ratio between area andperimeter�2 (Bogaert et al., 2000) for each patch (polygon) was also

Table 1Simplified shallow benthic habitats of Magdalena Bay.

Class number Main habitat characteristics

1 Low to medium cover each of macroalgae and rock withpresence of hard coral.

2 Low to high cover each of sand and pebbles with lowcover of rock.

3 Very high cover of seagrass with low cover of sand.4 Low to high cover of seagrass, medium to very high

cover of sand with presence of macroalgae.5 Presence to medium cover of macroalgae with medium

to very high cover of sand.6 Presence each of seagrass and macroalgae with very

high cover of sand.7 Low cover of macroalgae, medium cover each of pebbles

and sand.

Coverages are as follow: presence <5%, low �5 � 25%, medium >25 � 50%, high>50 � 75%, very high >75%. Seagrass coverage consisted of Zostera marina andRuppia maritima.

obtained. Compactness values were not normally distributed(p< 0.05) and none of several types of transformations changed thiscondition. A KruskaleWallis and a Tukey non-parametric multiplecomparison test (Zar, 1984) were used to determine whethercompactness was different between habitat types.

2.5. Selection of priority conservation areas

The habitat type, b-diversity and compactness rasters wereexported to a GIS (ArcMap 10.0). Due to the different types of datautilized, pixel values were firstly transformed to a standard scorescale between one and five, with one indicating a less suitablecharacteristic and five a highly desirable characteristic (Table 2). Forthe thematic map, habitats dominated by foundation species, such

Table 2Standard scores (1e5) assigned to habitat type, and b-diversity and compactnessvalue bins defined with the natural breaks/Jenks algorithm. One indicates a lessdesirable characteristic and five a highly desirable one.

Standard score Raster

Habitat type b-diversity values Compactness

1 Habitat 5 and 6 0.00e0.47 0.0001e0.00412 Habitat 2 0.48e1.45 0.0042e0.01383 Habitat 7 1.46e2.37 0.0139e0.02844 Habitat 1 2.38e3.20 0.0285e0.04815 Habitats 3 and 4 3.21e4.27 0.0482e0.0733

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as seagrasses, were deemed more important for conservation, andwere assigned the highest score; non-vegetated habitats wereassigned the lowest score. For the b-diversity and compactnessrasters, pixel values were classified into five categories each,defined with natural breaks/Jenks algorithm to minimize varianceand maximize differences within and between groups, respectively

Fig. 2. Benthic habitats of Magdalena Bay obtained from a supervised classification on a Gewith a group average linkagemethod, contextual editing, and similarity on the spectral signatwith presence of hard coral (% of cover of habitat¼ 0.05), habitat 2, low to high cover each of scover of seagrasswith lowcover of sand (% of cover of habitat¼ 2.0), habitat 4, low to high coveof habitat¼ 13.28), habitat 5, presence tomedium cover of macroalgaewith medium to very hmacroalgae with very high cover of sand (% of cover of habitat ¼ 45.55), habitat 7, low cover

(ESRI, 2010). Scores (1e5) were then assigned to obtained groups,where high values of compactness and b-diversity were considereddesirable.

Standardized rasters were used to perform a weighted overlayanalysis (ESRI, 2010) considering a weighting of 50% (habitat type),20% (b-diversity) and 30% (compactness), and a composite raster

oye-1 (RGB) mosaic using 60 training sites grouped with a BrayeCurtis similarity indexure between habitat classes.Habitat 1, low tomedium cover each of macroalgae and rockand and pebbles with low cover of rock (% of cover of habitat¼ 0.13), habitat 3, very highr of seagrass,medium to very high cover of sandwith presence ofmacroalgae (% of coverigh cover of sand (% of cover of habitat¼ 38.97), habitat 6, presence each of seagrass andof macroalgae, medium cover each of pebbles and sand (% of cover of habitat ¼ 0.02).

Fig. 4. Median of area/perimeter2 ratios for shallow benthic habitat patches inMagdalena Bay. Black lines represent upper and lower quartiles. Numbers above barsindicate the habitats where differences are significant (p < 0.05) when compared. Forhabitat description refer to Table 1.

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with pixel values grouped into low, medium and high priority forconservation categories produced.

3. Results

3.1. Habitat thematic map

Habitats 6 (presence each of seagrass and macroalgae withvery high cover of sand), and 5 (presence to medium cover ofmacroalgae with medium to very high cover of sand) were moreabundant and represented 84.52% of the total area. Habitats 1 (lowto medium cover each of macroalgae and rock with presence ofhard coral), 2 (low to high cover each of sand and pebbles with lowcover of rock) and 7 (low cover of macroalgae, medium cover eachof pebbles and sand) showed the lowest coverage (0.2% of the totalarea). Seagrass dominated habitats (habitats 3 and 4) represented15.28% of the mapped area (Fig. 2).

3.2. Habitat b-diversity and compactness

In general, b-diversity values ranged between 0 and 4.27, withhigh values obtained in theNorth and central areas of the Bay. Large,homogeneous areas (i.e. sandy and seagrass beds), with the samehabitat class showed lower values than areas with a similar extentwith a high variety of habitats (Fig. 3a and b). Habitat compactness(Fig. 4) differed between classes (X2 ¼ 160.24 p < 0.05). Habitat 6(presence each of seagrass and macroalgae with very high cover ofsand) showed the highest compactness value in the study area,while habitat 7 (low cover of macroalgae, medium cover each ofpebbles and sand) showed the lowest value with the exception ofthat with habitat 1 (low to medium cover each of macroalgae androck with presence of hard coral) where no differencewas observed

Fig. 3. (a) Section of the benthic habitat map of Magdalena Bay obtained from a Geoeye-1 (RRed square indicates the location of sections showed above.

(p > 0.05). Habitat 1 showed a lower compactness than habitat 5(presence to medium cover of macroalgae with medium to veryhigh cover of sand). Compactness of habitat 3 (very high cover ofseagrass with low cover of sand) was lower than compactness ofhabitats 4 (low to high cover of seagrass, medium to very high coverof sand with presence of macroalgae) and 5. All other combinationsshowed no statistical difference (p > 0.05).

3.3. Selection of PCOA

The frequency distribution of pixels categorized in low, mediumand high priority areas for conservation according to habitat type,b-diversity, and compactness is presented in Fig. 5(aec). Ninety five

GB) mosaic. (b) Section of the b-diversity results for the central area of Magdalena Bay.

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percent of pixels within a high priority category correspond tohabitats 3 and 4, 90% showed b-diversity values�2.38, and 64% hadcompactness values �0.0139. Medium priority pixels were gener-ally characterized by habitats 5 and 6 (66%), and 3 and 4 (28%). Fiftynine percent of pixels showed b-diversity values �2.38 and 75%showed compactness values <0.0139. Low priority pixels werecompletely characterized (100%) by habitats 5 and 6, and b-di-versity values < 2.38. Ninety two percent of pixels in this categoryshowed compactness values <0.0139.

High priority pixels are distributed on six regions along theNorth, West, and Southeast of the study area (Fig. 6aef). Thehighest density of pixels in this category is observed toward theNorth and North East of the bay (Fig. 6a, b and d). Therefore, thesethree regions are proposed as potential areas to be consideredwithin an MPA scheme.

4. Discussion

Anthropogenic activities and climate change related distur-bances make the allocation of resources for the management and

Fig. 5. Standardized frequency of pixels, grouped in intervals as defined in Table 2,which obtained a low, medium and high priority for conservation category. (a) Habitattype. (b) b-diversity values. (c) Compactness (a/p2) values.

conservation of coastal and marine ecosystems fundamentallyimportant. In this study, we illustrate the use of a cost-effectiveapproach for MPAs design using habitat type, b-diversity andcompactness. High b-diversity generally captures habitatcomplexity and environmental heterogeneity (McKnight et al.,2007; Reyers et al., 2002) which are important characteristics forconservation planning that in general have been overseen bymanagers. However, by only using the b-diversity model presentedin here, poses some limitations. For example, values <480 m fromthe borders of the image are affected. Additionally, large homoge-neous patches such as seagrass prairies, generally tend to havelower values. Therefore, to obtain a better representation of land-scape characteristics for conservation prioritization, the model wascomplemented by including habitat type and compactness.

Habitat value for conservation is difficult to assess and all habi-tats do not necessarily have the same importance (Mumby et al.,2008). For this model, habitat types which were assigned highscore values, correspond to structurally complex habitats such asseagrass andmacroalgae beds (i.e. habitats 3, 4 and 7). This allowedfor a better evaluation of both compact non-vegetated habitats(common in Magdalena Bay) within spatially heterogeneous re-gions and large homogeneous habitats dominated by foundationspecies. Structurally complex habitats are known to serve as a refugeand nursery for invertebrates and fish (Heck et al., 2003; Perssonet al., 2012) and provide sediment stabilisation and carbon andnutrient sequestration (Boström et al., 2006, 2011; Frost et al.,1999).Additionally, where uniform unvegetated substrates dominate, thephysical structure of seagrass beds resemble the ecological func-tions provided by mangroves and reefs (Sheridan, 1997).

Considering high habitat compactness as a suitable character-istic improved the assessment of areas with highly fragmentedhabitats, which tended to have lower compactness values. Thispattern has also been observed in forests (Chen et al., 1992) andwetland grass (Gabriel and Bodensteiner, 2002), where area to edgeratio decreases as fragmentation increases. Compactness may alsofunction as a surrogate of diversity and resiliency. Compact habitatsgenerally contain higher species richness in comparison to elon-gated ones (Forman, 2006), and under disturbance, compact hab-itats may develop an undisturbed zone which could help therecovery of disturbed areas (Bogaert et al., 2000).

While the choice of the weight for the layers in the model isarbitrary, designating a higher importance to habitat structure andheterogeneity entails the inclusion of natural processes that sustainbiodiversity and ecosystem persistence for the selection of thePCOA (Margules and Pressey, 2000; Reyers et al., 2002; Shelton,2010). These characteristics are important for a managementapproach based on resiliency (Holling, 1973) that is necessary givenchanging environmental conditions. In general, the values of thedatasets utilized distributed well within the 3 categories for con-servation obtained from themodel. Areas dominated by foundationspecies, andwith high b-diversity and compactness obtained a highpriority for conservationwhile those areaswithminimal vegetationcover and low values obtained a low priority status. Areas catego-rized with a medium priority displayed the full range of charac-teristics. However, few areas with low b-diversity, compactness ornon-dominated by foundation species also obtained a high con-servation priority status, which shows sensitivity of the model toareas with varied characteristics. Compactness showed the greaterspread of values within a high category which could be probablyrelated to fragmentation of seagrass and macroalgae beds.

Taking into consideration the landscape metrics discussed inthis study for spatial conservation prioritization allows for theproposal of management strategies, within an ecosystem-basedapproach, considering the characteristics that determine theecological processes in the ecosystem and therefore maintain

Fig. 6. Priority conservation areas map obtained from a weighted overlay analysis based on habitat type, b-diversity with a window of 0.23 km2 and compactness using a 50, 20 and30% weight, respectively. Black boxes indicate the areas shown on the right (a,b,c,d,e,f) where pixels with high conservation value are spatially grouped. Regions 6a, 6b and 6d areproposed to be considered within an MPA scheme.

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biodiversity for a particular region. If effective management wantsto be achieved, this information should be complemented with theneeds and condition of local stakeholders during the MPA’s design/placement process. In this sense, this methodology could also beused during the evaluation of previously placed protected areas andadapt their zonation according to human and climate induceddisturbances.

From the PCOA identification in Magdalena Bay, three regionswhich comprise w20% of Magdalena’s bay area are proposed to beconsidered as the basis of an MPA scheme. In general, these areasare characterized by structurally complex habitats, high environ-mental heterogeneity and less susceptibility to disturbance. Thesuggested area also represents a proportion of the total area thathas been considered sufficient to ensure MPA effectiveness else-where (Gladstone, 2007; Roberts, 2000). The systematic approachproposed here can improve habitat representation while reducing

the economic costs associated to the eventual implementation ofthe MPA (Malcolm et al., 2012). It also limits the size of the areaproposed to be under management, minimizing the disruptioncaused to local fishing activities. However, for this tool to provideeffective support, it will be necessary to include the opinions of allthe stakeholders that operate in the area. In this sense, the expe-rience of a volunteer no-take protected area, already establishedand enforced locally by the main fishing cooperative in the regionon the open sea side of the Bay, could prove fundamental during thedesign process of the MPA.

5. Conclusion

The methodology presented here is the first e to our knowledgee to combine habitat type, b-diversity modeling, and compactnessfor the systematic identification of priority areas for conservation.

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The integration of these three measurements addresses the prin-ciple of complementarity (Kirkpatrick, 1983), by selecting areaswith high habitat heterogeneity (hence species assemblages vari-ation) and biodiversity persistence; characteristics that are funda-mental for spatial conservation prioritization (Wilson et al., 2009).Integrating these landscape metrics also allows for a better evalu-ation of regions with varied landscape characteristics than by uti-lizing them individually.

Several tools can be used for spatial conservation planning (seeKlein et al., 2009; Merrifield et al., 2013; Teh et al., 2012). However,there is no “gold standard” methodology for the identification ofPCOA for the placement and design of MPAs as this depends onhuman and economic resources availability, data disponibility,technical and institutional capacity and local acceptance. Imple-menting the approach presented here to other areas and/or eco-systems should be relatively easy. Habitat maps based on GIS andremote sensing are becoming more readily available, scripts tocalculate habitat b-diversity can be produced by a person withprogramming skills, and compactness can be directly calculatedfrom a GIS.

By identifying the three areas in Magdalena Bay, whichaccording to the characteristics considered here are of high pri-ority for conservation, we increase the efficiency of the limitedconservation resources during the design process. This also limitsthe impact on fishing activities, which are the main source of in-come for the local community, associated to the implementationof a protected area. However, if effective management is going tobe achieved, the information provided by the model should becomplemented with local stakeholders’ needs and knowledge.This methodology also allows for changes in the zonation ofexisting protected areas, which for federal managed areas inMexico, should be reconsidered every five years according tolegislation.

Acknowledgments

We thank Eduardo Pacheco Góngora for his help adjusting thescripts for the b-diversity analysis and the anonymous reviewersfor their comments that improved the original manuscript. TheResearch grant CONACyT-SEMARNAT 108464 provided funding forthis research and a scholarship for EBF.

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