6. Identifying Priority Areas for Island Endemics Using Genetic Versus Speci_c Diversity

8/13/2019 6. Identifying Priority Areas for Island Endemics Using Genetic Versus Speci_c Diversity

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Identifying priority areas for island endemics using genetic versus specificdiversity The case of terrestrial reptiles of the Cape Verde Islands

Raquel Vasconcelos a,b,c,, Jos Carlos Brito a,b, Slvia B. Carvalho a, Salvador Carranza c, D. James Harris a,b

a CIBIO, Centro de Investigao em Biodiversidade e Recursos Genticos da Universidade do Porto, Instituto de Cincias Agrrias de Vairo, R. Padre Armando Quintas,

4485-661 Vairo, Portugalb Departamento de Biologia, Faculdade de Cincias da Universidade do Porto, Rua Campo Alegre s/n, 4169-007 Porto, Portugalc Institute of Evolutionary Biology (CSIC-UPF), Passeig Martim de la Barceloneta, 37-49, E-08003 Barcelona, Spain

a r t i c l e i n f o

Article history:

Received 2 August 2011Received in revised form 2 April 2012Accepted 19 April 2012Available online 5 July 2012

Keywords:

Chioninia

Ecological niche-based modelsESUs versus speciesHemidactylus

Protected areasTarentola

a b s t r a c t

Genetic diversity is critical for conservation of endemic populations. It enhances adaptation to rapid envi-ronmental changes and persistence over evolutionary time-scales. In small and isolated populations, suchas on islands, this is even more relevant. Nevertheless, few studies regarding the establishment of pro-tected areas (PAs) on islands have taken genetic diversity into account. The Cape Verde Islands are in abiodiversity hotspot andpresent to resource planners unique problems andpossibilities, hence are a goodcase study. This work primarily aims to compare targeting evolutionary significant units (ESUs) versusspecies in reserve selection algorithms for the conservation of the endemic Cape Verdean reptile diversityby assessing the PAs network adequacy, identifying its gaps, and optimizing it based on realistic (con-sidering areas inside PAs with lower cost) and ideal (considering all non-humanized areas with higherpotential for conservation) cost scenarios. Results clearly indicate that analyses targeting ESUs are moreeffective in the protection of genetic diversity and less costly in terms of selected area, in total and insidePAs. Results also indicate that most ESUs and species are insufficiently protected and that extra PAs areneeded on most islands to reach conservation targets. Surprisingly, the total area selected in ideal and

realistic prioritization scenarios are identical on most islands both for analyses targeting ESUs or species.Therefore the realistic scenario should be largely followed. The work provides an innovative methodo-logical framework for supporting the use of genetic diversity in reserve design and its results shouldassist in local-scale conservation planning.

2012 Elsevier Ltd. All rights reserved.

1. Introduction

Genetic diversity is critical for conservation of endemic popula-tions since it provides the raw material for the persistence of spe-cies over evolutionary time-scales, and is also of particularrelevance at present time-scale in terms of providing the basisfor adaptation to rapid environmental changes (Hglund, 2009).Genetic diversity is correlated with adaptive capacity of popula-tions and fitness (Soul, 1986). Furthermore, in small isolated pop-ulations, the synergy of genetic and demographic factorssubstantially increases their probability of extinction (Frankham,1997). The dynamics of isolated populations can often be observedon islands (e.g.Caujap-Castells et al., 2010), which are frequentlyaffected by catastrophic events, such as volcanic activity ordroughts that can cause bottleneck effects (Whittaker and Fernn-

dez-Palacios, 2007). Moreover, islands usually have higher num-bers of endemic species than equivalent continental areas(Whittaker and Fernndez-Palacios, 2007) and high levels ofuniqueness of genetic variation, especially on large or highly re-mote ones (Wilson et al., 2009). As a result, study and protectionof endemic island taxa and their genetic diversity, considering allevolutionarily significant units (ESUs), is particularly relevant.

Designation of protected areas (PAs) safeguards habitats impor-tant to wildlife and preserves genetic resources and species diver-sity, provides a baseline against which human-caused changes canbe measured, and allows evolutionary processes to continue with-out human disturbance (Quigg, 1978). The best way to representgenetic diversity in a subset of populations is to base conservationdecisions on known levels of diversity within, and distribution ofdiversity among, populations (Neel and Cummings, 2003). Never-theless, most studies focus on optimizing biodiversity representa-tion at species and/or habitat level (e.g. Cowling and Pressey, 2001;Cowling et al., 2003; Bonn and Gaston, 2005; Kremen et al., 2008),while studies accounting for intra-specific genetic variability interrestrial systems are scarce (e.g.Wei and Leberg, 2002; OMeally

0006-3207/$ - see front matter 2012 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.biocon.2012.04.020

Corresponding author at: CIBIO-UP, Instituto de Cincias Agrrias de Vairo, R.Padre Armando Quintas, 4485-661 Vairo, Portugal. Tel.: +351 252 660 414; fax:+351 252 661 780.

E-mail address:[email protected](R. Vasconcelos).

Biological Conservation 153 (2012) 276286

Contents lists available at SciVerse ScienceDirect

Biological Conservation

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and Colgan, 2005; Rissler et al., 2006; Grivet et al., 2008; Thomas-sen et al., 2010). To our knowledge, only four studies of this naturehave been performed on island-like system. Smith et al. (2000) andKahindo et al. (2007) studied mitochondrial lineages of avianspecies in African mountain regions, and considered distinctivelineages worthy of conservation.Moritz (2002)studied the conser-vation value of intraspecific mitochondrial lineages of rainforest

fauna from northeast Australian mountains, and Setiadi et al.(2009) tested whether the two disjunct blocks constituting aNational Park of an Indonesian island adequately captured thefull breadth of genetic diversity of endemic species of herpetofa-una. These works showed that the study of the distribution ofgenetic variation within species can provide useful informationfor biodiversity conservation. However, its concrete applicationin demonstrating how the selection of priority areas wouldperform comparatively to protecting species or habitats remainsunexplored.

Because financial resources for conservation are limited, sys-tematic methodologies and optimization algorithms have beendeveloped to optimize biodiversity representation and persistencewithin PAs (Moilanen et al., 2009). The establishment of PAs is usu-ally constrained by the existing reserve system (Pressey, 1994) andforms of land use that are, in the short term, financially more viablethan conservation (Ferrier et al., 2000). Implementation of newPAsin most developed countries is also usually hampered by high den-sities of human population and infrastructures. Cape Verde is anexception to some of those points, since most islands of the archi-pelago have less than 75 habitants/km2 (Lobban and Saucier, 2007)and few impacting human infrastructures. The PAs network in thecountry is sparse and was chosen in a non-systematic way, basedon ad hocpresences of endemic flora and nesting bird sites and alsoon scenic and recreational reasons (Anonymous, 2003). Currently,only four of the 46 legally proposed terrestrial PAs of the network(Anonymous, 2003) are lawfully established and only three ofthem have management programs, and can thus be consideredfully operational (Fig. 1), which correspond to merely 2.47% of

the area of the country (IUCN and UNEP-WCMC, 2011). Addition-ally, biodiversity inventories are still scarce, and distribution dataare still poorly documented. Hence, the degree that fully opera-tional PAs and the ones to be implemented serve to protect impor-tant elements of biodiversity at different scales, species and geneticdiversity, and the gaps in its representation are insufficiently un-known. Thus, there is still a real window of opportunity to enhancethe remaining 42 PAs which are not currently implemented forrepresenting and ensuring long-term persistence of endemicbiodiversity.

The Cape Verde archipelago is a biodiversity-rich area, includedin the Mediterranean biodiversity hotspot (Conservation Interna-tional, 2005). Among vertebrates, reptiles biodiversity in the coun-try stands out in total number of taxa and high level of endemism,

since it is the richest of all Macaronesian archipelagos (Vasconceloset al., in press). Contrary to other groups, all native taxa are ende-mic (Schleich, 1987) and have recently updated taxonomy, well-known genetic diversity and defined ESUs for conservation (Arnoldet al., 2008; Vasconcelos et al., 2010, 2012; Miralles et al., 2010),but were neglected in the PAs network design due to lack of distri-bution data. The group presents a manageable number of extanttaxa, 30, within only three genera: the HemidactylusandTarentolageckos and the Chioninia skinks (Vasconcelos et al., in press).Hence, Cape Verdean endemic reptiles are ideal models to studyreserve design and perform gap analyses taking into account ge-netic diversity.

The aim of this study is to compare conservation planning anal-ysis targeting ESUs or species as conservation features, with the

following specific objectives: (1) to assess the adequacy of thePAs network by quantifying the protection that it guarantees or

will guarantee and its gaps, and the amount that is still missingfor achieving conservation targets; (2) to map optimized priorityplanning units (PUs), by using two different reserve design costscenarios: a realistic (considering PUs inside PAs with lower cost)and an ideal scenario (considering all non-humanized PUs withhigher potential for conservation); (3) to evaluate the differencesin the prioritization exercises, by quantifying the amount of se-

lected PUs in total, and that will be inside PAs in each island, byassessing the percentage of target achievements for each conserva-tion feature, and by spatially depicting the combined outputs.

This work provides an innovative methodological frameworkfor testing the usefulness of targeting genetic diversity in reservedesign and its results contribute for local scale conservation plan-ning of endemic biodiversity on islands.

2. Materials and methods

2.1. Study area

Cape Verde is located in the Atlantic Ocean around 500 km offthe west coast of Africa (Fig. 1). With an area of 4067 km2, the

study area was divided into 76,414 grid cells, of 225 225 m each,hereafter referred as planning units (PUs), the units for reservedesign.

Data on the existing PAs was compiled from MAAP-DGA (2012)website. Digital maps of the terrestrial portions of the PAs networkwere created based on information available from governmentinternal reports (Fig.1; Appendix A).

2.2. Taxon occurrence data and distribution models

Given that only a small fraction of the territory was sampled(around 11%), and that sampled locations were spatially biased, itis most appropriate to use ecological models to predict potentialdistributions of occurrence, when attempting to identify priority

areas for conservation (Carvalho et al., 2010).

2.2.1. Taxon occurrence dataA total of 953 observations of all 30 extant Cape Verdean reptile

taxa from the most recent distribution atlas were used to developmodels (Vasconcelos et al., in press). For 752 observations, the geo-graphic location was recorded with a Global Positioning System(GPS) on the WGS84 datum, whereas the remaining 201 observa-tions were georeferenced using topographical maps to a precisionof 225 m. Given that there was spatial bias in survey effort that re-sulted in presence clumps, observations were removed from clus-ters of occurrences to decrease the level of spatial autocorrelationin taxon presences (for details see Brito et al., 2009). The NearestNeighbor Index was used to assess the degree of data clustering

(clustered if < 1; dispersed if > 1): 0.42, 0.66, 0.85 and 0.89 inChioninia delalandii, Hemidactylus boavistensis, Tarentola fogoensisand Tarentola substituta, respectively, and above 0.90 for theremaining taxa, indicating some degree of clustering for the formerfour species and dispersed distribution for the remaining ones.Spatial analyses were accomplished with Spatial Analyst exten-sion of ArcGIS 9.3 (ESRI, 2008). From the available observations,791 were used for developing distribution models for each taxon(for details seeAppendix B).

2.2.2. Environmental factorsFourteen ecogeographical variables (hereafter EGVs), were used

in the ecological models (Appendix C) and included altitude (Jarviset al., 2006), slope derived from altitude with the Slope function of

ArcGIS, normalized difference vegetation index (NDVI), and 11habitat types digitized from agro-ecological and vegetation zoning

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maps (for details on habitats seeVasconcelos et al., 2010). NDVI16-day L3 Global 250 m data series from 01.01.2006 to31.12.2008 were downloaded fromUSGS (2009) website, corre-sponding to the years when sampling was performed, and thenthe maximum of that data series was calculated to input into themodels. The Euclidean distance of each grid cell to the closest hab-itat-type was calculated for each individual habitat grid using theEuclidean Distance tool of ArcGIS. Advantages of using Euclideandistances to analyze habitat data include use of more than linear orpoint habitat features, absence of explicit error handling, andextraction of more information from data than classification-basedapproaches alone (Conner et al., 2003). Finally, the EGVs resolutionwas decreased from 0.00083 to a grid cell size average of 0.00211

(about 225 m) to match the observations resolution.

2.2.3. Predicted occurrencesModels were developed for each taxon (Appendices D and E)

using the Maximum Entropy approach (Phillipset al., 2006, Phillipsand Dudk, 2008). This modeling technique requires only presencedata as input, but consistently performs well in comparison toother methods (Elith et al., 2006), especially with low samplessizes (Wisz et al., 2008). Even so, for seven taxa with extremelylow sample size (n6 5) models were not developed. In these cases,the pixels of taxon occurrence and/or all pixels of the islet wherethe taxon occurs were used in subsequent analyses (Appendix B).

Reptile observations and EGVs were imported into MaxEnt 3.3software (Phillips et al., 2006). A total of 10 model replicates were

run with random seed which allows a different random training/testing data partition in each run. Observations for each replicate

were chosen by bootstrapping. Percentages assigned for testingmodels varied according to sample size: 10% for four taxa with lessthan 20 observations, 20% for 18 taxa with more than 20 observa-tions, and 15% for one taxon with only seven observations (Appen-dix B). Models were run with auto-features (Phillips et al., 2006),and the Area under the Curve (AUC) of the receiver-operating char-acteristics (ROC) plot was taken as a measure of individual modelfit (Fielding and Bell, 1997).

The individual model replicates (n= 10) were used to generatean average probability forecast of species occurrence (Marmionet al., 2009). Standard deviation between individual model proba-bilities of presence was used as an indication of prediction uncer-tainty (Buisson et al., 2010). Average models were reclassified to

display areas of probable absence and presence for each taxon.The 10th percentile training thresholds calculated by MaxEnt wereused, which considered the value of the 10 percentile species pres-ence record to define all areas with a lower predicted value as ab-sent, and with a higher value as present (Phillips and Dudk, 2008).This threshold was chosen because true absence data was notavailable to estimate adjusted thresholds for each taxon, whichwould allow optimizing omission and commission rates (Liuet al., 2005). Also, it provides more parsimonious models thanthe minimum training presence threshold, minimizing over-pre-dictions. To evaluate the models quality, the total observations(n= 953) were intersected with the threshold models to calculatethe percentage of correct classification of presences for each taxon(Appendix B).

Predicted occurrences of species that included more than onesubspecies were obtained by combining the individual-taxa mod-

Fig. 1. Location of the study area and distribution of the protected areas (PAs) in the Cape Verde Islands (see Appendix 1for PA designations).

278 R. Vasconcelos et al. / Biological Conservation 153 (2012) 276286


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els using the Combine and Reclassify tools of ArcGIS. For non-modeled taxa, predicted occurrences corresponded only to the pix-els were species were detected.

2.3. Conservation planning

A systematic approach was performed to identify priority PUs

for the conservation of Cape Verdean endemic reptiles. To includegenetic diversity into reserve design, conservation targets werefirst applied on ESUs. Then, the analysis was repeated targetingspecies to evaluate differences in the selection of priority areas,efficiency and protection of diversity.

2.3.1. Evolutionarily significant units

Considering the definition ofFraser and Bernatchez (2001) ESUs,the units for conservation action, are defined as lineages demon-strating highlyrestricted gene flow from other such lineages withinthe higher organizational level of species. Delimitation of ESUs forendemic species of each genus, based on independent mitochon-drial DNA networks and significant HudsonsSnnvalues, were per-formed on recently published papers that updated the taxonomyofthe three reptile groups based on molecular markers, populationand morphological analyses (seeArnold et al., 2008; Vasconceloset al., 2010; Miralles et al., 2010). Hence, the 23 reclassified modelswith predicted taxon occurrences (Appendices D and E) wereclippedinto individual files to correspond to the 31 previously iden-tified extant ESUs. Forexample, the reclassified model forC. delalan-diiwas clipped by Santiago, Fogo, Brava and Rombos shape files,respectively, to obtain the predicteddistributions of the four genet-ically identified lineages (see Table 1). In the case of two taxa(T. darwini andChioninia spinalis santiagoensis) with two ESUsoccur-ring on the same island (Santiago), the distribution data of lineageswas plotted over thereclassified models to definethe extentof occur-rence of each ESU and then models were clipped into two adjacentpolygons. For the seven taxa for which distribution models werenot developed, only observed records were accounted for reserve

selection. The ESU corresponding to the extinct Chioninia cocteiwasnot consideredin the analyses, thus 38 ESUs were considered in total.

2.3.2. Adequacy of the protected areas networkA gap analysis was performed to evaluate the adequacy of the

PAs network. The predictive models were intersected with PAspolygons to assess the percentages of eachESU/species distributionwhich was currently protected or that will be protected if the fullPAs network is implemented, and the amount of PUs still missingto reach conservation targets (see below).

The adequacy of the PAs network was also indirectly evaluatedby comparing solutions of reserve design algorithms using scenar-ios unconstrained or constrained to PAs (for details see Sections2.3.3. and 2.3.4.).

2.3.3. Optimized priority planning unitsA software for spatial conservation prioritization, ZONATIONv2.0

(Moilanen et al., 2009) was used to evaluate if the PAs networkwas optimal for protecting all ESUs of endemic reptiles from CapeVerde and to compare effectiveness of two analysis, one targetingESUs versus another targeting species as conservation features.ZONATIONuses a gradient-like iterative heuristic, which gives a solu-tion very close to the global optimal (van Teeffelen and Moilanen,2008) to produce a sequential removal of PUs throughout the plan-ning region. PUs with less conservation value are removed first,thus, PUs with highest rank have highest conservation value.

Target-based planning was chosen as PUs removing rule sincethe goal was to find the best solution in which the maximumnum-

ber of ESUs or species met conservation targets. Conservation tar-gets were set as 12% for this analysis because it appears to be

widely used in similar analyses (Wright and Mattson, 1996; Cantet al., 2004). Hence, any resource category with at least 12% of itsarea protected was considered adequately protected. The onlyexception was applied to taxa considered endangered (CriticallyEndangered or Endangered), according to IUCN Red List criteria(Vasconcelos et al., in press) to which a higher target was set(100%), following recommendations of similar works (Carvalho

et al., 2010; Jackson et al., 2004).In order to generate spatial aggregation into the solution, therule only remove from edges was selected. The warp factor wasset to one with an aggregation level of 0.04. The Boundary LengthPenalty (BLP), which devalues reserve structures with lots of edge,was chosen as the method for inducing reserve network aggrega-tion (Moilanen and Kujala, 2008).

Two cost scenarios, one realistic and one ideal, were simu-lated in each analysis, constrained and unconstrained by the46 PAs, respectively. In realistic scenarios, cells with main roadsand small and large urban areas and other infrastructures (with abuffer radius of 112 m or 1 km, respectively) were given a cost of100, with secondary roads a cost of 75, with PAs 1, and remainingcells 50. All different PA categories were thus treated with thesame weight. In ideal scenarios, PAs were not taken into account,thus cells with main roads and urban areas were given a cost of100, with secondary roads 50, and remaining cells 1. The minimumset of PUs with higher rank in the final solution, which assured thatall ESUs or species were represented with the desired target, wasselected for each scenario in reclassified binary files.

2.3.4. Differences in the prioritization exercisesThe selected PUs in each scenario (ideal or realistic) and anal-

yses targeting different conservation features (ESUs or species)were counted and intersected with PAs polygons using ArcGIS tocalculate the amount of PUs encompassed in the PAs network,and to identify differences in outputs according to cost scenariosand target analyses in total and per island/islet. The selected PUsin each cost scenario and target analyses were also intersected

with predicted occurrences to estimate the percentages of conser-vation targets achievement of each ESU and species. The outputcalculations of the two analyses (targeting ESUs or species) weresubtracted to quantify differences in amount and percentage of se-lected PUs between the two prioritization exercises per island/isletand per conservation feature.

To identify where PUs selected by realistic scenarios differedfrom ideal ones, these were combined for each analysis using Arc-GIS. To identify areas where the analysis targeting ESUs differedfrom the one targeting species, the analogous scenarios of eachanalysis were combined using ArcGIS.

3. Results

3.1. Evaluation of ecological niche-based models

The ROC plots exhibited high average AUCs with low standarddeviations (SD) for both training and test datasets in all modeltypes (Appendix B). Average AUCs for training and test datasetswere 0.985 0.003 and 0.970 0.018, respectively. Thresholdedmodels identified suitable cells for each species. The average per-centage of observations identified in suitable cells was 80.8%(Appendix B).

3.2. Adequacy of the protected areas network

Presently, with three PAs fully operational, only Chioninia spina-lis spinalis fulfils the conservation target (Table 1). All remaining

ESUs are insufficiently protected. When considering all PAs tobe implemented, these figures are quite different. In these



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circumstances, 15 of the 38 ESUs potential distributions (40%)considered in the analyses will have the targeted percentage ofits distribution inside PAs (Table 1). However, 9 of those 38 ESUs(24%) would not have a single PU inside a PA and only Hemidactylusbouvieri razoensis,Tarentola gigas brancoensisandT. g. gigaswouldbe fully protected (Table 1).

If conservation priorities targeted species instead of ESUs, thepresent PAs would fail to achieve conservation targets for all ofthem, and after the establishment of the full PAs network two ofthem would not have a single PU inside a PA (10%), and only sixout of 21 species (29%) would reach conservation targets (Table 1).

The PAs network adequacy was also indirectly evaluated by

comparing the ideal and realistic cost scenarios, unconstrainedand constrained to the PAs network, respectively (see Section3.4).

3.3. Optimized priority planning units

The maps with selected priority PUs for conservation, consider-ing realistic and ideal scenarios of analyses considering ESUs orspecies as conservation targets are presented in Fig. 2A and B,respectively.

3.4. Differences in the prioritization exercises

Overall, in both analyses targeting ESUs or species, more PUs intotal and inside PAs were selected in the realistic than in the

ideal scenarios (8127/8558 versus 8036/8458 total PUs and30.9/32.8% versus 21.2/21.9% of PUs inside PAs) (Table 2).

Table 1

Number (n) of planning units (PUs) where each evolutionarily significant unit (ESU) and species are predicted to occur, targeted for conservation, inside protected areas (PAs) fully

operational (present) or to be implemented (future), and missing to meet conservation targets (see Section 2 for details). Percentages (%) are given between brackets.

Taxon/ESU ESU Species Island/Islet Predicted Targeted Inside PAs Missing

Present Future

n n % n % n % n %

Hemidactylus lopezjuradoi F 1 1 (100) 0 (0.0) 0 (0.0) 1 (100.0)

H. boavistensis BV, S 5889 707 (12) 0 (0.0) 2986 (50.7) 0 (0.0)ESU Sal S 2225 267 (12) 0 (0.0) 517 (23.2) 0 (0.0)ESU Boavista BV 3664 440 (12) 0 (0.0) 2469 (67.4) 0 (0.0)

H. bouvieri SN, SA, SL, ra 111 111 (100) 1 (0.9) 109 (98.2) 2 (1.8)H. bouvieri,SN population SN 2 2 (100) 1 (50.0) 1 (50.0) 1 (50.0)H. bouvieri bouvieri SA 1 1 (100) 0 (0.0) 0 (0.0) 1 (100.0)H. bouvieri razoensis SL, ra 108 108 (100) 0 (0.0) 108 (100.0) 0 (0.0)Tarentola boavistensis BV 2994 359 (12) 0 (0.0) 1261 (42.1) 0 (0.0)T. bocagei SN 384 46 (12) 0 (0.0) 0 (0.0) 46 (100.0)T. fogoensis F 1099 132 (12) 17 (1.5) 17 (1.5) 115 (87.1)T. darwini ST 9801 1176 (12) 153 (1.6) 318 (3.2) 858 (73.0)

ESU North ST 3819 458 (12) 153 (4.0) 153 (4.0) 305 (66.6)ESU South ST 5982 718 (12) 0 (0.0) 165 (2.8) 553 (77.0)

T. substituta SV 1934 232 (12) 0 (0.0) 24 (1.2) 208 (89.7)T. raziana SL, ra, br 582 71 (12) 0 (0.0) 582 (100.0) 0 (0.0)T. caboverdiana SA 4180 502 (12) 0 (0.0) 97 (2.3) 405 (80.7)T. nicolauensis SN 2359 283 (12) 78 (3.3) 78 (3.3) 205 (72.4)

T. gigas

br, ra 153 153 (100) 0 (0.0) 153 (100.0) 0 (0.0)T. gigas brancoensis br 46 46 (100) 0 (0.0) 46 (100.0) 0 (0.0)T. gigas gigas ra 107 107 (100) 0 (0.0) 107 (100.0) 0 (0.0)T. rudis ST 2380 286 (12) 0 (0.0) 29 (1.2) 257 (89.8)T. maioensis M 2013 242 (12) 0 (0.0) 601 (29.9) 0 (0.0)T. protogigas F, B, ro 671 671 (100) 0 (0.0) 59 (8.8) 612 (91.2)T. protogigas protogigas F 4 4 (100) 0 (0.0) 0 (0.0) 4 (100.0)T. protogigas hartogi B, ro

ESU Brava B 608 73 (12) 0 (0.0) 0 (0.0) 73 (100.0)ESU Rombos ro 59 7 (12) 0 (0.0) 59 (100.0) 0 (0.0)

Chioninia vaillanti ST, F, ro 4084 4084 (100) 157 (3.8) 292 (7.1) 3792 (92.9)C. vaillanti vaillanti ST 3510 3510 (100) 157 (4.5) 233 (6.6) 3277 (93.4)C. vaillanti xanthotis F, ro 574 574 (100) 0 (0.0) 59 (10.3) 515 (89.7)C. delalandii ST, F, B, ro 7828 939 (12) 253 (3.2) 483 (6.2) 456 (48.6)

ESU Santiago ST 4541 545 (12) 167 (3.7) 338 (7.4) 207 (38.0)ESU Fogo F 2238 269 (12) 86 (3.8) 86 (3.8) 183 (68.0)ESU Brava B 990 119 (12) 0 (0.0) 0 (0.0) 119 (100.0)ESU Rombos ro 59 7 (12) 0 (0.0) 59 (100.0) 0 (0.0)

C. nicolauensis SN 1432 172 (12) 149 (10.4) 149 (10.4) 23 (13.3)C. fogoensis SA 3668 440 (12) 0 (0.0) 319 (8.7) 121 (27.5)C. stangeri SV, SL, ra, br 1046 1046 (100) 0 (0.0) 811 (77.5) 235 (22.5)

ESU Desertas SL, ra, br 811 811 (100) 0 (0.0) 811 (100.0) 0 (0.0)ESU S. Vicente SV 235 235 (100) 0 (0.0) 0 (0.0) 235 (100.0)

C. spinalis S, ST, F, M, BV 16,345 1961 (12) 303 (1.4) 4274 (26.1) 0 (0.0)C. spinalis salensis S 2356 283 (12) 0 (0.0) 465 (19.7) 0 (0.0)C. spinalis santiagoensis ST

ESU North ST 684 82 (12) 0 (0.0) 0 (0.0) 82 (100.0)ESU South ST 4056 487 (12) 0 (0.0) 0 (0.0) 487 (100.0)

C. spinalis spinalis F 2118 254 (12) 303 (14.3) 303 (14.3) 0 (0.0)C. spinalis maioensis M 1635 196 (12) 0 (0.0) 559 (34.2) 0 (0.0)C. spinalis boavistensis BV 5496 660 (12) 0 (0.0) 2947 (53.6) 0 (0.0)

Total 38 21 10 + 3 68,964 8276 (12) 1111 (1.6) 12,652 (18.3) 4376 (52.9)

Islands/Islets: SA, Santo Anto; SV; S. Vicente; SL, Santa Luzia; br, Branco; ra, Raso; SN, S. Nicolau; S, Sal; BV, Boavista; M, Maio; ST, Santiago; F, Fogo; B, Brava; ro, Rombos.



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In total, more PUs in total (5% on average) and lower percent-ages of PUs inside PAs (up to 7% less) were selected in the analysistargeting species than ESUs, comparing analogous scenarios (Ta-ble 2). Additionally, more ESUs and species reach conservation tar-gets more efficiently in the analysis targeting ESUs (Table 3). Apartfrom quantitative differences, the spatial configuration of the se-lected PUs differed considerably between the two target analysesin some islands (see Section3.4.1andFig. 3).

3.4.1. Differences per island/isletVariation in the amount of PUs (in total andwithin PAs) selected

within each island/islet was large. For four islands and three islets,almost the same amount of PUs was selected, both in total andwithin PAs, in both cost scenariosand target analyses (Santo Anto,S. Vicente, Sta. Luzia, Branco, Raso, S. Nicolau andRombos) whileforother islands a large difference was detected between solutions.

When just comparing results obtained with ideal and realisticcost scenarios, differences between the total number of selectedPUs was very low for most islands, both when targeting ESUs orspecies. Using ESUs as target units, solutions were almost identicalin all islands, except on Sal, where the ideal model would be moreeffective, since much less area would be needed to protect reptilediversity (Table 2). In this analysis, it was also on Sal where PUs se-lected by each scenario spatially coincided by the least amount,followed by Maio; in the remaining islands, concordance of PUs se-

lected by both scenarios was relatively high (Fig. 2A andAppendi-ces FJ). Using species as target units, solutions only substantiallydiffered on Boavista, Santiago, Fogo and Maio islands and only onBoavista did the ideal scenario perform better than the realisticone (Table 2). In these cases, it is again on these islands, especiallyMaio and Boavista, where the PUs selected by each scenario spa-tially coincided by the least amount (Fig. 2B). However, as ex-pected, differences between cost scenarios were higher whenconsidering PUs selected inside PAs, which were considerably lar-ger in the realistic scenario in several islands regardless of target-ing ESUs or species, except on the uninhabited islands/islets of thearchipelago (Rombos and Desertas group), where 100% of selectedPUs would be inside PAs regardless of the cost scenario and tar-geted feature for conservation (Table 2).

Comparing just targeted conservation features, they littlediffered in the above referred four islands and islets and presented

almost the same percentage of selected PUs inside PAs on five oth-ers (Santiago, Fogo, Brava, Boavista and Maio) in a least one costscenario. However, they differed in the number of total selectedPUs on six islands (Sal, Santiago, Boavista, Brava, Maio and Fogo),with the analyses targeting species selecting no PUs on Sal andmore total PUs than the analysis targeting EUSs in the latter fourislands. There were also noticeable differences in the number of se-lected PUs inside PAs. In the analysis using ESUs as targets, Salwould reach 100% of PUs inside PAs in the realistic scenario, whileFogo and Brava would present all PUs selected by both scenariosunprotected (Table 2andFig. 2A). On the contrary, in the analyses

using species as targets, Sal would not have a single PU inside PAsin both scenarios, while some PUs would be selected inside PAs onFogo, under the realistic scenario, although below the 12% thresh-old of protection (Table 2andFig. 2B).

Spatial agreement between analyses targeting species or ESUswas relatively high in most islands. However, on Sal analyses to-tally differ and on many northern and southern Santiago cellswhere PUs were selected only on the analyses targeting ESUs,and also in most area of Brava, where most PUs are only prioritizedby the analysis targeting species (Fig. 3).

3.4.2. Differences per conservation feature (ESUs or species)Considering ESUs, 19 out of the 38 presented no differences in

the amount of selected PUs to meet conservation targets between

analyses targeting different conservation features in both cost sce-narios (Table 3). However, the analysis targeting species was morecostly for 10 ESUs, and considerably more costly (selecting 50%more PUs) in at least one cost scenario for eight of them. In addi-tion to that, it failed to reach conservation targets for four ESUs(H. boavistensisandC. s. salensisfrom Sal and northern and south-ern ESUs ofC. s. santiagoensis from Santiago), completely failingtargets for three of them in both cost scenarios (Table 3). The anal-ysis targeting species was slightly more effective only for four ESUsconsidering at least one cost scenarios, and considerably better forone (Tarentola rudis), considering the realistic scenario only(Table 3).

Considering species, 12 out of the 21 presented no differencesbetween analyses targeting different conservation features in the

amount of selected PUs to meet conservation targets in both costscenarios (Table 3). However, the analysis targeting species was

A B

Fig. 2. Selected planningunits (PUs) necessary to reach conservation targets for reptile ESUs (A) and species (B) from Cape Verde Islands considering the realistic and idealscenarios inside and outside protected areas (see Section 2 and Appendices FJfor details).



7/11

less effective for six species and considerably less effective in atleast one cost scenario for five of them. Targeting species wastherefore slightly more effective only for three species, and consid-erably better for one (Table 3).

In summary, solutions found when targeting species failed toachieve targets for some ESUs and were generally more costly,but the inverse was not true, as solutions found when targeting

ESUs successfully achieved targets for all species and more effi-ciently, regardless of the cost scenario used.

4. Discussion

4.1. Evaluation of the methodology

Ecological niche-based models provided fairly robust predic-tions of occurrences and the reserve design algorithm identifiedpriority PUs for conservation of endemic reptiles. Hence, this studymay turn into an important tool in planning and designation of PAsin Cape Verde. Additionally, the novel approach used may proveuseful to other studies attempting to maximize representation ofgenetic diversity in conservation prioritization. It also showedthe increased importance of targeting ESUs instead of species asconservation features.

Several studies assumed predicted probabilities of occurrenceof taxa to be surrogates of probability of persistence, and targetedareas where probabilities were high (e.g. Margules and Stein, 1989;Williams and Arajo, 2000). In this study, the most effective selec-tion algorithm incorporated the most probable occurrence areas ofall ESUs, thus potentially enhancing taxa persistence even more.Nevertheless, potential pitfalls might have emerged because pat-terns of neutral variation, as measured by molecular markers,may not reflect levels of adaptive variation for all traits across allpopulations (Le Corre and Kremer, 2003). However, given the diffi-culty in measuring adaptive variation for wild species, molecularmarkers are valuable surrogates and, in some cases, may be conser-

vative estimates of the expectations of loss and recovery of quan-titative genetic variation (Lynch et al., 1999). Additionally,adaptive features may be best protected by maintaining the con-text for selection, such as heterogeneous landscapes (Hglund,2009). Since habitats in this archipelago are most different amongislands than within them (data not shown), targeting 12% of thearea of each island for conservation potentially enhances the coverof adaptive variation. Another question that might be addressed isif ecological models should have been based on ESUs instead oftaxa. Nevertheless, in doing so, sample sizes would have beengreatly reduced, which would probably compromise analyticalmethods chosen. More importantly, the comparison of targetingESUs instead of species would be biased, as some predictions ofoccurrence would be necessarily different from taxa predictions,

as in the case of species with no subspecies occurring on differentislands and subspecies with more than one ESU. Furthermore, inthose cases, the probability of occurrence of those ESUs might bemore related to isolation by the ocean, genetic drift and other fac-tors than with ecogeographical factors, as frequently they are notreciprocally monophyletic (seeArnold et al., 2008; Miralles et al.,2010; Vasconcelos et al., 2010), and do not present any evidencefor local adaptation, a determinant condition for modeling them(Pearman et al., 2010).

Concerning the representation targets set for each ESU and spe-cies, choosing the 12% target for non-endangered taxa makes theseresults comparable to other natural resources conservation studiesbut does not suggest that this figure has any established scientificvalidity to assure that populations selected for conservation are

viable. The question of which target should be used is a paramountin conservation planning but remains largely unsolved (Tear et al.,Table

2

Num

ber(n)oftotalplanningunits(PUs)andPUsinsidethe46protectedareas(PAs)oneachisland/isletavailableandselectedbyeachmodelscenarioand

targetanalysis,andsubtractedbetweenthetwotargetanalyses.Percentages

(%)aregivenbetweenbrackets.

ESU

stargetanalysis

Speciestargetanalysis

SpeciesESUstargetanalyses

Available

Idealscenario

Realisticscenario

Idealscenario

Realisticscenario

Idealscenario

Realisticscenario

Total

InsidePAs

Total

InsidePAs

Total

InsidePAs

Total

InsidePAs

Total

InsidePA

s

Total

InsidePAs

Total

InsidePAs

Is

land/Islet

n

n

%

n

n

%

n

n

%

n

n

%

n

n

%

n

n

%

n

n

%

SantoAnto

14,8

99

601

(4.0

)

50

5

7

(1.4

)

505

72

(14.3)

505

7

(1.4

)

505

73

(14.5

)

0

0

(0.0

)

0

1

(0.2

)

S.

Vicente

4275

58

(1.4

)

42

0

0

(0.0

)

419

19

(4.5)

420

0

(0.0

)

420

19

(4.5

)

0

0

(0.0

)

1

0

(0.0

)

SantaLuzia

656

656

(100.0

)

65

5

655

(100.0

)

655

655

(100.0)

655

655

(100.0

)

655

655

(100.0

)

0

0

(0.0

)

0

0

(0.0

)

B

ranco

49

49

(100.0

)

4

9

49

(100.0

)

49

49

(100.0)

49

49

(100.0

)

49

49

(100.0

)

0

0

(0.0

)

0

0

(0.0

)

R

aso

108

108

(100.0

)

10

8

108

(100.0

)

108

108

(100.0)

108

108

(100.0

)

108

108

(100.0

)

0

0

(0.0

)

0

0

(0.0

)

S.

Nicolau

6515

258

(4.0

)

33

4

11

(3.3

)

334

52

(15.6)

334

9

(2.7

)

334

52

(15.6

)

0

2

(0.6

)

0

0

(0.0

)

Sal

4187

862

(20.6

)

28

3

61

(21.6

)

374

374

(100.0)

0

0

(0.0

)

0

0

(0.0

)

283

61

(21.6

)

374

374

(100.0

)

B

oavista

11930

4743

(39.8

)

65

7

440

(67.0

)

655

655

(100.0)

855

589

(68.9

)

1173

1173

(100.0

)

198

149

(1.9

)

518

518

(0.0

)

M

aio

5141

1283

(25.0

)

24

2

90

(37.2

)

244

244

(100.0)

368

149

(40.5

)

356

356

(100.0

)

126

59

(3.3

)

112

112

(0.0

)

Santiago

18829

409

(2.2

)

408

4

225

(5.5

)

4085

225

(5.5)

3895

225

(5.8

)

3762

250

(6.6

)

189

0

(0.3

)

323

25

(1.1

)

Fogo

8841

1713

(19.4

)

52

1

0

(0.0

)

521

0

(0.0)

609

0

(0.0

)

536

15

(2.8

)

88

0

(0.0

)

15

15

(2.8

)

B

rava

1176

0

(0.0

)

11

9

0

(0.0

)

119

0

(0.0)

601

0

(0.0

)

601

0

(0.0

)

482

0

(0.0

)

482

0

(0.0

)

R

ombos

59

59

(100.0

)

5

9

59

(100.0

)

59

59

(100.0)

59

59

(100.0

)

59

59

(100.0

)

0

0

(0.0

)

0

0

(0.0

)

A

verage

5897

831

(39.7

)

61

8

131

(41.2

)

625

193

(56.9)

651

142

(39.9

)

658

216

(49.5

)

32.5

11.2

(1.3

)

33.2

22.8

(7.4

)

T

otal

76665

10799

(14.1

)

803

6

1705

(21.2

)

8127

2512

(30.9)

8458

1850

(21.9

)

8558

2809

(32.8

)

422

145

(0.7

)

431

297

(1.9

)



8/11


9/11

2005), although the solution might involve using variable targetsregarding taxon ranges (Rodrigues et al., 2004; Maiorano et al.,2007).

Regarding cost scenarios, the selected PUs of ideal models spa-tially coincided with the realistic model scenarios in most cases(Fig. 2). In addition, different scenarios presented identical effi-ciency (similar number of selected PUs) in most islands. These re-sults are surprising since ideal scenarios theoretically minimizecosts for PUs selection in comparison with scenarios constrainedby PAs, because PAs are generally biased for other factors rather

than protecting biodiversity. Three complementary rationalesmight explain this result. First, both scenarios are congruent inselecting many PUs outside PAs, in order to encompass 12% of mostESUs or species distributions (100% in threatened taxa), and inthose areas, selected PUs by both scenarios are likely to overlap.Second, in regions less affected by anthropogenic disturbance,selecting an ideal network that maximizes representation of diver-sity most times does not lead to significantly different results froma selection by chance (Bonn and Gaston, 2005). This pattern mightbe especially noticeable in small areas. In Cape Verde, where fewimpacting human infrastructures are present in most islands andPAs were designed using ad-hoccriteria, reptile distributions arelittle restricted by anthropogenic actions. Thus, some PUs insidePAs selected by the realistic scenarios (that prioritizes PUs inside

PAs) are likely to be also selected by the ideal models. This is mostnoticeable on islands like Boavista, Maio and Santiago. Third, alter-natively, the extensive overlap of solutions from different modelsmay suggest that PUs selected ad hoc for other endemic groupson which PAs locations were based on, such as birds, are also goodfor reptiles and vice-versa. Thus, reptiles may be good surrogates ofpriority PUs for endemic birds and flora, although they might notbe as good for other groups such as invertebrates (Rodrigues andGaston, 2001). In fact, some recent ad hocdata on endemic birdsconfirms several selected PUs outside PAs depicted by this workas important for conservation. For instance, the threatened CapeVerde cane warbler (Acrocephalus brevipennis) also occurs on thenortheast of Fogo, and a large colony of the Critically Endangeredpurple heron was confirmed (Ardea purpura) around Montanha,Santiago (seeAppendices I and J, respectively), followingHazevoet(2010). It would be important to cross-update information about

georeferenced nesting sites of the endemic birds and accurate dis-tribution maps of the endemic flora with the performed analyses,whenever they become available, to confirm this result.

Regarding the analyses using different conservation targets, it isquantitatively more effective and less costly to target ESUs since alower number of total PUs are selected (around 5% less) in compar-ison to targeting species. This is mainly because less PUs would beselected targeting ESUs on Brava, Boavista, Maio and Santiago. Theselection of less PUs on Brava is explained by very different conser-vation status and associated conservation targets (12% and 100%)

of the two subspecies ofTarentola protogigas, forcing the analysistargeting species to select much more PUs to protect the endan-gered species as a whole. This fact reinforces the importance ofusing subspecific taxonomy to evaluate conservation status andto prioritize areas for conservation (Neel and Cummings, 2003).On the other islands, that is explained by the lower number of tar-geted PUs per island when targeting ESUs due to the species frag-mentation among different islands/islets corresponding todifferent ESUs. Although the analysis targeting species presentedhigher total numbers of selected PUs inside some PAs (around 9%more) when comparing to the analysis targeting ESUs, the overallpercentage of PUs inside PAs is lower (Table 2). The first is ex-plained by the fact that aggregation of the selected PUs is necessar-ily easier when targeting fewer features (21 species versus 38

ESUs), what might seem a disadvantage of targeting ESUs, andthe latter because no PUs were selected in Sal in that analyses.However, the choice of targeting species instead of ESUs wouldalso necessarily imply failing to reach conservation targets for fourunique lineages, completely for three of them (two from Sal andone from northern Santiago; see Section 3.4.2.). Moreover, it wouldgenerally imply the selection of considerably more PUs per ESUand species. Hence the analyses targeting ESUs is also qualitativelymore effective for the conservation of diversity at different levels,with the additional advantage of considering its persistence.

4.2. Adequacy of the protected areas network and optimized priorityplanning units

Currently, Cape Verde presents the lowest proportion of land(about 2%) devoted to conservation in comparison to several other

A B

Fig. 3. Combination of selected planning units (PUs) necessary to reach conservation targets for reptiles ESUs and species from Cape Verde Islands considering the ideal (A,reddish colors) and realistic (B, bluish colors) scenarios inside and outside protected areas (see Section 2 for details). (For interpretation of the references to color in thisfigure legend, the reader is referred to the web version of this article.)



10/11

oceanic islands (40% on average;Caujap-Castells et al., 2010). Theimplementation of the full PAs network is thus needed to guaran-tee partial protection of the biodiversity of the endemic reptilesand their habitats (Tables 2 and 3). In addition, implementationof new PAs based on the proposed scenarios targeting ESUs, isneeded to fully protect the genetic diversity of these reptiles.Otherwise, nine ESUs and two species, Hemidactylus lopezjuradoi

andTarentola bocagei, both considered threatened species (follow-ingVasconcelos et al., in press), would fail completely to reach tar-gets and several other threatened taxa would not be adequatelyprotected (Hemidactylus bouvieri, Tarentola boavistensis, Tarentolaraziana, T. rudis, T. protogigas, Chioninia vaillanti, and Chioniniastangeri).

Reserve design analyses targeting ESUs indicated two main pat-terns in the Cape Verde Islands. On a group of islands, namelyDesertas group, Sal, Boavista, Maio and Rombos, designation ofnew PAs is not a priority, since PAs that are going to be imple-mented will guarantee total targeted protection of all endemicreptile species and ESUs occurring there and their habitats. Onthe remaining islands, the planned PAs are clearly insufficient,since about 60% of ESUs would not achieve conservation targets(Table 13andAppendices FJ). In the extreme case of Fogo andBrava islands, no scenario selected a single PU inside a PAusing ESUs as conservation targets (Table 2 and Appendix I).Recommended conservation actions are described inAppendix K.

This study addresses one of the major constraints of conserva-tion in the Cape Verde Islands, namelythe lack of basic informationin formats that policymakers and administrators can interpret anduse (Miller, 1993). It is expected that this innovating frameworkcan be applied to other island systems with well-know geneticdiversity such as the Canary Islands, where extensive work hasbeen carried out on the endemic reptiles (e.g.Brown and Pestano,1998; Carranza et al., 2002; Cox et al., 2010) or other island-likesystems, such as mountain ranges.

Acknowledgments

R.V. is grateful to I. Gomes from INIDA (Instituto Nacional deInvestigao e Desenvolvimento Agrrio) and A. Fernandes fromDGA (Direco Geral do Ambiente) for sharing the governmentinternal reports; to Dr. J. Spencer, Major A. Rocha, and Eng. J. And-rade from Direco de Servio e Cartografia e Cadastro, for helpingand facilitating digital data of roads and urban areas of Cape Verde.This study was partially supported by Fundao para a Cincia eTecnologia (FCT): SFRH/BD/25012/2005 and SFRH/BPD/79913/2011 (to R.V.), PTDC/BIA-BDE/74288/2006; J.C.B. and D.J.H. haveFCT contracts (Programa Cincia 2007 and 2008 Fundo SocialEuropeu); and Ministerio de Educacin y Ciencia CGL2009-11663/BOS.

Supplementary material

Supplementary data associated with this article can be found,in the online version, at http://dx.doi.org/10.1016/j.biocon.2012.04.020.

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6. Identifying Priority Areas for Island Endemics Using Genetic Versus Speci_c Diversity

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