Conservation Priorities for Prunus africana Defined with the Aid of Spatial Analysis of Genetic Data

16 Pages·2013·2.23 MB·English

Conservation Priorities for PrunusafricanaDefined with
the Aid of Spatial Analysis of Genetic Data and Climatic
Variables
Barbara Vinceti1*, Judy Loo1, Hannes Gaisberger1, Maarten J. van Zonneveld2,5, Silvio Schueler3,
Heino Konrad3, Caroline A. C. Kadu4, Thomas Geburek3
1Headquarters, Bioversity International, Rome, Italy, 2Regional Office for the Americas, Bioversity International, Cali, Colombia, 3Department of Genetics, Federal
ResearchandTrainingCentreforForests,NaturalHazardsandLandscape(BFW),Vienna,Austria,4DepartmentofBiochemistryandBiotechnology,SchoolofPureand
AppliedSciences,KenyattaUniversity,Nairobi,Kenya,5FacultyofBioscienceEngineering,GhentUniversity,Gent,Belgium
Abstract
ConservationprioritiesforPrunusafricana,atreespeciesfoundacrossAfromontaneregions,whichisofgreatcommercial
interestinternationallyandoflocalvalueforruralcommunities,weredefinedwiththeaidofspatialanalysesappliedtoaset
ofgeoreferencedmolecularmarkerdata(chloroplastandnuclearmicrosatellites)from32populationsin9Africancountries.
Two approaches for the selection of priority populations for conservation were used, differing in the way they optimize
representationofintra-specificdiversityofP.africanaacrossaminimumnumberofpopulations.Thefirstmethod(S1)was
aimedatmaximizinggeneticdiversityoftheconservationunitsandtheirdistinctivenesswithregardtoclimaticconditions,
the second method (S2) at optimizing representativeness of the genetic diversity found throughout the species’ range.
Populations in East African countries (especially Kenya and Tanzania) were found to be of great conservation value, as
suggested by previous findings. These populations are complemented by those in Madagascar and Cameroon. The
combinationofthetwomethodsforprioritizationledtotheidentificationofasetof6prioritypopulations.Thepotential
distribution of P. africana was then modeled based on a dataset of 1,500 georeferenced observations. This enabled an
assessmentofwhetherthepriority populationsidentifiedareexposedto threatsfrom agriculturalexpansionandclimate
change,andwhethertheyarelocatedwithintheboundariesofprotectedareas.Therangeofthespecieshasbeenaffected
by past climate change and the modeled distribution of P. africana indicates that the species is likely to be negatively
affected in future, with an expected decrease in distribution by 2050. Based on these insights, further research at the
regional andnational scaleisrecommended, inorder tostrengthen P.africana conservation efforts.
Citation:VincetiB,LooJ,GaisbergerH,vanZonneveldMJ,SchuelerS,etal.(2013)ConservationPrioritiesforPrunusafricanaDefinedwiththeAidofSpatial
AnalysisofGeneticDataandClimaticVariables.PLoSONE8(3):e59987.doi:10.1371/journal.pone.0059987
Editor:GiovanniG.Vendramin,CNR,Italy
ReceivedJune8,2012;AcceptedFebruary25,2013;PublishedMarch27,2013
Copyright: (cid:2)2013 Vinceti etal. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricteduse,distribution,andreproductioninanymedium,providedtheoriginalauthorandsourcearecredited.
Funding:ThisresearchwasfundedbytheAustrianDevelopmentAgency.ItistheresultofacollaborationbetweenBioversityInternationalandtheFederal
ResearchandTrainingCentreforForests,NaturalHazardsandLandscapeinAustria,andofapartnershipwithresearchinstitutionsinAfricancountries:Kenyatta
University,Nairobi,Kenya;UniversityoftheWitwatersrand,Johannesburg,SouthAfrica;SiloNationaldesGrainesForestieres,Madagascar;TanzaniaForestry
ResearchInstitute,Tanzania;InstituteofAgriculturalResearchforDevelopment(IRAD),Cameroon;FacultyofAgricultureandEnvironmentalScience,Bindura
University of Science Education, Zimbabwe; National Forestry Resources Research Institute, Uganda; Coordinador Nacional de la COMIFAC Ministerio de
AgriculturayBosques,EquatorialGuineawithintheframeworkoftheproject’DevelopmentofstrategiesfortheconservationandsustainableuseofPrunus
africanatoimprovethelivelihoodofsmall-scalefarmers’.Thefundershadnoroleinstudydesign,datacollectionandanalysis,decisiontopublish,orpreparation
ofthemanuscript.
CompetingInterests:Theauthorshavedeclaredthatnocompetinginterestsexist.
*E-mail:[email protected]
Introduction rather than intra-specific diversity indicators [8,11]; rare are the
cases in which morphological and demographic variables have
The identification of priority sites for conservation action beenintegratedwithgeneticparameterstodefinetheappropriate
remains a central issue in the implementation of conservation location of conservation units of threatened species [12]. In the
interventions,duetothefactthatresourcesareusuallylimitedand case of multi-taxon approaches, evidence shows that those based
competition for land is high. Different approaches for making onspeciesrichnessfailtorepresentrare,threatened,orgenetically
conservation choices when resources are scarce have been distinct species [13]. The use of range-weighted matrices is an
described previously [1–4]. Those currently proposed in the example of an approach that accounts for range sizes and the
literature are based on a combination of different criteria, higherprobabilityofextinctionforspeciesthataregeographically
including measures of diversity, assessments of risk status and restricted, compared tocongeners with wide distribution [14].
conservation costs [5], applicable at the level of vegetation type, Knowledgeofthedistributionofgeneticdiversityaddsvaluable
species, ormolecular diversity [6–10]. informationtosupportconservationeffortsbecausethecapacityof
Despite the recognized importance of evolutionary processes, a speciestoadapttochanging environmentalconditions depends
they have often been excluded in conservation assessments and on its heritable variation, which allows evolutionary processes to
planning, which are more frequently based on species richness, take place [15]. In the absence of data on the distribution of a
PLOSONE | www.plosone.org 1 March2013 | Volume 8 | Issue 3 | e59987
ConservationPrioritiesforPrunusafricana
species’ genetic variation, sites for conservation could be selected been highlighted [39]; the knowledge that has become available,
moreorlessuniformlythroughoutthespecies’naturalrange[16], suchasoccurrencedataandgeneticinformation,isanidealbase
if the environmental conditions are relatively uniform or if they to develop a continent-wide conservation strategy, starting with
followacontinuousgradient.Genecologicalapproacheshavebeen theidentification of key conservation sites.
used [17,18], basedon theassumption that tree geneticvariation The current study builds on molecular marker data for P.
followssomeofthepatternsofecologicalvariation[19].However, africana generated as part of two studies by Kadu etal. [40,41],
patterns are species-specific; species react differently to environ- which showed a clear phylogeographic pattern for the species,
mental gradients. The correlation between genetic diversity and suggestinganearlysplitof‘east’and‘west’typesduringsouthward
species richness is controversial [20], and species diversity and migration,followedbymorerecentsplittingeventsamongeastern
phylogeneticdiversityhavedifferentpatternsofspatialdistribution populations. These findings confirm the results from earlier pilot
[13]. Thus, when genetic data are available they provide more studiesbasedonmolecular markersshowingthestrongpartition-
precise information for decision-making [21]. They can also ingofgeneticvariationofP.africanaacrossgeographicaldistance,
support assessmentsof extinction risks[22]. due to its wide but disjunct distribution in Afromontane forest
Some key questions continue to pose challenges. Determining islands[42].Alltheseresultssuggestthataconsiderablenumberof
thenumberofpopulations,andthenumberofindividualswithina sites may be required for effective conservation of the genetic
population,thatareneededtocaptureusefulgeneticvariationina variationwithinthespecies.
species,orwithinpartofaspecies’range,isnotsimpleandthere We address a series of issues related to the definition of a
are many estimates in the published literature [23,24], which, in conservation strategy for P. africana, focusing primarily on the
some cases, are species-specific [25]. Another question relates to identification of priority populations for conservation. The
what criteria should be used, based on genetic parameters, to formulation of a conservation strategy requires understanding a
define priorities (mean number of alleles per locus, percentage of species’spatialdistribution.WegeneratedadistributionmapofP.
polymorphic loci, etc). In addition, the question about whether africana based on predictions of the potential species’ distribution
conservation emphasis should be higher in peripheral or central from records and environmental data from the same sites, using
populations, is not resolved and species-specific [26–31]. Gener- GIS(GeographicInformationSystem)[43–46].Theresultingmap
ally,diversityishigherinpopulationslocatedincentralpartsofthe indicatesareasofhighandlowprobabilityofoccurrencebasedon
distribution range. However, peripheral populations often have the species’ current ecological niche (a review of spatially explicit
valuableadaptivetraitsthatarespecifictomarginalenvironments methods usedby biogeographers canbefound in[46]).
[32], and species that have highly dispersed, isolated populations Weadoptedthenumberofalleles(i.e.,allelicrichness)asakey
maynothavearecognisablecentraliseddistribution.Furthermore, measurement to determine priorities in the conservation of the
issuesrelatedtopopulationsizematter.Smallpopulationsthatare genetic diversity in P. africana. This is a very informative
geographicallyandgeneticallyisolatedfromeachotherhavebeen parameter, as the number of alleles per locus is dependent on
shown to lose diversity more rapidly than larger populations or effective population size, therefore a good indicator of past
smallpopulations that arelinked bygeneflow [33]. demographic changes that would have affected genes associated
Theconceptoftheevolutionarilysignificantunit,definedas‘‘a with adaptive traits as well as neutral markers [47]. This
population that merits separate management and has a high parameterisconsideredidealespeciallyforhypervariablemarkers
priorityforconservation’’[34]hasbeenproposed[35].However, suchas nuclearmicrosatellites [48].
its identification is based on several parameters (e.g., morpholog- In order to define the location and number of populations
ical andphenologicaltraits,biochemicalandmolecular markers), neededtooptimallyconserveP.africana,weproposedanapproach
making the approach difficult to adopt in practice, especially at based on spatial analyses of genetic parameters and climatic
large spatial scales. It is known that a research–implementation variables. Geospatial methods can be applied to phylogeography
gap exists in conservation planning, for reasons that include the [46]andcanbeusedtocarryoutcomplexanalysesthatfacilitate
lack of dialogue between scientists and managers of natural the selection of priority populations for conservation of genetic
resources [36]; the gap can be widened if conservation methods resources[49–51].Accuratespatialinformationontheoccurrence
are perceived as toocostlyandsophisticated. of a species combined with climatic variables has also proven
Finally,conservationprioritieshaveusuallybeendefinedbased usefulforeffectivegenebankmanagement(e.g.,definitionofcore
on a static snapshot of the current situation, but it is increasingly collections, identification of collection gaps, etc.) [52]. Geospatial
recognized that estimates of species vulnerability to global approaches have been adopted for the conservation in situ of
environmental changes should be incorporated in conservation species for which it is not possible to rely on ex situ conservation
planningdecisions.Thiscanbeassessedintermsofpredictedloss strategies.Anexampleisthatofimportantcropwildrelatives,for
ofclimaticallysuitableareasforaparticularspecies[37],andmore which conservation can be achieved only by managing wild
recently looking separately at sensitivity, and adaptive capacity populations in situ, and which derive their properties from their
[38]. adaptationtoenvironmentalconditions,favouredbyevolutionary
AmongtheAfricanindigenoustreespecies,Prunusafricana(also processes [53,54].
known by its previous name, Pygeum africanum, Hook f.), with a More specifically, we proposed the selection of a core set of
broad but disjunct distribution across Afromontane regions, has prioritypopulationsbasedonacombinationoftwomethods:one
been studied due to its economic importance: occurrence data aimed at maximizing genetic diversity and distinctiveness of the
from herbaria offer wide coverage and recently genetic data conservationunitwithregardtoclimaticconditions(S1),theother
derived from a continent-wide collection have been made at optimizing representativeness of the genetic diversity found
available.Overthepastseveraldecades,productsfromP.africana’s throughout the species’ range (S2). Both approaches attempt to
bark extracts have been the most widely exported of any African maximize the species’ evolutionary potential through the identi-
treespeciesformedicinalpurposes,contributingtoitsoverexploi- ficationandconservation ofpopulationswiththehighestpossible
tation. Due to the threats to the species, mainly posed by levels of genetic variation. The assumption is that this variation
overexploitation, butalsobyagriculturalexpansionandexpected will allow evolutionary processes to take place and foster
environmental changes, the need for a conservation strategy has adaptation (e.g., [55–57]). These results were obtained through
PLOSONE | www.plosone.org 2 March2013 | Volume 8 | Issue 3 | e59987
ConservationPrioritiesforPrunusafricana
user-friendly,GIS-based,freeaccesssoftwareandconstituteafirst implement sustainable management options, including conserva-
level in the decision making process to which, subsequently, tion andappropriate domestication measures.
economic andother considerations could beadded [58].
Finally,weexaminedtheconservationstatusofthepopulations Materials and Methods
selected, based on the location of our proposed sites for
conservationvs.existingprotectedareas,andtheexpectedthreats Population Sampling and Data Source
posed by changing environmental conditions, using spatial TwomaindatasetsonP.africanawereusedinthisstudy:a)aset
analyses to build projected impacts of climate change on the of genetic data from 32 populations collected in the near range-
distribution of thespecies [59]. wide study by Kadu etal. [40,41], and b) a set of 1,500
georeferenced observations obtained from various sources (of
Prunus africana whichthe32populations above are a subset).
P.africanaisofgreatcommercialinterestduetothepreparation TheP.africanageoreferencedchloroplast(cpSSRs)andnuclear
ofmedicinalproductsfromitsbark,usedtotreatbenignprostatic microsatellites (nSSRs) consisted of: 7 chloroplast DNA loci from
hyperplasia. The species also plays an important ecological role, 582individualtrees;6nuclearlocifrom484individualtrees.The
providing food and home for pollinators and rare fauna, and DNA was isolated from leaf samples that were collected during
supporting vascular and non-vascular canopy epiphytes [60]. In 2007 and 2008in natural stands (ie, not planted) in 9 African
additiontolocaluseandtrade,thecollectionandprocessingofthe countries.Thesearefrom32accuratelygeoreferencedpopulations
barkhascreatedeconomicopportunitiesforruralcommunities.A (Table 1), spatially distributed in order to cover as extensively as
vastliteraturedescribesthevarioususesanditsimportanceinthe possible the species range, across Afromontane forests (it was not
preparation of medicinal products from its bark, marketed possible to gain access for sampling purposes to Ethiopia and
internationally [61–66]. Angola).Thesiteswerechosenbasedona)theirdegreeofisolation
The species has a broad but highly fragmented distribution, (selecting sites on well-separated mountain chains across the
spanningtheAfricancontinentfromSouthAfricatoEthiopiaand African continent), b) different ecological conditions (including
westtoCameroon,butislimitedtomontaneregionswhereitcan geological substrate), c) availability of logistical support for
be locally common [67]. Genetic considerations are particularly sampling, d) expected size of the populations (i.e., populations
relevantforthemanagementandconservationofP.africana,dueto expected to be too small were avoided a priori). Samples were
its close association with montane regions and low colonization collected by research partners from local institutions in different
potential [68]. The species has hermaphrodite flowers pollinated countries as indicatedinKadu etal. [40,41].
byinsects. Althoughself-fertile, itisusuallyoutcrossing; fruitsare Weassembledadatasetof1,500georeferencedobservationsof
dispersedbybirdsandmonkeys[69].Unsustainabledebarkingof P.africana(mostlyrecordedbetween1965and2010)thatcoverthe
P.africana,disproportionatelyaffecting andultimately causing the range of the species and enabled us to define its ecological niche
death of large, reproductively mature individuals [70] is likely to and tomodel itspotential distribution. We assembled the dataset
cause reduced seed dispersal and gene flow, increasing isolation accessing the following sources: Mpumalanga Tourism & Parks
andreducingviabilityofexistingpopulations.P.africanahasbeen Agency Lydenburg, South Africa (botanist Mervyn Lotter),
reportedasapioneer[71]orearlysuccessionalspecies,associated University of Bangor (John Hall), World Agroforestry Centre
with forestedgesanddisturbance [72]. (ICRAF), GBIF [75] and JSTOR Plant Science [76]. For the
Typically, the species is found where the annual temperature spatialanalysis,arastersizeof30minutes(about50kmnearthe
range is 18–26uC, mean annual rainfall ranges from 890 to equator) was used and molecular marker data were formatted in
2,600 mm, and at an elevation between 900 and 3,400 m, with sucha way toattribute coordinates toeachallele/haplotype.
increasingelevationrangetowardslowerlatitudes.Itsdistribution
range is limited by high temperatures and by insufficient Species Distribution Modeling
precipitation during the warmest months [39]. Moist conditions P. africana’s potential distribution was modeled using the
could trigger infestation of powdery mildew and occurrence of a distribution modeling program Maxent. This program uses an
stem borer, whose presence is indicated by resin exuded through algorithmofmaximumentropytocalculatetheecologicalnicheof
small bore holes [67]. Stem borers seem to be a serious problem a species and to define the areas of potential natural distribution
when the species is planted in lowland areas, as observed in [45,77].Theenvironmentalvaluesareextractedfromonlinegeo-
Cameroon [66]. referenced databases. A total of 21 environmental layers were
Overthepast40years,P.africanabarkharvesthasshiftedfrom included to build the potential natural distribution model under
subsistence and local use to large-scale commercial use for currentconditions:19BIOCLIMvariableswerederivedfromthe
international trade. Studies on the impacts of wild harvest on P. WorldClim database [78,79], soil data from the World Soil
africanapopulationshaveshownthatthepracticesadoptedandthe Resources Coverage map[80], and environmental data fromthe
quantities extracted are not sustainable [62,70]. Because of FAOMaponGlobalEcologicalZones[81].Forthepredictionof
concerns for the sustainability of the trade, the species has been species distribution, the threshold ‘‘Maximum sensitivity plus
assigned a vulnerable conservation status on theIUCN RedList, specificity’’withthreshold=0.180waschosen(fordetailssee[82]).
andwasproposedbyKenyaforCITESin1994[73],thenlistedin It should be noted that weather station coverage may not be
1995inCITESAppendixII.ImportofthebarkfromCameroon optimalandinterpolatedvaluescouldbeparticularlyproblematic
into the European Union was banned from November 2007 to inmontaneregionsduetofactorssuchasrainshadowsandfine-
December2010,whenCITESliftedthebansubjecttoareduced scale varying topography. However, WorldClim constitutes the
quota of150,000 kgfor2010and2011compared totwomillion best dataset available as it includes major climate databases from
kg of bark in 2005, reduced to one million kg in 2008 [74]. In manysources(e.g.,GlobalHistoricalClimatologyNetwork,FAO,
variousAfricancountries,policieshavebeenestablishedaimingto WMO, CIAT, R-HYdronet, and a number of additional minor
ensure the sustainable management of forests that contain P. databases). The geographic observations used for modeling the
africanastands.However,enforcementissuesandcontrolproblems potentialdistributionofP.africanawerefirstfilteredinDIVA-GIS
persist, and there is considerable urgency to identify and (www.diva-gis.org) [83] to detect outliers. All occurrence records
PLOSONE | www.plosone.org 3 March2013 | Volume 8 | Issue 3 | e59987
ConservationPrioritiesforPrunusafricana
Bioclim2.5 AllelicrichnessClimaticdistance(ranking)LCA(ranking)Cluster 26.11(15)3.00(10)4 29.89(9)3.67(7)3 23.47(19)2.50(12)4 27.31(13)2.83(11)3 28.10(11)3.50(8)4 30.26(8)3.33(9)1 31.90(6)3.83(6)2 35.96(1)4.00(5)4 31.91(5)3.67(7)4 21.85(23)2.33(13)1 20.85(24)2.17(14)1 24.98(17)2.17(14)2 435.96(1)4.50(3) 1.83(16)4 421.97(22)2.50(12) 1.67(17)4 22.93(20)2.50(12)4 14.80(27)3.00(10)2 17.96(26)1.83(16)1 24.66(18)3.00(10)2 27.87(12)2.50(12)2 1.17(18)2 1.00(19)1 26.86(14)3.00(10)2 232.90(2)3.67(7) 31.69(7)5.17(1)2 29.36(10)4.00(5)2 4.67(2)2 32.40(4)4.17(4)2 22.78(21)3.33(9)4 225.30(16)3.00(10) 18.94(25)2.00(15)2 occurrenceoflocallycommonalleles,similarityinallelicghestvalueoftheseparameters.Prioritypopulationsfor
nSSR GeneticdistancenCluster 152 202 192 132 194 192 182 172 202 154 184 124 122 63 253 63 72 181 121 164 114 64 34 154 104 202 202 202 202 191 131 201 hness,allelicrichness,opulationswiththehi
1). ricop
proposed(S LCA(ranking) 0.14(3) 0.00(4) 0.00(4) 0.14(3) 0.14(3) 0.43(1) 0.29(2) 0.00(4) 0.14(3) 0.14(3) 0.00(4) 0.14(3) 0.00(4) 0.14(3) 0.29(2) 0.14(3) 0.00(4) 0.14(3) 0.14(3) 0.14(3) 0.43(1) 0.29(2) 0.14(3) 0.14(3) 0.00(4) 0.00(4) 0.00(4) 0.00(4) 0.00(4) 0.00(4) 0.00(4) 0.00(4) duals,haplotypekingattributedt
PrunusafricanapopulationsofconservationprioritybasedonthefirstselectionmethodTable1. cpSSR GeneticHaplotypedistancerichnessNNameofpopulationCountrynCluster(ranking) 191Ngashie-MtOkuCameroon11.28(15) 2LowerMann’sSpring,MtCameroonCameroon1911.00(17) 3MtDanouaCameroon2011.00(17) 1854MokaEquatorialGuinea1.95(6) 5Chuka,CentralprovinceKenya2051.00(17) 1956Kinale,CentralprovinceKenya2.69(2) 207Kapcherop,CheranganiForest,RiftValleyKenya32.46(3) 8KakamegaForest,WesternProvinceKenya2051.16(16) 9Londiani,RiftValleyKenya2051.89(10) 10OlDanyoSambuk,CentralprovinceKenya1931.94(7) 11TaitaHills,CoastprovinceKenya2051.00(17) 12Lari,CentralprovinceKenya1231.00(17) 1651.16(16)13KibiriForest,WesternprovinceKenya 14MarovoayMadagascar54 3315LakatoforestMadagascar41.81(12) 16AntsahabiraokaMadagascar1841.00(17) 17NgelNyakiForestReserve,NigeriaNigeria911.00(17) 18MpumalangaSouthAfrica1951.90(9) 19KwaZulu-NatalSouthAfrica1751.81(13) 20MeruCatchmentReserveTanzania1931.97(5) 1721KilimanjaroCatchmentForestReserveTanzania52.96(1) 1422KindorokoCatchmentReserveTanzania21.00(17) 23ShumeMagambaCatchmentForestReserveTanzania2051.77(14) 24KidabagaTanzania1551.85(11) 1652.05(4)25UdzungwaTanzania 26KibaleForestNaturalParkUganda2011.00(17) 27KalinzuForestReserveUganda2011.00(17) 28BwindiForestUganda1911.28(15) 29MabiraForestUganda2011.93(8) 30NyangaNationalParkZimbabwe2051.00(17) 2051.00(17)31CashelValleyChimanimaniZimbabwe 32ChirindaForestReserveChipingeZimbabwe1951.00(17) Asubsetof32Prunusafricanapopulations,fromacross9Africancountriesischaracterizedbygeneticdata(numberofindivicomposition)andclimaticdata.Haplotype/allelicrichnessandpresenceoflocallycommonallelesareranked,withhighestranconservationarehighlightedinbold.doi:10.1371/journal.pone.0059987.t001
PLOSONE | www.plosone.org 4 March2013 | Volume 8 | Issue 3 | e59987
ConservationPrioritiesforPrunusafricana
were checked for inconsistency of their coordinates with [93] was carried out to test for significant differences among
administrative area level 1 (usually states or provinces) and for populations in thedistribution ofallelic richness.
extreme values in their climatic parameters (extreme values for a 3:Identificationofpopulationswithpresenceoflocallycommon
minimumof3outof19bioclimaticvariablesexamined)according alleles: locally common alleles are those repeatedly observed but
to the Reverse Jackknife method [84]). After the screening, three onlyinasmallarearelativetothespecies’distribution;theyareof
points were excluded and the final dataset included 1,500 high interest, especially if they are associated with adaptive traits
geographic observations. [94,91]. The definition considers as locally common those alleles
with a frequency higher than 5% in a local population and
Spatial Analysis for Selection of Priority Populations for occurringinlessthan25%ofallpopulationsexamined[51].The
distribution of locally common alleles was examined to further
Conservation
identify zones of high or unique intra-specific diversity. The
Two approaches toselect priority populations forconservation
average number of locally common alleles was calculated using
were used. One method (S1) maximizes genetic diversity and
GenAlEx6.5[95].Populationswererankedbasedonthisvariable.
distinctivenessoftheconservationunitbasedonacombinationof
4: Climate clustering: P. africana observations were clustered
genetic and climatic criteria. A second method (S2) is based
based on climatic data, assuming that natural populations from
exclusivelyongeneticdataandoptimizesrepresentativenessofthe
different climate zones would show variable adaptive traits not
geneticdiversityfoundthroughoutthespecies’range.Eachofthe
captured by the analyses of SSR markers data. The 1,500
selected priority populations wasfurtherevaluated forurgency of
observations were clustered on the basis of 19 bioclimatic
conservationactiononthebasisofcurrentprotectedstatusandthe
variables, extracted by point from the 2–5 minutes Wordclim
threat posed bypredicted future climate conditions.
dataset.Thefunction‘‘dist’’intheRstatisticsenvironment2.14.0
MethodS1. Weusedthefollowingstepstoaddpopulationsto
was used to calculate the Euclidian distance and hierarchical
thelistofprioritysites:populationswereclusteredbasedontheir
clustering was carried out using theUPGMA method. As forthe
genetic(chloroplast-andnuclear-based)andclimaticsimilarity;in
clusteringabove,basedongeneticsimilarity,weusedtheKelley-
eachgeneticcluster,thepopulationwithhighestrankinginallelic/
Gardner-Sutcliffe penalty function, in order to derive an optimal
haplotype richness and presence of locally common alleles was
number of clusters, and the cophenetic correlation coefficient to
selected. The second step identified populations in different
validate theclustering.
climatic clusters not represented in the selection above. In each
MethodS2. TheapproachS2wasbasedontheidentification
climatic cluster, the population with the highest rank in both
of a minimum number of populations needed to include all the
genetic parameters (allelic/haplotype richness and presence of
genetic diversity based on both chloroplast and nuclear markers.
locally common alleles) was added to the priorities for conserva-
The procedure adopted in DIVA-GIS is called ‘reserve selection’. It
tion.
generates a selection of grid cells (30 minute cell size) that are
1: Clustering of populations based on allelic composition: the complementarytoeachotherintermsofdiversityincludedineach
degreeofgeneticsimilaritybetweenpopulationswascalculatedin cell, and that captures the maximum amount of diversity in the
the R statistics environment version 2.14 [85] determining Nei’s smallest number of cells possible (see [96]). The algorithm also
distance [86,87] through the R function ‘‘dist.genpop’’ in the identifiespriorities,indicatingarankingforthegeographicunitsof
package adegenet version 1.3–4 [88]. Hierarchical clustering was interest. The first population chosen has the highest allelic
performed with the R function ‘‘hclust’’ in the package mva, richness; each successive population selected best complements
version 1.0–3, using the unweighted pair-group method of the intra-specific diversity already represented within the previ-
arithmetic averages (UPGMA). The Kelley-Gardner-Sutcliffe ouslyselected priority populations. The‘reserve selection’algorithm,
penalty function for a hierarchical cluster tree was calculated developedbyRebeloandSigfried[96],wasappliedtoacombined
usingthefunction‘‘kgs’’inthepackagemaptree[89]tosuggesta dataset,includingchloroplastandnuclearmolecularmarkersfrom
number of clusters in the dataset. The cophenetic distance was acrossthe32samplepopulationsstudied.Thisprocedureenabled
calculated with the function ‘‘cophenetic’’ in the package stats. a selection of cells/populations not only based on their diversity,
The cophenetic distance between two observations is defined as but alsoon differences/complementarity inallelic composition.
thedistance(orsimilarity)levelatwhichtwoobservationsbecome
part of the same cluster [90]. With the function ‘‘cor’’ in the Conservation and Threats
packagestats,version2.15.2,thecorrelationbetweenNei’sgenetic Themodeledpotentialdistributionwascombinedwithdataon
distance and the cophenetic distance was determined to validate the location of protected areas. The portion of P. africana’s
theclustering. potentialdistributionfoundwithinprotectedareaswasdetermined
2: Identification of populations with highest allelic/haplotype inordertoderiveanindicatorfortheinsituconservationstatusof
richness:thedatasetanalyzedinthisstudyincluded147allelesat6 thespecies.TheWorldDatabaseofProtectedAreas(WDPA)[97]
nSSRlociand19allelesat7cpSSRloci.Atotalof22multilocus was used to calculate the proportion of P. africana’s potential
haplotypes wereconstructed bycombiningsinglecpSSRloci.An distribution that falls within the boundaries of protected areas of
inherentdifficultywithmanydiversitystudiesarisesfromthewell- different types. WDPA includes detailed information on flora,
known property that diversity of a sample increases with sample fauna, and a wide range of climatic, environmental and
size[91,92].Resultsofanalysesmaythusdependonthenumber socioeconomic data for 741 protected areas across 50 countries.
ofsamplestakenwithineachsubunitofthestudyarea.Haplotype More detailed results were presented for three countries (Kenya,
richnessforcpSSRandallelicrichnessfornSSRweredetermined Uganda and Tanzania) that host high genetic diversity for P.
in DIVA-GIS, applying the rarefaction method to correct for africana,inordertoexaminemorecloselyspecificthreats,suchas
sample size bias, recalculating richness measured only within climate change andland conversion tocroplands.
subunits (30 min grid cell size) containing 7 or more trees LossofforestcoverinAfricahasbeensubstantialoverthepast
(equivalent to 7 haplotypes or 84 nuclear allele observations) (see 20years,withanaverageareaofca.3.7millionha/yearconverted
detailed methodology in [79]). Populations were then ranked tootherlandusesintheperiod1990–2010[98].TheGlobalLand
basedontheirallelic/haplotyperichness.TheKruskal-Wallistest Cover Map 2000 [99] was used to identify those areas with only
PLOSONE | www.plosone.org 5 March2013 | Volume 8 | Issue 3 | e59987
ConservationPrioritiesforPrunusafricana
naturalvegetation,excluding‘‘croplands’’(regionswithover50% The set of priority populations that combined highest value of
crop fields or pasture, equivalent to intensive cultivation and/or haplotyperichnessandhighestpresenceoflocallycommonalleles,
sown pasture) andother land uses (e.g.,urban settlements). ineach ofthe5 clusters identified,based on chloroplast markers,
Thepotentialthreatsfromchangesinclimaticconditionsatthe wasthefollowinginascendingclusternumber(Table1):pop.No.
regional scale were also assessed, by comparing the potential 1 (Ngashie - Mt Oku, Cameroon), 22 (Kindororo Catchment
distribution of P. africana under the current climate, based on the reserve, Tanzania), 7 (Cherangani Forest, Kenya), 15 (Lakato
species’ current distribution,with thepotential distribution under Forest,Madagascar),21(Kilimanjarocatchment,Tanzania).The
future climatic conditions. Future climate projections were set of priority populations that combined highest value of allelic
developed for 2050 under the A2 emission scenario (with richnessandhighestpresenceoflocallycommonalleles,ineachof
constantly increasing emission rate) from the average of three the 4 clusters identified based on nuclear markers, was the
differentGlobalCirculationModels(GCMs)downloadedfromthe following:pop.No.31(CashelValleyChiamanimani,Zimbabwe),
GCM Data Portal [100]: CCCMA-CGCM3.1-T63, HCCPR 13 (Kibiri Forest, Kenya), 15 (Lakato Forest, Madagascar), 25
HADCM3 and CSIRO-MK3.0. Finally, to determine the (Udzungwa, Tanzania). One of the priority populations above
potential distribution of P. africana during the peak of the last overlapped: 15(Lakato Forest,Madagascar).
glacial period (between 26,500 and19,000–20,000 years ago) the Aftertheselectionabove,additionalpopulationswereincluded
GCM ‘‘CCSM: Last glacial maximum (LGM; ,21,000 years intheprioritylistbasedontheanalysisofclimaticvariablesacross
BP)’’ downloaded from WORLDCLIM (http://www.worldclim. the sites where the species is found. Those populations occurring
org/past)wasused[Source:PaleoclimateModelingIntercompar- in areas having unique climatic conditions, not selected based on
isonProject Phase II(PMIP2)]. the previous criteria, were added among the priorities for
conservation. The rationale for this is that in the absence of
Results quantitativegeneticdata,distinctiveenvironmentalconditionscan
bea proxyfor useful adaptivevariation.
Distribution Range of Prunus africana and Spatial A total of4 distinct climate clusterswere identified using allP.
Analysis of Genetic Diversity africanaoccurrenceobservationsavailable(Fig.4).Thecophenetic
Based on our dataset of 1,500 georeferenced observations, the correlation coefficient obtained was 0.81, confirming the validity
presence of P. africana has been directly recorded in 22 African of the method adopted. While clusters 1, 2 and 4 correspond to
countries, while the modeled distribution extends to 34 countries climatic conditions with a broad distribution across the species
(Fig.1). range,andincludealargenumberoftheindividualobservations,
For the 32 populations sampled for genetic analyses, the cluster 3 characterizes a limited area, with very distinct climatic
clustering by similarity of allelic profile revealed 5 and 4 groups, features (low seasonality in temperature and high annual
respectively for chloroplast-based (Fig. 2a) and nuclear-based precipitation, between 2,400 and 3,000mm) (marked in yellow
(Fig.2b)SSRs.ThecopheneticcorrelationcoefficientsforcpSSRs in Fig. 4). Of the 32 populations for which genetic data were
and nSSRs were 0.79 and 0.73, indicating a good clustering available, populations in climate clusters 1 and 3 were not
structure in each case. The clustering results converged, showing representedintheselectionbasedongeneticparameters.Thus,in
that Madagascan populations are distinctive, and highlighting a eachofthesetwoclusters,thepopulationwithhighesthaplotype/
clear separation between East and West African populations. allelicrichnessandpresenceoflocallycommonalleleswasadded
Based on cpSSRs, these western populations showed a similar totheselectionofprioritypopulationsforP.africanaconservation
genetic profile to those found in Uganda (Fig. 2a). Populations (Table 1). The populations added were the following: No. 6
fromZimbabweandSouthAfricagroupedwithpopulationsfrom (Kinale, Kenya),andNo.4 (Moka,Equatorial Guinea).
KenyaandTanzania;Kenyaincludedpopulationsfrom2clusters, TheadoptionofapproachS1toselectpriorityareasgenerateda
while Tanzaniaincluded populations from3 clusters (Fig.2a). listof10prioritypopulations(highlightedinboldonthelefthand
Based on nSSRs, the grouping produced slightly different sideofTable1),thatwouldmaximizeinclusionofthegeneticand
results:thewesternpopulationsshowedasimilargeneticprofileto climaticdiversitymeasuredacrossthe32populationssampled.Six
thosefoundinUgandaandwesternKenya(Fig.2b).Populations of the 10 priority populations are located in Kenya (3) and
fromZimbabweandSouthAfricaclusteredtogetherandformeda Tanzania (3). The others are in Madagascar (1), Cameroon (1),
separategroupfrompopulationsinKenyaandTanzania(Fig.2b). Equatorial Guinea (1)andZimbabwe (1).
Kenya included populations from 2 clusters, while Tanzania The S2 approach generated a list of 19 priority populations
included populations from1cluster. presentedinTable2.Twooftheoriginal19prioritypopulations,
The spatial distributions of haplotype (chloroplast SSRs) inKakamegaandKibiriforests,fallwithinthesamegridcelldue
richnessandallelic(nuclearSSRs)richnessweredeterminedafter to their closeness, and are treated as one population; the final
rarefaction (Figure 3 a,b). Both types of markers point to number of priority populations is 18. Seven of the 18 priority
populations in East Africa as the ones with the highest allelic populations are located in Kenya, followed by Madagascar (3),
richness.TheKruskal-Wallistest(p-value ,0.0005)showedthat with 2 each in Tanzania (2), Zimbabwe (2) and Uganda (2), and
the32populationshadsignificantlydifferentdistributionsofallelic withoneeachinEquatorialGuinea(1)andCameroon(1).Being
richness. focused on representativeness of the genetic diversity found
The following populations had highest haplotype richness, in throughout the species’ range, approach S2 selects a larger
descendingorder(Table1):pop.No.21(Kilimanjarocatchment, numberofsitesamongthepriorities,andincludesalsooneofthe
Tanzania),6(Kinale,Kenya),and7(CheranganiForest,Kenya). two populations characterized by peculiar climatic conditions
Highest ranking populations for nuclear allelic richness do not (pop. No.4 inEquatorial Guinea).
overlap with those having highest haplotype richness. They are Acombinationofthetwoapproachesallowsafurtherselection
pop.No.8(KakamegaForest,Kenya),13(KibiriForest,Kenya), of6prioritypopulations,whichwereidentifiedasprioritiesusing
and 25(Udzungwa,Tanzania). Populations with locally common bothmethods.Thesepopulationsconstituteacoresetofproposed
alleles were located primarily in Kenya and Uganda, but also in conservation areas: 2 populations inKenya (pop. No.6,13),and
Tanzania andMadagascar (Table 1). one population each in Equatorial Guinea (pop. No. 4),
PLOSONE | www.plosone.org 6 March2013 | Volume 8 | Issue 3 | e59987
ConservationPrioritiesforPrunusafricana
Figure1.Prunusafricanaobservationsandmodeledpotentialdistribution.ProbabilityofoccurrenceofP.africanaisdeterminedonthe
basisofclimatic/environmentalparametersandindicatedbydifferentcolors,fromdarkbrown(highprobability)toyellow(lowprobability).
doi:10.1371/journal.pone.0059987.g001
Madagascar (pop. No. 15), Tanzania (pop. No. 22), and portion of the area with suitable climate for P. africana is not
Zimbabwe (pop. No. 31). Of the 32 populations sampled for covered by natural vegetation; large parts of it have been
geneticanalyses,21areincludedwithinofficialconservationareas, converted to cropland (Fig. 5a). Natural vegetation areas
and4othersareinsitesproposedforspecialprotection(Table3). correspond to just 21.5% of the species’ potential distribution; of
An assessment at the pan-regional level, indicates that protected this fraction, the portion covered by protected areas corresponds
areas cover 39% of the observed occurrences of P. africana and to 20.5% (Fig. 5b). This means that only ca. 4% of the potential
16.7% of its potential current distribution. Among the 6 distribution of the species in Kenya, Uganda and Tanzania is
populations that are selected as priorities by both approaches, 4 insideprotected areas.
are withinprotected areas. The predicted suitable habitat for the species, according to
However, a closer look at 3 countries (Kenya, Uganda and climate scenarios based on average values of three GCMs, is
Tanzania) where the sampled P. africana populations present the presented in Fig. 6a. This analysis indicated that a considerable
highesthaplotypeandallelicrichness,revealedthataconsiderable portion (53%) of the current range is expected to become
PLOSONE | www.plosone.org 7 March2013 | Volume 8 | Issue 3 | e59987
ConservationPrioritiesforPrunusafricana
Figure2.ClusteringofPrunusafricanapopulationsbasedonmolecularmarkerdata.The32populations,representedby30minutegrid
cells,aregroupedbyNei’sdistance,basedonsimilarityofhaplotypes(cpSSR)(2a)andsimilarityofnuclearmicrosatellite(nSSRs)alleliccomposition
(2b).
doi:10.1371/journal.pone.0059987.g002
unsuitable for P. africana by year 2050 (red areas), as a result of The modeled distribution of P. africana in 2050 in Kenya,
changing climate, with large portions of modeled distribution Uganda and Tanzania is predicted to be impacted by climate
disappearingfromthemap(e.g.,potentialrangeinAngola)anda change(Fig.5b),andtherangeofsuitablehabitatsisexpectedto
verymodestexpansionofthespeciestonewsuitablehabitats(1% decrease by 54% from 2010 to 2050; the part of the range
ofareaexpectedtobeoccupiedbythespeciesin2050),whilethe included in current protected areas is expected to shrink by 46%
blue areasindicate continued suitability forthenext 40years. by 2050.
Figure3.Prunusafricanahaplotyperichnessandallelicrichness.Haplotype(cpSSR)(3a)andallelic(nSSR)(3b)richnessaredeterminedfor32
populations,afterrarefaction,usinga30minutegridcellsize.
doi:10.1371/journal.pone.0059987.g003
PLOSONE | www.plosone.org 8 March2013 | Volume 8 | Issue 3 | e59987
ConservationPrioritiesforPrunusafricana
Figure4.Clusteringof1,500Prunusafricanaobservationsbasedonlevelofsimilarityofbioclimaticvariables.Bioclimaticvaluesfor19
variableswereassociatedwithallP.africanarecords.Bioclimaticvalueswereextractedfrom2.5minuterastersobtainedfromtheWorldclimwebsite.
Theobservationpointsaregrouped(eachclusterishighlightedwithadifferentcolour)byEuclideandistance.
doi:10.1371/journal.pone.0059987.g004
The current modeled potential distribution of P. africana was locatedatthemarginofthemodeleddistributionin2050.Ofthe6
compared with the modeled species distribution during the last prioritypopulations,2willbelocatedatthemarginofthemodeled
glacial maximum, ca. 21,000 years ago (Fig. 6b). The range is distribution: No.13in Kenya,andNo.22inTanzania.
estimated to have shrunk by approximately 45%; on this basis,
likelihood of future losses canbeestimated. Discussion
The modeled climate for 2050 in each sampled population is
Both approaches presented (S1 and S2) to select priority
reported inthelastcolumn ofTable 3.
populationsindicateahighconservationpriorityforpopulationsin
For twopopulations,No.11inKenyaandNo.29inUganda,
the eastern part of the distribution of P. africana, particularly in
thefuture climaticconditionsarepredicted tobecomeunsuitable
KenyaandTanzania,whichharboralargeportionofthegenetic
for P. africana. Another five populations (pop. No. 8 and 13 in
diversityfoundacrossthespecies’range.Atacountrylevel,Kenya
Kenya, No. 22 and 25 in Tanzania, No. 17 in Nigeria) will be
PLOSONE | www.plosone.org 9 March2013 | Volume 8 | Issue 3 | e59987
ConservationPrioritiesforPrunusafricana
Table2. Prunusafricana populations ofconservation priority based onthe secondselection method proposed (S2).
BothSSR
Code Nameofpopulation Country Reserveselection(priority)
8 KakamegaForest,WesternProvince Kenya 1
13 Kibiriforest,Westernprovince Kenya 1
6 Kinale,Centralprovince Kenya 2
31 CashelValleyChimanimani Zimbabwe 3
15 LakatoForest Madagascar 4
28 BwindiForest Uganda 5
5 Chuka,Centralprovince Kenya 6
20 MeruCatchmentReserve Tanzania 7
22 KindorokoCatchmentReserve Tanzania 8
4 Moka EquatorialGuinea 9
29 MabiraForest Uganda 10
12 Lari,Centralprovince Kenya 11
10 OlDanyoSambuk,Centralprovince Kenya 12
16 Antsahabiraoka Madagascar 13
30 NyangaNationalPark Zimbabwe 14
3 MtDanoua Cameroon 15
9 Londiani,RiftValley Kenya 16
11 TaitaHills,Coastprovince Kenya 17
14 Marovoay Madagascar 18
Prioritiesareidentifiedwithin32Prunusafricanapopulationsforwhichgeneticdataareavailable.Themethodisbasedonthe‘reserveselection’analysiscarriedoutin
DIVA-GIS.Themethodisaimedatenhancingcomplementaryofthegeneticdiversityrepresentedwithinthepopulationsselectedforconservationpriority.The18
populationsselectedforconservationpriorityarelisted(2oftheoriginalpopulationsfallwithinthesamegridcellduetotheircloseness,thereforearetreatedasone
populationandhavethesameranking).
doi:10.1371/journal.pone.0059987.t002
has unique opportunities to contribute to the conservation of the define conservation priorities. Populations with high diversity in
species, asdiscussed also inMuchugietal. [101]. neutral markers can be considered suitable candidates for high
The patterns of genetic variation found in P. africana adaptive variation as well. In addition, disjunctions in the
[40,42,101,102] are associated with the Afromontane habitats distribution range (like in the case of P. africana) are indicative of
occupied by the species, which play the role of islands of genetic isolation and we might expect to find local adaptive variation on
diversity [58]. The slight differences in clustering of populations thisbasis.
based on the two types of markers used (nuclear or chloroplast The approach proposed enables a reduction of the number of
SSRs)maybeexplainedbythefactthatcpDNAmarkerstendto priorities to a minimum set of core sites optimally distributed
reflect gene flow patterns that are more historically remote than acrosstherangeofP.africana.Inaddition,thecombinationofthe
thenuclearmarkers[103].TheRiftValleydisjunction-Albertine two methods described (S1 and S2) allows inclusion within
oreasternbranchdependingonthetypeofmarkerused-appears prioritiesofthosepopulationswithhighestgeneticdiversityacross
to have caused a major barrier between eastern and western genetically separate clusters, but also of populations with lower
populations[40]andexplainstherelatednessofpopulationsfrom diversity belonging to distinct climate clusters, potentially
Cameroon,UgandaandWesternKenya,whicharequitedifferent harbouring important adaptive properties. The clustering and
from those found in central Kenya. In addition, it is clear from ranking were obtained through a user friendly sequence of steps,
genetic analyses that populations from Madagascar are distinct with the support of freely available software, enabling conditions
and highly diverse [40,42,101]. Populations in Cameroon and fora wide uptake.
Equatorial Guinea, although not quite as diverse as those The results reveal that althoughthe species isnot indanger of
mentioned above, are also important as their environmental extinction, some important populations, with distinct characteris-
conditionsdivergesufficientlytoalmostcertainlyhavegivenriseto tics, are threatened and their loss would reduce the livelihood
variation ingenes controllingadaptive traits. potential for local people. Populations in Cameroon and
Obtaininggeneticinformationspecifictovaluabletraitsrequires Madagascar have been exposed to sustained high rates of
considerable time and cost but, as genomic tools develop, their exploitation [61,63]. Bark extraction has been also high, but less
potential for describing useful variation of expressed genes will intensive,inKenyaandontheislandofBioko(EquatorialGuinea)
likely be a breakthrough for conservation genetics [104]. The [61,106]. Debarking of P. africana often occurred within Afro-
approach for selecting priority populations for conservation montane forest habitats of global conservation significance
presented here is based on neutral molecular markers which are including in protected areas [107,108] and unpublished reports
extremelyusefulfordiscerninggeneflowandevidenceofhistoric indicate that harvest still occurs in such areas (pers. comm. with
events such as genetic bottlenecks [105] and are a useful basis to stakeholders). In addition, poor natural regeneration has been
PLOSONE | www.plosone.org 10 March2013 | Volume 8 | Issue 3 | e59987