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Ecology and Evolution. 2024;14:e70027.
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1 of 19
https://doi.org/10.1002/ece3.70027
www.ecolevol.org
Received:30January2024
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Revised:14June2024
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Accepted:7July2024
DOI: 10.1002/ece 3.70 027
RESEARCH ARTICLE
Population genomics and distribution modeling revealed the
history and suggested a possible future of the endemic Agave
aurea (Asparagaceae) complex in the Baja California Peninsula
Anastasia Klimova1,2 | Jesús Gutíerrez- Rivera1 | Alfredo Ortega- Rubio1 |
Luis E. Eguiarte2
This is an op en access arti cle under the ter ms of the CreativeCommonsAttributionLicense,whichpermitsuse,distributionandreproductioninanymedium,
provide d the original wor k is properly cited.
©2024TheAuthor(s).Eco logy an d EvolutionpublishedbyJohnWiley&SonsLtd.
1Centro de Investigaciones Biológicas del
NoroesteS.C.,LaPaz,Mexico
2Departamento de Ecología Evolutiva,
Instituto de Ecología, Universidad
NacionalAutónomadeMéxico,Ciudadde
México,Mexico
Correspondence
AnastasiaKlimova,Centrode
Investigaciones Biológicas del Noroeste
S.C.,LaPaz,Mexico.
Email: anastasia_aleksandrovna@hotmail.
com
Luis E. Eguiarte, Departamento de
Ecología Evolutiva , Instituto de Eco logía,
UniversidadNacionalAutónomade
México,CiudaddeMéxico,Mexico.
Email: fruns@unam.mx
Funding information
Instituto de Ecología, Universidad
NacionalAutónomadeMéxico,Grant/
AwardNumber:PAPIITIG200122
Abstract
Agaves are an outstanding arid- adapted group of species that provide a unique chance
tostudytheinfluenceofmultiplepotentialfactors(i.e.,geologicalandecological)on
plant population structure and diversification in the heterogeneous environment of
theBajaCaliforniaPeninsula.However,relativelylittleisknownaboutthephyloge-
ography of theendemicagave speciesof thisregion. Herein, we usedover 10,000
single-nucleotidepolymorphisms(SNPs)andspatialdatafromtheAgave aurea species
complex(i.e.,A. aurea ssp. aurea, A. aurea ssp. promontorii, and A. aurea var. capensis)
toresolvegeneticrelationshipswithinthiscomplexanduncoverfine-scalepopulation
structure,diversitypatterns,andtheirpotentialunderlyingdrivers.Analysesresolved
lowgeneticstructure withinthiscomplex,suggestingthatA. aurea is more likely to
representseveralcloselyrelatedpopulationsthanseparatespeciesorvarieties/sub-
species.Wefoundthatgeographicalandhistoricalecologicalcharacteristics—includ-
ingprecipitation,latitude,and past climatic fluctuations—haveplayed animportant
roleinthe spatialdistributionofdiversityand structureinA. aurea. Finally, species
distribu tion modeling resu lts suggested t hat climate change will becom e critical in
theextinction riskofA. aurea,with thenorthernmostpopulationbeing particularly
vulnerable.ThelowpopulationgeneticstructurefoundinA. aurea is consistent with
agave'slifehistory,and it is probably relatedtocontinuityofdistribution,relatively
low habitat fragmentation, and dispersion by pollinators. Together, these findings
have important implications for management and conservation programs in agave,
such as creating and evaluating protected areas and translocating and augmentation
of particular populations.
KEYWORDS
Agavoideae,BajaCaliforniaPeninsula,climatechange,genomicdiversity,pollinators,Sonoran
Desert
TAXONOMY CLASSIFICATION
Populationgenetics
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KLIMOVA et AL.
1 | INTRODUCTION
Arid landsare oneofthe mostwidespread ecosystemsworldwide
(Prăvălie, 2016; Ward, 2016), and due toanthropogenic activities,
theirbordersareexpanding(Liu&Xue,2020;Mirzabaevetal.,2019).
Despite the harsh environment, deserts are known for a large num-
berofendemicspeciesandhighplantfunctionaldiversity(Maestre
et al., 2012, 2021;S cherson et al ., 2020). Moreover, dry lands are
crucial inglobal biogeochemical cycles andEarth's energy balance
(Jickellsetal.,2005; Okin et al., 2004).
Althoughlivingindesertsishighlystressful,someplantgroups
have evolved un ique morpholog ical, physiologi cal, and behavio ral
adaptations,includingcrassulaceanacidmetabolism(CAM)photo-
synthe sis,delayedgerminat ion,clonality,extendedshallowroots ys-
tem, succulence, and production of particular heat- shock proteins,
which allow them to thrive in the harsh conditions of arid and semi-
aridareas(Smith,19 97;Wickens,1998;Ward,2016).Nevertheless,
due to the long generation time of many species, slow plant turn-
over, slow regeneration, and significant reliance on plant–plant inter-
actions(“nurseplants”),desertfloramaybeparticularlysensitiveto
theprojectedincreaseintemperatureandaridity(Brownetal.,2023;
Cody, 2000; Ravi et al., 2021). Moreover, it is quite p ossible that
desert plant species already operate close to their physiological lim-
its (Hantson et al.,2021;Madsen-Heppetal., 2023). For instance,
a recent simu lation stud y suggested t hat climate chan ge will be a
primary cause of cactus extinction risk, with over 60% of species
assessed being negatively impacted (Pillet et al., 2022). Climate
change isalso shifting the balance in plant–soil interactions when
anincrease inaridityreducesplantfungalsymbiontsandsubstan-
tially increases the proportion of fungal pathogens, which negatively
impactsplant'sfitness(Maestreetal.,2021;Pugnaireetal.,2019).
Someoftheiconicspeciesofthedesertsof NorthAmericaare
members of the Agavoideae (Asparagaceae), including agaves and
yuccas (Gentry,1978 , 1982). Agave is a genus of monocotyledons
native to th e arid lands of Nor th America (Egu iarte et al., 2021).
Agaves flourish in arid and semiarid areas, and in many regions,
they are dominant species that provide food and shelter for many
organisms(Gentry,1978 , 1982). The ge nus's great ecolog ical suc-
cesshasbeenlinkedtocrassulaceanacidmetabolism(CAM)andthe
generalistpollinationsystem(Eguiarteetal.,2021).Birds,bees,and
flies pollinate many agave species during the day, and nectar- feeding
bats pol linate them at night ( Rocha et al., 2006). In Mesoamerica
andAridoamerica,thegenusalsohasenormousculturalandeco-
nomic importance (Alducin-Martínez et al., 2022; Gentry, 1982).
Nevertheless, slow growth, low reproductive rates, the importance
ofplant–plantinteractions(i.e.,“nurseplantseffect”),andplant–pol-
linator interactions makeagaves especiallysusceptible toenviron-
mentaldisturbancesandpossiblytoclimatechange(Gómez-Ruiz&
Lacher, 2019;Martínez-Palaciosetal.,1999).
Recently(~7 Myaand~2.5 Mya),agavesexperiencedtwobursts
of evolutionary diversification that resulted in many endemic and
microendemic species, with countless forms of leaves, rosettes, and
inflorescences(Eguiarteetal.,2021;Gentry,1978, 1982;Good-Avila
et al., 2006; Jiménez-Ba rrón et al., 2020). On the B aja California
Peninsula (BCP hereaf ter), for example, a tot al of 23 Agave taxa
arefound,with22ofthembeingendemic(Trelease,1911;Webb&
Star r,2015).Surprisingly,therichdiversityofagavesintheBCPhas
been little studied (Gentry,1978; Webb& Starr,2015).For exam-
ple, almost all AgavetaxainBCPrepresentspecies/subspeciescom-
plexeswithuncleargeographicalboundariesorgeneticrelationships
betweenandwithinthem (Navarro-Quezada et al.,20 03;Webb&
Star r,2015).
The high bio logical diversity and en demism levels of flor a on
theBCParethoughttoarisefromtheprolongedisolation, peculiar
geography of the peninsula, and its high landscape heterogeneit y
(Garcillánetal.,2010;Grismer,2000; Riddle et al., 2000; Riemann
&Ezcurra, 20 07; Van Devender, 1990).The BCPlies betweenthe
Pacific O cean and the Gulf of C alifornia; it is one of t he longest
and the most isolated peninsulas, reaching a length of approxi-
mately1300 km(Dolbyetal.,2015).TheBCPismostly arid (~75%)
and forms part of the Sonoran Desert, with readily recognized
vegetation including different species of agave, columnar cacti
(e.g.,Pachycereus pringlei and Stenocereus thurberi),yuccas,and the
Boojumtree(Fouquieria columnaris)(Riemann&Ezcurra,2007).BCP
desert is relatively young and thought to have originated during
thelateMioceneandthePliocene,5–10millionyearsago,withthe
modernwarm-desertvegetationbecomingextensiveonlyapproxi-
mately6000–12,000 years ago,aftertheendof thelastglacialpe-
riod (A xelrod, 1978; Frenzel , 2005; Raven & Axel rod, 1978). A s a
consequence, a signal of relatively recent northward and southward
expansion from refugiahas been observed along theBCP inarid-
adapted and succulent plant taxa (De laRosa-Conroy et al., 2019;
Garricketal.,2009;Gutiérrez-Floresetal.,2016; Nason et al., 2002).
AmongtheuniquefloristicdiversityofBCP,Agave aurea stands
out(Figure 1).IntheBCP,A. aurea is the only representative of the
Campanifloraesection (Gentry,1978, 1982; Webb & St arr, 2015).
Thespeciesis foundonthewestside oftheSierraLaGiganta,ex-
tending south to theSierraLa Laguna and Cabo SanLucas (Webb
&Starr,2015).It is a relatively largeplant withatall,showyinflo-
rescence thatcan reach 8 meters. Theplant usedto be harvested
by indigeno us people as a so urce of food and fi ber. Nowadays, it
issometimesusedtomakea distilled alcoholicbeverage(a typeof
mezcal) andas an ornamentalplant. Theplantissaidtohavebeen
trialed as a commercial source of fiber, but yields were too low
(Gentr y,1978).
Currently, A. aureaisconsideredtobe a species complex, with
the taxonomic st atus of varieties/subspe cies being under debate
(Webb&Starr,2015).Initially,basedonflowercharacteristics,three
separate species, Agave aurea, Agave capensis, and Agave promon-
torii, were described (see Gentry,1978 , 1982).Recently,Webband
Starr(2015)indicatedthat,duetothesimilarityinvegetativechar-
acteristicsandthedif ferencesmainl yr elatedtosi zeandthepropen-
sityforclonalreproduction,thesethreespeciesshouldbereduced
tothe subspecies or varieties level.No genetic studies have been
donetoclarifytaxonomicuncertaintieswithintheA. aureacomplex,
yet.
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KLIMOVA et AL .
OfthethreevarietiesorsubspeciessensuWebbandStarr(2015),
A. aurea ssp. aureaisthe mos tcommo nanda bundant ,ex ten din gfr om
thewesternslopesoftheSierraLaGigantato the SierraLaLaguna
intheCapeRegion.Itiseasilyrecognizedbythelong,narrowgreen
leaves that a rch to form an ope n rosette and by t he bright yellow
flowers(Figure 1).Agave aurea var. capensis is a smaller plant with nar-
rowerleavesanddiffersfromother varietiesmainlybecause it pro-
liferatesbyaxillary sprouting, creatinglarge groupsof plants.Agave
aurea var. capensishasres trictedgeogr aphicdist ribut ionandcanon ly
befoundonthepeninsula'ssoutherntip.Agave aurea ssp. promonto-
rii,ontheotherhand,isalargeplantrestrictedtothenorthernSierra
La Lag una at elevations of 90 0–1800 m. Th e genetic relation ships
betweenthesevarietiesandtheirexactgeographicboundariesare
unclearandneedfurtherinvestigation(seeWebb&Starr,2015).
In addition to the uncertainty in species delimitation, there is a
growing concern for the conser vation of the flora and fauna of the
BCP(Benavidesetal.,2020;Dávilaetal.,2022;Klimova,Gutiérrez-
Rivera, et al., 2022; Klimova, Mondragón, et al., 2022; Riemann
& Ezcurra , 2005; Vanderplank et al., 2014). Like ot her arid areas
around th e globe, Baja Cali fornia has been heav ily influenced by
anthropogenic disturbances, such as overgrazing by free-roaming
livestock, off- road recreational vehicles, agriculture, and climate
change (Riemann & Ezcurra, 2005; Wehncke et al., 2014). In re-
cent decades, rapid development and human population growth
havegreatlyintensified threats to BCP's ecosystems, especially to
endemicandendangeredspecies (IUCN,2023;SEMARNAT,2010;
Wehnckeetal.,2 014).Amonghuman-inducedbiodiversitythreats,
climate change is predicted to play an increasingly important role
inbiodiversitydecline(Gao et al., 2020; Pinsky & Fredston,2022;
Urban,2015).Moreover,climatechangehasalreadyhadasignificant
negativeimpactonSonoran Desertvegetation,particularly on its
xericportion, leadingtoasubstantial decreaseinvegetationcover
(Hantsonetal.,2021).
Toaddresstheunresolvedissuesdescribedabove,wecombined
genome-wid e SNPs and spe cies distrib ution model ing (SDM) with
a thorough sampling of the Agave aurea complex i n the BCP.T he
datawereanalyzed ontwolevels.First,wetriedtoresolvegenetic
relationshipswithin this complexand,fromthere,delimitthegeo-
graphicboundaries of each group. We then focused on fine-scale
genetic analyses, uncovering the population structure and diver-
sity pat terns of A. aurea and investigating their potential underly-
ingdrivers.Second,wetriedtodeterminehowthefuturepotential
distribution ofthe A. aureacomplex willbealtered underdifferent
climate cha nge scenarios . Our main worki ng hypothese s were: (1)
Weexpected tofind genomic support for at least some currently
recognizedsubspecies/varietieswithinA. aureacomplex.(2)Asbats
are an important mediator of pollen dispersal in agaves, and agaves,
in general, present low genetic differentiation within species, we hy-
pothesizedthatforA. aurea,pollendispersalwouldnotberestricted
withingeographicregions,whichshouldbereflectedinoverallshal-
lowpopulation structure. (3) We expectedthat thedistributionof
genetic diversity and differentiation of A. aureawouldberelatedto
thegeography,ecological,andclimatichistoryofBCP.(4)Duetothe
projectedaridificationofBCP,wehypothesizedthatthefuturepo-
tentialdistributionofA. aureawillbenegativelyaffectedbyclimate
change.
FIGURE 1 MapofthesouthernBaja
CaliforniaPeninsula,Mexico,witha
backgroundrepresentingthetopography
oftheareaandblackdotsrepresenting29
sample site locations of Agave aurea sensu
WebbandStarr(2015).Samplingsite
abbreviationscanbefoundinTable S1;
thesubspeciesofA. aurea var. capensis
and A. aurea ssp. promontorii are coded
andcoloredasSLL_18_C(darkgreen)
andSLL_19_P(darkred),respectively.
Inset at the left corner is a picture of A.
aurea spp. aureacollectedinSierraLa
Giganta.Insetattherightcornerisamap
ofNorthAmerica,withtheBajaCalifornia
Peninsulahighlightedindarkblue.
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2 | MATERIALS AND METHODS
2.1 | Sample collection
Fresh leave tissue samples were collected from 29 geographic loca-
tionsacrossthesouthernpartof theBCPthatrepresentthe com-
pletedistributionrangeofAgave aureasensuWebbandStarr(2015)
(Figure 1, Table S1).Allthreerecognizedsubspecies(Gentry,1978;
Webb&Starr,2015)wereincludedintheanalysis.ForAgave aurea
va r. capensis, besides a ty pe localit y,seve ral more sites e xtracte d
fromtheGlobalBiodiversityInformationFacility(GBIF,2023)were
visited, butonly plants morphologicallysimilartoAgave aurea ssp.
aurea were found (rel atively large plan ts that did not form c lonal
clusters).Plants were assigned to subspecies based on their morphologi-
cal characteristics and the geographic locality where they were collected
(Gentr y,1978;Webb&Starr,2015).
Uponcollection,sampleswerepreservedinpaperbagsandkept
awayfromheatandsunlight;onceatthelaborator y,samples were
kept at −20°C until D NA extract ion. All the spec imens were col-
lected during fieldwork performed in 2023.
2.2 | DNA extraction and RADSeq
GenomicDNAfrom98individualsofAgave aureasensuWebband
Starr (2015) was extracted using a modified hexadecyltrimeth-
ylammonium bromide (CTAB) protocol from the frozen leaf tis-
sue disrupted with liquid nitrogen (Doyle & Doyle, 19 87; Klimova,
Gutiér rez-Rivera, et al., 2022; K limova, Mondrag ón, et al., 2022).
TheDNAqualitywascheckedona1%agarosegel.Samplesofade-
quatequalityandquantityweresenttotheUniversityofWisconsin
Gene CoreforRAD-Seqlibrary preparation(Andrewset al., 2016;
Elshire et al., 2011)usingtwomethylation-sensitiverestrictionen-
zymes(PstI/MspI)andsequencingontheIlluminaNovaSeq2 × 150
platform(Illumina,SanDiego,CA,USA).
The resulting paired- end reads were assessed for quality
usingFastQC (Andrews,2010)andfilteredusingthefastp(Chen
et al., 2018).Weremovedadapters,sequencesshorterthan55 bp,
reads with over five N, low- quality sequences, and trimmed
poly G and p oly X tails. We also f iltered low-complex ity reads,
where the complexity wasdefined as the percentage ofa base
that is different from its next base (base[i]! = base[i + 1]) (Chen
et al., 2018).Filteredreadsweredemultiplexedusingtheprocess_
radtagsfunctioninSTACKSv1.41(Catchenetal.,2013; Rochette
et al., 2019) and mapped to the Agave tequilana transcriptome
(GAHU00000000.1;Grossetal.,2013) using Burrows-Wheeler
Aligner(BWA)v0.7.13(Li&Durbin,2009).TheresultingSequence
AlignmentMap (SAM) files were converted to Binary Alignment
Map (BAM) format, sorted by coordinates, and indexed using
SAMtools(Daneceketal.,2021).SNPswerecalledusingbcftools
(Daneceketal.,2021).
The raw genotypes were filtered using VCFtools v0.1.16
(Daneceketal.,2 011).WeremovedSNPswithaminorallelecount
of <8,agenotypingrateoflessthan90%,amaximummeandepthof
150,aminimummeandepthof10,andamaximumnumberofalleles
of2.WeremovedlocithatdeviatedfromHardy–Weinbergequilib-
rium(p < .05,after Bonferronicorrection). Finally,we removed loci
in linkage disequilibrium (r2 > 0.2) using PL INK v1.90b6. 21(C hang
et al., 2015).
2.3 | Population structure
To investigate population structure and to understand the relation-
ships within the A. aureacomplexsensuWebbandStarr(2015), we
usedseveralcomplementaryapproaches.Weconductedaprincipal
component analysis (PCA) using the R package SNPRelate (Zheng
et al., 2012). We estimated individual admixture propor tions using
ADMIXTURE (Alexander et al., 20 09; Alexander & Lange, 2011).
Admix t urerun swe rep erfo rmedforancestr yclu ste rs(K)rangin gfr om
1 to 10, with 10 runs for each Kvalue.Theoptimalnumberofclusters
wasidentifiedbasedonthelowestcross-validationerror andvisual-
izedusing R. Wealso used fineRADstructure,aBayesianclustering
approachthatutilizeshaplotypelinkageinformationandsearchesthe
mostrecentcoalescence(commonancestry)amongthesampledindi-
viduals(Malinskyetal.,2018).A“coancestrymatrix”ofA. aurea speci-
mens was gen erated using STACKS's ‘po pulation’ modul e (Catchen
et al., 2013).Wesubsequentlyused10,000,000MarkovchainMonte
Carlo (MCMC) iterations witha burn-inof 5,000,000 and sampling
occurring every 10,000 iterations. A tree was constructed with
100,000hill-climbingiterations,andtheresultswerevisualizedusing
thescriptFINER ADSTRUCTUREPLOT.R,whichisavailableatht tp s : //
g i t h u b . c o m / m i l l a n e k / f i n e R A D s t r u c t u r e . We also reconstructed
the genealogical relationships among A. aurea individuals using an
neighbor-joining (NJ) tree estimated using the bitwise.dist func tion
wit hintheRpackagepopprandabootfu nction,using1000boots trap
replicates (Kamvar et al., 2015). The unrootedNJ tree among sam-
plingsitesbasedontheNeigeneticdistancewasestimatedwiththe
RpackagesStAMPP(StatisticalAnalysisofMixed-PloidyPopulations)
andape(Paradis&Schliep,2018;Pembletonetal.,2013).Finally,we
estimated pairwise genetic differentiation among subspecies, sam-
plingsites,andgeographicregions,usingWeirandCockerhamFST val-
uescalculated(Weir&Cockerham,198 4)intheRpackageStAMPP.
Confidence inter vals and p- valu eswerees tim atedb ase do nbo ots trap
resampling of individuals 100 times.
2.4 | Landscape genetics
Geographicisolationanddispersalbarriersareknowntocontribute
to the geographic structuring of genetic variation in many organisms
(Bradb urd et al., 2013; Lovel ess & Hamrick, 1984; Wright, 1949).
Therefore, we examined the relationship between genetic and
geographic distance between all pairs of sampling sites. Genetic
distance was based on pairwise FSTobtained using the R package
StAMPP.Geographicgreat-circledistanceamongsamplingsiteswas
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KLIMOVA et AL .
calculatedusingtheGeographicDistanceMatrixGeneratorversion
1.2.3 (Ersts,2013).The significanceofgenetic and geographicdis-
tanceassociationwascalculatedusingManteltestswiththeRpack-
ageade4(Dray&Dufour,2007)accordingtothemethodproposed
byRousset(1997 ),whichisbasedontheFST/(1 − FST)andthenatu-
rallogarithmofgeographicdistance(ln).
Thedivergencecanalsobeexplainedbylocaladaptationthatwill
show a corre lation betwee n genetic and envir onmental dist ances
(Frankhametal.,20 02).WeusedMantelandpartialManteltestsas
implementedintheRpackageVEGAN2.4-0(Oksanenetal.,2014)to
testforcorrelationsbetween geneticandenvironmentaldistances,
thelatterbeinggeneratedusingthe“dist”functioninRfromallthe
19bioclimatic variables downloaded from WorldClim v.2 (Hijmans
et al., 2005) as asetofrasterlayers.Mantel testswere performed
betweeneachgeneticandenvironmentaldistancematrix,andthese
analyseswere alsorepeatedas partial Mantel tests controlling for
geographic distance. Statistical significance was determined using
Pearson'stestsbasedon10,000permutations.Toavoidcollinearity
among eco logical var iables, we per formed a Ma ntel test bet ween
theclimaticvariablessignificantlycorrelatedwithgeneticdifferenti-
ationandusedonlythosevariablesthatdidnotcorrelatewitheach
other.Betweenhighly correlated variables,wechose one withthe
highest r- statistic and the lowest p-value(Table S3).
Finally,weusedtheclusteringmethodimplementedinTESS3R
that considers genetic and geographic data to determine the
most probable number ofclusters (K) ina geographic space (Caye
et al., 2015).WetestedK = 1to10with30replicatesofeachK and
keptthemostsupportedmodel(i.e.,“best,”basedoncross-entropy
scores)withineachofthe30replicates.Locationsonthemapwere
colored according to the resulting dominant ancestry cluster.
2.5 | Genome- wide diversity
To assess levels of genetic diversity within A. aureasensuWebband
Starr(2015),weestimatedmultilocusheterozygosity(MLH)usingthe
RpackageinbreedR(Stoffeletal.,2016)andtheinbreedingindicesFIS
and Fhat3usingPLINK2.0(Changetal.,2015).Thesediversitymetrics
werecalculatedattheindividuallevel.Tobetter understandthespa-
tialdistributionofgeneticdiversity,weplottedthediversityestimates
onamapusingtheRpackageggplot2(Wickham,2009).Wethenex-
ploredwhethergeographicalvariables(i.e.,elevationand latitude as
predictivevariables)wererelatedtothelevelsofgeneticdiversityby
usin gsimp leandqua dratic linearmo dels(LMs)i nt heRpackages tat s(R
Core Team, 2021).Wealsoestimatedthenumberofprivateallelesfor
eachsubspecies/geographicregionusingtheRpackagepoppr.
2.6 | Species distribution modeling (SDM)
Occurrence data for A. aureasensuWebbandStarr(2015) were
downloaded from the Global Biodiversity Information Facility
(GBIF,2021)andsupplementedwithourfieldsurveys.Weexcluded
locations that fell into the ocean or in human settlements, old
data (older than 1970), and coordinates with an uncertainty of
over200 m; data werealso filtered within theBIOMOD2package
(Thuiller,2003; Thuiller et al., 2009)usingthefilter.rasterfunction.
In total, 128 occurrence points were retained.
Weused the currentclimatic variablesataspatialresolutionof
30arc-sfromtheWorldClimtoestimatethecurrentpotentialdistri-
butionofA. aureasensuWebban dStarr(2015).Toavoidcollinearity
amongbioclimaticvariables,weusedPearson'scorrelationanalysis
tochoose onlyone variablefrom each pair of strongly associated
variables(i.e.,r > 0.75or−0.75).Atotalof10variableswereretained
aftercorrelationanalysis(Table S2).
For predictingthe future potentialdistributionof A. aurea, we
usedtheCoupledModelIntercomparisonProject(CMIP6)toaccess
theclimate modelsbasedontworepresentativeshared socioeco-
nomicpathways(SSP245andSSP585)foratimeperiodfrom2061
to 2080. Future climate models rely on diverse sets of codes and
are para meterized with slig htly differe nt conditions; the refore, as
sugges ted by Knutti e t al. (2013) and S anderson et a l. (2015), we
selectedthreedissimilarmodels(AustralianCommunityClimateand
EarthSystemSimulator-EarthSystemModel1.5(ACCESS-ESM1-5),
Model for Interdisciplinary Research on Climate, sixth version
(MIROC6), and Max-Planck Ins titute-Earth System Mod el version
1.2low resolution (MPI-ESM1-2-LR)).All climaticdata weredown-
loaded using R package geodata(RCoreTeam,2021).Thesameset
ofenvironmentalvariablesusedtoestimatethecurrentdistribution
of A. aureasensuWebbandStarr(2015)wasalsousedtopredictits
futurepotentialdistribution.
We performed the ensemble distribution modeling using
GeneralizedBoostedModels(GBM)(Ridgeway,1999)and Random
Forest (R F) (Breiman , 2001) algorit hms. The dist ribution mo deling
requiresthepresenceandabsenceofdata;we,therefore,randomly
generated 1000 pseudo-absence points and five pseudo-absence
data sets (Guisan et al., 2017).We built themodels using 80% of
the data (training set) and evaluated the model performance with
therestofthe20%ofthedata(evaluationset).Weraneachofthe
models10times.Weusedtwoevaluationmetricstodeterminethe
accuracyofthemodels:theareaunderthecurve(AUC)ofreceiver
operatingcharacteristics(ROC)andtrueskillsstatistics(TSS)(Khan
&Verma, 2022; Rather et al., 2022).Tovisualize andmeasurethe
range change for A. aureasensuWebbandStarr(2015)underfuture
climaticconditions,weusedtherange–sizefunctionimplementedin
theBIOMOD2package.
We also used SDM to predict climatically suitable areas for
A. aureasensuWebbandStarr(2015) under two different past
time periods (Mid-Holocene (MH) and Last Glacial Maximum
(LGM)). The ra ster layers were dow nloaded fro m WorldClim (Fick
& Hijmans, 2 017 ) and t wo different cli matic models wer e chosen
(Community Climate System Model, vers ion 4 (CCSM4) and Max
Planck Institute for Meteorology (MPI-M)-Earth System Model-P
Model(MPI-ESM-P)).Wegotaresolutionof30arc-secondsforMid-
Holocenedata,whereasforLGM,itwas2.5 min.TheSDMforpast
conditionswasimplemented,asdescribedabove.
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3 | RESULTS
We generated R ADSeq data for 98 A. aurea sensu Webb and
Starr (2015)individuals collectedfrom 29 locationsthat represent
the complete distribution range of the species. We included the
three subspecies/varieties recognized by Webb and Starr (2015)
(Figure 1, Table S1).ForA. aurea ssp. aurea, we included 87 samples,
comprising27samplingsitesfromtheCapeRegiontotheSierraLa
Giganta.ForA. aurea ssp. promontorii(n = 5),weincludedonesiteat
SierraLaLaguna,andforA. aurea va r. capensis(n = 6),onesampling
siteatthetypelocality CerrodelaZetaat theCapeRegion ofthe
BCP.
Fromatotalof547,875rawSNPscalledusingtheSamtools,after
filteringwithVCFtools,10,765high-qualitySNPsacrossallsubspe-
cies were retained. The mean individual depth among 98 individuals
was57.5(SD11.4).Theaveragemissingnessonaper-individualbasis
was1.4%.
3.1 | Population structure
To investigate population differentiation within A. aurea, we used
several complementary approaches: principal component analysis,
NJtree, ADMIXTURE, pairwise FST, and Bayesian clustering in fin-
eradstructure.Theoveralldivergencebetweenvarietieswaslow:A.
aurea ssp. aurea vs. A. aurea var. capensis, FST = 0.09; A. aurea ssp.
aurea vs. A. aurea ssp. promontorii, FST = 0.03;andA. aurea ssp. prom-
ontorii vs. A. aurea var. capensis, FST = 0.14;each estimate was sig-
nificant(p < .001).Althoughwithdifferentsensitivities,allmethods
agreed that the mor phologically de scribed varieties present very
shallowgeneticdivergenceamongthem(Figures 2–5).Forexample,
it was hard to identify any particular separated genetic group within
the individual-based NJ tree (Figure 2b). There were indications
that samples of A. aurea var. capensis were slightly differentiated
fromtherestofthespecimens,whichwasalsoconfirmedbyPCA.
However,thefirsttwoprincipalcomponentsexplainedonly6.65%
ofthevariance(Figure 2a).
The PCA further uncovered previously unrecognized regional
structuringrelatedtothegeologyandgeography ofthe BCP,sam-
ples clustered according to the mountain range: southern samples
collectedonandaroundSierraLaLagunavs.northernsamplescol-
lectedonSierraLaGiganta(Figure 2).Nevertheless,thegeneticdif-
ferentiationbetweenmountainrangesw asrelativelyl ow(FST = 0.03,
p < .01).These findings werealso visibleonanindividual-basedNJ
tree(Figure 2) and were more pronouncedwhen the tree was re-
constr ucted using th e populations (s ampling sites in stead of each
specimen, Figure 3).
ADMIXTURE'scross-validationerror(CVE)indicatedthatthebest
K value for A. aureasampleswas2(Figure S1).When the individual
ancestrieswereplotted(Figure 4),atthebestKvalue(K = 2),wefound
a north–south clinal clustering of the samples, with clear geographic
structuringintonorthern(SierraLa Giganta) andsouthern(SierraLa
LagunaandCapeRegion)groups.Furtherpartitioningofthesamples,
K = 3,indicatedthatthethreesouthernmostsamplingsites(including
the A. aurea var. capensis, SLL_18) had different genomic ancestry.
Moreover,severalnorthernmostsitesalsohaddifferentancestry,with
samples in the middle of the sampled region having mixed ancestry
(Figure 4).ADMIXTURE results were confirmed by the among sam-
pling sites FSTestimation(Figure S2);thevaluesrangedfromFST = 0.03
(between SLL_13 and SLL_11, separated by ~20 km) to FST = 0.26
(SLL_17andSLL_3,separatedby~141 km).
These patterns were reinforced with the fineradstructure anal-
ysis, which pointed to the presence of three main genetic clusters
(Figure 5). The fir st group comprised samples collected in Sierra
La Giga nta; the second g roup comprise d samples of A. aurea va r.
capensis and samples of A. aurea ssp. aurea(SLL_3, SLL_4,SLL_12,
SLL_15, an d SLL_14) collected i n the souther nmost part of S ierra
La Laguna; finally, the third cluster comprised samples of A. aurea
FIGURE 2 Populationgenetic
structure of Agave aureasensuWebb
andStarr(2015)fromBajaCalifornia
Peninsula,Mexico,basedon10,765
genome-wideSNPs.(a)Principal
componentanalysis(PCA)ofthe
individuals of A. aurea.(b)Neighbor-
joining(NJ)networkof98individualsof
A. aurea.PCAandNJtreetipsarecolored
accordingtothethreesubspeciesofA.
aureasensuWebbandStarr(2015),green
– A. aurea ssp. aurea,brown–A. aurea
ssp. promontorii,andblue–A. aurea var.
capensis.ShapesonPCAandNJtreetips
correspond to the mountain ranges from
where samples were collected, such as the
circleSierraLaLagunaandthetriangle
SierraLaGiganta.
(a) (b)
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KLIMOVA et AL .
ssp. promontorii and A. aurea ssp. aurea collected roughly on the cen-
tral and northern parts of theSierra LaLaguna(Figure 5). The co-
ancestrymatrixalsoshowedhighwithin-siterelatedness(Figure 5).
3.2 | Spatial structure
The cross- validation criterion recovered for A. aurea samples using
TESS3R did not exhibit a minimum value or ap lateau(Figure S3),
probably reflecting low differentiation within agave populations.
Therefore, we plotted the results of different Kvalues(fromK 2 to
3; Figure S4).AtK = 2,sampleswerepartitionedintogroupslocated
approximatelysouthandnorthoflatitude23°N,withsamplesofA.
aurea var. capensis (SLL _18)an d one site from A. aurea ssp. aurea
(SLL_3)beingtheonlysampleswithanancestryofover99%;therest
ofthesampleshadmixedancestry(Figure S4a,c).AtK = 3,wefound
supportforthedifferentiationbetweensouthernandnorthern(cor-
respondingtothe geologicalbreak between Sierra La Giganta and
Sierr aL aL agunamountainranges)samples,similartothere sult sob-
servedwithPCAandADMIXTURE(Figure S4b,d).Nevertheless,all
thesamplespresentedsomedegreeof mixedancestry(Figure S4).
ThethirdclustercomprisedonlythespecimensbelongingtoA. aurea
va r. capensis(SLL_18)(Figure S4b,d).
Surprisingly, wefoun da low relationship between genetic FST
and geogr aphic distance ( Mantel's r = 0.17, p = .04). Par tial Mantel
tests revealed significant associations between genetic distance
andseveralenvironmentalvariables relatedto temperature(BIO2,
BIO4,BIO6,andBIO7).Nevertheless,allthesevariablesalsopre-
sentedahighcorrelationwitheachother.Wedecidedtokeeponly
thevariablewiththehighestr- statistic and lower p-value(Table S3).
Specifically, BIO 4 (Temperature Seasonality (standard deviation
× 100)) correlated significantly with genetic distance in A. aurea
sensuWebbandStarr(2015),evenafter controlling by geographic
distanceamongsamples(PartialManteltest,r = 0.33,p = .002).
Takingtogether allthe abovepopulation genetic structureand
spatial analyses, we can conclude that within A. aureasensuWebb
andStarr(2015),thereare three maingeneticclusters,albeitwith
low diverge nce among them: (i ) samples of A. aurea var. capensis,
along with several samples of A. aurea ssp. aurea, restricted to the
southernmostregionoftheBajaCaliforniaPeninsula;(ii)samplesof
A. aurea ssp. aurea,withanaffinitytoSierraLaLaguna,andsamples
of A. aurea ssp. promontorii;and (iii)samplesofA. aurea ssp. aurea
distributedattheSierraLaGiganta.
3.3 | Diversity landscape across subspecies and
populations
Toexplorehowpopulationstructure,geography,andecologicalhis-
tory of th e BCP have influenc ed the genome-wid e variation in A.
aureasensuWebbandStarr(2015),wecomparedindividual mul-
tilocusheterozygosityand inbreeding indicesbetween subspecies,
populations(mountainranges),andsamplingsites.TheoverallMLH
was 0.21 (SD 0 .01), with the lowes t values (MLH = 0.17) fou nd in
individualsfromthesamplingsitesSM_8andSLL_16andthe high-
estvalue(MLH = 0.24)inSLL_13(Figure 6, Tables S4 and S5).There
was a signif icant (p < .005) d ifference in MLH b etween mounta in
ranges,withlowerheterozygosityfoundinSierraLaLaguna,i.e.,the
FIGURE 3 Populationgeneticstructure
of A. aureafromBajaCaliforniaPeninsula,
Mexico,basedon10,765genome-wide
SNPs,representedbyaNeighbor-joining
(NJ)networkof29samplingsites.NJtree
tips are colored according to the mountain
range:Blue–SierraLaLagunaand
brown–SierraLaGiganta.Samplingsite
abbreviationscanbefoundinTable S1.
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southernsamples (Figure 6, Table S5). Although F hat3 and FIS in-
breedingindiceswerecalculateddifferently,wefoundsimilarresults
forboth inbreedingestimates, withlowtomoderateinbreeding in
almost all individuals of A. aurea. Themean species-wideinbreed-
ingindex (Fhat3)was0.14(SD 0.05),withthe lowestvalues found
in samples from SLL_13 (0.05) and the highest inbreeding value
found inan individualfrom SM_8 (0.39). No significant difference
wasfoundfortheFhat3inbreedingindexbetweenmountainranges
(Table S6).Wealsofoundconsiderablelevelsofinbreedingmeasures
byFIS; the mean FIS was 0.14, ranging from FIS = 0.05inasamplefrom
theSLL_13toFIS = 0.31intheSM_8.TheinbreedingFIS was signifi-
cantlydifferentbetweenmountainranges,beinghigherintheSierra
LaLaguna,i.e.,thesouthernsamples(p < .001)(Table S6).
Wefurtherexploredtherelationshipsbetween the geneticdi-
versity of A. aureasensuWebbandStarr(2015) populatio ns and
BCP'sgeographyandecologicalhistory(Figure 6).Thegenomicdi-
versitychanged significantly withlatitude,both in their levels(i.e.,
MLHvs.latitudeR2 = .46,p < .0001)andininbreeding(Fhatvs.lati-
tude R2 = .3,p = .003;FIS vs. latitude R2 = .46,p < .001).Thecorrela-
tionswerenotlinear,aslowerheterozygosityandhigherinbreeding
werefoundattheextremesofspeciesdistributionlimits.Noeffect
ofe levat iononMLH(R2 = −.02,p = .61)oroninbreedingestimatedas
Fhat(R2 = −.01,p = .41)wasfound.
Wedid notfindprivatealleles in A. aurea ssp. promontorii, and
only one private allele was found in A. aurea va r. capensis, whereas
2781 private alleles were found in A. aurea ssp. aurea.Whensamples
were partitioned according to the mountain range, we found within
each sampled mountain range a considerable number of unique
alleles:290 private alleles were found forsamples from Sierra La
Laguna, and 239 alleles were found for samples from Sierra La
Giganta(Tables S4 and S6).Noprivatealleleswerefoundatthesite
level. These findings aligned with the detected population structure
andgeographypatternsoftheBajaCaliforniaPeninsula.
3.4 | Species distribution modeling
ThefinalensemblemodelshadanAUCof0.94andaTSSof0.99
on average. These scores indicate that our final model had high
accuracy in predicting the A. aureasensuWebbandStarr(2015)
distribution.Theimportanceoftheselectedbioclimaticvariables
varied between thealgorithms (Table S7).Forinstance, themain
variable explaining the distribution of A. aurea sensu Webband
Starr (2015) w as BIO-15 (Pre cipitation sea sonality), with im por-
tance scoresranging from0.18(in the caseofRF)to0.24(inthe
caseofGBM).Thesecondandthirdbestvariablesvariedbetween
models; forthe RF,it was BIO-16(Precipitationof wettest quar-
ter)andBIO-18(Precipitationof warmest quarter), and for GBM,
itwasBIO-10(Meantemperatureofwarmestquarter)andBIO-03
(Isothermality).Nevertheless,thethreemost influentialvariables
FIGURE 4 Populationgeneticstructureofthe98A. aureasamplescollectedintheBajaCaliforniaPeninsula,Mexico,basedon10,765
SNPs.Barplotsoftheindividualassignmentprobabilities(verticalaxis)forthenumberofgeneticclustersfromK = 2(a)toK = 3(b)inferred
usingtheprogramADMIXTURE.Sampleswereclusteredaccordingtosamplingsitesandarrangedfromthesouthernmostsamplingsite
(left)tothenorthernmostsites(right).AboveeachBarplot,theADMIXTUREQ- values represented as pie charts for each sampling site, for
the clustering of K = 2(c)andK = 3(d),plottedonastudyareamap.PopulationcodesasgiveninTable S1.
(a) (b)
(c) (d)
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KLIMOVA et AL .
contributed only 44% (GBM) and 35% (RF) to the explanatory
power of the model, indicating that we may have missed some im-
portant predictors of A . aureadistribution.
The final model revealed that under the current climate, the
areas havi ng suitable an d optimal cond itions for the g rowth of A.
aurea are the majority of the Cape Region, par ticularly the north-
ernendofSierraLaLaguna,aswellasthePacificcoastoftheCape
Region(Figure 7a).Theseresultsarecompatiblewiththerealdistri-
butionofthespecies(Webb&Starr,2015).
In general, the predictions of the ensemble models showed
that ther e would be a decreas e in the habitat suit ability for A .
aureasensuWebbandStarr(2015)underfutureclimaticscenar-
ios.However,therewereconsiderabledifferencesbetweenmod-
elsandSSPsinthepercentageofhabitatchange(Figures 7 and 8).
The ACCESS-ESM1-5 model produced themost catastrophic re-
su l t s , w iththeh i g hesthabi t a t l oss,wh e r e a sMIRO C 6 (SSP245)a n d
MPI-ESM1-2-LR(SSP245)projectedaslightgaininavailablehabi-
tat for A. aureasensuWebbandStarr(2015).Theresultsindicated
FIGURE 5 FineRADstructureanalysisofhaplotypesimilarityamongA. aureaspecimens.Aco-ancestrymatrixwasreconstructed
using10,765SNPs.Colorsindicatethescaleofrelatednessbetweenindividuals,withyellowrepresentinglowrelatednessandblue/black
indicatinghighrelatedness.Coloredboxesoverthephylogramcorrespondtothetwomaingeographicregions(SierraLaLagunainblueand
SierraLaGigantainbrown).SamplesarecodedasgiveninTable S1.
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KLIMOVA et AL.
thatby2061–2080,A. aureasensuWebbandStarr(2015)willun-
dergosignificantrangechangesfromashighas−42.5%underSSP
245(ACCESS-ESM1-5)toa slightgainintherange(+9.7%)under
SSP 245(MIROC6)(Figures 7 and 8). Except for the MPI-ESM1-
2-LRandMIROC6underthemediumpathway(SSP245),allmod-
elsagreedthattherewouldbeareductioninsuitableareasforA.
aureasensuWebbandStarr(2015)from −16.7to−42.5% when
comparedtocurrentlysuitablehabitat(Figures 7 and 8).Theob-
serveddiscrepanciesamongclimatemodelsareexpectedandre-
sult from different initial conditions, different parameterizations of
inter actionsbetweenE arth'sla nd,oce an,cryosphere,atmospher e
systems, anthropogenic activities, and different future emissions
assumptions(Merrifieldetal.,2023;Sandersonetal.,2015).
All models agreed that the areas likely to b ecome unsuitable
in the fut ure include a significant part of Sier ra La Giganta (that
currentlyhas genetically unique populations) and, to some extent,
thePacificcoastoftheCapeRegion(Figures 7 and 8). In contrast,
onlyatinyportionofthecurrentlyunsuitableareaswillbecomein-
creasinglysuitablecomparedtocurrenthabitatsuitability.Theseex-
pandingsuitableareaswouldincludeaportionoftheupperpartof
SierraLaLaguna,indicatingthatthespecieswouldprobablymigrate
upward(Figure 7).
Dur ingtheMid-Holocene(about6k ya),thepredictedgeograph-
ical dis tribution of A. aurea was narrower than the contemporary
distribution, with favorable habitatssituatedprimarily in theCape
Region, especially along the Gulf of California coast (Figure S5).
Furthe r back in time, dur ing the Last G lacial Maxim um (about 22
kya), A. aurea appears to have experienced poor ecological con-
ditions,as BIOMOD2 identified very low habitat suitability in the
studyareaforthisspecies(Figure S5).
FIGURE 6 Spatialdistributionofindividual-baseddiversityofA. aureasamples.(a)Multilocusheterozygosityand(c)Fhat3inbreedingindex.
(b)Relationship(quadraticregression)betweenMLHandlatitude(R2 = .46,p < .0001)and(d)betweenFhat3andlatitude(R2 = .3,p < .003).
(a) (b)
(c) (d)
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KLIMOVA et AL .
4 | DISCUSSION
4.1 | Relationships within A. aurea sensu Webb and
Starr (2015) complex
Thefirstaimofourstudywastoanalyze,usinggenomicmarkers,the
relationships among A. aureasubspecies.Wefound mixedsupport
for currently recognized taxonomic groups, withgenerally shallow
geneticdifferentiationamongmorphologicallyrecognizedvarieties.
For A. aurea var. capensis, there was a disagreement among the anal-
yses we conducted; some(i.e., TESS and PCA) indicated that only
thesamplescollectedattypelocalityCerrodelaZeta(SLL_18)had
unique genetic makeup and were differentiated from other sampling
sites. In co ntrast, AD MIXTURE , finerads tructure , and the NJ tree
groupedothersouthernmostsamplingsites(e.g.,SLL_3andSLL_4)
with A. aurea var. capensis,buteachmethodgroupedadifferentset
of sites. Independent of clustering, the divergence of A. aurea var.
capensis from other varieties was low, with only one private allele.
Thelownumberofprivateallelesmayalsobeexplainedbytherela-
tivelylowsamplesizeforA. aurea va r. capensis.
Furthermore, we found no evidence of divergence between
A. aurea ssp. promontorii and A. aurea ssp. aurea: all methods clus-
tered A. aurea ssp. promontorii with samples collected in and around
SierraLaLaguna.Nevertheless,onlyonepopulationofA. aurea ssp.
promontoriiwassampled,and itisnotimpossiblethatnonsampled
FIGURE 7 Current(a)andfuture(b–g)speciesdistributionmodels(SDMs)andtherespectivespatialshiftsforAgave aurea under
differentclimatechangescenariosandsharedsocioeconomicpathways(SSP245andSSP585).(b,c)SDMforA. aurea under future climate
scenariobasedontheACCESS-ESM1-5modelunderSSP245(b)andSSP585(c)intheyears2060–2080.(d,e)SDMforA. aurea under
futureclimatescenariobasedontheMIROC6modelunderSSP245(d)andSSP585(e)intheyears2060–2080.(f,g)SDMforA. aurea
underfutureclimatescenariobasedontheMPI-ESM1-2-LRmodelunderSSP245(f)andSSP585(g)intheyears2060–2080.Colors
correspondtothehighprobabilityofspeciespresence(orangeandred)tothelowprobability(darkblueandblue).
12 of 19
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KLIMOVA et AL.
individuals located at higher elevations may present a different ge-
neticcomposition.However,samplescollectedatlowerelevations
around the mountain rangeof SierraLaLagunaweremainly found
withinoratthebordersofarroyos(drystreams)andmayrepresent
plants whose seeds were dispersedby water pulses from higher
elevations. Alth ough the seed dispersion me chanisms in A. aurea
are still unknown, water pulses are important for seed dispersion
ofotherBCPplants(suchasBrahea armata)andmayhaveastrong
effect(Wehnckeetal.,2009).
Our data, therefore, lead us to conclude that A. aurea is more likely
to represent several closely related genetic populations than separate
speciesorvarieties/subspecies.Our resultsagreewith the studyon
the Agave deser ticomplex,wherea lowcorrelationbetweencurrent
taxono my and genetic diffe rentiation was foun d (Navarro-Quez ada
et al., 2003). Moreover, our gen etic data alig n with a previous m or-
phological revision of A. aureabyWebbandStarr(2015),whofound
thatthesesubspecies wereverysimilarinvegetative characteristics,
differing primarily in size and propensity to offset or remain soli-
tary.Additionally,thehybridizationbetweensubspeciesseemstobe
common, particularly in the southern part of the distributionrange
(Gentr y,1978).Nevertheless,furtherstudiesthatwouldincludemore
samples of A. aurea ssp. promontoriifromhigherelevationswillbe
neededtodecideonthetaxonomicstatuswithinA. aurea conclusively.
4.2 | Patterns of fine- scale population
genetic structure
Agaves are an intriguing arid- adapted group of species that provide
a unique opportunity to study the influence of multiple potential
factors(i.e.,geologicalandecological)onplantpopulationstruc-
ture and diversification in the heterogeneous environment of the
BCP (Eguia rte et al., 2021; Gentr y,1978; Web b & Starr, 2015).
Nevertheless, only one previous genetic study was carried out
onBCP'sagaves,anditwasmainlyfocusedonunravelingphylo-
genetic relationships within A. deser tispeciescomplex(Navarro-
Quezadaetal.,2003).Here,wegeneratedover10 Kgenome-wide
SNPs for A. aureasensuWebbandStarr(2015), which allowed
usto uncover,in some cases, unexpected patterns of fine-scale
differentiation.
We found evidence for three main genetic groups within
A. aureasensuWebbandStarr(2015), with previ ously unre-
ported genetic separation between the two main mountain
rangesintheregion,i.e.,SierraLaLagunavs.SierraLaGiganta.
Nevertheless, the genetic divergence among the identified
groups was relatively low (FST = 0.03), consistent with the
generally shallow population genetic structure found in other
Agavoideae(Eguiarteetal.,2013).Forexample,Yucca schidig-
erapopulationsintheBCP(FST = 0.067),aswellastheendemic
Yucca capensis(FST = 0.02) (De la Rosa-Conroy et al., 2019;
Luna-Ort iz et al., 2021). Moreover, two subspe cies of Aga ve
cerulatafromthenorthoftheBCPalsoshowedlowgenetic
differentiation (FST = 0.098; Navarro-Quezada et al., 2003), as
did populations of Agave palmeriinArizona(FST = 0.04;Lindsay
et al., 2018),A gave angustifolia, in the Sonora stateofMexico
(FST = 0.076;Klimova,Gutiérrez-Rivera,etal.,2022),andA gave
potatorum in southern Mexico (FST = 0.08; Ruiz-Mondragón
et al., 2023).
The shallow genetic differentiation found within A. aurea
sensuWebb& Starr(1985)coincideswiththeA gave life history:
FIGURE 8 Thepercentageof
distributionrangechangeinAgave aurea
sensuWebbandStarr(2015)under
future climate change in 2060–2080 and
different climate scenarios.
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KLIMOVA et AL .
outcrossingbreedingsystem,longgenerationtime,possibilityof
clonal reproduction, and involvement of long- distance pollinators
(batsandbirds)(Eguiarteetal.,2013, 2021).Apparently,nothing
is known yet a bout seed and p ollen disper sal in A. aurea sensu
WebbandSt arr(2015).Nevertheless,withintherangeofA. aurea,
the nectar-feeding bat Leptonycteris yerbabuenaecanbefound
(Arteagaetal.,2018).Thisbatspeciesisregardedasthemostim-
portant pollinator for the majorit y of Agaves(Fensteretal.,2004;
Flores-Abreu e t al., 2019; Trejo-Salazar etal., 2023), and it i s a
possiblepollinator of A. aurea.Moreover, L. yerbabuenae on the
BCPrepresents onepanmictic population (Arteaga et al.,2018),
suggesting that individuals can move over long distances, carr y-
ing polle n, homogenizing p opulations, a nd reducing the e ffects
of genetic drift and selection in agaves. Nevertheless, studies on
pollenandseeddispersalinagavesonBCPwillbeneededtoun-
derstandbetterthedrivers behindtheobservedpopulation ge-
netic structure.
Interestingly, several animal species display a genetic split
roughlynorthofLaPazcity(Dolbyetal.,2015; Riddle et al., 2000),
similar to the one found in A. aureasensuWebbandStarr(2015).
Thisgeneticsplithasbeenexplainedbyoneofthemajorvicariance
eventsontheBCP,thetemporary isolationofsouthernBaja(Cape
Region)fromtherestofthePeninsula,owingtooceanicinundation
oftheIsthmusofLaPazca.3 Ma(Riddleetal.,2000).Nevertheless,
the distribution patterns of many plant species do not agree with
thishypothesis(Arteagaetal.,2020;Garricketal.,2009;Gutiérrez-
Flores et al., 2016; Klimova e t al., 2018), sugges ting more recent
ecologicaleventsrelatedtoQuaternaryclimatefluctuations(Araya-
Donoso et al., 2022).Due to the low divergence between A. aurea
sensuWebbandStar r(2015)oneachmountainrange,wearguethat
thesplitisunlikelytohavebeencausedbythemillion-year-oldinun-
dationoftheIsthmusofLaPaz.
The divergence in A. aureasensuWebbandStarr(2015)may
have resulted from more recent climatic conditions on and around
each mountain range. Based on biotic characteristics, Sierra La
GigantaandtheCapeRegion(i.e.,SierraLaLagunaandsurround-
ing areas) are considered two different ecoregions (De La Luz
et al., 2008;González-Abrahametal.,2010),eachwithcharacter-
istic flora and climatic conditions.Moreover,Sierra La Gigantais
surroundedbyextremelyaridsandylow-elevationdesertareasof
theMagdalenaPlains,whichmay actas abarriertothedispersal
ofgenesandfortheestablishmentofseedlings.PopulationsofA.
aureasensuWebbandStarr(2015)inSierraLaGigantaarescat-
teredand canonlybe foundathilltops or neararroyos (Author's
observation), which may further preclude connectivity among
geographic regions. We alsofou nda signific antrelationship be-
tweengeneticdistanceandtemperature(particularlytemperature
seasonality), which suggests divergence among samples on each
mountain range and local adaptation. Further studies should delve
into the genomic divergence among the A. aurea populations and
searchfortheparticularlocithatmaycontributetotheobserved
differentiation pattern.
4.3 | Genomic diversity and possible route of
range expansion
Quaternary glacial–interglacial climate cycles with significant
temperature and precipitation changes have resulted in species
distributionshiftsacrosstheglobe(Hewitt,2000, 2004).Thisises-
peciallytrue forplants, giventhat their distributions, phenologies,
and physio logical tolera nces can be stron gly tied to precipi tation
or the frequency and severi ty of winter frost s (McAuliffe & Van
Devender, 1998; Van Devender, 2021).
InBCP,adramaticchangeinfloralcompositionhappenedsince
theLastGlacialMaximum(LGM;ca.21kya)(Butterfieldetal.,20 19;
Dolby et al.,2015; Van Devender, 1977 ), when a cooler and wet-
terenvironmentbegantotransitiontowarmeranddrierconditions,
and species once wid espread in the lowlands followed favorable
habitat , moved up in elevatio n and latitude, or sh eltered in scat-
tered oases (Butterfield et al., 2019; Grismer & McGuire, 1993;
Klimovaetal.,2017;McAuliffe&VanDevender,1998).Novelarid-
adapted communities replaced mesic woodland vegetation (Van
Devender, 1977 ). Mid-Holocene range shifts of Sonoran Desert
communitiesrecognizable from plant macrofossils in packratmid-
dens (Van Devender etal., 1994), and genetic dataofdesert plant
species p rovide stron g evidence of sout hward and nor thward ex-
pansion from refugia(Clark-Tapia & Molina-Freaner, 2003; De La
Rosa- Conroy et al., 2019;Garricketal.,2009; Nason et al., 2002).
Range exp ansions are usually d escribed by found er effects ,
whereafewindividuals(thefounders)leaveasourcepopulation,
colonizeanewneighboringarea,expand,andsendfurtherfound-
ers. This repeated process leads to reduced genetic diversity along
theexpansionaxis(Austerlitzetal.,1997;Slatkin&Excoffier,2012).
OurSNPdata forA. aureasensuWebbandStarr(2015)suppor t
theassumptionthatrangeexpansionhasplayedanimportantrole
inshapingspatialpatternsofintraspecificdiversity.However,the
northward expansion along the BCPinferred for two columnar
cacti(Clark-Tapia&Molina-Freaner,2003; Nason et al., 2002)was
not seen in A. aureasensuWebbandStarr(2015),nordidweob-
servetwoexpansionevent sfromdi fferentr efugia,aswasinfer red
for the desert Euphorbia lomelii(Garricketal.,2009).Thedecrease
indiversitywithincreasinganddecreasinglatitudesuggestsboth
southwardandnorthwardexpansionsofA. aureasensuWebband
Starr (2015) f rom a single refugium , located presuma bly in the
northern par t of the Cape Region, an area with high genetic diver-
sity,lowinbreeding,andsuitableecologicalconditionsaccording
to SDM. Th e best model exp laining diversi ty distribu tion (MLH
and inbreeding index) in A. aureasensuWebbandStarr(2015)
was a quadratic model with the highest diversit y and the lowest
inbreedingconcentratedapproximatelybetweenlatitudes24and
25. From there, the diversity steadily decreased toward the north
and south.
Furthersupportfortheabovescenariocomesfromthespecies
distributionmodelinganalysis.Theobservedreductioningeneticdi-
versityislocatedwithinanareawhereavailablesuitablehabitathas
14 of 19
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KLIMOVA et AL.
increased since the LGM (22 ka) andMid-Holocene (~ 6kya). Both
linesofevidence,geneticandclimatic,suggestarecentbidirectional
rangeexpansionofA. aurea.
4.4 | Climatic future for A. aurea sensu Webb and
Starr (2015) and conservation implications
Climate change is expectedto shift plantdistributionasspecies
expand to newlyfavorableareasanddeclineinincreasinglyhos-
tile locations. Ecosystemswhose functioning is mainly drivenby
precipitationshouldbe particularlyvulnerabletoclimatechange
(Tompkins&Adger,2004).Aridregionsrepresentthebestexam-
pleof highlyvulnerable ecosystemsbecause warming may drive
plant species to their physiological limits, and a decrease in pre-
cipitationwillaggravatesucheffects.Indeed,globalassessments
have ranked deserts and semideserts at the forefront of vulner-
abilitytoglobalclimatechange(Salaetal.,2000;Mirzabaevetal.,
2019).
Although authors like Tielbörger and Salguero-Gómez (2013)
argue that adaptations to lack of water and high temperatures com-
monly found in desert plants may result in the resilience of dryland
species to climate change, the current evidence of the Sonoran
Desertvegetationistellingacontrastingstory(Hantsonetal.,2021).
AsignificantdeclineintheNormalizedDifferenceVegetationIndex
(NDVI),vegetationcover,communitychanges,andspeciesdistribu-
tionshiftshavebeenobserved,withthemoststrikingchangesbeing
recordedinthelowlanddesertarea(Hantsonetal.,2021;Madsen-
Hepp et al., 2023). Moreover,other drivers of global change, such
asovergrazingbyfree-roaminglivestock,mismanagementpractices
in agriculture, and man- induced desertification, continuously in-
creasethepressureonaridecosystemsandmayleadtoirreversible
degradation(Carbonietal.,2023;Oswald&Harris,2023; Reynolds
et al., 20 07; Thornton et al., 2009).
Underalmostallclimatechangescenariosanalyzed,thesuitable
habitat forA. aureasensuWebbandStarr(2015)isexpectedtobe
reduced; this trend is particularlynotable under the high-end SSP
585. There we re exceptions in SDM re lated to particu lar models
(MPI-ESM1-2-LRandMIROC6)andtherelativelyoptimisticSSP245,
where A. aureawe r e s lightlygainin g n e w h a b it a t(~8%).Nevertheless,
wepredicted that, on average, by 2070, A. aurea would lose over
20%ofitscurrentlyavailablehabitat.Ourresultsareconsistentwith
the proposed hypothesis that warming temperatures and increased
water limitation negatively affect desert-adapted species (Bombi
et al., 2021;Hantsonetal.,2021;Vale&Brito,2015).
Moreover, climatechange isalso predicted to alterplant–plant
and plant–pollinator interactions, which are essential for agaves
(Bloisetal.,2013; Creech et al., 2023;Gómez-Ruiz&Lacher,2019).
Nurse plants are crucial for the establishment of agave species
(Rangel-Landa et al., 2015), with germination, growth, and sur-
vival pos itively affe cted by the pre sence of a nurse pl ant (Franco
&Nobel, 1988; Rangel- Landa et al., 2015).Meanwhile, thedisrup-
tion of plant–pollinator interactions may have a negative effect on
the sexu al reproduct ion, genetic var iability, and diff erentiation of
A. aureasensuWebbandStarr(2015),increasing its vulnerability
(Gómez-Ruiz&Lacher,2019).
Currently,agavesexperienceadiverserangeofthreats.Inmany
areas,thepredominantdangeristhedirecthumanextractionofwild
agavesusedasrawmaterialforalcoholicbeverage(mezcal)produc-
tion.Moreover,habitat degradation,landusechange, and agricul-
ture are con siderable th reats to agaves , affecti ng species in lar ge
partsofMexico(Delgado-Lemusetal.,2 014; Tetreault et al., 2021;
Valiente- Banuet, 2023).Onthe BCP,due to historically low popu-
lation density, agaves used to enjoy relatively low anthropogenic
pressure. Nevertheless, our results showed that future climates of
hotter and more arid conditions would not appear to favor Agave
aureasensuWebbandStarr(2015)asaconsiderablepartofth espe-
cies’currentlyfavorablehabitatisprojectedtodisappear.
Our study provides the first report on the population genom-
icsandspeciesdistribution modelinginformation inA. aurea sensu
Webb and Starr (2015), which may be used in conservation and
management.Wepropose toconsiderthe threeidentifiedgenetic
groups as separated genetic units or management units: the south-
ernmostpopulations,theplantsfromSierraLaGiganta,andplants
from the Cape Regiondistributed onandaroundSierraLa Laguna
(Moritz, 2004). This information is particularly important for the
southernmost and northernmost populations. First, these groups
have lower geneticdiver sity and increased inbreeding. Moreover,
the southernmost populations are under the heaviest anthropo-
genicimpact,asthey are located in an areaof fasturban develop-
ment. On the other hand, the northernmost populations are less
abundantand,basedonourdata,arevulnerabletoclimatechange.
Considering how lit tle is known about A . aurea sensu Webb and
Starr (2015), c onservati on actions are u rgently need ed to protect
thisspecies.Additionally,moreresearchisnecessarytounderstand
isolationbarriersandfactorsgoverningthisspecies'genomicstruc-
tureanddiversity.Forexample,anoutlieranalysismaypointtothe
genomic regions involved in the observed pattern of geographic
structuringandisolation-by-environment(IBE).
AUTHOR CONTRIBUTIONS
Anastasia Klimova:Conceptualization(equal);datacuration(equal);
formal analysis (lead); investigation (equal); methodology (equal);
software(equal);visualization(equal);writing–originaldraft(lead);
writing – r eview and editi ng (equal). Jesús Gutíerrez- Rivera: Data
curation (equal); methodology (equal); resources (equal); writing
– review and editing (equal). Alfredo Ortega- Rubio: Funding ac-
quisition(supporting); resources (equal); validation (equal); writing
– review and editing (equal). Luis E. Eguiarte: Conceptualization
(equal); funding acquisition (lead); project administration (equal);
resources(equal);supervision(equal);visualization(equal);writing–
reviewandediting(equal).
ACKNOWLEDGMENTS
TheauthorsaregratefultoAlfonsoMedelNarváezfromCentrode
InvestigacionesBiológicasdelNoroeste(CIBNOR)forcontributingto
|
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KLIMOVA et AL .
thecollectionoftheagavesamples.WethankErikaAguirre-Planter
for logistic support in processing and sequencing the samples.
FUNDING INFORMATION
ThisworkwasfundedinpartbyprojectPAPIITIG200122,UNAM,
toLuisE.EguiarteandRafaelLiraandbytheoperativebudgetofthe
InstitutodeEcología,UNAM.
CONFLICT OF INTEREST STATEMENT
None declared.
DATA AVAIL AB ILI T Y STAT E MEN T
All of the genotypes are available from Dryad (DOI: 10 .50 61/
dryad.0cfxpnw8t). Private access to download the data files URL:
h t t p s : / / d a t a d r y a d . o r g / s t a s h / s h a r e / C z a R Q Z l C 9 y C x l _ 7 M F J M o 2 W I l
a O u Z h 9 x 2 r r d P A P q 8 a A g .
BENEFIT SHARING
Benefits from this research accrue from the sharing of our data and
resultsonpublicdatabasesasdescribedabove.
ORCID
Anastasia Klimova https://orcid.org/0000-0002-1502-2910
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SUPPORTING INFORMATION
Additional supporting information can be found online in the
SupportingInformationsectionattheendofthisarticle.
How to cite this article: Klimova,A.,Gutíerrez-Rivera,J.,
Ortega-Rubio,A.,&Eguiarte,L.E.(2024).Populationgenomics
anddistributionmodelingrevealedthehistoryandsuggested
apossiblefutureoftheendemicAgave aurea(Asparagaceae)
complexintheBajaCaliforniaPeninsula.Ecology and Evolution,
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