Molecular Ecology (2008) 17, 3147–3159 doi: 10.1111/j.1365-294X.2008.03817.x

© 2008 The AuthorsJournal compilation © 2008 Blackwell Publishing Ltd

Blackwell Publishing LtdThe history of Seasonally Dry Tropical Forests in eastern South America: inferences from the genetic structure of the tree Astronium urundeuva (Anacardiaceae)

S. CAETANO,* D. PR ADO,† R . T. PENNINGTON,‡ S . BECK,§ A. OLIVEIRA-FILHO,¶ R . SPICHIGER*and Y. NACIRI**Laboratoire de Systématique et de Biodiversité, Unité de Phylogénie et Génétique Moléculaires, Conservatoire et Jardin botaniques, 1 Chemin de l’Impératrice, CP 60, CH-1292 Chambésy, Genève, Switzerland, †Cátedra de Botánica Morfológica y Sistemática, Facultad de Ciencias Agrarias, UNR, Casilla de Correo No. 14, S2125ZAA, Zavalla, Argentina, ‡Royal Botanic Garden Edinburgh, 20a Inverleith Row, Edinburgh EH3 5LR, UK, §Herbario Nacional de Bolivia, P.O. BOX 10077, Correo Central La Paz, Bolivia, ¶Departamento de Ciências Florestais, Universidade de Lavras, 37200-000, Lavras, MG, Brazil

Abstract

Today, the Seasonally Dry Tropical Forests (SDTF) of eastern South America occur as large,well-defined nuclei (e.g. Caatinga in the northeast) and as smaller enclaves within othervegetations (e.g. Cerrado and Chaco). In order to infer the way the present SDTF distributionwas attained, the genetic structure of Astronium urundeuva, a tree confined to SDTF, wasassessed using two chloroplast spacers and nine microsatellite loci. Five haplotypes wereidentified, whose distribution was spatially structured. The distribution of the two mostcommon and divergent haplotypes suggested former vicariance and progressive divergencedue to isolation. More recent range expansions of these two lineages subsequently occurred,leading to a secondary contact at the southern limit of the Caatinga SDTF nucleus. Themultilocus-Bayesian approach using microsatellites consistently identified three groups ofpopulations (Northeast, Central and Southwest). Isolation by distance was found in Northeastand Southwest groups whereas admixture was detected in the Central group, located at thetransition between Caatinga and Cerrado domains. All together, the results support theexistence of range expansions and secondary contact in the Central group. This study providesarguments that favour the existence of a previously more continuous formation of SDTF ineastern South America.

Keywords: chloroplast spacers, genetic boundaries, isolation by distance, microsatellites,Pleistocenic Arc, secondary contact, spatial analyses, vicariance events

Received 21 October 2007; revision received 24 February 2008; accepted 20 March 2008

Introduction

The present-day distribution of a taxon is both the outcomeof habitat preferences and colonization history. The wayspecies reacted to past climatic changes, such as thoseexperienced in the Pleistocene, reflects the dynamics ofecosystems in a long-term perspective (Schaal et al. 1998).In this context, exploring the genetic patterns of trees is ofgreat interest, because of the general attributes that makethem unique in terms of mode and tempo of evolution (see

review in Petit & Hampe 2006). How Pleistocenic climatefluctuations influenced different biomes in South Americais still poorly understood. Nearly all available data arebased on fossil pollen in the Amazon Basin (e.g. Colinvauxet al. 1996), and the few existing studies of molecularphylogeography of trees have focused mainly on rain-forestspecies (Caron et al. 2000; Cavers et al. 2003; Dutech et al.2003), or on species from the seasonally dry savannaformations of the Brazilian Cerrado (Lacerda et al. 2001;Collevatti et al. 2003; Ramos et al. 2007). Here, we willaddress the question of whether the Seasonally DryTropical Forests (SDTF) of eastern South America mayhave been more widespread during drier glacial climates,

Correspondence: Y. Naciri, Fax: +41 22 4185101; E-mail: yamama.naciri@ville-ge.ch

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as suggested by Prado & Gibbs (1993) and Pennington et al.(2000). We present the first range-wide phylogeographicalstudy for a SDTF tree, Astronium urundeuva (Allemão)Engl. [referred to by some authors (e.g. Santin & Leitão-Filho 1991) as Myracrodruon urundeuva Allemão], based ontwo chloroplast intergenic spacers and nine nuclearmicrosatellites.

In South America, SDTF form a long curve around theAmazon Basin (Fig. 1), with the extremities positionedat the Caatinga domain in northeastern Brazil and at theCaribbean coasts of Colombia and Venezuela. In between,these forests occur as isolated nuclei at the periphery of theChaco domain, in the Andean piedmont, in the inter-Andeanvalleys and in the Pacific coast (Pennington et al. 2000;Prado 2000). Moreover, they also occur as enclaves withinboth Cerrado (a savanna biome that covers the centralBrazilian plateau; Furley & Ratter 1988) and Chaco (an areaof woodlands and xeromorphic forests that occur on theless well-drained soils of Paraguay, Argentina and Bolivia;Spichiger et al. 1995) domains. Prado & Gibbs (1993)hypothesized the existence of a ‘Pleistocenic Arc’ to explainthe widespread distributions of a high number of treespecies occurring in several of the disjunct SDTF areas. Thisarc was defined as a more continuous expanse of SDTF that

stretched from the Brazilian Caatinga to the SDTF of theChaco domain, during the last glacial maximum (LGM),also possibly reaching the dry inter-Andean valleys ofBolivia, Peru and Ecuador (Prado & Gibbs 1993). Penningtonet al. (2000) furthermore suggested that SDTF species mayhave penetrated the Amazon basin during glacial periods.According to these authors, the currently scattered distri-bution of SDTF species in South America resulted from thefragmentation of this widespread SDTF formation, duringthe current wet interglacial. The alternative explanation ofrare and long-distance dispersal events (Gentry 1982;Naciri et al. 2006) has also been invoked to explain suchwide continental distributions of Neotropical SDTF species,especially by those who criticized the ‘Pleistocenic Arc’hypothesis (e.g. Mayle et al. 2004).

SDTF occur where the rainfall is less than 1600 mm/year(Gentry 1995). They are essentially tree-dominated witha continuous or almost continuous canopy, and a groundlayer in which grasses constitute a minor element (Mooneyet al. 1995). During the dry season the vegetation is semide-ciduous or deciduous, and more than 50% of the arborealcover can be lost (Murphy & Lugo 1986). Because SDTFoccupy highly favourable soils for agriculture, massivedestruction of this vegetation has been reported in SouthAmerica. This problem is further increased by the pres-ence of timber species of high commercial value, such asA. urundeuva (Santin 1989; IBAMA 1992), which contributesto SDTF being the most threatened tropical ecosystem(Prance 2006).

Testing the hypothesis of a previously more widespreadSDTF formation has received increasing attention fromecologists attempting to elucidate patterns of historicalvegetation changes in South America (Pennington et al.2000, 2004; Linares-Palomino et al. 2003; Bridgewater et al.2004; Spichiger et al. 2004; Oliveira-Filho et al. 2006; Queiroz2006). These studies are, for the most part, based on theassessment of floristic links among species assemblages ofSDTF areas. Here, we test the ‘Pleistocenic Arc’ hypothesisby exploring the chloroplast and nuclear genetic structureswithin A. urundeuva across its geographical range. Inagreement with phylogenetic studies that demonstratepre-Pleistocene origin for many SDTF species from diversegenera (Loxopterygium, Ruprechtia, Coursetia, Poissonia,Chaetocalyx and Nissolia; Pennington et al. 2004), we assumethat A. urundeuva is a relatively old species, probably pre-dating the Pleistocene. Moreover, according to severalauthors, the potential centre of the species origin is Brazil(e.g. Santin & Leitão-Filho 1991). Herein, we test twohypotheses of how the species may have attained its currentdistribution:

1 The species expanded its range by stepping-stone migra-tion through rare and isolated long-distance dispersalevents, via scattered SDTF patches within the Cerrado

Fig. 1 Schematic distributions of the Seasonally Dry TropicalForests (SDTF), the Cerrado and the Chaco in South America. Forthe SDTF, the different nuclei are numbered: 1, Caatinga; 2,Misiones; 3, Chiquitano; 4, Piedmont; 5, Bolivian, Peruvian andEcuadorean inter-Andean valleys; 6, Pacific coast of Peru andEcuador and the Galapagos Islands; 7, Caribbean coast ofColombia and Venezuela and the Dutch Antilles Islands (adaptedfrom Pennington et al. 2000).

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and the SDTF nuclei in the Chaco peripheral areas. Thispredicts strong genetic signs of founder events associ-ated with a very patchy distribution of the geneticvariability.

2 The species expanded through a more continuous SDTFformation — the Pleistocenic Arc of Prado & Gibbs (1993)— which predicts a more diffuse genetic structure, withthe same variability patterns being distributed overlarger distances.

In order to test these alternative hypotheses, we describethe distribution of the species’ genetic variability andevaluate its genetic structure. We also attempt to reconstructthe route of colonization and identify centres of geneticvariability.

Materials and methods

Species and study area

Astronium urundeuva (Anacardiaceae) is a tree speciesconfined to SDTF formations in eastern South America(Prado & Gibbs 1993), which vary from humid, denseformations to drier and more open areas (Muñoz 1990). Itreaches up to 1800 m into the Andes (Vargas-Salazar 1993).Although some monoecious individual trees can occasionallyoccur, the species is generally dioecious and hence typicallyoutcrossing (Freitas et al. 2004 and references herein). It is adeciduous, insect-pollinated species, and its small seedsare dispersed by wind (Muñoz 1990; Allen 1991).

Eastern South America is characterized by a ‘diagonal’of dry formations that includes three domains that interactin notoriously complicated relationships (Oliveira-Filhoet al. 2006): the Caatinga (northeast Brazil), the Cerrado(central Brazil) and the Chaco (northeast Argentina, westParaguay and south Bolivia). Forest from the Caatingadomain comprises one of the largest SDTF areas, in contrastwith the vegetation of the Cerrado and Chaco domains thatare floristically different from SDTF. However, within theCerrado and Chaco domains, small SDTF patches occur inareas where soils are different, or on well-drained levees ofgallery forests (Prado 1993a, b; Spichiger et al. 1995; Oliveira-Filho et al. 2006). Moreover, the peripheral areas of theChaco domain have been identified as important SDTFnuclei (Prado 2000). Here, we use the term ‘domain’ (e.g.Cerrado domain) to refer to the geographical area, and theterm ‘vegetation’ (e.g. chaco vegetation) to refer to thespecific vegetation types.

Nearly the whole range of A. urundeuva was coveredby sampling 53 natural populations in Argentina (nine),Bolivia (six), Brazil (22) and Paraguay (16). Because eachcountry required separate collecting and exporting permis-sions, plant specimens were collected by different collectorson several expeditions, from 2001 to 2005. Consequently, the

Brazilian populations were chosen according to a simplerandom scheme, whereas a clustered random design wasapplied to the others (Storfer et al. 2007). Geographicaldistances among populations ranged from 9 to 3798 km,and within each population adult individuals were selectedwithin a maximum range of 2 km. The nuclear geneticdiversity was estimated using 1048 individuals, and thechloroplast diversity was characterized through thesequencing of 379 of these individuals (Table S1, Supple-mentary material).

Chloroplast analyses

Genomic DNA was extracted using the DNeasy Plant Kit(Qiagen). The chloroplast data were based on the sequencingof two intergenic spacers: trnH-psbA (HA) and trnS-trnG(SG; Hamilton 1999a). Polymerase chain reactions (PCRs)were conducted in a 25 μL volume with 0.5 U Taq (ABgene),0.2 mm MgCl2, 0.4 mm of each primer, 0.2 mm of eachdNTP (QBiogene), and 1 μL template DNA of un knownconcentration, and following the cycling parametersproposed by Hamilton (1999a). Products were purified usingPrep-A-Gene kit and sequenced in an ABI 377 automatedsequencer, using Big Dye Terminator version 3.1 CycleSequencing Kit (Applied Biosystems). PCR repeatabilitywas checked for all individuals displaying poly A/Tvariants. Haplotype sequences were deposited in GenBankunder the accession nos EF513743–EF513748.

Nucleotide sequences of the chloroplast spacers werealigned using Clustal W (Thompson et al. 1994) implementedin bioedit program (Hall 1999) and revised manually.network software (Bandelt et al. 1999) was used to constructa median-joining network of the combined haplotypes.Both insertion–deletion events (indels) and microsatelliteswere taken into account, as they have been shown to providerelevant phylogeographical information (Walter & Epperson2001, 2005; Hamilton et al. 2003; Ingvarsson et al. 2003;Rendell & Ennos 2003). Geographical distribution of thechloroplast diversity was visualized by constructing ahaplotype map using arcmap GIS (Environmental SystemsResearch Institute).

Microsatellites analyses

Nuclear genetic variation was examined using nine micro-satellite loci. Six of these loci were previously described(Caetano et al. 2005), and the remaining were only optimizedfor multiplexing (Auru.A316, Auru.A361 and Auru.H207).Reactions were conducted in a 5 μL volume containing0.3 μL template DNA, 2.5 μL of multiplex PCR mix (Qiagen)and 0.5 μL of a primer mix corresponding to each multiplex.The following primer combinations containing 2 μm ofeach primer were used: MxI: Auru.A316, Auru.B209 andAuru.D167; MxII: Auru.A361, Auru.D094, Auru.D282 and

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Auru.H207; MxIII: Auru.A392 and Auru.E062. Cyclingconditions consisted of an initial denaturing step of15 min at 95 °C, followed by 35 cycles of 30 s at 94 °C, 90 sat 55 °C and 60 s at 72 °C, followed by a final elongationstep at 60 °C for 30min. Fluorescent PCR products wereanalyzed on an ABI377 automated sequencer, usingGenescan-400 Rox as individual size standard, andgenescan software (Applied Biosystems). Wheneverno allele was seen at a particular locus, the template wasre-amplified separately.

Allele frequencies were estimated using the Expectation-Maximization (EM) algorithm implemented in arlequinsoftware (Excoffier et al. 2005), which allows estimationof the frequency of null alleles. Linkage disequilibriumamong the loci was tested by means of 10 000 iterations andusing arlequin. A locus-by-locus analysis of variance(amova; Excoffier et al. 1992) was performed on all popula-tions, where the FST corresponds exactly to the parameter3W defined by Weir & Cockerham (1984).

Hierarchical statistical analyses

In order to describe the way genetic variability withinA. urundeuva is distributed, a hierarchical approach wasemployed that considered three levels of organization:(i) continental (differentiation among groups); (ii) regional(differentiation among populations within groups); and(iii) population (diversity within populations).

The way populations were grouped was first investigatedwith a fully Bayesian clustering approach, based on themultilocus microsatellite dataset. structure software(Pritchard et al. 2000) uses a Markov Chain Monte Carlo(MCMC) algorithm to cluster individuals into K panmiticgroups, by minimizing deviations from Hardy–Weinbergand linkage disequilibria. Ten independent runs for eachvalue of K (ranging from one to 12) were performed,assuming the admixture model with correlated allelefrequencies, and using burn-in and MCMC lengths of10 000 iterations each. Longer burn-in and MCMC iterationsdid not significantly change the results. The ΔK statisticwas calculated (Evanno et al. 2005), and samples wereplaced into the cluster for which they showed the highestpercentage of membership (q), averaging q over 10 runs. Toexplore whether substructure could be detected withineach main cluster, group datasets were analyzed inde-pendently. An analysis was performed at the continentallevel with barrier software (Manni et al. 2004) to locatepotential barriers to gene flow. The procedure starts byconnecting populations into a geometric network using aDelaunay triangulation, which allows the derivation of theVoronoï tessellation. Each edge is then associated with theestimated genetic distance (in this case pairwise FST), andMonmonier’s maximum difference algorithm identifiesthe zones where the genetic differences are the largest. The

genetic information is then overlaid onto a vegetationdistribution map. The way differences in sampling sizeinfluence the genetic structure was explored by performingboth analyses with the total dataset (53 populations, 1048individuals) and with a homogeneous dataset (45 popula-tions; 896 individuals) where populations with less than 18individuals were discarded and the ones having morethan 22 individuals (Pa_SL1, Pa_Mb1 and Pa_CLe) wererandomly re-sampled for sampling sizes ranging between20 and 22. Differentiation among the identified groupswas then quantified with amova, where we assessed thecomponent of genetic diversity attributable to the differencesamong groups.

The traditional pairwise FST was computed for bothchloroplast and microsatellites using arlequin, and theircorrelation with spatial distances in kilometres (rM-km)and their natural logarithms (rM-log) was estimated bymeans of a Mantel procedure, and tested with 9999 permu-tations in genalex software (Peakall & Smouse 2006). Formicrosatellites, rM-km and rM-log were estimated followingRousset (1997), using the linearized genetic distance FST/(1−FST) (Slatkin 1995). Isolation by distance (IBD) withineach group was additionally investigated for microsatelliteswith spatial autocorrelations, by calculating for each distanceclass, rS (Smouse & Peakall 1999), a correlation coefficientclosely related to Moran’s I, implemented in genalex. Thenumber of distance classes and their upper limits werechosen according to the geographical ranges of the groupunder examination, and 9999 permutations were used toset the upper and lower confidence limits at the 95% level.The patch size corresponds to the point at which the curvefirst crosses the x-axis (Sokal & Wartenberg 1983).

Within-population diversities were measured for thechloroplast data as the number of haplotypes, nucleotidediversity (π; Tajima 1983) and gene diversity (h; Nei 1987),computed using arlequin. Moreover, the observednumber of microsatellite alleles (NA), observed heterozy-gosity (HO) and expected heterozygosity (HE) corrected forsmall samples (Nei 1987) per locus and population wereestimated using arlequin. FIS per locus and populationwas estimated using fstat (Goudet 2001) as well as theallelic richness (RS), where n was set to 18, by discardingthe eight populations with less than 18 individuals(Pa_SL3:14; Pa_PuE:16; Pa_Alt:14; Pa_CMb:10; Bo_SJ1:16;Bo_SJ2:11; Br_Lim:12; Br_Pom:14). For each population,signs of bottleneck were investigated with the two-tailsWilcoxon signed-rank test, implemented in bottleneck soft-ware (Cornuet & Luikart 1996). Because most microsatellitedatasets better fit the two phase model (TPM) (di Rienzoet al. 1994), this model was selected with a small percentageof multistep changes (5–10%; σ2 = 30).

For multiple tests, the significance level was adjusted witha modified false discovery rate (FDR) method (Benjamini& Yekutieli 2001):

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k being the number of tests performed.

Results

Molecular diversity

Three haplotypes were detected at each chloroplast locus(Table 1), which resulted in five combined haplotypes,characterized by two indels of seven and six bp, fivenucleotide substitutions and one mononucleotide repeatdisplaying three variants. Haplotypes were not randomlydistributed in space (Fig. 2): the most frequent haplotype,AA, was reported in Bolivia, central and north Paraguayand in the three westernmost Brazilian populations (over arange of 1800 km), whereas the most divergent haplotype,AC, was present in all other Brazilian populations (1727 km).Haplotypes AB and BA both differed by a single indel fromhaplotype AA and were geographically more restricted,found in Argentina and Paraguay, respectively. The raresthaplotype CA was only found in one Paraguayan population(Pa_SL1).

All nine microsatellite loci were polymorphic (Table 2),although low polymorphism was reported at Auru.D167(four alleles), Auru.D094 and Auru.E062 (seven alleles each).Conversely, the most variable loci were Auru.A361 (33 alleles),Auru.D282 (32 alleles) and Auru.B209 (29 alleles). Significantlinkage disequilibrium was found for 79 tests (4.1%)after FDR correction (P < 0.006). The highly variable lociAuru.A361, Auru.D282 and Auru.B209 were involved in85% of the cases. It was therefore concluded that deviationswere produced by statistical bias due to insufficient averagesampling size and not by physical linkage between particularloci. Loci Auru.D094 and Auru.E062 displayed significantnegative FIS values at the population level (–0.16 and –0.21,respectively), while the highest positive values were foundat loci Auru.A316 and Auru.H207 (respectively, 0.37 and0.25; P < 0.001). The estimated frequency of null alleles wasvery high and highly significant at these two loci (averaged

overall populations: 0.18 ± 0.11 and 0.10 ± 0.08, respectively),and the observation of 26 null homozygotes at Auru.A316was a further evidence of the presence of such null alleles.The other two loci also affected by the presence of nullhomozygotes (Auru.A361: 4 and Auru.D167: 1) had muchlower but still significant frequencies of null alleles at the1% level (0.05 ± 0.05 and 0.06 ± 0.09, respectively; Table S2,Supplementary material). Because the presence of null allelescan substantially bias the genetic structure, a second analysisfor the clustering of populations was performed with sevenloci, omitting Auru.A316 and Auru.H207. Since outlier FSTvalues can also be indicative of balancing or spatiallyheterogeneous selection, which may consequently disruptthe genetic patterns observed (Beaumont & Nichols 1996),the FST distribution was analyzed. According to a non-parametric Kolmogorov–Smirnov one-sample test, FST valueswere normally distributed (P = 0.95), which allowed us todefine a classical 95% confidence interval (0.059–0.186).Three loci presented outlier FST values (Auru.A316,Auru.D167 and Auru.E062) and were removed in somesubsequent analysis.

Clustering of populations and differentiation of groups

The most likely number of clusters obtained on micro-satellites with structure for both biased and unbiaseddatasets was two, even when the loci outlined above wereexcluded (Auru.H207, Auru.A316, Auru.D167 and Auru.E062). Plotting the assignment probabilities showed thatall Argentinean, Bolivian and Paraguayan individualswere clustered together, whilst the Brazilian ones wereassigned to the other cluster. All the separate analyses ofthe Brazilian cluster resulted in a clear subdivision, withthe 10 northeasternmost populations being clusteredtogether. Assigning populations Br_Jav and Br_Boq to oneor the other subgroups depended, however, on the loci usedin the analyses. The subdivision within the Argentina–Bolivia–Paraguay (Ag-Bo-Pa) cluster was much moreinconsistent among analyses, with different number ofclusters found, depending on the dataset and the loci used.Accordingly, the ΔK within Argentina–Bolivia–Paraguay

α ( / )11

ii

k

=∑ ,

Table 1 Characterization of haplotypes found for Astronium urundeuva with trnH-psbA and trnS-trnG spacers

Haplotype

trn H-psb A (593bp) trn S-trn G (656bp)

GenBank Accession No. ID 36 S 341 GenBank Accession No. S 108 ID 253 ID 254 S 480 S 573 ID 576 S 601

AA EF513743 — T EF513746 C A — G A — GAB EF513743 — T EF513747 C A A G A — GAC EF513743 — T EF513748 A — — A C G TBA EF513744 C T EF513746 C A — G A — GCA EF513745 C G EF513746 C A — G A — G

Mutations are coded with S for substitution and ID for indel; positions are numbered from the end of the trnH and trnS primers.

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Fig. 2 Geographical distribution of the chloroplast haplotypes found within the 53 populations of Astronium urundeuva in eastern SouthAmerica. Each circle corresponds to one population and different colours were assigned to each haplotype: AA in black, AB dark grey,BA light grey, CA with black dots and AC in white. The median joining network is also included, in which figures the indels (in italics)and substitutions (underlined). Names starting with HA and SG refer, respectively, to mutations found within trnH-psbA and trnS-trnGspacers. The different dry formations are represented by different colours and motifs (modified from Pennington et al. 2000; Prado 2000;Queiroz 2006; Ratter et al. 2006; Spichiger et al. 2006).

Table 2 Patterns of variation at nine microsatellite loci used in the genetic analysis of the 53 populations and 1048 individuals of Astroniumurundeuva

Locus GenBank Accession No. NA Allele Size Null Allele HO HE FIS FST

Auru. A316 AY640259 16 290–320 0.181 ± 0.112 0.422 0.676 ± 0.139 0.371*** 0.197***Auru. A361 AY509816 33 226–292 0.050 ± 0.053 0.733 0.829 ± 0.104 0.126*** 0.101***Auru. A392 AY640260 18 166–202 0.016 ± 0.031 0.657 0.664 ± 0.155 0.013 0.084***Auru. B209 AY509817 29 186–254 0.033 ± 0.045 0.789 0.837± 0.062 0.056*** 0.082***Auru. D094 AY640267 7 109–127 0.010 ± 0.024 0.590 0.513 ± 0.128 −0.155*** 0.142***Auru. D167 AY640268 4 132–138 0.057 ± 0.091 0.352 0.378 ± 0.147 0.074* 0.260***Auru. D282 AY640270 32 185–265 0.020 ± 0.031 0.839 0.861 ± 0.062 0.029* 0.079***Auru. E062 AY640273 7 75–87 0.005 ± 0.014 0.627 0.520 ± 0.103 −0.205*** 0.029***Auru. H207 AY509818 23 135–183 0.102 ± 0.084 0.543 0.717 ± 0.122 0.253*** 0.151***Overall — 18.8 ± 11.2 — — 0.617 ± 0.161 0.661 ± 0.159 0.077*** 0.122***

NA, number of alleles; allele size in base pairs; null allele frequencies were averaged over populations ± standard deviation; HO, observed heterozygosity; HE, expected heterozygosity averaged over populations ± standard deviation. * and *** indicate significant values with α = 0.05 and 0.001, respectively.

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was in all cases much smaller than the ones found withinBrazil (e.g. with the unbiased samples and five loci:Br_ΔK = 91 vs. Ag-Bo-Pa_ΔK = 16). Some consistency wasstill observed in terms of clustering of populations, withArgentinean clustering with two Bolivian populations(Bo_SJ1 and Bo_SJ2) and two northern Paraguayanpopulations (Pa_Mj1 and Pa_Mj2). barrier results wereneither influenced by the sampling size nor by the presenceof null alleles, and two main genetic boundaries wereidentified: barrier A, isolating the 10 northeasternmostBrazilian populations; and barrier B, separating theremaining Brazilian populations from the others (see Fig. 3).When performing the analysis without the loci potentiallyunder selection, barrier A was no longer observed. Despitethis result, and because the Bayesian analyses still resultedin a strong differentiation within the main Brazilian cluster

when excluding these loci, we judged this differentiationpertinent, and therefore consider two Brazilian groups: thecentral (CE), formed by 12 central populations, and thenortheast (NE) group, constituted by the 10 northeasternpopulations. Since a much weaker signal of differentiationwas observed within the Argentina–Bolivia–Paraguay group(the southwest group, SW), unambiguously definingsubgroups was not possible with this set of loci.

Considering the population clustering into three groups(CE, NE and SW), the overall among-group differentiationwas high in comparison with the overall among-populationdifferentiation, whether with microsatellites (FST = 0.162and FCT = 0.105; P < 0.001) or with the chloroplast data(FST = 0.970 and FCT = 0.818; P < 0.001). With the chloroplast,the CE and NE groups were not significantly differentiated(0.145, P > 0.05; Table 3), and the highest differentiation

Fig. 3 Geographical distribution of the clustering results obtained with Bayesian analysis for the 53 populations of Astronium urundeuvain eastern South America: black symbols correspond to southwest (SW; circles and triangles representing the substructure observed);diamonds correspond to central (CE; the two white diamonds correspond to populations Br_Jav and Br_Boq, assigned to NE when puttingaside loci under selection); and white squares correspond to northeast (NE). Main genetic boundaries, A and B, were obtained withMonmonier’s maximum difference algorithm: light grey lines represent the Delaunay triangulation and dark grey lines the Voronoïtessellation. The different dry formations are represented by different colours and motifs (modified from Pennington et al. 2000; Prado2000; Queiroz 2006; Ratter et al. 2006; Spichiger et al. 2006). SDTF, Seasonally Dry Tropical Forests.

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was reported among SW and NE (0.891 P < 0.001). Thiswas also observed with microsatellites (0.117, P < 0.001),and the lowest differentiation was found between the SWand CE groups (0.097, P < 0.001). For the chloroplast, thesegroups were highly differentiated (0.765, P < 0.001), butthis remained lower than differentiation reported amongpopulations within each group (FSC = 0.838, P < 0.001). Forthe overall populations, a strong positive correlationbetween genetic and both geographical distances and theirnatural logarithm was found with both chloroplast spacers(rM-km = 0.59, rM-log = 0.61; P < 0.001) and microsatellites(rM-km = 0.75, rM-log = 0.70; P < 0.001).

Spatial genetic patterns within groups

For the chloroplast, the NE group was monomorphic forhaplotype AC, whereas the SW and CE groups were bothpolymorphic. Despite the high number of haplotypesexhibited in the SW group (four; Table 4), these differed byonly three mutations, whereas the CE group displayed twohaplotypes differing by six mutations.

With microsatellites, each group displayed more than90% of significant within-groups pairwise FST values, asreflected by the highly significant FST within groups(Table 4). When the logarithm of distances was used, therM increased within both the SW group (rM-km = 0.21,rM-log = 0.24; P ≤ 0.001) and the NE group (rM-km = 0.53,rM-log = 0.55; P ≤ 0.003), whereas it decreased for the CEgroup (rM-km = 0.57, rM-log = 0.51; P < 0.001). Both SWand NE correlograms shared common features (Fig. 4): (i)spatial autocorrelation was positive and significant up to400 km; (ii) nonsignificant autocorrelations were found atintermediate distances (600 km); and (iii) significant negativevalues were observed beyond 800 km. No significant spatialstructure was reported for the CE group. With the soleexception of Auru.E062, for which no significant correlationswere observed beyond 900 km, all correlograms departedsignificantly from randomness (Table 5).

Table 3 FCT values among Astronium urundeuva’s groupsestimated with the chloroplast data (above diagonal), and thenine microsatellite loci (below the diagonal). NS and *** indicatenon-significant and significant values with α = 0.001, respectively

SW CE NE

SW — 0.765*** 0.891***CE 0.097*** — 0.145NS

NE 0.117*** 0.101*** —

SW, southwest; CE, central; NE, northeast.

Table 4 Variability indices in the three Astronium urundeuva groups measured with microsatellites and the chloroplast SW, southwest; CE,central; NE, northeast

Group Nb Pops

Microsatellites Chloroplast

N NA Pr Alls Null Alls RS HO HE FIS FST N NH

SW 31 20.1 7.0 ± 1.1 30 0.041 ± 0.028 6.9 ± 0.5 0.659 ± 0.054 0.688 ± 0.036 0.045*** 0.060*** 281 4CE 12 19.9 6.8 ± 0.7 4 0.076 ± 0.027 6.5 ± 0.6 0.564 ± 0.026 0.640 ± 0.024 0.116*** 0.056*** 54 2NE 10 18.5 6.1 ± 0.8 6 0.076 ± 0.022 6.0 ± 0.7 0.551 ± 0.042 0.628 ± 0.049 0.122*** 0.079*** 45 1

N, number of individuals; NA and NH, number of alleles averaged over populations and number of haplotypes; Pr Alls, total number of private alleles; Null Alls, frequencies of null alleles averaged over populations ± standard deviation; RS, mean allelic richness averaged over populations ± standard deviation; HO and HE, observed and expected heterozygosities averaged over populations ± standard deviation. *** indicate significant values with α = 0.001.

Fig. 4 Correlograms of rS as a function ofdistance, in the three groups of Astroniumurundeuva: SW, southwest; CE, central;NE, northeast. Closed symbols indicatesignificance (α = 0.05) and open symbolsindicate nonsignificant values.

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Within-population diversity

Eight SW populations were polymorphic for the chloroplastmarkers (π varying between 0.182 ± 0.144 and 0.558 ± 0.045,and h between 0.0002 ± 0.0002 and 0.0005 ± 0.0004) and,with the exception of Pa_SL1 that displayed three haplotypes(AA, BA and CA), only two haplotypes were reported inthe remaining polymorphic populations.

For microsatellites, the SW populations also displayedthe highest mean diversity indices, most of them beingsignificantly different from those of the two other groups:RS, HO and HE when compared to NE, HO and HE whencompared to the CE group. The CE group was characterizedby intermediate diversities, although the indices were notsignificantly different from the NE’s (Table 4). Significantvalues of FIS overall loci were found within six populationsfrom the SW (19%), eight from the CE (67%) and six fromthe NE (60%; Table S3, Supplementary material). Eachdeviation could be explained by the presence of null allelesat frequencies higher than 0.05.

The TPM assumption for all loci revealed a possibletrace of recent bottleneck in four populations, two withinthe SW group (Pa_Mb1, P = 0.014 and Bo_CMt, P = 0.037) andtwo within the CE group (Br_Bar and Br_Jab; P = 0.037).

Discussion

Genetic differentiation within Astronium urundeuva

The chloroplast data are consistent with an early vicarianceof an ancestral Astronium urundeuva population. Continuedisolation of the resultant populations in the southwest andnortheast then allowed the divergence of the two mainhaplotypes, AA and AC. From this perspective, the centralgroup (CE) corresponds to the contact zone between these

two lineages after subsequent respective range expansions.The isolation-by-distance patterns (Sokal & Winkler 1987)reported in both SW and NE groups, and the admixturesigns (Belle et al. 2006) detected in the CE group, furthersupport this scenario, as does the finding of two poly-morphic populations for AA and AC haplotypes at thesouthern limit of the CE group (Caetano & Naciri, in press).Slatkin (1993) underlined that the lack of isolation-by-distance pattern in a species where the dispersal mechanismshould result in such a pattern is indicative of a recentinvasion, as it is hypothesized for the CE group.

Nuclear and chloroplast results did not coincide aboutthe location of the contact between the two divergentlineages, with the chloroplast indicating that this is locatedfurther south. This primarily reflects the characteristics ofeach marker, such as differential mutation rates and inher-itance mode. This result is also informative about the waypollen vs. seed flow influences population structure. Pollenflow is documented to be generally stronger than that ofseeds, and differentiation reported from maternally inheritedgenes (e.g. chloroplast) is generally greater than that frombiparentally inherited nuclear genes (e.g. microsatellites;Hamilton 1999b; Hu & Ennos 1999; Petit et al. 2005). Pollenflow has a homogenizing effect that dilutes the structureresulting from the movement of seeds. The pattern observedwithin the central group illustrates the colonization fin-gerprint (still observed with the chloroplast) being graduallyerased by current exchanges among populations (reflectedby microsatellites), probably favoured by the more con-tinuous SDTF distribution in this area. The Serra Geralof Tocantins and Goiás in the southwestern limit of theCaatinga domain could have played a role in the mainte-nance of the two divergent lineages by preventing seeddispersal, but the microsatellites clearly show that it doesnot represent a barrier to pollen flow.

Table 5 Mean spatial autocorrelation indices (rS) in the 53 populations of Astronium urundeuva for nine distance classes. Overallestimations were done with nine loci

Distance class limits, km

300 600 900 1200 1500 2000 2500 3000 4000

Auru. A316 0.57* 0.49* 0.30* 0.19* −0.08 −0.45* −0.38* −0.24* −0.22*Auru. A361 0.44* 0.31* 0.24* 0.22* −0.01 −0.13* −0.28* −0.31* −0.33*Auru. A392 0.55* 0.38* 0.03 −0.07 −0.28* −0.10 −0.13* −0.30* −0.12*Auru. B209 0.40* 0.36* 0.24* 0.33* −0.06 −0.32* −0.31* −0.32* −0.24*Auru. D094 0.77* 0.45* 0.59* 0.56* 0.27 −0.46* −0.64* −0.59* −0.73*Auru. D167 0.74* 0.35* 0.30* 0.00 −0.35* −0.17* −0.23* −0.32* −0.58*Auru. D282 0.42* 0.40* 0.35* 0.30* −0.02 −0.36* −0.36* −0.36* −0.28*Auru. E062 0.49* 0.26* 0.00 −0.07 −0.19 −0.13 −0.10 −0.10* −0.16Auru. H207 0.48* 0.33* 0.39* 0.25* 0.12 −0.19* −0.36* −0.42* −0.51*Overall 0.57* 0.37* 0.28* 0.18* −0.10 −0.27* −0.31* −0.31* −0.32*

*indicate significant values with α = 0.05.

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The Bayesian approach implemented in structurefirst separated populations into two major groups, whichcorresponded to barrier B in Fig. 3. The huge gap sepa-rating southwestern populations from those belongingto the Brazilian groups (> 1186 km) is the most likelyexplanation for such differentiation. The addition ofsupplementary populations in this area should result ina more clinal distribution of microsatellite variability.However, more intense sampling is not expected todramatically change the observed chloroplast pattern,given the location of the AA haplotype northwards ofthe sampling gap. Two other well supported clusterswithin Brazil (the central and northeast groups) werefound with and without the loci displaying outlier FST,indicating that the clustering was not biased by selection.However, the genetic barrier found between the twopopulations located 20 km apart (barrier A in Fig. 3) wasno longer resolved when excluding the loci potentiallyunder selection. This is due to genetic differentiationcomputed on the restricted batch of loci not being strongenough to counterbalance the effect of geographicaldistances.

The nuclear variation within A. urundeuva populationswas high, with differences among individuals accountingfor 88% of the total variation. This is not a surprising resultin a dioecious tropical tree species with allogamousbehaviour (Bawa 1992) and agrees with the results obtainedfor the same species on a more reduced geographicalrange, both with random amplification of polymorphicDNA (RAPD; Reis & Grattapaglia 2004) and isozymes(Moraes 1992; Lacerda 1997). Nevertheless, major differ-ences in terms of distribution of the nuclear diversity wereobserved between the northeast and the southwest. Thelowest indices reported in the northeast suggest pastdemographic reductions followed by a rapid range expan-sion throughout the Caatinga domain. Moreover, thehigher influence of genetic drift in this group also explainsthe more pronounced genetic differentiation found withmicrosatellites and the total lack of polymorphism withthe chloroplast. A possible explanation for the low nuclearvariability could be consistent with the increase of nullalleles (Callen et al. 1993; Lehmann et al. 1996) for nonfocalpopulations, because primers were designed from aunique Paraguayan individual (Caetano et al. 2005). Thepresence of such null alleles explains the high number ofHardy–Weinberg deviations within both Brazilian groups(more than 50%). High null-allele frequencies furthersupports the vicariance/differentiation scenario, accordingto which these populations would have been isolated fromthe focal ones (Paraguay) for a long period of time. Itremains, however, an insufficient explanation for the lowvariability in the northeast group, which was still the leastvariable when putting aside the two loci displaying highnull-allele frequencies.

The main pool of genetic variability for A. urundeuvawas identified in the southwest group, as shown by thehighest diversities reported at both nuclear and chloroplastlevels. This implies a more stable demographic historywith higher effective sizes within this group. Some internaldifferentiation was also observed, for example for theArgentinean populations that displayed a private haplotype(AB) differing by a single step mutation from the mostcommon one in the region (AA). Moreover, the Bayesiananalyses showed that these populations differed slightlyfrom nearly all other southwest populations. The peripheralareas of the Chaco domain have been characterized asimportant SDTF nuclei, such as Misiones in the south,Chiquitano in the north and Piedmont in the west (Prado2000). Hence, our results may indicate some weakand/or recent isolation of the Piedmont. In agreementwith the high genetic diversity found in this area, thewhole Chaco domain has been considered as a source ofdiversity (‘dispersal centre’) for a broader Neotropicalflora (Spichiger et al. 1995). These dispersal centres shouldnot, however, be confused with centres of species origin,as emphasized by Spichiger et al. (2004). Although thisstudy cannot corroborate the suggested Brazilian origin ofA. urundueva (Santin & Leitão-Filho 1991), it seems reason-able to assume that the ancient population(s) that gave riseto the two divergent lineages found in this study was locatedsomewhere in between their contemporary range limits.

Inferences on the history of SDTF

Due to the lack of palaeobotanical data from eastern SouthAmerica, the colonization scenario for A. urundeuva, andsubsequent any inferences on SDTF history, should betreated with caution. Moreover, the low variability foundwith the chloroplast does not allow straightforwardinferences on either colonization hypothesis.

The results gathered here cannot solely be explained byconsidering recent events, which agrees with the assumptionof a pre-Pleistocenic origin of the species. The presenceof the divergent haplotypes AA and AC in the oppositelimits of the species range, in the Chaco and Caatingadomains, respectively, as well as the IBD signs observedwithin these two areas, support a more ancient vicarianceevent (Slatkin 1993). Posterior range expansions of thesetwo divergent lineages are additionally suggested by theequivalent distances covered, by the secondary contactand the admixture detected in the Cerrado domain.

Explaining the expansion of the northeast lineage (AC)southwards is quite easy, due to the extant continuity ofthe SDTF in this area. In contrast, the spread northwardsof the southwest lineage (AA) is more difficult to explain,because SDTF only exists as small patches in the Cerradodomain. These range expansions could have occurred asfew individuals colonizing distant isolated SDTF patches

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(colonization hypothesis 1) or as a continuous ‘invasionfront’ (colonization hypothesis 2). Our data favour thehypothesis of a continuous ‘invasion front’. The fact thatonly one southwest haplotype (AA) is found further northlooks like the signature of a surfing haplotype on the waveof a range expansion (Klopfstein et al. 2006). If the coloni-zation has occurred by long-distance dispersal from thesouthwest group, it is odd that the frequent haplotype BAis not also found further northeast. According to theoreticalmodels of long-distance dispersal, we would have expectedhigher genetic patchiness (Ibrahim et al. 1996) which was,however, not observed here. The range expansion of theAA haplotype into the Cerrado domain as a continuous‘invasion front’, with contemporaneous expansion of AClineage southwards, appears a more parsimonious scenario.Therefore our results favour a more continuous SDTFformation, supporting the ‘Pleistocenic arc’ hypothesis ofPrado & Gibbs (1993) — provided that the range expansions,dated for instance with pollen cores, coincided with thePleistocene period (cf. Petit et al. 2002; Magri et al. 2006).

AcknowledgementsWe wish to thank Prof F. Méréles from Asuncion University(Paraguay), P. Silveira, K. Elizeche, M. Soloaga, L. Oakley and R.Santos for their help with sampling; Dr D. de Carvalho for the labfacilities at Lavras University (Brazil); H. Geser and L. Turin forhelp at the CJBG lab; Dr N. Wyler for the maps; Prof L. Excoffierand Dr E. Poloni for helpful discussions during this project; aswell as the three anonymous referees and Dr Rémy Petit for helpfulcomments on the manuscript. This work was supported by theSwiss National Foundation (grants n°3100A0/100806-1 & 2), theConservatoire et Jardin Botaniques of Geneva and the threefollowing Societies, which contributed to part of travel expenses:Société Académique de Genève, Société de Physique et d’HistoireNaturelle and the Swiss Zoological Society. Plant material wascollected and exported to the CJBG lab in agreement with eachcountry’s law in vigour at the time.

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Sofia Caetano worked on this study as part of her PhD, whichdealt with the phylogeography and population genetics of twoSeasonally Dry Tropical Forests trees, Astronium urundeuva andGeoffroea spinosa. Darién Prado is interested in biogeography,phytosociology, plant taxonomy and plant reproductive biologywith a special focus on grasslands and dry forest vegetation ofthe Neotropics. Toby Pennington studies the vegetation of theNeotropics. His research includes inventory, taxonomy, molecularsystematics, population genetics, biogeography and inference ofbiome history, with particular recent focus upon Seasonally DryTropical Forests. Stephan Beck works to build a national herbariumin Bolivia and coordinates, with researchers in Bolivia and in theUnited States, and the production of an annotated catalogue of theflora. Most of Ary Oliveira-Filho’s scientific production is onphytogeography, floristic patterns and vegetation ecology.Rodolphe Spichiger has been interested in tropical systematicsand floristics for a long time, working particularly on the flora ofParaguay. Yamama Naciri’s research focuses on populationgenetics of different organisms; among them plants, with a specialinterest in phylogeography.

Supplementary material

The following supplementary material is available for this article:

Table S1 Location of the 53 populations of Astronium urundeuva,and number of individuals used in the used microsatellite (Nm)and chloroplast (Nc) analyses

Table S2 Allele-frequency estimates for nine microsatellite loci in53 populations of Astronium urundeuva in South America

Table S3 Diversity indices per microsatellite locus in 53 popula-tions of Astronium urundeuva in South America

This material is available as part of the online article from:http://www.blackwell-synergy.com/doi/abs/10.1111/j.1365-294X.2008.03817.x(This link will take you to the article abstract).

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