Differentiation of metallicolous and non-metallicolous Salix caprea populations based on phenotypic characteristics and nuclear microsatellite (SSR) markers

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Differentiation of metallicolous and non-metallicolous Salixcaprea populations based on phenotypic characteristicsand nuclear microsatellite (SSR) markerspce_2170 1..15

MARKUS PUSCHENREITER1*, MINE TÜRKTAS2*†, PETER SOMMER1, GERLINDE WIESHAMMER1,GREGOR LAAHA3, WALTER W. WENZEL1 & MARIE-THERES HAUSER2

1Institute of Soil Science, Department of Forest and Soil Sciences, BOKU-University of Natural Resources and Applied LifeSciences, 2Department of Applied Genetics and Cell Biology, BOKU-University of Natural Resources and Applied LifeSciences, and 3Department of Landscape, Spatial and Infrastructure Sciences, University of Natural Resources and AppliedLife Sciences, A-1190 Vienna, Austria

ABSTRACT

The Salicaceae family comprises a large number of high-biomass species with remarkable genetic variability andadaptation to ecological niches. Salix caprea survives inheavy metal contaminated areas, translocates and accumu-lates Zn/Cd in leaves. To reveal potential selective effectsof long-term heavy metal contaminations on the geneticstructure and Zn/Cd accumulation capacity, 170 S. capreaisolates of four metal-contaminated and three non-contaminated middle European sites were analysed withmicrosatellite markers using Wright’s F statistics. The dif-ferentiation of populations North of the Alps are morepronounced compared to the Southern ones. By groupingthe isolates based on their contamination status, a weakbut significant differentiation was calculated betweenNorthern metallicolous and non-metallicolous popula-tions. To quantify if the contamination and genetic statusof the populations correlate with Zn/Cd tolerance and theaccumulation capacity, the S. caprea isolates were exposedto elevated Cd/Zn concentrations in perlite-based cultures.Consistent with the genetic data nested ANOVA analysesfor the physiological traits find a significant difference inthe Cd accumulation capacity between the Northern andSouthern populations. Our data suggest that natural popu-lations are a profitable source to uncover genetic mecha-nisms of heavy metal accumulation and biomassproduction, traits that are essential for improving phytoex-traction strategies.

Key-words: cadmium; cross-genera transferability; geneticdiversity; microsatellite; Salix caprea; soil contamination;zinc.

INTRODUCTION

Phytoextraction is an emerging technology targeting onheavy metal removal from soils by accumulation in theharvestable part of plants (McGrath & Zhao 2003). Fast-growing metal-accumulating trees were considered aspotential candidates for phytoextraction trials (e.g. Wie-shammer et al. 2007). Members of the Salicaceae familycomprise a large number of high-biomass species andhybrids with remarkable genetic variability (Pulford &Dickinson 2005). In previous field screenings, Salix capreawas identified as Zn/Cd-accumulating species (Lepp &Madejón 2007; Unterbrunner et al. 2007). Further potexperiments have shown that several isolates of S. capreaare able to accumulate large amounts of Zn (up to2210 mg kg-1) and Cd (up to 340 mg kg-1) in their leaves(Dos Santos Utmazian & Wenzel 2007; Dos Santos Utma-zian et al. 2007a; Wieshammer et al. 2007). In contrast withother tested Salix species, the growth of S. caprea isolateswas less affected by high metal concentrations in soils (DosSantos Utmazian & Wenzel 2007). However, it was unclearif the origin and the genetic makeup of S. caprea isolatescorrelate with Zn/Cd accumulation.

The only genetic analysis of S. caprea to date presented ahigh level of variation within European populations, reveal-ing a trend that the number of chloroplastic haplotypes inthe North is higher than in the South (Palmé, Semerikov &Lascoux 2003). Nuclear microsatellites, also known assimple sequence repeats (SSRs), are widely used becauseof their co-dominance and high variability for geneticmapping studies and population genetic analyses (Zhang& Hewitt 2003; Ellegren 2004; Schlötterer 2004; Kurodaet al. 2006). Although the number of SSR repeat motifs ishighly variable, the flanking regions are conservedbetween related species and used to design primers forcross-species/-genera amplification (Karhu, Dieterich &Savolainen 2000; Arnold et al. 2002; Clauss, Cobban &Mitchell-Olds 2002; Tuskan et al. 2004). For several speciesof the Salicaceae family, SSR makers have been developed(Dayanandan, Rajora & Bawa 1998; Rahman, Dayanandan& Rajora 2000; van der Schoot et al. 2000; Lian et al. 2001;

Correspondence: Marie-Theres Hauser. Fax: +43 1 360066392;e-mail: [email protected]

*Authors contributed equally.†Present address: Faculty of Engineering and Natural Sciences,Sabanci University, Orhanli, Tuzla, Istanbul, Turkey.

Plant, Cell and Environment (2010) doi: 10.1111/j.1365-3040.2010.02170.x

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Smulders et al. 2001; Barker et al. 2003; Stamati et al. 2003;Tuskan et al. 2004). In particular, 4200 Populus SSRs areavailable from the International Populus Genome Consor-tium. Since the sequencing of the P. trichocarpa genome hasbeen published, the chromosomal positions of the SSRs areknown (Tuskan et al. 2006). In addition, Populus shares ahigh degree of macrosynteny (shared order of genes onchromosomes between species, families and even genera)with the genus Salix (Hanley, Mallott & Karp 2006).Roughly 100 SSRs have been tested for cross-species/-genera amplifications in Salix species with a success rate of30–50% (Tuskan et al. 2004; http://www.ornl.gov/sci/ipgc/ssr_resource.htm). However, neither S. caprea was includedin this survey, nor the amplification products confirmedby either sequencing or evaluated for their degree ofpolymorphisms.

Most of the population genetic studies that address heavymetal tolerance and accumulation have focused on herba-ceous plants such as Thlaspi caerulescens (Meerts & vanIsacker 1997; Basic et al. 2006), T. pindicum and T. alpinum(Taylor & Macnair 2006) and Arabidopsis halleri (VanRossum et al. 2004; Pauwels et al. 2005, 2006, 2008). Heavymetal tolerance in A. halleri is largely a constitutive prop-erty and non-metallicolous populations also have a highfrequency of enhanced tolerance (Bert et al. 2000; Pauwelset al. 2006). Isolates of the facultative metallophyte T. caer-ulescense from normal soil had a lower tolerance but wereable to accumulate more Zn (Meerts & Van Isacker 1997;Escarré et al. 2000; Assunção et al. 2003; Frérot et al. 2003)and Basic et al. (2006) demonstrated that T. caerulescensepopulations with contrasting Cd accumulation capacitiescan be distinguished based on amplified fragment-lengthpolymorphism and chloroplast markers.

Quantitative trait loci (QTL) analyses with T. caerule-scens revealed that only few loci explain most of the vari-ance for Zn and Cd concentrations (Assunção et al. 2006;Deniau et al. 2006). A comparable analysis involving across between A. halleri and the non-tolerant A. lyratarelative identified only three major QTLs responsible forZn tolerance. One of the loci mapped to an alreadyknown gene involved in heavy metal uptake and thisheavy metal transporting ATPase 4 (HMA4) is known tobe higher expressed in A. halleri compared to Arabidopsis.lyrata sp. petraea (Courbot et al. 2007; Willems et al. 2007).Hanikenne et al. (2008) recently showed that the Znhyperaccumulation and tolerance to Cd and Zn dependon HMA4 and that its enhanced expression is due to acombination of modified cis-regulatory sequences andcopy number expansion.

To reveal potential effects of centuries of heavy metalcontaminations on the structure and accumulation capacityin S. caprea, the objectives of this study were:

• to evaluate the Zn/Cd accumulation potential of S. capreain standarized conditions;

• to investigate the differences in Zn/Cd accumulationin S. caprea isolates from contaminated and non-contaminated sites;

• to screen for the most effective isolates that exhibit a highZn/Cd phytoextraction efficiency (i.e. heavy metalconcentration ¥ leaf biomass).

• to establish microsatellite markers and elucidate thegenetic structure of central European S. caprea popula-tions from historic contaminated and non-contaminatedsites.

• to evaluate the genetic differentiation between popula-tions in the concept of local adaptation.

MATERIALS AND METHODS

Selection of S. caprea isolates

In this study, we used seven S. caprea populations fromthree countries in Central Europe. These were: in Austria[one metal contaminated (Arnoldstein – A) and two non-contaminated sites (Forchtenstein – F, Völkermarkt – V)],in the Czech Republic {two contaminated [Príbram – PR,Kutná Hora – KH] and one non-contaminated site [Prague(P)]} and in Slovenia [one contaminated (Mežica – M) site](Table 1). The contaminated sites were exposed to atmo-spheric heavy metal deposition for centuries because of theactivity of local metal smelters. In the territories of theCzech Republic, lead ores were mined and processedbetween the Middle Ages and the year 1994 (Czech Repub-lic Ministry of Environment 2005). The deposition of heavymetal in the area of Kutná Hora and Príbram has beenwell-documented (Grmela & Rapantova 2005). A similarsituation exists in Mežica, where Zn and Pb mining wasoperating for more than 300 years until 1989 (Prestor, Strucl& Pungartnik 2003; Druzina 2006). In Arnoldstein, Pb andZn smelting started at the end of the 15th century and wasactive until 1991. Information on the sites, their soil charac-teristics and the number of isolates is presented in Table 1(Supporting Information Table S1, Fig. S3).

Green cuttings of 20–25 individuals were collected andrepresent isolates. The group of isolates from each site con-stitutes a population. For the individual isolates, a soilsample (0–20 cm depth) below the canopy of the tree wasincluded. In case of small, i.e. few meters, distance betweenthe individual trees a composite soil sample was obtainedand analysed (Supporting Information Table S1).

Propagation and perlite-basedsoil-less cultures

Green cuttings with four internodes were used for vegetativepropagation in late June/early July 2004. The cuttingswere rooted in quartz sand for 6 weeks in a shadowedgreenhouse chamber (14/20 °C day/night temperature; 16 hlight period;>90% air moisture).The final number of isolatesper population that could be successfully propagated is pre-sented in Table 1. Up to 10 rooted cuttings with similarproperties, i.e. root length and diameter, were selected foreach isolate and transplanted into 1 L plastic pots con-taining perlite. Climatic conditions were as describedearlier, but with reduced air moisture (80%) and without

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shadowing. A Cd-free nutrient solution, containing normalZn concentration, was provided for a period of 8 weeks.Thisnutrient solution contained (in mM) 1000 Ca(NO3)2, 500Mg(SO4)2, 50 KH2PO4, 100 KCl, 5 H3BO3, 0.2 H24Mo7N6O24,10 MnSO4, 2.5 CuSO4, 0.25 NiSO4, 2.5 ZnSO4 and 50Fe (III)-EDDHA (ethylenediamine-di(o-hydroxyphenyl-acetic acid) (Shen, Zhao & McGrath 1997). Solution pH wasmaintained at around 6.0 with 1 mM MES as potassium salt.Each pot was surface-watered with 300 mL of this nutrientsolution twice a week, following the removal of excesspreviously-added solution from the saucer in which the potstood.After 8 weeks, three to five clones of each isolate withsimilar shoot length were selected and the height of theshoots was reduced to uniform length. Subsequently, theplants were provided twice a week with a modified solutioncontaining elevated concentrations of Zn (5 mg L-1) and Cd(0.5 mg L-1) for a further 8 weeks.

Analysis of plant and soil material

At harvest, plant material was divided into roots, shoots andleaves. Shoot length and number of leaves was quantified(Fig. S1). Leaf samples were washed with distilled waterbefore drying at 80 °C.After recording the DW, leaves wereground in a metal-free mill (IKA® – Werke, MF 10) anddigested in a mixture of HNO3 (puriss. p. a., Sigma-Aldrich,Vienna, Austria) and HClO4 (puriss. p. a., Sigma-Aldrich)(4:1, v/v) using an automated heating block (Digester DK42/26, Velp Scientifica, Milano, Italy). Soil samples wereair-dried and passed through a 2 mm stainless-steel mesh.The sieved fraction was analysed for pH and total (aquaregia extract; Österreichisches Normungsinstitut 1999) andlabile (1 M NH4NO3 extract, 1:2.5 m/v; DIN-DeutschesInstitut für Normung 1995) Zn and Cd concentrations (Sup-porting Information Table S1). Measurement of Zn and Cdconcentration was performed by flame atomic absorptionspectrometry (AAS, Perkin Elmer 2100, Waltham, MA,USA). For quality assurance, replicate samples, blanks andstandardized reference materials (Eurosoil 7; internal plantreference material) were included in all analyses.

DNA preparation

Genomic DNA was isolated from roughly 150 mg frozenleaves of 170 isolates after a modified protocol of Rogers &Bendich (1988). In brief, leaves were ground in liquid nitro-gen in 15 mL tubes. 5 mL of 2X CTAB (2% (w/v) hexade-cyltrimethylammonium bromide, Sigma-Aldrich), 100 mMTris/HCl pH 8.0, 20 mM ethylenediaminetetraacetic acid(EDTA), 1.4 M NaCl, 1% (w/v) polyvinylpyrrolidone(PVP40T, Sigma-Aldrich) was added, vortexed and incu-bated at 65 °C for 20 min. Equal amount of chloroform wasadded and vortexed for 2 min. Following centrifugation at4250 g for 10 min at 4 °C, the supernatant was transferred toa new tube and the DNA was precipitated by addition of 0.8volumes isopropanol (Carl-Roth, Karlsruhe, Germany).The pellet was washed with 70% ethanol and dissolved inTa

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Differentiation of Salix caprea populations 3

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100 mL TE (10 mM Tris-HCl pH 8.5, 1 mM EDTA) with3 mL RNase (10 mg/mL, DNase free, Roth). The integrityand the quantity of the genomic DNA were evaluated on0.8% (w/v) agarose gels containing 0.5 mg/mL of ethidiumbromide.

PCR amplification, electrophoresisand sequencing

PCR amplifications were carried out in a total volume of20 mL containing 20 ng genomic DNA, 200 mM of eachdNTP, 5 pmol of forward and reverse primers (SupportingInformation Table S2), 1 U TAQ polymerase and 1X PCRbuffer (10 mM Tris-HCl pH 8.5, 50 mM KCl, 2 mM MgCl2,0.15% Triton X-100). PCR was conducted in a MasterCycler-Gradient PCR machine (Eppendorf, Germany) withan initial cycle of 94 °C/3 min and 35 cycles of 15 s/50 °C,30 s/72 °C, 15 s/94 °C. Size and amount of the PCR productswere evaluated on 2% (w/v) agarose gels containing0.5 mg/mL of ethidium bromide.

SSR amplicons were directly sequenced with theDYEnamic ET Terminator Cycle Sequencing Kit (Amer-sham Pharmacia Biosciences, Pittsburgh, PA, USA) andaccording to the ABI Prism® BigDye® Terminator CycleSequencing Ready Reaction Kit on the ABI Prism 3100Genetic Analyzer (Applied Biosystems, Carlsbad, CA,USA). Sequences were analysed by BLAST algorithm(Altschul et al.1997) at NCBI (http://www.ncbi.nlm.nih.gov)and JGI (http://genome.jgi-psf.org/Poptr1/Poptr1.home.html). Sequence alignments were done using the MegAlignsoftware of the DNASTAR program package (DNASTARInc.,Madison,WI,USA).To evaluate the degree of polymor-phism in the S. caprea populations, SSR amplicons from 18isolates (three from six populations) were separated on 5%non-denaturing polyacrylamide gels,as described previously(Hauser et al. 1998).

Genotyping

One of each primer pair was labelled with 5-HEX(5-hexachlorofluorescin), 6-FAM (6-carboxylfluorescin) or5-NED (5-benzofluorotrichlorocarboxylfluorescin). PCRamplicons were diluted in distilled water with a ratio of 1:2to 1:10 (depending on the amplicon concentration). Formultiplexing, PCR amplicons with two different labels andfragment sizes were mixed 1:1. 1 mL and suspended in10.3 mL consisting of 10 mL Hi-Di™ formamide (AppliedBiosystems) and 0.3 mL of the size standard Genescan400HD-ROX (Applied Biosystems). After denaturation at94 °C for 3 min. and immediate chilling on ice, the productswere analysed with the ABI Prism 3100 Genetic Analyzer(Applied Biosystems). The chromatograms were analysedusing ABI Prism® Genotyper® 3.7 NT Software (AppliedBiosystems).

Statistical analyses

A nested analysis of variance (general linear model) wasperformed to detect significant differences between

populations. The factors were ‘contamination’ (which wasdefined ‘yes’ for contaminated sites and ‘no’ for non-contaminated) and ‘population within contamination’ (i.e.the factor ‘population’ was treated as a nested, that meanssecond hierarchy level below the factor ‘contamination’). Alevel of significance of a = 0.05 was used to determine sig-nificant effects. Pearson correlations were calculated andvisualized with Microsoft Excel 2002.The significance of thecorrelation r was evaluated with the Pearson’s table.

To assess the differences between the individual popula-tions, a post hoc comparison of means was performed usingthe Scheffé test (P < 0.05). Correlation analysis (Pearson r)was performed between leaf biomass and foliar Zn and Cdconcentration.All statistical analyses were performed usingStatistica 6.0 for Windows (StatSoft Inc. 2001).

Allele number and frequencies for each marker, popula-tion and categorical group, expected heterozygosity (Hexp),the global and pairwise F statistics were calculated with theprogram SPAGeDi (Spatial Pattern Analysis of GeneticDiversity), version 1.2 after Weir & Cockerham (1984).Thisprogram operates with genotype data of any ploidy level(Hardy & Vekemans 2002). The Weir & Cockerham algo-rithm for the F statistics is based on a nested anova, wherepopulations are weighted according to their sample size.Bonferroni correction was applied to correct for multiple-comparisons (Sokal & Rohlf 1995). Observed heterozygos-ity (Hobs) was computed using the MSA program (Dieringer& Schlötterer 2003). Significance (P values) of non-randomdistributions of specific alleles was calculated with a permu-tation test of 1000 replications in Microsoft Office® Exceland a Fisher’s exact test.

We assessed evidences of selection (i.e. adaptation in con-taminated populations) using the lnRH test and calculatedthe average gene diversity Hexp of each locus for the con-taminated (mc) and uncontaminated populations (mu) andthe formula Q = (1/(1 - Hexp))2 - 1 as described by Kauer,Dieringer & Schlötterer (2003). The Q values were normal-ized and Bonferroni corrected to get significance values(Supporting Information Table S3).

RESULTS

For all isolates tested in the perlite-based soil-less culture(n = 132), the average (mean � standard deviation) foliarconcentrations were 812 � 238 mg Zn kg-1 and 115 �

56.0 mg Cd kg-1. Mean leaf biomass was 2.91 �

1.55 g plant-1.

Influence of isolate origin

The factor ‘contamination’ had no significant influence onthe Cd and Zn concentration in leaves. In contrast, leaf DWof isolates from contaminated sites was significantly lowerthan for isolates from non-contaminated sites (Table 2).Thefactor ‘population within contamination’ had a significantinfluence on all the investigated variables (Table 2). In spiteof this, the differences of foliar Zn and Cd concentration(Fig. 1), as well as of leaf biomass (Fig. 2), shoot length and

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leaf number (Supporting Information Fig. S1) between theindividual populations were rather small and only a fewsignificant differences were detected. Comparing the resultsof the perlite-based soil-less culture with the soil character-istics of the isolate’s origin, we found a positive and signifi-cant trend. However, this correlation was only positive up toa contamination level of 20 mg kg-1 total Cd. Isolates fromsoils above this total Cd concentration did not increasetheir accumulation capacities in perlite-based soil-lesscultures (Supporting Information Fig. S5).

Comparison of individual isolatesand correlations

Figure 3 shows metal contents (metal concentration ¥ leafbiomass) in leaves of each isolate, which ranged from 704 to5560 mg Zn plant-1 and from 94.1 to 554 mg Cd plant-1,respectively. The highest total Cd and Zn contents werefound for an isolate from the non-contaminated populationPrague. Both the foliar concentration of Zn and Cd(r = 0.79) and the total content of Zn and Cd in leaves(r = 0.79) correlated significantly. The leaf biomass had ahigher influence (r = 0.85 and 0.58, respectively) on the totalfoliar Zn and Cd contents than the corresponding Zn andCd concentrations (r = 0.10 and 0.22, respectively).

Correlation between growth andZn/Cd concentration

A negative correlation was found between foliar Zn and Cdconcentration in leaves and the total leaf biomass (Fig. 4),indicating a decrease of shoot growth at increasing foliar Znand Cd concentration. This observation could be due to Znand/or Cd toxicity or might be caused by a ‘dilution effect’.Further potential signs of toxicity, i.e. chlorotic and necroticspots on leaves, were detected only for a few specimens andwere not correlated with foliar Zn and Cd concentrations(data not shown).

Comparing the results of the perlite-based soil-lessculture with the soil characteristics of the isolate’s origin, wefound that no significant dependency existed betweenbiomass production and the Zn and Cd levels in the isolatesderived from contaminated sites (Supporting InformationFig. S4). However, the biomass negatively correlates withthe much lower total Cd levels of uncontaminated soils.Thus the isolates of uncontaminated sites are more sensitiveto elevated Cd and Zn exposures while isolates from con-taminated sites seem to be more resistant to higher doses ofCd and Zn under soil-less condition and might have beenselected to withstand higher Cd and Zn concentrations.

Transferability of P. trichocarpa, P. nigra, S.lanata and S. burjatica SSR markers is low

To characterize the genetic structure of the S. caprea popu-lations, a total of 83 Populus ssp. and 10 Salix ssp. SSRs lociwere tested for cross-amplification (Table 3). Whereasaround 70% of the Populus and all Salix SSR could beamplified, only half of these amplicons had roughly theexpected fragment size and contained SSR repeats(Table 3).

Sequencing of the PCR-amplified loci revealed a highdegree of conservation in the flanking regions of the SSRsbetween genera and species. Interestingly, five of P. tri-chocarpa and three of the P. nigra loci lost completely therepeat region in S. caprea, although the rest of the sequencewas conserved. Four loci had altered repeat motives:While in P. trichocarpa, the repeat motif of ORPM_62was [AT]4.[ATTTT]3, it exists as [AT]2. . . [T]8 in S. caprea.ORPM_86 changed from [CTT]5 to [CTT]2[CAT]8,ORPM_137 from [AT]7 to [GT]4 and ORPM_312 from[CCT]6 to [CTT]5 in S. caprea. Six SSR amplicons had nohomology to any Populus SSR locus (in Table 3 indicated aswrong amplicons, Supporting Information Table S2). Only11 SSR loci were polymorphic enough to be used for thegenetic structure analyses of S. caprea. Assuming a high

Table 2. Main effects of the factors ‘contamination’ and ‘population within contamination’ determined by a nested analysis of variance(general linear model, type III decomposition). F values were treated as fixed. F values were calculated by dividing the mean square(MQ) of either factor by the mean square of the error. A significant effect is indicated by a P value below 0.05

Variable Factor SS* df MQ F P

Biomass (g plant-1) Constant 4 768 1 4 768 2 335 0.00Contamination 37.8 1 37.7 18.4 0.00Population within contamination 177 5 35.3 17.3 0.00Error 1 170 573 2.02 – –

Cd (mg kg-1) Constant 6 912 645 1 6 912 645 2 451 0.00Contamination 4 163 1 4 163 1.48 0.22Population within contamination 194 709 5 38 942 13.8 0.00Error 1 616 241 573 2 821 – –

Zn (mg kg-1) Constant 348 160 948 1 348 160 948 6 566 0.00Contamination 7 073 1 7 073 0.13 0.72Population within contamination 240 9978 5 481 996 9.09 0.00Error 30 384 698 573 53 027 – –

*Sum of squares.

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degree of synteny between P. trichocarpa and S. caprea,these loci are located on nine different chromosomes (Sup-porting Information Table S2). The alleles of these 11 locidiffered mainly in unit size, although some alleles did not fit

the standard unit series similar to previous reports for P.tremuloides (Cole 2005).

Beside the overall low transferability for Populus (below10%) and Salix SSRs (40%), the 11 SSR markers could also

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Figure 1. Foliar Zn (a) and Cd (b) concentration in S. caprea isolates derived from seven different populations. Boxes represent themedian (vertical solid line), the arithmetic mean (vertical dashed line), and 25–75% percentile. Whiskers represent the 90th and 10thpercentile. Significant differences were determined by a post hoc comparison of means (Scheffé test after nested anova; P < 0.05) and areindicated by different letters.

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be amplified in S. cinerea, S. fragilis, S. viminalis and S.purpurea.

Allelic variation of S. caprea is high and, forsome populations, specific

All 11 SSRs were highly polymorphic in 170 isolates. Intotal, 202 alleles were identified and their number per locusvaried from 5 to 30 (Table 4). The level of heterozygosity ofeach locus was between Hexp 0.34 and 0.89 (Table 4). Threehighly polymorphic SSR markers (WMPS_21, SB_38,gSIMCT024) were sufficient to unambiguously distinguishthe genotypes among individuals. Interestingly, none of the

isolates shared the same genotype. In contrast to some Salixspecies that spread clonally or have a mixed reproductionsystem, our genetic results are in accordance with the factthat no asexual propagation occurred in the sampled S.caprea populations. On average, 19 and 29% of the isolateshad more than two alleles or only one allele of a particularSSR, respectively (Table 4). Of the 1870 loci evaluated, 15and 2.8% had three and four alleles, respectively.Therefore,the genetic diversity and population differentiation was cal-culated with a program that accepts data sets containingindividuals with multialleles and different ploidy levels(Hardy & Vekemans 2002).

The occurrence of multiallele loci may be the results oflocal genome amplifications. To determine if the frequencyof multiallele loci is significantly different in contaminatedversus uncontaminated populations, we used the Student’st-test (Table 5). While no significance was detected if allloci were compared, contaminated populations had signifi-cantly more multiples alleles at loci ORPM_312 andSB_24, indicating that genome amplification may haveoccurred. Assuming a high degree of synteny between

lea

f b

iom

ass (

g p

lan

t–1)

0

1

2

3

4

5

6

ab ab

bc

ab

c

bc

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Hor

a

Pribra

m

Arnolds

tein

Mez

ica

Pragu

e

Forch

tens

tein

Völke

rmar

kt

Figure 2. Total amount of leaf biomass per plant for S. capreaisolates derived from seven different populations. Boxesrepresent the median (vertical solid line), the arithmetic mean(vertical dashed line), and 25–75% percentile. Whiskers representthe 90th and 10th percentile. Significant differences weredetermined by a post hoc comparison of means (Scheffé testafter nested anova; P < 0.05) and are indicated by differentletters.

Total Cd in leaves (mg plant–1

)

0 100 200 300 400 500 600

To

tal Z

n in

le

ave

s (

mg

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nt–

1 )

0

1000

2000

3000

4000

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Kutna Hora

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Mezica

Prague

Forchtenstein

Völkermarkt

Figure 3. Correlation between total Zn and total Cd in leavesof the individual isolates (n = 132; r = 0.79; P < 0.001).

leaf biomass (g DW plant–1

)

0 1 2 3 4 5 6

leaf C

d c

oncentr

ation (

mg k

g–1)

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)

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oncentr

ation (

mg k

g–1)

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(a)

(b)

Kutna Hora

Pribram

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Mezica

Prague

Forchtenstein

Völkermarkt

r = – 0.53

Figure 4. Relationship between the leaf biomass and the foliarCd (A) and Zn (B) concentration.

Differentiation of Salix caprea populations 7

© 2010 Blackwell Publishing Ltd, Plant, Cell and Environment

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Populus and Salix, these two loci are not on the samechromosome (Supporting Information Table S2). Anothermarker on the same chromosome, SB_24, does not showthis correlation indicating that partial genome duplicationmay have occurred at specific loci and chromosomalregions.

Furthermore, at two loci, specific alleles have beendetected for the Northern (ORPM_446/246, WMPS_19/174) and one (gSIMCT024/114) for the contaminated popu-lations (Table 6). Using Fisher’s exact and a permutationtest, the probability that these three alleles occurred bychance is below the significance level (Table 6).

Genetic differentiation of S. caprea isolates issignificant for the contamination status, thegeographic origin and for isolates with a highleaf biomass production

Differentiation within a random mating population is cor-related with a loss of genetic variation and a decrease inheterozygosity between subpopulations. To measure thedegree of differentiation between the geographically dis-tinct S. caprea populations Wright’s F statistics was used.Isolates from Prague (P) showed the highest average pair-wise FST values, and thus this population is significantlydifferent from all others. Because of the Prague popula-tions, the differentiation is more pronounced North of the

Alps (KH, PR, P) compared to the populations South of theAlps (A, M, V) (Table 7). The population of Forchtenstein(F) although situated North-East of the Alps is geneticallycloser to the Southern populations (Table 1).

By grouping the populations based on the contaminationstatus, a weak but significant differentiation was calculatedbetween metallicolous and non-metallicolous isolates(Table 8). This differentiation is mainly due to the markersORPM_62, WMPS_12, WMPS_21 and SB_38.

Separated FST values for individual loci were also calcu-lated by grouping Southern and Northern populations. Thedifferentiation was mainly due to the marker WMPS_12.Within the Northern populations, differentiation wasfound between the metal contaminated (KH, PR) andnon-contaminated (P) populations. However, the differen-tiation between Southern metallicolous (A, M) and non-metallicolous (V) populations was not significant, althoughit is significant between Arnoldstein (A) and Mežica (M),which are separated by the Karawanken mountains, but notbetween Arnoldstein (A) and Völkermarkt (V), which arein the same valley. These genetic data are consistent withthe geographic differentiation of the nested anova analysesfor the physiological traits that find a significant differencein the Cd accumulation capacity between the Northern andSouthern populations (Fig. 5).

Next, we grouped S. caprea individuals according to theirphysiological characteristics such as biomass (e.g. below and

Table 3. Summary of cross-genera and-species amplification of SSR markers inS. caprea

P. trichocarpa P. nigra S. burjatica S. lanata

SSRs tested 62 21 3 7Amplified 45 14 3 6Unexpected amplicon size 15 3 – 1Sequenced 23 11 3 4SSR present 12 7 3 4Wrong amplicon 7 – – –No repeat 5 3 – –Different repeat 4 – – –Polymorphic 3 4 3 1Success rate (%) 4.8 19.1 100 14.3

Table 4. Allelic variations, heterozygosity, FST and P-values and frequencies of multi- and single allele loci

LocusNo. ofAlleles Hobs Hexp

% multialleleloci

% singleallele loci FST P value

ORPM_62 14 0.49 0.74 20.00 50.63 0.035 0.0005ORPM_312 18 0.81 0.82 12.80 19.02 0.012 0.012ORPM_446 25 0.56 0.81 13.94 44.24 0.009 0.174WMPS_12 5 0.34 0.33 0.61 66.06 0.052 0.000WMPS_14 21 0.85 0.84 39.52 14.97 0.007 0.062WMPS_19 30 0.84 0.88 33.13 16.27 0.007 0.032WMPS_21 12 0.79 0.76 31.36 21.30 0.014 0.003SB_24 15 0.89 0.77 10.65 11.24 0.005 0.196SB_38 30 0.82 0.91 11.15 17.47 0.016 0.000SB_199 16 0.61 0.76 23.64 38.18 0.011 0.023gSIMCT024 18 0.85 0.80 9.41 14.71 0.009 0.040All loci 0.0143 0.000

8 M. Puschenreiter et al.

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above 5 mg kg-1 leaf dry weight). For this trait the markergSIMCT024 had a significant FST value.

This first genetic analysis of S. caprea with nuclear markershows that there is a weak but significant geographic dif-ferentiation of populations North and South the Alps.Moreover, S. caprea populations of contaminated areas aregenetically distinct. Apart from significant FST values forindividual SSR markers, our data suggest that local genomeamplifications might occur during adaptation to heavymetal contaminated environments.

DISCUSSION

We are aware of the fact that some significant limitationsmay be associated with soil-less experiments (e.g. regardingthe extrapolation of results to soil conditions). Some prob-lems associated with hydroponic and soil-lees studies havebeen previously discussed by Stoltz & Greger (2002),Watson, Pulford & Riddel-Black (2003) and Dos SantosUtmazian et al. (2007b). However, we have chosen thisapproach since Watson et al. (2003) also claim that soil-lesshydroponic screening tests comprise a method for differen-tiation between isolates under standardized conditions. Inorder to overcome problems that were found for S. capreawhen growing in nutrient solution (Dos Santos Utmazianet al. (2007b), we have decided to use a perlite-basedsoil-less culture.

The first pot experiments with S. caprea isolates fromcontaminated sites revealed a large Cd and Zn accumula-tion capacity of this species (Dos Santos Utmazian &Wenzel 2007; Dos Santos Utmazian et al. 2007a). Interest-ingly, similar concentrations were found in a specimen froma tree nursery (Wieshammer et al. 2007). Therefore, thequestion arose if the Cd and Zn accumulation capacity is aconstitutive or an adaptive property of S. caprea. The com-parison of seven populations from four contaminated andthree non-contaminated sites suggests that it is likely a con-stitutive property, because isolates obtained from non-contaminated sites had similar Zn and Cd concentrations intheir leaves compared with those from contaminated sites(Fig. 1). Landberg & Greger (1996) and Vysloužilová et al.(2006) have reported similar observations for other Salixspecies.

The Cd and Zn translocation factor was investigated forfour selected isolates: two with the lowest and two with thehighest foliar Cd and Zn concentration. For the two lowaccumulator isolates, the ratio was 0.6, but for the two high-accumulator isolates, the ratio was 1 and 1.8, respectively.This demonstrates the high capacity of some S. caprea iso-lates to translocate Cd from roots to shoots. Even highershoot:root ratios were reported for a S. caprea isolate grownin contaminated soil (up to 2.3; Dos Santos Utmazian &Wenzel 2007). Regarding the uptake of Cd, the correlationanalysis between foliar Cd and Zn or Ca concentrationssuggest that Cd is taken up more likely by the carrier systemfor Zn (r = 0.79) than for Ca (r = 0.51). Previous reportssuggest that Cd may be taken up by both Zn and Ca carriers(Welch & Norvell 1999; Zhao et al. 2002; Antosiewicz &Ta

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Differentiation of Salix caprea populations 9

© 2010 Blackwell Publishing Ltd, Plant, Cell and Environment

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Hennig 2004; Pittman et al. 2004; Korenkov et al. 2007, 2009;Parameswaran et al. 2007; Moreno et al. 2008; Mei et al.2009; Morel et al. 2009; Oomen et al. 2009; Küpper &Kochian 2010).

Based on the anova analysis with isolates grouped accord-ing to the contamination status, no significant difference wasfound for the foliar Zn and Cd concentration (Fig. 5). Nev-ertheless, signs of adaptation were revealed through the

significant positive correlation between the Cd concentra-tion of the soils and the isolates capability to accumulate Cdin leaves in soil-less perlite conditions. However, this corre-lation was only significant for total Cd concentrations of upto 20 mg kg-1 soil. Isolates from soils above this level did notexhibit a further enhancement of their Cd accumulationcapabilities (Fig. S5). Leaf biomass was, on average, largerfor the isolates from non-contaminated sites. These findingssuggest that: (1) most S. caprea isolates are good extractorsand accumulators of Zn and Cd; and (2) the contaminationof the site of isolate origin has a significant influence on foliarbiomass production and Cd but not Zn accumulation. Theconstitutive property of metal accumulation has been previ-ously reported for the hyperaccumulators A. halleri (Bertet al. 2000, 2002; Macnair 2002) and T. caerulescens (Meerts& Van Isacker 1997; Escarré et al. 2000). Our data suggest asimilar trait for S. caprea.

The significant influence of the factor ‘population’ onbiomass production and foliar Cd and Zn concentrationssuggests that the isolates from each site represent a distinctpopulation. This geographical difference was supported bygenetic analyses, which show that the Southern populationsare distinct from the Northern ones (Table 8). However, thedifferences between the populations are less pronounced(sometimes even not significantly different) than expected.The average foliar Cd concentration in the population withthe lowest mean value (Prague) is only 37% lower com-pared to the population with the highest mean value (Völk-ermarkt). For Zn, the difference between Mežica (highest)and Forchtenstein (lowest) is even less (22%).

Among the populations from non-contaminated sites,plants from Völkermarkt are characterized by higher Cd

Table 6. Frequencies and significance(P value) of population-specific allelesPopulations ORPM_446/246 WMPS_19/174 gSIMCT024/114

A 0.092 0.023 0.013M 0.038 0.077 0.019F 0.075 0.079 0.000V 0.139 0.024 0.000KH 0.000 0.000 0.058PR 0.000 0.000 0.077P 0.000 0.000 0.000P values 0.001 0.010 0.0

Table 7. Pairwise FST and P values amongS. caprea populations. FST values in bold anditalic are significant Bonferroni correctedand not significant, respectively

FST

A M V F KH PR P

PA 0.0118 0.0041 0.0076 0.0108 0.0108 0.0176M 0.0025** 0.0155 0.0035 0.0090 0.0117 0.0303V 0.3599 0.0121* 0.0136 0.0171 0.0272 0.0256F 0.0579 0.5586 0.0299* 0.0079 0.0123 0.0184KH 0.0085* 0.0365* 0.0039* 0.1053 0.0123 0.0309PR 0.0059* 0.0119* 0.0000** 0.0145* 0.0063* 0.0235P 0.0000** 0.0000** 0.0000** 0.0005** 0.0000** 0.0000**

**P < 0.05 with Bonferroni correction; *P < 0.05 without Bonferroni correction.

Table 8. Summary of the FST calculations for individual and allloci

Category FST P-value Bonferroni

Metallicolous versus non-metallicolousALL LOCI 0.0053 0.0003 *ORPM_62 0.0346 0.0009 *WMPS_12 0.0517 0.0007 *WMPS_21 0.0141 0.0035 *SB_38 0.0160 0.0003 *

North-SouthALL LOCI 0.005 0.0005 *ORPM_446 0.0145 0.0095WMPS_21 0.0112 0.0033 *

North: metallicolous versus non-metallicolousALL LOCI 0.0232 0.0000 *WMPS_14 0.0235 0.0043 *Dry weight*ALL LOCI 0.0124 0.0219gSIMCT024 0.0617 0.0007 *

*Isolates with leaf dry weights higher than 5 mg/kg were comparedto the rest.

10 M. Puschenreiter et al.

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concentrations than those from Prague or Forchtenstein.This may be explained by the rather short geographicaldistance between Völkermarkt and contaminated sites inthe vicinity (including Mežica and Arnoldstein). Thehypothesis that populations from Völkermarkt and Mežicaor Arnoldstein are genetically closely related was sup-ported by our genetic analysis that could not detect asignificant differentiation between Völkermarkt andArnoldstein and Völkermarkt and Mežica (Table 7).

Signs of differentiation and adaptation are best detect-able by genetic analyses with molecular marker such as theco-dominant nuclear SSRs. Since Tuskan et al. (2004) pub-lished that 30–50% of the Populus SSRs can be amplified indifferent Salix species, we used the rich source of mappedSSRs of the fully sequenced Populus genome (Tuskan et al.2006). Although 73% of the Populus SSRs could be ampli-fied in S. caprea, only 10% were polymorphic and could beused for the genetic analysis of our populations. This lowrate of transferability of Populus SSRs for Salix ssp. impliesthat Populus SSRs are not recommendable for wholegenome association studies in Salix.

To date, few data are available on the population struc-tures of S. caprea (Palmé et al. 2003) and other Salix species(Reisch, Schurm & Poschlod 2007). Our genetic analysisof seven middle European populations found a very lowlevel of differentiation with FST values between 0.0118 and0.052 (Tables 7 and 8) and agrees with data of maternally

inherited chloroplast markers (PCR-RFLPs) that revealeda low genetic differentiation (GST = 0.09) between S. capreapopulations (Palmé et al. 2003). Palmé et al. (2003) alsoreported that the genetic diversity was higher in the North-ern populations, a finding that is consistent to our significantdifferentiation of the populations North and South theAlps. Our data are also comparable with an SSR analysis onP. tremuloides populations, where the FST values variedbetween 0.006 and 0.045 (Cole 2005).

The frequency of multialleles differed between the popu-lations and loci. At two loci, it was significantly higherin population of contaminated areas, independent of thegeographic origin. This result may come from an ancientintrogression event with a polyploid Salix species and sub-sequent loss of most of the introduced chromosomes. Onthe other hand, the presence of multialleles might point tolocal genome amplifications, a phenomenon that has beenidentified in all the recently sequenced plant genomesincluding that of P. trichocarpa (Tuskan et al. 2006). DNAquantification with flow cytometry showed that S. capreahad the highest intraspecific variation of DNA contentamong Salix species (Thibault 1998). Correspondingly, ourflow cytometric DNA quantification of all S. caprea isolatesfound a similar variation (data not shown). Althoughfurther Southern blot or sequence analyses are needed,the data are indicative that S. caprea may use genenumber expansion in the adaptation process to specific

Figure 5. Comparison of leaf biomass and Zn/Cd concentration of Salix caprea between isolates from Northern (N) and Southern (S)populations as well as between specimen from contaminated (C) and non-contaminated (N) sites. Error bars represent the standard errorof the mean. A significant difference (P < 0.05) is indicated by an asterisk.

Differentiation of Salix caprea populations 11

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environments in a similar manner as it has been describedfor A. halleri by Hanikenne et al. (2008).

To test if other loci have signs of selection, the lnRH testwas used that compares significant reductions in variabilityof each locus in each population in relation to all loci (Sup-porting Information Table S3). Effects of demography, suchas bottlenecks, are removed by the lnRH test because thesewould affect all loci at the same degree. According to thisanalysis only for the WMPS_14 locus a sign of selectionwas revealed that lost the significance after Bonferroni.However, the lnRH test was designed for genome scansanalysing a large number of loci.With the moderate numberof loci used in this study, the chances to detect a genomicregion subjected to selection are small. It is also unclearhow the partial deviation from a strict diploid genomeaffects the FST – heterozygosity correlation and the lnRHvalue. A simulation test is, however, beyond the scope ofthis paper. Other signs of selection are the significantcorrelations between specific loci and physiological traits(Table 8), and the detection of three alleles that are specificfor the geographic localization and the contaminationstatus of the populations (Table 6).

To our knowledge, this is the first study on the populationstructure of S. caprea based on nuclear markers that asso-ciates phenotypic with genotypic variability. The weak indi-cations of differentiation between populations and amongisolates of specific physiological characteristics suggest thatselection might have shaped the populations. To increasethe resolution of the genetic analysis, a larger number ofpolymorphic sequences are needed. Our results exemplifythat deeper molecular analyses of isolates with contrastingperformance could be an effective measure for understand-ing the molecular basis of Zn/Cd tolerance, accumulation aswell as biomass production all traits that are essential forimproving the phytoextraction efficiency.

ACKNOWLEDGMENTS

We gratefully acknowledge Christian Schlötterer for hisvaluable advices and critical reading of the manuscript. Weare thankful to Claire Arnold and Nada Hamza for sharingsome of the S. burjatica and S. lanata primers, DanielDieringer for his help with the sequence analyser andsome statistical analyses and Agata Mansfeld for her kindintroduction into the polyacrylamide gel electrophoresis,Elisabeth Netherer for her help with the heavy metalquantifications and Joseph Glössl for his general support.We thank Ewald Brauner,Alex Dellantonio, Xiaojiang Gao,Evi Oburger, Gorana Todorovic and Siegfried Sowboda fortheir help in greenhouse and laboratory work.This study wassupported by the Vienna Fund for Science and Technology(WWTF LS-149) and the FWF (project L433_B17).

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Received 23 March, 2010; accepted for publication 14 April, 2010

SUPPORTING INFORMATION

Additional Supporting Information may be found in theonline version of this article:Figure S1. Shoot length (A) and leaf number (B) of S.caprea isolates. Boxes represent the median (vertical solidline), the arithmetic mean (vertical dashed line), and25–75% percentile. Whiskers represent the 90th and 10thpercentile. Significant differences were determined by apost hoc comparison of means (Scheffé test after nestedanova; P < 0.05) and are indicated by different letters.Figure S2. Geographic map indicating the location of theS. caprea populations. South the Alps in Slovenia thecontaminated population in Mežica – M, and in Austriathe contaminated population Arnoldstein – (A) and thenon-contaminated sites, Völkermarkt – V and Forchten-stein – F. North the Alps in the Czech Republic thetwo contaminated populations Príbram – PR and KutnáHora – KH and the non-contaminated site near Prague(P).Figure S3. The graphs show that the amount of labile Znand Cd depends with a significance level for Pearson’s cor-relation of P < 0.01 on the total Zn and Cd concentration inthe soil. In fact, 40% of the variation of labile Zn and 36%for Cd are explained by the total Zn and Cd concentration,respectively. Other factors influencing the labile heavymetal fractions are pH, content of clay, carbonate andorganic matter.Figure S4. Pearson’s correlations between the level of con-tamination where the isolate originated and the biomassproduction in perlite cultures exposed to elevated levels ofCd and Zn.A and B are the graphs for the uncontaminated,

14 M. Puschenreiter et al.

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and C and D for the contaminated sites. The only signifi-cant negative correlation between Cd concentration andbiomass production was seen at uncontaminated sites (A).Isolates from the contaminated sites did not show such acorrelation indicating that they might have been selected towithstand higher Cd and Zn concentrations. Note that twosoil samples from the uncontaminated sites had very highZn concentrations probably because of a nearby rusty fencethat leached into the soil (B).Figure S5. Pearson’s correlations between the level of soilcontamination and Cd concentration in leaves after theexposure of the isolates in perlite to Cd and Zn. Although asignificant trend was found between soil contamination andaccumulation capacity in perlite cultures for soil contami-nation below 20 mg kg-1 (A, B), above this soil Cd contami-nation level, the trend diminished.

Table S1. Summary of the soil characteristics for eachisolate and their growth and accumulation behaviour inperlite-based soil-less cultures exposed to elevated levels ofCd (0.5 mg L-1) and Zn (5 mg L-1).Table S2. Overview of the SSR marker characteristics.Table S3. Heterozygosities, lnRH and normalized lnRHvalues of the 11 loci and 7 populations. Loci with Norm.Q-values of larger +1.95 and smaller -1.95 have a significantreduced variability and thus might have been under selec-tion and contribute to adaptation in the contamination area.

Please note: Wiley-Blackwell are not responsible for thecontent or functionality of any supporting materials suppliedby the authors. Any queries (other than missing material)should be directed to the corresponding author for thearticle.

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Supplementary Material

Differentiation of metallicolous and non-metallicolous Salix caprea populations based on

phenotypic characteristics and nuclear microsatellite (SSR) markers

Markus Puschenreiter1*, Mine Türktaş2*, Peter Sommer1, Gerlinde Wieshammer1, Gregor Laaha3,

Walter W. Wenzel1 and Marie-Theres Hauser2

Supplementary Table 1: Summary of the soil characteristics for each isolate and their growth

and accumulation behaviour in perlite-based soil-less cultures exposed to elevated levels of Cd

(0.5 mg L-1) and Zn (5 mg L-1).

Soil sample Zn total Cd total pH Zn labile

Cd labile

Plant sample

Zn Perlite

Cd Perlite

Biomass

mg/kg mg/kg mg/kg mg/kg Perlite mg/kg mg/kg (g) Prag Nr mean SE mean SE mean SE P1-5, 21-22 S 87.4 0.41 5.83 0.85 0.016 1 888 87.2 119 16.93 3.52 0.23P1-5, 21-22 S 87.4 0.41 5.83 0.85 0.016 2 787 22.5 82.0 3.77 5.20 0.25P1-5, 21-22 S 87.4 0.41 5.83 0.85 0.016 3 773 17.8 68.8 6.39 4.10 0.44P1-5, 21-22 S 87.4 0.41 5.83 0.85 0.016 4 874 32.2 97.8 7.34 2.44 0.54P1-5, 21-22 S 87.4 0.41 5.83 0.85 0.016 5 797 37.9 121 22.4 3.10 0.90P6 S 67.9 0.26 6.06 0.37 0.008 6 638 10.0 72.6 2.57 2.99 0.44P7-8 S a 72.0 0.28 7.62 0.12 0.008 7 600 10.1 58.5 4.08 5.04 0.24P7-8 S b 72.0 0.28 7.62 0.12 0.005 8 577 8.8 46.5 1.76 5.29 1.15P9 S 67.5 0.29 6.57 0.20 0.006 9 680 35.1 68.0 5.28 2.24 0.43P10-11 S 67.5 0.26 6.48 0.17 0.005 10 1187 206 147 35.0 0.89 0.48P10-11 S 67.5 0.26 6.48 0.17 0.005 11 922 51.3 91.6 1.54 6.06 0.40P12-13 - S 59.9 0.19 5.43 0.65 0.016 12 799 65.8 90.8 9.42 4.20 1.10P12-13 - S 59.9 0.19 5.43 0.65 0.016 13 777 41.0 85.3 7.11 5.78 1.02P14-16+18S 64.6 0.22 5.43 0.80 0.019 14 678 21.6 71.5 7.22 4.35 0.51P14-16+18S 64.6 0.22 5.43 0.80 0.019 15 835 94.5 94.0 13.9 4.22 0.34P14-16+18S 64.6 0.22 5.43 0.80 0.019 16 913 44.0 141 31.1 2.53 0.53P17 S 60.3 0.19 5.37 0.73 0.019 17 634 16.4 61.4 1.73 4.75 0.35P14-16+18S 64.6 0.22 5.43 0.80 0.019 18 644 58.0 86.4 17.3 3.22 0.57P1-5, 21-22 S 87.4 0.41 5.83 0.85 0.016 21 752 67.7 96.6 12.8 4.22 1.20P1-5, 21-22 S 87.4 0.41 5.83 0.85 0.016 22 815 59.4 111 10.9 3.46 0.30Mezica S - M1-3, 21-25 1292 18.1 7.2 0.92 0.10 1 820 19.6 119 15.4 3.24 0.28S - M1-3, 21-25 1292 18.1 7.2 0.92 0.10 3 736 23.8 122 24.7 3.61 0.49S - M5 9534 59.4 7.2 29.4 0.63 4 845 216 146 55.6 3.36 1.02S - M7 404 7.65 7.27 0.70 0.07 7 608 39.6 86.4 2.9 5.35 0.21S - M9+10+11 2747 13.6 7.57 2.35 0.04 10 784 114 90.0 3.1 3.07 0.85S - M9+10+11 2747 13.6 7.57 2.35 0.04 11 1476 235 243 50.5 1.67 0.52S - M8, 12-14, 26 1508 15.3 6.94 1.33 0.07 13 570 23.3 76.0 3.9 4.63 0.31S - M15 3269 28.5 7.06 7.75 0.18 15 882 52.8 145 11.2 2.61 0.35S - M16 489 9.94 7.19 1.05 0.06 16 679 35.7 112 9.0 2.63 0.35S - M17 131 1.93 7.45 0.40 0.02 17 541 19.5 65.4 3.0 2.69 0.12S - M18 2267 35.3 6.64 6.99 0.20 18 686 178 86.7 38.1 1.74 1.49S - M19+20 1867 18.3 7.28 3.60 0.10 19 719 119 133 30.2 2.78 0.40

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S - M19+20 1867 18.3 7.28 3.60 0.10 20 991 50.8 116 19.3 1.16 0.35S - M1-3, 21-25 1292 18.1 7.2 0.92 0.10 21 1656 178 265 25.3 1.64 0.28S - M1-3, 21-25 1292 18.1 7.2 0.92 0.10 22 893 47.4 112 9.0 4.13 0.32S - M1-3, 21-25 1292 18.1 7.2 0.92 0.10 23 995 149 156 26.0 2.68 0.56S - M1-3, 21-25 1292 18.1 7.2 0.92 0.10 24 1228 51.4 207 16.3 1.55 0.17S - M1-3, 21-25 1292 18.1 7.2 0.92 0.10 25 1103 120 152 12.1 2.60 0.66S - M8, 12-14, 26 1508 15.3 6.94 1.33 0.07 26 959 64.3 140 9.4 2.89 0.29S - M18, 27 2267 35.3 6.64 6.99 0.20 27 1154 212 138 9.2 1.87 0.82 28 781 38.9 104 11.6 2.56 0.23Pribram PR1 S 276 7.35 4.88 25.7 1.68 1 765 46.7 182 29.1 1.01 0.08PR 2-6 202 4.70 5.5 13.1 1.22 2 743 28.4 90.8 6.51 1.74 0.26PR 2-6 202 4.70 5.5 13.1 1.22 3 751 45.1 85.4 6.35 2.91 0.32PR 2-6 202 4.70 5.5 13.1 1.22 4 929 63.8 111 16.8 1.74 0.65PR 2-6 202 4.70 5.5 13.1 1.22 5 722 64.4 65.3 21.6 2.80 0.55PR 2-6 202 4.70 5.5 13.1 1.22 6 614 33.4 52.9 2.88 3.21 0.28PR7-9 S 4182 35.8 6.35 36.5 1.01 7 936 26.6 137 0.56 1.40 0.35PR7-9 S 4182 35.8 6.35 36.5 1.01 8 648 47.7 67.7 10.8 3.59 0.47PR7-9 S 4182 35.8 6.35 36.5 1.01 9 670 38.7 84.1 10.5 2.50 0.25PR10-12 S a 4387 38.9 6.5 63.3 1.40 10 928 20.9 76.5 8.26 1.55 0.18PR10-12 S a 4387 38.9 6.5 63.3 1.40 11 807 63.0 141 9.29 1.25 0.18PR13 S 1139 10.3 7.35 2.01 0.13 13 715 55.5 65.2 8.44 2.74 0.33PR14-17 S 2091 12.6 7.25 1.20 0.11 14 838 90.8 134 52.6 1.65 0.52PR14-17 S 2091 12.6 7.25 1.20 0.11 15 859 57.3 87.6 3.84 2.00 0.12PR14-17 S 2091 12.6 7.25 1.20 0.11 16 624 47.2 67.4 6.97 2.78 0.16PR18-19 S 2962 30.0 5.95 221 4.69 18 879 67.8 129 14.8 1.98 0.11PR18-19 S 2962 30.0 5.95 221 4.69 19 774 75.9 86.9 12.2 1.78 0.09Arnoldstein A1-3, A21-S 2492 19.4 7.3 14.3 0.11 1 913 27.0 148 13.8 1.85 0.24A5, A22-S 1530 11.9 6.23 57.4 0.90 5 701 22.5 118 8.61 3.00 0.38A 6 - 8-S 1244 9.65 6.61 8.68 0.21 6 685 44.2 88.6 12.1 5.09 0.40A 6 - 8-S 1244 9.65 6.61 8.68 0.21 8 709 68.7 138 25.7 2.13 0.40A9-S 4465 13.5 7.89 35.1 0.17 9 590 45.9 79.0 7.97 3.84 0.22A10-16-S 1824 3.91 7.97 7.57 0.03 13 604 67.4 100 8.38 3.51 0.30A10-16-S 1824 3.91 7.97 7.57 0.03 11 545 23.8 78.6 1.64 3.37 0.09A10-16-S 1824 3.91 7.97 7.57 0.03 14 733 68.0 117 15.6 3.20 0.42A10-16-S 1824 3.91 7.97 7.57 0.03 15 900 85.9 192 25.0 2.34 0.37A17-S 1011 3.93 7.59 0.82 0.02 16 784 25.0 129 3.55 2.87 0.61A18-S 1898 12.0 7.44 10.0 0.15 19 903 130 153 30.5 2.82 0.51A19,20-S 1134 6.50 7.49 2.83 0.10 20 516 57.0 65.3 16.8 4.56 0.89A1-3, A21-S 2492 19.4 7.3 14.3 0.11 21 855 100 127 20.0 1.85 0.70A5, A22-S 1530 11.9 6.23 57.4 0.90 22 1062 90.2 169 27.1 3.13 0.63A19,20-S=A23 1134 6.50 7.49 2.83 0.10 23 751 36.5 122 7.68 2.47 0.09A1-3, 21, 24 S 2492 19.4 7.3 14.3 0.11 24 1192 57.0 248 15.1 1.52 0.10A18-S=A27 1898 12.0 7.44 10.0 0.15 27 1173 28.0 192 28.1 2.17 0.31A9-S=A28 4465 13.5 7.89 35.1 0.17 28 910 108 179 23.6 1.16 0.18Forchtenstein F2-4S 80.9 0.46 5.83 5.94 0.052 2 691 79.6 126 19.5 2.24 0.53F2-4S 80.9 0.46 5.83 5.94 0.052 3 606 32.4 72.1 3.13 3.23 0.31F5-9S 92.8 0.37 4.74 3.50 0.032 6 681 65.7 83.1 12.7 4.10 0.56F5-9S 92.8 0.37 4.74 3.50 0.032 7 570 21.7 60.1 4.28 4.40 0.19F5-9S 92.8 0.37 4.74 3.50 0.032 8 917 98.3 114 19.1 4.04 0.80F10-18S 89.0 0.59 4.3 8.03 0.049 10 804 47.4 96.6 16.6 2.90 0.52

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F10-18S 89.0 0.59 4.3 8.03 0.049 11 897 70.3 118 9.48 3.29 0.62F10-18S 89.0 0.59 4.3 8.03 0.049 12 707 58.0 79.3 13.9 3.68 0.35F10-18S 89.0 0.59 4.3 8.03 0.049 13 659 26.2 93.4 3.32 3.45 0.23F10-18S 89.0 0.59 4.3 8.03 0.049 14 797 110 125 26.4 2.75 0.41F10-18S 89.0 0.59 4.3 8.03 0.049 17 633 11.5 85.1 2.85 3.74 0.49F19-S 519* 0.50 5.14 76.45 0.028 19 632 37.5 61.8 4.76 3.20 0.29F20-S 228* 0.79 7.47 0.15 0.003 20 575 48.4 57.5 9.65 4.37 1.00Kutna Hora KH1-2 S 8603 72.00 6.42 82.99 1.206 1 924 29.2 133 9.55 1.74 0.38KH1-2 S 8603 72.00 6.42 82.99 1.206 2 745 74.1 76.2 6.96 2.40 0.34KH3-5, 21 S 2740 31.50 7.91 5.36 0.124 4 741 54.3 104 15.0 2.57 0.56KH3-5, 21 S 2740 31.50 7.91 5.36 0.124 5 650 19.9 61.0 4.88 2.37 0.49KH6 S 8220 84.00 7.14 14.65 0.378 6 862 61.5 110 17.0 1.66 0.36KH7 S 630 4.00 7.58 1.94 0.024 7 921 59.9 117 11.3 2.34 0.25KH8 S 5015 54.00 7.19 9.36 0.364 8 667 27.6 82.2 2.92 1.79 0.15KH10, 22 S 4814 61.00 7.02 18.52 0.673 10 618 33.1 47.3 5.42 3.91 0.46KH11-12S 4010 56.00 7.08 16.19 0.353 11 955 185 85.2 9.61 1.63 0.22KH13 S 505 4.50 8.34 1.06 0.005 13 767 152 84.5 16.0 2.27 0.26KH14 S 515 4.00 7.07 2.92 0.141 14 617 38.9 88.6 7.13 2.38 0.14KH15, 23 S 1801 14.00 5.12 12.74 0.272 15 931 72.2 119 26.9 1.21 0.19KH16-17 S 715 4.00 7.5 4.55 0.180 16 807 63.0 124 14.6 2.49 0.35KH16-17 S 715 4.00 7.5 4.55 0.180 17 699 46.4 68.2 9.00 1.54 0.30KH11-12S 4010 56.00 7.08 16.19 0.353 12 859 70.9 105 7.32 4.10 0.28KH18 S 109 1.50 7.67 1.685 0.045 18 768 107 123 21.8 2.81 0.33KH19-20 105 1.50 8.46 0.88 0.005 19 745 96.2 86.1 14.5 2.67 0.39KH19-20 105 1.50 8.46 0.88 0.005 20 667 21.8 116 7.73 2.26 0.28KH3-5, 21 S 2740 31.50 7.91 5.36 0.124 21 1021 31.1 252 12.5 1.44 0.13KH10, 22 S 4814 61.00 7.02 18.52 0.673 22 996 59.7 215 35.6 1.68 0.55KH15, 23 S 1801 14.00 5.12 12.74 0.272 23 1044 62.0 178 12.1 2.79 0.26Völkermarkt V1-5 63.5 0.58 6.8 0.58 0.002 1 1042 296 172 78.9 2.60 0.87V1-5 63.5 0.58 6.8 0.58 0.002 2 927 72.8 142 23.0 2.34 0.48V1-5 63.5 0.58 6.8 0.58 0.002 3 963 33.5 145 8.70 2.67 0.21V1-5 63.5 0.58 6.8 0.58 0.002 4 868 170 160 27.4 2.43 0.11V1-5 63.5 0.58 6.8 0.58 0.002 5 1116 261 190 28.6 1.14 0.47V6-11 74.4 0.46 6.7 0.72 0.010 7 925 158 154 34.8 2.56 0.64V6-11 74.4 0.46 6.7 0.72 0.010 9 664 84.6 103 20.7 3.05 0.41V6-11 74.4 0.46 6.7 0.72 0.010 10 939 76.1 147 16.8 2.63 0.26V6-11 74.4 0.46 6.7 0.72 0.010 11 888 64.7 159 6.21 0.82 0.14V12-21 58.4 0.45 6.5 1.40 0.001 12 794 22.7 117 9.19 2.55 0.31V12-22 58.4 0.45 6.5 1.40 0.001 13 897 107 178 36.9 2.40 0.59V12-23 58.4 0.45 6.5 1.40 0.001 14 766 37.9 110 9.39 3.21 0.26V12-24 58.4 0.45 6.5 1.40 0.001 15 972 189 151 30.8 3.08 0.72V12-25 58.4 0.45 6.5 1.40 0.001 16 606 32.3 82.0 1.33 4.62 0.10V12-26 58.4 0.45 6.5 1.40 0.001 17 942 225 160 58.2 3.19 0.73V12-27 58.4 0.45 6.5 1.40 0.001 18 914 62.9 196 20.6 1.14 0.28V12-28 58.4 0.45 6.5 1.40 0.001 19 1064 192 160 28.8 1.87 0.75V12-29 58.4 0.45 6.5 1.40 0.001 20 759 59.0 106 16.0 3.95 0.41V12-30 58.4 0.45 6.5 1.40 0.001 21 1009 150 109 12.7 2.23 0.75*) these two Zn soil concentrations are probably high because of a nearby rusty and leaching fence.

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4

Supplementary Table 2 Overview of the SSR marker characteristics

Locus Allele Size [bp]

Primer Sequence 5` - 3` Motif in

S. caprea

Motif in

P. trichocarpa

Chromsomal Position

ORPM_62 170-198 F: CGGAGTCAGCTTGAGGTAGC

R: CGGCAATATTGAGGAGAATGA

[AT]2.. [T]8 [AT]4..[ATTTT]3 I

16.99 Mb

ORPM_312 177-207 F: GTGGGGATCAATCCAAAAGA

R: CCCATATCAAACCATTTGAAAAA

[CTT]5 [CCT]6 VII

9.31 Mb

ORPM_446 220-258 F: GGGCTGCAGACAAATTAAGG

R: TGGGACATGCTCCATGGTAT

[CT]3..[CT]4 [CT]3..[CT]4 XIV – 3.69

Mb

Motif in

P. nigra

WMPS_12 146-155 F: TTTTTCGTATTCTTATCTATCC

R: CACTACTCTGACAAAACCATC

[CA]4 [CA]4 VI

11.36 Mb

WMPS_14 205-237 F: CAGCCGCAGCCACTGAGAAATC

R: GCCTGCTGAGAAGACTGCCTTGAC

1[CAG]21 1[CAG]28 V

12.53 Mb

WMPS_19 152-209 F: AGCCACAGCAAATTCAGATGATGC

R: CCTGCTGAGAAGACTGCCTTGACA

2[CAG]22 2[CAG]38 V

12.53 Mb

WMPS_21 157-184 F: TGCTGATGCAAAAGATTTAG

R: TTGGAACTTCAACATTCAGAT

[GCT]27 [GCT]45 II

5.05 Mb

Motif in

S. burjatica

SB_24 127-153 F: ACTTCAATCTCTCTGTATTCT

R: CTATTTATGGGTTGGTCGATC

3[TG]9 3[TG]9 XI

4.70 Mb

SB_38 91-151 F: CCACTTGAGGAGTGTAAGGAT

R: CTTAAATGTAAAACTGAATCT

[TG]27 [TG]27 IX

10.38 Mb

SB_199 95-127 F: CTATTTGGTCTCAATCACCTT

R: CTTTACCTCAGAAAATCCAGA

[TG]15 [TG]11CG[TG]6 XV

9.74 Mb

Motif in

S. lanata

gSIMCT024 96-124 F: CTCCCTTCACTTGCTCCAT

R: TAATACCAGCCCTTAAAGAAG

[CT]14 [CT]10 X

14.27 Mb

1Complete complex motif in S. caprea: [CAG]4CAT[CAG]12CAT[CAG]3 and in P. nigra:

[CAG]1CAT[CAG]12CAT[CAG]9CAT[CAG]3 2Complete complex motif in S. caprea: [CAG]2CAT[CAG]8CAT[CAG]10 and in P. nigra:

[CAG]3CAT[CAG]8CAT[CAG]8CAT[CAG]4CAA[CAG]3

3Complete complex motif in S. caprea: [TG]9A[TG]2A[TG]4 and in S. burjatica:

[TG]9A[TG]2A[TG]4..[TG]3

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Supplementary Table 3 Heterozygosities, lnRH and normalized lnRH values of the 11 loci

and seven populations. Loci with Norm.Θ values of larger +1.95 and smaller –1.95 have a

significant reduced variability and thus might have been under selection and contribute to

adaptation in the contamination area.

Populations Locus Hobs Hexp

A M V F KH PR P

µc µu lnRH Θ

Norm.Θ

ORPM_62 0.49 0.74 0.79 0.55 0.67 0.70 0.75 0.78 0.71 0,72 0,69 0,18 -0,30

ORPM_312 0.81 0.82 0.85 0.82 0.84 0.73 0.88 0.77 0.74 0,83 0,77 0,63 0,79

ORPM_446 0.56 0.81 0.86 0.80 0.79 0.75 0.80 0.79 0.80 0,81 0,78 0,33 0,07

WMPS_12 0.34 0.33 0.25 0.44 0.09 0.38 0.41 0.50 0.20 0,40 0,22 0,99 1,68

WMPS_14 0.85 0.84 0.81 0.86 0.84 0.85 0.77 0.86 0.90 0,83 0,86 -0,51 -1,97*

WMPS_19 0.84 0.88 0.86 0.88 0.85 0.86 0.88 0.89 0.89 0,88 0,87 0,17 -0,32

WMPS_21 0.79 0.76 0.75 0.75 0.75 0.71 0.75 0.76 0.82 0,75 0,76 -0,07 -0,90

SB_24 0.89 0.77 0.77 0.82 0.79 0.79 0.79 0.78 0.67 0,79 0,75 0,37 0,16

SB_38 0.82 0.91 0.90 0.93 0.82 0.90 0.92 0.89 0.89 0,91 0,87 0,74 1,07

SB_199 0.61 0.76 0.80 0.79 0.79 0.81 0.78 0.65 0.65 0,76 0,75 0,04 -0,63

gSIMCT024 0.85 0.80 0.82 0.74 0.73 0.84 0.84 0.83 0.76 0,82 0,78 0,45 0,35

all loci 0.71 0.77 0.77 0.76 0.72 0.76 0.78 0.77 0.73

* only significant without Bonferroni correction

Page 21

6

Kutna

Hor

a

Pribra

m

Arnold

stein

Mez

ica

Pragu

e

Forch

tens

tein

Völker

mar

kt

sho

ot

len

gth

(cm

)

0

20

40

60

80

Kutna

Hor

a

Pribra

m

Arnold

stein

Mez

ica

Pragu

e

Forch

tens

tein

Völker

mar

kt

leaf

num

ber

(-)

0

20

40

60

80

100

120

aa ab

b

a

ab

c

ac acb

acac

b

c

A

B

Supplementary Figure 1: Shoot length (A) and leaf number (B) of S. caprea isolates. Boxes

represent the median (vertical solid line), the arithmetic mean (vertical dashed line), and 25 –

75 % percentile. Whiskers represent the 90th and 10th percentile. Significant differences were

determined by a post hoc comparison of means (Scheffé test after nested analysis of variance;

p < 0.05) and are indicated by different letters.

Page 22

7

Supplementary Figure 2: Geographic map indicating the location of the S. caprea

populations. South the Alps in Slovenia the contaminated population in Mežica – M, and in

Austria the contaminated population Arnoldstein – (A) and the non-contaminated sites,

Völkermarkt - V and Forchtenstein – F. North the Alps in the Czech Republic the two

contaminated populations Příbram – PR and Kutná Hora – KH and the non-contaminated site

near Prague (P).

Page 23

8

y = 23.906x + 1093.4r = 0.406p<0.01

0

2000

4000

6000

8000

10000

12000

0 50 100 150 200 250

Zn labile [mg/kg]

tota

l Zn

[m

g/k

g]

y = 8.1922x + 10.079r = 0.3632

P<0.01

0

10

20

30

40

50

60

70

80

90

0 1 2 3 4 5 6

Cd labile [mg/kg]

tota

l C

d [

mg

/kg

]

Supplementary Figure 3: The graphs show that the amount of labile Zn and Cd depends

with a significance level for Pearson’s correlation of p<0.01 on the total Zn and Cd

concentration in the soil. In fact 40% of the variation of labile Zn and 36% for Cd are

explained by the total Zn and Cd concentration, respectively. Other factors influencing the

labile heavy metal fractions are pH, content of clay, carbonate and organic matter.

Page 24

9

A B

y = -2.9029x + 4.5286r = 0.3294p < 0.05

0

1

2

3

4

5

6

7

0.00 0.20 0.40 0.60 0.80 1.00

total Cd [mg/kg]

bio

mas

s / p

lan

t [g

]

y = 0.0011x + 3.2019r = 0.064p > 0.05

0

1

2

3

4

5

6

7

0 100 200 300 400 500 600

Total Zn [mg/kg]

bio

mas

s / p

lan

t [g

]

C D

y = -0.0087x + 2.8446r = 0.1386p > 0.05

0

1

2

3

4

5

6

7

8

0 10 20 30 40 50 60 70 80 90

total Cd [mg/kg]

bio

mas

s / p

lan

t [g

]

y = -1E-04x + 2.8907r = 0.1679p > 0.05

0

1

2

3

4

5

6

7

8

0 2000 4000 6000 8000 10000

total Zn [mg/kg]

bio

mas

s / p

lan

t [g

]

Supplementary Figure 4: Pearson’s correlations between the level of contamination where

the isolate originated and the biomass production in perlite cultures exposed to elevated levels

of Cd and Zn. A and B are the graphs for the uncontaminated, C and D for the contaminated

sites. The only significant negative correlation between Cd concentration and biomass

production was seen at uncontaminated sites (A). Isolates from the contaminated sites did not

show such a correlation indicating that they might have been selected to withstand higher Cd

and Zn concentrations.

Note that two soil samples from the uncontaminated sites had very high Zn concentrations

probably because of a nearby rusty fence that leached into the soil (B).

Page 25

10

y = 85.949x + 73.648r = 0.30181

p < 0.05

0

50

100

150

200

250

0.00 0.20 0.40 0.60 0.80 1.00

total Cd [mg/kg]

Cd

in le

aves

of

per

lite

cult

ure

s [m

g/k

g]

y = 3.5211x + 84.888r = 0.4177p < 0.01

0

50

100

150

200

250

300

0 5 10 15 20

total Cd [mg/kg]

Cd

in le

aves

of

per

lite

cult

ure

s [m

g/k

g]

y = -0.5456x + 140.83r = 0.1803p > 0.05

0

50

100

150

200

250

300

0 10 20 30 40 50 60 70 80 90

total Cd [mg/kg]

Cd

in le

aves

of

per

lite

cult

ure

s [m

g/k

g]

Supplementary Figure 5: Pearson’s correlations between the level of soil contamination and

Cd concentration in leaves after the exposure of the isolates in perlite to Cd and Zn. While a

significant trend was found between soil contamination and accumulation capacity in perlite

cultures for soil contamination below 20 mg kg-1 (A, B) above this soil Cd contamination

level the trend diminished.

A

B C