untitled ACTA BOT. CROAT. 75 (1), 2016 45 Acta Bot. Croat. 75 (1), 45–52, 2016 CODEN: ABCRA 25 DOI: 10.1515/botcro-2016-0005 ISSN 0365-0588 eISSN 1847-8476 Species delimitation and genetic diversity analysis in Salvia with the use of ISSR molecular markers Masoumeh Safaei1, Masoud Sheidai1*, Behnaz Alijanpoor2, Zahra Noormohammadi3 1 Faculty of Biological Sciences, Shahid Beheshti University, Tehran, Iran 2 Iran National Botanic Garden, Tehran, Iran 3 Department of Biology, School of Basic Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran Abstract – Thirty-nine plant specimens of six Salvia species were collected from different localities of the Alborz mountain region in Iran and studied for morphological and genetic variability and species relation- ship. Inter simple sequence repeats (ISSR) molecular markers showed a high degree of within-species and interspecifi c genetic variability in Salvia. Analysis of molecular variance and Hickory tests showed signifi - cant molecular difference among the studied populations. A principal coordinate analysis plot of morphologi- cal characters grouped the species into two distinct groups, supporting their taxonomic treatment. This was partly supported by ISSR networking. The Mantel test did not show a correlation between genetic distance and the geographical distance of the studied populations. STRUCTURE and reticulation analyses revealed some degree of gene fl ow among the species. The present study showed that ISSR molecular markers could be used in Salvia species delimitation along with morphological study. Key words: ISSR, network, population structure, Salvia * Corresponding author, e-mail: msheidai@yahoo.com Introduction The genus Salvia L. is the largest genus of plants in the mint family Lamiaceae, which contains about 900 species distributed throughout the Old and New World growing in temperate and subtropical areas (Standley and Williams 1973, Özdemir and Senel 1999). Within the Lamiaceae, Salvia is member of the tribe Mentheae within the subfam- ily Nepetoideae. It is one of several genera commonly re- ferred to as sage. Western Asia and Mediterranean regions have been considered as the original centers of the distribu- tion of Salvia (Wu and Li 1982). Salvia has undergone marked species radiations in three regions of the world: Central and South America (500 spp.), central Asia/Medi- terranean (250 spp.), and eastern Asia (90 spp.) (Walker et al. 2004). Salvia species are herbaceous, suffruticose or shrubby perennials, rarely biennial or annual, often strongly aromat- ic. These species are of horticultural, commercial and me- dicinal value. They contain monoterpene with antiseptic characteristics (Özdemir and Senel 1999) and the com- pounds obtained from these species decrease DNA synthe- sis in the cell, an important feature in the diagnosis and treatment of cancer. Many species of the Lamiaceae are ar- omatic and are often used as herbs, spices, folk medicines and fragrances. In addition, Salvia species are grown in parks and gardens as ornamental plants (Özdemir and Senel 1999). For example, S. nemorosa L., which is also included in the present study, is a popular herbaceous perennial spe- cies with both seed and vegetative propagated cultivars. S. spinosa L., another taxon discussed in the present work, contains essential oil in its aerial parts with antibacterial properties (Salehi-Sormaghi et al. 2006). There have been about 70 Salvia species reported from Flora Iranica (Hedge 1982) with 40% endemism. These species are distributed in subtropical, temperate, sub-arctic and arctic areas as well as in the tropical regions of Iran (Hedge 1982, Sheidai et al. 2010). Some of these species are very distinct while others show close affi nity to each other, and some of the species are in a state of evolutionary fl ux (Hedge 1982). Interspecifi c hybridization is suspected to be operative in this genus leading to a great morphologi- cal diversity (Hedge 1982). The occurrences of both natu- rally formed interspecifi c hybrids as well as artifi cial cross- es have been reported in Salvia. Successful crosses usually occur between closely related species. These hybrids usual- ly have intermediate morphological features (Tychonievich and Warner 2011, Radosavljević et al. 2012). Different molecular markers have been used in different investigations related to Salvia taxa: DNA barcoding meth- SAFAEI M., SHEIDAI M., ALIJANPOOR B., NOORMOHAMMADI Z. 46 ACTA BOT. CROAT. 75 (1), 2016 od (rpoB, rbcL, matK and trnH-psbA) (De Mattia et al. 2011), combination of chloroplast and nuclear ribosomal DNA sequences and allozyme (Sudarmono and Okada 2008), random amplifi ed polymorphic DNA (RAPD) and inter simple sequence repeats (ISSR) markers (Wang et al. 2011, Sepehry-Javan et al. 2012), chloroplast DNA regions of rbcL and trnL-F (Walker et al. 2004), nrDNA ITS se- quences (Zhan et al. 2012), PCR-RFLP (Karaca et al. 2008) and simple sequence repeats (SSR) markers (Radosavljević et al. 2011, 2012). The present study was performed to investigate genetic diversity and morphological variation in six Salvia species with the aim of producing data regarding their inter- and intra-population genetic structure and possible interspecies gene fl ow. Such information will be important in the con- servation of these medicinal plants and also in the provision of information about the evolution of the genus. We used ISSR molecular markers for genetic diversity analysis and for studying the species relationship, as these molecular markers were reported to be suitable for such in- vestigations (Wang et al. 2011, Sepehry-Javan et al. 2012). Material and methods Plant material Thirty-nine accessions of six Salvia species were col- lected from natural habitats in Iran (Tab. 1). Sampling was done in the Central Alborz region during 2013. Salvia spe- cies studied are: S. hypoleuca Benth., S. limbata C. A. Mey., S. nemorosa L., S. xanthocheiala Boiss. ex. Benth. (from the species group 1), and S. spinosa L., S. reuterana Boiss. (from the species group 3). The voucher specimens are de- posited in Herbarium of Shahid Beheshti University (HSBU) (Tab. 1). Morphological studies Morphological characters studied are: pedicel length, calyx length, stem leaf length, stem leaf width, bract length, fi lament length, anther length, corolla length, nut length, nut width, basal leaf length, basal leaf width, corolla color, corolla shape, bract shape, seed color, seed shape, bract col- or, corolla latex, leaf surface, calyx shape, and basal leaf shape. DNA extraction and ISSR assay Fresh leaves were collected randomly from plant speci- mens and dried in silica gel powder. Genomic DNA was extracted using the cetyltrimethyl ammonium bromide (CTAB) activated charcoal protocol (Sheidai et al. 2013). The quality of extracted DNA was examined by running on 0.8% agarose gel. Ten ISSR primers; (AGC)5GT, (CA)7GT, (AGC)5GG, UBC810, (CA)7AT, (GA)9C, UBC807, UBC811, (GA)9A and (GT)7CA custom synthesized by UBC (the University of British Columbia) were used. PCR reactions were per- formed in a 25 μL volume containing 10 mM Tris-HCl buf- fer at pH 8, 50 mM KCl, 1.5 mM MgCl2, 0.2 mM of each dNTP (Bioron, Germany), 0.2 μM of a single primer, 20 ng genomic DNA and 3 U of Taq DNA polymerase (Bioron, Germany). The amplifi cation reactions were performed in a Techne thermocycler (Germany) with the following pro- gram: a 5 min initial denaturation step at 94 °C, 30 s at 94 °C; 1 min at 50 °C and 1 min at 72 °C. The reaction was completed with a 7 min extension step at 72 °C. The amplifi cation products were visualized by running on 2% agarose gel, followed by the ethidium bromide stain- ing. The fragment size was estimated by using a 100 bp molecular size ladder (Fermentas, Germany). Data analyses Principal coordinate analysis (PCoA) was performed to group the plant specimens according to morphological characters and a principal components analysis (PCA bip- lot) was used to identify the most variable morphological characters among the studied species (Podani 2000). Mor- phological data were standardized (mean = 0, variance = 1) for these analyses (Podani 2000). ISSR bands obtained were coded as binary characters (presence = 1, absence = 0). Genetic diversity parameters were determined in each population. These parameters were Nei’s gene diversity (H), Shannon information index (I), number of effective alleles, and percentage of polymor- phism (Weising 2005, Freeland et al. 2011). Nei’s genetic distance was determined among the studied populations and used for clustering (Freeland et al. 2011, Weising 2005). For grouping of the plant specimens, the neighbor joining (NJ) clustering method and the neighbor-net meth- od of networking were performed after bootstrapping 100 times (Huson and Bryant 2006, Freeland et al. 2011). The Mantel test was performed to check correlation be- tween the geographical distance and the genetic distance of the studied species (Podani 2000). PAST ver. 2.17 (Hamer et al. 2001), DARwin ver. 5 (2012) and SplitsTree4 V4.13.1 (2013) programs were used for these analyses. Signifi cant genetic differences among the studied populations and provinces were determined by: 1 – Analysis of molecular variance (AMOVA) test (with 1000 permutations) with the use of GenAlex 6.4 (Peakall and Smouse 2006), and 2 – Nei’s Gst analysis of GenoDive ver.2 (2013) (Meirmans and Van Tienderen 2004). Furthermore, populations’ genet- ic differentiation was studied by G’st_est = standardized measure of genetic differentiation (Hedrick 2005), and D_ est = Jost measure of differentiation (Jost 2008). In order to overcome potential problems caused by the dominance of ISSR markers, a Bayesian program, Hickory (ver. 1.0) (Holsinger et al. 2003), was used to estimate pa- rameters related to genetic structure (theta B value). Three runs were conducted with default sampling parameters (burn-in = 50,000, sample= 250,000, thin = 50) to ensure consistency of results (Tero et al. 2003). The genetic structure of geographical populations and provinces was studied with two methods. First we carried out a Bayesian based model STRUCTURE analysis (Pritchard et al. 2000). For this analysis, data were scored as dominant markers (Falush et al. 2007). An Evanno test GENETIC DIVERSITY IN SALVIA ACTA BOT. CROAT. 75 (1), 2016 47 was performed on the STRUCTURE result to fi nd the prop- er number of K by using delta K value (Evanno et al. 2005). Secondly, we performed K-Means clustering as per- formed in GenoDive ver. 2. (2013). In this analysis, the op- timal clustering is the one with the smallest amount of vari- ation within clusters. This is calculated by using the within-clusters sum of squares. The minimization of the within-groups sum of squares that is used in K-Means clus- tering is, in the context of a hierarchical AMOVA, equiva- lent to minimizing the among-populations-within-groups sum of squares, SSDAP/WG (Meirmans 2012). We used two summary statistics to present K-Means clustering, 1- pseudo-F (Caliński and Harabasz 1974), and 2- Bayesian information criterion (BIC) (Schwarz 1978). The clustering with the highest value for pseudo-F is regarded as provid- ing the best fi t, while clustering with the lowest value for BIC is considered to provide the best fi t (Meirmans 2012). Similarly, non-metric multidimensional scaling (MDS) Tab 1. Salvia species studied, their locality and voucher number. Species Locality Latitude Longitude Height Voucher Number S. hypoleuca Benth. Porkan, Chalous 51.04.03 36.56.05 1613 HSBU 2012131 S. hypoleuca Polur1, Mazandaran 52.25.12 35.50.43 2273 HSBU 2012132 S. hypoleuca Polur2, Mazandaran 52.25.12 35.50.43 2273 HSBU 2012133 S. hypoleuca Chanar gharb, Firoozkooh 52.85.12 35.39.35 1572 HSBU 2012134 S. hypoleuca Dizin1, Tehran 51.25.19 36.00.58 3476 HSBU 2012135 S. hypoleuca Dizin2, Tehran 51.25.19 36.00.58 3476 HSBU 2012136 S. hypoleuca Jagrod1, Alborz 51.47.09 35.35.32 1235 HSBU 2012137 S. hypoleuca Jagrod2, Alborz 51.47.09 35.35.32 1235 HSBU 2012138 S. nemorosa L. Polur, Mazandaran 52.25.12 35.50.43 2273 HSBU 2012139 S. nemorosa Damavand1, Alborz 52.32.50 35.50.46 2221 HSBU 2012140 S. nemorosa Damavand2, Alborz 52.32.50 35.50.46 2221 HSBU 2012141 S. nemorosa Khojir1, Firoozkooh 51.43.19 35.39.71 1300 HSBU 2012142 S. nemorosa Khojir2, Firoozkooh 51.43.19 35.39.71 1300 HSBU 2012143 S. nemorosa Dizin1,Tehran 51.25.19 36.00.58 3476 HSBU 2012144 S. nemorosa Dizin2,Tehran 51.25.19 36.00.58 3476 HSBU 2012145 S. nemorosa Chanargharb1, Firoozkooh 52.85.12 35.39.35 1572 HSBU 2012146 S. limbata C.A.Mey Chanargharb2, Firoozkooh 52.85.12 35.39.35 1572 HSBU 2012147 S. limbata Khojir1, Firoozkooh 51.43.19 35.39.71 1300 HSBU 2012148 S. limbata Khojir2, Firoozkooh 51.43.19. 35.39.71 1300 HSBU 2012149 S. limbata Tochal1,Tehran 51.24.02 35.49.14 1857 HSBU 2012150 S. limbata Tochal2,Tehran 51.24.02 35.49.14 1857 HSBU 2012151 S. limbata Gulahak, Firoozkooh 52.07.35 35.39.31 2022 HSBU 2012152 S. xanthocheila Boiss. Laar1, Alborz 53.03.22 35.51.19 2163 HSBU 2012153 S. xanthocheila Laar2, Alborz 53.03.22 35.51.19 2163 HSBU 2012154 S. xanthocheila Dizin1,Tehran 51.25.19 36.00.58 3476 HSBU 2012155 S. xanthocheila Dizin2,Tehran 51.25.19 36.00.58 3476 HSBU 2012156 S. xanthocheila Emamzadeh Hashem, Mazandaran 52.20.21 35.46.48 2699 HSBU 2012157 S. spinosa L. Khojir1, Firoozkooh 51.43.19 35.39.71 1300 HSBU 2012158 S. spinosa Khojir2, Firoozkooh 51.43.19 35.39.71 1300 HSBU 2012159 S. spinosa Chitgar1, Tehran 51.12.29 35.46.47 1285 HSBU 2012160 S. spinosa Chitgar2, Tehran 51.12.29 35.46.47 1285 HSBU 2012161 S. spinosa Hesar1, Chalous 51.01.53 35.49.24 1394 HSBU 2012162 S. spinosa Hesar2, Chalous 51.01.53 35.49.24 1394 HSBU 2012163 S. reuteriana Boiss. Tochal1,Tehran 51.24.02 35.49.14 1857 HSBU 2012164 S. reuteriana Tochal2,Tehran 51.24.02 35.49.14 1857 HSBU 2012165 S. reuteriana Darakah, Tehran 51.23.48 48.25.45 1697 HSBU 2012166 S. reuteriana Porkan1, Chalous 51.04.03 36.56.05 1613 HSBU 2012167 S. reuteriana Porkan2, Chalous 51.04.03 36.56.05 1613 HSBU 2012168 S. reuteriana Chanar gharb, Firoozkooh 52.85.12 35.39.35 1572 HSBU 2012169 SAFAEI M., SHEIDAI M., ALIJANPOOR B., NOORMOHAMMADI Z. 48 ACTA BOT. CROAT. 75 (1), 2016 (Podani 2000) was performed to study the genetic distinct- ness of the provinces. Recently Frichot et al. (2013) introduced the statistical model called “latent factor mixed models (LFMM)”, which tests correlations between environmental and genetic varia- tion, while estimating the effects of hidden factors that rep- resent background residual levels of population structure. We used this method to check if the ISSR markers used here show any correlation with the environmental features of the species studied. The analysis was done with the LFMM program Version: 1.2 (2013). Results Morphometry The analysis of variance (ANOVA) test showed a sig- nifi cant difference (p < 0.05) for quantitative morphological characters among Salvia species. Moreover, PCoA plot of both quantitative and qualitative morphological characters separated the studied species into two distinct groups (Fig. 1). S. reuterana and S. spinosa of the species group 3, were placed close to each other and much separated from the other studied species of the species group 1 according to Flora Iranica (Hedge 1982). Within the species group 3, S. hypoleuca showed mor- phological similarity with S. nemorosa, while, S. limbata and S. xanthocheila, were placed closer to each other. PCA analysis revealed that the fi rst 3 components com- prised about 86% of total morphological variability. In the fi rst PCA components with about 45% of total variation, characters like calyx length, bract length, seed shape, calyx shape, and basal leaf shape showed the highest positive cor- relation (> 0.80). These morphological characters mainly separated S. spinosa and S. reuterana (species group 3) from the other species (Fig. 2). The nut width, basal leaf length, seed color, and leaf surface showed the highest positive correlation (> 0.70) with the second PCA component. These characters separat- ed S. hypoleuca and S. limbata from S. nemorosa and S. xanthocheila of the species group 1 (Fig. 2). ISSR analysis We obtained reproducible bands from almost all ISSR primers used and fi nally, a data matrix was formed. The de- trended correspondence analysis (DCA) plot revealed (not shown) scattered distribution of the ISSR loci studied, which indicated these loci are not linked and are suitable for population genetic structure analysis. Genetic diversity parameters determined in 6 studied species (Tab. 2) revealed that S. limbata had the highest level of genetic polymorphism (57.14%), while the lowest level of genetic polymorphism (28.57%) occurred in S. re- uterana. S. limbata also had the highest values for effective number of alleles (Ne = 1.256) and Shannon information index (I = 0.25). The AMOVA test revealed signifi cant molecular differ- ences (P = 0.01) among the studied species. It also revealed that 21% of total genetic variability occurred among the studied populations while 79% occurred within these spe- cies. Furthermore, pair-wise AMOVA test as well as non- metric MDS analysis revealed that most of the paired sam- ples comparisons differed signifi cantly from each other (P = 0.01) (Fig. 3). Tab. 2. Genetic diversity parameters determined in Salvia species. Na – no. of different alleles, Ne – no. of effective alleles, I – Shan- non’s information index, He – expected heterozygosity, UHe – un- biased expected heterozygosity, %P – percentage of polymorphic loci. Species N Na Ne I He UHe %P S. hypoleuca 8 0.78 1.119 0.14 0.083 0.088 38.46% S. nemorosa 7 0.96 1.183 0.20 0.125 0.134 47.25% S. limbata 7 1.15 1.256 0.26 0.163 0.175 57.14% S. xanthocheila 5 1.00 1.192 0.21 0.132 0.146 49.45% S. spinosa 6 0.87 1.237 0.22 0.145 0.158 42.86% S. reuterana 6 0.67 1.169 0.15 0.098 0.107 28.57% Fig. 1. Principal coordinate analysis plot of morphological charac- ters, separating two Salvia species groups from each other. Fig. 2. Principal components analysis biplot of morphological characters in Salvia species. Character numbers: 1) pedicel length, 2) calyx length, 3) stem leaf length, 4) stem leaf width, 5) bract length, 6) fi lament length, 7) anther length, 8) corolla length, 9) nut length, nut width, 10) basal leaf length, 11) basal leaf width, 12) corolla color, 13) corolla shape, 14) bract shape, 15) seed col- or, 16) seed shape, 17) bract color, 18) corolla latex, 19) leaf sur- face, 20) calyx shape, and 21) basal leaf shape, respectively. GENETIC DIVERSITY IN SALVIA ACTA BOT. CROAT. 75 (1), 2016 49 The Hickory test we used also produced a Theta B value of 0.25, which is a signifi cant value. Gst value (0.18, P = 0.001), and Hedrick, standardised fi xation index (G’st = 0.23, P = 0.001) as well as the Jost, differentiation index (D-est = 0.06, P = 0.001) showed that Salvia species are genetically differentiated. Nei’s genetic identity and the genetic distance deter- mined among the studied species are presented in On-line Suppl. Tab. 1. The results showed that the highest degree of genetic similarity (0.989) occurred between S. hypoleuca and S. limbata and then between S. limbata and S. spinosa (0.981). The lowest degree of genetic similarity occurred between S. nemorosa and S. reuterana (0.877). NJ tree based on Nei,s genetic distance (Fig. 4), showed that S. reuterana differed genetically from the other studied species, as it stands far from them. This dendrogram showed close genetic affi nity between S. hypoleuca and S. nemorosa, while S. xanthocheila joined them with some distance. The neighbor-net network obtained for all plant speci- mens revealed more detailed information about intraspecies genetic variability as well as the genetic affi nity between the studied species (Fig. 5). It showed that all plant speci- mens of S. reuterana were placed close to each other. This holds true for plant specimens of S. limbata. However, specimens studied from the other species showed genetic variability and were placed in different clusters. The network also showed genetic affi nity between spec- imens of S. hypoleuca and S. nemorosa (coded 1 and 2 re- spectively), and between S. xanthocheila and S. spinosa (coded 4 and 5 respectively). These results are in agreement with the NJ tree result. Fig. 4. Neighbor joining tree of inter simple sequence repeats data in the studied Salvia species. Fig. 5. NeighborNet tree of inter simple sequence repeats data in the studied Salvia species. Numbers at the tip of splits are boot- strap values. Fig. 3. Multidimentional scaling plots showing genetic distinctness of Salvia species. SAFAEI M., SHEIDAI M., ALIJANPOOR B., NOORMOHAMMADI Z. 50 ACTA BOT. CROAT. 75 (1), 2016 A Mantel test did not produce signifi cant correlation (r = 0.01, p = 0.63) between geographical distance and genet- ic distance of these species and therefore, no isolation by distance (IBD) exists between them. Salvia species studied are placed in two different spe- cies groups (namely species group 1 and 3 in Flora Iranica). In order to check if we have also two distinct genetic struc- tures, we performed K-Means clustering and Bayesian based method of STRUCTURE analysis. K-Means clustering result (On-line Suppl. Tab. 2), showed that the optimum number of genetic groups (num- ber of K) present in our data according to Calinski & Hara- basz’ pseudo-F is K = 2 (the highest value of pseudo-F = 7.218). On the other hand, according to Bayesian informa- tion criterion, K = 3 (the lowest value of BIC = 235.0). STRUCTURE analysis followed by Evanno test also produced delta K = 3 (On-line Suppl. Fig. 1). Therefore, we do have at least 2–3 genetic groups in the studied species. STRUCTURE plot (On-line Suppl. Fig. 2), which is based on Bayesian analysis, recognized 3 distinct genetic: 1- specimens having mostly blue colored segments (allelic combination) including S. hypoleuca and S. limbata; 2- specimens with blue and red segments, including S. nemorosa and S. xanthocheila; and 3- specimens having mostly green colored segments, including S. spinosa and S. reuterana. This genetic grouping is in agreement with the delta k of the Evanno test and K-Means clustering. Reticulation analysis (On-line Suppl. Fig. 3) showed that some degree of inter-specifi c gene fl ow occurs among members of the studied species and not a single species is entirely isolated from all other studied species. The analysis done by LFMM program produced a very low value of -log10 (p-value) not higher than 0.20 which were all non-signifi cant values (p >0.05). These results are summarized in a Manhattan plot, which is presented in On- line Suppl. Fig. 4. Discussion Morphological analyses of the studied Salvia species showed that they are well differentiated from each other both in quantitative measures (the ANOVA test result) and qualitative characters (The PCoA plot result). Moreover, the present study supports the taxonomic treatment of these taxa and the placing of them in two different species groups in Flora Iranica (Hedge 1982). In addition, PCA analysis suggests that characters like calyx length, bract length, seed shape, calyx shape, and bas- al leaf shape, could be used in species groups delimitation. AMOVA test revealed signifi cant molecular difference (P = 0.01) among the studied species. It also revealed that 21 % of total genetic variability occurred among the studied populations while, 79% occurred within these species. The possible reason for high within-species genetic variability is due to new mutations that occur and gradually accumu- late within populations of the certain species. Similar re- sults were reported in Salvia miltiorrhiza Bunge (Song et al. 2009). Both ISSR and SRAP molecular markers re- vealed the presence of a greater proportion of total genetic variation within S. miltiorrhiza populations, rather than among populations. The signifi cant pair-wise AMOVA test, as well as the non-metric MDS result, revealed the genetic distinctness of Salvia species studied. However, neighbor-net network and NJ tree results indicated a higher degree of genetic unifor- mity within S. reuterana and S. limbata than in the other studied species. An interesting result was obtained from K-Means clus- tering and STRUCTURE analysis, which showed that 2–3 genetic groups are present among the species studied. This is in agreement with species groups identifi ed in Salvia and also supports the AMOVA result showing genetic distinct- ness of the studied taxa. As we presented before, morphological and ISSR groupings separated S. spinosa, and S. reuterana of the spe- cies group 3 from the other studied species of the species group 1. However, the ISSR based grouping showed closer affi nity between S. hypoleuca and S. limbata, while a mor- phological grouping showed closer similarity between S. hypoleuca and S. nemorosa. Differences between the two groupings might be due to various reasons, such as the gene expression, environment, and gene introgressions as also reported in S. miltiorrhiza accessions and its varieties (Wang et al. 2011). Similar studies in populations of S. ja- ponica and some other Salvia species (Sudarmono and Okada 2008) did not show correlation between morpholog- ical variations and allozyme and DNA sequences. It was concluded that S. japonica is still at the early stage of spe- ciation process. Sympatry or co-occurrence of closely related species can either result from a sympatric speciation process or from secondary contact due to range expansion after spe- ciation. Under the allopatric scenario, genetic variation tends to be uniform across the genome due to a large pro- portion of the genome changing through a combination of divergent selection, differential response to similar selec- tive pressures and genetic drift (see for example Strasburg et al. 2012). In contrast, in the extreme case of sympatric speciation, gene fl ow between the incipient species can ho- mogenize most of the genome, except for loci that experi- ence strong divergent selection pressures or regions that are tightly linked with these loci (see for example, Strasburg et al. 2012, Via 2012). Different mechanisms, including isolation by distance, lack of gene fl ow, local adaptation, and genetic drift fol- lowed by strong selection pressure, are responsible for spe- cies/population differentiation and genetic divergence (Tero et al. 2003, Freeland et al. 2011, Frichot et al. 2013). In the present study, a Mantel test did not produce a signifi cant correlation between the geographical and the genetic dis- tance of these species and therefore, no isolation by dis- tance (IBD) exists between them. Moreover, the STRUC- TURE result indicated that a high degree of infra-specifi c genetic variability is present in Salvia species due to change in the allelic frequency and gene fl ow/admixture. However, a Manhattan plot did not show correlation between ISSR loci and environmental features of Salvia taxa. These re- GENETIC DIVERSITY IN SALVIA ACTA BOT. 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Population 1 2 3 4 5 6 1 0.000 0.989 0.965 0.978 0.972 0.889 2 0.010 0.000 0.969 0.981 0.977 0.892 3 0.035 0.031 0.000 0.967 0.966 0.877 4 0.021 0.018 0.033 0.000 0.973 0.888 5 0.027 0.022 0.033 0.027 0.000 0.921 6 0.117 0.113 0.130 0.117 0.081 0.000 On-line Suppl. Tab. 2. K-Means clustering of inter simple se- quence repeats (ISSR) data. Clustering statistics from k = 2 to k = 6. Asterisk (*) denotes best clustering according to Calinski & Harabasz’ pseudo-F: k = 2. Symbol “&” denotes best clustering ac- cording to Bayesian information criterion: k = 3. k SSD(T) SSD(AC) SSD(WC) r-squared pseudo-F BIC 2* 410.974 67.085 343.889 0.163 7.218 235.100 3& 410.974 98.270 312.704 0.239 5.657 235.056 4 410.974 121.766 289.208 0.296 4.912 235.673 5 410.974 142.352 268.623 0.346 4.504 236.457 6 410.974 162.407 248.567 0.395 4.312 237.094 On-line Suppl. Fig. 1. Delta K value of Evanno test. On-line Suppl. Fig. 2. STRUCTURE plot of Salvia species showing interspecifi c genetic variability and admixture. 1 SAFAEI M., SHEIDAI M., ALIJANPOOR B., NOORMOHAMMADI Z. ACTA BOT. CROAT. 75 (1), 2016 On-line Supplement Fig. 3. Reticulogram of Salvia species. Species numbers are: 1-8 = S. hypoleuca, 9-15 = S. limbata, 16-22 = S. nemorosa, 23-27 = S. spinosa, 28-33 = S. xanthocheila, 34-39 = S. reuterana, respectively. On-line Supplement Fig. 4. Manhattan plot of inter simple se- quence repeats (ISSR) data. 2