Caryologia. International Journal of Cytology, Cytosystematics and Cytogenetics 74(4): 11-20, 2021 Firenze University Press www.fupress.com/caryologia ISSN 0008-7114 (print) | ISSN 2165-5391 (online) | DOI: 10.36253/caryologia-1320 Caryologia International Journal of Cytology, Cytosystematics and Cytogenetics Citation: Fengzhen Chen, Dongmei Li, Mohsen Farshadfar (2021) Genetic vari- ations and interspesific relationships in Lonicera L. (Caprifoliaceae), using SCoT molecular markers. Caryolo- gia 74(4): 11-20. doi: 10.36253/caryolo- gia-1320 Received: May 21, 2021 Accepted: August 24, 2021 Published: March 08, 2022 Copyright: © 2021 Fengzhen Chen, Dong- mei Li, Mohsen Farshadfar. This is an open access, peer-reviewed article published by Firenze University Press (http://www.fupress.com/caryologia) and distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distri- bution, and reproduction in any medi- um, provided the original author and source are credited. Data Availability Statement: All rel- evant data are within the paper and its Supporting Information files. Competing Interests: The Author(s) declare(s) no conflict of interest. Genetic variations and interspesific relationships in Lonicera L. (Caprifoliaceae), using SCoT molecular markers Fengzhen Chen1, Dongmei Li2,*, Mohsen Farshadfar3 1 Peony Research Institute, Heze University, Heze , Shandong 274000, China 2 College of Horticulture Science and Engineering, Shandong Agricultural University, Taian, Shandong 271018, China 3 Department of Agriculture, Payame Noor University (PNU), Tehran, Iran *Corresponding author: E-mail:majidkhayatnezhad126@gmail.com; spfood3200@163. com Abstract. Lonicera L. (Caprifoliaceae) includes more than 200 species worldwide. The genus is mainly distributed in temperate to subtropical regions of the northern hemi- sphere: Europe, Russia, East Asia and North America. Some species are medicinal plants. Dried Lonicera flowers and buds are known as Flos Lonicera and have been a recognized herb in the traditional Chinese medicine for more than 1500 years. It has been applied for treatment of arthritis, diabetes mellitus, fever, and viral infections. Due to the importance of these plant species, we performed a combination of morpho- logical and molecular data for this species. For this study, we used 85 randomly col- lected plants from six species in 6 provinces. Amplification of genomic DNA using 10 primers produced 103 bands, of which 95 were polymorphic (90.98%). The obtained high average PIC and MI values revealed high capacity of SCoT primers to detect pol- ymorphic loci among Lonicera species. The genetic similarities of 6 collections were estimated from 0.67 to 0.90. According to the SCoT markers analysis, L. hypoleuca and L. iberica had the lowest similarity and the species of L. korolkowii and L. nummulari- ifolia had the highest similarity. The aims of present study are: 1) can SCoT markers identify Lonicera species, 2) what is the genetic structure of these taxa in Iran, and 3) to investigate the species inter-relationship? The present study revealed that SCoT markers can identify the species. Keywords: gene flow, genetic admixture, Lonicera, Network, population structure. INTRODUCTION Genetic diversity is a basic component of biodiversity and its conserva- tion is essential for long term survival of any species in changing environ- ments (Mills and Schwartz 2005, Tomasello et al. 2015). This is very impor- tant in fragmented populations because are more vulnerable due to the loss of allelic richness and increased population differentiation by genetic drift (decreases heterozygosity and eventual fixation of alleles) and inbreeding 12 Fengzhen Chen et al. depression (increases homozygosity within populations; Frankham 2005). Among different populations, genetic diversity is non randomly distributed and is affected by various factors such as geographic variations, breeding systems, dispersal mechanisms, life span, etc (Khatam- saz 1995; Ghahremaninejad and Ezazi 2009).Change in environmental conditions often leads to variation in genetic diversity levels among different populations and populations with low variability are generally consid- ered less adapted under adverse circumstances (Falk and Holsinger 1991, Olivieri et al. 2016). Most of the authors agree that genetic diversity is necessary to preserve the long-term evolutionary potential of a species (Falk and Holsinger 1991). In the last decade, experimental and field investigations have demonstrated that habitat frag- mentation and population decline reduce the effective population size. In the same way, most geneticists con- sider population size as an important factor for main- taining genetic variation (Turchetto et al. 2016). Lonicera L. (Caprifoliaceae) includes more than 200 species (Mabberley 2008) worldwide, with 19 spe- cies in the region of Flora Iranica (Wendelbo 1965). The genus is mainly distributed in temperate to subtropical regions of the northern hemisphere: Europe, Russia, East Asia, and North America (Hsu and Wang 1988; Mab- berley 2008). In the flora of Iran, the genus Lonicera is represented by nine species (Khatamsaz 1995; Ghahre- maninejad and Ezazi 2009) across the north, northwest and northeast of the country. Some species are medici- nal plants (Zeng et al. 2017). Dried Lonicera flowers and buds are known as Flos Lonicera and have been a recog- nized herb in the traditional Chinese medicine for more than 1500 years (Li et al. 2015). It has been applied for treatment of arthritis, diabetes mellitus, fever, and viral infections (Shang et al. 2011; Li et al. 2015). The plants are erect shrubs, occasionally small trees. Members of Lonicera are characterized by opposite, narrowly ellip- tic to obovate leaves, white, yellow, reddish, or purple- red corolla with capitate stigma (Judd et al. 2007), and undulate calyx margin. In Flora Iranica, Wendelbo (1965) classified 19 species of the Lonicera into two sub- genera (Chamaecerasus and Lonicera) and three sections, namely Isoxylosteum, Isika and Coeloxylosteum. The four studied species belong to subgenus Chamaecerasus and sections Isika and Coeloxylosteum. Molecular data have been obtained in phylogenetic studies and species divergence researches (Kazempour Osaloo et al. 2003, 2005). These data can also provide supportive and extra criteria for systematic classifica- tion of the studied species that have been based only on the morphological characters (Chase et al. 1993). The internal transcribed spacer (ITS) is the region of the 18S-5.8 S-26S nuclear ribosomal cistron (Baldwin et al. 1995). The spacers contain the signals needed to pro- cess the rRNA transcript (Baldwin 1992, Baldwin et al. 1995) and have often been used for inferring phylog- eny at the generic and infrageneric levels in plants (e.g. Baldwin 1992; Baldwin et al. 1995; Kazempour Osaloo et al. 2003, 2005). Theis et al. (2008) studied phylogenet- ics of the Caprifolieae and Lonicera (Dipsacales) on the basis of nuclear and chloroplast DNA sequences. Their analysis indicates monophyly in Lonicera and highlights instances of homoplasy in several morphological char- acters. Molecular phylogenetics of Lonicera in Japan has been studied by Nakaji et al. (2015) on the basis of chloroplast DNA sequences. According to the results, circumscription of the higher taxonomic groups for the Japanese species of Lonicera proposed by Hara in 1983 is fundamentally acceptable. Lonicera is well known for its taxonomic complexity resulting from overlapping mor- phological characters. With the progress in plant molecular biolog y, numerous molecular marker techniques have been developed and used widely in evaluating genetic diver- sity, population structure and phylogenetic relation- ships. In recent years, advances in genomic tools pro- vide a wide range of new marker techniques such as, functional and gene targeted markers as well as devel- op many novel DNA based marker systems (Wu et al. 2013). Start codon targeted (SCoT) polymorphism is one of the novel, simple and reliable gene-targeted marker systems. This molecular marker offers a simple DNA-based marker alternative and reproducible tech- nique which is based on the short conserved region in the plant genes surrounding the ATG (Collard and Mackill 2009) translation start codon (Collard and Mackill 2009). This technique involves a polymerase chain reaction (PCR) based DNA marker with many advantages such as low-cost, high polymorphism and extensive genetic information (Collard and Mackill 2009, Luo et al. 2011, Wu et al. 2013). The present investigation has been carried out to evaluate the genetic diversity and relationships among Lonicera species using new gene-targeted molecular markers, i.e. SCoT. This is the first study on the use of SCoT markers in Lonicera genus; Therefore, we per- formed molecular study of 85 specimens of 6 Lonicera species. We try to answer the following questions: 1) Is there infra and interspecific genetic diversity among studied species? 2) Is genetic distance among these spe- cies correlated with their geographical distance? 3) What is the genetic structure of populations and taxa? 4) Is there any gene exchange between Lonicera species in Iran? 13Genetic variations and interspesific relationships in Lonicera L. (Caprifoliaceae), using SCoT molecular markers MATERIALS AND METHODS Plant materials A total of 85 individuals were sampled represent- ing six geographical populations belong six Lonicera species (sp1= Lonicera caucasica; sp2= Lonicera iberica M. Bieb.; sp3= Lonicera nummulariifolia Jaub. et Spach; sp4= Lonicera bracteolaris Boiss. & Buhse; sp5= Lonicera korolkowii Stapf; sp 6= Lonicera hypoleuca Decne.) in East Azerbaijan, Guilan, Mazandaran, Tehran, Khorasan and Hormozgan Provinces of Iran during July-Agust 2017-2019. For morphometric and SCoT analysis we used 85 plant accessions (nine to eighteen samples from each populations) belonging to six different species with different eco-geographic characteristics were sampled and stored in -20 till further use. Voucher specimens are deposited in Herbarium of Azad Islamic University (HAIU). More information about geographical distribu- tion of accessions are in Table. 1. Morphological studies Nine to eighteen samples samples from each species were used for Morphometry. In total 17 morphological (9 qualitative, 8 quantitative) characters were studied. Data obtained were standardized (Mean= 0, variance = 1) and used to estimate Euclidean distance for clustering and ordination analyses (Podani 2000). Morphological characters studied are: corolla shape, bract shape, seed color, seed shape, bract color, leaf surface, calyx shape, basal leaf shape, pedicel length, calyx length, bract length, corolla length, basal leaf length, basal leaf width, corolla color, stem leaf length and stem leaf width. DNA extraction and SCoT assay Fresh leaves were used randomly from nine to eight- een plants in each of the studied populations. These were dried by silica gel powder. CTAB activated char- coal protocol was used to extract genomic DNA (Doyle and Doyle 1987). The quality of extracted DNA was examined by running on 0.8% agarose gel. A total of 25 SCoT primers developed by Collard and Mackill (2009), 10 primers with clear, enlarged, and rich polymorphism bands were chosen (Table 2). PCR reactions were car- ried in a 25μl volume containing 10 mM Tris-HCl buffer 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 amplifications, reactions were performed in Techne thermocycler (Germany) with the following program: 5 min initial denaturation step 94°C, followed by 40 cycles of 1 min at 94°C; 1 min at 52-57°C and 2 min at 72°C. The reaction was completed by final extension step of 7-10 min at 72°C. The amplification products were observed by running on 1% agarose gel, followed by the ethidium bromide staining. The frag- ment size was estimated by using a 100 bp molecular size ladder (Fermentas, Germany). Data analyses Morphological studies Morphological characters were f irst standard- ized (Mean = 0, Variance = 1) and used to establish Euclidean distance among pairs of taxa (Podani 2000). For grouping of the plant specimens, The UPGMA (Unweighted paired group using average) ordination methods were used (Podani 2000). ANOVA (Analysis of variance) were performed to show morphological differ- ence among the populations while, PCA (Principal com- ponents analysis) biplot was used to identify the most variable morphological characters among the studied populations (Podani 2000). PAST version 2.17 (Hammer et al. 2012) was used for multivariate statistical analyses of morphological data. Table 1. Voucher details of Lonicera species and relative genera examined in this study from Iran. Sp. Locality Sample size Latitude Longitude Altitude (m) Voucher no. L. caucasica Mazandaran, Chalus 18 34°52’393” 46°25’92” 1133 HIAU 201677 L. iberica M. Bieb. East Azerbaijan, Kaleybar, Road side 16 38°52’373” 47°23’92” 1144 HIAU 201683 L. nummulariifolia Jaub. et Spach Tehran, Alamut 14 33°52’353” 48°27’92” 1330 HIAU 201686 L. bracteolaris Boiss. & Buhse Guilan, Gole rodbar, Road sid 9 34°09’55” 47°55’49” 1600 HIAU 201689 L. korolkowii Stapf Khorasan, Bojnurd 15 320702.32 504432.6 2300 HIAU 201690 L. hypoleuca Decne. Hormozgan, Bandar Abbas, Siyahu 13 38°52’373” 47°23’92” 1144 HIAU 201695 14 Fengzhen Chen et al. Molecular analyses SCoT bands obtained were coded as binary char- acters (presence = 1, absence = 0) and used for genetic diversity analysis. Discriminatory ability of the used primers was evaluated by means of two parameters, polymorphism information content (PIC) and marker index (MI) to characterize the capacity of each primer to detect polymorphic loci among the genotypes (Pow- ell et al. 1996). MI is calculated for each primer as MI = PIC × EMR, where EMR is the product of the num- ber of polymorphic loci per primer (n) and the fraction of polymorphic fragments (β) (Heikrujam et al. 2015). The number of polymorphic bands (NPB) and the effec- tive multiplex ratio (EMR) were calculated for each primer. Parameter like Nei’s gene diversity (H), Shan- non information index (I), number of effective alleles, and percentage of polymorphism (P% = number of pol- ymorphic loci/number of total loci) were determined (Weising et al, 2005, Freeland et al. 2011). Shannon’s index was calculated by the formula: H’ = -Σpiln pi. Rp is defined per primer as: Rp = ∑ Ib, were “Ib” is the band informativeness, that takes the values of 1-(2x [0.5- p]), being “p” the proportion of each genotype contain- ing the band. The percentage of polymorphic loci, the mean loci by accession and by population, UHe, H’ and PCA were calculated by GenAlEx 6.4 software (Peakall & Smouse 2006). Nei’s genetic distance among popula- tions was used for Neighbor Joining (NJ) clustering and Neighbor-Net networking (Huson & Bryant 2006, Freeland et al. 2011). Mantel test checked the correla- tion between geographical and genetic distances of the studied populations (Podani 2000). These analyses were done by PAST ver. 2.17 (Hammer et al. 2012), DARwin ver. 5 (2012) software. AMOVA (Analysis of molecular variance) test (with 1000 permutations) as implemented in GenAlex 6.4 (Peakall and Smouse, 2006), and Nei,s Gst analysis as implemented in GenoDive ver.2 (2013) (Meirmans and Van Tienderen 2004) were used to show genetic difference of the populations. Moreover, popula- tions, genetic differentiation was studied by G’ST est = standardized measure of genetic differentiation (Hedrick 2005), and D_est = Jost measure of differentiation (Jost 2008). To assess the population structure of the Loni- cera accessions, a heuristic method based on Bayes- ian clustering algorithms were utilized. The clustering method based on the Bayesian-model implemented in the software program STRUCTURE (Pritchard et al. 2000; Falush et al. 2007) was used on the same data set to better detect population substructures. This clustering method is based on an algorithm that assigns genotypes to homogeneous groups, given a number of clusters (K) and assuming Hardy-Weinberg and linkage equilibrium within clusters, the software estimates allele frequencies in each cluster and population memberships for every individual (Pritchard et al. 2000). The number of poten- tial subpopulations varied from two to ten, and their contribution to the genotypes of the accessions was cal- culated based on 50,000 iteration burn-ins and 100,000 iteration sampling periods. The most probable number (K) of subpopulations was identified following Evanno et al. (2005). In K-Means clustering, two summary sta- tistics, pseudo-F, and Bayesian Information Criterion (BIC), provide the best fit for k (Meirmans, 2012). Table 2. SCoT primers used for this study and the extent of polymorphism. Primer name Primer sequence (5’-3’) TNB NPB PPB PIC PI EMR MI SCoT-1 CAACAATGGCTACCACCA 15 14 93.74% 0.47 5.66 17.56 5.67 SCoT-3 CAACAATGGCTACCACCG 13 12 92.31% 0.54 3.21 15.60 5.55 SCoT-6 CAACAATGGCTACCACGC 7 7 100.00% 0.47 4.32 9.55 3.45 SCoT-11 AAGCAATGGCTACCACCA 11 9 82.89% 0.43 5.56 6.34 5.11 SCoT-14 ACGACATGGCGACCACGC 10 10 100.00% 0.56 4.86 9.55 3.22 SCoT-15 ACGACATGGCGACCGCGA 9 8 84.99% 0.41 4.91 7.43 4.85 SCoT-16 CCATGGCTACCACCGGCC 8 8 100.00% 0.44 4.34 11.55 6.44 SCoT-17 CATGGCTACCACCGGCCC 16 16 100.00% 0.67 5.88 8.56 3.65 SCoT-18 ACCATGGCTACCACCGCG 13 13 100.00% 0.55 6.23 8.23 6.47 SCoT-19 GCAACAATGGCTACCACC 10 10 100.00% 0.59 6.25 9.7 5.87 Mean 10 9 90.98% 0.56 5 9.5 5.9 Total 103 95 Abbreviations: TNB = the number of total bands, NPB = the number of polymorphic bands, PPB (%) = the percentage of polymorphic bands, PI = polymorphism index, EMR = effective multiplex ratio, MI = marker index, PIC, polymorphism information content for each of CBDP primers. 15Genetic variations and interspesific relationships in Lonicera L. (Caprifoliaceae), using SCoT molecular markers Gene flow (Nm) which were calculated using POP- GENE (version 1.31) program (Yeh et al., 1999). Gene flow was estimated indirectly using the formula: Nm = 0.25(1 - FST)/FST. In order to test for a correlation between pair- wise genetic distances (FST) and geographical distances (in km) between populations, a Mantel test was per- formed using Tools for Population Genetic Analysis (TFP- GA; Miller, 1997) (computing 999 permutations). This approach considers equal amount of gene flow among all populations. (ii) Population assignment test based on maximum likelihood as performed in GenoDive ver. 2. (2013). The presence of shared alleles was determined by drawing the reticulogram network based on the least square method by DARwin ver 5. (2012). RESULTS Species identification and inter-relationship. Morphometry ANOVA showed significant differences (P <0.01) in quantitative morphological characters among the species studied. In order to determine the most variable char- acters among the taxa studied, PCA analysis has been performed. It revealed that the first three factors com- prised over 65% of the total variation. In the first PCA axis with 47% of total variation, such characters as seed shape, calyx shape, calyx length, bract length and basal leaf shape have shown the highest correlation (>0.7), seed color, leaf surface, corolla length and basal leaf length, were characters influencing PCA axis 2 and 3 respec- tively. Different clustering and ordination methods pro- duced similar results therefore, PCA plot of morpho- logical characters are presented here (Fig. 1). In general, plant samples of each species were grouped together and formed separate groups. This result show that both quan- titative and qualitative morphological characters separat- ed the studied species into distinct groups. In the studied specimens we did not encounter intermediate forms. Species identification and genetic diversity Ten SCoT primers were screened to study genetic relationships among Lonicera species; all the primers produced reproducible polymorphic bands in all 6 Loni- cera species. An image of the SCoT amplification gener- ated by SCoT-14 and SCoT-6 primer is shown in Figure 2. A total of 95 amplified polymorphic bands were gen- erated across 6 Lonicera species. The size of the ampli- fied fragments ranged from 100 to 2000 bp. The highest and lowest number of polymorphic bands were 16 for SCoT-17 and 7 for SCoT-6, on an average of 9 polymor- phic bands per primer. The PIC of the 10 SCoT primers ranged from 0.41 (SCoT-15) to 0.67 (SCoT-17) with an average of 0.56 per primer. MI of the primers ranged from 3.22 (SCoT-14) to 6.47 (SCoT-18) with an average of 5.9 per primer. EMR of the SCoT primers ranged from Figure 1. PCA plots of morphological characters revealing species delimitation in the Lonicera species. 16 Fengzhen Chen et al. 6.34 (SCoT-11) to 17.56 (SCoT-1) with an average of 9.5 per primer (Table 2). The primers with the high EMR values were considered to be more informative in distin- guishing the genotypes. The genetic parameters were calculated for all the 6 Lonicera species amplified with SCoT primers (Table 3). Unbiased expected heterozygosity (H) ranged from 0.13 (L. caucasica) to 0.33 (L. hypoleuca), with a mean of 0.21. A similar pattern was observed for Shannon’s informa- tion index (I), with the highest value of 0.34 observed in L. hypoleuca and the lowest value of 0.18 observed in L. caucasica with a mean of 0.28. The observed number of alleles (Na) ranged from 0.201 in L. bracteolaris to 0.892 in L. caucasica. The effective number of alleles (Ne) ranged from 1.00 (L. bracteolaris) to 1.138 (L. caucasica). AMOVA test showed significant genetic difference (P = 0.01) among studied species. It revealed that 53% of total variation was among species and 47% was within species (Table 4) Moreover, genetic differentiation of these species was demonstrated by significant Nei’s GST (0.66, P = 0.01) and D_est values (0.222, P = 0.01). These results revealed a higher distribution of genetic diversi- ty among Lonicera species compared to within species. Two major clusters were formed in UPGMA tree (Fig. 3). The first major cluster (A) contained two sub-clusters: L. nummulariifolia and L. korolkowii are separated from the other studied species and join the others with a great distance and comprised the first sub-cluster. The second sub-cluster was formed by L. caucasica; L. iberica and L. bracteolaris. The second major cluster also contained only 1 species of L. hypoleuca. In general, relationships obtained from SCoT data agrees well with species rela- tionship obtained from morphological. This is in agree- Figure 2. Electrophoresis gel of studied ecotypes from DNA frag- ments produced by SCoT-14, SCoT-6; sp1= L. caucasica; sp2= L. iberica M. Bieb.; sp3= L. nummulariifolia Jaub. et Spach; sp4= L. bracteolaris Boiss. & Buhse; sp5= L. korolkowii Stapf; sp 6= L. hypoleuca Decne; L = Ladder 100 bp, Arrows are representative of polymorphic bands Table 3. Genetic diversity parameters in the studied Lonicera species. SP N Na Ne I He UHe %P L. caucasica 18.000 0.892 1.138 0.18 0.141 0.13 28.63% L. iberica 16.000 0.244 1.032 0.26 0.23 0.18 55.53% L. nummulariifolia 14.000 0.314 1.044 0.26 0.18 0.23 39.38% L. bracteolaris 9.000 0.201 1.00 0.33 0.17 0.18 52.23% L. korolkowii 15.000 0.341 1.058 0.24 0.27 0.20 33.75% L. hypoleuca 13.000 0.567 1.062 0.34 0.324 0.333 64.73% Abbreviations: N = number of samples, Na= number of different alleles; Ne = number of effective alleles, I= Shannon’s information index, He = genetic diversity, UHe = unbiased gene diversity, P%= percentage of polymorphism, populations). Table 4. Analysis of molecular variance (AMOVA) of the studied species. Source df SS MS Est. Var. % ΦPT Among Pops 20 1991.364 70.789 12.154 53% 53%Within Pops 177 774.443 8.905 2.888 47% Total 197 2555.807 14.060 100% Abbreviations: df = degree of freedom, SS = sum of squared observations, MS = mean of squared observations, EV = estimated variance, ΦPT = proportion of the total genetic variance among individuals within an accession, (P < 0.001). 17Genetic variations and interspesific relationships in Lonicera L. (Caprifoliaceae), using SCoT molecular markers ment with AMOVA and genetic diversity parameters presented before. The species are genetically well dif- ferentiated from each other. These results indicate that SCoT molecular markers can be used in Lonicera species taxonomy. The Nm analysis by Popgene software also produced mean Nm= 0.186, that is considered very low value of gene flow among the studied species. Mantel test with 5000 permutations showed a signifi- cant correlation (r = 0.177, p=0.0002) between genetic dis- tance and geographical distance, so isolation by distance (IBD) occurred among the Lonicera species studied. Nei’s genetic identity and the genetic distance deter- mined among the studied species (Table not included). The results showed that the highest degree of genetic similarity (0.90) occurred between L. korolkowii and L. nummulariifolia. The lowest degree of genetic similar- ity occurred between L. hypoleuca and L. iberica (0.67). The low Nm value (0.186) indicates limited gene flow or ancestrally shared alleles between the species studied and indicating high genetic differentiation among and within Lonicera species. The species genetic STRUCTURE We performed STRUCTURE analysis followed by the Evanno test to identify the optimal number of genet- ic groups. We used the admixture model to illustrate interspecific gene flow or/and ancestrally shared alleles in the species studied. STRUCTURE analysis followed by Evanno test pro- duced ΔK =6 (Table 5). The STRUCTURE plot (Figure. 4) produced more detailed information about the genetic structure of the species studied as well as shared ances- tral alleles and/or gene flow among Lonicera species. This plot revealed that Genetic affinity between L. cauca- Figure 3. UPGMA tree of SCoT data revealing species delimitation in the Lonicera species. Branch support values are given as bootstrap (BP) value above branches. Figure 4. STRUCTURE plot of Lonicera species based on SCoT data. 18 Fengzhen Chen et al. sica and L. iberica (similarly colored, No. 1, 2), as well as L. nummulariifolia and L. korolkowii (sp No. 3,5) due to shared common alleles. This is in agreement with UPG- MA dendrogram presented before. The other species are distinct in their allele composition. DISCUSSION knowledge of the genetic variability and diver- sity within and among different populations is crucial for their conservation and management (e.g. Mills and Schwartz 2005; Khayatnezhad and Gholamin 2021; Guo et al. 2021; Ren et al. 2021). In the present study we used morphological and molecular (SCoT) data to evaluate species relationship in Lonicera. Morphological analy- ses of the studied Lonicera species showed that they are well differentiated from each other both in quantitative measures (the ANOVA test result) and qualitative char- acters (The PCA plot result). In addition, PCA analysis suggests that characters like bract length, stipule length, bract shape, calyx shape, petal shape, length and width of stem-leaf, length and width of petal could be used in species groups delimitation. Four species and 12 popula- tions of the genus Lonicera have been studied in terms of pollen and seed micro-morphology and molecular phylogeny (Amini et al. 2019). Their results showed that micro-morphological and molecular data provide relia- ble evidence for differentiation of some populations from others. Since Lonicera systematically is a problem genus, it is necessary to use alternative methods to distinguish its taxa. Statistical evaluation of taxa can be used for taxa delimitation. The present study intends to provide further evidence for taxonomists, so as to help them in separating these six species. Genetic structure and gene flow PIC and MI characteristics of a primer help in determining its effectiveness in genetic diversity analy- sis. Sivaprakash et al. (2004) suggested that the ability of a marker technique to resolve genetic diversity may be more directly related to the degree of polymorphism. Generally, PIC value between zero to 0.25 imply a very low genetic diversity among genotypes, between 0.25 to 0.50 shows a mid-level of genetic diversity and value ≥0.50 suggests a high level of genetic diversity (Tams et al. 2005; Hou et al. 2021; Huang et al. 2021; Khay- atnezhad and Gholamin 2020b). In this research, the SCoT primers’ PIC values ranged from 0.43 to 0.67, with a mean value of 0.56, which indicated a mid-ability of SCoT primers in determining genetic diversity among the Lonicera species. In the study conducted by Chen et al. (2012), 20 ISSR primers amplified 186 bands with 103 (54.63%) polymorphic bands and 58 sequence-related amplified polymorphism (SR AP) primer combinations ampli- fied 591 bands with 347 (55.46%) polymorphic bands. Both ISSR and SRAP analyses revealed a middle level of genetic diversity in Lonicera macranthoides cultivars. Smolik et al. (2006) found a level of similarity for 6 pop- ulations of Lonicera periclymenum ranging from 82.3% to 86.6%, indicating their closely related nature. ISSR amplification was used by Smolik et al. (2010) to analyze polymorphisms of microsatellite sequences in the honey- suckle genome and to evaluate genetic diversity among 14 Polish and Russian blue honeysuckle accessions. Ran- dom amplified polymorphic DNA (RAPD) analysis was used by Naugžemys et al. (2011) to assess the genetic relationships among 51 accessions of blue honeysuckle. The pairwise genetic distance (GDxy) values among studied accessions ranged from 0.054 to 0.479; the mean GDxy was 0.283. Knowledge of the content of second- ary metabolites in individual genotypes allows us to choose the best in Lonicera breeding programs in order to increase the nutritional value and health benefits. In conclusion, the results of this study showed that to evaluate the genetic diversity of the Lonicera genus, the primers derived from SCoT were more effective than the other molecular markers. Also, Lonicera ecotypes/ species were clearly separated from each other in the dendrogram and MDS, indicating the higher efficiency of SCoT technique in Lonicera species identification. ACKNOWLEDGMENT The authors are grateful to all colleagues in the labo- ratory of Plant Physiology in School of Horticulture for providing help and assistance. And thanks to Dr. Jianqiu Han in Shanghai Institute of Technoligy for modifying this paper. This study was supported by Natural Science TABLE 5 . K-Means clustering result of SCOT data. 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