MOLECULAR IDENTIFICATION OF Juniperus GENOTYPES FROM GILGIT-BALTISTAN USING CHLOROPLAST DNA BARCODES

INTRODUCTION

 The genus Juniperus L. (family Cupressaceae) comprises approximately 60–70 species of evergreen conifers distributed widely across the Northern Hemisphere, from the Arctic regions to subtropical and mountainous zones of Africa, Asia, and North America (Adams, 2011; Adams and Schwarzbach, 2011; Bejenaru et al., 2024). The members of this genus are major ecological constituents of forest habitats and are critical for maintaining ecosystem stability (Rahmonov et al., 2017, 2021; Uckele et al.,2026). They serve as vital soil stabilizers in arid and semi-arid environments, act as significant sinks for soil organic carbon, and provide essential shelter and habitat for a diverse range of fauna (Charro et al., 2017; Rostamikia et al., 2024). Beyond their ecological roles, Juniperus species are economically significant and widely utilized for their fragrant wood, therapeutic essential oils, and diverse medicinal applications (Tufail et al., 2023; Bozyel et al., 2024). Taxonomic differentiation within the genus remains difficult due to high morphological plasticity, similar phenotypic traits, and the effects of environmental factors on growth patterns (Knyazeva andHantemirova, 2020).

 In Pakistan, Juniperus species are mainly found in the north and west mountainous parts, such as Gilgit-Baltistan, Khyber Pakhtunkhwa, and Baluchistan. Juniperus forests of Ziarat, Baluchistan, and Gilgit-Baltistan are some of the oldest-known coniferous forests in the region, and the trees have been estimated to be more than 3,000 years old (Sarangzai et al., 2012; Shaheen et al., 2017). These forests also support the hydrological cycle, as well as counteracting the effects of climate change in the high-altitude ecosystems besides enhancing biodiversity conservation. Despite being ecologically and economically important, now these Juniperus populations are under the pressure of excessive overexploitation, grazing, habitat fragmentation, and climate stress which lead to a precipitous decrease in rates of regeneration and forest health (Gauquelin et al., 1999; Redmond et al., 2023).

 Correct identification of Juniperus species is a fundamental requirement for successful design of conservation and management practices (Shaheen et al., 2017; Irfan et al., 2021). Traditionally Juniperus taxonomic classification has been based on morphological features such as the morphology of leaves, cones, and seeds (Vasek, 1966; Köroğlu et al., 2018). However, these features have a high level of overlap among species as well as they are open to environmental forces and, consequently, morphological recognition under most circumstances cannot be relied upon (Knyazeva and Hantemirova, 2020). This has caused a great deal of taxonomic complexity, particularly in such complexes as J. excelsa, J. polycarpos and J. seravschanica which morphologically resemble but differ genetically (Douaihy et al., 2012).

 Over the past decades, molecular techniques have revolutionized plant systematics and taxonomy and provided plausible techniques of species identification and phylogenetic inference (Pyron, 2015; Zaman et al., 2025). One such technique is DNA barcoding, which has proven to be an effective tool for differentiating closely related taxa based on short and standardized DNA sequences (Hebert et al., 2003; Li et al., 2015). The concept of DNA barcoding is to have some loci, usually in the chloroplast genome in plants, which vary sufficiently to identify species but are maintained across species (Dong et al., 2012). Two of the most popular chloroplast markers to use in plant DNA barcoding are the matK (maturase K) and rbcL (ribulose-1,5-bisphosphate carboxylase/oxygenase large subunit) genes (Wattoo et al., 2016; Irfan et al., 2024). Their choice is motivated by their universality, high amplification success and good discriminatory power in a wide taxonomic group of plants (Group et al., 2009; Dormontt et al., 2018). Although these markers can face resolution challenges in Juniperus due to frequent hybridization and incomplete lineage sorting (Adams et al., 2016; Uckele et al., 2021), they remain the primary standardized tools for establishing a baseline molecular database for the Himalayan flora. The congruence between matK and rbcL results across our studied genotypes further justifies their application, demonstrating their reliability for consistent species-level identification within the specific geographic context of Gilgit-Baltistan (Song et al., 2019; Irfan et al., 2024).

 Several previous studies have stressed the usefulness of chloroplast DNA markers in clarifying phylogenetics and taxonomy on uncertainties between the conifers, especially Juniperus (Almerekova et al., 2024). Indeed, a whole chloroplast genome sequence of Pakistan populations of J. polycarpos confirmed the apparent genetic dissimilarity to justify its separation of other species close to it, such as J. excelsa and J. seravschanica (Irfan et al., 2021). Similarly, molecular analysis based on matK and rbcL sequencing has been successfully used to circumscribe J. oxycedrus subspecies and estimate genetic diversity of J. oxycedrus Mediterranean species (Aggag, 2020). All these studies testify to the effect that DNA barcoding used together with phylogenetic studies provide a sound basis of disclosing evolutionary associations amid morphologically congruent species (Almerekova et al., 2024; Irfan et al., 2024).

 Gilgit-Baltistan (GB), which is located at the intersection of the Himalaya, Karakoram, and Hindu Kush mountains, is one of the most diverse regions of Pakistan (Abbas et al., 2022). The topography, altitude gradients, and climatic variability are unique to it, and these features support excellent distribution of plant biodiversity including different endemic and relict species (Abbas et al., 2019; Abbas et al., 2022). Despite this, Juniperus species of this region and their evolutionary relationships have little molecular data and little understanding of the evolutionary relationships. An ordered molecular study is therefore required to determine the right Juniperus genotypes and be aware of their phylogenetic location in the genus. The objective of the current research was to determine the molecular identification of Juniperus species sampled in the under-explored Hunza Valley of GB using two chloroplast DNA barcodes of matK and rbcL. While 16 genotypes were initially collected to assess field-level morphological variation, four representative specimens were strategically selected for molecular analysis to provide a robust taxonomic verification across the observed phenotypic spectrum. This targeted selection ensures that the molecular data captures the core genetic identity of the population while avoiding redundancy, serving as a reliable baseline for the Juniperus stands of the region (Song et al., 2019; Aggag, 2020; Sajjad et al., 2024). Through the use of DNA barcoding for molecular identification, this study aims to resolve taxonomic ambiguity and provide baseline genetic information for the conservation and sustainable management of Juniperus forests in the Himalayan region of Pakistan.

MATERIALS AND METHODS

Study Area and samples collection: Gilgit-Baltistan is a region located in the northernmost part of Pakistan, situated at the crossroads of India, China, and Afghanistan. The region has a total area of approximately 72,971 km 2 (27,188 square miles) and is between the latitude of 37^o04 to 43^o04 and 72^o30 to 77^o50. Geographically, GB is a special trans-Himalayan area with a complicated topography and diverse geological layers and with specific climatic regimes. Some of the mountain ranges on the planet are the highest and include the Karakoram, Hindu Kush, and the Western Himalayas that characterize the region (Fig. 1; Table 1). In contrast to the cis-Himalayan areas under monsoon precipitation control, GB receives little monsoonal rainfall and is otherwise dominated by arid to semi-arid climatic regimes (Dani, 2001; Khan, 2012). For this study, 16 plant samples were gathered from different sites of Hunza valley, GB. The gathered specimens were at once put inside sterile polyethylene bags to avoid desiccation and contamination. Each sample was properly labeled, recorded, and shipped to the Molecular Genetics Laboratory, Department of Biotechnology and Genetic Engineering, for further molecular analyses. Out of 16 samples collected, 4 representative genotypes were selected based on distinct morphological variations (leaf shape, cone size) to cover the maximum taxonomic diversity of the region (Fig. 2).

Figure 1; Map of the study area, red dots showing the sample collection sites in Hunza, GB.

Table 1. Detailed description of collected species, with GPS coordinates and collection sites of different regions of study area.

Figure 2; Morphological comparison of Juniperus species collected from Hunza. The figure displays representative branches and foliage of: (A) Juniperus excelsa, showing mature scale-like leaves on elongated shoots; (B) Juniperus communis, displaying dense, prickly needle-like foliage; (C) Juniperus chinensis, highlighting scale leaves and a high density of mature seed cones (galbuli); and (D) Juniperus squamata, characterized by its distinctively "flaky" and clustered needle-like leaves.

 DNA extraction: Genomic DNA was isolated by the Cetyltrimethylammonium Bromide (CTAB) procedure with some modifications (increased incubation time and additional wash steps) (Doyle and Doyle, 1987). Dried leaf materials were finely crushed to powder in a sterile mortar and pestle. For every 100 mg of powdered material, 800 µL of pre-heated 2% CTAB extraction buffer (kept at 65°C) was added, followed by vigorous vortexing. The homogenate was incubated for 2 hours at 65°C. After incubation, 600 µL of phenol: chloroform: isoamyl alcohol (25:24:1, v/v/v) was added, and the contents were centrifuged at 13,000 rpm for 20 minutes. The obtained supernatant was pipetted out to another tube, combined with 500 µL of ice-cold isopropanol, and kept overnight at –20°C to precipitate DNA. Samples were then centrifuged at 13,000 rpm for 20 minutes, and the supernatant was discarded. The pellet of DNA was then washed three times with 70% ethanol, with each washing followed by centrifugation at 13,000 rpm for 3 minutes. Following the removal of remaining ethanol through air-drying of the inverted tubes, the pellet of DNA was dissolved in 80 µL double-distilled water (ddH₂O). The integrity and quality of isolated genomic DNA were evaluated by 1% agarose gel electrophoresis.

PCR amplification: The most extensively studied and best-characterized plant chloroplast DNA primers were selected for amplification (Table 2). DNA amplification was performed via polymerase chain reaction (PCR) using experimentally optimized conditions. Every 25 µL PCR reaction mixture was set up in a 200 µL PCR tube with the following components: 14 µL of double-distilled water (ddH₂O), 2 µL of genomic DNA template, 2.5 µL of 10× PCR buffer, 2 µL of MgCl₂, 2 µL of dNTPs, 0.5 U of Taq DNA polymerase, and 2 µL each of forward and reverse primers. Amplification was carried out using an Applied Biosystems 2720 Thermal Cycler with the following thermocycling protocol: 94°C for 5 minutes of initial denaturation, and then 35 cycles of 30 seconds at 94°C of denaturation, 40 seconds at 52°C of annealing, and 35 seconds at 72°C of extension, with a final 10-minute extension at 72°C. The amplified PCR products were examined by 1% agarose gel electrophoresis in TAE buffer to ensure successful amplification.

Table 2. DNA barcoding makers selected for the evaluation of Juniperus genotypes in the study area along with its forward and reverse primers.

 Nucleotide Sequencing and Analysis: Resultant PCR amplified products were sequenced through the Sanger sequencing method from Macrogn Inc. South Korea. And after successful sequencing, to ensure resulting sequences, accuracy and to recover numerous defined sequences were subjected to BLAST analysis in the NCBI GenBank database. Sequences of composed data were arranged via BioEdit version 7.2.5 (Hall, 1999) and Multiple sequence alignment was performed using MUSCLE and ClustalW. The resultant data was processed to compute the Kimura-2-parameter (K2P) distances for each region by MEGA version X (Kumar et al., 2018).

RESULTS

Molecular Identification of Juniperus species Using matK and rbcL Markers: In the current study, 4 out of 16 collected Juniperus samples from the various locations of the study area (GB) were evaluated through matK and rbcL markers to confirm their molecular identity using reference sequences databases. Genomic DNA extraction yielded high-quality and high-concentration templates (Figure 3A). Successful PCR amplification of the target chloroplast markers, matK and rbcL, was confirmed by the presence of distinct bands of the expected size (Figure 3B and C). The amplification and sequencing success rate were 100% while, the species identification success rate of selected DNA markers was greater than 90%.

Nucleotide BLAST analysis of candidate barcodes: BLAST analysis confirmed high homology between the query sequences and existing NCBI database records, enabling the identification of closely related reference sequences for phylogenetic comparison. The acquired sequence, made up of the entire nucleotide length, displayed a high level of similarity with reference sequences and exhibited very high percent identity and query coverage values. Percent identity was calculated, and an E-value of 0.0 was reflected, pointing to an extremely significant alignment. The final sequence alignment yielded a high-quality dataset of the expected length, providing a robust basis for subsequent phylogenetic characterization (Table 3).

Figure 3; Gel documentation of the genomic DNA and PCR of the selected Juniperus Genotypes. A) Genomic DNA, B) PCR results of matK marker, C) PCR results of rbcL marker. M=1kb DNA ladder, R1-R4 selected genotypes.

Table 3. Detailed description of query species, its successful sequence length, and BLASTn results characterizations.

Molecular characterizations of DNA barcodes: The final aligned dataset, comprising both query and reference sequences, exhibited high sequence homology and was utilized for subsequent phylogenetic reconstruction. The MEGA analysis comprised total aligned sequence length, conserved sites, variable sites, parsimony informative sites, and singleton sites in both the query species sequences and retrieved database sequences (Table 4).

Table 4. Descriptive information of DNA barcode sequences of both query sequences along with references database sequences in Molecular and evolutionary analysis.

DNA barcode-based similarities indexes: Based on molecular characterizations, the monophyletic clade of query sequences was confirmed with database references sequences using Mega (x) software. The evolutionary history was inferred using different methods like blast similarities and percent identities. The optimal tree is shown in different clade with bootstrap support. The bootstrap percentages (based on 1000 replicates) show the percentage of replicate trees in which the given taxa were found to be together. Evolutionary distances were computed using the Kimura 2-parameter (K2P) model and the number of base substitutions per site. The analysis comprised both query and reference nucleotide sequences, and all positions with less than full data were eliminated using the complete deletion option. In the resulting tree of phylogeny, the query sequences showed a strong evolutionary relationship with the corresponding reference sequences pulled from the database (Table 5).

Phylogeny of Juniperus species based on matK: Initially the matK sequences of all the query species along with references database sequences were utilized to construct the phylogeny. In the phylogenetic tree the query species showed evolutionary relationship with reference database sequences reported from the different parts of the world with strong bootstrap support (Annexure 1). The top most similar sequences from the database along with 4 query species sequences were further used to construct combined phylogenetic tree showing the evolutionary relationship among query species and available references database species. In the phylogenetic tree, query species (PZ049341.1-J. chinensis) showed close relationships with (MH659306.1-J. chinensis), (LC404753.1-J. chinensis), and (JQ512420.1- J. chinensis), and (NC065035.1-J. procumbens) with bootstrap support of 96. Similarly, the query species (PZ049345.1-J. communis) shared a clade with (MH121052.1, J. communis), (MK925622.1-J. communis), and (HQ245897.1-J. formosana). On the other hand, the query species (PZ049344.1- J. squamata) showed a close relationship with (NC044076.1-J. squamata) and (NC062329.1-J. przewalskii) with bootstrap value of 96. While, the query species (PZ049343.1-J. excelsa) shared clade with (HM024025.1- J. excelsa), and (HM024045.1- J. polycarpos) with bootstrap of 100. The outgroup species (HE966910.1, HM023994.1-Cupressus sempervirens) sequences make a separate clade from the query species sequences along with references database sequences in the phylogenetic tree (Fig. 4).

Phylogeny of Juniperus species based on rbcL: Initially the rbcL sequences of all the query species along with references database sequences were utilized to construct the phylogeny. In the phylogenetic tree the query species showed evolutionary relationship with reference database sequences reported from the different parts of the world with strong bootstrap support (Annexure 1). In the neighbour-joining tree, the query species (PZ049346.1-J. chinensis) showed a close relationship with (MH657749.1-J. chinensis), (PP356738.1-J. chinensis), and (OQ184424.1-J. chinensis). Similarly, the query specie (PZ049347.1-J. communis) shared clade with (PP374792.1-J. communis). On the other hand, the query specie (PZ049349.1-J. squamata) makes a close relationship with (NC044076.1-J. squamata), and (OQ184437.1-J. squamata). In the same tree, the query specie (PZ049348.1-J. excelsa) shared clade with (MN662645.1-J. excelsa), (AY380894.1-J. procera), and MN662648.1-Juniperus polycarpos) with bootstrap of 64. While the outgroup genus species (JX571816.1, JHE963440.1-Cupressus sempervirens) makes a separate clade in the phylogenetic tree (Fig. 5).

Table 5. A descriptive table showed the evaluated DNA barcodes in collected genotypes, their blast sequences similarities along with species authorities, Genebank Accession and geographic location of submitted sequences.

Figure 4: A representative NJ tree showed the phylogenetic relationship of query genotypes sequences with references database sequences, tree confirms the monophyly of query Juniperus species such as J. chinensis, J. communis, J. excelsa, and J. squamata with bootstrap of 64, 88, and 100.

Figure 5: A representative NJ tree showed the phylogenetic relationship of query genotypes sequences with references database sequences, tree confirms the monophyly of the query genotypes Juniperus species such as J. chinensis, J. communis, J. excelsa, and J. squamata with bootstrap of 64, and 94.

 DNA Barcode and QR-code of the Query species sequences: The DNA barcode from the matK and rbcL sequence was generated by DNA barcode generator (Bio-Rad) software (v 1.3) for their exact identification (Fig 6), and QR-code was generated online through bioinformatic tools (Fig 6).

Figure 6; Visual representation of DNA barcodes and corresponding QR codes generated from the sequences of the query species.

DISCUSSION

 The present study utilized two chloroplast DNA barcodes, matK and rbcL, to identify and establish the molecular identity of Juniperus genotypes collected from different regions of Gilgit-Baltistan. Both markers demonstrated high amplification and sequencing success, reflecting their reliability and universality for DNA barcoding in conifers. The findings revealed that all query genotypes shared high sequence similarity (>98%) with reference Juniperus species deposited in the NCBI GenBank database, confirming the accuracy of molecular identification.

 The matK and rbcL genes gave consistent and complementary data in circumscribing Juniperus species. The matK locus had greater interspecific variation than rbcL, consistent with previous findings which revealed that matK has greater nucleotide substitution rate and thus offers improved discrimination among closely related taxa (Irfan et al., 2021). On the other hand, the rbcL region, as being more conserved, is nonetheless applicable in resolving higher-level relationships due to its high universality and amplification efficiency. Similar findings were reported by (Aggag, 2020), further noting that the combination of both markers has increased species-level identification accuracy and phylogenetic resolution of morphologically similar plant groups.

 The BLAST and phylogenetic results unambiguously verified that the investigated genotypes represent four Juniperus species: J. chinensis, J. communis, J. excelsa, and J. squamata. These were well-supported by high bootstrap values (>90%) in the neighbor-joining trees, verifying their monophyletic origin and evolutionary proximity with reference species. The clustering pattern described here aligns well with earlier phylogenetic estimations of Juniperus, in which respective groupings were documented using chloroplast and nuclear markers (Mao et al., 2010; Adams and Schwarzbach, 2012, 2013; Adams et al., 2016). The same clustering pattern validates the reliability of matK and rbcL in reconstructing phylogenetic relations within the genus.

 The high level of sequence conservation in the rbcL and a relatively larger proportion of variable sites in matK is evidence of the evolutionary bottlenecks operating differently on those loci (Wattoo et al., 2016). The rbcL gene is functionally important in photosynthesis and thus puts the gene under strong purifying pressure, resulting in a reduced rate of variability, whilst matK, which encodes a maturase enzyme that splices RNA, evolves at a quicker rate and has more substitutions (Wolfe and DePamphilis, 1998; Barthet and Hilu, 2007; Christin et al., 2008). Such variation in the rate of substitution allows matK to be more useful in identifying the recent events of speciation and in discriminating closely related Juniperus species (Adams and Schwarzbach, 2012; Aggag, 2020; Irfan et al., 2021).

 Interestingly, phylogenetic trees that were derived during the current study depicted clear difference of the four Juniperus species of Gilgit-Baltistan into separate clades which were related to their morphological and ecological variations. As an example, J. chinensis and J. communis were closely related to the East Asia accessions, whereas J. excelsa and J. squamata were closely related to the Central Asian and Himalayan populations. This pattern of biogeography can be attributed to the historic paths of dispersal and the effect of glacial-interglacial cycles on Juniperus distribution over the trans-Himalayan area. Other Himalayan and Karakoram coniferous taxa have had similar biogeographic associations (Sarangzai et al., 2012; Irfan et al., 2021).

 Beyond simple taxonomic clustering, the identification of these four Juniperus species carries significant weight regarding the local ecology and genetic distinctiveness of the GB region. GB is a high-altitude, orogenic, confluence of Karakoram, Hindu Kush and Himalayan ranges. The genetic similarity of our samples to Central Asian and Himalayan accessions indicates that these populations might have served as glacial refugia, where unique genetic variations were formed as a result of the excessive environmental pressures and long-term seclusion in deep mountain valleys (Irfan et al., 2021; Abbas et al., 2022). Such Juniperus species are key taxa in the sub-alpine cold deserts of GB in which they are important in making soil stable and preserving moisture in an otherwise weak ecosystem (Redmond et al., 2023).

 The strong bootstrap and proximity to the authenticated database sequences confirm the validity of molecular data to be used in taxonomic confirmation. DNA barcoding is therefore an effective approach to the shortcomings of morphological identification that may be unraveled by environmental change or hybridizations (Miller, 2007; Sheth and Thaker, 2017). The findings of the study emphasize the utility of molecular barcoding in conjunction with morphological observation to effectively resolve taxon ambiguities within Juniperus, also found in other conifers such as Cedrus and Cupressus (Aggag, 2020).

 From a conservation perspective, the molecular verification of these genotypes is a critical step toward protecting the dwindling forest cover in northern Pakistan. The Juniperus forests in GB are extremely slow growing and their life span is very high, so they are very susceptible to anthropogenic impacts, including overgrazing, illicit logging to get fuel and changes in climate leading to habitat alteration (Irfan et al., 2021). Our results give a requisite ground upon which we can define Evolutionarily Significant Units (ESUs). Through the validation of the genetic identity of those species, conservationists can now formulate specific management plans that would then focus on preserving these particular high-altitude populations which might have specific local adaptations to be able to survive in the midst of the fast-paced climate change in the Trans-Himalayan region (Van, 2014; Shaheen et al., 2017).

 In addition, amplification of chloroplast markers on field-collected material indicates that experiments can be done on herbarium or preserved samples, which is advantageous in areas like GB where sampling and access is hard. The current results thus offer a useful genetic background on future genetic studies on population genetics, phylogeography and conservation of Juniperus species in Pakistan. On the whole, this paper confirms that matK and rbcL markers can be used to authenticate species and examine evolutionary history of Juniperus. The resulting molecular evidence does not only help to clear the taxonomic ambiguity among the sampled genotypes but also helps to understand Juniperus diversity and distribution in the Himalayan region at large. The further integration of DNA barcoding, chloroplast genome sequencing, and morphological data will further enable species demarcation and conservation measures of these economically and ecologically vital conifers.

 Although the phylogenetic tree produced superior bootstrap values on the recognized clades, the derivation of the results is treated with caution as far as strict monophyly is concerned. Considering the logistical difficulties and the inaccessibility of the high-altitude terrain of GB, the present study is a first step in molecular characterization on the basis of localized sampling attempt. This clustering is therefore seen as a sign of intimate evolutionary relationship as opposed to a conclusive proof of monophyletic relationship of these taxa. These results offer a general genetic context of the Juniperus species of the region but the evolutionary histories of the area and their intricate genetic origins need to be further researched with the inclusion of extended geographic coverage and more genomic signatures to clarify the diverse evolutionary histories of the genus Juniperus.

Conclusion: The present study established the molecular identity and taxonomic classification of Juniperus genotypes from the Gilgit-Baltistan region (Hunza valley) based on chloroplast DNA barcode (matK and rbcL). The two markers showed high amplification efficiency and could distinctly separate the different genotypes into four species namely J. chinensis, J. communis, J. excelsa and J. squamata. The matK was more varied and showed better species resolution as compared to rbcL. Phylogenetic analyses identified supported monophyletic clades in line with evolutionary connections recorded in world databases. The findings are a valid source of molecular data in proper species identification and it also forms a point of reference in future research on genetic diversity and conservation of Juniperus in the Himalayan region of Pakistan. However, the study is limited by its exclusive geographical focus on the Hunza region and the use of only two chloroplast markers (matK and rbcL); therefore, future research should involve broader sampling across the entire Gilgit-Baltistan region and incorporate nuclear markers or high-throughput sequencing to more comprehensively capture the genetic diversity and evolutionary complexity of these species.

Acknowledgement: All authors extend their sincere appreciation to the Department of Biotechnology and Genetic Engineering, Hazara University Mansehra, KP, Pakistan, for assistance and technical support in the experiment.

Author contributions: RN, SG, AU and IU conceived and designed the study. RN, IU, SI, S and MH performed the experiments. RN, SI, HA, MH and S contributed to data collection and analysis. RN, IU, HA and S assisted in manuscript drafting and reviewing. SG, AU, HA and IU provided supervision and critical revisions. All authors have read and agreed to the submitted version of the manuscript.

Conflicts of Interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Annexure 1

Figure A1: A representative NJ tree through matK, showed the phylogenetic relationship of query genotype sequences with references database sequences, tree confirms the monophyly of the query genotypes Juniperus chinensis with bootstrap of  94.

Figure A2: A representative NJ tree through rbcL, showed the phylogenetic relationship of query genotype sequences with references database sequences, tree confirms the monophyly of the query genotypes Juniperus chinensis with bootstrap of 92.

Figure A3: A representative NJ tree through matK, showed the phylogenetic relationship of query genotype sequences with references database sequences, tree confirms the monophyly of the query genotypes Juniperus communis with bootstrap of 100.

Figure A4: A representative NJ tree through rbcL, showed the phylogenetic relationship of query genotype sequences with references database sequences, tree confirms the monophyly of the query genotypes Juniperus communis with bootstrap of 64.

Figure A5: A representative NJ tree through matK, showed the phylogenetic relationship of query genotype sequences with references database sequences, tree confirms the monophyly of the query genotypes Juniperus excelsa with bootstrap of 85.

Figure A6: A representative NJ tree through rbcL, showed the phylogenetic relationship of query genotype sequences with references database sequences, tree confirms the monophyly of the query genotypes Juniperus excelsa with bootstrap of 100.

Figure A7: A representative NJ tree through matK, showed the phylogenetic relationship of query genotype sequences with references database sequences, tree confirms the monophyly of the query genotypes Juniperus squamata with bootstrap of 100.

Figure A8: A representative NJ tree through rbcL, showed the phylogenetic relationship of query genotype sequences with references database sequences, tree confirms the monophyly of the query genotypes Juniperus squamata with bootstrap of 50.