Introduction

Understanding the genetic and ecological characteristics of extremely rare plants or populations not only enhances our understanding of population dynamics, adaptation and evolution, but also provides vital information for biological conservation [1]. Effective population size is the fundamental factor in affecting the fate of any population, which describes the rate of genetic drift, inbreeding and population’s sensitivity to natural selection [2, 3]. Rare and endangered plants often tend to have small populations posing them vulnerable to environmental and genetic risk, thus loosing the extent of genetic variability [4]. This is because of the faster genetic drift occurring among them in comparison to larger populations that are connected with active gene flow. Inbreeding also represents potential threat for the persistence of small population, thereby resulting in low genetic diversity [5, 6]. The long-term survival of any species requires the maintenance of sufficient genetic diversity to serve as the raw material for evolutionary changes in natural populations. Therefore, a precise evaluation of the prevailing genetic status of critically endangered species and the degree of divergence among populations and, in particular, the extent to which those differences will affect survival and reproduction in various habitats is a necessary condition for successfully preventing their extinction [7, 8].

Decalepis arayalpathra (J. Joseph and V. Chandras.) Venter. (Syn. Janakia arayalpathra J. Joseph and V. Chandras.) is a perennial woody lateciferous under shrub of the family Apocynaceae. It is a narrow endemic species confined to few forest segments of Southern Western Ghats in Tirunelveli, Kanyakumari districts of Tamil Nadu and Thiruvananthpuram, Kollam districts of Kerala [9–11]. The plant is rare and distributed in grassy patches of exposed rocky slopes (Online Resource 1a), which grows in clumps without any firm holding and is exposed to heavy wind velocity, high temperature and moderately good rainfall almost throughout the year (Online Resource 1b). Popularly known as Amrithapala by the native Kani tribe, the plant is used as an effective remedy for peptic ulcer, cancer-like afflictions, stomachache and as a rejuvenating tonic [12]. The highly aromatic tuberous roots of the plant are rich in gastric antisecretory and antiulcer activities [13]. Recent chemical studies have also revealed that tuberous root oil of the plant contains higher amount of an industrially important flavour molecule, 2-hydroxy-4-methoxybenzaldehyde (96.8 %) [14].

In recent years, the rapid pace of deforestation in the Western Ghats has depleted the wild stands of D. arayalpathra. Poor fruit set, seed germination and rooting on stem cuttings has hampered its natural regeneration as well as conventional propagation [15]. This species was assessed as Critically Endangered—Globally (CR-G) due to its narrow distribution and fragmented population [16, 17]. The National Biodiversity Authority of India (NBA) declared it as a species of high conservation concern [18]. Furthermore; specific niche, extended flowering pattern, damage by fruit wasp, population bottleneck and root rot by fungus has endangered this species in its wild habitat [19, 20]. Consequently, an understanding of the genetic composition of the critically endangered D. arayalpathra populations is urgently needed for planning its conservation action.

The maintenance and restoration of population genetic analysis depends on several factors, among which the availability of markers with adequate variability is one of the most important and often difficult to achieve. This results in application of molecular methods and phylogenetic studies to design and implement conservation strategies [21, 22]. Molecular markers in combination with spatial statistical tools have contributed immensely to the understanding of the distribution of genetic diversity and differentiation, gene flow and population size implications for a wide range of plant species [4, 6, 23, 24]. Because of no affect by environmental or biological factors, the molecular random markers provide more precise understanding of genetic diversity through the identification of genomic segments that differentiate individuals or populations without the need for genetic information about the genome [1]. Random amplified polymorphic DNA (RAPD), a commonly used polymerase chain reaction (PCR)-based technique for detection of genetic variability due to its simplicity, low cost and unnecessary previous knowledge of the genome has been shown to be appropriate for rare and endangered species, because it requires only small amounts of tissue [25]. The use of inter-simple sequence repeats (ISSRs) is a molecular fingerprinting technique based on PCR that employs primers to amplify DNA sequences contained between microsatellites in genomes [26]. ISSR is a powerful tool for informing genetic conservation and sustainable use strategies and has been widely used in genetic diversity studies of species with conservation concerns based on relatively lower cost of analysis than amplified fragment length polymorphisms (AFLP) [27] and no need for other DNA sequence information compared to simple sequence repeats (SSRs) [28].

To the best of our knowledge, there have been no reports on molecular studies of genetic diversity and structure for the populations of this endangered plant species. The present study will be the first report employing 20 RAPD and 15 ISSR markers to investigate the genetic composition of nine wild D. arayalpathra populations distributed in different geographical regions in Tirunelveli and Kanyakumari districts of Tamil Nadu with the following specific objectives: (1) to document the level of genetic diversity among D. arayalpathra populations across the species geographic range, (2) to identify demographic patterns and evaluate the degree of genetic connectivity among populations, and (3) to discuss the implications for effective conservation according to the basic genetic information.

Materials and Methods

Population Sampling

D. arayalpathra is highly endangered with few populations which are exposed to multiple pressures that threaten the species existence. An appropriate permission was granted by the Tamil Nadu Biodiversity Board (Letter Ref No. TNBB/52/2011 dated September 28, 2011, for 1 year period from December 2011 to November 2012) and the Principal Chief Conservator of Forests & Chief Wildlife Warden, Tamil Nadu Forest Department (Letter Ref. No. WL5/23758/2011 dated December 5, 2011, for 1 year period from December 2011 to November 2012) to study the reproductive biology, conservation issues, problems in germination, process of multiplication, DNA barcoding etc. A total of 60 individuals corresponding to nine populations of different geographic regions were collected from different localities of Tirunelveli and Kanyakumari districts in Tamil Nadu, India (Online Resource 2, Table 1). Populations were sampled randomly; individuals 5–10 m from each other were chosen to avoid collecting the same plant. To minimize population damage and to avoid harvesting clonal ramets of this endangered species, we sampled only leaves of minimum possible individuals from each population. All of the samples were dried with silica gel and stored at −20 °C until subsequent DNA extraction. Vouchers for each population were deposited at the Foundation for Revitalisation of Local Health Traditions (FRLHT), Bangalore, India, for future reference.

Table 1 Geographic origin of the 60 individuals of Decalepis arayalpathra populations from Tamil Naidu
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DNA Extraction and Markers Genotyping

Total genomic DNA from individual accessions was extracted from the leaf tissues using the modified cetyl trimethyl ammonium bromide (CTAB) protocol with necessary modifications [29]. Isolated DNA was checked for its quality and quantity by running on 0.8 % agarose gel electrophoresis and spectrophotometric analysis (Nano Drop, ND-1000, USA), respectively.

A set of 45 ISSR primers procured from the University of British Columbia (Vancouver, Canada) and 30 RAPD primers procured from Operon Technologies Inc. (USA) were screened for their reproducible amplification. PCR amplifications for each primer set were carried out in a 25-μl volume solution containing 1× Taq DNA polymerase buffer, 200 μM each dNTPs (dATP:dTTP:dCTP:dGTP in 1:1:1:1 parts), 10 pmol of each primer, 1 unit of Taq DNA polymerase and 25 ng of template DNA. Amplifications were performed using DNA thermal cycler (Applied Biosystems, Bio-Rad) by optimizing the protocol with the following parameters: initial 5 min denaturation at 94 °C followed by 45 cycles, denaturation of 1 min at 94 °C, annealing of 1 min at 48–60 °C for ISSR and 36 °C for RAPD, extension of 2 min at 72 °C, with final extension of 7 min at 72 °C. Amplified PCR products were separated on 1.2 % agarose gel stained with ethidium bromide. A double-digested λ DNA (EcoRI and HindIII) was used as standard to assess the size of DNA bands.

Genetic Diversity Analysis

Only bands that could be unambiguously scored across all the sampled populations were used in the analysis. Each band is assumed to represent the phenotype at a single diallelic locus, because both markers are dominantly inherited. The reproducible ISSR and RAPD fragments were scored manually as present (1) or absent (0) for each accession, and binary qualitative data matrix was constructed for both the marker system. The binary data matrix was converted into genetic similarity matrix using the Jaccard coefficient in SPSS Statistics 17.0 software [30]. TREECON software [31] was used to assess the genetic relatedness within and among the populations using unweighted pair-group method with arithmetic average (UPGMA) based on pairwise Nei’s [32] genetic distance. The robustness of each UPGMA node was evaluated by a bootstrapping analysis of each data set over loci for 1000 replicates [33]. The hierarchical analysis of molecular variance (AMOVA) was applied to estimate variance components for markers genotype, partitioning the variations among populations and individuals using program GenAlEx version 6.5 [34]. The variance components were tested statistically by nonparametric randomization tests using 999 permutations. To assess the suitability of the marker system used, the performance of each marker was measured using polymorphic information content (PIC), marker index (MI) and resolving power (RP). The PIC of each locus was calculated using the formula PICi = 2fi (1- fi) [35]. The PIC of each primer was derived by finding the average PIC value from all loci of each primer. Marker index was calculated to characterize the capacity of primers to detect polymorphic loci among the genotypes. MI for each primer was calculated as a product of PIC and effective multiplex ratio (EMR) [36]. RP of each primer was calculated as RP = ΣIb [37].

Population Genetics Indices

Different genetic diversity parameters including the percentage of polymorphic loci (PPB), average gene diversity within population (H S) and total diversity (H T) at the population and species level were analysed using POPGENE version 1.32 [38] assuming Hardy-Weinberg equilibrium. Population differentiation was analyzed for polymorphisms between populations by genetic differentiation (Gst), which was estimated by Nei’s gene diversity statistics and Shannon’s information measure [39]. Gene flow (Nm) among these populations was quantified using the formula Nm = 0.5(1 − Gst)/Gst [40].

In order to access the spatial genetic structure, we used three complementary approaches: a principal coordinate analysis (PCoA), a clustering based on Bayesian models and a Mantel test. PCoA was conducted in GenAlEx version 6.5 based on covariance matrix (pooled data) with data standardization. To infer population structure and genetic admixtures with a Bayesian approach, STRUCTURE version 2.3.4 [41] was used based on individual multilocus genotypes to detect clusters of individuals that minimize Hardy-Weinberg and linkage disequilibria. An admixture model with correlated allele frequencies was performed [42], and default values were maintained for all other parameters. The number of populations, K = 1 to 9 was tested and determined assuming no prior information about the individuals sampled in each population. Ten independent runs were made to ensure consistent results, and for each run, a Markov chain Monte Carlo (MCMC) of 100,000 iterations was carried out after a burn-in period of 50,000 iterations. The most likely value for assigning K clusters was determined using the STRUCTURE HARVESTER program [43], which implements the Evanno method [44]. Further, a Mantel test [45] was performed using 10,000 permutations carried out in XLSTAT©-Pro version 7.5 (2004, Addinsoft Inc., Brooklyn, NY, USA); the significance level was set at α = 0.05 to compute the matrix correlation (r) between the similarity matrices generated from different assays to test the goodness of fit between the RAPD and ISSR marker system. Finally, isolation by distance-based geographic distance matrix was compared with genetic distance matrix using a Mantel test. Geographic distance matrix was realized based on latitude and longitude coordinates using the Geographic Distance Matrix Generator version 1.2.3 [46].

Results

Genetic Diversity

Fifteen ISSR and twenty RAPD primers were selected based on their reproducibility, clarity and highly polymorphic nature of product bands (Tables 2 and 3). The number of products generated by these arbitrary primers was found to range from 3 to 7 with sizes ranging from 200 to 3000 bp for ISSR and 2 to 11 with size ranges from 200 to 3530 bp for RAPD. ISSR and RAPD profiles of a representative gel of primers UBC 845 and OPA 11 amplification are shown in Figs. 1 and 2. Across 60 individuals representing nine wild populations, a total of 220 bands were recorded, with an average of 4.933 bands per primer for ISSR and 7.300 bands per primer for RAPD. The level of polymorphism found through both the marker system was very low. Out of 146 amplicon products generated through RAPD-PCR, 103 were found to be polymorphic accounting for 70.54 % polymorphism across the accessions (Table 3). While in the case of ISSR-PCR, a total of 74 amplicon products were recorded, among which, only 19 were polymorphic resulting in 25.67 % polymorphism across the accessions (Table 2). At the population level, the percentage of polymorphic loci per population ranged from 0 (TN) to 13.00 % (TK) with an average of 7.00 % for ISSR, and from 20.00 % (TAGM) to 51.00 % (TK) with an average of 29 % per population for RAPD (Table 4). PIC value was found to range from 0 (UBC 826, 828, 844) to 0.142 (UBC848) with an average 0.076 per primer for ISSR, and 0.006 (OPA12) to 0.339 (OPA10) with an average of 0.191 per primer for RAPD. Comparing the efficiency of the marker system used in the present study, RAPD markers were found to be more powerful than ISSR assay by showing greater discriminatory power among the accessions. EMR observed with RAPD analysis was high (23.413) in comparison to ISSR (12.817). Other marker parameters viz. MI and RP were also found to be comparable with MI = 4.674 and RP = 2.200 for RAPD and for ISSR assay, MI = 1.233 and RP = 0.618 (Tables 2 and 3).

Table 2 Characteristics of ISSR markers used in this study and their polymorphism indices
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Table 3 Characteristics of RAPD markers used in this study and their polymorphism indices
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Fig. 1
figure 1

ISSR profile of D. arayalpathra populations with primer UBC-845. Lanes M, λ DNA double-digested (EcoRI + HindIII) size markers, 1–60 accessions, as mentioned in Table 1

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Fig. 2
figure 2

RAPD profile of D. arayalpathra populations with primer OPA-11. Lanes M, λ DNA double-digested (EcoRI + HindIII) size markers, 1–60 accessions, as mentioned in Table 1

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Table 4 Intrapopulation statistics of nine population of D. arayalpathra
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Assuming Hardy-Weinberg equilibrium, Nei’s gene diversity (h) and Shannon’s information index (I) among all the populations were estimated as 0.103 ± 0.142 and 0.154 ± 0.210, respectively, for RAPD and 0.024 ± 0.050 and 0.036 ± 0.075, respectively, for ISSR. The values of h and I showed a similar trend to percentage of polymorphism (PPB), demonstrating relatively low level of genetic diversity. When calculated at the species level, the h and I values with RAPD marker system averaged 0.200 ± 0.184 and 0.289 ± 0.257, respectively, and 0.076 ± 0.134 and 0.118 ± 0.204, respectively, with ISSR assay. Among the nine populations investigated, population TK exhibited the highest genetic variability [ISSR (PPB = 13 %, h = 0.041, I = 0.063); RAPD (PPB = 51 %, h = 0.165, I = 0.254)], where as the population TN [ISSR (PPB = 0 %, h = 0, I = 0)] and TAGM [RAPD (PPB = 20 %, h = 0.087, I = 0.125)] exhibited the lowest variability as shown in Table 4.

The Jaccard coefficient among 60 accessions of D. arayalpathra ranged from 0.166 (Kanyakumari and Tirunelveli) to 0.800 (among several Tirunelveli populations) for ISSR marker, and from 0.361 (Kanyakumari and Tirunelveli population) to 0.900 (accessions from Kanyakumari population) for RAPD. Although ISSR and RAPD markers are related to different regions of the genome and generated dendrograms with different topologies, in general, both dendrograms showed the formation of the same groups, with a few exceptions (Online Resource 3 and 4). Despite the low bootstrap values for most of the nodes, it was possible to identify well-defined groups based on the banding patterns of the respective genomes. At the species level, both the markers clustered all the 60 genotypes into two main clusters (clusters I and II) with divergence into sub-clusters. Dendrogram generated through ISSR profiling framed cluster I into seven different groups and cluster II into four different groups according to their collection locations with a few exceptions, while RAPD profile framed cluster I into six different groups and cluster II into three different groups. All the populations from Kanyakumari and Tirunelveli districts were grouped into similar different sub-clusters. It was interesting to conclude that all the representatives from Nadukandanparai and Vistharmottai regions were grouped into individual similar cluster in both the dendrograms. All the twelve representatives from Maramalai population of Kanyakumari district formed single individual cluster in RAPD analysis (group IX), while in ISSR analysis, they diverged into two different groups viz. groups VI and X based on the molecular pattern showing similarity variance of 37 and 15 %, respectively, with the Yanaisethaoddaimadai populations of Kanyakumari district. Deducing the patterns realized through the pooled binary data from both the markers, all the 60 individuals were grouped into two main clusters viz. clusters I and II (Fig. 3). Cluster I again sub-clustered into six main groups viz. groups I, II, III, IV, V and VI, while cluster II sub-clustered into three different groups viz. groups VII, VIII and IX. Group IX framed all the representatives from Maramalai populations with 71 % similarity variance. Likewise, the dendrogram patterns observed through individual marker system, the combined analysis also depicted the similar grouping pattern according to their geographical locations. All the representatives from Vistharmottai population showed maximum similarity among themselves with 90 % bootstrap value. Also, the dendrogram realized through individual RAPD profile and combined ISSR + RAPD profile showed almost the similar clustering pattern with the grouping of Vistharmottai, Kuthuraikattimotai and Maramalai populations into three different clusters viz. groups VII, VIII and IX depicting the efficiency of the RAPD markers in D. arayalpathra populations.

Fig. 3
figure 3

Genetic similarity dendrogram of 60 D. arayalpathra accessions based on combined ISSR and RAPD band pattern (Nei and Li’s coefficient)

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Table 5 Population genetic structure of D. arayalpathra populations based on analysis of molecular variance
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Genetic Differentiation and Relationships

According to Nei’s gene diversity and AMOVA analyses, the percentages of genetic variation among populations were 45.00 % (Gst) and 36.00 % (total variance), respectively, with RAPD assay, while in ISSR, it was found to be 53.00 % (Gst) and 46.00 % (total variance), respectively (Table 5). Both the data separately indicated that the genetic differentiation was found mainly within the populations. Furthermore, the level of gene flow (Nm) was measured to be 1.85 (RAPD) and 0.29 (ISSR) individual per generation between populations, suggesting that gene exchange between populations was low. Genetic distance between populations of D. arayalpathra assayed with ISSR marker ranged from 0.024 (between KMM and KAS) to 0.127 (between TN and KMA), and the average Nei’s genetic identity was 0.927 (ranging from 0.880 to 0.975). In RAPD profile, the genetic distance between populations ranged from 0.038 (between KAS and KMA) to 0.179 (between TN and KAS), and the average Nei’s genetic identity was 0.899 (ranging from 0.836 to 0.962).

The UPGMA dendrogram, based on Nei’s (1978) unbiased genetic distance matrix realized through ISSR assays placed all the nine populations (TA, TN, TK, TAGM, KMM, TT, KKU, KAS, KMA) according to their geographical coordinates (Online Resource 5a). The first cluster grouped all the populations from Tirunelveli region, while the second cluster grouped the respective populations from Kanyakumari regions. The unbiased genetic distance matrix realized through RAPD assays showed the near similar topology by placing all the Tirunelveli populations into similar cluster with little exception (Online Resource 5b). KMM population from Kanyakumari and TT population from Tirunelveli regions showed little divergence from their geographical locality based on the genomic considerations. In agreement with the UPGMA cluster analysis, PCoA revealed that individuals formed cohesive clusters in populations with respect to their geographical localities. Overall, the populations were grouped into two main groups along each axis, which separated individuals of Tirunelveli regions from Kanyakumari populations. The first two components accounted for 26.95 % (axis 1 = 16.02 %, axis 2 = 10.93 %) of the total variability (Fig. 4).

Fig. 4
figure 4

Principal coordinate analysis of 60 individuals of D. arayalpathra populations based on covariance matrix (pooled data) with data standardization

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The Mantel test with 1000 permutations (p value = 0.001) revealed a positive correlation (r = 0.389, p = 0.033) between D. arayalpathra genetic and geographic distance matrix, thus evidencing a strong geographic pattern between genotypes (Online Resource 6). In addition, the cophenetic correlation between the ISSR and RAPD matrices was not significantly positive (r = 0.186, p = 0.127), while the combined ISSR + RAPD matrix showed strong positive correlation with both RAPD (r = 0.841, p = 0.001) and ISSR (r = 0.689, p = 0.001), thereby concluding the good fit of data with each other (Online Resource 7).

Population Structure

In the admixture analysis using STRUCTURE, the Bayesian posterior probability of data increased until K = 3. The highest peak in δK at K = 3 represents that the three genetic clusters were detected indicating all the 60 individuals of D. arayalpathra in the present study shared three genetic pools with few migrants and admixed individuals (Fig. 5a). This result was supported by the δK test [44], with the modal value of the rate of change of the likelihood distribution also being K = 3 (Fig. 6). With the exception of KAS and KMA, all the other populations presented considerable admixture with TK and TN presenting the highest degree among them (Fig. 5b).

Fig. 5
figure 5

Bayesian inference of the number of clusters (K) in D. arayalpathra populations (ISSR + RAPD assay). a Bar plot resulting from STRUCTURE analysis at K = 3. b Bar plot resulting from STRUCTURE analysis at K = 9. The genotype of each individual accession is represented by a vertical line divided into coloured segments, the lengths of which indicate the proportions of the genome attributed to the inferred clusters

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Fig. 6
figure 6

Results of the Bayesian assignment analysis using the STRUCTURE HARVESTER

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Discussion

Genetic Diversity

The level of genetic diversity highlights the population sizes, breeding systems, phylogenetic relationships, mutation and gene flow as the main factors which have gained increasing recognition, motivating numerous recent investigations of diversity [6, 47]. Many endangered species typically display low genetic variation [48]. Accordingly, the present study revealed low level of genetic diversity [h = 0.103 (RAPD), 0.024 (ISSR)] in wild D. arayalpathra populations with 24.48 % (ISSR) and 68.00 % (RAPD) of bands showing polymorphism at species level. Significantly under Hardy-Weinberg equilibrium, the observed level of heterozygosity in the populations was found to be higher (N a = 0.866 ± 0.137 for ISSR and N a = 1.287 ± 0.369 for RAPD) than expected (N e = 0.841 ± 0.086 for ISSR and N e = 1.173 ± 0.251 for RAPD), suspected to be due to an isolate-breaking effect. Clear evidence based on Bayesian and neighbour-joining analysis also suggested weak genetic divergence observed among the nine wild populations of D. arayalpathra.

Genetic diversity within population is significantly affected by the reproductive system in plants. In general, selfing plant species with relatively more homozygous individuals and reduced effective population size, usually display lower genetic variation and higher genetic differentiations than out crossers [48]. We accounted the possible reasons for low genetic diversity of D. arayalpathra populations as followed. Firstly, D. arayalpathra is self-compatible. Therefore, autogamy is likely to account for the low level of genetic variation within the species. Secondly, life history traits, ecological tolerance and seed dispersal mechanism are all closely related to the genetic diversity and structure of a plant species [49]. Specific niche, extended flowering pattern, very low seedling establishment, dispersal by wind, and damage by fruit wasp reinforce its endangered status in wild habitat. Finally, the high levels of genetic drift and inbreeding are the two major genetic consequences of small population size for long periods of time. In general, widespread species may have higher level of genetic variability than narrowly distributed ones [50, 51]. In D. arayalpathra, the narrow distribution range with rather small and isolated population due to high level of genetic drift and inbreeding noticed within the entire populations, makes this species more prone to suffer from low level of genetic diversity.

Population Genetic Differentiation

Plant species differ markedly in the way that genetic diversity is partitioned between populations. In the present study, both the Nei’s genetic diversity and Shannon’s diversity index analyses of both the marker systems gave similar indications about the nature of the genetic structure of the D. arayalpathra populations. The results of both ISSR (0.531) and RAPD (0.440) showed high values of genetic differentiation (G st) among populations. The proportion of diversity between populations based on the Shannon indices was similarly moderate, 15.00 % with RAPD and 4.00 % with ISSR. In general, genetic differentiation of plant populations reflects interactions among a range of different processes viz. evolutionary history, mutation, genetic drift, mating system, gene flow and selection [51]. The strong differentiation found in D. arayalpathra populations may be mainly due to genetic drift and limited gene flow. The estimate of Nm (ISSR = 0.289, RAPD = 0.847), derived in the present study, is too low to effectively prevent stochastic differentiation by genetic drift.

From Mantel test analysis, there was a significant relationship between the genetic distance and geographic distance among the nine populations (r = 0.389, p = 0.033) of D. arayalpathra. The UPGMA tree based on Nei’s [52] unbiased genetic distance matrix also supported the “isolation by distance” (IBD) model. For example, populations from Tirunelveli and Kanyakumari districts clustered according to their geographical locations, and the genetic distance between them is relatively small (Online Resource 5). Results obtained through different markers showed some discrepancies in the level of heterozygosity, PCoA clustering, UPGMA hierarchical clustering and STRUCTURE, suggesting that the manner of polymorphism differs because of marker specificity. Both the marker systems viz. ISSR and RAPD sample genomic regions with different evolutionary modes. Contrasting patterns have generally been found using multiple markers to detect genetic structure [8].

Implications for Conservation and Sustainable Utilization

Successful management strategies directed towards the conservation and sustainable utilization of threatened species depends on a good understanding of the distribution of genetic variation in the species [53]. These population genetic-structure characteristics have significant implications for conservation considerations. Smaller populations of endangered species are more prone to go extinct than large stable ones, thus requires special conservation concern [50]. Our findings would provide important genetic information for developing conservation strategies of D. arayalpathra.

Low genetic diversity concluded through the study and limited number of individuals examined during survey, identify the need to focus conservation activities on increasing the genetic diversity and population size of D. arayalpathra. Considering most of the genetic variation within populations and high genetic differentiation among populations, the conservation measures should therefore focus on establishing large numbers of seedlings, both in situ and ex situ methods. In general, in situ methods provide efficient tool to restore the suitable habitats and the effective population size compare to ex situ. With reference to our study, for populations with slightly higher level of genetic variation of different regions such as TK, TT, KMM and KMA as compared to others, we suggest that their habitats be protected. For ex situ conservation, we need to carefully design and establish a germplasm bank for this species. STRUCTURE analysis based on ISSR and RAPD data indicates that most of the populations from Kanyakumari districts showed genetically distinct group, suggesting a highly prioritized conservation concern. Additionally, for improving the reproductive fitness and evolutionary potential of D. arayalpathra, conserving a proportion of the mature individuals in populations, exploration of possible inbreeding depression and artificial increase of gene flow among populations are required.

Conclusions

In summary, our results concluded that genetic diversity was remarkably low both at the species and population levels, and that the genetic differentiation between populations was considerably high. AMOVA allocated the higher proportions of genetic variation within populations [(ISSR = 54 %) and (RAPD = 64 %)]. A significant correlation (r = 0.389) was found between genetic and geographic distances among the population. According to genetic data and field observations, narrow habitat preference, genetic drift due to a small and isolated population size, restricted gene flow, population bottleneck and human activity have shaped the current population structure. These results are integrated with our understanding of the current status of D. arayalpathra populations to assist in making effective conservation decisions.