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Article

Selection of Restoration Materials Based on Genetic Diversity and Structure of the Endangered Subalpine Conifer Taxus cuspidata, South Korea

by
Han-Na Seo
,
Jae-Hyun Park
,
Ji-Young Ahn
and
Hyo-In Lim
*
Department of Forest Bio Resources, National Institute of Forest Science, Suwon 16631, Republic of Korea
*
Author to whom correspondence should be addressed.
Forests 2026, 17(2), 285; https://doi.org/10.3390/f17020285
Submission received: 26 January 2026 / Revised: 13 February 2026 / Accepted: 17 February 2026 / Published: 23 February 2026
(This article belongs to the Special Issue Population Genetic Diversity and Conservation in Forests)
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Abstract

Taxus cuspidata is a threatened subalpine conifer in South Korea, necessitating evidence-based restoration strategies to counter the impacts of climate change. In this study, we assessed 13 natural populations using 15 polymorphic nuclear simple sequence repeat (nSSR) markers developed in Taxus species and spatial autocorrelation analysis to provide a scientific foundation for conservation. The results showed an intermediate level of genetic diversity, with the Mt. Gariwangsan population exhibiting higher diversity. This highlights its priority as a source for restoration materials. Bayesian clustering supported four distinct management units. Spatial autocorrelation analysis revealed significant positive genetic structure within approximately 50 m, indicating a localized genetic patch size. Based on these results, we suggest maintaining a minimum 50 m sampling distance during seed collection to avoid collecting closely related individuals and to reduce the risk of genetic homogeneity in restoration materials. Such restoration strategies informed by spatial genetic structure and broader genetic data are critical for enhancing the long-term resilience of T. cuspidata in the face of accelerating environmental shifts.

1. Introduction

In 2024, the global average temperature increased by 1.55 °C relative to the pre-industrial period (1850–1900), marking the first year exceeding the 1.5 °C threshold established by the Paris Agreement [1]. Rapid warming has intensified the impacts of climate change, including shifts in sea levels and an increased frequency of extreme events such as floods, heatwaves, and wildfires. These environmental fluctuations exert pressures on ecosystems that have historically adapted to specific environmental regimes [2].
Alpine and subalpine regions experience more extreme climatic conditions than lowlands, including lower temperatures and nutrient-poor soils. High-altitude flora has evolved unique genetic characteristics and species compositions in response to these extreme climatic conditions [3,4]. Due to their specialized habitat requirements and limited dispersal capabilities, subalpine plants are particularly vulnerable to climatic shifts [5,6,7,8]. As isolated high-altitude populations shift toward higher elevations or latitudes in response to climate change, their sizes often decrease. This reduction increases risks of inbreeding, genetic drift, and local extirpation [9,10,11].
These global patterns are particularly evident in the mountainous regions of the Korean Peninsula, where subalpine ecosystems are spatially restricted and climatically sensitive. The Baekdudaegan is a primary longitudinal mountain range spanning approximately 1000 km and encompassing numerous peaks ranging from 1500 to 2800 m in elevation. Except for Mt. Baekdu (2200–2800 m) and its immediate vicinity, the Baekdudaegan has been largely ice-free during past glacial–interglacial cycles and therefore functions as a critical glacial refugium for subarctic and temperate relict species [12]. Repeated bottlenecking events and subsequent range expansions over multiple climatic cycles have likely shaped contemporary genetic structure, contributing to patterns of allelic endemism and population differentiation [13]. Currently, alpine zones in Korea harbor 367 plant species, including 105 endemic taxa [14,15], and approximately 35% of Korea’s rare plants are concentrated in these high-altitude habitats [16,17].
There has been a substantial decline in the distribution area of subalpine coniferous forests in South Korea, driven by climate change-induced shifts in temperature and precipitation patterns [18,19]. Based on the Shared Socioeconomic Pathways scenarios, the survival likelihood of subalpine conifers, such as Pinus pumila and Abies nephrolepis, is predicted to diminish considerably in the coming decades [20]. In response, the Korea Forest Service has designated seven subalpine coniferous species, namely, A. koreana, A. nephrolepis, Picea jezoensis, Taxus cuspidata, Juniperus chinensis var. sargentii, Pinus pumila, and Thuja koraiensis, as priority species for conservation and restoration [21].
South Korea has implemented sequential national strategies for the conservation and restoration of endangered subalpine coniferous species through the 1st (2017–2021) and 2nd (2022–2026) Master Plans. The 2nd Master Plan aims to strengthen in situ and ex situ conservation by developing species-specific genetic restoration guidelines [22]. Under this framework, genetic diversity assessments will be used to prioritize restoration populations and guide restoration planning. For A. koreana and A. nephrolepis, collection strategies for restoration materials have already been established based on genetic evaluations, leading to the establishment of ex situ conservation sites [23,24,25]. However, such guidelines remain limited for T. cuspidata, highlighting the need to evaluate its genetic diversity and structure for the selection of suitable restoration materials.
Commonly known as Japanese yew, T. cuspidata Siebold & Zucc. is a dioecious gymnosperm and is a key endangered subalpine conifer species in South Korea. In natural populations, it grows into an evergreen tree reaching up to 20 m in height with a trunk diameter of 1 m. Its native range is restricted to Northeast Asia, including Korea, Japan, and parts of China and Russia. Within the Korean Peninsula, the species is primarily distributed along the subalpine and alpine ridges and slopes of the Baekdudaegan mountain range, extending from Mt. Seorak in the north to Mt. Halla in the south, at altitudes ranging from 700 to 2500 m [26,27]. T. cuspidata is highly valued for various applications, including ornamental use, high-quality timber for carving and construction, and as a medicinal resource [22]. The bark contains Taxol (paclitaxel), a potent anticancer compound that has been the focus of biochemical research [28].
T. cuspidata is categorized as Least Concern (LC) on the International Union for Conservation of Nature Red List and is also designated as LC in the National Red List of Vascular Plants in Korea [29,30,31]. Although the impact of global warming on T. cuspidata is less severe than on species such as P. jezoensis or P. pumila, it is expected to face range contraction. Similar to A. holophylla and P. koraiensis, T. cuspidata is vulnerable to impeded growth and fruiting commonly associated with increasing summer temperatures [32]. Natural populations face regeneration challenges characterized by poor seed production and extremely limited seedling recruitment, which hinders the formation of future populations [22,33]. These reproductive constraints impact natural population maintenance and generational transition, which may lead to a reduction in genetic diversity. As a keystone species in high-altitude coniferous forest ecosystems, the conservation and sustainable management of T. cuspidata are vital for ecosystem stability, necessitating active conservation and restoration efforts [22].
Located in Gangwon Province, South Korea, Mt. Gariwangsan (1561 m) is one of the representative natural habitats for T. cuspidata. Various indigenous and rare subalpine species, such as A. nephrolepis, A. holophylla, T. cuspidata, and Betula ermanii, are naturally distributed in this area [34]. Although it was designated as a Forest Genetic Resource Reserve in 2008, totaling 2475 ha, to ensure its protection, an area of 78.3 ha, comprising approximately 3% of the total reserve, was subjected to anthropogenic disturbance during the construction of a ski resort for the PyeongChang 2018 Olympics [35]. These damaged sites are now subject to an increased risk of landslides during summer precipitation, necessitating urgent ecological restoration. During the restoration of such disturbed forests, using genetically appropriate materials that preserve the genetic diversity of the source population is critical [36]. This approach safeguards the genetic architecture of the Mt. Gariwangsan population and supports the development of healthy, sustainable forests resilient to shifting environmental conditions. Therefore, there is a vital need to establish a restoration strategy centered on the genetic diversity of the Mt. Gariwangsan T. cuspidata population, with genetic analysis to provide a rigorous scientific foundation for this strategy.
While previous genetic evaluations of T. cuspidata populations in Korea were conducted using isozyme markers [37], isozyme markers generally exhibit relatively low levels of polymorphism and limited genomic coverage, constraining their ability to detect fine-scale population structure and relatedness. Therefore, the development of precise, specific restoration strategies, such as those required for the disturbed sites on Mt. Gariwangsan, necessitates the use of highly polymorphic markers such as simple sequence repeats (SSR). We aimed to answer the following three research questions: (1) What is the status of genetic diversity and the level of inbreeding within T. cuspidata populations in Korea? (2) What are the genetic relationships and structures among and within populations? and (3) What is the optimal strategy for selecting restoration materials based on these genetic assessments? The findings of this research enhance our understanding of the genetic characteristics of endangered subalpine plants in Korea and provide critical information for formulating and implementing robust conservation and management strategies for these coniferous populations.

2. Materials and Methods

2.1. Study Site and Plant Materials

A total of 426 individuals were sampled from 13 natural populations of T. cuspidata across its native range in South Korea (Table 1). The sampling sites included 11 mainland populations and two isolated island populations, namely, Mt. Hallasan (Jeju Island) and Mt. Seonginbong (Ulleung Island) (Figure 1). Population names were abbreviated using two-letter acronyms to facilitate identification. For each tree sampled, dendrometric data, including height and diameter at breast height, were recorded. To minimize the risk of sampling close relatives and to ensure the genetic independence of the samples, leaf tissues were collected from mature trees at least 50 m apart [24,25]. Sample sizes ranged from 17 to 62 individuals per population, depending on the tree density and accessibility. To analyze the fine-scale spatial genetic structure (SGS), intensive sampling was conducted within a 1-ha representative plot on Mt. Gariwangsan, where leaf samples from all 73 available individuals were collected. The geographical coordinates of all the sampled individuals were georeferenced using a portable Global Positioning System device (Garmin, eTrex Vista HCx, Olathe, KS, USA) (Table 1).
The Ulleung Island population (Mt. Seonginbong) has historically been classified as T. cuspidata var. latifolia (Pilg.) Nakai based on morphological traits such as broader leaves and seeds protruding beyond the aril [38]. However, previous morphological and DNA barcoding studies demonstrated substantial overlap with mainland populations and confirmed that Ulleung Island individuals form a monophyletic clade with T. cuspidata [39]. Therefore, consistent with the Silvics of Korea [27], the Ulleung Island population was treated as T. cuspidata in this study.

2.2. DNA Extraction and Microsatellite Amplification

Total genomic DNA was isolated from approximately 20 mg of fresh leaf tissue using the Exgene Plant SV Mini Kit (GeneAll, Seoul, Republic of Korea) according to the manufacturer’s protocol. The concentration and purity of the extracted DNA were determined using a NanoDrop 2000 spectrophotometer (Thermo Scientific, Wilmington, DE, USA). All DNA samples were subsequently diluted to a working concentration of 10 ng/μL and stored at −20 °C until further analysis.
To identify polymorphic markers for T. cuspidata, a total of 143 nuclear microsatellite (nSSR) markers previously developed for the genus Taxus were screened [40,41,42,43,44,45,46,47]. Initial screening was performed on four T. cuspidata individuals. From these, based on successful cross-species amplification, 17 markers were selected. After secondary screening using 8 individuals, 15 loci were selected based on their high polymorphism, clear allelic patterns, and consistency with target repeat motifs. For fluorescent labeling, an M13 (−19) sequence (5′-CACGACGTTGTAAAACGA-3′) was appended to the 5′ end of each forward primer.
Polymerase chain reaction (PCR) was performed in a 15 μL reaction volume containing 10 ng of template DNA, 1.5 μL of 10× PCR buffer including 25 mM MgCl2, 0.3 μL of 10 mM dNTPs, 0.3 μL of 10 μM primer mix, 0.6 μL of 10 μM M13 (−19) primer, and 0.5 U of Taq DNA polymerase (BioFACT™, Daejeon, Republic of Korea). The thermal cycling profile consisted of an initial denaturation at 94 °C for 5 min; followed by 10 cycles of 94 °C for 1 min, 60 °C for 1 min, and 72 °C for 1 min; then 25 cycles of 94 °C for 30 s, 50–60 °C for 1 min, and 72 °C for 1 min; with a final extension at 72 °C for 10 min. Annealing temperatures were optimized individually for each locus, and the specific conditions are provided in Table 2. The PCR products were analyzed via capillary electrophoresis on an ABI Prism 3730xl Genetic Analyzer (Applied Biosystems, Foster City, CA, USA). Each sample was prepared by mixing 1 μL of the PCR product with 9 μL of Hi-Di Formamide and 1 μL of GeneScan-500 ROX size standard (Life Technologies, Carlsbad, CA, USA). Allele sizes were determined using GeneMapper v6.0 (Life Technologies, Carlsbad, CA, USA). To ensure data quality, the presence of null alleles, large allele dropout, and scoring errors due to stuttering were evaluated using MICRO-CHECKER v2.2.3 [48]. Deviations from the Hardy–Weinberg equilibrium (HWE) were assessed for each population, and polymorphism information content (PIC) values were calculated for each locus using Cervus v2.0 [49].

2.3. Genetic Diversity and Population Differentiation

Genetic diversity indices for each population were estimated using GenAlEx v6.5 [50]. The mean number of alleles (A), effective number of alleles (Ae), observed heterozygosity (Ho), expected heterozygosity (He), and the fixation index (F) were calculated. Allelic richness (AR) was estimated using a minimum sample size of 13 individuals using FSTAT v2.9.4 [51].
To evaluate genetic differentiation among populations, we calculated pairwise FST and the standardized differentiation index G′ST [52], which is particularly suited for highly polymorphic markers like SSRs. Analysis of molecular variance (AMOVA) was performed to partition the total genetic variation among and within populations, with significance levels determined through 999 permutations in GenAlEx v6.5 [50].

2.4. Population Genetic Structure and Clustering Analyses

The genetic structure and potential clusters within the 13 populations were inferred. Principal Coordinates Analysis (PCoA) based on Nei’s genetic distance was performed to visualize the multidimensional relationships between populations. An Unweighted Pair Group Method with Arithmetic Mean (UPGMA) dendrogram was constructed based on Nei’s genetic distance using POPTREE2 [53], with node support evaluated through 1000 bootstrap replicates. A Bayesian clustering analysis was implemented in STRUCTURE v2.3.4 using an admixture model with independent allele frequencies [54]. The simulation was run for K values ranging from 1 to 10, with 10 independent replicates per K. Each run consisted of a burn-in period of 30,000 iterations followed by 30,000 Markov Chain Monte Carlo iterations. The optimal number of genetic clusters (K) was determined using the Evanno Delta K method via STRUCTURESELECTOR [55,56]. The Delta K method does not evaluate K = 1 [55].

2.5. Isolation by Distance and Demographic History

To test for Isolation by Distance (IBD), a Mantel test was performed in GenAlEx to assess the correlation between pairwise genetic distances (Nei’s distance) and geographic distances (m) among the 13 populations [50]. Despite its known limitations, including relatively low statistical power and sensitivity to complex spatial structure, the Mantel test is widely used to detect isolation by distance; therefore, it was employed to evaluate the relationship between genetic and geographic distances in this study. Recent demographic history was evaluated using BOTTLENECK v1.2.02 to detect potential reductions in effective population size [57]. We used the Two-Phase Mutation Model (TPM), which is recommended as more appropriate than the Stepwise Mutation Model (SMM) for most microsatellite datasets. The parameter settings (95% single-step and 5% multi-step mutations) followed previously published studies [57]. Significant deviations from the mutation-drift equilibrium (heterozygosity excess) were assessed using the Wilcoxon signed-rank test based on 10,000 simulations.

2.6. Genetic Spatial Autocorrelation Analysis

To provide a scientific basis for selecting restoration materials and recovering the genetic diversity of the disturbed sites on Mt. Gariwangsan, SGS was analyzed for 73 T. cuspidata individuals. Spatial autocorrelation was performed using GenAlEx v6.5 by calculating the autocorrelation coefficient (r) based on pairwise geographic and genetic distance matrices [50]. We defined 10 distance classes at 10 m intervals up to 100 m to ensure an even distribution of sample pairs across classes and to minimize stochastic noise. The 10 m interval was selected for this analysis based on the local spatial scale of tree distribution [22]. The statistical significance of the spatial structure was evaluated through 999 permutations to test the null hypothesis of no SGS (r = 0). 95% confidence intervals around the null hypothesis were estimated to determine the spatial extent of significant genetic clustering [58]. The spatial scale of significant positive autocorrelation was used as a reference for defining the minimum sampling distance for seed collection.

3. Results

3.1. Genetic Diversity and Population Genetic Differentiation

The genetic diversity parameters for the 13 T. cuspidata populations are summarized (Table 3). Across all the populations, the mean number of alleles (A) was 4.4 ± 0.2, with the highest value observed in SG (5.7) and the lowest in HL (2.8). The mean number of effective alleles (Ae) averaged 2.7 ± 0.1, ranging from 1.8 (HL) to 3.1 (TB). The mean number of allelic richness (AR) was 3.8 ± 0.5. The average observed heterozygosity (Ho) was 0.491 ± 0.017, peaking in GW (0.551) and reaching its minimum in HL (0.350). The expected heterozygosity (He), a key indicator of genetic diversity, also averaged 0.491 ± 0.016, with values ranging from 0.356 (HL) to 0.540 (BT). HL showed the lowest genetic diversity across all the estimated parameters. The mean fixation index (F) was −0.004 ± 0.017, ranging from −0.068 (DY) to 0.135 (TB). The polymorphism information content (PIC) ranged from 0.155 (T20) to 0.896 (TB50) (Table 2).
The molecular variance analysis indicated significant genetic differentiation among the 13 T. cuspidata populations (FST = 0.046, p < 0.001). The estimated number of migrants per generation (Nm) was 3.684. Pairwise FST values, which measure the degree of genetic divergence between populations, ranged from a minimum of 0.013 between SA and GB to a maximum of 0.147 between SB and HL, with a global mean of 0.046 (Table 4). HL population exhibited consistently higher-than-average FST values when compared to mainland populations. This included 0.093 with BT, 0.108 with JW, 0.085 with GW, 0.089 with DU, 0.085 with DY, and 0.092 with JR. Consistent with the FST results, the standardized differentiation index (G′ST) showed the lowest value between SA and GB (0.032) and the highest between HL and SB (0.392).
To evaluate the distribution of genetic variance among and within the studied populations, AMOVA was performed based on the 15 nSSR loci. Most of the total genetic variation (94%) was distributed within populations, while 6% of the variation was attributable to differences among populations (Table 5). Although the among-population component was relatively small, it was statistically highly significant (p = 0.001).

3.2. Population Genetic Structure and Clustering

To assess the multidimensional genetic relationships among the 13 T. cuspidata populations, a PCoA was performed (Figure 2a). The first two principal coordinates explained 41.8% of the total genetic variation, with the first axis (Coord. 1) accounting for 26.8% and the second axis (Coord. 2) accounting for 15.0%. Along the first axis, HL was positioned on the far right, whereas SB was located on the far left. The second axis further separated SG, which was located in the upper-right quadrant, relative to the mainland populations. In contrast, the remaining mainland populations, including GW, BT, BW, DU, TB, and others, formed a relatively tight central cluster.
A clustering analysis using the UPGMA based on Nei’s genetic distance was performed to infer the genetic relationships among the 13 T. cuspidata populations (Figure 2b). The resulting dendrogram showed population groupings into distinct clades. HL formed an independent basal branch with a bootstrap support value of 100%. SB diverged next, supported by a bootstrap value of 82%. DU and SG formed a separate cluster with moderate support (46%). In contrast, the remaining mainland populations, that is, BT, TB, BW, GB, SA, DY, GW, JW, and JR, exhibited relatively low bootstrap values ranging from 11% to 47% at their respective nodes, indicating weak hierarchical resolution among these populations.
To visualize the nationwide genetic patterns of T. cuspidata, a Bayesian clustering analysis was performed using STRUCTURE v2.3.4 (Figure 3). Following the Evanno method, the maximum value of Delta K was observed at K = 4 (Delta K = 7.24), suggesting the presence of four genetic clusters within the dataset. The bar plot at K = 4 showed distinct clustering patterns and admixture across the study area. HL and SG exhibited distinct genetic composition, represented in green. Among the mainland populations, SB stood out with a high proportion of a specific ancestral component (orange), while the remaining mainland populations, for example, SA, BT, GB, BW, JW, GW, DU, and TB, exhibited high levels of admixture.
A Mantel test was performed to examine the relationship between pairwise geographic distances (m) and Nei’s genetic distances among the 13 T. cuspidata populations (Figure 4). The analysis showed a significant positive correlation (Mantel r = 0.5436; R2 = 0.2955; p < 0.01), confirming a clear pattern of IBD across the species’ range in South Korea. This indicates that geographic distance explains approximately 29.6% of the observed genetic differentiation among the populations.
A bottleneck analysis was performed under the TPM to evaluate the demographic history of the 13 T. cuspidata populations (Table 6). The Wilcoxon signed-rank test identified a significant excess of heterozygosity in five populations: JR (p = 0.008), BT (p = 0.015), DU (p = 0.018), BW (p = 0.021), and SA (p = 0.047). For GW, although the Wilcoxon test was not significant (p = 0.068), a significant deviation was detected in the standardized differences test (p = 0.037). In contrast, no significant excess of heterozygosity was observed in the remaining seven populations, including SB, DY, HL, SG, and JW (p > 0.05).
SGS within the Gariwangsan population was assessed via spatial autocorrelation analysis (Figure 5). The autocorrelation coefficient (r) was calculated for 73 individuals across sequential distance classes of 10 m intervals. The analysis showed significantly positive r values within the first four distance classes: 0–10 m (r = 0.128), 10–20 m (r = 0.087), 20–30 m (r = 0.045), and 30–40 m (r = 0.058). In these classes, the observed r values exceeded the upper bound of the 95% null hypothesis confidence interval. This indicated that individuals located within 50 m of each other are genetically more similar than expected by chance. The r value intersected the confidence envelope at approximately 45 m, defining the genetic patch size for this population. Beyond 50 m, the r values fell within or below the 95% confidence intervals, showing no significant positive spatial structure. A significantly negative r value was observed at the 90 m distance class.

4. Discussion

4.1. Genetic Diversity

The genetic diversity of 13 T. cuspidata populations in South Korea (He = 0.491) was at an intermediate level compared to other coniferous species. While higher than Picea jezoensis (He = 0.406 [59]), this value is lower than those of A. koreana (He = 0.657 [25]) and A. nephrolepis (He = 0.791 [24]). T. cuspidata populations in China exhibited a lower diversity level (He = 0.38 [46]). In contrast, the western Himalayan yew (T. contorta) showed a higher expected heterozygosity (He = 0.541) than the Korean populations [60]. Similarly, the endangered T. globosa in Mexico also showed a higher diversity level (He = 0.68) [61]. The English yew (T. baccata L.), a representative European yew, exhibited exceptionally high genetic diversity (He = 0.901), significantly exceeding that of T. cuspidata in East Asia [62]. The observed diversity is lower than the expected ranges typically associated with the life-history traits of Taxus species (He = 0.610–0.680), such as long lifespan, outcrossing mating systems, and wind pollination [63]. A key finding is the prevalence of negative fixation indices (F) and heterozygote excess in several populations. As a dioecious species, T. cuspidata is subject to obligate outcrossing, which buffers the impacts of genetic drift, including within small, fragmented populations [64].
Despite the prevalence of outcrossing, the effective number of alleles (Ae) can be substantially reduced when the number of successful pollen donors is limited, a process influenced by stand density, age, structure, and environmental conditions [65]. Two potential explanations may account for the low Ae values observed in South Korean T. cuspidata populations, namely, restricted pollen dispersal and sex ratio imbalance. Our SGS analysis of the Mt. Gariwangsan population revealed high genetic similarity among individuals within a 50 m radius. This pattern suggests relatively fine-scale genetic structuring compared to the congeneric T. baccata. Despite reports of long-distance dispersal capacities, with pollen reaching over 700 m and seeds traveling beyond 100 m, the significant genetic relatedness observed among individuals within only 50 m at Mt. Gariwangsan supports the presence of localized genetic structure in the Korean yew population [66]. A skewed sex ratio could potentially influence Ae by limiting the diversity of nearby pollen donors, which may contribute to non-random mating and reduced genetic variation over time. To mitigate these effects, restoration strategies may benefit from considering a balanced sex ratio, strategic placement of male trees, and a minimum planting distance of at least 50 m to facilitate a broader range of pollen donors and enhance the genetic diversity of future generations.
While the genetic diversity of TB remains relatively high (He = 0.532), its comparatively high fixation index (F = 0.135) could be associated with reductions in the effective population size, potentially influenced by sex ratio imbalance. A restricted pool of pollen donors may contribute to non-random mating, necessitating further investigation into sex ratios to fully understand the genetic dynamics of T. cuspidata populations. Future restoration strategies should prioritize the selection of planting materials with a balanced sex ratio. However, a considerable challenge is the difficulty of identifying the sex of individuals in their natural habitats. T. cuspidata lacks distinct sexual dimorphism, and data collection is further hindered by the extreme height of mature trees and inconsistent seed production. The development of sex-specific DNA markers, as previously attempted for T. baccata, would be a critical tool for genetic diversity assessments and selecting materials for restoration programs [67].

4.2. Genetic Structure and Phylogeography

The genetic differentiation among South Korean T. cuspidata populations (FST = 0.046) is low according to Wright’s criteria [68]. AMOVA results further confirm this, indicating that 94% of the total genetic variation exists within populations, with only 6% among them. This level of differentiation is comparable to or lower than other major Korean conifers, such as P. jezoensis (FST = 0.102 [59]), A. koreana (FST = 0.053 [23]), and A. nephrolepis (FST = 0.049) [24], and significantly lower than congeneric species such as T. globosa (FST = 0.21 [61]) and T. baccata (FST = 0.164) [62]. According to a microsatellite metadata study, FST values for species with similar life-history traits, that is, long-lived perennials (0.190), wide distribution (0.250), outcrossing (0.220), and wind-dispersal (0.130), are typically higher [63]. The lower FST observed in T. cuspidata suggests that high levels of historical gene flow were once maintained across the Korean Peninsula.
However, FST values derived from highly polymorphic SSR markers can be mathematically constrained by high within-population heterozygosity (He), potentially leading to an underestimation of true differentiation [52,69]. In this context, the corrected G′ST values, which reached as high as 0.392 between HL and SB, provide stronger evidence of substantial genetic divergence and the existence of unique alleles not shared between populations. While the genetic framework remains similar, populations have accumulated distinct genetic compositions due to long-term geographical isolation, underscoring the value of each population as a distinct management unit (MU).
STRUCTURE (K = 4), PCoA, and UPGMA analyses corroborate that this genetic uniqueness is closely linked to geographic isolation. HL and SG are the most genetically distinct, reflecting their long-term isolation as volcanic islands [70]. This prolonged separation from mainland populations has strongly restricted gene flow, allowing for the development of independent genetic structures through sustained genetic drift. The lower genetic diversity observed in HL (He = 0.356) reflects the founder effect associated with this isolation. The divergence of the mainland SB as a separate cluster may stem from microtopographic isolation within the Baekdudaegan mountain range.
These structural insights provide essential guidelines for the restoration of T. cuspidata populations, including Mt. Gariwangsan. Relying solely on low FST values to justify mixing materials across regions poses a considerable risk of genetic pollution. This could destroy genetic compositions accumulated over millennia, or outbreeding depression caused by the introduction of maladapted alleles [36,71,72,73]. In this study, MUs are defined based on the combined evidence of (i) elevated G′ST values indicating substantial differentiation, (ii) consistent population separation in STRUCTURE, PCoA, and UPGMA analyses, and (iii) clear geographic isolation limiting gene flow. We propose the establishment of four distinct MUs, namely, (1) Baekdudaegan Mainland, (2) Mt. Sobaeksan, (3) Mt. Hallasan (Jeju Island), and (4) Mt. Seonginbong (Ulleung Island). Restoration efforts within each unit must prioritize local seeds to preserve the evolutionary potential of these populations. For island populations such as HL and SG, the influx of mainland materials should be strictly restricted to maintain their distinct evolutionary trajectories.

4.3. Conservation and Restoration Strategies

The results of the present study provide a robust scientific foundation for the genetic restoration of the damaged T. cuspidata population on Mt. Gariwangsan. Despite its designation as a Forest Genetic Resource Reserve in 2008, the site remains unrestored and vulnerable following the environmental disturbances caused by the 2018 PyeongChang Winter Olympic development [35]. Immediate restoration is imperative for ecological recovery and to mitigate risks of landslides and water pollution.
For the long-term persistence of T. cuspidata, in situ conservation should be the main priority. This process must involve demarcating and managing the current distribution range, using the extant genetic pool (He = 0.515) as a benchmark for restoration targets. Although bottleneck detection based on heterozygosity excess is sensitive to mutation model assumptions, the significant bottleneck signal observed in this population (p = 0.037 in the standardized differences test) suggests the potential need for conservation actions to prevent further genetic erosion.
A successful restoration process using genetic diversity requires securing appropriate restoration materials, implementing evidence-based selection of appropriate sites, and post-restoration monitoring [36]. Based on the four MUs identified in the present study, we propose a targeted strategy for selecting restoration materials. Since the genetic diversity of the Gariwangsan population (He = 0.515) is maintained at a level higher than the national average (0.491), we strongly recommend prioritizing the collection and use of restoration materials directly from the Gariwangsan population.
Sampling strategies must incorporate our SGS findings to avoid the selection of genetically similar individuals [23]. We propose a minimum distance of 50 m between mother trees when collecting seeds. This approach will minimize inbreeding depression and maximize the effective population size (Ne), thereby enhancing the adaptive capacity of restored populations. Implementing such genetically based restoration strategies for this endangered subalpine conifer will not only increase the success rate of habitat recovery but also secure the species’ evolutionary potential to adapt to the accelerating impacts of climate change. Importantly, post-restoration genetic monitoring should be considered a core component of restoration management. Long-term tracking of genetic diversity, allele frequency dynamics, and population structure will be essential for evaluating restoration outcomes and refining future conservation strategies.

5. Conclusions

In this study, we assessed the genetic diversity and population structure of T. cuspidata, an endangered coniferous species in South Korea, to establish a scientific basis for its conservation and restoration. South Korean yew populations exhibited an intermediate level of genetic diversity, with the Mt. Gariwangsan population identified as a valuable source of restoration materials. Although differentiation was relatively low, the corrected G′ST values and Bayesian clustering supported the establishment of four distinct MUs (Baekdudaegan Mainland, Mt. Sobaeksan, Mt. Hallasan (Jeju Island), and Mt. Seonginbong (Ulleung Island)) to preserve unique evolutionary lineages and prevent genetic pollution. The identification of a 50 m genetic patch size through spatial autocorrelation analysis offers a precise sampling guideline to minimize inbreeding and maximize the effective population size during habitat recovery. By providing genetic guidelines for selecting target populations and individuals, these results establish a robust evidence-based framework for the ‘2nd Master Plan for the Conservation and Restoration of Endangered Subalpine Coniferous Species (2022–2026)’. Future research should focus on assessing sex ratios within natural populations, examining sex-related genetic characteristics, and implementing genetic monitoring in restored sites to further refine restoration strategies. Ultimately, our findings underscore that using genetic diversity and genetic structure data is indispensable for optimizing the long-term restoration success and evolutionary resilience of this endangered subalpine conifer in the face of accelerating climate change.

Author Contributions

Conceptualization, H.-N.S. and H.-I.L.; methodology, H.-N.S. and H.-I.L.; validation, J.-Y.A. and H.-I.L.; formal analysis, H.-N.S.; investigation, H.-N.S., J.-H.P., J.-Y.A. and H.-I.L.; writing—original draft preparation, H.-N.S.; writing—review and editing, H.-N.S., J.-H.P., J.-Y.A. and H.-I.L.; supervision, H.-I.L.; funding acquisition, H.-I.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Institute of Forest Science (NIFoS) (grant number FG0802-2023-01-2024) of the Republic of Korea.

Data Availability Statement

The data presented in this study are not publicly available due to ongoing national research project constraints and the sensitive nature of location data for the endangered subalpine conifer, Taxus cuspidata. However, the datasets used to support the findings of this study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Geographic locations of the study sites for Taxus cuspidata in South Korea. Population abbreviations are presented in Table 1.
Figure 1. Geographic locations of the study sites for Taxus cuspidata in South Korea. Population abbreviations are presented in Table 1.
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Figure 2. Genetic relationships and population structure of 13 Taxus cuspidata populations in South Korea based on 15 nSSR markers. (a) PCoA based on Nei’s genetic distance, with percentages of total variation explained by the first and second coordinates indicated on the respective axes. (b) Genetic clustering of the 13 populations inferred by UPGMA analysis.
Figure 2. Genetic relationships and population structure of 13 Taxus cuspidata populations in South Korea based on 15 nSSR markers. (a) PCoA based on Nei’s genetic distance, with percentages of total variation explained by the first and second coordinates indicated on the respective axes. (b) Genetic clustering of the 13 populations inferred by UPGMA analysis.
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Figure 3. Genetic structure of 13 Taxus cuspidata populations based on Bayesian clustering analysis. (a) Delta K plot showing the optimal number of clusters at K = 4. (b) Individual assignment to four genetic clusters. Colors represent inferred genetic clusters, and each bar corresponds to an individual.
Figure 3. Genetic structure of 13 Taxus cuspidata populations based on Bayesian clustering analysis. (a) Delta K plot showing the optimal number of clusters at K = 4. (b) Individual assignment to four genetic clusters. Colors represent inferred genetic clusters, and each bar corresponds to an individual.
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Figure 4. Mantel test for IBD showing the relationship between pairwise geographic and Nei’s genetic distances for 13 Taxus cuspidata populations. The solid line represents the fitted linear regression.
Figure 4. Mantel test for IBD showing the relationship between pairwise geographic and Nei’s genetic distances for 13 Taxus cuspidata populations. The solid line represents the fitted linear regression.
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Figure 5. Spatial autocorrelogram showing the genetic correlation coefficient (r) as a function of geographic distance for Taxus cuspidata in Mt. Gariwangsan. The dashed red lines represent the 95% confidence interval for the null hypothesis of a random spatial distribution. Error bars indicate the 95% confidence intervals for r determined by bootstrapping.
Figure 5. Spatial autocorrelogram showing the genetic correlation coefficient (r) as a function of geographic distance for Taxus cuspidata in Mt. Gariwangsan. The dashed red lines represent the 95% confidence interval for the null hypothesis of a random spatial distribution. Error bars indicate the 95% confidence intervals for r determined by bootstrapping.
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Table 1. Sampling locations of 13 Taxus cuspidata populations in the Republic of Korea.
Table 1. Sampling locations of 13 Taxus cuspidata populations in the Republic of Korea.
PopulationCodeN 1LatitudeLongitudeAltitudeHeightDBH 2
Mt. SeoraksanSA62N38.18058E128.4201310018.342.0
Mt. BangtaesanBT17N37.89491E128.3556313198.349.8
Mt. GyebangsanGB34N37.72834E128.4654114748.757.8
Mt. BalwangsanBW41N37.60811E128.6702914197.958.2
Mt. JoongwangsanJW20N37.46366E128.5235012868.065.8
Mt. GariwangsanGW30N37.45138E128.5769914207.550.5
Mt. Duwi-bongDU39N37.21239E128.7540614177.756.5
Mt. TaebaeksanTB30N37.10813E128.9305514135.526.7
Mt. SobaeksanSB30N36.96144E128.4851113474.527.6
Mt. DeogyusanDY33N35.86298E127.7454215746.441.3
Mt. JirisanJR31N35.33692E127.7306616266.942.4
Mt. HallasanHL19N33.36155E126.5296316162.99.4
Mt. SeonginbongSG40N37.49772E130.863774894.010.0
1 Sample size. 2 Diameter at breast height.
Table 2. Characteristics of the 15 microsatellite loci used for Taxus cuspidata genetic analysis.
Table 2. Characteristics of the 15 microsatellite loci used for Taxus cuspidata genetic analysis.
PrimerPrimer Sequence (5′–3′)Repeat MotifProduct Size
(bp)		Tm
(°C)		PIC 1
Tach2F: GAACAAGTAGTTTTTCCATG
R: CTCATTCACTTGGTCATATCC		(AC)34(AG)22304–372500.867
Tach4F: CCGAAACTAATGTTATTCC
R: GTGTGGTAGTTAGAAAAGATG		(TG)46212–274500.653
Tc4F: GAATGCTTCCCACAATAG
R: AAACATGGTGGCTACACT		(GT)11112–152550.375
N-TC3F: TGCTATGGAATGAAGAATCCAA
R: GTTTCCGTGTGTGTTGTGTGTTTT		(AC)5157–165550.308
N-TC4F: ACATGGTGGCTACACTAGAGCAC
R: CAACCTAGTGAGGATCATACTTTCA		(AC)599–109550.365
NTWJ9F: CCTGCTACGTGTTTACACAC
R: CTTGTTAGGGCATTGAACAC		(ACAT)8200–320600.552
T2F: AACGTTGTAAATCATTTGGACTCA
R: CGGCATGAAATAGGATCAAAC		(AT)14138–184600.297
T20F: TCTTAGCCCTTTGGTTCTACACA
R: ATTCTAGAGGGTTGATGCGAGA		(TC)7174–180600.155
T38F: CAGATTTCAAACCTTTCGTGAG
R: ATCCATTTATGGCTTGGTGA		(CATA)10113–186600.266
TB50F: ACAAAGACTATGAGCTATGC
R: GAAAAGAGAATGTTGGGAG		(TC)8-(CT)13270–330600.896
TG111F: TATCCCACATTTAGCATTAG
R: ATAGAGCCGACCCATTCA		(CT)10101–111600.675
TG34F: CGTTGATTCCTTGGGAGAT
R: GTTGTCGTCGGAGAATACATC		(CT)10256–258600.511
gr1114F: AGACCCATCCAATATTTATAAAATGGT
R: AGAGACAACTTGAAAAGACCAGA		(TG)7–10106–112600.765
gr5502F: AGCGGTGCAGAGTTTGATGA
R: TTGTGGTCATTCGTTGGACA		(AGATA)5–6104–109600.321
gr907F: CATTGCGCCTCTTTGGAGTC
R: TGGCAGGCAGAATCAAAGGT		(CTT)6–7113–116600.274
1 Polymorphism information content.
Table 3. Summary of genetic diversity parameters for 13 Taxus cuspidata populations based on 15 microsatellite markers.
Table 3. Summary of genetic diversity parameters for 13 Taxus cuspidata populations based on 15 microsatellite markers.
PopulationN 1AAeARHoHeF
SA625.12.94.00.4770.4990.019 ***
BT174.02.83.80.5150.5400.056 *
GB345.02.84.10.5490.527−0.057
BW415.02.94.00.5470.526−0.059
JW203.52.43.30.4370.4800.050 *
GW304.32.73.70.5510.515−0.060
DU394.52.83.80.5400.519−0.057 **
TB304.93.14.20.4790.5320.135 ***
SB303.82.13.20.4500.401−0.052 ***
DY334.72.53.80.5140.485−0.068
JR313.92.53.40.5220.503−0.041
HL192.81.82.60.3500.3560.026
SG405.73.14.30.4580.5000.060 *
Mean ± SE32.4 ± 0.84.4 ± 0.22.7 ± 0.13.8 ± 0.50.491 ± 0.0170.491 ± 0.016−0.004 ± 0.017
1 N: sample size; A: number of alleles; Ae: number of effective alleles; AR: allelic richness; Ho: observed heterozygosity; He: expected heterozygosity; F: fixation index; * p < 0.05, ** p < 0.01, *** p < 0.001; Significant deviation from Hardy–Weinberg equilibrium.
Table 4. Pairwise genetic differentiation (FST, below diagonal) and standardized differentiation index (G′ST, above diagonal) among 13 populations of Taxus cuspidata.
Table 4. Pairwise genetic differentiation (FST, below diagonal) and standardized differentiation index (G′ST, above diagonal) among 13 populations of Taxus cuspidata.
SABTGBBWJWGWDUTBSBDYJRHLSG
SA–0.0690.0320.0490.0670.0920.1160.0410.1790.0620.1120.1920.143
BT0.027–0.0870.0470.1000.0700.0900.0330.1730.0900.1150.2680.165
GB0.0130.032–0.0750.0710.1230.1330.0630.2110.0690.0780.2300.160
BW0.0170.0220.024–0.0930.0640.0730.0480.1340.0760.1040.2330.131
JW0.0260.0400.0280.033–0.1310.1260.0900.2050.1290.1760.2990.189
GW0.0290.0280.0370.0220.044–0.0970.0770.1840.1250.1410.2500.177
DU0.0340.0320.0390.0240.0420.031–0.1110.2110.1270.1580.2630.088
TB0.0170.0200.0230.0190.0340.0270.034–0.1920.0540.0890.2040.129
SB0.0570.0590.0670.0440.0710.0600.0670.062–0.2190.2130.3920.303
DY0.0220.0330.0240.0260.0440.0390.0390.0220.072–0.0780.2420.121
JR0.0350.0390.0270.0320.0570.0430.0470.0300.0690.027–0.2680.196
HL0.0670.0930.0780.0790.1080.0850.0890.0710.1470.0850.092–0.179
SG0.0420.0520.0470.0390.0600.0520.0280.0400.0970.0390.0580.064–
Table 5. Analysis of molecular variance (AMOVA) for 13 populations of Taxus cuspidata in South Korea.
Table 5. Analysis of molecular variance (AMOVA) for 13 populations of Taxus cuspidata in South Korea.
Source of VariationDegrees of FreedomSum of SquaresMean SquaresEstimated VariationPercent of
Variation (%)
Among populations12246.85320.5710.2596 *
Within populations8393169.9963.7783.77894
Total8513416.8494.037100%
* p = 0.001.
Table 6. Bottleneck analysis for 13 Taxus cuspidata populations based on 15 microsatellite markers using the TPM.
Table 6. Bottleneck analysis for 13 Taxus cuspidata populations based on 15 microsatellite markers using the TPM.
PopulationLoci with Excess HeWilcoxon pDifference Test
SA7.700.0473T = 1.408, p = 0.07962
BT8.030.0151T = 2.289, p = 0.01104
GB8.060.0535T = 1.007, p = 0.15694
BW8.080.0206T = 1.093, p = 0.13719
JW8.020.1147T = 1.316, p = 0.09405
GW7.880.0677T = 1.781, p = 0.03742
DU7.410.0176T = 2.270, p = 0.01162
TB8.240.0603T = 1.431, p = 0.07615
SB7.920.7003T = −1.185, p = 0.11792
DY8.080.5548T = −0.357, p = 0.36058
JR7.880.0075T = 2.225, p = 0.01303
HL7.310.3349T = 0.084, p = 0.46646
SG7.790.4758T = −0.729, p = 0.23305
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Seo, H.-N.; Park, J.-H.; Ahn, J.-Y.; Lim, H.-I. Selection of Restoration Materials Based on Genetic Diversity and Structure of the Endangered Subalpine Conifer Taxus cuspidata, South Korea. Forests 2026, 17, 285. https://doi.org/10.3390/f17020285

AMA Style

Seo H-N, Park J-H, Ahn J-Y, Lim H-I. Selection of Restoration Materials Based on Genetic Diversity and Structure of the Endangered Subalpine Conifer Taxus cuspidata, South Korea. Forests. 2026; 17(2):285. https://doi.org/10.3390/f17020285

Chicago/Turabian Style

Seo, Han-Na, Jae-Hyun Park, Ji-Young Ahn, and Hyo-In Lim. 2026. "Selection of Restoration Materials Based on Genetic Diversity and Structure of the Endangered Subalpine Conifer Taxus cuspidata, South Korea" Forests 17, no. 2: 285. https://doi.org/10.3390/f17020285

APA Style

Seo, H.-N., Park, J.-H., Ahn, J.-Y., & Lim, H.-I. (2026). Selection of Restoration Materials Based on Genetic Diversity and Structure of the Endangered Subalpine Conifer Taxus cuspidata, South Korea. Forests, 17(2), 285. https://doi.org/10.3390/f17020285

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