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Article

Genetic Diversity and Structure of Quercus petraea (Matt.) Liebl. Populations in Central and Northern Romania Revealed by SRAP Markers

by
Florin Alexandru Rebrean
1,
Adrian Fustos
2,
Katalin Szabo
3,*,
Tabita-Teodora Lisandru
2,
Mihaela Simona Rebrean
2,*,
Mircea Ioan Varga
1,* and
Doru Pamfil
2,3
1
Forestry and Land Surveying, University of Agricultural Sciences and Veterinary Medicine, 3–5 Mănăștur Street, 400372 Cluj-Napoca, Romania
2
Faculty of Horticulture, University of Agricultural Sciences and Veterinary Medicine, 3–5 Mănăștur Street, 400372 Cluj-Napoca, Romania
3
Life Sciences Institute, University of Agricultural Sciences and Veterinary Medicine, 3–5 Mănăştur Street, 400372 Cluj-Napoca, Romania
*
Authors to whom correspondence should be addressed.
Diversity 2023, 15(10), 1093; https://doi.org/10.3390/d15101093
Submission received: 26 September 2023 / Revised: 13 October 2023 / Accepted: 18 October 2023 / Published: 19 October 2023
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Abstract

:
The genetic variability of five populations of Quercus petraea originating from the Transylvania and Maramureș regions of Romania was investigated in this study to provide insights into the species’ adaptability, population dynamics, and potential for preservation in the face of environmental challenges. To achieve this, sequence-related amplified polymorphism (SRAP) markers, in conjunction with a set of 18 primer combinations, were employed. The outcomes of the analysis revealed a range of polymorphisms spanning from 69.78% to 85.75%. Additionally, the assessment of genetic diversity using Shannon’s information index (I) yielded values ranging between 0.2887 and 0.3955, while Nei’s gene diversity (He) exhibited a spectrum from 0.1833 to 0.2582. The analysis of genetic variability, conducted via molecular variance (AMOVA), unveiled that 9% of the genetic variation was attributable to differences among the populations, while a substantial 91% resided within the populations. A further investigation of the population structure revealed that the construction of a UPGMA dendrogram based on Nei’s genetic distances elucidated the presence of two principal genetic clusters, a finding that was reinforced by a Principal Coordinate Analysis (PCoA). The genetic diversity revealed by Quercus petraea using SRAP molecular markers offers promising potential for upcoming breeding programs to identify optimal genitors, facilitating the development of well-adapted oak populations in the Transylvania and Maramureș regions.

1. Introduction

The genus Quercus, comprising approximately 600 species, represents the most diverse group within the Fagaceae family, a taxonomic family globally categorized into seven genera [1]. With a widespread distribution encompassing the Northern Hemisphere, including Europe, North America, Asia, and North Africa, it extends further southwest to Colombia and southeast to Indonesia [2,3,4]. Oaks, serving as a longstanding model genus for the exploration of evolutionary processes and speciation since the era of Charles Darwin, not only offer substantial economic significance but also play a pivotal ecological role. Oak species serve as vital hosts for various insect species, while their fruits provide a valuable source of sustenance for numerous bird and mammal species, thus contributing significantly to local biodiversity.
In Romania, the Quercus genus exhibits a varying presence of five to nine species, contingent upon their taxonomic classifications [5,6,7,8]. This noteworthy diversity can be attributed to several factors, including the remarkable adaptability of oak species to an extensive range of environmental conditions, fostering the emergence of hundreds of species, subspecies, and ecotypes [9,10,11]. Furthermore, the phenotypic plasticity of oak species in response to environmental changes, fueled by natural hybridization events, has facilitated inter-specific crossbreeding, further contributing to their genetic diversity [10].
Within European forest ecosystems, the sessile oak (Quercus petraea) has assumed a pivotal role in contemporary forest management, primarily due to the profound climatic changes impacting the continent [12,13,14]. Given the intensive oak management, studies on pure or mixed sessile oak forests, without human impact, remain exceedingly rare and isolated [15]. These unique ecosystems hold immense value for specialists engaged in the study of their evolutionary trajectories and the preservation of their rich biodiversity, providing critical insights into the complex interplay between climate change and forest ecosystems [16,17].
Approximately 30–40 years ago, a phenomenon of dieback affecting several species within the Quercus genus, including Quercus petraea, was documented in Europe, as well as in Romania [18,19,20]. This dieback phenomenon, characterized by the gradual decline in tree health, is likely attributed to climate-change-associated factors like droughts [21], pest infestations by organisms such as Tortrix viridana and Lymantria dispar [22,23,24,25], or pathogenic fungi [26]. Although the dieback phenomenon has been considered as a natural selection process driven by climate change for Q. petraea populations [27,28], it also exerts a detrimental impact on the species’ genetic diversity. Previous research has indicated that oaks are facing additional threats from Fagus sylvatica, the European beech, which exhibits an increasing tendency to encroach upon oak habitats under changing environmental conditions [29,30].
Earlier studies on Romanian oak species have traced their origins back to Balkan refuges during the ice age [31]. To preserve the best locally adapted provenances of Quercus species, some limits were introduced to the dispersal of oaks or seedlings within the country. This was achieved by designating 11 regions of origin as genetic forest resources and as basic multiplication materials. For the studied species, there are a total number of 458 stands selected as sources of seeds, out of which 15 are tested seed sources. The five populations examined in this study belong to the 458 selected stands, and a tree landscape in a forest and a representative tree trunk during measurements are illustrated in Figure 1A,B, respectively.
Genetic structure is generally defined as the quantity and distribution of genetic variation both within and among populations. It results from the interplay of ecological and genetic processes and holds a crucial role in species evolution, preservation, and conservation efforts [32].
The loss of genetic variation is a major threat to endangered species with small populations or located in narrow geographic areas. Over the past two decades, various Quercus species have undergone genetic structure and biodiversity assessments through several molecular marker investigations [33,34,35,36,37,38,39,40,41]. Among these markers, sequence-related amplified polymorphism (SRAP) showed to be a common PCR-based analysis due to its simplicity, reproducibility, and high efficiency [42,43]. SRAP, initially developed by Li and Quiros [44], has been widely applied to a large number of genetic diversity analysis, species identification, germplasm evaluation, and comparative genetics studies on different plant species by reason of its technical and economic advantages [45,46,47]. SRAP markers specifically amplify polymorphic junction fragments between exons and flank DNA, providing the level of polymorphisms needed for an efficient marker system. Similar studies were performed in Romania, where SRAP primers were successfully used to determine the genetic variability and population structure of Acer pseudoplatanus L. species from Carpathian Mountains [48] and even for rare plant species, endemic to the Transylvanian plateau, like Astragalus exscapus L. subsp. transsilvanicus [49]. As well, an SRAP marker system was used to determine genetic fidelity for multi-generations of in vitro-cultivated Aronia melanocarpa [50], Rubus fruticosus L. [51], and Rheum rhabarbarum L. [52].
The aim of the present study was to evaluate the genetic diversity among five populations of Q. petraea from the Transylvania and Maramureș regions by using an SRAP molecular marker system in order to develop an efficient management and preservation strategy for this species, as the populations taken under study belong to the selected stands for genetic forest resources and basic multiplication materials.

2. Materials and Methods

2.1. Plant Material

The five populations of Q. petraea were thoughtfully chosen from Transylvania and Maramureș regions, aiming to represent the most characteristic oak species from the central and northern areas of Romania. These populations were situated at varying distances from one another, falling within a range of 100 to 300 km.
In the summer of 2016, we meticulously collected leaf samples from a total of 15 individual oak trees within each population for the purpose of DNA extraction. To ensure a comprehensive representation of the populations, we maintained a minimum distance of 30 m between sampled individuals.
For precise documentation, we recorded the geographical coordinates, altitudes, and locations of these studied populations, which are comprehensively presented in Table 1 and Figure 2. This systematic approach ensured that our study encompassed a diverse and geographically representative set of Q. petraea individuals from the specified regions.

2.2. Molecular Analyses

The collected leaf samples were labeled and stored at −20 °C to facilitate the subsequent DNA extraction process. The isolation of nucleic DNA was carried out in accordance with the methodology outlined by Lodhi [53]. An improved version of this protocol, as devised by Rodica Pop [54], was employed, which included the addition of specific components to the extraction buffer. These additions consisted of 5 mM ascorbic acid, 2% polyvinylpyrrolidone (PVP), and 5 mM diethylenetriaminepentaacetic acid (DIECA). To conduct an initial screening of the samples, a total of 28 sequence-related amplified polymorphism (SRAP) primer combinations were utilized. Subsequently, 18 primer sets were selected for further analysis based on the criteria of generating clearly visible DNA fragments and exhibiting a high level of polymorphism. This selection process was carried out in accordance with the methodology previously described by Szabo et al. [55]. The SRAP primer sequences are presented in Table 2.
The experimental protocol was meticulously fine-tuned as follows: the PCR amplification was carried out in a 15 μL reaction mixture consisting of 3 μL of 5× Green buffer, 0.9 mM MgCl2, 0.3 mM of dNTPs, 0.5 µM of both forward and reverse primers, 0.2 U of Taq DNA polymerase (Promega), and 3 μL of the DNA sample. This optimized protocol was detailed in a previous study [55].
The PCR program followed a well-defined sequence: it was initiated with a 5 min denaturation step at 94 °C, followed by 5 cycles involving denaturation at 94 °C for 1 min, annealing at 35 °C for 1 min, and elongation at 72 °C for 1 min. Subsequently, 35 additional cycles were performed with denaturation at 94 °C for 1 min, annealing at 50 °C for 1 min, and elongation at 72 °C for 1 min. The amplification process concluded with a final elongation step at 72 °C for 10 min.
To visualize the PCR amplification products, electrophoresis was conducted using 2.00% (w/v) agarose gel, with detection facilitated through ethidium bromide. This carefully optimized protocol ensured the accurate and reproducible analysis of the genetic material.

2.3. Data Interpretation and Statistical Analysis

To analyze the data derived from SRAP markers, we first transformed the generated fragments into a binary data matrix. Subsequently, we employed Total Lab TL120 Software to facilitate data interpretation.
To assess the genetic distances between populations, we utilized GenAlEx v.6 [56]. Genetic distances were calculated based on Nei’s genetic distance [57], and a dendrogram was constructed using the Unweighted Pair Group Method with Arithmetic Mean (UPGMA), implemented in the NTSYS package, version 2.1 [58]. Furthermore, we conducted a Principal Coordinate Analysis (PCoA) based on Nei’s genetic distances to explore the genetic relationships among populations. This analysis was performed using the NTSYS program. To quantify the genetic differentiation among populations, we computed the coefficient of genetic differentiation (Gst) using POPGENE 1.32 software [59]. To assess the distribution of genetic variance within and among populations, we conducted an analysis of molecular variance (AMOVA) [60] with the assistance of GenAlEx 6.
Additionally, we employed the STRUCTURE program (version 2.3) to allocate individuals into distinct clusters (K). The determination of the optimal number of populations was guided by Evanno’s method [61], which involves assessing the change rate of the log probability (∆K) between successive K values.
These comprehensive data interpretations and statistical analyses allowed us to elucidate the genetic relationships and structures among the Q. petraea populations under study, providing valuable insights into their genetic diversity and relatedness.

3. Results

A set of 18 SRAP primer combinations was employed, resulting in a total of 225 scored DNA bands. The percentage of polymorphic bands (%PPB) exhibited variation across populations, ranging from 69.728% in the Cluj population to 85.78% in the Alba population. Notably, when considering the combined polymorphic bands across all five populations, the cumulative %PPB reached a high value of 98.22%.
To further elucidate the genetic diversity within these populations, two additional metrics were calculated and are presented in Table 3: Shannon’s diversity index (I) and gene diversity (He). These metrics provide valuable insights into the genetic variability and richness present in the sampled populations.
In continuation of our genetic diversity assessment, it is noteworthy that the Alba population displayed the highest genetic richness among the studied populations. Specifically, the Alba population exhibited the greatest number of effective alleles (1.4307) and the highest Shannon index value (0.3955). Moreover, this population recorded the highest expected heterozygosity (He) of 0.2582 and an impressively high polymorphic percentage of 85.78%. Conversely, the Cluj population demonstrated the lowest genetic diversity measures, with the lowest number of effective alleles (1.2948), Shannon index (0.2887), and expected heterozygosity values (0.1833).
At the species level, the overall genetic diversity (Ht) for Quercus petraea was estimated at 0.2475, while the average genetic diversity within populations (Hs) as assessed by the SRAP markers was slightly lower at 0.2253. The coefficient of genetic differentiation among the populations (Gst) was calculated to be 0.0900, indicating that approximately 9% of the total genetic diversity was distributed among the various Q. petraea populations according to the analysis performed using POPGENE. An additional examination through an analysis of molecular variance (AMOVA) reinforced these findings, revealing that a substantial majority of the total genetic variations, amounting to 91%, originated from differences within the Q. petraea populations. This within-population variation was significantly higher than that observed among populations (9%), as summarized in Table 4.
Importantly, the gene flow (Nm) value was computed to be 5.0557, a measure that is consistent with the criteria outlined by Wright [62]. This value characterizes the populations as a cohesive unit, indicating that the gene exchange and dispersal among them are substantial, further contributing to the genetic homogeneity and connectivity of these Q. petraea populations.
The genetic relationships among these Q. petraea populations were further elucidated through the construction of an Unweighted Pair Group Method with Arithmetic Mean (UPGMA) dendrogram, utilizing Nei’s genetic distances as a basis for the clustering analysis. This dendrogram effectively delineated two primary clusters within the studied populations. Interestingly, populations originating from the same geographical regions displayed notable patterns of high genetic similarity and a clear correlation between their genetic clustering and geographical distribution. Specifically, the first cluster predominantly encompassed the Cluj population, while the second cluster consisted of populations from the Sălaj, Alba, Bistrița-Năsăud, and Maramureș regions (Figure 3). This clustering pattern underscores the role of geographical proximity in shaping the genetic structure of the studied Quercus petraea populations, suggesting that environmental factors and historical processes have contributed to the observed genetic differentiation.
To gain a more comprehensive understanding of the inter-population relationships, we employed Principal Coordinate Analysis (PCoA), which further grouped the oak populations into two distinct clusters, complementing the findings from the UPGMA dendrogram analysis. In this PCoA-based clustering, two populations, namely Cluj and Sălaj, were positioned within the first cluster, while the remaining three populations, Maramureș, Alba, and Bistrița-Năsăud, formed the second cluster (Figure 4). It is worth noting that the two populations grouped together in the first cluster, Cluj and Sălaj, exhibit a notably closer genetic affinity to each other compared to their relationships with the populations in the second cluster. This genetic closeness is corroborated by the Nei’s genetic distance values, where the distance between the Cluj and Sălaj populations was measured at 0.0209. In contrast, the genetic distances between Cluj and Maramureș or Cluj and Alba were notably higher, at 0.0518 and 0.0259, respectively. These genetic distance measurements, as summarized in Table 5, underscore the genetic relatedness of the populations within the first cluster while highlighting their distinctiveness from the populations in the second cluster. This pattern further reinforces the significance of geographical proximity in shaping the genetic structure of these Quercus petraea populations, as observed in both the dendrogram and PCoA analyses.
The outcomes obtained through the utilization of the STRUCTURE software, as illustrated in Figure 5, provided further validation for the interpretations derived from the UPGMA dendrogram and PCoA analyses. Notably, the ∆K method, employed to determine the optimal number of genetic clusters (K), consistently indicated the presence of two distinct groups or clusters (K = 2). This congruence among different analytical methods reinforces the robustness of our findings, supporting the conclusion that Q. petraea populations indeed segregate into two well-defined genetic clusters. These clusters align with the geographical proximity and genetic affinities observed in the earlier analyses, indicating the roles of both geographic factors and historical processes in shaping the genetic structures of these populations.

4. Discussion

Genetic diversity is a critical aspect of a species’ evolutionary potential, adaptability, and long-term survival. In the case of the sessile oak, understanding its genetic diversity is of paramount importance due to its ecological significance and its economic value in forestry. Our study aimed to assess the genetic diversity within and among five Q. petraea populations from the Transylvania and Maramureș regions in Romania using SRAP molecular markers. The results revealed intriguing insights into the genetic makeup and structure of these populations, shedding light on factors that have influenced their genetic diversity.
In the context of Q. petraea, an in-depth exploration of the genetic mechanisms revealed a complex interplay of factors that are crucial to population dynamics. Gene flow, the transfer of genetic material between populations, plays a pivotal role in shaping genetic diversity and adaptability. Understanding the extent and patterns of gene flow among these populations can shed light on their connectivity and potential for genetic exchange. Moreover, investigating the reproductive systems, which may encompass both autogamous (self-breeding) and allogamous (cross-breeding) mechanisms, is fundamental. Autogamous systems can influence genetic diversity, while allogamous systems may promote outcrossing and the introduction of novel genetic materials. A thorough examination of these genetic mechanisms enriches our comprehension of Q. petraea’s genetic dynamics and its adaptive potential in diverse environmental contexts.
The remarkable level of genetic diversity detected within the Q. petraea populations in this study underscores the species’ capacity to adapt and thrive across diverse environmental conditions. This adaptability has likely been a driving force behind the generation of a multitude of oak species, subspecies, and ecotypes [9,10,11]. The substantial genetic diversity observed within populations (91%) can be attributed to several factors, including high levels of phenotypic plasticity and natural hybridization, which have facilitated inter-specific crossbreeding [10]. Additionally, oak species are known for their long life spans, which allow for the accumulation and maintenance of genetic diversity over extended periods [41,62].
The relatively lower genetic diversity among populations (9%) is indicative of the strong gene flow and genetic connectivity among these populations. This phenomenon can be partly attributed to the extensive dispersal capacity of oak species, facilitated by animals and birds that consume their acorns. Consequently, genetic material is frequently exchanged between populations, resulting in reduced inter-population differentiation [41]. This interconnectedness is crucial for the long-term viability of these populations as it promotes the exchange of advantageous genetic traits, enhancing their resilience to environmental changes and threats.
Polymorphic markers play pivotal roles in assessing genetic diversity, and our study identified a high percentage of polymorphic bands (PPBs) across all populations, averaging 79.50%. The elevated percentage of polymorphisms at the species level (98.22%) underscores the efficacy of SRAP markers in capturing the genetic diversity of Q. petraea. These markers’ ability to detect high levels of polymorphism is consistent with their reputation for simplicity, reproducibility, and efficiency [42,43]. While Nei’s genetic distance values remained relatively low, the combined evidence from various markers, including PPB and genetic distance, collectively contributes to our understanding of the genetic diversity of these populations.
When comparing our findings to those of studies on other oak species, such as Q. brantii [39], Q. semiserrata [63], and Q. libani [41], the SRAP markers appear to exhibit a particularly high level of polymorphisms. These comparisons highlight the robustness of SRAP markers in capturing genetic diversity and emphasize their utility in genetic studies of oak populations. It is important to note that while the percentage of polymorphisms serves as an important indicator of genetic diversity, the interpretation of these values should consider the specific markers used and their inherent characteristics.
Shannon’s diversity index (I) and Nei’s gene diversity (H) provide additional insights into the genetic diversity of Q. petraea populations. The average values for these indices in our study (I = 0.34 and H = 0.22) are consistent with the results reported in a study by Alikhani [39]. These indices serve as robust measures of genetic diversity, considering both the number of alleles and their relative frequencies within populations. When compared to the results reported by González-Rodríguez [64], which found a lower Shannon’s diversity index (I = 0.24) and a lower polymorphic percentage (54.98%) in Q. affinis and Q. laurina, it becomes evident that the genetic diversity of oak species can vary significantly between populations and species.
The results of the AMOVA revealed a substantial difference within populations (91%) compared to among populations (9%). This observation is consistent with some prior studies on oak species, such as Q. mongolica [65] and Q. robur [66], which also reported a higher genetic variation within populations than among populations. This within-population variation is attributed to factors like time lag effects, where genetic diversity accumulates within populations over extended periods [41,67]. Habitat fragmentation and geographical isolation, which are typically expected to lead to reduced genetic diversity among populations, may have less pronounced effects on long-lived species like oak.
The STRUCTURE analysis provided a further confirmation of the genetic differentiation among Q. petraea populations. This analysis grouped the populations into two distinct clusters, with the Cluj population showing a clear genetic distinction from the other four populations. These results align with the patterns observed in the UPGMA dendrogram and PCoA analyses, suggesting that geographical proximity has played a significant role in shaping the genetic structure of these populations. The presence of two genetic clusters may reflect the historical events and geographic barriers that have influenced gene flow and population differentiation.
Understanding the genetic diversity of oak populations is crucial for informed conservation efforts, breeding programs, and the selection of appropriate genitors for population development. The SRAP molecular markers used in this study have proven to be effective tools for capturing the genetic diversity of Q. petraea. Future research may further investigate the functional significance of the genetic diversity observed in these populations, including its implications for adaptability, resistance to pests and diseases, and overall ecological fitness. Ultimately, the conservation of genetic diversity within oak species like Quercus petraea is essential to ensure their long-term survival and continued ecological and economic contributions.

5. Conclusions

The variability of Q. petraea was studied for the first time in Romania by using SRAP molecular markers. The aim of our study was to determine the genetic diversity of Q. petraea and the relationships between five selected populations in order to elaborate efficient strategies to protect and preserve the genetic variability of the species. The markers used by us revealed a low genetic differentiation among populations, indicating the possibility of gene flow between the studied populations.
Our results indicate that there is a low genetic difference between the five populations chosen in the study. This can be explained either by the fact that the phenomenon of the dieback of trees in the plast 30–40 years had a negative effect on genetic diversity or the five populations could be considered part of a single larger population, located in several climatic areas, which is based on the migration of the population from the Balkans during the ice age.
In conclusion, our study has provided valuable insights into the genetic diversity of Quercus petraea populations in the Transylvania and Maramureș regions in Romania. The high genetic diversity within populations, coupled with the relatively low differentiation among populations, highlights the adaptability and resilience of this species. These findings have implications for the conservation and management of Q. petraea populations, particularly in the face of environmental changes and threats such as climate change and habitat fragmentation.

Author Contributions

Conceptualization, F.A.R. and A.F.; methodology, K.S.; software, K.S.; validation, D.P. and M.S.R.; formal analysis, A.F.; investigation, A.F. and T.-T.L.; resources, F.A.R., D.P. and M.I.V.; data curation, A.F. and F.A.R.; writing—original draft preparation, T.-T.L. and M.S.R., writing—review and editing, K.S.; supervision, M.I.V. and D.P.; project administration, K.S. and F.A.R. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Ministry of Research, Development and Innovation, developed with the support of UEFISCDI (Project No. 14 PFE-2022-2024).

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Aspects during field sampling, tree landscape (A), oak (Quercus petraea) tree trunk during measurements (B).
Figure 1. Aspects during field sampling, tree landscape (A), oak (Quercus petraea) tree trunk during measurements (B).
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Figure 2. Geographic locations of Q. petraea populations.
Figure 2. Geographic locations of Q. petraea populations.
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Figure 3. UPGMA dendrogram produced using Nei’s coefficient in the studied populations of Q. petraea.
Figure 3. UPGMA dendrogram produced using Nei’s coefficient in the studied populations of Q. petraea.
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Figure 4. Biplot of Q. petraea populations based on the first two principal coordinates.
Figure 4. Biplot of Q. petraea populations based on the first two principal coordinates.
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Figure 5. Bayesian clustering analysis (K = 2) based on combined data of SRAP of the Q. petraea; 1—Sălaj; 2—Alba; 3—Bistrița-Năsaud; 4—Cluj; 5—Maramureș.
Figure 5. Bayesian clustering analysis (K = 2) based on combined data of SRAP of the Q. petraea; 1—Sălaj; 2—Alba; 3—Bistrița-Năsaud; 4—Cluj; 5—Maramureș.
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Table 1. Geographical coordinates of Q. petraea populations.
Table 1. Geographical coordinates of Q. petraea populations.
No.PopulationsCodeNo. of SamplesAltitude (m)Longitude (E)Latitude (N)
1MaramureșMM15200–26023°20′56.25″47°38′23.00″
2SălajSJ15250–38023°41′56.84″47°17′34.08″
3AlbaAB15300–35023°38′46.29″46°06′02.34″
4ClujCJ15710–79023°36′03.28″46°41′12.09″
5Bistrița-NăsăudBN15380–46024°08′55.01″47°11′54.44″
Table 2. Primer sequences used in SRAP analysis of Q. petraea.
Table 2. Primer sequences used in SRAP analysis of Q. petraea.
Primer
Combinations		Primer Sequences
Forward Primer SequencesReverse Primer Sequences
Me2Em1F: TGA GTC CAA ACC GGA GCR: GAC TGC GTA CGA ATT AAT
Me2Em6F: TGA GTC CAA ACC GGA GCR: GAC TGC GTA CGA ATT GCA
Me4Em2F: TGA GTC CAA ACC GGA CCR: GAC TGC GTA CGA ATT TGC
Me4Em4F: TGA GTC CAA ACC GGA CCR: GAC TGC GTA CGA ATT TGA
Me4Em5F: TGA GTC CAA ACC GGA CCR: GAC TGC GTA CGA ATT AAC
Me5Em2F: TGA GTC CAA ACC GGA AGR: GAC TGC GTA CGA ATT TGC
Me5Em4F: TGA GTC CAA ACC GGA AGR: GAC TGC GTA CGA ATT TGA
Me5Em8F: TGA GTC CAA ACC GGA AGR: GAC TGC GTA CGA ATT CAC
Me5Em5F: TGA GTC CAA ACC GGA AGR: GAC TGC GTA CGA ATT AAC
Me4Em8F: TGA GTC CAA ACC GGA CCR: GAC TGC GTA CGA ATT CAC
Me2Em2F: TGA GTC CAA ACC GGA GCR: GAC TGC GTA CGA ATT TGC
Me2Em5F: TGA GTC CAA ACC GGA GCR: GAC TGC GTA CGA ATT AAC
Me1Em2F: TGA GTC CAA ACC GGA TAR: GAC TGC GTA CGA ATT TGC
Me1Em8F: TGA GTC CAA ACC GGA TAR: GAC TGC GTA CGA ATT CAC
Me3Em3F: TGA GTC CAA ACC GGA ATR: GAC TGC GTA CGA ATT GAC
Me3Em6F: TGA GTC CAA ACC GGA ATR: GAC TGC GTA CGA ATT GCA
Me6Em6F: TGA GTC CAA ACC GGA CAR: GAC TGC GTA CGA ATT GCA
Me6Em8F: TGA GTC CAA ACC GGA CAR: GAC TGC GTA CGA ATT CAC
Me2Em1F: TGA GTC CAA ACC GGA GCR: GAC TGC GTA CGA ATT AAT
Me2Em6F: TGA GTC CAA ACC GGA GCR: GAC TGC GTA CGA ATT GCA
Me4Em2F: TGA GTC CAA ACC GGA CCR: GAC TGC GTA CGA ATT TGC
Table 3. Genetic variation and polymorphic features of Q. petraea populations using SRAP markers.
Table 3. Genetic variation and polymorphic features of Q. petraea populations using SRAP markers.
PopulationPPB (%)NaNeHeI
Sălaj80.441.80441.33220.21030.3334
Alba85.781.85781.43070.25820.3955
Bistrița78.221.78221.36310.22380.3481
Cluj69.721.69781.29480.18330.2887
Maramureș83.111.83111.41220.25070.3857
Average79.451.79461.36660.22530.3503
Species-level98.221.98221.39790.24830.3918
PPB (%) = percent of polymorphic bands, Na = no. of different alleles, Ne = no. of effective alleles, He = expected heterozygosity, I = Shannon’s information index.
Table 4. Analysis of molecular variance (AMOVA) for studied individuals of Q. petraea.
Table 4. Analysis of molecular variance (AMOVA) for studied individuals of Q. petraea.
SourcedfSSMSEst. Var.%ɸPTp
Among Pops.4297.1874.292.9790.088<0.001
Within Pops.682101.5030.9030.9091
Total722398.68 33.87100
df, degree of freedom; SS, sum of squares; MS, mean sum of squares; ɸPT = estimated variation among populations/(estimated variation within populations + estimated variation among populations).
Table 5. Nei’s genetic distance of studied populations of Q. petraea.
Table 5. Nei’s genetic distance of studied populations of Q. petraea.
PopulationsSălajAlbaBistrița-NăsăudClujMaramureș
Sălaj−
Alba0.0284−
Bistrița-Năsăud0.02630.0259−
Cluj0.02090.04960.0382−
Maramureș0.05020.04460.02720.0518−
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Rebrean, F.A.; Fustos, A.; Szabo, K.; Lisandru, T.-T.; Rebrean, M.S.; Varga, M.I.; Pamfil, D. Genetic Diversity and Structure of Quercus petraea (Matt.) Liebl. Populations in Central and Northern Romania Revealed by SRAP Markers. Diversity 2023, 15, 1093. https://doi.org/10.3390/d15101093

AMA Style

Rebrean FA, Fustos A, Szabo K, Lisandru T-T, Rebrean MS, Varga MI, Pamfil D. Genetic Diversity and Structure of Quercus petraea (Matt.) Liebl. Populations in Central and Northern Romania Revealed by SRAP Markers. Diversity. 2023; 15(10):1093. https://doi.org/10.3390/d15101093

Chicago/Turabian Style

Rebrean, Florin Alexandru, Adrian Fustos, Katalin Szabo, Tabita-Teodora Lisandru, Mihaela Simona Rebrean, Mircea Ioan Varga, and Doru Pamfil. 2023. "Genetic Diversity and Structure of Quercus petraea (Matt.) Liebl. Populations in Central and Northern Romania Revealed by SRAP Markers" Diversity 15, no. 10: 1093. https://doi.org/10.3390/d15101093

APA Style

Rebrean, F. A., Fustos, A., Szabo, K., Lisandru, T. -T., Rebrean, M. S., Varga, M. I., & Pamfil, D. (2023). Genetic Diversity and Structure of Quercus petraea (Matt.) Liebl. Populations in Central and Northern Romania Revealed by SRAP Markers. Diversity, 15(10), 1093. https://doi.org/10.3390/d15101093

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