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Article

Genetic Diversity and Differentiation of Pedunculate Oak (Quercus robur L.) Populations at the Southern Margin of Its Distribution Range—Implications for Conservation

by
Lazar Kesić
1,
Klára Cseke
2,
Saša Orlović
1,
Dejan B. Stojanović
1,
Saša Kostić
1,
Attila Benke
2,
Attila Borovics
2,
Srđan Stojnić
1,* and
Evangelia V. Avramidou
3,*
1
Institute of Lowland Forestry and Environment, University of Novi Sad, Antona Čehova 13d, 21000 Novi Sad, Serbia
2
Forest Research Institute, University of Sopron, Várkerület 30/A, 9600 Sárvár, Hungary
3
Hellenic Agricultural Organization “ELGO-DIMITRA”, Institute of Mediterranean and Forest Ecosystems, Terma Alkmanos, Ilisia, 11528 Athens, Greece
*
Authors to whom correspondence should be addressed.
Diversity 2021, 13(8), 371; https://doi.org/10.3390/d13080371
Submission received: 17 June 2021 / Revised: 6 August 2021 / Accepted: 8 August 2021 / Published: 11 August 2021
(This article belongs to the Special Issue Feature Papers in Plant Diversity)
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Abstract

:
Understanding intraspecific genetic variation is one of the principal requirements for the evaluation of tree species capacity to cope with intensive climatic changes, as well as designing long-term conservation programs. Herein, we evaluated the genetic diversity and genetic structure of seven pedunculate oak (Quercus robur L.) populations, located at the southern margin of its distribution range on the Balkan Peninsula (Serbia). The objective of the study was to propose future in situ conservation measures aimed at protection of pedunculate oak adaptive and neutral genetic diversity at the species rear-edge. Genetic diversity and structure were estimated using twelve highly polymorphic simple sequence repeat (SSR) markers. The mean expected heterozygosity (He) was 0.769, allelic richness (AR) 9.63, and private allelic richness (pAR) 0.79, indicating high genetic diversity in the studied populations. Genetic differentiation among the populations was low (Fst = 0.032). Structure analysis, the unweighted pair group method with arithmetic mean (UPGMA) showed the existence of two gene pools unrelated to the populations’ area of occurrence. Taking into consideration the results of the current study and previous conservation activities on the pedunculate oak in Serbia, as well as the importance of rear-edge populations in the long-term conservation of the species genetic diversity, we suggested establishing three additional gene conservation units for securing long-term sustainability of the species.

1. Introduction

Pedunculate oak (Quercus robur L.) is widely distributed across Europe, from the Northern Iberian Peninsula, South Italy, and the Balkan Peninsula and Turkey to Southern Scandinavia, and from Scotland to Asia through the Ural and Caucasus Mountains [1]. In Southern Europe, the Quercus species survived the last glacial period in micro- and macrorefugia, from which the species were spreading to the north and east depending on climate occurrences [2]. According to Temunović et al. [3], the margin of pedunculate oak continuous distribution in southeastern Europe is located in the lowlands of the Pannonian Basin and floodplains of large rivers. Furthermore, according to Muir et al. [4] results showed that although in Europe Q. robur and Q. petraea hybridize extensively, the two species are separate taxonomic units; and populations from Serbia were close to populations from Slovenia. In Serbia, the majority of pedunculate oak stands is situated in the northern part of the country, along with the Sava and Danube rivers, and in Central Serbia (e.g., separate populations) [5,6].
In the past decades, pedunculate oak forests in Europe have been subjected to numerous stress factors, such as low underground water level [7], drought stress [8], pest and diseases [9,10], and climate change [11]. As a consequence of their negative impact, trees’ health status and growth rate have frequently deteriorated, sometimes resulting even in premature death [7,12]. Moreover, under the influence of intensive climate change, pedunculate oak forests may suffer significant damage from interacting causal agents, which have been shown to trigger a complex phenomenon of oak decline [13,14].
Genetic diversity is the basis of biodiversity, and it is very important for the adaptation, survival, and evolution of all living organisms in a changing climate [15,16]. Peripheral populations are defined as populations on the edge of species distribution that usually represent reservoirs of biodiversity [17], as well as a source of drought-resistant ecotypes [18]. For example, Erichsen et al. [19] reported notably higher genetic diversity of Fraxinus excelsior L. growing in the Hyrcanian forests (i.e., species rear-edge) than in European populations. In addition, Fagus sylvatica L. populations at the rear-edge showed higher resistance to xylem embolism in comparison to leading edge populations, indicating the potential of this species to adapt to different climatic conditions [20]. In this manner, peripheral populations at the rear-edge are responsible for long-term persistence of the species [21] and could save the species from extinction [22]. However, several studies have shown a negative effect of recent climate change on these populations. Indeed, growing under stronger selection pressures than core populations, rear-edge populations are frequently subjected to various biotic and abiotic stress factors which negatively affect their reproductive success and survival [23,24]. The problem becomes even more pronounced when these stresses act simultaneously or sequentially, chaining a large pool of negative factors. Therefore, peripheral populations have become the subject of conservation of genetic resources [25] and they were used in afforestation of other areas [26].
Molecular markers such as simple sequence repeats (SSRs) have a high rate of polymorphism, good reproducibility and codominant inheritance, making them ideal markers for studying genetic diversity [27]. Therefore, polymorphic SSR markers have proven to be a reliable tool for analyzing genetic diversity and population genetic structure in the case of many oak species [28,29,30]. Previous studies of the variability of pedunculate oak populations in Serbia were based on certain morphological and anatomical traits variation [5,6,31], failing to provide information on neutral genetic differences. Considering no consistent correlation between genetic variation and phenotypic variability [32], we evaluated the neutral genetic diversity of oak populations in Serbia, for the first time.
Bearing in mind the foregoing discussion, the aim of this paper was twofold: (1) to estimate the genetic diversity and structure of pedunculate oak populations in Serbia, and (2) to identify priority populations for conservation in order to preserve genetic diversity at the rear-edge of the species distribution range.

2. Materials and Methods

2.1. Plant Material and DNA Isolation

This study included seven natural pedunculate oak populations from Serbia (Table 1; Figure 1). In total, 210 adult trees (30 per population) were analyzed. Buds and flushing leaves were collected from trees located 50–100 m from each other. DNA was extracted from fresh plant material which was stored in deep freeze (−70.0 °C) before extraction. Total DNA extraction was carried out according to the DNeasy Plant Mini Kit protocol (Qiagen®, Hilden, Germany). DNA concentration was measured with BioSpec-nano (Shimadzu, Kyoto, Japan) and standardized to 10 ng µL −1.

2.2. PCR Amplification and SSR Markers

A set of twelve SSRs (Table 2) was used for genetic analysis. This set included eight SSR markers provided by Kampfer et al. [33]: QrZAG87, QrZAG30, QrZAG101, QrZAG11, QrZAG112, QrZAG96, QrZAG15, QrZAG20; three markers described by Steinkellner et al. [34]: QpZAG110, QpZAG1/5, QpZAG9; and finally, one marker described by Dow et al. [35]: MSQ13.
For SSR analysis, reactions were performed in a total volume of 15 µL for each marker separately containing 10 ng µL −1 of template DNA, 1× reaction buffer (Promega GoTaq G2 Flexi, 5× reaction buffer with no magnesium), 2 mM of MgCl2 (Promega) in the case of QrZAG11, 30, 87, 96, 101, 112, QpZAG110 and MSQ13, while 3 mM of MgCl2 in the case of QpZAG9, QpZAG1/5, QrZAG15 and 20, 12 μM of each dNTP (Promega dNTP mix, 10 mM each), 0.5 μM each of the reverse and forward primer, except MSQ13 where 0.34 μM each (Applied Biosystems Custom DNA oligos) and 0.5 unit polymerase (Promega GoTaq G2 Flexi, 5U/μL). Fluorescent dyes compatible with the Applied Biosystems G5 Matrix were used on the forward primer 5′ end. In order to multiplex the samples of different SSR loci by size and by dye, the following two mix combinations were set up: mix 1 contains the following six markers: QrZAG112 (NED), QrZAG96 (NED), QrZAG30 (VIC), QrZAG11 (VIC), QrZAG87 (PET), and QrZAG20(FAM)), while mix 2 contains the remaining six markers: QpZAG110 (NED), QpZAG1/5 (VIC), QpZAG9 (PET), MSQ13 (6FAM), QrZAG101 (NED), and QrZAG15 (6FAM).
PCR amplifications were performed in a Veriti 96-well Thermal Cycler (Applied Biosystems, Waltham, Massachusetts, USA) in the case of QrZAG11, 96, 112, QpZAG110 with the following steps: an initial denaturation for 3 min at 94 °C, followed by 30 cycles of 30 s at 94 °C, 30 s at 50 °C, 90 s at 65 °C, and finished with a final extension step at 65 °C for 15 min. In the case of QrZAG30, 87 and 101, a short protocol was applied following the modification of Neophytou et al. [36] that included an initial denaturation step 8 min at 94 °C, followed by 23 cycles of 15 s at 94 °C, 15 s at the optimum annealing temperature (Ta = 50 °C for QrZAG101 and Ta = 56 °C for QrZAG30 and 87), 15 s at 72 °C, and 10 additional cycles with reduced denaturation temperature (89 °C), without a final elongation step. The original, long protocol was more appropriate in case of the remaining markers, so the following PCR programming was used: an initial denaturation for 3 min at 95 °C, followed by 35 cycles of 50 s at 95 °C, 50 s at the optimum annealing temperature (Ta = 52 °C for MSQ13 and Ta = 65 °C for QpZAG9 and Ta = 55 °C for QpZAG1/5, QrZAG15 and 20), 105 s at 72 °C, finished with a final extension step at 72 °C for 10 min. SSR genotyping was performed on an ABI 3730 DNA Analyzer (Applied Biosystems, Waltham, MA, USA) by BIOMI Ltd. (Gödöllő, Hungary). Allele scoring was carried out using PeakScanner 1.0 software (Applied Biosystems, Waltham, MA, USA).

2.3. Data Analysis

2.3.1. Variation within Population

In order to describe variation among populations, effective numbers of alleles (Ne), observed heterozygosity (Ho), expected heterozygosity (He), and Shannon’s Information Index (I) software POPGENE ver. [37] was used. The number of alleles in a genotype (allelic richness (AR) and private allelic richness (pAR)) was calculated using HP-RARE software [38,39]. The inbreeding coefficient (Fis) was calculated for each locus with FSTAT software [40]. Frequency of null alleles was calculated with ML-Null software [41]. Micro-checker software was used to test presence of null alleles, long allele dropout, and scoring errors [42].

2.3.2. Variation among Populations

In order to estimate among-population genetic variation, we calculated Fst using FreeNA [43]. Means and significant values over the loci and populations were obtained by bootstrapping. GENEPOP was used to test genotypic disequilibrium between all pairs of loci, and Bonferroni correction was applied. The hierarchical distribution of genetic variation (AMOVA) [44] among and within the populations for twelve markers was assessed with GenAlEx software [45]. GENEPOP was also used to estimate the number of migrants (Nm) based on the private allele method. FreeNA software [43] was used to estimate Nei’s unbiased genetic distance and significance among populations [46]. We also estimated the Cavall–-Sforza and Edwards genetic distance for each pair of populations [43], with the ENA method (DCENA) and the INA method (DCINA). The dendrogram among populations was produced with UPGMA based on Nei’s genetic distance using POP-TREE2 software [47], and 1000 repetitions of bootstrapping were used.
STRUCTURE software was used to infer genetic structure and define the number of clusters (gene pool) in a dataset based on the Bayesian clustering method [48]. The most likely number of clusters (K) was defined by Structure Harvester [49]. For STRUCTURE analysis the following settings were used: burn-in 100000, MCMC 1000000, 20 repetitions per K. To detect structure among populations that are likely to be similar due to migration or shared ancestry, we used admixture model [50]. In order to test Cavalli–Sforza and Edward’s chord, FreeNA software was used [43].

3. Results

3.1. Variation and Genetic Diversity within Populations

In total, 253 alleles were obtained from the twelve SSR loci used in this study (Table 3). Several SSRs, QrZAG87, QrZAG30, QrZAG112, QrZAG101, and QrZAG11, contained more than 20 alleles. The number of alleles per locus (Na) ranged from a minimum of 11 (MSQ13) to a maximum of 36 (QrZAG30 and QrZAG11). Effective number of alleles (Ne) varied between 1.77 (QrZAG96) and 12.88 (QrZAG30), with an average value of 11.46 per locus. Expected heterozygosity (He) within population for all SSR loci studied was 0.77. Fis varied between 0.021 (QrZAG87) and 0.453 (QrZAG112), with an average value of 0.209 alleles per locus. The genetic differentiation (Fst) was 0.032; the highest one (0.065) was for loci QrZAG96, whereas the lowest was for loci QrZAG20 and QpZAG1/5 (0.017). The mean frequency of null alleles (fn) was 0.082 and ranged from 0.004 for locus QrZAG87, to 0.197 for locus QrZAG112 (Table 3).

3.2. Genetic Variation between and within Populations

The effective number of alleles (Ne) ranged between 5.642 in the population OD and 6.429 in the population AP, with an average of effective 6.07 alleles per individual population. The expected heterozygosity (He) varied between 0.740 (KL) and 0.793 (KU). The overall average allelic richness (AR) was 9.63; the highest one (10.47) was distributed in KU, whereas the lowest was in OD population (8.69). Private allelic richness (pAR) varied between 0.41 (MO) and 1.12 (KU) (Table 4).
The genetic differentiation between populations was low (Fst = 0.032, p > 0.05). AMOVA test proved that differentiation among populations was very low (2%), and 98% of the variation was within-population level, and statistically significant (p < 0.001, Table 5). Furthermore, the level of gene flow (Nm) was measured to be 9.82 individuals per generation between the populations.
The results from Structure Harvester [49] revealed the existence of two pedunculate oak gene pools in Serbia (K = 2) (Figure 2).
The results of STRUCTURE revealed two pedunculate oak gene pools in Serbia (Figure 3). Group I included populations from MO, OD, KL, whereas group II included population in KG, KU, AP, and BM.
Relationships among the populations were estimated with Cavalli–Sforza and Edward’s chord genetic distances (DC) [52] using the INA method computed in FreeNA (DCINA) (Table 6); the greatest distance occurred between the KL and BM (FSTENA = 0.028, DCINA = −0.349), and the smallest distance was between KG and KU (FSTENA = −0.0002), and for MO and KL (DCINA = 0.294).
The UPGMA dendrogram, based on Nei’s [46] unbiased genetic distance matrix, was created to depict the genetic relationship among the seven studied populations applying MEGA software [53]. The populations were clustered into two distinct groups. Group I consisted of three populations, in which OD and KL are more similar to each other, and MO slightly separated from the previous two populations. The populations KU and KG belong to Group II together with the population in AP. This appears genetically differentiated from the other two, whereas Group II also included the most northern population BM that forms a completely separated branch in the same cluster (Figure 4).

4. Discussion

Genetic diversity has a key role for forest tree population adaptation under changing climate, as well as planning future conservation programs. We found that Na ranged between 11 (MSQ13) and 36 (QrZAG30 and QrZAG11), with a mean value of 29.99. We observed higher Na than the one that was observed by Vranckx et al. [54] (Na = 21.26) considering several mutual loci (QpZAG9, QpZAG15, QpZAG110, MSQ13, and QrZAG112). Likewise, the Na on the QrZAG11 (36) was notably higher than previously reported for this species (i.e., Craciunesc et al. [55] and Pohjanmies et al. [56] found 18 and 20 alleles for QrZAG11, respectively).
Expected heterozygosity (He) across the studied pedunculate oak populations was similar to other populations at the south-east margin. For example, marker QrZAG87 had similar value of He (0.87) to those observed by Katičić–Bogdan et al. [57] (0.87), Morić [58] (0.88), and Muir and Schlötterer [59] (0.89). Based on QTL associated studies [60], locus QrZAG87 is considered to be linked to genome regions responsible for bud burst timing. Therefore, considerably high He value of this locus is probably the consequence of sampling procedure, since the material of both late (var. tardiflora) and early (var. praecox) flushing individuals were collected. According to Neophytou et al. [36], the same locus has higher contribution to intraspecific than interspecific provenance discrimination. Considering locus QrZAG96, the He observed in our study (0.40) was similar to that reported in Croatia (0.42) [58] and higher in comparison to southern population in Greece (0.33) and Bulgaria (0.39) [36]. In contrast, marker QpZAG110 had smaller He (0.62) in comparison to Greek (0.74) and Bulgarian (0.84) populations [36]. Marker QrZAG112 had equal He value (0.81) in our and other studies at the southern margin [36,55,57,58].
The mean AR in our populations (9.63) was lower than AR measured in Belgium (11.5) considering four mutual loci (QpZAG 9, QrZAG15, QrZAG112, MSQ 13) [54]. Moreover, according to Katičić–Bogdan et al. [57], the mean AR in Croatian pedunculate oak populations (12.77) was higher than in the Serbian (9.63), considering eight mutual loci (QpZAG9, QrZAG11, QrZAG30, QrZAG87, QrZAG96, QrZAG101, QrZAG112, and MSQ13). Considering our results, lower AR in comparison to those observed by the aforementioned authors could be explained by the founder effect impact [61]. It has been empirically shown that AR is more sensitive to the founder effects than He, meaning that the loss of rare alleles is likely to reduce AR to the larger extent than He (a rare allele which is lost will probably not reduce He as much as AR) [62,63]. In our case, it is possible that the measures on stands regeneration were not synchronized with the pedunculate oak seed years (as is the case with modern shelterwood system); therefore, the studied populations originate from the seeds collected from a limited number of mother trees.
The observed value of Fst (0.032) in Serbian pedunculate oak populations was lower than the ones previously reported for Bosnia and Herzegovina (0.051) [64] and Romania (0.052) [55], respectively. On the other hand, genetic differentiation was higher in Serbian populations than in other central European populations (0.024) [65]. Generally, peripheral populations have a tendency to show higher genetic differentiation than populations from the center of the species distribution [55,56,64]. For example, Jiménez et al. [66], reported higher genetic variation in marginal than central populations of Quercus suber L. Likewise, analysis of molecular variance (AMOVA) showed greater variability within (98%) than among populations (2%), which is commonly observed in geographically widespread tree species and populations, and mostly attributed to natural selection, different ecological conditions, and anthropogenic factor effects [6].
The, UMPGA and STRUCTURE analyses revealed that the studied populations were grouped into two different gene pools. Both gene pools consisted of geographically distinct populations; i.e., the first gene pool included populations MO, OD, and KL, whereas the second group consisted of geographically scattered populations KU, AP, KG (southernmost population), and BM (the northernmost). Low correlation between geographical and genetic distances could be explained by the anthropogenic impact on the current genetic structure of pedunculate oak, as well as human-assisted regeneration of these forests. Namely, situated on the border between the Austro-Hungarian and Ottoman empires, the northern part of Serbia (formerly part of Austro-Hungarian Monarchy; until 1918) has been subjected to turbulent historical occurrences over the centuries that frequently lead to intensive deforestation, and in some cases even forest devastation [67]. The first interim guideline for forest management (K.u.K. Őstereichischie–Ungarische Monarchie) was enacted in 1754, after which the legal regulations related to forest management changed several times depending on historical circumstances during 18th and 19th centuries [68]. The beginnings of modern management of pedunculate oak forests are linked to 1860, when the Forest Law of Austro-Hungarian Monarchy (passed in 1852) was extended to this region [69]. Nevertheless, although historical data on forest management in this area date back to the middle of the 18th century, there is no clear evidence on the origin of acorns used for regeneration of pedunculate oak forests. Likewise, although there is no evidence that populations were coppiced in the past, we assumed that coppicing was the simplest form of usage and forest reproduction, especially around settlements. This assumption can be supported by the fact that due to the excessive use of forests around settlements, the first Decree Law on forests has passed on 1755 to regulate the future management and protection of the forests in this region [69]. In contrast, certain records highlight that pedunculate oak forests in the area were largely preserved until second part of XIX century, primarily due to low population density, existence of impassable swamps on the large areas and lack of industrial wood processing, etc. (see [69]).
Even though some authors stated that the seed of Slavonian pedunculate oak (Quercus robur subsp. slavonica) was broadly used across Austro-Hungarian Monarchy [69,70], certain records also stressed that the afforestation of the nearby areas was done using seed originating from the Czech Republic and Slovakia [71]. In addition, the populations KL and KG are 97 and 90 years old, respectively, meaning that they were regenerated after World War I, when the Austro-Hungarian Empire collapsed, and the Kingdom of Yugoslavia was proclaimed (e.g., Northern part of Serbia was annexed to Serbia). Thus, it is very likely that these populations are established from the acorns collected in some of already existing populations, especially considering the fact that genetically close populations (OD to KL and KU to KG) are also the oldest ones, and theoretically could produce seed in that time (see [72,73]). Another reason to consider human-assisted regeneration in shaping pedunculate oak genetic differentiation also lies in the tendency of oak periodical yield; e.g., according to Gradečki–Poštenjak et al. [74] abundant yield could be expected every 4 years. Thus, due to the problematic acorn sourcing outside mast years, a common practice even today is that seed for afforestation is provided from other refugia, which are sometimes tens or even hundreds of kilometers away. For example, all pedunculate oak forests in Northern Serbia are managed by the single public enterprise for forest management and belong to the same provenance region (see Figure 1), thereby the production, market, and use of seed within this region is in accordance with national laws on forests and forest reproductive material, which are fully compliant with EU Directive 1999/105/EC.

Implications for Conservation

The most common approach in conservation of genetic diversity of forest trees is establishment of in situ dynamic conservation units. The prime objective of dynamic genetic conservation is not only to capture the current genetic diversity, but also to support continued evolutionary processes within populations [75]. Considering climate change as the major challenge for long-term conservation of forest genetic resources, European Forest Genetic Resources Programme (EUFORGEN) made pan-European recommendations aimed primarily at preserving adaptability of tree populations in the future, emphasizing the following activities: (1) identification of most vulnerable tree populations and species, (2) conservation of genetic diversity in marginal populations, and (3) conservation of adaptive diversity [76].
Our study revealed that the studied pedunculate oak populations were characterized by a high level of genetic diversity (e.g., expected heterozygosity), which is likely for Southern European tree populations as they are located in areas of glacial refugials [77]. According to Reed and Frankham [78], heterozygosity is tightly related to the overall population fitness, and therefore important for its capacity to adapt to changing environmental conditions. However, as found in Southern Europe, near the edge of the species environmental limits, these populations are supposed to be negatively threatened by climate change, imposing the need for implementation of measures aimed at conservation of their genetic diversity. So far, only one gene conservation unit (GCU) of pedunculate oak has been established in Serbia (population MO), in the framework of the European Forest Genetic Resources Programme (EUFORGEN). However, our results showed that this population handles the lowest pAR, as well as quite low AR and He values (e.g., only the populations OD and KL have lower AR and He, respectively), imposing the need to set aside additional conservation units. Considering the importance of AR and pAR for reliable diversity assessment and conservation purposes [30,65,79,80], we used these parameters as the most relevant criteria for prioritization of populations for conservation. The population that stood out by the highest values of He (0.793), AR (10.47), and pAR (1.12) was population KU; therefore, we suggest the establishment of an additional GCU to cover its genetic variability.
Furthermore, to ensure conservation of sizable amount of adaptive diversity throughout species distribution range, EUFORGEN recommended that at least one GCU per country and per environmental zone should be identified [76]. Moreover, in the case of marginal populations, it is recommended to duplicate the number of GCUs within each “country x environmental zone”. Bearing this in mind, we suggest establishing one GCU to cover the southernmost population KG, which is the only one situated southern of the Danube and Sava rivers, in the zone characterized by cool and moist climate (the Central Serbia region). Likewise, although the rest of the studied populations lie in the region with cool and dry climate, one more GCU should be established to cover the northernmost population BM for two reasons: (1) Establishing more GCUs along the transect from the North to the South will ensure the conservation of the species’ spatial genetic diversity, (2) the population BM belongs to Special Nature Reserve “Upper Danube”, which was officially designated UNESCO Biosphere Reserve in 2017 under the name “Bačko Podunavlje” [81], and will be part of the future transboundary Mura–Drava–Danube Biosphere Reserve.

5. Conclusions

In this study, twelve SSR markers were used to estimate the genetic diversity and genetic structure of seven pedunculate oak populations in Serbia, which are situated at the rear edge of the species distribution range. The pedunculate oak populations studied herein showed high genetic diversity, which was, as expected, more pronounced at the intra-population level than between different populations. Considering the results, we suggest a conservation strategy based on the establishment of additional in situ conservation units across the species distribution range in Serbia to cover all environmental zones, as well as populations characterized by exceptional genetic diversity. The evaluation of genetic diversity and structure of Q. robur populations at the rear edge allowed us to identify populations that are important for conservation. In addition, this genetic information could greatly improve knowledge of the species genetic resources which is available along the species distribution.

Author Contributions

S.S., L.K. and S.O. conceived the ideas and designed the experiment. L.K., D.B.S. and S.K. collected the samples. A.B. (Attila Benke) and A.B. (Attila Borovics) provided financial support. K.C. and L.K. performed laboratory analyses. E.V.A. and K.C. conducted statistical analyses. L.K. wrote the manuscript. Review and editing: all the authors. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Science Fund of the Republic of Serbia, PROMIS, #6066697, TreeVita and financial support of the Ministry of Education, Science and Technological Development of the Republic of Serbia and the Ministry of Agriculture of Hungary (“Kaán Károly Afforestation Project EGF/385/2020”).

Institutional Review Board Statement

“Not applicable” for studies not involving humans or animals.

Informed Consent Statement

“Not applicable” for studies not involving humans.

Data Availability Statement

The data presented in this study are available from the corresponing author upon request.

Acknowledgments

We acknowledge the “Vojvodina šume” public enterprise and “Srbija šume” public enterprise.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Eaton, E.; Caudullo, G.; Oliveira, S.; de Rigo, D. Quercus robur and Quercus petraea in Europe: Distribution, habitat, usage and threats. In European Atlas of Forest Tree Species; San-Miguel-Ayanz, J., de Rigo, D., Caudullo, G., Houston Durrant., T., Mauri, A., Eds.; Publication Office of the European Union: Luxembourg, 2016; p. e01c6df+. [Google Scholar]
  2. Birks, H.J.B.; Tinner, W. European tree dynamics and invasions during the Quaternary. In Introduced Tree Species in European Forests: Opportunities and Challenges; Krumm, F., Vítková, L., Eds.; European Forest Institute: Freiburg, Germany, 2016; pp. 22–24. [Google Scholar]
  3. Temunović, M.; Garnier-Géré, P.; Morić, M.; Franjić, J.; Ivanković, M.; Bogdan, S.; Hampe, A. Candidate gene SNP variation in floodplain populations of pedunculate oak (Quercus robur L.) near the species’ southern range margin: Weak differ-entiation yet distinct associations with water availability. Mol. Ecol. 2020, 29, 2359–2378. [Google Scholar] [CrossRef]
  4. Muir, G.; Fleming, C.C.; Schlötterer, C. Species status of hybridizing oaks. Nature 2000, 405, 1016. [Google Scholar] [CrossRef]
  5. Batos, B.; Vilotić, D.; Orlović, S.; Miljković, D. Inter and intra-population variation of leaf stomatal traits of Quercus robur L. in northern Serbia. Arch. Biol. Sci. 2010, 62, 1125–1136. [Google Scholar] [CrossRef]
  6. Ballian, D.; Memišević, M.; Bogunić, F.; Bašić, N.; Marković, M.; Kajba, D. Morphological variability of pedunculate oak (Quercus robur L.) in the region of Croatia and western Balkans. Šumarski List. 2010, 134, 371–386. [Google Scholar]
  7. Stojanović, D.B.; Matović, B.; Orlović, S.; Kržič, A.; Trudić, B.; Galić, Z.; Stojnić, S.; Pekeč, S. Future of the main important forest tree species in Serbia from the climate change perspective. SEEFOR 2014, 5, 117–124. [Google Scholar] [CrossRef] [Green Version]
  8. Madritsch, S.; Wischnitzki, E.; Kotrade, P.; Ashoub, A.; Burg, A.; Fluch, S.; Sehr, E.M. Elucidating drought stress tolerance in European oaks through cross-species transcriptomics. G3-Genes Genom. Genet 2019, 9, 3181–3199. [Google Scholar] [CrossRef] [Green Version]
  9. Dobreva, M.; Simov, N.; Georgiev, G.; Mirchev, P.; Georgieva, M. First record of Corythucha arcuata (Say) (Heteroptera: Tingidae) on the Balkan Peninsula. Acta Zool. Bulgar. 2013, 65, 409–412. [Google Scholar]
  10. Evans, M.R. Will natural resistance result in populations of ash trees remaining in British woodlands after a century of ash dieback disease? R. Soc. Open Sci. 2019, 6, 190908. [Google Scholar] [CrossRef] [Green Version]
  11. Kostić, S.; Levanič, T.; Orlović, S.; Matović, B.; Stojanović, D.B. Pendunctulate and Turkey oaks radial increment and stable carbon isotope response to climate conditions through time. Poplar 2019, 204, 29–35. [Google Scholar]
  12. Stojanović, D.B.; Levanič, T.; Matović, B.; Orlović, S. Growth decrease and mortality of oak floodplain forests as a response to change of water regime and climate. Eur. J. Forest. Res. 2015, 134, 555–567. [Google Scholar] [CrossRef]
  13. Thomas, F.M.; Blank, R.; Hartmann, G. Abiotic and biotic factors and their interactions as causes of oak decline in Central Europe. For. Pathol. 2002, 32, 277–307. [Google Scholar] [CrossRef]
  14. Bussotti, F.; Ferretti, M. Air pollution, forest condition and forest decline in Southern Europe: An overview. Environ. Poll. 1998, 101, 49–65. [Google Scholar] [CrossRef]
  15. Atkins, K.E.; Travis, J.M.J. Local adaptation and the evolution of species’ ranges under climate change. J. Theor. Biol. 2010, 266, 449–457. [Google Scholar] [CrossRef] [Green Version]
  16. Valladares, F.; Matesanz, S.; Guilhaumon, F.; Araújo, M.B.; Balaguer, L.; Benito-Garzón, M.; Cornwell, W.; Gianoli, E.; van Kleunen, M.; Naya, E.D.; et al. The effects of phenotypic plasticity and local ad-aptation on forecasts of species range shifts under climate change. Ecol. Lett. 2014, 17, 1351–1364. [Google Scholar] [CrossRef] [Green Version]
  17. Lira-Noriega, A.; Manthey, J.D. Relationship of genetic diversity and niche centrality: A survey and analysis. Evolution 2014, 68, 1082–1093. [Google Scholar] [CrossRef] [PubMed]
  18. Rose, L.; Leuchner, C.; Köckemann, B.; Buschmann, H. Are marginal beech (Fagus sylvatica L.) provenances a source for drought tolerant ecotypes. Eur. J. For. Res. 2009, 128, 335–343. [Google Scholar] [CrossRef] [Green Version]
  19. Erichsen, E.O.; Budde, K.B.; Sagheb-Talebi, K.; Bagnoli, F.; Vendramin, G.G.; Hansen, O.K. Hyrcanian forests—Stable rear-edge populations harbouring high genetic diversity of Fraxinus excelsior, a common European tree species. Divers. Distrib. 2018, 24, 1521–1533. [Google Scholar] [CrossRef] [Green Version]
  20. Stojnić, S.; Suchocka, M.; Benito-Garzón, M.; Torres-Ruiz, J.M.; Cochard, H.; Bolte, A.; Delzon, S. Variation in xylem vulnerability to embolism in European beech from geographically marginal populations. Tree Physiol. 2018, 38, 173–185. [Google Scholar] [CrossRef] [PubMed]
  21. Fady, B.; Aravanopoulos, F.A.; Alizoti, P.; Mátyás, C.; von Wühlisch, G.; Westergren, M.; Belletti, P.; Cvjetkovic, B.; Ducci, F.; Huber, G.; et al. Evolution-based approach needed for the conservation and silviculture of peripheral forest tree populations. For. Ecol. Manag. 2016, 375, 66–75. [Google Scholar] [CrossRef] [Green Version]
  22. Holliday, J.A.; Suren, H.; Aitken, S.N. Divergent selection and heterogeneous migration rates across the range of Sitka spruce (Picea sitchensis). Proc. R. Soc. B Biol. Sci. 2012, 279, 1675–1683. [Google Scholar] [CrossRef]
  23. Hódar, J.A.; Castro, J.; Zamora, R. Pine processionary caterpillar Thaumetopoea pityocampa as a new threat for relict Mediterranean Scots pine forests under climatic warming. Biol. Conserv. 2003, 110, 123–129. [Google Scholar] [CrossRef]
  24. Bigler, C.; Bräker, O.U.; Bugmann, H.; Dobbertin, M.; Rigling, A. Drought as an inciting mortality factor in Scots pine stands of the Valais, Switzerland. Ecosystems 2006, 9, 330–343. [Google Scholar] [CrossRef] [Green Version]
  25. Konnert, M.; Fady, B.; An Gomory, D.; A’hara, S.; Wolter, F.; Ducci, F.; Koskela, J.; Bozzano, M.; Maaten, T.; Kowalczyk, J. Use and transfer of forest reproductive material in Europe in the context of climate change. In European Forest Genetic Resources Programme (EUFORGEN); Bioversity International: Rome, Italy, 2015; Volume xvi, p. 75. [Google Scholar]
  26. Alleaume-Benharira, M.; Pen, I.R.; Ronce, O. Geographical patterns of adaptation within a species’ range: Interactions between drift and gene flow. J. Evol. Biol. 2006, 19, 203–215. [Google Scholar] [CrossRef]
  27. Kalia, R.K.; Rai, M.K.; Kalia, S.; Singh, R.; Dhawan, A.K. Microsatellite markers: An overview of the recent progress in plants. Euphytica 2011, 177, 309–334. [Google Scholar] [CrossRef]
  28. Mohammad-Panah, N.; Shabanian, N.; Khadivi, A.; Rahmani, M.S.; Emami, A. Genetic structure of gall oak (Quercus in-fectoria) characterized by nuclear and chloroplast SSR markers. Tree Genet. Genom. 2017, 13, 70. [Google Scholar] [CrossRef]
  29. An, M.; Deng, M.; Zheng, S.S.; Jiang, X.L.; Song, Y.G. Introgression threatens the geneticdiversity of Quercus austro-cochinchinensis (Fagaceae), an endangered oak: A case inferred by molecular markers. Front. Plant Sci. 2017, 8, 229. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  30. Carabeo, M.; Simeone, M.C.; Cherubini, M.; Mattia, C.; Chiocchini, F.; Bertini, L.; Caruso, C.; La Mantia, T.; Villani, F.; Mattioni, C. Estimating the genetic diversity and structure of Quercus trojana Webb populations in Italy by SSRs: Impli-cations for management and conservation. Can. J. For. Res. 2017, 47, 331–339. [Google Scholar] [CrossRef]
  31. Batos, B.; Miletić, Z.; Orlović, S.; Miljković, D. Variability of nutritive macroelements in pedunculate oak (Quercus robur L.) leaves in Serbia. Genetika 2010, 42, 435–453. [Google Scholar] [CrossRef]
  32. Villellas, J.; Berjano, R.; Terrab, A.; García, M.B. Divergence between phenotypic and genetic variation within populations of a common herb across Europe. Ecosphere 2014, 5, 1–14. [Google Scholar] [CrossRef] [Green Version]
  33. Kampfer, S.; Lexer, C.; Glossl, J.; Steinkellner, H. Brief report characterization of (GA) n microsatellite loci from Quercus robur. Hereditas 1998, 129, 1–86. [Google Scholar] [CrossRef]
  34. Steinkellner, H.; Lexer, C.; Turetschek, E.; Glössl, J. Conservation of (GA) n microsatellite loci between Quercus species. Mol. Ecol. 1997, 6, 1189–1194. [Google Scholar] [CrossRef]
  35. Dow, B.D.; Ashley, M.V.; Howe, H.F. Characterization of highly variable (GA/CT) n microsatellites in the bur oak, Quercus macrocarpa. Theor. Appl. Genet. 1995, 91, 137–141. [Google Scholar] [CrossRef]
  36. Neophytou, C.; Aravanopoulos, F.A.; Fink, S.; Dounavi, A. Detecting interspecific and geographic differentiation patterns in two interfertile oak species (Quercus petraea (Matt.) Liebl. and Q. robur L.) using small sets of microsatellite markers. For. Ecol. Manag. 2010, 259, 2026–2035. [Google Scholar] [CrossRef] [Green Version]
  37. Yeh Francis, C.; Yang, R.; Boyle Timothy, B.; Ye, Z.; Mao, J.X. POPGENE Version 1.32, the User-Friendly Shareware for Population Genetic Analysis; Molecular Biology and Biotechnology Centre, University of Alberta: Alberta, AB, Canada, 1999; Available online: https://sites.ualberta.ca/~fyeh/popgene.html (accessed on 6 December 2020).
  38. Kalinowski, S.T. hp-rare 1.0: A computer program for performing rarefaction on measures of allelic richness. Mol. Ecol. Resour. 2005, 5, 187–189. [Google Scholar] [CrossRef]
  39. Smouse, P.E.; Banks, S.C.; Peakall, R. Converting quadratic entropy to diversity: Both animals and alleles are diverse, but some are more diverse than others. PLoS ONE 2017, 12, e0185499. [Google Scholar] [CrossRef]
  40. Goudet, J. FSTAT (version 1.2): A computer program to calculate F-statistics. J. Hered. 1995, 86, 485–486. [Google Scholar] [CrossRef]
  41. Kalinowski, S.T.; Taper, M.L. Maximum likelihood estimation of the frequency of null alleles at microsatellite loci. Conserv. Genet. 2006, 7, 991–995. [Google Scholar] [CrossRef]
  42. Van Oosterhout, C.; Hutchinson, W.F.; Wills, D.P.; Shipley, P. MICRO-CHECKER: Software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 2004, 4, 535–538. [Google Scholar] [CrossRef]
  43. Chapuis, M.-P.; Estoup, A. Microsatellite null alleles and estimation of population differentiation. Mol. Biol. Evol. 2007, 24, 621–631. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Michalakis, Y.M.; Excoffier, L. A generic estimation of population subdivision using distances between alleles with special reference for microsatellite loci. Genetics 1996, 142, 1061–1064. [Google Scholar] [CrossRef] [PubMed]
  45. Peakall, R.O.D.; Smouse, P.E. GENALEX 6: Genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes 2006, 6, 288–295. [Google Scholar] [CrossRef]
  46. Nei, M. Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics 1978, 89, 583–590. [Google Scholar] [CrossRef]
  47. Takezaki, N.; Nei, M.; Tamura, K. POPTREE2: Software for constructing population trees from allele frequency data and computing other population statistics with Windows interface. Mol. Biol. Evol. 2010, 27, 747–752. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  48. Pritchard, J.K.; Stephens, M.; Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 2000, 155, 945–959. [Google Scholar] [CrossRef]
  49. Earl, D.A. STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 2012, 4, 359–361. [Google Scholar] [CrossRef]
  50. Falush, D.; Stephens, M.; Pritchard, J.K. Inference of population structure using multilocus genotype data: Linked loci and correlated allele frequencies. Genetics 2003, 164, 1567–1587. [Google Scholar] [CrossRef]
  51. Nei, M. Analysis of gene diversity in subdivided populations. Proc. Natl. Acad. Sci. USA 1973, 70, 3321–3323. [Google Scholar] [CrossRef] [Green Version]
  52. Cavalli-Sforza, L.L.; Edwards, A.W. Phylogenetic analysis. Models and estimation procedures. Am. J. Hum. Genet. 1967, 19, 233. [Google Scholar]
  53. Kumar, S.; Stecher, G.; Li, M.; Knyaz, C.; Tamura, K. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 2018, 35, 1547–1549. [Google Scholar] [CrossRef]
  54. Vranckx, G.; Jacquemyn, H.; Mergeay, J.; Cox, K.; Kint, V.; Muys, B.; Honnay, O. Transmission of genetic variation from the adult generation to naturally established seedling cohorts in small forest stands of pedunculate oak (Quercus robur L). For. Ecol. Manag. 2014, 312, 19–27. [Google Scholar] [CrossRef]
  55. Craciunesc, I.; Ciocîrlan, E.; Sofletea, N.; Curtu, A.L. Genetic diversity of pedunculate oak (Quercus robur L.) in Prejmer natural reserve. Bull. Transilv. Univ. Brasov. For. Wood Ind. Agric. Food Eng. Ser. II 2011, 4, 15. [Google Scholar]
  56. Pohjanmies, T.; Elshibli, S.; Pulkkinen, P.; Rusanen, M.; Vakkari, P.; Korpelainen, H.; Roslin, T. Fragmentation-related patterns of genetic differentiation in pedunculate oak (Quercus robur) at two hierarchical scales. Silva Fenn. 2016, 50, 1–15. [Google Scholar] [CrossRef] [Green Version]
  57. Katičić Bogdan, I.; Kajba, D.; Šatović, Z.; Schüler, S.; Bogdan, S. Genetic diversity of pedunculate oak (Quercus robur L.) in clonal seed orchards in Croatia, assessed by nuclear and chloroplast microsatellites. SEEFOR 2018, 9, 29–46. [Google Scholar] [CrossRef]
  58. Morić, M. Genetska Raznolikost Hrasta Lužnjaka (Quercus robur L.) u Pokusnim Nasadima s Potomstvom iz Odabranih Sjemenskih Sastojina. Ph.D. Thesis, University of Zagreb, Zagreb, Croatia, 2016. [Google Scholar]
  59. Muir, G.; Schloetterer, C. Evidence for shared ancestral polymorphism rather than recurrent gene flow at microsatellite loci differentiating two hybridizing oaks (Quercus spp.). Mol. Ecol. 2005, 14, 549–561. [Google Scholar] [CrossRef] [PubMed]
  60. Scotti-Saintagne, C.; Bodénès, C.; Barreneche, T.; Bertocchi, E.; Plomion, C.; Kremer, A. Detection of quantitative trait loci controlling bud burst and height growth in Quercus robur L. Theor. Appl. Genet. 2004, 109, 1648–1659. [Google Scholar] [CrossRef]
  61. Mandák, B.; Bímová, K.; PlačkovÁ, I.; Mahelka, V.; Chrtek, J. Loss of genetic variation in geographically marginal pop-ulations of Atriplex tatarica (Chenopodiaceae). Ann. Bot. 2005, 96, 901–912. [Google Scholar] [CrossRef] [Green Version]
  62. Leberg, P.L. Effects of population bottlenecks on genetic diversity as measured by allozyme electrophoresis. Evolution 1992, 46, 477–494. [Google Scholar] [CrossRef] [Green Version]
  63. Greenbaum, G.; Templeton, A.R.; Zarmi, Y.; Bar-David, S. Allelic richness following population founding events–a stochastic modeling framework incorporating gene flow and genetic drift. PLoS ONE 2014, 9, e115203. [Google Scholar] [CrossRef] [Green Version]
  64. Ballian, D.; Belletti, P.; Ferrazzini, D.; Bogunić, F.; Kajba, D. Genetic variability of pedunculate oak (Quercus robur L.) in Bosnia and Herzegovina. Period. Biol. 2010, 112, 353–362. [Google Scholar]
  65. Petit, R.J.; El Mousadik, A.; Pons, O. Identifying populations for conservation on the basis of genetic markers. Conserv. Biol. 1998, 12, 844–855. [Google Scholar] [CrossRef]
  66. Jiménez, P.; Agundez, D.; Alia, R.; Gil, L. Genetic variation in central and marginal populations of Quercus suber L. Silvae Genet. 1999, 48, 278–283. [Google Scholar]
  67. Vlatković, S. Šume Ravnog Srema, opšte karakteristike i šumovitost područja. In Monografija 250 Godina Šumarstva Ravnog Srema; Tomović, Z., Ed.; Javno preduzeće “Vojvodinašume”–Petrovaradin, Šumsko gazdinstvo: Sremska Mitrovica, Serbia, 2008; pp. 7–18. [Google Scholar]
  68. Erdeši, J.; Orlović, S.; Galić, Z.; Radosavljević, N. Istorijat šuma Ravnog Srema. In Monografija 250 Godina Šumarstva Ravnog Srema; Tomović, Z., Ed.; Javno preduzeće “Vojvodinašume”–Petrovaradin, Šumsko gazdinstvo: Sremska Mitrovica, Serbia, 2008; pp. 39–44. [Google Scholar]
  69. Plavšić, S.; Jezdić, D. Šumarstvo Srema u graničarsko doba. In Monografija 250 Godina Šumarstva Ravnog Srema; Tomović, Z., Ed.; Javno preduzeće “Vojvodinašume”–Petrovaradin, Šumsko gazdinstvo: Sremska Mitrovica, Serbia, 2008; pp. 45–54. [Google Scholar]
  70. Bordács, S.; Horváth, V.; Pintér, B. Development of Resources of Social Broadleaves’ Reproductive Material Production; OTKA Research Reports; Hungarian National Scientific Research Fund: Pécs, Hungary, 2008. [Google Scholar]
  71. Volner, H. Drvna industrija Slavonije s posebnim osvrtom na obitelj Gutmann do kraja 1918. godine. Hist. Zb. 2012, 65, 453–476. [Google Scholar]
  72. Martiník, A.; Dobrovolný, L.; Palátová, E. Tree growing space and acorn production of Quercus robur. Dendrobiology 2014, 71, 101–108. [Google Scholar] [CrossRef] [Green Version]
  73. Kamler, J.; Dobrovolný, L.; Drimaj, J.; Kadavý, J.; Kneifl, M.; Adamec, Z.; Knott, R.; Martiník, A.; Plhal, R.; Zeman, J.; et al. The impact of seed predation and browsing on natural sessile oak regeneration under different light conditions in an over-aged coppice stand. iForest 2016, 9, 569–576. [Google Scholar] [CrossRef] [Green Version]
  74. Gradečki-Poštenjak, M.; Novak Agbaba, S.; Licht, R.; Posarić, D. Dinamika plodnošenja i kvaliteta uroda sjemena hrasta lužnjaka (Quercus robur L.) u narušenim ekološkim uvjetima. Šumarski List 2011, 135, 169–181. [Google Scholar]
  75. Koskela, J.; Lefèvre, F.; Schueler, S.; Kraigher, H.; Olrik, D.C.; Hubert, J.; Ditlevsen, B. Translating conservation genetics into management: Pan-European minimum requirements for dynamic conservation units of forest tree genetic diversity. Biol. Conserv. 2013, 157, 39–49. [Google Scholar] [CrossRef] [Green Version]
  76. de Vries, S.M.; Alan, M.; Bozzano, M.; Burianek, V.; Collin, E.; Cottrell, J.; Ivankovic, M.; Kelleher, T.C.; Koskela, J.; Rotach, P.; et al. Pan-European strategy for genetic conservation of forest trees and establishment of a core network of dynamic conservation units. In European Forest Genetic Resources Programme (EUFORGEN); Bioversity International: Rome, Italy, 2015. [Google Scholar]
  77. Hampe, A.; Petit, R.J. Conserving biodiversity under climate change: The rear edge matters. Ecol. Lett. 2005, 8, 461–467. [Google Scholar] [CrossRef] [Green Version]
  78. Reed, D.H.; Frankham, R. Correlation between fitness and genetic diversity. Conserv. Biol. 2003, 17, 230–237. [Google Scholar] [CrossRef]
  79. Markert, J.A.; Champlin, D.M.; Gutjahr-Gobell, R.; Grear, J.S.; Kuhn, A.; McGreevy, T.J.; Roth, A.; Mark, J.; Bagley, M.; Nacci, D.E. Population genetic diversity and fitness in multiple environments. BMC Evol. Biol. 2010, 10, 1–13. [Google Scholar] [CrossRef] [Green Version]
  80. Porth, I.; El-Kassaby, Y.A. Assessment of the genetic diversity in forest tree populations using molecular markers. Diversity 2014, 6, 283–295. [Google Scholar] [CrossRef] [Green Version]
  81. 23 New Sites Added to UNESCO’s World Network of Biosphere Reserves. Available online: https://en.unesco.org/news/23-new-sites-added-unesco-s-world-network-biosphere-reserves (accessed on 14 June 2017).
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Figure 1. Geographical locations of pedunculate oak populations in the study.
Figure 1. Geographical locations of pedunculate oak populations in the study.
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Figure 2. Results from Structure Harvester analysis of pedunculate oak populations (K = 2).
Figure 2. Results from Structure Harvester analysis of pedunculate oak populations (K = 2).
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Figure 3. Results from structure analysis of pedunculate oak populations when K = 2.
Figure 3. Results from structure analysis of pedunculate oak populations when K = 2.
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Figure 4. UPGMA tree using Nei’s genetic distance. Bootstrap values are presented at the branch intersections.
Figure 4. UPGMA tree using Nei’s genetic distance. Bootstrap values are presented at the branch intersections.
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Table
Table 1. Population names, geographic coordinates, altitude, mean air temperature, and annual sum of precipitations.
Table 1. Population names, geographic coordinates, altitude, mean air temperature, and annual sum of precipitations.
Abb.PopulationLatitudeLongitudeAge of
Population		Altitude
(m a.s.l.)		Mean Air
Temperature (°C)		Annual Sum of Precipitation (mm)
BMBački Monoštor45°91′18°88′1158411.1613
APApatin45°62′18°94′1178811.2649
ODOdzaci45°46′19°16′1378511.2635
MOMorović44°92′19°12′1319311.5498
KLKlenak44°79′19°75′978611.6688
KUKupinovo44°74′19°99′1468311.6663
KGKragujevac44°14′21°09′9010911.8686
Table
Table 2. Analyzed loci of pedunculate oak populations, sequences of microsatellite primers, repeated motifs, fragment length (bp), and specific primer annealing temperatures (Ta).
Table 2. Analyzed loci of pedunculate oak populations, sequences of microsatellite primers, repeated motifs, fragment length (bp), and specific primer annealing temperatures (Ta).
LocusPrimer Sequences (5′→3′)Repeated MotifFragment Length (bp)Ta (°C)
QrZAG87TCCCACCACTTTGGTCTCTCA
GTTGTCAGCAGTGGGATGGGTA		(TC)20110–13156
QrZAG30TGCTCCGTCATAATCTTGCTCTGA
GCAATCCTATCATGCACATGCACAT		(GA)26172–24856
QrZAG101CCTGCACAATCAAATCCTTCACTT
GCCATGAACAACGGCGGTATCTAG		(TC)20(AC)15136–16050
QrZAG11CCTTGAACTCGAAGGTGTCCTT
GTAGGTCAAAACCATTGGTTGACT		(TC)22238–26750
QpZAG110GGAGGCTTCCTTCAACCTACT
GATCTCTTGTGTGCTGTATTT		(AG)15206–26250
QrZAG112TTCTTGCTTTGGTGCGCG
GTGGTCAGAGACTCGGTAAGTATTC		(GA)3285–9650
QrZAG96CCCAGTCACATCCACTACTGTCC
GGTTGGGAAAAGGAGATCAGA		(TC)20135–19455
MSQ13TGGCTGCACCTATGGCTCTTAGACACTCAGACCCACCATTTTTCC(TC)N222–24650
QrZAG15CGATTTGATAATGACACTATGG
CATCGACTCATTGTTAAGCAC		(GA)15144–21645
QrZAG20CCATTAQAAAGAAGCAGTATTTTGT
GCAACACTCAGCCTATATCTAGAA		(TC)20160–20048
QpZAG1/5GCTTGAGAGTTGAGATTTGT
GCAACACCCTTTAACTACCA		(GT)5(GA)9160–19055
QpZAG9GCAATTACAGGCTAGGCTGG
GTCTGGACCTAGCCCTCATG		(AG)12182–21065
Table
Table 3. Characteristics of twelve SSR loci across seven pedunculate oak populations.
Table 3. Characteristics of twelve SSR loci across seven pedunculate oak populations.
LocusNaNeIHoHeFstFisfn
QrZAG87248.4392.3330.8620.8740.0250.0210.004
QrZAG303612.8762.7490.5900.9200.0260.355 *0.163
QrZAG11369.3602.5130.6760.8830.0480.179 *0.087
QrZAG96181.7730.9070.2380.4040.0650.4120.102
QrZAG112235.3231.9990.4480.8090.0250.4530.197
QrZAG20186.3202.0160.6670.8400.0170.213 *0.090
QpZAG9157.3592.1160.6710.8620.0230.146 *0.058
MSQ13113.5231.4110.4780.6940.0400.211 *0.079
QrZAG15123.4321.4190.5710.6970.0530.200 *0.073
QpZAG1/5144.2061.6500.6900.7570.0170.1040.038
QrZAG101207.4402.2270.6520.8620.0310.0250.080
QpZAG110262.7321.5310.5950.6220.0220.0400.014
Mean29.9911.4641.9060.5950.7690.0320.2090.082
St. Dev.0.0120.4970.0600.0210.0170.0040.038
Legend: Na (number of alleles), Ne (number of effective alleles), I (Shannon’s information index), Ho (observed heterozygosity), He (Nei [51]; expected heterozygosity), Fst (Wright’s fixation index), Fis (inbreeding coefficient), fn (null allele frequency), * p < 0.05.
Table
Table 4. Genetic diversity parameters at the population level based on twelve SSR loci: Ne (number of effective alleles), He (expected heterozygosity), I (Shannon’s information index), AR (allelic richness), pAR (private allelic richness), Fst (Wright’s fixation index).
Table 4. Genetic diversity parameters at the population level based on twelve SSR loci: Ne (number of effective alleles), He (expected heterozygosity), I (Shannon’s information index), AR (allelic richness), pAR (private allelic richness), Fst (Wright’s fixation index).
PopulationNeHeIARpARFst
OD5.6420.7481.8208.690.59
KL5.8720.7401.8419.440.91
KU6.3160.7931.99810.471.12
BM6.1680.7831.9319.560.52
MO5.6480.7461.8108.780.41
AP6.4290.7901.99410.260.87
KG6.3820.7811.94910.211.10
Mean6.0650.7691.9069.630.790.032
Table
Table 5. Analysis of molecular variance (AMOVA) for 210 individuals in seven populations of pedunculate oak.
Table 5. Analysis of molecular variance (AMOVA) for 210 individuals in seven populations of pedunculate oak.
SourcedfSSMSEst. Var.%ΦsTp Value
Among Pops6123.87620.6460.29820.0250.001
Within Pops2032376.20011.70511.70598
Total2092500.076 12.003100
Table
Table 6. Corrected genetic differentiation (FST) between populations, using the INA method (above the diagonal) and the ENA method (below the diagonal), * p < 0.05 [43].
Table 6. Corrected genetic differentiation (FST) between populations, using the INA method (above the diagonal) and the ENA method (below the diagonal), * p < 0.05 [43].
FSTENA
DC INAPopulationODKLKUBMMOAPKG
OD–0.008 *0.010 *0.015 *0.004 *0.012 *0.004 *
KL0.308 *–0.015 *0.028 *0.015 *0.012 *0.015 *
KU0.342 *0.346 *–0.012 *0.017 *0.005 *0.0002 *
BM0.333 *0.349 *0.340 *–0.021 *0.0170.004
MO0.314 *0.291 *0.347 *0.307 *–0.018 *0.016 *
AP0.319 *0.318 *0.317 *0.326 *0.320 *–0.002 *
KG0.3070.329 *0.314 *0.315 *0.343 *0.328 *–
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Kesić, L.; Cseke, K.; Orlović, S.; Stojanović, D.B.; Kostić, S.; Benke, A.; Borovics, A.; Stojnić, S.; Avramidou, E.V. Genetic Diversity and Differentiation of Pedunculate Oak (Quercus robur L.) Populations at the Southern Margin of Its Distribution Range—Implications for Conservation. Diversity 2021, 13, 371. https://doi.org/10.3390/d13080371

AMA Style

Kesić L, Cseke K, Orlović S, Stojanović DB, Kostić S, Benke A, Borovics A, Stojnić S, Avramidou EV. Genetic Diversity and Differentiation of Pedunculate Oak (Quercus robur L.) Populations at the Southern Margin of Its Distribution Range—Implications for Conservation. Diversity. 2021; 13(8):371. https://doi.org/10.3390/d13080371

Chicago/Turabian Style

Kesić, Lazar, Klára Cseke, Saša Orlović, Dejan B. Stojanović, Saša Kostić, Attila Benke, Attila Borovics, Srđan Stojnić, and Evangelia V. Avramidou. 2021. "Genetic Diversity and Differentiation of Pedunculate Oak (Quercus robur L.) Populations at the Southern Margin of Its Distribution Range—Implications for Conservation" Diversity 13, no. 8: 371. https://doi.org/10.3390/d13080371

APA Style

Kesić, L., Cseke, K., Orlović, S., Stojanović, D. B., Kostić, S., Benke, A., Borovics, A., Stojnić, S., & Avramidou, E. V. (2021). Genetic Diversity and Differentiation of Pedunculate Oak (Quercus robur L.) Populations at the Southern Margin of Its Distribution Range—Implications for Conservation. Diversity, 13(8), 371. https://doi.org/10.3390/d13080371

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