Cent. Eur. J. Biol.• 6(4) • 2011 • 597-605 DOI: 10.2478/s11535-011-0034-8
Central European Journal of Biology
Genetic differences within natural and planted stands of Quercus petraea Research Article
Jiří Dostálek*, Tomáš Frantík, Miroslava Lukášová Silva Tarouca Research Institute for Landscape and Ornamental Gardening, CZ-252 43 Průhonice, Czech Republic Received 15 February 2011; Accepted 31 March 2011
Abstract: Five sessile oak [Quercus petraea (Matt.) Liebl.] stands from the Czech Republic were studied to learn about the impact of different types of forest management regimes on the genetic differences among tree populations and on population structures. One population had not been markedly affected by human activity, two populations represented unplanted stands that were extensively managed for a long period of time using the coppice system, and two populations were planted stands. Approximately 100 trees from each stand were mapped and subsequently genotyped using 10 nuclear microsatellite loci. We determined the spatial genetic structure of each population and the genetic differentiation among the populations. We found that: (i) the populations were genetically differentiated, but the differences between the unplanted and planted stands were not markedly significant; (ii) the genetic differentiation among the populations depended on the geographical distribution of the populations; (iii) within unplanted stands, a strong spatial genetic structure was seen; and (iv) within planted stands, no spatial genetic structure was observed. Our findings implies that the analysis of spatial genetic structure of the sessile oak forest stand can help reveal and determine its origin. Keywords: Quercus petraea • Genetic structure within a population • Genetic differences among populations • Microsatellites • Forest management © Versita Sp. z o.o.
1. Introduction Long-term human activity in European forests has influenced the genetic diversity of forest ecosystems [1] including forest trees [2]. Modified environmental conditions and forest structure, as well as the selective removal of trees during thinning and harvesting, can all potentially change the genetic structure of a forest [3,4]. Selectively favoring special phenotypes may strongly influence adaptive capability and economically important traits [3]. Artificial regeneration by means of sowing or intentional planting often results in changes in the genetic composition of the target species [5]. We can anticipate that a narrow selection of seed-producing trees will result in lower genetic variability in planted forest stands. Intensive forest management can also disturb the initial genetic structure of a population due to the planting of genotypes of alien origin within a species, i.e., seedlings from remote geographic regions within a country or from abroad. This can even result in an outbreeding depression as described, for example, by Woessner [6]. * E-mail:
[email protected]
To help protect the diversity of woody forest species, it is important to determine how forest management influences the genetic variability of these species. In this study we examined sessile oak, Quercus petraea, a woody species of high economic value commonly found in the Czech Republic and in most of Europe. Recently, a number of genetic studies have been performed, as genetic markers allow quick surveys of variation within and between populations [7-9]. However, most of these studies aimed to assess the genetic characteristics of natural or semi-natural populations. Studies concerning genetic structure and genetic variability at the population level were performed by several authors [10-13]. Finkeldey [11] analyzed 17 isoenzyme loci to reveal that differentiation among populations within a species is very low, reflecting the geographical location of populations to a certain extent. Bruschi et al. [14] also noticed a slight correlation between genetic distance and geographic distance among populations; they evaluated the diversity of several populations using both morphological and molecular
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markers. Using microsatellites, a low or not significant genetic differentiation among populations of Q. petraea was reported in France [15,16], Belgium [17], Ireland [18], Denmark [19], and the Czech Republic [20]. Similar results were also reported by Bakker et al. [12]. When analyzing the genetic structure of populations using enzyme-coding loci, Zanetto and Kremer [21] observed significant differences in the frequencies of alleles in oak populations. Streiff et al. [10] used isozymes and six microsatelite loci to study the diversity and fine-scale genetic structure within a native oak forest containing Quercus robur and Q. petraea in the northwest of France. Their study demonstrated that the stand had a significant but low spatial genetic structure that was greater for Q. petraea than for Q. robur. Cottrell et al. [13] compared by means of microsatellites the genetic structures of two British oak woods containing both Q. robur and Q. petraea. One stand contained unplanted natural woods managed by coppicing, while the second stand was influenced by human-mediated planting. Significant spatial genetic structure was found in both stands, with the most substantial spatial genetic structure occurring in unplanted forests. In general, these studies point to high genetic diversity in the stands that were investigated. This diversity was attributed to the high level of outcrossing that occurs in Quercus spp., particularly Q. petraea. In this study, we paid close attention to the relationship between genetic characteristics and management intensity and to the level of autochthony of the Q. petraea populations. An autochtonous stand is a conspecific stand that has spontaneously arisen at a given location. The high correlation between spatial genetic structure and forest management regime for Fagus sylvatica was mentioned by Gregorius and Kownatzki [22]. In our study, we examined five stands of Q. petraea in Czech Republic. We tested whether there were any differences in genetic characteristics among the populations that could be attributed to the various management regimes. The aim of this study was to answer the following questions: First, are there any genetic differences between natural and planted stands? Second, does the type of forest management regime influence the genetic structure of the stands? Third, is there existing relation between the origin of the stand and the genetic structure of the population?
2. Experimental Procedures 2.1 Localities
We choose five populations of Quercus petraea (Matt.) Liebl. that are managed using different forest 598
management schemes. Stands 1, 2 and 3 are located in the Křivoklátsko Protected Landscape Area, and stands 4 and 5 are located in the Podyjí National Park (Figure 1). Stand 1. Brdatka Nature Reserve (50º 02´ 56´´ N, 13º 53´24´´): This locality represents a natural forest that has not been influenced by human activity. The habitat is located above the Berounka River on an extremely jagged slope with a south to southeastern orientation and occasional rock outcrops. In this area, the oak population forms a layered dwarf thermophilous oak forest that is typical of a wild service-oak forest (Sorbo torminalis-Quercetum; Svoboda ex Blažková 1962). The dominant Q. petraea cover comprises 25%–50% of the tree layer, and the admixture of Pinus sylvestris L. cover is 5%–25% (for details see [23]). Stand 2. Červený Kříž Nature Reserve (49º 59´ 30´´ N, 13º 55´47´´): This locality represents a natural stand that has been managed for a long time using the coppice system. This habitat is located in a flat area. The oak population in the area is part of a thermophilous cinquefoils-oak forest (Potentillo albae-Quercetum Libbert 1933). The tree layer is composed almost exclusively of oak, with a cover of 75–100%. Individuals of Carpinus betulus L., Tilia cordata Mill., and Sorbus torminalis (L.) Crantz can be found only sporadically in this stand. A shrub layer is absent in this community primarily due to wild animal activity (for details see [23]). Stand 3. Křivoklát (50º 23´ 88´´ N, 13º 52´ 53´´): This locality represents a stand of Q. petraea planted approximately eighty years ago on municipal land in the town of Křivoklát. The tree layer is composed primarily of planted Q. Petraea with an admixture of C. betulus, Fagus sylvatica L., Larix decidua Mill., and P. sylvestris. Stand 4. Lipina (48º 52´ 45´´ N, 16º 01´ 13´´): The locality in the Podyjí National Park represents a seminatural unplanted stand on a slope of approximately 20º inclination that was extensively utilized by coppice management, at least for the past 500 years [24]. From 1951 to 1991, only sanitation cutting was carried out. Since 1991, the year when the Podyjí National Park was established, the stand has been preserved without intervention. The oak population here comprises part of the wild service-oak forest with S. torminalis and Vincetoxicum hirundinaria Med. (Sorbo torminalisQuercetum Svoboda ex Blažková 1962) transformed into an acidophilous woodrush oak forest with Festuca ovina L. (Luzulo albidae-Quercetum Hilitzer 1932). Oak is dominant in the tree layer, comprising 70–80% of the total canopy layer. The admixture consists of Tilia platyphyllos Scop., T. cordata Mill., Carpinus betulus L., Acer campestre L., Fraxinus excelsior L., and Sorbus aucuparia L. (for details see [24]).
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Figure 1.
Location of the studied sessile oak (Quercus petraea) vegetation in the Křivoklátsko Protected Landscape Area and Podyjí National Park: 1–Brdatka National Nature Reserve (natural stand), 2–Červený kříž Nature Reserve (unplanted stand managed with the coppice system), 3–Křivoklát (planted stand in the vicinity of the town), 4–Lipina (unplanted stand managed with the coppice system), 5–Podmolí (planted stand).
Stand 5. Podmolí, stand No. 55De2v (48º 49´ 49´´ N, 15º 57´ 37´´): This locality represents a stand planted approximately 12 years ago. The stand is composed of young trees where planted oak (Q. petraea) prevails with the admixture of C. betulus, Salix caprea L., Populus tremula L., and Betula pendula Roth.
2.2 Sampling of plant material
In July 2008 and 2009, several healthy leaves were collected from each of approximately 100 individuals registered on a spatial grid where the closest trees were an average of 5 m apart. Sampling area of individual stands was about 1 000 m2, containing approximately 120–150 trees. Samples in coppice stands were collected with consideration to avoid clonality. Individual trees were mapped using the polar method from the temporarily stabilized tops of polygonometric traverses using the universal Leica TC307 theodolite (accuracy of direction measuring 2 mgon, accuracy of length measuring 2 mm + 2 ppm). The polygonometric traverses (each in one locality) were fixed between connecting points determined using GPS receivers by the rapid static
method. Topcon Hiper+ GPS equipment was used, and GPS measurement was carried out on connecting points at the same time with a minimum observation time of 30 min. This technology allowed individual tree positions to be determined with accuracy of ±0.3 m.
2.3 DNA analysis
Young leaves were frozen to -80°C and lyophylized. DNA was extracted from the dry material using the DNeasy Plant Mini Kit (Qiagen). Microsatellite analysis was employed to genotype the individuals, and the primers for the amplification of polymorphic short sequence repeats (SSR) of the genus Quercus were adopted from the literature. The loci that were previously developed for Q. petraea were ssrQpZAG9, ssrQpZAG16, ssrQpZAG36, ssrQpZAG104, ssrQpZAG108, and ssrQpZAG110 [25]. The loci developed for Q. robur included ssrQrZAG11 and ssrQrZAG90 [26]. Finally, the loci developed for Q. macrocarpa included MSQ4 and MSQ13 [27]. The PCRs were done separately for each locus. DNA fragments were amplified by fluorescent-labeled 599
Genetic differences within natural and planted stands of Quercus petraea
primer pairs (reverse primers labeled with FAM, VIC, NED and PET dyes at the 5’-end). The mixture contained 8 ng of total DNA, 2 mM MgCl2, 100 μM of each dNTP, 0.2 μM of each primer, 0.32 U of Taq polymerase (Fermentas) and 1 x reaction buffer in a total volume of 10 μl. The PCR conditions included a preliminary denaturation for 4 min at 94°C followed by 30 cycles of denaturation for 45 s at 94°C, primer annealing for 45 s at 50°C, DNA synthesis for 30 s at 72°C, and a final extension for 10 min at 72°C. The fluorescently labeled PCR products were electrophoretically separated in the ABI 310 automatic genetic analyser (Applied Biosystems). The alleles were detected using GeneMapper software (Applied Biosystems).
2.4 Data processing
Genetic differentiation among populations was tested by Arlequin 3.11 [28]. Differentiation between all pairs of populations was evaluated using FST statistics (AMOVA procedure, 110 permutations). In order to graphically display differences between the populations, Genotype Assignment Procedure was used. In addition, the individual populations were evaluated using Wright`s fixation index FIS to evaluate deviations from the HardyWeinberg Equilibrium, which allowed us to estimate relevance of inbreeding (AMOVA procedure, 1000 permutations). The spatial genetic structure of the populations was assessed using SPAGeDi [29]. Genetic similarity of all pairs of individuals within the population was expressed using the kinship coefficient [30,31]. The matrix of these pairwise similarities was then correlated with the matrix of their spatial distances. In principle, the spatial genetic structure (SGS) of a population is indicated by a statistically significant decrease in genetic similarity that corresponds to the spatial distance between individuals. The kinship coefficient calculated according to Ritland [30] had a lower dispersion variance if compared to Natural Locality
Brdatka
the coefficient of Loiselle et al. [31]; however, it was sensitive to presence of alleles with low frequencies (less than 5%). Its value decreased with decreasing number of samples. Kinship coefficient calculated according to Loiselle et al. [31] had higher dispersion variance if compared to the coefficient of Ritland [30], but it was resistant to the incidence of low frequent alleles. Its values did not decrease significantly with decreasing number of samples. Therefore we choose to use the coefficient based on Loiselle et al. [31] for the evaluation of our data.
3. Results The basic genetic characteristics of the study populations are summarized in Table 1. In all populations, the values of the Wright´s fixation coefficients (FIS) were low. All values differed from the Hardy-Weinberg equilibrium. However, they were very close to zero, indicating a very small proportion of inbreeding.
3.1 Genetic differences
Genetic differentiation among all study populations were statistically significant; however, the index of genetic differentiation FST was low (Table 2), rarely exceeding 0.05, which indicates a low level of genetic divergence [32]. Pairwise genetic differentiation depended on geographic distances. The lowest FST value was found between the unplanted Lipina stand and the planted locality in Podmolí, with a distance of approximately 1 km (Figure 2). Very small differences were observed between the natural Brdatka stand and the planted Křivoklát stand, which were located approximately 2 km (Figure 3). Conversely, the FST value found between Brdatka and Červený Kříž was higher (their distance is approximately 7 km). The greatest genetic distances were found between populations from the Křivoklátsko
Coppice system Červený kříž
Planted Lipina
Křivoklát
Podmolí
n
104
104
96
104
96
A
18.0
17.3
19.1
19.1
19.4
Ho
0.8212
0.8001
0.8239
0.8135
0.7855
He
0.8739
0.8575
0.8742
0.8822
0.8777
FIS
0.0570
0.0429
0.0341
0.0427
0.0959
P
0.0001
0.001
0.01
0.00001
0.0001
Table 1.
Diversity statistics and Wright´s fixation coefficient of five studied populations of Quercus petraea.
A – number of alleles; Ho – observed heterozygosity; He – expected heterozygosity; FIS – Wright coefficient of fixation; P – level of significance
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Brdatka (Natural)
Červený kříž (Coppice system)
Křivoklát (Planted)
Červený kříž (Coppice system)
0.0178
Křivoklát (Planted)
0.0105
0.0155
Lipina (Coppice system)
0.0339
0.0509
0.0277
Podmolí (Planted)
0.0408
0.0462
0.0313
Table 2.
Lipina (Coppice system)
0.0065
Genetic differentiation (FST) between the studied populations of Quercus petraea. All values are significant at P=0.0001.
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-20
0
-150
-100
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0
0
0
-20
Podmolí
-60
Křivoklát
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-100 -80
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Lipina
Figure 2.
Brdatka
Podmolí
Assignment of individual Quercus petraea genotypes to the Lipina stand (unplanted stand, under the coppice management system) and the Podmolí stand (planted stand).
Protected Landscape Area (Brdatka, Červený kříž, and Křivoklát) with populations from the Podyjí National Park (including the Lipina and Podmolí localities), which are approximately 190 km apart. There was a distinct separation of multilocus genotypes from the Červený Kříž and Lipina populations (Figure 4). Similar results were obtained when comparing other pairs of geographically-distant populations.
Figure 3.
-100
Křivoklát
Assignment of individual Quercus petraea genotypes to the Brdatka stand (natural stand) and the Křivoklát stand (planted stand). In case of two outliers 6 loci were not possible to amplify. -80
-60
-40
0 0
-20
-40
-60
3.2 Spatial genetic structure
The genetic structure of the study populations is described in Figure 5. In Brdatka, a distinct spatial genetic structure was detected (Figure 5a). The genetic similarity of individuals was negatively correlated (P=0.02) with their spatial distance. The trees growing up to 8 m apart were genetically more similar than on average within the entire population. A genetic structure was also observed in Červený kříž and Lipina (both coppice system stands). There was a highly–significant correlation (P=0.0001) between
-20
Lipina
Lipina
-80
-100 Červený kříž
Červený kříž
Figure 4.
Lipina
Assignment of individual Quercus petraea genotypes to the Červený Kříž and Lipina stands (both unplanted stands were managed under the coppice management system). 601
Genetic differences within natural and planted stands of Quercus petraea
C
0,025
0,025
0,020
0,020
0,015
0,015
0,010
0,010
Genetic similarity
Genetic similarity
A
0,005 0,000 -0,005
0,005 0,000 -0,005
-0,010
-0,010
-0,015
-0,015
-0,020
-0,020
5
8
15
25
40
100
5
8
Distance class (m)
D
0,025
25
40
100
40
100
0,025
0,020
0,020
0,015
0,015
0,010
0,010
Genetic similarity
Genetic similarity
B
15
Distance class (m)
0,005 0,000 -0,005
0,005 0,000 -0,005
-0,010
-0,010
-0,015
-0,015
-0,020
-0,020
5
8
15
25
40
100
5
8
Distance class (m)
E
15
25
Distance class (m)
0,025 0,020
Genetic similarity
0,015 0,010 0,005 0,000 -0,005 -0,010 -0,015 -0,020 5
8
15
25
40
100
Distance class (m)
Figure 5.
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Relationship between genetic similarity and spatial distance between individuals within study populations of Quercus petraea. Dashed lines indicate 95% confidence limits under the null hypothesis (no spatial genetic structure) obtained after 1000 permutations of the multilocus genotypes. a) Brdatka (natural stand); b) Červený kříž (unplanted stand under coppice management); c) Lipina (unplanted stand under coppice management) – value for distance class ≤ 5 m is 0.09; the out-of-range value on the 5 m distance class is 0.09; d) Křivoklát (planted stand); e) Podmolí (planted stand).
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the decrease in genetic similarity between individuals and increasing spatial distance between them. Only trees growing within 15 m of each other (Červený kříž) and within almost 8 m of each other (Lipina) showed a significant genetic relationship. On the contrary, in the planted Křivoklát stand and in the Podmolí locality, no relationship between genetic affinity and spatial distance between individual trees was seen. The kinship coefficient of the closest trees was not significantly higher than the average similarity within the entire population.
4. Discussion 4.1 Genetic differentiation
We observed a low genetic differentiation among the populations of Q. petraea, which was similarly observed in other studies [18-20]. Our results showed that genetic differences among studied stands were statistically significant; however, the index of genetic differentiation FST was low in all cases. We observed differences in the genetic affinity of populations depending on their geographical distance – an increase in distance between populations coincident with a decrease in genetic distance. These spatial differences were more substantial than the differences that were revealed from a comparison of the stands under diverse management regimes. This confirms the data of Bodénès et al. [15] and Bruschi et al. [14]. Based on our results, we can assume that planting material obtained from a nearby area was used to produce the new forest. In general, all of the populations that we studied were slightly inbred. Most of them showed low FIS values (0.0341–0.0570, P=0.01–0.0001). Only the stand planted twelve years ago in the Podmolí locality had higher FIS values (0.0959, P=0.0001), which suggests a more significant degree of inbreeding. Similar results, including a case in which there was a more significant excess of homozygotes in one locality, were also found by Cottrell et al. [13]. A very low deviation from the Hardy-Weinberg equilibrium towards inbreeding was also seen by Finkeldey [11] and Bakker et al. [12].
4.2 Spatial genetic structure
We observed a spatial genetic structure within both the natural Brdatka stand and the stands that were extensively managed for a long period of time using the coppice system (Červený kříž and Lipina). Genetic similarity between the trees growing up to 8 m apart (in the Brdatka and Lipina localities) or up to 15 m apart (in the Červený kříž locality) was statistically significantly higher than the average similarity within the respective
population. For Q. petraea, Streiff et al. [10] showed that there was a markedly decrease in genetic structure when individual trees were more than 40 m apart. Cottrell et al. [13] showed that there was a genetic relationship between individuals of Q. petraea growing up to 80 m apart and for Q. robur growing up to 160 m apart. In contrast, Redkina et al. [33] used allozyme analysis to detect significant genetic similarity between Q. robur trees that were growing up to 12 m apart. Finally, Sork et al. [34] found significant near-neighbor autocorelation in Q. rubra trees growing 5 m apart, and Montalvo et al. [35] observed similar results in Q. chrysolepis Liebm. trees growing up to 4 m apart. Regarding the characteristics of the stands, we found spatial genetic structures at the shortest distances in habitats where the crowns were not very patulous. Significant genetic similarity was proven when trees were separated by a distance of approximately 4-times the length of the average radius of the crowns. We found this phenomenon in all of the stands with established spatial genetic structure (i.e., in Brdatka, Lipina, the average radius of the crowns was approximately 2 m and in Červený kříž, the average radius of the crown was about 3 m). We therefore postulate that the crown proportion plays an important role in closed stands where pollen and seed dispersal is limited. In more open stands where individual trees are not so close together, we expect the dispersal of both pollen and seeds to have a stronger effect.
4.3 Sampling design
To determine the minimum number of individuals needed to specify the genetic structure of a population, we compared the kinship coefficients for 100, 50, and 30 individuals. The analysis of 50 individuals was still sufficient to determine the spatial genetic structure in natural and semi-natural populations, but the statistical significance of the correlation was negatively affected. If 100 individuals were included in the calculation, the spatial genetic structure was revealed with a higher plausibility and infallibility. When analyzing only 30 individuals, the genetic structure was not proven. Moreover, we did not find significant spatial genetic structure within the trees growing at distances greater than 15 m. Thus, in case of less amount of individuals sampled, we recommend to choose spatially-close individuals, i.e., to collect samples from individuals growing just in a segment of the population. On the contrary, to delineate genetic differences between populations, it is advisable to collect samples evenly from the entire population. The analysis of 30 individuals from each population was sufficient to prove the genetic differences between populations. 603
Genetic differences within natural and planted stands of Quercus petraea
4.4 Informativeness of markers
In this study, ZAG110 and ZAG104 were the most suitable loci for determining the genetic structure of natural and semi-natural populations. These loci gave significant negative correlation between genetic similarity and distance of individuals. To determine the genetic differentiation between populations, MSQ13 and ZAG11 were the most appropriate loci. The total number of loci used for analysis can be less than 10 if the most appropriate markers are selected (i.e., approximately 5). However, the proper loci used to determine the genetic structure of the population and the genetic differences between populations did not overlap.
5. Conclusions We can make the following conclusions based on all of our analyses: (i) all studied populations were genetically differentiated, but there were no major effects that can be attributed to different management regimes; (ii) genetic
differentiation between populations corresponded to the geographic distances between localities; (iii) in natural, unplanted stands, the spatial genetic structure was documented; and (iv) in planted stands, there was no spatial genetic structure. Therefore, the analysis of spatial genetic structure of the sessile oak forest stand can help reveal and determine its origin.
Acknowledgements We wish to thank Pavel Moucha from the Křivoklátsko Landscape Protected Area and Petr Vančura from Podyjí National Park for their logistic support of our field work. Zdeněk Lukeš from Czech Technical University in Prague mapped the positions of individual trees. We are also grateful to Roman Businský, Jana Doudová and Markéta Pospíšková for field assistance. This project was supported by grant no. MZP0002707301 and by the Ministry of the Environment of the Czech Republic.
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