Subtle human impacts on neutral genetic diversity and spatial patterns of genetic variation in European beech (Fagus sylvatica)

Forest Ecology and Management 319 (2014) 138–149

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Subtle human impacts on neutral genetic diversity and spatial patterns of genetic variation in European beech (Fagus sylvatica) K.C. Rajendra a,1, Sarah Seifert a,1, , Kathleen Prinz a,c, Oliver Gailing a,b, Reiner Finkeldey a,⇑ a

Forest Genetics and Forest Tree Breeding, Georg-August-University Göttingen, Germany School of Forest Resources and Environmental Science, Michigan Technological University, USA c Institute of Systematic Botany with Herbarium Haussknecht and Botanical Garden, Friedrich-Schiller-University Jena, Germany b

a r t i c l e

i n f o

Article history: Received 17 October 2013 Received in revised form 3 February 2014 Accepted 3 February 2014 Available online 6 March 2014 Keywords: Microsatellite markers Spatial genetic structure Adaptation Global climate change Forest management

a b s t r a c t We aim to understand the role of past and ongoing anthropogenic impacts on genetic variation patterns at different spatial scales for the dominant tree species European beech (Fagus sylvatica L.) in Germany, a densely populated country with a long history of multiple human impacts on forests. Different types of human impact have likely influenced genetic variation patterns in beech: e.g. forest degradation and loss of forest cover over long time periods, intensive management and climate change. Former studies found generally high genetic diversity in European beech and indicated, based on limited sample sizes and few markers, no negative effects of management on genetic diversity. We investigated 30 beech stands with different management history located in three widely separated regions in Germany at six genomic and three gene-based microsatellite markers. High genetic diversity was found, but diversity levels were significantly different among regions. Genetic differentiation among stands and regions was generally low, but significant for most comparisons. The region in southern Germany was strongly differentiated from the other regions presumably due to different postglacial recolonization histories. Recent management activities had no significant impact on genetic diversity parameters but reduced small-scale spatial genetic structures (SGS) within stands. Long generation times, large effective population sizes, efficient gene flow and predominance of natural regeneration contributed to the maintenance of high genetic diversity throughout the Central European distribution of beech. Genetic diversity patterns of beech are remarkably unaffected by human impact although forested landscapes were strongly shaped by man for centuries. Ó 2014 Elsevier B.V. All rights reserved.

1. Introduction The structure and functioning of ecosystems and global biodiversity patterns are increasingly influenced by humans (Vitousek et al., 1997), and evolutionary changes of genetic structures in response to human interference in ecological processes are both strong and ubiquitous (Palumbi, 2001). Climate change due to human activities (Hoffmann and Sgro, 2011), industrial pollution and, most importantly, changing land use patterns can result in the extinction of populations or even species and shape genetic structures (Foley et al., 2005). Central Europe is one of the most densely populated regions on earth with a particularly long and intense history of human interventions on ecosystems. Untouched forest ecosystems do no longer ⇑ Corresponding author. Tel.: +49 5513912326; fax: +49 551391812326. 1  

E-mail address: rfi[email protected] (R. Finkeldey). These authors contributed equally to this work. We dedicate this publication to our late colleague and co-author, Dr. Sarah Seifert.

http://dx.doi.org/10.1016/j.foreco.2014.02.003 0378-1127/Ó 2014 Elsevier B.V. All rights reserved.

exist in this region, and the degree of human-caused forest fragmentation is higher in Europe than anywhere else (Wade et al., 2003). Intensive forest management resulted in a shift from mostly broadleaved trees dominating the natural cover to conifers, a trend which was inverted only during the past decades (Spiecker, 2003). In addition, thinning and harvesting operations had a great impact on forest regeneration. More recently, environmental conditions in European forests were modified by anthropogenic immissions and by global warming. The intensity and duration of human impacts on Central European forests suggests complex genetic responses of the tree species. Genetic drift due to fragmentation and conversion of land use, selection due to forest management practices, immissions and climate change, alteration of mating patterns and gene flow because of modified spatial distributions of trees, and even the complete disruption of the reproductive continuity of stands due to artificial regeneration affected the genetic structure of forest tree populations in Central Europe. Glaciations and postglacial remigration were the main natural processes leaving strong

K.C. Rajendra et al. / Forest Ecology and Management 319 (2014) 138–149

footprints on patterns of genetic variation in Central European ecosystems, resulting in genetic differentiation among widely separated regions due to different population histories (Petit et al., 2003). Human impact is expected to blur these patterns by decreasing the differentiation among regions, but increasing differentiation among populations within regions due to local fragmentation and opposing management regimes. European beech (Fagus sylvatica L.) is the most common deciduous tree species and covers a wide geographic range in Central Europe (Schmitz et al., 2004). Beech is wind-pollinated and monoecious with heavy fruits and limited seed dispersal. As a predominantly outcrossing tree species with efficient means of gene dispersal through pollen, it usually shows high genetic diversity within populations, but a rather low differentiation among populations (Austerlitz et al., 2000; Vornam et al., 2004; Buiteveld et al., 2007; Oddou-Muratorio et al., 2011). Palynological evidence and the low level of chloroplast polymorphism suggest that Central Europe was mostly recolonized from a single refugial region located in the Southeast of the continent (Magri et al., 2006). Beech is regarded as ‘‘the most successful European plant species’’ in the distribution area where it is dominant (Leuschner et al., 2006). The dominance of the species is mainly due to shade tolerance (Jarcuska, 2009). Although beech is highly competitive, it does not colonize all habitats in its distribution area; excluded are, for example, very dry soils. Concerning the long-term stability of beech ecosystems in Europe, there are today two main challenges: intensive management activities, among others due to the increasing need for non-fossil energy sources, and global climate change. Global climate change models predict less precipitation during the summer; the adaptation potential of beech to these conditions is under dispute (Ammer et al., 2005; Rennenberg et al., 2004; Gessler et al., 2007). High genetic diversity is important for evolutionary changes and adaptation, especially to cope with fast changes of environmental conditions (Amos and Harwood, 1998; Boshier and Amaral, 2004). It is even more important because global climate change will reduce intraspecific genetic diversity in many cases (Pauls et al., 2013). Additionally, forest management might affect genetic diversity of European beech forests. Phenotypic selection and impacts of forest management on the reproduction system by changing gene flow and the mating system may result in changes of genetic structures in the progeny generation (e.g., Finkeldey, 2002). Hosius et al. (2006) and Schaberg et al. (2008) reviewed the effects of various forest management types on genetic diversity and concluded that they are heterogeneous ranging from negative effects, no effects at all to even positive effects. Central European beech forests have been managed for centuries, but transfer of reproductive material resulting in a complete disruption of evolutionary processes has been rare due to the dominance of natural regeneration. The presumed absence of largescale seed transfer allows us to observe the effects of management practices, such as silvicultural techniques to promote forest regeneration, on intraspecific genetic variation patterns. So far, only few studies, mainly based on isozyme analyses, estimated the effects of silvicultural techniques on the genetic diversity in beech stands. These studies focused on the effect of thinning, soil amelioration and target diameter felling (Lauber et al., 1997; Janssen and Nowack, 2001; Finkeldey and Ziehe, 2004). In a more recent study, Buiteveld et al. (2007) investigated ten beech stands with a wider distribution in Europe in comparison to this survey. Using four microsatellite markers found no significant differences in genetic variation between stands exposed to different management intensities. In another investigation no effects of management activities on genetic diversity were found when one unmanaged old-growth beech stand and a post-harvest naturally regenerated stand were compared at four microsatellite markers and additionally at RAPD markers. However, a higher number of rare alleles were lost and a

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lower spatial structuring of genetic diversity was observed in the managed beech stand (Paffetti et al., 2012). Jump and Peñuelas (2006) investigated the effect of strong fragmentation on genetic diversity of beech in Spain. Gene diversity did not differ between stands in a continuous forest and strongly fragmented populations, but allelic richness decreased and inbreeding strongly increased in the remnant populations. These results suggested that population structure and breeding system are disrupted by forest fragmentation and that a negative impact on the persistence of these fragmented populations can be predicted. All former studies analyzing the genetic diversity of beech and/ or the impact of management on genetic diversity were limited to only a few regions and a small number of beech stands within these regions. Furthermore, only a restricted number of genetic markers were applied. Therefore, conclusions for beech stands in Central Europe drawn from these studies regarding the effect of management on the level and distribution of genetic diversity are limited. Our comprehensive study was designed to assess the effects of management on genetic diversity in multiple stands from different German regions and environments to give recommendations concerning the future management of European beech forests under climate change aspects. Three widely separated regions within the center of the distribution area were chosen for the investigation. Within each region, ten different beech stands were selected, comprising both managed beech stands and stands in forests that have been protected for at least 20 years (with one exception). These stands are defined as forests with low management intensity (called ‘‘unmanaged stands’’ in this study), because untouched beech forests do no longer exist in Central Europe. In total, around 3600 beech trees were part of this large investigation. As molecular markers, nine highly polymorphic microsatellite markers (or SSR markers) were carefully selected from a larger set of microsatellite markers (Pastorelli et al., 2003; Asuka et al., 2004; Vornam et al., 2004; Durand et al., 2010). All sampling sites have been chosen within the research project ‘Biodiversity Exploratories’ (www.biodiversity-exploratories.de) that was established to conduct and integrate biodiversity and ecosystem research at various levels and aims to explore drivers and functional consequences of biodiversity changes (Fischer et al., 2010). Specifically we address the following main questions. Are there differences in genetic diversity and spatial structure in European beech populations among geographically distant regions? Do management activities change the genetic diversity and/or the spatial genetic structure of European beech stands?

2. Materials and methods 2.1. Study areas and sampling Three widely separated study regions (exploratories), Schwäbische Alb (S. Alb, AEW), Hainich-Dün (Hainich, HEW) and Schorfheide-Chorin (S. Chorin, SEW) were investigated in Germany (Fig. 1a). Within each of these regions, ten plots were selected including managed and unmanaged beech stands (Fig. 1 and Table 1). Managed and unmanaged plots are not equally represented due to a low number of unmanaged stands in the respective regions. The proportion of unmanaged forests is reported as 7–8% for S. Alb and S. Chorin and 18% for the Hainich (Hessenmöller et al., 2011). Exhaustive sampling was done in most of the plots to collect fresh leaf samples. We collected 200 samples per plot from six plots in 2008 and 100–120 samples per plot from 24 plots in 2009. The sampling area depended on the density of the trees and ranged from 0.2 ha to 2 ha. Samples were collected from adult

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Fig. 1. Location of the studied regions (a) and the plots at Schorfheide-Chorin (b), Hainich-Dün (c) and Schwäbische Alb (d).

beech trees mostly above 10 cm diameter at breast height (dbh). In SEW-6, there were only 60 beech trees above 10 cm dbh in and around the plot. We therefore collected leaf samples from all available adult trees and their nearest saplings. Leaves were harvested and stored at 20 °C for DNA isolation. The location of all trees was recorded using a TruePulseÒ Laser Rangefinder (Laser Technology, Inc., Centennial, USA). Most of the studied plots are pure beech stands. Only three plots in S. Chorin comprised mixed forests with beech and pine (Pinus sylvestris) or oak species. The management intensities varied from implementation of logging and other silvicultural operations to strictly no management activities. Most of the managed forests are age-class forests with trees of similar age harvested in 80–120 years intervals. Few forests are managed as uneven-aged selection forests where single trees or small groups of trees are removed creating a forest with overlapping generations (Fischer et al., 2010). Target diameter felling has been practiced in the Hainich by selective removal of all trees above the target diameter of 60 cm. Unmanaged forests are typically characterized by wide diameter ranges of trees and multi-layered canopies. Unfortunately, nothing is known about planting in the past. Although natural regeneration has been the rule for beech forests, it is possible that some of the plots are the result of planting activities sometime in the past, including different forms of enrichment planting. Depending on the planting material, this procedure can have a strong impact on the genetic diversity and structure of the stand. In the S. Alb, plot AEW-8 has been unmanaged for around 80 years. AEW-7 is located in a nature conservation area established in 1938, but very limited management activities were conducted, mostly restricted to the removal of spruce trees to preserve the old beech forests. The date of the last management activity in AEW-9 is unknown, but the plot is very close to an area that has been protected for a long time, suggesting that AEW-9 has

been under very limited management. In the Schorfheide, all plots are located in a nature conservation area established in 1990. Therefore, the last management activities on these plots have been between 1975 and 1990. The Hainich national park was established in 1997. Two of the three unmanaged plots (HEW-10 and HEW-12) are located in a former restricted military area established in 1965. It is supposed that the management activities during the military utilization have been very limited. The military area was closed in 1995, thus management activities before the establishment of the national park in 1997 are unlikely. HEW-11 is also located in the national park today, but was managed before 1997. 2.2. DNA isolation and microsatellite analysis Total DNA was extracted from leaves using the DNeasy™ 96 PlantKit (Qiagen, Hilden, Germany). The amount and the quality of the DNA samples were analyzed using 0.8% agarose gel electrophoresis with 1 TAE as running buffer. DNA was stained with ethidium bromide, visualized by UV illumination and compared to a Lambda DNA size marker (Roche, Germany). Nine highly polymorphic microsatellite markers were used to screen all sampled trees. Four of them were originally developed for Fagus crenata (sfc markers, Asuka et al., 2004) and two of them were directly developed for F. sylvatica (FS 3-04, Pastorelli et al., 2003; mfs 11, Vornam et al., 2004). Additionally, three gene-based EST (Expressed Sequence Tag) microsatellite markers originally developed in Quercus robur (GOT066, FIR065, FIR004, located on two different linkage groups; Durand et al., 2010) were applied. Multiplexing of two to four primers, labelled with different fluorescent dyes, was performed to save time and costs for PCR amplification and subsequent capillary electrophoresis (set 1: all sfc loci, set 2: FS 3-04 and mfs 11, set 3: GOT006, FIR065, FIR004).

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K.C. Rajendra et al. / Forest Ecology and Management 319 (2014) 138–149 Table 1 General information about the investigated stands. Plots

Type

Latitude (N)

Longitude (E)

Altitude (m)

dbh (cm)

Age (years)

S. Alb AEW-4 AEW-5 AEW-6 AEW-19 AEW-23 AEW-39 AEW-40 AEW-7 AEW-8 AEW-9

M M M M M M M U U U

48°230 56.800 48°250 10.600 48°230 38.600 48°290 03.300 48°230 0900 48°220 32.800 48°290 58.600 48°230 46.500 48°220 57.300 48°220 9.600

9°140 41.400 9°240 52.900 9°260 45.400 9°180 40.300 9°290 16.700 9°140 11.600 9°200 5800 9°150 40.900 9°220 56.600 9°240 54.800

765 788 740 755 778 791 779 772 766 742

13.2 49.1 26.8 45.0 49.6 18.4 22.3 40.0 40.4 29.8

31–40 131–140 71–80 141–150 151–160 61–70 101–110 121–130 n.a n.a

767.6

33.5

416 435 379 503 402 451 466 378 414 333

16.7 37.0 26.9 24.5 29.1 15.5 28.8 27.4 26.0 27.5

417.7

25.9

56 64 55 58 67 67 79 98 79 80

20.1 41.1 52.1c 27.4 24.6 40.1 52.8 53.9 40.8 65.3

70.3

41.8

Mean Hainich HEW-5 HEW-6 HEW-7a HEW-8a HEW-9a HEW-18 HEW-21 HEW-10 HEW-11 HEW-12

M M M M M M M U U U

51°160 3.800 51°150 5000 51°70 51.900 51°210 20.900 51°70 48.900 51°200 13.100 51°110 39.400 51°50 2400 51°60 10.200 51°60 2.500

10°140 21.800 10°140 27.400 10°230 7.600 10°310 1.100 10°220 52.100 10°210 55.600 10°190 8.400 10°270 44.800 10°240 3.100 10°270 18.700

Mean S. Chorin SEW-4b SEW-5 SEW-6 SEW-30b SEW-31b SEW-38 SEW-7 SEW-8 SEW-9 SEW-46

M M M M M M U U U U

52°550 2.400 53°30 25.300 52°540 26.800 53°30 43.700 52°520 51.900 52°530 18.500 53°60 26.500 53°110 30.500 53°20 40.500 53°40 19.600

13°500 50.300 13°5300 7.300 13°500 30.100 13°550 20.500 13°540 12.100 13°400 14.700 13°410 39.900 13°550 49.200 13°480 36.400 13°460 38.800

Mean

n.a n.a n.a n.a n.a n.a n.a n.a n.a n.a

56 180 148 75 50 131 119d 143 140 158

M: managed, U: unmanaged, n.a: not available. a Selection cutting forest. b Pine-beech mixed stand. c Mean dbh of adult trees (N = 60). d Official age most probably not correct, stand is older.

PCR amplifications were conducted in a 15 ll volume containing 2 ll of genomic DNA (about 10 ng), 1 reaction buffer (0.8 M Tris–HCl pH 9.0, 0.2 M (NH4)2SO4, 0.2% w/v Tween-20; Solis BioDyne, Estonia), 2.5 mM MgCl2, 0.2 mM of each dNTP, 1 unit of Taq DNA polymerase (HOT FIREPolÒ DNA Polymerase, Solis BioDyne, Estonia), 0.3 lM of each forward and reverse primer. The PCR protocol consisted of an initial denaturation step of 95 °C for 15 min followed by 30 cycles of 94 °C for 1 min (denaturation), 47 °C (for the EST primer set 3) or 55 °C (for primer set 1 and 2) for 30 sec (annealing), 72 °C for 1 min (elongation) and a final extension step of 72 °C for 20 min. Microsatellite fragments were separated on an ABI PRISMÒ 3100 Genetic Analyzer (Applied Biosystems, Foster City, CA). Data were collected and aligned with the help of the internal size standard GS 500 ROX™ using GeneScan 3.7Ò (Applied Biosystems, Foster City, CA), and fragments were scored with the software Genotyper 3.7Ò (Applied Biosystems, Forster City, CA). 2.3. Data analysis Most of the genetic diversity parameters were calculated with GenAlEx 6.4 (Peakall and Smouse, 2006; total number of alleles (AT) and rare alleles (Arare; frequency <5%), average number of alleles per locus (NA) and effective number of alleles

[NE = 1/(1HS)]). Allelic richness (AR) was estimated with FSTAT 2.9.3 (Goudet, 1995) after correcting for the lowest sample size (96 diploid individuals) at all loci using the rarefaction index suggested by Petit et al. (1998). Maximum likelihood estimation of null allele frequency was executed following the expectation– maximization algorithm using GENEPOP 4.0 (Rousset, 2008). FSTAT 2.9.3 (Goudet, 1995) was used for the computation of Nei’s (1987) gene diversity estimates of heterozygosities, i.e., observed heterozygosity (HO) and gene diversity within populations (HS) for each locus and overall. Weir and Cockerham (1984) estimators of FIT, FST and FIS were calculated for each locus and overall. Jackknifing over samples and loci was performed for the F-statistics. Alleles were randomised 5000 times among individuals within populations and among the whole populations to test the locuswise global significance of FIS and FIT, respectively. The log-likelihood ratio (G) based on exact tests (Goudet et al., 1996) was performed by permuting genotypes 5000 times to test the significance of FST at each locus. Additionally, a matrix of pairwise genetic differentiation measures [FST/(1FST)] between all populations pairs was computed. The significance of the differentiation was tested with Arlequin 3.5 (Excoffier et al., 2005) by permuting the individuals 10,000 times between the pairs of populations. Measures of genetic diversity (AT, AR, HO, HS) and differentiation (FST and FIS) were calculated for all 30 beech stands. Their means

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for each region and over all regions (exploratories) were estimated and the differences between their means were tested with 5000 permutations with FSTAT 2.9.3 (Goudet, 1995). Likewise, differences of diversity estimates between managed and unmanaged populations were measured and tested by permutation tests (5000 permutations), separately for each exploratory and for all populations across exploratories. The genetic structure was estimated and analyzed using Arlequin 3.5 (Excoffier et al., 2005). Hierarchical analyses of molecular variance (AMOVA) were applied to partition the total variance within populations, among populations within exploratories and among exploratories (Weir and Cockerham, 1984; Excoffier et al., 1992). The significance of the differentiation was tested with 2024 permutations. Molecular genetic clustering among populations was done based on Nei’s (1972) genetic distances computed with GenAlEx 6.4 (Peakall and Smouse, 2006). Bayesian clustering approaches implemented in the software STRUCTURE 2.3.1 (Pritchard et al., 2000) were used to infer population structure applying the admixture model. The number of populations (K) was estimated with 10 replicates for K = 1 to K = 15 using 100,000 Markov Chain iterations and 10,000 iterations of burn-in periods. The best estimated K (DK) was calculated according to Evanno et al. (2005) using the web-based STRUCTURE HARVESTER program (Earl and von Holdt, 2012). The Unweighted Pair Group Method with Arithmatic Means (UPGMA) and a neighbor joining dendrogram based on Nei´s distance (Nei, 1972) were calculated with the software Populations, version 1.2.32 with 1,000 bootstrap replications (Langella, 1999). Dendrograms were visualized with the software TreeView version 1.6.6 (Page, 1996). SPAGeDi 1.3 (Hardy and Vekemans, 2002) was used to compute multilocus kinship coefficients to estimate the spatial genetic structure within stands. Kinship coefficients (Fij) are based on the probability of identity by descent of alleles for two homologous genes and were estimated as described by Loiselle et al. (1995). The extent of SGS was quantified using Sp statistics (Vekemans and Hardy, 2004) which is mainly based on the rate of decrease of pairwise kinship coefficients (Fij) between individuals with the logarithm of the distance. It is computed as Sp = bF/(F11) where bF is the regression slope of Fij over the natural logarithm of distance ln(dij) and F1 is the mean kinship of pairs of individuals belonging to the first distance class (Vekemans and Hardy, 2004). We applied equal intervals of 10 m for every distance class starting from zero and restricted the maximum distances from 40 to 120 m across the study populations according to the spatial distribution of the trees in the particular stand to estimate the plot-wise Sp statistic. For the fine-tuning of the information, the maximum distance was limited up to the distance interval which ensures the representation of at least 50% of the individual trees in pairwise comparisons and maintains the Coefficient of Variation (CV) 6 1 for the number of times each tree is represented (SPAGeDI manual). The average estimates of Fij over a set of distance classes (Fd) were plotted against the natural logarithm of distances to visualize the SGS. A permutation test (10,000 permutations of individuals among locations) was made to test the significance of the regression slope (bF). Spatial autocorrelograms showing the frequency distributions of bF at each distance class and 95% confidence intervals obtained after 10,000 permutations were compared to test the statistical significance of SGS under the null hypothesis of random distribution of genotypes within populations. All 29 beech stands were divided into the two groups ‘‘managed’’ and ‘‘unmanaged’’ to investigate the effects of management on SGS (Table 1). Further, we grouped the managed and unmanaged forest stands for each geographic region separately and their mean values were compared. All comparisons were performed by computing

student’s t-tests implemented in STATISTICA 6.0 (StatSoft, Oklahoma, USA). 3. Results 3.1. Microsatellite loci All investigated markers were highly polymorphic; the total number of alleles was between six and 25 (Table 2). Observed heterozygosity (HO) and genetic diversity (mean gene diversity within populations, HS) were generally high, but there were large differences among the nine loci with in general lower values for the EST-SSRs (Table 2). One of the EST-SSRs showed extremely low values for HO and HS (GOT066). The inbreeding coefficient was close to zero for most loci. However, it was significantly different from zero for two of the loci (Table 2). The total frequency of null alleles was estimated below 3% for all investigated loci except for FIR004 (8.4%). 3.2. Genetic diversity in the three exploratories Total number of alleles (AT), allelic richness (AR) and gene diversity (HS) were high for all investigated populations. The differences between the three exploratories were small but statistically significant. AR was significantly different (p < 0.05) among regions independent of the marker whereas HS was only significantly different at EST-SSRs (data not shown). The highest values for the different measurements of genetic diversity were found in S. Chorin, the lowest in the S. Alb (Table 3). Inbreeding coefficients (FIS) varied from 0.034 (SEW-6) to 0.054 (SEW-38) and were not statistically significant from zero (p > 0.05; Table 3). In total, 18 private alleles were present, differently distributed over the exploratories and the different plots. In eleven populations one private allele was found, in two populations two private alleles were found and in one population (SEW-6) three private alleles were present. The allele frequencies of all private alleles were low (0.004–0.017, Table 3). 3.3. Influence of management activities on genetic diversity indices Overall, there was no significant impact of management on total number of alleles (AT), rare alleles (Arare), allelic richness (AR), gene diversity (HS), inbreeding coefficient (FIS) and genetic Table 2 Population genetic parameters for the microsatellite loci (AT: total number of alleles, Arare: number of rare alleles (frequency <5%), NE: effective number of alleles, HO: observed heterozygosity, HS: mean gene diversity within populations, FIS: inbreeding coefficient).

*

Locus

AT

Arare

NE

HO

HS

FIS

sfc0018 sfc0161 sfc1063 sfc1143 FS3-04 mfs11

14 25 13 22 6 20

8 20 7 15 3 16

4.11 5.54 4.67 4.93 1.59 3.38

0.749 0.818 0.783 0.783 0.375 0.730

0.755 0.821 0.785 0.794 0.363 0.701

0.002 0.002 0.005 0.023** 0.033 0.051

Mean SSRs

16.7

11.5

4.04

0.706

0.703

0.003

FIR004 FIR065 GOT066

12 6 8

9 2 6

2.39 3.15 1.15

0.501 0.694 0.133

0.572 0.683 0.128

0.120*** 0.016 0.113

Mean EST-SSRs

8.7

5.7

2.23

0.442

0.461

0.037

Total mean

14

9.5

3.43

0.618

0.622

0.007*

p < 0.05. p < 0.01. *** p < 0.001. **

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Table 3 Population genetic parameters for the studied stands (M: managed stand, U: unmanaged stand, N: number of investigated trees, AT: total number of alleles, AR: allelic richness, HS: mean gene diversity within populations, FIS: inbreeding coefficient).

S. Alb AEW-4 AEW-5 AEW-6 AEW-19 AEW-23 AEW-39 AEW-40 AEW-7 AEW-8 AEW-9

Hainich HEW-5 HEW-6 HEW-7 HEW-8 HEW-9 HEW-18 HEW-21 HEW-10 HEW-11 HEW-12

S. Chorin SEW-4 SEW-5 SEW-6 SEW-30 SEW-31 SEW-38 SEW-7 SEW-8 SEW-9 SEW-46

Type

N

AT

Private alleles (No/allele frequency)

AR

HS

FIS

M M M M M M M U U U

100 198 100 100 100 100 100 100 199 100

65 86 71 69 72 67 69 71 80 66

1/0.01 2/0.01, 0.008 1/0.005 1/0.005 1/0.01 0 1/0.005 0 0 0

7.2 8.5 7.8 7.6 7.9 7.4 7.6 7.9 8.0 7.3

0.600 0.612 0.602 0.596 0.600 0.621 0.610 0.625 0.623 0.650

0.028 0.015 0.050 0.009 0.006 0.004 0.006 0.025 0.006 0.009

Mean

119.7

71.6

7.7

0.614

0.002

M M M M M M M U U U

100 200 100 100 100 100 100 200 99 100

74 84 73 77 76 73 70 74 68 77

8.2 8.3 8.0 8.5 8.4 8.1 7.7 7.6 7.5 8.5

0.606 0.615 0.633 0.627 0.614 0.622 0.631 0.628 0.613 0.630

0.020 0.012 0.010 0.018 0.001 0.011 0.015 0.033 0.018 0.026

Mean

119.9

74.6

8.1

0.622

0.007

M M M M M M U U U U

100 200 119 100 100 100 101 100 198 100

76 84 72 77 75 81 79 76 75 73

8.4 8.5 7.8 8.5 8.3 8.9 8.7 8.4 7.5 8.1

0.636 0.647 0.632 0.656 0.614 0.619 0.632 0.639 0.617 0.624

0.025 0.018 0.034 0.016 0.021 0.054 0.014 0.036 0.005 0.034

Mean

121.8

76.8

8.3

0.632

0.015

1/0.005 1/0.01 0 0 1/0.005 1/0.01 0 0 0 0

1/0.005 0 3/0.004, 0.004, 0.017 0 0 0 0 0 2/0.003, 0.003 1/0.01

Table 4 Influence of management activities on population genetic parameter (AT: total number of alleles, Arare: number of rare alleles (frequency <5%), AR: allelic richness, HO: observed heterozygosity, HS: mean gene diversity within population, FIS: inbreeding coefficient, FST: genetic differentiation). P-values < 0.05 are shown in bold face. No. of Populations

AT

Arare

AR

HO

HS

All regions Managed Unmanaged P-value

20 10

74.6 74.0 0.757

37.4 35.4 0.383

8.091 7.948 0.434

0.616 0.621 0.532

0.619 0.626 0.260

0.005 0.008 0.725

0.015 0.022 0.136

S. Alb Managed Unmanaged P-value

7 3

71.3 72.3 0.832

34.4 34.7 0.964

7.754 7.733 0.961

0.605 0.638 0.026

0.606 0.630 0.008

0.001 0.012 0.393

0.006 0.027 <0.001

Hainich Managed Unmanaged P-value

7 3

75.3 73.0 0.481

37.6 33.3 0.254

8.182 7.871 0.257

0.619 0.607 0.257

0.620 0.625 0.453

0.001 0.028 0.042

0.006 0.009 0.112

S. Chorin Managed Unmanaged P-value

6 4

77.5 75.8 0.723

40.7 37.5 0.271

8.433 8.205 0.414

0.625 0.620 0.689

0.635 0.626 0.363

0.015 0.009 0.694

0.013 0.018 0.463

differentiation (FST) (p > 0.05; Table 4). Looking at single regions, significantly lower values for observed heterozygosity (HO) and genetic diversity (HS) were found in the S. Alb for the managed beech stands. Inbreeding coefficients were significantly higher in managed stands than in unmanaged stands only in Hainich (Table 4). Genetic differentiation was significantly higher among unmanaged stands than among managed stands in the S. Alb (Table 4). There

FIS

FST

were no differences concerning EST-SSRs or genomic SSRs (data not shown). 3.4. Influence of management activities on spatial genetic structure Spatial genetic structure of the investigated stands was highly variable, ranging from very weak (Sp: 0.00004 in SEW-38) to

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medium (Sp: 0.03208 in AEW-7). The strongest spatial genetic structures (mean over all stands) were found in the S. Alb ðSp ¼ 0:01382Þ; followed by S. Chorin ðSp ¼ 0:0087Þ and Hainich ðSp ¼ 0:00626Þ. More detailed information about the spatial genetic structure for individual stands can be found in the Supplementary material (Table S1). Overall, management activities reduced the spatial genetic structure significantly. At the regional level, the different parameters estimating spatial genetic structure were lower in managed beech stands in all three exploratories, but a statistically significant effect existed only in the S. Alb (Table 5). Significant spatial genetic structure occurred up to a maximum distance of 30 m. Most of the managed stands had family structures up to 10 m only, whereas in most of the unmanaged stands, family structures up to 20 m or even 30 m were found (Supplementary material, Table S1). In two beech stands (HEW-5 and HEW-9) there was no significant structure in the first distance class, but in the second class (10–20 m). No significant spatial genetic structure was found in the three beech stands HEW-12, SEW-30 and SEW-38. An example illustrating the spatial autocorrelograms showing one managed and one unmanaged beech stand is presented in Fig. 2 (other autocorrelograms: Supplementary material, Fig. S1a–c). Four of the five longest extensions of spatial genetic structure (up to 30 m) were detected in unmanaged beech stands (Supplementary material, Table S1). 3.5. Genetic differentiation and cluster analysis The AMOVA revealed that 98% of the total genetic variation was within populations with only 1.17% among beech stands and 0.83% among exploratories. Almost all population pairs were significantly different using pairwise FST analysis, and most of them showed highly significant differentiation (Fig. 3). This is mainly caused by the genomic SSRs. With EST-SSRs only, around 50% of the population pairs were significantly different (data not shown). The analysis of Hedrick’s G‘ST and Jost’s D led to similar results (data not shown). In most cases, the differentiation between two populations of the same region was lower than between populations of different regions (Fig. 3). One clear exception is population SEW38 from S. Chorin that had very low differentiation to most of the stands in the Hainich region, but higher differentiation to the stands in S. Chorin. Within regions, the genetic differentiation was lowest in Hainich, followed by the S. Alb and highest in the region S. Chorin. Between regions, the differentiation was highest for Table 5 Influence of management on spatial genetic structures (df: degrees of freedom; F1: mean kinship coefficients at the first distance class, bF: regression slope of kinship coefficients over natural logarithm of distances; Sp: rate of decrease of relatedness over distances, dS: significant family structure up to this distance). P-values < 0.05 are shown in bold face. df All regions Managed Unmanaged P-value

27

S. Alb Managed Unmanaged P-value

8

Hainich Managed Unmanaged P-value

8

S. Chorin Managed Unmanaged P-value

8

F1

bF

Sp

dS (m)

0.0162 0.0351 0.002

0.0068 0.0141 0.012

0.00690 0.01477 0.011

0.0163 0.0570 <0.001

0.0088 0.0237 0.011

0.00893 0.02523 0.009

10–20 20–30

0.0175 0.0209 0.680

0.0057 0.0071 0.582

0.00584 0.00725 0.579

0–20 0–20

0.0144 0.0290 0.153

0.0055 0.0121 0.221

0.00561 0.01257 0.221

0–30 20–30

the pair S. Chorin/S. Alb and lowest for Hainich/S. Chorin (Fig. 3). These results were confirmed by analyzing Nei’s genetic distance (Supplementary material, Table S2). The structure analysis revealed similar values for K = 2 (23.842) and K = 5 (23.299). The S. Alb was clearly differentiated from the other two regions looking at K = 2 (Supplementary material, Fig. S2). For K = 5, the differentiation was not that obvious, but one of the clearly differentiated groups represents the S. Alb. Within this region, plot AEW-8 is differentiated from the other places (Supplementary material, Fig. S3). The results of the structure analysis were confirmed by a neighbor joining dendrogram based on Nei’s genetic distance (Fig. 4). The grouping of the plots was slightly different applying a UPGMA dendrogram (Supplementary material, Fig. S4). Almost all bootstrap values are low (data not shown). 4. Discussion 4.1. The influence of microsatellite loci on population genetic parameters The three EST-SSRs were originally developed for oak and four of the six genomic SSRs were developed for F. crenata. The transfer of microsatellite markers to other species often causes null alleles, even if the species are closely related (e.g., Vornam et al., 2004; Oddou-Muratorio et al., 2009). The presence of null alleles enhances the inbreeding coefficient and can therefore lead to misinterpretation. However, the chosen marker set is of a good quality for the analyses of this study, and null alleles were only found in one EST-SSR (FIR004) originally developed for oak (Durand et al., 2010). Genetic diversity varied largely between the nine markers. In general, the EST-SSRs showed lower values in comparison to the other SSR markers. One of the applied EST-SSRs (GOT066) showed extremely low variation in comparison to all other markers. This has to be taken into account comparing these results to other studies using microsatellite markers in beech. 4.2. Genetic diversity within the three exploratories The genetic diversity in all investigated plots is high (He = 0.623, excluding EST-SSRs: He = 0.703). However, other studies in F. sylvatica in Central Europe found even higher mean values (Vornam et al., 2004: He = 0.765; Buiteveld et al., 2007: He = 0.851; Oddou-Muratorio et al., 2011; He = 0.72; Bilela et al., 2012: He = 0.777; Piotti et al., 2012: He = 0.753) but applied only four or five microsatellite markers mostly different from the ones used in our study. Therefore, it is very difficult to compare the results of these different investigations. Seifert (2012) used the same set of microsatellite markers to investigate beech stands in northern Germany and found comparable values of genetic diversity (He = 0.619, excluding EST-SSRs: He = 0.689). The differences among regions in the genetic diversity indices are generally low, but significant. S. Chorin has the highest genetic diversity, although this region has the most fragmented beech forests in comparison to the other two regions. The higher genetic diversity is mainly caused by the gene-based EST-SSRs. Using the Blast2GO software (Conesa et al., 2005) FIR004 and GOT066 were functionally annotated as unknown proteins and FIR065 was annotated as one-helix protein 2, a transmembrane protein of photosystem I whose expression is induced upon light stress in Arabidopsis (Andersson et al., 2003). This is a remarkable result with regard to predicted climate change. Annual mean precipitation as well as the mean precipitation in the vegetation period is already relatively low in this region (annual mean: 507–642 mm, mean in the vegetation period: 239–300 mm) and in some parts the amount

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Fig. 2. Spatial distribution of trees at (a) AEW-5 (managed) and (b) AEW-8 (unmanaged). The size of the points is proportional to the dbh. Autocorrelograms between the mean kinship coefficients F(d) of pairs of individuals and each distance class for stand AEW-5 (c) and AEW-8 (d). The solid line is the observed value and the dotted lines are the 95% confidence intervals after 10,000 times randomization of individuals over locations (p: level of significance of the regression slope (bF), Sp: rate of decrease of relatedness over distances).

of precipitation in the vegetation period is already close to the minimum requirement of European beech (250 mm, Bolte et al., 2007). Additionally, the soil in this region has in general a low water storage capacity, varying from sandy loam to pure sand (Fischer et al., 2010). Climate change models predict decreasing precipitation for the summer months in Germany. Thus, especially in this region it might be necessary for beech populations to adapt to increasing drought stress. The high genetic diversity is a good basis for adaptation processes (e.g., Hamrick et al., 1992), although it is unlikely that the observed diversity is of direct significance for adaptation to a warmer climate. No or only very weak inbreeding was found in most of the beech stands (mean FIS = 0.015). In contrast, other studies in European beech reported relatively high inbreeding values (Buiteveld et al., 2007: FIS = 0.224; Paffetti et al., 2012: FIS = 0.24). Actually, a high inbreeding coefficient is not expected for a highly outcrossing and wind pollinated tree species. Buiteveld et al. (2007) discussed the results critically and concluded that null alleles have been found for at least two of the four applied microsatellite markers. One of these two markers showed extremely high FIS values (FS4–46: FIS = 0.38). The exclusion for these two markers reduced FIS drastically, but the values were still significantly different from zero. Paffetti et al. (2012) used the same marker set. Again, locus FS4-46 showed an extremely high inbreeding coefficient (FIS = 0.242). In agreement with the result of the present study, Lander et al. (2011) found no or low inbreeding values for most of the 52 investigated beech stands close to Mont Ventoux in France, even though

in some of the stands FIS values were significantly different from zero (FIS = 0.035). Thirteen microsatellite markers were applied in the latter investigation and only one showed high rates of null alleles and was therefore excluded from further analysis. It may be concluded that high inbreeding values for European beech in some studies are mainly due to the use of microsatellite markers with high frequencies of null alleles. We recommend therefore to select microsatellite markers carefully and to avoid the investigation of loci with high estimates of null alleles. In addition to the markers used in this study, a large number of additional microsatellite markers with low frequencies of null alleles have been suggested (e.g., Lander et al., 2011; Lefèvre et al., 2012). Jump and Peñuelas (2006) found significantly higher inbreeding in strongly fragmented beech forests using six microsatellite markers in Spain, close to the distribution limit of European beech. In our study, the comparison of a continuous forest (Hainich) and more fragmented regions (S. Alb and S. Chorin) showed no differences. Thus, it can be concluded that fragmentation had no or only moderate effects on genetic diversity and levels of inbreeding in Central Europe as result of large effective population sizes and effective means of gene dispersal in beech. 4.3. Influence of management activities on genetic diversity and spatial genetic structure We found no significant impact of management on genetic diversity in agreement with the results of Buiteveld et al. (2007),

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Fig. 3. Matrix of pairwise genetic differentiation (FST) (+: high significant differentiation (p < 0.001), N: not highly significant, *: unmanaged stands).

Fig. 4. Neighbor joining dendrogram based on Nei’s genetic distance. Plots of different regions are represented by circles for the S. Alb (A), rhombs for Hainich (H) and squares for S. Chorin (S).

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Shanjani et al. (2010, Fagus orientalis) and Paffetti et al. (2012). Some of the managed stands showed even higher genetic diversity than unmanaged stands of the same region. Several investigations showed a positive correlation between growth and vitality and heterozygosity of forest trees (e.g., Ziehe and Hattemer, 2002). Thus, the removal of slow growing trees with low individual heterozygosity in managed stands might have resulted in higher values for genetic diversity. Another investigation on beech also found a higher heterozygosity in thinned stands as compared to untreated stands (Lauber et al., 1997). However, most of the investigated beech stands were unmanaged since less than one beech generation. Therefore, the question remains open whether beech stands that have been unmanaged for several generations may show higher or lower values of genetic diversity. At the moment, it is not possible to conduct such an investigation in Germany because beech has been managed intensively for centuries and no primeval beech forests are left. Paffetti et al. (2012) found a high loss of rare alleles for a managed beech stand in comparison to an unmanaged one in Italy. On the contrary, there was no loss of rare alleles in our study. In the regions Hainich and S. Chrorin, the number of rare alleles was even higher in managed stands in comparison to the unmanaged stands, but these differences were statistically not significant. Buiteveld et al. (2007) also found no loss of rare alleles as a result of management for European beech. Additionally, no loss of private alleles was detected due to management in the present study. Indeed, most of the private alleles are present in the managed stands. In the different regions, significantly lower values for genetic diversity have been found for managed stands in the S. Alb. It is possible that this effect is due to management, but it can also be an artefact of the selection of the three unmanaged stands. After the analysis of these stands, old maps revealed that these areas were still grassland in 1820. Therefore, it is possible that these stands had been planted with material from diverse sources, inflating their diversities. In Hainich, the inbreeding coefficient was slightly and significantly higher in unmanaged stands as compared to managed stands. A new calculation excluding the microsatellite marker with null alleles (FIR004) revealed no significant difference between managed and unmanaged stands (data not shown). Therefore, the effect is most probably due to this single locus. In one of the managed stands in S. Chorin, only around 60 adult trees were available. Therefore, 60 additional young trees next to the mother trees were investigated. The management activities in this stand have not increased the inbreeding coefficient in the young beech trees (Table 3). Overall, significant differences in spatial genetic structure were observed between managed and unmanaged stands. The reduction of the extent of spatial genetic structures in managed stands is only significant for one of the regions, but a similar trend was found in the other two regions. Similar observations were made by Paffetti et al. (2012), who found very complex spatial genetic structures in an unmanaged beech stand, but not in a nearby managed stand. These complex family structures in unmanaged beech stands can be explained by non-random mating between closely related individuals and ineffective dispersal of beechnuts. Most beechnuts germinate in close distance to their mother trees, causing family structures. In managed beech stands, trees with superior growth traits are left in the stand, whereas nearby trees are removed for optimal growth conditions for the remaining trees. Thus, family structures are reduced in managed stands. Reduced family structures were also reported due to frequent thinning to remove undesirable trees by Dounavi et al. (2002; 2010). The more distinct effect in the S. Alb is probably attributed to the fact that the three unmanaged plots in the S. Alb have been unmanaged for around

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80 years, whereas the management of two plots in the Hainich stopped around 40–50 years ago, and the management in S. Chorin and in one plot in the Hainich stopped relatively recently. 4.4. Genetic differentiation and cluster analysis Most of the genetic variation was detected within populations. Nevertheless, genetic differentiation between almost all population pairs was significant. A similar result has been found by Lander et al. (2011). As expected, the largest differentiation was found between S. Chorin and the S. Alb. These two regions are far away from each other; the first one is located in northeastern Germany, the latter one in southern Germany (around 600 km). The genetic differentiation between Hainich and S. Alb is higher in comparison to Hainich and S. Chorin, although the distance of both pairwise comparisons is around 300 km. Neither the structure analysis nor the cluster analysis showed a clear differentiation among regions despite the high geographic distances between them. The differentiation between the S. Alb region and the other regions is most pronounced. A possible explanation is the postglacial recolonization history of European beech. There have been several refuges for beech during the last ice age, but most of the beech trees in Germany have most probably originated from only one refuge in Slovenia in the eastern Alps (Magri et al., 2006; Magri, 2008). However, there was also a refuge in the western Alps that may have contributed to the gene pool of F. sylvatica in southern Germany (Magri, 2008). Furthermore, adaptation processes are a possible explanation for the comparatively high genetic differentiation of the S. Alb from the other two regions. In comparison to the optimal growth conditions in Hainich, beech trees in the S. Alb had to adapt to the more difficult growth conditions of a low mountain range climate with a lower annual mean temperature (around 7 °C) and a higher number of days with a daily minimum temperature below 0 °C. A slight human impact on genetic differentiation can be found within regions. The genetic differentiation within Hainich is relatively low in comparison to the other two regions, most probably due to the different degree of forest fragmentation in the three regions. Hainich is the only region where most of the investigated stands are still located in a continuous forest area. Additionally, pure beech stands or mixed deciduous stands with a high amount of beech is the dominant forest type in Hainich. The Hainich region is one of the few regions in Germany where most of the deciduous forests have never been transformed into artificial conifer stands. Forests in the S. Alb and S. Chorin are more fragmented and the genetic differentiation is, as expected, higher within the regions in comparison to Hainich. In the S. Alb, there is still a high amount of spruce (Picea abies) today, whereas S. Chorin is dominated by pine forests (P. sylvestris) and has also a high amount of mixed deciduous forests dominated by oaks (Fischer et al., 2010). Concerning genetic differentiation, unexpected results have been found for a few of the investigated stands. Stand SEW-38 in the Schorfheide region is assigned to other stands in the Hainich region but not to the other Schorfheide stands in all cluster analyses. Indeed, the genetic distance of this stand to all Hainich plots is very low (Nei’s genetic distance: 0.009–0.023), whereas the distance to the other plots in the Schorfheide is higher (0.017– 0.036). In the dendrogram, SEW-38 groups with HEW-18. These two stands have the lowest observed genetic distance (0.009). Therefore, it might be suspected that SEW-38 has been planted with material from a region close to the Hainich. The three unmanaged stands in the S. Alb (AEW-7, 8 and 9) have relatively high genetic distances to each other and to the managed plots that are genetically closer to each other. Additionally, plot AEW-8 has high genetic distances to almost all other investigated

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beech stands (Nei’s genetic distance: 0.03–0.101). Old maps from 1820 showed for all three beech stands no forest, but grassland at this time, but no information is available about planting activities in this region. The closest deciduous forest was between 146 and 240 m away, but nothing is known about the composition of these deciduous forests.

Data accessibility  Microsatellite loci: corresponding cited publications.  Information about the study areas including exact location: Table 1.  Genotypes and position of all investigated individuals: DRYAD entry.

5. Conclusions A large-scale study on impacts of management on genetic diversity needs to consider other factors shaping genetic structures, including postglacial population history, as well. In contrast to former studies, three widely separated regions in Central Europe, a high number of stands per region allowing statistical comparisons, and a reasonable number of microsatellite markers were used in this investigation. The comparison of the different markers revealed that the results concerning genetic diversity and inbreeding can largely vary depending on the applied marker. It is therefore recommended to use a carefully selected and a large set of markers. All investigated beech stands contain high genetic diversity implicating a high potential to adapt to changing environmental conditions. The stands in the region in northeastern Germany will most probably suffer under more severe drought stress in the future than the other two regions. The result that this region harbors significantly higher genetic diversity is encouraging with regard to selective responses to predicted climate change. However, to assess the adaptability of European beech to climate change, adaptive genetic variation has to be analyzed additionally to neutral genetic variation. Currently, there are only a few attempts to investigate the molecular basis of adaptive genetic diversity of beech (Seifert et al., 2012). We confirmed former investigations and showed that management activities have no detectable negative impact on genetic diversity. Therefore, the life-history traits of European beech in combination with the management practices in Central Europe prevent a reduction of genetic diversity. The spatial genetic structure of beech stands is influenced by management in different intensities. In one region, the reduction of family structures was significant in managed stands, and the same tendency was also found for the other two regions. However, the breakup of family structures is a rather positive consequence of management because it can promote heterozygosity. Although there is most probably an effect on genetic differentiation due to forest fragmentation, there are still large effective population sizes and efficient gene flow to prevent negative effects on genetic diversity patterns. In general, European beech is remarkably unaffected by human impact in a region that has been strongly shaped by man for centuries. The establishment of strict protection areas in beech forests, often advocated by conservationists, is therefore not necessary to maintain the intraspecific diversity of the species. Nevertheless, the investigation of adaptive genetic diversity is strongly recommended for a comprehensive assessment of the effects of management practices, as well as a comparison with stands that are unmanaged since more than one or two generations.

Author contributions R.K.C. performed field and laboratory work, did most of the statistical analyses and helped to write the paper; the late S.S. assisted doing field and laboratory work, performed some of the statistical analyses and wrote most the paper; K.P. supported data analysis and helped to write the paper; O.G. and R.F. designed the experiment and helped to write the paper. All authors reviewed and edited the manuscript.

Acknowledgements We thank Alexandra Dolynska, Gerold Dinkel, Christine Radler and August Capelle for their technical support. Special thanks to everyone who helped conducting field work, especially Michael Zillmer, and to two anonymous reviewers. We thank the managers of the three exploratories, Swen Renner, Sonja Gockel, Kerstin Wiesner, and Martin Gorke for their work in maintaining the plot and project infrastructure; Simone Pfeiffer and Christiane Fischer giving support through the central office, Michael Owonibi for managing the central data base, and Markus Fischer, Eduard Linsenmair, Dominik Hessenmöller, Jens Nieschulze, Daniel Prati, Ingo Schöning, François Buscot, Ernst-Detlef Schulze, Wolfgang W. Weisser and the late Elisabeth Kalko for their role in setting up the Biodiversity Exploratories project. The work has been partly funded by the DFG Priority Program 1374 ‘‘Infrastructure-Biodiversity-Exploratories’’ (DFG Fi569/121). Field work permits were issued by the responsible state environmental offices of Baden-Württemberg, Thüringen, and Brandenburg (according to § 72 BbgNatSchG). Appendix A. Supplementary material Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.foreco.2014. 02.003. References Ammer, C., Albrecht, L., Borchert, H., et al., 2005. Future suitability of beech (Fagus sylvatica L.) in central Europe: critical remarks concerning a paper of Rennenberg et al. (2004). Allg. Forst Jagdztg. 176, 60–67. Amos, W., Harwood, J., 1998. Factors affecting levels of genetic diversity in natural populations. Philos. Trans. Royal Soc. London Ser. B-Biol. Sci. 353, 177–186. Andersson, U., Heddad, M., Adamska, I., 2003. Light stress-induced one-helix protein of the chlorophyll a/b binding family associated with photosystem I. Plant Physiol. 132, 811–820. Asuka, Y., Tani, N., Tsumura, Y., Tomaru, N., 2004. Development and characterization of microsatellite markers for Fagus crenata Blume. Mol. Ecol. Notes 4, 101–103. Austerlitz, F., Mariette, S., Machon, N., et al., 2000. Effects of colonization processes on genetic diversity: differences between annual plants and tree species. Genetics 154, 1309–1321. Bilela, S., Dounavi, A., Fussi, B., et al., 2012. Natural regeneration of Fagus sylvatica L. adapts with maturation to warmer and drier microclimatic conditions. For. Ecol. Manage. 275, 60–67. Bolte, A., Czajkowski, T., Kompa, T., 2007. The north-eastern distribution range of European beech – a review. Forestry 80, 413–429. Boshier, D., Amaral, W., 2004. Threats to forest ecosystems and challenges for the conservation and sustainable use of forest genetic resources. In: Vinceti, B., Amaral, W. (Eds.), Challenges in Managing Forest Genetic Resources for Livelihoods. IP-GRI, Rome, pp. 7–22. Buiteveld, J., Vendramin, G.G., Leonardi, S., Kamer, K., Geburek, T., 2007. Genetic diversity and differentiation in European beech (Fagus sylvatica L.) stands varying in management history. For. Ecol. Manage. 247, 98–106. Conesa, A., Götz, S., García-Gómez, J.M., Terol, J., Talón, M., Robles, M., 2005. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 21, 3674–3676. Dounavi, A., Koutsias, N., Ziehe, M., Hattemer, H.H., 2010. Spatial patterns and genetic structures within beech populations (Fagus sylvatica L.) of forked and non-forked individuals. Eur. J. Forest Res. 129, 1191–1202. Dounavi, K.D., Steiner, W., Maurer, WD., 2002. Effects of different silviculture treatments on the genetic structures of European beech populations (Fagus sylvatica L.). In: von Gadow, K., Nagel, J., Saborowski, J. (Eds.), Continuous Cover Forestry. Kluver Academic Publishers, Dordrecht, Boston, New York, pp. 81–90.

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