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Genomic signatures of natural selection at phenology-related genes in a widely distributed tree species Fagus sylvatica L



Diversity among phenology-related genes is predicted to be a contributing factor in local adaptations seen in widely distributed plant species that grow in climatically variable geographic areas, such as forest trees. European beech (Fagus sylvatica L.) is widespread, and is one of the most important broadleaved tree species in Europe; however, its potential for adaptation to climate change is a matter of uncertainty, and little is known about the molecular basis of climate change-relevant traits like bud burst.


We explored single nucleotide polymorphisms (SNP) at candidate genes related to bud burst in beech individuals sampled across 47 populations from Europe. SNP diversity was monitored for 380 candidate genes using a sequence capture approach, providing 2909 unlinked SNP loci. We used two complementary analytical methods to find loci significantly associated with geographic variables, climatic variables (expressed as principal components), or phenotypic variables (spring and autumn phenology, height, survival). Redundancy analysis (RDA) was used to detect candidate markers across two spatial scales (entire study area and within subregions). We revealed 201 candidate SNPs at the broadest scale, 53.2% of which were associated with phenotypic variables. Additive polygenic scores, which provide a measure of the cumulative signal across significant candidate SNPs, were correlated with a climate variable (first principal component, PC1) related to temperature and precipitation availability, and spring phenology. However, different genotype-environment associations were identified within Southeastern Europe as compared to the entire geographic range of European beech.


Environmental conditions play important roles as drivers of genetic diversity of phenology-related genes that could influence local adaptation in European beech. Selection in beech favors genotypes with earlier bud burst under warmer and wetter habitats within its range; however, selection pressures may differ across spatial scales.

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Local adaptation is one of the most important evolutionary mechanisms allowing species to thrive across heterogeneous environments [1]. Populations are said to be locally adapted when individuals from resident populations have higher fitness than individuals of the same species introduced from other habitats [2]. Knowledge about the extent of local adaptation and its underlying mechanisms in natural populations provides the basis for predicting responses to environmental fluctuations, including those associated with global climate change.

The genetic underpinnings of local adaptation, though, are poorly understood. Local adaptation would be expected to change allelic frequencies of genes affecting fitness in particular environments. However, most phenotypic traits related to adaptation are typically quantitative polygenic traits, which complicates the identification of genetic polymorphisms linked to adaptation [3, 4]. Local adaptation has been considered an important factor in maintaining genetic variation within species, but environmental heterogeneity also favors the evolution of adaptive phenotypic plasticity [1]. However, because phenotypic plasticity does not necessarily have to be adaptive [5], its interplay with adaptive traits confounds the ability to find genetic determinants of adaptation.

Local adaptation is best studied in long-lived sessile organisms with large effective population sizes spanning large, variable environments [6], such as forest trees [7]. In plants generally, the extent of any local adaptation can be assessed based on reciprocal common-garden experiments; however, phenotypic and genetic differentiation along native environmental gradients can also provide some evidence for local adaptation [3, 8]. Studying local adaptation in forest trees has been of interest for researchers for a long time, which is emphasized in many common-garden experiments established for various tree species, motivated mainly by the management of forest reproductive material and tree improvement programs. Such experimental sites are nowadays excellent platforms for studying local adaptation [9], allowing for the exploration of complex relationships between phenotype, genotype, and environment at the genomic level [4, 10]. Common-garden trials have the potential to provide the links between GPA (genotype-phenotype association) and GEA (genotype-environment association) studies [11], since the genomic diversity of populations can be associated to environmental variables existing at the place of the origin of each population. However, at the same time, this genomic diversity can be associated with phenotypes considered as a population response to environmental conditions existing at an experimental site.

Phenology of tree development is one example of a set of traits thought to be strongly indicative of adaptive fitness. Local population adaptations have been described among tree species for the timing of bud burst in relation to local climatic conditions varying along latitude or altitude [12,13,14]. As an example of a connection to fitness, bud burst phenology is impacted by the length of the growing season, and it has consequences for biomass production. Earlier bud burst and later bud set extend the growing season and increase net photosynthetic productivity, thus increasing an individual’s competitive ability [14, 15]. On the other hand, later bud burst and earlier bud set improve cold resistance [16]. Bud burst timing also regulates biological interactions between trees and associated species (pest insects, pathogens, mycorrhizal fungi), and phenological changes usually result in different synchrony with insect herbivores and fungal pathogens, which also leads to consequences for overall fitness [17, 18].

Modifications of the phenology of flowering, bud burst, or bud set have been observed in relation to global climate changes [19, 20]; however, the response of any individual tree species is hard to predict. While some authors suggest that a warming climate may promote earlier bud flashing with an associated extension to the growing season [15], others highlight the importance of photoperiod and of chilling requirements in some species, along with problems related to increased aridity of the environment [21]. The chance for late frost damage may increase due to the overall shift in bud burst dates [15, 22]. Nevertheless, observations of high phenological plasticity in forest trees suggests the potential for rapid population responses to variations in temperature [23,24,25].

The genetic background of bud burst phenology has been investigated in a number of tree species [26, 27]; see also review in Howe et al. [16]. In general, bud burst was found to be under strong genetic control with high heritability [16], increasing the prospects of identifying genomic regions controlling this trait. However, in oak (Quercus robur), it has been estimated that bud burst phenology, being considered a typical quantitative trait, is controlled by at least 12 unique genes or chromosomal regions [28]. If this complexity is representative of other species, it will mean that discoveries of genomic backgrounds to phenology will be complex. Indeed, functional genomics of phenology may actually be far more complicated than even this, as different genomic regions might play different roles in different populations subjected to different environmental pressures [25, 26, 29], which may be further confounded by possible epigenetic effects [30].

Next-generation sequencing approaches have provided a new handle on the genetic basis of such complex adaptive traits at whole-population levels [31,32,33]. Among these traits, phenology appears to be of primary interest in the literature. Attempts have been made to identify candidate genes responsible for bud burst phenology in the woody plants Vitis [34] and Ribes [35], but one of the first studies was performed on the tree species Quercus [36]. Derory et al. [36] identified approximately 190 genes down- or up-regulated during bud burst in sessile oak, which were therefore annotated as potential sources of signatures of selection. In a subsequent study, nine candidate genes were assessed for bud burst timing association [37]. Patterns of diversity among natural and segregating populations were, however, variable, making final inferences on the significance of respective genes difficult.

Other approaches have also been tested. Although genomic scans became attractive tools for studying genomic diversity in trees [38, 39], their applicability to the study of genomic signatures of adaptation has been debatable [8, 40, 41]. Instead, SNP arrays, exome, or whole-genome sequencing have been more suitable tools with which to conduct detailed identification [7, 42, 43]. Finally, several authors pointed out the necessity of studying intergenic allelic associations in candidate genes across a wide range of environments to test intergenic disequilibria and multilocus differentiation measures [29, 37].

Among the typical model tree species used for such studies, European beech (Fagus sylvatica L.) is widespread, and is one of the most important broadleaved trees in Europe. It has high importance not only economically, but also ecologically, being the dominant tree species in many forest ecosystems [44, 45]. As a late-successional tree species, F. sylvatica might be considered the perfect model species for studying local adaptation because of its importance as a component in its metapopulation dynamics.

The adaptation potential of beech to climate change has been widely discussed. While beech is considered a sensitive tree species in the context of predicted environmental changes, some authors conclude that beech will not lose its importance and adaptedness in the future [46,47,48,49,50,51]. However, changes in marginal beech populations have already been observed [52, 53], and different modeling studies predict range shifts for this species in the context of global warming [54, 55]. Therefore, a deeper understanding of its potential for adaptation to changing environmental conditions is required.

Beech bud burst phenology has been investigated thoroughly, and its adaptive importance is well-documented [56,57,58,59,60,61]. Recently, multiple SNP markers have been described in climate-related candidate genes in European beech or other Fagaceae [36, 62,63,64,65,66,67]. These SNPs have been successfully used to detect genetic variation showing signatures of selection in beech [68,69,70].

Detecting such signatures of selection is not a trivial task. In the last decade, several analytical methods have been developed to detect putative adaptive loci from genomic datasets, making it possible to assess adaptive genetic variation in natural populations [71,72,73]. Genotype-environment association analyses (GEA) are particularly promising for detecting these loci [72]. Unlike differentiation outlier methods, which identify loci with strong allele frequency differences among populations, GEA approaches directly associate allele frequencies and environmental conditions not only to detect genetic variants putatively under selection, but also to characterize the environmental conditions contributing to adaptive genetic variation [10, 71, 72, 74]. GEAs are especially promising because they are better able to detect relatively weak signals of selection compared to methods based on population differentiation [75,76,77]. In particular, multivariate GEA methods (which analyze many loci and environmental predictors simultaneously) identify how groups of loci covary in response to environmental predictors, and may reduce the need for multiple testing while potentially identifying polygenic selection [72, 78]. This is important because many adaptive processes are expected to result in weak multilocus signatures due to selection on standing genetic variation which has not yet led to allele fixation [3, 79, 80].

Environmental association studies such as these have been conducted in a variety of organisms, including forest trees. For instance, associations of genetic variation with temperature and precipitation have been detected in Alnus glutinosa [81], Populus balsamifera [82], Populus trichocarpa [83], Pseudotsuga menziesii [84], Quercus lobata [85], Quercus rugosa [86], Picea abies [87], and Pinus taeda [33]. More recently, SNPs in candidate genes that may be under climate selection have been found in European beech [63, 66,67,68,69, 88], and associations between these SNPs and environmental variables such as temperature, precipitation, and drought have been determined.

Less is known about the precise molecular basis of phenology-related traits; however, genomic resources facilitating such research in beech have been growing only recently. For instance, Lesur et al. [89] revealed a large set of genes involved in dormancy regulation in beech, and Mishra et al. [90] developed the reference genome for European beech, allowing the identification of SNP polymorphisms within well-defined gene models. Lalagüe et al. [63] and Müller et al. [65, 91] also analyzed a selection of candidate genes related to bud burst in European beech. While these studies are valuable, the field is nascent, and studies directly investigating relationships between genetic markers and phenology in European beech are scarce.

Here, we use a candidate gene approach to detect genotype-environment (GEA) and genotype-phenotype (GPA) associations to look further for genomic signatures of local adaptation of European beech to climate heterogeneity across Europe. Unlike genome-wide studies, candidate gene techniques focus on genes of a priori interest, and therefore are expected to enrich for the number of significant associations (e.g. [92]).

We focused on candidate genes differentially expressed during bud burst in European beech [89]. Based on the sequence capture approach, we generated a SNP dataset to: (i) detect a spatial pattern of genetic variation (geographic variables); (ii) identify SNPs associated with climatic variables existing at the original locations of populations; and (iii) find relationships between genetic polymorphisms and the subset of adaptive traits (such as spring and autumn phenology scores, survival rate, and tree height) measured at the location of a common-garden trial. We hypothesize that, given the variable pattern of climatic variables across the species’ range, the strength of selection will be variable in different parts of the species distribution. It is expected that, because we selected candidate genes related in some way to bud dormancy and its release, several identified SNPs will demonstrate associations to climate and phenology-related phenotypic variables.


Environmental variables

Principal component analysis revealed that much of the variation in environmental variables observed at the locations of sampled populations (Fig. 1) could be explained by the first three principal components (PC1, PC2, and PC3), which explained 85.21% of the total variance of the environmental variables data and were chosen as climate variables in further analyses. These climate indices describe the main spatial features of the climate in Europe (Table S3). In PC1 (43.05% of the variance), minimum temperature in the coldest month (BIO6), mean temperature in the driest quarter (BIO9), mean temperature in the coldest quarter (BIO11), precipitation in the driest month (BIO14), precipitation in the driest quarter (BIO17), and precipitation in the coldest quarter (BIO19) were all strong positive correlates, with loadings greater than 0.80. However, temperature seasonality (BIO4), annual temperature range (BIO7), and precipitation seasonality (BIO15) were the strongest negative correlates, with loadings under − 0.80. For PC2 (26.45% of the variance), precipitation in the wettest month (BIO13), precipitation in the wettest quarter (BIO16), and precipitation in the warmest quarter (BIO18) showed loading over 0.80. In PC3 (15.71% of variance), only mean diurnal range (BIO2) and maximum temperature in the warmest month (BIO5) were highly positively correlated (r > 0.80; p < 0.001). The biplot constructed by the two principal components showing populations and 19 environmental factors (as vectors) is presented in Fig. 2. The first principal component (PC1) separated Western European from Eastern European populations, which were characterized by a higher temperature in the coldest month (BIO6) and the driest quarter (BIO9), and also higher rainfall in the driest (BIO17) and coldest (BIO19) quarters. Along PC2, the Southern European populations occupying habitats that were characterized by greater rainfall in wettest (BIO16) and warmest (BIO18) quarters were separated from the remaining populations.

Fig. 1

Geographical distribution of the sampled provenances in Europe (black dots) and natural distribution of Fagus sylvatica (green)

Fig. 2

PCA plot based on 19 environmental variables (BIO1-BIO19) describing 47 European beech sampling sites

In general, the climate variables defined by PCs were significantly related to geographic variables, as indicated by canonical correlation analysis (Can R = 0.955; p < 0.0001; Table S4). Specifically, PC1 was significantly correlated with longitude (r = − 0.908, p < 0.001), PC2 had the strongest correlation with altitude (r = − 0.720; p < 0.001), and PC3 was strongly correlated with latitude (r = − 0.693; p < 0.001) (Table S4). The distribution of PC scores across provenance locations is presented in Figure S1. PC1 seems to represent the gradient from Atlantic to continental climate (mostly west-east direction), while PC2 and PC3 are more related to environmental variables varying along the north-south direction.

Phenotypic variables

Canonical correlation analyses, in general, indicated no relationship between phenotypic measures and climatic or geographic variables (Table S4). Only survival was found to be moderately correlated with PC3 (r = 0.411; p = 0.004). It should be noted that survival was also correlated with tree height and autumn phenology (r = 0,456; p = 0,001 and r = 0.343; p = 0,018) (Table S4). Based on spring (S) and autumn (A) phenology scores we calculated a synthetic variable (L = S - A), which is intended to mimic the length of the growing season. Provenances with the earliest growth in the spring and the latest growth cessation in autumn have the highest values of L. We found that this variable was strongly correlated with survival (r = 0.445; p = 0.002), but not with tree height (r = 0.227; p = 0.125).

Sequence capture data

An average of 5,636,543 raw sequence reads was generated per individual tree. More than 99% of reads were mapped to the reference genome of European beech (Mishra et al. 2018). Any sequence reads aligned with unique positions were subjected to SNP calling across individuals, identifying 15,742 SNPs (unfiltered). After applying the filtering criteria, as described in Material and methods, before LD pruning there were still 11,307 SNPs; however, after LD pruning a total of 5970 polymorphic SNPs were retained and used in further analyses.

From the initial set of 485 candidate genes used in the sequence capture experiment, and based on the reference genome [91], we identified 380 well-defined gene models, including 277 complete and 103 partially valid (without UTR sequences) gene models. Cumulative sequence length was equal to 1,014,286 bp (Table S5). The majority of gene models were supported by existing gene structure annotations of F. sylvatica. However, we found some annotation errors. In some cases, different initial contigs collapsed into the same gene model, or some initial candidate genes could not be identified in the F. sylvatica genome, likely due to gaps in the reference assembly.

We used only 2909 SNPs located within, or up to 500 bp from, 380 validated gene models for further analyses. By position relative to gene models, 2554 SNPs (87.79%) were located within candidate genes, with an average SNP density of 2.5 SNPs/kbp (or one SNP per 400 bp). 355 SNPs (12.21%) were found within a distance of 500 bp from gene models. Based on gene model annotations, 1889 (64.93%) were located in exons, 1511 (51.94%) in coding regions (CDS); 240 (8.25%) in 5′ untranslated regions (5′ UTRs), 138 (4.74%) in in three prime untranslated regions (3′ UTRs), and finally 665 (22.86%) in introns (Table 1).

Table 1 Number and percent of 2909 SNPs located in different genomic regions

The level of missing data was low because only 3 to 44 loci per individual were missing (an average of 20.39 SNPs per individual; 0.75%), and 0 to 4 individuals out of 92 had a missing genotype within a locus (an average of 0.723 individuals per locus; 0.75%). We identified only one example of copy number variation (CNV). This was found in gene FSB010001901 (GDSL esterase/lipase) that was either gained (4 cases) and lost (88 cases) among the 92 individuals.

Genetic diversity and structure

The mean genetic diversity of the 2909 SNP loci for the whole sampe was found to be 0.29, indicating a relatively high level of genetic diversity. An analysis of structure revealed the most likely number of genetic clusters as K = 3 (Figure S2), the first (K1) containing 39 individuals (present predominantly in Southeastern Europe), the second (K2) comprising 30 individuals (present predominantly in Western Europe), and the third (K3) containing only three individuals (Figure S3). An additional 23 individuals showed a mixed ancestry, with membership q-values lower than 70% in either of these three clusters.

The geographic distribution of clusters was non-random (Figure S4). There was a significant correlation of individual assignment probability (q-matrix) for the K1 cluster to longitude (r = 0.27; p = 0.009) and latitude (r = − 0.21; p = 0.042). This was in contrast to the K2 cluster, where longitude was negatively correlated (r = − 0.24; p = 0.022) and correlation with latitude was not significant (r = 0.17; p = 0.11). Within the K3 cluster, no significant correlations of q-values to geographic variables were observed.

Detection of outliers using LFMM

The LFMM analysis detected a total of 111 unique SNP loci showing significant association with one or more variables (Table S6). The greatest number of loci (79) was associated with phenotypic variables: spring phenology (8), height (18), and survival (53). Next, 45 SNPs appeared to be associated with geographic variables: longitude (37) and latitude (8). Finally, 36 SNP loci were associated with climate variables: PC1 (19), PC2 (12), and PC3 (5). However, there were no significant SNPs detected for altitude and autumn phenology. The highest number of common putative adaptive loci (14) was found for longitude and PC1, followed by height and survival (6).

Detection of outlier loci based on redundancy analysis

The RDA with 2909 SNPs was globally significant (ANOVA F = 1.15; p = 0.001) and explained about 8% of the variance (adj. R2 = 0.084) (Fig. 3A). This low explanatory power is not surprising because one might expect most of the SNPs in the dataset to be neutral and not to show relationships with the explanatory variables. Only the first two RDA axes were significant (RDA1, p = 0.007; RDA2, p = 0.020). Therefore, we considered the candidate loci only on the first two constrained canonical axes, which explained 18.02 and 16.57% of the genetic variation, respectively. Based on locus scores that were ± 3 SD from the mean loadings, 94 loci were identified as outliers (Fig. 3B, Table S6). The majority of candidate loci (48; 51.07%) were associated with phenotypic variables – height (21), survival (13), spring phenology (8), and autumn phenology (6). Of the remaining candidate loci, 27 (28.72%) were associated with climatic variables – PC1 (12), and PC3 (15). Finally, 19 SNPs (20.21%) were associated with a geographic variable (altitude).

Fig. 3

A Redundancy analysis (RDA) performed with 2909 SNPs (gray points in the center of plot) for 92 individuals (black circles) using geographic (altitude), climate (PC1, PC3) and phenotypic (spring phenology, autumn phenology, height, survival) variables considered as constraining variables. B Redundancy analysis performed using the 94 SNPs detected to be potentially associated with explanatory variables based on the RDA outlier detection approach

Among the 111 unique SNP outliers detected with LFMM, only 4 were among the 94 RDA outliers, and 7 additional ones were located within 150 bp of one of the RDA outliers. Thus, it might be considered that about 10% of the LFMM outliers were also detected by the RDA approach. However, when considering gene models, among all 162 genes that contained significant SNPs, 27 (16.7%) had SNPs identified by both LFMM and RDA methods. Considering both analytical methods, 19, 37, and 8 loci were related to altitude, longitude, and latitude, respectively; 65, 38, 16, and 6 loci were related to survival, height, spring, and autumn phenology, respectively; and finally 30, 12, and 20 loci were related to PC1, PC2, and PC3 climatic variables (Table S6). In summary, 63 loci (51 genes) were related to geographic variables, 58 loci (49 genes) to climatic variables, and 113 loci (90 genes) to phenotypic variables (Table S6).

Interestingly, there were five genes with significant SNPs related jointly to at least one variable from each of the three categories (geography, climate, phenotype), four genes related jointly to at least one geography and one phenotype variables, and four genes related jointly to at least one climate and phenotype variables. On the other hand, there were 17 genes related jointly to at least one geography and one climate variable, which is not surprising given significant correlations among these variables (Table S4).

Effect of geography, phenology and climate on adaptive genetic variation

A simple RDA model of combined geographic, climatic, and phenotypic variables (Model 1) explained a significant portion of variation in adaptive allele frequencies (adj. R2 = 0.15). Partial RDA was employed in order to separately determine the pure variation contributed by geographic, climatic, and phenotypic variables. Partitioning of the total variance indicated that the geographic variables accounted for 9.94% (p = 0.175) of the explainable total variance after removing the effect due to climatic and phenotypic variables (Model 2). The climate explained 8.70% (p = 0.406) of the total variance after the effect of geography and phenotypic traits was controlled (Model 3). However, the partial Model 4 showed that phenotypic variables explained 17.39% (p = 0.001) of the total explainable genetic variance after removing variance explained by geography and climate. In addition, 63.97% of the genetic variance could be explained by the joint effect of geography, phenotype and climate (Table 2).

Table 2 Partitioning of adaptive genetic variation using simple and partial redundancy analysis

We also tested the relationship between individual tree heterozygosities and geographic, climatic, and phenotypic variables. It appeared that the heterozygosity determined based on all 2909 SNPs was not related to any variable. However, the individual heterozygosity based on the subset of 201 significant SNPs appeared to be significantly related to PC1 (r = 0.3570; p = 0.0005), spring phenology (r = 0.3047; p = 0.0037), longitude (r = 0.2177; p = 0.0371), and altitude (r = 0.2933; p = 0.0045). Because some of the variables are correlated, we performed a multivariate regression analysis and found that the most significant model (adj. R2 = 0.2217; p < 0.0001) built only with significant variables included the three geographic variables (longitude, latitude, altitude) and spring phenology. All regression coefficients were positive, indicating that putative adaptive SNPs’ heterozygosity had a tendency to increase with longitude, latitude, altitude, and spring phenology scores (the higher score, the earlier phenology), and thus towards extreme climates and earlier bud flushing.

Additive polygenic scores

The relationships assessed between additive polygenic scores and the corresponding geographic, climatic, and phenotypic variables for each individual based on all 201 putative adaptive SNP loci are shown in Table 3 and Figures S4, S5. For almost all variables, the linear model had a lower AIC score and explained a similar proportion of variation as compared to a quadratic model (Table 3). Additive polygenic scores increased significantly with increasing latitude (p = 0.05), PC1 (p = 0.04), spring phenology (p = 0.01), and height (p = 0.01), but they decreased with decreasing longitude (p = 0.05) and PC3 (p = 0.03) (Figures S4, S5). The analyses revealed non-significant correlation of polygenic scores with altitude, PC2, autumn phenology, and survival.

Table 3 Correlation coefficients and p-values of the relationship between the additive polygenic scores and geographic, climate and phenotypic variables. The correlation coefficient and p-values were calculated following a linear and a quadratic model

Candidate SNPs under selection at a finer scale

Within the K2 cluster observed predominantly in Western Europe, RDA was not significant (ANOVA F = 1.01; p = 0.27) and the proportion of variance explained by the predictor variables was 10% (adjusted R2 = 0.10), thus we did not investigate candidate loci within this region. On the other hand, in the K1 cluster predominant in southeastern sampled populations, the proportion of total genetic variance explained by the predictor variables was similar (adj. R2 = 0.11), however the RDA was found to be significant (ANOVA F = 1.05; p = 0.004) (Fig. 4A). The proportion of total genetic variance explained by the predictor variables was lower than that observed for the total dataset. As with the broader scale, we considered candidate loci on the first two constrained axes, which explained 16.65 and 15.03% of the genetic variance, respectively. We detected 62 candidate loci (Fig. 4B). The majority of candidate loci (30; 48.39%) were associated with climate variables (PC1 (28) and PC3 (2)). Of the remaining candidate loci, 29 (46.77%) were associated with phenotypic variables (height (21), survival (5), spring phenology (1), and autumn phenology (2)) and 3 (4.84%) were associated with altitude. Only eight SNP loci detected over the regional scale were also significant at a global scale. However, 13 SNPs were located in 10 genes also identified by the global RDA.

Fig. 4

A Redundancy analysis (RDA) performed within the K1 genetic cluster with 2909 SNPs (gray in the center of plot) for 92 individuals (black circles) using geographic (altitude), climate (PC1, PC3) and phenotypic (spring phenology, autumn phenology, height, survival) variables as constraining variables on the first two ordination axes; B plot showing candidate loci detected based on locus scores ±3 SD from the mean loading on each RDA axis coloured by the most highly correlated explanatory variable

Functions of genes with significant SNPs

BLAST searches of candidate genes against the GenBank non-redundant protein database (NR) and also UniProt’s Swiss-Prot and TrEMBL protein databases revealed that most of the candidate genes in the current study had putative gene product names and general descriptions. We assigned functions to the candidate genes with the Gene Ontology (GO) classification, which provides a standardized set of terms to describe the genes and gene products of different species. Details of all genes initially selected for this study along with their product names and related GO terms can be found in Table S2.

All significant SNPs were related back to the gene through which they were found to assert their putative adaptive function. In total, the 255 unique significantly associated SNPs were distributed across 162 genes. Gene ontology annotation assigned at least one GO term for 116 (71.6%) of these genes. The total number of GO terms was 615. Among them, 86 genes were associated with 255 ‘biological process’ GO terms, 88 with 175 ‘molecular function’ GO terms, and 96 with 185 ‘cell component’ GO terms (Fig. 5). The most abundant GO slim terms were ‘response to stimulus’, ‘binding’, and ‘membrane’ in terms of biological process, molecular function, and cellular component, respectively.

Fig. 5

Gene ontology classification of genes with significant SNPs. The following GO slim terms contributed to the annotation of the 162 genes: biological process (black), molecular function (grey), cellular component (blue). The most abundant GO terms within the most common GO slim terms are indicated as white bars


Understanding the genomic background of adaptive traits is particularly important for forest tree species occupying wide and climatically variable geographic areas [93], such as Fagus sylvatica. Until now, there have been only a few attempts to investigate signatures of adaptation based on SNP polymorphism in candidate genes in beech. These attempts usually included a limited number of candidate genes and were focused on populations originating from relatively restricted areas [69, 91]. In this study, we applied a mixed approach where first we selected candidate genes differentially expressed during dormancy release, as identified by Lesur et al. [89], and supplemented these with a list of additional candidate genes known to be related to bud burst in plants. Then, we designed the sequence capture experiment to study the SNP polymorphism of these genes across a wide range of F. sylvatica distribution and their associations to geographic, climatic, and phenotypic variables.

Methodological constraints

There are two main weaknesses in our study: a relatively small sample size (mostly one sample per population), and the use of population-wide phenotypic measures instead of exact phenotypes of individual sequenced trees (which prevented us from performing typical GWAS analyses). These weaknesses could have restricted our ability to find significant genomic associations. However, because we used mostly one sample per population, such averages of population characteristics are better representations of populations than individual measures. Note also that survival can be assessed only at a population level. Nevertheless, despite these limitations, in combining the same univariate and multivariate approaches to find GEA and GPA, we identified 255 unique loci related to 162 genes exhibiting adaptive significance. These loci came from a dataset of 2909 SNPs derived from targeted sequencing of 380 phenology-related genes. These SNPs exhibit associations with environmental variables hypothesized to influence spatially varying selection in European beech, or associations with phenotypic traits (phenology, height, survival) related to adaptation. Moreover, different genotype-environment associations were identified within the Southeastern Europe region as compared to the entire sampled range of European beech, implying that selection pressures may differ across spatial scales. Our results demonstrate that, in beech, environmental conditions play important roles as drivers of genetic diversity of genes related to adaptive potential, thereby facilitating local adaptation of the species.

Genetic diversity and population structure

Genetic diversity in relevant genes is the basis for adaptation to environmental stress [94, 95]. In this study, we found that genetic diversity based on 2909 SNP markers was relatively high (He = 0.290) and comparable to other studies on beech [65, 91, 96, 97]. The high genetic diversity revealed for European beech might be a good basis for the capacity of adaptation to environmental stress. We observed that the individual heterozygosity estimated on the basis of significant SNPs increased with latitude, longitude, and altitude (i.e. towards extreme climates, where a future beech expansion is expected [98]) and earlier phenology, indicating the importance of relevant genes in local adaptation.

The STRUCTURE analysis revealed a weak population structure of beech in Europe. These findings are in line with recent studies at a regional scale reporting low population structure for beech populations in France [63], Germany [91, 97, 99] and Switzerland [100]. The low genetic structure of European beech in Central Europe may result from specific postglacial recolonization patterns from one predominant refugial area, and from high gene flow among populations [101, 102].

Detection of loci affected by selection

Many important phenotypic traits (e.g. flowering time, dormancy, chilling requirement, seed stratification) are under polygenic control, where several loci exert small effects [80, 103, 104]. Lesur et al. [89] identified in beech a number of genes differentially expressed during dormancy release, but, until now, it was unclear whether these genes were variable, and whether their variation was important for local adaptation.

Here, we found that 162 of these genes (among 380 tested) appeared to be significantly related to at least one of the explanatory variables related to geography, climate, or adaptive phenotypic traits. An attempt to relate the phenology-related genes to geographic, climatic, and a subset of adaptive traits seems reasonable because, in beech, phenology (in a broad sense) varies widely along geographic and climate clines [105].

Among climate variables, the largest number of outlier loci (30 SNPs) was related to the first principal component (PC1). Our results suggest that the first principal component of the PCA is an important predictor of genetic variation at candidate loci, and thus may be a potential driver of spatially variable selection for European beech. Indeed, this PC accounted for 43.05% of the variance among the 19 bioclimatic variables. Analysis of correlations between PC1 and bioclimatic variables (Table S3) suggests that the genes containing SNPs exhibiting strong associations with PC1 may therefore be under selection from temperature and water availability limitations. However, since PC1 was significantly correlated with several bioclimatic variables, it is difficult to ascertain exclusively the exact agent of selection responsible for the patterns observed in this study.

The timing of bud burst in populations of temperate trees is determined mainly by temperature and genetics [106,107,108,109,110]. It is possible that temperature may not be the only direct causative agent of selection, but instead, adaptation may be directly attributable to other variables that are correlated with temperature, such as water availability.

Another strategy to find genomic signatures of selection is to test for associations between genotypes and traits based on potentially differentiated populations growing in a common environment, as in common garden tests [11]. Such genotype–phenotype association analyses have the advantage of establishing more direct links between genes and traits that are potentially under selection [111]. In this study, in total 113 unique outliers were significantly associated with adaptive traits of European beech (autumn phenology, spring phenology, height, and survival), which explained 17.4% of the total outlier genetic variance after accounting for spatial and climate effects in a conditioned RDA. However, 16 SNPs were related directly to spring phenology. This number is higher than in the results of Müller and coauthors [91, 97], who reported only a few SNPs with significant association with bud burst in Germany. However, this may suggest significant differences in bud burst timing for the different populations in the whole natural range of European beech. This finding is in accordance with several other studies that revealed different bud burst timings for different beech populations/provenances [58, 60].

Additive effect of outliers on environmental and phenotypic correlations

To determine the extent to which individual outlier loci collectively mirror environmental variation across the European beech distribution range, we used a multilocus approach based on additive polygenic scores for phenotypic, geographic, and climate variables [112]. The strong positive correlation observed between individual polygenic scores calculated across all significant candidate loci and the first principal component is consistent with spatially varying selection across temperature and precipitation gradients, where different alleles are maintained in different environments. However, the quadratic model indicated optimum values of PC1 ≈ 2 maximizing additive polygenic scores (Figure S6), implying that the climatically suitable habitat for European beech is the area of Europe with higher temperature and precipitation (mostly across the area of Germany, Figure S1).

Genotypic variation at outlier loci detected in candidate genes should be compared with phenotypic data to establish associations between genotype, phenotype and fitness. The correlation between individual polygenic score and bud burst phenology appeared to be significant for both linear and quadratic models (Table 2). However, the phenotypic variation explained by significant SNPs associated with bud burst was low (linear model R2 = 6%; quadratic model R2 = 7%) but comparable to the results of other studies analyzing different traits and tree species [84, 113, 114]. Noticeably, additive polygenic scores generally increased with phenology scores, favoring individuals with earlier bud burst (Figure S5, S6).

The bud burst timing in deciduous trees has important fitness implications [23]. In this study, a positive correlation between individual polygenic score and bud burst phenology suggests that selection favors genotypes with earlier bud burst. Our results indicate that the warming climate in Europe might advantage northern-origin genotypes, which is further supported by a positive correlation between individual polygenic scores and latitude. It well corresponds with the predictions of Saltre et al. [98], i.e. that the climatically suitable habitat for European beech will shift north-eastward because the expected increase of temperature and precipitation at the range’s northern margins will increase survival and fruit maturation success.

Differentiation processes in European beech at regional scale

Within the K1 genetic cluster, predominant in southeastern populations, PC1 was the most strongly associated predictor of genetic variation at candidate loci, given both the number of candidate loci associated with climate predictors (PC1, PC2, PC3) and the strength of correlations. On the other hand, 45.8% of outliers at the regional scale were related to phenotypic variables. However, there was a lack of correspondence between the list of outlier SNPs at the regional and the broader scale. If taken as true positives, the genotype-environmental associations presented here likely represent loci affecting phenotypic traits to different degrees in different climatic conditions.

European beech can be considered as a late-flushing species growing under warm or mild climates, but not necessarily when exposed to colder climates within its range [59, 115]. This can be attributed to the known effects of winter chilling temperatures on the ability to bud burst in spring, where an increased duration of chilling temperatures reduces the thermal requirement for bud break [116,117,118,119]. As our beech samples related to K1 (predominantly southeastern populations) came from a different climate, we could then hypothesize that the lower number of genes involved in phenology compared to Europe as a whole may be explained by the delayed bud burst observed in beech [120]. However, a limited number of studies exist on the possible effect of water availability on bud burst timing of temperate forest trees [121, 122]. Both factors could potentially lead to adaptive divergence in phenology to cope with global warming conditions, and should be investigated further. The strong effect of PC1 (in terms of both the number of SNPs as well as the significance of correlations with additive polygenic scores) indicates that an environmental gradient affecting the selection of phenology-related genes lies across the western-eastern direction.

Physiological importance of the genes under selection

A number of candidate genes identified as significant in this study were already investigated in earlier works on European beech. These include: aldehyde dehydrogenase [62]; alcohol dehydrogenase [63]; protein phosphatase 2c [63, 65, 68]; chlorophyll a/b-binding protein [63, 70]; heat shock protein [63, 68, 69]; short-chain dehydrogenase reductase; 3-ketoacyl-CoA synthase 11; and dehydration-responsive element-binding protein 1b [68].

A detailed discussion of all genes found to be significantly associated to climate, geography, or phenotype variables is beyond the scope of this paper. However, we focused on a subset of genes significantly related to spring phenology. The monitoring of bud burst extended from the dormant bud stage (1) to the stage of leaves being stretched, smooth, and shiny (7). Therefore, the ‘bud-burst score’, as presented in this study, should be considered a complex phenotypic trait. Potential roles of specific genes controlling stages 1 and 2 (i.e. the dormant and the swelling bud) are related to release from ecodormancy. Conversely, genes preferentially expressed at the end of bud burst should be considered as candidate genes for the stages of leaf development and tissue elongation rather than for bud burst.

Cell defense and rescue-associated genes are expressed during the dormancy stage and at the onset of bud burst [36, 123,124,125,126]. This is confirmed in our study by functional analysis of genes with significant SNPs, where the most abundant GO slim term was ‘response to stimulus’, in terms of biological process (Fig. 5). Many of these genes encode proteins associated with detoxification, cold/drought hardiness, and pathogenesis, and were reported to be induced during dormancy in Quercus petraea [36] and Populus deltoides [127]. Growth inhibition occurs at the dormancy stage [128], as exemplified by the association between a gene encoding the protein TIFY9 and spring phenology, and also altitude. After dormancy release, buds remain cold-acclimated until a period of warm temperature results in deacclimation and bud break [129]. Therefore, pathogen-resistant proteins such us NDR1, GLOX, and phosphoribulokinase probably have antifreeze activity [130]. In addition, we found significant associations with genes encoding dehydrin (Dehydrin COR47), heat-shock proteins (22.0 kDa class IV heat shock protein, 18.1 kDa class I heat shock protein, Stromal 70 kDa heat shock-related protein, chloroplastic), heat stress transcription factor C-1 (HSFC1), and also 10 kDa chaperonin (mitochondrial) which are all related to phenological protection against desiccation and temperature stresses through the accumulation of dehydrins and heat-shock proteins at the onset of flushing.

We found a significant association between UDP-glycosyltransferase 83A1 and phenotypic and climatic variables. The UDP-G gene plays a role in carbohydrate metabolism, and was already reported to be related to bud burst, drought, and soil clay content [64, 89, 91]. It is also well-known that water stress enhances the production of reactive oxygen species in plants and increases susceptibility to pathogens [131]. On the one hand, oxidative stress, counteracted by the accumulation of dehydrins, heat shock proteins or sugars could contribute to the protection of cells against oxidation [132]. On the other hand, NDR, GLOX, and phosphoribulokinase genes may also contribute to defense against pathogens.

Genes associated with the gene ontology (GO) terms ‘regulation of growth’ and ‘regulation of unidimensional cell growth’ such as HD16 (Casein kinase 1-like protein HD16) and CLASP (CLIP-associated protein), respectively, may be related to the resumption of mitotic activity in meristematic cells. Indeed, it is well-known that bud burst is essentially driven by a resumption of mitotic activity in meristematic cells [133]. However, functions of these genes during bud burst are unclear. Casein kinases are involved in regulating flowering time through gibberellin signaling and are required for normal development of male floral organs and grains via the modulation of gibberellin signaling [134]. Nevertheless, CLIP-associated protein is required for cell morphogenesis and cell division [135, 136].

Activity of glycosyl hydrolases, such as probable glucan endo-1,3-beta-glucosidase A6, were also significantly associated with bud burst of European beech. This particular +enzyme is induced by gibberellic acid and is known to play a role in cell-wall mobilization and cell elongation [137] through hydrolysis of glycosidic bonds linking cell-wall components. During bud burst, induction of this enzyme could thus reflect the initiation of outgrowth taking place in early stages of bud burst. Another regulator of carbohydrate modification, namely a 1,4-alpha-glucan-branching enzyme, was also associated with bud burst phenology. Starch branching enzymes catalyze the formation of α-1,6 branches within α-glucan chains by cleaving internal α-1,4 links followed by re-attachment of the cleaved glucan to another chain via an α-1,6 linkage [138]. This enzyme was highly expressed in oak at the dormancy stage [139], suggesting that the hydrolysis of storage starch or glycogen is repressed in the quiescent buds. In contrast, the reduction in this gene expression upon bud swelling would indicate the onset of starch mobilization at this developmental stage.

In general, the contribution of genes essential for energy supply can be observed at the end of bud burst, as shown by genes encoding cytochrome P450 704B1, cytochrome b561 DOMON domain-containing protein At5g47530, and also CAB6A (chlorophyll a/b-binding protein 6A). In parallel, ribulose bisphosphate carboxylase/oxygenase activase (responsible for the activation of RuBisCO and RuBisCO large subunit-binding protein subunit beta, chloroplastic), was also found in this study, as well as that of two genes with the molecular function of glyceraldehyde-3-phosphate dehydrogenase (NAD+) (aldehyde dehydrogenase family 7 member A1 and aldehyde dehydrogenase family 2 member B4), as previously reported by Wang et al. [140]. Indeed, these authors demonstrated that enzyme activity of the glycolytic pathway increased at the release of dormancy. Regarding the developmental stages of spring phenology used in this study, this contribution of energy-related genes is undoubtedly caused by leaf development, which starts at stage 4, but mainly develops at stages from 5 to 7.


We investigated whether genes involved in the release of dormancy in Fagus sylvatica, a widely distributed forest tree species growing in various climates, show evidence of selection signatures in relation to geographic, climatic, or phenotypic variables related to adaptation. Using 380 candidate genes and a sequence capture approach, we identified 2909 associated SNPs and, based on two complementary analytical methods (LFMM, RDA), we narrowed this down to 201 SNPs within 162 (42.6%) genes associated with geographic, climatic, or population-wide phenotypic variables related to adaptation. This significantly extended the existing list of genes putatively identified as adaptive and related to spring phenology. A large proportion of significant genes seems not surprising given that the timing of spring phenology (or dormancy release, in general) varies along geographic and climate clines and has a strong adaptive importance. However, the variable pattern of significant loci across different spatial scales suggests that local adaptation may occur through multiple mechanisms, and that the importance of different genes may depend on the spatial and environmental context.

Material and methods

Plant material

We sampled one to four individuals (92 in total) of European beech originating from 47 provenances (geographic and climate data points) growing in the common-garden experimental site located in Siemianice Experimental Forest District in south-central Poland (Fig. 1; Table S1) [141]. Freshly developed leaves were sampled from individual plants in early spring 2015. After collection, leaf samples were labeled, dried, and kept at room temperature until DNA extraction.

Environmental data

Environmental data for the locations of the 47 provenances was downloaded from the WorldClim database [142]; from maps with a spatial resolution of 30 arc-seconds (approximately 1 km2) using DIVA GIS 7.5 software [143]. The characteristics of each location were quantified using a set of nineteen bioclimatic variables representing annual trends (e.g. mean annual temperature, annual precipitation), aspects of seasonality (e.g. annual range in temperature and precipitation), and extreme or potentially limiting environmental factors (e.g. temperature of the coldest and warmest months, and precipitation of the wettest and driest months). Since several bioclimatic variables are collinear in nature, we applied principal component analysis (PCA) to extract essential bioclimatic information with focused indices using the R package FactoMineR [144]. PCA is a useful approach in reducing the number of dimensions of explanatory variables with acceptable information loss under most conditions [145], and it has been used in other landscape genomics studies [29].

Phenotypic variables

The indices of spring and autumn phenology, tree height at 5 years of age, and survival in each provenance were obtained from Barzdajn and Rzeźnik [141]. These represent average and standardized observations. Spring phenology was assessed between 1995 and 1998 (on a seven point scale, assessed on a single day in each year), while autumn phenology, tree height, and survival were measured in 1998 [141]. Each individual sampled in this study was assigned a respective phenotypic index determined for its provenance (Table S1). To identify relationships between phenotypic, geographic, and climate variables, pairwise Pearson’s correlation and canonical correlation analyses (for the groups of variables) were performed, using STATISTICA software.

DNA isolation, candidate genes and genotyping

Total DNA was extracted from sampled individuals using the GeneMATRIX Plant & Fungi DNA Purification Kit (EURx, Poland) according to the manufacturer’s protocol with minor modifications. Extracted DNA was quantified using an Eppendorf BioPhotometer and Quantus Fluorometer (Promega). 100 ng of DNA from each tree was used for individual exome capture and sequencing.

We investigated the genetic variation that might be associated with geographic, climate, or phenotypic variables, using an exome capture approach [146] based on an initial set of 485 candidate genes (Supplementary Table S2). The candidate genes were selected as follows. Firstly, we created a set of 421 genes from the previously published F. sylvatica transcriptome (FSV2, [89]). These 421 genes were differentially expressed during bud dormancy release, as identified by the EdgeR method described by Lesur et al. [89]. Secondly, we chose 64 additional candidates based on their appearances in other published studies which aimed to identify genes of potential adaptive importance related to drought stress (53) and photoperiod (11) in beech and other species. These selected sequences from F. sylvatica [147], Populus trichocarpa [42, 148], and Pieca abies [149] were verified by BLAST software (Washington University Basic Local Alignment Search Tool Version 2.0) to find corresponding homologous sequences in the beech transcriptome (FSV2, [89]). Next, we ran BLAST searches to annotate the candidate genes. We aligned our sequences using BLASTX with an e-value cut-off of 0.0001 against GenBank’s non-redundant protein database (NR), UniProt Swiss-Prot, and TrEMBL protein databases. Information about these matching sequences was used to annotate our candidate genes. For significant matches in the databases, we extracted gene names, general descriptions, Gene Ontology (GO) categories, and additional information from the UniProt knowledge database.

DNA library preparation and exome capture was performed using the SeqCap EZ Library SR User’s Guide v5.1 (Nimblegen Roche). However, because the reference genome of F. sylvatica was not available at the time of the experiment set-up, the bait development performed in SeqCap EZ library preparation utilized only the published cDNA sequences for the 485 genes [89]. Following capture, 125 bp paired-end sequencing of 92 samples was performed on a single lane of Illumina HiSeq2500 sequencer. Sequence capture and sequencing was performed by IGA Technology Services (; Udine, Italy).

Sequence reads were assessed with FastQC software [150]. Adapter sequences were removed with cutadapt [151], and contaminant sequences (e.g. chloroplast genome) were removed with ERNE-FILTER [152]. Filtered, high-quality reads were mapped to the European beech reference genome version 1.3 [90] using Burrows–Wheeler Aligner (BWA-MEM algorithm) with default settings [153]. SAM files were converted to BAM files, sorted, and indexed using SAMtools v.0.1.19 [154]. SNPs were called with the Heap v.0.7.8 software [155] and stored in the vcf format.

Filtering of variants was carried out using VCFtools [156] with the following cut-offs: minimum depth of 10 reads; minor allele frequency > 5%; missing data per SNP < 25%. Contigs containing fewer than 10 base pairs per SNP were removed to control for mapping errors. Linkage disequilibrium (LD) among SNPs was accounted for using the LD pruning tool in Plink 2.0 [157, 158], where independent pairwise comparisons between each SNP were made and an r2 value was calculated. A cut-off of r2 > 0.5 was used, whereby one of a pair of SNPs was removed from the dataset if the coefficient of determination between the pair was > 0.5, thus removing SNPs showing strong signals of LD.

SnpEff software [159] was employed to classify SNPs according to their genomic regions and their positions relative to capture target regions. We identified the coordinates of targeted regions in the reference genome of European beech [90] based on gene models. Gene models were created by mapping and aligning candidate gene sequences to the genome using BLAT [160] and GMAP software [161] with 95% identity and 90% coverage cut-offs.

To improve gene annotations, we performed a manual curation of gene models. While sequence capture technology primarily targets exons, many functional elements are located outside the exonic regions. It is well-known that SNPs located in promoter and UTR regions may regulate gene expression. It is traditionally considered that introns are not as important as exons, but several studies have demonstrated some functional significance for introns [162, 163]. Many genome-wide association analyses (GWAS) and genotype-environment association analyses (GEA) have suggested strong associations between intergenic SNPs and phenotypic or environmental variables [43, 164]. Therefore, because these data should not be ignored in GEA studies [165], we analyzed SNPs located in exons, introns, and sequences up to 500 bp away from each candidate gene. Finally, the SNP dataset was filtered to remove remaining non-target variants using BEDtools v. 2.28.0 [166].

Copy number assessment

Copy number variations (CNVs) were identified using CNVkit [167], a piece of software designed to assess log2 copy ratios from targeted capture NGS data based on reads mapped to both on-target and off-target regions. CNVkit has the highest sensitivity and a typical specificity for small CNVs with sizes below 100 kb [168]. Therefore, CNVkit seemed to be the best suited to our data. CNVkit was run with the default parameters of the batch command after creating a flat reference genome as suggested in the manual using the command reference. A threshold of 0.2 was applied to identify the signals for amplification and deletions of the genes.

Genetic diversity and structure

Genetic diversity was estimated based on expected heterozygosity (He) defined as the probability that two randomly chosen alleles from the population are different using PowerMarker 3.25 software [169]. We investigated the genetic structure of sampled individuals by applying the Bayesian model-based clustering method implemented in the software STRUCTURE 2.3.4 [170]. The number of assumed populations (K) was set from 1 to 10, carried out each time in sets of 10 repeats, with a burn-in period of 50,000 iterations and 100,000 MCMC (Markov Chain Monte Carlo) repeats, and an admixture model with correlated frequencies. The optimal value of K was determined based on the ΔK method [171] using the software STRUCTURE HARVESTER 0.6.94 [172]. The probabilities of individuals’ assignments to particular clusters (q-matrix) were used to test the correlations with geographic variables of the origin of populations sampled.

Detection of outliers using LFMM

Because phenotypic variables were represented by population averages instead of individual measures, we applied the same approaches (LFMM and RDA) to detect loci with significant effects related to geographic, climate, and phenotypic variables. We used the latent factor mixed model (LFMM) approach [173] to find candidate loci under selection. According to P de Villemereuil, É Frichot, É Bazin, O François and OE Gaggiotti [76], LFMM is expected to provide the best compromise between power and error rate across different analytical scenarios. LFMM is also known to be less susceptible to both false negatives and false positives [173, 174] than other genotype-environment association (GEA) methods, such as Bayenv2 [175], because it does not rely on a specific demographic model when accounting for population structure [76, 174].

We employed an MCMC algorithm for regression analysis whereby potentially confounding population structure is modeled with unobserved (latent) factors [176]. As missing data can reduce the power of association studies [177, 178], we imputed the missing data based on the ancestry coefficients estimated by sNMF, using the “impute” function from the R package LEA [176]. In sNMF, we set K based on the number of distinct genetic clusters identified following the population genetic structure analysis and kept the best out of 10 runs based on a cross-entropy criterion. The MCMC algorithm was used for each of the geographic, climate, and phenotypic variables (i.e. longitude, latitude, altitude, PC1-PC3, spring and autumn phenology and height), using 50,000 steps for burn-in and 100,000 additional steps to compute LFMM parameters (z-scores) for all loci. The number of latent factors was set at the identified value of K. In order to compensate for run-to-run variation, the analysis was repeated over 10 independent runs and z-scores across runs were then combined in R using the LEA package [176]. The LEA package was also used to adjust p-values for multiple testing using the Benjamini–Hochberg procedure, and to calculate the genomic inflation factor to modify z-scores allowing for the control of the FDR, as described in E Frichot and O François [176]. A list of candidate loci with an FDR of 1% and adjusted p-values of < 0.001 was then generated for each explanatory variable.

Redundancy analysis to detect outlier loci

To test the multivariate relationships between genetic variation with geographic, climate and phenotypic variables, we conducted redundancy analysis (RDA) using the vegan 2.4–5 package in R [179]. RDA is a two-step process in which predictor (geographic, climatic, or phenotypic) variables and the response (the matrix of allele frequencies) variables are analyzed using multivariate linear regression, producing a matrix of fitted values. Next, PCA of the fitted values is used to produce constrained axes, which are linear combinations of the predictors [180].

RDA was used to estimate the proportion of variance in allele frequencies at SNPs across all sampling locations, which could be explained by geographic, climate, and phenotypic predictors based on the adjusted r2. We removed longitude, latitude, and PC2 variables from analyses because they were highly correlated (|r| > 0.7) [181] with PC1 (for longitude) and altitude (for latitude and PC2). Since the RDA method requires complete datasets, we performed an imputation of missing genotypes using the most common genotype at each SNP across all individuals. The global RDA’s significance was tested with an analysis of variance (ANOVA) following 1000 permutations. The explicative importance of the variables was represented as vectors in biplot graphs. Next, the candidate loci were identified based on locus scores (i.e. the loading of each locus in ordination space) separated by ±3 SD from the mean loading on the first two constrained ordination axes [77]. Predictors exhibiting the strongest associations with each candidate adaptive locus were identified using Pearson’s correlation coefficients (r). We performed a second RDA analysis using the same methods as described above, but this time within genetic groups identified by STRUCTURE independently in order to determine whether selection pressures vary between the genetic clusters.

Additive polygenic scores

The additive polygenic scores approach was used to assess the cumulative signal of candidate loci in response to environmental variation [112]. Firstly, alleles across all candidate loci that were associated with increasing values of a given geographic, climatic, or phenotypic variable (i.e. longitude, latitude, altitude, PCs, spring and autumn phenology, and also height) were identified based on the sign of their correlation between allele frequencies and the variables. Polygenic scores for each individual tree were obtained by summing the number of favored alleles for a given trait over loci. The correlation between individual additive polygenic scores and each geographic, climatic, or phenotypic variable was tested to evaluate how the cumulative signal of candidate adaptive alleles varied with each explanatory variable. Two models (a linear and a quadratic) were tested for each variable, and the best-fit model was determined based on the lowest Akaike information criterion (AIC) value.

Effect of geography, climate and phenotype on adaptive genetic variation

Partial redundancy analyses (pRDA) were performed on a combined dataset comprising outliers detected by both the RDA and the LFMM approaches to examine the relative contribution of geographic, climatic, and phenotypic variables, along with their combinations to adaptive genetic variation. Four different models were considered for partitioning variance components of the RDA (Table 3): Model 1 – a simple model with all geographic, climatic, and phenotypic variables given as explanatory variables; Model 2 – a partial model in which genetic data is explained by geographic data conditioning on climatic and phenotypic variables; Model 3 – a partial model where the genetic data is explained by climate conditioned on geographic and phenotypic variables; and Model 4 – a partial model in which the amount of genetic variation is explained by phenotypic variables conditioning on geographic and climatic data. Partial RDA was carried out using the vegan 2.4–5 package in R [179] with significance determined based on 999 permutations.

Availability of data and materials

Illumina data are publicly available at the NCBI BioProject PRJNA721723 ( Filtered SNP data in VCF format for each geographic context are available from the corresponding author upon request.


  1. 1.

    Kawecki TJ, Ebert D. Conceptual issues in local adaptation. Ecol Lett. 2004;7(12):1225–41.

    Article  Google Scholar 

  2. 2.

    Williams GC. Adaptation and natural selection: A critique of some current evolutionary thought.  Princeton: Princeton University Press; 2018;75.

  3. 3.

    Savolainen O, Lascoux M, Merila J. Ecological genomics of local adaptation. Nat Rev Genet. 2013;14(11):807–20.

    CAS  PubMed  Article  Google Scholar 

  4. 4.

    de Villemereuil P, Gaggiotti OE, Mouterde M, Till-Bottraud I. Common garden experiments in the genomic era: new perspectives and opportunities. Heredity. 2016;116(3):249–54.

    PubMed  Article  Google Scholar 

  5. 5.

    Van Kleunen M, Fischer M. Constraints on the evolution of adaptive phenotypic plasticity in plants. New Phytologist. 2005;166(1):49–60.

    Article  Google Scholar 

  6. 6.

    Leimu R, Fischer M. A meta-analysis of local adaptation in plants. PloS one. 2008;3(12):e4010.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  7. 7.

    MacLachlan IR, McDonald TK, Lind BM, Rieseberg LH, Yeaman S, Aitken SN. Genome-wide shifts in climate-related variation underpin responses to selective breeding in a widespread conifer. Proc Natl Acad Sci. 2021;118(10):e2016900118.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. 8.

    Lind BM, Menon M, Bolte CE, Faske TM, Eckert AJ. The genomics of local adaptation in trees: are we out of the woods yet? Tree Genet Genomes. 2018;14(2):29.

    Article  Google Scholar 

  9. 9.

    Mátyás C. Climatic adaptation of trees: rediscovering provenance tests. Euphytica. 1996;92(1):45–54.

    Article  Google Scholar 

  10. 10.

    Sork VL, Aitken SN, Dyer RJ, Eckert AJ, Legendre P, Neale DB. Putting the landscape into the genomics of trees: approaches for understanding local adaptation and population responses to changing climate. Tree Genet Genomes. 2013;9(4):901–11.

    Article  Google Scholar 

  11. 11.

    Depardieu C, Gérardi S, Nadeau S, Parent GJ, Mackay J, Lenz P, et al. Connecting tree-ring phenotypes, genetic associations, and transcriptomics to decipher the genomic architecture of drought adaptation in a widespread conifer. Mol Ecol. 2021.

  12. 12.

    Wright JW. Introduction to forest genetics. New York: Academic Press; 1996.

    Google Scholar 

  13. 13.

    Morgenstern EK. Geographic variation in forest trees: genetic basis and application of knowledge in silviculture. Vancouver: UBC Press; 1996.

  14. 14.

    Vitasse Y, Delzon S, Dufrêne E, Pontailler J-Y, Louvet J-M, Kremer A, et al. Leaf phenology sensitivity to temperature in European trees: do within-species populations exhibit similar responses? Agric Forest Meteorol. 2009;149(5):735–44.

    Article  Google Scholar 

  15. 15.

    Bennie J, Kubin E, Wiltshire A, Huntley B, Baxter R. Predicting spatial and temporal patterns of bud-burst and spring frost risk in north-West Europe: the implications of local adaptation to climate. Global Change Biol. 2010;16(5):1503–14.

    Article  Google Scholar 

  16. 16.

    Howe GT, Aitken SN, Neale DB, Jermstad KD, Wheeler NC, Chen THH. From genotype to phenotype: unraveling the complexities of cold adaptation in forest trees. Can J Botany-Revue Canadienne De Botanique. 2003;81(12):1247–66.

    CAS  Google Scholar 

  17. 17.

    Visser ME, Holleman LJM. Warmer springs disrupt the synchrony of oak and winter moth phenology. P Roy Soc B-Biol Sci. 2001;268(1464):289–94.

    CAS  Article  Google Scholar 

  18. 18.

    Ghelardini L, Santini A. Avoidance by early flushing: a new perspective on Dutch elm disease research. Iforest-Biogeosci Forestry. 2009;2(4):143–53.

    Article  Google Scholar 

  19. 19.

    Menzel A, Sparks TH, Estrella N, Koch E, Aasa A, Ahas R, et al. European phenological response to climate change matches the warming pattern. Global Change Biol. 2006;12(10):1969–76.

    Article  Google Scholar 

  20. 20.

    Nordli O, Wielgolaski FE, Bakken AK, Hjeltnes SH, Mage F, Sivle A, et al. Regional trends for bud burst and flowering of woody plants in Norway as related to climate change. Int J Biometeorol. 2008;52(7):625–39.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  21. 21.

    Polgar CA, Primack RB. Leaf-out phenology of temperate woody plants: from trees to ecosystems. New Phytologist. 2011;191(4):926–41.

    Article  Google Scholar 

  22. 22.

    Sangüesa-Barreda G, Di Filippo A, Piovesan G, Rozas V, Di Fiore L, García-Hidalgo M, et al. Warmer springs have increased the frequency and extension of late-frost defoliations in southern European beech forests. Sci Total Environ. 2021;775:145860.

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  23. 23.

    Kramer K, Ducousso A, Gomory D, Hansen JK, Ionita L, Liesebach M, et al. Chilling and forcing requirements for foliage bud burst of European beech (Fagus sylvatica L.) differ between provenances and are phenotypically plastic. Agric Forest Meteorol. 2017;234:172–81.

    Article  Google Scholar 

  24. 24.

    Vitasse Y, Bresson CC, Kremer A, Michalet R, Delzon S. Quantifying phenological plasticity to temperature in two temperate tree species. Funct Ecol. 2010;24(6):1211–8.

    Article  Google Scholar 

  25. 25.

    Varsamis G, Papageorgiou AC, Merou T, Takos I, Malesios C, Manolis A, et al. Adaptive Diversity of Beech Seedlings Under Climate Change Scenarios. Front Plant Sci. 2019;9:1918.

    PubMed  PubMed Central  Article  Google Scholar 

  26. 26.

    Jermstad KD, Bassoni DL, Jech KS, Wheeler NC, Neale DB. Mapping of quantitative trait loci controlling adaptive traits in coastal Douglas-fir. I. Timing of vegetative bud flush. Theoret Appl Genetics. 2001;102(8):1142–51.

    CAS  Article  Google Scholar 

  27. 27.

    Ducousso A, Guyon J, Krémer A. Latitudinal and altitudinal variation of bud burst in western populations of sessile oak (Quercus petraea (Matt) Liebl). Ann For Sci. 1996;53(2–3):775–82.

    Article  Google Scholar 

  28. 28.

    Scotti-Saintagne C, Bodenes C, Barreneche T, Bertocchi E, Plomion C, Kremer A. Detection of quantitative trait loci controlling bud burst and height growth in Quercus robur L. Theoret Appl Genet. 2004;109(8):1648–59.

    CAS  Article  Google Scholar 

  29. 29.

    Gugger PF, Fitz-Gibbon ST, Albarran-Lara A, Wright JW, Sork VL. Landscape genomics of Quercus lobata reveals genes involved in local climate adaptation at multiple spatial scales. Mol Ecol. 2021;30(2):406–23.

    CAS  PubMed  Article  Google Scholar 

  30. 30.

    Johnsen Ø, Dæhlen OG, Østreng G, Skrøppa T. Daylength and temperature during seed production interactively affect adaptive performance of Picea abies progenies. New Phytologist. 2005;168(3):589–96.

    Article  Google Scholar 

  31. 31.

    Harismendy O, Ng PC, Strausberg RL, Wang X, Stockwell TB, Beeson KY, et al. Evaluation of next generation sequencing platforms for population targeted sequencing studies. Genome Biol. 2009;10(3):R32.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  32. 32.

    Stapley J, Reger J, Feulner PG, Smadja C, Galindo J, Ekblom R, et al. Adaptation genomics: the next generation. Trends Ecol Evol. 2010;25(12):705–12.

    PubMed  Article  PubMed Central  Google Scholar 

  33. 33.

    Eckert AJ, van Heerwaarden J, Wegrzyn JL, Nelson CD, Ross-Ibarra J, Gonzalez-Martinez SC, et al. Patterns of population structure and environmental associations to aridity across the range of loblolly pine (Pinus taeda L., Pinaceae). Genetics. 2010;185(3):969–82.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. 34.

    Mathiason K, He D, Grimplet J, Venkateswari J, Galbraith DW, Or E, et al. Transcript profiling in Vitis riparia during chilling requirement fulfillment reveals coordination of gene expression patterns with optimized bud break. Funct Integr Genomics. 2009;9(1):81–96.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  35. 35.

    Hedley PE, Russell JR, Jorgensen L, Gordon S, Morris JA, Hackett CA, et al. Candidate genes associated with bud dormancy release in blackcurrant (Ribes nigrum L.). BMC Plant Biol. 2010;10:202.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  36. 36.

    Derory J, Leger P, Garcia V, Schaeffer J, Hauser MT, Salin F, et al. Transcriptome analysis of bud burst in sessile oak (Quercus petraea). New Phytologist. 2006;170(4):723–38.

    CAS  Article  Google Scholar 

  37. 37.

    Derory J, Scotti-Saintagne C, Bertocchi E, Le Dantec L, Graignic N, Jauffres A, et al. Contrasting relationships between the diversity of candidate genes and variation of bud burst in natural and segregating populations of European oaks. Heredity. 2010;104(5):438–48.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  38. 38.

    Parchman TL, Jahner JP, Uckele KA, Galland LM, Eckert AJ. RADseq approaches and applications for forest tree genetics. Tree Genet Genomes. 2018;14: 39.

  39. 39.

    Ulaszewski B, Meger J, Burczyk J. Comparative Analysis of SNP Discovery and Genotyping in Fagus sylvatica L. and Quercus robur L. Using RADseq, GBS, and ddRAD Methods. Forests. 2021;12(2):222.

    Article  Google Scholar 

  40. 40.

    Lowry DB, Hoban S, Kelley JL, Lotterhos KE, Reed LK, Antolin MF, et al. Breaking RAD: an evaluation of the utility of restriction site-associated DNA sequencing for genome scans of adaptation. Mol Ecol Resourc. 2017;17(2):142–52.

    CAS  Article  Google Scholar 

  41. 41.

    Catchen JM, Hohenlohe PA, Bernatchez L, Funk WC, Andrews KR, Allendorf FW. Unbroken: RADseq remains a powerful tool for understanding the genetics of adaptation in natural populations. Mol Ecol Resourc. 2017;17(3):362–5.

    CAS  Article  Google Scholar 

  42. 42.

    Evans LM, Slavov GT, Rodgers-Melnick E, Martin J, Ranjan P, Muchero W, et al. Population genomics of Populus trichocarpa identifies signatures of selection and adaptive trait associations. Nat Genet. 2014;46(10):1089–96.

    CAS  PubMed  Article  Google Scholar 

  43. 43.

    Zhang M, Suren H, Holliday JA. Phenotypic and genomic local adaptation across latitude and altitude in Populus trichocarpa. Genome Biol Evol. 2019;11(8):2256–72.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. 44.

    Packham JR, Thomas PA, Atkinson MD, Degen T. Biological Flora of the British Isles: Fagus sylvatica. J Ecol. 2012;100(6):1557–608.

    Article  Google Scholar 

  45. 45.

    Bolte A, Czajkowski T, Kompa T. The north-eastern distribution range of European beech - a review. Forestry. 2007;80(4):413–29.

    Article  Google Scholar 

  46. 46.

    Geßler A, Keitel C, Kreuzwieser J, Matyssek R, Seiler W, Rennenberg H. Potential risks for European beech (Fagus sylvatica L.) in a changing climate. Trees. 2006;21(1):1–11.

    Article  Google Scholar 

  47. 47.

    Kramer K, Degen B, Buschbom J, Hickler T, Thuiller W, Sykes MT, et al. Modelling exploration of the future of European beech (Fagus sylvatica L.) under climate change-range, abundance, genetic diversity and adaptive response. Forest Ecol Manag. 2010;259(11):2213–22.

    Article  Google Scholar 

  48. 48.

    Czucz B, Galhidy L, Matyas C. Present and forecasted xeric climatic limits of beech and sessile oak distribution at low altitudes in Central Europe. Ann Forest Sci. 2011;68(1):99–108.

    Article  Google Scholar 

  49. 49.

    Scharnweber T, Manthey M, Criegee C, Bauwe A, Schroder C, Wilmking M. Drought matters - declining precipitation influences growth of Fagus sylvatica L. and Quercus robur L. in North-Eastern Germany. Forest Ecol Manag. 2011;262(6):947–61.

    Article  Google Scholar 

  50. 50.

    Mette T, Dolos K, Meinardus C, Brauning A, Reineking B, Blaschke M, et al. Climatic turning point for beech and oak under climate change in Central Europe. Ecosphere. 2013;4(12):1–19.

    Article  Google Scholar 

  51. 51.

    Zimmermann J, Hauck M, Dulamsuren C, Leuschner C. Climate warming-related growth decline affects Fagus sylvatica, but not other broad-leaved tree species in central European mixed forests. Ecosystems. 2015;18(4):560–72.

    CAS  Article  Google Scholar 

  52. 52.

    Charru M, Seynave I, Morneau F, Bontemps JD. Recent changes in forest productivity: an analysis of national forest inventory data for common beech (Fagus sylvatica L.) in North-Eastern France. Forest Ecol Manag. 2010;260(5):864–74.

    Article  Google Scholar 

  53. 53.

    Peñuelas J, Boada M. A global change-induced biome shift in the Montseny mountains (NE Spain). Global Change Biol. 2003;9(2):131–40.

    Article  Google Scholar 

  54. 54.

    Meier ES, Edwards TC, Kienast F, Dobbertin M, Zimmermann NE. Co-occurrence patterns of trees along macro-climatic gradients and their potential influence on the present and future distribution of Fagus sylvatica L. J Biogeography. 2011;38(2):371–82.

    Article  Google Scholar 

  55. 55.

    Hanewinkel M, Cullmann DA, Schelhaas MJ, Nabuurs GJ, Zimmermann NE. Climate change may cause severe loss in the economic value of European forest land. Nat Climate Change. 2013;3(3):203–7.

    Article  Google Scholar 

  56. 56.

    Gomory D, Ditmarova L, Hrivnak M, Jamnicka G, Kmet’ J, Krajmerova D, et al. Differentiation in phenological and physiological traits in European beech (Fagus sylvatica L.). Eur J Forest Res. 2015;134(6):1075–85.

    Article  Google Scholar 

  57. 57.

    Chmura DJ, Rozkowski R. Variability of beech provenances in spring and autumn phenology. Silvae Genetica. 2002;51(2–3):123–7.

    Google Scholar 

  58. 58.

    Gomory D, Paule L. Trade-off between height growth and spring flushing in common beech (Fagus sylvatica L.). Ann Forest Sci. 2011;68(5):975–84.

    Article  Google Scholar 

  59. 59.

    Vitasse Y, Basler D. What role for photoperiod in the bud burst phenology of European beech. Eur J Forest Res. 2013;132(1):1–8.

    Article  Google Scholar 

  60. 60.

    Von Wuehlisch G, Krusche D, Muhs H-J. Variation in temperature sum requirement for flushing of beech provenances. Silvae Genetica. 1995;44(5–6):343–6.

    Google Scholar 

  61. 61.

    Kramer K, Buiteveld J, Forstreuter M, Geburek T, Leonardi S, Menozzi P, et al. Bridging the gap between ecophysiological and genetic knowledge to assess the adaptive potential of European beech. Ecol Model. 2008;216(3–4):333–53.

    Article  Google Scholar 

  62. 62.

    Seifert S, Vornam B, Finkeldey R. DNA sequence variation and development of SNP markers in beech (Fagus sylvatica L.). Eur J Forest Res. 2012;131(6):1761–70.

    CAS  Article  Google Scholar 

  63. 63.

    Lalagüe H, Csilléry K, Oddou-Muratorio S, Safrana J, de Quattro C, Fady B, et al. Nucleotide diversity and linkage disequilibrium at 58 stress response and phenology candidate genes in a European beech (Fagus sylvatica L.) population from southeastern France. Tree Genet Genomes. 2014;10(1):15–26.

    Article  Google Scholar 

  64. 64.

    Rellstab C, Zoller S, Walthert L, Lesur I, Pluess AR, Graf RE, et al. Signatures of local adaptation in candidate genes of oaks (Quercus spp.) with respect to present and future climatic conditions. Mol Ecol. 2016;25(23):5907–24.

    PubMed  Article  PubMed Central  Google Scholar 

  65. 65.

    Müller M, Seifert S, Finkeldey R. A candidate gene-based association study reveals SNPs significantly associated with bud burst in European beech (Fagus sylvatica L.). Tree Genet Genomes. 2015;11(6):1–13.

    Article  Google Scholar 

  66. 66.

    Cuervo-Alarcon L, Arend M, Müller M, Sperisen C, Finkeldey R, Krutovsky KV. A candidate gene association analysis identifies SNPs potentially involved in drought tolerance in European beech (Fagus sylvatica L.). Sci Rep. 2021;11(1):2386.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  67. 67.

    Pfenninger M, Reuss F, Kiebler A, Schönnenbeck P, Caliendo C, Gerber S, et al. Genomic basis of drought resistance in Fagus sylvatica. bioRxiv. 2020.12.04.411264.

  68. 68.

    Csilléry K, Lalagüe H, Vendramin GG, González-Martínez SC, Fady B, Oddou-Muratorio S. Detecting short spatial scale local adaptation and epistatic selection in climate-related candidate genes in European beech (Fagus sylvatica) populations. Mol Ecol. 2014;23(19):4696–708.

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  69. 69.

    Pluess AR, Frank A, Heiri C, Lalaguee H, Vendramin GG, Oddou-Muratorio S. Genome-environment association study suggests local adaptation to climate at the regional scale in Fagus sylvatica. New Phytologist. 2016;210(2):589–601.

    CAS  Article  Google Scholar 

  70. 70.

    Krajmerová D, Hrivnák M, Ditmarová Ľ, Jamnická G, Kmeť J, Kurjak D, et al. Nucleotide polymorphisms associated with climate, phenology and physiological traits in European beech (Fagus sylvatica L.). New Forests. 2017;48(3):463–77.

    Article  Google Scholar 

  71. 71.

    Schoville SD, Bonin A, François O, Lobreaux S, Melodelima C, Manel S. Adaptive genetic variation on the landscape: methods and cases. Ann Rev Ecol Evol Syst. 2012;43(1):23–43.

    Article  Google Scholar 

  72. 72.

    Rellstab C, Gugerli F, Eckert AJ, Hancock AM, Holderegger R. A practical guide to environmental association analysis in landscape genomics. Mol Ecol. 2015;24(17):4348–70.

    PubMed  Article  Google Scholar 

  73. 73.

    Jensen JD, Foll M, Bernatchez L. The past, present and future of genomic scans for selection. Mol Ecol. 2016;25(1):1–4.

    PubMed  Article  Google Scholar 

  74. 74.

    Joost S, Bonin A, Bruford MW, Despres L, Conord C, Erhardt G, et al. A spatial analysis method (SAM) to detect candidate loci for selection: towards a landscape genomics approach to adaptation. Mol Ecol. 2007;16(18):3955–69.

    CAS  PubMed  Article  Google Scholar 

  75. 75.

    De Mita S, Thuillet AC, Gay L, Ahmadi N, Manel S, Ronfort J, et al. Detecting selection along environmental gradients: analysis of eight methods and their effectiveness for outbreeding and selfing populations. Mol Ecol. 2013;22(5):1383–99.

    PubMed  Article  Google Scholar 

  76. 76.

    de Villemereuil P, Frichot É, Bazin É, François O, Gaggiotti OE. Genome scan methods against more complex models: when and how much should we trust them? Mol Ecol. 2014;23(8):2006–19.

    PubMed  Article  Google Scholar 

  77. 77.

    Forester BR, Lasky JR, Wagner HH, Urban DL. Comparing methods for detecting multilocus adaptation with multivariate genotype-environment associations. Mol Ecol. 2018;27(9):2215–33.

    CAS  PubMed  Article  Google Scholar 

  78. 78.

    Petit RJ, El Mousadik A, Pons O. Identifying populations for conservation on the basis of genetic markers. Conserv Biol. 1998;12(4):844–55.

    Article  Google Scholar 

  79. 79.

    Yeaman S, Whitlock MC. The genetic architecture of adaptation under migration-selection balance. Evolution. 2011;65(7):1897–911.

    PubMed  Article  Google Scholar 

  80. 80.

    Le Corre V, Kremer A. The genetic differentiation at quantitative trait loci under local adaptation. Mol Ecol. 2012;21(7):1548–66.

    PubMed  Article  Google Scholar 

  81. 81.

    De Kort H, Vandepitte K, Bruun HH, Closset-Kopp D, Honnay O, Mergeay J. Landscape genomics and a common garden trial reveal adaptive differentiation to temperature across Europe in the tree species Alnus glutinosa. Mol Ecol. 2014;23(19):4709–21.

    PubMed  Article  CAS  Google Scholar 

  82. 82.

    Keller SR, Levsen N, Olson MS, Tiffin P. Local adaptation in the flowering-time gene network of balsam poplar, Populus balsamifera L. Mol Biol Evol. 2012;29(10):3143–52.

    CAS  PubMed  Article  Google Scholar 

  83. 83.

    Geraldes A, Farzaneh N, Grassa CJ, McKown AD, Guy RD, Mansfield SD, et al. Landscape genomics of Populus Trichocarpa: the role of hybridization, limited gene flow, and natural selection in shaping patterns of population structure. Evolution. 2014;68(11):3260–80.

    PubMed  Article  Google Scholar 

  84. 84.

    Eckert AJ, Bower AD, Wegrzyn JL, Pande B, Jermstad KD, Krutovsky KV, et al. Association genetics of coastal Douglas fir (Pseudotsuga menziesii var. menziesii, Pinaceae). I. Cold-hardiness related traits. Genetics. 2009;182(4):1289–302.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  85. 85.

    Sork VL, Davis FW, Westfall R, Flint A, Ikegami M, Wang H, et al. Gene movement and genetic association with regional climate gradients in California valley oak (Quercus lobata nee) in the face of climate change. Mol Ecol. 2010;19(17):3806–23.

    PubMed  Article  Google Scholar 

  86. 86.

    Martins K, Gugger PF, Llanderal-Mendoza J, Gonzalez-Rodriguez A, Fitz-Gibbon ST, Zhao JL, et al. Landscape genomics provides evidence of climate-associated genetic variation in Mexican populations of Quercus rugosa. Evol Appl. 2018;11(10):1842–58.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  87. 87.

    Scalfi M, Mosca E, Di Pierro EA, Troggio M, Vendramin GG, Sperisen C, et al. Micro- and macro-geographic scale effect on the molecular imprint of selection and adaptation in Norway spruce. PloS one. 2014;9(12):e115499.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  88. 88.

    Cuervo-Alarcon L, Arend M, Muller M, Sperisen C, Finkeldey R, Krutovsky KV. Genetic variation and signatures of natural selection in populations of European beech (Fagus sylvatica L.) along precipitation gradients. Tree Genet Genomes. 2018;14(6):84.

    Article  Google Scholar 

  89. 89.

    Lesur I, Bechade A, Lalanne C, Klopp C, Noirot C, Leple JC, et al. A unigene set for European beech (Fagus sylvatica L.) and its use to decipher the molecular mechanisms involved in dormancy regulation. Mol Ecol Resourc. 2015;15(5):1192–204.

    CAS  Article  Google Scholar 

  90. 90.

    Mishra B, Gupta DK, Pfenninger M, Hickler T, Langer E, Nam B, et al. A reference genome of the European beech (Fagus sylvatica L.). Gigascience. 2018;7(6):giy063.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  91. 91.

    Muller M, Seifert S, Finkeldey R. Comparison and confirmation of SNP-bud burst associations in European beech populations in Germany. Tree Genet Genomes. 2017;13(3):59.

    Article  Google Scholar 

  92. 92.

    Ehrenreich IM, Hanzawa Y, Chou L, Roe JL, Kover PX, Purugganan MD. Candidate Gene Association mapping of Arabidopsis flowering time. Genetics. 2009;183(1):325–35.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  93. 93.

    Estravis-Barcala M, Mattera MG, Soliani C, Bellora N, Opgenoorth L, Heer K, et al. Molecular bases of responses to abiotic stress in trees. J Exper Botany. 2019;71(13):3765–79.

    Article  CAS  Google Scholar 

  94. 94.

    Jump AS, Marchant R, Penuelas J. Environmental change and the option value of genetic diversity. Trends Plant Sci. 2009;14(1):51–8.

    CAS  PubMed  Article  Google Scholar 

  95. 95.

    Potter KM, Jetton RM, Bower A, Jacobs DF, Man G, Hipkins VD, et al. Banking on the future: progress, challenges and opportunities for the genetic conservation of forest trees. New Forests. 2017;48(2):153–80.

    Article  Google Scholar 

  96. 96.

    Seifert S, Vornam B, Finkeldey R. A set of 17 single nucleotide polymorphism (SNP) markers for European beech (Fagus sylvatica L.). Conserv Genet Resourc. 2012;4(4):1045–7.

    Article  Google Scholar 

  97. 97.

    Müller M, Seifert S, Finkeldey R. Identification of SNPs in candidate genes potentially involved in bud burst in European beech (Fagus sylvatica L.). Silvae Genetica. 2015;64(1–6):1–20.

    Article  Google Scholar 

  98. 98.

    Saltré F, Duputié A, Gaucherel C, Chuine I. How climate, migration ability and habitat fragmentation affect the projected future distribution of European beech. Global Change Biol. 2015;21(2):897–910.

    Article  Google Scholar 

  99. 99.

    Rajendra KC, Seifert S, Prinz K, Gailing O, Finkeldey R. Subtle human impacts on neutral genetic diversity and spatial patterns of genetic variation in European beech (Fagus sylvatica). Forest Ecol Manag. 2014;319:138–49.

    Article  Google Scholar 

  100. 100.

    Pluess AR, Weber P. Drought-adaptation potential in Fagus sylvatica: linking moisture availability with genetic diversity and dendrochronology. PloS one. 2012;7(3):e33636.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  101. 101.

    Magri D, Vendramin GG, Comps B, Dupanloup I, Geburek T, Gomory D, et al. A new scenario for the quaternary history of European beech populations: palaeobotanical evidence and genetic consequences. New Phytologist. 2006;171(1):199–221.

    CAS  Article  Google Scholar 

  102. 102.

    Piotti A, Leonardi S, Buiteveld J, Geburek T, Gerber S, Kramer K, et al. Comparison of pollen gene flow among four European beech (Fagus sylvatica L.) populations characterized by different management regimes. Heredity. 2012;108(3):322–31.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  103. 103.

    Zhao K, Tung CW, Eizenga GC, Wright MH, Ali ML, Price AH, et al. Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nat Commun. 2011;2(1):1–10.

    Google Scholar 

  104. 104.

    Hendry AP. Key questions in the genetics and genomics of eco-evolutionary dynamics. Heredity. 2013;111(6):456–66.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  105. 105.

    Wuehlisch G, Krusche D, Muhs H-J. Variation in temperature sum requirement for flushing of beechprovenances. Silvae Genetica. 1995;44(5–6):343–6.

    Google Scholar 

  106. 106.

    Campbell RK, Sorensen FC. Cold-acclimation in seedling Douglas-fir related to phenology and provenance. Ecology. 1973;54(5):1148–51.

    Article  Google Scholar 

  107. 107.

    Chuine I, Cour P, Rousseau DD. Selecting models to predict the timing of flowering of temperate trees: implications for tree phenology modelling. Plant Cell Environ. 1999;22(1):1–13.

    Article  Google Scholar 

  108. 108.

    St Clair JB, Mandel NL, Vance-Borland KW. Genecology of Douglas fir in Western Oregon and Washington. Ann Botany. 2005;96(7):1199–214.

    Article  Google Scholar 

  109. 109.

    Harrington CA, Gould PJ, St Clair JB. Modeling the effects of winter environment on dormancy release of Douglas-fir. Forest Ecol Manag. 2010;259(4):798–808.

    Article  Google Scholar 

  110. 110.

    Robson TM, Garzon MB, Consortium BD. Phenotypic trait variation measured on European genetic trials of Fagus sylvatica L. Scientific Data. 2018;5(1):1–7.

    Article  Google Scholar 

  111. 111.

    Eckert AJ, Maloney PE, Vogler DR, Jensen CE, Mix AD, Neale DB. Local adaptation at fine spatial scales: an example from sugar pine (Pinus lambertiana, Pinaceae). Tree Genet Genomes. 2015;11: 42.

  112. 112.

    Gagnaire PA, Gaggiotti OE. Detecting polygenic selection in marine populations by combining population genomics and quantitative genetics approaches. Curr Zool. 2016;62(6):603–16.

    PubMed  PubMed Central  Article  Google Scholar 

  113. 113.

    González-Martínez SC, Wheeler NC, Ersoz E, Nelson CD, Neale DB. Association genetics in Pinus taeda LI wood property traits. Genetics. 2007;175(1):399–409.

    PubMed  PubMed Central  Article  Google Scholar 

  114. 114.

    Ingvarsson PK, Garcia MV, Luquez V, Hall D, Jansson S. Nucleotide polymorphism and phenotypic associations within and around the phytochrome B2 locus in European Aspen (Populus tremula, Salicaceae). Genetics. 2008;178(4):2217–26.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  115. 115.

    Čufar K, De Luis M, Saz MA, Črepinšek Z, Kajfež-Bogataj L. Temporal shifts in leaf phenology of beech (Fagus sylvatica) depend on elevation. Trees. 2012;26(4):1091–100.

    Article  Google Scholar 

  116. 116.

    Murray M, Cannell M, Smith R. Date of budburst of fifteen tree species in Britain following climatic warming. J Appl Ecol. 1989;26:693–700.

  117. 117.

    Falusi M, Calamassi R. Bud dormancy in beech (Fagus-Sylvatica L) - effect of chilling and photoperiod on dormancy release of beech seedlings. Tree Physiology. 1990;6(4):429–38.

    CAS  PubMed  Article  Google Scholar 

  118. 118.

    Heide OM. Dormancy release in beech buds (Fagus-Sylvatica) requires both chilling and long days. Physiologia Plantarum. 1993;89(1):187–91.

    Article  Google Scholar 

  119. 119.

    Caffarra A, Donnelly A. The ecological significance of phenology in four different tree species: effects of light and temperature on bud burst. Int J Biometeorol. 2011;55(5):711–21.

    PubMed  Article  Google Scholar 

  120. 120.

    Vitasse Y, Delzon S, Bresson CC, Michalet R, Kremer A. Altitudinal differentiation in growth and phenology among populations of temperate-zone tree species growing in a common garden. Can J Forest Res. 2009;39(7):1259–69.

    Article  Google Scholar 

  121. 121.

    Morin X, Roy J, Sonié L, Chuine I. Changes in leaf phenology of three European oak species in response to experimental climate change. New Phytologist. 2010;186(4):900–10.

    Article  Google Scholar 

  122. 122.

    Kuster TM, Dobbertin M, Gunthardt-Goerg MS, Schaub M, Arend M. A Phenological timetable of oak growth under experimental drought and air warming. PloS one. 2014;9(2):e89724.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  123. 123.

    Santamaria ME, Rodriguez R, Canal MJ, Toorop PE. Transcriptome analysis of chestnut (Castanea sativa) tree buds suggests a putative role for epigenetic control of bud dormancy. Ann Botany. 2011;108(3):485–98.

    CAS  Article  Google Scholar 

  124. 124.

    Falavigna VS, Porto DD, Buffon V, Margis-Pinheiro M, Pasquali G, Revers LF. Differential transcriptional profiles of dormancy-related genes in apple buds. Plant Mol Biol Report. 2014;32(4):796–813.

    CAS  Article  Google Scholar 

  125. 125.

    Andersen UB, Kjaer KH, Erban A, Alpers J, Hincha DK, Kopka J, et al. Impact of seasonal warming on overwintering and spring phenology of blackcurrant. Environ Exper Botany. 2017;140:96–109.

    Article  Google Scholar 

  126. 126.

    Liu G, Li W, Zheng P, Xu T, Chen L, Liu D, et al. Transcriptomic analysis of ‘Suli’ pear (Pyrus pyrifolia white pear group) buds during the dormancy by RNA-Seq. BMC Genomics. 2012;13(1):700.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  127. 127.

    Park S, Keathley DE, Han K-H. Transcriptional profiles of the annual growth cycle in Populus deltoides. Tree Physiol. 2008;28(3):321–9.

    CAS  PubMed  Article  Google Scholar 

  128. 128.

    Arora R, Rowland LJ, Tanino K. Induction and release of bud dormancy in woody perennials: a science comes of age. Hortscience. 2003;38(5):911–21.

    Article  Google Scholar 

  129. 129.

    Welling A, Palva ET. Molecular control of cold acclimation in trees. Physiologia Plantarum. 2006;127(2):167–81.

    CAS  Article  Google Scholar 

  130. 130.

    Hon W-C, Griffith M, Mlynarz A, Kwok YC, Yang DS. Antifreeze proteins in winter rye are similar to pathogenesis-related proteins. Plant Physiol. 1995;109(3):879–89.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  131. 131.

    Bray EA, Bailey-Serres J, Weretilnyk E. Responses to abiotic stresses. In: Buchanan BB, Gruissem W, Jones RL, editors. Biochemistry and Molecular Biology of Plants. Rockville: American Society of Plant Physiologists; 2000. p. 1158–203.

    Google Scholar 

  132. 132.

    Rhodes D, Hanson A. Quaternary ammonium and tertiary sulfonium compounds in higher plants. Ann Rev Plant Biol. 1993;44(1):357–84.

    CAS  Article  Google Scholar 

  133. 133.

    Horvath D. Bud Dormancy and Growth. In: Plant Developmental Biology - Biotechnological Perspectives: Volume 1. Edited by Pua EC, Davey MR. Springer, Berlin Heidelberg; 2010:53-70.

  134. 134.

    Dai C, Xue HW. Rice early flowering1, a CKI, phosphorylates DELLA protein SLR1 to negatively regulate gibberellin signalling. USA: EMBO J. 2010;29(11):1916–27.

  135. 135.

    Ambrose JC, Shoji T, Kotzer AM, Pighin JA, Wasteneys GO. The Arabidopsis CLASP gene encodes a microtubule-associated protein involved in cell expansion and division. Plant Cell. 2007;19(9):2763–75.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  136. 136.

    Kirik V, Herrmann U, Parupalli C, Sedbrook JC, Ehrhardt DW, Hülskamp M. CLASP localizes in two discrete patterns on cortical microtubules and is required for cell morphogenesis and cell division in Arabidopsis. J Cell Sci. 2007;120(24):4416–25.

    CAS  PubMed  Article  Google Scholar 

  137. 137.

    Hrmova M, Fincher GB. Structure-function relationships of β-D-glucan endo-and exohydrolases from higher plants. Plant Mol Biol. 2001;47(1):73–91.

    CAS  PubMed  Article  Google Scholar 

  138. 138.

    Ball SG, Morell MK. From bacterial glycogen to starch: understanding the biogenesis of the plant starch granule. Ann Rev Plant Biol. 2003;54(1):207–33.

    CAS  Article  Google Scholar 

  139. 139.

    Ueno S, Klopp C, Leple JC, Derory J, Noirot C, Leger V, et al. Transcriptional profiling of bud dormancy induction and release in oak by next-generation sequencing. BMC Genomics. 2013;14:236.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  140. 140.

    Wang SY, Jiao HJ, Faust M. Changes in metabolic enzyme activities during thidiazuron-induced lateral budbreak of apple. HortScience. 1991;26(2):171–3.

    CAS  Article  Google Scholar 

  141. 141.

    Barzdajn W, Rzeznik Z. Wstępne wyniki międzynarodowego doświadczenia proweniencyjnego z bukiem (Fagus sylvatica L.) serii 1993/1995 w Leśnym Zakłądzie Doświadczalnym Siemianice. Sylwan. 2002;146(2):149–64.

    Google Scholar 

  142. 142.

    Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A. Very high resolution interpolated climate surfaces for global land areas. Int J Climatol. 2005;25(15):1965–78.

    Article  Google Scholar 

  143. 143.

    Hijmans RJ, Guarino L, Mathur P. DIVA-GIS Version 7.5. Manual; 2012.

    Google Scholar 

  144. 144.

    Husson F, Josse J, Le S, Mazet J, Husson MF. Package ‘FactoMineR’. An R package. 2016;96:698.

    Google Scholar 

  145. 145.

    Bro R, Smilde AK. Principal component analysis. Analytical Methods. 2014;6(9):2812–31.

    CAS  Article  Google Scholar 

  146. 146.

    Kaur P, Gaikwad K. From genomes to GENE-omes: exome sequencing concept and applications in crop improvement. Front Plant Sci. 2017;8:2164.

    PubMed  PubMed Central  Article  Google Scholar 

  147. 147.

    Carsjens C, Ngoc QN, Guzy J, Knutzen F, Meier IC, Muller M, et al. Intra-specific variations in expression of stress-related genes in beech progenies are stronger than drought-induced responses. Tree Physiology. 2014;34(12):1348–61.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  148. 148.

    Street NR, Skogstrom O, Sjodin A, Tucker J, Rodriguez-Acosta M, Nilsson P, et al. The genetics and genomics of the drought response in Populus. Plant J. 2006;48(3):321–41.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  149. 149.

    Chen J, Tsuda Y, Stocks M, Kallman T, Xu N, Karkkainen K, et al. Clinal variation at phenology-related genes in spruce: parallel evolution in FTL2 and Gigantea? Genetics. 2014;197(3):1025–38.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  150. 150.

    Andrews S: FastQC: a quality control tool for high throughput sequence data. software. Available online at: 2010.

    Google Scholar 

  151. 151.

    Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17(1):10–2.

    Article  Google Scholar 

  152. 152.

    Del Fabbro C, Scalabrin S, Morgante M, Giorgi FM. An extensive evaluation of read trimming effects on Illumina NGS data analysis. PloS one. 2013;8(12):e85024.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  153. 153.

    Li H, Durbin R. Fast and accurate short read alignment with burrows-Wheeler transform. Bioinformatics. 2009;25(14):1754–60.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  154. 154.

    Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25(16):2078–9.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  155. 155.

    Kobayashi M, Ohyanagi H, Takanashi H, Asano S, Kudo T, Kajiya-Kanegae H, et al. Heap: a highly sensitive and accurate SNP detection tool for low-coverage high-throughput sequencing data. DNA Res. 2017;24(4):397–405.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  156. 156.

    Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, et al. The variant call format and VCFtools. Bioinformatics. 2011;27(15):2156–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  157. 157.

    Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Human Genet. 2007;81(3):559–75.

    CAS  Article  Google Scholar 

  158. 158.

    Chang CC, Chow CC, Tellier LCAM, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015;4(1):7 s13742-13015-10047-13748.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  159. 159.

    Cingolani P, Platts A, Wang LL, Coon M, Nguyen T, Wang L, et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff. Fly. 2012;6(2):80–92.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  160. 160.

    Kent WJ. BLAT - the BLAST-like alignment tool. Genome Res. 2002;12(4):656–64.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  161. 161.

    Wu TD, Watanabe CK. GMAP: a genomic mapping and alignment program for mRNA and EST sequences. Bioinformatics. 2005;21(9):1859–75.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  162. 162.

    Yi X, Liang Y, Huerta-Sanchez E, Jin X, Cuo ZXP, Pool JE, et al. Sequencing of 50 human exomes reveals adaptation to high altitude. Science. 2010;329(5987):75–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  163. 163.

    Rearick D, Prakash A, McSweeny A, Shepard SS, Fedorova L, Fedorov A. Critical association of ncRNA with introns. Nucleic Acids Res. 2011;39(6):2357–66.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  164. 164.

    Fahrenkrog AM, Neves LG, Resende MFR, Vazquez AI, de los Campos G, Dervinis C, et al. Genome-wide association study reveals putative regulators of bioenergy traits in Populus deltoides. New Phytologist. 2017;213(2):799–811.

    CAS  Article  Google Scholar 

  165. 165.

    Dou JZ, Wu DG, Ding L, Wang K, Jiang MH, Tai ES, et al. Using off-target data from whole-exome sequencing to improve genotyping accuracy, association analysis, and polygenic risk prediction. Genet Epidemiol. 2020;44(5):524–5.

    Google Scholar 

  166. 166.

    Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010;26(6):841–2.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  167. 167.

    Talevich E, Shain AH, Botton T, Bastian BC. CNVkit: Genome-Wide Copy Number Detection and Visualization from Targeted DNA Sequencing. PLoS Comput Biol. 2016;12(4):e1004873.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  168. 168.

    Zhao LL, Liu H, Yuan XG, Gao K, Duan JB. Comparative study of whole exome sequencing-based copy number variation detection tools. BMC Bioinformatics. 2020;21(1):97.

    PubMed  PubMed Central  Article  Google Scholar 

  169. 169.

    Liu KJ, Muse SV. PowerMarker: an integrated analysis environment for genetic marker analysis. Bioinformatics. 2005;21(9):2128–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  170. 170.

    Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000;155(2):945–59.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  171. 171.

    Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol. 2005;14(8):2611–20.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  172. 172.

    Earl DA, Vonholdt BM. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conservation Genet Resourc. 2012;4(2):359–61.

    Article  Google Scholar 

  173. 173.

    Frichot E, Schoville SD, Bouchard G, Francois O. Testing for associations between loci and environmental gradients using latent factor mixed models. Mol Biol Evol. 2013;30(7):1687–99.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  174. 174.

    Lotterhos KE, Whitlock MC. The relative power of genome scans to detect local adaptation depends on sampling design and statistical method. Mol Ecol. 2015;24(5):1031–46.

    PubMed  Article  Google Scholar 

  175. 175.

    Günther T, Coop G. Robust identification of local adaptation from allele frequencies. Genetics. 2013;195(1):205–20.

    PubMed  PubMed Central  Article  Google Scholar 

  176. 176.

    Frichot E, François O. LEA: AnRpackage for landscape and ecological association studies. Methods Ecol Evol. 2015;6(8):925–9.

    Article  Google Scholar 

  177. 177.

    Browning SR. Missing data imputation and haplotype phase inference for genome-wide association studies. Human Genetics. 2008;124(5):439–50.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  178. 178.

    Marchini J, Howie B. Genotype imputation for genome-wide association studies. Nat Rev Genet. 2010;11(7):499–511.

    CAS  PubMed  Article  Google Scholar 

  179. 179.

    Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, Minchin PR, O'Hara RB, Simpson GL, P Solymos et al. vegan: Community Ecology Package. R package v.2.5–7. 2020.

  180. 180.

    Van Den Wollenberg AL. Redundancy analysis an alternative for canonical correlation analysis. Psychometrika. 1977;42(2):207–19.

    Article  Google Scholar 

  181. 181.

    Dormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carre G, et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography. 2013;36(1):27–46.

    Article  Google Scholar 

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We thank Władysław Barzdajn from Poznań University of Life Sciences and the staff of Experimental Forest District in Siemianice for their support on the study site. We also thank our lab team members: Ewa Sztupecka and Katarzyna Meyza for their outstanding job in fieldwork and DNA isolations.


The study was supported by the National Science Center, Poland (2012/04/A/NZ9/00500), and the Polish Ministry of Education and Science under the program “Regional Initiative of Excellence” in 2019–2022 (grant no. 008/RID/2018/19).

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J.M. and J.B. designed the research; J. M and B.U. performed the field work; J.M. and B.U. analyzed the data; J.M. prepared initial draft of the paper; J.B. received funding and supervised research; all coauthors contributed to final version of the paper. The author(s) read and approved the final manuscript.

Corresponding author

Correspondence to Jaroslaw Burczyk.

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Ethics approval and consent to participate

Collection of plant samples from provenance trial in Siemianice Experimental Forest District was approved by prof. Władysław Barzdajn, Head of the Department of Silviculture, Faculty of Forestry, Poznan University of Life Sciences. All the experiments conducted on plants were carried out in accordance with guidelines of National Science Center, Poland.

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The authors declare that they have no competing interests.

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Supplementary Information

Additional file 1: Table S1.

Description of sampled populations. Table S2. List of 485 candidate genes and their functional annotations. Table S3. Correlations between 19 environmental variables (BIO1-BIO19) and the first three principal components. Table S4. Pearson’s correlations (below diagonal) between geographic, climate and phenotypic variables, and canocical correlation coefficients (above diagonal) among groups of variables. Coefficients significant at p < 0.05 are indicated by bold-italic face. Table S5. List of valid gene models and their coordinates in the reference genome of European beech (Mishra et al. 2018). Table S6. List of outlier loci and their functional annotation across the entire sampled geographic area and within regional groups.

Additional file 2: Figure S1.

Distribution of coefficients of three first principal components (PC1, PC2, PC3) determined based on 19 bioclimatic variables. Figure S2. Graphical method (as in Evanno et al. 2005) allowing for detection of the number of groups K using (A) ΔK and (B) the rate of change of the likelihood distribution (mean log-likelihood values). Figure S3. Bar plot of admixture proportions of individuals, inferred using K = 3 based on 2909 SNP loci. Individual’s proportions (q-values) are sorted within each cluster (cluster K1 – red, K2 – blue, K3 - green. Figure S4. Geographical distribution of European beech clusters according to the K = 3 model in STRUCTURE. Figure S5. Relationships between additive individual polygenic scores based on all 201 outlier markers and each of the explanatory variables: geographic (longitude, latitude, altitude), climate (PC1, PC2, PC3) and phenotypic (spring phenology, autumn phenology, height, survival). The solid line represents the regression line of the linear model. Figure S6. Relationships between additive individual polygenic scores based on all 201 outlier markers and each of the explanatory variables: geographic (longitude, latitude, altitude), climate (PC1, PC2, PC3) and phenotypic (spring phenology, autumn phenology, height, survival). The solid line represents the regression line of the quadratic model.

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Meger, J., Ulaszewski, B. & Burczyk, J. Genomic signatures of natural selection at phenology-related genes in a widely distributed tree species Fagus sylvatica L. BMC Genomics 22, 583 (2021).

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  • Forest tree
  • Genotype-environment association
  • Local adaptation
  • Sequence capture
  • Candidate genes
  • Bud-burst phenology