Genetic variation characterization of the boreal tree Acer ginnala population in response to environmental change of Northern China

Background: Acer ginnala is a deciduous shrub/small tree that primarily distributed across the northern regions of China. It comprises a foundation species in many terrestrial ecosystems and has signicant ornamental and economic value. Owing to its increased use as an economic resource, overexploitation and environmental destruction have resulted in the vulnerability of this species. Thus, the elucidation of the genetic differentiation and inuence of environmental factors on A. ginnala is very critical for its management and future utilization strategies. Results: Our results revealed that high genetic variation occurred in A. ginnala species while low genetic diversity was observed at the population level. Most differentiation has found among populations. A signicant correlation existed between genetic and environmental distances. Seven climate variables (bio1, bio2, bio3, bio4, bio13, bio15 and bio18) might explain the substantial levels of genetic variation (> 40%) in populations. The most suitable areas of this species appeared in Shaanxi, Shanxi, Anhui Provinces, and Northeastern China based on ENM results. Compared to the last interglacial (LIG) period, A. ginnala migrated toward Northern and Northeastern China, and extended to the most suitable areas during the last glacial maximum (LGM) period. Shanxi and Anhui Provinces might have served as refugium owing to their relatively high genetic variation. Conclusions: Low genetic diversity at the population level that may be the source of its vulnerability. Climate heterogeneity would play an important role in the pattern of genetic differentiation in A. ginnala populations. The A. ginnala population was isolated by a heterogeneous climate and subsequently began to adapt to local selection processes resulted in high genetic divergence. ginnala suggested the Mantel test and Spearman correlation between genetic and environmental distances. Habitat isolation or immigrant non-viability may have arisen from the local optimal for the environment; limiting the survival and reproduction of migrants

unknown. Being an important element of the forest community in northern regions of China, knowledge of genetic variation A. ginnala may reveal the evolutionary history and existing factors associated with this plant species [24][25][26]. Therefore, a comprehensive analysis of the genetic variation of A. ginnala may be invaluable, not only for the identi cation and development of this species, but also for its sustainable conservation and commercial utilization.
For this study, we presented the rst investigation of the genetic variation of A. ginnala across a wide range in China. The main objectives were to: estimate the genetic variability of A. ginnala populations and geographical groups; analyze the genetic structures and relationships of A. ginnala populations; verify the potential in uences of spatial and environmental factors on detected population differentiation patterns. The combined analysis of molecular markers and eco-geographical data provided bene cial data for the utilization and conservation of this wild plant germplasm.

Results
Genetic variation of A. ginnala populations A total of 170 bands were ampli ed with the sequence-related ampli ed polymorphism (SRAP) marker, and 100% of the bands were polymorphic. For the simple sequence repeats (SSR) markers, there were 177 polymorphic loci in 179 putative genetic loci, with the percentage of polymorphic bands (PPB) being 98.99%. According to the SRAP markers, the HHG population had the highest genetic diversity, followed by QLY and BYS. Based on the SSR markers, the highest genetic diversity was present in the PQG population, followed by XTS and BYS (Table 1).
At the species level, A. ginnala exhibited a high level of genetic diversity using the two types of markers (I SSR = 0.561, I SRAP = 0.5044; He SSR = 0.384, He SRAP = 0.3366), which was higher than the mean values at the population level (I SSR = 0.086, I SRAP = 0.057; He SSR = 0.056, He SRAP = 0.038) ( Table 1).
Through the STURCTURE results, we further analyzed the level of genetic diversity of the different groups. We combined two types of markers, Group I presented a relative genetic diversity, where the Shannon's Information index I was 0.087 SSR and 0.059 SRAP , and the expected heterozygosity He was 0.056 SSR and 0.039 SRAP , respectively (Table 1), following Group III and Group II (Table 1).
To further elucidate the relationships between the populations, a cluster analysis was implemented. The dendrogram constructed from the SRAP markers divided the 19 populations into three main clusters (Fig. 1A). Similar results were obtained with the SSR markers ( To reveal the patterns of genetic distribution of this species, we performed a population structure analysis using STURCTURE. The STRUCTURE results suggested that the best grouping number (K) was based on the ΔK, where all of the populations were divided into three groups, which was consistent with the cluster analysis (Fig. 2). Additionally, there were some admixed individuals among populations, which indicated that gene exchange occurred between them.
With SRAP markers, AMOVA analysis revealed that 88% (Φ ST SRAP = 0.88) of the total genetic variation was found between the populations (P = 0.01), whereas the remaining 12% of the total variation occurred within the populations (Table 2). According to the SSR markers, the genetic differentiation between the populations was 84% (Φ ST SSR = 0.84, P = 0.01), indicating that 16% of the total variance occurred within the populations. The two markers indicated a high level of interpopulation genetic differentiation and low level of intrapopulation genetic differentiation in A. ginnala (Table 2). Among the different groups, the variation was ~ 40% (Φ ST SSR = 0.40, Φ ST SRAP = 0.42, P = 0.01) of the total variation, while there was ~ 60% variation within the groups (Table 2).
In the DAPC analysis, which employed one discriminant function to distinguish ve principal components (PCs), three groups were present on the two main axes and a scatter plot of the discriminant analysis (Fig. 3). Every cluster was clearly differentiated using the two main DA eigenvalues and were represented according the provinces of origin. The DAPC results were similar to the STRUCTURE results; however, it was possible to assign admixed individuals to multiple clusters [27].
Predicted spatial distribution areas Within the 19 bioclimative variables, seven variables were selected. They were bio1 (Annual Mean Temperature), bio2 (Mean Diurnal Range), bio3 (Isothermality), bio4 (Temperature Seasonality), bio13 (Precipitation of Wettest Month), bio15 (Precipitation Seasonality), and bio18 (Precipitation of Warmest Quarter). Among these seven bioclimatic variables, the rst four (bio1, bio2, bio3, and bio4) were associated with the temperature dimension, while the last three (bio13, bio15, and bio18) were associated with the precipitation dimension. The explanatory percentage of the rst two PCs of the temperature dimension was estimated to be higher than that of the precipitation dimension, which suggested that the temperature might be more relevant in explaining the geographic distribution.
Ecological niche modeling (ENM) was further employed to predict the suitable distributions of A. ginnala,as well as to examine the key climatic variables in the prediction. Distribution models showed high discrimination performance (Fig. 4). The cross-validation area under the curve (AUC) value for all models was 0.989, indicating that 98.9% of the records were correctly predicted.
The ranges predicted by the seven bioclimatic variables (bio1, bio2, bio3, bio4, bio13, bio15, and bio18) were roughly consistent with the currently known distributions (Fig. 4A). The large areas of Shanxi and Shaanxi Provinces were high suitable regions for A. ginnala. Some areas of Jiangsu, Anhui, and Hubei were also the most suitable habitat for this species. Aside from these areas, there were small regions Northeast China that were suitable for the growth of A. ginnala.
In LIG, the potential distribution range expanded, compared with the current distribution. The most suitable areas (> 0.50) in Northeast China disappeared (Fig. 4B). The additional most suitable areas still existed and their extent was broader than currently. Compared with the LIG, the most suitable areas (> 0.50) increased signi cantly during the LGM, particularly in Shanxi and Shaanxi Provinces ( When conditioned on the geographic distribution, we found that more 40% of the variation (45.71% by SRAP data, 40.42% by SSR data) could be explained by climatic variables ( Table 3). The ANOVA further indicated that seven predictors (bio1, bio2, bio3, bio4, bio13, bio15, and bio18) signi cantly explained the genetic components of the population (P <0.0001), and that bio2 and bio3 had the highest explanatory proportions for predicting the genetic variation of the population. Three bioclimatic variables (bio1, bio2, and bio3) of the temperature dimension had signi cant F statistics (adjusted R 2 = 0.0259, 0.0400, and 0.0386, P < 0.05, Table 3) through SRAP data.

Discussion
Population genetic variation of A. ginnala A. ginnala contained a high genetic diversity at the species level (Table 1). Genetic diversity is the culmination of the long-term evolution of species or populations [28][29], which might be affected by multiple factors, such as the size of the geographical range, genetic exchange, environmental conditions, and species characteristics [30][31]. As relates to geographical distribution, A. ginnala has a relatively extensive distribution area, from Southwest to Northeast China. Within a larger distribution area, species can generally possess higher genetic diversity [31]. Being a perennial tree, longlived A. ginnala could provide more opportunities to accumulate mutations or specialized microstructures in different populations. Further, genetic diversity at the species level is also related to the breeding system, where A. ginnala is an insect-pollinated species [32][33] through sexual propagation through seeds. These reproductive characteristics have signi cance for maintaining genetic diversity in populations [31], which likely resulted in higher genetic diversity in this species.
However, genetic diversity at the population level was relatively lower (Table 1), which might have been initiated through two possible scenarios. Firstly, the inbreeding of plants can lower genetic diversity, which results from a reduction in population size, and leads to inbreeding depression [31,34]. Owing to human disturbance and destruction, A. ginnala population continued to dwindle, becoming smaller than previously. A degree of inbreeding of this species population could reduce plant tness, while promoting a further drop in abundance. Although A. ginnal is primarily insect-pollinated, self-pollination can occur. There are reports that some plants have suffered from a general decline in pollinator insects; a phenomenon referred to a pollination crises [35][36][37]. From the spring season, when A. ginnala owers, one might expect an elevated rate of self-pollination due to the presumable lack of effective pollinators [31]. Secondly, based on our eld investigations, more adults and fewer seedlings were found in A. ginnala communities. Those populations might be exposed to greater genetic drift effects, resulting in the observed low level of genetic diversity. Aside from anthropogenic factors, climate change may also be considered as a factor in the decline of this species. Low levels of genetic diversity might restrict the ability of a population to respond to changing environmental conditions [1,31,38]. Environmental heterogeneity at an over-all scale among A. ginnala populations was suggested by the Mantel test and Spearman correlation between genetic and environmental distances. Habitat isolation or immigrant non-viability may have arisen from the local optimal for the environment; limiting the survival and reproduction of migrants [1,[39][40].
Most genetic variation (> 80%) was observed between populations, whereas ~10% of the differentiation existed within populations.
This signi cant genetic variation among populations could be related to low gene ow (N m SSR = 0.085, N m SRAP = 0.063). The seeds of A. ginnala have a large wing and can be dispersed by the wind; the potential for exchange may be more sensitive to geographic barriers than that of wind-pollinated and wind-dispersed species. In the A. ginnala distribution areas, the discontinuous distribution of mountains, such as Taihang Mountains and Wuling Mountains, provide a complex landscape that likely blocks gene exchange among between populations [41][42]. These factors presumably isolated populations; thus, promoting population differentiation by limiting the potential for gene exchange. Interestingly, the Mantel test and Spearman correlation revealed no signi cant relationship between genetic and geographical distances. High genetic variation values (Table 2) pointed to habitat fragmentation and the presence of barriers to gene ow between populations. For A. ginnala, gene ow might also be obstructed by the pollination method. Being an insect pollinated species, pollen-mediated gene ow was generally limited, as the range of most pollinators can be less than 20 km [31], and they tend to visit neighboring plants [43]. A. ginnala populations may be geographically and/or ecologically too isolated to be connected via pollen exchange. The considerable genetic differences between populations might also be attributed to the absence of generative reproduction [44][45]. Field observations found that some populations ruled out generative reproduction, due to the absence of owering individuals, or their inability to produce fruit. The above factors might have inhibited gene ow and exacerbated genetic differences.
STURCTURE, UPGMA, and DPAC analyses suggested that all of the studied populations were divided into three clusters (Fig. 1, 2, and   3). The observed genetic differences between the three groups were relatively large and statistically signi cant. Low levels of migration between populations was reported above, thus e cient genetic exchange between the populations studied may be excluded.
Discontinuous mountains and/or climate heterogeneity can segment large populations into multiple-small fragmented populations, and enhance differentiation between populations and groups [42].
Population structure and dynamic history Historical events leave an indelible mark on patterns of genetic diversity found within plant species [46][47]. As hypothesized, Central and Western populations in China harbored relatively higher levels of genetic variation compared to Eastern populations. Further, the former areas served as large LGM refugia for many species in China; mainly boreal and temperate plants [47][48][49]. For this study, Group I and III contained relative genetic diversity among all examined populations ( Table 1). The Group I populations were located in the North Qinling Mountains and West Taihang Mountains, while the Group III populations were located in the South Qinling Mountains.
These mountainous regions comprised areas that were not heavily in uenced by the LGM [50]. In general, large portions of Central and Southwestern China served as plant refugia [47,48], due to their relatively stable climatic conditions [51]. All populations in the abovementioned areas were genetically more variable ( Table 1).
The ENM results provided a historical perspective for the interpretation of genetic data. During the LGM period, suitable habitats for A. ginnala were mainly in Shaanxi, Shanxi, and Anhui Provinces. Following the LGM period, suitable habitats for A. ginnala decreased dramatically in the regions indicated above, particularly in the Shanxi and Anhui provinces, which were regarded as refugia for A. ginnala (Fig. 4). Northern China once hosted temperate forests; however, this was replaced by tundra and taiga forests during the LGM period [52]. These temperate forests likely retreated to the South, and below 30°N [47,53]. In uence of environmental heterogeneity The genetic divergence of populations should be correlated with both geographic distance and environmental heterogeneity [40]. In the present study, geographic distance was not correlated with environmental distance. A signi cant correlation was found between genetic and environmental distances by Mantel test and Spearman correlation (Table 3). When considering the combined effects of environmental and geographic distances using MMRR, environmental distance was found to affect genetic distance signi cantly (Table 3). These results suggested that IBE (Isolation by Environment) might have played an important role in shaping the genetic divergence and adaptive divergence of populations, corresponding to the environmental heterogeneity that occurred for A. ginnala.
Seven environmental variables (bio1, bio2, bio3, bio4, bio13, bio15, and bio18) were separated into two categories: temperature and precipitation, and found to be a critical environmental factor that substantially explained the genetic variation of A. ginnala. Having signi cant F statistics, three temperature variables (bio1, bio2, and bio3) were the most important environmental factors that in uenced adaptive variation in A. ginnala. However, the second most important set of environmental factors, including bio4, bio13, bio15, and bio18, could also be important environmental elements that may have played key roles in driving adaptive divergence in this species.

Sample collection
Our study was conducted in accordance with the laws of the People's Republic of China, and eld collection was approved by Chinese

PCR ampli cation
Genomic DNA from the sampled individuals was extracted using the modi ed CTAB method [70]. The quality of the DNA was determined using an ultraviolet spectrophotometer and the electrophoresis on 0.8 % agarose gel [71]. Following extraction, the DNA was stored at -20 ℃ for further use.
Ten pairs of polymorphic SSR primers [42] with distinct bands and high stability were selected to amplify all individuals of the A.

Data analysis
Distinct and reproducible bands of each marker were scored as either 1 (present) or 0 (absent). The genetic diversity parameters, such as the percentage of polymorphic bands (PPB),, Shannon's Information index (I) and expected heterozygosity (He) were calculated using GENALEX [73].
STRUCTURE analysis [74], underlying the model Bayesian methods, was often used to delineate the clusters of genetically similar individuals. The presumed number of populations (K) was set from 2 to 19. For each run, the initial burn-in period was set to 100,000 with 500,000 Monte Carlo Markov Chain interactions. Ten independent runs were completed for each K value. The number of populations was determined using the DeltaK method (ΔK statistic).
According to the STRUCTRE results, the hierarchical analysis of molecular variance (AMOVA) was performed in GENALEX [73] to elucidate the extent of genetic variation between and within populations or groups.
Based on Nei's genetic distance, the phylogenetic relationship of populations was constructed by the unweighted pair-group method of arithmetic averages (UPGMA) using Molecular Evolutionary Genetics Analysis MEGA software [75].
To further con rm the cluster analysis and population genetic structure, a discriminant analysis of principal components (DAPC) was conducted using the R package ADEGENET [76]. For this study, the genetic data was initially transformed according Principal Component Analysis (PCA). These components explained most of the genetic variation, which was then used to perform linear Discriminant Analysis (DA), which provided variables that described the genetic groups that minimized the genetic variance within populations, while maximizing the variation between populations.
To investigate the in uence of the environment on A. ginnala variation, we extracted the environmental factors with DIVA-GIS software [77]     The designations employed and the presentation of the material on this map do not imply the expression of any opinion whatsoever on the part of Research Square concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. This map has been provided by the authors.