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Article

Plant Diversity Responses of Ulmus pumila L. Communities to Grazing Management in Hunshandak Sandy Land, China

by
Wenda Huang
1,*,
Yuanzheng He
1,
Xueyong Zhao
1,
Hongxiao Yang
2,
Honghao Gan
3 and
Xin Zhao
1
1
Naiman Desertification Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
2
College of Resource and Environment, Qingdao Agricultural University, Qingdao 266109, China
3
Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
*
Author to whom correspondence should be addressed.
Diversity 2023, 15(12), 1221; https://doi.org/10.3390/d15121221
Submission received: 14 September 2023 / Revised: 8 December 2023 / Accepted: 13 December 2023 / Published: 16 December 2023
(This article belongs to the Section Plant Diversity)
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Abstract

:
Biodiversity is sensitive to climate change and human activity. Grazing management practices have a profound impact on plant species–genetic diversity in grassland and woodland communities. In this study, we explored the responses of species and genetic diversity to grazing in Ulmus pumila L. communities in the Hunshandak Sandy Land, analyzed the relationship between species and genetic diversity, and revealed the effects of climate factors on them. We found that the dominant species were Spiraea trilobata, Caragana microphylla and Artemisia intramongolica in U. pumila communities. Plant species richness in the banned grazing (BG) and seasonal grazing (SG) communities was significantly higher than that in the delayed grazing (DG) community. Plant Simpson’s diversity index showed a downward trend with increasing grazing duration. There was no difference in allelic richness in nuclear DNA (nrDNA) of U (U. pumila) and chloroplast DNA (cpDNA) of NU (other dominant species besides U. pumila) among grazing management types. The expected heterozygosity of U in nrDNA and cpDNA was significantly affected by grazing management, and the trend was BG > SG > DG. The genetic diversity of U was lower than that of NU. The genetic diversity characteristics of U in cpDNA were lower than those in nrDNA. The analysis of molecular variance (AMOVA) showed that 98.08% of the variation in U and 95.25% of the variation in NU was attributed within populations and the differences within grazing management types were 13.35% in U and 24.08% in NU (p < 0.001). The species richness of communities was positively correlated with the genetic diversity of U, NU and all dominant species (U + NU) in communities. The nineteen climatic variables together explained 94.24% and 79.08% of the total variation in U and NU genetic and species diversity. The mean temperature of the warmest quarter and temperature seasonality were the main factors affecting genetic diversity (p = 0.046; 0.01), while the maximum temperature of the warmest month was the main factor affecting species diversity (p = 0.05).

1. Introduction

Biodiversity is a frequently employed term that encompasses various tiers of biological organization, including genes, species and ecosystems [1]. Plant diversity is one of the most important elements of biodiversity [2], and is easily restricted by the environment, particularly in the formation of genetic diversity and species diversity within communities [3]. Because changes in habitat characteristics disturb the niche width and population size of dominant plant species, the level of species diversity varies [4]. Similarly, the accumulation of genetic diversity of species in response to habitat change will improve the adaptability and viability of organisms [5]. Genetic variation within species could lead to differences in resource utilization, growth rate, and reproductive strategies among different individuals, thus affecting the structure and dynamics of the entire community. In turn, the ecological conditions of the changed community will accelerate the rate of evolution of different species to adapt to the environment [6]. Numerous studies have indicated that the relationship between species diversity and genetic diversity is intricate. This may be reflected as a parallel relationship due to drift, selection and species turnover [4,7], as genetic variation in a dominant species changes the biotic environment in the remaining species of the community and restricts community species diversity [7], or as the species diversity of communities affects the selection and genetic diversity levels of constituent populations [6,7,8]. The life type of the dominant species determines the composition of the community and determines the genetic diversity and species diversity within the community [7]. On the other hand, the patterns of genetic variation within species have an impact on the interactions between species and, as a result, influence community composition in diverse environments [9]. The study of species genetic diversity correlations in plant diversity is of great significance for biodiversity conservation planning. Studies on the interaction between genes in dominant species and populations of other species in the community can provide a basis for conceptual unification in biodiversity research.
Grazing is the most important and widespread land use and management practice in grassland ecosystems [10,11,12]. Grazing represents a multifaceted process where energy and nutrients move from the producer (plant) to the consumer (herbivore) level [13]. This intricate process involves various complicating elements, including the type of grassland, stocking rate, grazing intensity, type of livestock, and duration of the grazing season [14]. Grazing directly and indirectly affects both the structure and function of the ecosystem, as well as the biological relationship of the community, which are particularly important to the stability of grassland ecosystems [15,16]. Grasslands are lands that are extensively grazed throughout the world, including natural grasslands and savannas. These lands are not only an important source of livelihood for millions of pastoralists but also major wildlife habitats and conservation areas for plant genetic resources [13]. Optimizing grazing intensity and diversifying grazing type are key management measures for promoting grassland restoration, improving livestock production efficiency, and accomplishing grassland biodiversity conservation and sustainable development [14,16,17]. Moreover, rational grazing is beneficial for controlling understory woody vegetation and reducing forest fires, limiting the invasion of alien species and preventing declines in biodiversity [13], decreasing asymmetrical competition among plant species in the community [15,18] and protecting and utilizing animal and plant resources [16]. Improved grazing management regimes have been widely used in Inner Mongolia grasslands [12]. In view of the degradation of grassland caused by long-term overgrazing, ecological restoration measures have been implemented mainly by banning grazing, resting grazing, rotational grazing and seasonal grazing [14,17].
Hunshandak Sandy Land, situated in the eastern region of the Inner Mongolia Plateau, is one of China’s four major sandy land areas [19]. It serves as the transitional zone between a typical steppe region and a dry farming area in northern China [20]. Hunshandak Sandy Land falls within the middle temperate zone and experiences a semi-arid to arid continental monsoon climate, with an area of 3.8 × 104 km2 [19], altitude of 1100–1300 m, annual evaporation of 1680–2940 mm, annual average temperature of 1.8 °C and annual average rainfall of approximately 310 mm, mainly in summer, accounting for approximately 70% of the annual total [21]. The zonal soil is mainly sandy chestnut soil and aeolian sandy soil [22]. The most typical natural top-level vegetation community in Hunshandak Sandy Land is Ulmus pumila L. sparse forest grassland (Figure 1), which is a nonzonal hidden vegetation type [23]. The zonal vegetation of Hunshandak Sandy Land includes meadow steppe, steppe and desert steppe [24]. Vegetation growth is good, mainly Gramineae and Artemisia vegetation, and coverage is generally 30–50% [25]. The primary tree species in the region is U. pumila. The main shrubs are Caragana microphylla Lam., Spiraea aquilegifolia Pall. and Ribes diacanthum Pall. The main herbaceous plants are Leymus chinense (Trin.) Tzvel, Agropyron cristatum (L.) Beauv, Polygonum divaricatum L., Potentilla chinensis Ser., Artemisia frigida Willd., Chenopodium glaucum L., Setaria viridis (L.) Beauv and Leymus secalinus (Georgi) Tzvel [21,26]. Since the 1960s, due to unreasonable human activities, especially overgrazing, Hunshandak Sandy Land has been seriously degraded and desertified, and it has become one of the few areas in China with a desertification development rate that exceeds 4% [19]. This has not only reduced pasture productivity significantly and restricted the healthy development of the livestock industry, but also led to it becoming one of the main dust sources in Beijing and Tianjin [19]. To restore and manage the sandy ecosystem sustainably, the Inner Mongolia Autonomous Region launched the “Returning Pasture to Grassland” project in 2003 and began to implement the “three grazing policies” system, namely banning grazing, resting grazing and rotating grazing [27].
In Hunshandak Sandy Land, U. pumila sparse forest grassland is mainly distributed in the eastern part of Zhenglan Banner and Keshiketeng Banner. After a long period of vegetation succession, U. pumila sparse forest has become the top community and the most stable type of native vegetation in Hunshandak Sandy Land [28]. U. pumila sparse forest is a mixture of trees, shrubs and herbs with rich biodiversity and important ecological functions, providing feed for large herbivores [29]. However, in recent years, the phenomenon of overgrazing in U. pumila sparse forest grassland has been very common, the stocking rate has exceeded a reasonable level by 20% and the grassland has been seriously degraded [30]. Grassland degradation caused by overgrazing has prevented the natural regeneration of the U. pumila population, causing many shrub deaths and significantly reducing herb coverage, plant species and the proportion of perennial herbs [23].
Given the importance of these communities’ preservation and the income from grazing needed for sustaining nearby human populations, we have studied the influence of three possible grazing management practices on plant species diversity, as well as their influence on genetic diversity. Since genetic diversity and species diversity relationships can be intricate and both affected by levels of grazing, the main questions are as follows: (1) Is there a difference in the species diversity of U. pumila communities and the genetic diversity of U. pumila and dominant plant species in communities among grazing management types (banned grazing, seasonal grazing and delayed grazing)? If this is the case, what are the change trends and characteristics of species diversity and genetic diversity in Hunshandak Sandy Land? (2) Is there a correlation between the species richness and genetic diversity of U. pumila communities under different grazing management practices? If this is the case, how are they correlated? (3) Are the species diversity and genetic diversity (species level and community level) of U. pumila communities affected by regional bioclimatic factors in Hunshandak Sandy Land? If this is the case, which climatic factor influences the plant diversity of U. pumila communities in Hunshandak Sandy Land? How do they relate to each other? The aim of this study was to provide additional insights into the conservation of biodiversity within U. pumila communities. These findings have practical implications for restoring and managing grazing ecosystems in arid and semiarid regions.

2. Materials and Methods

2.1. Experimental Design

In the main distribution area of U. pumila sparse forest grassland, Zhenglan Banner and Keshiketeng Banner of Hunshandak Sandy Land, we selected 3 U. pumila sparse forest grassland fields with different grazing management types established in 2017; banned grazing fields (BG, communities 1–5), seasonal grazing fields (SG, communities 6–9) and delayed grazing fields (DG, communities 10–12) (Figure 2a). The BG fields were fenced year-round. The SG and DG fields were grazed with intensities of 10 cattle per 7 hm2 (1.4 cattle units/hm2). The SG fields were grazed in summer and autumn. The DG fields were interrupted from grazing for 45 days at the beginning of plant growth in spring (10 April to 25 May) and were grazed the remaining time. The vegetation cover of the three grazing fields was 93%, 81% and 67%, respectively. The dominant shrub in all three grazing fields was U. pumila seedlings, and the dominant herbs of the three grazing fields were Setaria trilobata L., Leymus secalinus (Georgi) and Artemisia intramongolica H. C. Fu.

2.2. Field Sampling and Climate Data Gathering

The U. pumila community consists of tree, shrub and herb layers. Although these layers are not at the same level, they influence each other, depend on each other and form an adaptation to growing space and light, water, nutrients, etc. The tree layer is the main body of the U. pumila community, and the shrub and herb layers belong to the understory vegetation. Each layer of the community has dominant species, namely U (U. pumila, tree layer) and NU (other dominant species besides U. pumila, shrub and herb layer).
The number of plants, vegetation cover, height and frequency of plants were determined in mid-August of 2018 and 2019. In each field type, we established a sizable quadrat measuring 100 m × 100 m to assess the species composition of all trees, shrubs and herbs. Within the large quadrat, we established five shrub quadrats measuring 10 m × 10 m and nine herb quadrats measuring 1 m × 1 m, as depicted in Figure 2b. The NU combinations of each grazing management type in this study were different, as shown in Table 1. A total of 48 U. pumila samples (U) and 72 other dominant plant samples (NU) were arbitrarily sampled from their young healthy leaves and promptly stored with silica gel in zip-lock plastic bags, preserving them for future DNA extraction. The sample information used in genetic diversity for each grazing management types shown in Table 1. Climatic data spanning the years 1970 to 2000 were obtained from the WorldClim data website https://www.worldclim.org/ (accessed on 8 November 2021). This dataset comprised 19 climatic variables, each providing insights into the region’s climate characteristics. These variables included annual mean temperature (AMT), mean diurnal range (MDR), isothermality (ISO), temperature seasonality (TS), max temperature of warmest month (WMT), min temperature of coldest month (CMT), temperature annual range (ART), mean temperature of wettest quarter (WQT), mean temperature of driest quarter (DQT), mean temperature of warmest quarter (TWQ), mean temperature of coldest quarter (CQT), annual precipitation (AP), precipitation of wettest month (WMP), precipitation of driest month (DMP), precipitation seasonality (PS), precipitation of wettest quarter (WQP), precipitation of driest quarter (DQP), precipitation of warmest quarter (PWQ) and precipitation of coldest quarter (CQP). These climatic data were crucial for assessing the influence of climatic factors on plant diversity in the Hunshandak Sandy Land region, shedding light on the intricate relationship between environmental conditions and biodiversity.

2.3. Molecular Methods

Total genomic DNA extraction was carried out using AxyPrep genomic DNA mini kits (Axygen Inc., Beijing, China), adhering to the manufacturer’s provided protocols. DNA quality assessment was performed using a 1.0% agarose gel. Referring to the Consortium for the Barcode of Life (CBOL), several pairs of chloroplast DNA (cpDNA) and nuclear DNA (nrDNA) primers were used [31,32,33] (Table 2). Polymerase chain reactions (PCRs) were conducted in a 25 µL reaction mixture containing 40 ng of genomic DNA, 1.0 U of Taq polymerase (Axygen Inc., Beijing, China), 3 mM MgCl2, 500 µM of each dNTP, 20 mM Tris-HCl (pH 8.3), 100 mM KCl and 0.3 µM of each primer. Amplification involved an initial denaturation at 94 °C for 3 min, followed by 30 cycles of 30 s at 94 °C, 30 s at an appropriate annealing temperature, 1 min at 72 °C, and a final extension step at 72 °C for 5 min. PCR products were assessed by 1.0% agarose gel electrophoresis. Subsequently, the products were purified using the AxyPrep PCR purification kit following the manufacturer’s protocol (Axygen Inc., Beijing, China), and DNA sequencing was carried out by the MEIJI sequencing company in Shanghai, China, employing the PCR primers as sequencing primers.

2.4. Data Analyses

Two diversity indices were used to estimate plant species diversity: species richness (SR) and Simpson’s diversity index (D). D = 1 − ∑ i S P i 2 , where Pi represents the proportion of each category i [34]. The importance value (IV) was calculated as the average of the relative height, relative frequency and relative coverage of the plant [35]. The measures of plant genetic diversity from U (U. pumila) and NU (other dominant species besides U. pumila) (Table 1) with different grazing management types were calculated with Arlequin 3.0 [36], including allelic richness (AR) and expected heterozygosity (HE), gene diversity (Gd), the number of haplotypes (H) and haplotype diversity (Hd) [37,38]. Genetic differentiation among populations in the different grazing management type groups was estimated by pairwise FST values [39]. Analysis of molecular variance (AMOVA) was conducted to assess the variation in U and NU among and within populations, and to compare the differences in U and NU populations within the same grazing management type and among grazing types using Arlequin 3.0 software [37]. We examined the connections between SR and plant genetic diversity (AR and HE) using Pearson correlation. Before conducting analysis of variance (ANOVA) and correlation analysis, the data underwent normality testing to confirm a normal distribution. ANOVA was conducted to determine differences between grazing management types and diversity indices. One-way (independent variables: grazing management types; dependent variable: plant species diversity indices) or two-way (independent variables: grazing management types × species; dependent variable: plant genetic diversity indices) ANOVA followed by Tukey’s post hoc test. All statistical analyses were performed using SPSS 19.0 for Windows (SPSS, Inc., Chicago, IL, USA). Redundancy analysis (RDA) was applied to assess the relative impact of the recorded climate variables on the plant diversity indices across 12 U. pumila communities. Initially, data underwent detrended correspondence analysis, indicating the suitability of RDA (gradient length < 3). To prevent overfitting caused by the extensive set of explanatory variables, a ‘forward selection’ approach was used to select the most influential variables during analysis. Prior to analysis, plant diversity indices and climate data were log-transformed (log (x + 1)). The RDA was conducted using CANOCO Version 4.5 [40], and all graphical representations were generated using Origin 2018.

3. Results

3.1. Effect of Grazing Intensity on Plant Species Diversity

Sixty-seven species were recorded in all grazing sites (Table 3). Specifically, there were 55 plant taxa in the banned grazing (BG) U. pumila communities, 39 plant taxa in the seasonally grazed (SG) U. pumila communities and 29 plant taxa in the delayed grazing (DG) U. pumila communities. The importance values of shrubs and herbs in the BG communities were similar, which were 49.89% and 50.11% of the total importance value, respectively, while those of the SG and DG communities were very close, and the trends were consistent, which were 61.29% and 38.71%, and 61.96% and 38.04%, for shrubs and herbs, respectively. Based on the species importance value, it was observed that U. pumila and its seedlings held the dominant position within all of the communities. In addition, S. trilobata, C. microphylla and A. halodendron were the dominant species in the BG, SG and DG communities, respectively. The results of one-way ANOVAs indicated that grazing management types had a significant impact on plant species richness (SR) and Simpson’s diversity index (D) (p < 0.001) (Figure 3). Specifically, the analysis revealed that the SR in the BG and SG communities was significantly higher than that in the DG communities, with values of 23.8 ± 2.280 and 24 ± 2.944, respectively (p < 0.05). The plant Simpson’s diversity index (D) showed a downward trend with increasing grazing management intensity. The values of SR and D in the DG community were the lowest (14.33 ± 2.08; 0.197 ± 0.002, p < 0.001).

3.2. Effect of Grazing Intensity on Plant Genetic Diversity

The genetic indices (AR and HE) of U (U. pumila) and NU (other dominant species besides U. pumila) from grazing management types are given in Figure 4 and Figure 5. The genetic diversity indices exhibited significant variations among different grazing management types, as indicated by two-way ANOVAs. Among the three grazing management types, the nrAR and HE of NU were the lowest (1.209 ± 0.084, 0.347 ± 0.041) in the SG communities and compared to those in the BG (1.622 ± 0.202, 0.416 ± 0.021) and DG communities (1.542 ± 0.057) (Figure 4 and Figure 5). However, there was no difference in AR in the nrDNA of U among grazing management types (Figure 4); in contrast, the HE of U in the nrDNA and cpDNA was significantly affected by grazing management, and the trend was BG > SG > DG (Figure 5). Comparing the genetic parameters of U and NU, the cpAR of U was noticeably lower than that of NU at all grazing management types (Figure 4). There was no marked difference in nrHE between U and NU; however, there was no inconsistency in the cpHE; that is, the value for U was higher than that for NU in the BG, and the opposite was true in the DG (Figure 5).
The comparative analysis results of the genetic diversity of chloroplast genes, nuclear genes and their combined genes at the species level are shown in Table 4. Data analysis showed that the genetic diversity of U was lower than that of NU. The genetic diversity characteristics of U in chloroplast genes were lower than those in nuclear genes, and most of the parameters in nuclear genes were the highest (Table 4). AMOVA showed that 1.92% of the variation in U and 4.75% of the variation in NU was attributed among populations, and within-population variation accounted for 98.08% and 95.25% of the total variation, respectively (Table 5). At the grazing management level, the differences among management types were 1.16% in U (p < 0.001) and 1.7% in NU (p = 0.004), and the differences within populations among grazing management types were 13.35% in U and 24.08% in NU (p < 0.001). The FST values for U and NU were 0.1219 and 0.2238 at the species level, respectively.

3.3. Correlations between Plant Species and Genetic Diversity

The correlation analyses revealed a positive association between species diversity and the genetic diversity of U, NU and all dominant species (U + NU) from grazing management communities, as depicted in Figure 6. The data clearly indicated a significant correlation between SR and allelic richness of U (UAR) (r = 0.90, p < 0.001) (Figure 6a), a significant correlation between SR and expected heterozygosity of NU (NUHE) (r = 0.77, p = 0.003) (Figure 6d), and no significant correlation between SR and other genetic diversity parameters (UHE, NUAR, CAR and CHE) (p > 0.05) (Figure 6b,c,e,f).

3.4. Correlations between Climatic and Species–Genetic Diversity

The plant diversities of U. pumila sparse forest grassland communities in Hunshandak Sandy Land were impacted by various climatic factors, including AMT, MDR, ISO, TS, WMT, CMT, ART, WQT, DQT, TWQ, CQT, AP, WMP, DMP, PS, WQP, DQP, PWQ and CQP (Figure 7). RDA revealed that the collective influence of the nineteen climatic variables accounted for 94.24% of the overall variation in the genetic diversity of U and NU (Figure 7a). Axis 1 and Axis 2 explained 76.04% and 18.2% of the total variation, respectively. Notably, among the nineteen climatic variables, only TWQ and TS were found to be statistically significant according to the Monte Carlo permutation test (p = 0.046; 0.01). The remaining variables did not demonstrate significant associations (all cases, p > 0.05). Specifically, TWQ and TS accounted for 26.6% and 17.1% of the total explained variation, respectively (Figure 7a). Additionally, the RDA indicated that the combined influence of the nineteen climatic variables accounted for 79.08% of the total variation in the species diversity of U. pumila communities. Axis 1 and Axis 2 explained 56.91% and 22.17% of the total variation, respectively (Figure 7b). Among these nineteen climatic variables, only WMT exhibited statistical significance based on the Monte Carlo permutation test (p = 0.05). Specifically, 20.7% of the total explained variation was attributed to WMT (Figure 7b).

4. Discussion

4.1. Responses of Species Diversity to Grazing Types

The community structure and composition of the U. pumila sparse forest grassland were affected by grazing type in Hunshandak Sandy Land. In this study, we found that the number of species in the BG communities was the highest, and that in the DG communities was the lowest. This was consistent with the results of most studies on sparse forest grasslands and grasslands [41,42,43]. The species diversity parameters (SR and D) in the DG were significantly lower than those in the BG and SG communities; nevertheless, there was no significant difference between the BG and SG communities. The reason is that the grazing time of the DG communities (continuous grazing for up to 320 days) each year is time unit longer than that of the BG and SG communities. Among the three grazing management policies implemented, the DG policy with the longest continuous grazing period reduced the species and quantity of plants and destroyed the ecological balance, indicating that the DG policy was the least beneficial for the restoration of U. pumila sparse forest grassland and grassland vegetation. The results of this study revealed that the species richness of the U. pumila sparse forest grassland community was relatively high under both non-grazed and seasonal grazing. In line with this, similar studies in grasslands found that non-grazed and seasonal grazing increased the level of species diversity in grassland communities [44,45,46].

4.2. Responses of Genetic Diversity and Difference to Grazing Types

Genetic data results indicated that the nuclear allelic richness (nrAR) of U (U. pumila) was not different under different grazing types, and the nuclear expected heterozygosity (nrHE) of all species was lower than the chloroplast expected heterozygosity (cpHE), indicating that the species chloroplast gene flow rates of U. pumila sparse forest grassland are faster than the nuclear gene flow rates. This is because the majority of species in the community are maternally inherited (gene flow by seeds or vegetative spread) [47], which is also in line with the natural phenomenon that most angiosperms mainly rely on maternal inheritance [48,49]. The genetic diversity parameters, including cpAR and HE in U and nrAR and HE in NU (other dominant species besides U. pumila), were consistently lower in the SG than in the BG. This suggests that species diversity and genetic diversity within U. pumila sparse forest grassland communities responded differently to various grazing types. Furthermore, at the species level, the genetic diversity of U was found to be lower than that of NU; this may be because, compared with U, the species used for genetic diversity analysis of NU came from the shrub and herb layer, which had more abundant species and larger sample size, and most of the species were dominant species in the community, with stronger environmental adaptability and more complex phylogenetic relationships, which may increase the overall genetic diversity level [1,50]. At the same time, most of the genetic diversity parameters of nuclear genes were higher than those of chloroplast genes in U and NU, indicating that the evolutionary adaptation of nuclear genes was stronger than that of chloroplast genes.
The AMOVA data revealed that the genetic differentiation of U and NU within populations in Hunshandak Sandy Land was high, both higher than 95%, explaining why the genetic differentiation of species in U. pumila sparse forest grassland was mainly within the populations. We discovered that the genetic differences between the U and shrub-grass layers under the same grazing management practices were larger than those between the different management practices. The overall variation rates were 13.35% and 24.08%, respectively, which indicated that grazing management easily caused genetic differentiation in the U. pumila sparse forest grassland community. This is consistent with some research findings, where grazing caused genetic differentiation of plant communities in Stipa grandis steppe (19.84%) and alpine meadow (>12%) [51,52].

4.3. Relationship between Species and Genetic Diversity

Numerous studies have investigated the relationship between plant species and genetic diversity, yielding inconsistent results. This relationship may take different forms: it could be parallel [4,53], where the genetic diversity of dominant species influences community species diversity [54], or it could be positive, where community species diversity affects the genetic diversity of dominant species [6]. In our study, species diversity at the U, NU and community levels was positively correlated with genetic diversity. The SR of the dominant plants (U and NU) was significantly positively correlated with AR and HE, respectively, indicating that SR and AR in the tree layer and SR and HE in the shrub-grass layer had significant interactions. Our results are supported by theoretical [6,52] and empirical [7,55] studies of species–genetic diversity relationships. Combined with relevant research results, our study shows that grazing can cause changes in the species composition and ecological niche of the U. pumila sparse forest grassland community and enhance intraspecific and interspecific competition for nutrients and space in microhabitats, thus stimulating and changing the genetic variation patterns of species and indirectly promoting genetic differences among different species in the community.

4.4. Effects of Climate Factors on Plant Species and Genetic Diversity

The RDA results showed that the contribution rate of 19 climate factors to the genetic diversity of the U. pumila sparse forest grassland community was as high as 94.24%, indicating that the genetic diversity was mainly affected by climate factors and that other environmental factors had little effect on the genetic diversity of the species. We screened TWQ and TS as the main climatic factors restricting the genetic diversity of U. pumila sparse forest grassland species. This is the same as the relationship between genetic diversity and climate factors of A. halodendron and C. microphylla populations in similar regions [56,57], indicating that temperature is the key factor affecting plant genetic diversity in sandy grasslands. In this study, the total contribution of 19 climatic factors to community species diversity was 79.08%, indicating that environmental factors have a major effect on the species diversity of the community. This is also consistent with the results on the effects of climate factors on the species diversity of forests [58,59]. The data in this paper suggested that the species diversity of U. pumila sparse forest grassland is significantly affected by WMT. This is consistent with the relationship between species diversity and temperature in plant communities on a large scale, where there was a significant positive correlation between plant species richness and average annual temperature in well-protected forests across four climate regions in China (p < 0.05) [58]. However, the results are inconsistent with similar studies on small scales, and there was a significant negative correlation between plant species richness and average annual temperature in warm temperate deciduous broad-leaved forest from Tuoliang National Nature Reserve in China (p < 0.05) [60]. This may be related to community type and regional microclimate.

5. Conclusions

The community structure, composition, species diversity and genetic diversity of the U. pumila sparse forest grassland in Hunshandak Sandy Land were all affected by grazing management type. The shrub layer was the main component of the vegetation community, and had higher species diversity and genetic diversity. Shrubs with strong resistance to grazing were S. trilobata, C. microphylla and A. halodendron. Grazing management easily caused genetic differentiation in the U. pumila sparse forest grassland community. A long continuous grazing period is not conducive to the conservation of species diversity in the U. pumila sparse forest grassland. The potential positive connections between species diversity and the genetic diversity of communities contributed to the substantial unification of biodiversity conservation at the gene and species levels. Both the genetic diversity and species diversity of U. pumila sparse forest grassland communities were easily affected by regional climate, and climate factors related to temperature played a leading role. In conclusion, we propose that the management strategy of U. pumila sparse forest grassland should be “conservation + utilization”. Scientific grazing management and sustainable use of grasslands are critical to maintaining the health of sandy ecosystems.

Author Contributions

W.H. designed the research, collected and analyzed the data and wrote the paper. Y.H. assisted with data analyses and conducted laboratory analysis. X.Z. (Xueyong Zhao) provided financial support. H.Y. and H.G. completed field sampling. X.Z. (Xin Zhao) conducted laboratory analysis. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by China National Key Research and Development Plan (2017FY100200) and the National Natural Science Foundation of China (41971144).

Data Availability Statement

The data presented in this study are available in this published paper.

Acknowledgments

The authors thank all the members of the 2017FY100205 project team, Naiman Desertification Research Station, and Key Laboratory of Stress physiology and Ecology, Gansu Province, Northwest Institute of Eco-Environment and Resources, China Academy of Sciences (CAS), for their help in field work and laboratory studies.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. The natural community of Ulmus pumila.
Figure 1. The natural community of Ulmus pumila.
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Figure 2. Sampling sites (a) and quadrat layout (b) of Ulmus pumila sparse forest grassland fields in Hunshandak Sandy Land.
Figure 2. Sampling sites (a) and quadrat layout (b) of Ulmus pumila sparse forest grassland fields in Hunshandak Sandy Land.
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Figure 3. The plant species diversity ((a) SR, species richness; (b) D, Simpson’s diversity index) of Ulmus pumila sparse forest grassland fields with different grazing management types. Values are mean ± SE. Different lowercase letters indicate significant differences (p < 0.05) among grazing management treatment types. BG, banned grazing fields; SG, seasonal grazing fields; DG, delayed grazing fields.
Figure 3. The plant species diversity ((a) SR, species richness; (b) D, Simpson’s diversity index) of Ulmus pumila sparse forest grassland fields with different grazing management types. Values are mean ± SE. Different lowercase letters indicate significant differences (p < 0.05) among grazing management treatment types. BG, banned grazing fields; SG, seasonal grazing fields; DG, delayed grazing fields.
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Figure 4. The plant allelic richness (AR) of Ulmus pumila communities with different grazing management types. Values are expressed as mean ± SE. Bars marked with distinct lowercase letters indicate significant differences among grazing management treatment types (p < 0.05). Bars marked with different capital letters signify significant variations between nuclear and chloroplast DNA genetic diversity treatments (p < 0.05). nrDNA and cpDNA, nuclear and chloroplast DNA. BG, banned grazing fields; SG, seasonal grazing fields; DG, delayed grazing fields; U, Ulmus pumila; NU, other dominant species besides Ulmus pumila; M, grazing management types; S, species; * and **, significant difference at p < 0.05 and p < 0.01 levels; n.s., no significant difference.
Figure 4. The plant allelic richness (AR) of Ulmus pumila communities with different grazing management types. Values are expressed as mean ± SE. Bars marked with distinct lowercase letters indicate significant differences among grazing management treatment types (p < 0.05). Bars marked with different capital letters signify significant variations between nuclear and chloroplast DNA genetic diversity treatments (p < 0.05). nrDNA and cpDNA, nuclear and chloroplast DNA. BG, banned grazing fields; SG, seasonal grazing fields; DG, delayed grazing fields; U, Ulmus pumila; NU, other dominant species besides Ulmus pumila; M, grazing management types; S, species; * and **, significant difference at p < 0.05 and p < 0.01 levels; n.s., no significant difference.
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Figure 5. The plant expected heterozygosity (HE) of Ulmus pumila communities with different grazing management types. The data is presented as mean ± SE. Bars with distinct lowercase labels indicate notable disparities among the various grazing management treatment types (p < 0.05). Meanwhile, bars designated with different uppercase labels indicate significant distinctions between treatments involving nuclear and chloroplast DNA genetic diversity (p < 0.05). nrDNA and cpDNA, nuclear and chloroplast DNA. BG, banned grazing fields; SG, seasonal grazing fields; DG, delayed grazing fields; U, Ulmus pumila; NU, other dominant species besides Ulmus pumila; M, grazing management types; S, species; * and **, significant difference at p < 0.05 and p < 0.01 levels; n.s., no significant difference.
Figure 5. The plant expected heterozygosity (HE) of Ulmus pumila communities with different grazing management types. The data is presented as mean ± SE. Bars with distinct lowercase labels indicate notable disparities among the various grazing management treatment types (p < 0.05). Meanwhile, bars designated with different uppercase labels indicate significant distinctions between treatments involving nuclear and chloroplast DNA genetic diversity (p < 0.05). nrDNA and cpDNA, nuclear and chloroplast DNA. BG, banned grazing fields; SG, seasonal grazing fields; DG, delayed grazing fields; U, Ulmus pumila; NU, other dominant species besides Ulmus pumila; M, grazing management types; S, species; * and **, significant difference at p < 0.05 and p < 0.01 levels; n.s., no significant difference.
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Figure 6. The correlation between species richness and genetic diversity. (between species richness (SR) and allelic richness of U. pumila (UAR) (a); expected heterozygosity of U. pumila (UHE) (b); allelic richness of other dominant species besides Ulmus pumila (NUAR) (c); expected heterozygosity of other dominant species besides Ulmus pumila (NUHE) (d); allelic richness of community (CAR) (e); expected heterozygosity of community (CHE) (f).
Figure 6. The correlation between species richness and genetic diversity. (between species richness (SR) and allelic richness of U. pumila (UAR) (a); expected heterozygosity of U. pumila (UHE) (b); allelic richness of other dominant species besides Ulmus pumila (NUAR) (c); expected heterozygosity of other dominant species besides Ulmus pumila (NUHE) (d); allelic richness of community (CAR) (e); expected heterozygosity of community (CHE) (f).
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Figure 7. RDA between plant diversity ((a) species level; (b) community level) of Ulmus pumila communities) and bioclimatic factors. AR, allelic richness; HE, expected heterozygosity; U, U. pumila; NU, non-U. pumila species; SSR, species richness of shrubs; HSR, species richness of herbs; CSR, species richness of communities; SD, Simpson’s diversity index of shrubs; HD, Simpson’s diversity index of herbs; CD, Simpson’s diversity index of communities.
Figure 7. RDA between plant diversity ((a) species level; (b) community level) of Ulmus pumila communities) and bioclimatic factors. AR, allelic richness; HE, expected heterozygosity; U, U. pumila; NU, non-U. pumila species; SSR, species richness of shrubs; HSR, species richness of herbs; CSR, species richness of communities; SD, Simpson’s diversity index of shrubs; HD, Simpson’s diversity index of herbs; CD, Simpson’s diversity index of communities.
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Table 1. Sample list for genetic analysis of Ulmus pumila communities from different grazing management types.
Table 1. Sample list for genetic analysis of Ulmus pumila communities from different grazing management types.
Grazing Management TypesNumber of Ulmus pumila
Samples		Species Name and Number of Other Dominant Species Samples besides U. pumila
BG 20Agropyron cristatum (L.) Gaertn. (2)
Artemisia frigida Willd. (3)
Artemisia halodendron Turcz. (4)
Artemisia scoparia Waldst. et Kit (4)
Caragana korshinskii Kom(2)
Carduus crispus L. (2)
Cleistogenes squarrosa (Trin.) Keng (2)
Setaria viridis (L.) Beauv. (4)
Spiraea trilobata L. (3)
SG 16Artemisia frigida Willd. (2)
Artemisia halodendron Turcz. (4)
Caragana microphylla Lam. (4)
Carex duriuscula C. A. Mey. (3)
Chenopodium acuminatum Willd. (3)
Leymus secalinus (Georgi) Tzvel. (2)
Setaria viridis (L.) Beauv. (2)
Spiraea trilobata L. (4)
DG12Artemisia halodendron Turcz. (8)
Corispermum mongolicum Iliin. (4)
Salix cheilophila Schneid. (2)
Salix gordejevii Y. L. Chang et Skv. (8)
BG, banned grazing; SG, seasonal grazing; DG, delayed grazing.
Table 2. Primer sequences in chloroplast and nuclear DNA of the selected species.
Table 2. Primer sequences in chloroplast and nuclear DNA of the selected species.
PrimerSequence (5′–3′)References
cpDNApsbA-trnHF: GTTATGCATGAACGTAATGCTC
R: CGCGCATGGTGGATTCACAATCC		Tate et al., 2003 [31]
trnL-trnFF: CGAAATCGGTAGACGCTACG
R: ATTTGAACTGGTGACACGAG		Taberlet et al., 1991 [32]
nrDNAITS1-ITS4F: AGGTGACCTGCGGAAGGATCATT
R: GGTAGTCCCGCCTGACCTGG		White et al., 1990 [33]
cpDNA, chloroplast DNA; nrDNA, nuclear DNA.
Table 3. Importance value of dominant species in elm Ulmus pumila communities with different grazing management types.
Table 3. Importance value of dominant species in elm Ulmus pumila communities with different grazing management types.
SpeciesLife-FormImportant Value (%)
BGSGDG
Setaria viridis (L.)Beauv.annual0.430.320.08
Corispermum mongolicum Iliin.0.250.500.33
Chenopodium aristatum L. 0.240.190.09
Bassia dasyphylla (Fisch. et C. A. Mey.) Kuntze0.040.080.06
Salsola collina Pall.0.020.140.07
Chenopodium acuminatum Willd.0.020.080.05
Artemisia palustris Linn.0.050.01
Echinops gmelini Turcz.0.010.01
Cannabis sativa L.0.010.05
Xanthium sibiricum Patrin ex Widder0.01
Agriophyllum squarrosum (Linn.) Moq. 0.07
Eragrostis pilosa (Linn.) Beauv 0.01
Artemisia scoparia Waldst. et Kitbiennial0.190.11
Artemisia sieversiana Ehrhart ex Willd.0.080.01
Lappula myosotis V. Wolf0.040.05
Sonchus oleraceus L.0.01 0.01
Dontostemon dentatus (Bunge) Ledeb.0.03 0.08
Carduus crispus L.0.53
Silene aprica Turcx. ex Fisch. et Mey.0.01
Agropyron cristatum (L.) Gaertn.perennial0.460.280.19
Artemisia frigida Willd.0.420.290.03
Carex duriuscula C. A. Mey.0.260.240.24
Cleistogenes squarrosa (Trin.) Keng0.260.140.02
Potentilla acaulis L.0.100.010.01
Medicago ruthenica (L.) Trautv.0.070.020.13
Carex tristachya Thunb.0.020.110.03
Bromus inermis Leyss.0.010.010.04
Leymus secalinus (Georgi) Tzvel.0.010.250.01
Allium tenuissimum L.0.050.01
Poa sphondylodes Trin.0.040.01
Potentilla bifurca Linn.0.040.02
Achnatherum sibiricum (L.) Keng0.020.09
Allium senescens L.0.020.04
Allium mongolicum Regel.0.010.02
Stipa grandis P.A. Smirn.0.05 0.01
Leymus chinensis (Trin.) Tzvel.0.02 0.09
Phragmites australis (Cav.) Trin. ex Steu. 0.080.01
Leontopodium leontopodioides (Willd.) Beauv.0.34
Dianthus chinensis L.0.10
Ferula bungeana Kitag.0.08
Heteropappus altaicus (Willd.) Novopokr.0.04
Oxytropis racemosa Turcz.0.03
Erodium stephanianum Willd.0.02
Psammochloa villosa (Trin.) Bor0.02
Astragalus laxmannii Jacquin0.02
Allium ramosum L.0.01
Oxytropis racemosa Turcz.0.01
Calamagrostis pseudophragmites (Haller f.) Koeler 0.02
Chamaerhodos canescens Krause 0.02
Thalictrum petaloideum L. 0.02
Polygonum sibiricum Laxm. 0.01
Polygonum divaricatum L. 0.09
Hedysarum gmelinii Ledeb. 0.04
Ptilotricum canescens (DC.) C. A. Mey. 0.02
Ulmus pumila L.shrub1.151.551.03
Artemisia halodendron Turcz.0.421.080.78
Spiraea trilobata L.2.580.98
Caragana microphylla Lam.0.031.40
Thymus mongolicus Ronn.0.010.02
Caragana korshinskii Kom.0.27
Lespedeza daurica (Laxm.) Schindl.0.03
Kochia prostrata (L.) Schrad.0.02
Cynanchum thesioides (Freyn) K. Schum.0.01
Berberis poiretii Schneid. 0.10
Salix gordejevii Y. L. Chang et Skv. 0.58
Salix cheilophila Schneid. 0.42
Hedysarum fruticosum.Pall. 0.04
BG, banned grazing fields; SG, seasonal grazing fields; DG, delayed grazing fields.
Table 4. Genetic diversity parameters at species level.
Table 4. Genetic diversity parameters at species level.
GeneSpeciesHHd ± SDAR ± SDHE ± SDGd ± SD
cpDNAU200.497 ± 0.1632.968 ± 0.6490.252 ± 0.1330.848 ± 0.079
NU340.584 ± 0.2333.467 ± 0.6390.435 ± 0.1870.847 ± 0.034
nrDNAU220.649 ± 0.1363.163 ± 1.020.281 ± 0.1850.905 ± 0.040
NU380.754 ± 0.1893.660 ± 1.0710.378 ± 0.2150.942 ± 0.034
COMU270.762 ± 0.2283.134 ± 1.0670.268 ± 0.1840.865 ± 0.018
NU450. 791 ± 0.2723.332 ± 1.0110.394 ± 0.2000.907 ± 0.012
U, U. pumila; NU, other dominant species besides Ulmus pumila; H, the number of haplotypes; Hd, the haplotype diversity; AR, allelic richness; HE, expected heterozygosity; Gd, gene diversity.
Table 5. Analysis of molecular variance (AMOVA) for Ulmus pumila and non-Ulmus pumila species.
Table 5. Analysis of molecular variance (AMOVA) for Ulmus pumila and non-Ulmus pumila species.
SpeciesSource of VariationdfPercentage of VariationFixation Indicesp
UAmong populations111.92FST = 0.0192p < 0.001
Within population3698.08
Total48
Among grazing types21.16FCT = 0.0116p < 0.001
Within population among grazing types913.35FSC = 0.1319p < 0.001
Total3687.82FST = 0.1219p < 0.001
NUAmong populations114.75FST = 0.0475p = 0.004
Within population13295.25
Total144
Among grazing types21.70FCT = 0.0170p = 0.003
Within population among grazing types924.08FSC = 0.2367p < 0.001
Total13277.62FST = 0.2238p < 0.001
U, U. pumila; NU, other dominant species besides Ulmus pumila.
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Huang, W.; He, Y.; Zhao, X.; Yang, H.; Gan, H.; Zhao, X. Plant Diversity Responses of Ulmus pumila L. Communities to Grazing Management in Hunshandak Sandy Land, China. Diversity 2023, 15, 1221. https://doi.org/10.3390/d15121221

AMA Style

Huang W, He Y, Zhao X, Yang H, Gan H, Zhao X. Plant Diversity Responses of Ulmus pumila L. Communities to Grazing Management in Hunshandak Sandy Land, China. Diversity. 2023; 15(12):1221. https://doi.org/10.3390/d15121221

Chicago/Turabian Style

Huang, Wenda, Yuanzheng He, Xueyong Zhao, Hongxiao Yang, Honghao Gan, and Xin Zhao. 2023. "Plant Diversity Responses of Ulmus pumila L. Communities to Grazing Management in Hunshandak Sandy Land, China" Diversity 15, no. 12: 1221. https://doi.org/10.3390/d15121221

APA Style

Huang, W., He, Y., Zhao, X., Yang, H., Gan, H., & Zhao, X. (2023). Plant Diversity Responses of Ulmus pumila L. Communities to Grazing Management in Hunshandak Sandy Land, China. Diversity, 15(12), 1221. https://doi.org/10.3390/d15121221

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