A Dataset for Spatial Variation of Species Diversity and Biomass of Subalpine Grasslands in Shanxi Province, China

Xu, M. H.^{1, 2 *}  Zhao, Z. T.¹

1. Institute of Geographical Science, Taiyuan Normal University, Jinzhong 030619, China;

2. Institute of Carbon Neutrality, Taiyuan Normal University, Jinzhong 030619, China

Abstract: Subalpine grasslands provide grazing for wildlife and grazing animals, so protecting their integrity is important. From July to August in 2016, experimental plots were established in the east of the Loess Plateau of China in high mountains with subalpine grasslands. A total of nine mountains were selected from the north to the south in the eastern part of the Plateau. For each mountain, a typical subalpine grassland was selected and surveys of species diversity and biomass were conducted at the plant community level. Six 1 m² quadrats were designed in each plot for measuring the height, abundance, coverage, and frequency of each plant species, and then α-diversity indices were calculated. Next, in order to calculate β- and γ-diversity indices, latitude and longitude were divided into five intervals using intervals of 0.5˚ and 0.45˚, respectively. Elevation was divided into six intervals of 100 m. Lastly, five of the six quadrats in each grassland were chosen and used to survey plant biomass. At the middle of biomass quadrats, 0.2 m × 0.2 m sub-quadrats were designed for measuring aboveground biomass by clipping aboveground plant parts and digging for belowground biomass. The resulting dataset includes subalpine grassland data for Shanxi province as follows: (1) the name of mountains and the geographical location of the experimental plots; (2) the indices of α-diversity; (3) the division of geographical intervals and the indices of β-diversity; (4) the indices of γ-diversity; (5) the indices of biomass; and (6) the correlations between α-diversity and biomass in these grasslands. The data formats are .shp and .xlsx files with a data size of 33.6 KB for a compressed file.

Keywords: subalpine grassland; species diversity; biomass; geographical gradient

DOI: https://doi.org/10.3974/geodp.2022.04.03

CSTR: https://cstr.escience.org.cn/CSTR:20146.14.2022.04.03

Dataset Availability Statement:

The dataset supporting this paper was published and is accessible through the Digital Journal of Global Change Data Repository at: https://doi.org/10.3974/geodb.2022.05.03.V1 or https://cstr.escience.org.cn/CSTR:20146.11.2022.05.03.V1.

1 Introduction

Plants serve vital roles in terrestrial ecosystems and provide humans with many ecological services including regulating climate, improving soil fertility, protecting biodiversity, and promoting productivity^[1,2]. Studies of plant species diversity and biomass have become important research topics related to ecology and geography, focusing on the spatial distribution of species diversity and biomass together with the correlation between species diversity and biomass along geographical gradients. Species diversity reveals the organizational levels of a community and induces changes in the functional characteristics of a biotic community; it can even alter a shortage of critical species in a community or the use patterns of environmental resources by species, thus leading to modifications in ecosystem structure and function^[3]. Measurements of species diversity are primarily conducted at three spatial scales^[4]. The first scale is within-habitat diversity, that is, α-diversity, which mainly focuses on the number of species in local homogeneous habitat; the second scale is between-habitat diversity, that is, β-diversity, which indicates differences of species composition among different habitats and communities; the last scale is regional diversity, that is, γ-diversity, which describes the number of species at regional or continental scales^[4].

Similar to species diversity, biomass is also a primary quantitative characteristic of ecosystems and reflects plants productivity^[5]. Biomass allocation among various organs mirrors the growth strategy a plant uses to adapt to an environment and plays a crucial role in the growth of plant individuals, species coexistence, and vegetation recovery after disturbance^[6]. Strategies of biomass allocation among leaves, stems, and roots, together with allometric relationships between plant organs provide a foundation in the study of species evolution, maintenance of diversity, and carbon cycling in ecosystems, and also are important to our understanding of the distribution of carbon in ecosystems and the function of carbon sinks^[7]. Biomass allocation, especially the models of allocation under the effects of different geographical gradients, is important in studies of biomass.

Subalpine grasslands are mainly distributed in high-elevation mountains where species diversity and biomass are obviously affected by the mountainous terrain. Latitude, longitude, and elevation are dominant terrain indicators of mountainous subalpine grasslands; they directly affect the spatial distribution of solar radiation and rainfall, and thus they result in an uneven distribution of soil moisture and temperature^[8,9]. Large areas of subalpine grasslands on the eastern part of the Loess Plateau have an abundance of species. These grasslands not only provide excellent natural pastures but also serve as famous eco-attractions,for example, Heyeping has been honored as the “plateau jade”, Shunwangping as the “Jiuzhaigou of north China”, and Wutai Mountain as “the roof of north China”^[10–12]. With a rapid development of tourism and pasture husbandry, subalpine grasslands have experienced extensive and severe degradation caused by humans in the mountain systems of Liuleng, Wutai, Lvliang, and Zhongtiao, where their environments are sensitive and fragile, grasslands degradation had been increasing, and biodiversity has been seriously threatened^[13,14]. Given this, nine typical subalpine grasslands were selected as research objects on the eastern part of the Loess Plateau. From the plant community level, we obtained a dataset of three types of subalpine grasslands in different mountains: (1) species diversity data (α, β, and γ diversity); (2) biomass data; (3) correlations between species diversity and biomass.

2 Metadata of the Dataset

The metadata of the In situ dataset of species diversity and biomass of subalpine meadows in Shanxi province of China^[15] are summarized in Table 1. This metadata include the full and short dataset names, authors, publication year of the dataset, temporal and spatial resolutions, as well as dataset format, size, file types, publisher, and data sharing policy, and so on.

Table 1  Metadata summary of the In situ dataset of species diversity and biomass of subalpine meadows in Shanxi province of China

3 Methods

3.1 Experimental Design

By comparing a vegetation-cover map of the Loess Plateau (Figure 1a) and a topographic map of Shanxi province (Figure 1b), experimental plots were selected in typical grasslands of subalpine belts at relatively high elevations (from 1,720 m to 3,045 m) in mountains along the eastern part of the Loess Plateau^[4]. These were investigated from July to August in 2016. In this region, nine subalpine grasslands (one subalpine grassland in each mountain) were successively surveyed in different mountain ranges moving from north to south. In total, nine mountains belonging to four mountain systems were surveyed. The names of these mountains were Dianding (DD) Mountain in the Liuleng Mountain system, Beitai (BT) and Dongtai (DT) mountains in Wutai Mountain system, Malun (ML), Heyeping (HY), Yunzhong (YZ), and Yunding (YD) mountains in the Lvliang Mountain system, and Shunwangping (SU) and Shengwangping (SE) mountains in the Zhongtiao Mountain system (Figure 1b).

Figure 1  Map of study area and plot distribution

(Notes: The study area covers the region with extensive vegetation cover on the eastern part of the Loess Plateau. Nine mountains with high elevations were selected as for placement of experimental plots from the north to the south in Shanxi province)

3.2 Measurement of Plant Diversity

On each of these nine mountains, six plots in 1m×1m were installed randomly in subalpine communities to survey plant diversity at a community scale or a total of 54 plots on all mountains. Each 1 m × 1 m quadrat frame was used as a measurement tool, and was divided into 100 uniform grids (0.1 m × 0.1 m) (Figure 2). In each grid, we measured plant height, abundance, coverage, and frequency of each species; the data were used to calculate species diversity indices in the plots. Meanwhile, we recorded the latitude, longitude, and elevation of each plot (Table 2).

Table 2  Plot location with LLE

3.3 Measurement of Plant Biomass

At each of nine mountain sites, we randomly located five plots for measuring plant diversity, with 45 plots in total. A 0.2 m × 0.2 m quadrat was placed in the center of each plot with its plants being evenly distributed, so that 45 quadrats were also acquired in total. We used quadrats to survey plant biomass at the community scale using the following method^[4]. In each quadrat, aboveground plant parts were clipped near the ground surface (Figure 3), and belowground plant parts were then excavated in the entire 0.2 m × 0.2 m quadrat to a depth of 0.2 m because most plants were very shallow rooted (Figure 4). Samples of aboveground plant parts and soil blocks with volumes of 0.2 m × 0.2 m × 0.2 m were sealed and brought to laboratory for post-sample processing. In this process, for aboveground plant parts, only live plants were retained. Samples of belowground plant parts were first sieved by a standard soil sieve with a bore diameter of 0.42 mm to eliminate stones and coarse debris from the soil. Then, the soil samples were sieved using a standard soil sieve with a bore diameter of 0.18 mm to eliminate fine roots with diameters no smaller than 0.18 mm. Next, live roots and treated stems and leaves were placed into an oven and dried for 48 h to constant weight at a temperature of 80 °C. In the last step, dry samples were weighed to calculate below- and above-ground plant biomass.

Figure 3  Clipping and gathering of aboveground plant parts

Figure 4  Digging and gathering of belowground plant roots

3.4 Data Analysis

(1) Calculations on α-, β-, and γ-diversity of plant communities

First, we calculated the importance values of plant species based on the relative height, abundance, coverage, and frequency of each species; then α-diversity indices were obtained, including the Patrick, Simpson, Shannon, and Pielou indices^[13]. Second, using the horizontal directions of latitude and longitude, the nine plots were divided into five latitudinal and five longitudinal intervals with 0.5° and 0.45° intervals arranged from south to north and from west to east, respectively; the nine plots were divided into six elevational intervals from low to high elevation with an interval of 100 m^[4]. Species in different plots were merged in each geographical interval. Unlike α-diversity, measurements of β-diversity could be separated into two methods: binary attribute data and quantitative data. Hence, we used two indices, the Cody and Sørenson indices, in the analysis of β-diversity based on binary attribute data; in addition, we used the Bray–Curtis Index based on quantitative data^[4]. Lastly, the total number of species present (i.e., total species richness) in each geographic interval (five latitudinal and longitudinal gradients together with six elevational gradients) was defined as an indicator of γ-diversity^[4].

(2) Calculations on biomass of plant community

Aboveground, belowground, and total biomass as well as the root-to-shoot ratio were adopted as biomass indicators ^[17,18].

4 Data Results and Validation

4.1 Data Composition

This dataset includes six types of data collected for the studied subalpine grasslands in Shanxi province as described below in six tables. Table 1 includes the names and geographical locations of mountains with experimental plots, including the Chinese and English names of mountains, abbreviations of these names, latitude, longitude, and elevation. Table 2 lists the α-diversity indices, including the Patrick, Simpson, Shannon, and Pielou indices. Table 3 provides the division of geographical intervals and the β-diversity indices, mainly covering the geographic gradient range along with the Cody, Sørenson, and Bray–Curtis indices. Table 4 has the γ-diversity indices, mainly covering the geographic gradient range and Richness Index. Table 5 provides the biomass indices, and mainly includes above- and below-ground biomass, total biomass, and the root-to-shoot ratio. Table 6 contains the data for correlations between α-diversity and biomass, which mainly cover the Patrick, Simpson, Shannon, and Pielou indices as well as above- and below-ground biomass, total biomass, and the root-to-shoot ratio. The .xlsx file-format data were from plot surveys and were summarized in an Excel file named “Diversity&BiomassSub-alpineMeadowsShanxi”. The .shp file-format data in GIS, which provide plot locations, were summarized in a file named “SampleSites”.

4.2 Data Products

At each mountain, six survey quadrats for measuring plant community species diversity were established. Four α-diversity indices were calculated by measuring plant growth, specifically the Patrick, Simpson, Shannon, and Pielou indices (Table 3). Based on the division on geographic intervals of different mountains, three β-diversity indices were calculated, specifically, the Cody, Sørenson, and Bray–Curtis indices (Table 4). In addition, one γ-diversity index was calculated for the Richness Index with the same way (Table 5).

Table 3  α-diversity indices of subalpine grasslands in Shanxi province

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 (Continued)

Table 4  β-diversity indices of subalpine grasslands in Shanxi province

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Table 5  γ-diversity indices of subalpine grasslands in Shanxi province

At each mountain, five survey quadrats were used to measure plant community biomass by measuring the weights of above- and below-ground plant parts, and four biomass indices were obtained as described above (Table 6). Because the plant biomass quadrats were designed as species diversity quadrats, the correlations between α-diversity and biomass could be calculated. Among these correlations, the distribution of eight indices included the Patrick, Simpson, Shannon, and Pielou indices, as well as above- and below-ground

Table 6  Biomass indices of subalpine grasslands in Shanxi province

biomass, total biomass, and the root–shoot ratio in different quadrats (Figure 5).

The following results were obtained from the above dataset. The α-diversity of subalpine grasslands presented unimodal change patterns with smaller values in the central mountains. The β-diversity had tendencies to decrease with increasing spatial gradients and amplitudes

Figure 5  Distribution Data for correlation between α-diversity and biomass of subalpine grasslands in Shanxi province

of variation that were greatest along latitudinal gradients. The γ-diversity generally conformed to unimodal change patterns in spatial distribution and the spatial distribution of biomass in subalpine grasslands tended to exhibit high geographical gradients where more biomass was allocated to belowground plant parts with increased spatial gradients^[4]. These results are described in detail in Xu et al.^[4].

4.3 Data Validation

The following conclusions were obtained from the dataset. The unique geomorphological structures with a series of basins between mountain systems from the east of the Loess Plateau have resulted in the subalpine grasslands being mostly distributed along latitudinal directions. As a result, the spatial distributions of species diversity and biomass were more evident along latitudinal gradients, and thus the response of aboveground biomass was more sensitive to variations of spatial gradients and species diversity^[4]. Meanwhile, by the published references ^{[12, 19, 20]} and the simulated warming experiment in Lvliang Mountain in 2016^[21–24], we concluded that species diversity of subalpine grasslands exerted a spatial pattern with an increase from north to south in the eastern Loess Plateau of China.

5 Discussion and Conclusion

Species distribution patterns are the result of many ecological processes, but researchers have come to a wide variety of conclusions during studies of species diversity and biomass together with their correlation, owing to discrepancies in study scales, projects, and areas^[25]. This was mainly reflected in: (1) topographical differences were eliminated by using studies at larger geographical scales, but concrete differences in community diversity were neglected when geographical units had small scales; (2) simulation studies were carried out in homogeneous habitats and humanly-modified communities at small scales, but the influence of stronger spatial heterogeneity caused by enlargements of scale were omitted in studies of diversity and productivity; and (3) in research studies conducted in mountainous areas, scales have been focused only on a single space level and lacked systematic research in mountains at overall spatial-scale hierarchies^{[9, 25]}. The conclusion of different distribution patterns as well as the interaction between species diversity and biomass from this dataset probably agreed with the biogeographic affinity hypothesis; that is, the ability of species to tolerate local climate probably developed under dual effects of Earth’s climates and species evolution in ecological niches^[26]. Meanwhile, this conclusion supports our common understanding that high levels of species diversity provide an important way for ecosystems to maintain biomass; that is, greater species diversity allows for accommodation to an environment by providing for species redundancy and functional complementation^[26]. Thereby, from a level of plant population in natural conditions, species diversity has important significance that contributes to the discussion of spatial distributions along with correlations of species diversity and biomass at various levels; this illuminates the internal mechanisms of functional relationships between biodiversity and ecosystems.

Author Contributions

Xu, M. H. designed the algorithms of dataset. Zhao, Z. T. contributed to the data processing and analysis. Xu, M. H. wrote the data paper.

Conflicts of Interest

The authors declare no conflicts of interest.

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