The functioning of alpine grassland ecosystems: climate outweighs plant species richness
Data files
Sep 18, 2023 version files 22.62 KB
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Data-_Cheng_et_al.__2023.xlsx
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README.md
Abstract
- The biodiversity–ecosystem functioning relationship has received significant attention in recent decades. It has been widely demonstrated that plant diversity plays a crucial role in enhancing the functioning of terrestrial ecosystems. However, few studies have tested the influence of plant species richness in mediating the impacts of climate on ecosystem functions at large spatial scales.
- To address this gap, we utilized data from field surveys across broad climatic gradients at the Qinghai-Tibetan Plateau, China. Our goal was to examine the importance of plant species richness for the functioning of alpine grassland ecosystems, specifically productivity and soil carbon sequestration.
- Our results showed strong positive correlations between ecosystem functioning and growing season precipitation as well as species richness. In contrast, there was a negative correlation with growing season temperature. Notably, the positive effect of growing season precipitation on ecosystem functioning outweighed the negative effect of growing season temperature. The indirect effects of growing season precipitation and temperature on ecosystem functioning through changes in species richness were weak. Furthermore, the inclusion of climate factors in the model weakened the relationships between species richness and ecosystem functioning.
- Synthesis. Our findings demonstrate that climate factors are more important than species richness for the provisioning of ecosystem functions at large spatial scales. In summary, our study underscores the importance of considering climate factors alongside species richness when assessing ecosystem functioning across extensive geographical areas.
README: The functioning of alpine grassland ecosystems: climate outweighs plant species richness
https://doi.org/10.5061/dryad.sqv9s4n8w
Give a brief summary of dataset contents, contextualized in experimental procedures and results.
Description of the data and file structure
For each site, we calculated the average values of species richness, aboveground net primary productivity, belowground plant biomass, soil organic carbon stock, and soil total nitrogen stock for the five quadrats. We hypothesized that changes in growing season precipitation and temperature would directly affect on grassland ecosystem functioning, including aboveground net primary productivity, belowground plant biomass, soil organic carbon stock, and soil total nitrogen stock, as well as indirectly through shifts in species richness. Based on our hypothesis, we provided an a priori structural equation model (SEM) (Figure 2) and then used our data to run the a priori SEM. We found it was an acceptable fit (Fisher's C = 3.94, P-value = 0.14). To further explore the importance of plant species richness in driving ecosystem functioning across our spatial climate gradient, we examined the role of species richness in mediating climate effects on grassland ecosystem functioning by comparing three SEMs: one with both climate factors and species richness, one without species richness, and one without climate factors. We tested the adequacy of all SEMs using Shipley's test of d-separation, Fisher's C statistic, and Akaike Information Criterion (AIC) (Burnham & Anderson, 2002). We conducted all SEMs using the piecewiseSEM package (Lefcheck et al., 2016) and visualized the data using the ggplot2 package in R version 3.6.1 (R Foundation for Statistical Computing, Vienna, Austria, 2021). Additionally, we employed ordinary least squares (OLS) regressions to test the relationships among climate factors (growing season precipitation and temperature), species richness, and grassland ecosystem functions (aboveground net primary productivity, belowground plant biomass, soil organic carbon stock, and soil total nitrogen stock). These regressions were fitted using the lm function of the stats package in R. We created 3-dimensional surfaces using SigmaPlot software to visualize the relationships between growing season precipitation, temperature, and grassland ecosystem functions. Prior to OLS regressions and SEMs, all variables were ln-transformed to ensure the normality of the model residuals.
Methods
Study area and sampling sites
The study area (32°11′-42°57N, 92°13′-108°46′E) is located at the eastern side of the Qinghai-Tibetan Plateau, China, approximately 4000 m above sea level (Figure 1a). This area is characterized by an arid and semi-arid climate, with mean annual air temperature of -1.1 °C and mean annual precipitation of 485 mm. The area has a typical continental monsoon climate, and 80-90% of precipitation occurs in the growing season from May to September. The dominant vegetation type in the study area is alpine grassland. These alpine grasslands have naturally developed with little human interference.
Field vegetation and soil survey
Field surveys were conducted in the summer of 2012, with a total of 100 sampling sites (100 m × 100 m) across natural grasslands that were representative of the area. For each site, we recorded the latitude, longitude, and elevation. At each site, five 1 × 1 m2 quadrats were established along a 100-m diagonal transect for which we investigated the height, cover, and density of all vascular plant species, as well as the above- and belowground plant biomass. The number of plant species in each quadrat was recorded as species richness. During the growing season, the aboveground plant parts within each quadrat were clipped at the soil surface, and the belowground plant biomass was collected from 0–5, 5–10, 10–20, 20–30, 30–50, 50–70 and 70–100 cm soil depth using a root auger (9 cm in diameter). For each quadrat, three root samples from each soil layer were pooled. Then, the roots were carefully washed from the soil. After harvesting, all plant materials were oven-dried at 65 °C for 48 h until a constant weight. Aboveground biomass with grazing exclusion was measured at the peak or at the end of the growing season, which was used as a proxy for grassland aboveground net primary production.
After harvesting the plant biomass, we used a soil auger (5 cm inner diameter) to collect three soil samples for each layer of 0–5, 5–10, 10–20, and 20–30 cm depth) in each quadrat. Soil samples from the same layer were mixed into one composite sample per quadrat. The soil samples were air-dried, weighed, sieved through a 2 mm mesh, handpicked to remove plant detritus, ground into a fine powder, and then used for measurement of soil nutrient properties. Soil organic carbon (SOC) content was measured using the potassium dichromate oxidation-external heating method, while soil total nitrogen (TN) content was measured by a Kjeltec2300 type fully automatic azotometer. Finally, for all quadrats, we collected one soil sample from each soil layer for bulk density using a cutting ring of 100 cm3 in volume. Soil bulk density (BD, g cm-3) was then calculated as the ratio of oven-dried (105°C) soil mass to the sample volume. Soil organic carbon stock (SOCs, g C m-2) and soil total nitrogen stock (TNs, g N m-2) were calculated.
Growing season climate data
We first obtained climate data from the National Meteorological Bureau of China database for 148 meteorological stations spread across the study region. To better reflect the climate experienced by the alpine vegetation and soil over the past decades, the gridded climate data include precipitation and air temperature during the growing seasons from 1981 to 2010, as plant and soil sampling was conducted during the growing season. The annual average growing season precipitation ranges from 48.9 to 556.5 mm, and annual average growing season temperature ranges from 4.8 to 19.5 °C (Figure 1b,c). And mean growing season precipitation was significantly negatively correlated with mean growing season temperature (Figure 1d). The climate variables were first compiled from daily climate raster surfaces interpolated using ANUSPLIN 4.37 and then extracted for each of our 100 study sites using ArcGIS software (Version 10.5) (ESRI, Redlands, CA, USA).