DOI: 10.5061/dryad.sqv9s4n8w
title: The functioning of alpine grassland ecosystems: climate outweighs plant species richness

description: 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.
description: 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
 m<sup>2</sup> 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
 cm<sup>3</sup> in volume. Soil bulk density (BD, g
 cm<sup>-3</sup>) 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<sup>-2</sup>) and soil total nitrogen stock (TNs, g N
 m<sup>-2</sup>) 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).
description: # The functioning of alpine grassland ecosystems: climate outweighs plant
 species richness
 [https://doi.org/10.5061/dryad.sqv9s4n8w](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<sup> </sup>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.