Study site
The study was carried out in the Qaidam Basin on the northeastern Tibetan Plateau (Fig. S1), which is the largest intermontane basin in western China (Liu et al.1998; Xia et al. 2001). The average altitude of the basin is approximately 3000 m, and the mountains rise to over 5000 m (Wang et al. 1999; Xia et al. 2001; Owen et al. 2006). The annual mean temperature is 2-4°C, the annual mean precipitation is less than 100 mm (Wang et al. 1999; Wang et al. 2005; Zhao et al. 2007), and the annual mean evaporation is more than 2500 mm. Overall, the basin is a region with extreme drought, cold temperatures and salinization (Wang et al. 2005). The plant community structure is simple, with low vegetation coverage. The predominant soil type is solonchak (Wang et al. 2018). The vegetation is characterized by xerophytes, dominated by rapidly regenerating grasses, herbs and short semishrubs (Zeng and Yang 2009). Local plant taxa include members of Leymus, Chenopodiaceae (Salsola abrotanoides, Kalidium gracile, Ceratoides latens, etc.), Compositae and Nitraria (Zhao et al. 2007; Wang et al. 2018). Specifically, the shrubland is dominated by shrubs and subshrubs, vegetation with low coverage (5%-30%), large areas of bare land and low diversity (3-7 species per quadrat). The arid grassland is characterized by grasses and annual forbs, and the vegetation has relatively high coverage (20%-50%) and diversity (6-12 species per quadrat) (DAHV and GSAHV 1996).
Soil seed bank sampling and aboveground vegetation investigation
We collected samples of the soil seed bank and investigated aboveground vegetation at 29 sites, including 21 shrubland sites and 8 arid grassland sites, in the Qaidam Basin during the peak growing season in August 2014.
For seed bank sampling, five plots (20 m × 20 m) were randomly selected at each site (200 m × 200 m). Five subplots (5 m × 5 m) were randomly distributed in each plot. Within each subplot, 10 soil cores (d=3.6 cm) were randomly extracted to a depth of 10 cm. The soil cores were divided into 2 layers, namely, the shallow layer (0-5 cm) and the second layer (5-10 cm), and we pooled 10 cores from each depth in each subplot into one soil sample. Overall, there were a total of 50 samples (5 plots × 5 subplots × 2 soil layers) from each site and 1450 samples (50 samples × 29 sites) for the entire study.
In the aboveground vegetation survey, the names and numbers of individuals were determined and recorded in five quadrats, which were randomly distributed at each of the sites where the seed bank and soil were sampled, resulting in a total of 145 quadrats (5 quadrats × 29 sites). The size of the quadrats varied with plant community type, as described in the appendix (Table S1).
Seedling emergence experiment
The seed germination experiment was carried out at the Research Station of Alpine Meadow and Wetland Ecosystems of Lanzhou University (Hezuo Branch), Gansu Province, China, which is also located on the northeastern Tibetan Plateau (34°55' N, 102°53' E, 2900 m). The seedling emergence method was used to determine seed bank composition (Thompson et al. 1997). The collected seed bank samples were sun dried and sieved (to 4 mm) to carefully remove plant fragments and coarse debris (Ma et al. 2013). Some seeds of desert species require exposure to low temperatures to break dormancy (Peters 2002); therefore, the samples were stored in a storeroom for the entire winter (Nov 2014-April 2015) at low temperatures to break seed dormancy (Ma et al. 2017). The germination period lasted from 1 May to 10 October 2015. The samples were spread evenly over 15-cm-deep sterilized sand on top of 10-cm-deep soil (>1.5 m), which improved the water storage capacity, in plastic pots. Thirty control pots containing sterilized sand were placed next to the experimental pots to test for wind-dispersed seeds. All pots were placed outside on the ground at the start of the experiment, watered every day to maintain moisture and monitored several times per week (Ma et al. 2011). The emerging seedlings were identified and removed. We identified and counted emerging seedlings at weekly intervals until the end of the experiment, when no more seedlings emerged for several consecutive weeks.
Analyses of soil environmental properties
Three soil cores were randomly extracted from each plot and mixed into one soil sample, and 5 mixed samples were collected from each site. Hence, there were 145 soil samples (5 samples × 29 sites) overall. The samples were used to analyse soil characteristics. After compositing, one of the subsamples was air dried and sieved (to <0.2 mm) to remove large pebbles and roots and was used to analyse soil pH, soil organic matter (SOM), total nitrogen (TN), and total phosphorus (TP). The other subsamples were stored at 4°C and used to analyse available nitrogen (AN), available phosphorus (AP) and soil moisture (SM). Soil moisture was determined gravimetrically after ~48 h of oven drying at 105°C. Soil pH was obtained by using a pH meter to measure a slurry of fresh soil and deionized water in a 1:2.5 ratio (Cahenzli et al. 2018). TN was measured by the Kjeldahl method (Institute of Soil Science, Academia Sinica 1978). TP was measured using molybdenum blue colorimetry after digestion by HCIO4-H2SO4 (Parkinson and Allen 2008). SOM was measured using the K2Cr2O7 method (Miller and Keeny 1982). AN was determined using a flow injection analyser (San++, Skalar, Netherlands). AP was measured by the Bray method (Bray and Kurtz 1945).
Data analyses
All statistical analyses were performed using R version 3.3.3 (R Core Team 2018). Variables of the seed bank, aboveground vegetation and soil environment factors among the 29 sites were assessed, and we used the average values of samples representing each site for the data analyses after all of the data were log transformed.
In our study area, saline-alkali soil is also one of the main characteristics, and many studies have found that soil pH is one of the main drivers of grassland diversity and composition (Basto et al. 2015b; Ma et al. 2017). To improve the robustness of the results, we used soil moisture and pH as the dependent variables. To determine how soil moisture and pH affect the seed bank and aboveground vegetation, we evaluated the relationships between the seed bank variables and soil moisture and soil pH, and the relationships between aboveground vegetation variables and soil moisture and soil pH using ordinary least squares (OLS) regression.
To identify the change in the composition of both the seed bank and aboveground vegetation, nonmetric multidimensional scaling (NMDS) was carried out using Bray-Curtis dissimilarity. Before we calculated Bray-Curtis dissimilarity matrixes, the species data of the seed bank and vegetation were converted to relative abundance data. Furthermore, we evaluated the influence of soil environmental factors on community composition. To determine whether the soil environment was correlated across the NMDS ordination, soil environmental factors were fitted as vectors to the NMDS ordination using the function ‘envfit’ from the vegan package (Oksanen et al. 2019).
To detect the threshold of composition of the seed bank and aboveground vegetation response to decreasing soil moisture, we carried out generalized linear and piecewise regression analyses. Sound evidence for a threshold response requires that one of the piecewise models provides the best fit to the data (Johnson and Omland 2004). The piecewise regression models were fitted using the ‘segmented’ package in R (Muggeo 2008).
To identify the best-fitting model for composition of the seed bank and aboveground vegetation at all sites along a soil moisture gradient, we used Akaike’s information criterion (AIC). We compared the AIC values of the models with linear and piecewise regression models. The models with the smallest AIC value were considered the best-fitting models (Bestelmeyer et al. 2011). The same method was used to identify the best-fitting model for soil environmental factors and composition similarity between the seed bank and aboveground vegetation along a soil moisture gradient.
As linear and piecewise regression models work on single response variables, we retained a 2-dimensional solution of NMDS in our analysis. We split the two NMDS dimensions (Delgado-Baquerizo et al. 2017; Ochoa-Hueso et al. 2017), with dimensions 1 and 2 representing composition as the dependent variable and soil moisture as the independent variable, respectively.
Similarity is the opposite of dissimilarity. Dissimilarity (Bray-Curtis distance) was calculated as
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where xij is the relative abundance of species i in community j, xik is the relative abundance of species i in community k, and n is the total number of species; a value of 0 represents the most similar communities, and 1 represents the most different (Basto et al. 2018).
Moreover, to exclude the possibility that the state transition from arid grassland to shrubland and the abrupt change in composition of aboveground vegetation are caused by the abrupt change in soil moisture, we further analysed the frequency distribution of the two ecosystem states (arid grassland and shrubland) and the change in composition of aboveground vegetation on the soil moisture isoline.