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Global Change Biology (2011) 17, 452–465, doi: 10.1111/j.1365-2486.2010.02253.x Community structure and composition in response to climate change in a temperate steppe H A I J U N Y A N G * w , M I N G Y U W U *, W E I X I N G L I U *, Z H E Z H A N G * w , N A I L I Z H A N G * and S H I Q I A N G WA N * z *State Key Laboratory of Vegetation Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing 100093, China, wGraduate School of Chinese Academy of Sciences, Yuquanlu, Beijing 100049, China, zKey Laboratory of Plant Stress Biology, College of Life Sciences, Henan University, Kaifeng, Henan 475004, China Abstract Climate change would have profound influences on community structure and composition, and subsequently has impacts on ecosystem functioning and feedback to climate change. A field experiment with increased temperature and precipitation was conducted to examine effects of experimental warming, increased precipitation and their interactions on community structure and composition in a temperate steppe in northern China since April 2005. Increased precipitation significantly stimulated species richness and coverage of plant community. In contrast, experimental warming markedly reduced species richness of grasses and community coverage. Species richness was positively dependent upon soil moisture (SM) across all treatments and years. Redundancy analysis (RDA) illustrated that SM dominated the response of community composition to climate change at the individual level, suggesting indirect effects of climate change on plant community composition via altering water availability. In addition, species interaction also mediated the responses of functional group coverage to increased precipitation and temperature. Our observations revealed that both abiotic (soil water availability) and biotic (interspecific interactions) factors play important roles in regulating plant community structure and composition in response to climate change in the semiarid steppe. Therefore these factors should be incorporated in model predicting terrestrial vegetation dynamics under climate change. Keywords: diversity, grassland, plant functional group, temperature, water availability Received 12 January 2010; revised version received 11 April 2010 and accepted 5 May 2010 Introduction Global climate warming resulting from anthropogenic activities has been observed, particularly in recent decades (IPCC, 2007). It is predicted that there will be concomitant shift in precipitation regimes at the global and regional scales with increasing occurrences of extreme rainfall events (Dore, 2005; IPCC, 2007). Given that both temperature and water availability are important abiotic factors to affect plant growth, climate change influences plant growth, alters interspecific relationships (Klanderud & Totland, 2005, 2007; Niu & Wan, 2008), and subsequently impacts plant community structure and composition (Harte & Shaw, 1995; Knapp et al., 2002; Klein et al., 2004). Changes in community structure and composition are likely to have consequent influences on ecosystem functioning (Hooper & Vitousek, 1997; Tilman et al., 1997) and potential feedback to climate change (Liston et al., 2002; Chapin et al., 2005). Thus, better understanding of plant community strucCorrespondence: Shiqiang Wan, tel. 1 86 10 6283 6512, fax 1 86 10 8259 6146, e-mail: [email protected] 452 ture and composition in response to climate change is crucial for projection of the impacts of climate change on terrestrial ecosystems. A growing body of evidence from model projections (Thuiller et al., 2005; Botkin et al., 2007), long-term observations (Chapin et al., 1995), natural gradients (Peñuelas et al., 2007), meta-analyses (Walker et al., 2006) and manipulative experiments (Chapin et al., 1995; Klein et al., 2004; Keryn & Mark, 2009) has demonstrated that climate warming has the potential to alter plant community structure and composition. Warming effects may vary with plant species (Bates et al., 2005; Prieto et al., 2009), functional groups, communities and sites (Walker et al., 2006; Jagerbrand et al., 2009) with general enhancement of shrubs and grasses (GR) and decrease in cryptogam coverage (Chapin et al., 1995; Walker et al., 2006). The different responses of plant species and/or functional groups can alter species competitive interactions and dominance hierarchies (Harte & Shaw, 1995; Klanderud & Totland, 2005; Niu & Wan, 2008), leading to increasing extinction risk of some plant species. In fact, rapid losses of plant species under climate warming have widely been observed in r 2010 Blackwell Publishing Ltd S T E P P E C O M M U N I T Y R E S P O N S E S T O C L I M AT E C H A N G E the Tibetan Plateau (Klein et al., 2004), salt marshes (Keryn & Mark, 2009) and Mediterranean shrubland (Prieto et al., 2009). Reduced species richness in high elevation ecosystem has been mainly ascribed to litter accumulation and suppression of plant growth due to heat stress (Klein et al., 2004). On the other hand, elevated temperature can indirectly impact plant community via altering species interactions (Niu & Wan, 2008) and belowground resources, such as water availability. For example, climate change indirectly affects subdominant species via altering competitive interactions with the dominant species (Engel et al., 2009; Kardol et al., 2010). In addition, warming can decrease soil water content and suppress plant growth by stimulating evapotranspiration (Niu et al., 2008) and impacting energy balance at the soil surface. Manipulative experiments have demonstrated water availability plays an important role in regulating the response of community composition to warming (Sternberg et al., 1999; Walker et al., 2006). Hence, elucidating the direct and indirect effects of warming on community will facilitate the mechanistic understanding of plant community structure and composition in response to climate warming, especially in semiarid region where water is limited. Alteration of precipitation regimes in concurrent with climate warming could also have profound influences on plant community structure and composition (Sternberg et al., 1999). Across a geographic gradient, plant species richness is positively dependent upon precipitation (Adler & Levine, 2007). Compositional change of plant community through time also increases with precipitation in Serengeti grasslands (Anderson, 2008). Manipulative experiments have shown that increased precipitation stimulates vegetation coverage and species richness in a calcareous grassland (Sternberg et al., 1999) and enhances diversity in an annual grassland (Zavaleta et al., 2003). Given the species-specific water sensitivity of plant growth, water availability may indirectly affect plant community structure and composition through changing interspecific relationships, which could regulate the response of plant community to climate change (Klanderud & Totland, 2007). In addition, species interactions among different trophic groups can also strongly impact the response of plant community to changing climate, overturning direct effects of changing climate on grassland within 5 years (Suttle et al., 2007). However, little attention has been paid to species interactions in both experimental studies and model projections (Klanderud & Totland, 2005). Distinguishing between the effects of abiotic and biotic factors on plant community would help us seek for the underlying mechanism for terrestrial vegetation in response to changing precipitation regimes. Moreover, r 2010 Blackwell Publishing Ltd, Global Change Biology, 17, 452–465 453 concurrent changes in global temperature and precipitation regimes may have potentially interactive effects on plant community. A modeling study has revealed strong dependence of species loss and turnover upon temperature and moisture conditions (Thuiller et al., 2005). The possible interaction between climate warming and changing precipitation regimes in regulating plant community structure and composition (Bates et al., 2005) may pose great challenges to simulation and projection of future dynamics of terrestrial biosphere under climate change. Located in the eastern part of the Eurasian grassland biome, the semiarid steppe in northern China with an area of 313 million hm2 accounts for 78% of the total grassland area in China, and plays an important role in supporting diverse species of plants and animals and serving the ecological environment and the development of socio-economics of the region (Kang et al., 2007). In addition, substantial changes in temperature and precipitation in this region have occurred (Zhai et al., 1999), and this ecosystem is sensitive to climate change (Christensen et al., 2004; Niu et al., 2008; Liu et al., 2009). In order to examine the potential effects of climate warming and increased precipitation on plant community structure and composition, a field experiment with increased temperature and precipitation has been conducted since April 2005. The specific questions addressed were: (1) How does climate change affect plant community structure and composition in the semiarid grassland? (2) Whether and how will abiotic [e.g., soil moisture (SM)] and biotic factors (interspecific interactions) mediate the response of plant community to climate warming and increased precipitation? Materials and methods Study site This study was conducted in a semiarid temperate steppe in Duolun County (42102 0 N, 116117 0 E, 1324 m a.s.l.), Inner Mongolia, China, which belongs to monsoon climate of moderate temperature zone. Long-term mean annual precipitation (MAP) and mean annual temperature (MAT) is approximately 383 mm and 2.1 1C, respectively. Ninety percent of the total precipitation is distributed from May to October and monthly mean temperature ranges from 17.5 1C in January to 18.9 1C in July. The soil in this area is classified as chestnut according to Chinese classification or Haplic Calcisols according to the FAO classification. The dominant plant species in this temperature steppe with relatively low primary productivity (approx. 100–200 g m2 yr1) were perennial herbs, including Stipa krylovii, Artimesia frigida, Potentilla acaulis, Cleistogenes squarrosa, Allium bidentatum and Agropyron cristattum. 454 H . Y A N G et al. Experimental design This experiment used a paired, nested design with four treatments (Niu et al., 2008; Liu et al., 2009). Precipitation was the primary factor and warming was nested with the precipitation treatment. There were three blocks with an area 44 28 m for each block. There was a pair of 10 15 m plots in each block, in which one plot was assigned as the increased precipitation treatment and the other one as the ambient precipitation treatment. Four 3 4 m plots were established in each 10 15 m plot with 1 m distance between the plots. The four plots were randomly assigned to warming and unwarmed control treatments with two replicates. Thus, there were totally 24 plots with six replicates for each treatment [control (C), warming (W), increased precipitation (P), and warming plus increased precipitation (WP)]. There were six sprinklers arranged in two rows in each of the precipitation treatment plot, with each sprinkler covering a circular area with a diameter of 3 m. A total amount of 120 mm precipitation was applied under the increased precipitation treatment in July and August with approx.15 mm week1. Each warmed subplot was heated continuously by a 165 15 cm MSR-2420 infrared radiators (Kalglo Electronics, Bethlehem, PA, USA) suspended 2.5 m aboveground since April 28, 2005 [the heaters were turned off over the winter (November 16–March 15) since 2007]. One ‘dummy’ heater with the same shape and size as the infrared radiator was used to simulate the shading effect of the infrared radiator in the unwarmed control subplot. Soil temperature (ST) and moisture ST at the depth of 10 cm was recorded using a CR1000 datalogger (Campbell Scientific, Logan, UT, USA) from June 4, 2005. SM (0–10 cm) was measured using a portable SM device (Diviner2000, Sentek Pty Ltd, Balmain, Australia) twice a month in 2005 and 2006 and four times a month in 2007, 2008 and 2009 during the growing seasons (from May to October). Vegetation and soil sampling and measurements Since the experiment was designed as a long-term manipulative experiment, all vegetation sampling was performed nondestructively. Vegetation sampling was carried out at the peak plant biomass in August in 2005 for the first time and repeated at the same time from 2006 to 2009. A visually estimated method was used to measure the changes in the community coverage. One permanent quadrat (1 1 m) was established in each subplot in June 2005. During the measurement, a 1 1 m frame with 100 equally distributed grids (10 10 cm) was put above the canopy in each quadrat. The coverage of each species was visually estimated in all the grids and summed as the species coverage in the quadrat. All the species coverage was summed as the coverage of different plant functional groups and the whole community. The species richness was recorded as the occurrence of the number of plant species in the quadrat. Canopy height of each species within a quadrat was calculated as the average of at least five random measurements of species’ natural height. Plants were divided into different functional groups on the basis of growth form: GR, legumes (LE), shrubs and semishrubs (SS) and nongraminous forbs (NF). Species richness (species number per quadrat), Shannon–Wiener index (H) and Pielou index evenness (E) were used to describe the patterns of plant community structure: H was calculated as: H 5SPi ln Pi and E was calculated as: E 5 (SPi ln Pi)/ln S, where Pi is the relative coverage of species i and S is species richness. In addition, we defined ‘dominant’ and ‘subdominant’ species according to the ‘50/20 rule’ (FICWD, 1989; Kardol et al., 2010). A. frigida dominated the plot across all treatments and counted for 450% of the total community coverage. S. krylovii, A. cristattum, Heteropappus altaicus and Phlomis umbrosa were defined as subdominant species. Soil samples were collected from one cylindrical soil core (15 cm in depth and 8 cm in diameter) in each subplot in August from 2006 to 2009. After removing roots and stones by sieving with 2 mm mesh, soil samples were stored in iceboxes and subsequently transferred to the laboratory. The fresh samples were divided into two subsamples. One subsample was used to measure the microbial variables, and the other subsample that was air-dried, finely ground and sieved with mesh o250 mm was used to measure soil organic C and total N and pH. Statistical analysis Four-way ANOVAs with a blocked nested design were performed to test the main and interactive effects of block, year, warming and increased precipitation on the vegetation variables, SM and ST. A General Linear Model (GLM) with a Duncan test was used to examine the statistical difference in the mean values of the treatments and specific comparisons between different groups of treatments were performed using LSMEANS statement of this procedure. Seasonal mean values of SM and ST, which were calculated from the monthly mean values, were used. Three-way ANOVAs with a blocked nested design were carried out to test the effects of block, warming, and elevated precipitation on the variables in each year owing to the interactions between year and manipulated treatments (warming or increased precipitation) on measured variables. Effects of block were tested together with the treatments in all the above analyses, but they were not discussed in this study. Stepwise multiple linear regressions with Po0.05 for inclusion in the mixed model were conducted to investigate which variable has the greatest effect on species richness and vegetation coverage, with SM, ST, soil organic C, total N and pH as the independent variables. Before regressions, collinearity was detected by calculating the variance inflation factor for each explanatory variable and VIFo5 for each independent variable, suggesting autocorrelation did not occur (Dobson, 2002). All the data were tested for normal distribution before statistical analyses, which were performed with SAS v.8.1 software (SAS Institute Inc., Cary, NC, USA). We used the constrained linear ordination technique redundancy analysis (RDA) to analyze the response of community r 2010 Blackwell Publishing Ltd, Global Change Biology, 17, 452–465 S T E P P E C O M M U N I T Y R E S P O N S E S T O C L I M AT E C H A N G E composition to treatments in 2009 (Teyssonneyre et al., 2002). In this analysis, species occurred infrequently in the plots were removed from the species data before ordination, which left 30 species for ordination, because rare species may have an unduly impact on the result of the analysis. The treatments (C, W, P and WP) were used as the environmental variables and block as the covariable (i.e. concomitant variable whose effect must be partialed out before estimating the effects of the treatment variables) within the model. The model was then tested using a restricted Monte Carlo permutation test (9999 random permutations) to test the null hypothesis that the treatments had no influences on the community plant composition. RDA and Monte Carlo permutation test were performed with the CANOCO 4.5 software. Furthermore, we conducted stepwise multiple linear regressions with Axes 1 and 2 scores as dependent variables and SM, ST, soil organic C, total N and pH as the independent variables to determine which variable has the greatest effect on community composition (Belote et al., 2004). Results Changes in soil microclimate There was strong interannual variability in ST at the depth of 10 cm (F4, 60 5 470.7, Po0.01), with the highest (15.13 1C) in 2007 and the lowest value (13.80 1C) in 2006 across all the treatments. Experimental warming and increased precipitation had significant effects on ST across the 5 years. Warming elevated ST by 0.98 1C (F1, 60 5 296.5, Po0.01) and increased precipitation reduced ST by 0.29 1C (F1, 60 5 26.6, Po0.01) over the whole experimental period, respectively (Fig. 1). Interactive effect of warming and increased precipitation on ST was observed across the 5 years (F1, 60 5 4.9, Po0.05). ST in the increased precipitation plots (14.4 0.2) was lower (Po0.05) than that in the control plots (14.6 0.2). The warmed plots (15.7 0.3) had higher ST than the control plots (Po0.01) and the increased precipitation plots (Po0.01). ST in the warming plus increased precipitation plots (15.3 0.3) was increased by 0.7 1C (Po0.01) compared with the control plots. In addition, the effects of warming and increased precipi- 455 tation on ST varied with year (F4, 60 5 2.7, Po0.05, F4, 60 5 4.8, Po0.01, respectively). Significant interannual variability in SM at the 0–10 cm depth was also observed across the whole experimental period (F4, 60 5 88.2, Po0.01). Warming decreased SM, on average, by 1.04% V/V (absolute difference, F1, 60 5 41.9, Po0.01) and increased precipitation improved SM by 1.23% V/V (F1, 60 5 57.6, Po0.01) across the 5 years. There was interactive effect of warming increased precipitation on SM (F1, 60 5 7.4, Po0.01). SM in the increased precipitation plots was 1.66% V/V (absolute difference), and this value was higher (Po0.01) than that in the control plots. SM in the warmed plots was decreased by 0.61% (Po0.05) and 2.27% V/V (Po0.05) relative to the control plots and the increased precipitation plots, respectively. SM in the warming plus increased precipitation plots was improved by 0.79% (Po0.01), which was greater than in the warmed plots, and was declined by 1.48% (Po0.01) relative to the increased precipitation plots (Fig. 1). In addition, the effect of increased precipitation on SM varied with the year (F4, 60 5 4.9, Po0.01), while no interactive effect of warming and year was observed (F4, 60 5 1.1, P40.05). Species diversity, evenness and species richness Mean Shannon–Wiener index (H), Pielou index evenness (E) and average species richness in the sampled quadrats (1 1 m) within the plots across all treatments and years were 1.64, 0.63 and 13.4, respectively (Table 1). H (F4, 60 5 19.2, Po0.01), E (F4, 60 5 18.7, Po0.01) and species richness (F4, 60 5 11.8, Po0.01; Table 2) varied with year, with the highest (1.89, 0.72) and lowest H and E values (1.31, 0.54) in 2007 and 2009, respectively. The highest (14.33) and lowest values (11.48) for species richness occurred in 2005 and 2009, respectively (Fig. 2). Increased precipitation enhanced species richness by 13.0% (F1, 60 5 24.8, Po0.01), but it did not affect H (F1, 60 5 0.2, P40.05) or E (F1, 60 5 0.9, P40.05; Table 2) across the five growing seasons. Experimental warming Fig. 1 Effects of experimental warming and increased precipitation on seasonal mean values of soil temperature ( 1C) and soil moisture (V/V %) at the depth of 10 cm from 2005 to 2009 (Mean 1 SE). C, control; W, warming; P, increased precipitation; WP, warming plus increased precipitation. r 2010 Blackwell Publishing Ltd, Global Change Biology, 17, 452–465 456 H . Y A N G et al. Table 1 List of species within the quadrats (1 1 m) across the 5 years Functional groups Species Abbreviation LHT Grasses Achnatherum sibiricum Agropyron cristatum Cleistogenes squarrosa Koeleria cristata Leymus chinensis Poa subfastigiata Setaria viridis Stipa krylovii Allium bidentatum Allium neriniflorum Allium ramosum Allium senescens Allium tenuissimum Androsace umbellata Artemisia capillaris Bupleurum scorzonerifolium Carex korshinskyi Chamaerhodos erecta Chenopodium aristatum Chenopodium glaucum Cymbaria dahurica Dianthus chinensis Dontostemon dentatus Fagopyrum sagittatum Gentiana dahurica Gentiana squarrosa Heteropappus altaicus Iris lacteal Iris tenuifolia Ixeris chinensis Leontopodium leontopodioides Phlomis umbrosa Potentilla acaulis Potentilla anserina Potentilla bifurca Potentilla betonicaefolia Potentilla multifida Potentilla tanacetifolia Salsola collina Saposhnikovia divaricata Saussurea japonica Scorzonera austriaca Silene conoidea Stellera chamaejasme Thalictrum petaloideum Sibbaldia adpressa Astragalus galacties Astragalus scaberrimus Gueldenstaedtia stenophylla Melilotoides ruthenica Artemisia frigida Lespedeza davurica Thymus serpyllum As Ac Cs Kc Lc Ps Sv Sk Ab An Ar As At Au Aca Bs Ck Ce Ca Cg Cd Dc Dd Fs Gd Gs Ha Ii It Ic Li Pu Pa Pan Pb Pb Pm Pt Sc Sd Sj Sa Sc Sc Tp Sab Ag As Gs Mr Af Ld Ts P P P P P P AB P P P P P P AB P P P AB AB AB P P AB AB P AB P P P P P P P P P P P P AB P AB P AB P P P P P P P P P P Nongraminous forbs Legumes Shrubs and semishrubs C (%) 5.99 2.41 7.26 3.23 1.91 4.85 4.25 3.36 1.91 1.40 0.46 3.27 1.31 2.22 51.33 Abbreviations used in the RDA analyses and the individual contribution value C (%) of the 15 most abundant species (contributing to 95% of the total coverage) to the total coverage are listed. life history traits (LHT) are listed. P, perennial plants; AB, annual & biennial. r 2010 Blackwell Publishing Ltd, Global Change Biology, 17, 452–465 S T E P P E C O M M U N I T Y R E S P O N S E S T O C L I M AT E C H A N G E 457 Table 2 Results (P-value) of four-way factorial ANOVA on the effects of year (Y), warming (W), increased precipitation (P) and their interactions on the species richness of community (SP); grass (GR); legumes (LE); shrubs and semishrubs (SS); nongraminous forbs (NF); Shannon–Wiener index (H) and Pielou index evenness (E) by calculating coverage Source of variation SP GR LE SS NF H E Block (B) Year (Y) P W PW YP YW YPW 0.0076 o0.0001 o0.0001 0.7108 0.2090 0.2632 0.0551 0.9106 o0.0001 0.0015 0.0027 0.0052 0.3825 0.5555 0.5634 0.8965 o0.0001 0.1881 0.3737 0.6207 0.0395 0.9251 0.5666 0.7295 o0.0001 0.4762 0.0396 0.0396 0.4889 0.4762 0.5960 0.9577 o0.0001 o0.0001 o0.0001 0.4866 0.9673 0.2982 0.1850 0.6697 0.0027 o0.0001 0.1807 0.3156 0.0283 0.0849 0.9595 0.9219 o0.0001 o0.0001 0.3363 0.3761 0.0442 0.0479 0.9344 0.7283 Fig. 2 Effects of experimental warming and increased precipitation on species richness of community (SP), Shannon–Wiener index (H) and Pielou index evenness (E) within the quadrats (1 1 m) from 2005 to 2009 (Mean 1 SE). See Fig. 1 for treatments abbreviations. had no effect on species richness (F1, 60 5 0.1, P40.05), H (F1, 60 5 0.7, P40.05) or E (F1, 60 5 0.8, P40.05; Table 2). No interactive effect of warming increased precipitation on species richness was detected across the 5 years (F1, 60 5 1.6, P40.05). However, there were interactive effects of warming increased precipitation on H (F1, 60 5 4.1, Po0.05) and E (F1, 60 5 4.2, Po0.05; Table 2). H in the warming plus increased precipitation plots (absolute value, 1.7 0.1) was 9.7% higher (Po0.05) than that in the increased precipitation plots (1.6 0.1), which did not differ from that in the control plots (P40.05). H in the warmed plots (1.6 0.1) was marr 2010 Blackwell Publishing Ltd, Global Change Biology, 17, 452–465 ginally different from that in the warming plus increased precipitation plots (P 5 0.08). Increased precipitation plots had 7.9% (Po0.05) and 7.7% (Po0.05) lower E than the control plots and the warming plus increased precipitation plots, respectively (Fig. 2). The effects of warming on H (F4, 60 5 0.2, P40.05) and of increased precipitation on species richness (F4, 60 5 1.3, P40.05) did not vary with year (Table 2). However, there were marginally significant effects of increased precipitation year on H (F4, 60 5 2.3, P 5 0.08) and warming year effects on species richness of community (F4, 60 5 2.4, P 5 0.06). Three-way ANOVAs analyses showed increased precipitation improved community species richness by 14.4% (F1, 12 5 4.4, Po0.05) and 24.9% (F1, 12 5 22.8, Po0.01) in 2008 and 2009, respectively. Warming reduced community species richness by 11.3% (F1, 12 5 6.7, Po0.01) in 2009 (Fig. 2). Neither warming nor increased precipitation affected species richness of community in other years (all P40.05). No interactive effect of warming and year on E was detected (F4, 60 5 0.2, P40.05), but the effect of increased precipitation on E varied with year (F4, 60 5 2.6, Po0.05). Increased precipitation marginally decreased E by 11.7% (F1, 12 5 4.3, P 5 0.06) in 2005 and significantly reduced it by 10.8% (F1, 12 5 5.7, Po0.05) in 2006 (Fig. 2). No main and interactive effects of increased precipitation and warming on E were observed in other years (all P40.05). Species richness of functional group Species richness of GR and NF in the sampled quadrats (1 1 m) showed strong interannual variability across the five consecutive growing seasons (GR, F4, 60 5 4.7, Po0.01; NF, F4, 60 5 9.5, Po0.01; Table 2), with the highest (3.46) and lowest GR richness (2.79) occurring in 2005 and 2009, respectively. The highest (8.54) and lowest richness (6.29) for NF were found in 2007 and 2009, respectively (Fig. 3). No interannual variation in 458 H . Y A N G et al. Fig. 3 Effects of experimental warming and increased precipitation on species richness of functional groups from 2005 to 2009 (Mean 1 SE). GR, grasses; LE, legumes; SS, shrubs and semishrubs; NF, nongraminous forbs. See Fig. 1 for treatments abbreviations. species richness of LE (F4, 60 5 1.6, P40.05) or SS (F4, 60 5 0.9, P40.05; Table 2) was observed. Increased precipitation elevated species richness of GR, SS and NF by 11.2% (F1, 60 5 9.4, Po0.01), 9.9% (F1, 60 5 4.3, Po0.05) and 19.1% (F1, 60 5 20.8, Po0.01; Table 2), respectively. Warming decreased GR species richness by 9.4% (F1, 60 5 8.1, Po0.01), but increased SS species richness by 9.9% (F1, 60 5 4.3, Po0.05) across the 5 years, respectively (Table 2; Fig. 3). Warming and increased precipitation interacted to affect species richness of LE (F1, 60 5 4.3, Po0.05). LE species richness in the increased precipitation plots (1.2 0.1) was marginally lower (P 5 0.05) than that in the control plots (1.6 0.2), which was not statistically distinguished from either the warmed or the warming plus increased precipitation plots (both P40.05; Fig. 3). No interactive effect of warming or increased precipitation with year on species richness of any plant functional group was detected. Coverage of plant functional groups The coverage of GR and SS fluctuated dramatically across the 5 years (GR, F4, 60 5 11.9, Po0.01; SS, F4, 60 5 20.6, Po0.01; Table 3), with the lowest (2.88%) and highest value (9.40%) for GR in 2007 and 2006, and the lowest (8.43%) and highest value (22.45%) for SS in 2007 and 2009, respectively (Fig. 4). Increased precipitation stimulated the coverage of GR and SS by 1.70% (absolute difference, F1, 60 5 6.6, Po0.05) and 4.79% (F1, 60 5 21.8, Po0.01; Table 3), respectively. In contrast, experimental warming decreased LE and SS coverage by 0.64% (F1, 60 5 4.1, Po0.05) and 3.25% (F1, 60 5 10.0, Po0.01), respectively, across the 5 years (Table 3; Fig. 4). Warming and Table 3 Results (P-value) of four-way factorial ANOVA on the effects of year (Y), warming (W), increased precipitation (P) and their interactions on the coverage of community (TC); grass (GR); legumes (LE); shrubs and semishrubs (SS) and nongraminous forbs (NF) Source of variation TC Block (B) Year (Y) P W PW YP YW YPW o0.0001 o0.0001 o0.0001 o0.0001 o0.0001 o0.0001 o0.0001 0.2212 o0.0001 0.1419 o0.0001 0.0115 0.2101 o0.0001 0.3790 o0.0001 0.0973 0.0455 0.0020 0.9510 0.2424 0.0270 0.2535 0.0043 0.8749 0.0146 0.2673 0.6100 0.6017 0.0005 0.0109 0.9741 0.7130 0.0414 0.8614 0.0435 0.2716 0.9462 0.2699 0.6323 GR LE SS NF increased precipitation interacted to affect the coverage of GR (F1, 60 5 5.0, Po0.05) and SS (F1, 60 5 8.5, Po0.01; Table 3). GR coverage in the warmed plots was lower by 2.87% (absolute difference; Po0.01), 3.02% (Po0.01), and 3.85% (Po0.01) than that in the control plots, the increased precipitation plots and the warming plus increased precipitation plots (Fig. 4), respectively. GR coverage was not statistically significant among the control plots, the increased precipitation plots and the warming plus increased precipitation plots (all P40.05). In contrast, SS coverage in the increased precipitation plots was enhanced by 7.48% (Po0.01), 7.51% (Po0.01), 4.75% (Po0.01) compared with that in the control plots, the warmed plots and the warming plus increased precipitation plots, respectively (Fig. 4). No significant differences in SS coverage were observed among the control plots, the warmed plots and the warming plus increased precipitation plots (all r 2010 Blackwell Publishing Ltd, Global Change Biology, 17, 452–465 S T E P P E C O M M U N I T Y R E S P O N S E S T O C L I M AT E C H A N G E 459 Fig. 4 Effects of experimental warming and increased precipitation on the coverages of plant functional groups from 2005 to 2009 (Mean 1 SE). See Fig. 1 for treatments abbreviations and Fig. 3 for functional groups abbreviations. P40.05). In addition, the effects of increased precipitation on NF coverage (F4, 60 5 5.4, Po0.01) and warming on SS (F4, 60 5 2.6, Po0.05) coverage varied with year (Table 3). Increased precipitation suppressed NF coverage by 4.15% (F1, 12 5 6.1, Po0.05) in 2005. In contrast, the same treatment enhanced NF coverage by 5.17% (F1, 12 5 9.9, Po0.01) in 2009. SS coverage was improved under increased precipitation by 6.79% (F1, 12 5 13.7, Po0.01), 4.65% (F1, 12 5 8.4, Po0.05) and 5.71% (F1, 12 5 5.7, Po0.05) in 2006, 2007 and 2009, respectively. However, warming reduced SS coverage by 8.16% (F1, 12 5 11.5, Po0.01) in 2009 (Fig. 4). No main and interactive effects of increased precipitation and warming on NF coverage were detected (all P40.05). Coverage of dominant species, subdominant species and main individual species Strong interannual variations in the coverages of dominant species (A. frigida; F4, 60 5 20.6, Po0.01) and subdominant species (F4, 60 5 7.6, Po0.01) were observed across the 5 years. Increased precipitation significantly elevated the dominant species and subdominant species coverages by 4.71% (absolute difference, F1, 60 5 21.3, Po0.01) and 1.71% (F1, 60 5 5.2, Po0.05), respectively. Experimental warming reduced the dominant species coverage by 3.36% (F1, 60 5 10.8, Po0.01; Fig. 5) and interacted with increased precipitation to affect the dominant species coverage (F1, 60 5 8.9, Po0.01). Dominant species coverage in the increased precipitation plots was 7.45% (absolute difference; Po0.01), 7.58% (Po0.01), and 4.89% (Po0.01) greater than that in the control plots, the warmed plots and the warming plus increased precipitation plots, respectively (Fig. 5). In addition, no differences in dominant species coverage r 2010 Blackwell Publishing Ltd, Global Change Biology, 17, 452–465 among the control plots, the warmed plots and the warming plus increased precipitation plots were detected (all P40.05). Year significantly interacted with warming to influence dominant species coverage (F4, 60 5 2.7, Po0.05) and with increased precipitation to affect subdominant species coverage (F4, 60 5 2.9, Po0.05). Patterns of the response of main individual species to treatments were idiosyncratic across the 5 years (Fig. 5). There were strong interannual variabilities in the coverage of S. krylovii (F4, 60 5 13.0, Po0.01) and A. cristattum (F4, 60 5 4.6, Po0.01) within GR and H. altaicus (F4, 60 5 3.2, Po0.05; Table 4) within NF. Increased precipitation significantly improved the coverage of A.cristatum, C. squarrosa, Artemisia capillaries and H. altaicus by 0.76% (F1, 60 5 5.6, Po0.05), 0.36% (F1, 60 5 8.9, Po0.01), 0.63% (F1, 60 5 8.4, Po0.01) and 1.46% (F1, 60 5 20.6, Po0.01), but suppressed the coverage of P. umbrosa and P. acaulis by 0.68% (F1, 60 5 4.8, Po0.05) and 1.39% (F1, 60 5 26.5, Po0.01) across the 5 years, respectively (Table 4; Fig. 5). Experimental warming or its interaction with year did not affect the coverage of any subdominant species (all P40.05). Significantly interactive effects of warming and increased precipitation on the coverage of A. cristattum (F1, 60 5 5.8, Po0.05) and P. tanacetifolia (F1, 60 5 5.2, Po0.05; Table 4) were observed. A. cristattum coverage in the warmed plots (absolute value, 1.3 0.3) was significantly and marginally lower that in the warming plus increased precipitation plots (2.7 0.5, Po0.01) and the control plots (2.3 0.5, P 5 0.05; Fig. 5), respectively. Increased precipitation plots (0.7 0.2) had higher (Po0.01) P. tanacetifolia coverage than the control plots (0.1 0.0), and P. tanacetifolia coverage in the warmed plots (0.5 0.2) was marginally higher 460 H . Y A N G et al. Fig. 5 Effects of warming and increased precipitation on the coverages of dominant species (A.f.), subdominant species (S.k., A.c., H.a. and P.u.) and some main species from 2005 to 2009 (mean 1 SE). See Fig. 1 for treatments abbreviations and Table 1 for abbreviations of species name. Table 4 Results (P-value) of four-way factorial ANOVA on the effects of year (Y), warming (W), increased precipitation (P) and their interactions on the coverage of dominant species (D.s.), subdominant species (Sub.s.) and some main species within the squares across the 5 years Source of variation D.s. Block (B) Year (Y) P W PW YP YW YPW Sub.s. S.k. A.c. C.s. A.g. M.r. P.u. P.a. H.a. A.c.a. P.t. o0.0001 0.0342 0.3999 o0.0001 0.0003 o0.0001 o0.0001 o0.0001 0.0002 o0.0001 0.0270 0.0017 o0.0001 o0.0001 o0.0001 0.0019 0.1115 0.1109 0.5374 0.0721 0.0743 0.0157 0.2689 0.3222 o0.0001 0.0242 0.7441 0.0201 0.0036 0.7193 0.7239 0.0311 o0.0001 o0.0001 0.0046 0.0573 0.0013 0.2113 0.1837 0.5327 0.1483 0.9606 0.1588 0.6928 0.0731 0.8978 0.4974 0.3947 0.0036 0.1069 0.2047 0.0175 0.7038 0.9775 0.1118 0.5253 0.1968 0.9806 0.9096 0.0243 0.5988 0.0229 0.0231 0.0423 0.5179 0.0363 0.8380 0.0182 0.5886 o0.0001 0.7582 0.9026 0.0361 0.9694 0.8583 0.8232 0.5681 0.8242 0.5439 0.9886 0.5175 0.9983 0.7635 0.5551 0.2319 0.3782 0.3236 0.5799 0.8138 0.6049 0.9522 0.9885 0.4404 0.9946 0.7791 0.9945 S.k., Stipa krylovii; A.c., Agropyron cristatum; C.s., Cleistogenes squarrosa; A.g., Astragalus galacties; M.r., Melilotoides ruthenica; P.u., Phlomis umbrosa; P.a., Potentilla acaulis; H.a., Heteropappus altaicus; A.c.a., Artemisia capillaries; P.t., Potentilla tanacetifolia. (P 5 0.07) than that in the control plots (Fig. 5). Effects of increased precipitation on the coverage of S. krylovii (F4, 60 5 3.0, Po0.05), A. cristattum (F4, 60 5 2.6, Po0.05), Astragalus galacties (F4, 60 5 2.7, Po0.05), P. umbrosa (F4, 60 5 3.1, Po0.05), and H. altaicus (F4, 60 5 6.84, Po0.01; Table 4) varied with year. No three-way interr 2010 Blackwell Publishing Ltd, Global Change Biology, 17, 452–465 S T E P P E C O M M U N I T Y R E S P O N S E S T O C L I M AT E C H A N G E active effects on the coverage of the main species were detected across the 5 years. Dependence of community composition upon environmental variables Environmental variables explained up to 25.2% of the total variability in community composition (RDA, F-value 5 2.25, Po0.05). The ordination diagram (Fig. 6) clearly reflected the divergence in community composition according to the triangle symbols of the related different treatments (C, W, P and WP). The warmed plots and the control plots had similar community composition – triangle symbols marking close distribution of these treatments in the diagram. The length and direction of the vectors representing each individual species in the diagram show the relevance of species to the respective treatments and axis. Axis 1 and 2 explained 22.6% and 2.4% of variations in community composition, respectively. Stepwise multiple regression analyses were conducted with Axis 1 and 2 scores as the dependent variables and SM, ST, soil organic C, total N and pH as the independent variables. SM was retained in the stepwise multiple regression model with Axis 1 scores and all variables were excluded with Axis 2 scores. Axis 1 scores showed positively linear dependence on SM (R2 5 0.43, F1, 22 5 16.7, Po0.01). Impact of water availability on species richness and vegetation coverage 461 treatments and the warmed plots as the dependent variables from 2006 to 2009, respectively. Only one variable, SM, was retained in the stepwise multiple regression model. Species richness was positively dependent on SM across all the treatments from 2006 to 2009 (R2 5 0.11, F1, 94 5 11.0, Po0.01, Fig. 7a). In addition, species richness of GR linearly increased with SM in the warmed plots across the 4 years (R2 5 0.38, F1, 22 5 13.8, Po0.01, Fig. 7b). Across all treatments from 2006 to 2009, SS coverage declined with ST (R2 5 0.30, F1, 94 5 39.4, Po0.01, Fig. 8a) and 11.8% variability in SS coverage was attributed to the fluctuation in SM. A combination of ST and SM explained 41.3% of the variation in SS coverage. In addition, SM alone explained 40.9% of the variation of LE coverage in the warmed plots over the 4 years (F1, 22 5 15.3, Po0.01, Fig. 8b). Discussion Effects of interannual fluctuation of precipitation on species richness and coverage In the present study, strong interannual variations in the measured variables were observed in the temperate steppe over the whole experimental period (Tables 2–4). Strong year-to-year fluctuations of plant coverage and aboveground net primary productivity with annual mean precipitation have been reported across different temporal and spatial scales (Sala et al., 1988; Stepwise multiple regression analyses were conducted with SM, ST, soil pH, soil organic C and total N as the independent variables, and species richness across all Fig. 6 Ordination diagram showing the result of redundancy analysis (RDA) of community composition. See Table 1 for abbreviations of species names. r 2010 Blackwell Publishing Ltd, Global Change Biology, 17, 452–465 Fig. 7 Dependence of community and grasses (GR) species richness on soil moisture at the 10 cm depth across all the treatments (a) and in the warmed plots (b) from 2006 to 2009, respectively. A, ambient temperature (open cycles); E, elevated temperature (filled cycles). 462 H . Y A N G et al. Fig. 8 Relationship between shrubs and semishrubs (SS) coverage and soil temperature (ST) across all the treatments (a), and the dependence of legumes (LE) coverage on soil moisture (SM) in the warmed plots (b) from 2006 to 2009, respectively. Heisler-White et al., 2008). Located in the semiarid region, water availability limits plant growth and ecosystem productivity (Niu et al., 2008) and plays a predominant role in mediating soil and microbial respiration and their responses to climate change in this system (Liu et al., 2009). It has been reported that January-July precipitation is mainly responsible for the fluctuations of community biomass production in the same region (Bai et al., 2004). Hence, the interannual variability in the measured variables could have been largely ascribed to year-to-year fluctuations of precipitation. In addition, the effect of simulated climate change on some measured variables varied with year (Tables 2 and 3). Therefore, it is expected that the magnitudes of changes in the vegetation variables under the increased precipitation are in line with the fluctuations of growing-season precipitation. However, our results indicated that the treatment effects on vegetation variables are not in accordance with this expectation. For example, total growing season precipitation in 2007 (194.04 mm) was only 56.1% of that in 2008 (346.19 mm), but community species richness was elevated by 14.4% in 2008 under the increased precipitation, while insignificant effect of increased precipitation on community species richness was detected in 2007 (Fig. 2). The magnitudes of the treatment-induced variability in the measured variables could have been mainly ascribed to the year-to-year changes in SM induced by respective treatments. The results are in good agreement with those of previous studies in the same experiment (Liu et al., 2009). Effects of experimental warming and increased precipitation on species richness Our findings that warming had little effect on species richness in the steppe community are in accordance with the results observed in a temperate grassland (Harmens et al., 2004) and an annual grassland (Zavaleta et al., 2003), but contrast with the results of some other studies where warming causes rapid loss of plant species (Klein et al., 2004; Keryn & Mark, 2009; Prieto et al., 2009). When analyzed by different functional groups, the warming-induced decline in species richness of GR was detected in this temperate steppe (Fig. 3). Klein et al. (2004) have ascribed warming-induced decrease in species richness to heat stress and warming-induced litter accumulation. However, in our system, experimental warming did not lead to litter accumulation (data not shown) owing to the low stature and coverage of plants in the system. Given the water limitation in this region, it is expected that experimental warming may indirectly affect richness of GR species via altering soil water availability. In contrast to the insignificant impacts of warming on species richness at the community level and negative effects on grass species richness, increased precipitation enhanced species richness at both community and functional group levels in this temperate steppe (Figs 2 and 3). The enhancement in species richness under increased precipitation observed in this study is consistent with those reported in a mesic grassland (Knapp et al., 2002), a calcareous grassland (Sternberg et al., 1999) and an annual grassland (Zavaleta et al., 2003). GR and NF could be primarily responsible for the response of species richness to increased precipitation. Water availability is one of the predominant limiting factors in semiarid region and can directly affect species richness by impacting the establishment and growth rates of species (Bazzaz, 1996). In our study, the observation that SM significantly contributed to the variability in species richness across all treatments and years (Fig. 7a) suggests water-mediated response of species richness to climate warming (Klanderud & Totland, 2007; Prieto et al., 2009). Reduction in soil water availability under warming (Niu et al., 2008) in this system could have indirectly affected species richness. Strong dependence of GR species richness upon SM in the warmed plots (Fig. 7b) supports the above speculation that water availability is an important factor in mediating plant diversity in response to climate change. r 2010 Blackwell Publishing Ltd, Global Change Biology, 17, 452–465 S T E P P E C O M M U N I T Y R E S P O N S E S T O C L I M AT E C H A N G E Effect of experimental warming on community composition Significant change in community composition induced by experimental warming is detected in semiarid steppe. Our observations are inconsistent with the results of some studies in arctic and subarctic sites (Wahren et al., 2005; Grime et al., 2008), a temperate grassland (Harmens et al., 2004) and bryophytes community of a limestone grassland (Bates et al., 2005) where no changes in community composition have been detected. Changes in community composition have been ascribed to alterations in competitive hierarchies and relative dominance of different plant species under warming (Chapin et al., 1995; Harte & Shaw, 1995; Klanderud & Totland, 2005; Niu & Wan, 2008; Post & Pedersen, 2008). Generally, warming directly enhances plant growth via stimulating metabolism and improving photosynthetic rates. The positive effects of warming on vegetation coverage (Harte & Shaw, 1995; Jagerbrand et al., 2009) have been reported in various ecosystems, especially in the arctic and alpine regions (Chapin et al., 1995; Walker et al., 2006; Post & Pedersen, 2008). On the other hand, changes in resources induced by warming can mediate or even offset the positive warming effects. Reduced soil water content under warming (Harte & Shaw, 1995; Niu et al., 2008) would exacerbate water limitation for plant growth, thus resulting in little or negative response of plant growth to elevated temperature, especially in semiarid region. In fact, negative warming effects on SS and LE coverage (Fig. 4) were observed in this system. Our results are in accordance with a study that reported reduced GR coverage in the warmed plots due to lower SM (Harte & Shaw, 1995). Moreover, negative dependence of SS coverage on ST across all treatments (Fig. 8a) and positive correlation of LE coverage with SM in the warmed plots (Fig. 8b) provide further experimental evidence in support of that there are indirect effects of warming on vegetation coverage via altering soil water availability. Reduced plant photosynthesis and gross ecosystem productivity (Niu et al., 2008) as well as soil respiration (Liu et al., 2009) due to exacerbated water limitation under warming have also been revealed in the same experiment. The observations indicate that soil water availability is an important factor in regulating plant community in arid and semiarid regions in response to climate warming. Changes in interspecific relationships under warming (Klanderud & Totland, 2007; Niu & Wan, 2008) can also mediate the response of plant community. Because of their intrinsic thermal sensitivity, plant species and functional groups can show different responses to r 2010 Blackwell Publishing Ltd, Global Change Biology, 17, 452–465 463 warming, contributing to the shifts in the competitive ability and relative dominance of among species and functional groups (Chapin et al., 1995; Harte & Shaw, 1995; Walker et al., 2006; Post & Pedersen, 2008). For example, dramatic decline in coverage of the dominant species under warming was observed in our system, whilst warming had no effect on subdominant species coverage. These results may be partly ascribed to the reduced interspecific competition (i.e., lower dominant species coverage). This explanation is in accordance with a study conducted in the old-field ecosystem that climate change indirectly affects subdominant species via dominant species effects on subdominant species (Engel et al., 2009; Kardol et al., 2010). In addition, alterations in interspecific relationship among other species were detected and could mediate the responses of plant community to climate change. For example, the negative relationships between the GR species (C. squarrosa) and several main nongraminous species (Supporting information, Table S1) suggest that these two functional groups may show opposite directions in response to climate warming. The observations in this study highlight the importance of incorporation of species interactions into the projection of plant community response to climate warming. Effect of increased precipitation on community composition The significant effect of increased precipitation on plant community composition across the 5 years is in accordance with the observations in a calcareous grassland (Sternberg et al., 1999), a California grassland (Harpole et al., 2007) and a Serengeti grassland (Anderson, 2008). The responses of different functional groups appeared to be driven by the dominant and subsdominant species and species interactions mediate their responses to increased precipitation. The enhanced coverage of species A. cristattum and C. squarrosa resulted in the increase in GR coverage (Fig. 5) irrespective of little changes in another subdominant GR species (S. krylovii). No changes in the coverage of two main LE species A. galacties and M. ruthenica were observed under the elevated precipitation (Fig. 5). This might be attributable to competitive interactions with the other species and their intrinsically low competitive abilities because the two main LE species have low stature and productivity. The negative relationships between the two main LE species coverages and the nongraminous species coverage (Table S2) suggest that the nongraminous species with higher stature suppress the response of LE to increased precipitation. The positive responses of SS to increased precipitation mainly result from the growth of species A. frigida, whose coverage increased 464 H . Y A N G et al. by 4.71% under the elevated precipitation across the 5 years (Fig. 5). Generally, forbs are highly diverse with respect to taxonomic point and cannot be considered as one plant functional group. The idiosyncratic, even contrasting responses of the main species in nongraminous functional group counteract with each other, leading to the insignificant response of this functional group to increased precipitation. For example, we found positive responses of A. capillaries and P. tanacetifolia coverage and negative responses of P. umbrosa and P. acaulis coverage (Fig. 5). The mean height of species A. capillaries, P. tanacetifolia, P. umbrosa and P. acaulis was 14.1, 8.6, 4.21 and 1.27 cm across all the treatments and years, respectively. Probably, higher plants capture more light per unit area and tend to benefit more from the increased precipitation and consequently suppress the growth of the low-stature plants via shading the lower canopy. The above observations suggest that species interactions play an important role in regulating plant community response to the increased precipitation. Conclusions and implications A previous modeling study (Ni & Zhang, 2000) has shown that MAP and MAT are projected to increase by 30–100 mm and 3 1C, respectively, in this century in the Inner Mongolia steppe. Our results showed that plant community structure in the temperate steppe has been altered under simulated climate change. Generally, species richness and vegetation coverage had contrasting responses to increased precipitation and warming. However, the amplitude of the response to increased precipitation was stronger than that to warming. Our findings are in concert with those in an old field grass in the United States where changes in precipitation dominated plant responses when compared with elevated temperature (Engel et al., 2009; Kardol et al., 2010). Therefore, the diversity and coverage in this steppe ecosystem will be enhanced in the future climate change scenarios with the predicted concurrent increases in precipitation and temperature. Alterations in temperate steppe community composition and diversity induced by climate change would facilitate the maintenance of diversity, ecosystem functioning and the development of the socio-economics, and better serve the ecological environment of the region. This study revealed that both soil water availability and species interactions play important roles in regulating plant community in response to climate change. The conclusion in this study is in well agreement with those in previous studies in the same experiment which have demonstrated mediation of water availability and species interactions in the responses of ecosystem C cycling to changing climate (Niu et al., 2008, 2009; Liu et al., 2009). These findings highlight the importance of incorporation of abiotic and biotic mediations in the model projection of climate change effects on biodiversity and terrestrial ecosystems (Shaver et al., 2000; Botkin et al., 2007). However, care should be taken when extrapolating from our findings to grassland ecosystems worldwide. The semiarid arid steppe in northern China where water is the predominant limiting factor may show different responses from grasslands in other regions where other factors rather than precipitation/ water availability are the primary limitation. In addition, short-term response of plant community to climate change may differ from the long-term response (Hollister et al., 2005) and the 5-year experimental period in this study is relatively short. Previous studies in the same experiment have demonstrated regulation of species composition change on ecosystem C cycling in response to atmospheric and climatic change (Niu et al., 2009, 2010). Therefore, understanding of the long-term effects of climate change on the diversity and community composition of semiarid temperate steppe of northern China will facilitate projection of C sequestration potential and its feedbacks to future climate warming. Acknowledgements This study was conducted as part of a comprehensive research project (Global Change Multi-factor Experiment – Duolun) sponsored by Institute of Botany, Chinese Academy of Sciences. The authors thank Jianyang Xia, Shuli Niu, Changhui Wang, Hongjun Wang and Xin Zhang for their helpful discussions and valuable advice on the manuscript. Special thanks give to Wenhua Xu and Zhuwen Xu for their help in data analysis and identifying plant species, respectively. Thanks two anonymous referees for their thoughtful and helpful comments to the earlier version of this manuscript. 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For each species pair, the upper number is Spearman’s rank correlation coefficient (r), and the lower number is P value. All the correlation coefficients and the P value (Po0.05) are showed. Table S2. Species correlation matrix of the increased precipitation plots across the 5 years. For each species pair, the upper number is Spearman’s rank correlation coefficient (r), and the lower number is P value. All the correlation coefficients and the P value (Po0.05) are showed. Please note: Wiley-Blackwell are not responsible for the content or functionality of any supporting materials supplied by the authors. Any queries (other than missing material) should be directed to the corresponding author for the article.