Community structure and composition in response toclimate change in a temperate steppeH A I J U N YA 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 dec-

ades (IPCC, 2007). It is predicted that there will be

concomitant shift in precipitation regimes at the global

and regional scales with increasing occurrences of ex-

treme rainfall events (Dore, 2005; IPCC, 2007). Given

that both temperature and water availability are im-

portant abiotic factors to affect plant growth, climate

change influences plant growth, alters interspecific re-

lationships (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 & Vitou-

sek, 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 struc-

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

(Penuelas 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, com-

munities 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 inCorrespondence: Shiqiang Wan, tel. 1 86 10 6283 6512, fax 1 86 10

8259 6146, e-mail: swan@ibcas.ac.cn

Global Change Biology (2011) 17, 452–465, doi: 10.1111/j.1365-2486.2010.02253.x

452 r 2010 Blackwell Publishing Ltd

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 com-

munity via altering species interactions (Niu & Wan,

2008) and belowground resources, such as water avail-

ability. For example, climate change indirectly affects

subdominant species via altering competitive interac-

tions 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 stimu-

lating evapotranspiration (Niu et al., 2008) and impact-

ing 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 com-

munity structure and composition in response to cli-

mate 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 (Stern-

berg et al., 1999). Across a geographic gradient, plant

species richness is positively dependent upon precipi-

tation (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 spe-

cies 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 in-

directly affect plant community structure and composi-

tion through changing interspecific relationships, which

could regulate the response of plant community to

climate change (Klanderud & Totland, 2007). In addi-

tion, 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 re-

sponse to changing precipitation regimes. Moreover,

concurrent changes in global temperature and precipi-

tation 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 warm-

ing 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 bio-

sphere 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 develop-

ment 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 precipita-

tion 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 (421020N, 1161170E, 1324 m a.s.l.), Inner Mon-

golia, 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 m�2 yr�1) were perennial herbs, including

Stipa krylovii, Artimesia frigida, Potentilla acaulis, Cleistogenes

squarrosa, Allium bidentatum and Agropyron cristattum.

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 453

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Experimental design

This experiment used a paired, nested design with four treat-

ments (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 week�1.

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 manipula-

tive experiment, all vegetation sampling was performed non-

destructively. 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 measure-

ments of species’ natural height.

Plants were divided into different functional groups on the

basis of growth form: GR, legumes (LE), shrubs and semi-

shrubs (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 5�SPi 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 sub-

sample 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 per-

formed to test the main and interactive effects of block, year,

warming and increased precipitation on the vegetation vari-

ables, 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 vegeta-

tion 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 vari-

able, suggesting autocorrelation did not occur (Dobson, 2002).

All the data were tested for normal distribution before statis-

tical analyses, which were performed with SAS v.8.1 software

(SAS Institute Inc., Cary, NC, USA).

We used the constrained linear ordination technique redun-

dancy analysis (RDA) to analyze the response of community

454 H . YA N G et al.

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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 compo-

sition. RDA and Monte Carlo permutation test were per-

formed 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 composi-

tion (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 re-

duced ST by 0.29 1C (F1, 60 5 26.6, Po0.01) over the

whole experimental period, respectively (Fig. 1). Inter-

active 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-

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 precipi-

tation 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 im-

proved 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 even-

ness (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.

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Table 1 List of species within the quadrats (1� 1 m) across the 5 years

Functional groups Species Abbreviation LHT C (%)

Grasses Achnatherum sibiricum As P

Agropyron cristatum Ac P 5.99

Cleistogenes squarrosa Cs P 2.41

Koeleria cristata Kc P

Leymus chinensis Lc P

Poa subfastigiata Ps P

Setaria viridis Sv AB

Stipa krylovii Sk P 7.26

Nongraminous forbs Allium bidentatum Ab P 3.23

Allium neriniflorum An P

Allium ramosum Ar P

Allium senescens As P

Allium tenuissimum At P

Androsace umbellata Au AB

Artemisia capillaris Aca P 1.91

Bupleurum scorzonerifolium Bs P

Carex korshinskyi Ck P

Chamaerhodos erecta Ce AB

Chenopodium aristatum Ca AB

Chenopodium glaucum Cg AB

Cymbaria dahurica Cd P

Dianthus chinensis Dc P

Dontostemon dentatus Dd AB

Fagopyrum sagittatum Fs AB

Gentiana dahurica Gd P

Gentiana squarrosa Gs AB

Heteropappus altaicus Ha P 4.85

Iris lacteal Ii P

Iris tenuifolia It P

Ixeris chinensis Ic P

Leontopodium leontopodioides Li P

Phlomis umbrosa Pu P 4.25

Potentilla acaulis Pa P 3.36

Potentilla anserina Pan P

Potentilla bifurca Pb P 1.91

Potentilla betonicaefolia Pb P

Potentilla multifida Pm P

Potentilla tanacetifolia Pt P 1.40

Salsola collina Sc AB

Saposhnikovia divaricata Sd P

Saussurea japonica Sj AB

Scorzonera austriaca Sa P

Silene conoidea Sc AB

Stellera chamaejasme Sc P

Thalictrum petaloideum Tp P 0.46

Sibbaldia adpressa Sab P

Legumes Astragalus galacties Ag P 3.27

Astragalus scaberrimus As P 1.31

Gueldenstaedtia stenophylla Gs P

Melilotoides ruthenica Mr P 2.22

Shrubs and semishrubs Artemisia frigida Af P 51.33

Lespedeza davurica Ld P

Thymus serpyllum Ts P

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.

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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 precipita-

tion 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 mar-

ginally different from that in the warming plus in-

creased 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 commu-

nity (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, respec-

tively. 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 de-

tected (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 signifi-

cantly 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 high-

est (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

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) 0.0076 o0.0001 o0.0001 o0.0001 o0.0001 0.0027 o0.0001

Year (Y) o0.0001 0.0015 0.1881 0.4762 o0.0001 o0.0001 o0.0001

P o0.0001 0.0027 0.3737 0.0396 o0.0001 0.1807 0.3363

W 0.7108 0.0052 0.6207 0.0396 0.4866 0.3156 0.3761

P�W 0.2090 0.3825 0.0395 0.4889 0.9673 0.0283 0.0442

Y�P 0.2632 0.5555 0.9251 0.4762 0.2982 0.0849 0.0479

Y�W 0.0551 0.5634 0.5666 0.5960 0.1850 0.9595 0.9344

Y�P�W 0.9106 0.8965 0.7295 0.9577 0.6697 0.9219 0.7283

Fig. 2 Effects of experimental warming and increased precipi-

tation 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 treat-

ments abbreviations.

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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 rich-

ness of LE (F1, 60 5 4.3, Po0.05). LE species richness in

the increased precipitation plots (1.2 � 0.1) was margin-

ally 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), re-

spectively. In contrast, experimental warming de-

creased 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

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 pre-

cipitation 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

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.

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 GR LE SS NF

Block (B) o0.0001 o0.0001 o0.0001 o0.0001 o0.0001

Year (Y) o0.0001 o0.0001 0.2212 o0.0001 0.1419

P o0.0001 0.0115 0.2101 o0.0001 0.3790

W o0.0001 0.0973 0.0455 0.0020 0.9510

P�W 0.2424 0.0270 0.2535 0.0043 0.8749

Y�P 0.0146 0.2673 0.6100 0.6017 0.0005

Y�W 0.0109 0.9741 0.7130 0.0414 0.8614

Y�P�W 0.0435 0.2716 0.9462 0.2699 0.6323

458 H . YA N G et al.

r 2010 Blackwell Publishing Ltd, Global Change Biology, 17, 452–465

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 andmain individual species

Strong interannual variations in the coverages of domi-

nant species (A. frigida; F4, 60 5 20.6, Po0.01) and sub-

dominant species (F4, 60 5 7.6, Po0.01) were observed

across the 5 years. Increased precipitation significantly

elevated the dominant species and subdominant spe-

cies coverages by 4.71% (absolute difference, F1, 60 5

21.3, Po0.01) and 1.71% (F1, 60 5 5.2, Po0.05), respec-

tively. 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). Domi-

nant 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

among the control plots, the warmed plots and the

warming plus increased precipitation plots were de-

tected (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 cover-

age 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 cover-

age 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 warm-

ing or its interaction with year did not affect the cover-

age of any subdominant species (all P40.05).

Significantly interactive effects of warming and in-

creased 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), respec-

tively. Increased precipitation plots (0.7 � 0.2) had high-

er (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

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.

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

r 2010 Blackwell Publishing Ltd, Global Change Biology, 17, 452–465

(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 inter-

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. Sub.s. S.k. A.c. C.s. A.g. M.r. P.u. P.a. H.a. A.c.a. P.t.

Block (B) o0.0001 0.0342 0.3999 o0.0001 0.0003 o0.0001 o0.0001 o0.0001 0.0002 o0.0001 0.0270 0.0017

Year (Y) o0.0001 o0.0001 o0.0001 0.0019 0.1115 0.1109 0.5374 0.0721 0.0743 0.0157 0.2689 0.3222

P o0.0001 0.0242 0.7441 0.0201 0.0036 0.7193 0.7239 0.0311 o0.0001 o0.0001 0.0046 0.0573

W 0.0013 0.2113 0.1837 0.5327 0.1483 0.9606 0.1588 0.6928 0.0731 0.8978 0.4974 0.3947

P�W 0.0036 0.1069 0.2047 0.0175 0.7038 0.9775 0.1118 0.5253 0.1968 0.9806 0.9096 0.0243

Y�P 0.5988 0.0229 0.0231 0.0423 0.5179 0.0363 0.8380 0.0182 0.5886 o0.0001 0.7582 0.9026

Y�W 0.0361 0.9694 0.8583 0.8232 0.5681 0.8242 0.5439 0.9886 0.5175 0.9983 0.7635 0.5551

Y�P�W 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.

460 H . YA N G et al.

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active effects on the coverage of the main species were

detected across the 5 years.

Dependence of community composition uponenvironmental 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 composi-

tion 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 distribu-

tion 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 andvegetation coverage

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

treatments and the warmed plots as the dependent

variables from 2006 to 2009, respectively. Only one vari-

able, SM, was retained in the stepwise multiple regres-

sion 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 onspecies 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;

Fig. 6 Ordination diagram showing the result of redundancy

analysis (RDA) of community composition. See Table 1 for

abbreviations of species names.

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).

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r 2010 Blackwell Publishing Ltd, Global Change Biology, 17, 452–465

Heisler-White et al., 2008). Located in the semiarid

region, water availability limits plant growth and eco-

system productivity (Niu et al., 2008) and plays a pre-

dominant 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 precipita-

tion. 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 grow-

ing-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 increasedprecipitation 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 (Zava-

leta 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 rich-

ness 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 warm-

ing-induced litter accumulation. However, in our sys-

tem, 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 con-

sistent 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 ob-

servation that SM significantly contributed to the varia-

bility 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 avail-

ability 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 mediat-

ing plant diversity in response to climate change.

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.

462 H . YA N G et al.

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Effect of experimental warming on communitycomposition

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 com-

munity 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 hier-

archies and relative dominance of different plant spe-

cies under warming (Chapin et al., 1995; Harte & Shaw,

1995; Klanderud & Totland, 2005; Niu & Wan, 2008; Post

& Pedersen, 2008). Generally, warming directly en-

hances 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 warm-

ing 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 tem-

perature, 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 warm-

ing 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

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. squar-

rosa) and several main nongraminous species (Support-

ing 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 commu-

nity response to climate warming.

Effect of increased precipitation on communitycomposition

The significant effect of increased precipitation on plant

community composition across the 5 years is in accor-

dance 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 spe-

cies A. cristattum and C. squarrosa resulted in the in-

crease in GR coverage (Fig. 5) irrespective of little

changes in another subdominant GR species (S. krylo-

vii). 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 attributa-

ble to competitive interactions with the other species

and their intrinsically low competitive abilities because

the two main LE species have low stature and produc-

tivity. 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

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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 nongrami-

nous functional group counteract with each other, lead-

ing to the insignificant response of this functional group

to increased precipitation. For example, we found po-

sitive responses of A. capillaries and P. tanacetifolia cover-

age and negative responses of P. umbrosa and P. acaulis

coverage (Fig. 5). The mean height of species A. capil-

laries, 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 in-

creased 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 contrast-

ing 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 domi-

nated 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 in-

creases in precipitation and temperature. Alterations

in temperate steppe community composition and di-

versity 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 regulat-

ing 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 spe-

cies 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 in-

corporation of abiotic and biotic mediations in the

model projection of climate change effects on biodiver-

sity and terrestrial ecosystems (Shaver et al., 2000;

Botkin et al., 2007). However, care should be taken when

extrapolating from our findings to grassland ecosys-

tems 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 addi-

tion, short-term response of plant community to climate

change may differ from the long-term response (Holl-

ister 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 spe-

cies 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 researchproject (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 andvaluable advice on the manuscript. Special thanks give toWenhua Xu and Zhuwen Xu for their help in data analysis andidentifying plant species, respectively. Thanks two anonymousreferees for their thoughtful and helpful comments to the earlierversion of this manuscript. This study was financially supportedby the Ministry of Science and Technology of China (2007CB106803), the National Natural Science Foundation of China(30821062, 30925009) and State Key Laboratory of Vegetation andEnvironmental Change.

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Supporting Information

Additional Supporting Information may be found in the

online version of this article:

Table S1. Species correlation matrix of the warmed 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.

Table S2. Species correlation matrix of the increased pre-

cipitation 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 coeffi-

cients and the P value (Po0.05) are showed.

Please note: Wiley-Blackwell are not responsible for the con-

tent 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.

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 465

r 2010 Blackwell Publishing Ltd, Global Change Biology, 17, 452–465