Download Community structure and composition in response to climate change

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
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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
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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 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.
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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.
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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. This study was financially supported
by the Ministry of Science and Technology of China (2007
CB106803), the National Natural Science Foundation of China
(30821062, 30925009) and State Key Laboratory of Vegetation and
Environmental 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 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
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