A positive correlation between plant diversity and productivity is indirectly caused by environmental factors driving spatial pattern of vegetation composition in semiarid sandy grassland

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In the past decade theoretical and experimental studies have greatly increased our knowledge of how plant diversity influences ecosystem productivity.Specifically, manipulative experiments changing plant diversity by drawing plant species from a random species pool have shown that increasing species diversity frequently increase productivity (Tilman et al., 1997(Tilman et al., , 2001;;Hector, 1998;Hector et al., 1999).These experiments studies are often performed in uniform environments with well mixed species pools (Zhou et al., 2006).However, studies in natural ecosystems report the different patterns of diversity-productivity, because field observations typically involve one or several environmental gradients (Cardinale et al., 2000;Zuo et al., 2011).Some studies have shown that environment gradient is thought to be an important factor influencing both species richness and the distribution of biomass in natural ecosystems (Maestre et al., 2006;Fornara and Tilman, 2009;Ma et al., 2010).In Chinese natural grasslands, a positive correlation between plant diversity and productivity is also formed due to the changes of climate and soil (Bai et al., 2007;Ma et al., 2010).This raises the question that if a positive relationship of diversity-productivity exists in natural ecosystems, how environmental factors influence species diversity and productivity (Waide et al., 1999;Cardinale et al., 2004).
Secondly, apart from species diversity, ecosystem productivity can also be strongly influenced by biotic attributes of plant communities such as species composition and evenness (Hooper et al., 2005).Within a given community, species number at the local Introduction

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Full scale may vary due to spatial stability of community compositions (Weigelt et al., 2008).
To further understand the mechanism driving the diversity-productivity relationships, it is necessary to consider all components of diversity, species richness and evenness (Isbell et al., 2008).In addition, in order to test the diversity-productivity relationship in grassland ecosystem, it is also necessary to consider the characteristics, structures and compositions of vegetation in specific regions (Cardinale et al., 2004;Ma et al., 2010).Previous study has also suggested that species compositions of plant communities may influence productivity independently of plant diversity (Kahmen et al., 2005a).
Thus a further study is called to assess how important environmental factors and vegetation composition is in influencing the diversity-productivity relationships (Chapin et al., 2000;Loreau et al., 2001;Kahmen et al., 2005a), in order to manage and restore natural grassland ecosystems with the goal of improving diversity, productivity and sustainability.Our previous study suggests that a combination of soil properties and topography features determines the vegetation pattern and composition along the environment gradient in sandy grasslands (mobile dune, semi-fixed dune, fixed dune, dry meadow, wet meadow and flood plain grassland) (Zuo et al., 2011).Here we use a multivariate model that examines and controls environmental variables statistically to determine the effects of vegetation composition and environment factors on the relationship of plant diversity-productivity in sandy grassland.We tested two hypotheses that 1) a positive correlation between plant diversity and productivity is along the environment gradient in sandy grassland; and 2) environment factors control the distributions and compositions of plant communities, which in turn control the pattern of species diversity and productivity.Introduction

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Full 2 Materials and methods

Experiment design
Vegetation survey in 60 sites were carried out in August and were selected from six typical vegetation types in the area of 20 × 50 km, including sand dunes (mobile dune, Introduction

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Full semi-fixed and fixed dune) and grasslands (dry meadow, wet meadow and flood plain grassland).At each site, a 30×30 m plot was established.Three random quadrats (1× 1 m) were placed in each plot to measure plant height (maximum), species abundance and plant cover.In each quadrat, the peak aboveground biomass, as a proxy for annual productivity was estimated by clipping all vegetation at ground level.The aboveground biomass was dried at 60 • for 48 h.
For each site a soil profile (20 cm in depth) was excavated to identify the soil type.Using a 3 cm diameter soil auger, one soil sample was collected within each quadrat at 0-20 cm depth for laboratory analysis.With the same auger at the same time, three additional samples were taken in each plot to measure soil water content (SW) at depths of 0-20, 20-40 and 40-60 cm.
Soil samples were hand-sieved through a 2-mm screen to remove roots and other debris.Soil particle size was determined by the pipette method in a sedimentation cylinder, using sodium hexametaphosphate as the dispersing agent (ISSCAS, 1978).Soil pH and electrical conductivity (EC) were measured in a 1 : 1 soil-water slurry and in a 1 : 5 soil-water aqueous extract, respectively.Soil organic carbon (C) was measured by the dichromate oxidation method of Walkey and Black (Nelson and Sommers, 1982) and total nitrogen (N) was determined by the Kjeldahl procedure (ISSCAS, 1978).

Plant diversity measures
The importance value of species (IV) in each plot was calculated using the formula IV = (relative abundance + relative height + relative cover of the plant)/3 (Zhang et al., 2005;He et al., 2007;Zuo et al., 2009).From the importance value of species, species diversity was calculated by the species richness, Shannon-Wiener index, Simpson ecological dominance index and Evenness index (Zhang et al., 2005).Introduction

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Aggregation of vegetation compositions and environment factors
To determine the effect of vegetation compositions and environmental factors on plant diversity and productivity, the ordination techniques of principal component analysis (PCA) and non-metric multidimensional scaling (NMDS) were used to aggregate environment factors and vegetation composition (Kahmen et al., 2005a).Using these approaches for 60 sites, we constructed the data matrixes of plant cover, soil properties and site characteristics.We used a square-root transformation data of plant cover and environment factor to improve normality of measured variables for the PCA and NMDS analyses.As a first step, using the PCA method, we aggregated soil properties and site characteristics data (ter Braak and Smilauer 2002).PCA is a method that reduces data dimensionality by performing a covariance analysis between factors.This procedure summarizes the information of the variables as four major axes of a standardized PCA, and creates composite independent variables (Kahmen et al., 2005a).PCAs were performed separately for soil properties and site characteristics.From each PCA, the axes explaining most of the total variance were extracted to form the new PCA-derived variables.These new PCA-derived variables were used in all consecutive analyses as independent parameters.Intra-set correlations from the PCA are used to assess the importance of soil properties and site characteristics.As a next step, the compositional differences among plant communities for the 60 investigated sites were analyzed using NMDS, with Bray-Curtis coefficient as distance measure (Kahmen et al., 2005a;Spiegelberger et al., 2006).NMDS is commonly regarded as the best and most robust unconstrained ordination method in community ecology (Minchin, 1987).The scores of the NMDS axes were used as parameters for vegetation composition (Kahmen et al., 2005a).To determine which species are mainly responsible for the compositional changes within the investigated communities (along the extracted NMDS axes), the linear regressions of each plant cover versus the scores of the NMDS axes were performed.Introduction

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Relationship among plant diversity, productivity, vegetation compositions and environmental factors
As a third step, least squares linear regressions were used to analyze the relationships between plant diversity measures (species richness, Shannon-Wiener index, Simpson ecological dominance index and Evenness index) and productivity, between plant diversity and vegetation compositions (NMDS axes), and between vegetation composition and productivity.In addition, multiple regression analyses were also performed separately for each diversity measure, vegetation composition and productivity, with one of the PCA constructed variable groups, soil variables and site characteristics (Kahmen et al., 2005a).Subsequently, we used a multiple stepwise regressions to test whether the PCA-derived variables were significant predictors for plant diversity, vegetation composition and productivity.For each dependent variable (diversity measures, NMDS1, NMDS2 and productivity), separated regression models were calculated for each parameter group, soil properties and site characteristics, respectively.

Influence of vegetation composition and environment factors on plant diversity and productivity
In a final path analysis, we used structural equation modeling (SEM) to examine the relationship between plant diversity and productivity, the influence of soil properties and site characteristics on vegetation composition, plant diversity and productivity, and the influence of vegetation composition on plant diversity and productivity.Starting and productivity is response variable, having no effect on environmental variables or vegetation composition.The descriptive statistical parameters, variance (ANOVA) procedures and Tukey's test, and regression analyses were performed using SPSS 16.0 software.PCA were performed using the CANOCO 4.5 software (ter Braak and Smilauer 2002).NMDS ordination techniques were applied using the program PC-ORD 5.0 software (McCune and Mefford 2006).The structural equation modeling was applied using AMOS 17.0 software (Arbuckle, 2008).

The relationship between vegetation patterns and environment factors
Ordination of the 60 plant communities using NMDS is depicted in Fig. 1.Based on plant species compositions, the 60 plots can be classified into six vegetation types in order of increasing species richness, Shannon-Wiener index and biomass: mobile dune, semi-fixed dune, fixed dune, dry meadow, wet meadow and flood plain grassland (Table 1, all P < 0.01).Our results showed that along a habitat gradient from mobile dune to flood plain grassland, mean species richness increased from 3 to 15 species per m 2 , and aboveground biomass increased from 31 to 391 g m −2 .NMDS also showed that a two-dimensional solution was sufficient to achieve low stress values (first axis/dimension = 49.13,R 2 = 0.28, P = 0.004; second axis/dimension = 31.66,R 2 = 0.42, P = 0.004) to explain vegetation composition (Fig. 1).
From the intra-set correlations of environmental factors with the first two axes of NMDS (Table A1), the first axis correlated significantly with soil type, soil organic C, total N, C/N, pH, EC and latitude (P < 0.01), and the second axis correlated significantly with soil type, soil organic C, total N, EC, soil water contents at three depths, very fine sand content and altitude (P < 0.01).These results explained 70 % of the species-environment relationship, indicating that environment gradients in relation to Introduction

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Full Based on this strong vegetation-environment relationship, we used the scores of the first two axes as parameters for plant species compositions (NMDS1 and NMDS2) in sandy grassland.The correlation analysis showed that plant diversity was correlated with species compositions (NMDS1) (Table A2).Several dominant plant species, such as Agriophyllum squarrosum, Artemisia halodendrom, Calamagrostis pseudophragmites, Carex dispalata, Digitaria ciliaris, Lespedeza davurica, Plantago asiatica, Potentilla bifurca, Salsola collina and Typha orientalis, showed a strong positive or negative relation with the NMDS1 and NMDS2, indicating that vegetation composition is closely related to the dominant species in plant communities (Table A3).

Changes of environmental factors
Soil organic C, total N, C/N, pH, EC, very fine sand and soil water contents (0-20, 20-40 and 40-60 cm) differed among six vegetation types (Table 1, all P < 0.01).Soil organic C, total N and soil water contents increased from the mobile dune to the flood plain grassland, but there were no significant differences in soil organic C and total N among dry meadow, wet meadow and flood plain grassland (P > 0.05) and were no significant differences in soil water contents among mobile dune, semi-fixed dune and the fixed dune (P > 0.05).There were differences in fine sand and altitude among six vegetation types (Table 1, P < 0.05), but not for coarse sand, silt and clay (all P > 0.05).Except for pH, soil properties had a high coefficient of variation (CV), indicating that the spatial distribution of soil properties is highly variable in the study area.

Aggregation of environmental factors
Four axes explaining 94.7 % of the total variance of all soil properties were extracted as independent variables from the PCA and labeled soil1-soil4 (Table A4).Soil1 Introduction

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Full accounted for 68.80 % of the total variance and was significantly positive correlated to soil type, soil C, total N, C/N, pH, EC, soil water contents at three depths, very fine sand and silt + clay (P < 0.01), and significantly negative correlated to coarse sand and fine sand (P < 0.01).Soil2 accounted for 15.40 % of the total variance and was significantly positive correlated with very fine sand and silt + clay (P < 0.01), and significantly negative correlated to coarse sand (P < 0.01).Soil3 explained 6.00 % of the total variance and was significantly positive correlated with coarse sand (P < 0.01), and significantly negative correlated to fine sand (P < 0.01).Soil4 explained 4.50 % of the variance and was significantly positive correlated with total N, silt + clay (P < 0.01), and significantly negative correlated to coarse sand (P < 0.01).
Two axes (site 1-2) were extracted from the PCA, explaining 100 % of the total site variation (Table A4).Site1 was significantly positive correlated to latitude and altitude (P < 0.01), which account for 99 % of the total variance of site characteristics.Site2 was significantly positive correlated to longitude and latitude (P < 0.01), which account for 1 % of the total variance of site characteristics.

The relationship between plant diversity and productivity
Overall, we found a positive correlation between plant diversity and productivity in sandy grassland (Fig. 2).Species richness and the Shannon-Wiener diversity index were significantly positive correlated to productivity (P < 0.01), and Simpson dominance index was significantly negative correlated to productivity (P < 0.01).Vegetation compositions represented as NMDS1 and NMDS2 were significantly positive correlated to productivity (P < 0.001, Fig. 2).

Relationships among environmental factors, plant diversity, vegetation composition and productivity
We found that environmental factors were correlated to plant diversity, vegetation composition and productivity in sandy grassland.Using multiple stepwise regression Introduction

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Full models, we also found that all explanations of soil parameter for the total variability in vegetation composition and productivity are over 43 % which is double than that for species diversity (Table 2).The parameter soil1 explained 20.9 % of the total variability in species richness (Table 2).Soil1 and soil4 explained 43.9 % of the total variability in NMDS1 and soil1, soil3 and soil4 explained 56.1 % of the total variability in NMDS2.In the regressions with either site characteristics as independent variables, the parameters site2 explained 31.0 % and 19.3 % of the variation in species richness and NMDS1 scores, respectively (Table 2).In addition, 11.4 % of the total variation in NMDS2 was explained by site1.For productivity, 62.7 % of total variation was explained by soil1, soil2 and soil3, and 22.5 % by site1 and site2 (Table 2).

Structural equation modeling (path analysis)
We used structural equation modeling to examine the direct and indirect correlations among plant diversity, productivity and environmental factors.Note that we only used species richness as a diversity measure, because this was the only variable which was significantly correlated to the soil and site parameters (P < 0.01).We used soil1, soil2, soil3, soil4, site1 and site2 as independent variables, and NMDS1, NMDS2, plant diversity and productivity as dependent variables (Table 2), to determine the initial structural equation modeling (Fig. 3a).Considering the effect of environment factors on plant diversity, vegetation composition and productivity, the initial model consisted of PCAderived soil and site parameters that were significantly correlated with the variables of plant diversity, NMDS1, NMDS2 and productivity in the multiple regression analyses (Table 2).Productivity and diversity were also hypothesized to be dependent on soil1, soil2, soil3, soil4, site1, site2, NMDS1 and NMDS2, and we structured the model including paths from those variables to plant diversity and productivity.This initial model was simplified by removing variables and paths according to the measures of fitting model (Table 3).All of the tested models were significant.The optimization model of structural equation with the best AIC and BCC values included variables soil1, soil4, site1, site2, NMDS1, NMDS2 and productivity, but excluded the Introduction

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Full relationship of soil1 with diversity, and of site1 with productivity, and of regression site2 with productivity (Table 3, Fig. 3b).The paths from soil1 and soil4 to vegetation compositions, from site2 to plant diversity, and from vegetation composition to plant diversity and productivity were significant (P < 0.01).Using this approach, however, the paths from plant diversity to productivity and from productivity to plant diversity were not significant (P > 0.05).Thus, according to the regression weights along paths, the relationship between diversity and productivity was a positive correlation, but was indirectly driven by vegetation composition.

Positive correlation between plant diversity and productivity in sandy grassland
We found a positive correlation between plant diversity and ecosystem productivity in sandy grassland, which is consistent with the finding from other experimental studies both in synthesized assemblages (Tilman et al., 1997(Tilman et al., , 2001;;Hector, 1998;Hector et al., 1999;Bai et al., 2007) and in natural grassland ecosystem (Bai et al., 2007;Ma et al., 2010).The multivariate regression analysis indicates that environmental factors and vegetation composition control both plant diversity and productivity (Table 2, Fig. 2.).However, the optimization model of structural equation indicates that vegetation composition rather than environmental factors influence both diversity and productivity in this sandy grassland system (Fig. 3b).Environment variables influence plant diversity and productivity, mostly via their direct effects on vegetation composition in sandy grassland.Our studies suggest that plant diversity and productivity both depending on vegetation composition increase consistently along the habitat gradient in sandy grassland which is closely correlated to the differences in soil properties and topographic features.Introduction

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Full This study supports a positive, rather than a humped-shaped pattern of diversityproductivity (Mittelbach et al., 2001;Gillman and Wright, 2006;Bai et al., 2007;Gross et al., 2009;Ma et al., 2010).An unimodal relationship between diversity-productivity, is often found in the temperate ecosystems, and the positive relationship is often found in tropical ecosystems (P ärtel et al., 2007).A meta-analysis also has supported the unimodal shape relationship from local to landscape scales, whereas a positive linear relationship is common at large spatial scale (Mittelbach et al., 2001).In Northern American grassland, Guo and Berry (1998) showed that, when the environmental gradients extend from extremely "poor" microhabitats to extremely "rich" microhabitats, a hump-shaped relationship can develop.However, other studies from semiarid grasslands in Europe and China contradict this hump-shaped relationship, and show that at the regional scale, the relationship between diversity-productivity is a positive pattern which is driven by an environmental gradient of climate and soil fertility (Hector et al., 1999;Bai et al., 2007;Ma et al., 2010).So there are the more positive patterns of diversity-productivity in grasslands, likely because of the effect of natural environment gradients at larger spatial scales.

Environmental factors explain the positive correlation between plant diversity and productivity
The positive pattern of diversity-productivity can occur when environmental conditions change from a small scale to a region scale and can promote species coexistence rather than competitive exclusion (Cardinale et al., 2000).At a Eurasian continent scale, a spatial gradient related to annual precipitation and soil nitrogen is thought to contribute to a positive relationship of plant diversity-productivity in grassland (Bai et al., 2007).Our results also suggest that environment factors are a better determiner for vegetation composition and productivity than for species diversity (Table 2).
In our study system, spatial patterns of soil and topography may reinforce species compositions at small scales, and spatial changes of habitats may result in the more homogeneous field-level productivity.The important gradient of soil properties that we Introduction

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Full found from mobile dune to flood plain grassland may determine the distribution patterns of plant communities (Zuo et al., 2011).So the particular pattern of vegetation composition contributes to the positive linear relationship in diversity-productivity at this region scale.This pattern also supports the findings of previous studies demonstrating that environmental factors are important drivers of species dissimilarity with increasing productivity (Chase and Leibold, 2002).
The effect of habitat change may also be important at regional scale, and is an alternative explanation of variations in diversity-productivity relationships among grasslands (Foster et al., 2007;Guo, 2007).Previously we have found that plant diversity and ecosystem productivity increased with the restoration of degraded vegetation in dune stabilization (Guo et al., 2008).In addition, the vegetation restoration of mobile dune also significantly enhances topsoil development by increasing the accumulation of carbon and total nitrogen (Li et al., 2009;Zuo et al., 2009).Thus, once speciespoor habitats (e.g.mobile dune) have been gradually transformed into diverse natural habitats such as semi-fixed dune and fixed dune, vegetation restoration may cause an increase in plant diversity and ecosystem productivity in sandy grassland.

Vegetation composition drives the positive correlation between plant diversity and productivity
Some studies have indicated that vegetation composition, in addition to species diversity, can strongly influence ecosystem productivity in grasslands (Cardinale et al., 2000;Hooper et al., 2005;Kahmen et al., 2005a;Ma et al., 2010).In our study, the gradient of soil properties drives vegetation composition, which in turn drives patterns in plant diversity and productivity.Our study is consistent with finding from other studies that vegetation composition is an important driver of ecosystem functioning in grassland ecosystems (Hooper et al., 2005;Kahmen et al., 2005a;Maestre et al., 2006).
Not surprisingly, we found that species compositions in plant communities changed from the pioneer plant species on mobile dune to hygrophytes in the flood plain grassland and that vegetation composition strongly varied with environmental conditions.Introduction

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Full Clearly, the occurrence of plant species at a site depends on the presence of suitable habitat, and local diversity and vegetation composition is strongly influenced by the number of habitat types, i.e. environmental heterogeneity.Therefore, niche differentiation between species may increase the collective performance of plant community across the habitat types, which further driving patterns of plant diversity and productivity.This is specifically indicated by that spatial heterogeneity that allows environmental resources to be used in spatially complementary ways utilized by different plant species (Cardinale et al., 2000).Thus, it is conceivable that the habitat variations caused by differences in soil properties and topography features, may affect species distributions and compositions in plant communities, and vegetation composition further drive plant diversity and productivity in the same positively correlated direction.
Our study demonstrates that vegetation composition, plant diversity and productivity changed consistently along an environment gradient in soil and topography in sandy grasslands.Although soil properties and topographic features are highly important basic factors for plant diversity and ecosystem functioning, their influences on plant diversity and productivity are indirect via driving the vegetation composition, supporting that vegetation composition of grassland ecosystem are an important parameter that is greatly driving the plant diversity and productivity.Thus to understand ecosystem functioning, we need to examine spatial patterns of plant diversity, vegetation composition and environment factors and how these factors influence productivity.In addition, to maintain the diversity and productivity in grassland ecosystem in semiarid area, it is necessary to conserve the sandy grassland habitats and promote the restoration succession of degraded vegetation by improvement of environment conditions.Introduction

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Full Discussion Paper | Discussion Paper | Discussion Paper | et al.
Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | from the most complex model that included all significant variables from the analyses of multiple stepwise regressions, model simplification was based on the significance of the regression weights.The competing models were compared by using the Chisquare test, Akaike information criterion (AIC), Browne-Cudeck criterion (BCC) and the squared multiple correlation (SMC)(Arbuckle, 2008;Kahmen et al., 2005a).Considering the complexity of structural equation modeling, the model postulated that diversity Introduction Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | soil and topographic factors (i.e.soil type, soil organic C, total N, C/N, pH, EC, soil water content, very fine sand content and altitude) are the key factors determining the distribution patterns of plant communities.
Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Table A1.Intra-set correlations of the environmental variables and cumulative percentage variance for the first two axes of NMDS in sandy grasslands.Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

Fig. 2 .Fig. 2 .
Fig.2.Relationships of different plant diversity measures and community composition to productivity in sandy grasslands (n=60) Fig. 2. Relationships of different plant diversity measures and community composition to productivity in sandy grasslands (n = 60).
The climate is temperate, semi-arid continental and monsoonal, receiving 360 mm in precipitation annually, with 75 % of the precipitation in the growing season of June to September.The annual mean open-pan evaporation is about 1935 mm.The annual mean temperature is around 6.4 • C, with the minimum monthly mean temperature of −13.1•C in January and the maximum of 23.7 • C in July.The annual mean wind velocity is in the range of 3.2 to 4.1 m s −1 , and the prevailing wind direction is northwest in winter and spring

Table A3 .
Relative presence and average cover of those plant species in all 60 sites that explain > 8 % of the variance of the non-metric multidimensional scaling (NMDS) axis and > 7 % of variability in productivity in simple linear regressions.+ and − signs represent the direction of the relationship.