# Climate variability decreases species richness and community stability in a temperate grassland

Yunhai Zhang, Michel Loreau, Nianpeng He, Junbang Wang, Qingmin Pan, Yongfei Bai, Junbang Han

► To cite this version:

Yunhai Zhang, Michel Loreau, Nianpeng He, Junbang Wang, Qingmin Pan, et al.. Climate variability decreases species richness and community stability in a temperate grassland. Oecologia, 2018, 1, pp.183 - 192. 10.1007/s00442-018-4208-1 . hal-02352812

HAL Id: hal-02352812

https://hal.science/hal-02352812v1

Submitted on 21 Jul 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Climate variability decreases species richness and community stability in a temperate grassland

Yunhai Zhang¹,⁴,ïD, Michel Loreau<fnref n="2" />, Nianpeng He³, Junbang Wang³, Qingmin Pan¹, Yongfei Bai¹, and Xingguo Han¹

¹State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, 100093 Beijing, China

²Centre for Biodiversity Theory and Modelling, Theoretical and Experimental Ecology Station, CNRS and Paul Sabatier University, 09200 Moulis, France

³Synthesis Research Center of Chinese Ecosystem Research Network, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 100101 Beijing, China

⁴School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA

## Abstract

Climate change involves modifications in both the mean and the variability of temperature and precipitation. According to global warming projections, both the magnitude and the frequency of extreme weather events are increasing, thereby increasing climate variability. The previous studies have reported that climate warming tends to decrease biodiversity and the temporal stability of community primary productivity (i.e., community stability), but the effects of the variability of temperature and precipitation on biodiversity, community stability, and their relationship have not been clearly explored. We used a long-term (from 1982 to 2014) field data set from a temperate grassland in northern China to explore the effects of the variability of mean temperature and total precipitation on species richness, community stability, and their relationship. Results showed that species richness promoted community stability through increases in asynchronous dynamics across species (i.e., species asynchrony). Both species richness and species asynchrony were positively associated with the residuals of community stability after controlling for its dependence on the variability of mean temperature and total precipitation. Furthermore, the variability of mean temperature reduced species richness, while the variability of total precipitation decreased species asynchrony and community stability. Overall, the present study revealed that species richness and species asynchrony promoted community stability, but increased climate variability may erode these positive effects and thereby threaten community stability.

<footnote>iD Yunhai Zhang: 0000-0003-0613-3624</footnote>

<footnote n="2">²Yongfei Bai, yfbai@ibcas.ac.cn; Xingguo Han, xghan@ibcas.ac.cn.
Yu-Long Feng.</footnote>

<footnote>**Author contribution statement** YZ, ML, and XH conceived and designed the experiments. YZ, NH, YB, and JW performed the experiments and collected the data. YZ analyzed the data. YZ, ML, and NH wrote the manuscript; other authors provided editorial advice.</footnote>

<footnote>**Conflict of Interest** The authors declare that they have no conflict of interest.</footnote>
**Keywords**

Biodiversity; Climate extremes; Inter-annual variability; Ecosystem function; Resilience

**Introduction**

Human activities, such as burning of fossil fuels, have significantly contributed to climate warming (IPCC 2013; Seneviratne et al. 2016). Global warming, however, increased not only the magnitude but also the probability of occurrence of climate extremes and unusual weather, i.e., climate variability (Min et al. 2011; Orlowsky and Seneviratne 2012; Putnam and Broecker 2017). For example, our planet has recently experienced more frequent and intense snow-storms, rainfall events, floods, heatwaves, droughts, and wildfires (Fischer and Knutti 2016; Wehner et al. 2017; Zhou et al. 2017). Moreover, the coupling of climate warming and drought can also lead to additional warming at a regional scale due to decreases in evapotranspiration (Berg et al. 2014; Diro and Sushima 2017), thereby increasing the variability of mean temperature and total precipitation (Berg et al. 2014). Recent climate models have also projected that, in addition to climate warming, the amplitude of inter-annual temperature oscillations will increase (Diro and Sushima 2017; Hawkins et al. 2014), meaning that high inter-annual climate variability will be superimposed on an unprecedented level of global warming in the coming decades. Because anomalies in average global temperature are much smaller than the expected changes in regional temperatures over most land areas (Orlowsky and Seneviratne 2012), the alterations in temperature and precipitation variability at regional and seasonal scales may be stronger than responses at global and annual scales (Donat et al. 2016).

Current increases in the magnitude of global climate changes are altering ecosystem functioning, causing biodiversity loss (Thomas et al. 2004; Walker et al. 2006) and species composition changes (Walker et al. 2006; Ma et al. 2017), increasing ecosystem vulnerability (Lesk et al. 2016), and threatening ecosystem production (Kennedy et al. 2003; Ma et al. 2017; Wagg et al. 2017). For example, climate warming significantly reduced species richness in a Tibetan grassland (Klein et al. 2004) and the aboveground net primary productivity (ANPP) of tallgrass prairie monoliths in Reno, Nevada, USA (Arnone et al. 2011). The decrease in the annual precipitation also caused species richness loss (Kennedy et al. 2003; Wilby and Shachak 2004) and ecosystem production reduction in the Kruger National Park, South Africa (Kennedy et al. 2003) and in a tallgrass prairie in northeastern Kansas, USA (Hoover et al. 2014).

Although it is important to explore the effects of changes in the mean value of climate variables (e.g., mean temperature and total precipitation) on ecosystem functioning, changes in climate variability may have stronger effects (Craine et al. 2012; Knapp et al. 2017; Reyer et al. 2013; Wilcox et al. 2017). For example, Knapp et al. (2002) found that increasing precipitation variability, while maintaining a constant precipitation amount by decreasing watering frequency, increased species richness but reduced community ANPP in a native grassland ecosystem in northeast Kansas, USA. Vasseur et al. (2014) reported that temperature variability increased the risk of species extinction more than warming.
Moreover, climate variability may tend to reduce community stability (defined as the inverse of inter-annual variability that measures the variation of community productivity over time). Although the total rainfall amount was not altered, increases in precipitation variability decreased community primary productivity (Gherardi and Sala 2015a) and tended to damage plant community stability in New Mexico, USA (Gherardi and Sala 2015b). Temperature variability weakened the diversity effects in aquatic microcosms (Petchey et al. 2002). The variabilities of temperature and precipitation often naturally covary. For instance, either extreme drought or intense snowstorm (i.e., precipitation variability) may largely change temperature, thereby increasing temperature variability. However, the lack of long-term data currently limits our understanding the response of natural communities to variability in both mean temperature and total precipitation (Knapp et al. 2017; Solow 2017). In particular, an important but unresolved question is how climate variability affects biodiversity, community stability, and their relationship.

Climate variability and biodiversity may not be fully independent, and both affect ecosystem functioning (Isbell et al. 2015b). In fact, a few previous studies have carefully separated the effects of climate variability and biodiversity on ecosystem functioning. Testing the effect of biodiversity on the residuals of community stability after controlling the effects of mean temperature and total precipitation variabilities, or employing structural equation modeling, are ways to explore the effect of climate variability on community stability through realistic species changes (Grace et al. 2015; Zhang et al. 2016).

Here, we used long-term data on monthly species richness (number of plant species m⁻²) and the ANPP over the growing season of 1982–2014 to analyze the responses of a temperate grassland ecosystem in northern China to the long-term inter-annual variability in mean temperature and total precipitation. We hypothesized that (1) climate variability decreases biodiversity in the temperate grassland and (2) climate variability may reduce community stability due to species richness loss.

# Methods

## Study site

This study was performed near the Inner Mongolia Grassland Ecosystem Research Station (116°14′E, 43°13′N), which is located in a temperate grassland in the Xilin River basin, Inner Mongolia, China. The C₃ perennial rhizome grass *Leymus chinensis* and the C₃ perennial bunchgrass *Stipa grandis* were dominant at the study site. The soil is classified as Haplic Calcisol and Calcic-Orthic Aridisol, according to FAO (Food and Agriculture Organization of the United Nations) and USA soil classification system, respectively. The long-term (1982–2014) mean annual temperature was 0.97 °C, with mean monthly temperatures ranging from – 21.4 °C in January to 19.7 °C in July, and mean annual precipitation was 345.2 mm with approximately four-fifths falling from May to September. In this region, the grassland often turns green in late April and yellow in early September (Zhang et al. 2018). In 1979, two 25-ha relative flat areas were fenced to exclude large grazing animals (Bai et al. 2004). In each area, an east–west transect of 200 × 100 m² was equally divided into five replicates (40 × 100 m² each) (Fig. S1).
## Field sampling

The ANPP of the community was estimated from aboveground plant biomass, which is an acceptable approximation for ANPP in this region, because aboveground plant tissues die during winter (Zhang et al. 2018). Due to changes in species composition throughout the growing season, plant aboveground biomass was measured on the 15th of every month throughout the growing season (i.e., from May to September) by clipping green parts of all vascular plants above the soil surface within a 1 × 1 m² plot within each block, over 1982–2014 (Fig. S1). All clipping plant materials were sorted into species, oven-dried at 65 °C for 48 h to a constant weight, and then weighed. Species richness was recorded in the same plot in which aboveground biomass was measured. As 10 plots were sampled each month (i.e., 5 plots per site × 2 sites = 10 plots), 50 plots were sampled each year (i.e., 10 plots per month × 5 months per year = 50 plots). Hence, species richness and community ANPP were estimated for 1650 plots (i.e., 50 plots per year × 33 years = 1650 plots).

## Community stability

Community temporal stability was defined as $\mu/\sigma$ (Lehman and Tilman 2000), where $\mu$ and $\sigma$ are the inter-annual mean and standard deviation of community ANPP for every month of the growing season over the 33 years, respectively. Community stability was natural logarithm transformed for homogeneity to meet the normality assumption required for further analyses. A larger community stability value indicates a smaller inter-annual variability of community production (Lehman and Tilman 2000; Zhang et al. 2016).

## Species asynchrony

Species asynchrony, which is the asynchronous dynamics across species to environmental

fluctuations and/or disturbances, was quantified as $1 - \frac{\sigma_{b_T}^2}{\left(\sum_{i=1}^N \sigma_{b_i}\right)^2}$ (Loreau and de

Mazancourt 2008), where $\sigma_{b_T}^2$ is the inter-annual variance of community ANPP and $\sigma_{b_i}$ is

the inter-annual standard deviation of ANPP of species *i* in a community with *N* species for every month of the growing season over the 33 years. Coexistent species tend to asynchronously respond to environmental fluctuations (Bai et al. 2004). Species asynchrony varies from zero, which indicates perfectly synchronized species fluctuations, to one, which indicates perfectly desynchronized species fluctuations.

## Climate data

Anomalies in mean temperature and total precipitation were determined based on 1961–1990 averages. Similar to community stability, the inter-annual coefficients of variation (CV) were calculated as $\sigma/\mu \times 100$, where $\mu$ and $\sigma$ were the inter-annual temporal mean and standard deviation of mean temperature or total precipitation for May (i.e., from 16th April to 15th May), June (i.e., from 16th May to 15th June), July (i.e., from 16th June to 15th July), August (i.e., from 16th July to 15th August), and September (i.e., from 16th August to 15th September) across 1982–2014. For homogeneity, the CV of mean temperature and total precipitation were logarithm transformed for further analyses.
## Statistical analysis

Repeated-measures analysis of variance (ANOVA) was used to test changes in either species richness or community ANPP differences between growing seasons. One-way ANOVAs were performed to explore the difference of species asynchrony and community stability among months within growing season. Residuals (Zhang et al. 2016) from the regressions of community stabilities on the CV of mean temperature or total precipitation were used to test for independent effects of diversity and asynchrony on community stability after accounting for climate variability.

Structural equation modeling (SEM) was employed to estimate the direct and indirect effects of climate variability on community stability through species richness and species asynchrony across the 33-year period. Data (sample size: 5 replicates per month per site × 5 months per site × 2 sites = 50) were fitted to the model using the maximum-likelihood estimation method. Adequacy of the model was determined using a Chi-squared test, the root-square-mean errors of approximation (RMSEA), and Akaike Information Criterion (AIC) values. Adequate model fits were indicated by a nonsignificant chi-squared test test (*P* > 0.05), low RMSEA (< 0.08), and low AIC. A nonsignificant chi-squared test test (*χ*² = 0.066, *P* = 0.797), RMSEA = 0.000, and AIC = 28.066 was reported for the final model.

AMOS™ 22.0 (Amos Development Co., Greene, Maine, USA) was used for the SEM analysis, and the SPSS® software package (SPSS 18.0 for windows, SPSS Inc., Chicago, IL, USA) was employed for all other tests.

## Results

### Climate change and its variability (CV)

Air mean temperature increased through time (Fig. 1a), while total precipitation did not show any temporal trends (Fig. 1c). The CV of mean temperature (Fig. 1b) and total precipitation (Fig. 1d) was relatively high in May (i.e., in spring).

### Community diversity, ANPP, and stability

Species richness (Fig. 2a) and community ANPP (Fig. 2b) peaked in August during the 1982–2014 period. The average rate of species richness loss was 0.8, 2.0, 2.4, 3.0, and 2.6 species m⁻² decade⁻¹ in May, June, July, August, and September, respectively (Fig. 2a; all *P* < 0.05). Community mean ANPP increased through time (i.e., from 1982 to 2014) in May (Fig. 2b; *F*₁,₃₂ = 8.4, *P* = 0.0069; *R*² = 0.21) and in September (*F*₁,₃₂ = 5.2, *P* = 0.0303; *R*² = 0.14).

Species asynchrony (Fig. 2c) and community stability (Fig. 2d) peaked in July. Community stability in August (i.e., period of peak species richness and ANPP) was significantly positively associated with species richness (Fig. 3a; *F*₁,₉ = 11.0, *P* = 0.0107; *R*² = 0.58) and species asynchrony (Fig. 3b; *F*₁,₉ = 21.8, *P* = 0.0016; *R*² = 0.73), and species richness was significantly positively associated with species asynchrony (Fig. 3c; *F*₁,₉ = 12.1, *P* = 0.0083; *R*² = 0.60). Moreover, species richness and species asynchrony throughout the growing season were significantly positively related to community stability (Fig. 3; All *P* < 0.0001).
These positive relationships during the peak biomass period and throughout the entire growing season illustrate that plant diversity may promote community stability via increasing species asynchrony in this temperate grassland.

## Effects of climate variability on the diversity-stability relationship

Community stability was significantly negatively associated with the *CV* of mean temperature (Fig. 4a; *F*<sub>1,49</sub> = 63.9, *P* < 0.0001; *R*<sup>2</sup> = 0.57) and total precipitation (Fig. 4b; *F*<sub>1,49</sub> = 68.7, *P* < 0.0001; *R*<sup>2</sup> = 0.59). Moreover, species richness was positively related to the residuals of community stability after controlling for the effect of the *CV* of mean temperature (Fig. 4c; *F*<sub>1,49</sub> = 16.2, *P* = 0.0002; *R*<sup>2</sup> = 0.25) and total precipitation (Fig. 4e; *F*<sub>1,49</sub> = 29.1, *P* < 0.0001; *R*<sup>2</sup> = 0.38). Species asynchrony was also positively related to the residuals of community stability after controlling for the effect of the *CV* of mean temperature (Fig. 4d; *F*<sub>1,49</sub> = 15.2, *P* = 0.0003; *R*<sup>2</sup> = 0.24) and total precipitation (Fig. 4f; *F*<sub>1,49</sub> = 14.0, *P* = 0.0005; *R*<sup>2</sup> = 0.23).

Results of the SEM revealed that the *CV* of mean temperature had direct negative effects on species richness (Fig. 5; standardized effect size: –0.45, *P* < 0.001), species asynchrony (standardized effect size – 0.06, *P* = 0.663), and community stability (standardized effect size – 0.15, *P* = 0.226). The *CV* of total precipitation directly significantly reduced species asynchrony (standardized effect size – 0.43, *P* = 0.002) and community stability (standardized effect size – 0.34, *P* = 0.007). Species richness promoted species asynchrony (standardized effect size 0.52; *P* < 0.001). Both species richness (standardized effect size 0.26; *P* = 0.006) and species asynchrony (standardized effect size 0.31; *P* = 0.009) were positively related to community stability. Thus, the SEM showed that species richness could promote community stability both directly and indirectly via an increase in species asynchrony, whereas climate variability might impair community stability both directly and indirectly.

## Discussion

Consistent with our hypothesis, this 33-year field experiment provided substantial evidence that climate variability might cause significant declines in species richness and community stability. Moreover, with the diversity effect on community stability due to species asynchrony, climate variability can increase the vulnerability of community production both directly and indirectly via decreased species richness and species asynchrony.

We also found that community aboveground primary productivity increased throughout the 33-year period, at both the beginning (i.e., May) and end (i.e., September) of the growing season. The changes in primary production reported in this study are consistent with the positive relationships between global warming and the advance of phenological events like spring leaf unfolding (Peñuelas et al. 2009; Wang et al. 2017), and between the extended length of the growing season and the delay of autumn leaf drop (Liu et al. 2016; Wang et al. 2017), which have been observed in recent decades.

Consistent with the previous studies on peak primary production (Hector et al. 2010; Gross et al. 2014; Tilman et al. 2014), species richness and species asynchrony significantly
enhanced community stability, and species richness was significantly positively associated with species asynchrony. Moreover, we found that species richness was positively associated with species asynchrony and community stability in the other months within the growing season as well as throughout the growing season. Species composition, plant phenological characteristics, and environmental factors might contribute to asynchronous responses of species throughout the growing season (de Mazancourt et al. 2013; Loreau and de Mazancourt 2013; Zhang et al. 2018). In the grassland examined here, the C₃ grass *Agropyron cristatum* and the forbs *Potentilla acaulis* with yellow flowers and *Pulsatilla turczaninovii* with white flowers contributed to community ANPP in May; the C₃ forb *Carex korshinskyi* and the C₃ grasses *A. cristatum*, *Koeleria cristata*, and *Poa subfastigiate* contributed to more than half of the community ANPP in June; the C₃ perennial rhizome grass *L. chinensis*, the C₃ grasses *A. cristatum* and *S. grandis*, and the colorful forbs *Iris tenuifolia*, *Thalictrum petaloideum*, and *Allium tenuissimum* started to flower in July; *L. chinensis*, the C₃ grasses *S. grandis* and *Achnatherum sibiricum*, the C₄ grass *Cleistogenes squarrosa*, some forbs like *Saussurea japonica* and *Allium condensatum*, and most annuals, such as *Salsola collina*, *Axyria amaranthoides*, *Chenopodium aristatum*, and *Artemisia sieversiana*, bloomed in August; most species died in September. These seasonal changes in species richness and composition suggest that, in natural communities, the diversity-dependent community stability is positively associated with asynchronous dynamics between species throughout the growing season. These positive relationships support the results of the previous studies (Bai et al. 2004; Cardinale et al. 2012; Tilman et al. 2014), showing that restoring and protecting biodiversity can provide sustainable ecosystem functioning.

Moreover, we found that climate variability negatively affected species richness, species asynchrony, and community stability. It has been reported that changes in climate variability may affect species distribution (Barga et al. 2017; Kelly and Goulden 2008; Reyer et al. 2013), thereby reducing local species richness. Particularly, we found that the variability of mean temperature directly decreased species richness, while the variability of total precipitation directly damaged species asynchrony and community stability (Fig. 5). According to the significant changes in climate variability, plant reproduction may be reduced due to severe physical environment restriction and high mortality due to physiological failure, e.g., failure in seedling establishment, that decrease species richness (Reyer et al. 2013). For example, Andrus et al. (2018) found that the deficit of soil moisture, which was directly affected by increased climate variability, was negatively associated with species recruitment. Concomitantly, plant community might display considerable reduction in production, thus increasing the inter-annual variability of community dynamics. As water is often limited in arid and semi-arid grasslands (Bai et al. 2004; Sala et al. 2012), precipitation variability may directly influence the asynchronous responses of species, on which root distribution may play an important role. The previous studies have showed that increases in precipitation variability decreased the biomass of grasses with shallow roots but enhanced tree productivity due to the deep distribution of their roots (Gherardi and Sala 2015b), thereby leading to decreased community stability (Gherardi and Sala 2015a). Based on the strong inter-annual precipitation fluctuations in our ecosystem, perennial grasses (e.g., *S. grandis* and *A. cristatum*) may have competitive advantages over forbs and annuals
in dry environmental conditions (Bai et al. 2004), because their generally deeper root
systems allow them exclusive access to deep soil water resources, which are expected to
increase in wet years (Zhang et al. 2015). In relatively wet years, annuals (e.g., *C. glaucum*
and *S. collina*) were favored and over shoot (Zhang et al. 2015), as their high growth rate
provides them advantage during months with above mean precipitation (i.e., under increased
precipitation variability), increasing the inter-annual variability of community production
(i.e., reducing community stability). Therefore, increases in mean temperature and total
precipitation variabilities might have negative impacts on biodiversity and community
stability.

Notably, this study showed that, even under climate variability, community stability could
benefit from greater biodiversity, which is consistent with the recent report synthesizing
global biodiversity and ecosystem functioning experiments that incorporated a random
assortment of plant species (Isbell et al. 2015a). We also found that species richness can still
promote community stability after removing the negative effects of climate variability. By
separating the effects of climate variability and species richness on community stability, we
found that species richness and species asynchrony were positively related to the residuals of
community stability after controlling for the effect of climate variability. Thus, biodiversity
and their asynchronous responses might provide insurance effects against climate variability
in natural communities to some extent (Martin and Watson 2016).

Conclusions

Our study showed that climate variability impaired community stability by decreasing
species richness and species asynchrony, suggesting that increasing climate variability has
negative impacts on ecosystem functioning. More important, irrespective of climate
variability, both species richness and species asynchrony enhanced community stability in
the temperate grassland examined here. Thus, urgent actions are needed to prevent further
climate warming, thereby decreasing climate variability (Ricke and Caldeira 2014), and to
maintain biodiversity to ensure the sustainable provision of ecosystem services as well as
regional and global food security.

**Supplementary Material**

Refer to Web version on PubMed Central for supplementary material.

Acknowledgements

We thank Shu Jiang, Zuozhong Chen, Dehua Huang, Shiping Wang, Chunmei Pan, Xiaoliang Wang, and others for their contributions to the long-term data collection at the Inner Mongolia Grassland Ecosystem Research Station, and two anonymous reviewers for their constructive comments that significantly improved the manuscript. This study was supported by the National Key R&D program of China (2016YFC0500700 and 2016YFC0500202), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB15010400), National Natural Science Foundation of China (31430016, 31361123001, 41573063, and 31570469), the TULIP Laboratory of Excellence (ANR-10-LABX-41), and by the BIOSTASES Advanced Grant, the European Research Council under the European Union's Horizon 2020 research and innovation programme (grant agreement number 666971), US National Science Foundation (DEB-1342754), and the International Postdoctoral Exchange Fellowship Program (20170070).
References

Andrus RA, Harvey BJ, Rodman KC, Hart SJ, Veblen TT. Moisture availability limits subalpine tree establishment. Ecology. 2018; 99:567–575. DOI: 10.1002/ecy.2134 [PubMed: 29469981]

Arnone JA, Jasoni RL, Lucchesi AJ, Larsen JD, Leger EA, Sherry RA, Luo YQ, Schimel DS, Verburg PSJ. A climatically extreme year has large impacts on C₄ species in tallgrass prairie ecosystems but only minor effects on species richness and other plant functional groups. J Ecol. 2011; 99:678–688. DOI: 10.1111/j.1365-2745.2011.01813.x

Bai YF, Han XG, Wu JG, Chen ZZ, Li LH. Ecosystem stability and compensatory effects in the Inner Mongolia grassland. Nature. 2004; 431:181–184. DOI: 10.1038/nature02850 [PubMed: 15356630]

Barga S, Dilts TE, Leger EA. Climate variability affects the germination strategies exhibited by arid land plants. Oecologia. 2017; 185:437–452. DOI: 10.1007/s00442-017-3958-5 [PubMed: 28936643]

Berg A, Lintner BR, Findell KL, Malyshev S, Loikith PC, Gentine P. Impact of soil moisture–atmosphere interactions on surface temperature distribution. J Clim. 2014; 27:7976–7993. DOI: 10.1175/jcli-d-13-00591.1

Cardinale BJ, Duffy JE, Gonzalez A, Hooper DU, Perrings C, Venail P, Narwani A, Mace GM, Tilman D, Wardle DA, Kinzig AP, et al. Biodiversity loss and its impact on humanity. Nature. 2012; 486:59–67. DOI: 10.1038/nature11148 [PubMed: 22678280]

Craine JM, Nippert JB, Elmore AJ, Skibbe AM, Hutchinson SL, Brunsell NA. Timing of climate variability and grassland productivity. Proc Natl Acad Sci USA. 2012; 109:3401–3405. DOI: 10.1073/pnas.1118438109 [PubMed: 22331914]

de Mazancourt C, Isbell F, Larocque A, Berendse F, De Luca E, Grace JB, Haegeman B, Wayne Polley H, Roscher C, Schmid B, Tilman D, et al. Predicting ecosystem stability from community composition and biodiversity. Ecol Lett. 2013; 16:617–625. DOI: 10.1111/ele.12088 [PubMed: 23438189]

Diro GT, Sushama L. The role of soil moisture–atmosphere interaction on future hot spells over North America as simulated by the Canadian regional climate model (CRCM5). J Clim. 2017; 30:5041–5058. DOI: 10.1175/jcli-d-16-0068.1

Donat MG, Lowry AL, Alexander LV, Ogorman PA, Maher N. More extreme precipitation in the world's dry and wet regions. Nat Clim Chang. 2016; 6:508–513. DOI: 10.1038/nclimate2941

Fischer EM, Knutti R. Observed heavy precipitation increase confirms theory and early models. Nat Clim Chang. 2016; 6:986–991. DOI: 10.1038/nclimate3110

Gherardi LA, Sala OE. Enhanced interannual precipitation variability increases plant functional diversity that in turn ameliorates negative impact on productivity. Ecol Lett. 2015a; 18:1293–1300. DOI: 10.1111/ele.12523 [PubMed: 26437913]

Gherardi LA, Sala OE. Enhanced precipitation variability decreases grass- and increases shrub-productivity. Proc Natl Acad Sci USA. 2015b; 112:12735–12740. DOI: 10.1073/pnas.1506433112 [PubMed: 26417095]

Grace JB, Scheiner SM, Schoolmaster DR, Jr. Structural equation modeling: building and evaluating causal modelsEcological statistics: contemporary theory and application. Fox GA, Negrete-Yankelevich S, Sosa VJ, editorsOxford Scholarship Online; Oxford: 2015. 168–199.

Gross K, Cardinale BJ, Fox JW, Gonzalez A, Loreau M, Polley HW, Reich PB, van Ruijven J. Species richness and the temporal stability of biomass production: a new analysis of recent biodiversity experiments. Am Nat. 2014; 183:1–12. DOI: 10.1086/673915 [PubMed: 24334731]

Hawkins E, Edwards T, McNeall D. Pause for thought. Nat Clim Chang. 2014; 4:154–156. DOI: 10.1038/nclimate2150

Hector A, Hautier Y, Saner P, Wacker L, Bagchi R, Joshi J, Scherer-Lorenzen M, Spehn EM, Bazeley-White E, Weilenmann M, Caldeira MC, et al. General stabilizing effects of plant diversity on grassland productivity through population asynchrony and overyielding. Ecology. 2010; 91:2213–2220. DOI: 10.1890/09-1162.1 [PubMed: 20836442]

Hoover DL, Knapp AK, Smith MD. Resistance and resilience of a grassland ecosystem to climate extremes. Ecology. 2014; 95:2646–2656. DOI: 10.1890/13-2186.1
IPCC. Climate change 2013: the physical science basis. contribution of working group I to the fifth assessment report of the Intergovernmental panel on climate change. Cambridge University Press; Cambridge, UK and New York, NY, USA: 2013.

Isbell F, Craven D, Connolly J, Loreau M, Schmid B, Beierkuhnlein C, Bezemer TM, Bonin C, Bruelheide H, de Luca E, Ebeling A, et al. Biodiversity increases the resistance of ecosystem productivity to climate extremes. Nature. 2015a; 526:574–577. DOI: 10.1038/nature15374 [PubMed: 26466564]

Isbell F, Tilman D, Polasky S, Loreau M. The biodiversity-dependent ecosystem service debt. Ecol Lett. 2015b; 18:119–134. DOI: 10.1111/ele.12393 [PubMed: 25430966]

Kelly AE, Goulden ML. Rapid shifts in plant distribution with recent climate change. Proc Natl Acad Sci USA. 2008; 105:11823–11826. DOI: 10.1073/pnas.0802891105 [PubMed: 18697941]

Kennedy AD, Biggs H, Zambatis N. Relationship between grass species richness and ecosystem stability in Kruger National Park, South Africa. Afr J Ecol. 2003; 41:131–140. DOI: 10.1046/j.1365-2028.2003.00391.x

Klein JA, Harte J, Zhao X-Q. Experimental warming causes large and rapid species loss, dampened by simulated grazing, on the Tibetan Plateau. Ecol Lett. 2004; 7:1170–1179. DOI: 10.1111/j.1461-0248.2004.00677.x

Knapp AK, Fay PA, Blair JM, Collins SL, Smith MD, Carlisle JD, Harper CW, Danner BT, Lett MS, McCarron JK. Rainfall variability, carbon cycling, and plant species diversity in a mesic grassland. Science. 2002; 298:2202–2205. DOI: 10.1126/science.1076347 [PubMed: 12481139]

Knapp AK, Avolio ML, Beier C, Carroll CJW, Collins SL, Dukes JS, Fraser LH, Griffin-Nolan RJ, Hoover DL, Jentsch A, Loik ME, et al. Pushing precipitation to the extremes in distributed experiments: recommendations for simulating wet and dry years. Global Change Biol. 2017; 23:1774–1782. DOI: 10.1111/gcb.13504

Lehman CL, Tilman D. Biodiversity, stability, and productivity in competitive communities. Am Nat. 2000; 156:534–552. DOI: 10.1086/303402 [PubMed: 29587515]

Lesk C, Rowhani P, Ramankutty N. Influence of extreme weather disasters on global crop production. Nature. 2016; 529:84–87. DOI: 10.1038/nature16467 [PubMed: 26738594]

Liu Q, Fu YH, Zhu Z, Liu Y, Liu Z, Huang M, Janssens IA, Piao S. Delayed autumn phenology in the Northern Hemisphere is related to change in both climate and spring phenology. Global Change Biol. 2016; 22:3702–3711. DOI: 10.1111/gcb.13311

Loreau M, de Mazancourt C. Species synchrony and its drivers: neutral and nonneutral community dynamics in fluctuating environments. Am Nat. 2008; 172:E48–E66. DOI: 10.1086/589746 [PubMed: 18598188]

Loreau M, de Mazancourt C. Biodiversity and ecosystem stability: a synthesis of underlying mechanisms. Ecol Lett. 2013; 16:106–115. DOI: 10.1111/ele.12073 [PubMed: 23346947]

Ma Z, Liu H, Mi Z, Zhang Z, Wang Y, Xu W, Jiang L, He J-S. Climate warming reduces the temporal stability of plant community biomass production. Nat Commun. 2017; 8 15378. doi: 10.1038/ ncomms15378

Martin TG, Watson JEM. Intact ecosystems provide best defence against climate change. Nat Clim Chang. 2016; 6:122–124. DOI: 10.1038/nclimate2918

Min S-K, Zhang X, Zwiers FW, Hegerl GC. Human contribution to more-intense precipitation extremes. Nature. 2011; 470:378–381. DOI: 10.1038/nature09763 [PubMed: 21331039]

Orlowsky B, Seneviratne SI. Global changes in extreme events: regional and seasonal dimension. Clim Change. 2012; 110:669–696. DOI: 10.1007/s10584-011-0122-9

Peñuelas J, Rutishauser T, Filella I. Phenology feedbacks on climate change. Science. 2009; 324:887–888. DOI: 10.1126/science.1173004 [PubMed: 19443770]

Petchey OL, Casey T, Jiang L, McPhearson PT, Price J. Species richness, environmental fluctuations, and temporal change in total community biomass. Oikos. 2002; 99:231–240. DOI: 10.1034/j.1600-0706.2002.990203.x

Putnam AE, Broecker WS. Human-induced changes in the distribution of rainfall. Sci Adv. 2017; 3:e1600871.doi: 10.1126/sciadv.1600871 [PubMed: 28580418]

Reyer CPO, Leuzinger S, Rammig A, Wolf A, Bartholomeus RP, Bonfante A, de Lorenzi F, Dury M, Gloning P, Abou Jaoudé R, Klein T, et al. A plant’s perspective of extremes: terrestrial plant
responses to changing climatic variability. Global Change Biol. 2013; 19:75-89. DOI: 10.1111/gcb.12023

Ricke KL, Caldeira K. Natural climate variability and future climate policy. Nat Clim Chang. 2014; 4:333-338. DOI: 10.1038/nclimate2186

Sala OE, Gherardi LA, Reichmann L, Jobbágy E, Peters D. Legacies of precipitation fluctuations on primary production: theory and data synthesis. Philos Trans Royal Soc B. 2012; 367:3135-3144. DOI: 10.1098/rstb.2011.0347

Seneviratne SI, Donat MG, Pitman AJ, Knutti R, Wilby RL. Allowable CO₂ emissions based on regional and impact-related climate targets. Nature. 2016; 529:477-483. DOI: 10.1038/nature16542 [PubMed: 26789252]

Solow AR. On detecting ecological impacts of extreme climate events and why it matters. Philos Trans Royal Soc B. 2017; 372 20160136. doi: 10.1098/rstb.2016.0136

Thomas CD, Cameron A, Green RE, Bakkenes M, Beaumont LJ, Collingham YC, Erasmus BFN, de Siqueira MF, Grainger A, Hannah L, Hughes L, et al. Extinction risk from climate change. Nature. 2004; 427:145-148. DOI: 10.1038/nature02121 [PubMed: 14712274]

Tilman D, Isbell F, Cowles JM. Biodiversity and ecosystem functioning. Annu Rev Ecol Evol Syst. 2014; 45:471-493. DOI: 10.1146/annurev-ecolsys-120213-091917

Vasseur DA, DeLong JP, Gilbert B, Greig HS, Harley CDG, McCann KS, Savage V, Tunney TD, O'Connor MI. Increased temperature variation poses a greater risk to species than climate warming. Proc Royal Soc Lond B. 2014; 281 20132612. doi: 10.1098/rspb.2013.2612

Wagg C, O'Brien MJ, Vogel A, Scherer-Lorenzen M, Eisenhauer N, Schmid B, Weigelt A. Plant diversity maintains long-term ecosystem productivity under frequent drought by increasing short-term variation. Ecology. 2017; 98:2952-2961. DOI: 10.1002/ecy.2003 [PubMed: 28869781]

Walker MD, Wahren CH, Hollister RD, Henry GHR, Ahlquist LE, Alatalo JM, Bret-Harte MS, Calef MP, Callaghan TV, Carroll AB, Epstein HE, et al. Plant community responses to experimental warming across the tundra biome. Proc Natl Acad Sci USA. 2006; 103:1342-1346. DOI: 10.1073/pnas.0503198103 [PubMed: 16428292]

Wang S, Zhang B, Yang Q, Chen G, Yang B, Lu L, Shen M, Peng Y. Responses of net primary productivity to phenological dynamics in the Tibetan Plateau, China. Agric For Meteorol. 2017; 232:235-246. DOI: 10.1016/j.agrformet.2016.08.020

Wehner MF, Arnold JR, Knutson T, Kunkel KE, LeGrande AN. Droughts, floods, and wildfires Climate science special report: fourth national climate assessment. Wuebbles DJ, Fahey DW, Hibbard KA, Dokken DJ, Stewart BC, Maycock TK, editorsVol. 1. US Global Change Research Program; Washington: 2017. 231-256.

Wilby A, Shachak M. Shrubs, granivores and annual plant community stability in an arid ecosystem. Oikos. 2004; 106:209-216. DOI: 10.1111/j.0030-1299.2004.13085.x

Wilcox KR, Shi Z, Gherardi LA, Lemoine NP, Koerner SE, Hoover DL, Bork E, Byrne KM, Cahill J, Collins SL, Evans S, et al. Asymmetric responses of primary productivity to precipitation extremes: a synthesis of grassland precipitation manipulation experiments. Global Change Biol. 2017; 23:4376-4385. DOI: 10.1111/gcb.13706

Zhang YH, Feng JC, Isbell F, Lü XT, Han XG. Productivity depends more on the rate than the frequency of N addition in a temperate grassland. Sci Rep. 2015; 5 12558. doi: 10.1038/srep12558

Zhang YH, Loreau M, Lü XT, He NP, Zhang GM, Han XG. Nitrogen enrichment weakens ecosystem stability through decreased species asynchrony and population stability in a temperate grassland. Global Change Biol. 2016; 22:1445-1455. DOI: 10.1111/gcb.13140

Zhang YH, He NP, Loreau M, Pan QM, Han XG. Scale dependence of the diversity-stability relationship in a temperate grassland. J Ecol. 2018; 106:1277-1285. DOI: 10.1111/1365-2745.12903

Zhou L, Wu J, Mo X, Zhou H, Diao C, Wang Q, Chen Y, Zhang F. Quantitative and detailed spatiotemporal patterns of drought in China during 2001-2013. Sci Total Environ. 2017; 589:136-145. DOI: 10.1016/j.scitotenv.2017.02.202 [PubMed: 28279878]
<figure><img src="image_2.png" /><figcaption>**Fig. 1.**</figcaption></figure>

Anomalies in mean monthly **a** temperature and **c** precipitation over 33 years (1982–2014) relative to the 1961–1990 average. The coefficients of variation (**CV**) of mean **b** temperature and **d** precipitation over 33 years (1982–2014) over the growing season. Note that the **CV** is expressed as a percentage (%) and is defined as the ratio of the standard deviation to the mean. Colors correspond to months during the growing season (i.e., from May to September); black corresponds to the mean temperature and precipitation over the growing season. Color version of this figure is available online
<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /><figcaption>**Fig. 2.**</figcaption></figure>

a Species richness (number of plant species m⁻²) and b community aboveground net primary productivity (ANPP) throughout the growing season from 1982 to 2014. Each line was the averages across 10 plots (i.e., 5 plots per site × 2 sites = 10 plots). c Species asynchrony and d community stability across 1982–2014. Bars with the same letter were not significantly different in Duncan's multiple range tests (*P* > 0.05). Error bars indicate 1 + SE. Different colors correspond to months during the growing season (i.e., from May to September). Color version of this figure is available online

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>
<figure><img src="image_9.png" /><figcaption>**Fig. 3.**</figcaption></figure>

Community stability as a function of a species richness and b species asynchrony. c Species
asynchrony as a function of species richness. Each point represents the value from one plot
across 1982–2014 and different colors correspond to months during the growing season (i.e.,
from May to September). Black regression indicates the relationship throughout the growing
season (n = 50). Color version of this figure is available online
<figure><img src="image_10.png" /><figcaption>Fig. 4.</figcaption></figure>

Relationship between community stability and the coefficients of variation (CV) of a mean temperature [stability = −1.1 × log₁₀(CV of temperature) + 1.9] and b total precipitation [stability = −1.6 × log₁₀(CV of precipitation) + 3.6]. The relationship between residuals of community stability [from a] and c species richness and d species asynchrony, after controlling for the effect of the CV of temperature. Relationship between residuals of community stability [from b] and e species richness and f species asynchrony, after controlling for the effect of the CV of precipitation. Each point represents the value from
one plot across 1982–2014. Black regression indicates the relationship throughout the
growing season (n = 50). Different colors correspond to months during the growing season
(i.e., from May to September). Color version of this figure is available online
<figure><img src="image_11.png" /><figcaption>**Fig. 5.**</figcaption></figure>

Structural equation model examining the effects of climate variability on species richness, species asynchrony, and community stability over 33 years (n = 50, 5 replicates/plots per month per site × 5 months × 2 sites). Numbers adjacent to arrows are standardized path coefficients, and width of the arrows indicates the strength of the relationship. Dashed and solid arrows indicate *P* > 0.05 and *P* < 0.01, respectively. Black and red arrows indicate positive and negative relationship, respectively. Percentages close to endogenous variables indicate the variance explained by the model (R²). Color version of this figure is available online