Submitted 22 March 2019Accepted 8 June 2019Published 15 July 2019

Corresponding authorsJian Sun sunjianigsnrraccnTao Zeng zengtaocdutcn

Academic editorJianhua Xu

Additional Information andDeclarations can be found onpage 12

DOI 107717peerj7272

Copyright2019 Liu et al

Distributed underCreative Commons CC-BY 40

OPEN ACCESS

The aridity index governs the variationof vegetation characteristics in alpinegrassland Northern Tibet PlateauBiying Liu12 Jian Sun23 Miao Liu2 Tao Zeng1 and Juntao Zhu2

1College of Earth Sciences Chengdu University of Technology Chengdu China2 Synthesis Research Centre of Chinese Ecosystem Research Network Key Laboratory of Ecosystem NetworkObservation and Modelling Institute of Geographic Sciences and Natural Resources Research ChineseAcademy of Sciences Beijing China

3 State Key Laboratory of Urban and Regional Ecology Research Center for Eco-environmental SciencesChinese Academy of Sciences Beijing China

ABSTRACTThe vegetation dynamic (eg community productivity) is an important index used toevaluate the ecosystem function of grassland ecosystem However the critical factorsthat affect vegetation biomass are disputed continuously and most of the debates focuson mean annual precipitation (MAP) or temperature (MAT) This article integratedthese two factors used the aridity index (AI) to describe the dynamics of MAP andMAT and tested the hypothesis that vegetation traits are influenced primarily by theAI We sampled 275 plots at 55 sites (five plots at each site including alpine steppe andmeadow) across an alpine grassland of the northern Tibet Plateau used correlationanalysis and redundancy analysis (RDA) to explore which key factors determine thebiomass dynamic and explained the mechanism by which they affect the vegetationbiomass in different vegetation types via structural equation modelling (SEM) Theresults supported our hypothesis in all of the environmental factors collected the AImade the greatest contribution to biomass variations in RDA and the correlationbetween the AI and biomass was the largest (R = 085 p lt 005) The final SEMalso validated our hypothesis that the AI explained 793 and 844 of the biomassvariations in the alpine steppe and the meadow respectively Furthermore we foundthat the soils with higher carbon to nitrogen ratio and soil total nitrogen had largerbiomass whereas soil organic carbon had a negative effect on biomass in alpine steppehowever opposite effects of soil factors on biomass were observed in an alpinemeadowThe findings demonstrated that the AI was the most critical factor affecting biomass inthe alpine grasslands and different reaction mechanisms of biomass response to the AIexisted in the alpine steppe and alpine meadow

Subjects Biogeography EcologyKeywords Aridity index Alpine grassland Aboveground biomass Species richness CoverageNorthern Tibet Plateau

INTRODUCTIONGrasslands are distributed widely in China and cover a total area of 4 million km2 (Sun Maamp Lu 2017) As one of the most widespread vegetation types and an important componentof the terrestrial ecosystem (Li et al 2016) grasslands occupy approximately 15 million

How to cite this article Liu B Sun J Liu M Zeng T Zhu J 2019 The aridity index governs the variation of vegetation characteristics inalpine grassland Northern Tibet Plateau PeerJ 7e7272 httpdoiorg107717peerj7272

km2 of the Tibetan Plateau Additionally alpine grasslands (alpine steppe and meadow)dominate the natural vegetation of the Tibetan Plateau and cover more than 60 of itsarea (Yang et al 2009) and alpine grassland plays an essential role in maintaining theecosystem functions of the Tibetan Plateau However due to the harsh climate of theTibetan Plateau the environmental factors under the equilibrium conditions of the fragilesurface system are usually in the critical threshold state (Sun et al 2019) Small fluctuationsof climate change will also generate strong responses in the ecosystem (Zhang et al 2017)and lead to changes in the pattern process and function of the plateau grassland ecosystemsthe manifestations of this including grassland degradation and disappearance (Li et al2013 Yu et al 2012) Consequently the response mechanism of alpine grasslands to soilproperties climate and other environmental factors is a new scientific problem facing theresearch on environmental change of the Tibetan Plateau

Biotic and abiotic factors constitute the fundamental forces that drive the quantitydistribution structure and diversity of vegetation communities Numerous studies haveinvestigated the effects of precipitation temperature different land use types and humandisturbance on soil properties biodiversity and biomass in grassland ecosystems (Redmannet al 1993 Sala et al 1988 Sun Cheng amp Li 2013) They found that increased variabilityin precipitation had a strong effect on some arid land ecological processes (Thomey etal 2015) Similarly precipitation influenced aboveground biomass (BIO) and primaryproductivity strongly in most alpine grasslands (Hu et al 2010 Huxman et al 2004OrsquoConnor Haines amp Snyman 2001 Yang et al 2009) Adler amp Levine (2007) hold thatwith increased mean annual precipitation (MAP) across a broad geographic gradient themean plant species richness increased significantly Additionally temperature is anothera critical factor and some simulated warming experiments in alpine grasslands haveshown that warming was associated with a decreased trend in aboveground net primaryproductivity and plant diversity (Cantarel Bloor amp Soussana 2013 White Bork amp Cahill2014) meanwhile a negative correlation was observed between BIO and temperaturewhen annual precipitation was held constant at about 50 mm (Epstein et al 1996 Salaet al 1988) It is important to note that responses of grassland to climate change alsovaried if soil properties are different (Noymeir 1973) for example by increasing soiltemperature reduced total plant community coverage in the peak growing peak season(Wang et al 2015) Further soil nitrogen also influenced species richness in a widerange of temperate grasslands (Critchley et al 2010) Previous studies have indicated thatplant-soil interactions were vital mechanisms in a grassland ecosystem (Orwin et al 2010)Nevertheless it is still controversial whether precipitation or temperature is the primarylimiting environmental factor for vegetative traits (Sun Qin amp Yang 2016 Sun Cheng ampLi 2013 Sun amp Qin 2016 Wu et al 2013) A limited number of studies has combinedwater and heat availability to address the contribution of climatic factors on vegetationcharacteristics This study integrated these environmental variables using the aridity index(AI) to describe the dynamics of precipitation and temperature This study also proposedthe hypothesis that the vegetation traits are influenced primarily by the AI Because of thesignificant differences in precipitation and temperature between different vegetation typeswe divided the research objects into steppe and meadow then compared the contributions

Liu et al (2019) PeerJ DOI 107717peerj7272 217

Figure 1 The distribution of sampled sites in alpine grasslands across northern Tibet Plateau The yel-low solid triangles represent the samples collected in alpine meadow while the black solid triangles repre-sent the samples collected in alpine steppe In addition the gray areas and the olive areas represent alpinemeadow and steppe respectively

Full-size DOI 107717peerj7272fig-1

of the critical environmental variables to vegetation biomass and explained the mechanismof environmental variables affecting biomass in these two vegetation types respectively

MATERIALS amp METHODSStudy areaThe study was conducted on the alpine grassland in the northern Tibet Plateau (8038ndash9201E 3122ndash3251N average altitude of sim4600 m) of southwestern China (Fig 1)The typical continental plateau climate is characterized by low temperature and limitedprecipitation with a short cool summer and long cold winter The annual precipitationranges from 7991 to 46527 mm and the mean annual temperature is 135 Alpinegrassland is one of the main ecosystems in the Tibetan Plateau where the vegetation isalpine meadow (dominated by Kobresia spp and Poa spp) and alpine steppe (dominatedby Stipa purpurea) (Sun et al 2009 Zeng Wu amp Zhang 2015) Alpine meadow has asub-frigid and semi-arid high-plateau monsoon climate and alpine steppe has a frigid andhigh-plateau dry climate Alpine meadow soil and alpine steppe soil are very compact andloosely structured respectively (Zeng et al 2018)

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Survey design and samplingPlant BIO were collected from 55 sites (30 sites in alpine steppe and 25 sites in alpinemeadow) along a transect at spatial intervals of 50 km in August 2012 (Fig 1 Table 1) Fiveplots (replicates) of 1 m times 1 m were randomly assigned to each sampling site (5 m2 area)Plant BIO in these quadrats was harvested by a clipping method flush with the ground Allspecies information including the vegetation coverage within each quadrat was identifiedby a field guide (Cai Huang amp Lang 1989) and the number of species in each quadratwas recorded to estimate diversity (Whittaker 1972) After the data were aggregated all ofthe plant samples were de-enzymed at 85 C for 30 min and oven-dried at 65 C until aconstant weight was achieved Thereafter the samples were weighed on an electronic scale(accurate to one one-hundredth of a gram)

Soil from the 0ndash30 cm layer was also sampled in the plots and soil samples were storedin a refrigerator at 4 C until measuring chemical properties We collected five soil coresusing a 5 cm diameter soil auger and mixed them in situ into one composite sample Thesoil organic carbon (SOC) and soil total nitrogen (STN) concentrations were determinedusing a vario MACRO cube elemental analyser (Ganjurjav et al 2016) The mean annualtemperature (MAT) and MAP were the mean values of temperature and precipitation in2012 obtained from the Worldclim database (URL httpwwwworldclimorg) and theoutliers of the meteorological data acquired has been modified or removed The databasementioned includes information on current global climate based on data collected from1950 to 2000 and interpolated to a resolution grid of 25 arc min (sim5 km2 grid cells atthe equator and smaller cells elsewhere) using information on latitude longitude andelevation (Hijmans et al 2005) The AI was calculated using the MAT and MAP dataaccording to Martonnersquos formula (De Martonne 1926)

AI =P

T+10(1)

where P is MAP (mm) and T is MAT (C)

Data analysisThe data package required in this study includes the target variable of BIO (BIO) and bioticfactors [coverage (COV) and species richness (SR)] and abiotic factors [(MAP MAT theAI STN SOC) and the soil carbon to nitrogen ratio (C N)] were selected to explore theresponse of BIO to these environment factors Table 1 shows the specific information ofthe database

First we conducted a Pearsonrsquos correlation analysis (Corrplot package) using R software2111 (R Core Team 2018) to screen out the variables that can reflect biomass changesSecond the redundancy analysis is a linear canonical ordination method that is relatedclosely to the potential explanatory variables and it is effective in evaluating the relationsamong multiple interacting variables (Tang et al 2017) Thus R (Vegan) was employed toexplore the relationships between plant biomass and the related variables that were filteredout and identify the key factor influencing BIO To verify that the AI produces a variation inbiomass significant effects of plant biomass and the AI between the two types of vegetationwere identified with an analysis of variance (ANOVA) in the R package (Psych)

Liu et al (2019) PeerJ DOI 107717peerj7272 417

Table 1 Description of the alpine grassland sampled sites across northern Tibet Plateau

Alpineecosystem

Descriptivestatistics

Longitude()

Latitude()

Altitude(m)

SOC()

STN()

CN MAT(C)

MAP(mm)

AI COV()

SR BIO(g mminus2)

Maximum 9095 3251 4953 469 015 5431 356 43650 4045 574 116 1016Minimum 8095 3136 4406 108 005 1031 minus007 10096 798 19 2 11Mean 8699 3188 4624 223 009 2894 180 29903 2574 266 64 324

Alpine steppe (n= 30)

Median 8725 3189 4600 212 009 2648 215 34337 2722 269 62 2227Maximum 9201 3243 4814 772 047 10126 344 46527 4853 824 116 1493Minimum 8038 3122 4374 026 004 691 minus070 7991 698 24 18 32Mean 8856 3182 4584 237 018 2782 080 35103 3397 437 69 594

Alpine meadow (n= 25)

Median 9133 3172 4586 217 016 1183 016 45142 4379 49 74 5690

NotesAI SOC C N STN SR BIO and COV represent aridity index soil organic carbon the soil carbon to nitrogen ratio soil total nitrogen species richness aboveground biomass and coverage respectively

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To demonstrate the reliability of our conclusions we only used the AI and remainingindependent variables collected to explore the mechanism of biomass variation Thestructural equation modelling (SEM) approach was adopted to explain the mechanismby which the key factors affect BIO both directly and indirectly in alpine steppe andmeadow SEM is a common multivariate technique that has been used in recent studies toevaluate explicitly the causal relations among multiple interacting variables (Sun Ma amp Lu2017) SEM differs from other modeling approaches as it tests both the direct and indirecteffects on pre-assumed causal relationships (Fan et al 2016) The standard estimate resultsexpress the influence on the BIO using a path coefficient generated by Amos software(1702 Amos Development Corporation Crawfordville FL USA)

RESULTSThe relationships between environmental factors and abovegroundbiomass across alpine grasslandsAccording to Fig 2 BIO was significantly correlated with four environmental variables atthe 95 confidence interval level including MAT (R=minus076 plt 005) MAP (R= 076plt 005) C N (R=minus052 plt 005) and the AI (R= 085 plt 005) in alpine grasslandsIn addition these environmental factors above were also closely related to each otherexample for AI it was significantly correlated with MAP (R=minus074 plt 005) and MAT(R= 097 plt 005)

Relative importance of the environmental factorsThe results showed that the interactions among MAP MAT C N and the AI were themain drivers of the response variable (Fig 3) explaining 7982 of the variation in BIOSpecifically the AI had the greatest influence explaining 7216 of the BIO followed byMAP which explained 5834 and MAT and C N which explained 5734 and 2625of the BIO respectively

The differences of aboveground biomass and the AI in the differentgrassland typesThe BIO and the AI exhibited large differences between alpine steppe and meadow (Fig 4and Table 1) The BIO ranged from 114 to 10165 g mminus2 in steppe with a mean of 324gmminus2 and 318 to 14930 g mminus2 in meadow with a mean of 594 g mminus2 (Table 1) the BIOof the latter was significantly higher than that of steppe (plt 001 Fig 4B) The AI betweensteppe and meadow was also entirely different (plt 005 Fig 4A) As shown in Table 1 theAI of alpine meadow largely was greater than 4379 whereas it was less than 2722 in alpinesteppe The regression analysis demonstrated that the BIO was exponential increasing withthe AI values in both alpine steppe (R2

= 075 plt 0001 Fig 5) and meadow (R2= 077

plt 0001 Fig 5) Notably the vegetation in the alpine steppe and meadow exhibiteddifferent response rates to the AI For BIO with the same increase of AI larger increaseswould be found in steppe than that in meadow and when the AI value is around 26 theBIO of the two tend to be equal

Liu et al (2019) PeerJ DOI 107717peerj7272 617

Figure 2 The correlationships of aboveground biomass with environmental factors The colored solidcircles represent the significant correlation (p lt 005) And the MAT MAP AI SOC C N STN and BIOrepresent mean annual temperature mean annual precipitation aridity index soil organic carbon the soilcarbon to nitrogen ratio soil total nitrogen and aboveground biomass respectively

Full-size DOI 107717peerj7272fig-2

Using structural equation modelling to explore the variations inaboveground biomass of alpine meadow and steppeThe SEM results explained 841 (Fig 6A) and 877 (Fig 6B) of the variation in theBIO in alpine steppe and meadow respectively Table 2 shows a summary of the directindirect and total effects of the variables Clearly in steppe and meadow all independentvariables included environmental factors (SOC STN C N and the AI) and biotic factors(coverage and species richness) both of which affected the dynamic change of BIO andall of the variables above are regulated directly or indirectly by the AI As the SEM modelresults show (Table 2 Fig 6) we also verified that the AI is the most critical factor affectingthe variation in BIO Furthermore the weights contributed up to 793 (path coefficient= 0793) of the variation in BIO in the alpine steppe (Fig 6A) and 844 (path coefficient= 0844) to the variation in BIO in alpine meadow (Fig 6B)

In addition the SEM showed that C N and STN had positive effects while SOC hadnegative effects on BIO in alpine steppe with path coefficients of 0331 0078 andminus0091respectively (Fig 6A) However in alpine meadow we observed the opposite effect as C Nand STN had negative effects on BIO while SOC had positive effects Their path coefficientswere minus0303 minus0161 and 0111 respectively (Fig 6B)

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Figure 3 The contributions () of the different critical environmental variables to abovegroundbiomass via redundancy analysis The MAT C N MAP and AI represent mean annual temperature thesoil ratio of carbon to nitrogen mean annual precipitation and the aridity index respectively

Full-size DOI 107717peerj7272fig-3

Figure 4 The variances of AI (A) and aboveground biomass (B) in alpine steppe andmeadow respec-tively and represent the significance at the 001 and 005 level

Full-size DOI 107717peerj7272fig-4

DISCUSSIONOur results demonstrated that the AI was the primary limiting environmental factor forvegetation traits (Figs 2 3 and 5) and this finding supported our hypothesis As we knowprecipitation and temperature are key factors that affect the vegetation dynamic and theaboveground biomassvegetation index increased or decreased significantly depending onthe more or less precipitation (Sun et al 2013 Sun amp Qin 2016 Sun Qin amp Yang 2016)

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Figure 5 Relationships between aboveground biomass and the aridity index (AI) The black solid cir-cle and hollow circle represent the samples collected in alpine steppe and alpine meadow respectively Thesolid lines represent the fitting curves of alpine steppe and the dotted lines represent the fitting curves ofalpine meadow

Full-size DOI 107717peerj7272fig-5

Figure 6 Using the SEM to analyze the directly and indirectly effects among variables in alpine steppe(Graph A) and alpine meadow (Graph B) respectively The standardized total coefficients are listed oneach significant path The thickness of the solid arrows reflects the magnitude of the standardized SEM co-efficients the black solid line represents the positive effect while the red solid line represents the negativeeffect The AI SOC C N STN SR BIO COV represent aridity index soil organic carbon the soil carbonto nitrogen ratio soil total nitrogen species richness aboveground biomass coverage respectively

Full-size DOI 107717peerj7272fig-6

and temperature (Sun Cheng amp Li 2013 Sun amp Qin 2016) in alpine grassland Althoughthe contributions of MAP and MAT to BIO were all higher than 57 in our experimentsthe AI had the greatest contribution (7216) on BIO (Fig 3) As to identifying the

Liu et al (2019) PeerJ DOI 107717peerj7272 917

complex network of causal relationships in ecosystems by SEM (Sun Ma amp Lu 2017) thedirect effects of the AI on plant growth and photosynthesis cannot be ignored (Chimneret al 2010 Shi et al 2014 Sun Cheng amp Li 2013) Different levels of the AI would leadto significant differences in plant photosynthetic rate (Liu amp Chen 1990) and a lowerAI level could reduce the stomatal opening of the leaves as a result the CO2 supply wasblocked reducing photosynthesis (Zhang et al 2011) However at this time plant leavescan stabilize the function of the photosynthetic mechanism by reducing the synergisticeffects of light capture heat dissipation and enzyme activity regulation to achieve theaccumulation of dry matter (Flexas amp Medrano 2002) When the AI was at a higherlevel plant leaves can coordinate the relationship between carbon assimilation and waterconsumption for transpiration through stomatal conductance regulation (Paoletti Raddiamp La Scala 1998) thus maintaining a high photosynthetic rate and water use efficiency andpromoting plant growth (Chengjiang amp Qingliang 2002) As a result if AI at a lower levelthe increases in BIO is minimum and once AI is larger than a certain point BIO wouldincrease rapidly (Fig 5) But in any case when the AI is within a certain range there arealways positive effects on aboveground biological vegetation traits (BIO species diversityand coverage) of the AI in both alpine steppe and alpine meadow (Fig 6)

However these positive effects do not equate to the same response mechanism ofvegetation traits to the AI in different grassland types on the northern Tibet Plateau Incontrast earlier studies revealed that grasslands with the different AI levels showed differentresponse patterns to vegetation biomass (Chen et al 2016) Consistent with our researchin an alpine steppe (the AI largely was less than 2722) our experiments showed that C Nand STN had positive effects while SOC had negative effect on BIO (Fig 6A) Howeverthe opposite effects of soil on BIO were detected in alpine meadow (the AI largely wasgreater than 4379) as C N and STN have negative effects while SOC has positive effects onBIO (Fig 6B) Generally plant growth in alpine grasslands was limited by soil N (Ladwiget al 2012 Lambers et al 2009) Illustrated by comparing the nitrogen concentration ofsub-humid and humid grasslands (009 for the alpine steppe and 018 for meadowTable 1) STN increases with the increase in water supply (Fig 6) due to the rainfall andbiological nitrogen fixation dominated by hydrothermal conditions (Aber et al 1998)However when the water supply exceeds the needs of plant growth the excessive watercontent inhibits the decomposition of organic matter and the STN increases further bythe rainfall and biological nitrogen fixation This ecological process can be harmful tovegetation in bottom land (Aber et al 1998) However these patterns do not apply to SOCbecause of its lower solubility (Hyun et al 1998) That is why the mean value of SOCwas largely the same (223 for alpine steppe and 237 for meadow) in two vegetationtypes despite gradual decline in soil organic matter from increased the AI This is becausethe increasing AI will intensify the activities of soil microorganisms and soil animals andaccelerate soil respiration inevitably leading to the acceleration of the release of CO2 fromthe soil carbon pool (Anderson 1992) and resulting in the imbalance between the input andoutput of soil carbon (Fig 6) In this case soil moisture was a major limiting factor andthe drier climate of the alpine steppe produced a lack of water-soluble organic carbon thatwas absorbed directly by the root and soil microorganisms constraining the development

Liu et al (2019) PeerJ DOI 107717peerj7272 1017

Table 2 Summary of the direct indirect and total effects of variables (AI STN SOC C N COV SR BIO) in the SEM of alpine steppe and alpine meadow Effectswere calculated with standardized path coefficients

A Direct Effect B Direct EffectVariable AI SOC STN CN COV SR Variable AI STN SOC CN COV SRSOC minus463 000 000 000 000 000 STN 449 000 000 000 000 000STN 423 000 000 000 000 000 SOC minus247 000 000 000 000 000CN 140 776 minus527 000 000 000 CN minus471 228 520 000 000 000COV 609 minus444 170 173 000 000 COV 733 minus058 371 minus452 000 000SR 814 minus092 minus043 000 000 000 SR 842 086 108 minus074 000 000BIO 109 minus358 469 270 357 179 BIO 105 minus047 minus023 041 731 182

C Indirect Effect D Indirect EffectVariable AI SOC STN CN COV SR Variable AI STN SOC CN COV SRSOC 000 000 000 000 000 000 STN 000 000 000 000 000 000STN 000 000 000 000 000 000 SOC 000 000 000 000 000 000CN minus582 000 000 000 000 000 CN minus026 000 000 000 000 000COV 200 134 minus091 000 000 000 COV 107 minus103 minus235 000 000 000SR 024 000 000 000 000 000 SR 048 minus017 minus038 000 000 000BIO 684 083 minus122 062 000 000 BIO 740 minus096 134 minus344 000 000

E Total Effect F Total EffectVariable AI SOC STN CN COV SR Variable AI STN SOC CN COV SRSOC minus463 000 000 000 000 000 STN 449 000 000 000 000 000STN 423 000 000 000 000 000 SOC minus247 000 000 000 000 000CN minus442 776 minus527 000 000 000 CN minus497 228 520 000 000 000COV 809 minus309 078 173 000 000 COV 840 minus161 136 minus452 000 000SR 838 minus091 minus044 000 000 000 SR 891 069 069 minus074 000 000

Alpinesteppe

BIO 793 minus276 347 331 357 179

Alpinemeado

w

BIO 844 minus143 111 minus303 731 182

NotesCorrelation is significant at the 005 levelCorrelation is significant at the 001 levelCorrelation is significant at the 0001 levelAI SOC C N STN SR BIO and COV represent aridity index soil organic carbon the soil carbon to nitrogen ratio soil total nitrogen species richness aboveground biomass and coverage respectively

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of root systems (Alexandre et al 2016 Kick Sauerbeck amp Fuumlhr 1964) In contrast in thehumid environment of alpine meadow the increase in water-soluble organic carbon in thesoil led to significant improvements in plant productivity (Atkinson Fitzgerald amp Hipps2010) This indicated that the AI (water and heat conditions) affected vegetation growthnot only directly but also indirectly by modifying soil properties (ie soil moisture andsoil nitrogen mineralization and availability) (Ruppert et al 2012 Shaver et al 2000 Wanet al 2005)

CONCLUSIONSIn this study we verified that the AI (integrated MAT and MAP) was the primary limitingenvironmental factor for vegetation traits in alpine grassland Additionally the different AIgovern the different response patterns of BIO in the alpine steppe and the alpine meadowrespectively The findings imply the important role of AI regulating vegetation dynamicswhich need to be explored deeply in future research Nevertheless due to the unevenlydistribution of the sampling sites throughout the vast region of northern Tibet plateauthe spatial heterogeneity of vegetation across regional environmental gradients limitedthis research Hence we should pay more attention to reduce spatial heterogeneity (ieoptimize sampling methods or analytical methods) to verify the effect of AI on variationsof biomass

ACKNOWLEDGEMENTSThe author thanks anonymous reviewers for providing invaluable comments on the originalmanuscript The author thanks the research team for the data collection of this study DrSun and Dr Zeng for their guidance of this paper and his girlfriend (Ms Zhang) for hersupport of his work

ADDITIONAL INFORMATION AND DECLARATIONS

FundingThis research was funded by the National Science Foundation of China (Grant No41871040 and 41501057) The funders had no role in study design data collection andanalysis decision to publish or preparation of the manuscript

Grant DisclosuresThe following grant information was disclosed by the authorsNational Science Foundation of China 41871040 41501057

Competing InterestsThe authors declare there are no competing interests

Author Contributionsbull Biying Liu conceived and designed the experiments performed the experiments analyzedthe data prepared figures andor tables authored or reviewed drafts of the paper

Liu et al (2019) PeerJ DOI 107717peerj7272 1217

bull Jian Sun conceived and designed the experiments performed the experiments analyzedthe data contributed reagentsmaterialsanalysis tools prepared figures andor tablesauthored or reviewed drafts of the paper approved the final draftbull Miao Liu analyzed the databull Tao Zeng contributed reagentsmaterialsanalysis tools approved the final draftbull Juntao Zhu contributed reagentsmaterialsanalysis tools authored or reviewed draftsof the paper

Data AvailabilityThe following information was supplied regarding data availability

The raw measurements are available as a Supplemental File

Supplemental InformationSupplemental information for this article can be found online at httpdxdoiorg107717peerj7272supplemental-information

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CurrieW Rustad L Fernandez I 1998 Nitrogen saturation in temperate forestecosystemmdashhypothesis revisited Bioscience 48921ndash934 DOI 1023071313296

Adler PB Levine JM 2007 Contrasting relationships between precipitation and speciesrichness in space and time Oikos 116221ndash232DOI 101111j0030-1299200715327x

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Cai Z Huang B Lang B 1989 Atlas of grassland and its main plants on qinghai-tibetplateau Beijing Agriculture Press

Cantarel AAM Bloor JMG Soussana JF 2013 Four years of simulated climate changereduces above-ground productivity and alters functional diversity in a grasslandecosystem Journal of Vegetation Science 24113ndash126DOI 101111j1654-1103201201452x

Chen Y Mu S Sun Z Gang C Li J Padarian J Groisman P Chen J Li S 2016 Grass-land carbon sequestration ability in china a new perspective from terrestrial aridityzones Rangeland Ecology and Management 6984ndash94DOI 101016jrama201509003

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Chengjiang R Qingliang X 2002 Effect of soil moisture on survival rate of Hippophaerhamnoides L and its stress resistance physiological characteristics Chinese Journalof Applied amp Environmental Biology 8341ndash345

Chimner RAWelker JM Morgan J Lecain D Reeder J 2010 Experimental manip-ulations of winter snow and summer rain influence ecosystem carbon cycling in amixed-grass prairie Wyoming USA Ecohydrology 3284ndash293 DOI 101002eco106

Critchley CNR Chambers BJ Fowbert JA Bhogal A Rose SC Sanderson RA 2010Plant species richness functional type and soil properties of grasslands and alliedvegetation in English Environmentally Sensitive Areas Grass amp Forage Science57(2)82ndash92 DOI 101046j1365-2494200200305x

DeMartonne E 1926 Lrsquoindice drsquoariditeacute Bulletin de lrsquoAssociation de geacuteographes francais33ndash5

Epstein HE LauenrothWK Burke IC Coffin DP 1996 Ecological responses ofdominant grasses along two climatic gradients in the great plains of the UnitedStates Journal of Vegetation Science 7777ndash788 DOI 1023073236456

Fan Y Chen JQ Shirkey G John RWu SR Park H Shao CL 2016 Applications ofstructural equation modeling (SEM) in ecological studies an updated reviewEcological Processes 5(1)19 DOI 101186s13717-016-0063-3

Flexas J Medrano H 2002 Drought-inhibition of photosynthesis in C-3 plantsstomatal and non-stomatal limitations revisited Annals of Botany 89183ndash189DOI 101093aobmcf027

Ganjurjav H Gao Q Gornish ES Schwartz MW Liang Y Cao X ZhangWZhang Y LiWWan Y Li Y Danjiu L Guo H Lin E 2016 Differential re-sponse of alpine steppe and alpine meadow to climate warming in the centralQinghaindashTibetan Plateau Agricultural amp Forest Meteorology 223233ndash240DOI 101016jagrformet201603017

Hijmans RJ Cameron SE Parra JL Jones PG Jarvis A 2005 Very high resolution in-terpolated climate surfaces for global land areas International Journal of Climatology251965ndash1978 DOI 101002joc1276

Hu Z Yu G Fan J Zhong HWang S Li S 2010 Precipitation-use efficiency alonga 4500-km grassland transect Global Ecology amp Biogeography 19842ndash851DOI 101111j1466-8238201000564x

Huxman TE SmithMD Fay PA Knapp AK ShawMR LoikME Smith SD TissueDT Zak JCWeltzin JF PockmanWT Sala OE Haddad BM Harte J Koch GWSchwinning S Small EEWilliams DG 2004 Convergence across biomes to acommon rain-use efficiency Nature 429651ndash654 DOI 101038nature02561

HyunH Chang AC Parker DR Page AL 1998 Cadmium solubility and phytoavail-ability in sludge-treated soil effects of soil organic carbon Journal of EnvironmentalQuality 27329ndash334

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Ladwig LM Collins SL Swann AL Xia Y AllenMF Allen EB 2012 Above- andbelowground responses to nitrogen addition in a Chihuahuan Desert grasslandOecologia 169177ndash185 DOI 101007s00442-011-2173-z

Lambers H Mougel C Jaillard B Hinsinger P 2009 Plant-microbe-soil interac-tions in the rhizosphere an evolutionary perspective Plant amp Soil 32183ndash115DOI 101007s11104-009-0042-x

Li XL Gao J Brierley G Qiao YM Zhang J Yang YW 2013 Rangeland degradationon the qinghai-tibet plateau implications for rehabilitation Land Degradation ampDevelopment 2472ndash80 DOI 101002ldr1108

Li Y Li Q Guo D Liang S Wang Y 2016 Ecological stoichiometry homeostasis ofLeymus chinensis in degraded grassland in western Jilin Province NE ChinaEcological Engineering 90387ndash391 DOI 101016jecoleng201601079

LiuMY Chen FY 1990 Effects of stomatal and nonstomatal factors on wheat photosyn-thesis under water stress Plant Physiology Communications 22477ndash486

Noymeir I 1973 Desert ecosystems environment and producers Annual Review ofEcology amp Systematics 425ndash51 DOI 101146annureves04110173000325

OrsquoConnor TG Haines LM Snyman HA 2001 Influence of precipitation and speciescomposition on phytomass of a semi-arid African grassland Journal of Ecology89850ndash860 DOI 101046j0022-0477200100605x

Orwin KH Buckland SM Johnson D Turner BL Smart S Oakley S Bardgett RD2010 Linkages of plant traits to soil properties and the functioning of temperategrassland Journal of Ecology 981074ndash1083 DOI 101111j1365-2745201001679x

Paoletti E Raddi P La Scala S 1998 Relationships between transpiration stomatladamage and leaf wettability in declining beech trees Chemosphere 36907ndash912DOI 101016S0045-6535(97)10146-1

R Core Team 2018 R a language and environment for statistical computing Vienna RFoundation for Statistical Computing Available at httpwwwr-projectorg

Redmann R Romo J Pylypec B Driver E 1993 Impacts of burning on primaryproductivity of Festuca and Stipa-Agropyron grasslands in central SaskatchewanAmerican Midland Naturalist 130262ndash273 DOI 1023072426126

Ruppert JC Holm A Miehe S Muldavin E Snyman HAWesche K Linstaumldter A 2012Meta-analysis of ANPP and rain-use efficiency confirms indicative value for degra-dation and supports non-linear response along precipitation gradients in drylandsJournal of Vegetation Science 231035ndash1050 DOI 101111j1654-1103201201420x

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Shaver GR Chapin FS Gurevitch J Harte J Henry GIP Jonasson S Melillo J Pitelka LRustad L Canadell J 2000 Global warming and terrestrial ecosystems a conceptualframework for analysis Bioscience 50871ndash882DOI 1016410006-3568(2000)050[0871GWATEA]20CO2

Shi YWang Y Ma Y MaW Liang C Flynn DFB Schmid B Fang J He JS 2014Field-based observations of regional-scale temporal variation in net primary

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Sun G Luo PWuN Qiu PF Gao YH Chen H Shi FS 2009 Stellera chamaejasmeL increases soil N availability turnover rates and microbial biomass in an alpinemeadow ecosystem on the eastern Tibetan Plateau of China Soil Biology amp Biochem-istry 4186ndash91 DOI 101016jsoilbio200809022

Sun J Cheng GW LiWP 2013Meta-analysis of relationships between environmentalfactors and aboveground biomass in the alpine grassland on the Tibetan PlateauBiogeosciences 101707ndash1715 DOI 105194bg-10-1707-2013

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Sun J Liu B You Y LiW LiuM Shang H He J-S 2019 Solar radiation regu-lates the leaf nitrogen and phosphorus stoichiometry across alpine mead-ows of the Tibetan Plateau Agricultural and Forest Meteorology 27192ndash101DOI 101016jagrformet201902041

Sun J Ma B Lu X 2017 Grazing enhances soil nutrient effects trade-offs betweenaboveground and belowground biomass in alpine grasslands of the Tibetan PlateauLand Degradation amp Development 29(2)337ndash348 DOI 101002ldr2822

Sun J Qin X 2016 Precipitation and temperature regulate the seasonal changesof NDVI across the Tibetan Plateau Environmental Earth Sciences 751ndash9DOI 101007s12665-015-5177-x

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