Patterns of plant carbon, nitrogen, and phosphorusconcentration in relation to productivity in China’sterrestrial ecosystemsZhiyao Tanga,1, Wenting Xub,1, Guoyi Zhouc,1, Yongfei Baib, Jiaxiang Lib,d, Xuli Tangc, Dima Chenb, Qing Liue,Wenhong Maf, Gaoming Xiongb, Honglin Heg, Nianpeng Heg, Yanpei Guoa, Qiang Guoa, Jiangling Zhua, Wenxuan Hanh,Huifeng Hub, Jingyun Fanga,b,2, and Zongqiang Xieb,2

aKey Laboratory for Earth Surface Processes of the Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing, China100871; bState Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, China 100093; cSouthChina Botanical Garden, Chinese Academy of Sciences, Guangzhou, China 510650; dCollege of Forestry, Central South University of Forestry and Technology,Changsha, China 410004; eKey Laboratory of Mountain Ecological Restoration and Bioresource Utilization, Chengdu Institute of Biology, Chinese Academyof Sciences, Chengdu, China 610041; fSchool of Life Science, Inner Mongolia University, Hohhot, China 010021; gKey Laboratory of Ecosystem NetworkObservation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China 100101;and hCollege of Resource and Environmental Sciences, China Agriculture University, Beijing 100193, China

Edited by F. Stuart Chapin III, University of Alaska, Fairbanks, AK, and approved October 6, 2017 (received for review February 13, 2017)

Plant nitrogen (N) and phosphorus (P) content regulate productivityand carbon (C) sequestration in terrestrial ecosystems. Estimates ofthe allocation of N and P content in plant tissues and the relationshipbetween nutrient content and photosynthetic capacity are critical topredicting future ecosystem C sequestration under global change. Inthis study, by investigating the nutrient concentrations of plantleaves, stems, and roots across China’s terrestrial biomes, we docu-ment large-scale patterns of community-level concentrations of C, N,and P. We also examine the possible correlation between nutrientcontent and plant production as indicated by vegetation gross pri-mary productivity (GPP). The nationally averaged community con-centrations of C, N, and P were 436.8, 14.14, and 1.11 mg·g−1 forleaves; 448.3, 3.04 and 0.31 mg·g−1 for stems; and 418.2, 4.85,and 0.47 mg·g−1 for roots, respectively. The nationally averaged leafN and P productivity was 249.5 g C GPP·g-1 N·y−1 and 3,157.9 g CGPP·g–1 P·y−1, respectively. The N and P concentrations in stems androots were generally more sensitive to the abiotic environment thanthose in leaves. There were strong power-law relationships betweenN (or P) content in different tissues for all biomes, which were closelycoupled with vegetation GPP. These findings not only provide keyparameters to develop empirical models to scale the responses ofplants to global change from a single tissue to the whole communitybut also offer large-scale evidence of biome-dependent regulation ofC sequestration by nutrients.

allocation | leaf nitrogen productivity | leaf phosphorus productivity |nutrient concentrations | plant stoichiometry

Plant growth is the dominant process that controls carbon (C)input to terrestrial ecosystems. It requires at least 16 ele-

ments in differing proportions (1). These elements are stronglycoupled with C-sequestration processes, such as plant primaryproduction and respiration (1, 2). C, nitrogen (N), and phos-phorus (P) are the most important limiting nutrients for C se-questration in ecosystems (3). C constitutes the basic structure ofplants and accounts for ca. 50% of plant biomass; N is an es-sential component of enzymes; and P is an essential element ofnucleic acids and membrane lipids (2). The concentrations of N([N]) and P ([P]) in plant tissues are also critical in controllingother ecological processes, such as grazing, parasitism, and de-composition (4).The productivity and C sequestration of ecosystems depend

largely on the availability of N and P to plants (3, 5, 6), especiallyunder future conditions of rising atmospheric CO2, N and Pdeposition, and global climate change (5–7). Estimates of N andP content, their allocations in different plant tissues, and therelationships between the metabolically active N and P content

and photosynthetic capacity are often used to predict the futureC sequestration of ecosystems under global change (7, 8).Large-scale patterns of plant stoichiometry have been explored at

the species and site level, mostly based on meta-analyses of surveys inthe literature (8–11). These studies report general patterns in which[N] and [P] increase with latitude and decrease with temperature andprecipitation (11–14). The patterns of species-level stoichiometry re-flect the replacement of species along environmental gradients (9, 11)but not the variations in nutrient concentrations within species (14,15). Site-level studies use the average nutrient concentrations ofdifferent species found within a site (13). However, these previousstudies cannot reflect patterns and processes at the community level,which depend on the abundance (or biomass) of different specieswithin a community (9). Within a site, leaf nutrient concentrationscould vary by an order of magnitude between species (16). Whenestimating ecosystem-level features, the over- or underrepresentationof any species may cause errors. Therefore, to better understand

Significance

Estimates of nutrient allocation in different plant tissues and therelationships between the nutrient contents and photosyntheticcapacity are critical to predicting ecosystem carbon sequestrationunder global change. Here, we provide an assessment of large-scale patterns of community-level nitrogen and phosphorusconcentrations in different plant tissues and then examine hownutrient allocations are coupled with plant productivity. The re-sults show that nutrient concentrations in leaves are less re-sponsive to abiotic environments than those in woody stems androots (stable leaf nutrient concentration hypothesis); the rela-tionships between vegetation primary productivity and leaf nu-trient contents are stronger when less nutrients are allocated tothe woody tissues (productivity–nutrient allocation hypothesis)and are stronger in deciduous than in evergreen vegetation(productivity–leaf lifespan hypothesis).

Author contributions: Z.T., J.F., and Z.X. designed research; Z.T., W.X., G.Z., Y.B., X.T., Q.L.,W.M., G.X., H. He, and Z.X. performed research; Z.T., W.X., J.L., D.C., W.M., G.X., Y.G.,Q.G., J.Z., and Z.X. analyzed data; and Z.T., W.X., G.Z., Y.B., N.H., W.H., H. Hu, J.F., and Z.X.wrote the paper.

The authors declare no conflict of interest.

This article is a PNAS Direct Submission.

Published under the PNAS license.1Z.T., W.X., and G.Z. contributed equally to this work.2To whom correspondence may be addressed. Email: jyfang@urban.pku.edu.cn or xie@ibcas.ac.cn.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1700295114/-/DCSupplemental.

Published online April 16, 2018.

www.pnas.org/cgi/doi/10.1073/pnas.1700295114 PNAS | April 17, 2018 | vol. 115 | no. 16 | 4033–4038

ECOLO

GY

SUST

AINABILITY

SCIENCE

SPEC

IALFEATU

RE

Dow

nloa

ded

by g

uest

on

June

20,

202

0 D

ownl

oade

d by

gue

st o

n Ju

ne 2

0, 2

020

Dow

nloa

ded

by g

uest

on

June

20,

202

0 D

ownl

oade

d by

gue

st o

n Ju

ne 2

0, 2

020

ecosystem processes, it is necessary to integrate abundance (or bio-mass) across a collection of species (14).With a few exceptions (17–19), large-scale patterns of terres-

trial plant stoichiometry have been documented primarily basedon a single tissue, particularly green or senesced leaves (8, 10–12). Leaves and woody stems play different roles in ecosystemfunction. Leaf N and P are critical for metabolism, whereaswoody stems provide storage for N and P, which are important inplant respiration and internal nutrient recycling (20, 21).According to environments and growth requirements, plants al-locate biomass and nutrients between woody stems and leaves(18, 19, 22, 23). The responses of plants and ecosystems to theirenvironments are simultaneously determined by all plant tissues(23). Compared with leaves, nutrient concentration and controlwithin woody stems are poorly known, although woody stemscontain most of the biomass and nutrients in plants (20). Ourunderstanding of N and P allocation between tissues is not welldeveloped. Community-level studies employ species abundance(or biomass) weighted mean concentrations of all species withinthe community (24), which is essential to determine the re-sponses of plant communities to their environment, either viachanges in concentration within a species or via changes inabundance or species replacements (9, 25).China is one of the largest countries in the world, containing

nearly all major biome types. The country has been experiencingsome of the heaviest, but unbalanced, N and P deposition (7, 26,27). It is critical to estimate nutrient storage and distribution tobetter understand how changes in nutrient availability will influ-ence future C sequestration (5–7). In this study, we explore thepatterns of community-level [C], [N], and [P] in China’s seventerrestrial biomes (evergreen broadleaf forest, deciduous broadleafforest, coniferous forest, mixed forest, evergreen shrubland, de-ciduous shrubland, and grassland) and their relationship to plantproductivity. Our study is based upon an intensive investigation of[C], [N], and [P] and biomass in different plant tissues—that is,leaves, stems, and roots, of 1,851 species from 4,159 sites acrossChina. The community means for [C], [N], and [P] were calculatedas the biomass-weighted mean of all of the species encountered ateach site (see Eq. 1 in Materials and Methods).Specifically, we explore the large-scale patterns of concentra-

tions (mg·g−1) of plant C, N, and P in relation to climate and soilcharacteristics, and we examine the allocation of nutrient contents(g·m−2) of N or P among different tissues, and their relationships

with vegetation gross primary productivity (GPP) for each biome.To approach these goals, we propose the following three hypoth-eses. First, [N] and [P] in leaves are more stable—that is, less re-sponsive to abiotic environments—than those in woody stems androots, because the nutrient reservoirs in woody stems buffer leafnutrient concentrations, allowing the maintenance of near-optimalstoichiometry in the metabolically more active leaves (20, 21, 28)(abbreviated as “stable leaf nutrient concentration hypothesis”).Second, the positive correlation between GPP and leaf nutrientcontents is stronger when less nutrients are allocated to the woodystems—that is, the relationship is stronger in grasslands than inshrublands and stronger in shrublands than in forests, because thephotosynthetic capacity depends on leaf nutrient contents (29–31)and woody stems provide nutrient reservoirs when leaf nutrientsare limited (20, 21) (abbreviated as “productivity–nutrient alloca-tion hypothesis”). Third, the GPP of deciduous vegetation is morestrongly regulated by leaf nutrient content than that of corre-sponding evergreen vegetation, because plants with long leaf life-spans (e.g., evergreen plants) can utilize leaf nutrients forphotosynthesis for a longer time than plants with short leaf life-spans (e.g., deciduous plants) (32, 33) (abbreviated as “pro-ductivity–leaf lifespan hypothesis”).

Results and DiscussionPatterns and Determinants of C, N, and P Concentrations. The geo-metric mean leaf [C], [N], and [P], weighted by species differencesin biomass, were 436.5, 14.14, and 1.11 mg·g−1 (C:N:P = 394:13:1),respectively. The mean leaf [N] and [P] at the community levelwere lower than those averaged across species (unweighted bybiomass) for the same area (18.6 mg·g−1 and 1.21 mg·g−1) (10),possibly because species with high [N] and [P] are generally rare inold-growth ecosystems (34). These differences indicated the im-portance of integrating species abundance for exploring large-scalepatterns of plant stoichiometry. The stems and roots had [C] valuessimilar to those of the leaves but lower [N] and [P] values (Table1). Among different biomes, the geometric mean leaf [C], [N], and[P] varied greatly, from 396.9 mg·g−1 in grasslands to 484.5 mg·g−1

in coniferous forests for [C], from 10.53 mg·g−1 in coniferous for-ests to 17.12 mg·g−1 in deciduous shrublands for [N], and from0.71 mg·g−1 in evergreen broadleaf forests to 1.33 mg·g−1 in de-ciduous broadleaf forests for [P] (Table 1).

Table 1. Geometric mean concentrations of C, N, and P for different tissues in different biomes in China

Leaf Stem Root

Biome type#

plots[C],

mg·g−1[N],

mg·g−1[P],

mg·g−1[C],

mg·g−1[N],

mg·g−1[P],

mg·g−1[C],

mg·g−1[N],

mg·g−1[P],

mg·g−1

Overall 4,159 436.5(366.1∼520.4)

14.16(7.73∼25.93)

1.11(0.61∼2.03)

448.1(383.0∼524.3)

3.04(1.27∼7.31)

0.31(0.13∼0.75)

417.8(339.9∼513.7)

4.87(2.20∼10.80)

0.47(0.22∼1.02)

Evergreenbroadleafforests

230 467.4(428.4∼509.8)

11.73(3.99∼34.54)

0.71(0.27∼1.88)

463.8(417.8∼514.9)

1.90(0.59∼6.08)

0.20(0.08∼0.49)

447.2(393.2∼508.6)

2.95(0.91∼9.52)

0.26(0.12∼0.61)

Deciduousbroadleafforests

716 450.9(407.5∼499.0)

16.59(8.99∼30.63)

1.33(0.77∼2.30)

449.1(411.8∼489.8)

2.96(1.30∼6.76)

0.33(0.14∼0.77)

429.1(377.7∼487.3)

5.06(2.21∼11.59)

0.57(0.25∼1.29)

Coniferous forests 931 484.5(432.9∼542.4)

10.53(5.67∼19.58)

1.09(0.59∼2.00)

477.8(433.3∼526.8)

1.84(0.85∼3.96)

0.25(0.09∼0.67)

465.2(416.1∼520.0)

3.30(1.56∼6.96)

0.41(0.17∼0.96)

Mixed forests 245 463.7(402.4∼534.4)

14.02(7.92∼24.82)

1.25(0.71∼2.18)

468.6(427.2∼514.0)

2.40(1.20∼4.78)

0.30(0.09∼0.95)

453.1(405.6∼506.1)

3.95(2.14∼7.30)

0.45(0.18∼1.14)

Evergreenshrublands

362 411.2(306.8∼551.1)

12.14(7.72∼19.11)

0.82(0.47∼1.42)

403.9(307.3∼530.8)

4.19(2.52∼6.94)

0.33(0.17∼0.64)

393.4(297.6∼520.1)

3.96(2.34∼6.70)

0.32(0.17∼0.62)

Deciduousshrublands

739 405.0(326.3∼502.8)

17.12(11.1∼26.4)

1.10(0.70∼1.82)

420.1(346.8∼508.9)

5.94(3.58∼9.85)

0.44(0.27∼0.70)

407.3(336.8∼492.6)

6.72(3.89∼11.60)

0.51(0.29∼0.89)

Grasslands 824 396.9(349.4∼450.8)

16.09(11.03∼23.46)

1.27(0.82∼1.98)

322.9(248.6∼ 419.3)

9.03(5.77∼14.13)

0.69(0.42∼1.13)

The values in parentheses indicate 95% confidence intervals.

4034 | www.pnas.org/cgi/doi/10.1073/pnas.1700295114 Tang et al.

Dow

nloa

ded

by g

uest

on

June

20,

202

0

Plant [C], [N], and [P] differed markedly among different cli-mates and soil nutrient regimes. For different tissues, [C] increasedand [N] and [P] decreased as temperature (Fig. 1) and pre-cipitation increased (Fig. S1). The climatic patterns of [N] and [P]were consistent with previous studies at the species level (11, 12).For most biomes, the mean [C] and [P] in different tissues in-creased with the increasing soil organic [C] and total [P] (Fig. S2).However, there was no consistent pattern of plant [N] with soiltotal [N] (Fig. S2), partly because plants with higher [N] are oftenmore abundant where the soil reactive N supply is limited relativeto other sources (35).The partitioning of environmental variables suggests that

vegetation types, climate, and soil nutrient concentrations to-gether explained 17.7–34.36% of the variance in [C], 14.8–36.2%of the variance in [N], and 21.2–25.3% of the variance in [P], indifferent tissues. The explanatory strength was higher for stemsand roots than for leaves, except that it was low for the stem [C](Fig. 2). Generally, soil and climate explained more variance in[P] than in [N], suggesting that elements with higher concen-trations (i.e., N) are less sensitive to environmental gradients (8).

Scaling and Allocation of N and P Between Different Tissues. Boththe concentration and content of the studied elements werehighly correlated between different tissues (Fig. 3). For theconcentration, based on the reduced major axis (RMA) analysis,the scaling slopes (bRMA) of leaf vs. stem, leaf vs. root, and stemvs. root were 0.73, 0.77, and 1.12 for [N] and 0.70, 0.78, and1.10 for [P], respectively, (Table S1). For the content, the cor-responding values were 0.91, 1.04, and 1.18 for N and 0.85, 1.03,and 1.23 for P, respectively (Table S1). The strong correlation ofnutrient concentration or content between different tissues ap-plied to all biomes.The strong correlation is important to scaling the responses of

plants to global change from a single tissue to the whole commu-nity. For all of the correlations, the slopes (bRMA) of leaves vs.

stems and leaves vs. roots were less than 1, and those of stems vs.roots were close to 1 (Table S1).Plants tend to allocate nutrients first to leaves to secure

growth (31) and are able to use nutrients stored in woody stemsto fulfill the needs of leaves when nutrients are limited (20).These results, together with the fact that the abiotic environmentcan explain more of the variance of nutrient concentrations instems and roots than in leaves (Fig. 2), suggest that metabolicallyactive elements are less sensitive to environmental gradients inthe more metabolically active tissues (e.g., leaves) than in the lessactive tissues (woody stems) (17, 28). Indeed, we observed lessvariable nutrient concentration in leaves (SE = 1.83 and 1.82 for[N] and [P]) than in stems (SE = 2.39 and 2.39) or roots (SE =2.22 and 2.18), supporting our first hypothesis (the stable leafnutrient concentration hypothesis).

Relationship Between N and P Contents and Plant Productivity.Whenall sites were pooled, the geometric mean leaf C, N, and P contentswere 824.6 kg C·ha−1, 27.4 kg N·ha−1, and 2.63 kg P·ha−1. Themean leaf element content varied from 164.8 kg C·ha−1 in de-ciduous shrublands to 3,401.1 kg C·ha−1 in coniferous forests for C,from 11.5 kg N·ha−1 in deciduous shrublands to 85.1 kg N·ha−1 inmixed forests for N, and from 0.98 kg P·ha−1 in deciduous shrub-lands to 7.66 kg P·ha−1 in coniferous forests for P (Table S2).Annual GPP (i.e., stand-level photosynthesis) increased with

both leaf N (R2 = 0.20, P < 0.001 for a power relationship) and P(R2 = 0.11, P < 0.001) content, consistent with previous studies atthe leaf or stand level (35, 36). However, the relationship was notconsistent for the different biomes. The relationship was positivefor deciduous shrublands (R2 = 0.25, P < 0.001 for N and R2 =0.19, P < 0.001 for P) and grasslands (R2 = 0.18, P < 0.001 for Nand R2 = 0.18, P < 0.001 for P), while it was not significant forevergreen shrublands, deciduous broadleaf forests, and mixedforests and was negative for evergreen broadleaf forests (R2 = 0.06,

Fig. 1. Changes in C, N, and P concentration in leaves, stems, and roots withmean annual temperature. Solid lines represent significant fits (P < 0.05),whereas dashed lines represent nonsignificant fits (P > 0.05).

Fig. 2. Venn diagrams illustrating the relative contribution of climate, soil,and vegetation to the variation in C, N, and P concentration in the leaves,stems, and roots in China’s terrestrial ecosystems. The numbers in each subsetfigure show the percentage of total variation explained by the model andvariations explained by independent effect of (a) vegetation type (green), (b)soil (pink), and (c) climate (blue), and the joint effect of (d) vegetation type andsoil, (e) climate and soil, (f) vegetation type and climate, and (g) all three fac-tors. The circle size indicates the relative contribution of the three factors.

Tang et al. PNAS | April 17, 2018 | vol. 115 | no. 16 | 4035

ECOLO

GY

SUST

AINABILITY

SCIENCE

SPEC

IALFEATU

RE

Dow

nloa

ded

by g

uest

on

June

20,

202

0

P = 0.003 for N and R2 = 0.05, P = 0.005 for P) and coniferousforests (R2 = 0.06, P < 0.001 for N and R2 = 0.19, P < 0.001 for P)(Fig. 4). These results support our hypotheses 2 (productivity–nutrientallocation correlation hypothesis) and 3 (productivity–leaf lifespanhypothesis), such that nutrient allocation and leaf life span influ-ence the relationship between leaf nutrient contents and GPP.We further estimated the annual leaf nitrogen (LNP) and

phosphorus productivity (LPP), which were calculated as theamount of annual GPP per unit leaf N or P. When all sites werepooled, the geometric mean LNP was 249.5 g C·g−1 N·y−1, withmean LNP varying from 141.6 g C·g−1 N·y−1 and 146.4 g C·g−1 N·y−1in coniferous and mixed forests, respectively, to 759.5 g C·g−1 N·y−1in evergreen shrublands. When all sites were pooled, the mean LPPwas 3,157.9 g C·g−1 P·y−1, with mean LPP varying from 1,367.1 gC·g−1 P·y−1 in coniferous forests to 10,140.7 g C·g−1 P·y−1 in ever-green shrublands (Table S3). Both the LNP and LPP varied withclimate. In general, LNP and LPP increased with both precipitationand temperature (Fig. 5). These results show that climate regulatesplant productivity in terms of LNP and LPP and that, with un-balanced N and P deposition (7) or even without additional N or P,global warming may enhance GPP in China’s terrestrial ecosystemsvia increasing LNP or LPP (36).

Potential Applications. Recent decades have witnessed a rapid in-crease in N and P deposition worldwide. However, it has also beenacknowledged that N and P availability for plant growth currentlylimit the responsiveness of terrestrial vegetation to CO2 enrichment

(5, 6), especially when the N and P deposition are not in balanceacross the globe (7). Large-scale studies on plant stoichiometry atthe community level have several practical applications related tothe widespread N deposition and N and P limitation. Especiallyunder rapid changes in climate (37) and land use (38), our resultsare important for predicting potential C sequestration in terrestrialecosystems and their underlying mechanisms (39, 40).First, community-level plant stoichiometry for different tissues

provides an approach to accurately estimate the storage of nu-trients within plant biomass (7). As an example, here we used thestoichiometry of different tissues in different vegetation types(Table 1), their biomass (39), and the distribution of vegetationtypes (41) to estimate the N and P stocks in different biomes inChina. The total quantities of N and P stored in living biomasswere 151.50 Tg N and 15.58 Tg P in these seven biomes, of which92.24 Tg N and 11.62 Tg P were stored in forests, 9.91 Tg N and0.65 Tg P were stored in shrublands, and 48.39 Tg N and 3.31 TgP were stored in grasslands.Second, community-level stoichiometry can be used to esti-

mate the C sequestration of ecosystems under N deposition (42).For example, using data on the spatial distribution of recent Ndeposition (43), the 15N-labeled retention and allocation of N indifferent pools and tissues within different biomes (44), and theshort-term changes in plant stoichiometry in response to N de-position (45), we estimated that N deposition had caused anadditional C sequestration of 28.1 Tg·y−1 in woody biomass inChina’s terrestrial ecosystems over 2011–2015, of which 20.3 Tg·y−1

were in forests and 7.7 Tg·y−1 in shrublands (for details, seeSupporting Information). To better understand the response of GPPto N and P deposition, it remains necessary for future studies tocompare the patterns and mechanism of GPP and leaf N or Pcontent in different biomes, dominated by species with contrastingecological characteristics.In summary, we provide an assessment of large-scale patterns

of plant stoichiometry in leaves, stems, and roots at the com-munity level in China’s terrestrial biomes. Our study showed thatbiomass-weighted mean plant [N] and [P] decreased, and [C]increased, with increasing mean annual temperature in all tis-sues. Along environmental gradients, [N] and [P] varied to agreater extent in stems and roots than in leaves, providing large-scale evidence that nutrient elements in more metabolicallyFig. 3. Relationships between N (or P) concentrations (A) or contents (B)

among tissues in different biomes in China.

Fig. 4. Relationship between leaf N (or P) content and annual GPP in dif-ferent biomes in China. Solid lines represent significant fits at P < 0.05,whereas dashed lines represent nonsignificant fits (P > 0.05).

4036 | www.pnas.org/cgi/doi/10.1073/pnas.1700295114 Tang et al.

Dow

nloa

ded

by g

uest

on

June

20,

202

0

active tissues (leaves) are less sensitive to environmental gradi-ents. A highly significant power-law scaling correlation was ob-served for N (and P) concentration and content betweendifferent tissues, which is useful for developing empirical modelsthat can scale the responses of plants to global change, from asingle tissue to the entire community. We also provide evidencethat, at large scales, the relationship between GPP and nutrient(N and P) content depends on the relative allocation of thesenutrients between leaf and woody tissues and that across China,leaf N and P productivity increase with annual precipitation andtemperature. These results suggest that, by increasing leaf N andP productivity, global warming may enhance gross primary pro-duction in China’s terrestrial ecosystems.

Materials and MethodsPlant and Soil Investigation and Sampling. The natural ecosystems in this studyinclude seven biomes (four forests, two shrublands, and one grassland)—namely, evergreen broadleaf forest (including two vegetation types, ever-green broadleaf forest and bamboo forest), deciduous broadleaf forest,coniferous forest (evergreen coniferous forest and deciduous coniferousforest), mixed forest, evergreen shrubland (evergreen broadleaf shrublandand evergreen coniferous shrubland), deciduous shrubland (deciduousbroadleaf shrubland and sparse shrubland), and grassland (grass-forb com-munity, meadow, steppe, and sparse grassland). These biomes were furtherclassified into 14 vegetation types, including 6 forest vegetation types,4 shrubland vegetation types, and 4 grassland vegetation types, consistentwith the level II classification used in China Cover (41). Because of a lack ofdata, Taiwan, Hong Kong, and Macao were not included in this study.

The landmass of China was divided into 35,800 grid cells with different areas(100–900 km2) according to vegetation diversity, of which 3–5% were selectedfor investigation. Eight sites within each investigation grid, for a total of13,030 sites, were established for the field investigation. At each site, the speciescomposition, biomass of different tissues and soil properties were investigated inthe summer (July–September) of 2011–2015. All of the investigations and sam-plings were conducted according to a standard protocol (46). Detailed in-formation on the inventory procedure, plot size, soil sample collection, andlaboratory analyses are provided by Tang et al., in this issue of PNAS (39).

For our study on stoichiometry, according to vegetation diversity, weselected two to three sites per investigation grid cell for plant sampling. Atforest sites, fully expanded leaves, stems, and roots were collected fromcommon tree species, with diameter at breast height greater than 5 cm; fullyexpanded sunlit leaves (at least 10 leaves for each species), stems (at leastthree cores for each species), and roots (at least three pieces for each species)were collected for dominant shrub species. At shrubland sites, fully expandedleaves, stems, and roots were collected for common shrub species. For thedominant herbaceous plants at the forest and shrubland sites, abovegroundand belowground biomass was also collected. At the grassland sites, the totalliving biomass of the leaves (aboveground) and roots (belowground) washarvested and fully mixed in each grassland site. The fully mixed harvestedleaves (or roots) were collected to represent community-level samples.

In total, plant samples from 4,159 sites were collected. These samples includedleaf, stem, and root samples from 1,851 species (535 trees, 723 shrubs, and593 herbaceous plants) from 2,234 forest and 1,101 shrubland sites and well-mixed leaf and root samples from 824 grassland sites (ref. 47 and Fig. 3). Allof the plant samples were oven-dried and ground after being transported to thelaboratory. The soil samples were air-dried and, after removal of roots, wereground to pass through a 100-mesh sieve for the elemental analyses.

Calculation of C, N, and P Concentrations at the Community Level. For each site,we calculated the community-level [C], [N], and [P].We define a community asall species co-occurring within a site. At each site, the community mean C, N,and P concentrations ([C]com, [N]com, [P]com) of the forests and shrublandswere calculated based on the relative biomass and nutrient concentrationsin the different tissues of each species. The relative biomass was estimatedby Tang et al., in this issue of PNAS (39). For each tissue, the communitymean [C], [N], and [P] ([C]com, [N]com, and [P]com) were calculated as follows:

½C,N,or   P�com=

Ps

i=1

�½C,N,or   P�i ×Bi�

Ps

i=1ðBiÞ

, [1]

where [C, N, or P]i is the [C], or [N], or [P] of the ith species; Bi is the tissue(leaf, stem, or root) biomass of the ith species; and s is the number of speciesin the community. The community mean above- and below-ground [C], [N],and [P] of the grassland communities were obtained from direct measure-ments of the mixed samples.

Estimation of C, N, and P Contents and Storage in Natural Ecosystems. TheC, N, or P content (DC, N, or P) at each forest and shrubland site was cal-culated based on the biomass and concentration of each species in dif-ferent tissues:

�DC,NorP

�=

X3

i=1

Xs

i=1

��Cij

�Nijor   Pij

��×Bij

�, [2]

where Bij is the biomass of the jth tissue (j = leaf, stem, or root) of the ithspecies, Cij (Nij or Pij) is the [C] ([N] or [P]) of the jth tissue of the ith species,and s is the number of species in the community. The C, N, or P contents ofthe grassland site were obtained from direct measurements of leaf or root[C], [N], or [P], multiplying by the leaf or root biomass of each site. The tissuebiomass was estimated by Tang et al., in this issue of PNAS (39).

We calculated the mean N (P) content for all sites belonging to the samelevel II vegetation type [i.e., the 14 level II landcover types (41)] andwithin thesame province. We then estimated the storage of N (P) in the living biomassby multiplying the content of N (P) and the area of each vegetation type ineach province. The area of each vegetation type in each province for2010 was calculated (41). Stocks were then summed for each of the sevenbiomes presented in this paper (e.g., deciduous broadleaf forest).

GPP. We used a model-based estimate of GPP for the year 2010 to explore therelationship between leaf N and P contents. The GPP was an average of threeecosystemmodels: CEVSA (the CarbonExchangebetweenVegetation, Soil and theAtmosphere model), BEPS (the Boreal Ecosystems Productivity Simulator model),and TEC (the Terrestrial Ecosystem Carbon Flux model). We used the most recentyear (2010) of GPP data after field sampling began. Formore detailed informationon the estimation of GPP, please refer to Chen et al. (37).

Calculation of Leaf N and P Productivity. Nutrient productivity is originallydefined as the amount of biomass produced per unit of nutrient in thebiomass and per unit of time (48). In this study, we used annual GPP as asurrogate for annual plant production, and then we defined LNP and LPP as

Fig. 5. Relationship of leaf N (A) and P (B) productivity to mean annualtemperature and log-transformed precipitation in China’s terrestrial biomes.

Tang et al. PNAS | April 17, 2018 | vol. 115 | no. 16 | 4037

ECOLO

GY

SUST

AINABILITY

SCIENCE

SPEC

IALFEATU

RE

Dow

nloa

ded

by g

uest

on

June

20,

202

0

the amount of annual GPP per unit leaf N or P—that is, LNP(LPP) = GPP/leafN (P), where GPP is the annual GPP (in Mg C·ha−1·y−1), and leaf N (or P) is theleaf N or P content (in Mg·ha−1) in the plot.

Scaling and Allocation of Nutrients Among Tissues. The statistics of correlationbetween plant [C], [N], and [P] in relation to climate and soil nutrient variableswere calculated using the software R with the basic and smatr packages(www.r-project.org/). Before the analysis, the concentrations were trans-formed to natural logarithms.

A scaling approach, Y = a × Xb, was applied to explore the allocation of N (P)in different tissues. The power function can be expressed in the form of a linearregression equation after a natural log transformation (lnY = a + b × lnX). TheRMA technique was applied to examine the correlations between the naturallog-transformed nutrient concentrations. A slope (b, bRMA) smaller than 1 in-dicates slower changes of [N] or [P] in Y than in X. In the RMA analyses, thetissues closer to the top (Eqs. 3–5) were set as the response variable for thecorrelation of the same nutrients among tissues:

ln½N  or  P�leaf = a+b× ln½N  or  P�root, [3]

ln½N  or  P�shoot = a+b× ln½N  or  P�root,   and [4]

ln½N  or  P�leaf = a+b× ln½N  or  P�shoot. [5]

RMA analyses were conducted for all of the pooled sites, as well as for sites ofdifferent biome types. A likelihood ratio test was used to assess the het-erogeneity between the RMA regression slopes of different groups.

ACKNOWLEDGMENTS. We thank Dr. Guirui Yu for organizing the terrestrialecosystem carbon projects and to all the field investigators, who made up agroup of more than 1,000 participants; Dr. Qiufeng Wang for her assistance inorganizing the project; and Drs. Lingli Liu, Jin-Sheng He, Yuanhe Yang,Zhiheng Wang, Shuqing Zhao, Biao Zhu, Shuijin Hu, and two anonymousreviewers for their insightful comments and suggestions on an earlier versionof this manuscript. This work was funded by Strategic Priority ResearchProgram of the Chinese Academy of Sciences Grant XDA05050000 andNational Natural Science Foundation of China Grant 31621091.

1. Agren GI (2008) Stoichiometry and nutrition of plant growth in natural communities.Annu Rev Ecol Evol Syst 39:153–170.

2. Elser JJ, et al. (2000) Nutritional constraints in terrestrial and freshwater food webs.Nature 408:578–580.

3. Agren GI, Wetterstedt JAM, Billberger MFK (2012) Nutrient limitation on terrestrialplant growth–Modeling the interaction between nitrogen and phosphorus. NewPhytol 194:953–960.

4. Sterner RW, Elser JJ (2002) Ecological Stoichiometry: The Biology of Elements fromMolecules to the Biosphere (Princeton Univ Press, Princeton).

5. Norby RJ, Warren JM, Iversen CM, Medlyn BE, McMurtrie RE (2010) CO2 enhancementof forest productivity constrained by limited nitrogen availability. Proc Natl Acad SciUSA 107:19368–19373.

6. Wieder WR, Cleveland CC, Smith WK, Todd-Brown K (2015) Future productivity andcarbon storage limited by terrestrial nutrient availability. Nat Geosci 8:441–447.

7. Peñuelas J, et al. (2013) Human-induced nitrogen-phosphorus imbalances alter nat-ural and managed ecosystems across the globe. Nat Commun 4:2934.

8. Han WX, Fang JY, Reich PB, Ian Woodward F, Wang ZH (2011) Biogeography andvariability of eleven mineral elements in plant leaves across gradients of climate, soiland plant functional type in China. Ecol Lett 14:788–796.

9. Elser JJ, Fagan WF, Kerkhoff AJ, Swenson NG, Enquist BJ (2010) Biological stoichi-ometry of plant production: Metabolism, scaling and ecological response to globalchange. New Phytol 186:593–608.

10. Tian D, et al. (2017) Global leaf nitrogen and phosphorus stoichiometry and theirscaling exponents. Natl Sci Rev, in press.

11. Han W, Fang J, Guo D, Zhang Y (2005) Leaf nitrogen and phosphorus stoichiometryacross 753 terrestrial plant species in China. New Phytol 168:377–385.

12. Reich PB, Oleksyn J (2004) Global patterns of plant leaf N and P in relation to tem-perature and latitude. Proc Natl Acad Sci USA 101:11001–11006.

13. Chen Y, Han W, Tang L, Tang Z, Fang J (2013) Leaf nitrogen and phosphorus con-centrations of woody plants differ in responses to climate, soil and plant growthform. Ecography 36:178–184.

14. McGroddy ME, Daufresne T, Hedin LO (2004) Scaling of C:N:P stoichiometry in forestsworldwide: Implications of terrestrial Redfield-type ratios. Ecology 85:2390–2401.

15. Agren GI, Weih M (2012) Plant stoichiometry at different scales: Element concen-tration patterns reflect environment more than genotype. New Phytol 194:944–952.

16. Kraft NJB, Valencia R, Ackerly DD (2008) Functional traits and niche-based treecommunity assembly in an Amazonian forest. Science 322:580–582.

17. Kerkhoff AJ, Fagan WF, Elser JJ, Enquist BJ (2006) Phylogenetic and growth formvariation in the scaling of nitrogen and phosphorus in the seed plants. Am Nat 168:E103–E122.

18. Yang X, et al. (2014) Scaling of nitrogen and phosphorus across plant organs inshrubland biomes across Northern China. Sci Rep 4:5448.

19. Zhao N, et al. (2016) Coordinated pattern of multi‐element variability in leaves androots across Chinese forest biomes. Glob Ecol Biogeogr 25:359–367.

20. Heineman KD, Turner BL, Dalling JW (2016) Variation in wood nutrients along atropical soil fertility gradient. New Phytol 211:440–454.

21. Yan Z, Li P, Chen Y, Han W, Fang J (2016) Nutrient allocation strategies of woodyplants: An approach from the scaling of nitrogen and phosphorus between twigstems and leaves. Sci Rep 6:20099.

22. Fortunel C, Fine PVA, Baraloto C (2012) Leaf, stem and root tissue strategies across758 Neotropical tree species. Funct Ecol 26:1153–1161.

23. Kleyer M, Minden V (2015) Why functional ecology should consider all plant organs:An allocation-based perspective. Basic Appl Ecol 16:1–9.

24. Muscarella R, Uriarte M (2016) Do community-weighted mean functional traits reflectoptimal strategies? Proc Biol Sci 283:20152434.

25. Chapin FS, 3rd (2003) Effects of plant traits on ecosystem and regional processes: Aconceptual framework for predicting the consequences of global change. Ann Bot 91:455–463.

26. Liu X, et al. (2013) Enhanced nitrogen deposition over China. Nature 494:459–462.

27. Mahowald N, et al. (2008) Global distribution of atmospheric phosphorus sources,concentrations and deposition rates, and anthropogenic impacts. Global BiogeochemCycles 22:GB4026.

28. Schreeg LA, Santiago LS, Wright SJ, Turner BL (2014) Stem, root, and older leaf N:Pratios are more responsive indicators of soil nutrient availability than new foliage.Ecology 95:2062–2068.

29. DeJong TM, Doyle JF (1985) Seasonal relationships between leaf nitrogen content(photosynthetic capacity) and leaf canopy light exposure in peach (Prunus persica).Plant Cell Environ 8:701–706.

30. Smith ML, et al. (2002) Direct estimation of aboveground forest productivity throughhyperspectral remote sensing of canopy nitrogen. Ecol Appl 12:1286–1302.

31. Sardans J, Peñuelas J (2013) Tree growth changes with climate and forest type areassociated with relative allocation of nutrients, especially phosphorus, to leaves andwood. Glob Ecol Biogeogr 22:494–507.

32. Reich PB, Walters MB, Kloeppel BD, Ellsworth DS (1995) Different photosynthesis-nitrogen relations in deciduous hardwood and evergreen coniferous tree species.Oecologia 104:24–30.

33. Reich PB, Walters MB, Ellsworth DS, Uhl C (1994) Photosynthesis-nitrogen relations inAmazonian tree species: I. Patterns among species and communities. Oecologia 97:62–72.

34. Menge DNL, Levin SA, Hedin LO (2008) Evolutionary tradeoffs can select against ni-trogen fixation and thereby maintain nitrogen limitation. Proc Natl Acad Sci USA 105:1573–1578.

35. Vitousek PM, Menge DNL, Reed SC, Cleveland CC (2013) Biological nitrogen fixation:Rates, patterns and ecological controls in terrestrial ecosystems. Philos Trans R SocLond B Biol Sci 368:20130119.

36. Agren GI, Kattge J (2016) Nitrogen productivity and allocation responses of 12 im-portant tree species to increased CO2. Trees 31:617–621.

37. Chen J, et al. (2014) Estimation of gross primary productivity in Chinese terrestrialecosystems by using VPM model. Quaternary Sciences 34:732–742.

38. Lu F, et al. (2018) Effects of national ecological restoration projects on carbon se-questration in China from 2001 to 2010. Proc Natl Acad Sci USA, 10.1073/pnas.17002941115.

39. Tang X, et al. (2018) Carbon pools across China’s terrestrial ecosystems: New esti-mations based on an intensive field inventory. Proc Natl Acad Sci USA, 10.1073/pnas.1700291115.

40. Chen S, et al. (2018) Plant diversity enhances productivity and soil carbon storage.Proc Natl Acad Sci USA, 10.1073/pnas.1700298114.

41. Land Cover Atlas of the People’s Republic of China Editorial Board (2017) Land CoverAtlas of the People’s Republic of China (1:1,000,000) (China Map Publishing House,Beijing).

42. de Vries W, Du E, Butterbach-Bahl K (2014) Short and long-term impacts of nitrogendeposition on carbon sequestration by forest ecosystems. Curr Opin Environ Sustain9–10:90–104.

43. Jia Y, et al. (2014) Spatial and decadal variations in inorganic nitrogen wet depositionin China induced by human activity. Sci Rep 4:3763.

44. Templer PH, et al. (2012) Sinks for nitrogen inputs in terrestrial ecosystems: A meta-analysis of 15N tracer field studies. Ecology 93:1816–1829.

45. Yang Y, Luo Y, Lu M, Schädel C, Han W (2011) Terrestrial C:N stoichiometry in re-sponse to elevated CO2 and N addition: A synthesis of two meta-analyses. Plant Soil343:393–400.

46. Fang J, Yu G, eds (2016) Protocols for the Status, Potential and Mechanisms of CarbonSink in China’s Ecosystems (Science Press, Beijing).

47. Fang J, Yu G, Liu L, Hu S, Chapin FS, III (2018) Climate change, human impact, andcarbon sequestration in China. Proc Natl Acad Sci USA, 10.1073/pnas.1700304115.

48. Ingestad T (1979) Nitrogen stress in birch seedlings II. N, K, P, Ca, and Mg nutrition.Physiol Plant 45:149–157.

49. Jones JB (2001) Laboratory Guide for Conducting Soil Tests and Plant Analysis (CRCPress, New York).

50. Heikkinen RK, Luoto M, Kuussaari M, Pöyry J (2005) New insights into butterfly-environment relationships using partitioning methods. Proc Biol Sci 272:2203–2210.

4038 | www.pnas.org/cgi/doi/10.1073/pnas.1700295114 Tang et al.

Dow

nloa

ded

by g

uest

on

June

20,

202

0

Correction

ECOLOGY, SUSTAINABILITY SCIENCECorrection for “Patterns of plant carbon, nitrogen, and phosphorusconcentration in relation to productivity in China’s terrestrial eco-systems,” by Zhiyao Tang, Wenting Xu, Guoyi Zhou, Yongfei Bai,Jiaxiang Li, Xuli Tang, Dima Chen, Qing Liu, Wenhong Ma,Gaoming Xiong, Honglin He, Nianpeng He, Yanpei Guo, QiangGuo, Jiangling Zhu, Wenxuan Han, Huifeng Hu, Jingyun Fang,and Zongqiang Xie, which was first published April 16, 2018;10.1073/pnas.1700295114 (Proc Natl Acad Sci USA 115:4033–4038).The authors note that, due to a printer’s error, two references

were omitted from the article’s references list. The referencesshould have appeared in the references list as references 37 and41, respectively. The complete references appear below.

The authors also note that, on page 4035, right column, thirdfull paragraph, lines 3–4, “previous studies at the leaf (35, 36) orstand level (37)” should instead appear as “previous studies atthe leaf or stand level (35, 36).”Additionally, on page 1 of the Supporting Information, left

column, second paragraph, line 3, “(ref. 27, in this issue ofPNAS)” should instead appear as “(41)”; in the same paragraph,line 19, “(47)” should instead appear as “(50)”; on the samepage, right column, second paragraph, line 3, “(43)” should in-stead appear as “(44)”; and in the same paragraph, line 5, “(43)”should instead appear as “(45).”The online version has been corrected.

1. Agren GI (2008) Stoichiometry and nutrition of plant growth in natural communities.Annu Rev Ecol Evol Syst 39:153–170.

2. Elser JJ, et al. (2000) Nutritional constraints in terrestrial and freshwater food webs.Nature 408:578–580.

3. Agren GI, Wetterstedt JAM, Billberger MFK (2012) Nutrient limitation on terrestrialplant growth–Modeling the interaction between nitrogen and phosphorus. NewPhytol 194:953–960.

4. Sterner RW, Elser JJ (2002) Ecological Stoichiometry: The Biology of Elements fromMolecules to the Biosphere (Princeton Univ Press, Princeton).

5. Norby RJ, Warren JM, Iversen CM, Medlyn BE, McMurtrie RE (2010) CO2 enhancementof forest productivity constrained by limited nitrogen availability. Proc Natl Acad SciUSA 107:19368–19373.

6. Wieder WR, Cleveland CC, Smith WK, Todd-Brown K (2015) Future productivity andcarbon storage limited by terrestrial nutrient availability. Nat Geosci 8:441–447.

7. Peñuelas J, et al. (2013) Human-induced nitrogen-phosphorus imbalances alter naturaland managed ecosystems across the globe. Nat Commun 4:2934.

8. Han WX, Fang JY, Reich PB, Ian Woodward F, Wang ZH (2011) Biogeography andvariability of eleven mineral elements in plant leaves across gradients of climate, soiland plant functional type in China. Ecol Lett 14:788–796.

9. Elser JJ, Fagan WF, Kerkhoff AJ, Swenson NG, Enquist BJ (2010) Biological stoichiometryof plant production: Metabolism, scaling and ecological response to global change.New Phytol 186:593–608.

10. Tian D, et al. (2017) Global leaf nitrogen and phosphorus stoichiometry and theirscaling exponents. Natl Sci Rev, in press.

11. Han W, Fang J, Guo D, Zhang Y (2005) Leaf nitrogen and phosphorus stoichiometryacross 753 terrestrial plant species in China. New Phytol 168:377–385.

12. Reich PB, Oleksyn J (2004) Global patterns of plant leaf N and P in relation to tem-perature and latitude. Proc Natl Acad Sci USA 101:11001–11006.

13. Chen Y, Han W, Tang L, Tang Z, Fang J (2013) Leaf nitrogen and phosphorusconcentrations of woody plants differ in responses to climate, soil and plantgrowth form. Ecography 36:178–184.

14. McGroddy ME, Daufresne T, Hedin LO (2004) Scaling of C:N:P stoichiometry inforests worldwide: Implications of terrestrial Redfield-type ratios. Ecology 85:2390–2401.

15. Agren GI, Weih M (2012) Plant stoichiometry at different scales: Element con-centration patterns reflect environment more than genotype. New Phytol 194:944–952.

16. Kraft NJB, Valencia R, Ackerly DD (2008) Functional traits and niche-based treecommunity assembly in an Amazonian forest. Science 322:580–582.

17. Kerkhoff AJ, Fagan WF, Elser JJ, Enquist BJ (2006) Phylogenetic and growth formvariation in the scaling of nitrogen and phosphorus in the seed plants. Am Nat 168:E103–E122.

18. Yang X, et al. (2014) Scaling of nitrogen and phosphorus across plant organs inshrubland biomes across Northern China. Sci Rep 4:5448.

19. Zhao N, et al. (2016) Coordinated pattern of multi‐element variability in leavesand roots across Chinese forest biomes. Glob Ecol Biogeogr 25:359–367.

20. Heineman KD, Turner BL, Dalling JW (2016) Variation in wood nutrients along atropical soil fertility gradient. New Phytol 211:440–454.

21. Yan Z, Li P, Chen Y, Han W, Fang J (2016) Nutrient allocation strategies of woodyplants: An approach from the scaling of nitrogen and phosphorus between twigstems and leaves. Sci Rep 6:20099.

22. Fortunel C, Fine PVA, Baraloto C (2012) Leaf, stem and root tissue strategies across758 Neotropical tree species. Funct Ecol 26:1153–1161.

23. Kleyer M, Minden V (2015) Why functional ecology should consider all plant organs:An allocation-based perspective. Basic Appl Ecol 16:1–9.

24. Muscarella R, Uriarte M (2016) Do community-weighted mean functional traits re-flect optimal strategies? Proc Biol Sci 283:20152434.

25. Chapin FS, 3rd (2003) Effects of plant traits on ecosystem and regional processes: Aconceptual framework for predicting the consequences of global change. AnnBot 91:455–463.

26. Liu X, et al. (2013) Enhanced nitrogen deposition over China. Nature 494:459–462.27. Mahowald N, et al. (2008) Global distribution of atmospheric phosphorus sources,

concentrations and deposition rates, and anthropogenic impacts. Global Bio-geochem Cycles 22:GB4026.

28. Schreeg LA, Santiago LS, Wright SJ, Turner BL (2014) Stem, root, and older leaf N:Pratios are more responsive indicators of soil nutrient availability than new foliage.Ecology 95:2062–2068.

29. DeJong TM, Doyle JF (1985) Seasonal relationships between leaf nitrogen content(photosynthetic capacity) and leaf canopy light exposure in peach (Prunus persica).Plant Cell Environ 8:701–706.

30. Smith ML, et al. (2002) Direct estimation of aboveground forest productivitythrough hyperspectral remote sensing of canopy nitrogen. Ecol Appl 12:1286–1302.

31. Sardans J, Peñuelas J (2013) Tree growth changes with climate and forest type areassociated with relative allocation of nutrients, especially phosphorus, to leavesand wood. Glob Ecol Biogeogr 22:494–507.

32. Reich PB, Walters MB, Kloeppel BD, Ellsworth DS (1995) Different photosynthesis-nitrogen relations in deciduous hardwood and evergreen coniferous tree species.Oecologia 104:24–30.

33. Reich PB, Walters MB, Ellsworth DS, Uhl C (1994) Photosynthesis-nitrogen rela-tions in Amazonian tree species: I. Patterns among species and communities.Oecologia 97:62–72.

34. Menge DNL, Levin SA, Hedin LO (2008) Evolutionary tradeoffs can select againstnitrogen fixation and thereby maintain nitrogen limitation. Proc Natl Acad SciUSA 105:1573–1578.

35. Vitousek PM, Menge DNL, Reed SC, Cleveland CC (2013) Biological nitrogen fixa-tion: Rates, patterns and ecological controls in terrestrial ecosystems. Philos Trans RSoc Lond B Biol Sci 368:20130119.

36. Agren GI, Kattge J (2016) Nitrogen productivity and allocation responses of 12 im-portant tree species to increased CO2. Trees 31:617–621.

37. Chen J, et al. (2014) Estimation of gross primary productivity in Chinese terrestrialecosystems by using VPM model. Quaternary Sciences, 34:732–742.

38. Lu F, et al. (2018) Effects of national ecological restoration projects on carbonsequestration in China from 2001 to 2010. Proc Natl Acad Sci USA 115:4039–4044.

39. Tang X, et al. (2018) Carbon pools across China’s terrestrial ecosystems: New esti-mations based on an intensive field inventory. Proc Natl Acad Sci USA 115:4021–4026.

40. Chen S, et al. (2018) Plant diversity enhances productivity and soil carbon storage.Proc Natl Acad Sci USA 115:4027–4032.

41. Land Cover Atlas of the People’s Republic of China Editorial Board (2017) LandCover Atlas of the People’s Republic of China (1:1,000,000) (China Map Pub-lishing House, Beijing).

42. de Vries W, Du E, Butterbach-Bahl K (2014) Short and long-term impacts of ni-trogen deposition on carbon sequestration by forest ecosystems. Curr Opin En-viron Sustain 9–10:90–104.

43. Jia Y, et al. (2014) Spatial and decadal variations in inorganic nitrogen wet depositionin China induced by human activity. Sci Rep 4:3763.

44. Templer PH, et al. (2012) Sinks for nitrogen inputs in terrestrial ecosystems: A meta-analysis of 15N tracer field studies. Ecology 93:1816–1829.

www.pnas.org PNAS | June 26, 2018 | vol. 115 | no. 26 | E6095–E6096

CORR

ECTION

45. Yang Y, Luo Y, LuM, Schädel C, HanW (2011) Terrestrial C:N stoichiometry in response toelevated CO2 and N addition: A synthesis of two meta-analyses. Plant Soil 343:393–400.

46. Fang J, Yu G, eds (2016) Protocols for the Status, Potential and Mechanisms of CarbonSink in China’s Ecosystems (Science Press, Beijing).

47. Fang J, Yu G, Liu L, Hu S, Chapin FS, III (2018) Climate change, human impact, and carbonsequestration in China. Proc Natl Acad Sci USA 115:4015–4020.

48. Ingestad T (1979) Nitrogen stress in birch seedlings II. N, K, P, Ca, and Mg nutrition.Physiol Plant 45:149–157.

49. Jones JB (2001) Laboratory Guide for Conducting Soil Tests and Plant Analysis (CRCPress, New York).

50. Heikkinen RK, Luoto M, Kuussaari M, Pöyry J (2005) New insights into butterfly-environmentrelationships using partitioning methods. Proc Biol Sci 272:2203–2210.

Published under the PNAS license.

Published online June 11, 2018.

www.pnas.org/cgi/doi/10.1073/pnas.1808126115

E6096 | www.pnas.org