• foliar nitrogen;
  • functional traits;
  • grassland;
  • leaf mass per area (LMA);
  • photosynthesis;
  • photosynthetic nitrogen use efficiency (PNUE)


  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information
  • • 
    Leaf mass per area (LMA), nitrogen concentration (on mass and area bases, Nmass and Narea, respectively), photosynthetic capacity (Amass and Aarea) and photosynthetic nitrogen use efficiency (PNUE) are key foliar traits, but few data are available from cold, high-altitude environments.
  • • 
    Here, we systematically measured these leaf traits in 74 species at 49 research sites on the Tibetan Plateau to examine how these traits, measured near the extremes of plant tolerance, compare with global patterns.
  • • 
    Overall, Tibetan species had higher leaf nitrogen concentrations and photosynthetic capacities compared with a global dataset, but they had a slightly lower Amass at a given Nmass. These leaf trait relationships were consistent with those reported from the global dataset, with slopes of the standardized major axes Amass–LMA, Nmass–LMA and AmassNmass identical to those from the global dataset. Climate only weakly modulated leaf traits.
  • • 
    Our data indicate that covarying sets of leaf traits are consistent across environments and biogeographic regions. Our results demonstrate functional convergence of leaf trait relationships in an extreme environment.


  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Leaf mass per area (LMA), nitrogen (N) concentration (on mass and area bases, Nmass and Narea, respectively), photosynthetic capacity (similarly, Amass and Aarea) and photosynthetic nitrogen use efficiency (PNUE, defined as photosynthetic capacity per unit leaf nitrogen) are fundamental leaf traits, playing key roles in plant functioning (Schulze, 1994; Grime et al., 1997; Mooney et al., 1999; Ackerly, 2004). As a reflection of the dry-mass cost of producing new leaves, LMA correlates positively with leaf lifespan (LL) and negatively with leaf N concentration across species (Reich et al., 1997; Westoby et al., 2002; Wright et al., 2004b). Leaf N concentration itself is strongly correlated with photosynthetic capacity (Field & Mooney, 1986; Evans, 1989; Reich et al., 1994), as N is essential for the synthesis of Rubisco, the key enzyme of photosynthesis (Field & Mooney, 1986; Taiz & Zeiger, 1998). This correlation provides a useful link between processes on short-term, leaf-level scales and long-term, plant- and stand-level scales, and has been used to estimate maximum CO2 uptake over a broad range of species (Schulze, 1994; Baldocchi & Harley, 1995; Harley & Baldocchi, 1995; Aber et al., 1996; Williams et al., 1997; Larocque, 2002). Understanding the relationships between these fundamental traits and their large-scale patterns is essential for scaling up ecophysiological processes from the leaf level to the ecosystem level and in predicting ecosystem functioning in response to environmental change (Ehleringer & Field, 1993; Peterson et al., 1999; Norby & Luo, 2004).

Understanding large-scale patterns of leaf functional traits is a challenging issue of great interest to both plant physiologists and ecologists (Körner, 1989; Yin, 1993; Niinemets, 2001; Reich et al., 2003; Chown et al., 2004; Reich & Oleksyn, 2004; Wright et al., 2005a,b). For example, in an examination of a global dataset, Reich et al. (1997) found that leaf traits such as photosynthetic rate and longevity scale predictably with one another, largely irrespective of environment or phylogeny. Wright et al. (2005b) similarly found that the effect of climate on the relationships among Amass, Nmass, LMA, leaf phosphorus (P), dark respiration rate (R) and LL was modest, although some patterns appeared. A recent study by Reich & Oleksyn (2004) further pursued the link between climate and leaf traits, finding that leaf N and P decreased with mean annual temperature (MAT) from the 5–10°C range to the warmest MAT. At very low MATs, however, the relatively scarce data available hindered arrival at any definitive conclusions.

The Tibetan Plateau is an ideal place for large-scale ecological studies, because it provides a unique opportunity to examine trends in a high-altitude, cold climate with very low MAT. The plateau represents one of the largest alpine grasslands in the world, yet its vegetation has been underrepresented in global-scale studies (e.g. Reich & Oleksyn, 2004; Wright et al., 2004b). Arctic and alpine plants have adapted to low temperatures, and thus are expected to have developed unique survival mechanisms (Chapin & Körner, 1995), enhancing the value of regional and global studies that include such plants. As the largest geomorphological unit on the Eurasian continent (Sun & Zheng, 1998), the Tibetan Plateau has a mean elevation of > 4000 m, with altitudes ranging from approx. 3000 to 8844 m. The plateau covers 12° of latitude and 28° of longitude, for a total area of approx. 2.5 × 106 km2, nearly one-quarter of the area of China. As a consequence of uplift in the past several million years (Zheng, 1996; Tapponnier et al., 2001), the Tibetan Plateau has had tremendous impact on the evolution and the development of species and ecosystems (Sun & Zheng, 1998), making it a center of differentiation for new species and a refuge for ancient species (Zhang et al., 1988; Hou & Chang, 1992). In addition, the Plateau is one of the main regions of low-latitude frozen soils in the world (Zhang et al., 1988; Molnar, 1989). Its alpine vegetation remains relatively undisturbed by humans, and thus the Plateau is an ideal region in which to study the responses of natural ecosystems to global climate change.

This study was designed to explore patterns of leaf functional traits in a high-elevation, low-temperature environment. Specifically, our study objectives were (i) to document the leaf functional traits of the flora in an understudied region over broad regional, elevational, and taxonomic ranges, and (ii) to examine how relationships among these traits, measured near the extremes of plant tolerance, compare with global patterns.

Materials and Methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Study sites

Sampling was performed and measurements were taken between late July and early August 2003 along a transect in the Central Tibetan Plateau (Fig. 1). The transect covers latitudes from 28.19 to 36.32°N and longitudes from 86.83 to 100.93°E, and is approx. 2000 km long and 250 km wide (Table 1). Climate variation along the transect is represented by a MAT range of −9.7 to 6.8°C, with mean annual precipitation (MAP) ranging from 239 to 534 mm, and elevation from 2934 to 5249 m (Table 1).


Figure 1. A vegetation map of the Tibetan Plateau, adapted from the Vegetation Map of China (Hou, 1982), showing the sample sites.

Download figure to PowerPoint

Table 1.  Description of 49 sites where leaf trait measurements were taken
SiteLongitude (°E)Latitude (°N)Altitude (m)MAT (°C)MAP (mm)Vegetation typeSpecies measured1
  • Data for latitude, longitude and altitude were obtained with Magellan GPS Field PRO V (Magellan System Corporation, San Dimas, CA, USA). Mean annual temperature (MAT) and mean annual precipitation (MAP) were calculated from 50-year averaged temperature and precipitation records (1951–2000) at 680 well-distributed climate stations across China based on a linear model using latitude, longitude, and altitude as variables. See Fig. 1 for site locations.

  • 1

    See Table 2 for definitions of species codes.

Q02100.4636.122934 2.63355SteppeAs, Ci, Ai, Pmu
Q04100.2236.003078 1.90362SteppeAs, Ai
Q06100.2335.763184 1.53389SteppeAi, Ic, Sp, Pm, En, Ls
Q07100.4935.573304 0.76431MeadowSp, Lv, Kp
Q09100.9335.353253 1.19468SteppeAs, Sp
Q10100.7735.083565−0.54508MeadowLv, Gs, Ssp, Pv
Q11100.8234.863650−0.88534MeadowLv, Gs, Ssp
Q12100.4034.453938−2.19562ScrubOo, So, Ppa, Lr
Q13100.2234.533727−0.78524MeadowLv, Kp, Gs, Pv, Lr, Ag, Pma, Ac, Ap, Pta, Pa, Pd
Q1699.9334.473930−1.96530ScrubSsu, Sa, Gs, Pv, Oo, So, Ppa
Q1898.9734.844518−6.01503MeadowKt, Sgr, Kr, Cm
Q2199.1835.364158−4.27446SteppeIc, Sp
Q2399.4835.444089−4.01454SteppeSp, Ag
Q2498.5834.994297−4.52446SteppeSgr, Fr
Q2598.4534.854219−3.78438SteppeEn, Cd, Sgl
Q2698.2534.884229−3.80425SteppeSp, Kk
Q3097.9934.584278−3.67432MeadowKt, Cm
Q3197.6634.205249−9.69534MeadowRr, Sgl, Pn, Sme
Q3297.0233.764589−4.05473MeadowKp, Mi, Kca
Q3496.3733.974229−2.10480MeadowGs, Oo, Kh
Q3596.2034.104363−2.99468MeadowKp, Kc, Oo, Kh, Gf, Rta
Q3795.8034.144226−2.20467SteppeSp, Of
Q3895.7033.954161−1.63470MeadowKp, Sc
Q3995.8833.734264−2.06468MeadowOf, Sc
Q4096.0133.604330−2.33467ScrubPpa, Lr, Cj
Q4396.7433.114238−1.40490ScrubGs, So
Q4496.9133.023901 0.63514MeadowLv, Kp, Asi
Q4796.7432.904286−1.49488MeadowGs, Oo, Ppa, Lr
Q4896.5632.593958 0.72505MeadowIc, Lv, Sc, Pal, Dc
X0196.5331.974167 0.05494ScrubPv, So, Ppa, Ga
X0296.3932.004191−0.11489MeadowRt, Sa, Pg, Ssp, So
X0396.5131.104631−1.87468ScrubRt, Ca, Rsp, Pi, Lg, Kpu, Sso, Ak, Rsp, So
X0494.9631.704336−0.58451MeadowRa, Smo, Bd, Pt, Csp, Lt, Rh, Ppa
X0693.7931.844014 1.25445MeadowBd, Lh, Gs
X0893.5431.854475−1.43412MeadowKp, Ppa
X1092.9031.844307−0.40408MeadowKsp, Kh
X1292.8731.834287−0.27409MeadowKp, Kh, Pd
X1990.8130.314328 1.19368ScrubPpa
XX190.4229.263667 7.01403ScrubSm
XX289.9529.333706 6.80390ScrubLm
XX386.8328.195100−5.65244ScrubHt, Ppa
XX486.8428.304622−1.20272ScrubPpa, Sc
XX587.0728.515242−6.91239MeadowPp, Sg, Ppa, Rta
XX688.1529.154080 3.75330ScrubSm

Natural vegetation types along the transect include alpine steppe, alpine meadow, alpine cushion vegetation and scrubland, which are representative of the Tibetan Plateau (Zhang et al., 1988). Alpine meadows, with perennial tussock grasses such as Kobresia pygmaea and Kobresia tibetica, and alpine steppes, with cold-xerophytic, short, dense tussock grasses such as Stipa purpurea, have extensive distributions, and are usually mixed with alpine forbs, including Polygonum viviparum and species of Gentiana and Pedicularis (Zhang et al., 1988). The scrublands are dominated by Salix oritrepha, Potentilla parvifolia, species of Rhododendron, and Sophora moorcroftiana.

Site selection and sampling

We selected 49 more or less evenly spaced sites along the transect by visual inspection of the vegetation, aiming to sample sites subject to minimal grazing and other anthropogenic disturbances (Table 1). Of the 49 sites, 12 were scrub, 12 steppe and 25 meadow. At each site, the dominant species were selected for in situ gas exchange measurement and ex situ chemical analysis. Nearly all measurements were taken at the flowering stage. In all, we investigated 74 species from 26 families over the 49 sites (Tables 1,2).

Table 2.  Species included in this study (74 in total), and average values of area-based light-saturated photosynthetic rate (Aarea), mass-based light-saturated photosynthetic rate (Amass), area-based leaf nitrogen concentration (Narea), mass-based leaf nitrogen concentration (Nmass), leaf mass per area (LMA) and photosynthetic nitrogen use efficiency (PNUE)
CodeSpecies1FamilyFG2LMA (g m−2)Aarea (µmol m−2 s−1)Amass (µmol g−1 s−1)Narea (g m−2)Nmass (mg g−1)PNUE (µmol g−1 N s−1)
  • 1

    Nomenclature follows that of Wu (1987). Family names follow current practice: Asteraceae = Compositae, Poaceae = Gramineae; Lamiaceae = Labiatae; Fabaceae = Leguminosae.

  • 2

    FG, functional group: H, herb, including annuals, biennials and perennial forbs; S, shrub, including deciduous shrubs and evergreen shrubs; G, grass, including graminoids and sedges.

  • LMA, leaf mass per area; Nmass and Narea, nitrogen concentration on mass and area bases, respectively; Amass and Aarea, photosynthetic capacity on mass and area bases, respectively; PNUE, photosynthetic nitrogen use efficiency.

AiAchnatherum inebriansPoaceaeG 4.6
AsAchnatherum splendensPoaceaeG112.615.90.153.5031.2 4.9
AgAconitum gymnandrumRanunculaceaeH 60.0 6.5
AcAnemone cathayensisRanunculaceaeH 56.313.70.241.7330.7 7.9
AkArenaria kansuensisCaryophyllaceaeH129.9 2.8
AsiArtemisia sieversianaAsteraceaeH 49.5 4.1
ApAstragalus porphyrocalyxFabaceaeH 5.1
BdBerberis diaphanaBerberidaceaeS101.2 5.0
CjCaragana jubataFabaceaeS 91.510.10.112.4927.2 4.1
CspCaragana sp.FabaceaeS 65.0 3.8
CmCarex moorcroftiiCyperaceaeG 59.913.90.221.9632.5 7.0
CaCotoneaster adpressusRosaceaeS 50.8 7.7
CdCremanthodium discoideumAsteraceaeH139.317.50.132.4617.7 7.1
DcDelphinium caeruleumRanunculaceaeH 84.724.20.282.0924.611.6
EnElymus nutansPoaceaeG 47.913.40.271.7036.5 8.8
FrFestuca rubraPoaceaeG 48.310.70.221.8738.7 5.7
GfGentiana farreriiGentianaceaeH 78.914.90.192.2228.2 6.7
GsGentiana stramineaGentianaceaeH 6.0
GaGnaphalium affineAsteraceaeH 63.921.70.332.4538.3 8.9
HtHippophae thibetanaElaeagnaceaeS 78.810.90.143.4443.7 3.2
IcIris chinensisIridaceaeH146. 5.9
KcaKobresia capillifoliaCyperaceaeG 53.0 4.1
KhKobresia humilisCyperaceaeG 58.6 7.0
KkKobresia kansuensisCyperaceaeG101.0 1.6
KpKobresia parvaCyperaceaeG 59.3 4.2
KrKobresia royleanaCyperaceaeG 45.711.10.230.9921.511.3
KpuKobresia pusillaCyperaceaeG 61.7 6.3
KspKobresia sp.CyperaceaeG 83.514.00.171.9823.7 7.0
KtKobresia tibeticaCyperaceaeG 75.711.00.142.2229.4 5.0
KcKoeleria cristataPoaceaeG 62.0 2.9
LgLagotis glaucaScrophulariaceaeH110.713.20.121.6915.3 7.8
LrLamiophlomis rotataLamiaceaeH 98.317.80.162.5726.8 7.4
LmLeptodermis microphyllaRubiaceaeS 63.9 5.7
LsLeymus secalinusPoaceaeG 98.820.00.202.5525.9 7.8
LvLigularia virgaureaAsteraceaeH 8.2
LhLonicera hispidaCaprifoliaceaeS129.0 2.7
LtLonicera tibeticaCaprifoliaceaeS 58.911.30.191.4624.8 7.7
MiMeconopsis integrifoliaPapaveraceaeH 80.621.30.262.9636.7 7.2
MtMicroula tibeticaBoraginaceaeH 9.9
OfOxytropis falcataFabaceaeH 5.9
OoOxytropis ochrocephalaFabaceaeH 7.0
PalPedicularis alaschanicaScrophulariaceaeH 54.711.80.211.5528.4 7.6
PiPedicularis integrifoliaScrophulariaceaeH 65.912.10.181.3119.8 9.3
PmuPeganum multisectumZygophyllaceaeH 5.4
PpPhyllophyton pharicumLamiaceaeH 73.3 7.0
PmaPolygonum macrophyllumPolygonaceaeH 56.913.40.232.1638.0 6.2
PvPolygonum viviparumPolygonaceaeH 65.312.40.192.1532.8 5.8
PaPotentilla anserinaRosaceaeH 6.8
PgPotentilla glabraRosaceaeS136.414.60.102.3217.0 6.3
PnPotentilla niviaRosaceaeH 57.6 4.7
PpaPotentilla parvifoliaRosaceaeS 86.611.70.131.8421.9 6.4
PtPrimula tanguticaPrimulaceaeH 45.612.40.271.0422.811.9
PtaPrzewalskia tanguticaSolanaceaeH 54.319.00.353.0856.8 6.2
PdPtilagrostis dichotomaPoaceaeG 65.6 4.4
RspRheum spiciformePolygonaceaeH 58.913.50.231.5426.1 8.8
RtaRheum tanguticumPolygonaceaeH115. 6.3
RrRhodiola rotundataCrassulaceaeH 57.7 3.2
RspRhododendron sp.EricaceaeS205.810.90.051.9012.2 5.8
RtRhododendron thymifoliumEricaceaeS113.2 4.3
RhRibes himalenseSaxifragaceaeS 6.0
RaRumex acetosaPolygonaceaeH 53.822.50.422.3343.3 9.7
SoSalix oritrephaSalicaceaeS 83.514.80.162.4930.2 5.9
SsoSalix soulieiSalicaceaeS 70.9 6.1
SspSalix sp.SalicaceaeS 74.416.70.221.7323.2 9.6
SgSaussurea glanduligeraAsteraceaeH 9.0
SgrSaussurea graminifoliaAsteraceaeH 70.7 4.9
SmeSaussurea medusaAsteraceaeH 65.6 3.1
SspSaussurea sp.AsteraceaeH
SsuSaussurea superbaAsteraceaeH 72.511.40.161.5521.4 7.4
SmSophora moorcroftianaFabaceaeS101.813.30.133.3432.8 4.1
SaSpiraea alpinaRosaceaeS 65.7 6.9
SmoSpiraea mongolicaRosaceaeS 8.0
ScStellera chamaejasmeThymelaeaceaeH 6.8
SpStipa purpureaPoaceaeG 80.5 4.8

Gas exchange, leaf carbon and nitrogen measurements

In situ photosynthetic rates of current-season leaves were measured at saturating light with two open path gas-exchange systems using red-blue light sources and CO2 mixers (LI-6400; Li-Cor Inc., Lincoln, NE, USA). The on-board pressure and temperature sensors on the LI-6400 corrected for any changes in air density resulting from changes in atmospheric pressure or air temperature, and provided the correct mole fraction of CO2 (Li-Cor Inc., 2002). Measurements were taken in the morning on clear days, on five to 10 plants of each of the dominant species at each site to account for idiosyncratic measurements. During the measurement at each site, leaf cuvette temperature was maintained at 22–25°C, depending on the external temperature, and relative humidity inside the leaf cuvette was kept at 45–65%. The reference CO2 concentration in the leaf cuvette was maintained at 360 µmol CO2 mol−1, and saturating photosynthetic photon flux density (PPFD, 400–700 nm) was set at 1500 µmol m−2 s−1. For grasses with needle-like leaves, four to six leaves were placed across the chamber, taking care to avoid self-shading. The leaf area enclosed in the leaf chamber was determined immediately with a portable leaf-area meter (AM200; ADC Bioscientific Limited, Herts, UK). For each gas exchange measurement, a subsample of leaf was taken, its fresh weight was determined with a balance (Acculab Lt-320; Acculab, Measurement Standards Inc., Danvers, MA, USA) and its leaf area was measured. Following photosynthesis measurements, leaves were placed in paper bags and dried in the sun. Leaf samples were oven-dried at 60°C in the laboratory and their dry masses were measured on a semianalytical balance (Sartorius AG, Goettingen, Germany). The two LMA measurements for the gas-exchange sample and subsample were averaged to yield a combined estimate of LMA.

Dried samples from each plant were ground using a ball mill (NM200; Retsch, Haan, Germany). Total carbon (C) and N concentrations were determined on 5–6 mg of homogenously ground material for each sample using an elemental analyzer (2400 II CHNS/O Elemental Analyzer; Perkin-Elmer, Boston, MA, USA) with a combustion temperature of 950°C and a reduction temperature of 640°C.

Climate data and statistical analyses

The climate data used in this study were from 50-year averaged temperature and precipitation records (1951–2000) at 680 well-distributed climate stations across China (Fang et al., 2001; Piao et al., 2003). We calculated MAT, mean growing season temperature (GST) (from May to August), MAP, and mean growing season precipitation (GSP) for each research site from the climate data, based on a linear model using latitude, longitude, and altitude as explanatory variables (Fang et al., 2001; Piao et al., 2003). It should be noted that MAT and GST, and MAP and GSP, were closely correlated (R2 = 0.97 and 0.96, respectively; P < 0.0001 for both correlations).

In our dataset, some species were frequently sampled. However, at some sites with very few species present, only one species could be sampled. To account for this variation in sample size and imbalance in the number of species per genus, we analyzed the data at three levels: (1) species-by-site level, with individual plant measurements averaged within species at each site to produce a species-by-site dataset; (2) species level, with measurements averaged within species to produce a dataset of species means; and (3) genus level, with measurements averaged within genera to produce a dataset of genus means. We used log10 transformation to normalize the distributions, a common practice in large-scale ecological studies (Sterner & Elser, 2002; McGroddy et al., 2004; Reich & Oleksyn, 2004; Wright et al., 2004b).

The influence of climate, plant functional group, and taxonomic identity on leaf traits was analyzed with general linear models (GLMs), using R and sequential (type-I) sums of squares (Ihaka & Gentleman, 1996; Schmid et al., 2002). The explanatory terms included MAT and MAP as climatic variables, grasses vs herbs vs shrubs as the functional group (FG) variable, plant family as the taxonomic variable, and interactions between these (MAT × FG, MAT × family, MAP × FG, and MAP × family). We switched the order of entry into the model for MAT and MAP to test the explanatory power of each ignoring the other (see e.g. Schmid et al., 2002). The significance of effects was tested with F-ratios between mean squares of explanatory terms and appropriate error terms. We also used GST and GSP to replace MAT and MAP, respectively. As the results were similar, for simplicity and for ease of comparison with other studies, we only present here the results with MAT and MAP.

The bivariate relationships of leaf traits were analyzed by fitting standardized major axis (SMA) lines to log-scaled variables (Wright et al., 2004a). Both correlation coefficients (r) and SMA slopes were calculated using a DOS-based computer package, (s)matr (Falster et al., 2003). In this program, heterogeneity between SMA slopes is tested via a permutation test. Where deemed nonheterogeneous, a common SMA slope is estimated using a likelihood-ratio method (Warton & Weber, 2002). Differences in SMA elevation (intercept) can then be tested with the SMA analog of standard analysis of covariance (ANCOVA).


  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Leaf traits of the Tibetan Plateau compared with global data

For all species, the average values of LMA, Nmass, Narea, Amass, Aarea and PNUE were 78.7 g m−2, 28.0 mg g−1, 2.1 g m−2, 0.16 µmol g−1 s−1, 12.7 µmol m−2 s−1 and 6.2 µmol g−1 s−1, and species varied c. 4-, 6-, 4-, 11-, 7-, and 8-fold, respectively (Tables 2, 3). Part of this variation could be explained by differences among plant functional groups (Table 3). Herbs had higher photosynthetic rate (by both Aarea and Amass) than shrubs and grasses, higher leaf N concentration (by Nmass) than shrubs, and higher PNUE than grasses. Comparison of the Tibetan data with the global dataset of Wright et al. (2004b) indicates that the present data are within the global ranges (Fig. 2). Overall, the Tibetan species had higher leaf N concentrations (by both Narea and Nmass) and photosynthetic rates (by both Aarea and Amass), but lower LMA than the average of the global dataset. For PNUE, mean values of the two datasets were not statistically different (Table 3). When individual functional groups were analyzed separately, the Tibetan grasses and herbs were found to have lower PNUE than in the global dataset, while the PNUE values for shrubs of the two datasets were similar.

Table 3.  Leaf traits of the plants on the Tibetan Plateau in comparison with the global dataset (Wright et al., 2004b)
Leaf traitGrowth formTibetWright et al.
  1. In multiple comparison tests, the Games–Howell method was used when variances were assumed to be heterogeneous by Levene's test, and Tukey's method was used when variances were homogeneous. Means followed by different lower-case or upper-case letters were statistically different at P < 0.05 among functional groups and between datasets, respectively.

  2. LMA, leaf mass per area; Nmass and Narea, nitrogen concentration on mass and area bases, respectively; Amass and Aarea, photosynthetic capacity on mass and area bases, respectively; PNUE, photosynthetic nitrogen use efficiency; SD, standard deviation.

Grass 4770.9aA20.82 125 95.0aA102.04
Herb 7178.3abA27.14 508 63.6bB 37.53
Shrub 3889.2bA29.47 733185.6cB160.97
NmassOverall15628.0A 7.992061 19.3B  9.81
Grass 4727.3abA 5.41  95 19.5aB  7.40
Herb 7130.3aA 9.25 379 28.1bA 10.84
Shrub 3824.6bA 6.82 625 14.8cB  8.69
NareaOverall156 2.1A 0.661975  1.9B  0.93
Grass 47 1.9aA 0.70  95  1.6aB  0.89
Herb 71 2.2aA 0.62 378  1.7aB  0.86
Shrub 38 2.1aA 0.64 621  2.1bA  0.96
AmassOverall142 0.16A 0.07 770  0.13B  0.10
Grass 40 0.14aA 0.06  37  0.20aB  0.10
Herb 65 0.20bA 0.08 141  0.25bB  0.13
Shrub 37 0.13aA 0.04 234  0.10cB  0.07
AareaOverall14312.7A 5.31 825 11.5B  5.93
Grass 40 9.6aA 4.64  44 16.6aB  8.28
Herb 6615.1bB 5.39 157 15.6aB  7.07
Shrub 3711.6aA 3.59 244 10.9bA  4.99
PNUEOverall143 6.2A 2.42 712  6.4A  3.53
Grass 40 5.4aA 2.42  37 11.7aB  6.65
Herb 66 6.9bA 2.57 139  8.7bB  3.24
Shrub 37 5.9abA 1.71 228  5.3cA  2.37

Figure 2. Leaf trait relationships for Tibetan species and from the global dataset of Wright et al. (2004b). The Aarea–LMA (photosynthetic capacity on an area basis–leaf mass per area) relationship for the global dataset was not significant (P > 0.05) and thus the regression line is not shown. ‘Slope’, difference in standardized major axis (SMA) slopes; ‘Elevation’, difference in SMA elevations; NS, not significantly different; *, significantly different (P < 0.05). LMA, leaf mass per area; Nmass and Narea, nitrogen concentration on mass and area bases, respectively; Amass and Aarea, photosynthetic capacity on mass and area bases, respectively.

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Leaf trait relationships across all species

Across species, all leaf traits were correlated with one another (Fig. 2). These trait relationships were consistent with previous results from the global dataset (Wright et al., 2004b). When the data from the present study were compared with the global dataset of Wright et al. (2004b), the SMA slopes for mass-based bivariate relationships, for example Amass–LMA, Nmass–LMA and AmassNmass, were found to be the same for the two datasets. However, on an area basis, the SMA slopes for LMA–Narea and NareaAarea differed between these two datasets (Fig. 3, Table 3). Furthermore, elevation shifts of the two datasets for Nmass–LMA and AmassNmass were both significant, indicating that Tibetan species tended to have a higher Nmass at a given LMA, and a lower Amass at a given Nmass (lower PNUE).


Figure 3. Leaf traits in relation to mean annual temperature (MAT). Regression lines are shown only for relationships that were significant at P < 0.05. LMA, leaf mass per area; Nmass and Narea, nitrogen concentration on mass and area bases, respectively; Amass and Aarea, photosynthetic capacity on mass and area bases, respectively.

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Climate modifications of leaf traits

At the species-by-site level, GLM analysis (Table 4) showed that the effect of MAT was significant for LMA and area-based traits (Narea and Aarea), but not for mass-based traits (Amass and Nmass). The effect of MAT was weak, explaining 4.0–6.5% of the total variation in leaf traits. As shown in Fig. 3, among all species, LMA, Aarea and Narea slightly increased with MAT (Fig. 3a,c,e), while other leaf traits did not shown any clear trend with MAT. FG and family were dominant factors, together explaining 25.3–60.7% of the total variation in leaf traits, and this result was independent of the sequence of entering the factors into the model. The effect of MAP was not significant for any of the leaf functional traits. Among the interaction terms, MAT × family had a significant influence on LMA and Narea, whereas MAT × FG had a significant influence on PNUE, demonstrating that the three relationships between the climatic variable MAT and the leaf traits LMA, Narea and PNUE differed among plant families or among plant functional groups. For example, LMA increased with MAT in Asteraceae, Poaceae, and Salicaceae, but decreased with MAT in Polygonaceae. Similarly, Narea increased with MAT in Asteraceae, Poaceae, Lamiaceae and Fabaceae, but decreased with MAT in Polygonaceae. When MAT and MAP were replaced with GST and GSP, essentially the same results were obtained (data therefore not shown).

Table 4.  Summary of general linear models for the effect of mean annual temperature (MAT), mean annual precipitation (MAP), functional group (FG) and family on leaf traits at three taxonomic levels
FactorSpecies by siteSpecies meanGenus mean
  1. Explanatory variables are listed in the order of their inclusion in the models. Leaf traits were log10-transformed before analysis. df, degrees of freedom; MS, mean squares; %SS, percentage of sum of squares explained (%). ***, P < 0.001; **, P < 0.01; *, P < 0.05.

  2. LMA, leaf mass per area; Nmass and Narea, nitrogen concentration on mass and area bases, respectively; Amass and Aarea, photosynthetic capacity on mass and area bases, respectively; PNUE, photosynthetic nitrogen use efficiency.

MAT 10.12*** 4.0 10.04 2.7 10.00 0.1
MAP 10.03 0.9 10.00 0.3 10.01 1.1
FG 20.09*** 6.2 20.05* 7.0   
MAT × FG 20.00 0.1 20.02 2.8   
MAP × FG 20.03 1.8 20.02 2.1   
MAT × family140.02* 9.8110.0217.1 80.0217.2
MAP × family100.01 2.8 60.01 4.3 50.02 9.5
Residuals980.0136.1250.0123.4 70.01 8.2
MAT 10.01 0.3 10.01 0.7 10.00 0.3
MAP 10.01 0.5 10.01 0.7 10.00 0.0
FG 20.10*** 8.8 20.07**11.6   
MAT × FG 20.00 0.2 20.01 1.0   
MAP × FG 20.01 1.4 20.01 1.4   
MAT × family140.01 5.9110.01 5.7 80.01 5.6
MAP × family100.01 4.7 60.00 2.4 50.01 7.1
Residuals980.0126.4250.0115.3 70.01 7.9
MAT 10.18*** 6.5 10.08** 6.6 10.01 0.9
MAP 10.00 0.1 10.02 1.9 10.01 1.7
FG 20.10*** 6.8 20.02 2.8   
MAT × FG 20.00 0.2 20.01 1.1   
MAP × FG 20.02 1.1 20.05** 7.5   
MAT × family140.02*11.8110.0112.1 80.0111.6
MAP × family100.01 2.3 60.01 4.9 50.01 5.0
Residuals980.0136.2250.0114.2 70.00 3.0
MAT 10.04 0.7 10.07 2.2 10.01 0.8
MAP 10.01 0.1 10.00 0.0 10.01 0.8
FG 20.46***16.8 20.31**20.1   
MAT × FG 20.08 2.9 20.08 5.3   
MAP × FG 20.08 3.0 20.00 0.3   
MAT × family140.02 5.8110.02 6.2 80.02 8.2
MAP × family100.01 2.1 60.04 8.2 50.0412.0
Residuals850.0344.3240.0323.0 70.0311.7
MAT 10.23** 4.2 10.21* 9.2 10.03 2.2
MAP 10.00 0.0 10.00 0.2 10.00 0.0
FG 20.67***24.0 20.22**19.7   
MAT × FG 20.09 3.1 20.02 1.7   
MAP × FG 20.03 1.2 20.01 1.1   
MAT × family140.03 8.6110.02 9.7 80.0421.3
MAP × family100.01 2.3 60.02 5.4 50.02 7.6
Residuals860.0345.1240.0327.8 70.0211.8
MAT 10.01 0.3 10.04 1.8 10.01 0.6
MAP 10.00 0.1 10.02 0.8 10.02 2.0
FG 20.21** 9.6 20.10*10.3   
MAT × FG 20.10* 4.5 20.05 4.8   
MAP × FG 20.03 1.5 20.02 2.1   
MAT × family140.03 8.7110.02 9.7 80.0110.3
MAP × family100.03 6.0 60.03 8.9 50.01 6.0
Residuals860.0353.7240.0331.5 70.0217.3

These patterns were generally similar for species means, except that LMA was no longer significantly affected by MAT (now averaged across sites for each species). However, at the genus mean level, the effects of all main factors (excluding the effect of family in Narea) and interactions were not significant. This is not surprising insofar as most genera occurred over a large range of sites and thus explanatory variables were also averaged over these large ranges of sites.


  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Overall patterns of leaf traits on the Tibetan Plateau

This work presents, to the best of our knowledge, the first large-scale survey of leaf functional traits on the Tibetan Plateau. Our data indicate that the leaf N concentrations and photosynthetic capacities of Tibetan plants are higher than the global average (Wright et al., 2004b). Furthermore, the leaf trait relationships were in agreement with those reported previously (Field & Mooney, 1986; Reich et al., 1997; Ackerly, 2004; Wright et al., 2004b).

Why do the Tibetan plants have overall higher leaf N and photosynthetic capacities? Functional group composition is likely a key factor. Most species we surveyed shed their leaves in winter, with only two evergreen species present. Previous studies have shown that evergreen plants usually have lower leaf N and photosynthetic capacities (Field & Mooney, 1986; Westoby et al., 2002; Wright et al., 2005a), so the dominance of deciduous species in our study, and in the flora of the Tibetan Plateau in general, may be one reason for high average leaf N concentrations and photosynthetic capacities. Another potential explanation for this high leaf N is the temperature–plant physiological hypothesis (TPPH) (Reich & Oleksyn, 2004), which predicts that leaf N should rise with decreasing temperature, as high leaf N may compensate for the low efficiency of physiological processes at low temperatures. Possibly because of the narrow MAT range (−9.7 to 6.8°C) in the current study, leaf N did not show any trend with decreasing temperature. However, when the leaf N data from Tibet were pooled with the dataset of Reich & Oleksyn (2004), the previously observed positive correlation between leaf N and mean annual temperature (MAT) at very low MATs disappeared (He et al., 2006). Thus, the TPPH is one potential explanation for higher leaf N concentrations on the Tibetan Plateau.

In recent years, several reports have documented global-scale variations in leaf functional traits and nutrient status (Reich et al., 1997; Sterner & Elser, 2002; McGroddy et al., 2004; Reich & Oleksyn, 2004; Wright et al., 2004b; Kerkhoff et al., 2005). A similar pattern of trait correlations is observed globally independent of growth form, biome or climate (Reich et al., 1997; Wright et al., 2004b). Despite the high altitude and low MAT of the Tibetan Plateau, which should exert strong evolutionary pressures on plant physiology, we found that interspecific leaf trait relationships on the Tibetan Plateau did not differ substantially from global patterns. Our results thus support the notion of convergent evolution in plant functioning (Reich et al., 1997), with data from near the lower temperature and elevation limits of plant tolerance.

Photosynthetic nitrogen use efficiency at high altitude

While shrubs on the Tibetan Plateau did not differ significantly in PNUE from shrubs in the global dataset, grasses and herbs had much lower PNUE in Tibet than globally. The lower intercept of the SMA regression line between Amass and Nmass for the Tibet data indicates that the Tibetan species had a lower Amass at a given Nmass, i.e. a lower PNUE (Table 3).

The Tibetan Plateau is characterized by both high altitude and low MAT. These characteristics are not independent, as MAT decreases with altitude. On the one hand, environmental conditions at higher altitudes are typically characterized by low MAT, low air pressure, high wind speed and high UV-B radiation (Friend & Woodward, 1990; Körner, 1999), all of which are considered to lower photosynthetic rates (Chapin et al., 1993). On the other hand, some studies have found that photosynthetic capacity at high altitude is comparable to that at low altitude (Körner, 1999). In addition, studies on alpine plants have revealed that leaf N concentration usually increases with increasing elevation (Körner & Diemer, 1987; Friend et al., 1989; Körner et al., 1989; Friend & Woodward, 1990; Westbeek et al., 1999). As a result, the PNUE of plants at high altitudes is predicted to be lower than that of plants at low altitudes.

The few studies investigating changes in PNUE along altitudinal gradients have supported this deduction (Körner & Diemer, 1987; Vitousek et al., 1990; Hikosaka et al., 2002). For example, Körner & Diemer (1987) found that in situ PNUE was 20–30% lower in many herbaceous species at an altitude of 2600 m than at 600 m in the Austrian Alps. Vitousek et al. (1990) also found that in situ PNUE of a Hawaiian tree species, Metrosideros polymorpha, decreased by half as altitude rose from 700 to 2500 m. In contrast, Terashima et al. (1993) showed that in situ PNUE of several herbaceous species at 4300 m in the Eastern Himalayas was comparable to that observed in lowland herbs. Based on a biochemical model, Terashima et al. (1995) argued that the biochemical suppression of photosynthesis should not be as large as has been supposed, because, with lowering of atmospheric pressure, the partial pressure of O2 decreases as well as that of CO2, which results in a reduction of photorespiration, partly compensating for the reduction in CO2 assimilation. Therefore, the effect of MAT at high altitudes may only partially contribute to this trend in PNUE.

In the present study, we observed a lower PNUE for the herbs and grasses on the Tibetan Plateau compared with the global average. N partitioning between photosynthetic and nonphotosynthetic structures (Loomis, 1997; Hikosaka, 2004) and N allocation within the photosynthetic apparatus (Hikosaka, 2004) may explain this decrease. Such partitioning differences could arise via alterations of leaf anatomy resulting from the falling temperature. Leaf thickness, palisade and parenchyma cell sizes, and the proportion of cell wall to cell volume may influence N partitioning, because these anatomical traits affect the ratio of cell wall mass to whole cell mass and thus the percentage of protein in each cell (Loomis, 1997). However, little information is available regarding the links between these anatomical traits and leaf N partitioning. These links will be the subject of future studies.

It is worth noting that our measurements were taken at local low air pressure. The Li-Cor 6400 photosynthetic system is designed to correct for any changes in air density resulting from changes in atmospheric pressure or air temperature, and provide the correct mole fraction of CO2 (Li-Cor Inc., 2002). In addition, the flow meter is a mass flow meter (not a volume flow meter). Thus the difference in gas exchange measurements between high and low altitudes is air pressure. We do not know how different the PNUE would be if we accounted for air pressure. This issue should be addressed in future studies.

Effects of climate on leaf traits in cold, extremely high-altitude environments

Whereas effects of climate on leaf traits were relatively small in our study, differences among plant functional groups and families were large, together explaining 25.3–60.7% of the total variation in the various leaf traits measured. If mean values for genera were used, the climate- and functional-group-related variations in leaf traits disappeared, indicating that different genera are not very specialized with regard to climatic preferences or functional group. In other words, these climate- and functional-group-related variations most likely reflect evolutionary processes occurring within genera at the intra- and interspecific levels.

Plant functional traits are considered to reflect adaptations to variation in the physical environment and ecophysiological as well as evolutionary trade-offs among different functions within a plant (Cornelissen, 1999; Ackerly et al., 2000; Westoby et al., 2002; Lavore et al., 2006). Thus, the responses of plant functional traits to climate, including responses to extreme low or high temperature, and to gradients of moisture availability, are associated with variations in life form and shifts in species composition (Chapin et al., 1993; Körner, 1999; Wright & Westoby, 2002; Cavender-Bares et al., 2004). Our results suggest that broad comparisons, at least in nontree plant species, should focus on intra- and interspecific variations, because data aggregation at the genus level may sacrifice too much information and thus not allow detection of macro-ecological patterns among climate, whole-plant functional type and leaf functional traits.

In the past 50 years, there have been numerous integrated surveys of forest and grassland resources in Tibet, with most of the work focusing on vegetation ecology (Chang & Gauch, 1986; Wang, 1988; Zhang et al., 1988). During the 1990s, long-term research on ecosystem structure and functioning began in the major vegetation types of the Tibetan Plateau (Li & Zhou, 1998), and recent studies have examined the productivity of its natural vegetation (Luo et al., 2002). In a new study on large-scale patterns of leaf N and P stoichiometry by Han et al. (2005), 14 out of 753 terrestrial plant species from across China were from Tibet. In spite of these efforts, the functional ecology of alpine plants on the Tibetan Plateau has been underrepresented in recent large-scale comparative studies, such as those of Reich & Oleksyn (2004) and Wright et al. (2004b).

Our study fills part of this information gap (Reich, 2005). The global uniqueness of the Tibetan Plateau with regard to its extremely high altitude and large size makes any global compilation incomplete without the inclusion of Tibetan data. More work on the ecology and evolution of plant traits in this region is therefore much needed to improve our understanding of global patterns of, in particular, plant C and N balance and allocation. Considering that global change may contribute to an upwards shift of climatic environments (Parmesan & Yohe, 2003), the Tibetan Plateau may be an important region for future research on plant acclimation and adaptation.

In conclusion, the general pattern of leaf trait relationships on the Tibetan Plateau is consistent with those reported previously on the global scale, providing additional support for convergent evolution in plant functioning. However, some patterns are unique to Tibet. First, overall leaf N concentrations and photosynthetic capacities were higher than the global average. This likely resulted from the dominance of deciduous species in our study, but low-temperature-associated chemical composition and physiological processes may also contribute to this pattern. Secondly, Tibetan species had a slightly lower PNUE, probably as a result of different N partitioning. Thirdly, even in a cold, extreme, high-altitude environment, the modulation of leaf traits by climate was weak, and the variations in leaf traits mainly occurred at the intra- and interspecific levels, not at the genus level.


  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

The authors thank members of the Peking University Expedition Team for the Tibetan Plateau, 2003, for assistance with field data collection. D. Ackerly, D. F. B. Flynn and three anonymous reviewers provided critical comments on an earlier version of the manuscript. This research was supported by grants from the National Natural Science Foundation of China (Grants 90411004, 40021101 and 90211016) to JSH and JYF, the State Key Basic Research and Development Plan (Project 2002CB412502) to JSH and Peking University Research Fund (Projects 211-II and 985-II) to JYF.


  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information
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Supporting Information

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Table S1 Dataset for photosynthetic rate, leaf nitrogen concentration, leaf mass per area and photosynthetic nitrogen use efficiency for each species at the 49 sites on the Tibetan Plateau, measured by JSH, WYZ, MZ and CYZ in 2003 (Department of Ecology, College of Environmental Sciences, Peking University, Beijing, China)

NPH1704sm_TableS1.xls41KSupporting info item