Journal of
Plant Ecology
PAGES 1–9
doi:10.1093/jpe/rtw069
available online at
www.jpe.oxfordjournals.org
Community-level trait responses
and intra-specific trait variability
play important roles in driving
community productivity in an
alpine meadow on the Tibetan
Plateau
Wei Li1,2,*, Jie Zhao3, Howard E. Epstein4, Guanghua Jing2,
Jimin Cheng1,2 and Guozhen Du5
1
State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, 26 Xinong
Road, Yangling 712100, China
2
Institute of Soil and Water Conservation, Chinese Academy of Sciences & Ministry of Water Resource, 26 Xinong Road,
Yangling 712100, China
3
College of Animal Science and Technology, Northwest A&F University, 22 Xinong Road, Yangling 712100, China
4
Department of Environmental Sciences, University of Virginia, 291 McCormick Road, Charlottesville, VA 22904–4123, USA
5
School of Life Sciences, Lanzhou University, 222 Tianshui Road, Lanzhou 730000, China
*Correspondence address. State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau,
Institute of Soil and Water Conservation, Chinese Academy of Sciences, No. 26 Xinong Road, Yangling, Shaanxi
Province 712100, China. Tel: +86-2987012272; Fax: +86-2987012210; E-mail: [email protected]
Abstract
Aims
Human activities have dramatically increased nutrient inputs to ecosystems, impacting plant community diversity, composition and functioning. Extensive research has shown that a decrease in species diversity
and an increase in productivity are a common phenomenon following
fertilization in grasslands ecosystem. The magnitude of the response of
species diversity and above-ground net primary productivity (ANPP) to
fertilization mainly depends on species traits (mean trait values) and traits
variability (plasticity). Our aim of this study was to examine (i) changes
of species diversity (species richness and Shannon–Wiener index) and
ANPP following fertilization; (ii) which species traits or communityweighted mean (CWM) traits can determine ANPP, as expected from
the ‘biomass ratio hypothesis’; and (iii) the relative role of intra-specific
and inter-specific trait variability in this process following fertilization.
Methods
We measured ANPP and four key plant functional traits: specific leaf
area (SLA), leaf dry matter content (LDMC), mature plant height (MPH)
and leaf nitrogen concentration (LNC) for 25 component species along
a fertilization gradient in an alpine meadow on the Tibetan Plateau. In
addition, trait variation of species was assessed using coefficients of
variation (CV), and we calculated the ratio of the CVintra to the CVinter.
Important Findings
Our results showed that: (i) fertilization significantly reduced
species richness and Shannon–Weiner diversity index, but significantly increased ANPP; (ii) there was a significant positive
correlation between ANPP and CWM–SLA and CWM–MPH,
yet there was no significant relationship between ANPP and
CWM–LNC or CWM–LDMC; (iii) intra-specific variability in
SLA and MPH was found to be much greater than inter-specific
variability, especially at the higher fertilization levels. We concluded that CWM–SLA and CWM–MPH can be used to assess
the impacts of species changes on ecosystem functioning, and
dominant species can maximize resource use through intra-specific variability in SLA and MPH to compensate for the loss of
species following fertilization, therefore maintaining high community productivity.
Keywords: biomass ratio hypothesis, fertilization, leaf dry matter
content, leaf nitrogen concentration, mature plant height, specific
leaf area, Tibetan Plateau
Received: 28 September 2015, Revised: 15 March 2016, Accepted:
24 June 2016
© The Author 2016. Published by Oxford University Press on behalf of the Institute of Botany, Chinese Academy of Sciences and the Botanical Society of China.
All rights reserved. For permissions, please email: [email protected]
Page 2 of 9
INTRODUCTION
Understanding the relationship between plant diversity and
ecosystem functioning (e.g. net primary productivity) is a
central issue in ecology (Chapin et al. 2000). Previous studies
have shown that diversity usually decreases, but productivity usually increases following nutrient addition (Clark and
Tilman 2008; Gough et al. 2000; Li et al. 2015). Numerous fertilization experiments have reported nearly ubiquitous negative productivity–diversity relationships (e.g. Chalcraft et al.
2008; Dickson et al. 2014). The traits of dominant species associated with competition ability play key roles in the response
of communities to fertilization (McGill et al. 2006; Shipley
et al. 2006; Violle et al. 2007), yet we still do not fully understand which traits and/or traits variability control community
productivity following fertilization in natural communities.
The ‘biomass ratio hypothesis’ proposed by Grime (1998)
postulates that the extent to which the traits of a species affect
ecosystem function is likely to be related to the contributions
of the species to the biomass of the community. The underlying assumption of this hypothesis is that the traits weighted
by the species abundance will scale up to the ecosystem function (Chapin et al. 2000; Díaz and Cabido 1997; Lavorel and
Garnier 2002). According to this hypothesis, the functioning
of ecosystems is determined to a large extent by the traits of
the dominant species. Several studies have tested this hypothesis for different ecosystem functions such as primary productivity (Garnier et al. 2004, 2007; Vile et al. 2006), nitrification
(Laughlin 2011) and litter decomposition (Garnier et al. 2004;
Tardif et al. 2014). The challenge is to identify the key functional traits of dominant species that have important effects
on ecosystems functioning (Chapin et al. 2002).
In majority of the studies that link functional traits and
ecosystem functioning, species have been described by mean
functional trait values, i.e. to have intra-specific variability
negligible compared with inter-specific variability (McGill et al.
2006; Shipley et al. 2006), therefore the relative importance of
intra-specific and inter-specific trait variability remains poorly
known. Plant species have to cope with temporal and spatial
environmental heterogeneity, which leads to trait variability
(Via et al. 1995). This variability is derived from two mechanisms. The first one is genetic variability,i.e. different genotypes
produce different phenotypes that are selected in different environments (Joshi et al. 2001). The second mechanism, phenotypic plasticity, is the ability of one genotype to express different
phenotypes depending on environmental conditions (Sultan
2004). Both mechanisms are important. Existing genetic variability and the time lag between the cue and the response can
be long, especially for long lived species. Conversely, phenotypic
plasticity allows for more flexible and faster response to environmental change (Valladares et al. 2007). In this study, we did
not distinguish between genetic and plastic effects (Albert et al.
2010). Several recent studies have shown that intras-pecific
variability can have significant effects on ecosystem functioning
(Jung et al. 2014; Lecerf and Chauvet 2008; Siefert et al. 2015).
Journal of Plant Ecology
The Tibetan Plateau is the youngest and highest plateau
in the world. Alpine meadows comprise the representative
vegetation on the plateau, and they are also very fragile and
sensitive ecosystems due to changes in global climate and
land use (Klein et al. 2007). Previously, a series of fertilization experiments were conducted in an alpine meadow on
the Tibetan Plateau in China to better understand the potential mechanism of species loss due to fertilization (e.g. Luo
et al. 2006; Li et al. 2015; Niu et al. 2008). However, to our
knowledge, no studies so far have explored the relationship
between community-weighted mean (CWM) trait values
and above-ground net primary productivity (ANPP), and the
relative role of intra-specific and inter-specific trait variability
along a fertilization gradient in this region.
We chose four functional traits, known to affect ecosystem
functioning at the leaf, whole plant, and ecosystem levels
(Cornelissen et al. 1999; Reich et al. 1992): specific leaf area
(SLA, the ratio of water-saturated leaf area to leaf dry mass),
leaf dry matter content (LDMC, the ratio of leaf dry mass to
water-saturated fresh mass), mature plant height (MPH, the
perpendicular distance between the upper foliage boundary
and the ground) and leaf nitrogen concentration (LNC). SLA
is closely related with relative growth rate and leaf net carbon
assimilation rate (Wright et al. 2001); it is also a good predictor of plant responses to resource availability (Grime 1977).
LDMC is associated with plant nutrient retention and water
(Poorter and Garnier 1999). MPH is directly related to light
interception and competitive ability (Westoby 1998); LNC is
closely correlated with concentrations of proteins involved in
photosynthesis as well as leaf growth and defense strategies
(Reich et al. 1992). Fast growing species from nutrient-rich
habitats usually have high SLA, high LNC, high MPH and
low LDMC, while the opposite characterizes species from
nutrient-poor habitats (Díaz et al. 2004). These characteristics
reflect a fundamental trade-off between traits related to nutrient conservation and traits related to nutrient acquisition and
turnover (Wright et al. 2004).
In this study, we tested the following predictions: (i) species diversity (species richness and Shannon–Wiener index)
should decrease, but ANPP should increase along an increasing fertilization gradient; (ii) changes in CWM traits in
response to fertilization may predominantly drive ANPP; (iii)
responses of intra-specific and inter-specific trait variability
should be different along an increasing fertilization gradient.
MATERIALS AND METHODS
Study site
The experiment was conducted at the Research Station
of Alpine Meadow and Wetland Ecosystems of Lanzhou
University (33°58′N, 101°53′E) on the eastern Tibetan
Plateau, 3500 m a.s.l., Gansu, China, The average yearly temperature is 1.2°C, ranging from −10°C in January to 11.7°C
in July, with ~270 frost days. Average annual precipitation
over the last 35 years is 620 mm, occurring mainly during the
Li et al. | Community-level trait responses and intra-specific trait variability
short, cool summer. The annual cloud-free solar radiation
is ~2580 h (Li et al. 2015). The vegetation, typical of Tibetan
alpine meadows, is dominated by clonal Kobresia graminifolia,
Elymus nutans, Festuca ovina, Poa poophagorum, Agrostis hugoniana, Saussurea nigrescens and Anemone rivularis (Li et al. 2015).
The soil type is subalpine meadow soil. The average soil
organic C (%), available N (kg−1), available P (kg−1) and PH is
1.6, 16.2, 2.1 and 7.1, respectively before applying fertilizers
(Li et al. 2015).
Experimental design
Thirty-six 4 m × 10 m plots composed of four fertilization
levels with nine replicates were distributed in nine columns
and four rows with a randomized block design. Each plot was
separated from the others by a 2 m buffer strip. The fertilization treatment was generated with different amounts of
(NH4)2 HPO4 fertilizer applied annually from 2007 to 2010 at
the beginning of the growing season (usually in the middle of
May). The fertilizer was applied during drizzly days to avoid
the need for watering (Li et al. 2015). Fertilizer applications
of 0, 15, 30 and 60 g m−2 yr−1 are hereafter referred to as
F0, F15, F30 and F60, which correspond to 0, 3.15, 6.3 and
12.6 g N m−2 yr−1 and 0, 3.5, 7.0 and 14.0 g P m−2 yr−1 (Li et al.
2011; 2015). Each plot was separated into two subplots: a 4
m × 4 m subplot for vegetation monitoring, and a 4 m × 6 m
subplot for individual plant sampling.
Vegetation monitoring
Above-ground biomass was sampled at its peak in late August
and early September from 2007 to 2010, which approximates
the ANPP in temperate grasslands (Sala and Austin 2000). At
the end of each growing season, one 0.25 m2 quadrat was
harvested from the 4 m × 4 m subplots in each plot. The quadrat location was randomly selected with the constraint that it
was at least 0.5 m from the margin to avoid edge effects. We
estimated the cover of each species before being harvesting
at ground level. All samples were dried at 80°C for 48 h, and
weighed to the nearest 0.01 g.
Leaf trait measurements
Following Pérez-Harguindeguy et al. (2013), four plant functional traits were measured in early September 2010 for 25
species (supplementary Table S1), representing at least 85%
of the peak standing biomass in this studied meadow. We randomly sampled two individuals and six mature leaves (three
leaves per individual) at flowering time for each of the 25 species in each 4 m × 6 m subplot to measure SLA and MPH (Li
et al. 2015). That is 18 individuals and 54 mature leaves were
measured for each of the 25 species, yet the real number of
samples will decline for fertilized plots because of the loss of
species following fertilization. MPH is the perpendicular distance between the upper foliage boundary and the ground.
Leaf area was measured by scanning the leaves with scanner
and analyzing the images with Image J (Rasband 2005), and
fresh weight of leaves were measured on a balance with an
Page 3 of 9
accuracy of 10−4 g (Acculab Lt-320; Acculab, Measurement
Standards, Inc., Danvers, MA). Following these measures,
leaves were placed in paper bags and dried in the sun. Leaf
samples were oven-dried at 80°C for 48 h in the laboratory
and their dry masses were measured on a semianalytical balance with an accuracy of 10−4 g (Sartorius AG, Goettingen,
Germany). Dried leaf samples were ground using a ball mill
(NM200; Retsch, Haan, Germany). Total N concentrations of
leaves were determined using an elemental analyzer (2400
II CHNS/O Elemental Analyzer; Perkin-Elmer, Boston, MA).
Data analysis
From the vegetation harvest data, we calculated the ANPP,
and two indices were selected to estimate diversity according to Pielou (1969). The first index is plant species richness, represented by the number of species recorded in each
quadrat. The second, Shannon–Weiner diversity index is:
S
H ¢ = -å Pi log 2Pi , where Pi is the cover proportion of species
i =1
represented by species i.
The CWM values for each trait were calculated for each plot
using species trait values and species relative cover following
S
Garnier et al. (2004) with: CWM = å Pi ´ traiti , where S is
i =1
the number of species in the sample, Pi is the relative cover of
species i and traiti is the trait value of species i.
We used one-way ANOVA to test the effect of fertilization
on species diversity (species richness and Shannon–Wiener
diversity index) and on the CWM for each functional trait.
Then, we performed linear regression modeling to test the
relationships between ANPP and species diversity and the
CWM for each trait. Correlations between CWMs of each
leaf trait were tested using Pearson correlation coefficients.
A principal component analysis (PCA) on standardized data
was conducted to analyze the overall pattern between community properties (species richness, Shannon–Wienner diversity index and ANPP) and the CWM of the four traits (SLA,
LNC, MPH and LDMC).
Trait variation of species was assessed using coefficients
of variation (CV) (Albert et al. 2010, 2011): within species,
CVintra was calculated using the ratio of the standard deviation (σ) to the mean (μ) of each species in each fertilization
level, and among species, CVinter, was assessed using the ratio
of the standard deviation (σ) to the mean (μ) of all species
in each fertilization level. In addition, we calculated the
ratio of the CVintra to the CVinter. If CVintra/CVinter > 1, this
was indicative of high variation within species, generated by
the existence of within-species trade-offs in functional traits,
which could maximize resource use and the maintain of high
productivity.
All variables met the statistical assumptions (residual normality, homogeneity of variance and data linearity) when
tested using the Shapiro–Wilk test and Levene’s test, respectively. All statistical analyses were performed using the
R 3.2.2 software (R Core Team, 2015)
Page 4 of 9
Figure 1: the changes of species richness (A), Shannon–Wiener
diversity index (B) and ANPP (C) along the fertilization gradients. F0,
F15, F30 and F60 represent (NH4)2HPO4 fertilizer applications of 0,
15, 30 and 60 g m−2 yr−1. Significant differences indicated by dissimilar letters above each bar were determined using Tukey’s honestly
significant difference (HSD) test (P < 0.05) after one-way ANOVA.
Journal of Plant Ecology
Figure 2: the relationship between ANPP and community-weighted
mean trait of leaf area per unit dry mass (CWM–SLA) (A), leaf nitrogen concentration (CWM–LNC) (B), mature plant height (MPH) (C)
and leaf dry matter content (CWM–LDMC) (D). Regression coefficient
(R2) and levels of significance (P-values). F0, F15, F30 and F60 represent (NH4)2HPO4 fertilizer applications of 0, 15, 30 and 60 g m−2 yr−1.
Li et al. | Community-level trait responses and intra-specific trait variability
RESULTS
Changes of species diversity and ANPP
As expected, species diversity always decreased with increased
artificial fertilization levels. Fertilization significantly reduced
species richness (Fig. 1A) and Shannon–Weiner diversity
index (Fig. 1B), but significantly increased ANPP (Fig. 1C).
Relationships between ANPP and CWM traits
Fertilization significantly increased CWM–SLA (supplementary Figure S1A) and CWM–MPH (supplementary Figure
S1C), but did not change CWM–LNC (supplementary Figure
S1B) and CWM–LDMC (supplementary Figure S1D). There
was a significant positive correlation between ANPP and
CWM–SLA (Fig. 2A) and CWM–MPH (Fig. 2C). However,
there were no significant relationships between ANPP and
CWM–LNC (Fig. 2B) and CWM–LDMC (Fig. 2D).
Relationships among species diversity, ANPP and
CWM traits and trait variability within species
and between species
Simple correlations (Table 1) and a PCA (Fig. 3) combining
data on community properties and CWM traits at the community level were conducted. The analyses revealed significant
correlations among ANPP, richness, Shannon–Weiner diversity index, CWM–SLA and CWM–MPH. Relationships among
CWM–SLA, CWM–LNC, CWM–MPH and CWM–LDMC were
also significant (Table 1). Figure 3 shows how these different
variables are associated in the multivariate analysis. The first
axis differentiates communities according to fertilization levels: the communities with higher fertilization levels showed
greater ANPP, CWM–SLA and CWM–MPH while the communities of lower fertilization levels showed higher species richness and Shannon–Wiener diversity index. The second axis is
mostly defined by CWM–LNC and CWM–LDMC.
We calculated the ratio of CVintra to CVinter: results showed
that inter-specific variability exceeded intra-specific variability for LNC and LDMC at each fertilization level. There was
greater trait variability within species than between species
for SLA and MPH, especially at the higher fertilization levels
(Table 2). The ratio of CVintra to CVinter had a increased trend
Page 5 of 9
with increased fertilization levels (Table 2), and this further
showed that dominant species can utilize intra-specific variability to maximize resource use.
DISCUSSION
Our study have shown that simple, quantitative plant functional traits (SLA and MPH) weighted by its relative abundance could predict ecosystem functioning (e.g. ANPP) in this
meadow community. Furthermore, the dominant species can
maximize resource use through intra-specific variability to
compensate for the loss of species in response to fertilization,
therefore maintaining high community productivity.
Plant ecologists have long concentrated on explaining the
relationship between productivity and diversity, and diversity
usually decreases along artificial fertilization gradients (Clark
and Tilman 2008; Silvertown et al. 2006). Consistent with
these studies, our results demonstrated that species richness
and Shannon–Wiener diversity index significantly decreased
with increased ANPP. Explanations for the decline in species
diversity due to nutrient enrichment mainly invoke competition (Hautier et al. 2009; Li et al. 2015; Rajaniemi 2002).
Fertilization can cause a switch in competition from mainly
below-ground (root competition) to above-ground (light
competition), in which the species with trait values that are
advantageous under the changed environment will exclude
other species; however, tests of the importance of particular functional traits (e.g. SLA, LNC, LDMC and MPH) along
fertilization gradients are rare. Numerous studies show that
the CWM traits obtained by taking the mean trait value for a
given species weighted by its relative abundance within the
community, then summed across all species can better predicts ecosystem function (Garnier et al. 2004; Laughlin 2011;
Tardif et al. 2014; Vile et al. 2006).
As proposed by the ‘biomass ratio hypothesis’ (Grime 1998),
our results showed that two of the four CWM traits were
strongly related to the ANPP. There was a significant positive
correlation between ANPP and CWM–SLA and CWM–MPH,
yet there was no significant relationship between ANPP and
CWM–LNC or CWM–LDMC. This result indicates that perhaps
Table 1: Pearson correlation coefficients between community properties (richness, Shannon–Wiener diversity index and ANPP) and
CWM of four functional traits
Shannon–Wiener diversity index
Richness (no.0.25 m−2)
Richness
ANPP
CWM–SLA
CWM–LNC
CWM–MPH
0.84
ANPP (g 0.25 m−2)
−0.83
CWM–SLA (cm2 g−1)
−0.59
−0.69
0.59
CWM–LNC (mg g−1)
−0.19
−0.20
0.19
0.69
CWM–MPH (cm)
−0.75
−0.83
0.74
0.79
0.41
CWM–LDMC (g g−1)
−0.11
−0.22
0.11
0.66
0.76
−0.86
0.53
Bold types indicate a significant correlation between community properties and CWM of three leaf trait by Pearson correlation test (P < 0.05).
CWM = community-weighted mean trait values; ANPP = above-ground net primary productivity; SLA = leaf area per unit dry mass; LDMC = leaf
dry matter content; LNC = leaf nitrogen concentration; MPH = mature plant height.
Page 6 of 9
Figure 3: principal component (PC) analysis combining data on
community properties (species richness, Shannon–Wienner diversity index and ANPP) and CWM of four functional traits (SLA, LNC,
MPH and LDMC). Only the first two axes (PC1 and PC2), which
account for 96.2% of the total variation, are retained here. The encircled groups of points represent four fertilization gradients. CWM,
community-weighted mean trait values; ANPP (g 0.25 m−2), aboveground net primary productivity; SLA (cm2 g−1), leaf area per unit
dry mass; LDMC (g g−1), leaf dry matter content; MPH (cm), mature
plant height; LNC (mg g−1), leaf nitrogen concentration. F0, F15, F30
and F60 represent (NH4)2HPO4 fertilizer applications of 0, 15, 30 and
60 g m−2 yr−1.
the variation in CWM–LNC or CWM–LDMC is not directly
linked to variation in ANPP, but rather controlled by other factors. Our results support the idea that the functional identities
(SLA and MPH) of the dominant species largely determine ecosystem functioning, yet subordinate species do not play a key
role driving ecosystem functions as the dominant ones does
(e.g. Garnier et al. 2004, 2007; Roscher et al. 2012; Vile et al.
2006). Polley et al. (2007) concluded that dominant species can
constrain the effects of species diversity on temporal variability
in biomass production in a tallgrass prairie. Cortez et al. (2007)
also found that functional traits of dominant species were tightly
linked with litter N concentration, and thereby to litter decay
and N loss rates. In addition, following fertilization, many subordinate species (forb species) gradually disappeared due to the
increased potential competition for soil and/or light resources,
but grasses increased dominance with greater growth rates and
greater heights in fertilized plots (Li et al. 2011). Our results are
consistent with McKenna and Shipley (1999) and Cornelissen
et al. (2003) who also showed that the functional identity of
grass species may be the main driver for community biomass
production in this alpine meadow.
At the species scale, SLA, LNC, MPH and LDMC reflect
fundamental trade-offs between rapid biomass production
Journal of Plant Ecology
(high SLA, high LNC, high MPH, low LDMC species) and efficient nutrient conservation (low SLA, low LNC, low MPH,
high LDMC species) (Poorter and de Jong 1999; Westoby
1998). However, at the plant community scale, our results do
not confirm previous findings about the relationships among
functional traits (but see Pérez-Ramos et al. 2012; Prieto et al.
2015). Our results showed that CWM–SLA, CWM–LNC,
CWM–LDMC and CWM–MPH were all positively correlated.
These contradictory results indicate that the relationships
among species mean trait values may not directly reflect the
relationships among CWM trait values, and future studies
also need to further clarify the link between species traits and
community functional parameters (i.e. traits values weighted
according to the relative abundance of species).
Changes in CWM trait values might be due to the replacement of species with different trait values (inter-specific
variability), or to changes in trait values within a species
(intra-specific variability), or to a combination of these two.
In order to determine the relationships between CWM trait
values and ANPP, we quantified the ratio between intra- and
inter-specific variability. Classic niche theory predicts that the
proportion of intra-specific variation compared with inter-specific variation showed vary with species richness (Violle et al.
2012). Consistent with niche theory, our results showed that
the ratios of intra- to inter-specific variability of SLA and MPH
variability increased with increasing productivity or decreasing species richness. In addition, we showed that intra-specific
variabilities of SLA and MPH were found to be much greater
than inter-specific variability at higher fertilization levels.
Our results also confirmed that the dominant species (Elymus
nutans, Anemone rivularis, Sphallerocarpus gracilis, Poa poophagorum) can maintain higher community productivity (or greater
resource use) through maximizing intra-specific variability to compensate the loss of species following fertilization.
Several other studies have recently found that species richness decreases as dominant species or clonal species increase in
response to fertilization and compete strongly with other species (Dickson et al. 2014; Isbell et al. 2013). We showed in this
study that changes in the values of CWM traits were mainly
due to changes in trait values within species (SLA and MPH)
across the fertilization gradient. Therefore, we suggest that
intra-specific variability could indeed play an important role
in community structure and dynamics.
All in all, our findings are in line with the idea that ecosystem functioning depends more on species functional
traits than on species richness (Li et al. 2013; Tilman et al.
1997). Our study, conducted on an alpine meadow, indicated that species can maximize resource use through
increased intra-specific variability in SLA and MPH to
compensate for the loss of species following fertilization,
therefore maintaining high community productivity. A further test is to assess which components of functional diversity are important to ecosystem functioning (Cadotte et al.
2009), and quantify the species turnover and intra-specific
variability along the environmental gradients (Kichenin
0.06
0.02
0.10
0.50
0.12
1.29
0.81
0.60
1.17
1.99
1.39
1.10
0.73
0.56
0.79
0.91
1.82
1.08
1.06
0.50
1.00
0.77
0.61
0.73
1.24
0.79
0.73
0.79
0.63
0.66
1.02
2.40
2.13
0.71
1.37
0.66
0.72
0.89
2.43
0.93
0.84
0.73
1.63
0.72
0.79
0.57
1.36
0.61
0.95
Anemone trullifolia
Aster alpinus
Delphinium kamaonense
Elymus nutans
Euphorbia micractina
Festuca ovina
Gentianopsis paludosa
Geranium pylzowianum
Halenia elliptica
Kobresia capillifolia
Koeleria cristata
Leontopodium nanum
Plantago depressa Willd.
Poa poophagorum
Potentilla fragarioides
Ranunculus tanguticus var.
capillaceus
Saussurea nigrescens
Saussurea stella
Sphallerocarpus gracilis
Thalictrum alpinum Linn.
Thermopsis lanceolata
Trollius farreri
1.96
0.03
0.05
0.03
0.08
0.00
0.06
0.05
0.06
0.05
0.01
0.08
0.05
0.01
0.01
0.03
0.00
0.04
0.02
0.06
0.01
0.11
0.07
0.02
0.1
0.07
0.05
0.04
0.10
0.03
0.02
0.06
0.18
0.02
0.04
0.07
0.04
0.13
0.01
0.04
0.06
0.04
0.04
0.12
0.13
F30
0.02
0.15
0.04
0.02
F60
0.66
0.33
0.64
0.53
0.25
0.33
0.29
0.66
0.31
0.56
0.26
0.29
0.45
0.50
0.48
0.38
0.35
0.28
0.64
0.40
0.28
0.20
0.28
0.26
0.53
F0
0.41
0.46
0.58
0.63
0.30
0.46
0.33
0.45
0.40
0.51
0.3
0.74
0.44
0.44
0.61
0.38
0.74
0.35
0.74
0.82
0.75
0.42
0.4
0.25
0.58
F15
LDMC (g g−1)
0.49
0.33
0.68
0.32
0.47
0.51
0.38
0.78
0.31
0.39
0.65
F30
0.52
0.21
0.59
0.51
F60
0.28
0.72
0.66
0.44
0.18
0.22
0.86
0.22
0.43
0.43
0.24
0.52
0.77
0.28
0.62
0.45
0.69
0.33
0.28
0.94
1.07
0.44
0.53
0.37
1.14
0.4
1.41
0.45
0.37
0.71
0.52
0.25
1.09
0.37
1.13
1.04
0.79
1.11
0.63
0.29
1.10
0.77
0.85
0.84
0.73
F15
0.47
0.14
1.36
0.40
0.36
F0
MPH (cm)
1.11
1.09
1.49
1.03
0.84
1.26
1.12
1.09
1.01
1.06
1.18
F30
Bold types indicate intra-specific variability exceeds intra-specific variability. SLA = leaf area per unit dry mass, LDMC = leaf dry matter content, LNC = leaf nitrogen concentration,
MPH = mature plant height. F0, F15, F30 and F60 represents (NH4)2HPO4 fertilizer applications of 0, 15, 30 and 60 g m−2 yr−1. CVinra = coefficients of variation within species;
CVinter = coefficients of variation among species.
0.69
1.06
1.16
1.25
0.27
1.09
0.20
0.08
0.09
0.05
0.06
2.14
2.13
1.06
1.55
2.00
0.02
0.03
0.01
0.11
1.11
2.01
0.48
0.39
Anemone rivularis
0.26
0.70
0.13
0.51
0.41
Anemone obtusiloba D. Don.
1.09
1.26
1.26
Agrostis hugoniana Rendle
F15
F0
F60
F15
F0
F30
LNC (mg g−1)
SLA (cm2 g−1)
Table 2: the ratio of the CVintra to the CVinter of four functional traits for each species in different fertilization gradients
1.11
1.20
1.17
1.05
F60
Li et al. | Community-level trait responses and intra-specific trait variability
Page 7 of 9
Page 8 of 9
Journal of Plant Ecology
et al. 2013). Plant strategies are an integration of multiple correlated traits (Craine 2009); single leaf functional
traits (SLA and MPH) do not fully reflect plant responses.
Further research should attempt to link other plant traits
(e.g. leaf phosphorus content and seed size) and functional
diversity to better understand the mechanisms of diversity
loss with fertilization.
Cortez J, Garnier E, Pérez-Harguindeguy N, et al. (2007) Plant traits,
litter quality and decomposition in a Mediterranean old-field succession. Plant Soil 296:19–34.
SUPPLEMENTARY MATERIAL
Díaz S, Cabido M (1997) Plant functional types and ecosystem function in relation to global change. J Veg Sci 8:463–74.
Supplementary material is available at Journal of Plant Ecology
online.
Díaz S, Hodgson JG, Thompson K, et al. (2004) The plant traits
that drive ecosystems: evidence from three continents. J Veg Sci
15:295–304.
FUNDING
Garnier E, Cortez J, Billes G, et al. (2004) Plant functional markers
capture ecosystem properties during secondary succession. Ecology
85:2630–7.
This study was supported by Natural Science Foundation
of China (41230852), Key Program of Chinese Academy of
Sciences (KJZD-EW-TZ-G10), Northwest A & F University
(Z109021107, Z109021307, QN2013070), West Light
Foundation of Chinese Academy of Sciences (K318021305),
Natural Science Foundation of Shaanxi Province
(2016JQ3008) and the China Scholarship Council.
ACKNOWLEDGEMENTS
We thank colleagues who helped with field work.
Conflict of interest statement. None declared.
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