Download Leaf trait variation captures climate differences but differs with

Journal of
Plant Ecology
Volume 8, Number 1,
Pages 61–69
February 2015
doi:10.1093/jpe/rtu009
Advance Access publication
25 August 2014
available online at
www.jpe.oxfordjournals.org
Leaf trait variation captures climate
differences but differs with species
irrespective of functional group
Guohong Wang1,*, Jinglan Liu2 and Tingting Meng 1
1
State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, The Chinese Academy of Sciences,
No. 20 Nanxincun, Xiangshan, 100093 Beijing, China
2
College of Natural Reserves, Beijing Forestry University, No. 35 Qinghuadonglu, Beijing 100083, China
*Correspondence address. State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, The
Chinese Academy of Sciences, No. 20 Nanxincun, Xiangshan, Beijing 100093, China Tel: +86-10-6283-6585;
Fax: +86-10-6259-0833; E-mail: [email protected]
Abstract
Aims
To clarify whether variation in leaf traits with climate differs with
scale, i.e. across species and within a species, and to detect whether
plant functional group affects species-specific response.
Methods
Leaf dry matter content (LDMC), specific leaf area (SLA), mass- and
area-based leaf N (Nmass, Narea) and leaf P concentrations (Pmass,
Parea) and leaf chlorophyll concentration (SPAD) were measured for
92 woody plant species in two botanical gardens in China. The two
gardens share plant species in common but differ in climate. Leaf
trait variation between the two gardens was examined via mean
comparison at three scales: all species together, species grouped
into plant functional groups and within a species. A meta-analysis
was performed to summarize the species-specific responses.
Important Findings
At the scale of all species together, LDMC, SLA, Pmass and Nmass were
significantly lower in the dry-cold habitat than in the wet-warm one,
whereas Narea and SPAD showed an inverse pattern, indicating a
significant environmental effect. The meta-analysis showed that the
above-mentioned patterns persisted for SLA, Narea and SPAD but
not for the other variables at the species-specific scale, indicating
that intraspecific variation affects the overall pattern of LDMC, Pmass
and Nmass and Parea. In terms of species-specific response, positive, negative or nonsignificant patterns were observed among the
92 species. Contrary to our prediction, species-specific responses
within a functional group were not statistically more similar than
those among functional groups. Our results indicated that leaf trait
variation captured climatic difference yet species-specific responses
were quite diverse irrespective of plant functional group, providing
new insights for interpreting trait variability with climate.
Keywords: chemical traits, leaf dry matter, specific leaf area,
intraspecific variation, life form, woody plants, botanical garden
Received: 26 August 2013, Revised: 17 June 2014, Accepted: 26
June 2014
Introduction
Plant functional traits can be defined as the morphological,
ecophysiological, biochemical, demographic or phenomenological characteristics of a plant (Diaz and Cabido 2001;
Lavorel et al. 2006). Trait variability with environment has
emerged as a hotspot of research because it can provide an
improved basis for modelling vegetation response to environmental change (Barboni et al. 2004; Lavorel and Garnier
2002; Neilson 1995; Prentice et al. 1992; Wright et al. 2005)
and can uncover mechanisms underlying plant invasion
(Funk 2008; Leishman et al. 2007; Ordonez et al. 2010; Sultan
2001), community assembly (Ackerly 2003; 2004; Kang et al.
2014; Keddy 1992; Uriarte et al. 2010; Weiher et al. 1998) and
plant evolution (Ackerly et al. 2000; 2006; Arntz and Delph
2001; Badyaev et al. 2005). In addition, trait variability among
species or life forms has been applied in plant functional
classification (Diaz and Cabido 1997; Gitay and Noble 1997;
Lavorel et al. 1997).
As has been well recognized since Darwin, however, plant
trait variability is determined by genetics and the environment (Desdevises et al. 2003; Dudley and Schmitt 1995;
Givnish 1987; Schilchting 1986). Thus, although comparisons
across species may be appropriate for examining an overall
© The Author 2014. Published by Oxford University Press on behalf of the Institute of Botany, Chinese Academy of Sciences and the Botanical Society of China.
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62
pattern of trait variation with climate (Bertiller et al. 2006;
He et al. 2006; Reich and Oleksyn 2004; Wright et al. 2004)
and for detecting an environmental filter in the context of
community assembly (Bello et al. 2005; Cingolani et al. 2007;
Cornwell and Ackerly 2009; Cornwell et al. 2006; Weiher
et al. 1998), using mean trait value means hiding intraspecific
variation (Albert et al. 2010; Fajardo and Piper 2011). Hence,
trait–climate relationships observed across species could be
inevitably confounded by plant phylogeny (Ackerly 2004;
Givnish 1987; Kroon et al. 2005; Watanabe et al. 2007). In
contrast, intraspecific comparisons are an alternative method
to study trait–climate relationships due to the minimization of
influence of genetic difference (Arntz and Delph 2001; Loreti
and Oesterheld 1996; Sultan et al. 1998; Vitasse et al. 2010;
Warren et al. 2006).
Therefore, for the same set of plant species in two contrasting habitats, we would hypothesize that plant trait variability
observed across species should be congruent with differences
in environments, yet variability observed within a species
should differ with species because of different genetic basis.
Nonetheless, plant trait variability should be similar within
a functional group as predicted by the hypothesis underlying
plant functional classification (Diaz and Cabido 1997; Gitay
and Noble 1997; Lavorel et al. 1997).
In this study, we measured several leaf functional traits
that characterize leaf structure properties and leaf chemical
components (Cornelissen et al. 2003) for 92 woody plant species cultivated in the Beijing Botanical Garden (BBG) and
in the Nanjing Botanical Garden (NBG). Both gardens have
large plant collections with relatively long cultivation histories, i.e. >50 years (Anonymous 1959a,1959b). In addition,
the two gardens have many plant species in common in their
collections but differ in climate, providing an opportunity to
examine trait–climate relationships for a wide array of plant
species at different scales. Specifically, we tested the hypothesis at three scales: i.e. all species together to test the effect of
climate, species grouped into plant functional groups to detect
the convergence among species within a functional group and
within a species to examine species-specific responses. We
attempted to answer the following questions: (i) is the leaf
trait variation observed across species congruent with the differences in climate between the two gardens? (ii) do species
differ from each other in terms of leaf trait variation? and (iii)
is there less trait variation within a plant functional group
than among plant functional groups?
Materials and Methods
Study area
The BBG (116°12.412′E, 39°59.216′N, 70–80 m alt.) and the
NBG (118°48′ E, 32°01′N, 30–50 m alt.) are nearly 950 km
apart. Their climatic conditions are temperate monsoon for
BBG and subtropical monsoon for NBG, representing a climatic difference varying from cool-dry with a shorter growing
season to warm-wet with a longer growing season. Specifically,
Journal of Plant Ecology
the mean annual temperature, mean annual precipitation and
frost-free period at BBG and at NBG between 1970 and 2000
are 12.3 and 15.4°C, 517 and 1031 mm, and 200 and 237 days,
respectively. BBG was established in 1955, and its total collection of woody plants was up to 1005 species belonging to 67
families and 65 genera (Anonymous 1959a). NBG was established in 1929, and its total collection of woody plants was
up to 1317 species belonging to 89 families and 354 genera
(Anonymous 1959b). Two comprehensive surveys conducted
in the urban areas of Beijing and Nanjing (mainly in various
gardens, including BBG and NBG) showed that the mean of
total soil N and available P in Beijing were approximately onehalf (0.66 vs. 1.5 g kg−1) and one-third (20 vs. 60.52 mg kg−1)
of those in Nanjing, respectively (Ma 2007; Wang et al. 2006)
Plant species
A total of 92 woody plant species, encompassing 8 gymnosperm species and 84 angiosperm species belonging to 44
families and 72 genera, were studied (Supplementary Table
S1). These species can be functionally grouped into deciduous
species (58 trees, 19 shrubs and 4 lianas) and evergreen species (6 trees, 4 shrubs and 1 liana).
Leaf functional traits
Leaf traits were measured in mid-July and early August in
BBG and NBG, respectively. In each garden, between 25 and
200 leaves were taken per species from at least five healthy
adult individuals. Leaves were collected from plants in fulllight situations. Leaf samples from different individuals were
pooled and then immediately placed into a fully wetted and
light-shaded paper bag and sealed in a plastic bag. We randomly selected 10 leaves for measuring leaf chlorophyll concentration (SPAD-502, Minolta Camera Co., Japan), with
three measurements of each leaf at different positions within
the lamina. The pooled leaves were divided into five subsamples (five replications). Although the five subsamples do not
necessarily match the five individuals, they may fairly reveal
the variation in leaf traits among the individuals of a species
between the gardens. Leaves in each subsample were scanned
(HP Scanjet 2000c), weighed and then dried at 65°C for 24 h,
weighed, ground and passed through an 80 mesh sieve for leaf
nitrogen (N) and leaf phosphorous (P) analysis. Leaf N was
determined colourimetrically using the automatic Kjeldahl
method (Kjektec System 1026 Distilling Unit, Sweden). Leaf P
was analysed by spectrophotometer. Seven leaf functional traits
were calculated/measured for the 92 species (Supplementary
Table S1): leaf dry matter content (LDMC), specific leaf area
(SLA), concentration of mass- and area-based leaf N (Nmass,
Narea), leaf P (Pmass, Parea) and leaf chlorophyll (SPAD: a relative
value of leaf chlorophyll concentration).
Data analysis
We compared the means of the leaf traits between gardens via
one-way analysis of variance (ANOVA) at various scales, i.e.
all species as a whole, species grouped into plant functional
Wang et al. | Relationship between leaf traits and climate63
groups and each of the 92 species. A meta-analysis was performed to summarize the species-specific responses.
The meta-analysis was performed using CMA Version 2
(Borenstein et al. 2005). Effect size was measured as Hedges’ d,
which was calculated based on trait mean, standard deviation
and sample size. A mixed-effect analysis was applied with the
assumption that there were random variations in effect sizes
among individual results. Tests for homogeneity of effect sizes
were based on the statistic Q with a significance level of PQ
>0.05. For given species, trait data observed at NBG were used
as the control, with BBG as the experimental group. Thus, a
positive effect size means that the trait value at BBG is higher
than at NBG, and vice versa with a negative effect size. A 95%
confidence interval without overlapping zero indicates that
the effect size is significant at P <0.05.
We calculated the difference in the mean trait value, a surrogate for trait plasticity, between the two gardens for each species:
∆T = TBBG − TNBG (1)
Prior to the calculation of ΔT, the leaf trait data were logarithm-transformed to ensure that the mean values for all leaf
traits were calculated at the same scale. A significant positive
ΔT, as determined by a one-way ANOVA, indicates that the
trait value at BBG is significantly higher than at NBG, and
vice versa with a positive ΔT. We used SLA as an example to
visually show the species-specific response (Fig. 1).
Given that different functional groups differed in sample size
(which here refers to species richness) and that the original
data did not necessarily meet the assumptions of conventional
ANOVA, one-way ANOVA in this study was performed via a
null model analysis which “does not assume that the data are
Response of SLA for individual species.
.4
normally distributed with equal variances among groups and is
less sensitive to unequal sample sizes among the groups” (Gotelli
and Entsminger 2004). In this case, the null hypothesis is that
the variation in leaf traits between the two gardens is no greater
than expected by chance. We conducted 1000 simulations and
then statistically compared the patterns in these randomized
data with those in the observed data. The null model analysis
was performed using EcoSim (Gotelli and Entsminger 2004).
Results
Leaf trait variation between the two gardens
For all species together, LDMC, SLA, Pmass and Nmass were significantly lower at BBG than at NBG, whereas Narea and SPAD
were significantly higher at BBG than at NBG. The variation
in Parea between the two gardens was not significant (Table 1).
Within a species, the meta-analysis showed that the effect
size was significantly negative for SLA, positive for Parea, Narea
and SPAD, but not significant for LDMC, Pmass and Nmass;
these results indicate that SLA was significantly lower at BBG
than at NBG, whereas Parea, Narea and SPAD were significantly
higher at BBG than at NBG. The difference in LDMC, Pmass and
Nmass between the two gardens was not significant (Table 2).
Species-specific variation
For a given leaf trait, positive, negative or nonsignificant ΔT
was observed among the 92 species. Of the 92 observations,
between 56 and 73.9% of ΔT were significant, the percentages varying with leaf traits. The ΔT values of LDMC, SLA and
mass-based leaf N and P were negative for most species but
positive for the rest. Area-based leaf N, P and SPAD showed
Significant response (P<0.05)
Non-significant (P>0.05)
78-81: Deciduous lianas
.2
0.0
-.2
82-92: Evergreens
1-58: Deciduous trees
-.4
59-77: Deciduous shrubs
-.6
0
10
20
30
40
50
60
70
80
90
No. of 92 woody plant species.
Figure 1: species within each a plant functional group were ranked in increasing order of the intraspecific response (ΔT = TBBG – TNBG) of SLA
to the climatic differences between the BBG and the NBG. Grey bars and black bars indicate significant (P < 0.05) and nonsignificant (P < 0.05)
responses, respectively.
64
Journal of Plant Ecology
Table 1: comparisons of leaf functional traits (mean ± SD) between the BBG and the NBG at the trait scale by replication (n) for all
species and for individual functional groups
All species
Traits
Garden
LDMC (g/kg−1)
BBG
NBG
2
−1
SLA (mm /mg )
BBG
NBG
−1
Pmass (g/kg )
BBG
NBG
−1
n
Trees
Mean ± SD
n
485
392.80 ± 56.87b
475
a
405.80 ± 84.42
485
b
13.79 ± 3.79
a
16.07 ± 5.96
475
1.58 ± 0.47
1.76 ± 0.70
420
401.19 ± 51.11b
a
345
b
292
b
23.02 ± 5.35
307
13.62 ± 3.27
a
15.54 ± 4.89
1.57 ± 0.49
1.73 ± 0.75
n
Mean ± SD
379.25 ± 65.30a
25
330.60 ± 40.23a
120
a
390.82 ± 74.18
25
309.35 ± 43.38a
115
b
25
17.09 ± 3.77a
a
25
17.79 ± 4.06a
b
19
1.50 ± 0.23b
a
23
1.97 ± 0.73a
b
22.08 ± 5.16
19
22.13 ± 2.98b
23.57 ± 2.84a
107
a
Mean ± SD
115
120
b
Lianas
105
a
23.41 ± 5.49
108
13.60 ± 4.82
17.17 ± 8.32
1.64 ± 0.45
1.79 ± 0.53
Nmass (g/kg )
BBG
NBG
438
23.90 ± 5.51a
305
23.90 ± 5.13a
109
24.00 ± 6.87a
24
Parea (mg/m−2)
BBG
430
123.76 ± 56.05a
304
125.29 ± 58.99a
107
125.62 ± 51.02a
19
88.96 ± 11.4a
NBG
420
120.17 ± 62.53a
292
119.11 ± 60.00a
105
127.99 ± 70.22a
23
115.91 ± 61.30a
BBG
434
1.75 ± 0.54a
307
1.82 ± 0.58a
108
1.65 ± 0.39a
19
1.31 ± 0.15a
438
b
305
b
109
b
Narea (g/m−2)
NBG
SPAD
BBG
NBG
434
n
418.54 ± 84.79
304
a
Mean ± SD
330
330
b
430
345
Shrubs
1.58 ± 0.49
a
50.63 ± 9.92
909
630
b
47.67 ± 9.94
865
580
Deciduous
LDMC (g/kg−1)
SLA (mm2/
mg−1)
−1
Pmass (g/kg )
a
48.89 ± 8.63
230
b
45.40 ± 8.45
237
Deciduous trees
24
1.35 ± 0.37a
a
49
52.78 ± 9.52a
b
48
53.37 ± 6.28a
1.58 ± 0.64
54.95 ± 11.64
52.06 ± 11.13
Deciduous shrubs
Deciduous lianas
BBG
430
395.63 ± 57.64b
315
403.51 ± 51.26b
95
382.67 ± 69.07a
20
333.07 ± 44.69a
NBG
415
411.15 ± 85.96a
295
425.35 ± 85.50a
100
389.04 ± 75.38a
20
312.32 ± 47.81a
BBG
430
14.34 ± 3.44b
315
14.06 ± 2.84b
95
14.68 ± 4.63b
20
17.24 ± 4.20a
NBG
415
16.87 ± 5.58a
295
16.10 ± 4.45a
100
18.98 ± 7.86a
20
17.67 ± 4.29a
385
b
277
b
94
b
14
1.55 ± 0.24a
a
17
1.97 ± 0.84a
b
22.59 ± 5.05
14
22.51 ± 3.28a
BBG
NBG
−1
1.60 ± 0.43
1.58 ± 0.46
a
1.77 ± 0.71
369
23.50 ± 5.28
279
a
1.74 ± 0.75
86
a
23.87 ± 5.41
95
1.61 ± 0.40
1.83 ± 0.56
Nmass (g/kg )
BBG
NBG
396
24.34 ± 5.38a
278
24.29 ± 4.81a
90
24.55 ± 7.20a
18
24.11 ± 2.40a
Parea (mg/m−2)
BBG
385
115.96 ± 45.32a
277
118.33 ± 50.50a
94
112.58 ± 27.84a
14
91.66 ± 11.85a
369
a
266
a
86
a
NBG
−2
Narea (g/m )
BBG
NBG
SPAD
BBG
NBG
388
266
b
1.57 ± 0.49
113.75 ± 56.59
a
1.71 ± 0.51
388
b
1.51 ± 0.40
396
a
48.99 ± 8.23
839
b
46.15 ± 8.01
800
Evergreens
LDMC (kg−1)
2
−1
SLA (mm /mg )
Pmass (g/kg )
Nmass (g/kg )
Parea (mg/m−2)
−2
Narea (g/m )
565
b
1.56 ± 0.39
a
48.32 ± 8.14
b
44.95 ± 8.88
Evergreen trees
95
90
190
197
119.95 ± 71.11a
14
1.33 ± 0.16a
b
18
1.40 ± 0.40a
a
39
49.80 ± 8.01a
b
38
51.56 ± 5.03a
1.57 ± 0.32
1.36 ± 0.38
50.98 ± 8.30
48.55 ± 7.89
Evergreen shrubs
Evergreen lianas
366.720 ± 42.86a
25
384.82 ± 42.86a
20
363.02 ± 40.55b
10
328.89 ± 10.98a
NBG
60
368.68 ± 61.73a
30
360.37 ± 54.56a
20
399.73 ± 69.02a
10
331.49 ± 37.66a
a
10
15.27 ± 1.47b
a
10
17.25 ± 2.50a
a
10
1.62 ± 0.30b
a
11
1.91 ± 0.23a
a
10
19.57 ± 2.04a
a
BBG
BBG
BBG
55
60
45
51
46
a
9.47 ± 3.56
a
10.53 ± 5.54
a
1.63 ± 0.56
a
1.70 ± 0.61
a
18.96 ± 4.00
30
22
21
23
7.94 ± 3.18
a
9.88 ± 5.88
a
1.50 ± 0.54
a
1.65 ± 0.91
a
19.03 ± 4.40
20
13
19
13
8.48 ± 0.57
8.15 ± 3.03
1.85 ± 0.70
1.63 ± 0.28
18.38 ± 4.55
20.68 ± 5.50
22
19.63 ± 7.10
19
21.41 ± 4.24
11
21.54 ± 3.45a
BBG
45
190.58 ± 88.40a
22
211.01 ± 91.35a
13
219.91 ± 77.61a
10
107.50 ± 27.82a
NBG
51
174.81 ± 78.32a
21
173.78 ± 66.82a
19
212.95 ± 89.17a
11
110.91 ± 13.00a
b
10
1.27 ± 0.08a
a
11
1.24 ± 0.20a
a
20
65.77 ± 4.60a
b
20
65.09 ± 6.74a
BBG
NBG
46
52
70
65
a
2.16 ± 0.59
a
2.13 ± 0.73
a
70.33 ± 5.89
b
66.36 ± 9.48
23
22
10
5
a
20
52
BBG
a
25
a
NBG
NBG
SPAD
610
1.77 ± 0.55
17
a
109.22 ± 48.77
55
NBG
−1
278
a
BBG
NBG
−1
279
114.87 ± 57.98
a
2.54 ± 0.28
b
2.14 ± 0.58
a
65.54 ± 3.27
b
47.62 ± 3.88
13
19
40
40
2.19 ± 0.48
2.62 ± 0.59
73.82 ± 4.42
69.34 ± 8.29
For each trait, mean ± SD marked with different letters indicate that the difference was significant at P < 0.05 (in bold). Those marked with the
same latter indicate a nonsignificant difference (P > 0.05).
Wang et al. | Relationship between leaf traits and climate65
Table 2: summary of trait variation within a species based on meta-analysis, including the number of species (n), overall effect sizes,
95% CIs and measures of data heterogeneity (Q) and its significance (PQ)
Mixed effects analysis
Heterogeneity test
Traits
n
Effect size
95% CIs
LDMC
92
−0.31
−0.80 to 0.18
P
0.21
Q
df
PQ
0.001
1
0.97
SLA
92
−1.88
−2.45 to −1.32
<0.001
0.10
1
0.75
Pmass
87
−0.36
−0.89 to 0.17
0.19
0.19
1
0.66
Nmass
87
−0.27
−0.73 to 0.19
0.25
0.37
1
0.54
Parea
87
0.75
0.10–1.40
0.02
0.05
1
0.83
Narea
87
1.01
0.47–1.55
<0.001
0.61
1
0.43
SPAD
86
0.84
0.44–1.24
<0.001
0.15
1
0.70
CI = confidence interval.
indicating that species-specific responses had little connection
with plant functional group responses.
Discussion
Leaf trait variation observed across species captures
the differences in climate between the gardens
Figure 2: percentage of different intraspecific responses of leaf traits
of 92 species, i.e. negative (P < 0.05), positive (P < 0.05) and nonsignificant (P > 0.05), to the climatic differences between the BBG and
the NBG. n = 86–92, varying with traits.
an inverse pattern (Fig. 2). This overall trend tended to be
invariant at the functional group scale (Fig. 3).
Do functional groups matter to species-specific
response?
The variation in leaf traits between the two gardens observed
for all species together was similar in all traits for the deciduous functional group, in most traits for trees, shrubs, deciduous trees and deciduous shrubs, in a few traits for evergreen
functional groups but in no traits for deciduous lianas
(Table 1). No significant difference in ΔT was observed among
functional groups (P ranges between 0.287 and 0.94, Table 3),
Multiscale comparisons have revealed a significant effect of the
environmental filter on leaf trait variation (Tables 1–3). The environment is colder and drier with lower soil N and P contents at
BBG than at NBG. Although irrigation may to some extent confound the rainfall effect, the difference in environmental conditions between the two gardens, especially the temperature and
soil conditions, should be plausibly regarded as one of the primary driving forces underlying the overall variation in leaf traits.
Lower SLA at BBG can be interpreted as a primary acclimation to dry stress for several reasons (Cunningham et al.
1999; Fonseca et al. 2000; Givnish 1987; Niinemets 2001).
Although area-based leaf N and P are predicted by SLA and
an offset effect between the leaf mass per area (LMA) and
the mass-based concentration, as revealed by the metaanalysis, higher Narea, Parea and SPAD at BBG nevertheless
suggests that plants may allocate more N and P per unit
light interception area to enhance light use efficiency as a
compensation for the suppression of photosynthesis in drier
and colder habitats, a mechanism revealed in previous studies (Byron et al. 2002; Cunningham et al. 1999; Wright et al.
2001). LDMC, often referred to tissue density (Wilson et al.
1999), has mixed results in its variation with climate. For
example, LDMC tends to increase as aridity increases, as
indicated by a global scale study (Niinemets 2001) and in
South Africa (Byron et al. 2002), but LDMC has no significant relationship with precipitation in SW Australia (Byron
et al. 2002) and in the Junggar Desert of China (Yao et al.
2010). In this case, LDMC was significantly higher at NBG
than at BBG, possibly because NBG has a longer growing
season in which foliage tends to persist longer and to allocate more mass to leaf construction. Robust leaf construction means high foliage tissue density or high toughness
66
Journal of Plant Ecology
Deciduous trees
80
80
Deciduous shrubs
Percentage of different responses (%)
Positive
Negative
Non-sig.
60
60
40
40
20
20
0
0
LDMC
80
LDMC
SLA Pmass Nmass Parea Narea SPAD
Deciduous lianas
SLA Pmass Nmass Parea Narea SPAD
Evergreens
80
60
60
40
40
20
20
0
0
LDMC
SLA Pmass Nmass Parea Narea SPAD
Leaf traits
LDMC
SLA Pmass Nmass Parea Narea SPAD
Leaf traits
Figure 3: percentage of different intraspecific responses, i.e. negative (P < 0.05), positive (P < 0.05) and nonsignificant (P > 0.05), of leaf traits
to the climatic differences between the BBG and the NBG at the scale of plant functional groups, i.e. deciduous trees (n = 55–58, varying with
traits, as in all other groups), deciduous shrubs (n = 17–19), deciduous lianas (n = 4) and evergreens (n = 7–11).
and hence high LDMC (Niinemets 2001; Shipley and Vu
2002; Wilson et al. 1999; Wright and Cannon 2001). Massbased leaf N and P, although with less of a clear pattern,
tend to decrease with increasing mean annual temperature
(Han et al. 2005; Reich and Oleksyn 2004). In this study, the
variations in the mass-based leaf N and P at the interspecific scale show an opposite pattern but are congruous with
the favouring of biogeochemical processes, such as mineralization of organic matter, in higher temperature habitats,
leading to higher soil N and P availability and hence a positive link between leaf N or P and temperature (Reich and
Oleksyn 2004). The variation in leaf N and P between the
two gardens can thus be ascribed to the differences in soil
N and P (Ma 2007; Wang et al. 2006) because leaf N and P
tend to be closely related to soil N and P availability (Foulds
1993; Gusewell 2004; Vitousek et al. 1995).
Species-specific variations in leaf traits are not
necessarily consistent with the overall pattern
The leaf trait variations observed within a species, however, are not necessarily consistent with the overall pattern.
Intraspecific variation can be ascribed to either plant plastic response or genetic differentiation or both (Ackerly 2003;
Geber and Griffen 2003; Nicotra et al. 1997; Reich et al. 2003).
As revealed by common-garden studies, plant traits in different genotypes tend to be highly correlated with the climate of
Wang et al. | Relationship between leaf traits and climate67
Table 3: effects of plant functional groups (deciduous trees,
shrubs and liana; deciduous vs. evergreen) on leaf trait variation
based on a null model analysis after 1000 simulations
Source
Growth forms
Evergreen vs. deciduous
Traits
P(observed
n
Fobserved
Fexpected
≥ expected)
LDMC
81
1.114
1.029
0.336
SLA
81
0.996
1.197
0.826
Pmass
80
0.346
1.023
0.705
Nmass
80
0.196
1.016
0.809
Parea
80
0.707
1.092
0.524
Narea
80
0.738
1.011
0.445
SPAD
79
0.796
1.018
0.444
LDMC
92
0.421
1.024
0.502
SLA
92
0.308
1.046
0.553
Pmass
91
0.005
0.939
0.94
Nmass
91
0.274
1.056
0.615
Parea
91
0.268
1.002
0.626
Narea
91
1.092
0.1
0.287
SPAD
86
0.309
1.099
0.587
origin (Benowicz et al. 2000; Oleksyn et al. 1998; Sandquist and
Ehleringer 1997). For this reason, the interactions between
plant acclimation and genetic differentiation may confound
the intraspecific response if the plant populations of interest
differ in genotype. In this study, because the plant genotypes
were unknown, we were unable to eliminate the possibility
that the plant populations of a species differed in genotype
between the two gardens. The intraspecific response observed
in this study can be plausibly interpreted as a collective effect of
plant acclimation and genetic differentiation. Thus, partitioning the intraspecific processes, which poses a major challenge
in the field (Reich et al. 2003), would provide critical evidence
to evaluate the relationship between leaf traits and climate.
Plant functional background does not affect the
trait–climate relationship
One theoretical basis underlying plant functional classification is that species within a functional group should have
more similar responses to an environmental gradient than
those in different functional groups (Diaz and Cabido 1997;
Gitay and Noble 1997; Lavorel et al. 1997). Our data tend to
be less consistent with this prediction.
First, our results showed that different functional groups
tended to respond to the environmental gradient in a similar
manner. For example, LDMC, SLA, Pmass and Nmass were significantly lower at BBG than at NBG for all functional groups.
Exceptions were only observed for Narea and Parea, whose variation was actually determined by an offset effect between LMA
and Pmass or Nmass. In this case, LMA tends to vary inversely
with the mass-based concentration. In addition, the trait variation for lianas, evergreen trees and shrubs less clearly showed
this pattern, likely due to the limited sample size.
Second, in terms of leaf trait variation, species within a
functional group were not necessarily more similar than
those among functional groups, suggesting that the species-specific response had little to do with plant life form or
functional group.
Given the limitations in our data, such as the confounding
effects among plant acclimation, genetic differentiation and
woody plant life forms, we should be cautious in the interpretation of the observed patterns, and we need further study to
test the generalities.
Conclusions
Leaf trait variation between the two gardens nicely echoes the
environmental differences between the two sites, indicating
an overall acclimation of plants to different climatic conditions. However, species-specific responses are quite diverse
and have little to do with plant life forms or functional groups.
Although differences in genotype among populations of a
species may confound plant acclimation, our species-specific
results offer an improved basis for interpreting trait–climate
relationships. The generality of our results should be tested
across the whole spectrum of plant life forms.
Supplementary material
Supplementary material is available at Journal of Plant Ecology
online.
Funding
Chinese National Basic Research Program (2014CB954201);
National Natural Science Foundation of China (30870398).
Acknowledgements
The authors thank BBG and NBG for plant leaf sampling. Our appreciation also goes to Yao Tingting, Sisi Jiang, Anqi Wang and Weikang
Zhang for assistance in leaf sampling and laboratory analysis.
Conflict of interest statement. None declared.
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