1. Introduction
Natural abundance of the carbon-13 isotope (
13C) can be affected by environmental factors such as the partial pressure of CO
2 and O
2 [
1], the irradiance level [
2,
3,
4] and air temperature [
5]. However, water availability is a predominant determinant of changes in carbon-13 isotope at regional and global scales [
1,
6,
7,
8]. This is due to the fact that water supply affects the stomatal conductance and photosynthesis of plants, which changes
13C/
12C ratios in the synthesized carbohydrates. Leaf δ
13C has also been correlated with leaf specific area and nitrogen concentration [
9,
10], water deficit [
11] and water use efficiency (WUE) [
12,
13,
14]. Hence the spatial pattern of leaf δ
13C, arising from changes in water availability, is of physiological and ecological interest particularly in the context of climate change.
At large geographical scales, precipitation has often been used as a surrogate of the effect of water availability on leaf δ
13C. A significant negative correlation is found between leaf δ
13C and mean annual precipitation (MAP) at a global scale [
1,
6]. The same relationship has also been found at regional scales in different climatic conditions, for instance, in northern temperate Australia [
15], both C
3 and C
4 vegetation in Southern Africa [
16],
Quercus suber in Mediterranean Portugal [
17] and
Metrosideros polymorpha in tropical Hawaii [
10]. In China, there have been studies on a range of terrestrial plants in rainforests [
18], in arid north and northeast China areas [
19,
20] and on high mountains [
21,
22]. However these studies do not focus on the driving forces or the effects on leaf δ
13C variation at a specieslevel acrossbiomes. Changes in leaf δ
13Care well correlated with spatial environmental variables, such as latitude (LAT), longitude (LON) and altitude (ALT), due to their co-variation with precipitation [
1,
23,
24,
25,
26,
27]. Shestakova
et al., (2014) investigated the spatial pattern of carbon isotope discrimination for three deciduous oaks and one evergreen oak along an aridity gradient, showing that evergreen oak was primarily related to temperature, whilst the spatial pattern for deciduous oaks was primarily dependent on precipitation [
28]. The total range of δ
13C values was 4.4‰ and 3.1‰ for
Quercus pubescens and
Quercus ilex, respectively, in Southern France with different levels of water availability [
29]. Klein
et al., also report that stomatal conductance and water use affect plant δ
13C for
Quercus calliprinos in Israel [
30]. By contrast, Donovan
et al., found no significant differences in δ
13C among three
Quercus species in the Southeastern USA [
31]. Hence different
Quercus species show a variety of δ
13C responses at a region scale; understanding the reasons for these variations could help determine how different species will respond to climate change and in particular increased drought conditions. An improved understanding of variations in δ
13C for selected species can also be used to build isoscapes [
32] and spatial models of precipitation [
33].
Oriental oak (
Quercus variabilis Bl.) is a deciduous broadleaf tree that is relatively abundant across temperate and subtropical areas in East Asia (24° N to 42° N; 96° E to 140° E), including the eastern part of Mainland China, Taiwan and Zhoushan islands, as well as Korea and Japan [
34].
Q. variabilis is found at altitudes ranging from sea-level to 2000 m above sea level (a.s.l.) in subtropical China. The associated forests are important for timber and cork production and ecosystem services, such as carbon sequestration, and water and soil conservation. Within the distribution area in Mainland China, mean annual temperature (MAT) and MAP range from 7.2 to 23.6 °C and 410 to 2000 mm, respectively [
34]. This provides an ideal situation for studying spatial patterns of leaf δ
13C variation within a single widespread species. The present study aims (1) to show the spatial pattern of variation in leaf δ
13C of
Q. variabilis within its distribution range across temperate-subtropical biomes and (2) to assess the correlation relationships between leaf δ
13C and climate variables.
3. Results
MAP of the sampled
Q. variabilis populations ranged from 495 mm to 1850 mm; the MATranged from 8.8 °C to 20.0 °C. The mean value for leaf δ
13C of the 25
Q. variabilis populations across eastern China was −27.4‰ ± 0.33‰, with the coefficient of variation of 5.73% (
Table 1). The lowest value of −30‰ was found at Dehua (DH), Fujian, where MAP was 1850 mm. The highest value of −24‰ was found at Anming (AN) and Kunming (KM), Yunnan, where MAP was approximately1000mm. Similar leaf δ
13C values were found in Sanmenxia (SM), Henan (MAP = 495 mm) and Pinggu (PG), Beijing (MAP = 542 mm) (
Supplementary Table S1).
Table 1.
Statistics of mean annual precipitation (MAP), mean annual temperature (MAT), the carbon content and the relative concentration of carbon 13 in the leaves δ13C of 25 Quercus variabilis across eastern China.
Table 1.
Statistics of mean annual precipitation (MAP), mean annual temperature (MAT), the carbon content and the relative concentration of carbon 13 in the leaves δ13C of 25 Quercus variabilis across eastern China.
| Mean | Standard Error | Maximum | Minimum | Coefficient of Variation(%) |
---|
MAP (mm) | 1065 | 75 | 1850 | 495 | 38 |
MAT (°C) | 14.7 | 0.5 | 20 | 8.8 | 20 |
C (mg g−1) | 493.1 | 3.4 | 519.1 | 462.5 | 3.4 |
δ13C (‰) | −27.4 | 0.3 | −24.3 | −30.4 | 5.7 |
Leaf δ
13C was significantly and positively correlated with latitude (
r2 = 0.48,
p = 0.0003), and significantly and negatively with MAT (
r2 = 0.40,
p = 0.0012) and MAP (
r2 = 0.675,
p < 0.0001) (
Figure 2). There was no significant trend with altitude and longitude. By stepwise multiple regression analyses, MAP alone could account for 67.5 of the variation in leaf δ
13C (
Table 2).
Figure 2.
Linear regressions of the relationship between leaf δ13C and five environmental variables: (a) latitude; (b) longitude; (c) altitude; (d) mean annual temperature; and (e) mean annual precipitation(n = 23, except for the sites at Anning and Kunming).
Figure 2.
Linear regressions of the relationship between leaf δ13C and five environmental variables: (a) latitude; (b) longitude; (c) altitude; (d) mean annual temperature; and (e) mean annual precipitation(n = 23, except for the sites at Anning and Kunming).
Table 2.
A simple stepwise multiple regression of leaf δ13C (‰) against mean annual precipitation, and a full model describing leaf δ13C (‰) in terms of latitude (LAT), longitude (LON), altitude (ALT), mean annual temperature (MAT), mean annual precipitation (MAP) (n = 23, excluding the populations at Anningand Kunming).
Table 2.
A simple stepwise multiple regression of leaf δ13C (‰) against mean annual precipitation, and a full model describing leaf δ13C (‰) in terms of latitude (LAT), longitude (LON), altitude (ALT), mean annual temperature (MAT), mean annual precipitation (MAP) (n = 23, excluding the populations at Anningand Kunming).
Methods | Equation | R2 | p |
---|
Full model | Leaf δ13C = −29.475 − 0.005 × LAT + 0.038 × LON + 0.000 × ALT + 0.032 × MAT − 0.003 × MAP | 0.680 | < 0.0001 |
Stepwise | Leaf δ13C= −24.889 − 0.003 × MAP | 0.675 | < 0.0001 |
4. Discussion
Large variations of leaf δ
13C have been observed for many species across their distribution range, showing plant adaptations to environmental change. In this study, the range of
Q. variabilis leaf δ
13C changed from −30‰ to −24‰, which is greater than within-country variations reported for other oak species. For example, evergreen oak species,
Q. suber leaf δ
13C varied from −28.58‰ to −24.58‰ in Mediterranean Portugal [
17], and the range of
Q. ilex δ
13C values was 3.1‰ for
Q. ilexin southern France[
29]. For deciduous oak species, ranges for leaf δ
13C of 1.9‰, 1.5‰, 0.7‰, and 4.4‰ have been reported for
Q. faginea,
Q. humilis,
Q. petraea [
28] and
Q. pubescens [
29], respectively. Plant carbon isotope discrimination is an ecophysiological property that can reflect the capacity of a species to cope with climatic stressors and, ultimately, define the range of their distribution. Therefore, the large range in the δ
13C values for
Q. variabilis population in this study demonstrates strong plasticity in response to the environment [
37]. According to Castillo
et al., spatial differences in carbon isotope levels at a regional scale can be used to model an area of similar precipitation [
33]. This study, which focuses on an individual species at a large regional scale, demonstrates how carbon isotope can be used to develop isoscape models and therebyrefine our understanding of biogeochemical cycles [
32].
Previous work has demonstrated that leaf δ
13C can be related to spatial variables, such as latitude, altitude, MAT and MAP [
1,
3,
6,
24,
26]. This paper shows that leaf δ
13C of a selected species has a significant positive correlation with latitude and a negative correlation with MAT and MAP, with no significant correlation with altitude and longitude (
Figure 2). Based on a Pearson correlation analysis, latitude is negatively correlated with MAT (
R = −0.86,
p < 0.0001) and MAP (
R = −0.73,
p < 0.0001) (
Supplementary Table S2). Although longitude was also negatively correlated with altitude, there was no correlation of altitude with MAT and MAP (
R = −0.74,
p < 0.0001) (
Supplementary Table S2). This suggests that the spatial variation of leaf δ
13C is primary caused by differences in MAT and MAP, which vary with latitude.
Although both MAT and MAP show significant influence on leaf δ
13C, MAP alone accounted for 67.5% of the variation in leaf δ
13C (
Table 2). Ferrio
et al., in a study of
Q. ilex in Spain, also showed that 59% of the variation in leaf δ
13C could be explained by changes in precipitation [
38]. Although this study focused on the response to mean annual precipitation, there is an argument that, for deciduous trees, a stronger relationship may be obtained from the mean precipitation in the growing season than over a calendar year.
In this paper, we used stepwise multiple regression analyses to minimize the problems with collinearity, for example MAT is positively correlated with MAP (
r = 0.76,
p < 0.0001). Our results suggest that MAP is the strongest predictor for leaf δ
13C, in line with other observations [
6,
9]. The soil water content determines the leaf stomatal conductance and photosynthesis, and thereby the water use efficiency (WUE) of plants [
39,
40,
41]. Environmentally induced increases in WUE, such as under drought, are expected to be accompanied by decreases in carbon isotope discrimination, because under drought, changes in p
i/p
a (ratio of intercellular to atmospheric partial pressures of CO
2) and WUE will be largely due to stomatal closure [
42].
The values of leaf δ
13C for the population at the Anning and Kunming sites in theYunnan province were −24.8 ‰ and −24.4‰, respectively. This is higher than in other sites. The two sites were excluded for the regression analyses because we assumed that those high values were not caused by climate factors. Yunnan province has some of the highest soil phosphorus concentrations in China [
43]. For example the soil P concentration at Anning and Kunming were 1.7 g kg
−1, and 1.2 g kg
−1, respectively, compared to 0.16–0.47 g kg
−1 at the other sites [
44]. P-rich soils, in addition to being high in phosphorus, contain relatively high levels of phosphate-associated elements such as Ca, Mg, Fe and Al, which can cause high concentrations N, P, Ca, Fe and Zn in the leaf. The concentrations of these elements in the Yunnan province were higher than those at P-deficient sites (
p < 0.05) [
44]. Organisms need to maintain the balance of internal element concentrations with respect to the environment (
i.e., homeostasis) [
45]. Zhou
et al., also reported that element ratios were more stable than individual element concentrations regardless of P-rich and P-deficient sites [
44]. Hence plants grown in the P-rich soil can fix more CO
2 through photosynthesis to improve the carbon content of plant bodies in order to maintain homeostatic regulation. The process can result in decreased fractionation and high leaf δ
13C. An alternative explanation is that the two sites are located in the high altitude mountains of a subtropical area, characterized by lower temperatures and precipitation levels than other sites at a subtropical latitude (
Supplementary Table S1). Low temperature and precipitation can decrease photosynthesis, transpiration and leaf conductance, resulting in a high value for leaf δ
13C [
36].