Changes in the Stoichiometry of Castanopsis Fargesii Along an Elevation Gradient in a Chinese Subtropical Forest

Changes in the stoichiometry of Castanopsis fargesii along an elevation gradient in a Chinese subtropical forest

Danping Liu1, Dexiang Zheng1, Yaoyao Xu1, Yifei Chen1, Hesong Wang2, Ku Wang3, Xiaoli Liao3, Changxiong Chen1, Jiangjiang Xia4 and Shaofei Jin3,5

1 College of Forestry, Fujian Agriculture and Forestry University, Fuzhou, China 2 School of Ecology and Nature Conservation, Beijing Forestry University, Beijing, China 3 Department of Geography, Minjiang University, Fuzhou, China 4 Key Laboratory of Regional Climate-Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China 5 Institute of Eco-Chongming, East China Normal University, Shanghai, China

ABSTRACT Elevation is important for determining the nutrient biogeochemical cycle in forest ecosystems. Changes in the ecological stoichiometry of nutrients along an elevation gradient can be used to predict how an element cycle responds in the midst of global climate change. We investigated changes in concentrations of and relationships between nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg) in the leaves and roots of the dominant tree species, Castanopsis fargesii, along an elevation gradient (from 500 to 1,000 m above mean sea level) in a subtropical natural forest in China. We analyzed correlations between C. fargesii’s above-ground biomass and stoichiometry with environmental factors. We also analyzed the soil and plant stoichiometry of this C. fargesii population. Our results showed that leaf N decreased while leaf K and Ca increased at higher elevations. Meanwhile, leaf P showed no relationship with elevation. The leaf N:P indicated that C. fargesii was limited by N. Elevation gradients contributed 46.40% of the total variance of ecological stoichiometry when assessing environmental factors. Our research may provide a theoretical basis for Submitted 16 September 2020 the biogeochemical cycle along with better forest management and fertilization for this Accepted 11 May 2021 C. fargesii population. Published 1 June 2021 Corresponding authors Dexiang Zheng, [email protected] Subjects Ecology, Plant Science, Soil Science, Biogeochemistry, Forestry Shaofei Jin, [email protected] Keywords Global warming, Subtropical evergreen broad-leaved forest, Elevation, Castanopsis Academic editor fargesii, Leaf stoichiometry Xugao Wang Additional Information and Declarations can be found on INTRODUCTION page 12 The global forest ecosystem is affected by increasing global temperatures caused by excess DOI 10.7717/peerj.11553 greenhouse gases from anthropogenic activities (Wang et al., 2016). Global warming Copyright has affected nutrient element cycling in forest ecosystems (Penuelas & Matamala, 1990; 2021 Liu et al. Penuelas & Matamala, 1993; Penuelas et al., 2020; Sardans et al., 2015) and its impact on Distributed under the biogeochemical cycle is difficult to ignore. Global warming may alter the distribution Creative Commons CC-BY 4.0 of nutrient elements by impacting plant metabolism, thus affecting nutrient transfer in OPEN ACCESS plant organs (Gavito et al., 2005; Jónsdóttir, Khitun & Stenström, 2005; Yan, Zhu & Yang,

How to cite this article Liu D, Zheng D, Xu Y, Chen Y, Wang H, Wang K, Liao X, Chen C, Xia J, Jin S. 2021. Changes in the stoichiometry of Castanopsis fargesii along an elevation gradient in a Chinese subtropical forest. PeerJ 9:e11553 http://doi.org/10.7717/peerj.11553 2017). Two the large-scale effects of global warming on element cycling have been studied to date: latitudinal effects over large areas (He et al., 2006; Han et al., 2011; Ordoñez et al., 2009; Zhang et al., 2012; Fang et al., 2019) and altitude effects. These are crucial factors for determining how variations in temperature and relative climatic changes drive ecological processes (Körner, 2007; Normand et al., 2009). Recently, studies on plant stoichiometry changes along elevation gradients have not shown uniform patterns of change. Changes in plant N has shown an increase (Richardson, Berlyn & Gregoire, 2001; Shi, Körner & Hoch, 2006), decrease (Li & Sun, 2016; Soethe, Lehmann & Engels, 2008; Van De Weg et al., 2009), or no linear correlation (Macek et al., 2012) with increasing elevation. Most studies have shown that plant P decreased with increasing elevation (Soethe, Lehmann & Engels, 2008; Tanner, Vitousek & Cuevas, 1998; Vitousek et al., 1992; Wang et al., 2018). Elevational gradients may lead to significant variations in regional microclimate and soil properties (Zeng et al., 2018; Chang et al., 2016) , which further affects the nutrient cycling of a plant-soil system in forest ecosystems. This suggests that there is closely-coupled element cycling between C, N, and P in the plant-soil system (Elser et al., 2000). Deng et al. (2008) demonstrated that plant P had a significant positive correlation with soil P in the subtropical forest karst area. Plant stoichiometry was also affected by environmental factors. Studying plant stoichiometry and its correlation with environmental factors may provide a theoretical understanding of plants’ nutritional requirements and their mutual feedback with the environment (Li et al., 2019). Reich & Oleksyn (2004) showed that leaf N and P increases from the tropics to mid-latitudes due to temperature-related plant physiological stoichiometry and soil substrate age; these elements decreased at higher latitudes because cold temperatures affect biogeochemistry. Ma et al. (2015) found that root N and P were negatively correlated with annual average temperature and annual precipitation. Many studies of plant stoichiometry have focused on C, N, and P but few have considered the stoichiometry of other crucial elements, including K, Ca, and Mg. Fagaceae is a vital subtropical plant in China that is important for maintaining and promoting the element cycling of subtropical forest ecosystems. Zheng et al. (2017) showed that there were significant differences between leaf and root C, N, and P in seedlings and young trees of Castanopsis fissa and other plant organs. Chang et al. (2013) showed that P was the most important element for limiting plant productivity in the subtropical forest ecosystem. Castanopsis fargesii is one of the main species of the subtropical forests of China; however, there is little information on its stoichiometry, which affects the regional forest ecosystem and element cycle characteristics. Previous studies have focused on the ecological characteristics of C. fargesii, including its community structure, photosynthetic characteristics, community biomass, and soil organic carbon (Song et al., 2003; Zhao et al., 2005; Qian et al., 2004; Gong et al., 2015; Dai et al., 2018). However, changes C. fargesii ’s stoichiometry along elevation gradients have not been well-studied (Liu et al., 2019; Dai et al., 2018). Temperatures are known to change with increasing elevation, even over short distances (Tan & Wang, 2016; Paudel et al., 2019). The elevational changes in temperature and soil nutrients may have an effect on plant stoichiometry (Normand et al., 2009; Sundqvist,

Liu et al. (2021), PeerJ, DOI 10.7717/peerj.11553 2/18 Sanders & Wardle, 2013; Yu et al., 2013). Thus, we conducted experiments with five elevation gradients as a proxy for the effects of global warming in a subtropical natural forest on Guoyan Mountain in southern China. We sought to investigate the plant (specifically in its leaves and roots) and soil stoichiometry (N, P, K, Ca, and Mg) in C. fargesii populations along elevation gradients and determine the relative importance of elevation to the total variation of ecological stoichiometry.

MATERIAL AND METHODS Study area description The study was conducted in the Guoyan Mountain Natural Reserve on Wuyi Mountain (17◦290 ∼118◦140E, 26◦380 ∼27◦120N) in the northwestern Fujian Province. Its peak elevation was 1,383.7 m (Fig. 1). The study area had a moderate-subtropical monsoon climate with an average annual temperature of 19 ◦C and an average annual rainfall of 2,051 mm. C. fargesii was one of the dominant species along the elevation gradients in the study area, which ranged from 500 to 1,000 m. Sampling Elevation gradients were established from 500 to 1,000 m and covered C. fargesii’s natural habitat. Forests were intentionally planted below this range according to our field observations. Five 30 m × 30 m plots were established at 100 m intervals (i.e., 500–600 m, 600–700 m, 700–800 m, 800–900 m, and 900–1,000 m, respectively, which were denoted as 500 m, 600 m, 700 m, 800 m, and 900 m). Three plots were established at 500 m where fewer C. fargesii were found and are close to bamboo plantations. Five C. fargesii specimens were selected randomly in each plot and leaf samples were collected from each tree that exhibited good growth conditions. Root samples were collected from the same five trees. Soil samples were collected from around each tree at three depths (0–20 cm, 20–40 cm, and 40–60 cm) using a soil sampling auger with a diameter of five cm. All samples from each plot were mixed thoroughly and stored at 4 ◦C before being transported to a laboratory. The specimens’ biodiversity index, height, and diameter at breast height (DBH) were also recorded (Table 1). Laboratory analyses Roots were deposited into 0.15 mm net bags and washed under running water. Fine roots (d < 2 mm) were separated. The leaves and roots were cleaned with distilled water and dried at 50 ◦C to a constant weight. All plant parts were ground prior to analysis. We removed stones and visible litter from the soil samples and then air-dried and sieved the soil through a two mm nylon mesh. We used 0.10 g of leaf and root material and 0.20 g of soil material to determine the N content using a Vario Max CN analyzer (Elementar, Germany). The leaf and soil P, K, Ca, and Mg concentrations were determined using Inductively Coupled Plasma- Mass Spectrometry (ICP-MS) (PE Optima 8000) following H2SO4/HClO4 and HF/HClO4 digestion.

Liu et al. (2021), PeerJ, DOI 10.7717/peerj.11553 3/18 A

Photo credit: Shaofei Jin. Photo credit: Shaofei Jin.

Figure 1 (A) Location of this study, (B) Ancient road in the Guoyan Mountain Natural Reserve, and (C) Photo of Castanopsis fargesii. Photo credit: Shaofei Jin. Map data c 2021 Google. Full-size DOI: 10.7717/peerj.11553/fig-1

Table 1 Importance value index, DBH, and tree height of C. fargesii at different elevations (n = 15).

Elevation Importance DBH Tree height (m) value index (cm) (m) 400 0 NA NA 500 38.70 15.50 ± 1.15 12.33 ± 0.76 600 43.46 17.06 ± 2.19 14.40 ± 0.80 700 26.09 14.22 ± 2.36 12.30 ± 3.99 800 40.53 13.00 ± 1.99 9.80 ± 2.22 900 71.12 10.58 ± 1.04 9.80 ± 0.94 1,000 0 NA NA Notes. DBH, diameter at breast height; NA, not available..

Liu et al. (2021), PeerJ, DOI 10.7717/peerj.11553 4/18 Estimating above-ground biomass of C. fargesii The above-ground biomass (AGB) of C. fargesii in each plot was estimated according to the 2006 IPCC Guidelines for National Greenhouse Gas Inventory (Eggleston et al., 2006). AGB was obtained as follows: Eqs. (1) and (2)

AGB = V ×n×S÷s×BCEFs (1)

V = g1.3 ×(H +3)×f3 (2)

where V is the volume of an individual C. fargesii; g 1.3 is the basal area of breast-height; H is the tree height; f3 is the experimental form factor; the value of the broad-leaved trees is 0.40 (Meng, 2006); S is the 1 hm2 of the stand area; s is the stand area of each plot; BCEFs is the biomass conversion and expansion factor of growing-stock. Its value is 0.66 in subtropical regions (Eggleston et al., 2006). Statistical analysis Leaf, root, and soil stoichiometry variations at different elevations were compared using one-way ANOVA. Multiple comparisons were performed using Tukey-HSD post hoc tests. The correlations between the environmental factors, plant tissues, and soil stoichiometry of the C. fargesii community were determined using redundancy analysis (RDA). All data were checked to assess whether they met the assumptions of homogeneity and normality. All analyses and figures were determined at a significance level of p < 0.05 using R software. All raw data are shown in Supplemental File 1.

RESULTS Variations in the plant stoichiometry of C. fargesii along the elevation gradients The mean N, P, K, Ca, and Mg concentrations in the leaves were 14.76 ± 1.60 mg/g, 2.00 ± 0.16 mg/kg, 41.59 ± 8.71 mg/kg, 20.68 ± 7.14 mg/kg, and 8.83 ± 1.51 mg/kg, respectively. The mean N, P, K, Ca, and Mg concentrations in the roots were 12.53 ± 1.91 mg/g, 1.96 ± 0.43 mg/kg, 18.56 ± 4.29 mg/kg, 10.89 ± 4.29 mg/kg, and 5.30 ± 1.56 mg/kg, respectively. Significant differences (P <0.05) in the leaf (Fig. 2) and root (Fig. 3) ecological stoichiometry were found among different elevation gradients. The linear regressions between the ecological stoichiometry and the elevation gradients are shown in Fig. S1 and Fig. S2, respectively. The greatest leaf N concentration was found at the lowest elevation. The mean leaf N:P along the elevation was 7.50 ± 0.63. A significantly positive correlation was found between leaf K and elevation. The leaf and root Ca increased significantly at higher elevations. Correlations between AGB and plant stoichiometry Variations in C. fargesii’s AGB at different elevations are shown in Fig. 4. The greatest AGB was found at 600 m. The AGB showed a significantly negative correlation with leaf Ca and no correlation with other leaf stoichiometry. The AGB also showed a significantly positive correlation with root K and root P. There was a significantly negative correlation between the AGB and root Ca (Fig. 5).

Liu et al. (2021), PeerJ, DOI 10.7717/peerj.11553 5/18 A 20 a B a C a ab 60 b b b b bc 2 15 bc c b b 40 b b 10 1 20

Leaf N(mg/g) 5 Leaf P(mg/kg) Leaf K(mg/kg)

0 0 0 500 600 700 800 900 500 600 700 800 900 500 600 700 800 900 Elevation(m) Elevation(m) Elevation(m)

D 40 a E a F a a ab ab ab b abc 7.5 30 ab 10 bc bc c bc 5.0 20 c 5 Leaf N:P 10 2.5 Leaf Ca(mg/kg) Leaf Mg(mg/kg)

0 0 0.0 500 600 700 800 900 500 600 700 800 900 500 600 700 800 900 Elevation(m) Elevation(m) Elevation(m)

G a H a a I a 0.6 b a 2 b b b b 1.0 ab b 0.4 bc c b 0.5 1 Leaf N:K Leaf N:Ca 0.2 Leaf N:Mg

0.0 0.0 0 500 600 700 800 900 500 600 700 800 900 500 600 700 800 900 Elevation(m) Elevation(m) Elevation(m)

J a K a L a a b ab 0.06 0.15 0.3 b b b ab ab b b 0.04 c 0.10 0.2 b Leaf P:K Leaf P:Ca 0.02 0.05 Leaf P:Mg 0.1

0.00 0.00 0.0 500 600 700 800 900 500 600 700 800 900 500 600 700 800 900 Elevation(m) Elevation(m) Elevation(m)

M4 a N a O a a a 10.0 4 ab 3 7.5 3 b bc ab b b 2 b 5.0 c c b 2 Leaf K:Ca Leaf K:Mg 1 2.5 Leaf Ca:Mg 1

0 0.0 0 500 600 700 800 900 500 600 700 800 900 500 600 700 800 900 Elevation(m) Elevation(m) Elevation(m)

Figure 2 Variations in the leaf N, P, K, Ca, and Mg concentrations (A–E) and the leaf N:P, N:K, N:Ca, N:Mg, P:K, P:Ca, P:Mg, K:Ca, K:Mg, and Ca:Mg ratios (F–O) at different elevations. The error bars represent standard deviation. Different letters indicate that there are significant differences among different elevations (p < 0.05). Full-size DOI: 10.7717/peerj.11553/fig-2

Relationships between soil stoichiometry and plant stoichiometry in the C. fargesii population There were significantly positive correlations between soil N, leaf Ca, and root Ca. There were significantly negative correlations between soil N, leaf N, and leaf Mg. However, there was no correlation between soil P and leaf and root nutrients. Soil K was found to be positively correlated with root P and negatively correlated with leaf Ca, leaf Mg, and root Ca. Soil Ca was found positively correlated with leaf N and leaf P. There was a significantly

Liu et al. (2021), PeerJ, DOI 10.7717/peerj.11553 6/18 A a B a C 30 ab b 3.0 15.0 b ab b 25 a a a 2.5 b b 12.5 b b 20 2.0 b 10.0 Root N (mg/g) Root P (mg/kg) Root K (mg/kg)

1.5 15

7.5

500m 600m 700m 800m 900m 500m 600m 700m 800m 900m 500m 600m 700m 800m 900m Elevation Elevation Elevation

D 20 b E a F 14 a a a 10.0 12 ab 15 a 10 7.5

a a 8

10 a Root N:P b b Root Ca (mg/kg) c Root Mg (mg/kg) 5.0 6 c b 5 2.5 4 500m 600m 700m 800m 900m 500m 600m 700m 800m 900m 500m 600m 700m 800m 900m Elevation Elevation Elevation 1.4 G a H a I a 3 1.2 ab 4 a a b a 1.0 2 c 3 a b Root N:K Root N:Ca 0.8 Root N:Mg 2 b b 1 d d 0.6 1

500m 600m 700m 800m 900m 500m 600m 700m 800m 900m 500m 600m 700m 800m 900m Elevation Elevation Elevation

J a K a L a 1.00 b 0.6 a ab

0.15 0.75 0.5 b b ab b 0.4 0.50 b Root P:K Root P:Ca Root P:Mg 0.3 0.10 b c 0.25 c c 0.2

500m 600m 700m 800m 900m 500m 600m 700m 800m 900m 500m 600m 700m 800m 900m Elevation Elevation Elevation

M a N 8 a O 5 a 5 ab a 4 4 6 a a a ab 3 a 3 b 4 Root K:Ca Root K:Mg 2 Root Ca:Mg 2 bc c bc c 2 1 1

500m 600m 700m 800m 900m 500m 600m 700m 800m 900m 500m 600m 700m 800m 900m Elevation Elevation Elevation

Figure 3 Variations in the root N, P, K, Ca, and Mg concentrations (A–E) and the root N:P, N:K, N:Ca, N:Mg, P:K, P:Ca, P:Mg, K:Ca, K:Mg, and Ca:Mg ratios (F–O) at different elevations. The error bars represent standard deviation. Different letters indicate that there are significant differences among different elevations (p < 0.05). Full-size DOI: 10.7717/peerj.11553/fig-3

positive correlation between soil Mg and leaf K, and a negative correlation between soil and leaf Mg, root N, and root K (Table 2). The changes in the soil stoichiometry of the C. fargesii community are shown in Fig. 6.

Liu et al. (2021), PeerJ, DOI 10.7717/peerj.11553 7/18 8 a

) 7

2 a b m

h 6 / t (

s 5 s a b c b c m 4 o i B

d 3 n

u c o

r 2 g - e

v 1 o b

A 0 5 0 0 6 0 0 7 0 0 8 0 0 9 0 0 E le v a t io n ( m )

Figure 4 Changes in the above-ground biomass of C. fargesii along the elevation gradients. The error bars represent the standard deviation. Different letters indicate that there are significant differences among different elevations (p < 0.05). Full-size DOI: 10.7717/peerj.11553/fig-4

RN= 0.24X+11.69 (mg/g); R2= 0.06 ns LN= -0.005X+14.78 (mg/g); R2= 3.41E-5 ns RP= 0.13X+1.50 (mg/kg); R2= 0.35*** LP= -0.005X+2.02 (mg/kg); R2= 0.003 ns 140 30 2 2 RK= 0.91X+15.35 (mg/kg); R = 0.17* t

t LK= -2.91X+92.99 (mg/kg); R =0.11 ns 2 n n 2 RCa= -1.48X+16.08 (mg/kg); R = 0.45*** e

e LCa= -3.02X+52.34 (mg/kg); R =0.18 *

120 i i 2 r 25 r

2 t RMg= -0.18X+5.95 (mg/kg); R = 0.05 ns t LMg= 0.13X+17.11 (mg/kg); R =0.005 ns u u N N

100 t f o

a 20 o e L R

80 f f o o

15 s s n 60 n o o i i t t a a

r 10 r t 40 t n n e e c c n n 5 o

20 o C C 0 0 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 Above-ground Biomass(t/hm2) A Above-ground Biomass(t/hm2) B

Figure 5 Linear regression between (A) leaf nutrients, (B) root nutrients (N, P, K, Ca, Mg) and above- ground biomass of the C. fargesii. LN, leaf N concentrations; LP, leaf P concentrations; LK, leaf K concentrations; LCa, leaf Ca concentrations; LMg, leaf Mg concentrations; RN, root N concentrations; RP, root P concentrations; RK, root K concentrations; RCa, root Ca concentrations; RMg, root Mg concentrations. ns, not significant, *: p < 0.05, **: p < 0.01, ***: p < 0.001. Full-size DOI: 10.7717/peerj.11553/fig-5

Relationships between environmental factors and leaf and root nutrients in the C. fargesii population RDA analysis identified two main axes of environmental variation that together accounted for 58.73% of the total variance of ecological stoichiometry (Fig. 7, Tables 3 and4). The first RDA axis contributed 38.99% of total variance which was explained by soil pH and elevation. The second RDA axis contributed 19.74% of total variance and was mainly explained by soil moisture and soil temperature. We show the ranking of environmental variables in the order of contribution in Table 3. Elevation, soil moisture, soil temperature, and soil pH contributed 46.4%, 33.8%, 16%, and 3.9%, respectively.

Liu et al. (2021), PeerJ, DOI 10.7717/peerj.11553 8/18 Table 2 Correlation analysis between soil stoichiometry and plant stoichiometry of C. fargesii.

Soil Leaf stoichiometry Root stoichiometry stoichiometry Leaf N Leaf P Leaf K Leaf Ca Leaf Mg Root N Root P Root K Root Ca Root Mg Soil N −0.46*** −0.16 0.17 0.44*** −0.21* −0.14 −0.056 0.028 0.4*** 0.012 Soil P 0.059 0.11 −0.048 0.2 0.026 −0.085 0.14 0.081 0.064 −0.0035 Soil K −0.29* −0.051 −0.011 −0.34** −0.35** −0.22* 0.66*** 0.059 −0.48*** −0.00094 Soil Ca 0.36** 0.37** −0.036 0.065 0.03 −0.25* 0.15 −0.024 −0.24* −0.12 Soil Mg −0.068 0.2 0.62*** −0.037 −0.52*** −0.35** −0.0079 −0.49*** 0.21* −0.091

Notes. *p < 0.05. **p < 0.01. ***p < 0.001 No asterisk denotes no significant correlation coefficient was found. DISCUSSION Plant stoichiometry responses to elevation We determined that the greatest leaf N concentration of C. fargesii was found at the lowest elevation (Fig. 2). This result was consistent with previous studies (Li & Sun, 2016; Soethe, Lehmann & Engels, 2008; Van De Weg et al., 2009). Lower temperatures were found at higher elevations, which decreased the decomposition and mineralization of organic matter (Hobbie et al., 2000; Hobbie, Nadelhoffer & Högberg, 2002; He et al., 2016) and thus decreased the availability of leaf N (Reich & Oleksyn, 2004). Leaf N is typically determined by plant growth characteristics, whereas leaf P is determined by plant growth and environment. Therefore, leaf N is more stable and consistent than leaf P (Chen et al., 2013). According to Güsewell (2004), leaf N:P <10 represents the N limitation, and N:P >20 represents the P limitation. C. fargesii was limited by N but not P, since N was lower than that in lower-elevation plants in China (Han et al., 2011; Zhang et al., 2012). There were significant differences in root N and P across elevations. Root N and P were greater than those of lower-elevation plants in China (Ma et al., 2015). There was a significantly positive correlation between leaf K and elevation, which was supported by the findings of Du et al. (2017). This indicates that the ecology at higher elevations did not restrict K uptake by plants. Leaf Ca increased with elevation except at 500 m. Root Ca increased at higher elevations. The lower temperatures at higher elevations promoted Ca uptake by plants, which aided in their defense against the cold (Plieth et al., 1999). C. fargesii required more Ca at higher elevations and we predict that its demands at the same elevation may decrease in the future with global warming and altered forest lines. Correlations between AGB and C. fargesii plant stoichiometry C. fargesii’s highest AGB (Fig. 4) and importance value (Table 1) were found at 600 m, indicating that this environment is more suitable for growth than those at other elevations. However, the AGB decreased as elevation increased and species’ tendency to migrate to a higher elevation was not noticeable despite being affected by warming temperatures and the tree line moving up. Leaf and root Ca were significantly negatively correlated with

Liu et al. (2021), PeerJ, DOI 10.7717/peerj.11553 9/18 A 6 A 6 A 6 BaBa BaBa B 6 BaBa B 6 B 6 AaBa AaBa C 125 AaBa CBaBa125 CBa125Ba BaBa Aa Aa Aa Aa Aa Aa Aa Aa Aa Aa Aa Aa 100 100 100 ABa DepthABa DepthABa Depth ABa DepthABa DepthABa BaDepth Ba DepthBa Depth Depth 4 4 4 4 4 4 Aa Aa Aa 0−20cm 0−20cm 0−20cm 0−20cm 75 0−20cmAa 750−20cmAaABa 75 AaAaABa 0−20cm AaABa 0−20cm 0−20cm Ab Ab Ab Ab Ab Ab Aa Aa Aa Aa Aa Aa Aa AaAa Aa AaAa Aa AaAa Aa Aa Aa Aa Aa ABa ABa ABa Aa Aa Aa AaAaAa Aa AaAaAa AaAa Aa Aab Aa Aab Aa Aab20−40cm Aa20−40cm Aa 20Aa−40cm Aa Aa Aa20−40cm 50 20−40cm 5020−40cm 50 20−40cm 20−40cm 20−40cm 2 Aa Aa 2 AaAaAa Aa 2 AaAaAa Aa AaAa 2 Ba ABa 2 AaBaAa ABa2 AaBaAa ABa AaAa 40−60cm 40−60cm 40−60cm 40−60cm 40−60cm 40−60cm 40−60cm 40−60cm 40−60cm Soil N(mg/g) Aa Soil N(mg/g) Aa Soil N(mg/g) Aa Aa Aa Aa Soil P(mg/kg) Soil P(mg/kg) Soil P(mg/kg) Soil K(mg/kg) 25 Soil K(mg/kg) 25 Soil K(mg/kg) 25

0 0 0 0 0 0 0 0 0 500 600 700500 800600 900700500 800600 900700 800 500900 600 700500 800600 900700500 800600 900700 800 900500 600 700500 800600 900700500 800600 900700 800 900 Elevation(m) Elevation(m) Elevation(m) Elevation(m)A 6 Elevation(m) Elevation(m)BaBa Elevation(m)B 6 Elevation(m) Elevation(m)AaBa C 125 BaBa Aa Aa 3 3 3 Aa Aa 100 D Ba D Ba D Ba E E EAa Aa F AaDepth F F Ba Ba Depth Ba Ba Depth Aa Aa ABa Aa ABa Aa Ba Aa Ba Aa Ba 20 20 4ABa 20 ABa ABa 4 Aa Aa Aa Aa 0−20cm 0−20cm 75 Aa AaABa 0−20cm Depth Depth Aa Depth Aa Ab Aa Ab Depth Depth20 Depth20 Aa 20 Aa Depth Depth DepthAa Aa Aa Aa ABa ABa ABa Aa Aa ABa Aa Aa Aa Aa AaAa 2 2 Aa 2 Aa Aa 15 15 Aa 15 Aab 20−40cm Aa Aa 20−40cm 50 20−40cm ABa ABaABa Ab ABaABa 0Ab−20cm ABa 0Ab−20cm 0−20cm2 Aa Aa AaAa 0−20cm 0−20cm 02−20cmBa AaABa AaAa Aa0−20cm Aa0−20cm 0−20cm ABa ABa ABa Aa Aa Aa 40−60cm 40−60cm 40−60cm Aa Soil N(mg/g) AaAa Aa Aa Aa Aa Aa Soil P(mg/kg) Aa Aa Aa Aa Aa Aa Soil K(mg/kg) 25 10 ABa Ba 10 ABa Ba 10 ABa Ba Aa 20−40cmAa 20−40cmAa 20−40cm Aa 20−40cmAa 10 20−Aa40cm 10Aa20−40cm 10 Aa 20−Aa40cm 20−40cm 20−40cm 1 1 AaAaAa 1 AaAaAaAa AaAaAaAa Aa ABb Aa ABb Aa Soil N:P ABbAa Aa Soil N:P Aa Soil N:P Aa Aa AaAa0 AaAa Aa Aa Aa 0 Aa Aa Aa Aa AaAa Aa Aa Aa 0 40−60cm 5 40−60cm 5Aa40Aa−60cm 5 AaAa 40−Aa60cmAa 40−Aa60cm Aa 40−60cmAa Aa Aa Aa 40−60cm 40−60cm 40−60cm Soil Ca(mg/kg) Soil Ca(mg/kg) Soil Ca(mg/kg) Soil Mg(mg/kg) Soil Mg(mg/kg) 500Soil Mg(mg/kg) 600 700 800 900 Aa 500Aa 600 700 Aa 800 900 500 600 700 800 900 Elevation(m) Elevation(m) Elevation(m) 0 0 0 0 0 0 0 0 0 500 600 700500 800600 900700500 800600 900700 800 900500 600 700500D 3 800600Ba 900700500 800600 900700 800 900500 600 E 700500 800600 900700500 800600Aa 900700 800 900 F Ba Aa Elevation(m) Elevation(m) Elevation(m) Elevation(m)Aa BaElevation(m) Elevation(m) Elevation(m)20 Elevation(m)ABa Elevation(m) Aa 20 ABa Depth Aa Depth Aa Depth 2 Aa 15 G Aa G Aa G Aa H H H BaABa ABaBa Ab I Ba0−20cmI I Ba Ba 0−20cm Ba Aa 0−20cm ABa 1.00 1.00 Aa1.00Aa BaBa BaBa BaBa Ba Ba ABaBa ABa ABa Aa Aa Aa 6 6 6 10 ABa Ba BaBa BaBa BaBa 0.10 0.10 0.10Aa Aa Aa Aa 20−40cm Aa 20−40cm 10 Aa 20−40cm Depth Depth Depth 1 AaAaDepthAa Aa Depth Depth ABa ABa DepthABb AaABa Depth Soil N:P DepthAa 0.75 0.75 Aa 0.75 Aa Aa Aa Aa Aa Aa Aa Aa 40−60cm 5 AaAa 40−60cm Aa Aa 40−60cm Soil Ca(mg/kg) Ab Aa Ab Aa 0−20cmAb Aa 0−20cm 0−20cm 0−20cm 0−20cm Soil Mg(mg/kg) 0−20cm 0−20cm 0−20cm 0−Aa20cm 4 4 Aa ABa4 Aa ABa Aa ABa ABa ABa ABa ABa ABa ABa ABa ABa ABa ABa ABa ABa Aa Aa Aa 0.50 0.50 Aa Aa 0.50 Aa Aa Aa Aa Aab Aab 20−40cmAab 20−40cmAa ABa 20−40cm0 Aa ABa Aa ABa20−40cm 20−40cm Aa Aa200−40cm Aa Aa Aa 20Aa−40cm 20−40cm 0 20−40cm 0.05 0.05 0.05 AaABa Aa AaABa Aa AaABa Aa Soil N:K Aa Soil N:K AaAaSoil N:K AaAa Aa Aa Aa Aa Aa Aa Aa Soil N:Ca Soil N:Ca Soil N:Ca 500 600 700 800 900 Soil N:Mg Aa Soil N:Mg 500Aa 600Soil N:Mg 700 Aa800 900 500 600 700 800 900 Aa Aa Aa Aa 40Aa−60cmAa 2 40−60cm Aa 2 40−60cm Aa 2 Aa 40−60cm 40−60cm 40−60cm 40−60cm 40−60cm 40−60cm AaAa Aa AaAa Aa AaAa Aa Ab Ab Ab Elevation(m) 0.25 0.25 0.25Elevation(m) Elevation(m) AabAa AabAa AabAa 0.00 0.00 0.00 0 0 G Aa 0 0.00 0.00H 0.00 Ba I 1.00 Ba BaBa Ba ABa Ba 500 600 700500 800600 900700500 800600 900700 800 500900 600 700500 0.10800600 900700500 800600 900700Aa 800 900500 600 6 700500 800600 900700500 800600 900700 800 900 Ba Depth Depth 0.75 ABa Depth Elevation(m) Elevation(m) Elevation(m) Elevation(m) Elevation(m) Elevation(m) Elevation(m) Elevation(m) Elevation(m)Aa Ab Aa 0−20cm 0−20cm 0−20cm 4 Aa ABa ABa ABa ABa Ba Ba Ba Aa Aa Aa ABa Aa Aa Aa Aa AaAa Aa 0.50 Aa Aa J J J K K K Aa Aab Aa L Aa 20−40cmL AaL ABa 20−40cm Aa Aa 20−40cm Ba Ba Ba 0.05 AaABa Aa Soil N:K AaAa Aa Aa Aa Aa Aa Aa Aa Aa

Aa Soil N:Ca Aa Aa 0.6 Aa 0.6 Aa 0.6 Aa Aa Aa Aa Aa Aa Soil N:Mg Aa 0.015 Ba 0.015 Ba 0.015 Ba Aa Aa Aa Aa Aa Aa Aa 40−60cm 2 Aa Aa 40−60cmAa 0.25 40−60cm ABa DepthABa DepthABa Depth AaAa Aa Depth Depth Depth Ab Depth Depth Depth ABa ABa ABa Aa AaAa AaAa Aa0.10 Aa Aa0.10 AabAaAa 0.10Aa Aa Aa Aa Aa Aa Aa Aa Aa Ab Aa Ab Aa Ab Aa ABa ABaABa ABa ABaABa0−20cmABa ABaABa 0−20cmAa 0−Aa20cmAa AaAa Aa 0−20cmAaAa 0−20cmAa Aa 0−20cmAa AbAa Aa AaAb 0−20cmAa Ab 0−20cm 0−20cm 0.010 0.010 0.010 0.4 0.4 0.00Aa Aa0.4 Aa Aa Aa Aa 0 0.00 Aa Aa Aa Aa Aa Aa Aa Aa Aa ABa Aa ABa20−40cmAa ABa20−Aa40cm 20−40cm 500 600 700 20800−40cm 900 20−40cm 20−40cm500 Aa600 700 800Aa20−90040cm Aa20−40cm 20500−40cm600 700 800 900

Soil P:K AaSoil P:K Aa Aa Soil P:K AaAa Aa AaAa Aa Elevation(m) 0.05 0.05 0.05Elevation(m) Elevation(m) Soil P:Ca Soil P:Ca Soil P:Ca 0.005 0.005 0.005 40−60cm 0.2 40−60cm 0.240−60cm 0.2 40−60cm Soil P:Mg 40−60cm Soil P:Mg 40−60cm Soil P:Mg 40−60cm 40−60cm 40−60cm J Ba K Aa Aa L Aa Aa Ba Aa Aa Aa Aa Aa 0.000 0.000 0.000 0.0 0.0 0.015 Ba0.0 0.00 0.000.6 Aa 0.00Aa Aa ABa Depth Depth Depth 500 600 700500 800600 900700500 800600 900700 800 900500 600 700500 800600 900700500 800600 900700 ABa800 900500 600 700500 800600Aa 900700500 800Aa600 900700 800 900 0.10 Aa Aa Aa Aa Ab Aa ABa ABaABa 0−20cm Aa Aa Aa 0−20cm Aa Aa Ab 0−20cm Elevation(m) Elevation(m) Elevation(m) Elevation(m)0.010 Elevation(m) Elevation(m) Elevation(m)0.4 Elevation(m)Aa AaElevation(m) Aa Aa Aa ABa Aa 20−40cm 20−40cm Aa 20−40cm

150 150 150 Soil P:K AaAa Aa 0.05 Soil P:Ca Ba Ba Ba 20 Aa 20 Aa20 Aa Aa Aa Aa Soil P:Mg M M M N N 0.005 N O 40−60cmO 0.2 O 40−60cm 40−60cm Ba Ba Ba Aa Aa Aa Aa Aa 0.6Aa Ba 0.6 Ba 0.6 Ba Depth Depth15 Depth15 AaAa Aa 15 AaAa DepthAa AaAa AaDepth Depth Depth Depth Depth 100 100 ABa100 ABa ABa Aa Aa Aa0.000Aa Aa AaAa Aa Aa 0.0 0.00 ABaABa ABaABa ABaABa Aa Aa AaAaAa AaAa AaAa Aa AaABaAa AaABaAa 0−20cm AaABa 0−20cm Aa 0−20cm Aa 500Aa 600 Aa700 0−Aa20cm800 900 0Aa−20cm 0−20cm500 600 700 800 0−20cm900 0−20cm 0500−20cm 600 700 800 900 ABbABb ABbABb ABbABb 0.4 0.4 0.4 Aa Aa Aa 10 Aa 10 Aa 10 Aa Elevation(m) Aa Aa AbElevation(m)Aa Ab Ab Elevation(m) Aa Aa Aa 20−40cm 20−40cm 20−40cm 20−40cm 20−40cm AaAaAa 20−40cmAa AaAaAa Aa AaAaAa20−40cmAa 20−40cm 20−40cm 50 Aa 50 Aa Aa50 Aa Aa Aa Aa Aa Aa Aa Aa Aa Soil K:Ca Soil K:Ca Soil K:Ca Soil K:Mg Soil K:Mg 150 Soil K:Mg Aa Aa AaAa Aa AaAa Aa Aa

Soil Ca:Mg Soil Ca:Mg 20 Aa Soil Ca:Mg Aa Aa 40−60cm 5 40−60cm 5 40−60cmM 5 Ba 40−60cm 0.2 40−60cm 0.2N 40−60cm Aa0.2 Aa 40−60cmAa 40−60cmAa O Aa40−60cm Aa Aa Aa Ba Aa Aa 0.6 Ba Depth 15 AaAa Aa Depth Depth 0 0 0 0 0 100 0 ABa 0.0 0.0 Aa 0.0 Aa Aa ABaABa Aa AaAa Aa AaABa 0−20cm Aa Aa 0−20cm 0−20cm 500 600 700500 800600 900700500 800600 900700 800 900500 600 700500 800600 900700500 800600 900700 ABbABb800 900500 600 700500 800600 900700500 800600 900700 800 900 0.4 Aa 10 Aa Aa Ab Elevation(m) Elevation(m) Elevation(m) Elevation(m) Aa Elevation(m) Elevation(m) 20−40cm Elevation(m) Elevation(m) Elevation(m) 20−40cm AaAaAa Aa 20−40cm 50 Aa Aa Aa Aa Soil K:Ca Soil K:Mg Aa Aa Aa 40−60cm 40−60cm Soil Ca:Mg 0.2 Aa Aa 40−60cm Aa 5

0 0 0.0 Figure 6 Variations in the soil N, P, K, Ca,500 and600 Mg700 contents800 900 (A–E) and the soil500 N:P,600 N:K,700 N:Ca,800 N:Mg,900 500 600 700 800 900 Elevation(m) Elevation(m) Elevation(m) P:K, P:Ca, P:Mg, K:Ca, K:Mg, and Ca:Mg ratios (F–O) at different elevations and soil depths. The error bars represent standard deviation. Different uppercase letters within the panels indicate that there are significant differences between different elevations (p < 0.05), and different lowercase letters within the panels indicate that there are significant differences between different soil depths (p < 0.05). Full-size DOI: 10.7717/peerj.11553/fig-6

C. fargesii AGB indicating that C. fargesii with a larger above-ground biomass had less demand for Ca. C. fargesii at a higher elevation may require more Ca than those at lower elevations in our research area. Root P and K were significantly positively correlated with AGB indicating that C. fargesii with a larger AGB had more demand for P and K. In general, P-limiting was found in the acidic soil of tropical and subtropical regions. However, we found that P-limiting did not exist in our research area and P was absorbed by plant roots as an essential nutrient in P-rich soils. Estimating the AGB of C. fargesii at different elevations and analyzing the relationship between AGB and the concentration of plant stoichiometry may provide a basis for C. fargesii management and fertilization in the future.

Liu et al. (2021), PeerJ, DOI 10.7717/peerj.11553 10/18 LCa 0.8 SMR

ELE

RCa LMg RMg RKSpH

LN LP

Axis 2 (19.74%) LK RP

TH DBH

ST -1.0 -0.8 Axis 1 (38.99%) 0.8

Figure 7 RDA analysis of environmental factors, leaf and root nutrient of the C. fargesii community in the Guoyan Mountain Natural Reserve. ST, soil temperature; ELE, elevation; SM, soil moisture; SpH, soil pH; LN, leaf N concentrations; LP, leaf P concentrations; LK, leaf K concentrations; LCa, leaf Ca concentrations; LMg, leaf Mg concentrations; RN, root N concentrations; RP, root P concentrations; RK, root K concentrations; RCa, root Ca concentrations; RMg, root Mg concentrations. Full-size DOI: 10.7717/peerj.11553/fig-7

Table 3 Summary of environmental factor at different elevation gradients.

Elevation Soil temperature Soil moisture Soil pH (m) (◦ C) (%) 500 17.13 ± 7.13 14.12 ± 4.12 5.34 ± .342 600 17.93 ± 7.93 15.98 ± 5.98 4.73 ± .738 700 16.40 ± 0.36 14.02 ± 4.02 4.67 ± .672 800 16.28 ± 6.28 13.04 ± 3.04 4.42 ± .424 900 14.70 ± 4.70 18.82 ± 8.82 4.70 ± .702

Table 4 Relative contribution of environmental variables to the variance of ecological stoichiometry of the C. fargesii community.

Order Variable Contribution (%) F p 1 Elevation 46.4 8.2 0.002 2 Soil moisture 33.8 8.0 0.002 3 Soil temperature 16.0 4.4 0.006 4 Soil pH 3.9 1.1 0.394

Liu et al. (2021), PeerJ, DOI 10.7717/peerj.11553 11/18 Relationships between the soil stoichiometry, environmental factors, and plant stoichiometry in the C. fargesii population Soil influences plant growth, productivity, and distribution (Condit et al., 2013) which are relevant to the biogeochemical cycling of nutrients in terrestrial ecosystems (Izquierdo, Houlton & van Huysen, 2013; Tian et al., 2010). Previous studies have demonstrated that soil P was strongly related to leaf N and P (Han et al., 2005; Hedin, 2004). However, we found that soil P showed no relationship with leaf stoichiometry, indicating that soil P did not govern nutrient accumulation in leaves from the study area. Leaf Ca and Mg were found to be negatively correlated with soil K. Previous studies have shown that a high concentration of one cation could cause an imbalance in the concentrations of other cations in the soil (Hailu et al., 2015). Therefore, Ca and Mg absorption by plants was negatively affected by the excess K in the soil (Hailu et al., 2015; Xue et al., 2019). There was a negative relationship between the soil and leaf N, but a previous study showed that soil N was not correlated with leaf N across Chinese grasslands (He et al., 2010). We explored the relative contribution of elevation gradients and soil microenvironment (soil temperature, soil pH, and soil moisture rate) to ecological stoichiometry variation. Our results show that elevation was the most important factor impacting the stoichiometry of C. fargesii. However, elevation had the least impact on stoichiometry variation for Pinus taiwanensis within our study area, which indicates that this evergreen broad-leaf species is more sensitive than coniferous trees. These results imply that the ecological stoichiometry of C. fargesii will alter during global warming.

CONCLUSIONS Leaf N, K, and Ca levels within C. fargesii were significantly related to elevation, while leaf P and Mg showed no relationship with elevation. The leaf N:P indicated that C. fargesii was limited by N in this subtropic forest ecosystem in China. Soil P showed no relationship with leaf stoichiometry. The elevation explained nearly half of the variation of ecological stoichiometry in C. fargesii. Our results may improve our understanding of the biogeochemical cycle for nutrients in the subtropical forests of China.

ACKNOWLEDGEMENTS We are grateful for the help from the state-owned forest farms of Shunchang County.

ADDITIONAL INFORMATION AND DECLARATIONS

Funding This study was supported by the National Natural Science Foundation of China (Nos. 32071760 and 41701099) and the Natural Science Foundation of Fujian Province (No. 2019J01768). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Liu et al. (2021), PeerJ, DOI 10.7717/peerj.11553 12/18 Grant Disclosures The following grant information was disclosed by the authors: National Natural Science Foundation of China: 32071760, 41701099. Natural Science Foundation of Fujian Province: 2019J01768. Competing Interests The authors declare there are no competing interests. Author Contributions • Danping Liu and Dexiang Zheng conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the paper, and approved the final draft. • Yaoyao Xu and Yifei Chen conceived and designed the experiments, performed the experiments, prepared figures and/or tables, and approved the final draft. • Hesong Wang and Ku Wang conceived and designed the experiments, authored or reviewed drafts of the paper, and approved the final draft. • Xiaoli Liao performed the experiments, authored or reviewed drafts of the paper, and approved the final draft. • Changxiong Chen conceived and designed the experiments, performed the experiments, authored or reviewed drafts of the paper, and approved the final draft. • Jiangjiang Xia conceived and designed the experiments, analyzed the data, authored or reviewed drafts of the paper, and approved the final draft. • Shaofei Jin conceived and designed the experiments, performed the experiments, analyzed the data, authored or reviewed drafts of the paper, and approved the final draft. Data Availability The following information was supplied regarding data availability: The raw data are available in the Supplementary File. Supplemental Information Supplemental information for this article can be found online at http://dx.doi.org/10.7717/ peerj.11553#supplemental-information.

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