Divergent effects of a 6-year warming experiment on the nutrient productivities of subtropical tree species

Forest Ecology and Management 461 (2020) 117952

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Divergent effects of a 6-year warming experiment on the nutrient productivities of subtropical tree species

T

Ting Wua,b,c,1, Shizhong Liua,b,1, Zhiyang Liea,b,c, Mianhai Zhenga,b, Honglang Duand, ⁎ Guowei Chua,b, Ze Menga,b, Guoyi Zhoua,b, Juxiu Liua,b, a Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems, South China Botanical Garden, Chinese Academy of Sciences, Xingke Road 723, Tianhe District, Guangzhou 510650, China b Center for Plant Ecology, Core Botanical Garden, Chinese Academy of Sciences, 723 Xingke Road, Tianhe District, Guangzhou 510650, China c University of Chinese Academy of Sciences, Beijing 100039, China d Jiangxi Provincial Key Laboratory for Restoration of Degraded Ecosystems & Watershed Ecohydrology, Nanchang Institute of Technology, Nanchang 330099, China

A R T I C LE I N FO

A B S T R A C T

Keywords: Biomass allocation Nitrogen productivity Phosphorus productivity Plant growth Subtropical forest Warming

Previous studies have mainly focused on the changes in plant growth, but few have quantified the alternations in nutrient productivities to explain the climate warming-induced shifts in species compositions and community structures of forest ecosystems. Here, we conducted a 6-year warming experiment by translocating model forest ecosystems from high-elevation sites to lower-elevation sites, to investigate the plant biomass allocation patterns and nutrient productivities of four native tree species. The results showed that warming increased the biomass of Schima superba, Syzygium rehderianum, and Itea chinensis, but decreased the biomass of Machilus breviflora. Seedlings of S. superba, S. rehderianum, and I. chinensis under warming allocated more biomass to stems at the expense of leaves and roots. In contrast, M. breviflora allocated 34% greater biomass to the roots. Warming increased the nitrogen productivity of S. rehderianum by 35%; however, it decreased the nitrogen and phosphorus productivity of I. chinensis and M. breviflora by approximately 40% and 60%, respectively. These results indicate that M. breviflora has a low fitness in long-term warming conditions due to its reduced biomass and nutrient productivities. Our findings may have important implications for better understanding the effects of warming on the species compositions of subtropical forests.

1. Introduction Plant nutrient productivity measures the rate of dry matter production per unit of nutrient in the plant biomass per unit of time (e.g., g dry weight g−1 nutrient day−1), quantifying the efficiency of nutrient conversion to plant biomass production (Ingestad, 1979). Importantly, it can reflect the fitness of plants under shifts in resource supplies (Eckstein and Karlsson, 2001). Plants with a high nutrient productivity are supposedly advantageous in nutrient-poor soil (Hirose, 2012). Nitrogen (N) and phosphorus (P) are major nutrients that can control plant growth. Therefore, knowing the changes in plant nitrogen productivity (NP) and phosphorus productivity (PP) could provide insight into how tree growth responds to various nutrient availabilities induced by warming, and has vital implications for forest management. However, few studies on how plant NP and PP respond to warming have been carried out in forest ecosystems.

In the past century, global mean temperature has increased by 0.85 °C (IPCC, 2014). The increasing temperature is an important driver of tree growth (Bowman et al., 2014; Li et al., 2017). The alteration in tree growth induced by warming can result in shifts in the demands for nutrients, including N and P (Way and Oren, 2010; Kuster et al., 2013). Whether the N and P in soils can meet the requirements of faster growth for tree species is mainly related to the availability of N and P in the soils (Dawes et al., 2017) and nutrient absorption capacities of the plants (Yu et al., 2019). Increases in N and P availability have been observed in some warming experiments, which were a result of the stimulation of litter decomposition (Liu et al., 2017), soil mineralization (Butler et al., 2012; Zhang et al., 2017), and so on. In contrast, Verburg et al. (1999) found that the supplies of N and P can be inhibited by declines in litter decomposition due to the reduced soil moisture caused by an exposure to soil warming. Eventually, the inconsistent shifts of N and P availability may induce morphological adjustments in plants

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Corresponding author at: No.723, Xingke Road, Tianhe District, Guangzhou, China. E-mail address: [email protected] (J. Liu). 1 Ting Wu and Shizhong Liu contributed equally to this work. https://doi.org/10.1016/j.foreco.2020.117952 Received 11 January 2020; Received in revised form 28 January 2020; Accepted 29 January 2020 0378-1127/ © 2020 Elsevier B.V. All rights reserved.

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Fig. 1. Chamber design (a) and the scheme of experimental design in our study (b).

2. Materials and methods

under warming (Kasurinen et al., 2016; Duan et al., 2018). Biomass allocation, between aboveground biomass and belowground biomass as well as among different organs, is a fundamental way for tree species to acclimate to the changing N and P availability caused by warming (Anderson et al., 2012; Nabais et al., 2014). For instance, a higher root to shoot (R/S) ratio under warming was observed by Zhou et al. (2012), indicating that increasing the biomass allocation to roots can enhance the absorption of water and nutrients. In addition, warming has been found to alter nutrient absorption and tree growth through changing the length and nutrient concentrations of the root (Pu et al., 2017; Yu et al., 2019). The warming-induced changes in the N and P availability and capacity of nutrient absorption eventually led to shifts in the N and P amounts in plants, as well as tree growth. The alternations in the plant growth and N and P amounts are interrelated. For instance, Dawes et al. (2017) reported that the positive effect of warming on plant growth promoted plant N amount. Additionally, Sardans et al. (2007) found that a decrease in the P amount in aboveground biomass might limit further plant growth. The balance between the response of plant growth and nutrient amounts to warming can affect plant nutrient productivity, including NP and PP. If warming enhances plant growth to a greater extent than N and P amounts, plant NP and PP are expected to be higher. Tropical tree species have narrower temperature tolerances than temperate tree species (Cunningham and Read, 2002). Therefore, the effects of warming on tropical trees might be more serious than those on temperate trees (Reed et al., 2012). However, most warming experiments have been conducted in temperate ecosystems (Way and Oren, 2010), creating an urgent need for warming experiments to be conducted in tropical forests (Cavaleri et al., 2015). In this study, we conducted a 6-year warming experiment to investigate the effects of long-term warming on nutrient productivities at the Dinghushan Biosphere Reserve in tropical China, which is experiencing an obvious enhancement in temperature (Zhou et al., 2011). According to Huang et al. (2013) and Mo et al. (2006), we know that this forest was Plimited with a high N deposition. Thus, the NP and PP of the tree species may differ in response to experimental warming in this tropical forest. We hypothesized that (1) warming would increase the plant biomass and R/S ratio of the tropical tree species, and (2) warming would have no influence on the plant NP, while it would increase the plant PP of the tree species, as the tropical area is N-rich but P-limited.

2.1. Study site This study was conducted at the Dinghushan Biosphere Reserve (DBR), which has an elevation range from 10 to 1000 m a.s.l. The DBR is located in the middle of Guangdong Province in southern China (23°09′N–23°11′N, 112°30′E–112°33′E). The climate is a typical tropical monsoon climate. The mean annual temperature is approximately 21 °C, ranging from the lowest mean temperature in January (12.6 °C) to the highest in July (28.0 °C). The mean annual precipitation is approximately 1700 mm, and almost 80% of all precipitation falls in the warm-wet season (April-September), with the remaining 20% falling in the cold-dry season (October-March). There are three major forest types at DBR: the monsoon evergreen broad-leaved forest, coniferous and broad-leaved mixed forest (CBMF), and mountain evergreen broadleaved forest (MEBF), which are located at the altitudes of 30, 300, and 600 m, respectively (Fang et al., 2015). In March 2012, we initiated a warming experiment by translocating model forest ecosystems from high-elevation sites to lower-elevations sites, with temperature as the main altered environmental factor. Model MEBF was translocated from the altitude of 600 m (control group) to 300 (warming group) and 30 m (warming group). Three open-top chambers were constructed at the high-elevation site (600 m), three at the middle-elevation site (300 m), and three at the low-elevation site (30 m). The three transplant sites were located in open areas and were exposed to natural light and rainfall conditions. Three 3 × 3 m chambers were located at the sites of 600, 300 and 30 m, respectively. The 0.8-m deep below-ground section in each chamber was surrounded by a concrete brick wall bound with ceramic tiles to prevent the lateral or vertical movement of water or elements from the surrounding soils (Fig. 1a). Three different layers of soils (0–20, 20–40, and 40–70 cm) were collected from the MEBF at an altitude of approximately 600 m, were homogenized separately, and then placed inside the open-top chambers at each site. Six native species of seedlings were selected from the MEBF. Schima superba and Syzygium rehderianum are distributed widely across the entire elevation gradient, while Machilus breviflora, Itea chinensis, Myrsine seguinii, and Ardisia lindleyana are only distributed between the altitudes of 700 and 400 m. In May 2013, six seedlings per species were transplanted into each open-top chamber in a randomized block design. Further detailed information is shown in Liu et al. (2017). The air temperature was monitored and recorded in each growth chamber using a HMP155A temperature probe. The soil temperature profile and soil moisture at 5 cm were recorded in each chamber using a 2

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Table 1 Equations for plant biomass estimation of four tree species exposed to warming. Species

Root

S. superba S. rehderianum I. chinensis M. breviflora

W W W W

= = = =

Stem 2.307 0.493 2.510 1.408

× × × ×

2

0.623

(D H) (D2H)0.687 (D2H)0.649 (D2H)0.646

W W W W

= = = =

Leaf 0.944 0.435 0.897 1.638

× × × ×

2

0.837

(D H) (D2H)0.888 (D2H)0.871 (D2H)0.723

W W W W

= = = =

0.483 0.370 1.225 0.135

× × × ×

(D2H)0.774 (D2H)0.786 (D2H)0.671 (D2H)1.015

Table 2 The concentrations of N, P, available N and available P (means ± standard errors, n = 3) in soils (0–10 cm) at different altitudes in the studied mountain evergreen broad-leaved forest. Time

Altitude (m)

N (g/kg)

P (g/kg)

Available N (mg/kg)

Available P (mg/kg)

a

June 2014

600 300 30

0.92 ± 0.05 0.92 ± 0.01 0.87 ± 0.02

0.25 ± 0.01 0.24 ± 0.01 0.25 ± 0.01

14.17 ± 1.09 12.20 ab ± 0.48 10.69b ± 0.56

1.26 ± 0.12 1.27 ± 0.12 1.23 ± 0.12

June 2015

600 300 30

1.19 ± 0.09 0.96 ± 0.09 0.95 ± 0.03

0.27 ± 0.02 0.25 ± 0.01 0.30 ± 0.02

13.00 ± 0.27 11.18 ± 1.29 10.78 ± 0.35

1.28 ± 0.20 1.25 ± 0.10 0.93 ± 0.14

December 2015

600 300 30

1.01 ± 0.03 1.06 ± 0.10 0.88 ± 0.01

0.26 ± 0.10 0.25 ± 0.01 0.25 ± 0.01

9.86 ± 0.24 9.07 ± 0.44 8.88 ± 0.20

1.20 ± 0.10 0.94 ± 0.23 0.85 ± 0.21

December 2017

600 300 30

1.28 ± 0.06 1.06 ± 0.04 1.02 ± 0.11

0.29 ± 0.01 0.26 ± 0.01 0.30 ± 0.03

10.05 ± 0.85 9.39 ± 0.51 13.21 ± 1.40

0.11 ± 0.01 0.10 ± 0.03 0.17 ± 0.06

Table 3 Results (P value) of repeated-measures ANOVA on the effects of warming (W), species (S), sampling time (T) and their interaction on tree growth, biomass allocation, nutrient amounts and nutrient productivity. Parameters 2

D H Biomass RMF SMF LMF R/S N amount P amount N productivity P productivity

W

S

W×S

T

W×T

S×T

W×S×T

< 0.01 < 0.001 < 0.001 < 0.001 ns ns < 0.01 < 0.01 < 0.01 < 0.01

< 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.01 < 0.01

< 0.001 < 0.001 < 0.001 < 0.01 ns ns < 0.001 < 0.001 ns < 0.001

< 0.001 < 0.001 < 0.001 < 0.001 ns 0.02 < 0.001 < 0.001 < 0.001 < 0.001

ns ns < 0.001 ns 0.04 ns ns ns < 0.01 ns

< 0.001 < 0.001 0.04 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 ns ns

ns ns ns ns 0.01 < 0.001 0.01 0.03 < 0.001 < 0.01

ns: no significance.

root-to-shoot (R/S) ratios were calculated as the root mass/(leaf mass + stem mass) (Fig. 1b). Subsamples of the leaves, stems, and roots of the harvested seedlings were used to determine the N and P concentrations. N concentrations were measured using the Kjeldahl method (Bremner and Mulvaney, 1982). P concentrations were measured photometrically, after the samples were digested with H2SO4-H2O2. The N and P amounts for each species were calculated as follows:

Campbell 109 constantan-copper thermocouple and time-domain reflectometer probes (CS616, Campbell, USA), respectively. Data were recorded every 30 min using Campbell Scientific (Logan, UT, USA) CR1000 data loggers, starting in May 2013. 2.2. Sample collection and measurement The basal diameter (D) and height (H) of all seedlings per species in each chamber were measured every December (dry season) and June (wet season) from 2014 to 2018. From each of the chambers, one or two seedlings of S. superba, S. rehderianum, I. chinensis, and M. breviflora were randomly selected and harvested in June 2014 and 2015, and 2018. Since M. seguinii and A. lindleyana grew slower than the others, we did not harvest them in this experiment. All harvested seedlings were separated into leaves, stems (including branches), and roots. The plant organs were dried at 65 °C for 72 h, after which the plant dry mass was determined. The biomass per seedling of each species was estimated according to the empirical equation B = a (D2H)b, where a and b were the regression coefficients (Table 1). The dry mass allocation was determined as follows. The fraction of dry mass allocated to the leaves (leaf mass fraction) was calculated as the leaf mass/total dry mass. The stem mass fraction and root mass fraction were calculated as the stem mass/total dry mass and root mass/total dry mass, respectively. The

N (g/plant) = N concentration leaf*biomass leaf + N concentration stem *biomass stem + N concentration root*biomass root, P (g/plant) = P concentration leaf*biomass leaf + P concentration stem *biomass stem + P concentration root*biomass root. The NP and PP were calculated from the N and P concentrations and the annual growth biomass, according to the following equations from Evans (1972):

3

NP =

LnNpool2 − LnNpool1 (B2 − B1) × T2 − T1 (Npool2 − Npool1)

PP =

Ln Ppool2-Ln Ppool1 (B2-B1) × T2-T1 (Ppool2-Ppool1)

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Fig. 2. The D2H of four tree species exposed to warming treatments from December 2014 to June 2018. Error bars indicate standard deviations. Different lowercase letters indicate significant differences among treatments in each sampling time. Data of plant height and base diameter are from Wu et al. (2019).

3. Results

where B, N pool, and P pool are the plant biomass, N amount, and P amount, respectively, at two consecutive harvests conducted at times T1 and T2. Soil samples were also collected from a depth of 0–10 cm in June (wet season) and December (dry season) from 2014 and 2018. N and P concentrations in the soil were analyzed in laboratory. The total N concentration was measured using the micro Kjeldahl method (Jackson, 1964), and the total P concentration was analyzed colorimetrically (Anderson and Ingram, 1989).

3.1. Environmental variables During the experimental period, translocating model forests from the altitude of 600 m to 300 and 30 m enhanced the average monthly air temperature by 0.84 and 1.42 °C, respectively. The average monthly air temperature was 0.58 °C higher at 30 m than at 300 m (Fig. S1a, P < 0.05). During the same period, the soil temperature and soil moisture at the 5 cm depth were also significantly affected by warming. When the model forests were translocated from the altitude of 600 m to 300 and 30 m, the average monthly soil temperature increased by 0.86 and 2.09 °C, respectively. The average monthly soil temperature was 1.23 °C higher at 30 m than at 300 m (Fig. S1b, P < 0.05). The average monthly soil moistures were 18.92%, 20.07% and 15.45% in the chambers at the altitudes of 600, 300, and 30 m, respectively (Fig. S1c, P < 0.05).

2.3. Statistical analysis Prior to the statistical analysis, the data were confirmed with the Kolmogorov-Smironv test for normality and Levene’s test for homogeneity of variance prior to statistical analysis. When the data did not conform to the assumption of normality and homogeneity of variances, it was logarithmically transformed. A repeated measures two-way ANOVA was used to evaluate the effects of the warming treatment and time on the soil moisture, soil temperature, and air temperature. A three-way ANOVA (TANOVA) was used to assess the effects of warming, species, sampling time, and their interactions on the D2H, biomass, root mass fraction, stem mass fraction, leaf mass fraction, R/S ratio, nutrient amounts, and nutrient productivity. Significant differences among treatments in each time were analyzed using a one-way ANOVA, followed by Tukey’s multiple comparison test. Percentage changes in the biomass, R/S ratio and nutrient productivity under warming treatments were quantified when the statistical analyses were significant. Differences were considered to be statistically significant at P < 0.05. Data were analyzed using SPSS 24.0 (SPSS Inc., Chicago, IL, United States).

3.2. Nutrient concentrations in soils The concentrations of N and available N in soils (0–10 cm) showed decreasing trend when studied forests were translocated from the altitude of 600 m to 300 and 30 m. For instance, the warming treatment at 30 m obviously reduced the concentration of available N compared to that at 600 m in June 2014. However, there were not inconsistent tendencies in the concentrations of P and available P in soils (Table 2). 3.3. Plant growth The warming, species, and sampling time all affected the D2H and 4

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Fig. 3. Leaf, stem and root biomass of four tree species exposed to warming treatments in June 2014 and 2015, and 2018, respectively. Different lowercase letters indicate significant differences of total biomass among treatments in each sampling time.

translocated from the altitude of 600 m to 300 and 30 m, the root mass fraction of I. chinensis was reduced during June 2014 and 2015 (Fig. 4c). However, warming induced a higher root mass fraction in M. breviflora during June 2018 (Fig. 4d). S. superba allocated more biomass to the stems at 30 m compared to that at 600 and 300 m during June 2014 and 2015 (Fig. 4e). S. rehderianum had a higher stem mass fraction at 30 m than at 600 and 300 m (Fig. 4f). The stem mass fraction of I. chinensis showed an increasing tendency at the altitudes of 300 and 30 m during June 2014 and 2015 (Fig. 4g). M. breviflora allocated more biomass to stems at 30 m than at 600 and 300 m during June 2018 (Fig. 4h). Warming at 300 and 30 m decreased the leaf mass fraction of S. superba and I. chinensis compared to that at 600 m (Fig. 4i and k). M. breviflora had a lower leaf mass fraction at 30 m than at 300 m during June 2018 (Fig. 4l). Overall, warming had no influence on the R/S ratio; however, the interactions between warming, species, and sampling time affected the R/S ratio (Table 3, P < 0.001). Warming induced a decline in the R/S ratios of S. superba and S. rehderianum at 30 m compared to that at 600 and 300 m during June 2014 and 2015 (Fig. 5a and b). A decreased R/S ratio of I. chinensis was observed at 300 and 30 m compared to that at 600 m during June 2014 and 2015 (Fig. 5c). Translocation from the altitude of 300 m to 30 m reduced the R/S ratio of I. chinensis; however, it enhanced the R/S ratio of M. breviflora by 34% during June 2018 (Fig. 5c and d).

biomass (Table 3, P < 0.01). Over time, translocation from the altitude of 600 m to 30 m increased the D2H of S. superba and S. rehderianum. The warming treatment at 300 m enhanced the D2H of S. superba and S. rehderianum between June 2016 and June 2018 (Fig. 2a and b). I. chinensis had a higher D2H when the model forests were translocated from the altitudes of 600 and 300 m to 30 m (Fig. 2c). However, the D2H of M. breviflora increased at 300 m compared to that at 600 m, but it decreased at 30 m compared to that at 600 and 300 m (Fig. 2d). The D2H was significantly higher in S. superba, S. rehderianum, and I. chinensis than in M. breviflora (Fig. 2). S. superba had a biomass approximately 150% higher at 30 m than 600 and 300 m during June 2014, and its biomass was increased by 90% at the altitude of 30 m compared to that at 600 and 300 m during June 2015 (Fig. 3a). Warming enhanced the biomass of S. rehderianum by approximately 200% and 90% at 300 and 30 m compared to that at 600 m, respectively, during June 2014 and 2015. S. rehderianum had an increase in biomass of 73% at 30 m compared to that at 600 m (Fig. 3b). Translocation from the altitudes of 600 m to 300 and 30 m increased the biomass of I. chinensis by approximately 90% and 110% during June 2014 and 2015, respectively. I. chinensis had an increase in biomass of 56% at 30 m compared to that at 600 m during June 2018 (Fig. 3c). However, warming decreased the biomass of M. breviflora by 62% at 30 m compared to that at 300 m during June 2018 (Fig. 3d). 3.4. Biomass allocation

3.5. Nutrient amounts and nutrient productivity The root mass fraction and stem mass fraction were strongly affected by the warming, species, and sampling time (Table 3, P < 0.001). Although warming did not influence the leaf mass fraction, the interactions between warming and time and between warming, species, and time did affect the leaf mass fraction (P < 0.05). The root mass fraction of S. superba and S. rehderianum showed a decreasing trend at the 30 m altitude compared to that at 600 and 300 m during June 2014 and 2015 (Fig. 4a and b). When model forests were

The N and P amounts in plants were significantly affected by the warming, tree species, and sampling time (Table 3, P < 0.01). For S. superba, warming enhanced the N amount at 30 m compared to that at 600 and 300 m (Fig. 6a). Over time, the N amount of S. rehderianum showed an increasing tendency at 30 m compared to that at 300 and 600 m (Fig. 6b). An increase in N amount of I. chinensis was observed at 300 and 30 m during June 2014 and 2015 and at 30 m during June 5

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Fig. 4. Plant biomass allocation of four tree species exposed to warming treatments in June 2014 and 2015, and 2018, respectively. Error bars indicate standard deviations. Different lowercase letters indicate significant differences among treatments in each sampling time. RMR: root mass ratio, SMR: stem mass ratio, LMR: leaf mass ratio.

PP at 300 and 30 m than 600 m during June 2018 (Fig. 8e and f). Warming decreased the NP and PP of M. breviflora by approximately 60% during June 2015 and 2018 (Fig. 8g and h).

2018 (Fig. 6c). However, the N amount of M. breviflora was negatively correlated with warming when model forests were translocated from the altitude of 300 m to 30 m (Fig. 6d). S. superba had a higher P amount at 30 m than at 600 and 300 m during June 2014 and 2018 (Fig. 7a). The shift in the P amount of S. rehderianum, I. chinensis, and M. breviflora was similar to that of the N amount under warming (Fig. 7b–d). Overall, the NP and PP were also influenced by the warming, tree species, and sampling time (Table 3, P < 0.01). When model forests were translocated from 600 m to 300 m, the NP of S. rehderianum significantly increased by 30% during June 2018 (Fig. 8c). The warming treatment at 30 m led to an increase in the NP of I. chinensis by 145% and 72% compared to that at 600 and 300 m during June 2015, respectively. However, I. chinensis had approximately 40% lesser NP and

4. Discussion 4.1. Biomass and biomass allocation The warming-induced effect on the total biomass was dependent upon the tree species and time. The changes in the total biomass of the selected four tree species were greatly associated with the tree growth caused by warming. The total biomass of S. superba, S. rehderianum, and I. chinensis were increased by warming, which supported our hypothesis. Similarly to our research, Li et al. (2017) also found that warming 6

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Fig. 5. Root/shoot mass (R/S) ratio of four tree species exposed to warming treatments in June 2014 and 2015, and 2018, respectively. Error bars indicate standard deviations. Different lowercase letters indicate significant differences among treatments in each sampling time.

patterns change with increasing temperatures, indicating morphological adjustments (Xu et al., 2016; Duan et al., 2018; Tedla et al., 2019). Morphological adjustments can be beneficial for tree species to acclimate to a warmer environment, since they are associated with multiple physiological process (Poorter et al., 2012). In our study, S. superba, S. rehderianum, and I. chinensis allocated more biomass to the stem and shoot at the expense of the root, which is the opposite to our hypothesis. The individuals may have been shaded by others due to our experimental design, therefore, they would allocate more biomass to stems and shoots to capture light and enhance their growth (Wang et al., 2011). This is consistent with Overdieck et al. (2007), who found that warming enhanced the stem biomass production to favour the growth of tree species. The reduced root biomass production of S. superba, S. rehderianum, and I. chinensis might be connected with the presence of sufficient resources required for their growth (Esmail and Oelbermann, 2011). A sufficient supply of resources may result from variation in the soil microbial communities and enzyme activities (Fang et al., 2015), and the higher nutrient uptake capacity (Sanders-DeMott et al., 2018) caused by warming. Decreased water availability, as a result of warming (translocation from 300 m to 30 m altitude), can lead to a higher biomass being allocated to roots, to seek for water resources (Wan et al., 2005; Niu et al., 2008). Additionally, reduced N concentrations in soils, in response to warming, might promote root growth during the later stages of the experiment. Therefore, M. breviflora had a higher root biomass production during June 2018. This result indicates that M. breviflora is more sensitive to decreased water availability and N

increased the total biomass of S. superba from coniferous and broadleaved mixed forest at the Dinghushan Biosphere Reserve. According to his study, the growth enhancement of S. superba was correlated with the increase in photosynthetic capacity (Li et al., 2016). Thus, increased photosynthetic rates of S. superba, S. rehderianum, and I. chinensis in our study may have induced the enhancement of their biomass. However, contrary to our hypothesis, the total biomass of M. breviflora at the 30 m elevation treatment showed a decreasing trend. The reductions in the soil moisture (Fig. S1 shown), N concentrations in the soil (Table 2 shown), and capacity of N absorption may have constrained the growth of M. breviflora. Abies fabri had a lower total biomass under warming (temperature increase 2 °C) in the eastern Tibetan Plateau, which was driven by reduced N concentrations (Yang et al., 2013). In addition, the canopy occupation, due to the faster growth of the other three tree species exposed to warming, can limit the light, which may be responsible for the decreased growth of M. breviflora. This result indicated that M. breviflora may have a lower growth rate and ability to compete for resources than the other tree species due to warming. Hence, our studies showed that the divergent effects of warming on tree growth were related to competition for resources in these studied forests. Importantly, future studies should focus on the shift in the growth of M. breviflora because of its reduced biomass induced by warming in subtropical forest. Biomass allocation patterns among various organs (i.e., leaf, stem, root, and root/shoot ratio) can indicate the coordination between the acquisition and utilization of resources by trees (Poorter et al., 2012). Many earlier studies have displayed that the biomass allocation 7

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Fig. 6. Nitrogen (N) amount in leaf, stem and root of four tree species exposed to warming treatments in June 2014 and 2015, and 2018, respectively. Different lowercase letters indicate significant differences of N amount among treatments in each sampling time. Data of N concentrations in leaves are from Wu et al. (2019).

Fig. 7. Phosphorus (P) amount in leaf, stem and root of four tree species exposed to warming treatments in June 2014 and 2015, and 2018, respectively. Different lowercase letters indicate significant differences of P amount among treatments in each sampling time. Data of P concentrations in leaves are from Wu et al. (2019).

8

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Fig. 8. Nitrogen (N) productivity and phosphorus (P) productivity of four tree species exposed to warming treatments in June 2015 and 2018. Different small letters above the error bars (standard deviations) indicate significant differences of N productivity and P productivity in each species.

4.2. Nutrient productivities

concentrations in soils than the other tree species, and thus allocates more biomass to the root. In our study, the lower biomass allocation to leaves contrasts with the results of Drake et al. (2018), who demonstrated that increasing temperatures positively affected the leaf biomass production due to higher rates of respiration. This shift in biomass allocation under elevated temperatures may decrease the overall C assimilation and respiration (Callaway et al., 1994; Olszyk et al., 2003). Additionally, future studies should investigate whether broader ranges of increasing temperatures could modify the biomass allocation patterns of M. breviflora. Based on our results, it is difficult to know the best biomass allocation strategy for adjusting to warming, thanks to the different strategies among various species.

The warming effects on the NP and PP were dependent upon the tree species and sampling time. The shifts in the NP and PP of the four tree species were greatly related to the plant growth as well as N and P amounts induced by warming (Suriyagoda et al., 2012; Huang et al., 2015). The faster growth of S. rehderianum and I. chinensis (from December 2015 to June 2018), induced by warming, may have contributed to the higher NP. This result did not support our hypothesis. This result is consistent with Weih and Karlsson (2001), who found that mountain birch achieved a higher NP under an elevated air temperature due to higher growth. Tateno and Takeda (2010) reported that NP reduced with the decreasing N availability. Reductions in the R/S ratio, root mass fraction, and N concentrations in the soils were observed in our study, thus, the increased NP of S. rehderianum and I. chinensis may 9

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Investigation. Mianhai Zheng: Writing - original draft. Honglang Duan: Writing - original draft. Guowei Chu: Project administration, Data curation. Ze Meng: Project administration, Data curation. Guoying Zhou: Project administration, Data curation. Juxiu Liu: Funding acquisition, Writing - original draft, Writing - review & editing.

have resulted from a higher capacity of acquiring N, including increased root length and higher nutrient concentrations in roots, similarly as proposed by Tateno and Takeda (2010) and Tang et al. (2016). This suggests that S. rehderianum and I. chinensis (during the early stages) had an advantage at acquiring N from soils to sustain their growth and a higher capacity for sequestering more carbon in response to warming (Weih and Karlsson, 1999; Eller and Oliveira, 2016). However, the decline in the leaf NP due to the reduced leaf mass fraction may have led to the lower NP of I. chinensis during June 2018 under warming, since a reduced leaf NP caused by warming can inhibit the photosynthetic N use efficiency, which can result in a lower NP (Wang et al., 2019). As for M. breviflora, the reductions in the plant growth and growth rate induced by warming probably led to its reduced NP. Contrary to our hypothesis, warming had a negative effect on the PP of I. chinensis and M. breviflora. As for I. chinensis, the decline in the PP may have been driven by a lower leaf PP (Wang et al., 2019), which is highly related to the lower amounts of biomass and P allocated to leaves under warming (Ryser et al., 1997). Reduced photosynthetic P use efficiency may have contributed to the decline in the PP of I. chinensis (Garnier et al., 1995). In addition, the lower R/S ratio and root mass fraction of I. chinensis caused by warming inhibited P absorption from soils, which contributed to the decreasing PP. The warming-induced negative effect on the PP of M. breviflora was caused by the decreased plant growth, which is consistent with Suriyagoda et al. (2012). Water limitations, nutrient constrains, and shading due to warming would account for the decline in the growth of M. breviflora, which would have then led to a decreased NP and PP. It is considered that P is the limiting nutrient in our study area (Mo et al., 2006; Vitousek et al., 2010; Huang et al., 2013); hence, the reduced PP of M. breviflora and I. chinensis suggested that they had a lower ability to acquire P from soils than the other tree species (Miller and Hawkins, 2007). A reduced NP and PP were found in M. breviflora, which indicated that it would lose the advantage for acquiring N in soils with reduced N concentrations and compete for P in P-limited soils. Based on our 6-year warming experiment, we found that I. chinensis showed a decreasing NP and PP in the later stages. Therefore, there is a need to conduct longterm warming experiments to explore tree growth in the future. To understand the reasons for changes in species compositions caused by warming in subtropical forest ecosystems, future studies should focus on the alternations in nutrient productivity, which can indicate the ability of the species to compete for nutrients.

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements This study was jointly funded by the Science and Technology Programs of Guangzhou City (201903010021), the Science and technology innovation project of Guangdong Province forestry (Grant No. 2019KJCX023), the National Natural Science Foundation of China (Grant Nos. 41977287, 31670487 and 41991285) and the Guangdong Hundred, Thousand and Ten Thousand Talents Program. Appendix A. Supplementary material Supplementary data to this article can be found online at https:// doi.org/10.1016/j.foreco.2020.117952. References Anderson, J.M., Ingram, J., 1989. Tropical soil biology and fertility. CAB International, Wallingford. Anderson, J.T., Panetta, A.M., Mitchell-Olds, T., 2012. Evolutionary and ecological responses to anthropogenic climate change. Plant. Physiol. 1604, 1728–1740. https:// doi.org/10.1104/pp.112.206219. Bowman, D.M.J.S., Williamson, G.J., Keenan, R.J., Prior, L.D., 2014. A warmer world will reduce tree growth in evergreen broadleaf forests: evidence from Australian temperate and subtropical eucalypt forests. Global. Ecol. Biogeogr. 23, 925–934. https:// doi.org/10.1111/geb.12171. Bremner, J.M., Mulvaney, C.S., 1982. Nitrogen-total. In: Page, A.L., Miller, R.H., Keeney, D.R. (Eds.), Methods of soil analysis, part 2, chemical and microbiological properties. Agronomy monograph No. 9, 2nd ed. American Society of Agronomy, Madison, Wisconsin, pp. 595–624. Butler, S.M., Melillom, J.M., Johnson, J.E., et al., 2012. Soil warming alters nitrogen cycling in a New England forest: implications for ecosystem function and structure. Oecologia. 168, 819–828. https://doi.org/10.1007/s00442-011-2133-7. Callaway, R.M., DeLucia, E.H., Schlesinger, W.H., 1994. Biomass allocation of montane and desert ponderosa pine: an analog for response to climate change. Ecol. 75, 1474–1481. https://doi.org/10.2307/1937470. Cavaleri, M.A., Reed, S.C., Smith, W.K., Wood, T.E., 2015. Urgent need for warming experiments in tropical forests. Glob. Change. Biol. 21, 2111–2121. https://doi.org/ 10.1111/gcb.12860. Cunningham, S., Read, J., 2002. Comparison of temperate and tropical rainforest tree species: photosynthetic responses to growth temperature. Oecologia 133, 112–119. https://doi.org/10.1007/s00442-002-1034-1. Dawes, M.A., Schleppi, P., Hättenschwiler, S., Rixen, C., Hagedorn, F., 2017. Soil warming opens the nitrogen cycle at the alpine treeline. Glob. Change. Biol. 23, 421–434. https://doi.org/10.1111/gcb.13365. Drake, J.E., Tjoelker, M.G., Aspinwall, M.J., Reich, P.B., Pfautsch, S., Barton, C.V.M., 2018. The partitioning of gross primary production for young Eucalyptus tereticornis trees under experimental warming and altered water availability. New. Phytol. 222, 1298–1312. https://doi.org/10.1111/nph.15629. Duan, H., Huang, G., Zhou, S., Tissue, D.T., 2018. Dry mass production, allocation patterns and water use efficiency of two conifers with different water use strategies under elevated [CO2], warming and drought conditions. Eur. J. Forest. Res. 137, 605–618. https://doi.org/10.1007/s10342-018-1128-x. Eckstein, R.L., Karlsson, P.S., 2001. Variation in nitrogen-use efficiency among and within subarctic graminoids and herbs. New Phytol. 150, 641–651. https://doi.org/10. 1046/j.1469-8137.2001.00130.x. Eller, C.B., Oliveira, R.S., 2016. Effects of nitrogen availability on the competitive interactions between an invasive and a native grass from Brazilian cerrado. Plant. Soil. 410, 63–72. https://doi.org/10.1007/s11104-016-2984-0. Esmail, S., Oelbermann, M., 2011. The impact of climate change on the growth of tropical agroforestry tree seedlings. Agroforestry. Syst. 83, 235. https://doi.org/10.1007/ s10457-011-9424-1. Evans, G., 1972. The Quantitiatives Analysis of Plant Growth. Blackwell Scientific Publications, Oxford. Fang, X., Zhao, L., Zhou, G., Huang, W., Liu, J., 2015. Increased litter input increases litter decomposition and soil respiration but has minor effects on soil organic carbon in

5. Conclusion Our results showed that the effects of warming on biomass allocation, NP, and PP were dependent upon the tree species. Warming increased the growth of S. superba, S. rehderianum, and I. chinensis, but decreased the growth of M. breviflora. Seedlings of S. superba, S. rehderianum, and I. chinensis allocated more biomass to stems at the expense of leaves and roots. In contrast, M. breviflora allocated more biomass to roots. In terms of plant NP, S. rehderianum benefitted from warming, but I. chinensis and M. breviflora would not be favoured under reduced N concentrations in soils caused by warming. I. chinensis and M. breviflora may lose the advantage to compete for P in P-limited soils because of a decreased PP under warming. Our results indicated that M. breviflora had a low fitness due to the reduced biomass, NP, and PP after nearly 6 years of exposure to warming. The species-specific responses of biomass allocation, NP, and PP to warming provide critical information concerning the species composition in tropical forests during future climatic warming. CRediT authorship contribution statement Ting Wu: Writing - original draft, Writing - review & editing. Shizhong Liu: Project administration, Data curation. Zhiyang Lie: 10

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