The vegetation characteristics of Pinus taiwanensis drive the changes in different components of soil respiration in Wuyi Mountain, Southeast China

doi:10.21203/rs.3.rs-3623156/v1

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The vegetation characteristics of Pinus taiwanensis drive the changes in different components of soil respiration in Wuyi Mountain, Southeast China

https://doi.org/10.21203/rs.3.rs-3623156/v1

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Background and Aims

Many driving variables have been used to model different soil respiration components. However, large uncertainty exists in the annual fluxes of soil respiration related to vegetation characteristics and soil properties.

Methods

Here, five Pinus taiwanensis forests along different elevations in the Wuyi Mountain were used to explore how the annual fluxes of total soil respiration (R_S), autotrophic respiration (R_A), and heterotrophic respiration (R_H) were regulated by vegetation characteristics, including vegetation carbon sequestration (Vco₂), 0-20 cm root biomass (W_R), litter production (P_L), leaf area index (LAI), and also soil properties, including soil temperature (ST), soil moisture (SM), fungal PLFAs and bacterial PLFAs.

Results

The results indicate that (1) the R_H was positively correlated with ST, P_L, W_R, and LAI, and negatively correlated with SM; and the R_A was positively related to the Vco₂ and LAI, and inversely correlated with fungal and bacterial PLFAs. (2) R_H is mainly determined by W_R, while R_Acan be comprehensively predicted through the Vco₂, and LAI; besides, the redundancy analysis indicated that W_R, Vco₂, and LAI together explained 83.06% of R_S, R_H, and R_A changes in different elevations.

Conclusion

Overall, our results suggested that vegetation characteristics attributes such as W_R, Vco₂, and LAI rather than soil properties and microbial community composition, were more important for explaining the annual fluxes of soil respiration components variance in five P. taiwanensis forests. Thus, soil respiration depends more on the plant capacity to fix organic carbon as an initial source of organic matter sustaining overall soil respiration.

annual flux

carbon sequestration

conifer forest

elevation gradient

root biomass

The mountain usually became a hotpot area to study soil carbon (C) cycling in long-term climate change due to the altitudinal gradients causing climatic variations in short geographic distances. Generally, the soil heterotrophic respiration and autotrophic respiration decline with elevation mainly caused by the temperature decrease (Zimmermann et al., 2015; Nottingham et al., 2015; 2019a; Tang et al., 2020; Zeeshan et al., 2021; Okello et al., 2023). However, not only soil temperature, but also vegetation type, structure (Dong et al., 2020), and soil microbial community (Whitaker et al., 2014) are important environmental factors influencing soil respiration along the elevation gradient, but deciphering their relative contribution mechanisms is challenging.

From the perspective of global data, the total below-ground C allocation (TBCA) plays an important role in controlling soil autotrophic respiration (R_A), accounting for 50% of the variation in R_A changes across the globe (Tang et al., 2020). Prior investigations also suggest that root biomass should be considered a major factor controlling soil respiration in different altitude alpine grasslands on the Tibetan Plateau (Zhao et al., 2017). Apart from root biomass, other studies indicated that soil respiration was strongly correlated with canopy photosynthesis and productivity (Högberg et al., 2001; Jia et al., 2018; Ma et al., 2020). Furthermore, the capacity to modify leaf, stem, and root allocation in the function of environmental changes is also an important vegetation characteristic directly linked with soil respiration process (Metcalfe et al., 2011; Han et al., 2014a; 2014b; Aubrey et al., 2018; Zhao et al., 2021). However, given that determining root types and production of roots is difficult, can explain because of there have not constant results of contribution in R_A to R_S in prior studies. For instance, the contribution of R_A to R_S varied from 31.8% (Wang et al., 2008) to 46% (Comeau et al., 2018) in the subtropical forest. On the one hand, the discrepancy might be due to the root system belonging to the different species and their functions are complex or to different litter production, that can either enhance or diminish the contribution of roots to total soil respiration (Raich & Tufekciogul, 2000)."Although some studies have observed that root biomass and tree height of dominant species could change the soil respiration along the different elevation gradients (Dong et al., 2020; Badraghi et al., 2021). However, it is largely unknown the dominant species community's characteristics, soil properties, and their interactive effects on soil autotrophic respiration. Dominant tree species identity has great impact on soil autotrophic respiration, due to the plant functional traits that drive C assimilation, transfer and emission collaborate with root activity, litter production and climate conditions (De Deyn et al. 2008; Cassart et al., 2021). Accurate knowledge of the predictions of soil autotrophic respiration changes require the monodominant species of characteristics to quantify these relationships.

In the last 30 years, the proportion of heterotrophic respiration in total soil respiration has increased rapidly with global warming (Bond-Lamberty et al., 2018; Ogle, 2018). The temperature, litter production, precipitation, leaf area index (LAI), nematode density, and soil F:B ratio are the most critical factors that predict the heterotrophic respiration variations in the world (He et al., 2022). Besides, previous studies suggest that vegetation attributes, such as root biomass and litter production, apart from determine autotrophic soil respiration, and more than soil microbial community composition, should be crucially considered to understand the variance of heterotrophic soil respiration among the different subtropical forests (Jenkins & Adams, 2010; Wei et al., 2015). However, with increasing elevation gradients, the vegetation characteristics and soil properties change faster, and some studies have found conflicting results in terms of the relationship between soil respiration and environmental factors (Zimmermann et al., 2010; Luan et al., 2014; Tian et al., 2016). Thus, how soil properties, vegetation characteristics and microbial community structure along the elevation gradients affect the annual flux of autotrophic respiration and heterotrophic respiration is still unclear (Yu et al., 2017). The main reason for this uncertainty may be the complex composition of plant communities in previous studies. Therefore, it is reasonable to assume that the monodominant species characteristics, including photosynthetic capability, root biomass, and litter production and their associated soil properties may play an essential role in driving the mechanism of soil respiration components changes.

The P. taiwanensis is a native and predominant species in mountain regions, across eastern and southern China. In the National Park of Wuyi Mountain, the P. taiwanensis is widely distributed from 1200 to 2000 m (a.s.l, above sea level). The uniformity of the P. taiwanensis distribution provides an opportunity to explore how the annual flux of soil respiration components responds to elevation change through vegetation characteristics and soil properties. We developed step regression analysis and redundancy analysis to test how the vegetation characteristics, including annual vegetation C sequestration, leaf area index, root biomass, and litter production, and also soil properties that include soil temperature, soil moisture, fungal PLFAs, bacterial PLFAs, and their interactions influences on the different annual flux of soil respiration components. The data were used to determine (1) how the elevational gradient affects the vegetation characteristics and the annual flux of soil respiration components, and (2) the main factors that drive the annual flux of total soil respiration (R_S), autotrophic respiration (R_A) and heterotrophic respiration (R_H) changes in different elevation environment.

2.1. Study Site Description

The study site is located in the National Park of Wuyi Mountain (117°24′13″-117°59′19″E, 27°31′20″-27°55′49″N). It is located in the humid warm subtropics in the southeast of China and has a mean annual precipitation of 1684–1780 mm and a mean annual temperature of 17–19ºC. The P. taiwanensis forests growing on Wuyi Mountain were selected for study because (1) this mountain is the highest peak in southeast mainland of China at 2160.8 m a.s.l and thus provided considerable climatic change along the elevational gradient, and (2) has the vertical zonation of P. taiwanensis forests along these elevational gradient ranges from 1200 m a.s.l to 2000 m a.s.l (Lyu et al., 2021). The major soil types of Wuyi Mountain are classified as Typic Haplohumults (300–500 m a.s.l), Typic Hapludults (500–900 m a.s.l.), Typic Palehumults (900–1300 m a.s.l.), Typic Dystrudepts (1300–1800 m a.s.l) and Histic Humuaquepts (1800–2100 m a.s.l.) (Chen, 2000).

2.2. Experiment design

Five P. taiwanensis forest sites were selected along an elevational gradient, located at intervals of 200 m from 1200 m a.s.l to 2000 m a.s.l, respectively. Three 20 m × 20 m plots were randomly established at each stablished level along the gradient. Forest canopy closure and stand density was calculated based on each forest plot. The stem diameter at breast height (DBH), and plant height (H) were measured form each trees (DBH ≥ 5 cm) in plot. Sample site information is shown in Table 1.

Table 1

The characteristics of *P. taiwanensis* sample sites along the Wuyi Mountain
Elevations	Soil type	Canopy closure (%)	Stand density (trees/ha)	Diameter of breast height (cm)	Height (m)	Mainly mixed species (Pinus taiwanensis : other)
1200 m	Typic Palehumults	86.67 ± 3.33a	575 ± 50b	28.02 ± 0.24a	19.42 ± 0.11a	Schima superba (1.62:1)
1400 m	Typic Dystrudepts	86.33 ± 3.18a	1525 ± 188a	17.57 ± 0.29b	18.10 ± 0.19b	Cunninghamia lanceolata (1.76:1)
1600 m	Typic Dystrudepts	90.33 ± 0.33a	1200 ± 288a	17.03 ± 0.33b	15.63 ± 0.17c	Cunninghamia lanceolata (5.07:1)
1800 m	Histic Humuaquepts	60.00 ± 5.77b	358 ± 102bc	17.20 ± 0.25b	7.07 ± 0.19d	Rhododendron fortune (2.23:1)
2000 m	Histic Humuaquepts	49.67 ± 0.33c	508 ± 154b	15.30 ± 0.35c	5.18 ± 0.20e	Rhododendron fortune (4.13:1)

2.3. Vegetation characteristic measurements

The total leaf biomass (W_L) of P. taiwanensis was calculated by using Eq. (1) (Hua et al., 2015), where the D and H are the diameters of tree breast height (DBH) and height per individual, respectively. Total leaf area per individual is calculated by the ratio of total leaf biomass per individual and leaf mass per area, i.e. A_L=W_L/LMA. The leaf area index (LAI) is the ratio between total leaf area of the sample plot and the sample plot area. Besides, three soil blocks (20cm depth and size 20cm×20cm) were randomly dug from each sample plot, and collected the fresh root. The P. taiwanensis root was identified, then put it into the ziplocked bag, and taken back to the laboratory, where it was washed and dried to obtain 0–20 cm root biomass (W_R).

$${\text{W}}_{\text{L}}=\frac{\text{0.04977}{\text{D}}^{\text{1.8394}}{\text{H}}^{\text{0.1207}}}{\text{1+1/(2.69418}{\text{D}}^{\text{-0.42942}}{\text{H}}^{\text{-0.22742}}\text{)}}\text{ (1)}$$

In July 2019 and December 2019, the Li-6800 (LICOR, Lincoln, Nebraska, USA) was used to measure the net photosynthetic rate (P_n, µmol⋅m^− 2⋅ s^− 1) of P. taiwanensis leaves at atmospheric temperature, and based 2 hours from 8:00 a.m. to 18:00. Three trees and total nine twigs have selected in each plot for the test. The light intensity was set to 2000 µmol⋅m^− 2⋅ s^− 1, and the CO₂ concentration was set to 400 µmol⋅mol^− 1. Besides, the leaf dark respiration rates (R_d) were measured after half an hour of dark adaptation. Thus, the total assimilation amount per hour of a single leaf (A_i, mmol·m^− 2·s^− 1) was calculated based on photosynthesis rates through a simple integration method (Zhang et al., 2013) (2).

$${\text{A}}_{i}\text{ =}\sum _{i=1}^{j}\text{(}{P}_{i+1}\text{+}{P}_{i}\text{)/2}\text{ⅹ}\text{(}{T}_{i+1}\text{+}{T}_{i}\text{)}\text{ }\text{×}\text{ }\text{3600/1000 (2)}$$

Where 3600 and 1000 indicate that 1 hour contains 3600 seconds and conversion rates between µmol and mol, respectively. P_i was the initial photosynthetic rate (µmol·m^− 2·s^− 1), and P_i+1 was the next photosynthetic rate. T_i is the instantaneous time of the initial measurement point and T_i+1 is the time of the next measurement point; j is the number of tests. To accurately study the annual vegetation carbon sequestration (Vco₂, g·m^− 2·yr^− 1) of P. taiwanensis, we used the annual sunshine duration (A_sh) and instantaneous C utilization efficiency (ICUE) to estimate. The A_sh was 959 h in the local areas and ICUE = P_n/(R_d+P_n)×100% (Frantz & Bugbee et al., 2005). Finally, the annual vegetation carbon sequestration of P. taiwanensis was calculated by using Eq. (3).

$${\text{V}}_{\text{C}{\text{O}}_{\text{2}}}\text{ = }{\text{A}}_{\text{i}}\text{ }\text{×}\text{ }\text{44/1000}\text{ }\text{×}\text{ }{\text{A}}_{\text{L}}\text{ }\text{×}\text{ }\text{SD}\text{ }\text{×}\text{ }{\text{A}}_{\text{sh}}\text{ }\text{×}\text{ }\text{ICUE}\text{ (3)}$$

Where A_L and SD indicate the total leaf area per individual and stand density in the sample site, respectively. The 44 is the molar mass of CO₂.

Nine nylon net bags (80 cm×80 cm) were randomly set in each sample site to collect litter production (P_L) every month from March 2019 to January 2021. The litter of P. taiwanensis was taken back to the laboratory and weighting after air drying.

2.4. Soil respiration measurements

In August 2018, three subplots (size 80 cm×80 cm) of control treatment (CT) and three subplots of root removal (RR) treatments were randomly installed in each forest sample plot, respectively. Two layers of nylon mesh (size 0.01 mm) were used in the trench (size 80 cm×80 cm, depth 60 cm) to isolate fine roots as RR treatments. Then, a PVC collar with a diameter of 20 cm and height of 10 cm was installed in each treatment, embedded about 6 cm in the soil layer. The different components of soil respiration (i.e. R_S, R_A, and R_H) were monitored on clear days once every month between March 2019 and January 2021 using a LI-COR 8100 infrared gas analyzer (LICOR, Lincoln, Nebraska, USA), respectively. Annual respiration (Rs) was estimated by summing daily soil respiration calculated with the following Eq. (4) (Lavigne et al., 2003; Laganière et al., 2012).

$${R}_{\text{S}}\text{= }\sum _{\text{daily}}{\text{}\text{e}}^{\text{bt}}\text{3600 ×}\text{ }\text{(12/1000000) (4)}$$

Where α is the mean respiration rate, and t is the soil temperature at a depth of 5 cm. Where 3600 and (12/1000000) indicate that 1 hour contains 3600 seconds and convert C from micromoles to grams, respectively.

2.5. Soil microbial community measurements

In December 2019 and August 2020, the soil was sampled by using a 2.5 cm diameter stainless steel soil cylinder from each subplot, and then mixed based on forest sample sites, respectively. Samples were collected from depths 0–10 cm, and distance 20 cm from the edge of the PVC collar. A total of 15 samples were collected and measured from five forest sample sites, and then used the mean values of PLFAs concentrations in two experiments for data analysis. We performed phospholipid fatty acid analysis (PLFA) and this method described by Bossio and Scow (1998) to estimate bacterial and fungal biomass in soil. We used standard nomenclature to express the abundance of individual fatty acids and calculated the concentration of each PLFA based on 19:0 internal standard concentrations (Zhang et al., 2016a). Total bacterial PLFAs was the summary of i14:0, i15:0, a15:0, i16:0, i17:0, a17:0, 16:1ω9c, 16:1ω7c, cy17:0 ω7c, 18:1ω7c, 18:1ω5c, cy19:0ω7c (Frostegård & Bååth, 1996). The fungal PLFAs were identified by 18:1ω9c, 18:2ω6, 9c, and 16:1ω5 (Nie et al., 2013).

2.6. Data Analysis

We compared the difference in vegetation characteristics along the different elevations with one-way ANOVA (α = 0.05) and performed in R software (R Core Team, 2018). We conducted a boxplot with "ggplot2" and "ggpubr" packages to test the variations of soil respiration components (R_S, R_A, and R_H) and vegetation traits (Vco₂, W_R, P_L, LAI, ) of P. taiwanensis at different elevations on the R software (R Core Team, 2018). The correlation matrix between the vegetation traits and soil respiration components analysis was performed on the R software (R Core Team, 2018) using the "Corrplot" package with a significance level of 0.05. Step regression analysis and redundancy analysis were implemented in IBM SPASS software and CANOCO for Windows version 5.0 (Ter Braak & Šmilauer 2012), respectively.

3.1. The vegetation characteristics and soil properties along the altitudinal gradients

Statistical analyses showed significant differences in vegetation characteristics of P. taiwanensis along the elevational gradient (Table 1, Fig. 1). From 1200 m to 2000 m, the annual vegetation C sequestration (Vco₂) of P. taiwanensis forest ranged from 2.82 to 20.67 t C·ha·yr^− 1, the leaf area index (LAI) ranged from 0.46 to 3.69 m²·m^− 2, the 0–20 cm root biomass (W_R) of the P. taiwanensis forest ranged from 1.19 t·ha^− 1 to 2.03 t·ha^− 1, and the litter production (P_L) ranged from 2.65 to 3.90 t·ha^− 1·yr^− 1 (Fig. 1). The Vco₂, W_R, and LAI at 1200 m, 1400 m, and 1600 m were significantly higher than those at 1800 m and 2000 m. The P_L was significantly highest at 1200 m and 1400 m (P < 0.05). Overall, the LAI, Vco₂, W_R, and P_L significantly decreased with increasing elevations. The most variables linked to plant production capacity tends to decrease with altitude.

The bacterial PLFAs of CT treatments varied from 19.23 to 32.04 nmol·g dry soil^− 1, and fungal PLFAs varied from 3.38 to 5.89 nmol·g dry soil^− 1 along the elevational gradient. The bacterial PLFAs and fungal PLFAs of RR treatments varied from 17.36 to 32.77 nmol·g dry soil^− 1, respectively. The bacterial PLFAs and fungal PLFAs were significantly different among the five elevations and highest at 2000 m (Fig. 2). The fungal PLFAs of RR treatments varied from 2.84 to 4.56 nmol·g dry soil^− 1, and there are no significant differences between five elevations (P = 0.064) (Fig. 2).

3.2. The annual flux of soil respiration components

R _S ranged from 0.635 to 1.141kg C m^− 2yr^− 1, the R_H ranged from 0.44 to 0.873 kg C m^− 2yr^− 1, and the R_A ranged from 0.147 to 0.397 kg C m^− 2yr^− 1 across five elevations (Fig. 3). R_S and R_H decreased with increasing elevations and showed significant differences. While the R_A has no significant statistical difference among the five elevations (Fig. 3). The R_S and R_H showed the highest values occur at 1200 m, while the lowest values are mainly at 1800 m (Fig. 3).

3.3. Relationships between soil respiration vs. vegetation characteristics and soil properties

The R_S and R_H were positively correlated with Vco₂, P_L, W_R, LAI, and soil temperature (ST), but inversely correlated with the fungal PLFAs, bacterial PLFAs, and soil moisture (SM) in P. taiwanensis forests across five elevations, respectively (Fig. 4). The R_A positively correlated with Vco₂ and LAI, but inversely correlated with the fungal PLFAs, and bacterial PLFAs (Fig. 4). The step regression analysis suggests that W_R and P_L provided adequate fit R_S changes in our data (R² = 0.80), and the Vco₂ and LAI explained 62% R_A changes along the different elevations (Table 1). All eight environmental factors (i.e., Vco₂, W_R, LAI, P_L, fungal PLFAs, bacterial PLFAs, ST, SM) explained 83.10% of the total variability in the soil respiration components (Fig. 5). The W_R (F = 20.8, p = 0.002), P_L (F = 3.3, p = 0.038), and Vco₂ (F = 3.2, p = 0.046) were strongly correlated with variability of the soil respiration components along the Wuyi Mountain (Fig. 5).

Different from the soil temperature and soil moisture, the vegetation characteristics of monodominant communities promote forest succession and development, which have stronger effects on soil respiration prediction. We demonstrated that the synergistic mechanism between the P. taiwanensis vegetation C sequestration of dominant species, litter production, root biomass, and soil properties can be seen as an effective way to explain the annual flux of soil respiration components changes along the different elevations in Wuyi Mountain.

4.1. Vegetation characteristics and soil respiration components change along the elevational gradient

Previous studies suggested that leaf C capture capacity at high altitudes was mainly limited by low temperatures and UV damage to leaf cell metabolism and gas exchange (Zhou et al., 2023). Consistent with our previous results, we found the C use efficiency of P. taiwanensis decreased with increasing elevations in Mt. Wuyi (Lyu et al., 2021). The decrease in vegetation C capture capacity may have caused the C storage decrease in the soil-plant system. Indeed, the vegetation characteristics of P. taiwanensis, including Vco₂, P_L, W_R, and LAI decreased significantly with increasing elevations (P < 0.05) (Fig. 1), which indicates that the growth of P. taiwanensis in Mt. Wuyi mainly limited by low temperature (Lyu et al., 2021), thus probably increase in root/shoot ratio to increase nutrient uptake. Besides, we found the R_S and R_H decreased with elevations (Fig. 3). Soil respiration rates of the four types of forest exhibited a similar trend with our result along the elevation in Shennongjia, Hubei (Ma et al., 2019). These results indicated that soil respiration was highly sensitive to elevation changes in subtropical forests. One possible reason is the effect of soil temperature and moisture on soil respiration along the mountain forest due to these factors can account for most of the variation in soil CO₂ efflux (Zimmermann et al., 2015; Zhao et al., 2017; Ma et al., 2019; Tang et al., 2020; Zeeshan et al., 2021; Okello et al., 2023). Compared with the Rs and R_H, R_A showed an increase at first and then decreased from 1200 m to 2000 m but with no statistical difference between the five elevations (Fig. 3). We speculate that due to the canopy closure of P. taiwanensis was highest at 1600 m with a higher supply of carbohydrates (Table 1). Given the plant C supply provides the main substrates in driving root metabolism (Li et al., 2010), thus, we found that Vco₂ and LAI have positively correlated with R_A, respectively (Fig. 4). The altitude environments may influence P. taiwanensis growth characteristics through the soil temperature and moisture, thus further modifying R_S, R_H, and R_A changes in Wuyi Mountain.

4.2. Relationships between vegetation characteristics and total soil respiration

Differences among vegetation characteristics in physiology may play critical roles in determining soil C cycling. Consistent with our results, Högberg et al. (2001) and Bahn et al. (2009) both found that photosynthates significantly affected soil respiration variations. In this study, although we indicated that Vco₂ strongly correlated with R_S (Fig. 4), the W_R and P_L have been considered as key predicting factors for R_S dynamics in different elevational gradients (Table 1). A potential cause for such a result is the biome type. The P. taiwanensis forest community composition in the current study is relatively simple, and the variation of Vco₂ is close (Table 1, Fig. 1). These results might reduce the sensitivity of R_S response to changes in the photosynthesis capacity of P. taiwanensis forest along the different elevations.

Previous studies also suggest that root biomass and litter production can be related to total soil respiration changes (Wang et al., 2015; Zhao et al., 2021; He et al., 2022). In line with our results, we also identified the positive relationships of R_S vs. W_R and R_S vs. P_L (Fig. 4), and indeed W_R and P_L were chosen as the best predictors of modeling (R² = 0.80) the R_S changes (Table 2). Root traits, i.e., root biomass, may be temporally and spatially dynamic along the different elevations. We found the W_R highest at 1200 m, with no significant difference between other sample sites (Fig. 2). Previous studies also observed that the diameter at breast height was positively correlated with fine root production (Cai et al., 2019). Therefore, we suggest that the W_R changes along Mt. Wuyi may be ascribed to the DBH of P. taiwanensis forest. Because, we found the DBH showed the highest at 1200 m and parallel the trend of W_R changes (Table 1, Fig. 1). In addition, similar to W_R, litter-derived respiration is part of the total soil respiration, and R_S generally be reported as linearly increased with litter production (Zhang et al., 2016b). This may be due, at least in part, to the litter production strongly influences soil C sources and regulates microbial activities in the forest ecosystem (Zhang et al., 2016b; Zhu et al., 2023).

Table 2

Stepwise regression analysis between soil respiration components and *P. taiwanensis* vegetation characteristics
Project	Formula	Standardized coefficients		R²	F	p
R_S	y = 1.25 + 2.72W_R + 1.20P_L	W_R	0.54	0.80	28.74	0.006
R_S	y = 1.25 + 2.72W_R + 1.20P_L	P_L	0.46	0.80	28.74	0.017
R_H	y = 1.47W_R + 3.15	W_R	0.81	0.62	24.17	0.001
R_A	y = 1.71 + 0.19Vco₂-0.97LAI	Vco₂	2.23	0.62	12.35	0.001
R_A	y = 1.71 + 0.19Vco₂-0.97LAI	LAI	-1.72	0.62	12.35	0.006

Besides, LAI is usually regarded as one of the key indicators that affect soil CO₂ efflux (Reichstein et al., 2003; Bond-Lamberty & Thomson, 2010; Li et al., 2010; Bolat & Öztürk, 2017; Yao et al., 2021). Consistent with prior studies, we observed that R_S linearly increased with increasing LAI. Theoretically, the LAI can be viewed as an indicator through the C assimilation and vegetation productivity capacity, directly or indirectly affecting the soil CO₂ efflux. On the other hand, it should be noted that LAI has a closely associated with soil temperature and soil moisture, and thus indirectly influences the R_S changes (Bolat & Öztürk, 2017). However, the relationships between soil respiration and LAI are not as stable. For instance, Reichstein et al. (2003) found that LAI strongly linear correlated with the standardized soil respiration rates, while other studies showed the LAI has a peaked relationship with total belowground C flux (TBCF) (Berryman et al., 2016). In any case, our results strongly suggested the importance of LAI for predicting R_S changes among the five P. taiwanensis forests along the different elevational gradients.

4.3. Relationships between soil properties and total soil respiration

It is not only our goal to review the biomass allocation (i.e., root biomass and litter production) by which vegetation characteristics can modify soil respiration, but also other soil properties (i.e., soil temperature, soil moisture, soil microbial community structure) are important. In the present work, we found positive correlations between R_S and soil temperature and negative correlations between R_S and soil moisture (Fig. 4). That is to say, soil temperature and moisture conditions significantly influence R_S in different elevations. Generally, soil temperature has a considerable effect on soil respiration and is the dominant driver of R_S in mountain forests (Zimmermann et al., 2015; Tang et al., 2020; Zeeshan et al., 2021; Okello et al., 2023). In this study, soil temperature showed a higher degree of correlation with Rs (R² = 0.84) (Fig. 4). Several potential mechanisms may be suggested to account for this. Firstly, although soil temperature decreases with increasing elevation, it is still controlled by other environmental factors. For instance, previous studies suggest that LAI can influence soil temperature by reducing solar radiation (Yan et al. 2013; Bolat & Öztürk, 2017). Secondly, some prior studies indicated that soil respiration includes a temperature-dependent model and a temperature-independent model (Vargas & Allen 2008; Geng et al., 2012). Moreover, the amount of C source input potentially varies with plant productivity rather than being limited by soil temperature, thus strongly affecting the soil CO₂ emitted to the atmosphere. Hence, our results suggest that to incorporate vegetation characteristics should be a vital dimension driver of R_S modeling efforts in the forest ecosystem.

The bacterial PLFAs and fungal PLFAs were inversely correlated with R_S along the different elevations (Fig. 4). It is likely due to the drop of microbial activity with altitude probably been mainly due to an increase of temperature limitation of biological activity, as observed in previous studies (Nottingham et al., 2019b). Consistent with our results, the fungal biomass of Cunninghamia lanceolata forests increased with increasing elevations at different altitudes in Wuyishan National Park (Xiong et al., 2022). But interestingly, our results indicated that R_S decreased with an increase in fungal PLFAs and bacterial PLFAs along the elevational gradient. We postulate that this pattern may be mainly offset by plant growth and soil organic matter input decreased along the elevational gradients. Thus, the microbial biomass-independent of the soil respiration was principally determined by substrate supply and very probably by the decrease of microbial community activity capacity under low temperature rather than by the pool size of microbial biomass content, as shown in previous cases (Wang et al., 2003; Birgé et al., 2015).

4.4 Difference regulation mechanism of R_H and R_A by vegetation characteristics

Our result demonstrates that vegetation characteristics and soil properties both account for 83.10% of R_S, R_H, and R_A variations along the different elevational P. taiwanensis forests (Fig. 5). However, R_H and R_A have different responses to the vegetation characteristics of P. taiwanensis. For instance, R_H is mainly determined by W_R, while R_A can be comprehensively predicted through the Vco₂ and LAI (Table 2). The results of our dataset indicate that the W_R has a greater contribution to R_H may be due to the large number of dead roots generated after root isolation, and the decomposition of these dead roots or root exudates potentially provides an available and sustainable substrate for microbial activities (Kuzyakov & Bol, 2006; Savage et al., 2018). From LAI and P_L related to higher R_S and R_H, the increase of LAI means the increase of canopy closure and litter production, which might prompt the ectotrophic mycorrhiza hyphae of existence to improve the heterotrophic respiration rate. Thus, the findings suggested that the fungal biomass can predict R_H under different elevational gradients (Fig. 4). Nevertheless, future studies should also consider the mycorrhizal types associated with species and the role of mycorrhizal biomass in soil respiration.

In the present work, the value of R_A changed by approximately 17.6%-41.6%, and there is no significant statistical difference among the five elevations. However, with the increase in elevations, plant root turnover and regeneration in soil are slower, which may increase the accumulation of soil C, and reduce the autotrophic respiration rate (McGroddy & Silver, 2000). Therefore, it is reasonable to believe that lower temperatures limited the root production and activity in P. taiwanensis forest, and finally caused an insignificant correlation between R_A and W_R. These results indicate that the vegetation characteristics controlled the R_A by significantly regulating the Vco₂ and LAI in the function of temperature in the P. taiwanensis forest across the altitudinal gradient. It was consistent with previous studies that seasonal changes in vegetation activity play a dominant role in the sensitivity of soil respiration component and highlights the critical ecological linkages between plant physiological processes and soil C cycling processes (Wang et al., 2010; Cassart et al., 2021).

The results of this study clearly showed that above-ground C sequestration, soil C source, and below-ground biomass allocation of P. taiwanensis forest were strongly related to annual soil respiration. However, the different components of soil respiration are difficult to quantify through the vegetation characteristics with the present facilities. To gain a precise understanding of the underlying mechanisms and the extent of soil respiration changes in this region, it is essential to conduct further research that focuses on analyzing shifts in microbial community species composition and explores the interactions with plant production and temperature variations.

We found that the altitude environment strongly affects the regulations of vegetation characteristics and soil respiration components in the P. taiwanensis forest. More than soil traits, plant related traits such as Vco₂, P_L, and W_R were be-well to predict the soil respiration component variations in different elevational gradients. This implies a general rule of the plant C fixation and allocation's role in soil respiration prediction processes.

Besides, the most critical driving factor that predicts the R_H is W_R, but Vco₂ and LAI directly control the R_A, respectively. Given the different mechanisms of R_H and R_A driven by vegetation characteristics, our findings highlighted that the soil respiration types (R_H and R_A) both depend on the system's capacity to produce more, with the logical and simple difference that the most direct variables are below-ground biomass and above-ground carbon capture capacity, respectively.

The results of this work offer a useful simplified model based on vegetation characteristics for predicting the complicated forest ecosystem C cycle models for different soil respiration components. We simply expect that under global change in this region, the shift of soil respiration would depend primarily on the effects on plant C fixation and allocation, and the feedback effect can be more or less neutral. Nevertheless, it is crucial for future research to clarify the impact of the overall microbial community on annual soil respiration fluxes, especially in light of the possibility that an increase in microbial biomass could potentially diminish its overall activity.

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.

Data availability

Data will be made available on request.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (32071555, 32001094), and the Key Public Welfare Project of Fujian Provincial Department of Science and Technology (2022R1002002). JP and JS were supported by the Spanish Government grants PID2020115770RB-I, TED2021-132627 B–I00 and PID2022-140808NB-I00, funded by MCIN, AEI/10.13039/ 501100011033 European Union Next Generation EU/PRTR, the Fundación Ramón Areces grant CIVP20A6621, and the Catalan Government grant SGR 2021–1333.The authors would like to express their gratitude to the editor and anonymous reviewers for their valuable comments and suggestions.

Aubrey, D.P., & Teskey, R.O. (2018). Stored root carbohydrates can maintain root respiration for extended periods. The New phytologist, 218: 142-152.
Badraghi, A., Ventura, M., Polo, A., Borruso, L., & Montagnani, L. (2021). Soil respiration variation along an altitudinal gradient in Italian Alps: disentangling forest structure and temperature effects. PloS ONE, 16: e0247893.
Bahn, M., Schmitt, M., Siegwolf, R.T., Richter, A., & Brüggemann, N. (2009). Does photosynthesis affect grassland soil-respired CO₂ and its carbon isotope composition on a diurnal timescale? The New phytologist, 182(2): 451-460.
Berryman, E.M., Ryan, M.G., Bradford, J.B., Hawbaker, T.J., & Birdsey, R. (2016). Total belowground carbon flux in subalpine forests is related to leaf area index, soil nitrogen, and tree height. Ecosphere, 7:e014018.
Bolat, İ., & Öztürk, M. (2017). Effects of altitudinal gradients on leaf area index, soil microbial biomass C and microbial activity in a temperate mixed forest ecosystem of Northwestern Turkey. Iforest - Biogeosciences and Forestry, 10: 334-340.
Bond‐Lamberty, B., Bailey, V.L., Chen, M., Gough, C.M., & Vargas, R. (2018). Globally rising soil heterotrophic respiration over recent decades. Nature, 560: 80-83.
Bossio, D.A., & Scow, K.M. (1998). Impacts of carbon and flooding on soil microbial communities: phospholipid fatty acid profiles and substrate utilization patterns. Microbial Ecology, 35: 265-278.
Cai, H., Li, F., & Jin, G. (2019). Fine root biomass, production and turnover rates in plantations versus natural forests: effects of stand characteristics and soil properties. Plant and Soil, 436: 463-474.
Cassart, B., Angbonga Basia, A., Jonard, M., & Ponette, Q. (2021). Functional traits drive the difference in soil respiration between Gilbertiodendron dewevrei monodominant forests patches and Scorodophloeus zenkeri mixed forests patches in the Central Congo basin. Plant and Soil, 460: 313-331.
Chen, J. (2000). Genetic characteristics and taxonomic classification of soils in the Mountain Wuyi. Chinese Journal of Soil Science. 31:97-101.
Comeau, L., Lai, D.Y., Cui, J.J., & Farmer, J. (2018). Separation of soil respiration: a site-specific comparison of partition methods. SOIL. 4: 141-152.
De Deyn, G.B., Cornelissen, J.H., & Bardgett, R.D. (2008). Plant functional traits and soil carbon sequestration in contrasting biomes. Ecology letters, 11: 516-531.
Dong, L., Zeng, W., Wang, A., Tang, J., Yao, X., & Wang, W. (2020). Response of soil respiration and its components to warming and dominant species removal along an elevation gradient in alpine meadow of the Qinghai-Tibetan Plateau. Environmental Science and Technology. 54:10472-10482.
Frantz, J.M., & Bugbee, B. (2005). Acclimation of plant populations to shade: photosynthesis, respiration, and carbon use efficiency. Journal of the American Society for Horticultural Science, 130: 918-927.
Frostegård, Å., & Bååth, E. (1996). The use of phospholipid fatty acid analysis to estimate bacterial and fungal biomass in soil. Biology and Fertility of Soils, 22: 59-65.
Geng, Y., Wang, Y., Yang, K., Wang, S., Zeng, H., Baumann, F., Kuehn, P., Scholten, T., & He, J. (2012). Soil respiration in Tibetan alpine grasslands: belowground biomass and soil moisture, but not soil temperature, best explain the large-scale patterns. PLoS ONE, 7: e34968.
Han, G., Luo, Y., Li, D., Xia, J., Xing, Q., & Yu, J. (2014a). Ecosystem photosynthesis regulates soil respiration on a diurnal scale with a short-term time lag in a coastal wetland. Soil Biology & Biochemistry, 68: 85-94.
Han, G., Xing, Q., Luo, Y., Rafique, R., Yu, J., & Mikle, N.L. (2014b). Vegetation types alter soil respiration and its temperature sensitivity at the field scale in an estuary wetland. PLoS ONE, 9: e91182.
He, Y., Ding, J., Dorji, T., Wang, T., Li, J., & Smith, P. (2022). Observation‐based global soil heterotrophic respiration indicates underestimated turnover and sequestration of soil carbon by terrestrial ecosystem models. Global Change Biology, 28: 5547-5559.
Hua, W., Qiu, T., Jiang, X., Gai, X., Huang, L. (2015). Study on model of individual biomass and carbon currency harvesting of Pinus taiwanensis. Forest Science and Technology, 8:6-10. doi:10.13456/j.cnki.lykt.2015.08.002.
Högberg, P., Nordgren, A., Buchmann, N., Taylor, A.F., Ekblad, A., Högberg, M.N., Nyberg, G., Ottosson-Löfvenius, M., & Read, D.J. (2001). Large-scale forest girdling shows that current photosynthesis drives soil respiration. Nature, 411: 789-792.
Jenkins, M.E., & Adams, M.A. (2010). Vegetation type determines heterotrophic respiration in subalpine Australian ecosystems. Global Change Biology, 16: 209-219.
Jia, X., Zha, T., Wang, S., Bourque, C.P., Wang, B., Qin, S., & Zhang, Y. (2018). Canopy photosynthesis modulates soil respiration in a temperate semi-arid shrubland at multiple timescales. Plant and Soil, 432: 437-450.
Kuzyakov, Y., & Bol, R. (2006). Sources and mechanisms of priming effect induced in two grassland soils amended with slurry and sugar. Soil Biology & Biochemistry, 38: 747-758.
Laganière, J., Paré, D., Bergeron, Y., & Chen, H.Y. (2012). The effect of boreal forest composition on soil respiration is mediated through variations in soil temperature and C quality. Soil Biology & Biochemistry, 53:18-27.
Lavigne, M.B., Boutin, R., Foster, R.J., Goodine, G., Bernier, P.Y., & Robitaille, G. (2003). Soil respiration responses to temperature are controlled more by roots than by decomposition in balsam fir ecosystems. Canadian Journal of Forest Research, 33:1744-1753.
Li, X., Fu, H., Guo, D., Li, X., & Wan, C. (2010). Partitioning soil respiration and assessing the carbon balance in a Setaria italica (L.) Beauv. Cropland on the Loess Plateau, Northern China. Soil Biology & Biochemistry, 42: 337-346.
Luan, J., Liu, S., Chang, S.X., Wang, J., Zhu, X., Liu, K., & Wu, J. (2014). Different effects of warming and cooling on the decomposition of soil organic matter in warm–temperate oak forests: a reciprocal translocation experiment. Biogeochemistry, 121: 551-564.
Lyu, M., Sun, M., Peñuelas, J., Sardans, J., Sun, J., Chen, X., Zhong, Q., & Cheng, D. (2021). Thermal acclimation of foliarcmetabolism in Pinus taiwanensis along an elevational gradient. Frontiers in Plant Science, 12:778045.
Ma, M., Zang, Z., Xie, Z., Chen, Q.S., Xu, W., Zhao, C., & Shen, G. (2019). Soil respiration of four forests along elevation gradient in northern subtropical China. Ecology and Evolution, 9: 12846-12857.
Ma, J., Liu, R., Li, C., Fan, L., Xu, G., & Li, Y. (2020). Herbaceous layer determines the relationship between soil respiration and photosynthesis in a shrub-dominated desert plant community. Plant and Soil, 449: 193-207.
McGroddy, M., & Silver, W.L. (2000). Variations in belowground carbon storage and soil CO₂ flux rates along a wet tropical climate gradient. BIOTROPICA, 32(4):614-624.
Metcalfe, D.B., Fisher, R.A., & Wardle, D.A. (2011). Plant communities as drivers of soil respiration: pathways, mechanisms, and significance for global change. Biogeosciences, 8: 2047-2061.
Nottingham, A. T., Raath, E., Reischke, S., Salinas, N., & Meir, P. (2019b). Adaptation of soil microbial growth to temperature: using a tropical elevation gradient to predict future changes. Global Change Biology, 25, 827-838.
Nottingham, A.T., Whitaker, J., Ostle, N., Bardgett, R.D., McNamara, N.P., Fierer, N., Salinas, N., Ccahuana, A.J., Turner, B.L., & Meir, P. (2019a). Microbial responses to warming enhance soil carbon loss following translocation across a tropical forest elevation gradient. Ecology letters. 22(11): 1889-1899.
Nottingham, A.T., Whitaker, J., Turner, B.L., Salinas, N., Zimmermann, M.B., Malhi, Y.S., & Meir, P. (2015). Climate warming and soil carbon in tropical forests: insights from an elevation gradient in the Peruvian Andes. Bioscience, 65(9): 906-921.
Nie, M., Pendall, E., Bell, C.W., Gasch, C.K., Raut, S., Tamang, S., & Wallenstein, M.D. (2013). Positive climate feedbacks of soil microbial communities in a semi-arid grassland. Ecology letters, 16: 234-241.
Ogle, K. (2018). Hyperactive soil microbes might weaken the terrestrial carbon sink. Nature, 560: 32-33.
Okello, J., Bauters, M., Verbeeck, H., Bodé, S., Kasenene, J.M., Françoys, A., Engelhardt, T., Butterbach‐Bahl, K., Kiese, R., & Boeckx, P. (2023). Temperature sensitivity of soil organic carbon respiration along a forested elevation gradient in the Rwenzori Mountains, Uganda. Biogeosciences. 20: 719-735.
R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
Reichstein, M., Rey, A.M., Freibauer, A., Tenhunen, J., Valentini, R., Banza, J., Casals, P., Cheng, Y., Grünzweig, J.M., Irvine, J.R., Joffre, R., Law, B.E., Loustau, D., Miglietta, F., Oechel, W.C., Ourcival, J., Pereira, J.S., Peressotti, A., Ponti, F., Qi, Y., Rambal, S., Rayment, M.B., Romanyà, J., Rossi, F., Tedeschi, V., Tirone, G., Xu, M., & Yakir, D. (2003). Modeling temporal and large‐scale spatial variability of soil respiration from soil water availability, temperature and vegetation productivity indices. Global Biogeochemical Cycles, 17:1104.
Savage, K., Davidson, E.A., Abramoff, R.Z., Finzi, A., & Giasson, M. (2018). Partitioning soil respiration: quantifying the artifacts of the trenching method. Biogeochemistry, 140: 53-63.
Tang, X., Pei, X., Lei, N., Luo, X., Liu, L., Shi, L., Chen, G., & Liang, J. (2020). Global patterns of soil autotrophic respiration and its relation to climate, soil and vegetation characteristics. Geoderma, 369: 114339.
Ter Braak CJF, Šmilauer P. (2012). Canoco reference manual and user’s guide: software for ordination, version 5.0. Ithaca: Microcomputer Power. p 496.
Tian Q, He H, Cheng W, Bai Z, Wang Y, Zhan X. (2016). Factors controlling soil organic carbon stability along a temperate forest altitudinal gradient. Scientific Reports, 6: 18783.
Vargas, R., & Allen, M.F. (2008). Environmental controls and the influence of vegetation type, fine roots and rhizomorphs on diel and seasonal variation in soil respiration. The New phytologist, 179: 460-471.
Wang, B., Zha, T., Jia, X., Gong, J., Wu, B., Bourque, C.P., Zhang, Y., Qin, S., Chen, G.P., & Peltola, H. (2015). Microtopographic variation in soil respiration and its controlling factors vary with plant phenophases in a desert–shrub ecosystem. Biogeosciences, 12: 5705-5714.
Wang, W., Dalal, R.C., Moody, P., & Smith, C. (2003). Relationships of soil respiration to microbial biomass, substrate availability and clay content. Soil Biology & Biochemistry, 35: 273-284.
Wang, X., Piao, S., Ciais, P., Janssens, I.A., Reichstein, M., Peng, S., & Wang, T. (2010). Are ecological gradients in seasonal Q₁₀ of soil respiration explained by climate or by vegetation seasonality. Soil Biology & Biochemistry, 42: 1728-1734.
Wang, X., Zhu, B., Wang, Y., & Zheng, X. (2008). Field measures of the contribution of root respiration to soil respiration in an alder and cypress mixed plantation by two methods: trenching method and root biomass regression method. European Journal of Forest Research, 127: 285-291.
Wei, H., Xiao, G., Guenet, B., Janssens, I.A., & Shen, W. (2015). Soil microbial community composition does not predominantly determine the variance of heterotrophic soil respiration across four subtropical forests. Scientific Reports, 5:7854.
Whitaker, J., Ostle, N., Nottingham, A.T., Ccahuana, A.J., Salinas, N., Bardgett, R.D., Meir, P., McNamara, N.P., & Austin, A.T. (2014). Microbial community composition explains soil respiration responses to changing carbon inputs along an Andes-to-Amazon elevation gradient. The Journal of Ecology, 102: 1058-1071.
Xiong, X., Lyu, M., Deng, C., Li, X., Lu, Y., Lin, W., Jiang, Y., & Xie, J. (2022). Carbon and nitrogen availability drives seasonal variation in soil microbial communities along an elevation gradient. Forests. 13:1657.
Yao, X., Chen, S., Ding, S., Zhang, M., Cui, Z., Linghu, S., & Xu, J. (2021). Temperature, moisture, hyperspectral vegetation indexes, and leaf traits regulated soil respiration in different crop planting fields. Journal of Soil Science and Plant Nutrition, 21: 3203-3220.
Yan, J., Yan, J., Chen, L., Li, J., & Li, H. (2013). Five‐Year Soil Respiration Reflected Soil Quality Evolution in Different Forest and Grassland Vegetation Types in the Eastern Loess Plateau of China. Clean-soil Air Water, 41: 680-689.
Yu, S., Chen, Y., Zhao, J., Fu, S., Li, Z., Xia, H., & Zhou, L. (2017). Temperature sensitivity of total soil respiration and its heterotrophic and autotrophic components in six vegetation types of subtropical China. The Science of the total environment, 607-608:160-167.
Zeeshan, M., Wenjun, Z., Chuansheng, W., Yan, L., Azeez, P.A., Qinghai, S., Yuntong, L., Yiping, Z., Zhiyun, L., & Liqing, S. (2021). Soil heterotrophic respiration in response to rising temperature and moisture along an altitudinal gradient in a subtropical forest ecosystem, Southwest China. The Science of the total environment, 151643.
Zhang, Q., Wu, J., Yang, F., Lei, Y., Zhang, Q., & Cheng, X. (2016a). Alterations in soil microbial community composition and biomass following agricultural land use change. Scientific Reports, 6: 36587.
Zhang Y, Fei S, Li Z, Meng C, Xu J. (2013). Carbon sequestration and oxygen release as well as cooling and humidification efficiency of the main greening tree species of Sha River, Chengdu. Acta Ecologica Sinica, 33:3878-3887.
Zhang, Y., Gan, Z., Li, R., Wang, R., Li, N., Zhao, M., Du, L., Guo, S., Jiang, J., & Wang, Z. (2016b). Litter production rates and soil moisture influences interannual variability in litter respiration in the semi-arid Loess Plateau, China. Journal of Arid Environments, 125: 43-51.
Zhao X, Liang N, Zeng J, & Mohti, A. (2021). A simple model for partitioning forest soil respiration based on root allometry. Soil Biology & Biochemistry, 152:108067.
Zhao, J., Li, R., Li, X., & Tian, L. (2017). Environmental controls on soil respiration in alpine meadow along a large altitudinal gradient on the central Tibetan Plateau. Catena, 159: 84-92.
Zimmermann, M.B., Davies, K.J., Zimmermann, V., & Bird, M.I. (2015). Impact of temperature and moisture on heterotrophic soil respiration along a moist tropical forest gradient in Australia. Soil Research, 53: 286-297.
Zimmermann, M.B., Meir, P., Bird, M.I., Malhi, Y.S., & Ccahuana, A.J. (2010). Temporal variation and climate dependence of soil respiration and its components along a 3000 m altitudinal tropical forest gradient. Global Biogeochemical Cycles, 24(4): GB4012.
Zhou, F., Matthew, C., Yang, P., Huang, Y., Nie, B., & Nan, Z. (2023). Leaf morphology, functional trait and altitude response in perennial vetch (Vicia unijuga A. Braun), alfalfa (Medicago sativa L.) and sainfoin (Onobrychis viciifolia Scop.). Planta, 257: 1-16.
Zhu, J., Wu, Q., Wu, F., & Ni, X. (2023). Partitioning of root, litter and microbial respiration by plant input manipulation in forests. Environmental Research Letters, 18: 024023.

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The vegetation characteristics of Pinus taiwanensis drive the changes in different components of soil respiration in Wuyi Mountain, Southeast China

Status:

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Abstract

Figures

1. Introduction

2. Materials and Methods

2.1. Study Site Description

2.2. Experiment design

2.3. Vegetation characteristic measurements

2.4. Soil respiration measurements

2.5. Soil microbial community measurements

2.6. Data Analysis

3. Results

3.1. The vegetation characteristics and soil properties along the altitudinal gradients

3.2. The annual flux of soil respiration components

3.3. Relationships between soil respiration vs. vegetation characteristics and soil properties

4. Discussions

4.1. Vegetation characteristics and soil respiration components change along the elevational gradient

4.2. Relationships between vegetation characteristics and total soil respiration

4.3. Relationships between soil properties and total soil respiration

4.4 Difference regulation mechanism of R_H and R_A by vegetation characteristics

5. Conclusions

Declarations

References

Status:

Version 1

The vegetation characteristics of Pinus taiwanensis drive the changes in different components of soil respiration in Wuyi Mountain, Southeast China

Status:

Version 1

Abstract

Figures

1. Introduction

2. Materials and Methods

2.1. Study Site Description

2.2. Experiment design

2.3. Vegetation characteristic measurements

2.4. Soil respiration measurements

2.5. Soil microbial community measurements

2.6. Data Analysis

3. Results

3.1. The vegetation characteristics and soil properties along the altitudinal gradients

3.2. The annual flux of soil respiration components

3.3. Relationships between soil respiration vs. vegetation characteristics and soil properties

4. Discussions

4.1. Vegetation characteristics and soil respiration components change along the elevational gradient

4.2. Relationships between vegetation characteristics and total soil respiration

4.3. Relationships between soil properties and total soil respiration

4.4 Difference regulation mechanism of RH and RA by vegetation characteristics

5. Conclusions

Declarations

References

Status:

Version 1

4.4 Difference regulation mechanism of R_H and R_A by vegetation characteristics