The Dominant Factor Affecting Soil Organic Carbon in Subtropical Phyllostachys edulis Forests Is Climatic Factors Rather Than Soil Physicochemical Properties

Abstract

Phyllostachys edulis, also known as moso bamboo, is widely distributed in China, has strong carbon sequestration potential, and contributes significantly to carbon sinks at the regional scale. However, the distribution and influencing factors of soil organic carbon (SOC) are unclear in bamboo forests at the regional scale. We selected six sites in Phyllostachys edulis forests in the northern subtropics, middle subtropics, and southern subtropics of China to determine the SOC contents and estimate its stocks. The relationships between the SOC and geographic position, climate, vegetation, and the soil’s physical and chemical characteristics were analyzed via Pearson correlation coefficients. Structural equation modeling (SEM) was established to reveal the direct and indirect effects of all factors on the SOC. The SOC content significantly decreased with an increase in soil depth in the subtropics. The Pearson correlation analysis and structural equation modeling results indicated that the climate was closely related to and had the most significant effect on the SOC in moso bamboo forests. The maximum effect values of the annual mean temperature (MAT) and annual mean precipitation (MAP) on SOC were −0.975 and 0.510, respectively. Elevation and latitude were strongly correlated with Phyllostachys edulis forests and 0–10 cm SOC content and significantly contributed to the SOC with effect values of 0.488 and 0.240, respectively. The soil’s physical properties and forest biomass had significant negative effects on the SOC. Both NH₄⁺-N and available phosphorus (SAP) were significantly and positively correlated with the SOC at different soil depths in moso bamboo forests to different degrees, but he soil’s chemical properties, in general, had no significant direct effect on the SOC.

1. Introduction

Accounting for one-third of the global land area, forests play a key role in the global carbon cycle as they account for 40% of the global soil carbon pool [1,2]. Small changes in the forest soil carbon pool capacity can greatly affect the global carbon balance and its distribution pattern [3]. Soil organic carbon (SOC) is an important indicator of soil quality dynamics and can directly affect soil fertility and productivity. Therefore, its distribution and influencing mechanisms have received much attention from researchers. Xie et al. [4] used data from the second soil survey of China to estimate the overall changes in SOC stocks from the 1980s to the 2000s and found that forest SOC showed an increasing trend with large regional differences. Forest SOC is affected by various factors, such as climate, altitude, soil type, forest age, and elevation. In northwest China, for example, organic carbon at a depth of 0–30 cm in natural forests is influenced to a large extent by the soil type, and the dominant factor in the carbon content of the 0–100 cm profile is the average temperature. The SOC in plantation forests varied greatly among the different altitudes and stand types [5]. A past study has shown that soil aggregates under different management practices have an important influence on SOC accumulation and storage [6]. The physical, chemical, and biological properties of soil affect the storage and transformation of the soil organic carbon pool to some extent. Past results show that the article, sand, and granule contents can significantly affect the stability of a soil organic carbon pool [7]. It is generally believed that the effect of the annual mean temperature (MAT) on the SOC under different climatic conditions is greater than that of the annual mean precipitation (MAP), and the SOC content increases with increasing latitude, showing an overall trend of tropical < subtropical < temperate [8,9]. Naoyuki et al. [10] used a random forest model to show that high latitudes accumulate more SOC reserves than other regions.

Phyllostachys edulis is an important forest resource in China. It covers 467.78 million hectares, accounting for 72.96% of China’s bamboo forest area, and is concentrated in subtropical areas [11]. Phyllostachys edulis has a strong carbon sequestration capacity due to its allometry growth, and its SOC pool accounts for 67–87% of that of moso bamboo forest ecosystems [12]. Qi et al. [13] found significant differences in the SOC content between differently managed moso bamboo forests, mainly in terms of its vertical distribution, seasonal dynamics, and relationship with soil properties. Among moso bamboo forests under different management measures, undisturbed and roughly managed moso bamboo forests have higher carbon sequestration potential than intensively managed ones [14]. In addition, the soil carbon pool dynamics of moso bamboo forests are also closely related to the soil’s properties (soil porosity, field capacity, and ammonium nitrogen) and have a sensitive response to the soil’s physicochemical characteristics [15]. However, there is a lack of research on the partitioning characteristics and influence mechanisms of SOC in moso bamboo forests at the regional scale. The present study investigates the regional distribution characteristics of the SOC content and stocks in unmanaged moso bamboo forests in a Chinese subtropical region and reveals the main influencing factors through structural equation modeling.

2. Material and Methods

2.1. Study Site Description

The study area is located in subtropical China (23°43′ N–31°49′ N, 105°01′ E–119°40′ E, Figure 1). The area is a monsoonal humid climate zone with annual mean precipitation ranges of 943–1212, 910–1175, and 1491–1510 mm, and annual mean temperature ranges of 12.98–15.75, 16.50–17.47, and 19.12–20.20 °C in the northern subtropics, middle subtropics and southern subtropics, respectively (Figure 2 and Figure 3). The frost-free period of the study area is 210 d. All climatic data were obtained from meteorological stations near the study area. The altitude of the area ranges between 120 and 2000 m, and the soil types in these sampling plots are mainly red, yellow, and yellow-brown soil. The zone is a concentrated distribution area of Phyllostachys edulis forests, with the main associated tree species being Cunninghamia lanceolata, Pinus massoniana, Liquidambar taiwaniana, etc. The main shrubs in the area are Camellia oleifera, Trachelospermum jasminoides, Rubus loropetalus, etc., and the herb layer includes Dicranopteris lineris, Digitaria sangiunalis, Pyrola calliantha, etc.

2.2. Experimental Design and Soil Sampling and Measurements

According to the natural geographical distribution characteristics of Phyllostachys edulis forests in six counties in the northern subtropical (Henan Xinyang, Zhejiang Anji), middle subtropical (Sichuan Changning, Hunan Taojiang), and southern subtropical (Guangdong Conghua, Guangdong Longmen) regions, four 20 m × 20 m sample plots were set in typical Phyllostachys edulis stands without management in each county.

The site factor survey and per-tree inspection were carried out in the sample plots with a detailed investigation of the latitude, longitude, elevation, slope, slope direction, and slope position, as well as the diameter at breast height (DBH), bamboo height, and stand density. The basic information is shown in

Table 1. Basic information of the sampled sites.

Region	Sample Site	Sample Plot	Longitude	Latitude	Altitude (m)	Slope (°)	Slope Orientation	Mean DBH (cm)	Mean Height (m)	Stand Density (Trees·hm−2)
Northern subtropics	XY	1	114°4′32″ E	31°49′5″ N	480	28	NE	6.86 ± 1.92	11.16 ± 2.45	7600
		2	114°4′36″ E	31°49′3″ N	500	39	N	4.76 ± 1.48	7.05 ± 1.50	4800
		3	114°4′36″ E	31°49′6″ N	540	28	SW	7.61 ± 1.71	10.78 ± 1.92	4050
		4	114°4′22″ E	31°49′4″ N	440	5	NW	8.52 ± 1.84	10.89 ± 1.98	5950
	AJ	5	119°40′34″ E	30°34′25″ N	110	46	W	8.83 ± 1.90	9.27 ± 1.79	3400
		6	119°36′09″ E	30°32′19″ N	290	31	N	8.89 ± 2.00	9.18 ± 1.95	2950
		7	119°34′45″ E	30°28′36″ N	370	48	E	7.51 ± 1.51	8.46 ± 1.47	2675
		8	119°34′50″ E	30°28′40″ N	720	22	S	7.50 ± 1.17	9.11 ± 1.72	2725
Middle subtropics	CN	9	105°1′19″ E	28°27′44″ N	900	4	E	9.27 ± 1.49	12.31 ± 1.40	6675
		10	105°0′46″ E	28°27′36″ N	890	3	SW	9.32 ± 1.44	12.59 ± 1.77	6400
		11	105°1′13″ E	28°28′17″ N	875	1	SW	9.20 ± 1.30	13.94 ± 1.69	4000
		12	105°1′35″ E	28°27′51″ N	845	3	E	8.75 ± 1.42	13.41 ± 2.46	5975
	TJ	13	112°5′29″ E	28°19′36″ N	240	29	E	12.17 ± 1.85	13.89 ± 1.59	3250
		14	112°4′58″ E	28°19′25″ N	290	5	E	9.37 ± 1.96	12.46 ± 2.46	3700
		15	112°4′11″ E	28°19′47″ N	300	28	SE	11.08 ± 1.57	16.51 ± 1.61	4700
		16	112°4′03″ E	28°19′40″ N	180	43	W	10.27 ± 2.09	14.04 ± 2.15	3900
Southern subtropics	LM	17	113°50′54″ E	23°38′20″ N	550	35	SE	8.79 ± 1.73	13.28 ± 1.95	5350
		18	113°51′28″ E	23°38′07″ N	530	37	N	9.78 ± 1.64	14.69 ± 1.78	4950
		19	113°50′50″ E	23°37′55″ N	590	14	N	9.59 ± 1.65	15.05 ± 2.03	3850
		20	113°50′34″ E	23°38′03″ N	540	16	S	8.91 ± 1.52	14.63 ± 1.77	4850
	CH	21	113°48′02″ E	23°44′22″ N	190	4	E	8.52 ± 1.73	12.49 ± 1.82	5875
		22	113°48′01″ E	23°44′22″ N	260	19	N	8.53 ± 1.56	12.79 ± 1.68	5300
		23	113°48′23″ E	23°44′12″ N	340	18	N	7.08 ± 2.61	11.89 ± 1.89	8575
		24	113°48′25″ E	23°43′41″ N	380	30	NE	8.07 ± 1.88	13.41 ± 2.63	3425

.

The investigation factors of the sample plots were substituted into the model of the individual plant biomass of moso bamboo; the individual plant biomass was estimated; and the total biomass of the sample plot was calculated [16]. The formula is as follows:

$$M=747.787D^{2.771}{\left(\frac{0.148A}{0.028+A}\right)}^{5.555}+3.772$$

where M is the stand biomass, D is the diameter at breast height, and A is the age of the bamboo.

Three sampling points were set up in each sampling plot, and natural soil was collected from the three layers of 0–10 cm, 10–30 cm, and 30–60 cm using cutting rings with a volume of 100 cm³ to determine the soil’s physical characteristics. Soil samples of 30 drills of 10 kg or more were taken from each sampling plot, brought back to the laboratory, and passed through 2 cm sieves to remove impurities such as coarse roots and rubble. They were then naturally dried and ground through 2 mm sieves to determine the soil’s chemical characteristics.

Soil bulk density (BD) was determined via the cutting-ring method. Soil water capacity (SWC) was determined via the drying method. After soaking the soil sample for 8 h, the mass of the soil sample was measured and the maximum water holding capacity was calculated. After standing for 2 h, the mass of the soil sample was measured and the capillary water capacity was calculated. After standing for 24 h, the mass of the soil sample was measured and the minimum water holding capacity was calculated. Soil pH was measured via the potentiometric method. An elemental analyzer (Costech ECS 4024 CHNSO) was used to determine the soil total carbon (TC) and total nitrogen (TN). Soil total phosphorus (TP) and available phosphorus (SAP) were determined using the Mo-Sb colorimetric method. Soil nitrate nitrogen (NO₃⁻-N) and ammonium nitrogen (NH₄⁺-N) were determined using a continuous flow analyzer (Seal AA3, Germany). Soil organic carbon (SOC) was determined via the K₂Cr₂O₇-H₂SO₄ oxidation method [17].

The following equation was used to calculate the SOC stocks of Phyllostachys edulis forests:

$$SOCS={\displaystyle \sum _{i=1}^{n}SOC\times B{D}_i\times D_i\times (1-G_i)\times 0.1}$$

(1)

where SOCS is the soil organic carbon stocks (t·hm⁻²), BD is the soil’s bulk density (g·cm⁻³), n is the soil layer (n = 3), SOC is the soil organic carbon (g·kg⁻¹), D is the thickness of each depth (cm), and G is the percentage of the volume of gravel with a diameter larger than 2 mm.

2.3. Structural Equation Modeling

A linear regression network among the latent variables, observed variables, and error variables was established via confirmatory factor analysis and path analysis to provide a structural equation model to reveal the total, direct, and indirect effects of the variables. χ²/df, CFI, TLI, and SRMR were used to evaluate the suitability of the structural equation model [18].

Measurement models are used to describe the relationships between latent and observed variables.

$$X=\stackrel{\wedge }{x}\xi +\delta$$

$$Y=\stackrel{\wedge }{y}\eta +\epsilon$$

where X and Y are exogenous and endogenous observed variable vectors, respectively; $\widehat{x}$ and $\widehat{y}$ are the factor load of X and Y, respectively; ξ and η are exogenous and endogenous latent variable vectors, respectively; and δ and ε are the measurement error of X and Y, respectively.

Structural models are used to describe complex relationships between latent variables.

$$\eta =M\eta +\mathsf{\Gamma }\xi +\zeta$$

where M is the structural coefficient matrix between endogenous latent variables, Γ is the structural coefficient matrix between endogenous and exogenous latent variables, and ζ is the residual.

2.4. Data Analysis

SPSS 26 was applied to perform one-way ANOVA and least-significant difference (LSD) tests for the SOC contents and storage. Pearson’s correlation coefficient was used to analyze the relationship between the environmental factors and the SOC. Amos 24.0 was used to develop the structural equation modeling to analyze the direct and indirect effects of the geographic position (latitude, longitude and elevation), climate (MAP and MAT), soil physical characteristics (SPC), soil chemical characteristics (SCC), and vegetation (stand biomass) on the SOC.

3. Results

3.1. Regional Distribution Characteristics of Soil Organic Carbon

The SOC content of Phyllostachys edulis forests showed a significant decrease with increasing soil depth (p < 0.05) (

Table 2. Regional distribution of the SOC in Phyllostachys edulis forests.

Soil Depth (cm)	Region (g·kg−1)
	Northern Subtropics	Middle Subtropics	Southern Subtropics
0–10	34.31 ± 2.94 Aa	31.41 ± 2.88 ABa	26.34 ± 1.24 Ba
10–30	20.44 ± 1.95 Ab	13.81 ± 1.13 Bb	15.04 ± 1.54 Bb
30–60	14.11 ± 1.73 Ac	6.97 ± 1.24 Bc	8.02 ± 0.95 Bc
0–60	22.95 ± 1.63 A	17.40 ± 1.64 B	16.47 ± 1.15 B

Note: Different lowercase letters indicate significant differences among the different soil horizons, and different capital letters indicate significant differences among the different regions (p < 0.05).

), and there were also significant differences in the same soil layer in different areas. The SOC content of the 0–10 cm soil layer showed an increase with latitude and was significantly higher (p < 0.05) in the northern subtropics than in the southern subtropics, with SOC contents of 34.31 g·kg⁻¹ and 26.34 g·kg⁻¹ being observed, respectively. The SOC contents of both the 10–30 cm and 30–60 cm soil layers were highest in the northern subtropics, at 20.44 g·kg⁻¹ and 14.11 g·kg⁻¹, respectively, which were significantly higher than those in the middle and southern subtropics (p < 0.05). The average SOC contents of the 0–60 cm soil layer in the northern, middle, and southern subtropics were 22.95 g·kg⁻¹, 17.40 g·kg⁻¹, and 16.47 g·kg⁻¹, showing a pattern of increasing with latitude. The regional distribution of the SOCS in Phyllostachys edulis forests was similar to the variation in the SOC. The SOCS in the 0–60 cm soil layer were the highest in the northern subtropics, followed by the southern subtropics, and were lowest in the middle subtropics, at 132.84 t·hm⁻², 96.79 t·hm⁻², and 82.80 t·hm⁻², respectively (Figure 4).

3.2. Bivariate Relationship between the SOC and Environmental Factors

The results of the correlation analysis showed that latitude was significantly positively correlated with the SOC content at different soil depths, and the SOC increased with increasing latitude (Figure 5). The effect of longitude on the SOC was uncertain, showing a negative correlation with the 0–10 cm soil layer and a positive correlation with the SOC at a depth of 30–60 cm. Elevation was significantly positively correlated with the SOC in the 0–10 cm and 10–30 cm soil layers. The MAT was highly significantly negatively correlated with the SOC at each soil depth. The MAP was significantly negatively correlated with the SOC at a depth of 0–10 cm. The Phyllostachys edulis forest biomass was significantly and highly significantly negatively correlated with the SOC at depths of 10–30 cm and 30–60 cm, respectively. The soil water capacity at a depth of 0–10 cm was significantly positively correlated with the SOC. The maximum water holding capacity (WHC_max), capillary water capacity (CWC), minimum water holding capacity (WHC_min), and soil bulk density (BD) were significantly or highly negatively correlated with the SOC. The TC was significantly or highly significantly positively correlated with the SOC. Nitrate nitrogen at a depth of 10–30 cm and ammonium nitrogen at a depth of 0–10 cm showed significant negative and positive correlations with the SOC. The SAP was significantly positively correlated and highly significantly positively correlated with the SOC at the 10–30 cm and 30–60 cm soil layers.

3.3. Effect Analysis of Factors Influencing SOC

The study used the χ²/DF, comparative fitness index (CFI), Tucker–Lewis index (TLI), and SRMR to describe the fitness of the SEM. The SEM explained about 36.1%, 30.1%, and 36.2% of the variation in the SOC at depths of 0–10 cm, 10–30 cm, and 30–60 cm, respectively (Figure 6).

The elevation showed a significant positive effect on the SOC in Phyllostachys edulis forests (

Table 3. Standardized total/direct/indirect effects of the SOC of Phyllostachys edulis forests as derived from structural equation modeling (SEM).

Effects	Soil Depth	Latitude	Longitude	Elevation	MAP	MAT	Biomass	SPC
Total effects	0–10 cm	0.240	−0.006	0.488	−0.269	−0.422	−0.189	−0.204
	10–30 cm	0.149	0.356	0.475	0.332	−0.861	−0.116	−0.329
	30–60 cm	0.182	0.465	0.416	0.510	−0.975	−0.269	−0.132
Direct effects	0–10 cm	0	0	0.369	−0.213	0	−0.189	−0.204
	10–30 cm	0	0	0.269	0.388	−0.479	−0.116	−0.329
	30–60 cm	0	0	0.365	0.462	−0.562	−0.269	−0.132
Indirect effects	0–10 cm	0.240	−0.006	0.120	−0.057	−0.422	0	0
	10–30 cm	0.149	0.356	0.207	−0.056	−0.382	0	0
	30–60 cm	0.182	0.465	0.051	0.048	−0.413	0	0

). The effect of elevation on the SOC showed a significant positive effect, with total effects of 0.488, 0.475, and 0.416 at depths of 0–10 cm, 10–30 cm, and 30–60 cm, respectively, showing a trend of decreasing with the increasing soil depth. The latitude and longitude have a significant positive effect on the SOC indirectly, mainly via influencing the climate and soil properties. The total effect of latitude was 0.240, 0.149, and 0.182 at depths of 0–10 cm, 10–30 cm, and 30–60 cm, respectively. The effect of longitude on the SOC at a depth of 0–10 cm was weak, and the total effect was only −0.006. The effect of longitude on the SOC at a depth of 0–10 cm was uncertain, with a total effect of only −0.006, while the total effect reached 0.356 and 0.465 at depths of 10–30 cm and 30–60 cm.

For a depth of 0–60 cm, the MAT and MAP mainly affected the SOC indirectly by influencing the Phyllostachys edulis stand’s biomass and soil properties. The MAT showed direct negative effects on the SOC, with the highest total effect among all of the factors, which were −0.422, −0.861, and −0.975 at depths of 0–10 cm, 10–30 cm, and 30–60 cm, respectively, and showed a pattern of increasing with the deepening of the soil’s depth. The MAP had a significant negative effect on the SOC in the 0–10 cm interval of soil, while it showed a positive effect on the SOC at depths of 10–30 cm and 30–60 cm. The total effect was −0.269, 0.332, and 0.510 at the depths of 0–10, 10–30, and 30–60 cm, respectively, indicating that increasing precipitation promotes organic carbon leaching from the topsoil.

The effects of the Phyllostachys edulis forest’s biomass on the SOC was negative in all soil layers, and its direct effect was significant in the 0–10 cm and 30–60 cm layers of soil, with total effects of −0.189 and −0.269, respectively. There were direct significant negative effects of the soil’s physical characteristics on the SOC at depths of 0–10 and 10–30 cm with effect values of −0.204 and −0.329, respectively. The soil’s chemical characteristics as a whole had no significant effect on the SOC of Phyllostachys edulis forests.

4. Discussion

4.1. Distribution of SOC in Phyllostachys edulis Forests

The SOC content of Phyllostachys edulis forests at the regional level all showed a significant decrease with increasing soil depth, which is consistent with the findings of Zhuo et al. [19] on the SOC content and density in the farming areas of northeastern and northern China and Qi et al. [20] on the SOC in Phyllostachys edulis forests of different management types. There were also significant differences in the regional distribution of the SOC in moso bamboo forests. Compared with the middle and southern subtropics, the northern subtropical moso bamboo forests had a higher SOC content. The SOC content in the southern subtropics is more similar to the results of Shang et al. [21] on the SOC in tropical rainforests.

The regional distribution of SOCS showed a similar pattern of variation to that of the SOC, which is generally consistent with the results of Wang et al. [22] and Bird et al. [23]. The SOCS of moso bamboo forests at depths of 0–10 cm, 10–30 cm, and 30–60 cm were 132.84 t·hm⁻², 82.80 t·hm⁻², and 96.79 t·hm⁻², with an average of 104.14 t·hm⁻² in the 0–60 cm interval of soil, which was higher than the national average SOC content (80 t·hm⁻²) [24]. The Chinese subtropical Phyllostachys edulis forest ecosystem clearly has a much larger soil carbon pool, and its soil carbon sink capacity should not be underestimated compared with the estimates of Wang et al. [25] for a subtropical Pinus massoniana plantation (51.51 t·hm⁻²) and Saimun et al. [26] for a tropical forest ecosystem (77.10 t·hm⁻²).

4.2. Influence of Climate and Geographical Position on the SOC in Phyllostachys edulis Forests

Studies have shown that the MAP has a significant positive effect on the SOC in tropical, warm–temperate, and cold–temperate plantations globally [27]. The same pattern was observed for Phyllostachys edulis forests in the subtropics, where the MAP increase favored SOC accumulation. The MAT was significantly negatively correlated with the SOC, and the SOC content decreased with increasing temperature, which is consistent with the results of Li et al. [28] for Chinese farmland and Fissore et al. [29] for North American forests, but different from the results of the relationship between the SOC and MAT in tropical rainforests. This may be related to the fact that long-term warming in tropical rainforests, despite accelerating underground carbon processes, has no significant effect on the SOCS. At the regional scale, the MAP and MAT were the dominant influences on the variation of the SOC in moso bamboo forests, which is consistent with the results of Li et al. [30] for subtropical forests.

Elevation and latitude are the main geographical factors affecting the SOC of Phyllostachys edulis forests. The SOC content of moso bamboo forests increased with elevation. This may be because the temperature is lower at higher altitudes, and most of the organic matter in plant residues is imported from the ground to the soil; thus, increasing the SOC content. Structural equation modeling further indicated that the SOC of moso bamboo forests was significantly and positively influenced by latitude. This result is consistent with the latitudinal pattern of the SOC in nine typical forest ecosystems in the North–South Transect of Eastern China (NSTEC) [31]. However, it was also found that the latitude was negatively correlated with the forest soil organic carbon in a large-scale region and could explain 20% of the variation in its content. It is speculated that this may be due to differences in the methods of land use, soil texture, and biological factors at the regional scale [32]. The effect of latitude on the SOC content of moso bamboo stands was mainly indirect. The study of SOC partitioning characteristics and the influencing factors in natural forests by Wang et al. [33] similarly confirmed this conclusion, probably because a high soil respiration rate accelerated the shift from forest SOC to CO₂ as the temperature gradually increased with decreasing latitude [34]. The influence of longitude on the SOC in Phyllostachys edulis forests is uncertain, which is consistent with the results of Zhou et al. [35] on soil carbon pools in northern China, and its influence mechanism is yet to be studied in depth.

4.3. Effects of Stand Biomass and Soil Physicochemical Characteristics on SOC in Phyllostachys edulis Forests

Both Phyllostachys edulis forests’ biomass and soil physicochemical characteristics were directly related to the SOC. The biomass of moso bamboo forests was significantly and negatively correlated with the SOC at a depth of 10–60 cm, which may be related to the fact that this soil layer is a concentrated area of the rhizome root of Phyllostachys edulis. The fine roots of moso bamboo absorbed more elements, such as soil carbon and phosphorus, which promoted root growth and microbial activity and, thus, accelerated the SOC consumption [36,37]. The invasion of broadleaf evergreen forests by moso bamboo significantly reduced the soil labile organic carbon content and, thus, changed the soil’s nutrient structure, suggesting that the root system of moso bamboo has a higher uptake efficiency compared to broadleaf species [38]. Achat et al. [39] found that conventional harvesting processes cut above-ground organic carbon and increase organic carbon in the bottom soil. Both were similar to the results of the present study.

WHC_max, WHC_min, CWC, and BD were all significantly negatively correlated with the SOC in Phyllostachys edulis forests, indicating that soil moisture increased the soil’s microbial activity and accelerated SOC degradation. In addition, the smaller the soil bulk density, the looser the soil texture and the higher the SOC content, which is consistent with the results of many studies [40,41]. However, the SWC was significantly positively correlated with the SOC, which may be related to the increase in fine roots and litter of plants with increasing soil water capacity; thus, leading to an increase in above-ground SOC input [42]. It has been suggested that the correlation between SOC and soil pH needs to be defined within a certain range to be meaningful [43]. Both NH₄⁺-N and SAP were significantly correlated with the SOC at all soil depths, which is similar to the results of Wang et al. [44] and Qi et al. [45].

5. Conclusions

The SOC content and storage of Phyllostachys edulis forests exhibit an obvious regional variation and increase with latitude. Climatic factors, especially the MAT and MAP, were found to be the dominant influencing factors of the SOC in moso bamboo forests at the regional scale, which showed that the SOC decreased with increasing temperature and increased with increasing precipitation. The geographical position (elevation, latitude, and longitude) promoted SOC accumulation mainly by influencing the temperature and precipitation, as well as the soil’s physical characteristics, among which elevation had the most directly significant effects on the SOC, while latitude and longitude indirectly affected the SOC by regulating the climate. Both Phyllostachys edulis forests’ biomass and soil physical characteristics showed direct negative effects on the SOC, indicating that they inhibited SOC sequestration. Although the soil’s chemical characteristics had no significant effect on the SOC in general, both NH₄⁺-N and the SAP were beneficial to the accumulation of SOC in moso bamboo forests. The conclusions of this study can provide a basic theoretical basis for the future management of subtropical moso bamboo forests in China.