Fractal scaling of particle-size distribution and associations with soil properties of Mongolian pine plantations in the Mu Us Desert, China

Mongolian pine plantations (MPPs) composed of Pinus sylvestris var. mongolica (P. sylvestris) are used for desertification control and restoration of degraded land in arid and semi-arid regions. We studied soil changes associated with P. sylvestris by comparing top (0–20 cm) and sub-top (20–40 cm) soil properties across 8 stand density gradients of MPPs ranging from 900 ± 5–2700 ± 50 trees ha–1. The study was conducted in the uncovered Sandy Land in the southern Mu Us Desert, China. The relationships between the volume fractal dimensions (D) of soil particle size distribution and soil physicochemical properties were evaluated. D was determined using a laser diffraction technique and soil properties were measured. In the top layer, P. sylvestris significantly positively affected soil physicochemical properties except for bulk density and total nitrogen. These effects were not observed in the sub-top soil layer. D values ranged from 1.52 ± 0.29–2.08 ± 0.06 and were significantly correlated with stand density. Significant correlations were observed between D and soil properties (except total nitrogen) in the top soil layer. Given these results, we concluded that D is a sensitive and useful index because it quantifies changes in soil properties that additionally implies desertification in the studied area.

Overcultivation, urbanization, and adverse climate variations, such as droughts and floods can result in the degradation of arid and semi-arid lands [1][2][3] . China has large areas of desertification (approximately 2.64 billion ha) because of overpopulation and insufficient natural resources 2 . Among the numerous desert areas, the Mu Us Desert in northern China is the places most seriously affected by desertification 4,5 . The Mu Us Desert is located on the southern Ordos Plateau and lies at the northern margin of the Asian summer monsoon 4 . The Mu Us Desert covers an area of approximately 4 million ha and is an important part of the farming and pastoral zone of China 5 . Desertification in the Mu Us Desert is primarily evident in the transformation of formerly anchored dunes into semi-anchored and mobile dunes 5 .
Vegetation cover loss and subsequent desertification results in degradation of several soil physicochemical properties 6 . Numerous means and methods, such as introducing mechanical sand barriers 7 , biological soil crust 8 , and afforestation have been carried out in an effort to restore soil fertility and modify sand areas. Afforestation is considered the most effective method for reducing wind damage and increasing biodiversity. For more than 50 years, through environmental management, afforestation has been used to control desertification and increase timber production in Sandy areas 2, 9 . Mongolian pines are an important species grown on Sandy Lands. Mongolian pines are a variety of Scots pine (P. sylvestris var. mongolica) that is naturally distributed in the Daxinganling mountains, Haila' er, Wangong, Cuogang, He' erhongde, Hunhe, and Ha' erhahe areas in the Inner Mongolian Autonomous Region and Hulunbeier Sandy plain of China (50°10′-53°33′N, 121°11′-127°10′E) and parts of Russia and Mongolia (46°30′-53°59′N, However, soil is a complex system in which many biological and physical components interact across space and time scales 19,20 . Between the 2 aforementioned approaches, individual fractions typically de-emphasize coarse fractions and emphasize fine particles. Textual analysis cannot provide complete information and this analysis results in a waste of soil data. Furthermore, the results are unsuitable for evaluating real soil systems such as desert soils that contain a large proportion of coarse particles 21 . Although SOC is widely used in soil quality assessment, this method is insensitive to environmental change over shorter time scales 22 . These traditional methodologies therefore cannot provide complete information and quantitatively represent fundamental attributes by use of a practical index. By contrast, fractal measures can use all soil particle-size distribution (PSD) information, including clay, silt, and sand particle data 23 . PSD is used in soil classification and the estimation of soil hydraulic properties, such as soil water retention curves, soil hydraulic conductivity, and soil bulk density (BD) [24][25][26][27] . Different PSD-driven sorption properties of soil affect the mineralization of decoupled carbon and nitrogen, as well as the activity of invertase and xylanase during organic matter decomposition [28][29][30] . Therefore, PSD is useful for understanding the physical and chemical processes of soil water and the development of soil nutrient cycles 31 . The volumetric distribution of soil particles is usually replaced by the mass distribution of soil particles when evaluating the soil fractal dimension 32 . However, the density of soil particles with different radii varies 33 . Therefore, the soil particle volumetric distribution can be used to directly calculate the soil volume fractal dimension (D). Laser diffraction is a useful technique that has been used to measure soil D, and it is a reliable method for estimating PSD 34 . The use of soil D is a new approach to describe the distribution of soil particles. Significant linear correlations have been found between D and various soil properties using this technique 21,23 . The method permits quantifying and integrating information on the biological, chemical, and physical characteristics of soil measured on different depths scales 31 .
Much additional information on the mutual relationships of MPPs and soil properties is needed. An effective index for quantifying MPPs effects on soil properties in desert areas should also be developed. This study evaluated soil status dynamics in forest ecosystems, particularly the effects that different stand densities of MPPs have on soil properties. We hypothesized that topsoil (0-40 cm) properties are affected by MPPs establishment and stand densities. Changes in top (0-20 cm) and sub-top (20-40 cm) soil properties were studied across a population density gradient of MPPs and in the referenced uncovered Sandy Land (CK) in Yulin City, Shaanxi Province (located in the southern Mu Us Desert, Northern China). The specific objectives were as follows: (1) to determine how changes in topsoil properties, including D and physicochemical properties vary with different stand densities of MPPs; and (2) to evaluate the possibility that D of soil PSD can be used as a practical index for quantifying variations in soil physicochemical properties and the implications of desertification. This study may improve the design and management of afforestation by using MPPs that increase soil nutrients and improve the physical structure of soil. These changes would also be beneficial to stand development. Table 1 shows the soil PSD in the different soil sampling plots, including the CK. Sand particles (50-2000 μm diameter) are the dominant soil particle class, and account for >70% of the total PSD. Clay (<2 μm) and silt (2-50 μm) contents were significantly lower than sand particles. The clay contents were less than 4.00% of total PSD.

PSD and fractal characteristics of topsoil properties in different MPPs.
In MPPs, clay and silt contents gradually increased with stand density. Compared with CK (1.18 ± 0.76% (top) and 2.32 ± 0.73% (sub-top), and 11.32 ± 0.76% (top) and 6.36 ± 0.74% (sub-top) for clay and slit contents separately) from P I (3.32 ± 0.84% (top) and 3.54 ± 0.47% (sub-top), 23.87 ± 0.78% (top) and 19.20 ± 0.39% (sub-top)) to P VIII (2.13 ± 0.05% (top) and 0.88 ± 0.73% (sub-top), 13.14 ± 0.03% (top) and 9.72 ± 0.72% (sub-top)), clay contents increased by as much as 182.26% and 52.15% for the top and sub-top layers, and by 80.64% for the top layer. Silt contents increased by as much as 110.94% and 201.98%, and by 16.22% and 52.92% for the top and sub-top layers, respectively. As a result, clay and silt content differences between MPPs and CK were high. Furthermore, sand particle content from P VIII to P I decreased. Compared with CK, sand particle content in P I and P VIII decreased by 20.19% (top) and 18.19% (sub-top), and by 3.27% (top) and 2.15% (sub-top) respectively. Meanwhile, sand content within the same plot increased from the top to sub-top layer, in addition to a decrease in silt and clay (expect P I , P II , P III , P IV , P V , and P VII ) contents. In contrast, clay contents of CK were increased with increasing soil depth.
D values were subsequently calculated with Eq. 1 based on the PSD data. The D values for the different plots are shown in Table 1. D of soil PSD ranged from 1.52 ± 0.29-2.01 ± 0.07 (top) and from 1.94 ± 0.12-2.08 ± 0.06 (sub-top) (except CK, which was 1.42 ± 0.25 and 1.71 ± 0.29 for the top and sub-top layers). Although there was a slight change in the value of D between MPPs, with increasing stand densities of MPPs, D values increased gradually. The D values of all MPPs were generally higher than CK in all topsoil layers. D values in the sub-top layer of all plots were higher than that of the top layers. Soils with greater clay and silt contents had higher D values, whereas soils with a greater amount of sand particles had lower D values (Table 1).

Physical properties of soil subsections in different MPPs. No significant variations in soil total
porosity (T P ) were noted among any of the MPPs in both top and sub-top layers (p > 0.05) (Fig. 1a). A significant difference was only observed between CK and MPPs. Capillary porosity (C P ), saturated soil moisture content (SMC), and BD showed significant differences in all layers among all MPPs (p < 0.05) ( Fig. 1b-d). P V , P VI , P VII , and P VIII had higher T P , C P , and SMC, and lower BD values compared with other plots in the top layer (p < 0.05). Meanwhile, P VI , P VII and P VIII had the lowest SMC, which ranged from 66.81 ± 2.45%-68.66 ± 3.21% in the sub-top layer. The CK soil had the lowest T P , C P and SMC, and had the highest BD values, which were 25.00 ± 2.30% (top) and 23.00 ± 2.02% (sub-top), 20.31 ± 2.01% (top) and 18.32 ± 1.86% (sub-top), 40.24 ± 3.62% (top) and 38.53 ± 4.21% (sub-top), and 1.72 ± 0.06 g.cm −3 (top) and 1.70 ± 0.02 g.cm −3 (sub-top).
T P , C P , SMC, and BD were significantly correlated with each other in the top layer (correlation coefficients ranged from 0.79-0.94, p < 0.01). In the sub-top layer, T P , SMC, and BD were significantly correlated with each other (correlation coefficients ranged from 0.75-0.77, p < 0.01); however, C P was not significantly correlated with SMC or BD (p > 0.05) ( Table 2).

Relationship between D and soil physicochemical properties of soil subsections in different
MPPs. Linear regression and correlation analysis were used to study the relationships between D and stand density, physical soil properties including T P , C P , SMC, and BD, and chemical soil properties including SOC and selected soil nutrients (Figs 3, 4 and 5; Tables 2 and 3). The results showed positive linear correlation between D values and stand density (top R 2 = 0.95, p < 0.01; sub-top R 2 = 0.84, p < 0.01). Furthermore, the D values were more affected by the top soil layer (Fig. 3).
A significant negative linear correlation was found between T P , SMC, and D values with R 2 ranging from 0.78-0.79, p < 0.01 (Fig. 4a,c). Lack of a significant correlation was noted between C P and D values in the sub-top layer, with R 2 = 0.19 (p > 0.05) (Fig. 4b). In contrast a positive linear correlation exists between SMC (sub-top), BD and D values (R 2 ranged from 0.80-0.90, p < 0.01) (Fig. 4c,d). This reverse correlation and the different variations in BD, T P , and C P were mutually verified. Pearson analysis results indicated strong correlations between soil D and selected soil physics properties (Table 2). D was significantly positively correlated with BD, and significantly negatively correlated with T P , C P , and SMC in the top layer. The correlation coefficients were 0.95, −0.89, −0.95, and −0.88, respectively (p < 0.01). D was significantly positively correlated with SMC and BD, and negatively correlated with T P and C P in the sub-top layer. The correlation coefficients were 0.92 and 0.90 (p < 0.01), and      Table 3).

Discussion
We investigated the effect of MPPs on topsoil properties and tested the feasibility of soil D as an indicator of soil property variation in the process of desert evolution. Consequently, the level of soil degradation and desertification in southern Mu Us Desert could be determined. Our main findings and analyses are discussed as follows.
Effects of MPPs on topsoil physicochemical properties. Plants affect soil properties, which in turn alter plant growth and interspecific competition. This process establishes a plant-soil feedback system [35][36][37][38][39] . Many physicochemical properties of soil, such as T P , C P , BD, SOC, N, P, K, and pH, are mainly determined by plant type and cover 36 . Soil plays an important role in the fertility and stability of forest ecosystems by supporting microorganism communities, which release nutrients necessary for vegetation development and improve the physical structure of the soil 40 . We found that soil physicochemical properties are improved by MPPs. These forests can protect the Sandy soil surface from wind erosion. For example, soil particles and dusts in airstreams are largely blocked by trees and undergrowth shrubs. Erosive force and carriage capability are absorbed by MPPs 41 . MPPs soil physical structure had good permeability, and nutrient losses due to wind erosion in the topsoil of CK were significantly higher than in the MPPs (Figs 1 and 2). Our findings are consistent with those of Huang et al. 42 , who found that the expansion of drylands, unprotected land, and erosion-induced land degradation may increase the extent of desertification. This expansion can also lead to SOC storage reduction and CO 2 emissions into the atmosphere, which contribute to global warming and form a positive feedback cycle. The Mu Us Desert has a typical arid and semi-arid continental monsoonal climate. The enhanced warming of arid and semi-arid areas will contribute to their degradation. Enhanced surface warming in drylands can be explained by surface processes 43 . In drylands, low soil moisture content limits evaporation and limited vegetation cover leads to low transpiration rates and C loss 44 . Vegetation can lower air temperature via transpiration 45 and by converting absorbed sunlight into chemical energy via photosynthesis to fix C 46 . This reduces the extra heating from increased greenhouse gases and results in lowered warming rates. We found that the presence of MPPs has a positive effect on topsoil properties, which is significant for managing the impact of climate warming on unprotected land. The C concentration in the topsoil decreased significantly in the CK compared to the Mu Us Lands with MPPs. This observation is consistent with previous observations  49 . Loss of soil C in the CK has been attributed to the effect of decreased organic matter inputs. Our data supports this mechanism since the C concentration in all particle-size fractions and in aggregates decreased in bare Sandy Land. These results are qualified with the observation that changes in BD may influence the interpretation of the C storage differences in BD values among MPPs and CK plots were large (see Fig. 1), with lower values in the CK and highest values in MPPs. In addition, compared to the CK, the increase in topsoil C in MPPs was associated with an increase in C concentration in both silt and sand particle-size fractions, and these increases were coincident with a decrease in the coarse sand fraction (Table 1). This decline in soil C stock might be ameliorated by adoption of improved afforestation practices. Thus, efforts should be made to retain as much plant cover as possible.
In previous studies, several processes were found to influence net C storage following pine afforestation of the Sandy Lands. As the forest grows, net C accumulation could occur from increased litter production and protection of soil organic matter by physical or biotic mechanisms 50 . Soil organic matter dynamics have been linked to changes in soil physical structure, especially aggregate formation 51 . To enhance soil C storage during afforestation of Sandy soils in semi-arid regions, disruption of vegetation should be minimized during the planting stage. These results are the same as those by Chen et al. 52 , who conducted research on organic carbon in soil physical fractions under different-aged plantations of Mongolian pine in the semi-arid region of Northeast China.
Our results are also consistent with those of a previous study conducted in the semi-arid Horqin Sandy Land of northern China 14 . The afforestation of areas with active sand dunes using MPPs had positive effects on SOC, N, and P accumulation in the plants and soil. Additionally, the greatest improvement of soil SOC and selected soil nutrients occurred in the upper soil layer after plantation establishment 14 .
Soil physical properties differ among topsoil layers, and these differences may affect precipitation infiltration and evaporation 53 . In the present study, sub-top soil layers had larger particle sizes (greater proportion of sand particles) than top soil layers (see Table 1), allowing for more rapid movement into deep soil layers. The results agree with those of Dai et al. 54 showing that the spatial variability of soil particle size and porosity result in differences in soil properties.
In the MPPs study area, the spatial pattern of SOC, soil P T , K T , N avi , P avi , and K avi distribution was consistent with distribution of T P and C P , suggesting the coupling of soil N, P, and K transformations, and the dependence of soil N, P, and K availability on soil water availability 55 . Water, SOC, N, P, and K are the main limiting factors for pine tree growth in the semi-arid area 56 . Regional ecosystem management must consider the availability and balance of these resources. Thus, protection of the litter layer is strongly recommended to ameliorate soil degradation and nutrient limitation in the study area since the litter layer was not only the main source of soil organic matter and available nutrients, but also a regulator of soil microbial activity 57,58 . Some beetle species live in the litter layer, and the decomposition of their bodies provides important nutrient resources in arid and semi-arid regions 21 .
Variations in soil properties differed among the stand densities of MPPs, indicating that an optimal stand density is needed for best results. We believe that P VIII (900 ± 5 trees.ha −1 ) is the optimal tree planting density. Under this density, we found the highest values of soil physicochemical properties, such as T P , C P , SOC, P T , K T , N avi , P avi , and K avi , whereas BD had the lowest values.

Soil D as a practical indicator for desertification in MPPs.
Soil texture classification is usually measured using the percentages of clay, silt, and sand within certain size ranges. Soil texture is critical for understanding the transportation and storage of soil water and nutrients, and the mineralization of organic matter content 59 . In this study of P. sylvestris plantations, the average D values continued to increase over time. This change led to optimal particle distribution of afforested Sandy Land compared to that of bare Sandy Land. The change was also beneficial by decreasing BD and increasing water infiltration. Such effects were more significant in the top layer of the soil profile. The strong correlation between D and the soil nutrients can be interpreted as being caused by an increase in fine soil particles and organic matter content. Given that soil clay particles bind nutrients in soil 60 , an increase in clay concentration enhances soil adhesive forces. Accordingly, the ability of soil to absorb water and the cation content in soil are both enhanced. Higher clay concentrations were found in MPPs soils than in CK soils. Clay is more easily eroded by runoff than sand, thereby enabling MPPs to act as a barrier to soil and wind erosion and enhancing the deposition of sediment carried by erosion processes 60 . Once the Sandy Land loses the protection of P. sylvestris, or wind velocity and precipitation exceed the threshold, accumulative fine particles can be quickly eroded and lost.
Linear regression and correlation analysis indicated that D values had a highly significant negative correlation with most of the selected soil properties. Fine fractions (clay and silt) are associated with fertile, hydrophilic, and biodiversity-rich soil systems; however, a different phenomenon was observed in the present study. The highest MPPs stand density (P I 2700 ± 50 trees.ha −1 ) had the highest D values. This may be because artificial forests with high stand density can effectively resist wind erosion. Wind erosion causes nutrient and functional losses and transports the fine soil particles, thereby reducing the water-holding capacity, depleting soil structure, and diminishing biological properties 61-63 . Fine particle losses caused by wind-induced erosion cause land degradation and desertification 28 . In general, soil D is closely related to soil functions, but the 2 parameters are interdependent. Given the capability of MPPs to reduce water and wind erosion, plantations can change the process and intensity of erosion. Different stand densities of MPPs change the movement and deposition of soil, thereby causing the redistribution of soil clay. Therefore, the soil particles and D vary within these MPPs, and the extent to which D reflects changes in soil nutrient content requires further study. Ecological systems are complex, and the estimation of soil D in different MPPs can help determine the changes in soil properties and vulnerability to desertification. Meanwhile, low D values are practical for suitable stand density of MPPs.
Further, unique among other soil nutrients, soil N T is an expectation. In this study, a non-significant relationship between D and N T was observed, corresponding with irregularities in N T values among the different stand densities of MPPs. Nitrogen turnover is complex because it combines nitrogen mineralization, ammonia volatilization, nitrification, and denitrification 12,16 . In forest ecosystems, soluble organic N and inorganic N (NH 4 + -N and NO 3 − N) are the major nitrogen sources available for plant growth 11 . Plants growing on mineral soils in the temperate zone do not efficiently utilize soluble organic N for growth, so soluble organic N is rarely reported in Sandy Land areas. The amounts of available inorganic forms of N in soils are generally small. A small pool of NO 3 − N may indicate either a low nitrification rate, a high rate of NO 3 − N uptake by plants, or rapid denitrification 12 . During our study, N T content in MPPs was higher than in the CK, indicating that MPPs improved N T in soils, although the degree of improvement was not significant.
Recommendations for further research. Several previous studies have proposed a combination of several physical, chemical, biological and biochemical properties as indicators of soil status 64 . Specific indicators of soil microbial activity have been proposed to assess soil status, including several enzyme activities specifically related to N, P, and C cycles, and some general microbial indicators, such as dehydrogenase activity and soil respiration 26 . However, lack of consideration for other major influencing factors and indexes, which consider both representativeness and comprehensiveness, limits the validity of these methods. Addressing the limitations of this study in future studies can provide a better understanding of soil improvement through use of xeric-adapted plant species such as P. sylvestris. This would provide guidance for more successful afforestation, combating desertification, and environmental protection in the arid and semi-arid regions of China 12, 13, 42, 65-69 .

Conclusions
The establishment of MPPs in the Mu Us Desert positively changed the topsoil properties. Soil clay and silt particle contents, T P , C P , SMC, SOC, and soil nutrients increased in MPPs compared with those in the CK. These increases were accompanied by a decrease in soil sand particle content and BD. With a decrease in stand density, soil physicochemical properties in all MPPs plots significantly decreased. Linear regression and correlation analysis showed that the D values had significant linear relationships with soil physicochemical properties (except for N T ), as well as stand densities in the top layer. R 2 values ranged from 0.54-0.95 (p < 0.05) and correlation coefficients ranged from 0.60-0.95 (p < 0.05). In the sub-top layer, the R 2 values (0.001-0.84) were lower and correlation coefficients ranged from 0.03-0.92. In summary, D was sensitive to soil coarsening and soil properties. Therefore, D can be used as a practical index to quantify changes in soil properties and indicate desertification vulnerability.
This research was limited by the omission of other soil depths and microelement levels. P. sylvestris is a shallow-rooted plant and 80% of its roots are found at 0-100 cm soil depth. Other soil nutrients, such as Ca, may have significant direct or indirect impact on plant growth and soil properties. Additionally, only 3 sampling points were used in the present study. Future studies should address these limitations.

Materials and Methods
Experiment site description. Mu Us Desert has an arid and semi-arid continental monsoonal climate, with an annual precipitation ranging from 200-400 mm, evaporation of 1800-2500 mm, and aridity of 1.0-2.5 70,71 . The Mu Us Desert has a low to moderate wind-energy environment 72 .
The Research Station (study site) is located on the Rare Psammophytes Protection Botanical Base (RPPBB) in Yulin City, which is the northernmost prefecture-level city of Shaanxi Province (38°20′11″N, 109°42′54″E) (Fig. 6). The study site area was 333.30 ha. The study site has a continental, monsoon-influenced semi-arid climate, with long, cold winters, and hot, humid summers. Annual precipitation is approximately 400 mm. Sunshine is abundant (annual accumulation of 2780 h). The mean annual temperature is 8.8 °C. The frost-free period is approximately 140 d. The RPPBB landscape is characterized by fixed sand dunes, which are classified as arenosol type of quartisamment (U.S. Soil Taxonomy) 21 . The soil pH value is 7.2 ± 0.5, and natural vegetation in the study area consists largely of Salix psammophila, Caragana korshinskii, Hedysarum scoparium, Artemisia ordosica, and Populus alba.
Sample plot investigation. The study was conducted from June 2013 to August 2013. A total of 24 MPPs sample plots 20 m × 20 m and with a stand density of 900 ± 5-2700 ± 50 trees.ha −1 were selected. 8 different density gradients were considered (3 sample plots were taken as reduplicates for each stand density), and each stand density of initial plantation area was 100 m × 100 m; initial planting time was in the year of 1989 (immature timber). These sample plots that were intact and unaffected by human disturbance. Within these plots, the dominant vegetation species was P. sylvestris, and understory species comprised a sparse grass-shrub layer. Herb cover was less than 30%, and the height was lower than 0.6 m. General information about the MPPs is presented in Table 4. Average tree height (H), diameter at breast height (DBH) and canopy size (C) were 10.05 m, 14.56 cm, and 3.14 m, respectively. For each plot, 3 soil sampling profiles (as reduplicates) were selected at random (not taken from the plot edge). Soil samples were collected for 2 layers: the top layer (0-20 cm) and the sub-top layer (20-40 cm). Soil samples of the 2 layers were also collected in the CK. D of soil PSD was calculated as follows (Eq. 1): where r is the soil particle size, R i is the soil particle size of grade i, R max is the maximum value of soil particle size, V(r < R i ) is the volume of soil particle size less than R i , and V T is the total volume of soil particles 21, 23, 25, 30 . Methods for soil property analysis. All the soil samples were dried naturally in the laboratory for 2 d. We carefully removed all plant stems, roots and tiny gravels, and then parts of the air-dried soil samples were hand sieved through 2.00 mm and 0.25 mm screens prior to laboratory analysis 21 .
Soil physical properties were analyzed using the following methods: (1) C P and SMC were measured through introduction of ring sampler; (2) T P was calculated using Eq. 2: where T P is the total porosity (%), BD is soil bulk density (g.cm −3 ), and ρ s is soil particle density which is equal to 2.73 g.cm −3 . BD was measured using the wax seal method (Eq. 3): where g 1 is the sample weight (g), g 2 is sample weight when completely wrapped by wax, g 3 is the original reading of electronic balance (g), g 4 is reading of electronic balance with the sample (g), ρ 1 is specific gravity of water (equal to 1.0 g.cm -3 ) and ρ 2 is specific gravity of wax (equal to 0.9 g.cm -3 ) 21 .
Soil chemical properties were analyzed through the following: (1) potassium dichromate wet combustion method for SOC; (2) micro-Kjeldahl's method for N T ; (3) Mo-Sb colorimetric method for P T ; (4) hydrofluoric and perchloric acid (HF-HCLO acid)-flame photometer method for K T ; (5) alkali diffusion method for N Avi ; (6) sodium bicarbonate (NaHCO 3 ) digestion-Mo-Sb colorimetric method for P Avi ; and (7) ammonium acetate digestion-flame photometer method for K Avi 21 . Statistical analysis. Data were analyzed using SPSS software version 21.0 (IBM Inc. NC, USA). The differences in selected soil physicochemical properties and D values among the MPPs were compared using multiple comparison and one-way analysis of variance. A least-significant difference test (at p < 0.05) was used to compare the means of soil variables. Pearson's correlation coefficient and a two-tailed test were used to distinguish correlation (significantly correlated at p < 0.05 (0.05 level) and p < 0.01 (0.01 level)) and significant differences (at the 0.05 level and 0.01 level). Simple linear regression and correlation analysis were performed using OriginLab OriginPro 9.0 software (OriginLab Inc., Northampton, MA, USA) to identify the relationships between D and the selected soil properties and stand density (at the 0.05 level and 0.01 level). Data processing and plotting were also completed using OriginLab OriginPro 9.0 software.  Table 4. General information of the different density of MPPs plots. Pn is the plot number, Sd is the stand density, H is the height, DBH is the diameter at breast height, H/DBH is the ratio of diameter at breast height to height, Cd is the canopy density, and C is the canopy size. Values in the parentheses indicate standard error (n = 3).