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Article

Response of Soil Enzyme Activities to Natural Vegetation Restorations and Plantation Schemes in a Landslide-Prone Region

by
Donglei Guo
1,2,
Yansheng Ou
1,2,
Xiaohe Zhou
2,
Xia Wang
2,
Yunfei Zhao
2,*,
Jia Li
2,
Jinjin Xiao
2,
Zhiguo Hao
2 and
Kaichang Wang
1,2
1
Key Laboratory of Strategic Mineral Resources of the Upper Yellow River, Ministry of Natural Resources, Lanzhou 730000, China
2
Gansu Key Laboratory for Environmental Pollution Prediction and Control, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
*
Author to whom correspondence should be addressed.
Forests 2022, 13(6), 880; https://doi.org/10.3390/f13060880
Submission received: 4 May 2022 / Revised: 29 May 2022 / Accepted: 1 June 2022 / Published: 5 June 2022
(This article belongs to the Section Forest Soil)
Browse Figures
Versions Notes

Abstract

:
Soil enzyme activities in different plantation types and land use patterns could indicate changes in soil quality. This research was aimed at exploring the dynamics of soil enzyme activities involved in carbon, nitrogen and phosphorus cycling, and their responses to changes in soil physicochemical properties resulting from natural vegetation restorations and plantation schemes. Knowing about the effects of soil physicochemical properties on soil enzyme patterns is crucial for understanding ecosystem functions and processes. The study selected four main land-use types (natural forestland, natural grassland, artificial forestland, and artificial grassland) and one control plot (bare land) in the West Qinling Mountains, China, which is a typical landslide region. We collected the soil samples from each land use type and tested their physicochemical properties and enzyme activities compared with control land. The results showed that both natural vegetation restoration and artificial plantation schemes have significant effects on enzyme activities. Soil physicochemical properties explained 92.2% of the variation in soil enzyme activities for natural vegetation restoration, while it only explained 77.8% of the variation in soil enzyme activities for plantation schemes. Furthermore, natural vegetation had a greater effect than the plantation schemes on soil enzyme activities.

1. Introduction

Vegetation restoration is the cheapest method to improve soil quality, and its additional benefits are of increasing carbon stocks [1]. Plant roots and leaf litter are the primary source of soil organic matter (SOM), which provides the main nutrients for the soil decomposers [2,3]. Differences in the amount and composition of above- and below-ground litter and root activity might result in differences in carbon inputs to soil. The quality and quantity of carbon inputs, litter fall, and root exudates from different vegetation types directly affect soil properties [4].
Soil enzymes, which have strong catalytic activities, are the main factors controlling biochemical processes, as they play important roles in SOM decomposition and soil nutrient cycling [5,6,7]. Previous studies have shown that soil enzyme activities are closely correlated with soil physicochemical properties, such as pH, soil water retention (SWR), and soil bulk density (BD) [6,8]. Human activities such as land use and fertilization can also significantly affect soil enzyme activities [9,10]. Land-use change drives variations in soil environmental conditions, which would affect the distribution and activities of soil enzymes.
Soil quality can be evaluated using a variety of physical (e.g., BD, aggregate stability), chemical (e.g., soil pH, SOM content), and biological (e.g., microbial biomass carbon and microbial enzyme activities) properties [7]. Soil physicochemical properties usually change slowly, and significant changes occur only over many years [11]. Soil enzyme activities respond more rapidly to small changes in soil conditions than do other soil properties. Therefore, soil enzyme activities are frequently used as a tool to measure the effects of different vegetative restoration strategies on soil quality [10,12,13]. Among all the soil enzymes, β-glucosidase (BG), urease (URE), and alkaline phosphatase (ALP) are associated with microbial mineralization and biogeochemical progresses and are generally used as indicators of microbial nutritional requirements in forest and grassland soils [14,15,16]. BG is involved in cellulose degradation and plays a key role in the conversion of cellulose into glucose. The product of its enzymatic hydrolysis is an energy source for microorganisms [17]. URE promotes the hydrolysis of nitrogen-containing organic matter and regulates the formation and availability of nitrogen in the soil [18,19]. ALP closely and sensitively reacts to changes in the external environment and is an indicator of organic phosphorus mineralization and biological activity in soil [19,20].
Landslide movements and vegetation restoration can greatly alter soil properties [21]. Surface disturbance and vegetation cover can change the soil conditions, while soil enzymes are mainly affected by soil physical and chemical properties. Thus, it is necessary to detect how soil nutrients affect enzyme activities under different types of vegetation cover in landslide-prone regions. To determine the relationship between soil physicochemical properties and enzyme activities, four vegetation cover land-use types (natural forestland, artificial forestland, natural grassland, and artificial grassland) were selected in the Wudu district, which is downstream of the Bailongjiang Basin and where geological disasters occur frequently. The objectives of this research were: (1) to investigate the changes in soil properties and soil enzyme activities under different types of vegetation restoration; (2) to explore which individual soil properties control the activities of soil enzymes; (3) to reveal the relationship between soil nutrients and enzyme activities.

2. Materials and Methods

2.1. Study Area

The Bailongjiang watershed, located in southern Gansu Province, is one of the most landslide-prone regions in the West Qinling Mountains, China. The special natural geological environment creates conditions in which geological disasters can occur, and such events threaten human lives and property. The local government undertakes a large number of vegetation protection measures every year to reduce soil erosion and prevent the occurrence of geological disasters. Up to 3051.68 ha of artificial forest was planted by the end of 2017, and more than 30% of artificial forest was ecological forest (http://www.longnan.gov.cn/, 2017 (accessed on 15 June 2019)).
The region has a subtropical semi-humid climate with an annual average temperature of 14.7 °C, which ranges from approximately 3.2 °C in January to 24.5 °C in July. Annual precipitation is 700–900 mm, of which approximately 50% falls between July and September. According to the classification system of the Food and Agriculture Organization (IUSS Working Group-FAO, 2006), the soil is an Alfisol, and the surface lithology consists of phyllite and limestone. The major cultivated plant species are Olea europaea, Zanthoxylum, Robinia pseudoacacia and Pinus tabulaeformis.

2.2. Experimental Design and Soil Sampling

Sampling was carried out in July and August of 2017, during the peak period for plant growth. The site selected for study was 3.2 km in length with an area of 2.8 km2 on a north-facing slope inclined at 20°. Four plots (10 m × 10 m) with different vegetation types (i.e., natural forestland, natural grassland, artificial forestland, and artificial grassland) and one area without vegetation (bare-land control) were selected for analyses on this site (Table 1). Five subplots (1 m × 1 m) were randomly selected within each plot. In each subplot, we collected soil samples using cylinders (5 cm in diameter and 5 cm in height) at two soil depths (0–10 cm and 10–20 cm). Eight soil core samples were collected along an “S”-shaped curve within each subplot and then mixed into one sample. Soil samples were stored in a portable refrigerator and then brought back to the laboratory. After removing visible plant and root residues, fresh soil samples were sieved through a <2 mm mesh and divided into two portions. One portion was air-dried for the determination of physicochemical properties, and the other portion was stored at 4 °C for the measurement of enzyme activities. All analyses were carried out in triplicate.

2.3. Laboratory Analyses

Soil pH was measured at 1:2.5 (soil/water); soil BD was obtained using the cutting ring method; and SWR was calculated with Equation (1). Soil organic carbon (SOC) was analyzed by the H2SO4-K2Cr2O7 oxidation method, total nitrogen (TN) was analyzed by the Kjeldahl method, and total phosphorus (TP) was determined colorimetrically after wet digestion of samples with H2SO4 + HClO4. Available nitrogen (AN) was determined with the micro-diffusion technique after samples were subjected to alkaline hydrolysis, available phosphorus (AP) was determined by the Olsen method, and available potassium (AK) was measured in 1 mol/L NH4OAc extracts by flame photometry. All the methods used for determining soil physicochemical properties referred to [22].
SWR = 0.003075(Sa) + 0.005886(Si) + 0.008039(Cl) + 0.002208(SOM) − 0.14340(BD)
where Sa, Si and Cl are the percentages of sand, silt and clay, respectively, in the soil [23].
The BG (EC 3.3.1.21), URE (EC 3.5.1.5), and ALP (EC 3.1.6.1) activities were quantified by colorimetrically detecting products released during incubation of the sample with an appropriate substrate under standard conditions. BG activity was measured using p-nitrophenyl-β-D-glucopyranoside (pNPG) as the substrate. After incubation at 37 °C for 1 h, the product, p-nitrophenol was detected by measuring absorbance at 410 nm [24]. URE activity was measured using urea as the substrate. After incubation at 37 °C for 24 h, the amount of NH4+ was determined by measuring absorbance at 578 nm [25]. ALP activity was measured using p-nitrophenyl phosphate (pNPP) as the substrate. After incubation at 37 °C for 1 h, the amount of p-nitrophenol was determined by measuring absorbance at 410 nm [25]. All enzyme activities are expressed as μg product per g oven-dry weight of sample per unit time.

2.4. Statistical Analyses

All results are reported as mean ± standard deviation. One-way analysis of variance (ANOVA), followed by the Duncan’s test, was used to evaluate significant differences at p < 0.05. The samples used to measure soil physicochemical properties and soil enzyme activities were taken from one plot only, therefore, there was no true replication (pseudoreplication). Because the main plots were large, it was not possible to have fully replicated treatments, and pseudoreplication involving sub-units within a uniform larger area was the only practical way to investigate the area. A redundancy analysis (RDA) was performed to evaluate the associations between enzyme activities and soil physicochemical properties.

3. Results

3.1. Differences in Soil Physicochemical Properties among Different Vegetation Covers

The soil under all types of vegetation cover was weakly alkaline, with pH values ranging from 7.26 to 7.92, lower than that in the control soil (8.11; Table 2). The mean pH was higher under APR land (7.64) than under NVR land (7.41). The range of BD under natural and artificial vegetation cover was 1.14–1.21 g/cm3 and 1.19–1.26 g/cm3, respectively. The SWR in NVR land ranged from 21.14% to 26.15%, markedly higher than the range in APR land (9.87%–11.06%). The results indicated that soil water retentions were significantly higher under vegetation cover than in bare land. The SOC content was 33.70 g/kg in natural forestland, 15.07 g/kg in artificial forestland, 19.56 g/kg in natural grassland, and 9.98 g/kg in artificial grassland, in comparison with only 2.27 g/kg in bare land. The pattern of TN was similar to that of SOC: 1.99–3.29 g/kg in NVR land; 1.19–1.51 g/kg in APR land; and only 0.51 g/kg in bare land. The TP did not differ significantly among the different types of vegetation cover (range, 0.62–0.70 g/kg), but was higher under vegetation of any type than in bare land (0.46 g/kg). Among the four vegetation cover types, natural forestland had the highest AN and AP contents (97.96 and 70.88 g/kg, respectively), artificial grassland had the lowest AN content (54.66 g/kg), and artificial forestland and grassland had the lowest AP contents (49.97 and 51.09 g/kg, respectively). The AK content was highest in natural grassland (117.07 g/kg), followed by natural forestland and artificial forestland (109.23 and 106.67 g/kg, respectively), and lowest in artificial grassland (85.82 g/kg).

3.2. Differences in Soil Enzyme Activities under Different Vegetation Covers

The soil enzyme activities varied among the different types of vegetation cover (Figure 1). The activities of BG and URE differed significantly (p < 0.05) among the four vegetation cover types. The highest BG and URE activities were in natural forestland (10.97 mg/kg pNP/h and 19.25 mg/kg NH4+/h, respectively), followed by natural grassland (6.87 mg/kg pNP/h and 14.21 mg/kg NH4+/h, respectively), artificial forestland (5.22 mg/kg pNP/h and 10.55 mg/kg NH4+/h, respectively), and then artificial grassland (3.31 mg/kg pNP/h and 1.75 mg/kg NH4+/h, respectively). The activities of ALP ranged from 40.69 (artificial forestland) to 70.62 (natural forestland) mg/kg pNP/h; ALP activities differed significantly (p < 0.05) between NVR land and APR land.

3.3. Relationships between Soil Properties and Enzyme Activities

RDA is an extension of multiple linear regressions and can be used to analyze the relationship between soil physicochemical properties and soil enzyme activities. In an RDA, the two variables are reflected in the same Cartesian coordinate system. Therefore, an RDA can intuitively reflect the relationship between soil physicochemical properties and soil enzyme activities [26]. The results of RDAs relating enzyme activities to different restoration types are shown in Table 3 and Table 4. There were corresponding correlations between soil enzyme activity and soil physicochemical properties. The first components of the RDA axes explained 92.2% of the variance in soil enzyme activities in NVR land, indicating that 92.2% of the variation in soil enzyme activities in NVR land was explained by soil physicochemical properties. For APR land, 77.8% of the variance in soil enzyme activities was explained by the first components of the RDA axes (Table 4), indicating that differences in soil physicochemical properties explained 77.8% of the variance in soil enzyme activities in APR land.
The first two axes accurately represented the relationship between soil enzyme activities and soil physicochemical properties. Therefore, a tri-plot of two axes of the RDA was constructed to further explore this relationship. In Figure 2a,b, the length of the arrow line and the cosine value of the included angle represent the degree of influence of a certain physicochemical factor on soil enzyme activity. The longest arrow lines in Figure 2a were for pH, SOC, and TN, indicating that these three factors had the greatest influence on soil activities in NVR land. There were significant positive correlations between soil enzyme activities and all soil nutrients except for AK, and negative correlations between soil enzyme activities and pH and BD. In Figure 2b, the longest arrow line was for AP, suggesting that AP was the dominant factor influencing soil enzyme activities in APR land. BG and URE had significant positive relationships with TN, SOC, and AK; negative relationships with pH and BD; and no relationship with AP (Figure 2). ALP had a significant positive relationship with AP, and a negative relationship with BD. All three enzyme activities were significantly positively correlated with AN and TP.
A partial Monte Carlo test was performed to analyze the contribution of soil physicochemical properties to variations in soil enzyme activities. The soil physicochemical factors were ranked, from strongest to weakest influence on soil enzyme activities, as follows: pH > SOC > TN > AN > TP > AP > BD >SWR > AK in NVR land; and AP > AN > SWR> TP > BD > AK > TN > pH > SOC in APR land (Table 5 and Table 6). The effects of soil physicochemical properties on enzyme activities were significant (p < 0.01), with pH, SOC, TN, AN, TP, and AP explaining 84.9%, 83.8%, 81.3%, 71.5%, 57.9%, and 48.8% of the variation, respectively, in soil enzyme activities in NVR land; and AP, AN, and SWR explaining 64.2%, 37.4%, and 31.7% of the variation, respectively, in soil enzyme activities in APR land.

4. Discussion

4.1. Soil Physicochemical Properties

The soil pH values were lower under all types of vegetation cover than in the control (bare land), consistent with the results of a previous study [11]. The low soil pH under vegetated land was related to the presence of acidic exudates secreted by plant roots and carbon dioxide produced by root respiration [27]. Soil BD is one of the indexes of soil mechanical resistance. It affects the distribution of soil porosity and also affects soil permeability and hydraulic conductivity. The soil BD was negatively correlated with SOC content in NVR land and APR land. A low BD can be attributed to a high SOC content in the soil [28], which increases the water holding capacity, thereby increasing SWR [29]. The SWR positively affects the growth and development of plants, and vegetation is the most important factor affecting SWR [30]. The SWR was higher in forestland than that in grassland under two restoration types, because trees had greater water holding capacity than did grasses, and this regulated the conditions.
Vegetation cover and land use have significant impacts on nutrient dynamics through organic matter inputs and decomposition. The vegetation cover determines not only the amount of organic matter inputs but also their location (as either aboveground litter deposited on the soil surface or root biomass in the subsoil) [31]. SOM, which is derived from plant residues, animal residues, and root exudates, is a crucial factor determining many soil physical attributes and chemical behaviors, such as soil aggregation, soil structure, and porosity [32]. Soil fertility is also closely connected to SOM content, the quantity and quality of which directly affect the availability of soil nutrients, including N and P [33,34], and SOC is a vital component in terms of improving soil productivity [35]. The relatively higher SOC and TN contents found in forestland than those in grassland under two restoration types were explained by the fact that trees generate more plant litter and root exudates than does herbage, leading to greater microbial biomass and increased SOC and TN contents in the soil [1]. The TP did not differ significantly among the four vegetation cover types because of its weak mobility, and it is always considered as a limiting factor for plant growth [36]. Available nutrients are produced by microbial decomposition of SOM and are readily taken up by plants. Organic forms of N, P and K are the major nutrient and energy sources for plants, and are involved in plant metabolism, starch synthesis, nitrate reduction, and sugar degradation [37].

4.2. Soil Enzyme Activities

Soil factors, such as pH, BD, SWR, SOC, and the contents of N, P, and other nutrients, show close relationships with the abundance and activities of microorganisms. Changes in these soil environmental factors can significantly affect the characteristics and activity of the microbial community by affecting soil physicochemical properties, thereby influencing soil enzyme activities [26,38]. Soil pH is a major factor affecting soil enzyme activities [39], because different soil enzymes have different pH optima for activity [40]. Previous studies have shown that pH is a dominant factor controlling enzyme activities both in forest and grassland soils [41,42]. In our study, soil pH was negatively correlated with soil enzyme activities, which contrasted with the findings of [40]. These different findings might be related to the scale of pH ranges in the different studies. In the present study, the soil pH range was 7.2–7.9, which was much higher than that in the study of Xu et al., (pH 4.0–8.5) [40]. As demonstrated by our results, the pH in NVR land is affected by organic acid inputs from the trees and grasses, and these acids affect the decomposition and mineralization process of SOM, which affects nutrient acquisition by microorganisms [43]. Soil BD reflects the degree of soil looseness and was negatively correlated with soil enzyme activities in our study, consistent with the results of [40]. The SWR was reported to be another major factor affecting soil enzyme activities [8]. In our study, SWR was significantly positively correlated with soil enzyme activities. A high SWR could increase the availability of nutrient resources to meet the physiological requirements of microbes, thereby driving increases in soil enzyme activities [44]. Enzymes are important soil components involved in soil nutrient transformation kinetics [11], and they regulate the processes of nutrient release from SOM [45].
Soil enzyme activity is a good indicator of microbial responses to changes in soil nutrient contents [1,46]. The results of the RDA shown in Table 5 and Table 6 demonstrated the close relationships between soil nutrients and soil enzyme activities. We found that soil enzyme activities were positively correlated with SOC content, consistent with the results of [10]. Organic carbon inputs improve soil enzyme activities in several ways: 1) it acts as a substrate and stimulates enzyme release; 2) it increases microbial biomass and activity; and 3) it binds to enzymes to form humus–protein complexes that protect enzymes [5,10]. We also found that TN, AN, TP, and AP affected the activities of soil enzymes, similar to the findings of [26]. The activities of BG and URE were most sensitive to TN and SOC, and ALP showed a significant positive relationship with AP. These were the most abundant nutrients in SOM; therefore, we consider that BG, URE, and ALP activities could be good indicators of soil C, N, and P dynamics. Allison et al. [47] reported that high enzyme activity indicates nutritional limitation, and a pattern of increasing enzyme activity with decreasing nutrient availability is sometimes found in soil. In our study, we found that soil enzyme activities had positive relationships with nutrient contents, consistent with the conclusion of Rodríguez-Loinaz et al. [2]. Soil enzymes decompose SOM to release specific nutrients, and these nutrients can act as substrates for enzymatic reactions. Meanwhile, the activities of these enzymes affect the supply and availability of corresponding nutrients in the soil, further affecting soil fertility [48].
APR and NVR are the two main types of vegetation restoration. The nutrient contents in soils are higher under NVR land than under APR land because human activities have caused fewer disturbances to the soil under NVR land. Fewer soil disturbances reduce nutrient loss and returns more plant and animal residues to the soil to increase SOM inputs [49]. We detected significant differences in enzyme activities among the different types of vegetation cover in this study. The different types of vegetation cover had different degrees of surface coverage, which affected the soil environmental conditions. The four types of vegetation cover might have produced different types and amounts of above- and below-ground litter, and may also have affected the below-ground microclimate via other mechanisms such as root activities [14]. This would undoubtedly change soil physicochemical properties, and directly influence enzyme activities [50].

5. Conclusions

The results showed that both natural vegetation restoration and artificial plantation reclamation have significant effects on soil physicochemical properties and enzyme activities. On the basis of the RDA results, the soil factors could be ranked from strongest to weakest influence on soil enzyme activities, as follows: pH > SOC > TN > AN > TP > AP > BD > SWR > AK in NVR land; and AP > AN > SWR > TP > BD > AK > TN > pH > SOC in APR land. Human activities can lead to alterations in soil physical and chemical properties and biological communities, indirectly affecting enzyme activity, so the main drivers of enzymatic activity in NVR and APR land show differences. The study concluded that soil enzyme activities can be used as an index to evaluate soil quality and can provide information about processes of soil nutrient cycling and the effects of vegetation restoration. Natural vegetation had a greater effect than the artificial plantation on soil enzyme activities in semi-humid regions.

Author Contributions

Conceptualization, D.G., Y.O. and Y.Z.; validation, X.Z. and J.L.; formal analysis, Y.O. and D.G.; investigation, X.W., Y.Z., J.X., Z.H. and K.W.; writing—original draft preparation, D.G. and Y.O.; writing—review and editing, D.G., X.Z., X.W. and Y.Z.; visualization, Y.O. and Y.Z.; supervision, Y.Z.; funding acquisition, D.G. and X.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was provided by the key project of Key Laboratory of Strategic Mineral Resources of the Upper Yellow River, MNR (GCZH2021143), the National Natural Science Foundation of China grant (NSFC 41971051) and the Second Tibetan Plateau Scientific Expedition and Research (2019QZKK0603; 2021QZKK0201).

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

NVRnatural vegetation restoration
APRartificial plantation reclamation
NFLnatural forestland
NGLnatural grassland
AFLartificial forestland
AGLartificial grassland
BDbulk density
SWRsoil water retention
SOCsoil organic carbon
TNtotal nitrogen
TPtotal phosphorus
ANavailable nitrogen
APavailable phosphorus
AKavailable potassium
BGβ-glucosidase
UREurease
ALPalkaline phosphatase

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Forests 13 00880 g001 550
Figure 1. Soil enzyme activities under different vegetation restoration: (a) β- glucosidase, (b) urease, (c) alkaline phosphatase. The abbreviations of different vegetation restoration sites are as follows: NFL, natural forestland; NGL, natural grassland; AFL, artificial forestland; AGL, artificial grassland; BL, bare land. Solid circles indicate the median, hollow circles indicate the measured value (n = 15 in four vegetation restoration sites and n = 5 in bare land). The line segments at the top and bottom represent the maximum and minimum values of the data, respectively.
Figure 1. Soil enzyme activities under different vegetation restoration: (a) β- glucosidase, (b) urease, (c) alkaline phosphatase. The abbreviations of different vegetation restoration sites are as follows: NFL, natural forestland; NGL, natural grassland; AFL, artificial forestland; AGL, artificial grassland; BL, bare land. Solid circles indicate the median, hollow circles indicate the measured value (n = 15 in four vegetation restoration sites and n = 5 in bare land). The line segments at the top and bottom represent the maximum and minimum values of the data, respectively.
Forests 13 00880 g001
Forests 13 00880 g002 550
Figure 2. Redundancy analyses (RDA) of soil physicochemical properties and soil enzyme activities in (a) NVR land, (b) APR land. In (a), 1–15 represent the sample sites of natural forestland, 16–30 represent the sample sites of natural grassland. In (b), 1–15 represent the sample sites of artificial forestland, 16–30 represent the sample sites of artificial grassland. The length of the arrow line and the cosine value of the included angle represent the degree of influence of a certain physicochemical factor on soil enzyme activity. BG, β-glucosidase; URE, urease; ALP, alkaline phosphatase; BD, bulk density; SWR, soil water retention; SOC, soil organic carbon; TN, total nitrogen; TP, total phosphorus; AN, available nitrogen; AP, available phosphorus; AK, available potassium.
Figure 2. Redundancy analyses (RDA) of soil physicochemical properties and soil enzyme activities in (a) NVR land, (b) APR land. In (a), 1–15 represent the sample sites of natural forestland, 16–30 represent the sample sites of natural grassland. In (b), 1–15 represent the sample sites of artificial forestland, 16–30 represent the sample sites of artificial grassland. The length of the arrow line and the cosine value of the included angle represent the degree of influence of a certain physicochemical factor on soil enzyme activity. BG, β-glucosidase; URE, urease; ALP, alkaline phosphatase; BD, bulk density; SWR, soil water retention; SOC, soil organic carbon; TN, total nitrogen; TP, total phosphorus; AN, available nitrogen; AP, available phosphorus; AK, available potassium.
Forests 13 00880 g002
Table
Table 1. Description of sampling sites.
Table 1. Description of sampling sites.
Land UseDominant VegetationLocationAltitude (m)Slope (o)Above-Ground Biomass (t ha−1)
Natural forestlandRobinia pseudoacacia, PopulusN 34°36′50″
E 105°42′52″		13802432.41
Natural grasslandPotentilla chinensis, Conyza CanadensisN 34°35′42″
E 105°43′38″		13421417.42
Artificial forestlandPinus tabulaeformis, Platycladus orientalisN 33°23′40″
E 104°49′08″		11722228.34
Artificial grasslandDigitaria sanguinalis, Cynodon dactylonN 33°24′33″
E 104°48′47″		10241615.24
Bare land—N 33°22′54″
E 104°48′49″		136818—
Table
Table 2. Soil physicochemical properties under different vegetation restoration types (N = 15).
Table 2. Soil physicochemical properties under different vegetation restoration types (N = 15).
NVRAPRBare LandFp
Forestland GrasslandForestland Grassland
pH7.26 ± 0.09 e7.55 ± 0.09 c7.35 ± 0.05 d7.92 ± 0.05 b8.11 ± 0.03 a266.708<0.001
BD (g cm−3)1.14 ± 0.05 d1.21 ± 0.02 c1.19 ± 0.01 c1.26 ± 0.04 b1.35 ± 0.02 a54.229<0.001
SWR (%)26.15 ± 2.64 a21.14 ± 2.21 b11.06 ± 0.97 c9.87 ± 1.60 c3.65 ± 0.10 d253.543<0.001
SOC (g kg−1)33.70 ± 3.14 a19.56 ± 1.19 b15.07 ± 1.14 c9.98 ± 1.96 d2.27 ± 0.10 e395.046<0.001
TN (g kg−1)3.29 ± 0.30 a1.99 ± 0.15 b1.51 ± 0.05 c1.19 ± 0.10 d0.51 ± 0.04 e419.200<0.001
TP (g kg−1)0.70 ± 0.04 a0.64 ± 0.02 a0.66 ± 0.02 a0.62 ± 0.04 a0.46 ± 0.01 a66.475<0.001
AN (mg kg−1)97. 96 ± 9.61 a82.22 ± 3.29 b62.63 ± 5.11 c54.66 ± 7.04 d16.40 ± 0.96 e195.493<0.001
AP (mg kg−1)70.88 ± 6.12 a64.42 ± 2.42 b49.97 ± 3.94 c51.09 ± 9.39 c10.95 ± 0.33 d112.281<0.001
AK (mg kg−1)109.23 ± 10.98 b117.07 ± 6.06 a106.67 ± 7.13 b85.82 ± 8.41 c38.94 ± 2.46 d106.795<0.001
Note: BD: bulk density; SWR: soil water retention; SOC: soil organic carbon; TN: total nitrogen; TP: total phosphorus; AN: available nitrogen; AP: available phosphorus; AK: available potassium. F test and p value are from the ANOVAs by SPSS 19.0. Different lowercase letters indicate significant differences (p < 0.05) between land use types.
Table
Table 3. Correlation of soil physicochemical factor with the axes by redundancy analysis (RDA) in NVR land.
Table 3. Correlation of soil physicochemical factor with the axes by redundancy analysis (RDA) in NVR land.
Axes ⅠAxes ⅡAxes ⅢAxes Ⅳ
pH−0.9592−0.14920.02320.0000
BD−0.6389−0.31930.13530.0000
SWR0.60190.5094−0.19260.0000
SOC0.9531−0.02900.15820.0000
TN0.9388−0.03560.28390.0000
TP0.7919−0.05490.34450.0000
AN0.8777−0.32930.26270.0000
AP0.7236−0.35100.38970.0000
AK−0.1368−0.83350.06740.000
Eigen values0.9220.0350.0030.025
Explained variation (%)92.295.796.098.6
Table
Table 4. Correlation of soil physicochemical factor with the axes by redundancy analysis (RDA) in APR land.
Table 4. Correlation of soil physicochemical factor with the axes by redundancy analysis (RDA) in APR land.
Axes ⅠAxes ⅡAxes ⅢAxes Ⅳ
pH0.1449−0.9461−0.25610.0000
BD−0.4284−0.7651−0.38130.0000
SWR0.60970.44620.20540.0000
SOC0.22660.84530.46170.0000
TN0.21480.92450.25020.0000
TP0.49940.52870.46960.0000
AN0.63670.65180.02790.0000
AP0.90800.03020.11390.0000
AK0.28380.83510.13510.0000
Eigen values0.7780.1380.0060.071
Explained variation (%)77.891.692.199.2
Table
Table 5. Importance and significance level of soil physicochemical properties in NVR land.
Table 5. Importance and significance level of soil physicochemical properties in NVR land.
Importance RankingSoil Physicochemical PropertiesExplanation of Environmental Factor/%Fp
1pH84.9158.00.002
2SOC83.8145.00.002
3TN81.3122.00.002
4AN71.570.10.002
5TP57.938.50.002
6AP48.826.70.002
7BD38.017.20.004
8SWR34.314.60.002
9AK4.11.20.288
Table
Table 6. Importance and significance level of soil physicochemical properties in APR land.
Table 6. Importance and significance level of soil physicochemical properties in APR land.
Importance RankingSoil Physicochemical PropertiesExplanation of Environmental Factor/%Fp
1AP64.250.10.002
2AN37.416.70.002
3SWR31.713.00.002
4TP23.48.50.004
5BD22.48.10.006
6AK15.95.30.020
7TN15.45.10.034
8pH14.04.60.036
9SOC13.94.50.030
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Guo, D.; Ou, Y.; Zhou, X.; Wang, X.; Zhao, Y.; Li, J.; Xiao, J.; Hao, Z.; Wang, K. Response of Soil Enzyme Activities to Natural Vegetation Restorations and Plantation Schemes in a Landslide-Prone Region. Forests 2022, 13, 880. https://doi.org/10.3390/f13060880

AMA Style

Guo D, Ou Y, Zhou X, Wang X, Zhao Y, Li J, Xiao J, Hao Z, Wang K. Response of Soil Enzyme Activities to Natural Vegetation Restorations and Plantation Schemes in a Landslide-Prone Region. Forests. 2022; 13(6):880. https://doi.org/10.3390/f13060880

Chicago/Turabian Style

Guo, Donglei, Yansheng Ou, Xiaohe Zhou, Xia Wang, Yunfei Zhao, Jia Li, Jinjin Xiao, Zhiguo Hao, and Kaichang Wang. 2022. "Response of Soil Enzyme Activities to Natural Vegetation Restorations and Plantation Schemes in a Landslide-Prone Region" Forests 13, no. 6: 880. https://doi.org/10.3390/f13060880

APA Style

Guo, D., Ou, Y., Zhou, X., Wang, X., Zhao, Y., Li, J., Xiao, J., Hao, Z., & Wang, K. (2022). Response of Soil Enzyme Activities to Natural Vegetation Restorations and Plantation Schemes in a Landslide-Prone Region. Forests, 13(6), 880. https://doi.org/10.3390/f13060880

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