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Article

Effects of Artificial Restoration and Natural Recovery on Plant Communities and Soil Properties across Different Temporal Gradients after Landslides

by
Sibo Chen
†,
Jinguo Hua
†,
Wanting Liu
,
Siyu Yang
,
Xiaoqi Wang
and
Wenli Ji
*
College of Landscape Architecture and Arts, Northwest A&F University, Xianyang 712100, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Forests 2023, 14(10), 1974; https://doi.org/10.3390/f14101974
Submission received: 19 August 2023 / Revised: 20 September 2023 / Accepted: 22 September 2023 / Published: 28 September 2023
(This article belongs to the Section Forest Ecology and Management)
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Versions Notes

Abstract

:
Landslides cause significant disturbances to mountainous ecosystems and human activities. Due to climate change, the frequency of landslides as secondary disasters has notably increased compared to the past. Further exploration is needed to understand the effects of different restoration methods on post-landslide plant communities and soil properties over different periods of time. In this regard, we selected Lantian County in the northern foothills of the Qinling Mountains as our study area. We conducted surveys on artificially restored and naturally recovered plots at 1, 6, and 11 years after landslide events. Undamaged areas were chosen nearby as control plots. We identified vegetation types and species diversity after artificial and natural recovery and further analyzed the impact of different restoration strategies on vegetation patterns and soil properties. The research results indicate that, compared with natural recovery, artificial restoration can more quickly improve vegetation and soil. With the increasing time gradient, the average ground cover of the herbaceous layer in natural recovery decreased gradually from 47% at year one to 34% at year eleven. In contrast, in artificial restoration, the average ground cover of the herbaceous layer increased from 27% at year one to 44% at year eleven. For the shrub layer, in natural recovery, the average ground cover gradually increased to 39% over eleven years. While in artificial restoration, the average ground cover for the shrub layer gradually increased to 46% over the same period. In the artificial restoration plots, soil pH gradually increased (from 6.2 to 8.2), while TN content gradually decreased (from 1.7 g/kg to 0.9 g/kg). Similarly, TK content decreased (from 22.4 g/kg to 14.5 g/kg), and AP content showed a decreasing trend (from 20.7 mg/kg to 11.4 mg/kg). In the natural recovery plots, DNA content gradually increased (from 3.2 μg/g/d to 142.6 μg/g/d), and SC content gradually increased as well (from 2.4 mg/d/g to 23.1 mg/d/g). In contrast, on sites undergoing natural recovery, the short-term restoration rates of vegetation and soil are lower, but they show greater stability over a longer time. This study provides a new perspective on vegetation restoration strategies and is expected to offer insights for the optimization of post-landslide recovery in the future.

1. Introduction

Landslides are among the most common natural disasters in mountainous regions and represent a natural geomorphic process that alters local topography and ecosystems through sudden displacements of rock masses and soil disturbance [1,2,3]. Landslides severely disrupt mountainous ecosystems, diminishing productivity, eroding the landscape, and causing soil erosion. They also lead to significant losses in human lives and economic resources [4,5,6]. On the other hand, the ecological succession following landslides is an integral component of forest succession. By disrupting plant communities and breaking the dominance of certain species over local ecosystems, landslides provide opportunities for other species to thrive, thus contributing to the maintenance of species diversity [1,7,8]. However, frequent landslides can exacerbate negative ecological impacts [9]. Recently, global warming has led to increased rainfall, resulting in a rising frequency of landslides in mountainous areas [10,11]. Consequently, selecting appropriate ecological restoration strategies after landslides has become a crucial issue.
Ecological restoration refers to the process of repairing damaged ecosystems, aiming to bring them back to their original state before the disturbance [12,13]. Ecological restoration can be categorized into natural recovery and artificial intervention [14]. Artificial intervention involves human-led ecosystem reconstruction [15]. In the context of post-landslide restoration, artificially restored vegetation often exhibits higher productivity, providing increased inputs of aboveground litter and root exudates [16,17]. In comparison to natural recovery, the vegetation under natural recovery may require a longer period of succession to restore the pre-landslide state [18]. For example, Peng et al conducted an analysis of the plant composition, vegetation structure, and plant functional types in areas affected by seven landslides in the Mediterranean region from 1996 to 2010. They found that the average plant height in areas undergoing natural recovery gradually increased from 14 cm to 131 cm over a span of 15 years [17]. In another study, Mi et al investigated the plant patterns in landslides on the Loess Plateau five years after natural recovery. They observed that the bottom and middle sections of the landslide belong to the dominant vegetation zones. The Margalef index values from the bottom to the top were 3.26, 3.91, and 2.17; the Shannon-Wiener index values were 2.56, 2.60, and 2.13; and the Pielou index values were 0.55, 0.56, and 0.46, respectively [6].
Some scholars argue that artificial restoration can rapidly increase vegetation cover on landslide-affected sites and effectively control soil structure and quality, thus enhancing biodiversity [14,19,20]. However, other researchers suggest that artificial restoration may have certain negative consequences. For instance, artificial forests could exacerbate soil moisture and nutrient loss, failing to effectively improve soil quality and structure [21,22]. Moreover, artificial restoration can incur high costs, demanding substantial human and financial resources from local administrative departments [6]. Natural recovery is the spontaneous process of vegetation restoration without human involvement [23]. This process is widely applicable and holds advantages in enhancing soil stability while incorporating greater amounts of organic matter during restoration [6,22,24]. Furthermore, natural recovery usually avoids species invasion, and the post-restoration community is primarily composed of local species. The unique succession patterns also contribute to greater species diversity [1,8].
Different types of vegetation restoration can influence indicators such as plant litter, canopy, biomass, and soil moisture, thereby exerting varying effects on soil properties [25]. Due to the intricate interactions between plants and microorganisms, vegetation can directly or indirectly alter soil properties and maintain soil fertility [26,27], thus effectively impacting and controlling the restoration of landslide-prone areas [28,29]. Wang et al. (2017) highlighted in their study that vegetation is widely used for slope reinforcement to control shallow landslides because plant root systems can increase soil shear strength by anchoring soil layers and altering hydrological characteristics. They found that tree roots provide significant additional cohesion to stabilize landslides, both under static and dynamic stresses [30].
Favorable soil structure enhances the retention and transport of fluids and organic and inorganic substances, supporting robust growth and development of vegetation roots [31]. It also helps prevent shallow landslides and other types of soil degradation [32,33]. In Wang et al.’s research, it was discovered that, compared to bare land, disaster-affected sites, under natural vegetation and planting conditions, showed a significant increase in soil total porosity, water-holding capacity, and infiltration rate, along with a significant decrease in bulk density after 8 years. Additionally, natural vegetation had a greater impact on soil structure aggregation and total porosity compared to artificial methods [22]. Blonska et al investigated changes in soil organic carbon and soil biochemical characteristics seven years after a landslide. They determined that soil physical, chemical, and biological properties in landslide areas exhibited higher diversity, with an increase in soil microbial biomass during the recovery of landslide-affected soil [34]. However, the process of vegetation succession is relatively lengthy, and at different stages, vegetation characteristics vary, leading to considerable differences in soil properties. These differences may evolve over time due to factors such as climate and environmental changes [35,36,37]. Yet, there is limited research comparing the vegetation patterns and soil properties of artificial restoration and natural recovery at different time gradients following landslides. Such studies are crucial for revealing the mechanisms underlying ecological restoration.
This study focuses on landslide-disturbed sites in Lantian County, investigating the vegetation community characteristics and soil properties one year, six years, and eleven years after landslides. By employing various analytical methods, a comparison is made between artificial restoration and natural recovery strategies. The main objectives of this study are as follows: (1) investigate changes in plant diversity under different restoration modes to identify dominant species; (2) evaluate variations in plant characteristics at different time gradients under distinct restoration modes; (3) assess the changes in soil physical, chemical, and biological properties under different restoration strategies; and (4) analyze the changes and impacts of different restoration modes over a longer period of time. Overall, the findings of this study offer valuable insights for optimizing vegetation restoration strategies and ensuring the effectiveness and sustainability of post-landslide land restoration. These insights hold practical significance for regional ecological construction.

2. Materials and Methods

2.1. Study Area

The study area is located in Lantian County, Xi’an City, Shaanxi Province, China (33°53′44″ N–34°4′56″ N, 109°33′57″ E–109°16′48″ E) (Figure 1). Situated in the middle latitude region of China, the area falls within a temperate semi-arid to semi-humid climate zone. The average annual temperature ranges from 12.0 to 13.6 °C, with an average annual precipitation of 600 to 750 mm. The region experiences an average of 2148.8 h of sunshine per year and has an average frost-free period of 212 days. Rainfall is primarily concentrated between July and September, accounting for 55% of the total annual rainfall. The dominant bedrock consists of gravel and clay, although soil and nutrient levels may vary across different locations. The most prominent soil types in Lantian County include Greyzems, Luvisols, Acrisols, and Ferralsols. Consequently, forested and barren lands cover a significant portion, totaling 62.8% of the county’s overall area. The predominant vegetation type is a mixed coniferous and broad-leaved forest, with a coverage rate of 44.7%.

2.2. Selection of Sample Sites

Vegetation surveys and soil sampling were conducted in Lantian County in areas where landslides occurred 1, 6, and 11 years prior (slides occurred in 2021, 2016, and 2011, respectively). The study distinguished between areas subject to artificial restoration and natural recovery, encompassing a total of 6 sample areas—3 that underwent artificial restoration and 3 undergoing natural recovery. The approach to artificial restoration primarily involved local authorities constructing interceptor dams (retaining walls) in the landslide areas. Additionally, trees and grass were planted to mitigate soil erosion and restore slope stability. However, variations in restoration styles or standards may exist among different locations. Additionally, we selected 6 undamaged sample areas near each of the 6 landslide sample areas for investigation (Figure 2). These areas served as references for post-restoration expected conditions in terms of plant richness and composition. In total, we established 36 sample plots for trees, 108 for shrubs, 108 for herbaceous plants, and 108 for aboveground biomass harvest. We also set up 108 soil collection points (specific layout details can be found in Figure 2 and Section 2.3). The age, coordinates, and restoration methods of the landslide areas were initially verified by the Lantian County Natural Resources Bureau and further confirmed through interviews with local villagers. Each of the 6 landslide sample areas had an area exceeding 1000 m2. The sample areas were subdivided into three parts: upslope, mid-slope, and downslope, to comprehensively and accurately survey the plant community composition within the landslide sample areas. The survey was conducted in October 2022, when plant annual growth had reached its peak. Information and locations for each experimental site are presented in Table 1.

2.3. Data Collection

Within each plot, vegetation data were measured and sampled using plots of various sizes: 10 m × 10 m for trees, 2 m × 2 m for shrubs, and 1 m × 1 m for herbaceous plants. For each plot, 3 tree plots (1 on the upper slope, 1 on the middle slope, and 1 on the lower slope), 9 shrub plots (3 on each slope), and 9 herbaceous plots (3 on each slope) were randomly established (Figure 2). The plots were spaced at least 10 m apart to mitigate mutual interference. All plant species within the plots were documented, including functional traits such as tree height (H) in meters, diameter at breast height (DBH) in centimeters, and canopy density (%) (CD) for trees; shrub height (H) in centimeters, diameter at breast height (DBH) in millimeters, and canopy coverage (%) (COV) for shrubs; and plant height (H) and quantity (NO.) for herbaceous species. For H measurement, a measuring tape and laser rangefinder were used; DBH was measured using a caliper; and CD and COV were determined based on the Braun–Blanquet scale. Leaf sampling involved collecting 3–5 leaves per plant species. On-site measurements of wet weight (WW) were conducted, followed by laboratory measurements of dry weight (DW), leaf area (LA), and leaf thickness (LT). Furthermore, all aboveground plants within the herbaceous plots were harvested to determine the aboveground biomass (W) of the herbaceous layer.
Surface debris and litter were removed from the plots, and undisturbed soil cores were collected using a 5 cm diameter soil auger, spanning the soil depth of 0–30 cm. Efforts were made to minimize disruption to the herbaceous layer while preserving the aboveground plant parts and community coverage. The soil cores were obtained in triplicate from the upper, middle, and lower slopes of each plot, and the soil from different layers was mixed to represent the overall soil conditions of the plot. Each soil sample was divided into two parts: one was left undisturbed for on-site measurements, while the other was sealed in sample bags and transported to the laboratory for analysis and testing. pH was determined through potentiometric analysis. Soil organic matter (SOM) content (%) was measured using the potassium dichromate oxidation method. Total nitrogen (TN) content (g/kg) was assessed using the Kjeldahl method. Total phosphorus (TP) content (g/kg) was determined by the antimony anti-colorimetric method. Total potassium (TK) content (g/kg) was measured using the sodium carbonate melting method. Available phosphorus (AP) content (mg/kg) was assessed by the Antimony anti-colorimetric method. Available potassium (AK) content (mg/kg) was determined using the NH4OAc extraction flame photometer method. Alkali nitrogen (AN) content (mg/kg) was quantified through the Alkali N-proliferation method. Cation exchange capacity (CEC) content (cmol/kg) was measured using the NaOAc method. Dehydrogenase (DHA) content (μg/g/d) was determined using the TTC colorimetric method. Protease (PRO) content (μg/g/2 h at 50 °C) was determined using the Roberts copper method. Urease (UE) content (mg/d/g) was quantified through the Indophenol blue method. Phosphatase (PHO) content (mg/g/d) was assessed using the phenyl phosphate colorimetric method. Sucrase (SC) content (mg/d/g) was measured utilizing the DNS method. Soil moisture content (SMC) (%) was evaluated using the drying method. Particle size distribution (PSD) was determined using the sieving method.

2.4. Data Analysis

The Shapiro—Wilk test was initially employed to test the normality of soil and vegetation attributes in different vegetation restoration ecosystems. Subsequently, in conjunction with field survey data, the importance value (IV) index of species was computed to determine the dominant species within different communities. The species richness index ( D m ) is useful for depicting the richness of species within a community, which can reflect the degree of post-landslide community restoration. The Simpson advantage degree Index ( D S ) provides a simple numerical representation of the dominance of species within a community. The Pielou evenness index ( J ) offers a clear depiction of the distribution of individual counts among all species in a community. The Shannon–Wiener diversity index ( H ) offers a reasonable measure of species diversity within a population, carrying objective evaluative significance. The above-mentioned indices determine the appearance, functionality, and structure of tree species communities under different post-landslide restoration conditions, as outlined in Table 2. The significance of the linear regression equation was determined through an F-test, with statistical significance at p = 0.05. ANOVA analysis was used to compare the mean values of various elements in the soil. All statistical analyses were performed using IBM SPSS 22 (IBM, Armonk, NY, USA), and data were generated and plotted using Origin 2021 (Origin Lab, Hampton, MA, USA).

3. Results

3.1. Species Composition and Diversity

Through field investigations, we identified different plant species on various restoration plots and time frames. In the artificial restoration plot one year after the landslide (hereafter referred to as the A1 plot in reference to Table 1), a total of 33 plant species were recorded. The most common families included Pinaceae, Meliaceae, Caprifoliaceae, Rosaceae, Asteraceae, and Poaceae. Among these, there were 9 tree species, 4 shrub species, and 20 herbaceous species. In the natural recovery plot one year after the landslide (hereafter referred to as the N1 plot in reference to Table 1), a total of 41 plant species were identified. Prominent families included Apocynaceae, Asteraceae, Poaceae, and Equisetaceae. There were 2 tree species, 3 shrub species, and 36 herbaceous species. However, it’s worth noting that Rhus chinensis Mill. And Diospyros lotus L. were among the very few surviving trees after the landslide, and therefore their species diversity was not included in the calculated weights. Moving to the artificial restoration plot six years after the landslide (hereafter referred to as the A6 plot in reference to Table 1), a total of 41 plant species were recorded. Dominant families included Fabaceae, Meliaceae, Ulmaceae, Asteraceae, and Poaceae. Within this plot, there were 6 tree species, 6 shrub species, and 29 herbaceous species. In the natural recovery plot six years after the landslide (hereafter referred to as the N6 plot in reference to Table 1), 33 plant species were observed. Prominent families included Pinaceae, Fabaceae, Rosaceae, Lardizabalaceae, Asteraceae, and Crassulaceae. There were 4 tree species, 3 shrub species, and 26 herbaceous species. Finally, in the artificial restoration plot eleven years after the landslide (hereafter referred to as the A11 plot in reference to Table 1), a total of 60 plant species were documented. Common families included Juglandaceae, Anacardiaceae, Fabaceae, Lardizabalaceae, Asteraceae, and Lamiaceae. There were 17 tree species, 13 shrub species, and 30 herbaceous species. In the natural recovery plot eleven years after the landslide (hereafter referred to as the N11 plot in reference to Table 1), a total of 54 plant species were identified. Dominant families included Fabaceae, Meliaceae, Rosaceae, Elaeagnaceae, Asteraceae, and Caryophyllaceae. Within this plot, there were 9 tree species, 8 shrub species, and 37 herbaceous species.
The importance values and names of the plant species in each plot are provided in Table 3 (only the top 18 species are listed). These data reflect the composition and quantity of the major species within the landslide area and represent the community types and structural characteristics of vegetation during the restoration process. Moreover, these species play a significant role as dominant species. It is evident that the number of tree species in the artificial restoration plots is consistently greater than in the natural recovery plots, with this trend being particularly pronounced in the one-year restoration plots. In the latter, Rhus chinensis Mill. stands out with a dominance value of 56.369%. Shrub species show a twofold difference between A6 and N6 plots, while shrub species diversity is relatively similar across other plots. Notably, the number of herbaceous species in the natural recovery plots is generally equal to or greater than that in the artificial restoration plots. This difference is especially pronounced in the A1 and N1 plots. In the tree layer, Juglans regia L. appears frequently in the importance values of various plots, with a cumulative importance value of 72.12%. Additionally, Quercus variabilis Blume, Pinus massoniana Lamb., Cotinus coggygria var. cinereus Engl., and Quercus robur L. have also appeared twice. In the shrub layer, Elaeagnus umbellata Thunb. has a high frequency of occurrence, with a cumulative importance value of 99.084%. Akebia trifoliata (Thunb.) Koidz. and Euonymus alatus (Thunb.) Siebold have also appeared multiple times. In the herbaceous layer, Artemisia annua L. appears five times in high frequency, but the highest cumulative importance value is attributed to Artemisia argyi H. Lév. & Vaniot, accounting for 36.289%. Artemisia lavandulifolia DC., Stellaria vestita Kurz, and Setaria viridis (L.) P. Beauv. also exhibit significant dominance.
Calculations of Margalef’s richness index, Simpson’s dominance index, Pielou’s evenness index, and Shannon–Wiener diversity index were conducted for various study sites, and the results are presented in Figure 3. Across nearly all groups of values, the α-diversity of the artificially restored sites was greater than that of the naturally recovering sites, while both were slightly lower than the α-diversity of the undisturbed reference areas. However, the diversity differences among different restoration periods shows varying trends. In the restoration sites one year after the landslide, the Margalef’s richness index for both the tree and shrub layers shows significant differences compared to the references (tree layer p = 0.000 < 0.05, shrub layer p = 0.009 < 0.05); Simpson’s dominance index exhibited significant differences only in the tree layer (p = 0.009 < 0.05); Pielou’s evenness index shows significant differences only in the herbaceous layer (p = 0.000 < 0.05); while the Shannon–Wiener diversity index did not exhibit noticeable differences in the tree, shrub, and herbaceous layers compared to the references. In the restoration sites six years after the landslide, the Simpson’s dominance index for the tree layer shows more pronounced differences compared to the references (p = 0.022 < 0.05); the Margalef’s richness index (p = 0.022 < 0.05) and Shannon–Wiener diversity index (p = 0.033 < 0.05) for the shrub layer exhibited significant differences; the herbaceous layer shows some significance in Pielou’s evenness index (p = 0.011 < 0.05). In the restoration sites eleven years after the landslide, only the Pielou’s evenness index for the tree layer (p = 0.012 < 0.05) and Shannon–Wiener diversity index for the shrub layer (p = 0.032 < 0.05) show significant differences compared to the references; the remaining α-diversity indices did not exhibit significant differences (p > 0.05). Comparing the restoration progress after one year, it is evident that the trends in various indices become more similar after six and eleven years of restoration, with this phenomenon being more pronounced at the eleven-year restoration sites.

3.2. Vegetation Patterns under Different Restoration Strategies

The analysis was performed on attributes of the tree layer, including tree height, diameter at breast height, canopy density, specific leaf area, leaf thickness, and leaf length, as depicted in Figure 4. In the one-year study area, all indicators of both human-assisted and naturally recovered sites were lower than those of the reference sites. Notably, the indicators in the human-assisted restoration sites surpassed those in the natural recovery sites. Within the human-assisted restoration sites, the linear trends of canopy density and specific leaf area show significant differences from the reference sites, exhibiting a higher degree of dispersion. However, based on the distribution of all indicators, the natural recovery sites displayed a distinct tendency for each indicator to fall within a smaller range. Remarkably, the trajectory of their regression lines closely resembled those of the reference sites. In the six-year study area, apart from canopy density and specific leaf area in the natural recovery sites, the regression lines of nearly all indicators generally converged and presented an organized arrangement. Surprisingly, the canopy density and specific leaf area distributions in the human-assisted restoration sites almost coincided with those of the reference sites. Conversely, in the distribution of leaf thickness and leaf length, the range at the restoration sites consistently appeared slightly smaller than at the reference sites. In the eleven-year study area, certain indicators in the restoration sites (e.g., leaf thickness, leaf length) even exceeded those in the reference sites. With respect to the duration of restoration, tree height and diameter at breast height exhibited gradual growth trends. Comparing the one-year, six-year, and eleven-year study areas, it became evident that the advantage of human-assisted restoration methods was gradually surpassed by natural recovery.
The analysis of various vegetation features in the shrub and herbaceous layers is presented in Figure 5. In the shrub layer, the vegetation height at the reference sites consistently exceeded that at the restoration sites following the landslide. With increasing time since restoration, the frequency distribution of vegetation height in the artificially restored sites tended to approach that of the reference sites, while the frequency in the naturally recovering sites gradually surpassed that of the reference sites. The basal stem diameter of vegetation exhibited certain dominance in sites A1, AR1, and N1, with the lowest dominance observed in site A11. Canopy coverage was relatively higher at sites AR1 and N1. The specific leaf area (SLA) of vegetation was lowest at site N11 and highest at site N6. Leaf water content was relatively consistent across all sites except for site A1, where it shows a distinct pattern. In the herbaceous layer, vegetation height dominance was prominent at site N1. Over time, the vegetation height at the post-landslide restoration sites exhibited an initial increase followed by a decrease. Herbaceous plant density was most pronounced in sites A6 and AR6, while it shows no significant dominance in the one-year restoration sites. Canopy coverage in the herbaceous layer gradually increased with restoration time, performing well in the six-year and eleven-year periods. Vegetation wet weight exhibited the highest frequency in site NR1, the lowest in site N1, and the difference between vegetation wet weight in the artificially restored sites and references was consistently smaller than that in the naturally recovering sites. Vegetation dry weight frequency was higher in site A1, lower in site AR6, and exhibited relatively similar fluctuations in other sites.

3.3. Soil Properties under Different Restoration Strategies

We conducted an analysis of various soil properties, including SOM, TN, TP, TK, AP, AK, AN, CEC, DHA, PRO, UE, PHO, SC, SMC, and pH. The results are presented in Figure 6. In both the one-year and six-year restoration sites, the content of SOM, TN, and TP show a trend where the gap between the artificially restored sites and the reference sites gradually decreased with increasing restoration time, even surpassing the reference sites in some cases. Across all sites, SOM, TN, and TP content consistently exhibited differences between artificially restored and naturally recovering sites, with this disparity being particularly noticeable in the one-year restoration sites. TK content exhibited a slight decrease trend from site NR1 to AR11, with the highest content in sites A1 and AR1. The TK content in the naturally recovering sites was consistently lower than or equal to that in the reference sites. AP content exhibited a relatively uniform distribution in the six-year restoration sites, with artificially restored sites generally having higher content than naturally recovering sites in other cases. AK content shows higher levels in the artificially restored sites compared to the reference sites, while the trends in the naturally recovering sites were less consistent. AN, CEC, and DHA content in the one-year restoration sites were consistently higher in the artificially restored sites compared to the reference sites and lower in the naturally recovering sites. In the six-year and eleven-year restoration sites, artificially restored sites show higher levels compared to the reference sites. CEC and DHA content in the naturally recovering sites were consistently lower than in the reference sites. In the one-year and eleven-year restoration sites, artificially restored sites had higher PRO, UE, and PHO content compared to the reference sites, with the PHO index showing a significant increase in site AR6 compared to N6. SC content exhibited a pattern where artificially restored sites had higher levels than the reference sites, while naturally recovering sites had lower levels consistent across the one-year, six-year, and eleven-year sites. SMC exhibited a more uniform distribution from site A1 to AR6, while sites N6 to NR11 show a varied pattern. In the one-year and six-year restoration sites, naturally recovering sites generally had proportions similar to the reference sites. The pH values in the naturally recovering sites were almost identical to the reference sites, while the pH values in the artificially restored sites were consistently close to or higher than the reference sites.
The analysis of differences in soil mineral particle size, mineralogical composition, and properties is presented in Figure 7. It is evident that with increasing time since the landslide, soil particles tend to become more sandy (0.100~2.000 mm). At the one-year restoration sites, the distribution of soil particles was generally similar. Among these, site N1 exhibits a trend of decreasing soil particle size, with the range of 0.500~2.000 mm representing the smallest value in this category, while the range of 0.002~0.020 mm had the highest soil particle content. In the six-year restoration sites, the changes in sites A6 and AR6, as well as N6 and NR6, were quite similar. In A6 and R6, which are the artificially restored sites, the distribution of smaller-diameter soil particles (<0.050 mm) was more widespread. Conversely, in N6 and NR6, which are the naturally recovering sites, the distribution of larger-diameter soil particles (>0.100 mm) was more pronounced. In the eleven-year restoration sites, it is apparent that the content of soil particles across different size fractions did not exhibit significant differences. However, the smaller soil particles (<0.020 mm) in both the artificially and naturally restored sites were greater than in the reference sites, while the larger soil particles (>0.250 mm) were slightly lower than in the reference sites.

4. Discussion

The composition, quantity, characteristics, and diversity of plant communities serve as crucial indicators of the complexity of community structure and function and also reflect the extent of vegetation restoration in damaged ecosystems [38]. The results of this study indicate that in the primary tree layer of the landslide site in Lantian County, apart from the artificially planted species such as Toona sinensis (Juss.) Roem., Zanthoxylum bungeanum Maxim., Gleditsia sinensis Lam., Pinus bungeana Zucc. ex Endl., and Pinus massoniana Lamb., there are also other rapidly growing tree species such as Rhus chinensis Mill., Diospyros lotus L., and Juglans regia L., which are adapted to impoverished conditions. In the shrub layer, species like Lonicera caerulea L., Elaeagnus umbellata Thunb., Spiraea × vanhouttei (Briot) Carrière, Berberis feddeana C. K. Schneid., and Rosa multiflora Thunb. are prevalent. The herbaceous layer comprises species like Artemisia lavandulifolia DC., Equisetum arvense L., Carex breviculmis R. Br., Echinochloa crus-galli (L.) P. Beauv., Elsholtzia ciliata (Thunb.) Hyl., and Artemisia argyi H. Lév. & Vaniot. Dominant families include Fabaceae, Pinaceae, Meliaceae, Juglandaceae, Rosaceae, Asteraceae, and Poaceae. While there is some overlap in species composition across different sites, the α-diversity in the artificially restored sites remains generally higher than that in the naturally recovering sites. This is particularly evident in the short period following the landslide, where the natural system is limited in its ability to rapidly reestablish species diversity. In contrast, human intervention can employ various tree planting techniques to create diverse landscapes at the restoration sites, aligning with predetermined objectives within a limited timeframe. However, over time, the advantage of α-diversity in artificially restored sites gradually diminishes until it reaches a state similar to that of natural recovery. This underscores the rationality and selectivity of natural recovery, allowing communities to rejuvenate and flourish after disturbance. Although human efforts can accelerate restoration progress, they cannot entirely alter the natural state. Early succession of vegetation is crucial for slope stability [39], highlighting the expedience and indispensability of artificial restoration on a temporal scale. Regarding restoration strategies, it is observable that artificial restoration focuses more on large-scale ecological restoration, primarily concentrating on tree planting and restoration. However, smaller-scale shrubs and herbaceous plants have not received sufficient attention. In contrast, naturally recovering sites tend to grow fast-growing annual and biennial herbaceous plants, followed by the development of dominant shrubs, which show significant short-term advantages. This indicates that as natural succession unfolds, plant communities transition gradually from herbaceous to shrub and tree dominance. While shrubs and herbaceous plants may have relatively less ecological importance and carbon sequestration capacity compared to trees, they remain indispensable in their natural state. There might also be a situation where trampling and surface sinking of herbaceous seeds occur more easily in the process of human restoration, possibly causing some plants to not exhibit their full potential. In the future, people may recognize the value of small-scale plants and choose rapidly growing species, extending beyond trees, to recreate natural landscapes more sensibly. In the six-year and eleven-year restoration sites, most α-diversity indicators closely converge, yet the majority of indicators in the artificially restored sites remain higher than in the naturally recovering sites. This emphasizes the significance of artificial restoration in landslide site recovery. While the α-diversity of plant communities in the naturally recovering sites gradually increases over time, aligning with that of artificial restoration, the short-term impact of artificial restoration on landslide sites is remarkable [19,40,41].
In the restoration of the one-year tree layer, we observed that various indicators of artificial restoration, such as tree species, tree height, diameter at breast height, canopy density, and leaf thickness, were all significantly greater than those of natural recovery. Furthermore, the range of these indicators in the artificially restored sites was notably larger than that of the naturally recovering sites, yet still exhibit substantial differences when compared to the reference sites. As restoration time extended, we observed a diminishing gap between the two approaches [24,42]. In the shrub and herbaceous layers, we confirmed the prior notion that the artificial restoration strategy leans more towards tree restoration, with lower indicators compared to the reference sites in the shrub and herbaceous layers. The natural recovery strategy demonstrates a succession from herbaceous to shrub and finally to tree growth. This is particularly evident in the data for shrub and herbaceous height at the one-year restoration sites. Shrubs and trees possess greater stability compared to surface-level grasses, and the regeneration rate of stable trees is notably slower [43]. This suggests that slope stability is established from top to bottom, with shallow-level stabilization occurring before deeper-level tree stability. While herbaceous plants can aid in erosion prevention, they may not be as effective in stabilizing the site against significant landslides [44]. This underscores the current tendency of human restoration methods towards rapid and stable recovery. We cannot pass judgment on the right or wrong of this restoration approach, but we can conclude that significant differences exist between artificial restoration and natural recovery.
In human efforts for vegetation restoration, methods like slope protection using measures such as anti-slide piles and anchoring are often employed to expedite the restoration process in a unified, simple, and rational manner. However, these approaches can easily lead to changes in the physical and chemical properties of the soil. Changes in soil structural characteristics are susceptible to influences from temporal and spatial factors, landscape environments, and climate variations [45]. Additionally, the growth of plant root systems and the decomposition of fallen leaves and branches can also contribute to soil structure improvement [42]. During the restoration process, some readily available soil indicators, such as soil organic matter (SOM), available phosphorus (AP), available potassium (AK), and available nitrogen (AN), may be initially lower in the early stages of natural recovery. However, with prolonged restoration, these indicators have the potential to surpass levels found at reference sites. This suggests that post-disturbance lands have disrupted their original ecological balance, creating diverse possibilities for the restoration process to promote vegetation growth and enhance biodiversity. In the context of shorter-term natural recovery, the content of SOM may be lower than that at reference sites. This could be due to the disturbance caused by earthquakes and geological hazards, leading to a significant loss of mature soil and a decrease in carbon storage in the surface soil layer. Additionally, lands affected by seismic landslides often exhibit an increase in coarse soil particles and a decrease in fine particles, which reduces soil carbon storage capacity. Increasing SOM can aid in optimizing soil structure, enhancing surface soil porosity, and improving water-holding capacity [46]. Research results indicate that in natural, undisturbed conditions, the proportion of sand particles in the soil is the highest. However, in the short-term artificial and natural recovery under vegetation, the particle distribution follows the sequence clay > silt > sand. Changes in soil cation exchange capacity and pH values are closely related, primarily due to the fact that soil pH is a critical factor influencing the quantity of variable charges on soil colloids. As soil pH increases, the quantity of variable charges on soil colloids also rises, consequently increasing the soil’s cation exchange capacity [47].
Plants and soil form an interconnected, interdependent system that collectively determines the ecological restoration of earthquake-induced landslide areas [48]. The growth of plant root systems and soil biota, through their physical interactions with the soil, helps protect it from erosion, thereby stabilizing soil structure [49]. The results of this study indicate that artificial restoration can effectively contribute to soil rehabilitation. However, if the root systems of native natural vegetation are deeper or denser than those of artificially planted vegetation, or if they possess both of these characteristics, native vegetation may play a more prominent role in stabilizing soil structure [50,51]. Simultaneously, in cases where the exact time of the landslide is unknown, the characteristics of the vegetation and soil within the affected area can be utilized to infer the timing of the landslide. This approach can provide valuable insights into the condition of the site and inform more informed restoration decisions. Nevertheless, as mentioned earlier, there is a need for further exploration of more effective restoration strategies. To this end, it is recommended to conduct thorough investigations of the dominant, fast-growing, and adaptive plant species within the landslide-affected area or neighboring lands prior to restoration. This information can serve as a foundation for restoration efforts, aiming to achieve similarity between the post-restoration vegetation community and the original state. Our focus should be on restoring the pre-existing natural state rather than simply pursuing entirely new vegetation forms.

5. Conclusions

In the process of restoration, tree species such as Toona sinensis (Juss.) Roem., Pinus bungeana Zucc. ex Endl., Pinus massoniana Lamb., Juglans regia L., Diospyros lotus L., as well as shrubs like Lonicera caerulea L., Elaeagnus umbellata Thunb., Rosa multiflora Thunb., and herbaceous plants including Artemisia lavandulifolia DC., Echinochloa crus-galli (L.) P. Beauv., and Elsholtzia ciliata (Thunb.) Hyl., exhibit rapid growth and play important roles. Families such as Fabaceae, Pinaceae, Meliaceae, Juglandaceae, Rosaceae, Asteraceae, and Poaceae are dominant within the landslide area. In general, artificial restoration strategies often commence with tree planting, followed by the restoration of shrubs and herbaceous plants. Natural recovery strategies, on the other hand, tend to prioritize the initial restoration of fast-growing herbaceous plants and shrubs, followed by the restoration of trees. This study shows artificial restoration has remarkable short-term effects, but long-term effects become similar to those of natural recovery. Future research needs to consider both natural processes and artificial facilitation to achieve more ideal ecological restoration outcomes. We should respect the natural restoration process, emphasize maintaining ecological balance during restoration, and ensure that the restored ecosystem can better adapt to the surrounding environment, thus laying a solid foundation for future ecological construction.

Author Contributions

Conceptualization, S.C.; methodology, S.C.; software, J.H. and S.C.; validation, W.L. and S.Y.; formal analysis, J.H. and S.C.; investigation, S.C., J.H. and W.L.; resources, S.C.; data curation, J.H., S.C. and W.L.; writing—original draft preparation, J.H. and S.C.; writing—review and editing, S.C., J.H., S.Y. and X.W.; visualization, S.Y. and X.W.; supervision, W.J.; project administration, W.J.; funding acquisition, W.J. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Shaanxi Academy of Forestry Technology Innovation Plan (NO: SXLK2021-0203) and China’s Ministry of Science and Technology’s Basic Science Resources Survey Special Project (NO: 2019FY101604).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data are not publicly available due to privacy or ethical restrictions.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Lopes, L.F.; Oliveira, S.C.; Neto, C.; Zezere, J.L. Vegetation Evolution by Ecological Succession as a Potential Bioindicator of Landslides Relative Age in Southwestern Mediterranean Region. Nat. Hazards 2020, 103, 599–622. [Google Scholar] [CrossRef]
  2. Kirschbaum, D.; Stanley, T.; Zhou, Y. Spatial and Temporal Analysis of a Global Landslide Catalog. Geomorphology 2015, 249, 4–15. [Google Scholar] [CrossRef]
  3. Guariguata, M.R. Landslide Disturbance and Forest Regeneration in the Upper Luquillo Mountains of Puerto Rico. J. Ecol. 1990, 78, 814–832. [Google Scholar] [CrossRef]
  4. Lin, W.-T.; Lin, C.-Y.; Tsai, J.-S.; Huang, P.-H. Eco-Environmental Changes Assessment at the Chiufenershan Landslide Area Caused by Catastrophic Earthquake in Central Taiwan. Ecol. Eng. 2008, 33, 220–232. [Google Scholar] [CrossRef]
  5. Sidle, R.C.; Gomi, T.; Akasaka, M.; Koyanagi, K. Ecosystem Changes Following the 2016 Kumamoto Earthquakes in Japan: Future Perspectives. Ambio 2018, 47, 721–734. [Google Scholar] [CrossRef]
  6. Mi, J.; Liu, R.; Zhang, S.; Hou, H.; Yang, Y.; Chen, F.; Zhang, L. Vegetation Patterns on a Landslide after Five Years of Natural Restoration in the Loess Plateau Mining Area in China. Ecol. Eng. 2019, 136, 46–54. [Google Scholar] [CrossRef]
  7. Li, B.; Zeng, T.; Ran, J.; Yue, B.; Zhang, M.; Shang, T.; Zhu, D. Characteristics of the Early Secondary Succession after Landslides in a Broad-Leaved Deciduous Forest in the South Minshan Mountains. For. Ecol. Manag. 2017, 405, 238–245. [Google Scholar] [CrossRef]
  8. Neto, C.; Cardigos, P.; Oliveira, S.C.; Zezere, J.L. Floristic and Vegetation Successional Processes within Landslides in a Mediterranean Environment. Sci. Total Environ. 2017, 574, 969–981. [Google Scholar] [CrossRef]
  9. Zhao, B.; Wang, Y.; Chen, M.; Luo, Y.; Liang, R.; Li, J. Typical Characteristics of Large-Scale Landslides in the Transition Belt between the Qinghai-Tibet Plateau and the Loess Plateau. Arab. J. Geosci. 2019, 12, 470. [Google Scholar] [CrossRef]
  10. Sloggy, M.R.; Suter, J.F.; Rad, M.R.; Manning, D.T.; Goemans, C. Changing Climate, Changing Minds? The Effects of Natural Disasters on Public Perceptions of Climate Change. Clim. Change 2021, 168, 25. [Google Scholar] [CrossRef]
  11. Bouwer, L.M. Have Disaster Losses Increased Due to Anthropogenic Climate Change? Bull. Am. Meteorol. Soc. 2011, 92, 39–46. [Google Scholar] [CrossRef]
  12. Aronson, J.; Clewell, A.; Moreno-Maeos, D. Ecological Restoration and Ecological Engineering: Complementary or Indivisible? Ecol. Eng. 2016, 91, 392–395. [Google Scholar] [CrossRef]
  13. Uprety, Y.; Asselin, H.; Bergeron, Y.; Doyon, F.; Boucher, J.-F. Contribution of Traditional Knowledge to Ecological Restoration: Practices and Applications. Ecoscience 2012, 19, 225–237. [Google Scholar] [CrossRef]
  14. Van Diggelen, R.; Grootjans, A.P.; Harris, J.A. Ecological Restoration: State of the Art or State of the Science? Restor. Ecol. 2001, 9, 115–118. [Google Scholar] [CrossRef]
  15. Chazdon, R.L. Beyond Deforestation: Restoring Forests and Ecosystem Services on Degraded Lands. Science 2008, 320, 1458–1460. [Google Scholar] [CrossRef]
  16. Deng, L.; Wang, K.-B.; Chen, M.-L.; Shangguan, Z.-P.; Sweeney, S. Soil Organic Carbon Storage Capacity Positively Related to Forest Succession on the Loess Plateau, China. Catena 2013, 110, 1–7. [Google Scholar] [CrossRef]
  17. Peng, X.; Dai, Q.; Ding, G.; Shi, D.; Li, C. Impact of Vegetation Restoration on Soil Properties in Near-Surface Fissures Located in Karst Rocky Desertification Regions. Soil Tillage Res. 2020, 200, 104620. [Google Scholar] [CrossRef]
  18. Shi, P.; Zhang, Y.; Li, P.; Li, Z.; Yu, K.; Ren, Z.; Xu, G.; Cheng, S.; Wang, F.; Ma, Y. Distribution of Soil Organic Carbon Impacted by Land-Use Changes in a Hilly Watershed of the Loess Plateau, China. Sci. Total Environ. 2019, 652, 505–512. [Google Scholar] [CrossRef]
  19. Xu, X.; Zhang, D. Comparing the Long-Term Effects of Artificial and Natural Vegetation Restoration Strategies: A Case-Study of Wuqi and Its Adjacent Counties in Northern China. Land Degrad. Dev. 2021, 32, 3930–3945. [Google Scholar] [CrossRef]
  20. Fu, B.; Wang, S.; Liu, Y.; Liu, J.; Liang, W.; Miao, C. Hydrogeomorphic Ecosystem Responses to Natural and Anthropogenic Changes in the Loess Plateau of China. In Annual Review of Earth and Planetary Sciences; Jeanloz, R., Freeman, K.H., Eds.; Annual Reviews: Palo Alto, CA, USA, 2017; Volume 45, pp. 223–243. ISBN 978-0-8243-2045-4. [Google Scholar]
  21. Jiang, W.; Yang, S.; Yang, X.; Gu, N. Negative Impacts of Afforestation and Economic Forestry on the Chinese Loess Plateau and Proposed Solutions. Quat. Int. 2016, 399, 165–173. [Google Scholar] [CrossRef]
  22. Wang, X.; Huang, Z.; Hong, M.M.; Zhao, Y.F.; Ou, Y.S.; Zhang, J. A Comparison of the Effects of Natural Vegetation Regrowth with a Plantation Scheme on Soil Structure in a Geological Hazard-Prone Region. Eur. J. Soil Sci. 2019, 70, 674–685. [Google Scholar] [CrossRef]
  23. Hu, P.; Xiao, J.; Zhang, W.; Xiao, L.; Yang, R.; Xiao, D.; Zhao, J.; Wang, K. Response of Soil Microbial Communities to Natural and Managed Vegetation Restoration in a Subtropical Karst Region. Catena 2020, 195, 104849. [Google Scholar] [CrossRef]
  24. Guo, J.; Gong, P. Forest Cover Dynamics from Landsat Time-Series Data over Yan’an City on the Loess Plateau during the Grain for Green Project. Int. J. Remote Sens. 2016, 37, 4101–4118. [Google Scholar] [CrossRef]
  25. Wang, B.; Zhang, G.-H.; Shi, Y.-Y.; Li, Z.-W.; Shan, Z.-J. Effects of Near Soil Surface Characteristics on the Soil Detachment Process in a Chronological Series of Vegetation Restoration. Soil Sci. Soc. Am. J. 2015, 79, 1213–1222. [Google Scholar] [CrossRef]
  26. Wang, B.; Liu, G.B.; Xue, S.; Zhu, B. Changes in Soil Physico-Chemical and Microbiological Properties during Natural Succession on Abandoned Farmland in the Loess Plateau. Environ. Earth Sci. 2011, 62, 915–925. [Google Scholar] [CrossRef]
  27. Deng, L.; Wang, K.; Zhu, G.; Liu, Y.; Chen, L.; Shangguan, Z. Changes of Soil Carbon in Five Land Use Stages Following 10 Years of Vegetation Succession on the Loess Plateau, China. Catena 2018, 171, 185–192. [Google Scholar] [CrossRef]
  28. Schlatter, D.C.; Bakker, M.G.; Bradeen, J.M.; Kinkel, L.L. Plant Community Richness and Microbial Interactions Structure Bacterial Communities in Soil. Ecology 2015, 96, 134–142. [Google Scholar] [CrossRef]
  29. Wang, J.; Zhao, W.; Zhang, X.; Liu, Y.; Wang, S.; Liu, Y. Effects of Reforestation on Plant Species Diversity on the Loess Plateau of China: A Case Study in Danangou Catchment. Sci. Total Environ. 2019, 651, 979–989. [Google Scholar] [CrossRef]
  30. Wang, S.; Meng, X.; Chen, G.; Guo, P.; Xiong, M.; Zeng, R. Effects of Vegetation on Debris Flow Mitigation: A Case Study from Gansu Province, China. Geomorphology 2017, 282, 64–73. [Google Scholar] [CrossRef]
  31. Kravchenko, A.N.; Negassa, W.C.; Guber, A.K.; Rivers, M.L. Protection of Soil Carbon within Macro-Aggregates Depends on Intra-Aggregate Pore Characteristics. Sci. Rep. 2015, 5, 16261. [Google Scholar] [CrossRef]
  32. Haile, S.G.; Nair, V.D.; Nair, P.K.R. Contribution of Trees to Carbon Storage in Soils of Silvopastoral Systems in Florida, USA. Glob. Change Biol. 2010, 16, 427–438. [Google Scholar] [CrossRef]
  33. Lin, Y.; Deng, H.; Du, K.; Li, J.; Lin, H.; Chen, C.; Fisher, L.; Wu, C.; Hong, T.; Zhang, G. Soil Quality Assessment in Different Climate Zones of China’s Wenchuan Earthquake Affected Region. Soil Tillage Res. 2017, 165, 315–324. [Google Scholar] [CrossRef]
  34. Blonska, E.; Lasota, J.; Piaszczyk, W.; Wiechec, M.; Klamerus-Iwan, A. The Effect of Landslide on Soil Organic Carbon Stock and Biochemical Properties of Soil. J. Soils Sediments 2018, 18, 2727–2737. [Google Scholar] [CrossRef]
  35. Liu, Z.; Fu, B.; Zheng, X.; Liu, G. Plant Biomass, Soil Water Content and Soil N:P Ratio Regulating Soil Microbial Functional Diversity in a Temperate Steppe: A Regional Scale Study. Soil Biol. Biochem. 2010, 42, 445–450. [Google Scholar] [CrossRef]
  36. Wilsey, B.J.; Barber, K.; Martin, L.M. Exotic Grassland Species Have Stronger Priority Effects than Natives Regardless of Whether They Are Cultivated or Wild Genotypes. New Phytol. 2015, 205, 928–937. [Google Scholar] [CrossRef] [PubMed]
  37. Wubs, E.R.J.; van der Putten, W.H.; Bosch, M.; Bezemer, T.M. Soil Inoculation Steers Restoration of Terrestrial Ecosystems. Nat. Plants 2016, 2, 16107. [Google Scholar] [CrossRef]
  38. Cheng, S.; Yu, H.; Yang, G.; Yang, R.; Liu, Y.; Zhang, Z.; Xiong, G.; Peng, B.; Gao, Y.; Li, J. Impacts of Wenchuan Earthquake-Induced Landslides on Tree Organ Nutrients. In Proceedings of the 2012 International Conference on Biomedical Engineering and Biotechnology, Washington, DC, USA, 28–30 May 2012; pp. 1518–1520. [Google Scholar]
  39. Walker, L.R.; Shiels, A.B. Landslide Ecology; Cambridge University Press: Cambridge, UK, 2012. [Google Scholar]
  40. Huang, Y.; Han, H.; Tang, C.; Liu, S. Plant Community Composition and Interspecific Relationships among Dominant Species on a Post-Seismic Landslide in Hongchun Gully, China. J. Mt. Sci. 2017, 14, 1985–1994. [Google Scholar] [CrossRef]
  41. Yang, Q.; Liu, S.; Hu, C.; Lin, Y.; Zhang, B.; Luo, M.; Peng, H. Ecological Species Groups and Interspecific Association of Vegetation in Natural Recovery Process at Xiejiadian Landslide after 2008 Wenchuan Earthquake. J. Mt. Sci. 2016, 13, 1609–1620. [Google Scholar] [CrossRef]
  42. Bienes, R.; Marques, M.J.; Sastre, B.; Garcia-Diaz, A.; Ruiz-Colmenero, M. Eleven Years after Shrub Revegetation in Semiarid Eroded Soils: Influence in Soil Properties. Geoderma 2016, 273, 106–114. [Google Scholar] [CrossRef]
  43. Kang, D.; Zou, S.; Ma, L.; Yin, C.; Zhu, D. Abiotic Regulation: Landslide Scale and Altitude Regulate Functional Traits of Regenerating Plant Communities After Earthquakes. Front. Ecol. Evol. 2022, 10, 846642. [Google Scholar] [CrossRef]
  44. Rengers, F.K.; McGuire, L.A.; Coe, J.A.; Kean, J.W.; Baum, R.L.; Staley, D.M.; Godt, J.W. The Influence of Vegetation on Debris-Flow Initiation during Extreme Rainfall in the Northern Colorado Front Range. Geology 2016, 44, 823–826. [Google Scholar] [CrossRef]
  45. Li, Y.Y.; Shao, M.A. Change of Soil Physical Properties under Long-Term Natural Vegetation Restoration in the Loess Plateau of China. J. Arid Environ. 2006, 64, 77–96. [Google Scholar] [CrossRef]
  46. Fu, X.; Shao, M.; Wei, X.; Horton, R. Soil Organic Carbon and Total Nitrogen as Affected by Vegetation Types in Northern Loess Plateau of China. Geoderma 2010, 155, 31–35. [Google Scholar] [CrossRef]
  47. Gruba, P.; Mulder, J. Tree Species Affect Cation Exchange Capacity (CEC) and Cation Binding Properties of Organic Matter in Acid Forest Soils. Sci. Total Environ. 2015, 511, 655–662. [Google Scholar] [CrossRef]
  48. Birkas, M.; Stingli, A.; Szemok, A.; Kalmar, T.; Bottlik, L. Soil Condition and Plant Interrelations in Dry Years. Cereal Res. Commun. 2008, 36, 15–18. [Google Scholar]
  49. Blouin, M.; Hodson, M.E.; Delgado, E.A.; Baker, G.; Brussaard, L.; Butt, K.R.; Dai, J.; Dendooven, L.; Peres, G.; Tondoh, J.E.; et al. A Review of Earthworm Impact on Soil Function and Ecosystem Services. Eur. J. Soil Sci. 2013, 64, 161–182. [Google Scholar] [CrossRef]
  50. Xu, C.; Gorum, T.; Tanyas, H. Editorial: Application of Remote Sensing and GIS in Earthquake-Triggered Landslides. Front. Earth Sci. 2022, 10, 964753. [Google Scholar] [CrossRef]
  51. Cao, S.; Chen, L.; Shankman, D.; Wang, C.; Wang, X.; Zhang, H. Excessive Reliance on Afforestation in China’s Arid and Semi-Arid Regions: Lessons in Ecological Restoration. Earth Sci. Rev. 2011, 104, 240–245. [Google Scholar] [CrossRef]
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Figure 1. Research scope map. (a) The location of Shaanxi Province in China. (b) Lantian County’s location in Shaanxi Province. (c) The study area and undamaged study area’s location in Lantian County.
Figure 1. Research scope map. (a) The location of Shaanxi Province in China. (b) Lantian County’s location in Shaanxi Province. (c) The study area and undamaged study area’s location in Lantian County.
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Figure 2. Sampled plot arrangement plan. (a) Landslide site diagram. (b) Rotational profile of the landslide area. (c) Layout of the plots. (1) Upper slope; (2) middle slope; (3) lower slope. In (c), I, II, and III represent three 10 m × 10 m tree plots, while the sizes of other plots can be referred to in the legend.
Figure 2. Sampled plot arrangement plan. (a) Landslide site diagram. (b) Rotational profile of the landslide area. (c) Layout of the plots. (1) Upper slope; (2) middle slope; (3) lower slope. In (c), I, II, and III represent three 10 m × 10 m tree plots, while the sizes of other plots can be referred to in the legend.
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Figure 3. α diversity in the vertical strata of each sample area. Groups: (a) one-year restoration sample area, (b) six-year restoration sample area, (c) eleven-year restoration sample area.   D m t -Margalef richness index for the tree; D s t -Simpson’s index of dominance for the tree; J t -Pielou’s uniformity index for the tree; H t -Shannon–Wiener diversity index for tree; D m s -Margalef richness index for the shrub; D s s -Simpson’s index of dominance for the shrub; J s -Pielou’s uniformity index for the shrub; H s -Shannon–Wiener diversity index for shrub; D m h -Margalef richness index for the herb; D s h -Simpson’s index of dominance for the herb; J h -Pielou’s uniformity index for the herb; H h -Shannon–Wiener diversity index for herb.
Figure 3. α diversity in the vertical strata of each sample area. Groups: (a) one-year restoration sample area, (b) six-year restoration sample area, (c) eleven-year restoration sample area.   D m t -Margalef richness index for the tree; D s t -Simpson’s index of dominance for the tree; J t -Pielou’s uniformity index for the tree; H t -Shannon–Wiener diversity index for tree; D m s -Margalef richness index for the shrub; D s s -Simpson’s index of dominance for the shrub; J s -Pielou’s uniformity index for the shrub; H s -Shannon–Wiener diversity index for shrub; D m h -Margalef richness index for the herb; D s h -Simpson’s index of dominance for the herb; J h -Pielou’s uniformity index for the herb; H h -Shannon–Wiener diversity index for herb.
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Figure 4. Vegetation pattern of the tree layer. In the one-year sample area: (a) distribution of tree height and diameter at breast height; (b) distribution of canopy density and specific leaf area; (c) distribution of leaf thickness and leaf length. In the six-year sample area: (d) distribution of tree height and diameter at breast height; (e) distribution of canopy density and specific leaf area; and (f) distribution of leaf thickness and leaf length. In the eleven-year sample area: (g) distribution of tree height and diameter at breast height; (h) distribution of canopy density and specific leaf area; (i) distribution of leaf thickness and leaf length.
Figure 4. Vegetation pattern of the tree layer. In the one-year sample area: (a) distribution of tree height and diameter at breast height; (b) distribution of canopy density and specific leaf area; (c) distribution of leaf thickness and leaf length. In the six-year sample area: (d) distribution of tree height and diameter at breast height; (e) distribution of canopy density and specific leaf area; and (f) distribution of leaf thickness and leaf length. In the eleven-year sample area: (g) distribution of tree height and diameter at breast height; (h) distribution of canopy density and specific leaf area; (i) distribution of leaf thickness and leaf length.
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Figure 5. Vegetation pattern of the shrub layer and herb layer. Groups: (a) vegetation pattern of the shrub layer; (b) vegetation patterns in the herb layer.
Figure 5. Vegetation pattern of the shrub layer and herb layer. Groups: (a) vegetation pattern of the shrub layer; (b) vegetation patterns in the herb layer.
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Figure 6. Various physical and chemical properties in the sample plots. Groups: (a) total organic matter content of the soil; (b) total nitrogen content of the soil; (c) total phosphorus content of the soil; (d) total potassium content of the soil; (e) effective phosphorus content of the soil; (f) effective potassium content of the soil; (g) alkaline nitrogen content of the soil; (h) cation exchange capacity content of the soil; (i) dehydrogenase content of the soil; (j) protease content of the soil; (k) urease content of the soil; (l) phosphatase content of the soil; (m) sucrase content of the soil; (n) moisture content of the soil; (o) pH content of the soil.
Figure 6. Various physical and chemical properties in the sample plots. Groups: (a) total organic matter content of the soil; (b) total nitrogen content of the soil; (c) total phosphorus content of the soil; (d) total potassium content of the soil; (e) effective phosphorus content of the soil; (f) effective potassium content of the soil; (g) alkaline nitrogen content of the soil; (h) cation exchange capacity content of the soil; (i) dehydrogenase content of the soil; (j) protease content of the soil; (k) urease content of the soil; (l) phosphatase content of the soil; (m) sucrase content of the soil; (n) moisture content of the soil; (o) pH content of the soil.
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Figure 7. Particle size distribution profile in each sample plot.
Figure 7. Particle size distribution profile in each sample plot.
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Table 1. General information about experimental sites.
Table 1. General information about experimental sites.
Sample AreasRestoration Period (Year)Slope AspectGradient
(°) C		Altitude
(m)		Restoration Method
A11South12~281240Artificial restoration
AR1/Southeast15~311157References
N11east21~39960Natural
NR1/Southeast16~38938References
A66Southeast33~521219Artificial restoration
AR6/South27~421238References
N66East24~45644Natural
NR6/Southeast16~34620References
A1111West20~361101Artificial restoration
AR11/South18~331103References
N1111East29~541084Natural
NR11/East22~391065References
Table 2. Summary of formulas.
Table 2. Summary of formulas.
NumberTitleIndexFormulaParameter
1Importance value I V I V = ( R c + R h + R f ) ÷ 300 R c : relative   coverage ;   R h :   relative   height ;   R f : relative frequency.
2Margalef richness index D m D m = M − 1 l n N M :   number   of   community   species ;   N : total individuals
3Simpson advantage degree index D s D s = 1 − ∑ n i n i − 1 N N − 1 n i :   number   of   individuals   of   species   i ;   N : total individuals
4Pielou evenness index J J = H l n M M : number of community species;
H : Shannon–Wiener diversity index
5Shannon–Wiener diversity index H H = − ∑ i = 1 M P i l n P i M : number of community species;
P i : Number of species i as a proportion of all species
Table 3. Dominant species and corresponding importance values (IV).
Table 3. Dominant species and corresponding importance values (IV).
One-Year Manual Restoration Sample AreaOne-Year Natural Recovery Sample Area
No.NameIV (%)Vegetation TypeNo.NameIV (%)Vegetation Type
1Toona sinensis (Juss.) Roem.47.347Tree1Rhus chinensis Mill.56.369Tree
2Zanthoxylum bungeanum Maxim.31.8872Diospyros lotus L.43.630
3Pinus armandii Franch.15.8413
4Toxicodendron vernicifluum (Stokes) F. A. Barkley12.7524
5Juglans regia L.12.6815
8Lonicera caerulea L.44.979Shrub8Elaeagnus umbellata Thunb.36.117Shrub
9Cornus alba L.26.5939Celastrus angulatus Maxim.33.769
10Kerria japonica (L.) DC.17.92310Akebia trifoliata (Thunb.) Koidz.30.113
11Euonymus alatus (Thunb.) Siebold10.50511
12Artemisia lavandulifolia DC.23.894Herb12Equisetum arvense L.9.193Herb
13Achnatherum chinense (Hitchc.) Tzvelev18.58413Artemisia annua L.5.361
14Artemisia annua L.10.61914Equisetum hyemale L.4.877
15Duchesnea indica (Andrews) Teschem.8.85015Artemisia lavandulifolia DC.4.752
16Phedimus aizoon (L.) ‘t Hart7.96516Arundinella hirta (Thunberg) Tanaka4.438
17Plantago asiatica L.7.08017Artemisia annua L.4.130
18Asplenium pekinense Hance4.42518Setaria viridis (L.) P. Beauv.3.833
Six-Year Manual Restoration Sample AreaSix-Year Natural Recovery Sample Area
No.NameIV (%)Vegetation TypeNo.NameIV (%)Vegetation Type
1Pinus massoniana Lamb.29.187Tree1Juglans regia L.50.922Tree
2Quercus variabilis Blume20.0902Pinus massoniana Lamb.24.730
3Cotinus coggygria var. cinereus Engl.19.8093Cotinus coggygria var. cinereus Engl.14.302
4Fraxinus stylosa Lingelsh. in Engler12.6614Broussonetia papyrifera (L.) L’Hér. ex Vent.10.047
5Quercus robur L.10.4715
8Berberis feddeana C. K. Schneid.44.852Shrub8Rosa multiflora Thunb.39.310Shrub
9Akebia trifoliata (Thunb.) Koidz.21.0399Elaeagnus umbellata Thunb.33.983
10Pueraria montana var. lobata (Willd.) Maesen & S. M. Almeida ex Sanjappa & Predeep20.40510Rubus parvifolius L.26.707
11Wikstroemia pilosa W. C. Cheng16.79811
12Elsholtzia ciliata (Thunb.) Hyl.13.162Herb12Artemisia argyi H. Lév. & Vaniot22.579Herb
13Humulus scandens (Lour.) Merr.10.50813Fragaria vesca L.9.754
14Lagopsis supina (Steph.) Ikonn.-Gal.6.64714Stellaria vestita Kurz8.969
15Artemisia argyi H. Lév. & Vaniot5.10515Artemisia annua L.5.314
16Stellaria vestita Kurz4.68916Artemisia lavandulifolia DC.4.661
17Carex agglomerata C. B. Clarke4.63217Setaria viridis (L.) P. Beauv.3.799
18Artemisia annua L.4.51218Artemisia dubia var. subdigitata (Mattf.) Y. R. Ling3.757
Eleven-Year Manual Restoration Sample AreaEleven-Year Natural Recovery Sample Area
No.NameIV (%)Vegetation TypeNo.NameIV (%)Vegetation Type
1Gleditsia sinensis Lam.15.208Tree1Quercus variabilis Blume38.025Tree
2Pinus bungeana Zucc. ex Endl.11.5802Phyllostachys sulphurea var. viridis R. A. Young18.147
3Prunus davidiana (Carrière) Franch.10.8553Ulmus pumila L.13.475
4Robinia pseudoacacia L.9.1634Sambucus williamsii Hance8.691
5Juglans regia L.8.5175Quercus robur L.6.510
8Spiraea × vanhouttei (Briot) Carrière27.920Shrub8Spiraea × vanhouttei (Briot) Carrière33.477Shrub
9Euonymus alatus (Thunb.) Siebold22.0129Elaeagnus umbellata Thunb.28.984
10Lespedeza bicolor Turcz.9.92610Wikstroemia nutans Champ. ex Benth.9.704
11Spiraea cantoniensis Lour.7.99111Spiraea cantoniensis Lour.8.493
12Carex breviculmis R. Br.15.528Herb12Echinochloa crus-galli (L.) P. Beauv.9.748Herb
13Chrysanthemum lavandulifolium (Fisch. ex Trautv.) Makino11.92713Artemisia capillaris Thunb.8.239
14Artemisia argyi H. Lév. & Vaniot8.54214Senecio scandens Buch.-Ham. ex D. Don7.113
15Phedimus aizoon (L.) ‘t Hart6.83015Artemisia argyi H. Lév. & Vaniot6.319%
16Artemisia eriopoda Bunge6.76916Rubia cordifolia L.5.917
17Anaphalis sinica Hance6.00317Aster altaicus Willd.4.663
18Cirsium japonicum Fisch. ex DC.3.96418Elsholtzia densa Benth.3.708
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MDPI and ACS Style

Chen, S.; Hua, J.; Liu, W.; Yang, S.; Wang, X.; Ji, W. Effects of Artificial Restoration and Natural Recovery on Plant Communities and Soil Properties across Different Temporal Gradients after Landslides. Forests 2023, 14, 1974. https://doi.org/10.3390/f14101974

AMA Style

Chen S, Hua J, Liu W, Yang S, Wang X, Ji W. Effects of Artificial Restoration and Natural Recovery on Plant Communities and Soil Properties across Different Temporal Gradients after Landslides. Forests. 2023; 14(10):1974. https://doi.org/10.3390/f14101974

Chicago/Turabian Style

Chen, Sibo, Jinguo Hua, Wanting Liu, Siyu Yang, Xiaoqi Wang, and Wenli Ji. 2023. "Effects of Artificial Restoration and Natural Recovery on Plant Communities and Soil Properties across Different Temporal Gradients after Landslides" Forests 14, no. 10: 1974. https://doi.org/10.3390/f14101974

APA Style

Chen, S., Hua, J., Liu, W., Yang, S., Wang, X., & Ji, W. (2023). Effects of Artificial Restoration and Natural Recovery on Plant Communities and Soil Properties across Different Temporal Gradients after Landslides. Forests, 14(10), 1974. https://doi.org/10.3390/f14101974

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