**1. Introduction**

The Soil and Water Conservation Demonstration Park (SWDP) is a major innovation of soil and water conservation in China. The ecosystem functions of SWDP include soil and water loss prevention, climate improvement, resource protection, etc., however, with the progress of the times, the goal of SWDP is not only limited to the improvement of park ecology through planting vegetation and construction projects but also plant restoration should become the focus of research. In order to better understand the role of vegetation in soil and water conservation projects, the changes of plant functional traits should not be ignored. The construction of Xindian SWDP can be traced back to 1952. After 68 years of continuous management, the vegetation coverage of the park has recovered from 5% to the current 75% and great changes have taken place in the ecology of the park during the 68 years of continuous management. Therefore, the study of plant functional traits reflecting the ecosystem change strategy is very important for soil and water conservation and restoration development.

As the largest terrestrial ecosystem, grassland ecosystem mainly distributes in ecologically fragile areas due to their special functional traits and strategies [1]. Functional traits

**Citation:** Duan, G.; Wen, Z.; Xue, W.; Bu, Y.; Lu, J.; Wen, B.; Wang, B.; Chen, S. Agents Affecting the Plant Functional Traits in National Soil and Water Conservation Demonstration Park (China). *Plants* **2022**, *11*, 2891. https://doi.org/10.3390/ plants11212891

Academic Editor: Martina Pollastrini

Received: 19 September 2022 Accepted: 26 October 2022 Published: 28 October 2022

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are biological attributes that directly or indirectly affect species fitness and endow plants with high adaptability to environmental changes [2,3]. Functional trait variability allows plants to minimize their building costs and maximize functional efficiency. Therefore, to ascertain plant functioning and their ecological strategies in soil and water conservation measures, it is critical to explore how functional traits vary across different measures [4], especially relevant under the ongoing climatic change scenario. For instance, Scots pine (*Pinus sylvestris* L.) has the ability to adjust its leaf/sapwood area ratio, leaf-specific hydraulic conductivity and total leaf area in response to drought [5].

The functional traits of grassland communities vary greatly due to latent influencing factors, such as topographic conditions, soil properties and plant diversity [6]. On the one hand, habitat heterogeneity, due to changes in environmental factors and topography, will lead to the differences in grassland composition [7]. For example, temperature, humidity and altitude will affect grassland vegetation population and change functional traits [8]. On the other hand, there is a complex relationship between soil properties and plant. Soil which controls water and nutrients is the most critical condition for plant growth [9,10]. Soil water content is also considered to be the main factor in determining the composition of grassland vegetation [11]. Lush plants also feed back to soil nutrients and avoid soil erosion through their leaves and roots [12,13]. In addition, high vegetation coverage will create a microclimate through changes to the environment temperature and humidity and then affect soil properties [14]. In recent years, correlation or causation was explained among plant functional traits and the influencing factors. However, how these factors work together to influence plant functional traits and how they interact with each other remains unclear [15]. Hence, to resolve this major problem, the research should quantify the factors that impact on plant functional traits.

Four categories of variables were represented using 29 different observational variables collected from 171 plots of 57 sites in Xindian National Soil and Water Conservation Demonstration Park. We used random forest (RF) algorithm to identify critical indicators of plant functional traits. The RF is a machine learning classification method that has been demonstrated as an efficient algorithm to obtain key factors [16]. Moreover, structural equation modeling (SEM) was used to quantify the influence of these factors on plant functional traits [17]. In this research, we employed the partial least squares structural equation modeling (PLS-SEM). By applying these methods, the main objectives of this study were as follows: (a) Select and quantify appropriate plant functional traits in the study area; (b) Discuss the interaction between topographic conditions, soil properties and plant diversity affecting plant functional traits; (c) Evaluate the effects of topographic conditions and diversity indexes on plant functional traits. The study on the above issues is helpful to understand the status quo and change rules of plant functional traits in the special ecosystem of Xindian National Soil and Water Conservation Demonstration Park; it is of great significance to understand the mechanism of plant community construction under the interaction of different factors.

#### **2. Materials and Methods**

### *2.1. Study Area*

Xindian Soil and WATER Conservation Demonstration Park, located on the left bank of the middle reaches of Wuding River, was built in 1952. It covers an area of 1.44 km<sup>2</sup> and is all composed of hills. The terrain is very broken, with 31 gullies that are 200 m long and cultivated land above 25 degrees accounting for 49% of the total cultivated land area (Figure S1). According to the observed data in 1952, the annual average soil loss amount was 19,900 t. Since 1952, 24 silt DAMS have been built in the demonstration park. At present, the control degree (through engineering measures and vegetation engineering) of the demonstration park has reached 80%, the forest and grass coverage rate has promoted from 5% to 75% and the sand blocking rate has reached 98%.

The soil texture is sandy loam with dense gullies, which has typical loess hilly and gully landform (Figure 1). It is a temperate continental semi-arid climate, with a monthly

mean temperature, ranging from −7.5◦ in January to 24◦ in July, mean annual temperature is 8.3◦ and the mean annual precipitation is 486 mm from 2010 to 2020, most of which occurs in the form of rainstorms from July to September [18]. *Plants* **2022**, *11*, 2891 4 of 17

**Figure 1.** Research area.

**Figure 1.** Research area.

The study area is dominated by grassland and accounts for more than 80% of the total vegetation area. Our research plots are located at an altitude of 850 m to 1287 m, with a slope range from 3 ◦C to 40 ◦C.

#### *2.2. Plot Survey, Sample Collection and Analysis*

There were one hundred and seventy-one herb plots (1 m × 1 m) from fifty-seven sampling sites in the study area (Figure 2). On each plot, we recorded the names, number, coverage, proportion of all herb species and plant height of each herb. The vegetation coverage was captured by Canon fisheye lens camera and processed using ArcGIS 10.6 to get the total coverage and dominant species coverage. After the investigation, ten well-lit and developed leaves were collected from dominant species for measuring the leaf thickness, area, and dry leaf weight of the species (Table S1). The remaining leaves were taken back to the laboratory for chemical element determination after drying.

Soil samples from the fifty-seven sites were also obtained at 11:00 a.m. to 15:00 p.m. from 29 July to 31 July 2020. Seven points were selected along an S-shape line in each plot (Figure 2). The sampling was collected from 0–30 cm and mixed. Then, the soil samples were required to pass a 2 mm sieve to remove impurities, the samples were taken back to the laboratory for subsequent analysis. Soil bulk density and soil water content were obtained from three samples along the diagonal in each grassland plot, using a cylindrical metal sampler (100 mm<sup>2</sup> ) [19].

The measurement of leaf traits was mainly carried out according to the literature [20,21]. A scanner was used to obtain the leaf area (10 replicates) and a vernier card was used to measure and record the leaf thickness. Then, the measured leaves were put into envelopes and the dry matter content of the leaves was obtained by drying method. The soil bulk density was determined by soil–core method. The SWC was calculated as the ratio of soil water mass to oven-dry weight [22,23]. The organic matter was assayed by dichromic oxidation method. The total nitrogen content was measured by Kjeldahl method using a FOSS Kjeltec 8400 Analyzer Unit (FOSS, Hillerod, Denmark) [24]. The total phosphorus was digested by H2SO4-HCIO<sup>4</sup> and measured by spectrophotometer [25]. Total carbon was measured from 1 mm screened samples using Liaui TOC II analyzer (ELMENTAR, Langenselbold, Germany). a slope range from 3 °C to 40 °C. *2.2. Plot Survey, Sample Collection and Analysis* There were one hundred and seventy-one herb plots (1 m × 1 m) from fifty-seven sampling sites in the study area (Figure 2). On each plot, we recorded the names, number, coverage, proportion of all herb species and plant height of each herb. The vegetation coverage was captured by Canon fisheye lens camera and processed using ArcGIS 10.6 to get the total coverage and dominant species coverage. After the investigation, ten well-lit and developed leaves were collected from dominant species for measuring the leaf thickness, area, and dry leaf weight of the species (Table S1). The remaining leaves were taken back to the laboratory for chemical element determination after drying.

The study area is dominated by grassland and accounts for more than 80% of the total vegetation area. Our research plots are located at an altitude of 850 m to 1287 m, with

*Plants* **2022**, *11*, x FOR PEER REVIEW 3 of 15

occurs in the form of rainstorms from July to September [18].

The soil texture is sandy loam with dense gullies, which has typical loess hilly and gully landform (Figure 1). It is a temperate continental semi-arid climate, with a monthly mean temperature, ranging from −7.5° in January to 24° in July, mean annual temperature is 8.3° and the mean annual precipitation is 486 mm from 2010 to 2020, most of which

**Figure 2.** Grassland plots position (a, b, and c) and soil collected in sampling plots. **Figure 2.** Grassland plots position (a, b, and c) and soil collected in sampling plots.

### *2.3. Variables*

**Figure 1.** Research area.

The SEM analysis method is a comprehensive technique that uses covariance matrix to analyze the relationships in multivariate data and identify the causality between observed variables and latent variables. In this study, we explained the relationships between the various influencing factors that pertain to plant functional traits by using latent variables and observational variables. The PLS-SEM was constructed by selecting three explanatory latent variables (topographic conditions, soil properties and vegetation structure) and one latent dependent variable (plant functional traits). Six explanatory observation variables were used to represent the latent variable characteristics of soil, namely soil organic matter content (SOM), soil water content (SWC), soil bulk density (BD), maximum soil water content (MWC), soil total phosphorus content (TP) and soil total nitrogen content (TN). Four topographic variables, namely altitude, slope, slope position and aspect, were chosen due to their effect on the hydrothermal conditions of sampling sites. In order to ensure the integrity of the vegetation diversity information, research needs of the integration of multiple levels and multiple diversity indexes [26]. We selected five different vegetation diversity indexes and two features as latent variables to illustrate vegetation structure, including the total number of plants (N), plants species index (S), Shannon–Wiener index (SHA), Simpson index (SIM), Margalef index (MAR) and Gleason index (GLE). Finally, 12 indexes, including leaf thickness, leaf dry weight, organic matter of leaves, total nitrogen content, total phosphorus content, nitrogen-to-phosphorus ratio, leaf tissue density, leaf area, specific leaf area, vegetation coverage, average plant height and ratio of dominant species were used to represent the observed variables of plant functional traits (Tables S2 and S3).
