Evaluation of Ecological Function Restoration Effect for Degraded Natural Forests in Xiaoxinganling, China

Abstract

To study the effect of ecological function restoration in degraded natural forests in Xiaoxinganling, Heilongjiang Province, China, we set up 90 plots of degraded natural forests of different types and ages in the Xinqing Group Branch of Yichun Forestry Industry Group in Xiaoxinganling, Heilongjiang Province, China. Moreover, 43 evaluation indexes, including litter characteristics, species diversity, soil physical properties, soil chemical properties, and canopy structural parameters, were selected to determine the effect of ecological function restoration in the degraded natural forests in the study area. Principal component analysis (PCA) was used to comprehensively evaluate the ecological function of the degraded forests. The results of the study showed that, based on the forest type scores, mixed coniferous and broadleaf forests, mixed coniferous forests, and broadleaf forests had higher scores, while the scores of pure Betula platyphylla forests, pure Larix gmelinii forests, and the three low-quality forests were lower. This showed that the ecological restoration effect for mixed forests in the Xinqing Group Branch of Yichun Sengyong Group of Xinjiangqing Group, Xiaoxinganling, Heilongjiang Province, China, was better than that for pure forests and low-quality forests. Based on forest age, the scores of near-mature forests and mature forests were generally higher than those of young forests and over-mature forests, indicating that the ecological restoration effect for near-mature forests and mature forests in Xinqing Group Branch of Yichun Forestry Industry Group in Xiaoxinganling, Heilongjiang Province, China, was better than that for young forests and over-mature forests. These evaluation results can provide a theoretical basis for subsequent research on the ecological restoration effect in degraded natural forests in Xiaoxinganling, Heilongjiang Province, China.

1. Introduction

Forests contribute to global sustainable development [1,2], and questions on how to sustainably manage forests, restore degraded forest ecosystems, and increase forest area coverage globally are among the current major issues pursued by the international community [3,4,5]. Accurate evaluation of degraded forest ecosystems is the first step to solving these problems, and it is also the prerequisite and foundation for forest ecosystem restoration [6,7]. This is because the accuracy and scientific authenticity of the evaluation index system directly affect the assessment of the degree of forest degradation and the formulation of restoration standards, as well as the promulgation and implementation of national restoration policies [8]. Several studies have evaluated the restoration effect for the ecological functions of degraded natural forests and demonstrated that an accurate and comprehensive evaluation of restoration effectiveness is a prerequisite for guiding the restoration process [9,10]. However, most of the studies on the effectiveness of forest vegetation restoration mainly focused on changes in the vegetation cover and the vegetation growth state [11,12,13]. Most studies evaluating the effect of forest ecological restoration focused on a single indicator, such as a single stand type and a single stand age [14,15]. Restoration of degraded natural forests mainly focuses on protecting the existing forest resources while improving the productivity of ecosystems, upgrading the quality of forest resources, and enhancing the biodiversity and stability of forests [16,17,18,19]. However, there is a lack of systematic analysis and evaluation of the restoration effect for the ecological functions of degraded natural forests, and few studies have evaluated the restoration effect for ecological functions of degraded natural forests of different stand ages and forest types [20,21].

There has been some progress in evaluating the effectiveness of forest ecological function restoration; for example, Gatica-Saavedra et al. [22] found that forest restoration assessments varied by region and were not related to the degree of degradation or restoration need. Yu [23] showed that, by 2030, the forest water conservation capacity of artificial restoration is expected to be about 7% higher than that of natural restoration. Moreover, Renato et al. [24] found that natural regeneration surpassed active restoration in achieving tropical forest restoration success for all three biodiversity groups (plants, birds, and invertebrates) and five vegetation structure measures (cover, density, litter, biomass, and height) included in the study. However, different studies used different methods in evaluating degraded forest ecosystems, resulting in different criteria for selecting and grading indicators for forest ecological restoration [25,26,27,28]. This shows the limitations in the current research, which restrict the development of ecological restoration practices and restoration ecology, thus necessitating harmonized criteria for evaluating the restoration effect for forest ecological function [29,30,31].

The aim of this study was to establish a reasonable and perfect evaluation index system for the restoration effect for ecological functions of degraded natural forests so as to comprehensively evaluate the restoration effect for ecological functions of forests in Xiaoxinganling [32,33,34]. The objects of the study were degraded natural forests of different forest types and ages in Xiaoxinganling. The scientific hypothesis was that, in terms of forest type, the ecological restoration effect for mixed forests would be better than that for pure forests and low-quality forests, and in terms of forest age, the ecological restoration effect for near-mature forests and mature forests would be better than that for young forests and over-mature forests. The study conclusions were consistent with the results of hypothesis testing. The results of the study can provide a scientific theoretical basis for subsequent studies on the ecological restoration effect in degraded natural forests in Xiaoxinganling, China.

2. Materials and Methods

2.1. Overview of Study Area

The study area was located in the Xinqing Group Branch of Yichun Forestry Group in Xiaoxinganling, Heilongjiang Province, China (longitude 129°33′44.236″–130°05′55.343″ E, latitude 48°08′06.152″–48°16′34.909″ N). The area has a typical continental humid monsoon climate, with cool and rainy summers and long, cold winters, with the highest temperature reaching 38 °C and the lowest temperature reaching −39 °C. The average annual temperature is 1.5 °C, and the annual precipitation time is about 130 days, with the precipitation mainly concentrated between July and September and an average annual precipitation of about 665 mm. The soil is dark brown loam, but some of the forested areas have valley meadows and swampy soil. The study site had a relatively complete structure of forest communities, and the herbaceous plants mainly included Carex callitrichos, Pinellia ternata, and other grasses of the forest. Shrubs mainly included Acanthopanax senticosus, Spodiopogon cotulifer, and Lonicera japonica. Tree species mainly included Quercus mongolica, Tilia tuan, Betula platyphylla, Fraxinus mandshurica, Acer mono, Picea asperata, Larix gmelinii, Abies delavayi, and Pinus koraiensis, among others.

2.2. Plot Description

There were 30 key sites in total, and three replicated plots were set up in each key site, totaling 90 plots, with the size of each plot being 20 m × 30 m. Field measurements and sample collection were conducted from May to June 2023, and the samples were screened to determine the ecological restoration effect of 43 evaluation indexes, such as the water-holding performance of the understory litter, soil physical and chemical properties, biodiversity, tree layer canopy parameters, and biodiversity status. A map of the study area and the locations of the key sites is shown in Figure 1, and the overview of sample plots is shown in

Table 1. The overview of the sample plots.

Stand Type	Forest Age	Average Diameter at Breast Height/cm	Average Tree Height/cm	Constriction (i.e., Degree of Depression)	Geolocation/E	Geolocation/N
Mixed red pine broadleaf forest	Young forest	13.40 ± 0.62	13.00 ± 1.67	0.35	129°33′44.236″	48°16′34.909″
	Middle-aged forest	15.30 ± 1.08	13.00 ± 1.65	0.34	129°33′44.789″	48°16′46.154″
	Nearly mature forest	21.20 ± 0.96	16.50 ± 1.25	0.34	129°33′47.774″	48°16′55.091″
	Mature forest	34.20 ± 1.23	18.20 ± 2.19	0.65	129°36′28.884″	48°15′41.210″
Mixed coniferous forests of cloud fir	Young forest	13.40 ± 1.05	13.00 ± 2.46	0.53	129°37′16.225″	48°15′38.105″
	Middle-aged forest	15.20 ± 0.62	13.10 ± 2.10	0.44	129°35′01.273″	48°19′39.782″
	Nearly mature forest	21.20 ± 1.48	18.20 ± 1.56	0.61	129°40′48.014″	48°14′35.158″
	Mature forest	23.10 ± 1.57	19.00 ± 1.70	0.75	129°41′09.190″	48°14′14.858″
Mixed larch conifer forest	Young forest	9.50 ± 0.56	7.50 ± 1.28	0.53	129°43′01.308″	48°12′15.848″
	Middle-aged forest	9.00 ± 0.82	8.00 ± 1.51	0.65	129°43′16.806″	48°09′18.865″
	Nearly mature forest	21.70 ± 1.10	19.80 ± 1.74	0.65	129°44′29.236″	48°08′08.398″
Mixed broad-leaved forest	Young forest	10.80 ± 0.36	10.30 ± 1.31	0.43	129°48′43.380″	48°07′41.136″
	Middle-aged forest	18.10 ± 1.25	15.00 ± 1.65	0.71	129°45′25.378″	48°06′26.496″
	Nearly mature forest	22.30 ± 1.71	15.80 ± 1.41	0.72	129°45′25.134″	48°06′45.467″
	Mature forest	18.60 ± 0.72	11.80 ± 1.35	0.56	129°54′02.134″	48°04′23.720″
	Over-mature forest	14.50 ± 0.87	14.10 ± 1.67	0.44	129°55′40.626″	48°04′43.576″
Mixed coniferous broadleaf forest	Young forest	11.00 ± 2.21	9.00 ± 2.00	0.53	129°55′33.596″	48°05′01.976″
	Middle-aged forest	16.00 ± 1.82	16.00 ± 1.66	0.71	130°05′55.343″	48°08′06.152″
	Nearly mature forest	22.00 ± 1.64	15.30 ± 1.57	0.78	130°00′14.043″	48°15′27.757″
	Mature forest	21.20 ± 3.49	17.30 ± 0.82	0.85	129°59′57.340″	48°15′19.465″
Mixed coniferous forest	Young forest	13.20 ± 1.70	13.10 ± 2.31	0.62	129°58′53.337″	48°15′29.958″
	Middle-aged forest	17.30 ± 1.44	15.60 ± 1.31	0.62	129°52′51.223″	48°18′41.037″
	Nearly mature forest	21.20 ± 1.28	16.50 ± 1.14	0.75	129°48′43.404″	48°17′48.245″
	Mature forest	36.00 ± 2.35	22.00 ± 2.44	0.81	129°32′12.818″	48°22′52.991″
	Over-mature forest	22.50 ± 2.08	19.00 ± 1.57	0.62	129°32′23.211″	48°22′53.217″
Birch pure forest	Middle-aged forest	16.50 ± 1.06	18.20 ± 2.16	0.71	129°32′40.027″	48°21′47.800″
Larch pure forest	Middle-aged forest	9.20 ± 1.25	8.10 ± 1.31	0.62	129°34′11.422″	48°21′38.616″
Low-quality coniferous broadleaf mixed forest	Middle-aged forest	12.70 ± 1.37	12.50 ± 2.00	0.44	129°34′10.009″	48°17′00.327″
Low-quality broad-leaved mixed forest	Middle-aged forest	11.90 ± 1.47	11.70 ± 1.90	0.34	129°34′53.609″	48°16′43.461″
Low-quality mixed coniferous forest	Middle-aged forest	13.70 ± 1.42	13.40 ± 2.08	0.33	129°38′21.977″	48°15′29.699″

.

2.3. Deadfall Collection and Measurement

Sampling of the semi-decomposed and semi-decomposed layers of understory litter was conducted in May–June 2023 using the “Z” sampling method, with a sample area of 30 cm × 30 cm. The samples were brought back to the laboratory, and the litter accumulation was obtained using the drying method, in which the dry matter mass of the litter was calculated. Briefly, the litter was oven-dried at 85 °C [11], and the dry matter mass was used to calculate the litter storage capacity. The water-holding capacity of the litter was measured using the soaking method, and the maximum water-holding capacity and the effective storage capacity of the litter were calculated based on the measured litter storage capacity. The soaking durations were 0.25, 0.5, 1, 2, 4, 8, and 24 h.

The following formulas were used to calculate each indicator of deadfall:

$$R_{\mathrm{hmax}}=(G_{24}-G_{\mathrm{d}})/G_{\mathrm{d}}\times 100\%$$

(1)

$$R_{\mathrm{sv}}=0.85\times R_{\mathrm{hmax}}-R_0$$

(2)

$$M_{\mathrm{hmax}}=R_{\mathrm{hmax}}\times M$$

(3)

$$M_{\mathrm{sv}}=R_{\mathrm{sv}}\times M$$

(4)

where $G_{\mathrm{d}}$ and $G_{24}$ represent the mass of dried litter and mass after 24 h of water immersion (g), respectively; $R_0$ represents the natural water-holding capacity (%); $R_{\mathrm{hmax}}$ denotes the maximum water-holding capacity (%); $R_{\mathrm{sv}}$ represents the effective storage capacity (%); $M$ is the storage capacity (t·hm⁻²); $M_{\mathrm{hmax}}$ represents the maximum water-holding capacity (t·hm⁻²); and $M_{\mathrm{sv}}$ is the effective storage capacity (t·hm⁻²).

The water-holding capacity and water uptake rate of the deadfall were calculated using the following equations:

$$\Delta W_{ij}=W_{i(j+1)}-W_{ij}$$

(5)

$$\Delta S_{ij}=\Delta W_{ij}/\Delta t_{ij}$$

(6)

where $\Delta W_{ij}$ represents the water uptake rate of the ith litter sample in the (j + 1) time period (g); $W_{i(j+1)}$ is the wet weight of the ith litter sample in the j + 1 time period (g); $W_{ij}$ represents the wet weight of the ith litter sample in the j time period (g); $\Delta S_{ij}$ denotes the water uptake rate of the ith litter sample in the (j + 1) time period (h) and (j + 1) time period (g·h⁻¹); and $\Delta t_{ij}$ denotes the ith deadwood sample at the j + 1 time period and j time period interval (h).

2.4. Determination of Soil Physical and Chemical Properties

Five soil sampling points were selected in each sample plot based on the “Z” sampling method. The thickness of the soil samples collected at each sampling point was 0–10 cm, and each soil sample was about 1 kg. The soil samples were mixed according to the quadratic method and brought back to the laboratory, where the samples were naturally dried, ground and sieved to analyze their chemical properties. The chemical properties of soil samples were determined as shown in

Table 2. Methods used to determine the soil chemical properties.

Property	Measurement Method(s)	Equipment/Medium
Organic matter mass fraction	Oil-bath potassium dichromate oxidation method	Oil bath
pH value	Water immersion method	Acidimeter
Total nitrogen mass fraction	Automatic Kjeldahl method	Automatic nitrogen determination instrument
Total phosphorus mass fraction	Acid-soluble molybdenum antimony colorimetric method	Atomic absorption spectroscopy analyzer
Total potassium mass fraction	Acid dissolution flame-photometric method	Flame photometer
Ammonium nitrogen mass fraction	Alkaline diffusion method	Diffusion dishes, thermostats
Effective phosphorus mass fraction	Sodium hydroxide leaching molybdenum antimony colorimetric method	Atomic absorption spectroscopy analyzer
Potassium fast-acting mass fraction	Ammonium acetate leaching flame-photometric method	Flame photometer

, and the physical properties of the soil were measured via the ring knife method, using a ring knife with a volume of 100 cm³. The soil samples were immersed in water for 24 h to saturation and their weight was measured with an electronic balance, after which the samples were oven-dried to a constant weight. The measured data were used to calculate the soil bulk weight, the maximum water-holding capacity, the water-holding capacity of the capillary tube, the non-capillary porosity, the capillary porosity, and the total porosity in each modified sample. The calculation formulas were as follows:

$$K=M_{\mathrm{pd}}/M_{\mathrm{pw}}$$

(7)

$$M_{\mathrm{d}}=M_{\mathrm{w}}\times K$$

(8)

$$D=M_{\mathrm{d}}/V$$

(9)

$$M_{\mathrm{hmax}}=(M_{12}-M_{\mathrm{d}})/M_{\mathrm{d}}\times 100\%$$

(10)

$$M_{\mathrm{hc}}=(M_2-M_{\mathrm{d}})/M_{\mathrm{d}}\times 100\%$$

(11)

$$P_{\mathrm{nc}}=(M_{\mathrm{hmax}}-M_{\mathrm{hc}})\times D\times 100\%$$

(12)

$$P_{\mathrm{c}}=M_{\mathrm{hc}}\times D\times 100\%$$

(13)

$$P_{\mathrm{t}}=P_{\mathrm{c}}+P_{\mathrm{nc}}$$

(14)

where $M_{\mathrm{pd}}$ and $M_{\mathrm{pw}}$ are the mass of dried soil and wet soil in the aluminum box (g); $M_{\mathrm{d}}$ and $M_{\mathrm{w}}$ represent the mass of dried soil and the mass of wet soil in the ring knife (g); $K$ is the moisture conversion coefficient; $V$ represents the volume of the ring knife (cm³); $D$ is the soil bulk weight (g·cm^-3); $M_{\mathrm{hmax}}$ and $M_{\mathrm{hc}}$ represent the maximum soil water-holding capacity and capillary water-holding capacity (%); $M_{12}$ and $M_2$ denote the mass of wet soil in the ring knife after 12 h of water absorption and the mass of wet soil after 2 h of placement on dry sand (g); and $P_{\mathrm{nc}}$, $P_{\mathrm{c}}$, and $P_{\mathrm{t}}$ represent the soil non-capillary porosity, capillary porosity, and total porosity (%), respectively.

2.5. Biodiversity Analysis

Various parameters of the trees, shrubs, and herbaceous vegetation in different sample plots were measured. A diameter at breast height (DBC) ruler and a tree height meter were used to measure the DBC and tree height (TH) of the retained trees in the sample plots, and the obtained measurements were used to calculate the growth rate for the DBC and TH of the retained trees in the consecutive years. The coordinates of the trees were determined using the total station. Furthermore, five sample plots of 5 m × 5 m were selected based on the “Z” method to investigate the shrub species, while five plots measuring 1.5 m × 1.5 m were used to investigate the herb species. The species richness index (S), Shannon–Wiener diversity index (H′), and Pielou evenness index (J) were calculated using the measurement data, which were used as the species diversity evaluation factors for ecological restoration. Each index was calculated according to the following formulas:

S = sum of all species in the survey sample

(15)

$$H^{\prime }=-{\displaystyle \sum _{i=1}^s{p}_i}\ln p_i$$

(16)

$$J=H^{\prime }/\ln S$$

(17)

where $p_i=n_i/N$ denotes the relative multiplicity of the ith species, $n_i$ denotes the number of individuals or the cover of species i, and N denotes the sum of the individuals or the cover of all species of the community where species i is located. The ground diameter, tree height, and growth of seedlings planted in the study area were measured, and the survival and successive growth rates of Siberian red pine, sphagnum pine, and Xing’an larch in the different renovation sample plots were calculated.

2.6. Determination of the Canopy Structure Parameters

Five trees were randomly selected in each experimental plot, and the selected trees were mainly natural mixed coniferous and broadleaf trees. The latitude, longitude, and altitude of the point where each tree was located were measured by Global Positioning System (GPS) to determine the due north direction. The data acquisition device, a Mini-O-Mount7MP (Regent, Vancouver, BC, Canada), was levelized on the trees to measure and record the distance of the lens from the ground, and the images from three different directions were collected and processed by the Winscanopy2010a canopy analyzer (Regent, Vancouver, BC, Canada). The data obtained were processed using XLScanopy (Regent, Vancouver, BC, Canada) to analyze the canopy structure. The measured canopy structure parameters were the forest gap fraction, openness, leaf inclination, leaf area index, direct fixation factor, indirect fixation factor, total fixation factor, direct radiation flux under the canopy, scattering radiation flux under the canopy, total radiation flux under the canopy, etc.

2.7. Evaluation of the Ecological Restoration Effect

The ecological restoration effect for the degraded natural forests in Xiaoxinganling, Heilongjiang Province, China, was comprehensively evaluated using principal component analysis. The evaluation procedure followed the assumption that there were n different types of land and m evaluation indicators, and their set constituted the original matrix $X$, as shown in the formula:

$$X={\left(x_{ij}\right)}_{m\times n} i=1,2,\cdots ,m;j=1,2,\cdots ,n$$

(18)

where $x_{ij}$ denotes the measured value of the $i$ indicator for the $j$ sample plot.

2.8. Standardized Raw Matrices

The raw data were standardized using Excel 2021 to eliminate the effect of the order of magnitude and scale. The forward indicators were standardized using Equation (19) while reverse indicators were standardized using Equation (20).

$$X_{ij}^{*}=\frac{X_{ij}}{\overline{X_j}}$$

(19)

$$X_{ij}^{*}=\frac{\overline{X_j}}{X_{ij}}$$

(20)

2.9. Determining the Principal Components

The standardized data were processed using SPSS26 software, and the first m principal components with ANOVA cumulative contributions ≥85% were selected to establish the relationship between the m principal components and the standardized variables, using the formula:

$${{\displaystyle Y}}_k={{\displaystyle b}}_{k1}{{\displaystyle X}}_1^{*}+{{\displaystyle b}}_{k2}{{\displaystyle X}}_2^{*}+\dots +{{\displaystyle b}}_{kp}{{\displaystyle X}}_p^{*}$$

(21)

2.10. Determination of Weights

The ratio of the contribution of the kth principal component to the total contribution of the selected m principal components was used to express the weight of each principal component, as shown in the formula:

$$w_k=\frac{{\lambda }_k}{{\displaystyle \sum _{k=1}^m{\lambda }_k}}$$

(22)

where w_k is the weight of the kth principal component and λ_k is the contribution of the kth principal component.

2.11. Evaluation Function for the Ecological Restoration

A comprehensive evaluation function was established based on the first m principal components determined in Equation (21) and the weights obtained in Equation (22), as follows:

$$F={\displaystyle \sum _{k=1}^m{w}_k{Y}_k}$$

(23)

where F indicates the comprehensive evaluation scores of different ecological restoration sample sites; the higher the score of a comprehensive evaluation, the better the ecological restoration effect of the sample site.

2.12. Hypothesis Checking

The Kruskal–Wallis test was used in this study. The Kruskal–Wallis test is a nonparametric test used to compare three or more independent samples. It can be used to test whether these samples are from the same aggregate or whether the median of one aggregate is different from the median of the others. The original hypothesis of the Kruskal–Wallis test is that all the samples are from the same aggregate, while the alternative hypothesis is that at least one of the samples is from a different aggregate.

3. Results

3.1. Ecological Factor Analysis

A comprehensive evaluation of degraded natural forests was conducted using different forest types at different growth stages in Yichun Sengyong Group Xinqing Group Branch, Xiaoxinganling, Heilongjiang Province, China. Several indicators were selected to establish a comprehensive evaluation system with the aim of achieving a comprehensive evaluation of the transformation effect at each sample site after ecological restoration. The total volume of the understorey litter, total maximum water-holding capacity, and total effective storage capacity of the degraded natural forests in the experimental area were measured from May to June 2023. The physical properties of soil (soil bulk density, the maximum water-holding capacity, capillary porosity, non-capillary porosity, and total porosity) and the chemical properties (soil pH value, the soil organic matter, the total nitrogen content, the hydrolyzed nitrogen content, the total phosphorus content, effective phosphorus content, total potassium content, and quick-acting potassium content) were also measured. The richness index (S), the diversity index (H′), and the Pielou uniformity index (J) were used to determine the diversity of the tree, shrub, and herbaceous species. Furthermore, the canopy structure parameters of the forest stand, including stand gap fraction, openness, leaf area index, leaf inclination angle, direct fixation factor, indirect fixation factor, overall fixation factor, direct radiation flux under the canopy, scattered radiation flux under the canopy, total radiation flux under the canopy, etc., were measured after ecological restoration. The woodland soil was slightly acidic after ecological restoration, with a pH value of approximately 6.0. The closer the pH value of the woodland soil is to 7.0, the more the acidity of the soil is neutralized and the more conducive it is to the growth of woodland vegetation. Therefore, the pH value is regarded as a positive index, and all the positive indexes in this study were standardized according to Equation (18). The soil bearing capacity of woodland soil indicates the firmness of the soil, and the higher the bearing capacity, the more unfavorable it is to the growth of woodland vegetation; therefore, the soil bearing capacity of woodland is regarded as an inverse indicator and was standardized according to Equation (19). The species richness data were standardized for each index after the transformation of degraded natural forests in the study area using SPSS26.0 analysis software and subjected to principal component analysis. The cumulative contribution rate of the first 11 principal components had a range of 87.14% > 85.00%; thus, these components could reflect the basic data, satisfying the description of the ecological restoration effect for degraded natural forests of different ages and types in Xiaoxinganling, Heilongjiang Province, China.

3.2. Principal Component Analysis

The factor loadings of the 11 principal components are shown in

Table 3. Factor loadings of each indicator in the 11 components after ecological restoration.

Factors	Indicators	Principal Components
		1.00	2.00	3.00	4.00	5.00	6.00	7.00	8.00	9.00	10.00	11.00
Soil chemical properties	All N	0.92	0.30	−0.02	−0.06	−0.01	0.13	−0.08	−0.03	−0.01	0.09	0.04
	Full P	0.91	0.26	0.00	−0.08	0.00	0.15	−0.10	−0.04	−0.03	0.11	0.05
	All K (music)	0.91	0.27	−0.01	−0.07	0.00	0.15	−0.10	−0.04	−0.02	0.10	0.04
	Quick-impact N	0.91	0.31	−0.02	−0.04	−0.01	0.12	−0.08	−0.03	−0.01	0.09	0.05
	Quick-impact P	0.76	0.55	−0.09	0.14	−0.07	−0.10	0.10	0.17	0.11	−0.06	0.06
	Quick-acting K	0.76	0.55	−0.10	0.12	−0.06	−0.09	0.12	0.17	0.10	−0.07	0.05
	Organic matter	0.69	−0.54	−0.35	0.05	0.17	−0.05	−0.06	0.03	−0.04	−0.03	0.11
	PH	0.66	−0.59	−0.05	−0.16	0.07	0.20	−0.13	0.01	0.20	0.08	0.09
Soil physical properties	Soil bearing capacity	0.65	−0.43	−0.40	0.16	0.23	−0.09	−0.06	0.04	−0.12	−0.08	0.08
	Maximum water-holding capacity	0.63	−0.23	−0.32	−0.22	0.19	−0.08	−0.38	−0.18	0.22	−0.01	−0.25
	Water-holding capacity of the capillary	0.61	0.56	−0.04	0.00	−0.33	−0.14	0.23	0.28	−0.03	−0.03	0.14
	Non-capillary porosity	0.57	0.54	−0.03	−0.02	−0.37	−0.14	0.24	0.29	−0.04	−0.03	0.15
	Capillary porosity	0.54	0.19	−0.22	0.15	0.51	0.03	−0.04	−0.35	0.13	0.03	−0.03
	Total porosity	0.46	−0.17	0.43	0.41	−0.44	0.19	0.09	−0.11	−0.04	0.16	−0.04
Undecomposed layer of litter	Storage capacity	−0.52	0.65	0.07	0.06	0.03	0.20	−0.10	−0.22	0.12	0.05	0.17
	Natural water-holding capacity	0.57	−0.63	−0.30	−0.02	0.20	−0.13	0.16	0.01	−0.07	0.09	0.07
	Maximum water-holding capacity	0.29	−0.62	0.05	0.20	0.33	0.24	0.29	0.08	−0.19	−0.11	−0.07
	Maximum water-holding capacity	−0.34	−0.61	0.13	0.20	0.25	0.27	0.00	0.18	−0.02	0.33	−0.02
	Effective storage capacity	0.03	0.60	0.18	−0.21	−0.13	0.18	0.17	0.01	−0.39	0.00	−0.40
Semi-decomposed layer of litter	Storage capacity	0.16	−0.59	−0.25	−0.57	−0.09	−0.19	0.10	0.10	0.17	−0.04	0.03
	Natural water-holding capacity	−0.14	0.59	−0.05	0.48	0.43	0.21	0.05	−0.25	0.13	−0.08	−0.09
	Maximum water-holding capacity	0.08	−0.58	0.21	0.38	−0.16	−0.20	−0.12	0.09	0.12	0.19	0.10
	Maximum water-holding capacity	0.43	−0.52	0.35	0.40	−0.18	0.20	0.22	−0.07	−0.08	0.05	−0.05
	Effective storage capacity	−0.43	0.43	−0.18	0.26	0.47	0.16	0.16	−0.11	0.18	0.07	0.29
Stand canopy structure	Forest gap fraction	0.38	−0.49	0.29	0.45	−0.14	0.33	0.28	−0.15	−0.05	−0.19	−0.07
	Kilowatt-hour	0.17	0.22	0.70	0.08	0.12	−0.45	−0.15	−0.05	−0.02	0.09	−0.11
	Divergence angle	0.12	−0.06	0.68	−0.19	0.41	−0.32	0.11	0.11	0.18	−0.16	0.05
	Leaf area index (TAI)	0.33	0.17	0.68	−0.17	0.23	−0.24	0.05	0.03	−0.03	−0.21	−0.04
	Direct fixed-point factor	0.07	0.08	0.68	0.23	0.29	0.26	−0.25	−0.02	−0.16	0.01	0.07
	Indirect fixed-point factor	−0.20	0.26	−0.66	0.10	−0.29	0.26	0.19	−0.17	0.02	0.14	0.18
	Total fixation factor	−0.09	−0.26	0.44	−0.36	−0.02	0.24	0.29	0.07	0.42	−0.31	0.16
	Direct radiation flux under the canopy	−0.13	0.09	0.34	−0.67	0.22	0.26	−0.05	−0.05	0.13	0.38	0.31
	Scattered radiation flux under the canopy	0.21	0.11	0.11	−0.64	0.20	0.26	0.32	0.08	−0.20	−0.04	0.11
	Total subcanopy radiant flux	−0.22	0.44	−0.21	0.49	0.46	−0.19	0.37	0.05	−0.03	0.07	0.07
Tree layer	Richness index	0.07	−0.01	0.42	0.30	−0.57	0.05	0.32	−0.06	0.32	0.28	0.00
	Diversity index	0.36	0.10	0.20	−0.34	0.44	0.16	0.40	−0.10	0.23	0.17	−0.22
	Uniformity index	−0.22	0.10	−0.12	−0.56	−0.29	0.63	−0.05	0.04	−0.09	−0.18	−0.14
Shrub layer	Richness index	0.14	−0.22	−0.09	−0.16	0.25	−0.08	0.53	−0.45	−0.36	0.07	0.02
	Diversity index	0.48	0.27	0.14	0.22	0.03	0.39	−0.49	−0.14	0.13	−0.21	−0.09
	Uniformity index	−0.02	0.16	−0.13	0.21	0.45	0.19	0.01	0.68	−0.04	−0.22	−0.08
Herbaceous layer	Richness index	−0.21	−0.21	−0.01	0.30	0.17	0.39	−0.06	0.64	0.05	0.12	0.06
	Diversity index	0.10	0.23	0.23	−0.20	0.20	−0.08	−0.28	0.22	−0.39	0.47	0.06
	Uniformity index	0.04	−0.17	0.30	0.08	−0.08	0.04	−0.22	−0.25	−0.36	−0.38	0.57

. The coefficient matrix score of each principal component after ecological restoration is shown in

Table 4. The coefficient matrix scores of the indicators in the 11 principal components after ecological restoration.

Factors	Indicators	Principal Components
		1.00	2.00	3.00	4.00	5.00	6.00	7.00	8.00	9.00	10.00	11.00
Soil chemical properties	All N	0.00	−0.03	0.07	0.02	−0.03	0.02	−0.11	−0.13	−0.27	−0.30	0.54
	Full P	0.01	−0.08	0.05	0.10	−0.05	−0.09	−0.06	0.05	0.09	0.15	0.10
	All K (music)	0.02	0.02	0.03	−0.17	0.06	0.11	0.16	0.04	−0.15	−0.03	0.11
	Quick-impact N	−0.01	−0.04	0.10	−0.10	−0.01	0.11	0.14	0.04	0.31	−0.24	0.15
	Quick-impact P	−0.02	0.01	−0.03	−0.15	−0.09	0.28	−0.03	0.02	−0.07	−0.14	−0.13
	Quick-acting K	−0.01	0.01	0.08	−0.18	0.07	0.12	−0.02	−0.02	0.09	0.30	0.30
	Organic matter	0.04	0.02	0.05	−0.09	0.14	0.07	0.20	−0.06	0.17	0.13	−0.21
	PH	0.00	0.09	0.04	−0.06	−0.04	0.08	0.08	0.01	−0.29	0.00	−0.38
Soil physical properties	Soil bearing capacity	0.03	−0.09	0.01	0.06	0.10	0.11	0.14	0.04	−0.14	−0.09	−0.07
	Maximum water-holding capacity	0.05	−0.03	0.10	0.11	−0.14	0.09	0.04	−0.06	−0.03	0.12	−0.04
	Water-holding capacity of the capillary	0.01	0.00	0.10	0.08	−0.18	0.02	0.15	−0.03	0.23	0.23	0.00
	Non-capillary porosity	0.01	−0.03	−0.02	−0.04	0.08	−0.04	0.26	−0.24	−0.27	0.05	0.02
	Capillary porosity	0.05	−0.08	0.08	0.11	−0.06	0.09	0.11	−0.04	−0.06	0.04	−0.05
	Total porosity	0.04	−0.07	0.07	0.12	−0.04	0.15	0.14	−0.08	−0.04	−0.15	−0.06
Undecomposed layer of litter	Storage capacity	0.06	−0.09	−0.07	−0.01	0.06	−0.06	0.08	0.00	−0.05	0.07	0.07
	Natural water-holding capacity	0.07	−0.09	−0.01	−0.04	0.02	0.09	−0.06	0.00	0.15	0.07	0.09
	Maximum water-holding capacity	0.05	0.04	0.03	0.06	0.01	0.17	−0.24	−0.07	0.10	−0.16	−0.09
	Maximum water-holding capacity	0.07	−0.08	−0.08	0.01	0.05	−0.02	−0.03	0.02	−0.03	−0.02	0.10
	Effective storage capacity	0.07	−0.06	−0.10	0.04	0.07	−0.04	−0.03	0.02	−0.09	−0.06	0.08
Semi-decomposed layer of litter	Storage capacity	−0.01	0.09	−0.01	0.13	0.13	0.10	0.02	−0.13	0.10	−0.07	−0.09
	Natural water-holding capacity	−0.05	0.09	0.02	0.02	0.01	0.09	−0.05	−0.12	0.09	0.04	0.16
	Maximum water-holding capacity	0.07	−0.03	−0.08	−0.06	0.06	−0.03	−0.19	−0.10	0.17	−0.01	−0.24
	Maximum water-holding capacity	−0.05	0.07	−0.04	0.07	0.15	0.07	0.08	−0.06	0.13	0.06	0.27
	Effective storage capacity	−0.02	0.06	−0.05	0.13	0.15	−0.09	0.18	0.03	−0.02	0.06	0.07
Stand canopy structure	Forest gap fraction	0.06	0.08	−0.01	0.00	−0.12	−0.06	0.12	0.16	−0.03	−0.02	0.14
	Kilowatt-hour	0.06	0.08	−0.01	0.00	−0.10	−0.06	0.11	0.15	−0.02	−0.03	0.13
	Divergence angle	0.06	0.03	−0.05	0.04	0.16	0.01	−0.02	−0.19	0.10	0.02	−0.03
	Leaf area index (TAI)	−0.04	−0.09	0.03	0.05	0.08	0.12	0.00	0.10	−0.02	0.27	−0.02
	Direct fixed-point factor	0.10	0.04	0.00	−0.02	0.00	0.07	−0.05	−0.02	−0.02	0.09	0.05
	Indirect fixed-point factor	0.08	0.08	−0.02	0.04	−0.02	−0.05	0.05	0.09	0.08	−0.05	0.05
	Total fixation factor	0.10	0.05	0.00	−0.01	0.00	0.06	−0.04	−0.02	−0.01	0.07	0.04
	Direct radiation flux under the canopy	0.10	0.04	0.00	−0.02	0.00	0.07	−0.05	−0.02	−0.01	0.08	0.03
	Scattered radiation flux under the canopy	0.08	0.08	−0.03	0.03	−0.02	−0.04	0.06	0.09	0.08	−0.05	0.04
	Total subcanopy radiant flux	0.10	0.04	0.00	−0.02	0.00	0.06	−0.04	−0.01	0.00	0.07	0.04
Tree layer	Richness index	0.00	0.02	−0.03	0.06	0.14	0.09	0.00	0.36	−0.03	−0.18	−0.08
	Diversity index	0.04	0.02	0.16	−0.05	0.07	−0.11	0.03	0.02	−0.02	−0.16	−0.04
	Uniformity index	0.01	−0.01	0.16	−0.05	0.13	−0.14	0.05	0.06	0.13	−0.13	0.05
Shrub layer	Richness index	−0.02	−0.03	0.00	0.08	0.05	0.17	−0.03	0.34	0.04	0.10	0.06
	Diversity index	0.02	0.03	0.17	0.02	0.04	−0.20	−0.07	−0.03	−0.01	0.07	−0.11
	Uniformity index	0.01	0.01	0.16	0.06	0.09	0.12	−0.12	−0.01	−0.12	0.01	0.07
Herbaceous layer	Richness index	0.01	0.03	0.05	−0.05	0.06	−0.04	−0.14	0.12	−0.29	0.38	0.05
	Diversity index	−0.02	0.04	−0.16	0.03	−0.09	0.12	0.09	−0.09	0.01	0.11	0.17
	Uniformity index	0.02	−0.09	−0.06	−0.15	−0.03	−0.09	0.05	0.05	0.13	−0.03	0.03

. They more intuitively reflect the importance of the evaluation indicators. Additionally, the ecological restoration effect at each sample plot with different forest types and ages was comprehensively evaluated. The factor scores and comprehensive ranking of the 11 principal components are shown in

Table 5. Comprehensive evaluation results for ecological restoration effects.

Forest Type	Forest Age	F1	F2	F3	F4	F5	F6	F7	F8	F9	F10	F11	Aggregate Score	Rankings
Mixed red pine broadleaf forest	Young forest	1.45	−0.95	−0.04	−0.19	−0.89	−1.58	−0.30	−1.48	1.14	−0.21	0.29	0.05	25
	Middle-aged forest	1.08	−1.51	0.21	−0.72	−0.95	1.34	0.34	0.04	−0.31	0.67	0.96	0.01	30
	Nearly mature forests	0.45	−1.30	−1.62	−1.25	−0.84	−1.24	0.08	1.78	0.42	1.14	−0.85	0.46	9
	Mature forest	0.27	−1.35	0.73	−0.92	1.13	−0.06	0.27	−0.85	0.67	0.13	1.78	0.04	26
Mixed coniferous forest of spruce (Pinus sylvestris)	Young forest	−0.12	−0.30	−0.39	−2.61	1.52	0.02	0.43	−0.94	−0.25	−0.82	−0.67	0.34	11
	Middle-aged forest	−0.24	−1.44	−1.70	−0.37	−0.81	−0.12	0.60	1.24	−0.49	−0.82	0.17	0.58	5
	Nearly mature forests	−0.82	−0.64	−0.82	−0.84	−0.18	−0.67	−0.51	−1.00	0.95	0.08	−1.49	0.64	3
	Mature forest	0.71	−1.25	1.11	0.70	−0.06	0.81	−0.03	−1.04	0.53	−1.79	−0.17	0.09	24
Mixed larch- conifer forest	Young forest	0.30	−0.72	0.79	0.81	−0.43	1.76	−0.95	−0.76	−1.69	0.83	−0.54	0.03	27
	Middle-aged forest	0.25	−0.56	−0.26	−0.17	−1.57	1.62	−0.82	−0.64	0.38	0.40	−0.43	0.19	19
	Nearly mature forests	−1.38	−0.31	1.27	−0.47	−0.65	−0.33	−1.27	0.78	−1.56	−1.16	1.16	0.48	7
Mixed broad- leaved forest	Young forest	0.55	0.43	−0.08	−0.16	1.96	0.61	−1.14	0.69	−1.02	−1.37	1.15	0.33	12
	Middle-aged forest	−0.04	0.31	−1.68	1.24	1.13	2.44	0.10	0.03	1.21	0.14	0.18	0.29	14
	Nearly mature forests	−0.32	0.60	−0.52	0.03	0.91	−0.51	−2.30	0.29	1.14	1.07	1.63	0.03	29
	Mature forest	0.39	−0.88	1.30	2.28	0.21	−0.97	−0.13	0.80	0.03	−0.16	−0.94	0.27	15
	Over-mature forest	0.48	−0.71	−0.04	1.65	0.06	−0.64	0.63	1.24	−0.62	0.76	−0.03	0.21	18
Mixed coniferous broadleaf forest	Young forest	1.89	1.28	−1.80	1.71	0.05	−0.72	0.06	−1.17	0.47	0.00	0.69	0.63	4
	Middle-aged forest	−1.55	0.43	0.80	−0.18	0.36	−0.16	1.02	0.11	2.28	0.33	1.18	0.03	28
	Nearly mature forest	1.07	0.39	1.30	−1.11	1.72	1.02	1.37	1.58	−0.17	1.89	−0.47	0.79	1
	Mature forest	−1.78	−0.10	−1.47	0.81	0.12	−0.05	1.56	0.67	−1.07	−0.18	−0.06	0.48	8
Mixed coniferous forest	Young forest	−1.29	−0.06	−0.36	0.90	1.22	−1.12	0.51	−0.94	−1.16	1.29	0.44	0.26	16
	Middle-aged forest	−1.82	−0.70	1.59	0.87	−0.09	−0.64	0.69	−1.11	0.40	0.49	−0.32	0.37	10
	Nearly mature forest	0.79	2.09	1.04	−0.79	−2.10	−0.44	1.25	−0.51	−0.71	1.46	1.13	0.52	6
	Mature forest	−0.87	1.41	−0.82	−0.27	−0.87	0.77	1.83	−1.16	−0.56	−1.54	0.32	0.13	23
	Over-mature forest	1.90	0.98	0.49	−0.03	0.27	−1.20	0.76	0.89	−0.52	−1.70	0.13	0.68	2
Birch pure forest (Betula alba var. vulgaris)	Middle-aged forest	0.21	0.61	−0.26	−0.57	1.06	−1.06	−1.05	−1.46	−1.78	0.29	−1.85	0.13	22
Larch pure forest	Middle-aged forest	−0.98	0.88	−0.32	−0.26	−1.38	−0.12	−1.91	1.11	−0.27	−0.29	1.00	0.32	13
Low-quality mixed coniferous broadleaf forest	Middle-aged forest	−0.24	1.56	0.04	−0.08	−0.32	0.83	−1.00	−0.11	0.15	1.19	−1.52	0.18	20
Low-quality mixed broad-leaved forest	Middle-aged forest	−0.28	1.29	0.99	−0.13	−0.04	−0.45	−0.45	1.09	1.70	−0.97	−1.43	0.25	17
Low-quality mixed coniferous forest	Middle-aged forest	−0.05	0.46	0.52	0.12	−0.46	0.87	0.37	0.83	0.72	−1.14	−1.36	0.17	21

.

3.3. Hypothesis Checking Analysis

The results of hypothesis checking are shown in

Table 6. Hypothesis checking for forest type.

Factors	Forest Type	N	Rank Mean	Kruskal–Wallis H	p
All N/g·kg3	Mixed forests	75	51.47	23.61	0.00
	Pure forest	6	13.83
	Low-quality forest	9	16.83
Full P/g·kg3	Mixed forests	75	52.65	33.83	0.00
	Pure forest	6	8
	Low-quality forest	9	10.89
All K (music)/g·kg3	Mixed forests	75	52.33	30.81	0.00
	Pure forest	6	10.67
	Low-quality forest	9	11.83
Quick-impact N/mg·kg3	Mixed forests	75	49.27	11.71	0.00
	Pure forest	6	39.33
	Low-quality forest	9	18.22
Quick-impact P/mg·kg3	Mixed forests	75	52.24	30.01	0.00
	Pure forest	6	10.83
	Low-quality forest	9	12.44
Quick-acting K/mg·kg3	Mixed forests	75	52.79	35.35	0.00
	Pure forest	6	12.83
	Low-quality forest	9	6.5
Organic matter/g·kg3	Mixed forests	75	50.75	19.90	0.00
	Pure forest	6	8.33
	Low-quality forest	9	26.56
PH	Mixed forests	75	49.55	12.15	0.00
	Pure forest	6	34.75
	Low-quality forest	9	18.94
Soil bearing capacity/g ·cm3	Mixed forests	75	52.65	33.92	0.00
	Pure forest	6	6.92
	Low-quality forest	9	11.67
Maximum water-holding capacity/%	Mixed forests	75	51.48	23.61	0.00
	Pure forest	6	14.67
	Low-quality forest	9	16.22
Water-holding capacity of the capillary/%	Mixed forests	75	51.85	26.83	0.00
	Pure forest	6	17.25
	Low-quality forest	9	11.39
Non-capillary porosity/%	Mixed forests	75	51.72	26.05	0.00
	Pure forest	6	15.67
	Low-quality forest	9	13.56
Capillary porosity/%	Mixed forests	75	52.04	28.46	0.00
	Pure forest	6	9.25
	Low-quality forest	9	15.17
Total porosity/%	Mixed forests	75	50.77	19.10	0.00
	Pure forest	6	26.33
	Low-quality forest	9	14.39
Storage capacity/t·hm−2	Mixed forests	75	52.54	32.71	0.00
	Pure forest	6	11.17
	Low-quality forest	9	9.72
Natural water-holding capacity/%	Mixed forests	75	49.36	9.99	0.01
	Pure forest	6	22.83
	Low-quality forest	9	28.44
Maximum water-holding capacity/%	Mixed forests	75	50.63	18.15	0.00
	Pure forest	6	27.17
	Low-quality forest	9	15
Maximum water-holding capacity/t·hm−2	Mixed forests	75	52.91	36.24	0.00
	Pure forest	6	9.83
	Low-quality forest	9	7.56
Effective storage capacity/t·hm−2	Mixed forests	75	52	29.26	0.00
	Pure forest	6	22.67
	Low-quality forest	9	6.56
Storage capacity/t·hm−2	Mixed forests	75	48.23	7.40	0.03
	Pure forest	6	18.83
	Low-quality forest	9	40.56
Natural water-holding capacity/%	Mixed forests	75	50.89	19.26	0.00
	Pure forest	6	19.17
	Low-quality forest	9	18.11
Maximum water-holding capacity/t·hm−2	Mixed forests	75	48.73	6.99	0.03
	Pure forest	6	31.83
	Low-quality forest	9	27.67
Maximum water-holding capacity/t·hm−2	Mixed forests	75	52.33	30.96	0.00
	Pure forest	6	14.08
	Low-quality forest	9	9.5
Effective storage capacity/t·hm−2	Mixed forests	75	48.65	6.61	0.04
	Pure forest	6	27.92
	Low-quality forest	9	30.94
Forest gap fraction/%	Mixed forests	75	48.86	7.54	0.02
	Pure forest	6	26.17
	Low-quality forest	9	30.39
Kilowatt-hour/%	Mixed forests	75	50.17	14.84	0.00
	Pure forest	6	27.92
	Low-quality forest	9	18.33
Divergence angle/%	Mixed forests	75	48.05	4.88	0.02
	Pure forest	6	38.92
	Low-quality forest	9	28.61
Leaf area index (TAI)	Mixed forests	75	49.25	10.27	0.01
	Pure forest	6	34.92
	Low-quality forest	9	21.28
Direct fixed-point factor	Mixed forests	75	49.04	8.33	0.02
	Pure forest	6	25.83
	Low-quality forest	9	29.11
Indirect fixed-point factor	Mixed forests	75	49.53	10.99	0.00
	Pure forest	6	21.17
	Low-quality forest	9	28.11
Total fixation factor	Mixed forests	75	48.69	7.13	0.03
	Pure forest	6	24.33
	Low-quality forest	9	33
Direct radiation flux under the canopy/mol·m−2·d−1	Mixed forests	75	48.49	6.65	0.04
	Pure forest	6	23.42
	Low-quality forest	9	35.28
Scattered radiation flux under the canopy/mol·m−2·d−1	Mixed forests	75	50.13	14.64	0.00
	Pure forest	6	28.08
	Low-quality forest	9	18.5
Total subcanopy radiant flux/mol·m−2·d−1	Mixed forests	75	48.19	5.75	0.03
	Pure forest	6	23.83
	Low-quality forest	9	37.56
Richness index	Mixed forests	75	48.55	7.44	0.02
	Pure forest	6	22.67
	Low-quality forest	9	35.28
Diversity index	Mixed forests	75	49.48	10.78	0.01
	Pure forest	6	30.33
	Low-quality forest	9	22.44
Uniformity index	Mixed forests	75	52.69	34.18	0.00
	Pure forest	6	10.67
	Low-quality forest	9	8.78
Richness index	Mixed forests	75	51.45	25.03	0.00
	Pure forest	6	10
	Low-quality forest	9	19.56
	Mixed forests	75	51.47	23.71	0.00
	Pure forest	6	19.33
	Low-quality forest	9	13.22
Uniformity index	Mixed forests	75	52.44	31.92	0.00
	Pure forest	6	10
	Low-quality forest	9	11.33
Richness index	Mixed forests	75	51	21.25	0.00
	Pure forest	6	22.92
	Low-quality forest	9	14.72
Diversity index	Mixed forests	75	50.45	16.27	0.00
	Pure forest	6	22.5
	Low-quality forest	9	19.56
Uniformity index	Mixed forests	75	51.81	27.66	0.00
	Pure forest	6	23.42
	Low-quality forest	9	7.61

and

Table 7. Hypothesis checking for forest age.

Factors	Forest Age	N	Rank Mean	Kruskal-Wallis H	p
All N/g·kg3	Young forest	18	15.86	20.19	0.00
	Nearly mature forest	18	38.61
	Mature forest	15	35.33
	Over-mature forest	6	23.75
Full P/g·kg3	Young forest	18	23.08	16.27	0.00
	Nearly mature forest	18	30.31
	Mature forest	15	41.1
	Over-mature forest	6	12.58
All K (music)/g·kg3	Young forest	18	21.78	11.91	0.01
	Nearly mature forest	18	30.89
	Mature forest	15	39.47
	Over-mature forest	6	18.83
Quick-impact N/mg·kg3	Young forest	18	23.89	22.54	0.00
	Nearly mature forest	18	35.72
	Mature forest	15	37.27
	Over-mature forest	6	3.5
Quick-impact P/mg·kg3	Young forest	18	25.92	9.85	0.02
	Nearly mature forest	18	30.25
	Mature forest	15	37.4
	Over-mature forest	6	13.5
Quick-acting K/mg·kg3	Young forest	18	23.94	7.11	0.04
	Nearly mature forest	18	32.25
	Mature forest	15	35.43
	Over-mature forest	6	18.33
Organic matter/g·kg3	Young forest	18	26.31	2.04	0.01
	Nearly mature forest	18	32.5
	Mature forest	15	30.27
	Over-mature forest	6	23.42
PH	Young forest	18	22	5.78	0.02
	Nearly mature forest	18	31.94
	Mature forest	15	34.83
	Over-mature forest	6	26.58
Soil bearing capacity/g ·cm3	Young forest	18	18.78	16.51	0.00
	Nearly mature forest	18	37.28
	Mature forest	15	35.7
	Over-mature forest	6	18.08
Maximum water-holding capacity/%	Young forest	18	25.64	2.57	0.00
	Nearly mature forest	18	28.42
	Mature forest	15	34.6
	Over-mature forest	6	26.83
Water-holding capacity of the capillary/%	Young forest	18	19.44	20.06	0.00
	Nearly mature forest	18	32.39
	Mature forest	15	41.93
	Over-mature forest	6	15.17
Non-capillary porosity/%	Young forest	18	20.06	14.62	0.00
	Nearly mature forest	18	38
	Mature forest	15	33
	Over-mature forest	6	18.83
Capillary porosity/%	Young forest	18	21.83	5.13	0.00
	Nearly mature forest	18	33.5
	Mature forest	15	31.47
	Over-mature forest	6	20.83
Total porosity/%	Young forest	18	17.94	13.21	0.00
	Nearly mature forest	18	35.78
	Mature forest	15	35
	Over-mature forest	6	26.83
Storage capacity/t·hm−2	Young forest	18	14.61	28.40	0.00
	Nearly mature forest	18	36.14
	Mature forest	15	41.9
	Over-mature forest	6	18.5
Natural water-holding capacity/%	Young forest	18	27.83	2.60	0.02
	Nearly mature forest	18	32.44
	Mature forest	15	29.8
	Over-mature forest	6	20.17
Maximum water-holding capacity/%	Young forest	18	22.89	10.60	0.01
	Nearly mature forest	18	39
	Mature forest	15	27.63
	Over-mature forest	6	20.75
Maximum water-holding capacity/t·hm−2	Young forest	18	16	18.86	0.00
	Nearly mature forest	18	38.03
	Mature forest	15	35.3
	Over-mature forest	6	25.17
Effective storage capacity/t·hm−2	Young forest	18	14.22	24.77	0.00
	Nearly mature forest	18	39.94
	Mature forest	15	35.4
	Over-mature forest	6	24.5
Storage capacity/t·hm−2	Young forest	18	20.5	8.95	0.03
	Nearly mature forest	18	31.31
	Mature forest	15	37.3
	Over-mature forest	6	26.83
Natural water-holding capacity/%	Young forest	18	26.39	1.23	0.05
	Nearly mature forest	18	32.64
	Mature forest	15	32.73
	Over-mature forest	6	28.58
Maximum water-holding capacity/t·hm−2	Young forest	18	26.06	5.77	0.02
	Nearly mature forest	18	35.78
	Mature forest	15	28.4
	Over-mature forest	6	19
Maximum water-holding capacity/t·hm−2	Young forest	18	22.17	8.32	0.04
	Nearly mature forest	18	35.36
	Mature forest	15	33.1
	Over-mature forest	6	20.17
Effective storage capacity/t·hm−2	Young forest	18	19.75	10.65	0.01
	Nearly mature forest	18	31.69
	Mature forest	15	38
	Over-mature forest	6	26.17
Forest gap fraction/%	Young forest	18	17.58	2.31	0.01
	Nearly mature forest	18	28.06
	Mature forest	15	34.1
	Over-mature forest	6	23.33
Kilowatt-hour/%	Young forest	18	20	1.04	0.00
	Nearly mature forest	18	29.17
	Mature forest	15	31.93
	Over-mature forest	6	24.17
Divergence angle/%	Young forest	18	27.11	2.58	0.04
	Nearly mature forest	18	33.22
	Mature forest	15	29.13
	Over-mature forest	6	21.67
Leaf area index (TAI)	Young forest	18	25.83	1.44	0.01
	Nearly mature forest	18	32.33
	Mature forest	15	29.4
	Over-mature forest	6	27.5
Direct fixed-point factor	Young forest	18	28.08	3.56	0.01
	Nearly mature forest	18	33.58
	Mature forest	15	28.53
	Over-mature forest	6	19.17
Indirect fixed-point factor	Young forest	18	21.31	4.00	0.00
	Nearly mature forest	18	34.19
	Mature forest	15	27.5
	Over-mature forest	6	19.25
Total fixation factor	Young forest	18	27.28	3.65	0.00
	Nearly mature forest	18	33.75
	Mature forest	15	29.17
	Over-mature forest	6	19.5
Direct radiation flux under the canopy/mol·m−2·d−1	Young forest	18	18.03	3.57	0.01
	Nearly mature forest	18	33.61
	Mature forest	15	28.57
	Over-mature forest	6	19.17
Scattered radiation flux under the canopy/mol·m−2·d−1	Young forest	18	21.47	3.19	0.00
	Nearly mature forest	18	31.72
	Mature forest	15	33.03
	Over-mature forest	6	20.33
Total subcanopy radiant flux/mol·m−2·d−1	Young forest	18	24.67	5.31	0.00
	Nearly mature forest	18	34.67
	Mature forest	15	31
	Over-mature forest	6	20
Richness index	Young forest	18	21.61	9.54	0.02
	Nearly mature forest	18	37.31
	Mature forest	15	30.27
	Over-mature forest	6	23.08
Diversity index	Young forest	18	21.31	2.16	0.04
	Nearly mature forest	18	30.47
	Mature forest	15	31.53
	Over-mature forest	6	20.33
Uniformity index	Young forest	18	22.92	3.32	0.05
	Nearly mature forest	18	34.42
	Mature forest	15	28.9
	Over-mature forest	6	21.25
Diversity index	Young forest	18	16.94	27.97	0.00
	Nearly mature forest	18	39.28
	Mature forest	15	37.6
	Over-mature forest	6	12.83
	Young forest	18	20.61	28.77	0.00
	Nearly mature forest	18	32.81
	Mature forest	15	48.63
	Over-mature forest	6	23.67
Uniformity index	Young forest	18	12.44	41.59	0.00
	Nearly mature forest	18	39.44
	Mature forest	15	42.87
	Over-mature forest	6	12.67
Richness index	Young forest	18	20.61	12.27	0.01
	Nearly mature forest	18	38.58
	Mature forest	15	29.97
	Over-mature forest	6	23
Diversity index	Young forest	18	24.56	4.58	0.01
	Nearly mature forest	18	35.14
	Mature forest	15	29.37
	Over-mature forest	6	23
Uniformity index	Young forest	18	18.69	20.73	0.00
	Nearly mature forest	18	35.81
	Mature forest	15	39.23
	Over-mature forest	6	13.92

. It can be concluded that the indicators of the ecological restoration effects for the mixed forest were obviously better than those for the pure forest and low-quality forest. The ecological restoration effects for the near-mature forest and mature forest were significantly better than those for the young forest and over-mature forest, and the corresponding hypothesis test significance p-values were less than 0.05, which was considered to be statistically significant.

4. Discussion

It was found that the ecological resilience of mixed forests had a higher score. The reason for this is that, compared with pure forests of a single species and low-quality forests and mixed forests of complex species [35], the soil fertility and the biodiversity of the forest stand are reduced [36], making the forest stand less resilient [37], which results in the reduction in the capacity of the forest stand to resist disasters and the capacity of the forest stand for ecological resilience [38]. In contrast, mixed forests make full use of the space of the forest stand, and their horizontal and vertical structures are more complex [39], with a more pronounced stratification phenomenon regarding light utilization in the forest [40,41]. The horizontal and vertical structures are more complex [42], the canopy is more obvious, the utilization rate of light in the forest is higher, the intensity of light in the forest decreases gradually with the height, the proportion of direct light in the forest is lower, and the proportion of scattered light in the forest is higher and reasonably distributed [43,44]. At the same time, the amount of forest litter on the surface of mixed forests is greater and more complex than that of pure forests [45], which is more conducive to increasing the number and species of soil microorganisms [46,47]. The decomposition of forest litter can improve the soil and effectively improve the physical and chemical properties of the soil [48,49]. Therefore, the ecological recovery ability of pure forest stands should be improved by reforming pure forests with reference to mixed forests with good overall scores [50]. The climate factor is a secondary factor affecting the evaluation of forest ecosystem resilience [51], and different climatic conditions have significant positive effects on forest ecosystem resilience [52,53]. At present, with global warming, forest ecosystem resilience has also changed, and many studies have shown that adaptive forest management is one of the most effective ways to cope with climate change—including strengthening the protection of natural forests, moderate replanting, controlling the density of forest stands, and formulating a scientific forest management policy—by improving the adaptability of forests to climate change and then improving the resilience of forest ecosystems [54,55].

Forests have diverse ecological restoration evaluation indicators due to their diversity [56], making it difficult to formulate a unified forest ecological restoration evaluation system [57,58]. In this study, we selected more representative indicators based on the comparative analysis of previous studies to establish an evaluation index system for ecological restoration in degraded forests in Xiaoxinganling, Heilongjiang Province, China. The 43 indicators selected in this study were correlated with each other, indicating that the index system could reflect the main characteristics of the forest ecological restoration system in a comprehensive way [59,60]. However, the evaluation index system had some shortcomings and requires in-depth research and exploration to (1) construct a long-term continuous observation network for evaluation indexes for the degraded forest ecosystem in China [61,62]—this would allow for long-term monitoring, comparison, and assessment of different stages of forest ecological restoration; (2) strengthen the research on the evaluation index system for forest ecological restoration in terms of forest ecosystem functions and services; (3) combine remote sensing analysis with sample plot investigation to analyze the spatial distribution of the evaluation index system for forest ecological restoration and its ecological process on a regional scale.

5. Conclusions

This study used degraded natural forests of different types and ages in the Xinqing Group Branch of Yichun Sengyong Group, Xiaoxinganling, Heilongjiang Province, China, as the research objects. Samples were collected, processed, and analyzed, and principal component analysis was applied to comprehensively evaluate the effect of ecological restoration for the degraded natural forests in the study area. The understory deadwood was evaluated to determine the ecological restoration effect. The 43 evaluation indexes, including the water-holding performance of the understory litter, soil physical and chemical properties, the canopy structure of the tree layer, and biodiversity, were selected to determine the effect of ecological restoration. We calculated the scores of 11 principal components selected after ecological restoration and found that they had weights of 0.25, 0.18, 0.11, 0.10, 0.09, 0.06, 0.06, 0.05, 0.04, 0.03, and 0.03. Furthermore, we utilized the comprehensive evaluation function to comprehensively evaluate the ecological restoration effect for degraded natural forests of different types and ages. The results showed that the comprehensive scores for the ecological restoration effects at each sample site were, in descending order: coniferous and broadleaf hybrid near-mature forests (0.79) > coniferous and broadleaf hybrid over-mature forests (0.68) > spruce mixed coniferous and broadleaf hybrid near-mature forests (0.64) > mixed coniferous and broadleaf hybrid young forests (0.63) > mixed spruce coniferous and broadleaf hybrid middle-aged forests (0.58) > coniferous and broadleaf hybrid near-mature forests (0.52) > mixed larch needle broadleaf hybrid near-mature forests (0.48), mixed needle broadleaf hybrid mature forests (0.48) > mixed red pine broadleaf near-mature forests (0.46) > mixed needle broadleaf hybrid middle-aged forests (0.37) > mixed cloud fir needle broadleaf hybrid young forests (0.34) > mixed broadleaf young forests (0.33) > mixed larch pure forests (0.32) > mixed broadleaf middle-aged forests (0.29) > mixed broadleaf mature forests (0.27) > conifer mixed young forests (0.26) > low-quality mixed broadleaf forests (0.25) > mixed broadleaf over-mature forests (0.21) > mixed larch middle-aged forests (0.19) > mixed broadleaf needle forests (0.18) > low-quality mixed broadleaf needle forests (0.17) > white birch pure forests (0.13), mixed mature forests (0.13) > mixed mature forests of cloud abies (0.09) > mixed mature forests of cloudy cold firs > mixed red pine broadleaf young forests (0.08) > mixed red pine broadleaf mature forests (0.07) > mixed larch needle broadleaf young forests (0.05) > mixed needle broadleaf middle-aged forests (0.04) > mixed broadleaf near-mature forest (0.03) > mixed red pine broadleaf middle-aged forest (0.01).

Based on the forest type score, it can be concluded that mixed coniferous and broadleaf forests, mixed coniferous forests, and mixed broadleaf forests scored higher, while pure white birch forests, pure larch forests, and the three low-quality forests scored lower. This indicated that the ecological restoration effect for mixed forests in the Xinqing Group Branch of Yichun Sengan Group, Xiaoxinganling, Heilongjiang Province, China, was better than that for pure forests and low-quality forests. Additionally, based on forest age, the scores for near-mature and mature forests were generally higher than those for juvenile and over-mature forests, indicating that the ecological restoration effect for near-mature forests and mature forests in the Xinqing Group Branch of Yichun Forestry Group in Xiaoxinganling, Heilongjiang Province, China, was better than that for young-aged forests and over-mature forests. Thus, these evaluation results provide a theoretical basis for subsequent research on the ecological restoration effect for degraded natural forests in Xiaoxinganling, Heilongjiang Province, China.