Short-Term Effects of Pine Plantations on Vegetation and Soil in Northern Mongolia

Abstract

This study looked at the impact of planting year differences on vegetation and soil parameters in Pinus sylvestris plantation forests in northern Mongolia. Tujiin nars region has three study sites: 18- to 20-year-old plantation forests planted in 2003, 2004, and 2005, as well as natural regeneration stand, natural forest, and steppe area. Three plots with distinct plantation stand types were constructed at each location to investigate changes in vegetation and soil attributes. Understory vegetation was comprised of 92 species of plants, including 4 shrubs, 1 semi-shrub, species, and 84 herb species (5 annuals and 87 perennials) belonging to 78 genera of 35 families. Species richness, total coverage, and biomass accumulation were significantly higher in the oldest plantation (2003). Soil pH ranged from 6.52 to 7.41, across plantations, with steppe and forest edge soils being alkaline and plantation soils slightly acidic. Soil temperature varied between 17.7 and 24.7 °C, where the lowest temperature was in the naturally regenerated stand and the highest was in the steppe plot. The average soil moisture varied from STP (5.1%) to MGS (12.0%) and decreased by 2.0% in the 2003 plantation forest. Available nitrogen, soil organic carbon, and carbon stock were higher in the top soil and decreased by depth of profile and differed in plantations by year of planting. Furthermore, the change in understory vegetation was significantly correlated with the soil moisture, fertility, and species composition was driven by over story density and crown parameters. Our findings revealed the importance of soil characteristics and understory vegetation in the effective restoration and management of Scots pine plantation in Mongolia and further management of planted Scots pine plantation in safeguarding resilient and productive forests in Mongolia.

1. Introduction

Mongolia is a low-forest cover country of 7.9% and only 67.5% of this is closed forest. Pinus sylvestris L. is one of the most economically important timber species even though the distribution is limited in Mongolia to the sub-taiga zone [1]. The southern border of P. sylvestris distribution dips into northern Mongolia with the main areas in the Khentii mountain range, along the ridge of Khantai–Buren–Buteeliin in the northwest to the Orkhon–Selenge catchment, and the south in the Onon–Balj–Barkhiin river basins, and to the northeast along the Bayan–Uul–Ereen davaa area [2]. Reforestation activity in Mongolia started in the 1970s and resulted in a National Forest Policy that considered reforestation and tree planting as key objectives. Restoration and reforestation activities encounter numerous challenges caused by both biotic and abiotic factors. Soil moisture is one of Mongolia’s most limiting environmental factors for tree growth and survival. Thus, the selection of appropriate seed sources that have advanced drought tolerance and growth performances could be the best option to promote the quality of planting stocks, obtain high survival of seedlings, and increase growth and productivity in large-scale rehabilitation and reforestation. This unique pine forest ecosystem plays an important role in environmental sustainability including biodiversity conservation, soil protection from erosion, wildlife habitat, and carbon sequestration [3]. The restoration project of the Scots pine forest of Tujiin nars was implemented between 2003 and 2005 with the financial support of the Yuhan Kimberly (YK) company as an example of forest restoration and afforestation in the forest steppe region of Mongolia. Over the last two decades, more than 21,000 ha of Scots pine plantations were established on clear-cuts and burnt forest areas using native tree species in the region [4]. The success of planting and reforestation depends upon many factors, including seed and seedling quality, site–species compatibility, and appropriate silvicultural practices [5].

Therefore, plantation forests often need maintenance such as thinning, which significantly influences forest soil, affecting root density, microbial communities, organic matter turnover, and nutrient budgets, which affect tree growth, understory vegetation composition, and the whole forest ecosystem [6,7,8]. Tree planting can change soil environments by affecting the soil temperature and moisture, bulk density, and soil organic carbon [9,10,11,12]. We evaluated the understory vegetation and soil parameters associated with 18- to 20-year-old plantation forests that were in 2016 and 2017 to offer answers to this topic. (1) What are the variations in understory vegetation composition and variety among plantations and other management types (plantations: BBS, MGS, BDS against natural stands: NRS, NFM, and steppe: STP)? (2) What is the correlation between soil parameters (pH, bulk density, moisture) and the growth of understory plants at various phases of forest restoration? Our findings will aid efforts to enhance the management of current plantations and urge planners to employ Scots pine only at the most appropriate densities and locations in future afforestation.

2. Materials and Methods

2.1. Study Area and Site Description

This study area is located in the territory of Tujiin nars Nature Conservation Park, which administratively belongs to Selenge province (50°10′ and 50°12′ N, 106°14′ and 106°31′ E) in northern Mongolia with an elevation ranging from 650 to 750 m asl (Figure 1). The park stretches approximately 33 km from east to west and covers an area of 73,000 ha, of which 45,800 ha are natural pine forests and 21,000 ha are Scots pine plantations [4]. Tujiin nars lies within the northern temperate and boreal forest that is distributed along the southern edge of the Siberian Taiga, at the forest steppe transitional zone called sub-taiga forests [13]. According to the updated world map of the Köppen–Geiger climate classification [14], the region lies within the transition climatic zone between a cool continental climate (Dwc) and a cold semi-arid climate (Bsk), with small pockets exhibiting a temperate continental climate (Dwb). The main tree species in this forest include Siberian larch (Larix sibirica Ldb.), Scots pine (Pinus sylvestris L.), Siberian pine (Pinus sibirica Du Tour.), Asian white birch (Betula platyphylla Sukaczev.), and European aspen (Populus tremula L.) [15].

The mean annual temperature was 0.6 ± 0.002 °C and the mean annual precipitation was 280.3 ± 7.3 mm with a precipitation peak between June and August (Figure 2) according to the nearest meteorological station located at Sukhbaatar (50°14′35.063″ N; 106°10′23.008″ E, 621 asl). The soils in the study site are Podzols (arenic) (also called Derno-Forest soils in Mongolia), which were derived from sandy sediments and loess. The sandy loess stratum, which provides the parent materials, is extremely thick and is widely distributed in the study region. The soil is characterized by relatively good water retention but is poorly vegetated due to the climate [16].

2.2. Measurements and Sampling Design

2.2.1. Sampling Design

Sample plots for comparative growth analyses were established in three Scots pine plantations, and each plot had four replicates. One additional sample plot was established in a naturally regenerated young stand for a total of six plots. Each 20 m × 20 m (400 m²) sized sample plot was established in a representative part of each treatment for analyzing forest understory vegetation and soil properties. This dimension encapsulates the regional variability in soil characteristics while being feasible for thorough sampling as well as being sufficiently extensive to encompass the impact of individual trees (taking into consideration crown spread and root zones) on soil characteristics. The geographical location and site description of the sample plots are illustrated in

Table 1. Geographical location of the sites.

№	Plot ID	Plot Definition	Coordinates		Altitude (m)
1	BBS	2003 plantation stand	50°11′26.7″ N	106°26′31.8″ E	720
2	MGS	2004 plantation stand	50°10′10.6″ N	106°24′49.0″ E	712
3	BDS	2005 plantation stand	50°11′25.4″ N	106°28′42.8″ E	708
4	NRS	Natural regeneration stand	50°10′59.9″ N	106°26′24.6″ E	714
5	NFM	Natural forest edge	50°12′19.9″ N	106°26′32.8″ E	666
6	STP	Steppe area	50°13′0.95″ N	106°26′21.4″ E	624

.

2.2.2. Vegetation Survey

Investigation of herbal plant cover and species diversity was carried out in 6 sample plots established in the forest plantations, a naturally regenerated young stand, and at the forest edge and steppe area (Table 1). Vegetation was surveyed in 1 m² quadrat sample plots at the center and each of the opposite corners of the sample plots. A 1 m² square using stakes, string, or any other method to delineate the area was clearly marked off. All plant species were identified within the plot. For this method, you are interested in the foliar cover—the amount of the plot covered by the leaves or above-ground parts of the plants. For each species present in the plot, its abundance and coverage was estimated based on visual estimates. The Braun–Blanquet method uses a cover-abundance scale, which is a set of categories that describe the proportion of the area covered by each species [17]. Species presence, number, and coverage data were used to estimate species richness and vegetative cover. The species richness was defined as the number of species per plot [18]. Vegetation cover was estimated as the ratio of the vertical projection of exposed leaf area to the area of each quadrat. Importance value, species diversity indices, and similarity indices were calculated to compare vegetation change in the sample plots [19]. Importance value (IV) was calculated with relative frequency and relative coverage to identify indicator species [20]. Difference in species diversity was assessed by species richness, the Shannon–Weiner index [21], and an evenness index (J) was calculated as the ratio of observed density (H′) to maximum density (H_max) [22]. Simpson’s index (D) was calculated to measure community diversity [23]. The Shannon index provides a comprehensive understanding of the community structure’s overall complexity and uncertainty. The Simpson index offers a viewpoint that emphasizes the influence of numerically dominant species and the dominance of species. Together, these indices allow for a more nuanced interpretation of biodiversity, revealing both the richness and evenness of the species present as well as the degree of dominance by particular species. This comprehensive approach is especially crucial when evaluating the efficacy of restoration initiatives or contrasting ecological states across various habitats.

Bray and Curtis (BC) [24] and Sorensen (SS) [25] indices of similarity were calculated for all pairs of plots, including naturally regenerated stand, plantation forest, forest edge, and steppe [26].

After the vegetation survey, all individuals were harvested (including roots) in each quadrat and weighed to obtain fresh weight. Above- and below-ground components were separated and oven-dried at 80 °C for 72 h to estimate dry biomass. The nomenclature of the species followed the Conspectus of the Vascular Plants of Mongolia [27], which was based on the APG III [28] of plant classification. Collected materials were identified based on the Key to the Vascular Plants of Mongolia [29,30]. Families and species were listed in accordance with APG IV [31]. The ecological groups of plants by ref. [32] and the information on the conservation status was based on the International Union for Conservation of Nature (IUCN) Red List, Urgamal et al. [27], Nyambayar et al. [33], the Red Data Book of Mongolia [34], and the Appendix to the Mongolian Law of Natural Plants [35].

2.2.3. Soil Survey

Soil sampling was conducted relative to the two soil sampling plots at each site. Representative 20 × 20 cm subplots were excavated to a depth of 100 cm. Morphological characteristics were recorded and soil samples taken from the horizons of the excavated profiles at 0–5 cm, 5–10 cm, and at 10 cm intervals from 10 to 100 cm (total of 122 samples). Undisturbed soil cores were taken from the upper part of each horizon to determine bulk density (BD) and soil moisture (in total 198 samples), using 5 cm tall metal cylinders, 95 cm³ in volume. Soil temperature was measured at each depth of the soil profile with an accuracy of ±0.2 °C. The soil temperature readings were conducted on 24–25 July 2020, between 12:00 PM and 4:00 PM, during the peak summer temperature period. The measurements were punctual and taken at each depth of the soil profile with three repetitions using the Digital Thermometer HI98501 Checktemp (Hanna Instruments Inc., Seoul, South Korea), which has an accuracy of ±0.2 °C. A total of 33 soil temperature data points were collected for each soil profile.

Soil samples were air-dried, sieved through a 2 mm sieve, and stored at room temperature. The samples were subjected to the following physical and chemical analyses [36]. Particle size was determined by pipette [37]; pH was determined on a 1:2.5 air-dried soil/distilled water mixture using a glass electrode pH meter [38]. Electrical conductivity (EC) was determined for a 1:5 air-dried soil/distilled water mixture using a platinum electrode. Soil organic carbon was measured by the Walkley and Black [39] method and organic carbon stock was determined according to Batjes [40]. Calcium carbonate content was determined by the volumetric method [41]. Available phosphorus (P₂O₅) was measured by molybdenum blue colorimetry, after (NH₄)₂CO₃ digestion [42]. Nitrate nitrogen (NO₂-N) was determined using a CH₃COONa digestion and spectrocolorimetry. Potassium (K₂O) was analyzed by flame spectrometry [43].

2.3. Statistical Analysis

The SAS software package, version 9.4 [44], was used for statistical analysis. A one-way analysis of variance (ANOVA) was adopted to assess the significance of differences among stands, year of planting (YoP) on the stand characteristics, vegetation (species richness, coverage), as well as soil properties (moisture content, pH, soil organic carbon). Duncan’s multiple range test (DMRT) was used for multiple comparisons among the 2020 data. Correlation between above-ground and below-ground understory biomass of different plots and regression analysis between climatic characteristics and diameter increment were carried out using IBM SPSS Statistics (Version 27) [45]. Numerous visualizations of PCA scatter plots and heatmaps illustrate the relationships between variables (soil characteristics and understory vegetation values by plots) in the dataset, while integrating statistical methods through the ggplot2, pheatmap, complexheatmap, and tidyverse packages in the R (R Core Team, 2023) [46]. The combination of SAS, SPSS, and R capitalizes on the strengths of each platform—including detailed correlation analysis, robust statistical testing, and sophisticated visualization—to produce a comprehensive and multifaceted comprehension of forest restoration dynamics.

3. Results

3.1. Understory Vegetation Composition and Its Change

Understory vegetation was comprised of 92 species of plants, including 4 shrubs, 1 semi-shrub, species, and 84 herb species (5 annuals and 87 perennials) belonging to 78 genera of 35 families. The dominant families were Asteraceae (15 species, 16.3%), Rosaceae (10 species, 10.8%), Ranunculaceae, Poaceae, and Fabaceae (each with 7 species, 7.6%) followed by Lamiaceae (4 species, 4.3%), Iridaceae (3 species, 3.3%), and Caryophyllaceae (3 species, 3.3%). Dominant species were mesophytes (36 species, 39.1%), xerophytes (30 species, 32.6%), petrophytes (4.35%), and gigrophytes (1.08%). Mesophyte species dominated in the plantations (BBS 50%, MGS 56.7%, BDS 52%) and the naturally regenerated stand (NRS 52%); xerophytes were greater in the natural forest edge (NFM 52.6%) and the steppe (STP 46.9%) plots (Table S1).

Species richness was significantly different between the six plots (p = 0.0352), with higher total values registered in BBS (32 species) and the lowest total richness and cover detected in the NRS (8 species). Total plant coverage was significantly different (p = 0.0001); BBS had the greatest (58.5%) and MGS had the lowest (26.5%) cover (Figure 3).

Above-ground (AGB) and below-ground (BGB) biomass accumulation was highest in BBS (AGB = 1159.6 g m⁻², BGB = 3325 g m⁻²). The lowest above-ground biomass was observed in the NRS (AGB = 44.0 g m⁻²) and the lowest below-ground biomass was observed in BDS (BGB = 137.0 g m⁻²) (Figure 4).

There were few significant correlations between stand characteristics and species richness, plant coverage, and above- and below-ground biomass (

Table 2. Correlations between tree variables with species richness, plant coverage, and above–below-ground biomass.

Variables	BBS				MGS				BDS				NRS
	SR	PC	AGB	BGB	SR	PC	AGB	BGB	SR	PC	AGB	BGB	SR	PC	AGB	BGB
DBH	−0.14	−0.11	0.5	0.5	0.21	0.21	−0.5	−0.51	0.16	0.16	0.48	0.59	0.38	0.31	0.03	0.26
Height	−0.2	−0.21	0.45	0.56	0.28	0.28	−0.619 *	−0.59	−0.1	−0.1	0.04	0.21	0.3	0.43	0.16	0.39
BA	−0.18	−0.15	0.48	0.49	0.18	0.18	−0.39	−0.38	0.2	0.2	0.46	0.57	0.47	0.23	−0.1	0.17
Vol	−0.17	−0.15	0.53	0.57	0.19	0.19	−0.46	−0.44	0.06	0.06	0.45	0.6	0.43	0.28	−0.02	0.25
CL	−0.2	−0.15	0.627 *	0.653 *	0.4	0.4	−0.57	−0.56	−0.33	−0.34	−0.02	0.14	0.29	0.42	0.16	0.41
CD	0.18	0.23	−0.06	−0.01	0.33	0.33	−0.34	−0.4	0.27	0.27	0.33	0.52	0.15	0.18	0.09	0.37
CPA	0.14	0.19	−0.17	−0.1	−0.1	−0.1	−0.637 *	−0.781 **	0.49	0.48	0.1	0.29	0.22	0.27	0.08	0.35

Note: * p < 0.05, ** p < 0.01, vegetation variables: SR—species richness, PC—plant coverage, AGB—above-ground biomass, BGB—below-ground biomass, tree variables: DBH—diameter at breast height, Height—tree height, BA—tree basal area, Vol—growing stock, CL—tree crown length, CD—tree crown diameter, CPA—crown projection area. Plots: BBS—2003 plantation stand, MGS—2004 plantation stand, BDS—2005 plantation stand, NRS—natural regeneration stand.

). Tree crown length and tree height were positively correlated with above- and below-ground biomass but negatively correlated with crown projection area. Correlations between the soil pH and bulk density understory biomass were negative (

Table 3. Correlations between soil variables with species richness, plant coverage, and above–below ground biomass of studied sample plots.

Variables	BBS				MGS				BDS
	SR	PC	AGB	BGB	SR	PC	AGB	BGB	SR	PC	AGB	BGB
pH	0.23	0.266	−0.645 *	−0.744 **	−0.034	−0.033	−0.212	−0.099	−0.392	−0.396	−0.297	−0.195
SOC	−0.111	−0.155	0.302	0.442	−0.149	−0.148	0.083	0.232	−0.273	−0.272	0.341	0.471
BD	0.23	0.266	−0.645 *	−0.744 **	0.096	0.095	−0.078	−0.23	0.34	0.333	−0.382	−0.45
SM	−0.135	−0.172	−0.149	−0.013	−0.157	−0.156	0.165	0.317	−0.25	−0.25	0.339	0.497
Variables	NRS				NFM				STP
	SR	PC	AGB	BGB	SR	PC	AGB	BGB	SR	PC	AGB	BGB
pH	−0.294	−0.616 *	0.122	−0.094	−0.425	−0.252	−0.126	−0.238	0.162	0.268	−0.227	−0.371
SOC	0.657 *	0.447	−0.222	−0.082	0.517	0.235	0.241	0.376	−0.095	−0.217	0.315	0.439 **
BD	−0.036	−0.749 **	−0.529	−0.627 *	−0.57	−0.297	−0.25	−0.41	−0.036	0.074	−0.123	−0.243
SM	0.35	0.683 *	0.184	0.309	0.609 *	0.328	0.27	0.396	0.205	0.085	0.411 **	0.468

Note: * p < 0.05, ** p < 0.01, vegetation variables: SR—species richness, PC—plant coverage, AGB—above-ground biomass, BGB—below-ground biomass, soil variables: SOC—soil organic carbon, BD—soil bulk density, SM—soil moisture. Plots: BBS—2003 plantation stand, MGS—2004 plantation stand, BDS—2005 plantation stand, NRS—natural regeneration stand, NFM—natural forest edge, STP—steppe area.

). Soil moisture was positively correlated with plant cover in the naturally regenerated (NRS) plot, species richness in the near forest (NFM) plot, and above-ground biomass in the steppe plots (STP). Plant coverage was negatively correlated with the soil pH, bulk density, and soil moisture at the NRS plot. Soil organic carbon was positively correlated with above-ground biomass in the steppe plot.

Importance values were used to describe and compare the species dominance, with the highest IV index considered to be most “important” in a specific plot. Agrimonia pilosa, Festuca valesiaca, and Linum sibiricum species had high IV values and dominated in the 2003 plantation named a BBS plot. Phlomis tuberosa and Sibbaldia adpressa species dominated in the 2004 plantation, NGS. Therefore Cleistogenes squarrosa, Cirsium esculentum, and Elymus sibirica species dominated and had a high IV in the 2005 plantation, BDS (

Table 4. Quantitative analysis for IV of herbaceous vegetation in BBS, MGS, and BDS plots.

Plots	BBS			MGS			BDS
Species	RF, %	RC, %	IV, %	RF, %	RC, %	IV, %	RF, %	RC, %	IV, %
Achillea asiatica Serg.	17	0.2	8.4	0	0	0	0	0	0
Agrimonia pilosa Ledeb.	17	2.1	9.4	0	0	0	0	0	0
Artemisia commutata Bess.	17	0.2	8.4	0	0	0	0	0	0
Artemisia integrifolia L.	17	1	8.9	0	0	0	0	0	0
Cirsium esculentum (Siev.) C.A.Mey.	0	0	0	0	0	0	4.8	2.4	3.6
Cleistogenes squarrosa (Trinius) Keng.	0	0	0	0	0	0	4.8	4.8	4.8
Elymus sibiricus L.	0	0	0	0	0	0	4.8	2.4	3.6
Festuca valesiaca Gaud.	17	2.1	9.4	0	0	0	0	0	0
Galatella dahurica DC.	17	1	8.9	0	0	0	0	0	0
Heteropappus hispidus (Thunbg.) Less.	17	1	8.9	0	0	0	4.8	2.4	3.6
Iris tigrida Bunge ex Ledebour.	0	0	0	0	0	0	4.8	2.4	3.6
Leptopyrum fumarioides (L.) Reichb.	0	0	0	0	0	0	4.8	1	2.9
Linum sibiricum DC.	17	2.1	9.4	0	0	0	0	0	0
Papaver nudicaule Ldb.	17	0.6	8.6	0	0	0	0	0	0
Phlomis tuberosa L.	0	0	0	7.1	2.7	4.9	4.8	2.4	3.6
Potentilla acaulis L.	0	0	0	0	0	0	4.8	2.4	3.6
Sedum aizoon L.	17	0.4	8.5	0	0	0	0	0	0
Sibbaldia adpressa Bunge.	0	0	0	7.1	5.4	6.3	0	0	0
Trifolium lupinaster L.	17	1	8.9	0	0	0	0	0	0

Note: Vegetation variables: RF, %—relative frequency, RC, %—relative coverage, IV, %—importance value. Plots: BBS—2003 plantation stand, MGS—2004 plantation stand, BDS—2005 plantation stand.

).

But in the naturally regenerated and natural plots, the IV values and dominant species were a little bit different. Elymus sibiricus and Phlomis tuberosa species had high IV values and dominated in the naturally regenerated stand (NRC). Allium bidentatum, Carum carvi, Fragaria orientalis, and Phlomis tuberosa species dominated in the natural forest edge (NFM), but Cleistogenes squarrosa, Festuca valesiaca, Patrinia rupestris, Linum sibiricum, Allium linare, and Caragana microphylla species dominated and had a high IV in the steppe area, STP (

Table 5. Quantitative analysis for IV of herbaceous vegetation in NRS, NFM, and STP plots.

Plots	NRS			NFM			STP
Species	RF, %	RC, %	IV, %	RF, %	RC, %	IV, %	RF, %	RC, %	IV, %
Allium bidentatum Fisch.ex.Prokh.	0	0	0	10	6.3	8.2	0	0	0
Allium linare L.	0	0	0	0	0	0	13	2.6	7.5
Caragana microphylla Lam.	0	0	0	0	0	0	13	2.6	7.5
Carum carvi L.	0	0	0	10	1.6	5.8	0	0	0
Cirsium esculentum (Siev.) C.A.Mey.	0	0	0	0	0	0	13	1.3	6.9
Cleistogenes squarrosa (Trinius) Keng.	0	0	0	0	0	0	13	2.6	7.5
Cymbaria dahurica L.	0	0	0	0	0	0	13	1.8	7.2
Elymus sibiricus L.	10	8.9	9.4	0	0	0	0	0	0
Festuca valesiaca Gaud.	0	0	0	0	0	0	13	2.6	7.5
Fragaria orientalis Losinsk.	0	0	0	0	0	0	13	1.3	6.9
Galatella dahurica DC.	0	0	0	10	1.6	5.8	13	1.3	6.9
Galium verum L.	0	0	0	0	0	0	13	1.3	6.9
Inula britannica L.	0	0	0	0	0	0	13	0.3	6.4
Leontopodium ochroleucum Beauverd.	0	0	0	0	0	0	13	1.3	6.9
Lespedeza davhurica (Laxm.) Schlinder.	0	0	0	0	0	0	13	0.3	6.4
Linum sibiricum DC.	0	0	0	0	0	0	13	2.6	7.5
Patrinia rupestris (Pall.) Dufr.	0	0	0	0	0	0	13	5.1	8.8
Phlomis tuberosa L.	10	3.3	6.7	10	1.6	5.8	13	1.3	6.9
Potentilla acaulis L.	10	0	5	0	0	0	0	0	0
Plantago major L.	0	0	0	0	0	0	13	1.3	6.9
Potentilla acaulis L.	0	0	0	10	1.6	5.8	13	1.3	6.9
Stellera chamaejasme (L.) Rydb.	0	0	0	0	0	0	13	0.3	6.4
Thalictrum petaloideum L.	10	0.4	5.2	0	0	0	0	0	0

Note: Vegetation variables: RF, %—relative frequency, RC, %—relative coverage, IV, %—importance value. Plots: NRS—natural regeneration stand, NFM—natural forest edge, STP—steppe area.

).

There were significant differences among plots in the diversity index (H′), evenness index (J), and Simpson index (D), as shown in

Table 6. Species diversity indices of studied plots.

Diversity Indices	BBS	MGS	BDS	NRS	STP	NFM
Shannon index (H′)	0.8156 a	0.172 c	0.2632 bc	0.5671 ab	0.2959 bc	0.4871 abc
Evenness index (J)	0.1458 cd	0.4966 b	0.1720 a	0.1108 cd	0.2127 c	0.0869 d
Simpson index (D)	1.1638 c	0.5674 ab	0.7895 a	0.0855 ab	0.2988 ab	0.440 bc

Note: Different letters indicate significant difference at 5%, BBS—2003 plantation stand, MGS—2004 plantation stand, BDS—2005 plantation stand, NRS—natural regeneration stand, NFM—natural forest edge, STP—steppe area.

. The diversity Shannon index in BBS has the highest value (0.8156), indicating the most diverse plot, MGS has the lowest value (0.172), showing the least diversity. The evenness index for MGS has the highest evenness (0.4966), suggesting a more balanced species distribution. NFM has the lowest evenness (0.0869), indicating dominance by a few species. The Simpson index for BBS has the highest value (1.1638), showing the least dominance and highest diversity. NRS has the lowest value (0.0855), indicating high dominance by a few species. As a result, BBS is the most diverse plot across all indices. MGS has high evenness but low diversity, suggesting fewer species are distributed evenly. NRS shows high dominance, with a few species dominating the BBS-1 plot, which was significantly higher than other plots, while the lowest was MGS-1; on the contrary, MGS had the highest evenness index and the lowest was NFM (Table 6).

The table presents similarity coefficients (Bray–Curtis and Sorensen) for two plot groups, assessing ecological similarity based on species composition. Bray–Curtis (BC) dissimilarity coefficient: Assesses the dissimilarity between two groups (values approaching 0 signify greater similarity). BBS, MGS, BDS, NRS: 0.30 (moderate dissimilarity). STP and NFM: 0.28 (marginally more analogous than the initial group). Sorensen (Ss) coefficient: Assesses similarity as a percentage, with larger values signifying increased similarity. BBS, MGS, BDS, NRS: 20.83% (little resemblance). STP and NFM: 46.81% (moderate resemblance, surpassing the initial group). STP and NFM have greater ecological similarity to one another than to the group comprising “BBS, MGS, BDS, NRS”. The Bray–Curtis values correspond with the Sorensen percentages, indicating that the second group exhibits greater similarity (

Table 7. Similarity coefficients (%) of studied plots.

Similarity Coefficient, %	BBS, MGS, BDS, NRS	STP and NFM
Bray and Curtis (BC)	0.30	0.28
Sorensen (Ss)	20.83	46.81

Notes: BBS—2003 plantation stand, MGS—2004 plantation stand, BDS—2005 plantation stand, NRS—natural regeneration stand, NFM—natural forest edge, STP—steppe area.

).

3.2. Changes in Chemical and Physical Properties of the Top Soil

The soil pH significantly differed in the top soil of the plots, with a lower pH in the soils of the BBS (6.10) than in the soils of the other plots (

Table 8. Soil properties of studied plots (n = 48; depth = 0–30 cm).

Variables	Unit	BBS	NRS	STP	BDS	NFM	MGS	F Value
pH		6.10 ± 0.11 d	6.51 ± 0.07 c	7.00 ± 0.47 b	7.39 ± 0.03 a	6.77 ± 0.13 bc	6.88 ± 0.11 bc	13.14 ***
AN (N-NO−3)	mg kg−1	2.99 ± 0.54 ab	4.07 ± 0.62 a	2.88 ± 0.58 b	3.17 ± 0.58 ab	3.22 ± 0.51 ab	3.56 ± 0.81 ab	1.51 ns
OC	g kg−1	26.7 ± 12.89 a	14.6 ± 7.52 ab	8.7 ± 4.04 b	15.2 ± 7.61 ab	15.8 ± 3.53 ab	16.7 ± 5.43 ab	1.8 ns
SOCs	mg ha−1	22.2 ± 6.95 a	10.9 ± 0.84 bc	8.1 ± 1.51 c	14.5 ± 3.02 abc	16.5 ± 5.83 ab	15.3 ± 4 abc	3.8 *
AP (P2O5)	mg kg−1	26.4 ± 9.32 a	17.3 ± 4.42 a	20.1 ± 7.37 a	23.9 ± 5.99 a	14.7 ± 5.34 a	21.2 ± 8.69 a	1.1 ns
AK (K2O)	mg kg−1	173.5 ± 72.9 a	119.3 ± 36.4 ab	108.5 ± 35.5 ab	84.1 ± 36.3 b	119.3 ± 53.9 ab	94.9 ± 27 ab	1.36 ns
Sand (2–0.05 mm)	%	69.5 ± 0.73 b	69.5 ± 3.91 b	82.9 ± 0.82a	76.4 ± 6.47ab	75.9 ± 6.93ab	73.3 ± 3.59b	3.93 *
Silt (0.05–0.002 mm)	%	19.8 ± 1.94 a	17.9 ± 7.54 a	8.4 ± 1.59 b	11.6 ± 4.89 ab	12.7 ± 5.62 ab	15.3 ± 3.71 ab	2.38 ns
Clay (<0.002 mm)	%	10.8 ± 1.32 ab	12.6 ± 3.64 a	8.6 ± 1.37 b	12 ± 1.8 ab	11.4 ± 1.74 ab	11.4 ± 0.53 ab	1.48 ns
BD	g cm−3	1.25 ± 0.2 a	1.24 ± 0.2 a	1.46 ± 0.07 a	1.46 ± 0.11 a	1.42 ± 0.14 a	1.35 ± 0.12 a	1.41 ns
SM	%	10.6 ± 1.3 a	8.25 ± 1.97 ab	5.15 ± 2.23 b	7.97 ± 2.72 ab	9.41 ± 2.62 ab	12.06 ± 4.33 a	2.35 ns
ST	°C	18.65 ± 1.62 c	17.74 ± 0.74 c	24.68 ± 2.56 a	21.22 ± 0.85 b	21.53 ± 1.27 b	18.09 ± 0.94 c	9.96 ***

Notes: Values are mean ± standard deviation, * p < 0.05, *** p < 0.001; different letters within a row indicate significant differences (p < 0.05) among the different treatments based on the one-way ANOVA result, followed by the Duncan’s multiple range test result. AN—available nitrogen, AP—available phosphorous, AK—available potassium, OC—organic carbon, SOCs—soil organic carbon stock, BD—bulk density, SM—soil moisture, ST—soil temperature.

) and the top soil pH of the 2005 plantation (BDS) was 7.39, which was higher than in the soils of the STP plot (7.00). The similarity in pH values between the BBS and NRS plots indicates that the soil pH in the plantation forest is becoming similar to the naturally regenerated forests, but on the other hand, the similarity between BDS (2005 plantation) and STP shows slow recovery after disturbance. However, the other soil chemical properties are not significant while the soil temperature significantly differed in the top soil. The soil temperature varied between 17.7 and 24.7 °C, where the lowest temperature was in the naturally regenerated stand and the highest was in the steppe plot, and generally, the air temperature in the plantation forests was lower than in the steppe and at the forest edge (

Table 9. Soil properties of studied plots (n = 36; depth = 30–60 cm).

Variables	Unit	BBS	NRS	STP	BDS	NFM	MGS	F Value
pH		6.63 ± 0.12 e	6.79 ± 0.06 d	7.61 ± 0.02 a	7.28 ± 0.07 b	7.13 ± 0.01 c	6.91 ± 0.04 d	62.8 ***
AN (N-NO−3)	mg kg−1	2.64 ± 0.7 b	1.58 ± 0.18 b	4.29 ± 1.44 a	1.73 ± 0.38 b	2.03 ± 0.18 b	1.81 ± 0.18 b	4.45 *
OC	g kg−1	9.97 ± 1.48 a	6.38 ± 1.46 b	2.19 ± 0.45 c	5.93 ± 1.64 b	6.02 ± 2.16 b	5.14 ± 0.75 bc	5.99 **
SOCs	mg ha−1	14.63 ± 1.78 a	10.03 ± 2.23 b	3.31 ± 0.64 c	9.22 ± 2.58 b	9.31 ± 3.25 b	8.02 ± 1.04 b	5.95 **
AP (P2O5)	mg kg−1	39.19 ± 1.74 a	31.81 ± 3.62 b	14.03 ± 3.87 d	24.07 ± 2.07 c	17.66 ± 1.2 cd	20.68 ± 4.04 cd	19.9 ***
AK (K2O)	mg kg−1	103.04 ± 0 a	74.13 ± 5.11 b	70.52 ± 0 b	70.52 ± 8.85 b	77.75 ± 5.11 b	70.52 ± 0 b	14.7 ***
Sand (2–0.05 mm)	%	72.14 ± 1.38 c	76.53 ± 1.72 c	83.36 ± 1.19 a	80.92 ± 1.82 ab	79.46 ± 3.01 ab	82.38 ± 3.29 a	7.06 **
Silt (0.05–0.002 mm)	%	12.69 ± 1.38 a	9.56 ± 0.66 ab	5.13 ± 0.6 c	7.81 ± 0.91 bc	8.93 ± 2.45 abc	7.32 ± 2.6 bc	4.71 *
Clay (<0.002 mm)	%	15.17 ± 0 a	13.91 ± 1.23 a	11.52 ± 0.6 b	11.27 ± 0.91 b	11.61 ± 0.7 b	10.3 ± 0.69 b	11.2 ***
BD	g cm−3	1.47 ± 0.04 b	1.57 ± 0.01 a	1.52 ± 0.02 ab	1.55 ± 0.02 a	1.55 ± 0.02 a	1.57 ± 0.03 a	4.56 *
SM	%	7.65 ± 0.73 a	6.11 ± 0.79 b	5.3 ± 0.5 b	4.86 ± 0.78 bc	4.44 ± 0.83 c	3.96 ± 0.38 c	7.5 **
ST	°C	15.71 ± 0.37 b	15.38 ± 0.51 b	19.77 ± 0.45 a	18.86 ± 0.44 a	19.49 ± 0.33 a	15.76 ± 0.55 b	43.5 ***

Notes: Values are mean ± standard error, * p < 0.05; ** p < 0.01; and *** p < 0.001; different letters within a row indicate significant differences (p < 0.05) among the different treatments based on the one-way ANOVA result, followed by the Duncan’s multiple range test result. AN—available nitrogen, AP—available phosphorous, AK—available potassium, OC—organic carbon, SOCs—soil organic carbon stock, BD—bulk density, SM—soil moisture, ST—soil temperature.

). The soil temperature of the BBS stands were 2.3 °C higher than in the naturally regenerated stand (NRS). The average soil moisture of the soils in the different sample plots varied from STP (5.1%) to MGS (12.0%) and the soil moisture decreased by 2.0% in the 2003 plantation forest. The soil bulk density was significantly different among the plots with lower values in BBS and NRS (1.23–1.25 g cm⁻³) than other plots. The bulk density of the 2005 plantation and steppe plots was similar, averaging 1.46 g cm⁻³.

3.2.1. Changes in Chemical and Physical Properties of the Subsoil

There were statistically significant differences in the organic carbon content (p < 0.01) and carbon stock (p < 0.01) between the plots and a lower content in the steppe plots (STP). Even so, no significant differences were observed (p = 0.548) in soil organic carbon and carbon stock among the plots, although there was a slight decrease in organic carbon stock (Table 9). The mean values of soil organic carbon and carbon stock of the plantation forests (5.93 g kg⁻¹; 9.08 mg ha⁻¹) were higher than those of the steppe (2.19 g kg⁻¹; 3.31 mg ha⁻¹). The highest organic carbon (9.97 g kg⁻¹), available phosphorus (39.19 mg kg⁻¹), and potassium (103.0 mg kg⁻¹) were recorded in the soil of the BBS plot. The lowest contents of carbon (2.19 g kg⁻¹) and available phosphorus (14.03 mg kg⁻¹) were found in the soil of the STP plots, MGS, with the lowest available potassium content in the MGS and BDS plots (

Table 10. Soil properties of studied plots (n = 48; depth = 60–100 cm).

Variables	Unit	BBS	NRS	STP	BDS	NFM	MGS	F Value
pH		6.81 ± 0.02 d	6.78 ± 0.03 d	7.66 ± 0.01 a	7.06 ± 0.01 c	7.26 ± 0.11 b	6.67 ± 0.03 d	62.0 ***
AN (N-NO−3)	mg kg−1	2.2 ± 0.34 b	1.75 ± 0.19 b	3.89 ± 0.39 a	1.41 ± 0.05 b	1.97 ± 0.19 b	1.41 ± 0.05 b	8.17 ***
OC	g kg−1	14.9 ± 2.33 ab	15.1 ± 1.73 ab	10.6 ± 1.82 ab	15.4 ± 0.71 ab	13.2 ± 1.92 ab	18 ± 0.85 a	1.21 ns
SOCs	mg ha−1	23.1 ± 3.58 ab	24.3 ± 2.71 ab	16.9 ± 2.91 b	24.7 ± 0.97 ab	21.1 ± 3.19 ab	29.5 ± 1.41 a	1.44 ns
AP (P2O5)	mg kg−1	31.1 ± 3.41 a	27.1 ± 2.05 ab	15.4 ± 1.65 c	19.8 ± 2.32 bc	21.4 ± 2.19 abc	20.8 ± 3.01 bc	3.95 *
AK (K2O)	mg kg−1	85.4 ± 4.06 a	84.1 ± 2.71 a	59.6 ± 0 c	70.5 ± 0 b	78.6 ± 2.71 ab	59.6 ± 0 c	12.8 ***
Sand (2–0.05 mm)	%	85 ± 1.25 a	88.1 ± 0.21 a	85.5 ± 0.42 a	87.3 ± 0.36 a	87.3 ± 0.47 a	88.1 ± 0.27 a	2.22 ns
Silt (0.05–0.002 mm)	%	3.53 ± 0.76 a	4.86 ± 0.2 a	3.77 ± 0.3 a	3.8 ± 0.44 a	3.66 ± 0.29 a	3.66 ± 0.24 a	0.61 ns
Clay (<0.002 mm)	%	11.4 ± 0.68 a	7.01 ± 0.4 c	10.6 ± 0.33 a	8.8 ± 0.09 b	8.95 ± 0.36 b	8.22 ± 0.09 bc	9.75 ***
BD	g cm−3	1.55 ± 0.01 c	1.6 ± 0.01 ab	1.59 ± 0.002 ab	1.6 ± 0.02 ab	1.58 ± 0.01 bc	1.63 ± 0.002 a	4.54 **
SM	%	3.67 ± 0.51 ab	4.43 ± 0.1 a	3.95 ± 0.27 ab	3.67 ± 0.25 ab	3.02 ± 0.28 b	3.21 ± 0.03 ab	1.32 ns
ST	°C	14.3 ± 0.26 c	12.9 ± 0.44 d	18.2 ± 0.25 a	16.4 ± 0.51 b	17.4 ± 0.49 ab	13.2 ± 0.41 cd	30.9 ***

Notes: Values are mean ± standard error, * p < 0.05; ** p < 0.01; and *** p < 0.001; different letters within a row indicate significant differences (p < 0.05) among the different treatments based on the one-way ANOVA result, followed by the Duncan’s multiple range test result. AN—available nitrogen, AP—available phosphorous, AK—available potassium, OC—organic carbon, SOCs—soil organic carbon stock, BD—bulk density, SM—soil moisture, ST—soil temperature.

). Also, there were statistically significant differences in the soil pH (p < 0.001) and soil temperature (p < 0.001) between the plots, with a lower pH in the soils of the BBS (6.10) than in the soils of the other plots. The soil pH of the subsoil was the same situation as the soil pH in the top soil.

There were statistically significant differences in the soil clay (p < 0.001) and soil temperature (p < 0.001) between the plots, with a higher clay in the soils of the BBS and NRS (15.1; 13.9%) than in the soils of the other plots (Table 9). The soil temperature of the NFM and STP stand were 4.1 °C higher than in the naturally regenerated stand (NRS), BBS, and MGS. In the 60–100 cm soil layer, statistically significant differences were observed among plots in the soil pH (p < 0.001), available phosphorus (p < 0.001), and available potassium (p < 0.001). The soil pH was significantly higher in the STP plot (7.61) compared to the other plots (Table 10). Regarding the soil physical properties, significant differences were also found in the soil clay content (p < 0.001) and soil temperature (p < 0.001) among the plots. The highest clay content was recorded in the BBS plot (14.3%), which was significantly greater than that in the other plots.

3.2.2. Statistical Analysis of the Understory Vegetation Composition and Soil Chemical and Physical Properties Between Different Plantation Plots

According to our PCA analysis, it was particularly evident that variations in the soil chemical and physical characteristics are positively different in the BBS, MGS, and NFM than in the other plots, but in particular, the understory vegetation data show STP and BDS are more different than other plots (PCA 1 33.2%, PCA 2 18.2%) and the soil physical properties are interrelated, with PCA 1 explaining the main patterns of variation (50.4%); PCA 1 and PCA 2 together explain 84.4% of the total variance, indicating a good representation of the soil physical variation, while PCA 3 adds another 11.1%, mainly capturing the variation in the particle size (Figure 5A and

Table 11. Soil properties’ complete PCA analysis summary.

Variable	PCA1	PCA2	PCA3
Soil moisture (%)	0.49408002	−0.36913745	−0.25806503
Bulk density (g/cm3)	0.44905773	0.42026906	0.42973202
Soil temperature (°C)	−0.44739513	0.53199237	−0.08915193

).

Moreover, when we used the heatmap approach, we obtained a clear indication that in all plots. From this, the vegetative characteristics of the BBS and MGS plots are higher lacking of species richness. Although SOM, EC, AP, AK Clay, and SM are higher for the soil features in the BBS and BDS plots, the STP and NFM plots appear to be the lowest in all soil characteristics (Figure 5B,C). The scatter plot in Figure 5A illustrates a positive linear correlation between the Shannon index (H′) and pH. The R² value of 0.27 signifies that 27% of the variation in the Shannon index is elucidated by the pH. The dark area surrounding the trend line denotes the 95% confidence interval. The intervals expand near the extremes of the pH, signifying more uncertainty in predictions for extremely high or low pH levels. Although the association is positive, the p-value (0.289) indicates that the correlation lacks statistical significance, perhaps due to the limited sample size (n = 6). The trend line indicates that for each unit increase in the pH, the Shannon index rises by roughly 0.26 units, as shown by the regression slope. As the pH rises from 6.8 to 8.2, the Shannon index correspondingly increases, signifying that mildly alkaline soils (elevated pH) foster enhanced species diversity.

We also analyzed some soil variables in relation to the understory vegetation diversity, as represented in the PCA-derived scatter plot in Figure 6. Shannon index versus pH: the graph indicates a positive association (R² = 0.27). BBS exhibits the maximum diversity (0.82) at pH 8.2, while BDS demonstrates the lowest diversity (0.26) at pH 6.8. The linear relationship implies that diversity grows by 0.26 units for each unit rise in the pH. Confidence intervals expand at pH extremes, signifying increased uncertainty. Slightly alkaline environments (pH 7.5–8.2) seem to promote greater species diversity (Figure 6a). Shannon index versus soil organic matter (SOM): indicates a positive correlation trend (R² = 0.22). BBS exhibits the highest diversity with the highest SOM (35%), while BDS has the lowest diversity with the lowest SOM (15%). Each 1% increment in SOM is associated with a 0.016 rise in the Shannon index. Increased organic matter content enhances species diversity, likely due to enhanced soil structure and nutrient accessibility (Figure 6b). Shannon index versus electrical conductivity (EC): demonstrates a tendency of positive association (R² = 0.24). BBS exhibits the maximum diversity at EC 0.8, while BDS displays the lowest diversity at EC 0.3. The linear connection suggests that diversity grows by 0.62 units for each unit increase in EC. Moderate electrical conductivity levels seem advantageous for species variety, potentially signifying appropriate food availability (Figure 6c). All three soil factors (pH, SOM, EC) exhibit positive associations with species diversity. BBS consistently exhibits the greatest values across all metrics, while BDS consistently displays the lowest values. Other plots (MGS, NRS, NFM, STP) typically aggregate within intermediate ranges. Moderate R² values (0.22–0.27) indicate that soil factors account for around 22%–27% of the variation in diversity. Additional unquantified variables are expected to affect diversity. The robust interconnection among soil metrics indicates comprehensive soil quality impacts. Improved soil conditions (elevated pH, soil organic matter, electrical conductivity) typically foster greater biodiversity (Figure 6).

4. Discussion

The establishment of productive forest plantations is becoming an important silvicultural issue in Mongolia. In the study region, plantations are primarily monocultures, consisting only of Scots pine (P. sylvestris L.). Scots pine is a light-demanding tree species that plays an important role not only in the domestic wood industry, but also in forest ecosystem sustainability in Mongolia. Several open questions regarding Scots pine in Mongolia include whether to plant or rely on natural regeneration; which regeneration method produces the greatest understory diversity; the optimal spacing for planting; and the effect of planting tree on the growth and development of the over story pine, understory diversity, and soil properties.

Soil water content decreased with the increasing tree age, while the thickness of the surface dry layer increased, leading to significantly greater soil drying. In the 4-year-old forests, soil moisture was adequate, and seasonal rainfall could partially compensate for the soil water deficit. However, in 9-year-old forests, water deficit became a serious concern at high tree densities, where seasonal rainfall did not completely offset the soil water deficit at densities greater than 400 trees per hectare (trees ha⁻¹). Consequently, the soil remained relatively dry at the end of the rainy season, even after more than 640 mm of rainfall. The 15- and 30-year-old forests also experienced significant drought due to their drying effect on the soil. Overall, the trees promoted soil water loss, creating a serious imbalance between the water supply and demand in this desert environment. High-density planting accelerated the deterioration in the water environment (i.e., soil drying) and threatened the future survival of the trees and other plants. Thus, ecological managers must reduce tree planting and test the effectiveness of reducing the density to 333 trees ha⁻¹ during the young stage [47]. Natural regeneration in the Tujiin nars Scots pine forests has been impeded by fires and grazing, increasing the emphasis on planting to regenerate these forests. Although this study was not designed to directly compare planting with natural regeneration, we included comparisons of the planted stands to a naturally regenerated stand of approximately the same age to highlight different management effects. On one hand, several studies [48,49,50,51] have noted the importance of establishing forest plantations with native tree species, which can have a highly diverse understory of indigenous species. On the other hand, Hanter [52] and Hartley [53] stated that plantation forests typically are less favorable as a habitat than naturally regenerated stands for a wide range of taxa, particularly in the case in the even-aged, single-species stands. In our case, species richness (30.57%–75.18%) and plant coverage (18.4%–48.3%) were higher in the planted forests than in the naturally regenerated stand (SR-68.16% and PC-48.3%) (Figure 3). The contrary effects may be due to legacy effects of previous land use [54,55]. For example, Hedman et al. [56] found that understory diversity was greater in pine plantations established after harvesting a forest than on old agricultural fields.

Optimal spacing at planting and during subsequent tending must balance several sometimes-competing factors. Stock ability, a concept of optimal growing space [57], depends upon species traits and site quality. Additionally, optimal planting density must consider competing vegetation to ensure that site resources are captured by the target planted species. Managing stand density at lower levels has been proposed as an adaptation to climate change-driven increases in aridity, which is an important consideration in Mongolia [48,58].

Greater diversity and unique composition in the treatments compared to the undisturbed control suggested a link between resource heterogeneity and biodiversity [59] or legacy effects associated with different treatment changes in the understory plant community, which was partly supported by our study. The Shannon index was highly variable among treatments (Table 7) and generally indicated low species richness, which did not exceed 0.8156 in the BBS.

Open conditions after the removal of previous stands provided an opportunity for non-forest understory species to establish. Changes in species composition and a stable existence of invasive plant species from different ecological groups tend to persist during the initial stage of forest plantation establishment [60].

The main living forms were mesophytes and xerophytes, which are quite fit for relatively barren soils and dry climate. Furthermore, the abundant herbaceous species with high plant coverage from steppe and forest meadow ecological groups were found not only in the STP and NFM, but also in other stands. This high proportion of herbaceous species adapted to the steppe and forest meadow ecosystem within planted forests, and their stable existence in the plantations, indicated a potential risk of replacing the Scots pine ecosystem with steppe ecosystems.

Tree crowns produce shade and more appropriate microclimatic conditions for the growth of forest and forest meadow vegetation as they get bigger over time. Thus, comparisons of understory composition and biodiversity in plantations with other types of forests heavily depend on the plantation age.

Forest management significantly affects forest soils, particular soil organic matter, a key component of sustainability [61,62]. Soil is a crucial component in ecosystems, serving as a major storage and source of plant-available nutrients [8]. Change in the soil properties in our study mainly occurred in the top soil layers (0 to 20 cm). Significant increases in the soil bulk density (1.25 ± 0.11 to 1.46 ± 0.06 g cm⁻³), organic carbon (8.85 ± 2.55 to 15.45 ± 4.32 g kg⁻¹), soil organic carbon stock (8.87 ± 1.34 to 13.39 ± 2.98 mg ha⁻¹), available phosphorus (21.17 ± 5.02 to 26.42 ± 5.38 mg kg⁻¹), and soil moisture (8.6 ± 1.0 to 12.0 ± 2.5%) were observed in plantations compared to NRS and NFM. The results of the assessments showed a slight increase in the soil temperature throughout the soil profile and a sharp decrease in the moisture content of the upper soil layer in the 2003 plantations.

Restoration objectives include maintaining the pH levels between 7.5 and 8.2, enhancing soil organic matter to 30%–35%, sustaining electrical conductivity around 0.7–0.8, and demonstrating potential for improvement in all parameters through biological diversity strategies. Additional plots may gain from specific soil enrichment. Furthermore, we propose that a larger sample size be utilized to enhance the statistical significance, alongside temporal monitoring to evaluate seasonal fluctuations, the examination of additional environmental factors, and a comprehensive analysis of species composition. Future research directions include long-term monitoring of soil diversity relationships, investigation of species-specific responses to soil conditions, analysis of soil microbial communities, assessment of restoration success using these parameters as indicators, and examination of climate change effects on soil diversity relationships.

The results indicate that interventions to elevate the soil pH, such as liming, in acidic plots like BDS may promote species diversity. Plots with an intermediate pH (e.g., NRS, NFM) may require supplementary soil amendments to enhance conditions for biodiversity. Additional soil variables (e.g., soil organic matter, electrical conductivity) could be integrated into the analysis to enhance the comprehension of their collective impact on diversity. Alterations in soil characteristics and biodiversity across time should be observed to document temporal dynamics.

Examining the reactions of particular species to pH and other soil properties will help one identify the main elements affecting diversity.

Soil organic carbon (SOC) levels in plantation forests are higher compared to grasslands and naturally regenerated forests. In low-carbon soils, forest restoration accumulates significantly more carbon than natural regeneration [63].

Carbon stocks generally decrease with soil depth, as SOC concentrations below 30 cm are less influenced by management practices due to lower carbon inputs and higher decomposition rates in deeper layers. Near the soil surface, SOC concentrations increase non-linearly, driven by carbon inputs from plant residues, roots, and favorable conditions such as optimal temperature and moisture [64]. Maintaining optimal soil moisture levels is crucial for enhancing carbon sequestration and reducing greenhouse gas emissions [65].

Drought conditions, which reduce soil moisture, have a significant negative impact on carbon sequestration. Conversely, in some regions, high soil moisture levels promote carbon accumulation. For example, elevated soil moisture in areas like the Qinghai–Tibetan Plateau, Xinjiang, and Northwest China enhances carbon sink activity, contributing to net ecosystem productivity increases of up to 3.0 g C m² per year [66]. These findings collectively emphasize the strong connection between soil moisture and SOC dynamics. The results from previous studies align with our findings, which establish a clear relationship between soil moisture levels and carbon dynamics, highlighting the importance of soil moisture in SOC accumulation and loss.

The correlation between soil organic carbon (SOC) in forest plots and carbon sequestration trends demonstrates that SOC functions as a crucial reservoir for atmospheric carbon, especially in tropical wet and moist forest ecosystems [67]. The density and diversity of trees in these forest plots positively correlate with soil carbon levels, indicating that healthier and more diversified forests can improve carbon sequestration [68]. Our findings indicate that plantation stands (BBS, MGS, BDS) typically exhibit elevated SOC levels in comparison to natural regeneration stands (NRS) and steppe regions (STP). The detailed BBS exhibits the highest soil organic carbon (SOC) at 14.63 ± 1.78 mg ha⁻¹, followed by NRS at 10.03 ± 2.23 mg ha⁻¹. STP exhibits the lowest SOC (3.31 ± 0.64 mg ha⁻¹), underscoring the restricted carbon storage potential of steppe regions.

This tendency corresponds with Lal’s (2004) findings, which highlight that planted trees elevate soil organic carbon levels through augmented organic matter inputs and diminished soil erosion [69]. SOC functions as a primary reservoir for atmospheric carbon, playing a crucial role in global carbon sequestration initiatives [67]. Elevated soil organic carbon levels in plantation stands indicate that afforestation and reforestation initiatives are pivotal in alleviating climate change through the augmentation of soil carbon sequestration. The documented reduction in soil organic carbon with increasing soil depth underscores the significance of surface soil layers in carbon sequestration, corroborated by worldwide soil profile investigations [70]. Plantation stands (e.g., BBS, MGS) likely enhance soil organic carbon (SOC) levels by augmenting litterfall and diminishing competition among trees, as observed by the user. This corresponds with observations that plantation can augment soil organic matter inputs and enhance soil quality indices [71]. The relationship between soil moisture and SOC dynamics underscores the significance of water availability in carbon sequestration. For instance, BBS, exhibiting elevated soil moisture (SM = 7.65 ± 0.73%), also demonstrates the greatest levels of SOC. This association aligns with research indicating that soil moisture favorably affects SOC buildup by enhancing microbial activity and organic matter breakdown [71]. The findings endorse the utilization of planted forests as carbon farming zones, especially in degraded environments such as steppes (STP). Policies that advocate for afforestation and reforestation can elevate soil organic carbon levels and aid in achieving carbon sequestration objectives under frameworks such as the Paris Agreement. Plantation and soil moisture management must be incorporated into forest management plans to enhance SOC levels and carbon sequestration. The favorable correlation between soil organic carbon (SOC) and tree diversity emphasizes the necessity of reconciling biodiversity protection with carbon sequestration initiatives. Mixed-species plantations can fulfil both goals. The investigation underscores the essential function of plantation forests in augmenting soil organic carbon levels and facilitating carbon sequestration. The identified trends in the plots offer significant insights for carbon farming and silvicultural management, in accordance with global climate change mitigation strategies. Subsequent studies ought to concentrate on refining these approaches to enhance carbon sequestration and biodiversity preservation. We will require the implementation outlined below: Initial planting density: determined by site characteristics and management objectives. Thinning schedule: adjusted according to crown competition and understory growth. Monitoring protocol: systematic evaluation of essential indicators. Adaptive management: a flexible approach to evolving circumstances. Future study necessitates long-term monitoring of growth responses to varying densities, understory development patterns, alterations in soil properties, and the implications of climate change

Furthermore investigated should be the financial consequences of carbon sequestration possibilities, biodiversity preservation successes, and delivery of ecosystem services.

These suggestions establish a framework for effective forest management, recognizing the necessity for site-specific modifications and continuous study to enhance strategies in response to evolving problems and possibilities.

5. Conclusions

Mongolia has committed to the Bonn Challenge, aiming for 1.5 million hectares to undergo restoration by 2030. At the same time, existing forests are threatened by increasing aridity and drought, wildfires, and grazing encroachment, which impedes natural regeneration. Successful artificial regeneration and sustainable management of native forests are critical for meeting the Bonn Challenge commitment and to maintaining biodiversity and protecting land from degradation. The forest of Tujiin nars is an important genetic resource of natural Scots pine in Mongolia, and efforts by national and local governments, external donors, university, and local communities have been directed toward the restoration of this vital resource. Over the last two decades, more than 21,000 ha of clear-cuts and burnt forest areas have been restored.

The survival and establishment of Scots pine at Tujiin nars have been successful, with rates over 80%. Understory plant diversity varied among the plots with no discernible trend due to planting, indicating the strength of legacy effects. These results provide a foundation for developing restoration and management guidelines for native Scots pine forests in Mongolia, suggesting that lowered stand densities may be a useful adaptation to increased aridity under climate change. The research highlights the essential importance of soil characteristics and understory flora in the effectiveness of Scots pine restoration and sustainable forest management. These findings offer practical guidance for modifying forest management strategies in response to climate change and safeguarding the resilience of indigenous forests in Mongolia.