Next Article in Journal
Origanum vulgare subsp. virens (Hoffmanns. & Link) Bonnier & Layens Essential Oils: Chemotypes and Bioactivity as Antifungal, Antifeeding and Enzyme Inhibitors
Previous Article in Journal
Identification of Chalcone Synthase Genes and Their Responses to Salt and Cold Stress in Poncirus trifoliata
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Plants
Volume 14
Issue 19
10.3390/plants14193004
plants-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Soil Microbial Communities Significantly Changed Along Stand Ages in Masson Pine (Pinus massoniana Lamb.) Plantation

by
Weijun Fu
1,†,
Bingyi Wang
1,*,†,
Dunzhu Li
2 and
Yong Zhang
1,3,*,†
1
State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, China
2
College of Jiyang, Zhejiang A&F University, Zhuji 311800, China
3
Zhejiang Provincial Management Station of Public Welfare Forest and State-owned Forest Farms, Hangzhou 310020, China
*
Authors to whom correspondence should be addressed.
†
Current Address: National Key Laboratory for Development and Utilization of Forest Food Resources, Zhejiang A&F University, Hangzhou 311300, China
Plants 2025, 14(19), 3004; https://doi.org/10.3390/plants14193004
Submission received: 20 August 2025 / Revised: 25 September 2025 / Accepted: 26 September 2025 / Published: 28 September 2025
(This article belongs to the Special Issue Plant-Soil-Microbe Interactions for the Health of Agroforest Ecosystems)
Browse Figures
Versions Notes

Abstract

Soil microbial communities are important for nutrient cycling regulation in forest ecosystems. However, limited knowledge exists regarding the characteristics of these microbial communities in Masson pine (Pinus massoniana Lamb.) plantations of different stand ages. In this study, four planted Masson pine stands (8-year-old, 12-year-old, 22-year-old, and 38-year-old stands) and one natural broadleaved forest stand (as a control) with three replications, were selected in the Laoshan Forest Farm, Qiandao Lake Town, Zhejiang Province, China. Soil physicochemical properties were measured and their effects on soil microbial communities were studied. Amplicon-based high-throughput sequencing was employed to process raw sequence data for soil microbes. It is worth noting that significant differences (p < 0.05) in soil bacterial genera were observed among different stand age groups. Total nitrogen (TN), total phosphorus (TP), total potassium (TK), available potassium (AK), soil organic carbon (SOC), and soil bulk density (BD) were identified as the primary factors influencing bacterial community distribution (p < 0.05). Available nitrogen (AN), SOC, TN, and TK showed significant correlations with soil fungal communities (p < 0.05). These findings underscore the crucial role of soil physicochemical properties in shaping soil microbial community composition in Masson pine plantations.

1. Introduction

Soil microorganisms significantly contribute to organic matter decomposition, enhancing soil structure and fertility and participating in essential processes like soil formation, nutrient cycling, and sustainable plant development [1,2,3]. Therefore, the community structure of soil microorganisms serves as a vital indicator of terrestrial ecosystem dynamics such as biochemical cycling and material exchanges on Earth [4,5,6,7]. Meanwhile, vegetation plays a pivotal role as primary producers, providing essential nutrients to soil microorganisms. These microorganisms function as decomposers, converting organic matter into vital nutrients [8,9]. Therefore, comprehending the diversity and community composition of soil microorganisms is crucial for elucidating the intricate feedback mechanisms among forest tree species [10], microorganisms, and soil dynamics [11]. Studies have demonstrated that environmental factors such as soil nutrient contents [12], pH levels [13], and forest/plantation age [14] regulate the structure of soil microbial communities. Previous research has also confirmed that alterations in soil organic carbon (SOC) and total nitrogen (TN) levels can significantly impact the composition of soil bacterial communities [15]. Moreover, processes like mineralization and patchy nutrient utilization patterns executed by soil microorganisms substantially influence vegetation’s nutrient uptake capacity [16].
According to the 9th China Forest Resources Inventory (2014–2018), the national forest coverage rate was about 22.96% [17], corresponding to a total area of 220 million hectares, with an estimated national forest stock volume of 17.56 billion cubic meters [18]. Forest ecosystems serve a critical function in maintaining the homeostasis of global ecological equilibria, exerting regulatory influences across multiple biogeochemical and ecological processes [19], while plant/microbe interactions are indispensable for soil fertility and the functionality of forest ecosystems. Therefore, identifying the shifts in soil microbial communities holds significant ecological importance for understanding forest ecosystems [20]. However, limited research has been conducted on the variations in soil microbial diversity and community composition across different age stages of Masson pine (Pinus massoniana Lamb.) plantations (MPPs), particularly within subtropical coastal regions of China.
Previous studies have revealed that vegetation composition affects soil microbial activity in coniferous forests [21,22]. Moreover, metagenomic analysis has shown a progressive increase in forest-associated soil bacteria and fungi with advancing forest age [23,24]. Additionally, changes in soil enzyme activity resulting from the impact of increasing plantation age on microbial community structure can subsequently affect soil nutrient status [25,26]. However, large-scale afforestation efforts have led to the establishment of MPPs with varying ages [27], further influencing the overall soil microbial community structure within these ecosystems [28]. Therefore, accurately assessing variations in soil microbial diversity [29] in MPPs of different ages is crucial for comprehending their growth conditions and understanding the status of soil nutrients [30,31,32].
This study investigated the soil microbial community structure and physicochemical properties of MPPs across different age classes (8-year-old young forest, 12-year-old middle-aged forest, 22-year-old mature forest, and 38-year-old over-mature forest), with natural broadleaved forest (NBLF) soil samples serving as the control group. We hypothesized that (1) Forest stand age exerts significant influences on both the physicochemical properties of soil and the diversity of microbial community composition; (2) the effects of forest age on soil bacterial and fungal communities are non-unidirectional and exhibit complex regulatory patterns; and (3) the key driving factors governing soil bacterial and fungal communities differ substantially in their underlying mechanisms.

2. Results

2.1. Physical and Chemical Properties of the Soil

As depicted in Table 1, the soil pH value of MPPs across different forest ages was consistently acidic. Soil organic matter was significantly higher (p < 0.05) in both the mature (MPP (38-year-old)) and young forest (MPP (8-year-old)) stages compared to other stages. The contents of soil total nitrogen, phosphorus, and potassium were slightly higher in mature forests, although not statistically significant (p > 0.05).

2.2. Soil Microbial Community Structure Variation Along the Stand Ages

The Venn analysis of soil bacterial and fungal operational taxonomic units (OTUs) in the soils of MPPs with different stand ages revealed that bacteria and fungi shared 16,350 OTUs, including 11,603 bacterial OTUs and 4747 fungal OTUs (Figure 1a,b). Among these, 374 bacterial OTUs and 55 fungal OTUs were shared across all different stand ages, respectively. The identified bacterial OTUs spanned 37 phyla, 97 orders, and 171 genera, while the fungal OTUs encompassed 31 phyla, 72 orders, and 602 genera. The number of age-specific bacterial and fungal OTUs initially increased, then decreased, before increasing again as forest aged. In comparison, the number of these OTUs exceeded that observed in natural forests.
The top 20 dominant phyla of bacterial and fungal communities in soil across different stand ages are shown in Figure 1c,d. Among soil bacteria, Acidobacteria and Proteobacteria exhibited a higher relative abundance, ranging from 30.18% to 36.78% and 29.36% to 36.96%, respectively. While there was no significant change in the relative abundance of Acidobacteria or Proteobacteria with the increase of forest age (p > 0.05), Actinobacteria showed a significant increasing trend (p < 0.05, Table 2).
Among fungi, Basidiomycota and Ascomycota were identified as the dominant phyla, accounting for 34.85–65.35% and 19.44–51.91% of the relative abundance, respectively. As forest age increased, a decreasing trend was observed regarding the relative abundance of Ascomycota. Overall, the dominant phyla of both bacteria and fungi did not show significant changes while variations in their respective relative abundances were noted.
Further comparisons were conducted between dominant bacterial and fungal genera in the soils of Masson pine under different stand ages (Figure 1e,f). Variations in the distribution of dominant bacterial and fungal genera were observed in Masson pine soils with different forest ages. Regarding bacterial genera, Palsa_187, UBA11361, and URHD0088 were predominantly enriched in the soil of 12-year-old forest, while Edaphobacter_415416, Bradyrhizobium, Tyrphobacter, and CF_113 were enriched in the 8-year-old forest. Interestingly, the bacterial genera in the soils of 22-year-old and 38-year-old forests exhibited similar distribution, with Gp1_AA17, Hypericibacter, and Binatus being the main enriched genera. At the fungal genera level, Wilcoxina and Tomentella were significantly enriched in the soil of 8-year-old forest, while the soil of 12-year-old forest exhibited a higher abundance of enriched genera. In comparison, Scleroderma and Sympodiella were enriched in the soil of 22-year-old forest, whereas Rhizopogon and Penicillium were primarily enriched in the soil of 38-year-old forest.

2.3. Structural Diversity of Soil Microbial Community

The Kruskal–Wallis H-test indicated significant differences (p < 0.05) in the dominant bacterial phyla (Actinobacteria) among Masson pine of different forest ages (Table 2), whereas the dominant fungal phyla did not show significant differences (p > 0.05). Additionally, the bacterial α diversity of Masson pine was found to be significantly higher than that of natural broadleaved forests, as confirmed by a two-factor analysis of variance (p < 0.05, Table 3).
The β-diversity of soil bacterial and fungal communities was further investigated using non-metric multidimensional scaling (NMDS) to explore the variations across different forest ages (Figure 2). The NMDS analysis revealed that soil exhibited higher similarity within the same forest age, while more pronounced differences were observed in different forest ages. Figure 2a clearly demonstrated notable variations in bacterial community across different forest ages. Importantly, the analysis also revealed that the younger forests exhibited greater divergence in bacterial community structures. The divergence in bacterial community structure increased with forest ages. Similarly, the youngest subgroup (8-year-old forest) exhibits the greatest disparity in fungal community structure when compared to other subgroups.

2.4. The Associations Between Soil Microbial Communities and Soil Properties

Soil physical and chemical parameters such as bulk density (BD), total nitrogen (TN), and soil organic matter (SOC) showed significant positive correlations with bacterial genera such as Vermiphilus and Acidoferrum (Figure 3), but the relationship between available phosphorus (AP), total nitrogen (TN), SOC, available nitrogen (AN), and bacterial genera exhibited an inverse pattern compared to the previous findings. Conversely, the correlations between BD, total phosphorus (TP), total potassium (TK), Density, Coverage, Slope, available potassium (AK), AP, TN, SOC, AN, and fungal genera generally demonstrated contrasting tendencies, as depicted in Figure 3b. The relationships between BD, TP, TK, Density, Canopy, and Tricellula, Chaetomium Myxospora and other fungal genera exhibited significantly negative correlations. Overall, the associations between bacterial and fungal genera and soil physicochemical properties demonstrated distinct trends.
As depicted in Table 4 and Figure 3c, a significant correlation (p < 0.05) was observed between SOC and bacteria, as well as AP and bacteria. Additionally, a significant correlation (p < 0.05) was found between AN and the soil fungal community. Overall, SOC and available nutrients play the most prominent role in shaping soil bacterial and fungal communities.
The structural equation modeling was employed to reveal the influences of environmental factors (soil physicochemical properties, vegetation factors, and slope in this study) on soil microbial community structure, with results illustrated in Figure 4a–d. As depicted in Figure 4a, TN, TP, TK, AK, SOC, and BD exhibited significantly negative effects on the bacterial community structure (p < 0.001). Figure 4b indicates the direct effects on soil bacteria, ranked as follows: Density > SOC > TP > TN > TK > AP > BD > AK > AN > Canopy > Coverage > Slope. According to Figure 4c, TK, AK, SOC had highly significant negative effects on fungi community (p < 0.001), whereas capacity, Slope, AP, AN, and Canopy demonstrated highly significant positive impacts (p < 0.001). Figure 4d outlines the direct effects of each factor on soil fungi, ranked as follows: Density > Coverage > Slope > Canopy > TK > SOC > AN > TP > TN > BD > AP > AK.
The RDA1 and RDA2 collectively accounted for 33.84% and 33.75% of the total variation in bacterial and fungal community structures, respectively. For bacterial communities, SOC and vegetation cover were identified as the primary environmental factors influencing their distribution (p < 0.05). Similarly, SOC, TN, and TK emerged as the main environmental factors influencing the distribution of fungal communities (p < 0.05). Bacterial communities in natural forests were positively correlated with Density, TP, BD, TK, TN, and AN and negatively correlated with SOC and AP. Soil fungal communities across different forest ages demonstrated positive associations with TP, BD, Density, Canopy, and TK and negative associations with AN, SOC, AP, AK, Slope, and Coverage.

3. Discussion

3.1. Soil Microbial Community Structure Along the Stand Ages

This study aimed to characterize the variations in soil microbial communities and edaphic properties across different stand age classes of MPPs, focusing on dominant taxonomic groups, diversity indices, and key drivers influencing bacterial and fungal assemblages. Forest ecosystems serve as critical niches for soil microbial proliferation [33], which are crucial for maintaining soil quality, fertility, and vegetation health [5]. Prior research has established that the diversity of soil bacterial and fungal communities directly modulates the stability and functional processes of soil ecosystems [34]. The number of specific OTUs for both bacteria and fungi initially increased with forest ages, followed by a decrease. Overall, the number of OTUs was significantly higher than that observed in natural broadleaved forests. These findings confirm that stand age strongly influences the community structure of soil microorganisms while highlighting the fundamental role played by soil in driving changes within these microorganisms [22], which are consistent with our research findings. In addition, the growth of soil microorganisms is significantly influenced by soil pH [35]. The soil environment in Masson pine forests provides favorable conditions for bacterial growth [36], which contributes to the aforementioned findings. Moreover, compared to over-mature forests, middle-aged Masson pine forests exhibit higher diversity and abundance of bacteria and fungi. This could be attributed to the substantial impact of tree age on the interaction between vegetation and soil microorganisms. As forest age increases, Masson pines require more nutrients, thereby affecting the growth of soil microorganisms and ultimately exerting a negative influence on the microbial community in the soil [37]. Notably, both bacterial and fungal diversity and abundance in MPPs exceeded those documented in natural forest systems [38,39]. This discrepancy may be linked to the age structure of natural forests in the study region, which have remained unmanaged since establishment, leading to a generalized over-mature state. Such long-term lack of anthropogenic intervention is presumably a major contributor to reduced soil microbial diversity and abundance in natural forests [40,41].
The composition and structural complexity of soil microbial communities have been demonstrated to indirectly impact soil quality, thereby impeding the growth of forest vegetation [1]. In Masson pine forests of varying ages, Acidobacteria and Proteobacteria are identified as the dominant phyla among soil bacteria, while Ascomycota represents the dominant phylum among fungi. It is postulated that these dominant phyla exhibit enhanced adaptability within Masson pine forests, given the faster growth rate observed in bacterial communities under nutrient-rich conditions [42]. Consequently, Acidobacteria also emerges as a predominant phylum in Chinese fir and native pine forests of different ages [43]. Furthermore, these dominant phyla likely play a pivotal role as primary decomposers in complex organic matter degradation within soils, thus establishing their prominence within Masson pine forests [44]. The results of cluster analysis and RDA analysis demonstrate significant changes in bacterial composition with forest age in Masson pine forests, exhibiting higher bacterial diversity within the genus compared to natural forests. This disparity can be attributed to the increased abundance of nutrient-rich and eutrophic bacteria and fungi in the soil environment of resource-rich plantation forests [29], leading to variations in soil microbial community composition. in resource-rich plantation environments, which consequently lead to alterations in soil microbial community composition. Forest succession processes are known to influence plant community structure and organic carbon levels, thereby impacting microbial community activity [45]. Further research is essential for understanding the complex interactions between forest types and soil microbial activity.
The composition of soil microbial communities profoundly influences their dynamics and ecological functions [7]. The NMDS analysis revealed that bacteria and fungi in soils of the same forest age exhibited high similarity but significantly differed from those present in natural forests. Notably, bacterial communities displayed more pronounced variations among the different ages of Masson pine forests, while fungal communities showed relatively stable patterns across forest age. These findings suggest that tree species and age may play a crucial role as they create distinct internal environments, making conditions within Masson pine forest ecosystems more favorable for bacterial growth [46,47].

3.2. Factors Influencing the Dominant Flora and Diversity of Microbial Community Structure in Soil

Soil microorganisms play a pivotal role in soil ecosystems and exhibit high sensitivity to environmental fluctuations. Research findings have demonstrated that TN, TP, TK, AK, SOC, and Density exert a significantly negative influence on bacterial community indicators (p < 0.001), with soil organic carbon and vegetation coverage emerging as the primary environmental factors influencing bacterial community distribution (p < 0.05). These outcomes can be attributed to the substantial correlation between bacterial diversity and soil properties, particularly carbon and nitrogen elements [48]. The interplay of nitrogen (N) and carbon (C) elements synergistically affects bacteria’s assimilation of C while promoting N availability. Moreover, soil pH regulates changes in bacterial communities by modulating the living environment of soil bacteria through its impact on the efficacy of N and C within the soil matrix [49]. The above results occurred because bacteria and fungi are adapted to different environmental conditions, and the contents of C, N, and P in soil indirectly influence microbial growth.
The results of this study demonstrate a significant correlation (p < 0.05) between AN, SOC, TN, TK, and soil fungal community, supporting our hypothesis that the main influencing factors for bacteria and fungi are not entirely identical. Soil total nitrogen content is a crucial determinant of soil fungal abundance and diversity due to their wider pH tolerance range [50] and ability to acquire C in more diverse forms [24,32], making them more susceptible to higher N content [50]. Overall, soil physicochemical properties and environmental factors exert substantial impacts on the microbial community composition in soils across different forest ages [51]. Soil exhibits strong spatiotemporal variability in biological, physical, and chemical properties, creating a diverse and complex environment for microorganisms. The joint effects of vegetation density, terrain, and afforestation duration lead to variations in soil properties across different forest ages. As soil properties at different layers serve as primary drivers of changes in microbial community structure, the compositions of soil bacterial and fungal communities are consequently significantly influenced by soil nutrient contents. This knowledge motivates us to further investigate the mechanisms underlying interactions among soil/vegetation/microorganisms towards enhancing forest ecosystem functionality and contributing towards achieving carbon neutrality.

4. Materials and Methods

4.1. The Study Area and Sampling

The study area was situated in the Laoshan Forestry Field (29°33′30″ N, 119°02′55″ E) of Qiandao Lake, Hangzhou City, China. The area falls within the subtropical monsoon zone, characterized by a substantial annual precipitation of 1430 mm. The mean annual temperature was 17.1 °C, accompanied by an average of 1951 h of sunshine per year. The approximate average elevation is 150 m above sea level. The original vegetation was represented by typically subtropical evergreen broadleaved forest (Cyclobalanopsis glauca and Castanopsis sclerophylla species), with MPPs as the main forest communities since 1980s.
A “space-for-time substitution” method was applied to study the soil microbial community in MPPs with different stand ages. To minimize variation among different Masson pine stands, plantations with consistent soil type, slope incline, and topography were selected. The selected MPPs were planted in 1986, 2002, 2012, and 2016 after clearly cutting the original broadleaved forest. The stands consisted of a juvenile forest (8 years old, since 2016), a middle-aged forest (12 years old, since 2012), a mature forest (22 years old, since 2002), and an over-mature forest (38 years old, since 1986). Moreover, a nearby natural broadleaved forest (NBLF) with dominating Cyclobalanopsis glauca and Castanopsis sclerophylla was selected as the control. After a rigorous evaluation of the sites, three replicated plots (20 m × 20 m per each) for each age class of MPP and NBLF were randomly established, resulting in a total of 15 plots. A comprehensive survey was conducted to assess the vegetation density of Masson pine forests with a diameter at breast height (DBH) ≥ 3 cm, by measuring parameters such as DBH, tree height, and crown width on a tree-by-tree basis within each plot. Soil samples from a depth of 0–10 cm were collected. Soil samples weighing 500 g each, free from gravel, were transported to the laboratory. The main characteristics of the studied sites are summarized in Table 5.

4.2. Soil Properties Analysis

Soil physicochemical properties were determined according to the standard methods of Agricultural Chemistry Committee of China [52]. Soil organic matter was quantified using the potassium dichromate externally heated sulfuric acid oxidation method. Bulk density was measured by cutting ring method. The TN and TP were determined by semi-micro Kjeldahl method and alkali melt/molybdenum/antimony resistance colorimetric method, respectively. Alkali melt-flame spectrophotometry was used to assess TK. The available nitrogen (AN) was determined by alkaline hydrolysis diffusion method. Available phosphorus (AP) was extracted by sodium bicarbonate and then determined by molybdenum/selenium/antimony colorimetric method. Available potassium (AK) was extracted by ammonium acetate and determined by flame photometry method. Soil pH was measured potentiometrically with a water/soil ratio of 2.5:1.

4.3. Gene Sequencing Analysis

The soil DNA was extracted using the cetyltrimethyl ammonium bromide standard method [53]. The purity and concentration of DNA were then assessed by 1% agarose gel electrophoresis. To determine the purity and concentration of DNA, PCR amplification targeting the V3-V4 region of the bacterial 16S rRNA gene was performed with primers 341F (5′-CCTAYGGGRBGCASCAG-3′) and 806R (5′-GGACTACNNGGGTATCTAAT-3′) For PCR amplification of fungal rDNA ITS1 region, primers ITS1F (5′-CTTGGTCATTTAGAGGAAGTAA-3′) and ITS1R (5′-GCTGCGTTCTTCATCGATGC-3′) were used. Aliquots were pooled based on PCR product concentration and subsequently purified. Library preparation was carried out using Illumina TruSeq® DNA PCR-Free Sample Preparation Kit. Sequencing was conducted on the NovaSeq 6000 PE250 platform [53].
The raw sequences were processed using the QIIME2 software package. Initially, sequences were trimmed to a length of 200 bp using cutadapt (v1.10), Pear (v1.9.4), and Prinseq-liteV 0.20.4 [54]. Primer sequences were eliminated using cutadapt, followed by removal of tail region sequences with slightly lower quality values utilizing Prinseq-lite V0.20.4. Subsequently, paired-end reads were merged through Pear (v1.9.4) [55]. After eliminating single and chimeric sequences, high-quality sequences exhibiting ≥97% similarity were clustered into operational taxonomic units (OTUs) using Usearch (v5.2).

4.4. Statistical Analysis

Analysis of variance (ANOVA) was applied to investigate the impact of forest ages on soil physicochemical properties, the abundance and diversity of soil microbial communities. Prior to ANOVA analysis, the normality and homogeneity of variances were carried out in the data, with logarithmic transformations applied if necessary to ensure adherence to a normal distribution. When significance was observed based on the F statistic, mean comparisons were performed using Tukey’s test.
Statistical analyses were conducted using IBM SPSS Statistics 19.0 software, while R (ver. 3.6.1) was used to plot relative abundance, heat map, Venn analysis, redundancy analysis (RDA), and correlation heat map for analyzing the composition of soil microbial communities. To assess differences in microbial community composition among sample groups, a permutation-based multivariate analysis of variance with permutational multivariate analysis (PERMANOVA) was performed after applying Hellinger transformation to the OTU table. These results were further employed to analyze the structure of soil microbial community composition. QIIME2 software was used to assess α-diversity (Ace, Chao1, Pielou, Shannon index) and β-diversity of soil fungal communities, further examine the diversity and differences among soil microorganisms [56]. Additionally, structural equation modeling was conducted to reveal the correlation between soil microbial community structure and physicochemical properties.

5. Conclusions

This study explores the disparities in soil microbial community structural characteristics and soil properties among Masson pine forests of varying ages. Our findings reveal that distinct bacterial and fungal taxa exhibit differential roles across different forest ages, with the highest soil microbial abundance observed in the mature stage of Masson pine forests. Furthermore, significant differences (p < 0.05) in soil bacterial genera among forests of different ages, with the most abundant species of dominant soil bacterial genera found in the middle-aged forest (12 years). Additionally, bacterial and fungal diversity and abundance were higher in young and middle-aged Masson pine forests compared to over-mature forest. Moreover, both soil bacterial and fungal communities showed significant correlations (p < 0.05) with soil AP and SOC contents. Therefore, it is imperative to implement further soil fertility management and tree age control measures in order to effectively enhance the soil microbial activity within the Masson pine forest ecosystem, thereby making a significant contribution towards augmenting its carbon sequestration capacity.

Author Contributions

Conceptualization, W.F. and B.W.; Software, B.W.; Validation, W.F., B.W., and D.L.; investigation, B.W.; Resources, W.F. and B.W.; Data curation, W.F. and B.W.; Writing—original draft preparation, W.F. and B.W.; Writing—review and editing, W.F.; Supervision, W.F.; Project administration, Y.Z.; Funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the State Key Laboratory of Subtropical Silviculture (SKLSS-KF2022-07).

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

The authors want to express our gratitude to the staff of Laoshan Forest Farm in Qiandao Lake Town, Hangzhou City, Zhejiang Province, for their invaluable assistance during our fieldwork.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. Luo, X.; Guo, Y.P.; Xu, F.Y.; Wang, S.; Tao, Y.Y.; YangZ, M.M. Soil horizons regulate bacterial community structure and functions in Dabie Mountain of the East China. Sci. Rep. 2023, 13, 15866. [Google Scholar] [CrossRef]
  2. Miao, D.; Peng, X.; Teng, Q.M.; Sardar, M.F.; Zhang, Q.; Zhu, X.T.; Li, Y. Different contributions of bacterial and fungal communities to nitrogen mineralization in Moso bamboo-invaded subtropical forests. J. Soil Sediment. 2023, 23, 1123–1134. [Google Scholar] [CrossRef]
  3. Wu, J.; Jiao, Z.; Zhou, J.; Guo, F.; Ding, Z.; Qiu, Z. Analysis of bacterial communities in rhizosphere soil of continuously cropped healthy and diseased konjac. World J. Microb. Biot. 2017, 33, 134. [Google Scholar] [CrossRef]
  4. Jaeger, A.; Hartmann, M.; Conz, R.F.; Six, F.; Solly, E.F. Drought-induced tree mortality in Scots pine mesocosms promotes changes in soil microbial communities and trophic groups. Appl. Soil Ecol. 2024, 194, 105198. [Google Scholar] [CrossRef]
  5. Fang, X.; Zhang, J.; Meng, M.; Guo, X.; Wu, Y.; Liu, X.; Zhao, K.; Ding, L.; Shao, Y.; Fu, W. Forest-type shift and subsequent intensive management affected soil organic carbon and microbial community in southeastern China. Eur. J. Forest Res. 2017, 136, 689–697. [Google Scholar] [CrossRef]
  6. Wang, H.K.; Wu, C.S.; Liu, J.; Chen, Q.; Li, C.; Shu, C.J.; Zhang, Y.; Liu, Y.Q. Changes in soil microbial communities induced by warming and N deposition accelerate the CO2 emissions of coarse woody debris. J. For. Res. 2023, 34, 1051–1063. [Google Scholar] [CrossRef]
  7. Wan, X.; Zhou, R.Y.; Liu, S.; Xing, W.; Yuan, Y.D. Seasonal Changes in the Soil Microbial Community Structure in Urban Forests. Biology 2024, 13, 31. [Google Scholar] [CrossRef]
  8. He, Y.; Wu, B.; Wang, C.X.; Zhi, Y.Y.; Shi, Y.J.; Fu, W.; Zhang, Y. Spatial variability and influencing factors of soil organic carbon, soil total nitrogen and C:N ratio in Chinese hickory plantations of southeastern China. J. Soil Sci. Plant Nutri. 2025, 25, 3323–3337. [Google Scholar] [CrossRef]
  9. Zhang, J.H.; Li, M.; Jia, K.L.; Zheng, G.Q.; Long, X. Seasonal variation rather than stand age determines bacterial diversity in the rhizosphere of wolfberry (Lycium barbarum L.) associated with soil degradation. J. Soils Sediments 2018, 18, 1518–1529. [Google Scholar] [CrossRef]
  10. Li, L.; Wang, D.; Liu, X.; Zhang, B.; Liu, Y.Z.; Xie, T.; Du, Y.; Pan, G. Soil organic carbon fractions and microbial community and functions under changes in vegetation: A case of vegetation succession in karst forest. Environ. Earth Sci. 2014, 71, 3727–3735. [Google Scholar] [CrossRef]
  11. Okie, J.D.; Van Horn, D.J.; Storch, D.; Barrett, J.E.; Gooseff, M.N.; Kopsova, L.; Takacsvesbach, C.D. Niche and metabolic principles explain patterns of diversity and distribution: Theory and a case study with soil bacterial communities. Pro. Roy. Soc. B Biol. Sci. 2015, 282, 20142630. [Google Scholar] [CrossRef]
  12. Constancias, F.; Terrat, S.; Saby, N.P.A.; Horrigue, W.; Villerd, J.; Guillemin, J.; Biju-Duval, L.; Nowak, V.; Dequiedt, S.; Ranjard, L.; et al. Mapping and determinism of soil microbial community distribution across an agricultural landscape. Microbiology 2015, 4, 505–517. [Google Scholar] [CrossRef]
  13. Lv, Y.; Fu, W.; Zhou, K.; Lao, J.; Zhi, Y. Effects of reduced fertilization combined with titanium gypsum on soil arsenic&cadmium and water spinach (Ipomoea aquatica Forsk) growth. Pol. J. Environ. Stud. 2023, 32, 2763–2773. [Google Scholar]
  14. Li, C.; Jia, Z.H.; Zhai, L.; Zhang, B.; Peng, X.N.; Liu, X.; Zhang, J. Effects of mineral-solubilizing microorganisms on root growth, soil nutrient content, and enzyme activities in the rhizosphere soil of Robinia Pseudoacacia. Forests 2021, 12, 60. [Google Scholar] [CrossRef]
  15. Husson, O. Redox potential (Eh) and pH as drivers of soil/plant/microorganism systems: A transdisciplinary overview pointing to integrative opportunities for agronomy. Plant Soil 2013, 362, 389–417. [Google Scholar] [CrossRef]
  16. Adomako, M.O.; Roiloa, S.; Yu, F.H. Potential roles of soil microorganisms in regulating the effect of soil nutrient heterogeneity on plant performance. Microorganisms 2022, 10, 2399. [Google Scholar] [CrossRef]
  17. Li, F.; Chen, L.; Zhang, J.B.; Yin, J.; Huang, S.M. Bacterial Community Structure after Long-term Organic and Inorganic Fertilization Reveals Important Associations between Soil Nutrients and Specific Taxa Involved in Nutrient Transformations. Front. Microbiol. 2017, 8, 187. [Google Scholar] [CrossRef] [PubMed]
  18. Dong, L.; Li, J.; Sun, J.; Yang, C. Soil degradation influences soil bacterial and fungal community diversity in overgrazed alpine meadows of the Qinghai-Tibet Plateau. Sci. Rep. 2021, 11, 11538. [Google Scholar] [CrossRef]
  19. Duan, X.; Zou, H.; Wang, L. Exploration of Institutional Mechanisms for the Construction and Development of the Yangtze River Economic Belt. Prog. Geosci. 2015, 34, 1377–1387. (In Chinese) [Google Scholar]
  20. Lladó, S.; López-Mondéjar, R.; Baldrian, P. Forest Soil Bacteria: Diversity, Involvement in Ecosystem Processes, and Response to Global Change. Microbiol. Mol. Biol. Rev. 2017, 81, e00063-16. [Google Scholar] [CrossRef]
  21. Zhang, S.N.; Tang, K.; Gao, H.Y.; Wei, Y.F.; Zhang, L. Characterization of soil bacterial communities and their driving roles in birch woodlands. For. Engin. 2024, 40, 27–35. (In Chinese) [Google Scholar]
  22. Mao, L.; He, X.X.; Ye, S.M.; Wang, S.Q. Soil Aggregate-Associated Carbon-Cycle and Nitrogen-Cycle Enzyme Activities as Affected by Stand Age in Chinese Fir Plantations. J. Soil Sci. Plant Nutri. 2023, 23, 4361–4372. [Google Scholar] [CrossRef]
  23. Ming, A.; Yang, Y.J.; Liu, S.; Nong, Y.; Li, H.; Tao, Y.; Sun, D.; Lei, L.; Zeng, J.; An, N. The impact of near natural forest management on the carbon stock and sequestration potential of Pinus massoniana (Lamb.) and Cunninghamia lanceolata (Lamb.) Hook. plantations. Forests 2019, 10, 626. [Google Scholar] [CrossRef]
  24. Gao, Y.; Cheng, J.M.; Zhao, Y.; Su, J.S. Carbon storage in biomass, litter, and soil of different plantations in a semiarid temperate region of northwest China. Ann. For. Sci. 2014, 71, 427–435. [Google Scholar] [CrossRef]
  25. Wang, C.Q.; Xue, L.; Dong, Y.; Hou, L.; Wei, Y.; Chen, J.; Jiao, R. Contrasting Effects of Chinese Fir Plantations of Different Stand Ages on Soil Enzyme Activities and Microbial Communities. Forests 2019, 10, 11. [Google Scholar] [CrossRef]
  26. Mao, P.; Guo, L.M.; Gao, Y.; Qi, L.; Cao, B.H. Effects of Seed Size and Sand Burial on Germination and Early Growth of Seedlings for Coastal Pinus thunbergii Parl. in the Northern Shandong Peninsula, China. Forests 2019, 10, 281. [Google Scholar] [CrossRef]
  27. Chen, L.C.; Wang, S.L.; Wang, Q.K. Ecosystem carbon stocks in a forest chronosequence in Hunan Province, South China. Plant Soil. 2016, 409, 217–228. [Google Scholar] [CrossRef]
  28. Nie, M.; Meng, H.; Li, K.; Wan, J.R.; Quan, Z.X.; Fang, C.M.; Chen, J.K.; Li, B. Comparison of Bacterial and Fungal Communities Between Natural and Planted Pine Forests in Subtropical China. Curr. Microbiol. 2012, 64, 34–42. [Google Scholar] [CrossRef]
  29. Zhang, H.Y.; Bao, W.K.; Hu, B.; Hu, H. Research progress on the effect of vegetation type change on soil microbial carbon utilization efficiency. J. Ecol. 2023, 43, 6878–6888. [Google Scholar]
  30. Štursová, M.; Bárta, J.; Šantrůčková, H.; Baldrian, P. Small-scale spatial heterogeneity of ecosystem properties, microbial community composition and microbial activities in a temperate mountain forest soil. FEMS Microbiol. Ecol. 2016, 92, fiw185. [Google Scholar] [CrossRef]
  31. Zhou, J.; Zhang, M.; Raza, S.T.; Yang, S.; Liu, J.; Cai, M.; Xue, S.M.; Wu, J.P. Fungal but not bacterial β-diversity decreased after 38-year-long grazing in a southern grassland. Plant Soil. 2023, 507, 181–193. [Google Scholar] [CrossRef]
  32. Fu, W.J.; Dong, J.Q.; Ding, L.Z.; Yang, H.S.; Ye, Z.Q.; Zhao, K.L. Spatial correlation of nutrients in a typical soil-hickory system of southeastern China and its implication for site-specific fertilizer application. Soil Till. Res. 2022, 217, 105265. [Google Scholar] [CrossRef]
  33. Barnard, R.L.; Osborne, C.A.; Firestone, M.K. Responses of soil bacterial and fungal communities to extreme desiccation and rewetting. ISME J. 2013, 7, 2229–2241. [Google Scholar] [CrossRef]
  34. Gu, Y.; Wu, L.; Lin, D.; Zou, B.; Wang, S.; Zhou, L.; He, J.Z. Soil bacterial community composition of typical subtropical forests in Fujian Province. J. Fujian Norm. Univ. 2024, 40, 52–59. (In Chinese) [Google Scholar]
  35. Hu, W.G.; Ran, J.Z.; Dong, L.W.; Du, Q.; Ji, M.F.; Yao, S.R.; Sun, Y.; Gong, C.M.; Hou, Q.Q.; Gong, H.Y.; et al. Aridity-driven shift in biodiversity-soil multifunctionality relationships. Nat. Commun. 2021, 12, 5350. [Google Scholar] [CrossRef]
  36. Jiang, A.J.; Dong, Y.; Ju, L.; Zhou, S.; Nie, T.; Li, C. Effects of sealing on soil bacterial community characteristics in different grassland types. J. Grassland. 2022, 30, 2600–2608. (In Chinese) [Google Scholar]
  37. Jiang, X.W.; Ma, D.L.; Qiang, S.Y.; Zhang, D.Y.; Sun, H.Z. Characteristics of soil bacterial and fungal community of typical forest in the Greater Khingan Mountains based on high-throughput sequencing. Microbiol. China 2021, 48, 1093–1105. (In Chinese) [Google Scholar]
  38. Hu, Y.B.; Liang, C.; Jin, J.; Wang, X.; Ye, Z.; Wu, J. Effects of long-term grass cultivation on soil fungal communities and enzyme activities in hickory plantation forests. Environ. Sci. 2023, 44, 2945–2954. (In Chinese) [Google Scholar]
  39. Hou, Z.N.; Zhang, X.; Wang, R.; Li, A.; Li, X.; Wei, Y. Changes in soil microbial communities during decomposition of alpine forest apomictic litter at different altitudes. J. China Agric. Univ. 2024, 29, 36–46. (In Chinese) [Google Scholar]
  40. Kong, W.B.; Qiu, L.P.; Ishii, S.; Jia, X.X.; Su, F.Y.; Song, F.; Hao, M.D.; Shao, M.A.; Wei, X.R. Contrasting response of soil microbiomes to long-term fertilization in various highland cropping systems. ISME Commu. 2023, 3, 1–14. [Google Scholar] [CrossRef] [PubMed]
  41. Wu, F.; Dong, M.; Liu, Y.; Ma, X.; An, L.; Young, J.; Feng, H.Y. Effects of long-term fertilization on AM fungal community structure and Glomalin-related soil protein in the Loess Plateau of China. Plant Soil. 2011, 342, 233–247. [Google Scholar] [CrossRef]
  42. Zhang, Y.; Dong, K.; Gao, Q.; Liu, S.; Ganjurjav, H.; Wang, X.; Su, X.K.; Wu, X.Y. Soil bacterial and fungal diversity differently correlated with soil biochemistry in alpine grassland ecosystems in response to environmental changes. Sci. Rep. 2017, 7, 43077. [Google Scholar] [CrossRef]
  43. Zheng, W.; Gong, Q.; Zhao, Z.; Liu, J.; Zhai, B.; Wang, Z.; Li, Z. Changes in soil bacterial community structure and enzyme activities after intercrop mulch with cover crop for eight years in an orchard. Eur. J. Soil Biol. 2018, 86, 34–41. [Google Scholar] [CrossRef]
  44. Kong, W.Y.; Wang, X.H.; Dong, K.; Ge, Y.X. Ground cover under ancient oil pine and its effect on soil bacterial community diversity. J. Beijing Agri. Coll. 2021, 36, 101–106. (In Chinese) [Google Scholar]
  45. Zhang, Y.L.; Liu, L.; Liu, S.; Fang, W.; Luo, Z.; Hong, X.; Cheng, X.R. Effects of community structure of Cunninghamia lanceolata sprouting forests with different densities on ecosystem carbon density at the early stage of succession. Chinese J. Appl. Ecol. 2024, 35, 289–297. [Google Scholar]
  46. Shao, P.S.; Xie, H.T.; Bao, X.L.; Liang, C. Variation of microbial residues forest secondary succession in topsoil and subsoil. Acta Pedol. Sin. 2021, 58, 1050–1059. (In Chinese) [Google Scholar]
  47. McGee, K.M.; Porter, T.M.; Wright, M.; Hajibabaei, M. Drivers of tropical soil invertebrate community composition and richness across tropical secondary forests using DNA metasystematics. Sci. Rep. 2020, 10, 18429. [Google Scholar] [CrossRef]
  48. Yao, L.; Wang, D.; Kang, L.; Wang, D.; Zhang, Y.; Hou, X.; Guo, Y.J. Effects of fertilizations on soil bacteria and fungi communities in a degraded arid steppe revealed by high through-put sequencing. PeerJ 2018, 6, e4623. [Google Scholar] [CrossRef] [PubMed]
  49. Yang, F.; Jiang, H.; Chang, G.; Liang, S.; Ma, K.; Cai, Y.; Tian, B.M.; Shi, X.J. Effects of Rhizosphere Microbial Communities on Cucumber Fusarium wilt Disease Suppression. Microorganisms 2023, 11, 1576. [Google Scholar] [CrossRef]
  50. Yang, Y.; Li, H.L.; Ma, K.L.; Yu, F.F.; Niu, S.Q. Effects of continuous cropping on the physicochemical properties, microbial activity and community characteristics of inter-root soil of Codonopsis pilosula. Environ. Sci. 2023, 44, 6387–6398. (In Chinese) [Google Scholar]
  51. Agricultural Chemistry Committee of China. Conventional Methods of Soil and Agricultural Chemistry Analysis; Science Press: Beijing, China, 1983. (In Chinese) [Google Scholar]
  52. Hu, M.H.; Zhou, S.; Xiong, X.; Wang, X.; Sun, Y.; Meng, Z.; Hui, D.F.; Zhang, D.Q.; Deng, Q. Dynamics of soil microbial communities involved in carbon cycling along three successional forests in southern China. Front. Microbiol. 2024, 14, 1326057. [Google Scholar] [CrossRef]
  53. Luo, Z.; Viscarra-Rossel, R.A.; Qian, T. Similar importance of edaphic and climatic factors for controlling soil organic carbon stocks of the world. Biogeosciences 2021, 18, 2063–2073. [Google Scholar] [CrossRef]
  54. Klindworth, A.; Pruesse, E.; Schweer, T.; Peplies, J.; Quast, C.; Horn, M.; Glöckner, F.O. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 2013, 41, e1. [Google Scholar] [CrossRef] [PubMed]
  55. Lu, J.; Salzberg, S.L. Ultrafast and accurate 16S rRNA microbial community analysis using Kraken 2. Microbiome 2020, 8, 124. [Google Scholar] [CrossRef] [PubMed]
  56. Barnett, S.E.; Buckley, D.H. Simulating metagenomic stable isotope probing datasets with MetaSIPSim. BMC Bioinform. 2020, 21, 37. [Google Scholar] [CrossRef]
Plants 14 03004 g001
Figure 1. The OTU number of soil bacteria (a) and fungi (b) in forests of different ages based on Venn analysis. The relative proportion of the top twenty soil bacterial (c) and fungal (d) phyla in forests of different ages. The clustering heatmaps of the most important twenty soil bacteria (e) and fungi (f) genera in forests of different ages.
Figure 1. The OTU number of soil bacteria (a) and fungi (b) in forests of different ages based on Venn analysis. The relative proportion of the top twenty soil bacterial (c) and fungal (d) phyla in forests of different ages. The clustering heatmaps of the most important twenty soil bacteria (e) and fungi (f) genera in forests of different ages.
Plants 14 03004 g001
Plants 14 03004 g002
Figure 2. Non-metric multidimensional scaling (NMDS) analysis of soil bacterial communities (a) and fungal communities (b).
Figure 2. Non-metric multidimensional scaling (NMDS) analysis of soil bacterial communities (a) and fungal communities (b).
Plants 14 03004 g002
Plants 14 03004 g003
Figure 3. The correlation between soil bacteria genera and environmental factors (soil physicochemical properties, vegetation factors, and slope) (a) and soil fungus genera and environmental factors (b). Correlation heatmap for soil microbia and fungi with environmental factors based on Mantel test (c). *: stands for significant difference at p < 0.05. **: stands for significant difference at p < 0.01; ***: stands for significant difference at p < 0.001.
Figure 3. The correlation between soil bacteria genera and environmental factors (soil physicochemical properties, vegetation factors, and slope) (a) and soil fungus genera and environmental factors (b). Correlation heatmap for soil microbia and fungi with environmental factors based on Mantel test (c). *: stands for significant difference at p < 0.05. **: stands for significant difference at p < 0.01; ***: stands for significant difference at p < 0.001.
Plants 14 03004 g003
Plants 14 03004 g004
Figure 4. Structural equation model diagrams: (a) soil bacteria of Masson pine plantations and (c) soil fungi of Masson pine plantations. Figure (b,d) depict the standard effect diagrams within the structural equation model diagrams. (e) RDA of soil bacterial communities and (f) RDA of soil fungal communities. * p < 0.05; ** p ≤ 0.01; *** p ≤ 0.001.
Figure 4. Structural equation model diagrams: (a) soil bacteria of Masson pine plantations and (c) soil fungi of Masson pine plantations. Figure (b,d) depict the standard effect diagrams within the structural equation model diagrams. (e) RDA of soil bacterial communities and (f) RDA of soil fungal communities. * p < 0.05; ** p ≤ 0.01; *** p ≤ 0.001.
Plants 14 03004 g004
Table 1. Soil properties of forests at different ages.
Table 1. Soil properties of forests at different ages.
Stand TypespH ValueOrganic Matter
(g·kg−1)		Total Nitrogen (g·kg−1)Total Phosphorus
(g·kg−1)		Total Potassium
(g·kg−1)
MPP(8-year-old)4.96 ± 0.08 b 56.97 ± 0.86 a 1.31 ± 0.09 a 2.23 ± 1.08 a 23.74 ± 1.29 b
MPP(12-year-old)5.11 ± 0.09 a 26.16 ± 3.62 b 2.17 ± 0.14 a 1.96 ± 0.29 b 36.06 ± 5.71 a
MPP(22-year-old)4.79 ± 0.08 c 34.26 ± 3.06 b 1.33 ± 0.55 a 1.89 ± 0.20 b 34.46 ± 1.18 a
MPP(38-year-old)4.93 ± 0.13 b 63.88 ± 17.11 a 1.84 ± 0.79 a 2.26 ± 0.53 ab 36.57 ± 5.14 a
NBLF4.78 ± 0.08 c 56.48 ± 19.12 a 1.47 ± 0.21 a 2.33 ± 0.77 a 35.14 ± 2.13 a
Mean ± SD, n = 3. Different lowercase letters indicate significant differences between forest stands of different ages at a significance level of (p < 0.05). MPP: Masson pine plantation; NBLF: natural broadleaved forest.
Table 2. The dominant phyla of soil microorganisms.
Table 2. The dominant phyla of soil microorganisms.
H-Valuep-Value
BacteriaAcidobacteria4.8000.308
Proteobacteria8.9000.063
Actinobacteria9.5000.049 *
FungiAscomycota8.8000.066
Basidiomycota5.6890.224
Mucoromycota4.8000.308
* indicates significant differences (p < 0.05).
Table 3. Soil microbial community and α-diversity indexes.
Table 3. Soil microbial community and α-diversity indexes.
Microbial CategoryStand TypesChao1 IndexShannon IndexSimpson Index
BacteriaMPP(8-year-old)2137.48 ± 212.46 ab9.80 ± 0.29 a0.997 ± 0.0008 a
MPP(12-year-old)2254.65 ± 91.45 a10.00 ± 0.10 a0.998 ± 0.0002 a
MPP(22-year-old)1748.06 ± 335.69 ab9.66 ± 0.20 ab0.998 ± 0.0003 a
MPP(38-year-old)1836.43 ± 623.81 ab9.65 ± 0.42 ab0.997 ± 0.0006 a
NBLF1457.00 ± 451.92 b9.11 ± 0.36 b0.996 ± 0.0008 b
FungiMPP(8-year-old)476.00 ± 74.36 b4.91 ± 0.53 a0.89 ± 0.05 a
MPP(12-year-old)598.67 ± 86.00 b5.84 ± 0.35 a0.95 ± 0.02 a
MPP(22-year-old)513.33 ± 66.88 b5.09 ± 0.95 a0.90 ± 0.05 a
MPP(38-year-old)841.33 ± 244.39 a5.80 ± 0.88 a0.92 ± 0.03 a
NBLF625.00 ± 65.80 ab5.56 ± 1.02 a0.92 ± 0.05 a
The presence of distinct lowercase letters indicates statistically significant differences in α diversity index among different forest ages (p < 0.05). MPP: Masson Pine plantation; NBLF: natural broadleaved forest.
Table 4. Correlation results between soil microbial community structure and environmental factors.
Table 4. Correlation results between soil microbial community structure and environmental factors.
Environmental PropertiesBacterial Community StructureFungal Community Structure
rprp
SOC0.2660.028 *−0.1050.789
BD−0.0670.7260.1070.155
Slope0.1570.0680.0430.319
Density0.0690.330.1900.145
Canopy−0.0590.593−0.0120.446
Coverage0.1850.1530.1920.133
AN−0.0510.5710.4350.025 *
AP0.3790.015 *−0.1290.835
AK0.2580.0510.1400.126
TN0.0000.477−0.0730.601
TP−0.0210.5220.1890.115
TK−0.2070.877−0.2130.982
The r value in correlation analysis represents the strength of the correlation, while the p value indicates its statistical significance (p < 0.05). * indicates significant differences (p < 0.05).
Table 5. Main site conditions (altitude, slope aspect, mean DBH, mean tree height and stand density) of the MPP and NBLF.
Table 5. Main site conditions (altitude, slope aspect, mean DBH, mean tree height and stand density) of the MPP and NBLF.
StandsAltitude
(m)		Slope AspectMean Tree DBH
(cm)		Mean Tree Height
(m)		Stand Density
(Plant/ha)
MPP(8-year-old)146Northwest13.312.61800
MPP(8-year-old)145North14.813.51600
MPP(8-year-old)141North14.313.61700
MPP(12-year-old)139Southeast14.514.82400
MPP(12-year-old)158Southeast12.015.62500
MPP(12-year-old)148Southeast13.59.82200
MPP(22-year-old)148Southwest19.230.92300
MPP(22-year-old)141Northwest25.223.92000
MPP(22-year-old)129Northwest29.925.41900
MPP(38-year-old)139Southeast33.929.01800
MPP(38-year-old)144Northwest26.528.61900
MPP(38-year-old)150Northeast26.123.81800
NBLF126North14.621.12400
NBLF128North26.630.42000
NBLF131North16.422.01600
MPP: Masson pine plantation; NBLF: natural broadleaved forest. DBH: Diameter at breast height.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Fu, W.; Wang, B.; Li, D.; Zhang, Y. Soil Microbial Communities Significantly Changed Along Stand Ages in Masson Pine (Pinus massoniana Lamb.) Plantation. Plants 2025, 14, 3004. https://doi.org/10.3390/plants14193004

AMA Style

Fu W, Wang B, Li D, Zhang Y. Soil Microbial Communities Significantly Changed Along Stand Ages in Masson Pine (Pinus massoniana Lamb.) Plantation. Plants. 2025; 14(19):3004. https://doi.org/10.3390/plants14193004

Chicago/Turabian Style

Fu, Weijun, Bingyi Wang, Dunzhu Li, and Yong Zhang. 2025. "Soil Microbial Communities Significantly Changed Along Stand Ages in Masson Pine (Pinus massoniana Lamb.) Plantation" Plants 14, no. 19: 3004. https://doi.org/10.3390/plants14193004

APA Style

Fu, W., Wang, B., Li, D., & Zhang, Y. (2025). Soil Microbial Communities Significantly Changed Along Stand Ages in Masson Pine (Pinus massoniana Lamb.) Plantation. Plants, 14(19), 3004. https://doi.org/10.3390/plants14193004

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop