Unlocking mechanisms for soil fertility enhancement in tropical forests restored from non-native rubber plantations: Bacteria as the key drivers

Unlocking mechanisms for soil fertility enhancement in tropical forests restored from non-native rubber plantations: Bacteria as the key drivers | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Land Degradation & Development This is a preprint and has not been peer reviewed. Data may be preliminary. 12 February 2025 V1 Latest version Share on Unlocking mechanisms for soil fertility enhancement in tropical forests restored from non-native rubber plantations: Bacteria as the key drivers Authors : Xiaoyi Cai , Chunfeng Chen 0000-0002-2269-0191 [email protected] , Ashutosh Kumar Singh , Xiaojin Jiang , and wenjie Liu 0000-0002-9918-3462 Authors Info & Affiliations https://doi.org/10.22541/au.173936246.66909425/v1 441 views 329 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Forest restoration is a proven method to rehabilitate eroded soil. However, how the soil microenvironment of forest restoration affects microbial communities and soil fertility at the aggregate scale remains unclear, hindering the ecological well-being of development in the degraded lands in the Xishuangbanna region. To address this, soil samples were collected from a tropical primary forest (tropical rainforest, RF), an artificial monoculture forest (rubber monoculture, RM), and two restored forests (JRM: natural restoration of RM; JRC: natural restoration of RM with Camellia sinensis intercropping) and analyzed for soil physicochemical properties and microbial communities. Our results indicated that restored forest soils have higher levels of soil fertility compared to RM (i.e., SOC: 1.74–2.03 times; TN: 1.51–1.70 times; TP: 1.48–1.52 times), and the soil fertility increased as the size of soil aggregates decreased. The microbial alpha diversity and the complexity of microbial networks were higher in the restored forests than in RM. Microbial alpha diversity and co-occurrence network complexity increased as soil aggregate size decreased. These changes were significantly correlated with pH, electrical conductivity (EC), and soil fertility. Compared with fungi, bacterial network complexity was significantly associated with most soil fertility factors, and bacterial r-strategists increased in restored forests compared with RM. In addition, the random forest model and partial least squares path model further confirmed that forest types ( P 0.05; total effect: –0.07) significantly positively influenced soil fertility by inducing soil pH, EC, and bacterial communities but not fungal communities. These results suggest that forest restoration can foster conducive soil conditions that enhance the growth of soil microbes, especially the bacterial community, to participate in soil nutrient cycling and accumulation. However, JRC exhibited greater potential for increasing soil fertility than JRM, although both restorations played comparable roles in improving microbial community characteristics. In conclusion, the results of this study suggest that forest restoration in abandoned rubber plantations plays an essential role in improving soil fertility, but this depends on the restored forest communities and soil microbial community characteristics. 1. Introduction Soil microorganisms dominate soil health and play a crucial role in sustaining biogeochemical cycling and regulating the response of soil ecosystems to anthropogenic activities and environmental changes (Qiu et al. 2021; Coban, De Deyn, and van der Ploeg 2022). Managing and maintaining the soil microbial communities with reasonable structure can promote the restoration process and overall health of degraded ecosystems (Ma et al. 2022; Pedrinho et al. 2024). In recent years, soil erosion has become one of the greatest challenges facing humanity (Coban, De Deyn, and van der Ploeg 2022; Hartmann and Six 2023), causing severe disasters (e.g., a decline in soil fertility and microbial diversity) and eventually influencing the associated ecosystem services and land productivity, such as soil organic matter sequestration and soil nutrient cycling (Qiu et al. 2021; Zhu et al. 2022; Philippot et al. 2024). Erosion has impacted a staggering 84% of the earth’s land, with over one-third of the global soil experiencing degradation (Borrelli et al. 2017). Xishuangbanna is recognized as the most biodiverse zone in China and is part of the Indo-Burma biodiversity hotspot (Myers et al. 2000). To meet the financial requirements and address the demand for latex, rubber plantations have expanded and occupy approximately 22.14% of the Xishuangbanna area, while the area of primary rainforests has dwindled to just 3.60% (Warren-Thomas, Dolman, and Edwards 2015; Zhang, Corlett, and Zhai 2019). In addition, the crisis of grievous soil erosion and structural degradation, soil nutrient loss, soil microbial community structure simplification, and biodiversity decline have started to the emerge due to single-vegetation composition, thinner ground cover, and heavy seasonal rainfall intensity in the rubber plantation area (Liu et al. 2016; Zhu et al. 2018; Cai et al. 2024). Consequently, native forest restoration may be an alternative for reconstructing the structure of soil microbial communities and mitigating soil quality decline and biodiversity loss in erosively degraded lands (Teng et al. 2019; Xu et al. 2022; Kang et al. 2024). However, it remains unclear how stepwise forest restoration, which gradually alters vegetation structure, affects soil fertility by affecting soil microbial communities (Chen et al. 2023a; Zhang et al. 2024a). Soil aggregates, the basic units of soil structure and the soil quality indicators, play an important role in protecting soil organic matter, determining nutrient adsorption and desorption, influencing microbial community structure, and decreasing runoff and erosion (Six et al. 2004). The formation and breakdown of soil aggregates are influenced by various factors, such as anthropogenic management and intensive land use; deforestation of primary forests and planting of monocultures can significantly decrease the aggregation of soil particles (Gholoubi, Emami, and Caldwell 2019; Blanco-Canqui 2024), which further affects the soil fertility and microbial community within aggregates (Gholoubi, Emami, and Caldwell 2019; Rui et al. 2022). As the primary driver of the soil’s main nutrient (e.g., C, N, and P) cycle, microorganisms affect the storage and utilization of soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) (Liao et al. 2018; Baumert et al. 2021). Additionally, different sizes of soil aggregates provide heterogeneous microenvironments and habitats that differ in the physicochemical properties of soil microbes, which further affect their role and behavior in C, N, and P cycling (Liao et al. 2018; Chen et al. 2023a). However, little is known about how microbial co-occurrence patterns change and the link between soil microbial communities and soil nutrients (e.g., C, N, and P) in different-sized soil aggregates in response to primary tropical rainforest conversion and restoration processes. Forest restoration also plays an essential role in regulating soil microbial communities and their distribution (Turley et al. 2020; Zhang et al. 2021), which significantly influences soil aggregation and soil nutrient cycling processes (Hu et al. 2023). A previous study demonstrated that forest restoration, such as the Mallotus paniculatus - Millettia leptobotrya - Syzygium oblatum chronosequence of restoration stages, changed the bacterial composition from being dominated by oligotrophic bacteria to being copiotrophic bacteria and increased soil fertility (Wang et al. 2022). Zhang et al. (2024a) found that the three stages of forest restoration of farmland resulted in increased soil aggregate stability, multifunctionality, and complexity of the soil bacterial community. Another study found that forest restoration promoted soil fertility and enzyme activities and that fungi were more susceptible to soil aggregates than bacteria (Chen et al. 2023a). Chen et al. (2023b) demonstrated that forest restoration after agricultural abandonment significantly increased soil fertility and aggregate stability but decreased fungal-bacterial network complexity (Chen et al. 2023b). This influence of forest restoration on microbial communities may be due to recovered tree species, soil types, and microclimate. Hence, there is an urgent need for further investigation to comprehend the dynamic changes in microbial communities following forest restoration efforts in the soil-depleted land of the Xishuangbanna region. In this study, three forest types were studied, including rubber monoculture plantation (RM), jungle rubber mixed plantation (JRM), and jungle rubber × Camellia sinensis mixed plantation (JRC). We used a tropical rainforest (RF) as a control. We aimed to (1) investigate the dynamics of soil physicochemical properties and soil bacterial and fungal communities in bulk soil and different-sized soil aggregates under different forest restorations following rubber monoculture plantation land, and (2) identify the key influencing factors of soil fertility during forest restoration. We were particularly interested in investigating whether forest restoration improves soil fertility and in delving into the impact of microbial alpha diversity, composition, and co-occurrence networks in regulating soil fertility. 2. Materials and methods 2.1. Study area and sampling The study area is located in four long-term forest monitoring stations in the Xishuangbanna Tropical Botanical Garden (XTBG; 21°55′39″N, 101°15′55″E) in Yunnan Province, SW China. The mean annual temperature ranged from 24 to 29°C at these stations, whereas the average yearly precipitation was 1475 mm. The soils are laterites (Oxisols), which are developed from arenaceous shale sediments (Chen et al. 2019). The RF is a climatic climax community that occurs in humid valleys, low-altitude lands, and low hills. Rubber trees at RM were cultivated with a 2.5 m × 3.0 m spacing on forest land after complete clear-cutting of the native rainforest. Each pair of double rows was separated by a gap of 18.0 m. The synthetic fertilizer, which contains N, P, and K, was applied at a rate of 200 kilograms per hectare per year in mid-October in RM. Rubber tapping was performed on alternate days, starting in late April and continuing through mid-November annually. Shrubs and herbs are regularly cleared by using herbicides. JRM and JRC communities were established in aged rubber plantations and aged rubber × tea plant ( Camellia sinensis ) intercropped plantations, respectively. All rubber trees were planted in 1962, after the deforestation of a native rainforest. In 2005, tea trees were planted in 18-m-wide gaps between the rows of rubber trees. Additionally, these two plantations have experienced natural succession for ten years. Four sampling plots (20 × 20 m) were established at each forest site in February 2023. Within each plot, five soil cores were collected from the topsoil (0–20 cm depth) and thoroughly mixed to form a composite soil sample and then passed through a 4 mm sieve to eliminate roots and rocks. The samples were stored at 4 °C for approximately 7 days, allowing soil moisture content to decrease to around 10% in preparation for the soil aggregate fractionation experiments. To isolate aggregates and minimize the disturbance of soil microbes, a modified dry-sieving method was used (Wang et al. 2015; Yuan et al. 2021; Zhang et al. 2024b): (i) > 2 mm (large macroaggregate, LMA); (ii) 0.25–2 mm (small macroaggregate, SMA); (iii) < 0.25 mm (microaggregate, MI). In addition, we maintained the bulk soil for analysis. Bulk soil and separated soil aggregates were freeze-dried and divided into two parts. One part was stored at –80°C until DNA extraction, and the other was stored at 4°C for soil properties analysis. The pH and electrical conductivity (EC) were measured in a soil: water ( w / v ) mixture at a ratio of 1:2 using a pH and EC glass-electrode meter (AccumetAB15/15, Fisher Scientific, UK). SOC content was determined using a dichromate oxidation method described by Vitti et al. (2016). TN was measured using the CN-analyzer (Vario MAX CN, Elementar Analysensysteme GmbH, Germany). TP was analyzed using inductively coupled plasma-atomic emission spectrometry (ICP-AES; ICAP6300, Thermo Fisher Scientific, USA) after HNO 3 –HF–HClO 4 pre-digestion. 2.2 DNA extraction and Illumina sequencing DNA was extracted from each subsample (0.5 g) using the CTAB/SDS method according to the manufacturer’s instructions. The bacteria-specific V4 hypervariable region of the 16S rRNA gene was amplified with the primer pairs 341F/806R (5′-CCTAYGGGRBGCASCAG-3′/5′-GGACTACNNGGGTATCTAAT-3′). The ITS1 region of fungi was amplified using the primer pairs ITS1F/ITS2R (5′-CTTGGTCATTTAGAGGAAGTAA-3′/5′-GCTGCGTTCTTCATCGATGC-3′). The amplicon mixture was analyzed using the Illumina NovaSeq platform (Illumina, San Diego, CA, USA). The 16S V4 and ITS sequences were analyzed and filtered using QIIME 2. After quality control and denoising, the sequencing data were merged with FLASH 1.2.11. Sequences that were at least 97% similar were assigned to the same operational taxonomic unit (OTU). Phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) (https://picrust.github.io/picrust/) was utilized to predict bacterial function, and Fungi Functional Guild (FUNGuild) v.1.0 (https://github.com/UMNFuN/FUNGuild) was applied to predict fungal function. A total of 5,510,394 and 5,519,118 high-quality reads were obtained from bacteria (67,323–97,189 reads per sample) and fungi (70,976–96,109 reads per sample), respectively, in 48 soil samples. In total, 51,007 and 15,683 bacterial and fungal OTUs were obtained from 48 soil samples, respectively. The raw sequence data were deposited in the NCBI SRA database (Accession Numbers: PRJNA1200975 and PRJNA1201057). 2.3 Microbial network construction The Spearman’s correlation coefficient (r) was used to construct co-occurrence networks of bacteria and fungi (Jiao et al. 2021). To diminish the redundancy of rare OTUs, OTUs that occurred in less than eight samples in the microbial datasets were removed. Correlations of |r| > 0.8 and P < 0.01 were used to construct networks. The parameters of networks were evaluated using the ’igraph’ package in R. Furthermore, sub‐networks of individual soil samples were obtained using the subgraph function in the ’igraph’ package as described by Ma et al. 2016. The topological parameters of the sub-networks in each sample, including nodes, edges, average degree, and modularity, were calculated. The networks were visualized using the Gephi 0.10.1. 2.4. Data analysis Given that all data satisfied the criteria for homogeneity of variance and normality, they were directly applied to the statistical analysis. The effects of forest type and soil aggregates on soil properties and relative abundance of microbial phyla were evaluated using mixed-effect analysis of variance (ANOVA) and Duncan’s test. Alpha diversity indices, including the Chao1 and Shannon indices, were calculated using the ’vegan’ package in R. Principal coordinates analysis (PCoA), permutational multivariate analysis (PERMANOVA), and Bray-Curtis dissimilarity were used to investigate the dissimilarities in microbial community structures (’vegan’ package). Microbial diversity was compared using Duncan’s test or the Wilcoxon test. We applied the Mantel test (’vegan’ package) and Pearson’s correlation analysis (’psych’ package) to examine the relationship between soil properties and microbial composition and the correlations within the soil properties, respectively. Pearson’s correlation analysis (’psych’ package) was used to estimate the relationship between soil properties and microbial community parameters. We applied the random forest analysis (’rfPermute’ package) to estimate the importance of forest types, soil aggregates, pH, EC, bacterial community indices, and fungal community indices for soil fertility. In addition, redundancy analysis (RDA) (’vegan’ package) was applied to explore the relationships between soil properties and soil microbial communities. Finally, the Partial Least Squares Path Modeling (PLS-PM) was constructed in the ’plspm’ package to determine the main path of forest types, soil aggregates, pH, EC, bacterial communities, and fungal communities to the soil fertility. A goodness of fit index (GFI) > 0.36 was used to in examine model fitting (Zhu et al. 2022; Wang et al. 2023a). 3. Results 3.1. Soil physicochemical properties in different-sized soil aggregates among different forest types There were significant variations in soil properties across different forest types and soil aggregate sizes (Table 1). The soil pH of the bulk soil and soil aggregates was in the decreasing order of RF > JRC > RM ≥ JRM, and the EC of the bulk soil and soil aggregates exhibited a descending order of RF > JRM ≥ JRC ≥ RM (Table 1). SOC, TN, and TP in the bulk soil and soil aggregates decreased in the order of RF > JRC ≥ JRM ≥ RM (Table 1). The decreasing order of C:N, C:P, and N:P ratios in the bulk soil was JRC ≥ RF ≥ JRM ≥ RM (Table 1). In all forest types, soil properties did not differ significantly among different-sized soil aggregates, with a decreasing trend of MI, SMA, and LMA for all variables (Table 1). Notably, pH and EC were significantly positively correlated with SOC, TN, and TP in the bulk soil and different-sized soil aggregates (Figure S2). 3.2. Soil microbial diversity in different-sized soil aggregates among different forest types The bacterial alpha diversity of the bulk soil and soil aggregates decreased in the following order: JRC ≥ JRM ≥ RF > RM, and the fungal Chao1 index followed the same trend (Figure 1). The bacterial alpha diversity (Chao1 and Shannon indexes) of the different soil aggregate sizes within the same forest type was in the decreasing order of MI ≥ SMA ≥ LMA, and this trend was also reflected in the fungal Chao1 index (Figure 1c). Fungal Shannon index of the different soil aggregate sizes exhibited the decreasing order of MI ≥ SMA ≥ LMA in the RM, JRM, and JRC, whereas the decreasing order in RF was opposite (Figure 1d). Soil pH, EC, SOC, TN, and TP were significantly and positively correlated with bacterial Shannon and fungal Chao1 indices (Figure 4a). In addition, SOC was significantly positively correlated with the bacterial Shannon index for different soil aggregate sizes (Figure S1a, b, and d). The microbial compositions (beta diversity) in bulk soil differed significantly between forest types (Figure 1a and b). The bacterial compositions between the soil aggregate sizes showed no significant differences in RF, RM, and JRC, but there were significant differences between LMA and other aggregate sizes in JRM (Figure 1c). Notably, the fungal compositions of LMA and MI were significantly different in RF, RM, and JRM (Figure 1d). Furthermore, there was a significant and positive correlation observed between microbial compositions and the pH, EC, SOC, TN, and TP (Figures 4b and S2). 3.3. Microbial presence and relative abundance in different-sized soil aggregates among different forest types The presence and abundance of bacteria and fungi varied with forest restoration and aggregate particle size (Figure 2, Tables S1 and S2). In the bulk soil, the relative abundance of Acidobacteria and Chloroflexi showed a decline in the following sequence: RM ≥ JRM ≥ JRC > RF (Figure 2a and Table S1). In contrast, the abundance of Bacteroidota, Actinobacteria, Latescibacterota, and Methylomirabilota showed a decline in the following sequence: RF ≥ JRC ≥ JRM ≥ RM (Figure 2a and Table S1). The decreased sequence of Proteobacteria abundance in the bulk soil was JRC ≥ JRM ≥ RF > RM (Figure 2a and Table S1). Additionally, the relative abundances of Acidobacteria, Verrucomicrobiota, and Chloroflexi increased with the larger aggregate sizes within the same forest type, whereas Proteobacteria and Actinobacteria showed the opposite trend (Figure 2a and Table S1). For fungi, the abundance of Basidiomycota showed a decline in the following sequence: JRC > JRM > RM > RF in the bulk soil, while Ascomycota showed a decrease in sequence of RM ≥ JRM > JRC ≥ RF (Figure 2b and Table S2). The abundances of Ascomycota and Basidiomycota increased with an increase in aggregate size in the same forest type, whereas Mortierellomycota exhibited the opposite trend (Figure 2b and Table S2). Notably, the relative abundances of Acidobacteria and Chloroflexi were significantly negatively correlated with pH, EC, SOC, TN, and TP in bulk soil and soil aggregates (Figures 4a and S3). In contrast, Latescibacterota, Methylomirabilota, and Nitrospirota were positively correlated with these soil physicochemical variables. Furthermore, the abundances of Proteobacteria, Bacteroidota, Actinobacteriota, and Firmicutes were partly significantly and positively correlated with soil properties in the bulk soil and soil aggregates (Figure S3). For fungi, the relative abundances of Ascomycota, Basidomycota, and Glomeromycota were partly significantly and negatively correlated with pH, EC, SOC, TN, and TP in different-sized soil aggregates, whereas Mortierellomycota and Rozellomycota were positively correlated with these soil physicochemical attributes (Figures 4a and S4). 3.4. Soil microbial networks and functions in different-sized soil aggregates among different forest types The characteristics of bacterial and fungal co-occurrence networks varied with forest types and soil aggregate sizes (Figure 3, Tables S3 and S4). Among the forests, JRC exhibited more complex bacterial and fungal network indices than JRM and RM (Figure 3a). The percentage of positive links in bacterial networks showed a decline in the sequence of JRC > RF > JRM > RM, whereas the percentage of positive links in fungal networks decreased in the sequence of JRM > RF > JRC > RM (Table S3). In bulk soil, the bacterial network exhibited more complex indices than the fungal network (Figure 3b and Table S4). The microbial network complexity increased as the size of the soil aggregates decreased, whereas the positive link percentage of the microbial networks followed the opposite trend (Figure 3b and Table S4). Pearson correlation coefficients indicated that bacterial network complexity exhibited a significant correlation with soil properties (Figures 4a and S5). The bacterial and fungal functions were predicted using PICRUSt and FUNGuild, and these functions showed different trends in different forest types and soil aggregate sizes (Figures S6 and S7). In case of bacteria, similar functional characteristics (e.g., Carbohydrate Metabolism, Amino Acid Metabolism, etc.) were observed for different forest types in the bulk soil and soil aggregates (Figures S6a and S7c). However, there was no difference in the function of fungi in bulk soil among the different forest types (Figure S6b). Undefined saprotroph, soil saprotroph, and plant saprotroph in the bulk soil showed a decline sequence: RF > JRC > RM > JRM (Figure S6b). Furthermore, within the same forest type, the relative abundances of fungal parasites and endophyle decreased in the following order: MI > SMA > LMA (Figure S7d). 3.5. Comprehensive relationships between abiotic factors, biotic factors, and soil fertility The random forest and RDA models indicated that the forest types, pH, EC, and bacterial composition were significantly associated with soil fertility (SOC, TN, and TP) (Figure 5a, b, and Table S5). Soil pH and EC were also significantly associated with both bacterial and fungal community compositions at all soil aggregate sizes in the different forest types (Figure 5b and Table S5). Therefore, we constructed a PLS-PM to explore the key pathways affecting soil fertility (Figure 6a). Generally, forest types directly regulate soil pH and EC, the bacterial community, and the fungal community (Figure 6a). Forest types or soil pH and EC directly positively regulated soil bacteria and further positively regulated soil fertility (Figure 6a and b). Notably, soil aggregates did not significantly influence abiotic and biotic factors and soil fertility (Figure 6a and b). 4. Discussion Forest restoration is a key approach for rehabilitating degraded rubber lands (Zeng et al. 2021). Several recent studies investigated the causal factors that might affect the soil fertility and microbial communities in the rubber or rubber-restored agroforestry systems, such as compound fertilizer, sulfur powder, plant litter, herbicides, domestic garbage, etc. (Chen et al. 2019; Song et al. 2019; Zhu et al. 2019; Zeng et al. 2021; Lan et al. 2022). Notably, a higher diversity of small microfauna (i.e., bacteria, fungi, nematodes, etc.) in the restored rubber land had significantly positive effects on soil multifunctionality (Wang, Mishra, and Yang 2023b). Our previous study partially supported this result (Cai et al. 2024). In this study, we demonstrated the effects of soil microbial communities on soil fertility in different forest types, which also showed different patterns in different-sized soil aggregates. In particular, our data support the idea that soil fertility differs distinctly in response to different forest types and is more likely to be regulated by microbial communities. This highlights the fact that enhancing soil fertility by regulating the microbial community structure is vital to improving soil health and maintaining the functionality of ecosystems. Clarifying how microorganisms participate in nutrient cycling within bulk soil and soil aggregates during the restoration of degraded rubber lands can help restore soil ecosystem services and guide sustainable forest management. 4.1 Soil physicochemical properties In this study, an increase in soil fertility (SOC, TN, and TP) was noted in the bulk soil of restored forests compared to that in RM, which is supported by other studies in rubber-based agroforestry systems (Chen et al. 2019; Zeng et al. 2021). In rubber monoculture plantations, serious soil degradation and erosion can lead to unfavorable soil conditions, such as high losses of fertile topsoil and nutrients, carbon loss, and biodiversity decline (Coban, De Deyn, and van der Ploeg 2022). The restoration process may induce alterations in soil properties through the following ways: First, higher plant input (e.g., litter nutrient input), fewer field management activities (e.g., the application of compound fertilizer and sulfur powder), and a more reasonable soil hydrological structure can increase pH, EC, and soil fertility in the restored forest soil (Zhu et al. 2019; Zeng et al. 2021). Second, owing to higher vegetation diversity and more plant organic matter inputs (Zhu et al. 2019), JRC exhibited higher soil fertility in all soil aggregate sizes than in JRM. Cui et al. (2020) and Chen et al. (2022) also found that forest restoration can significantly improve soil structural stability and increase soil organic matter and nutrients. In our study, SOC, TN, and TP were higher in microaggregates than in macroaggregates, which is similar to previous findings (Han et al. 2021; Han et al. 2024). These results can be ascribed to the greater availability of resources (nutrients) in smaller soil aggregates, which led to greater microbial abundance and higher extracellular enzyme activity. 4.2 Relationship of microbial diversity, microbial composition, and soil fertility Soil microbial communities are vital in regulating the synthesis and decomposition of soil organic matter (Liu et al. 2023). The microbial alpha diversity in the bulk soil was higher in restored forests than in rubber monoculture in our study. These results were similar to those reported in previous studies (Liu et al. 2023; Wang, Mishra, and Yang 2023b; Sun et al. 2024), indicating that forest restoration has a positive impact on microbial survival and growth. In this context, previous studies have revealed that a high soil microbial diversity is conducive to multiple soil functions and improves overall soil quality (Wang et al. 2023c). We found that the relative abundance of several microbial phyla (Bacteroidota, Actinobacteria, Proteobacteria, Latescibacterota, Methylomirabilota, and Basidiomycota) increased with forest restoration and showed a positive correlation with soil fertility. These results suggest that restored forests provide a favorable soil microenvironment for these microbes and eventually promote the execution of specific functions to increase soil fertility. Generally, Bacteroidota, Actinobacteria, Proteobacteria, and Ascomycota are considered fast-growing copiotrophic organisms (r-strategy) and exist in favorable environments with abundant organic matter. Acidobacteriota, Chloroflexi, and Basidiomycota are oligotrophic organisms (K-strategy) that can grow on hemicellulose or cellulose and mineralize recalcitrant SOC (Yang et al. 2022). Bacteroidota, Actinobacteria, and Proteobacteria depolymerize fresh organic matter, regulate soil carbon, nitrogen, and phosphorus cycles, and eventually increase soil fertility (Trivedi, Anderson, and Singh 2013; Zhang et al. 2022a; Chen et al. 2023; Liu et al. 2023). Furthermore, the high abundance of Latescibacterota and Methylomirabilota in restored forests may result from the accumulation of plant polysaccharides (Robbins et al. 2021; Xu et al. 2023) and high P availability in soils (Xie et al. 2023), respectively. Consistent with previous studies (Li et al. 2022; Liu et al. 2023; Zhang et al. 2024a), we found that the abundance of Acidobacteriota and Chloroflexi decreased with forest restoration and were negatively correlated with pH, EC, and soil fertility. Therefore, we assume that a suitable soil microenvironment may be an important factor in regulating microbial community composition and that high pH, EC, and nutrient-rich conditions may suppress the growth of Acidobacteriota and Chloroflexi. In addition to bacteria, Basidiomycota, Ascomycota, and Mortierellomycota were the dominant fungal communities. The main fungal phylum changed from Ascomycota in rubber monoculture land to Basidiomycota in restored forests, consistent with the results reported by Liu et al. (2020) and He et al. (2022). These results can be attributed to an increase in the recalcitrant organic matter content in the litter, leading to an increase in the Basidiomycota abundance (Jiang et al. 2021; Yang et al. 2022). The abundance of Mortierellomycota was the highest in RF and was not significantly different form the other forests. A previous study revealed that Mortierellomycota can dissolve mineral phosphorus in the soil and enhance soil nutrient content by producing and releasing oxalic acid (Wang et al. 2020). Hence, we can assume that the high TP content in rainforest soil promoted the proliferation of Mortierellomycota. In our study, the bacterial and fungal communities exhibited different alpha diversities and relative abundances among the soil aggregates. In particular, we found a higher diversity of microbial communities in the microaggregates than in the macroaggregates. This distribution pattern of microbial communities is likely due to larger specific surface areas and more soil nutrients in smaller aggregates (Chen et al. 2023). Moreover, this pattern of bacterial phylum distribution in the soil aggregates was consistent across all forests. Specifically, copiotrophic bacteria, which have an affinity for nutrient-rich environments, were highly abundant in the smaller aggregates, whereas oligotrophic bacteria exhibited the opposite trend. Therefore, we assumed that forest type influenced microenvironmental heterogeneity at the aggregate level. Hence, different microenvironments, owing to aggregate size, affect microbial functions, such as soil nutrient cycling. Together, these results revealed that the dominant microbial phyla in decomposed exogenous organic matter accelerated soil nutrient cycling depending on forest restoration and soil aggregate levels. Furthermore, the random forest model and PLS-PM analysis showed that soil fertility was significantly influenced by forest types, soil pH and EC, and bacterial communities. This indicates that changes in the soil microenvironment through forest restoration may improve the complexity of the bacterial communities and increase soil fertility in rubber plantations. These results are similar to those of previous studies (Liu et al. 2019; Fu et al. 2020; Zhang et al. 2024c), in which pH, EC, SOC, and other soil nutrients were significantly correlated with bacterial communities. These microbe-mediated improvements in soil nutrients may be attributed to the following processes: On the one hand, the forest restoration process produces large amounts of exogenous organic matter (e.g., plant litter and residue) and increases soil pH, which can enhance exogenous nutrient availability, stimulate microbial decomposition, and convert the exogenous nutrient into soil nutrients (Liu et al. 2019; Liu et al. 2023). On the other hand, previous studies have revealed that soil bacteria exhibit heightened sensitivity to fluctuations in their environment than fungi and that bacterial communities are easier to reconstruct because of their small body sizes (Huo et al. 2023; Cai et al. 2024). By combining the distribution and functions of microbial phyla in different forest types, we speculate that the soil bacterial community could adapt more quickly to environmental changes during forest restoration. Hence, bacterial phyla can reshape more rational community structures (i.e., r-strategists oriented) to perform their functions to accelerate the decomposition and mineralization of organic matter (Yang et al. 2022). 4.3 Relationship of microbial networks, functions, and soil fertility Generally, complex microbial networks directly reflect the adaptability of soil microbes to the soil environment and their feedback in biomass production, respiration, and carbon use efficiency, thus influencing soil nutrient cycling and subsequent functions (Maynard, Crowther, and Bradford 2017; Qiu et al. 2021). In our study, forest restoration exhibited more complex microbial networks and a higher positive link percentage (i.e., higher interspecific communication and cooperation) than rubber monoculture plantations. These may be likely due to alterations in the availability of soil nutrients (Qiu et al. 2021). Nutrient availability diminished at the outset of rainforest conversion to rubber plantations but showed a gradual increase as rubber lands were transformed back into restored forests. Previous studies have indicated that this complex co-occurrence network structure, interspecific communication, and cooperation of microbes can accelerate the decomposition of exogenous organic matter and nutrient circulation in the soil (Liu et al. 2023; Wang et al. 2023c). Therefore, greater co-occurrence network complexity enhances microbial adaptability and the capacity to adapt to future environmental changes in restored forests. In our study, small soil aggregates had more complex microbial co-occurrence network structures and a higher percentage of negative links than large aggregates. This may provide more stability to microbes under limited nutrient environments in small aggregates and is more conducive to their survival and development (Hernandez et al. 2021). Such a distribution of microbes in small soil aggregates may further accelerate the cycling process of soil nutrients and increase the level and availability of soil nutrients in these soil aggregates (Wang et al. 2023c). Unlike previous studies (Yang et al. 2022; Yang et al. 2024), the complexity of fungal networks was not significantly associated with most soil fertility factors compared with bacteria. This may be attributed to the consensus that bacteria are more sensitive to changes in the microenvironment, whereas fungi have a higher tolerance to environmental changes (Yuste et al. 2011; Barnard, Osborne, and Firestone 2013; Sun et al. 2017). In addition, we found that some functional microbes, such as fungal parasites and soil saprophytic fungi, were more concentrated in small aggregates. Previous studies have found that fungal parasites and soil saprophytic fungi play an important role in plant nutrient absorption (metabolism) functions and humus decomposition, respectively (Zhang et al. 2022b; Wei et al. 2024). These results suggest that higher complexity and stable microbial co-occurrence patterns in small soil aggregates support more diverse functions of microbes to strengthen nutrient turnover in the plant-soil-microbe system. 5. Conclusions In this study, we investigated the effects of forest restoration on soil fertility, microbial diversity, and co-occurrence patterns (Figure 7). The results indicated that restored forest soils exhibited higher levels of SOC, TN, and TP than rubber (monoculture) plantations. Moreover, bacterial and fungal alpha diversities were higher in restored forest soils, particularly in small soil aggregates, and were positively correlated with soil fertility, revealing the positive effects of microbial diversity on soil quality. Microbial network analysis revealed that the interactions among microbial communities and their complex network structures in the restored forest bulk soil and soil microaggregates contributed to diverse microbial functions, such as efficient soil nutrient cycling. The random forest model and PLS-PM further confirmed that forest types, optimal pH and EC environments, and reasonable bacterial community structure were the key drivers of soil fertility. Among the restored forests, the JRC exhibited more complex microbial communities and demonstrated greater potential for increasing soil fertility than the JRM. In summary, this study revealed the effects of forest restoration and soil intrinsic heterogeneity (associated with soil aggregates) on the microbial community and soil fertility and suggests that JRC is a more efficient restoration method than JRM for soil ecological restoration degraded by rubber monoculture. Acknowledgements We extend our sincere thanks to the Institutional Center for Shared Technologies and Facilities of Xishuangbanna Tropical Botanical Garden, CAS for their invaluable assistance with the chemical analysis. We are also grateful to the Xishuangbanna Station for Tropical Rainforest Ecosystem Studies for providing us with the meteorological data. This research was supported by the National Natural Science Foundation of China (32001221 and 32371608), the Yunnan Fundamental Research Projects (202201AT070212 and 202001AU070136), the Yunnan Revitalization Talent Support Program (Grant Nos: XDYC-QNRC-2022-0021), and the Chinese Academy of Sciences “Light of West China” program. References Barnard, R. L., C. A. Osborne, and M. K. Firestone. 2013. ”Responses of soil bacterial and fungal communities to extreme desiccation and rewetting.” The ISME Journal 7, no. 11: 2229-2241. https://doi.org/10.1038/ismej.2013.104. Baumert, V. L., S. J. Forstner, J. H. Zethof, C. Vogel, J. Heitkötter, S. Schulz, I. Kögel-Knabner, and C. W. Mueller. 2021. ”Root-induced fungal growth triggers macroaggregation in forest subsoils.” Soil Biology and Biochemistry 157: 108244. https://doi.org/10.1016/j.soilbio.2021.108244. Blanco-Canqui, H. 2024. ”Do cover crop mixtures improve soil physical health more than monocultures?.” Plant and Soil 495: 99-112. https://doi.org/10.1007/s11104-023-06086-4. Borrelli, P., D. A. Robinson, L. R. Fleischer, E. Lugato, C. Ballabio, C. Alewell, K. Meusburger et al. 2017. ”An assessment of the global impact of 21st century land use change on soil erosion.” Nature Communications 8, no. 1: 1-13. https://doi.org/10.1038/s41467-017-02142-7. Cai, X., C. Chen, A. K. Singh, X. Zhu, and W. Liu. 2024. ”Anthropogenic restoration exhibits more complex and stable microbial co-occurrence patterns than natural restoration in rubber plantations.” Science of the Total Environment 948: 174935. https://doi.org/10.1016/j.scitotenv.2024.174935. Chen, C., W. Liu, J. Wu, X. Jiang, and X. Zhu. 2019. ”Can intercropping with the cash crop help improve the soil physico-chemical properties of rubber plantations?.” Geoderma 335: 149-160. https://doi.org/10.1016/j.geoderma.2018.08.023. Chen, P., L. Wang, J. Y. Li, W. Wan, R. Q. Zhang, Y. Liu, and Z. G. Li. 2023a. ”Response of soil aggregate-associated fertility and microbial communities to afforestation in the degraded ecosystem of the Danjiangkou Reservoir, China.” Plant and Soil 501, no. 1: 171-189. https://doi.org/10.1007/s11104-023-05973-0. Chen, S., X. Feng, Q. Lin, C. Liu, K. Cheng, X. Zhang, R. Bian et al. 2022. ”Pool complexity and molecular diversity shaped topsoil organic matter accumulation following decadal forest restoration in a karst terrain.” Soil Biology and Biochemistry 166: 108553. https://doi.org/10.1016/j.soilbio.2022.108553. Chen, Y., J. Chi, X. Lu, Y. Cai, H. Jiang, Q. Zhang, and K. Zhang. 2023b. ”Fungal-bacterial composition and network complexity determine soil multifunctionality during ecological restoration.” Catena 230: 107251. https://doi.org/10.1016/j.catena.2023.107251. Coban, O., G. B. De Deyn, and M. van der Ploeg. 2022. ”Soil microbiota as game-changers in restoration of degraded lands.” Science 375, no. 6584: abe0725. https://doi.org/10.1126/science.abe0725. Cui, Y., X. Wang, X. Zhang, W. Ju, C. Duan, X. Guo, Y. Wang, and L. Fang. 2020. ”Soil moisture mediates microbial carbon and phosphorus metabolism during vegetation succession in a semiarid region.” Soil Biology and Biochemistry 147: 107814. https://doi.org/10.1016/j.soilbio.2020.107814. Fu, D., X. Wu, Q. Qiu, C. Duan, and D. L. Jones. 2020. ”Seasonal variations in soil microbial communities under different land restoration types in a subtropical mountains region, Southwest China.” Applied Soil Ecology 153: 103634. https://doi.org/10.1016/j.apsoil.2020.103634. Gholoubi, A., H. Emami, and T. Caldwell. 2019. Deforestation effects on soil aggregate stability quantified by the high energy moisture characteristic method. Geoderma 355: 113919. https://doi.org/10.1016/j.geoderma.2019.113919. Han, C., W. Zhou, Y. Gu, J. Wang, Y. Zhou, Y. Xue, Z. Shi, and K. H. Siddique. 2024. ”Effects of tillage regime on soil aggregate-associated carbon, enzyme activity, and microbial community structure in a semiarid agroecosystem.” Plant and Soil 498, no. 1: 543-559. https://doi.org/10.1007/s11104-023-06453-1. Han, S., M. Delgado-Baquerizo, X. Luo, Y. Liu, J. D. Van Nostrand, W. Chen, J. Zhou, and Q. Huang. 2021. ”Soil aggregate size-dependent relationships between microbial functional diversity and multifunctionality.” Soil Biology and Biochemistry 154: 108143. https://doi.org/10.1016/j.soilbio.2021.108143. Hartmann, M., and J. Six. 2023. ”Soil structure and microbiome functions in agroecosystems.” Nature Reviews Earth & Environment 4, no. 1 (2023): 4-18. https://doi.org/10.1038/s43017-022-00366-w. He, G., T. Peng, Y. Guo, S. Wen, L. Ji, and Z. Luo. 2022. ”Forest succession improves the complexity of soil microbial interaction and ecological stochasticity of community assembly: Evidence from Phoebe bournei -dominated forests in subtropical regions.” Frontiers in Microbiology 13: 1021258. https://doi.org/10.3389/fmicb.2022.1021258. Hernandez, D. J., A. S. David, E. S. Menges, C. A. Searcy, and M. E. Afkhami. 2021. ”Environmental stress destabilizes microbial networks.” The ISME Journal 15, no. 6: 1722-1734. https://doi.org/10.1038/s41396-020-00882-x. Hu, Y., G.Yu, J. Zhou, K. Li, M. Chen, M. Abulaizi, M. Cong, Z. Yang, X. Zhu, and H. Jia. 2023. ”Grazing and reclamation-induced microbiome alterations drive organic carbon stability within soil aggregates in alpine steppes.” Catena 231: 107306. https://doi.org/10.1016/j.catena.2023.107306. Huo, X., C. Ren, D. Wang, R. Wu, Y. Wang, Z. Li, D. Huang, and H. Qi. 2023. ”Microbial community assembly and its influencing factors of secondary forests in Qinling Mountains.” Soil Biology and Biochemistry 184: 109075. https://doi.org/10.1016/j.soilbio.2023.109075. Jiang, S., Y. Xing, G. Liu, C. Hu, X. Wang, G.Yan, and Q. Wang. 2021. ”Changes in soil bacterial and fungal community composition and functional groups during the succession of boreal forests.” Soil Biology and Biochemistry 161: 108393. https://doi.org/10.1016/j.soilbio.2021.108393. Jiang, Y., S. Li, A. D. Barnes, J. Liu, G. Zhu, L. Luan, F. Dini-Andreote, S. Geisen, and B. Sun. 2023. ”Unraveling the importance of top-down predation on bacterial diversity at the soil aggregate level.” Geoderma 439: 116658. https://doi.org/10.1016/j.geoderma.2023.116658. Kang, H., Y. Xue, Y. Cui, D. L. Moorhead, H. Lambers, and D. Wang. 2024. ”Nutrient limitation mediates soil microbial community structure and stability in forest restoration.” Science of the Total Environment 935: 173266. https://doi.org/10.1016/j.scitotenv.2024.173266. Lan, G., C. Yang, Z. Wu, R. Sun, B. Chen, and X. Zhang. 2022. ”Network complexity of rubber plantations is lower than tropical forests for soil bacteria but not for fungi.” Soil 8, no. 1: 149-161. https://doi.org/10.5194/soil-8-149-2022. Li, J., M. Li, L. Zhao, X. Sun, M. Gao, L. Sheng, and H. Bian. 2022. ”Characteristics of soil carbon emissions and bacterial community composition in peatlands at different stages of vegetation succession.” Science of the Total Environment 839: 156242. https://doi.org/10.1016/j.scitotenv.2022.156242. Liao, H., Y. Zhang, Q. Zuo, B. Du, W. Chen, D. Wei, and Q. Huang. 2018. ”Contrasting responses of bacterial and fungal communities to aggregate-size fractions and long-term fertilizations in soils of northeastern China.” Science of the Total Environment 635: 784-792. https://doi.org/10.1016/j.scitotenv.2018.04.168. Liu, C., Y. Jin, Y. Hu, J. Tang, Q. Xiong, M. Xu, F. Bibi, and K. C. Beng. 2019. ”Drivers of soil bacterial community structure and diversity in tropical agroforestry systems.” Agriculture, Ecosystems & Environment 278: 24-34. https://doi.org/10.1016/j.agee.2019.03.015. Liu, J., X. Jia, W. Yan, Y. Zhong, and Z. Shangguan. 2020. ”Changes in soil microbial community structure during long‐term secondary succession.” Land Degradation & Development 31, no. 9: 1151-1166. https://doi.org/10.1002/ldr.3505. Liu, J, J. Wang, S. J. Morreale, R. L. Schneider, Z. Li, and G. L. Wu. 2023. ”Contributions of plant litter to soil microbial activity improvement and soil nutrient enhancement along with herb and shrub colonization expansions in an arid sandy land.” Catena 227: 107098. https://doi.org/10.1016/j.catena.2023.107098. Liu, W., C. Zhu, J. Wu, and C. Chen. 2016. ”Are rubber-based agroforestry systems effective in controlling rain splash erosion?.” Catena 147: 16-24. https://doi.org/10.1016/j.catena.2016.06.034. Ma, L., C. Zhang, X. Xu, C. Wang, G. Liu, C. Liang, X. Zuo, C. Wang, Y. Lv, and R. Wang. 2022. ”Different facets of bacterial and fungal communities drive soil multifunctionality in grasslands spanning a 3500 km transect.” Functional Ecology 36, no. 12: 3120-3133. https://doi.org/10.1111/1365-2435.14220. Maynard, D. S., T. W. Crowther, and M. A. Bradford. 2017. ”Competitive network determines the direction of the diversity–function relationship.” Proceedings of the National Academy of Sciences 114, no. 43: 11464-11469. https://doi.org/10.1073/pnas.171221111. Myers, N., R. A. Mittermeier, C. G. Mittermeier, G. A. Da Fonseca, and J. Kent. 2000. ”Biodiversity hotspots for conservation priorities.” Nature 403, no. 6772: 853-858. https://doi.org/10.1038/35002501. Pedrinho, A., L. W. Mendes, A. P. de Araujo Pereira, A. S. F. Araujo, A. Vaishnav, D. G. Karpouzas, and B. K. Singh. 2024. ”Soil microbial diversity plays an important role in resisting and restoring degraded ecosystems.” Plant and Soil : 1-25. https://doi.org/10.1007/s11104-024-06489-x. Philippot, L., C. Chenu, A. Kappler, M. C. Rillig, and N. Fierer. 2024. ”The interplay between microbial communities and soil properties.” Nature Reviews Microbiology 22, no. 4: 226-239. https://doi.org/10.1038/s41579-023-00980-5. Pioli, S., J. Sarneel, H. J. Thomas, X. Domene, P. Andrés, M. Hefting, T. Reitz et al. 2020. ”Linking plant litter microbial diversity to microhabitat conditions, environmental gradients and litter mass loss: Insights from a European study using standard litter bags.” Soil Biology and Biochemistry 144: 107778. https://doi.org/10.1016/j.soilbio.2020.107778. Qiu, L., Q. Zhang, H. Zhu, P. B. Reich, S. Banerjee, M. G. A. van der Heijden, M. J. Sadowsky et al. 2021. ”Erosion reduces soil microbial diversity, network complexity and multifunctionality.” The ISME Journal 15, no. 8: 2474-2489. https://doi.org/10.1038/s41396-021-00913-1. Robbins, S. J., W. Song, J. P. Engelberts, B. Glasl, Beate M. Slaby, J. Boyd, E. Marangon et al. 2021. ”A genomic view of the microbiome of coral reef demosponges.” The ISME Journal 15, no. 6: 1641-1654. https://doi.org/10.1038/s41396-020-00876-9. Rui, Z., X. Lu, Z. Li, Z. Lin, H. Lu, D. Zhang, S. Shen et al. 2022. ”Macroaggregates serve as micro-hotspots enriched with functional and networked microbial communities and enhanced under organic/inorganic fertilization in a paddy topsoil from Southeastern China.” Frontiers in Microbiology 13: 831746. https://doi.org/10.3389/fmicb.2022.831746. Santonja, M., A. Rancon, N. Fromin, V. Baldy, S. Hättenschwiler, C. Fernandez, N. Montès, and P. Mirleau. 2017. ”Plant litter diversity increases microbial abundance, fungal diversity, and carbon and nitrogen cycling in a Mediterranean shrubland.” Soil Biology and Biochemistry 111: 124-134. https://doi.org/10.1016/j. soilbio.2017.04.006. Six, J., H. Bossuyt, S. Degryze, and K. Denef. 2004. ”A history of research on the link between (micro) aggregates, soil biota, and soil organic matter dynamics.” Soil and Tillage Research 79, no. 1: 7-31. https://doi.org/10.1016/j.still.2004.03.008. Song, H., D. Singh, K. W. Tomlinson, X. Yang, M. C.e Ogwu, J. F. Slik, and J. M. Adams. 2019. ”Tropical forest conversion to rubber plantation in southwest China results in lower fungal beta diversity and reduced network complexity.” FEMS microbiology ecology 95, no. 7: fiz092. https://doi.org/10.1093/femsec/fiz092. Sun, D., Y. Huang, Z. Wang, X. Tang, W. Ye, H. Cao, and H. Shen. 2024. ”Soil microbial community structure, function and network along a mangrove forest restoration chronosequence.” Science of the Total Environment 913: 169704. https://doi.org/10.1016/j.scitotenv.2023.169704. Sun, S., S. Li, B. N. Avera, B. D. Strahm, and B. D. Badgley. 2017. ”Soil bacterial and fungal communities show distinct recovery patterns during forest ecosystem restoration.” Applied and Environmental Microbiology 83, no. 14: e00966-17. https://doi.org/10.1128/AEM.00966-17. Teng, M., C. Huang, P. Wang, L. Zeng, Z. Zhou, W. Xiao, Z. Huang, and C. Liu. 2019. ”Impacts of forest restoration on soil erosion in the Three Gorges Reservoir area, China.” Science of the Total Environment 697: 134164. https://doi.org/10.1016/j.scitotenv.2019.134164. Trivedi, P., I. C. Anderson, and B. K. Singh. 2013. ”Microbial modulators of soil carbon storage: integrating genomic and metabolic knowledge for global prediction.” Trends in microbiology 21, no. 12: 641-651. https://doi.org/10.1016/j.tim.2013.09.005. Turley, N. E., L. Bell‐Dereske, S. E. Evans, and L. A. Brudvig. 2020. ”Agricultural land‐use history and restoration impact soil microbial biodiversity.” Journal of Applied Ecology 57, no. 5: 852-863. https://doi.org/10.1111/1365-2664.13591. Vitti, C., A. M. Stellacci, R. Leogrande, M. Mastrangelo, E. Cazzato, and D. Ventrella. 2016. ”Assessment of organic carbon in soils: A comparison between the Springer–Klee wet digestion and the dry combustion methods in Mediterranean soils (Southern Italy).” Catena 137: 113-119. https://doi.org/10.1016/j.catena.2015.09.001. Wang, R., M. Dorodnikov, S. Yang, Y. Zhang, T. R. Filley, R. F. Turco, Y. Zhang, Z. Xu, H. Li, and Y. Jiang. 2015. ”Responses of enzymatic activities within soil aggregates to 9-year nitrogen and water addition in a semi-arid grassland.” Soil Biology and Biochemistry 81: 159-167. https://doi.org/10.1016/j.soilbio.2014.11.015. Wang, S., J. Cheng, T. Li, and Y. Liao. 2020. ”Response of soil fungal communities to continuous cropping of flue-cured tobacco.” Scientific Reports 10, no. 1: 19911. https://doi.org/10.1038/s41598-020-77044-8. Wang, S., S. Zhao, B. Yang, K. Zhang, Y. Fan, L. Zhang, and X. Yang. 2022. ”The carbon and nitrogen stoichiometry in litter-soil-microbe continuum rather than plant diversity primarily shapes the changes in bacterial communities along a tropical forest restoration chronosequence.” Catena 213: 106202. https://doi.org/10.1016/j.catena.2022.106202. Wang, W., S. Mishra, and X. Yang. 2023b. Seasonal difference in soil health indicators mediating multidiversity-multifunctionality relationship depends on body size of soil organisms: Evidence from rubber plantation agroforestry system. Soil Biology and Biochemistry 178: 108968. https://doi.org/10.1016/j.soilbio.2023.108968. Wang, X., Q. Zhang, Z. Zhang, W. Li, W. Liu, N. Xiao, H. Liu et al. 2023c. ”Decreased soil multifunctionality is associated with altered microbial network properties under precipitation reduction in a semiarid grassland.” iMeta 2, no. 2 (2023): e106. https://doi.org/10.1002/imt2.106. Wang, Y., C. He, Y. Shi, H. Li, Y. Tang, B. Chen, Q. Ke, L. Wu, and L. Chen. 2023a. ”Short-term cultivation limiting soil aggregate stability and macronutrient accumulation associated with glomalin-related soil protein in Eucalyptus urophylla × Eucalyptus grandis plantations.” Science of the Total Environment 878: 163187. https://doi.org/10.1016/j.scitotenv.2023.163187. Warren‐Thomas, E., P. M. Dolman, and D. P. Edwards. 2015. ”Increasing demand for natural rubber necessitates a robust sustainability initiative to mitigate impacts on tropical biodiversity.” Conservation Letters 8, no. 4: 230-241. https://doi.org/10.1111/conl.12170. Wei, Y., L. Chen, Z. Yin, Q. Feng, H. Xi, C. Zhang, K. Gan, and T. Yong. 2024. ”Differences in soil fungal communities under salinity gradients in arid and semiarid regions.” Global and Planetary Change 236: 104425. https://doi.org/10.1016/j.gloplacha.2024.104425. Xie, L., W. Li, X. Pang, Q. Liu, and C. Yin. 2023. ”Soil properties and root traits are important factors driving rhizosphere soil bacterial and fungal community variations in alpine Rhododendron nitidulum shrub ecosystems along an altitudinal gradient.” Science of the Total Environment 864: 161048. https://doi.org/10.1016/j.scitotenv.2022.161048. Xu, J., Y. Zhang, C. Huang, L. Zeng, M. Teng, P. Wang, and W. Xiao. 2022. ”Forest restoration shows uneven impacts on soil erosion, net primary productivity and livelihoods of local households.” Ecological Indicators 134: 108462. https://doi.org/10.1016/j.ecolind.2021.108462. Xu, Z., R. Sun, T. He, Y. Sun, M. Wu, Y. Xue, F. Meng, and J. Wang. 2023. ”Disentangling the impact of straw incorporation on soil microbial communities: enhanced network complexity and ecological stochasticity.” Science of the Total Environment 863: 160918. https://doi.org/10.1016/j.scitotenv.2022.160918. Yang, H., Li C., L. Che, Y. Su, and Y. Li. 2024. ”Nutrients addition decreases soil fungal diversity and alters fungal guilds and co-occurrence networks in a semi-arid grassland in northern China.” Science of the Total Environment 926: 172100. https://doi.org/10.1016/j.scitotenv.2024.172100. Yang, Y., Y. Dou, B. Wang, Z. Xue, Y. Wang, S. An, and S. X. Chang. 2023. ”Deciphering factors driving soil microbial life‐history strategies in restored grasslands.” imeta 2, no. 1: e66. https://doi.org/10.1002/imt2.66. Yang, Y., Y. Shi, J. Fang, H. Chu, and J. M. Adams. 2022. ”Soil microbial network complexity varies with ph as a continuum, not a threshold, across the north china plain.” Frontiers in Microbiology 13: 895687. https://doi.org/10.3389/fmicb.2022.895687. Yuan, Y., Y. Li, Z. Mou, L. Kuang, W. Wu, J. Zhang, F. Wang et al. 2021. ”Phosphorus addition decreases microbial residual contribution to soil organic carbon pool in a tropical coastal forest.” Global Change Biology 27, no. 2: 454-466. https://doi.org/10.1111/gcb.15407. Yuste, J. C., J. Penuelas, M. Estiarte, J. GARCIA‐MAS, S. Mattana, R. Ogaya, M. Pujol, and J. Sardans. 2011. ”Drought‐resistant fungi control soil organic matter decomposition and its response to temperature.” Global Change Biology 17, no. 3: 1475-1486. https://doi.org/10.1111/j.1365-2486.2010.02300.x. Zeng, H., J. Wu, X. Zhu, A. K. Singh, C. Chen, and W. Liu. 2021. ”Jungle rubber facilitates the restoration of degraded soil of an existing rubber plantation.” Journal of Environmental Management 281: 111959. https://doi.org/10.1016/j.jenvman.2021.111959. Zhang, P., P. Guan, C. Hao, J. Yang, Z. Xie, and D. Wu. 2022b. ”Changes in assembly processes of soil microbial communities in forest-to-cropland conversion in Changbai Mountains, northeastern China. ” Science of the Total Environment 818: 151738. https://doi.org/10.1016/j.scitotenv.2021.151738. Zhang, J. Q., R. T. Corlett, and D. Zhai. 2019. After the rubber boom: good news and bad news for biodiversity in Xishuangbanna, Yunnan, China. Regional Environmental Change 19, no. 6: 1713-1724. https://doi.org/10.1007/s10113-019-01509-4. Zhang, M., X. Song, X. Wu, F. Zheng, S. Li, Y. Zhuang, X. Man, and A. Degré. 2024b. ”Microbial regulation of aggregate stability and carbon sequestration under long-term conservation tillage and nitrogen application.” Sustainable Production and Consumption 44: 74-86. https://doi.org/10.1016/j.spc.2023.11.022. Zhang, Q., H. Liu, Y. Liang, Y. Liang, Y. Zhao, Z. Wang, Y. Li et al. 2024a. ”The multifunctionality of soil aggregates is related to the complexity of aggregate microbial community during afforestation.” Catena 236: 107737. https://doi.org/10.1016/j.catena.2023.107737. Zhang, Q., X. Wang, Z. Zhang, H. Liu, Y. Liu, Y. Feng, G. Yang, C. Ren, and X. Han. 2022a. ”Linking soil bacterial community assembly with the composition of organic carbon during forest succession.” Soil Biology and Biochemistry 173: 108790. https://doi.org/10.1016/j.soilbio.2022.108790. Zhang, S., Q. Deng, Y. P. Wang, J. Chen, M. Yu, X. Fang, H. He et al. 2021. ”Linkage of microbial living communities and residues to soil organic carbon accumulation along a forest restoration gradient in southern China.” Forest Ecosystems 8: 1-13. https://doi.org/10.1186/s40663-021-00334-8. Zhang, X., Z. Ji, X. Yang, J. Huang, Y. Zhang, H. Zhou, Y. Qu, and J. Zhan. 2024c. ”Deciphering the spatial distribution and function profiles of soil bacterial community in Liao River estuarine wetland, Northeast China.” Marine Pollution Bulletin 199: 115984. https://doi.org/10.1016/j.marpolbul.2023.115984. Zhu, M., X. Cao, Y. Guo, S. Shi, W. Wang, and H. Wang. 2022. ”Soil P components and soil fungi community traits in poplar shelterbelts and neighboring farmlands in northeastern China: Total alterations and complex associations.” Catena 218: 106531. https://doi.org/10.1016/j.catena.2022.106531. Zhu, X., W. Liu, H. Chen, Y. Deng, C. Chen, and H. Zeng. 2019. ”Effects of forest transition on litterfall, standing litter and related nutrient returns: Implications for forest management in tropical China.” Geoderma 333: 123-134. https://doi.org/10.1016/j.geoderma.2018.07.023. Zhu, X., W. Liu, X. Jiang, P. Wang, and W. Li. 2018. ”Effects of land‐use changes on runoff and sediment yield: Implications for soil conservation and forest management in Xishuangbanna, Southwest China.” Land Degradation & Development 29, no. 9: 2962-2974. https://doi.org/10.1002/ldr.3068. 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Collection Land Degradation & Development Keywords forest restoration rubber plantation soil aggregates soil fertility soil microbes Authors Affiliations Xiaoyi Cai Xishuangbanna Tropical Botanical Garden Chinese Academy of Sciences Key Laboratory of Tropical Forest Ecology View all articles by this author Chunfeng Chen 0000-0002-2269-0191 [email protected] Xishuangbanna Tropical Botanical Garden Chinese Academy of Sciences Key Laboratory of Tropical Forest Ecology View all articles by this author Ashutosh Kumar Singh Xishuangbanna Tropical Botanical Garden Chinese Academy of Sciences Key Laboratory of Tropical Forest Ecology View all articles by this author Xiaojin Jiang Xishuangbanna Tropical Botanical Garden Chinese Academy of Sciences Key Laboratory of Tropical Forest Ecology View all articles by this author wenjie Liu 0000-0002-9918-3462 Xishuangbanna Tropical Botanical Garden Chinese Academy of Sciences Key Laboratory of Tropical Forest Ecology View all articles by this author Metrics & Citations Metrics Article Usage 441 views 329 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Xiaoyi Cai, Chunfeng Chen, Ashutosh Kumar Singh, et al. 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