Contrasting fungal community succession and assembly processes between cut slope and natural soils along an altitudinal gradient in subalpine forests

During the highway construction, forest ecosystems are usually fragmented, leaving numerous bare cut slopes. External- soil spray seeding (ESSS) is widely utilized to restore these slopes, but our understanding regarding soil fungal communities during such a process still limited, particularly across an altitude gradient. Using high-throughput sequencing technology, we investigated the spatial shifts in the composition, diversity, molecular ecological network and assembly processes of fungal communities in both natural forest (NS) and cut slope (CS) along an altitude gradient. Results revealed significant differences in both the β-diversity and composition of fungal communities between CS and NS at each altitude site. The relative abundances of Ascomycota in CS decreased with the altitude, while those of Basidiomycota showed an opposite trend along the altitude gradient. Network analysis indicated a lower stability of fungal networks in CS than NS. Deterministic and stochastic processes jointly drive fungal community construction, with stochastic processes playing a major role. The construction of the fungal community is significantly correlated with soil pH and NH 4 +-N. This study improves our understanding about how soil fungal communities are restored in cut slopes in subalpine forests across altitude gradients. It also provides critical scientific guidance for restoring ecological functions on cut slopes formed during infrastructure development in forests. Contrasting fungal community succession and assembly processes between cut slope and nat- ural soils along an altitudinal gradient in subalpine forests Yuxiang Hua,#, Haijun Liao b,#, Chaonan Li b, Zhe Fenga, Yongping Koua,* a CAS Key Laboratory of Mountain Ecological Restoration and Bioresource Utilization & Ecological Restora- tion and Biodiversity Conservation Key Laboratory of Sichuan Province & China-Croatia ”Belt and Road” Joint Laboratory on Biodiversity and Ecosystem Services, Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610213, China b Security and Protection Key Laboratory of Sichuan Province, Mianyang Normal University, Mianyang 621000, China

During the highway construction, forest ecosystems are usually fragmented, leaving numerous bare cut slopes. External-soil spray seeding (ESSS) is widely utilized to restore these slopes, but our understanding regarding soil fungal communities during such a process still limited, particularly across an altitude gradient. Using high-throughput sequencing technology, we investigated the spatial shifts in the composition, diversity, molecular ecological network and assembly processes of fungal communities in both natural forest (NS) and cut slope (CS) along an altitude gradient. Results revealed significant differences in both the β- diversity 1 Posted on 25 Feb 2025 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.174048708.86546752/v1 — This is a preprint and has not been peer-reviewed. Data may be preliminary. and composition of fungal communities between CS and NS at each altitude site. The relative abundances of Ascomycota in CS decreased with the altitude, while those of Basidiomycota showed an opposite trend along the altitude gradient. Network analysis indicated a lower stability of fungal networks in CS than NS. Deterministic and stochastic processes jointly drive fungal community construction, with stochastic processes playing a major role. The construction of the fungal community is significantly correlated with soil pH and NH4+-N. This study improves our understanding about how soil fungal communities are restored in cut slopes in subalpine forests across altitude gradients. It also provides critical scientific guidance for restoring ecological functions on cut slopes formed during infrastructure development in forests.

soil fungi; cut slope; subalpine forest ecosystem; community structure; community assembly process

Soil fungi exert pivotal functions within forest ecosystems, fulfilling substantial ecological roles such as the decomposition of litter, the cycling of nutrients, and the regulation of plant growth (Peay et al., 2016; Anthony et al., 2024). To a certain extent, soil fungal dynamics also serve as an important indicator for the health and stability of forest ecosystems because they are sensitive and capable of rapidly responding to environmental changes (Huang et al., 2023). The construction of highways can lead to cut slopes, which

in landscape connectivity degradation (Forman, 1995; Forman, 2000), biodiversity loss and the decline in ecosystem quality (Banerjee et al., 2018). The external-soil spray seeding (ESSS) technique is widely used to restore cut slopes, which is a technology of spraying an artificial soil mixture onto the slope surface to establish a soil layer (Chen et al., 2019; Li et al., 2021). It involves applying a blend of cement, backfilled soil, inorganic fertilizer, grass seeds, and soil hydrates onto the cut slopes (Fu et al., 2018; Ai et al., 2020). While the ESSS is effective in stabilizing bare cut slope and restoring aboveground plants, it also exerted impacts on soil microbes and nutrient cycling, and the growth of aboveground plants (Xu et al., 2017; Liao et al., 2023a). Previous studies have revealed the dynamics of soil fungi and their ecological mechanisms during the simulation of the ESSS based restoration process (Liao et al., 2024). Yet, our knowledge of fungal community composition and assembly processes in cut slope soils restored through the ESSS still remains elusive, especially across an altitude gradient. The implementation of ecological restoration measures alters soil environmental conditions, influencing not only the composition of fungal communities but also their assembly processes (Wang, K. et al., 2022). Compared to community assembly, numerous studies have focused on the correlation between community composition and environmental factors in the process of ecological restoration. Community assembly studies are beneficial for understanding and predicting community dynamics during restoration and can facilitate ecological restoration (Wainwright et al., 2018). Previous research on ecosystem restoration in desertified areas has shown that both deterministic and stochastic processes drive soil fungal community assembly (Gong et al., 2023). However, different restoration measures may lead to varying environmental changes, affecting fungal community distribution differently. During the natural recovery of bare patches, factors such as moisture, organic matter, and organic carbon content are significantly correlated with soil fungal community composition (Zhao et al., 2019). In contrast, restoration techniques involving soil improvement and plant cover have more diverse impacts on fungal community composition, including changes in soil nutrient levels, pH, vegetation type, and root characteristics (Montiel-Rozas et al., 2018; Hu et al., 2020; Wang, Y. et al., 2022; Cheng et al., 2024). Soil factors, such as pH (Tripathi et al., 2018), soil carbon (Ning et al., 2019), and nitrogen (Liang et al., 2020), are critical indicators influencing community assembly. Additionally, biological factors like competition and predation also affect species survival and distribution within communities (Arnillas et al., 2021; Huang et al., 2021). Nevertheless, the dominant factors governing fungal community composition and assembly mechanisms following ESSS restoration remain unclear. Restoration can shift soil quality and microbial community (Harris, 2009). Studies show that during the ecological restoration, compared with bacteria, soil fungi exhibit a slower succession rate and a more con- sistent composition, suggesting higher resilience to environmental changes (Jin et al., 2024). However, the resilience and response of fungal community to cut slope, especially under harsh conditions such as alti- 2 Posted on 25 Feb 2025 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.174048708.86546752/v1 — This is a preprint and has not been peer-reviewed. Data may be preliminary. tudinal gradients and rapid environmental changes, are poorly understood. Previous studies identified soil temperature, moisture, pH, mineral nutrients and C:N ratio as key factors controlling fungal community altitudinal distribution (Lazzaro et al., 2015; Siles and Margesin, 2016; Ji et al., 2021). However, the impact of altitudinal gradients on fungal community structure and assembly processes in the cut slope ecological recovery phase remained unexplored. The subalpine forests, averaging 3,000 meters in altitude and frequently exposed to extreme conditions like low temperatures, oxygen deficiency, and strong winds (Pang et al., 2002), face challenges in restoring cut slope ecology, impacting forest ecological function. However, our knowledge on soil fungal community structure, molecular ecological network characteristics, assembly processes and key influencing factors in cut slopes along subalpine forest altitudinal gradients was significantly lacking. In this study, fungal communities in cut slope soil and natural subalpine forest soil at three altitudes (2900 m, 3102 m, and 3194 m) along the Wenma highway in subalpine forests were investigated. Soil samples from cut slopes (CS) and natural forests (NS) were collected. By utilizing high-throughput sequencing and bioinformatics analysis, we aimed to: (1) explore changes in soil fungal community structure, molecular ecological network characteristics and assembly processes along altitudinal gradients in both CS and NS, and (2) identify key factors influencing these structures and processes. Our findings contribute to a better understanding of the successional patterns and the possible determinants of fungal communities during the recovery of degraded ecosystems along altitudinal gradients. 2. Materials and Methods 2.1. Site Description and Soil Sampling The study area is in the Miyaluo of Lixian County, Sichuan, Southwest China (31°42’38” N - 31deg47’55” N, 102deg41’40” E - 102deg44’23” E). The mountains of western Sichuan have a monsoon climate with strong sunshine, cold, dry, and windy conditions (Zhang et al., 2018). Precipitation is concentrated from May to October, averaging 600-1100 mm annually. Average annual temperature is 8.9 , with monthly temperatures ranging from -8.0 in January to 12.6 in July. The soils in our study area are typical brown forest soils. We selected three altitudinal locations to sample along the Wenma highway (2900 m, 3102 m and 3194 m). The cut slopes formed in 2015 and underwent ESSS-based restoration. In October 2018, we collected 36 soil samples from the 0-10 cm depth cut slopes (CS) and adjacent natural subalpine forest soils (NS). At each altitude, we employed an ’S-shaped’ sampling method to mitigate the influence of hydrological conditions on soil properties (Fu et al., 2018; Ai et al., 2020). Six sample plots were identified along the route at each site, and five soil cores (0-10 cm depth) were collected per plot using quadrat sampling. The cores were mixed after removing impurities, yielding six independent biological replicates per elevation. The soil was sieved to 2.0 mm and divided: one part was stored at 4 for soil property analysis, and the other was freeze-dried (-20 ) for genomic DNA extraction. 2.2. Soil property measurements Soil pH and conductivity (CD) were measured in soil-water slurry (1:5, soil/water) using a pH meter. Soil moisture content (MC) was assessed by drying the soil in an oven at 105 until a consistent mass was obtained. Soil temperature (ST) was measured in situ during the sampling using an earth thermometer. Soil total organic carbon (TOC) was assessed using the dichromate oxidation-titration method (Nelson and Sommers, 1996). Total soil nitrogen (TN) was assessed using the Kjeldahl method (Bremner, 1965). Soil ammonium (NH4+-N) and nitrate nitrogen (NO 3--N) were measured by indophenol blue and phenol disulfonic acid methods, respectively (Wang, Y.S. et al., 2017). Soil total phosphorus (TP) was measured by H 2SO4-HClO4 digestion followed by molybdenum blue colourimetric method (Murphy and Riley, 1962). Soil available phosphorus (SAP) was measured by molybdenum-antimony inverse colourimetry (Olsen, 1954). 2.3. DNA extraction, fungal ITS amplification and sequencing Genomic DNA was extracted from 0.25 g of soil using the DNeasy PowerSoil kit (QIAGEN GmbH, Hilden, Germany). Primers gITS7F (GTGARTCATCGARTCTTTG) and ITS4R (TCCTCCGCT- 3 Posted on 25 Feb 2025 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.174048708.86546752/v1 — This is a preprint and has not been peer-reviewed. Data may be preliminary. TATTGATATATGC) were used for polymerase chain reaction (PCR) amplification (Whitman et al., 2018). The PCR reaction (25 μL) consisted of 1 μL of DNA template (˜20 ng DNA), 1 μL each of 10 μM forward and reverse primers, 9.5 μL of H 2O, and 12.5 μL of MasterMix (PCR buffer, DNA polymerase, and dNTP and Mg2+) (CWBIO China) (Liao et al., 2023b). The amplification program was as follows: 5 min at 94 for initial denaturation, 35 cycles of amplification (30 s at 94 , 30 s at 56 , 45 s at 68 ), and a final extension for 10 min at 72 . The PCR products were then analyzed using a NanoDrop ND-1000 spectrophotometer (Nan- oDrop Technologies Inc., Wilmington, DE, USA). Then each sample was combined in equimolar amounts and sequenced using the Illumina NovoSeq platform. In view of the failure of some molecular experiments, only 33 soil samples were ultimately used for sequencing. 2.4. Bioinformatics analysis Pair-end reads were demultiplexed using the Sabre software (https://github.com/najoshi/sabre; accessed date: 22 December 2019) based on barcode sequences. This resulted in two FASTQ files for each soil sample. These files were subsequently converted to the format required by QIIME2 (version 2019.10) (Bolyen et al., 2019) for merging (VSEARCH algorithm) (Rognes et al., 2016), quality filtering with default parameters and denoising (deblur algorithm) (Amir et al., 2017). During the denoising, sequences were trimmed to 235 bps and the first 35 bps from the 5’ end were removed, yielding a final sequence of 200 bps. After the denoising, all amplicon sequence variants (ASVs) were taxonomically annotated to the UNITE v2019.02.02 database using a classify-sklearn algorithm (Nilsson et al., 2018). ASVs appearing only once across all samples or failing to be annotated at the Kingdom taxonomic level were removed. To account for sequencing depth differences, each soil sample was rarefied to 8375 sequences. A phylogenetic tree was constructed using QIIME2 built-in tools. The above procedures generated ASV matrices, taxonomic classifications table and phylogenetic trees for downstream statistical analysis. 2.5 Community assembling process The mean nearest taxon distance index (MNTD) was employed to quantify phylogenetic diversity within communities, as calculated by the “microeco” R package (Kembel et al., 2010). This index represents the mean evolutionary distance between each species within a community and its closest relative (Fine and Kembel, 2011). Subsequently, we calculated the standardized effect sizes of the Mean Nearest Taxon Distance index (ses.MNTD). A negative value of ses.MNTD indicates that species tended to be more closely related within a community, whereas a value greater than zero indicates that species tended to be more distantly related (Miller et al., 2017). The Beta Mean Nearest Taxon Distance metric ( β MNTD) was calculated based on phylogenetic turnover between pairs of communities using the “picante” package (v1.8) in R (Kembel et al., 2010; Stegen et al., 2012). The Beta Nearest Taxon Index ( β NTI) and Bray-Curtis- based Raup-Crick (RCbray) indices, calculated using the “microeco” package, were used to determine the fungal community assembly processes (Liu et al., 2021). We used the standardized effect size of β NTI to distinguish deterministic and stochastic assembly processes (Stegen et al., 2013; Tripathi et al., 2018). For the β NTI metrics, weighted |β NTI| > 2 implies dominance of deterministic processes, while |β NTI| 0.95 for dispersal limitation, RCbray < -0.95 for homogeneous dispersal, and |RCbray| < 0.95 for drift and other processes. 2.6. Statistical analysis Differences in fungal community compositions and β MNTD were visualized using Principal Coordinate Analysis (PCoA) and then tested using Analysis of Similarities based on Bray-Curtis distance (ANOSIM). A Wilcoxon rank-sum test was employed to compare the individual soil properties, observed ASVs (α -diversity metric), the Bray-Curtis distances ( β -diversity metric) of fungal communities, the relative abundances of differential lineages, ses.MNTD, and β NTI. A Venn diagram was employed to illustrate the unique ASV numbers in each group and the shared ASV numbers between or among groups. We used the “vegan” package (v2.5 - 6) in R to carry out a partial Mantel test with 999 permutations, quantifying the Spearman correlation between each environmental variable and matrices of Bray-Curtis dissimilarity and β NTI. To identify key factors impacting the fungal community, a redundancy analysis (RDA) was used, and the 4 Posted on 25 Feb 2025 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.174048708.86546752/v1 — This is a preprint and has not been peer-reviewed. Data may be preliminary. contribution of variables to community variations was estimated using the “envfit()” function of the vegan R package (v2.6.4) (Dixon, 2003). Using the MENA (Molecular Ecological Network Analysis Pipeline, http://ieg4.rccc.ou.edu/mena.), soil fungal molecular ecological networks were constructed for NS and CS at different altitudes, including those at elevations of 2900 m, 3102 m, and 3194 m, with a cut-off value of 0.92. Methods based on Random Matrix Theory (RMT) were used to automatically determine the cut-off value. The visualization was conducted using the Gephi-0.10.1 software.

Differences in soil properties between NS and CS, and among altitudes No significant differences in soil TP and AP existed between NS and CS at any elevation (Fig. S1). At 3102 m and 3194 m, the pH, ST and MC in CS were higher than those in NS, while the NH 4+-N and TN levels in CS were lower than those in NS. Soil ST decreased with the altitude. Regarding NS, soil pH, MC, ST and TP declined with the altitude and then increased (Fig. S1). Soil pH, MC and AP at CS were higher at 3102 and 3194 m than at 2900 m. Instead, soil ST, TN and NH 4+-N in CS decreased with the altitude (Fig. S1). Variations in fungal diversity characteristics and abundance between NS and CS soils across different eleva- tions PCoA analysis revealed significant differences ( p < 0.001) in fungal communities between NS and CS, as well as among three altitudes (Fig. 1B, Tab. S1). The dominant taxa in NS and CS, and among differ- ent altitudes, included Ascomycota , Basidiomycota ,Glomeromycota , Mortierellomycota , Mucoromycota ,Rozellomycota and Chytridiomycota (Fig. 1C). In NS soils, Ascomycota abundance decreased while Ba- sidiomycota abundance increased with altitude. Conversely, in CS soils, the opposite trend is observed: Ascomycota abundance increased and Basidiomycota abundance decreased with altitude (Fig. 1C). ASV distribution patterns differed between NS and CS across elevations. NS had fewer unique ASVs than CS at all altitudes (Fig. 1D, E, F). CS at 3102 m contained the most unique ASVs, while NS at 2900 m had the fewest (Fig. 1D, E, F). The number of ASVs shared between NS and CS was highest at 2900 m and lowest at 3102 m (Fig. 1D - F). 3.3. Molecular ecological network analysis The clustering coefficient of fungal network in NS was higher than that in CS across all elevations (Tab. S2). Positive links in the network of CS were the highest at 2900 m. CS soil fungal network modularity was lowest at 3194 m (Tab. S2). Compared to 3102 m, the NS fungal network showed reduced network complexity at 2900 m and 3194 m, exhibiting lower degrees, fewer nodes and links, decreased positive interactions, a reduced average degree (avgK), and a lower clustering coefficient (Tab. S2). 3.4. Community assembly mechanisms of soil fungal communities in NS and CS A phylogenetic null model revealed a higher MNTD and ses.MNTD in NS than those in CS at 3194 m. PCoA analysis revealed significant differences (p < 0.001) in β MNTD between NS and CS, as well as among different altitudes (Fig. 3B, Tab. S4). The β MNTD of NS was significantly higher than that of CS at both altitudes of 2900 m and 3102 m (Fig. 3C). The β NTI of NS was significantly greater than that of CS at 3102 m (Fig. 3D). The proportion of dispersal limitation tended to increase with altitudes in NS, whereas it was highest at 3102 m in CS (Fig. 3E). Meanwhile, heterogeneous selection was higher in NS than in CS at any altitude. In addition, ecological drift in NS decreased with altitude, whereas ecological drift in CS had the greatest effect at 3194 m (Fig. 3E). 3.5. Linkage between fungal community and environmental factors in NS and CS RDA showed that ST (R2 = 0.793, p = 0.001), MC (R 2 = 0.7663, p = 0.001), pH (R 2 = 0.7443, p = 0.001), TOC (R 2 = 0.619, p = 0.001), NO 3--N (R 2 = 0.595,p = 0.001), NH 4+-N (R 2 = 0.415, p = 0.001) and AP (R2 = 0.208, p = 0.023) were the key factors driving differences in the fungal communities of NS and CS soils (Fig. 4). MC, pH, and ST were the key factors driving fungal community changes in CS soils (Fig. 4), 5 Posted on 25 Feb 2025 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.174048708.86546752/v1 — This is a preprint and has not been peer-reviewed. Data may be preliminary. while TOC, NO 3--N, NH4+-N and AP were the main factors driving fungal community changes in NS (Fig. 4). Spearman rank correlations revealed a significant positive correlation between β MNTD, β NTI and RCbray and MC, ST, pH, TN, NO 3--N and NH 4+-N (p < 0.05) (Fig. 5). MNTD exhibited a significant negative correlation with pH and CD ( p < 0.05). RCbray showed a significant negative relationship with TP ( p < 0.05). Primarily, RCbray,β MNTD, and β NTI were correlated with soil pH ( p < 0.05).

Cut slopes and natural forests form significantly different fungal communities characteristics and diversity, with different trends as with altitudinal increases Our research findings indicate that at any altitude, there is no difference in the α -diversity of soil fungi between CS and NS. This is inconsistent with the previous research findings in the cut slope restoration of subtropical evergreen and deciduous forests (Wang et al., 2024a; Wang et al., 2024b). This inconsistent

may be caused by environmental heterogeneity, such as the lower pH (< 5) in the subtropical evergreen and deciduous forests (Wang et al., 2024a). The β -diversity of fungal communities varied significantly in CS and NS. This may have been due to ESSS remediation altering soil properties. This is consistent with previous research findings that fungal β -diversity is driven by environmental factors (Wang, J.M. et al., 2017). Meanwhile, the divergence in fungal β -diversity across different altitudes is likely attributable to the variations in ecological conditions at these altitudes (Liu et al., 2018). We found that Ascomycota and Basidiomycota were the dominant taxa in CS and NS. This is consistent with previous research findings that Ascomycota and Basidiomycota are the dominant fungal taxa in forest ecosystems (Li et al., 2022; Manici et al., 2024b). CS treatment reduced the relative abundance of Basidiomycota and increased the relative abundance ofAscomycota . This trend is also evident along the altitudinal gradient. The edaphic variables are crucial in shaping soil fungal community structure (Mukhtar et al., 2021). Studies have found that soil water content (SWC) plays a critical role in shaping the composition of cut slope fungal communities along altitudinal gradients (Wang et al., 2024b). Furthermore, Ascomycota possess a broader enzyme spectrum and exhibit greater metabolic adaptability compared to Basidiomycota (Manici et al., 2024a; Manici et al., 2024b).Ascomycota are also more capable of adapting to environments in the early stages of restoration than Basidiomycota (Manici et al., 2024a). 4.2. Cut slopes and natural forests form significantly different fungal networks, with different trends as with altitudinal increases Our results showed that the CS fungal community network connectivity (clustering coefficient) was signifi- cantly lower than that of NS at any altitude, which may be indicate that reduced network stability of soil fungal communities in cut slopes (Price et al., 2021). Moreover, fungal network structures also underwent significant changes along the altitude gradient. Predominantly positive links existed between soil fungal communities in CS at an altitude of 2900 m. This may reflect commonly preferred conditions or cooperative behaviors between fungal communities in CS soils (Deng et al., 2012; Ren et al., 2018). Such interactions likely promote a wider range of ecological niches and enhance environmental adaptability (Du et al., 2020; He et al., 2022). The fungal networks under CS treatment exhibited the lowest modularity at 3194 m, indicating that CS fungal communities may have reduced adaptability to environmental changes at such altitudes (Guo and Gong, 2023). This indicates that the impact of altitude gradients on fungal network structure needs to be considered during the restoration process. 4.3. Different assembly processes of soil fungal communities in cut slopes and natural forests. Fungal communities were influenced by both stochastic (dispersal limitation and ecological drift) and deter- ministic (heterogeneous selection) processes, while stochastic processes prevailed. Fungi are predominantly dispersed by various media, such as wind, water, and animals (Peay et al., 2010; Kang et al., 2022). This dispersal leads to a heterogeneous distribution pattern across spatial distances (Evans et al., 2017; Ning et al., 2020). Meanwhile, undominated process contributed more in CS. This may imply that the fungal commu- 6 Posted on 25 Feb 2025 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.174048708.86546752/v1 — This is a preprint and has not been peer-reviewed. Data may be preliminary. nity in CS is more susceptible to the effect of drift. The fungal communities in such fragmented landscapes may be more susceptible to ecological drift (Siqueira et al., 2020). In addition, heterogeneous selection contributed more in NS than in CS. This phenomenon may have been related to different environmental conditions between regions (Urbanov´ a et al., 2015; Wang, J.M. et al., 2017). The relative contributions of these processes vary significantly along the altitudinal gradient. Heterogeneous selection peaks at 2900 m and reaches its lowest point at 3102 m for both NS and CS soils. This can be attributed to the diverse ecological conditions posed by the altitudinal gradient (Liu et al., 2018), leading to environmental filtering and niche partitioning among soil fungi (Bahram et al., 2016). Along the altitudinal gradient, the role of stochastic processes in CS increased and then decreased. This may stem from the harsh environmental conditions at high altitudes, leading to random changes in fungal populations where certain species more adapted to high altitudes emerge (Li et al., 2023). Notably, ecological drift (undominated) in CS dominated fungal community shaping at high altitude. This suggests that at 3194 m, the shaping of fungal communities in CS soils was less influenced by environmental factors and interspecific interactions, and was mainly attributed to ecological differences among individuals and potential dispersal mechanisms (Louca et al., 2018). 4.4. Different drivers shaping soil fungal communities in NS and CS Our research found that different soil factors dominated the variation and assembly processes of soil fungal communities in NS and CS. In NS soils, fungal communities were primarily correlate with TOC, NO 3--N, NH4+-N and AP. This aligned with a previous study, confirming the link between fungal communities and soil nutrients (Lauber et al., 2008; Kuramae et al., 2011). Conversely, fungal communities in CS were mainly associate with ST, MC and pH. Meanwhile, we found that after ESSS restoration, ST, MC, and pH increased, differing from NS soils. These changes may have resulted from cement-induced alkalization and hardening (Mahedi et al., 2020), and water-retaining agent-mitigated moisture loss (Li et al., 2021). Further correlation analysis revealed the influence of environmental factors on the assembly process of fungal communities. The soil pH and NH 4+-N played pivotal roles in regulating the assembly of soil fungal communities. This may have been due to the significant impact of soil pH on biogeochemical processes, which affected microbial cell metabolism and, consequently, microbial growth (Waldrop et al., 2006). Additionally, NH 4+-N, as a rapidly utilizable nitrogen source for microorganisms in the environment, significantly influenced microbial growth and reproduction, thereby shaping the microbial communities in the environment (Cai et al., 2022). In summary, the study uncovers the impact of changes in soil pH and NH 4+-N on the composition and assembly processes of fungal communities. These findings can provide guidance for improving soil properties and thereby promoting fungal recovery.

This research revealed the composition, diversity, ecological networks, and assembly processes of soil fungal communities in natural forests (NS) and cut slopes (CS) along altitudinal gradients during early recovery. β -diversity differed significantly between NS and CS. Specific taxa showed distinct trends along altitudes. Network analysis showed CS had lower connectivity than NS, and at 3194 m CS fungal communities were less resistant to environmental changes. Fungal community assembly was driven by both deterministic and stochastic processes, with stochastic ones more dominant. The variation of these processes along the gradient was clarified and relevant environmental factors were identified. Managing soil properties may aid the restoration of cut slope fungal communities. The findings can guide soil property improvement, which supports fungal community restoration, optimizes soil conditions, and promotes fungal restoration crucial for ecological balance and functions. Funding This research was financially supported by Sichuan Science and Technology Program (24NSFSC2583); the National Natural Science Foundation of China (32171550); the Key Projects of Chengdu Institute of Biology, Chinese Academy of Sciences (QYJC2024-3); the National Key R&D Program of China (2024YFF1306700) and the Youth Innovation Promotion Association, Chinese Academy of Sciences (2021371). 7 Posted on 25 Feb 2025 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.174048708.86546752/v1 — This is a preprint and has not been peer-reviewed. Data may be preliminary. Data Availability The raw reads were submitted in the European Nucleotide Archive under accession number PRJNA1079527 (http://www.ebi.ac.uk/ena/data/view/PRJNA1079527). Author contributions YPK conceived the project. YPK and CNL collected samples. HJL and ZF performed the experiments. YXH and HJL carried out bioinformatic and statistical analyses. YXH prepared the original draft. YPK and CNL acquired funding. All authors read, edited, and approved the final manuscript. Conflict of interest statement The authors declare no competing interests.

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The standardized effect size measurement of the mean nearest taxon distance (MNTD) and the means standardized effect size measurement of the mean nearest taxon distance (ses.MNTD) and β -mean nearest taxon distance ( β MNTD)-based (A), the PCoA within the natural (NS) and cut slope (CS) at different altitudinal (B), the β -mean nearest taxon distance ( β MNTD) within the natural (NS) and cut slope (CS) at different altitudinal (C), the β -nearest taxon index ( β NTI) between natural (NS) and cut slope (CS) at different altitudinal (D), and the community assembling processes within each natural (NS) 13 Posted on 25 Feb 2025 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.174048708.86546752/v1 — This is a preprint and has not been peer-reviewed. Data may be preliminary. and cut slope (CS) at different altitudinal (E). Different fill colors in panel (C) indicates different community assembly processes. * p < 0.05. ** p < 0.001, **** p < 0.001, **** p < 0.0001. Fig. 4. Redundancy analysis (RDA) of fungal communities in natural (NS) and cut slope (CS) soils at different altitudinal. Fig. 5. Pearson correlations between soil properties and fungi community assembly processes. Numbers in each cell of this figure represent correlation coefficients (the R values shown in the legend). Bray: Bray- Curtis distance; ST: soil temperature; MC: moisture content; TOC: total organic carbon; NO 3--N: nitrate nitrogen; AP: soil available phosphorus; NH 4+-N: ammonium nitrogen; AP: soil available phosphorus; TP: total phosphorus; CD: conductivity. * p < 0.05. ** p < 0.001, *** p < 0.001. 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