Environmental Drivers of Soil Microbial Diversity and Metabolic Potential Across the Western Himalayan Treeline

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Asif Chowdhary, Sarbani Roy, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7306065/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Although soil microbial communities in high-altitude environments are a vital component of ecosystem functioning, their structural and functional responses to elevation and environmental heterogeneity are poorly known. This study explored the composition, diversity, and functional potential of the soil microbial community along elevation in the western Himalayan ecotone, using high-throughput metagenome sequencing. Physicochemical profiling based on 13 parameters revealed pronounced spatial variability among soil samples. A total of 4,394 species were identified, with a resilient core microbiome comprising 1,772 shared species across all samples. The bacterial genera Azorhizobium , Buchnera , Shewanella , and Dictyoglomus were found to be dominant, indicating key roles in nitrogen fixation, metal cycling, and cellulose degradation. Functional annotation identified 193 MetaCyc pathways and over 149,000 gene families, with significant variation in pathway richness and composition along elevation. Vegetation transition zones showed the highest functional diversity and unique pathway presence. Core metabolic pathways such as nucleotide biosynthesis, folate metabolism, and fatty acid synthesis were highly enriched across sites. Stress-related pathways were found to be more pronounced as elevation increases. Redundancy analysis revealed iron, moisture content, and electrical conductivity as major environmental drivers of both microbial composition and functional traits. These findings emphasise the ecological importance of environmental gradients in shaping microbial communities and their functions, offering critical insights into microbial adaptation and ecosystem processes in mountain treeline environments. Microbial diversity Treeline ecotone Metagenomics Functional genes Soil environment Western Himalayas Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction The distribution of microbial communities along elevation gradients and the underlying drivers of these patterns are fundamental concerns of biogeography (Hanson et al., 2012; Xiong et al., 2022). Microbes play a vital role in the biosphere by regulating key soil biogeochemical processes such as decomposition and nutrient transformation, thereby influencing the composition and dynamics of the plant community (Aislabie et al., 2013; Basu et al., 2021). Microbes play a vital role in the biosphere by regulating key soil biogeochemical processes such as decomposition and nutrient transformation, thereby influencing the composition and dynamics of the plant community (Aislabie et al., 2013; Basu et al., 2021). Despite their ecological significance, our understanding of how microbial communities vary with elevation, particularly with their functional roles in soil processes, remains limited and often contradictory. Currently, many studies have focused on microbial diversity across elevation and detected many noticeable trends in soil microbial α-diversity, but most of the results were inconsistent (Singh et al., 2012). Since the first study on microbial elevational distribution by Bryant et al. (2008), research on microbial diversity across elevations has expanded significantly. While many studies have observed clear trends in soil microbial α-diversity with elevation, their findings remain inconsistent (Fu et al., 2023; Peng et al., 2022; Singh et al., 2013; Wang et al., 2018). For example, some studies report a hump-shaped pattern of microbial α-diversity, often attributed to the mid-domain effect (Miyamoto et al., 2014; Singh et al., 2013). Others have identified U-shaped or declining diversity patterns, which are largely associated with soil pH variation (Shen et al., 2021; Wang et al., 2015). However, despite the increasing number of studies along broad elevational gradients, microbial diversity within ecologically critical transition zones (treeline ecotones) remains largely unexplored. These narrow yet climatically sensitive interfaces between subalpine forest and alpine tundra are often overlooked, even though they are likely to harbour distinct microbial communities shaped by abrupt environmental transitions. The alpine treeline ecotone is defined as the upper limit of the forest in the high-mountain ecosystem, representing dynamic interfaces between subalpine and alpine zones where climatic and environmental gradients strongly influence microbial biodiversity patterns (Kui et al., 2019). Unique ecological characteristics, including the freeze-thaw cycle, high solar radiation, low nutrient supply, and limited water availability, primarily characterise these sites. Due to these extreme environmental conditions and narrow ecological margins at alpine treeline ecotones, even slight environmental changes can induce significant ecosystem shifts, such as species migration and replacement (Kupfer & Cairns, 1996; Liu et al., 2009). Among the most sensitive indicators of such environmental shifts are soil microbial communities (De Vries & Shade, 2013). As elevation increases, changes in environmental variables such as temperature, soil properties, and moisture availability, vegetation community structure can alter the soil microbiota composition and functional potential. Despite growing recognition of microbial contributions to ecosystem processes, their diversity and distribution across elevation gradients, especially in alpine treeline ecotones, remain understudied. This study was conducted on the alpine treeline ecotone in the Western Himalayas. While the region has been relatively well studied for vegetation dynamics, climate-driven treeline shift, and plant biodiversity (Kumar & Khanduri, 2024; Maletha et al., 2022; Rawat et al., 2021), but soil microbial community, particularly in the sensitive transitional zone between subalpine and alpine ecosystems, have received far less attention. Most microbial ecology research in the Himalayas has either concentrated on broad elevational gradients, specific functional groups, or high-altitude alpine soil (Kumar et al., 2019; Mushtaq et al., 2023; Rathore et al., 2022), with minimal focus on the ecotone zone where abrupt shifts in vegetation, soil condition, and climate converge. Soil physicochemical properties and alternative vegetation types along the elevation in the treeline ecotone, spanning subalpine to alpine, may alter the microbial community structure and their functional potential. Therefore, this study aims to characterise and compare the taxonomic and functional diversity of soil microbial communities across a subalpine to alpine elevation gradient, providing insights into how soil characteristics and microbial communities respond to ecological transitions shaped by altitude and the vegetation types in the Western Himalayas. 2. Materials and Methods 2.1. Sample collection and site description The treeline forest soil samples were collected from Chhitkul, Himachal Pradesh, India (longitude 78.441541 E, latitude 31.344609 N), in August 2023. The average annual rainfall of the study site is between 816 mm, and the yearly temperature is below 10ºC. Treeline in this part of the Western Himalayas is inhabited by Betula utilis forests, making a mixed community with Rhododendron campanulatum (Singh et al., 2010). Above the treeline, diverse herbaceous species thrive, providing a contrast to the subalpine woody vegetation, whereas sub sub-treeline region is covered with dense Pinus gerardiana and Pinus wallichiana timberline forest. A systematic sampling approach was employed to investigate the soil microbial diversity along an elevation gradient in the treeline ecotone. Five samples were collected along the gradient from 3390 m to 3795 m (each sample after 100 m), covering the lower timberline forest (S1), treeline forest (mixed tree-shrub community, S2), subalpine region (S3), alpine treeline ecotone (S4), as well as the herbaceous alpine zone (S5) (Detailed site descriptions are given in Table 1 ). With the help of a soil digger, three replicates from standardised depths of 0–10 cm, 10–20 cm, and 20–30 cm were obtained at each site and then mixed as described by Carter and Gregorich (2007). Three replicates of soil samples were collected from each site. To prevent contamination, surface debris such as vegetation and litter was carefully removed before sampling. GPS coordinates, altitude, slope, and aspect were recorded for each. The collected soil samples were kept in sterile airtight polybags and transported to the laboratory. The samples were sieved through a 2-mm mesh and homogenised. Then, soil samples were divided into two parts; one part was air-dried to analyse soil physicochemical properties, while the other was stored at -80ºC for DNA extraction. Table 1 Details of the locations of sampling sites. Samples Vegetation type Longitude (E) Latitude (N) Altitude (m) Slope (º) Aspect S1 Timberline forest 78.442277 31.348311 3390 20 N S2 Mixed shrub-tree community 78.442665 31.345995 3486 40 N S3 Subalpine treeline 78.441541 31.344609 3591 45 N S4 Alpine treeline ecotone 78.440893 31.343271 3694 45 N S5 Herbaceous alpine 78.440176 31.340606 3795 35 NW 2.2. Analysis of soil physicochemical properties The pH level and electrical conductivity (EC) of the soil samples were assessed in a 1:2.5 (w/v) soil-to-water suspension employing an automated LCD pH meter (PF20 Standard Kit, Mettler Toledo, USA). The total nitrogen (N) concentration was determined with the Kjeldahl digestion technique, using a Kjeldahl apparatus, with the alkaline permanganate method. The total organic carbon (TOC) was evaluated employing a TOC analyser (TOC-L CPH/CPN, Shimadzu, Japan) after the elimination of inorganic carbon in alignment with the EPA Method. The quantification of available phosphorus (P) was executed through a UV-Visible spectrophotometer using Bray’s P-1 method due to acidic soil conditions (Bray & Kurtz, 1945). Available potassium (K) was determined using an atomic absorption spectrophotometer (Analyst 600, Perkin-Elmer, USA). Further, micronutrients - sulphur (S), zinc (Zn), boron (B), iron (Fe), manganese (Mn), and copper (Cu) concentrations, were analysed using Inductively Coupled Plasma Mass Spectrometry (ICP-MS) following microwave-assisted acid digestion employing nitric and hydrochloric acids, under U.S. EPA Method 3052. 2.3. Soil DNA extraction and sequencing A HiPurA® Soil DNA Extraction Kit (HiMedia Laboratories Private Limited, Maharashtra, India) was used to extract genomic DNA from treeline soil samples following the manufacturer’s instructions. The DNA concentration and quality were measured by a Jenway Genova Nano Microvolume Spectrophotometer and 1.2% agarose gel electrophoresis. The DNA from three replicates of each site was pooled together for sequencing. The paired-end libraries were prepared using the Twist NGS Library Preparation Kit (Twist Biosciences Corporation, South San Francisco, CA, USA). Adapters containing the full complement of sequencing primer hybridisation sites were ligated to the fragments. Following the quality and quantity assessment of the libraries, these libraries were subjected to Shotgun sequencing through the Illumina NovaSeq 6000 platform. Finally, raw metagenomics datasets were deposited in the NCBI Sequence Read Archive (SRA) database with a Bio Project ID: PRJNA1279859. 2.4. Metagenomic assembly and gene annotation Firstly, adaptors and low-quality metagenomic reads (quality score < 20) were trimmed out using Trimmomatic v0.39 (Bolger et al., 2014). Subsequently, contigs and scaffolds were assembled for each sample using Megahit v1.2.9 (Li et al., 2015) with default parameters, specifying a minimum contig size of 200 bp. Completeness of assemblies and read representation were calculated using BUSCO v5 (Manni et al., 2021). The sequences with an identity and coverage ≥ 90% were clustered to construct a non-redundant sequence catalogue using CD-HIT v4.8.1 (Fu et al., 2012). Taxonomic classification was performed using Kraken2 v2.1.3 (Wood & Salzberg, 2014), a k-mer-based classifier, against the standard Kraken2 database. To assess the functional capacity of the communities, functional classification for the high-quality reads was assigned using HUNAnN (HMP Unified Metabolic Analysis Network) version 3.9 (Beghini et al., 2021). HUMAnN maps reads to gene families using the UniRef90 database and reconstructs pathway abundance using the MetaCyc pathway database. Metabolic pathways significantly associated with elevation were identified using the MaAsLin2 (Multivariable Association with Linear Model 2) pipeline through the R package Maaslin2 v1.12.0 (Mallick et al., 2021). 2.5. Statistical analysis and diversity assessment Beta diversity analysis was performed in R version 4.3.2 using the vegan and phyloseq packages based on Bray-Curtis distance (Ricotta & Podani, 2017) and analysed by principal coordinate analysis (PCoA). Various diversity indices, such as observed species, Chao1, Shannon diversity index, and Simpson index, were computed for each sample and visualised using the R package ggplot2. The "redundancy analysis (RDA)" function from the vegan package (Oksanen et al., 2013) in R, the RDA was employed to examine various correlations between environmental factors and community composition at the genus level, with environmental determinants integrated into the ordination plots using the vegan package in R, incorporating 999 permutations. Significant environmental vectors (p < 0.05) were fitted via the envfit function and depicted on the ordination plot. To investigate specific relationships between soil characteristics and metabolic pathways, Spearman's rank correlations were calculated between standardised soil parameters and pathway abundances, subsequently visualised using the pheatmap package in R (Kolde & Kolde, 2015). 3. Results 3.1. Soil physicochemical properties The physicochemical characteristics of soil exhibited considerable variability across the five sampling locations, as exhibited in boxplots (Fig. 1 ). Soil pH values fluctuated between 4.56 (S4) and 5.16 (S1), indicating a predominantly acidic environment with minimal fluctuations. EC demonstrated moderate uniformity, with values ranging from 349.0 µS/cm (S4) to 512.0 µS/cm (S2), signifying comparable ionic strength across sites. Moisture content (MC) displayed pronounced variability, ranging from 22.76% (S1) to 27.51% (S5). Total organic carbon (TOC) values varied between 12.34% (S2) and 15.29% (S3), while total nitrogen content extended from 544.93 kg/ha (S4) to 777.72 kg/ha (S3), suggesting distinct site-specific disparities in organic matter. Macronutrient concentrations also exhibited variation: phosphorus levels ranged from 14.05 mg/kg (S2) to 43.68 mg/kg (S5), potassium levels spanned from 96.42 mg/kg (S3) to 241.15 mg/kg (S4), and sulphur concentrations varied from 12.56 ppm (S2) to 24.36 ppm (S5), indicating heterogeneity in nutrient accessibility. Concentrations of trace elements also varied significantly among the sampling sites. Zinc levels ranged from 0.34 ppm (S4) to 4.19 ppm (S5), boron concentrations varied from 42.77 ppm (S4) to 184.51 ppm (S3), iron levels fluctuated between 2.86 ppm (S4) and 63.85 ppm (S1), manganese concentrations ranged from 0.32 ppm (S3) to 1.25 ppm (S5), and copper levels extended from 0.10 ppm (S4) to 0.30 ppm (S5). These findings underscore the significant spatial heterogeneity present in the chemical characteristics of soil, which may have implications for nutrient cycling and microbial communities. 3.2. Analysis of Illumina sequencing data A total of 285,026,676 raw reads of 5 libraries were obtained from Illumina sequencing. After filtering out low-quality reads, 282,911,610 clean reads were identified, which account for more than 99.25% of the raw reads (Table 2 ). After assembly, 43,12,977 contigs were assembled into 5 metagenome assemblies. N50 value among metagenome assemblies ranged from 433bp for S5 to 489bp in S2. The N50 statistics revealed that, on average, for 5 assemblies, more than half of the contigs were longer than 466bp. The longest contig of all genes was 16,697bp, and the shortest was set as 200bp. After the removal of redundancy, 41,19,679 sequences remain across the assemblies, with the highest in S4 (10,62,346) and the lowest in S5 (6,12,744). Table 2 Assembly statistics of metagenome assemblies. Sample Raw reads Clean reads %age clean reads GC content (%) Assembled Contigs Nonredundant sequences N50(bp) Seq > 1k S1 56134062 55746986 99.31 62 784047 775649 460 26386 S2 65455854 64946284 99.22 61 1116306 1036971 489 49691 S3 41423454 41026752 99.04 61 641228 631969 478 23301 S4 70391262 70018786 99.47 60 1155621 1062346 472 44838 S5 51622044 51172802 99.13 63 615775 612744 433 10284 Total 285026676 282911610 99.26 61.4 4312977 4119679 466 30900 3.3. Microbial community composition along elevation Principal coordinate analysis (PCoA) of soil microorganisms was performed to compare the microbial community among different samples (Fig. 2 A). The two main coordinates explained 87.99% of the microbial community changes among all the samples, of which PC1 explained 72.36% of the variation; PC2 explained 15.63% of the variation. Meanwhile, PCoA analysis and UPGMA-based hierarchical clustering of the samples revealed that they clustered into two distinct groups. Samples S2, S3, and S4 were closely related due to their similar microbial community structures, while S1 and S5 were distantly placed (Fig. 2 A and B). This suggests that the structure of the microbial community in the Betula utilis forests in the western Himalayan treeline ecotone represents similarity among them, while communities below and above were significantly distinct from them. For the total microbial community, 47 phyla, 93 classes, 200 orders, 417 families, 1367 genera, and 4394 species were detected by metagenomic sequencing across five samples (Fig. 2 C, Table 3 ). Among all samples, 3225 species were detected, with the greatest number of species found in S4, followed by S5 (3196), S2 (3162), and S1 (3137). At the same time, the lowest number of species was detected in S3 (2791). Out of 4394 species, 1772 were common in all the soil samples (Fig. 2 C). Moreover, alpha diversity indices revealed significant differences among different microbial communities. Sample S1 exhibits the highest diversity and evenness (Shannon index = 3.98, Simpson index = 0.88) (Fig. 2 E, Supplementary file-I Sheet1). In terms of species richness, S2 had the highest estimated richness (Chao1 = 3351.03) with observed species count (2734), suggesting a more complex community structure compared to others. (Detailed krona plots of all 5 communities are given in the Supplementary file - II, from plot1 to plot5). Table 3 Total microbial community statistics across all five soil samples. Samples Phyla Classes Orders Families Genus Species S1 39 79 169 351 1046 3137 S2 41 78 168 350 1046 3162 S3 38 78 164 328 952 2791 S4 41 83 173 356 1073 3225 S5 39 78 170 351 1052 3196 Community 47 93 200 417 1367 4394 3.4. Bacterial community composition PCoA analysis at the phylum level was performed to compare the bacterial community in different samples. The two main coordinates explained more than 87% of the change among the samples (Fig. 3 A). In this S2, S3, and S4 are placed distantly from S1 and S5, suggesting that the structure of the bacterial community was altered along the elevation. A total of 4234 species of 35 phyla of bacteria were detected in five samples, of which S5 possessed the highest number of bacterial species (3125) and S3 the lowest (2754) (Supplementary file-I Sheet2). Among all, the dominant phyla across samples were Pseudomonadota, Myxococcota, Dictyoglomota, Desulphobacterota, Actinomycetota, Spirochetata, Plenktomycetota, Bacteroidota, and Bacillota, with relative abundance greater than 0.5 per cent (Fig. 3 B). The sum of these phyla accounted for more than 70% of the bacteriome, in which Pseudomonadota (42.6%) accounted for the largest proportion, followed by Myxococcota (12.6%) and Dictyoglomota (4.6%) (Supplementary file-I Sheet3). Moreover, the relative abundance of Bacillota, Bacteroidota, and Desulphobacterota successively increased, while Psuedomonadota, Myxococcota and Spirochetota significantly declined along the increase in elevation. The microbial community was analysed at the genus level to gain more insight into the variation in microbial abundance and composition along the elevation gradient in the treeline ecotone. Bacterial community composition across elevation showed broadly consistent trends, dominated by a few abundant genera such as Azorhizobium, Buchnera, Shewanella , and Dictyoglomus (Fig. 3 D, Supplementary file-I Sheet4). Whereas, a substantial portion of the community (more than 70%) comprised other low-abundant genera. Azorhizobium maintains relatively high dominance in all samples, indicating its ecological dominance in the system. Further, very few species of archaea were reported in all soil samples. The highest 4 species of archaea were reported in S3, followed by S1 and S5 (3 species), S2 (2 species) and S4 (1 species). 3.5. Eukaryotic taxonomic composition A total of 160 species of eukaryotes belonging to 101 genera of 12 phyla were detected across all five soil samples (Supplementary file-I Sheet5). The highest number of eukaryotic read counts was observed in S2 (105) and the lowest in S3 (57) (Fig. 4 A, Supplementary file-I Sheet6). Species richness varied considerably in different metagenomes, with the highest diversity observed in sample S5, which contained 71 eukaryotic species, while the lowest diversity was recorded in S3, with only 37 species (Supplementary file-I Sheet5). Three species were shared among all 5 soil samples, while a higher number of eukaryotic species were exclusive among different elevations. Soil sample S5 was found to possess the highest number of exclusive eukaryotic species (24), whereas S1 and S3 possessed the least (6 species) (Fig. 4 B). Taxonomic composition at the higher ranks revealed three major eukaryotic groups that dominated all metagenomes: Fungi, Viridiplantae (green plants), and Metazoa (animals). Based on the read counts, fungi were the dominant group among eukaryotes in extreme microbiomes (S1 and S5), whereas Viridiplantae and Metazoans were highest at the ecotones (S2 and S4). 3.6. Potential functional pathways in the Himalayan treeline Functional annotation for 43,12,997 assembled sequences across assemblies was assigned using HUMAnN v3.9 based on the UniRef90 database to compare the relative abundance of potential functions along elevation in the treeline microbiome. A total of 193 MetaCyc level 3 metabolic pathways were annotated from 5 samples. S2 was found to have the largest number of pathways, 179, and S1 has the lowest, 55. As shown in the Venn diagram (Fig. 5 A), out of 193 total pathways, 38 pathways were shared among all five communities. The largest number of exclusive pathways was found to be 25 in S2, while there were only 2, 4, and 3 unique pathways in microbial communities S3, S4, and S5, respectively. Among level 3 MetaCyc annotated pathways most abundant were nucleotide biosynthesis and salvage pathways (pyrimidine deoxyribonucleotide phosphorylation, adenosine deoxyribonucleotide de novo biosynthesis II, and guanosine deoxyribonucleotide de novo biosynthesis II), folate derivatives and amino acid metabolism pathways (folate transformation II), and fatty acid biosynthesis (cis-vaccenate biosynthesis) pathways (Fig. 5 B and C). Furthermore, assembled sequences were assigned to a total of 1,49,196 gene families, out of which 103 were shared among all microbial communities (Fig. 6 A). Among all S2 exhibits the highest number of exclusive gene families, i.e. 69,581, which is followed by S4 (21,329), S3 (11,270), and S5 (7758), respectively. S1 showed the smallest number of unique gene familes i.e. 7228. Among the most abundant gene families were those involved in core cellular functions such as transcription, translation, and energy metabolism (DNA binding, DNA binding TF activity, rRNA binding, etc.) (Fig. 6 B and C). Multivariable association analysis was conducted on level 3 MetaCyc pathways to identify pathways with significant differences in abundance with elevation and between forested and alpine ecosystems. Persistent differences in pathway patterns in correspondence with elevation were revealed (Fig. 7 A). Higher elevations are primarily associated with ethanolamine-utilisation, ornithine degradation, proline metabolism and rubisco shunt pathways, with effect size coefficients (EC) 2.06, 1.57, 1.49, and 1.05, respectively. While tetrapyrole (EC = -1.07), inosine-5-phosphate (EC = -0.90), guanosine (EC = -0.84), and adenosine biosynthesis (EC = -0.59) pathways are prominent in lower elevations. Additionally, comparing the pathway abundance in forested and alpine microbiomes, the alpine community is primarily associated with oleate biosynthesis-IV (EC = 1.68), fatty acid elongation (EC = 1.63), phosphopantothenate (EC = 1.54), and L-isoleucine biosynthesis pathways (EC = 1.53). In contrast, the forested microbiome was mainly associated with guanosine, adenosine, and glycogen degradation (EC value − 4.22, -3.97, -1.94, respectively), as well as flavin and chorismate biosynthesis pathways with EC values equal to -2.47 and − 1.57, respectively (Fig. 7 B). 3.7. Effect of environmental factors on microbial community composition and functional pathways RDA and Spearman’s correlation analysis were employed to elucidate the environmental determinants that influence microbial composition and functional pathways within soil samples. Following Spearman’s correlation analysis, the environmental variables exhibiting high correlations were excluded from further investigation. The findings from RDA showed that the two RDA components, RDA1 and RDA2, account for 82.2% and 9.3%, respectively, of the total variance in microbial communities (Fig. 8 A), alongside 45.1% and 33.7% in functional pathways (Fig. 8 C). Furthermore, we assessed the relative contributions of environmental variables in modulating microbial communities and functional pathways. Iron (Fe), electrical conductivity (EC), and moisture content (MC) were identified as the primary contributors to the structure of microbial communities, displaying relative contributions of 21.51%, 16.13%, and 15.59%, respectively (Fig. 8 B), whereas Fe (12.81%), zinc (Zn) (11.17%), and MC (10.77%) were the most influential on the functional pathway associations with soil (Fig. 8 E). Moreover, Spearman’s correlation analysis elucidated the associations between level 3 MetaCyc functional pathway traits and environmental factors (Fig. 8 D). The analysis demonstrated that pathways associated with nucleotide biosynthesis exhibited positive correlations with both MC and EC. The ethanolamine utilization pathway (r = 0.974) revealed a substantial positive correlation with MC, while the biosynthetic pathways for adenosine and guanosine deoxyribonuclease (r = 0.5 and r = 0.6, respectively) displayed positive correlations with N (Supplementary file-I Sheet7). Additionally, the super pathways of adenosine nucleotide de novo biosynthesis-II (r = 1) and pyrimidine nucleobases salvage (r = 0.90) were positively correlated with EC. Conversely, the glycolysis pathway (r = -0.9), along with the putrescine biosynthesis, lysine biosynthesis, and pyruvate fermentation pathways, demonstrated significant negative correlations with the environmental TOC, Cu, and Zn, respectively (Supplementary file-I Sheet7). 4. Discussion Vegetation and elevation gradients are key drivers shaping soil microbial community structure in mountain treeline ecotones (Xu et al., 2014). The structure of soil microbial communities is influenced by various abiotic and biotic factors, such as soil properties, climate, and vegetation types, along an elevation gradient in mountains (Cui et al., 2016; Sun et al., 2020). The present study comprehensively analyses soil microbial communities along an elevational gradient in the Western Himalayan treeline ecotone, deciphering the distinct patterns in their composition, diversity, and taxonomic structure. Moreover, a detailed analysis of soil physicochemical variables revealed significant spatial heterogeneity, providing critical insights into the environmental factors that regulate microbial community structure and metabolic capabilities across different elevations. The observed variability in soil properties across the five sampling sites underscores the spatial heterogeneity of the edaphic environment at different elevations in the region. Soil pH values consistently indicated an acidic environment (pH range 4.56 to 5.16) (Fig. 1 ), a characteristic commonly associated with mountain ecosystems and known to affect microbial diversity, nutrient solubility, and enzymatic activity (Rousk et al., 2010; Tripathi et al., 2018). Despite relatively consistent EC values, indicating similar ionic strengths, other parameters, such as MC, TOC, and total N, exhibited substantial differences among sites. The elevated TOC and N levels at specific sites (e.g., S3) may reflect localised enrichment through litter deposition or slower decomposition rates at higher elevations (alpine), both of which are known to shape microbial community assembly by promoting copiotrophic taxa (Bahram et al., 2018; Fierer et al., 2007). Macronutrient concentrations (N, P, K) also displayed site-specific differences. Such heterogeneity likely modulates microbial metabolic pathways and plant-microbe interactions by altering nutrient availability, especially in nutrient-limited soils (Dalling et al., 2016). For instance, elevated P and S levels at site S5 may enhance microbial pathways related to phosphorus solubilisation and sulphur oxidation, processes that have been linked to microbial niche differentiation in complex soil environments (Li et al., 2014). Furthermore, the distribution of trace elements, including iron, zinc, boron, manganese, and copper, illustrates the geochemical diversity across sites. Elevated Fe at S1 and B at S3 may result from differences in mineral composition or microclimatic conditions affecting element mobilisation. While these micronutrients are essential for microbial enzymatic functions, their excess concentrations can be inhibitory or toxic, potentially acting as selective forces that shape microbial community composition and functionality (Delgado-Baquerizo et al., 2016; Giller et al., 1998). Such microenvironmental gradients may lead to local adaptation of microbial taxa and contribute to the spatial partitioning of microbial functional traits, consistent with niche theory and biogeographic structuring observed in other mountainous soil systems (Looby & Martin, 2020). Microbial community composition and diversity are associated with soil health and vegetation type (Han et al., 2021). Diverse studies have shown significant differences in microbial biomass and composition depending on soil traits and vegetation canopy (Kara et al., 2008; Ren et al., 2018). Our findings highlight the substantial impact of elevation and associated environmental factors on microbial groups in alpine treeline ecosystems. PCoA and UPGMA clustering showed a prominent bifurcation in microbial community composition, with mid-elevation microbiomes clustering together, distinct from S1 and S5 (Fig. 2 A and B). This segregation suggests that microbial community composition is not consistent along elevation, likely due to variations in temperature and soil physicochemical properties, influenced by vegetation types, which is consistent with various previous studies (Ren et al., 2018). Taxonomic analysis revealed a resilient core microbiome (1772 common species in all samples) with few exclusive taxa (Fig. 2 C), in line with the previous reports of diversity analysis along the elevation gradient (Adamczyk et al., 2019; Gu et al., 2024; Peng et al., 2022). Among the five microbiomes, the highest species richness was observed in S4, followed by S2, while S3 exhibited the lowest. This trend aligns with findings from other montane ecosystems, where microbial diversity peaks at the transition zone between tree and herbaceous ecosystems (He et al., 2008; Hu et al., 2018; Shen et al., 2021). Pseudomonadota, Myxococcota, and Dictyoglomota were the dominant taxa at the phylum level, representing over 70% of the bacterial assemblage across samples. Notably, the relative abundance of Bacillota, Bacteroidota, and Desulphobacterota exhibited an upward trend with increasing elevation, whereas Pseudomonadota and Myxococcota experienced a decline. These alterations may signify adaptive responses to fluctuating environmental parameters, including temperature and nutrient availability, along the elevation gradient (Salinas et al., 2021). Furthermore, an analysis at the genus level disclosed the dominance of Azorhizobium, Buchnera, Shewanella , and Dictyoglomus . The persistent occurrence of Azorhizobium , a well-known nitrogen-fixing bacterium, indicates a strong potential for biological nitrogen input, which is critical in nutrient-limited high-altitude soils and may support plant–microbe symbioses in these ecosystems (Lüttge, 2008). The dominance of Buchnera , Shewanella , and Dictyoglomus suggests active insect–microbe interactions, redox-driven metal cycling, and organic matter decomposition, respectively. This highlights the community’s functional adaptation to nutrient dynamics and environmental stressors. Also, eukaryotic diversity exhibited variability across samples, with S3 demonstrating the greatest richness. Furthermore, the considerable representation of low-abundance bacterial and fungal genera highlights a complex and diverse microbial community, which may contribute to the resilience of the ecosystem (Chen et al., 2024; Pedrinho et al., 2024). Therefore, this predominant functional diversity likely plays a crucial role in maintaining ecosystem stability, facilitating nutrient cycling, and enabling microbial responses to environmental fluctuations across the treeline ecotone. Different metabolic pathways drive specific physiological functions, enabling microbes to adapt to various environmental conditions. These functional adaptations shape microbial community structure and ecosystem roles, especially across environmental gradients. The functional annotation of the treeline soil microbiomes reveals a highly diverse and spatially structured functional framework, reflecting ecological adaptations to elevation gradients and vegetation zones. The identification of 193 level 3 MetaCyc metabolic pathways, with only 38 shared across all five sites (Fig. 5 A), indicates substantial functional turnover along elevation as observed previously (Dragone et al., 2022; Li et al., 2025). The highest pathway richness in S2, along with its large number of unique pathways, suggests it as a metabolically versatile community potentially shaped by transitional environmental conditions or mixed vegetation influence (Bahram et al., 2018; Chen et al., 2024). The dominance of nucleotide biosynthesis and salvage pathways, folate metabolism, and fatty acid biosynthesis among the most abundant functions highlights a focus on core cellular processes that ensure DNA replication, amino acid processing, and membrane formation, essential for microbial survival under fluctuating environmental pressures (Goyal et al., 2021; Wani et al., 2022). This foundational metabolic activity supports microbial community persistence and adaptability in the dynamic treeline ecosystem. The gene family annotation further supports this view, with mostly exclusive genes, only 103 shared among all communities. The prevalence of gene families related to transcription, translation, and energy metabolism further emphasises the importance of fundamental biological processes across all communities. Further, varying environmental conditions from the subalpine and alpine zones are regarded as important factors regulating soil microbial extracellular enzymatic activity (Liu et al., 2023; Ren et al., 2021). Multivariable association analysis reveals clear functional stratification with elevation as well as different vegetation zones. High-elevation communities are enriched in stress-related and specialised pathways such as ethanolamine utilisation, ornithine and proline degradation, and the rubisco shunt, indicative of adaptations to cold, nutrient-limited, and low-oxygen conditions. Conversely, lower elevations support biosynthesis-heavy functions like tetrapyrrole and purine nucleotide production, reflecting richer nutrient availability and potentially higher microbial growth rates. Moreover, comparative analysis between forested and alpine zones uncovers ecosystem-specific metabolic profiles. Alpine soils are enriched in pathways involved in lipid metabolism and amino acid biosynthesis, reflecting microbial strategies to maintain membrane integrity and biosynthesis under extreme conditions. Forested soils, on the other hand, are associated with carbohydrate degradation and purine biosynthesis, consistent with higher organic matter inputs and more active microbial proliferation. The results from the RDA and Spearman’s correlation collectively highlight the pivotal role of abiotic factors in shaping the microbial taxonomic and functional structure of treeline soil communities. The high explanatory power of the RDA components underscores a strong environmental filtering effect across the elevation gradient. Specifically, iron, electrical conductivity, and moisture content emerged as dominant drivers influencing both microbial composition and metabolic function. These findings are consistent with previous studies that identified redox-active elements (such as Fe and Zn) and hydrological variables (like MC) as major regulators of soil microbial diversity and functional potential in alpine and forested ecosystems (Fierer et al., 2012; Liu et al., 2023; Shen et al., 2021). Spearman’s correlation further supports this, revealing distinct associations between environmental parameters and key metabolic pathways. The strong positive relationship between MC and the ethanolamine utilisation pathway (r = 0.974) suggests microbial adaptations for utilising alternative nitrogen sources in moist conditions, a trait known to confer ecological advantage under fluctuating nutrient availability (Krysenko & Wohlleben, 2022). Similarly, positive correlations between EC and pathways such as pyrimidine nucleobase salvage (r = 0.90) and adenosine nucleotide biosynthesis-II (r = 1) point to increased osmotic stress tolerance and heightened DNA repair or replication activities in response to saline or ion-rich conditions (Zhang et al., 2021). Conversely, pathways involved in glycolysis and amino acid biosynthesis (e.g., putrescine and lysine) showed significant negative correlations with TOC, Cu, and Zn. These inverse relationships may reflect metal toxicity or carbon-saturation effects suppressing specific microbial metabolic strategies (Li et al., 2022; Ma et al., 2022). Overall, these findings underscore how edaphic variables modulate microbial ecological functions, leading to distinct metabolic adaptations in treeline soils. Therefore, the microbial communities in mountain environments are not only structured by elevation-related climatic gradients but are finely tuned to local soil chemistry and microhabitat conditions. 5. Conclusion This study provides a comprehensive insight into the microbial community structure and functional potential in the treeline ecotone of the western Himalaya. The marked spatial differences in soil properties were found to significantly impact both taxonomic composition and metabolic functionality of the microbial community. High-throughput metagenomic analysis revealed a resilient core microbiome with distinct site-specific taxa. Notable dominance of bacterial groups such as Azorhizobium , Buchnera , and Shewanella was found, reflecting their adaptive roles in nitrogen fixation, organic matter decomposition, and redox-sensitive metal cycling. Functional profiling identified fundamental cellular pathways, including nucleotide biosynthesis, folate metabolism, and fatty acid biosynthesis, as prevalent across all elevations, emphasising the importance of foundational metabolic processes for microbial community survival under harsh environmental conditions. Additionally, stress-adaptive pathways such as ethanolamine utilisation and amino acid degradation were elevated in high-elevation communities, suggesting functional plasticity in response to a cold climate. Redundancy analysis and correlation modelling identified iron, moisture content, and electrical conductivity as principal environmental determinants of microbial community composition and functional gene prevalence. Collectively, these results elucidate the complex interrelationship between edaphic variables and microbial functional ecology, providing a valuable framework for elucidating ecosystem dynamics and biogeochemical cycling in high-altitude habitats amidst shifting climatic paradigms. Declarations All authors have read, understood, and have complied as applicable with the statement on “Ethical responsibilities of Authors” as found in the Instructions for Authors. Competing interest: The authors declare no competing interest. ACKNOWLEDGEMWNTS The authors are grateful to the Central University of Punjab for providing the necessary infrastructure and financial support to conduct the study. VS, HSG, MAC, and SR thank CSIR and UGC for providing financial support toward their PhD degree. Funding: Not Applicable. Author Contribution Conceptualisation and overall supervision: PB; Data analysis: VS, HSG, MAC; Writing- original draft: VS; Writing- review and editing: PB, HSG, MAC, SR, and VS. 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Environmental microbiology , 23 (2), 1020-1037. Additional Declarations No competing interests reported. Supplementary Files SupplementaryfileI.xlsx SupplemenrtaryfileII.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7306065","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":501354228,"identity":"a402b9b2-864f-4937-bcac-348a4a00e1a6","order_by":0,"name":"Vikas Sharma","email":"","orcid":"","institution":"Central University of Punjab","correspondingAuthor":false,"prefix":"","firstName":"Vikas","middleName":"","lastName":"Sharma","suffix":""},{"id":501354229,"identity":"2d8b8500-9027-4fde-bf80-d764678c05e0","order_by":1,"name":"Hari Shankar Gadri","email":"","orcid":"","institution":"Central University of Punjab","correspondingAuthor":false,"prefix":"","firstName":"Hari","middleName":"Shankar","lastName":"Gadri","suffix":""},{"id":501354230,"identity":"0db450f9-ea27-41b2-a51c-f167eb9b2b28","order_by":2,"name":"Md. 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(EC = Electrical Conductivity; TOC = Total Organic Carbon; N = Available Nitrogen; P = Available Phosphorous; K = Available Potassium; S = Available Sulphur; Zn = Available Zink; B = Available Boron; Fe = Available Iron; Mn = Available Manganese; Cu = Available Copper)\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7306065/v1/624b303637d41bbd437a5e0a.png"},{"id":91706805,"identity":"cb87f6bf-2000-4e98-8e10-14d2e2fbd552","added_by":"auto","created_at":"2025-09-19 11:42:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":477022,"visible":true,"origin":"","legend":"\u003cp\u003eTotal microbiome diversity along elevation. (A) Bray-Curtis PCoA plot of microbial communities at the species level among 5 samples. (B) Phylogenetic clustering of samples based on microbial communities. (C) A Venn diagram of exclusive and shared microbial species among five soil samples. (D) PCoA plot of species of different microbial communities. (E) Comparison of the alpha diversity of microorganisms across communities based on Shannon and Simpson indices.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7306065/v1/8730353ffca7e70fb5da3655.png"},{"id":91705885,"identity":"006014ee-b5ea-4e3b-b918-ce821dd3dbb6","added_by":"auto","created_at":"2025-09-19 11:34:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":336758,"visible":true,"origin":"","legend":"\u003cp\u003eThe microbial composition of bacterial taxa in five soil samples. (A) Bray-Curtis PCoA plot of the bacterial community at the phylum level. (B) Bacterial community structure at the phylum level. (C) Venn diagram of exclusive and shared bacterial species across five samples. (D) Composition of bacterial community at the genus level.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7306065/v1/b542b6c4b87b9337f98eb3ad.png"},{"id":91707011,"identity":"caadbae7-ab93-4b6c-b0bc-fd5c58951a8f","added_by":"auto","created_at":"2025-09-19 11:50:26","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":229961,"visible":true,"origin":"","legend":"\u003cp\u003eTaxonomic composition of eukaryotes. (A) Read count-based abundance of various dominant eukaryotic groups in different samples. (B) A Venn diagram of exclusive and shared eukaryotic species among samples.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7306065/v1/931f6f316ba1ca034ff570f4.png"},{"id":91706806,"identity":"a8243373-3f47-4b7a-b34f-ddc667dd3e5b","added_by":"auto","created_at":"2025-09-19 11:42:26","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":501443,"visible":true,"origin":"","legend":"\u003cp\u003ePotential functional pathways depiction (A) Venn diagram of shared and exclusive functional pathways among samples, (B) Top 10 most abundant pathways across samples and their relative abundance, (C) Heat map of top 30 most abundant pathways across microbial communities.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7306065/v1/6493ca84c5741d8a021f27e9.png"},{"id":91707896,"identity":"c745f89c-3f8e-4916-8205-f2729710e830","added_by":"auto","created_at":"2025-09-19 11:58:26","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":446960,"visible":true,"origin":"","legend":"\u003cp\u003eGene family depiction across microbial communities (A) Venn diagram of shared and exclusive gene families, (B) Top 15 most abundant gene families across samples and their relative abundance, (C) Heat map of top 15 most abundant gene families across microbial communities.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7306065/v1/c0bfd30efa2b7171ba0cb4d6.png"},{"id":91705887,"identity":"307e10d6-541b-43f1-a6ec-54cff7b92161","added_by":"auto","created_at":"2025-09-19 11:34:26","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":830688,"visible":true,"origin":"","legend":"\u003cp\u003eMaAsLin2-based multivariable association analysis of level 3 MetaCyc pathways (A) Bar plot representing top pathway association with elevation, (B) Bar plot representing top pathways enriched in forested and alpine microbiomes.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7306065/v1/d6211e1082d7ca0614922583.png"},{"id":91705883,"identity":"3d079ef3-3401-4a95-a9df-e937cc1c745f","added_by":"auto","created_at":"2025-09-19 11:34:26","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":568706,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of environmental variables on microbial community composition and pathway abundance. Redundancy analysis plot of the correlation of environmental variables with microbial community (A) and pathways abundance (C). Relative contribution of environmental variables in shaping microbial community (B) and pathways abundance (E). (D) The heatmap of the correlation between the MetaCyc pathway trait and the physicochemical properties of soil (The significant correlations are presented with asterisks, * p \u0026lt; 0.05, **p \u0026lt; 0.01).\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-7306065/v1/9a6a307381463c57d72cae2a.png"},{"id":93520680,"identity":"eb5cfaf2-af9b-45f9-ba5d-25447c64613e","added_by":"auto","created_at":"2025-10-14 17:46:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4605376,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7306065/v1/0bd7b6aa-61b0-40fd-82be-a0f563c30637.pdf"},{"id":91705880,"identity":"aecb4f22-650e-4b9f-824f-bef672e1fb2c","added_by":"auto","created_at":"2025-09-19 11:34:26","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":33324,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryfileI.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7306065/v1/99faa4421e52ec1e81a202c8.xlsx"},{"id":91705889,"identity":"4dd5a91a-33c9-4570-b2e7-5e0506b7d99b","added_by":"auto","created_at":"2025-09-19 11:34:26","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2129537,"visible":true,"origin":"","legend":"","description":"","filename":"SupplemenrtaryfileII.docx","url":"https://assets-eu.researchsquare.com/files/rs-7306065/v1/0fca2609a2b1b186459eca74.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Environmental Drivers of Soil Microbial Diversity and Metabolic Potential Across the Western Himalayan Treeline","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe distribution of microbial communities along elevation gradients and the underlying drivers of these patterns are fundamental concerns of biogeography (Hanson et al., 2012; Xiong et al., 2022). Microbes play a vital role in the biosphere by regulating key soil biogeochemical processes such as decomposition and nutrient transformation, thereby influencing the composition and dynamics of the plant community (Aislabie et al., 2013; Basu et al., 2021). Microbes play a vital role in the biosphere by regulating key soil biogeochemical processes such as decomposition and nutrient transformation, thereby influencing the composition and dynamics of the plant community (Aislabie et al., 2013; Basu et al., 2021). Despite their ecological significance, our understanding of how microbial communities vary with elevation, particularly with their functional roles in soil processes, remains limited and often contradictory. Currently, many studies have focused on microbial diversity across elevation and detected many noticeable trends in soil microbial α-diversity, but most of the results were inconsistent (Singh et al., 2012). Since the first study on microbial elevational distribution by Bryant et al. (2008), research on microbial diversity across elevations has expanded significantly. While many studies have observed clear trends in soil microbial α-diversity with elevation, their findings remain inconsistent (Fu et al., 2023; Peng et al., 2022; Singh et al., 2013; Wang et al., 2018). For example, some studies report a hump-shaped pattern of microbial α-diversity, often attributed to the mid-domain effect (Miyamoto et al., 2014; Singh et al., 2013). Others have identified U-shaped or declining diversity patterns, which are largely associated with soil pH variation (Shen et al., 2021; Wang et al., 2015). However, despite the increasing number of studies along broad elevational gradients, microbial diversity within ecologically critical transition zones (treeline ecotones) remains largely unexplored. These narrow yet climatically sensitive interfaces between subalpine forest and alpine tundra are often overlooked, even though they are likely to harbour distinct microbial communities shaped by abrupt environmental transitions.\u003c/p\u003e\u003cp\u003eThe alpine treeline ecotone is defined as the upper limit of the forest in the high-mountain ecosystem, representing dynamic interfaces between subalpine and alpine zones where climatic and environmental gradients strongly influence microbial biodiversity patterns (Kui et al., 2019). Unique ecological characteristics, including the freeze-thaw cycle, high solar radiation, low nutrient supply, and limited water availability, primarily characterise these sites. Due to these extreme environmental conditions and narrow ecological margins at alpine treeline ecotones, even slight environmental changes can induce significant ecosystem shifts, such as species migration and replacement (Kupfer \u0026amp; Cairns, 1996; Liu et al., 2009). Among the most sensitive indicators of such environmental shifts are soil microbial communities (De Vries \u0026amp; Shade, 2013). As elevation increases, changes in environmental variables such as temperature, soil properties, and moisture availability, vegetation community structure can alter the soil microbiota composition and functional potential. Despite growing recognition of microbial contributions to ecosystem processes, their diversity and distribution across elevation gradients, especially in alpine treeline ecotones, remain understudied.\u003c/p\u003e\u003cp\u003eThis study was conducted on the alpine treeline ecotone in the Western Himalayas. While the region has been relatively well studied for vegetation dynamics, climate-driven treeline shift, and plant biodiversity (Kumar \u0026amp; Khanduri, 2024; Maletha et al., 2022; Rawat et al., 2021), but soil microbial community, particularly in the sensitive transitional zone between subalpine and alpine ecosystems, have received far less attention. Most microbial ecology research in the Himalayas has either concentrated on broad elevational gradients, specific functional groups, or high-altitude alpine soil (Kumar et al., 2019; Mushtaq et al., 2023; Rathore et al., 2022), with minimal focus on the ecotone zone where abrupt shifts in vegetation, soil condition, and climate converge. Soil physicochemical properties and alternative vegetation types along the elevation in the treeline ecotone, spanning subalpine to alpine, may alter the microbial community structure and their functional potential. Therefore, this study aims to characterise and compare the taxonomic and functional diversity of soil microbial communities across a subalpine to alpine elevation gradient, providing insights into how soil characteristics and microbial communities respond to ecological transitions shaped by altitude and the vegetation types in the Western Himalayas.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Sample collection and site description\u003c/h2\u003e\u003cp\u003eThe treeline forest soil samples were collected from Chhitkul, Himachal Pradesh, India (longitude 78.441541 E, latitude 31.344609 N), in August 2023. The average annual rainfall of the study site is between 816 mm, and the yearly temperature is below 10\u0026ordm;C. Treeline in this part of the Western Himalayas is inhabited by \u003cem\u003eBetula utilis\u003c/em\u003e forests, making a mixed community with \u003cem\u003eRhododendron campanulatum\u003c/em\u003e (Singh et al., 2010). Above the treeline, diverse herbaceous species thrive, providing a contrast to the subalpine woody vegetation, whereas sub sub-treeline region is covered with dense \u003cem\u003ePinus gerardiana\u003c/em\u003e and \u003cem\u003ePinus wallichiana\u003c/em\u003e timberline forest.\u003c/p\u003e\u003cp\u003eA systematic sampling approach was employed to investigate the soil microbial diversity along an elevation gradient in the treeline ecotone. Five samples were collected along the gradient from 3390 m to 3795 m (each sample after 100 m), covering the lower timberline forest (S1), treeline forest (mixed tree-shrub community, S2), subalpine region (S3), alpine treeline ecotone (S4), as well as the herbaceous alpine zone (S5) (Detailed site descriptions are given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). With the help of a soil digger, three replicates from standardised depths of 0\u0026ndash;10 cm, 10\u0026ndash;20 cm, and 20\u0026ndash;30 cm were obtained at each site and then mixed as described by Carter and Gregorich (2007). Three replicates of soil samples were collected from each site. To prevent contamination, surface debris such as vegetation and litter was carefully removed before sampling. GPS coordinates, altitude, slope, and aspect were recorded for each. The collected soil samples were kept in sterile airtight polybags and transported to the laboratory. The samples were sieved through a 2-mm mesh and homogenised. Then, soil samples were divided into two parts; one part was air-dried to analyse soil physicochemical properties, while the other was stored at -80\u0026ordm;C for DNA extraction.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDetails of the locations of sampling sites.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSamples\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVegetation type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLongitude (E)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLatitude (N)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAltitude (m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSlope (\u0026ordm;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAspect\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTimberline forest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e78.442277\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.348311\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3390\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMixed shrub-tree community\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e78.442665\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.345995\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3486\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSubalpine treeline\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e78.441541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.344609\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3591\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAlpine treeline ecotone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e78.440893\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.343271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3694\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHerbaceous alpine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e78.440176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.340606\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3795\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNW\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Analysis of soil physicochemical properties\u003c/h2\u003e\u003cp\u003eThe pH level and electrical conductivity (EC) of the soil samples were assessed in a 1:2.5 (w/v) soil-to-water suspension employing an automated LCD pH meter (PF20 Standard Kit, Mettler Toledo, USA). The total nitrogen (N) concentration was determined with the Kjeldahl digestion technique, using a Kjeldahl apparatus, with the alkaline permanganate method. The total organic carbon (TOC) was evaluated employing a TOC analyser (TOC-L CPH/CPN, Shimadzu, Japan) after the elimination of inorganic carbon in alignment with the EPA Method. The quantification of available phosphorus (P) was executed through a UV-Visible spectrophotometer using Bray\u0026rsquo;s P-1 method due to acidic soil conditions (Bray \u0026amp; Kurtz, 1945). Available potassium (K) was determined using an atomic absorption spectrophotometer (Analyst 600, Perkin-Elmer, USA). Further, micronutrients - sulphur (S), zinc (Zn), boron (B), iron (Fe), manganese (Mn), and copper (Cu) concentrations, were analysed using Inductively Coupled Plasma Mass Spectrometry (ICP-MS) following microwave-assisted acid digestion employing nitric and hydrochloric acids, under U.S. EPA Method 3052.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Soil DNA extraction and sequencing\u003c/h2\u003e\u003cp\u003eA HiPurA\u0026reg; Soil DNA Extraction Kit (HiMedia Laboratories Private Limited, Maharashtra, India) was used to extract genomic DNA from treeline soil samples following the manufacturer\u0026rsquo;s instructions. The DNA concentration and quality were measured by a Jenway Genova Nano Microvolume Spectrophotometer and 1.2% agarose gel electrophoresis. The DNA from three replicates of each site was pooled together for sequencing. The paired-end libraries were prepared using the Twist NGS Library Preparation Kit (Twist Biosciences Corporation, South San Francisco, CA, USA). Adapters containing the full complement of sequencing primer hybridisation sites were ligated to the fragments. Following the quality and quantity assessment of the libraries, these libraries were subjected to Shotgun sequencing through the Illumina NovaSeq 6000 platform. Finally, raw metagenomics datasets were deposited in the NCBI Sequence Read Archive (SRA) database with a Bio Project ID: PRJNA1279859.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Metagenomic assembly and gene annotation\u003c/h2\u003e\u003cp\u003eFirstly, adaptors and low-quality metagenomic reads (quality score\u0026thinsp;\u0026lt;\u0026thinsp;20) were trimmed out using Trimmomatic v0.39 (Bolger et al., 2014). Subsequently, contigs and scaffolds were assembled for each sample using Megahit v1.2.9 (Li et al., 2015) with default parameters, specifying a minimum contig size of 200 bp. Completeness of assemblies and read representation were calculated using BUSCO v5 (Manni et al., 2021). The sequences with an identity and coverage\u0026thinsp;\u0026ge;\u0026thinsp;90% were clustered to construct a non-redundant sequence catalogue using CD-HIT v4.8.1 (Fu et al., 2012). Taxonomic classification was performed using Kraken2 v2.1.3 (Wood \u0026amp; Salzberg, 2014), a k-mer-based classifier, against the standard Kraken2 database. To assess the functional capacity of the communities, functional classification for the high-quality reads was assigned using HUNAnN (HMP Unified Metabolic Analysis Network) version 3.9 (Beghini et al., 2021). HUMAnN maps reads to gene families using the UniRef90 database and reconstructs pathway abundance using the MetaCyc pathway database. Metabolic pathways significantly associated with elevation were identified using the MaAsLin2 (Multivariable Association with Linear Model 2) pipeline through the R package Maaslin2 v1.12.0 (Mallick et al., 2021).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Statistical analysis and diversity assessment\u003c/h2\u003e\u003cp\u003eBeta diversity analysis was performed in R version 4.3.2 using the vegan and phyloseq packages based on Bray-Curtis distance (Ricotta \u0026amp; Podani, 2017) and analysed by principal coordinate analysis (PCoA). Various diversity indices, such as observed species, Chao1, Shannon diversity index, and Simpson index, were computed for each sample and visualised using the R package ggplot2. The \"redundancy analysis (RDA)\" function from the vegan package (Oksanen et al., 2013) in R, the RDA was employed to examine various correlations between environmental factors and community composition at the genus level, with environmental determinants integrated into the ordination plots using the vegan package in R, incorporating 999 permutations. Significant environmental vectors (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were fitted via the envfit function and depicted on the ordination plot. To investigate specific relationships between soil characteristics and metabolic pathways, Spearman's rank correlations were calculated between standardised soil parameters and pathway abundances, subsequently visualised using the pheatmap package in R (Kolde \u0026amp; Kolde, 2015).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Soil physicochemical properties\u003c/h2\u003e\u003cp\u003eThe physicochemical characteristics of soil exhibited considerable variability across the five sampling locations, as exhibited in boxplots (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Soil pH values fluctuated between 4.56 (S4) and 5.16 (S1), indicating a predominantly acidic environment with minimal fluctuations. EC demonstrated moderate uniformity, with values ranging from 349.0 \u0026micro;S/cm (S4) to 512.0 \u0026micro;S/cm (S2), signifying comparable ionic strength across sites. Moisture content (MC) displayed pronounced variability, ranging from 22.76% (S1) to 27.51% (S5). Total organic carbon (TOC) values varied between 12.34% (S2) and 15.29% (S3), while total nitrogen content extended from 544.93 kg/ha (S4) to 777.72 kg/ha (S3), suggesting distinct site-specific disparities in organic matter. Macronutrient concentrations also exhibited variation: phosphorus levels ranged from 14.05 mg/kg (S2) to 43.68 mg/kg (S5), potassium levels spanned from 96.42 mg/kg (S3) to 241.15 mg/kg (S4), and sulphur concentrations varied from 12.56 ppm (S2) to 24.36 ppm (S5), indicating heterogeneity in nutrient accessibility. Concentrations of trace elements also varied significantly among the sampling sites. Zinc levels ranged from 0.34 ppm (S4) to 4.19 ppm (S5), boron concentrations varied from 42.77 ppm (S4) to 184.51 ppm (S3), iron levels fluctuated between 2.86 ppm (S4) and 63.85 ppm (S1), manganese concentrations ranged from 0.32 ppm (S3) to 1.25 ppm (S5), and copper levels extended from 0.10 ppm (S4) to 0.30 ppm (S5). These findings underscore the significant spatial heterogeneity present in the chemical characteristics of soil, which may have implications for nutrient cycling and microbial communities.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Analysis of Illumina sequencing data\u003c/h2\u003e\u003cp\u003eA total of 285,026,676 raw reads of 5 libraries were obtained from Illumina sequencing. After filtering out low-quality reads, 282,911,610 clean reads were identified, which account for more than 99.25% of the raw reads (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). After assembly, 43,12,977 contigs were assembled into 5 metagenome assemblies. N50 value among metagenome assemblies ranged from 433bp for S5 to 489bp in S2. The N50 statistics revealed that, on average, for 5 assemblies, more than half of the contigs were longer than 466bp. The longest contig of all genes was 16,697bp, and the shortest was set as 200bp. After the removal of redundancy, 41,19,679 sequences remain across the assemblies, with the highest in S4 (10,62,346) and the lowest in S5 (6,12,744).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssembly statistics of metagenome assemblies.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSample\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRaw reads\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eClean reads\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e%age clean reads\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGC content (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAssembled Contigs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNonredundant sequences\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eN50(bp)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eSeq\u0026thinsp;\u0026gt;\u0026thinsp;1k\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e56134062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55746986\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e99.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e784047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e775649\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e460\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e26386\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e65455854\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64946284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e99.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1116306\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1036971\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e489\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e49691\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e41423454\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41026752\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e99.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e641228\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e631969\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e478\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e23301\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e70391262\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70018786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e99.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1155621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1062346\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e472\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e44838\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e51622044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51172802\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e99.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e615775\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e612744\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e433\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e10284\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e285026676\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e282911610\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e99.26\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e61.4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e4312977\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e4119679\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e466\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e30900\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Microbial community composition along elevation\u003c/h2\u003e\u003cp\u003ePrincipal coordinate analysis (PCoA) of soil microorganisms was performed to compare the microbial community among different samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The two main coordinates explained 87.99% of the microbial community changes among all the samples, of which PC1 explained 72.36% of the variation; PC2 explained 15.63% of the variation. Meanwhile, PCoA analysis and UPGMA-based hierarchical clustering of the samples revealed that they clustered into two distinct groups. Samples S2, S3, and S4 were closely related due to their similar microbial community structures, while S1 and S5 were distantly placed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and B). This suggests that the structure of the microbial community in the \u003cem\u003eBetula utilis\u003c/em\u003e forests in the western Himalayan treeline ecotone represents similarity among them, while communities below and above were significantly distinct from them.\u003c/p\u003e\u003cp\u003eFor the total microbial community, 47 phyla, 93 classes, 200 orders, 417 families, 1367 genera, and 4394 species were detected by metagenomic sequencing across five samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Among all samples, 3225 species were detected, with the greatest number of species found in S4, followed by S5 (3196), S2 (3162), and S1 (3137). At the same time, the lowest number of species was detected in S3 (2791). Out of 4394 species, 1772 were common in all the soil samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Moreover, alpha diversity indices revealed significant differences among different microbial communities. Sample S1 exhibits the highest diversity and evenness (Shannon index\u0026thinsp;=\u0026thinsp;3.98, Simpson index\u0026thinsp;=\u0026thinsp;0.88) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE, Supplementary file-I Sheet1). In terms of species richness, S2 had the highest estimated richness (Chao1\u0026thinsp;=\u0026thinsp;3351.03) with observed species count (2734), suggesting a more complex community structure compared to others. (Detailed krona plots of all 5 communities are given in the Supplementary file - II, from plot1 to plot5).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTotal microbial community statistics across all five soil samples.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSamples\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePhyla\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eClasses\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOrders\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFamilies\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGenus\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSpecies\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3137\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3162\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e952\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2791\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e356\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3225\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3196\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCommunity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e417\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4394\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Bacterial community composition\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003ePCoA analysis at the phylum level was performed to compare the bacterial community in different samples. The two main coordinates explained more than 87% of the change among the samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). In this S2, S3, and S4 are placed distantly from S1 and S5, suggesting that the structure of the bacterial community was altered along the elevation. A total of 4234 species of 35 phyla of bacteria were detected in five samples, of which S5 possessed the highest number of bacterial species (3125) and S3 the lowest (2754) (Supplementary file-I Sheet2). Among all, the dominant phyla across samples were Pseudomonadota, Myxococcota, Dictyoglomota, Desulphobacterota, Actinomycetota, Spirochetata, Plenktomycetota, Bacteroidota, and Bacillota, with relative abundance greater than 0.5 per cent (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The sum of these phyla accounted for more than 70% of the bacteriome, in which Pseudomonadota (42.6%) accounted for the largest proportion, followed by Myxococcota (12.6%) and Dictyoglomota (4.6%) (Supplementary file-I Sheet3). Moreover, the relative abundance of Bacillota, Bacteroidota, and Desulphobacterota successively increased, while Psuedomonadota, Myxococcota and Spirochetota significantly declined along the increase in elevation. The microbial community was analysed at the genus level to gain more insight into the variation in microbial abundance and composition along the elevation gradient in the treeline ecotone. Bacterial community composition across elevation showed broadly consistent trends, dominated by a few abundant genera such as \u003cem\u003eAzorhizobium, Buchnera, Shewanella\u003c/em\u003e, and \u003cem\u003eDictyoglomus\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, Supplementary file-I Sheet4). Whereas, a substantial portion of the community (more than 70%) comprised other low-abundant genera. \u003cem\u003eAzorhizobium\u003c/em\u003e maintains relatively high dominance in all samples, indicating its ecological dominance in the system. Further, very few species of archaea were reported in all soil samples. The highest 4 species of archaea were reported in S3, followed by S1 and S5 (3 species), S2 (2 species) and S4 (1 species).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.5. Eukaryotic taxonomic composition\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eA total of 160 species of eukaryotes belonging to 101 genera of 12 phyla were detected across all five soil samples (Supplementary file-I Sheet5). The highest number of eukaryotic read counts was observed in S2 (105) and the lowest in S3 (57) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, Supplementary file-I Sheet6). Species richness varied considerably in different metagenomes, with the highest diversity observed in sample S5, which contained 71 eukaryotic species, while the lowest diversity was recorded in S3, with only 37 species (Supplementary file-I Sheet5). Three species were shared among all 5 soil samples, while a higher number of eukaryotic species were exclusive among different elevations. Soil sample S5 was found to possess the highest number of exclusive eukaryotic species (24), whereas S1 and S3 possessed the least (6 species) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Taxonomic composition at the higher ranks revealed three major eukaryotic groups that dominated all metagenomes: Fungi, Viridiplantae (green plants), and Metazoa (animals). Based on the read counts, fungi were the dominant group among eukaryotes in extreme microbiomes (S1 and S5), whereas Viridiplantae and Metazoans were highest at the ecotones (S2 and S4).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.6. Potential functional pathways in the Himalayan treeline\u003c/h2\u003e\u003cp\u003eFunctional annotation for 43,12,997 assembled sequences across assemblies was assigned using HUMAnN v3.9 based on the UniRef90 database to compare the relative abundance of potential functions along elevation in the treeline microbiome. A total of 193 MetaCyc level 3 metabolic pathways were annotated from 5 samples. S2 was found to have the largest number of pathways, 179, and S1 has the lowest, 55. As shown in the Venn diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), out of 193 total pathways, 38 pathways were shared among all five communities. The largest number of exclusive pathways was found to be 25 in S2, while there were only 2, 4, and 3 unique pathways in microbial communities S3, S4, and S5, respectively. Among level 3 MetaCyc annotated pathways most abundant were nucleotide biosynthesis and salvage pathways (pyrimidine deoxyribonucleotide phosphorylation, adenosine deoxyribonucleotide de novo biosynthesis II, and guanosine deoxyribonucleotide de novo biosynthesis II), folate derivatives and amino acid metabolism pathways (folate transformation II), and fatty acid biosynthesis (cis-vaccenate biosynthesis) pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB and C). Furthermore, assembled sequences were assigned to a total of 1,49,196 gene families, out of which 103 were shared among all microbial communities (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Among all S2 exhibits the highest number of exclusive gene families, i.e. 69,581, which is followed by S4 (21,329), S3 (11,270), and S5 (7758), respectively. S1 showed the smallest number of unique gene familes i.e. 7228. Among the most abundant gene families were those involved in core cellular functions such as transcription, translation, and energy metabolism (DNA binding, DNA binding TF activity, rRNA binding, etc.) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB and C).\u003c/p\u003e\u003cp\u003eMultivariable association analysis was conducted on level 3 MetaCyc pathways to identify pathways with significant differences in abundance with elevation and between forested and alpine ecosystems. Persistent differences in pathway patterns in correspondence with elevation were revealed (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Higher elevations are primarily associated with ethanolamine-utilisation, ornithine degradation, proline metabolism and rubisco shunt pathways, with effect size coefficients (EC) 2.06, 1.57, 1.49, and 1.05, respectively. While tetrapyrole (EC = -1.07), inosine-5-phosphate (EC = -0.90), guanosine (EC = -0.84), and adenosine biosynthesis (EC = -0.59) pathways are prominent in lower elevations. Additionally, comparing the pathway abundance in forested and alpine microbiomes, the alpine community is primarily associated with oleate biosynthesis-IV (EC\u0026thinsp;=\u0026thinsp;1.68), fatty acid elongation (EC\u0026thinsp;=\u0026thinsp;1.63), phosphopantothenate (EC\u0026thinsp;=\u0026thinsp;1.54), and L-isoleucine biosynthesis pathways (EC\u0026thinsp;=\u0026thinsp;1.53). In contrast, the forested microbiome was mainly associated with guanosine, adenosine, and glycogen degradation (EC value \u0026minus;\u0026thinsp;4.22, -3.97, -1.94, respectively), as well as flavin and chorismate biosynthesis pathways with EC values equal to -2.47 and \u0026minus;\u0026thinsp;1.57, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.7. Effect of environmental factors on microbial community composition and functional pathways\u003c/h2\u003e\u003cp\u003eRDA and Spearman\u0026rsquo;s correlation analysis were employed to elucidate the environmental determinants that influence microbial composition and functional pathways within soil samples. Following Spearman\u0026rsquo;s correlation analysis, the environmental variables exhibiting high correlations were excluded from further investigation. The findings from RDA showed that the two RDA components, RDA1 and RDA2, account for 82.2% and 9.3%, respectively, of the total variance in microbial communities (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA), alongside 45.1% and 33.7% in functional pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). Furthermore, we assessed the relative contributions of environmental variables in modulating microbial communities and functional pathways. Iron (Fe), electrical conductivity (EC), and moisture content (MC) were identified as the primary contributors to the structure of microbial communities, displaying relative contributions of 21.51%, 16.13%, and 15.59%, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB), whereas Fe (12.81%), zinc (Zn) (11.17%), and MC (10.77%) were the most influential on the functional pathway associations with soil (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003eMoreover, Spearman\u0026rsquo;s correlation analysis elucidated the associations between level 3 MetaCyc functional pathway traits and environmental factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD). The analysis demonstrated that pathways associated with nucleotide biosynthesis exhibited positive correlations with both MC and EC. The ethanolamine utilization pathway (r\u0026thinsp;=\u0026thinsp;0.974) revealed a substantial positive correlation with MC, while the biosynthetic pathways for adenosine and guanosine deoxyribonuclease (r\u0026thinsp;=\u0026thinsp;0.5 and r\u0026thinsp;=\u0026thinsp;0.6, respectively) displayed positive correlations with N (Supplementary file-I Sheet7). Additionally, the super pathways of adenosine nucleotide \u003cem\u003ede novo\u003c/em\u003e biosynthesis-II (r\u0026thinsp;=\u0026thinsp;1) and pyrimidine nucleobases salvage (r\u0026thinsp;=\u0026thinsp;0.90) were positively correlated with EC. Conversely, the glycolysis pathway (r = -0.9), along with the putrescine biosynthesis, lysine biosynthesis, and pyruvate fermentation pathways, demonstrated significant negative correlations with the environmental TOC, Cu, and Zn, respectively (Supplementary file-I Sheet7).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eVegetation and elevation gradients are key drivers shaping soil microbial community structure in mountain treeline ecotones (Xu et al., 2014). The structure of soil microbial communities is influenced by various abiotic and biotic factors, such as soil properties, climate, and vegetation types, along an elevation gradient in mountains (Cui et al., 2016; Sun et al., 2020). The present study comprehensively analyses soil microbial communities along an elevational gradient in the Western Himalayan treeline ecotone, deciphering the distinct patterns in their composition, diversity, and taxonomic structure. Moreover, a detailed analysis of soil physicochemical variables revealed significant spatial heterogeneity, providing critical insights into the environmental factors that regulate microbial community structure and metabolic capabilities across different elevations.\u003c/p\u003e\u003cp\u003eThe observed variability in soil properties across the five sampling sites underscores the spatial heterogeneity of the edaphic environment at different elevations in the region. Soil pH values consistently indicated an acidic environment (pH range 4.56 to 5.16) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), a characteristic commonly associated with mountain ecosystems and known to affect microbial diversity, nutrient solubility, and enzymatic activity (Rousk et al., 2010; Tripathi et al., 2018). Despite relatively consistent EC values, indicating similar ionic strengths, other parameters, such as MC, TOC, and total N, exhibited substantial differences among sites. The elevated TOC and N levels at specific sites (e.g., S3) may reflect localised enrichment through litter deposition or slower decomposition rates at higher elevations (alpine), both of which are known to shape microbial community assembly by promoting copiotrophic taxa (Bahram et al., 2018; Fierer et al., 2007). Macronutrient concentrations (N, P, K) also displayed site-specific differences. Such heterogeneity likely modulates microbial metabolic pathways and plant-microbe interactions by altering nutrient availability, especially in nutrient-limited soils (Dalling et al., 2016). For instance, elevated P and S levels at site S5 may enhance microbial pathways related to phosphorus solubilisation and sulphur oxidation, processes that have been linked to microbial niche differentiation in complex soil environments (Li et al., 2014). Furthermore, the distribution of trace elements, including iron, zinc, boron, manganese, and copper, illustrates the geochemical diversity across sites. Elevated Fe at S1 and B at S3 may result from differences in mineral composition or microclimatic conditions affecting element mobilisation. While these micronutrients are essential for microbial enzymatic functions, their excess concentrations can be inhibitory or toxic, potentially acting as selective forces that shape microbial community composition and functionality (Delgado-Baquerizo et al., 2016; Giller et al., 1998). Such microenvironmental gradients may lead to local adaptation of microbial taxa and contribute to the spatial partitioning of microbial functional traits, consistent with niche theory and biogeographic structuring observed in other mountainous soil systems (Looby \u0026amp; Martin, 2020).\u003c/p\u003e\u003cp\u003eMicrobial community composition and diversity are associated with soil health and vegetation type (Han et al., 2021). Diverse studies have shown significant differences in microbial biomass and composition depending on soil traits and vegetation canopy (Kara et al., 2008; Ren et al., 2018). Our findings highlight the substantial impact of elevation and associated environmental factors on microbial groups in alpine treeline ecosystems. PCoA and UPGMA clustering showed a prominent bifurcation in microbial community composition, with mid-elevation microbiomes clustering together, distinct from S1 and S5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and B). This segregation suggests that microbial community composition is not consistent along elevation, likely due to variations in temperature and soil physicochemical properties, influenced by vegetation types, which is consistent with various previous studies (Ren et al., 2018). Taxonomic analysis revealed a resilient core microbiome (1772 common species in all samples) with few exclusive taxa (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), in line with the previous reports of diversity analysis along the elevation gradient (Adamczyk et al., 2019; Gu et al., 2024; Peng et al., 2022). Among the five microbiomes, the highest species richness was observed in S4, followed by S2, while S3 exhibited the lowest. This trend aligns with findings from other montane ecosystems, where microbial diversity peaks at the transition zone between tree and herbaceous ecosystems (He et al., 2008; Hu et al., 2018; Shen et al., 2021).\u003c/p\u003e\u003cp\u003ePseudomonadota, Myxococcota, and Dictyoglomota were the dominant taxa at the phylum level, representing over 70% of the bacterial assemblage across samples. Notably, the relative abundance of Bacillota, Bacteroidota, and Desulphobacterota exhibited an upward trend with increasing elevation, whereas Pseudomonadota and Myxococcota experienced a decline. These alterations may signify adaptive responses to fluctuating environmental parameters, including temperature and nutrient availability, along the elevation gradient (Salinas et al., 2021). Furthermore, an analysis at the genus level disclosed the dominance of \u003cem\u003eAzorhizobium, Buchnera, Shewanella\u003c/em\u003e, and \u003cem\u003eDictyoglomus\u003c/em\u003e. The persistent occurrence of \u003cem\u003eAzorhizobium\u003c/em\u003e, a well-known nitrogen-fixing bacterium, indicates a strong potential for biological nitrogen input, which is critical in nutrient-limited high-altitude soils and may support plant\u0026ndash;microbe symbioses in these ecosystems (L\u0026uuml;ttge, 2008). The dominance of \u003cem\u003eBuchnera\u003c/em\u003e, \u003cem\u003eShewanella\u003c/em\u003e, and \u003cem\u003eDictyoglomus\u003c/em\u003e suggests active insect\u0026ndash;microbe interactions, redox-driven metal cycling, and organic matter decomposition, respectively. This highlights the community\u0026rsquo;s functional adaptation to nutrient dynamics and environmental stressors. Also, eukaryotic diversity exhibited variability across samples, with S3 demonstrating the greatest richness. Furthermore, the considerable representation of low-abundance bacterial and fungal genera highlights a complex and diverse microbial community, which may contribute to the resilience of the ecosystem (Chen et al., 2024; Pedrinho et al., 2024). Therefore, this predominant functional diversity likely plays a crucial role in maintaining ecosystem stability, facilitating nutrient cycling, and enabling microbial responses to environmental fluctuations across the treeline ecotone.\u003c/p\u003e\u003cp\u003eDifferent metabolic pathways drive specific physiological functions, enabling microbes to adapt to various environmental conditions. These functional adaptations shape microbial community structure and ecosystem roles, especially across environmental gradients. The functional annotation of the treeline soil microbiomes reveals a highly diverse and spatially structured functional framework, reflecting ecological adaptations to elevation gradients and vegetation zones. The identification of 193 level 3 MetaCyc metabolic pathways, with only 38 shared across all five sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), indicates substantial functional turnover along elevation as observed previously (Dragone et al., 2022; Li et al., 2025). The highest pathway richness in S2, along with its large number of unique pathways, suggests it as a metabolically versatile community potentially shaped by transitional environmental conditions or mixed vegetation influence (Bahram et al., 2018; Chen et al., 2024). The dominance of nucleotide biosynthesis and salvage pathways, folate metabolism, and fatty acid biosynthesis among the most abundant functions highlights a focus on core cellular processes that ensure DNA replication, amino acid processing, and membrane formation, essential for microbial survival under fluctuating environmental pressures (Goyal et al., 2021; Wani et al., 2022). This foundational metabolic activity supports microbial community persistence and adaptability in the dynamic treeline ecosystem. The gene family annotation further supports this view, with mostly exclusive genes, only 103 shared among all communities. The prevalence of gene families related to transcription, translation, and energy metabolism further emphasises the importance of fundamental biological processes across all communities. Further, varying environmental conditions from the subalpine and alpine zones are regarded as important factors regulating soil microbial extracellular enzymatic activity (Liu et al., 2023; Ren et al., 2021). Multivariable association analysis reveals clear functional stratification with elevation as well as different vegetation zones. High-elevation communities are enriched in stress-related and specialised pathways such as ethanolamine utilisation, ornithine and proline degradation, and the rubisco shunt, indicative of adaptations to cold, nutrient-limited, and low-oxygen conditions. Conversely, lower elevations support biosynthesis-heavy functions like tetrapyrrole and purine nucleotide production, reflecting richer nutrient availability and potentially higher microbial growth rates. Moreover, comparative analysis between forested and alpine zones uncovers ecosystem-specific metabolic profiles. Alpine soils are enriched in pathways involved in lipid metabolism and amino acid biosynthesis, reflecting microbial strategies to maintain membrane integrity and biosynthesis under extreme conditions. Forested soils, on the other hand, are associated with carbohydrate degradation and purine biosynthesis, consistent with higher organic matter inputs and more active microbial proliferation.\u003c/p\u003e\u003cp\u003eThe results from the RDA and Spearman\u0026rsquo;s correlation collectively highlight the pivotal role of abiotic factors in shaping the microbial taxonomic and functional structure of treeline soil communities. The high explanatory power of the RDA components underscores a strong environmental filtering effect across the elevation gradient. Specifically, iron, electrical conductivity, and moisture content emerged as dominant drivers influencing both microbial composition and metabolic function. These findings are consistent with previous studies that identified redox-active elements (such as Fe and Zn) and hydrological variables (like MC) as major regulators of soil microbial diversity and functional potential in alpine and forested ecosystems (Fierer et al., 2012; Liu et al., 2023; Shen et al., 2021). Spearman\u0026rsquo;s correlation further supports this, revealing distinct associations between environmental parameters and key metabolic pathways. The strong positive relationship between MC and the ethanolamine utilisation pathway (r\u0026thinsp;=\u0026thinsp;0.974) suggests microbial adaptations for utilising alternative nitrogen sources in moist conditions, a trait known to confer ecological advantage under fluctuating nutrient availability (Krysenko \u0026amp; Wohlleben, 2022). Similarly, positive correlations between EC and pathways such as pyrimidine nucleobase salvage (r\u0026thinsp;=\u0026thinsp;0.90) and adenosine nucleotide biosynthesis-II (r\u0026thinsp;=\u0026thinsp;1) point to increased osmotic stress tolerance and heightened DNA repair or replication activities in response to saline or ion-rich conditions (Zhang et al., 2021). Conversely, pathways involved in glycolysis and amino acid biosynthesis (e.g., putrescine and lysine) showed significant negative correlations with TOC, Cu, and Zn. These inverse relationships may reflect metal toxicity or carbon-saturation effects suppressing specific microbial metabolic strategies (Li et al., 2022; Ma et al., 2022). Overall, these findings underscore how edaphic variables modulate microbial ecological functions, leading to distinct metabolic adaptations in treeline soils. Therefore, the microbial communities in mountain environments are not only structured by elevation-related climatic gradients but are finely tuned to local soil chemistry and microhabitat conditions.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study provides a comprehensive insight into the microbial community structure and functional potential in the treeline ecotone of the western Himalaya. The marked spatial differences in soil properties were found to significantly impact both taxonomic composition and metabolic functionality of the microbial community. High-throughput metagenomic analysis revealed a resilient core microbiome with distinct site-specific taxa. Notable dominance of bacterial groups such as \u003cem\u003eAzorhizobium\u003c/em\u003e, \u003cem\u003eBuchnera\u003c/em\u003e, and \u003cem\u003eShewanella\u003c/em\u003e was found, reflecting their adaptive roles in nitrogen fixation, organic matter decomposition, and redox-sensitive metal cycling. Functional profiling identified fundamental cellular pathways, including nucleotide biosynthesis, folate metabolism, and fatty acid biosynthesis, as prevalent across all elevations, emphasising the importance of foundational metabolic processes for microbial community survival under harsh environmental conditions. Additionally, stress-adaptive pathways such as ethanolamine utilisation and amino acid degradation were elevated in high-elevation communities, suggesting functional plasticity in response to a cold climate. Redundancy analysis and correlation modelling identified iron, moisture content, and electrical conductivity as principal environmental determinants of microbial community composition and functional gene prevalence. Collectively, these results elucidate the complex interrelationship between edaphic variables and microbial functional ecology, providing a valuable framework for elucidating ecosystem dynamics and biogeochemical cycling in high-altitude habitats amidst shifting climatic paradigms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAll authors have read, understood, and have complied as applicable with the statement on \u0026ldquo;Ethical responsibilities of Authors\u0026rdquo; as found in the Instructions for Authors.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eCompeting interest:\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMWNTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to the Central University of Punjab for providing the necessary infrastructure and financial support to conduct the study. VS, HSG, MAC, and SR thank CSIR and UGC for providing financial support toward their PhD degree.\u003c/p\u003e\n\u003ch2\u003eFunding:\u003c/h2\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eConceptualisation and overall supervision: PB; Data analysis: VS, HSG, MAC; Writing- original draft: VS; Writing- review and editing: PB, HSG, MAC, SR, and VS.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThe authors are grateful to the Central University of Punjab for providing the necessary infrastructure and financial support to conduct the study.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eRaw metagenomic sequence data is uploaded to NCBI under Bio Project ID: PRJNA1279859.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdamczyk, M., Hagedorn, F., Wipf, S., Donhauser, J., Vittoz, P., Rixen, C., Frossard, A., Theurillat, J.-P., \u0026amp; Frey, B. 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Salinity controls soil microbial community structure and function in coastal estuarine wetlands. \u003cem\u003eEnvironmental microbiology\u003c/em\u003e,\u003cem\u003e 23\u003c/em\u003e(2), 1020-1037. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Microbial diversity, Treeline ecotone, Metagenomics, Functional genes, Soil environment, Western Himalayas","lastPublishedDoi":"10.21203/rs.3.rs-7306065/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7306065/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAlthough soil microbial communities in high-altitude environments are a vital component of ecosystem functioning, their structural and functional responses to elevation and environmental heterogeneity are poorly known. This study explored the composition, diversity, and functional potential of the soil microbial community along elevation in the western Himalayan ecotone, using high-throughput metagenome sequencing. Physicochemical profiling based on 13 parameters revealed pronounced spatial variability among soil samples. A total of 4,394 species were identified, with a resilient core microbiome comprising 1,772 shared species across all samples. The bacterial genera \u003cem\u003eAzorhizobium\u003c/em\u003e, \u003cem\u003eBuchnera\u003c/em\u003e, \u003cem\u003eShewanella\u003c/em\u003e, and \u003cem\u003eDictyoglomus\u003c/em\u003e were found to be dominant, indicating key roles in nitrogen fixation, metal cycling, and cellulose degradation. Functional annotation identified 193 MetaCyc pathways and over 149,000 gene families, with significant variation in pathway richness and composition along elevation. Vegetation transition zones showed the highest functional diversity and unique pathway presence. Core metabolic pathways such as nucleotide biosynthesis, folate metabolism, and fatty acid synthesis were highly enriched across sites. Stress-related pathways were found to be more pronounced as elevation increases. Redundancy analysis revealed iron, moisture content, and electrical conductivity as major environmental drivers of both microbial composition and functional traits. These findings emphasise the ecological importance of environmental gradients in shaping microbial communities and their functions, offering critical insights into microbial adaptation and ecosystem processes in mountain treeline environments.\u003c/p\u003e","manuscriptTitle":"Environmental Drivers of Soil Microbial Diversity and Metabolic Potential Across the Western Himalayan Treeline","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-19 11:34:21","doi":"10.21203/rs.3.rs-7306065/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8c416e7c-53d6-43f4-8ae8-aba4f409062d","owner":[],"postedDate":"September 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-14T17:38:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-19 11:34:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7306065","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7306065","identity":"rs-7306065","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}