Seasonal shifts in vegetation, soil properties, and microbial communities in Western Himalyan forests

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To enhance the resilience of high-altitude forests, improved conservation and management programs are a key. For the programs to be effective, knowledge on the seasonal effects on vegetation, soil properties and microbial communities in high altitude forests is needed, but this information is uncertain across high altitude forest types. Methods To fill the gap, we examined the seasonal variation in vegetation, soil and microbial communities by determining vegetation diversity indices, soil properties, soil metagenomic analysis across 10 distinct forest types during winter, 2023, and summer, 2024 respectively. Results Summer showed greater species richness ( p < 0.001), Shannon ( p < 0.001), and Simpson diversity ( p < 0.008), while winter had greater evenness ( p < 0.005) and community maturity ( p < 0.02). Soil pH was 2–3% greater ( p < 0.05) in summer (winter:6.04–7.5; summer:5.9–7.9), with 125–130% greater ( p < 0.05) microbial biomass carbon (MBC) but 30–35% lesser ( p < 0.05) soil moisture and 20–25% lesser ( p < 0.05) soil organic carbon (SOC). Microbial α-diversity was greater in summer (Shannon: p < 0.006; richness: p 0.05). Beta diversity showed high dissimilarity in conifers (0.509) versus MWB (Mix Mekha Wali Burmi Forest, Surgan) and OQMF ( Olea and Quercus Mix Forest, Baandi) (0.035). Conclusions Structural Equation Modelling (SEM) showed stronger summer plant-soil-microbe linkages (summer R²=0.78 vs winter 0.71), demonstrating microbial resilience and the need for seasonal forest management. Himalaya Forests Seasons Vegetation Soil microbial diversity Metagenome Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction The Western Himalayan forests of Azad Jammu and Kashmir, Pakistan, provide a wide range of ecological services while supporting the livelihoods of local communities (Shaheen et al. 2024). These forests are characterized by diverse geography and climatic conditions, which support a variety of forest types, each dominated by specific tree species (Sharma et al. 2022a). Seasonal variations impact the ecological dynamics of these forests (Manral et al. 2023). as changes in temperature and precipitation patterns can influence vegetation development, soil properties, and microbial diversity. For example, snowfall plays a key role in maintaining soil moisture and regulating forest health in the Himalayas. It’s decline can lead to increased dryness and shifts in species composition (Han et al. 2021). Understanding the seasonal impacts on vegetation, soil properties, and microbial diversity in the Western Himalayan forests of Azad Kashmir is essential for developing effective conservation strategies and sustainable management, thus ensuring the long-term health of these ecosystems (Kattel and Conservation 2022). These forests are biodiversity hotspots, providing vital ecosystem services like carbon sequestration, water regulation, and nutrient cycling (Kumar et al. 2023; Ramachandra and Bharath 2020). Key soil parameters such as pH, microbial biomass carbon (MBC) and soil organic carbon (SOC) are critical indicators of soil fertility and ecosystem productivity, with seasonal variations influencing microbial activity and nutrient availability. Additionally, soil moisture plays a pivotal role in regulating microbial communities and plant growth, and is particularly important in mountainous regions where water availability fluctuates with altitude and precipitation patterns. Previous studies have highlighted the ecological importance of these forests in maintaining biodiversity, regulating hydrological cycles, and providing habitats for diverse plant and animal species. Forests dominated by Abies pindrow , Cedrus deodara , and Pinus wallichiana are crucial for supporting local livelihoods through timber and non-timber forest products. However, these ecosystems are vulnerable to climate change, deforestation, and overgrazing, which threaten their sustainability and the services they provide. Recent advances in metagenomics have enabled deeper insights into microbial diversity and functional potential. Alpha diversity metrics indicate microbial richness within specific niches, while beta diversity compares community shifts across seasons and elevations. We hypothesized that species richness, Shannon-Wiener and Simpson diversity indices, microbial diversity and microbial biomass carbon (MBC) would be greater, wheres community maturity, soil moisture, and soil organic carbon (SOC) would be lesser in summer than winter. To test these hypotheses, structural equation modelling (SEM) was performed to examine the seasonal effects on vegetation, soil properties, and microbial diversity across 10 diverse forest types of the Western Himalayas during winter, 2023, and summer, 2024. Integrating soil biogeochemical properties with metagenomic approaches can enhance our understanding of microbial resilience and ecosystem functioning under environmental stress, benefitting the generation of targeted conservation strategies for the Western Himalayas. 2. Materials and Methods 2.1. Study area This study was conducted in the Neelum, Jhelum, and Muzaffarabad valleys in the Western Himalayan region of Azad Jammu and Kashmir (AJK), Pakistan, in the winter of 2023 and summer of 2024,. The site is characterized by a temperate climate, with mean annual temperatures of 15–20°C (Khan et al. 2024), and mean annual precipitation of 1200–1500 mm (Ullah et al. 2022). Cold, snowy winters and warm, rainy summers influence the vegetation development, soil properties, and microbial diversity (Malik et al. 2023). Ten forest types were studied across an altitudinal range of 1,021–2,816 m above sea level (34°5'19.7088-34°56'2.616 latitude, 741326.4-735128.08 longitude) and were named according to the dominant tree species present, namely, 1) Abies Forest, Phulawai (ADF), 2) Mix Mekha Wali Burmi Forest, Surgan (MWB), 3) Picea and Taxus Forest, Brathwaar Gali (PTDF), 4) Aesculus Forest, Khapi (KAD), 5) Temperate Mix Forest Salmia Chikar (TMF), 6) Pinus and Aesculus Forest, Sinjli (PADF), 7) Quercus Forest, Upper Neelum (QDF), 8) Pinus and Cedrus Forest, Lubgran (PCDF), 9) Olea and Quercus Mix Forest, Baandi (OQMF), and 10) Pure Pine Forest, Dewliyan (PPDF) ( Fig. 1). 2.2. Vegetation survey and measurements A plot of one ha (100 m×100 m) was marked in each forest type for measurements in winter 2023 and summer 2024. Five random 10 m×10 m quadrats were selected in each plot to measure density, cover, and frequency of each tree and shrub species, and a 1 m × 1 m quadrat within the quadrat was selected to measure density, cover, and frequency of each herbaceous species. Species diversity was determined using established ecological indices, namely, the Shannon-Wiener index (H′) for species diversity, Simpson’s index (D) for dominance, Pielou’s evenness index (J) for species distribution uniformity, richness index (SR) for species abundance. Shannon diversity index was calculated as: H′ = -∑pi log pi where: Pi = ni/N, N=∑ni = total number of individuals of all species; ni = number of individuals of one species. Simpson diversity index was calculated as: D= ∑ni(ni-1)/N(N-1) where ni = number of individual species; N = total number of individuals of all species. Species evenness was calculated as (Pielou, 1975): J = H/LogS where H = Shannon’s diversity index; S = total number of species in a community. Species richness was calculated as (Menhinick, 1964): D = S/ √N where S = total number of species; N = total number of individuals. Species maturity was determined by the method of Pichi – Sermolli’s (1948): Species Maturity = F/S where F = total frequency of a community; S = total species of a community. 2.3. Soil sampling and analysis Five replicates of soil samples were collected from each of two soil depths, 0–10 cm, and 10–20 cm, in each 10 m×10 m quadrat using a soil auger of 12.6 cm diameter (Eijkelkamp, Giesbeek, Netherlands), stored in polyethylene bags and transported to the laboratory. A total of 200 soil samples were collected, 100 in each season. Soil samples were air-dried, ground, and passed through a 2-mm sieve for physical analysis, including soil EC, pH, soil texture, soil moisture content (SMC), and bulk density (BD), and chemical analysis, including soil organic carbon (SOC), total phosphorus (TP), total potassium (TK), total nitrogen (TN), available phosphorus (AP) and potassium (AK), cation exchange capacity (CEC), and the microelements, iron (Fe), copper (Cu), manganese (Mn) and zinc (Zn) (Almas et al. 2023). The samples from upper soil depth in each forest were combined, so that there were a total of 20 samples; part of each samples was stored at -20˚C for soil microbial biomass carbon (MBC) and part was stored at -80˚C for metagenomic analysis (Majorbio Company, Shanghai, China; methodology explained in supplementary file) (Lepcha and Devi 2020). 2.4. Metagenomics analysis 2.4.1 DNA extraction and quality control Genomic DNA was extracted from soil samples using the DNeasy PowerSoil Kit (Qiagen). DNA integrity and quality were assessed via 1% agarose gel electrophoresis. Fragmentation to ~ 350 bp used a ultrasonicator (Covaris M220, Woburn MA, USA). Paired-end (PE) libraries were constructed by ligating a "Y" adapter, followed by magnetic bead screening to remove linker autolinkers. PCR amplification enriched the library templates, and denaturation with sodium hydroxide yielded single-stranded DNA for sequencing. Bridge PCR amplified the library, forming dense clusters of identical DNA amplicons on a sequencing chip. Illumina sequencing used modified DNA polymerase and fluorescently labelled dNTPs. Each cycle incorporated a single base, detected via laser scanning, followed by fluorophore cleavage to enable subsequent cycles. Index tags were added to distinguish samples, and raw sequencing data were stored in FASTQ format (FQ1 and FQ2 files for paired-end reads). Raw sequencing data were evaluated for quality using fastp (v0.20.1) and adapter sequences and low-quality reads (average quality < 20, length < 50 bp) were removed. Base quality distribution and A/T/G/C content were analyzed to ensure data integrity. Clean data were retained for downstream analysis. 2.4.2 Assembly and gene prediction Clean reads were assembled into contigs using appropriate splicing software, retaining contigs ≥ 300 bp. Open reading frames (ORFs) were predicted using MetaGene or Prodigal (v2.6.3), with ORFs ≥ 100 bp translated into amino acid sequences. ORFs from all samples were clustered using CD-HIT (90% identity, 90% coverage) to construct a non-redundant gene set. The longest sequence in each cluster was selected as the representative. 2.4.3 Taxonomic and functional annotation The non-redundant gene set was aligned to the NR database using DIAMOND (blastp, E-value ≤ 1e-5). Taxonomic annotations were derived from the NR database, and species abundances were determined at various taxonomic levels (Domain to Order). Sequences were aligned to the eggNOG database using DIAMOND (blastp, E-value ≤ 1e-5) to assign Clusters of Orthologous Groups (COGs), and to the Kyoto Encyclopedia of Genes and Genomes (KEGG) genes database (blastp, E-value ≤ 1e-5) to annotate pathways, modules, and orthologs (KOs). In addition, sequences were aligned to the CAZy database using hmmscan (E-value ≤ 1e-5) to identify carbohydrate-active enzymes (CAZymes), including glycoside hydrolases (GHs), glycosyltransferases (GTs), and polysaccharide lyases (PLs). 2.5. Statistical analyses Two-way analysis of variance (ANOVA) in Graphpad prism (version 8.0.1) compared soil variables among forests and Tukey’s test separated means. A t-test compared soil variables between soil depths within a forest type and a p < 0.05 was accepted as significant. R Studio (version 4.4.2) was used for Spearman's rank correlation to test the relationship between vegetation diversity indices in both seasons, Beta diversity distance heatmaps were created in R Studio to determine microbial communities, KEGG, COG and CAZy similarities among forest types and seasons. The non-parametric Wilcoxon test compared KEGG, COG, CAZy and non-redundant (NR) microbial community abundance between seasons and the non-parametric Kruskal-Wallis test compareed community abundance among forest types. The structural equation model (SEM) quantified the relative contributions of plant diversity, soil properties, and microbial communities (MBC, functional pathways, dominant phyla) to ecosystem multifunctionality. Several software tools were used in the metagenomics study, including Fastp(version 0.20.0) for read preprocessing, BWA (version 0.7.9a) for alignment, Megahit (version 1.1.2) for assembly, CD-HIT (version 4.6.1) for clustering, Diamond (version 0.8.35) for annotation, and databases such as NR (version 20200604), KEGG (version 94.2), and eggnog (version 4.5.1) for taxonomic and functional analysis. 3. Results 3.1. Seasonal Variations in Vegetation Patterns and Diversity In summer, species richness ranged from 1.08 to 2.88, which was greater ( p < 0.001) than the 0.69 to 1.72 in winter. Shannon diversity, a measure of community diversity considering richness and evenness, was generally greater ( p < 0.001) in summer than winter. In summer, it ranged from 2.77 to 3.63, with PTDF displaying the highest diversity, and in winter it ranged from 2.13 to 3.01, with TMF displaying the highest diversity. Simpson diversity, which measures dominance, ranged from 0.81 to 0.93 in winter, with OQMF and TMF the highest values, and from 0.92 to 0.96 in summer ( p < 0.008), with PTDF the highest. Pielou's evenness, which measures the eveness of species distribution, did not differ between seasons in some forest types. In winter ( p < 0.005), evenness ranged from 0.79 to 0.91, with KAD the highest, while in summer it ranged from 0.83 to 0.91, with PPDF the highest. The community maturity index ranged from 34.7 to 63.6 in winter, with ADF the highest, while in summer the index was lesser ( p < 0.02) and ranged from 29.0 to 55.2, with PPDF the highest ( Tab. S1, 2) . Species richness (SR) and diversity indices of vegetation were greater in summer than in winter, which reflects the greater plant productivity due to suitable weather conditions. In Spearman's rank correlation analysis ( Fig. S1 ) , a positive correlation emerged between Shannon diversity and Simpson diversity ( p < 0.001) and between SR and Simpson diversity ( p < 0.05) in winter. In contrast, a negative correlation emerged between community maturity (MI) and SR in winter ( p < 0.05). 3.2 Seasonal variations in soil properties 3.2.1 Soil pH and EC The study revealed significant seasonal and spatial variations in soil pH across the 10 forest types. In winter, the minimum soil pH was 6.04 in MWB, while the maximum pH was 7.5 in TMF, for the 0–10 cm and 10–20 cm depths, respectively. In summer, the minimum pH was 5.9 in MWB and the maximum pH was 7.9 in PPDF, for the same depth layers. Notably, soil pH was higher in summer than winter ( p 0.05). Additionally, Soil pH displayed a non-significant increasing trend with depth ( p > 0.05). In winter, the minimum EC was 0.090 dS/m in PCDF, while the maximum was 0.83 dS/m in KAD in the 10–20 cm and 0–10 cm depths, respectively. In summer, the minimum EC was 0.12 dS/m in PTDF, and the maximum was 0.87 dS/m in KAD, for the same depth layers. Soil EC has non-significant decrease( p > 0.05) with depth and was greater ( p < 0.05) in summer than winter (Fig. 2 ). 3.2.2 Soil moisture, bulk density and texture Soil moisture content was greater in winter than summer and decreased with depth. In winter, the minimum was 6.84% in PCDF, while the maximum was 23.5% in MWB, in 10–20 cm and 0–10 cm depths, respectively. In summer, at these depths, the minimum moisture content was 4.7% in OQMF, and the maximum was 16.0% in KAD. Soil moisture content decreased ( p < 0.05) with depth and was lesser ( p < 0.05) in summer than winter. Soil bulk density increased ( p < 0.05) with depth, ranging from 0.44 g/cm³ in PCDF to 1.14 g/cm³ in PADF for the 0–10 cm and 10–20 cm depths, respectively. For soil texture, clay content ranged from 25% in KAD to 43% in PCDF, sand content from 6% in PCDF to 46% in PTDF, and silt content from 29% in PTDF to 53% in PPDF ( p < 0.001; Fig. 2 ) . 3.2.3 Soil organic carbon (SOC) and total nitrogen (TN) SOC and TN exhibited seasonal and depth-related trends. In winter, SOC ranged from 36.1 g/kg in TMF to 113.6 g/kg in MWB for the 10–20 cm and 0–10 cm depths, respectively and in summer, from 20.4 g/kg in PADF to 93.7 g/kg in MWB for the same depth layers. SOC decreased ( p < 0.05) with depth, and was lesser ( p 0.05) between seasons but decreased ( p = 0.01) with soil depth (Fig. 3 ). SOC(Mg/ha) and TN(Mg/ha) stock, following the same trend with soil depth and season was also calculated ( Tab. S3,4 ). 3.2.4 Total and available phosphorus and potassium Soil total phosphorus (TP) ranged from 0.263 g/kg in TMF to 0.920 g/kg in MWB in the 10–20 cm and 0–10 cm layers, respectively, and decreased ( p 0.05) between seasons or soil depth.. It ranged from 0.76 g/kg in TMF to 2.77 g/kg in MWB in winter and from 0.56 g/kg in TMF to 2.57 g/kg in MWB in summer (Fig. 3 ). Available phosphorus (AP) decreased ( p < 0.01) with soil depth and was greater ( p = 0.05) in summer than winter, with a minimum of 0.00075 g/kg in PPDF and a maximum of 0.0110 g/kg in QDF in winter, and a minimum of 0.0028 g/kg in TMF and a maximum of 0.0113 g/kg in QDF in summer. AK did not differ ( p > 0.05) between seasons or depth. In winter it ranged from a minimum of 0.131 g/kg in TMF to a maximum of 0.362 g/kg in KAD, and in summer from 0.109 g/kg in PPDF to 0.314 g/kg in KAD ( Fig. S2 ). TK(Mg/ha) ,TP(Mg/ha), AP(Mg/ha) and AK(Mg/ha) stock, following the same trend with soil depth and season was also calculated ( Tab. S3,4 ). 3.2.5 Cation exchange capacity (CEC), microbial biomass carbon (MBC), and micronutrients CEC was greater ( p < 0.0001 ) in summer than winter, with a minimum of 16 meq/100g in TMF and a maximum of 26 meq/100g in MWB in winter, and a minimum of 21 meq/100g in TMF and a maximum of 31 meq/100g in MWB in summer. MBC was greater ( p < 0.0001- p = 0.01) in summer than winter, with minimum of 0.402 g/kg in OQMF, and a maximum of 1.27 g/kg in KAD in summer and a minimum of 0.251 g/kg in PCDF and a maximum of 0.480 g/kg in ADF in winter ( Fig. S2 ). All micronutrients decreased ( p < 0.0001- p = 0.01) with depth. Iron (Fe) ranged from 2411 mg/kg in KAD to 3205 mg/kg in QDF for the 10–20 cm and 0–10 cm depths, respectively; manganese (Mn) ranged from 165 mg/kg in KAD to 487 mg/kg in PADF; zinc (Zn) ranged from 25.5 mg/kg in TMF to 55.2 mg/kg in QDF; and copper (Cu) ranged from 1.25 mg/kg in ADF to 22.7 mg/kg in PTDF ( Fig. S3 ). 3.3 Seasonal variations in microbial alpha diversity Shannon diversity was greater in all forest types in summer than winter, ranging from 6.55 in PPDF to 6.88 in ADF in summer and from 4.97 in ADF to 5.78 in PCDF in winter.There was no significant difference ( p > 0.05) between forest types but differed ( p < 0.05) between seasons. In winter, microbial Simpson diversity ranged from 0.0160 in PCDF to 0.0358 in ADF; whereas in summer, the diversity was generally lesser, ranging from 0.0059 in ADF to 0.0137 in TMF. There was no difference ( p > 0.05) among forest types there was a difference ( p 0.05) among forest types, but was greater ( p < 0.05) in summer than winter. The Sobs index, which represents observed species richness, displayed similar trends to Chao index, ranging from 17067 in OQMF to 21081 in PCDF in winter and from 25362 in PCDF to 27933 in TMF in summer (Fig. 4 ). 3.4 Seasonal variations in taxon function beta diversity 3.4.1 COG functional beta diversity The Bray-Curtis dissimilarity matrices and corresponding heatmap ( Fig. 5) revealed the functional beta diversity of microbial communities across the 10 forest types. In winter, there was moderate to high dissimilarity among the forest types, with distinct clustering. ADF and PADF displayed the smallest dissimilarity (Bray-Curtis distance = 0.055), indicating similar functional profiles, while PPDF and ADF displayed the largest dissimilarity (Bray-Curtis distance = 0.161), indicating large differences in potential function between them. MWB and OQMF exhibited moderate dissimilarity (Bray-Curtis distance = 0.109), suggesting a partial overlap in functional traits. Both ADF, PADF, KAD, and QDF, and PTDF, PADF, MWB, and KAD formed tight clusters, indicating functional similarity. These groups may share similar environmental conditions or microbial adaptations. MWB, OQMF, PTDF, PCDF, TMF, and PPDF formed a separate cluster with high internal dissimilarity, reflecting diverse functional profiles within this group. In summer, the microbial functional profiles exhibited more distinct groupings than in winter, with new clustering patterns emerging. PTDF and PADF exhibited the smallest dissimilarity (Bray-Curtis distance = 0.052), indicating similar functional profiles in summer. PPDF and OQMF also displayed low dissimilarity (Bray-Curtis distance = 0.075), suggesting shared functional traits between them. The largest dissimilarity occurred between PPDF and PCDF (Bray-Curtis distance = 0.166), indicating large differences in their functional potential. PPDF exhibited greater dissimilarity with other forest types in winter than in summer, indicating seasonal changes in its functional potential, in temperature, precipitation, or nutrient availability. MWB and OQMF displayed consistent functional similarity between seasons, suggesting resilience to seasonal climatic changes. 3.4.2 CAZy Functional Beta Diversity The Bray-Curtis dissimilarity matrices and corresponding heatmaps ( Fig. 5) revealed the functional beta diversity of microbial communities based on carbohydrate-active enzymes (CAZy) profiles. In winter, the CAZy functional profiles exhibited moderate to high dissimilarity across forest types, with distinct clustering patterns. ADF and PADF displayed the smallest dissimilarity (Bray-Curtis distance = 0.075), indicating that these forests harbor microbial communities with similar carbohydrate-active enzymatic capabilities during winter. PPDF and ADF displayed the greatest dissimilarity (Bray-Curtis distance = 0.175), indicating large differences in CAZy potential between them. MWB and OQMF exhibited moderate dissimilarity (Bray-Curtis distance = 0.111), suggesting a partial overlap in CAZy functional traits. ADF, PADF, KAD, and QDF formed a tight cluster, while MWB, OQMF, PTDF, PCDF, TMF, and PPDF formed a separate cluster with large internal dissimilarity, reflecting diverse CAZy profiles within this group. In summer, the CAZy functional profiles displayed more distinct groupings than in winter, with new clustering patterns emerging in the heatmap. PTDF and PADF exhibited the smallest dissimilarity (Bray-Curtis distance = 0.064), indicating similar CAZy profiles during summer and that these forests share microbial communities with similar carbohydrate-active enzymatic functions. PPDF and OQMF also displayed small dissimilarity (Bray-Curtis distance = 0.088), suggesting shared CAZy traits between these forest types, while the greatest dissimilarity occurred between PPDF and PCDF (Bray-Curtis distance = 0.186). 3.4.3 KEGG beta diversity The beta diversity analysis revealed distinct patterns among the microbial communities in both seasons. In winter, the greatest dissimilarity was observed between ADF and PPDF (0.087), indicating large differences in their microbial composition, whereas, the smallest dissimilarity was between MWB and OQMF (0.0346), suggesting a close relationship between these communities. In summer, the greatest dissimilarity was between PADF and QDF (0.097), while the smallest dissimilarity was between MWB and OQMF (0.035), consistent with the winter findings, indicating stable relationships between these communities between seasons. The pairwise dissimilarity findings (PDF) for both seasons were consistent, with values ranging from 0.02 to 0.08. The KEGG beta diversity analysis for both winter and summer provides insights into microbial communities dynamics in response to seasonal changes. The consistent small dissimilarity between MWB and OQMF between seasons suggests a stable microbial community, possibly due to similar environmental conditions or functional roles. The large dissimilarity in specific pairs, such as ADF and PPDF in winter and PADF and QDF in summer, indicates a shift in microbial composition, likely driven by seasonal environmental factors. These findings contribute to our understanding of microbial ecology and highlight the importance of seasonal variations in microbial community studies ( Fig. S4) . 3.4.4 NR beta diversity The winter NR beta diversity analysis revealed significant differences in microbial community structures among the 10 forest types. The greatest dissimilarity was observed between PTDF and PPDF (0.509), indicating substantial differences in their microbial composition; whereas, the smallest dissimilarity was between MWB and KAD (0.146). The pairwise comparisons indicated the variability in microbial diversity, with notable differences between PTDF and other forest types, such as ADF (0.385) and TMF (0.424). In summer, the greatest dissimilarity in NR beta diversity patterns was noted between PTDF and PPDF (0.509), consistent with the winter findings, indicating persistent differences in microbial profiles between these forest types. The pairwise comparisons also revealed large differences between PPDF and other forest types, including ADF (0.423) and TMF (0.388) ( Fig. S4) . 3.5. Seasonal impact on taxon functional composition 3.5.1 COG functional composition among forest types and between seasons The relative abundance of COG (clusters of orthologous groups) functional categories was calculated for each forest type and season. The dominant COG categories across all forest types and seasons included R (general function prediction), E (amino acid transport and metabolism), C (energy production and conversion), and G (carbohydrate transport and metabolism), which collectively represented most of the functional potential in the microbial communities. R was the most relatively abundant COG category, ranging from 0.093 to 0.105 across forest types and seasons. E and C also displayed high relative abundances, ranging from 0.073 to 0.101 and 0.069 to 0.082, respectively, while G exhibited moderate abundances, ranging from 0.069 to 0.078. Seasonal comparisons revealed minor fluctuations in the relative abundances of COG categories, but no consistent patterns were observed across forest types. In the ADF, the relative abundance of R was greater in winter (0.105) than summer (0.093), whereas in the MWB, it was marginally greater in summer (0.101) than winter (0.095) Similar trends were observed for dominant COG categories, such as E and C, which exhibited minimal seasonal differences among forest types. The relative abundances of COG categories did not differ (p > 0.05) among forest types, suggesting that the functional potential of microbial communities was relatively consistent across forests and may indicate functional redundancy within microbial communities. This suggests that different microbial taxa can perform similar metabolic functions, thereby maintaining ecosystem processes despite variations in vegetation composition and environmental conditions. The high relative abundance of generalist functions (e.g., R) further supports this premise, as these functions are essential for microbial survival and adaptation across diverse habitats (Fig. 6 ) . 3.5.2 CAZy functional composition among forest types and between seasons The functional composition of microbial communities, as inferred from CAZy (carbohydrate-active enzymes) families, was calculated for each forest type and season.The dominant CAZy families across all forest types and seasons included GT41 (glycosyltransferase family 41), GT4 (glycosyltransferase family 4), GT2_glycos_transf_2 (glycosyltransferase family 2), and CE1 (carbohydrate esterase family 1), which collectively represented most of the carbohydrate-active enzymatic potential in the microbial communities. GT41 was the most abundant CAZy family, with relative abundances ranging from 0.057 to 0.131. GT4 and GT2_Glycos_transf_2 also displayed high relative abundances, ranging from 0.045 to 0.076 and 0.041 to 0.063, respectively. CE1 exhibited moderate abundances, ranging from 0.042 to 0.058, indicating the importance of carbohydrate esterification in these forest ecosystems. Seasonal comparisons revealed minor fluctuations in the relative abundances of CAZy families, but no consistent pattern was observed across forest types. In ADF, the relative abundance of GT41 was greater in winter (0.131) than summer (0.063); whereas in MWB, the relative abundance was greater in summer (0.100) than winter (0.087). Similar trends were observed for dominant CAZy families, such as GT4 and GT2_Glycos_transf_2, which exhibited minimal seasonal variation across forest types. The high relative abundance of generalist families (e.g., GT41) further supports this premise, as these families are essential for microbial survival and adaptation across diverse habitats. The general lack of differences in the relative abundances of CAZy families across forests and between seasons indicates there may be functional redundancy within the microbial communities and that different microbial taxa can perform similar carbohydrate-active enzymatic functions, thereby maintaining ecosystem processes despite variations in vegetation composition and environmental conditions. It also indicates that the microbial carbohydrate-active enzymatic potential is resilient to variations in vegetation composition and seasonal environmental conditions (Fig. 6 ) . 3.5.3 KEGG level 3 functional composition among forest types and between seasons The functional composition of microbial communities, as inferred from KEGG level 3 pathways, was calculated for each forest type and season. The dominant pathways across all forest types and seasons included metabolic pathways, biosynthesis of secondary metabolites, microbial metabolism in diverse environments, and two-component system, which collectively represented most of the functional potential in the microbial communities. Metabolic pathways were the most abundant pathway, with relative abundances ranging from 0.165 to 0.172 across forest types and seasons. Biosynthesis of secondary metabolites and microbial metabolism in diverse environments also were relatively abundant, ranging from 0.068 to 0.072 and 0.057 to 0.060, respectively. Two-component systems exhibited moderate abundances, ranging from 0.022 to 0.032, indicating the importance of signal transduction in these forest ecosystems. Seasonal comparisons revealed minor fluctuations in the relative abundances of pathways, but no consistent pattern was observed across forest types. In the ADF, the relative abundance of metabolic pathways was slightly greater in winter (0.169) than summer (0.166), while in the MWB, it was marginally greater in summer (0.168) than winter (0.166). Similar trends were observed for other dominant pathways, such as biosynthesis of secondary metabolites and microbial metabolism in diverse environments, with minimal seasonal variation across forest types. The was no difference (p > 0.05) in the relative abundances of KEGG level 3 pathways among forest types or between seasons, which may indicate functional redundancy within the microbial communities and suggests that different microbial taxa can perform similar metabolic functions. The high relative abundances of generalist pathways (e.g., metabolic pathways) further supports this premise, as these pathways are essential for microbial survival and adaptation across diverse habitats ( Fig. S5) . 3.5.4 Microbial community composition at the phylum level The dominant phyla across all forest types and seasons included Actinomycetota, Pseudomonadota, and Acidobacteriota, accounting for most of the microbial community composition. Actinomycetota was the most abundant phylum, with the relative abundance ranging from 0.164 to 0.558, while Pseudomonadota ranged from 0.120 to 0.392 and Acidobacteriota ranged from 0.043 to 0.216. Other notable phyla included Verrucomicrobiota, Candidatus_Rokubacteria, and Chloroflexota. Seasonal comparisons revealed fluctuations in the relative abundances of microbial phyla, but no consistent patterns were observed across forest types. In ADF, the relative abundance of Actinomycetota was greater in summer (0.451) than in winter (0.164), but in the PTDF, it was greater in winter (0.558) than summer (0.268). Overall, the relative abundances of microbial phyla and the composition of microbial communities were consistent and did not differ (p > 0.05) among forest types and between seasons. The lack of significant difference suggests that the microbial community structure was resilient to variations in seasonal vegetation composition and environmental conditions, which may play a critical role in maintaining ecosystem functions, such as nutrient cycling and organic matter decomposition ( Fig. S5) . 3.6. Seasonal drivers of Ecosytem: Structural equation model (SEM) The structural equation models revealed distinct seasonal patterns, with summer showing significantly stronger relationships between microbial α-diversity (R²=0.64 vs winter's 0.52) and microbial biomass carbon (R²=0.45 vs 0.35) with ecosystem functioning. Taxonomic (R²=0.43 vs 0.36) and functional diversity (R²=0.72 vs 0.63) were more strongly linked to multifunctionality in summer, while winter exhibited weaker but more stable soil-microbe interactions. Both models ( Fig. S6) showed excellent fit (summer CFI = 0.99, RMSEA = 0.01; winter CFI = 0.98, RMSEA = 0.03), confirming robust seasonal dynamics in microbial-ecosystem relationships 4. Discussion The Western Himalayan forests of Azad Jammu and Kashmir (AJK), Pakistan, represent a unique and ecologically important region characterized by diverse forest types and large seasonal variations. This study investigated the seasonal impacts on vegetation patterns, soil characteristics, and microbial diversity across 10 forest types during winter, 2023, and summer, 2024. The findings revealed seasonal and spatial variations in vegetation, soil properties, and microbial community composition, providing valuable insights into the ecological dynamics of high altitude forests. 4.1 Seasonal variations in vegetation patterns and diversity The analysis demonstrated clear seasonal shifts in vegetation communities, with greater species richness and diversity indices in summer than winter. This trend aligns with findings from other temperate and Himalayan ecosystems, where seasonal changes in temperature and precipitation influenced vegetation dynamics. For example, studies in the Eastern Himalayas (Sharma et al. 2022b) and European temperate forests also reported greater species richness and diversity during warmer months due to favorable growth conditions (Tinya et al. 2021). The positive correlations between Shannon and Simpson diversity indices in both seasons indicate that as species richness increased, dominance by a few species decreased leading to a more equally distributed community structure. This finding is consistent with observations in other forest ecosystems, where increased resource availability in summer promoted greater species evenness (Gu et al. 2023). However, the lesser community maturity during summer indicated that younger, less stable communities may dominate in warmer months, potentially due to rapid growth and turnover of species. This contrasts with studies in tropical forests, where community maturity remained relatively stable across seasons (McDowell et al. 2020). The decline in maturity in the present study may reflect the impact of environmental stressors, such as increased herbivory or competition, which can disrupt the establishment of mature plant communities. These findings underscore the importance of seasonal monitoring to understand the long-term impacts of climate change on forest ecosystems, particularly in regions like the Western Himalayas, where seasonal variations are pronounced. 4.2 Soil properties and nutrient dynamics Soil properties exhibited seasonal and depth-related variations, reflecting the influence of environmental conditions on soil processes. Soil pH was generally greater in summer than winter, except in MWB and QDF, which suggests that higher temperatures and greater microbial activity may increase soil alkalinity. This aligns with studies in temperate forests, where seasonal warming increased soil pH due to enhanced microbial mineralization of organic matter (Yin et al. 2024b). Conversely, the decrease in soil moisture content and SOC in summer highlights the impact of plant development and microbial decomposition, which deplete soil resources. Similar trends were reported in Mediterranean forests, where summer droughts reduced soil moisture content and accelerated organic matter decomposition (Najera et al. 2020). The consistent decrease in SOC and total nitrogen (TN) with depth and season emphasizes the role of surface organic matter in maintaining soil fertility. This is consistent with findings in other mountainous regions, where topsoil layers were critical for nutrient cycling and carbon storage (Tian et al. 2020). The increase in available phosphorus (AP) and decrease in available potassium (AK) during summer, despite the stable total phosphorus (TP) and decrease in total potassium (TK), suggest that microbial activity and root exudates may enhance nutrient availability in warmer months. These findings are consistent with previous studies that seasonal changes in temperature and moisture influenced nutrient cycling and microbial activity in forest soils (Yin et al. 2024a). The greater cation exchange capacity (CEC) and microbial biomass carbon (MBC) in summer further support the role of microbial communities in nutrient mobilization and soil health, as observed in other forest ecosystems (Ngaba et al. 2024). 4.3 Microbial diversity and functional composition The metagenomic analysis revealed seasonal shifts in microbial diversity, with greater Shannon and Chao diversity indices in summer than in winter. This increase in microbial diversity during summer likely reflects the more favorable environmental conditions for microbial growth and activity. Similar patterns were reported in temperate forests, where higher temperatures and increased soil moisture availability enhanced microbial diversity (Nottingham et al. 2018). The consistent low dissimilarity between MWB and OQMF across seasons suggests stable microbial compositions, possibly due to similar environmental conditions or functional roles. This is consistent with studies in mixed forests, where vegetation type strongly influenced microbial community composition (Zubek et al. 2024). In contrast, the high dissimilarity between PTDF and PPDF highlights the distinct microbial communities in these forest types, likely driven by differences in vegetation and soil properties, as observed in coniferous versus mixed forests (Lladó et al. 2018). The functional composition of microbial communities, as inferred from COG, CAZy, and KEGG pathways, displayed strong consistency among forest types and between seasons. The dominance of generalist functions, such as metabolic pathways and carbohydrate-active enzymes, indicate functional redundancy within microbial communities, which may enhance ecosystem resilience by ensuring that critical metabolic functions are maintained despite variations in environmental conditions. Similar findings were reported in other forest ecosystems, where functional redundancy stabilized nutrient cycling and organic matter decomposition under changing environmental conditions (Dhyani et al. 2022). The lack of significant differences in functional composition across seasons and forest types further supports the premise that microbial communities in the Western Himalayas are highly adaptable and resilient, as was observed in other high-altitude ecosystems (Bhadouria et al. 2023). 4.4 SEM interpreting Seasonal Shifts: Microbial ecology between summer activity and winter resilience The findings align with and expand upon previous studies of seasonal microbial dynamics in mountain ecosystems. The observed summer increase in microbial α-diversity and functional activity corroborates work by (Zhou et al. 2020) in alpine soils, who similarly found warmer temperatures enhanced microbial metabolic rates and diversity. Our results showing stronger soil-microbe decoupling in summer mirror findings from (Baldrian et al. 2012) in Himalayan meadows, where reduced moisture availability during warm periods weakened microbial-SOC relationships. The winter patterns of microbial dormancy and carbon stabilization support the cold storage hypothesis proposed by (Manzoni et al. 2014) demonstrating how low-temperature conditions preserve microbial biomass and organic matter. Notably, the seasonal microbial functional resilience importance, echoes global meta-analyses by (Bahram et al. 2018), suggesting a universal feature of temperate mountain ecosystems. However, our observation that multifunctionality remains strongly microbial-linked in both seasons contrasts with some boreal studies (Fierer et al. 2012), possibly reflecting the unique transitional nature of Himalayan forests. These comparisons highlight how our findings both confirm general ecological principles about seasonal microbial ecology while revealing ecosystem-specific adaptations to the Himalayan environment. 4.5 Ecological implications and conservation strategies The findings of this study have important implications for the conservation and sustainable management of Western Himalayan forests. The seasonal variations in vegetation, soil properties, and microbial diversity demonstrate the dynamic nature of these ecosystems and their sensitivities to environmental changes. The greater species richness and microbial diversity in summer than in winter underscores the importance of preserving these forests as biodiversity hotspots, as emphasized in other Himalayan regions (Meena et al. 2024). However, the decline in community maturity and SOC in summer raises concerns about the long-term impacts of climate change on ecosystem stability, as reported in other temperate forests (Liu et al. 2024). The functional redundancy in microbial communities suggests that these ecosystems may be resilient to environmental changes, but this resilience may be challenged by ongoing climate warming and anthropogenic activities. Conservation strategies should focus on maintaining soil health, preserving biodiversity, and mitigating the impacts of climate change. This could include promoting sustainable land-use practices, enhancing soil organic matter through afforestation, and implementing climate-adaptive management strategies, as recommended in studies from other mountainous regions (Kougioumoutzis et al. 2024). 5. Conclusions and future directions This study revealed seasonal and spatial variations in vegetation, soil properties, and microbial diversity across the Western Himalayan forests of Azad Jammu and Kashmir. The findings emphasized that species richness, diversity indices (Shannon-Wiener and Simpson), and microbial biomass carbon (MBC) were greater, while soil moisture and soil organic carbon (SOC) were lesser in summer than winter, highlighting the strong influence of seasonal climatic conditions. Microbial alpha diversity was greater by 20–25% in summer than winter, with no significant change in functional profiles, indicating functional stability despite seasonal shifts. Beta diversity analysis revealed distinct microbial community structures, with small dissimilarity in mixed forests (e.g., MWB and OQMF), but large dissimilarity in coniferous forests, underscoring the role of forest type in shaping microbial communities. The structural equation models (SEMs) revealed that Western Himalayan forests maintain ecosystem stability through complementary seasonal microbial regimes. The consistency of our results with global patterns (summer, activity peaks and winter, carbon sequestration) suggests Himalayan microbes follow fundamental biogeochemical rules, while the ecosystem's exceptional multifunctionality across seasons may represent a unique adaptation to extreme altitudinal gradients. These insights strengthen arguments by (Manzoni et al. 2014) for incorporating seasonal microbial dynamics into mountain conservation planning, particularly under climate change scenarios predicted to disrupt traditional seasonal patterns in high-altitude regions. To ensure the long-term sustainability of these ecosystems, adaptive management strategies must prioritize seasonal monitoring and conservation efforts. Future research should focus on identifying the specific environmental drivers behind the observed patterns, particularly the interplay between SOC, vegetation diversity, and microbial functional pathways. Integrating advanced techniques such as meta-transcriptomics and metabolomics could provide deeper insights into microbial functional dynamics and their role in ecosystem processes Additionally, the impacts of anthropogenic activities, such as grazing and deforestation, on ecological dynamics should be investigated, as these factors have altered ecosystem functioning in other regions. By addressing these knowledge gaps, we can develop more effective strategies to enhance the resilience of these ecologically vital forests in the face of global environmental change. Declarations Competing interests: The authors declare that they have no competing interests. Acknowledgement: This work was supported by the Natural Science Foundation of China (31961143012), the Science-based Advisory Program of The Alliance of National and International Science Organizations for the Belt and Road Regions (ANSO-SBA-2023-02). Author contributions: H.A. did the conceptualization, field work and laboratory analyses, data analyses, original draft, and revision. M.R, A.W., W.W. processed the data. A.A.D edited and reviewed the final version of the manuscript. All the authors participated in editing the initial draft and approved the final submission version. Z.S. contributed to the conceptualization, manuscript revision, funding acquisition, and supervision. Data availability Not applicable. Code availability Not applicable. Ethics approval Not applicable. Consent to participate Not applicable. Consent for publication Not applicable. Conflicts of interest The authors declare that they have no conflict of interest. References Aguirre J (2023) The Kjeldahl Method. The Kjeldahl Method: 140 Years. Springer. Almas M, Sikandar S, Rubab U, Anjum SJJKJoA (2023) Fertility Status of Agricultural Soils from Muzaffarabad Division of Azad Jammu and Kashmir. Planta Animalia 3: 31-35. 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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-6344148","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":438246154,"identity":"e2df7c63-61bb-49a9-abd4-2cf9a7c5cc1e","order_by":0,"name":"Huma Ali","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Huma","middleName":"","lastName":"Ali","suffix":""},{"id":438246155,"identity":"b37599ec-0f93-47bb-a2b1-f84080735cb9","order_by":1,"name":"Muhammad Rafiq","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Rafiq","suffix":""},{"id":438246156,"identity":"ae8b97e2-5c8a-4f61-b6c0-e64469ef1972","order_by":2,"name":"Muhammad Manzoor","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Manzoor","suffix":""},{"id":438246157,"identity":"2bc114b7-d0b0-4b4d-9795-2b721cba1a25","order_by":3,"name":"Syed Waseem Gillani","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Syed","middleName":"Waseem","lastName":"Gillani","suffix":""},{"id":438246158,"identity":"1df61c33-22ce-4fbc-8e2d-cebd344379f9","order_by":4,"name":"Allan Degen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Allan","middleName":"","lastName":"Degen","suffix":""},{"id":438246159,"identity":"0c32525a-1f45-4cb3-a531-41182b7be2e8","order_by":5,"name":"Awais Iqbal","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Awais","middleName":"","lastName":"Iqbal","suffix":""},{"id":438246160,"identity":"99e67359-14a0-4725-9231-3f2a1c63eae0","order_by":6,"name":"Wenyin Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Wenyin","middleName":"","lastName":"Wang","suffix":""},{"id":438246161,"identity":"0a541ce2-005e-40ce-a4ea-9cca0ba1b737","order_by":7,"name":"Muhammad Khalid Rafiq","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Khalid","lastName":"Rafiq","suffix":""},{"id":438246162,"identity":"db89b9e5-6097-4af0-93f0-e0c69fb87c6f","order_by":8,"name":"Zhanhuan Shang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYDACCTB5IIGfHcxgJkGLZDPJWgwOE6tFfnbzs4df/tzJMz7M/kyCocI6sYH97AG8WgzuHDM3luF5Vmx2mCFNguFMemIDT14Cfi0SCWbSEhKHE7cdZjgmwdh2OLFBgscAv8NmpH+TljA4nLi5mbFNgvEfEVoYbuSYSX5IOJy4gZmZTYKxgQgtBjdyyqQZDhxOnHGYjdki4Vi6cRtPDkGHbZP88edwYn97+8MbH2qsZfvZzxBwGBAw88BYCUDMRlA9EDD+IEbVKBgFo2AUjFwAAFRJRUoShRT/AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-6069-4708","institution":"Lanzhou University","correspondingAuthor":true,"prefix":"","firstName":"Zhanhuan","middleName":"","lastName":"Shang","suffix":""}],"badges":[],"createdAt":"2025-03-31 11:03:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6344148/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6344148/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40793-025-00842-y","type":"published","date":"2026-01-06T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81380676,"identity":"9cd19efa-a05b-4f5c-a0ae-3175ba6d24de","added_by":"auto","created_at":"2025-04-25 12:39:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1656070,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e. Map of study area with forest types, \u003cstrong\u003eB\u003c/strong\u003e\u003cem\u003e. Abies\u003c/em\u003e forest (ADF), \u003cstrong\u003eC\u003c/strong\u003e. Mix Mekha Wali Burmi forest (MWB), \u003cstrong\u003eD\u003c/strong\u003e. \u003cem\u003ePicea\u003c/em\u003e and \u003cem\u003eTaxus\u003c/em\u003e forest (PTDF), \u003cstrong\u003eE\u003c/strong\u003e. \u003cem\u003eAesculus \u003c/em\u003eforest (KAD), \u003cstrong\u003eF\u003c/strong\u003e. Temperate mix forest (TMF), \u003cstrong\u003eG\u003c/strong\u003e. \u003cem\u003ePinus\u003c/em\u003eand \u003cem\u003eAesculus\u003c/em\u003e forest (PADF), \u003cstrong\u003eH\u003c/strong\u003e. \u003cem\u003eQuescus\u003c/em\u003e forest (QDF), \u003cstrong\u003eI\u003c/strong\u003e.\u003cem\u003ePinus\u003c/em\u003e and \u003cem\u003eCedrus\u003c/em\u003e forest (PCDF), \u003cstrong\u003eJ\u003c/strong\u003e. \u0026nbsp;Pure \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u003cbr\u003e\n pine forest (PPDF).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6344148/v1/e0cc04faf31cb2fcc2b73647.png"},{"id":81379923,"identity":"feb645f8-819e-4735-ba67-a0b6d54cece1","added_by":"auto","created_at":"2025-04-25 12:31:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":173410,"visible":true,"origin":"","legend":"\u003cp\u003eA, B, C, D, E, F, G, H presenting means ±SD of soil pH, electric conductivity (EC, dS/m), moisture content (%), bulk density (BD, g/cm\u003csup\u003e3\u003c/sup\u003e) and texture (% clay, sand or silt) at 0 - 10 cm and 10 - 20 cm in winter and summer in 10 forest types. Means with different superscripts differ from each other (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). Asterisks on shoulder lines indicate statistically significant differences between 0-10 cm and 10-20 cm in the same forest type: *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6344148/v1/2959de04df624cda41ef3973.png"},{"id":81379922,"identity":"50e407f5-48f5-451c-81ca-9f015c9006d8","added_by":"auto","created_at":"2025-04-25 12:31:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":166622,"visible":true,"origin":"","legend":"\u003cp\u003eI, J, K, L, M, N, O presenting means ±SD of soil organic carbon (SOC, g/kg), total nitrogen (TN, g/kg), total potassium (TK, g/kg) and total phosphorus (TP, g/kg) at 0-10 cm and 10-20 cm in winter and summer. Within the forest types, means with different superscripts differ from each other (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). Asterisks on shoulder lines indicate statistically significant differences between 0-10 cm and 10-20 cm in the same forest type: *\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6344148/v1/066eaf3d0d8bfd44d6cf7385.png"},{"id":81379926,"identity":"bb0a61a3-5865-47dd-b5b7-900519733418","added_by":"auto","created_at":"2025-04-25 12:31:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":110856,"visible":true,"origin":"","legend":"\u003cp\u003eSoil microbial alpha diversity(Shanon,Simpson,Chao,Sobs) in 10 Himalayan forests based on the non-parametriic Wilcoxon and Kruskal-Wallis tests. There was no difference among forest types (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05), but there was a difference between seasons (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6344148/v1/1b868dd4f80487092fe13522.png"},{"id":81379929,"identity":"a56a211f-e535-454c-8975-f272f1433601","added_by":"auto","created_at":"2025-04-25 12:31:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":155870,"visible":true,"origin":"","legend":"\u003cp\u003eClusters of orthologous groups\u003cstrong\u003e (\u003c/strong\u003eCOG) and carbohydrate active enzymes (CAZy) functional beta diversity in 10 forest types in winter and summer.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6344148/v1/d20f44f6d98a0a829d82c714.png"},{"id":81379930,"identity":"7efa530c-0992-47e7-bc8e-c91641be1823","added_by":"auto","created_at":"2025-04-25 12:31:06","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":243787,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA. \u003c/strong\u003eClusters of orthologous groups\u003cstrong\u003e (\u003c/strong\u003eCOG) functional community abundance, \u003cstrong\u003eB. \u003c/strong\u003eCarbohydrate active enzymes (CAZy ) enzyme families in 10 forest types and two seasons, R: General function prediction only, E: Amino acid transport and metabolism, C: Energy production and conversion, G: Carbohydrate transport and metabolism: Signal transduction mechanisms, H: Coenzyme transport and metabolism, I: Lipid transport and metabolism, P: Inorganic ion transport and metabolism, M: Cellwall/membrane/envelope biogenesis, K: Transcription, J: Translation, ribosomal structure and biogenesis, O: Posttranslational modification, protein turnover, chaperones, L:Replication, recombination and repair, V: Defense mechanisms, Q: Secondary metabolites biosynthesis, transport and catabolism, S: Function unknown, X: Mobilome: prophages, transposons, F: Nucleotide transport and metabolism, U: Intracellular trafficking, secretion, and vesicular transport, D: Cell cycle control, cell division, chromosome partitioning,\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6344148/v1/de95ffd3d9935524cb6ad0eb.png"},{"id":101876649,"identity":"0622f77b-ed12-4fd3-a6b1-deb81e13d877","added_by":"auto","created_at":"2026-02-04 14:23:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3707896,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6344148/v1/d8722c13-3135-4948-a58a-c45b6f51cb76.pdf"},{"id":81380679,"identity":"1daeeca3-6027-42ed-853e-3b832d837889","added_by":"auto","created_at":"2025-04-25 12:39:06","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":2344398,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-6344148/v1/376f0b7112fc3a40f1071110.docx"}],"financialInterests":"","formattedTitle":"Seasonal shifts in vegetation, soil properties, and microbial communities in Western Himalyan forests","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe Western Himalayan forests of Azad Jammu and Kashmir, Pakistan, provide a wide range of ecological services while supporting the livelihoods of local communities (Shaheen et al. 2024). These forests are characterized by diverse geography and climatic conditions, which support a variety of forest types, each dominated by specific tree species (Sharma et al. 2022a). Seasonal variations impact the ecological dynamics of these forests (Manral et al. 2023). as changes in temperature and precipitation patterns can influence vegetation development, soil properties, and microbial diversity. For example, snowfall plays a key role in maintaining soil moisture and regulating forest health in the Himalayas. It\u0026rsquo;s decline can lead to increased dryness and shifts in species composition (Han et al. 2021).\u003c/p\u003e \u003cp\u003eUnderstanding the seasonal impacts on vegetation, soil properties, and microbial diversity in the Western Himalayan forests of Azad Kashmir is essential for developing effective conservation strategies and sustainable management, thus ensuring the long-term health of these ecosystems (Kattel and Conservation 2022). These forests are biodiversity hotspots, providing vital ecosystem services like carbon sequestration, water regulation, and nutrient cycling (Kumar et al. 2023; Ramachandra and Bharath 2020). Key soil parameters such as pH, microbial biomass carbon (MBC) and soil organic carbon (SOC) are critical indicators of soil fertility and ecosystem productivity, with seasonal variations influencing microbial activity and nutrient availability. Additionally, soil moisture plays a pivotal role in regulating microbial communities and plant growth, and is particularly important in mountainous regions where water availability fluctuates with altitude and precipitation patterns.\u003c/p\u003e \u003cp\u003ePrevious studies have highlighted the ecological importance of these forests in maintaining biodiversity, regulating hydrological cycles, and providing habitats for diverse plant and animal species. Forests dominated by \u003cem\u003eAbies pindrow\u003c/em\u003e, \u003cem\u003eCedrus deodara\u003c/em\u003e, and \u003cem\u003ePinus wallichiana\u003c/em\u003e are crucial for supporting local livelihoods through timber and non-timber forest products. However, these ecosystems are vulnerable to climate change, deforestation, and overgrazing, which threaten their sustainability and the services they provide. Recent advances in metagenomics have enabled deeper insights into microbial diversity and functional potential. Alpha diversity metrics indicate microbial richness within specific niches, while beta diversity compares community shifts across seasons and elevations.\u003c/p\u003e \u003cp\u003eWe hypothesized that species richness, Shannon-Wiener and Simpson diversity indices, microbial diversity and microbial biomass carbon (MBC) would be greater, wheres community maturity, soil moisture, and soil organic carbon (SOC) would be lesser in summer than winter. To test these hypotheses, structural equation modelling (SEM) was performed to examine the seasonal effects on vegetation, soil properties, and microbial diversity across 10 diverse forest types of the Western Himalayas during winter, 2023, and summer, 2024. Integrating soil biogeochemical properties with metagenomic approaches can enhance our understanding of microbial resilience and ecosystem functioning under environmental stress, benefitting the generation of targeted conservation strategies for the Western Himalayas.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study area\u003c/h2\u003e \u003cp\u003eThis study was conducted in the Neelum, Jhelum, and Muzaffarabad valleys in the Western Himalayan region of Azad Jammu and Kashmir (AJK), Pakistan, in the winter of 2023 and summer of 2024,. The site is characterized by a temperate climate, with mean annual temperatures of 15\u0026ndash;20\u0026deg;C (Khan et al. 2024), and mean annual precipitation of 1200\u0026ndash;1500 mm (Ullah et al. 2022). Cold, snowy winters and warm, rainy summers influence the vegetation development, soil properties, and microbial diversity (Malik et al. 2023). Ten forest types were studied across an altitudinal range of 1,021\u0026ndash;2,816 m above sea level (34\u0026deg;5'19.7088-34\u0026deg;56'2.616 latitude, 741326.4-735128.08 longitude) and were named according to the dominant tree species present, namely, 1) \u003cem\u003eAbies\u003c/em\u003e Forest, Phulawai (ADF), 2) Mix Mekha Wali Burmi Forest, Surgan (MWB), 3) \u003cem\u003ePicea\u003c/em\u003e and \u003cem\u003eTaxus\u003c/em\u003e Forest, Brathwaar Gali (PTDF), 4) \u003cem\u003eAesculus\u003c/em\u003e Forest, Khapi (KAD), 5) Temperate Mix Forest Salmia Chikar (TMF), 6) \u003cem\u003ePinus\u003c/em\u003e and \u003cem\u003eAesculus\u003c/em\u003e Forest, Sinjli (PADF), 7) \u003cem\u003eQuercus\u003c/em\u003e Forest, Upper Neelum (QDF), 8) \u003cem\u003ePinus\u003c/em\u003e and \u003cem\u003eCedrus\u003c/em\u003e Forest, Lubgran (PCDF), 9) \u003cem\u003eOlea\u003c/em\u003e and \u003cem\u003eQuercus\u003c/em\u003e Mix Forest, Baandi (OQMF), and 10) Pure Pine Forest, Dewliyan (PPDF) (\u003cb\u003eFig.\u0026nbsp;1).\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Vegetation survey and measurements\u003c/h2\u003e \u003cp\u003eA plot of one ha (100 m\u0026times;100 m) was marked in each forest type for measurements in winter 2023 and summer 2024. Five random 10 m\u0026times;10 m quadrats were selected in each plot to measure density, cover, and frequency of each tree and shrub species, and a 1 m \u0026times; 1 m quadrat within the quadrat was selected to measure density, cover, and frequency of each herbaceous species.\u003c/p\u003e \u003cp\u003eSpecies diversity was determined using established ecological indices, namely, the Shannon-Wiener index (H\u0026prime;) for species diversity, Simpson\u0026rsquo;s index (D) for dominance, Pielou\u0026rsquo;s evenness index (J) for species distribution uniformity, richness index (SR) for species abundance.\u003c/p\u003e \u003cp\u003eShannon diversity index was calculated as:\u003c/p\u003e \u003cp\u003eH\u0026prime; = -\u0026sum;pi log pi\u003c/p\u003e \u003cp\u003ewhere: Pi\u0026thinsp;=\u0026thinsp;ni/N, N=\u0026sum;ni\u0026thinsp;=\u0026thinsp;total number of individuals of all species; ni\u0026thinsp;=\u0026thinsp;number of individuals of one species.\u003c/p\u003e \u003cp\u003eSimpson diversity index was calculated as:\u003c/p\u003e \u003cp\u003eD= \u0026sum;ni(ni-1)/N(N-1)\u003c/p\u003e \u003cp\u003ewhere ni\u0026thinsp;=\u0026thinsp;number of individual species; N\u0026thinsp;=\u0026thinsp;total number of individuals of all species.\u003c/p\u003e \u003cp\u003eSpecies evenness was calculated as (Pielou, 1975):\u003c/p\u003e \u003cp\u003eJ\u0026thinsp;=\u0026thinsp;H/LogS\u003c/p\u003e \u003cp\u003ewhere H\u0026thinsp;=\u0026thinsp;Shannon\u0026rsquo;s diversity index; S\u0026thinsp;=\u0026thinsp;total number of species in a community.\u003c/p\u003e \u003cp\u003eSpecies richness was calculated as (Menhinick, 1964):\u003c/p\u003e \u003cp\u003eD\u0026thinsp;=\u0026thinsp;S/\u003cb\u003e\u0026radic;N\u003c/b\u003e\u003c/p\u003e \u003cp\u003ewhere S\u0026thinsp;=\u0026thinsp;total number of species; N\u0026thinsp;=\u0026thinsp;total number of individuals.\u003c/p\u003e \u003cp\u003eSpecies maturity was determined by the method of Pichi \u0026ndash; Sermolli\u0026rsquo;s (1948):\u003c/p\u003e \u003cp\u003eSpecies Maturity\u0026thinsp;=\u0026thinsp;F/S\u003c/p\u003e \u003cp\u003ewhere F\u0026thinsp;=\u0026thinsp;total frequency of a community; S\u0026thinsp;=\u0026thinsp;total species of a community.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Soil sampling and analysis\u003c/h2\u003e \u003cp\u003eFive replicates of soil samples were collected from each of two soil depths, 0\u0026ndash;10 cm, and 10\u0026ndash;20 cm, in each 10 m\u0026times;10 m quadrat using a soil auger of 12.6 cm diameter (Eijkelkamp, Giesbeek, Netherlands), stored in polyethylene bags and transported to the laboratory. A total of 200 soil samples were collected, 100 in each season. Soil samples were air-dried, ground, and passed through a 2-mm sieve for physical analysis, including soil EC, pH, soil texture, soil moisture content (SMC), and bulk density (BD), and chemical analysis, including soil organic carbon (SOC), total phosphorus (TP), total potassium (TK), total nitrogen (TN), available phosphorus (AP) and potassium (AK), cation exchange capacity (CEC), and the microelements, iron (Fe), copper (Cu), manganese (Mn) and zinc (Zn) (Almas et al. 2023). The samples from upper soil depth in each forest were combined, so that there were a total of 20 samples; part of each samples was stored at -20˚C for soil microbial biomass carbon (MBC) and part was stored at -80˚C for metagenomic analysis (Majorbio Company, Shanghai, China; methodology explained in supplementary file) (Lepcha and Devi 2020).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Metagenomics analysis\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 DNA extraction and quality control\u003c/h2\u003e \u003cp\u003eGenomic DNA was extracted from soil samples using the DNeasy PowerSoil Kit (Qiagen). DNA integrity and quality were assessed via 1% agarose gel electrophoresis. Fragmentation to ~\u0026thinsp;350 bp used a ultrasonicator (Covaris M220, Woburn MA, USA). Paired-end (PE) libraries were constructed by ligating a \"Y\" adapter, followed by magnetic bead screening to remove linker autolinkers. PCR amplification enriched the library templates, and denaturation with sodium hydroxide yielded single-stranded DNA for sequencing.\u003c/p\u003e \u003cp\u003eBridge PCR amplified the library, forming dense clusters of identical DNA amplicons on a sequencing chip. Illumina sequencing used modified DNA polymerase and fluorescently labelled dNTPs. Each cycle incorporated a single base, detected via laser scanning, followed by fluorophore cleavage to enable subsequent cycles. Index tags were added to distinguish samples, and raw sequencing data were stored in FASTQ format (FQ1 and FQ2 files for paired-end reads). Raw sequencing data were evaluated for quality using fastp (v0.20.1) and adapter sequences and low-quality reads (average quality\u0026thinsp;\u0026lt;\u0026thinsp;20, length\u0026thinsp;\u0026lt;\u0026thinsp;50 bp) were removed. Base quality distribution and A/T/G/C content were analyzed to ensure data integrity. Clean data were retained for downstream analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 Assembly and gene prediction\u003c/h2\u003e \u003cp\u003eClean reads were assembled into contigs using appropriate splicing software, retaining contigs\u0026thinsp;\u0026ge;\u0026thinsp;300 bp. Open reading frames (ORFs) were predicted using MetaGene or Prodigal (v2.6.3), with ORFs\u0026thinsp;\u0026ge;\u0026thinsp;100 bp translated into amino acid sequences. ORFs from all samples were clustered using CD-HIT (90% identity, 90% coverage) to construct a non-redundant gene set. The longest sequence in each cluster was selected as the representative.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3 Taxonomic and functional annotation\u003c/h2\u003e \u003cp\u003eThe non-redundant gene set was aligned to the NR database using DIAMOND (blastp, E-value\u0026thinsp;\u0026le;\u0026thinsp;1e-5). Taxonomic annotations were derived from the NR database, and species abundances were determined at various taxonomic levels (Domain to Order). Sequences were aligned to the eggNOG database using DIAMOND (blastp, E-value\u0026thinsp;\u0026le;\u0026thinsp;1e-5) to assign Clusters of Orthologous Groups (COGs), and to the Kyoto Encyclopedia of Genes and Genomes (KEGG) genes database (blastp, E-value\u0026thinsp;\u0026le;\u0026thinsp;1e-5) to annotate pathways, modules, and orthologs (KOs). In addition, sequences were aligned to the CAZy database using hmmscan (E-value\u0026thinsp;\u0026le;\u0026thinsp;1e-5) to identify carbohydrate-active enzymes (CAZymes), including glycoside hydrolases (GHs), glycosyltransferases (GTs), and polysaccharide lyases (PLs).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Statistical analyses\u003c/h2\u003e \u003cp\u003eTwo-way analysis of variance (ANOVA) in Graphpad prism (version 8.0.1) compared soil variables among forests and Tukey\u0026rsquo;s test separated means. A t-test compared soil variables between soil depths within a forest type and a p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was accepted as significant. R Studio (version 4.4.2) was used for Spearman's rank correlation to test the relationship between vegetation diversity indices in both seasons, Beta diversity distance heatmaps were created in R Studio to determine microbial communities, KEGG, COG and CAZy similarities among forest types and seasons. The non-parametric Wilcoxon test compared KEGG, COG, CAZy and non-redundant (NR) microbial community abundance between seasons and the non-parametric Kruskal-Wallis test compareed community abundance among forest types. The structural equation model (SEM) quantified the relative contributions of plant diversity, soil properties, and microbial communities (MBC, functional pathways, dominant phyla) to ecosystem multifunctionality. Several software tools were used in the metagenomics study, including Fastp(version 0.20.0) for read preprocessing, BWA (version 0.7.9a) for alignment, Megahit (version 1.1.2) for assembly, CD-HIT (version 4.6.1) for clustering, Diamond (version 0.8.35) for annotation, and databases such as NR (version 20200604), KEGG (version 94.2), and eggnog (version 4.5.1) for taxonomic and functional analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Seasonal Variations in Vegetation Patterns and Diversity\u003c/h2\u003e \u003cp\u003eIn summer, species richness ranged from 1.08 to 2.88, which was greater (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) than the 0.69 to 1.72 in winter. Shannon diversity, a measure of community diversity considering richness and evenness, was generally greater (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in summer than winter. In summer, it ranged from 2.77 to 3.63, with PTDF displaying the highest diversity, and in winter it ranged from 2.13 to 3.01, with TMF displaying the highest diversity. Simpson diversity, which measures dominance, ranged from 0.81 to 0.93 in winter, with OQMF and TMF the highest values, and from 0.92 to 0.96 in summer (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.008), with PTDF the highest.\u003c/p\u003e \u003cp\u003ePielou's evenness, which measures the eveness of species distribution, did not differ between seasons in some forest types. In winter (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.005), evenness ranged from 0.79 to 0.91, with KAD the highest, while in summer it ranged from 0.83 to 0.91, with PPDF the highest. The community maturity index ranged from 34.7 to 63.6 in winter, with ADF the highest, while in summer the index was lesser (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.02) and ranged from 29.0 to 55.2, with PPDF the highest (\u003cb\u003eTab. S1, 2)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eSpecies richness (SR) and diversity indices of vegetation were greater in summer than in winter, which reflects the greater plant productivity due to suitable weather conditions. In Spearman's rank correlation analysis (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/b\u003e, a positive correlation emerged between Shannon diversity and Simpson diversity (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and between SR and Simpson diversity (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in winter. In contrast, a negative correlation emerged between community maturity (MI) and SR in winter (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Seasonal variations in soil properties\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Soil pH and EC\u003c/h2\u003e \u003cp\u003eThe study revealed significant seasonal and spatial variations in soil pH across the 10 forest types. In winter, the minimum soil pH was 6.04 in MWB, while the maximum pH was 7.5 in TMF, for the 0\u0026ndash;10 cm and 10\u0026ndash;20 cm depths, respectively. In summer, the minimum pH was 5.9 in MWB and the maximum pH was 7.9 in PPDF, for the same depth layers. Notably, soil pH was higher in summer than winter (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), except for MWB and QDF (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Additionally, Soil pH displayed a non-significant increasing trend with depth (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eIn winter, the minimum EC was 0.090 dS/m in PCDF, while the maximum was 0.83 dS/m in KAD in the 10\u0026ndash;20 cm and 0\u0026ndash;10 cm depths, respectively. In summer, the minimum EC was 0.12 dS/m in PTDF, and the maximum was 0.87 dS/m in KAD, for the same depth layers. Soil EC has non-significant decrease(\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) with depth and was greater (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in summer than winter (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Soil moisture, bulk density and texture\u003c/h2\u003e \u003cp\u003eSoil moisture content was greater in winter than summer and decreased with depth. In winter, the minimum was 6.84% in PCDF, while the maximum was 23.5% in MWB, in 10\u0026ndash;20 cm and 0\u0026ndash;10 cm depths, respectively. In summer, at these depths, the minimum moisture content was 4.7% in OQMF, and the maximum was 16.0% in KAD. Soil moisture content decreased (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with depth and was lesser (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in summer than winter. Soil bulk density increased (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with depth, ranging from 0.44 g/cm\u0026sup3; in PCDF to 1.14 g/cm\u0026sup3; in PADF for the 0\u0026ndash;10 cm and 10\u0026ndash;20 cm depths, respectively. For soil texture, clay content ranged from 25% in KAD to 43% in PCDF, sand content from 6% in PCDF to 46% in PTDF, and silt content from 29% in PTDF to 53% in PPDF (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Soil organic carbon (SOC) and total nitrogen (TN)\u003c/h2\u003e \u003cp\u003eSOC and TN exhibited seasonal and depth-related trends. In winter, SOC ranged from 36.1 g/kg in TMF to 113.6 g/kg in MWB for the 10\u0026ndash;20 cm and 0\u0026ndash;10 cm depths, respectively and in summer, from 20.4 g/kg in PADF to 93.7 g/kg in MWB for the same depth layers. SOC decreased (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with depth, and was lesser (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in summer than winter due to rapid plant growth. TN ranged from 2.12 g/kg in PPDF to 9.26 g/kg in MWB in winter and from 2.02 g/kg in PPDF to 9.16 g/kg in MWB in summer. TN did not differ (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) between seasons but decreased (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) with soil depth (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). SOC(Mg/ha) and TN(Mg/ha) stock, following the same trend with soil depth and season was also calculated (\u003cb\u003eTab. S3,4\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.2.4 Total and available phosphorus and potassium\u003c/h2\u003e \u003cp\u003eSoil total phosphorus (TP) ranged from 0.263 g/kg in TMF to 0.920 g/kg in MWB in the 10\u0026ndash;20 cm and 0\u0026ndash;10 cm layers, respectively, and decreased (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with soil depth but did not differ between seasons. Soil total potassium (TK) has did not differ (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) between seasons or soil depth.. It ranged from 0.76 g/kg in TMF to 2.77 g/kg in MWB in winter and from 0.56 g/kg in TMF to 2.57 g/kg in MWB in summer (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAvailable phosphorus (AP) decreased (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.01) with soil depth and was greater (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.05) in summer than winter, with a minimum of 0.00075 g/kg in PPDF and a maximum of 0.0110 g/kg in QDF in winter, and a minimum of 0.0028 g/kg in TMF and a maximum of 0.0113 g/kg in QDF in summer. AK did not differ (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) between seasons or depth. In winter it ranged from a minimum of 0.131 g/kg in TMF to a maximum of 0.362 g/kg in KAD, and in summer from 0.109 g/kg in PPDF to 0.314 g/kg in KAD (\u003cb\u003eFig. S2\u003c/b\u003e). TK(Mg/ha) ,TP(Mg/ha), AP(Mg/ha) and AK(Mg/ha) stock, following the same trend with soil depth and season was also calculated (\u003cb\u003eTab. S3,4\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.2.5 Cation exchange capacity (CEC), microbial biomass carbon (MBC), and micronutrients\u003c/h2\u003e \u003cp\u003eCEC was greater (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.0001\u003cem\u003e)\u003c/em\u003e in summer than winter, with a minimum of 16 meq/100g in TMF and a maximum of 26 meq/100g in MWB in winter, and a minimum of 21 meq/100g in TMF and a maximum of 31 meq/100g in MWB in summer. MBC was greater (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001- \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) in summer than winter, with minimum of 0.402 g/kg in OQMF, and a maximum of 1.27 g/kg in KAD in summer and a minimum of 0.251 g/kg in PCDF and a maximum of 0.480 g/kg in ADF in winter (\u003cb\u003eFig. S2\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eAll micronutrients decreased (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001- \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) with depth. Iron (Fe) ranged from 2411 mg/kg in KAD to 3205 mg/kg in QDF for the 10\u0026ndash;20 cm and 0\u0026ndash;10 cm depths, respectively; manganese (Mn) ranged from 165 mg/kg in KAD to 487 mg/kg in PADF; zinc (Zn) ranged from 25.5 mg/kg in TMF to 55.2 mg/kg in QDF; and copper (Cu) ranged from 1.25 mg/kg in ADF to 22.7 mg/kg in PTDF (\u003cb\u003eFig. S3\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Seasonal variations in microbial alpha diversity\u003c/h2\u003e \u003cp\u003eShannon diversity was greater in all forest types in summer than winter, ranging from 6.55 in PPDF to 6.88 in ADF in summer and from 4.97 in ADF to 5.78 in PCDF in winter.There was no significant difference (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) between forest types but differed (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between seasons.\u003c/p\u003e \u003cp\u003eIn winter, microbial Simpson diversity ranged from 0.0160 in PCDF to 0.0358 in ADF; whereas in summer, the diversity was generally lesser, ranging from 0.0059 in ADF to 0.0137 in TMF. There was no difference (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) among forest types there was a difference (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between seasons.\u003c/p\u003e \u003cp\u003eThe Chao diversity index, an estimator of total species richness, did not differ (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) among forest types, but was greater (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in summer than winter. The Sobs index, which represents observed species richness, displayed similar trends to Chao index, ranging from 17067 in OQMF to 21081 in PCDF in winter and from 25362 in PCDF to 27933 in TMF in summer (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Seasonal variations in taxon function beta diversity\u003c/h2\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1 COG functional beta diversity\u003c/h2\u003e \u003cp\u003eThe Bray-Curtis dissimilarity matrices and corresponding heatmap (\u003cb\u003eFig.\u0026nbsp;5)\u003c/b\u003e revealed the functional beta diversity of microbial communities across the 10 forest types. In winter, there was moderate to high dissimilarity among the forest types, with distinct clustering. ADF and PADF displayed the smallest dissimilarity (Bray-Curtis distance\u0026thinsp;=\u0026thinsp;0.055), indicating similar functional profiles, while PPDF and ADF displayed the largest dissimilarity (Bray-Curtis distance\u0026thinsp;=\u0026thinsp;0.161), indicating large differences in potential function between them. MWB and OQMF exhibited moderate dissimilarity (Bray-Curtis distance\u0026thinsp;=\u0026thinsp;0.109), suggesting a partial overlap in functional traits.\u003c/p\u003e \u003cp\u003eBoth ADF, PADF, KAD, and QDF, and PTDF, PADF, MWB, and KAD formed tight clusters, indicating functional similarity. These groups may share similar environmental conditions or microbial adaptations. MWB, OQMF, PTDF, PCDF, TMF, and PPDF formed a separate cluster with high internal dissimilarity, reflecting diverse functional profiles within this group. In summer, the microbial functional profiles exhibited more distinct groupings than in winter, with new clustering patterns emerging. PTDF and PADF exhibited the smallest dissimilarity (Bray-Curtis distance\u0026thinsp;=\u0026thinsp;0.052), indicating similar functional profiles in summer. PPDF and OQMF also displayed low dissimilarity (Bray-Curtis distance\u0026thinsp;=\u0026thinsp;0.075), suggesting shared functional traits between them. The largest dissimilarity occurred between PPDF and PCDF (Bray-Curtis distance\u0026thinsp;=\u0026thinsp;0.166), indicating large differences in their functional potential. PPDF exhibited greater dissimilarity with other forest types in winter than in summer, indicating seasonal changes in its functional potential, in temperature, precipitation, or nutrient availability. MWB and OQMF displayed consistent functional similarity between seasons, suggesting resilience to seasonal climatic changes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2 CAZy Functional Beta Diversity\u003c/h2\u003e \u003cp\u003eThe Bray-Curtis dissimilarity matrices and corresponding heatmaps (\u003cb\u003eFig.\u0026nbsp;5)\u003c/b\u003e revealed the functional beta diversity of microbial communities based on carbohydrate-active enzymes (CAZy) profiles. In winter, the CAZy functional profiles exhibited moderate to high dissimilarity across forest types, with distinct clustering patterns. ADF and PADF displayed the smallest dissimilarity (Bray-Curtis distance\u0026thinsp;=\u0026thinsp;0.075), indicating that these forests harbor microbial communities with similar carbohydrate-active enzymatic capabilities during winter. PPDF and ADF displayed the greatest dissimilarity (Bray-Curtis distance\u0026thinsp;=\u0026thinsp;0.175), indicating large differences in CAZy potential between them. MWB and OQMF exhibited moderate dissimilarity (Bray-Curtis distance\u0026thinsp;=\u0026thinsp;0.111), suggesting a partial overlap in CAZy functional traits.\u003c/p\u003e \u003cp\u003eADF, PADF, KAD, and QDF formed a tight cluster, while MWB, OQMF, PTDF, PCDF, TMF, and PPDF formed a separate cluster with large internal dissimilarity, reflecting diverse CAZy profiles within this group. In summer, the CAZy functional profiles displayed more distinct groupings than in winter, with new clustering patterns emerging in the heatmap. PTDF and PADF exhibited the smallest dissimilarity (Bray-Curtis distance\u0026thinsp;=\u0026thinsp;0.064), indicating similar CAZy profiles during summer and that these forests share microbial communities with similar carbohydrate-active enzymatic functions. PPDF and OQMF also displayed small dissimilarity (Bray-Curtis distance\u0026thinsp;=\u0026thinsp;0.088), suggesting shared CAZy traits between these forest types, while the greatest dissimilarity occurred between PPDF and PCDF (Bray-Curtis distance\u0026thinsp;=\u0026thinsp;0.186).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e3.4.3 KEGG beta diversity\u003c/h2\u003e \u003cp\u003eThe beta diversity analysis revealed distinct patterns among the microbial communities in both seasons. In winter, the greatest dissimilarity was observed between ADF and PPDF (0.087), indicating large differences in their microbial composition, whereas, the smallest dissimilarity was between MWB and OQMF (0.0346), suggesting a close relationship between these communities. In summer, the greatest dissimilarity was between PADF and QDF (0.097), while the smallest dissimilarity was between MWB and OQMF (0.035), consistent with the winter findings, indicating stable relationships between these communities between seasons. The pairwise dissimilarity findings (PDF) for both seasons were consistent, with values ranging from 0.02 to 0.08.\u003c/p\u003e \u003cp\u003eThe KEGG beta diversity analysis for both winter and summer provides insights into microbial communities dynamics in response to seasonal changes. The consistent small dissimilarity between MWB and OQMF between seasons suggests a stable microbial community, possibly due to similar environmental conditions or functional roles. The large dissimilarity in specific pairs, such as ADF and PPDF in winter and PADF and QDF in summer, indicates a shift in microbial composition, likely driven by seasonal environmental factors. These findings contribute to our understanding of microbial ecology and highlight the importance of seasonal variations in microbial community studies (\u003cb\u003eFig. S4)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e3.4.4 NR beta diversity\u003c/h2\u003e \u003cp\u003eThe winter NR beta diversity analysis revealed significant differences in microbial community structures among the 10 forest types. The greatest dissimilarity was observed between PTDF and PPDF (0.509), indicating substantial differences in their microbial composition; whereas, the smallest dissimilarity was between MWB and KAD (0.146). The pairwise comparisons indicated the variability in microbial diversity, with notable differences between PTDF and other forest types, such as ADF (0.385) and TMF (0.424). In summer, the greatest dissimilarity in NR beta diversity patterns was noted between PTDF and PPDF (0.509), consistent with the winter findings, indicating persistent differences in microbial profiles between these forest types. The pairwise comparisons also revealed large differences between PPDF and other forest types, including ADF (0.423) and TMF (0.388) (\u003cb\u003eFig. S4)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Seasonal impact on taxon functional composition\u003c/h2\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e3.5.1 COG functional composition among forest types and between seasons\u003c/h2\u003e \u003cp\u003eThe relative abundance of COG (clusters of orthologous groups) functional categories was calculated for each forest type and season. The dominant COG categories across all forest types and seasons included R (general function prediction), E (amino acid transport and metabolism), C (energy production and conversion), and G (carbohydrate transport and metabolism), which collectively represented most of the functional potential in the microbial communities. R was the most relatively abundant COG category, ranging from 0.093 to 0.105 across forest types and seasons. E and C also displayed high relative abundances, ranging from 0.073 to 0.101 and 0.069 to 0.082, respectively, while G exhibited moderate abundances, ranging from 0.069 to 0.078. Seasonal comparisons revealed minor fluctuations in the relative abundances of COG categories, but no consistent patterns were observed across forest types.\u003c/p\u003e \u003cp\u003eIn the ADF, the relative abundance of R was greater in winter (0.105) than summer (0.093), whereas in the MWB, it was marginally greater in summer (0.101) than winter (0.095) Similar trends were observed for dominant COG categories, such as E and C, which exhibited minimal seasonal differences among forest types. The relative abundances of COG categories did not differ (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) among forest types, suggesting that the functional potential of microbial communities was relatively consistent across forests and may indicate functional redundancy within microbial communities. This suggests that different microbial taxa can perform similar metabolic functions, thereby maintaining ecosystem processes despite variations in vegetation composition and environmental conditions. The high relative abundance of generalist functions (e.g., R) further supports this premise, as these functions are essential for microbial survival and adaptation across diverse habitats (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e3.5.2 CAZy functional composition among forest types and between seasons\u003c/h2\u003e \u003cp\u003eThe functional composition of microbial communities, as inferred from CAZy (carbohydrate-active enzymes) families, was calculated for each forest type and season.The dominant CAZy families across all forest types and seasons included GT41 (glycosyltransferase family 41), GT4 (glycosyltransferase family 4), GT2_glycos_transf_2 (glycosyltransferase family 2), and CE1 (carbohydrate esterase family 1), which collectively represented most of the carbohydrate-active enzymatic potential in the microbial communities. GT41 was the most abundant CAZy family, with relative abundances ranging from 0.057 to 0.131. GT4 and GT2_Glycos_transf_2 also displayed high relative abundances, ranging from 0.045 to 0.076 and 0.041 to 0.063, respectively. CE1 exhibited moderate abundances, ranging from 0.042 to 0.058, indicating the importance of carbohydrate esterification in these forest ecosystems.\u003c/p\u003e \u003cp\u003eSeasonal comparisons revealed minor fluctuations in the relative abundances of CAZy families, but no consistent pattern was observed across forest types. In ADF, the relative abundance of GT41 was greater in winter (0.131) than summer (0.063); whereas in MWB, the relative abundance was greater in summer (0.100) than winter (0.087). Similar trends were observed for dominant CAZy families, such as GT4 and GT2_Glycos_transf_2, which exhibited minimal seasonal variation across forest types. The high relative abundance of generalist families (e.g., GT41) further supports this premise, as these families are essential for microbial survival and adaptation across diverse habitats. The general lack of differences in the relative abundances of CAZy families across forests and between seasons indicates there may be functional redundancy within the microbial communities and that different microbial taxa can perform similar carbohydrate-active enzymatic functions, thereby maintaining ecosystem processes despite variations in vegetation composition and environmental conditions. It also indicates that the microbial carbohydrate-active enzymatic potential is resilient to variations in vegetation composition and seasonal environmental conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section3\"\u003e \u003ch2\u003e3.5.3 KEGG level 3 functional composition among forest types and between seasons\u003c/h2\u003e \u003cp\u003eThe functional composition of microbial communities, as inferred from KEGG level 3 pathways, was calculated for each forest type and season. The dominant pathways across all forest types and seasons included metabolic pathways, biosynthesis of secondary metabolites, microbial metabolism in diverse environments, and two-component system, which collectively represented most of the functional potential in the microbial communities.\u003c/p\u003e \u003cp\u003eMetabolic pathways were the most abundant pathway, with relative abundances ranging from 0.165 to 0.172 across forest types and seasons. Biosynthesis of secondary metabolites and microbial metabolism in diverse environments also were relatively abundant, ranging from 0.068 to 0.072 and 0.057 to 0.060, respectively. Two-component systems exhibited moderate abundances, ranging from 0.022 to 0.032, indicating the importance of signal transduction in these forest ecosystems. Seasonal comparisons revealed minor fluctuations in the relative abundances of pathways, but no consistent pattern was observed across forest types.\u003c/p\u003e \u003cp\u003eIn the ADF, the relative abundance of metabolic pathways was slightly greater in winter (0.169) than summer (0.166), while in the MWB, it was marginally greater in summer (0.168) than winter (0.166). Similar trends were observed for other dominant pathways, such as biosynthesis of secondary metabolites and microbial metabolism in diverse environments, with minimal seasonal variation across forest types. The was no difference (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) in the relative abundances of KEGG level 3 pathways among forest types or between seasons, which may indicate functional redundancy within the microbial communities and suggests that different microbial taxa can perform similar metabolic functions. The high relative abundances of generalist pathways (e.g., metabolic pathways) further supports this premise, as these pathways are essential for microbial survival and adaptation across diverse habitats (\u003cb\u003eFig. S5)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section3\"\u003e \u003ch2\u003e3.5.4 Microbial community composition at the phylum level\u003c/h2\u003e \u003cp\u003eThe dominant phyla across all forest types and seasons included Actinomycetota, Pseudomonadota, and Acidobacteriota, accounting for most of the microbial community composition. Actinomycetota was the most abundant phylum, with the relative abundance ranging from 0.164 to 0.558, while Pseudomonadota ranged from 0.120 to 0.392 and Acidobacteriota ranged from 0.043 to 0.216. Other notable phyla included Verrucomicrobiota, Candidatus_Rokubacteria, and Chloroflexota.\u003c/p\u003e \u003cp\u003eSeasonal comparisons revealed fluctuations in the relative abundances of microbial phyla, but no consistent patterns were observed across forest types. In ADF, the relative abundance of Actinomycetota was greater in summer (0.451) than in winter (0.164), but in the PTDF, it was greater in winter (0.558) than summer (0.268). Overall, the relative abundances of microbial phyla and the composition of microbial communities were consistent and did not differ (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) among forest types and between seasons. The lack of significant difference suggests that the microbial community structure was resilient to variations in seasonal vegetation composition and environmental conditions, which may play a critical role in maintaining ecosystem functions, such as nutrient cycling and organic matter decomposition (\u003cb\u003eFig. S5)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Seasonal drivers of Ecosytem: Structural equation model (SEM)\u003c/h2\u003e \u003cp\u003eThe structural equation models revealed distinct seasonal patterns, with summer showing significantly stronger relationships between microbial α-diversity (R\u0026sup2;=0.64 vs winter's 0.52) and microbial biomass carbon (R\u0026sup2;=0.45 vs 0.35) with ecosystem functioning. Taxonomic (R\u0026sup2;=0.43 vs 0.36) and functional diversity (R\u0026sup2;=0.72 vs 0.63) were more strongly linked to multifunctionality in summer, while winter exhibited weaker but more stable soil-microbe interactions. Both models (\u003cb\u003eFig. S6)\u003c/b\u003e showed excellent fit (summer CFI\u0026thinsp;=\u0026thinsp;0.99, RMSEA\u0026thinsp;=\u0026thinsp;0.01; winter CFI\u0026thinsp;=\u0026thinsp;0.98, RMSEA\u0026thinsp;=\u0026thinsp;0.03), confirming robust seasonal dynamics in microbial-ecosystem relationships\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe Western Himalayan forests of Azad Jammu and Kashmir (AJK), Pakistan, represent a unique and ecologically important region characterized by diverse forest types and large seasonal variations. This study investigated the seasonal impacts on vegetation patterns, soil characteristics, and microbial diversity across 10 forest types during winter, 2023, and summer, 2024. The findings revealed seasonal and spatial variations in vegetation, soil properties, and microbial community composition, providing valuable insights into the ecological dynamics of high altitude forests.\u003c/p\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Seasonal variations in vegetation patterns and diversity\u003c/h2\u003e \u003cp\u003eThe analysis demonstrated clear seasonal shifts in vegetation communities, with greater species richness and diversity indices in summer than winter. This trend aligns with findings from other temperate and Himalayan ecosystems, where seasonal changes in temperature and precipitation influenced vegetation dynamics. For example, studies in the Eastern Himalayas (Sharma et al. 2022b) and European temperate forests also reported greater species richness and diversity during warmer months due to favorable growth conditions (Tinya et al. 2021). The positive correlations between Shannon and Simpson diversity indices in both seasons indicate that as species richness increased, dominance by a few species decreased leading to a more equally distributed community structure. This finding is consistent with observations in other forest ecosystems, where increased resource availability in summer promoted greater species evenness (Gu et al. 2023). However, the lesser community maturity during summer indicated that younger, less stable communities may dominate in warmer months, potentially due to rapid growth and turnover of species. This contrasts with studies in tropical forests, where community maturity remained relatively stable across seasons (McDowell et al. 2020). The decline in maturity in the present study may reflect the impact of environmental stressors, such as increased herbivory or competition, which can disrupt the establishment of mature plant communities. These findings underscore the importance of seasonal monitoring to understand the long-term impacts of climate change on forest ecosystems, particularly in regions like the Western Himalayas, where seasonal variations are pronounced.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Soil properties and nutrient dynamics\u003c/h2\u003e \u003cp\u003eSoil properties exhibited seasonal and depth-related variations, reflecting the influence of environmental conditions on soil processes. Soil pH was generally greater in summer than winter, except in MWB and QDF, which suggests that higher temperatures and greater microbial activity may increase soil alkalinity. This aligns with studies in temperate forests, where seasonal warming increased soil pH due to enhanced microbial mineralization of organic matter (Yin et al. 2024b). Conversely, the decrease in soil moisture content and SOC in summer highlights the impact of plant development and microbial decomposition, which deplete soil resources. Similar trends were reported in Mediterranean forests, where summer droughts reduced soil moisture content and accelerated organic matter decomposition (Najera et al. 2020). The consistent decrease in SOC and total nitrogen (TN) with depth and season emphasizes the role of surface organic matter in maintaining soil fertility. This is consistent with findings in other mountainous regions, where topsoil layers were critical for nutrient cycling and carbon storage (Tian et al. 2020). The increase in available phosphorus (AP) and decrease in available potassium (AK) during summer, despite the stable total phosphorus (TP) and decrease in total potassium (TK), suggest that microbial activity and root exudates may enhance nutrient availability in warmer months. These findings are consistent with previous studies that seasonal changes in temperature and moisture influenced nutrient cycling and microbial activity in forest soils (Yin et al. 2024a). The greater cation exchange capacity (CEC) and microbial biomass carbon (MBC) in summer further support the role of microbial communities in nutrient mobilization and soil health, as observed in other forest ecosystems (Ngaba et al. 2024).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Microbial diversity and functional composition\u003c/h2\u003e \u003cp\u003eThe metagenomic analysis revealed seasonal shifts in microbial diversity, with greater Shannon and Chao diversity indices in summer than in winter. This increase in microbial diversity during summer likely reflects the more favorable environmental conditions for microbial growth and activity. Similar patterns were reported in temperate forests, where higher temperatures and increased soil moisture availability enhanced microbial diversity (Nottingham et al. 2018). The consistent low dissimilarity between MWB and OQMF across seasons suggests stable microbial compositions, possibly due to similar environmental conditions or functional roles. This is consistent with studies in mixed forests, where vegetation type strongly influenced microbial community composition (Zubek et al. 2024). In contrast, the high dissimilarity between PTDF and PPDF highlights the distinct microbial communities in these forest types, likely driven by differences in vegetation and soil properties, as observed in coniferous versus mixed forests (Llad\u0026oacute; et al. 2018).\u003c/p\u003e \u003cp\u003eThe functional composition of microbial communities, as inferred from COG, CAZy, and KEGG pathways, displayed strong consistency among forest types and between seasons. The dominance of generalist functions, such as metabolic pathways and carbohydrate-active enzymes, indicate functional redundancy within microbial communities, which may enhance ecosystem resilience by ensuring that critical metabolic functions are maintained despite variations in environmental conditions. Similar findings were reported in other forest ecosystems, where functional redundancy stabilized nutrient cycling and organic matter decomposition under changing environmental conditions (Dhyani et al. 2022). The lack of significant differences in functional composition across seasons and forest types further supports the premise that microbial communities in the Western Himalayas are highly adaptable and resilient, as was observed in other high-altitude ecosystems (Bhadouria et al. 2023).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec35\" class=\"Section2\"\u003e \u003ch2\u003e4.4 SEM interpreting Seasonal Shifts: Microbial ecology between summer activity and winter resilience\u003c/h2\u003e \u003cp\u003eThe findings align with and expand upon previous studies of seasonal microbial dynamics in mountain ecosystems. The observed summer increase in microbial α-diversity and functional activity corroborates work by (Zhou et al. 2020) in alpine soils, who similarly found warmer temperatures enhanced microbial metabolic rates and diversity. Our results showing stronger soil-microbe decoupling in summer mirror findings from (Baldrian et al. 2012) in Himalayan meadows, where reduced moisture availability during warm periods weakened microbial-SOC relationships. The winter patterns of microbial dormancy and carbon stabilization support the cold storage hypothesis proposed by (Manzoni et al. 2014) demonstrating how low-temperature conditions preserve microbial biomass and organic matter. Notably, the seasonal microbial functional resilience importance, echoes global meta-analyses by (Bahram et al. 2018), suggesting a universal feature of temperate mountain ecosystems. However, our observation that multifunctionality remains strongly microbial-linked in both seasons contrasts with some boreal studies (Fierer et al. 2012), possibly reflecting the unique transitional nature of Himalayan forests. These comparisons highlight how our findings both confirm general ecological principles about seasonal microbial ecology while revealing ecosystem-specific adaptations to the Himalayan environment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec36\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Ecological implications and conservation strategies\u003c/h2\u003e \u003cp\u003eThe findings of this study have important implications for the conservation and sustainable management of Western Himalayan forests. The seasonal variations in vegetation, soil properties, and microbial diversity demonstrate the dynamic nature of these ecosystems and their sensitivities to environmental changes. The greater species richness and microbial diversity in summer than in winter underscores the importance of preserving these forests as biodiversity hotspots, as emphasized in other Himalayan regions (Meena et al. 2024). However, the decline in community maturity and SOC in summer raises concerns about the long-term impacts of climate change on ecosystem stability, as reported in other temperate forests (Liu et al. 2024). The functional redundancy in microbial communities suggests that these ecosystems may be resilient to environmental changes, but this resilience may be challenged by ongoing climate warming and anthropogenic activities. Conservation strategies should focus on maintaining soil health, preserving biodiversity, and mitigating the impacts of climate change. This could include promoting sustainable land-use practices, enhancing soil organic matter through afforestation, and implementing climate-adaptive management strategies, as recommended in studies from other mountainous regions (Kougioumoutzis et al. 2024).\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions and future directions","content":"\u003cp\u003eThis study revealed seasonal and spatial variations in vegetation, soil properties, and microbial diversity across the Western Himalayan forests of Azad Jammu and Kashmir. The findings emphasized that species richness, diversity indices (Shannon-Wiener and Simpson), and microbial biomass carbon (MBC) were greater, while soil moisture and soil organic carbon (SOC) were lesser in summer than winter, highlighting the strong influence of seasonal climatic conditions. Microbial alpha diversity was greater by 20\u0026ndash;25% in summer than winter, with no significant change in functional profiles, indicating functional stability despite seasonal shifts. Beta diversity analysis revealed distinct microbial community structures, with small dissimilarity in mixed forests (e.g., MWB and OQMF), but large dissimilarity in coniferous forests, underscoring the role of forest type in shaping microbial communities. The structural equation models (SEMs) revealed that Western Himalayan forests maintain ecosystem stability through complementary seasonal microbial regimes. The consistency of our results with global patterns (summer, activity peaks and winter, carbon sequestration) suggests Himalayan microbes follow fundamental biogeochemical rules, while the ecosystem's exceptional multifunctionality across seasons may represent a unique adaptation to extreme altitudinal gradients. These insights strengthen arguments by (Manzoni et al. 2014) for incorporating seasonal microbial dynamics into mountain conservation planning, particularly under climate change scenarios predicted to disrupt traditional seasonal patterns in high-altitude regions.\u003c/p\u003e \u003cp\u003eTo ensure the long-term sustainability of these ecosystems, adaptive management strategies must prioritize seasonal monitoring and conservation efforts. Future research should focus on identifying the specific environmental drivers behind the observed patterns, particularly the interplay between SOC, vegetation diversity, and microbial functional pathways. Integrating advanced techniques such as meta-transcriptomics and metabolomics could provide deeper insights into microbial functional dynamics and their role in ecosystem processes Additionally, the impacts of anthropogenic activities, such as grazing and deforestation, on ecological dynamics should be investigated, as these factors have altered ecosystem functioning in other regions. By addressing these knowledge gaps, we can develop more effective strategies to enhance the resilience of these ecologically vital forests in the face of global environmental change.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u0026nbsp; The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u003c/strong\u003e This work was supported by the Natural Science Foundation of China (31961143012), the Science-based Advisory Program of The Alliance of National and International Science Organizations for the Belt and Road Regions (ANSO-SBA-2023-02).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e H.A. did the conceptualization, field work and laboratory analyses, data analyses, original draft, and revision. M.R, A.W., W.W. processed the data. A.A.D edited and reviewed the final version of the manuscript. All the authors participated in editing the initial draft and approved the final submission version. Z.S. contributed to the conceptualization, manuscript revision, funding acquisition, and supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e The authors declare that they have no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAguirre J (2023) The Kjeldahl Method. \u0026nbsp;The Kjeldahl Method: 140 Years. 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Soil Biology and Biochemistry 193: 109405.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Himalaya, Forests, Seasons, Vegetation, Soil microbial diversity, Metagenome","lastPublishedDoi":"10.21203/rs.3.rs-6344148/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6344148/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and aims\u003c/h2\u003e \u003cp\u003eThe Western Himalayan Forest ecosystem faces unprecedented pressure of climate change and anthropogenic activities. To enhance the resilience of high-altitude forests, improved conservation and management programs are a key. For the programs to be effective, knowledge on the seasonal effects on vegetation, soil properties and microbial communities in high altitude forests is needed, but this information is uncertain across high altitude forest types.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eTo fill the gap, we examined the seasonal variation in vegetation, soil and microbial communities by determining vegetation diversity indices, soil properties, soil metagenomic analysis across 10 distinct forest types during winter, 2023, and summer, 2024 respectively.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSummer showed greater species richness (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Shannon (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and Simpson diversity (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.008), while winter had greater evenness (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.005) and community maturity (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.02). Soil pH was 2\u0026ndash;3% greater (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in summer (winter:6.04\u0026ndash;7.5; summer:5.9\u0026ndash;7.9), with 125\u0026ndash;130% greater (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) microbial biomass carbon (MBC) but 30\u0026ndash;35% lesser (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) soil moisture and 20\u0026ndash;25% lesser (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) soil organic carbon (SOC). Microbial α-diversity was greater in summer (Shannon: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.006; richness: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.006) while functional profile was stable (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Beta diversity showed high dissimilarity in conifers (0.509) versus MWB (Mix Mekha Wali Burmi Forest, Surgan) and OQMF (\u003cem\u003eOlea\u003c/em\u003e and \u003cem\u003eQuercus\u003c/em\u003e Mix Forest, Baandi) (0.035).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eStructural Equation Modelling (SEM) showed stronger summer plant-soil-microbe linkages (summer R\u0026sup2;=0.78 vs winter 0.71), demonstrating microbial resilience and the need for seasonal forest management.\u003c/p\u003e","manuscriptTitle":"Seasonal shifts in vegetation, soil properties, and microbial communities in Western Himalyan forests","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-25 12:31:01","doi":"10.21203/rs.3.rs-6344148/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":"838a2a48-27ee-46f2-b8ee-367b544e3051","owner":[],"postedDate":"April 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-04T14:23:11+00:00","versionOfRecord":{"articleIdentity":"rs-6344148","link":"https://doi.org/10.1186/s40793-025-00842-y","journal":{"identity":"environmental-microbiome","isVorOnly":false,"title":"Environmental Microbiome"},"publishedOn":"2026-01-06 00:00:00","publishedOnDateReadable":"January 6th, 2026"},"versionCreatedAt":"2025-04-25 12:31:01","video":"","vorDoi":"10.1186/s40793-025-00842-y","vorDoiUrl":"https://doi.org/10.1186/s40793-025-00842-y","workflowStages":[]},"version":"v1","identity":"rs-6344148","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6344148","identity":"rs-6344148","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}