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Article

Effects of Fire on Soil Bacterial Communities and Nitrogen Cycling Functions in Greater Khingan Mountains Larch Forests

by
Yang Shu
1,2,
Wenjie Jia
1,
Pengwu Zhao
1,2,
Mei Zhou
1,2 and
Heng Zhang
1,3,*
1
Forestry College, Inner Mongolia Agricultural University, Hohhot 010011, China
2
National Orientation Observation and Research Station of the Saihanwula Forest Ecosystem in Inner Mongolia, Chifeng 024000, China
3
National Field Scientific Observation and Research Station of the Greater Khingan Forest Ecosystem, Genhe 022350, China
*
Author to whom correspondence should be addressed.
Forests 2025, 16(7), 1094; https://doi.org/10.3390/f16071094
Submission received: 29 May 2025 / Revised: 29 June 2025 / Accepted: 1 July 2025 / Published: 2 July 2025
(This article belongs to the Section Forest Soil)
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Abstract

Investigating the effects of fire disturbance on soil microbial diversity and nitrogen cycling is crucial for understanding the mechanisms underlying soil nitrogen cycling. This study examined the fire burn site of the Larix gmelinii forest in the Greater Khingan Mountains, Inner Mongolia, to analyze the impact of varying fire intensities on soil nitrogen, microbial communities, and the abundance of nitrogen cycle-related functional genes after three years. The results indicated the following findings: (1) Soil bulk density increased significantly following severe fires (7.06%~10.84%, p < 0.05), whereas soil water content decreased with increasing fire intensity (6.62%~19.42%, p < 0.05). The soil total nitrogen and ammonium nitrogen levels declined after heavy fires but increased after mild fires; (2) Mild fire burning significantly increased soil bacterial diversity, while heavy fire had a lesser effect. Dominant bacterial groups included Xanthobacteraceae, norank_o_norank_c_AD3, and norank_o_Elsterales. Norank_o_norank_c_AD3 abundance decreased with burn intensity (7.90% unburned, 3.02% mild fire, 2.70% heavy fire). Conversely, norank_o_Elsterales increased with burning (1.23% unburned, 5.66% mild fire, 5.48% heavy fire); (3) The abundance of nitrogen-fixing nifH functional genes decreased with increasing fire intensity, whereas nitrification functional genes amoA-AOA and amoA-AOB exhibited the opposite trend. Light-intensity fires increased the abundance of denitrification functional genes nirK, nirS, and nosZ, while heavy fires reduced their abundance; (4) The correlation analysis demonstrated a strong association between soil bacteria and denitrification functional genes nifH and amoA-AOA, with soil total nitrogen being a key factor influencing the nitrogen cycle-related functional genes. The primary bacterial groups involved in soil nitrogen cycling were Proteobacteria, Actinobacteria, and Chloroflexi. These findings play a critical role in promoting vegetation regeneration and rapid ecosystem restoration in fire-affected areas.

1. Introduction

Escalating fires pose mounting threats to forest ecosystems under anthropogenic climate change. Global warming is projected to increase the frequency, spatial extent, and intensity of wildfires [1], with profound implications for ecosystem recovery, soil microbiome functionality, and microbe-mediated biogeochemical cycling [2]. Enhanced fire-favorable weather conditions [3,4,5] have already driven measurable increases in burned area (BA) and fire-derived carbon emissions across key forested regions over the past two decades [6,7]. Wildfires had long-lasting effects on soil properties and vegetation cover for many years. After a fire, the soil pH value significantly increased, while phosphorus (P) and calcium (Ca) accumulated. The soil organic carbon (SOC) content was also increased in fire-affected areas. Furthermore, fire inhibited catalase activity while enhancing peroxidase activity [8,9]. Babur et al. have also found that fires increased soil carbon stocks, total nitrogen (TN), pH, and metabolic quotient (qCO2) while decreasing microbial biomass carbon (MBC) and microbial biomass nitrogen (MBN) [10].
Current studies indicate that the cycles of carbon, nitrogen, and phosphorus in forests and soils are highly sensitive to fire disturbances [11], and the extent of their impact largely depends on fire severity, recurrence, and post-fire recovery time [12,13]. Previous studies by Vourlitis [14], Pellegrini [15], and Kolka [16] showed that severe fire disturbances can alter microbial communities and nitrogen biogeochemical cycles. Koyama et al. [17] observed that the total nitrogen mineralization rate decreased shortly after a fire due to soil carbon loss limitations. However, as post-fire vegetation regenerated and litter input increased, the total nitrogen mineralization rate gradually increased. Previous studies have also suggested that forest fires can enhance the nitrogen availability and circulation, with potential effects lasting decades [18]. DeLuca et al. [19] reported that the areas in the Rocky Mountains of the United States that experienced one or two forest fires over a century exhibited higher soil nitrification activity, total nitrification, and net nitrification than fire-unaffected areas. It has been found that fires of different intensities significantly affect soil microbial biomass and microbial respiration [10,20]. Knelman et al. further suggested that the higher fire intensities reduced the soil microbial biomass, enzyme activity, and microbial community diversity to different extents [21]. Fire-produced ash can inhibit soil enzyme activity, thereby influencing microbial metabolic processes. Palviainen et al. discovered that the moderate-intensity fires in subarctic pine forests significantly reduced the nitrogen pool in soil biomass, while the nitrogen reservoir changes within the soil and ecosystem remained minimal [18].
Soil, hosting one of the greatest microbial diversities on Earth, provides critical forest ecosystem services including primary productivity, nutrient cycling, litter decomposition, and plant resilience [22,23]. These microorganisms are indispensable for ecosystem processes and play a central role in plant–soil–microbe interactions [24,25]. Studies have indicated that the post-fire recovery trajectory of ecosystems is closely associated with the taxonomic and functional succession of soil microorganisms [26]. However, little information is available on the recovery of microbial functional groups after fire disturbances. Among soil functions, nitrogen cycling is one of the most vital processes conducted by microorganisms, driving nitrogen transformations through enzyme production [27]. Fire can directly affect microbial activity by altering the soil temperature, with burning temperatures reaching 1500 °C, causing massive microbial mortality and impeding short-term recovery [28]. Fire can also significantly increase the abundance of nirK, nosZ, narG, and nifH genes in the soil, thereby reducing the total nitrogen mineralization rate [29]. However, Sun et al. reported that fire did not affect the relative abundance of nitrogen-fixing nifH genes [30]. Additionally, previous research has indicated that fire can increase the relative abundance of nitrite-reducing nirS genes and decrease nitrate-reducing narG genes in surface soils [31]. Liu et al. confirmed that the repeated fire disturbances in coniferous forest soils resulted in a decrease in denitrifying microbial communities and denitrification genes (narG, nirK, and nirS) with the increasing soil depth [32]. Docherty et al. investigated the interactive effects of fire intensity, nitrogen deposition, precipitation, and CO2 on post-fire plant biomass and microbial communities and found that low-intensity fires had no significant impact on soil microorganisms [33]. However, nitrogen deposition increased soil NH4+, which enhanced NH3 availability, increased the abundance of ammonia-oxidizing bacteria (AOB), and elevated the nitrification rate.
In the Greater Khingan Mountains forest region of Inner Mongolia, large areas of land have been severely affected by wildfires. After fire accidents, the exposed surface can accelerate the continuous melting and downward movement of the permafrost layer, intensifying the decomposition and loss of soil organic matter. This process directly alters the soil microhabitats, carbon and nitrogen levels, and composition and structure of the original vegetation, thereby disrupting the soil microbial functions and vegetation regeneration strategies in burned areas [34]. Currently, the ecological functions of the Daxing’an Mountain forest region are significantly weakened, causing a series of severe environmental problems. However, the mechanisms by which forest fire disturbance modulates the abundance dynamics of soil microbial nitrogen cycling genes and their functional potential in driving nitrogen transformations remain unclear at the molecular level. Therefore, this study aimed to analyze the effects of varying fire intensities on soil physicochemical properties, microbial diversity, and abundance of microbial nitrogen cycling genes in burned larch forests in the Greater Khingan Mountains Range of Inner Mongolia, based on field monitoring, high-throughput sequencing, and quantitative analysis. These findings could provide essential data to support the reconstruction of soil nitrogen pools and the restoration of microbial carbon and nitrogen cycling functions in fire-affected areas.

2. Materials and Methods

2.1. Overview of the Study Area

The Greater Khingan Mountains forest region in Inner Mongolia is located in northern China and serves as one of the eight major state-owned forest areas. The study site is under the jurisdiction of the Genhe Forestry Administration Bureau within the Inner Mongolia Greater Khingan Mountain Range Key State-owned Forest Management Bureau, with geographical coordinates ranging from 120°41′30″ to 122°42′30″ E and 50°25′30″ to 51°17′00″ N. The region exhibits a cold temperate continental monsoon climate, characterized by long, cold, dry winters with snow depths of 20–50 cm and short, cool summers. Annual precipitation ranges from 450 to 550 mm, predominantly occurring in July and August. The mean annual temperature is −3.5 °C, with extreme minima reaching −52.3 °C and a frost-free period of 70–130 days. Significant diurnal temperature variations occur, and annual surface evaporation (800–1200 mm) notably exceeds precipitation. The predominant soil types are brown coniferous forest soils. The soil parent material of the studied plots is slope sediment, and the topography is mountainous terrain. Forest coverage exceeds 62%, supporting lush vegetation and abundant forest resources. The dominant tree species is Larix gmelinii, representing a typical coniferous forest zone. Other major arboreal species include Larix gmelinii, Pinus pumila, Betula platyphylla, Quercus mongolica, and Pinus sylvestris. The shrub layer is primarily composed of Vaccinium uliginosum, Ledum palustre, Betula fruticosa, Rosa davurica, Rhododendron simsii, and Vaccinium vitis-idaea, while the herbaceous layer mainly consists of Carex spp., Epilobium angustifolium, Vicia pseudorobus, and Sanguisorba officinalis.

2.2. Research Methodology

The selected experimental area was a fire disturbance site in Shangyanggeqi Forest Farm based on fire records from 2019, covering a burned area of 120.15 ha (Figure 1). The fire was caused by a lightning strike, and the fire disturbance intensity was classified based on the proportion of unburned, damaged standing trees: 30% for mild fire disturbance and >70% for heavy fire disturbance [35]. The characteristics of different fire disturbance intensities are listed in Table 1. To conduct the study, 30 m × 30 m plots were established, with three replicate plots for each fire disturbance intensity. Moreover, control plots were established in unburned areas with similar site conditions and vegetation, which were located 100–150 m from the burned area, resulting in a total of nine plots. Detailed information for each plot is presented in Table 2.

2.2.1. Sample Collection

The soil samples used in this study were collected in July 2022. Nine standard plots (30 × 30 m) were established based on different fire intensities. Soil samples were collected from six sampling points arranged in an S-shaped pattern within each plot. At each point, soil profiles were excavated to extract specimens from two distinct horizons: the A horizon (0–10 cm, humus layer) and the B horizon (10–20 cm, illuvium, Bw), with parent material occurring below a 20 cm depth. Before taking soil, the dead branches, leaves, and debris were removed using a sterilized carbon-steel shovel to minimize cross-contamination. Soil collection adhered to a bottom-to-top sequence (i.e., layer B before layer A) to preserve horizon integrity. Visible gravel, roots, and organic residues were excluded during sampling. A minimum of 200 g of soil was collected from each genetic horizon. All samples were immediately sealed in sterile, self-sealing polyethylene bags. A total of 108 soil samples were transported to the laboratory in insulated containers with refrigerant packs (≤4 °C) for laboratory testing and analysis.
This study employed several analytical methods to assess soil properties. Soil bulk density was determined using the ring knife method. Soil moisture content was measured via the drying differential method, soil pH was assessed with a glass electrode (soil:water = 2.5:1), and soil organic carbon was quantified by potassium dichromate oxidation. Total nitrogen was analyzed using the Kai-type nitrogen determination method, while ammonium nitrogen and nitrate nitrogen levels were measured using intermittent element analyzers (CleverChem 380, HH, Germany). Furthermore, soil available phosphorus was quantified by sodium bicarbonate extraction-molybdenum antimony colorimetry, while soil available potassium was measured using ammonium acetate (NH4OAc) extraction-flame photometry [36].

2.2.2. DNA Extraction and Illumina MiSeq Sequencing

Total microbial genomic DNA was extracted from the soil samples using an E.Z.N.A.® Soil DNA Kit (Omega Bio-tek, Norcross, GA, USA) following the manufacturer’s instructions. DNA quality and concentration were assessed using 1.0% agarose gel electrophoresis and a NanoDrop® ND-2000 spectrophotometer (Thermo Scientific Inc., Waltham, MA, USA) before storage at −80 °C for further use. The hypervariable V3–V4 region of the bacterial 16S rRNA gene was amplified using the primer pair 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) with an ABI GeneAmp® 9700 PCR thermocycler (ABI, MA, USA) [37]. PCR products were electrophoresed on 2% agarose gels, purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, CA, USA), and quantified with a Quantus™ Fluorometer (Promega, Madison, WI, USA). DNA purity was assessed using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA), concentration was measured with a TBS-380 mini-fluorometer (Turner BioSystems, CA, USA), and integrity was evaluated by 1% agarose gel electrophoresis (5 V/cm, 20 min). Quantitative fluorescence PCR (ABI GeneAmp 9700, ABI, MA, USA) was used to amplify genes related to soil nitrogen cycling, including nitrogen fixation (nifH), nitrification (amoA-AOA and amoA-AOB), and denitrification (nirS, nirK, and nosZ), using the primers listed in Table 3. The PCR amplification system consisted of an initial denaturation at 94 °C for 5 min, followed by 36 cycles of denaturation at 95 °C for 5 s and annealing/extension at 60 °C for 60 s. The amplification products were validated by 2% agarose gel electrophoresis, purified, and subsequently used for sequencing library construction using a NEXTFLEX Rapid DNA-Seq Kit. Metagenomic sequencing was performed using the Illumina NovaSeq platform following the standard protocols of Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China).

2.2.3. Statistical Analysis

Bioinformatic analysis of the soil microbiota was performed using the Majorbio Cloud platform (https://cloud.majorbio.com). Based on OTU information, rarefaction curves and alpha diversity indices, including observed OTUs, Chao1 richness, Shannon index, and Good’s coverage, were calculated using Mothur v1.30.1 [38].
Microbial community similarity was analyzed via principal coordinate analysis (PCoA) using Bray–Curtis dissimilarity (Vegan v2.5-3 package). Linear discriminant analysis (LDA) effect size (LEfSe) (http://huttenhower.sph.harvard.edu/LEfSe) (accessed on 20 June 2024) was used to identify significantly abundant bacterial taxa (from phylum to genus) among the different groups (LDA score > 2, p < 0.05) [39]. Distance-based redundancy analysis (db-RDA) was performed using the Vegan v2.5-3 package to examine the influence of soil physicochemical properties on bacterial community structure, with forward selection based on Monte Carlo permutation tests (permutations = 9999). Linear regression analysis was used to assess the associations between the major physicochemical properties identified in db-RDA and microbial alpha diversity indices. Co-occurrence networks were constructed to explore the internal community relationships across samples [40], with the correlations between the nodes considered statistically robust if the Spearman’s correlation coefficient was >0.6 or <−0.6 and the p-value was <0.05.

3. Results

3.1. Effects of Fire Intensity and Soil Physicochemical Properties

The study of the physicochemical properties of soil layers A and B under varying fire intensities (Table 4) revealed significant differences in the soil bulk density between severely burned and unburned conditions (p < 0.05). Soil moisture content varied significantly across all fire intensities but presented no significant differences between soil layers (p > 0.05). In soil layer A, both soil pH and organic carbon exhibited significant differences across different fire intensities (p < 0.05). These findings indicated that fire intensity had a significant impact on soil properties at a depth of 0–10 cm.
The soil pH and organic carbon in layers A and B differed significantly between the lightly and severely burned conditions (p < 0.05). The total nitrogen and ammonium nitrogen decreased after severe burning but increased with light burning, whereas the fire enhanced the soil nitrate nitrogen content, which increased with fire intensity. Total nitrogen and nitrate nitrogen in soil layers A and B showed significant differences across fire intensities (p < 0.05). The ammonium nitrogen in layer A varied significantly across fire intensities (p < 0.05), whereas in layer B, there were significant differences only between the lightly burned and unburned conditions (p < 0.05).
The soil available phosphorus in layer A showed significant differences between severely burned, lightly burned, and unburned conditions (p < 0.05), whereas no significant differences were observed in layer B (p > 0.05). In contrast, soil available potassium did not vary significantly across different fire intensities (p > 0.05). For the same fire intensity, the available phosphorus exhibited significant differences between layers A and B (p < 0.05).

3.2. Effect of Fire Intensity on Soil Bacterial Community Diversity

3.2.1. Soil Bacterial Alpha Diversity Index Analysis

As shown in Table 5, the soil bacterial Ace and Chao1 indices in lightly burned plots differed significantly from those in both severely burned and unburned plots (p < 0.05), indicating that light burning significantly increased the total number of soil bacteria and bacterial richness, whereas severe burning had a lesser effect. Additionally, the Ace and Chao1 indices were higher in layer B than in layer A, suggesting that the soil bacteria are primarily concentrated in layer B.
The Shannon index of soil bacteria in the lightly burned plots differed significantly from that in the unburned plots (p < 0.05), indicating that light burning significantly enhanced soil bacterial community diversity, while severe burning had a lesser effect. Although the bacterial community diversity was highest in layer B, the differences were not statistically significant (p > 0.05).
The Venn diagram illustrates the OTU composition across different samples (Figure 2a), revealing a total of 1102 OTUs for soil bacteria in the fire-affected samples. The number of OTUs specific to soil bacteria in layers A and B of heavily burned samples was 19, whereas that in the lightly burned samples was 2. These results suggest that a fire of a certain intensity increases the total number of soil bacterial species. To compare the changes in the soil bacterial community structure across the fire-affected sites with different fire intensities, PCoA was performed using the Bray–Curtis algorithm (Figure 2b). The analysis further indicated that the soil bacterial community composition differed between burned and unburned sites.

3.2.2. Structural Composition and Differential Analysis of Soil Bacterial Communities

Analysis of the soil bacterial alpha diversity under different burn intensities revealed that the bacterial communities were significantly affected by burn intensity (p < 0.05), whereas the soil layers had no significant impact (p > 0.05). The sequencing identified bacteria from 29 phyla, 71 classes, 160 orders, 247 families, and 404 genera, with the sequences unclassified at the phylum level grouped under “others”. As shown in Figure 3, the five dominant phyla in the samples were Proteobacteria, Actinobacteria, Acidobacteria, Chloroflexi, and Firmicutes. The relative abundance of Proteobacteria increased with burn intensity, ranking from the highest to the lowest: 35.72% in the high-intensity burns, 33.72% in the low-intensity burns, and 30.85% in the unburned samples. The relative abundance of Actinobacteria also increased with burning, with the highest abundance in the low-intensity burn (23.34%), followed by the high-intensity burn (22.79%) and the unburned samples (18.62%). Acidobacteria exhibited a decreasing trend, with relative abundances of 19.61% in the unburned samples, 18.16% in the low-intensity burns, and 18.10% in the high-intensity burns. Similarly, Chloroflexi declined with increasing burn intensity, with relative abundances of 11.63% in the unburned samples, 9.16% in the low-intensity burns, and 6.75% in the high-intensity burns. Firmicutes exhibited the opposite trend, with the highest abundance in the high-intensity burn (3.81%), followed by the unburned samples (3.44%), and the lowest in the low-intensity burn (2.22%), indicating that the high-intensity fire increased its abundance while the low-intensity fire decreased it.
As shown in Figure 4, the five dominant bacterial families in the samples were Xanthobacteraceae, norank_o_norank_c_AD3, norank_o_Elsterales, norank_o_Acidobacteriales, and Acidothermaceae. The relative abundance of Xanthobacteraceae increased with burning, ranking the highest in low-intensity burns (16.15%), followed by high-intensity burns (15.06%), and unburned samples (14.65%). Norank_o_norank_c_AD3 showed a decreasing trend with increasing burn intensity, with relative abundances of 7.90% in the unburned samples, 3.02% in the low-intensity burns, and 2.70% in the high-intensity burns. Norank_o_Elsterales increased with burning, reaching 5.66% for low-intensity burns, 5.48% for high-intensity burns, and only 1.23% for unburned samples. Norank_o_Acidobacteriales decreased with burning, with a relative abundance of 4.69% in the unburned samples, 3.88% in the high-intensity burn, and 3.46% in the low-intensity burn, demonstrating the greatest decline in the low-intensity burn. Acidothermaceae exhibited the opposite trend, with the highest abundance in the high-intensity burn (4.37%), followed by the unburned samples (3.54%), and the lowest in the low-intensity burn (3.25%), indicating that the high-intensity fire increased its abundance while the low-intensity fire reduced it.
As shown in Figure 5, the five dominant bacterial genera in the samples were Bradyrhizobium, norank_f_Xanthobacteraceae, norank_f_norank_o_norank_c_AD3, norank_f_norank_o_Elsterales, and norank_f_norank_o_Acidobacteriales. The relative abundance of Bradyrhizobium was the highest in the low-intensity burns (9.46%), followed by the unburned samples (9.29%) and high-intensity burns (7.83%), indicating that the low-intensity burns increased in abundance, whereas the high-intensity burns decreased it. Norank_f_Xanthobacteraceae exhibited an increasing trend with the burn intensity, with the relative abundances of 6.39% in the high-intensity burn, 5.93% in the low-intensity burn, and 4.65% in the unburned samples. In contrast, norank_f_norank_o_norank_c_AD3 decreased with burn intensity, with the highest abundance in unburned samples (7.90%), followed by low-intensity burns (3.02%) and high-intensity burns (2.70%). norank_f_norank_o_Elsterales increased with burning, with the highest abundance in the low-intensity burn (5.66%), followed by the high-intensity burn (5.48%) and the unburned samples (1.23%). Norank_f_norank_o_Acidobacteriales exhibited a decreasing trend, with a relative abundance of 4.69% in the unburned samples, 3.88% in the high-intensity burns, and 3.46% in the low-intensity burns, showing the greatest decline in low-intensity burns.
At the phylum level, Chloroflexi was significantly affected by fire intensity (p < 0.05), whereas Actinobacteria and Chloroflexi were significantly influenced by soil depth (p < 0.05). At the family level, norank_o_norank_c_AD3 and norank_o_Elsterales were significantly affected by fire intensity (p < 0.05), whereas norank_o_norank_c_AD3, norank_o_Elsterales, and Acidothermaceae were significantly affected by soil depth (p < 0.05). At the genus level, norank_f_norank_o_norank_c_AD3 and norank_f_norank_o_Elsterales were affected by the interaction between the fire intensity and soil depth (Table 6).

3.3. Effect of Flaming Intensity on Functional Genes of Soil Microbial Nitrogen Cycling

As shown in Table 7, the abundance of the nitrogen-fixing nifH functional gene decreased with increasing burn intensity in the following order: unburned > low-intensity burn > high-intensity burn. In contrast, the nitrification functional genes amoA-AOA and amoA-AOB exhibited the opposite trend, increasing with the burn intensity. The low-intensity burns enhanced the abundance of the denitrification functional genes nirK, nirS, and nosZ, whereas the high-intensity burns reduced their abundance. The significance analysis revealed that the differences in the abundance of soil microbial nitrogen cycling functional genes were primarily significant between the high-intensity burn and unburned plots (p < 0.05). Moreover, high-intensity burns significantly affected the nitrogen cycling functional gene abundance in the 10–20 cm soil layer, whereas low-intensity burns only affected the 0–10 cm layer.

3.4. Soil Microbial Communities and Environmental Factors Correlate with Functional Genes of the Nitrogen Cycle

3.4.1. Soil Microbial Communities and Functional Gene Correlations for Microbial Nitrogen Cycling

As shown in Figure 6 at the phylum level, Proteobacteria exhibited a significant positive correlation with the soil nitrification amoA-AOA functional gene (p < 0.05) with a correlation coefficient of 0.386, whereas Actinobacteria demonstrated a highly significant positive correlation (p < 0.01) with a correlation coefficient of 0.527. Chloroflexi showed a highly significant negative correlation with amoA-AOA (p < 0.01), with a correlation coefficient of −0.489. Correlations between other bacterial phyla and functional soil nitrogen cycling genes were not significant (p > 0.05).
At the family level (Figure 6), norank_o_norank_c_AD3 exhibited a highly significant negative correlation with the soil nitrification amoA-AOA functional gene (p < 0.01), with a correlation coefficient of −0.679. Norank_o_Elsterales showed a highly significant positive correlation with the soil nitrogen-fixing nifH functional gene (p < 0.01, r = 0.499) and a significant positive correlation with the soil nitrification amoA-AOA functional gene (p < 0.05, r = 0.378). Norank_o_Acidobacteriales demonstrated significant negative correlations with amoA-AOA (p < 0.05), with correlation coefficients of −0.385, respectively. Correlations between other bacterial families and functional soil nitrogen cycling genes were not significant (p > 0.05).
At the genus level (Figure 6), Bradyrhizobium exhibited a significant positive correlation with the soil denitrification nirK functional gene (p < 0.05, r = 0.422). Norank_f_Xanthobacteraceae showed a significant positive correlation with the soil nitrification amoA-AOA functional gene (p < 0.05, r = 0.368). Norank_f_norank_o_norank_c_AD3 demonstrated a highly significant negative correlation with amoA-AOA (p < 0.01, r = −0.679). Norank_f_norank_o_Elsterales exhibited a highly significant positive correlation with the soil nitrogen-fixing nifH functional gene (p < 0.01, r = 0.499) and a significant positive correlation with amoA-AOA (p < 0.05, r = 0.378). Norank_f_norank_o_Acidobacteriales showed significant negative correlations with amoA-AOA (p < 0.05), with correlation coefficients of −0.385, respectively. Correlations between other bacterial genera and functional soil nitrogen cycling genes were not significant (p > 0.05).

3.4.2. Correlation Between Soil Environmental Factors and Functional Genes of Microbial Nitrogen Cycling in Soils

As shown in Figure 7, the soil nitrogen-fixing nifH functional gene exhibited a significant positive correlation with total soil nitrogen (p < 0.05) and a significant negative correlation with soil moisture content (p < 0.05). The soil nitrification amoA-AOB functional gene showed a significant negative correlation with total soil nitrogen (p < 0.05) and a significant positive correlation with soil nitrate nitrogen and available potassium (p < 0.05). The soil nitrification amoA-AOA functional gene displayed highly significant positive correlations with soil nitrate–nitrogen and available potassium (p < 0.01) and a highly significant negative correlation with soil moisture content (p < 0.01). Additionally, it exhibited significant positive correlations with the soil bulk density, pH, ammonium nitrogen, and available phosphorus (p < 0.05). The soil denitrification nirK functional gene showed highly significant positive correlations with soil organic carbon, total soil nitrogen, and ammonium nitrogen (p < 0.01), along with a significant positive correlation with soil moisture content (p < 0.05). The soil denitrification nosZ functional gene showed no significant correlation with soil physicochemical properties (p > 0.05). The soil denitrification nirS functional gene exhibited a highly significant positive correlation with total soil nitrogen (p < 0.01) and a significant negative correlation with soil nitrate nitrogen and available phosphorus (p < 0.05).
As shown in Figure 8, the first two axes of the RDA together explained 82.99% of the total variance. Based on the length of the arrows, soil organic carbon and total soil nitrogen were identified as the primary factors influencing the functional genes involved in the soil microbial nitrogen cycle.

4. Discussion

4.1. Analysis of the Effect of Different Burning Intensities on the Physicochemical Properties of Soil

This study suggested that fire significantly affected the soil bulk density (p < 0.05) in plots with varying fire intensities compared to unburned plots, with the bulk density increasing as the fire intensity increased, which was consistent with the findings of Chen et al. [41] and Hubbert et al. [42]. This increase can be attributed to the collapse of soil aggregates, destruction of soil organic carbon, and clogging of soil pores by ash and dispersed clay particles from vegetation combustion [43]. However, some studies have reported either a decrease or no change in soil bulk density following fire disturbance. For instance, Mikita-Barbato et al. reported that fire did not affect soil bulk density, in contrast with our results, which could be due to the differences in soil types and the lack of significant changes in post-fire soil organic carbon content [44]. In this study, increased soil bulk density was accompanied by decreased soil porosity, leading to reduced soil moisture content. Similarly, Weninger et al. [45] reported a decline in soil moisture content following a fire disturbance, and Granged et al. [46] found comparable results in Mediterranean soils subjected to prescribed burning. Mataix-Solera et al. suggested that this reduction in soil moisture content was caused by the formation of hydrophobic layers on mineral particles during the combustion of organic matter [47].
This study discovered that low-intensity fires increased soil organic carbon content, while high-intensity fires led to a decrease. Granged et al. similarly reported a significant reduction in soil organic carbon following high-intensity fires [46]. This discrepancy may be due to the incomplete combustion of dead leaves and branches during low- and medium-intensity fires, leaving behind ash rich in residual carbon, which was subsequently leached into the soil by rainfall, thereby increasing the soil carbon content.
Moya et al. [48] found no significant effect of fire disturbance on soil nitrogen content. However, this study revealed that the trend in the total soil nitrogen content following fire disturbances of varying intensities closely mirrored that of organic carbon. High-intensity fires led to a reduction in total nitrogen in the upper soil layers, whereas low-intensity fires resulted in an increase. Similarly, Dzwonko et al. reported a significant decrease in total nitrogen in soils beneath Scots pine forests following high-intensity wildfires, likely because of the substantial loss of soil organic matter [49]. The increase in total nitrogen observed after the low-intensity fires may be attributed to the deposition of nitrogen-rich ash and the higher mineralization rate of litter, which releases more nitrogen, providing a better explanation for the increase in soil total nitrogen in this study.
The content of ammonium nitrogen in forest soils is primarily influenced by ammonification and nitrogen fixation, whereas nitrification affects nitrate-nitrogen levels. This study found that fire significantly affected soil ammonium and nitrate-nitrogen levels (p < 0.05). After a fire, the nitrate–nitrogen content increased significantly and continued to rise with fire intensity, which was consistent with the findings of Albert–Belda et al. [50]. Additionally, the soil available phosphorus and potassium levels significantly increased following the fire disturbances, likely because of the influx of nutrient-rich ash from the combustion of vegetation and organic matter, which enhanced soil nutrient availability. Fernández-García et al. [51] similarly reported the higher available phosphorus content in the surface soil of the burned plots compared to the unburned areas after the high-intensity wildfires. Moya et al. [48] observed a significant increase in available phosphorus in the 0–10 cm soil layer of the burned forests compared to the control plots.

4.2. Analysis of the Effect of Different Fire Intensities on the Diversity of Soil Microbial Communities

This study indicated that fire altered the community structure and diversity of soil microorganisms. The richness and diversity indices of soil bacteria were higher in the slightly burned plots than in the unburned plots, indicating that the mild fire enhanced bacterial richness and diversity, which is consistent with previous studies [52]. Although richness and diversity were also higher in the highly burned plots than in the unburned plots, the differences were not significant, in contrast to the findings of Cui et al. [53]. This discrepancy may be due to variations in climatic conditions and soil types, which could influence the heat tolerance of the soil microorganisms in different regions.
This study suggested that Proteobacteria, Actinobacteria, Acidobacteria, and Chloroflexi were the dominant bacterial phyla, exhibiting higher abundances across different fire intensities, consistent with previous studies [54]. Proteobacteria rapidly responded to environmental disturbances and became the dominant bacterial group, which is consistent with our finding that their relative abundance increased with fire intensity. As the key participants in soil carbon, nitrogen, and sulfur cycles [55], Proteobacteria thrived in fire-affected soils, where ash could provide rich nutrients and promote their abundance. The relative abundance of Chloroflexi significantly varied across the fire intensities, which was likely due to (1) their high sensitivity to temperature, as Mao Jin’s study reported a negative correlation between Chloroflexi and soil temperature [56], and (2) their adaptation to nutrient-poor environments [57], leading to a negative correlation with soil nutrients, which this study also confirmed. Temperature may be the primary factor influencing Chloroflexi distribution across soil layers. Norank_o_norank_c_AD3 at the family level and norank_f_norank_o_Elsterales at the genus level showed significant differences across the fire intensities (p < 0.05), indicating that fire significantly affected soil bacterial diversity.

4.3. Analysis of the Effect of Different Fire Intensity on the Functional Genes of Soil Microbial Nitrogen Cycling

Soil microorganisms produce enzymes that drive the nitrogen cycle and are highly sensitive to temperature. This study demonstrated that the functional genes involved in soil nitrogen cycling (nifH for nitrogen fixation; nirS, nirK, and nosZ for denitrification; and amoA-AOA and amoA-AOB for nitrification) exhibited different trends under varying fire intensities. The abundance of the nifH gene, as an indicator of nitrogen fixation activity, increased following the fire, with the most significant effect observed under low-intensity fire, suggesting that the low-intensity fire enhanced nitrogen fixation. Previous studies have shown that forest fires can promote nitrogen-fixing and light-loving vegetation because low-intensity fires can improve soil nutrients and foster the growth of nitrogen-fixing plants [58]. However, She et al. reported that high-intensity fires weakened nitrogen fixation, which is in contrast to the findings of this study [59]. This discrepancy may be attributed to the recovery of nitrogen-fixing vegetation and microorganisms in severely burned plots, leading to enhanced nitrogen fixation.
Correlation analysis revealed a close relationship between soil bacteria and the nitrogen-fixing nifH functional gene. Unclassified_k_Fungi at the phylum level, norank_o_Elsterales of the phylum Proteobacteria at the family level, and norank_f_norank_o_Elsterales of the phylum Proteobacteria at the genus level exhibited a significant positive correlation with nifH, indicating their involvement in the nitrogen-fixing process in post-fire soil. The microorganisms most likely influencing nifH abundance across different fire intensities were norank_o_Elsterales at the family level and norank_f_norank_o_Elsterales at the genus level, both belonging to the phylum Proteobacteria.
Soil nitrification amoA-AOA and amoA-AOB functional genes served as indicators of soil nitrification. Zhang et al. [60] found that fire significantly increased the abundance of these genes, with their ratio rising from 23.9 to 48.1. Similarly, this study observed an increase in amoA-AOA and amoA-AOB abundances with fire intensity. The possible reasons for this increase include that: (1) wildfires elevate soil temperatures, creating a favorable environment for nitrifying bacteria; (2) post-fire vegetation ash is rich in carbon and nitrogen promotes microbial growth and reproduction; and (3) biochar as a fire byproduct improves soil aeration, enhances oxygen availability for nitrifying bacteria, and also adsorbs natural nitrification inhibitors such as phenolic compounds [19]. Correlation analysis revealed a strong association between soil bacteria and the amoA-AOA and amoA-AOB genes. Among the top five most abundant bacterial phyla, Actinobacteria and Chloroflexi, along with norank_o_Elsterales (family level) and norank_f_norank_o_Elsterales (genus level) within Proteobacteria, were significantly positively correlated with these genes, indicating their direct role in post-fire soil nitrification. Conversely, Chloroflexi, the family norank_o_norank_c_AD3 within Chloroflexi, and genus norank_f_norank_o_norank_c_AD3 within the family norank_o_norank_c_AD3 exhibited significant negative correlations, suggesting an indirect influence on nitrification. The microorganisms that influenced amoA-AOA and amoA-AOB gene abundance under varying fire intensities included Chloroflexi, the family norank_o_norank_c_AD3 within Chloroflexi, the family norank_o_Elsterales within Proteobacteria, and the genus norank_f_norank_o_norank_c_AD3 within the family norank_o_norank_c_AD3.
This study found that severe fire significantly reduced the abundance of denitrification nirS, nirK, and nosZ functional genes, whereas mild fire increased their abundance, likely due to its effect on organic matter content. Mild fires accelerate the decomposition of recalcitrant components in surface litter, increasing the available soil nutrients and nitrate levels, which promotes the growth of soil denitrifying bacteria [61]. Similarly, Cao Linhua [62] reported a significant positive correlation between soil denitrifying bacterial functional genes and organic matter, whereas Li Hang et al. [63] identified the total nitrogen as a major factor influencing soil denitrification, which was similar to the findings of this study. Because soil denitrification can primarily occur under anoxic conditions, the post-fire increase in soil bulk density reduced the oxygen content, creating favorable conditions for denitrification. Correlation analysis further indicated that soil bacteria were closely associated with the nirS, nirK, and nosZ functional genes involved in soil denitrification.

5. Conclusions

This study demonstrated that fire intensity significantly alters soil physicochemical properties and microbial communities in Larix gmelinii forests. Soil bulk density, pH, nitrate nitrogen, available potassium, and available phosphorus increased with escalating fire severity. In contrast, soil organic carbon, total nitrogen, and ammonium nitrogen rose after mild fires but declined following severe burns. Mild fires enhanced microbial diversity and richness, with Proteobacteria, Actinobacteria, and Acidobacteria dominating bacterial communities across fire intensities. Fire critically regulated soil nitrogen cycling via microbial functional genes: Mild fires significantly increased nitrogen fixation (nifH) and denitrification (nirK, nirS, nosZ) gene abundances, while nitrification genes (amoA-AOA and amoA-AOB) also rose, indicating stimulated nitrification. Key bacterial taxa driving these processes included unclassified_o_Elsterales (Proteobacteria) for nitrogen fixation and unclassified_c_AD3 (Chloroflexi) combined with unclassified_o_Acidobacteriales (Acidobacteria) for nitrification. Total nitrogen content emerged as the primary driver of nitrogen cycling functional genes. Future research should prioritize long-term monitoring of post-fire soil microbial and nutrient dynamics to elucidate legacy ecological mechanisms.

Author Contributions

Y.S. organized the data and wrote the manuscript. Y.S. and H.Z. helped with literature searches and analysis. H.Z., P.Z., Y.S. and M.Z. revised the manuscript and language editing. W.J. helped with field experiments and field investigations. All co-authors contributed to the editing of the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

National Natural Science Foundation of China (NSFC) Project (32460392); Scientific Research Support Funds for Introduction of High-level Talents to Institutions at the Autonomous Region Level in 2022 (DC2300001275). Scientific research start-up project for the introduction of high-level/excellent doctoral talents (NDYB2022-18).

Data Availability Statement

The raw reads were deposited into the NCBI sequence read archive. (SRA) database (Accession Number: SRP499782, SRP499761, SRP581037).

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.

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Figure 1. Location map of the study area.
Figure 1. Location map of the study area.
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Figure 2. Effect of fire intensity on soil bacterial community structure. (a) Venn diagram, (b) PCoA analysis diagram.
Figure 2. Effect of fire intensity on soil bacterial community structure. (a) Venn diagram, (b) PCoA analysis diagram.
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Figure 3. Relative abundance of soil bacterial phyla in fire samples of different intensities.
Figure 3. Relative abundance of soil bacterial phyla in fire samples of different intensities.
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Figure 4. Relative abundance of soil bacterial families in fire samples of different intensities.
Figure 4. Relative abundance of soil bacterial families in fire samples of different intensities.
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Figure 5. Relative abundance of soil bacterial genera in fire samples of different intensities.
Figure 5. Relative abundance of soil bacterial genera in fire samples of different intensities.
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Figure 6. Correlation of soil bacterial phyla, families, and genera with functional nitrogen cycle genes. Note: * indicates p < 0.05, ** indicates p < 0.01,*** indicates p < 0.001.
Figure 6. Correlation of soil bacterial phyla, families, and genera with functional nitrogen cycle genes. Note: * indicates p < 0.05, ** indicates p < 0.01,*** indicates p < 0.001.
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Figure 7. Correlation analysis between soil physicochemical properties and soil nitrogen cycle functional genes.
Figure 7. Correlation analysis between soil physicochemical properties and soil nitrogen cycle functional genes.
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Figure 8. RDA of soil physicochemical properties and functional genes during soil nitrogen cycling. Note: the blue arrow represents soil microbial nitrogen cycling genes, while the red arrow indicates other environmental factors in the soil.
Figure 8. RDA of soil physicochemical properties and functional genes during soil nitrogen cycling. Note: the blue arrow represents soil microbial nitrogen cycling genes, while the red arrow indicates other environmental factors in the soil.
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Table 1. Fire disturbance classification characteristics.
Table 1. Fire disturbance classification characteristics.
Diagnostic PropertyFire Intensity
UnburnedMild FireHeavy Fire
Type of fire disturbance/Surface fireSurface and crown fires
Victimization of standing timber/≤30%≥70%
Severity/Bark and stems disturbed and scorched, tree still covered with green leavesCrown burned, no green leaf cover
Fire disturbance level/The organic matter layer is well preserved and charred to a depth of a few millimetersAsh deposits and charred organic matter up to several centimeters thick
Table 2. Summary of detailed information on fire sites.
Table 2. Summary of detailed information on fire sites.
Plot TypeLatitude and LongitudeElevation AspectSlopeVegetation TypeSoil TypeC/N
Unburned121°41′19″ E–50°52′49″ N1146.4Southwestern4°Larix gmelinii—Rhododendron simsiiBrown coniferous forest soils19.27
Mild fire121°41′19″ E–50°52′49″ N1130.7Southwestern6°Larix gmelinii—Rhododendron simsiiBrown coniferous forest soils15.07
Heavy fire121°41′19″ E–50°52′49’ N1144.9Southwestern4°Larix gmelinii—Rhododendron simsiiBrown coniferous forest soils21.36
Table 3. Primer information on nitrogen cycle genes.
Table 3. Primer information on nitrogen cycle genes.
Target GenePrimer
nirSCd3aF: GTSAACGTSAAGGARACSGG
R3cd:GASTTCGGRTGSGTCTTGA
nirKnirk876: ATYGGCGGVCAYGGCGA
nirk1040: GCCTCGATCAGRTTRTGGT
nosZnosz2F:CGCRACGGCAASAAGGTSMSSGT
nosz2R: CAKRTGCAKSGCRTGGCAGAA
nifHMMF2: TNATCACCKCNATCACTTCC
MMR1: CGCCGGACKWGACGATGTAG
amoA-AOBamoa1F: GGGGTTTCTACTGGTGGT
amoa2R: CCCCTCKGSAAAGCCTTCTTC
amoA-AOAArch-AmoAF:STAATGGTCTGGCTTAGACG
Arch-AmoAR:GCGGCCATCCATCTGTATGT
Table 4. Analyses of soil physicochemical properties.
Table 4. Analyses of soil physicochemical properties.
Fire IntensitySoil Depth
(cm)		Heavy FireMild FireUnburned
Soil Properties
Bulk density (g/cm3)A (0–10)0.91 ± 0.04 Aa0.88 ± 0.03 ABa0.85 ± 0.06 Ba
B (10–20)0.92 ± 0.07 Aa0.90 ± 0.04 Aa0.83 ± 0.04 Ba
Moisture content (%)A (0–10)34.21 ± 3.20 Ca37.38 ± 2.39 Ba40.03 ± 0.64 Aa
B (10–20)31.42 ± 2.65 Ca34.46 ± 3.32 Ba38.99 ± 1.95 Aa
pHA (0–10)5.64 ± 0.20 Aa5.40 ± 0.17 Ba5.13 ± 0.12 Ca
B (10–20)5.13 ± 0.23 Ab5.25 ± 0.07 Ab5.14 ± 0.09 Aa
Organic carbon (g/kg)A (0–10)82.66 ± 8.32 Ba102.54 ± 17.15 Aa94.44 ± 14.98 Aa
B (10–20)46.33 ± 9.45 Ab35.17 ± 7.70 Ab43.70 ± 13.03 Ab
Total nitrogen (g/kg)A (0–10)3.06 ± 0.26 Aa4.68 ± 0.33 Ba3.71 ± 0.45 Ca
B (10–20)2.95 ± 0.27 Aa4.28 ± 0.29 Ba3.34 ± 0.34 Ca
Ammonium nitrogen (mg/kg)A (0–10)57.73 ± 4.85 Ca64.91 ± 2.55 Aa60.46 ± 6.12 Ba
B (10–20)21.19 ± 3.59 Bb25.77 ± 7.85 Ab22.21 ± 7.94 Bb
Nitrate nitrogen (mg/kg)A (0–10)5.58 ± 0.26 Aa4.33 ± 0.36 Ba3.77 ± 0.28 Ca
B (10–20)2.98 ± 0.14 Ab2.37 ± 0.18 Bb2.21 ± 0.15 Cb
Available phosphorus (mg/kg)A (0–10)26.68 ± 5.81 Aa19.83 ± 6.06 Ba18.61 ± 3.19 Ba
B (10–20)16.02 ± 4.56 Ab11.80 ± 3.92 Ab14.38 ± 3.32 Ab
Available potassium (mg/kg)A (0–10)297.70 ± 62.97 Aa276.01 ± 28.24 Aa265.23 ± 41.50 Aa
B (10–20)325.74 ± 31.59 Aa312.05 ± 77.47 Aa252.71 ± 40.02 Aa
Note: Uppercase letters denote significant differences (p < 0.05) between fire intensity levels within the same soil layer; lowercase letters indicate significant differences between soil layers under identical fire intensity.
Table 5. Analysis of soil bacterial alpha diversity indices.
Table 5. Analysis of soil bacterial alpha diversity indices.
Soil Depth
(cm)		Fire IntensityAceChao1ShannonSimpson
A (0–10)Heavy fire1464.34 ± 167.97 Ba1442.82 ± 170.70 Ba5.38 ± 0.23 ABa0.016 ± 0.006 Aa
Mild fire1713.57 ± 124.22 Aa1721.07 ± 120.85 Aa5.43 ± 0.16 Aa0.019 ± 0.004 Aa
Unburned1401.52 ± 212.35 Ba1413.62 ± 203.37 Ba5.09 ± 0.23 Ba0.015 ± 0.005 Aa
B (10–20)Heavy fire1490.60 ± 112.24 Aa1489.29 ± 119.23 Aa5.38 ± 0.13 ABa0.018 ± 0.002 Aa
Mild fire1743.42 ± 100.79 Aa1753.87 ± 95.51 Aa5.60 ± 0.04 Aa0.021 ± 0.002 Aa
Unburned1464.76 ± 107.23 Ba1477.74 ± 129.78 Ba5.14 ± 0.27 Ba0.016 ± 0.006 Aa
Note: Uppercase letters denote significant differences (p < 0.05) between fire intensity levels within the same soil layer; lowercase letters indicate significant differences between soil layers under identical fire intensity.
Table 6. Effects of fire intensity, soil depth, and their interactions on soil bacterial composition.
Table 6. Effects of fire intensity, soil depth, and their interactions on soil bacterial composition.
Classification NameFire IntensitySoil Depth
FpFp
PhylumProteobacteria1.40p = 0.252.19p = 0.15
Actinobacteriota1.89p = 0.1711.56p < 0.05
Acidobacteriota0.21p = 0.810.05p = 0.82
Chloroflexi5.08p < 0.0564.01p < 0.05
Firmicutes0.60p = 0.560.39p = 0.54
FamilyXanthobacteraceae0.34p = 0.710.46p = 0.50
norank_o_norank_c_AD318.19p < 0.0564.12p < 0.05
norank_o_Elsterales25.06p < 0.0522.94p < 0.05
norank_o_Acidobacteriales1.23p = 0.310.13p = 0.72
Acidothermaceae0.68p = 0.526.02p < 0.05
GenusBradyrhizobium0.86p = 0.441.48p = 0.24
norank_f_Xanthobacteraceae2.17p = 0.140.56p = 0.46
norank_f_norank
_o_norank_c_AD3		18.19p < 0.0564.12p < 0.05
norank_f_norank
_o_Elsterales		25.06p < 0.0522.94p < 0.05
norank_f_norank
_o_Acidobacteriales		1.23p = 0.310.13p = 0.72
Table 7. Effects of different fire intensities on the abundance of functional genes during soil microbial nitrogen cycling.
Table 7. Effects of different fire intensities on the abundance of functional genes during soil microbial nitrogen cycling.
Soil Nitrogen Fraction Soil DepthHFLFUC
nifH0–10 cm2.91 × 103 ± 8.09 × 102 Ba8.44 × 103 ± 3.56 × 102 ABa1.13 × 104 ± 4.49 × 103 Aa
10–20 cm5.68 × 103 ± 2.46 × 103 Aa7.55 × 103 ± 2.27 × 103 Aa7.91 × 103 ± 4.23 × 103 Aa
amoA-AOA0–10 cm1.25 × 102 ± 2.37 × 101 Aa1.73 × 101 ± 1.65 × 100 Bb1.28 × 101 ± 2.26 × 100 Bb
10–20 cm1.04 × 102 ± 1.51 × 101 Aa8.40 × 101 ± 6.71 × 100 Aa4.30 × 101 ± 9.58 × 100 Ba
amoA-AOB0–10 cm2.33 × 105 ± 7.75 × 104 Aa7.70 × 102 ± 5.12 × 101 Ba2.02 × 102 ± 1.04 × 102 Ba
10–20 cm3.92 × 105 ± 2.04 × 105 Aa6.51 × 102 ± 2.39 × 102 Ba5.01 × 102 ± 1.74 × 102 Ba
nirK0–10 cm5.14 × 106 ± 1.93 × 106 Ba6.41 × 107 ± 2.69 × 107 Aa5.24 × 107 ± 2.56 × 107 Aa
10–20 cm2.04 × 106 ± 7.78 × 105 Ba4.13 × 107 ± 1.22 × 107 Aa2.74 × 107 ± 9.55 × 106 Aa
nirS0–10 cm6.15 × 104 ± 4.40 × 104 Aa3.75 × 105 ± 7.41 × 104 Aa3.55 × 105 ± 8.99 × 104 Aa
10–20 cm6.41 × 104 ± 3.27 × 104 Ba4.25 × 105 ± 4.78 × 104 Aa3.72 × 105 ± 6.48 × 105 ABa
nosZ0–10 cm1.10 × 106 ± 1.91 × 105 Ca6.61 × 106 ± 2.15 × 105 Aa2.75 × 106 ± 3.26 × 105 Ba
10–20 cm1.64 × 106 ± 7.06 × 105 Aa3.90 × 106 ± 8.07 × 105 Ab2.66 × 106 ± 9.74 × 105 Aa
Note: Uppercase letters denote significant differences (p < 0.05) between fire intensity levels within the same soil layer; lowercase letters indicate significant differences between soil layers under identical fire intensity.
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Shu, Y.; Jia, W.; Zhao, P.; Zhou, M.; Zhang, H. Effects of Fire on Soil Bacterial Communities and Nitrogen Cycling Functions in Greater Khingan Mountains Larch Forests. Forests 2025, 16, 1094. https://doi.org/10.3390/f16071094

AMA Style

Shu Y, Jia W, Zhao P, Zhou M, Zhang H. Effects of Fire on Soil Bacterial Communities and Nitrogen Cycling Functions in Greater Khingan Mountains Larch Forests. Forests. 2025; 16(7):1094. https://doi.org/10.3390/f16071094

Chicago/Turabian Style

Shu, Yang, Wenjie Jia, Pengwu Zhao, Mei Zhou, and Heng Zhang. 2025. "Effects of Fire on Soil Bacterial Communities and Nitrogen Cycling Functions in Greater Khingan Mountains Larch Forests" Forests 16, no. 7: 1094. https://doi.org/10.3390/f16071094

APA Style

Shu, Y., Jia, W., Zhao, P., Zhou, M., & Zhang, H. (2025). Effects of Fire on Soil Bacterial Communities and Nitrogen Cycling Functions in Greater Khingan Mountains Larch Forests. Forests, 16(7), 1094. https://doi.org/10.3390/f16071094

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