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Article

Variations in the Diversity and Biomass of Soil Bacteria and Fungi under Different Fire Disturbances in the Taiga Forests of Northeastern China

by
Zhichao Cheng
1,2,†,
Song Wu
3,†,
Jun Du
2,*,
Hong Pan
1,
Xinming Lu
1,
Yongzhi Liu
2 and
Libin Yang
1,*
1
Key Laboratory of Biodiversity, Institute of Natural Resources and Ecology, Heilongjiang Academy of Sciences, Harbin 150040, China
2
Heilongjiang Huzhong National Nature Reserve, Huzhong 165038, China
3
Science and Technology Innovation Center, Institute of Scientifc and Technical Information of Heilongjiang Province, Harbin 150028, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Forests 2023, 14(10), 2063; https://doi.org/10.3390/f14102063
Submission received: 8 September 2023 / Revised: 12 October 2023 / Accepted: 13 October 2023 / Published: 16 October 2023
(This article belongs to the Section Forest Soil)
Browse Figures
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Abstract

:
Fire is a crucial disturbance factor for the structure and function of forest ecosystems, as it directly or indirectly affects plant growth, animal life and soil biogeochemical properties. Here, the effects of different fire severities and key driving environmental factors on soil microbial diversity and biomass were investigated in taiga forests that had undergone light, moderate or heavy fires, more recently or in the past, with unburned taiga forest included as a control (CK). The sample sites were located in the Greater Khingan mountains in the northeast of China. Critical soil parameters were determined, and bacterial and fungal content was inferred from determined phospholipid fatty acids (TPLFAs). The results showed that (1) all three fire severities significantly increased the concentrations of soil microbial carbon (MBC), moisture content (MC) and total nitrogen content (TN), but they significantly decreased soil available potassium (AK) content compared with the CK. (2) Recent light and moderate fires significantly decreased the Simpson and Shannon indices of soil microbial communities compared to CK; moderate fire did not change the Menhinick and Margalef indices. (3) Following moderate fire disturbance, there were no significant differences (p > 0.05) in relative abundance of total soil bacteria (Ba), Gram-positive bacteria (G+), Gram-negative bacteria (G−) and content of TPLFAs compared to the control, either as a result of more recent fires or earlier fires. (4) Redundancy analysis revealed that soil MC, TN, soil organic carbon (SOC), available P (AP) and alkaline N (AN) all strongly significantly affected the composition of the microbial communities, with a total explanation of 85.16% of the data. The species diversity and biomass of Ba, G+, G− and TPLFAs were in accordance with the intermediate disturbance hypothesis. The change pattern of soil fungi was in accordance with their own characteristics of reproduction and growth, which was in line with k-selection and ecological countermeasures.

1. Introduction

Forest ecosystems play an important role in local water storage and soil conservation, and are indispensable in global biogeochemical cycles [1]. Forest ecosystems are threatened by fire, which present as a principal disturbance factor; forest fire occurrence is increasing as a result of higher global temperatures and extreme weather conditions [2]. To some degree, the occurrence of forest fires can play a certain role in forest ecosystem “cleaning”; this is because a past fire results in plant seeds and seedlings having easier access to the soil and the circulation of nutrients is accelerated [3], promoting plant seed germination and growth [4]. Thus, forest fires can be conducive to the growth and development of forests, their recovery, and succession [5,6,7]. However, forest fires can negatively affect the aboveground vegetation, wildlife, ecosystems, soil characteristics, and the atmospheric environment [8]. Fires can indirectly change the composition of vegetation communities, soil nutrition conditions, and soil microbial community compositions [9].
Soil microorganisms play cardinal functions in nutrient element cycles, energy flow and information transfer, and undertake many functions in forest ecosystems, especially in the process of vegetation renewal and succession. The consequences of forest fires on the soil microbiome mainly result from the ultra-high temperatures that directly kill soil microorganisms [10]. In addition, indirect effects include shifts in soil microorganisms by altered soil nutrients availability and microenvironments [11]. As forest fires affect the soil’s physicochemical properties and the microorganisms present, there can be consequences on nitrogen fixation, mineral nutrient transformation and other processes that regulate vegetation, often promoting forest recovery [12]. The period of recovery can be long or short, but eventually the forest may be restored to the pre-fire levels [13].
In the Huzhong National Nature Reserve forest in the Greater Khingan mountains in northeast China, from 1966 to present, 25 incidents of fires have been recorded, with a total fire area of 7227 hectares, covering on average 289 hectares per incident. The fires destroyed 3627 hectares of forest area, which seriously damaged the ecological environment and led to serious direct and indirect economic losses [14]. How to quickly renew and restore the fire sites is not only an important ecological problem in this region, but also affects the sustainable development of the local economy. Previous studies on forest fire sites in this area have mainly focused on the recovery of aboveground vegetation after a fire [15], the transformation of trace metals [16], the variability of spatial patterns produced by forest fires [17], and their effect on the growth of abundant tree species such as Larix gmelinii [18]. However, fewer studies have investigated the dynamic process of soil microorganisms in post-fire forest soil, taking into consideration the severity of the fire and the time allowed for restoration. This was the subject of the present study, for which we selected past and recent fire sites with different fire severities in the Huzhong National Nature Reserve of Greater Khingan study area. We determined the variations in soil microbial diversity and biomass and established the relationship with environmental influences under two-factor interactions. The outcomes provide a theoretical basis for an in-depth understanding of responses by cold-temperate forest ecosystems towards fire disturbances and the driving mechanisms of forest recovery.

2. Materials and Methods

2.1. Sample Sites Description

The Greater Khingan Mountains are the largest cold-temperate coniferous forest ecosystem in northeast China. The climate is a typical cold-temperate continental monsoon climate with a temperature range between −35.8 °C in January and 24.5 °C in July. The annual precipitation ranges from 398 to 688 mm, with an average annual precipitation of 458.3 mm. The dominant vegetation consists of Larix gmelinii, Betula platyphylla, and Pinus sylvestris var. mongolica. The selected research site is located in Heilongjiang Huzhong National Nature Reserve (122°12′16.3″–122°21′7.8″ E, 53°26′30.6″–53°28′6.3″ N; Figure 1), covering 63 km from north to south and 32 km from east to west. The reserve (total area 167,213 hm2) is nearly fully covered by forest (166,725 hm2 woodland) and represents one of the most primitive and complete cold-temperate zone coniferous forests in China.

2.2. Sample Plots

In the nature reserve, areas were selected where fires occurred in 2000 to represent past fire sites, denoted in the text by “00”, and in 2010 for recent fire sites, denoted by “10”. The severity of these fires was classified according to the classification standard presented in Table 1 as light (L), moderate (M) or heavy (H). A nearby area without fire damage was selected as the control (CK), with the same altitude, vegetation composition and similar environmental conditions as the fire sites. Thus, a total of seven sites were sampled: 00L, 00M, 00H, 10L, 10M, 10H and CK. For each site, three sample plots (20 m × 20 m) were identified with a spacing of at least 20 m along the diagonal in each sample site, totaling 21 plots.

2.3. Soil Sample Collection and Experimental Methods

The five-point method was used to collect soil samples (depth: 0–10 cm) from each sample plot. After removing humus, plant roots and stones, 2.5 kg of soil was collected, sieved through a 2 mm mesh, mixed and stored in Ziploc plastic bags on ice for immediate transfer to the laboratory. Here, the soil was divided into two parts, of which one was stored at −20 °C for microbial determination, and the other was air-dried to determine the soil physicochemical properties.

2.4. Determination of Soil Physicochemical Properties

The microbial carbon (MBC) of the soil was determined following extraction by chloroform fumigation, and the leachate was measured with a Vario TOC meter (Elementar, Langenselbold, Germany), as previously described [20]. The soil pH was measured with a water/soil ratio of 2.5:1 [21]. A fraction of the soil was then dried in a drying oven at 110 °C for 24 h to a constant weight (two weightings with a precision electronic balance of 0.01 g differing by <0.1%), and the weight difference pre- and post-drying was used to calculate the moisture content (MC) [22]. Available potassium (AK) was determined by a flame photometer, as described [23], for which 2.5 g of air-dried soil was added to 25 mL 1 mol/L ammonium acetate, incubated for 30 min under rotation, and filtered prior to measurement. Soil organic carbon (SOC) and total nitrogen (TN) were measured with a CN element analyzer (Elementar Vario ELIII, Elementar, Germany), for which air-dried soil was ground, passed through a 0.25 mm mesh, and 0.1 g was used for determination [24]. Available phosphorus (AP) was determined by NaHCO3 leaching followed by flame photometry [23], and soil alkaline nitrogen (AN) was determined by the alkaline distillation method, as described in [23].

2.5. Phospholipid Fatty Acid Detection

The detection of phospholipid fatty acids was accomplished following methyl esterification in KOH–methanol solution, as described [25], with inclusion of nineteen-alkanoic acids (19:0) as an internal standard. Detection was performed by gas chromatography (Agilent 6850), and from the phospholipid fatty acid compositions, microbes were identified using the Sherlock Microbial Identification System (v4, Element). The concentration of each fatty acid was calculated based on the concentration of the carbon 19:0 internal standard and expressed as nmol/g. The characteristic fatty acids indicative of total soil bacteria, Gram-positive bacteria, Gram-negative bacteria and fungi were referred to based on the literature [26,27,28]. The content of PLFAs in the samples was calculated according to the following formula [29]:
CFA = [Target Response/(19:0)Response] × (19:0)Concentration × [Dissolved sample volume/Sample dry weight × MFA]
where CFA is the content of fatty acids (nmol/g), Response is the response value of the biomarker, 19:0 is the internal standard C19:0 (ng/μL), MFA is the molar mass of the fatty acid (g/mol), the Dissolved sample volume is in μL, and the Sample dry weight is in g. The microorganisms in the soil were characterized with reference to the phospholipid fatty acid markers as per the literature [30], as summarized in Table 2.

2.6. Data Analysis

The Shannon–Wiener diversity index H, Simpson dominance index D, Margalef richness index D, and Menhinick richness index E were used to analyze the microbial community diversity and characterize its alpha diversity. These were calculated as follows:
Shannon–Wiener (H): H = ∑(Pi)(lnPi)
Simpson (D): D = 1 − ΣPi2
Margalef (M): M = (S − 1)/lnN
Menhinick   ( E ) :   E = S / √ N
where Pi is the proportion of i characteristic fatty acids to the total characteristic fatty acids, Pi = Ni/N; N is the total number of characteristic fatty acids, and Ni is the proportion of the number of i fatty acids to the total number of characteristic fatty acids; and S is the total number of characteristic fatty acid species in the community.
Microsoft Excel 2016 was used to organize the data, and SPSS25.0 was used to test for normal distribution of the data and for ANOVA Chi-squared tests. The differences in soil microbial alpha diversity, soil physicochemical properties and microbial PLFA content observed with different fire severities were analyzed by one-way ANOVA and multiple comparisons (DUNCAN method) at a significance level of p < 0.05. Redundancy analysis (RDA) was used to describe the relationship between soil microorganisms and environmental factors by analyzing the differences in microbial community structure among different intensities of the fire sites using the vegan package in R 3.6.0. The relationships of soil microbial structure and environmental factors were also analyzed by RDA.

3. Results

3.1. The Soil Physicochemical Parameters of Different Fire Severities

The physicochemical properties differed in the forest soils collected from past and more recent fire sites and also between fire intensities (Table 3, p < 0.05). Higher concentrations of soil MBC, MC, TN, SOC, AP and AN were detected in fire sites of L, M and H severity compared to the CK, with a stronger difference for the 2000 fire sites compared to the 2010 fire sites in most cases. The concentrations of AK were lower than in CK, and this was more so in the 2000 fire site samples.
When comparing the former (2000) fire sites with the more recent (2010) sites, independent of fire severity, the concentrations of soil physicochemical properties differed significantly (Table 3, p < 0.05). The concentrations of soil MBC were higher in 00L, 00M and 00H than in CK, as were the moisture content. The highest values were obtained for 00L. The soil AK content of all samples was lower than CK, lower in 2000 than in 2010 fires, and lower in L than in H fires. The soil SOC content of 00L, 00M, 00H, and 10L were higher than CK, but they were unchanged or lower in 10M and 10H (Table 3, p < 0.05), with the highest values recorded for 00M. The soil AN content of soils after the 2010 fires did not differ from the control (p > 0.05), but they were higher (p < 0.05) in the three soils after the 2000 fires.

3.2. Differences in Microbial Diversity of the Soils in Fire Sites

Four microbial community indices were determined for the seven soil sample types. the Shannon and Simpson indices of the detected soil microbial community were significantly lower for 10L and 10M that had more recently been exposed to fire compared to CK, but there were no differences for the other samples (Table 4, p < 0.05). The Menhinick and Margalef indices varied more widely, with higher values for the 2000 fire sites compared to the 2010 fire sites (Table 4).
In the earlier 2000 fire sites, Menhinick and Margalef indices were both highest following light fires and lowest following moderate fires (Table 4, p < 0.05). These indices for 00M did not differ from the CK (p > 0.05). For the more recent fire sites, the Menhinick and Margalef indices were highest following heavy fires and lowest following light fires.

3.3. Changes in the Content and Community Structure of Microbial PLFAs in Soils of the Fire Sites

The variation in soil microbial PLFA content in the different fire sites is shown in Figure 2. In the past (2000) fire sites, the contents of Ba (Figure 2a), fungi, (Figure 2b), G+ (Figure 2d), G− (Figure 2e) and TPLFAs (Figure 2g) were significantly lower (p < 0.05) in the soil of L and H compared to CK and M. In the more recent fire sites (2010), these contents decreased significantly (p < 0.05) with fire severity from L to H, except for fungi, which were found to be highest in 10M (Figure 2b). The ratio between bacteria and fungi was found to be significantly increased in 00H (Figure 2c). Under moderate fire disturbance, the contents of soil Ba, G+, G− and TPLFAs did not vary significantly (p > 0.05) between the past and more recent fire sites. The ratio of G+/G− did not vary compared to CK (Figure 2f).

3.4. Correlation Analysis of Factors Affecting Soil Microbial Communities in the Fire Sites

Correlation analysis was performed between the four diversity indices and the soil physicochemical parameters (Table 5). The Shannon index of the soil community correlated strongly (p < 0.01) and positively with the concentrations of MC, TN, SOC, and AN. The Simpson index correlated strongly (p < 0.01) and positively with the concentrations of TN and AN, and strongly (p < 0.01) and negatively with the concentration of AP; at a lower level of significance it correlated positively with moisture content MC (p < 0.05, Table 5). The Menhinick index correlated with all parameters except SOC. A very strong (p < 0.001) negative correlation was demonstrated with AP; positive correlations were also found with concentrations of MBC, MC, TN and AN, while negative correlations were demonstrated with pH and AK. The Margalef index mimicked the correlations observed for Menhinick.
Redundancy analyses were carried out for the different soil microbial contents and the soil physicochemical properties of the fire sites. From Figure 3 it can be seen that the degree of explanation was 67.09% for the first and 18.07% for the second dimension, giving a total degree of explanation of 85.16%. Soil microbial community of 00L, 00M, and 00H was positively correlated with SOC, MC, TN, and AN. For the 2010 fire sites, the soil microbial community of 10L was positively correlated with AP, while that of 10M was positively correlated with pH and AK.
As summarized in Table 6, soil MC, TN, SOC, AP and AN produced the highest significance for effects on the soil microbial community composition (p < 0.001), indicating that these physicochemical properties of the soil collected after fires of different severity had the most severe effect on the structure of the soil microbial community.
The results of the correlation analysis between the content of phospholipid fatty acids of the analyzed soil types and the soil physicochemical properties is summarized in a heatmap (Figure 4). MBC significantly and negatively correlated with the content of Ba, G+, G− and TPLFAs (p < 0.05). The contents of soil MC and TN correlated negatively with the content of Ba, Fu, G− and TPLFAs (p < 0.05), and they correlated positively with the B/F and G+/G− ratios. The soil pH showed positive correlations with the contents of Ba, Fu, G+, G− and TPLFAs, and similar correlations were observed for AK. The contents of soil TN with negative correlations for Ba, Fu, G− and TPLFAs and positive correlations for the B/F ratio and the G+/G− ratio. The contents of soil SOC with negative correlations for Fu, and positive correlations for the B/F. The content of soil AP strongly (p < 0.001) positively correlated with Ba, G+, G− and TPLFAs. Finally, the content of AN correlated strongly (p < 0.001) negatively with Fu and weaker with Ba, G+, G− and TPLFAs, producing a positive correlation with the B/F ratio (Figure 4). This confirms that soil physicochemical properties influence the microbial composition of the soil of past fire sites.

4. Discussion

4.1. Effects of Forest Fires on Soil Physicochemical Properties

Forest fires consume the dead litter layer and change the physicochemical properties of forest soil through oxidation, volatilization, convection of ash particles and leaching [11]. This study demonstrates that fires also cause significant changes in soil AN, TN, SOC, AK and AP, detectible after one and even two decades.
In this study, we found that both soil AN and TN contents were higher (p < 0.05) in the soil from past fire sites compared to the control, which is consistent with previous studies [30,31]. Burning may have converted most of the soil organic nitrogen into inorganic nitrogen, and any ammonium nitrogen generated would have been adsorbed by the negatively charged minerals and organic matter, resulting in nitrogen being retained in the soil [32,33], and this may be behind the higher contents of AN and TN.
We further report that soil SOC in the more recent fire sites showed a tendency of increasing and then decreasing with increasing fire severity, which was consistent with previous reports [34,35]. There may be two reasons for this as follows: (1) After a light fire, unburned residues combine with mineral soil, which may protect the biochemical decomposition of organic carbon into less decomposable forms [34]. (2) In addition, a light fire may increase the amount of reactive organic matter in the soil, enhancing the effect of soil reactive organic carbon, and this may also result in an increase in the content of reactive organic carbon in the soil [11]. The decrease in SOC following moderate fires may explained by burning of dead wood and humus on the forest floor, resulting in bare mineral soil and soil erosion, which may be responsible for a decrease in organic matter content [36].
Mcintosh et al. found that soil AK content was significantly reduced after fire [37,38], and similar findings were observed in the present study. This may be attributed to the large damage to forests caused by forest fires, resulting in a reduction in forest canopy area and a decrease in forest closure. When the surface litter is burned, it results in a large area of bare ground, which will be subject to oxidation, volatilization, leaching, and other high-severity erosion and loss, resulting in the loss of potassium [39].
In addition, the soil AP content of the sites more recently hit by fire investigated here was significantly increased compared to the control, similar to findings reviewed by Certini and Liu [32,39]. This may be due to the accelerated mineralization of organic phosphorus by the fire, and as leaves and other debris are transformed into ash, which replenished the phosphorus in the soil, hence increasing the AP content.

4.2. Effects of Forest Fires on Soil Microbial Diversity

Changes in soil physicochemical properties caused by forest fires may lead to the disappearance or reduction in certain microbial species, and this may have a large impact on the alpha diversity of soil microorganisms [11,40]. Water is the main carrier of soluble minerals and carbon that function as nutrients. Of these, nitrogen is a typical growth-limiting nutrient in forest soil. Significant correlations were identified between MC, SOC, TN and AN with soil microbial alpha diversity in published studies [41,42]. In the present study, TN, AN and MC were found to be positively correlated with soil microbial alpha diversity.
We found that the Shannon index and Simpson index of the soil microbial community of soil damaged by light and moderate fires occurring 10 years ago were significantly lower than in the control soil (p < 0.05), although heavy fires did not result in significant differences (p > 0.05). The decrease in diversity may be caused by direct killing of soil microorganisms by the fire that permanently reduced the diversity of the community [10]. Following a heavy fire, organic material that is difficult to decompose may be mineralized, increasing the effective amount of nutrients in the soil, such as AN, TN, etc., which allows the soil microbial communities to diversify. The soil microbial diversity would then more rapidly recover [43]. The Shannon index and Simpson index of the sites suffering from fire 20 years ago were not significantly different from the control group (p > 0.05), indicating that over this period of time, the total number of soil microbial species and the number of dominant species had been re-established to pre-fire levels [44].
We found that that the Menhinick and Margalef indices increased significantly with an increase in fire severity in recent fire sites. This may be because fire disturbances may have increased the heterogeneity of the forest soil, and at the same time they may have maintained or even increased microbial richness by increasing the competitive capacity of soil microorganisms against external environmental resources [45,46,47]. The observed changing patterns in Menhinick and Margalef indices of the soil communities in the past fire sites were consistent and correlated with the levels of soil TN, AN and MC. Possibly, soil water content and soil nitrogen can promote the increase in some microbial abundance [48,49]. In addition, we found that the Menhinick and Margalef indices in soil of past and more recent moderate fire sites were similar to those of the control sample, which is consistent with the “intermediate disturbance hypothesis theory” proposed by Connell from the point of view of the species richness and diversity of the biological communities [50].

4.3. Effects of Forest Fires on the Microbial Composition of Soils

The effect of fire on soil microbial composition and structure is the combined result of high temperatures that eliminate certain microorganisms followed by the reassembling of a new microbial community over time. The new communities may adapt to the changed soil characteristics and thus results in a different structure of the microbial community [10]. The severity of a fire determines the nature and severity of the changes in the physicochemical properties and microenvironment of the soil, resulting in heterogeneous habitats, which in turn affect the local microbial community structure and composition in the soil. We demonstrated that the contents of Ba, G+, G− and TPLFAs in the more recent fire sites had decreased significantly with an increase in fire severity, which is consistent with the findings of Hebel et al. [51]. With increasing fire severity, the metabolic processes of soil microorganisms are disrupted, and irrespective of heat, oxidative damage can occur to macromolecules (proteins and nucleic acids) that can result in increased microbial mortality [52].
In this study, the soil contents of MC, TN, AK, SOC, AP and AN were identified as the most important factors affecting the changes in soil microbial community structure after a fire disturbance. Among them, AP and AK were significantly positively correlated with all determined types of soil microorganisms, while TN and AN were significantly negatively correlated, which was consistent with the results of Yang et al. and Wang et al. [29,53]. The burning of vegetation produces ash which is rich in potassium and provides effective nutrients for plant recovery and growth [54]; at the same time, fire burning induces organisms to produce more extracellular phosphatases to enhance the net phosphorus mineralization rate, and this further increases the efficiency of nutrient turnover, which is conducive to promoting the growth and reproduction of soil microorganisms [39,55]. Prayogo and colleagues reported that excess nitrogen would reduce the soil carbon excitation and nitrogen excitation effects, resulting in the weakening of soil microbial respiration [56], the reduction in soil organic carbon mineralization rate, and the slowing down of organic carbon conversion [57]. Such processes would inhibit the recovery of soil microorganisms, which may be the reason for the significant negative correlation between the soil microbial biomass and nitrogen in the present study.
The ratio of G+/G− is an indicator of microbial community structure. Gram-positive bacteria tend to utilize inert carbon sources, while Gram negatives are more likely to utilize fresh plant residues [58]; therefore, the G+/G− ratio is often used to reflect changes in substrate quality. In this study, no significant difference in G+/G− was observed between the past or more recent fire sites, nor was there an effect of the fire severities, suggesting that the decomposition strategy of the microbial community on soil organic matter was restored to the pre-fire level in all samples.
The intermediate disturbance hypothesis describes that biological communities maintain a high species diversity under moderate disturbance [50]. Our observations suggest that under moderate fire disturbance, the contents of soil Ba, G+, G− and TPLFAs were similar to those of the control soil in both past and more recent fire sites. This indicates that the soil microbial biomass of the fire sites, except for fungi, was restored to the previous pre-fire level after a moderate fire disturbance. The failure of fungi to fully recover may be related to the ecological response of fungal taxa. Fungi have been shown to be more severely affected by fire disturbances, and their populations recover more slowly than bacteria; fungi are characterized as slow-growing, k-responsive microbial taxa, while bacteria are mainly r-responsive microbial taxa with faster growth and turnover [59,60]. Fungi generally reproduce by spores, and they take a longer time to germinate and form a fungal body after a fire [61]; in contrast, bacterial reproduction is typically faster so that a bacterial population can regrow in a shorter period of time [62].
The intermediate disturbance hypothesis was originally proposed to explain the effects on the species level of biomes; our experimental results were obtained with TPLFAs from which bacterial taxa were identified. Nevertheless, the observed patterns of changes in the biomass of bacteria and other taxa after a moderate fire disturbance were still found to be consistent with the intermediate disturbance hypothesis. Whether this insight can serve as an enrichment and extension of the content and theory of the intermediate disturbance hypothesis needs to be corroborated by further in-depth studies.

5. Conclusions

(1)
Soil microbial biomass carbon (MBC), moisture content (MC), and total nitrogen (TN) all increased significantly in the soil of the investigated fire sites compared to control soil, but the content of available potassium (AK) decreased significantly.
(2)
The species diversity of soil microorganisms and the contents of soil Ba, G+, G− and TPLFAs were restored to pre-fire levels after a moderate fire.
(3)
Soil MC, TN, SOC, AP and AN being the main influencing factors of the soil microbial community composition.
The determination of soil microbial biomass and diversity using the phospholipid fatty acid method of identification, as applied here, is not equivalent to a taxonomic species determination, determining the microbial community composition in an alternative manner. This is different from the representation of species in the intermediate disturbance hypothesis, and further research is needed to determine whether the pattern of changes in species diversity observed in this study is truly consistent with the intermediate disturbance hypothesis.

Author Contributions

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

Funding

This research was funded by the Forestry and Grassland Ecological Protection and Restoration Funds project (GZCG2023-024); Foundation of Heilongjiang Academy of Sciences (KY2023ZR03).

Data Availability Statement

Data are contained within the article.

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 a potential conflict of interest.

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Figure 1. Location of the research area and sample plot.
Figure 1. Location of the research area and sample plot.
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Figure 2. Total phospholipid fatty acids (PLFAs) and microbial compositions of soils with different fire severities. CK: control (no fire history); 00L: light fire in year 2000; 00M: moderate fire in 2000; 00H: heavy fire in 2000; 10L: light fire in 2010; 10M: moderate fire in 2010; 10H: heavy fire in 2010. Different letters within above a column indicate a statistical difference (p < 0.05). (a): Bacteria PLFAs content in fire sites; (b): Fungi PLFAs content in fire sites; (c): Bacteria/Funfi PLFAs content in fire sites; (d): Gram-positive bacteria PLFAs content in fire sites; (e): Gram-negative bacteria PLFAs content in fire sites; (f): Gram-positive bacteria/Gram-negative bacteria PLFAs content in fire sites; (g): Total PLFAs content in fire sites.
Figure 2. Total phospholipid fatty acids (PLFAs) and microbial compositions of soils with different fire severities. CK: control (no fire history); 00L: light fire in year 2000; 00M: moderate fire in 2000; 00H: heavy fire in 2000; 10L: light fire in 2010; 10M: moderate fire in 2010; 10H: heavy fire in 2010. Different letters within above a column indicate a statistical difference (p < 0.05). (a): Bacteria PLFAs content in fire sites; (b): Fungi PLFAs content in fire sites; (c): Bacteria/Funfi PLFAs content in fire sites; (d): Gram-positive bacteria PLFAs content in fire sites; (e): Gram-negative bacteria PLFAs content in fire sites; (f): Gram-positive bacteria/Gram-negative bacteria PLFAs content in fire sites; (g): Total PLFAs content in fire sites.
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Figure 3. Relationships between soil physicochemical properties and soil microbial community as determined by RDA. CK: control; 00L: light fire in 2000; 00M: moderate fire in 2000; 00H: heavy fire in 2000; 10L: light fire in 2010; 10M: moderate fire in 2010; 10H: heavy fire in 2010; SOC: soil organic content; MC: moisture content; TN: total nitrogen content; AN: alkaline nitrogen content; MBC: microbial carbon; AK: available potassium; AP: available phosphorus.
Figure 3. Relationships between soil physicochemical properties and soil microbial community as determined by RDA. CK: control; 00L: light fire in 2000; 00M: moderate fire in 2000; 00H: heavy fire in 2000; 10L: light fire in 2010; 10M: moderate fire in 2010; 10H: heavy fire in 2010; SOC: soil organic content; MC: moisture content; TN: total nitrogen content; AN: alkaline nitrogen content; MBC: microbial carbon; AK: available potassium; AP: available phosphorus.
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Figure 4. Heatmap of correlation between soil microbial community structure and environmental factors. Ba: total bacteria; Fu: fungi; B/F:L ratio of bacteria/fungi; G+: Gram-positive bacteria; G−: Gram-negative bacteria; G+/G−: ratio of Gram-positive/Gram-negative bacteria; TPLFAs: total PLFAs. Statistical significance is indicated as *, ** and *** at the level of 0.05, 0.01 and 0.001, respectively.
Figure 4. Heatmap of correlation between soil microbial community structure and environmental factors. Ba: total bacteria; Fu: fungi; B/F:L ratio of bacteria/fungi; G+: Gram-positive bacteria; G−: Gram-negative bacteria; G+/G−: ratio of Gram-positive/Gram-negative bacteria; TPLFAs: total PLFAs. Statistical significance is indicated as *, ** and *** at the level of 0.05, 0.01 and 0.001, respectively.
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Table 1. Characteristics of the sites with different severities of fire damage [19].
Table 1. Characteristics of the sites with different severities of fire damage [19].
SeverityGrade Index (k·Wm−1)Burned Dead Wood RatioDelineation Criteria
L (Light fire)350–750<30%Understory shrub was partially burned (less than 50%) with tree trunk blackening up to 2 m in height
M (Moderate fire)750–350030%–70%The litter layer and half-rot layer was burned, with unchanged color below the half-rot layer.
H (Heavy fire)>3500>70%All undergrowth was burned and tree trunks are blackened to a height of 5 m or more.
Table 2. The PLFA biomarkers used to characterize the microbes.
Table 2. The PLFA biomarkers used to characterize the microbes.
Microbial GroupDetected PLFA Biomarker
Unspecified bacteria (BA)12:0, 13:0, 14:0, 15:0, 16:0, 18:0, 22:0, 24:0
Gram-positive bacteria (G+)11:0 iso, 11:0 anteiso, 12:0 iso, 12:0 anteiso, 13:0 iso, 13:0 anteiso, 14:1 iso w7c, 14:0 iso, 14:0 anteiso, 15:1 iso w9c, 15:1 iso w6c, 15:1 anteiso w9c, 15:0 iso, 15:0 anteiso, 16:0 iso, 16:0 anteiso, 17:1 iso w9c, 17:0 iso, 17:0 anteiso, 18:0 iso, 19:0 iso, 19:0 anteiso, 20:0 iso, 22:0 is0
Gram-negative bacteria (G−)13:1 w5c, 13:1 w4c, 13:1 w3c, 12:0 2OH, 14:1 w7c, 14:1 w5c, 15:1 w9c, 15:1 w8c, 15:1 w7c, 15:1 w6c, 15:1 w5c, 14:02OH, 16:1 w9c, 16:1 w7c, 16:1 w6c, 16:1 w4c, 16:1 w3c, 17:1 w9c, 17:1 w8c, 17:1 w7c, 17:1 w6c, 17:0 cyclo w7c, 17:1 w5c, 17:1 w4c, 17:1 w3c, 16:0 2OH, 18:1 w8c, 18:1 w7c, 18:1 w6c, 18:1 w5c, 18:1 w3c, 19:1 w9c, 19:1 w8c, 19:1 w7c, 19:1 w6c, 19:0cyclo w9c, 19:0 cyclo w7c, 19:0 cyclo w6c, 20:1 w9c, 20:1 w8c, 20:1 w6c, 20:1 w4c, 20:0 cyclo w6c, 21:1 w9c, 21:1 w8c, 21:1 w6c, 21:1 w5c, 21:1 w4c, 21:1w3c, 22:1w9c, 22:1w8c, 22:1 w6c, 22:1 w5c, 22:1 w3c, 22:0 cyclo w6c, 24:1 w9c, 24:1 w7c
Unspecified Fungi (FU)18:1ω9c, 23:0
Table 3. Physicochemical properties in the sampled forest soils.
Table 3. Physicochemical properties in the sampled forest soils.
SampleMBC mg/kgMCpHAK mg/kgTN g/kgSOC g/kgAP mg/kgAN mg/kg
CK149.97 ± 33.56 b0.04 ± 0.004 g6.79 ± 0.03 a213.05 ± 19.19 a1.69 ± 0.01 d97.05 ± 3.41 e11.74 ± 0.69 e96.14 ± 11.33 b
00L1092.24 ± 267.27 a0.47 ± 0.013 b6.11 ± 0.02 bc9.33 ± 0.87 d3.28 ± 0.52 c193.22 ± 2.42 c6.3 ± 0.69 f253.89 ± 20.4 a
00M1143.35 ± 94.89 a0.6 ± 0.054 a6.7 ± 0.21 a10.04 ± 1.4 d4.84 ± 0.06 a304.86 ± 0.84 a22.6 ± 1.18 c300.73 ± 74.27 a
00H849.63 ± 94.06 a0.42 ± 0.014 c6.36 ± 0.41 abc24.94 ± 1.46 d4.28 ± 0.14 b235.56 ± 4.7 b11.15 ± 1.16 e285.21 ± 54.59 a
10L268.37 ± 23.67 b0.16 ± 0.002 e6.64 ± 0.02 ab140.07 ± 5.2 b1.95 ± 0.06 d122.98 ± 5.96 d46.9 ± 0.83 a89.37 ± 12.3 b
10M847.07 ± 369.5 a0.12 ± 0.006 f6.53 ± 0.05 ab80.43 ± 6.38 c1.71 ± 0.06 d93.16 ± 6.49 e18.21 ± 0.66 d82.13 ± 24.22 b
10H349.99 ± 12.5 b0.27 ± 0.03 d5.84 ± 0.4 c80.97 ± 11.17 c2.96 ± 0.43 c76.06 ± 3.24 f27.48 ± 1.57 b148.16 ± 17.29 b
CK: control (no fire history); 00L: light fire in year 2000; 00M: moderate fire in 2000; 00H: heavy fire in 2000; 10L: light fire in 2010; 10M: moderate fire in 2010; 10H: heavy fire in 2010; MBC: microbial carbon; MC: moisture content; AK: available potassium; TN: total nitrogen, SOC: soil organic carbon, AP: available phosphorous; AN: alkaline nitrogen. Different superscript letters within a column indicate a statistical difference (p < 0.05).
Table 4. Alpha diversity of microbial communities in the soil samples.
Table 4. Alpha diversity of microbial communities in the soil samples.
SampleShannonSimpsonMenhinickMargalef
CK3.41 ± 0.04 ab0.95 ± 0.00 a27.7 ± 0.35 d40.47 ± 0.35 d
00L3.45 ± 0.04 a0.95 ± 0.00 a36.24 ± 2.04 a49.65 ± 2.37 a
00M3.47 ± 0.01 a0.95 ± 0.00 a28.3 ± 0.6 d41.08 ± 0.61 d
00H3.47 ± 0.03 a0.95 ± 0.00 a33.4 ± 1.22 b46.45 ± 1.33 b
10L3.22 ± 0.02 c0.93 ± 0.00 b25.12 ± 0.8 e37.93 ± 0.78 e
10M3.18 ± 0.01 c0.93 ± 0.00 b27.17 ± 0.12 d39.95 ± 0.12 d
10H3.37 ± 0.06 b0.95 ± 0.00 a30.8 ± 0.39 c43.65 ± 0.4 c
CK: control (no fire history); 00L: light fire in year 2000; 00M: moderate fire in 2000; 00H: heavy fire in 2000; 10L: light fire in 2010; 10M: moderate fire in 2010; 10H: heavy fire in 2010. Different superscript letters within a column indicate a statistical difference (p < 0.05).
Table 5. Relationships of the soil physicochemical parameters and soil microbial diversity indices.
Table 5. Relationships of the soil physicochemical parameters and soil microbial diversity indices.
IndexMBCMCpHAKTNSOCAPAN
Shannon0.300.64 **−0.20−0.390.71 ***0.62 **−0.58 **0.76 ***
Simpson0.200.50 *−0.34−0.300.58 **0.39−0.64 **0.63 **
Menhinick0.50 *0.55 **−0.54 *−0.61 **0.50 *0.32−0.67 ***0.57 **
Margalef0.50 *0.55 *−0.53 *−0.61 **0.49 *0.32−0.66 **0.56 **
Statistical significance is indicated as *, ** and *** at the level of 0.05, 0.01 and 0.001, respectively.
Table 6. Significance of the soil physicochemical properties and soil microbial community structure.
Table 6. Significance of the soil physicochemical properties and soil microbial community structure.
Soil Factorr2p
MBC0.27540.055
MC0.66150.001 ***
pH0.1380.248
AK0.39670.016 *
TN0.68370.001 ***
SOC0.54540.003 **
AP0.80980.001 ***
AN0.72590.001 ***
Statistical significance is indicated as *, ** and *** at the level of 0.05, 0.01 and 0.001, respectively.
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Cheng, Z.; Wu, S.; Du, J.; Pan, H.; Lu, X.; Liu, Y.; Yang, L. Variations in the Diversity and Biomass of Soil Bacteria and Fungi under Different Fire Disturbances in the Taiga Forests of Northeastern China. Forests 2023, 14, 2063. https://doi.org/10.3390/f14102063

AMA Style

Cheng Z, Wu S, Du J, Pan H, Lu X, Liu Y, Yang L. Variations in the Diversity and Biomass of Soil Bacteria and Fungi under Different Fire Disturbances in the Taiga Forests of Northeastern China. Forests. 2023; 14(10):2063. https://doi.org/10.3390/f14102063

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Cheng, Zhichao, Song Wu, Jun Du, Hong Pan, Xinming Lu, Yongzhi Liu, and Libin Yang. 2023. "Variations in the Diversity and Biomass of Soil Bacteria and Fungi under Different Fire Disturbances in the Taiga Forests of Northeastern China" Forests 14, no. 10: 2063. https://doi.org/10.3390/f14102063

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