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Article

Consecutive Fertilization-Promoted Soil Nutrient Availability and Altered Rhizosphere Bacterial and Bulk Fungal Community Composition

by
Wenbo Wang
1,
Yuanyuan Yang
2,
Jinge Li
3,
Pengtu Bu
2,
Aijun Lu
2,
Hao Wang
2,
Wenxing He
1,
Ramon Santos Bermudez
1 and
Jian Feng
2,*
1
School of Biological Science and Technology, University of Jinan, Jinan 250022, China
2
Liaoning Academy of Forestry Sciences, Shenyang 110032, China
3
College of Life Sciences, Shandong Normal University, Jinan 250014, China
*
Author to whom correspondence should be addressed.
Forests 2024, 15(3), 514; https://doi.org/10.3390/f15030514
Submission received: 10 January 2024 / Revised: 3 March 2024 / Accepted: 6 March 2024 / Published: 10 March 2024
(This article belongs to the Section Forest Soil)
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Abstract

:
Fertilization is an important measure to quickly supplement the soil nutrients required for plantation productivity. However, the response patterns of the microbial community and functional taxa in Larix plantation root, rhizosphere, and bulk soil to short-term and consecutive fertilization have rarely been reported. In this study, we assessed Larix root, rhizosphere, and bulk soil microbial community on days 0, 5, 15, and 30 after the first inorganic fertilization and after three consecutive years of fertilization. The bacterial 16S and fungal ITS high-throughput sequencing technology were used to monitor changes in microbial community composition and potential functional groups, as well as changes in soil nutrient content and enzyme activity to evaluate the status of plantation soil productivity. Consecutive fertilization treatment significantly increased the available nitrogen, phosphorus, and potassium (NPK) content and soil enzyme activity. The nonmetric multidimensional scaling (NMDS) and analysis of similarities (ANOSIM) results showed that there were significant differences in microbial community composition in root samples, rhizosphere soil, and bulk soil samples. The dominant microbial taxa were different between root and soil microbial community composition. Consecutive fertilization treatment had little effect on endophytic microbial community but significantly increased the abundance of Gaiellales in rhizosphere soil and Mortierella in bulk soil. The redundancy analysis (RDA) and co-occurrence network analyses showed that Gaiellales and Mortierellales had significant positive correlations with soil nutrient content and enzyme activity. The fungal functional group compositions were significantly affected by consecutive fertilization treatment and the proportions of ectomycorrhizal and saprotroph significantly decreased, but the proportion of endophyte significantly increased in bulk soil samples. Our results suggested that consecutive fertilization may promote soil nutrient availability by increasing the abundance of Gaiellales and Mortierella. Consecutive fertilization maintained the balance of the soil microbiota under Larix plantation and had a positive effect on promoting soil nutrient availability. This study provided a theoretical basis for consecutive fertilization to promote soil nutrient availability through specific microbial groups.

1. Introduction

In the intensive cultivation process of artificially fast-growing plantations, fertilization is an important measure to quickly supplement the soil nutrients required to improve productivity [1,2]. Research has found that the effect of fertilization on soil nutrient availability depends on plant type and microbial community [3]. At present, relevant research mainly focuses on the effects of fertilization on soil chemical properties, as well as the response of microbial community diversity, composition, and nutrient cycling function [4,5,6]. Further, the response patterns of microbial communities in Larix plantation root, rhizosphere, and bulk soil to short-term and consecutive fertilization have rarely been reported. A previous study found that nitrogen fertilization increased rhizosphere soil microbial alpha diversity and significantly changed fungal community composition [7]. The application of nutrients can alter the structure of soil bacterial communities and the relative abundance of specific microbial groups [8]. At present, it is unclear how consecutive fertilization regulates key microbial groups, improves soil enzyme activity and nutrient availability, and affects the internal microecological mechanisms of soil nutrient availability.
Fertilization measures play an important role in the sustainable management of Larix plantations [3]. In particular, the nutrient availability of nitrogen (N), phosphorus (P), and potassium (K) elements is important for maintaining the health and sustainable management of a plantation [9,10]. Fertilization not only directly provides nutrients for plants, but also partially circulates and transforms nutrients under the mediation of soil microorganisms. Microorganisms play an important role in promoting the mineralization of N, P, K elements in soil, and work as a key driving factor in mediating the biogeochemical cycle of elements [5,11,12]. On the one hand, soil microorganisms gradually release available nutrients from organic matter by producing a variety of decomposition enzymes, providing more available nutrients for trees, thus increasing primary productivity [13]. On the other hand, fertilization weakens the nutrient competition between microorganisms and plants in the soil by directly providing a large amount of nutrients, inhibiting the absorption and transport potential of nutrients by microorganisms, and promoting the accumulation of effective nutrients in the soil [14]. Research had found that long-term chemical fertilization could significantly stimulate most functional genes involved in the C, N, and P cycles. Moreover, there was a significant correlation between the abundance of functional genes and the availability of nutrients and enzyme activity, indicating that long-term fertilization could accelerate the turnover of soil nutrients [15]. The response patterns and characteristics of soil nutrient cycling function of taxa to long-term fertilization are related to the efficient utilization of fertilizer nutrients, and warrant more attention [6,16].
A previous study found that the response of microbial communities to fertilization was significantly influenced by time, and the response modes of rhizosphere and bulk microorganisms were different [17,18]. The rhizosphere is an important area of plant ecosystems, which affects plant growth by controlling the chemical composition of plant nutrients. Regulating the rhizosphere environment through agronomic measures can improve nutrient utilization efficiency [19]. The plant root system establishes resource-rich areas with different properties from bulk soil, generating additional complexity and selectively recruiting specific microbial groups and soil organic matter in the rhizosphere [20]. Based on this, Larix root, rhizosphere, and bulk soil microbial community on days 0, 5, 15, and 30 after the first fertilization and after three consecutive years of fertilization were selected as the research object. The bacterial 16S and fungal ITS high-throughput sequencing technology in combination with soil nutrient content determination and enzyme activity analysis were used to analyze the effects of consecutive fertilization (three years) and short-term (less than 30 days) fertilization measures on soil nutrient content, enzyme activity characteristics, microbial community composition, and functional characteristics of root, rhizosphere, and bulk soil. The research hopes to reveal the internal relationship among the increase of soil-available nutrients caused by consecutive fertilization and the microbial community. We assume that consecutive fertilization measures will induce the enrichment of specific groups in the soil microbial community, increase extracellular enzyme activity, and promote the effective transformation and release of N, P, K nutrition. The research results will provide a theoretical microbiological basis for promoting soil nutrient availability by consecutive fertilization.

2. Materials and Methods

2.1. Study Site and Sample Collection

The study area is located in Qingyuan Manchu Autonomous County, Fushun City, Liaoning Province, Longgang Branch of Changbai Mountain (42°20′ N, 124°49′ E, and 400–600 m a.s.l.). This area belongs to the continental monsoon climate of the temperate zone, with cold winters and hot summers. The annual average temperature is 5.3 °C, the extreme highest temperature is 37.2 °C, the extreme lowest temperature is −37.6 °C, and the frost-free period is about 130 days. The number of total sunshine hours throughout the year is 2419, with an average annual precipitation of 806.5 mm. The soil is a typical brown forest soil classified as Udalfs with silt loam textures according to the USDA soil taxonomy [21].
Three sampling points were randomly selected from the unfertilized (1.42°20′58″ N, 124°49′25″ E; 2.42°22′27″ N, 124°51′19″ E; 3.42°21′40″ N, 124°51′17″ E) and three-year continuous fertilization areas (1.42°22′7″ N, 124°51′14″ E; 2.42°21′49″ N, 124°51′15″ E; 3.42°22′17″ N, 124°51′18″ E) in the Larix seed orchard. The plantation density and age at each sampling point were basically the same, which were 238/ha and 36 years, respectively. In every area we randomly selected three sampling points and pooled them as a composite sample. The size of each plot was 20 m × 30 m. The fertilization method adopted circular ditch fertilization. The depth of the circular ditch for fertilization treatment was about 15 cm and the width was about 10 cm. The fertilization period was in June, and the frequency was once a year. The fertilization treatment measures in this study were urea (CO(NH2)2, total nitrogen > 46%) 0.4 kg/tree/year, ammonium phosphate ((NH4)2HPO4; N-P2O5-K2O, 18-46-0) 0.6 kg/tree/year, potassium chloride (KCl > 61%; K + S Minerals and Agriculture GmbH) 0.6 kg/tree/year. The fertilization regime for three-year continuous fertilization groups was the same. Samples were collected at a soil depth of 15 cm. Sampling was conducted on day 0 in the unfertilized area marked as unfertilized treatment groups and after fertilization began on the 5th, 15th, and 30th days marked as short-term fertilization treatment groups (less than 30 days). The sample types were divided into Larix root samples, rhizosphere soil samples, and bulk soil samples. Bulk soil samples were collected distant from Larix roots. Rhizosphere soil samples were collected after gentle shaking treatment. Root samples were obtained after brushing and rinsing with water in the laboratory [22]. The samples used for DNA extraction were stored at −80 °C until they were sent for sequencing. Other samples used for enzyme assays were stored at 4 °C. The remaining soil samples were dried at 65 °C to constant weight for measuring of the chemical properties.

2.2. Soil Chemical Property Analysis

The total N content of the soil was determined using a C and N element analyzer (Leco CNS-2000, Leco Corporation, St Joseph, MI, USA). The total P and total K content in the soil was determined using the combustion method (2400 Ⅱ, CHNS/O PerkinElmer, Boston, MA, USA). The amount of soil organic carbon (SOC) was determined using an organic carbon analyzer (Multi N/C 3000, Analytik Jena AG, Jena, Germany). The effective P content in soil was determined using the molybdenum blue colorimetric method. The amount of potassium available in the soil was measured by a flame photometer method. The soil nitrate nitrogen and ammonium nitrogen content was determined by a flow analyzer (San++ System, Skalar, Breda, Holland). The pH values were determined in deionized water (1:2.5 for soil sample, m/v) [23].

2.3. Soil Enzyme Activity Assays

The soil cellulase, soil urease, soil amylase, soil acid, and alkaline phosphatase activities before and after fertilization were determined using Solarbio soil enzyme activity detection kits (Solarbio, Beijing, China; BC0150; BC0120; BC1920; BC0140; BC0280). The soil cellulase activity was determined by anthrone colorimetric method [24]. The activity of soil amylase was estimated using 3,5-dinitrosalicylic acid [25]. The soil urease activity was determined using indophenol blue colorimetric method. Acid and alkaline phosphatase activity was determined by the disodium phenyl phosphate colorimetric method [26]. The urease activity was expressed as g−1 soil d−1 μg−1 NH3-N. The activity of cellulase was defined as g−1 soil d−1 mg−1 glucose. The amylase activity was expressed as g−1 soil d−1 μmol−1 maltose. The activity of acid phosphatase and alkaline phosphatase was defined as g−1 soil d−1 μmol−1 phenol.

2.4. Soil Microbiomes DNA Extraction, PCR Amplification, and High-Throughput Sequencing

According to the manufacturer’s instructions, total microbial community genomic DNA was extracted from 500 mg soil samples using the MP FastDNA SPIN Kit for Soil (Omega Bio-tek, Norcross, GA, USA). Duplicate DNA extractions were performed for each sample and pooled. The quality of the extracted DNA was tested on 1% agarose gels and NanoDrop 2000 UV-vis spectrophotometer (Thermo Scientific, Wilmington, NC, USA). The V3–V4 region of the bacterial 16S rRNA gene was amplified using primer pairs 799F (5′-AACMGGATTAGATACCCKG-3′) and 1193R (5′-ACGTCATCCCCACCTTCC-3′), and the fungal ITS1 region was amplified using primer pairs ITS1F (5′-CTTGGTCATTTAGAGGAAGTAA-3′) and ITS2R (5′-GCTGCGTTCTTCATCGATGC-3′). PCR reactions were performed with TaKaRa rTaq DNA Polymerase, 20 μ reaction system with the following program: 3 min of denaturation at 95 °C, 35 cycles (fungi) or 13 cycles (bacteria) of 95 °C for 30 s, 55 °C for 30 s, 72 °C for 45 s, and a final extraction at 72 °C for 10 min. PCR products were extracted from 2% agarose gels and purified by the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA). Purified amplicons were combined at equimolar concentrations and paired-end sequenced on the Illumina MiSeq-PE300 platform (Illumina, San Diego, CA, USA) at Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China).

2.5. Bioinformatics Analysis

The Fastp software (version 0.20.0, https://github.com/OpenGene/fastp, accessed on 15 October 2022) and FLASH software (version 1.2.7, http://www.cbcb.umd.edu/software/flash, accessed on 19 October 2022) were used to control the quality of the original sequencing and splice paired-end reads. Finally, quality control and filtering were performed on the obtained sequences. The raw data generated by sequencing in this study are publicly available in the NCBI Sequence Read Achieve (SRA) database with an accession number SRP465537. Operational taxonomic units (OTUs) were selected with 97% similarity of clustered reads and then were checked for chimaeras using the UPARSE (v.7.0.1090, https://drive5.com/uparse/, accessed on 3 November 2022) pipeline in USEARCH v.11.0 software before generating an OTU count table. The bacterial and fungal taxonomy of each OUT representative sequence were analyzed against the 16S rRNA database (Silva v138) and ITS database (unite 8.0) using confidence threshold of 0.7. Then, statistical analysis of community structure was conducted on species annotations at various classification levels. The number of considered sequences in every sample was unified by minimum number of sample sequences. Based on FUNGuild (https://github.com/UMNFuN/FUNGuild, accessed on 9 November 2022), fungal functional groups have been identified [27], and this functional prediction method has been widely used to understand soil fungal communities [28].

2.6. Statistical Analysis

The soil nutrient content and enzyme activity statistical analyses in our study were performed by SPSS software (version 20.0, IBM, Armonk, NY, USA). In our results, differences were considered statistically significant when p < 0.05. The relative abundance of microbial taxa was transformed into square root to meet the normality and homogeneity of variances for ANOVA before analysis. Statistical differences among means were evaluated using Tukey’s multiple comparison. The repeated measures analysis of variance (RMANOVA) was used to test significant differences among sampling treatments and time in soil nutrient content, enzyme activity, relative abundances of microbial taxa, and phosphorus metabolism function genes [29]. The nonmetric multidimensional scaling (NMDS) plots were used to visualize the separation of soil microbial community composition in different samples based on Bray–Curtis distance matrix [30]. Analysis of similarities (ANOSIM) was used for differential testing of microbial community composition in inter group samples [31]. Redundancy analysis (RDA) was performed using the vegan package in R (4.0.2) software to examine the relationships among microbial community, soil enzyme activity, and nutrient content [32]. Each co-occurrence network was constructed using 15 samples. The rhizosphere soil microbial community co-occurrence network included the samples which were collected on days 0, 5, 15, and 30 and after three consecutive years of fertilization. And it was the same as the bulk soil microbial community co-occurrence network. Co-occurrence network analyses of bacterial and fungal taxa were conducted using the Python package ‘networkx’ (version 1.11) [33]. To simplify pairwise comparisons and reduce the complexity of co-occurrence network, only the orders with relative abundance of top 50 were selected for network analyses [34]. The relationships with positive (r > 0.3) and negative (r < −0.3) are shown in the network diagrams.

3. Results

3.1. Soil Chemical Property and Enzyme Activity

The RMANOVA results showed that there was no difference in the content of total N, total P, total K, and SOC with different soil types and sampling times (p > 0.05) (Figure 1a–d). But the available P, available K, nitrate, and ammonium content was significantly affected by soil type and sampling time (p < 0.05) (Figure 1e–h). In different soil types, the average content of available P, available K, nitrate, and ammonium was higher in rhizosphere soil than in bulk soil. The results indicated that the content of available P, available K, nitrate, and ammonium exhibited different responses to fertilization treatment between bulk soil and rhizosphere soil.
The content of SOC was only significantly affected by interaction between soil type and time (p < 0.05) (Figure 1c). Consecutive fertilization significantly increased the content of nitrate both in rhizosphere soil and bulk soil (p < 0.05). Compared with the unfertilized treatment group, consecutive fertilization treatment increased the content of available P and available K in rhizosphere soil (p < 0.05). It is worth noting that the content of available P, available K, and nitrate showed different short-term (less than 30 days) and long-term effects. The content of available P, available K, and nitrate showed a short-term increase and a long-term decreasing trend.
Based on the results of RMANOVA there were no significant differences in the activities of acid phosphatase and alkaline phosphatase between rhizosphere and bulk soil (p > 0.05) (Figure 2a,b). The activities of cellulase and urease were significantly influenced by soil types and sampling time (p < 0.05) (Figure 2c,d). Compared with bulk soil, consecutive fertilization significantly increased the activities of alkaline phosphatase, cellulase, and amylase in rhizosphere soil (p < 0.01) (Figure 2b,c,e). The effect of consecutive fertilization on urease was opposite.
The activities of alkaline phosphatase and amylase were significantly affected by sampling time and interaction between soil type and time (p < 0.05). The activities of alkaline phosphatase and amylase showed a significant time effect (p < 0.05). Compared with unfertilized treatment, consecutive fertilization significantly increased the activities of urease and amylase in bulk soil (p < 0.05). The activities of alkaline phosphatase, cellulase, urease, and amylase showed different short-term (less than 30 days) and long-term effects. Except for cellulase, the activity of other three enzymes showed a short-term decrease and a long-term increasing trend.

3.2. Composition Characteristics of Bacterial and Fungal Community

The NMDS and ANOSIM analysis results showed that there were significant differences in bacterial community composition between root samples and other samples (R = 0.42, p = 0.001) (Figure 3a). Then, the detailed comparative analyses were checked between consecutive fertilization treatment groups and short-term fertilization treatment groups in bulk soil and rhizosphere soil samples. The results suggested that there were significant differences only in rhizosphere soil bacterial community composition between consecutive fertilization treatment groups and short-term fertilization treatment groups (R = 0.29, p = 0.026) (Figure 3b).
In terms of fungal community composition, the NMDS and ANOSIM analysis results showed that there were also significant differences between root samples and other samples (R = 0.33, p = 0.001) (Figure 3c). Then, the detailed comparative analyses were checked between consecutive fertilization treatment groups and short-term fertilization treatment groups in bulk soil and rhizosphere soil samples. The results indicated that only significant differences exist in the composition of bulk soil fungal communities between consecutive fertilization treatment groups and short-term fertilization treatment groups (R = 0.37, p = 0.009) (Figure 3d).
In all samples, the bacterial community was dominated by the phyla Proteobacteria (41.78%), Actinobacteriota (37.33%), and Acidobacteriota (6.89%), which accounted for 86.00% of the total bacterial abundance (Figure 4a). In the short-term fertilization treatment groups, by comparing root samples with other soil samples, it was found that the average abundance of Proteobacteria in the root (58.04%) was significantly higher than that in bulk soil (32.12%) and rhizosphere soil samples (37.79%). While the average abundance ratio of Actinobacteria was the opposite. After comparing the bacterial composition between the consecutive fertilization treatment groups and the first fertilization treatment groups, the consecutive fertilization treatment decreased the proportions of Proteobacteria both in bulk soil and rhizosphere soil (p < 0.05). After the first fertilization, the relative abundance of Proteobacteria in rhizosphere soil significantly showed an increasing trend over time.
In all samples, the fungal community was dominated by the phyla Ascomycota (49.36%), Basidiomycota (29.39%), and Mortierellomycota (8.16%), which accounted for 86.91% of the total fungal abundance (Figure 4b). By comparing the root samples with other soil samples, it was found that the average abundance of Ascomycota in the root (70.38%) was significantly higher than that in bulk soil (40.49%) and rhizosphere soil samples (37.42%). The average abundance ratio of Basidiomycota was the opposite. After comparing the fungal composition between the consecutive fertilization treatment groups and the first fertilization treatment groups, it was found that consecutive fertilization significantly increased the proportion of Mortierellomycota in bulk soil (p < 0.05). After the first fertilization, the relative abundance of Ascomycota in bulk soil significantly decreased over time (p < 0.05). The relative abundance of Mortierellomycota was significantly increased in all soil samples (p < 0.01).
At the bacterial order level, there were significant differences between root samples and soil samples (Figure 5a). In the root samples, the bacterial community was dominated by Rhizobiales (21.66%), Burkholderiales (15.64%), and Corynebacteriales (6.24%). But in the soil samples, the dominant bacterial groups were Gaiellales (21.37%), Rhizobiales (18.52%), and Burkholderiales (8.74%). The results showed that the average abundance of Gaiellales in roots was significantly lower than that of soil samples (p < 0.01), while the average abundance of Frankiales was higher than that of soil samples. Consecutive fertilization significantly increased the relative abundance of Rhizobiales in root samples and Gaiellales in rhizosphere soil (p < 0.05). There were no significant differences of dominant bacterial groups within 30 days after the first fertilization.
There were significant differences in fungal order composition between root samples and soil samples (Figure 5b). In the root samples, the fungal community was dominated by Helotiales (54.06%), Agaricales (10.21%), and Atheliales (4.31%). But in the soil samples, the dominant fungal groups were Atheliales (15.44%), Mortierellales (12.01%), and Hypocreales (8.00%). The results showed that the average abundance of Helotiales in roots was significantly higher than that of soil samples (p < 0.01), while the average abundance of Atheliales was lower than that of soil samples. Consecutive fertilization significantly reduced the relative abundance of Atheliales in soil samples, but only significantly increased the relative abundance of Mortierellales in bulk soil (p < 0.05). After the first fertilization, the relative abundance of Mortierellales in soil samples gradually increased over time, while Helotiales gradually decreased.
Based on the results of NMDS analysis, we conducted detailed analysis on bacterial rhizosphere soil samples (Figure S1a) and fungal bulk soil samples (Figure S1b) with significant differences after fertilization. The results indicated that Gaiellales, Rhizobiales, and Burkholderiales were the dominant orders in the bacterial community. Compared with unfertilized treatment group and short-term fertilization treatment groups, consecutive fertilization significantly increased the abundance of Gaiellales (p < 0.05).
At fungal order level, Atheliales, Mortierellales, and Hypocreales were the dominant groups in the composition of fungal community. Compared with unfertilized treatment group and short-term fertilization treatment groups, consecutive fertilization significantly increased the abundance of Mortierellales (p < 0.05). And over time after fertilization, the relative abundance of Mortierellales significantly increased (p < 0.05).

3.3. Correlations between Microbial Composition and Soil Nutrient Content, Enzyme Activity

According to the results of redundancy analysis, bacterial community compositions at order level were only significantly correlated with pH (R2 = 0.47, p = 0.02). Among the bacterial orders, the relative abundance of Gaiellales was positively correlated with soil available phosphorus and total phosphorus content, while Rhizobiales was opposite (Figure 6a). In terms of soil enzyme activity, bacterial community compositions were significantly correlated with urease and amylase (R2 = 0.53, p = 0.01; R2 = 0.46, p = 0.03) (Figure 6b). It is worth noting that the relative abundance of Gaiellales was positively correlated with urease, amylase, and alkaline phosphatase.
The RDA results showed that there were no significant corrections between fungal community composition and soil nutrient content (Figure 6c). The relative abundance of Mortierellales was positively correlated with pH and total phosphorus content (R2 = 0.22, p = 0.20; R2 = 0.14, p = 0.38). In terms of soil enzyme activity, fungal community compositions at the order level were significantly correlated with the activities of alkaline phosphatase and amylase (R2 = 0.48, p = 0.01; R2 = 0.63, p = 0.003) (Figure 6d). In more detail, the activities of alkaline phosphatase and amylase were positively correlated with the relative abundance of Mortierellales, while Atheliales was opposite. These results suggested that Gaiellales and Mortierellales had positive effects on soil nutrient release.
The co-occurrence network analyses were performed to show the interactions among soil nutrient content, enzyme activity, and microbial order groups in different samples. The results indicated that the content of soil AP, APO, TPO, SOC, TN, and ammonium was significantly corrected with bacterial taxa (Figure 7a). Among them, there were significant positive correlations between the abundance of Gaiellales and the content of soil-available phosphorus (p < 0.05). The activity of acid phosphatase, alkaline phosphatase, amylase, and urease were significantly corrected with bacterial taxa (Figure 7b). Importantly, there were significant positive correlations between the abundance of Gaiellales and the activity of amylase and urease (p < 0.05). The content of soil AP, APO, TPO, pH, SOC, TN, ammonium, and nitrate were significantly correlated with fungal taxa (Figure 7c). Among them, there were significant positive correlations between the abundance of Mortierellales and the content of soil nitrate (p < 0.05). The activity of acid phosphatase, alkaline phosphatase, amylase, cellulase, and urease was significantly corrected with fungal taxa (Figure 7d). Importantly, there were significant positive correlations between the abundance of Mortierellales and the activity of amylase and alkaline phosphatase (p < 0.05). This indicated that Gaiellales and Mortierellales may play important roles in soil nutrient release during consecutive fertilization.

3.4. Microbial Community Potential for Phosphorus Metabolism Function

Compared with unfertilized treatment group and short-term fertilization (less than 30 days) treatment groups, consecutive fertilization significantly decreased the predicted proportions of ectomycorrhizal and saprotroph, but significantly increased the proportion of endophyte in bulk soil samples (p < 0.01) (Figure S2). Over time, the proportions of ectomycorrhizal gradually increased from 18.38% to 24.31% after fertilization. In contrast, the proportions of saprotroph significantly decreased from 24.43% to 18.58% after fertilization (p < 0.05). These results suggested that the predicted fungal functional group compositions were significantly affected by consecutive fertilization treatment.

4. Discussion

4.1. Consecutive Fertilization Alters Soil Properties, Rhizosphere Soil Bacterial, and Bulk Soil Fungal Community Composition

Our results suggested that the average content of available phosphorus, available potassium, nitrate, and ammonium was higher in rhizosphere soil than in bulk soil. This could be explained by the fact that the enrichment of a large number of functional microbial groups related to nutrient mineralization in the rhizosphere soil, through which organic matter was metabolized and converted into mineral elements [35,36]. Compared with the unfertilized treatment group, consecutive fertilization treatment increased the content of nitrate, available phosphorus, and available potassium in rhizosphere soil. Microorganisms mainly promoted the mineralization of organic matter by secreting extracellular enzymes [37]. Adequate mineral nutrients in the rhizosphere soil provided important guarantees for plantation soil productivity. Research has shown that long-term application of inorganic fertilizers only slightly increases the microbial biomass in the soil, but greatly increases the activity of soil enzymes [38]. Our results showed that consecutive fertilization significantly increased the activities of urease and amylase in bulk soil versus unfertilized treatment. The study found that consecutive fertilization had different effects on rhizosphere soil and bulk soil enzyme activity [39]. In our study, consecutive fertilization significantly increased the activity of alkaline phosphatase, cellulase, and amylase in rhizosphere soil versus bulk soil. The previous study had found that alkaline phosphatase was more important than acid phosphatase in regulating soil P availability [40,41]. And this was consistent with our RDA results; there were higher correlations between alkaline phosphatase activity and microbial composition.
The NMDS and ANOSIM results showed that there were significant differences in bacterial and fungal community composition between Larix root samples and soil samples. For bacterial community, the average abundance of Proteobacteria in Larix root (58.04%) was significantly higher than that in bulk soil (32.12%) and rhizosphere soil samples (37.79%). For fungal community, the average abundance of Ascomycota in Larix root (70.38%) was significantly higher than that in bulk soil (40.49%) and rhizosphere soil samples (37.42%). Compared with other microbial phyla, Proteobacteria and Ascomycota have faster evolutionary rates and higher specific diversity tends to accumulate in nutrient-rich root and rhizosphere soils [42,43]. Research found that fertilization had less impact on the composition of root microbial community versus rhizosphere and bulk soils. This may be due to the fact that plant genotypes have a higher impact on the assembly of endophytic bacterial communities than soil chemical properties [44]. Both plant genotypes and fertilization measures have a significant impact on the rhizosphere microbial community, and plant genotypes can affect fertilization effectiveness, shaping different microbial community structures [45]. Only when the yield of root exudates is low or stopped, will the nutritional differences caused by fertilization become more important [46]. In this study there were significant differences only in rhizosphere soil bacterial community composition between consecutive fertilization treatment groups and short-term fertilization treatment groups. These findings were consistent with the results of previous studies. Under consecutive fertilization conditions, the correlation between rhizosphere bacterial community composition and soil nutrient concentration was greater than that with bulk soil. And as nutrient availability increases, consecutive fertilization increased the abundance of rhizosphere bacteria and altered the composition. RDA results indicated that soil pH, organic matter, and available phosphorus concentration were the most important factors in the formation of plant rhizosphere bacterial communities [47]. On the other hand, only significant differences exist in the composition of bulk soil fungal communities between consecutive fertilization treatment groups and short-term fertilization treatment groups. Due to the rhizosphere effect, the fungal community in rhizosphere soil was less affected by fertilization than in bulk soil [48]. One possible reason was that although consecutive fertilization had a strong impact on the fungal community, as the fungi became closer to the plant roots, the rhizosphere effect of the plant itself cushioned the strong impact of fertilization [44].
The structure and activity of soil microbial communities did not always show significant changes in the short term [49,50], indicating that microbial communities responded differently to short-term and long-term nutrient additions. Our study also found that there were significant differences in rhizosphere soil bacterial and bulk soil fungal community composition between long-term and short-term fertilization treatment groups. This indicated that certain groups of rhizosphere soil bacterial and bulk soil fungal communities played important roles in soil nutrients releasing and plantation fruiting. However, more detailed research is needed on the underlying microbial mechanisms.

4.2. Abundance of Soil Nutrients Releasing Relevant Microbes Are Driven by Fertilization Treatment

Our results showed that the average abundance of Gaiellales in roots was significantly lower than that of soil samples, while the average abundance of Frankiales was higher than that of soil samples. One possible reason is that Gaiellales and Frankiales belong to the Actinobacteria phylum, they occupy similar ecological niches, have a competitive relationship, and Frankiales form symbiotic root nodules with many plants and can be extensively colonized in the roots [51]. Consecutive fertilization significantly increased the relative abundance of Rhizobiales in root samples and Gaiellales in rhizosphere soil. Compared with unfertilized treatment groups and short-term fertilization treatment groups, consecutive fertilization significantly increased the abundance of Gaiellales. Gaiella is a genus of Gaiellales, several researchers revealed that they were widely distributed in the rhizosphere of different plant species and could reduce the content of nitrate, helping plants better absorb nutrients from the soil [52,53,54]. Another study also found that there was a significant positive correlation between crop yield and the abundance of Gaiellalea [55]. The RDA and co-occurrence network analyses both indicated that the relative abundance of Gaiellales was positively correlated with soil-available phosphorus, urease, and amylase. Hence, consecutive fertilization may indirectly promote soil nutrient release by increasing the abundance of Gaiellales.
In terms of fungal community composition, the results showed that consecutive fertilization significantly increased the relative abundance of Mortierellales in bulk soil. Compared with unfertilized treatment group and short-term fertilization treatment groups, consecutive fertilization significantly increased the abundance of Mortierellales. And over time after fertilization, the relative abundance of Mortierellales significantly increased. The previous study found that consecutive fertilization significantly increased the abundance of Mortierella in soil and certain specific Mortierella had important contributions to soil nutrient transformation and availability [38,56]. The RDA results showed that the relative abundance of Mortierellales was positively correlated with pH and total phosphorus content. And the activities of alkaline phosphatase and amylase were positively correlated with the relative abundance of Mortierellales. Osorio and Habte [57] found that Mortierella had the potential to release various organic acids in different soils to dissolve soil phosphorus. The co-occurrence network analyses also showed that there were significant positive correlations between the abundance of Mortierellales and the activity of amylase and alkaline phosphatase. Mortierella has been reported as a potential biocontrol fungi against several plant pathogens and pests [58,59], and has also been positively correlated with mineral fertilizers [38]. This indicated that Mortierellales play important roles in nutrient releasing and soil microecological balance.
The FUNGuild results indicated that compared with unfertilized treatment group and short-term fertilization (less than 30 days) treatment groups, consecutive fertilization significantly decreased the predicted proportions of ectomycorrhizal and saprotroph, but significantly increased the predicted proportion of endophyte in bulk soil samples. These results were consistent with previous studies [60,61,62]. It was reported that Larix plantations were ectomycorrhizal (ECM)-dominant forests [30]. In our study, the proportions of ECM in consecutive fertilization treatments were lower than unfertilized treatment group and short-term fertilization (less than 30 days) treatment groups, but significantly increased the predicted proportion of endophyte. This may be because consecutive fertilization treatments increased nutrient storage in the soil and then the high nutrient availability inhibited the growth and functions of ECM [63]. Mycorrhizal symbiotic structure is an important component of plant functional traits, and almost all tree species are associated with endophyte and ectomycorrhizal. The research found that epiphyte tree species tended to directly utilize inorganic nutrients, while ECM tree species tended to utilize organic nutrients [64]. This may be due to the long-term NPK fertilization measures supplementing the soil with a large amount of inorganic nutrients, enriching endophytic bacteria, and inhibiting the abundance of ECM. Previous studies have suggested that endophyte and pathogenic microbes share the same ecological niche and compete with pathogens in plants for space and nutrition, leading to their death without normal nutrient supply, enhancing the host’s ability to resist diseases. Therefore, consecutive fertilization is of great significance for maintaining the balance of the soil microbiota under Larix plantation.
Our study found that consecutive fertilization treatment significantly increased the available NPK content, soil enzyme activity, and the abundance of Gaiellales in rhizosphere soil and Mortierella in bulk soil. Therefore, our study suggested that consecutive fertilization may promote the release of effective nutrients in the soil by increasing the abundance of Gaiellales and Mortierella, providing sufficient nutrients for the plantation cultivation.

5. Conclusions

This study defined that the responses of rhizosphere soil bacterial and bulk soil fungal communities and functional taxa associated with nutrient release to fertilization treatment and time differed in the Larix plantation. There were significant differences in microbial community composition in root samples, rhizosphere soil, and bulk soil samples. Three consecutive years of fertilization treatment significantly increased the available NPK content, soil enzyme activity, the abundance of Gaiellales in rhizosphere soil and Mortierella in bulk soil, which may play an important role in soil nutrient release during consecutive fertilization. Consecutive fertilization is beneficial for maintaining the balance of the soil microbiota under Larix plantation. Short-term and continuous fertilization have different effects on microbial community composition, and consecutive fertilization had a positive effect on soil nutrient cycling. Our research provided a theoretical basis for consecutive fertilization to promote nutrient availability by increasing specific microbial taxa abundance.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f15030514/s1, Figure S1. Bacterial community structure in rhizosphere soil (a) and fungal community structure in bulk soil (b). The top 3% abundance of microbial taxa were shown in the figure. RS: Rhizosphere soil; BS: Bulk soil; RSCF: Rhizosphere soil after consecutive fertilization; BSCF: Bulk soil after consecutive fertilization; The number after the letter represents the number of days after the first fertilization. Figure S2. Variations in bulk soil fungal functional groups compositions inferred by FUNGuild. BS: Bulk soil; BSCF: Bulk soil after consecutive fertilization; The number after the letter represents the number of days after the first fertilization. BSCF: Bulk soil after consecutive fertilization. Table S1. Microbial co-occurrence network properties in Figure 6a. Table S2. Microbial co-occurrence network properties in Figure 6b. Table S3. Microbial co-occurrence network properties in Figure 6c. Table S4. Microbial co-occurrence network properties in Figure 6d.

Author Contributions

Conceptualization, W.W. and J.F.; methodology, W.W.; software, W.W.; formal analysis, R.S.B.; investigation, W.W., Y.Y., P.B., A.L., H.W. and J.F.; resources, J.F.; data curation, W.W. and J.L.; writing—original draft preparation, W.W.; writing—review and editing, W.W., J.L. and J.F; visualization, W.W.; supervision, J.L., W.H., Y.Y. and P.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research was financially supported by the National Key Research and Development Program of China (Project no. 2022YFD220030204), the National Natural Science Foundation of China (Project no. 32201546), and Shandong Province Double Hundred Talent Plan (No. WSG20200001).

Data Availability Statement

All sequence data were deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) database.

Acknowledgments

We thank Majorbio (Shanghai) for sequencing services.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Dynamic changes of soil nutrient content. Values are presented as the means ± standard deviation (SD). The results of repeated measures analysis of variance (RMANOVA) are provided. S, soil type; T, sampling time; NS, not significant (p > 0.05); *, p < 0.05. The number of repetitions was three (n = 3); CF, consecutive fertilization. (a) total N content, (b) total P content, (c) SOC content, (d) total K content, (e) available P content, (f) available K content, (g) nitrate content, (h) ammonium content, (i) pH value.
Figure 1. Dynamic changes of soil nutrient content. Values are presented as the means ± standard deviation (SD). The results of repeated measures analysis of variance (RMANOVA) are provided. S, soil type; T, sampling time; NS, not significant (p > 0.05); *, p < 0.05. The number of repetitions was three (n = 3); CF, consecutive fertilization. (a) total N content, (b) total P content, (c) SOC content, (d) total K content, (e) available P content, (f) available K content, (g) nitrate content, (h) ammonium content, (i) pH value.
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Figure 2. Dynamic changes of soil enzyme activity. Values are presented as the means ± standard deviation (SD). The results of repeated measures analysis of variance (RMANOVA) are provided. S, soil type; T, sampling time; NS, not significant (p > 0.05); *, p < 0.05. The number of repetitions was three (n = 3); CF, consecutive fertilization. (a) acid phosphatase activity, (b) alkaline phosphatase activity, (c) cellulase activity, (d) urease, (e) amylase.
Figure 2. Dynamic changes of soil enzyme activity. Values are presented as the means ± standard deviation (SD). The results of repeated measures analysis of variance (RMANOVA) are provided. S, soil type; T, sampling time; NS, not significant (p > 0.05); *, p < 0.05. The number of repetitions was three (n = 3); CF, consecutive fertilization. (a) acid phosphatase activity, (b) alkaline phosphatase activity, (c) cellulase activity, (d) urease, (e) amylase.
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Figure 3. Non-metric multidimensional scaling (NMDS) analysis based on Bray–Curtis distance matrix of microbial community composition. (a) All bacterial samples, (b) rhizosphere soil bacterial samples, (c) all fungal samples, (d) bulk soil fungal samples. R: root; RS: rhizosphere soil; BS: bulk soil; RCF: root after consecutive fertilization; RSCF: rhizosphere soil after consecutive fertilization; BSCF: bulk soil after consecutive fertilization. The number after the letter represents the number of days after the first fertilization.
Figure 3. Non-metric multidimensional scaling (NMDS) analysis based on Bray–Curtis distance matrix of microbial community composition. (a) All bacterial samples, (b) rhizosphere soil bacterial samples, (c) all fungal samples, (d) bulk soil fungal samples. R: root; RS: rhizosphere soil; BS: bulk soil; RCF: root after consecutive fertilization; RSCF: rhizosphere soil after consecutive fertilization; BSCF: bulk soil after consecutive fertilization. The number after the letter represents the number of days after the first fertilization.
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Figure 4. Bacterial community (a) and fungal community (b) composition at the phylum level in different soil samples. The top 1% average abundance of microbial taxa is shown in the figure. R: root; RS: rhizosphere soil; BS: bulk soil; RCF: root after consecutive fertilization; RSCF: rhizosphere soil after consecutive fertilization; BSCF: bulk soil after consecutive fertilization. The number after the letter represents the number of days after the first fertilization.
Figure 4. Bacterial community (a) and fungal community (b) composition at the phylum level in different soil samples. The top 1% average abundance of microbial taxa is shown in the figure. R: root; RS: rhizosphere soil; BS: bulk soil; RCF: root after consecutive fertilization; RSCF: rhizosphere soil after consecutive fertilization; BSCF: bulk soil after consecutive fertilization. The number after the letter represents the number of days after the first fertilization.
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Figure 5. Bacterial community (a) and fungal community (b) composition at the order level in different soil samples. The top 3% average abundance of microbial taxa is shown in the figure. R: root; RS: rhizosphere soil; BS: bulk soil; RCF: root after consecutive fertilization; RSCF: rhizosphere soil after consecutive fertilization; BSCF: bulk soil after consecutive fertilization. The number after the letter represents the number of days after the first fertilization.
Figure 5. Bacterial community (a) and fungal community (b) composition at the order level in different soil samples. The top 3% average abundance of microbial taxa is shown in the figure. R: root; RS: rhizosphere soil; BS: bulk soil; RCF: root after consecutive fertilization; RSCF: rhizosphere soil after consecutive fertilization; BSCF: bulk soil after consecutive fertilization. The number after the letter represents the number of days after the first fertilization.
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Figure 6. Redundancy analysis (RDA) to check correlations among microbial community composition, soil nutrient content, and enzymatic activity. (a) Rhizosphere soil bacterial community and soil nutrient, (b) rhizosphere soil bacterial community and soil enzymes, (c) bulk soil fungal community and soil nutrient, (d) bulk soil fungal community and soil enzymes. TN: total nitrogen, TP: total phosphorus, TPO: total potassium, SOC: soil organic carbon, AP: available phosphorus, APO: available potassium, RS: rhizosphere soil; BS: bulk soil; RSCF: rhizosphere soil after consecutive fertilization; BSCF: bulk soil after consecutive fertilization. The number after the letter represents the number of days after the first fertilization. Red arrows indicate soil nutrient content or enzymatic activity; blue arrows indicate microbial taxa abundance.
Figure 6. Redundancy analysis (RDA) to check correlations among microbial community composition, soil nutrient content, and enzymatic activity. (a) Rhizosphere soil bacterial community and soil nutrient, (b) rhizosphere soil bacterial community and soil enzymes, (c) bulk soil fungal community and soil nutrient, (d) bulk soil fungal community and soil enzymes. TN: total nitrogen, TP: total phosphorus, TPO: total potassium, SOC: soil organic carbon, AP: available phosphorus, APO: available potassium, RS: rhizosphere soil; BS: bulk soil; RSCF: rhizosphere soil after consecutive fertilization; BSCF: bulk soil after consecutive fertilization. The number after the letter represents the number of days after the first fertilization. Red arrows indicate soil nutrient content or enzymatic activity; blue arrows indicate microbial taxa abundance.
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Figure 7. Co-occurrence network analysis of microbial taxa, soil nutrient content, and enzyme activity. (a) Rhizosphere soil bacterial taxa and soil nutrient, (b) rhizosphere soil bacterial taxa and soil enzymes, (c) bulk soil fungal taxa and soil nutrient, (d) bulk soil fungal taxa and soil enzymes. The color of each line indicates positive or negative correlation, red: positive correlation between order, blue: negative correlation between order. The size of each node in the figure represents the species abundance. The thickness of each line indicates the size of the correlation coefficient, and the thicker the line, the higher the correlation between species. TN: total nitrogen, TP: total phosphorus, TPO: total potassium, SOC: soil organic carbon, AP: available phosphorus, APO: available potassium. The network’s properties are shown in Supplementary Tables S1–S4.
Figure 7. Co-occurrence network analysis of microbial taxa, soil nutrient content, and enzyme activity. (a) Rhizosphere soil bacterial taxa and soil nutrient, (b) rhizosphere soil bacterial taxa and soil enzymes, (c) bulk soil fungal taxa and soil nutrient, (d) bulk soil fungal taxa and soil enzymes. The color of each line indicates positive or negative correlation, red: positive correlation between order, blue: negative correlation between order. The size of each node in the figure represents the species abundance. The thickness of each line indicates the size of the correlation coefficient, and the thicker the line, the higher the correlation between species. TN: total nitrogen, TP: total phosphorus, TPO: total potassium, SOC: soil organic carbon, AP: available phosphorus, APO: available potassium. The network’s properties are shown in Supplementary Tables S1–S4.
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MDPI and ACS Style

Wang, W.; Yang, Y.; Li, J.; Bu, P.; Lu, A.; Wang, H.; He, W.; Santos Bermudez, R.; Feng, J. Consecutive Fertilization-Promoted Soil Nutrient Availability and Altered Rhizosphere Bacterial and Bulk Fungal Community Composition. Forests 2024, 15, 514. https://doi.org/10.3390/f15030514

AMA Style

Wang W, Yang Y, Li J, Bu P, Lu A, Wang H, He W, Santos Bermudez R, Feng J. Consecutive Fertilization-Promoted Soil Nutrient Availability and Altered Rhizosphere Bacterial and Bulk Fungal Community Composition. Forests. 2024; 15(3):514. https://doi.org/10.3390/f15030514

Chicago/Turabian Style

Wang, Wenbo, Yuanyuan Yang, Jinge Li, Pengtu Bu, Aijun Lu, Hao Wang, Wenxing He, Ramon Santos Bermudez, and Jian Feng. 2024. "Consecutive Fertilization-Promoted Soil Nutrient Availability and Altered Rhizosphere Bacterial and Bulk Fungal Community Composition" Forests 15, no. 3: 514. https://doi.org/10.3390/f15030514

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