Phosphorus Cycling Dominates Microbial Regulation of Synergistic Carbon, Nitrogen, and Phosphorus Gene Dynamics During Robinia pseudoacacia Restoration on the Loess Plateau

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The study is interesting and has a robust content, but it needs to be improved on several points before it can be published. They are listed below:

Abstract:

• The abstract mentions that the synergistic interactions of the C, N and P cycles and their driving factors are still ‘insufficiently understood’, but does not clarify which specific gap is being addressed in this study. It is necessary to highlight the scientific problem more precisely, differentiating it from previous studies.
• The results are described in a qualitative and generic way. Expressions such as “significant synergistic changes” and “pivotal role in nutrient dynamics” lack numerical data or levels of statistical significance.

Introduction:

• The introduction contains a lot of disconnected information, especially in the second half, where there is an extensive review of the importance of nutrient cycles and the influence of soil microbiota. However, the connection between these concepts and the study proposal is not well established. My suggestion is to reduce the generic literature review and focus only on the aspects that support the study's hypothesis.
• The hypothesis presented (‘We expect the functional genes of C, N and P to show significant synergistic changes, regulated by central genes of each cycle’) is vague and does not offer a precise direction for the study. The hypothesis does not specify which genes are expected to be the main regulators. Which environmental factors (pH, humidity, organic matter content?) are most relevant to this regulation? What patterns of change are expected over time?
• The manuscript states that it intends to investigate the factors that drive the synergistic changes in the C, N and P genes, but it doesn't make it clear how this will be done. What exactly will be analyzed? Will the study only assess the relative abundance of genes or also their expression and functionality? How will metagenomic analysis be used to answer this question? Will any inferences be made about metabolic pathways and their interactions? What are the predictions about the temporal patterns observed throughout the different stages of restoration?
• The introduction lacks focus and clarity in defining the scientific problem, resulting in a scattered and unconvincing argument. In addition, the hypothesis and objectives need to be more specific, clearly establishing the expected mechanisms and the methods used to test them.

Material and Methods:

• The study mentions that areas of Robinia pseudoacacia at different ages (10, 20, 30 and 40 years) were analysed, but does not explain why these ages were chosen or what the scientific basis is for considering these stages of restoration as representative. In addition, a control group was included (farmland - FL), but there is no justification for choosing this area over other possible reference conditions (e.g. natural vegetation or other land use).
• The manuscript mentions that the soil was collected at a depth of 0-10 cm, but does not explain why this layer was chosen. Does this represent the main zone of microbial activity? In addition, the criteria for locating the sampling points within each area are unclear. Was a randomized design made? Was the variability of the terrain, such as slope and topography, taken into account?
• The manuscript mentions that metagenomic sequencing was carried out, but the description is superficial and omits essential technical details, such as: Which DNA extraction kit was used? Which sequencing platform was used? What criteria were used to filter the reads? The study mentions that sequences shorter than 50 bp were discarded, but what other quality control steps were applied?
• The section mentions that redundancy analysis (RDA) and structural equation modeling (PLS-SEM) were used, but does not explain how these analyses were applied and which variables were included in the models. In addition, was the normality of the data checked before applying ANOVA? The criteria for including genes in the network analysis are not detailed. What was the abundance threshold used to define the presence of a gene in the co-occurrence?

Results and Discussion:

• The study mentions significant changes in various soil parameters and in the microbiota, but does not present P values, confidence intervals or statistical metrics for many comparisons. Some of these should appear. Always include complete statistical values for each statement of significance (e.g. F and P for ANOVA, R² for regressions, coefficients and 95%CI for PLS-SEM).
• There is no structured separation between the main aspects of the results (e.g. changes in the soil, microbiota, and gene functionality). The transition between paragraphs is abrupt, making it difficult to understand the line of reasoning.
• The discussion lacks an in-depth analysis of the biological mechanisms behind the observed changes. There is no convincing explanation as to why certain functional genes of the phosphorus cycle had high centrality in the gene network. The relationship between soil properties and microbial changes is presented descriptively, without a robust mechanistic approach.
• The manuscript mentions previous studies, but without an in-depth comparative analysis. There is no discussion of discrepancies or similarities between the findings of this study and existing literature.
• The Discussion does not explore the impact of the findings on ecological restoration and soil management. There is no mention of strategies that could be derived from this study to optimize the recovery of degraded areas. The text suggests that the results are ‘important for restoration’, but does not explain how they can be applied in practice.

Conclusion:

• In the conclusions, the text repeats some points from the Results and Discussion without a concise reformulation.
• The conclusion does not reinforce how the results respond to the objectives and hypotheses set out in the introduction.
• The manuscript's Conclusion needs substantial improvements to make it clearer, more impactful and more scientifically relevant. Currently, the section lacks objective summarisation, an explicit connection with the objectives, practical implications and recommendations for future studies.

 

Author Response

Comments 1:

The abstract mentions that the synergistic interactions of the C, N and P cycles and their driving factors are still ‘insufficiently understood’, but does not clarify which specific gap is being addressed in this study. It is necessary to highlight the scientific problem more precisely, differentiating it from previous studies.

Response 1:

We have revised the abstract to explicitly highlight the specific knowledge gap being addressed, namely the insufficient understanding of functional gene-level coordination and their driving factors in C, N, and P cycles. This distinction differentiates our study from previous research that primarily focused on bulk soil nutrients and microbial communities.

Revised text (Abstract: L16-21):

“Carbon (C), nitrogen (N), and phosphorus (P) are key soil nutrients whose synergistic interactions regulate ecosystem nutrient cycling, yet the functional gene-level coordination and driving factors of these cycles remain poorly understood. This study addresses this gap by investigating the dynamic changes in C, N, and P cycling functional genes and their microbial and environmental drivers across Robinia pseudoacacia plantations of different restoration stages (10, 20, 30, and 40 years) on the Loess Plateau.”

Comments 2:

The results are described in a qualitative and generic way. Expressions such as “significant synergistic changes” and “pivotal role in nutrient dynamics” lack numerical data or levels of statistical significance.

Response 2:

We have replaced generic descriptions such as “significant synergistic changes” and “pivotal role in nutrient dynamics” with specific numerical results, including increases in STC, SOC, SAP, pH, and SWC (P < 0.05) and strong correlations between microbial shifts and functional gene variations (P < 0.01). This ensures that key findings are presented with appropriate statistical rigor.

Revised text (Abstract: L25-28):

“Compared to farmland, STC, SOC, SAP, pH, and SWC significantly changed (P < 0.05) with restoration age, directly shaping key microbial groups such as Proteobacteria, Acidobacteria, Actinobacteria, and Chloroflexi. These microbial shifts were strongly correlated with the synergistic changes in C, N, and P functional gene abundance (P < 0.01).”

Comments 3:

The introduction contains a lot of disconnected information, especially in the second half, where there is an extensive review of the importance of nutrient cycles and the influence of soil microbiota. However, the connection between these concepts and the study proposal is not well established. My suggestion is to reduce the generic literature review and focus only on the aspects that support the study's hypothesis.

Response 3:

We have streamlined the literature review in the second half of the introduction to focus more directly on the aspects relevant to our study hypothesis. We removed general discussions about the importance of nutrient cycles and soil microbiota that did not directly relate to the study’s objectives. The revised introduction now places a greater emphasis on the current knowledge gaps regarding synergistic changes in C, N, and P cycling functional genes during ecological restoration, which is the central focus of our study.

Revised text (Section 1: L86-90):

“Despite these findings, current studies on the mechanisms of synergistic changes in functional genes during ecological restoration are limited and inconclusive. Investigating these changes and their drivers is essential for understanding soil nutrient cycling and the impacts of restoration on nutrient dynamics.”

Comments 4:

The hypothesis presented (‘We expect the functional genes of C, N and P to show significant synergistic changes, regulated by central genes of each cycle’) is vague and does not offer a precise direction for the study. The hypothesis does not specify which genes are expected to be the main regulators. Which environmental factors (pH, humidity, organic matter content?) are most relevant to this regulation? What patterns of change are expected over time?

Response 4:

We revised the hypothesis to clarify which genes are expected to play key roles in the synergistic changes observed in C, N, and P cycling. We specifically mentioned the phosphorus-cycling genes, such as pqqC and spoT, as central regulators, which will drive the synergistic interactions between the C, N, and P cycles. Additionally, we highlighted the key environmental factors (such as SOC, STC, SAP, pH) that we expect to influence these changes during restoration

Revised text (Section 1: L111-118):

“We hypothesize that C, N, and P functional genes exhibit significant synergistic changes during R. pseudoacacia restoration, primarily driven by key phosphorus-cycling genes due to their central role in linking the three cycles. Furthermore, we expect that these synergistic changes are regulated by distinct environmental factors within each cycle (e.g., SOC and STC in the carbon cycle, STN in the nitrogen cycle, and SAP and pH in the phosphorus cycle), as well as by shared microbial drivers.”

Comments 5:

The manuscript states that it intends to investigate the factors that drive the synergistic changes in the C, N and P genes, but it doesn't make it clear how this will be done. What exactly will be analyzed? Will the study only assess the relative abundance of genes or also their expression and functionality? How will metagenomic analysis be used to answer this question? Will any inferences be made about metabolic pathways and their interactions? What are the predictions about the temporal patterns observed throughout the different stages of restoration?

Response 5:

• Clear Methodology for Analyzing Synergistic Changes:

To address your concerns about how the study will analyze the synergistic changes in C, N, and P functional genes, we clarified the methods used to assess gene abundance, expression, and functionality. We specified that the study will integrate metagenomic sequencing with soil physicochemical analysis, and will focus on assessing not only the relative abundance of genes but also their expression and functionality across different restoration stages. Additionally, we mentioned the use of co-occurrence network analysis, redundancy analysis (RDA), and Partial Least Squares Structural Equation Modeling (PLS-SEM) to identify drivers and interactions within the nutrient cycles.

Revised text (Section 1: L119-126):

“To explore the driving factors of these synergistic changes, we will integrate metagenomic sequencing with soil physicochemical analysis. This study will assess not only the relative abundance of key genes in C, N, and P cycling but also their expression and functionality across different restoration stages. Co-occurrence network analysis and redundancy analysis (RDA) will be used to understand how soil properties shape gene dynamics, and Partial Least Squares Structural Equation Modeling (PLS-SEM) will be employed to uncover causal relationships. Additionally, metabolic pathways involved in carbon fixation, nitrogen mineralization, and phosphorus solubilization will be investigated.”

• Addressing Temporal Patterns and Predictions:

In response to your request for clearer expectations regarding the temporal patterns observed throughout the different stages of restoration, we included specific details about the changes expected over time and how they relate to microbial community composition and functional gene expression. The revised introduction now explicitly states that the study will examine these patterns across four restoration stages: 10, 20, 30, and 40 years.

Revised text (Section 1: L113-115):

“These changes are expected to vary across restoration stages, influenced by soil physicochemical properties that shape microbial community composition and functional gene expression at each stage.”

Comments 6:

The introduction lacks focus and clarity in defining the scientific problem, resulting in a scattered and unconvincing argument. In addition, the hypothesis and objectives need to be more specific, clearly establishing the expected mechanisms and the methods used to test them.

Response 6:

• Clarified the Scientific Problem：

We have restructured the introduction to emphasize the knowledge gap in understanding the synergistic interactions of C, N, and P functional genes and their driving mechanisms during R. pseudoacacia restoration (see Introduction, Paragraph 3). 

• Refined the Hypothesis:

The revised version explicitly states that P-cycling genes, particularly pqqC, play a central role in linking the C and N cycles, and we hypothesize that their synergistic changes are influenced by both cycle-specific (e.g., SOC and STC for C, STN for N, SAP and pH for P) and shared environmental factors.

Revised text (Section 1: L111-121):

“We hypothesize that C, N, and P functional genes exhibit significant synergistic changes during R. pseudoacacia restoration, primarily driven by key phosphorus-cycling genes due to their central role in linking the three cycles. These changes are expected to vary across restoration stages, influenced by soil physicochemical properties that shape microbial community composition and functional gene expression at each stage. Furthermore, we expect that these synergistic changes are regulated by distinct environmental factors within each cycle (e.g., SOC and STC in the carbon cycle, STN in the nitrogen cycle, and SAP and pH in the phosphorus cycle), as well as by shared microbial drivers. To explore the driving factors of these synergistic changes, we will integrate metagenomic sequencing with soil physicochemical analysis. This study will assess not only the relative abundance of key genes in C, N, and P cycling but also their expression and functionality across different restoration stages”

• Specified the Research Objectives and Methods

We now clearly define the research objectives, which include analyzing soil properties, microbial communities, and metagenomic functional genes across different restoration stages. Additionally, we detail our approach, including metagenomic sequencing, co-occurrence network analysis, redundancy analysis (RDA), and Partial Least Squares Structural Equation Modeling (PLS-SEM) to identify causal relationships between environmental factors, microbial composition, and gene dynamics

Revised text (Section 1: L122-126):

“Co-occurrence network analysis and redundancy analysis (RDA) will be used to understand how soil properties shape gene dynamics, and Partial Least Squares Structural Equation Modeling (PLS-SEM) will be employed to uncover causal relationships. Additionally, metabolic pathways involved in carbon fixation, nitrogen mineralization, and phosphorus solubilization will be investigated.”

Comments 7:

The study mentions that areas of Robinia pseudoacacia at different ages (10, 20, 30 and 40 years) were analysed, but does not explain why these ages were chosen or what the scientific basis is for considering these stages of restoration as representative. In addition, a control group was included (farmland - FL), but there is no justification for choosing this area over other possible reference conditions (e.g. natural vegetation or other land use).

Response 7:

• Justification for Selecting 10, 20, 30, and 40-Year-Old R. pseudoacacia Plantations：

We selected these plantation ages based on afforestation records from local forestry departments and growth cone measurements. These ages represent key stages in R. pseudoacacia restoration, capturing the early (RP10), mid (RP20), late (RP30), and mature (RP40) succession stages of plantation development. Previous studies on ecological restoration in this region have also used similar age classifications to examine vegetation dynamics, soil development, and microbial succession.

Revised text (Section 2.1: L141-146):

"The study selected R. pseudoacacia plantations with ages of 10 years (RP10), 20 years (RP20), 30 years (RP30), and 40 years (RP40) based on afforestation records and growth cone measurements. These ages represent distinct stages in ecological succession, capturing early (RP10), mid (RP20), late (RP30), and mature (RP40) restoration phases, which are widely used in previous studies to assess soil and microbial dynamics during R. pseudoacacia restoration (K. Li et al., 2021; Ren et al., 2018)."

• Justification for Using Farmland (FL) as a Control：

Farmland was chosen as the control because it represents the pre-afforestation land-use type in this region and provides a baseline for evaluating the impact of R. pseudoacacia restoration. Unlike natural vegetation, which is sparse and fragmented due to historical degradation, farmland offers a standardized reference for assessing changes in soil properties, microbial communities, and functional genes.

Revised text (Section 2.1: L146-150):

"Farmland (FL) was selected as the control because it represents the dominant land use before afforestation and serves as a baseline for evaluating the impact of R. pseudoacacia restoration. Compared to sparse and fragmented natural vegetation, farmland provides a more standardized reference for assessing changes in soil properties, microbial communities, and functional genes."

Comments 8：

The manuscript mentions that the soil was collected at a depth of 0-10 cm, but does not explain why this layer was chosen. Does this represent the main zone of microbial activity? In addition, the criteria for locating the sampling points within each area are unclear. Was a randomized design made? Was the variability of the terrain, such as slope and topography, taken into account?

Response 8:

• Justification for Sampling Depth (0–10 cm)：

The 0–10 cm soil layer was chosen as it is the most biologically active zone, where the highest microbial activity, organic matter decomposition, and root interactions occur. Previous studies have also demonstrated that the topsoil layer is most responsive to ecological restoration, showing significant changes in nutrient cycling and microbial dynamics.

Revised text (Section 2.1: L152-157):

"Soil samples were collected from the 0–10 cm layer, as this depth is the most biologically active zone, where microbial activity, organic matter decomposition, and root interactions are most pronounced (Tian et al., 2021). Previous studies have demonstrated that this topsoil layer is highly responsive to ecological restoration, exhibiting significant changes in nutrient cycling and microbial dynamics."

• Clarification of Sampling Design and Site Variability Consideration：

We used an "S"-shaped sampling pattern to account for spatial heterogeneity within each subplot. To minimize terrain variability, plots were selected on slopes with similar aspects and gradients, avoiding extreme topographical variations. This design ensures the representativeness of our sampling while reducing confounding effects caused by microtopography.

Revised text (Section 2.1: L157-162):

"To account for spatial heterogeneity, soil sampling was conducted using an 'S'-shaped pattern within each 20 m × 20 m subplot, with 10 sampling points per subplot. To minimize the influence of terrain variability, plots were selected on slopes with similar aspects and gradients, avoiding extreme topographical variations. This approach ensures that observed differences in soil and microbial properties are primarily driven by restoration age rather than microtopography."

Comments 9:

The manuscript mentions that metagenomic sequencing was carried out, but the description is superficial and omits essential technical details, such as: Which DNA extraction kit was used? Which sequencing platform was used? What criteria were used to filter the reads? The study mentions that sequences shorter than 50 bp were discarded, but what other quality control steps were applied?

Response 9:

• DNA Extraction, Sequencing Platform and Library Preparation：

We now specify that DNA was extracted using the PowerSoil DNA Isolation Kit (MO BIO Laboratories, Inc., Carlsbad, CA, USA), following the manufacturer’s protocol. We also have added details that sequencing was conducted on the Illumina HiSeq 2500 platform. The sequencing process involved ultrasonic fragmentation (Covaris M220, Woburn, MA, USA) of the DNA to approximately 400 bp, followed by paired-end (PE) library construction, bridge PCR, and sequencing.

Revised text (Section 2.2: L187-188; L192-194):

“DNA was extracted from 0.5 g of soil using the Power Soil DNA Isolation Kit (MO BIO Laboratories, Inc., Carlsbad, CA, USA), following the manufacturer’s instructions.”

“The sequencing process involved ultrasonic fragmentation (Covaris M220, Woburn, MA, USA) of the DNA to approximately 400 bp, followed by paired-end (PE) library construction, bridge PCR, and sequencing using the Illumina HiSeq 2500 platform.”

• The method for gene assembly, clustering, and functional annotation (Section 2.3):

We have clarified that raw sequencing data were filtered using fastp (version 0.20.0). In addition to removing sequences shorter than 50 bp, we now specify that sequences with an average quality score below 20 and those containing ambiguous bases (denoted as “N”) were removed. High-quality reads were assembled into contigs using Megahit (version 1.1.2), and only contigs >300 bp were retained for further analysis. Open reading frames (ORFs) were predicted using Prodigal (version 2.6), and only genes >100 bp were selected. We included information that predicted gene sequences were clustered into a non-redundant gene set using CD-HIT (version 4.6.1) with 95% identity and 90% coverage threshold. Functional annotation was performed by aligning genes to the KEGG database using DIAMOND (version 0.8.35). Taxonomic classification was conducted by comparing non-redundant gene sets to the NR database.

Comments 10:The section mentions that redundancy analysis (RDA) and structural equation modeling (PLS-SEM) were used, but does not explain how these analyses were applied and which variables were included in the models. In addition, was the normality of the data checked before applying ANOVA? The criteria for including genes in the network analysis are not detailed. What was the abundance threshold used to define the presence of a gene in the co-occurrence?

Response 10:

• Clarification of RDA and PLS-SEM Applications:

We now provide a detailed description of RDA, specifying how it was applied to examine the influence of soil factors (STC, STN, STP, SOC, SAP, SWC, pH, and SBD) on functional gene abundance and microbial diversity indices. We also explicitly describe the PLS-SEM model and the variables included to assess causal relationships.

Revised text (Section 2.4: L233-239):

“To investigate the relationships between soil physicochemical properties and microbial functional genes, redundancy analysis (RDA) was performed using the "vegan" package in R. The first RDA assessed the influence of soil factors (STC, STN, STP, SOC, SAP, SWC, pH, and SBD) on C, N, and P functional gene abundance, while the second RDA analyzed how soil properties shape microbial community composition and diversity indices (Shannon index, Simpson index, and Pielou index).”

• Clarification of RDA and PLS-SEM Applications:

We have clarified that Shapiro-Wilk tests were conducted to assess data normality, and if violated, data were log-transformed before performing one-way ANOVA and Duncan’s multiple range test.

Revised text (Section 2.4: L217-221):

“Prior to statistical tests, the Shapiro-Wilk test was used to assess data normality. If data did not meet normality assumptions, they were log-transformed before performing one-way ANOVA and Duncan’s multiple range test in OriginLab to determine significant differences in C, N, and P functional genes across restoration stages.”

• Inclusion Criteria for Genes in Network Analysis:

We now specify that only genes with a relative abundance >0.01% and detected in at least 50% of samples were included in the co-occurrence network, reducing bias from low-abundance genes.

Revised text (Section 2.4: L226-227):

“To minimize bias from sparse matrices, only genes with a relative abundance above 0.01% and detected in at least 50% of samples were included in the network analysis.”

• Edge Definition in Co-Occurrence Networks:

The co-occurrence network edges now follow a clear threshold: Spearman correlation >0.7 and FDR-adjusted P <0.01, ensuring statistical robustness.

Revised text (Section 2.4: L227-229):

“Spearman correlation analysis was performed to identify robust associations between genes, with edges representing correlations above 0.7 (FDR-adjusted P <0.01).”

• Expanded Network Analysis Details:

We have included additional explanations on network topology attributes (connectivity, clustering coefficient, path length, graph density, modularity) and clarified that network visualization was conducted using Gephi and the "pheatmap" package.

Revised text (Section 2.4: L229-233):

“The "igraph" package was used to calculate network topology attributes, including average connectivity, clustering coefficient, average path length, graph density, and modularity. The final network was visualized and edited in Gephi, and the top 30 C, N, and P functional genes based on centrality were displayed in a heatmap using the "pheatmap" package.”

Comments 11:

The study mentions significant changes in various soil parameters and in the microbiota, but does not present P values, confidence intervals or statistical metrics for many comparisons. Some of these should appear. Always include complete statistical values for each statement of significance (e.g. F and P for ANOVA, R² for regressions, coefficients and 95%CI for PLS-SEM).

Response 11:

We have incorporated p-values for all statements regarding significant changes in soil physicochemical properties, ensuring clarity and accuracy in statistical reporting. We also have updated one-way ANOVA and Duncan’s multiple range test results, ensuring that all significant variations are properly marked in figures using different lowercase letters (p < 0.05). Besides, We have explicitly reported R², p-values, and SRMR values in the PLS-SEM model results (calculated using SmartPLS software with Bootstrapping algorithm).

Revised text (Section 3.1: L249-253, L261-262, ):

“Soil nutrient levels also change significantly: STC and SOC increase notably with restoration age compared to FL (rising from 5.83 g kg^-1 to 22.99 g kg^-1 and from 49.85 mg kg^-1 to 323.60 mg kg^-1, respectively) (p<0.01). STN and DON levels also increase, with STN rising from 0.28 g kg^-1 to 1.06 g kg^-1 and DON from 11.13 mg kg^-1 to 46.86 mg kg^-1 (p<0.05). Although STP does not show a significant change (p>0.05), SAP decreases overall (p<0.05).”

“Different lowercase letters from one-way ANOVA and Duncan’s multiple range test indicate significant variations among R. pseudoacacia plantation sites (p<0.05).”

“Proteobacteria showed higher abundance in plantations than in FL, while Acidobacteria abundance did not vary significantly across plots (p>0.05).”

“The red line represents a positive correlation between variables, with p < 0.05. The green line represents a negative correlation between variables, with p < 0.05. The gray line indicates that there is no significant correlation between variables, with p > 0.05. SRMR (Standardized Root Mean Square Residual) is an indicator for evaluating the goodness of fit of the model. An SRMR < 0.08 indicates a good model fit, SRMR < 0.10 means the model fit is acceptable, and SRMR > 0.10 means the model fit is not ideal.”

Comments 12:

There is no structured separation between the main aspects of the results (e.g. changes in the soil, microbiota, and gene functionality). The transition between paragraphs is abrupt, making it difficult to understand the line of reasoning.

Response 12:

We appreciate your comments regarding the lack of structured separation between the main aspects of the results and the abrupt transitions between paragraphs. We have carefully revised the Results section to improve the clarity and flow of the text, ensuring a more logical and coherent presentation of our findings.

• Structured Separation of Results:

We have reorganized the Results section into distinct subsections, each focusing on a specific aspect of the study. This restructuring ensures that each major finding is presented in a separate, well-defined section, making it easier for readers to follow the logical progression of the results.

Revised text (Section 3):

The Changes in Soil Physicochemical Properties and Nutrient Dynamics (L246)

The Changes in Soil Microbial Composition (L267)

Co-occurrence Network Analysis of C, N, and P Cycling Genes (L282)

Changes in C, N, and P Cycling Functional Genes (L298)

Driving Factors of Synergistic Changes in C, N, and P Cycling Functional Genes (349)

Structural Equation Modeling (SEM) (L435)

• Improved Transitions Between Paragraphs:

We have added transitional sentences and phrases to improve the flow between paragraphs and subsections. For example:

After discussing soil physicochemical properties, we introduced the microbial composition section with a sentence like: "In addition to changes in soil properties, the microbial composition also exhibited significant shifts with increasing restoration years." Similarly, before introducing the co-occurrence network analysis, we added: "To further understand the interactions among nutrient cycling processes, we conducted a co-occurrence network analysis of genes involved in C, N, and P cycling." These transitions help to connect the different aspects of the results and provide a clearer narrative.

Revised text:

"In addition to changes in soil properties, the microbial composition also exhibited significant shifts with increasing restoration years." (Section 3.2: L268-269)

"To further understand the interactions among nutrient cycling processes, we conducted a co-occurrence network analysis of genes involved in C, N, and P cycling." (Section 3.3: L283-284)

• Clarification of Key Findings:

We have revised the text to ensure that the key findings are presented more clearly and concisely. For example: In the section on soil physicochemical properties, we explicitly state the trends observed. In the microbial composition section, we highlight the dominant phyla and their changes over time. This approach ensures that the most important results are immediately apparent to the reader.

Revised text:

“Soil pH, SBD and ST decreased substantially with increasing R. pseudoacacia restoration years, while Clay increased (Table 1).” (Section 3.1: L247-248)

" At the phylum level, Actinobacteria was the most abundant across all plots (relative abundance >35%), followed by Proteobacteria (>25%) and Acidobacteria (>10%) (Fig. 1)." (Section 3.2: L271-272)

• Enhanced Visual Aids:

We have referenced tables and figures more effectively within the text to support the results. For example: When discussing soil properties, we direct readers to Table 1 for detailed data. When describing the co-occurrence network, we refer to Fig. 2a and Fig. 2b to illustrate the connections among genes. This helps readers to easily locate and interpret the visual data that supports our findings.

Revised text (Section 3.3: L284-290):

“Soil pH, SBD and ST decreased substantially with increasing R. pseudoacacia restoration years, while Clay increased (Table 1).”

Focus on Synergistic Changes:

We have emphasized the synergistic changes in C, N, and P cycling genes throughout the Results section. For example: In the section on functional genes, we explicitly state how gene abundance trends vary across pathways and restoration stages. In the SEM section, we highlight the role of microbial diversity and community composition in driving these synergistic changes. This focus ensures that the central theme of the study—synergistic nutrient cycling—is consistently highlighted.

Revised text:

“Gene abundance trends varied across pathways within each cycle as R. pseudoacacia restoration progressed. For example, total gene abundance in carbon cycling pathways was generally lower in R. pseudoacacia plantations than in FL and followed a pattern of initial increase and subsequent decline with increasing restoration age (Fig. S6a). In contrast, nitrogen cycling pathways displayed more diverse trends, with gene abun-dance varying significantly over time (Fig. S6b). Similarly, phosphorus cycling pathways showed distinct gene abundance patterns across different stages of restoration, reflecting the dynamic and complex nature of nutrient cycling during ecological recovery (Fig. S6c)” (Section 3.4: L303-310)

“Overall, the results from the structural equation model highlight the varying impacts of soil physicochemical properties, microbial composition, and microbial diversity on the synergistic changes in soil C, N, and P functional genes. Restoration age consistently shows a positive correlation with the changes in these genes, whereas soil physicochemical properties tend to exhibit negative effects. Microbial diversity generally exerts a weak positive influence, but the impact of microbial composition on gene synergism varies across the C, N, and P cycles. These findings underscore the complexity of soil-microbe interactions and their role in driving nutrient cycling during forest restoration.” (Section 3.6: L460-467)

Comments 12:

The discussion lacks an in-depth analysis of the biological mechanisms behind the observed changes. There is no convincing explanation as to why certain functional genes of the phosphorus cycle had high centrality in the gene network. The relationship between soil properties and microbial changes is presented descriptively, without a robust mechanistic approach.

Response 12:

We sincerely appreciate your having pointed out the issues. In fact, the previous discussion did feature rather broad descriptions and repetitive elaborations of the results. Consequently, we have significantly deepened the mechanistic analysis of phosphorus cycle gene centrality and soil-microbe interactions. Key revisions include:

• Structured Separation of Results:

Added enzymatic details: The pqqC gene encodes pyrroloquinoline quinone (PQQ)-dependent glucose dehydrogenase, which oxidizes glucose to gluconic acid, acidifying the rhizosphere and solubilizing inorganic phosphorus (e.g., Ca₃(PO₄)₂).

Revised text (Section 4.2: L534-537):

“The pqqC gene, encoding pyrroloquinoline quinone (PQQ)-dependent glucose dehydrogenase, plays a pivotal role in phosphorus solubilization by oxidizing glucose to gluconic acid, which acidifies the rhizosphere and releases inorganic phosphorus from mineral complexes (e.g., Ca₃(PO₄)₂) (Langhans et al., 2022).”

Cross-cycle integration: Highlighted how pqqC links carbon metabolism (GAPDH, ppdK) to phosphorus availability by generating gluconate, a carbon source that fuels microbial growth.

Revised text (Section 4.2: L537-542):

“This process directly links carbon metabolism (GAPDH, ppdK) to phosphorus availability: gluconic acid not only releases phosphate but also provides carbon skeletons for microbial growth, amplifying cross-cycle synergies (He et al., 2024). The centrality of pqqC (Fig. 2b) reflects its dual role in alleviating phosphorus limitation and fueling microbial carbon demand, critical in nutrient-poor Loess Plateau soils (Du et al., 2023).”

Polyphosphate dynamics: Explained ppk1 (synthesizes polyphosphate) and spoT (degrades polyphosphate) as microbial "P batteries" that buffer phosphorus fluctuations.

Revised text (Section 4.2: L542-548):

“In parallel, ppk1 and spoT, key genes in polyphosphate synthesis and degradation (Fig. S5), exhibited fluctuating abundance during restoration (Fig. 3c), reflecting dynamic shifts in soil phosphorus availability influenced by pH and STP (Fig. 4d). These genes are integral to microbial strategies for maintaining phosphorus balance, ppk1 (polyphosphate kinase) synthesizes polyphosphate during phosphorus surplus, while spoT (polyphosphate hydrolase) degrades it under scarcity, acting as a microbial "P battery" (Dai et al., 2024).”

• Mechanistic Soil-Microbe Interactions

pH-driven microbial shifts: Gradual acidification suppresses alkaliphilic taxa (e.g., Nitrososphaera) while favoring acid-tolerant Acidobacteria and Actinobacteria, directly linking pH to pqqC activity.

Revised text (Section 4.3: L573-579):

“Soil pH emerges as a master regulator, shaping microbial composition and enzymatic activity. Gradual acidification during restoration (Table 1) suppresses alkaliphilic taxa like Nitrososphaera while favoring acid-tolerant Acidobacteria and Actinobacteria  (Bastida et al., 2021; K. Li et al., 2021; C. Luo et al., 2020). This pH shift enhances pqqC-mediated phosphorus solubilization but inhibits alkaline-active phosphatases like phoR, creating divergent trends in phosphorus cycling pathways (Kielak et al., 2017; Tian et al., 2021).”

SOC quality and microbial succession: Labile carbon fuels Proteobacteria-dominated pathways early on, while recalcitrant carbon selects for Actinobacteria lignin degradation later (Lines 11–14).

Revised text (Section 4.3: L579-584):

“Concurrently, soil organic carbon (SOC) quality dictates microbial succession. Labile carbon in early stages fuels copiotrophic Proteobacteria, which dominate carbon cycling genes (PC, ACO) (Tao et al., 2023). As SOC becomes recalcitrant, oligotrophic groups like Actinobacteria and Chloroflexi thrive, activating ligninolytic enzymes (e.g., laccases) and shifting gene abundance toward degradation pathways (Fig. S6a; Zhang et al., 2024).”

Nutrient limitation feedbacks: SAP decline correlates with phoP upregulation, illustrating direct soil-to-gene regulation.

Revised text (Section 4.3: L588-591):

“These adaptations are mechanistically linked to soil properties: SAP decline correlates strongly with phoP upregulation, illustrating a direct soil-to-gene regulatory pathway (Fig. 5c; Mosley et al., 2022).”

Comments 13:

The manuscript mentions previous studies, but without an in-depth comparative analysis. There is no discussion of discrepancies or similarities between the findings of this study and existing literature.

Response 13:

We have incorporated a detailed comparative analysis with prior studies, emphasizing agreements and novel insights:

• Similarities with Existing Literature (Section 4.3):

STC-STN correlation: Aligns with Séneca et al. (2021) on plant-microbe carbon-nitrogen coupling but highlights Actinobacteria-driven lignin degradation as a unique driver .

Revised text (Section 4.3: L598-601):

“Our findings both align with and diverge from prior work on nutrient cycling during ecological restoration. The strong correlation between STC and STN (Fig. S2a) echoes Séneca et al. (2021), who attributed such synergy to plant-microbe car-bon-nitrogen coupling in reforested systems.”

SAP decline: Mirrors Mehnaz et al. (2019) in agroforestry systems but links it to pqqC-polyphosphate competition, a novel mechanism.

Revised text (Section 4.3: L607-609):

“Notably, the decline in SAP despite stable STP (Table 1) mirrors observations by Mehnaz et al. (2019) in agroforestry systems, where microbial immobilization and plant uptake reduce available phosphorus.”

• Contrasts with Prior Models

Contrasts with nitrogen-centric models in grasslands (Kuypers et al., 2018), emphasizing calcium-rich loess soils' unique phosphorus limitations.

Revised text (Section 4.3: L654-656):

“Our work demonstrates phosphorus’s pivotal role in loess soils. This aligns with but extends prior studies (e.g., Séneca et al., 2021; Kuypers et al., 2018), which emphasized nitrogen or carbon dominance in other ecosystems.”

Comments 14:

The Discussion does not explore the impact of the findings on ecological restoration and soil management. There is no mention of strategies that could be derived from this study to optimize the recovery of degraded areas. The text suggests that the results are ‘important for restoration’, but does not explain how they can be applied in practice.

Response 14:

We've added Section 4.4, which details actionable ecological restoration strategies. These include microbial inoculation, SOC management, pH adjustment, and gene - based monitoring. While these strategies are designed for calcium - dominated ecosystems, they can be adapted to similar degraded areas.

Revised text (Section 4.4: L614-634):

“4.4. Practical Implications for Ecological Restoration and Soil Management

The findings offer actionable strategies to enhance restoration outcomes in degraded ecosystems:

Promoting Phosphorus-Solubilizing Microbes: Inoculating soils with pqqC-rich Actinobacteria or Proteobacteria strains could accelerate Phosphorus availability in calcium-rich soils. This approach has succeeded in agroecosystems (Langhans et al., 2022) and could be adapted for reforestation.

Managing SOC Quality: Early-stage addition of labile carbon (e.g., compost) could boost Proteobacteria-driven nutrient cycling, while later-stage incorporation of lignin-rich mulch may sustain Actinobacteria activity, balancing short- and long-term carbon sequestration.

pH Monitoring and Adjustment: Liming soils in early restoration could temporarily enhance alkaline phosphatase activity (phoR), while gradual acidification via organic amendments (e.g., pine needles) may later favor pqqC-mediated phosphorus release.

Gene-Based Monitoring: Tracking functional genes (pqqC, ppk1, glnA) via metagenomics could serve as a biomarker for restoration progress, enabling targeted interventions in nutrient-limited areas.

These strategies address the Loess Plateau’s specific challenges but are adaptable to other calcium-dominated ecosystems. By linking microbial gene networks to practical management, this study bridges the gap between theoretical ecology and on-ground restoration.”

Comments 15:

In the conclusions, the text repeats some points from the Results and Discussion without a concise reformulation.

Response 15:

We reduced the repetitive descriptions of the results and discussions, and condensed the research findings into three key points, with the emphasis on the validation of the hypothesis.

Revised text (Section 5: L642-653):

“Phosphorus Genes as Network Keystones: The centrality of pqqC, ppk1, and spoT in the co-occurrence network (Fig. 2b) highlights their dual role in phosphorus solubilization and cross-cycle integration. pqqC bridges carbon metabolism (via gluconic acid production) and phosphorus availability, while ppk1 and spoT buffer phosphorus fluctuations through polyphosphate storage and mobilization.

Soil-Microbe Feedbacks: Soil acidification and organic carbon (SOC) shifts drove microbial succession, favoring Actinobacteria and Acidobacteria, which dominated lignin degradation and nitrogen mineralization. These shifts were mechanistically linked to functional gene expression, particularly in phosphorus cycling (Fig. 5c).

Nutrient Limitation Drives Synergy: Declining available phosphorus (SAP) triggered compensatory upregulation of phosphorus-scavenging genes (phoP, pstB), coupling phosphorus scarcity to enhanced carbon and nitrogen cycling efficiency.”

Comments 16:

The conclusion does not reinforce how the results respond to the objectives and hypotheses.

Response 16:

We started with a direct statement indicating that the research results are consistent with the research objectives and hypotheses, and presented the research results point by point to correspond with the research objectives.

Revised text (Section 5: L637-640):

“This study aimed to elucidate the mechanisms driving synergistic interactions among carbon (C), nitrogen (N), and phosphorus (P) cycling functional genes during Robinia pseudoacacia restoration on the Loess Plateau. Our hypothesis—that phosphorus cycle genes act as central hubs mediating cross-cycle nutrient dynamics—was strongly supported by the data.”

Comments 17:

The manuscript's Conclusion needs substantial improvements to make it clearer, more impactful and more scientifically relevant. Currently, the section lacks objective summarisation, an explicit connection with the objectives, practical implications and recommendations for future studies.

Response 17:

We have made substantial revisions to the "Conclusion" section to make it clearer and establish a clear connection with our initial research objectives. We have also added the differences between this study and previous ones, as well as suggestions for future research.

Revised text (Section 5: L654-661):

“Our work demonstrates phosphorus’s pivotal role in loess soils. This aligns with but extends prior studies (e.g., Séneca et al., 2021; Kuypers et al., 2018), which emphasized nitrogen or carbon dominance in other ecosystems. The integration of metagenomics, network analysis, and structural equation modeling provided a novel framework to unravel soil-microbe-gene interactions. While this study clarifies short-term nutrient dynamics, long-term effects of microbial succession and climate variability require further investigation. Future work should prioritize multi-decadal gene expression tracking and field trials of proposed management strategies to validate their efficacy across diverse ecosystems.”

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

This study contributes to elucidate how soil factors (soil physicochemical properties) and soil microbial communities (meta-genomic sequencing, along with redundancy analysis and Partial Least Squares Structural Equation Modeling) collectively drive synergistic changes in C, N,and P cycling genes as restoration progresses in Robinia pseudoacacia plantations on the Loess Plateau (China).

Despite the extensive research on soil nutrients and microbial communities during R. pseudoacacia restoration, the authors claim that previous research has focused on individual nutrient cycles and justify the novelty of the study on the consideration of synergistic interactions among C, N and P cycling functional genes.

According to the results of this study, soil pH, soil water content, soil total and organic carbon and soil available phosphorus influence the microbial community structure that drives the interaction among C, N and P cycling genes. The study finds that the release of bioavailable P through microbial activity directly influences C and N cycles, particularly Actinobacteria and P cycling genes such as pqqC, ppk1 and spot playing a major contribution.

General concept comments

My main concern with the study is how the authors interpret the results regarding STP and SAP. Their results show no significant change in STP and a decrease in SAP among  R. pseudoacacia sites (Table 1). However, the authors discuss their results as an “enhanced SAP” (L455). Further, the authors state that “carbon accumulation supplies energy for microbes, promoting phosphorus mineralization and solubilization and thus increasing its bioavailability” (LL 457-458). And they conclude that “The release of bioavailable phosphorus through microbial activity directly influences C and N cycling, reinforcing the interconnectedness of these nutrient cycles.” (L643 and 644). I think how the above mentioned discussion and conclusion correlate to SAP results shown in Table 1 and Fig. S2e, needs a proper clarification by the authors.

Specific comments

The Introduction is weak. If there is extensive research on soil nutrients and microbial communities during R. pseudoacacia restoration (LL. 94-96), the reader would appreciate some outlines on that.

The description of Materials and Methods regarding the soil physicochemical analysis is confusing specially in relation to what is described in Supplementary Materials Text S1. Moreover, the quality of the text in the mentioned Supplementary Materials Text S1is unacceptable.

Fig S2, axis need units.

Author Response

Comments 1:

My main concern with the study is how the authors interpret the results regarding STP and SAP. Their results show no significant change in STP and a decrease in SAP among R. pseudoacacia sites (Table 1). However, the authors discuss their results as an “enhanced SAP” (L455). Further, the authors state that “carbon accumulation supplies energy for microbes, promoting phosphorus mineralization and solubilization and thus increasing its bioavailability” (LL 457-458). And they conclude that “The release of bioavailable phosphorus through microbial activity directly influences C and N cycling, reinforcing the interconnectedness of these nutrient cycles.” (L643 and 644). I think how the above mentioned discussion and conclusion correlate to SAP results shown in Table 1 and Fig. S2e, needs a proper clarification by the authors.

Response 1:

Thank you for your valuable feedback. We acknowledge the inconsistency in our interpretation of soil available phosphorus (SAP) trends and appreciate the opportunity to clarify our discussion and conclusions.

Revisions Made:

• Clarification of SAP Trends:

In Table 1 and Fig. S2e, SAP shows a decreasing trend across R. pseudoacacia restoration sites, which contradicts our previous statement regarding "enhanced SAP." We have revised the manuscript to ensure that our discussion accurately reflects the observed SAP decline.

Revised text (Section 4: L494-496):
" Although STP remains stable (Table 1), available SAP declines (Fig. S2e, p < 0.05), likely due to increased plant uptake and microbial immobilization (Mehnaz et al., 2019)."

• Reinterpretation of Microbial Influence on Phosphorus Cycling:

While microbial activity contributes to phosphorus mineralization and solubilization, our results indicate that SAP availability is constrained as restoration progresses. The initial statement suggesting an increase in bioavailable phosphorus has been adjusted to emphasize microbial competition for phosphorus rather than a net increase in availability.

Revised text (Section 4: L496-499):
“This highlights a critical trade-off: while carbon inputs fuel microbial phosphorus mineralization (Pan et al., 2024), rising phosphorus demand from both plants and microbes reduces SAP availability, creating a nutrient limitation feedback loop (Liang et al., 2020).”

• Modification of the Conclusion:

We have substantially revised the contents of both the Discussion and Conclusion sections. In the Discussion section, we have further elaborated in detail on the impacts of the changes in soil SOC and SAP on the coordinated changes of functional genes, as well as how these elements are interconnected. In the Conclusion section, we have clarified our research objectives and provided a point-by-point summary in light of the previous hypotheses, making our conclusions more concise.

Revised text (Section 4.2: L553-567; Section 5: L636-652):
"Carbon and nitrogen cycling genes exhibit distinct but interconnected trends. Carbon cycling genes, such as PC (pyruvate carboxylase) and ACO (aconitase), initially increase in abundance as labile carbon from root exudates fuels microbial activity but decline in later stages as soil organic carbon (SOC) transitions to recalcitrant forms like lignin (Zhang et al., 2024). This shift favors Actinobacteria-driven lignin degradation pathways, altering the functional gene landscape. Nitrogen cycling genes, such as glnA (glutamine synthetase) and nasB (nitrate reductase), display divergent patterns tied to nitrogen demand. For example, glnA peaks during mid-restoration (Fig. 3a), supporting amino acid synthesis amid rapid plant growth, but declines as nitrogen limitation eases (Zhang et al., 2020). These dynamics highlight the interdependence of nutrient cycles: phosphorus availability regulates ATP synthesis, which in turn drives carbon fixation and nitrogen assimilation (Kuypers et al., 2018). Declining soil available phosphorus (SAP) in later stages further intensifies this integration, triggering compensatory upregulation of high-affinity phosphorus transporters (pstB) and mineralization genes (phoR), thereby coupling phosphorus scarcity to enhanced microbial scavenging and cross-cycle coordination (Wu et al., 2024)."

“This study aimed to elucidate the mechanisms driving synergistic interactions among carbon (C), nitrogen (N), and phosphorus (P) cycling functional genes during R. pseudoacacia restoration on the Loess Plateau. Our hypothesis—that phosphorus cycle genes act as central hubs mediating cross-cycle nutrient dynamics—was strongly supported by the data. Key findings include:

Phosphorus Genes as Network Keystones: The centrality of pqqC, ppk1, and spoT in the co-occurrence network (Fig. 2b) highlights their dual role in phosphorus solubilization and cross-cycle integration. pqqC bridges carbon metabolism (via gluconic acid production) and phosphorus availability, while ppk1 and spoT buffer phosphorus fluctuations through polyphosphate storage and mobilization.

Soil-Microbe Feedbacks: Soil acidification and organic carbon (SOC) shifts drove microbial succession, favoring Actinobacteria and Acidobacteria, which dominated lignin degradation and nitrogen mineralization. These shifts were mechanistically linked to functional gene expression, particularly in phosphorus cycling (Fig. 5c).

Nutrient Limitation Drives Synergy: Declining available phosphorus (SAP) triggered compensatory upregulation of phosphorus-scavenging genes (phoP, pstB), coupling phosphorus scarcity to enhanced carbon and nitrogen cycling efficiency.”

Comments 2:

The Introduction is weak. If there is extensive research on soil nutrients and microbial communities during R. pseudoacacia restoration (LL. 94-96), the reader would appreciate some outlines on that.

Response 2:

We acknowledge that the original introduction in the manuscript (Line 119) was overly general. To address this, we have incorporated a more detailed discussion of previous studies, focusing on changes in soil stoichiometry, enzyme activity, and microbial composition during the ecological restoration of Robinia pseudoacacia. This revision enriches the introduction and provides a clearer context for our research.

Revised text (Section 1: L95-104):
“Extensive research has recently focused on soil nutrients and microbial communities during R. pseudoacacia restoration. For example, studies have shown that R. pseudoacacia, as a nitrogen-fixing plant, enhances soil organic matter, organic nitrogen, and inorganic nitrogen levels compared to grass vegetation (Zhang et al., 2019). It also increases microbial biomass carbon (MBC) and alkaline phosphatase activity while decreasing urease activity, though it does not affect dehydrogenase activity (Vlachodimos et al., 2013). Additionally, R. pseudoacacia restoration has been shown to have a less pronounced effect on the dissimilarity of rare microbial communities compared to abundant taxa (Li et al., 2024). On the Loess Plateau, the C:N:P stoichiometry of soil microbes under R. pseudoacacia is nutrient-dependent rather than homeostatic (Zhang et al., 2019).

Comments 3:

The description of Materials and Methods regarding the soil physicochemical analysis is confusing specially in relation to what is described in Supplementary Materials Text S1. Moreover, the quality of the text in the mentioned Supplementary Materials Text S1 is unacceptable.

Response 3:

We thoroughly reviewed the descriptions of soil physicochemical analysis methods in both the main text and the supplementary materials and identified inconsistencies, such as the discrepancy in the soil-to-water ratio for pH measurement (1:5 w/v in the main text vs. 1:2.5 w/v in the supplementary materials). These errors have been corrected to ensure consistency. Additionally, we have revised the language in the main text to make the description of soil physicochemical analysis methods more concise and clear. In the supplementary materials, we have reorganized the methods into a structured, point-by-point format to enhance clarity, accuracy, and readability.

Revised text (Section 2.1: L166-185 and Supplementary Materials Text S1)：

Main Text (Revised):

“Soil water content (SWC) was determined by oven-drying the samples at 105°C to a constant weight (Bao, 2000). Soil bulk density (SBD) was calculated as the ratio of the soil mass to the total volume of the core (g·cm⁻³) after oven-drying at 105°C to a constant weight (Bao, 2000). Soil pH was measured using a pH meter (Model PHS-2, INESA Instrument, Shanghai, China) after shaking a soil-distilled water suspension (1:5 w/v) at 200 rpm for 30 minutes (Peng and Wang, 2016). Soil temperature (ST) and clay content were determined using a laser particle size analyzer (Mastersizer 2000, Malvern Instruments, UK), and the distribution of soil particles was analyzed by laser scattering (Faé et al., 2019). Soil organic carbon (STC) was measured by dry combustion using a TOC-TN analyzer (TOC-L CPH, Shimadzu Corp., Kyoto, Japan). Total nitrogen (STN) and total phosphorus (STP) concentrations were determined using a continuous flow analyzer (AA3; Germany) following wet digestion with K₂SO₄·5H₂O (10:1 w/w)-H₂SO₄ and HClO₄-H₂SO₄, respectively (Liu et al., 2013). Total dissolved nitrogen (TDN) was analyzed using a TOC-TN analyzer (TOC-L CPH, Shimadzu Corp., Kyoto, Japan) (Jones and Willett, 2006). Ammonium nitrogen (NH₄⁺-N, SAN) and nitrate nitrogen (NO₃⁻-N, SNN) were measured using a continuous flow analyzer (AA3; Germany) after extracting fresh soil with 2 M KCl (Baldrian et al., 2008). Dissolved organic nitrogen (DON) was calculated as the difference between TDN and inorganic nitrogen (NH₄⁺-N and NO₃⁻-N) (Ren et al., 2018). Soil available phosphorus (SAP) was extracted with ammonium lactate solution and determined by spectrophotometry and flame photometry (Tian et al., 2021).”

Supplementary Materials Text S1：

Soil physicochemical properties were analyzed following established methods:

Soil Water Content (SWC): Soil samples were oven-dried at 105°C to a constant weight, and the water content was calculated (Bao, 2000).

Soil Bulk Density (SBD): Soil cores were oven-dried at 105°C to a constant weight, and the bulk density was calculated as the ratio of soil mass to the total volume of the core (g·cm⁻³) (Bao, 2000).

Soil pH: Soil pH was measured using a pH meter (Model PHS-2, INESA Instrument, Shanghai, China). A soil-distilled water suspension (1:5 w/v) was shaken at 200 rpm for 30 minutes before measurement (Peng and Wang, 2016).

Soil Temperature (ST) and Clay Content: A laser particle size analyzer (Mastersizer 2000, Malvern Instruments, UK) was used to determine soil temperature and clay content. The distribution of soil particles was analyzed by laser scattering (Faé et al., 2019).

Soil Organic Carbon (STC): Soil organic carbon was measured by dry combustion using a TOC-TN analyzer (TOC-L CPH, Shimadzu Corp., Kyoto, Japan).

Total Nitrogen (STN) and Total Phosphorus (STP): Total nitrogen was determined by wet digestion with K₂SO₄·5H₂O (10:1 w/w)-H₂SO₄, and the digested solution was analyzed using a continuous flow analyzer (AA3; Germany) (Liu et al., 2013). Total phosphorus was determined by wet digestion with HClO₄-H₂SO₄, and the digested solution was analyzed using a continuous flow analyzer (AA3; Germany) (Liu et al., 2013).

Total Dissolved Nitrogen (TDN): TDN was analyzed using a TOC-TN analyzer (TOC-L CPH, Shimadzu Corp., Kyoto, Japan) (Jones and Willett, 2006).

Ammonium Nitrogen (NH₄⁺-N, SAN) and Nitrate Nitrogen (NO₃⁻-N, SNN): Fresh soil samples were extracted with 2 M KCl, and the extracts were analyzed using a continuous flow analyzer (AA3; Germany) (Baldrian et al., 2008).

Dissolved Organic Nitrogen (DON): DON was calculated as the difference between TDN and inorganic nitrogen (NH₄⁺-N and NO₃⁻-N) (Ren et al., 2018).

Soil Available Phosphorus (SAP): SAP was extracted with ammonium lactate solution and determined by spectrophotometry and flame photometry (Tian et al., 2021).

 

Author Response File: Author Response.pdf