the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 28 May 2025
Soil microbial necromass shapes global carbon stocks in agricultural and natural ecosystems
Abstract. Soil carbon (C) plays an essential role in regulating global C cycle and climate. Microbial necromass is an important component of soil C, and yet the relative contribution of microbial necromass in shaping the global C stocks in agricultural and natural ecosystems worldwide remains virtually unknown. In this study, we compiled data on fungal and bacterial necromass along with soil organic carbon (SOC) from the 0–20 cm soil layer across 486 study sites (145 agricultural and 341 natural ecosystems) to evaluate the relative contribution of fungal necromass C (FNC) and bacterial necromass C (BNC) to SOC and the FNC/BNC ratio, after accounting for other biotic and abiotic factors. Our results indicated that, in both agricultural and natural ecosystems, the contribution of FNC to SOC significantly exceeded that of BNC, with FNC contributing approximately twice as much as BNC to SOC. However, the contributions of FNC and BNC to SOC were markedly higher in agricultural ecosystems than those in natural ecosystems, with a contrasting trend in the FNC/BNC ratio. Soil physicochemical properties (C/N and clay) were the most important predictors of the contributions of FNC and BNC to SOC in both ecosystems, while geographical factor (elevation) was the most important predictor of the FNC/BNC ratio. Our study enhances the current level of understanding regarding microbially mediated biogeochemical cycling and SOC dynamics, underscoring the critical role of microbial necromass in the global C cycle.
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Status: final response (author comments only)
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RC1: 'Comment on essd-2025-229', Anonymous Referee #1, 18 Aug 2025
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AC1: 'Reply on RC1', Jingli Lu, 16 Oct 2025
Dear Editor,
We would like to thank you, and the reviewers for the contributions to this manuscript. The constructive feedback has been extremely helpful. We have accepted the vast majority of the changes suggested and made the appropriate changes to the study. We believe that the manuscript is considerably clearer and more impactful as a result.
Attached please find our point-by-point responses to the reviewer’s comments.
We thank you for your consideration and hope you will find this version suitable for publication in Earth System Science Data.
Best regards,
Zhiqiang Wang, and on behalf of all co-authors
Sichuan Zoige Alpine Wetland Ecosystem National Observation and Research Station, Southwest Minzu University
Chengdu, 610041, PR China
E-mail: wangzq@swun.edu.cn
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AC1: 'Reply on RC1', Jingli Lu, 16 Oct 2025
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RC2: 'Comment on essd-2025-229', Anonymous Referee #2, 30 Oct 2025
The paper presents an excellent and timely study, offering a comprehensive global-scale analysis of the contributions of fungal and bacterial necromass carbon (FNC and BNC) to soil organic carbon (SOC) across agricultural and natural ecosystems. The manuscript is well-written, methodologically rigorous, and addresses a topic of significant importance in soil biogeochemistry. The findings provide valuable insights into microbial-mediated carbon stabilization mechanisms in terrestrial ecosystems. I suggest this study highly suitable for publication in ESSD, however, some questions should be resolved before final acceptance.
Major concerns
In Section 2.1, The authors should justify the use of interpolated data (e.g., for MAT, MAP, and soil properties) obtained from public databases. Please address the potential uncertainties and describe any steps taken to validate these values against site-specific conditions or to quantify the associated error in the analysis.
Section 3.2 presents a highly detailed and, at times, repetitive description of the results. This level of minutia can obscure the key findings for the reader. To improve clarity and impact, I strongly recommend that the authors streamline this section. The text should be condensed to focus on the primary results, avoiding a minute description of every statistical outcome. Reorganizing the content into clearer thematic paragraphs would also significantly enhance its readability.
Meanwhile, I suggest the authors separately describe the effects of driving factors on the contributions with agricultural and natural ecosystems. Also, in the section 4.2, the authors should better discuss it separately about agricultural and natural ecosystems.
The Discussion would benefit from a sharper focus on the novelty of this study. Currently, the overemphasis on aligning with previous findings (e.g., Lines 305–306, 340–341) detracts from highlighting the new insights. This is apparent in Section 4.1, where the interpretation of results, such as the elevated FNC and BNC in agricultural ecosystems, needs more mechanistic depth. The authors should use their own analytical evidence (e.g., from BRT and SEM on C/N ratio and clay content) to explain these patterns, rather than merely stating them. The discussion should use prior literature to frame the study's unique conclusions, not just to confirm them.
Minor concerns
Line 21: Delete this sentence.
Lines 78–81: Suggest change into “Previous studies indicated that the contributions of FNC and BNC to SOC depended on the type of ecosystems (Wang et al., 2021a; Cao et al., 2023; Xu et al., 2024).”
Lines 126–127: Natural ecosystems include grasslands and forests. What habitats does the agricultural ecosystem consist of? Please clarify this carefully.
Lines 182–183: Why is the threshold for the variance inflation factor set at 3.3 instead of the more common 5 or 10 that we commonly used?
Lines 230–233: Suggest delete this sentence. Just provide an objective description of the result, without delving into other details.
Lines 286–296: This section contains too much overlap with the introduction and results sections. Suggest delete it.
Lines 300–302: Delete this sentence.
Citation: https://doi.org/10.5194/essd-2025-229-RC2 -
AC2: 'Reply on RC2', Jingli Lu, 05 Nov 2025
Dear Editor,
We would like to thank you, and the reviewers for the contributions to this manuscript. The constructive feedback has been extremely helpful. We have accepted all the changes suggested and made the appropriate changes to the study. We believe that the manuscript is considerably clearer and more impactful as a result.
Attached please find our point-by-point responses to the reviewer’s comments.
We thank you for your consideration and hope you will find this version suitable for publication in Earth System Science Data.
Best regards,
Zhiqiang Wang, and on behalf of all co-authors
Sichuan Zoige Alpine Wetland Ecosystem National Observation and Research Station, Southwest Minzu University
Chengdu, 610041, PR China
E-mail: wangzq@swun.edu.cn
Response to reviewer’s comments
Responses to the Reviewer’s comments
The paper presents an excellent and timely study, offering a comprehensive global-scale analysis of the contributions of fungal and bacterial necromass carbon (FNC and BNC) to soil organic carbon (SOC) across agricultural and natural ecosystems. The manuscript is well-written, methodologically rigorous, and addresses a topic of significant importance in soil biogeochemistry. The findings provide valuable insights into microbial-mediated carbon stabilization mechanisms in terrestrial ecosystems. I suggest this study highly suitable for publication in ESSD, however, some questions should be resolved before final acceptance.
Response: We sincerely thank the reviewer for the positive and encouraging comments on our manuscript. We particularly appreciate the reviewer's recognition of the global-scale analysis, methodological rigor, and significance of our study to the field of soil biogeochemistry.
We have carefully considered all the points raised by the reviewer. In the sections below, we provide a point-by-point response to the specific questions and have revised the manuscript accordingly to address them. We believe that these revisions have further strengthened the quality and clarity of our work.
Major concerns
In Section 2.1, The authors should justify the use of interpolated data (e.g., for MAT, MAP, and soil properties) obtained from public databases. Please address the potential uncertainties and describe any steps taken to validate these values against site-specific conditions or to quantify the associated error in the analysis.
Response: Thanks for this important suggestion. We agree that acknowledging and addressing the potential uncertainties associated with these datasets is crucial for the robustness of our global-scale analysis. Below, we provide a justification for their use and describe the steps we took to mitigate potential issues.
The primary rationale for employing globally interpolated datasets (e.g., WorldClim, SoilGrids) was to ensure consistent, continuous, and spatially complete coverage of environmental variables across all 486 globally distributed sites. The original publications from which microbial necromass data were extracted frequently did not report the full suite of climatic and soil variables required for our unified analysis. By using these standardized, high-resolution global datasets, we maintained methodological consistency and mitigated potential biases arising from missing data.
We acknowledge that interpolated data inherently contain uncertainties. To address this, we took the following steps:
(1) We exclusively used globally recognized and widely cited databases (e.g., WorldClim v2.1 with a 30-arc second resolution, SoilGrids 2.0), which represent the current state-of-the-art in global spatial interpolation and are extensively used in global ecological and biogeochemical studies (e.g., Lu et al., 2022; Ren et al., 2024; Shi et al., 2025; Zhou et al., 2025).
(2) After retrieving missing value from gridded data, we typically calibrate them against field-reported values via a field-anchored bias correction (i.e., a site- or region-specific “delta” adjustment) to minimize errors introduced by gridded data.
(3) Our statistical approach inherently accounts for data uncertainty. The performance metrics of our models (e.g., the R² values ranging from 23% to 66% in our Boosted Regression Tree analysis, as shown in Figure 4) already reflect the unexplained variance, which partly incorporates the measurement and interpolation errors of all input variables. The fact that we still identified strong and significant drivers suggests that the signals we detected are robust enough to overcome the background noise, including potential errors from interpolation.
In response to the reviewer's comment, we have revised the Section 2.1 (Data collection) in the revised manuscript to explicitly address this point. The added text reads:
“We supplemented missing climatic and soil variables using high-resolution, globally interpolated datasets to ensure consistent spatial coverage across all sites. After retrieving missing value from gridded data, we typically calibrate them against field-reported values via a field-anchored bias correction (i.e., a site- or region-specific “delta” adjustment) to minimize errors introduced by gridded data. While the use of such data introduces inherent uncertainties, these databases are widely adopted in global-scale ecological analyses and provide the most feasible approach for a unified assessment.”
For further detail, please see Lines 156–163 of the revised manuscript.
Section 3.2 presents a highly detailed and, at times, repetitive description of the results. This level of minutia can obscure the key findings for the reader. To improve clarity and impact, I strongly recommend that the authors streamline this section. The text should be condensed to focus on the primary results, avoiding a minute description of every statistical outcome. Reorganizing the content into clearer thematic paragraphs would also significantly enhance its readability.
Response: Thank you for this constructive comment. As recommended, we have reorganized the Section 3.2 as followings:
“Soil physicochemical factors were the most important influence on the contributions of FNC and BNC to SOC across both ecosystem types (Figures 3a–d, 4a–d). Specifically, they explained 16% and 17% of the variance in the contributions of FNC and BNC to SOC in agricultural ecosystems, respectively (Figures 3a, c), and 20% and 24% in natural ecosystems (Figures 3b, d). BRTs corroborated this pattern, with soil physicochemical factors showing the highest relative influence (51% for FNC, and 44% for BNC) in agricultural systems and 44% in natural systems (Figures 4a–d). All BRT models were significant (P < 0.001), with explained variance 36–66%. While soil factors dominated overall, responses to individual variables differed between ecosystems. In detail, in agricultural systems, the C/N ratio ranked third for FNC after clay and SOC (Figure 4a), whereas C/N was the top predictor for FNC in natural systems and for BNC in both ecosystems (Figures 4b–d). Consistently, linear models showed declines in the contributions of FNC and BNC with increasing C/N in both ecosystems (Figures S5g, S6g). SEMs yielded convergent results, indicating both direct and indirect pathways (Figures 5a–d, 6a–d). Notably, the direct and total effects of soil physicochemical factors on FNC were negative in agricultural but positive in natural ecosystems (Figures 5a, b, 6a, b), whereas the effects on BNC were negative in both ecosystem types (Figures 5c, d, 6c, d).
Our results indicated that geographical factors were the most important contributors to explain the FNC/BNC ratio in both agricultural and natural ecosystems, accounting for 21% and 10% of the explained variance in the FNC/BNC ratio, respectively (Figures 3e, f). The results of the BRTs suggested that geographical factors played a similar role in explaining the FNC/BNC ratio (Figures 4e, f). In the BRT models, geographical factors emerged as the primary influencers of the FNC/BNC ratio in agricultural and natural ecosystems, accounting for 32% and 44% of the variance in each case, respectively (Figures 4e, f). To be more specific, elevation was the most significant geographical factors influencing the FNC/BNC ratio in both ecosystems (Figures 4e, f). Moreover, the FNC/BNC ratio in agricultural and natural ecosystems show significantly increased with an increase elevation (Figure S7a). The results of SEMs also indicated that geographical factors were the most influential factors for the FNC/BNC ratio in agricultural and natural ecosystems, exerting both direct and indirect effects on this ratio (Figures 5e, 6e), with the standardized total effect being positive (Figures 5f, 6f).”.
For further details, please see Lines 235–267 of the revised manuscript.
Meanwhile, I suggest the authors separately describe the effects of driving factors on the contributions with agricultural and natural ecosystems. Also, in the section 4.2, the authors should better discuss it separately about agricultural and natural ecosystems.
Response: Thanks for the constructive comment. As recommended, we have separately described the effects of driving factors on the contributions with agricultural and natural ecosystems as followings:
“While soil factors dominated overall, responses to individual variables differed between ecosystems. In detail, in agricultural systems, the C/N ratio ranked third for FNC after clay and SOC (Figure 4a), whereas C/N was the top predictor for FNC in natural systems and for BNC in both ecosystems (Figures 4b–d). Consistently, linear models showed declines in the contributions of FNC and BNC with increasing C/N in both ecosystems (Figures S5g, S6g). SEMs yielded convergent results, indicating both direct and indirect pathways (Figures 5a–d, 6a–d). Notably, the direct and total effects of soil physicochemical factors on FNC were negative in agricultural but positive in natural ecosystems (Figures 5a, b, 6a, b), whereas the effects on BNC were negative in both ecosystem types (Figures 5c, d, 6c, d).”.
For further details, please see Lines 242–252 of the revised manuscript.
In addition, we have reorganized and discussed it separately about agricultural and natural ecosystems in the Section 4.2. The revised text now reads:
“In agricultural ecosystems, high soil N levels primarily result from fertilization (Chen et al., 2020). In contrast, natural ecosystems experience minimal anthropogenic disturbance, N often acts as the key limiting factor for microbial activity (Elser et al., 2007). Under N-limited conditions, microbes (both fungi and bacteria) allocate more energy and C resources to the synthesis of N-acquiring enzymes (e.g., proteases and chitinases). This shift in metabolic strategy reduces the C allocated to biomass synthesis, thereby diminishing the amount of C ultimately converted into microbial necromass (Mooshammer et al., 2014; Liu et al., 2024). Thus, although microbial community composition differs between natural and agricultural ecosystems, the regulatory role of soil C/N ratio in shaping their structure and function remains consistent (Han et al., 2024). In our study, soil clay content was identified as the predominant factor governing the contribution of FNC to SOC in agricultural ecosystems (Figure 4a), with this contribution increasing concomitantly with clay content (Figure S5d). This suggests that soils with higher clay and silt contents generally accumulate greater amounts of microbial residues, particularly those derived from fungi, which can be attributed to the promotion of stable organo-mineral complex formation by abundant fine soil particles (Six et al., 2006 and Liang et al., 2017). Furthermore, although agricultural management practices often disturb soil structure, they simultaneously enhance clay enrichment and aggregate formation, thereby providing effective physical protection for the long-term stabilization of fungal-derived C (Chen et al., 2020; Mou et al., 2021; Zhou et al., 2023).”.
For further details, please see Lines 344–365 of the revised manuscript.
The Discussion would benefit from a sharper focus on the novelty of this study. Currently, the overemphasis on aligning with previous findings (e.g., Lines 305–306, 340–341) detracts from highlighting the new insights. This is apparent in Section 4.1, where the interpretation of results, such as the elevated FNC and BNC in agricultural ecosystems, needs more mechanistic depth. The authors should use their own analytical evidence (e.g., from BRT and SEM on C/N ratio and clay content) to explain these patterns, rather than merely stating them. The discussion should use prior literature to frame the study's unique conclusions, not just to confirm them.
Response: Following the constructive comment, we have reorganized and revised some parts of Section 4.1 in the manuscript. The updated text now reads:
“Although this general pattern has been reported in previous studies (Liang et al., 2019; Wang et al., 2021a; Zhang et al., 2023; Ding et al., 2024), the systematic differences in the magnitude of these contributions between agricultural and natural ecosystems—and their underlying drivers—have remained poorly understood. Our study not only confirms the broad pattern but also elucidates these ecosystem-level disparities and their environmental determinants. Consistent with our finding that the contribution of fungal necromass carbon (FNC) to SOC exceeded that of bacterial necromass carbon (BNC) in both ecosystem types (Table 1), the predominance of fungal necromass may be attributed to its more recalcitrant cell wall composition (e.g., chitin) and slower decomposition rate (Wang et al., 2021a). Our BRT and SEM analyses further identified soil clay content and C/N ratio as key drivers of FNC accumulation (Figs. 4a, 5a), reinforcing the importance of organo-mineral associations in the stabilization of fungal-derived carbon.”.
For further details, please refer to Lines 275–287 of the revised manuscript.
Furthermore, we have reorganized and revised the specific paragraph (contained the content in Lines 340–341 of the original manuscript), strengthening the support for our findings by integrating relevant pre-existing literature. The updated text now reads:
“Furthermore, nutrient-rich conditions prevalent in agricultural systems (e.g., due to fertilization) often select for bacterial-dominated communities, as many bacteria exhibit r-strategist traits that support rapid growth under high resource availability. In contrast, natural ecosystems—characterized by lower nutrient availability and greater resource heterogeneity—tend to favor fungal dominance, since fungi often function as K-strategists with higher efficiency in decomposing complex organic matter under resource-limited conditions (Strickland & Rousk, 2010; Yu et al., 2022). This shift in microbial community composition is reflected in our results, which show a significantly higher FNC/BNC ratio in natural ecosystems across our global dataset (Figure 2c, Table 1). A high FNC/BNC ratio signifies a fungal-dominated decomposition pathway. Fungal necromass—rich in recalcitrant compounds such as chitin—is more resistant to decay, and fungal hyphae play a key role in the formation of stable soil aggregates that physically protect organic matter from degradation (Lenardon et al., 2007). This pathway promotes the formation of stable, long-turnover SOC pools essential for long-term carbon sequestration (Six et al., 2006; Lehmann et al., 2020). Furthermore, fungi generally exhibit higher carbon use efficiency than bacteria, meaning a larger proportion of assimilated carbon is allocated to biomass production (and subsequently necromass) rather than being respired as CO₂ (Wang & Kuzyakov, 2024). Thus, the fungal-driven pathway characteristic of natural ecosystems represents a highly efficient conversion of plant litter into persistent soil organic matter (Kallenbach et al., 2016; Malik et al., 2016). Conversely, the lower FNC/BNC ratio observed in agricultural ecosystems reflects a bacterial-dominated pathway, accelerated by practices such as tillage and nutrient amendments. This pathway is associated with faster carbon cycling and greater carbon loss through respiration. Although microbial necromass can accumulate under these conditions—sometimes contributing more significantly to a reduced total SOC pool—the resulting carbon is often less stabilized (Zhou et al., 2023). Therefore, the FNC/BNC ratio serves not merely as a descriptive metric, but as a functional biomarker that elucidates fundamental differences in the stability and persistence of SOM between managed agricultural systems and natural ecosystems.”.
For further details, please refer to Lines 306–335 of the revised manuscript.
Minor concerns
Line 21: Delete this sentence.
Response: Done.
Lines 78–81: Suggest change into “Previous studies indicated that the contributions of FNC and BNC to SOC depended on the type of ecosystems (Wang et al., 2021a; Cao et al., 2023; Xu et al., 2024).”
Response: Thanks. We have rewritten in the revised manuscript (Lines 77–79).
Lines 126–127: Natural ecosystems include grasslands and forests. What habitats does the agricultural ecosystem consist of? Please clarify this carefully.
Response: Thanks. We have explicitly classified the agricultural ecosystem into dry land, irrigated cropland, and submerged paddy. For further details, please refer to Lines 123–124 of the revised manuscript.
Lines 182–183: Why is the threshold for the variance inflation factor set at 3.3 instead of the more common 5 or 10 that we commonly used?
Response: Thank you for this insightful and important comment. The choice of a more conservative Variance Inflation Factor (VIF) threshold of 3.3, as opposed to the more commonly cited values of 5 or 10, was a deliberate decision to ensure the robustness and reliability of our models by more rigorously minimizing multicollinearity.
The detailed justification for selecting this specific threshold of 3.3 as following:
(1) Conventional Thresholds and Their Implications. (a) VIF < 10: This is a very lenient standard, more common in earlier statistical applications. It implies that 90% of an independent variable's variance can be explained by the other independent variables (since 1 - 1/10 = 0.9). In modern research demanding higher model precision, this threshold is often considered too permissive and may fail to effectively eliminate problematic collinearity. (b) VIF < 5: This is a moderate and frequently used standard, deemed acceptable in many fields. It indicates that up to 80% of a variable's variance is explained by others. This threshold often provides a reasonable balance in many situations.
(2) Rationale for a Stricter Threshold (VIF < 3.3). Our reference to Kock (2015) is pivotal here. This literature advocates for and substantiates the necessity of a stricter VIF threshold, primarily based on the following points: (a) although this study ultimately uses BRTs and SEM, the threshold proposed by Kock (2015) was initially developed within the context of Partial Least Squares Structural Equation Modeling (PLS-SEM) for comprehensive collinearity assessment. This concept has since been adopted by numerous researchers and applied to a wider range of multivariate statistical models as a gold standard for ensuring predictor independence; (b) A variable with a VIF of 5 still has 20% of its variance inflated by other variables in the model. This remains a non-negligible proportion that can distort regression coefficient estimates, making them unstable and difficult to interpret. Setting the threshold at 3.3 ensures that no more than approximately 30% of any predictor's variance is explained by others (1 - 1/3.3 ≈ 0.7). This more effectively guarantees that the influence of each predictor on the response variable is relatively independent, leading to more reliable and trustworthy model outcomes; (c) In ecology and environmental sciences, many environmental drivers (e.g., temperature, precipitation, soil nutrients) are inherently correlated. Employing a strict VIF threshold proactively addresses these issues during the variable selection stage. This ensures that the "most important factors" subsequently identified in the Boosted Regression Trees and Structural Equation Models are genuinely influential, not merely appearing significant due to high correlations with other excluded variables. This significantly strengthens the robustness of the study's conclusions.
Therefore, our selection of the threshold value of 3.3 was not an arbitrary choice, but was grounded in established literature and driven by our commitment to more stringent criteria for data integrity and model robustness.
We sincerely hope this clarification adequately addresses your concern.
Lines 230–233: Suggest delete this sentence. Just provide an objective description of the result, without delving into other details.
Response: Thanks. We have deleted the sentence, and revised the respective section to provide a more objective description of the results. For further details, please refer to Lines 235–252 of the revised manuscript.
Lines 286–296: This section contains too much overlap with the introduction and results sections. Suggest delete it.
Response: Done.
Lines 300–302: Delete this sentence.
Response: Done.
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AC2: 'Reply on RC2', Jingli Lu, 05 Nov 2025
Data sets
Soil microbial necromass shapes global carbon stocks in agricultural and natural ecosystems Jingli Lu https://dx.doi.org/10.6084/m9.figshare.28827386
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The study worked out the fungal and bacterial necromass contribution to SOC and interpreted the variability with climate, geographical and soil conditions. The data are useful and provide key reference for global ecosystem study of SOC storage. However, as the authors often mentions their work was very similar, or consistent, with others work already published. The shortage may be not robust information about their soil samples, particularly agricultural soils not classified and sampling condition not clearly defined. It could be improved if the authors could add samples from managed grasslands, wetlands and divided agricultural soil into dry land and irrigated as well as submerged paddy. The discussion need rewrite, reorganized and better presented with statistical analysis.
Specific comments as follows:
Title: Suggest to change as "Microbial necromass contribution to topsoil organic carbon storage of natural and agricultural ecosystems".
INTRO
Line 47-51: Soil organic matter, the material containing organic carbon as the core element but preserved in soil matrix, is truly formed based on plant biomass (material), even the organic pollutants in soil based on fossil plant material (coal and petro-oil). It is not contradictory that SOM is composed of plant derived and microbes-derived organics, the letter is formed of metabolic residues upon microbial processing of plant material and preserved through interaction with mostly soil mineral matrix. That is to say, microbial necromass is indirectly derived from plant material, or microbial products via processing plant material. Both plant derived or microbial derived organic carbon could be stable in soil conditions preservation could be allowed. “Stable” here could be changed into “with long turnover time”.
Line 55-58: Microbial biomass carbon generally possessed generally about 2% to SOC, but microbial necromass could predominate (as high as 80% to SOC) in soils low in SOC. For the general estimation (2%) of microbial biomass C percentage to SOC (often termed microbial quotient), maybe cite a recent study(Topsoil microbial biomass carbon pool and the microbial quotient under distinct land use types across China: A data synthesis, Soil Science and Environment, 2:5. Doi:10.48130/SSE-2023-0005)
Line 60-63: many studies…., add description of the study conditions such as land use, management, climate change, regional, etc.
Line 64-65: With the distinct roles of fungi and bacteria in decomposing organic matter and stabilizing organic carbon in soil, the relative contribution to SOC of fungal and bacterial necromass C could be used to track the dynamics of SOC storage (Malik et al., 2016).
Line 66-68: Vague sentence, suggest to delete.
Line 71-74: As bacterial amino-sugars is degradable rather than fungal chitin or β-glucans, fungal necromass existed in soil generally with longer turnover time than bacterial necromass.
Line 74-78: The sentence may not be correct here. By definition, microbial necromass is not microbial biomass.
Line 78-82: Pls delete “Previous studies have also indicated that”.
Line 82-86: May be changed into“ However, few studies on fungal and bacterial necromass carbon and their contribution to SOC has been reported for ecosystems under human interference (Chen et al., 2020)“. The citation of (Chen et al., 2020) may not be appropriate here.
Line88-90：Pls consider to change. The agricultural ecosystems are typical of plant litter derived of single crops under human management (Bohan et al., 2013)
Line 90-92: In contrast, natural ecosystems display greater diversity in plant litter and root deposits (Wu et al., 2019).
Line 93-95: In such ecosystems, fungal mycelial networks and stable soil aggregates are enhanced, leading to higher FNC contributions to SOC (Sanaullah et al., 2020; Sae-Tun et al., 2022).
Line 95-98: in comparison to bacterial?
Line 98-102: These statements may be of questions, or not sufficient. For example, why diverse plant input lead to rich soil cellulose content, why not diverse SOM composition?
Line 102-106: The issue is important, but the rational is not strong as your overview of studies. Could you focus on why FNC and BNC across global ecosystems or why the ratio is important across the global ecosystems?
Line 112-113: delete the sentence as is already ascertained above.
MM
Line 117-118: to be deleted
Line 119: by December 31, 2021?
Line 121-123: why not fungal derived glomalin-related proteins? And, why “fungal necromass, bacterial necromass included in your search engine?
Line 123-130: studies search and screening procedure are not well described. I suppose you first collected all the studies indexed of the keywords, then you made a rough compilation. Secondly, filter the compiled studies with “topsoil”; followed by paired data of fungal and bacterial necromass or so, further divided your filtered studies into ecosystem categories, finally you excluded those potentially disturbed ecosystems from the natural category. Pls do organize clearly your work flow and display in a flow chart or in an order of steps.
A question，here you claim that only data of topsoil (0-20cm) were collected. As I experienced, different depth intervals of topsoil were used for agroecosystems and natural ecosystems, mostly 0-30cm for dry croplands while often undiscerned for natural ecosystems. You may mention these different usages though not critical for your relative contribution and ratio estimation. But this may affect estimation of mass abundance of microbial necromass in soil.
Another key question: did you screen the data for collecting and measurement standard protocol? Is the similar season or growing period among all the samples? All the samples were treated with a consistent procedure (for example sample preparation, shipping and storage, and analysis condition)
Line 130-133: What about the time span of the published studies? How did you check if repeated data in publications of a same study but in different journals and/or years?
Line 134-137: Pls describe the calculation of FNC and BNC respectively, and number the equations.
Line 138-140: Pls clarify the unit of the calculated contents.
Line 141-145: How did you obtain the information of soil temperature. As I know well, soil temperature is not normally recorded while in field sampling. However, soil moisture content data could be available in most sampling procedure or lab measurement before further analysis for specific purposes. In addition, what kind of information for microbial or plant factors.
Line 151: what is the spatial distance of 30 x 30 arc sec ? Is such grid resolution comparable to the site specific climate data?
Line 153-158: Use of data of soil temperature and soil properties digested from the GEO-based data base is questionable for the studied soil in your database.
Line 161-162: data of microbial biomass carbon and nitrogen is not eligible from the geo-database. These varies very much from site to site, or from time to time.
Results
Line 224-226: How did you get these values? Calculation using the numbers you provided in the preceding sentences does not yield the same values (2.23 for agricultural but 2.09 for natural). If the calculation correct, there is significant but slight difference in FNC/BNC ratio between agricultural and natural ecosystems.
The samples of agricultural ecosystem not clearly defined. Dry croplands, irrigated croplands, rain-fed dry lands and waterlogged paddies? Also, the cultivation history is important, at least need to clarify those shortly shifted from natural ecosystem, for example from grassland.
Line 228 the subheading of “ Effects of the driving factors on…” may not be proper for this is a synthesis of data in arbitrary studies without certain treatments. Could be change into “Driving factors of the change in fungal and bacterial necromass contribution to SOC and their ratio”. But this context should be presented in Discussion part, not the direct results presented here.
I suggest you could split your result into two subheadings: 3.1 Fungal and bacterial necromass contribution to SOC; 3.2 Ratio of Fungal and bacterial necromass. In 3.1, you may provide more detailed information of the variation of fungal and bacterial necromass content and the contribution to SOC, among samples, ecosystem types and or other dimension (for example, regionally). In 3.2, provide the ratio variance among the systems, but also digest the relations to SOC level. Possibly, you could align your correlation to these variance to digest the driving factors, respectively.
Discussion
This part not well organized, often repeating the statement of results.
Line 286-296: Not a single independent paragraph.
Line 286-288: Move to INTRO;
Line 288-289: Move to Results part;
Line 289-296: Most are repeated Results context. Delate.
Line 298-299: the subheading is many times repeated in this paper. May use something different, may be like ”Fungal necromass Greater contribution to SOC by fungal necromass than by bacterial one.”
Line 300-302 Should included in INTRO, not repeated here.
Line 302-305: Avoid repeated statement of result. But you need specify the range of the ratio difference among the samples and between your two sets of ecosystems. It may not be true fungal necromass contribution twice as much as bacterial across samples.
Line 305: if this sentence correct, then what is your study’s novelty? If the following discussion about the factors are new, then you may say “ The similar variance feature been reported in previous studies, but the reasons unknown. In this study…….
Line 308-319: Unfortunately, the discussion are weak, just using some knowledge from publications not with your own analysis or statistical attribution.
Line 320-323: If the finding is new, you may rewrite like: In this study we found higher microbial necromss contribution in agricultural system than in natural ecosystems.
Line 324-326: You could use this reason for lower contribution in natural ecosystem but not ending with “potentially resulting in a greater proportion of microbially derived C within SOC (Angst et al., 2021).”.
Line 328-337: The second reason for higher microbial necromass contribution pointed to high quality substrates in agricultural systems, with lower C/N ratio generally. Could you use a correlation respectively of these necromass contribution values to the soils C/N ratio? Lower C/N ratio in agricultural soils is driven by the N fertilization, not necessarily by high quality substrate like legume residue. In fact, agricultural residues are often high C/N ratio, for example wheat straw is over 30.
Line 340-342: may be not the difference between the two microbial groups but the difference in microbial behavior between the two systems, which you mentioned later.
Line 344-349: These are very weak nor robust.
Line 351-359: These are not sound knowledge. Should link the ratio difference to the difference in SOM accumulation between natural and agricultural systems.
The contents in 4.2 should be sued in discussion part 4.1. When the reason of the changes is in discussion, you present these results from statistics to support or to cohere your finding. Not presented separately while leaving your discussion often pale.
Subheading 4.3, statement about limitations are honest. But need to mention that sampling conditions may not be comparable so as to the large variability.
Conclusions
Line 429-430: why not “ FNC two times as much as BNC..”
Line 432-434: significantly but slightly
Line 434-437: no evidence of “consistent trends”, as for the large variability.
Line 437-440: Mention about added value of your study compared to previous study, or future perspectives.