Urbanization Imprint on Soil Bacterial Communities in Forests and Grasslands

Round 1

Reviewer 1 Report

The manuscript is well managed - the structure of the introduction and discussion, the number of references, the design of the experiment, and the presentation of the results. However, I have a few questions and comments about the data analysis and a few general minor notes.

Revisions and the authors' subsequent responses to revisions are better done if the manuscript is provided with line numbers.

Introduction and Materials and Methods: CO2, K2SO4, K2Cr2O7, H2SO4 should be with appropriate use of lower index: CO₂, K₂SO₄, K₂Cr₂O₇, H₂SO₄

Poa annua should be in italics, and all species names of organisms should be in full scientific names at first appearance.

Statistical analysis: Some indices were calculated, but the method of their further analysis is not described – was it ANOVA and some post hoc tests? (In results - Impact of urbanization on soil bacterial diversity and Figure 2 – what type of statistical test did you use?).

RDA: At what taxonomic level were OTUs used in ADONIS and RDA analysis? I have the following concern: If you worked at the level of orders (~20) or even phyla (~10), and you used around 20 explanatory variables in the analysis, then you de facto turned the constrained analysis into an unconstrained analysis (the number of explanatory variables must be less than a number of dimensions given by taxa). I recommend at least using stepwise selection for RDA analysis and limiting it to a few of the most important environmental variables.

Results: “The relative abundance of Acidobacteria decreased significantly…” – I think the description of the statistical analysis that led to these results is missing. Please keep in mind that the relative abundances of individual taxa are correlated, so this is a multiple-testing problem and perhaps some form of p-value adjustment would be appropriate.

Discussion: correct “in in” on “in”

Conclusions: Some words are in different font/size, correct

Figure S1. The structural equation model (SEM) is very good, reconsider moving it to the main results (maybe instead of RDA) and describe it in Materials and Methods.

Author Response

The manuscript is well managed - the structure of the introduction and discussion, the number of references, the design of the experiment, and the presentation of the results. However, I have a few questions and comments about the data analysis and a few general minor notes.

Response: We appreciate the reviewer’s time and effort for review of our manuscript. Thanks for the postive and detailed comments which helps significantly improve the manuscript.

Revisions and the authors' subsequent responses to revisions are better done if the manuscript is provided with line numbers.

Response: Thanks for the suggestion. We agree with the reviewer that it is better for revisions if the manuscript is provided with line numbers. However, we tried to add line numbers but failed, it may be due to template format of Forests

Introduction and Materials and Methods: CO2, K2SO4, K2Cr2O7, H2SO4 should be with appropriate use of lower index: CO2, K2SO4, K2Cr2O7, H2SO4

Response: Changed as suggested. The lower index was used in CO₂, K₂SO₄, K₂Cr₂O₇, H₂SO_4.

Poa annua should be in italics, and all species names of organisms should be in full scientific names at first appearance.

Response: Changed as suggested, and Poa annua was changed in italics.

Statistical analysis: Some indices were calculated, but the method of their further analysis is not described – was it ANOVA and some post hoc tests? (In results - Impact of urbanization on soil bacterial diversity and Figure 2 – what type of statistical test did you use?).

Response: Thanks for the comment. Yes, one-way analysis of variance (ANOVA) was used to test the differences, and homogeneity of variances were checked. We added the information in Statistical analysis as follows: ‘Soil properties and alpha diversity indexes including Richness, Shannon, Chao1 and Simpson were calculated in vegan with R software [46]. The differences were tested using one-way analysis of variance (ANOVA) and followed by post-hoc Tukey Honestly Significant Difference (HSD) tests for significance. Data were transformed (natural log, square root, or rank) when required to meet assumptions of normality and homogeneity of variance.’

RDA: At what taxonomic level were OTUs used in ADONIS and RDA analysis? I have the following concern: If you worked at the level of orders (~20) or even phyla (~10), and you used around 20 explanatory variables in the analysis, then you de facto turned the constrained analysis into an unconstrained analysis (the number of explanatory variables must be less than a number of dimensions given by taxa). I recommend at least using stepwise selection for RDA analysis and limiting it to a few of the most important environmental variables.

Response: Thanks for the comment, the ADONIS and RDA analysis were conducted at the OTU level, not at the level of orders or phyla. There were about 242,617 and 296,924 OTUs in forestland and grassland, respectively. The number of OTUs were much larger than that of explanatory variables.

We stated in the legend of Fig. 4 as ‘Distance based redundancy analysis (db-RDA) showing the relationships between bacterial communities and environmental factors in forestland (a) and grassland (b) at the OTU level.’ We also revised the Statistical analysis as: ‘The nonparametric multivariate analysis of variance (ADONIS) was used to evaluate the significant differences of microbial community structure among different treatments at the OTU level using vegan package in R [43]. The distance based redundancy analysis (db-RDA) was conducted to analyze the relationships between bacterial communities and environmental factors at the OTU level in forestland and grassland using Canoco 5.0 (Microcomputer Power, Ithaca, NY, United States).’

Results: “The relative abundance of Acidobacteria decreased significantly…” – I think the description of the statistical analysis that led to these results is missing. Please keep in mind that the relative abundances of individual taxa are correlated, so this is a multiple-testing problem and perhaps some form of p-value adjustment would be appropriate.

Response: Thanks for the comment. We added the description of the statistical analysis behind the title Table S2 as follows: ‘Significant differences (p < 0.05) among treatments are shown with different letters. The differences were tested using one-way analysis of variance (ANOVA) and followed by post-hoc Tukey Honestly Significant Difference (HSD) tests for significance.’

Discussion: correct “in in” on “in”

Response: Changed as suggested.

Conclusions: Some words are in different font/size, correct

Response: Changed as suggested.

Figure S1. The structural equation model (SEM) is very good, reconsider moving it to the main results (maybe instead of RDA) and describe it in Materials and Methods.

Response: Thanks for the comment. We moved SEM to the main results as Fig. 5, and added the description of SEM in Results and Materials and Methods

Reviewer 2 Report

Review of ms forests-2028343

‘Urbanization imprint on soil bacterial communities in forests and grasslands’ by Gao et al. for publication in Forests

This manuscript reports on a study on the soil bacterial communities in forests and grasslands at 18 sites located in ether urban, suburban or rural locations. The topic of this study is highly relevant and looking at urbanisation effects in different nature types or land covers (forests and grasslands) is novel. The design of the study seems sound, but the data analysis is superficial, a little old-fashioned and limited. Moreover, a lot of information is missing regarding methods and statistics. The discussion can benefit from clarifications, better reasonings and positioning in a broader context; the novelty of the study and its implications should be highlighted. The ms has high potential, but I think it needs serious revisions. My detailed comments are below.

 

Abstract:

-        ‘soil bacterial diversity was higher in urban areas than those in’: remove those

-        from ‘in addition to urbanization…’ and onwards: no causal relationships were assessed so, replace influencing and affecting by ‘related with’

Intro:

-        the term land use or land-use type are not correctly used.

-        ‘such as human activity, the temperature rise…’: temperature rise is already mentioned in the sentence before and is not urban-specific, do you mean the urban heat island effect? which is specific for urban sites in contrast to other ecosystems; also soil moisture evaporation is not urban-specific, so please clarify. other urban-specific factors which are not mentioned, is sealing, compaction, physical disturbance and the use of technosols

-        ‘In addition the urban ring roads…’: sentence not correct + what aspect of the urban ring roads? their proximity, surface area? please clarify

-        key questions: all question are too vague, should be rephrased more in function of the study, e.g., question 1 “do soil bacterial diversity and community composition change along a rural-suburban-urban gradient?” or “in relation with urbanization intensity. Please, also mention the hypotheses. why would the effect of urbanization on soil bacterial communities depend on the land cover type or ecosystem?

Material & methods

-        general remark: urban-suburban-rural should be considered land use categories and grassland and forestland land cover types. urban-suburban-rural is not really a gradient but three land use types or categories; the urbanization intensity is a gradient.

-        please describe the soil types in these sites

-        “Fine Resolution Observation and Monitoring-Global Land Cover System (Finer Resolution Observation and Monitoring-Global Land Cover System)”? why repeated?

-        why was urbanisation intensity calculated in four spatial extents? why would 5 km still be relevant?

-        Plot is generally considered a lower spatial level than site, so I would describe it as 18 sampling sites in which 3 sampling plots were sampled in grassland and 3 in plots in forest; also, regarding the ‘five soils’: ‘soils’ cannon be collected, these should be called soil samples

-        randomly collected? how large was the area in which the five soil samples were randomly selected? how was this randomisation done?

-        ‘soils from the same plot were homogenized’? not clear what is meant: were all 5 samples from each of the three forests and grassland coming from one of the 18 plots pooled? that would yield 18 pooled soil samples? not 36? this is very confusing, please clarify this paragraph on soil sampling

-        ‘the grasslands that along…’: remove ‘that’

-        info on soil sampling is missing: was litter layer removed? how was this done in grassland? 0-10 cm of the mineral topsoil or incl. the organic layer? which soil corer was used? how far from the nearest tree?

-        more info on the grassland and forest vegetation composition in the different sites and plots is needed: how did that vary along the gradient? particularly regarding the proportion of conifers

-        how long were the soil samples air dried? were constant masses achieved?

-        what is soil biomass N and P, is it the same as the generally used terms soil organic N and soil organic P? anyway, where does the M stand for in SMBN and SMBP – does not make any sense.

-        ‘chloroform fumigation-K2SO4 extraction-flow injection nitrogen analyzer’ and ‘Pi determination plus Pi correction method’: unclear what is meant; please describe more in detail how it is measured.

-        heavy metals: ‘measured on the digestion solution’: which digestion? why these metals? please clarify

-        Fig.1: 18 sites are shown but in the text these are called plots. please be consistent in the use of the correct terms; please zoom in more on the areas where the 18 sites are located because it is too small to be able to see the urban intensity around the sitse

-        2.2: soil dna was extracted on 0.5 g soil: air-dried soil I guess? which kit was used?

-        2.3: results were considered: ‘were’ is missing

-        why were the sequences log-transformed?

-        regarding the manova adonis: clarify treatments, do the authors mean land use/cover and urban-suburban-rural? given that this is an observational study, one cannot use the term ‘treatment’. also, adonis cannot discriminate between differences in centroid location and differences in spreading. a significant result could thus be the result of different spread between the levels of the tested factor and not of differences in centroid location. to overcome this, please test the spread effect. so one needs to test for multivariate homogeneity of group dispersions eg by betadisper in R

-        how were alpha diversity indices compared? what is the added value of calculating four different alpha diversity indices? if there is no, better reduce to one index; if there is, please clarify

-        NMDS and RDA can be done perfectly in R, why in Canoco while adonis was done in R?

-        which environmental factors were tested in the RDA, please list them for clarity

-        info on which test was used to test for differences in soil properties is lacking; idem for tests on relationships with urbanization intensity

-        model on explanation of alpha diversity by the environmental variables is missing

-        relationships of beta-diversity with differences in urbanisation intensity or other environmental variables are not tested

-        given that the grassland and forest plots are co-located in sites (6 urban, 6 suburban and 6 rural), beta-diversity could be calculated between grassland and forest based on the pairs per site and this would yield 18 beta-diversity values which can be compared for differences between urban-suburban-rural or in relation with the sites’ urbanisation intensity.

Results

-        table 1 does not list the soil properties along the urbanisation intensity gradient, then I would expect data in function of urbanisation intensity as calculated as a continuous variable (R1 to R4), instead table 1 provides the properties for the two different land cover types in each of the three land use categories.

-        in table 1: I guess SWC is soil water content? was it measured in this study? how? this is not mentioned in the material & methods.

-        ‘The soil TOC, TN and SWC decreased significantly with the increase of urbanization intensity in both forestland and grassland (p < 0.05, Table 1)’ and ‘The soil pH tended to increase along

-        the urbanization intensity gradient in both forestland and grassland (Table 1).’: these relationships are not shown in table 1

-        table 2: avoid the use of the term treatment; remove letters if there are no signif differences (eg. TP, SMBN, As)

-        ‘, which might receive high amounts of heavy metals from human activities (e.g., coal burning, automobile exhaust, the construction materials in construction of the city).’: move to discussion section + add reference

-        ‘The data in figure 2 showed the diversity indexes along the urbanization intensity gradient in forestland and grassland.’: no gradient in urbanisation intensity is shown, instead these are the land use and land cover categories

-        Fig. 2: test results are presented but mention the type of test used in the caption, was it ANOVA with post-hoc Tukey? was a homogeneity of variances checked? also, the way the outcome of the pairwise comparisons are displayed in the graph is too complex, it is easier to use letters

-        3.3: ‘urbanisation induced shifts…’ and ‘urbanization changed…’: no causal relationship, instead use related with or coincided with; also in 3.4: was determined by, influenced, affcting -> related with

-        ‘The relative abundance of Acidobacteria decreased significantly with the process of urbanization’: how was this tested? and was this tested for each phylum? also, the process of urbanisation was not tested, but the urbanisation intensity, which is the result of the urbanisation process.

-        The NMDS analysis further showed the variations in the bacterial community structure along the urbanization intensity gradients (Fig. 3c and 3d): again, I can’t see an urbanisation gradient in the figure, only the three land use categories urban-suburban-rural for each of the two land covers grassland and forest; please rephrase

-        Fig 3 and 4: makes more sense to have the urban sites red and the rural sites green

Discussion:

-        4.1: “Our results showed that the diversity of soil bacterial communities increased along the rural-suburban-urban gradient in both forestland and grassland, particularly in forests, where significant increases in bacterial diversity were detected (Fig. 1)”: not true: diversity differed between the categories urban-suburban-rural; moreover, differences were not significant for grassland, so one cannot conclude that bacterial diversity depended on the categories considered in grassland; thus, only in forests, this pattern was detected.

-        ‘helps maintain’: maintaining or to maintain

-        ‘Taken together, all these findings suggest that the higher bacterial diversity might be

-        due to more disturbance in urban areas’: this cannot be drawn as a conclusion from the paragraph. this is not supported by the literature or own data mentioned.

-        ‘Meanwhile, the bacterial community composition was shifted by urbanization in both forest and grassland, which was consistent with our hypothesis.’: there are no hypotheses mentioned before; add them to the intro

-        ‘SEM analysis showed that the relative abundance of Proteobacteria was strongly correlated with soil nutrients and soil heavy metals in both forestland and grassland (Fig. S1).’?? was structural equation modelling applied or scanning electron microscopy? this was not mentioned in the material and methods. Also, looking at Fig. S1, it seems to me that this SEM structure is doubtful, but please explain the choices the authors made. The metals are part of the  ‘metal’ factor and so are ‘R1 to R4’ of urban and TOC, TN and TP of ‘soil’, but these are just multiple representatives of the same ‘proxy’. What are ‘spatial’, ‘metal’, ‘soil’ and ‘urban’ anyway? why were these SEMs only done for Acidobacteria and Proteobacteria? how can soil properties and the SWC influence the urban intensity? why were soil pH, N/P and C/N not included? what is the R2?

-        ‘SEM analysis showed that the relative abundance of Proteobacteria was strongly correlated with soil nutrients’: are TOC, TN and TP soil nutrients for bacteria?

-        The heavy metals (e.g., Cd, Pb, and Zn) were significantly enriched in urban soils (Table 2)’: not entirely true, only for Cd, Pb and Zn in forests while in grassland only Cd was slightly enhanced in the urban category. please be more specific and explain why this is different between grassland and forest.

-        ‘The higher diversity of bacterial communities helps maintain ecosystem stability due to the high functional redundancy of bacteria [15, 45]. Therefore, in the present study, the bacterial diversity was generally higher in grass soils than that in forest soils, which could be tentatively related to an increasing human management intensity in grass soils’: how can functional redundancy explain higher diversity in grass? this makes no sense.

-        ‘The significant lower moisture of urban grass soils might be due to the soil moisture evaporation caused by the urban heat island effect, as well as increase of impermeable surface in urban areas [19]. In comparison with forests, grasslands are managed more intensively, such as grazing, fertilization, irrigation, cutting or reseeding [59]. Thus, the grass soils have more homogeneous bacterial communities along urbanization gradients, which may due to an increasing human management intensity in grasslands.’. it is still not clear why the effect of urbanisation is expressed differently in forests than in grasslands. please elaborate on this. that this would be related to differences in management, is very speculative and is not endorsed by the data. other plausible explanation is the lack of heavy metal contamination of grassland soils, but also differences in temperature, former land use, in soil texture, in changes of vegetation cover with urbanisation for forests but not for grassland, … also differences in dispersal could lay at the basis of the difference in response to urbanisation. there are many variables not considered, so difficult to say that this different response to urbanisation is caused by differences in management, so write more carefully and focus more on the processes involved in how it would affect the bacterial community, and remove from conclusion and abstract.

-        relevance and novelty of the study results, implications of the results regarding ecosystem functioning or recommendations for further study are missing. please discuss.

 

Author Response

‘Urbanization imprint on soil bacterial communities in forests and grasslands’ by Gao et al. for publication in Forests

This manuscript reports on a study on the soil bacterial communities in forests and grasslands at 18 sites located in ether urban, suburban or rural locations. The topic of this study is highly relevant and looking at urbanisation effects in different nature types or land covers (forests and grasslands) is novel. The design of the study seems sound, but the data analysis is superficial, a little old-fashioned and limited. Moreover, a lot of information is missing regarding methods and statistics. The discussion can benefit from clarifications, better reasonings and positioning in a broader context; the novelty of the study and its implications should be highlighted. The ms has high potential, but I think it needs serious revisions. My detailed comments are below.

 Response: We appreciate the reviewer’s time and effort for review of our manuscript. Thanks for the positive and detailed comments which helps significantly improve the manuscript. According to the comments, we revised our methods, statistics and discussion in the revised manuscript.

Abstract:

-        ‘soil bacterial diversity was higher in urban areas than those in’: remove those

Response: Changed as suggested, ‘those’ was removed.

-        from ‘in addition to urbanization…’ and onwards: no causal relationships were assessed so, replace influencing and affecting by ‘related with’

Response: Changed as suggested, ‘influencing’ and ‘affecting’ were replaced by ‘related with’.

Intro:

-        the term land use or land-use type are not correctly used.

Response: Thanks for the comment. We revised ‘land use’ to ‘land cover’ types.

-        ‘such as human activity, the temperature rise…’: temperature rise is already mentioned in the sentence before and is not urban-specific, do you mean the urban heat island effect? which is specific for urban sites in contrast to other ecosystems; also soil moisture evaporation is not urban-specific, so please clarify. other urban-specific factors which are not mentioned, is sealing, compaction, physical disturbance and the use of technosols

Response: Thanks for the comments. Yes, we mean the urban heat island effect here， and we revised ‘temperature rise’ to ‘urban heat island effect’. We agree with the reviewer that soil moisture evaporation is not urban-specific, so we deleted ‘soil moisture evaporation’ from this sentence. In addition, urbanization is associated with a variety of specific factors, including the urban heat island effect, pollution, human population and the anthropogenic disturbances such as soil sealing, compaction and the use of technosols (Yan et al., 2016; Liu et al., 2022; Nawaz et al., 2013; Lu et al., 2020).

Therefore, we revised the sentence as follows: ‘In contrast to other ecosystems, soil bacteria in urban ecosystems can be disturbed by a lot of external factors linked to urbanization, such as city population, urban heat island effect, soil sealing, soil compaction, physical disturbance and the use of technosols [14, 30-32].’

Reference:

30. Liu, L.; Barberán, A.; Gao, C.; Zhang, Z.; Wang, M.; Wurzburger, N.; Wang, X.; Zhang, R.; Li, J.; Zhang, J., Impact of urbanization on soil microbial diversity and composition in the megacity of Shanghai. Land Degradation & Development 2022, 33 (2), 282-293.
31. Nawaz, M. F.; Bourrie, G.; Trolard, F., Soil compaction impact and modelling. A review. Agronomy for sustainable development 2013, 33 (2), 291-309.
32. Lu, C.; Kotze, D. J.; Setälä, H. M., Soil sealing causes substantial losses in C and N storage in urban soils under cool climate. Science of the Total Environment 2020, 725, 138369.
33. Yan, B.; Li, J.; Xiao, N.; Qi, Y.; Fu, G.; Liu, G.; Qiao, M., Urban-development-induced changes in the diversity and composition of the soil bacterial community in Beijing. Scientific Reports 2016, 6 (1), 1-9.

-        ‘In addition the urban ring roads…’: sentence not correct + what aspect of the urban ring roads? their proximity, surface area? please clarify

Response: Thanks for the comments. Ring roads are considered as indicators of urban expansion, and the gradient of urban ring roads is a suitable way to represent the stages of urban development (Yan et al., 2016). Ring roads are associated with residential areas of different ages, population densities, and related socioeconomic variables.

We revised the sentence as follows: ‘In addition, urban green space soils from different ring roads were investigated, and it is found that urban development changes the bacterial diversity and community composition [14].’

Reference:

Yan, B.; Li, J.; Xiao, N.; Qi, Y.; Fu, G.; Liu, G.; Qiao, M., Urban-development-induced changes in the diversity and composition of the soil bacterial community in Beijing. Scientific Reports 2016, 6 (1), 1-9.

 

-        key questions: all question are too vague, should be rephrased more in function of the study, e.g., question 1 “do soil bacterial diversity and community composition change along a rural-suburban-urban gradient?” or “in relation with urbanization intensity. Please, also mention the hypotheses. why would the effect of urbanization on soil bacterial communities depend on the land cover type or ecosystem?

Response: Thanks for the comments. We added hypotheses and revised key questions as follows: ‘We aimed to answer the following key questions: (1) Do soil bacterial diversity and community composition change along a rural-suburban-urban gradient? (2) Will bacterial communities of forestland and grassland respond to urbanization differently or not? (3) What are the main drivers of these changes?

We hypothesized that urbanization would affect both the bacterial diversity and community composition, but such effects might be different between forests and grasslands.’

Material & methods

-        general remark: urban-suburban-rural should be considered land use categories and grassland and forestland land cover types. urban-suburban-rural is not really a gradient but three land use types or categories; the urbanization intensity is a gradient.

Response: Thanks for the comments. We agree with the reviewer that urban-suburban-rural should be considered land use categories and grassland and forestland land cover types. Thus, we were careful in the use of ‘gradient’ in the revised manuscript.

To better represent the impact of urbanization intensity, 4 urbanization coefficients R1, R2, R3 and R4 were used in this study which represent the urbanization intensity at 250m, 1km, 2km and 5km resolution, respectively. In the present study, the urbanization intensity was calculated based on the impervious surface of the city using ArcGIS. The intensity of urbanization is determined by the proportion of pixels that fall into the impervious surface in the overlay window of 30 m × 30 m (Yu et. al., 2018), and then resample them to 250 m, 1000 m, 2000 m and 5000 m resolution in ArcGIS 10.4. The 4 urbanization coefficients from rural to urban were showed in the table as follows, the results showed that the four urbanization indexes were all have good gradients from rural to urban.

	R1	R2	R3	R4
Rural	0.06	0.04	0.00	0.10
Suburban	0.25	0.20	0.13	0.18
Urban	0.37	0.46	0.68	0.45

 

-        please describe the soil types in these sites

Response: Thanks for the comments. We added soil types as follows: ‘The soil type is red and the soil texture is sandy and loam.’

-        “Fine Resolution Observation and Monitoring-Global Land Cover System (Finer Resolution Observation and Monitoring-Global Land Cover System)”? why repeated?

Response: Thanks for the comments. We deleted the repeated sentence ‘(Finer Resolution Observation and Monitoring-Global Land Cover System)’.

-        why was urbanisation intensity calculated in four spatial extents? why would 5 km still be relevant?

Response: Thanks for the comments. To better represent the impact of urbanization intensity, 4 urbanization coefficients R1, R2, R3 and R4 were used in this study which represent the urbanization intensity at 250m, 1km, 2km and 5km resolution, respectively. In the present study, the urbanization intensity was calculated based on the impervious surface of the city using ArcGIS. The intensity of urbanization is determined by the proportion of pixels that fall into the impervious surface in the overlay window of 30 m × 30 m (Yu et. al., 2018), and then resample them to 250 m, 1000 m, 2000 m and 5000 m resolution in ArcGIS 10.4.

Our results showed that the four urbanization indexes were all have good correlations with soil bacterial community, particularly for R3. The results suggests that the calculation of urbanization intensity was reasonable in this study.

-        Plot is generally considered a lower spatial level than site, so I would describe it as 18 sampling sites in which 3 sampling plots were sampled in grassland and 3 in plots in forest; also, regarding the ‘five soils’: ‘soils’ cannon be collected, these should be called soil samples

Response: Thanks for the suggestion. We revised the sentence as follows: ‘Soil samples were collected in April, 2019, and there were 18 sampling sites in this study (6 urban sites, 6 suburban sites, 6 rural sites). In each sampling site, 3 soil sampling plots (10 × 10 m2) of forestlands and 3 sampling plots (10 × 10 m2) of grasslands were randomly established, and soil samples (0-20 cm) were collected randomly at five points in each sampling plot.’

-        randomly collected? how large was the area in which the five soil samples were randomly selected? how was this randomisation done?

Response: The 10 × 10 m² sampling plots were established within forests and grasslands, respectively. Three replicated plots were randomly established for each sampling site. At each 10 × 10 m² sampling plot, soil was sampled at five points randomly. Soil samples from the same site were homogenized, thus, a total of 36 soil samples were collected (18 forest and 18 grass soils).

We revised the sentence as follows: ‘Soil samples were collected in April, 2019, and there were 18 sampling sites in this study (6 urban sites, 6 suburban sites, 6 rural sites). In each sampling site, 3 soil sampling plots (10 × 10 m2) of forestlands and 3 sampling plots (10 × 10 m2) of grasslands were randomly established, and soil samples (0-20 cm) were collected randomly at five points in each sampling plot.’

-        ‘soils from the same plot were homogenized’? not clear what is meant: were all 5 samples from each of the three forests and grassland coming from one of the 18 plots pooled? that would yield 18 pooled soil samples? not 36? this is very confusing, please clarify this paragraph on soil sampling

Response: Sorry for not describe the soil sampling clearly. We revised the sentence as follows: ‘Soil samples were collected in April, 2019, and there were 18 sampling sites in this study (6 urban sites, 6 suburban sites, 6 rural sites). In each sampling site, 3 soil sampling plots (10 × 10 m2) of forestlands and 3 sampling plots (10 × 10 m2) of grasslands were randomly established, and soil samples (0-20 cm) were collected randomly at five points in each sampling plot. Soil samples from the same site were homogenized, thus, a total of 36 soil samples were collected (18 forest and 18 grass soils).’

-        ‘the grasslands that along…’: remove ‘that’

Response: Removed as suggested.

-        info on soil sampling is missing: was litter layer removed? how was this done in grassland? 0-10 cm of the mineral topsoil or incl. the organic layer? which soil corer was used? how far from the nearest tree?

Response: Yes, the litter layer was removed before soil sampling in forests. In grassland, the plants were removed before soil sampling. Sorry for not check the manuscript carefully, actually we sampled the 0-20 cm surface soil samples in forests and grassland. The soil auger (3.8 cm diameter) was used for soil sampling. The sampling point was at least 1 m apart from the nearest tree.

We revised the information on soil sampling as follows: ‘In each sampling site, 3 soil sampling plots (5 × 5 m) of forestlands and 3 sampling plots (5 × 5 m) of grasslands were randomly established. In each plot, surface (0-20 cm) soil samples were collected at five points randomly with at least 1 m apart using a Dutch auger (5.0 cm diameter). Soil samples from the same site were homogenized and pooled into one composite sample. Thus, a total of 36 soil samples (18 forest and 18 grass soils) were collected and sealed in plastic bags immediately, transported in a cold container to the laboratory. The soil samples were sieved (2 mm) to remove roots and stones, and then were subdivided into three portions’

-        more info on the grassland and forest vegetation composition in the different sites and plots is needed: how did that vary along the gradient? particularly regarding the proportion of conifers

Response: Thanks for the suggestion. Slight differences of vegetation composition in the different sites were observed along the gradient in grassland and forests. We agree with the reviewer that different plant communities will harbor different bacterial communities. Previous study showed that the composition of urban plant communities were relatively homogeneous, with a high proportion of exotic species (Aronson et al., 2014). Plants could affect belowground microbes directly and indirectly via soil physicochemical properties and litterfall (Hooper et al., 2000; Liu et al., 2020). Thus, it is important to consider the effect of plant communities on bacterial communities.

Unfortunately, we do not have the detailed data of plant diversity in the present study, we only record the vegetation composition roughly. We think we will add sampling of plant diversity and composition in the future study. Before soil sampling, we tried to select similar vegetation composition in the different sites along the rural-suburban-urban gradient. Finally, we selected evergreen and deciduous mixed forests along the gradient, which were dominated by Cinnamomum camphora, Phoebe zhennan, Cunninghamia lanceolate and Pinus massoniana. The grasslands were dominated by Poa annua, Cynodon dactylon, Alopecurus aequalis, Trifolium repens, Viola philippica, Oxalis corniculata.

In this study, we used land use types (forestland and grassland) to represent a range of human disturbances and management impacts, and used rural-suburban-urban gradient to reflect the differences of population density and its induced environmental change. Our results showed that soil physico-chemical (e.g., TOC, TN and SWC), and bacterial communities were apparently different along the rural-suburban-urban gradient, particularly in forests. Thus, we think our sampling sites are reasonable.

Reference:

Aronson, M. F., La Sorte, F. A., Nilon, C. H., Katti, M., Goddard, M. A., Lepczyk, C. A. Clarkson, B. (2014). A global analysis of the impacts of urbanization on bird and plant diversity reveals key anthropogenic drivers. Proceedings of the Royal Society B: Biological Sciences, 281(1780), 20133330.

Hooper, D. U., Bignell, D. E., Brown, V. K., Brussard, L., Dangerfield, J. M., Wall, D. H., … Lavelle, P. (2000). Interactions between aboveground and belowground biodiversity in terrestrial ecosystems: patterns, mechanisms, and feedbacks. Bioscience, 50(12), 1049–1061.

Liu, L., Zhu, K., Wurzburger, N., & Zhang, J. (2020). Relationships between plant diversity and soil microbial diversity vary across taxonomic groups and spatial scales. Ecosphere, 11(1), e02999.

-        how long were the soil samples air dried? were constant masses achieved?

Response: Yes, the soil samples achieved constant masses, as they were air dried for about 20 days in the laboratory where had good ventilated condition.

-        what is soil biomass N and P, is it the same as the generally used terms soil organic N and soil organic P? anyway, where does the M stand for in SMBN and SMBP – does not make any sense.

Response: SMBN is soil microbial biomass nitrogen, and SMBP is soil microbial phosphorus. We revised the sentence as follows: ‘one was stored at 4°C for measuring soil microbial biomass (N and P)’.

-        ‘chloroform fumigation-K2SO4 extraction-flow injection nitrogen analyzer’ and ‘Pi determination plus Pi correction method’: unclear what is meant; please describe more in detail how it is measured.

Response: Sorry for not describe the method of Soil microbial biomass nitrogen and phosphorus clearly. Three replicates were conducted for the estimation of microbial biomass (N and P). Soil microbial biomass (N and P) were determined by fumigation extraction method (Anderson et al., 1994; Devi et al., 2006). Soil microbial biomass nitrogen (SMBN) was determined by microkjeldahl method (Bremner et al., 1982), and soil microbial phosphorus (SMBP) was determined by ammonium molybdate stannous chloride method (Sparling et al., 1985).

SMBN = K_EN Í1:46

SMBP = K_EP Í 2:5

K_EN and K_EP are the difference between C, N and P extracted from fumigated and unfumigated soils.

We revised the sentence as follows: ‘Soil microbial biomass nitrogen (SMBN) was determined by microkjeldahl method [39], and soil microbial phosphorus (SMBP) was determined by ammonium molybdate stannous chloride method [40].’

Reference:

Devi, N. B.; Yadava, P., Seasonal dynamics in soil microbial biomass C, N and P in a mixed-oak forest ecosystem of Manipur, North-east India. Applied Soil Ecology 2006, 31 (3), 220-227.

Anderson, J. M.; Ingram, J. S., Tropical soil biology and fertility: a handbook of methods. Soil Science 1994, 157 (4), 265.

Bremner, J.; Mulvaney, C., Total nitrogen. In. AL Page (ed.) Methods of Soil Analysis. Part 2. Chemical and Microbiological Methods. Amer. Soc. Agron. pp: 1982.

Sparling, G.; Whale, K.; Ramsay, A., Quantifying the contribution from the soil microbail biomass to the extractable P levels of fresh and air-dried soils. Soil Research 1985, 23 (4), 613-621.

-        heavy metals: ‘measured on the digestion solution’: which digestion? why these metals? please clarify

Response: According to previous study, the concentrations of heavy metal elements, such as As, Cd, Cr, Cu, Mn, Ni, Pb and Zn, could be affected by urbanization (Hu et al., 2018; Luo et al., 2015). We revised the method of heavy metals as follows: ‘Concentrations of heavy metal elements (As, Cd, Cr, Cu, Mn, Ni, Pb and Zn) were analyzed using a strong acid (HNO3–HClO4) pseudo-total digestion method [39], determined by Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-OES; Optima 7000 DV, PerkinElmer, USA)’

Reference:

Hu, Y., Dou, X., Li, J., et al. Impervious surfaces alter soil bacterial communities in urban areas: a case study in Beijing, China[J]. Frontiers in Microbiology, 2018, 9: 226.

Luo, X.-S., Xue, Y., Wang, Y.-L., et al. Source identification and apportionment of heavy metals in urban soil profiles[J]. Chemosphere, 2015, 127: 152-157.

-        Fig.1: 18 sites are shown but in the text these are called plots. please be consistent in the use of the correct terms; please zoom in more on the areas where the 18 sites are located because it is too small to be able to see the urban intensity around the sits

Response: Thanks for the comment, we revised the ‘plots’ to ‘sites’ in the text. We zoomed in more on the areas where the 18 sites are located in Fig. 1.

-        2.2: soil dna was extracted on 0.5 g soil: air-dried soil I guess? which kit was used?

Response: Soil DNA was extracted from fresh soil. The DNA extraction kit was FastDNA™ Spin Kit for Soil (MP Biomedicals, USA). We revised the sentence as follows: ‘Soil DNA was extracted from fresh 0.5g soil using FastDNA™ Spin Kit for Soil (MP Biomedicals, USA) following the manufacturer’s instructions.’

-        2.3: results were considered: ‘were’ is missing

Response: ‘were’ was added as suggested.

-        why were the sequences log-transformed?

Response: To improve normality, the OTU table (number of sequence) was standardized and log-transformed in NMDS analysis. We also used OTU table that was not log-transformed for NMDS analysis, and there was no different in results of NMDS.

-        regarding the manova adonis: clarify treatments, do the authors mean land use/cover and urban-suburban-rural? given that this is an observational study, one cannot use the term ‘treatment’. also, adonis cannot discriminate between differences in centroid location and differences in spreading. a significant result could thus be the result of different spread between the levels of the tested factor and not of differences in centroid location. to overcome this, please test the spread effect. so one needs to test for multivariate homogeneity of group dispersions eg by betadisper in R

Response: Thanks for the suggestion, we removed the term ‘treatment’ and revised the description of adonis as follows: ‘The nonparametric multivariate analysis of variance (ADONIS) was used to evaluate the significant differences of microbial community structure along urban-suburban-rural gradient at the OTU level using vegan package in R’.

-        how were alpha diversity indices compared? what is the added value of calculating four different alpha diversity indices? if there is no, better reduce to one index; if there is, please clarify

Response: The differences of alpha diversity indices were tested using one-way analysis of variance (ANOVA) and followed by post-hoc Tukey Honestly Significant Difference (HSD) tests for significance. Data were transformed (natural log, square root, or rank) when required to meet assumptions of normality and homogeneity of variance. To better explore the changes in the bacterial diversity along urban-suburban-rural gradient, we analyzed the four alpha diversity indexes including Richness, Shannon, Chao1 and Simpson in the present study.

-        NMDS and RDA can be done perfectly in R, why in Canoco while adonis was done in R?

Response: Yes, the ADONIS analysis was used to evaluate the significant differences of microbial community structure at the OTU level using vegan package in R. We agree with reviewer that NMDS and RDA can be done perfectly in R, but we are used to doing NMDS and RDA analysis in Canoco. We think we will try to do NMDS and RDA analysis in R in next study.

-        which environmental factors were tested in the RDA, please list them for clarity

Response: The environmental factors were pH, TOC, TN, TP, C/N, N/P, SWC, heavy metal elements (As, Cd, Cr, Cu, Mn, Ni, Pb and Zn), urbanization coefficients R1, R2, R3 and R4.

-        info on which test was used to test for differences in soil properties is lacking; idem for tests on relationships with urbanization intensity. model on explanation of alpha diversity by the environmental variables is missing

 

Response: We added the information as follows: ‘Soil properties and alpha diversity indexes including Richness, Shannon, Chao1 and Simpson were calculated in vegan with R software [46]. The differences were tested using one-way analysis of variance (ANOVA) and followed by post-hoc Tukey Honestly Significant Difference (HSD) tests for significance. Data were transformed (natural log, square root, or rank) when required to meet assumptions of normality and homogeneity of variance.’

-        relationships of beta-diversity with differences in urbanisation intensity or other environmental variables are not tested. given that the grassland and forest plots are co-located in sites (6 urban, 6 suburban and 6 rural), beta-diversity could be calculated between grassland and forest based on the pairs per site and this would yield 18 beta-diversity values which can be compared for differences between urban-suburban-rural or in relation with the sites’ urbanisation intensity.

Response: Thanks for the suggestion. The beta-diversity was calculated between grassland and forest based on the pairs per site using the vegan (v2.5-7) and phyloseq (v1.38.0) packages in R software. Then the yield 18 beta-diversity values were used to compared for differences between urban-suburban-rural (Fig. S1)

Results

-        table 1 does not list the soil properties along the urbanisation intensity gradient, then I would expect data in function of urbanisation intensity as calculated as a continuous variable (R1 to R4), instead table 1 provides the properties for the two different land cover types in each of the three land use categories.

Response: Thanks for the suggestion. We agree with the reviewer that it is better to show our data in a continuous urbanisation intensity. But R1 to R4 were actually represent the same thing but with different resolution (Table S1), which represent the urbanization intensity at 250m, 1km, 2km and 5km resolution, respectively. Therefore, we revised the data of table 1 along urbanisation intensity from U1 to U3. U1, U2 and U3 represent urbanisation intensity coefficients of rural, suburban and urban sites, respectively

-     in table 1: I guess SWC is soil water content? was it measured in this study? how? this is not mentioned in the material & methods.

Response: Yes, SWC represents soil water content. Soil water content was measured in this study by oven-drying the fresh soil for 48 h at 105°C. We added the information in the material & methods as follows: ‘Soil water content (SWC, %, g of water per 100 g dry soil) was measured by oven-drying the soil for 48 h at 105°C.’

-        ‘The soil TOC, TN and SWC decreased significantly with the increase of urbanization intensity in both forestland and grassland (p < 0.05, Table 1)’ and ‘The soil pH tended to increase along the urbanization intensity gradient in both forestland and grassland (Table 1).’: these relationships are not shown in table 1

Response: Thanks for the comment, we revised the sentence as follows: ‘The soil TOC, TN and SWC tended to decrease with the increase of urbanization intensity in both forestland and grassland (Table 1), and their lowest values were observed at the urban sites (UF and UG). The soil pH increased along the urbanization intensity gradient in both forestland and grassland (Table 1).’

-        table 1: avoid the use of the term treatment; remove letters if there are no signif differences (eg. TP, SMBN, As)

Response: Thanks for the comment, we removed the term treatment, and revised as: ‘Significant differences (p < 0.05) along the urbanization intensity gradient are shown with different letters.’ In addition, we removed letters where there are no significant differences in table 1.

-        ‘, which might receive high amounts of heavy metals from human activities (e.g., coal burning, automobile exhaust, the construction materials in construction of the city).’: move to discussion section + add reference

Response: The sentence was removed to the discussion section and revised as follows: ‘The heavy metals (e.g., Cd, Pb, and Zn) were significantly enriched in urban soils (Table 1) and had strong correlations with bacterial community composition structure and diversity (Fig. 4) in both forestland and grassland. The heavy metal enrichment in soil may be caused by construction debris and garbage generated during the construction process of the city [50]. In addition, a series of human activities, such as coal burning and automobile exhaust, are also the main sources of heavy metals in the urban soil [28, 51].’

Reference:

28. Zhang, K.; Delgado-Baquerizo, M.; Zhu, Y.-G.; Chu, H., Space is more important than season when shaping soil microbial communities at a large spatial scale. Msystems 2020, 5 (3), e00783-19.
29. Gao, X.; Gu, Y.; Xie, T.; Zhen, G.; Huang, S.; Zhao, Y., Characterization and environmental risk assessment of heavy metals in construction and demolition wastes from five sources (chemical, metallurgical and light industries, and residential and recycled aggregates). Environmental Science and Pollution Research 2015, 22 (12), 9332-9344.
30. Luo, X.-S.; Xue, Y.; Wang, Y.-L.; Cang, L.; Xu, B.; Ding, J., Source identification and apportionment of heavy metals in urban soil profiles. Chemosphere 2015, 127, 152-157.

-        ‘The data in figure 2 showed the diversity indexes along the urbanization intensity gradient in forestland and grassland.’: no gradient in urbanisation intensity is shown, instead these are the land use and land cover categories

Response: Thanks for your suggestion, we replaced ‘rural-suburban-urban gradient’ with ‘land cover categories’. The title of the figure was revised as follows: ‘Box plot indicating bacterial alpha diversity indexes of richness (a), Shannon (b), chao1 (c) and Simpson (d) in different land cover categories.’

-        Fig. 2: test results are presented but mention the type of test used in the caption, was it ANOVA with post-hoc Tukey? was a homogeneity of variances checked? also, the way the outcome of the pairwise comparisons are displayed in the graph is too complex, it is easier to use letters.

Response: Yes, one-way analysis of variance (ANOVA) was used to test the differences, and homogeneity of variances were checked. We added more information in Statistical analysis as follows: ‘The differences were tested using one-way analysis of variance (ANOVA) and followed by post-hoc Tukey Honestly Significant Difference (HSD) tests for significance. Data were transformed (natural log, square root, or rank) when required to meet assumptions of normality and homogeneity of variance.’

-        3.3: ‘urbanisation induced shifts…’ and ‘urbanization changed…’: no causal relationship, instead use related with or coincided with; also in 3.4: was determined by, influenced, affcting -> related with

Response: Thanks for your suggestion, we replaced ‘urbanisation induced shifts, changed, was determined by, influenced and affcting’ with ‘related with or coincided with’.

-        ‘The relative abundance of Acidobacteria decreased significantly with the process of urbanization’: how was this tested? and was this tested for each phylum? also, the process of urbanisation was not tested, but the urbanisation intensity, which is the result of the urbanisation process.

Response: Yes, significant differences were tested for each bacterial phylum and order, the results were showed in table S2. The differences were tested using one-way analysis of variance (ANOVA) and followed by post-hoc Tukey Honestly Significant Difference (HSD) tests for significance. Data were transformed (natural log, square root, or rank) when required to meet assumptions of normality and homogeneity of variance. We revised this sentence as follows: ‘The relative abundance of Acidobacteria decreased significantly from rural forests to urban forests (p < 0.05)’.

-        The NMDS analysis further showed the variations in the bacterial community structure along the urbanization intensity gradients (Fig. 3c and 3d): again, I can’t see an urbanisation gradient in the figure, only the three land use categories urban-suburban-rural for each of the two land covers grassland and forest; please rephrase

Response: Thanks for the comment. We revised this sentence as: ‘The NMDS analysis further showed the variations in the bacterial community structure in the three land use categories of forestland and grassland’

-        Fig 3 and 4: makes more sense to have the urban sites red and the rural sites green

Response: Thanks for your suggestion, we revised the urban sites red and the rural sites green in Fig 3 and 4.

Discussion:

-        4.1: “Our results showed that the diversity of soil bacterial communities increased along the rural-suburban-urban gradient in both forestland and grassland, particularly in forests, where significant increases in bacterial diversity were detected (Fig. 1)”: not true: diversity differed between the categories urban-suburban-rural; moreover, differences were not significant for grassland, so one cannot conclude that bacterial diversity depended on the categories considered in grassland; thus, only in forests, this pattern was detected.

Response: Thanks for the comment, we agree with the reviewer that only significant increase of soil bacterial diversity was detected in forests. Thus, we revised the sentence as follows: ‘Our results showed that the diversity of soil bacterial communities increased significantly from rural forests to urban forests, and an increasing trend was also observed in soil bacterial diversity from rural grasslands to urban grasslands (Fig. 1).’

-        ‘helps maintain’: maintaining or to maintain

Response: Changed as suggested.

-        ‘Taken together, all these findings suggest that the higher bacterial diversity might be due to more disturbance in urban areas’: this cannot be drawn as a conclusion from the paragraph. this is not supported by the literature or own data mentioned.

Response: Thanks for the comment, we agree with the reviewer that this sentence was inappropriate here, thus we deleted the sentence in the revised manuscript.

-        ‘Meanwhile, the bacterial community composition was shifted by urbanization in both forest and grassland, which was consistent with our hypothesis.’: there are no hypotheses mentioned before; add them to the intro

Response: We added the hypotheses in the introduction as follows: ‘We hypothesized that urbanization would affect both the bacterial diversity and community composition, but such effects might be different between forests and grasslands.’

-        ‘SEM analysis showed that the relative abundance of Proteobacteria was strongly correlated with soil nutrients and soil heavy metals in both forestland and grassland (Fig. S1).’?? was structural equation modelling applied or scanning electron microscopy? this was not mentioned in the material and methods. Also, looking at Fig. S1, it seems to me that this SEM structure is doubtful, but please explain the choices the authors made. The metals are part of the  ‘metal’ factor and so are ‘R1 to R4’ of urban and TOC, TN and TP of ‘soil’, but these are just multiple representatives of the same ‘proxy’. What are ‘spatial’, ‘metal’, ‘soil’ and ‘urban’ anyway? why were these SEMs only done for Acidobacteria and Proteobacteria? how can soil properties and the SWC influence the urban intensity? why were soil pH, N/P and C/N not included? what is the R2?

Response: SEM was Structural equation model. We added the information in the material & methods as follows: ‘Structural equation model (SEM) analysis was constructed to analyze both the direct and indirect effects of urbanization on bacterial communities by using the lavaan package in R [44]. To improve normality, all data that used in SEM were standardized and transformed. We then generated a priori model, which included soil SWC, spatial (spatial variables), soil (soil nutrients: TOC, TN, TP), metal (soil heavy metal: As, Pb, Zn, Cu, Cd, Cr), urban (urban impervious surface values: R1, R2, R3, and R4 represent the 250 m, 1 km, 2 km, and 5 km resolutions in the calculation of urbanization intensity in ArcGIS 10.4, respectively), and bacterial community (diversity index and dominant group abundance). Spatial analysis was performed by using the dbmem () function in the adespatial package in R (distance-based Moran feature vector graph analysis) [45], and the selected significant factors were used for further analysis. The regression weights, correlations and covariances were calculated using the maximum likelihood estimation method. The model was tested by the chi-square goodness-of-fit statistic and its associated p value [46]. The model fit was improved iteratively through removing or adding relationships between observed variables, minimizing the probability of spurious results because of multicollinearity, until the Chi-square test of the model is not significant (P>0.05).’

spatial represents the spatial variables, the spatial analysis was performed by using the dbmem () function in the adespatial package in R (distance-based Moran feature vector graph analysis). soil represents soil nutrients. metal represents soil heavy metal: As, Pb, Zn, Cu, Cd, Cr. urban represents urban impervious surface values, R1, R2, R3, and R4 represent the 250 m, 1 km, 2 km, and 5 km resolutions in the calculation of urbanization intensity in ArcGIS 10.4, respectively.

In the beginning, we tried to put all the ten phyla and bacterial alpha diversity indexes (richness, Shannon, chao1 and Simpson) in the SEM analysis, but the figure was too big and complex. Thus, to reduce the complexity of SEM, we only analysed the soil environmental factors effects on bacterial diversity and most dominant two phyla (Acidobacteria and Proteobacteria) abundance in forestland and grassland.

R1, R2, R3, and R4 represent the 250 m, 1 km, 2 km, and 5 km resolutions in the calculation of urbanization intensity, respectively. We reanalysed the SEM analysis in the revised manuscript, which include soil pH, N/P and C/N (Fig. S1).

Reference:

45. Eisenhauer, N.; Bowker, M. A.; Grace, J. B.; Powell, J. R., From patterns to causal understanding: structural equation modeling (SEM) in soil ecology. Pedobiologia 2015, 58 (2-3), 65-72.
46. Dray, S.; Pélissier, R.; Couteron, P.; Fortin, M.-J.; Legendre, P.; Peres-Neto, P. R.; Bellier, E.; Bivand, R.; Blanchet, F. G.; De Cáceres, M., Community ecology in the age of multivariate multiscale spatial analysis. Ecological Monographs 2012, 82 (3), 257-275.
47. Grace, J. B., Structural equation modeling for observational studies. The Journal of Wildlife Management 2008, 72 (1), 14-22.

-        ‘SEM analysis showed that the relative abundance of Proteobacteria was strongly correlated with soil nutrients’: are TOC, TN and TP soil nutrients for bacteria?

Response: We reanalysed the SEM analysis in the revised manuscript, which also include soil pH, N/P and C/N (Fig. S1). Soil represents TOC, TN, TP, N/P, C/N and SWC.

-        The heavy metals (e.g., Cd, Pb, and Zn) were significantly enriched in urban soils (Table 2)’: not entirely true, only for Cd, Pb and Zn in forests while in grassland only Cd was slightly enhanced in the urban category. please be more specific and explain why this is different between grassland and forest.

Response: Thanks for the comment. Previous study showed that the land cover types have a significant impact on soil process, thereby affecting the migration and redistribution of heavy metals (Zhang et al., 2020; Kerr et al., 2017). We found that the heavy metalswere significantly enriched in urban forest soils, however, there were only slight variations of heavy metals between rural and urban grasslands (Table 1), which indicated that forests may play more important role in absorbing heavy metals than grassland in urban areas. We revised the sentence as follows: ‘The heavy metals (e.g., Cd, Pb, and Zn) were significantly enriched in urban forest soils (Table 1).’

Reference:

Kerr, J.G., Cooke, C.A. 2017. Erosion of the Alberta badlands produces highly variable and elevated heavy metal concentrations in the Red Deer River, Alberta. Sci. Total Environ. 596, 427-436.

Zhang, Y., Zhang, X., Bi, Z., Yu, Y., Shi, P., Ren, L., Shan, Z., 2020. The impact of land use changes and erosion process on heavy metal distribution in the hilly area of the Loess Plateau, China. Science of the Total Environment 718, 137305.

-        ‘The higher diversity of bacterial communities helps maintain ecosystem stability due to the high functional redundancy of bacteria [15, 45]. Therefore, in the present study, the bacterial diversity was generally higher in grass soils than that in forest soils, which could be tentatively related to an increasing human management intensity in grass soils’: how can functional redundancy explain higher diversity in grass? this makes no sense.

Response: Thanks for your suggestion, we realized that our previous explanation was not reasonable. Therefore, we further search literatures and revised this paragraph as follows: ‘Our results revealed that the grass soils persistently showed a significantly higher bacterial diversity than forest soils under each urbanization level (Fig. 1). This was consistent with previous studies which showed that land use affected the bacterial diversity [56-57]. Nacke et al [58] also found the higher bacterial diversity in grass soils than in forest soils. Different with forests, grasslands have more intensive management practices including fertilization, irrigation, grazing, cutting or reseeding [59]. The fertilization may change soil nutrient status, and further soil microbial diversity in grasslands [60].’

Reference:

56. Yuan, Y.; Si, G.; Wang, J.; Luo, T.; Zhang, G., Bacterial community in alpine grasslands along an altitudinal gradient on the Tibetan Plateau. FEMS microbiology ecology 2014, 87 (1), 121-132.
57. Kaiser, K.; Wemheuer, B.; Korolkow, V.; Wemheuer, F.; Nacke, H.; Schöning, I.; Schrumpf, M.; Daniel, R., Driving forces of soil bacterial community structure, diversity, and function in temperate grasslands and forests. Scientific Reports 2016, 6 (1), 1-12.
58. Nacke, H.; Thürmer, A.; Wollherr, A.; Will, C.; Hodac, L.; Herold, N.; Schöning, I.; Schrumpf, M.; Daniel, R., Pyrosequencing-based assessment of bacterial community structure along different management types in German forest and grassland soils. PloS one 2011, 6 (2), e17000.
59. Poeplau, C., Grassland soil organic carbon stocks along management intensity and warming gradients. Grass and Forage Science 2021, 76 (2), 186-195.
60. Leff, J. W.; Jones, S. E.; Prober, S. M.; Barberán, A.; Borer, E. T.; Firn, J. L.; Harpole, W. S.; Hobbie, S. E.; Hofmockel, K. S.; Knops, J. M. H.; McCulley, R. L.; La Pierre, K.; Risch, A. C.; Seabloom, E. W.; Schütz, M.; Steenbock, C.; Stevens, C. J.; Fierer, N., Consistent responses of soil microbial communities to elevated nutrient inputs in grasslands across the globe. Proceedings of the National Academy of Sciences 2015, 112 (35), 10967-10972.

-        ‘The significant lower moisture of urban grass soils might be due to the soil moisture evaporation caused by the urban heat island effect, as well as increase of impermeable surface in urban areas [19]. In comparison with forests, grasslands are managed more intensively, such as grazing, fertilization, irrigation, cutting or reseeding [59]. Thus, the grass soils have more homogeneous bacterial communities along urbanization gradients, which may due to an increasing human management intensity in grasslands.’. it is still not clear why the effect of urbanisation is expressed differently in forests than in grasslands. please elaborate on this. that this would be related to differences in management, is very speculative and is not endorsed by the data. other plausible explanation is the lack of heavy metal contamination of grassland soils, but also differences in temperature, former land use, in soil texture, in changes of vegetation cover with urbanisation for forests but not for grassland, … also differences in dispersal could lay at the basis of the difference in response to urbanisation. there are many variables not considered, so difficult to say that this different response to urbanisation is caused by differences in management, so write more carefully and focus more on the processes involved in how it would affect the bacterial community, and remove from conclusion and abstract.

relevance and novelty of the study results, implications of the results regarding ecosystem functioning or recommendations for further study are missing. please discuss.

Response: Thanks for your suggestion, we added more information in the discussion. In addition, we added recommendations for further study in the discussion. We revised as follows: ‘The significant lower moisture of urban grass soils might be due to the soil moisture evaporation caused by the urban heat island effect, as well as increase of impermeable surface in urban areas [19]. In comparison with forests, grasslands are managed more intensively, such as grazing, fertilization, irrigation, cutting or reseeding [59], which indicating that these human managements may affect soil properties, thus affect soil microbial communities in grasslands [60]. However, there are still many variables, such as vegetation composition and diversity, temperature, differences in dispersal along the rural-suburban-urban gradient, should be considered in future study to explore why forest and grass soil bacteria respond to urbanization differently. In addition, urban forests and grasslands play a positive role in alleviating the impacts of urbanization, which has multiple ecological benefits, such as regulation of urban microclimate, absorption of carbon and release of oxygen, and decrease of heat island effect [15, 65]. Therefore, our data contributes to understanding the effect of urbanization on soil bacterial community, which will be helpful for effective management of urban greenspace ecosystems.’

Reviewer 3 Report

The work is really very good. Correctly written.

Below I give a comment on the review:
- Introduction - short, concise, contains the most relevant information about the research problem based on the latest literature.
- Materials and methods are correctly, clearly described
statistical analysis was also correctly done.
- The results represent the most relevant values obtained from the study
- I really like the discussion section. It is very good that, the authors divided it into two smaller parts, which definitely facilitates the reception of the work.

There is a number "1" at the end of the article, which must be removed before the paper is published. 

1. The purpose of the study was to determine whether urbanization affects bacterial diversity or community composition, or both? Do the bacterial communities of forest and grassland areas respond to urbanization differently or not, and what are the main factors causing these changes? 2 In my opinion, the topic is original, very relevant in the particular field of environmental or forest microbiology. However, this is an article for people in the field, scientists.  3. Compared to other works, the study data clearly shows that urbanization causes large and significant changes in the composition and diversity of bacteria in forest areas, where urbanization itself has a relatively weaker effect on soil bacteria in other areas such as grasslands.  4 The paper does not need to be improved.  5. the conclusions are correctly written.  6. In my opinion, the references in the paper are appropriate.

 

Author Response

The work is really very good. Correctly written.
Below I give a comment on the review:
- Introduction - short, concise, contains the most relevant information about the research problem based on the latest literature.
- Materials and methods are correctly, clearly described
statistical analysis was also correctly done.
- The results represent the most relevant values obtained from the study
- I really like the discussion section. It is very good that, the authors divided it into two smaller parts, which definitely facilitates the reception of the work.

Response: We appreciate the reviewer’s time and effort for review of our manuscript. Thanks for the postive and detailed comments which helps significantly improve the manuscript.

 

There is a number "1" at the end of the article, which must be removed before the paper is published. 

Response: Removed as suggested.

1. The purpose of the study was to determine whether urbanization affects bacterial diversity or community composition, or both? Do the bacterial communities of forest and grassland areas respond to urbanization differently or not, and what are the main factors causing these changes? 2 In my opinion, the topic is original, very relevant in the particular field of environmental or forest microbiology. However, this is an article for people in the field, scientists.  3. Compared to other works, the study data clearly shows that urbanization causes large and significant changes in the composition and diversity of bacteria in forest areas, where urbanization itself has a relatively weaker effect on soil bacteria in other areas such as grasslands.  4 The paper does not need to be improved.  5. the conclusions are correctly written.  6. In my opinion, the references in the paper are appropriate.

Response: Thanks again for the reviewer’s time and effort for review of our manuscript. Thanks for the postive comments which helps significantly improve the manuscript.

Round 2

Reviewer 1 Report

I am satisfied with the answers of the authors