Effects of Understory Shrub Biomass on Variation of Soil Respiration in a Temperate-Subtropical Transitional Oak Forest
Round 1
Reviewer 1 Report
The article entitled "Seasonal patterns and controls of soil respiration in a temperate-subtropical transitional oak forest" by Liu et al. needs an accurate review.
The goal of the manuscript is very interesting, but I do not think it has been fully achieved, considering the work in its entirety. The authors focus their attention on the coefficient of variation of both respiration and temperature and the soil's water content, often linking these coefficients rather than the real data. Given the large number of data points, perhaps a more detailed analysis of the relationships between the measured variables and between these and the type of vegetation above could be more appropriate, perhaps using more targeted statistical tests such as, for example, spatial statistics such as GLS that take into account spatial autocorrelation, an almost certain phenomenon in this type of experimental design.
In the results, in particular subsection 3.2 and 3.3, the text follows pedestrianly what is observed either in figure (3) or in table (1). Therefore, the text of these two subsections should be assembled while omitting figure and table. Figures 4 and 5 are also useless for the purpose of displaying the results.
Also the spatial variability model appears to be poorly exposed both in the results and discussion. On the contrary, I think that if all the data collected were considered to realize the model, such model should be illustrated and discussed in detail.
Bibliographic citations could be numerically reduced.
Author Response
1. The goal of the manuscript is very interesting, but I do not think it has been fully achieved, considering the work in its entirety. The authors focus their attention on the coefficient of variation of both respiration and temperature and the soil's water content, often linking these coefficients rather than the real data. Given the large number of data points, perhaps a more detailed analysis of the relationships between the measured variables and between these and the type of vegetation above could be more appropriate, perhaps using more targeted statistical tests such as, for example, spatial statistics such as GLS that take into account spatial autocorrelation, an almost certain phenomenon in this type of experimental design.
Response: We accepted the reviewer’s suggestions. We have complemented an analysis of the spatial autocorrelation using Geostatistical Techniques. See lines 162-175 in page 5 (Method) and lines 253-265 on page 9(Results).
2. In the results, in particular subsection 3.2 and 3.3, the text follows pedestrianly what is observed either in figure (3) or in table (1). Therefore, the text of these two subsections should be assembled while omitting figure and table. Figures 4 and 5 are also useless for the purpose of displaying the results.
Response: We accepted the reviewer’s suggestion. We have rewritten the Result Section based on the suggestions from all reviewers. Also, we have deleted the Figure 4 and 5 and corresponding text in the Results in the updated version.
3. Also the spatial variability model appears to be poorly exposed both in the results and discussion. On the contrary, I think that if all the data collected were considered to realize the model, such model should be illustrated and discussed in detail.
Response: Many thanks for the reviewer’s good advice, and we have added a Table (Table 4) showing the spatial autocorrelation of soil respiration and other environment factors in our updated manuscript.
4. Bibliographic citations could be numerically reduced.
Response: We accepted the reviewer’s advice, and we have decreased the number of literature cited in the updated manuscript.
Reviewer 2 Report
The topic is actual – soil carbon emissions from forest ecosystems and its special and temporal variability is important research question especially under climate change conditions.
The major concern of the presented study is lack of real replications. Authors use one plot in a temperate-subtropical transitional oak forest in China. The measurements were recorded one vegetation season as well. As replicates the repeated measurement during the season were used, which are depended variables.
Other comments:
In methods section there was not mentioned total measurements made during the year.
What was diurnal variation in soil respiration? What time the measurements were done during the day?
Figure 3,4,5,6. Show all result of the analyses even It is not significant.
Figure 4 and 5. It is unclear how it was calculated CV according each month. As I understood 21 measurement was done for all season. So, how many measurements it was done during one month?
Author Response
1. The major concern of the presented study is lack of real replications. Authors use one plot in a temperate-subtropical transitional oak forest in China. The measurements were recorded one vegetation season as well. As replicates the repeated measurement during the season were used, which are depended variables.
Response: Many thanks for the good suggestions for our experiment design. Replication is a general role in most of the ecological experiments. However, Most studies on the spatial variation of soil respiration have always been conducted based on a large plot (>1000 m2) (for example, Fang et al., 2017, Plant and soil; Søe et al., 2005, Tree Physiology). In the field natural forest ecosystems, it is impossible to find three or more large plots (>1000 m2) with the similar soil, vegetation, and topography characteristics due to the high spatial heterogeneity in forest ecosystems. In terms of the depended variables due to replicated measurements, we studied the autocorrelation of all environmental factors using Geostatistical Techniques.
2. In methods section there was not mentioned total measurements made during the year.
Response: We agreed with the reviewer’s suggestion, and we have added some information on the measurement. See line 115 on page 3.
3. What was diurnal variation in soil respiration? What time the measurements were done during the day?
Response: Thanks for your suggestion. Soil respiration usually follows the diurnal pattern of soil temperature, with higher values at noon and lower values at dawn and sunset. In this study, we measured the soil respiration from 9:00 to 12:00 a.m. in each campaign to represent the average level per day. We have complemented some information in the Method Section.
4. Figure 3,4,5,6. Show all result of the analyses even It is not significant.
Response: Thanks for your suggestion. We have reorganized the Result section based on the comments of all the reviewers. Part of the non-significant relationships were displayed in some figures, in order to highlight and illustrate the differential effects of different environment factors.
5. Figure 4 and 5. It is unclear how it was calculated CV according each month. As I understood 21 measurement was done for all season. So, how many measurements it was done during one month?
Response: Thanks for your suggestion. We have rewritten the Result section based on the comments of all the reviewers. We redefined the calculation method of the temporal and spatial variation of soil respiration in the Method section. Both the content and Figures 4-5 were deleted in our updated manuscript. See line 144-154 on page 4.
Reviewer 3 Report
The authors studied the seasonal and spatial patterns of soil respiration in a large forested plot in China. The study itself has merit, but the analysis and reporting of the analysis is flawed with one large misunderstanding.
Spatial variability (CVspatial) in soil respiration should be measured as the variation across space (i.e., 48 subplots) during a fixed time frame (n = 21 for the 21 different measurement time periods). Temporal variability in soil respiration (CVtemporal) should, in contrast, be measured across time (i.e., 21 time periods) at one fixed subplot location (n = 48 for each subplot). The authors flipped these around throughout the paper.
These things would need to be fixed, and this is a suggested way of doing that:
1. Create two different CV variables/symbols. One for spatial and one for temporal. In section 2.5, specifically state how the two different CV variables were calculated and their respective sample sizes.
2. Results Section 3.1
a. Rename as “Seasonal patterns of soil temperature, moisture, and respiration and their spatial variability”
b. Change “CV” to CVspatial symbol to demonstrate that the CVs that are recorded on figure 2 are the spatial variability (i.e., as measured across space during one time period).
3. Results Section 3.2
a. Rename “Effects of soil temperature and moisture on spatial variation of soil respiration”
b. Change “CV” to CVspatial symbol throughout
4. Results Section 3.3
a. Rename “Temporal variation of soil respiration”
b. Change to temporal variation and CVtemporal symbol in first three sentences
c. Make fourth sentence start a new section called “Spatial variation in environmental factors during July 2014”
i. Table 4 should be split into two tables. One table can have soil temperature, moisture, respiration and summaries of temporal CV (measured across time at each subplot, then averaged). This table would correspond with section “temporal variation of soil respiration”** The other table would show the summary stats (mean, CVspatial) for the environmental factors (SB, GB, UB, FB) as measured across subplots in July. This table would correspond with “spatial variation in environmental factors during july 2014” section
5. Results section 3.4
a. Drop the analysis represented in the first paragraph and figures 4 and 5, or change to a correlation analysis table done between soil temperature, moisture, and respiration for each individual time period (n = 48 for each analysis). A table with columns for each correlation (STxSR SMxSR and STxSM) and a row for each measurement period (21). This would then meld with section 3.2. It does not make sense as it is to all of a sudden be analyzing data for each month individually without supporting that in the methods. Months are artificial constructs in this analysis
b. The second paragraph and figure 6 belong in a section called “Effects of environmental factors on the temporal variation in soil respiration” since these data represent the average across time at each of the 48 subplots.
i. If you wanted to do a more meaningful analysis where you are actually looking at the effect of the environmental factors on the spatial variation in soil respiration, just analyze environmental factors versus soil respiration during the soil respiration campaign in July that was closest to when you measured the environmental factors.
6. Results section 3.5
a. Rename as: “Structural equation modeling of the temporal variation in soil respiration rates”
b. You didn’t mention doing a Chi-squared test in methods. Please include more details about SEM in methods.
c. Again, since this is the n = 48 data with variation measured across all 21 time periods at each individual subplot, this is actually explaining the TEMPORAL variation in soil respiration, not spatial.
d.
**How was soil temp/moisture/respiration min and max measured in this table compared to section 3.1? The numbers don’t match
· In all figures, make the dots much smaller so that the error bars are visible.
· What is understory biomass (UB)? Is it SB + GB? How did you separate grasses out from the flowering plants? None of this is explained by methods
· Another thing to remember is that CV and mean values are always going to be related (e.g., Figure 2), since you use the mean to calculate CV.
Author Response
1. The authors studied the seasonal and spatial patterns of soil respiration in a large forested plot in China. The study itself has merit, but the analysis and reporting of the analysis is flawed with one large misunderstanding. Spatial variability (CVspatial) in soil respiration should be measured as the variation across space (i.e., 48 subplots) during a fixed time frame (n = 21 for the 21 different measurement time periods). Temporal variability in soil respiration (CVtemporal) should, in contrast, be measured across time (i.e., 21 time periods) at one fixed subplot location (n = 48 for each subplot). The authors flipped these around throughout the paper.
Response: Thanks for your good comments and constructive suggestions. We have redefined the calculation method of the temporal and spatial variation of soil respiration in the Method section, and reorganized the Result section based on the reviewer’s comments. See line 144-154 on page 4, and all the Results.
2. These things would need to be fixed, and this is a suggested way of doing that:
Create two different CV variables/symbols. One for spatial and one for temporal. In section 2.5, specifically state how the two different CV variables were calculated and their respective sample sizes.
Response: We accepted the reviewer’s suggestion. We have defined the calculation method in the updated manuscript. See line 144-154 on page 4, and all the Results.
3. Results Section 3.1 a. Rename as “Seasonal patterns of soil temperature, moisture, and respiration and their spatial variability” b. Change “CV” to CVspatial symbol to demonstrate that the CVs that are recorded on figure 2 are the spatial variability (i.e., as measured across space during one time period).
Response: We accepted the reviewer’s good suggestions. We have changed the subtitle and illustration based on the regenerated method and result.
4. Results Section 3.2 a. Rename “Effects of soil temperature and moisture on spatial variation of soil respiration” b. Change “CV” to CVspatial symbol throughout
Response: We accepted the reviewer’s good suggestions. We have changed the subtitle and illustration based on the regenerated method and result.
5. Results Section 3.3 a. Rename “Temporal variation of soil respiration”. b. Change to temporal variation and CVtemporal symbol in first three sentences. c. Make fourth sentence start a new section called “Spatial variation in environmental factors during July 2014”
Response: We accepted the reviewer’s good suggestions. We have renamed the subtitle and illustration based on the regenerated method and result. A new subsection on the effects of understory plant on spatial variation of soil respiration was organized as the Result 3.6.
6. Table 4 should be split into two tables. One table can have soil temperature, moisture, respiration and summaries of temporal CV (measured across time at each subplot, then averaged). This table would correspond with section “temporal variation of soil respiration”** The other table would show the summary stats (mean, CVspatial) for the environmental factors (SB, GB, UB, FB) as measured across subplots in July. This table would correspond with “spatial variation in environmental factors during july 2014” section
Response: We accepted the reviewer’s good suggestions. We have separated the originated Table 4 into two new Tables. The first Table states soil temperature, moisture, respiration, and summaries of their temporal variation and the second Table introduce the understory plant and corresponding spatial variation.
7. Results section 3.4 a. Drop the analysis represented in the first paragraph and figures 4 and 5, or change to a correlation analysis table done between soil temperature, moisture, and respiration for each individual time period (n = 48 for each analysis). A table with columns for each correlation (STxSR SMxSR and STxSM) and a row for each measurement period (21). This would then meld with section 3.2. It does not make sense as it is to all of a sudden be analyzing data for each month individually without supporting that in the methods. Months are artificial constructs in this analysis. b. The second paragraph and figure 6 belong in a section called “Effects of environmental factors on the temporal variation in soil respiration” since these data represent the average across time at each of the 48 subplots. i. If you wanted to do a more meaningful analysis where you are actually looking at the effect of the environmental factors on the spatial variation in soil respiration, just analyze environmental factors versus soil respiration during the soil respiration campaign in July that was closest to when you measured the environmental factors.
Response: Thanks for your good suggestions. We have modified the manuscript based on these comments. First, we deleted the content of seasonal patterns of spatial variation of soil respiration as illustrated in Figure 4 and 5. Second, we renamed the subtitle of the effects of soil temperature, moisture on the temporal variation of soil respiration. Third, we analyzed the relationships between soil respiration in July and understory plant biomass, and added it as a new section in our Results.
8. Results section 3.5 . a. Rename as: “Structural equation modeling of the temporal variation in soil respiration rates”. b. You didn’t mention doing a Chi-squared test in methods. Please include more details about SEM in methods. c. Again, since this is the n = 48 data with variation measured across all 21 time periods at each individual subplot, this is actually explaining the TEMPORAL variation in soil respiration, not spatial.
Response: Thanks for your good suggestion. First, we renamed the subtitle “Structural equation modeling of the temporal variation in soil respiration rates”. Second, we added some description on the SEM analysis in our Method section; see lines 155-175 on page 5.
9. How was soil temp/moisture/respiration min and max measured in this table compared to section 3.1? The numbers don’t match
Response: Thanks for your good suggestion. The maximum and minimum values of the variables in the updated Table 2 were calculated based on the mean value of 21 measurements in each of the 48 subplots. However, the maximum and minimum values of variables in the updated Figure 4 were calculated using the mean value of 48 subplots in each of the 21 measurements.
10. In all figures, make the dots much smaller so that the error bars are visible.
Response: We accepted the reviewer’s suggestion. We regenerated all the illustrations in our updated manuscript.
11. What is understory biomass (UB)? Is it SB + GB? How did you separate grasses out from the flowering plants? None of this is explained by methods
Response: We accepted the reviewer’s suggestion. We complemented some information on the calculation of understory biomass in the Method. See lines 136-138 on page 4.
Round 2
Reviewer 1 Report
The article entitled "Seasonal patterns and controls of spatial variation of soil respiration in a temperate-subtropical transitional oak forest" by Liu et al. has been re-submitted to Forests after a substantial revision. I think that in the present form the manuscript could be published.
Reviewer 2 Report
The manuscript was improved substantially. The geostatistical analyses partly solve problems of autocorrelation in special analyses and I accept it.
Reviewer 3 Report
Thank you for making the requested changes. Your additions helped improve this manuscript, and made it a more fair representation of the data that you collected.
