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Peer review report 1 on “Comprehensive synthesis of spatial variability in carbon flux across monsoon Asian forests”
Agricultural and Forest Meteorology 217 (2016) 518
Contents lists available at ScienceDirect
Agricultural and Forest Meteorology journal homepage: www.elsevier.com/locate/agrformet
Peer Review Report
Peer review report 1 on “Comprehensive synthesis of spatial variability in carbon flux across monsoon Asian forests” 1. Original Submission 1.1. Recommendation Major Revision 2. Comments to Author: Review on AGRFORMET-D-16-00281 (Kondo et al) General comments: The authors investigated the relationships between carbon fluxes and climate variables in East Asian forest ecosystems by merging eddy covariance, inventory and supplementary dataset. In particular, the authors split their analysis between boreal/temperate and tropical forests which exhibited the opposite correlations between NEE and GPP. The topic falls on the scope of AgForMet and the C flux variability in East Asia has been less explored in the community. The authors conducted a comprehensive analysis on the component carbon fluxes in East Asia. However, I do have several major concerns as follows. Three key findings in this study are 1) temperature mainly controlled spatial variability of carbon fluxes, 2) component carbon fluxes were scaled with GPP, and 3) NEP was positively (negatively) correlated to component fluxes in mid-high latitude forest (low latitude forest) due to the regional contrast in N deposition and stand age. The 1 st and 2nd findings are well expected as a) Luyssaeart et al. (2007) reported mean annual temperature largely explained annual GPP across global forests, and b) Litton et al. (2007) reported component carbon fluxes are positively scaled to gross carbon input, GPP across global forest ecosystems. Thus, mean annual temperature largely altered annual GPP, which again scaled component fluxes, which agrees with the 1st and 2nd findings. I think the authors did not report enough evidence to support the 3rd finding. Among 22 sites, only three (N deposition) and four sites (land use change) were mentioned. Also, it is unclear whether N deposition and land use change really led the opposite correlation between NEE and component C fluxes. For example, higher N loading to the ecosystem tends to reduce soil respiration and increase NPP (Janssens et al., 2010 Nature Geoscience), thus increase NEE (more C uptake), which implies positive correlation between NEE and soil respiration. It is not the case in this manuscript (Fig. 3c). Thus, I am skeptical whether higher N loading could explain the negative correlation between NEE and component C fluxes in low latitude forest. Finally, I left with this question: what is the most significant contribution in this manuscript to the community.
DOI of published article: http://dx.doi.org/10.1016/j.agrformet.2016.10.020. http://dx.doi.org/10.1016/j.agrformet.2016.11.256 0168-1923/
It is unclear which “variability” was mainly investigated. The authors tended to argue “spatial variability of C fluxes” were investigated, but I do not think it is the case. There is no “spatial” context in their analysis. At most, across site variability in C fluxes were investigated. Apparently, the authors used single year flux data for each site, then make correlation analysis across sites (there was no description on the used site years data in Methods). I am skeptical on this analysis as within site variability (e.g. normal year vs climate extreme year such as El Nino and Li Nina) could be greater than across site variability. The authors should report the used site year information in Table 3, and clarify which variability was studied. R2 represents linearity, not correlation, between two variables. The authors said “positive correlations between. . .." using R2, which is incorrect. Use R if the authors want to analyze correlation. When looking at Fig. 4, it seems there is no significant correlation between GPP and NEP, GPP and deltaABM, GPP and deltaSOMp for sub-tropical forests. Significant correlation only appears when combining both sub- and tropical forests together. This should be discussed. Specific comments: L1: The “variability” is unclear. Is it spatial or temporal? Seasonal or interannual? L19: employ − > employed L24: I don’t this study investigated “spatial variability” which associates with maps of 2D or 3D analysis. Rather, I would say this study investigated carbon flux variability across sites. L34: Unclear “common variability” L35-36: This is very confusing sentence. “Boreal and temperate forests in the mid-high latitudes revealed the positive relationship between GPP and NEP." L37-38: Again, it should be reworded. Also, I don’t think the authors provided direct evidence about “regional contrast in N deposition and stand age." L194: So were the other correlations statistically significant in Fig. 2a, b, c? L199-200: Is there statistically significant difference between two groups? L210: Mark "*" for p < 0.05 in Fig. 3. L323: High N deposition and more disturbance enhanced NEE (carbon sink) while reducing GPP. What is the behind mechanism? Fig. 2 Does carbon flux represent single year sum value for each site?. Table 3 Add available site years info. Anonymous Available online 5 December 2016
Contents lists available at ScienceDirect
Agricultural and Forest Meteorology journal homepage: www.elsevier.com/locate/agrformet
Peer Review Report
Peer review report 1 on “Comprehensive synthesis of spatial variability in carbon flux across monsoon Asian forests” 1. Original Submission 1.1. Recommendation Major Revision 2. Comments to Author: Review on AGRFORMET-D-16-00281 (Kondo et al) General comments: The authors investigated the relationships between carbon fluxes and climate variables in East Asian forest ecosystems by merging eddy covariance, inventory and supplementary dataset. In particular, the authors split their analysis between boreal/temperate and tropical forests which exhibited the opposite correlations between NEE and GPP. The topic falls on the scope of AgForMet and the C flux variability in East Asia has been less explored in the community. The authors conducted a comprehensive analysis on the component carbon fluxes in East Asia. However, I do have several major concerns as follows. Three key findings in this study are 1) temperature mainly controlled spatial variability of carbon fluxes, 2) component carbon fluxes were scaled with GPP, and 3) NEP was positively (negatively) correlated to component fluxes in mid-high latitude forest (low latitude forest) due to the regional contrast in N deposition and stand age. The 1 st and 2nd findings are well expected as a) Luyssaeart et al. (2007) reported mean annual temperature largely explained annual GPP across global forests, and b) Litton et al. (2007) reported component carbon fluxes are positively scaled to gross carbon input, GPP across global forest ecosystems. Thus, mean annual temperature largely altered annual GPP, which again scaled component fluxes, which agrees with the 1st and 2nd findings. I think the authors did not report enough evidence to support the 3rd finding. Among 22 sites, only three (N deposition) and four sites (land use change) were mentioned. Also, it is unclear whether N deposition and land use change really led the opposite correlation between NEE and component C fluxes. For example, higher N loading to the ecosystem tends to reduce soil respiration and increase NPP (Janssens et al., 2010 Nature Geoscience), thus increase NEE (more C uptake), which implies positive correlation between NEE and soil respiration. It is not the case in this manuscript (Fig. 3c). Thus, I am skeptical whether higher N loading could explain the negative correlation between NEE and component C fluxes in low latitude forest. Finally, I left with this question: what is the most significant contribution in this manuscript to the community.
DOI of published article: http://dx.doi.org/10.1016/j.agrformet.2016.10.020. http://dx.doi.org/10.1016/j.agrformet.2016.11.256 0168-1923/
It is unclear which “variability” was mainly investigated. The authors tended to argue “spatial variability of C fluxes” were investigated, but I do not think it is the case. There is no “spatial” context in their analysis. At most, across site variability in C fluxes were investigated. Apparently, the authors used single year flux data for each site, then make correlation analysis across sites (there was no description on the used site years data in Methods). I am skeptical on this analysis as within site variability (e.g. normal year vs climate extreme year such as El Nino and Li Nina) could be greater than across site variability. The authors should report the used site year information in Table 3, and clarify which variability was studied. R2 represents linearity, not correlation, between two variables. The authors said “positive correlations between. . .." using R2, which is incorrect. Use R if the authors want to analyze correlation. When looking at Fig. 4, it seems there is no significant correlation between GPP and NEP, GPP and deltaABM, GPP and deltaSOMp for sub-tropical forests. Significant correlation only appears when combining both sub- and tropical forests together. This should be discussed. Specific comments: L1: The “variability” is unclear. Is it spatial or temporal? Seasonal or interannual? L19: employ − > employed L24: I don’t this study investigated “spatial variability” which associates with maps of 2D or 3D analysis. Rather, I would say this study investigated carbon flux variability across sites. L34: Unclear “common variability” L35-36: This is very confusing sentence. “Boreal and temperate forests in the mid-high latitudes revealed the positive relationship between GPP and NEP." L37-38: Again, it should be reworded. Also, I don’t think the authors provided direct evidence about “regional contrast in N deposition and stand age." L194: So were the other correlations statistically significant in Fig. 2a, b, c? L199-200: Is there statistically significant difference between two groups? L210: Mark "*" for p < 0.05 in Fig. 3. L323: High N deposition and more disturbance enhanced NEE (carbon sink) while reducing GPP. What is the behind mechanism? Fig. 2 Does carbon flux represent single year sum value for each site?. Table 3 Add available site years info. Anonymous Available online 5 December 2016