the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 04 Jul 2025
The Use of Newly Assimilated Photosynthates by Soil Autotrophic and Heterotrophic Respiration on a Diurnal Scale
Abstract. The regulatory role of plant carbohydrate status and root exudation on soil CO2 efflux has been demonstrated, yet the underlying mechanisms, particularly through root respiration, remain largely theoretical. In this study, we analyzed the cospectral variation of soil autotrophic (Ra) and heterotrophic (Rh) respiration components with key physiological and environmental factors, including gross primary productivity (GPP), photosynthetically active radiations (PAR), soil temperature (Ts) and volumetric water content (VWC), to evaluate their relative contributions in a subtropical mature shortleaf pine forest in the southern United States. The findings reveal a strong diurnal relationship between Rh and both GPP and PAR, in contrast to the weaker and more variable associations observed with Ra. This suggests that substrate availability was a key limitation of Rh on a diurnal basis, and that recently assimilated carbohydrates were directly discharged into the soil via root and mycorrhizal exudates. The consistent 2–4 hour time lag between Rh relative to GPP is consistent with the propagation rate of phloem pressure-concentration waves. While a diurnal peak in Rh-Ts covariance was also detected, the time lag of Rh in relation to Ts varied between positive and negative values, precluding this from being a causal relationship. Ra had a similarly strong cospectral peak with GPP as Rh, but with inconsistent lag, likely because of carbon availability from local starch reserves.
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Status: closed
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RC1: 'Comment on egusphere-2025-2849', Anonymous Referee #1, 21 Jul 2025
- AC1: 'Reply on RC1', Moeka Ono, 13 Sep 2025
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RC2: 'Comment on egusphere-2025-2849', Anonymous Referee #2, 13 Aug 2025
This manuscript presents an investigation into the diurnal coupling between photosynthesis and soil respiration components (autotrophic - Ra, heterotrophic - Rh) in a pine forest. The use of cospectral analysis to explore time lags is a relevant approach. The authors found a strong diurnal link between Rh and GPP/PAR with a consistent 2-4 hour lag, contrasted with weaker/more variable links for Ra，which is potentially significant for understanding rapid C cycling. However, the manuscript in its current form has substantial weaknesses in clarity, methodology description, figure interpretation, and discussion depth that prevent publication.
Major revisions are required.
Major comments:
- The separation standards regarding campaigns (especially C3, C4, and C5, C6) is valid and severe. The manuscript fails to adequately define what these campaigns represent (e.g., different seasons? specific meteorological conditions?), and why they were chosen? The method for partitioning soil CO2 efflux is fundamental to the entire study's conclusions but is either missing or described insufficiently. Without this, the validity of the Ra and Rh data, and thus the core findings, is questionable.
- Figures 3-7 are currently incomprehensible to the reader, as noted. The primary issue is the lack of explanation for the "Period" axis. This presumably represents the period of the cyclic components identified by the cospectral analysis (e.g., Wavelet Coherence? Cross-Wavelet Transform? Other?). How were the parameters chosen (e.g., wavelet type, wavelet power) in the cospectral analysis. Without this fundamental explanation in the caption, methodology, or axis label, the figures convey no meaningful information for general readers. The x-axis labels in Figs 6 & 7 are absent. Furthermore, the meaning of "cospectral peak" and "lag" in the context of these figures needs clearer explanation.
- The concluding statement ("These findings highlight the tight coupling between plant carbon status and soil microbial activity...") is not directly supported by the data presented. The study measures Rh, which includes microbial respiration, but it does not measure any specific microbial activity parameters (e.g., biomass, composition, enzyme activity, substrate use efficiency). Attributing the Rh signal directly to "soil microbial activity" without this link is speculative. The mechanism proposed (direct exudation of recent assimilates) is plausible but remains inferred, not proven, by the Rh-GPP lag correlation.
- The Discussion primarily restates results without sufficient context or mechanistic depth. Crucially, it lacks comparison with previous studies. How do the observed lags (2-4 hours for Rh-GPP) compare to other forest ecosystems? Are they faster/slower? How do they align with known phloem transport speeds or exudation dynamics reported elsewhere? The discussion of Ra's inconsistency is underdeveloped. What does "carbon availability from local starch reserves" mean mechanistically? Why would this buffer Ra differently than Rh? The dismissal of temperature needs more nuance – why might the lag vary? Could other factors (e.g., moisture, labile C pulses) interact with temperature differently at different times?
Citation: https://doi.org/10.5194/egusphere-2025-2849-RC2 - AC2: 'Reply on RC2', Moeka Ono, 13 Sep 2025
Status: closed
-
RC1: 'Comment on egusphere-2025-2849', Anonymous Referee #1, 21 Jul 2025
This study focuses on the diurnal coupling mechanism between soil autotrophic respiration (Ra), heterotrophic respiration (Rh), and photosynthetic carbon allocation in subtropical coniferous forests. It fills the gap in traditional soil respiration models that overlook the dynamic regulation of carbohydrates, and particularly reveals the rapid response of Rh to newly assimilated photosynthates (with a 2-4 hour lag), providing a new perspective for understanding the immediate driving mechanisms of soil carbon fluxes. However, there are still some uncertainties, as detailed below:
(1) It is necessary to clarify the criteria for determining the stable period (3-6 months) of Rh after the insertion of deep collars, supplement the correlation analysis between the vertical distribution of fine root biomass and collar depth, and explain whether the rhizosphere priming effect has been effectively excluded. The potential biases of the root exclusion method (such as interference of dead root decomposition on Rh) are only briefly mentioned in the "Limitations" section, without discussing whether the differences in the Rh:SR ratio (59%-86%) in the supplementary materials compared with other studies stem from methodological factors.
(2) Description of wavelet analysis parameters: The basis for selecting the period parameters of the Morlet wavelet and the specific threshold for excluding data outside the "cone of influence (COI)" should be supplemented to ensure the reproducibility of the results.
(3) Calculation of model residuals: The differences in the estimation of Q10 parameters in Formula (1) between daily and weekly scales need to be quantified, and an explanation should be provided for the use of two time windows (day/week) and their impact on residual results.
(4) Seasonal differences in lag time: The slight advance (+0.64 hours) of Rh relative to GPP in C2 (dormant season) in Figure 8 needs further explanation, whether it is related to microbial metabolic lag under low temperatures or the release of root carbon reserves.
(5) Fluctuation mechanism of Ra: Combined with the positive and negative alternation of Ra lags in Figures S10-S11, it is suggested to supplement the measured data of non-structural carbohydrates (such as starch) in different seasons to verify the "reserve buffer" hypothesis.
(6) Interaction of temperature and humidity: Did the high VWC values (42-43%) in C3-C4 inhibit the response of respiration to substrates? It is necessary to analyze the synergistic effect of VWC and GPP to avoid the limitation of single-factor interpretation.
(7) Differences from early studies: A clearer comparison should be made between the results of this study (2-3 meters tree distance) and the "tree distance gradient method", attributing the differences to vegetation types (coniferous vs. deciduous) or methodologies (root exclusion vs. spatial substitution).
(8) It is recommended to discuss the stability of this mechanism under extreme climates (such as drought), and infer the impact of water stress on carbon allocation based on the data of C5 (VWC=10.4%).
(9) The differences in Rh:SR ratios (47%-86%) among various campaigns in Table 1 require ANOVA analysis to clarify seasonal significance.
(10) Soil temperature and humidity were only measured at a 5cm depth, while 84% of fine roots are distributed in the 0-30cm layer (Figure S1). The association between deep root activity and carbon allocation is ignored, which may underestimate the impact of vertical gradients on Ra/Rh.
(11) Root exudate concentration or isotope labeling data were not measured, so it is impossible to directly prove that the "2-4 hour lag" is caused by carbon input, and the evidence chain is incomplete only through indirect correlation inference.
(12) When using the Q10 model to isolate temperature effects, the differences in parameter estimation between daily-scale (rRh_day) and weekly-scale (rRh_week) were not quantified. If the weekly-scale parameters smooth out short-term fluctuations, it may artificially amplify the correlation between residuals and GPP, leading to an overestimation of the "substrate-driven" conclusion. However, the actual situation is that I am not familiar with this content, and I would ask other reviewers to comment on it.
(13) In cross-wavelet transformation (XWT), the determination of the "5% significance level" is based on the white noise assumption, but the relationship between soil respiration and environmental factors may have red noise characteristics, leading to misjudgment of significantly correlated regions.
(14) The statement that "the coupling strength between Rh and GPP is 1.2-2.6 times that of Ra" lacks statistical significance testing.
(15) In the seasonal difference analysis, what are the sample sizes of the growing season and dormant season groups respectively?
(16) The observed lagged association between Rh and GPP may be driven by third-party factors (such as Ts) (Figure S12 shows that GPP lags behind Ts by 3.5 hours), and confounding effects have not been excluded through methods such as structural equation modeling.
(17) If Rh depends on root exudates and Ra originates from immediate carbon allocation, Ra should respond to GPP more rapidly than Rh, but the results show the opposite. This contradiction has not been reasonably explained, which may be due to methodological biases.
(18) The study only infers that root exudates drive Rh through the "2-4 hour lag consistent with the propagation rate of phloem pressure-concentration waves", but fails to explain in depth. For example, how do microorganisms quickly utilize newly input carbon sources (such as whether specific functional flora are involved in activation)? Do the chemical compositions of root exudates (such as the proportion of sugars and amino acids) change with photosynthetic dynamics, and what are their differential impacts on Rh?
(19) Why does the significant correlation between rRh_day and GPP in the supplementary materials (Figures S2 and S6) still exist in the dormant season (such as C2), which contradicts the common sense that "carbon input is more active in the growing season", and this abnormal phenomenon has not been discussed.
(20) The high variability in the lag time between Ra and GPP (-1.8 to +4.8 hours) is only attributed to "starch reserves", but without data support.
(21) The differences in carbon reserve strategies between coniferous and broad-leaved trees have not been discussed. For example, coniferous trees retain leaves throughout the year, which may have a more stable Ra regulation mechanism.
(22) The study mentions that the coupling between Rh and GPP in this research is stronger than that of Ra, which is opposite to the results of temperate forest studies by Savage et al. (2013), but only attributes it to "vegetation type differences" without in-depth analysis.
(23) In Table 1, the proportion of Ra in C1 (growing season) reaches 53%, while that in C6 (dormant season) is only 15%. Why is Rh still significantly coherent with GPP in the dormant season (Figure S4)? The "source of surplus carbon in non-growing seasons" has not been explained; moreover, the proportion of "surplus carbon" has not been quantified (such as estimated through the flux balance of GPP and Ra/Rh), and only qualitative descriptions are provided, lacking data support.
(24) The reason why the correlation between VWC and respiration in wavelet analysis is extremely weak (VWC-related subgraphs in Figures S2 and S6) has not been explained, which contradicts the general cognition that "soil moisture affects microbial activity", and it is only mentioned in passing as "weak and unstable".
(25) The study indirectly infers photosynthetic carbon input through GPP and PAR, but key indicators such as the concentration, flux, and chemical composition of root exudates, the content of non-structural carbohydrates in leaves and roots, and the allocation path of newly assimilated carbon to Ra and Rh have not been directly measured, resulting in a lack of direct evidence for relevant conclusions.
Citation: https://doi.org/10.5194/egusphere-2025-2849-RC1 - AC1: 'Reply on RC1', Moeka Ono, 13 Sep 2025
-
RC2: 'Comment on egusphere-2025-2849', Anonymous Referee #2, 13 Aug 2025
This manuscript presents an investigation into the diurnal coupling between photosynthesis and soil respiration components (autotrophic - Ra, heterotrophic - Rh) in a pine forest. The use of cospectral analysis to explore time lags is a relevant approach. The authors found a strong diurnal link between Rh and GPP/PAR with a consistent 2-4 hour lag, contrasted with weaker/more variable links for Ra，which is potentially significant for understanding rapid C cycling. However, the manuscript in its current form has substantial weaknesses in clarity, methodology description, figure interpretation, and discussion depth that prevent publication.
Major revisions are required.
Major comments:
- The separation standards regarding campaigns (especially C3, C4, and C5, C6) is valid and severe. The manuscript fails to adequately define what these campaigns represent (e.g., different seasons? specific meteorological conditions?), and why they were chosen? The method for partitioning soil CO2 efflux is fundamental to the entire study's conclusions but is either missing or described insufficiently. Without this, the validity of the Ra and Rh data, and thus the core findings, is questionable.
- Figures 3-7 are currently incomprehensible to the reader, as noted. The primary issue is the lack of explanation for the "Period" axis. This presumably represents the period of the cyclic components identified by the cospectral analysis (e.g., Wavelet Coherence? Cross-Wavelet Transform? Other?). How were the parameters chosen (e.g., wavelet type, wavelet power) in the cospectral analysis. Without this fundamental explanation in the caption, methodology, or axis label, the figures convey no meaningful information for general readers. The x-axis labels in Figs 6 & 7 are absent. Furthermore, the meaning of "cospectral peak" and "lag" in the context of these figures needs clearer explanation.
- The concluding statement ("These findings highlight the tight coupling between plant carbon status and soil microbial activity...") is not directly supported by the data presented. The study measures Rh, which includes microbial respiration, but it does not measure any specific microbial activity parameters (e.g., biomass, composition, enzyme activity, substrate use efficiency). Attributing the Rh signal directly to "soil microbial activity" without this link is speculative. The mechanism proposed (direct exudation of recent assimilates) is plausible but remains inferred, not proven, by the Rh-GPP lag correlation.
- The Discussion primarily restates results without sufficient context or mechanistic depth. Crucially, it lacks comparison with previous studies. How do the observed lags (2-4 hours for Rh-GPP) compare to other forest ecosystems? Are they faster/slower? How do they align with known phloem transport speeds or exudation dynamics reported elsewhere? The discussion of Ra's inconsistency is underdeveloped. What does "carbon availability from local starch reserves" mean mechanistically? Why would this buffer Ra differently than Rh? The dismissal of temperature needs more nuance – why might the lag vary? Could other factors (e.g., moisture, labile C pulses) interact with temperature differently at different times?
Citation: https://doi.org/10.5194/egusphere-2025-2849-RC2 - AC2: 'Reply on RC2', Moeka Ono, 13 Sep 2025
Data sets
Software from: The Use of Newly Assimilated Photosynthates by Soil Autotrophic and Heterotrophic Respiration on a Diurnal Scale Moeka Ono https://github.com/moekaono/CRK_cont_SR/tree/main/Data
Model code and software
Software from: The Use of Newly Assimilated Photosynthates by Soil Autotrophic and Heterotrophic Respiration on a Diurnal Scale Moeka Ono https://github.com/moekaono/CRK_cont_SR/tree/main/Codes
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This study focuses on the diurnal coupling mechanism between soil autotrophic respiration (Ra), heterotrophic respiration (Rh), and photosynthetic carbon allocation in subtropical coniferous forests. It fills the gap in traditional soil respiration models that overlook the dynamic regulation of carbohydrates, and particularly reveals the rapid response of Rh to newly assimilated photosynthates (with a 2-4 hour lag), providing a new perspective for understanding the immediate driving mechanisms of soil carbon fluxes. However, there are still some uncertainties, as detailed below:
(1) It is necessary to clarify the criteria for determining the stable period (3-6 months) of Rh after the insertion of deep collars, supplement the correlation analysis between the vertical distribution of fine root biomass and collar depth, and explain whether the rhizosphere priming effect has been effectively excluded. The potential biases of the root exclusion method (such as interference of dead root decomposition on Rh) are only briefly mentioned in the "Limitations" section, without discussing whether the differences in the Rh:SR ratio (59%-86%) in the supplementary materials compared with other studies stem from methodological factors.
(2) Description of wavelet analysis parameters: The basis for selecting the period parameters of the Morlet wavelet and the specific threshold for excluding data outside the "cone of influence (COI)" should be supplemented to ensure the reproducibility of the results.
(3) Calculation of model residuals: The differences in the estimation of Q10 parameters in Formula (1) between daily and weekly scales need to be quantified, and an explanation should be provided for the use of two time windows (day/week) and their impact on residual results.
(4) Seasonal differences in lag time: The slight advance (+0.64 hours) of Rh relative to GPP in C2 (dormant season) in Figure 8 needs further explanation, whether it is related to microbial metabolic lag under low temperatures or the release of root carbon reserves.
(5) Fluctuation mechanism of Ra: Combined with the positive and negative alternation of Ra lags in Figures S10-S11, it is suggested to supplement the measured data of non-structural carbohydrates (such as starch) in different seasons to verify the "reserve buffer" hypothesis.
(6) Interaction of temperature and humidity: Did the high VWC values (42-43%) in C3-C4 inhibit the response of respiration to substrates? It is necessary to analyze the synergistic effect of VWC and GPP to avoid the limitation of single-factor interpretation.
(7) Differences from early studies: A clearer comparison should be made between the results of this study (2-3 meters tree distance) and the "tree distance gradient method", attributing the differences to vegetation types (coniferous vs. deciduous) or methodologies (root exclusion vs. spatial substitution).
(8) It is recommended to discuss the stability of this mechanism under extreme climates (such as drought), and infer the impact of water stress on carbon allocation based on the data of C5 (VWC=10.4%).
(9) The differences in Rh:SR ratios (47%-86%) among various campaigns in Table 1 require ANOVA analysis to clarify seasonal significance.
(10) Soil temperature and humidity were only measured at a 5cm depth, while 84% of fine roots are distributed in the 0-30cm layer (Figure S1). The association between deep root activity and carbon allocation is ignored, which may underestimate the impact of vertical gradients on Ra/Rh.
(11) Root exudate concentration or isotope labeling data were not measured, so it is impossible to directly prove that the "2-4 hour lag" is caused by carbon input, and the evidence chain is incomplete only through indirect correlation inference.
(12) When using the Q10 model to isolate temperature effects, the differences in parameter estimation between daily-scale (rRh_day) and weekly-scale (rRh_week) were not quantified. If the weekly-scale parameters smooth out short-term fluctuations, it may artificially amplify the correlation between residuals and GPP, leading to an overestimation of the "substrate-driven" conclusion. However, the actual situation is that I am not familiar with this content, and I would ask other reviewers to comment on it.
(13) In cross-wavelet transformation (XWT), the determination of the "5% significance level" is based on the white noise assumption, but the relationship between soil respiration and environmental factors may have red noise characteristics, leading to misjudgment of significantly correlated regions.
(14) The statement that "the coupling strength between Rh and GPP is 1.2-2.6 times that of Ra" lacks statistical significance testing.
(15) In the seasonal difference analysis, what are the sample sizes of the growing season and dormant season groups respectively?
(16) The observed lagged association between Rh and GPP may be driven by third-party factors (such as Ts) (Figure S12 shows that GPP lags behind Ts by 3.5 hours), and confounding effects have not been excluded through methods such as structural equation modeling.
(17) If Rh depends on root exudates and Ra originates from immediate carbon allocation, Ra should respond to GPP more rapidly than Rh, but the results show the opposite. This contradiction has not been reasonably explained, which may be due to methodological biases.
(18) The study only infers that root exudates drive Rh through the "2-4 hour lag consistent with the propagation rate of phloem pressure-concentration waves", but fails to explain in depth. For example, how do microorganisms quickly utilize newly input carbon sources (such as whether specific functional flora are involved in activation)? Do the chemical compositions of root exudates (such as the proportion of sugars and amino acids) change with photosynthetic dynamics, and what are their differential impacts on Rh?
(19) Why does the significant correlation between rRh_day and GPP in the supplementary materials (Figures S2 and S6) still exist in the dormant season (such as C2), which contradicts the common sense that "carbon input is more active in the growing season", and this abnormal phenomenon has not been discussed.
(20) The high variability in the lag time between Ra and GPP (-1.8 to +4.8 hours) is only attributed to "starch reserves", but without data support.
(21) The differences in carbon reserve strategies between coniferous and broad-leaved trees have not been discussed. For example, coniferous trees retain leaves throughout the year, which may have a more stable Ra regulation mechanism.
(22) The study mentions that the coupling between Rh and GPP in this research is stronger than that of Ra, which is opposite to the results of temperate forest studies by Savage et al. (2013), but only attributes it to "vegetation type differences" without in-depth analysis.
(23) In Table 1, the proportion of Ra in C1 (growing season) reaches 53%, while that in C6 (dormant season) is only 15%. Why is Rh still significantly coherent with GPP in the dormant season (Figure S4)? The "source of surplus carbon in non-growing seasons" has not been explained; moreover, the proportion of "surplus carbon" has not been quantified (such as estimated through the flux balance of GPP and Ra/Rh), and only qualitative descriptions are provided, lacking data support.
(24) The reason why the correlation between VWC and respiration in wavelet analysis is extremely weak (VWC-related subgraphs in Figures S2 and S6) has not been explained, which contradicts the general cognition that "soil moisture affects microbial activity", and it is only mentioned in passing as "weak and unstable".
(25) The study indirectly infers photosynthetic carbon input through GPP and PAR, but key indicators such as the concentration, flux, and chemical composition of root exudates, the content of non-structural carbohydrates in leaves and roots, and the allocation path of newly assimilated carbon to Ra and Rh have not been directly measured, resulting in a lack of direct evidence for relevant conclusions.