the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 27 Jun 2024
Exploring effects of variation in plant root traits on carbon emissions from estuarine marshes
Abstract. Estuarine marshes are crucial components of coastal environments around the world and provide numerous ecosystem services, such as carbon sequestration. Plant-microbe interactions are potential key drivers of organic carbon cycling in these ecosystems, but their contribution to the ecosystem-level carbon balance has been rarely quantified so far. This is partly due to the substantial intra- and interspecific variation of plant traits that are affecting microbial functions. Traits such as root oxygen loss and root exudation, for instance, modify soil heterotrophic respiration, but may strongly differ between plant species. Moreover, the non-linearity of the relationships between soil carbon fluxes and effects of plant-microbe interactions may require an explicit representation of trait variation for correctly estimating the carbon balance of estuarine marshes in ecosystem models. However, modelling approaches in this regard so far mostly represent plants as a set of traits that are based on average values of different individuals or species, thus not capturing trait variation. In this study, we implemented a key plant trait, the modification of soil oxygen concentration, into a simple model of heterotrophic respiration in estuarine marsh soils. We then compared two model configurations, one with and one without explicit representation of variation in soil oxygen levels, to estimate the effect on simulated heterotrophic respiration. We found a 10 % reduction in the average respiration rate and a deviation from the median of +33 % /-47 % within the first and third quartile of the distribution in the approach that accounted for trait variation. This illustrates the potentially large impacts that may arise from spatial heterogeneity of plant species or changing community composition of plants on the carbon balance of estuarine marshes. We thus suggest implementing trait variation in marsh ecosystem models.
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RC1: 'Comment on egusphere-2024-1756', Anonymous Referee #1, 28 Jul 2024
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The goal of this study was to determine how root oxygen loss (ROL) affects wetland soil respiration. This is an important and timely topic because wetland plant ROL clearly affects soil biogeochemical processes, as documented by many of the papers that the authors cite. This study falls short, however, because it is overly simplistic and does not take into account that ROL is spatially or temporally heterogenous. Moreover it considers oxygen in air rather than the dissolved phase that is typical of wetland rhizospheres. Root oxygen loss is generally in to saturated, anoxic soils where heterotrophic microbes would compete with abiotic oxidative processes. This may be outside of the scope of the study but important to consider because increasing oxygen may not increase heterotrophic respiration linearly. Regardless the models seem to better describe O2 penetration into wetland surface soils (top few mm-cm) during periods of drying than O2 release into the rhizosphere. It lacks the spatial complexity of O2 diffusion in a soil matrix and how the concentration will change with distance from the root and time since release. The study assumes that respiration is a function of bulk SOC. This excludes the plant trait of root carbon exudation and differences in bioavailability of recent exudates vs. existing rhizosphere organic matter. Respired CO2 will be released into a saturated matrix where it will become part of the DIC pool and not necessarily contribute to carbon emissions (as described in the title). There is not a clear link to plant traits either as the model simulation uses an average plant type (L137).
The authors should see Zhou et al Biogeochemistry (2024) 167:945–963 “Simulated plant‑mediated oxygen input has strong impacts on fine‑scale porewater biogeochemistry and weak impacts on integrated methane fluxes in coastal wetlands”.
L54 and L91 – write out the model names before using the acronym
L111. How was the activation energy of the reaction determined (Easx)? Did this vary as a function of C reactivity? The value is in table 1 – is there a reference?
L115. How were p and Dliq determined? The values are in table 1 – are there references?
Equation 4. Why use parameters for oxygen in air? Wetland soils are generally saturated below the top few cm and gases would be dissolved in a liquid phase. The movement of the liquid phase would depend on advection (eg due to tides, groundwater, etc). A simplifying assumption could be made for marsh interior areas that solute movement is primarily driven by diffusion – but that would still be diffusion in a liquid until the liquid is in contact with the atmosphere. The parameterization used in the model seems more appropriate for surficial soils (top few cm) that are better drained and in contact with the atmosphere rather than the rhizosphere where ROL would be more important.
Section 2.1. It is not clear how these experiments mimic conditions with and without ROL. The first one varies SOC content and holds ROL constant. However, root exudate C and O2 release are spatially and temporally variable. Moreover the bioavailability of exudates would be different than bulk SOC. There is clearly value in varying one variable at a time (e.g., the fraction of bioavailable C) while holding the other (O2) constant, but this does not necessarily mimic ROL.
L137. What is the average plant type and how is average plant ROL known?
L141. This needs more justification because diffusion rates in air are greater than in solution.
L146 More details about the experiments that the model was based on are needed. Incubations were under aerobic conditions but was the water flow static or flow-through? If the latter, were conditions monitored to prevent resource limitation – or inhibition by the build up of metabolites? Were potential microbial metabolites monitored to support the assertion of aerobic conditions (e.g., H2S)? Were the soils saturated? What was the salinity? Did the DO levels mimic the rhizosphere? What were the SOC levels? The reader is referred to 2 other papers to learn about the methods; including a brief version of that information and the rationale for how the incubations support the present study would be valuable.
Citation: https://doi.org/10.5194/egusphere-2024-1756-RC1 -
AC1: 'Reply on RC1', Youssef Saadaoui, 07 Sep 2024
reply
We thank the reviewer for important and helpful comments which will allow us to improve our manuscript. Below, we show the reviewers’ comments in italic text, and our responses to all raised concerns are formatted as standard text.
“The goal of this study was to determine how root oxygen loss (ROL) affects wetland soil respiration. This is an important and timely topic because wetland plant ROL clearly affects soil biogeochemical processes, as documented by many of the papers that the authors cite.”
We are glad that the reviewer appreciates the topic of our manuscript, the key role of ROL in the biogeochemistry of wetlands, such as estuarine marshes.
“This study falls short, however, because it is overly simplistic and does not take into account that ROL is spatially or temporally heterogenous.“
Our model represents a highly simplified approach of soil biogeochemical processes, but, in our opinion, this is not necessarily a shortcoming of our study.
Plants are usually represented by average functional types in ecosystem models of estuarine marshes, meaning a uniform base rate of root oxygen loss (ROL). In reality, plant species and individuals may strongly differ in ROL, which likely affects soil heterotrophic respiration rate. In contrast to other aspects of the complex biogeochemistry in estuarine marsh soils, impacts of plant trait diversity have been little considered in modelling approaches so far. Thus, the goal of our study is to assess the potential effect of variation in ROL due to plant trait diversity compared to a uniform ROL for all plants at a given location.
We do not, however, aim to provide an accurate estimate of soil carbon fluxes for a given marsh ecosystem. This would require the inclusion of a multitude of physiological and biogeochemical processes in the model, including the effect of spatial and temporal heterogeneity of O2 concentration, as mentioned by the reviewer. Since these processes are associated with considerable parameter uncertainty, it is not guaranteed that simulating them will substantially improve the assessment of the effect of plant ROL variation, which is the focus of our study.
To make our results more reliable, we carried out additional sensitivity analysis and tested the effect of uncertain parameter values on our main results. Moreover, we will clarify in the next version of our manuscript that we model soils under tidal influence, with fractions of both aerobic and anaerobic conditions, not the permanently water-logged part where aerobic respiration plays little role.
“Moreover it considers oxygen in air rather than the dissolved phase that is typical of wetland rhizospheres.”
We agree with the reviewer; the DAMM model that we use as a basis for our modelling approach indeed only considers diffusion of oxygen in soil air, thereby neglecting the substantially slower diffusion in water. We focus, however, on aerobic respiration that requires the occurrence of aerobic microsites close to the root surface. In a revised manuscript, we will clarify this point and state that we neglect anaerobic processes in our study.
“Root oxygen loss is generally into saturated, anoxic soils where heterotrophic microbes would compete with abiotic oxidative processes. This may be outside of the scope of the study but important to consider because increasing oxygen may not increase heterotrophic respiration linearly.”
We thank the reviewer for this suggestion. The basis for our results (Fig. 3) is the variation in the concentration of oxygen close to the sites of microbial respiration, not the flux of ROL that generates these concentration values. In the next version of the manuscript, we will describe more clearly that we do not mechanistically model the diffusion of oxygen from the root to the site of respiration, but simply assume a range of possible oxygen values at these sites. We will also discuss the important topic of competition for oxygen between microbes and abiotic processes.
“Regardless the models seem to better describe O2 penetration into wetland surface soils (top few mm-cm) during periods of drying than O2 release into the rhizosphere. It lacks the spatial complexity of O2 diffusion in a soil matrix and how the concentration will change with distance from the root and time since release.”
As described above, we do not explicitly consider all aspects of oxygen diffusion in our approach, since we aim at representing a range of possible concentration values caused by plant diversity and their effect on aerobic respiration. We agree with the reviewer that a spatially explicit model of oxygen distribution would be required to accurately estimate all soil carbon fluxes, not only CO2 from aerobic respiration but, in particular, methane fluxes.
“The study assumes that respiration is a function of bulk SOC. This excludes the plant trait of root carbon exudation and differences in bioavailability of recent exudates vs. existing rhizosphere organic matter.”
We are thankful to the reviewer for highlighting the role of root exudates. Indeed, variation in root exudation between plants may, too, have a substantial impact on the carbon balance of estuarine marshes. In our study, however, we focus on ROL since estimates on root exudation rates are not yet available for the locations where the soil samples were collected. We will discuss this aspect in more detail in a revised version of the manuscript.
“Respired CO2 will be released into a saturated matrix where it will become part of the DIC pool and not necessarily contribute to carbon emissions (as described in the title).”
We agree with the reviewer that CO2 produced by respiration may be taken up again by other processes before being emitted from the soil. However, as the concentration of carbon in the DIC pool increases, the disequilibrium to the surface air will become stronger, leading to an ultimate increase in CO2 emissions in the steady state unless DIC is stored in other long-term pools. We will revise the title of the manuscript to be more precise.
“There is not a clear link to plant traits either as the model simulation uses an average plant type (L137).”
In our approach, we represent both an average plant type and a range of individual plants that differ only in their rate of ROL. As described above, the different fluxes of oxygen from root to soil are, however, not mechanistically simulated, but instead a range of oxygen concentration values approximates the effect of variation in plant ROL.
“The authors should see Zhou et al Biogeochemistry (2024) 167:945–963 ’Simulated plant‑mediated oxygen input has strong impacts on fine‑scale porewater biogeochemistry and weak impacts on integrated methane fluxes in coastal wetlands’”.
We thank the reviewer for pointing us to the recently published paper by Zhou et al. We agree that a mechanistic, spatially and temporally explicit modeling approach of pore water biogeochemistry is important for an accurate estimation of methane release from saturated soil. Our study, however, aims at a different process, aerobic respiration close to the root surfaces, and thus makes simplifying assumptions regarding the saturated part of the soil (i.e. we neglect anaerobic processes). We did not find estimates of CO2 release due to aerobic respiration in Zhao et al., but their setup of different oxygen saturation values in the pore water (white boxes in Fig. 4 c1) seems to be conceptually similar to our approach: They test for the effects of variation (spatial heterogeneity) compared to a homogeneous distribution. We thus think that our approach can be seen as complementary.
“L54 and L91 – write out the model names before using the acronym”
We will ensure that all model names are written out in full upon first mentioning, writing "Dual Arrhenius and Michaelis-Menten (DAMM) kinetics model", for example.
“L111. How was the activation energy of the reaction determined (EaSx)? Did this vary as a function of C reactivity? The value is in Table 1 – is there a reference?”
“L115. How were p and Dliq determined? The values are in Table 1 – are there references?”
We thank the reviewer for pointing this out. The reference for these three parameter values is the paper by Davidson et al. (2012). Since we did not examine the temperature response of the respiration rate in our study, we did not recalibrate the value of EaSx. Moreover, we also kept the original values of p and Dliq, since these are constant multipliers of the concentration of soluble substrate as a function of total substrate, and are thus not likely to alter substantially the effect of oxygen on the reaction rate. To test for the effect of uncertain parameters, we ran an additional sensitivity analysis; the results are shown below in Tab. 1 We found that the modelled absolute respiration rate is relatively sensitive to the parameter Ea, while p and Dliq have little effect. However, the parameter variation has no effect on the difference between the average trait approach and the diverse trait approach since the relative response to parameter variation is the same. Moreover, the parameters p and Dliq have the same relative effect since they are constant multipliers of the substrate conversion. We will include this in a revised version of our manuscript.
Table 1: Results of the Sensitivity Analysis. Respiration (in mg C cm-3h-1) is calculated at the middle of the range of substrate concentration (0.15 g C cm-3)
Parameter
Respiration
(average approach)Respiration
(diverse approach)% Difference to control
Control
0.0166
0.0151
-
Ea lower bound
0.0254
0.0232
53.2%
Ea upper bound
0.0108
0.00986
-34.7%
p lower bound
0.0157
0.0143
-5.6%
p upper bound
0.0173
0.0157
4.1%
Dliq lower
0.0157
0.0143
-5.6%
Dliq upper
0.0173
0.0157
4.1%
“Equation 4. Why use parameters for oxygen in air? Wetland soils are generally saturated below the top few cm, and gases would be dissolved in a liquid phase.”
We acknowledge the reviewer's concern regarding the use of parameters for oxygen diffusion in air, since this would indeed not be appropriate to describe diffusion of oxygen in water, i.e. in permanently water-logged soil. In our study, however, we focus on soils in estuarine marshes, which are characterized by tidal dynamics and regular fluctuations in water table and soil moisture distribution. In our opinion, the simplified representation of oxygen availability used in the DAMM model is sufficient to account for the variation in oxygen concentration due to alternating water saturation and the effects on aerobic respiration rate. As written above, we consider only respiration at aerobic microsites and how increased oxygen availability by ROL may affect this. Oxygen is emitted along the entire root length in several wetland plant species, such as Phragmites australis and Spartina alterniflora, facilitating aerobic respiration in otherwise anaerobic environments (Armstrong and Armstrong, 2005; Colmer, 2003). We do not consider the process of CO2 production by respiration in entirely anoxic conditions. We agree with the reviewer that in these conditions, the role of ROL for methane and sulphate dynamics is more relevant. We will add these clarifications to a revised version of the manuscript.
“Section 2.1. It is not clear how these experiments mimic conditions with and without ROL.”
We thank the reviewer for this point and we agree that the description should be extended. In a revised version of the manuscript, we will explain more clearly that we use the existing scheme of the DAMM model for oxygen diffusion to approximate the effect of plant aerenchyma on soil oxygen concentration via ROL (see also the paragraph before Sect. 2.1.), rather than explicitly simulating the spatial and temporal heterogeneity of ROL. We assume that negligible ROL corresponds to fully water-saturated soil in the model, while dry soil would correspond to the maximum possible ROL. We will point out that the latter condition is unlikely to occur in nature, but the uncertainty regarding the effects of ROL on soil oxygen availability (e.g. (Colmer, 2003)) would make setting a different upper limit arbitrary.
“L137. What is the average plant type and how is average plant ROL known?”
In our model, the "average plant type" corresponds to the representation of plants that are often used in ecosystem-scale models of estuarine marshes, where inter-and intra-specific functional diversity of plant traits, including ROL, is aggregated into average traits, applied to one or a few plant functional types. The little available data on ROL in marsh soils of the Elbe estuary currently do not allow us to compute an accurate average value of ROL, which is why we assume that a soil water saturation of 30% is representative of the average conditions in the rhizosphere in the part that is not permanently water-logged, including an intermediate value of ROL. We conducted a sensitivity analysis to test the effect of uncertainty in the average value of ROL on our estimates by varying the assumed soil water saturation (Theta) by ±20%. The mean respiration over the range of substrate concentrations changed by -17% and +5% for higher and lower Theta for the average trait approach and by -15% and +1% for the diverse one. The mean impact on the difference between the approaches was larger, ranging from -51% at lower to +42% at higher Theta, which means that ROL becomes less important at drier soil conditions. We will include these results in the revised manuscript.
L146 More details about the experiments that the model was based on are needed. Incubations were under aerobic conditions but was the water flow static or flow-through? If the latter, were conditions monitored to prevent resource limitation – or inhibition by the build-up of metabolites? Were potential microbial metabolites monitored to support the assertion of aerobic conditions (e.g., H2S)? Were the soils saturated? What was the salinity? Did the DO levels mimic the rhizosphere? What were the SOC levels? The reader is referred to 2 other papers to learn about the methods; including a brief version of that information and the rationale for how the incubations support the present study would be valuable.
We thank the reviewer for raising these points. We agree that the description of the experiment was not self-explanatory. Water was added at the beginning of the incubation, therefore there was no water flow during the experiment. Microbial metabolites were not measured. Soils were not water-saturated but adjusted to 60% water-holding capacity at the beginning of the experiment. Oxygen limitation during the experiment was prevented by flushing the headspace with synthetic air.
We will include the following information in a revised version:
For the soil incubation experiment, 20 g dry mass equivalent of sieved soil samples (2 mm) were adjusted to 60% water holding capacity. The water-adjusted samples were placed in 1000 ml flasks and incubated in the dark at 20 °C for a period of up to 465 days. Headspace samples (1 ml) were collected at regular time intervals for CO2 measurements by gas chromatography (JAS 6890N, Germany). To maintain a higher pressure within the flasks compared to the surrounding atmosphere and to prevent oxygen deficiency due to excessive CO2 build-up (> 2.5% in headspace), synthetic air was added when necessary.
Regarding the first part of the modelling approach, we will add: ICBM is a classical and widely used 2-pool model following first-order kinetics of decomposition, with a fast and slow carbon pool. The decomposition rate constants and humification rate constant used in that model were calibrated against the CO2 concentrations measured in the lab incubation study data mentioned above by using a gradient descent optimization method. Methodological details can be found in (Knoblauch et al., 2013) and (Beer et al., 2022)
The incubations and the subsequent ICBM simulations were carried out to estimate characteristic respiration rates for soils in the marshes of the Elbe estuary for a wide range of substrate concentrations. These were then used to calibrate the DAMM model and carry out our analysis of ROL variation effects. We will make this point more clear in a revised version of the manuscript.
References :
Armstrong, J. and Armstrong, W.: Rice: Sulfide-induced barriers to root radial oxygen loss, Fe2+ and water uptake, and lateral root emergence, Ann. Bot., 96, 625–638, https://doi.org/10.1093/aob/mci215, 2005.
Beer, C., Knoblauch, C., Hoyt, A. M., Hugelius, G., Palmtag, J., Mueller, C. W., and Trumbore, S.: Vertical pattern of organic matter decomposability in cryoturbated permafrost-affected soils, Environ. Res. Lett., 17, 104023, https://doi.org/10.1088/1748-9326/ac9198, 2022.
Colmer, T. D.: Long-distance transport of gases in plants: A perspective on internal aeration and radial oxygen loss from roots, https://doi.org/10.1046/j.1365-3040.2003.00846.x, 1 January 2003.
Knoblauch, C., Beer, C., Sosnin, A., Wagner, D., and Pfeiffer, E. M.: Predicting long-term carbon mineralization and trace gas production from thawing permafrost of Northeast Siberia, Glob. Chang. Biol., 19, 1160–1172, https://doi.org/10.1111/gcb.12116, 2013.
Citation: https://doi.org/10.5194/egusphere-2024-1756-AC1 -
RC2: 'Reply on AC1', Anonymous Referee #1, 28 Sep 2024
reply
Thank you to the authors for replying to my comments and suggestions. Unfortunately many of my initial concerns remain. Below are the authors comments followed by a - with my reply.
Authors: Plants are usually represented by average functional types in ecosystem models of estuarine marshes, meaning a uniform base rate of root oxygen loss (ROL). In reality, plant species and individuals may strongly differ in ROL, which likely affects soil heterotrophic respiration rate. In contrast to other aspects of the complex biogeochemistry in estuarine marsh soils, impacts of plant trait diversity have been little considered in modelling approaches so far. Thus, the goal of our study is to assess the potential effect of variation in ROL due to plant trait diversity compared to a uniform ROL for all plants at a given location.
- The primary issue with the paper is the insistence that the model mimics plant traits and ROL. The title of the article is: “Exploring effects of variation in plant root traits on carbon emissions from estuarine marshes”. Yet, the authors do not characterize plant functional, phenotypic, or genetic traits nor do they link those traits to ROL. They do not measure ROL nor how it differs across different species, growth stages, or tidal periods. Oxygen input in the model is described as: “To represent root oxygen loss in the model, we use the dependence of soil oxygen concentration on soil moisture that is already implemented in the model according to Eq. (4): [𝑂2] = 𝐷𝑔𝑎𝑠 × 0.209 × 𝑎4 /3 , (4) where Dgas is the diffusion coefficient for O2 in air, 0.209 is the volume fraction of O2 in air, and a is the air-filled porosity of the soil…” Equation 4 does not have any plant component much less a relationship to functional, phenotypic, or genetic traits. It is purely diffusive gas flux in a wet, porous medium. This parameterization is completely divorced from the ideas that: “The amount of O2 taken up by plant shoots and released from roots to flooded soils depends upon many above and belowground factors, including the numbers, types (e.g., adventitious, laterals) and lengths of roots, the magnitude and distribution of their pore-space and tissue respiratory demand, the degree and distribution of barriers to impede ROL, the numbers of the aerial shoots in capacity for O2 uptake, the porosity and O2 demand within these shoots, their lengths and the degree of submergence of the aerial shoots, the submerged soil O2 demand, their microbial activity, their physical properties (i.e., O2 diffusivity being lower in clay than sandy soils) and temperature” (https://doi.org/10.3390/plants10112322); or that the ROL is related to plant height (https://doi.org/10.1016/S0302-3524(81)80104-1); or that ROL can differ even between two populations of the same marsh grass species (https://doi.org/10.1016/j.scitotenv.2017.02.147). There are many more examples from the published literature that the parameterization in equation 4 is not representative of ROL. The authors offer this disclaimer: “While this is not a mechanistic representation, the effect of plant aerenchyma on the diffusion of oxygen from the atmosphere into the soil is similar to the effect of increased air-filled pore space during decreasing soil moisture.” There is no evidence presented in this manuscript to support this idea.
Authors: We do not, however, aim to provide an accurate estimate of soil carbon fluxes for a given marsh ecosystem. This would require the inclusion of a multitude of physiological and biogeochemical processes in the model, including the effect of spatial and temporal heterogeneity of O2 concentration, as mentioned by the reviewer. Since these processes are associated with considerable parameter uncertainty, it is not guaranteed that simulating them will substantially improve the assessment of the effect of plant ROL variation, which is the focus of our study.
- I agree with the authors that estimating soil C fluxes resulting from ROL would require a more complex model. However I disagree with the sentiment that it is not worth exploring how physiological processes (which are plant traits) affect ROL and soil C respiration because doing so would introduce uncertainty.
Authors: To make our results more reliable, we carried out additional sensitivity analysis and tested the effect of uncertain parameter values on our main results. Moreover, we will clarify in the next version of our manuscript that we model soils under tidal influence, with fractions of both aerobic and anaerobic conditions, not the permanently water-logged part where aerobic respiration plays little role.
- I support the addition of uncertainty analyses however there is not enough information to understand what was/will be done.
Authors: We agree with the reviewer; the DAMM model that we use as a basis for our modelling approach indeed only considers diffusion of oxygen in soil air, thereby neglecting the substantially slower diffusion in water. We focus, however, on aerobic respiration that requires the occurrence of aerobic microsites close to the root surface. In a revised manuscript, we will clarify this point and state that we neglect anaerobic processes in our study.
- This response does not address the original critique that the study focuses on oxygen in air and not in the dissolved phase. The water content of wetland soils is high – by definition. The argument that they focus on aerobic microsites is not convincing because (1) the potential extent of such sites is not estimated, and (2) just because a space is aerobic does not mean it is dry. Aerobic heterotrophic respiration occurs in the ocean, lakes, rivers, and the saturated sediments underneath water masses. The authors cannot ignore gas transport in water if their results are supposed to be representative of the rhizosphere in a wetland. If the authors maintain that this work is only related to aerobic respiration in dry spaces then they must reframe and make it clear that their results are only relevant to surficial soils during ebb tides.
Authors: As described above, we do not explicitly consider all aspects of oxygen diffusion in our approach, since we aim at representing a range of possible concentration values caused by plant diversity and their effect on aerobic respiration. We agree with the reviewer that a spatially explicit model of oxygen distribution would be required to accurately estimate all soil carbon fluxes, not only CO2 from aerobic respiration but, in particular, methane fluxes.
- This goes back to my primary issue with the paper – no data are presented linking plant traits or diversity to variations in ROL. Because of this it is completely unclear how the modeled variations in dry soil oxygen levels relate to variability in plant phenotypic, functional, or genetic traits.
Authors: We agree with the reviewer that CO2 produced by respiration may be taken up again by other processes before being emitted from the soil. However, as the concentration of carbon in the DIC pool increases, the disequilibrium to the surface air will become stronger, leading to an ultimate increase in CO2 emissions in the steady state unless DIC is stored in other long-term pools. We will revise the title of the manuscript to be more precise.
- The authors should calculate the disequilibrium reactions in a porous medium before estimating C emissions.
Authors: In our approach, we represent both an average plant type and a range of individual plants that differ only in their rate of ROL. As described above, the different fluxes of oxygen from root to soil are, however, not mechanistically simulated, but instead a range of oxygen concentration values approximates the effect of variation in plant ROL.
- Please provide support linking the range of O2 concentrations used to variation in ROL.
Authors: In our model, the "average plant type" corresponds to the representation of plants that are often used in ecosystem-scale models of estuarine marshes, where inter-and intra-specific functional diversity of plant traits, including ROL, is aggregated into average traits, applied to one or a few plant functional types.
- This is very vague.
Authors: The little available data on ROL in marsh soils of the Elbe estuary currently do not allow us to compute an accurate average value of ROL, which is why we assume that a soil water saturation of 30% is representative of the average conditions in the rhizosphere in the part that is not permanently water-logged, including an intermediate value of ROL. We conducted a sensitivity analysis to test the effect of uncertainty in the average value of ROL on our estimates by varying the assumed soil water saturation (Theta) by ±20%. The mean respiration over the range of substrate concentrations changed by -17% and +5% for higher and lower Theta for the average trait approach and by -15% and +1% for the diverse one. The mean impact on the difference between the approaches was larger, ranging from -51% at lower to +42% at higher Theta, which means that ROL becomes less important at drier soil conditions. We will include these results in the revised manuscript.
- Why restrict data on ROL to just the Elbe estuary? Please provide data supporting that the soils in the rhizosphere have 30% water content. Is this marsh environment being modeled very high in the tidal frame and rarely inundated? I appreciate that the authors varied water content but this still does not account for O2 (or CO2) exchange between dry and wet porespaces.
Authors: We thank the reviewer for raising these points. We agree that the description of the experiment was not self-explanatory. Water was added at the beginning of the incubation, therefore there was no water flow during the experiment. Microbial metabolites were not measured. Soils were not water-saturated but adjusted to 60% water-holding capacity at the beginning of the experiment. Oxygen limitation during the experiment was prevented by flushing the headspace with synthetic air.
We will include the following information in a revised version:
For the soil incubation experiment, 20 g dry mass equivalent of sieved soil samples (2 mm) were adjusted to 60% water holding capacity. The water-adjusted samples were placed in 1000 ml flasks and incubated in the dark at 20 °C for a period of up to 465 days. Headspace samples (1 ml) were collected at regular time intervals for CO2 measurements by gas chromatography (JAS 6890N, Germany). To maintain a higher pressure within the flasks compared to the surrounding atmosphere and to prevent oxygen deficiency due to excessive CO2 build-up (> 2.5% in headspace), synthetic air was added when necessary.
- This experiment is a very artificial representation of a wetland soil. Soil structure is disrupted by drying, sieving, and rewetting, which will affect microbial metabolisms and gas fluxes. The bottles were held under constant conditions for over a year! Wetland soils experience regular tidal, seasonal, and annual cycles – which are not mimicked here. Moreover microbial community composition and metabolisms likely changed substantially over one year and may no longer be representative of natural communities.
Citation: https://doi.org/10.5194/egusphere-2024-1756-RC2
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RC2: 'Reply on AC1', Anonymous Referee #1, 28 Sep 2024
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AC1: 'Reply on RC1', Youssef Saadaoui, 07 Sep 2024
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