the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Upscaling microbial stoichiometric adaptability in SOM turnover: The SESAM Soil Enzyme Steady Allocation Model (v3.0)
- 1Max Planck Institute for Biogeochemistry, Hans-Knöll-Straße 10, 07745 Jena, Germany
- 2Centre for Environmental and Climate Science (CEC) at Lund University, Sölvegatan 37, Lund, Sweden
- 1Max Planck Institute for Biogeochemistry, Hans-Knöll-Straße 10, 07745 Jena, Germany
- 2Centre for Environmental and Climate Science (CEC) at Lund University, Sölvegatan 37, Lund, Sweden
Abstract. Understanding the coupling of nitrogen (N) and carbon (C) cycles of land ecosystems, requires understanding microbial element use efficiencies of soil organic matter (SOM) decomposition. Whereas important controls of those efficiencies by microbial community adaptations have been shown at the scale of a soil pore, a simplified representation of those controls is needed at the ecosystem scale. However, without abstracting from the many details, models are not identifiable, i.e. can not be fitted without ambiguities to observations. There is a need to find, implement, and validate abstract simplified formulations of theses processes.
Therefore, we developed the SESAM model as an ab- straction of the more detailed soil enzyme allocation model (SEAM) model and tested, whether it can provide the same decadal-term predictions. SEAM explicitly models community adaptation strategies of resource allocation to extracellular enzymes and enzyme limitations on SOM decomposition. It thus provides a scaling from representing several microbial functional groups to a single holistic microbial community. Here we further abstracted the model using quasi-steady-state assumption for extracellular enzyme pools to derive the SESAM model.
SESAM reproduced the priming effect, the SOM banking mechanism, and the damping of fluctuations of carbon use efficiency with microbial competition as predicted by SEAM and other more detailed models. This development is an important step towards more parsimonious representation of soil microbial effects in global land surface models.
Thomas Wutzler et al.
Status: open (until 29 Aug 2022)
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RC1: 'Comment on egusphere-2022-359', Anonymous Referee #1, 05 Aug 2022
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In this manuscript, Wutzler et al. present a simplification of the previously published model SEAM by assuming a quasi-steady state for the extracellular enzyme pool. The SESAM model builds upon the SEAM model with additional modifications to the enzyme allocation mechanism for the decomposition of litter and residue pools. The main results include reducing SEAM model complexity while retaining long-term (decadal) SOM dynamics (C and N) and microbial adaptation to nutrient-limited conditions using overflow respiration and dynamic enzyme allocation. Overall, I appreciate how the authors retained the effects of microbial diversity and kept a simpler model structure. Moreover, I admire the authors for their thoroughness of the analysis, especially on the bias with quasi-state assumptions. The methods and results are sound; however, the introduction and discussion need some attention.
The major shortcoming of the manuscript is the writing style. The discussion section needs significant revision. I have the following suggestion to improve the quality of the manuscript further; afterward, the manuscript should be reconsidered for publication.
- Consider modifying the title because the manuscript is more of a simplification of SEAM rather than upscaling. Or be specific what is being upscaled.
- Abstract and Introduction:
- The first paragraph of the abstract deals with the spatial scale of the microbial processes, and then SESAM is proposed to fix issues with temporal scale (decadal dynamics). Please fix this. The same issue is in the introduction; first, the spatial scale is introduced, then a solution for the temporal scale. A better explanation is needed to link how pore-scale processes affect SOM dynamics at different time scales to motivate the simplification of SEAM.
- P1L22: What is that discrepancy? Provide examples with references. Some introductory sentences are missing on how current "global models" implement microbial processes and what is already feasible. Without this context, the development of the proposed model does not make sense.
- Define what the temporal scale of processes is. Authors often use short-term and long-term vaguely.
- Methods:
- sensitivity analyses often depend on the sample size. I wonder if 5000 was sufficient.
- The maintenance does not require N in SEAM/SESAM, right? What would be consequence of if it did?
- Discussion:
- The first paragraph in most discussion sections reads as the introduction. This should be deleted or put in context with the manuscript's results.
- Discussion 4.1
- L5 "At the heart of the interactions are soil microbial processes, and hence, these processes need to be represented in models of SOM dynamics". Is that a good enough reason?
- L6 check patters,
- L7 Which microbial processes are being referred to here? Please be specific.
- L8 missing subject.
- L9: "Many of these processes work on small" define small
- L11: In my opinion, "abstract" is misused throughout the manuscript. I would prefer simplification of the model rather than abstraction. Quasi-state assumption leads to a simpler model structure. All models are abstract anyway.
- L12: what is meant by mean effects?
- P13L3: "neglects smoothing dynamics that occurs when explicitly modeling DOM and enzyme pools". Add reference.
- P13L7: it is confusing to read input along with fluxes because 'decomposition functions' are functions of stocks, not inputs. If you mean litter input, then write litter input/s.
- P13L8: "The fluctuation analysis revealed…." Convoluted sentence. Split into two sentences.
- P13L11: What are those certain conditions?
- Discussion 4.2
- "Competition between microbial groups and adaptation of the microbial community is one of those detailed processes that have been shown to exert strong control on decadal-term SOM dynamics". Which of those detailed processes? Also, add references for "strong control on decadal-term SOM dynamics"
- P14L5: delete "instead of respiring it to the atmosphere after those pulses"
- Delete: "Studying and discussing how these pathways can be modeled and clarified using SESAM warrants a dedicated manuscript"
- P14L13-14, why and how is this related to the results from this manuscript?
- Discussion 4.3
- "short time scales" vague
- "Hence, microbial parameters need to be constrained by inverting models to larger scale observations". What is meant by inverting here?
- "Currently, the free air enrichment time series are running about 20 years are getting long enough to calibrate and test models at decadal time scale" This sentence seems to be disconnected from the entire paragraph.
- Discussion 4.4
- "We think of ways how let it change together with othermicrobial properties of enzyme allocation". How does this sentence contribute to the manuscript?
- The third paragraph in this section is not a discussion of results. It can go in the outlook, but it can also be removed entirely.
- It would be interesting to see how the sensitivity of selected parameters varies in the short-term simulation, e.g., for the time scale of Figure 4, substrate pulse simulation. This would add interesting analysis to the sensitivity section comparing short and long-term sensitivity patterns in parameter space.
- Outlook: Delete it or rewrite it. In its current shape, it is not adding anything to the story. It reads like a to-do list.
- Figures:
- SOM should be the sum of all C pools, right? Even if microbial biomass, DOM, and enzyme pools are small.
- Figure 4: How does CUE compare with different model formulations SEAM/SESAM/ SESAM_NoEnzFlux for this simulation?
- Figure 6. Why are seasonal patterns for SESAM not shown?
- Appendix:
- A3b It is best to avoid syn_Enz, because when discussing NoEnzFluz scenario, it gets confusing that dec will zero.
- I strongly advise using a formal writing approach as text occasionally reads colloquial, and often author deviates from the main ideas. Streamlining the text and avoiding such diversion would help the reader better understand take-home messages .
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RC2: 'Comment on egusphere-2022-359', Nadezda Vasilyeva, 12 Aug 2022
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General: The study presents an upscaling of a short-term (enzyme turnover time) SEAM model of C and N dynamics in soil to a decadal scale. This is done by one of the valid procedures - the simplification of short-term model equations, while retaining discussed microbial mechanisms. The study shows that the performed modification of the model equations did not change long-term effects of those mechanisms in specific scenarios, and the SESAM model is interesting for further studiyng the effects of these mechanisms on regional scale.
The authors mention having already tried implementing SESAM into a land model and that this trial initiated several reformulations of model aspects. It would be interesting to know which kind of aspects were revealed necessary to reformulate.
The basis of the model in adapting microbial consumption of SOM driven by their stoichiometry and resulting CUE as emergent property of microbial community is a very senseful approach and a valuable result. While it looks questionable whether the stoichiometrically excess C should indeed go into respiration overflow, this mechanism is worth testing.
Minor questions:
p.2 l.20: it would be good somewhere in the beginning to explain in a few words what is “banking mechanism” same as done for “N priming”.
p.5 l.10 “..SEAM required model parameters for enzyme turnover.'' You could make it more clear - that SESAM now requires only one instead of two enzyme parameters in SEAM, if this is what you mean.
p.7 l.24: “with scenario of varying initial C/N ratio with otherwise very low rate of L input” not very clear what is meant here by “otherwise”.
p.10 Figure 3: the legend covers the figure
p.11 Figure 4: what do numbers 50,70,90 in the legend mean? is it C/N ratio if yes, why is it so high?
p.13 l.6: “The mostly concave functions of decomposition according to Michaelis-Menten kinetics yield a lower flux of the average input compared to the average of the fluxes on varying input. Hence, we expected slightly higher decomposition rates and lower stocks with the average litter input scenario.” Not very clear how the averaging of inputs should cause ‘higher decomposition’.
p.14 l.12: “While the relative changes in SOM pools are so small that are very hard to directly measure, changes can potentially be detected by a changing C/N ratio of the total SOM”. What result of SESAM model one can expect in the scenario of no litter input on decadal scale for C/N of the pools and C stock?
p.15 l.27: here for the first time “conserving CUE” is used. The CUE was discussed as an emergent property of the model. How to interpret “conserving”? Does it mean optimal for survival?
Typos:
p.2 l.19: an model
p.2 l.30: “ allows exploring consequences soil microbial stoichiometry for SOM cycling” sentence is not consistent
p.3 l.4: “several SOM by several pools”
p.6 l.1: “..production and turnover or organic matter” did you miss the word “enzyme” to “production and turnover”? What “or organic matter” refers to?
p.6 l.14: contrained
p.7. l.5: input of input
p.7 l.23: “gm2yr−1” divide by meter
p.14 l.8: SUM
p.15 l.3: because its range it rather
Thomas Wutzler et al.
Thomas Wutzler et al.
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