the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 10 Apr 2026
Microbial carbon use efficiency emerges from interactions between soil structure and life-history strategies
Abstract. Microbial carbon use efficiency (CUE) is believed to be a key regulator of the accumulation of persistent soil organic carbon. Microbial processing of organic carbon in soil occurs in the pore network, the properties of which are known to affect microbial activity. However, the effects of interactions between pore architecture, and microbial life-history strategies on carbon dynamics, and house these interactions are modulated by moisture regime, remain poorly understood.
Here, we use a spatially explicit model based on a cellular automaton, applied to realistic soil pore networks derived from X-ray tomographic images, to investigate how pore geometry and organic matter distribution shape microbial dynamics and CUE. The model explicitly represents r- and K-strategists that are either motile or immobile, and have different resource requirements for growth, maintenance and motility. The constraints imposed on substrate diffusion and the spatial domain in which motile bacteria can effectively disperse by the moisture regime are explicitly represented in the model, in order to capture how moisture regulate decomposer access to resources.
The simulations show that pore architecture has a significant effect on microbial CUE. Micropores promoted high CUE and a dominance of K-strategists by limiting substrate diffusion, reducing pore connectivity, and constraining microbial access to spatially isolated resources. In contrast, macropores with high connectivity and greater substrate accessibility favor r-strategists, rapid biomass turnover and lower CUE. Motility emerges as advantageous in connected, carbon-rich macropores, but energetically unfavourable in confined or poorly connected micropores, where immobile strategies outperform motile ones.
Overall, our results indicate that microbial CUE is not an intrinsic property of life-strategies alone, but emerges from the interaction between microbial life-strategies and pore-scale physical constraints. This study suggests that soil structure influences microbial strategies by limiting resource availability and dispersal.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2026-1818', Sara König, 12 May 2026
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RC2: 'Comment on egusphere-2026-1818', Anonymous Referee #2, 18 May 2026
I have reviewed the manuscript “Microbial carbon use efficiency emerges from interactions between soil structure and life-history strategies” for publication in SOIL. In this article, the authors present a spatially explicit model based on a cellular automaton framework and use it to investigate the effects of soil pore structure, moisture and selected microbial traits on microbial CUE. They find that the configuration of the pore space is a dominant control on CUE, as it modulates the relative performance of traits such as microbial life-history strategy and motility.
The paper is well written and nicely illustrates the use of a relatively simple model to study processes and effects in the soil system at a scale that remains difficult to observe or manipulate directly. It thereby enhances our understanding of the system even in the absence of measured data, and it will be relevant for both future experimental and modelling work in the field. Therefore, the article should spark interest among many readers of SOIL and the special issue.
Below, I provide some comments to hopefully enhance the clarity of the text and to expand some aspects of the discussion. In particular, I would suggest a more explicit presentation of the processes and update rules of the cellular automaton to improve reproducibility. Otherwise, I have no major concerns regarding this study and I am looking forward to the final version of the article.
Specific comments, line by line:
L164ff.: While this is a nice conceptual description of the automaton, it would be difficult for me to reproduce an update step based on the provided information. I strongly recommend providing a structured overview of all processes and rules being observed during one update/iteration, e.g., in a table or set of equations (at the very least, this should be included in the supplement). Of course, it would be optimal to make the underlying code accessible as well, if feasible.
L175: In my opinion, this equation is not explicit enough. I suggest writing the actual equation/update rule using full notation.
L183ff: I am somewhat confused about the role of the resource matrix and its interaction with microbes. How exactly do (grid) cells that contain microbes accumulate resources? Do they simply “ingest” all available resource at their current location at each step, and the resulting microbial resource level is checked against the various thresholds? This should be clarified.
L188: What happens between maintenance and reproduction, so for Sd < x < Sr? Microbes could move I suppose, and they still lose carbon via maintenance?
L191: Again, this equation could be improved. For example, in L175, “Resource” at a location xy was a function of iteration i, but here it is a function of microbial entities (parent, descendant) and iteration t. This should be harmonized and made more explicit, see my comments above. Also, how is the location of the descendant microbe chosen? Is overcrowding ever an issue?
L213: The study design could be described more clearly. Currently, I cannot confidently enumerate all the studied combinations.
L218: Do you have a sense of the timescale of 200 iterations? Since the spatial scale is well defined and many of the processes have some constraints (diffusion, microbial growth, etc), I wonder if something can be said about the temporal dimension as well (and if there are potentially some mismatches that must be considered during interpretation).
L220ff.: I greatly appreciate the amount of effort invested into performing the sensitivity analysis. It is unfortunate that all of it is hidden in the supplement. Of course, you cannot include all of these observations and discussions in the main text, but I would encourage you to summarize the most important results and implications at least briefly. As it stands, the outcome of the sensitivity analysis is barely mentioned again.
L246ff: I appreciate the transparency regarding the limitation of necromass recycling. You suggest that it should be kept in mind when interpreting model outcomes, yet you do not explore it much in the discussion. For example, do you expect this choice to favour certain traits more than others in your simulations (e.g., r vs K, mobile vs immobile)? How could this be addressed?
L260: Please explicitly state all statistical analyses you performed. For example, you show the results of Tukey tests, but you do not specify the underlying analysis (I assume an ANOVA, but is it with a single factor of 4 levels or two factors with 2 levels each, and what are the results?).
L264: Again, the correct interpretation of this equation is somewhat hindered by the lack of an explicit/rigorous notation (or perhaps just my confusion with the resource matrix). I would appreciate some clarification of the processes involved (e.g., “assimilation”)
L389: While you provide a detailed and insightful discussion, I am missing a paragraph on the relation of your model to other cellular automata that have been used in this context, some of which you cite in the introduction. From a modelling point of view, do you identify certain advantages or disadvantages of your approach, and do any opportunities arise from these in combination with others? I would enjoy a few sentences on the matter.
L501ff.: I assume that you reach these conclusions based on your sensitivity analyses? It would be worth pointing this out and elaborating a bit. As it stands, the central role of these parameters comes as a bit of a surprise after just the main text, at least for me.
L531ff.: I think you raise an important point here that could be elaborated a bit. While I understand the choice of focussing on the traditional r- and K-strategist distinction, I believe that the spatially explicit nature of your model enables a much more nuanced analysis of microbial traits. In particular, I took from your sensitivity analysis that the detailed interplay of several traits that may or may not align with this axis must be considered. One potential extension that I would emphasize is the role of extracellular enzymes and associated microbial traits, as a spatially explicit analysis of the return on investment could be very valuable (similar to motility).
Fig 1, Fig 2: I think a scale bar could be useful to make the size of the spatial domain immediately discernible from the figure.
Fig 5, Fig 6: You focus on CUE as the key outcome, but I am curious whether you also looked at absolute carbon fluxes (e.g. respiration) per microbial group? This could also reveal interesting patterns. Along these lines, are all available resources typically depleted by the end of the simulations?
Minor comments & typos:
L43: Typo “... spatial access the have the OC” should be “... spatial access they have to the OC”
L59: Typo “... been classified into according to, ...” should be “... been classified according to, ...”
L82: add commas around “particularly pore size distribution and connectivity”
L246: Typo “cellular resource resource” delete one resource
L373ff: Typo “motile cells r” and “motile cells K” should be given proper names here and throughout
L571: Doubled contributions of NN, but only 5 unique contributions despite 6 authors.
Citation: https://doi.org/10.5194/egusphere-2026-1818-RC2
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In this paper, the authors present a mechanistic spatially-explicit model simulating microbial dynamics and carbon consumption in a realistic pore space with different moisture levels, integrating different microbial life and motility strategies. This allows to analyze trade-offs between such strategies in regard to resource use efficiency, and how those are shaped by pore space. It is well written and highly relevant in regard to ongoing developments of carbon cycle models. It also includes an extensive supplement with more simulation results and sensitivity analysis, which is very nice.
I very much like how you justify all your assumptions made. For many model applications, we do not have reliable data, but we still can learn a lot applying them based on assumptions – very good job! This is a nice example on how simplified mechanistic models can help to understand interactions and trade-offs, which cannot be easily observed in experiments and thus add up on experimental work. I hope to see more on your model in the future!
Some things remain unclear to me, which I describe further below.
Minor comments:
#42 capacity instead of capactity
#43 the end of the sentence is somehow strange
#59 delete ‘into’
#120 please also cite the scientific publication to the soil structure library https://doi.org/10.5194/soil-8-507-2022
#144 I would suggest to revise the sentence in something like “All resources were placed either in small (S) or large (L) pores as defined before”
#217 I do not understand why you refer to Figure 2 here
#217 also I would not mention the numbers for low and high bacterial densities here, as your justification for this comes only later in the text, just mention ‘two different microbial densities (low and high)’
#246 resource resource?
#372ff check for the right references for figure 6b and c, I guess you mean a and b (there is no c)
Figure 2: revise description upper right to ‘growing in a small pore’ instead of pores; decide to call it either strategist or strategists (its not consistent)
Figure 3: what are the shades, the 10 different images or the 20 runs or both? Please mention this
Figure 6: in the legend you use the term ‘mobile’ while you usually refer to the cells as ‘motile’