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the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Modeling Microbial Regulatory Feedback in Organic Matter Decomposition Identifies Copiotrophic Traits as Key Drivers of Positive Priming
Abstract. Microbial decomposition of complex soil organic matter (OM) is often regulated by labile organic carbon inputs, a phenomenon known as priming, which plays a critical role in belowground biogeochemical cycling. However, the strength and direction of microbial priming of soil OM pools varies significantly across ecosystems. A generalizable mechanistic framework explaining the factors that lead to accelerated (positive priming) or impeded (negative priming) rates of OM decomposition is still lacking. In this work, we conceptualize priming as a microbial feedback loop that optimizes the costs and benefits of maximizing growth rate, specifically, the cost of exoenzyme synthesis for decomposing complex OM versus the benefit of energy acquisition from labile OM. We examined the impacts of microbial growth traits and interactions on priming by employing a cybernetic modelling approach, which predicts complex microbial growth patterns by accounting for dynamic metabolic regulations. We simulated microbial priming across ecological community configurations composed of degraders and non-degraders with either oligotrophic or copiotrophic growth traits, resulting in seven combinations that included both single functional groups (degraders with either growth trait) and binary functional groups (combinations of degraders and non-degraders, or degraders only, with differing or common traits). Configurations with only non-degraders were excluded, as they are irrelevant for studying priming in OM decomposition. Monte Carlo simulations for these scenarios revealed: (1) positive priming is prevalent, while negative priming occurs sporadically under specific parameter settings; (2) positive priming is more frequently observed in microbial systems with copiotrophic degraders than those with oligotrophic degraders; (3) the presence of copiotrophic non-degraders suppresses positive priming, whereas the presence of oligotrophic non-degraders promotes it; and (4) the temporal dynamics of priming is also influenced by microbial growth traits and interactions. These findings highlight the driving role of microbial functional traits and interactions in priming. Most strikingly, our simulations predicted a dramatic positive priming effect triggered by the addition of a small amount (i.e., less than 10 %) of labile OM, with no notable changes observed beyond this point. As we used a generalized microbial model, we hypothesize that our findings may reflect common features of OM priming across diverse microbial systems and environments. Overall, this work, combining new theories and models, significantly enhances our understanding of priming by providing model-generated and empirically testable hypotheses on the mechanisms governing it.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
Journal article(s) based on this preprint
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2026-1571', Anonymous Referee #1, 21 Apr 2026
- AC1: 'Reply on RC1', Hyun-Seob Song, 07 Jul 2026
-
RC2: 'Comment on egusphere-2026-1571', Anonymous Referee #2, 02 Jun 2026
Summary.
In this modeling paper, the authors simulated how the priming effect would vary depending on microbial growth traits, specifically differences between copiotrophs and oligotrophs. In the main text, the authors present four model simulations: two single functional group simulations (either copiotrophic or oligotrophic degraders) and two binary functional group simulations (a copiotrophic degrader paired with an oligotrophic non-degrader, or vice versa). The authors use a technique called cybernetic modelling: in their model, complex OM can be degraded to labile OM via the production of exoenzymes, which can then be consumed by microbes, stimulating microbial growth and further exoenzyme production in a feedback loop. The modelling technique allows for microbes to incorporate trade-offs in growth vs. enzyme production over finite time horizons (e.g. allocating energy to enzyme production to favor future growth outcomes, or vice versa). Overwhelmingly, the authors found cumulative positive priming in their model results, with interesting variations across different functional group simulations, with negative priming only occasionally observed when oligotrophic degraders are present. However, temporal modelling reveals that positive priming initially dominates while negative priming starts to become more prevalent as time goes on (complex OM is depleted), suggest the potential for priming effects to invert over time in real ecosystems.
General comments.
This was a very interesting and thorough modelling paper which prompted me to think about microbial regulation of the priming effect. Especially interesting was the authors’ finding that magnitude/direction of priming effects may be partially explained by the presence/absence of non-degrading microbes in a system, which is something experimentalists do not generally measure. Surely these results will prompt some interesting experiments and future modelling work.
As I am not a modeler myself, many of my comments are rooted in exploring the biological realism of the model, and I encourage the authors to be explicit in what the model is simulating biologically where they can. The manuscript would also benefit from some more research into what has been found empirically, and discussion of if/why this varies from the model outcomes. Some general points that I think could be clarified/reviewed in the manuscript:
- Define the explicit difference between copiotrophs and oligotrophs in the model (and if/how this varies from classical definition). Consider using more explicit terminology than “microbial growth traits”
- Review the presence of positive vs. negative priming in the literature and how this compares to the model
- Review the proportion of labile vs. complex OM in real ecosystems and in priming experiments and how this compares to the model (I think 10% labile OM is actually quite large)
Specific comments.
As there were no line numbers in the submitted manuscript, I’ve just grouped my in-line comments by the section. If further review is needed, it would be good if the authors could submit a numbered manuscript.
Abstract:
Less than 10% of what?
I think the abstract should include some brief discussion of the biological relevance of the findings. The finding that copiotrophs induce positive priming is consistent with the idea that priming effect is a byproduct of microbial nutrient mining, right?
Introduction:
Paragraph 1:
What is debated? Whether priming occurs? There is no evidence provided to suggest that priming does not occur.
Paragraph 3:
This would be a good paragraph to provide some reasons why a microbe would allocate C/energy to growth vs exoenzyme production. This would be a good place to bring in stoichiometry.
Paragraph 4:
This is an interesting topic. Is there any information known about the proportion of degrading to non-degrading microbes in real ecosystems? This would be a good place to look into that and bring it in.
Paragraph 7:
This paragraph should include a statement precisely defining cybernetic modelling. Most biogeochemists/ecologists won’t know what that is.
Which enzymes/enzyme classes can microbes produce in this model? I’m sure this is addressed later but some brief discussion in the intro would also be good.
Methods:
Section 2.1:
In the model, is it possible to vary the “quality” of complex OM such that “energy return from degrading complex OM” can vary? Otherwise how does energy return vary? Is the energy return just different for the different types of organisms (e.g. copiotroph, degrader, etc.) that are modeled?
Section 2.2:
To help the reader follow equations, consider providing units when defining each term in the equations (e.g. some terms are in unit mass, others are in unit mass per unit time, etc). It could be good to put these units along with the definition of each term in Table S1 for easy access to the interested reader.
I don’t think Eiand eiare defined anywhere. These are the exoenzyme and endoenzyme concentrations right?
Section 2.3:
“Complex OM degradation kinetic parameters were also fixed”… this means that the only thing impacting the energy return from complex OM is microbial growth traits, right? I would make this explicit in Section 2.1 where you talk about energy return.
“Only exoenzyme and endoenzyme synthesis kinetic parameters are randomized in Monte Carlo simulations”… biomass yield (YX,i) is randomized according to Table S1. This isn’t an enzyme synthesis parameter, is it?
The parameters that distinguish between copiotrophs and oligotrophs in the model are kS,iand KS,i. From the supplementary table, copiotrophs have a higher rate of maximum OM uptake (about doubled) and a much higher saturation constant (one order of magnitude). My conclusion is that copiotrophs are defined in the model by their ability to uptake more labile OM than oligotrophs. I think a sentence like the previous explicitly explaining what differentiates copiotrophs and oligotrophs (biologically, not mathematically) in this model is important. (Also, a sentence about how the fixed numbers for the two differentiating parameters were chosen.) The differentiation between copiotrophs and oligotrophs in the model does not match precisely with biological definitions of copiotrophy and oligotrophy, which is OK, but it needs to be defined.
Section 2.4:
This section lost me, and I don’t have the expertise to comment on it. A number of terms (A, Bui, pui) are not defined explicitly, but maybe we are getting outside the realm of precise physical definitions here and it’s not as important.
Results:
Section 3.1:
There are no instances of negative priming in the CDG model. Is it mathematically impossible to have negative priming based on the input parameters in the CDG model? If so, why, and is there a biological basis for this? Perhaps there will be more discussion of this later…
“Owing to the inherent growth traits of copiotrophs and oligotrophs, the population level of CDG is lower at pure complex OM while the population level of ODG is higher.” This sentence should be rephrased to be more explicit. For example (not sure if this interpretation is totally correct), “Because copiotrophs have higher labile OM uptake rates than complex OM decomposition rates relative to oligotrophs, oligotrophs are better able to sustain population levels under pure complex OM, resulting in greater biomass under pure OM in ODG than CDG.”
Section 3.3:
I would like to see some discussion of the actual timing in this section. The time is in hours. After how many hours did you start to observe a positive priming effect? After how many hours did the positive priming effect go away?
Discussion:
Section 4.1:
This less than 10% number needs a bit more qualifying. 10% labile OM seems very large to me. Imagine if I suddenly added 10% of the carbon pre-existing in a soil sample as labile glucose. I would see a huge priming effect. In nature, labile C (e.g. as root exudates) is not added to systems at such high rates. In artificial substrate addition experiments, I think it will also be rare to see rates of substrate addition this high.
The finding of overwhelmingly positive priming effects should be discussed within the context of the existing empirical literature. I think positive priming effects are probably found more often than negative priming effects, but not overwhelmingly so as in the model. What does this mean? Are we measuring the priming effect inaccurately? Is the model predicting priming inaccurately? Or are there certain systems where negative priming may be more likely, where this model does not apply as well?
Section 4.2:
Interesting discussion of the temporal dynamics. This suggests to me that soils which are receiving constant labile C inputs (i.e. anywhere with plants) will be less likely to exhibit a positive priming effect than soils which are, say, newly colonized by plants. Or a seasonal aspect – perhaps a greater priming effect in the beginning of the growing season than the end?
Section 4.3:
“Oligotrophs can deplete labile OM to concentrations well below the threshold required for copiotrophs to persist…” I’m not sure I follow this argument, as it applies to this system. Oligotrophs have lower rates of labile OM uptake – wouldn’t that leave more OM left over for the copiotrophic degraders than if it was a copiotrophic non-degrader instead of an oligotrophic non-degrader?
Section 4.4:
It seems like the argument employed in this section (that CDG dominates OND biomass because OND do not consume enough OM to capitalize on priming by CDG) is at odds with the argument employed in the previous section (that OND deplete OM to low enough concentrations that CDG struggle to persist).
Section 4.5:
See my earlier comments on the wording of “small amount” re. 10%. It might be interesting to have a section focusing on just what happens between 0 and 10% labile OM, or even 0 and 5%.
Citation: https://doi.org/10.5194/egusphere-2026-1571-RC2 - AC2: 'Reply on RC2', Hyun-Seob Song, 07 Jul 2026
Peer review completion
Post-review adjustments
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2026-1571', Anonymous Referee #1, 21 Apr 2026
This manuscript presents a novel theoretical framework for positive and negative priming effects, grounded in microbial growth traits (copiotrophs versus oligotrophs), dynamic metabolic regulation, and interactions between degraders and non‑degraders. The use of cybernetic modelling to capture resource‑allocation trade‑offs is a major strength. To this reviewer's knowledge, the combination of optimal‑control‑derived cybernetic laws with Monte Carlo ensembles across community configurations is new in the priming literature. The work explores multiple functional‑group setups, generates clear testable hypotheses (most notably that <10% labile organic matter [OM] addition triggers near‑maximal positive priming), and offers a substantial advance for biogeochemical modelling. The manuscript is well‑written logically structured and moves the field beyond correlative trait–priming links toward a process‑based regulatory mechanism. This reviewer has no major scientific concerns and recommends publication after revisions. The points below are intended to stimulate constructive discussion.
First, the balance between generalisation and oversimplification in the representation of microbial traits warrants discussion. Although the copiotroph-oligotroph distinction provides a useful conceptual scaffold, real soil communities occupy a continuum and exhibit substantial metabolic flexibility. In the model (page 7), these traits are enforced by fixing the kinetic parameters governing labile-OM uptake (maximum rate kS,i and saturation constant KS,i) and by imposing a binary exoenzyme‑synthesis rule (degraders: dEi/dt ≠ 0; non‑degraders: dEi/dt = 0). While this simplifies interpretation, it also raises the question of whether such predefined boundaries might artificially constrain the emergence of negative priming, particularly given that some copiotrophs can exhibit slow growth under stressful conditions. The authors should thus discuss the sensitivity of their results to the specific threshold values chosen for kS,i and KS,i, and whether relaxing these contrasts (e.g. allowing partial overlap or condition‑dependent shifts) would alter the qualitative patterns. Relatedly, Section 4.6 correctly highlights the omission of nutrient stoichiometry, nitrogen limitation and mineral protection, factors known to modulate microbial strategies and priming responses (page 17). A short additional paragraph, or ideally a supplementary sensitivity test exploring how relaxing the fixed complex OM parameters (kZ, KZ, YS) or introducing a simple nitrogen co‑limitation term affects the copiotroph/oligotroph contrast, would substantially strengthen the manuscript. Such an analysis would directly address a common critique of purely carbon‑centric priming models and help clarify whether the observed trait‑based patterns are robust to broader biogeochemical constraints.
Second, the definition of non‑degraders and the treatment of metabolic “cheating” require clarification. Non‑degraders are implemented as completely unable to synthesise exoenzymes (“non‑degraders (dEi/dt = 0)”, page 7). In nature, many microbes produce low baseline levels or can switch strategies. This simplification is acceptable for tractability but becomes relevant given for the counterintuitive result (page 10) that oligotrophic non-degraders (OND) promote positive priming when paired with copiotrophic degraders (CDG). The R* explanation (page 14) is plausible, but establishing a more explicit mechanistic link between OND's slow substrate uptake and increased exoenzyme investment by CDG would provide a clearer explanation of the feedback. In addition, the model assumes a well‑mixed batch system, while many cited empirical contexts (e.g. hyporheic zones, rhizosphere; pages 3, 13) are transport-limited. A brief discussion of how advective/diffusive constraints might shift the ~10 % threshold or temporal dynamics would improve translation to field conditions. A supplementary figure showing example time series of E (exoenzyme), e (endoenzyme), S (labile substrate) and Z (complex OM) for one representative parameter set would nicely illustrate the regulatory loop.
Third, the temporal dynamics of priming warrant closer examination. Figures 2–4 are clear, but adding a small inset or secondary y‑axis to show the fractional contribution of each functional group to total priming (CDG–OND versus ODG–CND) would make interaction effects even more intuitive. Specifically, Figure 4 shows negative instantaneous priming emerging late and at high n (labile fraction). The authors attribute this to a switch toward accumulated labile-OM consumption (page 13). It would be helpful to comment on whether explicit maintenance respiration, cell death or necromass recycling would shift the timing or magnitude of this transition, as the current model lacks these terms.
Fourth, the apparent threshold behaviour deserves additional sensitivity analysis. The saturation of positive priming beyond ~10% labile OM is striking and empirically testable. However, the threshold depends on the fixed assumptions YS>1 (“YS must be greater than 1”, page 7) and fixed complex-OM kinetics (“the complex OM degradation kinetic parameters … were also fixed”, page 7). Although limitations are acknowledged, a brief sensitivity analysis, e.g. varying YS from 2 to 10, would confirm that the ~10% threshold is a general feature rather than a model artefact. For clarity, “less than 10 %” should be explicitly defined as the proportion of labile OM within the total OM mixture on pages 2 and 12.
Minor comments include equation formatting, clarity, missing parameter tables and typographical issues:
- It would be helpful to state explicitly whether the cybernetic time horizon in Eq. (9) (Δt) is fixed or state-dependent, and to indicate how sensitive the results are to this value.
- For Eq. (11) and following, defining ΔZ explicitly as the mass or amount of carbon (or OM) mineralised would help readers distinguish between changes in mineralisation fluxes and changes in substrate pools. In terms of terminology, the use of “amended” (page 11) and “control” (at various instances throughout the text) is consistent. However, to avoid ambiguity regarding the mineralisation of added labile organic matter (OM), it would be better to specify that “degraded complex OM” (page 9) refers exclusively to the organic matter that occurs naturally in soil. This would strengthen the clarity of the priming interpretation.
- The authors may consider rephrasing the text of Eq. (12), “The threshold above is low enough that it will no longer drive priming”, to “… low enough that priming becomes negligible”.
- In Eqs. (13) and (14), the index j=2, 3, … and the summation should explicitly state the time discretisation used (e.g. the timestep Δt or the discrete time grid) to improve clarity and reproducibility.
- The caption of Figure 3 states that “markers are individual runs with an overall negative priming effect”. The top panel of Figure 3A displays markers with negative relative priming (Prel). The authors should clarify whether all markers shown correspond to simulations with an overall negative priming effect, or only a subset of runs. This would avoid ambiguity in interpreting the patterns displayed.
- The long sentence on page 15, “This is because ODG is at a greater disadvantage in a labile OM-rich environment compared to OND because although both are unable to competitively utilize the surplus labile OM, ODG must continue synthesizing exoenzymes to maintain the labile OM level in the environment, incurring additional metabolic costs”, would benefit from splitting or rephrasing for readibility.
- The authors may consider revising the phrase on page 5, “degraders and non-degraders, each with either copiotrophic or oligotrophic growth traits”, which repeats the abstract wording almost verbatim (“degraders and non-degraders with either oligotrophic or copiotrophic growth traits” on page 2).
- Tables S1 (parameter ranges) and S2 (summary of outcomes) are referenced, but this reviewer failed to find them attached to the preprint. To facilitate public discussion, either these tables should be provided, or the key randomised ranges and outcomes should be listed in the main text together with the random sampling procedure and seed to ensure reproducibility.
Citation: https://doi.org/10.5194/egusphere-2026-1571-RC1 - AC1: 'Reply on RC1', Hyun-Seob Song, 07 Jul 2026
-
RC2: 'Comment on egusphere-2026-1571', Anonymous Referee #2, 02 Jun 2026
Summary.
In this modeling paper, the authors simulated how the priming effect would vary depending on microbial growth traits, specifically differences between copiotrophs and oligotrophs. In the main text, the authors present four model simulations: two single functional group simulations (either copiotrophic or oligotrophic degraders) and two binary functional group simulations (a copiotrophic degrader paired with an oligotrophic non-degrader, or vice versa). The authors use a technique called cybernetic modelling: in their model, complex OM can be degraded to labile OM via the production of exoenzymes, which can then be consumed by microbes, stimulating microbial growth and further exoenzyme production in a feedback loop. The modelling technique allows for microbes to incorporate trade-offs in growth vs. enzyme production over finite time horizons (e.g. allocating energy to enzyme production to favor future growth outcomes, or vice versa). Overwhelmingly, the authors found cumulative positive priming in their model results, with interesting variations across different functional group simulations, with negative priming only occasionally observed when oligotrophic degraders are present. However, temporal modelling reveals that positive priming initially dominates while negative priming starts to become more prevalent as time goes on (complex OM is depleted), suggest the potential for priming effects to invert over time in real ecosystems.
General comments.
This was a very interesting and thorough modelling paper which prompted me to think about microbial regulation of the priming effect. Especially interesting was the authors’ finding that magnitude/direction of priming effects may be partially explained by the presence/absence of non-degrading microbes in a system, which is something experimentalists do not generally measure. Surely these results will prompt some interesting experiments and future modelling work.
As I am not a modeler myself, many of my comments are rooted in exploring the biological realism of the model, and I encourage the authors to be explicit in what the model is simulating biologically where they can. The manuscript would also benefit from some more research into what has been found empirically, and discussion of if/why this varies from the model outcomes. Some general points that I think could be clarified/reviewed in the manuscript:
- Define the explicit difference between copiotrophs and oligotrophs in the model (and if/how this varies from classical definition). Consider using more explicit terminology than “microbial growth traits”
- Review the presence of positive vs. negative priming in the literature and how this compares to the model
- Review the proportion of labile vs. complex OM in real ecosystems and in priming experiments and how this compares to the model (I think 10% labile OM is actually quite large)
Specific comments.
As there were no line numbers in the submitted manuscript, I’ve just grouped my in-line comments by the section. If further review is needed, it would be good if the authors could submit a numbered manuscript.
Abstract:
Less than 10% of what?
I think the abstract should include some brief discussion of the biological relevance of the findings. The finding that copiotrophs induce positive priming is consistent with the idea that priming effect is a byproduct of microbial nutrient mining, right?
Introduction:
Paragraph 1:
What is debated? Whether priming occurs? There is no evidence provided to suggest that priming does not occur.
Paragraph 3:
This would be a good paragraph to provide some reasons why a microbe would allocate C/energy to growth vs exoenzyme production. This would be a good place to bring in stoichiometry.
Paragraph 4:
This is an interesting topic. Is there any information known about the proportion of degrading to non-degrading microbes in real ecosystems? This would be a good place to look into that and bring it in.
Paragraph 7:
This paragraph should include a statement precisely defining cybernetic modelling. Most biogeochemists/ecologists won’t know what that is.
Which enzymes/enzyme classes can microbes produce in this model? I’m sure this is addressed later but some brief discussion in the intro would also be good.
Methods:
Section 2.1:
In the model, is it possible to vary the “quality” of complex OM such that “energy return from degrading complex OM” can vary? Otherwise how does energy return vary? Is the energy return just different for the different types of organisms (e.g. copiotroph, degrader, etc.) that are modeled?
Section 2.2:
To help the reader follow equations, consider providing units when defining each term in the equations (e.g. some terms are in unit mass, others are in unit mass per unit time, etc). It could be good to put these units along with the definition of each term in Table S1 for easy access to the interested reader.
I don’t think Eiand eiare defined anywhere. These are the exoenzyme and endoenzyme concentrations right?
Section 2.3:
“Complex OM degradation kinetic parameters were also fixed”… this means that the only thing impacting the energy return from complex OM is microbial growth traits, right? I would make this explicit in Section 2.1 where you talk about energy return.
“Only exoenzyme and endoenzyme synthesis kinetic parameters are randomized in Monte Carlo simulations”… biomass yield (YX,i) is randomized according to Table S1. This isn’t an enzyme synthesis parameter, is it?
The parameters that distinguish between copiotrophs and oligotrophs in the model are kS,iand KS,i. From the supplementary table, copiotrophs have a higher rate of maximum OM uptake (about doubled) and a much higher saturation constant (one order of magnitude). My conclusion is that copiotrophs are defined in the model by their ability to uptake more labile OM than oligotrophs. I think a sentence like the previous explicitly explaining what differentiates copiotrophs and oligotrophs (biologically, not mathematically) in this model is important. (Also, a sentence about how the fixed numbers for the two differentiating parameters were chosen.) The differentiation between copiotrophs and oligotrophs in the model does not match precisely with biological definitions of copiotrophy and oligotrophy, which is OK, but it needs to be defined.
Section 2.4:
This section lost me, and I don’t have the expertise to comment on it. A number of terms (A, Bui, pui) are not defined explicitly, but maybe we are getting outside the realm of precise physical definitions here and it’s not as important.
Results:
Section 3.1:
There are no instances of negative priming in the CDG model. Is it mathematically impossible to have negative priming based on the input parameters in the CDG model? If so, why, and is there a biological basis for this? Perhaps there will be more discussion of this later…
“Owing to the inherent growth traits of copiotrophs and oligotrophs, the population level of CDG is lower at pure complex OM while the population level of ODG is higher.” This sentence should be rephrased to be more explicit. For example (not sure if this interpretation is totally correct), “Because copiotrophs have higher labile OM uptake rates than complex OM decomposition rates relative to oligotrophs, oligotrophs are better able to sustain population levels under pure complex OM, resulting in greater biomass under pure OM in ODG than CDG.”
Section 3.3:
I would like to see some discussion of the actual timing in this section. The time is in hours. After how many hours did you start to observe a positive priming effect? After how many hours did the positive priming effect go away?
Discussion:
Section 4.1:
This less than 10% number needs a bit more qualifying. 10% labile OM seems very large to me. Imagine if I suddenly added 10% of the carbon pre-existing in a soil sample as labile glucose. I would see a huge priming effect. In nature, labile C (e.g. as root exudates) is not added to systems at such high rates. In artificial substrate addition experiments, I think it will also be rare to see rates of substrate addition this high.
The finding of overwhelmingly positive priming effects should be discussed within the context of the existing empirical literature. I think positive priming effects are probably found more often than negative priming effects, but not overwhelmingly so as in the model. What does this mean? Are we measuring the priming effect inaccurately? Is the model predicting priming inaccurately? Or are there certain systems where negative priming may be more likely, where this model does not apply as well?
Section 4.2:
Interesting discussion of the temporal dynamics. This suggests to me that soils which are receiving constant labile C inputs (i.e. anywhere with plants) will be less likely to exhibit a positive priming effect than soils which are, say, newly colonized by plants. Or a seasonal aspect – perhaps a greater priming effect in the beginning of the growing season than the end?
Section 4.3:
“Oligotrophs can deplete labile OM to concentrations well below the threshold required for copiotrophs to persist…” I’m not sure I follow this argument, as it applies to this system. Oligotrophs have lower rates of labile OM uptake – wouldn’t that leave more OM left over for the copiotrophic degraders than if it was a copiotrophic non-degrader instead of an oligotrophic non-degrader?
Section 4.4:
It seems like the argument employed in this section (that CDG dominates OND biomass because OND do not consume enough OM to capitalize on priming by CDG) is at odds with the argument employed in the previous section (that OND deplete OM to low enough concentrations that CDG struggle to persist).
Section 4.5:
See my earlier comments on the wording of “small amount” re. 10%. It might be interesting to have a section focusing on just what happens between 0 and 10% labile OM, or even 0 and 5%.
Citation: https://doi.org/10.5194/egusphere-2026-1571-RC2 - AC2: 'Reply on RC2', Hyun-Seob Song, 07 Jul 2026
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Firnaaz Ahamed
Emily B. Graham
Timothy D. Scheibe
Hyun-Seob Song
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
This manuscript presents a novel theoretical framework for positive and negative priming effects, grounded in microbial growth traits (copiotrophs versus oligotrophs), dynamic metabolic regulation, and interactions between degraders and non‑degraders. The use of cybernetic modelling to capture resource‑allocation trade‑offs is a major strength. To this reviewer's knowledge, the combination of optimal‑control‑derived cybernetic laws with Monte Carlo ensembles across community configurations is new in the priming literature. The work explores multiple functional‑group setups, generates clear testable hypotheses (most notably that <10% labile organic matter [OM] addition triggers near‑maximal positive priming), and offers a substantial advance for biogeochemical modelling. The manuscript is well‑written logically structured and moves the field beyond correlative trait–priming links toward a process‑based regulatory mechanism. This reviewer has no major scientific concerns and recommends publication after revisions. The points below are intended to stimulate constructive discussion.
First, the balance between generalisation and oversimplification in the representation of microbial traits warrants discussion. Although the copiotroph-oligotroph distinction provides a useful conceptual scaffold, real soil communities occupy a continuum and exhibit substantial metabolic flexibility. In the model (page 7), these traits are enforced by fixing the kinetic parameters governing labile-OM uptake (maximum rate kS,i and saturation constant KS,i) and by imposing a binary exoenzyme‑synthesis rule (degraders: dEi/dt ≠ 0; non‑degraders: dEi/dt = 0). While this simplifies interpretation, it also raises the question of whether such predefined boundaries might artificially constrain the emergence of negative priming, particularly given that some copiotrophs can exhibit slow growth under stressful conditions. The authors should thus discuss the sensitivity of their results to the specific threshold values chosen for kS,i and KS,i, and whether relaxing these contrasts (e.g. allowing partial overlap or condition‑dependent shifts) would alter the qualitative patterns. Relatedly, Section 4.6 correctly highlights the omission of nutrient stoichiometry, nitrogen limitation and mineral protection, factors known to modulate microbial strategies and priming responses (page 17). A short additional paragraph, or ideally a supplementary sensitivity test exploring how relaxing the fixed complex OM parameters (kZ, KZ, YS) or introducing a simple nitrogen co‑limitation term affects the copiotroph/oligotroph contrast, would substantially strengthen the manuscript. Such an analysis would directly address a common critique of purely carbon‑centric priming models and help clarify whether the observed trait‑based patterns are robust to broader biogeochemical constraints.
Second, the definition of non‑degraders and the treatment of metabolic “cheating” require clarification. Non‑degraders are implemented as completely unable to synthesise exoenzymes (“non‑degraders (dEi/dt = 0)”, page 7). In nature, many microbes produce low baseline levels or can switch strategies. This simplification is acceptable for tractability but becomes relevant given for the counterintuitive result (page 10) that oligotrophic non-degraders (OND) promote positive priming when paired with copiotrophic degraders (CDG). The R* explanation (page 14) is plausible, but establishing a more explicit mechanistic link between OND's slow substrate uptake and increased exoenzyme investment by CDG would provide a clearer explanation of the feedback. In addition, the model assumes a well‑mixed batch system, while many cited empirical contexts (e.g. hyporheic zones, rhizosphere; pages 3, 13) are transport-limited. A brief discussion of how advective/diffusive constraints might shift the ~10 % threshold or temporal dynamics would improve translation to field conditions. A supplementary figure showing example time series of E (exoenzyme), e (endoenzyme), S (labile substrate) and Z (complex OM) for one representative parameter set would nicely illustrate the regulatory loop.
Third, the temporal dynamics of priming warrant closer examination. Figures 2–4 are clear, but adding a small inset or secondary y‑axis to show the fractional contribution of each functional group to total priming (CDG–OND versus ODG–CND) would make interaction effects even more intuitive. Specifically, Figure 4 shows negative instantaneous priming emerging late and at high n (labile fraction). The authors attribute this to a switch toward accumulated labile-OM consumption (page 13). It would be helpful to comment on whether explicit maintenance respiration, cell death or necromass recycling would shift the timing or magnitude of this transition, as the current model lacks these terms.
Fourth, the apparent threshold behaviour deserves additional sensitivity analysis. The saturation of positive priming beyond ~10% labile OM is striking and empirically testable. However, the threshold depends on the fixed assumptions YS>1 (“YS must be greater than 1”, page 7) and fixed complex-OM kinetics (“the complex OM degradation kinetic parameters … were also fixed”, page 7). Although limitations are acknowledged, a brief sensitivity analysis, e.g. varying YS from 2 to 10, would confirm that the ~10% threshold is a general feature rather than a model artefact. For clarity, “less than 10 %” should be explicitly defined as the proportion of labile OM within the total OM mixture on pages 2 and 12.
Minor comments include equation formatting, clarity, missing parameter tables and typographical issues:
- It would be helpful to state explicitly whether the cybernetic time horizon in Eq. (9) (Δt) is fixed or state-dependent, and to indicate how sensitive the results are to this value.
- For Eq. (11) and following, defining ΔZ explicitly as the mass or amount of carbon (or OM) mineralised would help readers distinguish between changes in mineralisation fluxes and changes in substrate pools. In terms of terminology, the use of “amended” (page 11) and “control” (at various instances throughout the text) is consistent. However, to avoid ambiguity regarding the mineralisation of added labile organic matter (OM), it would be better to specify that “degraded complex OM” (page 9) refers exclusively to the organic matter that occurs naturally in soil. This would strengthen the clarity of the priming interpretation.
- The authors may consider rephrasing the text of Eq. (12), “The threshold above is low enough that it will no longer drive priming”, to “… low enough that priming becomes negligible”.
- In Eqs. (13) and (14), the index j=2, 3, … and the summation should explicitly state the time discretisation used (e.g. the timestep Δt or the discrete time grid) to improve clarity and reproducibility.
- The caption of Figure 3 states that “markers are individual runs with an overall negative priming effect”. The top panel of Figure 3A displays markers with negative relative priming (Prel). The authors should clarify whether all markers shown correspond to simulations with an overall negative priming effect, or only a subset of runs. This would avoid ambiguity in interpreting the patterns displayed.
- The long sentence on page 15, “This is because ODG is at a greater disadvantage in a labile OM-rich environment compared to OND because although both are unable to competitively utilize the surplus labile OM, ODG must continue synthesizing exoenzymes to maintain the labile OM level in the environment, incurring additional metabolic costs”, would benefit from splitting or rephrasing for readibility.
- The authors may consider revising the phrase on page 5, “degraders and non-degraders, each with either copiotrophic or oligotrophic growth traits”, which repeats the abstract wording almost verbatim (“degraders and non-degraders with either oligotrophic or copiotrophic growth traits” on page 2).
- Tables S1 (parameter ranges) and S2 (summary of outcomes) are referenced, but this reviewer failed to find them attached to the preprint. To facilitate public discussion, either these tables should be provided, or the key randomised ranges and outcomes should be listed in the main text together with the random sampling procedure and seed to ensure reproducibility.