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.
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.