Abstract
AbstractMicrobial priming, characterized by significant changes in organic matter (OM) decomposition rates due to minor external treatments with the addition of labile OM, exerts a significant impact on biogeochemical cycles in ecosystems. Priming can take many forms, including positive priming (increased OM decomposition rates), negative priming (decreased OM decomposition rates), and everything in between. Currently, we lack generalizable frameworks that can mechanistically explain these diverse patterns of priming, making it challenging to identify its governing factors. In this work, we theorized priming to result from a biogeochemical feedback loop regulated by microorganisms optimizing the balance between cost and benefit towards maximizing their growth rates, i.e., the cost of exoenzyme synthesis for decomposing complex OM and the benefits of energy acquisition from microbial growth on labile OM. Accordingly, we examined the impacts of microbial growth traits and interactions on priming employing a cybernetic approach, which specializes in predicting complex microbial growth patterns through a regulatory feedback loop. Using the cybernetic model, we simulated the occurrence of priming driven by microorganisms in the following four distinct settings: copiotrophic degraders independently, oligotrophic degraders independently, a consortium of copiotrophic degraders and oligotrophic non-degraders, and a consortium of oligotrophic degraders and copiotrophic non-degraders. Comprehensive Monte Carlo simulations using these four models revealed several critical aspects of priming, including: (1) positive priming is a dominant phenomenon in general, while negative priming can also occur sporadically under specific parameter settings, (2) positive priming is more frequently observed in microbial systems with copiotrophic degraders than with oligotrophic degraders, (3) the presence of copiotrophic non-degraders suppresses positive priming, while the presence of oligotrophic non-degraders promotes positive priming, and (4) the evolution of priming over time is also influenced by microbial growth traits and interactions. Most strikingly, all four models predicted a dramatic positive priming effect triggered by the addition of a small amount (i.e., less than 10%) of labile organic matter, with no notable changes observed beyond this point. Together with other findings summarized above, this might represent a key feature of microbial priming that might be commonly observed across microbial systems with diverse growth traits as supported by literature data. 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 priming.
Publisher
Cold Spring Harbor Laboratory