An Optimal Control Theory-based Microbial Regulation Model to Offer a
Generalizable Theory for Priming Effects
Priming leads to the significant changes in the decomposition rate of
organic matter (OM) in natural ecosystems induced by minimal treatments.
A fundamental understanding of priming effects is critical to accurately
predict biogeochemical dynamics and carbon/nitrogen OM cycles in natural
ecosystems. However, we poorly understand how the priming effect is
mechanistically induced and what factors govern the process among
microbial activities and environmental constraints. Here, we propose a
generalizable theory to collectively explain diverse patterns of priming
effects via the cybernetic approach that accounts for regulation as key
features of microbial growth. The cybernetic model treats microorganisms
as dynamic systems that optimally regulate metabolic functions with
respect to environmental conditions to safeguard their survival.
Motivated by priming phenomenon observed in the hyporheic corridor of a
riverine ecosystem, we formulated our model to investigate how the
addition of exogenous labile OM primes the microbial respiration of
polymeric OM. Our model accounts for interspecies interactions between
various assortments of microbial groups with distinct metabolic traits
to enable prediction of both increase (positive priming) and decrease
(negative priming) of OM turnover using the same model structure. Our
modeling framework reveals that: (1) the priming effects are
manifestations of microbial regulatory response to diverse environmental
conditions, and (2) priming magnitude and direction are highly dependent
on the polymeric OM richness and the extent of treatment with labile OM.
Beyond elucidating qualitative understanding of the phenomenon, our
model also suggests that interspecies interactions between microbial
groups with distinct metabolic traits (i.e., population turnover,
sensitivity to labile OM, and efficiency in degrading polymeric OM)
potentially drive the priming effects. By integrating contextual
knowledge and a generalizable theory, our holistic modeling framework is
effective for investigation and prediction of biogeochemical dynamics of
natural ecosystems across diverse biological and environmental settings.