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Representation of dormant and active microbial dynamics for ecosystem modeling.

Wang G, Mayes MA, Gu L, Schadt CW - PLoS ONE (2014)

Bottom Line: However, global ecosystem models typically ignore microbial dormancy, resulting in notable model uncertainties.Our new model shows that the exponentially-increasing respiration from substrate-induced respiration experiments can only be used to determine the maximum specific growth rate and initial active microbial biomass, while the respiration data representing both exponentially-increasing and non-exponentially-increasing phases can robustly determine a range of key parameters including the initial total live biomass, initial active fraction, the maximum specific growth and maintenance rates, and the half-saturation constant.Our new model can be incorporated into existing ecosystem models to account for dormancy in microbially-driven processes and to provide improved estimates of microbial activities.

View Article: PubMed Central - PubMed

Affiliation: Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America ; Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America.

ABSTRACT
Dormancy is an essential strategy for microorganisms to cope with environmental stress. However, global ecosystem models typically ignore microbial dormancy, resulting in notable model uncertainties. To facilitate the consideration of dormancy in these large-scale models, we propose a new microbial physiology component that works for a wide range of substrate availabilities. This new model is based on microbial physiological states and the major parameters are the maximum specific growth and maintenance rates of active microbes and the ratio of dormant to active maintenance rates. A major improvement of our model over extant models is that it can explain the low active microbial fractions commonly observed in undisturbed soils. Our new model shows that the exponentially-increasing respiration from substrate-induced respiration experiments can only be used to determine the maximum specific growth rate and initial active microbial biomass, while the respiration data representing both exponentially-increasing and non-exponentially-increasing phases can robustly determine a range of key parameters including the initial total live biomass, initial active fraction, the maximum specific growth and maintenance rates, and the half-saturation constant. Our new model can be incorporated into existing ecosystem models to account for dormancy in microbially-driven processes and to provide improved estimates of microbial activities.

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Steady state active fraction (rss) and substrate saturation level () as a function of α and β; α  =  mR/(μG+mR), μG and mR (h−1) are maximum specific growth rate and specific maintenance rate for active microbial biomass, respectivly; β denotes the ratio of dormant specific maintenance rate to mR.
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pone-0089252-g002: Steady state active fraction (rss) and substrate saturation level () as a function of α and β; α  =  mR/(μG+mR), μG and mR (h−1) are maximum specific growth rate and specific maintenance rate for active microbial biomass, respectivly; β denotes the ratio of dormant specific maintenance rate to mR.

Mentions: Assuming the input (Is) is time-invariant, we can obtain the steady state solution to the above new MEND model (see Equations S2-3(a–e) in Appendix S2). Fig. 2 shows the steady state active fraction (rss) and substrate saturation level () as a function of the two physiological indices, i.e., α (0–0.5) and β (0–1). Both rss and positively depend on α and β and rss≥ for any combinations of α and β. If we consider two extreme values of β→0 or β→1, the rss and (see Equations S2-4 and S2-5 in Appendix S2) can be simplified to


Representation of dormant and active microbial dynamics for ecosystem modeling.

Wang G, Mayes MA, Gu L, Schadt CW - PLoS ONE (2014)

Steady state active fraction (rss) and substrate saturation level () as a function of α and β; α  =  mR/(μG+mR), μG and mR (h−1) are maximum specific growth rate and specific maintenance rate for active microbial biomass, respectivly; β denotes the ratio of dormant specific maintenance rate to mR.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3928434&req=5

pone-0089252-g002: Steady state active fraction (rss) and substrate saturation level () as a function of α and β; α  =  mR/(μG+mR), μG and mR (h−1) are maximum specific growth rate and specific maintenance rate for active microbial biomass, respectivly; β denotes the ratio of dormant specific maintenance rate to mR.
Mentions: Assuming the input (Is) is time-invariant, we can obtain the steady state solution to the above new MEND model (see Equations S2-3(a–e) in Appendix S2). Fig. 2 shows the steady state active fraction (rss) and substrate saturation level () as a function of the two physiological indices, i.e., α (0–0.5) and β (0–1). Both rss and positively depend on α and β and rss≥ for any combinations of α and β. If we consider two extreme values of β→0 or β→1, the rss and (see Equations S2-4 and S2-5 in Appendix S2) can be simplified to

Bottom Line: However, global ecosystem models typically ignore microbial dormancy, resulting in notable model uncertainties.Our new model shows that the exponentially-increasing respiration from substrate-induced respiration experiments can only be used to determine the maximum specific growth rate and initial active microbial biomass, while the respiration data representing both exponentially-increasing and non-exponentially-increasing phases can robustly determine a range of key parameters including the initial total live biomass, initial active fraction, the maximum specific growth and maintenance rates, and the half-saturation constant.Our new model can be incorporated into existing ecosystem models to account for dormancy in microbially-driven processes and to provide improved estimates of microbial activities.

View Article: PubMed Central - PubMed

Affiliation: Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America ; Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America.

ABSTRACT
Dormancy is an essential strategy for microorganisms to cope with environmental stress. However, global ecosystem models typically ignore microbial dormancy, resulting in notable model uncertainties. To facilitate the consideration of dormancy in these large-scale models, we propose a new microbial physiology component that works for a wide range of substrate availabilities. This new model is based on microbial physiological states and the major parameters are the maximum specific growth and maintenance rates of active microbes and the ratio of dormant to active maintenance rates. A major improvement of our model over extant models is that it can explain the low active microbial fractions commonly observed in undisturbed soils. Our new model shows that the exponentially-increasing respiration from substrate-induced respiration experiments can only be used to determine the maximum specific growth rate and initial active microbial biomass, while the respiration data representing both exponentially-increasing and non-exponentially-increasing phases can robustly determine a range of key parameters including the initial total live biomass, initial active fraction, the maximum specific growth and maintenance rates, and the half-saturation constant. Our new model can be incorporated into existing ecosystem models to account for dormancy in microbially-driven processes and to provide improved estimates of microbial activities.

Show MeSH