English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

. Leading an effective response to the accelerating crisis ofanthropogenic climate change will require improved understanding of globalcarbon cycling. A critical source of uncertainty in Earth system models(ESMs) is the role of microbes in mediating both the formation anddecomposition of soil organic matter, and hence in determining patterns ofCO2 efflux. Traditionally, ESMs model carbon turnover as a first-orderprocess impacted primarily by abiotic factors, whereas contemporarybiogeochemical models often explicitly represent the microbial biomass andenzyme pools as the active agents of decomposition. However, the combinationof non-linear microbial kinetics and ecological heterogeneity across spaceand time guarantees that upscaled dynamics will violate mean-fieldassumptions via Jensen's inequality. Violations of mean-field assumptionsmean that parameter estimates from models fit to upscaled data (e.g., eddycovariance towers) are likely systematically biased. Likewise, predictionsof CO2 efflux from models conditioned on mean-field values will also bebiased. Here we present a generic mathematical analysis of upscalingMichaelis–Menten kinetics under heterogeneity and provide solutions indimensionless form. We illustrate how our dimensionless form facilitatesqualitative insight into the significance of this scale transition and arguethat it will facilitate cross-site intercomparisons of flux data. We alsoidentify the critical terms that need to be constrained in order to unbiasparameter estimates.

Theoretical insights from upscaling Michaelis–Menten microbial dynamics in biogeochemical models: a dimensionless approach