Simulating long-term responses of soil organic matter turnover to substrate stoichiometry by abstracting fast and small-scale microbial processes: the Soil Enzyme Steady Allocation Model (SESAM; v3.0)
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Media type:
E-Article
Title:
Simulating long-term responses of soil organic matter turnover to substrate stoichiometry by abstracting fast and small-scale microbial processes: the Soil Enzyme Steady Allocation Model (SESAM; v3.0)
Published in:
Geoscientific Model Development, 15 (2022) 22, Seite 8377-8393
Language:
English
DOI:
10.5194/gmd-15-8377-2022
ISSN:
1991-9603
Origination:
Footnote:
Description:
<jats:p>Abstract. Understanding the coupling of nitrogen (N) and carbon (C) cycles of land ecosystems requires understanding microbial element use
efficiencies of soil organic matter (SOM) decomposition. Whereas important controls of those efficiencies by microbial community adaptations have
been shown at the scale of a soil pore, a simplified representation of those controls is needed at the ecosystem scale. However, without abstracting
from the many details, models are not identifiable; i.e. they cannot be fitted without ambiguities to observations. There is a need to find, implement,
and validate abstract simplified formulations of theses processes. Therefore, we developed the Soil Enzyme Allocation Model (SEAM). The model explicitly represents community adaptation strategies of resource
allocation to extracellular enzymes and enzyme limitations on SOM decomposition. They thus provide an abstraction from several microbial functional
groups to a single holistic microbial community. Here we further simplify SEAM using a quasi-steady-state assumption for extracellular
enzyme pools to derive the Soil Enzyme Steady Allocation Model (SESAM) and test whether SESAM can provide the same decadal-term predictions as SEAM. SESAM reproduced the priming effect, the SOM banking mechanism, and the damping of fluctuations in carbon use efficiency with microbial competition
as predicted by SEAM and other more detailed models. This development is an important step towards a more parsimonious representation of soil
microbial effects in global land surface models.
</jats:p>