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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Medientyp:
E-Artikel
Titel:
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)
Erschienen in:
Geoscientific Model Development, 15 (2022) 22, Seite 8377-8393
Sprache:
Englisch
DOI:
10.5194/gmd-15-8377-2022
ISSN:
1991-9603
Entstehung:
Anmerkungen:
Beschreibung:
Abstract. Understanding the coupling of nitrogen (N) and carbon (C) cycles of land ecosystems requires understanding microbial element useefficiencies of soil organic matter (SOM) decomposition. Whereas important controls of those efficiencies by microbial community adaptations havebeen shown at the scale of a soil pore, a simplified representation of those controls is needed at the ecosystem scale. However, without abstractingfrom 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 resourceallocation to extracellular enzymes and enzyme limitations on SOM decomposition. They thus provide an abstraction from several microbial functionalgroups to a single holistic microbial community. Here we further simplify SEAM using a quasi-steady-state assumption for extracellularenzyme 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 competitionas predicted by SEAM and other more detailed models. This development is an important step towards a more parsimonious representation of soilmicrobial effects in global land surface models.