Ecological stoichiometry as a foundation for omics-enabled biogeochemical models of soil organic matter decomposition

Graham, Emily B.; Hofmockel, Kirsten S.

doi:10.1007/s10533-021-00851-2

Ecological stoichiometry as a foundation for omics-enabled biogeochemical models of soil organic matter decomposition

Journal Article · Mon Nov 01 00:00:00 EDT 2021 · Biogeochemistry

DOI:https://doi.org/10.1007/s10533-021-00851-2· OSTI ID:2583172

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Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Washington State Univ., Richland, WA (United States)
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Iowa State Univ., Ames, IA (United States)

Coupled biogeochemical cycles drive ecosystem ecology by influencing individual-to-community scale behaviors; yet the development of process-based models that accurately capture these dynamics remains elusive. Soil organic matter (SOM) decomposition in particular is influenced by resource stoichiometry that dictates microbial nutrient acquisition (‘ecological stoichiometry’). Despite its basis in biogeochemical modeling, ecological stoichiometry is only implicitly considered in high-resolution microbial investigations and the metabolic models they inform. State-of-science SOM decomposition models in both fields have advanced largely separately, but they agree on a need to move beyond seminal pool-based models. This presents an opportunity and a challenge to maximize the strengths of various models across different scales and environmental contexts. To address this challenge, we contend that ecological stoichiometry provides a framework for merging biogeochemical and microbiological models, as both explicitly consider substrate chemistries that are the basis of ecological stoichiometry as applied to SOM decomposition. We highlight two gaps that limit our understanding of SOM decomposition: (1) understanding how individual microorganisms alter metabolic strategies in response to substrate stoichiometry and (2) translating this knowledge to the scale of biogeochemical models. We suggest iterative information exchange to refine the objectives of high-resolution investigations and to specify limited dynamics for representation in large-scale models, resulting in a new class of omics-enabled biogeochemical models. Assimilating theoretical and modelling frameworks from different scientific domains is the next frontier in SOM decomposition modelling; advancing technologies in the context of stoichiometric theory provides a consistent framework for interpreting molecular data, and further distilling this information into tractable SOM decomposition models.

View Accepted Manuscript (DOE)

Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: AC05-76RL01830

OSTI ID:: 2583172

Report Number(s):: PNNL-SA--159593

Journal Information:: Biogeochemistry, Journal Name: Biogeochemistry Journal Issue: 1 Vol. 157; ISSN 0168-2563; ISSN 1573-515X

Publisher:: SpringerCopyright Statement

Country of Publication:: United States

Language:: English

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