Abstract
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.
Highlights
The world’s soils contain a pool of carbon (C) that is larger than vegetation and atmospheric stocks combined, with 1500 Gt C stored in the top one meter alone (Batjes 2016)
We suggest iterative information exchange to refine the objectives of highresolution investigations and to specify limited dynamics for representation in large-scale models, resulting in a new class of omics-enabled biogeochemical models
Given the increase in disturbances and novel environments created by changes in our global climate, the shortcomings of predicting soil organic matter (SOM) decomposition beyond localized scales point to a need for scalable spatial and biogeochemical processes in SOM decomposition models (Todd-Brown et al 2013)
Summary
The world’s soils contain a pool of carbon (C) that is larger than vegetation and atmospheric stocks combined, with 1500 Gt C stored in the top one meter alone (Batjes 2016). At coarser levels of molecular resolution, biogeochemical models predict SOM decomposition using nutrient-regulated exchange between lumped SOM pools with specific chemical attributes stoichiometries of microbial biomass and Fatichi et al (2019) have proposed direct representation of microbial communities involved in coupled SOM and nutrient cycling, an approach that could aid in increasing the accuracy of SOM decomposition predictions and become a useful tool for empiricists to interpret multidimensional microbial data.
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