Notebook for "Fluxes vs. Pools: Connecting Temperature Dependence and Sensitivity of Soil Carbon Dynamics Across Timescales""

This Data Resource includes the Mathematica notebook developed to generate the Figures for the manuscript titled \"Fluxes vs. Pools: Connecting Temperature Dependence and Sensitivity of Soil Carbon Dynamics Across Timescales\". The Notebook can be run using Mathematica or through a Wolfram Engine. The abstract of the manuscript:

As global temperature (T) regimes shift, there is growing interest in understanding the biotic and abiotic mechanisms driving short- and long-term changes in soil organic carbon (SOC) dynamics. Inconsistent terminology—particularly the use of T sensitivity and dependence—and varying methodological emphases on SOC pools versus fluxes can hinder the integration of experimental results with process-based models aimed at mechanistic insights into SOC-T responses. Here, we clarify the distinction between T dependence (e.g., Q10 formulations of fluxes) and T sensitivity (i.e., T the derivative of SOC fluxes or pools), and demonstrate how T responses of SOC fluxes are related to that of SOC pools. We apply this framework to analyze SOC dynamics using experimental data and a SOC model, examining both steady-state (quasi-static) and transient responses, including the change in heterotrophic respiration following a step-change in T. Our analysis reveals that the T dependence of SOC pools emerges from the T dependence of individual fluxes and is determined, at steady state, by ratios of Q10 values. This underscores the need to measure multiple SOC pools and fluxes, and to use process-based models, in order to estimate the Q10s accurately. Recognizing heterotrophic respiration as an emergent process, we showed that its short-term T response is influenced by the Q10 ratio of microbial uptake and maintenance processes, while the long-term decline arises from mass-balance constraints. Our results offer a mechanistic basis for integrating flux- and pool-based studies and emphasize the importance of combining data and models to quantify SOC-T responses across temporal scales.