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Unravelling biosphere feedback mechanisms is crucial for predicting the impacts of global warming. Soil priming, an effect of fresh plant-derived carbon (C) on native soil organic carbon (SOC) decomposition, is a key feedback mechanism that could release large amounts of soil C into the atmosphere. However, the impacts of climate warming on soil priming remain elusive. Here, we show that experimental warming accelerates soil priming by 12.7% in a temperate grassland. Warming alters bacterial communities, with 38% of unique active phylotypes detected under warming. The functional genes essential for soil C decomposition are also stimulated, which could be linked to priming effects. We incorporate lab-derived information into an ecosystem model showing that model parameter uncertainty can be reduced by 32–37%. Model simulations from 2010 to 2016 indicate an increase in soil C decomposition under warming, with a 9.1% rise in priming-induced CO₂emissions. If our findings can be generalized to other ecosystems over an extended period of time, soil priming could play an important role in terrestrial C cycle feedbacks and climate change.

 

Incorporating microbial processes into soil biogeochemical models has received growing interest. However, determining the parameters that govern microbially driven biogeochemical processes typically requires case‐specific model calibration in various soil and ecosystem types. Here each case refers to an independent and individual experimental unit subjected to repeated measurements. Using the Microbial‐ENzyme Decomposition model, this study aimed to test whether a common set of microbially‐relevant parameters (i.e., generalized parameters) could be obtained across multiple cases based on a two‐year incubation experiment in which soil samples of four distinct soil series (i.e., Coland, Kesswick, Westmoreland, and Etowah) collected from forest and grassland were subjected to cellulose or no cellulose amendment. Results showed that a common set of parameters controlling microbial growth and maintenance as well as extracellular enzyme production and turnover could be generalized at the soil series level but not land cover type. This indicates that microbial model developments need to prioritize soil series type over plant functional types when implemented across various sites. This study also suggests that, in addition to heterotrophic respiration and microbial biomass data, extracellular enzyme data sets are needed to achieve reliable microbial‐relevant parameters for large‐scale soil model projections.

 

Whether and how CO 2 and nitrogen (N) availability interact to influence carbon (C) cycling processes such as soil respiration remains a question of considerable uncertainty in projecting future C–climate feedbacks, which are strongly influenced by multiple global change drivers, including elevated atmospheric CO 2 concentrations (eCO 2 ) and increased N deposition. However, because decades of research on the responses of ecosystems to eCO 2 and N enrichment have been done largely independently, their interactive effects on soil respiratory CO 2 efflux remain unresolved. Here, we show that in a multifactor free-air CO 2 enrichment experiment, BioCON (Biodiversity, CO 2 , and N deposition) in Minnesota, the positive response of soil respiration to eCO 2 gradually strengthened at ambient (low) N supply but not enriched (high) N supply for the 12-y experimental period from 1998 to 2009. In contrast to earlier years, eCO 2 stimulated soil respiration twice as much at low than at high N supply from 2006 to 2009. In parallel, microbial C degradation genes were significantly boosted by eCO 2 at low but not high N supply. Incorporating those functional genes into a coupled C–N ecosystem model reduced model parameter uncertainty and improved the projections of the effects of different CO 2 and N levels on soil respiration. If our observed results generalize to other ecosystems, they imply widely positive effects of eCO 2 on soil respiration even in infertile systems. 

Global soil organic carbon (SOC) stocks may decline with a warmer climate. However, model projections of changes in SOC due to climate warming depend on microbially-driven processes that are usually parameterized based on laboratory incubations. To assess how lab-scale incubation datasets inform model projections over decades, we optimized five microbially-relevant parameters in the Microbial-ENzyme Decomposition (MEND) model using 16 short-term glucose (6-day), 16 short-term cellulose (30-day) and 16 long-term cellulose (729-day) incubation datasets with soils from forests and grasslands across contrasting soil types. Our analysis identified consistently higher parameter estimates given the short-term versus long-term datasets. Implementing the short-term and long-term parameters, respectively, resulted in SOC loss (–8.2 ± 5.1% or –3.9 ± 2.8%), and minor SOC gain (1.8 ± 1.0%) in response to 5 °C warming, while only the latter is consistent with a meta-analysis of 149 field warming observations (1.6 ± 4.0%). Comparing multiple subsets of cellulose incubations (i.e., 6, 30, 90, 180, 360, 480 and 729-day) revealed comparable projections to the observed long-term SOC changes under warming only on 480- and 729-day. Integrating multi-year datasets of soil incubations (e.g., > 1.5 years) with microbial models can thus achieve more reasonable parameterization of key microbial processes and subsequently boost the accuracy and confidence of long-term SOC projections.

 

Oxygen (O₂) limitation contributes to persistence of large carbon (C) stocks in saturated soils. However, many soils experience spatiotemporal O₂ fluctuations impacted by climate and land‐use change, and O₂‐mediated climate feedbacks from soil greenhouse gas emissions remain poorly constrained. Current theory and models posit that anoxia uniformly suppresses carbon (C) decomposition. Here we show that periodic anoxia may sustain or even stimulate decomposition over weeks to months in two disparate soils by increasing turnover and/or size of fast‐cycling C pools relative to static oxic conditions, and by sustaining decomposition of reduced organic molecules. Cumulative C losses did not decrease consistently as cumulative O₂exposure decreased. After >1 year, soils anoxic for 75% of the time had similar C losses as the oxic control but nearly threefold greater climate impact on a CO₂‐equivalent basis (20‐year timescale) due to high methane (CH₄) emission. A mechanistic model incorporating current theory closely reproduced oxic control results but systematically underestimated C losses under O₂ fluctuations. Using a model‐experiment integration (ModEx) approach, we found that models were improved by varying microbial maintenance respiration and the fraction of CH₄production in total C mineralization as a function of O₂availability. Consistent with thermodynamic expectations, the calibrated models predicted lower microbial C‐use efficiency with increasing anoxic duration in one soil; in the other soil, dynamic organo‐mineral interactions implied by our empirical data but not represented in the model may have obscured this relationship. In both soils, the updated model was better able to capture transient spikes in C mineralization that occurred following anoxic–oxic transitions, where decomposition from the fluctuating‐O₂treatments greatly exceeded the control. Overall, our data‐model comparison indicates that incorporating emergent biogeochemical properties of soil O₂variability will be critical for effectively modeling C‐climate feedbacks in humid ecosystems.

 

Soil carbon (C) and nitrogen (N) cycles and their complex responses to environmental changes have received increasing attention. However, large uncertainties in model predictions remain, partially due to the lack of explicit representation and parameterization of microbial processes. One great challenge is to effectively integrate rich microbial functional traits into ecosystem modeling for better predictions. Here, using soil enzymes as indicators of soil function, we developed a competitive dynamic enzyme allocation scheme and detailed enzyme‐mediated soil inorganic N processes in the Microbial‐ENzyme Decomposition (MEND) model. We conducted a rigorous calibration and validation of MEND with diverse soil C‐N fluxes, microbial C:N ratios, and functional gene abundances from a 12‐year CO₂ × N grassland experiment (BioCON) in Minnesota, USA. In addition to accurately simulating soil CO₂fluxes and multiple N variables, the model correctly predicted microbial C:N ratios and their negative response to enriched N supply. Model validation further showed that, compared to the changes in simulated enzyme concentrations and decomposition rates, the changes in simulated activities of eight C‐N‐associated enzymes were better explained by the measured gene abundances in responses to elevated atmospheric CO₂concentration. Our results demonstrated that using enzymes as indicators of soil function and validating model predictions with functional gene abundances in ecosystem modeling can provide a basis for testing hypotheses about microbially mediated biogeochemical processes in response to environmental changes. Further development and applications of the modeling framework presented here will enable microbial ecologists to address ecosystem‐level questions beyond empirical observations, toward more predictive understanding, an ultimate goal of microbial ecology.

 

Global soil carbon (C) stocks are expected to decline with warming, and changes in microbial processes are key to this projection. However, warming responses of critical microbial parameters such as carbon use efficiency (CUE) and biomass turnover (rB) are not well understood. Here, we determine these parameters using a probabilistic inversion approach that integrates a microbial‐enzyme model with 22 years of carbon cycling measurements at Harvard Forest. We find that increasing temperature reduces CUE but increases rB, and that two decades of soil warming increases the temperature sensitivities of CUE and rB. These temperature sensitivities, which are derived from decades‐long field observations, contrast with values obtained from short‐term laboratory experiments. We also show that long‐term soil C flux and pool changes in response to warming are more dependent on the temperature sensitivity of CUE than that of rB. Using the inversion‐derived parameters, we project that chronic soil warming at Harvard Forest over six decades will result in soil C gain of <1.0% on average (1st and 3rd quartiles: 3.0% loss and 10.5% gain) in the surface mineral horizon. Our results demonstrate that estimates of temperature sensitivity of microbial CUE and rB can be obtained and evaluated rigorously by integrating multidecadal datasets. This approach can potentially be applied in broader spatiotemporal scales to improve long‐term projections of soil C feedbacks to climate warming.