Soil enzymes as indicators of soil function: A step toward greater realism in microbial ecological modeling

Abstract

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 CO 2 × N grassland experiment (BioCON) in Minnesota, USA. In addition to accurately simulating soil CO 2 fluxes 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 CO 2 concentration. 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.

Challenges in upscaling laboratory studies to ecosystems in soil microbiology research

“…Such fast data accumulation has profoundly advanced the understanding of changes in soil microbial communities under various environment conditions as well as their ecological functions (Luo et al, 2016 ; Smercina et al, 2021 ). For example, emerging studies provide compelling evidence that explicit incorporation of microbial processes into mechanistic modeling has substantially improved projections of soil carbon (C) and nutrient cycling and reduced model uncertainties (Wang, Gao, et al, 2021 ; Wieder et al, 2015 ). These studies have spurred laboratory‐scale and field‐scale research to explore the patterns and mechanisms of soil microbial communities, functions, and the ecological implications.…”

“…(2) Exploring useful proxies (e.g., genes, enzymes, and other functional traits) for the species‐rich soil microbial communities (Treseder et al, 2012 ; Trivedi et al, 2013 ). For example, by representing various C‐N‐associated enzyme‐mediated processes into the Microbial‐Enzyme Decomposition model as a proxy of microbial processes, Wang, Gao, et al ( 2021 ) profoundly improved model simulations of soil C and N cycling. (3) Applying emerging advanced data‐analytic approaches (e.g., hierarchical random‐matrix, eco‐evolutionary dynamics, machine learning, ecological networks) for integrating the mechanistic understanding of key microbial processes with data from multiple scales (Matchado et al, 2021 ; Zhang et al, 2022 ).…”

Latitudinal shifts of soil microbial biomass seasonality

“…The inconsistency between MBC and Rh at a seasonal scale indicates that microbial physiology may play a predominant role in affecting the SOC mineralization ( 46 , 47 ). The inconsistency between MBC and Rh also challenges the traditional view that declining microbial biomass leads to a possible decline in Rh ( 48 , 49 ).…”

A modified version of RothC to model the direct and indirect effects of rice straw mulching on soil carbon dynamics, calibrated in a Mediterranean citrus orchard

“…A proper calibration of 𝑘 𝑖 is especially important for the HUM pool, since it is the most important parameter for the determination of long term SOC stock in RothC (Cagnarini et al, 2019). Since 𝑘 𝑖 is an implicit representation of the degradation activity of the soil biota, to improve its representation in soil carbon cycling models, and explicit representation of the biological and ecological processes in the soil should be considered; various studies are already going in that direction (Allison et al, 2010;Wang et al, 2015Wang et al, , 2022Sulman et al, 2018), but still need further testing before being used to predict SOC changes in field conditions.…”