Background/Question/Methods Food web models can estimate the effect of soil organisms on the flow of carbon and nitrogen through the soil system. These models use field estimates of biomass along with feeding relationships and physiological parameters to calculate the consumption rates of individual taxa. They can be used to (1) explore changes in community composition, (2) calculate the direct and indirect effects of individual taxa, and (3) predict aggregate properties, such as carbon and nitrogen mineralization. We developed an R package, called soilfoodwebs, to facilitate and standardize these calculations. The package can be used to analyze food webs at equilibrium, explore parameter and structural uncertainty, and simulate models over time. We collected published soil food web data from the scientific literature and summarized the effects of individual taxa on decomposition and nutrient cycling using our new R package.
Results/Conclusions Soil food web models are effective at calculating the effects of individual organisms on decomposition and nutrient mineralization rates. Theoretical analyses demonstrate that the results are sensitive to the food web resolution, with the errors in mineralization rate up to 60% occurring when top predators with different physiology are combined at higher trophic levels and errors of up to 900% when microbes with different biomass stoichiometry are combined. Across all the published food webs available to us, microorganisms had the largest effects on carbon and nitrogen cycling, with direct and indirect effects up to ±100%. Single celled predators typically increased nutrient mineralization, up to 40%, while higher trophic levels had variable effects based on the food web and their biomasses. Simulations over time demonstrated the cumulative effects of soil organisms on system properties. For example, oribatid mites have a trivial direct effect on decomposition annually, but over 50-years they can reduce litter accumulation by 6%. Overall, we argue that soil food web model analyses are most useful for characterizing the effects of different organisms across systems or experimental treatments.