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Recent work suggests that metabolic activation and deactivation of microbes in soil strongly influences soil carbon (C) dynamics and climate feedbacks. However, few soil C models consider these transitions. We hypothesized that microbes’ capacity to enter and exit dormancy in response to unfavorable and favorable environmental conditions decreases the sensitivity of microbial biomass and cumulative respiration to environmental stress. To test this hypothesis, we collected data from a rewetting experiment and used it to design and parameterize dormancy in an existing microbe-based soil C model. Then we compared predictions of microbial biomass and soil heterotrophic respiration (RH) under simulated cycles of stressful (dryness) and favorable (wet pulses) conditions. Because the influence of moisture on microbial processes in soil generally depends on temperature, we collected data and tested predictions at different temperatures. When dormancy was not taken into account, simulated microbial biomass and cumulative microbial respiration over five years were lower and decreased faster under lengthening drying-wetting cycles. Differences due to dormancy increased with temperature and with the length of the dry periods between wetting events. We conclude that ignoring both the capacity of microbes to enter and exit dormancy in response to the environment and the consequences of these metabolic responses for soil C cycling results in predictions of unrealistically low RH under warming and drying-wetting cycles.

Accumulation of soil carbon (C) in ecosystems could help mitigate climate change, particularly in agricultural landscapes, but accumulation rates are thought to depend on land cover and management practices. To uncover relationships between soil properties and vegetation management strategies in the Midwestern USA, we examined soil C and nitrogen (N) content on lands under five management types that produced different levels of plant species diversity: remnant prairies, high-, medium- and low-diversity grassland restorations, and crop fields. We examined the relationships of soil C, labile C, and N with plant species richness, aboveground biomass production and silt + clay content on land under each of the five management types, in two non-adjacent counties. Field plots were located in sites predicted to have mid-range soil C contents for each county (2.0–2.5% or 2.4–2.9%) based on a SOLIM (Soil Land Inference Model) C model ( Libohova, 2010 ), in an effort to limit variability among sites. However, we found that soil texture data from the Soil Survey Geographic Database (SSURGO; the base data layer for SOLIM) frequently overestimated silt + clay content relative to the hydrometer-measured silt + clay content of our collected soil samples. We found that the low-diversity restorations (which were the most productive sites) had the greatest soil C and N content over the full soil profile (to 90 cm depth). On average soil C was 1.3 and 1.9 times greater, and soil N was 3.7 and 1.5 times greater, in the low diversity restorations compared to remnant prairies in Newton and Lee Counties, respectively. This was likely a consequence of (and a driver of) the high biomass production of the grasses that tended to dominate these fields. We attribute low soil C content in the remnant prairies and high diversity restorations in large part to the patterns of soil texture that led to the historical patterns of land use change, in which less productive areas were left as prairie and the more productive areas tilled. Labile C was highest in the low-diversity restorations at the 0–15 and 15–30 cm depths in both counties. Labile C was greatest from 0–15 cm and decreased with soil depth. Patterns of soil C across the intensely cultivated Midwestern landscape depend on the interaction between the suitability of soil for cultivation, and historical land management in terms of both the soil and vegetation. Our results suggest that the more heavily managed and productive fields in this region often harbor the greatest soil C, and that this soil C cannot be accurately predicted from the SSURGO database, highlighting the challenges and opportunities associated with maintaining and sequestering soil C in the Midwestern agricultural landscape.

Although potential enzyme activity measurements have a long history of use as an indicator of microbial activity, current methods do not provide accurate estimates of in situ activity. In the field, diffusion rates typically limit the rate at which enzymes can pair with substrates. However, the common laboratory practice of creating soil slurries removes all diffusion constraints. In addition, temperature strongly affects in situ enzyme activities, but is rarely considered in enzyme assays. To address these limitations, we developed a new protocol to measure the moisture and temperature sensitivity of enzyme activities. We incorporated sensitivity data obtained using this protocol into a model to estimate the effects of temperature and moisture on in situ β-glucosidase enzyme activity, recognizing that other factors such as substrate concentrations and diffusion constraints also affect in situ enzyme activities. Soil samples were collected from the Boston-Area Climate Experiment every two weeks over a 10-week period to track enzyme dynamics as field temperature and moisture changed. Precipitation inputs to an old-field were manipulated to produce drought (50% ambient precipitation), ambient, and wet (150% ambient precipitation) treatments. Temperature sensitivity of β-glucosidase was determined by assaying for the enzyme in soil slurries at three different temperatures (15, 25 and 35 °C). Moisture sensitivity was determined by exposing soils to different moisture levels in the lab and adding substrate to homogenized dry or moist soils instead of slurries. Temperature sensitivity was calculated as Q 10 and moisture sensitivity was calculated using a linear regression for each field treatment at each sample collection date. Moisture sensitivity varied significantly among the five sample dates and treatments, whereas temperature sensitivity remained stable. At almost every time point, β-glucosidase activity responded more strongly to increased moisture in soils of drought plots than in soils of ambient and wet plots. We estimated in situ β-glucosidase activity in the fall using the temperature and moisture sensitivities. Estimates that used only temperature or only moisture sensitivity suggested that ambient plots had the highest activity, followed by wet and then drought plots. Estimates based on both temperature and moisture suggested that β-glucosidase activity responded primarily to changes in temperature, except when soils were dry, with water potentials below −1 MPa. These results demonstrate that low soil moisture can strongly limit in situ enzyme activity in soils, negating any positive effect of warming. This study provides a template for parsing out the role of specific abiotic drivers on in situ enzyme activities, which could lead to the explicit incorporation of enzymes in biogeochemical models, improving upon the ability of current models to predict rates of biogeochemical processes in dynamic environments.