Rising temperatures are expected to reduce global soil carbon (C) stocks, driving a positive feedback to climate change 1-3. However, the mechanisms underlying this prediction are not well understood, including how temperature affects microbial enzyme kinetics, growth efficiency (MGE), and turnover 4,5. Here, in a laboratory study, we show that microbial turnover accelerates with warming and, along with enzyme kinetics, determines the response of microbial respiration to temperature change. In contrast, MGE, which is generally thought to decline with warming 6-8 , showed no temperature sensitivity. Using a microbial-enzyme model, we show that temperature-sensitive microbial turnover promotes soil C accumulation with warming, in contrast to reduced soil C predicted by traditional biogeochemical models. Furthermore, the effect of increased microbial turnover differs from the effects of reduced MGE, causing larger increases in soil C stocks. Our results demonstrate that the response of soil C to warming is affected by changes in microbial turnover. This control should be included in the next generation of models to improve prediction of soil C feedbacks to warming.

Elevated CO2 and nitrogen (N) addition directly affect plant productivity and the mechanisms that allow tidal marshes to maintain a constant elevation relative to sea level, but it remains unknown how these global change drivers modify marsh plant response to sea level rise. Here we manipulated factorial combinations of CO2 concentration (two levels), N availability (two levels) and relative sea level (six levels) using in situ mesocosms containing a tidal marsh community composed of a sedge, Schoenoplectus americanus, and a grass, Spartina patens. Our objective is to determine, if elevated CO2 and N alter the growth and persistence of these plants in coastal ecosystems facing rising sea levels. After two growing seasons, we found that N addition enhanced plant growth particularly at sea levels where plants were most stressed by flooding (114% stimulation in the + 10 cm treatment), and N effects were generally larger in combination with elevated CO2 (288% stimulation). N fertilization shifted the optimal productivity of S. patens to a higher sea level, but did not confer S. patens an enhanced ability to tolerate sea level rise. S. americanus responded strongly to N only in the higher sea level treatments that excluded S. patens. Interestingly, addition of N, which has been suggested to accelerate marsh loss, may afford some marsh plants, such as the widespread sedge, S. americanus, the enhanced ability to tolerate inundation. However, if chronic N pollution reduces the availability of propagules of S. americanus or other flood-tolerant species on the landscape scale, this shift in species dominance could render tidal marshes more susceptible to marsh collapse.

Carbon use efficiency (CUE), the proportion of carbon (C) consumed by microbes that is converted into biomass, is an important parameter for soil C models with explicit microbial controls. While often considered as a single parameter, CUE is an emergent property of multiple microbial processes, including assimilation efficiency, biomassspecific respiration, enzyme production, and respiratory costs of enzyme production. These processes occur over variable time scales and imply different fates for C, and the same emergent CUE value can result when C is allocated in fundamentally different ways (e.g. a high investment in enzyme production vs. a high assimilation cost). We developed a model that represents the individual processes underlying emergent CUE to test how shifts in microbial allocation alter equilibrium soil C pool sizes. We found that an increase in emergent CUE that results from a change in assimilation efficiency, biomass specific respiration, or respiration costs from enzyme production causes soil organic C (SOC) to decline, while the same change in emergent CUE resulting from a change in enzyme production causes SOC to increase. We also used the model to test the sensitivity of CUE from isotopic C tracer estimates to changes in microbial allocation processes. We found that these estimates do not account for the same microbial processes represented by emergent CUE in models. We propose that considering microbial processes explicitly rather than representing CUE as a single parameter can improve data-model integration. In addition, modeling microbial processes explicitly will account for a wider range of possible outcomes from shifts in microbial C allocation, such as when increased SOC results from increasing CUE.

The efficiency with which microbes use substrate (Carbon Use Efficiency or CUE) to make new microbial biomass is an important variable in soil and ecosystem C cycling models. It is generally assumed that CUE of microbial activity in soils is low, however measured values vary widely. It is hypothesized that high values of CUE observed in especially short-term incubations reflect the build-up of storage compounds in response to a sudden increase in substrate availability and are therefore not representative of CUE of microbial activity in unamended soil. To test this hypothesis, we measured the 13 CO 2 release from six position-specific 13 C-labeled glucose isotopomers in ponderosa pine and piñon-juniper soil. We compared this position-specific CO 2 production pattern with patterns expected for 1) balanced microbial growth (synthesis of all compounds needed to build new microbial cells) at a low, medium, or high CUE, and 2) synthesis of storage compounds (glycogen, tri-palmitoyl-glycerol, and polyhydroxybutyrate). Results of this study show that synthesis of storage compounds is not responsible for the observed high CUE. Instead, it is the position-specific CO 2 production expected for balanced growth and high CUE that best matches the observed CO 2 production pattern in these two soils. Comparison with published studies suggests that the amount of glucose added in this study is too low and the duration of the experiment too short to affect microbial metabolism. We conclude that the hypothesis of high CUE in undisturbed soil remains viable and worthy of further testing.

Mixing models are powerful tools for identifying biogeochemical sources and determining mixing fractions in a sample. However, identification of actual source contributors is often not simple, and source compositions typically vary or even overlap, significantly increasing model uncertainty in calculated mixing fractions. This study compares three probabilistic methods, Stable Isotope Analysis in R (SIAR), a pure Monte Carlo technique (PMC), and Stable Isotope Reference Source (SIRS) mixing model, a new technique that estimates mixing in systems with more than three sources and/or uncertain source compositions. In this paper, we use nitrate stable isotope examples (δ 15 N and δ 18 O) but all methods tested are applicable to other tracers. In Phase I of a three-phase blind test, we compared methods for a set of six-source nitrate problems. PMC was unable to find solutions for two of the target water samples. The Bayesian method, SIAR, experienced anchoring problems, and SIRS calculated mixing fractions that most closely approximated the known mixing fractions. For that reason, SIRS was the only approach used in the next phase of testing. In Phase II, the problem was broadened where any subset of the six sources could be a possible solution to the mixing problem. Results showed a high rate of Type I errors where solutions included sources that were not contributing to the sample. In Phase III some sources were eliminated based on assumed site knowledge and assumed nitrate concentrations, substantially reduced mixing fraction uncertainties and lowered the Type I error rate. These results demonstrate that valuable insights into stable isotope mixing problems result from probabilistic mixing model approaches like SIRS. The results also emphasize the importance of identifying a minimal set of potential sources and quantifying uncertainties in source isotopic composition as well as demonstrating the value of additional information in reducing the uncertainty in calculated mixing fractions.