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  Modeling ecosystem-scale carbon dynamics in soil: The microbial dimension

  In predicting how soil C fluxes and stocks will change with the environment, models are a critical tool for integrating datasets with theory. Models developed in the 1980's were based on 1st order kinetics of C-pools defined by turnover time. However, new models generally include microbes as decomposers although they vary in the number and nature of microbial pools. They don't, however, integrate modern omics-based datasets because models have coarse resolution and need to function even in the absence of community data—geographically or into the future. There are several issues new models must address to be valuable for large-scale synthesis. First, how to incorporate microbes and their activities—how many pools of organisms? How should they be defined? How should they drive C-cycling? Should their synthesis of degradative enzymes be treated implicitly or explicitly? Second, carbon use efficiency (CUE)—the partitioning of processed C between respiration and re-synthesis into biomass. This term is critical because the size of the biomass influences its rate of organic matter processing. A focus has been on CUE's temperature sensitivity—most studies suggest it declines as temperature rises, which would limit decomposition and organic matter loss. The final novel modeling element I discuss is “priming”—the effect of fresh inputs on decomposition of native organic matter (OM). Priming can either repress or accelerate the breakdown of native OM. But whether, and how, to capture priming effects in soil organic matter models remains an area of exploration.
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  Microbial carbon use efficiency along an altitudinal gradient

  Soil microbial carbon-use efficiency (CUE), described as the ratio of growth over total carbon (C) uptake, i.e. the sum of growth and respiration, is a key variable in all soil organic matter (SOM) models and critical to ecosystem C cycling. However, there is still a lack of consensus on microbial CUE when estimated using different methods. Furthermore, the significance of many fundamental drivers of CUE remains largely unknown and inconclusive, especially for tropical ecosystems. For these reasons, we determined CUE and microbial indicators of soil nutrient availability in seven tropical forest soils along an altitudinal gradient (circa 900–2200 m a.s.l) occurring at Taita Hills, Kenya. We used this gradient to study the soil nutrient (N and P) availability and its relation to microbial CUE estimates. For assessing the soil nutrient availability, we determined both the soil bulk stoichiometric nutrient ratios (soil C:N, C:P and N:P), as well as SOM degradation related enzyme activities. We estimated soil microbial CUE using two methods: substrate independent 18O-water tracing and 13C-glucose tracing method. Based on these two approaches, we estimated the microbial uptake efficiency of added glucose versus native SOM, with the latter defined by 18O-water tracing method. Based on the bulk soil C:N stoichiometry, the studied soils did not reveal N limitation. However, soil bulk P limitation increased slightly with elevation. Additionally, based on extracellular enzyme activities, the SOM nutrient availability decreased with elevation. The 13C-CUE did not change with altitude indicating that glucose was efficiently taken up and used by the microbes. On the other hand, 18O-CUE, which reflects the growth efficiency of microbes growing on native SOM, clearly declined with increasing altitude and was associated with SOM nutrient availability indicators. Based on our results, microbes at higher elevations invested more energy to scavenge for nutrients and energy from complex SOM whereas at lower elevations the soil nutrients may have been more readily available.