Dynamic changes of soil microorganisms in rotation farmland at the western foot of the Greater Khingan range

Crop rotation and other tillage systems can affect soil microbial communities and functions. Few studies have reported the response of soil spatial microbial communities to rotation under drought stress. Therefore, the purpose of our study was to explore the dynamic changes of the soil space microbial community under different drought stress-rotation patterns. In this study, two water treatments were set up, control W1 (mass water content 25%–28%), and drought W2 (mass water content 9%–12%). Four crop rotation patterns were set in each water content, spring wheat continuous (R1), spring wheat-potato (R2), spring wheat-potato-rape (R3) and spring wheat-rape (R4), for a total of eight treatments (W1R1, W1R2, W1R3, W1R4, W2R1, W2R2, W2R3, W2R4). Endosphere, rhizosphere and bulk soil of spring wheat in each treatment were collected, and root space microbial community data were generated. The soil microbial community changed under different treatments and their relationship with soil factors were analyzed using a co-occurrence network, mantel test, and other methods. The results revealed that the alpha diversity of microorganisms in the rhizosphere and bulk soil did not differ significantly, but it was significantly greater than in the endosphere. The bacteria community structure was more stable, fungi alpha-diversity significant changes (p < 0.05), that were more sensitive to the response of various treatments than bacteria. The co-occurrence network between fungal species was stable under rotation patterns (R2, R3, R4), while the community stability was poor under continuous cropping pattern (R1), and interactions were strengthened. Soil organic matter (SOM), microbial biomass carbon (MBC), and pH value were the most important factors dominating the bacteria community structural changed in the endosphere, rhizosphere, and bulk soil. The dominant factor that affected the fungal community structural changed in the endosphere, rhizosphere, and bulk soil was SOM. Therefore, we conclude that soil microbial community changes under the drought stress-rotation patterns are mainly influenced by soil SOM and microbial biomass content

Evaluating Alternative Ebullition Models for Predicting Peatland Methane Emission and Its Pathways via Data–Model Fusion

Understanding the dynamics of peatland methane (CH4) emissions and quantifying sources of uncertainty in estimating peatland CH4 emissions are critical for mitigating climate change. The relative contributions of CH4 emission pathways through ebullition, plant-mediated transport, and diffusion, together with their different transport rates and vulnerability to oxidation, determine the quantity of CH4 to be oxidized before leaving the soil. Notwithstanding their importance, the relative contributions of the emission pathways are highly uncertain. In particular, the ebullition process is more uncertain and can lead to large uncertainties in modeled CH4 emissions. To improve model simulations of CH4 emission and its pathways, we evaluated two model structures: (1) the ebullition bubble growth volume threshold approach (EBG) and (2) the modified ebullition concentration threshold approach (ECT) using CH4 flux and concentration data collected in a peatland in northern Minnesota, USA. When model parameters were constrained using observed CH4 fluxes, the CH4 emissions simulated by the EBG approach (RMSE = 0.53) had a better agreement with observations than the ECT approach (RMSE = 0.61). Further, the EBG approach simulated a smaller contribution from ebullition but more frequent ebullition events than the ECT approach. The EBG approach yielded greatly improved simulations of pore water CH4 concentrations, especially in the deep soil layers, compared to the ECT approach. When constraining the EBG model with both CH4 flux and concentration data in model–data fusion, uncertainty of the modeled CH4 concentration profiles was reduced by 78 % to 86 % in comparison to constraints based on CH4 flux data alone. The improved model capability was attributed to the well-constrained parameters regulating the CH4 production and emission pathways. Our results suggest that the EBG modeling approach better characterizes CH4 emission and underlying mechanisms. Moreover, to achieve the best model results both CH4 flux and concentration data are required to constrain model parameterization

Evaluating Alternative Ebullition Models for Predicting Peatland Methane Emission and Its Pathways via Data–Model Fusion

Understanding the dynamics of peatland methane (CH4) emissions and quantifying sources of uncertainty in estimating peatland CH4 emissions are critical for mitigating climate change. The relative contributions of CH4 emission pathways through ebullition, plant-mediated transport, and diffusion, together with their different transport rates and vulnerability to oxidation, determine the quantity of CH4 to be oxidized before leaving the soil. Notwithstanding their importance, the relative contributions of the emission pathways are highly uncertain. In particular, the ebullition process is more uncertain and can lead to large uncertainties in modeled CH4 emissions. To improve model simulations of CH4 emission and its pathways, we evaluated two model structures: (1) the ebullition bubble growth volume threshold approach (EBG) and (2) the modified ebullition concentration threshold approach (ECT) using CH4 flux and concentration data collected in a peatland in northern Minnesota, USA. When model parameters were constrained using observed CH4 fluxes, the CH4 emissions simulated by the EBG approach (RMSE = 0.53) had a better agreement with observations than the ECT approach (RMSE = 0.61). Further, the EBG approach simulated a smaller contribution from ebullition but more frequent ebullition events than the ECT approach. The EBG approach yielded greatly improved simulations of pore water CH4 concentrations, especially in the deep soil layers, compared to the ECT approach. When constraining the EBG model with both CH4 flux and concentration data in model–data fusion, uncertainty of the modeled CH4 concentration profiles was reduced by 78 % to 86 % in comparison to constraints based on CH4 flux data alone. The improved model capability was attributed to the well-constrained parameters regulating the CH4 production and emission pathways. Our results suggest that the EBG modeling approach better characterizes CH4 emission and underlying mechanisms. Moreover, to achieve the best model results both CH4 flux and concentration data are required to constrain model parameterization

Evaluating alternative ebullition models for predicting peatland methane emission and its pathways via data–model fusion

Understanding the dynamics of peatland methane (CH4) emissions and quantifying sources of uncertainty in estimating peatland CH4 emissions are critical for mitigating climate change. The relative contributions of CH4 emission pathways through ebullition, plant-mediated transport, and diffusion, together with their different transport rates and vulnerability to oxidation, determine the quantity of CH4 to be oxidized before leaving the soil. Notwithstanding their importance, the relative contributions of the emission pathways are highly uncertain. In particular, the ebullition process is more uncertain and can lead to large uncertainties in modeled CH4 emissions. To improve model simulations of CH4 emission and its pathways, we evaluated two model structures: (1) the ebullition bubble growth volume threshold approach (EBG) and (2) the modified ebullition concentration threshold approach (ECT) using CH4 flux and concentration data collected in a peatland in northern Minnesota, USA. When model parameters were constrained using observed CH4 fluxes, the CH4 emissions simulated by the EBG approach (RMSE = 0.53) had a better agreement with observations than the ECT approach (RMSE = 0.61). Further, the EBG approach simulated a smaller contribution from ebullition but more frequent ebullition events than the ECT approach. The EBG approach yielded greatly improved simulations of pore water CH4 concentrations, especially in the deep soil layers, compared to the ECT approach. When constraining the EBG model with both CH4 flux and concentration data in model–data fusion, uncertainty of the modeled CH4 concentration profiles was reduced by 78 % to 86 % in comparison to constraints based on CH4 flux data alone. The improved model capability was attributed to the well-constrained parameters regulating the CH4 production and emission pathways. Our results suggest that the EBG modeling approach better characterizes CH4 emission and underlying mechanisms. Moreover, to achieve the best model results both CH4 flux and concentration data are required to constrain model parameterization

Implementing an integrated meter and sensor system (IMSS) in existing social housing stock

The current rollout of smart meters for gas and electricity, both in the UK and internationally, will help suppliers to better forecast demand and supply accurate bills to consumers. However, even with an in-home display (IHD), the benefits of a smart meter to a domestic customer are limited by the so-called ‘double invisibility’ of energy [1] and the standardisation of IHD design for an imagined home ‘micro-resource manager’ [2]. Furthermore, low-income households may be limited in the benefits they can reap from such systems; already living within a tight budget, suggestions for further energy-related cost savings may be detrimental to their health and wellbeing. This makes it important that the impact of actions taken to save energy is communicated. This can be done using indoor environmental measures, including carbon dioxide, relative humidity and temperature, as part of an integrated meter and sensor system (IMSS) and an associated IHD or digital application. Such a system gives users the ability to make informed decisions about their energy use and indoor environmental health. This paper explores the potential barriers to implementing an IMSS in practice. It explains how an IMSS was designed, based on a review of meter and sensor systems; details the process is taken to trial the IMSS in 19 social housing properties in the English Midlands; and makes recommendations for a larger scale rollout of IMSSs. The paper also reviews current progress in cloud storage and security as relevant to IMSSs and smart metering

Transient dynamics of terrestrial carbon storage : mathematical foundation and its applications

Terrestrial ecosystems have absorbed roughly 30 % of anthropogenic CO2 emissions over the past decades, but it is unclear whether this carbon (C) sink will endure into the future. Despite extensive modeling and experimental and observational studies, what fundamentally determines transient dynamics of terrestrial C storage under global change is still not very clear. Here we develop a new framework for understanding transient dynamics of terrestrial C storage through mathematical analysis and numerical experiments. Our analysis indicates that the ultimate force driving ecosystem C storage change is the C storage capacity, which is jointly determined by ecosystem C input (e.g., net primary production, NPP) and residence time. Since both C input and residence time vary with time, the C storage capacity is time-dependent and acts as a moving attractor that actual C storage chases. The rate of change in C storage is proportional to the C storage potential, which is the difference between the current storage and the storage capacity. The C storage capacity represents instantaneous responses of the land C cycle to external forcing, whereas the C storage potential represents the internal capability of the land C cycle to influence the C change trajectory in the next time step. The influence happens through redistribution of net C pool changes in a network of pools with different residence times. Moreover, this and our other studies have demonstrated that one matrix equation can replicate simulations of most land C cycle models (i.e., physical emulators). As a result, simulation outputs of those models can be placed into a three-dimensional (3-D) parameter space to measure their differences. The latter can be decomposed into traceable components to track the origins of model uncertainty. In addition, the physical emulators make data assimilation computationally feasible so that both C flux- and pool-related datasets can be used to better constrain model predictions of land C sequestration. Overall, this new mathematical framework offers new approaches to understanding, evaluating, diagnosing, and improving land C cycle models.This work was partially done through the working group, Nonautonomous Systems and Terrestrial Carbon Cycle, at the National Institute for Mathematical and Biological Synthesis, an institute sponsored by the National Science Foundation, the US Departmernt of Homeland Security, and the US Department of Agriculture through NSF award no. EF-0832858, with additional support from the University of Tennessee, Knoxville, Research in Yiqi Luo EcoLab was financially supported by US Department of Energy grants DE-SC0008270, DE-SC0014085, and US National Science Foundation (NSF) grants EF 1137293 and OIA-1301789.Ye

Large-scale prediction of long non-coding RNA functions in a coding–non-coding gene co-expression network

Although accumulating evidence has provided insight into the various functions of long-non-coding RNAs (lncRNAs), the exact functions of the majority of such transcripts are still unknown. Here, we report the first computational annotation of lncRNA functions based on public microarray expression profiles. A coding–non-coding gene co-expression (CNC) network was constructed from re-annotated Affymetrix Mouse Genome Array data. Probable functions for altogether 340 lncRNAs were predicted based on topological or other network characteristics, such as module sharing, association with network hubs and combinations of co-expression and genomic adjacency. The functions annotated to the lncRNAs mainly involve organ or tissue development (e.g. neuron, eye and muscle development), cellular transport (e.g. neuronal transport and sodium ion, acid or lipid transport) or metabolic processes (e.g. involving macromolecules, phosphocreatine and tyrosine)