The microbial metabolic quotient (MMQ), microbial respiration per unit of biomass, is a fundamental factor controlling heterotrophic respiration, the largest carbon flux in soils. The magnitude and controls of MMQ at regional scale remain uncertain. We compiled a comprehensive data set of MMQ to investigate the global patterns and controls of MMQ in top 30 cm soils. Published MMQ values, generally measured in laboratory microcosms, were adjusted on ambient soil temperature using long‐term (30 yr) average site soil temperature and a Q10 = 2. The area‐weighted global average of MMQ_Soil is estimated as 1.8 (1.5–2.2) (95% confidence interval) μmol C·h−1·mmol−1 microbial biomass carbon (MBC) with substantial variations across biomes and between cropland and natural ecosystems. Variation was most closely associated with biological factors, followed by edaphic and meteorological parameters. MMQ_Soil was greatest in sandy clay and sandy clay loam and showed a pH maximum of 6.7 ± 0.1 (mean ± se). At large scale, MMQ_Soil varied with latitude and mean annual temperature (MAT), and was negatively correlated with microbial N:P ratio, supporting growth rate theory. These trends led to large differences in MMQ_Soil between natural ecosystems and cropland. When MMQ was adjusted to 11°C (MMQ_Ref), the global MAT in the top 30 cm of soils, the area‐weighted global averages of MMQ_Ref was 1.5 (1.3–1.8) μmol C·mmol MBC−1·h−1. The values, trends, and controls of MMQ_Soil add to our understanding of soil microbial influences on soil carbon cycling and could be used to represent microbial activity in global carbon models.

A mechanistic understanding of microbial assimilation of soil organic carbon is important to improve Earth system models' ability to simulate carbon-climate feedbacks. A simple modelling framework was developed to investigate how substrate quality and environmental controls over microbial activity regulate microbial assimilation of soil organic carbon and on the size of the microbial biomass. Substrate quality has a positive effect on microbial assimilation of soil organic carbon: higher substrate quality leads to higher ratio of microbial carbon to soil organic carbon. Microbial biomass carbon peaks and then declines as cumulative activity increases. The simulated ratios of soil microbial biomass to soil organic carbon are reasonably consistent with a recently compiled global data set at the biome level. The modelling framework developed in this study offers a simple approach to incorporate microbial contributions to the carbon cycling into Earth system models to simulate carbon-climate feedbacks and explain global patterns of microbial biomass.

Abstract. Over the past 4 decades, a number of numerical models have been developed to quantify the magnitude, investigate the spatial and temporal variations, and understand the underlying mechanisms and environmental controls of methane (CH 4 ) fluxes within terrestrial ecosystems. These CH 4 models are also used for integrating multi-scale CH 4 data, such as laboratory-based incubation and molecular analysis, field observational experiments, remote sensing, and aircraft-based measurements across a variety of terrestrial ecosystems. Here we summarize 40 terrestrial CH 4 models to characterize their strengths and weaknesses and to suggest a roadmap for future model improvement and application. Our key findings are that (1) the focus of CH 4 models has shifted from theoretical to site-and regional-level applications over the past 4 decades, (2) large discrepancies exist among models in terms of representing CH 4 processes and their environmental controls, and (3) significant datamodel and model-model mismatches are partially attributed to different representations of landscape characterization and inundation dynamics. Three areas for future improvements and applications of terrestrial CH 4 models are that (1) CH 4 models should more explicitly represent the mechanisms underlying land-atmosphere CH 4 exchange, with an emphasis on improving and validating individual CH 4 processes over depth and horizontal space, (2) models should be developed that are capable of simulating CH 4 emissions across highly heterogeneous spatial and temporal scales, particularly hot moments and hotspots, and (3) efforts should be invested to develop model benchmarking frameworks that can easily be used for model improvement, evaluation, and integration with data from molecular to global scales. These improvements in CH 4 models would be beneficial for the Earth system models and further simulation of climate-carbon cycle feedbacks.

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Objective This study investigated if resveratrol ameliorates diabetic cardiomyopathy by targeting associated oxidative stress mechanisms. Method Type 1 diabetes mellitus (DM) in FVB mice was induced by several intraperitoneal injections of a low dose of streptozotocin. Hyperglycemic and age-matched control mice were given resveratrol (10 mg/kg per day) for 1 month and subsequently monitored for an additional 6 months. Mice were assigned to four groups: control, resveratrol, DM, and DM/resveratrol. Cardiac function and blood pressure were assessed at 1, 3, and 6 months after DM induction. Oxidative damage and cardiac fibrosis were analyzed by histopathology, real-time PCR, and Western blot. Result Mice in the DM group exhibited increased blood glucose levels, cardiac dysfunction, and high blood pressure at 1, 3, and 6 months after DM induction. Resveratrol did not significantly affect blood glucose levels and blood pressure; however, resveratrol attenuated cardiac dysfunction and hypertrophy in DM mice. Resveratrol also reduced DM-induced fibrosis. In addition, DM mice hearts exhibited increased oxidative damage, as evidenced by elevated accumulation of 3-nitrotyrosine and 4-hydroxynonenal, which were both attenuated by resveratrol. Mechanistically, resveratrol increased NFE2-related factor 2 (Nrf2) expression and transcriptional activity, as well as Nrf2's downstream antioxidative targets. Conclusion We demonstrated that resveratrol prevents DM-induced cardiomyopathy, in part, by increasing Nrf2 expression and transcriptional activity.

Northern peatlands contain a vast pool of soil carbon that may be vulnerable to atmospheric release under changing environmental conditions, potentially causing a positive feedback to the climate system (Frolking et al., 2011;Nichols & Peteet, 2019;Yu, 2012). The magnitude of carbon emissions and their mechanistic responses to changing environments are elusive due to the complexity of hydrologic and biogeochemical processes in peatland systems (Blodau, 2002). Methane (CH 4 ) is one of the key carbon forms leaving peatlands under anaerobic conditions. Given the high radiative warming potential of CH 4 compared to CO 2 (Neubauer & Megonigal, 2015), it is critically important to accurately predict future CH 4 emissions from global peatlands. Peatlands are typically formed over millennial timescales due to organic carbon inputs, long-term water saturation and low temperatures and thus store major amounts of terrestrial carbon (Yu, 2012). It is expected that hydrological and biogeochemical dynamics play important roles affecting CH 4 fluxes from peatlands, but many of these key processes are missing in current Earth system models (Bohn et al., 2015). Therefore, to build a better predictive capacity for CH 4 dynamics in peatlands, it is necessary to fully consider the processes and environmental conditions controlling CH 4 processes, particularly the

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