Anthropogenic nitrogen (N) deposition is expected to increase substantially and continuously in the future. Soil N availability regulates microbial communities and the decomposition and formation of soil organic matter, which have great impacts on global carbon (C) cycling. We conducted a meta-analysis based on 454 N-addition experiments in order to synthesize the patterns and mechanisms of responses by soil microbial communities to N addition in various biomes (i.e., boreal forest, temperate forest, tropical/subtropical forest, grassland, and desert). Results showed that the effects of N addition on the total microbial biomass varied depending on biome types, methodologies (fumigation–extraction technique vs. total phospholipid fatty acid), and N-addition rates. Nitrogen addition consistently decreased the microbial C:N and fungi to bacteria ratio (F:B), but increased Gram positive bacteria to Gram negative bacteria ratio (GP:GN) among biome types and N-addition rates. Nitrogen addition increased soil N availability and thereby resulted in soil acidification. Regression technique and principal component analyses showed that the shifts in the F:B and GP:GN mainly resulted from enhanced N availability due to N addition rather than soil acidification. When the N addition rate is lower than 100 kg N ha−1 year−1, about ten times higher than of global normal rate, the positive response of microbial growth was found. Overall, these findings revised the previous notion that N addition inhibited the microbial growth. Microbial species shifts might accentuate or mitigate the effects of alterations in microbial biomass at the ecosystem level, highlighting the critical role of microbial community composition in soil ecosystem functions under N deposition scenarios.

Patterns and mechanisms of responses by soil microbial communities to nitrogen addition

The availability of nitrogen (N) is a critical control on the cycling and storage of soil carbon (C). Yet, there are conflicting conceptual models to explain how N availability influences the decomposition of organic matter by soil microbial communities. Several lines of evidence suggest that N availability limits decomposition; the earliest stages of leaf litter decay are associated with a net import of N from the soil environment, and both observations and models show that high N organic matter decomposes more rapidly. In direct contrast to these findings, experimental additions of inorganic N to soils broadly show a suppression of microbial activity, which is inconsistent with N limitation of decomposition. Resolving this apparent contradiction is critical to representing nutrient dynamics in predictive ecosystem models under a multitude of global change factors that alter soil N availability. Here, we propose a new conceptual framework, the Carbon, Acidity, and Mineral Protection hypothesis, to understand the effects of N availability on soil C cycling and storage and explore the predictions of this framework with a mathematical model. Our model simulations demonstrate that N addition can have opposing effects on separate soil C pools (particulate and mineral-protected carbon) because they are differentially affected by microbial biomass growth. Moreover, changes in N availability are frequently linked to shifts in soil pH or osmotic stress, which can independently affect microbial biomass dynamics and mask N stimulation of microbial activity. Thus, the net effect of N addition on soil C is dependent upon interactions among microbial physiology, soil mineralogy, and soil acidity. We believe that our synthesis provides a broadly applicable conceptual framework to understand and predict the effect of changes in soil N availability on ecosystem C cycling under global change.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/gcb.13980

Nitrogen limitation of decomposition and decay: How can it occur?

Demand of all living organisms on the same nutrients forms the basis for interspecific competition between plants and microorganisms in soils. This competition is especially strong in the rhizosphere. To evaluate competitive and mutualistic interactions between plants and microorganisms and to analyse ecological consequences of these interactions, we analysed 424 data pairs from 41 (15)N-labelling studies that investigated (15)N redistribution between roots and microorganisms. Calculated Michaelis-Menten kinetics based on K(m) (Michaelis constant) and V(max) (maximum uptake capacity) values from 77 studies on the uptake of nitrate, ammonia, and amino acids by roots and microorganisms clearly showed that, shortly after nitrogen (N) mobilization from soil organic matter and litter, microorganisms take up most N. Lower K(m) values of microorganisms suggest that they are especially efficient at low N concentrations, but can also acquire more N at higher N concentrations (V(max)) compared with roots. Because of the unidirectional flow of nutrients from soil to roots, plants are the winners for N acquisition in the long run. Therefore, despite strong competition between roots and microorganisms for N, a temporal niche differentiation reflecting their generation times leads to mutualistic relationships in the rhizosphere. This temporal niche differentiation is highly relevant ecologically because it: protects ecosystems from N losses by leaching during periods of slow or no root uptake; continuously provides roots with available N according to plant demand; and contributes to the evolutionary development of mutualistic interactions between roots and microorganisms.

Competition between roots and microorganisms for nitrogen: mechanisms and ecological relevance

Soil net nitrogen mineralization rate (Nmin ), which is critical for soil nitrogen availability and plant growth, is thought to be primarily controlled by climate and soil physical and/or chemical properties. However, the role of microbes on regulating soil Nmin has not been evaluated on the global scale. By compiling 1565 observational data points of potential net Nmin from 198 published studies across terrestrial ecosystems, we found that Nmin significantly increased with soil microbial biomass, total nitrogen, and mean annual precipitation, but decreased with soil pH. The variation of Nmin was ascribed predominantly to soil microbial biomass on global and biome scales. Mean annual precipitation, soil pH, and total soil nitrogen significantly influenced Nmin through soil microbes. The structural equation models (SEM) showed that soil substrates were the main factors controlling Nmin when microbial biomass was excluded. Microbe became the primary driver when it was included in SEM analysis. SEM with soil microbial biomass improved the Nmin prediction by 19% in comparison with that devoid of soil microbial biomass. The changes in Nmin contributed the most to global soil NH4 + -N variations in contrast to climate and soil properties. This study reveals the complex interactions of climate, soil properties, and microbes on Nmin and highlights the importance of soil microbial biomass in determining Nmin and nitrogen availability across the globe. The findings necessitate accurate representation of microbes in Earth system models to better predict nitrogen cycle under global change.

Microbes drive global soil nitrogen mineralization and availability.

Soils contain more carbon than plants or the atmosphere, and sensitivities of soil organic carbon (SOC) stocks to changing climate and plant productivity are a major uncertainty in global carbon cycle projections. Despite a consensus that microbial degradation and mineral stabilization processes control SOC cycling, no systematic synthesis of long-term warming and litter addition experiments has been used to test process-based microbe-mineral SOC models. We explored SOC responses to warming and increased carbon inputs using a synthesis of 147 field manipulation experiments and five SOC models with different representations of microbial and mineral processes. Model projections diverged but encompassed a similar range of variability as the experimental results. Experimental measurements were insufficient to eliminate or validate individual model outcomes. While all models projected that CO2 efflux would increase and SOC stocks would decline under warming, nearly one-third of experiments observed decreases in CO2 flux and nearly half of experiments observed increases in SOC stocks under warming. Long-term measurements of C inputs to soil and their changes under warming are needed to reconcile modeled and observed patterns. Measurements separating the responses of mineral-protected and unprotected SOC fractions in manipulation experiments are needed to address key uncertainties in microbial degradation and mineral stabilization mechanisms. Integrating models with experimental design will allow targeting of these uncertainties and help to reconcile divergence among models to produce more confident projections of SOC responses to global changes.

/pdf/multiple-models-and-experiments-underscore-large-uncertainty-2jq779xu0f.pdf

Multiple models and experiments underscore large uncertainty in soil carbon dynamics

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.

Global pattern and controls of soil microbial metabolic quotient

The datasets of National Centers for Environmental Prediction (NCEP I and II) and Climatic Research Unit – NCEP (CRUNCEP) were evaluated by comparing against observational data from the National Oceanic and Atmospheric Administration (NOAA) at monthly and annual scales, site and regional scales. The evaluations were conducted for air temperature (maximum, mean, and minimum) and precipitation at the 90 largest cities across the United States. At annual scale, NCEP I, NCEP II and CRUNCEP were slightly yet significantly underestimated for mean temperature (1%) and maximum temperatures (1%). The NCEP I was significantly underestimated for minimum temperature (1%) while overestimated for total precipitation (16%); the NCEP II was significantly underestimated for minimum temperature (2%) and total precipitation (2%); the CRUNCEP was significantly overestimated for minimum temperature (2%) and total precipitation (6%). At monthly scale, three datasets were underestimated for a majority of months in terms of monthly mean temperature, monthly maximum temperature, monthly minimum temperature, and monthly total precipitation except an overestimation of CRUNCEP on minimum temperature (48%) and monthly total precipitation (43%). At the seasonal scale, three datasets had smaller biases in summer season and larger biases in winter season for temperature, while larger biases in summer season and smaller biases in winter seasons for precipitation. Overall, the CRUNCEP dataset had a slightly better performance than NCEP I and NCEP II datasets. This multi-scale evaluation of the three most-widely used regional climate datasets provides insightful information for atmospheric science studies, particularly urban heat impact investigation.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2016JD026165

Multiscale evaluation of NCEP and CRUNCEP data sets at 90 large U.S. cities

The mixed quantum-classical dynamical simulation is essential for studying nonadiabatic phenomena in photophysics and photochemistry. In recent years, many machine learning models have been developed to accelerate the time evolution of the nuclear subsystem. Herein, we implement long short-term memory (LSTM) networks as a propagator to accelerate the time evolution of the electronic subsystem during the fewest-switches surface hopping (FSSH) simulations. A small number of reference trajectories are generated using the original FSSH method, and then the LSTM networks can be built, accompanied by careful examination of typical LSTM-FSSH trajectories that employ the same initial condition and random numbers as the corresponding reference. The constructed network is applied to FSSH to further produce a trajectory ensemble to reveal the mechanism of nonadiabatic processes. Taking Tully's three models as test systems, we qualitatively reproduced the collective results. This work demonstrates that LSTM can be applied to the most popular surface hopping simulations.

/pdf/fewest-switches-surface-hopping-with-long-short-term-memory-352h27jf.pdf

Fewest-Switches Surface Hopping with Long Short-Term Memory Networks.

The mixed quantum-classical dynamical simulation is essential to study nonadiabatic phenomena in photophysics and photochemistry. In recent years, many machine learning models have been developed to predict potential energy surfaces in the ground and excited electronic states eﬀectively, which accelerates the time evolution of the nuclear subsystem. Herein, we implement long short-term memory (LSTM) networks as a propagator to accelerate the time evolution of the electronic subsystem during the fewest-switch surface hopping (FSSH) simulations. A small number of reference trajectories are generated using the original FSSH method, and then the LSTM networks can be constructed, followed by careful examination of typical LSTM-FSSH trajectories that employ the same initial condition and random numbers as the corresponding reference. The constructed network is applied to FSSH to further produce a trajectory ensemble to reveal the mechanism of nonadiabatic processes. Taking Tully’s three models as test systems, the collective results can be reproduced qualitatively. This work demonstrates that LSTM is applicable to the most popular surface hopping simulations.

Diandong Tang

Papers

Global pattern and controls of soil microbial metabolic quotient

Multiscale evaluation of NCEP and CRUNCEP data sets at 90 large U.S. cities

Fewest-Switches Surface Hopping with Long Short-Term Memory Networks.

Fewest-Switch Surface Hopping with Long Short-Term Memory Networks