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• Precipitation was repackaged to simulate 4-level of precipitation variability. • Extra-extreme precipitation variability scenario potentially reduced N 2 O emissions. • Effects of precipitation variability on N 2 O fluxes had high interannual variations. • Soil, microbes, functional genes, and plants explained 61% variation in N 2 O fluxes. High temporal precipitation variability, characterized by less frequent but larger-magnitude precipitation events, is increasing. However, how precipitation variability affects N 2 O emissions and the underlying mechanisms remain unclear, especially at multiple-year scales. We conducted a 3-year manipulative experiment in which the same long-term mean growing season precipitation total was repackaged into events of inversely varying magnitude and frequency to simulate four levels of precipitation variability (extra-extreme, extreme, medium, and normal) in a semiarid grassland. Cumulative N 2 O emissions was the smallest under the extra-extreme precipitation variability scenario (6 very large rainfall events), 26% less than emission under the other three treatments (10, 16, and 24 rainfall events) over the three growing seasons. However, this difference was almost entirely due to three sampling events in the third year. Plant community (biomass and biodiversity), soil abiotic properties (water, dissolved organic carbon and pH), soil microbial biomass (carbon, nitrogen and the ratio), and soil functional genes (archaeal and bacterial amoA, nirS, nirK, narG , and nosZ) explained 61% of the variation in N 2 O emissions in response to the precipitation variability. Structural equation modelling indicated that the precipitation variability had direct positive effects, and indirect negative effects via soil abiotic properties, on soil functional genes that ultimately had positive effects on N 2 O emissions. The reduction in N 2 O emission in the extra-extreme precipitation variability scenario in the third year was due to low levels of soil water content, soil pH, aboveground biomass, and microbial biomass carbon simultaneously. Our results suggest that at the multi-year timescale, semiarid grasslands may have negative feedbacks to future precipitation regimes with higher variability. [Display omitted] [ABSTRACT FROM AUTHOR]

• N 2 O flux is sensitive and insensitive to drought and heat wave, respectively. • Soil water content is a first-order predictor of seasonal variation of N 2 O flux. • Inter-annual variations of N 2 O flux are impacted by precipitation distribution. • Archaeal amoA and nirK mainly regulates climate extremes effects on N 2 O flux. Nitrous oxide (N 2 O), the third most important greenhouse gas, contributes to the increasing frequency and severity of climate extremes. Disentangling feedbacks of climate extremes on terrestrial N 2 O emission is important for forecasting future climate changes. Here, we experimentally imposed extreme drought and heat wave events during three years in a semiarid grassland to investigate the responses of N 2 O flux. We identified that N 2 O flux suppression during droughts was mediated by soil water content (SWC), microbial biomass carbon (MBC), soil inorganic nitrogen (SIN) and dissolved organic carbon (DOC) contents, and the abundance of archaeal amoA, nirK, and narG. However, bacterial amoA, nirS, and nosZ remained stable. Upon rewetting following droughts, the SWC, SIN, DOC, archaeal amoA, nirK, narG, and resultant N 2 O fluxes recovered to the magnitude of the ambient control. In contrast, heat waves alone or in combination with drought did not impact N 2 O fluxes or the underlying physical, chemical and microbial states. Stepwise multiple linear regression suggested that SWC, DOC, and MBC were the key factors regulating immediate responses of N 2 O flux to climate extremes while the major factors regulating seasonal mean N 2 O flux in response to climate extremes were archaeal amoA abundance, nirK abundance, and MBC. Our results suggest that N 2 O fluxes were sensitive to droughts but insensitive to heat waves. Soil moisture induced changes in substrate availability, and the community size of total and functional microorganisms in soil jointly regulated N 2 O responses to climate extremes. The relative importance of regulating factors shifted at different timescales. [ABSTRACT FROM AUTHOR]

Increasing heavy rainfalls can strongly affect ecosystem nitrogen (N) cycling processes and thereby alter soil N fluxes and pools. However, the effects of heavy rainfalls on soil N fluxes and pools are poorly understood, particularly with regards to high rainfall timing under field conditions. We conducted a 3-year (2014–2016) manipulative experiment in which heavy rainfall was imposed in middle (plant peak growing stage) or late (plant senescent growth stage) growing season in a semiarid grassland of Inner Mongolia, China to explore the responses of N 2 O fluxes and soil total N contents and the underlying microbial mechanisms. Mid-season heavy rainfall promoted soil N 2 O emissions by 65% on average across the three years, attributable mainly to increases in denitrifying nirK and nirS abundances induced large denitrification at higher soil water contents. However, archaeal and bacterial amoA and narG genes did not change significantly due probably to counteracting effects of increased soil water content (positive) and soil pH (negative). Mid-season heavy rainfall led to 17% reduction in soil total N by the end of the last year of the study (2016), partly due to the enhanced accumulated N 2 O emissions over the three years. In contrast, late-season heavy rainfall did not change N 2 O emissions and soil total N contents even though soil water content, soil pH and nirK and nirS abundance were significantly increased, perhaps due to limitation by low temperature. Timing of the heavy rainfall events during the plant growing season strongly influenced their impacts on soil N fluxes and pools and heavy rainfalls in the peak stage of plant growth may potentially cause a positive feedback to global warming and exacerbate N limitation in terrestrial ecosystems. • N 2 O flux and soil total N responses to heavy rainfall depended on seasonal timing. • Mid-season heavy rainfall increased N 2 O flux by 65% due to higher denitrification. • Enhanced N 2 O emissions coincided with 17% of reduction in soil total N. • Late-season heavy rainfall did not change N 2 O emissions and soil total N contents. [ABSTRACT FROM AUTHOR]

Aim: Species–area relationships (SAR) are widely utilized for estimating the species richness and its spatial turnover across various scales. Despite the prevalent characterization of SAR using the power law in many microbial community studies, its efficacy remains unvalidated. This study aims to characterize the microbial SAR and its mechanisms in alpine grassland soils on the Qinghai‐Tibet Plateau (QTP). Location: Qinghai‐Tibet Plateau, China. Time Period: August 2014. Major Taxa Studied: Soil bacteria. Methods: Soil samples were collected from five alpine grassland sites on the QTP. Employing a nested sampling strategy at each site, soil samples were collected in plot sizes ranging from 0.5 × 0.5 m2 to 2048 × 2048 m2. Soil bacterial communities were analysed by sequencing 16S ribosomal RNA gene amplicons using an Illumina MiSeq. Results: The bacterial SAR exhibited a logarithmic power law (R2: 0.952–0.999), outperforming the power law (R2: 0.701–0.852). Consequently, the most widely adopted power law led to an overestimation of species richness by up to 15.07% in areas >256 × 256 m2, and the regional maximum theoretical richness based on Chao1 by up to 9.88%. Mechanistically, the passive sampling hypothesis was refuted through the rarefied species richness analysis, and the disproportionate effect hypothesis was rejected based on analyses of the effective numbers of species number conversions for the probability of interspecific encounters (SPIE). Notably, Pearson and multiple linear regression analyses indicated that the spatial turnover of bacterial richness was determined by the environmental heterogeneity (R2: 0.855–0.999), rather or better than environmental variables themselves, supporting the 'environment heterogeneity hypothesis'. Main Conclusions: Soil bacterial SAR in alpine grasslands exhibited a logarithmic power relationship. Spatial turnover was primarily governed by the environmental heterogeneity. In contrast, the traditional power law leads to an overestimation of soil bacterial diversity at the regional scale. [ABSTRACT FROM AUTHOR]

Against a backdrop of rising temperature, large portions of the western United States are experiencing fewer, larger and less frequent precipitation events. How such temporal 'repackaging' of precipitation alters the magnitude and timing of seasonal maximum gross primary productivity (GPPmax) remains unknown. Addressing this knowledge gap is critical, since changes to GPPmax magnitude and timing can impact a range of ecosystem services and management decisions.Here we used a field‐based precipitation manipulation experiment in a semi‐arid mixed annual/perennial bunchgrass ecosystem with mean annual precipitation ~384 mm to investigate how temporal repackaging of a fixed total seasonal precipitation amount impacts seasonal GPPmax and its timing.We found that temporal repackaging of precipitation profoundly influenced the seasonal timing of GPPmax. Many/small precipitation events advanced the seasonal timing of GPPmax by ~13 days in comparison with climatic normal precipitation. Conversely, few/large events led to deeper soil water infiltration, which delayed the timing of GPPmax by up to 16 days in comparison with climatic normal precipitation, and altered end‐of‐season community composition by increasing the diversity of shallow‐rooted annual plants. While GPPmax magnitude did not differ across precipitation treatments, it was positively correlated with the abundance and biomass of deeper‐rooted perennial bunchgrasses. The sensitivity of plant growth, biomass accumulation and plant life histories to the timing and magnitude of precipitation events and the resulting temporal patterns of soil moisture regulated ecosystem responses to altered precipitation patterns.Our results highlight the sensitivity of semi‐arid grassland ecosystem to the temporal repackaging of precipitation. We find that already‐observed and model‐forecasted shifts toward few/large precipitation events could drive significant delays in the timing of peak productivity for this ecosystem. Adaptive land management frameworks should consider these findings since shifts in peak ecosystem productivity would have major implications for multiple land user communities. Additional research is needed to better understand the role of climate, community composition and soil properties in mediating variability in the seasonal timing of maximum ecosystem productivity. A free Plain Language Summary can be found within the Supporting Information of this article. [ABSTRACT FROM AUTHOR]

• Grassland degradation is predictable on a large scale by Random Forest. • Specific microbial groups, mainly Solirubrobacter, were screened as credible biomarkers. • Grassland degradation exhibited a longitudinal pattern and was correlated with current Net primary productivity. Extensive grassland degradation under climate change and intensified human activities has threatened ecological security and caused a variety of environmental problems. However, it is still challenging to predict the grassland degradation status on a large scale because it is a multi-factorial phenomenon with complex changes in ecosystem structure and function, which is hard to be fully characterized through mechanism models. The emergence of machine learning algorithms provides a potential to model complex systems and mine information from multi-source data without elucidating underlying mechanisms. Here, we utilized random forest and neural network algorithms to predict the grassland degradation represented by the net primary productivity (NPP) changing rate based on multi-source data including geographic, meteorological, plant traits, land use type and microbial variables in the Chinese Northern grassland. Particularly, the microbial roles in determining the degradation status were concerned. Results show that a high prediction precision was achieved by random forest model, rather than by neural network model, with a mean relative error of 16.9% and a mean square error of 9.273e-05. Besides identified longitude, arid index and current NPP state, specific soil microbial groups, mainly Solirubrobacter, were screened as credible biomarkers. Regarding model fitting, geographic, meteorological and plant variables explained 61.8% of the total variance, which was enhanced up to 72.8% by the rest microbial markers. These findings provide a theoretical basis to establish a pre-warning system for grassland management and policy-making. [ABSTRACT FROM AUTHOR]

• Non-linear patterns of microbial N genes occurred along precipitation gradient. • Breaking points were clearly related to certain soil types. • Attribution of different environmental factors varied by ecosystem. Despite the importance of microorganisms in soil nitrogen (N) cycling, studies on spatial patterns of microbial N-cycling gene abundances in temperate grasslands are still lacking, whose productivity is limited by N. Here, we investigated N functional genes in soil microorganisms from 60 sites in temperate grasslands across 1661 km in Inner Mongolia, China. Abundances of all N functional genes and 16S rDNA tended to decrease from northeast to southwest, consistent with the changing tend of precipitation but contrary to that of temperature. Non-linear saturation curves dominated patterns for abundances of most N functional genes and 16S rDNA along precipitation as they increased with the rising precipitation when <288–343 mm, but remained stable after these breaking points. Interestingly, these breaking points were clearly related to certain soil types, e.g. nifH did not change with precipitation in Chernozems and Gleysols, but increased with it in other soil types. Non-linear saturation patterns were also observed for most N functional gene and 16S rDNA abundances with temperature and soil total organic carbon. In contrast, no consistent spatial pattern for ratios of N functional genes: 16S rDNA was observed, which varied greatly by different N functional genes. Moreover, decay relationships were discovered for the abundance-based matrix of N functional genes over geographic distance. From the temperate meadows to typical steppes and to temperate desert steppes, the influence of relatively long-term environmental variables increased, but that of short-term ones decreased. Overall, we revealed non-linear patterns of N functional gene abundances along precipitation with a clear relationship with soil type, and discovered that attributions of geographic distance, plant community, historical-contingency and contemporary-disturbance to N functional gene community similarity decay were ecosystem–dependent. [ABSTRACT FROM AUTHOR]

• Soil EEAs and bacterial community composition responded differently to drought. • Soil water content was most important in affecting responses. • Changes in bacterial community composition were more persistent than soil EEAs. Extreme drought can strongly impact belowground communities and biogeochemical processes, including soil microbial community composition and extracellular enzyme activities (EEAs), which are considered key agents in ecosystem carbon (C) and nutrient cycling. However, our understanding of how seasonal timing of drought during the growing season affects soil microbial communities and their activity remains notably poor. In this study, we investigated the responses of soil physicochemical properties, EEAs, and bacterial community composition to extreme-duration drought imposed in the early-, mid-, or late-stages of the growing season in a semiarid grassland ecosystem in Inner Mongolia, China. Compared with the ambient control, the activities of C-, nitrogen (N)-, and phosphorus (P)-acquisition enzymes were significantly decreased in the mid- and/or late-stages of drought. Bacterial community diversity also significantly decreased in the mid- and late-stage drought treatments. Soil water content was the most important factor explaining changes in soil EEAs and bacterial community composition. At the end of the growing season, the activities of C-, N-, and P-acquisition enzymes had mostly recovered, while the bacterial community diversity in the mid- and late-stage drought treatments was still lower than the ambient control. Overall, our study demonstrates that the effects of extreme drought on soil EEAs and bacterial community composition depend on the timing of drought. Our results highlight that understanding the effects of extreme-duration drought at different stages of the growing season may play a vital role in predicting the responses of belowground function to global changes in grassland ecosystems. [ABSTRACT FROM AUTHOR]

Grassland remote sensing (GRS) is an important research topic that applies remote sensing technology to grassland ecosystems, reflects the number of grassland resources and grassland health promptly, and provides inversion information used in sustainable development management. A scientometrics analysis based on Science Citation Index-Expanded (SCI-E) was performed to understand the research trends and areas of focus in GRS research studies. A total of 2692 papers related to GRS research studies and 82,208 references published from 1980 to 2020 were selected as the research objects. A comprehensive overview of the field based on the annual documents, research areas, institutions, influential journals, core authors, and temporal trends in keywords were presented in this study. The results showed that the annual number of documents increased exponentially, and more than 100 papers were published each year since 2010. Remote sensing, environmental sciences, and ecology were the most popular Web of Science research areas. The journal Remote Sensing was one of the most popular for researchers to publish documents and shows high development and publishing potential in GRS research studies. The institution with the greatest research documents and most citations was the Chinese Academy of Sciences. Guo X.L., Hill M.J., and Zhang L. were the most productive authors across the 40-year study period in terms of the number of articles published. Seven clusters of research areas were identified that generated contributions to this topic by keyword co-occurrence analysis. We also detected 17 main future directions of GRS research studies by document co-citation analysis. Emerging or underutilized methodologies and technologies, such as unmanned aerial systems (UASs), cloud computing, and deep learning, will continue to further enhance GRS research in the process of achieving sustainable development goals. These results can help related researchers better understand the past and future of GRS research studies. [ABSTRACT FROM AUTHOR]

Grazing is expected to exert a substantial influence on antibiotic resistance genes (ARGs) in grassland ecosystems. However, the precise effects of grazing on the composition of ARGs in grassland soils remain unclear. This is especially the case for grassland soils subject to long-term grazing. Here, we investigated ARGs and bacterial community composition in soils subject to long-term historic grazing (13–39 years) and corresponding ungrazed samples. Using a combination of shotgun metagenomics, amplicon analyses and associated soil physicochemical data, we provide novel insights regarding the structure of ARGs in grassland soils. Interestingly, our analysis revealed that long-term historic grazing had no impacts on the composition of ARGs in grassland soils. An average of 378 ARGs, conferring resistance to 14 major categories of antibiotics (80%), were identified in both grazing and ungrazed sites. Actinobacteria , Proteobacteria and Acidobacteria were the most prevalent predicted hosts in these soils and were also shown to harbour genetic capacity for multiple-resistant ARGs. Our results suggested that positive effects of bacterial community composition on ARGs could potentially be controlled by affecting MGEs. Soil properties had direct effects on the composition of ARGs through affecting the frequency of horizontal gene transfer among bacteria. Twelve novel ARGs were found in S. grandis steppe grasslands, indicating that different vegetation types might induce shifts in soil ARGs. Collectively, these findings suggest that soil properties, plants and microorganisms play critical roles in shaping ARG patterns in grasslands. Together, these data establish a solid baseline for understanding environmental antibiotic resistance in grasslands. Unlabelled Image • ARGs related with resistance to cephalosporin and tetracycline were dominant. • ARGs driven by factors affecting the distribution of soil microorganisms in grasslands. • Long-term historic grazing had no effect on ARGs in grassland soils. • ARGs shaped by the initial plant, soil environmental parameters and microbiomes in grassland. [ABSTRACT FROM AUTHOR]

• Drought reduced the soil respiration (SR), especially during the treatment. • Heat wave, alone or combined with drought, had little effect on SR. • Seasonal dynamic of SR was regulated soil moisture and microbial activity. • Inter-annual variation in SR was positively related to plant biomass and structure. Increasing frequency and magnitude of climate extremes could fundamentally affect terrestrial carbon (C) cycling. However, as the second-largest terrestrial C flux, soil respiration (SR) responses to climate extremes are not well understood. Here, we investigated the effects of drought, heat wave and drought plus heat wave on SR in a semiarid grassland during the growing season over 3 years. The results indicated that drought consistently reduced the growing-season mean SR, especially during the period of drought treatment, while heat wave, alone or when combined with drought, had little effect on SR. The decreased SR under drought at the intra-annual timescale could be primarily attributable to restriction of low soil moisture to microbial biomass, as there were no consistent changes in plant community (aboveground biomass, richness and abundance). In contrast, the inter-annual variation in SR was positively related to plant community abundance, richness and aboveground biomass in additional to soil water availability, but was not significantly affected by microbial biomass. Our study highlighted that incorporating microbial biomass in C cycling models can improve simulation of seasonal dynamics of soil respiration while incorporating plant community characteristics can benefit prediction of variation in SR across multiple years in semiarid grasslands. [ABSTRACT FROM AUTHOR]