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Purpose: The escalation of nitrogen (N) deposition has resulted in phosphorus (P) limitation in alpine grasslands on the Qinghai–Tibetan Plateau (QTP). However, the impact of N deposition affects soil P transformations in alpine grasslands, and whether there is a universal pattern of N-induced soil P fraction change in terrestrial ecosystems is still not well understood. Methods: We performed field experiments in two alpine grasslands on the QTP and a meta-analysis including 1033 records worldwide to analyze the responses of soil P fractions to N addition. Results: We found that N addition significantly altered soil P fractions in alpine meadow, whereas it induced a minor response in alpine steppe. The N addition induced a decrease in soil inorganic phosphorus (Pi) in the alpine meadow, resulting from occluded P (i.e., C.HCl-Pt and residual-Pt). Though N addition did not change total organic P (Po) concentration, there were remarkable changes among soil organic P fractions (C.HCl-Po, NaOH-Po, and NaHCO3-Po) in the alpine meadow, with an increase in NaOH-Po but a decrease in C.HCl-Po. Soil inorganic P in the alpine meadow was associated with Ca2+ and soil pH that was also reduced by N addition. By contrast, meta-analysis results showed that N addition significantly increased the lnRR of NaOH-Pi, but decreased lnRR of C.HCl-Pt and marginally reduced lnRR of NaHCO3-Po across all terrestrial ecosystems. Among multiple environmental and experimental variables, soil pH, mean annual temperature (MAT), mean annual precipitation (MAP), N forms, and soil phosphatase activity mainly drove the response of NaHCO3-Po to N addition at the large scale. Structural equation model (SEM) further showed that soil phosphatase activity was the main direct factor controlling NaHCO3-Po response. Conclusions: Our results suggest that soil P fractions are more sensitive to N addition in alpine meadow than in alpine steppe. The reduction of inorganic P fractions and uneven changes of organic P fractions in alpine meadow suggested that N addition may accelerate inorganic P dissolution but depress organic P mineralization. Environmental factor (e.g., MAP) and experimental variables (N rate) affected soil P fractions in response to N addition mediated by soil pH and enzymatic activities. Collectively, these findings improved our understanding of the consequences of N addition on soil organic and inorganic P transformations and predicted the trajectory of soil phosphorus fraction change under increasing N deposition. [ABSTRACT FROM AUTHOR]

Soil nematodes play pivotal roles in the soil food web. However, the trophic regulation on nematodes in the soil micro‐food web and its temporal dynamics are less explored. Here, we investigated the seasonal dynamics of soil nematode community in an alpine grassland on the Qinghai–Tibet Plateau at non‐degraded (ND), moderately degraded (MD) and severely degraded (SD) stages, either under open‐top chamber warming (W) or not (NW). Nematode richness was higher in SD than in MD and ND that did not differ, accompanied by a clear shift from the bottom‐up regulation via belowground productivity in ND to the top‐down regulation via omnivore and predator nematodes in SD. The intensified top‐down regulation in SD was explained by increased proportions of predator and omnivore nematodes, likely induced by the expanded soil pore size in SD. As a result, accelerated temporal turnover of nematode community was observed under grassland degradation, which were 0.006 (slope, p = 0.78), 0.045 (p = 0.07), and 0.077 (p = 0.001) in ND, MD, and SD, respectively. Moreover, grassland degradation strengthened the association between nematode density and ecosystem respiration, whose slope was higher in SD (0.0025, p < 0.05) than in MD (0.0008, p < 0.01) or ND (insignificant). In contrast, warming did not affect the temporal turnover of the nematode community, richness, nor its role in ecosystem respiration. Overall, we revealed the accelerated temporal turnover of nematode community and its strengthened role in determining ecosystem respiration under alpine grassland degradation, suggesting that trophic cascade changes under ecosystem degradation may alter the ecosystem functioning. [ABSTRACT FROM AUTHOR]

The grassland in the Hindu Kush Himalayan (HKH) region is one of the largest and most biodiverse mountain grassland types in the world, and its ecosystem service functions have profound impacts on the sustainable development of the HKH region. Monitoring the spatiotemporal distribution of grassland aboveground biomass (AGB) accurately and quantifying its response to climate change are indispensable sources of information for sustainably managing grassland ecosystems in the HKH region. In this study, a pure vegetation index model (PVIM) was applied to estimate the long-term dynamics of grassland AGB in the HKH region during 2000–2018. We further quantified the response of grassland AGB to climate change (temperature and precipitation) by partial correlation and variance partitioning analyses and then compared their differences with elevation. Our results demonstrated that the grassland AGB predicted by the PVIM had a good linear relationship with the ground sampling data. The grassland AGB distribution pattern showed a decreasing trend from east to west across the HKH region except in the southern Himalayas. From 2000 to 2018, the mean AGB of the HKH region increased at a rate of 1.57 g/(m2·yr) and ranged from 252.9 (2000) to 307.8 g/m2 (2018). AGB had a positive correlation with precipitation in more than 80% of the grassland, and temperature was positively correlated with AGB in approximately half of the region. The change in grassland AGB was more responsive to the cumulative effect of annual precipitation, while it was more sensitive to the change in temperature in the growing season; in addition, the influence of climate varied at different elevations. Moreover, compared with that of temperature, the contribution of precipitation to grassland AGB change was greater in approximately 60% of the grassland, but the differences in the contribution for each climate factor were small between the two temporal scales at elevations over 2000 m. An accurate assessment of the temporal and spatial distributions of grassland AGB and the quantification of its response to climate change are of great significance for grassland management and sustainable development in the HKH region. [ABSTRACT FROM AUTHOR]

Extreme climatic events are likely to intensify under climate change and can have different effects on ecosystems depending on their timing and magnitude. Understanding how productivity responds to extreme precipitation patterns requires assessing stability and vulnerability during critical growing periods at the plant community level. In this study, we experimentally imposed two contrasting types of extreme precipitation patterns, including extreme drought (excluding all rainfall for 30 consecutive days) during early-, mid-, and late-stages of the growing season, and heavy rainfall (adding 14.1 mm of rainfall every day for 20 consecutive days) during mid- and late-stages of the growing season over four years (2013–2016) in a steppe community in Inner Mongolia, China. We found that extreme drought and heavy rainfall had no effect on community aboveground net primary productivity (ANPP), species richness, and dominance at any stage of the growing season. Community stability in response to extreme drought was mainly driven by compensation among species and the stability of dominant species, while the compensatory effect among species and functional groups, and the stability of dominant species contributed to the community stability in response to heavy rainfall. Overall, our findings indicate that the responses of the ecosystem to intra-seasonal contrasting extreme precipitation patterns can be driven by similar stability mechanisms and suggest that semiarid temperate steppe communities may have strong initial resistance to more frequent extreme climatic events in the future. [ABSTRACT FROM AUTHOR]

Purpose: Grasslands are the largest type of terrestrial ecosystem on the earth, providing rich and unique ecosystem services. However, climate change and human activities have triggered a global degradation of grasslands, which has become a major ecological crisis. In this study, a scientometric analysis was performed to explore the hotspots and frontiers of global grassland degradation research. Materials and methods: Two methods involving visualization were used to analyze these data: document co-citation analysis and burst analysis based on the papers indexed in the Web of Science (WOS) during 1970–2020. Results and discussion: A total of 3580 research papers related to grassland degradation research and 54,666 references were included. The results showed that Harris's paper in 2010 had the strongest burst value of 26.2, far larger than any other, which shows that this paper was a turning point in the research process. The document co-citation network was divided into 14 main theme clusters. The most influential and emerging research theme clusters were including alpine meadow, grazing exclusion, alpine region, and human activities. Alpine meadow was the largest cluster lasting from 2010 to 2020, indicating that this topic is still active in grassland degradation research. Furthermore, research focus has transferred toward grasslands in Qinghai-Tibetan Plateau. The topic of grazing exclusion is both classic and currently active as it lasted as a research hotspot for 15 years (2004–2018). However, the extent and state of grazing effects research are unclear. Conclusions: As the first scientometric review on grassland degradation research, our study identified the research hotspots and their shifts over the past 50 years, pointing to some potential research frontiers in the future. The scientometric analysis is a useful tool for a quantitative evaluation of research hotspots and trends of global grassland degradation. [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]

Eddy covariance measurements also produce information on how ecosystem respond to the changes in climate, which is useful for assessing ecosystem carbon sequestration ([10]), water and energy balance ([8]), resource use efficiency ([15]) and ecosystem feedback to climate change ([11]; [18]; [28]). Ecologic vulnerable areas (EVAs) are the regions where ecosystems are fragile and vulnerable to suffer from degradation with external disturbances, e.g. environmental changes and human activities ([7]; [20]). Therefore, clarifying the variations and controls of ecosystem carbon and water fluxes is an effective approach to clarifying how ecosystem respond to global change in EVAs ([2]). [Extracted from the article]

Droughts and heat waves are increasing in magnitude and frequency, altering the carbon cycle. However, understanding of the underlying response mechanisms remains poor, especially for the combination (hot drought). We conducted a 4‐year field experiment to examine both individual and interactive effects of drought and heat wave on carbon cycling of a semiarid grassland across individual, functional group, community and ecosystem levels. Drought did not change below‐ground biomass (BGB) or above‐ground biomass (AGB) due to compensation effects between grass and non‐grass functional groups. However, consistently decreased BGB under heat waves limited such compensation effects, resulting in reduced AGB. Ecosystem CO2 fluxes were suppressed by droughts, attributed to stomatal closure‐induced reductions in leaf photosynthesis and decreased AGB of grasses, while CO2 fluxes were little affected by heat waves. Overall the hot drought produced the lowest leaf photosynthesis, AGB and ecosystem CO2 fluxes although the interactions between heat wave and drought were usually not significant. Our results highlight that the functional group compensatory effects that maintain community‐level AGB rely on feedback of root system responses, and that plant adjustments at the individual level, together with shifts in composition at the functional group level, co‐regulate ecosystem carbon sink strength under climate extremes. • It is indispensable to explore the mechanisms and impacts of climate extremes on the carbon cycle given they are increasing in magnitude and frequency.• Aboveground biomass responses to climate extremes rely on the functional group compensatory effects and feedbacks of root system responses.• Plant physiological adjustments and shifts in community composition co‐regulate ecosystem CO2 fluxes under climate extremes. [ABSTRACT FROM AUTHOR]

Aims Prediction of changes in ecosystem gross primary productivity (GPP) in response to climatic variability is a core mission in the field of global change ecology. However, it remains a big challenge for the model community to reproduce the interannual variation (IAV) of GPP in arid ecosystems. Accurate estimates of soil water content (SWC) and GPP sensitivity to SWC are the two most critical aspects for predicting the IAV of GPP in arid ecosystems. Methods We took a widely used model Biome-BGC as an example, to improve the model performances in a temperate grassland ecosystem. Firstly, we updated the estimation of SWC by modifying modules of evapotranspiration, SWC vertical profile and field capacity. Secondly, we modified the function of controlling water–nitrogen relation, which regulates the GPP–SWC sensitivity. Important Findings The original Biome-BGC overestimated the SWC and underestimated the IAV of GPP sensitivity, resulting in lower IAV of GPP than the observations, e.g. it largely underestimated the reduction of GPP in drought years. In comparison, the modified model accurately reproduced the observed seasonal and IAVs in SWC, especially in the surface layer. Simulated GPP–SWC sensitivity was also enhanced and became closer to the observations by optimizing parameter controlling nitrogen mineralization. Consequently, the model's capability of reproducing IAV of GPP has been largely improved by the modifications. Our results demonstrate that SWC in the surface layer and the consequent effects on nitrogen availability should be among the first considerations for accurate modeling IAV of GPP in arid ecosystems. [ABSTRACT FROM AUTHOR]

• 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]

Highlights • Grazing interacted extreme drought timing to regulate steppe ecosystem CO 2 exchange. • Extreme drought reduced CO 2 uptake in a fenced steppe regardless of seasonal timing. • Early-season drought caused largest declines in CO 2 uptake in a grazed steppe. Abstract Climate models predict a substantial increase in the frequency of extreme drought, suggesting subsequent impacts on the carbon (C) and water cycles. Although many studies have investigated the impacts of extreme drought on ecosystem functioning, it remains unknown how the timing of extreme drought within a growing season may affect carbon and water cycling. Here we conducted a 3-year field experiment to investigate the influence of seasonal drought timing on ecosystem carbon and water exchange by excluding rainfall (for consecutive 30 days) during three periods of the growing season (May–June, July–August and August–September) in fenced and grazed sites of a semiarid temperate steppe in Inner Mongolia, China. In the fenced steppe, extreme drought reduced growing-season net CO 2 uptake regardless of drought timing, while in the grazed steppe, early-growing season drought caused relatively larger reductions to net CO 2 uptake than drought imposed later in the season. The effect of extreme drought on evapotranspiration (ET) was similar to that of CO 2 exchange at the fenced site, with consistent reductions of seasonally-integrated ET for all treatments compared with the ambient condition. In contrast, at the grazed site, the response of ET to extreme drought was more variable, possibly due to the absence of litter and greater bare ground. Surprisingly, both gross and net carbon uptake declined with increasing ET at the grazed site, while the fenced site showed the positive water-carbon linkage typically seen in semiarid ecosystems. The different responses of CO 2 and water exchanges for the fenced and grazed sites were regulated predominately by soil temperature and soil water content. Together, our results show that drought timing within the growing season can significantly alter drought impacts on ecosystem water and CO 2 exchanges, and that grazing management may further mediate the response. [ABSTRACT FROM AUTHOR]

• Seasonal timing strongly regulates the response of N 2 O emissions to extreme droughts. • Early drought affected N 2 O emission with high interannual variability. • Middle drought consistently suppressed N 2 O emissions. • Late drought had little effect on N 2 O fluxes. Terrestrial ecosystems are important sources of nitrous oxide (N 2 O), a powerful greenhouse gas which can be strongly impacted by increasing droughts in association with climate change. However, detailed information on whether and how drought timing regulates N 2 O fluxes is still lacking. Here, we conducted a 3-year field experiment on a semiarid grassland in which extreme drought was imposed in either early-, mid-, or late-growing seasons repeatedly from 2014 to 2016. We found that early drought affected N 2 O emission with high interannual variability (increased, decreased and unchanged N 2 O emission in 2014, 2015, and 2016, respectively), coincident with changes in inorganic nitrogen (SIN), dissolve organic carbon (DOC), microbial biomass carbon (MBC), and soil functional genes (bacterial amoA , nirK , nirS , and nosZ). However, middle drought consistently suppressed N 2 O emissions due to simultaneous decreases in MBC, DOC and the abundances of archaeal amoA , nirK , and narG genes , causing the largest reduction in N 2 O emissions across the three years. In contrast, late drought had little effect on N 2 O fluxes, even though DOC and SIN decreased and the abundance of nirK , nirS , and nosZ increased. As a result, soil organic C and mineral N availability and functional gene abundances were not always robust factors for predicting N 2 O emissions under droughts across all treatments, except for abundance of AOA and nosZ. Our results highlight the vital role of seasonal timing in regulating the response of N 2 O emissions to extreme droughts. [ABSTRACT FROM AUTHOR]

Abstract: Given the important contributions of semiarid region to global land carbon cycle, accurate modeling of the interannual variability (IAV) of terrestrial gross primary productivity (GPP) is important but remains challenging. By decomposing GPP into leaf area index (LAI) and photosynthesis per leaf area (i.e., GPP_leaf), we investigated the IAV of GPP and the mechanisms responsible in a temperate grassland of northwestern China. We further assessed six ecosystem models for their capabilities in reproducing the observed IAV of GPP in a temperate grassland from 2004 to 2011 in China. We observed that the responses to LAI and GPP_leaf to soil water significantly contributed to IAV of GPP at the grassland ecosystem. Two of six models with prescribed LAI simulated of the observed IAV of GPP quite well, but still underestimated the variance of GPP_leaf, therefore the variance of GPP. In comparison, simulated pattern by the other four models with prognostic LAI differed significantly from the observed IAV of GPP. Only some models with prognostic LAI can capture the observed sharp decline of GPP in drought years. Further analysis indicated that accurately representing the responses of GPP_leaf and leaf stomatal conductance to soil moisture are critical for the models to reproduce the observed IAV of GPP_leaf. Our framework also identified that the contributions of LAI and GPP_leaf to the observed IAV of GPP were relatively independent. We conclude that our framework of decomposing GPP into LAI and GPP_leaf has a significant potential for facilitating future model intercomparison, benchmarking and optimization should be adopted for future data‐model comparisons. [ABSTRACT FROM AUTHOR]

Purpose: Understanding the biogeographic distribution of soil fungal communities is crucial for assessing the impacts of environmental factors on terrestrial biodiversity and ecosystem functioning. Here, we investigated spatial variations of fungal communities across three different types of temperate grasslands along a transect in the Inner Mongolia, China. The aims were to understand the biogeographic patterns of fungi and key drivers shaping soil fungal communities in temperate grasslands. Materials and methods: The composition and diversity of soil fungal community across 30 sites of the meadow steppe, typical steppe, and desert steppe along a 1200-km transect were studied through pyrosequencing. The relationships between fungal communities and environmental and biotic factors were assessed. Results and discussion: The results showed that the fungal community along this transect exhibited strong dependence on soil moisture content and nitrate concentration, while the fungal alpha diversity was positively correlated with precipitation and below-ground biomass. Drier environment has resulted in a shift towards an Ascomycota-dominating fungal community. Conclusions: Our findings suggest that the distribution and community structure of soil fungal communities are primarily driven by precipitation. Plant biomass and soil nutrient status which are also influenced by precipitation are also predictors of fungal community. Our results provide important implications for understanding the linkages among environmental factors and soil fungal communities in Eurasian steppe ecosystems. [ABSTRACT FROM AUTHOR]

Purpose: In this study, a process-based denitrification-decomposition (DNDC) model was parameterized and validated with CO fluxes data measured using eddy covariance (EC) technique in the meadow and desert steppes of Inner Mongolia grasslands, China. The validated model was applied to predict the influence of moderate climate change on carbon (C) budget of the two steppes over the next 30 years. Materials and methods: A series of parameters associated with plant growth including maximum biomass production, C/N ratio of plant tissue, and accumulating thermal degree days based on local observations were adjusted to meet the requirements of adapting DNDC for the two targeted steppes. Daily weather data for the studied year together with soil properties for the study sites were employed as inputs to simulate the grass growth and soil C dynamics. A baseline and five alternative climate scenarios were utilized to estimate the effect of climate change on the ecosystem C dynamics for the next 30 years. The coefficient of determination ( R ), root mean square error (RMSE), relative mean deviation (RMD), and modeling efficiency (EF) were used to assess the DNDC model based on the EC measurements. Results and discussion: The DNDC model successfully captured the maximum and minimum soil surface temperature and the effects of rainfall pulse on soil water content (SWC). Validation of ecosystem daily CO fluxes against the EC observed values resulted in satisfactory results in both of the meadow and the desert steppe. Additionally, the comparison of monthly simulated results is better than that of daily results. Between 2011 and 2040, if local climate becomes wetter, both steppes would become C sinks, leading to a great net primary productivity (NPP) of 821 and 828 kg C ha year sequestered in soil organic C (SOC), respectively. If local climate becomes drier, they would become C-sources, having the lowest NPP with 166 and 121 kg C ha year lost from the soil C pool, respectively. Conclusions: The validated DNDC model can be used to predict C dynamics in two semiarid grassland ecosystems. For the upcoming 30 years, if the local climate became wetter, the meadow and desert steppes would act as C sink, absorbing CO from the atmosphere and increase the C sequestration potential. However, both steppes would act as C source, releasing CO to the atmosphere, and decrease the C sequestration when the local climate becomes drier. [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]

Purpose: Carbon (C) dynamics in grassland ecosystem contributes to regional and global fluxes in carbon dioxide (CO) concentrations. Grazing is one of the main structuring factors in grassland, but the impact of grazing on the C budget is still under debate. In this study, in situ net ecosystem CO exchange (NEE) observations by the eddy covariance technique were integrated with a modified process-oriented biogeochemistry model (denitrification-decomposition) to investigate the impacts of grazing on the long-term C budget of semiarid grasslands. Materials and methods: NEE measurements were conducted in two adjacent grassland sites, non-grazing (NG) and moderate grazing (MG), during 2006-2007. We then used daily weather data for 1978-2007 in conjunction with soil properties and grazing scenarios as model inputs to simulate grassland productivity and C dynamics. The observed and simulated CO fluxes under moderate grazing intensity were compared with those without grazing. Results and discussion: NEE data from 2-year observations showed that moderate grazing significantly decreased grassland ecosystem CO release and shifted the ecosystem from a negative CO balance (releasing 34.00 g C m) at the NG site to a positive CO balance (absorbing −43.02 g C m) at the MG site. Supporting our experimental findings, the 30-year simulation also showed that moderate grazing significantly enhances the CO uptake potential of the targeted grassland, shifting the ecosystem from a negative CO balance (57.08 ± 16.45 g C m year) without grazing to a positive CO balance (−28.58 ± 14.60 g C m year) under moderate grazing. The positive effects of grazing on CO balance could primarily be attributed to an increase in productivity combined with a significant decrease of soil heterotrophic respiration and total ecosystem respiration. Conclusions: We conclude that moderate grazing prevails over no-management practices in maintaining CO balance in semiarid grasslands, moderating and mitigating the negative effects of global climate change on the CO balance in grassland ecosystems. [ABSTRACT FROM AUTHOR]

Abstract: Precipitation quantity and timing have been shown to fundamentally influence grassland ecosystem CO2 exchange by both model simulation and field observation. Carbon dioxide exchange and patterns of variation (magnitude and timing) in precipitation were observed to determine their relationship in typical steppe in Inner Mongolia Plateau during four years (2003–2006) with different rainfall patterns. The response of ecosystem-level CO2 fluxes to rainfall pulse events was also examined. The four years studied were classified into three levels of rainfall amount (100%, 2004; reduced by 70% in 2005 and by 45% in 2006 compared with 2004) and two temporal patterns of rain events (average temporal patterns in 2004 and altered precipitation timing in 2003, a year with precipitation fell during the first half of the growing season, compared with 2004). The grassland ecosystem switched from a carbon sink (2004) to carbon sources (2005 and 2006) under reduced rainfall amounts. Carbon sink reduced by 34% under variational rainfall timing conditions (2003). There was a significant positive correlation between annual rainfall and above-ground NPP across the study (P =0.05, r 2 =0.9). Antecedent soil water before rainfall pulse and rainfall pulse size are important in controlling the response of CO2 exchange to rainfall in this steppe. After rainfall pulse events, CO2 fluxes had 4 to 5days of system-level hysteresis and the increase degree of CO2 exchange was different among the four years. Precipitation pulse size not only affected the duration of CO2 fluxes, but also instant flux rate. Our results suggest that CO2 exchange in temperate steppe will be more sensitive to altered rainfall timing and more affected by changes in rainfall quantity, with a longer dry season resulting in significant C losing from Eurasia grassland in Inner Mongolia Plateau. [Copyright &y& Elsevier]

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]

• Structural stable state triggers a change in functional stable state. • Long-term ungrazed steppe had an abrupt transition in plant functional type and followed a shift in ecosystem function. • Moderate grazed activity no abrupt transitions for stable states. Theoretical models suggest that ecosystems can be found in one of several possible alternative stable states, and a shift in structural stable state (SSS) can trigger a change in functional stable state (FSS). But we still lack the empirical evidence to confirm these states and transitions, and to inform the rates of change. Here, a 30-yr dataset from long-term ungrazed and grazed temperate grasslands was analyzed to determine whether abrupt transitions of SSS and FSS can occur. We found that the long-term ungrazed grassland experienced abrupt transitions in the dominant plant functional type (shift in SSS) that was followed by a transition between carbon sink and source 1–2 year later (shift in FSS). A directional shift in precipitation and temperature accounted for 40% of the variation in the SSS transition, while the SSS transition explained 20% of the variation in the FSS transition. In contrast, no abrupt transitions for SSS and FSS were observed in the long-term moderately grazed grassland. These findings provide important insight into the interacting effects of climate change and livestock grazing on ecosystem transitions in temperate grasslands. Moderate utilization of production in ecosystems that have co-evolved with herbivores can offset structural and functional transitions induced by climate change. [ABSTRACT FROM AUTHOR]

The alpine grassland on the Qinghai-Tibet Plateau covers an area of about 1/3 of China's total grassland area and plays a crucial role in regulating grassland ecological functions. Both environmental changes and irrational use of the grassland can result in severe grassland degradation in some areas of the Qinghai-Tibet Plateau. However, the magnitude and patterns of the physical and anthropogenic factors in driving grassland variation over northern Tibet remain debatable, and the interactive influences among those factors are still unclear. In this study, we employed a geographical detector model to quantify the primary and interactive impacts of both the physical factors (precipitation, temperature, sunshine duration, soil type, elevation, slope, and aspect) and the anthropogenic factors (population density, road density, residential density, grazing density, per capita GDP, and land use type) on vegetation variation from 2000 to 2015 in northern Tibet. Our results show that the vegetation index in northern Tibet significantly decreased from 2000 to 2015. Overall, the stability of vegetation types was sorted as follows: the alpine scrub > the alpine steppe > the alpine meadow. The physical factors, rather than the anthropogenic factors, have been the primary driving factors for vegetation dynamics in northern Tibet. Specifically, meteorological factors best explained the alpine meadow and alpine steppe variation. Precipitation was the key factor that influenced the alpine meadow variation, whereas temperature was the key factor that contributed to the alpine steppe variation. The anthropogenic factors, such as population density, grazing density and per capita GDP, influenced the alpine scrub variation most. The influence of population density is highly similar to that of grazing density, which may provide convenient access to simplify the study of the anthropogenic activities in the Tibet plateau. The interactions between the driving factors had larger effects on vegetation than any single factor. In the alpine meadow, the interaction between precipitation and temperature can explain 44.6% of the vegetation variation. In the alpine scrub, the interaction between temperature and GDP was the highest, accounting for 27.5% of vegetation variation. For the alpine steppe, the interaction between soil type and population density can explain 29.4% of the vegetation variation. The highest value of vegetation degradation occurred in the range of 448–469 mm rainfall in the alpine meadow, 0.61–1.23 people/km2 in the alpine scrub and –0.83–0.15 °C in the alpine steppe, respectively. These findings could contribute to a better understanding of degradation prevention and sustainable development of the alpine grassland ecosystem in northern Tibet. [ABSTRACT FROM AUTHOR]

[Display omitted] Structural information of grassland changes on the Tibetan Plateau is essential for understanding alterations in critical ecosystem functioning and their underlying drivers that may reflect environmental changes. However, such information at the regional scale is still lacking due to methodological limitations. Beyond remote sensing indicators only recognizing vegetation productivity, we utilized multivariate data fusion and deep learning to characterize formation-based plant community structure in alpine grasslands at the regional scale of the Tibetan Plateau for the first time and compared it with the earlier version of Vegetation Map of China for historical changes. Over the past 40 years, we revealed that (1) the proportion of alpine meadows in alpine grasslands increased from 50% to 69%, well-reflecting the warming and wetting trend; (2) dominances of Kobresia pygmaea and Stipa purpurea formations in alpine meadows and steppes were strengthened to 76% and 92%, respectively; (3) the climate factor mainly drove the distribution of Stipa purpurea formation, but not the recent distribution of Kobresia pygmaea formation that was likely shaped by human activities. Therefore, the underlying mechanisms of grassland changes over the past 40 years were considered to be formation dependent. Overall, the first exploration for structural information of plant community changes in this study not only provides a new perspective to understand drivers of grassland changes and their spatial heterogeneity at the regional scale of the Tibetan Plateau, but also innovates large-scale vegetation study paradigm. [ABSTRACT FROM AUTHOR]

Climate change and grassland degradation have become great challenges for ecosystem multifunctionality, which is mediated by microbial communities and their interactions. However, influences of warming and grassland degradation on soil microbial interactions and their relationship with ecosystem multifunctionality still remain vague, and it is unclear whether warming could exacerbate or mitigate degradation from these prospects. In this study, we conducted a field study in alpine grasslands on the Qinghai-Tibet Plateau to investigate the effects of warming and grassland degradation (i.e., non-degraded, ND; moderately degraded, MD; and severely degraded, SD) on soil microbial network properties and their association with ecosystem multifunctionality comprising nutrient provision, biomass production, and ecosystem processes indices. Results showed that microbial network complexity, characterized by edge number, node degree, graph diameter, and graph density, was 109.5∼238.8 % higher in MD than in ND or SD, likely explained by the intermediate disturbance hypothesis. Warming stimulated the network complexity by 78.7 ∼ 92.8 % in MD due to the prevalence of prokaryote and the escalation of their negative interactions, likely implying the fiercer resource competition that may restrain the grassland restoration; while warming weakened it by 35.4 ∼ 59.9 % and 35.0 ∼ 47.4 % in ND and SD, respectively, due to declines in all interaction types, indicating undermined structure of microbial interactions that may prevent the restoration of degradation grassland. Consistently, the ecosystem multifunctionality was not stimulated by warming significantly in various degradation stages. Interestingly, the microbial network complexity, rather than diversity, was positively correlated with the ecosystem's multifunctionality under control, which was eliminated by warming. Overall, warming changed the microbial network complexity by making the strong stronger in MD while the weak weaker in ND and SD. These divergent changes by warming decouple associations of microbial network complexity with ecosystem multifunctionality. Altered microbial network complexity under future warming scenarios may tend to hamper the multifunctionality restoration of alpine grasslands, though with different mechanisms in various degradation stages. • The network is dominated by positive intra-fungi's interactions despite its lower richness. • Warming interacts with grassland degradation to alter microbial network complexity. • Ecosystem multifunctionality declines in the severely-degraded alpine grassland. • Network complexity correlated with multifunctionality, but its decoupled under warming. [ABSTRACT FROM AUTHOR]

The plant productivity and species richness (P-SR) relationship has been a central topic in ecology for decades. It is increasingly recognized that climate resources (CR, temperature and precipitation) might affect the P-SR relationship as (1) the response of plant productivity and species richness to CR are different; (2) plant-plant interactions change along the CR gradient. However, little is known about how these effects mediate the shape and strength of the P-SR relationship from local-site to regional scales. Here, we proposed a cross-scale framework to disentangle the climatic control over the P-SR relationship in grasslands using the HerbDivNet dataset, which covers 28 natural grasslands in 19 countries and 6 continents with wide precipitation and temperature gradients. Our results showed that, at the local-site scale, a humped-back P-SR relationship is common at sites under poor and rich CR conditions, whereas both the shape and strength of the P-SR relationship strongly vary among sites under moderate CR condition. However, at the regional scale, the humped-back P-SR relationship is only observed under poor CR condition. We suggested that the asynchronous responses of productivity and species richness to increasing CR is an abiotic driver of the inconsistent P-SR relationships along the climatic gradient in addition to the changing plant-plant interactions suggested by the stress-gradient hypothesis. These findings help to reconcile the long-standing debate over the P-SR relationship and highlight the importance of CR conditions in developing or comparing grassland ecosystem theories and management at small scales. • Climate effects cause inconsistent P-SR relationships from local to regional scales. • Asynchronous response of P and SR to CR is an abiotic driver of P-SR relationship. • Unimodal P-SR relationship at regional scale only exists with poor CR. • None unimodal P-SR relationships at site scale are mostly observed with moderate CR. • Shape and strength of P-SR relationship strongly vary among sites with moderate CR. [ABSTRACT FROM AUTHOR]

• Temperate and alpine grasslands were weak carbon sinks. • Elevated water consistently increased carbon fluxes in temperate and alpine grasslands. • Increased temperature reduced carbon fluxes in temperate grasslands and increased carbon fluxes in alpine grasslands. Temperate grassland (TG) and alpine grassland (AG) are two distinct grassland types with different climatic backgrounds and adaptation patterns. Little is known about the universal and divergent responses of TGs and AGs to climate change, particularly with regard to the carbon (C) fluxes. The large-scale responses of the C fluxes in different grasslands to climate change remain unclear, with only a few comparative studies of distinct responses of the C fluxes in TGs and AGs to climate change hinders our understanding of this subject. In this study, we conducted a large-scale transect study across the Mongolian, Loess, and Tibetan Plateaus in China to reveal the similarities and differences in the responses of the C fluxes in TGs and AGs to climate change. We used C flux data measured by eddy covariance from ChinaFLUX and published literature, covering the period from 2003 to 2020. The results showed that TGs and AGs in China were weak C sinks. Net ecosystem productivity in TGs and AGs exhibited opposite trends with increasing latitude, longitude, and altitude. Elevated water (mean annual precipitation and soil water content) consistently increased the C fluxes. Conversely, increased temperature (mean annual temperature and mean annual soil temperature) reduced the C fluxes in TGs and increased the C fluxes in AGs. The water factors promoted the C fluxes in both TGs and AGs by improving the leaf area indices. Conversely, the temperature factors had a strong direct, negative effect on the C fluxes in TGs and a weak direct, positive effect on the C fluxes in AGs, emphasizing the divergent patterns of the C fluxes in TGs and AGs and their responses to climate change. Overall, this study enhances our knowledge on C sinks and various grassland hydrothermal sensitivities. [ABSTRACT FROM AUTHOR]

• Dominant factor for the IAVs of ET varied among the four grassland ecosystems. • Net radiation dominated the IAV of ET at the alpine marsh meadow. • Water availability mainly regulated the IAVs of ET for relatively arid sites. • Environmental responses of surface conductance varied across the grasslands. Understanding the long-term variation in evapotranspiration (ET) for the spatially distributed grasslands is crucial for the accurate prediction of ET response to climate change. In this study, we analyzed the interannual variability (IAV) of ET and its responses to environmental conditions at four grassland ecosystems across a wide range of climatic and biome conditions based on the long-term (9–11 years) eddy-covariance measurements. The four ecosystems encompassed the most prevalent grassland vegetations in China, containing a typical temperate steppe, an alpine meadow-steppe, an alpine shrubland meadow, and an alpine marsh meadow. The IAVs of annual ET at the typical temperate steppe and the alpine meadow-steppe were primarily affected by either change in precipitation (P) or relative humidity (RH). Leaf area index (LAI) was the dominant factor controlling the IAVs of annual and growing-season ET at the alpine shrubland meadow, and the IAV of LAI was significantly correlated with P variation. As to the alpine marsh meadow, net radiation turned to be the dominant factor for the IAV of annual ET, additionally with significant effects from water supply condition (P and RH) on the IAV of growing-season ET. Similar environmental responses were also found for the IAVs of mean surface conductance (g s) across the sites. Specifically, annual and growing-season mean g s significantly increased with increases in LAI at the alpine shrubland meadow, and appeared to be more sensitive to changes in water availability and VPD at the typical temperate steppe and the alpine meadow-steppe. Significant linear relationships were also observed among the IAVs of mean Priestley-Taylor coefficient (α = ET/ET eq , where ET eq is the equilibrium evaporation), decoupling coefficient (Ω), and g s on both the annual and growing-season basis in this study. Moreover, the variabilities of annual mean g s , Ω , and α further demonstrated the energy-limited conditions at the alpine marsh meadow, and the overall water-limited conditions at the other three grasslands. This study reveals the divergent environmental responses of long-term ET variations over grassland ecosystems, and contributes to the comprehensive understanding on the ET process and modeling efforts as well. [ABSTRACT FROM AUTHOR]

Biological nitrogen fixation, conducted by soil diazotrophs, is the primary nitrogen source for natural grasslands. However, the diazotrophs in grassland soils are still far from fully investigated. Particularly, their regional-scale distribution patterns have never been systematically examined. Here, soils (0–5 cm) were sampled from 54 grasslands on the Tibetan Plateau to examine the diazotroph abundance, diversity, and community composition, as well as their distribution patterns and driving factors. The diazotroph abundance was expressed as nifH gene copies, measured using real-time PCR. The diversity and community composition of diazotrophs were analyzed through MiSeq sequencing of nifH genes. The results showed that Cyanobacteria (47.94%) and Proteobacteria (45.20%) dominated the soil diazotroph communities. Most Cyanobacteria were classified as Nostocales which are main components of biological crusts. Rhizobiales, most of which were identified as potential symbiotic diazotrophs, were also abundant in approximately half of the soil samples. The soil diazotroph abundance, diversity, and community composition followed the distribution patterns in line with mean annual precipitation. Moreover, they also showed significant correlations with prokaryotic abundance, plant biomass, vegetation cover, soil pH values, and soil nutrient contents. Among these environmental factors, the soil moisture, organic carbon, available phosphorus, and inorganic nitrogen contents could be the main drivers of diazotroph distribution due to their strong correlations with diazotroph indices. These findings suggest that autotrophic and symbiotic diazotrophs are the predominant nitrogen fixers in Tibetan grassland soils, and highlight the key roles of water and nutrient availability in determining the soil diazotroph distribution on the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

• Belowground biomass was positively sensitive to early and middle droughts. • Belowground biomass was negatively sensitive to late drought. • Aboveground biomass was insensitive to all droughts. • CO 2 fluxes had the largest sensitivity to early drought. • CO 2 fluxes had the lowest sensitivity to middle drought. Quantifying the sensitivity of ecosystems to droughts, particularly with different seasonal timing, could improve our predictions of ecosystem-climate feedbacks, but few experiments have explicitly addressed seasonal timing per se effects on ecosystem sensitivity to droughts. Here, we present a seasonal timing × drought manipulation experiment to examine sensitivity (relative change in response parameters to the relative change in precipitation) of key ecosystem processes (community biomass and ecosystem CO 2 fluxes) to pulse-drought with different seasonal timing (early, middle or late) on a temperate semiarid grassland. We found belowground and total biomass were positively sensitive (i.e. ecological processes promoted by droughts and vice versa) to early and middle droughts but negatively sensitive to late drought while aboveground biomass was insensitive to all droughts. Ecosystem CO 2 fluxes had the largest negative sensitivity to early drought and smallest negative sensitivity to middle drought, although gross ecosystem production showed larger negative response to droughts than ecosystem respiration, leading to reduction in net ecosystem production, regardless of seasonal timing. Our results highlight the crucial role of seasonal drought timing in regulating sensitivity of key carbon cycle processes to droughts and suggest that droughts at plant peak stage cause the least detrimental ecological consequences. [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]

Abstract: Few studies have examined the long-term responses of soil labile organic carbon (C) and nitrogen (N) and microbial activities to climate change in semiarid and arid regions. Here we investigated soil extractable organic carbon (EOC) and nitrogen (EON), microbial biomass and microbial metabolic activities at two depths of 0–10 and 10–20cm in response to single and combined effects of warming and increased precipitation in a semiarid grassland of northern China since April 2005. Soil EOC and EON pools were measured using KCl and hot water extractions, and microbial metabolic activities were measured using MicroResp. Results showed that warming had no effects on EOC, EON and microbial biomass C (MBC) and N (MBN) in the two extracts as well as the ratio of MBC to MBN at the two depths, but increased precipitation significantly increased MBC, MBN, EON and microbial quotient at the 0–10cm depth. Warming significantly decreased microbial metabolic activities at both soil depths, but significantly increased microbial metabolic diversity (H) and evenness (E) at the 10–20cm depth. Increased precipitation significantly decreased microbial metabolic activities, but significantly increased H and E at the two depths. Warming and increased precipitation significantly interacted to affect microbial metabolic activities at the two depths as well as H and E at the 10–20cm depth. Redundancy analysis determined that microbial quotient, i.e., the ratio of MBC to total C, pH and NH4 +–N greatly accounted for the variances in the soil microbial metabolic profiles, but the ratio of EOC to EON, moisture and microbial quotient largely accounted for the variances in the soil microbial metabolic profiles specifically at the 10–20cm depth, implying that microbial physiology such as microbial quotient rather than the amounts of labile organic C and N pools exerted more influence on driving the patterns of microbial metabolic profiles. Our results indicated that soil EOC and EON, microbial biomass and microbial metabolic activities at the two depths differentially responded to warming and increased precipitation in this semiarid region. [Copyright &y& Elsevier]

• 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]