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Attention mechanisms have significantly boosted the performance of video classification neural networks thanks to the utilization of perspective contexts. However, the current research on video attention generally focuses on adopting a specific aspect of contexts (e.g., channel, spatial/temporal, or global context) to refine the features and neglects their underlying correlation when computing attentions. This leads to incomplete context utilization and hence bears the weakness of limited performance improvement. To tackle the problem, this paper proposes an efficient attention-in-attention (AIA) method for element-wise feature refinement, which investigates the feasibility of inserting the channel context into the spatio-temporal attention learning module, referred to as CinST, and also its reverse variant, referred to as STinC. Specifically, we instantiate the video feature contexts as dynamics aggregated along a specific axis with global average and max pooling operations. The workflow of an AIA module is that the first attention block uses one kind of context information to guide the gating weights calculation of the second attention that targets at the other context. Moreover, all the computational operations in attention units act on the pooled dimension, which results in quite few computational cost increase (< 0.02%). To verify our method, we densely integrate it into two classical video network backbones and conduct extensive experiments on several standard video classification benchmarks. The source code of our AIA is available at https://github.com/haoyanbin918/Attention-in-Attention. [ABSTRACT FROM AUTHOR]

In recent years, vision Transformers and MLPs have demonstrated remarkable performance in image understanding tasks. However, their inherently dense computational operators, such as self-attention and token-mixing layers, pose significant challenges when applied to spatio-temporal video data. To address this gap, we propose PosMLP-Video, a lightweight yet powerful MLP-like backbone for video recognition. Instead of dense operators, we use efficient relative positional encoding to build pairwise token relations, leveraging small-sized parameterized relative position biases to obtain each relation score. Specifically, to enable spatio-temporal modeling, we extend the image PosMLP’s positional gating unit to temporal, spatial, and spatio-temporal variants, namely PoTGU, PoSGU, and PoSTGU, respectively. These gating units can be feasibly combined into three types of spatio-temporal factorized positional MLP blocks, which not only decrease model complexity but also maintain good performance. Additionally, we enrich relative positional relationships by using channel grouping. Experimental results on three video-related tasks demonstrate that PosMLP-Video achieves competitive speed-accuracy trade-offs compared to the previous state-of-the-art models. In particular, PosMLP-Video pre-trained on ImageNet1K achieves 59.0%/70.3% top-1 accuracy on Something-Something V1/V2 and 82.1% top-1 accuracy on Kinetics-400 while requiring much fewer parameters and FLOPs than other models. The code is released at https://github.com/zhouds1918/PosMLP_Video. [ABSTRACT FROM AUTHOR]

Efficient action recognition aims to classify a video clip into a specific action category with a low computational cost. It is challenging since the integrated spatial-temporal calculation (e. g., 3D convolution) introduces intensive operations and increases complexity. This paper explores the feasibility of the integration of channel splitting and filter decoupling for efficient architecture design and feature refinement by proposing a novel spatio-temporal collaborative (STC) module. STC splits the video feature channels into two groups and separately learns spatio-temporal representations in parallel with decoupled convolutional operators. Particularly, STC consists of two computation-efficient blocks, i.e., $\text {S}_{\mathrm{ T}}$ and $\text {T}_{\mathrm{ S}}$ , where they extract either spatial (${S}_{\cdot }$) or temporal (${T}_{\cdot }$) features and further refine their features with either temporal ($\cdot _{T}$) or spatial ($\cdot _{S}$) contexts globally. The spatial/temporal context refers to information dynamics aggregated from temporal/spatial axis. To thoroughly examine our method’s performance in video action recognition tasks, we conduct extensive experiments using five video benchmark datasets requiring temporal reasoning. Experimental results show that the proposed STC networks achieve a competitive trade-off between model efficiency and effectiveness. [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]

• 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]

• 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]

[Display omitted] • Severely altered precipitation regime induced NO 3 – and DOC redistribution between soil layers in the alpine meadow. • Topsoil microorganisms are more sensitive to severe changes in precipitation regime in the alpine meadow. • The main edaphic regulators of microbial communities are heterogeneous in soil layers in the alpine meadow. • Microbes at different soil layers have divergent modes of regulating GHG fluxes in the alpine meadow. How soil microbial communities regulate greenhouse gas (GHG) emissions as precipitation events are "repackaged" from many small events to a few larger events has received significant attention. Previous studies have quantified the net fluxes of GHG across the soil/atmosphere interface; however, they have not investigated GHG fluxes in soil profiles under altered precipitation regimes. This study investigated the joint regulation of topsoil and subsoil microorganisms on GHG emissions under changes in precipitation regime in an alpine meadow. Severely altered precipitation regimes have led to changes in NO 3 –, DOC, and soil moisture among different soil layers, which can be attributed to a combination of substrate migration and changes in carbon and nitrogen mineralization rates. Topsoil microorganisms exhibit high sensitivity to severe changes in the precipitation regime, which can affect their biomass, microbial composition, and metabolic activities. In contrast, the activity of subsoil microorganisms remains relatively stable. This was likely due to the heterogeneity of dominant regulators across soil layers. Under the severely altered precipitation regime, carbon dioxide (CO 2) emissions significantly decreased by approximately 11.08%. This decrease was primarily due to a reduction in microbial respiration in the topsoil. The decrease in both topsoil and subsoil methane (CH 4) uptake likely caused the inhibition of the CH 4 sink from the surface (by approximately 11.60%). While a 1.75-fold increase in nitrous oxide (N 2 O) emissions under the severe changes in the precipitation regime might be primarily related to the increase in subsoil N 2 O emissions caused by the increasing abundance of subsoil denitrifiers, as well as the addition of subsoil nitrate content and soil moisture. This study revealed the divergent modes of microbial communities at different soil layers regulating GHG fluxes, which could guide the improvement of ecological models predicting GHG emissions in response to altered precipitation regimes. [ABSTRACT FROM AUTHOR]

In alpine meadows, plants and soil microbes typically engage in competition for nitrogen (N) under N-deficient conditions. However, the acquisition and distribution of N among soil microbes and plants under alpine meadow degradation and climate warming induced by global climate change are still uncharacterized. In this study, we isotope labeled inorganic (NH4+-15N, NO3−-15N) and organic (glycine-15N) N in both degraded and non-degraded plots by using open-top chambers (OTC) to mimic increasing air temperatures. After 6 h, the 15N contents in soil microbes and plants were measured to investigate the effects of degradation and rising air temperature on N allocations in the ecosystems studied. Results showed that alpine meadow degradation significantly reduced soil microbial N accumulation by 52% compared to those in non-degraded plots. In non-degraded plots, warming significantly lowered the organic N levels of soil microbes by 49%, whereas in degraded ones, it reduced both NH4+-15N and NO3−-15N recovery by 80% and 45% on average but increased glycine-15N recovery by 653%. Meanwhile, warming decreased the plant recovery of NH4+-15N and NO3−-15N by 75% and 45% but increased the recovery of glycine-15N by 45% in non-degraded plots. Conversely, in degraded plots, warming markedly lowered NH4+-15N recovery by 40% but increased glycine-15N recovery by 114%. Warming mitigates the effects of alpine meadow degradation on nitrogen allocation among soil microbes and plants. In unwarmed plots, degradation significantly elevated the total 15N recovery ratio of soil microbes to plants by 60%. However, in warmed plots, the impact of degradation on this ratio was reduced. The responses of the 15N recovery ratio of soil microbes and plants to rising temperatures were closely related to alpine meadow quality. In non-degraded areas, warming enhanced the recovery ratio for NH4+-15N by 165% but reduced it for glycine-15N by 66%. Conversely, in degraded plots, warming decreased the recovery ratio for NH4+-15N by 66% but increased it for glycine-15N by 232%. This indicates that warming can increase carbon limitation for soil microbes in degraded alpine meadows, and the restoration of degraded alpine meadows should prioritize restoring carbon accumulation. [ABSTRACT FROM AUTHOR]

The semantic matching problem detects whether the candidate text is related to a specific input text. Basic text matching adopts the method of statistical vocabulary information without considering semantic relevance. Methods based on Convolutional neural networks (CNN) and Recurrent networks (RNN) provide a more optimized structure that can merge the information in the entire sentence into a single sentence-level representation. However, these representations are often not suitable for sentence interactive learning. We design a multi-dimensional semantic interactive learning model based on the mechanism of multiple written heads in the transformer architecture, which not only considers the correlation and position information between different word levels but also further maps the representation of the sentence to the interactive three-dimensional space, so as to solve the problem and the answer can select the best word-level matching pair, respectively. Experimentally, the algorithm in this paper was tested on Yahoo! and StackEx open-domain datasets. The results show that the performance of the method proposed in this paper is superior to the previous CNN/RNN and BERT-based methods. [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]

• Dynamic scene graph is adaptive to different questions. • Word-level co-attention mechanism to refine node features and edge features. • Dynamic scene graph is beneficial to the logistic reasoning. • The proposed method experimentally outperforms the best results on the GQA dataset. In visual question answering task, it is vital to learn the semantic interactions between the question and target objects in the input image. Existing scene graph-based methods generally extract global features from the image and then perform feature fusion with the question representation. However, the scene graph constructed by these methods only obtains the abstract semantic features from the image, but does not consider the influence of the positional words and semantic information in question. In this paper, we propose a Question-aware Dynamic Scene Graph (QDSG) method. Firstly, we adopt a scene graph of the initial state based on the local attribute features of the image target. Then we design a dynamic scene graph adaptive to different questions based on the initial scene graph, which is used a word-level co-attention mechanism to refine node features and edge features. Finally, iterative reasoning is performed on the refined scene graph and the correct answer is predicted by using the graph attention network model. The proposed method is sufficient to learn the semantic local features to generate the interactive scene graph between the image and question, which is beneficial to the logistic reasoning depending on the adaptive graph refinement. The proposed method outperforms the comparative performance when compared with state-of-the-art models on the GQA dataset and its semantic and structural type datasets. [ABSTRACT FROM AUTHOR]

Urolithiasis was a global disease and it was more common in southern China. This study looked into the association between daily temperature and urolithiasis hospital admissions in Ganzhou, a large prefecture-level city in southern China. In Ganzhou City from 2016 to 2019, a total of 60,881 hospitalized cases for urolithiasis from 69 hospitals and meteorological data were gathered. The effect of high ambient temperature on urolithiasis hospital admissions was estimated using a distributed lag nonlinear model. Stratified analysis was done to examine sex differences. The study found that in Ganzhou of China, the exposure–response curves approximated a "J" shape which across genders were basically similar. The maximum lag effect occurred on the second day after high temperatures for males but on the third day for females. Compared to the 10 °C reference temperature and considering the cumulative lag effect of 10 days, the relative risks of the daily mean temperature at the 95th percentile on the total, male, and female hospital admissions for urolithiasis were 2.026 (95% CI: 1.628, 2.521), 2.041 (95% CI: 1.603, 2.598), and 2.030 (95% CI: 1.552, 2.655), respectively, but the relative risks between sex were not statistically significant (p = 0.977). Urolithiasis morbidity risk in China could be exacerbated by high temperatures. The effect of high temperature on urolithiasis was similar across genders. [ABSTRACT FROM AUTHOR]

Background: With an increasing incidence of obesity, the relationship between obesity and body image has become a hot research topic worldwide. From high school to university, young people experience changes in their social environment. University students have a high incidence of eating disorders and insufficient physical activity. The purpose of this study was to explore the relationship between body dissatisfaction, insufficient physical activity, and disordered eating behaviors among university students in southern China.Methods: In total, 1296 university students aged 18-23 years were recruited for this study. The participants completed anthropometric measurements, the Physical Activity Rating scale-3 (PARS-3), and the Chinese-Dutch Eating Behavior Questionnaire (C-DEBQ). The ideal weight and silhouette were reported by university students using a questionnaire.Results: Compared with men, young women had a higher level of body dissatisfaction. For men, body mass index (BMI; β = 0.76, P <  0.01), physical activity score (β = - 0.11, P <  0.01), and restrained eating score (β = 0.10, P <  0.01) were the significant factors predictive of body dissatisfaction. For women, BMI (β = 0.57, P <  0.01), muscle mass (β = 0.12, P <  0.01), physical activity score (β = - 0.11, P <  0.01), and restrained eating score (β = 0.09, P <  0.01) were the significant factors predictive of body dissatisfaction.Conclusions: University students with high body dissatisfaction had lower physical activity scores and higher restrained eating scores. The data presented here highlight the impact of university students' body dissatisfaction on physical activity deficiency and disordered eating behaviors in China. [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]

Numerical research on natural convection heat transfer in Toco Toucans beak is carried out in this work to determine the optimal direction for their geometric evolution. The isothermal contours, streamlines, and Nusselt number are obtained. The results show that the entrainment effect near the distal region is stronger than that at the proximal region, leading to the accumulation of the thermal boundary layer near the proximal region. The air flowing upward from the mandible accumulates thermal energy at the lower beak teeth, which increases the air velocity at the maxilla. This process improves the adverse effects of preheating on the air and enhances maxillary heat transfer. Based on the analysis of the flow and temperature fields, it is determined that rounding beak surface is necessary to achieve a more uniform Nusselt number distribution in the direction of gravity. Subsequent research revealed that rounding the beak surface increased the average Nusselt number by 0.5%, indicating that the current shape is very close to the optimal cross-sectional profile. The heat transfer mechanism of the beak was quantitatively elucidated, providing a reference for the study of the beaks of other species in ecology. • Abstract the heat transfer structure of a toucan's beak into a numerical model for natural convection. • Quantitatively describes the flow conditions of the boundary layer around the beak. • The beak of Toco toucans is anticipated to evolve toward a more rounded shape. [ABSTRACT FROM AUTHOR]

This study used the time series data of Ganzhou city to explore the individual and interaction effects of temperature and humidity on COPD death, and identify vulnerable subgroups of the population. We collected daily COPD mortality and meteorological data in Ganzhou from 2016 to 2019. The nonlinear distribution lag model was used to examine the associations and interaction between daily mean temperature and humidity and COPD mortality. For the total population, male and 65 years old or above, the relative risk (RR) for COPD mortality could be significant at extremely low temperature (3.3 ℃), reaching 1.799 (95% confidence interval [CI]: 1.216, 2.662), 1.894 (95% CI: 1.164, 3.084) and 1.779 (95% CI:1.185, 2.670). Also, at extremely low humidity (47.8%), the risk reached 1.888 (95% CI: 1.217, 2.930), 1.837 (95% CI: 1.066, 3.165) and 2.166 (95% CI: 1.375, 3.414). The cumulative COPD death risk for females was 3.524 (95% CI: 1.340, 9.267) at high temperature (30.7 ℃), 1.953(95% CI: 1.036, 3.683) at low humidity (47.8%) and 1.726 (95% CI: 1.048, 2.845) at high humidity (96.7%). For the total COPD deaths and subgroups, the interaction effects between daily temperature and humidity were not significant (p > 0.05). Both extremely low temperature and low humidity increased the risk of COPD death in Ganzhou city, especially for males and people over 65 years old. Females were more sensitive to extremely high temperature and humidity. Patients with COPD should pay attention to self-protection under extreme temperature and humidity weather conditions. [ABSTRACT FROM AUTHOR]

Objective: To investigate the relationship between the aspartate aminotransferase to alanine aminotransferase ratio (AAR) and the prognosis of IgA nephropathy (IgAN).Clinical, pathological and follow-up data of 271 patients with IgAN from January 1, 2013, to July 31, 2023, were collected. A 50% decrease in estimated glomerular filtration rate (eGFR) or end-stage renal disease (ESRD) was used as renal composite end point events. A receiver operating characteristic (ROC) curve was plotted to predict the composite end point events by AAR. The optimal cutoff value of 1.24 was determined, and patients were allocated to high AAR and low AAR groups. Kaplan‒Meier (K‒M) curves and Cox proportional hazard models were used to evaluate the predictive effect of AAR on renal composite end point events.After a mean follow-up of 29 months, 39 patients achieved renal composite end point events. Among them, 9 and 30 patients in the low and high AAR groups achieved renal composite end point events, respectively, with a significant difference (P < 0.001). After adjustment for confounding factors, AAR was found to be an independent prognostic factor for renal composite end point events (HR = 3.283, 95% CI: 1.489–7.238, P = 0.003). Kaplan‒Meier analysis showed that high AAR was associated with achieving renal composite end point events in patients with IgAN. Moreover, the clinical features in the high AAR group were more severe. Further subgroup analysis showed that high AAR had a better predictive effect in patients with more severe clinicopathological manifestations.AAR is an independent prognostic factor in patients with IgAN.Methods: To investigate the relationship between the aspartate aminotransferase to alanine aminotransferase ratio (AAR) and the prognosis of IgA nephropathy (IgAN).Clinical, pathological and follow-up data of 271 patients with IgAN from January 1, 2013, to July 31, 2023, were collected. A 50% decrease in estimated glomerular filtration rate (eGFR) or end-stage renal disease (ESRD) was used as renal composite end point events. A receiver operating characteristic (ROC) curve was plotted to predict the composite end point events by AAR. The optimal cutoff value of 1.24 was determined, and patients were allocated to high AAR and low AAR groups. Kaplan‒Meier (K‒M) curves and Cox proportional hazard models were used to evaluate the predictive effect of AAR on renal composite end point events.After a mean follow-up of 29 months, 39 patients achieved renal composite end point events. Among them, 9 and 30 patients in the low and high AAR groups achieved renal composite end point events, respectively, with a significant difference (P < 0.001). After adjustment for confounding factors, AAR was found to be an independent prognostic factor for renal composite end point events (HR = 3.283, 95% CI: 1.489–7.238, P = 0.003). Kaplan‒Meier analysis showed that high AAR was associated with achieving renal composite end point events in patients with IgAN. Moreover, the clinical features in the high AAR group were more severe. Further subgroup analysis showed that high AAR had a better predictive effect in patients with more severe clinicopathological manifestations.AAR is an independent prognostic factor in patients with IgAN.Results: To investigate the relationship between the aspartate aminotransferase to alanine aminotransferase ratio (AAR) and the prognosis of IgA nephropathy (IgAN).Clinical, pathological and follow-up data of 271 patients with IgAN from January 1, 2013, to July 31, 2023, were collected. A 50% decrease in estimated glomerular filtration rate (eGFR) or end-stage renal disease (ESRD) was used as renal composite end point events. A receiver operating characteristic (ROC) curve was plotted to predict the composite end point events by AAR. The optimal cutoff value of 1.24 was determined, and patients were allocated to high AAR and low AAR groups. Kaplan‒Meier (K‒M) curves and Cox proportional hazard models were used to evaluate the predictive effect of AAR on renal composite end point events.After a mean follow-up of 29 months, 39 patients achieved renal composite end point events. Among them, 9 and 30 patients in the low and high AAR groups achieved renal composite end point events, respectively, with a significant difference (P < 0.001). After adjustment for confounding factors, AAR was found to be an independent prognostic factor for renal composite end point events (HR = 3.283, 95% CI: 1.489–7.238, P = 0.003). Kaplan‒Meier analysis showed that high AAR was associated with achieving renal composite end point events in patients with IgAN. Moreover, the clinical features in the high AAR group were more severe. Further subgroup analysis showed that high AAR had a better predictive effect in patients with more severe clinicopathological manifestations.AAR is an independent prognostic factor in patients with IgAN.Conclusion: To investigate the relationship between the aspartate aminotransferase to alanine aminotransferase ratio (AAR) and the prognosis of IgA nephropathy (IgAN).Clinical, pathological and follow-up data of 271 patients with IgAN from January 1, 2013, to July 31, 2023, were collected. A 50% decrease in estimated glomerular filtration rate (eGFR) or end-stage renal disease (ESRD) was used as renal composite end point events. A receiver operating characteristic (ROC) curve was plotted to predict the composite end point events by AAR. The optimal cutoff value of 1.24 was determined, and patients were allocated to high AAR and low AAR groups. Kaplan‒Meier (K‒M) curves and Cox proportional hazard models were used to evaluate the predictive effect of AAR on renal composite end point events.After a mean follow-up of 29 months, 39 patients achieved renal composite end point events. Among them, 9 and 30 patients in the low and high AAR groups achieved renal composite end point events, respectively, with a significant difference (P < 0.001). After adjustment for confounding factors, AAR was found to be an independent prognostic factor for renal composite end point events (HR = 3.283, 95% CI: 1.489–7.238, P = 0.003). Kaplan‒Meier analysis showed that high AAR was associated with achieving renal composite end point events in patients with IgAN. Moreover, the clinical features in the high AAR group were more severe. Further subgroup analysis showed that high AAR had a better predictive effect in patients with more severe clinicopathological manifestations.AAR is an independent prognostic factor in patients with IgAN. [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]

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]

• Droughts increased CH 4 uptake in a wet year in the semiarid grassland. • Magnitude and pathways of drought effects on CH 4 uptake depend on seasonal timing. • Positive effects of early drought on CH 4 uptake were the least. • Plant composition regulated CH 4 uptake in response to droughts. Increasingly frequent and severe droughts are occurring in multiple seasons of a year in many dryland ecosystems, with unknown impacts on the role of drylands in cycling of methane (CH 4), a potent greenhouse gas. In particular, there is limited understanding of how drought occurring at different times within the growing season regulates biological CH 4 uptake, and how these responses are mediated by plant community composition. Here, we quantify how drought timing and plant community composition regulate CH 4 uptake in a semiarid grassland. We employ a field experiment in which droughts were imposed in early, middle, or late growing season in three different communities (two graminoids, two shrubs and their mixture), respectively. All three droughts increased CH 4 uptake, but the effect size and pathway varied with seasonal timing. Early and middle drought increased CH 4 uptake through increasing both soil pomA abundance and diffusivity resulting from reduced soil water content (SWC), while late drought increased CH 4 uptake only by reducing SWC. Overall, early drought had the least positive effects on CH 4 uptake because it excluded the least precipitation and therefore had smaller impacts on SWC. Besides, plant composition did not affect CH 4 uptake under normal environment but regulated CH 4 uptake in response to droughts due to different response of plant composition to droughts. Early and middle drought had larger positive effects on CH 4 uptake in shrub communities than the other two communities, consistent with larger reductions in SWC and larger increases in pomA abundance, respectively. In contrast, late drought had consistent effects on CH 4 uptake across three communities. Our results suggest that the magnitude and pathways of extreme drought effects on CH 4 uptake are strongly co-regulated by seasonal timing and plant composition. [ABSTRACT FROM AUTHOR]

This study characterized the effect of green manures (February orchid, hairy vetch, rattail fescue and a no-green-manure control) and the termination method (flail or disk) on nutrient contents, enzyme activities, microbial biomass, microbial community structure of rhizosphere soil and vegetative growth of walnut tree. All three selected green manures significantly enhanced the water content, organic C, total N and available P. The rattail fescue significantly decreased the mineral N. Total organic C, total N, mineral N and available P were significantly greater under flail than under disk. Hairy vetch and February orchid significantly improved levels of soil β-glucosidase, N-acetyl-glucosaminidase and acid phosphatase activity, whereas rattail fescue improved only β-glucosidase activity. All of the green manures significantly decreased phenoloxidase activity. β-glucosidase, N-acetyl-glucosaminidase and acid phosphatase activities were significantly greater under flail relative to disk. The termination method had no significant effect on phenoloxidase activity. The different types of green manures and termination methods significantly altered the soil microbial biomass and microbial community structure. The green-manure treatments were characterized by a significantly greater abundance of Gram-positive (Gram +) bacteria, total bacteria and saprophytic fungi compared to the control. Hairy vetch significantly decreased the abundance of arbuscular mycorrhizal fungi (AMF) while February orchid and rattail fescue increased their abundance compared to the no-green-manure treatment. The abundance rates of Gram+ bacteria, actinomycetes, saprophytic fungi and AMF were significantly greater in soils under flail than under disk. In terms of vegetative growth of walnut tree, hairy vetch showed the greatest positive effects. The growth of walnut tree was significantly greater under flail relative to disk. Our results indicate that green-manure application benefits the rhizosphere soil micro-ecology, rhizosphere soil nutrient contents and tree growth. Overall, the hairy vetch and flail combined treatment is recommended for walnut orchards in northern China. [ABSTRACT FROM AUTHOR]

Sustainable development goals (SDGs) emphasize a holistic achievement instead of cherry-picking a few. However, no assessment has quantitatively considered the evenness among all 17 goals. Here, we propose a systematic method, which first integrates both the evenness and the overall status of all goals, to distinguish the ideal development pathways from the uneven ones and then revisit the development trajectory in China from 2000 to 2015. Our results suggest that, despite the remarkable progress, a bottleneck has occurred in China since 2013 due to the stagnant developments in some SDGs. However, many far-reaching policies in China have been targeting these deficiencies since then, providing a perspective on how a country approaches sustainable development by promoting evenness among all SDGs. Our results also indicate that regions with the slowest progress are the developed provinces, owing to the persistent uneven status of all goals. Our study demonstrates the importance of adopting evenness in assessing and guiding sustainable development. [ABSTRACT FROM AUTHOR]

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]

• 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]

Plant roots play a crucial role in acquiring available nutrients and regulating microbial decomposition, leading to faster nutrient cycling in the rhizosphere than in bulk soil. With increasing nitrogen (N) loading, rhizosphere processes may become more crucial than bulk soil processes for accurately predicting nutrient cycling process changes. However, it remains unclear how the rhizosphere and bulk soil extracellular enzyme activities (EEAs) respond to N addition on a global scale. Therefore, we used a global meta-analysis to examine the effects of N addition on EEAs involved in carbon (C), N, and phosphorus (P) acquisition in paired rhizosphere and bulk soils, based on 1992 observations from 40 studies. Overall, N addition significantly increased β-xylosidase (BXYL) activity by 17.0% in bulk soil, together with urease (URE) activity in both the rhizosphere (25.3%) and bulk soil (33.6%). In addition, N addition marginally significantly decreased phenol oxidase (PHEO) activity in the rhizosphere (−18.4%), as well as peroxidase (PERO) activity in both the rhizosphere (−12.6%) and bulk soil (−13.6%). The reductions may be caused by fungi: bacteria ratio and Gram+ bacteria: Gram− bacteria ratio (F: B and G+: G−, respectively) reductions. We also found that EEAs (e.g., acid/alkaline phosphatase (AP) and PHEO) did not always have a simple linear relationship with higher N addition rate and duration. Additionally, the responses of β-1,4-N-acetyl-glucosaminnidase (NAG) and PHEO significantly increased with increase in mean annual temperature (MAT), whereas those of microbial biomass carbon (MBC) and microbial biomass nitrogen (MBN) significantly decreased with increase in mean annual precipitation (MAP). Moreover, the responses of PHEO and PERO significantly decreased along altitudinal gradients. Furthermore, we found that the N sensitivity of rhizosphere C-acquisition (BG, PHEO, and PERO) and P-acquisition (AP) enzyme activities was significantly lower than that of bulk soil, but no significant difference was noted in N-acquisition enzyme activities except for the NAG which exhibited marginal significant differences between the rhizosphere and bulk soil. Our results suggest that EEAs in the rhizosphere and bulk soil have different response patterns, and these differences might mainly be caused by soil nutrient availability and microbial attributes. These results deepen our understanding of rhizosphere and bulk soil nutrient cycling processes under increasing N loading. They also provide extensive evidence and a basis for the parameterization of global nutrient cycling models that incorporate rhizosphere responses to climate change. • Response pattern of extracellular enzyme activities (EEAs) in the rhizosphere and bulk soil to nitrogen (N) addition on a global scale. • EEAs (e.g., AP and PHEO) did not always have a simple linear relationship with higher N addition. • N sensitivity of rhizosphere C-acquisition (BG, PHEO, and PERO) and P-acquisition (AP) EEAs was significantly lower than that of bulk soil. • N sensitivity of rhizosphere N-acquisition EEAs (LAP and URE) showed insignificant difference compared to that of bulk soil. • N sensitivity of NAG activity showed marginal significance between the rhizosphere and bulk soil. [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]

Soil microbes play a crucial role in myriad ecological processes in terrestrial ecosystem. With increasing nitrogen (N) loading, phosphorus (P) may become more limiting for soil microbes and these processes. However, it remains unclear to what extent P addition impacts soil microbial communities and respiration at global scale, especially under different N loadings. Therefore, we used a global meta-analysis to examine the effects of phosphorus addition on soil microbes based on 2293 paired observations from 129 studies in the world. Overall, P addition increased significantly total as well as fungal, bacterial, and actinomycete (ACT) phospholipid fatty acids (PLFAs), together with Gram+ bacteria (G+) and Gram- bacteria (G-) abundance regardless of N input or not. The increments were more pronounced under higher P addition rate or places with higher mean annual temperature or mean annual precipitation. Moreover, the fungi: bacteria ratio significantly decreased along elevational gradients. Furthermore, higher P addition frequency tended to have significantly more ACT PLFAs, as well as higher G+:G-, but significantly lower fungi: bacteria ratio (F:B). However, the responses of P addition on bacterial PLFAs and F:B were larger in forest than grassland, and cropland and varied with P fertilizer forms. In addition, the responses of soil organic carbon (SOC) contents was positively correlated with those of microbial biomass carbon (MBC) and bacterial PLFAs, and all these three parameters, in addition to fungal PLFAs, correlated positively with the response of soil respiration (Rs). Our results suggest that phosphorus addition had globally positive effects on soil microbes with different N loadings, and the positive effects of soil microbial abundance tended to promote heterotrophic respiration (Rh) and Rs. These results deepen our understanding of soil microbial community structure and function dynamics under increasing P deposition. They also provide extensive evidences and bases for parameterization of soil carbon cycling models incorporating microbial responses under global climate change. • We found phosphorus addition had globally positive effects on soil microbes. • The soil microbial biomass positively correlated with phosphorus addition rate, mean annual temperature (MAT) and mean annual precipitation (MAP). • The responses of soil microbes varied with P addition forms and ecosystem types. • The positive effects of soil microbes tended to promote Rs and Rh. [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]

• A drought index was obtained for drought monitoring based on soil moisture series. • Drought characteristics (e.g., return period) were presented on a regional scale. • Wavelet analysis was adopted to study the area extent characteristics of drought. As the critical parameter of the agricultural drought process, soil moisture (SM) has the potential to monitor agricultural drought, which can provide practical support for water management. In this study, a drought index, namely Soil Dryness Index (SDI), was proposed based on long-term gridded ERA-5 land SM, and its performance was evaluated. Furthermore, we employed the run theory and copula to study the spatiotemporal drought characteristics based on this index in Inner Mongolia. Although the return period of drought events has been assessed in many studies, the drought tends to occur with a nonstationary frequency. The wavelet transform was then applied to drought area proportion time series in sub-regions to study the temporal characteristics of droughts. The results show that SDI performs well in monitoring agricultural drought. There is strong heterogeneity in the spatial distribution of drought events. Frequent and low-severity droughts dominated arid and semi-arid regions, while some high-severity droughts mainly occurred in humid and semi-humid regions (mainly in Hulunbuir and Xing'an). The droughts, especially those with high severity, have become much more frequent in the last two decades. The period from 2000 to 2010 had seen more frequent droughts in recent 40 years, and most 20-year return period droughts occurred during this period. These findings could contribute to a better understanding of spatiotemporal patterns of agricultural droughts and provide a reference for future drought trends in Inner Mongolia. [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]

• A sustainable livelihoods indicator across a geographical gradient of aridity was developed. • Sustainable livelihoods vary greatly across dryland zones. • Semi-arid zones have the most vulnerable livelihood rather than the arid zones. • The lack of physical capital and social capital is an important obstacle to sustainable livelihoods development in drylands. • Call for integrated land management and planning approach in drylands. Drylands cover about 41% of the Earth's land surface and are inhabited by more than two billion people, who rely on the diversified ecosystem services provided by drylands for their livelihoods. Achieving sustainable livelihoods (SLs) is a key component of achieving the sustainable development goals set by the agenda in 2030. Although it has aroused extensive interest, research on SLs in drylands at a regional scale is still limited. This paper aims to address this research gap by evaluating SLs through a geographic gradient of aridity in Inner Mongolia. A sustainable livelihood index (SLI) was developed using a wide range of indicators in a sustainable livelihood framework (SLF). The weight of the indicators was determined by the entropy weight method, and the characteristics of the spatial distribution of the SLI were analyzed. The results showed that the SLI varies greatly across aridity zones. In terms of livelihood assets, the SLI in the dry sub-humid zone was 15% higher than in the arid zones, while, surprisingly, semi-arid zones were found to have the most vulnerable livelihoods (rather than the arid zones). The reason for this is that land management and planning approaches are necessary in drylands. In further detail, Moran's I index illustrated that the overall performance of the SLI of each league or city has a positive spatial correlation, while through local spatial correlation it was found that Hinggan and Chifeng are hot spot areas and Hohhot is a cold spot area. The lack of physical and social capital is an important obstacle for SLs. Based on the analysis of SLs in Inner Mongolia, the characteristics of the sustainable development of local residents were revealed. In this paper, we call for an integrated (i.e., focusing on natural and human capital) land management and planning approach for drylands to reflect the nature of the tightly coupled socio-ecological systems. [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]