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Climate change scenarios forecast increased aridity in large areas worldwide with potentially important effects on nutrient availability and plant growth. Plant nitrogen and phosphorus concentrations (plant [N] and [P]) have been used to assess nutrient limitation, but a comprehensive understanding of drought stress on plant [N] and [P] remains elusive. We conducted a meta‐analysis to examine responses of plant [N] and [P] to drought manipulation treatments and duration of drought stress. Drought stress showed negative effects on plant [N] (−3.73%) and plant [P] (−9.18%), and a positive effect on plant N : P (+ 6.98%). Drought stress had stronger negative effects on plant [N] and [P] in the short term (< 90 d) than in the long term (> 90 d). Drought treatments that included drying–rewetting cycles showed no effect on plant [N] and [P], while constant, prolonged, or intermittent drought stress had a negative effect on plant [P]. Our results suggest that negative effects on plant [N] and [P] are alleviated with extended duration of drought treatments and with drying–rewetting cycles. Availability of water, rather than of N and P, may be the main driver for reduced plant growth with increased long‐term drought stress.

Compound soil drought and heat extremes are expected to occur more frequently with global warming, causing wide-ranging socio-ecological repercussions. Vegetation modulates air temperature and soil moisture through biophysical processes, thereby influencing the occurrence of such extremes. Global vegetation cover is broadly expected to increase under climate change, but it remains unclear whether vegetation greening will alleviate or aggravate future increases in compound soil drought-heat events. Here, using a suite of state-of-the-art model simulations, we show that the projected vegetation greening will increase the frequency of global compound soil drought-heat events, equivalent to 12–21% of the total increment at the end of 21st century. This increase is predominantly driven by reduced albedo and enhanced transpiration associated with increased leaf area. Although greening-induced transpiration enhancement has counteracting cooling and drying effects, the excessive water loss in the early growing season can lead to later soil moisture deficits, amplifying compound soil drought-heat extremes during the subsequent warm season. These changes are most pronounced in northern high latitudes and are dominated by the warming effect of CO 2 . Our study highlights the necessity of integrating vegetation biophysical effects into mitigation and adaptation strategies for addressing compound climate risks. This study finds that vegetation greening is expected to exacerbate compound soil drought-heat extreme events, as enhanced transpiration during the early growing season depletes soil moisture leading to deficits in the subsequent warm season.

National parks face ecological threats from climate change and human activities. Sanjiangyuan National Park (SNP), a major ecological area in China, lacks a systematic evaluation of its ecological environmental quality changes and their driving factors. This study explores these dynamics to provide a scientific basis for regional ecological management. By constructing the remote sensing ecological index (RSEI) and using the optimal multivariate-stratification geographical detector (OMGD) model, we assessed ecological changes from 2014 to 2024. The results showed the RSEI remained stable at approximately 0.66, peaking at 0.732 in 2022, indicating a general improvement in ecological quality. The vegetation coverage rate (NDVI) increased from 0.591 to 0.680. Driving factor analysis revealed considerable regional variation, with temperature and human activities as the primary drivers. Higher RSEI values were associated with conditions where precipitation was moderate (~100 mm), evapotranspiration levels were high (>50 mm), temperatures were above average (>4 °C), and nighttime light indices were low (<0.6). These findings suggest that specific combinations of these factor thresholds may enhance ecological quality, informing protection strategies for SNP and providing a reference for similar plateau ecosystems.

Plant transpiration is a key flux of surface water loss to the atmosphere, determining the available surface water for ecosystem and human use. However, over China, the magnitude of transpiration and its spatial pattern remain poorly understood due to a lack of constraints from in situ observations. Here we compile 34 plot‐scale annual transpiration measurements across China to evaluate the performance of four transpiration products, and then produce a new constrained transpiration map for China. The transpiration map reveals an annual value of 209.0 mm yr−1, while the four transpiration products have a large spread of values (173.4–307.9 mm yr−1), especially in the wet southeastern regions. Land surface models exhibit large biases in modeled transpiration (−62.8% to 49.1%) compared to our constrained transpiration, and tend to underestimate transpiration in wet regions while overestimating it in dry regions. This behavior introduces bias in runoff projections which has ramifications for regional water resource management policies. Plain Language Summary Transpiration from plants is a very important part of the hydrological cycle but, in China, the way in which it varies from one place to another is poorly understood because there is a lack of direct measurements, especially in remote areas. We took 34 measurements made by other teams in different areas of China and combined these with four transpiration products to produce a more accurate map of transpiration across China. We found that the irrigation of crops, which is common in some areas, had a large effect on how transpiration varies from place to place. We compared our new map of transpiration with simulations made by the most up‐to‐date land surface models, in the situation where the concentration of CO2 in the atmosphere is increasing. They do not agree well and this disagreement leads to variations in runoff projects which will have implications for national water resource management. Key Points We produce a constrained transpiration map for China by combining 34 plot‐scale observations and four independent transpiration products Human irrigation plays a critical role in the spatial variation of transpiration Land surface models introduce bias in runoff projections due to poor performance of transpiration simulation

Desertification control and scientific evaluation of desert ecosystem sustainability are important issues for countries along the Silk Road Economic Belt. Fractional vegetation coverage (FVC) is used as a quantitative indicator to describe the vegetation coverage of desert ecosystems. Although satellite remote sensing technology has been widely used to retrieve FVC at the regional and global scale, the authenticity evaluation of the inversion results has been flawed. To gain insight into the composition, structure and changes of desert vegetation, it is important to assess the accuracy of FVC and explore the relationship between FVC and meteorological factors. Therefore, we adopted unmanned aerial vehicle remote sensing (UAVRS) technology to verify the inversion results and analyse the practicability of MODIS-NDVI (where NDVI = normalized difference vegetation index) products in desert areas. To provide a new method for the estimation of vegetation coverage in the natural state, the relationships between vegetation coverage and four meteorological factors, namely, land surface temperature, temperature, precipitation and evaporation were analysed. The results showed that using the original MODIS-NDVI data product with a spatial resolution of 250 m to invert vegetation coverage is practical in desert areas (coefficient of determination (R2) = 0.83, root mean square error (RMSE) = 0.052, normalized root mean square error (NRMSE) = 42.94%, mean absolute error (MAE) = 0.007) but underestimates vegetation coverage in the study area. MODIS-NDVI data products are different from the real NDVI in the study area. Correcting MODIS-NDVI data products can effectively improve the accuracy of the inversion. When extracting vegetation coverage in this area, the scale has little effect on the results. There is a significant correlation between precipitation, evaporation and FVC in the area, but the interaction of temperature and land surface temperature with precipitation and evaporation also has a considerable impact on FVC, and evaporation has a substantial impact on FVC values inverted from MODIS-NDVI data (FVCM), When exploring the relationship between vegetation coverage and meteorological elements, if vegetation coverage is retrieved from MODIS-NDVI data products or MODIS-NDVI data, when considering temperature and precipitation, the effect of evaporation should also be considered. In addition, meteorological factors can be used to predict FVC (R2 = 0.7364, RMSE = 0.0623), which provides a new method for estimating FVC in areas with less manual intervention.

Increasing atmospheric CO2 causes substantial spatial and seasonal changes in air temperature and precipitation through its radiative (RAD) and vegetation physiological (PHY) effects. However, it remains poorly understood on how these two effects impact the integrated climate zone shifts over China. Here, we disentangle the RAD and PHY effects on the shifts of Köppen‐Geiger climate zones from pre‐industrial to 4 × CO2 in China using nine Earth system models. We find that climate zone changes over approximately 56.1% of China, and PHY contributes 15.2% of such changes at 4 × CO2. PHY shifts regional climate to warmer and wetter classifications, shrinking (−42.8%) the arid zone distributions and promoting (26.8%) the tropical zone northward extensions. Our findings highlight the critical role of vegetation in reshaping the overall climate zone distributions, yet introduce potential risk to climate mitigation and adaptation. Plain Language Summary Ongoing rise in atmospheric CO2 not only alters the magnitudes of temperature and precipitation, but also shifts their spatial and seasonal variations, reshaping climate zone distributions. Vegetation amplifies global warming and modulates precipitation through its physiological response (PHY) to rising CO2, but how this process affects the local climate zone shifts is understudied, especially in China. Using nine CMIP6 Earth system models, we find that climate zone undergoes shifts across 56.1% of China at 4 × CO2, and PHY contributes 15.2% of such changes. PHY aids the northward expansions of tropical and temperate zones, and generally shifts the regions to warmer and wetter climate from pre‐industrial to 4 × CO2. Importantly, PHY diminishes (−42.8%) the arid zone coverage, indicating an important role of vegetation to alleviate water stress in those regions. Strong PHY contributions to the total climate zone area changes caused by rising CO2 emerges in Shandong, Shanxi, Ningxia, Guangdong and Guangxi at 4 × CO2. Our results clarify the climate zone shifts at 4 × CO2 and unravel the role of vegetation physiological response on shifting future climate zone distributions in China, providing new perspective on the role of vegetation in climate system. Key Points Climate zone shifts across 56.1% of land area in China at 4 × CO2, 15.2% of which is contributed by vegetation CO2 physiological forcing CO2 physiological forcing shifts the regions to a warmer climate through lifting temperature, expanding tropical zone northward Vegetation acts to shrink arid zone as precipitation increases through CO2 physiological forcing

Accurate prediction of reference evapotranspiration (ET0) using integrated deep learning approaches with limited meteorological data is highly significant for efficient water resource utilization and management in arid regions. Nevertheless, parameter optimization is frequently overlooked in current research, leading to unsatisfactory estimation accuracy that cannot meet practical application requirements. To overcome this limitation, a CNN–BiLSTM–attention hybrid model is constructed by combining the powerful feature-extraction capability of CNN and excellent sequence-processing performance of BiLSTM, followed by the integration of an attention mechanism. Five metaheuristic algorithms, namely the osprey optimization algorithm (OOA), grey wolf optimization (GWO), whale optimization algorithm (WOA), particle swarm optimization (PSO), and northern goshawk optimization (NGO), are adopted to optimize the key parameters of the proposed model. The developed hybrid models are then applied to ET0 estimation in Linze County, China. The results demonstrate that the error indices of these models vary within the ranges of MAPE [14.28%, 14.48%], MAE [0.4270, 0.4482], RMSE [0.5596, 0.5844], and NMSE [0.0490, 0.0577]. Overall, the OOA–CNN–BiLSTM–attention model exhibited the most robust and consistent estimation performance across multiple evaluation metrics among the investigated models.

Background High Mobility Group Box 1 (HMGB1) was first identified as a nonhistone chromatin-binding protein that functions as a pro-inflammatory cytokine and a Damage-Associated Molecular Pattern molecule when released from necrotic cells or activated leukocytes. HMGB1 consists of two structurally similar HMG boxes that comprise the pro-inflammatory (B-box) and the anti-inflammatory (A-box) domains. Paradoxically, the A-box also contains the epitope for the well-characterized anti-HMGB1 monoclonal antibody “2G7”, which also potently inhibits HMGB1-mediated inflammation in a wide variety of in vivo models. The molecular mechanisms through which the A-box domain inhibits the inflammatory activity of HMGB1 and 2G7 exerts anti-inflammatory activity after binding the A-box domain have been a mystery. Recently, we demonstrated that: 1) the TLR4/MD-2 receptor is required for HMGB1-mediated cytokine production and 2) the HMGB1–TLR4/MD-2 interaction is controlled by the redox state of HMGB1 isoforms. Methods We investigated the interactions of HMGB1 isoforms (redox state) or HMGB1 fragments (A- and B-box) with TLR4/MD-2 complex using Surface Plasmon Resonance (SPR) studies. Results Our results demonstrate that: 1) intact HMGB1 binds to TLR4 via the A-box domain with high affinity but an appreciable dissociation rate; 2) intact HMGB1 binds to MD-2 via the B-box domain with low affinity but a very slow dissociation rate; and 3) HMGB1 A-box domain alone binds to TLR4 more stably than the intact protein and thereby antagonizes HMGB1 by blocking HMGB1 from interacting with the TLR4/MD-2 complex. Conclusions These findings not only suggest a model whereby HMGB1 interacts with TLR4/MD-2 in a two-stage process but also explain how the A-box domain and 2G7 inhibit HMGB1.

Drought is increasing in frequency and severity, exacerbating food and water security risks in an era of continued global warming and human population growth. Here, we analyzed a severe summer drought affecting the US Northern Plains region in 2017. We examined the spatial pattern and seasonal progression of vegetation productivity and water use in the region using satellite-based estimates of field-scale (30 m) cropland evapotranspiration (ET), county level annual crop production statistics, and GOME-2 satellite observations of solar-induced chlorophyll fluorescence (SIF). The cropland ET record shows strong potential to track seasonal cropland water demands spatially, with strong correspondence to regional climate variables in the Northern Plains. The GOME-2 SIF record shows significant but limited correlations with finer scale climate variability due to the coarse sensor footprint, but captured an anomalous regional productivity decline coincident with drought related decreases in crop production and ET. The drought contributed to an overall 25% reduction in cropland ET, 6% decrease in crop production, and 11% reduction in SIF productivity over the region from April to September in 2017 relative to the longer (2008-2017) satellite record. More severely impacted agricultural areas indicated by the US Drought Monitor exceptional drought (D4) category represented 11% of the region and showed much larger anomalous ET (20%-81%) and productivity (11%-73%) declines. The regional pattern of drought impacts indicated more severe productivity and ET reductions in the north central and southern counties with extensive agriculture, and less impact in the western counties of the Northern Plains. This study provides a multiscale assessment of drought related impacts on regional productivity and ET over a crop intensive region, emphasizing the use of global satellite observations capable of informing regional to global scale water and food security assessments.

As a key indicator of terrestrial carbon cycle, carbon use efficiency (CUE) represents the efficiency of carbon fixation and carbon allocation strategy, shaping ecosystem function and services. Siberia, as a major carbon sink, is experiencing rapid and dramatic climate change, which triggers complex ecological responses and leads to uncertainties in its carbon balance evaluations. However, the spatiotemporal variations of CUE and the underlying mechanisms remain rarely studied, limiting our understanding of Siberian carbon cycle under climate change. This study investigates the spatial variations of CUE across Siberian ecosystem and employs random forest and SHapley Additive exPlanation (SHAP) to analyze the intrinsic driving mechanisms from 2001 to 2020. The results indicate that the annual mean CUE over Siberia from 2001 to 2020 is 0.60 ± 0.07, with notable ecosystem-specific variations ranging from grasslands (GRA) (0.64 ± 0.08) to closed shrublands (CSH) (0.52 ± 0.06). Vapor pressure deficit (VPD) primarily shapes the CUE spatial variations, and its interactions with mean annual temperature (MAT) and shortwave radiation (SW) synergistically distribute CUE over Siberia. Rather than non-significant trends from 2001 to 2020, CUE exhibits significant decreasing trends over Siberian regions under both SSP1-2.6 and SSP5-8.5 from 2021 to 2100, with substantial fluctuations within this period. Moreover, under SSP5-8.5, CUE experiences twice the decreasing rate of that from SSP1-2.6, indicating more vulnerable responses of Siberian ecosystems to climate change under a higher warming projection. Our results provide valuable insights into the dynamics of CUE in Siberia and offer scientific guidance for climate adaptation strategies in this region.