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Urbanization, a dominant global demographic trend, leads to various changes in environments (e.g., atmospheric CO₂ increase, urban heat island). Cities experience global change decades ahead of other systems so that they are natural laboratories for studying responses of other nonurban biological ecosystems to future global change. However, the impacts of urbanization on vegetation growth are not well understood. Here, we developed a general conceptual framework for quantifying the impacts of urbanization on vegetation growth and applied it in 32 Chinese cities. Results indicated that vegetation growth, as surrogated by satellite-observed vegetation index, decreased along urban intensity across all cities. At the same time, vegetation growth was enhanced at 85% of the places along the intensity gradient, and the relative enhancement increased with urban intensity. This growth enhancement offset about 40% of direct loss of vegetation productivity caused by replacing productive vegetated surfaces with nonproductive impervious surfaces. In light of current and previous field studies, we conclude that vegetation growth enhancement is prevalent in urban settings. Urban environments do provide ideal natural laboratories to observe biological responses to environmental changes that are difficult to mimic in manipulative experiments. However, one should be careful in extrapolating the finding to nonurban environments because urban vegetation is usually intensively managed, and attribution of the responses to diverse driving forces will be challenging but must be pursued.

Urban green spaces have many vital ecosystem services such as air cleaning, noise reduction, and carbon sequestration. Amid these great benefits from urban green spaces, the cooling effects via shading and evapotranspiration can mitigate the urban heat island effect. The impact of urban green spaces (UGSs) on the urban thermal environment in Beijing was quantified as a case study of metacities using four metrics: Land surface temperature (LST), cooling intensity, cooling extent, and cooling lapse. Three hundred and sixteen urban green spaces were extracted within the 4th ring road of Beijing from SPOT 6 satellite imagery and retrieved LST from Landsat 8 remote sensing data. The results showed that the cooling intensity of green spaces was generally more prominent in the areas with denser human activities and higher LST in this metacity. Vegetation density is always the dominant driver for the cooling effect indicated by all of the metrics. Furthermore, the results showed that those dispersive green spaces smaller than 9 ha, which are closely linked to the health and well-being of citizens, can possess about 6 °C of cooling effect variability, suggesting a great potential of managing the layout of small UGSs. In addition, the water nearby could be introduced to couple with the green and blue space for the promotion of cooling and enhancement of thermal comfort for tourists and residents. As the severe urban heating threatens human health and well-being in metacities, our findings may provide solutions for the mitigation of both the urban heat island and global climate warming of the UGS area customized cooling service.

Urban heat island (UHI) is one major anthropogenic modification to the Earth system that transcends its physical boundary. Using MODIS data from 2003 to 2012, we showed that the UHI effect decayed exponentially toward rural areas for majority of the 32 Chinese cities. We found an obvious urban/rural temperature “cliff” and estimated that the footprint of UHI effect (FP, including urban area) was 2.3 and 3.9 times of urban size for the day and night, respectively, with large spatiotemporal heterogeneities. We further revealed that ignoring the FP may underestimate the UHI intensity in most cases and even alter the direction of UHI estimates for few cities. Our results provide new insights to the characteristics of UHI effect and emphasize the necessity of considering city- and time-specific FP when assessing the urbanization effects on local climate.

As a fundamental aspect of the urban form, building height is a key attribute for reflecting human activities and human-environment interactions in the urban context. However, openly accessible building height maps covering the whole China remain sorely limited, particularly for spatially informed data. Here we developed a 1 km × 1 km resolution building height dataset across China in 2017 using Spatially-informed Gaussian process regression (Si-GPR) and open-access Sentinel-1 data. Building height estimation was performed using the spatially-explicit Gaussian process regression (GPR) in 39 major Chinese cities where the spatially explicit and robust cadastral data are available and the spatially-implicit GPR for the remaining 304 cities, respectively. The cross-validation results indicated that the proposed Si-GPR model overall achieved considerable estimation accuracy (R 2  = 0.81, RMSE = 4.22 m) across the entire country. Because of the implementation of local modelling, the spatially-explicit GPR outperformed (R 2  = 0.89, RMSE = 2.82 m) the spatially-implicit GPR (R 2  = 0.72, RMSE = 6.46 m) for all low-rise, mid-rise, and high-rise buildings. This dataset, with extensive-coverage and high-accuracy, can support further studies on the characteristics, causes, and consequences of urbanization. Measurement(s) 1 km gridded building height across China in 2017 Technology Type(s) Sentinel-1 SAR; Spatially-informed Gaussian Process Regression Factor Type(s) Sentinel-1 SAR Sample Characteristic - Location China

Significance The Mongolian Plateau, composed mainly of Inner Mongolia in China and the Republic of Mongolia, has been experiencing remarkable lake shrinkage during the recent decades because of intensive human activities and climate changes. This study provides a comprehensive satellite-based evaluation of lake shrinkage across the plateau, and finds a greater decreasing rate of the number of lakes in Inner Mongolia than in Mongolia (34.0% vs. 17.6%) between the late 1980s and 2010, due mainly to an unsustainable mining boom and agricultural irrigation in the former. Disastrous damages to the natural systems are threatening the livelihood of local people, and we thus call for an urgent action to prevent further deterioration. Lakes are widely distributed on the Mongolian Plateau and, as critical water sources, have sustained Mongolian pastures for hundreds of years. However, the plateau has experienced significant lake shrinkage and grassland degradation during the past several decades. To quantify the changes in all of the lakes on the plateau and the associated driving factors, we performed a satellite-based survey using multitemporal Landsat images from the 1970s to 2000s, combined with ground-based censuses. Our results document a rapid loss of lakes on the plateau in the past decades: the number of lakes with a water surface area >1 km ² decreased from 785 in the late 1980s to 577 in 2010, with a greater rate of decrease (34.0%) in Inner Mongolia of China than in Mongolia (17.6%). This decrease has been particularly pronounced since the late 1990s in Inner Mongolia and the number of lakes >10 km ² has declined by 30.0%. The statistical analyses suggested that in Mongolia precipitation was the dominant driver for the lake changes, and in Inner Mongolia coal mining was most important in its grassland area and irrigation was the leading factor in its cultivated area. The deterioration of lakes is expected to continue in the following decades not only because of changing climate but also increasing exploitation of underground mineral and groundwater resources on the plateau. To protect grasslands and the indigenous nomads, effective action is urgently required to save these valuable lakes from further deterioration.

Detailed and accurate urban landscape mapping, especially for urban blue-green-gray (UBGG) continuum, is the fundamental first step to understanding human–nature coupled urban systems. Nevertheless, the intricate spatial heterogeneity of urban landscapes within cities and across urban agglomerations presents challenges for large-scale and fine-grained mapping. In this study, we generated a 3 m high-resolution UBGG landscape dataset (UBGG-3m) for 36 Chinese metropolises using a transferable multi-scale high-resolution convolutional neural network and 336 Planet images. To train the network for generalization, we also created a large-volume UBGG landscape sample dataset (UBGGset) covering 2,272 km 2 of urban landscape samples at 3 m resolution. The classification results for five cities across diverse geographic regions substantiate the superior accuracy of UBGG-3m in both visual interpretation and quantitative evaluation (with an overall accuracy of 91.2% and FWIoU of 83.9%). Comparative analyses with existing datasets underscore the UBGG-3m’s great capability to depict urban landscape heterogeneity, providing a wealth of new data and valuable insights into the complex and dynamic urban environments in Chinese metropolises.

Porcine deltacoronavirus (PDCoV) can experimentally infect a variety of animals. Human infection by PDCoV has also been reported. Consistently, PDCoV can use aminopeptidase N (APN) from different host species as receptors to enter cells. To understand this broad receptor usage and interspecies transmission of PDCoV, we determined the crystal structures of the receptor binding domain (RBD) of PDCoV spike protein bound to human APN (hAPN) and porcine APN (pAPN), respectively. The structures of the two complexes exhibit high similarity. PDCoV RBD binds to common regions on hAPN and pAPN, which are different from the sites engaged by two alphacoronaviruses: HCoV-229E and porcine respiratory coronavirus (PRCoV). Based on structure guided mutagenesis, we identified conserved residues on hAPN and pAPN that are essential for PDCoV binding and infection. We report the detailed mechanism for how a deltacoronavirus recognizes homologous receptors and provide insights into the cross-species transmission of PDCoV. As a potential zoonotic pathogen, porcine deltacoronavirus (PDCoV) has been shown to cause febrile illness in humans. Here, Ji et al . report the structures of PDCoV spike protein bound to porcine and human aminopeptidase receptors, pointing to the likely underlying mechanism of PDCoV zoonotic transmission.

Transportation networks threaten global forests, but prior assessments have been regional or limited to single metrics (e.g., forest cover). Here, we present a global analysis of multidimensional road effects on forests, using high-resolution remote sensing data and a Grid-wise Environmental Matching for Background Reference (GEM-BR) strategy. We detect 18.6% lower forest cover, 2.7 m shorter canopy height, 52.2 gC m -2 yr -1 reduced net primary productivity, and 23.0 patches per km 2 higher fragmentation within 1 km of roads compared to reference areas. Impacts extend up to 5 km with a clear distance decay effect, totaling 4.26 million km 2 of forest loss—equivalent to 10.7% of the 2020 global forest extent. The Global South (tropics accounting for 54.8%) faces severe, worsening degradation (2000–2020), while the Global North shows milder impacts, with partial recovery. Critically, 89% of grid cells exhibit conflicting long-term trends across metrics, highlighting the inadequacy of cover-only assessments. We further find that road-linked degradation is tightly coupled with local human activity, and that global protected areas have insufficient capacity to curb ongoing degradation. Differences in impacts among regions suggest that road-linked forest degradation is tied to governance choices—urging integrated transport-forest planning to balance development and conservation. Transportation infrastructure threatens forests, but past studies have been regional or focused on single metrics like forest cover. This global analysis finds road-adjacent forests lose cover, height, and productivity and become more fragmented.

Leaf rust, caused by Puccinia triticina Eriksson ( Pt ), is one of the most severe foliar diseases of wheat. Breeding for leaf rust resistance is a practical and sustainable method to control this devastating disease. Here, we report the identification of Lr47 , a broadly effective leaf rust resistance gene introgressed into wheat from Aegilops speltoides . Lr47 encodes a coiled-coil nucleotide-binding leucine-rich repeat protein that is both necessary and sufficient to confer Pt resistance, as demonstrated by loss-of-function mutations and transgenic complementation. Lr47 introgression lines with no or reduced linkage drag are generated using the Pairing homoeologous1 mutation, and a diagnostic molecular marker for Lr47 is developed. The coiled-coil domain of the Lr47 protein is unable to induce cell death, nor does it have self-protein interaction. The cloning of Lr47 expands the number of leaf rust resistance genes that can be incorporated into multigene transgenic cassettes to control this devastating disease. Leaf rust is one of the most severe foliar diseases of wheat. Here, the authors report the cloning of Lr47 , a broadly effective leaf rust resistance gene introgressed into wheat from Aegilops speltoides , and show it encodes a coiled-coil nucleotide-binding leucine-rich repeat protein.

Systemic acquired resistance (SAR) is an inducible disease resistance phenomenon in plant species, providing plants with broad-spectrum resistance to secondary pathogen infections beyond the initial infection site. In Arabidopsis , SAR can be triggered by direct pathogen infection or treatment with the phytohormone salicylic acid (SA), as well as its analogues 2,6-dichloroisonicotinic acid (INA) and benzothiadiazole (BTH). The SA receptor non-expressor of pathogenesis-related protein gene 1 (NPR1) protein serves as a key regulator in controlling SAR signaling transduction. Similarly, in common wheat ( Triticum aestivum ), pathogen infection or treatment with the SA analogue BTH can induce broad-spectrum resistance to powdery mildew, leaf rust, Fusarium head blight, and other diseases. However, unlike SAR in the model plant Arabidopsis or rice, SAR-like responses in wheat exhibit unique features and regulatory pathways. The acquired resistance (AR) induced by the model pathogen Pseudomonas syringae pv. tomato strain DC3000 is regulated by NPR1 , but its effects are limited to the adjacent region of the same leaf and not systemic. On the other hand, the systemic immunity (SI) triggered by Xanthomonas translucens pv. cerealis ( Xtc ) or Pseudomonas syringae pv. japonica ( Psj ) is not controlled by NPR1 or SA, but rather closely associated with jasmonate (JA), abscisic acid (ABA), and several transcription factors. Furthermore, the BTH-induced resistance (BIR) partially depends on NPR1 activation, leading to a broader and stronger plant defense response. This paper provides a systematic review of the research progress on SAR in wheat, emphasizes the key regulatory role of NPR1 in wheat SAR, and summarizes the potential of pathogenesis-related protein ( PR ) genes in genetically modifying wheat to enhance broad-spectrum disease resistance. This review lays an important foundation for further analyzing the molecular mechanism of SAR and genetically improving broad-spectrum disease resistance in wheat.