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Shrub encroaching on grasslands threatens grassland ecosystems and negatively affects human land use and livelihood. However, existing research on this subject is limited, having focused on small areas, thus hindering a comprehensive understanding of the diverse patterns of large-scale shrub invasions into grasslands. In recent decades, a significant encroachment of shrubs into Xilin Gol grassland has been observed in China, resulting in considerable damage to the grasslands. Therefore, this study aimed to investigate the rapid expansion of shrub-encroached grassland (SEG) from 1990 to 2020 in the Xilin Gol League, Inner Mongolia Autonomous Region, China, using long-term Landsat images. The spatial distribution characteristics of the new SEG areas were analyzed. The main factors driving the shrub encroachment in the study area were identified using a geographic detector. Our results revealed a continuous expansion of SEG in the study area over the past three decades, marked by an increase of 48.76 × 103 km2 and an average annual gain rate of 9.41 %. The increased SEG was primarily owing to grassland transition. The sprawl of the SEG was the most prominent in the western and northern regions of the study area, extending to gentle slopes of approximately 5° and low-middle elevations between 600–1300 m above mean sea level. Shrub encroachment was primarily influenced by surface temperature, wind speed, and relative humidity, with elevation, slope, and precipitation exerting relatively weaker influences. Rather than being driven by a singular factor, the expansion was a result of the combined influence of various factors. This study provides a valuable case study for understanding shrub invasion dynamics in arid and semi-arid regions globally.

Residential quarters are a critical component of urban areas, which significantly affects people's health and quality of life. In recent decades, the high-density and crowded residential quarters have become the mainstream organizational model of construction in China, because of the rapid and massive urbanization in many cities. This results in the contradiction between the eco-environment needs and the accommodation needs. Therefore, a systematic evaluation of the eco-environment in residential quarters is urgently needed to address the drawbacks of current residential quarters' planning and design. This paper aims to explore ways to improve the eco-environment with residential quarters from the perspective of China. We first used the Remote Sensing Ecological Index (RSEI) to evaluate the eco-environmental quality of 3, 756 residential quarters in the main urban areas of Xi 'an, China. Then, we quantitatively analyzed the landscape patterns of the impervious surfaces in these residential quarters. Finally, we characterized the impacts of residential quarters layout on the ecological environment by using the vegetation coverage (FVC), the normalized difference bare soil index (NDBSI), building density (BD), the residential density (RD), and the population density (PD) as main factors. The results are as follows: (1) Only 20.5 % of residential quarters with good eco-environments were distributed between the Second and Third Ring Road, indicating the need for improvement in creating livable eco-environments. (2) The shape of the impervious surface of excellent ecological quarters is complex and broken, the average patch area is smaller, and the degree of aggregation and combination of patches is lower compared to residential quarters with poor ecological quality. Therefore, it is recommended to reduce the connectivity of the impervious surface to improve the eco-environment of residential quarters. (3) Among the factors affecting RSEI of residential quarters, FVC, and NDBSI contribute more than other factors. For the construction of excellent eco-environments in residential quarters, we suggest that vegetation coverage should be no less than 0.4, the impervious surface coverage should be no more than 0.53, the building density should be between 0.28 ∼ 0.35, residential density should be no more than 0.22, and the population density of residential quarter should be under 0.29. Overall, for open and low-density residential quarters, the living environment can be improved by enriching vegetation and reducing impervious surfaces, while compact, high-density residential quarters can create a livable eco-environment by optimizing the impervious surface landscape patterns.

Over the past decades, cities worldwide have experienced a sharp decline in urban ecological quality (UEQ) due to rapid urbanization accompanied. However, the spatiotemporal dynamics of UEQ and its associated multi-scale impacts of natural and human factors on UEQ changes were still unknown. Thus, based on the Remote Sensing-based Ecological Index, Random Forest regression, and Geographical Detector, taking the downtown area of Xi’an City, China, as an example, we revealed the spatiotemporal dynamics of UEQ from 2000 to 2019 and quantified the impacts of 19 natural and human factors on UEQ changes at 30 scales. Results showed that 1) from 2000 to 2019, the UEQ in the downtown area of Xi'an experienced a sharp decrease at first, and then gradually recovered. But the recovered UEQ was much lower than the lost UEQ, indicating the big loss of UEQ. 2) The identified driving factors had significant impacts on local UEQ within 1300 m. 3) Elevation, temperature, precipitation, slope, and GDP were more important than other factors in impacting UEQ changes. 4) Natural conditions and climate change had the greatest impact on the regional UEQ, followed by social changes and urban built environment. Altogether, our results revealed the complex impacts of natural-anthropogenic factors on UEQ changes, including impact scale, impact strength, and impact interaction, thus can assist local government in optimizing urban planning and improving livable cities.

Vegetation phenology reflects the temporal dynamics of vegetation growth and is an important indicator of climate change. However, differences consistently exist in land surface phenology derived at different spatial scales, which hinders the understanding of phenological events and integration of land surface phenology products from different scales. The Qinling Mountains are a climatic and geographical transitional region in China. To better understand the spatial scale effect issues of land surface phenology in mountainous ecosystems, this study up-scaled vegetation start of season (SOS) and end of season (EOS) in the Qinling Mountains derived from three different Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) products to four scales (i.e., 2 km × 2 km, 4 km × 4 km, 6 km × 6 km, and 8 km × 8 km) using the spatial averaging method. Then, similarities and differences between the up-scaled SOSs/EOSs were examined using the simple linear regression, cumulative distribution function, and absolute difference. Finally, the random forest model was used to reveal the major factors influencing the spatial scale effect of land surface phenology in Qinling Mountains. Results showed that the derived basic SOS/EOS datasets using the same filtering method from the 250 m and 500 m NDVI datasets were consistent in spatial distribution, while the results from the 1000 m NDVI dataset differed. For both the basic and the up-scaled datasets, the land surface phenology derived from the Savitzky-Golay-filtered NDVI showed an advance in SOS, but a delay in EOS, compared to those derived from the asymmetric Gaussian- and double logistic-filtered NDVI. The up-scaled SOS was greatly impacted by both NDVI resolution and the filtering methods. On the other hand, EOS was mostly impacted by the filtering methods. Moreover, up-scaled SOSs usually had larger differences compared to up-scaled EOSs. While different filtering methods sometimes amplified the absolute differences between different SOS/EOS across scales, the upscaling reduced the differences. Influence factor analysis showed that spatial variations observed in SOS in Qinling Mountains were mainly caused by forest cover, uneven distribution of spring precipitation, and annual precipitation, while spatial variations in aspect, winter temperature, and autumn precipitation all strongly influenced the observed EOS across scales in the study area. These findings enhance our understanding of the effects of observational scale on vegetation phenology in mountain ecosystems and provide a reference for phenology modeling in mountainous areas.

Poly(ethylene terephthalate) (PET) is a major plastic polymer utilized in the single-use and textile industries. The discovery of PET-degrading enzymes (PETases) has led to an increased interest in the biological recycling of PET in addition to mechanical recycling. IsPETase from Ideonella sakaiensis is a candidate catalyst, but little is understood about its structurefunction relationships with regards to PET degradation. To understand the effects of mutations on IsPETase productivity, we develop a directed evolution assay to identify mutations beneficial to PET film degradation at 30 °C. IsPETase also displays enzyme concentration-dependent inhibition effects, and surface crowding has been proposed as a causal phenomenon. Based on total internal reflectance fluorescence microscopy and adsorption experiments, IsPETase is likely experiencing crowded conditions on PET films. Molecular dynamics simulations of IsPETase variants reveal a decrease in active site flexibility in free enzymes and reduced probability of productive active site formation in substrate-bound enzymes under crowding. Hence, we develop a surface crowding model to analyze the biochemical effects of three hit mutations (T116P, S238N, S290P) that enhanced ambient temperature activity and/or thermostability. We find that T116P decreases susceptibility to crowding, resulting in higher PET degradation product accumulation despite no change in intrinsic catalytic rate. In conclusion, we show that a macromolecular crowdingbased biochemical model can be used to analyze the effects of mutations on properties of PETases and that crowding behavior is a major property to be targeted for enzyme engineering for improved PET degradation. [ABSTRACT FROM AUTHOR]