Global aging is getting worse, especially in China, a country with a large population. It is urgently needed to plan the site of new urban elderly care facilities for an aging society. Based on point of interest data and machine learning algorithms, we established a site selection model of urban elderly care facilities for Wuhan in China and selected potential optimal sites for new urban elderly care facilities. We found that 2059 of the 31,390 grids with a resolution of 500 m × 500 m of Wuhan are priority layout grids for new urban elderly care facilities. A total of 635 priority grids were further selected based on the agglomeration degree of the aging population in each street. They are mainly distributed in the areas with a concentrated aging population within the Second Ring Road around the urban centers. Additionally, some outer suburban streets with a relatively high aging degree also require immediate facility construction. The point of interest data and machine learning algorithms to select the location of urban elderly care facilities can optimize their overall configuration and avoid the subjectivity of site selection to some degree, provide empirical support for how to achieve a good configuration of “population–facilities” in space, and continuously improve the science of the spatial allocation of elderly care facilities.

Clarifying the response of soil microbial communities to vegetation restoration is essential to comprehend biogeochemical processes and ensure the long-term viability of forest development. To assess the variations in soil microbial communities throughout the growth of Pinus armandii plantations in the karst region, we utilized the “space instead of time” approach and selected four P. armandii stands with ages ranging from 10 to 47 years, along with a grassland control. The microbial community structure was determined by conducting Illumina sequencing of the 16 S rRNA gene and the ITS gene, respectively. The results demonstrated that afforestation with P. armandii significantly influenced soil microbial communities, as indicated by notable differences in bacterial and fungal composition and diversity between the plantations and the control. However, soil microbe diversity did not display significant variation across stand ages. Moreover, the bacterial community exhibited higher responsiveness to age gradients compared to the fungal community. Soil physicochemical factors play a critical role in elucidating microbial diversity and community composition variations during restoration processes. TN, AN, TP, AP, SOC, AK, and pH were the most significant influencing factors for the composition of bacterial community, while TC, SOC, pH, and TC_a were the most significant influencing factors for the composition of fungal community. Our findings indicate substantial changes in soil bacterial and fungal communities across successive stages of development. Additionally, the changes in dominant bacteria and fungi characteristics across the age gradient were primarily attributed to variations in the prevailing soil conditions and chemical factors.

Global historical land use datasets have been widely used in global or regional environmental change studies. Historical pasture data are essential components of these spatially explicit global datasets, and their uncertainties have not been well evaluated. Using the livestock-based historical pasture dataset for the Tibetan Plateau (TP), we evaluated the uncertainties of these representative global historical land use datasets in pasture reconstruction for the TP over the past 300 years in terms of pasture area estimation and spatial pattern mapping. We found that only the Sustainability and the Global Environment (SAGE) dataset can roughly reflect the temporal and spatial characteristics of historical pasture changes on the TP. The History Database of the Global Environment (HYDE) version 3.2 and the Pongratz Julia (PJ) datasets overestimated pasture area for the TP dramatically, with a maximum area ratio of about 221% and 291%, respectively, and the Kaplan and Krumhardt 2010 (KK10) dataset underestimated pasture area for the TP dramatically, with a minimum area ratio of only 9%. As for the spatial pattern, all these global datasets overestimated the spatial scope of grazing activities obviously. The KK10 dataset unreasonably allocated pasture to forest areas in southeastern Tibet because only climate and soil factors were considered in assessing land suitability for grazing. Using population to estimate pasture area and only using natural factors to allocate pasture area into grids is unsuitable for the TP historical pasture reconstruction. In the future, more information directly related to grazing activities, e.g., the number of livestock and its spatial distribution, and social-cultural factors, including technology and diet, should be used for area estimation and spatial pattern mapping to improve the accuracy of pasture data in these global datasets.

Areal changes in alpine lakes on the Tibetan Plateau (TP) are reliable indicators of climate change and anthropogenic disturbance. This study used long-term Landsat images and meteorological records to monitor the temporal evolution patterns of lakes within the Changtang National Nature Reserve between 1972 and 2021 and examine the climatic and anthropogenic impacts on lake area changes. The results revealed that the area of Lake LongmuCo and Lake Jiezechaqia significantly expanded by 12.81% and 12.88% from 1972 to 2021, respectively. After 1999, Lake LongmuCo and Lake Jiezechaqia entered into a period of rapid expansion. During 1972–2021, the annual mean temperature significantly increased at a rate of 0.05 °C/a, while the change in annual precipitation was not significant. The temperature change was a major contributor to the observed changes of Lake LongmuCo and Lake Jiezechaqia between 1972 and 2021, while human intervention also played a vital role during 2013–2021. The glaciers around these two lakes decreased by 21.81%, and the increase in water supply from warming-triggered glacier melting was a reason of expansion of Lake LongmuCo and Lake Jiezechaqia. The areas of the two artificial salt lakes affiliated with Lake LongmuCo and Lake Jiezechaqia were 0.24 km² and 2.67 km² in 2013 and rose to 0.51 km² and 9.80 km² in 2021, respectively. In particular, the continuous exploitations of salt lakes to extract lithium resources have retarded the rate of expansion of Lake LongmuCo and Lake Jiezechaqia. The dams constructed by industrial enterprises have blocked the expansion of Lake LongmuCo to the south. This paper sheds new light on the influences of recent human intervention and climatic variation on alpine lakes within the TP. Due to the importance of alpine lakes in the TP, we need more comprehensive and in-depth efforts to protect the lake ecosystems within the national nature reserves.

As a large-scale afforestation initiative, the Grain for Green (GFG) program was implemented for more than decade in the hilly Loess Plateau of Northern Shaanxi starting in 1999 and inevitably affected plant biomass production. This study evaluated the trend and magnitude of gross primary production (GPP) changes with VPM in the study area over 2000–2012 and quantitatively identified GFG’s contribution. The results indicate that the regional mean GPP was 529.6 to 854.7 g C m⁻² yr⁻¹, which significantly increased by 1.63% per year during the period. At the pixel level, 80% of the pixels presented an increasing trend in annual GPP, and in nearly half of the region, the increasing trend was statistically significant. Spatial analyses showed that the GPP increased more significantly in the gully areas of the middle north area, while no evident change trend was observed in the southern forestland areas. Statistical data from relevant yearbooks and interpreted data from high-resolution images show that the GFG program promoted vegetation restoration. Taking the increased area of forestland to indicate the achievements of GFG, we estimated, using regression analyses, that the afforestation contributed 40%–60% to the increased GPP in the region during 2000–2012. Overall, the ecosystem had a positive response to the Grain for Green (GFG) program in Northern Shaanxi.