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The largest area of tropical rainforests in China is on Hainan Island, and it is an important part of the world’s tropical rainforests. The structure of the tropical rainforests in Hainan is complex, the biomass density is high, and conducting ground surveys is difficult, costly, and time-consuming. Remote sensing is a good monitoring method for biomass estimation. However, the saturation phenomenon of such data from different satellite sensors results in low forest biomass estimation accuracy in tropical rainforests with high biomass density. Based on environmental information, the biomass of permanent sample plots, and forest age, this study established a tropical rainforest database for Hainan. Forest age and 14 types of environmental information, combined with an enhanced vegetation index (EVI), were introduced to establish a tropical rainforest biomass estimation model for remote sensing that can overcome the saturation phenomenon present when using remote sensing data. The fitting determination coefficient R2 of the model was 0.694. The remote sensing estimate of relative bias was 2.29%, and the relative root mean square error was 35.41%. The tropical rainforest biomass in Hainan Island is mainly distributed in the central mountainous and southern areas. The tropical rainforests in the northern and coastal areas have been severely damaged by tourism and real estate development. Particularly in low-altitude areas, large areas of tropical rainforest have been replaced by economic forests. Furthermore, the tropical rainforest areas in some cities and counties have decreased, affecting the increase in tropical rainforest biomass. On Hainan Island, there were few tropical rainforests in areas with high rainfall. Therefore, afforestation in these areas could maximize the ecological benefits of tropical rainforests. To further strengthen the protection, there is an urgent need to establish a feasible, reliable, and effective tropical rainforest loss assessment system using quantitative scientific methodologies.

Based on the panel data of water and latrine improvement in rural China from 2003 to 2016, this paper explores the spatiotemporal evolution pattern of rural sanitation facilities and analyzes the spatial heterogeneity of influencing factors of rural sanitation facilities by using the Geographically Weighted Regression (GWR) model. The conclusions are as follows: the gap between the western and the eastern regions of China is gradually narrowing; the spatial differences of rural environmental sanitation facilities in provinces were obvious, showing high-high and low-low agglomeration types. Additionally, years of education per capita, population density, and government investment all have a significant positive impact on the improvement of water and latrines. And the proportion of the minority population has a significant negative impact on the improvement of water. The net income per capita, traffic density, and residential investment per capita are significantly positively correlated with the improvement of water and latrines. But the difference is that the impact on the improvement of water is an obviously east-west band and decreases successively, and the impact on the improvement of latrines shows a dual pattern of polarization between north and south.

How coniferous forests evolved in the Northern Hemisphere remains largely unknown. Unlike most groups of organisms that generally followa latitudinal diversity gradient, most conifer species in the Northern Hemisphere are distributed in mountainous areas at middle latitudes. It is of great interest to know whether the midlatitude region has been an evolutionary cradle or museum for conifers and how evolutionary and ecological factors have driven their spatiotemporal evolution. Here, we investigated the macroevolution of Pinus, the largest conifer genus and characteristic of northern temperate coniferous forests, based on nearly complete species sampling. Using 1,662 genes from transcriptome sequences, we reconstructed a robust species phylogeny and reestimated divergence times of global pines. We found that ∼90% of extant pine species originated in the Miocene in sharp contrast to the ancient origin of Pinus, indicating a Neogene rediversification. Surprisingly, species at middle latitudes are much older than those at other latitudes. This finding, coupled with net diversification rate analysis, indicates that the midlatitude region has provided an evolutionary museum for global pines. Analyses of 31 environmental variables, together with a comparison of evolutionary rates of niche and phenotypic traits with a net diversification rate, found that topography played a primary role in pine diversification, and the aridity index was decisive for the niche rate shift. Moreover, fire has forced diversification and adaptive evolution of Pinus. Our study highlights the importance of integrating phylogenomic and ecological approaches to address evolution of biological groups at the global scale.

Under global warming, the understanding of regional drought heterogeneity, drivers, and future persistence remains limited. Utilizing the Temperature Vegetation Dryness Index (TVDI, 2001–2020) and combining Theil–Sen trend analysis, Mann–Kendall test, partial correlation analysis, and Hurst exponent, this study analyzes global drought patterns, drivers, and persistence. Global drought changes exhibit significant spatial heterogeneity, exemplified by persistent intensification in Europe and an initial increase followed by subsequent mitigation in the Amazon. In terms of driving factors, drought is positively correlated with temperature in high-latitude regions (e.g. North America and Siberia), while it is primarily controlled by precipitation in arid regions. Compared to the period of 2001–2010, drought intensification became more widespread during 2011–2020, and its persistent nature suggests that most affected areas will continue to face sustained drought risks in the future. These findings underscore the necessity for region-specific adaptation strategies and provide valuable insights for drought risk assessment.

Against the backdrop of rapid development in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), the spatiotemporal evolution of Production-Living-Ecological Land (PLEL) in its peri-urban regions has emerged as a critical research focus for achieving regional coordinated development. This study employs Huiyang District as a representative case study area, utilizing multisource geospatial data and analytical methodologies including land use dynamic degree, land transfer matrix, binary logistic regression, and ROC analysis to systematically investigate PLEL’s spatiotemporal evolution patterns and underlying driving mechanisms from 2007 to 2023. The empirical results demonstrate that: (1) During the observation period, aggregate production and ecological land areas exhibited a declining trend, while living land manifested persistent expansion, reflecting accelerated urbanization process and intensified population concentration. Specifically, between 2007 and 2015, production and ecological land areas decreased significantly while living land expanded markedly, reflecting accelerated urbanization. From 2015 to 2023, the decline in production land substantially moderated, whereas ecological land diminution accelerated. Living land maintained a consistent expansion throughout both periods. (2) Spatial analysis revealed a stable concentric pattern emerged, characterized by living land at the urban core, encircled by production land, with ecological land occupying the peripheral zones. Notable land conversion dynamics were observed between ecological and production land, revealing the tension between conservation and development. Living land conversions to other categories remained negligible, with its expansion predominantly sourced from production land, suggesting urban expansion has largely occurred through agricultural land appropriation. (3) Driving factor analysis identified population size as the predominant influence. Following GBA development, the impact of population size on living land expansion intensified. Model validation through ROC curve analysis confirmed robust predictive performance. This research systematically elucidates PLEL’s spatiotemporal evolution patterns and driving mechanisms within a GBA peripheral urban unit, thereby contributing scientific insights for territorial spatial optimization in rapidly urbanizing regions.

ContextUrban agglomeration is an advanced spatial organization of cities, usually caused by urbanization processes when cities develop to a certain level - typically associated with higher population density and a certain density of built environment. However, compared with various studies focusing on specific cities, urban agglomerations are still understudied, especially for the quantitative identification of spatiotemporal evolution of urban agglomerations.ObjectivesThis study aims to identify the boundary of urban agglomerations in China from 2000 to 2012, and to explore the temporal evolution and spatial difference of urban agglomerations.MethodsFirstly, the core zone of urban agglomerations was identified using an appropriate threshold of the digital number (DN) of nighttime light. Secondly, the mean patch area and gravity model were used to determine the affected zone of urban agglomerations. Thirdly, spatiotemporal contrast was conducted focusing on the 23 main urban agglomerations in China.ResultsBy 2012, the most highly developed Yangtze River Delta and Pearl River Delta urban agglomerations met the standard of world level, with the Beijing–Tianjin–Hebei urban agglomeration for regional level, as well as 11 urban agglomerations for sub-regional level. Regional differences in urban agglomerations between southern and northern China, or between coastal and inland China remained stable over the study period of 2000–2012. Compared with the western urban agglomerations, the outward expansion of eastern urban agglomerations decelerated. From 2000 to 2012, the overall development mode of urban agglomerations shifted from the core-expansion to the peripheral-development, together with slower expansion of urban agglomerations after 2006.ConclusionsNighttime light data are effective in exploring the spatiotemporal evolution of urban agglomerations.

Background Population ageing is an increasingly severe global issue. And this has been posing challenges for public health policies and medical resource allocation There are various features of population ageing in different regions worldwide. Methods All data were obtained from the health data of World Bank Open Data. Quantile linear regression was used to subtly measure the common variation tendency and strength of the global ageing rate and ageing population. The Bayesian space-time hierarchy model (BSTHM) was employed to assess the detailed spatial temporal evolution of ageing rate and ageing population in global 195 countries and regions. Results Annual growth of the ageing (65 and above) rate occurred on six continents: Europe (0.1532%), Oceania (0.0873%), Asia (0.0834%), South America (0.0723%), North America (0.0673%) and Africa (0.0069%). The coefficient of variation of the global ageing rate increased from 0.54 in 1960 to 0.69 in 2017. The global ageing rate and ageing population increased over this period, correlating positively with their quantiles. Most countries (37/39) in Europe belong to the top level with regard to the ageing rate, including the countries with the greatest degree of ageing—Sweden, Germany, Austria, Belgium and the UK—whose spatial relative risks of ageing are 3.180 (3.113–3.214), 3.071 (3.018–3.122), 2.951 (2.903–3.001), 2.932 (2.880–2.984) and 2.917 (2.869–2.967), respectively. Worldwide, 44 low ageing areas which were distributed mainly in Africa (26 areas) and Asia (15 areas) experienced a decreasing trend of ageing rates. The local trends of ageing population in the 195 areas increased. Conclusions The differentiation of global population ageing is becoming increasingly serious. Globally, all 195 areas showed an increasing local ageing trend in absolute terms, although there were 44 low-ageing areas that experienced a decreasing local trend of ageing rate. The statistical results may provide some baseline reference for developing public health policies in various countries or regions, especially in less-developed areas.

There are significant differences in the dominant driving factors of rocky desertification evolution in different historical periods in southwest karst mountainous areas. However, previous studies were mostly conducted in specific periods. In this study, taking Bijie City as an example, the spatial and temporal evolution pattern of rocky desertification in Bijie City in the recent 35 years was analyzed by introducing the feature space model and the gravity center model, and then the dominant driving factors of rocky desertification in the study area in different historical periods were clarified based on GeoDetector. The results were as follows: (1) The point-to-point B (bare land index)-DI (dryness index) feature space model has high applicability for rocky desertification monitoring, and its inversion accuracy was 91.3%. (2) During the past 35 years, the rocky desertification in Bijie belonged to the moderate rocky desertification on the whole, and zones of intensive and severe rocky desertification were mainly distributed in the Weining Yi, Hui, and Miao Autonomous Region. (3) During 1985–2020, the rocky desertification in Bijie City showed an overall weakening trend (‘weakening–aggravating–weakening’). (4) From 1985 to 2020, the gravity center of rocky desertification in Bijie City moved westward, indicating that the aggravating degree of rocky desertification in the western region of the study area was higher than that in the eastern region. (5) The dominant factors affecting the evolution of rocky desertification in the past 35 years shifted from natural factor (vegetation coverage) to human activity factor (population density). The research results could provide decision supports for the prevention and control of rocky desertification in Bijie City and even the southwest karst mountainous area.

To discuss the coupling coordination relationship among tourism carbon emissions, economic development and regional innovation it is not only necessary to realize the green development of tourism economy, but also great significance for the tourism industry to take a low-carbon path. Taking the 30 provinces of China for example, this paper calculated the tourism carbon emission efficiency based on the super-efficiency Slacks based measure and Data envelope analyse (SBM-DEA) model from 2007 to 2017, and on this basis, defined a compound system that consists of tourism carbon emissions, tourism economic development and tourism regional innovation. Further, the coupling coordination degree model and dynamic degree model were used to explore its spatiotemporal evolution characteristics of balanced development, and this paper distinguished the core influencing factors by Geodetector model. The results showed that (1) during the study period, the tourism carbon emission efficiency showed a reciprocating trend of first rising and then falling, mainly due to the change of pure technical efficiency. (2) The coupling coordination degree developed towards a good trend, while there were significant differences among provinces, showing a gradient distribution pattern of decreasing from east to west. Additionally, (3) the core driving factors varied over time, however, in general, the influence from high to low were as follows: technological innovation, economic development, urbanization, environmental pollution control, and industrial structure. Finally, some policy recommendations were put forward to further promote the coupling coordination degree.

The static and dynamic properties of self-bound quantum droplets in a one-dimensional Bose-Bose mixture are discussed in the spirit of the Hartree–Fock-Bogoliubov theory. This latter enables us to provide beyond the Lee-Huang-Yang (LHY) quantum corrections to the equation of state at both zero and finite temperatures. In the uniform case our results for the ground-state energy and the critical temperature are confirmed through comparison with Quantum Monte-Carlo simulation and with available theoretical results. The density profiles are supported by numerical simulations of the generalized Gross-Pitaevskii equation which selfconsistently includes higher-order terms originating from the normal and anomalous fluctuations under the local density approximation. We show that the density exhibits a dip near its center in the flat-top plateau region for large interspecies interactions. We exemplify the impact of the beyond LHY corrections on the spatiotemporal evolution of the self-bound droplet in the presence of excitation induced by periodic density modulation. It is found that higher-order corrections may lead to the formation of a train of small droplets. We then extend our study for the case of inhomogeneous droplets in quasi one-dimensional Bose mixtures.