Urban vitality is a crucial indicator of a city’s sustainable development and the quality of life of its residents. Investigating the spatiotemporal patterns and influencing mechanisms of urban vitality is essential for optimizing the built-environment and improving governance. Using the central urban area of Guiyang, China, as a case study, this research integrates multi-source urban sensing data to investigate the spatiotemporal patterns of urban vitality and their driving factors. Geographically weighted regression (GWR) and machine learning combined with SHapley Additive exPlanations (SHAP) are applied to capture spatial heterogeneity, nonlinear relationships, and threshold effects among influencing variables. Results show that urban vitality exhibits a Y-shaped, single-core, multi-center, and clustered spatial configuration, with slightly higher intensity on weekdays and similar diurnal rhythms across weekdays and weekends. The effects of influencing factors display strong spatial non-stationarity, characterized by a concentric gradient radiating outward from the historic Laocheng core. Building density (BD), residential point density (RED), normalized difference vegetation index (NDVI), and road density (RD) emerge as the dominant contributors to urban vitality, while topographic conditions play a relatively minor role. The relationships between key landscape and built-environment variables and urban vitality are highly nonlinear, with distinct threshold effects. By integrating spatial econometric modeling and explainable machine learning, this study advances methodological approaches for urban vitality research and provides practical insights for landscape-oriented urban planning and human-centered spatial design.

In order to elucidate the factors regulating nutrient dynamics in plant–soil interactions across various latitudes within the karst climax community, this study focused on the karst forest climax community in Guizhou Province, Southwest China. We analyzed and compared the differences in carbon, nitrogen, and phosphorus content, as well as stoichiometry, in plant leaves and soils under various growing conditions. Additionally, redundancy analyses were conducted to investigate the stoichiometric correlations between plants and soil. The research findings indicate the following: (1) Leaf carbon content (LCC) and the carbon-to-nitrogen ratio (LCN) exhibit significant differences across various latitudes, with the lowest values observed in high-latitude regions. (2) Soil organic carbon (SOC) and the carbon-to-nitrogen ratio (SCN) also show significant variations across latitudes, with the lowest concentrations found in high-latitude regions and the highest in low-latitude regions. (3) The variability in leaf nutrient element ratios among karst region climax communities is greatest in low-latitude areas. This study found that the carbon content (LCC), nitrogen content (LNC), and carbon-to-nitrogen ratio (LCN) of leaves in karst climax community plants decrease as latitude increases. This suggests that plants regulate the nutrient utilization efficiency of carbon content (LCC), nitrogen content (LNC), and phosphorus content (LPC) in their leaves to maintain the nutrients necessary for their growth and development along the latitudinal gradient. The sensitivity of soil organic carbon (SOC), carbon-to-nitrogen (SCN), and carbon-to-phosphorus (SCP) ratios to latitudinal changes were particularly pronounced in the karst climax community. Additionally, plant leaf stoichiometry was significantly influenced by soil phosphorus content (SPC) in mid- and high-latitude regions, while factors other than soil nitrogen content (SNC) had a more substantial impact on plant leaf stoichiometry in low-latitude areas. The findings of this study are highly significant for guiding nutrient management in karst forest ecosystems and for the restoration of degraded karst forest vegetation.

Pedicularis henryi is a hemiparasitic species within Orobanchaceae (Lamiales). In this study, the mitochondrial genome of P. henryi was assembled and characterized. The mitogenome is 251,317 bp in length with a GC content of 44.32%, containing 36 protein-coding genes, 24 tRNAs, and three rRNAs. Codon usage analysis revealed a marked preference for A/U-terminated codons. A total of 196 repetitive elements were identified, with interspersed repeats as the most abundant type. We detected 293 C-to-U RNA editing sites across 31 protein-coding genes, predominantly causing non-synonymous substitutions. Eighteen chloroplast-derived fragments totaling 35,894 bp were found, accounting for 15.0% of the mitogenome. Nucleotide diversity analysis among three Pedicularis species showed an average π of 0.0018, with core respiratory genes highly conserved. Synteny analysis revealed extensive structural rearrangement in P. henryi compared to P. chinensis. This study provides mitochondrial genomic resources for Orobanchaceae and insights into mitogenome evolution in hemiparasitic plants.

Although the coupling coordination relationship (CCR) between ecological environment and economic development has received extensive scholarly attention, investigations into the underlying mechanisms of this coupling coordination remain insufficient. Taking the Chishui River Basin (CRB) in Southwest China as the study area, this study integrates remote sensing data and county-level statistical datasets. Firstly, the quality of the ecological environment and economic development level of the CRB are systematically evaluated. Secondly, an improved coupling coordination degree model (ICCDM) is adopted to quantify the CCR between the ecological environment and economic development, as well as its spatiotemporal evolution characteristics. Finally, an obstacle degree model and panel Tobit model are employed to explore the influencing factors of the CCR from both intrinsic and extrinsic perspectives. The results show that during the study period, both the ecological environment index (EEI) and the economic development index (EDI) in the CRB exhibited upward trends, with pronounced inter-county disparities. The CCR between ecological environment and economic development was continuously optimized, and the coupling coordination degree (CCD) displayed a distinct spatial gradient pattern of downstream regions > midstream regions > upstream regions. Obstacle degree analysis identifies significant heterogeneity in the obstacle factors for CCR improvement across the basin: Renhuai and Zunyi are dominated by ecological environment constraints, while 11 counties including Chishui and Xishui are mainly restricted by economic development constraints. Industrial structure, ecological endowment, industrialization level and government capacity are vital positive driving factors for the CCR in the CRB, whereas Terrain conditions act as a key negative restraining factor. This study indicates that the overall coupling coordination level between ecological environment and economic development in the CRB is still relatively low and requires further enhancement. Therefore, region-specific differentiated regulation strategies are urgently needed to achieve high-level coordinated development between the ecological environment and economy in the CRB.

Green development constitutes a critical pathway toward achieving high-quality development in karst mountainous regions; however, the internal coordination mechanisms and influencing factors governing green development in these areas remain inadequately understood. This study employs Bijie City, a representative karst mountainous region in Southwest China, as a case study and utilizes county-level panel data spanning from 2013 to 2024 to construct a comprehensive evaluation index system for green development. This system encompasses three dimensions: green production, green ecology, and green living. The study applies the CRITIC weighting method, the coupling coordination degree model, the obstacle degree model, and a fixed-effects model to systematically analyze the temporal evolution of green development levels, internal coordination relationships, and key influencing factors across the eight counties of Bijie City. The findings reveal that (1) overall green development levels improved across the eight counties, with the average county-level green development index (GDI) increasing from 0.407 to 0.650. Among the sub-indices, the green living index (GLI) exhibited the most substantial increase, followed by the green production index (GPI), while the green ecology index (GEI) showed the least growth. (2) The coupling coordination degree among green production, green ecology, and green living generally increased across the counties, indicating a progressive enhancement in the internal coordination of green development. (3) The primary obstacles to advancing green development shifted from green living to green ecology and green production over the study period. Industrial structure and population urbanization emerged as significant positive drivers of green development, whereas land urbanization and population density exerted notable negative effects. These results suggest that green development in Bijie City has entered a new phase characterized by internal structural reorganization and the increasing prominence of deep-seated obstacles. Consequently, future governance efforts should prioritize ecological restoration and the optimization of production structures, while implementing differentiated governance strategies tailored to local conditions. This study contributes to a deeper understanding of green development in karst mountainous areas and offers a theoretical foundation and empirical reference for promoting green transformation and high-quality development in similar contexts.