Search Results (102)

Urban trees deliver multiple ecosystem services. However, rapid climate change may alter species-specific growth suitability, necessitating climate-informed planting and management. We developed 1 km grid-based ensemble species distribution models (ensemble SDMS) for 18 tree species widely planted in South Korean cities and projected growth suitability under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 across four future periods (2021–2040, 2041–2060, 2061–2080, 2081–2100) relative to a historical baseline (2000–2019). We quantified multidimensional redistribution signals from SDM outputs, including binary suitable area changes, centroid displacement, latitudinal boundary shifts, and mean suitability changes, using multivariate climatic predictors and complementary environmental variables. These indicators were integrated to classify species responses into four management-relevant types: Stable, Northward Expansion, Poleward Shift, Range Contraction. Model performance was generally high (AUC = 0.74–0.97). Although the median change in suitable area remained near 0%, interspecific variability increased toward later periods and under stronger forcing, with the largest dispersion under SSP3-7.0 (2041–2060). Stable type was most frequent overall (36.8–63.2%), but Northward Expansion increased to 42.1% under late-century SSP3-7.0, and Range Contraction reached 36.8% under mid-century SSP3-7.0. This indicator-based typology provides a practical basis for decision-support tools to prioritize climate-adaptive urban tree selection, replacement, and monitoring. Full article

The stability of solar-dependent energy systems is vital for urban sustainability, but it is increasingly threatened by compound energy risks (CERs), events where low photovoltaic generation coincides with high electricity demand. This study addresses a critical knowledge gap by disentangling the co-evolving structural and magnitude drivers of these events to identify their propagation pathways and the most vulnerable districts. To achieve this, a novel hybrid framework was developed to provide a high-resolution, spatiotemporal assessment of both risk dimensions across Singapore’s 41 districts. Structural risk was mapped by integrating an undirected co-occurrence network, quantified using Mutual Information (MI), with a directed influence network derived from Bayesian Network Theory (BNT). Concurrently, magnitude risk was assessed through a copula-based analysis of joint probabilities for historical and future climate conditions, using Singapore’s new V3 dataset under multiple Shared Socioeconomic Pathways (SSPs). The results reveal a significant shift in the compound energy risk landscape. Structurally, the network of risk propagation evolves from a historically diffuse configuration to a consolidated system dominated by clusters of 8 to 9 highly interconnected districts under the SSP245 scenario. Under the high-diffusion SSP585 scenario, this evolution is expanded by the addition of 4 more districts. At the same time, the magnitude of risk intensifies across identified hotspot districts. This synthesis uncovers a critical feedback dynamic: districts such as 29, 36, and 40 not only serve as key structural hubs but also experience sharp increases in event probability, with their return periods for extreme compound events collapsing from over 50 years historically to the 10–20-year range. This forms a self-reinforcing loop of systemic vulnerability. These findings indicate that Singapore’s energy security will become increasingly exposed to climate-driven risks that propagate through this consolidated network, requiring targeted spatial adaptation to ensure long-term grid sustainability. Full article

Climate change can alter both the amount and timing of inflows to water supply reservoirs while also increasing heat-driven demand and the likelihood of stressful warm-season conditions. Climate-driven changes in inflow to Eagle Mountain Lake Reservoir (Texas, USA) were quantified by integrating (i) a calibrated SWAT model evaluated at four USGS stream gauges, (ii) statistically downscaled CMIP6 daily precipitation and minimum/maximum temperature at seven stations/grid points for a historical baseline (2003–2022) and two future windows (2031–2050 and 2081–2100) under SSP1-2.6, SSP2-4.5, and SSP5-8.5, and (iii) observed reservoir operations (lake level, water supply releases, and flood discharge; 1990–2022). A standard watershed climate workflow is reframed through an operations-focused lens, wherein projected inflow changes are translated into decision-relevant indicators via the utilization of observed thresholds and operating mode signals. Included within this framework are spring refill-season inflow shifts, a hot–dry month metric, and storage threshold performance measures, which are coupled with screening-level probabilities linked to multi-year inflow deficits. Across models and stations, mean annual temperature increases by 0.7–1.9 °C in the 2030s and by 0.7–6.1 °C in the 2080s, while annual precipitation changes remain uncertain (−24% to +55%). Daily projections show a strong increase in extreme heat days (daily Tmax above the historical 95th percentile), from about 18 days yr⁻¹ historically to about 30–33 days yr⁻¹ in the 2030s and about 34–82 days yr⁻¹ by the 2080s. Hot–dry months (monthly mean Tmax above the historical 90th percentile and monthly precipitation below the historical median) increase modestly by mid-century and rise to about 1.5 months yr⁻¹ on average by the 2080s under SSP5-8.5. SWAT simulations indicate that the mean annual inflow declines by 17–20% across scenarios, with the largest reductions during the spring refill period (March–June). Historical operations show that hot–dry months are associated with approximately double the mean water supply release (7.2 vs. 3.5 m³/s) and a lower monthly minimum lake level (about 0.30 m; about 1.0 ft lower on average). Flood discharges occur almost exclusively when lake elevation is at or above about 197.8 m and follow multi-day rainfall clusters (cross-validated AUC = 0.99). Together, these results indicate that earlier-season inflow reductions and more frequent hot–dry stress will tighten the operational margin between refill, summer demand, and flood management, underscoring the need for adaptive drought response triggers and integrated drought–flood planning for the Dallas–Fort Worth region. Full article

Heatwaves are intensifying across China under global warming. Although previous SSP-RCP studies project more frequent and intense events, systematic evaluations of exposure mechanisms among the elderly in China remain limited. The purpose of the paper is to reveal the spatiotemporal dynamics and inequality of heatwave exposure among China’s urban elderly and to disentangle the driving influences of climate change, ageing, and urbanization. Historical and future heatwaves across China are identified and analyzed, exposure inequality is evaluated using the Gini coefficient, and the relative contributions of key drivers are quantified through factor separation. Results showed that heatwave frequency and duration increased from 2000 to 2019, with high-risk provinces clustering in the Yangtze River Basin, North China Plain, and Sichuan Basin. Future projections indicate substantial growth in elderly exposure to heatwaves, while under the SSP3-70 scenario, inter-provincial inequality in exposure tends to alleviate rather than intensify. Climate change was identified as the dominant driver, while ageing amplified risks and urbanization partly mitigated growth. These findings highlighted the urgent need for place-based adaptation and health protection strategies, aligned with climate mitigation, demographic transition, and sustainable urban planning. Full article

Amid China’s dual-carbon goals and widening regional disparities, land-use/cover change (LUCC)-induced volatility in carbon storage (CS) has emerged as a binding constraint on emission reduction and the low-carbon transition in resource-based regions. Yet integrated historical-scenario assessments and rigorous evidence on spatial-heterogeneity mechanisms remain limited, which hampers targeted spatial governance. Using Shanxi Province, a resource-based province, as the study area, this study develops a coupled PLUS-InVEST framework under SSP-RCP scenarios. It integrates spatial autocorrelation, geographically weighted regression (GWR), and structural equation modeling (SEM) to characterize spatiotemporal responses of CS to LUCC and to identify underlying drivers. The results indicate that: (1) Regional CS follows an inverted U-shaped trajectory, initially increasing due to ecological restoration projects and subsequently declining owing to industrial development and urban expansion; (2) By 2030, forestland expansion under SSP126 is projected to enhance CS, whereas accelerated urbanization under SSP585 is expected to intensify CS losses; (3) Significant spatial clustering of CS remains consistent from historical periods to future projections, underscoring its sensitivity to topography, vegetation patterns, and human activities; and (4) CS is jointly shaped by natural and anthropogenic drivers, with DEM and slope providing stable protection, while population density and transport-network configuration cause ongoing disturbances. The study provides an integrated historical-scenario assessment and reveals the underlying mechanisms for resource-based regions, offering quantitative evidence to support optimization of the Ecological Conservation Redline, managing urban growth boundaries, and implementing zoned ecological restoration. Full article

(1) Background: Housing instability, a key social determinant of health, disproportionately affects people who use drugs (PWUD), increasing their risk for adverse outcomes. This study explores the relationship between housing status and drug-related health outcomes among PWUD in an urban setting in the Southeastern United States (US) and determines the cutoff point for the Housing Instability Scale (HIS). (2) Methods: We conducted a cross-sectional survey from July to November 2024 among adult PWUD. Recruitment was through syringe services programs (SSPs), nightlife venues, and community outreach. HIS was used to assess housing status, while cluster analysis and Gaussian Mixture Modeling (GMM) were used to suggest a potential cutoff. Logistic regression models were employed to examine associations between housing instability and health outcomes. (3) Results: Among 164 participants (mean age = 41.2; 79.9% Black/African American), the average HIS score was 3.23. Cluster analysis suggested a singular cutoff, while GMM suggested four levels of housing instability. Multivariate logistic regression revealed that housing instability was significantly associated with infections (AOR = 1.55, p = 0.064), blackouts (AOR = 1.47, p = 0.0457), and seizures (AOR = 1.28, p = 0.0667). Overdose and wounds showed no significant association. SSP use, opioid use, and Xanax use were also identified as potential predictors. Full article

To address the problems of low efficiency and poor real-time performance in underwater acoustic modeling, as well as the requirement of maximizing search coverage for underwater target search path planning, this paper proposed an efficient path planning method based on Sound Speed Profile (SSP) clustering. Firstly, the SSPs were dimensionally reduced via Empirical Orthogonal Function (EOF) decomposition, and the sea area was divided into 10 acoustic sub-areas using K-means clustering after fusing geographic coordinates and terrain information, thereby constructing a block-wise sound field model. Secondly, with the active sonar equation as the core, sonar parameters such as the noise level and target strength were solved, respectively, to generate a spatial distribution matrix of search distances. Finally, an Improved Ant Colony Optimization (IACO) algorithm was modified by dynamically setting the pheromone evaporation rate and improving the heuristic information for search path optimization. Numerical experiments showed that clustering significantly improves the efficiency of sound field modeling, reducing the time consumption of the transmission loss calculation from 24.74 h to 10.84 min. The IACO increased the average search coverage from 47.96% to 86.01%, with an improvement of 79.34%. The performance of IACO is superior to those of the compared algorithms, providing support for efficient underwater target search. Full article

Forest fire regimes are undergoing systematic reorganization under climate change, particularly in monsoon–human coupled ecosystems such as Southeastern China, where risk dynamics remain poorly quantified. This study proposes a meteorology-driven machine learning model designed to assess long-term forest fire risk. Using kernel density estimation and standard deviational ellipse analysis, we assessed the spatiotemporal patterns of fire risk during the observational period and their future shifts across the SSP1-2.6 and SSP5-8.5 scenarios. The results indicate a significant overall decline in fire frequency from 2008 to 2024 (−467.3 fires/year, representing an annual average reduction of 10.8%, p < 0.001), which is attributed primarily to enhanced regional fire prevention and control measures, yet with a notable reversal after 2016 in Guangdong and Fujian. Fires are highly seasonal, with 74% occurring in the dry season (December–March). The meteorologically driven random forest model exhibited excellent performance (R² = 0.889), validating meteorological conditions as key drivers of regional fire dynamics. It is projected that intensified warming (+5.5 °C under SSP5-8.5) and increased precipitation variability (+23%) are likely to drive pronounced northward and inland migration in high-risk zones. Our projections indicate that by the end of the century, high-risk area coverage could expand to 19.2%, with a shift from diffuse to clustered patterns, particularly in Jiangsu and Zhejiang. These findings underscore the critical role of hydrothermal reconfiguration in reshaping fire risk geography and highlight the need for dynamic, region-specific fire management strategies in response to compound climate risks. Full article

Cleptoparasitic bees of the subgenus Stelis (Stelidomorpha) occur mainly in the Mediterranean and Middle East. In this study, we elevate Stelis aegyptiaca ssp. canaria Warncke, 1992 to species rank (S. canaria Warncke, 1992) and describe two new species, Stelis alainensis Kasparek sp. nov. and Stelis surica Kasparek sp. nov., both discovered in Oman and the United Arab Emirates. Morphological differences between these species and their closest relatives (S. aegyptiaca Radoszkowski, 1876, S. pentelica Mavromoustakis, 1963, and S. nasuta (Latreille, 1809)) are corroborated by genetic divergence in the mitochondrial COI barcode region, with Kimura 2-parameter (K2P) distances of 7.6–15.2%. A notable case is Stelis nasuta, which shows deep genetic subdivision into three clusters: (1) Iberian Peninsula and North Africa, (2) southeastern France, Italy, and the Balkans, (3) eastern Balkans, Turkey, and the Levant. Moderate genetic K2P distances of 2.9–3.3% complicated species delimitation. Analyses with ABGD, ASAP, bPTP, and RESL algorithms consistently supported recognition of these lineages as putative species. As multivariate analyses of 11 morphometric traits revealed no consistent diagnostic differences, we treat these lineages as phylospecies rather than formal taxa. Our findings demonstrate that bee diversity in the Palaearctic remains underestimated, and that expanded sampling and integrative approaches continue to reveal hidden lineages. Full article

Rapid climate change has exacerbated global ecosystem degradation, leading to habitat fragmentation and landscape connectivity loss. Constructing ecological networks (EN) with resilient conduction functions and conservation priorities is crucial for maintaining regional ecological security and promoting sustainable development. However, the spatiotemporal modeling and dynamic resilience assessment of EN under the combined impacts of future climate and land use/land cover (LULC) changes remain underexplored. This study focuses on the Central Yunnan Urban Agglomeration (CYUA), China, and integrates landscape ecology with complex network theory to develop a dynamic resilience assessment framework that incorporates multi-scenario LULC projections, multi-temporal EN construction, and node-link disturbance simulations. Under the Shared Socioeconomic Pathways and Representative Concentration Pathways (SSP-RCP) scenarios, we quantified spatiotemporal variations in EN resilience and identified resilience-based conservation priority areas. The results show that: (1) Future EN patterns exhibit a westward clustering trend, with expanding habitat areas and enhanced connectivity. (2) From 2000 to 2040, EN resilience remains generally stable, but diverges significantly across scenarios—showing steady increases under SSP1-2.6 and SSP5-8.5, while slightly declining under SSP2-4.5. (3) Approximately 20% of nodes and 40% of links are identified as critical components for maintaining structural-functional resilience, and are projected to form conservation priority patterns characterized by larger habitat areas and more compact connectivity under future scenarios. The multi-scenario analysis provides differentiated strategies for EN planning and ecological conservation. This framework offers adaptive and resilient solutions for regional ecosystem management under climate change. Full article

This study presents a comprehensive spatiotemporal assessment of climatic and bioclimatic conditions across Türkiye for both a historical reference period (1995–2014) and future projections (2020–2099) under two Shared Socioeconomic Pathways (SSP2-4.5 and SSP3-7.0) scenarios using the regional climate model (RCM) COSMO-CLM to downscale large-scale signals to a regional scale at high resolution (0.11). A comparison of the model with ERA5-Land reanalysis data revealed annual biases of +1.41 °C (warm) and −0.28 mm/day (dry), emphasizing the importance of bias correction in regional climate assessments. Bias-corrected future projections indicate a marked warming trend and significant decline in precipitation, especially after the 2060s, with pronounced spatial variability across regions. The most intense warming period of the century is the 2060–2079 period, with an anticipated increase of 0.109 °C/year under the SSP3-7.0 scenario, while, under the SSP2-4.5, it is the 2040–2059 period with an increase of 0.068 °C/year. Bioclimatic variables further illustrate shifts in temperature extremes, seasonal variability, and precipitation patterns. Coastal regions are expected to experience a delay in the onset of wet seasons of 1–2 months, while high-altitude zones show earlier shifts of up to 4 months. Four distinct clusters were identified by using k-means clustering method, each with unique temporal and spatial evolution under both SSP scenarios. Clusters 1 and 2, which predominantly represent continental and interior regions, exhibit a strong association with earlier precipitation onset. Notably, arid and semi-arid conditions expand northward, replacing temperate zones in Central Anatolia. Overall, findings suggest that Türkiye is undergoing a substantial climatic transition toward hotter and drier conditions, regardless of the emission scenario. This study has critical implications for ecological resilience, agricultural sustainability, and water resource management, and offers valuable information for targeted climate adaptation strategies and land-use planning in vulnerable regions of Türkiye. Full article

Climate change has caused considerable uncertainty to oilseed rape production. However, the climatic suitability for oilseed rape cultivation and its future changing trend remain unclear, specifically in Jiangsu Province—a major oilseed rape producing-region in China. Based on the past 50 years (1969–2018) of daily meteorological data from 13 meteorological stations in the province, this study established a climate suitability assessment model for oilseed rape cultivation. Temperature, precipitation, and sunlight were comprehensively analyzed, with suitable zones delineated through GIS spatial analysis and the natural break method. With the incorporation of SSP2-4.5 climatic scenario simulation data, the study projected the evolving trends of oilseed rape cultivation climatic suitability zones from 2024 to 2050 in the province. The findings reveal that over the past five decades, the climatic suitability for oilseed rape planting in the province has demonstrated the following patterns: temperature suitability increased by 0.02 per decade, precipitation suitability declined by −0.01 per decade, sunlight suitability decreased by −0.01 per decade, and comprehensive suitability rose by 0.01 per decade. High climatic suitability with the index of 0.80–1.00 was predominantly clustered in the central region, while moderate suitability zones with the index of 0.50–0.80 were mainly found in its northern and southern regions. Unsuitable zones with the index of 0.00–0.50 were mainly confined to the northern and southern extremities of the province. Under future climate scenarios, oilseed rape planting suitability is projected to improve significantly, with highly suitable zones expanding, particularly into the central and parts of the northern Jiangsu. Moderately suitable zones also will be extended, including potential areas such as the parts of Lianyungang and Wuxi. Unsuitable zones will be reduced, with only limited areas like southern Wuxi retaining lower suitability. Future temperature increases in Lianyungang are expected to be in favor of oilseed rape production. However, excessive precipitation in the southern region will require enhanced drainage measures. Improved temperature and precipitation conditions in Xuzhou are anticipated to boost the climatic suitability. Overall, oilseed rape planting climatic factors in the central and northern regions are projected to improve, enabling production expansion, while the southern region will face the challenge of excessive precipitation in Jiangsu Province. Full article

Background/Objectives: Food selectivity is a prevalent and challenging issue in childhood, particularly in children with autism spectrum disorder (ASD), which may result in restricted dietary patterns and nutrient deficiencies. This study aimed to identify high-risk subgroups of children by combining food selectivity, diet, BMI, gastrointestinal symptoms, sensory processing, and parental feeding practices in children with ASD and in typically developing children (TDC). Methods: To achieve this aim, we ran a cross-sectional, survey-based study, including 408 children (aged 3 to 12.11 years), with gender-matched groups. Both parents completed a survey on children’s diet, anthropometric curves, gastrointestinal symptoms, and the Brief Autism Mealtime Behavior Inventory (BAMBI), Short Sensory Profile (SSP), and Caregiver’s Feeding Style Questionnaire (CFSQ). Data analysis included comparative tests, correlations, and k-means cluster analysis. Results: Children with ASD exhibited significantly greater sensory processing difficulties, higher food refusal, limited food variety in the diet, and autism-related mealtime characteristics compared with TDC across all age groups. Caregivers of children with ASD reported higher controlling and contingency management feeding practices compared to the parents of the TDC. We found a strong correlation between sensory sensitivities and feeding issues. Notably, Body Mass Index (BMI) was not significantly associated with dietary restriction or gastrointestinal symptoms. Cluster analysis revealed a high-risk sub-phenotype in both groups of children with some differences, characterized by high food selectivity, taste, tactile, and smell sensitivity, gastrointestinal symptoms, and overactive parental practices. Conclusions: The early identification of this subgroup might foster more tailored, multidisciplinary, and effective assessment and clinical intervention. Full article

Aiming at the problem of accuracy degradation caused by the mixing of spatiotemporal disturbance patterns in sound speed profile (SSP) inversion using the traditional geographic grid division method, this study proposes an acoustic stability pre-clustering framework that integrates principal component analysis and machine learning clustering. Disturbance mode principal component analysis is first used to extract characteristic parameters, and then a machine learning clustering algorithm is adopted to pre-classify SSP samples according to acoustic stability. The SSP inversion experimental results show that: (1) the SSP samples of the South China Sea can be divided into three clusters of disturbance modes with statistically significant differences. (2) The regression inversion method based on cluster attribution reduces the average error of SSP inversion for data from 2018 to 1.24 m/s, which is more than 50% lower than what can be achieved with the traditional method without pre-clustering. (3) Transmission loss prediction verification shows that the proposed method can produce highly accurate sound field calculations in environmental assessment tasks. The acoustic stability pre-clustering technology proposed in this study provides an innovative solution for the statistical modeling of marine environment parameters by effectively decoupling the mixed effect of SSP spatiotemporal disturbance patterns. Its error control level (<1.5 m/s) is 37% higher than that of the single empirical orthogonal function regression method, showing important potential in underwater acoustic applications in complex marine dynamic environments. Full article

Background/Objectives: Chinese cabbage (Brassica rapa ssp. Pekinensis, AA) growth and development is highly sensitive to cold temperatures. Prolonged low-temperature exposure during early growth stages can induce premature bolting, which reduces market quality and yield. Methods: Here, using comparative leaf RNA-seq transcriptome analysis of plants grown at 6, 9, 12, and 15 °C, we explored key genes and metabolic pathways regulating Chinese cabbage cold response. Results: RNA-seq transcriptome analysis identified a total of 1832 differentially expressed genes (DEGs) in the three comparison groups, with 5452, 1861, and 752 DEGs specifically expressed in the A6_vs_A15, A9_vs_A15, and A12_vs_A15 groups, respectively. KEGG enrichment analysis of DEGs showed that sulfur metabolism, secondary metabolites biosynthesis and photosynthesis pathways were mostly affected by cold stress. K-means clustering revealed distinct expression profiles among the DEGs enriched in cold stress response-associated clusters. Subsequently, DEGs were divided into 18 modules by WGCNA, whereupon co-expression genes that clustered into similar modules exhibited diverse expression and were annotated to various GO terms at different temperatures. Module-trait association analysis revealed M1, M2, M3, and M6 modules as key clusters potentially linked to vernalization-related processes. These modules harbored candidate hub genes encoding transcription factors (including MYB, bZIP, and WRKY), protein kinases, and cold-stress-responsive genes. Additionally, phenotypic analysis showed that 12 °C to 15 °C supported optimal growth, whereas <9 °C temperature inhibited growth. Physiological measurements showed increased antioxidant enzyme activity and proline accumulation at 6 °C. Conclusions: Overall, our study provides a set of candidate cold-stress-responsive genes and co-expression modules that may support cold stress tolerance breeding in Chinese cabbage. Full article