Search Results (135)

Land cover, topography, precipitation, and socio-economic factors exert both direct and indirect influences on urban land surface temperatures. Within the broader context of global climate change, these influences are magnified by the escalating intensity of the urban heat island effect. However, the interplay and underlying mechanisms of natural and socio-economic determinants of land surface temperatures remain inadequately explored, particularly in the context of cold-region cities located in the northern temperate zone of China. This study focuses on Changchun City, employing multispectral remote sensing imagery to derive and spatially map the distribution of land surface temperatures and topographic attributes. Through comprehensive analysis, the research identifies the principal drivers of temperature variations and delineates their seasonal dynamics. The findings indicate that population density, night-time light intensity, land use, GDP (Gross Domestic Product), relief, and elevation exhibit positive correlations with land surface temperature, whereas slope demonstrates a negative correlation. Among natural factors, the correlations of slope, relief, and elevation with land surface temperature are comparatively weak, with determination coefficients (R²) consistently below 0.15. In contrast, socio-economic factors exert a more pronounced influence, ranked as follows: population density (R² = 0.4316) > GDP (R² = 0.2493) > night-time light intensity (R² = 0.1626). The overall hierarchy of the impact of individual factors on the temperature model, from strongest to weakest, is as follows: population, night-time light intensity, land use, GDP, slope, relief, and elevation. In examining Changchun and analogous cold-region cities within the northern temperate zone, the research underscores that socio-economic factors substantially outweigh natural determinants in shaping urban land surface temperatures. Notably, human activities catalyzed by population growth emerge as the most influential factor, profoundly reshaping the urban thermal landscape. These activities not only directly escalate anthropogenic heat emissions, but also alter land cover compositions, thereby undermining natural cooling mechanisms and exacerbating the urban heat island phenomenon. Full article

Artificial reservoirs in arid regions provide unique ecological environments for studying the spatial and functional dynamics of plankton communities under the combined stressors of climate change and anthropogenic activities. This study conducted a systematic investigation of the phytoplankton community structure and its environmental drivers in 17 artificial reservoirs in the Ili region of Xinjiang in August and October 2024. The Ili region is located in the temperate continental arid zone of northwestern China. A total of 209 phytoplankton species were identified, with Bacillariophyta, Chlorophyta, and Cyanobacteria comprising over 92% of the community, indicating an oligarchic dominance pattern. The decoupling between numerical dominance (diatoms) and biomass dominance (cyanobacteria) revealed functional differentiation and ecological complementarity among major taxa. Through multivariate analyses, including Mantel tests, principal component analysis (PCA), and redundancy analysis (RDA), we found that phytoplankton community structures at different ecological levels responded distinctly to environmental gradients. Oxidation-reduction potential (ORP), dissolved oxygen (DO), and mineralization parameters (EC, TDS) were key drivers of morphological operational taxonomic unit (MOTU). In contrast, dominant species (SP) were more responsive to salinity and pH. A seasonal analysis demonstrated significant shifts in correlation structures between summer and autumn, reflecting the regulatory influence of the climate on redox conditions and nutrient solubility. Machine learning using the random forest model effectively identified core taxa (e.g., MOTU1 and SP1) with strong discriminatory power, confirming their potential as bioindicators for water quality assessments and the early warning of ecological shifts. These core taxa exhibited wide spatial distribution and stable dominance, while localized dominant species showed high sensitivity to site-specific environmental conditions. Our findings underscore the need to integrate taxonomic resolution with functional and spatial analyses to reveal ecological response mechanisms in arid-zone reservoirs. This study provides a scientific foundation for environmental monitoring, water resource management, and resilience assessments in climate-sensitive freshwater ecosystems. Full article

Dutch elm disease is one of the most devastating plant diseases, primarily spread through bark beetles. Scolytus scolytus is a key vector of this disease. In this study, distribution data of S. scolytus were collected and filtered. Combined with environmental and climatic variables, an ensemble model was developed using the Biomod2 platform to predict its potential geographical distribution in China. The selection of climate variables was critical for accurate prediction. Eight bioclimatic factors with high importance were selected from 19 candidate variables. Among these, the three most important factors are the minimum temperature of the coldest month (bio6), precipitation seasonality (bio15), and precipitation in the driest quarter (bio17). Under current climate conditions, suitable habitats for S. scolytus are mainly located in the temperate regions between 30° and 60° N latitude. These include parts of Europe, East Asia, eastern and northwestern North America, and southern and northeastern South America. In China, the low-suitability area was estimated at 37,883.39 km², and the medium-suitability area at 251.14 km². No high-suitability regions were identified. However, low-suitability zones were widespread across multiple provinces. Under future climate scenarios, low-suitability areas are still projected across China. Medium-suitability areas are expected to increase under SSP370 and SSP585, particularly along the eastern coastal regions, peaking between 2041 and 2060. High-suitability zones may also emerge under these two scenarios, again concentrated in coastal areas. These findings provide a theoretical basis for entry quarantine measures and early warning systems aimed at controlling the spread of S. scolytus in China. Full article

Climatic oscillations in the Quaternary are altering the performance of angiosperms, while the species’ distribution is regarded as a macroscopic view of these spatial and temporal changes. Oaks (Quercus L.) are important tree models for estimating the abiotic impacts on the distribution of forest tree species. In this study, we modeled the past, present, and future suitable habitat for two varieties of deciduous oaks (Quercus serrata and Quercus serrata var. brevipetiolata), which are widely distributed in China and play dominant roles in the local forest ecosystem. We evaluated the importance of environmental factors in shaping the species’ distribution and identified the “wealthy” habitats in harsh conditions for the two varieties. The ecological niche models showed that the suitable areas for these two varieties are mainly concentrated in mountain ranges in central China, while Q. serrata var. brevipetiolata is also widely distributed in the middle-east mountain range. The mean temperature of the coldest quarter was identified as the critical factor in shaping the habitat availability for these two varieties. From the last glacial maximum (LGM) to the present, the potential distribution range of these two sibling species has obviously shifted northward and expanded from the inferred refugia. Under the optimistic (RCP2.6), moderate (RCP 4.5)-, and higher (RCP 6.0)-concentration greenhouse gas emissions scenarios, our simulations suggested that the total area of suitable habitats in the 2050s and 2070s will be wider than it is now for these two varieties of deciduous oaks, as the distribution range is shifting to higher latitudes; thus, low latitudes are more likely to face the risk of habitat losses. This study provides a case study on the response of forest tree species to climate changes in the north temperate and subtropical zones of East Asia and offers a basis for tree species’ protection and management in China. Full article

Urban thermal environment challenges in China have made outdoor thermal comfort a key factor in evaluating spatial quality and livability. Building layout not only affects internal performance but also shapes the microclimate of surrounding outdoor spaces. The climatic characteristics of temperate monsoon climate regions significantly impact residents’ outdoor activities. Most existing studies focus solely on either the external thermal environment or the buildings themselves in isolation. This study focuses on Beijing, a representative city in the temperate monsoon climate zone, and explores block-scale spatial optimization using computational typology. The objective is to balance architectural performance with outdoor thermal comfort in both winter and summer. Optimization targets include the Universal Thermal Climate Index (UTCI), winter sunshine duration, and summer solar radiation. Results show winter UTCI can be optimized to −6.13 °C to −1.18 °C and summer UTCI to 28.19 °C to 29.17 °C, with greater optimization potential in winter (23.5% higher). Synergistic relationships are observed between winter comfort and sunshine duration (coefficient: 0.777) and between summer comfort and solar radiation (coefficient: 0.947). However, trade-offs exist between seasonal comfort indicators, with strong conflicts between winter and summer objectives. Two distinct form types—“low-south-high-north enclosed” for winter and “high-rise point-type low-density” for summer—are identified as effective for seasonal adaptation. The study proposes an integrated method combining data-driven generation, multi-objective optimization, and clustering-based decision-making. This approach moves beyond traditional empirical design, offering a quantitative and adaptable strategy for climate-responsive urban block planning and supporting low-carbon urban transformation. Full article

The Northeastern China Gravity Monitoring Network (NCGMN; 40–50°N), a pioneering time-variable gravity monitoring system in high-latitude cold-temperate environments, serves as a critical infrastructure for geodynamic investigations of the Songliao Basin, Changbai Mountain volcanic zone, and northern Tan-Lu Fault Zone. To address the data reliability challenges posed by nonlinear dynamic drifts in spring-type relative gravimeters during mobile surveys, this study quantifies—for the first time—the non-smooth normal distribution characteristics of such drifts using the inaugural 2015 dataset from two CG-5 instruments. Results demonstrate a 7–15% reduction in mean dynamic drift rates compared to static conditions, with spatiotemporal variability governed by multi-physics field coupling (terrain undulation, thermal fluctuation, and barometric perturbation). A comprehensive correction framework—integrating a gravimetric line drift rate computation, multi-model validation, and absolute datum cross-validation—reveals gravity value discrepancies up to ±10 μGal across models. The innovative hybrid scheme combines local drift preprocessing (initial-point modeling, line fitting, variance-sum optimization) with global adjustment optimization, achieving the significant suppression of nonlinear drift errors. The variance-sum optimal and Bayesian adjustment hybrid synergizes local variance minimization and global temporal correlation priors, delivering the following: (1) 34% and 29% reductions in segment self-difference standard deviations versus classical and Bayesian adjustments; (2) 24% and 14% decreases in segment residual standard deviations; (3) 12% and 6% improvements in absolute datum cross-validation precision. This study establishes a foundation for the reliable extraction of μGal-level gravity signals, advancing high-precision gravity monitoring of seismicity, volcanic unrest, and fault zone deformation in complex terrains. By harmonizing local-scale accuracy with network-wide consistency, the framework sets a new benchmark for time-variable gravity studies in challenging environments. Full article

The cold temperate forest ecosystem is a crucial ecological zone in China, significantly impacted by human activities. To understand the impact of restoration on soil microbial communities following disturbance, this study employed high-throughput sequencing technology to systematically examine the assembly patterns and processes of soil microbial communities under two restoration modes (nature restoration (NR) and artificial restoration (AR)) in this forest ecosystem. The results indicated that the concentrations of total nitrogen (TN), alkaline hydrolysable nitrogen (AN), dissolved organic carbon (DOC) and soil organic carbon (SOC) were significantly higher in soils under natural restoration compared to artificial restoration. The α-diversity of soil bacteria remained unchanged, while soil fungal α-diversity changed significantly across different restoration modes. Furthermore, different restoration modes significantly alter the β-diversity of soil microbial (bacterial and fungal) communities. The relative abundance of soil microbial (bacterial and fungal) changed significantly across different forest restoration strategies, i.e., the relative abundance of Pajaroellobacter increased in natural restoration compared to that in natural forest; similarly, both Podila and Russula showed higher relative abundances in natural restoration than those in natural forest. Furthermore, analysis of variance for differences between groups shows that Incoybe plays a crucial role in artificial restoration. Community assembly analyses indicated that that soil microbial (bacterial and fungal) communities were primarily driven by deterministic processes in both restoration models. In short, our study improves our comprehension of how soil microbial communities respond to different restoration methods in temperate forest ecosystems, providing valuable insights for their sustainable management. Full article

Phytolith carbon sequestration has been recognized as an important mechanism for long-term carbon sequestration in forest ecosystems. Conducting relevant research in cold temperate regions that are sensitive to climate change can reveal their unique mechanisms as a stable and long-term carbon pool, fill key blind spots in global carbon cycling models, and provide necessary scientific support for developing climate-resilient ecological strategies and carbon neutrality pathways. In this study, we focused on the Larix gmelinii forest ecosystem and investigated the latitudinal spatial characteristics of soil phytolith and phytolith-occluded carbon (phytOC) in Eastern China. We analyzed the factors that influenced their accumulation and assessed their storage potential across different climatic zones. Our findings revealed an exponential increase in soil phytolith content with increasing latitude in Eastern China. Additionally, the content of soil phytoliths in tropical and subtropical forests was significantly lower than in the cold temperate forests. It was also found that soil phytOC content increased linearly with latitude and was significantly higher in cold temperate zones than in the other climatic zones. The order of soil phytOC storage was tropical (0.23 t ha⁻¹) < middle temperate (0.24 t ha⁻¹) < subtropical (0.27 t ha⁻¹) < cold temperate (1.20 t ha⁻¹). Soil phytolith and phytOC content were significantly negatively correlated with temperature and precipitation. pH, organic matter, and nutrients of soil significantly influenced the formation and accumulation of soil phytoliths. It can provide a scientific basis for the quantitative evaluation of forest soil carbon pool. Full article

Larch shoot blight, caused by the fungus Neofusicoccum laricinum, threatens larch (Larix spp.) forests across northeastern China, jeopardizing both timber productivity and ecological stability. This study aimed to investigate the genomic diversity, population structure, and potential adaptive mechanisms of N. laricinum across contrasting climatic regions. To achieve this, we conducted whole-genome resequencing of 23 N. laricinum isolates collected from three major provinces—Heilongjiang, Inner Mongolia, and Jilin—that represent distinct climatic zones ranging from cold-temperate to relatively warmer regions. We identified ~219.1 K genetic variants, offering a detailed portrait of the pathogen’s genomic diversity. Population structure analyses, including principal component analysis and phylogenetic tree, revealed clear genetic differentiation aligning with geographic origin and climate. Functional annotation (GO and KEGG) highlighted enrichment in metabolic, stress-response, and membrane transport pathways, suggesting potential adaptation to varied temperature regimes and environmental pressures. Moreover, region-specific variants—particularly missense and stop-gain mutations—were linked to genes involved in ATP binding, oxidoreductase activity, and cell division, underscoring the fungus’s capacity for rapid adaptation. Collectively, these findings fill a critical gap in the population genetics of N. laricinum and lay a foundation for future disease management strategies to larch shoot blight under changing climatic conditions. Full article

Background: The positive effects of urban green space (UGS) on public health and well-being have been confirmed. However, most previous studies on the health benefits of UGS have focused on the influencing factors, mechanisms, and different groups of people, with little attention paid to regional heterogeneity. Methods: Using provincial-level panel data from China (2007–2020), this study measures residents’ comprehensive health levels (CHLs) through factor analysis encompassing physiological, mental, and social dimensions. Fixed-effects models and panel quantile regressions are employed to analyze UGS–health associations across climatic zones and health status quantiles. Results: The CHL of residents in China has improved as a whole, but with some provinces showing a declining or unpredictable trend. The results of the effects of UGS on the health status of urban residents were inconsistent. Overall, the amount of UGS is positively related to the CHL of the inhabitants (Coef. = 0.113; p < 0.01). In addition, the health-promoting effect of UGS is significantly stronger in provinces with a higher health level than in provinces with a lower health level, and no positive effect was observed in the provinces with the lowest health level. Increasing the amount of UGS can effectively improve the CHL of residents in the mid-temperate (Coef. = 0.189; p < 0.05) and warm temperate (Coef. = 0.135; p < 0.05) regions, but no health-promoting effect was found in the subtropical regions. Conclusions: This study expands our scientific understanding of the effects of UGS on the comprehensive health status of urban residents. Full article

The ladybird beetle, Propylea japonica Thunberg (Coleoptera: Coccinellidae), is a widely distributed natural predator that is crucial in controlling various agricultural pests in China. Despite frequent references to its remarkable thermotolerance, the molecular mechanisms underlying its thermotolerance remain poorly understood. Here, we investigated metabolomic changes in P. japonica following exposure to acute heat stress (AHS) lasting 1 h at 39 °C and 43 °C in populations from Zhengzhou (ZZ, warm temperate climate zone) and Shenzhen (SZ, subtropical climate zone), representing distinct northern and southern Chinese ecosystems. A total of 4165 and 4151 metabolites were detected in positive and negative ion modes, respectively. The high proportion of lipid and lipid-like metabolites (35.5%) and the top 20 pathways containing the highest number of metabolites, implying membrane fluidity modulation and energy metabolism restructuring, served as the core adaptive mechanism in P. japonica populations confronting thermal stress. The SZ25 vs. SZ39 exhibited a significantly higher number of differentially expressed metabolites (DEMs), which were predominantly enriched in the purine and tryptophan metabolism pathways. This indicated that these pathways orchestrate thermal adaptation in the SZ population by coordinating energy metabolism reprogramming, orchestrating antioxidant defense mechanisms, and modulating neuroendocrine homeostasis dysregulation. Additionally, the starch and sucrose, arachidonic acid, and fructose and mannose metabolism pathways were also implicated. This study enhances our understanding of P. japonica thermotolerance and provides a valuable reference for thermotolerance mechanisms in other insects. Full article

High soil sulfate levels can inhibit crop growth and accelerate concrete infrastructure degradation, highlighting the critical importance of rapid and accurate sulfate content determination. Nevertheless, conventional analytical techniques are laborious and intricate, and delays in processing may result in alterations to the material, owing to oxidation. We recognized the accuracy, reproducibility, and non-invasiveness of mid-infrared (MIR) spectroscopy as a rapid and straightforward technique for soil analysis. In this study, soil samples were collected from two depths (0–20 cm and 20–40 cm) across three regions in China: the arid northwestern region, the cold-temperate northeastern zone, and the subtropical southwestern region. One group was mixed with Na₂SO₄ (a readily soluble salt) at mass fractions ranging from 0.1% to 7%, while the other group was mixed with FeS₂ (a sulfide) at mass fractions ranging from 1% to 70%. This study aimed to develop a mid-infrared spectroscopy-based method for analyzing soluble sulfate and sulfide in soil. Three chemometric methods were evaluated: partial least squares regression (PLSR), principal component regression (PCR), and multivariate linear regression (MLR). Results showed that the MLR model provided superior predictive performance. For the 20–40 cm sodium sulfate-mixed soil from the arid northwestern region, the MLR model exhibited the best performance with an Rp² of 0.9535, an RMSEP of 0.0030, an RPD of 4.96, and an RPIQ of 6.26. For the 20–40 cm iron disulfide-mixed soil from the cold-temperate northeastern region, the MLR model demonstrated superior results with Rp², RMSEP, RPD, and RPIQ values of 0.9590, 0.042, 5.97, and 10.94, respectively. For the 0–20 cm iron disulfide-mixed soil from the subtropical southwestern region, the MLR model achieved the best performance with an Rp² of 0.9848, an RMSEP of 0.0025, an RPD of 14.20, and an RPIQ of 25.48. Despite regional variations in soil properties, this study successfully predicted sulfate and sulfide contents in soils from diverse areas using mid-infrared spectroscopy combined with appropriate chemometric methods. This approach provides reliable technical support for soil sulfate detection and offers significant practical value for soil assessment in both agricultural production and engineering construction. Full article

Studying spatial distribution patterns and intraspecific and interspecific associations of tree species is crucial for understanding the maintenance of biodiversity and offering insights into community dynamics and stability. The Shennongjia National Park, located in the transition zone between the (sub)tropics and the temperate climate, holds great significance for understanding how species interact with each other and coexist within forest communities. We used data from a fully mapped 25 ha montane deciduous broad-leaved forest dynamic plot at Shennongjia (SNJ) National Park, central China, to conduct a community-level evaluation of spatial distribution patterns and intraspecific and interspecific associations. We analyzed the spatial distribution patterns of 20 dominant species with univariate and bivariate g(r) functions, as well as intraspecific and interspecific associations across different life-history stages. We assessed the relative contributions of underlying processes in community assembly with three models: complete spatial randomness (CSR), heterogeneous Poisson (HP), and antecedent condition (AC). The results showed that all 20 tree species exhibited aggregated distribution patterns within a 100 m scale. After excluding the influence of environmental heterogeneity, the degree of aggregation decreased, and with the increasing spatial scale from 0 to 100 m, the distribution gradually shifted from aggregated to random or uniform appearance. Positive associations were common in different life-history stages. Negative associations were common across different species, while most of the intraspecific and interspecific associations turned out to be irrelevant when environmental heterogeneity was excluded. We concluded that habitat heterogeneity and dispersal limitation may primarily determine the spatial distribution of species in subtropical montane deciduous broad-leaved forests. This indicates that species distribution may align with environmental patterns, and interspecific correlations may exist. However, the exact responses of these species to environmental changes remain uncertain. Upcoming management approaches ought to concentrate on ongoing observation, which is crucial for mitigating how climate change might affect species distribution and community interactions, thus guaranteeing enduring stability and the conservation of biodiversity. Full article

Air pollution poses a pressing global challenge, particularly in rapidly industrializing nations like China where deteriorating air quality critically endangers public health and sustainable development. To address the heterogeneous patterns of air pollution across diverse geographical and climatic regions, this study proposes a novel CNN-LSTM-KAN hybrid deep learning framework for high-precision Air Quality Index (AQI) time-series prediction. Through systematic analysis of multi-city AQI datasets encompassing five representative Chinese metropolises—strategically selected to cover diverse climate zones (subtropical to temperate), geographical gradients (coastal to inland), and topographical variations (plains to mountains)—we established three principal methodological advancements. First, Shapiro–Wilk normality testing (p < 0.05) revealed non-Gaussian distribution characteristics in the observational data, providing statistical justification for implementing Gaussian filtering-based noise suppression. Second, our multi-regional validation framework extended beyond conventional single-city approaches, demonstrating model generalizability across distinct environmental contexts. Third, we innovatively integrated Kolmogorov–Arnold Networks (KANs) with attention mechanisms to replace traditional fully connected layers, achieving enhanced feature weighting capacity. Comparative experiments demonstrated superior performance with a 23.6–59.6% reduction in Root-Mean-Square Error (RMSE) relative to baseline LSTM models, along with consistent outperformance over CNN-LSTM hybrids. Cross-regional correlation analyses identified PM2.5/PM10 as dominant predictive factors. The developed model exhibited robust generalization capabilities across geographical divisions (R² = 0.92–0.99), establishing a reliable decision-support platform for regionally adaptive air quality early-warning systems. This methodological framework provides valuable insights for addressing spatial heterogeneity in environmental modeling applications. Full article

The family Rissoinidae represents a significant component of microgastropod diversity, with a global distribution spanning temperate to tropical zones and encompassing over 300 recorded species. Hainan Island, the largest island in the South China Sea, harbors a rich diversity of mollusks, but the family Rissoinidae remains poorly studied in this region. Here, we report three rissoinid species and one newly recorded species from Hainan Island, providing detailed taxonomic descriptions supported by SEM imaging. For the first time, we provide the mitochondrial genomes of Rissoina cardinalis and Phosinella seguenziana, analyzing their genome structure and nucleotide composition, thereby addressing the existing knowledge gap in Rissoinidae research. A phylogenetic tree of the family Rissoinidae was reconstructed using the COI gene, clarifying the intergeneric relationships within the family. Notably, the genus Rissoina is revealed as a non-monophyletic group, likely due to the limitations of single-gene analyses in providing adequate phylogenetic information. Full article