Search Results (108)

The Chongming Dongtan wetland, a representative coastal wetland in East Asia, faces a significant ecological threat from the invasive species Spartina alterniflora. The mixed ecotone formed between this invasive species and the native Phragmites australis serves as a highly sensitive and critical indicator of alterations in wetland ecosystem structure and function. Using spring and autumn Sentinel-2 imagery from 2016 to 2023, this study developed an integrated method that combines a three-dimensional feature space with multi-threshold Otsu segmentation to accurately extract the mixed S. alterniflora–P. australis ecotone. The spatiotemporal dynamics of the mixed ecotone were analyzed at multiple temporal scales using a centroid migration model and a newly defined Seasonal Area Ratio (SAR) index. The results suggest that: (1) Near-infrared reflectance and NDVI were identified as the optimal spectral indices for spring and autumn, respectively. This approach led to a classification achieving an overall accuracy of 87.3 ± 1.4% and a Kappa coefficient of 0.84 ± 0.02. Notably, the mixed ecotone was mapped with producers’ and users’ accuracies of 85.2% and 83.6%. (2) The vegetation followed a distinct land-to-sea ecological sequence of “pure P. australis–mixed ecotone–pure S. alterniflora”, predominantly distributed as an east–west trending belt. This pattern was fragmented by tidal creeks and micro-topography in the northwest, contrasting with geometrically regular linear features in the central area, indicative of human engineering. (3) The ecotone showed continuous seaward expansion from 2016 to 2023. Spring exhibited a consistent annual area growth of 13.93% and a stable seaward centroid migration, whereas autumn exhibited significant intra-annual fluctuations in both area and centroid, likely influenced by extreme climate events. (4) Analysis using the Seasonal Area Ratio (SAR) index, defined as the ratio of autumn to spring ecotone area, revealed a clear transition in the seasonal competition pattern in 2017, initiating a seven-year spring-dominant phase after a single year of autumn dominance. This spring-dominated era exhibited a distinctive sawtooth fluctuation pattern, indicative of competitive dynamics arising from the phenological advancement of P. australis combined with the niche penetration of S. alterniflora. This study elucidates the multiscale competition mechanisms between S. alterniflora and P. australis, thereby providing a scientific basis for effective invasive species control and ecological restoration in coastal wetlands. Full article

Urbanization in mountainous regions alters hydrologic systems, yet the spatial patterning of residential (RA) and non-residential (NRA) areas in response to hydrologic constraints remains poorly quantified. In this study, we analyzed how such constraints shaped the distinct locational logic of RA and NRA expansion in the mountainous Kunming Core Region (KCR), Southwest China, from 1975 to 2020. Using the Global Human Settlement Layer (GHS-BUILT-S) built-up fraction data and its functionally classified RA and NRA layers at 100 m resolution, we quantified multi-decadal urban land changes via regression and centroid migration analyses. Six hydrologic factors, namely altitude, slope, surface roughness, distance to river (DTR), depth to water table (DTWT) and vegetation root depth (VRD), were derived from global terrain, groundwater, and rooting depth datasets, and harmonized to a common grid. Results show a two-phase urbanization pattern: moderate, compact growth before 1995 followed by rapid, near-exponential expansion, dominated by RA. RA consistently clustered in hydrologically favorable zones (low–moderate roughness, mid-altitudes, lower slopes, proximal rivers, shallow–moderate DTWT, moderate VRD), whereas NRA expanded into more hydrologically variable terrain (higher roughness, intermediate DTR, deeper DTWT, higher altitudes, deeper VRD). Contribution-weighting analysis revealed a temporal shift in dominant drivers: for RA, from river proximity and slope in 1975 to terrain roughness in 2020; for NRA, from vegetation root depth and moderate topography to root depth plus altitude. Geographic centroids of both RA and NRA migrated northeastward, indicating coordinated yet functionally distinct peri-urban and corridor-oriented growth. These findings provide a hierarchical, factor-based framework for integrating hydrologic constraints into risk-informed land-use planning in topographically complex basins. Full article

Sthenoteuthis oualaniensis is a major commercial species in the high-seas fisheries of the northwestern Indian Ocean. However, its spatiotemporal distribution exhibits strong uncertainty under climate and environmental variability, complicating the understanding of fishing ground dynamics. To investigate the spatiotemporal distribution of S. oualaniensis under climate change, this study analyzed commercial fishing data from 2016 to 2024. The results indicate that the core distribution area of the species is consistently concentrated within 14–19° N and 61–65° E. From 2016 to 2024, the fishing ground expanded annually and shifted overall toward the east and north, with its centroid showing a persistent northeastward trajectory. Global spatial autocorrelation analysis revealed positive and significant Moran’s I values for all years, demonstrating a strong spatial clustering pattern. Hotspot analysis shows that high-abundance areas were primarily located north of 14° N, with an overall northeastward migration trend. Hotspots expanded continuously from 2016 to 2023, but sharply contracted in 2024, shifting further northeast and becoming restricted to 63–68° E and 19–21° N. The GAM results indicate that CPUE in the region is jointly influenced by spatiotemporal drivers and multiple environmental factors. These findings confirm that the spatiotemporal distribution and population dynamics of S. oualaniensis are shaped by the combined effects of environmental variability and temporal–spatial factors, with environmental influences playing a particularly crucial role. Full article

Rapid urbanization complicates the precise, timely quantification of urban spatial morphology. This study examined urban spatial morphology in Fujian Province, integrating DMSP-OLS and NPP-VIIRS nighttime light imagery from 1992 to 2022 to extract the built-up urban footprint via the constructed VMNUI. This method achieved an overall accuracy >0.95 and a Kappa coefficient of 0.80 when the results were compared against land use samples. Utilizing Centroid Migration Analysis, clustering, Geographical Detector, and GTWR, we quantitatively analyzed Fujian’s urban spatial form and its driving mechanisms. The results indicate that the calibration and integration of NTL data effectively resolved saturation and overflow issues in the DMSP data, revealing an urban expansion rate of 3.79%, which centered on coastal areas. Geographical Detector analysis identified fixed-asset investment (q = 0.83), population (0.80), precipitation (0.78), and highway density (0.76) as dominant factors; GDP ∩ fixed-asset investment yielded the strongest interaction (0.873). GTWR further identified that slope aspect, GDP, and secondary industry share accelerated expansion in eastern Fujian, whereas population, urbanization rate, and mean temperature were key drivers of expansion in the west. This study analyzed the spatiotemporal evolution patterns and driving mechanisms of urban spatial form development in Fujian Province over a long period, and based on the results, actionable, science-based optimization strategies with practical implications are proposed for sustainable development in the region. Full article

The genus Dysosma, a group of perennial herbaceous plants with significant medicinal value and a relatively narrow ecological niche, is potentially at risk due to the combined pressures of habitat degradation and climate change. As their habitats continue to degrade, all species of this genus have been included in the National Key Protected Wild Plants List (Category II). Investigating the impacts of climate change on the distribution of Dysosma resources is vital for their sustainable utilization. In this study, the potential distribution dynamics of seven Dysosma species under current and three future climate scenarios (SSP126, SSP245, SSP585) were quantified using 534 occurrence points and 25 environmental variables in a MaxEnt model, accompanied by the ecological niche overlap index (Schoener’s D), dynamic metrics (relative change rate [RCR], change intensity [CI], stability index [SI], spatial displacement rate [SDR]), and centroid migration analysis. The results indicated that (1) areas of high habitat suitability were consistently concentrated in the mountainous and hilly regions of southwestern Guizhou, Chongqing, and Hubei, with the minimum temperature of the coldest month (Bio6) and the mean diurnal temperature range (Bio2) being identified as the primary driving factors. (2) The future suitable habitat areas remained highly stable overall (SI > 97.89%), though dynamic changes varied across scenarios. Under SSP126, only slight fluctuations were observed, with an average CI of approximately 3.78% and RCR ranging from −0.46% to 1.97%. Under the SSP245 scenario, suitable habitat areas showed a continuous, slight expansion (RCR = 0.45% to 1.54%), whereas under the high-emission SSP585 scenario, a typical “mid-term expansion–late-term contraction” pattern was observed, with RCR shifting from positive (1.32%, 1.44%) to negative (−0.92%). The SI reached its lowest value of 97.89% in the late term, and the spatial displacement rate increased, signaling a reorganization of the distribution pattern. (3) High ecological niche differentiation was observed within the genus, with the highest overlap index being only 0.562, and approximately one-third of species pairs exhibiting completely non-overlapping niches. (4) Dysosma tsayuensis, a niche-specialist species, exhibited a distribution that was highly dependent on the annual mean ultraviolet-B radiation (UVB, contribution rate 52.9%), displaying an adaptation strategy markedly different from that of conservative species. (5) Centroid analysis indicated that, although the overall centroid remained stable in Guizhou, the presence of niche-specialist species under the high-emission SSP585 scenario resulted in migration paths opposite to those observed under other scenarios. The findings reveal the potential vulnerability and differential response patterns of Dysosma species under rapid climate warming, thereby providing a scientific basis for targeted conservation, in situ and ex situ conservation strategies, and population restoration. Full article

Harvest-disrupting rain events (HDREs) are prolonged cloudy–rainy spells during winter wheat maturity that impede harvesting and drying, induce pre-harvest sprouting and grain mould, and threaten food security in the Huang–Huai–Hai Plain (HHHP), China’s core winter wheat region. Using daily meteorological records (1960–2019), remote sensing-derived land-use data and topography, we develop a hazard–exposure–vulnerability framework to quantify HDRE risk and its drivers at 1 km resolution. Results show that HDRE risk has increased markedly over the past six decades, with the area of medium-to-high risk rising from 26.9% to 73.1%. The spatial pattern evolved from a “high-south–low-north” structure to a concentrated high-risk belt in the central–northern HHHP, and the risk centroid migrated from Fuyang (Anhui) to Heze (Shandong), with an overall displacement of 124.57 km toward the north–northwest. GeoDetector analysis reveals a shift from a “humidity–temperature dominated” mechanism to a “sunshine–humidity–precipitation co-driven” mechanism; sunshine duration remains the leading factor (q > 0.8), and its interaction with relative humidity shows strong nonlinear enhancement (q = 0.91). High-risk hot spots coincide with low-lying plains and river valleys with dense winter wheat planting, indicating the joint amplification of meteorological conditions and underlying surface features. The results can support regional decision-making for harvest-season early warning, risk zoning, and disaster risk reduction in the HHHP. Full article

The Maximum Entropy (MaxEnt) model, integrated with ArcGIS (a geographic information system), was employed to project potential species distribution under current conditions and future climate scenarios (SSP1–2.6, SSP2–4.5, SSP5–8.5) for the 2050s, 2070s, and 2090s. Model optimization involved testing 1160 parameter combinations. The optimized model (FC = LQ, RM = 0.1) exhibited significantly improved predictive performance, with an average AUC of 0.967. Under current conditions, the estimated core suitable habitat spans 35.62 × 10⁴ km², primarily located in southern China. Future projections indicated a non-linear trajectory: an initial contraction of total suitable area by mid-century, followed by a substantial expansion by the 2090s, particularly under high-emission scenarios. Simultaneously, the distribution centroid shifted northwestward. The primary factors influencing distribution were the annual mean temperature (Bio1, 41.1%) and the precipitation of the coldest quarter (Bio19, 20.0%). These findings establish a critical scientific basis for developing climate-adaptive conservation strategies, including the identification of priority climate refugia in Fujian province, China, and planning for assisted migration to northwestern regions. Full article

Understanding alpine plants’ survival and reproduction is crucial for their conservation in climate change. This study, based on 273 valid distribution points, utilizes the MaxEnt model to predict the potential habitat and distribution dynamics of Kobresia pygmaea under both current and future climate scenarios (SSP126, SSP245, SSP370, SSP585), while clarifying the key factors that influence its distribution. The study indicates that elevation (3527.99–6054.54 m) is the dominant factor influencing its distribution. The current suitable habitat is primarily concentrated in southern and central Tibet, northwestern Sichuan, and southern Qinghai on the Tibetan Plateau, with a total area of 1.13 × 10⁵ km², of which high- and moderate-suitability areas account for 1.76 × 10⁴ km² and 3.2 × 10⁴ km², respectively. Under future climate scenarios (2050s–2070s), the overall distribution pattern remains concentrated on the Tibetan Plateau, but the suitable area exhibits a trend of initial expansion followed by contraction. By the 2050s, the total suitable area increases across all scenarios, with the most pronounced expansion under SSP126. By the 2070s, however, the total suitable area decreases under high-emission scenarios, declining by 9.50% under SSP370 and 6.76% under SSP585, respectively. The reduction in high-suitability areas is more severe, with a maximum decline of 58.75% under SSP3-7.0. Dynamic change analysis shows that approximately 70% of the current high-suitability areas remain stable by the 2050s, with range expansion occurring under low-emission scenarios toward southeastern Tibet, northwestern Sichuan, and southern Golog in Qinghai. In contrast, habitat contraction intensifies by the 2070s, particularly under the SSP5-8.5 scenario, where the reduced area reaches 1.6 times the current high-suitability extent. Centroid shift analysis indicates that the distribution center of suitable habitats migrates northward or northeastward, with a maximum displacement of 206.51 km under the SSP1-2.6 scenario by the 2050s. The results suggest that short-term climate warming may alleviate low-temperature constraints, facilitating the upward and poleward expansion of Kobresia pygmaea into higher-elevation areas. However, prolonged and intensified warming will likely lead to degradation of core habitats, posing a significant threat to its long-term persistence. This study provides a scientific basis for the conservation of alpine ecosystems on the Tibetan Plateau and for developing adaptive management strategies under climate change. Full article

Afforestation plays a vital role in reshaping land systems and enhancing carbon sequestration, particularly in ecologically fragile regions. However, the carbon implications and spatial dynamics of large-scale planted-forest (PF) expansion in the Yellow River Basin (YRB) remain insufficiently understood. Focusing on the YRB, this study integrates multi-source land-use, forest type, and carbon datasets to evaluate land-use transitions (2000–2020) and quantify changes in total ecosystem carbon (TEC), aboveground carbon (AGC), and PF-derived AGC (PF-AGC) from 2005 to 2020 under the IPCC-based accounting framework. The results show cumulative land-use conversion of 118,481 km², with forest land expanded to 11.89% of the basin, mainly due to afforestation efforts in the middle reaches. TEC followed a rise–decline–rebound trajectory, yielding a net gain of 1.96 × 10⁸ t, while AGC increased by 4.37 × 10⁸ t. With the expansion of PF, PF-AGC contributed 1.60 × 10⁸ t (36.61% of AGC gains), primarily sourced from grassland (40.51%), natural forests (35.15%), and cropland (23.56%). PFs were dominated by young stands (≤40 years), spatially clustered in the middle–lower reaches, and exhibited higher carbon sink potential than natural forests. Spatially, AGC and PF distributions underwent staged reconfiguration. Standard deviational ellipse and centroid analyses revealed eastward shifts and axis changes in AGC, and southwestward migration of PFs, indicating PF expansion as a major driver of carbon redistribution. These findings clarify the forest age–land-use–carbon nexus and highlight the spatial impact of afforestation, offering critical insights for region-specific low-carbon strategies and sustainable land governance in the YRB. Full article

China’s rapid economic growth since its reform and opening-up has come at the cost of worsening atmospheric pollution. This study investigates the spatiotemporal evolution and driving mechanisms of PM_2.5 concentrations in Shandong province, a key industrial region, during 2014–2023, using comprehensive air quality monitoring, meteorological observations, and socioeconomic datasets. Through spatial analysis and geodetector methods, we identify that (1) The annual PM_2.5 concentration decreases significantly by 50.9%; spatially, heterogeneity is observed with the western urban agglomeration experiencing more severe pollution, while the eastern coastal urban agglomeration exhibits better air quality. (2) Gravity model analysis shows that the centroids of PM_2.5 pollution undergo distinct migration phases. (3) PM_2.5 levels show a distinct seasonal pattern, peaking in winter at a level 143.7% higher than the summer average. (4) The meteorological driving factors are primarily air temperature (r = 0.511) and wind speed (r = −0.487), while the socioeconomic factors are tertiary industry production (r = −0.971), particulate matter emissions (r = 0.956), and sulfur dioxide emissions (r = 0.938). Concurrently, the combined effect of tertiary industry production and PM emissions account for 99.5% of PM_2.5 variability. Notably, we validate an Environmental Kuznets Curve relationship (R² = 0.805) between economic development and air quality improvement, demonstrating that clean production policy integration can reconcile environmental and economic objectives. These findings provide empirical evidence supporting circular economy strategies for air pollution mitigation in industrializing regions. Full article

Chinese hawthorn (Crataegus pinnatifida) offers both medicinal and nutritional benefits due to its bioactive compounds. However, the combined effects of environmental and anthropogenic factors on its geographic distribution and medicinal quality remain insufficiently explored. Species distribution modeling (Biomod2) was integrated with ArcGIS-based spatial analysis to systematically model suitable habitats for C. pinnatifida across varying environmental and anthropogenic conditions, while concurrently assessing the spatial distribution patterns of medicinally active compounds. Furthermore, multi-scenario climate simulations were utilized to predict the impacts of future climate change on the spatial distribution patterns of this species. Anthropogenic activities strongly influence C. pinnatifida habitat suitability, followed by BIO18 (Precipitation of Warmest Quarter) and BIO4 (Temperature Seasonality). Bioactive compound heterogeneity is driven by anthropogenic activities, AWC (Soil Available Water Content), BIO12 (Annual Precipitation), and BIO4. High-suitability/high-quality areas are concentrated in Shandong Province. Under SSP126 (low emissions), high-suitability areas remain stable (2050s–2090s) with northeastward centroid shifts; under SSP585 (high emissions), they contract with southeastward centroid migration. Shandong is the optimal long-term cultivation region across scenarios. These findings establish a critical basis for cultivating C. pinnatifida with enhanced medicinal properties, developing adaptive management strategies to strengthen conservation, and ensuring sustainable utilization amid global climate change. Full article

Populus adenopoda, an endemic tree species in China with considerable ecological and industrial value, is threatened by climate change-induced habitat loss. Understanding its spatial response is critical for conservation. This study employed the MaxEnt model with 181 occurrence records and seven environmental variables to project its current and future suitable habitats under multiple climate scenarios (SSP126, SSP245, SSP370, SSP585 for the 2050s and 2090s). The model exhibited high predictive performance (AUC = 0.947 and TSS = 0.817). Annual precipitation and the minimum temperature of the coldest month were the dominant factors shaping its distribution. Currently, the total suitable habitat spans approximately 228.19 × 10⁴ km², predominantly in subtropical China. Future projections consistently revealed a stark degradation of highly suitable habitat, with losses of up to 78.81% under SSP585 by the 2090s, partially offset by an expansion of low-suitability areas. A pronounced northwestward shift of the habitat centroid indicates a potential migration toward higher elevations. These results provide a critical scientific foundation for developing climate-adaptive conservation strategies, including identifying priority areas and planning assisted migration, to ensure the long-term sustainability of P. adenopoda. Full article

Alexandrium spp., globally recognized as harmful algal bloom (HAB) species, pose severe threats to marine ecosystems, fisheries, and public health. Based on 469 occurrence records and 24 marine environmental variables, this study employed the Biomod2 ensemble modeling framework to predict the potential distribution of Alexandrium spp. under current and future climate scenarios, and to assess the role of key environmental factors and the spatiotemporal dynamics of habitat centroid shifts. The results revealed that (1) the ensemble model outperformed single models (AUC = 0.998, TSS = 0.977, Kappa = 0.978), providing higher robustness and reliability in prediction; (2) salinity range (bio18, 19.1%) and mean salinity (bio16, 5.8%) were the dominant factors, while minimum temperature (bio23) also showed strong constraints, indicating that salinity determines “whether persistence is possible,” while temperature influences “whether blooms occur”; (3) under present conditions, high-suitability habitats are concentrated in Bohai Bay, the Yangtze River estuary to the Fujian coast, and parts of Guangdong; (4) climate change is predicted to drive a southward shift of suitable habitats, with the most pronounced expansion under the high-emission scenario (RCP8.5), leading to the emergence of new high-risk areas in the South China coast and adjacent South China Sea; (5) centroid analysis further indicated a pronounced southward migration under RCP8.5 by 2100, highlighting a regional reconfiguration of ecological risks. Collectively, salinity and temperature are identified as the core drivers shaping the ecological niche of Alexandrium spp., and future warming is likely to exacerbate HAB risks in southern China. This study delineates key prevention regions and proposes a shift from reactive to proactive management strategies, providing scientific support for HAB monitoring and marine ecological security in China’s coastal waters. Full article

The outburst of Zonag Lake in the permafrost region of the Qinghai-Tibet Plateau (QTP) has significantly altered the local environment, particularly affecting surface conditions and permafrost dynamics. By employing remote sensing and GIS tools, this study analyzed the spatial and temporal variations in surface environmental changes (surface temperature, vegetation, and dryness) within the Zonag–Salt Lake basin. The results indicate that the outburst caused higher surface temperatures and reduced vegetation cover around Zonag Lake. Analysis using the Temperature–Vegetation Dryness Index (TVDI) reveals higher dryness levels in downstream areas, especially from Kusai Lake to Salt Lake, compared to the upstream Zonag Lake. Temporal trends from 2000 to 2023 show a decrease in average Land Surface Temperature (LST) and an increase in the Normalized Difference Vegetation Index (NDVI). Geographical centroid shifts in environmental indices demonstrate migration patterns influenced by seasonal climate changes and the outburst event. Desertification around Zonag Lake accelerates permafrost development, while the wetting environment around Salt Lake promotes permafrost degradation. The Zonag Lake region is also an ecologically significant area, serving as a key calving ground for the Tibetan antelope (Pantholops hodgsonii), a nationally protected species. Thus, the environmental changes revealed in this study carry important implications for biodiversity conservation on the Tibetan Plateau. These findings highlight the profound impact of the Zonag Lake outburst on the surface environment and permafrost dynamics in the region, providing critical insights for understanding environmental responses to lake outbursts in high-altitude regions. Full article

Global climate change poses a significant management challenge for vital transboundary resources like the Pacific saury (Cololabis saira). To address this, we developed an innovative framework that uses high-resolution Automatic Identification System (AIS) data and deep learning to define species distribution, which then informs a robust Ensemble Species Distribution Model (ESDM). The model (TSS > 0.89, AUC > 0.97) identifies sea surface temperature (SST) and chlorophyll-a (CHL) as key habitat drivers. Projections under future climate scenarios reveal two critical threats: (1) a continuous northeastward migration of the habitat’s centroid, exceeding 400 km by 2100 under a high-emission SSP5-8.5 scenario, and (2) a drastic contraction of highly suitable habitat (suitability > 0.8), shrinking by up to 94% under the high-emission SSP3-7.0 scenario. By directly linking key oceanographic features to these climate-driven risks, this study delivers an essential scientific decision-support tool for management bodies like the North Pacific Fisheries Commission (NPFC) to develop climate-adaptive strategies. Full article