Search Results (132)

The distribution of Itea yunnanensis, a shrub species in the genus Itea of the family Iteaceae, is primarily concentrated in the Hengduan Mountains region of China, where it faces severe threats from global climate change. However, systematic research on the species’ distribution patterns, climatic response mechanisms, and future suitable habitat dynamics remains insufficient. This study aims to assess the spatiotemporal evolution and driving mechanisms of I. yunnanensis-suitable habitats under current and future climate change scenarios to reveal the migration patterns of its distribution centroid and ecological thresholds, and to enhance the reliability and interpretability of predictions through model optimization. For MaxEnt modeling, we utilized 142 georeferenced occurrence records of I. yunnanensis alongside environmental data under current conditions and three future Shared Socioeconomic Pathways (SSPs: SSP1-2.6, SSP2-4.5, SSP5-8.5). Model parameter optimization (Regularization Multiplier, Feature Combination) was performed using the R (v4.2.1) package ‘ENMeval’. The optimized model (RM = 3.0, FC = QHPT) significantly reduced overfitting risk (ΔAICc = 0) and achieved high prediction accuracy (AUC = 0.968). Under current climate conditions, the total area of potential high-suitability habitats for I. yunnanensis is approximately 94.88 × 10⁴ km², accounting for 9.88% of China’s land area, with core areas located around the Hengduan Mountains. Under future climate change, the suitable habitats show significant divergence, area fluctuation and contraction under the SSP1-2.6 scenario, and continuous expansion under the SSP5-8.5 scenario. Meanwhile, the species’ distribution centroid exhibits an overall trend of northwestward migration. This study not only provides key spatial decision-making support for the in situ and ex situ conservation of I. yunnanensis, but also offers an important methodological reference for the adaptive research on other ecologically vulnerable species facing climate change through its optimized modeling framework. Full article

Rural post-poverty alleviation areas are not on a solid developmental footing and therefore remain at risk of returning to poverty in the midst of rapid urbanization. Vulnerability assessment of socio-ecological systems is critical for identifying risks and enhancing resilience in rural areas transitioning out of poverty. Based on research data from 2012, 2017, and 2022 in the post-poverty alleviation areas of Hunan Province, this research establishes a Vulnerability-Scoping-Diagram (VSD) assessment framework for rural development vulnerability and Spatially-Explicit-Resilience-Vulnerability (SERV) analysis model from a socio-ecological system perspective. It comprehensively analyzes the spatial and temporal variations of the Rural Development Vulnerability Index (RDVI) in the study area. Geodetector is used to explore the main factors influencing the spatial and temporal variability of RDVI, and vulnerability type zones are classified by combining the dominant elements method. The findings indicate that: (1) The rural development vulnerability index of post-poverty alleviation areas in Hunan Province has obvious characteristics of spatial and temporal differentiation. The RDVI in western Hunan and southern Hunan is always high, while the RDVI in ChangZhuTan and Dongting Lake regions decreases year by year. (2) The RDVI of post-poverty alleviation areas in Hunan Province is determined by the three dimensions of exposure, sensitivity, and adaptability, exhibiting significant spatial and temporal variations. (3) Spatial autocorrelation analysis showed that areas with similar rural socio-ecological vulnerability in post-poverty alleviation areas of Hunan Province were significantly clustered spatially. (4) The core influencing factors of RDVI in Hunan’s post-poverty alleviation areas have shifted from natural disaster risk to multiple risk dimensions encompassing social resource load and ecological environment risk superimposition, resulting in more complex and diversified influencing factors. (5) By combining results from the RDVI assessment with the dominant elements method, the regions can be classified into multiple vulnerability type districts dominated by multiple elements or single-element dominance, leading to corresponding development suggestions. The study aims to examine the process of changes in vulnerability within rural development in post-poverty alleviation areas and its causal factors from a socio-ecological system perspective. This will provide a foundation for policy formulation to consolidate the results of post-poverty alleviation and promote the sustainable development of rural areas. Full article

Our study aims to analyze the spatiotemporal dynamics of six lakes in Ifrane National Park (Morocco) using remote sensing and satellite imagery over the period 2000–2024. Spectral indices such as NDWI, MNDWI, EWI, AWEI, and ANDWI were employed to extract water bodies from Landsat images, while the NDVI index was used to identify irrigated agricultural lands. Additionally, the SPEI and RDI indices were applied to assess the impact of climate fluctuations on the hydrological evolution of the lakes. The results reveal an alarming reduction in lake surface areas, with some lakes having completely dried up. This decline is correlated with decreased precipitation and the expansion of irrigated agricultural lands, highlighting the impact of human activities. The analysis of hydrological correlations between lakes demonstrates significant interactions, although some indices show disparities. The rapid expansion of agricultural land, particularly arboriculture, increases pressure on water resources. These changes threaten local biodiversity and heighten the socio-economic vulnerability of surrounding populations. Full article

Fire-driven land cover change has generated a paradox: while habitat fragmentation from agriculture, livestock, and urban expansion has reduced natural fire occurrences, human-induced ignitions have increased wildfire frequency and intensity. In northern Colombia’s Magdalena Department, most of the territory faces moderate to high wildfire risk, especially during recurrent dry seasons and periods of below-average precipitation. However, knowledge of wildfire spatiotemporal occurrence and its drivers remains scarce. This work addresses this gap by identifying fire-prone zones and analyzing the influence of climate and vegetation in the Magdalena Department. Fire-prone zones were identified using the Getis–Ord Gi* method over fire density and burned area data from 2001 to 2023; then, they were analyzed with seasonally aggregated hydroclimatic indices via logistic regression to quantify their influence on wildfires. Vegetation susceptibility was assessed using geostatistics, obtaining land cover types most affected by fire and their degree of fragmentation. Fire-prone zones in the Magdalena Department covered ~744.35 km² (3.21%), with a weak but significant (τ = 0.20, p < 0.01) degree of coincidence between classification based on fire density, as pre-fire variable, and burned area, as a post-fire variable. Temporally, fire probability increased during the dry season, driven by short-lagged precursors such as Dry Spell Length and precipitation from the preceding wet season. Fire-prone zones were dominated by pastures (62.39%), grasslands and shrublands (19.61%) and forests (15.74%), and exhibited larger, more complex high-risk patches, despite similar spatial connectedness with non-fire-prone zones. These findings enhance wildfire vulnerability understanding, contributing to risk-based territorial planning. Full article

Constructing regional ecological security patterns (ESP) and enhancing carbon sequestration are essential for achieving China’s dual-carbon goals. However, rapid urbanization has intensified landscape fragmentation, disrupted ecosystem flows, and significantly altered urban ecological networks, weakening their carbon sink functions. Using the Chengdu metropolitan area (CMA) as a case study, a time-series ESP from 2000 to 2020 was constructed. Morphological Spatial Pattern Analysis (MSPA), the Minimum Cumulative Resistance (MCR) model, the gravity model, and complex network theory were employed to assess the spatiotemporal evolution and carbon sequestration capacity of the ecological network. Results revealed continuous declines in ecological sources and corridors, an initial rise then stabilization in resistance, and weakening connectivity, particularly in central and western subregions. Reductions in modularity and topological indices reflected lower ecological stability and greater vulnerability. Forest land served as the primary carbon sink, closely associated with multiple topological metrics; grassland sequestration correlated with clustering, while water bodies were positively linked to centrality measures. Adding 10 stepping-stone nodes and 45 corridors could enhance carbon sequestration by approximately 4156 Mg C yr⁻¹, with forests contributing 94.8% by 2020. This study provides scientific support for resilient regional ESP construction and dual-carbon strategy advancement. Full article

The Hubei section of the Yangtze River Economic Belt (YREB) has an important strategic position as the core zone of the central part of the YREB, and the advantages and disadvantages of its ecological environment are closely related to the development quality of the whole YREB. Moreover, the systematic assessment of ecological vulnerability is of great significance to regional ecological environmental protection, the rational exploitation and utilization of resources, and sustainable development. Based on the pressure–state–response–management model, this study analyzes the spatial and temporal evolution characteristics of the ecological vulnerability of the Hubei section of the YREB and its influencing factors using G1–CRITIC–game theory combination weighting, the Theil index, and the Ridge regression model. The results show that from 2010 to 2023, the area was characterized by medium ecological vulnerability, with an average area share of 58.2%; the degree of vulnerability rose and then fell; the ecological environment gradually improved; and there was an overall spatial distribution pattern of high in the central part and low in the east and west. On the trend of vulnerability transformation, 62.2% of the area remained unchanged, 21% of the area shifted to low vulnerability, and 16.8% of the area increased in vulnerability level. The Theil index decreased and then rose, the degree of spatial agglomeration was floating in a “V” shape, and the spatial pattern of vulnerability was essentially the same in the hot- and cold-spot areas. Among the six ecological functional protection zones, the soil preservation function zone exhibited the lowest average ecological vulnerability index (EVI) at 0.371. From 2010 to 2023, the water source conservation function zone demonstrated a significant decline in EVI, while the remaining zones showed a gradual upward trend in EVI. The human disturbance index was the main driver affecting the change in ecological vulnerability, and the pressure layer was the key influence criterion layer. This study can provide a reasonable evaluation model and analytical framework for the scientific and objective assessment of ecological vulnerability. Full article

Unique, small-scale tectonic and geological systems are occasionally vulnerable to natural hazards. Although the combination of such systems with rapid socio-ecological change can enhance the risk of disasters, such synergistic impacts have not been well studied. The primary goal of this study was to investigate the potential synergistic impact of land deformation and rapid socio-ecological changes on disaster risk in lowland alluvial regions of a collision zone in the Gorontalo Regency of Gorontalo Province, Indonesia. In this region, socio-ecological changes such as urbanization and rapid lake shrinkage are significant. Frequent occurrence of flood hazards threatens local livelihood. Differential interferometric synthetic aperture radar analysis of Sentinel-1 C-band data from April 2020 to April 2023 was applied to assess land deformation. Thereafter, supervised classification of moderate and high spatiotemporal resolution optical satellite time series was used to assess the relationship between land deformation and built-up area. The findings revealed both significant land deformation and rapid socio-ecological changes. Vertical deformation rates were as high as ~6 cm/year and were primarily attributable to tectonic activity; they were particularly apparent in rapidly developing and highly populated residential areas. Rapid shrinkage of a lake resulted from the local geological system and socioeconomic changes in the region, which together possibly exacerbated the hazard risk because of their effects on land deformation. These results indicate the potential danger to both infrastructure and human inhabitants at a regional level due to the synergistic effects of natural processes and socio-ecological changes. The study design and data that were used facilitated a comprehensive assessment of the potential impacts on disaster risk. These findings are expected to be integrated into locally specific hazard (e.g., flood inundation and ground fissuring) risk mitigation and management strategies. Full article

Drought raises significant challenges and consequences in the socioeconomic environment in Dobrogea, Romania. This research aimed to assess the spatiotemporal dynamics of agrometeorological droughts from 2001 to 2021 using a multi-index approach that includes the Vegetation Health Index (VHI) and Standardized Precipitation Evapotranspiration Index (SPEI). Severe-to-extreme drought events were detected in 2001, 2007, 2012, 2015, 2016, 2019, and 2020, when temperatures in the area reached as high as 40.91 °C. Regarding area coverage, 2012 and 2020 were the worst drought years, with 66% and 71% of the region affected. Mild and moderate droughts were consistently identified across almost the entire period, while normal wet conditions were indicated in 2004–2006. The spatial analysis and the drought frequency maps revealed that the central, southern, and northwestern areas were particularly vulnerable, underlining the need for targeted drought mitigation measures. The trend analysis results indicated a nonuniform spatial feature of the negative (drying)/positive (wetting) trends at the regional level, with statistically significant trends identified only over small areas. Further results showed a robust relationship among the VHI and SPEI, particularly on 1-month and seasonal timescales. The extended correlation analysis results showed very strong positive relationships among all the vegetation indices, positive relations with rainfall, and strong negative ties with land surface temperature. Moreover, the seasonal VHI proved to be effective for drought monitoring across areas with diverse crop types. The results we obtained are consistent with previous studies on the incidence of drought in the area and hold practical significance for decision-makers responsible for drought management planning within Dobrogea, including setting up an early warning system using the VHI. Full article

Studying the spatiotemporal trends and influencing factors of vegetation coverage is essential for assessing ecological quality and monitoring regional ecosystem dynamics. The existing research on vegetation coverage variations and their driving factors predominantly focused on inland ecologically vulnerable regions, while coastal areas received relatively little attention. However, coastal regions, with their unique geographical, ecological, and anthropogenic activity characteristics, may exhibit distinct vegetation distribution patterns and driving mechanisms. To address this research gap, we selected Shandong Province (SDP), a representative coastal province in China with significant natural and socioeconomic heterogeneity, as our study area. To investigate the coastal–inland differentiation of vegetation dynamics and its underlying mechanisms, SDP was stratified into four geographic sub-regions: coastal, eastern, central, and western. Fractional vegetation cover (FVC) derived from MOD13A3 v061 NDVI data served as the key indicator, integrated with multi-source datasets (2000–2023) encompassing climatic, topographic, and socioeconomic variables. We analyzed the spatiotemporal characteristics of vegetation coverage and their dominant driving factors across these geographic sub-regions. The results indicated that (1) the FVC in SDP displayed a complex spatiotemporal heterogeneity, with a notable coastal–inland gradient where FVC decreased from the inland towards the coast. (2) The influence of various factors on FVC significantly varied across the sub-regions, with socioeconomic factors dominating vegetation dynamics. However, socioeconomic factors displayed an east–west polarity, i.e., their explanatory power intensified westward while resurging in coastal zones. (3) The intricate interaction of multiple factors significantly influenced the spatial differentiation of FVC, particularly dual-factor synergies where interactions between socioeconomic and other factors were crucial in determining vegetation coverage. Notably, the coastal zone exhibited a high sensitivity to socioeconomic drivers, highlighting the exceptional sensitivity of coastal ecosystems to human activities. This study provides insights into the variations in vegetation coverage across different geographical zones in coastal regions, as well as the interactions between socioeconomic and natural factors. These findings can help understand the challenges faced in protecting coastal vegetation, facilitating deeper insight into ecosystems responses and enabling the formulation of effective and tailored ecological strategies to promote sustainable development in coastal areas. Full article

Heilongjiang Province, a major grain-producing region in China, faces ecological vulnerabilities that directly affect its sustainable development. A scientific assessment of the spatiotemporal characteristics of ecological vulnerability and its influencing factors in Heilongjiang is crucial for a deeper understanding of environmental issues and provides theoretical support for enhancing regional ecological governance capabilities. The SRP model, combined with the AHP-CRITIC weighting method, was employed to assess Heilongjiang Province’s ecological vulnerability’s temporal and regional differentiation trends between 2000 and 2020. The aggregation kinds of ecological vulnerability were examined using spatial autocorrelation. GeoDetector was used to determine the main elements affecting ecological vulnerability in the province. Additionally, the ecological vulnerability status in 2030 was predicted using the CA-Markov model. The findings indicate that (1) the average EVI values for Heilongjiang Province during the three periods were 0.323, 0.317, and 0.347, respectively, indicating a medium level of ecological vulnerability across the province; the ecological vulnerability initially decreased and then worsened. Spatially, the distribution followed a pattern of “high in the east and west, and low in the north and south”. (2) Spatial agglomeration is evident, with high-high (H-H) aggregation primarily occurring in heavily and extremely vulnerable areas characterized by high human activity, while low–low (L-L) aggregation is mainly found in mildly and marginally vulnerable areas with a favorable natural background. (3) Biological abundance, net primary productivity, dry degree, and PM_2.5 were the main drivers of ecological vulnerability, with interactions between these factors amplifying their impact on ecological vulnerability. (4) The CA-Markov model prediction results indicated an upward trend in the overall ecological vulnerability of Heilongjiang Province by 2030, reflecting a decline in the ecological environment. The study indicates that the ecological vulnerability of Heilongjiang Province is closely linked to its natural geographic conditions and is influenced through the interplay of several environmental elements. Based on the vulnerability zoning results, this paper proposes governance recommendations for regions with different vulnerability levels, aiming to provide theoretical support for future ecological restoration and sustainable development. Full article

The early growth phase is a critical period for the development of the chicken gut microbiome. In this study, the spatiotemporal diversity of the gastrointestinal microbiota, shifts in taxonomic composition, and relative abundances of the main bacterial taxa were characterized in Kadaknath, a high-value indigenous Indian chicken breed, using sequencing of the V3–V4 region 16S rRNA gene. To assess microbiome composition and bacterial abundance shifts, three chickens per growth phase (3, 28, and 35 days) were sampled, with microbiota analyzed from three gut regions (crop, small intestine, and ceca) per bird. The results revealed Firmicutes as the most abundant phylum and Lactobacillus as the dominant genus across all stages. Lactobacillus was particularly abundant in the crop at early stages (3 and 28 days), while the ceca exhibited a transition towards the dominance of genus Phocaeicola by day 35. Microbial richness and evenness increased with age, reflecting microbiome maturation, and the analyses of the microbial community composition revealed distinct spatiotemporal differences, with the ceca on day 35 showing the highest differentiation. Pathogen analysis highlighted a peak in poultry-associated taxa Campylobacter, Staphylococcus, and Clostridium paraputrificum in 3-day-old Kadaknath, particularly in the small intestine, underscoring the vulnerability of early growth stages. These findings provide critical insights into age-specific microbiome development and early life-stage susceptibility to pathogens, emphasizing the need for targeted interventions to optimize poultry health management and growth performance. Full article

In-depth analysis of the evolution of ecosystem services (ESs) in the basin at different spatial scales, scientific identification of ecosystem service clusters, and revelation of their spatial and temporal characteristics as well as coupling mechanisms of interactions are the key prerequisites for effective implementation of ES management. This paper assessed the spatial and temporal changes of six key ESs covering food provisioning (FP), water yield (WY), soil retention (SR), water conservation (WC), habitat quality (HQ), and carbon sequestration (CS) in the Xijiang River Basin (XRB), China, between 2000 and 2020. Given that the scale effects of ESs and their spatial heterogeneity in the XRB are still subject to large uncertainties, a combination of Spearman correlation analysis and geographically weighted regression (GWR) modelling systematically revealed the trade-offs and synergistic relationships between ESs and the scale effects from a grid, watershed, and county perspective. Additionally, we applied the self-organizing mapping (SOM) method to identify multiple ecosystem service bundles (ESBs) and propose corresponding sustainable spatial planning and management strategies for each cluster. The results reveal the following key findings: (1) Spatial distribution and heterogeneity: The six ESs demonstrated pronounced spatial variability across the study area during the two-decade period from 2000 to 2020. The downstream areas had higher levels of ESs, while the upstream regions showed comparatively lower levels. This trend was particularly evident in areas with extensive arable land, higher population density, and more developed economic activity, where ESs levels were lower. (2) Trade-offs/synergies: The analysis highlighted the prevalence of synergistic effects among ESs, with food provisioning-related services exhibiting notable trade-offs. Trade-off/Synergistic effects were weaker at the grid scale but more pronounced at the sub-basin and county scales, with significant spatial heterogeneity. (3) Identification of ESBs: We identified five distinct ESBs: the HQ-CS synergy bundle (HCSB), the integrated ecological bundle (IEB), the agricultural bundle (AB), the key synergetic bundle lacking HQ (KSB), and the supply service bundle (SSB). These clusters suggest that the overall ecological environment of the study area has significantly improved, the supply functions have strengthened, and ecosystem vulnerability has been effectively mitigated. Building upon the identified multi-scale spatiotemporal heterogeneity patterns of ESBs in the XRB, this study proposes an integrated framework for territorial spatial planning and adaptive land management, aiming to optimize regional ecosystem service provisioning and enhance socio-ecological sustainability. Full article

Railway infrastructure faces significant operational threats due to ground deformation risks from natural and anthropogenic sources, posing serious challenges to safety and maintenance. Traditional monitoring methods often fail to capture the complex spatiotemporal patterns of railway deformation, leading to delayed responses and increased risks of infrastructure failure. To address these limitations, this study introduces InSAR-RiskLSTM, a novel framework that leverages the high-resolution and wide-coverage capabilities of Interferometric Synthetic Aperture Radar (InSAR) to enhance railway deformation risk prediction. The primary objective of this study is to develop an advanced predictive model that accurately captures both temporal dependencies and spatial susceptibilities in railway deformation processes. The proposed InSAR-RiskLSTM framework integrates Long Short-Term Memory (LSTM) networks with spatial attention mechanisms to dynamically prioritize high-risk regions and improve predictive accuracy. By combining image-based spatial attention for deformation hotspot identification with advanced temporal modeling, the approach ensures more reliable and proactive risk assessment. Extensive experiments on real-world railway datasets demonstrate that InSAR-RiskLSTM achieves superior predictive performance compared to baseline models, underscoring its robustness and practical applicability. The results highlight its potential to contribute to proactive railway maintenance and risk mitigation strategies by providing early warnings for infrastructure vulnerabilities. This work advances the integration of image-based methods within cyber–physical systems, offering practical tools for safeguarding critical railway networks. Full article

This study assessed soil erosion intensity and soil properties across the Crveni Potok catchment in Serbia, a region of diverse morphology, geology, pedology, and vegetation. Soil samples were collected using a regular grid approach to identify the underlying factors contributing to erosion and the most vulnerable areas. Based on ¹³⁷Cs activities and the profile distribution (PD) model, severe erosion (>10 t ha⁻¹ y⁻¹) was predicted at nearly 60% of the studied locations. The highest mean erosion rates were detected for the lowest altitude range (300–450 m), Rendzic Leptosol soil, and grass-covered areas. A significant negative correlation was found between the erosion rates, soil organic matter, and indicators of soil structural stability (OC/clay ratio and S_t), indicating that the PD model successfully identifies vulnerable sites. The PD and RUSLE (revised universal soil loss equation) models provide relatively similar mean erosion rates (14.7 t ha⁻¹ y⁻¹ vs. 12.7 t ha⁻¹ y⁻¹) but significantly different median values (13.1 t ha⁻¹ y⁻¹ vs. 5.5 t ha⁻¹ y⁻¹). The model comparison revealed a positive trend. The observed inconsistencies were interpreted by the models’ spatiotemporal frameworks and RUSLE’s sensitivity to input data quality. Land use stands out as a significant factor modifying the variance of erosion rate, highlighting the importance of land management practices in mitigating erosion. Full article

Forests are critical ecosystems that regulate climate, preserve biodiversity, and support human livelihoods by providing essential resources. However, they are increasingly vulnerable due to the growing impacts of deforestation and habitat fragmentation, which endanger their value and long-term sustainability. Assessing forest and deforestation fragmentation is vital for promoting sustainable logging, guiding ecosystem restoration, and biodiversity conservation. This study introduces an advanced approach that integrates the Local Connected Fractal Dimension (LCFD) with near real-time (NRT) land use and land cover (LULC) data from the Dynamic World dataset (2017–2024) to enhance deforestation monitoring and landscape analysis. By leveraging high-frequency, high-resolution satellite imagery and advanced imaging techniques, this method employs two fractal indices, namely the Fractal Fragmentation Index (FFI) and the Fractal Fragmentation and Disorder Index (FFDI), to analyze spatiotemporal changes in the forest landscape and enhance deforestation monitoring, providing a dynamic, quantitative method for assessing forest fragmentation and connectivity in real time. LCFD provides a refined assessment of spatial complexity, localized connectivity, and self-similarity in fragmented landscapes, improving the understanding of deforestation dynamics. Applied to Nigeria’s Okomu Forest, the analysis revealed significant landscape transformations, with peak fragmentation observed in 2018 and substantial recovery in 2019. FFI and FFDI metrics indicated heightened disturbances in 2018, with FFDI increasing by 75.2% in non-deforested areas and 61.1% in deforested areas before experiencing rapid declines in 2019 (82.6% and 87%, respectively), suggesting improved landscape connectivity. Despite minor fluctuations, cumulative deforestation trends showed a 160.5% rise in FFDI from 2017 to 2024, reflecting long-term stabilization. LCFD patterns highlighted persistent variability, with non-deforested areas recovering 12% connectivity by 2024 after a 38% reduction in 2019. These findings reveal the complex interplay between deforestation and landscape recovery, emphasizing the need for targeted conservation strategies to enhance ecological resilience and connectivity. Fractal indices offer significant potential to generate valuable insights across multiple spatial scales, thereby informing strategies for biodiversity preservation and adaptive landscape management. Full article