Search Results (102)

Global climate change is accelerating and human pressures are intensifying, exerting profound impacts on biodiversity and ecosystem service functions. The accurate prediction of species distributions has thus become a critical research direction in ecological conservation and restoration. This study selected Puccinellia tenuiflora, a species distributed across China, as its research subject. Utilizing 169 occurrence records and 10 environmental variables, we applied a parameter-optimized MaxEnt model to simulate the species’ current and future (2050s–2090s) potential suitable habitats under the SSP126, SSP370, and SSP585 scenarios. The results identified the human footprint index (HFI, 43.3%) and temperature seasonality (Bio4, 26.9%) as the dominant factors influencing its distribution. The current suitable area is primarily concentrated in northern China, covering approximately 258.26 × 10⁴ km². Under all future scenarios, a contraction of suitable habitat is projected, with the most significant reduction observed under SSP585 by the 2090s (a decrease of 56.2%). The distribution centroid is projected to shift northeastward by up to 145.36 km. This study elucidates the response mechanism of P. tenuiflora distribution to climate change and human activities. The projected habitat contraction and spatial displacement highlight the potential vulnerability of this species to future climate change. These findings, derived from a rigorously optimized and spatially validated model, provide a scientific basis for the conservation, reintroduction, and adaptive management of P. tenuiflora under climate change. Full article

Obesity is an epidemic with myriad health effects, but little is understood regarding individual obese phenotypes and how they may respond to therapy. Epigenetic changes associated with obesity have been detected in blood, liver, pancreas, and adipose tissues. Previous work using human organoids found that dietary glucose hyperabsorption is a steadfast trait in cultures derived from some obese subjects, but detailed transcriptional or epigenomic features of the intestinal epithelia associated with this persistent phenotype are unknown. This study evaluated differentially expressed genes and relative chromatin accessibility in intestinal organoids established from donors classified as non-obese, obese, or obese hyperabsorptive by body mass index and glucose transport assays. Transcriptomic analysis indicated that obese hyperabsorptive subject organoids have significantly upregulated dietary nutrient absorption transcripts and downregulated type I interferon targets. Chromatin accessibility and transcription factor footprinting predicted that enhanced HNF4G binding may promote the obese hyperabsorption phenotype. Quantitative RT-PCR assessment in organoids representing a larger subject cohort suggested that intestinal epithelial expression of CUBN, GIP, SLC5A11, and SLC2A5 were highly correlated with hyperabsorption. Thus, the obese hyperabsorption phenotype was characterized by transcriptional changes that support increased nutrient uptake by intestinal epithelia, potentially driven by differentially accessible chromatin. Recognizing unique intestinal phenotypes in obesity provides a new perspective in considering therapeutic targets and options with which to manage the disease. Full article

Under the combined influence of global climate change and intensified human activities, ecosystem services (ESs) are undergoing substantial transformations. Identifying their nonlinear driving mechanisms is crucial for promoting regional sustainable development. Taking Anhui Province as a case study, this research evaluates the spatial patterns and temporal dynamics of six key ecosystem services from 2000 to 2020—namely, biodiversity maintenance (BM), carbon fixation (CF), crop production (CP), net primary productivity (NPP), soil retention (SR), and water yield (WY). The InVEST and CASA models were employed to quantify service values, and the XGBoost–SHAP framework was used to reveal the nonlinear response paths and threshold effects of dominant drivers. Results show a distinct “high in the south, low in the north” spatial gradient of ES across Anhui. Regulatory services such as BM, NPP, and WY are concentrated in the southern mountainous areas (high-value zones > 0.7), while CP is prominent in the northern and central agricultural zones (>0.8), indicating a clear spatial complementarity of service types. Over the two-decade period, areas with significant increases in NPP and CP accounted for 50% and 64%, respectively, suggesting notable achievements in ecological restoration and agricultural modernization. CF remained stable across 98.3% of the region, while SR and WY exhibited strong sensitivity to topography and precipitation. Temporal trend analysis indicated that NPP rose from 395.83 in 2000 to 537.59 in 2020; SR increased from 150.02 to 243.28; and CP rose from 203.18 to 283.78, reflecting an overall enhancement in ecosystem productivity and regulatory functions. Driver analysis identified precipitation (PRE) as the most influential factor for most services, while elevation (DEM) was particularly important for CF and NPP. Temperature (TEM) and potential evapotranspiration (PET) affected biomass formation and hydrothermal balance. SHAP analysis revealed key threshold effects, such as the peak positive contribution of PRE to NPP occurring near 1247 mm, and the optimal temperature for BM at approximately 15.5 °C. The human footprint index (HFI) exerted negative impacts on both BM and NPP, highlighting the suppressive effect of intensive anthropogenic disturbances on ecosystem functioning. Anhui’s ES exhibit a trend of multifunctional synergy, governed by the nonlinear coupling of climatic, hydrological, topographic, and anthropogenic drivers. This study provides both a modeling toolkit and quantitative evidence to support ecosystem restoration and service optimization in similar transitional regions. Full article

This study examined the spatiotemporal dynamics and driving mechanisms of ecosystem service value (ESV) in Wuhan from 1985 to 2020. Using multi-temporal land-use data, remotely sensed vegetation indices, and socioeconomic statistics, we estimated the ESV with an improved equivalent-factor method and analyzed its drivers using a Geodetector and geographically weighted regression (GWR). Over the 35-year period, total ESV for Wuhan showed a mildly declining trend, decreasing from CNY 37.464 billion in 1985 to CNY 36.439 billion in 2020. Waterbodies contributed the largest share of ESV, followed by croplands and forests. In the urban core, ESV declined significantly, with low-value zones expanding outward from the city center. Spatial autocorrelation analysis revealed significant “high–high” and “low–low” clustering. Geodetector results indicated slope, elevation, and normalized difference vegetation index (NDVI) as the primary natural drivers, with human footprint, gross domestic product (GDP), and population density acting as important socioeconomic auxiliaries. Interactions between natural and socioeconomic factors substantially increased the explanatory power. Furthermore, GWR revealed pronounced spatial heterogeneity in the sign and magnitude of the factor effects across the study area, underscoring the complexity of ESV drivers. These findings provide quantitative evidence to support spatially differentiated ecological planning and conservation strategies during urbanization in Wuhan and the broader mid-Yangtze region. Full article

Understanding the spatial distribution and socio-cultural perceptions of threatened raptors is essential for evidence-based conservation in biodiverse yet understudied regions such as Nigeria. This study combines species distribution modelling with community-based surveys to explore the ecological and human dimensions influencing raptor conservation. To investigate the influence of anthropogenic pressures on threatened raptors’ reporting rates, we modelled the relationship between the reporting rate (RR) and two key predictors: the Human Footprint Index and population density. Concurrently, 318 questionnaires were administered across multiple sites to assess public perceptions and attitudes toward raptors. Results indicate that there was a notable reduction in the RR of threatened raptor species with an increase in population density (Estimate = −0.085, SE = 0.028, t = −3.056, p = 0.002). In socio-cultural analyses, sentiment analysis revealed that more than 60% of respondents with higher knowledge of raptors often held more negative perceptions, typically associated with poultry predation and cultural beliefs. In contrast, individuals with limited knowledge frequently exhibited more positive (50%) attitudes. Interestingly, areas with high raptor abundance were associated with more negative community perceptions, suggesting that human–wildlife conflict plays a significant role in shaping attitudes. These findings highlight the complexity of human–raptor interactions and the need for conservation strategies that extend beyond formal protected areas. We advocate for an integrated approach that combines ecological modelling with culturally sensitive education and community-based interventions to foster coexistence and support raptor conservation in Nigeria and similar socio-ecological landscapes. Full article

Vegetation is a fundamental component of terrestrial ecosystems. Understanding how vegetation changes and what drives these evolutions is crucial for developing a high-quality ecological environment and addressing global climate change. Extensive evidence has shown that China has undergone substantial vegetation changes, characterized primarily by greening. To quantify vegetation dynamics in China and assess the contributions of various drivers, we explored the spatiotemporal variations in the kernel Normalized Difference Vegetation Index (kNDVI) from 2001 to 2020, and quantitatively separated the influences of climate and human factors. The kNDVI time series were generated from the MCD19A1 v061 dataset based on the Google Earth Engine (GEE) platform. We employed the Theil-Sen trend analysis, the Mann-Kendall test, and the Hurst index to analyze the historical patterns and future trajectories of kNDVI. Residual analysis was then applied to determine the relative contributions of climate change and human activities to vegetation dynamics across China. The results show that from 2001 to 2020, vegetation in China showed a fluctuating but predominantly increasing trend, with a significant annual kNDVI growth rate of 0.002. The significant greening pattern was observed in over 48% of vegetated areas, exhibiting a clear spatial gradient with lower increases in the northwest and higher amplitudes in the southeast. Moreover, more than 60% of vegetation areas are projected to experience a sustained increase in the future. Residual analysis reveals that climate change contributed 21.89% to vegetation changes, while human activities accounted for 78.11%, being the dominant drivers of vegetation variation. This finding is further supported by partial correlation analysis between kNDVI and temperature, precipitation, and the human footprint. Vegetation dynamics were found to respond more strongly to human influences than to climate drivers, underscoring the leading role of human activities. Further analysis of tree cover fraction and cropping intensity data indicates that the greening in forests and croplands is primarily attributable to large-scale afforestation efforts and improved agricultural management. Full article

As human activities increasingly encroach on ecologically sensitive lake zones, China’s lake-ring urban agglomerations struggle to balance the intensifying human footprint (HF) and declining habitat quality (EQ). Addressing the spatiotemporal interactions between HF and EQ is essential for achieving human–environment coordination. This study examined five major freshwater lake-ring urban agglomerations in China during the period from 2000 to 2020 and developed an HF–EQ assessment framework. First, the coupling coordination degree (CCD) model quantified the spatiotemporal coupling between HF and EQ. Second, GeoDetector identified how HF and EQ interact to influence CCD. Finally, the four-quadrant static model and CCD change rate index formed a dual-dimensional management framework. The results indicate that the spatiotemporal evolution patterns of HF and EQ are highly complementary, exhibiting a significant coupling interaction. High-CCD zones expanded from lakeside urban areas and transport corridors, while low-CCD zones remained in remote, forested areas. HF factors such as GDP, land use intensity, and nighttime lights dominated CCD dynamics, while EQ-related factors showed increasing interaction effects. Five human–environment coordination zones were identified based on the static and dynamic characteristics of HF and EQ. Synergy efficiency zones had the highest coordination with diverse land use. Ecological conservation potential zones were found in low-disturbance hilly regions. Synergy restoration zones were concentrated in croplands and urban–rural fringe areas. Imbalance regulation zones were in forest areas under development pressure. Conflict alert zones were concentrated in urban cores, transport corridors, and lakeshore belts. These findings offer insights for global human–environment coordination in lake regions. Full article

(1) Background: As a prominent zoonotic parasitic disease, fascioliasis threatens the sustainable development of animal husbandry and public health. Current research focuses mainly on individual species (parasite or intermediate host), neglecting systematic evaluation of the transmission chain and exposure risks to animal husbandry. Thus, comprehensive studies are urgently needed, especially in the ecologically fragile alpine region of the Qinghai-Tibet Plateau; (2) Methods: Distribution data of Radix spp. and Fasciola hepatica in the Qinghai-Tibet Plateau and adjacent areas were gathered to establish a potential distribution model, which was overlaid on a map of livestock farming in the region; (3) Results: The key environmental factors influencing Radix spp. distribution were temperature seasonality (21.4%), elevation (16.4%), and mean temperature of the driest quarter (14.7%). For F. hepatica, the main factors were elevation (41.3%), human footprint index (30.5%), and Precipitation of the driest month (12.1%), with all AUC values exceeding 0.9. Both species exhibited extensive suitable habitats in Qinghai and Tibet, with higher F. hepatica transmission risk in Qinghai than Tibet; (4) Conclusions: The significant transmission risk and its impacts on the livestock industry in the Qinghai-Tibet Plateau highlight the need for proactive prevention and control measures. This study provides a scientific foundation for targeted alpine diseases control, establishes an interdisciplinary risk assessment framework, fills gaps in high-altitude eco-epidemiology, and offers insights for ecological conservation of the plateau. Full article

Solanum aculeatissimum is a herbaceous to semi-woody perennial plant native to the Brazilian ecosystem. It has naturalized extensively in southwestern China, posing significant threats to local biodiversity. This study systematically screened and integrated 100 distribution records from authoritative databases, including the Chinese Virtual Plant Specimen Database, the Global Biodiversity Information Facility, and Chinese Natural Museums. Additionally, 23 environmental variables were incorporated, comprising 19 bioclimatic factors from the World Climate Dataset, 3 topographic indicators, and the Human Footprint Index. The objectives of this research are as follows: (1) to simulate the plant’s current and future distribution (2050s/2070s) under CMIP6 scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5); (2) to quantify changes in the distribution range; and (3) to determine the migration trajectory using MaxEnt 3.4.4 software. The findings reveal that human pressure (contributing 79.7%) and isothermality (bioclimatic factor 3: 10.1%) are the primary driving forces shaping its distribution. The core suitable habitats are predominantly concentrated in the provinces of Yunnan, Guizhou, and Sichuan. By 2070, the distribution center shifts northeastward to Qujing City. Under the SSP5-8.5 scenario, the invasion front extends into southern Tibet, while retreat occurs in the lowlands of Honghe Prefecture. This study underscores the synergistic effects of socioeconomic development pathways and bioclimatic thresholds on invasive species’ biogeographical patterns, providing a robust predictive framework for adaptive management strategies. Full article

The sustainable development of the Guanzhong Plain Urban Agglomeration (GPUA), which is a pivotal Belt and Road hub, is critical for achieving the UN’s 17 SDGs. Based on the ecological footprint (EF) model, this study innovatively constructs a three-dimensional framework integrating natural and human-made capital, using the Gini coefficient and spatiotemporal analysis to evaluate resource allocation fairness in the GPUA from 2005 to 2022. Key findings include the following: (1) EF and GDP grew continuously at annual rates of 11.43% and 11.87%, while ecological carrying capacity (EC) stabilized, pushing the GPUA toward its ecological threshold under the Environmental Kuznets Curve (EKC). Moreover, the increasing Ecological Pressure Index (EPI) shows that after 2014, the GPUA has trended toward “extremely unsafe” status. (2) The ecological carrying capacity Gini coefficient (G₁, 0.1710–0.6060) fluctuated significantly, while the economic contribution Gini coefficient (G₂, 0.1039–0.3519) showed a narrow upward trend; since 2015, the comprehensive Gini (G < 0.4) indicates that the EF aligns with its EC and economic contribution. (3) The GPUA shows fair resource allocation. Tongchuan, Baoji, and Xianyang are low economic contribution and high ecological contribution; Xi’an and Yangling Demonstration Zone are high economic contribution and low ecological contribution; Weinan is low ecological contribution and low economic contribution. These findings provide critical insights for hub urban agglomerations to achieve the 17 SDGs through fair ecological resource allocation and sustainable development. Full article

The complex interaction between nature and human factors has led to frequent forest fires, but their combined effects in different areas remain unclear. Taking the Northeast China forest as the study area, this study integrates structural equation modeling (SEM) and Vine Copula analysis to quantify these drivers over 2001–2022. Results show that 70.42% of forest fires were caused by humans, clustering in populated low-elevation areas. SEM revealed partial correlations of 0.48 (weather conditions) and 0.59 (human activities) with forest fire frequency; canopy moisture was negatively correlated with fire (−0.38). Vine Copula indicated a joint probability of 0.32 between the human footprint index (HFI) and forest fires under high temperatures. This study can provide a framework for region-specific fire management in temperate forests by combining the effects of various influences. Full article

Biodiversity loss is a global environmental concern, mainly driven by human-induced factors, encompassing both direct and indirect drivers. This study investigates the long-term relationship between either the Human Footprint Index (HFI), which measures the extent of human pressures (i.e., direct drivers), or the Gross Domestic Product (GDP), a measure of economic growth (i.e., indirect driver) and biodiversity change, using bird population trends as indicators. The analysis was based on time-series data for Portugal (2004–2023) aggregated at national and sub-national scales, representative of different socio-economic contexts. Multi-species indices were regressed against either the HFI or GDP using Autoregressive Distributed Lag (ARDL) to identify long-run relationships. Bird population trends varied by species group (common, agricultural, and forest birds) and socio-economic context underscoring the importance of sub-national assessments. The HFI and GDP had varying predictive value across species groups and socio-economic contexts, with the HFI showing greater consistency, particularly as a predictor for agricultural birds. While most models showed a negative association between species abundance and either the HFI or GDP, revealing a signal of socio-economic pressures on bird populations at sub-national scales, some models suggested mixed results, indicating that conservation policies must take local contexts into account. Full article

The relocation of residents from high-risk areas is a critical measure to address safety and development issues in the floodplain regions of Henan Province in China. Whether the old villages can be reclaimed as farmland after demolition concerns Henan Province’s ability to maintain its farmland red line. This paper integrated multiple remote sensing indices and proposed a remote sensing identification method for monitoring the progress status of village relocation and reclamation that adapted to data characteristics and application scenarios. Firstly, it addressed the issue of missing target bands in GF-2 (GaoFen-2) by employing a band downscaling method; secondly, it combined building and vegetation indices to identify changes in land cover in the old villages within the floodplain, analyzing the implementation effects of the relocation and reclamation policies. Results showed that using a Random Forest regression model to generate a 4 m resolution shortwave infrared band not only retains the original target band information of Landsat-8 but also enhances the spatial detail of the images. Based on the optimal thresholds of multiple remote sensing indices, combined with human footprint data and POI (Points of Interest) identified village boundaries, the overall accuracy of identifying the progress status of resident relocation and reclamation reached 93.5%. In the floodplain region of Henan, the implementation effect of resident relocation was relatively good, with an old village demolition rate of 77%, yet the farmland reclamation rate was only 23%, indicating significant challenges in land conversion, lagging well behind the pilot program schedule requirements. Overall, this study made two primary contributions. First, to distinguish between rural construction and bare soil, thereby improving the accuracy of construction land extraction, an Enhanced Artifical Surface Index (EASI) was proposed. Second, the monitoring results of land use changes were transformed from pixel-level to village-level, and this framework can be extended to other specific land use change monitoring scenarios, demonstrating broad application potential. Full article

Floods are considered to be among the most dangerous and destructive geohazards, leading to human victims and severe economic outcomes. Yearly, many regions around the world suffer from devasting floods. The estimation of flood aftermaths is one of the high priorities for the global community. One such flood took place in northern Libya in September 2023. The presented study is aimed at evaluating the flood aftermath for Derna city, Libya, using high resolution GEOEYE-1 and Sentinel-2 satellite imagery in Google Earth Engine environment. The primary task is obtaining and analyzing data that provide high accuracy and detail for the study region. The main objective of study is to explore the capabilities of different algorithms and remote sensing datasets for quantitative change estimation after the flood. Different supervised classification methods were examined, including random forest, support vector machine, naïve-Bayes, and classification and regression tree (CART). The various sets of hyperparameters for classification were considered. The high-resolution GEOEYE-1 images were used for precise change detection using image differencing (pixel-to-pixel comparison and geographic object-based image analysis (GEOBIA) for extracting building), whereas Sentinel-2 data were employed for the classification and further change detection by classified images. Object based image analysis (OBIA) was also performed for the extraction of building footprints using very high resolution GEOEYE images for the quantification of buildings that collapsed due to the flood. The first stage of the study was the development of a workflow for data analysis. This workflow includes three parallel processes of data analysis. High-resolution GEOEYE-1 images of Derna city were investigated for change detection algorithms. In addition, different indices (normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), transformed NDVI (TNDVI), and normalized difference moisture index (NDMI)) were calculated to facilitate the recognition of damaged regions. In the final stage, the analysis results were fused to obtain the damage estimation for the studied region. As the main output, the area changes for the primary classes and the maps that portray these changes were obtained. The recommendations for data usage and further processing in Google Earth Engine were developed. Full article

Extracting building outlines from 3D models poses significant challenges stemming from the intricate diversity of structures and the complexity of urban scenes. Current techniques heavily rely on human expertise and involve repetitive, labor-intensive manual operations. To address these limitations, this paper presents an innovative automatic technique for accurately extracting building footprints, particularly those with gable and hip roofs, directly from 3D data. Our methodology encompasses several key steps: firstly, we construct a triangulated irregular network (TIN) to capture the intricate geometry of the buildings. Subsequently, we employ 2D indexing and counting grids for efficient data processing and utilize a sophisticated connected component labeling algorithm to precisely identify the extents of the roofs. A single seed point is manually specified to initiate the process, from which we select the triangular facets representing the outer walls of the buildings. Utilizing the projection histogram method, these facets are grouped and processed to extract regular building footprints. Extensive experiments conducted on datasets from Nanjing and Wuhan demonstrate the remarkable accuracy of our approach. With mean intersection over union (mIOU) values of 99.2% and 99.4%, respectively, and F1 scores of 94.3% and 96.7%, our method proves to be both effective and robust in mapping building footprints from 3D real-scene data. This work represents a significant advancement in automating the extraction of building footprints from complex 3D scenes, with potential applications in urban planning, disaster response, and environmental monitoring. Full article