Search Results (4,395)

Accidents between vehicles and wildlife (WVCs) represent a complex management challenge, requiring integrated strategies that balance biodiversity conservation with public security and socio-ecological resilience. However, existing GIS hotspot analyses often identify spatial patterns without quantifying the structural landscape drivers that compel animal–road interactions. This study aims to identify “ecological traps” through an integrated landscape diagnostic framework combining Kernel Density Estimation (KDE) for statistical hotspot identification and landscape metrics (FRAGSTATS) for structural diagnosis, using the wild boar (Sus scrofa) as a focal species. An exploratory case analysis of high-collision locations was conducted, utilizing a high-quality dataset of 161 precisely georeferenced incidents recorded between 2015 and 2020 within the most critical municipalities of the Province of Avellino (Southern Italy). Results highlight two primary hotspots: the Guardia Lombardi-Conza corridor and the Avellino Nord-Pratola Serra axis. Quantitative analysis reveals that 39.1% of incidents occurred in non-irrigated arable lands and 19.9% in broad-leaved forests, with 52.8% of events situated within 500 m of river systems, which function as primary ecological movement corridors. Furthermore, fragmentation indices (Patch Density, Edge Density) were significantly higher in these focus areas, confirming that habitat isolation and the loss of core patches force animals to traverse infrastructure. These findings underscore the urgency of evidence-based spatial planning, offering a methodological framework with potential applicability for prioritizing mitigation actions, such as ecological corridors and intelligent signaling, to enhance the resilience of socio-ecological systems. This framework provides a scalable model for sustainable land management, ensuring that biodiversity conservation is integrated into long-term infrastructure governance. Full article

Accurate and up-to-date information is essential for environmental monitoring, particularly in regions characterized by complex topography and heterogeneous landscapes. This study presents a multisource remote sensing–based approach for forest vegetation classification on La Palma Island (Canary Islands, Spain), which was further used to illustrate its potential for monitoring the temporal dynamics of different forest habitat types. Very high-resolution multispectral data from the WorldView-2/3 satellites were used, complemented by multispectral and LiDAR data acquired by an unmanned aerial vehicle (UAV). Four target forest vegetation types were mapped within a six-class classification scheme that also included “Other vegetation” and “Soil/Others” as non-target/background classes. The performance of ten supervised classification algorithms was evaluated, including Minimum Distance, Mahalanobis Distance, Parallelepiped, Spectral Angle Mapper, Maximum Likelihood, Naïve Bayes, K-Nearest Neighbors, Random Forest, Support Vector Machine, and the transformer-based deep learning model SegFormer. The results indicate that Random Forest achieved the highest overall accuracy, while Support Vector Machine and SegFormer also showed competitive performance, particularly when spectral information was integrated with vegetation indices and topographic variables. The study provides practical evidence on the selection of input data and classifiers for detailed forest vegetation mapping in a large and topographically complex island. Full article

Nature reserves (NRs) sustain biodiversity and multiple ecosystem services (ESs), yet assessments commonly stop at administrative boundaries and seldom examine how service relationships and associated factors vary across surrounding landscape gradients. We assessed carbon storage (CS), habitat quality (HQ), soil retention (SR), and water yield (WY) in 54 NRs in Jiangxi Province, China, and nested 5 and 10 km surrounding zones for 2000, 2010, and 2020. The InVEST model, a comprehensive ecosystem service index (CESI), correlation analysis, RMSE, and GeoDetector were used to characterize service dynamics, trade-offs/synergies, and spatial associations. CS and HQ increased slightly, whereas SR and WY rose until 2010 and subsequently declined. Mean CESI decreased outward from NR interiors (0.53) to the 5 km (0.50) and 10 km zones (0.49), although WY was higher outside NRs in 2020. CS–HQ and SR–WY were predominantly synergistic, while HQ–SR showed the strongest trade-off. Dominant associated factors and their interactions varied among services and spatial zones. These findings reveal context-dependent cross-boundary differentiation in ecosystem-service patterns, supporting coordinated management of NRs and their surrounding landscapes and service-specific conservation priorities. Full article

Rapid urbanization has led to habitat fragmentation in peri-urban areas, continuously eroding the ecological foundation of sustainable food systems in urban–rural transition zones and posing a real threat to regional food security. Against the backdrop of urbanization disturbances, traditional nature-based solutions have limitations in addressing socioecological nonlinear responses, whereas digital tools offer new governance pathways for enhancing food system resilience. To elucidate the intrinsic mechanisms through which digital technology empowers the resilience of peri-urban food systems, this study, which is grounded in ecological wisdom theory, constructs a system dynamics model that integrates “digital technology-ecological perception-ecological wisdom capital” in a three-dimensional linkage. This model simulates the dynamic process through which sustainable food systems in urban–rural transition zones resist the risks of habitat fragmentation and achieve synergistic steady-state evolution. According to the simulation results, a synthesized steady-state transition in sustainable food systems can be regarded as a self-organizing phase transition process. During resource metabolism, system elements show strong nonlinear symbiotic and mutually beneficial features. Further, there is a significant time-lag effect on improving food system resilience through digital technology empowerment and policy coordination. Also, the effects of governance are not immediately visible. Further, as an important instrumental empowerment carrier, urban–rural spatial and information barriers can be broken through means like digital ecological monitoring. Moderate investment in this regard can promote the acceleration of the system’s self-organizing phase transition. Also, this can enhance resilience against disturbance from habitat fragmentation while ensuring food production and supply. Finally, the ecological carrying capacity of core food production spaces does not increase monotonically. This means that the system possesses an adaptive cyclical fluctuation mechanism, with a periodic oscillatory evolution of carrying capacity. This study breaks through static analytical paradigms, fills the quantitative research gap on the resilience evolution of peri-urban food systems driven by the integration of digital technology and ecological wisdom, and can provide scientific evidence and decision-making support for food–ecological collaborative governance in China’s urban–rural transition zones. Full article

Xinjiang’s distinctive geography and climate provide important habitats for Falconidae species, yet their climate-driven habitat shifts and conservation gaps remain poorly understood. Using 2731 validated occurrence records and 26 environmental predictors, including bioclimatic, land-use, topographic, hydrological, anthropogenic, and Normalized Difference Vegetation Index (NDVI) predictors, we applied an optimized Maximum Entropy (MaxEnt) framework to project suitable habitats for seven falconid species under current conditions and three Shared Socioeconomic Pathway scenarios (1–2.6, 2–4.5, and 5–8.5) for 2041–2060, 2061–2080 and 2081–2100. Barycenter migration analysis, the Habitat Quality module of the Integrated Valuation of Ecosystem Services and Tradeoffs framework, and protected-area overlays were further integrated to identify conservation priorities. All models showed high discriminatory performance, with mean areas under the receiver operating characteristic curve exceeding 0.90. Current suitable habitats were mainly concentrated along river corridors and mountain foothills in the southern Altai, central-western Tianshan and northern Kunlun regions. Future responses were strongly species-specific. By 2081–2100 under Shared Socioeconomic Pathway 5–8.5, suitable habitat increased by 154.6% for Falco peregrinus and 79.5% for Falco tinnunculus but declined by 81.1% for Falco vespertinus; Falco subbuteo also showed overall expansion, with its suitable-habitat barycenter shifting by up to 291.9 km. Mean relative habitat quality was 0.514 [standard deviation = 0.185], while suitable habitat outside protected areas ranged from 35,346 to 304,866 km² among species, and high-quality priority conservation gaps reached 102,489 km² for Falco cherrug. These results demonstrate that climatic suitability does not necessarily correspond to high habitat quality or adequate protection and support species-specific, climate-adaptive conservation strategies for falconids in Xinjiang. Full article

Cliff habitats are characterized by limited and heterogeneous water availability, requiring plants to develop adaptive strategies to cope with drought stress. Opisthopappus longilobus, a cliff-endemic Asteraceae species restricted to the Taihang Mountains of northern China, has evolved under persistent water-limited conditions and represents a valuable model for investigating the molecular mechanisms underlying drought adaptation. However, the transcriptional regulatory networks involved in its drought response remain largely unexplored. In this study, we performed RNA sequencing of O. longilobus leaves under control and drought conditions to investigate drought-responsive regulatory networks. Six RNA-seq libraries were generated, and a total of 5260 differentially expressed genes (DEGs) were identified in response to drought stress. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed that these DEGs were mainly associated with phytohormone signal transduction, stress-responsive regulation, defense responses, metabolic reprogramming, and transcriptional regulation. Notably, multiple transcription factor families, including MYB, ERF, and ABF, were enriched among drought-responsive genes, suggesting their involvement in drought adaptation. Furthermore, quantitative RT-PCR was used to validate the RNA-seq results. Among the drought-responsive transcription factors, an R2R3-MYB transcription factor, OlMYB35, was identified as a candidate regulator and was further demonstrated to play a positive role in drought response through transient transformation assays. Taken together, this study provides new insights into drought-responsive regulatory mechanisms in O. longilobus and identifies OlMYB35 as a promising candidate gene for further functional validation and potential application in stress-resilient chrysanthemum breeding. Full article

Araucaria angustifolia (Bertol.) Kuntze, a critically endangered tree species, plays an irreplaceable ecological role in its native habitats. Under global climate change, identifying the drivers governing its geographic distribution and assessing climate-related threats can provide scientific guidance for the long-term conservation and habitat restoration of this species. In this study, a total of 287 valid occurrence records from 27 countries were compiled. Combined with 14 screened environmental variables, an optimized Maximum Entropy (MaxEnt) model was used to predict the potential suitable habitats of A. angustifolia under historical climate conditions (1970–2000), as well as under low-emission (SSP126) and high-emission (SSP585) scenarios for the future periods of 2050, 2070, and 2090. Under historical climatic conditions, the average training AUC value from 10 replicate model runs was 0.979, indicating excellent and reliable model performance. Globally, the species has 1.91 × 10⁶ km² of moderately suitable habitat and 0.95 × 10⁶ km² of highly suitable habitat, with a total suitable habitat area of 2.86 × 10⁶ km², accounting for only 1.92% of the global terrestrial area. Mean annual temperature (bio1), mean temperature of the coldest quarter (bio11), and annual temperature range (bio7) are the dominant environmental variables shaping the distribution of A. angustifolia, followed by annual precipitation (bio12). Under future climate scenarios, the overall suitable habitats of A. angustifolia exhibit a slight contracting trend, whereas their spatial distribution patterns remain relatively stable. Full article

Urbanization and agricultural expansion have led to widespread habitat loss and fragmentation, profoundly altering avian community structure. To understand how different habitat types shape bird diversity and assembly rules, we investigated bird communities across four major habitats (farmland, forest, urban, and wetland) in Henan province. Our results showed that wetland habitats supported the highest species richness, while forest habitats exhibited the highest Shannon–Wiener diversity and evenness. Rarefaction curves indicated that wetland and forest communities harbored the greatest species diversity even with increased sampling effort, while farmland communities showed early saturation. Venn diagram analysis revealed that 30.0% of species were unique to wetlands, 25.0% to forests, 8.3% to urban areas, and only 5.0% to farmland, with limited species overlap across habitats. Rank-abundance distribution (RAD) models varied significantly by habitat: the lognormal distribution best described farmland communities, preemption fit forest and wetland communities, and the Mandelbrot distribution best fit urban communities. Indicator species analysis identified Spilopelia chinensis as a significant indicator for farmland habitats (Indicator Value = 0.938, p < 0.01), reflecting its strong association with open agricultural landscapes. NMDS ordination (Stress = 0.152) showed partial overlap in community composition across habitats, while redundancy analysis (RDA) revealed that longitude (15.3% variance explained), latitude, altitude, and temperature collectively shaped bird community structure. These findings highlight that habitat type is a key driver of avian diversity and community assembly, with forests and wetlands serving as critical refuges for diverse bird communities. Urban and farmland habitats, while supporting lower diversity, host specialized species adapted to human-altered conditions. Our study emphasizes the need for multi-habitat conservation strategies to maintain avian biodiversity in fragmented landscapes. Full article

Concurrent high river water temperature (RWT), low dissolved oxygen (DO), and low river flow (RF) can degrade water quality and aquatic habitat, but their joint probability is rarely quantified in a fully trivariate framework. This study develops a semiparametric trivariate drawable-vine (D-vine) copula model that uses Gaussian kernel density estimation (GKDE) margins with parametric pair-copulas to estimate compound thermal–oxygen–low-flow hazards. The framework provides conditional exceedance probabilities and AND- and OR-joint return periods (RPs) for monthly synchronized RWT–RF–DO states. The analysis uses 117 synchronized monthly triplets from the Tungabhadra and 152 from the Yamuna. Kendall’s <!-- MathType@Translator@5@5@MathML2 (no namespace).tdl@MathML 2.0 (no namespace)@ --> Full article

Although asymbiotic orchid seed germination was first achieved in vitro in 1922, the prediction of germination requirements under in vitro conditions still remains complicated. To address this, we developed a machine learning framework to classify the ex situ asymbiotic germination potential of wild orchids into four discrete groups: Low (0–30%), Mid (31–50%), High (51–80%), and Max (81–100%). Models were trained on a dataset of 203 species, utilizing seed morphometrics—specifically, the embryo-to-testa (E:S) length ratio—alongside core ecological traits (subfamily, growth habit, habitat, and climate zone), as well as chemical scarification duration as a proxy of seed permeability. Validation leveraged novel germination and trait data from 26 taxa from Greece (17) and Armenia (9), published here for the first time. To mitigate class imbalance and prevent algorithmic bias toward highly germinating species, we applied inverse frequency weighting during training. Iterative testing of six algorithms revealed that the “Step 4” feature matrix (excluding climate zone and pretreatment duration) yielded the optimal predictive balance. K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) emerged as the superior models, achieving overall accuracies of 44.4% and 61.1%, respectively, with both achieving 100% accuracy for low-germinating species. Finally, we synthesized a novel database compiling new seed morphometrics from Armenia (17 taxa), Greece (52 taxa), and the data from the literature (479 taxa). After filtering previously utilized species, we generated a prediction pool of 361 orchid taxa. Applying our Step 5 KNN and SVM models to forecast their germination behavior revealed distinct variations linked to ecological profiles. This high-accuracy framework, particularly for low-germinability groups, offers a powerful screening tool for ex situ conservation planning. The final trained models are compiled in the publicly available R (v. 4.6.0) package OrchidGermClass. Full article

Understanding how multiple stressors operating across spatial scales shape riverine biota is essential for advancing sustainable ecosystem management. This study evaluated the relative importance of hydromorphological alterations and land-use changes for benthic invertebrate (n = 77) and fish assemblages (n = 92) in large Slovenian rivers. Multivariate analyses were employed to disentangle unique and shared effects of stressors at reach, segment, and catchment scales while accounting for natural variability. The results revealed a hierarchical structuring of stressors, with regional factors shaping local conditions. Fish assemblages were predominantly associated with local-scale stressors, particularly at the segment scale, whereas benthic invertebrates were more strongly associated with shared stressor effects across multiple spatial scales. Stressors explained substantially more variability in fish (43.5%) than in benthic invertebrates (19.9%). Catchment-scale land use showed higher explanatory power than local riparian land use. Typological variables moderately improved model performance, emphasizing the contribution of natural environmental gradients. Our findings suggest that large river management may benefit from a multi-scale perspective that prioritizes segment-scale habitat conditions for fish and coordinated catchment-to-local strategies for benthic invertebrates, with potential benefits for ecosystem resilience and long-term sustainability. Full article

Intertidal habitat heterogeneity can shape macrobenthic communities, but its links to assembly processes and network stability remain uncertain. Macrobenthos were surveyed at 22 sites across muddy sediment (MA), oyster farming (OF), eastern rocky reef (EA), and southern rocky reef (SA) habitats around Nan’ao Island. Habitat type explained 25.5% of compositional variation, whereas observed richness and evenness showed no consistent among habitat differences. Mean between-habitat Bray-Curtis dissimilarity (0.730) and turnover (0.623) exceeded their within-habitat values (0.634 and 0.555), indicating that species replacement, rather than nested species loss, dominated community differentiation. Functional richness differed among habitats, whereas most other functional indices remained comparatively stable. Neutral-model fits were stronger in EA and SA (R² = 0.501 and 0.475, respectively) than in MA and OF, and null-model partitioning indicated undominated processes in MA and OF but greater homogenizing dispersal in rocky habitats. SA contained the largest co-occurrence network (24 nodes, 81 edges), although robustness was highest in OF. Exploratory PLS-SEM showed positive associations between habitat heterogeneity and environmental filtering (β = 0.591, p = 0.004) and between turnover and potential network stability (β = 0.593, p = 0.022), whereas the assembly–stability association was nonsignificant (β = 0.044, p = 0.856). These cross-sectional associations support a conceptual pathway linking habitat-related environmental gradients, species turnover, assembly shifts, and network reorganization. Full article

Winter water allocation in regulated plain river networks requires an operational link between hydrological low water benchmarks and replenishment decisions. This study focused on the Lixiahe plain river networks; utilizing winter water level records from nine stations during 2006–2021, five hydrological methods were applied to derive candidate water level benchmarks. A one-dimensional MIKE 11 model was applied to simulate total upstream replenishment scenarios of 40–400 m³/s and to establish station-specific stage–discharge relationships under fixed downstream boundaries and structure operation rules. Within the simulated range, the candidate benchmarks corresponded to scenario-specific replenishment requirements of 153.94–268.49 m³/s. The framework supports comparison of winter water allocation strategies and identification of controlling stations, but ecological sufficiency requires independent evidence from species, habitat, thermal, dissolved oxygen, and water quality responses. Full article

Climate and land-use change are shifting the spatial distribution and habitat suitability of many plant species, particularly those with narrow ecological niches and limited ranges. Dracaena cochinchinensis (Lour.) S.C.Chen is a medicinally important and nationally protected plant species distributed in tropical and subtropical southern China. Predicting its habitat suitability under current and future climate scenarios is essential for understanding its responses to environmental change and guiding conservation and resource management. In this study, an optimized maximum entropy model was used to predict the current habitat suitability of D. cochinchinensis in China and to assess changes in habitat suitability under future climate scenarios in the 2090s. The model showed reliable predictive performance for estimating species distribution. Under current conditions, suitable habitats were concentrated in southern China, particularly southern Yunnan, Guangxi, Guangdong, Hainan, Taiwan, and parts of Fujian, covering 23.93 × 10⁴ km² (8.31% of the study area). Mean temperature of the driest quarter (Bio9), temperature annual range (Bio7), and precipitation seasonality (Bio15) were the dominant predictors, highlighting the importance of thermal conditions in shaping species distribution, whereas lithology (Lith) and soil type (ST) were the major non-climatic contributors influencing habitat suitability. Under future scenarios, suitable habitats showed spatial redistribution rather than uniform expansion or contraction. The spatial comparison among future scenarios identified stable habitats in the central and southwestern parts of Hainan Island, south-central Yunnan, and western Guangxi, with habitat losses mainly occurring in coastal South China and limited expansion in southwestern regions. Habitat centroid shifts were scenario dependent, with an eastward shift under SSP1-2.6 and northwestward shifts under SSP3-7.0 and SSP5-8.5, with migration distances ranging from 60.09 to 165.77 km. These results indicate that climate change may substantially reorganize the distribution pattern of D. cochinchinensis in southern China. Therefore, future conservation planning should prioritize current high-suitability areas, potential macroclimatically stable areas, and emerging suitable habitats to support long-term preservation and sustainable utilization of nationally protected medicinal species. Full article

Stored-product insects threaten global food security, yet the environmental mechanisms governing their responses to climate change remain poorly understood. Existing pest distribution projections rarely integrate diurnal thermal variability with agricultural land use. Here, we show that diurnal thermal variability, together with agricultural land use, is a major determinant of habitat suitability for three globally important Callosobruchus pests across the Middle East. Using optimized species distribution models integrating climate, topography, and cropland under contrasting CMIP6 climate scenarios, we demonstrate that mean diurnal temperature range and cropland consistently emerge as the strongest predictors across all species, revealing the importance of daily thermal fluctuations beyond mean warming alone. Under the low-emission scenario (SSP1-2.6), suitable habitat by mid-century expands substantially for C. chinensis and C. phaseoli, while remaining little changed overall for C. maculatus, for which comparable local expansion and contraction largely offset one another; under the high-emission scenario (SSP5-8.5), gains are reduced, and localized contractions occur, particularly for C. chinensis and C. maculatus, the latter shifting to a slight net loss in total suitable area. Persistent climatic refugia remain along Mediterranean and Red Sea coastal regions, whereas habitat losses are concentrated in the northern Gulf lowlands and Zagros foothills. Our findings identify diurnal thermal variability as an overlooked dimension of stored-product pest ecology and show that integrating agricultural landscapes with climate projections can improve forecasts of future pest risk, providing a framework for climate-informed surveillance, biosecurity, and adaptation. Full article