Search Results (905)

Understanding moose calving space use in disturbed landscapes can inform forestry and wildlife management. GPS collars were deployed on 89 female moose between 2018 and 2022 in the Cree traditional territory of Eeyou Istchee in northern Quebec, Canada. We compared space use and fidelity during a 7-day period following video- and movement-estimated parturition dates to equivalent period measures in late winter and summer. We also evaluated calving site selection comparing the use and availability of terrain, land cover, road density, and fire and forestry disturbance. Female space use during the 7-day post-calving period was confined to 0.04 km², comparable to 7-day space use in winter, but smaller than 7-day space use in summer. Females observed calving across multiple seasons expressed low calving site fidelity, calving a median distance of 4.00 km from previously used sites, which was lower than summer range fidelity and similar to winter range fidelity. No differences were observed in calving site space use and fidelity between a southern forestry-affected area and a northern fire-affected area or with the extent of disturbance. Female moose exhibited individual variability in calving site selection, with overall preferences for elevated areas with mixed-wood or broadleaf forests and low road densities. Females were not observed calving in areas disturbed by forestry or fires within the last year, but some females preferentially selected 10- to 15-year-old burns as calving sites. Because female moose used and remained highly localized around dispersed, annually variable calving sites, protection of moose calving habitat should move beyond the specific sites where moose are observed calving to the general habitats preferentially used by moose during this critical life history stage. Full article

Intensifying climate change poses a major challenge to biodiversity conservation by weakening the ability of protected area systems to support species movement and ecological processes. However, protected area network planning has paid limited attention to the integration of climate connectivity and network resilience. Taking the highly urbanized Yangtze River Delta (YRD) as a case study, this study developed an integrated framework for climate-connected protected area network optimization. Specifically, climate refugia potential and species distribution probability were integrated to identify source areas, climate connectivity corridors were delineated by coupling landscape resistance with temperature gradients, and complex-network-based resilience analysis was applied to evaluate network responses under multiple disturbance and recovery scenarios. The results showed that: (1) climate stability, climate heterogeneity, and species distribution probability generally exhibited a south-to-north decreasing pattern, and 205 source areas were identified, mainly concentrated in the western and southern mountainous regions; (2) 459 climate connectivity corridors were extracted, forming a network backbone in the western and southern mountains, whereas corridors were relatively sparse in the plains and highly urbanized coastal areas; and (3) the network was highly vulnerable under critical-node-targeted, human-pressure-oriented, and climate-risk-oriented attack scenarios, while critical-node-priority recovery was the most effective strategy for restoring network function. These findings provide scientific support for cross-regional coordination, restoration prioritization, and long-term adaptive management in climate-connected protected area network planning. Full article

This study examines the landscape characteristics of high-suitability habitats for the Black-tailed Godwit (Limosa limosa) during the non-breeding season in inland and coastal wetlands of the middle and lower Yangtze River regions, and seeks to elucidate the distribution patterns and their drivers. Using the MaxEnt model and landscape analysis, the following conclusions were obtained: (1) High-suitability habitats for the Black-tailed Godwit cover approximately 128,800 km² and are primarily distributed across the middle and lower Yangtze River regions. (2) The dominant environmental variables were identified as elevation, distance to water source, slope, distance to paddy field, land use classification, and minimum temperature of the coldest month. (3) Landscape fragmentation, habitat connectivity, human disturbance, and climate change were found to be associated with the shift in the Black-tailed Godwit’s distribution from coastal to inland areas. (4) The distribution of the Black-tailed Godwit in the Nanji Wetland showed significant moderate positive correlation with shallow-water area (r = 0.38, p < 0.05) and significant moderate negative correlation with deep-water area (r = −0.48, p < 0.01). (5) At large spatial scales (coastal and inland wetlands), habitat connectivity and fragmentation were found to exert a greater influence, whereas at smaller spatial scales (Nanji Wetland) land use areas (wetlands and shallow-water areas) and food resources were found to exert greater influence on the Black-tailed Godwit’s distribution. This study synthesizes findings from multiple sources and aims to provide a reference for the conservation of the Black-tailed Godwit. Full article

Large-scale excavation for pumped-storage hydropower stations (PSPSs) in mountainous areas substantially alters slope soils and accelerates ecological degradation, yet quantitative multi-indicator assessments for such projects remain limited. This study integrates field surveys, laboratory analyses, and multi-temporal remote-sensing data to evaluate the disturbance-induced evolution of soil properties at two representative PSPSs in China. Soil bulk density and porosity measurements revealed significant compaction on disturbed slope surfaces, particularly on soil-dominated slopes. Key nutrient indicators, including organic matter, alkali-hydrolysable nitrogen, available phosphorus, and available potassium, showed consistent declines relative to adjacent undisturbed habitats. A comprehensive ecological degradation indicator (EDI) was constructed using five vegetation and soil spectral indices (RVI, NDVI, SAVI, SBI, and SM) weighted through the analytic hierarchy process. Time-series EDI mapping (2019–2023) demonstrated a progressive increase in moderately to extremely degraded areas during intensive construction stages. The results highlight the strong spatial heterogeneity of disturbance effects and underscore the necessity of soil-focused restoration strategies. This integrated assessment framework provides a scientific basis for guiding near-natural restoration and long-term soil–vegetation management in PSPS infrastructure landscapes. Full article

Wildfires are an increasingly recurrent disturbance in Mediterranean forest landscapes, yet fire severity assessment remains limited in data-scarce regions such as Lebanon. This study aims to assess wildfire severity patterns and identify the main environmental drivers influencing fire severity across the forests of Akkar, northern Lebanon, within a Digital Landscapes framework. Fire severity was mapped using the Differenced Normalized Burn Ratio (dNBR) derived from multi-temporal Landsat-8 imagery (2013–2024) processed in Google Earth Engine. Vegetation productivity was assessed through annual Net Primary Productivity (NPP), while topographic variables (elevation, slope, and aspect) were derived from a Digital Elevation Model. The results reveal heterogeneous fire severity patterns over the study period and pronounced spatial variability in NPP, with no consistent linear relationship between productivity and fire severity. Principal Component Analysis (PCA) was applied to explore multivariate relationships between fire severity, productivity, and terrain. PCA results show that the first two components explain 77.4% of the total variance, indicating that fire severity is primarily structured by topographic factors, particularly elevation and solar exposure, while vegetation productivity plays a secondary role. These findings highlight the dominant influence of terrain on wildfire severity in Mediterranean mountainous landscapes, and demonstrate the value of integrating remote sensing, cloud-based platforms, and multivariate analysis for fire assessment in data-scarce regions. The study contributes to the advancement of Digital Landscapes approaches by providing a scalable and data-driven framework for understanding fire dynamics and supporting future landscape management and risk assessment strategies. Full article

Of the many clinical phenotypes of obesity, the most prevalent are metabolically “healthy” (MHO) and metabolically “unhealthy” (MUO) obesities, the latter being associated with a range of comorbidities, including type 2 diabetes mellitus (T2DM). The underlying causes of different obesity phenotypes and the mechanisms of conversion of one phenotype into another have yet to be fully elucidated. However, increasing evidence suggests the key role of low-grade metabolic inflammation (metaflammation) in the pathogenesis of obesity and metabolic dysfunction. The review presents a comprehensive description of changes in immune cell populations and pro-inflammatory mediators, as well as a detailed comparative mapping of the adipose tissue immune landscape during MHO/MUO transition. Based upon a conceptual model for the intensification of metaflammation during MHO progression and conversion to MUO, a pattern of dynamical changes that accompany MHO/MUO transition is described. Though many parameters demonstrate significant differences in multiple cross-sectional and some longitudinal studies, only a few of them (CRP, IL-6, IL-17A, absolute counts of leukocytes and neutrophils) meet the criteria of a validated biomarker in clinical setting. A lack of standardization in MHO definition and heterogeneity in the severity of MUO make the search for predictive biomarkers a challenge. The review also discusses the mechanisms underlying metabolic memory and the incomplete reversibility of metabolic disturbances after bariatric surgery. Full article

Reservoir drawdown zones are repeatedly affected by water-level fluctuations and anthropogenic regulation, making vegetation recovery an important issue for ecological restoration and sustainable reservoir management. This study focused on Cynodon dactylon, a dominant herbaceous species in the drawdown zones of five reservoirs in the Jinsha River Basin, southwestern China. Drawing on the existing concept of stress memory, which emphasizes the retained effects of previous environmental stress exposure on subsequent plant responses, we established an integrated assessment framework based on species dominance, functional traits, landscape pattern indices, and the soil seed bank. This framework was used to evaluate variation in the stress memory of C. dactylon across different successional stages and inundation gradients. The results showed that the overall stress memory of C. dactylon increased with successional progression in both the upper and lower zones, indicating continuous adaptive accumulation under long-term hydrological disturbance. The memory reflected by individual component indicators also generally increased, although their accumulation patterns varied among indicators. These findings suggest that dominance, functional traits, landscape pattern, and the soil seed bank can jointly characterize the adaptive responses of C. dactylon during vegetation recovery. Overall, the stress memory framework provides a systematic approach for identifying stage-specific vegetation changes, evaluating restoration potential, and informing ecological restoration and sustainable management in reservoir drawdown zones. Full article

Reservoirs supplying urban areas are often managed according to their primary function, which may constrain ecological functioning. This paper presents a multiscale environmental assessment of the Serra Serrada Reservoir, NE Portugal, to identify environmental pressures and mitigation measures across landscape, catchment, reservoir-surroundings, reservoir, and urban scales. The assessment was based on field observations, available site-specific information, and technical data. Key pressures included marginal habitat disturbance, seasonal water-level fluctuations, drawdown-zone exposure, and urban water demand. Among the mitigation measures identified, urban water-efficiency interventions were further examined as a demand-side response in selected public buildings. These buildings accounted for about 31,500 m³ of annual water consumption, with potential savings of 1.5–74.4%. The case study highlights the value of linking environmental mitigation with urban water-efficiency measures. Full article

Understanding the environmental drivers underlying the spatial heterogeneity of soil organic carbon (SOC) in mountainous regions remains a major challenge in digital soil mapping. This study investigated the spatial distribution and driving mechanisms of SOC contents in a typical subtropical mountainous area using an integrated modeling and interpretation framework based on 132 soil samples. The SOC content in Yangshan County ranged from 3.33 to 50.00 g kg⁻¹, with a coefficient of variation of 48.64%, indicating a moderate level of variability across the study area. Six mainstream modeling approaches were compared, including multiple linear regression (MLR), geographically weighted regression (GWR), Cubist, eXtreme Gradient Boosting (XGBoost), random forest (RF), and a hybrid RF-GWR model. The results showed that RF outperformed traditional linear methods and other machine learning approaches, achieving an R² of 0.45 and RMSE of 7.78 g kg⁻¹, while the hybrid model further improved prediction accuracy (R² = 0.48). Then, spatial mapping revealed a clear elevational gradient, with higher SOC values concentrated in forested mountainous areas in the north and lower values distributed across low-elevation cultivated and disturbed zones. SHAP analysis identified intrinsic soil properties, particularly total nitrogen (TN) and cation-exchange capacity (CEC), as dominant controls on SOC contents. When extended to prediction datasets, relative humidity (RH) and mean annual precipitation (MAP) showed greater importance on SOC, suggesting an amplification of climatic factors at the broader scale. Subsequently, hotspot analysis of GeoShapley components further revealed the spatial differentiations in group indicators, with overall contributions ranked as soil physicochemical properties (36.4%) > geographic conditions (21.1%) > climate (17.4%) > organisms (12.9%) > parent material (12.1%). Soil properties formed clustered hotspots overlaid on carbonate-dominated areas, while geographic conditions and climate primarily acted as spatial modulators, generating localized zones of intensified or weakened influence across the landscape. The integrated framework proposed in this study has potential applicability across broader regions. These findings provided a scientific basis for the localized interpretation of environmental drivers of SOC and offered valuable support for region-specific land management and sustainable decision-making. Full article

The activity of an Apennine wolf (Canis lupus italicus) family inhabiting the natural reserve Calanchi di Atri in central Italy was monitored during the post-reproductive period (May–October) of two consecutive years (2023–2024), using ten camera trap sites. Detections were classified into adults and pups. Although records cover a limited period and focus on a single pack, they allowed the detection of variations in the spatiotemporal activity of the wolf family. In the first year, wolf activity peaked in summer, with adults frequently supervising pups at rendezvous sites. In the second year, activity by both adults and pups declined significantly and was accompanied by an evident shift in territory use. In addition to potential intrinsic factors, such as individual variability and litter dynamics, these variations may also reflect increased environmental stressors and anthropogenic disturbance. These findings provide insights into how wolves adapt their behavior in human-modified landscapes and highlight the importance of integrating human–wildlife dynamics into conservation and management strategies. Full article

Arrhythmogenic cardiomyopathy (ACM) is a genetic myocardial disorder marked by progressive cardiomyocyte loss, fibro-fatty replacement, ventricular arrhythmias, and risk of sudden cardiac death. Traditionally considered a structural and electrical disease driven by desmosomal dysfunction, emerging evidence redefines ACM as an inflammatory cardiomyopathy in which immune activation plays a central role. This review integrates genetic, molecular, experimental, and clinical data to highlight inflammation as a unifying feature of ACM. Desmosomal gene variants impair cell adhesion and also activate cardiomyocyte-intrinsic inflammatory pathways, including nuclear factor of kappa B (NFκB) and glycogen synthase kinase 3β (GSK3β) signaling, promoting cytokine release, immune cell recruitment, and fibrotic remodeling. Preclinical studies suggest inflammation precedes structural changes, indicating it may be an initiating event rather than a secondary response. Clinical and pathological findings support this model, with inflammatory infiltrates, circulating cytokines, and autoantibodies observed across disease stages. These processes often present as episodic “hot phases” resembling myocarditis, thus complicating diagnosis. The inflammatory landscape involves both innate and adaptive immunity, along with stromal and neuronal remodeling, contributing to arrhythmogenesis through gap junction disruption, calcium-handling abnormalities, and fibrosis. Environmental factors such as exercise, stress, and metabolic disturbances further modulate inflammatory pathways and disease expression. Therapeutically, this evolving perspective supports immunomodulatory approaches, including inhibition of NFκB, GSK3β, and cytokine signaling. Early clinical data on immunosuppressive and cytokine-directed therapies are promising, especially during active inflammatory phases, while gene-based strategies specifically address the underlying genetic defects. In conclusion, ACM should be recognized as an inflammatory cardiomyopathy shaped by interactions between genetic susceptibility and immune dysregulation. Integrating genetic and immunologic profiling may improve diagnosis, risk stratification, and treatment, ultimately leading to refined personalized therapeutic strategies. Full article

Soil organic matter (SOM) dynamics in tropical montane ecosystems remain poorly understood, particularly regarding the relative importance of particulate versus mineral-associated fractions under varying disturbance regimes. This study investigated SOM fraction distribution across the Ericaceous and Afroalpine belts of Bale Mountains National Park, Ethiopia, an Andosol-dominated landscape subject to recurrent fire. Using a stratified sampling design (n = 30 plots) across four vegetation classes (Ericaceous belt, fragmented Ericaceous belt, herbaceous and heathland, and giant Lobelia areas), three fire history categories (<10, 10–25, and >25 years since fire), and three topographic positions (northern slopes, southern slopes, and central plateau), we quantified coarse particulate organic matter (cPOM: 149–2000 μm), fine particulate organic matter (fPOM: 53–149 μm), and mineral-associated organic matter (MAOM: <53 μm). Particulate fractions dominated the SOM pool, with cPOM and fPOM together accounting for >99% of measured organic carbon. Multivariate ordination revealed a primary gradient (PC1, 61.7%) contrasting particulate-dominated soils in less disturbed areas with relatively MAOM-enriched soils in fire-impacted and fragmented zones. A global comparison reveals a profound stability gap: the Bale Mountains utilize <2% of the mineral stabilization potential of comparable Andosols, demonstrating that extreme fire frequency (<25 yr return interval) overrides even the most reactive mineralogy. We critically evaluate whether standard size-based fractionation adequately captures mineral-associated carbon in volcanic soils and discuss methodological limitations. These results provide baseline data for conservation planning in this biodiversity hotspot and underscore the need for fire management strategies that balance ecological integrity with carbon storage objectives. Full article

Monitoring boreal above-ground biomass (AGB) change requires approaches that are both measurement-based and spatially explicit. We integrated permanent sample plots from Quebec and Ontario with Landsat-7 spectral trajectories (1999–2023) to quantify non-fire-related AGB change after excluding wildfire-affected intervals and to evaluate whether annualized AGB change can be predicted from spectral change at the plot-interval scale. Tree height was estimated using a multilayer perceptron model (R² = 0.83) and combined with species-specific allometry to derive plot-level AGB and interval ΔAGB. These estimates were aggregated to ecodistricts using effective sample sizes and confidence intervals. Across well-sampled ecodistricts, mean annualized ΔAGB ranged from −0.82 to +3.54 t ha⁻¹ yr⁻¹, with lower or negative changes mainly occurring in eastern regions. Spectral indices derived from NIR–SWIR bands showed relatively stronger associations with ΔAGB than greenness-based indices, consistent with the sensitivity of moisture- and disturbance-related metrics to canopy stress, including defoliation. An XGBoost ensemble correctly predicted the direction of change in 77% of intervals. These results provide a measurement-constrained and scalable framework for monitoring non-fire-related biomass change and supporting greenhouse-gas reporting across boreal forest landscapes. Full article

The degradation of forest landscapes in West Africa, particularly in Togo, threatens ecological and socio-economic sustainability. This study analyzes the spatio-temporal dynamics of land use in the central plains of Togo between 1991 and 2022, and projects its evolution for 2030 and 2050 to guide restoration strategies. The methodology integrates the interpretation of Landsat images (1991, 2005, 2022) and the analysis of indicators, including conversion rates and the anthropization index. Prospective modeling (Markov chains and neural networks) follows a trend scenario. The results reveal a sharp decline in natural forest formations: dense semi-deciduous and dense dry forests (−50.55%) and woodlands (−62.06%), converted mainly to cropland, plantations, and built-up areas. Shrub/tree savannas, the dominant class, represent a transitional stage resulting from forest degradation. The average annual deforestation rate is 0.75%. The ecological disturbance index increased from 0.24 (1991) to 0.45 (2005), and then to 0.56 (2022), reflecting increased human impact and fragmentation. Projections indicate that these trends will continue, highlighting the growing vulnerability of ecosystems and the need to integrate this dynamic into sustainable management and restoration policies. Full article

This paper introduces the Quantum-Inspired Impulsive Continuous Hopfield Network (Q-ICHN), a novel hybrid control framework designed to handle non-smooth, high-energy perturbations in nonlinear dynamical systems. Standard Continuous Hopfield Networks (CHNs) rely on sigmoidal activation functions that are prone to gradient saturation, which leads to an insufficient corrective response when the system undergoes large deviations from equilibrium. To overcome this shortcoming, the proposed Q-ICHN adopts a wave-packet-based activation function grounded in the stationary Schrödinger equation, yielding a non-monotonic and oscillatory activation profile that sustains effective compensatory dynamics across a broad range of states. Furthermore, the proposed framework incorporates Madelung’s quantum potential into the control architecture, thereby enabling a fundamental reshaping of the system’s energy landscape. Specifically, this induces a tunneling-like mechanism that allows the system to circumvent local minima and rapidly recover from impulsive disturbances, manifested as a sharpened attractor structure in the phase-space domain. Together, these properties yield enhanced convergence behavior and improved robustness over traditional neural control approaches. To rigorously assess its merits, the performance of the Q-ICHN is evaluated through a large-scale benchmark involving 20 established control methods, including Sliding Mode Control (SMC), Model Predictive Control (MPC), and Backstepping. The experimental results obtained across 20 heterogeneous scenarios demonstrate that the proposed model achieves a 48% reduction in Mean Squared Error (MSE) relative to the classical ICHN. In addition, the Q-ICHN exhibits improved smoothness, reflected in a 30% reduction in jerk with respect to high-gain robust controllers, and enhanced reliability, validated by superior spectral purity and a 34% reduction in integrated variance under stochastic perturbations. Collectively, these results underscore the potential of quantum-inspired activation mechanisms to favorably balance control responsiveness and harmonic stability, providing a robust framework for handling both continuous dynamics and impulsive effects. Full article