Search Results (775)

Forest management strongly influences understory plant communities, yet their responses to management remain context-dependent and scale-sensitive. This review synthesizes current knowledge on understory vegetation in managed forests by combining a bibliometric analysis of global research trends (1986–2025) with a comprehensive qualitative evaluation of empirical studies. The bibliometric assessment identified 726 publications, with a marked increase in output after 2017 and a predominance of research articles within Environmental Sciences, forestry, and biodiversity conservation. Research activity is geographically concentrated in North America and Europe, while other biomes remain comparatively underrepresented. The classical review highlights canopy structure and light availability as primary drivers of understory diversity. Management interventions such as thinning, harvesting, gap creation, and prescribed fire frequently increase local (α) species richness and vegetation cover in the short term by promoting early-seral and light-demanding species. However, these gains are often accompanied by compositional shifts toward ruderal and generalist taxa, reductions in bryophytes and forest specialists, and declines in spatial heterogeneity at broader scales. Consequently, β-diversity and landscape-level distinctiveness may decrease despite stable or elevated plot-level richness. Management intensity, retention of structural legacies (e.g., large trees and deadwood), soil and hydrological conditions, landscape context, and interactions with global change drivers (e.g., nitrogen deposition and climate warming) strongly mediate vegetation responses. Low-to-moderate-intensity systems that maintain structural heterogeneity and approximate natural disturbance regimes appear most effective at balancing production objectives with biodiversity conservation. Understory vegetation responses to forest management reflect trade-offs between short-term richness increases and long-term risks of compositional homogenization and specialist decline. Evaluating biodiversity outcomes therefore requires multi-scale, trait-informed approaches that move beyond species richness alone. This synthesis provides an integrative framework to support evidence-based, biodiversity-oriented forest management in increasingly human-modified landscapes. Full article

Improving the understanding of hydrological drought mechanisms is paramount for drought resistance and early warning in a changing environment. The Upper Yellow River Basin (UYRB), the primary water-producing region of the Yellow River Basin, experiences hydrological droughts that are jointly influenced by climate variability and human activities. This study systematically investigated the spatiotemporal evolution of hydrological droughts in the UYRB and elucidated their underlying mechanisms from the perspectives of sub-basin contributions, reservoir regulation, and large-scale climate drivers. We found an increasing trend in yearly drought severity from 1956 to 2010 under the natural scenario, but large reservoirs significantly reduced the severity. The headwater region, Tao River Basin, and interval region 2 were identified as the key areas in drought formation of the whole UYRB. Reservoirs generally increased monthly drought intensity in summer and autumn but decreased drought intensity in spring and winter. For drought events lasting for a longer time, reservoirs interrupted their continuity, which reduced the average severity and duration but exaggerated peak intensity, especially for extreme events. The impacts of different reservoirs on drought variations showed distinct differences due to different operation regulations. Cascade reservoir regulation weakened and altered the relationships between hydrological drought and climate indices, including PDO, AMO, NAO, and ENSO, at the 8–16 and 32–64 month timescales. This finding indicates that the effects of large-reservoir regulation should be removed using naturalized streamflow when identifying teleconnection drivers of hydrological drought and conducting drought early warning. These findings provide new insights into the mechanisms governing hydrological drought under the combined influences of climate change and reservoir regulation and offer a scientific basis for drought early warning, reservoir operation, and integrated water resources management. Full article

As a globally climate-sensitive region and ecological security barrier, the spatiotemporal dynamics of net ecosystem productivity (NEP) on the Qinghai–Tibetan Plateau are of great significance for understanding carbon cycling in alpine ecosystems. However, due to insufficient representation of parameter heterogeneity in models and unclear nonlinear attribution of complex environmental factors, substantial uncertainties remain in the spatiotemporal patterns and driving mechanisms of NEP in this region. Therefore, this study first employed an improved Carnegie–Ames–Stanford Approach (CASA) model to assess NEP on the Qinghai–Tibetan Plateau from 2000 to 2022 and characterize its spatiotemporal evolution, and subsequently applied the optimal multivariate-stratification geographical detector (OMGD) to quantify the independent and synergistic driving effects of hydrothermal conditions, extreme climate events, and human activities on NEP variations. The results indicate that: (1) from 2000 to 2022, vegetation NEP on the Qinghai–Tibetan Plateau exhibited a southeast-to-northwest decreasing spatial heterogeneity pattern, with a multi-year mean value of 219.61 g C·m⁻²; (2) during the study period, NEP showed an overall increasing trend (at a rate of 1.596 g C·m⁻²·yr⁻¹), with 52.5% of the region experiencing significant increases, primarily concentrated in the central–eastern humid regions and alpine meadow areas; and (3) among individual factors, the growing season length was the primary driver of NEP, in addition to temperature and precipitation, while human activities exerted negligible influence; under interaction effects, the hydrothermal synergistic enhancement (0.79 < q < 0.89) exhibited the highest explanatory power. These results show that carbon sequestration in alpine ecosystems is governed by nonlinear hydrothermal interactions and provide a scientific basis for assessing carbon sink resilience in the “Asian Water Tower” under global warming. Full article

This study investigates the empirical links between climatic/socio-economic factors and Consumer Price Food Indices (CPFIs) across 47 advanced and emerging economies from 2001 to 2022. Utilizing a balanced panel dataset compiled from the World Bank’s World Development Indicators, FAO databases, and UNDP Human Development Reports, the analysis applies fixed-effects and random-effects panel data models to evaluate the drivers of food price dynamics. The empirical results reveal that socio-economic variables, specifically the Human Development Index (HDI), food price inflation, and agricultural productivity, are consistently associated with food indices, underscoring the critical role of structural development and nominal inflationary pressures. Climatic factors, particularly temperature anomalies, also exert a significant impact: a one-degree Celsius increase in temperature anomalies is associated with a 0.82-unit rise in the food price index level, whereas expansions in forest area and agricultural land are linked to price reductions. These findings indicate that food price dynamics are shaped by the intricate interplay of climate variability and socio-economic conditions. Consequently, policy recommendations emphasize targeted investments in socio-economic development, climate adaptation measures (including support for climate-resilient agriculture), and sustainable land management (forest conservation and optimized land use) to enhance food system resilience and support long-term food security. Full article

Recent advances in numerical modelling, remote sensing, and artificial intelligence are bringing a transformation in our ability to assess sediment transport. Nevertheless, climate change is fundamentally altering sediment production, transport, and deposition through intensifying hydrological extremes, sea-level rise, changing storm regimes, cryosphere degradation, and increasing human modification of sediment pathways. These interacting drivers challenge conventional sediment transport assessment, which has largely evolved within separate fluvial, estuarine, coastal, and marine disciplines and often lacks an integrated perspective capable of representing source-to-sink sediment connectivity under non-stationary environmental conditions. Although significant advances have been made in process-based numerical modelling, Earth observation, and artificial intelligence (AI), these approaches are commonly reviewed independently, limiting their collective application to regional climate-responsive sediment assessment. This review examines state-of-the-art process-based numerical models, observational tools, and machine-learning approaches for sediment transport from source-to-sink. A transparent benchmarking scheme is used to compare leading modelling systems (e.g., AdH, SRH-2D, FLO-2D, HEC-RAS, TELEMAC, Delft3D, EFDC, SCHISM, XBeach, ROMS), highlighting differences in dimensionality, sediment-process representation, computational demands, and climate-scenario readiness. Remote sensing (optical, SAR, LiDAR, UAV) and AI/ML/DL methods (e.g., random forests) are reviewed as complementary tools that enhance model parametrization, improve validation, and address uncertainty in data-limited regions. A reproducible bibliometric synthesis based on Dimensions.ai records (2000–2026) reveals accelerating growth in sediment-transport research, with strong recent expansion in coastal, estuarine, and data-driven modelling applications. Major challenges include cohesive sediment physics, cross-environment coupling, limited long-term validation datasets, and the need for scalable workflows compatible with climate-model forcing. In this manuscript, we analyse and propose a future roadmap for near-term integration of satellite–field data streams, medium-term development of hybrid physics–AI models, and long-term coupling of sediment modules within Earth-system and regional climate frameworks. Collectively, this review provides a foundation for next-generation, climate-responsive sediment transport assessment supporting sustainable river basin and coastal management. Full article

Ecological resilience is a critical indicator of forest ecosystem stability and the capacity to respond to disturbance. Under intensifying climate change and human activities, accurately evaluating forest ecological resilience is important for ecosystem restoration and sustainable management. This study developed a satellite time−series−based framework for assessing ecological resilience from the complementary perspectives of resistance and recovery. Taking the Qianshan region, a typical forest area in the northeastern forest belt, as a case study, MODIS Normalized Difference Vegetation Index (NDVI) time−series data from 2005 to 2024 were analyzed. The Breaks For Additive Season and Trend (BFAST) algorithm was used to detect vegetation breakpoints, after which ecological resistance and recovery were quantified using breakpoint magnitude and post−disturbance NDVI growth rate. The optimal−parameter−based geographical detector (OPGD) was further applied to identify the spatial drivers of resistance and recovery and their interaction effects. Approximately 21% of the pixels in the Qianshan region experienced at least one breakpoint during the study period, and more than 80% of the disturbed pixels contained only one detected breakpoint. More than 70% of the disturbed pixels subsequently exhibited vegetation recovery, and most recovered pixels had normalized recovery values between 0.40 and 1.00. In contrast, ecological resistance was generally low and varied substantially among land−cover types. Forests exhibited higher resistance but lower recovery, whereas grasslands and croplands showed lower resistance but stronger post−disturbance recovery. Among the individual factors, precipitation and slope had relatively high explanatory power for the spatial differentiation of recovery. Factor interactions substantially enhanced explanatory power, with the interaction between precipitation and elevation exerting the strongest influence on resistance and the interaction between precipitation and slope exerting the strongest influence on recovery. Although mining density had relatively limited explanatory power at the regional scale, mining activities caused non−negligible localized impacts, particularly in open−pit mining areas. The proposed framework provides a practical basis for long−term monitoring, ecological restoration, and differentiated forest management in disturbance−prone regions. Full article

Within the framework of the dual carbon strategy, investigating the spatiotemporal characteristics and driving factors of carbon sequestration in metropolitan areas through land use analysis is important for mitigating climate change and promoting regional ecological protection and sustainable development. On the basis of land use time points for five phases from the Jinan metropolitan area (JMA) covering the period from 2000 to 2024, the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model was coupled with the extreme gradient boosting (XGBoost)–Shapley Additive exPlanations (SHAP) and multiscale geographically weighted regression (MGWR) models to explore the spatiotemporal variations in carbon storage and its driving factors. In the last 24 years, cropland has been the predominant land use category in the JMA, representing almost 62% of the overall area. Throughout the five periods, the transition from cropland to construction land predominated, resulting in an 11.78% reduction in farmland and a 49.73% expansion in construction land. Between 2000 and 2024, carbon storage in the JMA decreased overall, with a total reduction of 3.70 Tg. The occupation of farmland for construction purposes was the primary cause of the decrease in carbon storage. The spatial pattern of carbon storage was similar to that of land use in the JMA, characterized by a distribution pattern with elevated values in the southeast and reduced values in the northwest. The SHAP analysis results demonstrated that the contributions of driving factors such as elevation, vegetation coverage, human footprint, and population density were generally high, making them the main drivers affecting carbon storage, with a significantly greater contribution of natural factors than human activity factors. The MGWR model results revealed that the digital elevation model and fractional vegetation cover positively influenced carbon storage in the JMA, whereas the population density imposed a negative effect. These results could guide the judicious allocation and utilisation of resources in urban regions, the establishment of ecological conservation areas, and the advancement of regional sustainability. Full article

The Inner Mongolia Autonomous Region, located in northern China, spans a vast territory. It serves as a crucial transitional zone connecting three major ecosystems—forest, grassland, and desert—and constitutes an essential component of the nation’s ecological security barrier. Over the past three decades, significant changes have occurred in the region’s land use structure and landscape patterns. Based on long-term time-series land use data, this study systematically analyzed land cover changes and landscape diversity characteristics by dividing the region into climatic zones, ecological zones, and administrative areas. Additionally, a driver analysis framework based on the eXtreme Gradient Boosting (XGBoost) model was constructed, and the SHapley Additive exPlanations (SHAP) method was employed to quantitatively identify the contribution rates of driving factors influencing the Shannon Diversity Index (SHDI). Results indicate pronounced spatial heterogeneity in land-use structure dynamics across regions. Among all land use types, grasslands, croplands, and bare land exhibited the most pronounced changes under the combined influence of natural environmental variations and human disturbances. Against this backdrop of landscape pattern evolution, the regional Shannon Diversity Index (SHDI) also showed differentiated variation trends. Based on SHAP contribution analysis, population density (POP) was identified as the primary driver affecting SHDI changes, with a contribution rate of 29.9%, followed by precipitation (PRE, 19.5%), elevation (DEM, 17.2%), and GDP (11.9%). Further SHAP response analysis revealed the nonlinear effects and characteristics of different driving factors on SHDI, indicating that the proposed framework can effectively elucidate the driving mechanisms of landscape changes. The findings provide scientific support for developing sustainable and differentiated land-use management strategies and optimizing ecological conservation and restoration measures across regions with diverse ecological backgrounds. Full article

Climate change and human activities are widely recognized as major drivers of global biodiversity loss. The Brown Eared Pheasant (Crossoptilon mantchuricum), a ground-dwelling avian species unique to China, is confined to a highly fragmented range and faces persistent long-term threats. To devise robust and site-specific preservation strategies, it is imperative to elucidate the species’ sensitivity to climate change and pinpoint high-priority protection areas. In this study, we integrated field surveys, ensemble species distribution modelling, Marxan systematic conservation planning, and GAP analysis to assess habitat suitability changes under future climate scenarios and identify conservation gaps for the Brown Eared Pheasant. Our results demonstrate that the species’ distribution is primarily driven by annual temperature range, precipitation in the coldest and wettest quarters, and elevation, underscoring its highly restricted climatic niche. Based on current climatic conditions, highly suitable habitats are primarily distributed across specific mountainous regions: Hebei Province (Xiaowutai Mountains), Shanxi Province (the Taihang, Lvliang, and Taiyue mountain systems), Shaanxi Province (Huanglong Mountains), and northwestern Beijing (the Donglingshan area). Simulations conducted across prospective climatic scenarios suggest a continued decline in suitable habitat, which becomes increasingly restricted to higher latitudes and mountainous regions. According to our GAP analysis, current protected area networks fail to encompass 72.27% of the priority conservation zones identified, with significant gaps in the southern Lvliang, Taiyue, and Huanglong Mountains. The outcomes of this research establish a robust empirical foundation with which to inform prospective preservation strategies and habitat stewardship for the Brown Eared Pheasant. Full article

Mangroves provide important ecological and socioeconomic services but are among the most threatened coastal ecosystems, impacted by human activities and natural events, especially climate-related processes. Remote Sensing (RS) has become a key tool for monitoring changes in mangrove coverage and identifying drivers of transformation. In Brazil, which hosts the second-largest mangrove area globally, RS has generated important insights; however, gaps remain in its application for analyzing internal changes in these environments. This study presents a bibliometric analysis of research on Brazilian mangroves over the past 20 years. Data were collected from Scopus and Web of Science databases following PRISMA guidelines, and selected articles were systematically analyzed. Results show that, despite a limited number of publications, studies address diverse topics, reflected in a wide range of journals. Only 32% of studies were conducted in protected areas, although 87% of Brazilian mangroves are located within them. Landsat imagery was the most commonly used data source, likely due to its long-term availability and free access, while NDVI was the main vegetation index applied. Overall, these findings provide useful information for researchers, policymakers, and society, enhancing understanding of scientific knowledge on Brazilian mangroves and supporting the use of RS for ecosystem monitoring and management. Full article

Artificial light at night (ALAN) is an increasing component of urban environmental change, affecting vegetation dynamics and ecosystem functioning. Satellite nighttime light (NTL) data serve as proxies for urbanization and artificial illumination, aiding the analysis of vegetation responses to human pressures. However, NTL–vegetation relationships are often poorly synthesized, and ALAN is rarely included in frameworks linking urban vegetation, climate, and human drivers. Drawing on a 2014–2025 Scopus and Web of Science search, this review of 22 articles categorizes findings as (i) Lights Track Urbanization, (ii) Vegetation Modulates Light, and (iii) ALAN Shapes Ecology. Results show strong geographical concentration in China, followed by the United States, and high heterogeneity in sensors, metrics, and methods. Increasing nighttime radiance is consistently associated with vegetation decline and higher environmental pressure, while vegetation modulates light through canopy structure and phenology. ALAN effects on plant phenology are reported but vary relative to climatic drivers and are highly context-dependent. Despite these advances, the field remains methodologically inconsistent and geographically biased. This review highlights the need for harmonized multi-sensor frameworks that integrate radiance, vegetation, and climate data to improve assessments of urban environmental change and to support biodiversity conservation and light-sensitive urban planning, thereby preserving ecosystem service functions. Full article

Global climate change and rapid urbanization have intensified extreme precipitation events, making urban pluvial flood risk management particularly urgent. However, existing research exhibits theoretical and methodological limitations in reconciling pluvial flood resilience within high-density urban development, especially lacking a quantitative framework for human mobility resilience based on natural hydrological units. To address this, this study takes 48 natural catchment areas in Shenzhen as research units. By integrating mobile phone signaling data, remote sensing imagery, and multi-dimensional spatial form indicators, it constructs a post-disaster recovery curve based on population dynamics to quantify human mobility resilience. Using Principal Component Analysis (PCA), Ordinary Least Squares (OLS) regression, and the K-means clustering method, this research analyzes the mechanisms through which green infrastructure, road network structure, and three-dimensional building morphology influence resilience. The results show that three-dimensional building morphology is the core driver of post-disaster recovery capacity, with Floor Area Ratio (FAR), Standard Deviation of Building Height (SDBH), and Building Coverage Ratio (BCR) significantly promoting recovery, while Building Shape Coefficient (BSC) exerts an inhibitory effect. A key comparative analysis reveals that the model based on natural catchment areas has significantly better explanatory power than the model using stormwater management units, and the dominant factors differ: building morphology factors are more prominent in natural hydrological units, whereas road network structure factors are more significant in stormwater management units. This study confirms that natural geographic boundaries can more authentically reveal the intrinsic “morphology–resilience” relationship, providing an important theoretical and empirical basis for optimizing the planning and management units of sponge cities in high-density urban areas. Full article

Urban expansion has profoundly altered ecosystem carbon dynamics, yet its associations with changes in aboveground biomass (AGB) and belowground biomass (BGB) remain insufficiently understood at the national scale. This study investigated urban expansion areas in 352 Chinese cities from 2000 to 2020 using 1-km-resolution AGB and BGB datasets. By integrating temperature, precipitation, potential evapotranspiration, and atmospheric CO₂ concentration, we developed the Enhanced Aboveground Biomass Deviation Index (EAGB) and Enhanced Belowground Biomass Deviation Index (EBGB) to identify areas showing positive and negative biomass responses during urban expansion and to explore their spatial patterns and driving mechanisms. Between 2000 and 2020, China’s urban land expanded by 52,635 km², corresponding to an average annual expansion rate of 4.1%. Areas showing positive biomass responses during urban expansion accounted for 22,376 km² (42.5%) for AGB and 23,569 km² (44.8%) for BGB, mainly concentrated in East and Central China. In contrast, areas showing negative biomass responses covered 5233 km² (9.9%) for AGB and 3911 km² (7.4%) for BGB, primarily distributed in Northwest and Southwest China. During urban expansion, biomass declines were more frequently observed in rapidly expanding small- and medium-sized cities in Northwest and North China, whereas larger cities generally exhibited more widespread biomass gains. Climatic factors, particularly precipitation and atmospheric CO₂ concentration, together with human activities, were the dominant drivers of the observed spatial heterogeneity. Moreover, the dominant driving mechanisms shifted from the combined influence of climatic factors and human activities during 2000–2010 to the combined influence of climatic and topographic factors during 2010–2020. These findings provide a new framework for assessing biomass responses during urban expansion and offer scientific support for urban land-use optimization and ecological conservation. Full article

Climate change is progressing most rapidly in the high-latitude regions of the Northern Hemisphere, where boreal and Arctic ecosystems are experiencing significant environmental changes and transformations. These ecosystems are being altered by rising temperatures, changing precipitation patterns characterized by more intense rainfall events, decreasing snow and ice cover, and increasing floods, heat waves and fires. These changes pose significant risks to ecosystems, human health, and animal health, especially in the boreal zone. A narrative literature review was conducted across major scientific databases, including Scopus, Web of Science, PubMed, and Google Scholar, covering publications up to 2026. The evidence was combined thematically across key climate hazards and health domains, including infectious and non-communicable diseases, and mental health outcomes. The review shows that accelerating warming in northern regions is changing species distribution, increasing the risk of zoonoses and vector-borne diseases, and increasing cardiovascular, respiratory, and mental health issues. Extreme heat and cold events are important drivers of morbidity and mortality, while floods and wildfires contribute to long-term psychological distress. Vulnerable populations, including elderly adults and socioeconomically disadvantaged groups, are mostly affected. This review identifies key knowledge gaps that could be filled by developing evidence-based public health adaptation plans in high-latitude regions. Full article

This study investigates lichen colonisation on a medieval basalt Armenian cross-stone (khachkar) in the Noratus cemetery, the largest extant concentration of memorial khachkars in Armenia, integrating fine-scale vegetation sampling, microhabitat assessment, and multivariate analysis. A preliminary survey of c. 30 cross-stones confirmed that the selected one was representative of the site. Thirty-six evenly spaced relevés of 10 cm² were recorded across the surface, with lichens identified macroscopically in situ and supported by laboratory-verified reference material from outside the protected area. Microhabitat descriptors were recorded, and ecological preferences were assessed using indicator values. The main environmental drivers at Noratus appear to be high solar irradiation, pronounced aridity and especially elevated nutrient deposition. Numerical classification and ordination revealed three main lichen communities, structured primarily by micro-scale water availability, exposure, and nutrient enrichment, which occupy different portions of the monument. The absence of endolithic lichens, together with the dry–cold climate of Noratus and the resistance of basalt to bioweathering, suggests limited structural impact. However, pronounced chromatic alteration was observed, producing a characteristic polychromy on the cross-stones. Lichen removal is likely to be problematic due to the extent of the cemetery, the prevalence of vegetative reproduction, and rapid recolonisation of nitrophilous species. This is the first study ever carried out on lichen colonisation of Armenian cross-stones, which are inscribed on the UNESCO List of the Intangible Cultural Heritage of Humanity. The results provide a baseline for future monitoring and support a conservation approach balancing material risk assessment with aesthetic considerations. Full article