Introduction Fusarium proliferatum is a globally distributed fungal pathogen of major agricultural significance, responsible for considerable crop losses and the production of hazardous mycotoxins that endanger food security and human health. Climate change is expected to modify the geographic distribution of plant pathogens, allowing their spread into previously unsuitable regions. Methods This study employed the Maximum Entropy (MaxEnt) species distribution modeling approach to evaluate the potential impacts of climate change on the global distribution of F. proliferatum under different Representative Concentration Pathway (RCP) emission scenarios. A total of 347 species occurrence records were obtained from the Global Biodiversity Information Facility (GBIF) and spatially filtered to minimize sampling bias. Bioclimatic variables, primarily temperature-related factors, were identified as key environmental determinants through systematic variable selection and correlation analysis. Model performance was evaluated using the Area Under the Curve (AUC) metric. Results The MaxEnt model demonstrated excellent predictive accuracy (AUC = 0.844). Current distribution maps revealed high environmental suitability in tropical and subtropical regions, with moderate suitability in temperate zones. Future projections for 2050 and 2070 under both moderate (RCP 2.6) and severe (RCP 8.5) emission scenarios indicated notable poleward range expansion, particularly into northern Europe, northern Asia, and northern North America. The most substantial distributional shifts occurred under the severe emission scenario for 2070, showing extensive expansion of highly suitable environments into previously marginal regions. Temperature seasonality was identified as the most influential limiting factor globally. Discussion These findings suggest that ongoing climate change will substantially broaden the geographic range of F. proliferatum , heightening mycotoxin contamination risks in new agricultural areas and threatening food security in temperate zones historically unexposed to this pathogen. The study provides critical insights for developing proactive surveillance, biosecurity policies, and adaptive management strategies to mitigate the escalating risks posed by this economically important fungal pathogen under future climatic conditions.

Introduction Vegetation phenology serves as a sensitive indicator of terrestrial ecosystem responses to climate change. Over the past few decades, research on phenological changes, especially in forest ecosystems, has expanded substantially. Methods This study conducts a systematic bibliometric analysis of global literature on vegetation phenology and climate change from 1995 to 2024, based on 599 publications retrieved from the Web of Science Core Collection and Scopus. Using VOSviewer, CiteSpace, Bibliometrix (R package), and SciMAT, we examined publication trends, geographic distribution, institutional collaboration, author contributions, and thematic evolution. Results Results reveal a rapid rise in research since 2012, with peaks in 2022 and 2024. China leads in publication volume, whereas the United States dominates in total and average citations, reflecting greater academic influence. The University of the Chinese Academy of Sciences and Beijing Normal University emerge as leading institutions. Collaboration networks highlight four major international clusters, with China and the United States at the core. Keyword co-occurrence analysis identifies central topics such as “climate change,” “spring phenology,” “NDVI,” and “remote sensing,” while new themes emerging after 2017 include “urban vegetation phenology,” “grassland ecosystems,” and “extreme climate events.” Thematic evolution indicates a shift from foundational topics such as “MODIS” and “plant growth” toward more integrated themes such as “climate impacts,” “phenological modeling,” and “ecosystem responses.” Co-citation analysis identifies 13 research clusters, including “temperate forest phenology,” “winter wheat,” and “urban spring vegetation phenology.” Recent clusters emphasize high-resolution data sources and urbanization effects, reflecting a transition toward finer-scale and application-oriented research. Discussion Influential works by Richardson, Piao, and others form the intellectual foundation of the field and continue to shape its development. Overall, this study provides a comprehensive overview of the evolution and trajectory of vegetation phenology research under climate change. The findings underscore the growing integration of interdisciplinary approaches, finer-scale observations, and an increasing focus on climate-vegetation feedbacks, offering guidance for future research and strategies to enhance ecosystem adaptation.

Oak ( Quercus ) diversity is a defining feature of North American forests, comprising over 75% of the region's vegetation. Oaks play a vital role in supporting numerous wildlife species by providing essential food and habitat resources, while also contributing to carbon storage, soil stabilization, and cultural and economic values. Arkansas, with its highly diverse biogeography, offers suitable conditions for a wide range of oak species, leading to the formation of varied vegetation communities. The purpose of this study is to assess the diversity (species richness) of the Quercus genus across the state of Arkansas using multi species niche modeling. Geographic coordinates of 5,987 herbarium records, representing 26 native Oak species, were compiled. We employed an ensemble species distribution modeling approach to predict suitable habitat areas for oak species and to identify hotspots. Our results showed that mean temperature of driest month, precipitation of warmest month, and mean diurnal range contributed > 80% the model prediction. The highest oak richness in Arkansas was found in Crowley's Ridge and Mississippi Valley Loess Plains, each supporting 20 Quercus species, followed by Arkansas River Valley and Ozark highlands with 18 species each. Considering growing concerns over oak forest decline due to anthropogenic and climate change, this study offers valuable insights for conservation, assessment, management strategies, and silviculture planning of oak species in the region.

Participatory forest management constitutes an important pathway toward sustainable and inclusive forest governance, mediated by local social, cultural, and institutional dynamics. This study presented pioneering research that applied the theory of planned behavior (TPB) to investigate the intentions of non-state forest owners in the Czech Republic to involve the public in forest management planning. Data were obtained from 92 valid responses and analyzed using covariance-based structural equation modeling (CB-SEM). The results showed that attitudes, subjective norms, and perceived behavioral control had a positive influence on behavioral intention. However, only subjective norms had a statistically significant effect ( β = 0.61, p = 0.021), while the effects of attitudes ( β = 0.10, p = 0.515) and perceived behavioral control (β = 0.26, p = 0.277) were not significant. The model explained 86.31% of the variance in behavioral intention, demonstrating strong explanatory power despite the modest sample size. These findings provide preliminary evidence that social expectations from professional peers, local communities, and family networks play a dominant role in shaping forest owners’ willingness to adopt participatory planning approaches. Theoretically, this study extends the TPB framework by showing that normative influence predominates when attitudes and perceived control are already favorable, underscoring the importance of social legitimacy in structured institutional contexts. From a policy perspective, the results highlight the need to strengthen collective norms and institutional support through forest owner associations, peer learning, and transparent governance mechanisms. Such strategies can enhance social legitimacy and foster broader, more sustainable public participation in forest management planning.

The innovation here is the new classification of aquatic vegetation based on the association level using unmanned aerial vehicle (UAV)-mounted sensing technology, and a light detection and ranging (LiDAR) method to acquire point cloud data and high-resolution red, green, and blue (RGB) imagery. This research focuses on aquatic vegetation in the littoral zone of East Lake Taihu. By innovatively introducing UAV and LiDAR provide clear single images of both exterior and atmospheric surfaces by using a point cloud canopy height model (PCHM), VDVI (visible-band difference vegetation index, spectral information) and a decision tree classification model for littoral aquatic vegetation at the association level. In terms of data processing, improving data reliability through point cloud gridding and alignment with field quadrats. After integrating point cloud and optical image data, we interpret canopy height and spectral information of aquatic associations by precisely identifying and mapping vegetation types to their individual vegetation associations. This is the first study to achieve fine-scale classification of aquatic vegetation at the association level in lakeshore wetlands based on UAV-LiDAR fusion technology. Results showed the classification accuracy for these associations ranging from 79.80% to 97.40%. The higher canopy associations have greater classification accuracy with an overall classification accuracy of 87.93% and a kappa coefficient of 0.855. The new association classification method can improve data results on scientific management of littoral aquatic ecosystems.

Wildfires present a significant threat to ecosystems, property, and human life in Kazakhstan. Understanding fire hazards is essential for effective management and mitigation of these risks. This study develops a comprehensive fire hazard index for Kazakhstan by integrating static, long-term landscape factors with dynamic, real-time weather and vegetation conditions. The static component employs a machine learning approach, specifically the Random Forest algorithm, trained on a dataset that includes topographic variables derived from the SRTM DEM, land cover classifications from MODIS Terra/Aqua LULC products, and historical fire occurrence data from NASA FIRMS. This model quantifies the inherent fire susceptibility of various landscapes based on these enduring characteristics. The dynamic component captures short-term fluctuations in fire risk by incorporating satellite-derived vegetation information and meteorological observations. The MODIS-derived Normalized Difference Vegetation Index (NDVI) serves as a proxy for fuel availability and moisture content. Spatially interpolated weather data such as temperature, humidity, wind speed, and precipitation provide the necessary meteorological context. The dynamic index is calculated using a modified Canadian Fire Weather Index (FWI) system, specifically adapted to account for the influence of live fuel moisture, as indicated by NDVI, on fire ignition and spread dynamics. The final fire risk index is created by additively combining the static and dynamic components, offering a spatiotemporal perspective on fire risk. This integrated approach allows for the assessment of both the underlying susceptibility of a landscape to fire and the immediate effects of weather and vegetation conditions. The resulting high-resolution fire hazard maps are intended to inform fire management decisions, optimize resource allocation for fire prevention and suppression efforts, and support targeted interventions in high-risk areas. This research underscores the value of combining machine learning techniques with remotely sensed data for enhanced fire risk assessment in Kazakhstan, facilitating more proactive and effective fire management strategies.

Forest management in Central Europe is affected by numerous biotic and abiotic factors, and wildlife damage to forest stands is one of the major ones. While damage to young forest stands is predominantly attributed to wild ungulates, the potential negative impact of the European hare ( Lepus europaeus ) on tree plantations has been largely overlooked. Therefore, this study aimed to quantify hare damage in comparison with ungulates, determine which developmental stages of trees are most attractive to hares, compare browsing preferences among key commercial species, and assess the effectiveness of commonly used protection measures. We evaluated 209 calamity clearcuts with 75,912 seedlings to determine the proportion and structure of browsing damage. Research revealed a substantial proportional damage caused by the European hare, accounting for 10.12%, while wild ungulates were responsible for 7.11% of browsing damage. The species distribution played a crucial role in the damage rate. Hares caused the greatest browsing damage on silver birch ( Betula pendula —37%), while ungulates preferred Scots pine ( Pinus sylvestris —29%). Moreover, browsing intensity caused by hares was seasonally affected with a significantly higher damage ratio in autumn (<14%) compared to the spring season (<2%) when clearcuts offer enough alternative fodder opportunities. Common protection measures, such as coating and fencing, proved largely ineffective against hare browsing. Surprisingly, sycamore maple ( Acer pseudoplatanus ) and European beech ( Fagus sylvatica ) were more heavily damaged by hares when coated than unprotected seedlings. The results showed that the European hare plays a crucial role in clear-cut reforestation, highlighting the need for enhanced focus on protective measures, including population management and the use of specialized fencing made of dense wire mesh compared to the usual protection against wild ungulates.

Alpine wetlands play a vital role in water storage, ecosystem services, biodiversity conservation, material recycling, climate change mitigation, and environmental purification. At present, these high-elevation and high-latitude ecosystems are facing dual threats from the combined impacts of climate change and human activities. This perspective reveals that climate-related factors such as phenological changes and plant migrations, as well as human activities like agricultural reclamation, have significantly damaged these ecosystems. To address alpine wetland degradation, multiple strategies are proposed through integrated approaches. First, exploring the synergistic application of multiple restoration techniques, including ditch-filling, terrain-leveling, invasive species removal, and vegetation restoration. Second, establishing long-term ecological monitoring frameworks, including extreme climate and water level monitoring, vegetation and soil survey, among others. Third, sustained investments in scientific research and active public awareness and stakeholder engagement. Through these comprehensive efforts, integrating traditional ecological knowledge with modern restoration techniques, we can jointly safeguard biodiversity, maintain ecosystem services, mitigate the degradation of alpine wetlands and preserve these high-value ecological systems for future generations.

Introduction White pine blister rust (WPBR) disease, caused by an invasive fungal pathogen ( Cronartium ribicola J.C. Fisch), has long been the primary biotic threat to eastern white pine in Canada. A hybridization program initiated in Ontario, Canada in the 1950s aimed to transfer blister rust resistance from Himalayan blue pine to eastern white pine, resulting in WPBR-resistant interspecific hybrids. Metabolic adjustments related to disease resistance may cause trade-offs with tolerance to abiotic stress (e.g., frost, heat, drought). To evaluate the adaptive potential of WPBR-resistant hybrids, it is crucial to understand how morphological and physiological traits change during multi-generation backcrossing, as these shifts may influence both growth performance and resilience to climate change. Methods We assessed changes in photosynthetic-related traits, as well as needle morphology and resistance to xylem cavitation of eastern white pine and Himalayan blue pine, and their hybrids with varying levels of white pine parentage ranging from 50 to 87.5%. Results and discussion Needle length and specific leaf area ( SLA ) decreased linearly by increasing eastern white pine parentage; inversely, needle density increased by increasing eastern white pine parentage. Variations in needle morphology were not translated into variations in light-saturated photosynthesis (Amax), mesophyll conductance (g m ), maximum rate of carboxylation (V cmax ), and maximum rate of electron transport (J max ). Photosynthetic nitrogen use efficiency ( PNUE ) decreased, while water use efficiency ( WUE i ) increased with increasing eastern white pine parentage. Increasing needle density and declining PNUE reflect greater investment in structural tissue, which is commonly associated with frost and drought tolerance. Also, Himalayan blue pine and hybrids were more resistant to xylem cavitation than eastern white pine. Hybrid pines recovered most of their eastern white pine morpho-physiological characteristics after two rounds of backcrossing. Consequently, WPBR-resistant interspecific hybrids should have integrated stress tolerance traits of eastern white pine enabling them to adapt to abiotic and biotic stresses in Canadian boreal forests.

This study evaluated the influence of demographic variables on Czech citizens’ behavioral intentions (IN) to participate in forest management planning, by examining differences in the Theory of Planned Behavior (TPB) model across demographic groups. Structural Equation Modeling (SEM) with multi-group analysis was applied to test how age, gender, education, income, occupation, area of residence, and forest ownership moderated the relationships among attitude (AT), subjective norms (SN), and perceived behavioral control (PBC). The results revealed that gender, education, area of residence, and income significantly moderated the influence of TPB constructs on participatory intention, while age, occupation, and forest ownership inquiry showed weaker or non-significant effects. Females exhibited stronger SN effects (β = 0.929), whereas males relied more heavily on PBC (β = 1.065). Higher-income respondents (>1,600 EUR /month) demonstrated a stronger PBC effect (β = 0.597), while lower-income participants were more influenced by SN (β = 0.933). Participants with a high school education or less were slightly more influenced by SN (β = 0.902) compared to college-educated respondents (β = 0.703). Additionally, suburban and rural residents were slightly more influenced by PBC (β = 0.671), whereas urban residents were more influenced by SN (β = 0.629). These findings highlight the importance of demographic and experiential diversity in shaping environmental behavior and provide actionable insights for developing inclusive and effective participatory forest management strategies.