Search Results (205)

The time fluctuations of forest fires occurring in Basilicata, a region situated in Southern Italy, between 2004 and 2023 were investigated using various analytical approaches. Analysis revealed a clustering of fire occurrences over time, as indicated by a significantly high coefficient of variation. This suggests that the fire sequence does not follow a Poisson distribution and instead exhibits a clustered structure, largely driven by the heightened frequency of events during the summer seasons. The analysis of monthly forest fire occurrences and total burned area indicates a significant correlation between the two. This correlation is reinforced by shared patterns, notably an annual cycle that appears to be influenced by meteorological factors, aligning with the yearly fluctuations in the region’s weather conditions typical of a Mediterranean climate. Furthermore, the relationship between the Standardized Precipitation Evapotranspiration Index (SPEI) and forest fires revealed that the accumulation period of the SPEI corresponds to the cycle length of the fires: longer cycles in fire occurrences align with higher accumulation periods in SPEI data. Full article

In this study, a large portion of data on the chemical composition of precipitation falling in the South Baikal region shows the main factors determining their formation in 2017–2024. Taking into account the high variability of meteorological conditions in the region, both in time and in space, a method of observing the chemical composition of atmospheric precipitation has been developed, which makes it possible to determine its composition depending on the conditions of air mass formation. Using statistical analysis, marker substances characterizing the main groups of sources influencing the composition of atmospheric precipitation were identified. Joint analysis of air mass trajectories and data on chemical composition of precipitation allowed for establishing the areas of location of potential sources of precipitation pollution. All precipitation events were categorized based on the similarity of air mass formation conditions and chemical composition. Precipitation composition data collected on the shores of Lake Baikal reflect the influence of different types of pollutants such as industrial emissions, motor vehicles, dust storms, and forest fires. The results of the study are relevant for air quality assessment in the region and demonstrate the potential of using precipitation chemistry data to understand the long-range transport of pollutants, which contributes to sustainable development by increasing the availability of air quality data in ecologically significant regions such as Lake Baikal. Full article

Investigating the effects of fire disturbance on soil microbial diversity and nitrogen cycling is crucial for understanding the mechanisms underlying soil nitrogen cycling. This study examined the fire burn site of the Larix gmelinii forest in the Greater Khingan Mountains, Inner Mongolia, to analyze the impact of varying fire intensities on soil nitrogen, microbial communities, and the abundance of nitrogen cycle-related functional genes after three years. The results indicated the following findings: (1) Soil bulk density increased significantly following severe fires (7.06%~10.84%, p < 0.05), whereas soil water content decreased with increasing fire intensity (6.62%~19.42%, p < 0.05). The soil total nitrogen and ammonium nitrogen levels declined after heavy fires but increased after mild fires; (2) Mild fire burning significantly increased soil bacterial diversity, while heavy fire had a lesser effect. Dominant bacterial groups included Xanthobacteraceae, norank_o_norank_c_AD3, and norank_o_Elsterales. Norank_o_norank_c_AD3 abundance decreased with burn intensity (7.90% unburned, 3.02% mild fire, 2.70% heavy fire). Conversely, norank_o_Elsterales increased with burning (1.23% unburned, 5.66% mild fire, 5.48% heavy fire); (3) The abundance of nitrogen-fixing nifH functional genes decreased with increasing fire intensity, whereas nitrification functional genes amoA-AOA and amoA-AOB exhibited the opposite trend. Light-intensity fires increased the abundance of denitrification functional genes nirK, nirS, and nosZ, while heavy fires reduced their abundance; (4) The correlation analysis demonstrated a strong association between soil bacteria and denitrification functional genes nifH and amoA-AOA, with soil total nitrogen being a key factor influencing the nitrogen cycle-related functional genes. The primary bacterial groups involved in soil nitrogen cycling were Proteobacteria, Actinobacteria, and Chloroflexi. These findings play a critical role in promoting vegetation regeneration and rapid ecosystem restoration in fire-affected areas. Full article

This study explores the spatiotemporal patterns and drivers of deforestation and forest degradation along the politically sensitive Iraq–Turkey border within the Duhok Governorate between 2015 and 2024. Utilizing paired remote sensing (RS) and high-end machine learning (ML) methods, forest dynamics were simulated from Sentinel-2 imagery, climate datasets, and topographic variables. Seven ML models were evaluated, and XGBoost consistently outperformed the others, yielding predictive accuracies (R²) of 0.903 (2015), 0.910 (2019), and 0.950 (2024), and a low RMSE (≤0.035). Model interpretability was further improved through the application of SHapley Additive exPlanations (SHAP) to estimate variable contributions and a Generalized Additive Model (GAM) to elucidate complex nonlinear interactions. The results showed distinct temporal shifts; climatic factors (rainfall and temperature) primarily influenced vegetation cover in 2015, whereas anthropogenic drivers such as forest fires (NBR), road construction (RI), and soil exposure (BSI) intensified by 2024, accounting for up to 12% of the observed forest loss. Forest canopy cover decreased significantly, from approximately 630 km² in 2015 to 577 km² in 2024, mainly due to illegal deforestation, road network expansion, and conflict-induced fires. This study highlights the effectiveness of an ML-driven RS analysis for geoinformation needs in geopolitically complex and data-scarce regions. These findings underscore the urgent need for robust, evidence-based conservation policies and demonstrate the utility of interpretable ML techniques for forest management policy optimization, providing a reproducible methodological blueprint for future ecological assessment. Full article

Soil respiration (Rs) is a significant contributor to the global carbon cycle, with its two main sources—microbial (heterotrophic, Rh) and plant root (autotrophic, Ra) respiration—being sensitive to various environmental factors. This study investigates the impact of ecosystem disturbances (Ds), including fire, biogenic (insects and pathogens), and harvesting, on soil respiration in Russia’s forest ecosystems. We introduced response factors to account for the effects of these disturbances on Rh over three distinct stages of ecosystem recovery. Our analysis, based on data from case studies, remote sensing data, and the national forest inventory, revealed that Ds increase Rh by an average of 2.1 ± 3.2% during the restoration period. Biogenic disturbances showed the highest impacts, with average increases of 16.5 ± 3.2%, while the contributions of clearcuts and wildfires were, on average, less pronounced—2.0 ± 3.1% and 0.8 ± 3.3%, respectively. These disturbances modify forest soil dynamics by affecting soil temperature, moisture, and nutrient availability, influencing carbon fluxes over varying timescales. This research underscores the role of ecosystem disturbances in altering soil carbon dynamics and highlights the need for improved data and monitoring of forest disturbances to reduce uncertainty in soil carbon flux estimates. Full article

Forest fires are one of the significant factors affecting forest ecosystems globally, with their impacts on soil microbial community structure and function drawing considerable attention. This study focuses on the short-term effects of different fire intensities on soil bacterial community structure and function in Abies (Pinus densata) forests within the Birishen Mountain National Forest Park in southeastern Tibet. High-throughput sequencing technology was employed to analyze soil bacterial community variations under unburned (C), low-intensity burn (L), moderate-intensity burn (M), and high-intensity burn (S) conditions. The results revealed that with increasing fire severity, the dominant phylum Actinobacteriota significantly increased, while Proteobacteria and Acidobacteriota markedly decreased. At the genus level, the relative abundance of Bradyrhizobium declined significantly with higher fire severity, whereas Arthrobacter exhibited a notable increase. Additionally, soil environmental factors such as available phosphorus (AP), dissolved organic carbon (DOC), C/N ratio, and C/P ratio displayed distinct trends: AP content increased with fire severity, while DOC, C/N ratio, and C/P ratio showed decreasing trends. Non-metric Multidimensional Scaling (NMDS) analysis indicated significant differences in soil bacterial community structures across fire intensities. Diversity analysis demonstrated that Shannon and Simpson indices exhibited regular fluctuations correlated with fire severity and were significantly associated with soil C/N ratios. Functional predictions revealed a significant increase in nitrate reduction-related bacterial functions with fire severity, while nitrogen-fixing bacteria declined markedly. These findings suggest that forest fire severity profoundly influences soil bacterial community structure and function, potentially exerting long-term effects on nutrient cycling and ecosystem recovery in forest ecosystems. Full article

Wildfire is a critical driver of ecological processes in western U.S. forests, but recent shifts in climate, land use, and fire suppression have altered forest structure and disturbance regimes. Understanding post-fire recovery is essential for land management, particularly across complex montane landscapes like the southern Rocky Mountains. We assessed forest recovery in montane conifer forests, ranging from ponderosa pine to spruce-fir, following a large mixed-severity fire using field-based forest stand data and remotely sensed Leaf Area Index (LAI) measurements. Our objectives were to determine whether LAI is a meaningful proxy for post-fire vegetative recovery and how recovery patterns vary by forest type, burn severity, and abiotic factors. Stand characteristics predicted crown burn severity inconsistently and did not predict soil burn severity. LAI correlated strongly with live overstory tree density and shrub cover (R² = 0.70). Recovery trajectories varied by forest type, with lower-severity burns generally recovering four years post-fire, while high-severity burns showed delayed recovery. Regeneration patterns were strongly influenced by climate, with higher seedling densities occurring at wetter sites. Our findings highlight the utility of LAI as a proxy for vegetative recovery and underscore the importance of forest type, fire severity, and climatic factors when assessing post-fire resilience. Full article

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

Wildfires in the Mediterranean basin, particularly in Portugal, pose significant ecological challenges by altering landscapes and ecosystems. This study examines vegetation recovery in Serra do Açor seven years after the 2017 wildfires, using remote sensing and field data to analyze post-fire dynamics. The primary goal was to assess whether fire severity, measured via the dNBR index from Sentinel-2 imagery, impacts vegetation recovery or if site-specific factors and pre-fire floristic composition are more influential. Randomly assigned plots based on previous land use and fire severity were analyzed for floristic attributes. To quantify and classify cover changes, a supervised classification methodology based on the random forest algorithm was applied to Sentinel-2 data. The results showed no clear link between fire severity and recovery; instead, local factors like soil and topography, along with dominant pre-fire species, influenced recovery. Acacia and eucalyptus communities grew faster and increased the occupied area but exhibited lower diversity than native vegetation communities. Supervised classifications achieved high accuracy (Kappa > 0.90), showing increased shrubland areas and expansion of eucalyptus and acacia. The study highlights the methodology’s effectiveness and potential for broader applications in future research. Full article

The focus of this study was the Intercontinental Biosphere Reserve of the Mediterranean (IBRM, part of the biodiversity hotspot of the Mediterranean Basin) and the Orchidaceae family, which is under-studied in the Moroccan part of the IBRM. For this reason, an inventory of Orchidaceae diversity and factors that could influence their in situ conservation was undertaken, employing a series of field surveys conducted in the Northern Moroccan IBRM ecosystems. In total, 42 sites were surveyed in four protected areas of the Moroccan part of the IBRM. In total, 21 Orchidaceae species and subspecies (taxa) belonging to seven genera were identified, including Orchis spitzelii subsp. cazorlensis, as newly recorded in Morocco, as well as several new reports for different sites and/or areas surveyed, thus updating the previous knowledge of Moroccan Orchidaceae. Most of the Orchidaceae taxa were found in limited numbers of individuals (<30) and were restricted in a few sites (1–3) or a single area; thus, they were assessed as poorly conserved due to the scarcity of rainfall coupled with human pressures, such as the abstraction of surface water, forest fires, and the conversion of protected forests to Cannabis farms. The enforcement of existing laws, the adoption of strategies to combat desertification and forest fires, the prohibition of Cannabis farming, and raising awareness among the local population could reduce the pressures on the protected Orchidaceae members and their habitats, thereby contributing to their conservation. Full article

Wildfires frequently occur, posing a significant threat to the operational stability of transmission lines across mountainous forest areas. Therefore, this paper reviews numerous studies conducted by domestic and international scholars on the gap breakdown tests and discharge mechanisms of transmission lines under simulated wildfire conditions. It analyses and summarizes the physical parameter measurement methods commonly used in current experiments. Combining the results of existing experiments, this study analyzes the discharge mechanisms, including the research progress made in numerical simulations. The conclusion is that existing tests are limited in their measurement methods of the physical quantities related to breakdown characteristics, and it is not easy to strictly control experimental variables when considering complex factors. Numerical simulations mainly focus on multi-physical field simulations, which consider the characteristics of vegetation fires in short gaps. The synergistic mechanism of environmental factors on gap breakdown characteristics remains unclear. This paper points out the breakdown characteristics and discharge mechanisms derived from existing experiments and numerical simulations under various influencing factors, highlighting their applicability and limitations, which differ from complex actual transmission lines in the environment. Then, we look forward to the future development of simulation test platforms that could better reflect the actual transmission line corridor environment, incorporating multi-parameter measurement and in-depth numerical simulation works that consider climate and terrain factors. Full article

Ongoing climate change has intensified fire disturbances in boreal forests globally, posing significant risks to forest ecosystem structure and function, with the potential to trigger major regime shifts. Understanding how environmental factors regulate the resilience of key structural and functional parameters is critical for sustaining and enhancing ecosystem services under global change. This study analyzed the resilience of forest ecosystems following three representative extreme fires in the Greater Xing’an Mountains (GXM) via the temporal evolution of the leaf area index (LAI), net primary productivity (NPP), and evapotranspiration (ET) as key indicators. A comprehensive wall-to-wall assessment was conducted, integrating gradient boosting machine (GBM) modeling with Shapley Additive Explanation (SHAP) to identify the dominant factors influencing postfire resilience. The results revealed that NPP demonstrated stronger resilience than ET and LAI, suggesting the prioritization of functional restoration over structural recovery in the postfire landscape of the GXM. The GBM-SHAP model explained 45% to 69% of the variance in the resilience patterns of the three parameters. Among the regulatory factors, extreme precipitation and temperature during the growing season were found to exert more significant influences on resilience than landscape-scale factors, such as burn severity, topography, and prefire vegetation composition. The spatial asynchrony in resilience patterns between structural and functional parameters highlighted the complex interplay of climatic drivers and ecological processes during post-disturbance recovery. Our study emphasized the importance of prioritizing functional restoration in the short term to support ecosystem recovery processes and services. Despite the potential limitations imposed by the coarse spatial granularity of the input data, our findings provide valuable insights for postfire management strategies, enabling the effective allocation of resources to increase ecosystem resilience and facilitating long-term adaptation to changing fire regimes. Full article

The semi-deciduous Brazilian Atlantic Forest has faced intense fragmentation, impacting Metrodorea nigra St. Hill., a fly-pollinated and autochorous tree. We investigated population structure, inbreeding, and spatial genetic structure (SGS) across adult (Adu) and juvenile (Juv) generations in three fragmented populations of M. nigra in Ribeirão Preto, São Paulo, Brazil. We tested whether the magnitude of these effects could result from its mating system, seed dispersal, anthropogenic disturbances, matrix, and fragment size. Populations affected by selective logging, fire, and trail openings include M13-Rib (84 ha) and FAC-Crav (8 ha), both surrounded by sugar cane and BSQ-Rib (3 ha) in an urban matrix. We evaluated phenological events and germination rates in the BSQ-Rib fragment. We sampled leaves and amplified their DNA using ISSR (UBC 1, 2, 820, 834, 851, 858, 860, 886) and SSR (Mtn 1, 3, 13, 16, 19, 87, 95) molecular markers. Fst, PCoA, and AMOVA values suggest a lack of generational isolation, with most variance within generations. Inbreeding values were significant in all populations (Fis and Fit, p = 0.001), probably intensified by natural seed dispersal and pollinator behavior favoring geitonogamy. However, fragmentation, anthropogenic disturbances, and the surrounding matrix influenced SGS. The urban BSQ-Rib fragment recorded the highest SGS values (26 m Juv, 24 m Adu [ISSR]; 7 m Juv, 9 m Adu [SSR]), which may result in low fruit and seed production and germination rates. Despite being the largest fragment, M13-Rib shows SGS in the first distance class (19 m Juv, 24 m Adu [ISSR]; 0 m Juv, and 10 m Adu [SSR]), possibly due to selective logging and fire. FAC-Crav, a more conserved fragment, showed no SGS in adults but punctual SGS in juveniles (27 m [ISSR] and 8 m [SSR]), pointing to it as a promising source for seed collections for reforestation purposes. In summary, inbreeding in M. nigra, influenced by pollinator behavior and seed dispersal, along with fragmentation, anthropogenic disturbances, and the surrounding matrix, are critical in shaping SGS. These factors potentially impact the reproductive success of M. nigra and their long-term survival in the face of climate change. Full article

This study applied various machine learning techniques, including shrinkage methods, XGBoost, CSR, and random forest, to forecast ultrafine particulate matter (PM2.5) concentrations in Seoul, South Korea. The analysis incorporated key variables known to significantly influence PM2.5 levels, including meteorological data, coal-fired power generation, and PM2.5 concentrations in Dalian, China. Using daily data from 1 January 2018 to 30 June 2023, this study employed the Boruta algorithm, a variable selection technique based on the random forest model, to identify the most influential predictors for predicting PM2.5 concentrations. Out-of-sample multi-period forecasts were evaluated for each model using the RMSE, MAE, and Giacomini–White test to determine the most effective forecasting approach. It was found that the random forest model with the Boruta algorithm outperformed all other models, achieving improvements of 4% to 17% in the RMSE and 4% to 16.5% in the MAE across all forecast horizons. The results indicate that the random forest model and its variant incorporating the Boruta algorithm provided superior short-term forecasting performance. In particular, the Boruta algorithm highlighted the lagged variables of temperature, PM2.5 concentration, mean humidity, and Dalian PM2.5 concentration as critical factors for the accurate prediction of PM2.5 levels in Seoul. These findings underscore the utility of data-driven approaches to improve air quality forecasting and management. Full article

This study investigates the latency of lightning-caused fires in the boreal coniferous forests of the Greater Khingan Mountains, employing advanced machine learning techniques to analyze the relationship between meteorological factors, lightning characteristics, and fire ignition and smoldering processes. Using the Random Forest Model (RFM) combined with Recursive Feature Elimination with Cross-Validation (RFECV) and SHapley Additive exPlanations (SHAP), the study identifies key factors influencing fire latency. Two methods, Min distance and Min latency, were used to determine ignition lightning, with the Min distance method proving more reliable. The results show that lightning-caused fires cluster spatially and peak temporally between May and July, aligning with lightning activity. The Fine Fuel Moisture Code (FFMC) and precipitation were identified as the most influential factors. This study underscores the importance of fuel moisture and weather conditions in determining latency of lightning-caused fire, offering valuable insights for enhancing early warning systems. Despite limitations in data resolution and the exclusion of topographic factors, this study advances our understanding of lightning-fire latency mechanisms and provides a foundation for more effective wildfire management strategies under climate change. Full article