Search Results (153)

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

Wildfires critically affect ecosystems, carbon cycles, and public health. COVID-19 restrictions provided a unique opportunity to study human activity’s role in wildfire regimes. This study presents a comprehensive evaluation of pandemic-induced wildfire regime changes across global fire-prone regions. Using MODIS data (2010–2022), we analyzed fire patterns during the pandemic (2020–2022) against pre-pandemic baselines. Key findings include: (a) A 22% global decline in wildfire hotspots during 2020–2022 compared to 2015–2019, with the most pronounced reduction occurring in 2022; (b) Contrasting regional trends: reduced fire activity in tropical zones versus intensified burning in boreal regions; (c) Stark national disparities, exemplified by Russia’s net increase of 59,990 hotspots versus Australia’s decrease of 60,380 in 2020; (d) Seasonal shifts characterized by December declines linked to mobility restrictions, while northern summer fires persisted due to climate-driven factors. Notably, although climatic factors predominantly govern fire regimes in northern latitudes, anthropogenic ignition sources such as agricultural burning and accidental fires substantially contribute to both fire incidence and associated emissions. The pandemic period demonstrated that while human activity restrictions reduced ignition sources in tropical regions, fire activity in boreal ecosystems during these years exhibited persistent correlations with climatic variables, reinforcing climate’s pivotal—though not exclusive—role in shaping fire regimes. This underscores the need for integrated wildfire management strategies that address both human and climatic factors through regionally tailored approaches. Future research should explore long-term shifts and adaptive management frameworks. Full article

(1) Background: Changes in the spatial, temporal, and magnitude-related patterns of fires caused by humans are expected to exacerbate with climate change, significantly impacting ecosystems and societies worldwide. However, our understanding of fire regimes in many regions remains limited, largely due to the inherent complexity of fire as an ecological process. Pyrogeography, combined with unsupervised learning methods and the availability of long-term satellite data, offers a robust framework for approaching this problem. The purpose of the study is to identify the pyroregions of the Argentine Gran Chaco, the world’s largest continuous tropical dry forest region. (2) Methods: Using globally available fire occurrence datasets, we computed five fire metrics, related to the extent, frequency, intensity, size, and seasonality of fires at three spatial scales (5, 10, and 25 km). In addition, we tested two widely used cluster algorithms, the K-means algorithm and the Gaussian Mixture Model (GMM). (3) Results and Discussion: The identification of pyroregions was dependent on the clustering algorithm and scale of analysis. The GMM algorithm at a 25 km scale ultimately demonstrated more coherent ecological and spatial distributions. GMM identified six pyroregions, which were labeled based on three metrics in the following order: annual burned area (categorized in low, regular or high), interannual variability of fire (rare, occasional, frequent), and fire intensity (low, moderate, intense). The values were as follows: LRM (22% of study area), ROI (19%), ROM (14%), LOM (10%), ROL (9%), and HFL (4%). (4) Conclusions: Our study provides the most comprehensive delineation of the Argentine Gran Chaco’s Dry Forest pyroregions to date, and highlights both the importance of determining the optimal scale of analysis and the critical role of clustering algorithms in efforts to accurately characterize the diverse attributes of fire regimes. Furthermore, it emphasizes the importance of integrating fire ecology principles and fire management perspectives into pyrogeographic studies to ensure a more comprehensive and meaningful characterization of fire regimes. Full article

Fire regime (FR) is a key element in the study of ecosystem dynamics, supporting natural resource management planning by identifying gaps in fire patterns in time and space and planning to assess ecological conditions. Due to the insufficient consideration of integrated characterization factors, especially the insufficient research on fire season types (FST), the current understanding of the spatial heterogeneity of fire patterns in China is still limited, and it is necessary to use FST as a key dimension to classify FR zones more accurately. This study extracted 13 fire characteristic variables based on Moderate Resolution Imaging Spectroradiometer (MODIS) burned area data (MCD64A1), active fire data (MODIS Collection 6), and land cover data (MCD12Q1) from 2001 to 2023. The study systematically analyzed the frequency, intensity, spatial distribution and seasonal characteristics of fires across China. By using data normalization and the k-means clustering algorithm, the study area was divided into five types of FR zones (FR 1–5) with significant differences. The burned areas of the five FR zones account for 67.76%, 13.88%, 4.87%, 12.94%, and 0.55% of the total burned area across the country over the 23-year study period, respectively. Among them, fires in the Northeast China Plain and North China Plain cropland areas (FR 1) exhibit a bimodal distribution, with the peak period concentrated in April and June, respectively; the southern forest and savanna region (FR 2) is dominated by high-frequency, small-scale, unimodal fires, peaking in February; the central grassland region (FR 3) experiences high-intensity, low-frequency fires, with a peak in April; the east central forest region (FR 4) is characterized by low-frequency, high-intensity fires; and the western grassland region (FR 5) experiences low-frequency fires with significant inter-annual fluctuations. Among the five zones, FST consistently ranks within the top five contributors, with contribution rates of 0.39, 0.31, 0.44, 0.27, and 0.55, respectively, confirming that the inclusion of FST is a reasonable and necessary choice when constructing FR zones. By integrating multi-source remote sensing data, this study has established a novel FR classification system that encompasses fire frequency, intensity, and particularly FST. This approach transcends the traditional single-factor classification, demonstrating that seasonal characteristics are indispensable for accurately delineating fire conditions. The resultant zoning system effectively overcomes the limitations of traditional methods, providing a scientific basis for localized fire risk warning and differentiated prevention and control strategies. Full article

Climate change has led to the global intensification of wildfire activity, and South Korea has also experienced a marked increase in fire danger. To understand these developments, it is essential to examine both temporal trends and spatial patterns in fire-conducive climate conditions. In this study, we investigated the temporal and spatial characteristics of wildfire danger across South Korea between 2004 and 2023 using the Fire Weather Index (FWI). The analysis of long-term climate trends revealed region-specific patterns of increasing temperatures, decreasing precipitation, and declining wind speeds, which collectively contribute to an increased risk of wildfires. The FWI exhibited strong seasonal variations, with significant increases observed in spring, particularly in May, over the most recent decade. Statistical analyses confirmed a strong correlation between high FWI percentiles and wildfire occurrence, particularly noting an increased frequency of large-scale fires (>100 ha) in the highest FWI bins. The spatial analysis further highlighted that certain provinces, including Gangwon State, Gyeongsangbuk-do, Chuncheongbuk-do, and Gyeonggi-do, experienced disproportionately high increases in FWI values. These findings suggest that the FWI can serve as a robust framework for wildfire danger assessment in South Korea, particularly when supported by region-specific calibration and long-term validation under varying climatic conditions. Full article

Increasing wildfire activities across the Great Plains has raised concerns about the effectiveness and safety of prescribed fire as a land management tool. This study analyzes wildfire records from 1992 to 2020 to assess spatiotemporal patterns in wildfire risk and evaluate the role of prescribed fires through the combined analysis of wildfire and prescribed fire data. Results show a threefold increase in both wildfire frequency and area burned, with fire size increasing from east to west and frequency rising from north to south. Wildfire seasons are gradually occurring earlier due to climate change. Negative correlation between prescribed fires in spring and wildfires in summer indicated the effectiveness of prescribed fire in mitigating wildfire risk. Drought severity accounted for 51% of the interannual variability in area burned, while grass curing accounted for 60% of monthly variability of wildfires in grasslands. The ratio of wildfire area burned to total area burned (dominated by prescribed fires) declined from over 20% in early March to below 1% by early April. The results will lay a foundation for the development of a localized fire risk assessment tool that integrates various long-term, mid-term, and short-term risk factors, and support more effective fire management in this region. Full article

Global forest wildfires are increasing in both frequency and intensity, resulting in significant ecological degradation and posing substantial threats to human health. This study focused on the Dongjiang River Basin in southern China and investigated the seasonal and spatial distribution patterns of forest wildfires in the research region from 2003 to 2023 using geographic information system technology. This study employed the random forest (RF) model, a machine learning algorithm, to predict the danger level of wildfire across different seasons and quantitatively interpret the seasonal wildfire driving mechanisms using the SHapley Additive exPlanations (SHAP) values. The results indicated that forest wildfires in the Dongjiang Basin were predominantly concentrated in the eastern region of the Dongjiang Basin, with significant seasonal variation in the spatial distribution. The frequency of fire events exhibited distinct seasonal patterns, with higher incidence in spring and winter and relatively lower frequency in summer and autumn. The random forest model demonstrated high predictive accuracy for the wildfire danger in all the seasons. Furthermore, the analysis of the driving factors showed that, despite some seasonal variability, the underlying mechanisms of wildfire occurrence could be effectively quantified using the SHAP values. Notably, the Normalized Difference Vegetation Index and anthropogenic disturbances consistently emerged as the dominant driving forces behind forest wildfires across all the seasons. Full article

The red imported fire ant (Solenopsis invicta, RIFA) is a globally invasive species with strong sensitivity to environmental conditions. This study investigated the seasonal dynamics and colony structure of RIFA over the course of one year across two typical habitats in South China: Camellia oleifera plantations and fishponds. The results revealed clear seasonal patterns in caste composition. Worker abundance peaked during winter (December–January), while reproductive individuals (queens, males, and alates) emerged primarily in spring and early summer (March–May). Colony biomass, worker number, and individual dry weight were significantly higher in C. oleifera plantations, whereas fishpond habitats exhibited greater numbers of larvae and male alates, suggesting different reproductive allocation strategies across habitats. An analysis of caste composition indicated that adult workers were dominant in both habitats, but the proportion of pupae was notably higher in fishpond colonies, especially in spring. Significant correlations were found between colony metrics and nest characteristics, including a negative relationship between worker body length and colony biomass. Environmental factor analysis showed that air pressure positively influenced worker numbers, while temperature was negatively associated with them. Precipitation and humidity played key roles in regulating larval and pupal populations. Overall, RIFA exhibited strong seasonal patterns and ecological plasticity in response to habitat differences and environmental variables. These findings provide insights into the species’ invasion biology and inform habitat-specific monitoring and management strategies. Full article

Vegetation changes in savanna ecosystems are playing an increasingly important role in shaping tick populations and the spread of tick-borne diseases, with consequences for both wildlife and livestock health. This study examines how factors such as climate variability, land use, vegetation structures, and host availability influence tick survival, distribution, and behavior. As grasslands degrade and woody plants become more dominant, ticks are finding more suitable habitats, often supported by microclimatic conditions that favor their development. At the same time, increased contact between domestic and wild animals is facilitating the transmission of pathogens. This review highlights how seasonal patterns, fire regimes, grazing pressure, and climate change are driving shifts in tick activity and expanding their geographical range. These changes increase the risk of disease for animals and humans alike. Addressing these challenges calls for integrated management strategies that include vegetation control, host population monitoring, and sustainable vector control methods. A holistic approach that connects ecological, animal, and human health perspectives is essential for effective disease prevention and long-term ecosystem management. Full article

Understanding the characteristics of wildfires in North China is critical for advancing regional fire danger prediction and management strategies. This study employed satellite-based burned area products of the Global Fire Emissions Database (GFED) and reanalysis of climate datasets to investigate the spatiotemporal characteristics of wildfires, as well as their relationships with fire danger indices and climatic drivers. The results revealed distinct seasonal variability, with the maximum burned area extent and intensity occurring during the March–April period. Notably, the fine fuel moisture code (FFMC) demonstrated a stronger correlation with burned areas compared to other fire danger or climate indices, both in temporal series and spatial patterns. Further analysis through the self-organizing map (SOM) clustering of FFMC composites then revealed six distinct modes, with the SOM1 mode closely matching the spatial distribution of burned areas in North China. A trend analysis indicated a 7.75% 10a⁻¹ (p < 0.05) increase in SOM1 occurrence frequency, associated with persistent high-pressure systems that suppress convective activity through (1) inhibited meridional water vapor transport and (2) reduced cloud condensation nuclei formation. These synoptic conditions created favorable conditions for the occurrence of wildfires. Finally, we developed a prediction model for burned areas, leveraging the strong correlation between the FFMC and burned areas. Both the SSP245 and SSP585 scenarios suggest an accelerated, increasing trend of burned areas in the future. These findings emphasize the importance of understanding the spatiotemporal characteristics and underlying causes of wildfires, providing critical insights for developing adaptive wildfire management frameworks in North China. 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

Based on statistical analyses applied to official data from the Digital Atlas of Disasters in Brazil over the last 25 years, we evidenced a consistent intensification in the annual occurrence of natural disasters in the state of Pará, located in the eastern Brazilian Amazon. The quantitative comparison between the averages of the most intense period of disasters (2017 to 2023) and the earlier years (1999 to 2016) revealed a remarkable percentage increase of 473%. Approximately 81% of the state’s municipalities were affected, as indicated by disaster mapping. A clear seasonal pattern was observed, with Hydrological disasters (Inundations, Flash floods, and Heavy rainfall) peaking between February and May, while Climatological disasters (Droughts and Forest fires) were most frequent from August to October. The catastrophic impacts on people and the economy were documented, showing a significant rise in the number of homeless individuals and those directly affected, alongside considerable material damage and economic losses for both the public and private sectors. Furthermore, we conducted a comprehensive composite analysis on the tropical ocean–atmosphere dynamic structure that elucidated the various triggering mechanisms of disasters arising from Inundations, Droughts, and Forest fires (on seasonal scale), and Flash floods and Heavy rainfall (on sub-monthly scale) in Pará. The detailed characterization of disasters on a municipal scale is relevant in terms of the scientific contribution applied to the strategic decision-making, planning, and implementation of public policies aimed at early risk management (rather than post-disaster response), which is critical for safeguarding human well-being and strengthening the resilience of Amazonian communities vulnerable to climate change. Full article

High-severity wildfires create heterogeneous patterns of vegetation across burned landscapes. While these spatial patterns are well-documented, less is known about the short- and long-term effects of large-scale high-severity wildfires on insect community assemblages and dynamics. Ants are bottom-up indicators of ecosystem health and function that are sensitive to disturbance and fill a variety of roles in their ecosystems, including altering soil chemistry, dispersing seeds, and serving as a key food resource for many species, including the federally endangered Jemez Mountain salamander (Plethodon neomexicanus). We examined the post-fire effects of the 2011 Las Conchas Wildfire on ant communities in the Valles Caldera National Preserve (Sandoval County, New Mexico, USA). We collected ants via pitfall traps in replicated burned and unburned sites across three habitats: ponderosa pine forests, mixed-conifer forests, and montane grassland. We analyzed trends in species richness, abundance, recruitment, loss, turnover, and composition over five sequential years of post-fire succession (2011–2015). Ant foraging assemblage was influenced by burn presence, season of sampling, and macrohabitat. We also found strong seasonal trends and decreases over time since fire in ant species richness and ant abundance. However, habitat and seasonal effects may be a stronger predictor of ant species richness than the presence of fire or post-fire successional patterns. Full article

Global climate change is leading to increased wildfire activity in many parts of the world, and with increasing development, a heightened threat to communities in the wildland urban interface. Evaluating the potential for fire to affect communities and critical infrastructure is essential for effective response decision-making and resource prioritization, including evacuation planning, with changing weather conditions during the fire season. Using a receptor–pathway–source assessment framework, we estimate the potential source area from which a wildfire could spread to a community in British Columbia by projecting fire growth outward from the community’s perimeter. The outer perimeter of the source area is effectively an evacuation trigger line for the forecast period. The novel aspects of our method are inverting fire growth in both space and time by reversing the wind direction, the time course of hourly weather, and slope and aspect inputs to a time-evolving fire growth simulation model Prometheus. We also ran a forward simulation from the perimeter of a large fire that was threatening the community to the community edge and back. In addition, we conducted a series of experiments to examine the influence of varying environmental conditions and ignition patterns on the invertibility of fire growth simulations. These cases demonstrate that time-evolving fire growth simulations can be inverted for practical purposes, although caution is needed when interpreting results in areas with extensive non-fuel cover or complex community perimeters. The advantages of this method over conventional simulation from a fire source are that it can be used for pre-attack planning before fire arrival, and following fire arrival, it does not require having an up-to-the-minute map of the fire location. The advantage over the use of minimum travel time methods for inverse modeling is that it allows for changing weather during the forecast period. This procedure provides a practical tool to inform real-time wildfire response decisions around communities, including resource allocation and evacuation planning, that could be implemented with several time-evolving fire growth models. Full article

Studies of the impact of biomass burning and the emissions of trace gases from biomass burning, especially using long-term observations, are scarce in China. We utilize solar absorption spectra obtained via ground-based high-resolution Fourier transform infrared (FTIR) spectroscopy to retrieve the atmospheric total columns and vertical profiles of carbon monoxide (CO), formaldehyde (H₂CO), and hydrogen cyanide (HCN) in Hefei, China. Seasonal and interannual variability in the three gases from 2016 to 2022 are analyzed. Atmospheric CO shows significant seasonal variations, peaking during spring and winter, and declining during summer, with a seasonal amplitude of 8.07 × 10¹⁷ molecules cm⁻² and a seasonal variability of 29.35%. H₂CO and HCN have similar seasonal patterns to each other, with high concentrations in summer and low concentrations in winter. The seasonal amplitude of H₂CO and HCN are 1.89 × 10¹⁶ molecules cm⁻² and 2.32 × 10¹⁵ molecules cm⁻², respectively, with a seasonal variability of 133.07% and 34.69%, respectively. The means of the annual variation rate for CO, H₂CO, and HCN are (−2.67 ± 2.88)% yr⁻¹, (2.52 ± 12.48)% yr⁻¹ and (−3.48 ± 7.26)% yr⁻¹, respectively. To assess the influence of biomass burning on the variations in column concentrations of the three gases, the correlation between CO, H₂CO, and HCN was analyzed. The months during which the monthly correlation coefficient between CO and H₂CO with HCN exceeds 0.8, and the fire radiative power (FRP) observed by satellites is larger than its monthly average are regarded as a biomass-burning occurrence in Anhui province. Additionally, the enhancement ratios of ΔH₂CO/ΔCO and ΔHCN/ΔCO were calculated for the periods impacted by the biomass burning. Finally, backward trajectory cluster analysis and the potential source contribution function (PSCF) calculation identified the air mass transport pathways and the potential source areas at the Hefei site. Full article