Periodic to infrequent fire in the 71 million ha sagebrush ecosystem of North America historically shifted dominance from sagebrush to herbaceous vegetation, and limited conifer encroachment. Large wildfire years in recent decades, the proliferation of fire-adapted invasive annual grasses, and the decline of sagebrush habitat and associated wildlife have resulted in calls for fire exclusion in sagebrush communities. However, the role and effect of fire in sagebrush communities varies by a host of factors. We synthesized the literature on fire in sagebrush communities to examine how and why fire effects vary and identify the effects of fire on wildlife and biodiversity, and how restoration efforts influence fire effects. Communities in good condition pre-fire are likely to be in good condition post-fire and vice-versa. Cooler, wetter communities are more resilient and periodic fire is essential for limiting conifers in these communities. In hotter, drier communities, fire has a greater potential to promote invasive annual grasses. Post-fire restoration that establishes native vegetation can mediate negative impacts and promote positive outcomes, but are less likely to be successful in the hotter, drier communities. Infrequent fire, especially if it creates a mosaic of different aged burns and unburned areas, is vital to the sustainability of cooler, wetter sagebrush communities, promoting biodiversity and wildlife, but invasive plants and more frequent fire have made fire detrimental in hotter, drier communities. Variables influencing fire effects can be used to prioritize areas for wildfire suppression and pre- and post-fire treatments to improve resilience and limit annual grass invasion.
For plants that resprout after fire, burning creates a strong before-after contrast in which lost biomass must be rebuilt under altered environmental conditions. In species that regenerate through protected belowground buds (basal resprouters), postfire resprouting may lead to increases in subindividual variability through the activation of dormant buds. In contrast, in species where regeneration depends on the survival of stem buds (aerial resprouters), fire might impose a filter favoring well protected buds that might lead to a reduction in variability. We tested whether frequent low-severity fires act as a phenotypic filter in apical resprouters, reducing variability in bud protection and associated leaf traits. We studied the shrub Palicourea rigida Kunth (Rubiaceae) in the Brazilian Cerrado, and sampled three populations with contrasting fire histories. We quantified bud mortality, bud protection (using terminal stem diameter and height above ground), and leaf phenotype (leaf area, dry mass, moisture content and specific leaf area), and assessed fire-driven changes in phenotypic variability at subindividual and population scales. In high fire frequency populations, bud mortality strongly depended on terminal stem diameter; while in the fire-excluded population it depended solely on bud height. These patterns were associated with reduced variability in stem diameter and leaf traits under frequent fires at both population and subindividual scales. Leaf phenotypes also differed between fire histories, with frequently burned populations showing more conservative leaf traits. Frequent low-severity fires can reduce subindividual phenotypic variability in aerial resprouters by selectively removing poorly protected buds. This contrasts with the increased subindividual variability reported for basal resprouters and highlights that fire modifies plant variability and that the direction of change depends on resprouting strategy, and ultimately, on fire regime. Incorporating subindividual variability into fire ecology provides new insights into how disturbance shapes plant form, function and resilience.
Although large grazers are well known to alter fire regimes, small herbivore effects on fire have received comparatively little attention. The gopher tortoise (Gopherus polyphemus) is a herbivorous reptile that acts as an ecosystem engineer in upland, fire-dependent ecosystems of the southeastern U.S. Many animals rely on their deep burrows for refuge from extreme temperatures and fire. At the same time, gopher tortoise's burrowing and foraging activities may decrease fire intensity and severity by reducing plant biomass and/or by altering the flammability of the adjacent plant community. We examined the spatial scale and evidence for each mechanism underlying potential fire effects in sandhill at Archbold Biological Station in south-central Florida. We selected 30 existing burrows varying in activity status (active, inactive, abandoned) as well as non-mound control points in relatively open microsites. We characterized plant biomass and community composition within 15 m of mounds and control points and quantified 11 fire-related traits for 23 common plant species. Our analysis of pre- and post-fire drone imagery from an earlier fire in our study area found a localized reduction in fire severity within about 2 m of tortoise burrows. Our analysis of contemporary burrows showed that mounds of both active and inactive tortoise burrows had lower plant and litter cover than abandoned mounds and the vegetation matrix beyond the mound itself. Tortoise effects on community-level flammability were minor and unlikely to modify fire intensity. Overall, the highly localized soil disturbance associated with burrowing is likely the primary means by which gopher tortoises may decrease fire severity surrounding their burrows. Critically, our study highlights how small animals can potentially shape fire behavior via direct reduction of fuel loads.
The rising frequency of large-scale, destructive wildfires has significantly affected not only natural ecosystems but also critical infrastructure, human lives, and properties. Given the devastating and often irreversible environmental and financial consequences, researchers from diverse fields are actively seeking solutions to improve resilience against wildfires. Fire suppression, one of the most effective strategies to mitigate wildfire damage, relies heavily on rapid and high-fidelity forecasting of fire spread. These predictions are essential for planning evacuations by state or local emergency management agencies, implementing preemptive de-energization strategies for electric utilities, and coordinating fire containment efforts by firefighting teams. However, a significant bottleneck across all these planning processes is the significant computational burden imposed by high-resolution wildfire modeling, the demand for improved predictive accuracy, and the need to integrate diverse and large-scale datasets. Since response time is crucial for wildfire risk management, this paper proposes a deep learning-based surrogate model to predict fire spread in just a fraction of a second. We developed and trained a convolutional neural network (CNN) model that efficiently predicts wildfire propagation. The proposed model demonstrates high efficiency, achieving an F1 score of 0.92. The contributions of this paper are twofold: (1) a fast, high-resolution CNN model that can support wildfire-related public safety power shutoff (PSPS) planning for electric utilities, and (2) a practical tool for firefighting and evacuation teams to support rapid and data-informed risk assessment.
In the western United States, there has been a significant increase in both the size and number of wildfires associated with the increasing drought conditions. The six largest wildfires in U.S. history have occurred in the last seven years. Four of the six fires were in California (2017 Tubbs Fire, 2018 Camp Fire, 2020 Bay Area Fire, and 2021 Dixie Fire), where the mountains create complex atmospheric flows that lead to terrain-induced wildfires from dry, downslope winds. To investigate the health outcomes associated with smoke exposure the spatiotemporal distribution of smoke plume concentrations is required. Air quality monitors are sparse and do not quantify pollutant concentrations solely from smoke plumes. To estimate smoke impacts, the monitoring data can be supplemented with information from satellites, wildfire emissions inventories, dispersion models, and chemical transport models. Results of air quality modeling efforts to simulate wildfire smoke plume transport in the western U.S. and estimate smoke exposure are presented here. The focus of this work is on two recent modeling efforts (1) a novel smoke exposure modeling framework that provides estimates by fuel type, fire size, and plume age and (2) gap-filling approaches that leverage machine learning algorithms to increase the usability of satellite aerosol remote sensing products for estimating wildfire smoke exposure.
Building fire key factors are the fundamental control variables that govern both the initiation of fires and dynamics of propagation. The accurate identification of key factors in building fires is crucial for enhancing the effectiveness of fire prevention strategies. To improve the accuracy of key factor identification in building fires, a novel K-shell Entropy Gravity (KEG) algorithm that integrates multiple topological metrics is proposed in this study. First, a complex network is constructed to characterize the relationships among accident factors, where nodes represent influencing factors and edges denote their co-occurrence in fire incidents. Subsequently, considering the positional importance and core connectivity of nodes, the information influence and irreplaceability of nodes, as well as the collaborative coupling and nonlinear characteristic among multiple indicators, a composite attribute integrating K-shell value, information entropy difference, and total shortest path length is developed to quantify node importance, thereby capturing both the local coreness and the global influence of nodes within the network. Then, these metrics are incorporated into an established gravity-based model to comprehensively assess the influential scope of each node, and the results are employed to identify the key factors. Finally, the proposed method is compared with baseline methods based on the Susceptible-Infected-Recovered (SIR) model and network robustness evaluation using the California Building Fire Dataset (2012-2024). In addition, a sensitivity analysis is performed to investigate how the removal of key factors affects accident propagation. To further verify the robustness of this method, fire data from Alaska are applied for comparison, and an ablation experiment is designed. The results indicate that the KEG algorithm achieves superior accuracy in identifying critical factors and offers a reliable analytical tool for developing targeted fire prevention and mitigation strategies.
Avoiding human fatalities during wildfires is a key public policy objective. Outward road access, or the number of egress routes, is widely assumed to influence wildfire fatalities, yet few studies have quantified if or when this factor becomes critical. To address this gap, we assembled a dataset on community-level wildfire fatality counts and combined it with nationally consistent community egress for the United States, finding that cumulative fatalities are sharply concentrated in communities with very few exits, declining steeply to roughly six nonresidential roads, beyond which additional routes confer minimal further risk reduction. Extending this analysis nationally, we mapped all small communities (<50,000 residents) to identify geographic confluence of limited egress and high wildfire hazard, highlighting regions where road constraints could directly amplify fatalities. Across the United States, 17.7 million people live in communities below this critical egress threshold, including 2.5 million in high wildfire hazard areas. Although most high-risk communities are in the western United States, unexpected hotspots appear in Oklahoma, Florida, and Hawai'i. As wildfire hazard continues to expand with climate change, fuel accumulation, and development in the wildland-urban interface, even more communities may be at risk. Targeted investment in road infrastructure, improved evacuation communication and preparedness, and development of preplanned refuge options together offer complementary and actionable pathways to reduce wildfire fatalities and build nationwide resilience.
Fire investigators conduct post-fire scene work with potential exposure to combustion byproducts, damaged structures, and psychosocial stressors, yet data on exposure reduction practices and health characteristics remain limited. The objective of this analysis is to characterize training, protective practices, self-reported exposures, and health indicators among U. S. fire investigators. As part of a national photovoice study, fire investigators completed a confidential pre-interview survey between April and November 2025. Survey items assessed demographics, occupational history, use of personal protective equipment and respiratory protection, exposures, safety training, decontamination practices, and self-reported health conditions and behaviors. Fifty-six investigators from 24 states participated (mean 12.6 years of experience); 71.4% were public investigators and 29.6% had a second job. Personal protective equipment use during investigations was reported as often or always by 92.9%, while 60.7% reported often or always using respiratory protection. Exposures reported as often or always included soot (89.3%), burned debris (73.2%), smoke (67.9%), and hazardous substances (60.7%). Psychosocial stressors occurred often or always for 28.6%. Preliminary exposure reduction training was reported by 12.5, and 56.4% indicated their agency lacked investigator-specific decontamination practices. Self-rated health was reported as very good or good by 83.9%. Self-reported conditions included cardiovascular disease (21.4%), musculoskeletal disorders (17.9%), mental health conditions (12.5%), and cancer (5.4%). U. S. fire investigators report frequent post-fire exposures and variability in respiratory protection use, exposure reduction training, and investigator-specific decontamination policies. This pilot study data can inform occupational health guidance and future research on exposure assessment, health monitoring, and control strategies.
The 2023 Canadian fire season was record-breaking in terms of burned area and carbon emissions. Here, we present estimates of the regional climate-cooling effect from postfire surface albedo changes, which have historically partially offset the warming influence of fire emissions by wildfires. We estimate that the 2023 fires generated a time-integrated climate cooling of -3.41 W m-2 of burned area (95% CI: -4.39 to -2.43) over a 70-y period. We show that the climate-cooling impact has weakened on average by 29% since the 1960s due to changes in snow cover and duration. Collectively, this result implies that modern-day boreal fires are on average twice as likely to result in a net climate-warming influence.
Tracing the earliest evidence of burning in archaeological contexts is essential for understanding the emergence of fire use-an innovation that underpinned critical behavioral and biological developments in the genus Homo. However, identifying unambiguous traces of early fire use remains challenging. To enhance detection of incipient burning in early occupation layers, we introduce a rapid, non-invasive protocol based on bone luminescence properties, validated through comparison with Fourier Transform Infrared spectroscopy (FTIR). Using these methods, we provide evidence for fire use in two Early Pleistocene (Acheulean) deposits at Wonderwerk Cave (South Africa), extending the chronology of one of the world's earliest paleo-fire records. This combined approach improves the resolution with which early fire use can be identified and opens new avenues for investigating the emergence of pyrotechnology in deep time.
Recent surges in wildfire emissions have exacerbated surface ozone pollution in the United States. Using deep learning, we developed a gapless daily surface ozone dataset at 1-kilometer resolution for 2003-2024. This dataset revealed a reversal in national policy-relevant ozone trends that had gone undetected by the sparse monitoring network: from -0.65 parts per billion (ppb) per year (2003-2015) to +0.13 ppb per year (2015-2024). The reversal was primarily driven by increasing wildfire emissions, offsetting 3.9 years of mitigation progress. Premature deaths from fire-sourced ozone have increased by 318 deaths per year since 2013, with post-2013 mortality 46% higher than pre-2013 mortality. During 2022-2024, wildfire emissions exposed 43 million people to nonattainment conditions, effectively preventing a 4-ppb tightening of the ozone standard. These results underscore the growing challenges of sustaining air quality progress as wildfires intensify under climate change.
The rapid increase in electric bike (e-bike) use has led to a rise in lithium-ion battery fires, which present significant hazards. Beyond thermal injury, these fires emit toxic gases such as hydrogen fluoride (HF), capable of causing severe chemical inhalation injury. The pulmonary effects of inhaled hydrofluoric acid are not well characterised in the literature. Two cases of severe lung injury occurred following indoor e-bike battery fires. The first patient sustained 32% total body surface area (TBSA) burns and developed acute respiratory distress syndrome with radiological evidence of chemical pneumonitis, necessitating prolonged mechanical ventilation and resulting in persistent pulmonary impairment. The second patient sustained 44% TBSA burns and experienced rapidly progressive respiratory failure that was disproportionate to typical smoke inhalation injury. Despite maximal supportive therapy, including extracorporeal membrane oxygenation, the patient died from catastrophic pulmonary failure. Lithium-ion battery fires present both thermal and chemical hazards, especially in enclosed environments. These cases highlight the importance of maintaining a high index of suspicion for toxic inhalation injury, promptly recognising disproportionate respiratory failure, and monitoring for biochemical indicators of hydrofluoric acid exposure as the prevalence of lithium-ion battery use increases.
Artificial intelligence (AI) is reshaping decision-support systems across multiple domains, including risk management and urban safety. Urban villages, characterized by high population density and informal infrastructure, are particularly vulnerable to fire hazards. This study presents an AI-driven fire risk forecasting framework based on an Improved Grey Wolf Optimizer (IGWO) and a Long Short-Term Memory (LSTM) neural network, further enhanced by an incremental learning strategy. IGWO improves hyperparameter convergence and avoids local optima, while the incremental component allows real-time model updates without full retraining. Using real fire incident data from 55 urban villages in Beijing, the proposed IGWO-LSTM-IL model achieves a 92.57% reduction in mean squared error compared to baseline LSTM. The model demonstrates high predictive accuracy, stability, and adaptability, making it a practical tool for intelligent fire risk monitoring and urban safety systems within the scope of AI-transforming urban infrastructure.
Periungual warts, caused by Human Papillomavirus (HPV) infection, are a therapeutic challenge due to their proximity to the nail matrix and poor drug penetration. Conventional treatments, such as cryotherapy and laser, carry a high risk of nail damage and recurrence, highlighting the need for safer and more effective alternatives. Two patients with refractory periungual warts were included, who had failed multiple previous traditional treatments. One was a 38-year-old male with a 2-year history of periungual warts on the right great toe, and the other was a 60-year-old female with a 1-year history of periungual warts on the right middle finger. Both patients showed no improvement after repeated cryotherapy, topical antiviral drugs, or oral medication. The diagnosis of refractory periungual warts was confirmed clinically based on the characteristic verrucous lesions adjacent to and partially embedded under the nail plate, coupled with a history of treatment failure. Based on the "Wen Tong Qu Shi Du" (warming dredging to dispel dampness and toxin) theory, a combined therapy was adopted: filiform fire needle (0.30 mm × 40 mm, heated to incandescence with an alcohol lamp) puncturing the core of the wart every 2 weeks, combined with daily soaking in warming-yang and collateral-dredging TCM decoction (10 g Ramulus Cinnamomi, 6 g Rhizoma Zingiberis, 15 g Poria Cocos, 20 g Coix Seed, 12 g Radix Angelicae Sinensis, 9 g Flos Carthami, 15 g Radix Isatidis, 15 g Folium Isatidis; decocted to 200 mL, soaked at 40-45°C for 20 minutes each time). The 38-year-old male patient achieved complete wart shedding after 3 treatments (6 weeks), and the 60-year-old female patient achieved complete wart shedding after 4 treatments (8 weeks). No adverse reactions such as nail matrix damage or local infection occurred in either patient during treatment. During the 6-month follow-up, there was no recurrence, and the nail bed was smooth with normal nail plate growth. The combination of filiform fire needle and warming-yang and collateral-dredging TCM soak shows significant efficacy in treating refractory periungual warts. The filiform fire needle directly destroys wart tissue and activates the local immune microenvironment based on the "resolving fire stagnation" principle, while the TCM soak improves the "dampness-stasis-toxin accumulation" pathological state through "warming yang to resolve dampness + dredging collaterals to dissipate stagnation", achieving both symptom and root cause treatment. This therapy avoids the risk of nail matrix damage from traditional physical treatments and overcomes the limitations of poor drug penetration in antiviral therapy, providing a safe and effective TCM external treatment option for clinical practice.
Fires are a major disturbance in the terrestrial ecosystem carbon cycle, releasing CO2 while converting part of biomass into pyrogenic carbon (PyC). PyC is increasingly recognized as a persistent carbon pool, yet it remains largely overlooked in the global carbon budget, and its large-scale and long-term dynamics remain poorly quantified. We quantified the spatiotemporal patterns of PyC production in China from 1901 to 2020 by integrating newly reconstructed century-scale burned area data and fuel-type-specific PyC production factors. Mean annual PyC production was 0.8 Tg C yr-1 for forests and 0.0059 Tg C yr-1 for non-forest fires, corresponding to 17.8% and 12.3% of associated CO2 emissions. PyC production increased from 1901 to 2004 and declined thereafter, with Northeast China dominating national totals. This study provides the first century-long assessment of PyC dynamics in China and delivers gridded, uncertainty-quantified PyC estimates that can be incorporated into fire CO2 inventories and carbon-accounting assessments.
Forest fires are becoming increasingly common worldwide, posing a threat to the environment, economy, and society. Spatiotemporal analysis of forest fires is important to understand their characteristics and causes and to inform decision-making. This type of analysis requires the availability of a number of factors that contribute to fire occurrence, such as land use, environment, climate, and human activities, at high spatial and temporal resolutions. The South American Amazon rainforest covers a large area, and acquiring a useful dataset for analysis requires extensive effort and computer-intensive processing. This study investigates potential data sources, establishes a methodology, and prepares a dataset of attributes useful for spatiotemporal fire analysis. We provide a raster-based dataset that includes fires, land use, environment, and climate factors at a spatial resolution of 500 m and monthly temporal resolution from 2001 to 2020, which facilitates the analysis of forest fires in the Amazon. Moreover, because data sources and implementation procedures are detailed, this work also encourages similar research in other parts of the world.
As climate change increases the frequency and severity of disasters, proactive planning for post-disaster housing recovery is essential to mitigate long-term social and economic disruption. Computational models can support this planning by simulating potential recovery trajectories, yet many existing approaches are limited by overwhelming data requirements or narrow applicability to past events. Here, we introduce RAAbIT (Recovery Assessment using Agent-based Tools), a novel agent-based model designed to simulate housing recovery using data available within weeks of a disaster. RAAbIT models individual households, insurers, and contractors as agents governed by empirical behavior rules, and incorporates modifiable system-level constraints, such as contractor availability, to reflect context-specific recovery dynamics. We demonstrate the model's utility by hindcasting two California wildfires-the 2017 Tubbs Fire in Santa Rosa and the 2018 Camp Fire in Paradise-and capturing their divergent recovery trajectories. Despite similar hazards, the two communities experienced significantly different reconstruction outcomes, with Santa Rosa rebuilding 57% of destroyed homes and Paradise only 9% within five years. RAAbIT can reproduce temporal and spatial patterns of recovery observed in building permit and construction data. By balancing generalizability with data realism, RAAbIT provides a flexible and transferable tool for post-disaster recovery planning, supporting more effective decision-making under uncertainty and enhancing community resilience in the face of escalating climate risks.
This study explores the dynamics of soil organic carbon (SOC) in karst ecosystems under prescribed fire and seasonal variations, emphasizing the role of deeper soil horizons often overlooked in previous research. We investigated the influence of fire and seasonality on extracellular polymeric substances (EPS) production at two soil depths (0-10 cm and 20-30 cm) in burned and unburned soils. EPS-protein content ranged from 3.09 to 7.52 mg kg-1 in burned soils and 3.09 to 5.34 mg kg-1 in unburned soils, with higher concentrations at 20-30 cm in burned soils during the dry season. EPS-polysaccharide content was highest in unburned soils (9.92 mg kg-1) at 0-10 cm during the wet season. Amino sugar levels varied significantly, with Glucosamine concentrations reaching 99.96 mg kg-1 in burned soils at 20-30 cm in the dry season. Microbial biomass carbon (MBC) and nitrogen (MBN) displayed significant seasonal and depth-dependent variations, with higher MBC:SOC ratios in burned soils during the dry season at 0-10 cm. Enzyme activities, including acid phosphatase, β-glucosidase, and N-acetylglucosaminidase, were higher in unburned soils, indicating more active nutrient cycling. Structural equation modeling and Random Forest analysis identified soil nutrients, EPS-protein, and microbial biomass as key drivers of the EPS-C:SOC ratio. These findings highlight the importance of deeper soils in SOC stabilization and provide new insights into the impacts of fire and seasonality on microbial processes, enhancing our understanding of carbon sequestration in karst ecosystems.
The increasing frequency and severity of wildfires has necessitated assessing fire effects on soil systems. Variation in fuel loads and fire effects create landscape mosaics with distinct abiotic properties, a heterogeneity that has been dubbed pyrodiversity. Expanding on the pyrodiversity framework, the pyrodiversity-biodiversity hypothesis posits that pyrodiversity increases niche diversity, thereby promoting biodiversity. This hypothesis, however, has remained untested for soil microbes and microeukaryotes. We explored this hypothesis for soil fungal communities using pre- and post-fire data from three empirical fuel load manipulations and across a total of five different vegetation contexts. We first compared pre- and post-fire abiotic heterogeneity to test whether fuel load manipulations would lead to greater environmental heterogeneity, particularly in soil properties. We then tested whether such manipulations led to greater fungal biodiversity as measured by fungal richness, β-diversity, and community dispersion. Labile abiotic soil resource (e.g. plant available phosphorus and inorganic nitrogen) heterogeneity increased post-fire, but this effect depended on the experimental context. In contrast, we observed little evidence for pyrodiversity-associated increases in post-fire fungal richness or diversity; community dispersion increased only in the study with the most extreme fuel load manipulations. Although our analyses did not clearly answer whether pyrodiversity begets biodiversity, our results highlight the nuances of soil responses to fire. Pyrodiversity-biodiversity linkages appear to depend on the system and on the diversity metric: the hypothesis had no support based on fungal richness, but community dispersion provided some support, even if only in one experiment. Understanding system-specific responses may be particularly important as fires increase in systems where they have been suppressed or have been historically rare.
Redox recycling of manganese (Mn) plays a key role in organic matter decomposition and nutrient cycling in terrestrial vegetated ecosystems, and it is expected to be changed by fires. This study revealed how Mn is oxidized during vegetation burning, by characterizing the chemical speciation of Mn in fire ash from wildland fires and laboratory burning and evaluating the factors governing its average oxidation state (AOS) and speciation. Manganese in wildland fire ash from different ecosystems showed variable AOS that ranges from 2.5 to 3.3. Laboratory burning experiments showed that Mn oxidation was primarily controlled by fire thermal intensity (temperature × duration) and burning completeness. As heating time increased from 5 min to 5 h at 550 and 700 °C, Mn AOS in the lab-burned vegetation ash increased from 2.7 to 4.0 and the oxidation rate was faster at higher temperature. Diverse Mn species can present in wildland fire ash and differ structurally from biogenic Mn oxides. The oxidized Mn species enable fire ash to mediate oxidative degradation of catechol, demonstrating its potential in mediating organic matter decomposition. This study revealed a new paradigm of Mn redox recycling, as compared to the microbe-mediated Mn redox cycling in the absence of fires.