Intensity Of Wildfires Research Articles

Wildfire Detection Using Convolutional Neural Networks and PRISMA Hyperspectral Imagery: A Spatial-Spectral Analysis

The exacerbation of wildfires, attributed to the effects of climate change, presents substantial risks to ecological systems, infrastructure, and human well-being. In the context of the Sustainable Development Goals (SDGs), particularly those related to climate action, prioritizing the assessment and management of the occurrence and intensity of extensive wildfires is of utmost importance. In recent times, there has been a significant increase in the frequency and severity of widespread wildfires worldwide, affecting several locations, including Australia, Italy, and the United States of America. The presence of complex phenomena marked by limited predictability leads to significant negative impacts on biodiversity and human lives. The utilization of satellite-derived data with neural networks, such as convolutional neural networks (CNNs), is a potentially advantageous approach for augmenting the monitoring capabilities of wildfires. This research examines the generalization capability of four neural network models, namely the fully connected (FC), one-dimensional (1D) CNN, two-dimensional (2D) CNN, and three-dimensional (3D) CNN model. Each model’s performance, as measured by accuracy, recall, and F1 scores, is assessed through K-fold cross-validation. Subsequently, T-statistics and p-values are computed based on these metrics to conduct a statistical comparison among the different models, allowing us to quantify the degree of similarity or dissimilarity between them. By using training data from Australia and Sicily, the performances of the trained model are evaluated on the test dataset from Oregon. The results are promising, with cross-validation on the training dataset producing mean precision, recall, and F1 scores ranging between approximately 0.97 and 0.98. Especially, the fully connected model has superior generalization capabilities, whilst the 3D CNN offers more refined and less distorted classifications. However, certain issues, such as false fire detection and confusion between smoke and shadows, persist. The aforementioned methodologies offer significant perspectives on the capabilities of neural network technologies in supporting the detection and management of wildfires. These approaches address the crucial matter of domain transferability and the associated dependability of predictions in new regions. This study makes a valuable contribution to the ongoing efforts in climate change by assisting in monitoring and managing wildfires.

Wildfire exposure and academic performance in Brazil: A causal inference approach for spatiotemporal data

As the frequency and intensity of wildfires are projected to globally amplify due to climate change, there is a growing need to quantify the impact of exposure to wildfires in vulnerable populations such as adolescents. In our study, we applied rigorous causal inference methods to estimate the effect of wildfire exposure on the academic performance of high school students in Brazil between 2009 and 2015. Using longitudinal data from 8,183 high schools across 1,571 municipalities in Brazil, we estimated that the average performance in most academic subjects decreases under interventions that increase wildfire exposure, e.g., a decrease of 1.8 % (p = 0.01) in the natural sciences when increasing the wildfire density from 0.0035 wildfires/km2 (first quantile in the sample) to 0.0222 wildfires/km2 (third quartile). Furthermore, these effects considerably worsened over time. Our findings highlight the adverse impact of wildfires on educational outcomes.

Changes in European fire weather extremes and related atmospheric drivers

Recent destructive fire seasons around the world have increased concern that climate change is escalating the frequency, intensity, and extent of wildfires. While the contributions of individual factors can be debated, the scientific literature concludes that fire weather is one prominent driver of fire activity. Our analysis of the relationship between fire weather extremes and burned area in Europe revealed that the number of extreme fire weather days per year correlates positively with the annual burned area over most of the study domain. The evidence presented in our study strongly suggests that fire weather extremes in Europe have become more frequent in recent decades while affecting increasingly larger areas and occurring earlier and later in the fire season. Increases in fire weather extremes occurred abruptly in several parts of Europe, as evidenced by the detection of statistically significant change-points in the time series of the annual extreme fire weather days. Our analysis revealed that, at the regional scale, significant change-points occurred around the late 1990s and mid-2000s in the Mediterranean, marking an abrupt rise in the median of extreme fire weather days per year. We found that high-latitude blocking and sub-tropical ridging drive fire weather extremes in northern and southern Europe, respectively. The results of our study suggest that fire weather extremes are today more likely to occur than in the past, and the increasing occurrence of extreme fire weather could lead to rapidly reaching adaptation limits, thereby reversing currently stagnating or even declining trends reported for fire activity in Europe. Corresponding with extreme fire weather years reported here, recent destructive fire seasons exemplify the limits of fire suppression and the need to adapt to the reality of more frequent extreme fire weather.

Acute Health Effects of Wildfire Smoke Exposure During a Compound Event: A Case-Crossover Study of the 2016 Great Smoky Mountain Wildfires.

In 2016, unprecedented intense wildfires burned over 150,000 acres in the southern Appalachian Mountains in the United States. Smoke from these fires greatly impacted the region and exposure to this smoke was significant. A bidirectional case-crossover design was applied to assess the relationship between PM2.5 (a surrogate for wildfire smoke) exposure and respiratory- and cardiovascular-related emergency department (ED) visits in Western North Carolina during these events. For 0-, 3-, and 7-day lags, findings indicated a significant increase in the odds of being admitted to the ED for a respiratory (ORs: 1.055, 95% CI: 1.048-1.063; 1.083, 1.074-1.092; 1.066, 1.058-1.074; respectively) or cardiovascular event (ORs: 1.052, 95% CI: 1.045-1.060; 1.074, 1.066-1.081; 1.067, 1.060-1.075; respectively) for every 5μg/m3 increase in PM2.5 over a chosen cutpoint of 20.4μg/m3. For all endpoints assessed except for emphysema, there were statistically significant increases in odds from 5.1% to 8.3%. In general, this increase was most pronounced 3days after exposure. Additionally, individuals aged 55+ generally experience higher odds of heart disease at the 3- and 7-day lag points, and Black/African Americans generally experience higher odds of asthma at the 3-day lag point. In general, larger fires and increased numbers of fires within counties resulted in higher health burden at same day exposure. In a secondary analysis, the odds of an ED visit increased by over 40% in several cases among people exposed to days above the Environmental Protection Agency 24-hr PM2.5 standard of 35μg/m3. Our findings provide new understanding on the health impacts of wildfires on rural populations in the southeastern US.

Developing risk assessment framework for wildfire in the United States – A deep learning approach to safety and sustainability

The frequency and intensity of wildfires have significantly increased in the United States over recent decades, posing profound threats to community safety and ecological sustainability. The escalating losses of human life, property, and biodiversity demand a proactive approach to wildfire prediction and management. This study proposes a highly efficient deep learning framework, utilizing a geospatial database of wildfire incidents in the United States from 1992 to 2018, aimed at bolstering our collective resilience against such disasters. The framework comprises two components: firstly, leveraging deep neural networks (DNN), the cause and size of potential wildfires are predicted, achieving accuracy rates of 76.9% and 76.4% for 5-class classification respectively. Secondly, a forecast model using long short term memory networks (LSTM) and an autoencoder is used to anticipate the likelihood of imminent wildfires, focusing on highly at-risk regions such as California. A specific model created to perform one-week forecasts for California achieved a coefficient of determination (R2) and root-mean-square error (RMSE) of 0.90 and 49.5076, respectively. These predictive models offer a significant step towards advancing community safety and environmental sustainability by facilitating timely and effective responses, thereby mitigating the catastrophic effects of wildfires on human life, properties, and delicate ecosystems.

Natural-Language-Processing-Enabled Quantitative Risk Analysis of Aerial Wildfire Operations

Aerial wildfire operations are high risk and account for a large number of firefighter deaths. The increasing intensity of wildfires is driving a surge in aerial operations, as well as interest to improve system safety and performance. In this work, wildfire aviation mishaps documented using the Aviation Safety Communiqué (SAFECOM) system are analyzed using a previously developed framework for hazard extraction and analysis of trends. Hazards and specific failure modes are extracted from the narrative data in SAFECOM forms using natural language processing techniques. Metrics for each hazard (including the frequency, rate, and severity) are calculated. We examine whether these metrics change over time and whether they are related to metadata, such as region and aircraft type. The results of the hazard analysis are presented in a risk matrix, identifying the highest and lowest risk hazards based on the rate of occurrence and average severity. The analysis of all SAFECOM reports indicated that the jumper operations hazards were classified as high risk; whereas the hydraulic fluid malfunctions, bucket or tank failures, retardant loading and jettison failures, prescribed burn operations, cargo letdown failures, and severe weather were classified as serious risk. However, when applied to a specific operational scenario, risk levels change across hazards.

Satellite Evidence of HONO/NO2 Increase With Fire Radiative Power

AbstractWildfires are important sources of atmospheric reactive nitrogen. The reactive nitrogen species partitioning generally depends on fire characteristics. One reactive nitrogen compound, nitrous acid (HONO), is a source of hydroxyl radicals and nitric oxide, which can impact the oxidizing capacity of the atmosphere and fire plume chemistry and composition. We study the Australian wildfire season of 2019–2020, known as Black Summer, where numerous large and intense wildfires burned throughout the continent. We use HONO and nitrogen dioxide (NO2) from the TROPOspheric Monitoring Instrument (TROPOMI) and fire radiative power (FRP) from the Visible Infrared Imaging Radiometer Suite to investigate HONO and NO2 relationships with fire characteristics. The ratio of HONO to NO2 increases linearly with FRP both in Australia and globally. Both Australian and global fire relationships depend strongly on land cover type. These relationships can be applied to emission inventories to improve wildfire emission representation in models.

Abrupt, climate-induced increase in wildfires in British Columbia since the mid-2000s

In the province of British Columbia, Canada, four of the most severe wildfire seasons of the last century occurred in the past 7 years: 2017, 2018, 2021, and 2023. To investigate trends in wildfire activity and fire-conducive climate, we conducted an analysis of mapped wildfire perimeters and annual climate data for the period of 1919–2021. Results show that after a century-long decline, fire activity increased from 2005 onwards, coinciding with a sharp reversal in the wetting trend of the 20th century. Even as precipitation levels remain high, moisture deficits have increased due to rapid warming and increased evaporative demand. Bottom-up factors further influence fire activity, as the legacy of past wildfires, insect outbreaks, and land-use practices continually influence fire regimes. The compound effects of climate-induced moisture changes and altered fuels now force British Columbians to confront the harsh reality of more frequent years of intense and prolonged wildfire activity.

Illuminating Wildfire Risk

As climate change increases the frequency and intensity of wildfires, optics is helping researchers better understand the threats—and reduce the impacts.

Community‐Based Fire Management in East and Southern African Savanna‐Protected Areas: A Review of the Published Evidence

AbstractThe introduction of fire suppression policies and expansion of exclusionary protected areas in East and Southern African savannas have engendered a wildfire paradox. Outside protected areas, livestock have replaced fire as the dominant fuel consumer. Inside their boundaries, wildfire intensity has increased due to accumulating flammable biomass. Community‐Based Fire Management (CBFiM) is recognized as an alternative fire management strategy to address the wildfire paradox and promote equitable fire governance across conservation landscapes. Yet, there has been little investigation into the implementation and effectiveness of CBFiM across East and Southern Africa's savanna‐protected areas. Here we employ a social‐ecological systems framework to develop a systematic map of the published literature on the framing and features of CBFiM in this context. We characterize the challenges and opportunities for their design and implementation, focusing on the relationship between governance systems and community participation in fire management. We find that CBFiM projects are commonly governed by the state and international non‐governmental organisations who retain decision‐making power and determine access to savanna resources and fire use. Existing CBFiM projects are limited to communal rangelands and are developed within existing Community‐Based Natural Resource Management programs prioritizing fire prevention and suppression. Planned CBFiM projects propose an exclusive early‐dry season patch mosaic burning regime to incorporate indigenous fire knowledge into modern management frameworks, but evidence of indigenous and local peoples' involvement is scarce. To provide equitable fire management, CBFiM projects need to address inequalities embedded in protected area governance and centralized suppression policies, and account for changing state‐society and intra‐society relations across the region.

Disrupting the Grid

Abstract Experiences of fires are mediated by energy infrastructures and refracted through social inequality and difference. In California, a state marked by increasingly intense and frequent wildfires, the grid is a source of fire risk, with historically marginalized groups bearing the brunt of exposures to wildfire smoke. Drawing on research conducted by one of the co-authors in collaboration with California's Karuk Tribe and Blue Lake Rancheria Tribes, this empirically grounded review article expands our understanding of grids. Extant scholarship presents the grid as a networked infrastructure mediating access to energy and one's relationship to a collective and the state. We extend this analysis by highlighting the diverse and unevenly distributed forms of risk entangled with the electric grid, focusing on those related to fire and smoke. We conclude by considering alternative infrastructural arrangements entailing different relationships to the grid with potential for more just futures in the context of climate change.

Deciphering the role of fungi in post-fire succession in Canada's Great Lakes–St. Lawrence forest region

Fungi are important drivers of forest health, as well as culturally important for many Indigenous communities. In Canada, increasingly intense and frequent wildfires affect fungal substrates and habitats, environmental conditions, and physiological responses of hosts with which fungi may interact. In Ontario, aggressive fire suppression raises the question of how fungal regeneration, and taxonomic compositions, may be affected when fires do appear on the landscape. This study investigates the fungal regrowth post-fire above and below-ground and the role fungal species play in post fire ecological succession in Southeastern Ontario. The study sites are two black oak savannas in restoration, previously prescribed a burn (Northumberland County, 2022 and 2023), and a recent wildfire burned mixed deciduous forest (Centennial Lake, 2023). We collected fruiting body and soil samples for fungal identification via observational and DNA methods. Above ground vegetation assays were made at 9 burned plots within these three sites and another 8 adjacent unburned (control) plots. We extracted soil DNA and will amplify fungal ITS2 amplicons by PCR. Then, we will sequence the amplified DNA using MiSeq to compare burned and unburned soil fungi. Lastly, we will use statistical methods to differentiate effects from fire and soil texture and type in burned and control plots. Thus far, we found that Pyropyxis rubra, a pyrophilous ascomycete fungus, is extremely abundant just weeks after the lightning-induced fire at Centennial Lake. Our combined observational and quantitative descriptions of above-and below ground soil fungi provide a deeper understanding of how fire affects species composition, fungal reproduction, and population dynamics in Ontario. This research may help inform other successional processes, and fungal harvesting of culturally important species.

Low‐ and high‐intensity fire in the riparian savanna: Demographic impacts in an avian model species and implications for ecological fire management

Abstract Climate change is driving changes in fire frequency and intensity, making it more urgent for conservation managers to understand how species and ecosystems respond. In tropical monsoonal savannas—Earth's most fire‐prone landscapes—ecological fire management aims to prevent intense wildfires late in the dry season through prescribed low‐intensity burns early in the dry season. Riparian habitats embedded within tropical savannas represent critical refuges for biodiversity, yet are particularly fire‐sensitive. Better understanding of the impact of fire—including prescribed burns—on riparian habitats is therefore key but requires long‐term detailed post‐fire monitoring of species' demographic rates, as effects may persist and/or be delayed. We analyse impacts of (prescribed) low‐intensity and (prescribed but escaped) high‐intensity fire in northern Australian riparian and adjacent savanna habitat. We quantify multi‐year impacts on density, survival, reproduction and dispersal of an endangered riparian bird, the western purple‐crowned fairy‐wren (Malurus coronatus coronatus), in a well‐studied individually marked population. Following low‐intensity fire, bird density was reduced by >20% in burnt compared to adjacent unburnt riparian habitat for ≥2.5 years. This was a result of reduced breeding success and recruitment for 2 years immediately following fire, rather than mortality or dispersal of adults. In contrast, high‐intensity fire (in a dry year) resulted in a sharp decline in population density by 50% 2–8 months after fire, with no signs of recovery after 2.5 years. The decline in density was due to post‐fire adult mortality, rather than dispersal. Breeding success of the (few) remaining individuals was low but not detectably lower than in unburnt areas, likely because breeding success was poor overall due to prevailing dry conditions. Synthesis and applications. Even if there is no or very low mortality during fire, and no movement of birds away from burnt areas, both low‐ and high‐intensity fire in the riparian zone reduce population density. However, the mechanism by which this occurs, and recovery time, differs with fire intensity. To minimise impacts of fire on riparian zones in tropical savannas, we suggest employing low‐intensity prescribed burns under optimal conditions shortly after the breeding season.

Spatial heterogeneity in global atmospheric CO during the COVID–19 lockdown: Implications for global and regional air quality policies

The COVID–19 lockdown (LD) provided a unique opportunity to examine the changes in regional and global air quality. Changes in the atmospheric carbon monoxide (CO) during LD warrant a thorough analysis as CO is a major air pollutant that affects human health, ecosystem and climate. Our analysis reveals a decrease of 5–10% in the CO column during LD (April–May 2020) compared to the pre-lockdown (PreLD, March 2020) periods in regions with high anthropogenic activity, such as East China (EC), Indo-Gangetic Plain (IGP), North America, parts of Europe and Russia. However, this reduction did not occur in the regions of frequent and intense wildfires and agricultural waste burning (AWB). We find high heterogeneity in the CO column distributions, from regional to city scales during the LD period. To determine the sources of CO emissions during LD, we examined the ratios of nitrogen dioxide (NO2), sulfur dioxide (SO2) to CO for major cities in the world. This facilitated the identification of contributions from different sources; including vehicles, industries and biomass burning during LD. The comparison between CO levels during the LD and PreLD periods indicates a notable reduction in the global tropospheric CO, but no significant change in the stratosphere. It is found that CO emissions decreased during LD in the hotspot regions, but rebounded after the LD restrictions were lifted. This study, therefore, highlights the importance of policy decisions and their implementations in the global and regional scales to improve the air quality, and thus to protect public health and environment.

Projection of Future Fire Emissions Over the Contiguous US Using Explainable Artificial Intelligence and CMIP6 Models

AbstractIncreasing temperature and water cycle changes due to warming climate may increase the frequency and intensity of wildfires. Fire emission projections are useful for informing strategies for adaptation and mitigation of fire impacts on societies and ecosystems. Here, we construct a neural network (NN) model explained by the Shapley Additive explanation to predict fire PM2.5 emissions change and understand their drivers over the contiguous US (CONUS) in the mid‐21st century under a high greenhouse gas emissions scenario (SSP5‐8.5). Using future meteorology and leaf area index (LAI) simulated by eight global climate models from the Coupled Model Intercomparison Project Phase 6, future population density, and present‐day land use and land cover (LULC) as input to the NN model, the total fire PM2.5 emissions over CONUS are projected to increase by 4%–75% (model spread). Among different regions, fire emissions in the western US are projected to increase more significantly in June‐July‐August than in other seasons and regions, with the median ratios of future to present‐day fire emissions ranging from 1.67 to 2.86. The increases in fire emissions are mainly driven by increasing normalized temperature (23%–29%) and decreasing soil moisture (2%–10%) in the future. When future LULC change is considered, the projected fire emissions further increase by 58%–83% over the western US compared to projections without LULC change because of future increases in vegetation fraction. The results highlight the important role of warmer temperature, decreasing soil moisture, and LULC change in increasing fire emissions in the future.

Disturbance and disease: host-parasite interactions in freshwater streams remain stable following wildfire.

Increases in the intensity and frequency of wildfires highlight the need to understand how fire disturbance affects ecological interactions. Though the effects of wildfire on free-living aquatic communities are relatively well-studied, how host-parasite interactions respond to fire disturbance is largely unexplored. Using a Before-After-Control-Impact design, we surveyed 10 stream sites (5 burned and 5 unburned) in the Willamette River Basin, Oregon and quantified snail host infection status and trematode parasite community structure 1year before and two years after historic wildfires. Despite the severity of the wildfires, snail host populations did not show significant shifts in density or size distributions. We detected nine taxa of trematode parasites and overall probability of infection remained consistent over the three-year study period. However, at the taxon-specific level, we found evidence that infection probability by one trematode decreased and another increased after fire. In a larger dataset focusing on the first year after fire (9 burned, 8 unburned sites), we found evidence for subtle differences in trematode community structure, including higher Shannon diversity and evenness at the burned sites. Taken together, host-parasite interactions were remarkably stable for most taxa; for trematodes that did show responses, changes in abundance or behavior of definitive hosts may underlie observed patterns. These results have implications for using parasites as bioindicators of environmental change and suggest that aquatic snail-trematode interactions may be relatively resistant to wildfire disturbance in some ecosystems.

Assessing spatial and temporal variation in obligate resprouting, obligate seeding, and facultative seeding shrub species in California’s Mediterranean-type climate region

Mediterranean-climate region (MCR) shrublands have evolved a set of regeneration strategies in response to periodic, high intensity wildfires: obligate seeding (OS), obligate resprouting (OR), and facultative seeding (FS) species. In the North American MCR, data on their spatial and temporal variability is currently lacking, which is a significant information gap for resource managers. We developed a multinomial model using dynamic and static variables to predict the distribution of the three shrub post-fire regeneration strategies, plus trees and herbs, in southern California. Cross-validation showed 50% of the predicted values for each of the five plant groups were within 8–24 percent of the actual value. We assessed variation in shrub regeneration strategies in three ways. First, we found the three major shrub community types (mixed chaparral, chamise-redshank chaparral, and coastal sage scrub) had proportionally greater biomass of FS (37–43% of total biomass) than OR or OS. Second, we assessed the spatial variability using (a) moisture availability (climatic water deficit and solar radiation) and found FS accounted for an increasingly greater proportion of total aboveground live biomass as water became limited, while OR biomass decreased; and (b) ecosystem productivity (NDVI) which showed the proportion of OS, OR, and tree biomass increased with productivity (with a corresponding dramatic decrease in herb biomass). We also assessed temporal variability using time since fire and found OS represented an initially small proportion of total biomass immediately post-fire (8–10%) which increased with time since fire (13–17%). Third, spatial outputs from the multinomial model indicated FS dominated pixels were most widely distributed across the study area (~3.4 million ha, 71%), compared to OR and OS covering 5% each. FS also occurred more frequently on warmer, south facing slopes and summits whereas OR preferred cooler, north facing slopes and valleys (p < 0.0001). Better understanding the distribution of OS, OR, and FS shrubs is important for resource management, including quantifying shrubland carbon storage and identifying areas for post-fire restoration, as described in a case study of the Bobcat Fire on the Angeles National Forest.

Ground-based and AIRS carbon monoxide behavior at El Arenosillo observatory (Southwestern Europe)

Carbon monoxide (CO) observations registered at El Arenosillo observatory (Southwestern Europe), during three years (2019–2022) were used to explore levels, temporal variations and patterns. The seasonal mean hourly oscillated between 127 ± 21 and 97 ± 14 ppb, with extreme peaks measured under wildfire plume arrival. A remarkable monthly evolution was observed with peaks in cold months (135 ± 20 ppb in January) and minimum in summer (90 ± 11 ppb in June). Seasonal daily patterns showed a different nocturnal behavior depending on the season, with a clear maximum at 7:00–9:00 UTC, a decrease during diurnal time, and a minimum at 17:00–18:00 UTC. To determine the potential trends from 2002 to 2022, the observations of the AIRS (Atmospheric Infrared Sounder) instrument and the emissions from the global emission inventory of CAMS (Copernicus Atmosphere Monitoring Service) were used. A downward trend for emissions of −31% decade−1 was found, whereas the decrease from AIRS was −9.8% decade−1. The effort to reduce CO emissions is being countered by other processes. The CO behavior was investigated under weather conditions governed by the synoptic and mesoscale processes. Background levels (∼90 ppb) were obtained under Atlantic airflows, while an increase of ∼40% was observed with the impact of urban plumes from continental areas. CO even lower than 90 ppb was obtained under non-pure sea-land breezes, whereas, with pure breezes, reached peaks above 120 ppb, affecting the atmosphere at the surface and up to 1–1.2 km in the air. The extreme level was registered under wildfire plume arrival, with peaks exceeding 350 ppb. Climate change is modifying weather patterns and modulating breezes; in addition, it is also increasing the frequency and intensity of wildfires. These processes could be counteracting the efforts carried out to reduce emissions.

Post-fire wood mulch negatively affects the moss biocrust cover and its positive effects on microbial diversity in a semi-arid Mediterranean forest

Mulches are highly effective in mitigating the risk of erosion generated after wildfires in fire-prone ecosystems. Despite being a technique commonly used, it remains completely unexplored how mulches interact with the positive effects of the emergent moss biocrust on the recovery and resilience of soils and their microbial communities. For this purpose, the effects of wood-based mulch were assayed on the soil stability and moisture improvement, the nutrient inputs, and the response of microbial biomass, activity, composition, and diversity. Soils were studied after one year of wood mulch application at two rates representing possible scenarios, “Rate 1” (65 % of soil cover), and “Rate 2” (100 % of cover), and “Control” soils without mulch. The biocrust development had a positive impact on soil aggregate stability and moisture retention before mulch application. However, one year after the mulch application the biocrust cover was drastically inhibited, especially at the highest rate of mulch. Independently of the biocrust presence, soils at Rate 1 registered a tendency to higher nitrogen content, available phosphorous, basal respiration, and microbial biomass carbon, suggesting an incipient recovery of soil conditions and soil functionality. However, the microbial community composition became more homogeneous and less diverse under the mulch presence (regardless of the application rate), and the positive effect of moss biocrust emergence on the microbial diversity was diluted after one year. The fungal community was particularly sensitive to the wood mulch presence, increasing in richness in response to fresh wood incorporation to soils, in particular saprotrophs and yeasts. The fungal and bacterial compositional shifts after the mulch application reveal an incipient wood decomposition stage, but the transitory loss in beta diversity after the moss biocrust suppression warns about the necessity of including the microbial diversity information into post-fire management planning. Studying the effects of forest management on the above and belowground soil communities is essential to understanding the resistance and resilience of semi-arid forests to the increasing intensity and severity of wildfires.

Estimating proximity effects to wildfire fuels treatments on house prices in Cibola National Forest, New Mexico, USA

Forested landscapes in the Western United States are subject to growing size and severity of wildfires, in part due to historical management strategies focusing on wildfire suppression. Forest restoration treatments and fuels reductions, including thinning and prescribed burning, can reduce the frequency and intensity of wildfires. Extensive restoration and fuels treatment efforts are underway across many areas in the Southwestern United States, including New Mexico. The tradeoff between amenity values provided by forested landscapes and the wildfire risk associated with forested landscapes is becoming increasingly important to understand as development in the wildland-urban interface increases. Understanding how house proximity, relative to forest restoration or fuels treatments, is capitalized into home sale prices can provide useful information about how individuals value forested landscapes that have been altered to reduce wildfire risk or severity. We use a Hedonic Property Model to estimate the average treatment effect of proximity to forest restoration or fuel treatments in New Mexico, United States. We use matching methods to estimate the average treatment effect of proximity to forest restoration. We find that proximity to the forest has a positive amenity value; however, proximity to recent forest restoration or fuel treatments results in a decrease in house sale prices. We combine the results of our two models and calculate that homes not within one kilometer of a treated forest and within one kilometer of Cibola National Forest sell for an average $73,626 premium. The average premium drops to $22,996 for homes within one kilometer of a forest that has been recently treated and within one kilometer of Cibola National Forest.