Spatial and temporal pattern of wildfires in California from 2000 to 2019

Does socioeconomic and environmental burden affect vulnerability to extreme air pollution and heat? A case-crossover study of mortality in California

BACKGROUND

Extreme heat and air pollution is associated with increased mortality. Recent evidence suggests the combined effects of both is greater than the effects of each individual exposure. Low neighborhood socioeconomic status ("socioeconomic burden") has also been associated with increased exposure and vulnerability to both heat and air pollution. We investigated if neighborhood socioeconomic burden or the combination of socioeconomic and environmental exposures ("socioenvironmental burden") modified the effect of combined exposure to extreme heat and particulate air pollution on mortality in California.

METHODS

We used a time-stratified case-crossover design to assess the impact of daily exposure to extreme particulate matter <2.5 μm (PM2.5) and heat on cardiovascular, respiratory, and all-cause mortality in California 2014-2019. Daily average PM2.5 and maximum temperatures based on decedent's residential census tract were dichotomized as extreme or not. Census tract-level socioenvironmental and socioeconomic burden was assessed with the CalEnviroScreen (CES) score and a social deprivation index (SDI), and individual educational attainment was derived from death certificates. Conditional logistic regression was used to estimate associations of heat and PM2.5 with mortality with a product term used to evaluate effect measure modification.

RESULTS

During the study period 1,514,292 all-cause deaths could be assigned residential exposures. Extreme heat and air pollution alone and combined were associated with increased mortality, matching prior reports. Decedents in census tracts with higher socioenvironmental and socioeconomic burden experienced more days with extreme PM2.5 exposure. However, we found no consistent effect measure modification by CES or SDI on combined or separate extreme heat and PM2.5 exposure on odds of total, cardiovascular or respiratory mortality. No effect measure modification was observed for individual education attainment.

CONCLUSION

We did not find evidence that neighborhood socioenvironmental- or socioeconomic burden significantly influenced the individual or combined impact of extreme exposures to heat and PM2.5 on mortality in California.

IMPACT

We investigated the effect measure modification by socioeconomic and socioenvironmental of the co-occurrence of heat and PM2.5, which adds support to the limited previous literature on effect measure modification by socioeconomic and socioenvironmental burden of heat alone and PM2.5 alone. We found no consistent effect measure modification by neighborhood socioenvironmental and socioeconomic burden or individual level SES of the mortality association with extreme heat and PM2.5 co-exposure. However, we did find increased number of days with extreme PM2.5 exposure in neighborhoods with high socioenvironmental and socioeconomic burden. We evaluated multiple area-level and an individual-level SES and socioenvironmental burden metrics, each estimating socioenvironmental factors differently, making our conclusion more robust.

Evaluating building-level tree cover change in Southern California wildland-urban interface using high-resolution satellite imagery

Wildfire risks are increasing in wildland-urban interface (WUI) areas due to climate change, fuel accumulation, and human settlement development patterns. Building proximity to tree cover is a major factor in igniting buildings and the specific spatial patterns of buildings further influence fire behavior. While tree cover reduction is one approach to mitigating this risk, our understanding about how tree cover specifically changes around individual buildings remains limited, particularly in the complex social dynamic of urbanization, landscaping practices, and new construction. In this study, coupling high-resolution NAIP imagery with Microsoft building footprints, we examined tree cover change within a 10m buffer around buildings from 2010 to 2022 in Southern California's WUI. Our analysis focused on how these changes differed across building types, categorized by their spatial arrangement, and the impact of new construction on existing buildings. Our findings indicate that 69 % of buildings showed an increase in tree cover within the 10 m buffer, with an average increase of 5.69 %. Notably, very dense and dense clustered buildings, which dominated the study area, exhibited the highest level of tree cover increase. Furthermore, old buildings in proximity to new construction demonstrated a greater increase in tree cover compared to those not near new development. Given the increasing wildfire risk in this area, these findings highlight the importance of community-wide vegetation management to prevent structure-to-structure wildfire spread, and new development policies should consider the possible impact that new construction may have on existing patterns of tree cover and wildfire risk.

Global lightning-ignited wildfires prediction and climate change projections based on explainable machine learning models

Wildfires pose a significant natural disaster risk to populations and contribute to accelerated climate change. As wildfires are also affected by climate change, extreme wildfires are becoming increasingly frequent. Although they occur less frequently globally than those sparked by human activities, lightning-ignited wildfires play a substantial role in carbon emissions and account for the majority of burned areas in certain regions. While existing computational models, especially those based on machine learning, aim to predict lightning-ignited wildfires, they are typically tailored to specific regions with unique characteristics, limiting their global applicability. In this study, we present machine learning models designed to characterize and predict lightning-ignited wildfires on a global scale. Our approach involves classifying lightning-ignited versus anthropogenic wildfires, and estimating with high accuracy the probability of lightning to ignite a fire based on a wide spectrum of factors such as meteorological conditions and vegetation. Utilizing these models, we analyze seasonal and spatial trends in lightning-ignited wildfires shedding light on the impact of climate change on this phenomenon. We analyze the influence of various features on the models using eXplainable Artificial Intelligence (XAI) frameworks. Our findings highlight significant global differences between anthropogenic and lightning-ignited wildfires. Moreover, we demonstrate that, even over a short time span of less than a decade, climate changes have steadily increased the global risk of lightning-ignited wildfires. This distinction underscores the imperative need for dedicated predictive models and fire weather indices tailored specifically to each type of wildfire.

Heat-Related Illness in California Firefighters, 2001-2020

BACKGROUND

Firefighters have a higher rate of heat-related illness (HRI) compared to other occupations. Given the changing climate, firefighters' risk of occupational HRI merits attention. Therefore, we aimed to identify demographic, temporal, and geographic risk factors associated with occupational HRI in California firefighters between 2001 and 2020.

METHODS

Within the California Workers' Compensation Information Systems (WCIS), we identified firefighters from 2001 to 2020 using industry and class codes and assigned occupation titles using the NIOSH Industry and Occupation Computerized Coding system (NIOCCS). HRI claims among firefighters were identified using International Classification of Diseases (ICD) Ninth or Tenth revision codes, WCIS nature and cause of injury codes, and keywords. We calculated HRI incidence rates adjusted by sex, age, year, and county. Estimates of California firefighter employment were obtained from the American Community Survey.

RESULTS

We identified 2185 firefighter HRI claims between 2001 and 2020 (305.5 claims/100,000 firefighters, 90% CI: 278.7-740.7). Firefighters aged 18 to 29 years had a statistically significant higher risk of HRI compared to those aged 40 to 49 years (rate ratio = 3.5, 90% CI: 3.1-3.9). The HRI rate increased over time, and the risk from 2016 to 2020 was 1.8 times higher than it was from 2001 to 2005 (90% CI: 1.7-1.9). Northern California counties, including Shasta (2313.9) and Sacramento (1772.1), had the highest HRI rates.

CONCLUSIONS

Firefighters in certain demographic groups and northern California counties were at highest risk of HRI. With rising temperatures and larger wildfires, additional prevention efforts are needed to reduce HRI in California firefighters.

Temporal and spatial pattern analysis of escaped prescribed fires in California from 1991 to 2020

Background

Prescribed fires play a critical role in reducing the intensity and severity of future wildfires by systematically and widely consuming accumulated vegetation fuel. While the current probability of prescribed fire escape in the United States stands very low, their consequential impact, particularly the large wildfires they cause, raises substantial concerns. The most direct way of understanding this trade-off between wildfire risk reduction and prescribed fire escapes is to explore patterns in the historical prescribed fire records. This study investigates the spatiotemporal patterns of escaped prescribed fires in California from 1991 to 2020, offering insights for resource managers in developing effective forest management and fuel treatment strategies.

Results

The results reveal that the months close to the beginning and end of the wildfire season, namely May, June, September, and November, have the highest frequency of escaped fires. Under similar environmental conditions, areas with more records of prescribed fire implementation tend to experience fewer escapes. The findings revealed the vegetation types most susceptible to escaped prescribed fires. Areas with tree cover ranging from 20 to 60% exhibited the highest incidence of escapes compared to shrubs and grasslands. Among all the environmental conditions analyzed, wind speed stands out as the predominant factor that affects the risk of prescribed fire escaping.

Conclusions

These findings mark an initial step in identifying high-risk areas and periods for prescribed fire escapes. Understanding these patterns and the challenges of quantifying escape rates can inform more effective landscape management practices.

Supplementary Information

The online version contains supplementary material available at 10.1186/s42408-024-00342-3.

Development of over 30-years of high spatiotemporal resolution air pollution models and surfaces for California

California's diverse geography and meteorological conditions necessitate models capturing fine-grained patterns of air pollution distribution. This study presents the development of high-resolution (100 m) daily land use regression (LUR) models spanning 1989-2021 for nitrogen dioxide (NO2), fine particulate matter (PM2.5), and ozone (O3) across California. These machine learning LUR algorithms integrated comprehensive data sources, including traffic, land use, land cover, meteorological conditions, vegetation dynamics, and satellite data. The modeling process incorporated historical air quality observations utilizing continuous regulatory, fixed site saturation, and Google Streetcar mobile monitoring data. The model performance (adjusted R2) for NO2, PM2.5, and O3 was 84 %, 65 %, and 92 %, respectively. Over the years, NO2 concentrations showed a consistent decline, attributed to regulatory efforts and reduced human activities on weekends. Traffic density and weather conditions significantly influenced NO2 levels. PM2.5 concentrations also decreased over time, influenced by aerosol optical depth (AOD), traffic density, weather, and land use patterns, such as developed open spaces and vegetation. Industrial activities and residential areas contributed to higher PM2.5 concentrations. O3 concentrations exhibited no significant annual trend, with higher levels observed on weekends and lower levels associated with traffic density due to the scavenger effect. Weather conditions and land use, such as commercial areas and water bodies, influenced O3 concentrations. To extend the prediction of daily NO2, PM2.5, and O3 to 1989, models were developed for predictors such as daily road traffic, normalized difference vegetation index (NDVI), Ozone Monitoring Instrument (OMI)-NO2, monthly AOD, and OMI-O3. These models enabled effective estimation for any period with known daily weather conditions. Longitudinal analysis revealed a consistent NO2 decline, regulatory-driven PM2.5 decreases countered by wildfire impacts, and spatially variable O3 concentrations with no long-term trend. This study enhances understanding of air pollution trends, aiding in identifying lifetime exposure for statewide populations and supporting informed policy decisions and environmental justice advocacy.

Assessment of the suitability of drought descriptions for wildfires under various humid temperate climates in Japan

Drought is the primary driver of wildfires in humid regions, and the main drought drivers for wildfire occurrence and spread vary across different humid climatic areas. This study explores the suitability of different drought descriptions for wildfires under various humid temperate climates in Japan. Based on wildfire data from 1995 to 2012, statistical and correlation analyses were conducted to examine the performance of effective humidity (EH) and soil moisture (SM) as indicators of atmospheric and soil drought. EH is used for nationwide wildfire and drought warnings in Japan. The results show that EH is significantly influenced by seasonal and regional factors, with its ability to assess drought for wildfire varying accordingly, whereas SM demonstrates a more consistent ability to assess drought across different seasons and regions. Correlation analysis revealed that atmospheric drought better explains the drought conditions for wildfire ignition in 11 prefectures, mainly concentrated in the northern regions along the Sea of Japan. In contrast, the correlation coefficients for SM were higher in 33 prefectures, particularly along the Pacific coast, indicating that soil drought better explains the drought conditions for burned areas in these prefectures.

Advancements in remote sensing for active fire detection: A review of datasets and methods

This study comprehensively and critically reviews active fire detection advancements in remote sensing from 1975 to the present, focusing on two main perspectives: datasets and corresponding instruments, and detection algorithms. The study highlights the increasing role of machine learning, particularly deep learning techniques, in active fire detection. Looking forward, the review outlines current challenges and future research opportunities in remote sensing for active fire detection. These include exploring data quality management and multi-modal learning, developing spatiotemporally explicit models, investigating self-supervised learning models, improving explainable and interpretable models, integrating physical-process based models with machine learning, and building digital twins to replicate wildfire dynamics and perform what-if scenario analysis. The review aims to serve as a valuable resource for informing natural resource management and enhancing environmental protection efforts through the application of remote sensing technology.

Mortality attributable to PM2.5 from wildland fires in California from 2008 to 2018

In California, wildfire risk and severity have grown substantially in the last several decades. Research has characterized extensive adverse health impacts from exposure to wildfire-attributable fine particulate matter (PM2.5), but few studies have quantified long-term outcomes, and none have used a wildfire-specific chronic dose-response mortality coefficient. Here, we quantified the mortality burden for PM2.5 exposure from California fires from 2008 to 2018 using Community Multiscale Air Quality modeling system wildland fire PM2.5 estimates. We used a concentration-response function for PM2.5, applying ZIP code-level mortality data and an estimated wildfire-specific dose-response coefficient accounting for the likely toxicity of wildfire smoke. We estimate a total of 52,480 to 55,710 premature deaths are attributable to wildland fire PM2.5 over the 11-year period with respect to two exposure scenarios, equating to an economic impact of $432 to $456 billion. These findings extend evidence on climate-related health impacts, suggesting that wildfires account for a greater mortality and economic burden than indicated by earlier studies.

Establishing the relationship between wildfire smoke and performance metrics on finished beef cattle in Western Rangelands

Identifying causal relationships is complicated. Researchers usually overlook causality behind relationships which can generate misleading associations. Herein, we carefully examine the parametric relationship and causality between wildfire smoke exposure and animal performance and behavior metrics over a period of 2 yr in Reno, Nevada. The animals in the 2020 smoke season were grain-finished (n = 12) and grass-finished (n = 12), whereas the animals during the 2021 season were fed under the same diet but finished with either a hormonal implant (n = 9), or without (n = 9). The dataset included daily records of feed intake (FI), body weight (BW), water intake (WI), average daily gain (ADG), and WI behavior (time spent drinking [TSD]; water intake events [WIE]; no-WIE [NWIE]). Variable tree length Bayesian additive regression trees (BART) were utilized to investigate the relationships between air quality index (AQI), particulate matter 2.5 μm (PM2.5) and 10 μm (PM10), NO2, SO2, Ozone, and CO levels in the air (sensors < 1.6 km from animals) with the animal data. Additionally, linear mixed models with a 7-d lag were used to evaluate parametric relationships among the same variables. All statistical analyses were performed on R Statistical Software (R Core Team 2023). Under the linear mixed model with a 7-d lag, significant positive and negative associations were found for all parameters examined (P < 0.05). Negative associations were found between FI, WI, ADG, BW, WIE, NWIE, TSD, and PM2.5 (P < 0.05) for at least one animal group. Positive linear associations between wildfire smoke parameters and the metrics evaluated were more variable and dependent on year, treatment, and smoke parameters. When examining the credible intervals and the variable importance in the BART, relationships were more difficult to identify. However, some associations were found for Ozone, AQI, NO2, CO, and PM10 (P < 0.05). Overall, our results carefully examine the relationship between smoke parameters and cattle performance and present interesting pathways previously unexplored that could guide early culling/finishing of animals to avoid economic losses associated with performance decrease in response to wildfire smoke exposure. Though interesting associations are found under linear mixed models, causality is difficult to establish, which highlights the need for controlled exposure experiments.

Exposure to wildfires and health outcomes of vulnerable people: Evidence from US data

This paper investigates the causal effect of wildfire exposure on birth outcomes and older people's health outcomes in United States (US). The study focuses on three sub-questions for each health outcome: (1) the causal effect of each of the five largest wildfires on individual health, (2) the causal impact of multiple large wildfires on individual health outcomes, and (3) the causal influence of wildfires larger than different sizes within different distances of counties on health outcomes at the county level. The analysis exploits data from National Vital Statistics System, Behavioural Risk Factor Surveillance System and FIRESTAT. In terms of birth outcomes, the findings show that the largest wildfire slightly increased the risk of other circulatory or respiratory anomalies. Multiple large wildfires moderately raised the risk of prematurity and led to a small decline in the probability of getting omphalocele and cleft lip. The county-level analysis suggests an increased risk of macrosomia following maternal exposure to wildfires. As for the elderly aged 65 + , the results indicate that exposure to multiple massive wildfires led to frequent occurrence of asthma symptoms, while the largest wildfire led to sleeping difficulty caused by asthma symptoms. The number of days older people experienced psychological problems was increased following exposure to multiple large wildfires.

Testing the contribution of dispersal to microbial succession following a wildfire

IMPORTANCE

Identifying the mechanisms underlying microbial community succession is necessary for predicting how microbial communities, and their functioning, will respond to future environmental change. Dispersal is one mechanism expected to affect microbial succession, yet the difficult nature of manipulating microorganisms in the environment has limited our understanding of its contribution. Using a dispersal exclusion experiment, this study isolates the specific effect of environmental dispersal on bacterial and fungal community assembly over time following a wildfire. The work demonstrates the potential to quantify dispersal impacts on soil microbial communities over time and test how dispersal might further interact with other assembly processes in response to environmental change.

Spatial and temporal patterns of indicators of climate change and variability in the Arab world in the past four decades

A comprehensive assessment of the spatial and temporal patterns of the most common indicators of climate change and variability in the Arab world in the past four decades was carried out. Monthly maximum and minimum air temperature and precipitation amount data for the period 1980-2018 were obtained from the CHELSA project with a resolution of 1 km2, which is suitable for detecting local geographic variations in climatic patterns. This data was analyzed using a seasonal-Kendall metric, followed by Sen's slope analysis. The findings indicate that almost all areas of the Arab world are getting hotter. Maximum air temperatures increased by magnitudes varying from 0.027 to 0.714 °C/decade with a mean of 0.318 °C/decade while minimum air temperatures increased by magnitudes varying from 0.030 to 0.800 °C/decade with a mean of 0.356 °C/decade. Most of the Arab world did not exhibit clear increasing or decreasing precipitation trends. The remaining areas showed either decreasing or increasing precipitation trends. Decreasing trends varied from -0.001 to -1.825 kg m-2/decade with a mean of -0.163 kg m-2/decade, while increasing trends varied from 0.001 to 4.286 kg m-2/decade with a mean of 0.366 kg m-2/decade. We also analyzed country-wise data and identified areas of most vulnerability in the Arab world.

Historical spatiotemporal changes in fire danger potential across biomes

This study 1) identifies the seasons and biomes that exhibit significant (1980-2019) changes in fire danger potential, as quantified by the Canadian Fire Weather Index (FWI); 2) explores what types of fire behavior potentials may be contributing to changes in fire danger potential, as quantified by the United States Energy Release Component (ERC) and the Ignition Component (IC); 3) provides spatiotemporal insight on how fire danger potential and fire behavior potential are responding in relation to changes in seasonal precipitation totals and seasonal mean air temperature across biomes. Time series of these fire potentials, as well as seasonal mean temperature, and seasonal precipitation totals are generated using data from the 0.25° ECMWF spatial resolution Reanalysis 5th Generation (ERA5) and the Climatic Research Unit gridded Time Series (CRU TS). The Mann-Kendall test is then applied to identify significant spatiotemporal trends across each biome. Results indicate that the September-November season (SON) exhibits the greatest rate of increase in fire danger potential, followed by the June-August season (JJA), December, January-February season (DJF), and March-May season (MAM), and this is predominant over the Tropical and Subtropical Moist Broadleaf Forest Biome, as well as all vegetation types of the temperate biomes. Similarly, the temperate biomes experience the greatest rate of increase in fire intensity potential and ignition potential, but prevalent during the DJF and MAM seasons. Furthermore, there is a significant positive correlation between fire danger potential and seasonal mean air temperature during JJA in the Northern Hemisphere for the temperate biomes in North America and Europe, as well as the Tropical and Subtropical biomes in Africa. Our analysis provides quantitative insight as to how fire danger potential and fire behavior potential have been responding to changes in seasonal mean temperature and seasonal precipitation totals across different ecoregions around the world.

Rapid Expansion and Adaptability of California Medical Disaster Teams

The California Medical Assistance Team (CAL-MAT) program is coordinated by the California Emergency Medical Services Authority (EMSA). The program was developed to deploy and support medical personnel for disaster medical response. During the coronavirus disease (COVID-19) pandemic, the program and missions grew rapidly in response to medical surge, programs for testing and vaccination, and other concurrent disasters. CAL-MAT enrollment increased 10-fold from approximately 200 members at the beginning of 2020, to an estimated 2200 members by June 2021. This article describes the flexible use of a state-managed disaster medical response program within California and some of the challenges associated with rapid expansion and varied demands during the COVID-19 surges of March 2020-March 2022. CAL-MAT may serve as a model for development of similar state-sponsored or other disaster medical response teams.

Forest fire threatens global carbon sinks and population centres under rising atmospheric water demand

Levels of fire activity and severity that are unprecedented in the instrumental record have recently been observed in forested regions around the world. Using a large sample of daily fire events and hourly climate data, here we show that fire activity in all global forest biomes responds strongly and predictably to exceedance of thresholds in atmospheric water demand, as measured by maximum daily vapour pressure deficit. The climatology of vapour pressure deficit can therefore be reliably used to predict forest fire risk under projected future climates. We find that climate change is projected to lead to widespread increases in risk, with at least 30 additional days above critical thresholds for fire activity in forest biomes on every continent by 2100 under rising emissions scenarios. Escalating forest fire risk threatens catastrophic carbon losses in the Amazon and major population health impacts from wildfire smoke in south Asia and east Africa.

Temporal and spatial patterns of fire activity in three biomes of Brazil

The trade-off between conservation of natural resources and agribusiness expansion is a constant challenge in Brazil. The fires used to promote agricultural expansion increased in the last decades. While studies linking annual fire occurrence and rainfall seasonality are common, the relationship between fires, land use, and land cover remains understudied. Here, we investigated the frequency of the fires and performed a trend analysis for monthly, seasonal, and annual fires in three different biomes: Cerrado, Pantanal, and Atlantic Forest. We used burned area and integrated models in distinct scales (interannual, intraseasonal, and monthly) using Probability Density Functions (PDFs). The best fitting was found for Generalized Extreme Values (GEV) distribution at all three biomes from the several PDFs tested. We found the most fire in the Pantanal (wetlands), followed by Cerrado (Brazilian Savanna) and Atlantic Forest (Semideciduous Forest). Our findings indicated that land use and land cover trends changed over the years. There was a strong correlation between fire and agricultural areas, with increasing trends pointing to land conversion to agricultural areas in all biomes. The high probability of fire indicates that expanding agricultural areas through the conversion of natural biomes impacts several natural ecosystems, transforming land cover and land use. This land conversion is promoting more fires each year.

HexFire: A Flexible and Accessible Wildfire Simulator

As fire frequency and severity grow throughout the world, scientists working across a range of disciplines will increasingly need to incorporate wildfire models into their research. However, fire simulators tend to be highly complex, time-consuming to learn, and difficult to parameterize. As a result, embracing these models can prove impractical for scientists and practitioners who are not fire specialists. Here we introduce a parsimonious wildfire simulator named HexFire that has been designed for rapid uptake by investigators who do not specialize in the mechanics of fire spread. HexFire should be useful to such nonspecialists for representing the spread of fire, interactions with fuel breaks, and for integrating wildfire into other types of ecological models. We provide a detailed description of the HexFire simulator's design and mechanisms. Our heuristic fire spread examples highlight the flexibility inherent in the model system, demonstrate that HexFire can generate a wide range of emergent fire behaviors, and illustrate how HexFire might be coupled with other environmental models. We also describe ways that HexFire itself might be altered or augmented. HexFire can be used as a proxy for more detailed fire simulators and to assess the implications of wildfire for local ecological systems. HexFire can also simulate fire interactions with fuel breaks and active fire suppression.

Projected U.S. drought extremes through the twenty-first century with vapor pressure deficit

Global warming is expected to enhance drought extremes in the United States throughout the twenty-first century. Projecting these changes can be complex in regions with large variability in atmospheric and soil moisture on small spatial scales. Vapor Pressure Deficit (VPD) is a valuable measure of evaporative demand as moisture moves from the surface into the atmosphere and a dynamic measure of drought. Here, VPD is used to identify short-term drought with the Standardized VPD Drought Index (SVDI); and used to characterize future extreme droughts using grid dependent stationary and non-stationary generalized extreme value (GEV) models, and a random sampling technique is developed to quantify multimodel uncertainties. The GEV analysis was performed with projections using the Weather Research and Forecasting model, downscaled from three Global Climate Models based on the Representative Concentration Pathway 8.5 for present, mid-century and late-century. Results show the VPD based index (SVDI) accurately identifies the timing and magnitude short-term droughts, and extreme VPD is increasing across the United States and by the end of the twenty-first century. The number of days VPD is above 9 kPa increases by 10 days along California’s coastline, 30–40 days in the northwest and Midwest, and 100 days in California’s Central Valley.

Impacts from Wildfires on Livestock Health and Production: Producer Perspectives

Simple Summary

Wildfires are increasing in frequency and severity across the Western United States. Efforts to understand the health impacts on humans are widespread and expanding; however, very little is known about the impact of wildfires and smoke exposure on livestock. This work presents the results of a survey of cattle, sheep, and goat producers in California, Oregon, and Nevada, on their experiences during the 2020 wildfire season. While few direct impacts of fires were reported among the 70 responses, 26% of respondents reported they had to evacuate livestock and 19% reported pasture losses. Indirect losses from smoke exposure, including pneumonia and reproductive losses were reported more broadly. This preliminary work highlights the need to better understand impacts of wildfires on livestock and how policy changes can help support the livestock production industry through these crises.

Abstract

Wildfires are increasing in frequency and severity across the Western United States. However, there is limited information available on the impacts these fires are having on the livelihood of livestock producers and their animals. This work presents the results of a survey evaluating the direct and indirect impacts of the 2020 wildfire season on beef cattle, dairy cattle, sheep, and goat, producers in California, Oregon, and Nevada. Seventy completed surveys were collected between May and July 2021. While dairy producers reported no direct impacts from the fires, beef, sheep, and goat producers were impacted by evacuations and pasture lost to fires. Only beef producers reported losses due to burns and burn-associated deaths or euthanasia. Dairy, beef, sheep, and goat producers observed reduced conception, poor weight gain, and drops in milk production. All but dairy producers also observed pneumonia. Lower birthweights, increased abortion rates, and unexplained deaths were reported in beef cattle, sheep, and goats. This work documents the wide-ranging impacts of wildfires on livestock producers and highlights the need for additional work defining the health impacts of fire and smoke exposure in livestock, as well as the policy changes needed to support producers experiencing direct and indirect losses.