Human influence on California fire regimes

Protected areas as hotspots of wildfire activity in fire-prone Temperate and Mediterranen biomes

The European Union has recently passed the Nature Restoration Law which, among others, seeks to increase the cover of forest reserves protected for biodiversity and, globally, the Kunming-Montreal Global Biodiversity Framework similarly seeks to expand protected areas. Here we test whether a trade-off exists between protected areas expansion and fire activity, leading to a higher exposure to fire for the population in protected areas, because they often harbor more biomass and occur in remote areas. We analyzed forest fires affecting 14,892,174 ha, and intersecting 10,999 protected areas, across fire-prone European Temperate and Mediterranean forest biomes, and in similar ecosystems within California, Chile and Australia. Protected areas were being disproportionally affected by fire within most Temperate biomes, and fire severity was 20 % higher within protected areas also in Mediterranean biomes. Population in the periphery of forest areas was up to 16 times more likely to be exposed to large wildfires when their environment was within, or near, protected areas. Differences in manageable factors such as fuel loads and road density were primary drivers of the divergence in burned area across protection status, with abiotic factors playing also significant roles. The importance of fuel loads indicates that current plans for expanding strictly protected areas, where no human intervention is allowed, may be particularly problematic from a fire perspective. Wildfire prevention and mitigation must be central goals in the development of conservation/restoration programs to diminish population exposure and fire severity.

Household needs among wildfire survivors in the 2017 Northern California wildfires

Wildfires are impacting communities globally, with California wildfires often breaking records of size and destructiveness. Knowing how communities are affected by these wildfires is vital to understanding recovery. We sought to identify impacted communities' post-wildfire needs and characterize how those needs change over time. The WHAT-Now study deployed a survey that was made publicly available for communities affected by the October 2017 Northern California wildfires or the accompanying smoke at beginning approximately four months post-fire with the vast majority completed by nine months post-fire. Among other questions, the survey asked an adult household member to report on their households' greatest need both one-week post-fire and at the time of survey. A total of 1461 households responded to these questions. Households reported many types of needs, with 154 responses that did not directly name needs but rather described how their households had been affected, which we classified as impacts. Four major themes were identified: physical, health, air, and information, each representing an array of varied specific needs or impacts. Physical needs (e.g. housing, food) were the most common (cited by more than 50% during the fires and about a third at the time of survey). The need for clean air was strong during the fires, but not months later, at the time of survey. In contrast, health needs were reported by a quarter of households during the fires. Needs that were reported at both times were categorized as 'persistent', and there were more persistent mental health needs over time compared to other health themes. Understanding the needs and impacts that arise during wildfires, their diversity and duration, and how they change over time is crucial to identifying types of assistance that are most needed during recovery efforts and when they are needed. Results presented here along with other wildfire needs assessments can be utilized to improve disaster preparedness, including for wildfire recovery.

Impacts of ash-induced environmental alkalinization on fish physiology, and their implications to wildfire-scarred watersheds

Changes in land use, a warming climate and increased drought have amplified wildfire frequency and magnitude globally. Subsequent rainfall in wildfire-scarred watersheds washes ash into aquatic systems, increasing water pH and exposing organisms to environmental alkalinization. In this study, 15 or 20 °C-acclimated Chinook salmon (Oncorhynchus tshawytscha) yearlings were exposed to an environmentally-relevant ash concentration (0.25 % w/v), increasing water pH from ∼8.1 to ∼9.2. Salmon experienced significant disturbance to blood plasma pH (pHe) and red blood cell intracellular pH (RBC pHi) within 1 h, but recovered within 24 h. Impacts on plasma ion concentrations were relatively mild, and plasma glucose increased by 2- to 4-fold at both temperatures. Temperature-specific differences were observed: 20 °C salmon recovered their pHe more rapidly, perhaps facilitated by higher basal metabolism and anaerobic metabolic H+ production. Additionally, 20 °C salmon experienced dramatically greater spikes in plasma total ammonia, [NH3] and [NH4+] after 1 h of exposure that decreased over time, whereas 15 °C salmon experienced a gradual nitrogenous waste accumulation. Despite pHe and RBC pHi recovery and non-lethal nitrogenous waste levels, we observed 20 % and 33 % mortality in 15 and 20 °C treatments within 12 h of exposure, respectively. The mortalities cannot be explained by high water pH alone, nor was it likely to be singularly attributable to a heavy metal or organic compound released from ash input. This demonstrates post-wildfire ash input can induce lethal yet previously unexplored physiological disturbances in fish, and further highlights the complex interaction with warmer temperatures typical of wildfire-scarred landscapes.

Changes in aerosol properties at the El Arenosillo site in Southern Europe as a result of the 2023 Canadian forest fires

From the beginning of May 2023 to the end of August 2023, the Northern Hemisphere experienced significant wildfire activity with the most widespread fires occurring in Canada. Forest fires in Canada destroyed more than 15.6 million hectares of forests. These wildfires worsened air quality across the region and other parts of the world. The smoke reached southern Europe by the end of June 2023. To better understand the consequences of such forest fires far from the site of origin, aerosol optical, microphysical and radiative properties were analyzed during this event for southern Europe using data from the Visible Infrared Imaging Radiometer Suite (VIIRS), TROPOspheric Monitoring Instrument (TROPOMI), and Aerosol Robotic Network (AERONET). TROPOMI aerosol index (AI) and the carbon monoxide (CO) product confirm that the smoke originated directly from these forest fires. AERONET data from the El Arenosillo site in southern Spain showed maximum aerosol optical depth (AOD) values on June 27 reached 2.36. Data on Angstrom Exponent (AE), aerosol volume size distribution (VSD), single scattering albedo (SSA), fine mode fraction (FMF), volume particle concentration, effective radius (REff), absorption AOD (AAOD), extinction AE (EAE) and absorption AE (AAE) showed that fine-mode particles with carbonaceous aerosols contribution predominated in the atmosphere above the El Arenosillo site. Direct aerosol radiative forcing (DARF) at the top (DARFTOA) and bottom of atmosphere (DARFBOA) were -103.1 and -198.93 Wm-2, respectively. The atmospheric aerosol radiative forcing (DARFATM) was found to be 95.83 Wm-2 and with a heating rate 2.69 K day-1, which indicates the resulting warming of the atmosphere.

Playing with fire: Socioeconomic factors and deliberate wildfires in Texas

Deliberately setting fires, also known as arson, is one of the primary causes of human-caused wildfires in the US. Arson fires pose a significant threat, contributing to the human-caused wildfire problem in the southern US. Since more than 90% of wildfires in the South are human-caused, it is particularly important to examine the factors that influence the distribution of arson wildfires in this region. While most research has combined arson fires together with other human-caused wildfires, a smaller body of studies has focussed specifically on fires started by arson. The focus of this study is to understand the key factors associated with the distribution of intentional wildfires in Texas. We examine the relationship between a combination of key socioeconomic factors and environmental factors and, arson wildfires using Negative Binomial models. As expected, our results indicate that road density, unemployment rate, and crime rate are positively associated with arson fires, and the number of law enforcement officers and wage rate are negatively associated with arson wildfire incidences. The higher percentage of forest cover in a county and higher temperature regimes are also positively associated with arson fire occurrences. As such, we conclude that arson wildfires are more likely in areas with high accessibility to wildlands. Similarly, counties with high unemployment rates and consequently high crime rates are more likely to suffer from arson wildfires. Our results clearly show that a higher presence of law enforcement officers in a county deter arsonists. Similarly, financial well-being as indicated by the higher wage rates in a county plays an apparent role in curbing arson wildfire occurrences. Thus, the results from this study provide valuable insights to policymakers, land managers, and fire prevention specialists to develop targeted interventions aimed at reducing arson risk in Texas and beyond.

The importance of geography in forecasting future fire patterns under climate change

An increasing amount of California's landscape has burned in wildfires in recent decades, in conjunction with increasing temperatures and vapor pressure deficit due to climate change. As the wildland-urban interface expands, more people are exposed to and harmed by these extensive wildfires, which are also eroding the resilience of terrestrial ecosystems. With future wildfire activity expected to increase, there is an urgent demand for solutions that sustain healthy ecosystems and wildfire-resilient human communities. Those who manage disaster response, landscapes, and biodiversity rely on mapped projections of how fire activity may respond to climate change and other human factors. California wildfire is complex, however, and climate-fire relationships vary across the state. Given known geographical variability in drivers of fire activity, we asked whether the geographical extent of fire models used to create these projections may alter the interpretation of predictions. We compared models of fire occurrence spanning the entire state of California to models developed for individual ecoregions and then projected end-of-century future fire patterns under climate change scenarios. We trained a Maximum Entropy model with fire records and hydroclimatological variables from recent decades (1981 to 2010) as well as topographic and human infrastructure predictors. Results showed substantial variation in predictors of fire probability and mapped future projections of fire depending upon geographical extents of model boundaries. Only the ecoregion models, accounting for the unique patterns of vegetation, climate, and human infrastructure, projected an increase in fire in most forested regions of the state, congruent with predictions from other studies.

Time since fire as a driver of taxonomic and phylogenetic patterns of grassland plant communities

Fire plays a key role in grasslands, determining the distribution and evolution of species and boundaries with neighboring ecosystems. Evidence of community-wide responses to fire is largely based on taxonomic and functional descriptors, while the phylogenetic dimension is overlooked. Here we evaluated how the taxonomic and phylogenetic structure of grassland plant communities responded to a time since fire (TSF) gradient. We sampled 12 communities in Southern Brazil under varying TSF and calculated taxonomic species richness (S) and dominance (D), phylogenetic diversity (PD), and mean phylogenetic distances (MPD). We used Structural Equation Models to test the relationships between the environmental gradient and community descriptors. Communities with longer TSF presented higher PD and MPD but lower species richness and increased taxonomic dominance. These sites were dominated by monocots, specifically C4 grasses, but also presented exclusive clades, whereas recently-burned sites presented lower taxonomic dominance and more species distributed in a wider variety of clades. Our results indicate that these scenarios are interchangeable and dependent on fire management. Fire adaptation was not constrained by phylogenetic relatedness, contrasting with previous findings for tropical savannahs and indicating that temperate and tropical non-forest ecosystems from South America respond differently to fire, possibly due to different evolutionary histories.

Rangewide occupancy of a flagship species, the Coastal California Gnatcatcher (Polioptila californica californica) in southern California: Habitat associations and recovery from wildfire

The Coastal California Gnatcatcher (Polioptila californica californica), a federally threatened species, is a flagship species for regional conservation planning in southern California (USA). An inhabitant of coastal sage scrub vegetation, the gnatcatcher has declined in response to habitat loss and fragmentation, exacerbated by catastrophic wildfires. We documented the status of gnatcatchers throughout their California range and examined post-fire recovery of gnatcatchers and their habitat. We used GIS to develop a habitat suitability model for Coastal California Gnatcatchers using climate and topography covariates and selected over 700 sampling points in a spatially balanced manner. Bird and vegetation data were collected at each point between March and May in 2015 and 2016. Presence/absence of gnatcatchers was determined during three visits to points, using area searches within 150 x 150 m plots. We used an occupancy framework to generate Percent Area Occupied (PAO) by gnatcatchers, and analyzed PAO as a function of time since fire. At the regional scale in 2016, 23% of the points surveyed were occupied by gnatcatchers, reflecting the effect of massive wildfires in the last 15 years. Similarly, PAO in the post-fire subset of points was 24%, with the highest occupancy in unburned (last fire <2002) habitat. Positive predictors of occupancy included percent cover of California sagebrush (Artemisia californica), California buckwheat (Eriogonom fasciculatum), and sunflowers (Encelia spp., Bahiopsis laciniata), while negative predictors included laurel sumac (Malosma laurina) and total herbaceous cover; in particular, non-native grasses. Our findings indicate that recovery from wildfire may take decades, and provide information to speed up recovery through habitat restoration.

Ungulates mitigate the effects of drought and shrub encroachment on the fire hazard of Mediterranean oak woodlands

Climate change is increasing the frequency of droughts and the risk of severe wildfires, which can interact with shrub encroachment and browsing by wild ungulates. Wild ungulate populations are expanding due, among other factors, to favorable habitat changes resulting from land abandonment or land-use changes. Understanding how ungulate browsing interacts with drought to affect woody plant mortality, plant flammability, and fire hazard is especially relevant in the context of climate change and increasing frequency of wildfires. The aim of this study is to explore the combined effects of cumulative drought, shrub encroachment, and ungulate browsing on the fire hazard of Mediterranean oak woodlands in Portugal. In a long-term (18 years) ungulate fencing exclusion experiment that simulated land abandonment and management neglect, we investigated the population dynamics of the native shrub Cistus ladanifer, which naturally dominates the understory of woodlands and is browsed by ungulates, comparing areas with (no fencing) and without (fencing) wild ungulate browsing. We also modeled fire behavior in browsed and unbrowsed plots considering drought and nondrought scenarios. Specifically, we estimated C. ladanifer population density, biomass, and fuel load characteristics, which were used to model fire behavior in drought and nondrought scenarios. Overall, drought increased the proportion of dead C. ladanifer shrub individuals, which was higher in the browsed plots. Drought decreased the ratio of live to dead shrub plant material, increased total fuel loading, shrub stand flammability, and the modeled fire parameters, that is, rate of surface fire spread, fireline intensity, and flame length. However, total fuel load and fire hazard were lower in browsed than unbrowsed plots, both in drought and nondrought scenarios. Browsing also decreased the population density of living shrubs, halting shrub encroachment. Our study provides long-term experimental evidence showing the role of wild ungulates in mitigating drought effects on fire hazard in shrub-encroached Mediterranean oak woodlands. Our results also emphasize that the long-term effects of land abandonment can interact with climate change drivers, affecting wildfire hazard. This is particularly relevant given the increasing incidence of land abandonment.

Evidence of increasing wildfire damage with decreasing property price in Southern California fires

Across the Western United States, human development into the wildland urban interface (WUI) is contributing to increasing wildfire damage. Given that natural disasters often cause greater harm within socio-economically vulnerable groups, research is needed to explore the potential for disproportionate impacts associated with wildfire. Using Zillow Transaction and Assessment Database (ZTRAX), hereafter "Zillow", real estate data, we explored whether lower-priced structures were more likely to be damaged during the most destructive, recent wildfires in Southern California. Within fire perimeters occurring from 2000-2019, we matched property price data to burned and unburned structures. To be included in the final dataset, fire perimeters had to surround at least 25 burned and 25 unburned structures and have been sold at most seven years before the fire; five fires fit these criteria. We found evidence to support our hypothesis that lower-priced properties were more likely to be damaged, however, the likelihood of damage and the influence of property value significantly varied across individual fire perimeters. When considering fires individually, properties within two 2003 fires-the Cedar and Grand Prix-Old Fires-had statistically significantly decreasing burn damage with increasing property value. Occurring in 2007 and later, the other three fires (Witch-Poomacha, Thomas, and Woolsey) showed no significant relationship between price and damage. Consistent with other studies, topographic position, slope, elevation, and vegetation were also significantly associated with the likelihood of a structure being damaged during the wildfire. Driving time to the nearest fire station and previously identified fire hazard were also significant. Our results suggest that further studies on the extent and reason for disproportionate impacts of wildfire are needed. In the meantime, decision makers should consider allocating wildfire risk mitigation resources-such as fire-fighting and wildfire structural preparedness resources-to more socioeconomically vulnerable neighborhoods.

The adoption of climate-smart agriculture to address wildfires in the Maya Golden Landscape of Belize: Smallholder farmers' perceptions

Ecosystems around the globe are enduring wildfires with greater frequency, intensity, and severity and this trend is projected to continue as a result of climate change. Climate-smart agriculture (CSA) has been proposed as a strategy to prevent wildfires and mitigate climate change impacts; however, it remains poorly understood as a strategy to prevent wildfires. Therefore, the authors propose a multimethod approach that combines mapping of wildfire susceptibility and social surveys to identify priority areas, main factors influencing the adoption of CSA practices, barriers to their implementation, and the best CSA practices that can be implemented to mitigate wildfires in Belize's Maya Golden Landscape (MGL). Farmers ranked slash and mulch, crop diversification, and agroforestry as the main CSA practices that can be implemented to address wildfires caused by agriculture in the MGL. In order to reduce wildfire risk, these practices should, be implemented in agricultural areas near wildlands with high wildfire susceptibility and during the fire season (February-May), in the case of slash and mulch. However, socio-demographic and economic characteristics, together with a lack of training and extension services support, inadequate consultation by agencies, and limited financial resources, hinder the broader adoption of CSA practices in the MGL. Our research produced actionable and valuable information that can be used to design policies and programs to mitigate the impacts of climate change and wildfire risk in the MGL. This approach can also be used in other regions where wildfires are caused by agricultural practices to identify priority areas, barriers and suitable CSA practices that can be implemented to mitigate wildfires.

Association of Wildfire Exposure While Recovering From Lung Cancer Surgery With Overall Survival

Importance

With a changing climate, wildfire activity in the US has increased dramatically, presenting multifaceted and compounding health hazards. Individuals discharged from the hospital following surgical resection of non-small cell lung cancer (NSCLC) are potentially at higher risk from wildfires' health hazards.

Objective

To assess the association between wildfire exposure and postoperative long-term overall survival among patients with lung cancer in the US.

Design, Setting, and Participants

In this cohort study, individuals who underwent curative-intent NSCLC resection between January 1, 2004, and December 31, 2019, were selected from the National Cancer Database. Daily wildfire information was aggregated at the zip code level from the National Aeronautics and Space Administration Fire Information for Resource Management System. The data analysis was performed between July 19, 2022, and April 14, 2023.

Exposure

An active wildfire detected at the zip code of residence between 0 and 3, 4 and 6, or 7 and 12 months after NSCLC surgery.

Main Outcome

Overall survival was defined as the interval between age at hospital discharge and age at death, last contact, or study end, whichever came first. Cox proportional hazards were used for estimating hazard ratios (HRs) adjusted for sex, region, metropolitan category, health insurance type, comorbidities, tumor size, lymph node involvement, era, and facility type.

Results

A total of 466 912 individuals included in the study (249 303 female and [53.4] and 217 609 male [46.6%]; mean [SD] age at diagnosis, 67.3 [9.9] years), with 48 582 (10.4%) first exposed to a wildfire between 0 and 3 months, 48 328 (10.6%) between 4 and 6 months, and 71 735 (15.3%) between 7 and 12 months following NSCLC surgery. Individuals exposed to a wildfire within 3 months (adjusted HR [AHR], 1.43; 95% CI, 1.41-1.45), between 4 and 6 months (AHR, 1.39; 95% CI, 1.37-1.41), and between 7 and 12 months (AHR, 1.17; 95% CI, 1.15-1.19) after discharge from the hospital following stage I to III NSCLC resection had worse overall survival than unexposed individuals.

Conclusions

In this cohort study, wildfire exposure was associated with worse overall survival following NSCLC surgical resection, suggesting that patients with lung cancer are at greater risk from the health hazards of wildfires and need to be prioritized in climate adaptation efforts.

Unique challenges posed by fire disturbance to water supply management and transfer agreements in a headwaters region

As a headwaters region, Colorado is a critical source of water for surrounding states and Mexico. But fuel densification and shifts in hydrometeorological processes, such as climate aridification and precipitation sharpening, are causing increasingly severe and erratic wildfire behavior and post-disturbance geomorphic hazards in and downstream of its forested source water areas. Human development patterns and inter and intra-state water rights agreements further complicate resource management. This is prompting land managers to consider progressive planning and management tools to mitigate fire-related degradation of water supply and irrigation systems. This narrative review examines aspects of Colorado's geography, demography, and hydrology that make its water supply systems and transfer agreements particularly vulnerable to landscape disturbance and then provides hazard mitigation recommendations. Readers are introduced to Colorado's water supply portfolio including how water is moved, stored, treated, and consumed; why those systems are vulnerable to wildfire disturbance; and how risk can be reduced before and after fires occur. Lessons learned are applicable to other source water areas facing similar challenges. By synthesizing our review findings, we identified numerous research and programmatic gaps including the need for more interdisciplinary studies; a lack of explicit research into how disturbance-driven hydromodification may hinder the ability of headwater regions to exercise their water rights and fulfill water transfer agreements (crucial for reducing potential future water conflict); an unresolved debate regarding the potential effects of forest treatments on water yield; and the need for additional funding to roll out tools and educational programs to communities experiencing severe wildfire activity for the first time.

A firebreak placement model for optimizing biodiversity protection at landscape scale

A solution approach is proposed to optimize the selection of landscape cells for inclusion in firebreaks. It involves linking spatially explicit information on a landscape's ecological values, historical ignition patterns and fire spread behavior. A firebreak placement optimization model is formulated that captures the tradeoff between the direct loss of biodiversity due to the elimination of vegetation in areas designated for placement of firebreaks and the protection provided by the firebreaks from losses due to future forest fires. The optimal solution generated by the model reduced expected losses from wildfires on a biodiversity combined index due to wildfires by 30% relative to a landscape without any treatment. It also reduced expected losses by 16% compared to a randomly chosen solution. These results suggest that biodiversity loss resulting from the removal of vegetation in areas where firebreaks are placed can be offset by the reduction in biodiversity loss due to the firebreaks' protective function.

Use of machine learning as a tool for determining fire management units in the brazilian atlantic forest

Geoprocessing techniques are generally applied in natural disaster risk management due to their ability to integrate and visualize different sets of geographic data. The objective of this study was to evaluate the capacity of classification and regression tree (CART) to assess fire risk. MCD45A1 product of the burnt area, relative to a 16-year period (2000-2015) was used to obtain a fire occurrence map, from center points of the raster, using a kernel density approach. The resulting map was then used as a response variable for CART analysis with fire influence variables used as predictors. A total of 12 predictors were determined from several databases, including environmental, physical, and socioeconomic aspects. Rules generated by the regression process allowed to of define different risk levels, expressed in 35 management units, and used to produce a fire prediction map. Results of the regression process (r = 0.94 and r² = 0.88) demonstrate the capability of the CART algorithm in highlighting hierarchical relationships among predictors, while the model's easy interpretability provides a solid basis for decision making. This methodology can be expanded in other environmental risk analysis studies and applied to any area of the globe on a regional scale.

Systematic Mapping and Review of Landscape Fire Smoke (LFS) Exposure Impacts on Insects

Landscape fire activity is changing in many regions because of climate change. Smoke emissions from landscape fires contain many harmful air pollutants, and beyond the potential hazard posed to human health, these also have ecological impacts. Insects play essential roles in most ecosystems worldwide, and some work suggests they may also be sensitive to smoke exposure. There is therefore a need for a comprehensive review of smoke impacts on insects. We systematically reviewed the scientific literature from 1930 to 2022 to synthesize the current state of knowledge of the impacts of smoke exposure from landscape fires on the development, behavior, and mortality of insects. We found: (1) 42 relevant studies that met our criteria, with 29% focused on the United States of America and 19% on Canada; (2) of these, 40 insect species were discussed, all of which were sensitive to smoke pollution; (3) most of the existing research focuses on how insect behavior responds to landscape fire smoke (LFS); (4) species react differently to smoke exposure, with for example some species being attracted to the smoke (e.g., some beetles) while others are repelled (e.g., some bees). This review consolidates the current state of knowledge on how smoke impacts insects and highlights areas that may need further investigation. This is particularly relevant since smoke impacts on insect communities will likely worsen in some areas due to increasing levels of biomass burning resulting from the joint pressures of climate change, land use change, and more intense land management involving fire.

Beyond Particulate Matter Mass: Heightened Levels of Lead and Other Pollutants Associated with Destructive Fire Events in California

As the climate warms, wildfire activity is increasing, posing a risk to human health. Studies have reported on particulate matter (PM) in wildfire smoke, yet the chemicals associated with PM have received considerably less attention. Here, we analyzed 13 years (2006-2018) of PM_2.5 chemical composition data from monitors in California on smoke-impacted days. Select chemicals (e.g., aluminum and sulfate) were statistically elevated on smoke-impacted days in over half of the years studied. Other chemicals, mostly trace metals harmful to human health (e.g., copper and lead), were elevated during particular fires only. For instance, in 2018, lead was more than 40 times higher on smoke days on average at the Point Reyes monitoring station, due mostly to the Camp Fire, burning approximately 200 km away. There was an association between these metals and the combustion of anthropogenic material (e.g., the burning of houses and vehicles). Although still currently rare, these infrastructure fires are likely becoming more common and can mobilize trace metals in smoke far downwind, at levels generally unseen except in the most polluted areas of the country. We hope a better understanding of the chemicals in wildfire smoke will assist in the communication and reduction of public health risks.

Systems in Flames: Dynamic Coproduction of Social-Ecological Processes

Ecologists who study human-dominated places have adopted a social-ecological systems framework to recognize the coproduced links between ecological and social processes. However, many social scientists are wary of the way ecologists use the systems concept to represent such links. This wariness is sometimes due to a misunderstanding of the contemporary use of the systems concept in ecology. We aim to overcome this misunderstanding by discussing the contemporary systems concept using refinements from biophysical ecology. These refinements allow the systems concept to be used as a bridge rather than a barrier to social-ecological interaction. We then use recent examples of extraordinary fire to illustrate the usefulness and flexibility of the concept for understanding the dynamism of fire as a social-ecological interaction. The systems idea is a useful interdisciplinary abstraction that can be contextualized to account for societally important problems and dynamics.

Spatial Predictions of Human and Natural-Caused Wildfire Likelihood across Montana (USA)

Spatial wildfire ignition predictions are needed to ensure efficient and effective wildfire response, and robust methods for modeling new wildfire occurrences are ever-emerging. Here, ignition locations of natural and human-caused wildfires across the state of Montana (USA) from 1992 to 2017 were intersected with static, 30 m resolution spatial data that captured topography, fuel availability, and human transport infrastructure. Once combined, the data were used to train several simple and multiple logistic generalized linear models (GLMs) and generalized additive models (GAMs) to predict the spatial likelihood of natural and human-caused ignitions. Increasingly more complex models that included spatial smoothing terms were better at distinguishing locations with and without natural and human-caused ignitions, achieving area under the receiver operating characteristic curves (AUCs) of 0.84 and 0.89, respectively. Whilst both ignition types were more likely to occur at intermediate fuel loads, as characterized by the local maximum Normalized Difference Vegetation Index (NDVI), naturally-ignited wildfires were more locally influenced by slope, while human-caused wildfires were more locally influenced by distance to roads. Static maps of ignition likelihood were verified by demonstrating that mean annual ignition densities (# yr−1 km−1) were higher within areas of higher predicted probabilities. Although the spatial models developed herein only address the static component of wildfire hazard, they provide a foundation upon which dynamic data can be superimposed to forecast and map wildfire ignition probabilities statewide on a timely basis.

Study of Driving Factors Using Machine Learning to Determine the Effect of Topography, Climate, and Fuel on Wildfire in Pakistan

As the climate changes with the population expansion in Pakistan, wildfires are becoming more threatening. The goal of this study was to understand fire trends which might help to improve wildland management and reduction in wildfire risk in Pakistan. Using descriptive analyses, we investigated the spatiotemporal trends and causes of wildfire in the 2001–2020 period. Optimized machine learning (ML) models were incorporated using variables representing potential fire drivers, such as weather, topography, and fuel, which includes vegetation, soil, and socioeconomic data. The majority of fires occurred in the last 5 years, with winter being the most prevalent season in coastal regions. ML models such as RF outperformed others and correctly predicted fire occurrence (AUC values of 0.84–0.93). Elevation, population, specific humidity, vapor pressure, and NDVI were all key factors; however, their contributions varied depending on locational clusters and seasons. The percentage shares of climatic conditions, fuel, and topographical variables at the country level were 55.2%, 31.8%, and 12.8%, respectively. This study identified the probable driving factors of Pakistan wildfires, as well as the probability of fire occurrences across the country. The analytical approach, as well as the findings and conclusions reached, can be very useful to policymakers, environmentalists, and climate change researchers, among others, and may help Pakistan improve its wildfire management and mitigation.

Mapping the wildland-urban interface in California using remote sensing data

Due to the mixed distribution of buildings and vegetation, wildland-urban interface (WUI) areas are characterized by complex fuel distributions and geographical environments. The behavior of wildfires occurring in the WUI often leads to severe hazards and significant damage to man-made structures. Therefore, WUI areas warrant more attention during the wildfire season. Due to the ever-changing dynamic nature of California's population and housing, the update frequency and resolution of WUI maps that are currently used can no longer meet the needs and challenges of wildfire management and resource allocation for suppression and mitigation efforts. Recent developments in remote sensing technology and data analysis algorithms pose new opportunities for improving WUI mapping methods. WUI areas in California were directly mapped using building footprints extracted from remote sensing data by Microsoft along with the fuel vegetation cover from the LANDFIRE dataset in this study. To accommodate the new type of datasets, we developed a threshold criteria for mapping WUI based on statistical analysis, as opposed to using more ad-hoc criteria as used in previous mapping approaches. This method removes the reliance on census data in WUI mapping, and does not require the calculation of housing density. Moreover, this approach designates the adjacent areas of each building with large and dense parcels of vegetation as WUI, which can not only refine the scope and resolution of the WUI areas to individual buildings, but also avoids zoning issues and uncertainties in housing density calculation. Besides, the new method has the capability of updating the WUI map in real-time according to the operational needs. Therefore, this method is suitable for local governments to map local WUI areas, as well as formulating detailed wildfire emergency plans, evacuation routes, and management measures.

Assessment of Climate Change and Human Activities on Vegetation Development in Northeast China

Vegetation in Northeast China (NEC) has faced dual challenges posed by climate change and human activities. However, the factors dominating vegetation development and their contribution remain unclear. In this study, we conducted a comprehensive evaluation of the response of vegetation in different land cover types, climate regions, and time scales to water availability from 1990 to 2018 based on the relationship between normalized difference vegetation index (NDVI) and the standardized precipitation evapotranspiration index (SPEI). The effects of human activities and climate change on vegetation development were quantitatively evaluated using the residual analysis method. We found that the area percentage with positive correlation between NDVI and SPEI increased with time scales. NDVI of grass, sparse vegetation, rain-fed crop, and built-up land as well as sub-humid and semi-arid areas (drylands) correlated positively with SPEI, and the correlations increased with time scales. The negatively correlated area was concentrated in humid areas or areas covered by forests and shrubs. Vegetation water surplus in humid areas weakens with warming, and vegetation water constraints in drylands enhance. Moreover, potential evapotranspiration had an overall negative effect on vegetation, and precipitation was a controlling factor for vegetation development in semi-arid areas. A total of 53% of the total area in NEC showed a trend of improvement, which is mainly attributed to human activities (93%), especially through the implementation of ecological restoration projects in NEC. The relative role of human activities and climate change in vegetation degradation areas were 56% and 44%, respectively. Our findings highlight that the government should more explicitly consider the spatiotemporal heterogeneity of the influence of human activities and water availability on vegetation under changing climate and improve the resilience of regional water resources. The relative proportions and roles map of climate change and human activities in vegetation change areas provide a basis for government to formulate local-based management policies.

Combining Participatory Mapping and Geospatial Analysis Techniques to Assess Wildfire Risk in Rural North Vietnam

Participatory mapping (PM) is a valuable research tool for assessing fire risk, especially in regions where data are difficult to collect or inconsistent; in such areas, the integration between crowdsourced data and geospatial techniques plays a fundamental role in gathering more consistent and reliable information. This study combines a participatory (community-based) mapping approach with geospatial techniques to assess fire risk in Van Chan district, northern Vietnam, an area where the economy relies mainly on forestry activities. Local stakeholders designed a map of wildfires, which was modelled as a function of a set of physical and socio-economic variables. A fire-probability map of the district was obtained and compared with MODIS data (2000-2020). The results suggest that higher fire probability occurs in areas with lower human pressure, and they provide information on related socio-economic drivers that affect this phenomenon. This study highlights the importance of combining participatory approaches and geospatial techniques to assess fire dynamics and prevent wildfires in terms of understanding and predicting the risks. The involvement of local communities is fundamental to this innovative participatory approach with regard to better supporting decision-making and prevention actions and to developing fire control management guidelines.

Biophysical controls over fire regime properties in Central Portugal

The concept of fire regime can be used to describe, with different degrees of complexity, the spatial and temporal patterns of fires and their effects within a given area and over a given period. In this work, we explore the relations between fire regime and a set of potential biophysical controls at a local scale, for 972 civil parishes in central Portugal. The fire regime was characterized with reference to a 44-year period (1975-2018) using three properties: cumulative percentage of parish area burned, area-weighted total number of wildfires, and the Gini concentration index of burned area over time. Potential control variables included topography, seasonal temperature and rainfall, and land use/land cover type and patch fragmentation. Ordinal logistic regression was used to model the relations between the fire regime properties and the potential control factors. Results show that the fire regime properties have important spatial contrasts within the study area, and that land use/land cover distribution, spring rainfall and summer temperatures are the major controls over their variability. The percentage of each parish occupied by shrubland and spontaneous herbaceous vegetation is the single most important factor influencing cumulative percentage of parish area burned and the Gini concentration index of burned area, whereas spring rainfall is the foremost factor regarding area-weighted total number of wildfires. Along with the role of spring rainfall in promoting fuel availability later in the year, our results highlight the importance of the speed of regrowth of shrubland and spontaneous herbaceous vegetation after burning, pointing out the need of tailoring fuel management strategies to the properties of each parish.

Plant-water sensitivity regulates wildfire vulnerability

Extreme wildfires extensively impact human health and the environment. Increasing vapour pressure deficit (VPD) has led to a chronic increase in wildfire area in the western United States, yet some regions have been more affected than others. Here we show that for the same increase in VPD, burned area increases more in regions where vegetation moisture shows greater sensitivity to water limitation (plant-water sensitivity; R2 = 0.71). This has led to rapid increases in human exposure to wildfire risk, both because the population living in areas with high plant-water sensitivity grew 50% faster during 1990-2010 than in other wildland-urban interfaces and because VPD has risen most rapidly in these vulnerable areas. As plant-water sensitivity is strongly linked to wildfire vulnerability, accounting for ecophysiological controls should improve wildfire forecasts. If recent trends in VPD and demographic shifts continue, human wildfire risk will probably continue to increase.

Human ignitions on private lands drive USFS cross-boundary wildfire transmission and community impacts in the western US

Wildfires in the western United States (US) are increasingly expensive, destructive, and deadly. Reducing wildfire losses is particularly challenging when fires frequently start on one land tenure and damage natural or developed assets on other ownerships. Managing wildfire risk in multijurisdictional landscapes has recently become a centerpiece of wildfire strategic planning, legislation, and risk research. However, important empirical knowledge gaps remain regarding cross-boundary fire activity in the western US. Here, we use lands administered by the US Forest Service as a study system to assess the causes, ignition locations, structure loss, and social and biophysical factors associated with cross-boundary fire activity over the past three decades. Results show that cross-boundary fires were primarily caused by humans on private lands. Cross-boundary ignitions, area burned, and structure losses were concentrated in California. Public lands managed by the US Forest Service were not the primary source of fires that destroyed the most structures. Cross-boundary fire activity peaked in moderately populated landscapes with dense road and jurisdictional boundary networks. Fire transmission is increasing, and evidence suggests it will continue to do so in the future. Effective cross-boundary fire risk management will require cross-scale risk co-governance. Focusing on minimizing damages to high-value assets may be more effective than excluding fire from multijurisdictional landscapes.

Extreme pre-fire drought decreases shrub regeneration on fertile soils

Extreme drought and increasing temperatures can decrease the resilience of plant communities to fires. Not only may extremely dry conditions during or after fires lead to higher plant mortality and poorer recruitment, but severe pre-fire droughts may reduce the seed production and belowground vigor that are essential to post-fire plant recovery, and may indirectly facilitate invasion. We studied survival, recruitment, and growth of shrubs and herbs in chaparral (shrubland) communities in Northern California after a 2015 fire that immediately followed California's extreme 3-yr drought. We followed the same protocols used to study similar, adjacent communities after a 1999 fire that did not follow a drought, and we compared the two recovery trajectories. Overall, the 2015 fire was not more severe than the 1999 fire, yet it caused higher mortality and lower growth of resprouting shrubs on fertile (sandstone) soils. In contrast, the 2015 fire did not affect the mortality or growth of resprouting shrubs on infertile (serpentine) soils, the density of shrub seedlings, or the richness or cover of native herbs differently than the 1999 fire. However, the 2015 fire facilitated a massive increase in exotic herbaceous cover, especially on fertile soils, possibly portending the early stages of a type conversion to exotic-dominated grassland. Our findings indicate that the consequences of climate change on fire-dependent communities will include effects of pre-fire as well as post-fire climate, and that resprouting shrubs are particularly likely to be sensitive to pre-fire drought.

Vegetation-type conversion of evergreen chaparral shrublands to savannahs dominated by exotic annual herbs: causes and consequences for ecosystem function

Woody, evergreen shrublands are the archetypal community in mediterranean-type ecosystems, and these communities are profoundly changed when they undergo vegetation-type conversion (VTC) to become annual, herb-dominated communities. Recently, VTC has occurred throughout southern California chaparral shrublands, likely with changes in important ecosystem functions. The mechanisms that lead to VTC and subsequent changes to ecosystem processes are important to understand as they have regional and global implications for ecosystem services, climate change, land management, and policy. The main drivers of VTC are altered fire regimes, aridity, and anthropogenic disturbance. Some changes to ecosystem function are certain to occur with VTC, but their magnitudes are unclear, whereas other changes are unpredictable. I present two hypotheses: (1) VTC leads to warming that creates a positive feedback promoting additional VTC, and (2) altered nitrogen dynamics create negative feedbacks and promote an alternative stable state in which communities are dominated by herbs. The patterns described for California are mostly relevant to the other mediterranean-type shrublands of the globe, which are biodiversity hotspots and threatened by VTC. This review examines the extent and causes of VTC, ecosystem effects, and future research priorities.

Trends in Fire Danger and Population Exposure along the Wildland-Urban Interface

The increased risk of wildfires and associated smoke exposure in the United States is a growing public health problem, particularly along the Wildland-Urban Interface (WUI). Using the measure of fire danger, the Energy Release Component, we define fire danger as the onset and duration of fire season, in the continental US, between 1979 and 2016. We then combine the measure of fire danger with census data to quantify changes in population fire exposure across the WUI. We determined that the largest increases in fire danger were observed in the Southwest, Intermountain, and Pacific Southwest regions. The increased fire danger, specifically during peak fire season, accounted for 6.1 more fires each year and 78,000 more acres burned each year, underscoring the link between fire danger and the risks of large fire occurrence and burn acreage. Finally, we observed significant population growth (121.2% between 1990 and 2010) within high-danger WUI areas, further implying significant increases in potential fire exposure.

Relationships of climate, human activity, and fire history to spatiotemporal variation in annual fire probability across California

In the face of recent wildfires across the Western United States, it is essential that we understand both the dynamics that drive the spatial distribution of wildfire, and the major obstacles to modeling the probability of wildfire over space and time. However, it is well documented that the precise relationships of local vegetation, climate, and ignitions, and how they influence fire dynamics, may vary over space and among local climate, vegetation, and land use regimes. This raises questions not only as to the nature of the potentially nonlinear relationships between local conditions and the fire, but also the possibility that the scale at which such models are developed may be critical to their predictive power and to the apparent relationship of local conditions to wildfire. In this study we demonstrate that both local climate-through limitations posed by fuel dryness (CWD) and availability (AET)-and human activity-through housing density, roads, electrical infrastructure, and agriculture, play important roles in determining the annual probabilities of fire throughout California. We also document the importance of previous burn events as potential barriers to fire in some environments, until enough time has passed for vegetation to regenerate sufficiently to sustain subsequent wildfires. We also demonstrate that long-term and short-term climate variations exhibit different effects on annual fire probability, with short-term climate variations primarily impacting fire probability during periods of extreme climate anomaly. Further, we show that, when using nonlinear modeling techniques, broad-scale fire probability models can outperform localized models at predicting annual fire probability. Finally, this study represents a powerful tool for mapping local fire probability across the state of California under a variety of historical climate regimes, which is essential to avoided emissions modeling, carbon accounting, and hazard severity mapping for the application of fire-resistant building codes across the state of California.

Site fidelity and behavioral plasticity regulate an ungulate's response to extreme disturbance

With rapid global change, the frequency and severity of extreme disturbance events are increasing worldwide. The ability of animal populations to survive these stochastic events depends on how individual animals respond to their altered environments, yet our understanding of the immediate and short-term behavioral responses of animals to acute disturbances remains poor. We focused on animal behavioral responses to the environmental disturbance created by megafire. Specifically, we explored the effects of the 2018 Mendocino Complex Fire in northern California, USA, on the behavior and body condition of black-tailed deer (Odocoileus hemionus columbianus). We predicted that deer would be displaced by the disturbance or experience high mortality post-fire if they stayed in the burn area. We used data from GPS collars on 18 individual deer to quantify patterns of home range use, movement, and habitat selection before and after the fire. We assessed changes in body condition using images from a camera trap grid. The fire burned through half of the study area, facilitating a comparison between deer in burned and unburned areas. Despite a dramatic reduction in vegetation in burned areas, deer showed high site fidelity to pre-fire home ranges, returning within hours of the fire. However, mean home range size doubled after the fire and corresponded to increased daily activity in a severely resource-depleted environment. Within their home ranges, deer also selected strongly for patches of surviving vegetation and woodland habitat, as these areas provided forage and cover in an otherwise desolate landscape. Deer body condition significantly decreased after the fire, likely as a result of a reduction in forage within their home ranges, but all collared deer survived for the duration of the study. Understanding the ways in which large mammals respond to disturbances such as wildfire is increasingly important as the extent and severity of such events increases across the world. While many animals are adapted to disturbance regimes, species that exhibit high site fidelity or otherwise fixed behavioral strategies may struggle to cope with increased climate instability and associated extreme disturbance events.

Exploring the multidimensional effects of human activity and land cover on fire occurrence for territorial planning

The strong link between climate change and increased wildfire risk suggests a paradigm change on how humans must co-exist with fire and the environment. Different studies have demonstrated that human-induced fire ignitions can account for more than 90 % of forest fires, so human co-existence with wildfires requires informed decision making via preventive policies in order to minimize risk and adapt to new conditions. In this paper, we address the multidimensional effects of three groups of drivers (human activity, geographic and topographic, and land cover) that can be managed to assist in territorial planning under fire risk. We found critical factors of strong interactions with the potential to increase the likelihood of starting a fire. Our solution approach included the application of a Machine Learning method called Random Undersampling and Boosting (RUSBoost) to assess risk (fire occurrence probability), which was subsequently accompanied by a sensitivity analysis that revealed interactions of various levels of risk. The prediction performance of the proposed model was assessed using several statistical measures such as the Receiver Operating Characteristic curve (ROC) and the Area Under the Curve (AUC). The results confirmed the high accuracy of our model, with an AUC of 0.967 and an overall accuracy over test data of 93.01 % after applying a Bayesian approach for hyper-parameter optimization. The study area to test our solution approach comprised the entire geographical territory of central Chile.

Human and climate drivers of global biomass burning variability

Biomass burning is one of the most critical factors impacting vegetation and atmospheric trends, with important societal implications, particularly when extreme weather conditions occur. Trends and factors of burned area (BA) have been analysed at regional and global scales, but little effort has been dedicated to study the interannual variability. This paper aimed to better understand factors explaining this variation, under the assumption that the more human control of fires the more frequently they occur, as burnings will be less dependent of weather cycles. Interannual variability of BA was estimated from the coefficient of variation of the annual BA (BA_CV) estimated from satellite data at 250 m, covering the period from 2001 to 2018. These data and the explanatory variables were resampled at 0.25-degree resolution for global analysis. Relations between this variable and explanatory factors, including human and climate drivers, were estimated using Random Forest (RF) and generalized additive models (GAM). BA_CV was negatively related to BA_Mean, implying that areas with higher average BA have lower variability as well. Interannual BA variability decreased when maximum temperature (TMAX) and actual and potential evapotranspiration (AET, PET) increased, cropland and livestock density increased and the human development index (HDI) values decreased. GAM models indicated interesting links with AET, PET and precipitation, with negative relation with BA_CV for the lower ranges and positive for the higher ones, the former indicating fuel limitations of fire activity, and the latter climate constrains. For the global RF model, TMAX, AET and HDI were the main drivers of interannual variability. As originally hypothesised, BA_CV was more dependent on human factors (HDI) in those areas with medium to large BA occurrence, particularly in tropical Africa and Central Asia, while climatic factors were more important in boreal regions, but also in the tropical regions of Australia and South America.

Prediction of regional wildfire activity in the probabilistic Bayesian framework of Firelihood

Modeling wildfire activity is crucial for informing science-based risk management and understanding the spatiotemporal dynamics of fire-prone ecosystems worldwide. Models help disentangle the relative influences of different factors, understand wildfire predictability, and provide insights into specific events. Here, we develop Firelihood, a two-component, Bayesian, hierarchically structured, probabilistic model of daily fire activity, which is modeled as the outcome of a marked point process: individual fires are the points (occurrence component), and fire sizes are the marks (size component). The space-time Poisson model for occurrence is adjusted to gridded fire counts using the integrated nested Laplace approximation (INLA) combined with the stochastic partial differential equation (SPDE) approach. The size model is based on piecewise-estimated Pareto and generalized Pareto distributions, adjusted with INLA. The Fire Weather Index (FWI) and forest area are the main explanatory variables. Temporal and spatial residuals are included to improve the consistency of the relationship between weather and fire occurrence. The posterior distribution of the Bayesian model provided 1,000 replications of fire activity that were compared with observations at various temporal and spatial scales in Mediterranean France. The number of fires larger than 1 ha across the region was coarsely reproduced at the daily scale, and was more accurately predicted on a weekly basis or longer. The regional weekly total number of larger fires (10-100 ha) was predicted as well, but the accuracy degraded with size, as the model uncertainty increased with event rareness. Local predictions of fire numbers or burned areas also required a longer aggregation period to maintain model accuracy. The estimation of fires larger than 1 ha was also consistent with observations during the extreme fire season of the 2003 unprecedented heat wave, but the model systematically underrepresented large fires and burned areas, which suggests that the FWI does not consistently rate the actual danger of large fire occurrence during heat waves. Firelihood enabled a novel analysis of the stochasticity underlying fire hazard, and offers a variety of applications, including fire hazard predictions for management and projections in the context of climate change.

Managing fires in a changing world: Fuel and weather determine fire behavior and safety in the neotropical savannas

Fire is an important ecological disturbance, but anthropogenic wildfires increasingly threaten native ecosystems and human lives. In fire-prone ecosystems, zero-fire policies have been replaced by active fire management to reduce the risk of wildfires and improve ecological outcomes. The environmental drivers of fire behavior are widely known, but climate change and deforestation are changing their roles, making fires less predictable. Thus, reassessing the main determinants of fire behavior is preeminent to allow for safe and adaptive uses of fire in protected areas (PA). We did this research in collaboration with PA managers during the initial implementation of a pilot Integrated Fire Management (IFM) program in the Brazilian savanna. The program mainly aimed to prevent large wildfires in the late-dry season and included prescribed burns during the rainy, early- and mid-dry seasons to create vegetation patch mosaics with different fire histories. We assessed fire behavior and its environmental drivers during prescribed fires in the mid-dry season (MF) and experimental late-dry season fires (LF) (emulating wildfires). We applied Linear Models to test for differences in fire intensity, heat released, combustion factor and flame height between fire seasons and to check the influence of meteorological and fuel conditions in these parameters. LF had a significantly higher fire intensity (3508 vs. 895 kW m^-1), heat released (5537 vs. 3329 kW m^-2), combustion factor (90 vs. 51%) and flame height (2.5 vs. 1.9 m) than MF. Relative humidity, air temperature, wind speed and fuel load were the best predictors of fire behavior, corroborating previous research. Air temperature and relative humidity pushed the seasonal differences in fire behavior while wind speed and fuel load showed similar effects across seasons. Our results emphasize the importance of considering primarily environmental variables during fire management planning, especially in the current climate changing world where extreme events and seasonal weather fluctuations are constantly defying our knowledge about fire behavior.

Identifying Key Drivers of Wildfires in the Contiguous US Using Machine Learning and Game Theory Interpretation

Understanding the complex interrelationships between wildfire and its environmental and anthropogenic controls is crucial for wildfire modeling and management. Although machine learning (ML) models have yielded significant improvements in wildfire predictions, their limited interpretability has been an obstacle for their use in advancing understanding of wildfires. This study builds an ML model incorporating predictors of local meteorology, land-surface characteristics, and socioeconomic variables to predict monthly burned area at grid cells of 0.25° × 0.25° resolution over the contiguous United States. Besides these predictors, we construct and include predictors representing the large-scale circulation patterns conducive to wildfires, which largely improves the temporal correlations in several regions by 14%-44%. The Shapley additive explanation is introduced to quantify the contributions of the predictors to burned area. Results show a key role of longitude and latitude in delineating fire regimes with different temporal patterns of burned area. The model captures the physical relationship between burned area and vapor pressure deficit, relative humidity (RH), and energy release component (ERC), in agreement with the prior findings. Aggregating the contribution of predictor variables of all the grids by region, analyses show that ERC is the major contributor accounting for 14%-27% to large burned areas in the western US. In contrast, there is no leading factor contributing to large burned areas in the eastern US, although large-scale circulation patterns featuring less active upper-level ridge-trough and low RH two months earlier in winter contribute relatively more to large burned areas in spring in the southeastern US.

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

The environmental pollution, property losses and casualties caused by wildfires in California are getting worse by the year. To minimize the interference of wildfires on economic and social development, and formulate targeted mitigation strategies, it is imperative to understand the scale and extent of previous wildfire occurrences. In this study, we first investigated the temporal distributions of past wildfires in California divided by size and causes and analyzed the changes observed in the past two decades against the last century. The trend of wildfires in different time scales (yearly and monthly), as well as the distribution of wildfires across different spatial scales (administrative units, climate divisions in California from 2000 to 2019) were also studied. Furthermore, to extract the significant variables on the risk of wildfire occurrence, multivariate analyses of environmental and human-related variables with wildfire densities were carried out. The results show that the wildfire density distribution of the burned area in California conforms to the characteristics of the Pareto distribution. Over the past two decades, the frequency of small (< 500 acres), human-caused wildfires has increased most rapidly, and they are widely distributed in central and western California. The wildfire season has lengthened and the peak months have been advanced from August to July. In terms of the variables related to the risk of wildfire occurrence, the temperature, vapor pressure deficit, grass cover, and the distance to roads are crucial. This study reveals the relationship between environmental and social background conditions and the spatial-temporal distribution of wildfires, which can provide a reference for wildfire management, the formulation of future targeted wildfire emergency plans, and the planning of future land use in California.

Contrasting Geographic Patterns of Ignition Probability and Burn Severity in the Mojave Desert

The extent and frequency of fire has increased in many arid systems over the last century, with a large proportion of area in some regions undergoing transitions to novel conditions. Portions of the Mojave Desert in southwestern North America have undergone such transitions, most often from woody to herbaceous-dominated systems. These transitions have often been attributed to the proliferation of invasive annual grasses that promote more frequent fire, but recent evidence indicates that transitions can also occur independent of fire frequency if burn severity is high. In addition, high probability of ignition (i.e., potentially high fire frequency) and high burn severity may not always be geographically related. Therefore, our goals were to: (1) map potential burn severity, fire frequency, and probability of ignition across the Mojave; and, (2) evaluate spatial association among predicted burn severity, fire frequency and probability of ignition. We first mapped perimeters of 250 wildfires &gt; 405 ha that occurred from 1972 to 2010, then extracted data on fire frequency (number of times burned from 1972 to 2010), burn severity (the difference Normalized Burn Ratio), and 15 predictor variables representing physiography, climate, ignition, and vegetation. Maximum entropy was used to predict probability of ignition and Random Forest models were used to predict dNBR and fire frequency. Areas with high burn severity and high ignition probability had opposite spatial trends; areas with high burn severity were predicted to predominantly be in the northwest part of the region whereas areas with high ignition probability were predicted to be in the northeast. The models indicate the existence of a number of spatially structured but temporally dynamic fire regimes throughout the Mojave Desert. Two prevalent and ecologically significant regimes include one with frequent fires of low to moderate severity and another with infrequent fire of high severity. Areas with high fire frequency are currently limited in extent (&lt;1% total area). However, cover of invasive grasses can remain high decades after a burn of high or moderate severity, so grass-fire cycles could develop in areas where there may be expectations of infrequent fire as well as those with relatively high fire frequency.

Are Climate Factors Driving the Contemporary Wildfire Occurrence in China?

Understanding the drivers of wildfire occurrence is of great value for fire prevention and management, but due to the variation in research methods, data sources, and data resolution of those studies, it is challenging to conduct a large-scale comprehensive comparative qualitative analysis on the topic. China has diverse vegetation types and topography, and has undergone rapid economic and social development, but experiences a high frequency of wildfires, making it one of the ideal locations for wildfire research. We applied the Random Forests modelling approach to explore the main types of wildfire drivers (climate factors, landscape factors and human factors) in three high wildfire density regions (Northeast (NE), Southwest (SW), and Southeast (SE)) of China. The results indicate that climate factors were the main driver of wildfire occurrence in the three regions. Precipitation and temperature significantly impacted the fire occurrence in the three regions due to the direct influence on the moisture content of forest fuel. However, wind speed had important influence on fire occurrence in the SE and SW. The explanation power of the landscape and human factors varied significantly between regions. Human factors explained 40% of the fire occurrence in the SE but only explained less than 10% of the fire occurrence in the NE and SW. The density of roads was identified as the most important human factor driving fires in all three regions, but railway density had more explanation power on fire occurrence in the SE than in the other regions. The landscape factors showed nearly no influence on fire occurrence in the NE but explained 46.4% and 20.6% in the SE and SW regions, respectively. Amongst landscape factors, elevation had the highest average explanation power on fire occurrence in the three regions, particularly in the SW. In conclusion, this study provides useful insights into targeted fire prediction and prevention, which should be more precise and effective under climate change and socio-economic development.

Toward Best Management Practices for Ecological Corridors

Ecological corridors are one of the best, and possibly only viable, management tools to maintain biodiversity at large scales and to allow species, and ecological processes, to track climate change. This document has been assembled as a summary of the best available information about managing these systems. Our aim with this paper is to provide managers with a convenient guidance document and tool to assist in applying scientific management principles to management of corridors. We do not cover issues related to corridor design or political buy in, but focus on how a corridor should be managed once it has been established. The first part of our paper outlines the history and value of ecological corridors. We next describe our methodologies for developing this guidance document. We then summarize the information about the impacts of linear features on corridors and strategies for dealing with them—specifically, we focus on the effects of roads, canals, security fences, and transmission lines. Following the description of effects, we provide a summary of the best practices for managing the impacts of linear barriers. Globally, many corridors are established in the flood plains of stream and rivers and occur in riparian areas associated with surface waters. Therefore, we next provide guidance on how to manage corridors that occur in riparian areas. We then segue into corridors and the urban/suburban environment, and summarize strategies for dealing with urban development within corridors. The final major anthropic land use that may affect corridor management is cultivation and grazing agriculture. We end this review by identifying gaps in knowledge pertaining to how best to manage corridors.

Does short-interval fire inhibit postfire recovery of chaparral across southern California?

Regrowth after fire is critical to the persistence of chaparral shrub communities in southern California, which has been subject to frequent fire events in recent decades. Fires that recur at short intervals of 10 years or less have been considered an inhibitor of recovery and the major cause of 'community type-conversion' in chaparral, primarily based on studies of small extents and limited time periods. However, recent sub-regional investigations based on remote sensing suggest that short-interval fire (SIF) does not have ubiquitous impact on postfire chaparral recovery. A region-wide analysis including a greater spatial extent and time period is needed to better understand SIF impact on chaparral. This study evaluates patterns of postfire recovery across southern California, based on temporal trajectories of Normalized Difference Vegetation Index (NDVI) derived from June-solstice Landsat image series covering the period 1984-2018. High spatial resolution aerial images were used to calibrate Landsat NDVI trajectory-based estimates of change in fractional shrub cover (dFSC) for 294 stands. The objectives of this study were (1) to assess effects of time between fires and number of burns on recovery, using stand-aggregate samples (n = 294) and paired single- and multiple-burn sample plots (n = 528), and (2) to explain recovery variations among predominant single-burn locations based on shrub community type, climate, soils, and terrain. Stand-aggregate samples showed a significant but weak effect of SIF on recovery (p < 0.001; R² = 0.003). Results from paired sample plots showed no significant effect of SIF on dFSC among twice-burned sites, although recovery was diminished due to SIF at sites that burned three times within 25 years. Multiple linear regression showed that annual precipitation and temperature, chaparral community type, and edaphic variables explain 28% of regional variation in recovery of once-burned sites. Many stands that exhibited poor recovery had burned only once and consist of xeric, desert-fringe chamise in soils of low clay content.

Planning for Dynamic Connectivity: Operationalizing Robust Decision-Making and Prioritization Across Landscapes Experiencing Climate and Land-Use Change

Preserving landscape connectivity is one of the most frequently recommended strategies to address the synergistic threats of climate change, habitat fragmentation, and intensifying disturbances. Although assessments to develop plans for linked and connected landscapes in response to climate and land-use change have been increasingly employed in the last decade, efforts to operationalize and implement these plans have been limited. Here, we present a framework using existing, available biological data to design an implementable, comprehensive multispecies connectivity plan. This framework uses a scenario-based approach to consider how ecosystems, habitats, and species may need to adapt to future conditions with an ensemble of connectivity models. We use the south coast ecoregion of California as an example to evaluate and prioritize linkages by combining linked metapopulation models and key landscape features (e.g., conservation planning status and implementation feasibility) to identify and prioritize a multispecies linkage network. Our analyses identified approximately 30,000 km2 of land, roughly one-fifth of our study area, where actions to preserve or enhance connectivity may support climate adaptation, nearly half of which is already conserved. By developing and implementing a dynamic connectivity assessment with an eye towards projected changes, our analysis demonstrates how dynamic connectivity can be integrated into feasible regional conservation and management plans that account for demographic as well as landscape change. We observed overlap across multiple models, reinforcing the importance of areas that appeared across methods. We also identified unique areas important for connectivity captured by our complementary models. By integrating multiple approaches, the resultant linkage network is robust, building on the strengths of a variety of methods to identify model consensus and reduce uncertainty. By linking quantitative connectivity metrics with prioritized areas for conservation, our approach supports transparent and robust decision-making for landscape planning, despite uncertainties of climate and land-use change.