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Background California in the year 2020 experienced a record breaking number of large fires. Here, we place this and other recent years in a historical context by examining records of large fire events in the state back to 1860. Since drought is commonly associated with large fire events, we investigated the relationship of large fire events to droughts over this 160 years period. Results This study shows that extreme fire events such as seen in 2020 are not unknown historically, and what stands out as distinctly new is the increased number of large fires (defined here as > 10,000 ha) in the last couple years, most prominently in 2020. Nevertheless, there have been other periods with even greater numbers of large fires, e.g., 1929 had the second greatest number of large fires. In fact, the 1920’s decade stands out as one with many large fires. Conclusions In the last decade, there have been several years with exceptionally large fires. Earlier records show fires of similar size in the nineteenth and early twentieth century. Lengthy droughts, as measured by the Palmer Drought Severity Index (PDSI), were associated with the peaks in large fires in both the 1920s and the early twenty-first century.

Anthropogenic drivers of global change include rising atmospheric concentrations of carbon dioxide and other greenhouse gasses and resulting changes in the climate, as well as nitrogen deposition, biotic invasions, altered disturbance regimes, and land-use change. Predicting the effects of global change on terrestrial plant communities is crucial because of the ecosystem services vegetation provides, from climate regulation to forest products. In this paper, we present a framework for detecting vegetation changes and attributing them to global change drivers that incorporates multiple lines of evidence from spatially extensive monitoring networks, distributed experiments, remotely sensed data, and historical records. Based on a literature review, we summarize observed changes and then describe modeling tools that can forecast the impacts of multiple drivers on plant communities in an era of rapid change. Observed responses to changes in temperature, water, nutrients, land use, and disturbance show strong sensitivity of ecosystem productivity and plant population dynamics to water balance and long-lasting effects of disturbance on plant community dynamics. Persistent effects of land-use change and human-altered fire regimes on vegetation can overshadow or interact with climate change impacts. Models forecasting plant community responses to global change incorporate shifting ecological niches, population dynamics, species interactions, spatially explicit disturbance, ecosystem processes, and plant functional responses. Monitoring, experiments, and models evaluating multiple change drivers are needed to detect and predict vegetation changes in response to 21st century global change.

The wildland-urban interface (WUI) is the area where houses and wildland vegetation meet or intermingle, and where wildfire problems are most pronounced. Here we report that the WUI in the United States grew rapidly from 1990 to 2010 in terms of both number of new houses (from 30.8 to 43.4 million; 41% growth) and land area (from 581,000 to 770,000 km²; 33% growth), making it the fastest-growing land use type in the conterminous United States. The vast majority of new WUI areas were the result of new housing (97%), not related to an increase in wildland vegetation. Within the perimeter of recent wildfires (1990–2015), there were 286,000 houses in 2010, compared with 177,000 in 1990. Furthermore, WUI growth often results in more wildfire ignitions, putting more lives and houses at risk. Wildfire problems will not abate if recent housing growth trends continue.

State and federal agencies have reported fire causes since the early 1900s, explicitly for the purpose of helping land managers design fire-prevention programs. We document fire-ignition patterns in five homogenous climate divisions in California over the past 98 years on state Cal Fire protected lands and 107 years on federal United States Forest Service lands. Throughout the state, fire frequency increased steadily until a peak c. 1980, followed by a marked drop to 2016. There was not a tight link between frequency of ignition sources and area burned by those sources and the relationships have changed over time. Natural lightning-ignited fires were consistently fewer from north to south and from high to low elevation. Throughout most of the state, human-caused fires dominated the record and were positively correlated with population density for the first two-thirds of the record, but this relationship reversed in recent decades. We propose a mechanistic multi-variate model of factors driving fire frequency, where the importance of different factors has changed over time. Although ignition sources have declined markedly in recent decades, one notable exception is powerline ignitions. One important avenue for future fire-hazard reduction will be consideration of solutions to reduce this source of dangerous fires.

Conservation practitioners responsible for maintaining biodiversity and ecosystem services within protected areas require information about how dominant plant species may reassemble under rapid global change. Although species’ distribution models (SDMs) alone do not account for multiple threats or species population dynamics, they can provide robust assessments of where species may persist or disperse to in the future, especially if carefully constructed and thoroughly evaluated. We used ensemble SDMs to evaluate how climate change may alter suitable habitat for six dominant plant species representing different life forms within the Golden Gate Biosphere Network (GGBN), located along the central to northern coast of California. We trained the models on presence-absence data and 23 environmental predictors, including climate, topography, and soils, using six algorithms. We projected habitat suitability to late-century climate conditions using three GCMs under RCP 8.5 and summarized areas of agreement, expansion, and refugia. Model results can be summarized into several lessons learned, many of which are consistent with previous research. The first is that projected habitat may either expand or contract, and the direction of change varies by individual species, even within the same genus. Many projected changes reflected species’ relationships with key climate variables relative to their future projected trends. Disagreement across scenarios was largely driven by uncertainty in projected precipitation changes, while non-climatic variables, particularly soils, were also important in mediating projected habitat change. Contrary to common assumptions, projected habitat shifts were not always upslope or poleward. Finally, although species face multiple threats from other global changes, SDMs can provide a valuable baseline for conservation decisions within small reserves, particularly through identification of refugia and comparison of model scenarios. However, habitat changes within protected areas may not reflect dynamics elsewhere across species’ ranges, underscoring the need for multi-scale conservation planning.

Understanding where and how fire patterns may change is critical for management and policy decision-making. To map future fire patterns, statistical correlative models are typically developed, which associate observed fire locations with recent climate maps, and are then applied to maps of future climate projections. A potential source of uncertainty is the common omission of static or dynamic vegetation as predictor variables. We therefore assessed the sensitivity of future fire projections to different combinations of vegetation maps used as explanatory variables in a statistically based fire modeling framework. We compared models without vegetation to models that incorporated static vegetation maps and that included output from a dynamic vegetation model that imposed three scenarios of fire and one scenario of land use change. We mapped projected future probability of all and large fires (> = 40 ha) under two climate scenarios in a heterogeneous study area spanning a large elevational gradient in the Sierra Nevada, California, USA. Results showed high model sensitivity to the treatment of vegetation as a predictor variable, particularly for models of large fire probability and for models accounting for wildfire effects on vegetation, which lowered future fire probability. Some scenarios resulted in opposite directional trends in the extent and probability of future fire, which could have serious implications for policy and management resource allocation. Model sensitivity resulted from high relative importance of vegetation variables in the baseline models and from large predicted changes in vegetation, particularly when simulating wildfire. Although statistical fire models often omit vegetation due to uncertainty, model sensitivity demonstrated here suggests a need to account for that uncertainty. Coupling statistical and processed based models may be a promising approach to reflect a more plausible range of scenarios.

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 wildland-urban interface (WUI) is the spatial manifestation of human communities coupled with vegetated ecosystems. Spatial delineation of the WUI is important for wildfire policy and management, but is typically defined according to spatial relationships between housing development and wildland vegetation without explicit consideration of fire risk. A fire risk-based definition of WUI can enable a better distribution of management investment so as to maximize social return. We present a novel methodological approach to delineate the WUI based on a fire risk assessment. The approach establishes a geographical framework to model fire risk via machine learning and generate multi-scale, variable-specific spatial thresholds for translating fire probabilities into mapped output. To determine whether fire-based WUI mapping better captures the spatial congruence of houses and wildfires than conventional methods, we compared national and subnational fire-based WUI maps for Chile to WUI maps generated only with housing and vegetation thresholds. The two mapping approaches exhibited broadly similar spatial patterns, the WUI definitions covering almost the same area and containing similar proportions of the housing units in the area under study (17.1% vs. 17.9%), but the fire-based WUI accounted for 13.8% more spatial congruence of fires and people (47.1% vs. 33.2% of ignitions). Substantial regional variability was found in fire risk drivers and the corresponding spatial mapping thresholds, suggesting there are benefits to developing different WUI maps for different scales of application. We conclude that a dynamic, multi-scale, fire-based WUI mapping approach should provide more targeted and effective support for decision making than conventional approaches.

Increasing numbers of homes are being destroyed by wildfire in the wildland-urban interface. With projections of climate change and housing growth potentially exacerbating the threat of wildfire to homes and property, effective fire-risk reduction alternatives are needed as part of a comprehensive fire management plan. Land use planning represents a shift in traditional thinking from trying to eliminate wildfires, or even increasing resilience to them, toward avoiding exposure to them through the informed placement of new residential structures. For land use planning to be effective, it needs to be based on solid understanding of where and how to locate and arrange new homes. We simulated three scenarios of future residential development and projected landscape-level wildfire risk to residential structures in a rapidly urbanizing, fire-prone region in southern California. We based all future development on an econometric subdivision model, but we varied the emphasis of subdivision decision-making based on three broad and common growth types: infill, expansion, and leapfrog. Simulation results showed that decision-making based on these growth types, when applied locally for subdivision of individual parcels, produced substantial landscape-level differences in pattern, location, and extent of development. These differences in development, in turn, affected the area and proportion of structures at risk from burning in wildfires. Scenarios with lower housing density and larger numbers of small, isolated clusters of development, i.e., resulting from leapfrog development, were generally predicted to have the highest predicted fire risk to the largest proportion of structures in the study area, and infill development was predicted to have the lowest risk. These results suggest that land use planning should be considered an important component to fire risk management and that consistently applied policies based on residential pattern may provide substantial benefits for future risk reduction.

As extreme wildfires increase globally, understanding their causes is critical for effective management. While climate and housing growth are commonly linked to rising fire activity, the role of specific ignition sources—particularly human-caused—remains understudied. Analyzing a 79-year dataset (1940–2019) from U.S. Forest Service regions across the continental United States, we found that different ignition sources in different regions have been a major driver of wildfire trends, accounting for 60%–80% of the interannual variation in fire frequency and approximately 20% in area burned across most U.S. regions. Lightning and campfires were the dominant sources in western regions, while arson drove fire activity east of the Mississippi River. Trends also varied significantly by region and over time, with housing growth explaining more in terms of fire frequency and climate primarily influencing area burned. Importantly, frequent fires often originated from different sources than those causing the largest areas burned. Prevention of human-caused ignitions, such as campfires and arson, could offer efficient and effective strategies to mitigate wildfire impacts on human and natural systems under changing climate and land-use conditions.