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Fire is an integral component of ecosystems globally and a tool that humans have harnessed for millennia. Altered fire regimes are a fundamental cause and consequence of global change, impacting people and the biophysical systems on which they depend. As part of the newly emerging Anthropocene, marked by human-caused climate change and radical changes to ecosystems, fire danger is increasing, and fires are having increasingly devastating impacts on human health, infrastructure, and ecosystem services. Increasing fire danger is a vexing problem that requires deep transdisciplinary, trans-sector, and inclusive partnerships to address. Here, we outline barriers and opportunities in the next generation of fire science and provide guidance for investment in future research. We synthesize insights needed to better address the long-standing challenges of innovation across disciplines to (i) promote coordinated research efforts; (ii) embrace different ways of knowing and knowledge generation; (iii) promote exploration of fundamental science; (iv) capitalize on the “firehose” of data for societal benefit; and (v) integrate human and natural systems into models across multiple scales. Fire science is thus at a critical transitional moment. We need to shift from observation and modeled representations of varying components of climate, people, vegetation, and fire to more integrative and predictive approaches that support pathways toward mitigating and adapting to our increasingly flammable world, including the utilization of fire for human safety and benefit. Only through overcoming institutional silos and accessing knowledge across diverse communities can we effectively undertake research that improves outcomes in our more fiery future.

Non-native, invasive Bromus tectorum (cheatgrass) is pervasive in sagebrush ecosystems in the Great Basin ecoregion of the western United States, competing with native plants and promoting more frequent fires. As a result, cheatgrass invasion likely alters carbon (C) storage in the region. Many studies have measured C pools in one or more common vegetation types: native sagebrush, invaded sagebrush and cheatgrass-dominated (often burned) sites, but these results have yet to be synthesized. We performed a literature review to identify studies assessing the consequences of invasion on C storage in above-ground biomass (AGB), below-ground biomass (BGB), litter, organic soil and total soil. We identified 41 articles containing 386 unique studies and estimated C storage across pools and vegetation types. We used linear mixed models to identify the main predictors of C storage. We found consistent declines in biomass C with invasion: AGB C was 55% lower in cheatgrass (40 ± 4 g C/m2) than native sagebrush (89 ± 27 g C/m2) and BGB C was 62% lower in cheatgrass (90 ± 17 g C/m2) than native sagebrush (238 ± 60 g C/m2). In contrast, litter C was >4× higher in cheatgrass (154 ± 12 g C/m2) than native sagebrush (32 ± 12 g C/m2). Soil organic C (SOC) in the top 10 cm was significantly higher in cheatgrass than in native or invaded sagebrush. SOC below 20 cm was significantly related to the time since most recent fire and losses were observed in deep SOC in cheatgrass >5 years after a fire. There were no significant changes in total soil C across vegetation types. Synthesis and applications. Cheatgrass invasion decreases biodiversity and rangeland productivity and alters fire regimes. Our findings indicate cheatgrass invasion also results in persistent biomass carbon (C) losses that occur with sagebrush replacement. We estimate that conversion from native sagebrush to cheatgrass leads to a net reduction of C storage in biomass and litter of 76 g C/m2, or 16 Tg C across the Great Basin without management practices like native sagebrush restoration or cheatgrass removal.

Biological invasions are a leading cause of rapid ecological change and often present a significant financial burden. As a vibrant discipline, invasion biology has made important strides in identifying, mapping, and beginning to manage invasions, but questions remain surrounding the mechanisms by which invasive species spread and the impacts they bring about. Frequent, multiscalar ecological monitoring such as that provided through the National Ecological Observatory Network (NEON) can be an important tool for addressing some of these questions. We articulate a set of major outstanding questions in invasion biology, consider how NEON data science is positioned to contribute to addressing these questions, and provide suggestions to help equip a growing contingent of NEON data users in solving invasion biology problems. We demonstrate these ideas through four case studies examining the mechanisms of plant invasions in the U.S. Intermountain West. In Case Study I, we evaluate the relationships between native species richness, non‐native species richness, and probability of invasion across scales. In Case Studies II and III, we explore the relationship between environmental factors and non‐native species presence to understand invasion mechanisms. Case Study IV outlines a method for improving the ability to distinguish invasive plants from native vegetation in remotely sensed data by leveraging temporal patterns of phenology. There are many novel elements in the NEON sampling design that make it uniquely poised to shed light on the mechanisms that can help us understand invasibility, prediction, and progression, as well as on the variability, longevity, and interactions of multiple invasive species’ impacts. Thus, knowledge gained through analysis of NEON data is expected to inform sound decision‐making in unique ways for managers of systems experiencing biological invasions.

Losses from natural hazards are escalating dramatically, with more properties and critical infrastructure affected each year. Although the magnitude, intensity, and/or frequency of certain hazards has increased, development contributes to this unsustainable trend, as disasters emerge when natural disturbances meet vulnerable assets and populations. To diagnose development patterns leading to increased exposure in the conterminous United States (CONUS), we identified earthquake, flood, hurricane, tornado, and wildfire hazard hotspots, and overlaid them with land use information from the Historical Settlement Data Compilation data set. Our results show that 57% of structures (homes, schools, hospitals, office buildings, etc.) are located in hazard hotspots, which represent only a third of CONUS area, and ∼1.5 million buildings lie in hotspots for two or more hazards. These critical levels of exposure are the legacy of decades of sustained growth and point to our inability, lack of knowledge, or unwillingness to limit development in hazardous zones. Development in these areas is still growing more rapidly than the baseline rates for the nation, portending larger future losses even if the effects of climate change are not considered., Key Points More than half of the structures in the conterminous United States are exposed to potentially devastating natural hazardsGrowth rates in hazard hotspots exceed the national trendRisk assessments can be improved by considering multiple hazards, mitigation history and fine‐scale data on the built environment

Increasing fire impacts across North America are associated with climate and vegetation change, greater exposure through development expansion, and less-well studied but salient social vulnerabilities. We are at a critical moment in the contemporary human-fire relationship, with an urgent need to transition from emergency response to proactive measures that build sustainable communities, protect human health, and restore the use of fire necessary for maintaining ecosystem processes. We propose an integrated risk factor that includes fire and smoke hazard, exposure, and vulnerability as a method to identify ‘fires that matter’, that is, fires that have potentially devastating impacts on our communities. This approach enables pathways to delineate and prioritise science-informed planning strategies most likely to increase community resilience to fires.

Wildfires are becoming more frequent in parts of the globe, but predicting where and when wildfires occur remains difficult. To predict wildfire extremes across the contiguous United States, we integrate a 30‐yr wildfire record with meteorological and housing data in spatiotemporal Bayesian statistical models with spatially varying nonlinear effects. We compared different distributions for the number and sizes of large fires to generate a posterior predictive distribution based on finite sample maxima for extreme events (the largest fires over bounded spatiotemporal domains). A zero‐inflated negative binomial model for fire counts and a lognormal model for burned areas provided the best performance. This model attains 99% interval coverage for the number of fires and 93% coverage for fire sizes over a six year withheld data set. Dryness and air temperature strongly predict extreme wildfire probabilities. Housing density has a hump‐shaped relationship with fire occurrence, with more fires occurring at intermediate housing densities. Statistically, these drivers affect the chance of an extreme wildfire in two ways: by altering fire size distributions, and by altering fire frequency, which influences sampling from the tails of fire size distributions. We conclude that recent extremes should not be surprising, and that the contiguous United States may be on the verge of even larger wildfire extremes.

Wildfires are becoming more frequent in parts of the globe, but predicting where and when wildfires occur remains difficult. To predict wildfire extremes across the contiguous United States, we integrate a 30 year wildfire record with meteorological and housing data in spatiotemporal Bayesian statistical models with spatially varying nonlinear effects. We compared different distributions for the number and sizes of large fires to generate a posterior predictive distribution based on finite sample maxima for extreme events (the largest fires over bounded spatiotemporal domains). A zero-inflated negative binomial model for fire counts and a lognormal model for burned areas provided the best performance. This model attains 99% interval coverage for the number of fires and 93% coverage for fire sizes over a six year withheld data set. Dryness and air temperature strongly predict extreme wildfire probabilities. Housing density has a hump-shaped relationship with fire occurrence, with more fires occurring at intermediate housing densities. Statistically, these drivers affect the chance of an extreme wildfire in two ways: by altering fire size distributions, and by altering fire frequency, which influences sampling from the tails of fire size distributions. We conclude that recent extremes should not be surprising, and that the contiguous United States may be on the verge of even larger wildfire extremes.

Deforestation and fragmentation influence the microclimate, vegetation structure, and composition of remaining patches of tropical forest. In the southern Amazon, at the frontier of cropland expansion, forests are converted and fragmented in a pattern that leaves standing riparian forests whose dimensions are mandated by the Brazilian National Forest Code. These altered riparian forests share many characteristics of well-studied upland forest fragments, but differ because they remain connected to larger areas of forest downstream, and because they may experience wetter soil conditions because reduction of forest cover in the surrounding watershed raises groundwater levels and increases stream runoff. We compared forest regeneration, structure, composition, and diversity in four areas of intact riparian forest and four areas each of narrow, medium, and wide altered riparian forests that have been surrounded by agriculture since the early 1980s. We found that seedling abundance was reduced by as much as 64% and sapling abundance was reduced by as much as 67% in altered compared to intact riparian forests. The most pronounced differences between altered and intact forest occurred near forest edges and within the narrowest sections of altered riparian forests. Woody plant species composition differed and diversity was reduced in altered forests compared to intact riparian forests. However, despite being fragmented for several decades, large woody plant biomass and carbon storage, the number of live or dead large woody plants, mortality rates, and the size distribution of woody plants did not differ significantly between altered and intact riparian forests. Thus, even in these relatively narrow forests with high edge: area ratios, we saw no evidence of the increases in mortality and declines in biomass that have been found in other tropical forest fragment studies. However, because of the changes in both species community and reduced regeneration, it is unclear how long this relative lack of change will be sustained. Additionally, Brazil recently passed a law in their National Forest Code allowing narrower riparian buffers than those studied here in restored areas, which could affect their long-term sustainability.

At the global scale, the population density of coastal areas is nearly three times that of inland areas, and consequently, land development represents a threat to coastal ecosystems. It is critical to understand how increasing urbanization affects coastal watersheds. To that end, the objective of this study was to examine the influence of urban development on stream water quality and hydrology in a coastal setting, a scenario that has received less attention than other physiographic regions. Stream hydrologic, physicochemical, and microbial data were collected in watersheds near Apalachicola, Florida with a range of impervious surfaces from 0 to 15%. Watersheds with greater impervious cover exhibited higher pH, specific conductance, and temperature, elevated nutrient concentrations and loads (Cl−,NO3−,SO42−,Na+,K+,Mg2+,Ca2+, and total phosphorus), higher bacterial concentrations (fecal coliform and Escherichia coli), and increased maximum flow and hydrograph flashiness. Different responses to development here compared to other physiographic regions included lower total suspended solid concentrations, higher total dissolved solid concentrations, and a lack of response of base flow to increased urbanization. Additionally, Na+ and Cl− concentrations were elevated to a greater extent than is often the case in non-coastal areas. In the coming years, urban development is projected to increase substantially in coastal zones and thus there is risk of further stream degradation in coastal watersheds. Copyright © 2011 John Wiley & Sons, Ltd.

It is widely recognized that forest and water resources are intricately linked. Globally, changes in forest cover to accommodate agriculture and urban development introduce additional challenges for water management. The U.S. Southeast typifies this global trend as predictions of land-use change and population growth suggest increased pressure on water resources in coming years. Close attention has long been paid to interactions between people and water in arid regions; however, based on information from regions such as the Southeast, it is evident that much greater focus is required to sustain a high-quality water supply in humid areas as well. To that end, we review hydrological, physicochemical, biological, and human and environmental health responses to conversion of forests to agriculture and urban land uses in the Southeast. Commonly, forest removal leads to increased stream sediment and nutrients, more variable flow, altered habitat and stream and riparian communities, and increased risk of human health effects. Although indicators such as the percentage of impervious cover signify overall watershed alteration, the threshold to disturbance, or the point at which effects can been observed in stream and riparian parameters, can be quite low and often varies with physiographic conditions. In addition to current land use, historical practices can greatly influence current water quality. General inferences of this study may extend to many humid regions concerning climate, environmental thresholds, and the causes and nature of effects.

Urbanization in the southeastern U.S. has progressed rapidly due to economic development and population growth. This is particularly the case in the Piedmont physiographic region of Georgia where an interdisciplinary group of researchers conducted a series of studies, collectively known as the West Georgia Project, to evaluate the causes and consequences of urbanization associated with a mid-size city (

Large fires account for the majority of burned area and are an important focus of fire management. However, ‘large’ is typically defined by a fire size threshold, minimizing the importance of proportionally large fires in less fire-prone ecoregions. Here, we defined ‘large fires’ as the largest 10% of wildfires by ecoregion (n = 175,222 wildfires from 1992 to 2015) across the United States (U.S.). Across ecoregions, we compared fire size, seasonality, and environmental conditions (e.g., wind speed, fuel moisture, biomass, vegetation type) of large human- and lighting-started fires that required a suppression response. Mean large fire size varied by three orders of magnitude: from 1 to 10 ha in the Northeast vs. >1000 ha in the West. Humans ignited four times as many large fires as lightning, and were the dominant source of large fires in the eastern and western U.S. (starting 92% and 65% of fires, respectively). Humans started 80,896 large fires in seasons when lightning-ignited fires were rare. Large human-started fires occurred in locations and months of significantly higher fuel moisture and wind speed than large lightning-started fires. National-scale fire policy should consider risks to ecosystems and economies by these proportionally large fires and include human drivers in large fire risk assessment.

Humans use combustion for heating and cooking, managing lands, and, more recently, for fuelling the industrial economy. As a shift to fossil-fuel-based energy occurs, we expect that anthropogenic biomass burning in open landscapes will decline as it becomes less fundamental to energy acquisition and livelihoods. Using global data on both fossil fuel and biomass burning emissions, we tested this relationship over a 14 year period (1997–2010). The global average annual carbon emissions from biomass burning during this time were 2.2 Pg C per year (±0.3 s.d.), approximately one-third of fossil fuel emissions over the same period (7.3 Pg C, ±0.8 s.d.). There was a significant inverse relationship between average annual fossil fuel and biomass burning emissions. Fossil fuel emissions explained 8% of the variation in biomass burning emissions at a global scale, but this varied substantially by land cover. For example, fossil fuel burning explained 31% of the variation in biomass burning in woody savannas, but was a non-significant predictor for evergreen needleleaf forests. In the land covers most dominated by human use, croplands and urban areas, fossil fuel emissions were more than 30- and 500-fold greater than biomass burning emissions. This relationship suggests that combustion practices may be shifting from open landscape burning to contained combustion for industrial purposes, and highlights the need to take into account how humans appropriate combustion in global modelling of contemporary fire. Industrialized combustion is not only an important driver of atmospheric change, but also an important driver of landscape change through companion declines in human-started fires.This article is part of the themed issue ‘The interaction of fire and mankind’.

Climate and land use models predict that tropical deforestation and conversion to cropland will produce a large flux of soil carbon (C) to the atmosphere from accelerated decomposition of soil organic matter (SOM). However, the C flux from the deep tropical soils on which most intensive crop agriculture is now expanding remains poorly constrained. To quantify the effect of intensive agriculture on tropical soil C, we compared C stocks, radiocarbon, and stable C isotopes to 2 m depth from forests and soybean cropland created from former pasture in Mato Grosso, Brazil. We hypothesized that soil disturbance, higher soil temperatures (+2°C), and lower OM inputs from soybeans would increase soil C turnover and deplete C stocks relative to nearby forest soils. However, we found reduced C concentrations and stocks only in surface soils (0-10 cm) of soybean cropland compared with forests, and these differences could be explained by soil mixing during plowing. The amount and Δ14C of respired CO2 to 50 cm depth were significantly lower from soybean soils, yet CO2 production at 2 m deep was low in both forest and soybean soils. Mean surface soil δ13C decreased by 0.5‰ between 2009 and 2013 in soybean cropland, suggesting low OM inputs from soybeans. Together these findings suggest the following: (1) soil C is relatively resistant to changes in land use and (2) conversion to cropland caused a small, measurable reduction in the fast-cycling C pool through reduced OM inputs, mobilization of older C from soil mixing, and/or destabilization of SOM in surface soils.