Wildfires Are Burning Less Frequently and More Severely in the Western US: An Integrative Approach to Calculating Fire-Regime Departures

doi:10.21203/rs.3.rs-5270701/v1

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Wildfires Are Burning Less Frequently and More Severely in the Western US: An Integrative Approach to Calculating Fire-Regime Departures

https://doi.org/10.21203/rs.3.rs-5270701/v1

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Background

Changing climate, vegetation, and fire exclusion are altering and homogenizing fire regime attributes compared to historical conditions. Fire-regime changes are commonly quantified using departure metrics, which are often based on measures of central tendency (i.e., the mean). These metrics can mischaracterize complex changes to the distributional characteristics of fire regime attributes.

Results

Here we develop a fire regime departure metric that quantifies non-parametric distributional changes to fire regime attributes. We use this departure metric to compare fire frequency and severity between historical (~ 1600–1880) and contemporary (1980–2021) time periods in western US forests. Our analysis revealed that 89% of western US forests are experiencing less frequent fire and that departures in fire severity tend to increase with human land use intensity. We also evaluated prioritization within the Wildfire Crisis Mitigation plan and found that priority landscapes are, on average, more departed than non-priority landscapes. We found that previously developed fire regime departure metrics underestimate departures in frequent fire forests and overestimate departures in infrequent fire forests.

Conclusions

By leveraging our distributional metrics, land managers can more effectively target restoration efforts, such as intentional fire use and mechanical thinning, to restore historical fire regimes and bolster the resilience of fire-prone landscapes.

disturbance regimes

Earth Mover’s Distance

fire modeling

fire regimes

historical fire regimes

management

wildfire

Wasserstein metric

Wildfire is a critical ecosystem process that influences the composition, structure, and spatial patterns of forests in the western US and elsewhere (Balch et al. 2017; Coop et al. 2020; Sugihara et al. 2006). Lightning- and human-ignited wildfire have influenced western US forests for centuries to millennia, as evidenced by the numerous tree species that have evolved fire-adapted traits such as thick bark, resprouting, and serotiny (Jager et al. 2021; Keeley and Pausas 2022; Schieck and Song 2006; Hood et al. 2018). As a result, distinct forest types often have characteristic fire regimes that emerge from differences in climate and fire-adaptations. For example, dry and warm forests dominated by ponderosa pine generally experienced frequent (< 35-year return interval), low-severity surface fire, whereas cold forests dominated by lodgepole pine experienced infrequent (> 100-year return interval), high-severity stand-replacing fire (Agee 1993; Sugihara et al. 2006).

Fire regimes in the western US have been altered since the late-1800s due to the cessation of Indigenous burning, land use change, extensive livestock grazing, and persistent fire suppression (Cooper 1960; Eisenberg et al. 2019; Hessburg et al. 2021; Kreider et al. 2024). These actions and policies reduced fire frequency and annual area burned in western US forest ecosystems (Bowman et al. 2011; Kreider et al. 2024; Roos et al. 2021; Swetnam et al. 2016) and result in a contemporary fire deficit (Marlon et al. 2012; McClure et al. in press). The exclusion of fire has also resulted in the accumulation of fuels beyond historical norms (Hagmann et al. 2021). When fires inevitably occur, they often burn at a higher severity (i.e., more tree mortality) than was characteristic of historical fire regimes, particularly in forests characterized by low- to moderate-severity fire (Iglesias et al. 2022; Parks et al. 2023; Williams et al. 2023).

Understanding the extent to which contemporary fire regimes (1985-present) are departed from historical conditions (~ 1600–1880) is critical for the management of forested landscapes (Keane et al. 2009; Keane et al. 2011; Whitlock et al. 2010). Historical baselines allow managers to place current fire regimes in context (Higgs et al. 2014) to better understand how altered fire regimes may result in novel post-fire forest trajectories (Buma et al. 2019; Higgs et al. 2014; Turner and Seidl 2023; Coop et al. 2020). Despite widespread acknowledgement that fire regime attributes are complex and best characterized by distributions (e.g., Sugihara et al. 2006; Agee 1993; Pickett and White 1986), developing distributional methods to quantify departures has proven challenging (Buma et al. 2019). To date, fire regime departure metrics generally lie on measures of central tendency (i.e., the mean). For example, fire regime departure metrics are often quantified by comparing the historical and contemporary mean fire return interval (MFRI) and mean fire severity (Mallek et al. 2023; Donato et al., 2023; Reilley et al. 2017; Haugo et al., 2019; Parks et al. 2023). In addition to departures based on means, there are also metrics that account for variability in fire-attributes, for example, historical range of variation (HRV; Keane et al., 2009; Turner & Seidl, 2023). HRV describes the range of a chosen statistic (e.g., MFRI) over periods of time, where the range can be compared to the contemporary period to assess departures while accounting for variability over time (Keane et al. 2009; Whitlock et al. 2010; Haugo et al. 2019). However, this approach has generally been applied to attribute variability over time but not across space (e.g., Buma et al., 2019; Marlon et al., 2012; Swetnam et al., 2016).

Measures of central tendency inherently mask spatial variability in fire regimes. For example, MFRI and mean fire severity collapse the spatial variance of fires into characteristic mean values on the landscape. Consider a hypothetical landscape that historically exhibited a broad range of fire frequencies, but contemporary fire exclusion has homogenized fire frequency (Fig. 1a). In this hypothetical landscape, the mean values for number of fire events are identical, yet the distribution of fire frequencies substantially differ between time periods (Fig. 1b). Similar patterns could be evident in terms of fire severity over the hypothetical landscape. More broadly, departure metrics based on measures of central tendency do not quantify changes to the distribution of fire regime attributes and thus may obscure important changes in fire regime characteristics (Buma et al. 2019). As spatial variability in fire regimes contribute to ecosystem resilience and function (Buma et al. 2019; Hessburg et al. 2021; Jager et al. 2021; Martin and Sapsis 1992), capturing this variability is crucial to forming a more complete understanding of fire regime departures (Buma et al. 2019).

Here, we developed a fire regime departure metric that accounts for changes in fire frequency and severity (independently and combined). We used the Earth Mover’s Distance (EMD; i.e., the Wasserstein metric), a non-parametric measure of distributional dissimilarity (Dobrushin 1970; Kantorovich and Rubinstein 1958; Vaserstein 1969), to calculate distributional differences in fire regime attributes. Using this metric, we quantify departures in fire frequency and severity over western US forests between historical (~ 1600–1880) and contemporary (1985–2021) time periods. We summarize our findings across the western US at multiple spatial scales, including a standardized hexagonal grid (hexels) and federally designated firesheds. We demonstrate that our metric captures new and informative distributional departures with examples from the Kalmiopsis Wilderness (Oregon, USA) and Olympic National Park (Washington, USA). As previous research indicates that human land uses influence fire regimes (Parks et al. 2014; Syphard et al. 2017), we also evaluate how fire regime departures relate to various measures of human influence (e.g., percent of public vs private lands) across the western US. Our results provide land managers and researchers with a more nuanced understanding of fire regime departures in western US forests.

For forests in the western US, we characterized contemporary (1985–2021) fire regime distributions with satellite derived fire severity data and fire perimeters from the Monitoring Trends in Burn Severity (Eidenshrink et al. 2007) program. Historical distributions (36 simulated years approximating 1600–1880) were characterized using the LANDFIRE BioPhysical Setting product (BPS), which contains information regarding the average frequency of fires and proportion of those fires that burned under low, mixed, and high severity (LANDFIRE, 2020). Both the historic and contemporary datasets were 30-m resolution. We calculated the EMD between historical and contemporary distributions for fire frequency and fire severity, creating a distributional fire frequency departure (FFD), and distributional fire severity departure (FSD), which we then combined to obtain a multivariate distributional fire regime departure index (MFRD).

Study Area

We aggregated fire frequency and severity data estimated at 30-m resolution pixels (0.09 ha) to multiple spatial containers including hexagonal grids (hexel), firesheds (Ager et al. 2021), protected areas, among others (see data availability) across the western US. For simplicity, we walk through our analysis and main results using hexels. Our chosen hexel size (150,000 ha) is the median mapped areal coverage among biophysical settings (BPS) according to LANDFIRE (LANDFIRE 2020), which maps pre-euro American settlement vegetation with expert and data derived fire regime simulation models developed over the mapped extent. Hexels were not analyzed if they contained no contemporary forest fires and if they had greater than 50% overlap with the ocean, Canada, or Mexico. We also removed hexels if they contained less than 10% forested area, as fire regime departures are only calculated for forested pixels.

Frequency and severity distributions

For each hexel, we randomly sampled 0.1% of forested 30m pixels and iterated our analysis 100 times to capture variation and acknowledge the inherent spatial autocorrelation and pseudo replication associated with gridded spatial data (workflow of our analysis is shown in Supplemental 1; sensitivity tests for changes in parameters, such as the number of iterations, are in Appendix D). We defined forest as those pixels labeled as “Conifer,” “Hardwood,” “Conifer-Hardwood,” or “Hardwood-Conifer” by LANDFIRE Existing Vegetation Type and BPS. We filtered to forested areas because satellite-derived Composite Burn Index (CBI) is not appropriate for use in non-forest systems (Parks et al. 2019). To produce contemporary fire frequency and severity distributions, we first processed the western US fire perimeters from Monitoring Trends in Burn Severity (MTBS) using the approach described by Parks, et al., (2019) to obtain bias corrected CBI maps of every mapped fire greater than 404 hectares from 1985–2021. Parks et al., (2019) is a predictive Random Forest (Breiman 2001) model for satellite derived CBI using multiple spectral, climatic, and geographic variables in Google Earth Engine (Gorelick et al. 2017). These gridded CBI maps approximate field-based CBI measurements (Key and Benson 2006). Then we mosaicked individual fire maps into yearly fire maps from 1985–2021 and filtered to pixels (30m resolution) with > 0.35 pre-fire NDVI as an additional step to remove non-forest (Parks et al. 2023).

We then counted the number of times each pixel burned (CBI values > 0) from 1985–2021 to calculate fire frequency across the study area, from which we produced the contemporary fire frequency distribution within each hexel. Similarly, we extracted fire severity estimates for each burned pixel, which we then produced distributions for each hexel, with 16 evenly spaced bins with values ranging from 0–3.

To obtain historical fire frequency, we first extracted the mean fire return interval (MFRI) from the gridded BPS layer at each sampled pixel. Then we randomly sampled from a binomial distribution at each pixel with parameters n = 36, P = 1 / MFRI, n represents the number of years in the contemporary period and P is the yearly probability of fire in the pixel given its biophysical setting. We then summarized these historical fire frequencies into a distribution for each hexel.

To infer the severity of each estimated historical fire, we assigned a historical severity class of low, mixed, or high, via weighted random selection with weights given by the percent likelihood for each severity class as described by LANDFIRE BPS. For each assigned fire severity class, we then drew randomly from a uniform distribution within the bounds of CBI and thresholds between severity classes determined in Appendix B. For example, if we selected a low severity fire, we drew from a uniform distribution with a lower bound of zero and an upper bound of 1.75, the value determined from our threshold calculation. This produces an estimated CBI for each estimate fire. With these estimated fire severities, we then created fire severity distributions using the same 16 bins as described for contemporary fires for each hexel.

Fire-regime Departure

To quantify fire-attribute departures for frequency and severity in each hexel, we first rescaled our distributions by the historical mean and standard deviation, producing Z-scores. Then we computed the Earth Mover’s Distance (full description of EMD in appendix A) on each attribute independently, effectively quantifying the difference between the historic and contemporary distributions.

EMD can only be positive, so we modified attribute departures to indicate the net direction of change. We simply added a sign based on the change in percent burned at higher severity and MFRI. Positive values represent lower fire frequency, and higher proportion of high severity fire in the contemporary period compared to historical, respectively. This produces signed distributional fire frequency (FFD) and distributional fire severity departures (FSD).

Lastly, we calculated a multivariate distributional fire regime departure index (MFRD) by adding the fire frequency and severity departures together using Euclidean distance \(\:\sqrt{{\text{F}\text{F}\text{D}}^{2}+{\text{F}\text{S}\text{D}}^{2}}\). Because we simulate fire regime attributes using 100 iterations, the key outputs for each hexel are the median normalized departures for FFD, FSD, and MFRD. See our data availability statement for additional datasets and statistics for each spatial container.

Analysis Summaries

We summarized FFD, FSD, and MFRD by hexel, then grouped by state in the western US to analyze statewide patterns of fire regime departure. Next, we analyzed relationships between MFRD and human influence. For each hexel, we calculated the average human footprint (Venter et al. 2016), the proportion covered by public lands, and the proportion covered by Wilderness and National Park lands (U.S. Geological Survey 2022). Human footprint combines eight human pressures (e.g., structures) into one characteristic score of human influence on landscapes (Venter et al. 2016). Human footprint, proportion of public lands, and proportion covered by Wilderness and National Park all serve as indirect measures of fire exclusion policy and changes to the timing and frequency of anthropogenic ignitions (Balch et al. 2017; Boerigter et al. 2024). Each dataset was binned into five evenly spaced bins. We then calculated pairwise Wilcoxon rank sum tests to assess whether there were significant differences in fire regime departure between bins.

Additionally, to gain an alternative perspective on contemporary human use and its relationship to MFRD, we examined whether firesheds designated as priority landscapes are more departed than non-priority landscapes (US Forest Service, 2023). The fireshed dataset delimits landscapes with similar wildfire risks, land tenure, and planned management (Ager et al. 2021). Priority landscapes are designated by the US federal government as the firesheds (Ager et al. 2021) with highest need for future fire risk mitigation and the ability of communities to enact fire mitigation strategies (US Forest Service, 2023). For this analysis, we analyzed firesheds and then grouped them into priority and non-priority firesheds, determined by those with greater than 50% priority landscape coverage. We then calculated Wilcoxon rank sum tests between priority and non-priority firesheds to assess whether there are differences between these groups based on MFRD.

We compared our distributional metrics to a commonly used suite of departure metrics based on means – the Fire-Regime Condition Class (FRCC; Barrett et al., 2010). In each hexel, we used the fire frequency and severity information generated above to calculate historical and contemporary MFRI and proportion burned at high severity (Barrett et al. 2010; Johnson and Gutsell 1994). These values were then used to calculate FRCC fire frequency departure (FFD_cc), fire severity departure (FSD_cc) and fire regime departure (FRD_cc) following the formulas in Barrett et al., 2010. Because FRCC applies no sign to their statistics, we converted FFD and FSD into absolute values. Next, we transformed these absolute values, along with MFRD and the FRCC departure statistics, into percentiles (denoted by a percentile subscript) relative to the western US, placing them all on the same 0-100 scale. We then subtracted the relevant FRCC departure from the relevant distributional departure (e.g., MFRD_percentile – FRD_{cc.percentile} = Δ_percentile) to assess the difference in percentile between our metrics and FRCC. This process was performed on all datasets (supplemental 3)

Finally, we provide two granular examples of our results within the Kalmiopsis wilderness and Olympic National Park. In these examples, we discuss the historical and contemporary MFRI, FFD_percentile, FFD_{cc.percentile} and the underlying fire frequency distributions.

Package Credits

Spatial data was manipulated using the R packages sf and terra (Hijmans et al. 2022; Pebesma 2018; R Core Team 2022). Tabular data was manipulated using tidyverse, data.table (Barrett et al. 2023; Wickham et al. 2019), and overall analysis was performed with foreach, doParallel, and units (Daniel, Corporation, et al., 2022; Daniel, Ooi, et al., 2022; Pebesma et al., 2016). Figures were generated with Rcolorbrewer, ggplot2 (Neuwirth, 2022; Wickham, 2016) and in the Julia language with the packages OptimalTransport, Gadfly, and Distributions (Bezanson et al., 2017; Jones et al., 2021; Lin et al., 2023; Zhang et al., 2022). Calculation of EMD used the transport package in R (Schuhmacher et al., 2023).

Fire Frequency and Severity Departures

Overwhelmingly (89% of hexels), forested landscapes across the western US are burning less frequently and more severely today than in the historical period (Fig. 2). These departures were most prevalent in California and southern Oregon, however all states showed notable departures (Fig. 2, 3A). Although there are hexels in the other quadrants of Fig. 2, they do not appear to have strong spatial patterning (Fig. 3A).

Fire-Regime Departure metric

A map of the distributional, multivariate fire regime departure metric (MFRD; Fig. 3B) shows similar patterns as those described by our distributional fire attribute departures. California is statistically more departed than other states (pairwise Wilcoxon rank sum test: P < 0.001). Full pairwise comparisons among states can be found in Appendix C. California, Oregon, and Nevada are the three most departed western States while Colorado, Utah, and Idaho are the three least departed states (Supplemental 3).

Relationship between Departure and Human Influence

We found that fire regime departures increased with land use intensity (as measured by the human footprint; Fig. 4A). Furthermore, fire regime departures decreased as the percent coverage of public lands (Fig. 4B) and the percent coverage of wilderness and national park increased (Fig. 4C; pair-wise Wilcoxon rank sum tests in Appendix C).

We found that priority landscapes are burning less frequently and more severely (Fig. 5A and B) than non-priority firesheds. Accordingly, priority landscapes have larger fire regime departures than non-priority landscapes (Fig. 5C, Wilcoxon rank sum test, W = 67132, P value ≤ 0.001).

Comparison to Fire-Regime Condition Class (FRCC)

We found that the difference in percentiles (Δ_percentile) for fire frequency departures between FRCC (FFD_cc) and our metrics (FFD) are higher in frequent fire forests of California, Arizona, and New Mexico, meaning our metric of fire frequency departure is reporting relatively larger departures in those regions than FRCC. We also found negative changes in infrequent fire forests of western Washington, Nevada, and the Central Rocky Mountains, meaning that FRCC suggests larger fire regime departures than our fire frequency metric. The mean Δ_{percentile.frequency}, was 0 (SD = 0.339, MAE = 0.270). For fire severity (Fig. 6B), we found that the region near Yellowstone National Park has high Δ_{percentile.severity} and there are large negative values near the Colorado Plateau and Eastern Cascades. Mean Δ_{percentile.severity} was 0 (SD = 0.293, MAE = 0.221). For MFRD (Fig. 6C), we see the combined effects of both frequency and severity differences, and they show large positive differences in the Yellowstone region and large negative differences in the Colorado Plateau. The mean Δ_{percentile.regime} was 0 (SD = 0.270, MAE = 0.207).

Forested landscapes of the western US are burning less frequently and more severely today compared to the historical reference period that roughly spans 1600–1880 (Fig. 2). Our results provide critical new context to observations of increased wildfire activity over the past 40 years (Higuera et al. 2023; Parks and Abatzoglou 2020). Although annual area burned increased across the western US since 1985, contemporary fire frequency is still well below levels indicated by models of 17th -20th century fire regimes based on paleoecological data (Marlon et al. 2012; McClure et al. in press). This paradoxical observation of rapid contemporary increases in fire activity that still remains below historical levels, is documented by previous research (Donato et al. 2023; Hagmann et al. 2021; Haugo et al. 2019; Williams et al. 2023). This widespread fire deficit (Parks, Miller, et al. 2015) is understood to be increasing the incidence of uncharacteristically severe fires across the western US (Donato et al. 2023; Haugo et al. 2019; Williams et al. 2023). Our analyses show that decreases in fire frequency correspond strongly with increases in fire severity, supporting this contention (Fig. 3).

Our distributional approach captures information about changes to fire regime attribute distributions not captured by comparing means. To demonstrate this, we contrast our distributional departure metrics to Fire Regime Condition Class’s (Barrett et al. 2010) mean departure statistics (Fig. 6) and show that the magnitude and spatial patterning of departures differ between our metrics and FRCC. To unpack the mechanisms driving both positive and negative percentile differences between these metrics, we examine two protected areas, Olympic National Park and the Kalmiopsis Wilderness.

First, Olympic National Park has a historical MFRI of 309 years, a contemporary MFRI of ~ 16,000 years, and a corresponding FRCC fire frequency departure (FFD_cc) in the 90th percentile of western US wilderness areas (Fig. 7). Meanwhile, our distributional fire frequency departure (FFD) was a substantially lower 14th percentile departure. FRCC departure is high because as mean fire frequency approaches zero, which is common for infrequent fire ecosystems, the MFRI approaches infinity — inflating the mean departure statistic. Inflated departure statistics could lead to suboptimal resource allocation if they are used to guide management prioritization (Donato et al. 2023; Haugo et al. 2019). Our distributional approach mitigates this issue, better reflecting the small distributional shift between time periods (Fig. 7).

The Kalmiopsis Wilderness of Southwest Oregon historically burned frequently, with a MFRI of 13 years (LANDFIRE 2020). The contemporary MFRI is 19 years. The similarity of these values suggests a high frequency fire regime that minimally changed from 17th -20th century conditions. This is reflected by a FFD_cc with an 11th percentile departure. Upon closer inspection, it is apparent that the contemporary fire frequency of the Kalmiopsis has become more homogenous with time (Fig. 8). FFD captures the homogenization of the fire frequency distribution resulting in a higher 38th percentile fire frequency departure.

While our west-wide results for each fire regime departure statistic are consistent with previous research (Donato et al. 2023; Hagmann et al. 2021; Haugo et al. 2019; Williams et al. 2023), our results are sensitive to the underlying assumptions and limitations of LANDFIRE BPS (LANDFIRE 2020). LANDFIRE BPS uses expert, paleoecological, and simulations to estimate historical fire regimes, and with these data sources, comes uncertainty and assumptions that can change our fire regime departure results. For example, we detected lower frequency and lower severity than historical patterns in the region south of Yellowstone National Park (Fig. 3A), yet this area is known to be experiencing more frequent, high severity reburns (Turner et al. 2019; 2022). This result is due, in part, to LANDFIRE BPS not reporting any low severity fire in this ecosystem, despite it being a known component of fire regimes in many cool dry forests and parklands (Cansler et al. 2018; LANDFIRE 2020; Spies et al. 2018; Turner et al. 2022). To improve upon these assumptions, we recommend the use of more robust historical models (Keane et al. 2011; Seidl et al. 2014).

Our approach also has inherent limitations. First, our dataset simulated fire frequency and severity independently. Simulating the codependence of frequency and severity would quantify changes in the covariance between multiple fire attributes, potentially exposing novel fire regime departures (Parks et al. 2015; Steel et al., 2015). Future research that characterizes joint frequency and severity distributions of contemporary and historical fire regimes is needed (Keane et al. 2011; Seidl et al. 2012; Seidl et al. 2014; Turner et al. 2022). Earth Mover’s Distance could be readily applied to joint distributions.

Further, we simulated frequency and severity with a binomial and a piecewise-uniform distribution, respectively. These are simple models of fire frequency and severity that do not reflect the regulation of fuels by repeated wildfires (Parks et al. 2015) nor gradients in fire severity as a result (Agee 1993; Sugihara et al. 2006). Our historical distributions are estimates, and future studies should refine these distributions to better reflect real systems.

Despite these limitations, our study is an important step towards the development of statistically robust and ecologically meaningful, multivariate distributional departure metrics relevant to ecology, management, and future research. Metrics that characterize distributional changes in fire regimes provide a more nuanced characterization of departures than those based on means (Buma et al. 2019; Sugihara et al. 2006). Recognizing changes in the statistical distributions of fire regimes is essential, as these shifts, when combined with land management practices, can highlight key opportunities for restoring historical fire dynamics and supporting ecosystem resilience.

Areas with high contemporary human land use have the highest departures. We found higher departures in areas of high human footprint, lower public land coverage, and lower Wilderness and National Park coverage (Fig. 4). These patterns are likely due in part to fire exclusion. Fire exclusion policies have been, and continue to be, implemented in areas with high human footprint due to the risk wildfires pose to communities and societal values (Iglesias et al. 2022; Miller 2006; Wagtendonk 2007; Boerigter et al. 2024). The deficit of wildland fire leads to increasing fuel accumulation and subsequently larger and more severe wildfires which further threaten communities, encouraging further fire suppression (Higuera et al. 2023; Kreider et al. 2024; McLauchlan et al. 2020) - responsible for the fire suppression paradox (Calkin et al. 2015; Cohen 2008; Kreider et al. 2024).

Federally prioritized landscapes provide another lens through which to assess the relationship between fire regime departure and human influence (Fig. 5). Priority landscapes designated in the Wildfire Crisis Implementation Plan were identified based on future community vulnerability and capacity to mitigate wildfire hazard US Forest Service, 2022). Our results show that priority landscapes have higher fire regime departures than non-priority landscapes, in part because of the prevalence of large priority landscapes in California (Fig. 5), where the highest departure values in the western US occur. Fire regime departure and community risk are seemingly interlinked. We can, however, break the link between human influence and fire regime departures by implementing intentional fire management plans to return frequent, low intensity fires to fire prone forests (Ager, Evers, et al. 2021; Barros et al. 2021; Iglesias et al. 2022; Krawchuk et al. 2023; Syphard et al. 2013; North et al. 2024; Dunn et al. 2020).

Our findings point directly toward an evidence-based management toolkit to address departed fire frequency and severity: prescribed burns, cultural burns, and managed wildfire, alone and in combination with hand thinning or mechanical fuel treatments. These interventions have been shown to reduce fuel loads, thereby reducing wildfire severity, helping to restore ecosystems (Davis et al. 2023; 2024; Hessburg et al. 2005; Kalies and Yocom Kent 2016). However, increased temperatures and drought from climate change are expected to reduce the windows of safe fire weather for beneficial fire use, which will complicate the implementation of these restoration practices (Keyser and Westerling 2019; Parks and Abatzoglou 2020; Schuurman et al. 2022; Swain et al. 2023). In such cases, our approach offers a distributional, historical context that can help managers prioritize fire and fuel management actions that support ecosystem resilience under future climates (Davis et al. 2023; Schuurman et al. 2022).

EMD: Earth Mover’s Distance

MFRI: Mean Fire Return Interval Departure

BPS: BioPhysical Setting

HRV: Historical Range of Variation

MTBS: Monitoring Trends in Burn Severity

CBI: Composite Burn Index

NDVI: Normalized Difference Vegetation Index

FRCC: Fire Regime Condition Class

FFD_cc: FRCC Fire Frequency Departure

FSD_cc: FRCC Fire Severity Departure

FRD_cc: FRCC Fire Regime Departure

FFD: Distributional Fire Frequency Departure

FSD: Distributional Fire Severity Departure

MFRD: Multivariate Distributional Fire Regime Departure

SD: Standard Deviation

MAE: Mean Absolute Error

Availability of data and materials

We provide analyzed hexels, wilderness areas, protected areas, firesheds, HUC8 and HUC10 datasets as GeoPackage files (.gpkg, similar to GeoJSON). Within each GeoPackage, we report all statistics described in Supplemental 2. Additionally, we provide R scripts for the full analysis and each databases analysis at https://github.com/souma4/Fire_Regime_Departure. In tandem, we provide a database of every dataset, with each dataset containing maps of BPS, fire frequency, and average fire severity, and figures that show the historical and contemporary frequency and severity distributions for all landscapes within. This database is available at [Database Placeholder].

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Authors contributions

Conceptualization: J.R.C., S.A.P., and S.Z.D. Methodology: J.R.C., S.A.P., T.J.H, C.A.C., S.Z.D. Formal Analysis: J.R.C. Data Curation: J.R.C., S.A.P. Visualization: J.R.C., S.A.P., S.Z.D., T.J.H., C.A.C. Writing---original draft: J.R.C. Writing---review and editing: S.Z.D., S.A.P., C.A.C., T.J.H.

Acknowledgements

This research was funded by the USDA Forest Service, Rocky Mountain Research Station, Aldo Leopold Wilderness Institute. The findings and conclusions in this publication are those of the authors and should not be construed to represent any official USDA or U.S. Government determination or policy. The U.S. Geological Survey Northwest Climate Adaptation Science Center also partially supported the research, through support for TH. We thank Zachary L. Steel for helping with the initial justification of the project, Kori Blankenship, and Leonardo Frid for help with LANDFIRE. We also thank Erik Borke at the University of Montana Writing and Public Speaking Center and Svetlana Yegorova for their help with writing.

Agee, James K. 1993. Fire Ecology of Pacific Northwest Forests. Washington D.C.: Island Press.
Ager, Alan A., Michelle A. Day, Chris Ringo, Cody R. Evers, Fermin J. Alcasena, Rachel M. Houtman, Michael Scanlon, Michael Scanlon, and Tania Ellersick. 2021. “Development and Application of the Fireshed Registry.” RMRS-GTR-425. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. https://doi.org/10.2737/RMRS-GTR-425.
Ager, Alan A., Cody R. Evers, Michelle A. Day, Fermin J. Alcasena, and Rachel Houtman. 2021. “Planning for Future Fire: Scenario Analysis of an Accelerated Fuel Reduction Plan for the Western United States.” Landscape and Urban Planning 215 (November):104212. https://doi.org/10.1016/j.landurbplan.2021.104212.
Balch, Jennifer K., Bethany A. Bradley, John T. Abatzoglou, R. Chelsea Nagy, Emily J. Fusco, and Adam L. Mahood. 2017. “Human-Started Wildfires Expand the Fire Niche across the United States.” Proceedings of the National Academy of Sciences 114 (11): 2946–51. https://doi.org/10.1073/pnas.1617394114.
Barrett, S, D Havlina, J Jones, W Hann, C Frame, D Hamilton, K Schon, T Demeo, L Hutter, and J Menakis. 2010. “Interagency Fire Regime Condition Class(FRCC) Guidebook, Version 3.0.” USDA Forest Service, US Department of the Interior, and The Nature Conservancy.
Barrett, Tyson, Matt Dowle, and Arun Srinivasan. 2023. Data.Table: Extension of `data.Frame`. https://r-datatable.com.
Barros, Ana, Derek Churchill, Aleksandar Dozic, Chuck Hersey, Garrett Meigs, Amy Ramsey, Annie Smith, and Andrew Spaeth. 2021. “Prioritizing for Dual Benefits of Forest Health and Wildfire Response.” Washington State Department of Natural Resources.
Boerigter, Clare E., Sean A. Parks, Jonathan W. Long, Jonathan D. Coop, Melanie Armstrong, and Don L. Hankins. 2024. “Untrammeling the Wilderness: Restoring Natural Conditions through the Return of Human-Ignited Fire.” Fire Ecology 20 (1): 76. https://doi.org/10.1186/s42408-024-00297-5.
Bowman, David M. J. S., Jennifer Balch, Paulo Artaxo, William J. Bond, Mark A. Cochrane, Carla M. D’Antonio, Ruth DeFries, et al. 2011. “The Human Dimension of Fire Regimes on Earth.” Journal of Biogeography 38 (12): 2223–36. https://doi.org/10.1111/j.1365-2699.2011.02595.x.
Breiman, Leo. 2001. “Random Forests.” Machine Learning 45 (1): 5–32. https://doi.org/10.1023/A:1010933404324.
Buma, B., B. J. Harvey, D. G. Gavin, R. Kelly, T. Loboda, B. E. McNeil, J. R. Marlon, et al. 2019. “The Value of Linking Paleoecological and Neoecological Perspectives to Understand Spatially-Explicit Ecosystem Resilience.” Landscape Ecology 34 (1): 17–33. https://doi.org/10.1007/s10980-018-0754-5.
Calkin, David E., Matthew P. Thompson, and Mark A. Finney. 2015. “Negative Consequences of Positive Feedbacks in US Wildfire Management.” Forest Ecosystems 2 (1): 9. https://doi.org/10.1186/s40663-015-0033-8.
Cansler, C. Alina, Donald McKenzie, and Charles B. Halpern. 2018. “Fire Enhances the Complexity of Forest Structure in Alpine Treeline Ecotones.” Ecosphere 9 (2): e02091. https://doi.org/10.1002/ecs2.2091.
Clark, James S., Louis Iverson, Christopher W. Woodall, Craig D. Allen, David M. Bell, Don C. Bragg, Anthony W. D’Amato, et al. 2016. “The Impacts of Increasing Drought on Forest Dynamics, Structure, and Biodiversity in the United States.” Global Change Biology 22 (7): 2329–52. https://doi.org/10.1111/gcb.13160.
Cohen, Jack. 2008. “The Wildland Urban Interface Fire Problem.” Forest History Today, 2008.
Coop, Jonathan D, Sean A. Parks, Camille S Stevens-Rumann, Shelley D Crausbay, Philip E Higuera, Matthew D Hurteau, Alan Tepley, et al. 2020. “Wildfire-Driven Forest Conversion in Western North American Landscapes.” BioScience 70 (8): 659–73. https://doi.org/10.1093/biosci/biaa061.
Cooper, Charles F. 1960. “Changes in Vegetation, Structure, and Growth of Southwestern Pine Forests since White Settlement.” Ecological Monographs 30 (2): 129–64. https://doi.org/10.2307/1948549.
Davis, Kimberley T., Jamie Peeler, Joseph Fargione, Ryan D. Haugo, Kerry L. Metlen, Marcos D. Robles, and Travis Woolley. 2024. “Tamm Review: A Meta-Analysis of Thinning, Prescribed Fire, and Wildfire Effects on Subsequent Wildfire Severity in Conifer Dominated Forests of the Western US.” Forest Ecology and Management 561 (June):121885. https://doi.org/10.1016/j.foreco.2024.121885.
Davis, Kimberley T., Marcos D. Robles, Kerry B. Kemp, Philip E. Higuera, Teresa Chapman, Kerry L. Metlen, Jamie L. Peeler, et al. 2023. “Reduced Fire Severity Offers Near-Term Buffer to Climate-Driven Declines in Conifer Resilience across the Western United States.” Proceedings of the National Academy of Sciences 120 (11): e2208120120. https://doi.org/10.1073/pnas.2208120120.
Dobrushin, R. L. 1970. “Prescribing a System of Random Variables by Conditional Distributions.” Theory of Probability & Its Applications 15 (3): 458–86. https://doi.org/10.1137/1115049.
Donato, Daniel C., Joshua S. Halofsky, Derek J. Churchill, Ryan D. Haugo, C. Alina Cansler, Annie Smith, and Brian J. Harvey. 2023. “Does Large Area Burned Mean a Bad Fire Year? Comparing Contemporary Wildfire Years to Historical Fire Regimes Informs the Restoration Task in Fire-Dependent Forests.” Forest Ecology and Management 546 (October):121372. https://doi.org/10.1016/j.foreco.2023.121372.
Dunn, Christopher J., Christopher D. O’Connor, Jesse Abrams, Matthew P. Thompson, Dave E. Calkin, James D. Johnston, Rick Stratton, and Julie Gilbertson-Day. 2020. “Wildfire Risk Science Facilitates Adaptation of Fire-Prone Social-Ecological Systems to the New Fire Reality.” Environmental Research Letters. 15: 025001. 15:025001. https://doi.org/10.1088/1748-9326/ab6498.
Eidenshrink, Jeff, Brian Schwind, Ken Brewer, Zhi-Liang Zhu, Brad Quayle, and Stephen Howard. 2007. “A Project for Monitoring Trends in Burn Severity.” Fire Ecology 3:3–21. https://doi.org/10.4996/fireecology.0301003.
Eisenberg, Cristina, Christopher L. Anderson, Adam Collingwood, Robert Sissons, Christopher J. Dunn, Garrett W. Meigs, Dave E. Hibbs, et al. 2019. “Out of the Ashes: Ecological Resilience to Extreme Wildfire, Prescribed Burns, and Indigenous Burning in Ecosystems.” Frontiers in Ecology and Evolution 7 (November). https://doi.org/10.3389/fevo.2019.00436.
Gorelick, Noel, Matt Hancher, Mike Dixon, Simon Ilyushchenko, David Thau, and Rebecca Moore. 2017. “Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone.” Remote Sensing of Environment, Big Remotely Sensed Data: tools, applications and experiences, 202 (December):18–27. https://doi.org/10.1016/j.rse.2017.06.031.
Hagmann, R. K., P. F. Hessburg, S. J. Prichard, N. A. Povak, P. M. Brown, P. Z. Fulé, R. E. Keane, et al. 2021. “Evidence for Widespread Changes in the Structure, Composition, and Fire Regimes of Western North American Forests.” Ecological Applications 31 (8): e02431. https://doi.org/10.1002/eap.2431.
Haugo, Ryan D., Bryce S. Kellogg, C. Alina Cansler, Crystal A. Kolden, Kerry B. Kemp, James C. Robertson, Kerry L. Metlen, Nicole M. Vaillant, and Christina M. Restaino. 2019. “The Missing Fire: Quantifying Human Exclusion of Wildfire in Pacific Northwest Forests, USA.” Ecosphere 10 (4): e02702. https://doi.org/10.1002/ecs2.2702.
Hessburg, Paul F., James K. Agee, and Jerry F. Franklin. 2005. “Dry Forests and Wildland Fires of the Inland Northwest USA: Contrasting the Landscape Ecology of the Pre-Settlement and Modern Eras.” Forest Ecology and Management 211 (1–2): 117–39. https://doi.org/10.1016/j.foreco.2005.02.016.
Hessburg, Paul F., Susan J. Prichard, R. Keala Hagmann, Nicholas A. Povak, and Frank K. Lake. 2021. “Wildfire and Climate Change Adaptation of Western North American Forests: A Case for Intentional Management.” Ecological Applications 31 (8): e02432. https://doi.org/10.1002/eap.2432.
Higgs, Eric, Donald A Falk, Anita Guerrini, Marcus Hall, Jim Harris, Richard J Hobbs, Stephen T Jackson, Jeanine M Rhemtulla, and William Throop. 2014. “The Changing Role of History in Restoration Ecology.” Frontiers in Ecology and the Environment 12 (9): 499–506. https://doi.org/10.1890/110267.
Higuera, Philip E, Maxwell C Cook, Jennifer K Balch, E Natasha Stavros, Adam L Mahood, and Lise A St. Denis. 2023. “Shifting Social-Ecological Fire Regimes Explain Increasing Structure Loss from Western Wildfires.” PNAS Nexus, February, pgad005. https://doi.org/10.1093/pnasnexus/pgad005.
Hijmans, R. J., R. Bivand, and M.D. Sumner. 2022. “Terra: Spatial Data Analysis.” R. https://cran.r-project.org/package=terra.
Hood, Sharon M., J. Morgan Varner, Phillip van Mantgem, and C. Alina Cansler. 2018. “Fire and Tree Death: Understanding and Improving Modeling of Fire-Induced Tree Mortality.” Environmental Research Letters 13 (11): 113004. https://doi.org/10.1088/1748-9326/aae934.
Iglesias, Virginia, Jennifer K. Balch, and William R. Travis. 2022. “U.S. Fires Became Larger, More Frequent, and More Widespread in the 2000s.” Science Advances 8 (11): eabc0020. https://doi.org/10.1126/sciadv.abc0020.
Iglesias, Virginia, Natasha Stavros, Jennifer K. Balch, Kimiko Barrett, Jeanette Cobian-Iñiguez, Cyrus Hester, Crystal A. Kolden, et al. 2022. “Fires That Matter: Reconceptualizing Fire Risk to Include Interactions between Humans and the Natural Environment.” Environmental Research Letters 17 (4): 045014. https://doi.org/10.1088/1748-9326/ac5c0c.
Jager, Henriette I., Jonathan W. Long, Rachel L. Malison, Brendan P. Murphy, Ashley Rust, Luiz G. M. Silva, Rahel Sollmann, et al. 2021. “Resilience of Terrestrial and Aquatic Fauna to Historical and Future Wildfire Regimes in Western North America.” Ecology and Evolution 11 (18): 12259–84. https://doi.org/10.1002/ece3.8026.
Johnson, E. A, and S. L. Gutsell. 1994. “Fire Frequency Models, Methods and Interpretations.” Advances in Ecological Research 25:239–87.
Kalies, Elizabeth L., and Larissa L. Yocom Kent. 2016. “Tamm Review: Are Fuel Treatments Effective at Achieving Ecological and Social Objectives? A Systematic Review.” Forest Ecology and Management 375 (September):84–95. https://doi.org/10.1016/j.foreco.2016.05.021.
Kantorovich, L. V, and S. G Rubinstein. 1958. “On a Space of Totally Additive Functions.” Vestnik of the St. Petersburg University: Mathematics 13 (7): 52–59.
Keane, Robert E., Geoffrey J. Cary, and Mike D. Flannigan. 2011. “Challenges and Needs in Fire Management: A Landscape Simulation Modeling Perspective.” In Landscape Ecology in Forest Management and Conservation: Challenges and Solutions for Global Change, edited by Chao Li, Raffaele Lafortezza, and Jiquan Chen, 75–98. Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-642-12754-0_4.
Keane, Robert E., Paul F. Hessburg, Peter B. Landres, and Fred J. Swanson. 2009. “The Use of Historical Range and Variability (HRV) in Landscape Management.” Forest Ecology and Management 258 (7): 1025–37. https://doi.org/10.1016/j.foreco.2009.05.035.
Keeley, Jon E., and Juli G. Pausas. 2022. “Evolutionary Ecology of Fire.” Annual Review of Ecology, Evolution, and Systematics 53 (1): 203–25. https://doi.org/10.1146/annurev-ecolsys-102320-095612.
Key, Carl H., and Nathan C. Benson. 2006. “Landscape Assessment (LA).” In: Lutes, Duncan C.; Keane, Robert E.; Caratti, John F.; Key, Carl H.; Benson, Nathan C.; Sutherland, Steve; Gangi, Larry J. 2006. FIREMON: Fire Effects Monitoring and Inventory System. Gen. Tech. Rep. RMRS-GTR-164-CD. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. p. LA-1-55 164. https://www.fs.usda.gov/research/treesearch/24066.
Keyser, Alisa R., and A. LeRoy Westerling. 2019. “Predicting Increasing High Severity Area Burned for Three Forested Regions in the Western United States Using Extreme Value Theory.” Forest Ecology and Management 432 (January):694–706. https://doi.org/10.1016/j.foreco.2018.09.027.
Krawchuk, Meg A., J Hudec, and G. W. Meigs. 2023. “Manager’s Brief: Integrating Fire Refugia Concepts and Data into Vegetation Management Decisions A Case Study on the Gifford Pinchot National Forest, Little White Salmon Project Area, Washington.” Little White Salmon Project Area.
Kreider, Mark R., Philip E. Higuera, Sean A. Parks, William L. Rice, Nadia White, and Andrew J. Larson. 2024. “Fire Suppression Makes Wildfires More Severe and Accentuates Impacts of Climate Change and Fuel Accumulation.” Nature Communications 15 (1): 2412. https://doi.org/10.1038/s41467-024-46702-0.
LANDFIRE. 2020. “LANDFIRE 2020 Biophysical Settings (BPS) CONUS.” LANDFIRE, Earth Resources Observation and Science Center (EROS), U.S. Geological Survey. https://www.landfire.gov.
Marlon, Jennifer R., Patrick J. Bartlein, Daniel G. Gavin, Colin J. Long, R. Scott Anderson, Christy E. Briles, Kendrick J. Brown, et al. 2012. “Long-Term Perspective on Wildfires in the Western USA.” Proceedings of the National Academy of Sciences 109 (9): E535–43. https://doi.org/10.1073/pnas.1112839109.
Martin, R. E, and D. B. Sapsis. 1992. “Fires as Agents of Biodiversity: Pyrodiversity Promotes Biodiversity.” Proc. of the Symp. on Biodiversity in Northwestern California.
McClure, Emma J., Jonathan D. Coop, Christopher H. Guiterman, Ellis Q. Margolis, and Sean A. Parks. in press. “Contemporary Fires Are Less Frequent but More Severe in Dry Conifer Forests of the Southwestern United States.”
McLauchlan, Kendra K., Philip E. Higuera, Jessica Miesel, Brendan M. Rogers, Jennifer Schweitzer, Jacquelyn K. Shuman, Alan J. Tepley, et al. 2020. “Fire as a Fundamental Ecological Process: Research Advances and Frontiers.” Journal of Ecology 108 (5): 2047–69. https://doi.org/10.1111/1365-2745.13403.
Miller, Carol. 2006. “Wilderness Fire Management in a Changing World.” International Journal of Wilderness 12 (1): 18–21.
North, Malcolm P., Sarah M. Bisbing, Don L. Hankins, Paul F. Hessburg, Matthew D. Hurteau, Leda N. Kobziar, Marc D. Meyer, Allison E. Rhea, Scott L. Stephens, and Camille S. Stevens-Rumann. 2024. “Strategic Fire Zones Are Essential to Wildfire Risk Reduction in the Western United States.” Fire Ecology 20 (1): 50. https://doi.org/10.1186/s42408-024-00282-y.
Parks, Sean A., and J. T. Abatzoglou. 2020. “Warmer and Drier Fire Seasons Contribute to Increases in Area Burned at High Severity in Western US Forests From 1985 to 2017.” Geophysical Research Letters 47 (22): e2020GL089858. https://doi.org/10.1029/2020GL089858.
Parks, Sean A., Lisa M. Holsinger, Kori Blankenship, Gregory K. Dillon, Sara A. Goeking, and Randy Swaty. 2023. “Contemporary Wildfires Are More Severe Compared to the Historical Reference Period in Western US Dry Conifer Forests.” Forest Ecology and Management 544 (September):121232. https://doi.org/10.1016/j.foreco.2023.121232.
Parks, Sean A., Lisa M. Holsinger, Michael J. Koontz, Luke Collins, Ellen Whitman, Marc-André Parisien, Rachel A. Loehman, et al. 2019. “Giving Ecological Meaning to Satellite-Derived Fire Severity Metrics across North American Forests.” Remote Sensing 11 (14): 1735. https://doi.org/10.3390/rs11141735.
Parks, Sean A., Lisa M. Holsinger, Carol Miller, and Cara R. Nelson. 2015. “Wildland Fire as a Self-Regulating Mechanism: The Role of Previous Burns and Weather in Limiting Fire Progression.” Ecological Applications 25 (6): 1478–92. https://doi.org/10.1890/14-1430.1.
Parks, Sean A., Carol Miller, Marc-André Parisien, Lisa M. Holsinger, Solomon Z. Dobrowski, and John Abatzoglou. 2015. “Wildland Fire Deficit and Surplus in the Western United States, 1984–2012.” Ecosphere 6 (12): 1–13. https://doi.org/10.1890/ES15-00294.1.
Parks, Sean A., Marc-André Parisien, Carol Miller, and Solomon Z. Dobrowski. 2014. “Fire Activity and Severity in the Western US Vary along Proxy Gradients Representing Fuel Amount and Fuel Moisture.” PLOS ONE 9 (6): e99699. https://doi.org/10.1371/journal.pone.0099699.
Pebesma, E. 2018. “Simple Features for R: Standardized Support for Spatial Vector Data.” The R Journal 10 (1): 439–46. https://doi.org/10.32614/RJ-2018-009.
Pickett, Steward T.A., and P.S. White. 1986. The Ecology of Natural Disturbance and Patch Dynamics. https://shop.elsevier.com/books/the-ecology-of-natural-disturbance-and-patch-dynamics/pickett/978-0-08-050495-7.
R Core Team. 2022. “R: A Language and Environment for Statistical Computing.” Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
Roos, Christopher I., Thomas W. Swetnam, T. J. Ferguson, Matthew J. Liebmann, Rachel A. Loehman, John R. Welch, Ellis Q. Margolis, et al. 2021. “Native American Fire Management at an Ancient Wildland–Urban Interface in the Southwest United States.” Proceedings of the National Academy of Sciences 118 (4): e2018733118. https://doi.org/10.1073/pnas.2018733118.
Schieck, Jim, and Samantha J Song. 2006. “Changes in Bird Communities throughout Succession Following Fire and Harvest in Boreal Forests of Western North America: Literature Review and Meta-Analyses” Canadian Journal of Forest Research 36:20.
Schuurman, Gregor W, David N Cole, Amanda E Cravens, Scott Covington, Shelley D Crausbay, Cat Hawkins Hoffman, David J Lawrence, et al. 2022. “Navigating Ecological Transformation: Resist–Accept–Direct as a Path to a New Resource Management Paradigm.” BioScience 72 (1): 16–29. https://doi.org/10.1093/biosci/biab067.
Seidl, Rupert, Werner Rammer, Robert M. Scheller, and Thomas A. Spies. 2012. “An Individual-Based Process Model to Simulate Landscape-Scale Forest Ecosystem Dynamics.” Ecological Modelling 231 (April):87–100. https://doi.org/10.1016/j.ecolmodel.2012.02.015.
Seidl, Rupert, Werner Rammer, and Thomas A. Spies. 2014. “Disturbance Legacies Increase the Resilience of Forest Ecosystem Structure, Composition, and Functioning.” Ecological Applications 24 (8): 2063–77. https://doi.org/10.1890/14-0255.1.
Spies, Thomas A., Paul F. Hessburg, Carl N. Skinner, Klaus J. Puettmann, Matthew J. Reilly, Raymond J. Davis, Jane A. Kertis, Jonathan W. Long, and David C. Shaw. 2018. “Chapter 3: Old Growth, Disturbance, Forest Succession, and Management in the Area of the Northwest Forest Plan.” In: Spies, T.A.; Stine, P.A.; Gravenmier, R.; Long, J.W.; Reilly, M.J., Tech. Coords. 2018. Synthesis of Science to Inform Land Management within the Northwest Forest Plan Area. Gen. Tech. Rep. PNW-GTR-966. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station: 95-243. 966:95.
Steel, Zachary L., Hugh D. Safford, and Joshua H. Viers. 2015. “The Fire Frequency-Severity Relationship and the Legacy of Fire Suppression in California Forests.” Ecosphere 6 (1): art8. https://doi.org/10.1890/ES14-00224.1.
Sugihara, Neil, Jan van Wagtendonk, and JoAnn Fites-Kaufman. 2006. “Fire as an Ecological Process.” In Fire in California’s Ecosystems, 58–74.
Swain, Daniel L., John T. Abatzoglou, Crystal Kolden, Kristen Shive, Dmitri A. Kalashnikov, Deepti Singh, and Edward Smith. 2023. “Climate Change Is Narrowing and Shifting Prescribed Fire Windows in Western United States.” Communications Earth & Environment 4 (1): 1–14. https://doi.org/10.1038/s43247-023-00993-1.
Swetnam, Thomas W., Joshua Farella, Christopher I. Roos, Matthew J. Liebmann, Donald A. Falk, and Craig D. Allen. 2016. “Multiscale Perspectives of Fire, Climate and Humans in Western North America and the Jemez Mountains, USA.” Philosophical Transactions of the Royal Society B: Biological Sciences 371 (1696): 20150168. https://doi.org/10.1098/rstb.2015.0168.
Syphard, Alexandra D., Jon E. Keeley, Anne H. Pfaff, and Ken Ferschweiler. 2017. “Human Presence Diminishes the Importance of Climate in Driving Fire Activity across the United States.” Proceedings of the National Academy of Sciences of the United States of America 114 (52): 13750–55. https://doi.org/10.1073/pnas.1713885114.
Syphard, Alexandra D., Avi Bar Massada, Van Butsic, and Jon E. Keeley. 2013. “Land Use Planning and Wildfire: Development Policies Influence Future Probability of Housing Loss.” PLOS ONE 8 (8): e71708. https://doi.org/10.1371/journal.pone.0071708.
Turner, Monica G., Kristin H. Braziunas, Winslow D. Hansen, and Brian J. Harvey. 2019. “Short-Interval Severe Fire Erodes the Resilience of Subalpine Lodgepole Pine Forests.” Proceedings of the National Academy of Sciences 116 (23): 11319–28. https://doi.org/10.1073/pnas.1902841116.
Turner, Monica G., Kristin H. Braziunas, Winslow D. Hansen, Tyler J. Hoecker, Werner Rammer, Zak Ratajczak, A. Leroy Westerling, and Rupert Seidl. 2022. “The Magnitude, Direction, and Tempo of Forest Change in Greater Yellowstone in a Warmer World with More Fire.” Ecological Monographs 92 (1): e01485. https://doi.org/10.1002/ecm.1485.
Turner, Monica G., and Rupert Seidl. 2023. “Novel Disturbance Regimes and Ecological Responses.” Annual Review of Ecology, Evolution, and Systematics 54 (1): null. https://doi.org/10.1146/annurev-ecolsys-110421-101120.
US Forest Service, “Confronting the Wildfire Crisis.” 2023. FS-1187f. USDA Forest Service.
US Forest Service, “Wildfire Crisis Implementation Plan.” 2022. USDA Forest Service, US Department of the Interior, and The Nature Conservancy. https://www.fs.usda.gov/sites/default/files/fs_media/fs_document/WCS-Implementation-Plan.pdf.
U.S. Geological Survey. 2022. “Protected Areas Database of the United States (PAD-US).” U.S. Geological Survey data release: Gap Analysis Project (GAP),. https://doi.org/10.5066/P9Q9LQ4B.
Vaserstein, L. N. 1969. “Markov Processes over Denumerable Products of Spaces, Describing Large Systems of Automata.” Probl. Peredachi Inf. 5.
Venter, Oscar, Eric W. Sanderson, Ainhoa Magrach, James R. Allan, Jutta Beher, Kendall R. Jones, Hugh P. Possingham, et al. 2016. “Global Terrestrial Human Footprint Maps for 1993 and 2009.” Scientific Data 3 (1): 160067. https://doi.org/10.1038/sdata.2016.67.
Wagtendonk, Van. 2007. “The History and Evolution of Wildland Fire Use.” Fire Ecology 3 (2): 3–17. https://doi.org/10.4996/fireecology.0302003.
Whitlock, Cathy, Philip E. Higuera, David B. McWethy, and Christy E. Briles. 2010. “Paleoecological Perspectives on Fire Ecology: Revisiting the Fire-Regime Concept” The Open Ecology Journal 3 (2): 6–23. https://doi.org/10.2174/1874213001003020006.
Wickham, Hadley, Mara Averick, Jennifer Bryan, Winston Chang, Lucy D’Agostino McGowan, Romain François, Garrett Grolemund, et al. 2019. “Welcome to the Tidyverse.” Journal of Open Source Software 4 (43): 1686. https://doi.org/10.21105/joss.01686.
Williams, J. N., H. D. Safford, N. Enstice, Z. L. Steel, and A. K. Paulson. 2023. “High-Severity Burned Area and Proportion Exceed Historic Conditions in Sierra Nevada, California, and Adjacent Ranges.” Ecosphere 14 (1): e4397. https://doi.org/10.1002/ecs2.4397.

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Wildfires Are Burning Less Frequently and More Severely in the Western US: An Integrative Approach to Calculating Fire-Regime Departures

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Fire-regime Departure

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Fire Frequency and Severity Departures

Fire-Regime Departure metric

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