Wildland fire reburning trends across the US West suggest only short-term negative feedback and differing climatic effects

Fires are widespread agents of change in the world's forests (Williams et al 2016). Most ecosystems contain many species adapted to their local fire regime as a result (Buma et al 2013). However, if fire rates increase, burns should begin to intersect with recent fires, termed 'reburning,' 'short-interval fires,' or more broadly 'interacting disturbances.' Short interval fires can have an outsized impact on ecosystem functioning due to a lack of adaptation to the increased fire frequency (Walker et al 2018), sometimes resulting in forest loss (Jackson et al 2009). As a result, the impacts of reburning over relatively brief intervals are a major research focus (Buma et al 2013, Donato et al 2016, Hart et al 2019, Stevens-Rumann and Morgan 2019, Turner et al 2019). Rapid, directional ecosystem changes, such as converting those forests to alternate ecological regimes (e.g. grasslands), occur via several mechanisms: reducing the efficacy of adaptive resilience mechanisms (e.g. serotiny, Buma and Wessman 2011; resprouting, Fairman et al 2019), interval squeeze (Enright et al 2015), reseeding failure (Bowman et al 2014), decreased organic layer depth (Brown and Johnstone 2012), and changes in species flammability producing positive or negative feedbacks with future fire probability (e.g. Brooks et al 2004, Paritsis et al 2015).

How the rate of reburning is changing, and where, is therefore an important question. While not all portions of the world are expected to see increasing fire rates, most temperate latitudes are likely to see major increases in fire frequency (Mortiz et al 2012). Previous work suggests increasing fire rates could limit future fire occurrence—a negative feedback (Parks et al 2015). This feedback can be useful, as prescribed burning can limit 'unplanned' fires (Price et al 2015). But for how long? It is vital that we understand the prevalence, trends, and correlating factors with reburning in recently burned forest ecosystems, but while there are many excellent local and landscape scale investigations of reburning trends and feedback mechanisms (e.g. Parks et al 2016, Harvey et al 2016, Erni et al 2017, Tepley et al 2018, Hart et al 2019), a broad-scale assessment of trends has not been attempted. It is important to determine if trends and feedbacks suggested at finer scales do indeed scale up to broad regional patterns.

Mechanistically, much has been learned about reburning and feedback mechanisms. Climate and topography strongly influence recovering vegetation that ultimately fuels subsequent fires (Coppoletta et al 2016, Erni et al 2017, Grabinski et al 2017, Parks et al 2018) and climate (especially inter-annual moisture deficits, Westerling 2016) affects flammability as well. Extreme fire conditions, such as intense drought, can overwhelm negative feedbacks on burning associated with low fuel loads (Parks et al 2018, Tepley et al 2018), and increasing aridity will lead to increasing burn probabilities in many locations (Coppoletta et al 2016, Littell et al 2016, Keyser and Westerling 2017). Looking past the first fire, however, suggests that fuel limitations immediately after a burn will limit reburn activity—at least until the landscape regenerates biomass (Heon et al 2014, Coppoletta et al 2016). Conceptually, fine-scale studies suggest that climate-vegetation dynamics should result in differential feedbacks—moisture driving increased fire frequency in productive systems that are primarily fuel limited (with droughts limiting reburning; Parks et al 2018), and drought driving increased fire frequency in systems that have substantial biomass but are primarily ignition/condition limited. The role of topography is realized at finer scales, with southerly aspects and steeper slopes generally drier and less productive and playing into the pattern of burning in any given event (Harris and Taylor 2017). These factors driving feedbacks between fires have been well documented in local studies (for a review, see Prichard et al 2017).

Despite this, the prevalence and temporal nature of reburning likelihood—how long the negative feedback lasts, and when/where it disappears—has not been explored at broad scales. In other words, the ability of previous studies to scale up their conclusions, that there is generally a significant feedback on fire likelihood for ∼10–20 years (e.g. Harvey et al 2016, Parks et al 2018), is unknown. Here we explore that potential for scaling previous, foundational, and site-specific work to the wildland forests of the Western US. We explicitly do not look at managed areas, deforested areas, or landscapes with intense human presence, which dramatically alters fire likelihood (e.g. Fusco et al 2016). The data presented here are the first to document reburning trends at this scale, providing context for this important driver of long-lasting ecosystem change.

We use the longest spatially-explicit, high resolution fire history mapping (1984–2016, 30 m resolution) available to test that intuition across the Western US. Our primary questions are: Are increasing fire rates inhibiting reburning frequency and at what timescales? Are those reburns associated with differing topographic contexts, and is that association changing with time? Are reburns associated with droughtier conditions, and is that relationship changing with time?

The analysis region spans the western US (figure 1). The analysis was limited to reburning in wildland forests. This was done using remote sensing derived landcover classification and land use maps (anthromes: Ellis and Ramankutty 2008, agriculture fraction: Ramankutty et al 2010). The anthrome dataset was created using population density, land use, and land cover. To focus on forests and limit human influence, we restricted the analysis to pixels classified as 'remote forests' or 'wild forests' indicating little to no human presence. This was done to avoid unintentional topographic/climatic/ecoregion bias that could introduced by reburning associated with human causes (Fusco et al 2016). We eliminated areas with >1% agriculture in the pixel, a conservative step intended to remove any spurious reburning associated with farming.

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2.1. Data sets

2.2. Null model

A reburn is an overlap between a fire in any given focal year and the fire footprints of previous fires. However, one has to also define an interval in the past that constitutes a short-interval fire (for all fires are essentially reburns over some time period). To determine if observed reburning rates are different from a purely random distribution of fires in a given area, a null model for each year was created. First, the percentage of landscape burned in the past interval under consideration was calculated as:

$$\begin{eqnarray*}&&{B}_{interval}=\displaystyle \frac{\sum BurnPoint{s}_{{interval}}}{TotalPoints}* 100,\end{eqnarray*}$$

where BurnPoints_interval are all points with an observed fire during the temporal interval prior to the focal year, and TotalPoints are all points in the dataset. This results in a percentage area burned in the preceding time period under consideration. For example, for a 10-year interval, all burned points in the previous 9 years would be summed and the percentage of landscape burned in that time period calculated. Second, the percentage of landscape burned in the focal year t, denoted B_t, was calculated as:

$$\begin{eqnarray*}&&{B}_{t}=\displaystyle \frac{BurnPoint{s}_{t}}{Total\,Points}* 100,\end{eqnarray*}$$

where BurnPoints_t is the number of fires in the focal year. This results in the percentage area burned in the focal year.

B_interval and B_t both represent percentages of the landscape. These two percentages were randomly distributed in 10 000 cell environments, creating two independent sets of simulated burn locations at the same percentage as observed fires. Expectations for reburning frequency was calculated as the number of cells which overlapped, i.e. the sets intersected:

$$\begin{eqnarray*}&&ReburnEvents=Count\,of\,{B}_{interval}\,\displaystyle \cap \,{B}_{t}.\end{eqnarray*}$$

And the null expected reburn percentage for that focal year was calculated as:

$$\begin{eqnarray*}&&ReburnPercen{t}_{null}=\displaystyle \frac{ReburnEvents}{{B}_{t}}.\end{eqnarray*}$$

This process was repeated 10 000 times for each focal year, and the mean and 95% percentiles calculated.

This null model naturally produces increasing reburn rates with time, a simple consequence of the increase in overall fire rate (p < 0.001, r² = 0.85). What is of interest is the rate of change, and in particular if the rate of increase driven by increasing fire rates (the null model) is matched by observed changes in reburn rates. Rates of change in the null and observed models were compared with a standard ANCOVA (two-tailed). Differences between the null and observed values were also assessed via subtracting the observed rate from the mean null rate, with 95% confidence intervals constructed from the 0.05% and 0.95% quantiles of the null rate distribution. For the ecoregion scale, the process was identical but constrained to only points and percentages within the focal ecoregion.

2.3. Interval

Overall, fires impacted 15.2% of wildland forests between 1984 and 2016 (figures 1, S2), with 92.5% burned once versus 7.4% burning twice and 0.2% burning thrice. Reburns were spread across the region, but primarily concentrated in southern California, the southern desert mountains, and the Idaho portion of the Rockies (table S1). Although reburns occurred on significantly steeper slopes compared to unburned locations (p < 0.05, figure S4 inset) and trended towards southwest aspects (though not significantly, figure S5), the influence of topography on the interval between repeat fires was muted. Overall then, reburns in the observational period were associated with steep, south facing slopes and drought in most ecoregions, though some ecoregions tended to reburn in wetter conditions.

Trends in reburning proportion: overall reburn proportions increased significantly (p < 0.05) regardless of how a reburn is classified (5, 10, 20, or 25 years between fires (figures 2; S3). At the ecoregion level, sample size is significantly smaller, and short-interval burns (<10 years) only increased significantly in the Idaho Batholith (∼1% per decade increase, p = 0.02, r² = 0.36). Reburns should increase because of increasing fire rates, of course, and so were tested against the null model. At a 5 year reburn interval, the difference between observed reburning rates and the null model results increased with time, suggesting fuel limitation. The rate of observed reburn frequency is nearly zero (p > 0.05) despite rapidly increasing fire frequency overall (0.2% per year). Put another way, the slope of the null model is significantly higher than the slope of the observed change (ANCOVA, p < 0.05). Meaningfully, this negative feedback disappeared when looking at the 20 year interval trends (0.38% per year, p = 0.09; F = 3.39_1,12), where reburn rates increased just as fast as the increasing fire frequency would predict assuming no interactions (slopes not significantly different: ANCOVA, p >0.05; figure 2). 10 and 25 year intervals agreed with this trend of declining inhibition as intervals increase (figure S3).

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Reburning Interval Analysis: for the mixed model, which focused on the interval between fires, the most parsimonious model form included (topographic) slope as a fixed effect and SPEI as a random slope varying by ecoregion (i.e. effect magnitude and direction varying by ecoregion). Aspect was not included in the most parsimonious model. Steeper slopes had longer intervals across all ecoregions (figure S4). While the effect sizes of SPEI on reburn interval overlapped zero for many ecoregions, several estimates were significantly different from zero (figure 3). Coastal regions generally experienced shorter reburn intervals when conditions were wetter (higher SPEI was associated with shorter return intervals), whereas the estimate for several interior regions was the opposite, with wetter conditions (higher SPEI) favoring longer reburn intervals.

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This study demonstrates that reburns are increasing significantly at very broad scales, agreeing with expectations that as fire frequency increases so will short-interval events. However, it also demonstrates a short-term negative feedback that is limiting extremely short-interval fires in most locations. This suggests that patterns observed at finer scales (local, landscape, and regional scale studies, such as Parks et al 2015) scale to wildland forests of the US West, and that interpretations of the mechanisms posited by those studies likely apply across broader regions.

The substantial increase in fires across the West has been well documented (Dennison et al 2014, Westerling 2016), and with it an expectation for increasing reburns. Reburning now occurs within 7.4% of total observed fires in US West forests. Given the increased rate of total fires, the significant increase in observed reburns is in line with expectations. The observed rate is less than anticipated due to chance in the short-term, however. This is likely associated with fuel limitations (e.g. Parks et al 2015, Harvey et al 2016, Prichard et al 2017), as the effect is strongest for the shorter time period and gets progressively weaker when longer intervals are considered. However, even the longest intervals considered here (25 years) are still short relative to the life history and reproductive timelines of dominant tree species across North America (Buma et al 2013). A 20 year reburn interval still poses a threat to many species whose resilience mechanisms cannot cope with such frequent fires, such as lodgepole pine (Pinus contorta), a dominant species across North American mountain forests (Buma et al 2013). Thus, the increase in the rate of fire does appear to pose a risk via reburning at broad scales, despite short-term negative feedbacks.

These results suggest 20 year interval reburn rates will continue to increase as fast as the overall fire rate, whereas 5 and 10 year intervals are currently partially inhibited by previous fires. On a yearly basis, there are few significant differences between the null and expected early in the record due to low fire activity overall and subsequent high variability in the null model; in the last decade there are several years with significantly lower reburning than expected (figure 2, bottom) corresponding to recent years of very high fire activity.

Drought strongly influences the interval between fires in many ecoregions (figure 3). Droughty conditions had an opposite effect depending on ecoregion; short intervals were associated with less droughty conditions in more coastal areas. We interpret this as a function of productivity of fuels (review: Prichard et al 2017, also explored in Parks et al 2018), potentially due to increasing fine/grassy fuels associated with wetter conditions and corresponding to a reduction in tree cover with fire (Grabinski et al 2017). The drier conditions in interior forests are consistent with this concept as well—fuels (in the form of woody debris) do exist but require extreme drought to carry fires, and the low productivity in general limits fine fuel accumulation, at least over this observational period. It should be noted that drought/moisture conditions vary over multiple timescales, some of which may be longer than the timescale of observation (1984–2016) and the role of drought over longer time periods should be explored with other site-specific proxies. Overall, divergence in terms of reburn response to drought between coastal California forests and the remainder of the West may be significant in the future as climate shifts. Demonstrating that these feedbacks hold over the entire US West is valuable and useful in constraining future broad-scale modeling efforts, as well as validating inferences from finer scale studies.

Positive severity feedbacks (Barker and Price 2018) have been observed at finer scales and the role of management can be critical in managing these complex interactions between climate and topographical tendencies towards reburning (reported here, Prichard et al 2017), the local specifics of recovering vegetation (e.g. invasive species; Brooks et al 2004), and human actions (management/land use change; Thompson et al 2007, Fusco et al 2016). Prescribed fire may be a useful tool towards managing reburning impacts after a major wildfire (Barker and Price 2018) by strongly reducing fuel loads and avoiding intense reburns that may trigger forest loss (Buma et al 2013, Enright et al 2015). Biome types can influence the efficacy of prescribed fire in reducing future fires via fuel reductions, the same mechanism indicated here (Price et al 2015). An interesting question is the role of historical fire suppression (early to late 20th century) and its role in reburn probabilities; fires in suppressed areas can be more intense due to increased fuels (with high variability; Steel et al 2015), but the impacts on reburn probabilities after that fire are unknown. Fire prediction and effects modeling must take these spatial and temporal interactions into account, rather than naïve non-spatial modeling, because of the strong role of fire history in future burning at any given locations.

This study demonstrates an increase in reburning across the US West, but also ample evidence for negative feedbacks. It suggests that feedback mechanisms proposed at finer scales are indeed relevant across extremely broad regions, but that those feedbacks are relatively short-lived. Overall, as fires increase so do reburns, and with them new challenges for ecosystems and society.

The authors thank three anonymous reviewers for providing constructive critiques of the manuscript. Funding: the work was partially supported by NSF 1737706; Author contributions: Buma conceived of and led the analysis and writing. Weiss and Lucash conducted the MTBS data compilation and Hayes contributed to the data processing. Weiss and Lucash helped write the paper; Competing interests: Authors declare no competing interests; Data and materials availability: all data is publicly available at https://doi.org/10.25412/iop.11704068.v1, from the MTBS program and ASTER mission (cited in main text), compiled and distilled data (as txt file) is posted on IOP Publishing, and all R code from Buma.