ArticlePDF Available

Repeated fires reduce plant diversity in low-elevation Wyoming big sagebrush ecosystems (1984 – 2014)


Abstract and Figures

Sagebrush is one of the most imperiled ecosystems in western North America, having lost about half of its original 62 million hectare extent. Annual grass invasions are known to be increasing wild-fire occurrence and burned area, but the lasting effects (greater than five years post-fire) that the resulting reburns have on these plant communities are unclear. We created a fire history atlas from 31 yr (1984-2014) of Landsat-derived fire data to sample along a fire frequency gradient (zero to three fires) in an area of northern Nevada that has experienced frequent fire in this time period. Thirty-two percent of our study area (13,000 km 2) burned in large fires (over 404 ha) at least once, 7% burned twice, and 2% burned three or more times. We collected plant abundance data at 28 plots (N = 7 per fire frequency), with an average time since fire of 17 yr. We examined fire's effect on plant diversity using species accumulation curves, alpha diversity (Shannon's dominance, Pielou's evenness, and number of species), and beta diversity (Whit-taker, Simpson, and Z indexes). For composition, we used non-metric multidimensional scaling. We then used PERMANOVA models to examine how disturbance history, temperature, precipitation, and aridity around the time of the fire affected subsequent community composition and diversity. One fire fundamentally changed community composition and reduced species richness, and each subsequent fire reduced richness further. Alpha diversity decreased after one fire. Beta diversity declined after the third fire. Cover of exotics was 10% higher in all burned plots, and native cover was 20% lower than in unburned plots, regardless of frequency. PERMANOVA models showed fire frequency and antecedent precipitation as the strongest predictors of beta diversity, while time since fire and vapor pressure deficit for the year of the fire were the strongest predictors of community composition. Given that a single fire has such a marked effect on species composition, and repeated fires reduce richness and beta diversity, we suggest that in lower elevation big sagebrush systems fire should be minimized as much as possible, perhaps even prescribed fire. Restoration efforts should be focused on timing with wet years on cooler, wetter sites.
Content may be subject to copyright.
Repeated res reduce plant diversity in low-elevation Wyoming
big sagebrush ecosystems (19842014)
Department of Geography, University of Colorado Boulder, GUGG 110, 260 UCB, Boulder, Colorado 80309 USA
Citation: Mahood, A. L., and J. K. Balch. 2019. Repeated res reduce plant diversity in low-elevation Wyoming big
sagebrush ecosystems (19842014). Ecosphere 10(2):e02591. 10.1002/ecs2.2591
Abstract. Sagebrush is one of the most imperiled ecosystems in western North America, having lost
about half of its original 62 million hectare extent. Annual grass invasions are known to be increasing wild-
re occurrence and burned area, but the lasting effects (greater than ve years post-re) that the resulting
reburns have on these plant communities are unclear. We created a re history atlas from 31 yr (1984
2014) of Landsat-derived re data to sample along a re frequency gradient (zero to three res) in an area
of northern Nevada that has experienced frequent re in this time period. Thirty-two percent of our study
area (13,000 km
) burned in large res (over 404 ha) at least once, 7% burned twice, and 2% burned three
or more times. We collected plant abundance data at 28 plots (N =7 per re frequency), with an average
time since re of 17 yr. We examined res effect on plant diversity using species accumulation curves,
alpha diversity (Shannons dominance, Pielous evenness, and number of species), and beta diversity (Whit-
taker, Simpson, and Zindexes). For composition, we used non-metric multidimensional scaling. We then
used PERMANOVA models to examine how disturbance history, temperature, precipitation, and aridity
around the time of the re affected subsequent community composition and diversity. One re fundamen-
tally changed community composition and reduced species richness, and each subsequent re reduced
richness further. Alpha diversity decreased after one re. Beta diversity declined after the third re. Cover
of exotics was 10% higher in all burned plots, and native cover was 20% lower than in unburned plots,
regardless of frequency. PERMANOVA models showed re frequency and antecedent precipitation as the
strongest predictors of beta diversity, while time since re and vapor pressure decit for the year of the re
were the strongest predictors of community composition. Given that a single re has such a marked effect
on species composition, and repeated res reduce richness and beta diversity, we suggest that in lower ele-
vation big sagebrush systems re should be minimized as much as possible, perhaps even prescribed re.
Restoration efforts should be focused on timing with wet years on cooler, wetter sites.
Key words: Artemisia tridentata ssp. wyomingensis; biodiversity; Bromus tectorum; cheatgrass; community composition;
re; re frequency; repeated re; sagebrush.
Received 5 October 2018; revised 3 January 2019; accepted 11 January 2019. Corresponding Editor: Debra P. C. Peters.
Copyright: ©2019 The Authors. This is an open access article under the terms of the Creative Commons Attribution
License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Wildre activity has been increasing across the
western United States since the 1980s (Westerling
et al. 2006, Dennison et al. 2014, Westerling 2016,
Balch et al. 2017), and this is leading to concern
among land managers in the U.S. Great Basin
(Miller et al. 2013, Integrated Rangeland Fire
Management Strategy Actionable Science Plan
Team 2016, Chambers et al. 2017). This trend will
likely continue as rising temperatures and more
frequent drought events increase the probability
of re (Krawchuk et al. 2009, Moritz et al. 2012,
Liu et al. 2013), and as these climatic factors com-
bine with increased human ignition pressure
(Balch et al. 2017) and land use change (Bowman 1February 2019 Volume 10(2) Article e02591
et al. 2011) to increase the length of the re sea-
son (Wotton and Flannigan 1993, Jolly et al.
2015). This increased re activity is one con-
tributing factor to the loss of approximately half
of the area of sagebrush (Artemisia tridentata
Nutt.) shrubland communities, which once occu-
pied over 600,000 km
in the western United
States. Much of this land is now dominated by
cheatgrass (Bromus tectorum L.) (Bradley and
Mustard 2008), an introduced annual grass
(Davies 2011). This in turn is initiating a positive
feedback, wherein invading plants increase the
probability of re, and increased re activity
stimulates more annual grass invasion (DAnto-
nio and Vitousek 1992, Brooks et al. 2004, Balch
et al. 2013). The result is a re return interval that
has decreased from a historical range of 100
342 yr for intact sagebrush (Baker 2006,
Bukowski and Baker 2013) to 78 yr in invaded
areas (Balch et al. 2013), to as low as 35yrin
cheatgrass-dominated areas in the Snake River
Plain (Whisenant 1990). This increase in re activ-
ity results in more areas that are burned multiple
times, and the lasting effect this has on plant com-
munitiesbiodiversity and composition is rela-
tively unknown. There are relatively few studies
on the impacts of re after more than 5 yr (but
see Beck et al. 2009, Reed-Dustin et al. 2016), and
fewer still that analyze the impacts of repeated
res in the same location (Miller et al. 2013).
There are at least 40 vertebrate species of con-
servation concern associated with sagebrush
habitats (Rowland et al. 2006), including the
greater sage grouse (Centrocercus urophasianus).
Greater sage grouse depends on sagebrush for its
habitat and has been a management priority by
land managers (Chambers et al. 2017). Optimal
shrub cover for sage grouse is 1525% with over
15% bunchgrasses and forbs (Beck et al. 2009).
Fire is one of the top 2 threats to the greater sage
grouse in the western part of its range (Brooks
et al. 2015), and the loss of sagebrush due to
wildre has contributed strongly to its popula-
tion declines over the past 30 yr (Coates et al.
2016). Land management agencies have linked
re management with long-term conservation
goals focused on sagebrush ecosystems and the
greater sage grouse (Chambers et al. 2017).
There is emerging consensus among research-
ers and land managers that lower elevation
Wyoming big sagebrush (A. tridentata ssp.
wyomingensis Beetle & Young) ecosystems are not
resilient to re (Chambers et al. 2014) and should
be prevented from burning whenever possible,
while higher elevation Mountain big sagebrush
(A. tridentata ssp. vaseyana) ecosystems may still
recover naturally (Hanna and Fulgham 2015) or
with restoration by seeding (Knutson et al.
2014). Several authors have recommended
attempting to reduce the size and frequency of
wildre, and stopping the use of prescribed re
(Whisenant 1990, Baker 2006, Lesica et al. 2007,
Beck et al. 2009), while also reducing grazing
(Shinneman and Baker 2009, Ellsworth and
Kauffman 2013). Others have urged caution with
the use of prescribed re (Davies et al. 2009,
Reed-Dustin et al. 2016, Shinneman and McIlroy
2016). There has been disagreement in the past
about the historical re return interval for
Wyoming big sagebrush. It has been character-
ized as being every 35100 yr (Schmidt et al.
2002), every 100240 yr (Baker 2006), to every
171342 yr (Bukowski and Baker 2013). This dis-
crepancy has important management implica-
tions, leading to disagreement as to which
stressors or disturbances (e.g., grazing, re) need
to be increased or decreased in order to manage
for healthy sagebrush ecosystems. The lower
estimations imply the system is re-dependent
and requires frequent burning in order to persist,
while the upper estimates suggest re sensitivity.
Wyoming big sagebrush assemblages are gen-
erally agreed to be an endangered ecosystem and
re, and the invasive plants that generally colo-
nize afterward are thought to be two major dri-
vers of declining biodiversity in this system
(Davies et al. 2011). Cover of introduced annual
grass species has been mostly observed to be
negatively related to species richness and native
diversity (Davies 2011, Gasch et al. 2013, Bansal
and Sheley 2016), but over a 45-yr period, Ander-
son and Inouye (2001) found that while intro-
duced annual grass cover was negatively
correlated with cover of native species, species
richness was unrelated. While re is strongly cor-
related with annual grass cover in this system at
regional scales (Balch et al. 2013), it has also been
shown to be an unimportant predictor variable
for both exotic cover and species richness in east-
ern Washington (Mitchell et al. 2017).
Post-re communities of introduced annual
grasses are affected by both re frequency and 2February 2019 Volume 10(2) Article e02591
time since re. Cheatgrass cover can increase ini-
tially after re, then stabilize above its pre-re
cover after 25 yr (Reed-Dustin et al. 2016), but
positive linear relationships between time since
re and cheatgrass cover have also been
observed (Shinneman and Baker 2009), as well as
areas where cheatgrass declined and was
replaced by perennial grasses (West and Yorks
2002, Hanna and Fulgham 2015). Pre-re com-
munity composition might explain the inconsis-
tency in results. Cheatgrass can come to
dominate areas with re-intolerant natives post-
re, but in areas with pre-re populations of re-
tolerant species (e.g., Poa secunda J. Presl), these
species can regenerate following re (Davies
et al. 2012, but see Bagchi et al. 2013).
Precipitation, temperature, and aridity affect
both the re occurrence and the subsequent
recovery of plant communities. Unlike most
forested systems in the western United States,
burned area in Great Basin sagebrush systems is
best predicted by antecedent precipitation (Abat-
zoglou and Kolden 2013, Pilliod et al. 2017). Pre-
cipitation also drives the invasion of cheatgrass
into lower elevation sagebrush systems (Cham-
bers et al. 2007), which increases the probability
of re for several years due to the persistence of
the litter it leaves behind (Pilliod et al. 2017).
Cheatgrass invasion increases the continuity of
fuels (Davies and Nafus 2013) and burned area
(Balch et al. 2013), thereby reducing the number
of unburned patches that provide the native seed
sources critical for recolonizing burned areas.
Unburned patches are essential for sagebrush
regeneration as almost every species in this
genus is a seed obligate and the seeds generally
fall no more than 30 m from the mother plant
(Meyer 1994). Once established, a sagebrush
seedling needs to be able to withstand drought
conditions in the summer to survive and be
recruited into the population (Meyer 1994).
Here, we explored how sagebrush community
composition and diversity responded to increas-
ing re disturbance by constructing a re history
atlas and sampling plant communities that
burned zero to three times between 1984 and
2014 in the Central Basin and Range ecoregion.
We constrained soil, ecological site type, eleva-
tion, and climate, and sampled blocks of plots
stratied along a gradient of zero to three res.
Our rst hypothesis was that community
composition would change drastically between
unburned and burned plots, but remain similar
between burned plots of different re frequencies.
This was our expectation because in the Great
Basin there are vast areas of sagebrush which are
generally unburned in the last 30+yr, and burned
areas are almost always completely dominated by
cheatgrass, along with a handful of exotic forb
species and a native grass, P. secunda. These cheat-
grass-dominated areas all appear very similar,
regardless of re frequency (Fig. 1). But we sus-
pected that there would be a signal on plant diver-
sity after multiple res, due to selective pressure
against re-intolerant plants. Thus, our second
hypothesis was that alpha diversity (the Shannon-
Weaver index, Pielous evenness, and the number
of species in a sampling unit), beta diversity (con-
tinuity or turnover of species between plots), and
the extrapolated species richness (with plots
pooled by re frequency) would decrease with
increasing re frequency. Our third hypothesis
was that cheatgrass abundance would have a neg-
ative relationship with plant diversity. Our fourth
hypothesis was that temperature, vapor pressure
decit, and precipitation around the time of the
re would exert a lasting inuence over post-re
community composition and diversity. This is
based on evidence that the effects of introducing
species at the beginning of secondary succession
can be long-lasting (Veen et al. 2018), and in this
system, the assemblage of species that are able to
successfully colonize an area after a re depends
on their abilities to compete for moisture and tol-
erate drought (Meyer 1994).
Study area
We conducted the study in a 13,000 km
region in northern Nevada (Fig. 2). The region
has hot, dry summers and cold, wet winters.
Annual precipitation averages 293 mm, falling
mostly from November to May. Mean tempera-
tures range from 21.8°C in July to 1.4°Cin
December (PRISM Climate Group 2016). The
region consists of mountain ranges that run
northsouth, and the sagebrush ecosystems gen-
erally lie on the lower slopes of the mountains;
our sites ranged from 1272 to 1696 m in elevation
(median: 1458, SD: 99). From 1984 to 2014, 32%
(4096 km
) of the study area burned in large res 3February 2019 Volume 10(2) Article e02591
(over 500 acres) at least once, 7% burned twice,
and 2% burned three or more times (Appendix
S1: Table S1).
Site selection
We used a block sampling design, with each
block containing one site from each of four re
frequencies (zero to three), and all res occurring
at least ve years before the study. We used
geospatial data representing ecosystem state fac-
tors (sensu Amundson and Jenny 1997) to design
a sampling scheme that constrained all other fac-
tors. We used the LANDFIRE (Rollins 2009) bio-
physical setting layer to eliminate all vegetation
types except big sagebrush shrubland. The
LANDFIRE data have 6268% classication
accuracy for shrublands (Zhu et al. 2006). We
used soil data from the Natural Resource Conser-
vation Service to include only areas in the Loamy
810 precipitation zone (Soil Survey Staff, Natu-
ral Resources Conservation Service, United
States Department of Agriculture (USDA) 2016).
We chose this particular zone simply because it
was the most common type in our study area
within the big sagebrush shrubland biophysical
setting. We used the Land Treatment Digital
Library to exclude areas that had undergone
intensive restoration activities (Pilliod and Welty
2013). Excluding private and military land, and
areas more than 5 miles from a road eliminated
impractical plot locations and held human inu-
ence somewhat constant.
Fig. 1. Plot photographs taken from study block 4 in Kings River Valley, west of Orovada, Nevada. We show
these to illustrate the apparent similarity between plots with different re frequencies, and why we thought spe-
cies composition would not change dramatically between one and three res, while also hypothesizing that
diversity would decline. 4February 2019 Volume 10(2) Article e02591
We accounted for additional, unknown distur-
bances such as grazing by using a block sam-
pling design and stratifying our statistical
analyses by these blocks. Long-term grazing data
were not available. Therefore, we assumed that
plots within blocks were close enough together
that they had experienced similar grazing pres-
sure. Additionally, we visually assessed the
impact of grazing on-site, aggregated what
records we could for the allotments in our study
(billed animal unit months (AUM) provided by
the Bureau of Land Management), and normal-
ized AUM by unit area and included these data
in our statistical modeling.
Once we constrained the area to a consistent
sampling space, we used Landsat-derived re
data to stratify the space along a re frequency
gradient. To generate re history maps, we rst
extracted only the values two to four (low, med-
ium, and high severity) from each yearly burn
severity mosaic from the Monitoring Trends in
Burn Severity (MTBS; Eidenshink et al. 2007)
project, as these were the values where one can
be reasonably certain that they actually burned.
Unburned patches and post-re green-up, which
could be caused by a response to re or an
unburned patch, were excluded. To generate re
frequency maps, we reclassied each yearly layer
to a binary grid, and summed all 31 layers. To
avoid areas with less certain re frequencies, we
then converted the MTBS re perimeter polygons
to layers of re frequency to extract only the grid
cells where the frequency from the polygons
matched the frequency from the reclassied ras-
ter grid. To generate last-year-burned maps, we
reclassied each severity mosaic (values two to
four) to the re year, and calculated the maxi-
mum year for the entire time period for each
pixel. To eliminate areas that had burned more
recently than 2014, we masked pixels that
burned in 2015 according to the MODIS MCD64
burned area product (Giglio et al. 2009).
Kolden et al. (2015) have brought up several
shortcomings for the use of the MTBS burn
Inset (C)
Study Area (A)
Fig. 2. The extent of the study area is shown in (A). The striping from the scanner line correction failure from
Landsat 7 is clearly visible, and those areas were avoided in our sampling. Darker shading indicates higher re
frequency. The potential range is 05res, although areas with more than three res were extremely rare (0.2%
of total area). We sampled frequencies 03. The placement of the study area within the Central Basin and Range
ecoregion is shown in (B). A detail of one of the study blocks is represented in (C). 5February 2019 Volume 10(2) Article e02591
severity mosaics, in particular inconsistent
development of class thresholds and a lack of
empirical relationships between the classied
values and ecological metrics. Because we only
used these data to get a more precise estimate of
re occurrence (i.e., we used it to eliminate areas
of uncertainty) rather than using the severity
data as an independent variable for analysis, we
thought it sufcient to use these data in this
state. Another shortcoming that should be noted
is that there is no practical way for us to know
what these sites looked like before the earliest
res in the re record. The fact that our
unburned control plots were all mature sage-
brush is one piece of evidence suggesting these
sites were mature sagebrush pre-re, but we
cannot be 100% certain, and this is a shortcom-
ing of all chronosequence studies (Walker et al.
We selected seven blocks in our sampling
space in accessible areas where there was a range
of re frequencies and unburned areas for con-
trols within close proximity (0.510 km). Within
each block, we created spatially balanced ran-
dom points (Theobald et al. 2007) for each re
frequency, and sampled one plot for each re his-
tory class within the block. At each block, we
rst sampled the unburned control plot to con-
rm that the area was indeed the correct vegeta-
tion type, and then sampled burned plots. After
navigating to the predetermined coordinates for
each plot, we rst conrmed the physical charac-
teristics (soil type, lack of obvious restoration,
lack of obvious overgrazing) were within the
constraints of our sampling design. If a predeter-
mined point was not suitable (e.g., soil was too
rocky or sandy, an unburned control plot had
obviously burned, or it was the wrong ecological
site type), we referred to georeferenced PDFs of
our re history atlas that we accessed with a sim-
ple application (Avenza Maps https://www.ave on a mobile device and located
nearby areas within the site that were suitable.
When a suitable area was found, we used a ran-
dom number generator to pick a random bearing
and a random distance, and navigated to the
new plot location.
We sampled 28 plots that fell along a gradient
of re frequency (zero to three res; N =seven
plots per frequency) and a range of times since
re (431 yr; mean =17.6, SD =6.6; Fig. 2).
Because most of the re effects research in this
system has been done within ve years of a re,
we aimed to have the time since re of all of the
plots greater than or equal to 5 yr. We encoun-
tered 53 plant species12 were introduced and
41 were native (Appendix S1: Table S2).
Plot establishment
We used GPS to navigate to predetermined
plot locations. Upon arrival, we established a
permanent marker at the southwest corner of the
plot. We recorded the slope, aspect, distance to
the nearest A. tridentata individual or other shrub
species, the topographic curvature of the site
(convex, concave, at), evidence of ecological
restoration, grazing signs, and evidence of past
res. We then delineated a 50 950 meter plot,
and placed pin ags at nine randomly deter-
mined 1 m
subplots within the plot with a mini-
mum spacing of 3 m. Pilliod and Arkle (2013)
found this sampling density sufcient for this
ecosystem, if supplemental methods are used to
estimate disparate functional groups like trees
and shrubs. Hence, we used the point-quarter
method as a supplement to estimate shrub cover
(see Pilliod and Arkle (2013) for detailed
Vegetation sampling
To explore how re frequency inuences com-
munity composition and diversity, we measured
the occurrence and abundance of all species. We
identied and recorded occupancy data for every
species within each subplot, and took a pho-
tograph from nadir with an Olympus Stylus
TG-870 digital camera to be analyzed later for
percent cover.
We used Samplepointsoftware (Booth et al.
2006) to analyze the digital photographs for per-
cent cover. We prepared photographs for analy-
sis by cropping them to the 1 91 m area of the
subplot. Then, we used Samplepoint to overlay a
regular grid of 100 points on each picture, and at
each point identied whether it was litter, bare
ground, rock, dung, or a plant. If it was a plant,
we identied it to species with the aid of the
occupancy data recorded at the plot. These data
were then converted to percent cover. If we
recorded a species as present within the subplot,
but it was missed by the photographic analysis,
we recorded it as 0.5% cover. 6February 2019 Volume 10(2) Article e02591
Environmental data
Aspect was converted to folded aspect (folded
aspect =|180 |aspect 225||; McCune and
Keon 2002). This results in an approximation of
heat load ranging from zero (northeast) to 180
(southwest). Elevation was extracted from 10-m
resolution digital elevation models. The study
sites were situated among six grazing allotments.
To learn how climate before, during, and after
the re event affected the subsequent community
composition and diversity, we extracted monthly
maximum vapor pressure decit, maximum tem-
perature, and precipitation for the years before,
during, and after the most recent re at each plot.
Maximum temperature and maximum vapor
pressure decit were averaged for the entire year
before, during, and after, and precipitation was
averaged for the two winters (NovemberMay)
prior and one after. We used monthly data pro-
vided by the PRISM Climate Group (2016) for all
climate variables. Variables used in modeling are
provided in Table 1. We also sampled soil C and
N (see Mahood (2017) for detailed methods).
Statistical analysis
Community composition and environmental
variables.To analyze how re frequency affects
community composition, we used non-metric
multidimensional scaling (NMDS). We ran a
rank correlation test for re history gradients
against a matrix of relative cover of species per
plot to determine the best hierarchical clustering
method for creating a dissimilarity matrix. We
used this index for NMDS to examine how those
re history characteristics affected the oristic
composition. To assess which species and envi-
ronmental variables had the most inuence on
community composition, we added those vari-
ables to the ordinations using the envtfunc-
tion from Vegan, with 9999 permutations and
stratied by the study block. Then, we grouped
species by their biogeographical origin (i.e.,
native or exotic), and used Tukeys test to assess
how re frequency inuenced native cover, exo-
tic plant cover, and cheatgrass abundance.
Species richness, alpha diversity, and beta
diversity.We created species accumulation
curves grouped by re frequency to assess how
re frequency affected species richness. This is dif-
ferent from alpha diversity in that the species
accumulation curve is estimating number of spe-
cies across all of the sites within each group with
each added plot, as opposed to simply calculating
a diversity index for each plot. We used the sam-
ple-based rarefaction method (Chiarucci et al.
2008, Oksanen et al. 2018, R Core Team 2016). We
used Tukeys honestly signicant difference test
(hereafter, Tukeys test) to see whether different
re frequencies inuenced alpha diversity (the
Shannon-Weaver index, Pielous evenness, and
number of species per plot). There are several
ways to quantify beta diversity, most of which are
grouped into measures of continuityand mea-
sures of gain and loss(Koleff et al. 2003). We
used the Zindex and Whittakersoriginalbeta
diversity index for continuity measures, and
Simpsons index (based on G. Gaylord Simpsons
asymmetric index (Simpson 1943) and modied
by Lennon et al. (2001), not to be confused with
Edward H. Simpsons index (1949)) for a measure
of gain and loss. To see how beta diversity dif-
fered between re frequencies, we modeled the
homogeneity of dispersion of those matrices
(Anderson et al. 2006) and ran pairwise permuta-
tion tests (Legendre et al. 2011) on these models
Table 1. Variables used in PERMANOVA models.
Variable Abbreviation Source
Time since re TSF MTBS
Fire frequency FF MTBS
Maximum vapor
pressure decit
Year of re vpdmax_during PRISM
Year before re vpdmax_before PRISM
Year after re vpdmax_after PRISM
Maximum temperature
Year of re tmax_during PRISM
Year before re tmax_pre PRISM
Year after re tmax_after PRISM
2 yr before re
ppt_2pre PRISM
1 yr before re
ppt_1pre PRISM
After re
ppt_post PRISM
Folded aspect Field
Slope Field
Elevation USGS
Animal unit months
per hectare
AUM_ha BLM 7February 2019 Volume 10(2) Article e02591
with 9999 permutations, stratied by the study
blocks. To assess the inuence of cheatgrass
abundance on alpha and beta diversity, we used
linear mixed models (Pinheiro et al. 2018) with
the study block as a random effect. We included
elevation as a xed effect in addition to cheat-
grass due to its strong correlation with tempera-
ture and moisture availability, and ecosystem
resistance and resilience (Chambers et al. 2014).
We ensured that predictors had no multi-
collinearity using a variable ination factor test
(Fox and Weisberg 2011), and used the partial
coefcient of determination (Jaeger et al. 2016) to
determine the cheatgrass component of the
model. To aid visualization, we removed the par-
tial effects of elevation from the dependent vari-
ables (Hohenstein and Kliegl 2018).
Modeling which re and climate variables drive
post-re composition and diversity.To assess how
pre- and post-re climate, along with soil and
other environmental variables (Table 1) affected
post-re community composition and diversity,
we used permutational multivariate analysis of
variance (PERMANOVA). PERMANOVA uses a
dissimilarity matrix as the response variable and
columns from a separate data frame as the pre-
dictors. It makes the assumption that groups
being modeled have homogeneous dispersions.
If the test is run on groups with heterogeneous
dispersions, it is vulnerable to type 1 error
(Anderson and Walsh 2013). To account for this,
we built multivariate homogeneity of groups dis-
persions (MHGD) models on our community
clustering and beta diversity matrices grouped
by block, re frequency, and burned vs unburned.
We then ran ANOVAs and Tukeystestoneach
model, with Pvalues below 0.05 considered to be
an indication of heterogeneous dispersions. After
removing variables with multicollinearity, we
built PERMANOVA models with both commu-
nity clustering and beta diversity matrices using
an additive model-building process, with 9999
permutations, and stratifying the permutations by
the study blocks, with the aim of producing parsi-
monious models.
Code availability
Data and code to reproduce the analysis are
available at
ff_study and is on the dryad data repository
Community composition fundamentally changed
after one fire
The rank index test showed the Kulczynski
index to have the most consistent high scores
across gradients of re history characteristics, so
we used this index for our hierarchical clustering
and NMDS analyses. Non-metric multidimen-
sional scaling (non-metric t, R
=0.992, linear
t, R
=0.972) showed seven unburned plots
clustered around high abundances of A. triden-
tata, and 18 burned plots clustered around B. tec-
torum (Fig. 3). Two thrice-burned plots were
dominated by exotic annual forbs (Sisymbrium
altissimum L. and Erodium cicutarium (L.) LHer.
ex Aiton), and one was dominated by the native
perennial grass P. secunda (these are the three
thrice-burned plots outside of the burned
ellipse). The ordination showed a clear separa-
tion between burned and unburned plots, but
re frequency was not signicantly correlated
with the ordination, nor were any environmental
For the Tukeys tests of exotic versus native
cover, there were differences between unburned
and burned plots (P<0.05) for both exotic
(increased by 10%) and native cover (decreased
by 20%), and no differences among the burned
plots (Fig. 4A, B). After dividing the mean cover
estimates into native and exotic life form groups
(annual and perennial graminoids and forbs, and
shrubs), we saw lower native shrub cover for
burned plots re (243%), coupled with higher
annual grass cover (414%; Fig. 5).
Plant biodiversity decreased with each successive
We found a decline in plant diversity at sites
that had burned more frequently. Species rich-
ness estimates declined as re frequency
increased (Fig. 6; Appendix S1: Table S3). The
number of species and the Shannon-Weaver
index were higher in unburned plots, but the dif-
ferences were not signicant, and Pielous even-
ness was not different between frequencies
(Fig. 4CE). All three indexes of beta diversity
followed very similar patterns, so we only report
on Whittakers index here. It was not different
between zero and two res, and lower for thrice-
burned plots (Fig. 4F), meaning that there is less 8February 2019 Volume 10(2) Article e02591
dissimilarity within the group of thrice-burned
plots and more dissimilarity within the other
Alpha diversity and evenness decreased with
cheatgrass abundance
Cheatgrass abundance had a negative relation-
ship with the Shannon-Weaver diversity (P0.05,
partial R
=0.65) and Pielous evenness (P0.05,
partial R
=0.51), a weak negative relationship
with the number of species (P<0.05, partial
=0.24), and no relationship to beta diversity
(P>0.5, partial R
=0.08; Fig. 7, Table 2). Eleva-
tion was important in all models except Pielous
evenness (Table 2).
Different climate and fire variables predict post-
fire composition and diversity
PERMANOVA models showed that re history
and environmental factors inuenced commu-
nity composition and beta diversity differently.
ANOVAs and TukeystestsonMHGDmodels
showed no heterogeneity in groups dispersions
for both beta diversity and hierarchical clustering
(P>0.05 for all models). Community composition
after re was most affected by re frequency, time
since re, maximum vapor pressure decit of the
year of the re, and the interaction between re
frequency and time since re (Table 3, R
The relatively low amounts of variation accounted
for by the individual variables indicate these are
subtle effects. Beta diversity on the other hand was
inuenced most by winter precipitation one and
two years prior to the re, re frequency, and
the interaction between winter precipitation one
year prior and max temperature for the year
after the re (Table 4, R
=0.62). Here, the effect
was more pronounced, as more variation
accounted for by the three most statistically signif-
icant variables (re frequency and precipitation
one and two winters prior to the re).
The purpose of this study is to assess how
Wyoming big sagebrush plant communities
−2 −1 0 1
Fig. 3. Ordination plot of non-metric multidimensional scaling conducted on plant community data using Kul-
czynski hierarchical clustering. Ellipses represent the 95% condence interval around plots grouped by whether
or not they had burned. Species signicantly (P<0.05) correlated with the ordination are shown, with arrows
scaled by the strength of the correlation. Species are listed by their USDA plant codes. ARTRW8 is Artemisia tri-
dentata ssp. wyomingensis; POSE is Poa secunda; ELEL5 is Elymus elymoides; SIAL2 is Sisymbrium altissimum; BRTE
is Bromus tectorum; CETE5 is Ceratocephalum testiculatum; ERCI6 is Erodium cicutarium. 9February 2019 Volume 10(2) Article e02591
respond to being burned repeatedly before
returning to their prior condition. The combina-
tion of a 32-yr re history atlas and the use of the
RRQRR (Theobald et al. 2007) to randomly strat-
ify the sampling blocks over a large area pro-
vides broad-scale statistical inference for the
lower elevation (<1500 m) portion of the Wyom-
ing big sagebrush ecosystem. These lower
elevation sites generally experience higher tem-
peratures and lower soil moisture, and it is well
documented that they have lower resilience after
wildres (Chambers et al. 2014). We did not
detect recovery of Wyoming big sagebrush at our
sites, and also found that while the cover of Bro-
mus tectorum does not change with successive
res, the number of species in the species pool
does decrease and that biodiversity decreases
with cover of B. tectorum. The results of this
study may seem to conict with other recent
studies documenting Wyoming big sagebrush
sagebrush recovery in the Great Basin (Ellsworth
et al. 2016, e.g., Shinneman and McIlroy 2016).
But all of the studies we are aware of showing
sagebrush recovery were conducted at cooler,
wetter sites, where Wyoming big sagebrush is
more resilient after re (Chambers et al. 2014).
Coupling the 30+yr re history atlas created
here with intensive eld sampling offers a
unique opportunity to explore plant diversity
and composition changes in areas that have rela-
tively high re frequencies, such as grass-domi-
nated or grass-invaded areas (Balch et al. 2013).
As annual grass invasions and their alterations to
re regimes are a global phenomenon (DAnto-
nio and Vitousek 1992, Brooks et al. 2004), this
type of study design will be useful for
D. Pielou’s evenness E. Number of species F. Beta diversity (Whittaker)
A. Native cover B. Exotic cover C. Shannon−weaver dominance
0123 0123 0123
0123 0123 0123
Fire frequency
Fig. 4. Alpha diversity (Shannons index of proportional abundance, Pielous index of evenness, and the num-
ber of species per plot), beta diversity (Whittakers indexthe values are a unitless index of dissimilarity), and
native and exotic plant cover, all grouped by re frequency. Shading indicates signicantly different groups as
determined by Tukeys test. 10 February 2019 Volume 10(2) Article e02591
understanding the consequences of changing re
regimes in other regions. Additionally, new algo-
rithms are being developed that will lead to more
accurate and precise re data products (Haw-
baker et al. 2015), leading to more nuanced re
history atlases and thus more precise sampling
straticationsespecially now that burn severity
information can be easily incorporated (Eiden-
shink et al. 2007).
Community composition fundamentally changes
after one fire
In lower elevation A. tridentata ssp. wyomin-
gensis systems, our results show that one re can
convert this shrub-dominated system to one
composed mainly of introduced annual grasses
and forbs, and we demonstrate that this new
state can persist for decades with little sign of
recovery to its prior condition. While almost all
of our burned plots were dominated by cheat-
grass, several thrice-burned plots were domi-
nated by P. secunda or exotic annual forbs (see
Fig. 3, where there are three plots that are out-
side the condence envelope containing all other
burned plots). This corroborates previous work
showing that re can push cheatgrass-invaded
grassland and shrubland communities into those
dominated by cheatgrass, P. secunda, and exotic
forbs, while uninvaded sites, or sites that are
invaded but still have signicant bunchgrass
communities, can persist in a state of native
bunchgrasses and forbs (Davies et al. 2012, Reis-
ner et al. 2013, Condon and Pyke 2018). Other
studies have found that topography can be a
mediating factor, with native bunchgrasses more
likely to persist on steeper, more north-facing
slopes in the face of invasion and disturbance
(Rodhouse et al. 2014, Reed-Dustin et al. 2016).
One hypothesis that we were not able to test in
this study is that increasing re frequency may
select for more re-resilient plant functional
traits. More research is needed to investigate the
relationship between re frequency and func-
tional traits. While it has been demonstrated that
B. tectorum establishes immediately post-re and
can persist in the shorter term (Davies et al. 2012,
Hanna and Fulgham 2015), we show that this
Fire frequency
Percent cover
Origin and life form
Exotic annual forb
Exotic annual grass
Exotic perennial forb
Exotic perennial grass
Native annual forb
Native perennial forb
Native perennial grass
Native shrub
Fig. 5. Percent cover of life form groups, grouped
by re frequency. Of the two most dominant life form
groups, exotic annual grass is >99% cheatgrass, and
native shrub is >99% Wyoming big sagebrush.
Fig. 6. Species accumulation curves for re fre-
quency. Vertical lines represent the conditioned stan-
dard deviation around species richness and are jittered
for visibility. 11 February 2019 Volume 10(2) Article e02591
novel grass state can persist for long periods (i.e.,
>17 yr), corroborating recent work (Reed-Dustin
et al. 2016). If there was recovery, our study
design would have enabled us to detect it, as
Wyoming big sagebrush has been found to
recover from disturbance in as little as nine
(Wambolt et al. 2001) to 20 yr (Shinneman and
McIlroy 2016) following re, and our re history
atlas goes back 32 yr.
Biodiversity decreases with each subsequent fire
Here, we show that over a three decade period
repeated res had long-lasting effects on commu-
nity composition and biodiversity in Wyoming
big sagebrush ecosystems. Species richness
declined with increasing re frequency, but
measures of alpha and beta diversity decreased
after one and three res, respectively (Fig. 4A, B).
Species accumulation curves demonstrated that
repeated res are decreasing the overall pool of
species from which an individual patch might
draw from. So while there may not have been sig-
nicant differences in alpha diversity as re fre-
quency increased, as the number of species each
plot can draw from decreased, this signal mani-
fested itself when beta diversity declined after
three res.
We found negative relationships between
cheatgrass abundance and alpha and beta diver-
sity, as we hypothesized, but no relationship
between cheatgrass abundance and the number
of res. Establishment and dominance of
C. Number of species D. Beta diversity (Whittaker)
A. Shannon−weaver dominance B. Pielou’s evenness
0 20 40 60 0 20 40 60
0 20 40 60 0 20 40 60
Cheatgrass cover
Elevation−adjusted values
Fig. 7. Scatter plots for a) Shannon-Weaver, b) Pielous evenness, c) number of species, and d) Whittakers beta
diversity as predicted by Bromus tectorum cover and elevation. Lines are predictions from linear mixed effects
models with study block as a random effect. The x-axis is cheatgrass cover, and the y-axis is the value of the index
with the effect of elevation removed (Hohenstein and Kliegl 2018). 12 February 2019 Volume 10(2) Article e02591
cheatgrass after re are well documented (Whi-
senant 1990, Balch et al. 2013), and the relation-
ship between re and species richness is clear
from this work. This implies that once an area is
invaded by cheatgrass, the competitive effects
from its increased abundance combine with its
effect on re frequency to exclude species that
either cannot compete for moisture or cannot
survive re. It should be noted that because we
selected sites that had burned at least three times
since 1984, we may have biased our results to be
applicable to only those areas that are susceptible
to initiating a grassre cycle.
Time since fire and vapor pressure deficit drive
community composition
PERMANOVA models showed that re his-
tory and climate variables affect diversity and
Table 2. Results of linear mixed models testing the relationship between diversity indexes and cheatgrass abun-
dance, while accounting for elevation. Study block was the random effect. Partial coefcient of determination
was calculated from Jaeger et al. (2016).
Independent variable
Dependent variable
B. tectorum cover 0.017*** (0.003) 0.008*** (0.002) 0.071*** (0.027) 0.002 (0.001)
Elevation 0.189*** (0.062) 0.046 (0.029) 1.700*** (0.642) 0.065*** (0.021)
Intercept 1.214*** (0.103) 0.652*** (0.052) 8.538*** (1.121) 0.288*** (0.037)
Partial R
,B. tectorum cover 0.65 0.51 0.24 0.08
Notes: BD, Beta Diversity (Whitaker); NS, Number of Species; PE, Pielous evenness; SW, Shannon-Weaver.
Table 3. PERMANOVA results for re history and environmental factors inuencing post-re community
Variable df SumsOfSqs MeanSqs F.Model R
TSF 1 0.1457 0.1457 2.0189 0.0713 0.0082
FF 1 0.2342 0.2342 3.2448 0.1146 0.0485
vpdmax_during 1 0.2243 0.2243 3.1081 0.1098 0.0016
tmax_during 1 0.0952 0.0952 1.3186 0.0466 0.1358
tmax_pre 1 0.0776 0.0776 1.0757 0.0380 0.1790
AUM_ha 1 0.2038 0.2038 2.8232 0.0997 0.2111
TSF:FF 1 0.1246 0.1246 1.7262 0.0610 0.0389
Residuals 13 0.9383 0.0722 0.4591
Total 20 2.0436 1.0000
Table 4. PERMANOVA results for re history and environmental factors inuencing post-re beta diversity
(Whittakers index).
Variable df SumsOfSqs MeanSqs F.Model R
FF 1 0.2110 0.2110 2.4408 0.0783 0.0070
ppt_1pre 1 0.5236 0.5236 6.0581 0.1943 0.0014
tmax_after 1 0.1075 0.1075 1.2438 0.0399 0.0646
ppt_2pre 1 0.3993 0.3993 4.6199 0.1482 0.0134
TSF 1 0.1450 0.1450 1.6779 0.0538 0.2493
Folded aspect 1 0.1105 0.1105 1.2780 0.0410 0.4735
Elevation 1 0.0442 0.0442 0.5108 0.0164 0.9526
ppt_1pre:tmax_after 1 0.1162 0.1162 1.3447 0.0431 0.0255
Residuals 12 1.0371 0.0864 0.3849
Total 20 2.6943 1.0000 13 February 2019 Volume 10(2) Article e02591
community composition differently. Composi-
tion was found to be inuenced by both re fre-
quency and time since re, and high vapor
pressure decit the year of the re. This suggests
that drought stress exerts a signicant inuence
on the particular plant species that will survive
and persist after a re, and this effect can still be
detected decades after the re burned. Shinne-
man and McIlroy (2016) also found that climatic
variables around the time of the re inuence the
eventual composition; namely, winter precipita-
tion the year after the re was benecial for sage-
brush recovery, but winter precipitation 2 yr
later had a negative effect. Elevation and recov-
ery have been shown to be positively related in
this system (Knutson et al. 2014), and most of the
studies showing fast recovery times were done at
higher elevations and latitudes (Wambolt et al.
2001, Hanna and Fulgham 2015, Ellsworth et al.
2016), in areas with long-term grazing exclusion
(Ellsworth et al. 2016), or on sites that were
specically selected because their topographic
position was such that there was potential for
sagebrush recovery (Shinneman and McIlroy
2016). Here, we found that on low-elevation sites,
even after an average of 17 yr, post-re sage-
brush cover was very low (<6%; also see Reed-
Dustin et al. 2016). These differences in recovery
rates (i.e., 920 yr at cooler sites vs no detectable
recovery at hotter sites) could be due to a slow-
ing down of recovery rates as the system loses
resilience with increasing drought stress at hotter
sites, while cooler sites have not yet experienced
sufcient drought stress to hamper recovery
(sensu van de Leemput et al. 2018).
Fire frequency and antecedent precipitation drive
beta diversity
Beta diversity was most heavily inuenced by
re frequency, precipitation for the two wet sea-
sons prior to the re, and an interaction between
antecedent precipitation and maximum tempera-
ture for the year after the re. Antecedent precip-
itation has been shown in other studies to be an
important predictor of re occurrence and
burned area in this system (Abatzoglou and Kol-
den 2013, Balch et al. 2013). Since this is a fuel-
limited system, high precipitation increases ne
fuel loads and continuity (Davies and Nafus
2013), leading to higher re probability, more
homogeneously burning res, and larger extents.
Increased ne fuel loads could also be the driv-
ing factor behind decreasing diversity. Following
highly contiguous and extensive res, there
would be fewer unburned patches as seed
sources, which are essential for the seed-obligate
sagebrush to reestablish quickly (Shinneman and
McIlroy 2016). In addition, Wyoming big sage-
brush is an opportunist in reproduction, setting
most of its seed in wet years (Meyer 1994) during
the short window in early spring when enough
water is available in the soil for plants to uptake
nutrients (Ryel et al. 2010, Schlaepfer et al. 2014).
So, in the years that Wyoming big sagebrush is
maximizing its expenditure on reproductive
resources, increased horizontal fuel continuity of
invasive annual grasses (Davies and Nafus 2013)
(1) increases the probability of burning and (2)
increases interspecic competition for resources
post-re. This may result in a more homoge-
neous post-re landscape populated mostly by
re-tolerant plants.
Management implications
This work adds to the existing body of litera-
ture that suggests that in low-elevation
(<1700 m) Wyoming big sagebrush systems
wildre should be minimized due to the negative
effects of single and repeated res on community
composition and biodiversity. The reality is that
wildre cannot be prevented, but re suppres-
sion policies and practices could be crafted to
maximize the number and size of unburned
patches within burns to increase the probability
that Wyoming big sagebrush and other native
seed obligates recover post-re. These results
also imply that prescribed burning is a risky
proposition with potentially disastrous conse-
quences for biodiversity and ecosystem structure
and function. However, we did not directly
assess the inuence of prescribed res in this
study. Prescribed res typically are conducted at
a cooler time of year outside of or at the shoulder
of the re season, and may have different ecolog-
ical effects due to the phenological stage plants
would be in at this different time of year, as well
as the lower burn severity that would be
expected due to cooler ambient air temperatures
and higher soil moisture. At a cooler, wetter site
where grazing has been excluded since 1994,
Ellsworth et al. (2016) detected the recovery of
sagebrush 17 yr after prescribed res were 14 February 2019 Volume 10(2) Article e02591
conducted in late September 1997, which is the
natural end of the re season at that location.
Two other studies at higher latitudes concluded
that prescribed burning to be an unwise action
even at those wetter sites. Beck et al. (2009) stud-
ied an area in southeast Idaho that was burned
in late August 1989 by prescribed re 14 yr post-
re for its utility in improving sage grouse habi-
tat. They recommended against prescribed res
due to the lack of recovery of sagebrush. Wam-
bolt et al. (2001) found minimal benet to the
herbaceous plant community at 13 sites that had
burned in prescribed res in western Montana,
with little shrub recovery 615 yr after re. Thus,
there is conicting evidence on the use of pre-
scribed res for management objectives even at
cooler wetter sites, providing less optimism for
the use of prescribed res in the lower elevation
portion of the Wyoming big sagebrush ecosys-
tems studied here. Future research could focus
on comparing low-elevation Wyoming big sage-
brush sites that have been burned in prescribed
res in the past paired with nearby areas that
burned in wildres, with particular emphasis on
teasing out the effects of seasonality and burn
Our results from PERMANOVA modeling sug-
gest that the success of post-re restoration efforts
will depend not only on elevation and topo-
graphic conditions (Arkle et al. 2014), but also the
climatic conditions that occur around the time of
the re. This could mean that in a very dry year,
less money is spent on restoration efforts on low-
elevation sites, focusing instead on higher eleva-
tion sites and cooler aspects, and in wet years,
more funding is directed toward those more vul-
nerable low-elevation, southwest-facing sites.
Disagreement on the actual historical re rota-
tion limits our ability to determine whether
Wyoming big sagebrush is re-sensitive or re-
resistant. However, this question may be irrele-
vant given the disruption and interaction
between invasive annual grasses and res. We
demonstrate that when both re and invasive
annual grasses operate in conjunction, sagebrush
is re-sensitive. Moreover, we show that an alter-
nate exotic grass state can persist for 17 yr post-
re even with only a single burn. This makes the
use of prescribed burning problematic, as the risk
of a re-prone grassland establishing after a re
likely outweighs the potential benets of a
prescribed re. Our results are specic to lower
elevation (<1700 m), dryer, hotter Wyoming big
sagebrush sites, and it remains to be explored
how sagebrush at higher elevations and latitudes
responds to increasing re frequency, and how it
will respond under future climate change scenar-
ios. However, if temperatures continue to rise as
projected in this region (Garn et al. 2014), those
areas may also become susceptible to a strong
grassre cycle. Overall, this effort demonstrates
that sagebrush communities are vulnerable to
repeated res (Seipel et al. 2018), which should
be taken into account in land management deci-
sions (Chambers et al. 2017) that attempt to con-
serve or restore these valuable ecosystems, and
the threatened species that they harbor.
We are grateful for the assistance of Nick Whittemore
and Kathleen Weimer for their assistance in the eld
and in the laboratory. We thank Tom Veblen and Car-
son Farmer for comments on previous versions of the
from three anonymous reviewers, which greatly
improved the paper. Thanks to Max Joseph for help
with the data analysis. We are also very grateful for the
support of the Nevada Bureau of Land Management
and the Central Nevada Interagency Dispatch Center.
This work was funded by the National Aeronautics and
Space Administration Terrestrial Ecology Program
under Award NNX14AJ14G and start-up funding from
the Department of Geography at CU Boulder. Publica-
tion of this chapter was funded by the University of
Colorado Boulder Libraries Open Access Fund.
Abatzoglou, J. T., and C. A. Kolden. 2013. Relation-
ships between climate and macroscale area burned
in the western United States. International Journal
of Wildland Fire 22:10031020.
Amundson, R., and H. Jenny. 1997. On a state factor
model of ecosystems. BioScience 47:536543.
Anderson, M. J., K. E. Ellingsen, and B. H. McArdle.
2006. Multivariate dispersion as a measure of beta
diversity. Ecology Letters 9:683693.
Anderson, J. E., and R. S. Inouye. 2001. Landscape-
scale changes in plant species abundance and
biodiversity of a sagebrush steppe over 45 years.
Ecological Monographs 71:531556.
Anderson, M. J., and D. C. I. Walsh. 2013. PERMA-
NOVA, ANOSIM, and the Mantel test in the face of 15 February 2019 Volume 10(2) Article e02591
heterogeneous dispersions: What null hypothesis
are you testing? Ecological Monographs 83:557
Arkle, R., D. Pilliod, S. Hanser, M. L. Brooks, J. C.
Chambers, J. B. Grace, K. C. Knutson, D. A. Pyke, J.
L. Welty, and T. A. Wirth. 2014. Quantifying
restoration effectiveness using multi-scale habitat
models: implications for sage-grouse in the Great
Basin. Ecosphere 5:132.
2013. Assessing resilience and state-transition mod-
els with historical records of cheatgrass Bromus
tectorum invasion in North American sagebrush-
steppe. Journal of Applied Ecology 50:11311141.
Baker, W. L. 2006. Fire and restoration of sagebrush
ecosystems. Wildlife Society Bulletin 34:177185.
Balch, J. K., B. A. Bradley, J. T. Abatzoglou, R. C. Nagy,
E. J. Fusco, and A. L. Mahood. 2017. Human-
started wildres expand the re niche across the
United States. Proceedings of the National Acad-
emy of Sciences USA 114:29462951.
Balch, J. K., B. A. Bradley, C. M. DAntonio, and J.
omez-Dans. 2013. Introduced annual grass
increases regional re activity across the arid west-
ern USA (19802009). Global Change Biology
Bansal, S., and R. L. Sheley. 2016. Annual grass inva-
sion in sagebrush steppe: the relative importance
of climate, soil properties and biotic interactions.
Oecologia 181:543557.
Beck, J. L., J. W. Connelly, and K. P. Reese. 2009. Recov-
ery of greater sage-grouse habitat features in
Wyoming big sagebrush following prescribed re.
Restoration Ecology 17:393403.
Booth, D. T., S. E. Cox, and R. D. Berryman. 2006. Point
sampling digital imagery with Samplepoint. Envi-
ronmental Monitoring and Assessment 123:97108.
Bowman, D. M. J. S., et al. 2011. The human dimension
of re regimes on Earth. Journal of Biogeography
Bradley, B. A., and J. F. Mustard. 2008. Comparison of
phenology trends by land cover class: a case study
in the Great Basin, USA. Global Change Biology
Brooks, M. L., C. M. D. Antonio, D. M. Richardson, J.
B. Grace, J. E. Keeley, J. M. DiTomaso, R. J. Hobbs,
M. Pellant, and D. Pyke. 2004. Effects of invasive
alien plants on re regimes. BioScience 54:677688.
Brooks, M. L., J. R. Matchett, D. J. Shinneman and P. S.
Coates. 2015. Fire Patterns in the Range of the
Greater Sage-Grouse, 19842013Implications for
Conservation and Management: U.S. Geological
Survey Open-File Report 2015-1167. Page 66.
Bukowski, B., and W. L. Baker. 2013. Historical re
regimes, reconstructed from land-survey data, led
to complexity and uctuation in sagebrush land-
scapes. Ecological Applications 23:546564.
Chambers, J. C., B. A. Bradley, C. S. Brown, C. DAnto-
nio, M. J. Germino, J. B. Grace, S. P. Hardegree, R.
F. Miller, and D. A. Pyke. 2014. Resilience to stress
and disturbance, and resistance to Bromus tectorum
L. invasion in cold desert shrublands of western
North America. Ecosystems 17:360375.
Chambers, J. C., B. A. Roundy, R. R. Blank, S. E. Meyer,
and A. Whittaker. 2007. What makes Great Basin
sagebrush ecosystems invasible by Bromus tecto-
rum? Ecological Monographs 77:117145.
Chambers, J. C., et al. 2017. Science Framework for
Conservation and Restoration of the Sagebrush
Biome: linking the Department of the Interiors
Integrated Rangeland Fire Management Strategy
to Long-Term Strategic Conservation Actions. Part
1. Science basis and applications. Gen. Te:213.
Chiarucci, A., G. Bacaro, D. Rocchini, and L. Fattorini.
2008. Discovering and rediscovering the sample-
based rarefaction formula in the ecological litera-
ture. Community Ecology 9:121123.
Coates, P. S., M. A. Ricca, B. G. Prochazka, M. L.
Brooks, K. E. Doherty, T. Kroger, E. J. Blomberg, C.
A. Hagen, and M. L. Casazza. 2016. Wildre, cli-
mate, and invasive grass interactions negatively
impact an indicator species by reshaping sage-
brush ecosystems. Proceedings of the National
Academy of Sciences USA 113:1274512750.
Condon, L. A., and D. A. Pyke. 2018. Fire and grazing
inuence site resistance to Bromus tectorum through
their effects on shrub, bunchgrass and biocrust
communities in the Great Basin (USA). Ecosystems
DAntonio, C. M., and P. M. Vitousek. 1992. Biological
invasions by exotic grasses, the grass/re cycle,
and global change. Annual Review of Ecological
Systems 23:6387.
Davies, K. W. 2011. Plant community diversity and
native plant abundance decline with increasing
abundance of an exotic annual grass. Oecologia
Davies, G. M., J. D. Bakker, E. Dettweiler-Robinson,
P. W. Dunwiddie, S. A. Hall, J. Downs, and J.
Evans. 2012. Trajectories of change in sagebrush
steppe vegetation communities in relation to multi-
ple wildres. Ecological Applications 22:1562
Davies, K. W., C. S. Boyd, J. L. Beck, J. D. Bates, T. J.
Svejcar, and M. A. Gregg. 2011. Saving the sage-
brush sea: an ecosystem conservation plan for big
sagebrush plant communities. Biological Conserva-
tion 144:25732584.
Davies, K. W., and A. M. Nafus. 2013. Exotic annual
grass invasion alters fuel amounts, continuity and 16 February 2019 Volume 10(2) Article e02591
moisture content. International Journal of Wildland
Fire 22:353358.
Davies, K. W., T. J. Svejcar, and J. D. Bates. 2009. Inter-
action of historical and nonhistorical disturbances
maintains native plant communities. Ecological
Applications 19:15361545.
Dennison, P. E., S. C. Brewer, J. D. Arnold, and M. A.
Moritz. 2014. Large wildre trends in the western
United States, 19842011. Geophysical Research
Letters 41:29282933.
Eidenshink, J., B. Schwind, K. Brewer, Z.-L. Zhu, B.
Quayle, and S. Howard. 2007. A project for Moni-
toring Trends in Burn Severity. Fire Ecology 3:321.
Ellsworth, L. M., and J. B. Kauffman. 2013. Seedbank
responses to spring and fall prescribed re in
mountain big sagebrush ecosystems of differing
ecological condition at Lava Beds National Monu-
ment, California. Journal of Arid Environments
Ellsworth, L. M., D. W. Wrobleski, J. B. Kauffman, and
S. A. Reis. 2016. Ecosystem resilience is evident
17 years after re in Wyoming big sagebrush
ecosystems. Ecosphere 7:112.
Fox, J., and S. Weisberg. 2011. An R companion to
applied regression. Second edition. Sage, Thou-
sand Oaks, California, USA.
Garn, G., G. Franco, H. Blanco, A. Comrie, P. Gonza-
lez, T. Piechota, R. Smyth and R. Waskom. 2014.
Southwest: The Third National Climate Assess-
ment. Pages 462486 in J. M. Melillo, T. C. Rich-
mond, and G. W. Yohe, editors. Climate change
impacts in the United States: The Third National
Climate Assessment. U.S. Global Change Research
Program, Washington, D.C., USA.
Gasch, C. K., S. F. Enloe, P. D. Stahl, and S. E. Williams.
2013. An aboveground belowground assessment
of ecosystem properties associated with exotic
annual brome invasion. Biology and Fertility of
Soils 49:919928.
Giglio, L., T. Loboda, D. P. Roy, B. Quayle, and C. O.
Justice. 2009. An active-re based burned area
mapping algorithm for the MODIS sensor. Remote
Sensing of Environment 113:408420.
Hanna, S. K., and K. O. Fulgham. 2015. Post-re vege-
tation dynamics of a sagebrush steppe community
change signicantly over time. California Agricul-
ture 69:3642.
Hawbaker, T. J., S. Stitt, Y.-J. Beal, G. Schmidt, J. Falgout,
B. Williams and J. Takacs. 2015. Provisional burned
area essential climate variable (BAECV) algorithm
description. United States Geological Survey.
Hohenstein, S. and R. Kliegl. 2018. Remef: remove par-
tial effects. R package version https://
Integrated Rangeland Fire Management Strategy
Actionable Science Plan Team. 2016. The integrated
rangeland re management strategy actionable
science plan. Page 128. U.S. Department of the Inte-
rior, Washington, D.C., USA.
Jaeger, B. C., L. J. Edwards, K. Das, and P. K. Sen. 2016.
An R
statistic for xed effects in the generalized
linear mixed model. Journal of Applied Statistics
Jolly, W. M., M. A. Cochrane, P. H. Freeborn, Z. A. Hol-
den, T. J. Brown, G. J. Williamson, and D. M. J. S.
Bowman. 2015. Climate-induced variations in glo-
bal wildre danger from 1979 to 2013. Nature
Communications 6:7537.
Knutson, K. C., D. A. Pyke, T. A. Wirth, R. S. Arkle, D.
S. Pilliod, M. L. Brooks, J. C. Chambers, and J. B.
Grace. 2014. Long-term effects of seeding after
wildre on vegetation in Great Basin shrubland
ecosystems. Journal of Applied Ecology 51:1414
Kolden, C. A., A. M. S. Smith, and J. T. Abatzoglou.
2015. Limitations and utilisation of Monitoring
Trends in Burn Severity products for assessing
wildre severity in the USA. International Journal
of Wildland Fire 24:10231028.
Koleff, P., K. J. Gaston, and J. J. Lennon. 2003. Measur-
ing beta diversity for presence-absence data. Jour-
nal of Animal Ecology 72:367382.
Krawchuk, M. A., M. A. Moritz, M. A. Parisien, J. Van
Dorn, and K. Hayhoe. 2009. Global pyrogeogra-
phy: the current and future distribution of wildre.
PLoS ONE 4:e5102.
Legendre, P., J. Oksanen, and C. J. F. ter Braak. 2011.
Testing the signicance of canonical axes in redun-
dancy analysis. Methods in Ecology and Evolution
Lennon, J. J., P. Koleff, J. Greenwood, and K. J. Gaston.
2001. The geographical structure of british bird dis-
tributions: diversity, spatial turnover and scale.
Journal of Animal Ecology 70:966979.
Lesica, P., S. V. Cooper, and G. Kudray. 2007. Recovery
of big sagebrush following re in southwest Mon-
tana. Rangeland Ecology & Management 60:261
Liu, Y., S. L. Goodrick, and J. A. Stanturf. 2013. Future
U.S. wildre potential trends projected using a
dynamically downscaled climate change scenario.
Forest Ecology and Management 294:120135.
Mahood, A. L. 2017. Long-term effects of repeated res
on the diversity and composition of Great Basin
sagebrush plant communities. Dissertation,
University of Colorado Boulder, Boulder, Color-
ado, USA.
tds/111 17 February 2019 Volume 10(2) Article e02591
McCune, B., and D. Keon. 2002. Equations for poten-
tial annual direct incident radiation and heat load.
Journal of Vegetation Science 13:603606.
Meyer, S. E. 1994. Germination and establishment ecol-
ogy of big sagebrush: implications for community
restoration. Pages 244251 in Symposium on
management, ecology, and restoration of
lntermountain annual rangelands, boise, id, May
1821, 1992.
Miller, R. F., J. C. Chambers, D. A. Pyke, F. B. Pierson,
C. J. Williams. 2013. A review of re effects on veg-
etation and soils in the Great Basin Region:
response and ecological site characteristics. General
Technical Report RMRS-GTR-308. U.S. Department
of Agriculture, Forest Service, Rocky Mountain
Research Station, Fort Collins, Colorado, USA.
Mitchell, R. M., J. D. Bakker, J. B. Vincent, and G. M.
Davies. 2017. Relative importance of abiotic, biotic,
and disturbance drivers of plant community struc-
ture in the sagebrush steppe. Ecological Applica-
tions 27:756768.
Moritz, M. A., M.-A. Parisien, E. Batllori, M. A. Kraw-
chuk, J. Van Dorn, D. J. Ganz, and K. Hayhoe.
2012. Climate change and disruptions to global re
activity. Ecosphere 3:49.
Oksanen, J., et al. 2018. vegan: community ecology
package. R package version 2.5-3. https://CRAN.
Pilliod, D. S., and R. S. Arkle. 2013. Performance of
quantitative vegetation sampling methods across
gradients of cover in Great Basin plant communi-
ties. Rangeland Ecology & Management 66:634
Pilliod, D. S. and J. L. Welty. 2013. Land Treatment
Digital Library, Reston, Virginia, USA. https://ltdl.
Pilliod, D. S., J. L. Welty, and R. S. Arkle. 2017. Rening
the cheatgrass-re cycle in the Great Basin: precipi-
tation timing and ne fuel composition predict
wildre trends. Ecology and Evolution 7:8126
Pinheiro, J., D. Bates, S. DebRoy, D. Sarkar, and R Core
Team. 2018. nlme: linear and nonlinear mixed
effects models. R package version 3.1-137. https://
PRISM Climate Group. 2016. PRISM gridded climate
data. Oregon State University, Corvallis, Oregon,
R Core Team. 2016. R: a language and environment for
statistical computing. R Foundation for Statistical
Computing, Vienna, Austria.
alez, and T. J. Rodhouse.
2016. Long-term re effects on native and invasive
grasses in protected area sagebrush steppe. Range-
land Ecology & Management 69:257264.
Reisner, M. D., J. B. Grace, D. A. Pyke, and P. S.
Doescher. 2013. Conditions favouring Bromus tecto-
rum dominance of endangered sagebrush steppe
ecosystems. Journal of Applied Ecology 50:1039
Rodhouse, T. J., K. M. Irvine, R. L. Sheley, B. S. Smith, S.
Hoh, D. M. Esposito, and R. Mata-Gonzalez. 2014.
Predicting foundation bunchgrass species abun-
dances: sagebrush steppe. Ecosphere 108:116.
Rollins, M. G. 2009. LANDFIRE: a nationally consis-
tent vegetation, wildland re, and fuel assessment.
International Journal of Wildland Fire 18:235249.
Rowland, M. M., M. J. Wisdom, L. H. Suring, and C.
W. Meinke. 2006. Greater sage-grouse as an
umbrella species for sagebrush-associated verte-
brates. Biological Conservation 129:323335.
Ryel, R. J., A. J. Lefer, C. Ivans, M. S. Peek, and M. M.
Caldwell. 2010. Functional differences in water-use
patterns of contrasting life forms in Great Basin
steppelands. Vadose Zone Journal 9:548.
Schlaepfer, D. R., W. K. Lauenroth, and J. B. Bradford.
2014. Natural regeneration processes in big sage-
brush (Artemisia tridentata). Rangeland Ecology &
Management 67:344357.
Schmidt, K. M., J. P. Menakis, C. C. Hardy, W. J. Hann
and D. L. Bunnell. 2002. Development of course-
scale spatial data for wildland re and fuel man-
agement. General Technical Report RMRS-GTR-87.
U.S. Department of Agriculture, Forest Service,
Rocky Mountain Research Station, Fort Collins,
Colorado, USA.
Seipel, T., L. J. Rew, K. T. Taylor, B. D. Maxwell, and E.
A. Lehnhoff. 2018. Disturbance type inuences
plant community resilience and resistance to Bro-
mus tectorum invasion in the sagebrush steppe.
Applied Vegetation Science 21:385394.
Shinneman, D. J., and W. L. Baker. 2009. Environmen-
tal and climatic variables as potential drivers of
post-re cover of cheatgrass (Bromus tectorum)in
seeded and unseeded semiarid ecosystems. Inter-
national Journal of Wildland Fire 18:191202.
Shinneman, D. J., and S. K. McIlroy. 2016. Identifying
key climate and environmental factors affecting
rates of post-re big sagebrush (Artemisia triden-
tata) recovery in the northern Columbia Basin,
USA. International Journal of Wildland Fire
Simpson, G. G. 1943. Mammals and the nature of con-
tinents. American Journal of Science 241:131.
Simpson, E. H. 1949. Measurement of diversity. Nature
Soil Survey Staff, Natural Resources Conservation
Service, United States Department of Agriculture
(USDA). 2016. Web Soil Survey. https://websoil 18 February 2019 Volume 10(2) Article e02591
Theobald, D. M., D. L. Stevens, D. White, N. S. Urqu-
hart, A. R. Olsen, and J. B. Norman. 2007. Using
GIS to generate spatially balanced random survey
designs for natural resource applications. Environ-
mental Management 40:134146.
van de Leemput, I. A., V. Dakos, M. Scheffer, and E. H.
van Nes. 2018. Slow recovery from local distur-
bances as an indicator for loss of ecosystem resili-
ence. Ecosystems 21:141152.
Veen, G. F., W. H. van der Putten, and T. M. Bezemer. 2018.
Biodiversity-ecosystem functioning relationships
in a long-term non-weeded eld experiment. Ecol-
ogy 99:18361846.
Walker, L. R., D. A. Wardle, R. D. Bardgett, and B. D.
Clarkson. 2010. The use of chronosequences in
studies of ecological succession and soil develop-
ment. Journal of Ecology 98:725736.
Wambolt, C. L., K. S. Walhof, and M. R. Frisina. 2001.
Recovery of big sagebrush communities after burn-
ing in south-western Montana. Journal of environ-
mental management 61:243252.
West, N. E., and T. P. Yorks. 2002. Vegetation responses
following wildre on grazed and ungrazed sage-
brush semi-desert. Journal of Range Management
Westerling, A. L. 2016. Increasing western US forest
wildre activity: sensitivity to changes in the
timing of spring. Philosophical Transactions of the
Royal Society B: Biological Sciences. https://doi.
Westerling, A. L., H. G. Hidalgo, D. R. Cayan, and T.
W. Swetnam. 2006. Warming and earlier spring
increase western U.S. forest wildre activity.
Science 313:940943.
Whisenant, S. G. 1990. Changing re frequencies on
Idahos Snake River plains: ecological and
management implications. Pages 410 in E. D.
McArthur, E. M. Romney, S. Smith, and P. T. Tuel-
ler, editors. Proceedings of the Symposium on
Cheatgrass Invasion, Shrub Die-Off, and Other
Aspects of Shrub Biology and Management. Forest
Service General Technical Report INT-276. Inter-
mountain Research Station, Las Vegas, Nevada,
Wotton, B. M., and M. D. Flannigan. 1993. Length of
the re season in a changing climate. The Forestry
Chronicle 69:187192.
Zhu, Z., J. Vogelmann, D. Ohlen, J. Kost, X. Chen and
B. Tolk. 2006. Mapping existing vegetation compo-
sition and structure for the LANDFIRE prototype
project. Pages 197215 in Gen. tech. rep. rmrs-gtr-
175. U.S. department of agriculture, forest service,
rocky mountain research station, Fort Collins, Col-
orado, USA.
Additional Supporting Information may be found online at:
2591/full 19 February 2019 Volume 10(2) Article e02591
... While wildfires are becoming more frequent in many environments globally (Cattau et al., 2020;Collins et al., 2021;Jones et al., 2022;Levine et al., 2022;Mahood & Balch, 2019;Stewart et al., 2021;Westerling et al., 2006), in others their frequency has remained unchanged (McWethy et al., 2018) or even reduced, for example, as a result of rapid fire suppression (Palmquist et al., 2014;Zackrisson, 1977), or because of agricultural expansion and intensification (Andela et al., 2017). Such differences suggest that changes in fire frequency may not be spatially uniform across ecosystems (Baker & Williams, 2018). ...
... (1) fire-sensitive animal populations Ward et al., 2020;Whelan, 1995); (2) key parts of plant life cycles (Mahood & Balch, 2019) such as the ability to naturally regenerate (Day et al., 2020;Enright et al., 2015;Keeley & Pausas, 2019;Le Breton et al., 2022), and the ability to continue to successfully resprout following burning (Fairman et al., 2019); and (3) the potential to trigger regime shifts (Flores & Holmgren, 2021) such as through increasing flammability (Zylstra, 2018;Zylstra et al., 2022), impairing forest resilience (Hart et al., 2018), and in some cases resulting in ecosystem collapse (Bergstrom et al., 2021;Lindenmayer et al., 2011). Understanding the effects of fire frequency as part of fire regimes is also critical to determine the impacts of repeated fires on resource availability like supplies of timber Cyr et al., 2009;Levine et al., 2022). ...
Full-text available
Fire is a key ecosystem process with more than half the world's land surface potentially subject to fire. A key aspect of fire ecology is the fire regime, with fire frequency an important component. Fire frequency appears to be increasing in some ecosystems, but decreasing in others. Such temporal and spatial variability in fire frequency highlights the importance of more effectively quantifying spatiotemporal changes in fire frequency for particular environments. We modeled changes in fire frequency over the past 40 years (1981–2020) in a 4.64 million ha area in Victoria, Australia. We quantified regional variation in the number of fires (hereafter termed fire frequency) during two 20‐year time periods (1981–2000 vs. 2001–2020), employing the Interim Biogeographic Regionalisation for Australia (IBRA), a standardized regionalization of Australia's terrestrial landscapes. We also quantified the climate and environmental factors influencing fire frequency in each IBRA subregion. Our empirical analyses revealed that fire frequency in Victoria was heterogeneous in both time and space. Wildfire frequency changed between 1981 and 2020, with the past 20 years (2001–2020) experiencing a substantially greater number of fires relative to the 20 years prior (1981–2000). Changes in fire frequency were not spatially uniform, with increases more pronounced in some IBRA subregions than others. Climate and topographic factors influenced the frequency of wildfires, but their effects manifested differently in different IBRA subregions. For example, fire frequency was associated with increasing rainfall deficit deviation in four IBRA subregions, but an opposite trend characterized two others. Associations between fire frequency and increasing temperature deviation also varied from negative to positive across subregions. We also found evidence of elevation, slope, and aspect effects, but these too varied between IBRA subregions. The complex spatiotemporal changes in fire frequency quantified in this study, and the complex between‐region differences in the factors associated with the number of fires, have major implications for biodiversity conservation, resource availability (e.g., timber yields), and ecosystem integrity. In ecosystems subjected to repeated fires at short intervals, new rapid detection and swift suppression technologies may be required to reduce the risks of ecosystem collapse as high‐severity wildfires increase in frequency.
... Our data show that a second fire occurrence did not significantly alter plant biomass, cover, or density, when compared to the effects of the initial fire ( Fig. 1). Initial desert fires remove significant shrub cover, leaving space and soil resources that promote invasion success that leads to loss of plant community diversity (Brooks 2000, DeFalco et al. 2003Mahood and Balch 2019). The secondary fires occurred 5 years after the initial burns, which is within the potential fire frequency window being observed in these deserts (Balch et al. 2013). ...
Full-text available
Background Wildfire regimes are changing dramatically across North American deserts with the spread of invasive grasses. Invasive grass fire cycles in historically fire-resistant deserts are resulting in larger and more frequent wildfire. This study experimentally compared how single and repeat fires influence invasive grass-dominated plant fuels in the Great Basin, a semi-arid, cold desert, and the Mojave, a hyper-arid desert. Both study sites had identical study designs. In the summer of 2011, we experimentally burned half of each experimental block, the other half remaining as an unburned control. Half of the burned plots were reburned 5 years later to simulate increasing burn frequency. We estimated non-woody plant biomass, cover, and density in plots from 2017 to 2020. Results Biomass did not vary between sites, but there was higher plant cover and lower plant density at the Mojave site than at the Great Basin site. Plant biomass, density, and cover varied significantly across the years, with stronger annual fluctuations in the Great Basin. At both desert sites, fire increased plant density and biomass but had no effect on the cover. The effect of fire on plant cover varied significantly between years for both deserts but was greater in the Great Basin than in the Mojave site. Repeat fires did not amplify initial fire effects. Conclusions The results suggest that in general annual fluctuations in fine fuel production and fluctuations in response to fire were more apparent at the Great Basin site than at the Mojave site, with no immediate compounding effect of repeat fires at either site.
... This implies a dynamic whereby communities enter the grass-fire cycle abruptly following fire and dominance of annual grasses is maintained through repeated burning. This paradigm is pervasive, with many emphasizing the importance of fire as a driver of change (Barker et al., 2019;Mahood and Balch, 2019). The spread of annual grass dominance has been attributed to fires igniting in heavily infested communities and spreading into adjacent, intact communities (Balch et al., 2013). ...
Full-text available
Sagebrush ecosystems of western North America are experiencing widespread loss and degradation by invasive annual grasses. Positive feedbacks between fire and annual grasses are often invoked to explain the rapid pace of these changes, yet annual grasses also appear capable of achieving dominance among vegetation communities that have not burned for many decades. Using a dynamic, remotely-sensed vegetation dataset in tandem with remotely-sensed fire perimeter and burn severity datasets, we examine the role of fire in transitions to and persistence of annual grass dominance in the U.S. Great Basin over the past 3 decades. Although annual grasses and wildfire are so tightly associated that one is rarely mentioned without the other, our findings reveal surprisingly widespread transformation of sagebrush ecosystems by invasive annual grasses in the absence of fire. These findings are discussed in the context of strategic management; we argue a pivot from predominantly reactive management (e.g., aggressive fire suppression and post-fire restoration in heavily-infested areas) to more proactive management (e.g., enhancing resistance and managing propagule pressure in minimally-invaded areas) is urgently needed to halt the loss of Great Basin sagebrush ecosystems.
... Once covering more than 600,000 km 2 , 60-90% of the sagebrush steppe has been lost, fragmented, or degraded (Noss et al. 1995;Knick et al. 2003). Due to these disturbances, the sagebrush steppe ecosystem is facing a loss in native plant species and genetic diversity (McArthur & Fairbanks 2001;Mahood & Balch 2019), resulting in reduced ecosystem services such as water and soil retention and plant productivity (Kachergis et al. 2011;Nichols et al. 2021). These changes also threaten animal populations, including species of conservation concern, such as the greater sage-grouse (Centrocercus urophasianus) (Knick et al. 2003). ...
Full-text available
Restoration planning requires a reliable seed supply, yet many projects occur in response to unplanned events. Identifying regions of greater disturbance risk could efficiently guide seed procurement. Using fire in U.S. Cold Deserts as an example, we demonstrate how historic disturbance can inform seed production choices. We compared differences in fire frequency, area burned, and percent of area burned among different management areas, identifying regions of particular need. We also present a case study focused on fire occurrence within important wildlife habitat, specifically looking at the greater sage‐grouse priority areas for conservation (PACs) within the Northern Basin and Range ecoregion. We used geospatial seed transfer zones as our focal management areas. We broadly considered generalized provisional seed transfer zones, created using climate and stratified by ecoregion, but also present results for empirical seed transfer zones, based on species‐specific research, as part of our case study. Historic fire occurrence was effective for prioritizing seed transfer zones: 23 of 132 provisional seed transfer zones burned every year, and, within each ecoregion, two provisional seed transfer zones comprised ≧50% of the total area burned across all years. Fire occurrence within PACs largely reflected the seed transfer zone priorities found for the ecoregion as a whole. Our results demonstrate that historic disturbance can be used to identify regions that encounter regular or large disturbance. This information can then be used to guide seed production, purchase, and storage, create more certainty for growers and managers, and ultimately increase restoration success.
... However, wildfire suppression, overgrazing, and the expansion of juniper (Juniperus spp.) and pinyon (Pinus spp.) into shrublands, coupled with progressive increases in stand density, have lengthened fire return intervals (Chambers et al. 2014a;Miller et al. 2019). This departure from historical conditions is a concern in sagebrush ecosystems due to the loss of critical wildlife habitat, reductions in biodiversity (Davies et al. 2011;Mahood and Balch 2019), loss of perennial native shrub and herbaceous cover (Ellsworth et al. 2020, and increased runoff and soil erosion ) associated with increased wildfire intensity and frequency. ...
Full-text available
Background Native pinyon ( Pinus spp.) and juniper ( Juniperus spp.) trees are expanding into shrubland communities across the Western United States. These trees often outcompete with native sagebrush ( Artemisia spp.) associated species, resulting in increased canopy fuels and reduced surface fuels. Woodland expansion often results in longer fire return intervals with potential for high severity crown fire. Fuel treatments are commonly used to prevent continued tree infilling and growth and reduce fire risk, increase ecological resilience, improve forage quality and quantity, and/or improve wildlife habitat. Treatments may present a trade-off; they restore shrub and herbaceous cover and decrease risk of canopy fire but may increase surface fuel load and surface fire potential. We measured the accumulation of surface and canopy fuels over 10 years from ten sites across the Intermountain West in the Sagebrush Steppe Treatment Evaluation Project woodland network ( ), which received prescribed fire or mechanical (cut and drop) tree reduction treatments. We used the field data and the Fuel Characteristic Classification System (FCCS) in the Fuel and Fire Tools (FFT) application to estimate surface and canopy fire behavior in treated and control plots in tree expansion phases I, II, and III. Results Increased herbaceous surface fuel following prescribed fire treatments increased the modeled rate of surface fire spread (ROS) 21-fold and nearly tripled flame length (FL) by year ten post-treatment across all expansion phases. In mechanical treatments, modeled ROS increased 15-fold, FL increased 3.8-fold, and reaction intensity roughly doubled in year ten post-treatment compared to pretreatment and untreated controls. Treatment effects were most pronounced at 97th percentile windspeeds, with modeled ROS up to 82 m min ⁻¹ in mechanical and 106 m min ⁻¹ in prescribed fire treatments by 10 years post-treatment compared to 5 m min ⁻¹ in untreated controls. Crown fire transmissivity risk was eliminated by both fuel treatments. Conclusions While prescribed fire and mechanical treatments in shrublands experiencing tree expansion restored understory vegetation and prevented continued juniper and pinyon infilling and growth, these fuel treatments also increased modeled surface fire behavior. Thus, management tradeoffs occur between desired future vegetation and wildfire risk after fuel treatments.
... The summer burn plots obtained the lowest α-diversity values, which can be attributed to species migration to unoccupied areas facilitated by fire or the limited impact on the seed bank due to low-intensity PBs. Similar shifts in species composition shortly after disturbances, particularly fires, have been documented in previous studies [71][72][73]. This dominance can be influenced by factors such as fire seasonality, intensity, and post-fire recovery dynamics of the vegetation community [74,75]. ...
Full-text available
Worsening climate change and increasing temperatures generate more sever and extended wildfires, raising concerns about ecosystem services. Prescribed burns (PB) are used to reduce forest fuel loads. Improving knowledge regarding the vegetation response after PB is essential for generating common points for monitoring ecological burning effects and generating a protocol or practice guide. We compared the PB seasonality of low-intensity (spring, summer, and autumn) and unburned areas in a total of 12 plots in Pinus nigra Arnold ssp. salzmannii Mediterranean forest. Our vegetation analysis was short term (one year after each PB). We analyzed vegetation coverage, α-diversity (Pielou, Simpson, and Shannon’s index), life forms, and fire-adapted traits using the Canfield transect method, followed by statistical analyses such as non-metric multidimensional scaling (NMDS) and two-way ANOVA. α-diversity was significantly decreased (>55% of dissimilarity) in the burned plots during each season, with the lowest values after summer PB (69% of dissimilarity) when comparing the burned and unburned plots. There was a significant increase in hemicryptophytes (15−20%) and geophyte coverage (from 6% to 14%, or from 4% to 8% in certain cases) in the burned plots after PB seasonality; however, the phanerophytes were reduced (from 13% to 5%). Resprouters were more dominant after PB (an increase of 15–20%), which indicates that resprouters have a faster recovery and generate a fuel load quickly for highly flammable species such as Bromus after low-intensity burning. This suggests that low-intensity prescribed burning may not be the best methodology for these resprouting species. This study helps to understand how burning in the early season can affect inflammable vegetation and the change in fuel that is available in semi-arid landscapes. This is key to achieving the basis for the development of a standardized system that allows for the efficient management of forest services in order to reduce wildfire risks. One objective of this line of research is to observe the effects of recurrent burning in different seasons on vegetation, as well as plant−soil interaction using the microbial and enzyme soil activity. Keywords: prescribed burning; vegetation parameters; biodiversity; preventive tools; ecological effects; Mediterranean forest; forest management; vegetation response
... However, declining, degraded, and increasingly fragmented sagebrush landscapes present challenges for rangeland management that targets both recovery of sagebrush (Artemisia spp.) and restoration of species-specific habitat. Fire is a disturbance factor with the potential to influence habitat suitability via direct loss of vegetation Remington et al., 2021) as well as through effects to plant community composition and structure (Nelson et al., 2014;Mahood and Balch 2019), soil properties (Sankey et al., 2012;Nichols et al., 2021), and long-term vegetation recovery trajectories (Bates et al., 2020;Schlaepfer et al., 2021). Wildfire is a natural part of sagebrush ecosystems. ...
Wildfires are increasingly modifying wildlife habitat in the western United States and managers need ways to scope the pace and degree to which post-fire restoration actions can re-create habitat in dynamic landscapes. We developed a spatially explicit state-transition simulation model (STSM) to project post-fire revegetation and the potential for sage-grouse habitat restoration in sagebrush ecosystems. The model included annual fires, annual grass invasion, conifer encroachment, and projected annual vegetation growth caused by natural regeneration as well as sagebrush seeding and planting. We cross-referenced resulting vegetation maps with greater sage-grouse (Centrocercus urophasianus) habitat needs and evaluated trajectories of potential habitat at three Priority Areas for Conservation in the Great Basin. We compared outcomes among different types of revegetation actions (natural regeneration, seeding, planting), treatment durations, and treatment area sizes. In all scenarios, sagebrush cover was generally insufficient to meet sage-grouse needs for at least a decade post-fire, and the best habitat classes declined or remained at low proportions of landscapes for >50 years post-fire. Under current fire patterns, the pace of habitat restoration is likely to lag behind losses from wildfires. Our results indicate additional efforts beyond sagebrush revegetation actions (e.g., fire suppression, invasive grass treatment) will likely be necessary to maintain and restore areas to meet sage-grouse habitat needs in burned landscapes. Our results also underscore the need for broad-scale habitat restoration strategies that expand the ability to reestablish sagebrush in large, burned areas, as well as strategies for defining which areas should be prioritized for revegetation within the biome. Our landscape models and resulting vegetation maps can be integrated with other restoration prioritization or wildlife monitoring tools that support land manager decision-making. By gauging potential benefits of restoration decisions, our approach can provide information to aid choices on where to invest time, money, and effort and how best to mitigate losses and plan long-term restoration and recovery for landscapes across the sagebrush biome.
... These changes likely affect the ability of seedlings to establish and grow (Rhoades et al., 2021), with subsequent impacts on hydrology and the trajectory of ecosystem recovery. Widespread regeneration failures and state transitions in the forests and shrublands of the western US (Mahood and Balch, 2019;Shriver et al., 2019) are likely to lead to alterations in the pools and fluxes of soil nutrients (Mahood et al., 2022), for which the role of EMF reductions are largely unexplored. Moreover, the full extent of post-fire changes to microbial communities and their impacts on soil health, vegetation recovery, water quality, and source-water yields remain to be identified. ...
Full-text available
Agricultural production in the western United States relies on water supplies from mountain source-water systems that are sensitive to impacts from wildfire and a changing climate. The resultant challenges to water supply forecasting directly impact agricultural producers and irrigation managers who rely on snowmelt and streamflow forecasts for crop selection and irrigation scheduling. To date, much research has focused on source-water system processes and agricultural production separately, but in this short communication we highlight a substantial need for new research connecting these disparate systems to improve forecasting accuracy. We identify key knowledge and data gaps regarding the functioning of source watersheds and their contributions to agricultural water resources with associated uncertainties in the context of wildfire and changing climate. In doing so, we encourage researchers, resource managers, and agricultural producers to consider the interdependency of water supply source and sink relationships through improved observations, monitoring, and modeling to ensure sustainable food production in the western US.
... Given the variability in fire effects, it follows that postfire grazing along these same climate and disturbance gradients produces a range of outcomes for plant communities and herbivores (Waterman and Vermeire 2011;Scasta et al. 2016;Fuhlendorf and Engle 2004;Vermeire et al. 2018). Areas with more frequent historical fire return intervals, higher levels of evolutionary grazing pressure, and more productive areas may require less recovery time or even benefit from fire-grazing interactions compared to warmer, drier sites where fire and grazing pressures were historically less frequent or intense (Seefeldt et al. 2007;Clark et al. 2016a;Bates et al. 2019;Mahood and Balch 2019;Innes and Zouhar 2018). In the Pacific Northwest bunchgrass prairie of Oregon, USA, fire and grazing promote fire-tolerant forb species, altering plant cover and composition but not diversity or richness after ten years (Watson et al. 2021), while the shrub-steppes of the Great Basin, USA, post-fire grazing was associated with increased invasive annual grass cover (Condon and Pyke 2018). ...
Full-text available
Fire and grazing play an important role in managed rangeland ecosystems. These disturbances interact to shape plant communities and outcomes for rangeland biodiversity and livestock production. However, managers have a limited toolbox to reach vegetation management goals in shrub-steppe ecosystems. It is commonly assumed that deferring grazing for up to two growing seasons after a fire is necessary to ensure plant community recovery. We report on a 4-year long replicated experiment comparing the effects of season of fire (spring or fall) and sheep grazing deferment on sagebrush-steppe rangelands in east Idaho, USA. Deferment treatments included either no grazing for one or two growing seasons after fire, or no deferment, in which domestic sheep returned in the season after fire. We found no evidence that grazing deferment affected plant community composition in any plots compared to no grazing deferment, but deferred areas did have more litter relative to grazed areas 4 years after the fire. Burn treatment had a consistent effect on plant communities. One year after fire, spring and fall burned plots had 24 and 30% reductions in native shrub cover, respectively. After 4 years, fall and spring burned areas had moderate, statistically significant (14 and 9%, respectively) reductions in litter. Burned areas had less than 6% increases in perennial native grass cover, but no significant increases in invasive plants. The effects of fire and grazing management tools is context-dependent across climatic gradients in the sagebrush biome. At our study site, prescribed fire seasonality and grazing deferment have important economic but relatively moderate ecological implications.