Study area
The Greater Yellowstone Ecosystem (GYE) comprises 89,000 km2 (YNP 2017) of mostly
federally managed land centered on Yellowstone and Grand Teton National Parks (Figure
1). Greater Yellowstone has cold, snowy winters and mild summers, with most annual
precipitation falling as snow. Average summer temperature (1981-2010) is 12.3 °C, and
annual precipitation averages 644 mm at centrally located Old Faithful in Yellowstone
National Park (WRCC 2021). The region is expected to get warmer and drier over the
21st century, with lengthening fire seasons and harsher conditions for germination and
establishment of young tree seedlings (Westerling et al. 2011; Romme and Turner 2015).
Since 1950, Greater Yellowstone has warmed +1.3 °C, and annual snowfall has decreased
by 25% (Hostetler et al. 2021). Soils are primarily derived from highly infertile,
volcanic rhyolite; slightly less infertile andesite; or sedimentary parent materials
(Despain 1990).
Subalpine forests cover much of the GYE between ~1900-3000 m elevation and
historically recovered rapidly after infrequent severe fire due to prevalent
serotinous lodgepole pine with its fire-stimulated canopy seed bank (Turner et al.
1999). Stand-level percent serotiny of lodgepole pine is highest at lower elevations
(up to ~2300-2400 m; Tinker et al. 1994, Schoennagel et al. 2003). Approximately
one-third of 1984-2010 area burned in US Northern Rocky Mountains subalpine forests
was stand-replacing (Harvey et al. 2016a), and 19-25% of 1984-2020 short-interval area
burned in Northwest US forests was stand-replacing in both fires (Harvey et al. 2023).
Mean aboveground biomass in lodgepole pine-dominated forests averages 139 Mg ha-1
(live tree) and 98 Mg ha-1 (dead woody) across a 300-year chronosequence, and stand
density stabilizes to approximately 1,200 stems ha-1 after 200 years of stand
development (Kashian et al. 2005; 2013).
Other tree species in the subalpine zone include Douglas-fir (Pseudotsuga
menziesii var. glauca) and quaking aspen (Populus tremuloides) at lower elevations,
Engelmann spruce (Picea engelmannii) and subalpine fir (Abies lasiocarpa) at higher
elevations, and whitebark pine (Pinus albicaulis) near upper treeline (Baker 2009).
Douglas-fir, Engelmann spruce, subalpine fir, and non-serotinous lodgepole pine rely
on wind dispersal from nearby live seed sources, and most seeds fall within 50 m of a
live tree (McCaughey and Schmidt 1987; Gill et al. 2021). Whitebark pine and quaking
aspen can disperse over longer distances (Turner et al. 2003; Lorenz et al. 2011), and
aspen can also resprout after fire (Baker 2009).
Reburn and plot selection
We identified recent, large fires (1994-2018; > 404 ha) that severely burned
subalpine forests at both short (< 30-year; n = 16 reburns) and long (>
125-year) intervals (Figure 1a; Appendix S1; Eidenshink et al. 2007). In 2021, we
sampled 22 plot pairs (1-2 pairs per reburn) that each consisted of a 0.25-ha
short-interval plot burned twice as stand-replacing fire and a topographically
similar, nearby 0.25-ha long-interval plot burned as stand-replacing in the same
recent fire (Figure 1c). These data were augmented with paired short- and
long-interval post-fire plot data collected in 2000, 12 years after the 1988 fires
(Schoennagel et al. 2003; Braziunas et al. 2023). Together, these datasets included 33
plot pairs (n = 66 plots) in 27 reburns widely distributed throughout the GYE and
representing 7-28-year short fire return intervals, 3-27 years since most recent fire,
1798-2769 m in elevation, 0-356 ° aspect, and 0-25 ° slope (Figure 1, Appendix
S2:Table S1 and Figure S1).
Field data collection
Forest recovery and fuels were sampled in 0.25-ha plots following standard methods
(Turner et al. 2019; Nelson et al. 2016). Sapling/seedling (< 1.4 m height), tree
(> 1.4 m height), and standing dead (> 1.4 m height) stem density were tallied
in three parallel 2-m x 50-m belt transects. At 5-m intervals, we measured height,
crown base height, and diameter at breast height (DBH) of the closest live tree by
species; height and DBH of the closest standing dead snag; height of the closest
sapling/seedling by species; and cover and average height of shrubs by species in
0.25-m2 quadrats (n = 25 quadrats for 6.25-m2 per plot). Downed woody and forest floor
fuels were quantified with five 20-m Brown’s planar intersect transects (Brown 1974)
oriented randomly from plot center (total length = 100 m per plot). We recorded 1-h
(< 0.64 cm diameter) and 10-h (0.64-2.54 cm) fuels along the first 3 m, 100-h
(2.54-7.60 cm) fuels along the first 10 m, and sound and rotten coarse woody debris
(> 7.6 cm diameter, 1000-h fuel) along the full 20 m. Litter and duff depth were
recorded at 2-m intervals at three locations per transect (n = 15 measurements per
plot). At plot center we measured aspect, slope, and distance to unburned live forest
edge. If live edge was not visible or too far to measure in the field, this distance
was estimated in ArcGIS Desktop 10.6 from aerial imagery and burn severity perimeters.
Field data from 2000 included live stem densities by species counted in four parallel
2-m x 50-m belt transects spaced 25 m apart (Schoennagel et al. 2003).
Biomass and fuels calculations
We derived live tree, dead snag, lodgepole pine sapling, and shrub aboveground
biomass using allometric equations (Appendix S1). Snag biomass was summarized by size
classes corresponding to downed wood (i.e., 1-, 10-, 100-, and 1000-h based on DBH).
Canopy fuel load and bulk density were estimated from conifer tree crown biomass. Dead
woody fuel biomass was computed for 1-, 10-, 100-, and 1000-h pools following Brown
(1974) and correcting for slope. Litter and duff biomass were quantified based on
average depth and bulk densities for lodgepole pine forest types (Brown et al. 1982;
Nelson et al. 2016).
Question 1: Effects of interacting drivers on forest regeneration
We tested whether live stem densities (including all seedlings, saplings, and
trees) were lower in short- versus long-interval fire with a one-sided, paired
Wilcoxon signed rank test (n = 33 pairs, lower densities expected in reburns).
Differences were also evaluated by species. For lodgepole pine, which was present in
all plots, a two-sided, paired Wilcoxon signed rank test was used. For other species,
which were absent from many plots and exhibited high variance relative to mean values,
differences in presence and density between pairs were tested with zero-inflated
negative binomial regression models adjusted for matched data (McElduff et al. 2010;
Abadie and Spiess 2022). Simulated model residuals were evaluated to determine that
these distributions appropriately represented underlying data (Appendix S2:Figures
S2-S3). Subsequent analyses only used live conifer stem densities (i.e., excluding
aspen).
Post-fire climate was characterized with water-year (October-September) climate
water deficit and summer (June-August) vapor pressure deficit (VPD; Harvey et al.
2016b; Stevens-Rumann et al. 2018; Davis et al. 2019). We used 4-km resolution climate
data (TerraClimate; Abatzoglou et al. 2018) and summarized 30-year normal (1989-2018)
and 3-year post-fire anomaly (z-score relative to normal; Appendix S2:Figures S4-S7).
We assessed whether differences in conifer stem density were associated with
warmer-drier climate using Spearman’s rank correlations because pairwise bivariate
distributions were not normal.
The relative importance of drivers of post-fire stem density was tested with
multiple linear regression models (n = 66 observations). Predictors included climate
(climate water deficit normal and post-fire summer VPD anomaly), short- versus
long-interval fire, lower (< 2350) versus higher elevation as a proxy for
stand-level serotiny, topography (heat load index and topographic position index;
Appendix S1), and distance to live edge. Continuous predictors were not strongly
correlated (Pearson’s |r| < 0.5) and were rescaled to have a mean of 0 and standard
deviation of 1. Conifer stem density was log10-transformed to meet assumptions of
linearity, normality, and equal variance, which were assessed with residual and
quantile-quantile plots (Appendix S2:Figures S10-S11). We fit a full model including
interactions between each predictor and short- versus long-interval fire. We used
exhaustive model selection to identify the most important factors based on model
Bayesian Information Criterion (BIC), retaining all models with difference in BIC <
2 (see Appendix S2:Table S2 for additional models).
Question 2: Forest biomass and fuels after short- versus long-interval fire
We assessed whether total live and dead tree biomass were lower in short- versus
long-interval fire with one-sided, paired t-tests (n = 22 pairs for live and n = 21
for dead fuels, lower biomass expected following reburns). Individual fuel pool
differences were tested using either two-sided, paired t-tests or two-sided, paired
Wilcoxon signed rank tests. Fuels were transformed as needed to meet normality based
on quantile-quantile plots (Appendix S2:Figure S12), and a Wilcoxon test was used if
transformations did not result in normal distributions. Trees (> 1.4 m height),
canopy fuels, and 1-h and 10-h snags were absent from > 40% of plots and were not
tested for differences. Finally, biomass pools were averaged over 0-10, 10-20, and
20-30 years since fire to explore trajectories of biomass change and recovery
following short- versus long-interval fire.
All analyses and visualizations were performed in ArcGIS Desktop 10.6 and R 4.1.3
(R Core Team 2022). See Appendix S1 for supplemental detail on methods and R
packages.
See published manuscript for additional information and appendices. This
repository includes field data and code for reproducing all analyses in
data_code_deposit.zip.
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