2.2 Field Methods and Calculation of Independent Variables
To examine changes in species diversity from before to after the 2011
Horseshoe Two Fire, we remeasured vegetation during the summers of 2017
and 2018 in 138 plots established and first measured in 2002 and 2003
(Poulos et al., 2007; Figure 1). The distribution of plots was initially
stratified using vegetation cover types (Kubler, 2000; Taylor et al., in
press). Sample points were placed in the center point of homogeneous
areas of a cover type larger than 1800 m2. Highly
dissected terrain with vertical rhyolitic towers made random or
systematic sampling impossible, as we were limited to sites <
30° slope that were accessible by foot. Nevertheless, the plot network
spanned gradients of vegetation types, topography, and fire severity
arising from the Horseshoe Two Fire (Figure 2).
In the initial 2002-3 survey, woody vegetation at each selected point
was sampled in 5 x 25 m belt transects established parallel to the slope
contour. The location (GPS) and azimuth (º) of each belt transect were
recorded. We measured the basal diameter of all shrubs and trees of each
genet > 10 cm, counted individual juvenile plants
(<10 cm basal diameter), tallied shrubs including cacti
(stems), and estimated percent cover of each woody species in one of six
cover classes (<1, 1-4, 5-24, 25-49, 50-74,75-100 %).
Post-fire plot remeasurements in 2017-18 were identical to those
employed in 2002-3 in the same 5 x 25 m belt transects.
Using Ward clustering with NbClust (3.0) in the vegan package (2.5-7) in
R (R Core Team, 2020), 138 plots were clustered into three vegetation
types: juniper woodlands (n=59), piñon woodlands (n=39), and pine-oak
forest (n=40) (M. Freiburger et al., personal communication). Juniper
woodlands were characterized by alligator juniper (Juniperus
deppeana Steud.), Emoryi oak (Quercus emoryi Torr.),
three-leaved sumac (Rhus trilobata Nutt.), Palmer’s century plant
(Agave palmeri Engelm.), catclaw mimosa (Mimosa
aculeaticarpa Benth.), and twistspine pricklypear (Opuntia
macrorhiza Englem.). Piñon woodlands were dominated primarily by border
piñon (Pinus discolor Bailey and Hawksw), Toumey oak (Q.
toumeyi Sarg.), pointleaf manzanita (Arctostaphylos pungensKunth), Wheeler’s sotol (Dasylirion wheeleri Wats.), Garry’s
silktassel (Garrya wrightii Torr.), and sacahuista (Nolina
macrocarpa S. Watson). Pine-oak forests were composed mainly of
Chihuahua pine (P. leiophylla var. chihuahuana (Engelm.)
Shaw), Apache pine (P. engelmannii Carr.), Arizona pine (P.
arizonica Engelm.), Arizona white oak (Q. arizonica Sarg.),
silverleaf oak (Q. hypoleucoides Camus), Arizona madrone
(Arbutus arizonica (Gray) Sarg.), and Douglas-fir
(Pseudotsuga menziesii (Mirb.) Franco).
We estimated fire severity of the 2011 Horseshoe Two Fire for each of
the 138 plots using raster delta normalized burn ratio (dNBR; Eidenshink
et al., 2007), a Landsat ETM+ derived product that estimates absolute
change in fire severity from before to after a fire. The Normalized Burn
Ratio is calculated from ETM+ bands 4 and 7 as (ETM4 – ETM7)/ (ETM4 +
ETM7); ETM4 represents the near-infrared spectral range (0.76 -0.90 μm)
and ETM7 the shortwave infrared spectral range (2.08 – 2.35 μm).
Differenced NBR images (pre-fire NBR minus post-fire NBR) are referred
to as dNBR images. Pre-fire Landsat ETM+ images are from the month
before the fire and post-fire images are from 6 months after the fire
for dNBR calculation. We selected dNBR rather than a relativized
remotely sensed fire severity estimator because absolute change provides
a more reliable measure than relative change for asessing vegetation
cover change occurred in response to the Horseshoe Two Fire. We acquired
dNBR data from the Monitoring Trends in Burn Severity data distribution
site (https://www.mtbs.gov/), extracting a value for each plot with the
point sampling tool in QGIS (QGIS Development Team, 2020). In some
cases, dNBR was used as a continuous independent variable; in others,
dNBR fire severity classes (none, low, moderate, and high; MTBS,
www.mtbs.gov) were employed for analyses.
We used elevation and the topographic relative moisture index
(TRMI; Parker 1982) as independent woody
plant diversity predictors, as past research has revealed their
importance of in regulating woody plant species composition (Poulos et
al., 2007). Elevation was extracted in QGIS (QGIS, 2020) for each plot
from 30-m resolution digital elevation models (DEM)
(https://lpdaac.usgs.gov); TRMI was calculated from field measured
topographic position (ridge, upper elevation, mid elevation, lower
elevation, and valley), slope direction (in degrees), slope steepness
(in degrees), and surface shape (convex, convex-straight, straight,
concave-straight, and concave). TRMI provides a quantitative xeric to
mesic continuum among plots, independent of elevation. We also extracted
and used the terrain ruggedness index (TRI) from the 30-m DEMS in QGIS,
which is defined as the mean difference between a central plot pixel and
its surrounding 8 pixels.