Supplementary material: Supplementary figures and tables
Figure S1.
The Colorado Front Range Corridor study area defined by red line in (a), and significant driving
variables (b - i) used to model the WUI probability of occurrence surface (Figure S2) using
logistic regression, b) aspect1, c) distance to road2, d) NLCD 20063, e) distance to urban area4, f)
land ownership5, g) slope6, h) vegetation status7, i) elevation8.
1
Calculated using the aspect function in ArcGIS 10.1 from 30-m elevation (Figure S1i) data
Calculated using the Euclidean distance function in ArcGIS 10.1 from the road network at 1: 1
million scale from http://www.nationalatlas.gov/
3
Downloaded from http://www.mrlc.gov/ at 30-m resolution
4
Calculated using the Euclidean distance function in ArcGIS 10.1 from urban extent extracted
from NLCD 2006
5
Download from http://consbio.org/products/projects/pad-us-cbi-edition
6
Calculated using the slope function in ArcGIS 10.1 from elevation (Figure S1i) data
7
Reclassified from the NLCD 2006 data
8
Download from http://ned.usgs.gov/ at 30-m resolution
2
Figure S2.
WUI probability of occurrence surface (POS) at 90-m spatial resolution
Figure S3.
Natural vegetation community type (a) and fire regime zone map (b) used in the CHANGE
model simulations
Figure S4.
Example successional pathway diagram for the ponderosa pine community type. In this pathway,
high-severity fires can occur in any successional stage, initiating a transition to the grass/forb
stage. Non-lethal surface fires maintain the pre-fire successional stage and delay ingrowth and
understory succession. Moderate severity fire can change the forest to a less dense successional
stage. When fire does not occur for 40 years, understory reestablishment moves the forest to a
more dense successional stage. Probability of high-severity fires generally decrease with stage
age, and increase with stand density. Ystart is the stand age at which the forest enters the
successional stage. Last indicates how long the forest will remain in the successional stage.
Figure S5.
Average 5-year net demand for land-cover and land-use (LCLU) classes and the WUI from 2005
to 2050. Demand for LCLU classes was derived from data published by Sleeter et al. (2012) and
Sohl et al. (2014) based on the A2 emissions scenario.
Figure S6.
Characteristics of burned WUI from 1984 to 2010. Burned WUI was extracted by overlaying the
initial WUI map for 2005 and burned patches derived from the MTBS dataset from 1984 to
2010. This figure shows that burned WUI areas were located at lower elevations (upper panel)
and more in mixed conifer and ponderosa pine forests than other vegetation types (lower panel).
Abbreviations for vegetation types for lower panel are: decid_Shrub = deciduous shrubs;
Hardwood = hardwood forests; MixedConifers = Mixed coniferous forests; MixForest = mixed
hardwood and coniferous forests; PIPO = ponderosa pine forests; PJW = pinion juniper
woodlands.
Figure S7.
Relative influences of abiotic variables on burn rate in the WUI area. The regression analysis of
abiotic influences on burn rate (BR) was based on 1000 random points extracted from the
simulated future WUI in 2050. Abiotic variables included distance to nearest road (D2Rd) and
distance to urban (D2Ub), elevation (DEM), aspect (ASP), and slope (SLOP). Relative
contributions of abiotic influence on burn rate were calculated as the proportion of the variation
explained by each individual variable, and were normalized to 100%.
Table S1
Coefficients and standard errors for the logistic regression model used to generate the WUI
probability of occurrence surface (Figure S2).
Variables
coefficients
standard errors
z value
p-value
-3.392
0.295
-11.50
<0.001***
Intercept
-0.0022
0.00013
-16.39
<0.001***
Distance to Road
-0.00040
0.000051
-7.88
<0.001***
Distance to urban
0.0021
0.00017
12.24
<0.001***
Elevation
0.14
0.066
2.12
<0.05*
Aspect
-3.05
0.18
-17.01
<0.001***
Public land1
2
0.11
8.87
<0.001***
Natural vegetation 0.97
1: Land ownership is a categorical variable, and private land is used as reference category.
2: Vegetation status is a categorical variable, and non-vegetation is used as reference category.
Table S2
Fire regime zones and associated fire parameters under static and changing fire regimes. Static fire regime scenarios used the current
fire regime which was calculated from 1984 to 2010 MTBS data. Projections of change in the MFR from the current fire regime to
2050 were calculated from published literature (Litschert et al. 2012). Fire size distribution remains unchanged between static versus
changing fire regime scenarios. MFR: mean fire rotation; MFS: mean fire size; s.d.: standard deviations. See Figure S3b for a map of
fire regime zones.
Fire regime zone
MFR for static
fire regime
(years)
MFS (s.d.)
(ha)
MFR in 2050
for changing
fire regime
(years)
9999
2000
Elevation
(meters)
Non-vegetated area
Great plain shrubland
9999
2000
0
190(1640)
NA
<1524
Great plain grassland
2700
59 (283)
2700
Low montane zone
180
130 (94)
45
1524 – 1981
Montane zone
250
165 (1681)
61
1981-2590
Subalpine zone
1150
317(9910)
280
2590-3352
<1524
pre-European fire regimes
NA
Frequent (MFR: < 30 years), high
severity fire
Frequent (MFR: < 30 years), high
severity fire
Frequent (MFR: < 30 years), low
severity surface fire
Mixed severity fire (MFR: 30 - 150
years)
Infrequent (MDR: >150), stand
replacing fire
Reference:
Litschert SE, Brown TC, Theobald DM (2012) Historic and future extent of wildfires in the Southern Rockies Ecoregion, USA. For.
Ecol. Manage. 269:124-133