Mapping American black bear habitat shifts in Washington state following wildfires

Item

Identifier
Thesis_MES_2022Su_KlimM
Title
Mapping American black bear habitat shifts in Washington state following wildfires
Date
September 2022
Creator
Klim, Michelle
extracted text
Mapping American black bear habitat shifts in Washington state following wildfires

by
Michelle Klim

A Thesis
Submitted in partial fulfillment
Of the requirements for the degree
Master of Environmental Studies
The Evergreen State College
August 2022

© 2022 by Michelle Klim. All rights reserved.

This Thesis for the Master of Environmental Studies Degree
by
Michelle Klim

has been approved for
The Evergreen State College
by

_______________________________
Kevin Francis, Ph. D.
Member of Faculty

_______________________________
Date

ABSTRACT
Mapping American black bear habitat shifts in Washington state following wildfires

Michelle Klim
After decades of fire suppression, wildfires in the Pacific Northwest have increased in size and
quantity in recent years. Little is known about how these fires may be impacting black bear
(Ursus americanus) habitat. My research examined wildfire-related land change and its impact
on plausible black bear habitat in Washington state from 2010 and 2020. Using GAP program
habitat maps, LANDFIRE disturbance data, and MTBS wildfire severity data, I created a series
of maps in ArcGIS Pro to identify where wildfire-related habitat change may be occurring and its
potential impact on habitat concentration areas (HCAs). I found that all fires impacted black bear
habitat in the short-term by reducing cover and food resources, but that location and size of the
fires determined how severe these impacts were. Fires that fell completely within the HCAs
generally had enough surrounding habitat that their impact was minimal. Fires that fell outside of
the HCAs, but along travel corridors, likely had more impact since suitable habitat along the
corridors is sparce. While this study can provide insight into how bears may be affected by
habitat change due to wildfires, additional studies will be needed to understand these impacts.
Future studies should include telemetry data from populations most at risk for exposure to fire,
habitat assessments following those fires, and physical observations of these populations.

Table of Contents
List of Figures ................................................................................................................................. v
Acknowledgements ........................................................................................................................ vi
Introduction ..................................................................................................................................... 1
Literature Review............................................................................................................................ 3
Introduction ................................................................................................................................. 3
Fire .............................................................................................................................................. 3
Regimes in Washington .......................................................................................................... 3
American black bear ................................................................................................................... 7
Habitat and Disturbance .......................................................................................................... 7
Home Ranges ........................................................................................................................ 10
Tracking and Modeling ......................................................................................................... 10
Methods......................................................................................................................................... 14
Results and Discussion ................................................................................................................. 16
State Level Patterns................................................................................................................... 16
Individual Fires / Case Studies ................................................................................................. 22
Conclusion .................................................................................................................................... 43
Bibliography ................................................................................................................................. 45
Appendices .................................................................................................................................... 56
Appendix A: Gap Reclassification............................................................................................ 56
Appendix B: LANDFIRE Disturbance Reclassification .......................................................... 64
Appendix C: Resistance Values ................................................................................................ 65
Appendix D: Land Classification Change 2001-2021 .............................................................. 67
Appendix E: Data Sources ........................................................................................................ 71
Appendix F: Base Maps ............................................................................................................ 73

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List of Figures
Map 1: Land classification change with fire perimeters 2001-2020 ............................................ 18
Map 2: Vegetation types within fire perimeters 2010-2020 ......................................................... 21
Map 3: Carlton Complex, 2015 .................................................................................................... 23
Map 4: Lime Belt Fire, 2015......................................................................................................... 25
Map 5: Tunk Block Fire, 2015 ...................................................................................................... 26
Map 6: North Star Fire, 2015 ........................................................................................................ 28
Map 7: Buck Creek Fire, 2016 ...................................................................................................... 30
Map 8: Hayes Two Fire, 2016 ...................................................................................................... 32
Map 9: Norse Peak Fire, 2017 ...................................................................................................... 34
Map 10: Jolly Mountain Fire, 2017 .............................................................................................. 36
Map 11: Diamond Creek Fire, 2017 ............................................................................................. 38
Map 12: South Spokane GAP Classification ................................................................................ 42

v

Acknowledgements
Growing up in the Rust Belt, I never imagined that I would end up here, on the opposite side of
the country, studying wildlife that I had not seen until I was 22. My love for forests began with
running and eventually carried me to an internship at the Great Smoky Mountains National Park,
where I saw burned land and bears for the first time. I was curious about where the bears went
after the fire and how they survived, but I didn’t give it much thought. Now I’m here, knowing a
lot more about black bears and fire than I did then. I want to thank everyone who has helped
guide me here; though there are too many people to name, I appreciate everyone’s role in my life
that has led me here. Most importantly I thank my partner Vanessa for bringing me balance,
encouraging me to take breaks, get outside, and do what I genuinely want—I could not have
done this without you.
“Last but not least, I wanna thank me” – Snoop Dogg

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Introduction
American black bears (Ursus americanus) have been extensively studied in Washington
State. They have a broad distribution throughout Washington (Hummel et al., 1991; Johnson &
Cassidy, 1997), are associated with forested habitats (WDFW, n.d.), and display wide-ranging
space-use patterns (WDFW, n.d.). Studies on the eastside of the Cascades found that important
habitats included riparian forest, deciduous forest, and montane-high elevation forest. Dry nonforest habitats such as shrub steppe were ranked as low use by bears (Lyons et al., 2003; Gaines
et al., 2005).
However, researchers in Washington State have not adequately studied black bear habitat
in relation to fire and vegetation changes. Within the past decade, there have been 19 fires in
Washington larger than 200 km2, the size of the average female black bear home range (Koehler
& Pierce, 2003). In this thesis research, I wanted to understand black bear habitat in dynamic
fire-shaped environments. How have wildfires changed the landscape in Washington State and
have these changes been significant enough to impact American black bear habitat? I answered
these questions by assessing land change in relation to fire and comparing habitat concentration
areas (HCA) for black bears from 2010 to 2020.
I used a combination of habitat models to assess the plausible habitat of the American
black bear in Washington State. Based on the parameters set by Washington Wildlife Habitat
Connectivity Working Group (Working Group), I assessed habitat change since their last
published map (2010). To do this, I updated the GAP Program habitat maps following methods
outlined by McKerrow et al. (2014). Once the land cover layers were updated, I completed a
habitat analysis following steps outlined by Working Group (2010). I then compared these
changes to fire severity data from a select number of fires within the last ten years (MTBS).

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Based on recent wildfire data, I predicted that wildfires have created a shift in black bear habitat
by transitioning forested areas to grass or shrub-land and removing cover and potential food
sources. However, this was not the case as land cover change showed areas of ecological
succession near areas of burned habitat, meaning areas that were previously classified as
grassland transitioned to shrubland, and areas that were previously classified as shrubland
transitioned to forest. This suggests that while fire may have removed cover in one area, other
areas were developing more cover or potential food sources.
The consequences of wildfire on black bear habitat can be beneficial, depending on
severity, frequency, and size. Fires that reduce food resources, cover, and potential den sites may
negatively impact black bears within the first year following the fire (Bogener, 2003;
Daubenmire, 1968; French & French, 1996; Hamilton, 1981). Specifically, severe fires that
remove large amounts of snags, coarse woody debris, and vegetative cover are most likely to
negatively impact black bears (Bull et al., 1997; Davis, 1996; Hall, 1976; Jonkel & Cowan,
1971). However, in areas where vegetation growth favors fire, fire exclusion may have adverse
effects on foraging (Unsworth et al., 1989). For example, certain shrubs, like blueberry and
blackberry produce the most fruit several years after a fire (Landers, 1987). In the absence of
fire, these shrublands may be shaded out by developing forests, diminishing a food source. In
fact, a study by Potter and Kessell (1985) found that black bears showed the lowest preference
for foraging in unburned communities and the highest preference for foraging in communities
burned 10 years prior. Fires that create a mosaic of burned and unburned areas are most
beneficial (Allen, 1987; Bendell, 1974; Cunningham et al., 2003; Kelleyhouse, 1979).

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Literature Review
Introduction
Wildfires in the Pacific Northwest have grown in size and quantity in recent years
(Halofsky, 2020). To better understand wildfires and their impact on black bear habitat, I
reviewed literature on fire regimes in Washington and how land change is mapped. This research
shows that fire regimes in Washington range from infrequent high-intensity fires to frequent lowseverity fires. Changes from these fires are mapped using LANDFIRE disturbance data and
comparing satellite imagery from before the fire. I then reviewed literature on black bear
habitats, their responses to disturbance, their behaviors, and tracking. Black bear habitat
preferences have been found to vary geographically, but their response to disturbance is
generally the same. Black bears tend to prefer areas with a mosaic of land cover from shrublands
to forests, depending on the season and food sources. They tend to avoid developed or populated
areas, but will venture out if their food is scarce. They are usually tracked using a combination of
GPS collars and on the ground observations.

Fire
Regimes in Washington
Fire regimes are characterized by the intensity of a fire and the frequency of burn. They
are typically described by a combination of forest types and the known or hypothesized effects of
fires in them. Fire severity is the effect that fire has on an ecosystem, including anything of
value: vegetation, soils, streams, timber, wildlife habitat, and human communities (Tappeiner II
et al., 2015). It is commonly correlated with intensity; however, not every intense and standreplacing crown fire is severe, nor is every low-intensity surface fire harmless to an ecosystem.
Severity is rooted in the intersection of plant and ecosystem adaptations of fire: the intensity of a
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particular fire and the resistance and resilience of plants, soils, and other parts of the ecosystem
to that fire (Kilgore, 1981).
Agee (1993) classified several forest types with respect to fire frequency, intensity, and
perceived intensity. However, there are important aspects of a fire regime that cannot be
documented by mean fire return interval alone (Agee, 1993; Baker and Ehle, 2001). Actual fire
occurrence and severity could vary throughout forests that are in the same severity classification.
This can happen when applied to areas of large forest types and areas that vary in topography
and microclimate, species composition and stand structure, total amount and arrangement of
fuels, and probabilities of ignition or spread.
An infrequent high-severity fire regime creates major shifts in the structure, composition,
and function of a forest. These forests have generally productive stands, with a high
accumulation of fuels, and where seasonally dry weather promotes sufficiently low fuel
moisture. Fires spread rapidly during the hottest and driest parts of the season and burn large
areas with patch sizes ranging from 10-10,000 acres. Historically, major fires occurred at least
every 100 years. Washington has several examples of infrequent high-severity fire regimes such
as Douglas fir/western hemlock forests, lodgepole pine forests, and true fir forests. Douglas
fir/western hemlock forests and coastal redwood forests in the western slopes of the Cascades
and Olympic Mountains of Oregon and Washington fall into this regime. Between standreplacing events, surface fire occurred sporadically associated with drier microsites and
indigenous burning practices (Peter and Harrington, 2014). Lodgepole pine forests in the
Cascades regenerate quickly after stand-replacing fires and form dense stands that susceptible to
severe fires after 60 years. The quantity of dead wood generated by self-thinning, insect
mortality, and understory growth of trees and shrubs make this forest type susceptible to fire.

4

True fir forests occurring at subalpine elevations with a persistent snowpack and short growing
season generally regenerate slowly after a fire (greater than 400 years). Fire may occur during
extended years of drought, possibly associated with subtle changes in climate (Tappeiner II et al.,
2015).
Fires in the mixed-frequency and severity fire regime generally cause ongoing temporal
and spatial shifts in the character of a forest. This creates more variability than is found in either
high- or low-severity regimes. Mixed fire regimes have regular fire within parts of the landscape,
with mean fire return intervals (MFRI) of less than 20 years (Sensenig et al., 2013), but less
frequent fires in other parts of the forests, ranging from 25-100 years, depending on the season
and weather conditions. Mixed severity fires reduce stand density on average but leave patches
of trees unburned as well. Biomass is accumulated relatively quickly between fires. Plant
communities in topographic positions with higher probabilities of burning tend to burn most
frequently, creating an ecological memory in the landscape (Tappeiner II et al., 2015). Dry
Douglas fir forest in the eastern Cascades, west-central Cascades and Sound Trough have a fire
return interval of 70-100 years. The forest structure is often patchy, with regeneration occurring
in openings caused by fires, often with a hardwood component.
Frequent low-severity fire regimes, also known as understory regimes, have historically
occurred on drier sites, with short return intervals ranging from 5-25 years. Drier sites have
lower productivity with a high chance for ignition and little time for major fuel accumulation
between fires. Fire seasons are generally long and widespread with periodic fuel accumulation to
bark beetles or other insects. Forest types in this regime include ponderosa pine forests, mixedconifer forests on the east side of the Cascades, and oak woodland forests along the fringes of
valleys in western Washington. Sites with annual fire seasons that are dominated by ponderosa

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pine, with grass and shrub understories, normally have return intervals of 5-15 years. Mixedconifer forests on the east sides of the Cascades are composed of ponderosa pine, sugar pine,
Douglas fir, incense cedar, and white fir. Oak woodland forests had low-intensity fires that
burned through an understory dominated by grasses. These fires were typically initiated by
Native Americans for food and wildlife habitat, which could account for the short return interval
of <25 years. With fire exclusion policies, Douglas fir trees have often invaded these sites,
overtopping, and killing oak trees (Tappeiner II et al., 2015).
Mapping
McKerrow et al. (2014) used land cover and disturbances to update the National Gap
Analysis Program’s Species Habitat Map. They relied on deductive modeling of habitat
attributes using these products to create models of habitat availability. They tested the integration
of the Multi-Resolution Landscape Characterization Consortium’s National Land Cover
Database 2011 and LANDFIRE’s Disturbance Products to update the species models. The
update approach was tested in three geographic areas. NLCD products were used to identify
areas where the cover type mapped in 2011 was different from what was in the 2001 land cover
map. Satellite imagery from Google Earth and ArcGIS basemaps were used as reference imagery
to label areas identified as “changed” to the appropriate class. Areas that were mapped as water
or urban in the updated NLCD map were accepted without further validation and recoded to the
corresponding GAP class. LANDFIRE’s Disturbance products were used to identify changes that
are the result of recent disturbance to inform the reassignment of areas to their updated thematic
label. Areas that were changed in the 2011 NLCD map but having no record of disturbance were
reclassified using the nearest neighbor function. Once land cover was updated, they ran a habitat
species model for three species created by GAP. To compare how land change may have

6

impacted habitat they ran the model for the 2001 NLCD map and the updated land cover map
they created. This analysis showed that the three species were impacted by land cover change
from recent disturbances (McKerrow et al., 2014).

American black bear
Habitat and Disturbance

Habitat diversity is important as bears require a mosaic of vegetation. Preference is given
to mesic over xeric sites and forest over open areas (Unsworth et al., 1989). Habitat use is
dictated by seasonal food production (Amstrup & Beecham, 1976; Hatler, 1972). Meadows are
generally preferred for foraging on grass and forbs during spring (Gill & Beck, 1990). Riparian
habitat, avalanche chutes, and early-successional habitat created by logging or fire are preferred
in the summer (Fuller & DeStefano, 2003; Hamer, 1995). Mature forest containing hard mast is
preferred during fall (Elowe & Dodge, 1989; Litvaitis, 2001).

Habitat modification has a greater effect on American black bears than direct mortality
from wildfires (Yellowstone National Park, 1991). Fires that create patches of burned and
unburned areas are most beneficial (Allen, 1987; Bendell, 1974; Cunningham et al., 2003;
Kelleyhouse, 1979). Fires that reduce food resources, cover, and potential den sites may
negatively impact black bears in the short-term (Bogener, 2003; Daubenmire, 1968; French &
French, 1996; Hamilton, 1981). A severe fire that removes large amounts of snags, coarse woody
debris, and vegetative cover would most likely negatively affect American black bears (Bull et
al., 1997; Davis, 1996; Hall, 1976; Jonkel & Cowan, 1971). However, fire exclusion may have
adverse impacts on foraging in areas where vegetation growth favors fire (Unsworth et al.,
1989). Huckleberries and blueberries are more productive on recently burned sites compared to

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unburned sites. Logging treatments that include severe soil scarification or slash burns may also
reduce berry yields. In areas where timber harvesting favors berry production, lack of cover in
early postfire years may limit its use.

Potter and Kessell (1985) modeled potential feeding and reproductive habitat utilization
for large mammals in any homogenous forest community. They examined wildlife use of an
unburned habitat and habitats at 0, 10, and 25 postfire years. American black bear showed the
lowest preference for foraging in unburned communities and the highest preference for foraging
in the postfire year 10 community.

Wildfires, prescribed burns, and thinning treatments can cause significant changes to
habitat conditions such as increasing fragmentation (Mitchell & Powell 2003), and reducing
cover (White et al., 2001; Tredick et al. 2016). Forage availability varies with precipitation
patterns and rate of vegetation maturation, which results in seasonal shifts in forage consumption
(Pelchat and Ruff, 1986; Auger et al., 2005). Post-disturbance recovery of vegetation can vary by
burn severity, plant species, and climatic conditions (Bartel et al., 2016). These disturbances
have the potential to create unfavorable environmental conditions, at least short-term, as they
reduce forage availability, horizontal cover, and basal area, which could result in area avoidance
by bears until adequate vegetation recovery has occurred (Mitchell et al., 2005; Baruch-Mordo et
al., 2014).

A study by Cunningham and Ballard (2004) found that the largest impact of wildfire was
lack of recruitment of cubs in the yearling age class over a period of 4 years after a fire in central
Arizona. It was suggested that continued poor recruitment could result in a population decline if
vegetation regeneration is prolonged. They suggested an altered hunting strategy, which could be

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useful in managing populations in Washington. The most supported models suggested that black
bears were more likely to select bed sites with a combination of low horizontal visibility and
high basal area. Black bears were found to use all disturbed sites to varying degrees, although
48% of bed sites were in undisturbed habitat (Bard & Cain 2020). Site selection was most
strongly related to decreased visibility due to obstruction from boulders, vegetation cover, and
downed logs (Bard & Cain, 2020).

The impacts of roads on black bears are determined by location, road structure, amount of
traffic, and timing of road use. In the northern Cascade Range of Washington, roads consistently
had a negative impact on habitat used by female American black bears (Gaines et al., 2005).
Roads may not be problematic if they are gated to reduce vehicular traffic and maintained as
linear wildlife openings (Gaines et al., 2005; Frederick & Meslow, 1977; Lyons et al., 2003).

Den types vary geographically; however, den sites located in dead- and live-tree cavities
are preferred across the American Black Bear's range (Bull et al., 1996, 1997). Denning periods
depend on the length of winter but typically occur October-May. In the northeastern Cascade
Range of Washington, females entered dens approximately 1 week earlier in the fall and left dens
1 week later in the spring than males (Gaines, 2003). Pregnant females will den longer (up to
247 days in one bear in Alaska) (Schwartz et al., 1987).

Black bears are common throughout Washington except for the non-forested areas of the
Columbia basin. Black bears live in a diverse array of forested habitats in the state, from coastal
rainforests to the dry woodlands of the Cascades’ eastern slopes. In general, black bears are
strongly associated with forest cover, but they do occasionally use relatively open country, such
as clearcuts and the fringes of other open habitat (WDFW, nd).

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Home Ranges
Home range size, distribution within home ranges, and density of black bears are
determined by sex, habitat quality, population density, distribution of food, breeding season,
and topography (Amstrup & Beecham, 1976; Archmabault et al., 1990; Elowe & Dodge, 1989).
Adult males have the largest home range followed by adult females, yearling males, and
yearling females (Powell et al., 1997). Size and distribution is the greatest in the summer, during
breeding season, for adult males and largest for adult females and cubs from September until
October during high food abundance. All bears reduce their range size in late Fall through
Spring during denning (Powell et al., 1997).
Females that are related usually have overlapping home ranges (Amstrup & Beecham;
Horner & Powell, 1990; Jonkel & Cowan, 1971). Subadult males and females may be allowed to
stay on their mothers' home ranges for their first year of independence before dispersing
(Kolenosky et al., 1987). When female yearlings separate from their mothers at 16-17 months
of age, they live alone within their native home range. Mothers may shift their territories away
from their daughters, possibly to avoid overcrowding (Rogers, 1987).
Tracking and Modeling
In New Mexico, Bard and Cain (2020) used a combination of GPS location data and a
use/available study design. By using a combination of GPS tracking with on the ground
observations, they were able to identify den and bed sites and gather their attributes. GPS collars
to track wildlife is common practice, but the rate in which data is collected varies by the

10

objective of the study. To conserve battery, it is best practice to use GPS collars with motion
sensors so that they are only collecting data when there is activity.
Tracking efforts in Yosemite have involved ground-based telemetry techniques to collect
locations on radio-collared bears (Matthews et al., 2006). Telemetry locations were collected
from two or more locations using the loudest signal to determine azimuths (Springer, 1979)
using a handheld receiver. The location error was then determined using the location error
method (Zimmerman & Powell 1995). To measure location error, the distance between the actual
location and estimated location were measured. Ground-based telemetry efforts were restricted to
the Valley because of limited road availability. To generate home range estimates of radiocollared bears in areas outside of the Valley, aerial telemetry was used (Matthews et al., 2006).
Location data was collected during 24-hour monitoring events for 30 seconds in 15-minute
intervals. During monitoring, motion sensors were used to monitor pulse rate to determine bear
activity (Ayres et al. 1986). Movements were then quantified by measuring distance traveled
between two locations collected in 1-hour intervals during the monitoring event. Matthews et al.
(2006) found that adult male bears were significantly more active during nocturnal and diurnal
periods, whereas adult female, subadult male, and subadult female showed no significant
difference in diurnal and nocturnal activity. Adult females were shown to be more active during
nocturnal periods when they were located only in natural areas. Diurnal patterns of bears could
be explained by foraging behavior (Bacon & Burghardt, 1976; Lariviere et al., 1994). Bears rely
on visual cues for foraging, making daylight more efficient. Bears who have been found foraging
in developed areas may display nocturnal behavior to avoid human harassment (Ayres et al.
1986; Lariviere et al. 1994; Pelton 2000). Human activity and use of developed areas in

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Yosemite Valley by bears result in behavioral differences between bears near humans and bears
in areas with less human impact (Matthews et al., 2006).
Washington Wildlife Habitat Connectivity Working Group (2010) created a state analysis
for certain focal species, one being the American black bear. For their analysis, they consulted
habitat and wildlife specialists to inform on important habitat attributes for each species. Studies
by Cushman et al. (2006, 2008) were used to identify important attributes for black bears.
Attributes that were considered were: distance from roads, human population, habitat type,
elevation, and slope. Habitat attributes were then weighted for each species based on their
importance and used to determine landscape-resistance. This helped inform on where black bears
would most likely not be found, which was determined to be the inverse of ideal habitat.
Cushman et al. (2006) used genetic distance metrics to test landscape-resistance. They used
elevation, slope, roads, and land cover to develop their hypotheses. The models most supported
by genetic distance data showed strong relationships with forest cover and mid-elevations, with
variable support for different levels of road factors and no relationship with slope (Cushman et
al., 2006). The best supported model had high road resistance, which was then used to identify
corridors. In 2008, Cushman et al. used the model to identify potential corridors for American
black bears between forested portions of the Canadian border down to the northern boundary of
Yellowstone National Park. They identified three categories of potential barriers along the
movement corridors: gaps in federal ownership that contain freeways and major highways; areas
within federal ownership where major highways cross the corridor; and areas where major
corridors parallel highways (Cushman et al., 2008).
Studies of American black bears in Washington have focused on habitat preference
related to human activities and habitat modeling (Cushman et al., 2006, 2008). These studies

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helped inform important habitat attributes that were used to create a map of predicted habitat
concentration areas (HCAs) for black bears (Working Group, 2010). To understand black bear
habitat in dynamic fire-shaped environments I asked: How have wildfires changed the landscape
in Washington State and have these changes been significant enough to impact American black
bear habitat? I answered these questions by assessing land change in relation to fire and
comparing habitat concentration areas (HCAs) for black bears from 2010 to 2020.

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Methods
The following methods were adapted from Working Group’s 2010 Statewide Analysis.
To ensure the exact methods were followed I had intended to use the model (Gnarly Landscape
Utilities, 2010) that was created as a product of their analysis in lieu of individual calculations in
ArcGIS Pro. However, due to updates in software and the need for updated code, I chose to
follow the steps outlined below.
Three national datasets were used to update land cover to 2021. The Ecological Systems
map (GAP, 2011) was the primary base layer and was updated with Fuel Disturbance data
(Landfire, 2020) to make reclassifying disturbed areas possible. Current Land Cover (NLCD,
2019) was used for comparison of forest harvest and regeneration areas. Additional base layers
used for the analysis include elevation (National Elevation Dataset, n.d.), slope, roads (TIGER,
2000), and housing density (U.S. Census, 2000) all of which were maintained from WWHWG’s
2010 analysis. Although roads and housing density may have significantly impacted habitat
concentration areas for black bears, land use layers should have reflected at least some of that
change by reclassifying areas as urban or developed. Previous analysis included forest structure
data, such as canopy cover and height, but was excluded from this analysis due to incongruencies
experienced in the 2010 analysis.
Once data was obtained and imported into ArcGIS Pro v2.9.2 it was projected onto a
World Topographic Map using NAD 1983 (2011) State Plane Washington South FIPS 4602
(meters). GAP (2011) was reclassified to the Working Group classifications listed in Appendix
A. It was then combined with Fuel Disturbance (LANDFIRE, 2020) and filtered to target areas
in which disturbance occurred. Forest areas identified as disturbed were recoded into the
appropriate ecosystem classification based on the time and severity of the disturbance (Appendix

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B). Areas that were not designated as forested habitat or shrub initially were not recoded
according to disturbance. Large or severely disturbed areas were compared to NLCD (2019) to
designate the appropriate ecosystem classification. The updated land use layer was converted
from 30m cells to 100m cells to match the other base layers.
Each base layer was reclassified according to resistance values provided by Working
Group (Appendix C). The resistance layers were combined, and resistance was calculated by
summing their resistance values and adding one to account for Euclidian Distance. Suitable
habitat was identified as areas with a resistance value of ≤ 6, a home range radius of 2.6 km, a
moving window threshold of 0.5, and a minimum patch size of 200 km2.
To better understand how specific fires may have shaped black bear habitat, I looked at
wildfire severity data and compared it to vegetation change. I focused on fires that have occurred
within the past 10 years and fell within or near the habitat concentration areas. By looking at
specific fires, I was able to see what vegetation change was a result of natural processes and what
vegetation change was a result of human error in mapping.

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Results and Discussion
State Level Patterns
Large wildfires are becoming increasingly common in the western United States. High
intensity wildfires can impact habitat by changing vegetation growth, allowing for invasive
vegetation establishment, removing tree coverage used for denning, and damaging food
resources for black bears (Halofsky et al., 2020). How have wildfires changed the landscape in
Washington State? Have these changes been significant enough to impact American black bear
habitat? While the largest of these fires fell outside the ideal habitat area for black bears, a few
fell completely or partially within these areas (Map 1). To determine if these fires have impacted
black bear habitat, I compared land classifications from 2001 to 2020 (Appendix A). Areas that
were previously classified as wet or dry forest and then reclassified to either shrub- or grassdominated would have the most impact on black bears by removing cover used for denning sites.
Likewise, areas that transitioned from grass- or shrub-dominated to wet or dry forest could
provide more habitat for bears by providing cover and food sources.
The largest fires within the past 10 years fell on the east side of the Cascades between or
partially within HCAs. These include the Lime Belt, Tunk Block, North Star, and the Carlton
Complex fires. Nearly all the remaining fires fell within the HCAs along the Cascades. These
include the Diamond Creek, Jolly Mountain, Norse Peak, and Buck Creek fires. Hayes Two fell
completely within the HCA in the Olympic Peninsula.
To understand wide-scale landscape changes in Washington State, I compared land
change from GAP v.2.2 to GAP v3.0 (Map 1). The largest change, covering 56634.3 km2, was
from wet forest to dry forest. Grass-dominated to shrub-dominated was the next largest change,

16

accounting for 44577.9 km2. Areas classified as wet forest include mesic forests and mixed
hardwood-conifer forest (Appendix A). American black bears prefer mesic or xeric sites
(Unsworth, 1989). Changes from wet to dry forest could be significant because of loss of cover
for denning and loss of food resources. Changes from grass- to shrub-dominated show evidence
of succession, which may provide more habitat for black bears in the future.
Major landscape changes such as fires may not worsen black bear habitat. Fires that
contain a mosaic of burned and unburned areas are preferred (Allen, 1987; Bendell, 1974;
Cunningham et al., 2003; Kelleyhouse, 1979). A closer look at fires in or near HCAs within the
past 10 years shows that fires had a significant short-term impact on black bears, but may have
been beneficial in the long-term.

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Map 1: Land change with fire perimeters 2001-2020

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My study builds upon and updates previous analysis on habitat concentration areas for
black bears in Washington state. In 2010, Washington Wildlife Habitat Connectivity Group
(Working Group) conducted a statewide analysis of focal species habitat concentration areas,
landscape resistance, and habitat connectivity. Their model modified existing habitat
connectivity models (Singleton et al., 2002; Cushman et al., 2006) with local research on
resource selection (Koehler & Pierce, 2003; Lyons et al., 2003; Gaines et al., 2005). Since its
publication in 2010 there have been 19 fires larger than 200 km2 (Landfire, 2020). This analysis
used the same parameters as Working Group with updated vegetation and wildfire data to
determine if and how wildfire may have impacted black bear habitat concentration areas (HCAs).
Land cover change was visible when comparing the GAP/LANDFIRE National
Terrestrial Ecosystems data [Previously GAP’s National Land Cover Dataset] from v2.2 (2001)
to v3.0 (2011). Landsat imagery used for the GAP v2.2 analysis layer was collected from 19992001, while the GAP v3.0 was updated with NLCD data and compared to satellite imagery to
verify changes (Homer et al., 2015). Comparison of GAP v2.2 and the updated GAP v3.0 layers
showed over 290,161 km2 of land cover change to and from grass-dominated, shrub-dominated,
and forest classification layers (Map 1). Appendix A shows the differences in classifications
from GAP v2.2 to v3.0 and the approximate area that changed.
To understand the potential role of fire in these landscape changes, I added a layer
showing outlines of fires larger than 200 km2 from 2010-2021 (Map 1). I found that most of
these fires occurred on the east side of the Cascades, just outside of black bear habitat
concentration area (HCA). The fires that did occur within the HCA were generally small or low
severity. To see how fire has changed land classifications I compared land change within fire
perimeters to their fire severity maps. I focused on fires that fell within or bordered the HCA that

19

have occurred within the last 10 years (Maps 3 to 11). In the following section, I provide a map
and overview of the key aspects of each fire, the dominant habitat type preceding the fire, major
vegetation changes within the last decade in which the fire occurred, and the likely short- and
long-term implications for black bear habitat. Short-term changes were defined as anything that
was impacted for less than a year. Long-term changes were defined as anything that was
impacted longer than a year.

20

Fire Locations
Vegetation
Type2010-2020
Map 2: Vegetation
typesand
within
fire perimeters

This map shows the vegetation type prior to each fire, as classified by Working Group.
West of the Cascades vegetation was classified as wet forest, wetland, and grass dominated. East
of the Cascades, where the largest fires occurred, vegetation was classified as dry forest, grassdominated, and shrub-dominated with patches of agriculture. These fires bordered habitat
concentration areas but did not fall directly within.

21

Individual Fires / Case Studies
The Carlton Complex (Map 4) burned from July 14, 2014 to August 24, 2014. It included
Stokes, Gold Hikes, French Creek, and Cougar Flat fires—all of which started as the result of
lightning strikes. It burned 256,108 acres and destroyed nearly 300 homes. The primary fuels
were timber (grass and understory) and dry forests such as ponderosa pine and Douglas-fir. The
high severity area in the northeast portion of the fire transitioned from dry forest to shrubdominated and from sparsely vegetated to shrub-dominated. On the westernmost portion of the
fire vegetation transitioned from dry forest to grass-dominated. The fire spread rapidly due to
strong winds and heavy fuels (Prichard et al., 2020). It did not pose a significant risk to black
bear habitat because of its location, which was outside two HCAs and not a predicted travel
corridor.

22

Map 3: Carlton Complex, 2015

23

Both the Lime Belt (Map 4) and Tunk Block (Map 5) fires were a part of the Okanogan
Complex which covered Omak, Tonasket, and Okanogan. Lime Belt burned from August 14,
2015 to September 30, 2015 and covered 133,428 acres. It burned mostly at low-moderate
severity. The westernmost portion of the fire showed the highest burn severity and fell within the
HCA. Tunk Block burned from August 13, 2015 to October 15, 2015 and covered 213,138
acres. It burned at low severity with some high severity areas (Inciweb, Tunk Block Fire, 2015).
Both fires were ignited by an unknown source. The vegetation burned consisted of shrub-steppe,
ponderosa pine, and Douglas-fir, which are fire adapted and dependent (BAER, Lime Belt Fire,
2015). While short-term there may have been impact on black bear habitat due to the highest
severity burn occurring within the HCA (habitat concentration area), long term vegetation
growth would be improved. Both fires fall within possible linkage zones as determined by
Working Group (2010). This fire may have changed where the linkage zone was drawn. Areas
that had severe burns may have been impacted short-term because of lack of cover and food
resources. However, long term vegetation growth would be improved because of the fire
dependency of this habitat type, which thrives with more frequent burns (Agee, 1993).

24

Map 4: Lime Belt Fire, 2015

Orange indicates vegetation change from sparsely vegetated to shrub dominated. Dark
brown indicates change from shrub-dominated to dry forest. Yellow indicates change
from sparsely vegetated to shrub-dominated. Beige indicates change from dry forest to
shrub-dominated. Bright green indicates change from shrub-dominated to grassdominated.

25

Map 5: Tunk Block Fire, 2015

26

North Star fire (Map 6) burned 217,619 acres from August 13, 2015 to September 28,
2015 and was human-caused. It burned forested areas on the Colville Reservation at moderate to
high severity. The vegetation that burned at high severity was classified as dry-forest such as
ponderosa pine and Douglas-fir (Inciweb, North Star Fire, 2015). A majority of the land
transitioned from dry forest to shrub-dominated. A small portion in the northwest portion of the
fire, shown in dark brown, transitioned from shrub-dominated to dry forest. A significant portion
of the fire fell within the HCA and likely had short-term impacts on black bears. Areas with low
to moderate severity burning may have provided enough cover for black bears to forage once
new vegetation grew. The portion of the North Star fire that fell within the HCA was classified
as high severity. This portion of the fire would impact habitat preference because of loss of
coverage and food resources.

27

Map 6: North Star Fire, 2015

28

Buck Creek fire (Map 7) burned from July 22, 2016 to August 30, 2016. It covered
around 3,500 acres in the old growth areas of Glacier Peak Wilderness. It burned at moderate to
high severity and was ignited by lightning. The area burned was primarily spruce, western
hemlock, and ponderosa pine. The areas that burned at high severity transitioned from wet forest
to grass-dominated, while areas that burned at low severity transitioned from wet forest to dry
forest. While it fell completely within the HCA, its impact to black bears would be minimal
because of its location and size; it was located completely within the HCA and was smaller than
the average female home range, leaving the bears ample options for navigation.

29

Map 7: Buck Creek Fire, 2016

30

Hayes Two fire (Map 8) burned from July 21, 2016 to August 28, 2016 and covered
around 3,000 acres. It was ignited by a lightning strike in Olympic National Park 20 miles south
of Port Angeles. The fire burned at high severity along a ridge line, consuming rotten and dead
trees (Inciweb, Hayes Two, 2016). Most of the land, and all of which burned at high severity,
transitioned from wet forest to grass dominated. Areas that burned at low severity transitioned
from wet forest to dry forest. Because this was a small fire fell completely within the HCA, its
impact to black bears would be minimal. Hayes Two burned rotten and dead trees, which
provided enough fuel to classify this fire as high severity, due to crown burning. Like Buck
Creek fire, it also fell completely within the HCA. Due to it being surrounded by suitable habitat,
it likely had minimal impact on black bears.

31

Map 8: Hayes Two Fire, 2016

32

Norse Peak fire (Map 9) burned from August 11, 2017 to November 1, 2017 in Mt.
Baker-Snoqualmie National Forest and Okanogan-Wenatchee National Forest. It was started by
lightning strike and burned 55,290 acres, with the highest severity in the wilderness interior. The
vegetation burned consisted of dry-forest types such as Douglas-fir, grand fir, mountain
hemlock, pacific fir, subalpine fir, and western hemlock. It burned around a portion of the Pacific
Crest Trail between Crystal Mountain and Cougar Valley, which has high foot traffic. Due to the
severity and location of the fire, there is a high risk of introduction or spread of invasive plant
species. Bare soil exposure also provides prime habitat for weed establishment, since the weeds
cannot be shaded out by native vegetation (USDA Forest Service, 2017). It likely had a
significant short-term impact on black bear habitat due to its location and severity, which likely
removed denning habitat for bears by removing downed trees that bears typically take shelter in
(Bull et al., 1997).

33

Map 9: Norse Peak Fire, 2017

34

Jolly Mountain fire (Map 10) burned from August 11, 2017 to November 2, 2017 in
Wenatchee National Forest. It was started by lightning and covered over 36,000 acres. It burned
a combination of whitebark pine, subalpine fir, huckleberry, Douglas fir, bitter cherry, and
beaked hazelnut. It burned at moderate to high severity (BAER, Jolly Mountain Fire, 2017).
Vegetation change was a mix of wet forest to dry forest and wet forest to grass-dominated. The
loss of food sources such as huckleberry and beaked hazelnut likely impacted black bears.
However, since the fire fell on the outer edge of the HCA, the impact may have been minimal.

35

Map 10: Jolly Mountain Fire, 2017

36

Diamond Creek fire (Map 11) burned from July 23, 2017 to mid-September in Pasayten
Wilderness. It started due to an improperly extinguished campfire. It covered 128,272 acres,
most of which burned severely. The vegetation burned consisted of mountain larch, whitebark
pine, subalpine fir, Englemann spruce, lodgepole pine, Alaskan yellow cedar, silver fir, and
mountain hemlock (BAER, Diamond Creek Fire,2017). Areas that burned at high severity were
reclassified from dry forest to dry forest. The fire fell completely within the HCA and likely
impacted black bear habitat by damaging possible denning sites and removing food resources.

37

Map 11: Diamond Creek Fire, 2017

38

Short-term habitat-related fire effects were classified as effects that persisted less than a
year. This includes reduction of food resources, cover, and potential den sites (Bogener, 2003;
Daubenmire, 1968; French & French, 1996; Hamilton, 1981). Availability of forage may
decrease in the short-term, but may begin to increase one year following a fire. As production of
early-seral vegetation increases, more food and cover become available (Cunningham et al.,
2006). As the canopy closes in later stages of succession, availability of some foods may
decrease; however, cover and potential den sites increase (Kellyhouse, 1979; Keyser and Ford,
2006).
Long-term effects were classified as effects that persisted longer than a year. These
include forest regeneration, invasive species, and loss of food resources. Potter and Kessell
(1985) found that black bears showed the lowest preference for foraging in unburned
communities and the highest preference for foraging in the postfire year 10 community. This
could be because American black bears require a mosaic of successional stages for foraging,
cover, and denning, so fires that create patches of burned and unburned habitat are most
beneficial (Bendell, 1974; Cunningham et al, 2003; Kellyhouse, 1979; Kovalchick and
Clausnitzer, 2004).
Although these fires varied in size, intensity, and location they all impacted black bear
habitat short-term by either removing food resources or damaging denning sites. The fires that
occurred outside of the HCA may have impacted areas that were important transportation
corridors for black bears. For instance, the Tunk Block and North Star fires fell between two
HCAs that were previously found to be potential travel corridors by Working Group (2010).
These corridors connect the HCAs from the Cascade Range to eastern Washington. Fires with
high intensity had the most impact on habitat due to their locations within the HCA and their loss

39

of tree cover. Nearly all these fires had high tree cover and fuel loading which allowed the
flames to creep into tree crowns and reduce cover at least short-term.
Not all fires shown in Map 1 were used to update GAP v3.0 because they lacked intensity
information that was used to reclassify vegetation. However, most of these fires fall outside of
the habitat concentration area because they were previously classified as non-ideal habitat for
black bears. Fires that occurred along the center of the Cascades would have had the biggest
impact on black bear habitat. These fires showed changes from wet forest to grass-dominated,
suggesting a loss of cover but increase in spring forbs that bears may forage on.
Changes in classifications were likely due to more than disturbance, such as changes in
satellite imagery, mapping methods, and human error. The goal of the GAP v3.0 update was to
“generate a detailed land cover product representing the 2011 timeframe. Differences between
the 2011 GAP maps do not always represent on-the-ground changes in vegetation communities”
(USGS, Gap Analysis Program, 2011). Some of the differences may be the product of
corrections to misclassifications in the original 2001 map. An area that contained the same
vegetation in 2001 and 2011 but was incorrectly mapped in 2001 would show up as changed.
The concepts of the Ecological Systems used to define the map legend are variable with a range
of physiognomic and phenological conditions possible in a single system. There are cases where
Ecological System land cover class may have remained “unchanged”, but the general land cover
class had changed between 2001 and 2011. For example, some areas correctly mapped as shrub
in the NLCD Layer (based on the NLCD definition) are best mapped as the Northern Rocky
Mountain Ponderosa Pine Woodland and Savanna ecological system in the GAP map and
therefore the 2001 Woodland and Savanna label would be retained (USGS, 2011). The most
notable example of this is visible in Map 12, in an area south of Spokane. In the 2001 Statewide
40

Analysis it was classified as grassland (Working Group, 2010). However, in GAP v3.0 it was
classified as Columbia Plateau Scabland Shrubland, Intermountain Basins Big Sagebrush Steppe,
and Northern Rocky Mountain Montane-foothill Deciduous Shrubland- all of which were
reclassified to shrub-dominated in the 2001 analysis. This area was classified as herbaceous
according to NLCD in 2019, suggesting that the land cover had not actually changed, but there is
a difference in classification definitions between NLCD and GAP. While using NLCD to assess
land change would leave less room for error, it only contains 16 land cover classes not suitable
for habitat assessment.

41

Map 12: South Spokane GAP Classification

42

Conclusion
In 2010 HCAs covered 53,071 km2 of the project area (Working Group). By using
updated GAP Analysis (2020) vegetation data along with disturbance data (LANDFIRE, 2020), I
was able to recalculate HCAs following the same methods used by Working Group. I found that
HCAs cover roughly 45,063 km2 of the assessment area, suggesting a loss of 8,008 km2 in
suitable habitat.
However, because land change detected from GAP v2.2 to v3.0 contained mostly
undisturbed areas, this could be a result of reclassification and not an indicator of on-the-ground
change. To further understand how much of the change was due to human error or remapping
efforts a comparison of satellite imagery and land cover layers should be done. To improve
habitat assessment more detailed vegetation data should be used in conjunction with disturbance
data. Visually comparing NLCD classifications to GAP classifications proved helpful in
identifying possible land change, but the differences in classifications are enough that they may
skew habitat assessments for more specialized species. As such, GAP’s habitat maps would best
suit such analyses.
While this study can provide insight into how bears may be affected by change in habitat
due to wildfires, additional studies will be needed to assess how they may be affected. Future
studies should include telemetry data from populations most at risk for exposure to fire, habitat
assessments following those fires, and physical observations of these populations. Telemetry
data would allow for a more accurate representation of where black bears reside in Washington,
as well as any changes in habitat preference following wildfires. Habitat assessments following
fires would give a more comprehensive view of the vegetation present post-burn and how long
vegetation recovery takes. If a fire occurred in an area and that area still had black bear
43

visitation, physical observation could provide insight into how the changed habitat is being
utilized. In combination telemetry data, habitat assessments, and physical observations would
give a clearer picture as to what is actually happening following a fire. This could change our
understanding of where habitat concentration areas are and where the linkage zones pass
through. This would allow for wildlife management following wildfires, especially near
residential areas.

44

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55

Appendices
Appendix A: Gap Reclassification
WHCWG

WHCWG Classification Value

1

Agriculture

2

3

4

Urban/Developed

GAP Ecosystem Classifications (2011)

556

Cultivated Cropland

557

Pasture/Hay

580

Quarries, Mines, Gravel Pits and Oil Wells

581

Developed, Open Space

582

Developed, Low Intensity

583

Developed, Medium Intensity

584

Developed, High Intensity

510

North Pacific Maritime Eelgrass Bed

552

Unconsolidated Shore

578

Open Water (Brackish/Salt)

579

Open Water (Fresh)

380

North Pacific Coastal Cliff and Bluff

381

North Pacific Maritime Coastal Sand Dune
and Strand

434

Columbia Plateau Vernal Pool

456

Inter-Mountain Basins Alkaline Closed
Depression

Water

Sparsely Vegetated

56

5

458

Inter-Mountain Basins Playa

529

Rocky Mountain Cliff, Canyon and Massive
Bedrock

531

North Pacific Montane Massive Bedrock,
Cliff and Talus

532

North Pacific Serpentine Barren

533

North Pacific Active Volcanic Rock and
Cinder Land

543

Columbia Plateau Ash and Tuff Badland

545

Inter-Mountain Basins Active and Stabilized
Dune

546

Inter-Mountain Basins Cliff and Canyon

565

Disturbed, Non-specific

308

North Pacific Alpine and Subalpine Dry
Grassland

502

Rocky Mountain Alpine Fell-Field

506

North Pacific Dry and Mesic Alpine DwarfShrubland, Fell-field and Meadow

507

Rocky Mountain Alpine Tundra/Fellfield/Dwarf-shrub Map Unit

549

Rocky Mountain Alpine Bedrock and Scree

551

North Pacific Alpine and Subalpine Bedrock
and Scree

554

North American Alpine Ice Field

Alpine

57

6

7

265

Columbia Basin Foothill Riparian Woodland
and Shrubland

266

Great Basin Foothill and Lower Montane
Riparian Woodland and Shrubland

269

Northern Rocky Mountain Lower Montane
Riparian Woodland and Shrubland

270

Rocky Mountain Lower Montane Riparian
Woodland and Shrubland

272

Rocky Mountain Subalpine-Montane
Riparian Woodland

274

North Pacific Lowland Riparian Forest and
Shrubland

275

North Pacific Montane Riparian Woodland
and Shrubland

439

Rocky Mountain Subalpine-Montane
Riparian Shrubland

562

Introduced Riparian and Wetland Vegetation

268

Northern Rocky Mountain Conifer Swamp

273

North Pacific Hardwood-Conifer Swamp

276

North Pacific Shrub Swamp

397

North Pacific Bog and Fen

398

Rocky Mountain Subalpine-Montane Fen

431

North Pacific Intertidal Freshwater Wetland

Riparian

Wetland

58

8

Grass-dominated

432

Temperate Pacific Freshwater Emergent
Marsh

433

Temperate Pacific Freshwater Mudflat

438

Rocky Mountain Alpine-Montane Wet
Meadow

440

Temperate Pacific Montane Wet Meadow

443

North American Arid West Emergent Marsh

455

Temperate Pacific Tidal Salt and Brackish
Marsh

508

Temperate Pacific Intertidal Mudflat

513

Temperate Pacific Freshwater Aquatic Bed

306

Columbia Basin Foothill and Canyon Dry
Grassland

307

Columbia Basin Palouse Prairie

309

North Pacific Montane Grassland

311

Northern Rocky Mountain Lower Montane,
Foothill and Valley Grassland

314

Northern Rocky Mountain Subalpine-Upper
Montane Grassland

319

North Pacific Herbaceous Bald and Bluff

321

Willamette Valley Upland Prairie and
Savanna

323

Rocky Mountain Subalpine-Montane Mesic
Meadow

59

9

441

Willamette Valley Wet Prairie

487

Columbia Plateau Steppe and Grassland

497

Inter-Mountain Basins Semi-Desert
Grassland

558

Introduced Upland Vegetation - Annual
Grassland

559

Introduced Upland Vegetation - Perennial
Grassland and Forbland

567

Harvested Forest - Grass/Forb Regeneration

571

Recently burned grassland

573

Recently burned forest

182

Columbia Plateau Western Juniper Woodland
and Savanna

184

Inter-Mountain Basins Curl-leaf Mountain
Mahogany Woodland and Shrubland

310

North Pacific Montane Shrubland

312

Northern Rocky Mountain Montane-Foothill
Deciduous Shrubland

313

Northern Rocky Mountain Subalpine
Deciduous Shrubland

320

North Pacific Hypermaritime Shrub and
Herbaceous Headland

430

North Pacific Avalanche Chute Shrubland

Shrub-dominated

60

10

457

Inter-Mountain Basins Greasewood Flat

484

Inter-Mountain Basins Mat Saltbush
Shrubland

485

Inter-Mountain Basins Mixed Salt Desert
Scrub

489

Inter-Mountain Basins Big Sagebrush
Shrubland

490

Inter-Mountain Basins Big Sagebrush Steppe

491

Inter-Mountain Basins Montane Sagebrush
Steppe

493

Columbia Plateau Low Sagebrush Steppe

494

Columbia Plateau Scabland Shrubland

498

Inter-Mountain Basins Semi-Desert Shrub
Steppe

561

Introduced Upland Vegetation - Shrub

568

Harvested Forest-Shrub Regeneration

572

Recently burned shrubland

54

East Cascades Oak-Ponderosa Pine Forest
and Woodland

57

North Pacific Dry Douglas-fir-(Madrone)
Forest and Woodland

58

North Pacific Oak Woodland

137

Middle Rocky Mountain Montane Douglasfir Forest and Woodland

Dry Forest

61

11

138

Northern Rocky Mountain Dry-Mesic
Montane Mixed Conifer Forest

141

Northern Rocky Mountain Ponderosa Pine
Woodland and Savanna

142

Northern Rocky Mountain Western Larch
Savanna

145

Inter-Mountain Basins Aspen-Mixed Conifer
Forest and Woodland

147

Northern Rocky Mountain Subalpine
Woodland and Parkland

148

Rocky Mountain Aspen Forest and
Woodland

149

Rocky Mountain Lodgepole Pine Forest

150

Rocky Mountain Poor-Site Lodgepole Pine
Forest

151

Rocky Mountain Subalpine Dry-Mesic
Spruce-Fir Forest and Woodland

174

North Pacific Wooded Volcanic Flowage

563

Introduced Upland Vegetation - Treed

569

Harvested Forest - Northwestern Conifer
Regeneration

136

East Cascades Mesic Montane MixedConifer Forest and Woodland

140

Northern Rocky Mountain Mesic Montane
Mixed Conifer Forest

152

Rocky Mountain Subalpine Mesic Spruce-Fir
Forest and Woodland

Wet Forest

62

166

North Pacific Broadleaf Landslide Forest and
Shrubland

167

North Pacific Dry-Mesic Silver Fir-Western
Hemlock-Douglas-fir Forest

168

North Pacific Hypermaritime Sitka Spruce
Forest

169

North Pacific Hypermaritime Western Redcedar-Western Hemlock Forest

170

North Pacific Lowland Mixed HardwoodConifer Forest and Woodland

171

North Pacific Maritime Dry-Mesic Douglasfir-Western Hemlock Forest

172

North Pacific Maritime Mesic-Wet Douglasfir-Western Hemlock Forest

173

North Pacific Mesic Western Hemlock-Silver
Fir Forest

177

North Pacific Maritime Mesic Subalpine
Parkland

178

North Pacific Mountain Hemlock Forest

260

East Gulf Coastal Plain Near-Coast Pine
Flatwoods - Open Understory Modifier

63

Appendix B: LANDFIRE Disturbance Reclassification
Time Since Disturbance

Disturbance Severity

High

2-5 Years

Medium

Low

High

6-10 Years

Medium

Low

From Habitat Type

To Habitat Type

Wet Forest

Grass-dominated

Dry Forest

Grass-dominated

Shrub-dominated

Grass-dominated

Wet Forest

Grass-dominated

Dry Forest

Grass-dominated

Shrub-dominated

Grass-dominated

Wet Forest

Dry Forest

Dry Forest

Dry Forest

Shrub-dominated

Grass-dominated

Wet Forest

Shrub-dominated

Dry Forest

Shrub-dominated

Shrub-dominated

Shrub-dominated

Wet Forest

Shrub-dominated

Dry Forest

Shrub-dominated

Shrub-dominated

Shrub-dominated

Wet Forest

Dry Forest

Dry Forest

Dry Forest

Shrub-dominated

Shrub-dominated

Areas classified as disturbed by LANDFIRE Fuel Disturbance (2020) were reclassified
according to the above table, which updated the 2011 GAP layer.

64

Appendix C: Resistance Values
Spatial data layers and included factors

Resistance value

land cover/land-use
agriculture

100

urban/developed

200

water

100

sparsely vegetated

1

alpine

0

riparian

0

wetland

0

grass-dominated

1

shrub-dominated

1

dry forest

1

wet forest

0
Elevation (meters)

0-250

5

>250-500

5

>500-750

4

>750-1000

3

>1000-1500

2

>1500-2000

1

>2000-2500

0

>2500-3300

1

>3300

100

65

slope (degrees)
0-20

0

>20-40

1

>40

3

Housing density (acres per dwelling unit)
>80

0

>40 <80

10

>20 <40

10

>10 <20

10

<10

100

Road type and distance (meters)*
freeway >500-1000 buffer

10

freeway > 0-500 buffer

50

freeway centerline

1000

major highway > 500-100 buffer

5

major highway > 0-500 buffer

10

major highway centerline

100

secondary highway > 500-1000 buffer

4

secondary highway > 0-500 buffer

8

secondary highway centerline

50

local road > 500-1000 buffer

1

local road > 0-500 buffer

2

local road centerline

3

66

Appendix D: Land Classification Change 2001-2021
Classvalue

Name

122 No Change
121 Wet Forest->Dry Forest
Grass-dominated->Shrub89
dominated
Shrub-dominated->Grass99
dominated
Sparsely Vegetated->Dry
50
Forest
Agriculture->Shrub19
dominated
Shrub-dominated->Dry
100
Forest
Wet Forest->Shrub120
dominated
Dry Forest->Shrub110
dominated
Wet Forest113
>Urban/Developed
90 Grass-dominated->Dry Forest
Wet Forest->Grass119
dominated
Sparsely Vegetated->Wet
51
Forest
Agriculture12
>Urban/Developed
111 Dry Forest->Wet Forest
109 Dry Forest->Grass-dominated
Sparsely Vegetated->Shrub49
dominated
Agriculture->Grass18
dominated
Shrub-dominated->Wet
101
Forest
Sparsely Vegetated->Grass48
dominated
Shrub-dominated93
>Urban/Developed
Grass-dominated83
>Urban/Developed
117 Wet Forest->Riparian
Grass-dominated82
>Agriculture

From

To

Same
Wet Forest

Same
Dry Forest

Grass-dominated

Shrub-dominated

Shrub-dominated

Grass-dominated

Sparsely
Vegetated

Dry Forest

Agriculture

Shrub-dominated

Shrub-dominated

Dry Forest

Wet Forest

Shrub-dominated

Dry Forest

Shrub-dominated

Wet Forest

Urban/Developed

Grass-dominated

Dry Forest

Wet Forest

Grass-dominated

Sparsely
Vegetated

Wet Forest

Agriculture

Urban/Developed

Dry Forest
Dry Forest
Sparsely
Vegetated

Wet Forest
Grass-dominated

Agriculture

Grass-dominated

Shrub-dominated

Wet Forest

Sparsely
Vegetated

Grass-dominated

Shrub-dominated

Urban/Developed

Grass-dominated

Urban/Developed

Wet Forest

Riparian

Grass-dominated

Agriculture

67

Shrub-dominated

Area
(km2)
1182580
56634.3
44577.9
42627.1
40077.6
31411
25435.5
22153.9
21234.6
20491.2
20191.8
18077.8
18016.8
17294.7
15511.4
12489
11798.7
11788.3
8752.5
8425.9
8111.4
6709.2
6500.4
6074

45 Sparsely Vegetated->Alpine
92
103
43
71
107
118
91
70
97
63
20
21
42
16
73
72
22
81
69
112
114
62
87
94
29
31
98

Shrub-dominated>Agriculture
Dry Forest>Urban/Developed
Sparsely Vegetated>Urban/Developed
Riparian->Wet Forest
Dry Forest->Riparian
Wet Forest->Wetland
Grass-dominated->Wet
Forest
Riparian->Dry Forest
Shrub-dominated->Riparian
Riparian->Urban/Developed
Agriculture->Dry Forest
Agriculture->Wet Forest
Sparsely Vegetated>Agriculture
Agriculture->Riparian
Wetland->Urban/Developed
Wetland->Agriculture
Urban/Developed>Agriculture
Wetland->Wet Forest
Riparian->Shrub-dominated
Wet Forest->Agriculture
Wet Forest->Water
Riparian->Agriculture
Grass-dominated->Riparian
Shrub-dominated->Water
Urban/Developed->Shrubdominated
Urban/Developed->Wet
Forest
Shrub-dominated->Wetland

44 Sparsely Vegetated->Water
67
64
33
77
84

Riparian->Wetland
Riparian->Water
Water->Urban/Developed
Wetland->Riparian
Grass-dominated->Water

Sparsely
Vegetated

Alpine

Shrub-dominated

Agriculture

Dry Forest

Urban/Developed

Sparsely
Vegetated
Riparian
Dry Forest
Wet Forest

Urban/Developed
Wet Forest
Riparian
Wetland

Grass-dominated

Wet Forest

Riparian
Shrub-dominated
Riparian
Agriculture
Agriculture
Sparsely
Vegetated
Agriculture
Wetland
Wetland

Dry Forest
Riparian
Urban/Developed
Dry Forest
Wet Forest
Agriculture
Riparian
Urban/Developed
Agriculture

Urban/Developed Agriculture
Wetland
Riparian
Wet Forest
Wet Forest
Riparian
Grass-dominated
Shrub-dominated

Wet Forest
Shrub-dominated
Agriculture
Water
Agriculture
Riparian
Water

Urban/Developed Shrub-dominated
Urban/Developed Wet Forest
Shrub-dominated
Sparsely
Vegetated
Riparian
Riparian
Water
Wetland
Grass-dominated

68

Wetland
Water
Wetland
Water
Urban/Developed
Riparian
Water

4448.5
4404.7
4191.4
3891.1
3142.9
2893.9
2686
2476
2129.1
2098.1
2058.9
1735.3
1727.9
1625.3
1539.3
1528.6
1490.1
1466.8
1382.6
1307.1
1239.9
1203.3
1189.9
1125.9
961.3
950.8
915.5
859.8
842.2
792.3
785.9
778.9
769.6
766.6

Urban/Developed->Dry
Forest
Sparsely Vegetated46
>Riparian
30

55 Alpine->Sparsely Vegetated
95
61
68
80
28
17
41
78
88
74
39
13
60
40
115
79
85
106
36
47
23
102
108
32
38
116
37
105
104
26
27
14

Shrub-dominated->Sparsely
Vegetated
Alpine->Wet Forest
Riparian->Grass-dominated
Wetland->Dry Forest
Urban/Developed->Grassdominated
Agriculture->Wetland
Water->Wet Forest
Wetland->Grass-dominated
Grass-dominated->Wetland
Wetland->Water
Water->Shrub-dominated
Agriculture->Water
Alpine->Dry Forest
Water->Dry Forest
Wet Forest->Sparsely
Vegetated
Wetland->Shrub-dominated
Grass-dominated->Sparsely
Vegetated
Dry Forest->Alpine
Water->Riparian

Urban/Developed Dry Forest
Sparsely
Vegetated
Alpine
Shrub-dominated
Alpine
Riparian
Wetland

Riparian
Sparsely
Vegetated
Sparsely
Vegetated
Wet Forest
Grass-dominated
Dry Forest

Urban/Developed Grass-dominated
Agriculture
Water
Wetland
Grass-dominated
Wetland
Water
Agriculture
Alpine
Water
Wet Forest
Wetland
Grass-dominated

Dry Forest
Water
Sparsely
Sparsely Vegetated->Wetland
Vegetated
Urban/Developed->Water
Urban/Developed
Dry Forest->Agriculture
Dry Forest
Dry Forest->Wetland
Dry Forest
Water->Agriculture
Water
Water->Grass-dominated
Water
Wet Forest->Alpine
Wet Forest
Water->Wetland
Water
Dry Forest->Sparsely
Dry Forest
Vegetated
Dry Forest->Water
Dry Forest
Urban/Developed->Riparian Urban/Developed
Urban/Developed->Wetland
Urban/Developed
Agriculture->Sparsely
Agriculture
Vegetated

69

Wetland
Wet Forest
Grass-dominated
Wetland
Water
Shrub-dominated
Water
Dry Forest
Dry Forest
Sparsely
Vegetated
Shrub-dominated
Sparsely
Vegetated
Alpine
Riparian
Wetland
Water
Agriculture
Wetland
Agriculture
Grass-dominated
Alpine
Wetland
Sparsely
Vegetated
Water
Riparian
Wetland
Sparsely
Vegetated

723.3
676.1
669.9
647.9
638.9
627.6
621.3
600.3
589.7
564.8
556.9
511
495.3
485.9
482.6
463.4
437.7
411
393.2
385.7
383
366.5
348.3
320.3
320.1
281.4
271.4
249.8
246
236.4
221
210.5
168.3
133.2
95.4

59 Alpine->Shrub-dominated
96 Shrub-dominated->Alpine
86 Grass-dominated->Alpine

Alpine
Shrub-dominated
Grass-dominated

Shrub-dominated
84.1
Alpine
74.8
Alpine
59.5
Sparsely
34 Water->Sparsely Vegetated
Water
Vegetated
53.9
Riparian->Sparsely
Sparsely
65
Riparian
Vegetated
Vegetated
40.1
58 Alpine->Grass-dominated
Alpine
Grass-dominated
33.6
54 Alpine->Water
Alpine
Water
28.7
Sparsely
75 Wetland->Sparsely Vegetated Wetland
Vegetated
19.5
Urban/Developed->Sparsely
Sparsely
24
Urban/Developed
Vegetated
Vegetated
18.6
35 Water->Alpine
Water
Alpine
8.1
53 Alpine->Urban/Developed
Alpine
Urban/Developed
7
15 Agriculture->Alpine
Agriculture
Alpine
6.1
66 Riparian->Alpine
Riparian
Alpine
5.3
56 Alpine->Riparian
Alpine
Riparian
4.1
57 Alpine->Wetland
Alpine
Wetland
4
76 Wetland->Alpine
Wetland
Alpine
2.6
52 Alpine->Agriculture
Alpine
Agriculture
0.8
25 Urban/Developed->Alpine
Urban/Developed Alpine
0.8
Red rows highlight changes that could be due to disturbance, such as fire. Orange rows highlight
areas that could be re-establishing post-disturbance. Gray rows highlight classes that have been
reclassified as developed. Rows below the bold black line are changes that are smaller than the
average American black bear home range (200 km2). It is important to note that all changes may
not be reflective of on-the ground change, but reclassifications or improvements in satellite
imagery.

70

Appendix E: Data Sources
Land Cover/ Land-Use and Forest Structure
Theme: Gap Analysis Program National Terrestrial Ecosystems
Source: Gap Analysis Project, USGS
Format: raster
Cell size: 30 meters
Publication date: 2011
Landsat acquisition period: ~2001
Online linkages: https://www.usgs.gov/programs/gap-analysis-project/science/land-cover-vision

Theme: NLCD Land Cover
Source: Multi-Resolution Land Characteristics (MRLC) Consortium
Format: raster
Cell size: 30 meters
Publication date: 2019
Online linkages: https://s3-us-west2.amazonaws.com/mrlc/nlcd_2019_land_cover_l48_20210604.zip

Theme: Existing Vegetation Type (EVT)
Source: LANDFIRE (Landscape Fire and Resource Management Planning Tools Project)
Format: raster
Cell size: 30 meters
Publication date: 2016
Online linkages: https://landfire.gov/bulk/downloadfile.php?FNAME=US_200_mosaicLF2016_EVT_200_CONUS.zip&TYPE=landfire

Theme: National Vegetation Classification (NVC)
Source: LANDFIRE (Landscape Fire and Resource Management Planning Tools Project)
Format: raster
Cell size: 30 meters
Publication date: 2016 REMAP

71

Acres per Dwelling Unit (Housing Density)
Theme: Housing Density 2010
Source: Natural Resource Ecology Lab, Colorado State University, Fort Collins, CO
Format: raster
Cell size: 100 meters
Publication date: 2011

Elevation
Theme: National Elevation Dataset (NED)
Source: US Geological Survey
Format: raster, elevation unit meters
Cell size: 30 meters

72

Appendix F: Base Maps

73

74