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Title
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Making Connections Analyzing Habitat Connectivity for the Gray Wolf in Coastal Washington
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Creator
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Pushee, Marisa
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Date
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2020
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extracted text
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MAKING CONNECTIONS
ANALYZING HABITAT CONNECTIVTY FOR THE GRAY WOLF IN
COASTAL WASHINGTON
by
Marisa Pushee
A Thesis
Submitted in partial fulfillment
of the requirements for the degree
Master of Environmental Studies
The Evergreen State College
September 2020
ã 2020 by Marisa Pushee. All rights reserved.
This Thesis for the Master of Environmental Studies Degree
by Marisa Pushee
has been approved for The Evergreen State College
by
_______________________________
Timothy Quinn, PhD
Member of the Faculty
__________________________
Date
ABSTRACT
Making Connections:
Analyzing Habitat Connectivity for the Gray Wolf in Coastal Washington
Marisa Pushee
The Gray Wolf (Canis lupus) is a federally-listed endangered species in the U.S. that requires
large habitat ranges and is highly human-avoidant. The compounding impacts of climate change
and increasing land development in Washington State threaten to further impede Wolf recovery.
As a result, preserving Wolf habitat and movement routes over the long-term may be essential to
achieving Washington’s recovery goals as outlined in the state’s Wolf Conservation and
Management Plan. Identifying and maintaining core habitat and corridors may assist wildlife
managers in mitigating the impact of both anthropogenic development and climate change on
Wolves. These efforts may also assist land-use managers and planners in considering how
landscape configuration could serve to minimize human-Wolf conflicts in the future. One of the
first steps in this type of habitat planning is understanding how the Gray Wolf utilizes the
landscape. To do this, I estimated habitat suitability scores and landscape resistance scores for
land cover, streams, roads, elevation bands, buildings, and other landscape features by consulting
Wolf experts and conducting a literature review on Wolf habitat use. Habitat suitability scores
describe Wolf habitat quality and landscape resistance scores indicate the degree to which
landscape features impede or direct Wolves as they move through landscape. The results of this
work can be used to conduct landscape permeability analyses and inform recommendations for
Wolf conservation. In collaboration with the Washington Wildlife Habitat Connectivity Working
Group, this project will contribute to the ongoing analysis of habitat connectivity for the Gray
Wolf in Washington State as well as support the state’s goal for Wolf recovery.
Table of Contents
List of Figures…………………………………………………………………………………….vi
List of Tables…………………………………………………………………………………….vii
Acknowledgements……………………………………………………………………………..viii
CHAPTER 1: INTRODUCTION….……………………………………………………………...1
Introduction………………………………………………………………………………..1
Wolves in Washington…………………………………………………………………….2
CHAPTER 2: LITERATURE REVIEW………….………………………………………………7
Introduction………………………………………………………………………………..7
Habitat Fragmentation…………………………………………………………………….8
Habitat Connectivity and Genetic Drift…………………………………………………...9
Wildlife Corridors and Crossing Structures……………………………………………...11
Concerns and Criticisms………………………………...……………………………….12
The Gray Wolf…………………………………………………………………………...14
Habitat Connectivity in Washington State……………………………………………….18
Gaps in the Research……………………………………………………………………..20
Human-Carnivore Conflict………………………………………………………………22
Conclusion……………………………………………………………………………….23
CHAPTER 3: METHODS…………….…………………………………………………………24
Introduction………………………………………………………………………………24
Washington Wildlife Habitat Connectivity Working Group…………………………….24
The Study Area…………………………………………………………………………..25
Determining Habitat Suitability and Landscape Resistance Scores……………………..27
Literature Review………………………………………………………………………..28
Soliciting Expert Opinion………………………………………………………………..28
CHAPTER 4: RESULTS……………………………….………………………………………..30
Habitat Suitability Scores………………………………………………………………..30
Landscape Resistance Scores…………………………………………………………….34
CHAPTER 5: DISCUSSION………………………………..…………………………………...38
Bibliography……………………………………………………………………………………..40
iv
Appendix A: Scoring Instructions……………………………………………………………….49
Appendix B: NOAA Regional Land Cover Classification Scheme……………………………..51
Appendix C: Literature Review of Gray Wolf Landscape Use………………………………….56
v
List of Figures
Figure 1: Pack distribution of the Gray Wolf in Washington State……………………………….4
Figure 2: Gray Wolf movement patterns before and after corridor restoration………………….17
Figure 3: MaxEnt distribution model for coastal and interior Wolves within the area of the
natural re-colonization zone……………………………………………………………………...18
Figure 4: Habitat concentration areas and fracture zones for large carnivores in
Washington………………………………………………………………………………...…….19
Figure 5: Study area for the coastal Washington connectivity analysis………………………...26
vi
List of Tables
Table 1: Habitat Suitability Scores for the Gray Wolf…………………………………………..30
Table 2: Landscape Resistance Scores for the Gray Wolf……………..………………………...34
Appendix Table 1: Literature Review of Gray Wolf Landscape Use……………………………56
vii
Acknowledgements
I owe my gratitude to a number of people who have supported me throughout the thesis process.
I’d like to thank my reader, Tim Quinn, for his exceptional guidance. I’d also like to thank the
MES faculty and staff, including John Withey, Mike Ruth, Kevin Francis, Andrea Martin, and
Averi Azar. Thank you to the Washington Wildlife Habitat Connectivity Working Group,
especially Kelly McAllister and Glen Kalisz with Washington Department of Transportation, for
welcoming me into this project. Thank you also to Gregg Kurz with U.S. Fish and Wildlife
Service, Julia Smith with Washington Department of Fish and Wildlife, and an anonymous Wolf
expert for their integral input on this project. Thank you to my coworkers and managers at Wolf
Haven International and Sustainability in Prisons Project. A special thanks to my MES
colleagues, including my peer reviewers Christine Davis and Sam Alfieri. Finally, thank you to
my friends and family for their support, including my mom, my partner Preston, and my dog
Hiji.
viii
CHAPTER 1: INTRODUCTION
Introduction
On an ever-increasing scale, anthropogenic development and climate change
detrimentally impact global biodiversity (LeCraw et al., 2014). Human populations continue to
grow, development expands, landscape becomes fragmented, and biodiversity is often reduced
(Brodsky and Safronova, 2017). Identifying and maintaining core wildlife habitat and wildlife
corridors provides a potential management strategy to help mitigate these impacts. However,
landscape fragmentation is a complex issue. Promoting landscape connectivity has the potential
to contribute to conserving native flora and fauna, but designing and building functional
corridors and crossing structures requires an understanding of animal movement ecology In cases
where connectivity does address all the life history requirements of a species, additional
conservation strategies may be needed. Further, wildlife corridors and crossing structures
encompass a wide range of structures, landscape features, and purposes.
A focal species approach offers one method for improving landscape connectivity. A
focal species approach can serve multiple benefits, especially if that focal species serves as an
umbrella species and impacts a range of other wildlife. Maintaining connectivity for species
requiring large areas may also benefit a larger suite of species that use similar habitats at smaller
spatial scales. Additionally, creating core areas and corridors to protect apex predators or other
keystone species can similarly multiply the conservation effects of single species management.
For example, the Gray Wolf in the greater Yellowstone Ecosystem presents a compelling case
for the disproportionate effects of a single apex predator on ecosystem structure and function
(Wilmers and Getz, 2005). The reintroduction of the Gray Wolf in Yellowstone National Park
(YNP) included the regrowth of Aspen (Populus tremuloides) stands and Willow (Salix
1
melanopsis), especially in riparian areas, as well as an increase in Beaver (Castor canadensis)
population as a result of the Wolf’s impact on elk (Cervus canadensis) populations and habitat
use (Smith et al, 2003; Smith et al, 2009).
For this thesis, I examined the Gray Wolf as a focal species for mapping habitat
connectivity in coastal Washington State, including the Olympic Peninsula and expanding to the
Cascades and south to the Columbia River. I developed habitat suitability and landscape
resistance scores for landscape factors based on peer-reviewed literature and expert opinion. This
project has the potential to support wildlife managers in navigating potential conflict areas
between the Gray Wolf and humans in western Washington. In this thesis, I first provide
background on the Gray Wolf in Washington State followed by a literature review with a focus
on habitat connectivity and the Gray Wolf. I then discuss my use of a targeted literature review
on the Gray Wolf’s landscape use as well as inclusion of expert opinion in developing habitat
suitability and landscape resistance scores. The detailing of my methodology is followed by the
resulting scores from my project and a discussion of the potential uses and impacts of this work
including conserving potential Gray Wolf habitat and mitigating human-Wolf conflict.
Wolves in Washington
The Gray Wolf has a long and complex history in Washington State. Common to
Washington prior to 1800 and persisting at least until the 1850s, the Gray Wolf was extirpated
from the state in the 1930s (Wiles et al., 2011; Scheffer, 1995). Euro-American colonization of
the pacific northwest resulted in Wolf control through the fur trade and bounty incentives
(Harding, 1909; Adamire, 1985). While scattered Wolf sightings persisted throughout the
following decades, they were largely unsubstantiated (Wiles et al., 2011).
The Gray Wolf recolonized eastern Washington in 2008 likely from populations in
2
Canada, Montana, and Idaho, and Wolf management has been challenging in the years since
(Wiles et al., 2011). After their return to Washington State, tensions around the Gray Wolf in
Washington have been high, especially in eastern Washington where the Gray Wolf is most
prevalent and most likely to conflict with humans when they prey on livestock in open range
areas. Managing the Gray Wolf on land grazed by livestock is often logistically difficult,
expensive, and deeply controversial. Despite intense conflict with some human interests,
however, the Gray Wolf plays an important role in the ecosystem (Shepherd and Whittington,
2006). As an apex predator and keystone species, the Gray Wolf directly and indirectly affects
the composition, structure, and functioning of ecosystems as well as the provisioning of
ecosystem goods and services (Callan, 2013). Ecosystem services, including supporting,
provisioning, regulating, and cultural services can be critical for serving human needs as well as
maintaining functional ecosystems (Millennium Ecosystem Assessment, 2002). By maintaining
the Gray Wolf population in Washington State, wildlife managers may be able to increase the
resilience of ecosystems to human stressors such as climate change and habitat conversion.
Nonetheless, conflict between humans and endangered species, like the Wolf, present difficult
and ongoing challenges to many stakeholders.
The Gray Wolf is expanding its range in Washington State. At the end of 2019, The
Washington Department of Fish and Wildlife (WDFW) counted 108 known Gray Wolves in 21
packs and the Confederated Tribes of the Colville Reservation counted an additional 37 Wolves
in five packs (WDFW, 2020). The Gray Wolf is currently most prevalent in the northeastern
region of Washington State (Figure 1). Compared to the eastern side of Washington State, the
western side is more heavily populated with humans, has more infrastructure, and is more
fragmented, making much of the potential habitat less ideal for the Gray Wolf. Despite western
3
Washington’s higher level of anthropogenic development, the landscape still offers potential
suitable habitat for the Gray Wolf, a habitat generalist. One major barrier to Wolves colonizing
western Washington is Interstate 90, a major east-west highway that acts as a barrier to many
wildlife species, including the Gray Wolf. However, recently constructed wildlife crossing
structures may eventually aid in Wolf dispersal.
Figure 1: Pack distribution of the Gray Wolf in Washington State (WDFW, 2020).
Despite management challenges surrounding the Gray Wolf, the species offers an
instructive candidate for mapping habitat connectivity in western Washington. Because the Gray
Wolf is highly human-avoidant and thus tends to constrain its movement near anthropogenic
development (Rio-Maior et al., 2018), preserving habitats and travel routes may be essential for
meeting and maintaining the state’s recovery goals for the Gray Wolf. Careful selection of Wolf
core areas and travel corridors also provides a proactive management approach to avoid or
minimize unwanted human-Wolf interactions. Wolf conservation efforts will need to consider
human development in addition to prey resources and habitat suitability when evaluating
potential habitat in support of stable Wolf populations. A key component of coexistence will
4
likely require minimizing or avoiding human activity within carnivore habitat (Rio-Maior et al.,
2018), and discouraging wolf use of human habitat.
In addition to preserving potential Wolf habitat separate from humans on the landscape,
maintaining likely travel routes and general connectivity may be critical for Gray Wolf recovery
in areas inhabited by humans (Clark et al. 1996, Noss et al. 1996, Weaver et al. 1996). As human
development increases, the persistence of Wolves will likely depend on their ability to move
through the western Washington landscape to meet a variety of needs including finding food,
dispersing, mating, and rearing pups, while avoiding areas that lead to human-Wolf conflict. As
outlined in Washington’s Wolf Conservation and Management Plan, WDFW aims to restore a
self-sustaining Wolf population in the state (Wiles et al., 2011). This would ideally include at
least 15 breeding pairs for a minimum of three years with a minimum of four pairs in eastern
Washington, four in the North Cascades, four in the South Cascades, and an additional three
throughout the state (Wiles et al., 2011). However, apex predators like the Gray Wolf are
especially vulnerable to the impacts of habitat fragmentation, because they have requirements for
large contiguous areas with low potential for human contact (LeCraw et al, 2014). Wolves living
within fragmented patches can become isolated from larger populations and may be at risk of
environmental, demographic and genetic (genetic drift and inbreeding depression) stochasticity.
Additionally, as climate change impacts increase, improving species resilience now through
careful planning may help ensure that Gray Wolf populations can adapt to future environmental
disruptions.
As Washington continues to develop, wildlife managers and landscape planners will need
to identify strategies to mitigate the impacts of anthropogenic activity on the most vulnerable
wildlife. Perhaps more importantly, in order to meet recovery goals for the Gray Wolf, the state
5
may need to proactively identify and protect Wolf habitat, while at the same time considering
how to manage the landscape in order to minimize conflict.
Further study on the effects of habitat fragmentation on the Gray Wolf may prove useful
in mitigating human-Wolf conflict. Since the Gray Wolf is human-avoidant and sensitive to
habitat fragmentation, human-caused landscape fragmentation can reduce Wolf habitat
suitability, which in the extreme includes direct mortality of Wolves by humans (Gude et al.
2012; Hanley, 2019). On the other hand, the Gray Wolf is also resilient and may recover once
human exploitation is reduced or discontinued (Hayes and Harestad 2000, Fuller et al., 2003).
This thesis contributes to Washington’s recovery goals for the Gray Wolf by identifying
areas to prioritize protection and restoration of potential Wolf habitat and connectivity. Drawing
from the literature and expert opinion, I developed habitat suitability and landscape resistance
scores for the Gray Wolf. These scores can be used to map habitat suitability and landscape
connectivity, and may also be useful in identifying areas for acquisitions as well as focusing
investment in constructing crossing structures or improving existing wildlife corridors for linear
barriers such as major roads, large rivers, streams, and other features.
6
CHAPTER 2: LITERATURE REVIEW
Introduction
Anthropogenic development and climate change will increase the challenges that many
species face. As greenhouse gas emissions alter global climates, the distribution of vegetation
community changes as a result (Williamson et al., 2016). Additionally, the increase in lands to
support human activities impacts ecosystem health and stability, leading to decline as a result of
pollution, loss in biodiversity, and habitat fragmentation (Hautier et al., 2015). Kabenick and
Jennings (2017) state that we are in the midst of the sixth major extinction event reaching as high
as 150 species per day. Others describe the current crises in terms of “a global wave of
anthropogenically driven biodiversity loss,” noting both species and population extirpations as
well as declines in local species abundance caused by habitat conversion including both habitat
loss and fragmentation, over exploitation, invasive species, and disease (Dirzo et al., 2014).
Abrahms (2017) suggests approaching these impacts of climate change from a place of
adaptation, where wildlife managers consider the benefits of both maintaining wildlife reserves
and connecting existing habitat, while monitoring and evaluating outcomes in order to optimize
future success in a changing environment. This adaptive management approach could prove
beneficial for many conservation initiatives. For example, the western U.S. has witnessed an
increase in drought and wildfires, which may ultimately transform once forested areas into shrub
or grasslands. Where drought and fire occur in proximity to endangered species (Williamson et
al., 2016), these species may need to alter their ranges in order to survive. Many species have
already begun to shift the timing of their lifecycles, including migration, as well as their
geographic ranges in response to changes in temperature (Abrahms, 2017). Additionally, where
vegetation community shifts are more subtle, climate change may decrease habitat quality by
7
decreasing access to much needed resources. Isolated populations are most at risk when they lack
the ability to travel to other areas in search of habitat. Additional stressors like anthropogenic
development and exotic species invasions compound the effects of climate change on vulnerable
native species. In order to maintain native species populations and biodiversity, wildlife
managers will need to consider local climate projections and how vulnerable species respond to
changes, and account for uncertainty in their predictions (Abrahms, 2017).
Landscape fragmentation can have some of the most severe impacts on large carnivores
like the Gray Wolf. As a result of their sensitivity to and avoidance of anthropogenic activity,
carnivore conservation efforts will need to consider human development when evaluating the
potential for carnivores to establish stable populations (Rio-Maior et al., 2018). In this literature
review, I focus on: 1) the effects of habitat conversion and fragmentation on Gray Wolf
populations, 2) genetic problems associated with small and isolated populations, and 3) how
connectivity can address small population issues. Finally, I apply these concepts to recovery of
the Gray Wolf in Washington State.
Habitat Fragmentation
Anthropogenic development has resulted in widescale habitat loss and fragmentation for
a wide variety of species in many parts of the world (Ezard and Travis, 2006). These threats are
part of a larger group of related anthropogenic activities that contribute to shifts in climate and
increased frequency of large-scale disturbance events, spread of invasive species, the overharvesting of natural resources, and pollution that combine to threaten global biodiversity (Brody
and Safronova, 2017; Reed F. Noss, 1987).
Two of the primary threats of habitat fragmentation include a decrease in habitat area and
isolation of existing habitat patches (Fahrig and Merriam, 1994). For example, high-traffic roads
8
not only reduce habitat, they can also act as effective barriers to movements by preventing
animal crossings, and by causing high levels of mortality to crossing animals (Forman, 2002).
Fragmented landscapes are often depicted as a series of isolated patches of habitat that each
contain relatively small populations of individuals of a particular species. To a lesser or greater
degree, depending on population size and connectivity among patch populations, these isolated
populations are subject to stochastic forces associated with the small population paradigm. These
stochastic forces include environmental, demographic and genetic issues (Ezard and Travis,
2006).
Habitat Connectivity and Genetic Drift
Landscape connectivity can be crucial for promoting species resilience to stochastic
processes affecting small populations, including genetic drift and inbreeding depression, by
promoting the exchange of genes among populations. Genetic drift, also referred to as genetic
stochasticity, is a key factor in maintaining biodiversity and genetic variation in species
populations (Cortazar-Chinarro et al., 2017). Genetic drift is the process whereby alleles are lost
to a population from generation to generation due to chance events, thereby decreasing the
genetic variation of a population (Cortazar-Chinarro et al., 2017). Loss of alleles in small
populations also increases the probability of inbreeding depression—that is the mating of closely
related individuals, which can result in loss of adaptive potential as well as increased sensitivity
to environmental and demographic stochasticity (Weckworth et al., 2013). Genetic variation
(sometimes measured by allele frequency) is thought to confer advantages to species by virtue of
the fact that variation can better equip species to survive in a rapidly changing environment.
Thus, small populations, i.e., those isolated in disconnected habitat patches, experience genetic
drift faster than larger connected populations.
9
Additionally, habitat availability and effective population size have a strong influence
over genetic variability (Weckworth et al., 2013). Habitat geometry, including size, shape, and
connectivity among patches (via immigration and emigration) affects population size and thus
rates of genetic drift. Further, effective population size (Ne), which considers the number of
sexually reproducing adults in a population (rather than the overall population size), is an
important parameter in determining population dynamics (Weckworth et al., 2013). An abrupt
change in habitat availability can surpass thresholds after which a species cannot persist.
Species’ responses to habitat loss may depend on both the amount of habitat that is lost as well as
the pattern in which it is lost (Ezard and Travis, 2006). Factors contributing to extinction and
thresholds at which species become functionally extinct are species-specific. Since these
processes are typically unknown to wildlife managers, it may be critical to err on the side of
caution to ensure that species viability thresholds are not crossed (Ezard and Travis, 2006).
Finally, it is important to keep in mind that the complex relationships between ecological (biotic)
and landscape (abiotic) processes are often not well-understood (Ezard and Travis, 2006).
Because habitat quality and quantity, and population size are commonly related to species
viability, wildlife managers and landscape planners need to consider multiple factors when
designing landscapes to maintain connectivity. Habitat connectivity is a relative term and is both
species and landscape specific, that is – connectivity is a function of species ecology and
landscape features (Tischendorf and Fahrig, 2000). Habitat fragmentation is likely a greater
threat to viability of species that rely on long distance movements e.g., migration to complete
their life history, such as Monarch Butterfly (Danaus plexippus) or Salmon (Salmo salar), and
long-distance dispersal from natal home ranges for species like the Gray Wolf (Mech et al.,
2001).
10
Wildlife Corridors and Crossing Structures
Wildlife corridors typically refer to naturally occurring landscape features that promote
species movement, whereas wildlife crossing structures are man-made features that are designed
to facilitate species movement. Corridors can be defined as linear patches that are either left
intact post land-use change or reconstructed after a disturbance in order to maintain or reestablish connectivity between formerly connected habitat patches (Mech et al., 2001). Wildlife
crossing structures consist of a relatively narrow patch of landscape that wildlife use to travel
from one patch of larger habitat to another. Culverts are also one type of crossing structure and
are utilized as corridors by some aquatic species (Beier and Noss, 1998). While corridors and
crossing structures vary widely in their design, they perform the same primary function, enabling
wildlife to travel in order to locate mates, food, and other resources (Beier and Loe, 1992).
For some species, these landscape features primarily serve as dispersal corridors and for
others they function as linear habitats. Dispersal corridors link otherwise unconnected landscape
patches, providing a travel route from one habitat patch to another. In contrast, linear habitats
function as core habitat space rather than as a route solely for movement (Beire and Loe, 1992),
although they may also function as such. For example, wildlife corridors may serve as linear
habitat for amphibians or reptiles, which have relatively small home ranges. However, for large
mammals like the Gray Wolf, corridors are designed to move species through the landscape
rather than providing habitat per se and thus serve to provide dispersal routes for species that
require exceptionally large and relatively undisturbed areas. When corridors and crossing
structures are successful in facilitating species movement, they contribute to overall landscape
connectivity (Tischendorf and Fahrig, 2000).
11
Although corridors and crossing structures are becoming increasingly popular tools to
mitigate population impacts (e.g., increases in demographic, environmental, and genetic
stochasticity) of habitat loss and fragmentation, the variety in their structure and implementation
reflects a wide range of different objectives. While some initiatives focus on constructing
structures designed to prioritize a suite of focal species, others seek to improve existing crossing
structures over constructing new ones. Corridor design may also reflect the needs of the species
the corridor is meant to serve, the type of movement it is intended to facilitate, and other
considerations including: the proximity, type and intensity of human activity, and ownership and
current and future management plans, and the availability and distribution of suitable habitat
(Beier and Noss, 1998).
Concerns and Criticisms
While wildlife crossing structures have their fair share of advocates, they have also
received criticism. Concerns around improving landscape connectivity include the threat of
spreading invasive species and wildfires, and the impacts of increasing edge habitat and edge
predators (Beier and Noss, 1998). The cost to state agencies of preventing the spread of disease
has also been cited as an additional concern (Beier and Noss, 1998). Further, there is an inherent
risk of not knowing enough to make crossing structures work for a focal species. Criticisms
surrounding the use of crossing structures for species like the Gray Wolf run even deeper.
Some researchers have proposed translocation as an alternative to maintaining and
constructing wildlife corridors and crossing structures, but this management strategy is not
without its own shortcomings, especially for the Gray Wolf. For this family-oriented pack
species, removal of individuals from their territory can lead to homing behaviors as well as a
disruption of multi-generational hunting strategies that may be region-specific (Bradley et al.,
12
2005). Simberloff and Cox (1987) suggest that while translocation, as opposed to improving
connectivity, may be a viable option to address threats of inbreeding in isolated
populations/fragmented habitat, the scale of translocation required to combat inbreeding may be
too large and expensive of an undertaking. Translocation can also be detrimental to many
species, especially social animals that are removed from their family units.
In a comparison of translocation efforts across the northwestern United States,
translocated Wolves demonstrated strong homing tendencies resulting in either returning to
capture sites or traveling toward them (Bradley et al., 2005). Additionally, translocated Wolves
had lower success rates forming packs and had lower survival rates than non-translocated
Wolves, with government removal following depredation events as the primary cause of
mortality (Bradley et al., 2005).
Critics have also expressed concerns that funneling financial resources into constructing
and maintaining crossing structures and corridors will take money away from other much needed
conservation initiatives. Importantly, landscape connectivity is just one of many tools needed in
order to maintain biodiversity and healthy wildlife populations. Crossing structures and corridors
alone cannot address the ever-expanding problem of habitat loss and fragmentation. However,
since the potential negative impacts of losing corridors is unknown, maintaining existing
corridors that survived land-use development is worth the added cost according to Beier and
Noss (1998). And although more research is needed, current findings demonstrate the positive
impact of corridors in facilitating species dispersal, thereby expanding habitat range and
promoting genetic diversity in species of concern.
While wildlife crossing structures and corridors are widely used, they will likely remain
controversial until we know more about their contribution to conservation efforts. Studies of
13
their effectiveness are limited in number and are often restricted to a single species. Not
surprisingly, research has yet to determine the full ecological effects of corridors (Shepherd and
Whittington, 2006). However, studies like the one detailed in the next section clearly
demonstrate the potential benefits of connectivity for the Gray Wolf (Shepherd and Whittington,
2006).
The Gray Wolf
The Gray Wolf serves as a unique focal species, because of its importance to and impact
on the environment and because of its sensitivity to habitat fragmentation. Although the Gray
Wolf is a habitat generalist, they are territorial and require large home ranges (Mech and Boitani
2003). Because they are territorial, high dispersal rates help stabilize Gray Wolf populations
(Carroll et al, 2014).
The Gray Wolf plays a critical role as an apex predator and indicator species (Ripple et
al., 2015). Apex predators like Wolves are of critical import to ecosystems due to their impact on
trophic levels through a trophic cascades effect (Fortin et al., 2005). Changes in Gray Wolf
populations at the top of the food chain influence changes in their prey (mostly members of the
Cervidae family) as well as additional lower trophic levels, thereby influencing the greater
ecosystem. In Yellowstone National Park (YNP), the Gray Wolf has been shown to impact
scavenger food webs by providing additional food left uneaten from their kills (Wilmers and
Getz, 2005). When the Gray Wolf was absent from the landscape, YNP saw a reduction in winter
carrion, which had the potential to lead to genetic bottlenecking for scavenger species like the
Bald Eagle (Haliaeetus leucocephalus) and the Red Fox (Vulpes vulpes) (Wilmers and Getz,
2005). This influence of the Gray Wolf may also mitigate some impacts of the shorter winters
experienced in YNP as a result of climate change (Wilmers and Getz, 2005). Researchers predict
14
that early snow thaw may lead to a decline in late-winter carrion in the absence of the Gray Wolf
(Wilmers and Getz, 2005). Wolves mitigate this impact of climate change by providing
additional carrion for scavenger species (Wilmers and Getz, 2005). The impact of Wolf behavior
on other trophic cascades has also been documented on Isle Royale where Wolves have been
shown to limit Moose (Alces alces) abundance and thereby increase productivity of fir (Abies
sp.) trees (Post et al., 1999). Considering the cascading impacts of maintaining stable Wolf
populations detailed above, recovery of the Gray Wolf may be very valuable.
The Endangered Species Act (ESA) has played a large role in defining the path to recovery
for the Gray Wolf. The spatial distribution of Wolves is of particular important in terms of
maintaining stable populations. The ESA emphasizes the importance of considering a species’
role on the landscape in addition to the importance of conserving variation among populations of
species (Carroll et al., 2009). Language in the ESA has been disputed, though. The Endangered
Species Act (ESA) was distinct in that it included language on recovery over a “significant
portion of a species’ range” (Vucetich et al., 2006).
There has been some dispute over interpretation of range in the ESA and whether it refers to
current or historic range (Vucetich et al., 2006). Some interpret the language in the ESA to a
species’ range at time of listing, but this reading is deeply problematic. There is an inherent
fallacy in interpreting the ESA’s definition of range to mean “currently occupied” range when so
many endangered species are imperiled because of habitat loss. Vucetich et al. (2006) draw
attention to the idea of delisting the gray Wolves when they only occupy 5% of their historic
range: they argue that because Gray Wolf ecology is not homogenous, species density depends
on prey availability, and local extinction/recolonization may be frequent, true recovery may
require that Gray Wolves occupy a far larger extent of their historic range than is typically
15
discussed (Vucetich et al., 2006). This interpretation does not account for the spatial elements of
population dynamics or the impoverished conditions of many current landscapes (Carroll et al.,
2009; Gilpin, 1987). The definition of a species’ historic range is further complicated by the
question of what period in history to consider. It is not only important that Washington state have
Wolves, but where in the state those Wolves occur (Carroll et al., 2009).
Anthropogenic development can greatly impact the Gray Wolf. Gray Wolves are
sensitive to habitat fragmentation at various spatial scales and the presence of humans (i.e.
human-avoidant). Moreover, many people are intolerant of Wolves living around them. Wolf
mortality from humans can significantly impact the growth and size of Wolf populations (Gude
et al. 2012; Hanley, 2019). Additionally, state agencies may underestimate the number of
Wolves that are killed by humans each year (Treves et al., 2017). Some studies suggest that for
Wolf recovery to be successful in the western United States, wildlife managers will need to
factor a greater consideration for the impact of illegal hunting and vehicle collisions on Wolf
populations (Treves et al., 2017, Hanley, 2019). On the other hand, the Gray Wolf is extremely
resilient and populations can recover rapidly once human exploitation is reduced or discontinued
(Hayes and Harestad 2000, Fuller et al. 2003). Further, Wolf populations impacted by humans
will recover faster when immigration occurs from neighboring areas (Hayes and Harestad 2000,
Larivière et al. 2000, Hanley, 2019).
Habitat connectivity can have positive population-level effects for Wolves. Shepherd and
Whittington (2006) studied how wildlife corridors in Jasper National Park, Alberta, Canada
impacted the movement of Wolves in the area. They used winter track counts of Wolves and
their prey (deer and elk) before and after the construction of a 330 m (average width) corridor
16
through the center of a golf course. Figure 2 shows Gray Wolf movement one year before (left)
compared to two years after (right) corridor construction (Shepherd and Whittington, 2006).
Figure 2: Gray Wolf movement patterns one year before corridor restoration (left) compared to
two years after (right) (Shepherd and Whittington, 2006).
Shepherd and Whittington’s study concluded that the Gray Wolf likely selected trails that
were the least used by humans (2006). They also found that Wolves used the corridor in order to
access prey, but not for feeding or resting, that is, the corridor was used for movement, but not
habitat (Shepherd and Whittington, 2006). These considerations could be instrumental in
navigating landscape connectivity for the Gray Wolf in Washington State.
Gray Wolves in Washington State may especially be of genetic importance. In a genetic
study of Wolves in the PNW, Hendricks et al. (2019) found the first genetic admixture between
different lineages, including coastal British Columbia (BC) and Northern Rocky Mountain
(NRM) Wolves. The ideal ranges for these two genetic lineages are displayed in Figure 3.
17
Figure 3: MaxEnt distribution model for coastal and interior Wolves within the area of the
natural re-colonization zone. Warmer colors indicate more suitable habitat for interior Wolves
and cooler colors indicate more suitable environment for coastal Wolves (Hendricks et al., 2019).
Hendricks et. al’s study (2019) highlights the importance of Washington’s Wolf
population as well as the potential value in characterizing the landscape in coastal Washington
with attention to Gray Wolf habitat suitability and landscape resistance. Gene flow between
NRM and BC Wolf populations that is occurring in WA state may increase with genetic
variation and enhance these Wolves’ ability to adapt (Hendricks et al., 2019).
Habitat Connectivity in Washington State
Washington State’s concern for wildlife habitat conservation exemplifies the growing
trend for environmental initiatives as the state demonstrates a priority for maintaining and
fostering biodiversity and ecosystem health (WWHCWG, 2019). The Washington Wildlife
Habitat Connectivity Working Group is part of this effort and an example of how state agencies,
nonprofits, and individuals are working together to promote habitat connectivity. Since 2007, the
18
Working Group has promoted a collaborative and science-based approach to identifying where to
restore wildlife habitat (WWHCWG, 2019). The Working Group’s current project is to map
habitat connectivity in coastal Washington State for a suite of focal species, drawing on speciesexperts from across the state.
Even before 2007, Peter Singleton (2002), current Working Group member, performed a
series of connectivity analyses for the state. Figure 4 shows Singleton’s course scale statewide
analysis of habitat concentration and fracture zones for large carnivores in Washington.
Singleton’s work now serves as a foundation for state connectivity work, having established
initial permeability scores for large carnivores in Washington, including Gray Wolves, in 2002.
Figure 4: Coarse scale analysis of habitat concentration areas and fracture zones for large
carnivores in Washington State (Singleton, 2002).
Singleton’s project used least-cost analysis with attention to landscape cover, human
population, roads, slope and elevation to evaluate landscape permeability for large carnivores
(2002). As a part of this project, he identified habitat concentration areas (HCAs) and fracture
19
zones for large carnivores in Washington. While Singleton’s project provided a substantial boost
to this type of work, updates in available analyses and changes to the landscape in the last two
decades underscore a need to continue this work.
Additionally, Washington’s Department of Transportation (WSDOT), along with partners
like the U.S. Forest Service (USFS) and local conservation nonprofits including Conservation
Northwest, have been working to support wildlife conservation in Washington (WSDOT, 2000).
A current focus includes Interstate 90, which runs east-west through the Cascade Mountains
dividing U.S. Forest Service lands and private timberlands for north-south migration and
dispersal of wildlife. One initiative, Snoqualmie Pass Habitat Linkage project, was designed to
develop crossing structures both above and below I-90 that would promote species passage
across I-90. From 1998 to 2000, WSDOT performed an assessment of GIS least-cost path data,
road kill distribution, camera surveys, existing documentation of how wildlife used bridges and
culverts, and winter snow animal tracking to inform corridor location, design, and
implementation (WSDOT, 2000). Additionally, the recent I-90 Snoqualmie Pass Habitat Linkage
demonstrates how the state is adapting to help combat the impact of anthropogenic development
on wildlife (WSDOT, 2000). In the face of impacts from climate change and anthropogenic
development, these infrastructure changes may prove critical in ensuring that Washington
maintains viable wildlife populations.
Gaps in the Research
Though well-studied compared to many species, the Gray Wolf can prove to be a
challenging subject for research. Because large carnivores require relatively large areas, most
studies of Gray Wolf movements are limited by small sample sizes - a common problem in
ecology. Due to these small sample sizes, it can be difficult to do studies with high statistical
20
power. As a result, small, individual studies may not reveal detailed habitat use patterns suitable
for informing useful management actions such as corridor creation. Gray Wolves can also be a
challenging species to study because they are human avoidant, highly intelligent, and can be
difficult to collar and track. Moreover, because of these constraints, research projects involving
Wolves can be very costly. Further, when the Gray Wolf has a protected status like in
Washington state, their locations can be considered sensitive information and therefore can be
difficult for researchers to access. Finally, the application of connectivity to large mammalian
carnivores can be controversial and thus understudied.
A better understanding of how the Gray Wolf uses the landscape could help wildlife
managers mitigate potential human-carnivore conflict. In support of better understanding,
managers may be able to promote coexistence by identifying core habitat areas with attention to
breeding pairs and den sites as well as corridors for dispersal movements (Rio-Maior et al.,
2018). For example, Rio-Maior et al. (2018) noted that most research focused on coexistence has
not provided what they call prediction maps to visualize how avoidance of human-related
activities impact the spatial distribution of large carnivores and their habitats. By using the
literature to inform and predict species movements and computer software like GIS or other
mapping tools, landscape planners and wildlife managers can model how anthropogenic activity
might constrain habitat for human-avoidant species like the Gray Wolf.
In another study, McGuire et al. (2016) measured the projected success or failure of
landscape across the country to provide adequate landscape connectivity in the face of climate
change. Their analysis suggests that some areas in Washington, most notably southeastern WA,
have higher rates of general landscape connectivity than the eastern United States (McGuire et
al., 2016). Compared to many other more fragmented and warmer areas in the country,
21
Washington State has maintained large tracts of natural areas. However, there remain many areas
of Washington that may increasingly lack connectivity due to climate change (McGuire et al.,
2016). McGuire et al.’s study, while considering the current impact of anthropogenic
development, does not consider potential impacts of continued human population growth or
increased rates of severe climate disasters (2016). It may be increasingly important to consider
projected population growth in WA and subsequent development as well as the increase of
wildfire and flooding in the state for predictive mapping.
Human-Carnivore Conflict
Research into information that Way and Bruskotter (2012) call human dimensions data
reveals that attitudes toward Wolves largely depends on context. For example, public support of
Wolf management strategies varies depending on what impact Wolf populations have on the
humans who live in close proximity to Wolves. Because of this, recolonization of coastal
Washington by the Gray Wolf may reveal a range of pro and anti-Wolf sentiment in the region.
Context will be of great import when considering Gray Wolf recolonization of western
Washington and may help state agencies implement adaptive management strategies. Because
candid management can include some of the most divisive practices, including aerial shooting
and foot-hold traps, knowing how the local public would react to these management policies may
aid government agencies in their Wolf management (Way and Bruskotter, 2012). In Washington
State, western Washington is typically more liberal than eastern Washington, but also has little
experience living alongside Wolves. Residents in western Washington may not currently support
lethal removal of Wolves as practiced in eastern Washington, but their stance may change when
Wolves enter their more immediate landscape. Regardless of their stance on lethal removal,
residents in coastal Washington will likely want some management strategy to balance human
22
and Wolf needs. Because of this, early and targeted outreach may be a critical tool for state
agencies and wildlife organizations. A planned and timely outreach campaign could also help
state agencies identify local stakeholder concerns and help them understand and implement
strategies to avoid, minimize, and mitigate conflict (Way and Bruskotter, 2012).
Conclusion
In the face of climate change and growing anthropogenic development, conserving and
restoring wildlife habitat and connectivity may be critical for the survival of many species. The
Gray Wolf offers just one example of the importance of connectivity. Many threatened species
are greatly impacted by habitat fragmentation, but identifying and conserving wildlife corridors
as well as constructing wildlife crossing structures to connect otherwise isolated habitat patches
may improve connectivity. Promoting connectivity may thereby enhance species viability, by
increasing population size and gene flow among metapopulations and thus decreasing genetic
drift and inbreeding depression. While maintaining large reserves is often preferable to
connecting smaller patches, conserving core wildlife habitat and connecting corridors can play
an important role in connecting fragmented habitat and linking otherwise isolated species
populations (Beier and Noss, 1998; Cortazar-Chinarro, 2017).
23
CHAPTER 3: METHODS
Introduction
The goal of this research was to begin the process of identifying priority habitat and
connectivity linkages for the Gray Wolf in coastal Washington, including the south Cascades
from approximately Mount Rainier to the Columbia River down to southwestern Washington
and the Olympic Peninsula. My thesis supports the work of the Washington Wildlife Habitat
Connectivity Working Group (WWHCWG) with the goal of conserving the Gray Wolf by
proactively addressing methods for avoiding human-Wolf conflict. This chapter describes the
methods I used to determine habitat suitability and landscape resistance scores based on land
cover and landscape factors.
Washington Wildlife Habitat Connectivity Working Group
My thesis builds on the continuing work by WWHCWG (hereafter the Working Group),
a collaborative effort to map habitat connectivity in the state for a variety of species. This
collaboration increases communication across agencies and provides opportunities to learn from
diverse perspectives and experiences. In 2010, the Working Group performed a statewide
connectivity analysis. Based on this initial effort, the Working Group identified the need for
further analysis in the coastal Washington region. In 2018, the Working Group began using
Linkage Mapper, a core-corridor approach, along with Omniscape, a coreless approach
(WWHCWG, 2019). For the Coastal Washington Habitat Connectivity Modeling pilot, the
Working Group identified their goal as: “Promoting the long-term viability of wildlife
populations in Washington State through a science-based, collaborative approach that identifies
opportunities and priorities to conserve and restore habitat connectivity.” They define their
vision as: “Permanent protection of a robust, validated network of connected habitats to
24
accommodate species movements, range shifts, and continued ecological functions that
maximize retention of biodiversity and ecological integrity in light of existing land-use pressures
and climate change.” (WWHCWG, 2019).
The Study Area
The analysis area for this project includes the Southern Cascades Mountains from
approximately Mount Rainier to the Columbia River down to southwestern Washington and the
Olympic Peninsula, and encompasses part of a potential connective path from Eastern
Washington to Coastal Washington (Figure 5). The Working Group concluded that their earlier
statewide analysis was too general and that a finer-scale approach was needed for this region.
This area is especially important for the Gray Wolf because this species is anticipated to
recolonize western Washington (Hendricks et al., 2018). Understanding how Wolves use a
human-dominated landscape will be beneficial for wildlife managers in their efforts to inform
human-carnivore conflict mitigation strategies.
25
Figure 5: Study area for the coastal Washington connectivity analysis (WWHCWG, 2019).
The Working Group takes a conservation-based approach to landscape management. For
this project, the group began with 188 potential species, then narrowed their selection down to 21
“focal species” based on the following considerations: federal listing status, Washington State
listing status, NatureServe global rank, the Washington National Heritage Program state rank,
the International Union of Conservation of Nature’s red list, WDFW priority Species, WDFW
species of greatest conservation need, population size, population trends, climate vulnerability,
estimated percent of planning area comprised of species range, estimated size of species home
range, and summary of conservation concern (Southwest Washington Habitat Connectivity
Assessment, 2019). The Working Group decided on Cougar (Puma con color), Fisher (Pekania
Pennanti), Western Gray Squirrel (Scuirus griseus), American Beaver (Castor canadensis), and
Mountain Beaver (Aplodontia rufa). The Gray Wolf was carefully considered and initially won
26
approval for becoming a focal species, but was not included in the final analysis because it does
not currently occupy the coastal Washington landscape and because public opinion of the Wolf is
so varied in Washington State. I elected to study landscape permeability for the Gray Wolf
because it meets the criteria for a focal species in that their presence in an ecosystem has farreaching impacts. Additionally, as a student I have more freedom to explore this species than
either the Working Group or state agencies involved in connectivity work. My work lacks the
visibility and impact that comes from being a member of the Working Group. My hope is that I
can contribute to ongoing work in Washington State to conserve Wolves and their habitat.
Determining Habitat Suitability and Landscape Resistance Scores
For this project, I utilized surface resistance values as indicators of landscape
permeability for the Gray Wolf in coastal Washington. I took a focal species approach to habitat
connectivity modeling (Southwest Washington Habitat Connectivity Assessment, 2019). To
determine habitat and resistance values, I utilized the landscape factors outlined by the Working
Group for their connectivity analysis of coastal Washington. These landscape factors were
organized into seven categories including: land cover (38 types), streams size (4 ranges of order),
slope (3 categories), road types (16 designations), elevation (4 categories), building density (5
categories), and other miscellaneous (9 types) (see appendix A for a full description of the 38
land cover classes). From the Working Group’s initial designation, I expanded several of the
categories, including slope, elevation, and stream order, in order to offer a finer scale analysis,
which could help me identify exactly where Wolf experts might disagree in their scoring. In
total, I utilized 111 landscape factors.
I determined habitat suitability and landscape resistance scores for landscape factors by:
1) reviewing literature on Gray Wolf habitat use with a special focus on forested habitats, as well
27
as examining other connectivity analyses for similar species and 2) cataloguing expert opinion
from three Wolf biologists (Tables 1 and 2). This information was organized into a series of
Excel spreadsheets. I then calculated the mean, standard deviation, and coefficient of variation
based on the four scores, which included my literature informed scores along with scores from
the three species experts. Habitat suitability scores are ranked on a scale of 0 to 1 with 0
reflecting unusable habitat and 1 as optimal habitat. Landscape resistance scores are ranked on a
scale of 0 to 100 with a score of 0 indicating that the landscape feature class has no resistance,
i.e. would not impede movement. A resistance score of 100 indicates that the landcover class or
feature fully impedes Gray Wolf movement.
Literature Review
From the literature, I catalogued studies that evaluated how the Gray Wolf uses the
landscape. I primarily relied on studies that used radio collar telemetry data from Gray Wolves in
the U.S. and Europe. Additionally, I drew on other Gray Wolf connectivity analyses and from
relevant work on large carnivores. I used this literature review (Appendix C) to determine Gray
Wolf habitat suitability and landscape resistance for a total of 111 landscape classes. These
classes were determined by the Working Group, which drew from available data for GIS
mapping. Based on my review, I found that some classes were better informed than others, and
some classes were not informed by the review. I also referenced the Working Group’s work on
other focal species, with particular attention to their Cougar resistance values, as a point of
comparison for my Wolf analysis.
Soliciting Expert Opinion
In an effort to better inform my literature review, I consulted with Wolf experts in order
to inform habitat suitability and landscape resistance scores. I reached out to biologists and
28
researchers who had a knowledge of both the Gray Wolf and the Washington State landscape. I
followed the general process that the Working Group used for working with species experts.
Each species group used slightly different methods for determining resistance scores. However,
most species groups relied on several species experts, each of whom developed their own scores.
Those scores were then compared and discussed, and modified to reflect the best judgment of the
group. The scores from each species group were also compared to those of the other focal
species in order to ensure that results from each group could be modeled together. I followed a
similar trajectory with species experts for the Gray Wolf. Along with scoring instructions
(Appendix A) and National Oceanic and Atmospheric Association (NOAA) land cover
classification data (Appendix B), I provided Wolf experts with my draft scores based on the
literature review as well as the draft Cougar scores from Working Group’s Cougar team;
examples that were intended to serve as points of reference for Wolf experts. With this
information to guide their process, each of the three Wolf experts determined their scores based
on their professional knowledge of both the species and the Washington landscape.
29
CHAPTER 4: RESULTS
Habitat Suitability Scores
Three out of the four experts that I contacted responded, resulting in a 75% response rate.
The three participating species experts included Gregg Kurz, Branch Manager of Listing and
Recovery with U.S. Fish and Wildlife; Julia Smith, Wolf Coordinator with Washington
Department of Fish and Wildlife; and an anonymous Wolf expert. The use of expert opinion is
not quantitative (Beier et al., 2011) and often is nonrepeatable due to a lack of standardized
sourcing. However, there is great value in mining the knowledge and experience of species
experts for the purpose of predictive modeling.
I included my owns scores with those from the three outside experts, and from the four
scores, I calculated the mean, standard deviation, and coefficient of variation (Tables 1 and 2).
The mean values, which incorporate the input from each expert, would be used in the final
mapping output. The standard deviation and coefficient of variation illuminate the level of
concurrence between experts for each class.
Table 1: Table of habitat suitability scores for the Gray Wolf with consideration for 111
landscape covers and features. These scores were provided by the following experts: expert 1Marisa Pushee (based on a review of the literature), expert 2- Julia Smith with WDFW, expert 3anonymous Wolf expert, expert 4- Gregg Kurz with USFWS. Following each individual’s
scores, are the mean of the four scores, the standard deviation (SD), and the coefficient of
variation (CV) for each class of land cover/landscape feature.
Land cover/feature
High intensity developed
Medium intensity developed
Low intensity developed
Developed open space
Prairie/native grassland
Cultivated
Pasture/Hay
1
0.00
0.00
0.10
0.20
0.30
0.15
0.10
Expert
2
3
0.00
0.00
0.00
0.00
0.15
0.10
0.15
0.10
0.50
0.10
0.15
0.00
0.15
0.10
4
0.00
0.01
0.20
0.30
0.35
0.20
0.20
Mean
0.00
0.00
0.14
0.19
0.31
0.13
0.14
SD
CV
0.00
0.01
0.05
0.09
0.17
0.09
0.05
NA
2.00
0.35
0.46
0.53
0.69
0.35
30
Table 1 continued
Deciduous Forest
Evergreen Forest
Mixed Forest
Scrub/Shrub
Palustrine Forested Wetland
Palustrine Scrub/Shrub Wetland
Palustrine Emergent Wetland
Estuarine Forested Wetland
Estuarine Scrub/Shrub Wetland
Estuarine Emergent Wetland
Unconsolidated Shore, Riverine
Bare land
Freshwater
Palustrine Aquatic Bed
Estuarine Aquatic Bed
Snow/Ice
Sparse Forest (CANCOV<10)
Open Forest (CANCOV 10-39)
Broadleaf, Sap/pole, mod/closed
Broadleaf, sm/med/lg, mod/closed
Mixed, sap/pole, mod/closed
Mixed, sm/med, mod/closed
Mixed, lg + giant, mod/closed
Conifer, sap/pole, mod/closed
Conifer, sm/med, mod/closed
Conifer, lg, mod/closed
Conifer, giant, mod/closed
Unconsolidated shore, coastal
Saltwater
Dunes
Dry Douglas Fir
Oak Woodland
Prairie
Highly structured agriculture
0.99
0.99
0.99
0.80
0.60
0.50
0.50
0.50
0.50
0.20
0.90
0.20
0.01
0.10
0.10
0.10
0.20
0.20
0.90
0.90
0.90
0.90
0.90
0.90
0.90
0.90
0.90
0.20
0.01
0.10
0.80
0.80
0.20
0.02
1.00
1.00
1.00
0.90
0.70
0.40
0.40
0.70
0.40
0.40
1.00
0.10
0.01
0.05
0.05
0.10
0.30
0.50
0.95
0.95
0.95
0.95
0.95
0.95
0.95
0.95
0.95
0.20
0.01
0.20
0.99
0.99
0.50
0.05
0.99
0.99
0.99
0.40
0.60
0.60
0.60
0.60
0.60
0.60
0.20
0.00
0.00
0.20
0.20
0.00
0.20
0.60
0.90
0.90
0.90
0.90
0.90
0.90
0.90
0.90
0.90
0.20
0.00
0.00
0.80
0.80
0.10
0.00
1.00
1.00
1.00
0.90
0.60
0.60
0.45
0.60
0.50
0.30
1.00
0.30
0.00
0.10
0.10
0.10
0.50
0.70
0.90
0.90
0.90
0.90
0.90
0.90
0.90
0.90
0.90
0.30
0.00
0.10
0.90
0.90
0.40
0.00
1.00
1.00
1.00
0.75
0.63
0.53
0.49
0.60
0.50
0.38
0.78
0.15
0.01
0.11
0.11
0.08
0.30
0.50
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.23
0.01
0.10
0.87
0.87
0.30
0.02
0.01
0.01
0.01
0.24
0.05
0.10
0.09
0.08
0.08
0.17
0.39
0.13
0.01
0.06
0.06
0.05
0.14
0.22
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.05
0.01
0.08
0.09
0.09
0.18
0.02
0.01
0.01
0.01
0.32
0.08
0.18
0.18
0.14
0.16
0.46
0.50
0.86
1.15
0.56
0.56
0.67
0.47
0.43
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.22
1.15
0.82
0.10
0.10
0.61
1.35
31
Table 1 continued
Built Linear Features
Transmission lines < 100 volts
Transmission lines 100-220 volts
Transmission lines 221-287 volts
Transmission lines 288-345 volts
Transmission lines > 345 volts
Active rail lines
Abandoned rail lines
Rail bank
0.50
0.50
0.50
0.50
0.50
0.00
0.20
0.40
0.00
0.00
0.00
0.00
0.00
0.00
0.30
0.30
0.10
0.50
0.50
0.50
0.50
0.00
0.00
0.00
0.35
0.35
0.35
0.35
0.35
0.00
0.10
0.30
0.24
0.34
0.34
0.34
0.34
0.00
0.15
0.25
0.23
0.24
0.24
0.24
0.24
0.00
0.13
0.17
0.96
0.70
0.70
0.70
0.70
NA
0.86
0.69
Streams by Order
Order 1
Order 2
Order 3
Order 4
Order 5
Order 6
Order 7
Order 8
Order 9
Order 10
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.08
0.08
0.08
0.08
0.08
0.08
0.08
0.08
0.08
0.08
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.67
0.67
0.67
0.67
0.67
0.67
0.67
0.67
0.67
0.67
Slope
0-20 degrees
21-30 degrees
31-40 degrees
41-50 degrees
51-60 degrees
61-70 degrees
71-80 degrees
81-90 degrees
1.00
0.90
0.90
0.90
0.90
0.90
0.90
0.90
1.00
1.00
1.00
0.70
0.50
0.50
0.30
0.30
0.90
0.90
0.90
0.90
0.90
0.70
0.10
0.10
1.00
0.95
0.95
0.85
0.85
0.85
0.65
0.45
0.98
0.94
0.94
0.84
0.79
0.74
0.49
0.44
0.05
0.05
0.05
0.10
0.19
0.18
0.36
0.34
0.05
0.05
0.05
0.11
0.25
0.24
0.73
0.78
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.05
0.00
0.00
0.02
0.00
0.00
0.02
0.00
0.00
1.35
NA
NA
0.00
0.00
0.00
0.00
0.00
0.00
NA
0.00
0.00
0.00
0.00
0.00
0.00
NA
0.00
0.00
0.00
0.00
0.00
0.00
NA
Roads
Highways:50 - 500 vehicles/day
Highways: 501 - 1,000 vehicles/day
Highways: 1,001 - 2,000
vehicles/day
Highways: 2,001 - 5,000
vehicles/day
Highways: 5,001 - 10,000
vehicles/day
Highways: >10,001 vehicles/day
32
Table 1 continued
Forest—Unpaved
Paved—Unknown
Paved—urban
Rural—Unknown
All trails
0.30
0.20
0.02
0.20
0.99
0.40
0.10
0.00
0.30
0.99
0.90
0.00
0.00
0.00
0.00
0.30
0.25
0.01
0.20
1.00
0.48
0.14
0.01
0.18
0.75
0.29
0.11
0.01
0.13
0.50
0.60
0.81
1.28
0.72
0.67
Elevation
0-500 ft
501-1000 ft
1001-1500 ft
1501-2000 ft
2001-2500 ft
2501-3000 ft
3001-3500 ft
3501-4000 ft
4001-4500 ft
4501-5000 ft
5001-5500 ft
5501-6000 ft
6001-6500 ft
6501-7000 ft
7001-7500 ft
7501-8000 ft
8001-8500 ft
8501-9000 ft
9001-9500 ft
9501-10000 ft
10001-10500 ft
10501-11000 ft
11001-11500 ft
11501-12000 ft
12001-12500 ft
12501-13000 ft
13001-13500 ft
13501-14000 ft
14001-14500 ft
0.90
0.90
0.90
0.90
0.90
0.90
0.90
0.90
0.90
0.90
0.90
0.90
0.90
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.50
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.90
0.90
0.90
0.90
0.90
0.90
0.90
0.90
0.90
0.70
0.70
0.70
0.50
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.10
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.95
0.95
0.95
0.95
0.95
0.95
0.95
0.95
0.95
0.90
0.90
0.90
0.85
0.50
0.50
0.50
0.50
0.50
0.38
0.38
0.38
0.38
0.38
0.38
0.38
0.38
0.38
0.38
0.38
0.58
0.58
0.58
0.58
0.58
0.58
0.58
0.58
0.58
0.14
0.14
0.14
0.24
0.37
0.37
0.37
0.37
0.37
0.19
0.19
0.19
0.19
0.19
0.19
0.19
0.19
0.19
0.19
0.19
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.06
0.16
0.16
0.16
0.28
0.75
0.75
0.75
0.75
0.75
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.50
Building Density
No buildings
Isolated buildings
Clusters of buildings
High density buildings
1.00
0.20
0.02
0.00
1.00
0.50
0.20
0.00
1.00
0.20
0.10
0.00
1.00
0.20
0.05
0.00
1.00
0.28
0.09
0.00
0.00
0.15
0.08
0.00
0.00
0.55
0.85
NA
33
Landscape Resistance Scores
The following landscape resistance scores are ranked on a scale of 0 to 100 with a score
of 0 indicating that the landscape feature class has no resistance value, i.e. would not impede
movement, for the Gray Wolf. A resistance score of 100 indicates that the landcover class or
feature fully impedes Gray Wolf movement. The following tables follow the structure of the
habitat tables above. The four Gray Wolf experts are listed in the following order: Marisa Pushee
(based on literature review), Julia Smith with WDFW, anonymous Wolf expert, and Gregg Kurz
with USFWS. From the four experts’ landscape resistance scores, I calculated the mean, standard
deviation, and coefficient of variation. The mean values, which incorporate the input from each
expert, would be used in the final mapping output. The standard deviation and coefficient of
variation illuminate the level of concurrence between experts for each class.
Table 2: Table of landscape resistance scores for the Gray Wolf with consideration for 111
landscape covers and features. These scores were provided by the following experts: expert 1Marisa Pushee (based on a review of the literature), expert 2- Julia Smith with WDFW, expert 3anonymous Wolf expert, expert 4- Gregg Kurz with USFWS. Following each individual’s
scores, are the mean of the four scores, the standard deviation (SD), and the coefficient of
variation (CV) for each class of land cover/landscape feature.
Land cover/feature
High intensity developed
Medium intensity developed
Low intensity developed
Developed open space
Cultivated
Pasture/Hay
Grassland
Deciduous Forest
Evergreen Forest
Mixed Forest
Scrub/Shrub
Palustrine Forested Wetland
Palustrine Scrub/Shrub Wetland
1
100
70
40
30
35
35
20
1
1
1
1
1
1
Expert
2
3
99 100
60
95
30
75
30
80
30
90
30
60
0
30
0
1
0
1
0
1
0
20
0
5
0
5
4
99
60
35
25
20
20
10
1
1
1
1
2
1
Mean
SD
99.50
71.25
45.00
41.25
43.75
36.25
15.00
0.75
0.75
0.75
5.50
2.00
1.75
0.58
16.52
20.41
25.94
31.46
17.02
12.91
0.50
0.50
0.50
9.68
2.16
2.22
CV
0.01
0.23
0.45
0.63
0.72
0.47
0.86
0.67
0.67
0.67
1.76
1.08
1.27
34
Table 2 continued
Palustrine Emergent Wetland
Estuarine Forested Wetland
Estuarine Scrub/Shrub Wetland
Estuarine Emergent Wetland
Unconsolidated Shore, Riverine
Bare Land
Freshwater
Palustrine Aquatic Bed
Estuarine Aquatic Bed
Snow/Ice
Sparse Forest (CANCOV<10)
Open Forest (CANCOV 10-39)
Broadleaf, Sap/pole, mod/closed
Broadleaf, sm/med/lg, mod/closed
Mixed, sap/pole, mod/closed
Mixed, sm/med, mod/closed
Mixed, lg + giant, mod/closed
Conifer, sap/pole, mod/closed
Conifer, sm/med, mod/closed
Conifer, lg, mod/closed
Conifer, giant, mod/closed
Unconsolidated shore, Coastal
Saltwater
Dunes
Dry Douglas Fir
Oak Woodland
Prairie
Highly structured agriculture
5
1
1
5
1
20
25
25
25
5
10
20
1
1
1
1
1
1
1
1
1
35
20
20
1
1
2
40
10
0
0
10
0
0
30
25
25
5
0
0
0
0
0
0
0
0
0
0
0
10
40
0
0
0
0
30
5
5
5
5
5
10
20
20
20
10
10
10
1
1
1
1
1
1
1
1
1
10
20
70
1
1
25
50
4
1
1
4
1
7
15
15
15
5
2
2
1
1
1
1
1
1
1
1
1
20
15
15
1
1
1
35
6.00
1.75
1.75
6.00
1.75
9.25
22.50
21.25
21.25
6.25
5.50
8.00
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
18.75
23.75
26.25
0.75
0.75
7.00
38.75
2.71
2.22
2.22
2.71
2.22
8.30
6.46
4.79
4.79
2.50
5.26
9.09
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.50
11.82
11.09
30.38
0.50
0.50
12.03
8.54
0.45
1.27
1.27
0.45
1.27
0.90
0.29
0.23
0.23
0.40
0.96
1.14
0.67
0.67
0.67
0.67
0.67
0.67
0.67
0.67
0.67
0.63
0.47
1.16
0.67
0.67
1.72
0.22
Built Linear Features
Transmission lines < 100 volts
Transmission lines 100-220 volts
Transmission lines 220-287 volts
Transmission lines 287-345 volts
Transmission lines > 345 volts
Active rail lines
Abandoned rail lines
Rail bank
2
2
2
2
2
50
2
1
0
0
0
0
0
30
0
0
5
5
5
5
5
30
5
40
2
2
2
2
2
10
1
1
2.25
2.25
2.25
2.25
2.25
30.00
2.00
10.50
2.06
2.06
2.06
2.06
2.06
16.33
2.16
19.67
0.92
0.92
0.92
0.92
0.92
0.54
1.08
1.87
35
Table 2 continued
Streams by Order
Order 1
Order 2
Order 3
Order 4
Order 5
Order 6
Order 7
Order 8
Order 9
Order 10
1
1
1
1
1
1
5
5
5
5
0
0
0
0
0
0
5
5
5
5
5
5
5
5
5
5
5
5
5
5
1
1
1
1
1
1
3
3
5
5
1.75
1.75
1.75
1.75
1.75
1.75
4.50
4.50
5.00
5.00
2.22
2.22
2.22
2.22
2.22
2.22
1.00
1.00
0.00
0.00
1.27
1.27
1.27
1.27
1.27
1.27
0.22
0.22
0.00
0.00
Slope
0-20 degrees
21-30 degrees
31-40 degrees
41-50 degrees
51-60 degrees
61-70 degrees
71-80 degrees
81-90 degrees
1
1
1
1
1
1
1
2
0
0
0
10
20
20
30
30
1
1
1
1
1
1
60
95
1
1
1
1
1
1
5
20
0.75
0.75
0.75
3.25
5.75
5.75
24.00
36.75
0.50
0.50
0.50
4.50
9.50
9.50
27.22
40.53
0.67
0.67
0.67
1.38
1.65
1.65
1.13
1.10
20
50
10
30
10
40
15
30
13.75
37.50
4.79
9.57
0.35
0.26
60
50
60
40
52.50
9.57
0.18
80
70
60
50
65.00
12.91
0.20
90
90
80
70
82.50
9.57
0.12
100
1
20
30
10
1
99
0
10
30
10
0
90
1
20
95
20
1
99
1
10
20
7
1
97.00
0.75
15.00
43.75
11.75
0.75
4.69
0.50
5.77
34.49
5.68
0.50
0.05
0.67
0.38
0.79
0.48
0.67
Roads
Highways:50 - 500 vehicles/day
Highways: 501 - 1,000
vehicles/day
Highways: 1,001 - 2,000
vehicles/day
Highways: 2,001 - 5,000
vehicles/day
Highways: 5,001 - 10,000
vehicles/day
Highways: >10,001 vehicles/day
Forest Unpaved
Paved Unknown
Paved Urban
Rural Unknown
All trails
36
Table 2 continued
Elevation
0-500 ft
501-1000 ft
1001-1500 ft
1501-2000 ft
2001-2500 ft
2501-3000 ft
3001-3500 ft
3501-4000 ft
4001-4500 ft
4501-5000 ft
5001-5500 ft
5501-6000 ft
6001-6500 ft
6501-7000 ft
7001-7500 ft
7501-8000 ft
8001-8500 ft
8501-9000 ft
9001-9500 ft
9501-10000 ft
10001-10500 ft
10501-11000 ft
11001-11500 ft
11501-12000 ft
12001-12500 ft
12501-13000 ft
13001-13500 ft
13501-14000 ft
14001-14500 ft
Building Density
No buildings
Isolated buildings
Clusters of buildings
High density building
1
1
1
1
1
1
1
1
1
1
1
1
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
5
5
5
5
5
5
5
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
20
20
20
80
80
80
80
80
80
80
80
80
80
80
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
5
5
5
5
5
5
5
5
5
5
5
5
5
5
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
3.00
3.00
3.00
8.75
8.75
8.75
23.75
23.75
23.75
25.00
25.00
25.00
25.00
25.00
25.00
25.00
25.00
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.50
0.50
4.69
4.69
4.69
8.54
8.54
8.54
37.72
37.72
37.72
36.74
36.74
36.74
36.74
36.74
36.74
36.74
36.74
0.67
0.67
0.67
0.67
0.67
0.67
0.67
0.67
0.67
0.67
0.67
0.67
1.56
1.56
1.56
0.98
0.98
0.98
1.59
1.59
1.59
1.47
1.47
1.47
1.47
1.47
1.47
1.47
1.47
1
50
80
100
0
0
70
100
1
25
90
100
1
10
20
99
0.75
21.25
65.00
99.75
0.50
21.75
31.09
0.50
0.67
1.02
0.48
0.01
37
CHAPTER 5: DISCUSSION
The statistics displayed in Tables 1 and 2 demonstrate the degree of concurrence among
experts on habitat suitability and landscape resistance for the Gray Wolf. In each of these tables,
the coefficient of variation (CV) suggests where Wolf experts most strongly agreed or disagreed.
Classes with a high CV likely require further exploration and conversation among experts. Some
of these discrepancies may have resulted from varying interpretations of the landscape classes,
whereas others may have resulted from differing opinions on how the Gray Wolf uses the
landscape. In both the literature review and expert feedback, roads were revealed to be one of the
most interesting categories, as they can facilitate Wolf movement but also pose a mortality risk.
Additionally, a comparison of the standard deviation across all CV values for habitat suitability
(0.370) and for landscape resistance (0.464) suggests that Gray Wolf experts tended to agree
more on habitat suitability than they did on landscape resistance as a whole. These results may in
part be due to habitat suitability being more widely studied than landscape permeability.
While the next steps of this work were beyond the scope of this thesis, these steps should
include continuing conversations with the Gray Wolf experts who participated in the work in an
attempt to find common ground on scoring. Following these conversations and the fine tuning of
habitat suitability and landscape resistance scores, the results can then be used in Esri
Geographic Information Systems (GIS) maps. Additional components for consideration could
also include prey distribution and sociological factors including land ownership and social
tolerance of Wolves. This visualization and spatial analysis will be useful for informing Wolf
management and conservation issues. In particular, these maps can help explore questions about
how the Gray Wolf will use this landscape as core habitat and as movement habitat. In turn this
38
information may be critical for engaging stakeholders in finding creative ways to manage the
landscape in ways that minimize human-Wolf conflict.
39
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48
Appendix A:
The following scoring instructions were sent to prospective Gray Wolf experts in order to guide
them in the scoring process.
Scoring Instructions
Goals:
This project builds on the current coastal connectivity mapping project by the Washington
Wildlife Habitat Connectivity Working Group (WWHCWG). The Working Group’s project
seeks to identify areas with high ecological value for conserving and/or restoring conditions that
promote desirable wildlife movements. The Working Group intends to support the broadest
possible range of native species by analyzing habitat for a suite of focal species, which includes
Cougar, Fisher, Western Gray Squirrel, American Beaver, and Mountain Beaver.
My thesis project is intended to complete this process for the Gray Wolf. The goal of my work is
to both build on the Working Group’s analyses and to help managers understand how different
landscape configurations could help reduce the potential for future human-Wolf conflict in
coastal Washington.
Process:
The attached Excel sheet lists land cover types, e.g., forest types, agricultural land types, etc.,
and land features, which include attributes of land cover types, (e.g., elevation, slope, and
presence of linear features such as streams, roads, transmission lines, and railroad tracks). Each
land cover type has a value to Wolves as habitat for meeting life requirements (feeding,
breeding, rearing, resting). Each land feature can affect the quality of the habitat in general terms
(elevation or slope) and in more site-specific ways like the presence of roads or streams.
We would like you to rank habitat value of land cover and land features as the first step in the
process. Habitat values are ranked on a scale of 0 to 1, where a value of 0 indicates no habitat
value, and 1 indicates ideal habitat. For example, using the Working Group’s Cougar scores as a
proxy, “high intensity developed” land cover ranked as a 0 habitat score for Cougars whereas
“prairie” ranked as 0.38 (medium value) and “conifer, large, mod/closed” ranked as a 1 as ideal
cougar habitat. Similarly, the highways as a land feature with more than 500 cars per day has no
habitat value (score of 0) for Cougars whereas all trails ranked as idea habitat.
The second task is to rank land cover and land features by resistance. Resistance values range
from 1 to 100 and indicate the degree to which land cover and land features facilitate or impede
species movements. High resistance scores e.g., 100, indicates that land cover or feature strongly
impedes a species movement. For example, “high intensity developed” land and “highways with
greater than 10,001 vehicles per day” were both ranked very high (100) for resistance scores for
cougars. Broadleaf, mixed, and conifer forest categories all received a resistance score of 1 as
they maximally facilitate cougar movement.
49
In the attached spreadsheet, you will find the Working Group’s draft values for the habitat and
resistance scores for cougar as well as my initial draft values for gray wolves. My Gray Wolf
values are based on my ongoing literature review and will also be informed by input from Wolf
experts including yourself. The species scores for this project are relational and as such I am
asking you as a wolf expert assign habitat and resistance scores for Wolves based on the Cougar
scores, which are provided as a proxy.
I value your expert opinion in determining these scores. Please use your judgement to best fill
out each score even if you are more confident in some of your scoring decisions than others. For
this project, you don’t need empirical information to support your scoring and I don’t expect you
to refer to the literature. I am placing value on your judgement as a Wolf expert.
For my thesis, I will be averaging scores from four Wolf experts with local knowledge to
Washington state and comparing those totals with the results from my literature review. I will
also report simple metrics of uncertainty across experts by the calculating measures of dispersion
around mean estimates.
Please let me know if I can credit you for your input or if you would prefer to remain
anonymous. Let me know if you have any questions. Thank you for participating in this work.
50
Appendix B:
The following land cover classification data scheme was shared with Wolf experts in order to
help inform their decisions on determining habitat and landscape resistance scores.
Coastal Change Analysis Program (C-CAP) NOAA Office for Coastal Management
Regional Land Cover Classification Scheme
The following information provides a description of land cover classes used with NOAA’s
Coastal Change Analysis Program (C- CAP) Regional land cover products. These classes have
been targeted as important indicators of coastal ecosystems and have been identified as features
that can be consistently and accurately derived primarily through remote-sensing means.
These descriptions have been revised from those originally published in NOAA Coastal Change
Analysis Program (C-CAP): Guidance for Regional Implementation.
Unclassified
Background (0) – areas within the image file limits but containing no data values.
Unclassified (1) – areas in which land cover cannot be determined; these include clouds and deep
shadow.
Developed Land
Developed, High Intensity (2) – contains significant land area and is covered by concrete,
asphalt, and other constructed materials. Vegetation, if present, occupies less than 20 percent of
the landscape. Constructed materials account for 80 to 100 percent of the total cover. This class
includes heavily built-up urban centers and large constructed surfaces in suburban and rural areas
with a variety of land uses.
Developed, Medium Intensity (3) – contains areas with a mixture of constructed materials and
vegetation or other cover. Constructed materials account for 50 to 79 percent of total area. This
class commonly includes multi- and single-family housing areas, especially in suburban
neighborhoods, but may include all types of land use.
Developed, Low Intensity (4) – contains areas with a mixture of constructed materials and
substantial amounts of vegetation or other cover. Constructed materials account for 21 to 49
51
percent of total area. This subclass commonly includes single-family housing areas, especially in
rural neighborhoods, but may include all types of land use.
Developed, Open Space (5) – contains areas with a mixture of some constructed materials, but
mostly managed grasses or low-lying vegetation planted in developed areas for recreation,
erosion control, or aesthetic purposes. These areas are maintained by human activity such as
fertilization and irrigation, are distinguished by enhanced biomass productivity, and can be
recognized through vegetative indices based on spectral characteristics. Constructed surfaces
account for less than 20 percent of total land cover.
C-CAP Regional Land Cover Classification Scheme ─ 2
Agricultural Land
Cultivated Crops (6) – contains areas intensely managed for the production of annual crops.
Crop vegetation accounts for greater than 20 percent of total vegetation. This class also includes
all land being actively tilled.
Pasture/Hay (7) – contains areas of grasses, legumes, or grass-legume mixtures planted for
livestock grazing or the production of seed or hay crops, typically on a perennial cycle and not
tilled. Pasture/hay vegetation accounts for greater than 20 percent of total vegetation.
Grassland
Grassland/Herbaceous (8) – contains areas dominated by grammanoid or herbaceous
vegetation, generally greater than 80 percent of total vegetation. These areas are not subject to
intensive management such as tilling but can be utilized for grazing.
52
Forest Land
Deciduous Forest (9) – contains areas dominated by trees generally greater than 5 meters tall
and greater than 20 percent of total vegetation cover. More than 75 percent of the tree species
shed foliage simultaneously in response to seasonal change.
Evergreen Forest (10) – contains areas dominated by trees generally greater than 5 meters tall
and greater than 20 percent of total vegetation cover. More than 75 percent of the tree species
maintain their leaves all year. Canopy is never without green foliage.
Mixed Forest (11) – contains areas dominated by trees generally greater than 5 meters tall, and
greater than 20 percent of total vegetation cover. Neither deciduous nor evergreen species are
greater than 75 percent of total tree cover. Both coniferous and broad-leaved evergreens are
included in this category.
Scrub Land
Scrub/Shrub (12) – contains areas dominated by shrubs less than 5 meters tall with shrub
canopy typically greater than 20 percent of total vegetation. This class includes tree shrubs,
young trees in an early successional stage, or trees stunted from environmental conditions.
Barren Land
Barren Land (20) – contains areas of bedrock, desert pavement, scarps, talus, slides, volcanic
material, glacial debris, sand dunes, strip mines, gravel pits, and other accumulations of earth
material. Generally, vegetation accounts for less than 10 percent of total cover.
Tundra (24) – is categorized as a treeless region beyond the latitudinal limit of the boreal forest
in pole-ward regions and above the elevation range of the boreal forest in high mountains. In the
United States, tundra occurs primarily in Alaska.
Perennial Ice/Snow (25) – includes areas characterized by a perennial cover of ice and/or snow,
generally greater than 25 percent of total cover.
C-CAP Regional Land Cover Classification Scheme ─ 3
Palustrine Wetlands
Palustrine Forested Wetland (13) – includes tidal and nontidal wetlands dominated by woody
vegetation greater than or equal to 5 meters in height, and all such wetlands that occur in tidal
areas in which salinity due to ocean-derived salts is below 0.5 percent. Total vegetation coverage
is greater than 20 percent.
53
Palustrine Scrub/Shrub Wetland (14) – includes tidal and nontidal wetlands dominated by
woody vegetation less than 5 meters in height, and all such wetlands that occur in tidal areas in
which salinity due to ocean-derived salts is below 0.5 percent. Total vegetation coverage is
greater than 20 percent. Species present could be true shrubs, young trees and shrubs, or trees
that are small or stunted due to environmental conditions.
Palustrine Emergent Wetland (Persistent) (15) – includes tidal and nontidal wetlands
dominated by persistent emergent vascular plants, emergent mosses or lichens, and all such
wetlands that occur in tidal areas in which salinity due to ocean-derived salts is below 0.5
percent. Total vegetation cover is greater than 80 percent. Plants generally remain standing until
the next growing season.
Estuarine Wetlands
Estuarine Forested Wetland (16) – includes tidal wetlands dominated by woody vegetation
greater than or equal to 5 meters in height, and all such wetlands that occur in tidal areas in
which salinity due to ocean-derived salts is equal to or greater than 0.5 percent. Total vegetation
coverage is greater than 20 percent.
Estuarine Scrub/Shrub Wetland (17) – includes tidal wetlands dominated by woody
vegetation less than 5 meters in height, and all such wetlands that occur in tidal areas in which
salinity due to ocean-derived salts is equal to or greater than 0.5 percent. Total vegetation
coverage is greater than 20 percent.
Estuarine Emergent Wetland (18) – Includes all tidal wetlands dominated by erect, rooted,
herbaceous hydrophytes (excluding mosses and lichens). These wetlands occur in tidal areas in
which salinity due to ocean-derived salts is equal to or greater than 0.5 percent and are present
for most of the growing season in most years. Total vegetation cover is greater than 80 percent.
Perennial plants usually dominate these wetlands.
Barren Land
Unconsolidated Shore (19) – includes material such as silt, sand, or gravel that is subject to
inundation and redistribution due to the action of water. Substrates lack vegetation except for
pioneering plants that become established during brief periods when growing conditions are
favorable.
Water and Submerged Lands
Open Water (21) – includes areas of open water, generally with less than 25 percent cover of
vegetation or soil.
Palustrine Aquatic Bed (22) – includes tidal and nontidal wetlands and deepwater habitats in
which salinity due to ocean-derived salts is below 0.5 percent and which are dominated by plants
that grow and form a continuous cover principally on or at the surface of the water. These
54
include algal mats, detached floating mats, and rooted vascular plant assemblages. Total
vegetation cover is greater than 80 percent.
C-CAP Regional Land Cover Classification Scheme ─ 4
Estuarine Aquatic Bed (23) – includes tidal wetlands and deepwater habitats in which salinity
due to ocean-derived salts is equal to or greater than 0.5 percent and which are dominated by
plants that grow and form a continuous cover principally on or at the surface of the water. These
include algal mats, kelp beds, and rooted vascular plant assemblages. Total vegetation cover is
greater than 80 percent.
55
Appendix C:
Appendix Table 1: Literature review for Gray Wolf landscape use. This table displays a limited
selection of the sources that I used in my literature review to inform my habitat suitability and
landscape resistance scores. While this table is not comprehensive, it provides some amount of
transparency in how I cataloged and tracked previous research by organizing studies with
attention to their date, location, main conclusions, habitat type, methods, and other
considerations. Additional sources that I referenced to determine my habitat suitability and
landscape resistance scores can be found in the bibliography of this thesis.
Source and
study location
/environment
Ciucci et al.,
2018
Apennines,
Italy (Pollino
National Park)
Main conclusions of
the study
Habitat type
Methods
Wolves preferentially
located rendezvous
sites close to
meadows, wetlands or
other water sources,
and forests,
(some variability
concerning forest
types, canopy closure,
soil type, and
topography)
Boreal and
temperate
ecosystems in
North America
Snow tracking
Avoided areas
featuring high
densities of humans,
paved roads, and trails
Other
considerations
Documented
rendezvous sites
Howling
surveys
Telemetry (5
wolves in 6
packs)
Wolves living in
humandominated
landscapes
10
environmental,
topographic,
and
anthropogenic
variables in
GIS
Selected for higher
forest cover and rough
terrain enhanced
concealment and
ensured reduced
accessibility by
humans
Selected open areas
and (at coarse grain)
areas of high density
of dirt roads and trails
Selected for forest
cover, avoided lowuse anthropogenic
56
Demma and
Mech, 2009
Northeastern
Minnesota,
Superior
National Forest
linear features and
rough terrain-revealed trade-offs in
selection decisions
across spatial and
temporal scales
Mean daily range
overlap was 22% (SE
= 0.02)
Average daily range
overlap was greater
(t216 = -2.12, P =
0.04) for breeders
(25%, SE = 0.03, n =
143) than nonbreeders
(16%, SE = 0.03, n =
75)
Wolves used MCP
areas of 100-396 km2
Homesites made up
an average of 31%
(SE = 5, n + 6) of
each Wolf’s GPS
locations
Breeding wolves (2F,
1M) were present at
homesites on 81100% of days &
nonbreeder use was
more varied
1,300-km2 area in
the central
Superior National
Forest
May-July 2003
and 2004,
researchers
trapped,
immobilized,
and examined 8
Wolves
Researchers
fitted wolves
with store-onboard and
remote
downloadable
GPS radio
collars; 6
Televit collars
at 10 min
intervals as
well as 1 ATS
and 1 Vectronic
collar at 15 min
intervals each
Prey included
white tailed
deer, which
occurred at a
density of 1215/10 km2
Wolves were
1-2 years old
(only 1 showed
signs of
breeding)
Excluded data
from the first 5
days post
capture
Plotted GPS
data in ArcMap
Hebblewhite
Wolves strongly
Subarctic climate 16 wolves from
and Merrill,
avoided steeper slopes dominated by
five packs with
2008
and strongly selected
Lodgepole Pine at GPS collars
for areas closer to
lower elevations
Banff National ‘hard’ edges
and Engelmann
RSF-models of
Park in Alberta,
Spruce in higher
GPS-data
Canada
Selected burned and
ones below the
treeline, above
alpine areas during
summer, but selected which is
Objective: to
extend the
application of
mixed-effect
RSFs--resource
selection by the
Gray Wolf
57
burns less and
avoided alpine
completely in the
winter
primarily rocks
and ice
Stronger avoidance of
rock during winter
Open conifer and
cutblocks areas were
selected during
summer, but were as
equally avoided as
forested habitats
during winter
Summer, during the
day: correlation
between all Wolves
within packs, ρ(pack),
and between Wolves
for a specific pack,
ρ(wolf, pack), were
similar, 0·62 and 0·69
Summer, night:
Wolves within packs
were less correlated
than with other packs
(0·15 vs. 0·55)
Winter: different
packs were not
correlated during
either night or day (ρ
= 0·11, 0·03); Wolves
within a specific pack
were highly correlated
(ρ = 0·909, 0·907).
ρ(wolf, pack) >
ρ(pack)
As human activity
increased, packs were
constrained to select
58
areas closer to human
activity at home-range
scale
James and
Smith, 2000
Northeastern
Alberta,
Canada
At high human
activity levels, the
response differed
depending on time of
day
Wolf locations were
closer (134 m) than
random to linear
corridors
Wolf predation sites
were not significantly
closer to corridors
than were wolf
locations or random
points
(56 degrees N,
112 degrees W)
encompassed
approximately
20,000 km2 of
boreal mixed
wood and
peatland
vegetation
From 1994 to
1997,
researchers
placed VHF
collars on 20
Wolves from 7
packs and on 3
lone wolves
Radio-collared
Wolves were
located every
2-3 weeks and
Significant point type
researchers
by habitat interaction Wetlands were
collected
(F1,2173 = 9.2, P =
dominated by
additional
Black
Spruce/
0.002)
locations twice
Black SpruceWithin caribou range, Tamarack, ferns, a day for 15
consecutive
telemetry locations of and bogs
days during
Wolves were on
average 134 m closer Well-drained sites winters of 1996
and 1997
to corridors than were were dominated
random points (F1,1197 by Aspen, White
Spruce, and Jack Examined the
= 9.3, P = 0.002)
distribution of
Pine
2,616 telemetry
Significant pack effect
locations of
(F7,1197 = 5.7, P <
caribou, 27
0.001)
caribou
mortality sites,
Outside caribou
592 telemetry
range, significant
locations of
point type by pack
Wolves, and 76
interaction (F6,976 =
sites where
5.8, P<0.001)
Wolves had
preyed on large
ungulates
Elevation ranged
from 500-700 m
Researchers
tested the
hypothesis that
linear corridors
(roads, seismic
lines, power
lines, and
pipeline right
of ways)
affect caribou
and wolf
activities
25,500 km of
the 26,850 km
of linear
corridors
studied were
seismic lines or
pipeline right
of ways (a few
gravel roads
and one paved
road)
59
Kunkel and
Pletscher, 2000
Northwestern
Montana and
southeastern
British
Columbia,
Canada
Within their home
ranges, Wolves
selected areas that
facilitated travel
including lower snow
depths and more
vegetative cover or
that enhanced
encounters with prey
Wolves selected
topographic, cover,
and slope similar to
those selected by prey
within their range
Killed deer in areas
with greater hidingstalking cover, less
slope and closer to
water than expected
Hiding cover was 1.2
times greater at kill
sites than along travel
routes
1,024 to 1,375 m
in elevation
Transitional
between the
northern Pacific
coastal and the
continental types
Dense Lodgepole
Pine forests
dominated most
of the valley with
additional
occurrence of
Alpine Fir,
Spruce, Western
Larch, and
Douglas-fir
relative to
linear corridors
in caribou
range
Captured and
1990-1996
radio-tagged 30
Wolves in 3-4
Objective:
packs
determine
effects of
Followed Wolf spatial and
travel routes on habitat features
skis and
on hunting
snowshoes
success of
Wolves
Spatial analysis
in GIS
Meadows and
riparian areas also
dispersed
throughout
Kill sites had 37%
less slope than travel
routes
Lleneza et al.,
2012
Predator concealment
was more important
than prey
detectability—Wolves
were more successful
in dense stalkinghiding cover
Predictors related with Galicia (NW
landscape attributes
Spain); covering
(altitude, roughness
c. 30,000 km2
and refuge) strongly
Data on the
distribution of
Wolves came
from regional
Humandominated
landscape with
human
60
NW Iberian
Peninsula
Mech et al.,
1998
Minnesota
determined Wolf
occurrence, followed
by humans and food
availability
patchy and
heterogeneous
landscape of
cropland, pasture,
scrub, semiVariance partitioning natural deciduous
analysis revealed that forest (Quercus
the three most
robur, Quercus
important components pyrenaica and
determining Wolf
Betula alba) and
forest plantations
occurrence were
related with: (1) the
(Eucalyptus spp.
and Pinus spp.).
joint effects of the
three predictor
Cover percentage
groups, (2) the joint
effect of humans and
of pastures and
crops in Galicia is
landscape attributes
and (3) the pure effect 39%, 23% for
of landscape attributes forest plantations
and 26.6% for
Mean altitude had the scrublands, which
have been
highest proportion of
transformed by
independent
human activities.
contribution to
Less than 10% of
explaining the
this area is
probability of Wolf
occupied by
occurrence (35.6%),
followed by density of woodland
deciduous forest
buildings (23.8%),
and most of them
density of horses
have been
(13.4%) and density
managed for
of roads (11.2%)
timber harvest.
Wolves showed a
strong positive
selection towards
elevated and hardly
accessible sites as
well as areas where
vegetation structure
provided refuge
Primary threat to
About 46% of
Wolves, which is
Minnesota was
associated with high
considered
road densities, is the
accessibility that
Wolf surveys
carried out in
the summerautumn periods
(breeding and
pre-dispersal
periods)
between 1999
and 2003
settlements (>
10 buildings)
widely
scattered (1
human
settlement/km2;
c. 50% of
human
settlements of
Spain are
Wolf presence located in
was determined Galicia) and a
mean human
by indirect
signs such as
population
density around
feces and
ground scratch 93 inhabitants
marks,
km2.
excluding
tracks owing to The percentage
the difficulty of of people
differentiating
living in small
dog tracks from villages in
wolf tracks
Galicia (< 10
buildings) is
16.5%,
whereas this
percentage for
the overall
country is four
times lower.
Researchers did
not consider
unpaved roads
Wolf
distribution
(relative to road
density) was
mapped by the
The mean
density of
roads was 0.36
km/km2
61
Theuerkauf et
al., 2003
The Bialowieza
Forest, Poland
roads allow humans to An area of
kill Wolves (shooting, 100,576 km2
snaring, and trapping)
The area
Road densities may be inhabited by
associated with
wolves totaled
different types of land 59,900 km2
use
The region was
primarily
coniferous and
deciduous forest,
but the southern
and western
portions also
contained
brushlands,
scattered old
fields, and
pastures
three coauthors
who had
knowledge of
Wolf
distribution
based on
previous
experience
Daily activity patterns
of Wolves in the
study area were
mainly shaped by
their pattern of
hunting prey (rather
than human activity or
other factors)
Radio tracked
11 Wolves
Wolves were active
45% of the day on
average with activity
highest at dusk and
dawn
Hourly activity and
distance traveled was
highest 2 hrs. before
Wolves made a kill
Transition zone
between boreal
and temperate
climate. Forest
consists of
deciduous,
coniferous, and
mixed tree
stands
Human
density—
approx. 7
inhabitants/km2
in the
Bialowieza
Forest and 70
inhabitants/km2
in the region
Surveyed 112
local canid
trappers by
mail and
telephone in
1982 and 1983
During 24 hr.
radio tracking
session of
usually 6 days,
researchers
noted locations
every 15 min
(1996-1999) or
every 30 min
(1994-1996)
The peripheral
and disjunct
parts of the
Wolf range
varied in size
from 686 to
9,915 km2 and
density of
roads averaged
0.54 km/km2
The two
contiguous
regions
uninhabited by
Wolves had
mean road
densities of
0.88 and 0.81
km/km2, and
the part of the
primary range
devoid of
wolves, >0.83
km/km2
The density of
forest roads
suitable for 2wheel drive cars
was about 1.2
km/km2 in the
commercial
forest, but only
about 0.1 km/km2
are intensively
used by the public
62
Activity and
movement were
highest in March
during mating season
Theurekauf et
al., 2007
Bieszczady,
Poland
Did not find a
correlation between
human activity and
temporal activities of
Wolves where Wolves
have the opportunity
to avoid contact with
humans
Wolves avoided the
area around main
public roads more at
night (up to a distance
of 1.5 km) than in the
day (up to 0.5 km)
surrounding
the study area
Bieszcz
Mountains
Southeastern
Poland
62% of the area
Wolves avoided a 0.5- was forested
km area around
secondary public
Forest mainly
roads and paved forest consisted of
roads both at night
Beech, Fir,
and in the day but did Spruce, and Grey
not avoid the
Alder
surroundings of set
The degree of
Human activity is
forest
unlikely to be the
fragmentation
reason for nocturnal
was 74%
activity in Wolves
Whittington et
al., 2005
Wolves moved at any
time of the day with a
major peak of the
distance travelled per
hour around dawn and
a small peak in the
early night
Wolves selected low
elevations, shallow
slopes, and southwest
aspects
Jasper lies in the
confluence of
several valleys-valley bottoms
are dominated by
Radio tracked
wolves from 3
packs: 24 hr
radio tracking
sessions in
2002-2006
(usually one
session each
month for each
Wolf)
Wolves were
hunted until
1998 in the
study area
Used a
magnetic
counter card
placed in forest
roads to
document
human activity
Paved road
density as 0.64
km/ km2
(considered the
threshold for
Wolf
occurrence)
Human density
was higher
than in any
other Wolf
study
Recorded the
Researchers
movements of
simplified
two Wolf packs Wolf paths into
a series of
points
63
The town of
Jasper in Jasper
National Park
Alberta,
Canada
Selected areas within
25 m of roads, trails,
and the railway line
and more strongly
selected low-use roads
and trails compared to
high-use roads and
trails
open Lodgepole
Pine forests that
are interspersed
with Douglas fir,
Aspen, Poplar,
White Apruce,
and small
meadow
complexes. Sides
One pack strongly
of the valley are
dominated by
avoided distances
between 26 and 200 m Englemann
spruce and
of high-use trails;
otherwise, the Wolves subalpine Fir.
weakly selected or
avoided this distance
Snow depths
along the valley
class
bottoms range
Both packs avoided
from 5 to 40 cm
areas of high road and
The study area
trail density
contained 759 km
The use of roads and
of trails and 292
km of roads
trails was negatively
coupled with road
including a
density
railway line
Selected to be close to
areas of low human
activity but far from
high human activity
areas
Wolves traveled
within 25 m of roads,
trails, and railway
lines 21% of the time
and traveled through
the forests, rivers, and
meadows the other
79% of the time
Both Wolf packs
traveled five times
farther on low-use
trails than high-use
The territories of
both packs
extended between
20 and 50 km
along the three
valleys that
converge upon
the town of Jasper
The study area
included a portion
of these two pack
territories,
approximately 20
km each side of
Jasper (52052' N,
118005' W,
elevation 970-
for two winters
(1999-2000)
Snow tracking
and
simultaneously
recording
positions with a
hand-held
global
positioning
system
Used matched
case-controlled
logistic
regression to
compare
habitat
covariates of
Wolf paths
(cases) to
multiple paired
random
locations
(controls)
separated by
100 m, which
produced 481
wolf points for
pack 1 and 467
wolf points for
pack 2
Wolves in this
study were not
subjected to
legal or illegal
hunting.
Wolves were
subject to
mortality from
collisions with
vehicles and
trains.
Researchers
note the
association that
researchers
found between
roads, trails,
and topography
could create
conservative
estimates of
road and trail
avoidance
The number of
Wolves in Pack
1 ranged from
seven to ten
individuals
The number of
wolves in Pack
2 ranged from
two to three
individuals
64
trails, yet only Pack 2
traveled farther on
low use roads than on
high use roads (Pack 2
rarely traveled on
high use roads and the
railway line)
Other variables
ranked from most to
least important
included: low-use
roads, railways, highuse trails, and highuse roads
Zimmerman et
al., 2014
Scandinavia
At the site scale
(approximately
0.1 km2), Wolves
selected for roads
when traveling, nearly
doubling their travel
speed
At the patch scale
(10 km2), house
density rather than
road density was a
significant negative
predictor of Wolf
patch selection
At the home range
scale (approximately
1000 km2), breeding
Wolves increased
2800 m above sea
level)
A major
transportation
highway (not
divided or
fenced) with
substantial
freight-truck
traffic runs
through the study
area from
northeast to west.
The outer limits
of the study area
coincided with
park boundaries,
prominent
geographic
features, and
Wolf territorial
boundaries
Secondary
highways extend
throughout Jasper
National Park.
While the study
area encompassed
2900 km2, only
572 km2 lay
below 1600 m
where 99% of
wolf movements
occurred
Within the Wolf
breeding range in
south-central
parts of the
Scandinavian
Peninsula
(Sweden and
Norway); 59–
62°N, 10–15°E,
approximately
100000 km2.
Wolf territories
were primarily
covered by boreal
coniferous forest
dominated by
Scots Pine and
Norway Spruce
with some
Jasper received 1
288 788 vehicles
in 2000, a 22%
increase from
1990
Analyzed the
summer
movements of
19 GPScollared
resident
Wolves in
relation to
roads
Seasonal
variation in traffic
volume
Differentiated
between
breeding and
nonbreeding
Wolves
Behavioral
response of
Scandinavian
Wolves to
roads is a
complex
process
dependent on
time of day,
road type,
behavioral
state,
reproductive
65
gravel road use with
increasing road
availability
deciduous
species, including
Birch and Aspen
Of all 3154 hourly
steps used in the SSF
models, 328 (10.4%)
ended on gravel roads
and 30 (1.0%) on
main roads
Mire, agricultural
fields, open areas
(e.g. mountains,
boulder fields),
and built-up areas
were also
represented (in
that order)
While resting during
day time, Wolves
preferred intermediate
distances to gravel
roads, and they were
1.4 times more likely
to bed at distances of
1–1.5 km from the
closest gravel road as
compared to directly
at the road
Selected day bed sites
far away from main
roads and at
intermediate distances
to houses, with a peak
at 2 km from the
closest house.
Main road density
averaged 0.19 ±
0.02 km/km2, and
the maximum
distance to main
roads ranged
from 3.72 to
14.88 km
status, and
spatial scale
Human density
within the
distribution of the
Scandinavian
wolf population is
low, including
vast areas with <1
person per km2
House densities
within the
territories
averaged 3.0 ±
0.4 per km2
Gravel road
densities were on
average 4.6 times
higher than main
road densities and
the maximum
distance to gravel
roads within
territories ranged
from 1.25 to 6.09
km
66
Thesis_MES_2020_MarisaP