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A TEMPORAL ANALYSIS OF ELK MOVEMENT IN RELATION
TO WASHINGTON’S TRANSPORTATION INFRASTRUCTURE
by
Molly Tyler Sullivan
A Thesis
Submitted in partial fulfillment
of the requirements for the degree
Master of Environmental Studies
The Evergreen State College
June 2014
©2014 by Molly Tyler Sullivan. All rights reserved.
This Thesis for the Master of Environmental Studies Degree
by
Molly Tyler Sullivan
has been approved for
The Evergreen State College
by
________________________
Dina Roberts, Ph. D.
Member of the Faculty
________________________
Date
ABSTRACT
A Temporal Analysis of Elk Movement in Relation to Washington’s
Transportation Infrastructure
Molly Tyler Sullivan
Many studies show that roads negatively affect wildlife, but many
questions remain unanswered, such as exactly how roads influence species like
elk (Cervus elaphus) (Montgomery et al. 2013). This often makes mitigation
measures difficult to design and implement. However, to offset consequences of
roads, mitigation techniques including wildlife crossings have been built into
roads to facilitate wildlife movement. In partnership with the Washington State
Department of Transportation, this study used data from the combination of
motion-triggered cameras deployed around three underpasses in Washington
State, Washington State Patrol Collision records, Washington State Department of
Transportation Carcass Removal Information and locations of three elk in the
Upper Snoqualmie Valley fitted with GPS collars to understand how existing
infrastructure may facilitate wildlife movement. I compiled and statistically
analyzed this information to understand whether different light levels, seasons and
traffic volumes affect elk movement through underpasses below grade, elkvehicle collisions at grade, and elk movement in relation to Interstate 90.
Ultimately I discovered that elk used underpasses, and elk were involved in
collisions on highways most frequently at night when traffic volumes were
typically low. Since elk are normally most active during dawn and dusk, the
observed patterns suggest that another factor may be influencing elk movement
near roads, causing a shift from their normal behavior. Elk used underpasses
most frequently during the fall and summer, likely in response to heightened
activity during the mating and growing seasons, though the seasonal patterns of
collisions were less well defined. Additionally, elk demonstrated a highly
correlated albeit non-linear relationship between average distance to the road and
light level according to varying traffic volumes. Despite available habitat on
either side of the highway, collared elk remained close to the road but rarely
crossed it. However, underpasses studied did reveal their effectiveness in
allowing safe passage below the highway. This should be considered in future
studies and transportation projects aiming to understand best practices for
ameliorating effects of roads on wildlife movement, especially those intending to
reduce habitat fragmentation and collisions between humans and elk. How well
best practices focused on reducing collisions between humans and elk translate to
other wildlife should be the basis of further study.
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Table of Contents
Chapter 1: Literature Review...................................................................................1
Introduction..........................................................................................................1
Roads: Their History and Evolution ....................................................................2
Road Ecology.......................................................................................................4
Effects of Roads on Wildlife and Humans...........................................................6
Economic Costs................................................................................................7
Road Mortality: WVCs ....................................................................................7
Habitat loss .......................................................................................................9
Habitat Alteration .............................................................................................9
Road Affinity and Avoidance.........................................................................10
Landscape Connectivity and Fragmentation ..................................................11
Barrier Effects ................................................................................................13
Human Access/Exploitation ...........................................................................14
Mitigation Techniques .......................................................................................15
Crossing Structures.........................................................................................16
Other Mitigation Techniques..........................................................................19
Evaluating Mitigation Strategies........................................................................23
Elk ......................................................................................................................27
Elk in Relation to Roads and Underpasses.....................................................29
Literature Review Summary and Thesis Research Questions ...........................34
Chapter 2: Analysis of Elk Movement in Relation to Transportation Infrastructure
at Grade and below Grade .....................................................................................37
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Introduction: Roads and Wildlife.......................................................................37
Study Area..........................................................................................................39
Upper Snoqualmie Valley Site .......................................................................40
Cowlitz River Valley Site...............................................................................43
Methods..............................................................................................................45
Origination of the Study .................................................................................45
Identifying Underpasses for Analysis and Camera Set Up ............................46
Camera Data and Elk Activity at Underpasses ..............................................47
Elk-Vehicle Collision Data ............................................................................53
Carcass Removal Data....................................................................................55
Telemetry Data ...............................................................................................56
Traffic Data ....................................................................................................57
Results................................................................................................................58
Camera Data and Elk Activity at Underpasses ..............................................58
Elk-Vehicle Collisions ...................................................................................62
Telemetry........................................................................................................64
Discussion ..........................................................................................................65
Camera Data and Elk Activity at Underpasses ..............................................66
Elk-Vehicle Collisions (EVC)........................................................................72
Telemetry........................................................................................................78
Conclusion .........................................................................................................79
Chapter 3: Conclusions and Management Implications ........................................82
Conclusions........................................................................................................82
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Management Implications..................................................................................84
Recommendations for Future Research .........................................................85
References............................................................................................................115
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List of Figures
Figure 1: Total Collisions versus Wildlife-Vehicle Collisions..............................93!
Figure 2: Study Areas in the Upper Snoqualmie Valley and Cowlitz River Valley
...............................................................................................................................94!
Figure 3: Study Area in the Upper Snoqualmie Valley.........................................95!
Figure 4: Study Area in the Cowlitz River Valley ................................................96!
Figure 5: Number of Detections versus Individual Elk Using Underpasses .........97!
Figure 6: Categories of Twilight............................................................................98!
Figure 7: Frequency of Elk Detections using Three Different Underpasses .........99!
Figure 8: Total Frequencies of Detections of Elk Movement through Underpasses
.............................................................................................................................100!
Figure 9: Total Frequencies of Elk Movement through Underpasses by Season103!
Figure 10: Total Elk-Vehicle Collisions in Study Areas .....................................105!
Figure 11: Statewide Records of Elk-Vehicle Collisions and Carcass Removals
.............................................................................................................................106!
Figure 12: Elk-Vehicle Collisions in Study Areas ..............................................107!
Figure 13: Frequency of Elk-Vehicle Collisions .................................................111!
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List of Tables
Table 1: Goodness-of-fit test Using Light Level and Site...................................101!
Table 2: Results of a 2-Way ANOVA using Light Level and Site .....................102!
Table 3: Results of a Chi-square Goodness-of-fit Test using Season and Site ...104!
Table 4: Results of a 2-Way ANOVA using Light Levels and Collisions..........108!
Table 5: Student-Newman-Keuls Mean Separation Test of Collisions...............109!
Table 6: Student-Newman-Keuls Mean Separation Test of Traffic Volumes
during Elk-Vehicle Collisions .............................................................................110!
Table 7: A Chi-square Goodness-of-fit Test using Season and Site ...................112!
Table 8: Elk Distances to the Highway ...............................................................113!
Table 9: A Chi-square Goodness-of-fit Test using Light Level and GPS Points114!
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Acknowledgements
This thesis represents countless hours of personal work, forged
connections with the Washington State Department of Transportation, diligent
help from professors and students within the Masters of Environmental Studies
Program, and the support of my family and friends. You all are such a critical
component to this accomplishment, and I am deeply grateful for all of you.
Thank you to the Washington State Department of Transportation and the
Upper Snoqualmie Valley Elk Management Group for collaborate efforts, and
sharing of data. Special thanks to Kelly McAllister, wildlife biologist, for
supporting my efforts this past year, encouraging and listening to my ideas and
helping me formulate an important research question. Your guidance and
kindness has been unfathomable. Thank you to Mark Bakeman, biologist, for
statistical guidance, and reviews that you provided me. Your positive attitude and
willingness to help made this thesis come to life. Thank you to Marion Carey,
fish and wildlife program manager, for supporting my internship in the
Environmental Services Office and Jeff Dreier for helping me with collecting
camera data with no less than a positive attitude and fascination for nature.
Gratitude is especially given to those of you who offered additional help
and support. Marc Hayes, biologist at the Washington Department of Fish and
Wildlife, for the wonderful statistical help and pointed comments and revisions to
my manuscript. I still do not comprehend how you can do so much in a day.
Martha Henderson, director of the Graduate Program on the Environment at The
Evergreen State College for sponsoring my internship with the Department of
Transportation. Dina Roberts, biologist and faculty, for being my thesis reader
and reviewing my progress this past year.
I would also like to extend my extreme gratitude to the friends and family
who have supported me these past few years. Being relatively nomadic, those
friends and pseudo-family I have been lucky enough to find in Washington have
helped me establish roots here. My family, especially my Mom and Dad, for
supporting me at all times and believing in me even when I did not, and especially
for teaching me to always strive to be extraordinary. To Mary Kay, who has
loved and encouraged me at all times from this life, and beyond; I will always be
your girl on fire. I am eternally grateful for the unconditional love and support
you all have given me, what incredible examples you have set.
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Last, but certainly not least, thank you to the wildlife whose presence and
mysteries have kept me so intrigued all these years. As Hilty et al. (2006) put so
eloquently, “May wildlife and wild places be a cherished and significant part of
their futures.” I hope that this thesis is only the beginning of my efforts to
contribute to such wildlife and wild places.
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Chapter 1: Literature Review
Introduction
Roads and transportation infrastructure span a large part of the United
States, with public roads alone accounting for over 6.3 million km (Forman et al.
2003). This allows for settlement of remote areas, availability of goods, and
communication. Such roads are an integral part of the development of the United
States. However, with this large network of roads continually growing across the
landscape, its negative impacts must also be analyzed, especially for wildlife.
Wildlife evolved in specific habitats, none of which originally included such
extensive human-made barriers. Therefore conflicts between wildlife and
transportation infrastructure are growing. Despite our extensive roadway system
in North America, only in the past few decades have researchers examined the
negative impacts associated with the barriers that roads present to wildlife.
Therefore this literature review begins with a brief overview of the history of
roads and road ecology. Then I discuss current knowledge of negative impacts of
roads for wildlife, and mitigation techniques and methods for evaluating roadcrossing success. With an understanding of the challenges that roads pose to
wildlife and how wildlife managers employ current mitigation measures, specific
case studies will be analyzed. Because collisions with elk (Cervus elaphus) are
frequent and highly detrimental in Washington State, I will focus on this species.
Specifically, studies involving elk and transportation infrastructure will be
discussed, as well as the lack of sufficient monitoring of the temporal aspects of
elk movement. In conclusion, despite the growing body of research dedicated to
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road ecology, it will become clear that much more work needs to be conducted
before we fully understand the effects of roads.
Roads: Their History and Evolution
A historical background of road and highway systems in North America is
important for understanding road ecology and the various impacts of roads on
wildlife. Therefore, a brief overview of the history of the road system in the
United States is presented here. Road systems are important for most societies to
function and communicate properly. In fact, roads have been integral parts of
many societies for thousands of years, ranging from footpaths for humans, to dirt
roads for moving armies and supplies, and finally to paved roads for motorized
vehicles (Forman et al. 2003). Roads have similarly taken various shapes and
sizes across the United States.
Though some paths were in place before European settlement, most
current roads take their shapes from roads designed over 100 years ago (Forman
et al. 2003). These early roads were concentrated in the eastern United States and
spread outwards to export extractive resources. Therefore, most early roads were
smaller, following the contour of the land (Forman et al. 2003) or game trails used
by animals for movement and migration.
By the early 1900s, railroads had expanded rapidly across the US, and
dominated most modes of transportation leaving little interest in road building
(Forman et al. 2003), except near farmlands to haul grain. As automobiles
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became more common, roads quickly transformed from dirt, to oiled gravel, to
asphalt (Forman et al. 2003).
Roads were heavily used in World War I to transport goods made in
factories (Forman et al. 2003). After the war, the building of roads surged, due in
large part to the implementation of the gas tax. In 1916 the U.S. Congress created
the Federal-Aid Highway Program bringing with it the creation of state highway
departments. By World War II suburban areas were common, and household cars
used to transport people to and from these areas became a necessity. The
resulting interstate highway program was created as a solution for the expanding
presence of automobiles, trucks, and military strategies. As of 1991 due to the
Intermodal Surface Transportation Efficiency Act, the responsibilities of roads
were passed to local government authorities instead of federal agencies (Forman
et al. 2003). Therefore, the myriads of roads today are owned by many different
entities.
It is impressive how quickly roads spread across the US. In just over a
century the entire landscape was drastically altered to include the vast network of
highways and roads. New roads continue to be built, and existing roads continue
to be widened (Forman et al. 2003), accommodating the nation’s growing
population and transportation needs. With the sharp transition from a once wideopen landscape, it is easy to imagine that animals evolving over thousands of
years in such areas would find the new changes in the landscape difficult to
maneuver.
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The combination of a quickly growing road network, increasing human
population size, and the fact that most roads followed the same paths of least
resistance that many migratory animals utilize has proven to be literally lethal to
wildlife. Though the negative influences of roads on wildlife were not initially
taken into account, a new field of study has formed in the past few decades to
address the many concerns that roads cause: road ecology, which will be further
discussed in detail (Forman et al. 2003).
Road Ecology
Road ecology studies the influences and relationships of roads on
surrounding environments and organisms (Forman et al. 2003). Road ecology can
be focused on small-scale issues, such as a segment of road and its impact on one
species, or road ecology can focus on large-scale issues, like the impediment of
gene flow in animals across a road-divided landscape. Road ecology can even be
addressed on a global scale, due to the vast network of roads stretching across the
globe. Roads are somewhat ironic because though they connect people to almost
anywhere on Earth, roads also drastically segregate many wild animal populations
(Forman et al. 2003). Overall the diverse field of road ecology has grown in the
past few decades and now focuses on important and prevalent issues (van der Ree
et al. 2011) on many scales.
Road ecology is an extremely interdisciplinary field, requiring knowledge
of: hydrology, microclimates, wind, weather, vegetation, biodiversity, wildlife,
landscape ecology and habitat fragmentation (Forman et al. 2003). My thesis will
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focus on aspects of wildlife biology, specifically wildlife movement and wildlifevehicle collisions (WVCs).
The US supports many herds of large mammals, and when these animal
habitats overlap with human presence, problems like WVCs can occur (Forman et
al. 2003). Instances of road-kill have been documented since the 1920s, with
injury to humans driving further interest in reducing these occurrences (Forman et
al. 2003). Applications of road impacts mitigation really began in the 1960s when
bridges were constructed in France to allow games species safe travel over
highways (Bielsa and Pineau 2007). Since then Europe has set many precedents
for road ecology spurred by stringent motorist safety laws (Forman et al. 2003).
In the United States in the 1970s, several wildlife overpasses were built in Utah
and New Jersey and much research on deer and highways was conducted
(Carbaugh et al. 1975). Florida gained prestige in the road ecology community in
the 1980s and 1990s after building 23 underpasses, widening bridges for water
movement purposes, and reducing instances of WVCs involving the endangered
Florida Panther (Forman et al. 2003). The extensive collaboration of different
entities, amount of funding, and public involvement focused on reducing vehiclecaused-panther-mortalities illustrates the interdisciplinarity of road ecology.
Studies in road ecology tend to focus on the most noticeable and costly
effects of roads: WVCs and habitat fragmentation. Therefore, many experiments
and projects since the 1990s have focused attention and research towards these
areas. Road ecology issues have since been highlighted at international
conferences, and the number of peer-reviewed papers that appear in scientific
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journals is growing (Forman et al. 2003). Most research, as evaluated by Taylor
and Goldingay (2010) has been conducted in North America (51%) and Europe
(25%) with an emphasis on mammals (53%), showing a distinct bias in this
growing body of literature. Still in its infancy, road ecology has far-reaching
implications for many disciplines. Therefore its gain in momentum needs to
continue and expand into even more sectors so that studies can move beyond
asking basic questions, and begin understanding the dynamic processes that make
up this interesting field. After basic knowledge is acquired, more effective
mitigation and planning processes can occur, thereby lessening the tension
between overlapping human and wildlife habitats.
Effects of Roads on Wildlife and Humans
Scientists and transportation agencies are trying to gain a better
understanding of the vast consequences of transportation infrastructure on
wildlife, though only in the past few decades has serious thought been given to
this problem. It is important to note that though roads take up a small portion of
space on a landscape due to their linearity, their effects are startlingly
disproportionate as they permeate far into adjacent landscapes (Jackson 2000,
Frair et al. 2008). In fact, up to 20% of land mass in the US may be considered
“road-effect zones” (Forman and Alexander 1998). Increasing distance and width
of roads in addition to more people has led to an incredible loss in landscape
connectivity, and remains a leading cause of habitat fragmentation (Beckmann
and Hilty 2010). Roads and highways cause many issues for wildlife, though the
extent and severity is different for each species and each population. The
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negative effects of roads include: economic costs, road mortality in the form of
WVCs, habitat loss, habitat alteration, road avoidance and affinity, landscape
connectivity and fragmentation, barrier effects and human access/exploitation.
Each of these will be discussed subsequently in further detail.
Economic Costs
The overlap of human and animal habitats results in costly consequences.
Such consequences include WVCs in the US that annually result in ~200 human
fatalities, 29,000 human injuries and over $1 billion in property damage (White
2007). Other estimates are even higher, including one in 2007 in the US where
over $8 billion in vehicle damage occurred as the result of 1 to 2 million accidents
involving large mammals (Beckmann and Hilty 2010). Given the severity and
costs of WVCs, it is in the best interest of states and motorists to reduce instances
of collisions. Therefore, though roads have many other negative effects as listed
and explained below, most states and agencies seek to reduce WVCs because of
the impacts on human life and wildlife-related deaths, and the financial impacts.
Road Mortality: WVCs
Over 1 million vertebrates are killed on roads every day in the US (Lalo
1987). Road-associated mortality can have significantly detrimental effects on
certain wildlife populations (Jaeger et al. 2005). It is important to understand that
while other forms of vehicle-related crashes have remained relatively stable or in
some cases declined, WVCs continue to rise (Huijser and McGowen 2010), with a
doubling in fatal animal-vehicle collisions (AVC) since just 1990 (Sullivan 2011).
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Several factors likely contribute to this upward trend including: 1) Increasing deer
populations, 2) Increasing road traffic (Huijser and McGowen 2010), or 3)
Suitable habitat adjacent to roads (Gunson et al. 2011). Washington State
recorded at least 14,969 deer collisions and 415 elk collisions between 2000-2004
(WSDOT 2013a). Overall, an obvious increasing trend exists in WVC
occurrences (Hughes et al. 1996). Road mortality rates are far higher than natural
rates of mortality, and potentially pose serious threats to different species of
vulnerable wildlife (Ciuti et al. 2012).
Though current estimates for collision mortality are already substantial,
actual collisions are likely much higher due to selected data limitations. Road-kill
is one way of enumerating WVCs, though it remains only a portion of the
cumulative effects that roads have on wildlife (Forman and Alexander 1998).
Limitations to using road-kill estimates include the fact that: 1) Many collisions
and carcasses are not reported (WSDOT 2013a), 2) Some animals die out of sight
of the roadway, and are therefore not seen (Prosser et al. 2008), 3) The time
between death and pickup is often substantial (Prosser et al. 2008), 4)
Maintenance crews are not required to report species picked up 5) Some species
are incorrectly recorded or misidentified 6) Many systems do not categorize by
species, but rather by general groupings and 7) Records only include statemaintained roads (WSDOT 2013a). Given these limitations, it is likely that actual
numbers of wildlife mortalities are much higher than currently reported, as found
in other studies (Teixera et al. 2013). Other equally important, yet sometimes
ignored effects of roads are listed below.
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Habitat loss
Roads have a direct effect on landscapes in the form of habitat loss. Not
only do roads change habitat directly where the actual road is located, but the
habitat adjacent to the road also subsequently changes (Forman et al. 2003).
Roads and the impacted surrounding habitat, or edge, fragment once continuous
landscapes which can negatively influence wildlife species differently according
to their behavioral responses (Beckmann and Hilty 2010). Direct habitat loss
from the 6.25 million km of public roads in the US is substantial, not including
ongoing efforts to lengthen and widen existing roads. In addition to the overall
loss of habitat, roads fragment landscapes causing edge effects that can extend
anywhere from 10 to 100s of meters away from each road. Because of this, few
places in the US lack the influence of roads in some way (Ament et al. 2008).
Therefore, roads change landscapes not only through the physical placement of
the road causing direct habitat loss, but also through habitat alternation due to
edge effects, as will be explained below further.
Habitat Alteration
Roads can cause landscape changes, leading to increased or decreased
habitat quality for surrounding wildlife. Roads sometimes increase surrounding
habitat quality by creating additional habitat, or allowing certain species to move
and detect prey more easily (Forman et al. 2003). For example, additional
microhabitats like perches or nesting space are created along roads for birds
(Forman et al. 2003). Additionally, excess nitrogen along roadways can facilitate
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some plant growth and subsequent insect populations. In fact, many roadsides
that are regularly maintained result in high-quality habitat suitable for many
species (Forman et al. 2003). For example, grazers may be attracted to medians
that are planted with nutritious grasses or shrubs and maintained regularly.
Therefore, although roads can sometimes improve surrounding habitats, the net
effect of attracting wildlife to roadsides is problematic.
More often roads result in decreased habitat quality. For example,
increased impermeable surfaces lead to more runoff, and contamination of
adjacent habitats (Coffin 2007, Jackson 2000). Such impermeable surfaces also
lead to increased concentrations of chemicals like heavy or harmful gases like
carbon dioxide and ozone (Coffin 2007) that can negatively impact wildlife
dependent on contaminated resources. The noise and unnatural light from roads
can also influence wildlife negatively (Jaeger et al. 2005). Existing plant
communities can be altered, either due to runoff or the introduction of invasive
species (Forman et al. 2003). Therefore, not only do roads divide once
contiguous landscapes, they also have far-reaching effects extending into their
adjacent environments. These effects often influence the behavior of animals
residing in habitat adjacent to roads, causing either an attraction to, or avoidance
of, the road.
Road Affinity and Avoidance
Because of the resulting increased or decreased habitat quality that roads
produce, many species respond differently to road presence. Increased habitat
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resulting in vegetation near roads can attract ungulates and small mammals, which
are often hit by vehicles (Forman et al. 2003). Resulting road kill attracts
scavengers such as eagles, coyotes, bears, and wolverines (Forman et al. 2003),
which may also increase the likelihood of mortality in these species as well. In
contrast, realizing that many predators avoid roads, moose for example, take
advantage by giving birth closer to roads, increasing calf survivability (Berger
2007). More often, however, roads deter animals, effectively isolating species
that require large tracts of land. Many migratory species like ungulates move
long distances between summer and winter ranges to avoid harsh conditions and
find scarce resources (Beckmann and Hilty 2010). The added difficulty of
moving across roads is challenging for many species. Additionally, some species,
especially predators, find the noise and light from cars disturbing and avoid roads
and surrounding habitats altogether (Jackson 2000). Overall, a high density of
existing roads in an area can exacerbate animal road affinity or avoidance,
creating a distinctive faunal assemblage in road-associated habitats. As further
explained below, addition of new roads continues to fragment landscapes,
reducing overall habitat connectivity which can be detrimental to animals that are
strongly influenced by roads.
Landscape Connectivity and Fragmentation
Landscape connectivity is an important factor in maintaining animals’
basic needs, and when fragmentation occurs, many negative effects on wildlife
are observed. Landscape connectivity is the ability of a landscape to provide
animal passage and for other large-scale ecological processes to occur (Knaapen
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et al. 1992). Many mobile species require access to various habitats to meet their
needs. Barriers to this movement result in increased mortality rates, decreased
fecundity, smaller populations and decreased viability (Forman et al. 2003).
When landscapes are fragmented it makes it difficult for species to repopulate
following declines or maintain access to resources (Forman et al. 2003). Overall
reduced landscape connectivity is especially detrimental to species requiring
ample foraging area, species that disperse to establish new home ranges, for
species that migrate (Forman et al. 2003) or for species and populations that
exhibit metapopulation structure (Beckmann and Hilty 2010).
Habitat fragmentation reduces landscape connectivity, which imposes
many effects on wildlife. Specifically habitat fragmentation results when a
continuous habitat is divided into differently sized patches (Hilty et al. 2006).
Since species have different habitat requirements, fragmentation affects
organisms differently. For example, Brown-headed Cowbirds thrive in
fragmented habitats because they can easily find passerine nests to lay their eggs
in, much like a parasite (Donovan et al. 1997). However, some species like the
Spotted Owl can only survive in large continuous landscapes (Lamberson et al.
1994). In fact for rare species habitat fragmentation is a substantial factor in
population decline (Forman et al. 2003). So, while habitat fragmentation can
create suitable habitat for some species, more often it results in less available,
lower-quality habitat that has been detrimental to many vulnerable species.
Habitat fragmentation is a widely studied phenomenon, and it is a key
component in the field of island biogeography (Hilty et al. 2006). Studies based
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on Island Biogeography Theory demonstrate that larger islands contain more
species, islands closer to the mainland are more diverse, small islands are more
prone to species extinctions, and islands near the mainland will have lower rates
of species extinction (Hilty et al. 2006). Landscapes that are fragmented by roads
are often likened to islands. Larger fragmented landscapes found closer to one
another usually contain more species, and have more diversity, though current
research shows that some landscapes can be linked together via corridors to
conserve biodiversity (Hilty et al. 2006). However as landscapes become more
fragmented and the area available to wildlife becomes smaller and farther apart,
species may become prone to extinction. Wildlife isolated to small islands, or
very fragmented landscapes are not as likely to persist because of lack of
resources and lack of gene flow. Much like islands, these landscapes fragmented
by roads can pose serious barriers to wildlife movement. Ultimately, roads
impact population isolation, and potentially play a huge role in the negative
effects of fragmentation and resulting barrier effects for wildlife.
Barrier Effects
Barrier effects resulting from the presence of roads are often difficult to
see, and are therefore poorly understood (Forman et al. 2003). Speciation is one
such effect, resulting in the evolution of new species following the isolation of
subpopulations. This isolation can also lead to inbreeding depression, resulting in
the continuation of less viable genes and the production of weaker populations of
animals (Forman et al. 2003). Roads can also affect individual animal behavior
(Forman et al. 2003) though more study should be dedicated to this area.
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Specifically, roads can cause animals to change normal behaviors such as mating,
birthing, and migration. These behavioral changes can also be attributed to
increased human presence. Roads allow human access into once isolated areas,
and this human presence can act as a barrier for animals, which will be explained
below. Overall, these lesser-seen but equally important effects of road barriers
can affect populations greatly.
Human Access/Exploitation
The main purpose of roads is to allow human access into certain areas,
which also allows for human exploitation into previously isolated areas. Human
presence alone can deter animals, along with the added competition of harvesting
resources, changing the functionality of the landscape, introducing non-native
species and even increasing hunting pressures (Jackson 2000, Bonnot et al. 2013).
Animals are subject to the pressures that anthropogenic alterations have on the
landscape, and roads greatly facilitate such rapid change and human colonization.
As demonstrated above, roads greatly influence natural landscapes and
resulting wildlife habitats. Few positive ecological effects of roads exist as most
contribute to the reduction of habitat quality and increased conflict between
humans and animals. Knowledge of these negative effects has resulted in the
creation and implementation of many mitigation techniques. These mitigation
techniques serve to reduce wildlife mortality on roads, and better connect
landscapes (Forman et al. 2003), which is important because the increasing road
network in the US will likely only exacerbate the aforementioned effects.
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Mitigation Techniques
Understanding issues in road ecology is a complex process, making the
design of mitigation techniques diverse. Since transportation infrastructure not
only presents a barrier to individual animals, the issue must be understood on a
larger scale (Jackson 2000) and analyzed for higher order effects as well (van der
Ree et al. 2011). The initial construction of roads and highways serves as only
the beginning to the issue of fragmentation (Jackson 2000). The addition of
longer, wider roads increases habitat fragmentation and exacerbates problems
with wildlife (Jackson 2000).
Transportation agencies must now mitigate for negative impacts to
wildlife and humans caused by both old and new roads. Without a proper
understanding of the impacts that roads have on wildlife, roads and highways
were placed in unsuitable areas for wildlife. Now that thousands of miles of roads
cover vast stretches of the globe, it is necessary to both understand how to build
better infrastructure for wildlife movement for the future, as well as deal with
existing infrastructure. Mitigation techniques often involve changing
infrastructure, or altering motorist behavior or animal behavior, or both (Glista et
al. 2008, Clevenger and Ford 2010). Therefore, besides planning, collecting data
and implementing proper road designs (Clevenger and Ford 2010), the following
techniques attempt to mitigate the impacts that roads and highways currently pose
on wildlife: constructing species-specific crossing structures, fencing, lighting,
road removal, altering motorist behavior, and evaluating road-kill hotspots. Each
of these mitigation methods will be discussed in sequence.
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Crossing Structures
Several types of wildlife crossings exist, intended to allow wildlife
movement between segregated habitats (Clevenger and Ford 2010). Wildlife
crossings are widely cited as an appropriate mitigation technique, though few
studies have actually provided solid evidence of their effectiveness (Beier and
Noss 1998). In general, crossings above or below roadways provide connections
between habitats for animals thereby reducing WVCs and increasing motorist
safety (Clevenger and Ford 2010). It is important to recognize that using crossing
structures to connect fragmented landscapes is dependent upon targeted species
and the surrounding environment (Clevenger and Ford 2010). Since each wildlife
crossing structure is unique and costly, it is necessary to fully understand the
effectiveness of this expensive mitigation strategy. Finances often dictate the
extent of mitigation measures, so that many effective measures are rarely
implemented due to associated costs (Glista et al. 2008). The following are
several types of crossing structures that help mitigate the negative influences of
roads on wildlife.
Overpasses
Wildlife overpasses are large structures, usually spanning roadways instead of
entire landscapes, allowing large- and medium-sized wildlife species access to
adjacent habitats (Clevenger and Ford 2010). Some overpasses are used strictly
for animals, while others accommodate humans as well. These overpasses allow
animals to cross over highways, and are usually very effective when combined
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with fencing to keep wildlife off the adjacent road. While construction costs are
relatively high, overpasses effectively link ecological processes across a
landscape, acting as “landscape connectors” (Forman et al. 1997). Overpasses
can be designed for a specific species to maximize effectiveness. Some of the
most recognizable and impressive overpasses designed for wildlife mitigation are
located in Banff, Canada. Banff is a leader in designing and implementing
wildlife crossing structures, with the building of over 24 crossing structures
during the twinning process of the Trans-Canada Highway (TCH) (Ford et al.
2010).
Bridges
Landscape bridges are just one type of overpass. They tend to be large, with the
ability to provide connectivity for many animals (Clevenger and Ford 2010).
Landscape elements like vegetation can be included in some types of bridge
designs to help facilitate acceptance and movement of animals underneath or
across the structure (Clevenger and Ford 2010). Notable wildlife bridges include
those built along I-75 for the Florida Panther (Jansen et al. 2010), and those
located in Banff National Park along the TCH (Ford et a. 2010).
Canopy Crossings
Canopy crossings are used in forested habitats for arboreal or semi-arboreal
species (Clevenger and Ford 2010). They can include the use of ropes or cables
across roads. These innovative structures are especially important for animals
like squirrel gliders (Petaurus norfolcensis), in areas where road length exceeds
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their gliding abilities (van der Ree et al. 2010). Without habitat connectivity
measures, these animals are effectively isolated to fragmented canopy stands.
Viaducts
Viaducts are elevated roadways usually used for wetland habitats (Clevenger and
Ford 2010, Jackson and Griffin 2000). Because viaducts span valleys or gorges,
they help keep hydrological flows intact, and can be a low impact solution to
habitat connectivity needs in riparian areas (Clevenger and Ford 2010, Jackson
and Griffin 2000). Viaducts are typically more open structures that incorporate
vegetation, so they are especially functional for animals found in riparian areas
(Jackson and Griffin 2000).
Underpasses
There are many different types and sizes of underpasses, serving a range of
species. Underpasses differing in size and allowance of water flow can provide
targeted movement for animals and humans (Clevenger and Ford 2010). They
can be large enough to naturally mimic terrain preferred by certain animals,
though often these effective structures are expensive to implement (Glista et al.
2008).
Culverts
Culverts used as wildlife crossings are typically used by small- and medium-sized
animals residing in riparian habitats (Clevenger and Ford 2010). Dry platforms,
walkways and ramps are all forms of modifications that can be applied to culverts
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to further increase wildlife use (Clevenger and Ford 2010). Different types of
culverts such as drainage culverts, upland culverts and oversized stream culverts
can be used in different habitats (Jackson 2000). Typically, pipe culverts are used
by amphibians, in contrast to box culverts that are used by more species because
they only conduct water during heavy rains (Glista et al. 2008). Though many
culverts are not big enough for larger animals, and can become blocked and
require regular maintenance, they are often an economical solution (Glista et al.
2008).
Other Mitigation Techniques
Fencing
Fencing has been a key mitigation strategy for many years. Fencing is often
considered a critical component to helping funnel animals into crossing structures.
A study in Banff National Park showed that implementation of fencing resulted in
an 80% reduction of WVCs (Clevenger et al. 2001). Whether or not fencing acts
as a complete barrier in keeping animals off roads, it has its limitations too.
Fencing is expensive and requires regular maintenance. Additionally, it usually
only inhibits larger animal passage, it can cause an “end-of-the-fence” problem,
and animals can become trapped in areas if they accidentally get around the
fencing (Clevenger and Ford 2010). These “end-of-the-fence-problems” occur
when animals can easily enter the roadway once the fence stops. Increased
amounts of WVCs at these locations are good indicators of this problem. This
can be mitigated using additional techniques, like using fencing to funnel animals
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into underpasses (Clevenger et al. 2001). Animals trapped inside of fences are
especially dangerous, so steel swing gates, hinged metal doors, earthen ramps and
jump-outs are commonly used to reduce negative effects of fencing (Clevenger
and Ford 2010). Despite end-of-the-fence issues, some areas have shown reduced
costs associated with fencing by only using partial fencing (Ascensao et al. 2013).
They showed nearly the same level of effectiveness with 75% fencing as 100%
fencing previously had, though such results should be interpreted and
implemented with caution.
Lighting
Since many WVCs occur at night, with some areas reporting that over 80% of
collisions with deer occur from sunset to sunrise (Carbaugh et al. 1975, Reed and
Woodard 1981), it is important to have mitigation measures directed at different
levels of light. Though the effectiveness of highway lighting to decrease
accidents is not completely understood, it has long been thought of as an
expensive but possibly useful way to increase motorist visibility (Reed and
Woodard 1981). Overall, Reed and Woodard (1981) did not show that crossingsper-accident were different when lights were on or off, though motorists did
reduce their speed when deer simulations were placed in view. In fact, Reed and
Woodard (1981) found that more deer crossed the highway after it was
illuminated. It is still possible that different intensities or types of light might
influence wildlife deterrence from highways (Blackwell and Seamans 2008).
Overall, lighting has not been shown to effectively reduce WVCs.
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Road Removal
Since roads are one of the main impacts on wildlife and habitat, some restoration
techniques have focused on removing roads altogether (Switalski and Nelson
2011). Researchers in the Northern Rocky Mountains discovered that black bears
were found more frequently on closed roads, or recontoured roads, suggesting that
this might be an effective habitat restoration mitigation measure.
Nonstructural Methods
In areas where crossing structures are not feasible, other nonstructural methods
can be utilized to deter animals from using roads and adjacent habitats if needed.
Methods include: olfactory repellents, ultrasound, road lighting, population
control, and habitat modification (Glista et al. 2008).
Motorist Behavior
Changing motorist behavior is just as important as wildlife management in many
environments. Though several studies have tried to decipher the exact
implications of speed limits, amount of light, and traffic volumes, much is left to
discover. Overall the use of signs, speed bumps, reduced speed (high-speed
traffic is one of main causes of WVCs), wildlife crossing signs, and flashing
lights have all been implemented to make drivers more alert (Glista et al. 2008).
Human behaviors may be important, too, as Neumann et al. (2012) suggested due
to evidence showing that collisions likely happened in more human-modified
areas with higher traffic speeds. Indeed traffic speed is important, especially as
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drivers tend to increase speed at night (Ramp et al. 2006) when many animals
may be closer to roads.
Road-kill Hotspots
Areas with high rates of collisions and road-kill are often chosen as a priority for
mitigation placement. This is indeed a powerful tool to understand WVC impacts
on a large-scale, but care should be extended when interpreting the effects on
individual populations. Today’s road-kill estimates may not truly represent the
extent of impact for some species, especially for populations that have been
declining for decades due to WVCs (Eberhardt et al. 2013). Overall, using roadkill hotspots is an important technique, but more parameters should be included
depending upon specific species.
Effects of roads on landscapes, humans, and animals are substantial.
Therefore, many mitigation measures ranging in cost and effectiveness are
employed to combat problems caused by overlapping human and wildlife
habitats. Understanding animal needs and the type of surrounding habitat is
crucial for implementing the most effective mitigation measure. In fact,
evaluating effectiveness remains an important component to project success
measurements, though this is difficult to quantify.
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Evaluating Mitigation Strategies
Because wildlife crossing structures are usually unique to a specific
landscape, target species and purpose, measuring effectiveness can be
challenging. However, some of the techniques listed below may be used to help
researchers gain a better understanding of the overall effectiveness of corridors.
Visual Observation
Some early studies of road ecology in general used human observation to detect
wildlife presence. This technique is time-intensive, expensive, and could cause
animals to avoid areas where humans are present. Though visual observation is a
good way to understand animal behaviors at crossing structures, this technique is
now infrequently used (K. McAllister, personal communication, 2013).
Track Pads
Track pads can be used to detect wildlife moving through structures (K.
McAllister, personal communication, 2013). They can be set up for days at a time
with little cost, and are relatively non-invasive. Identifying animals from track
pads requires someone skilled at reading tracks to decipher differences among
species and count individuals. This technique is often used in tandem with other
identification methods.
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Cameras and Video
Motion-triggered cameras are now frequently used to detect animals moving
through structures (Jackson 2000, Bissonette and Rosa 2012). Cameras record
time and temperature in addition to images of wildlife. These cameras require
little maintenance and are mostly non-invasive, making them ideal for
observational purposes. Some older cameras may produce a red flash or click
when photos are taken, possibly frightening animals. Most animals do not notice
the cameras, especially if they are deployed effectively. For species with nonunique pelage, these cameras cannot be used to determine number of animals
using structures, only frequencies. For other species like jaguars, which can be
identified based on unique patterns of pelage, cameras can be used to identify
individual animals non-invasively and therefore provide density estimates of
relatively elusive animals (Soisalo and Cavalcanti 2006). Motion-triggered
cameras are often used in tandem with other methods. Drawbacks of cameras
include difficultly in placement to accurately record warm-blooded animals, and
vandalism (Jackson 2000). One of the most important constraints of these
cameras is the resulting data, which often exhibits a lack of variance estimates,
which is critical for applying diverse statistical analyses of such data (Bengsen et
al. 2011). Regardless, these cameras are a good way to monitor structure use and
activity.
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DNA Analysis
Hair snags using barbed wire are used to gain genetic information on species or
numbers of individuals using crossing structures (Clevenger and Ford 2010). For
example studies in Banff National Park (BNP), the North American epicenter of
road mitigation techniques (Sawaya et al. 2013), have used hair snags. Using hair
snag DNA analysis, they found that crossing structures allowed sufficient
demographic connectivity for bears in BNP. Of these bears, almost 20% of each
population used the crossing structures. Hair snag analyses are now being used
for bears and cougars in Washington State (R. Beausoleil, personal
communication, 2013). This technique usually involves stringing barbed wire
perpendicular to an animal’s path. As an animal moves over or under the wire,
hair becomes caught on the barbed wire. Hair tufts can later be gathered and
further sampled in a lab. This technique is minimally invasive.
VHF and GPS Collars
Other monitoring techniques include use of very high frequency (VHF) radio
collars and global positioning system (GPS) radio collars to track individual
animal movement despite their relation to the structure (Montgomery et al. 2013,
Gagnon et al. 2007a, Gagnon et al. 2007b). Attaching collars to animals requires
finding animals, immobilizing them, and fitting collars onto them, which is
relatively invasive. Collars typically cost several thousand dollars and can be
used anywhere from days to years, depending on battery life and size which must
be appropriately sized for the animal depending upon its mass. VHF collars rely
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on a signal that is emitted from the collar that a researcher must find. GPS collars
use satellites to triangulate an animal’s position that can be downloaded at
different frequencies to a computer. Having continuous data on animal locations
allows researchers to see what types of habitats animals reside in or move
through, in comparison to when such animals move through crossing structures.
Collars are often used in road ecology studies with wildlife.
Many monitoring techniques like motion-triggered cameras and GPS or
VHF radio collars are now being implemented by several different agencies.
Overall, some monitoring techniques are outdated, while others continuously
improve. Often times using a combination of techniques proves adequate.
Regardless of monitoring technique, today’s interagency collaboration indicates
that issues in road ecology are finally being addressed. Even a decade ago, road
ecology was not widely recognized. Now almost one third of US agencies have
employed some type of wildlife mitigation measure (Clevenger and Ford 2010).
In the past solid knowledge about mitigation techniques was rarely based on
research (Forman et al. 2003). One study used a survey to assess national park
management units’ level of concern about roads. Results showed that many
respondents thought that WVCs affected populations of wildlife, though there was
little systematically gathered supporting evidence (Ament et al. 2008). The field
of road ecology has much room for improvement in both conducting research
systematically, and disseminating results and information to the public. No doubt
as wildlife management, technology, transportation, and intercollaboration
increase in efficacy, advances and knowledge in this field are sure to emerge.
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These advances will help agencies create solutions for both humans, and specific
types of wildlife. Elk are one wildlife species that many transportation agencies
try to effectively manage because collisions can be highly detrimental, sometimes
resulting in human fatalities. Therefore, general information regarding elk, as
well as more in-depth studies examining the effects of roads and safe crossing
structures will be further discussed.
Elk
In the US, and especially in Washington State, elk are of special interest in
studies regarding road ecology. Elk are commonly studied because: 1) Their
populations are abundant, 2) They migrate and occupy a wide range of habitats, 3)
Their large body size poses a significant threat to motorists if hit, and 4) They are
a managed game species of high value to the hunting community. Collisions with
elk can be expensive due to the level of physical damage and sustained injuries.
Therefore, many studies in the US focus on elk, with the goal of reducing WVCs
and their associated costs and dangers.
Elk were once widespread across North America before European
settlement, with estimates around 10 million (Rocky Mountain Elk Foundation
2013). About 1 million elk remain in the western US with a few herds in the east
and south. Elk are divided into six subspecies: Rocky Mountain, Roosevelt’s,
Tule, Manitoban, Merriam’s, and Eastern though the latter two are extinct (Rocky
Mountain Elk Foundation 2013). Despite this reduction in historic numbers, the
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current estimate of elk in Washington State, which is limited to Rocky Mountain
and Roosevelt’s subspecies, is over 60,000 individuals (USFWS 2013).
Many elk in Washington State are highly mobile. They often travel long
distances for foraging or to mate. Depending on whether a herd is resident or
migratory, some elk travel long distances due to the effects of seasonal
fluctuations on available forage resources (Hobbs et al. 1981). Many herds
migrate to find suitable habitats containing grasses, forbs, shrubs, tree bark or
twigs (Rocky Mountain Elk Foundation 2013). Studies show that elk dedicate
most of their time to feeding with peak intensities around dusk and dawn, shifting
only for seasonal fluctuations (Green and Bear 1990). Such important foraging
needs and physiological changes also depend on the energy requirements of elk in
terms of seasonal activities including mating and calving (Fancy and White 1985).
For example, bull antlers grow and harden by late summer, allowing for proper
defense by the rut in the fall. Males may move through habitats to gather and
protect harems of females and calves, resulting in newborn calves the following
summer (Rocky Mountain Elk Foundation 2013). As previously discussed, elk
are very mobile species, and occupy many different types of suitable habitat in the
US, anywhere from rainforests to desert valleys.
Elk are impressive creatures, were named “Wapiti” by Native Americans
for their white rumps, and can weigh anywhere from 225 kg (females) to 315 kg
(males) with newborn calves weighing up to 16 kg (Rocky Mountain Elk
Foundation 2013). Besides the sustenance that their harvested meat provides,
these animals have long held spiritual importance for many Native American
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tribes. Today elk hunting draws considerable attention and is a critical asset to
wildlife departments that benefit fiscally from purchased tags. These animals are
an important part of many different cultures and agencies for various reasons,
making WVCs with elk particularly detrimental.
All in all, the combination of sizeable populations, high degrees of
mobility and large body sizes makes elk relatively dangerous to motorists.
Though many recent studies of traffic flow and effects of roads have given rise to
a body of literature focused on techniques for monitoring wildlife-roads effects,
data addressing elk-road effects lack the diversity to effectively answer critical
questions. Much more research is necessary to understand exactly how roads
affect elk, and how different populations and individual elk respond to
anthropogenic disturbances generated from roads. To date, productive but only
limited study has been dedicated to the topic of elk movement in relation to roads,
and elk behavior in relation to mitigation efforts like underpasses.
Elk in Relation to Roads and Underpasses
Many studies show that roads negatively affect wildlife, but many
questions remain unanswered, such as exactly how roads influence elk
(Montgomery et al. 2013). Several studies have attempted to isolate factors that
negatively influence elk movement near roads. Other studies examine elk
movement at wildlife crossing structures to gain a sense of influential factors. A
comparison of these factors between elk movement at roads and underpasses
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helps researchers understand whether or not underpasses alleviate some
disturbances generated from roads.
Factors Affecting Elk Movement near Roads
Identifying and understanding the negative effects of roads that inhibit elk
movement is a primary concern. So far, influential factors including road type,
season, gender, traffic volume and temporal shifts in behavior have been studied.
For example, Montgomery et al. (2013) examined elk over many years to better
understand how elk responded to roads according to road type, season and sex
finding that road type did in fact influence elk space use, with differences
according to seasons and sex. Overall elk home ranges were situated more
closely to roads without public vehicle traffic, and avoided primary and tertiary
roads with high traffic levels. Females and males avoided such active roads at
different times of the year. Males avoided busy roads in the summer when
vehicle traffic peaked, and females avoided busy roads during spring and autumn
in conjunction with calving and mating. Therefore, though high traffic volumes
remained relatively consistent throughout the year, elk still avoided primary and
tertiary roads, suggesting that they did not habituate to the disturbance.
Interestingly, researchers found that when traffic levels increased, elk would more
likely use habitat that had a visual barrier to even primary roads, though they
tended to avoid habitat that was clearly visible from roads. This could be a result
of increased hunting pressures in areas with greater road access, or a result of
calving and subsequent caring and protection of their young (Montgomery et al.
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2013). Interestingly the results showed a possible coping mechanism for elk in
relation to roads, though the negative influence of roads was clear.
Gagon et al. (2007a) also studied how traffic affected elk distribution and
crossings in relation to highways. Using traffic recorders and GPS relocation
points, they found that elk moved away from roads during times of high traffic.
When traffic levels subsided, elk moved closer to roads with suitable habitat
quality. This could be explained by the possible habituation of elk to road
disturbances, or due to the high quality riparian meadow habitat adjacent to the
road. Because elk still utilized habitat close to roads, the likelihood of WVCs
increased. Though some studies show higher rates of WVCs during periods of
high traffic (Gunson et al. 2003) possibly due to migration needs, low traffic
collisions could be a result of high quality habitat near roads. Therefore, Gagnon
et al. (2007a) also clearly shows the negative influence of roads on elk.
Many studies show that elk behavior changes at night, and recognize the
need for temporal analysis of elk use (Montgomery et al. 2013, Gagnon et al.
2007a). Millspaugh (1999) showed that elk move closer to roads with suitable
adjacent habitat when traffic volume is lower at night. Other studies have also
shown that elk move closer to roads at night, and may exhibit diurnal movement
patterns when close to low-traffic roads (Ager et al. 2003). Studies of similar
animals like moose have also shown temporal adjustments due to anthropogenic
disturbances (Neumann et al. 2013). Grizzly bears in Canada were also shown to
adjust their behavior, moving nearer to and across roads with less traffic at night
(Northrup et al. 2012). As demonstrated above, during times of heavy traffic
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volume, roads created a barrier for many animals. Since this barrier might cause
animals to change the timing of some activities, additional research analyzing
temporal aspects is needed so that transportation planners can implement effective
mitigation techniques, and so that drivers may be more aware of the possibility of
wildlife presence on roadways during certain times.
Factors Affecting Use of Wildlife Structures
To offset consequences of roads, several wildlife crossings have been built
into roads to facilitate wildlife movement. Important factors including traffic
volume, temporal shifts in behavior, and adjacent habitat quality have been the
subjects of research at underpasses. Gagnon et al. (2007b) stresses the importance
of studying the influence of traffic on wildlife at underpasses, especially because
previous studies have hypothesized that high traffic volumes completely inhibit
animals crossing highways (Mueller and Berthound 1997). Therefore,
understanding if this factor also inhibits elk use of underpasses is critical in
determining their effectiveness for alleviating negative effects of roads. Gagnon
et al. (2007b) studied how traffic affected elk use of an underpass and found that
higher levels of traffic did not deter elk use of such structures. While traffic has
been shown to discourage elk from moving over highways, it does not seem to
influence elk use of underpasses, thereby increasing the effectiveness of the
underpass for linking habitats once segregated by roads. Though some animals
were repelled at times possibly due to noise from larger vehicles, most animals
traveling in herds followed the lead elk through the underpass (Gagnon et al.
2007b). Overall this study suggests that high volumes of traffic known to deter
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elk from crossing highways do not influence elk use of underpasses (Gagnon et al.
2007b, Dodd and Gagnon 2011). Another study demonstrated that elk typically
used underpasses at night when traffic volume was lower (Servheen et al. 2003).
Similarly Dodd et al. (2006a) found that traffic influenced elk crossings, with
more crossings occurring at lower traffic volumes. Since traffic volume changes
throughout the day, some studies have focused on the temporal patterns of elk
movements at these underpasses.
A temporal understanding of elk movement is important to understand
because it may account for observed behavioral shifts. Looking specifically at
underpasses, Servheen et al. (2003) found that ungulates tended to move through
more frequently during crepuscular periods (dawn and dusk). Since elk are most
active during dawn and dusk normally, this indicates that elk are using
underpasses within their normal hours of activity. However, any deviation from
their normal activity pattern could indicate that animals are changing their
behavior to accommodate for selected anthropogenic disturbances found at these
underpasses.
Seasonality and associated quality habitat is another important factor in
determining effectiveness of underpasses. Researchers found that high passage
rates in spring and summer might be attributed to the forage found in riparian
meadows (Gagnon et al. 2007b). If elk are attracted to high quality riparian
habitat at underpasses, it may influence passage rates and mask disturbances that
would make elk otherwise avoid areas near roads.
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Though abundant wildlife crossings are lacking where elk can be studied,
the few studies that have examined these interactions have proven valuable. At a
basic level, researchers understand that roads can influence habitat use (Lyon
1979, Jones and Hudson 2002), isolate populations influencing genetics (Forman
et al. 2003), and can cause WVCs (Forman et al. 2003). Researching how
mitigation measures, such as safe crossing structures, can alleviate some of these
pressures is important. In fact, current studies have demonstrated that roads do in
fact negatively influence wildlife movement, and that wildlife structures intending
to alleviate this are effective. These recent research efforts provide a beginning
understanding of elk behavior in response to this infrastructure. More research
needs to be conducted to obtain a finer-scale resolution of temporal elk movement
so that we may predict when elk use these structures and when they might avoid
them. With this knowledge scientists and transportation agencies can continue
refining ways to better provide safe crossing opportunities for elk.
Literature Review Summary and Thesis Research Questions
The advent and expansion of roads in the US was a sharp transition for the
landscape, resulting in fragmented habitats that often harm wildlife or cause
conflicts with humans. Though roads are necessary for human transport and
obtaining resources, many negative effects exist on wildlife. These include
economic costs, road mortality in the form of WVCs, habitat loss, habitat
alteration, road avoidance and affinity, landscape connectivity and fragmentation,
barrier effects and human access/exploitation. Hence, a myriad of mitigation
techniques including overpasses, underpasses, culverts, bridges, canopy crossings
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and viaducts have been designed and implemented to allow wildlife movement
over or under roads safely.
Current research needs include addressing animal behavior at structures
already in place. Research regarding species that pose serious safety risks like elk
because of their high mobility, large body size, and abundance in Washington
State should be prioritized. Knowing exactly when elk use safe crossing
structures will help researchers not only evaluate current underpass effectiveness,
but will also aid in developing mitigation techniques refined to focus in the
problem time intervals. Though much knowledge is known about when elk are
typically most active, monitoring their activity in relation to underpasses and
roads is important to understand what influences these safe crossing opportunities
have on elk. If activity levels at underpasses are not the same as normal activity
patterns, this may suggest that elk are responding to some anthropogenic
influence caused by roads. Therefore, using multiple data sources regarding elk,
underpass use, and collisions the following research aims to:
1.) Summarize and analyze temporal patterns based on light levels of elk use
of underpasses and collisions with vehicles at three underpasses in
Washington State
2.) Summarize and analyze seasonal patterns of elk use of underpasses and
collisions with vehicles at three underpasses in Washington State
3.) Analyze the influence of traffic levels on elk use of underpasses and elkvehicle collisions near three underpasses in Washington State
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Prominent gaps exist in the literature, which hinders understanding about the
temporal and seasonal influences of safe crossing structures and elk movement.
Therefore, using a combination of data sources, I seek to address a substantial
portion of these gaps, and discover how and when elk use safe crossing
opportunities, when they do not, and how other factors, such as traffic volume
may influence this. Ultimately the information I have analyzed will be given to
and used by the Washington State Department of Transportation (WSDOT) so
that the needs of elk may be better understood and addressed in current
retrofitting projects and future construction efforts.
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Chapter 2: Analysis of Elk Movement in Relation to Transportation
Infrastructure at Grade and below Grade
Introduction: Roads and Wildlife
Conflicts arising from the dual needs of human transportation and wildlife
habitat requirements can be dangerous, even lethal (Hilty et al. 2010). In fact,
today it is likely that roads with vehicles surpass hunting as the largest source of
vertebrate mortality (Forman and Alexander 1998). Despite this fact,
transportation infrastructure continues to increase, especially the construction of
roads for human mobility, transportation of goods and resources, military
operations and economic development even though it is increasingly understood
to negatively impact wildlife (Forman et al. 2003). In response to these
increasingly obvious impacts, the field of road ecology has emerged as an
important area of study to gain a better understanding of how roads affect
wildlife, which species are most at risk, how roads impact wildlife habitat
connectivity, and how to begin mitigating for the harmful effects on wildlife
(Forman et al. 2003). Habitat fragmentation and wildlife-vehicle collisions
(WVCs) are two of the biggest consequences of roads often studied in this
discipline.
The vast road network in the US increasingly fragments habitats and
landscapes. Gunson et al. (2011) concluded that as roads continue transecting
landscapes, avoidance of natural areas and wildlife species is increasingly
difficult, especially since up to 20% of landmass in the US may already be
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considered “road-effect zones” (Forman and Alexander 1998). Habitat
fragmentation associated with roads dissecting natural landscapes is especially
dangerous when large mobile animals enter roadways. Collisions between large
animals and vehicles often result in injury or death to humans and wildlife, and
also contribute to property damage.
In the US, the number of collisions with animals is concerning, and often
results in substantial property damage. Collisions with animals are estimated to
be around 300,000 per year according to national crash databases (Huijser et al.
2008). Due to issues with collecting and reporting this information, this estimate
is likely conservative. Actual numbers of collisions between vehicles and large
animals are estimated to orders of magnitude greater than 1-2 million per year,
most of which do not result in serious injury (95.4%) (Huijser et al. 2008). The
resulting 26,000 injuries and 200 deaths per year are a significant issue, especially
considering total WVCs continue to rise in comparison to all other types of
collisions (Figure 1).
For other wildlife-related collisions, regardless of injury or death,
property damage may be substantial. Collisions with larger animals typically
result in greater property damage, between $3,000-4,000 for elk (Cervus elaphus)
collisions (Huijser et al. 2008). Overall, nationwide WVCs are estimated to cost
> 8 billion dollars each year when factoring in vehicle repair costs, medical costs,
towing, law enforcement, monetary value for the animal, and carcass removal and
disposal.
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To reduce costs to humans and wildlife from WVCs, mitigation efforts
have been developed. Mitigation measures including underpasses are necessary
to minimize associated injury, death and property damage resulting from WVCs.
Studies have shown that underpasses may provide safe crossing opportunities for
wildlife like elk (Barrueto et al. 2014, Dodd et al. 2006b, Dodd et al. 2007,
Gagnon et al. 2007a, Gagnon et al. 2007b). To better understand how existing
infrastructure may facilitate wildlife movement, my research focused on elk
movement at grade and below grade in relation to three underpasses in two
geographically separate areas in Washington State. Specifically, I conducted a
temporal analysis examining when elk are most likely to cross at grade and be hit
by vehicles, and when they are most likely to use underpasses. Understanding
patterns of when elk cross at grade or below grade will help researchers better
predict WVCs, and design mitigation measures to prevent future issues.
This study uses existing underpasses in Washington State to analyze elk
movement according to light level, seasonality and traffic volume. Ultimately the
goal of my research is to gain further understanding of the impacts of roads so
that mitigation efforts might help make our roadways safer for multiple species.
Study Area
For this study, I drew data from individual elk from two of Washington
State’s elk herds managed by the Washington State Department of Fish and
Wildlife (WDFW). One of the herds studied, the North Rainier Herd, was located
in North Bend in the Upper Snoqualmie Valley. The second site located between
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Randle and Packwood in the Cowlitz River Valley supports elk from the South
Rainier Elk Herd. Elk were studied at three different underpasses in the Upper
Snoqualmie Valley and the Cowlitz River Valley, and along I-90 and US-12
highways within 20-32 km of each underpass, as subsequently explained (Figure
2).
Upper Snoqualmie Valley Site
North Bend is located in the Upper Snoqualmie Valley in Western
Washington, 50 km east of Seattle (latitude: 47.493831; longitude: -121.786247)
in the Cascade Range foothills. Average annual precipitation is 137.5cm. With
less than 5,000 residents, this town in King County is made up of many private
agricultural holdings as well as some housing, subdivision and commercial
buildings all of which are connected by Interstate 90 (Henceforth, I-90) (US
Census Bureau, Spencer 2002). I-90 is the longest interstate highway in the US,
spanning from the East in Boston, MA to Washington State, at its westernmost
extent, and remains the only interstate highway to span the Cascade Mountain
Range in northwest Washington. This route has been an important part of travel
in Washington since before the original Oregon Trail Pioneers, when Native
Americans favored it as a cross-mountain trail. Today I-90 is the most heavily
used transportation corridor to connect eastern and western Washington for
recreational, commercial, and commuter trips.
I-90 runs to the south of the town of North Bend with three lanes of traffic
in each direction, and an annual average daily traffic (AADT) volume of about
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29,000 vehicles (WSDOT 2013b). It parallels the South Fork of the Snoqualmie
River, crossing over it in several places. The river provides ample riparian habitat
for wildlife in Snoqualmie Valley. Forests adjacent to I-90 near North Bend are
dominated by western hemlock (Tsuga heterophylla), Pacific silver fir (Abies
amabilis), mountain hemlock (Tsuga mertensiana) and Douglas fir (Pseudotsuga
menziesii). Mount Si and Rattlesnake Ridge are nearby, adding to the region’s
diverse habitat, suitable for a myriad of species. Megafauna species such as black
bear (Ursus americanus), black-tailed deer (Odocoileus hemionus columbianus),
bobcat (Lynx rufus), cougar (Puma concolor), and elk are known to reside in this
area. Historical elk presence in the Cascades has been debated, with some
believing that elk did not exist prior to introductions in the 1900s (Bradley 1982).
Other records support elk presence in Washington before European settlement,
with Puyallup Tribe members actively managing elk habitat with fire near Mount
Rainier (Schullery 1984).
Regardless of their historical presence, Rocky Mountain elk were
transplanted from Yellowstone National Park in Montana to the Cascades in the
early 1900s. One elk herd examined in this thesis is part of the North Rainier Elk
Herd found on Game Management Unit (GMU) 460, located in the Snoqualmie
valley. According to the Washington State Elk Herd Plan produced by WDFW,
little population data are available for the Snoqualmie valley sub-herd of the
North Rainier Elk Herd. WDFW personnel estimated that the Snoqualmie valley
sub-herd comprised 125 elk in 1989, with numbers increasing to 175 elk in 2000.
Today, elk population numbers in the Snoqualmie valley sub-herd are recovering
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well. According to the Upper Snoqualmie Valley Elk Management Group’s 2012
research and management committee annual report, the population estimate for
elk in the valley was 428 individuals in 2011. Nonetheless, this elk population
still faces some challenges.
Elk in the North Rainier Herd are subject to several sources of mortality
including predation, hunting, and roads. Predation by cougars and black bears
occurs on both adult and juvenile elk. Additionally, both state and tribal hunting
continues in this area. Road kill is the third major source of mortality for elk in
this area. I-90 poses a barrier to elk, though some elk have been seen to utilize
habitat on both sides of the I-90, making crossings over the highway that are
dangerous to motorists as well as elk (Starr, 2012). Several WVCs and carcass
removals are reported in this area every year.
In an effort to understand how roads affect wildlife, and how animalvehicle collisions may be reduced, WSDOT began monitoring wildlife use of
underpasses in 2010. Specifically on I-90 in the vicinity of North Bend, several
underpasses are equipped with motion-triggered cameras to capture animal
movement around and through transportation infrastructure (Figure 3). Each
camera monitors a bridge underpass adjacent to the South Fork of the Snoqualmie
River. For this study, I used data from cameras located at mileposts 31.6 and 38
to collect information related to elk movement. Cameras were located within
16km of each other along this stretch of I-90 south of North Bend.
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North Bend Site (Milepost 31.6)
The underpass along I-90 at milepost 31.6 has a total of four motion-triggered
cameras. I-90 crosses over the South Fork of the Snoqualmie River in the form of
a steel and concrete bridge built in 1976. Surrounding habitat includes riparian
areas, and mixed use residential. Two cameras on the east side of the river are
located along a heavily used recreational trail that sees little wildlife use. The
other two cameras are located on the west side, with a view of the bridge’s
abutment and pier. Both cameras are within 100m of the river. Tall chain link
fence (1.8m) exists along each side of the river, but parts of the fence are in
disrepair, and wildlife can be found on both sides of the fence.
North Bend Site (Milepost 38)
The underpass along I-90 at milepost 38 has a total of four cameras. I-90 crosses
over the South Fork of the Snoqualmie River in the form of a concrete and steel
bridge as well, built in 1976, with cameras monitoring both the east and west
sides of the divided highway. Surrounding vegetation includes riparian habitat.
Tall barbed wire fence (0.9m) exists near the structures along the associated
riprap. The fence, in many areas, is in disrepair.
Cowlitz River Valley Site
Randle and Packwood are small towns located in Lewis County in western
Washington, connected by US-12 in the Cowlitz River Valley. US-12 is a 2-lane
undivided highway running east and west across Washington. Annual AADT is ~
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3,000 vehicles. Cora Bridge, a steel and concrete bridge, was built in 1948 to
span the width of the Cowlitz River where US-12 crosses over. Though not
originally intended to facilitate safe animal movement, several species are
observed to move safely under the highway via use of this underpass.
The area surrounding Cora Bridge is made up of riparian habitat, with
occasional flooding during severe storms. Average annual precipitation is
152.4cm (Huang et al. 2002). The U.S. Forest service owns much land in this
area, in addition to state and privately owned industrial forestland. Many small
private holdings are found along the Cowlitz River where the terrain is mostly
flat. Residential and agricultural developments are common. Though the area
adjacent to Cora Bridge is dominated by grassland habitat, the larger area
supports western hemlock, Pacific silver fir, and mountain hemlock. This area
also supports megafauna including black-tailed deer in addition to domestic
livestock (Huang et al. 2002).
Elk from the South Rainier herd occur in this area between Randle and
Packwood, located within the Packwood GMU 516. Roosevelt elk once
dominated this area, but their extirpation by the 1900s led to translocations of
Rocky Mountain Elk from Yellowstone National Park to increase elk numbers.
Population estimates from 1996-1998 surveys show elk numbers ranging from 28
to 95 individuals in the GMU 516. Surveys have differed dramatically between
years, with the estimate in 2009 for the entire South Rainier Herd at about 1,000
individuals. Sources of elk mortality are mostly attributed to hunting, though
habitat modification and collisions with vehicles are also potential sources of
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mortality. The underpass at Cora Bridge is monitored by WSDOT for wildlife
use with three motion-triggered cameras (Figure 4). Two cameras are located on
the east side of the river, and one on the west side of the river. Tall grasses and
Himalayan blackberry dominate this area. Easy access to the Cowlitz River also
allows for considerable use by humans. No fencing exists along the highway, and
the cattle fence surrounding the adjacent property is in disrepair. Elk have been
observed jumping over it easily.
Methods
Origination of the Study
In response to WSDOT’s Habitat Connectivity policy directive stating the
agency’s role in protecting ecosystem health, Julia Kintsch and Dr. Patricia
Cramer were hired by WSDOT to conduct a study that ranked a sample of
existing bridges and culverts in Washington State on their ability to allow wildlife
passage. Using motion-triggered cameras at culverts and bridges, Kintsch and
Cramer (2011) developed a Passage Assessment System (PAS) allowing the
Transportation Department to understand where roads facilitated or prohibited
animal movement. Using this tool, WSDOT continues to monitor many bridges
in Washington. My research is a continuation of those monitoring efforts, and
borrows many methods in terms of set up, analysis, and one study site based on
this research report’s parameters. The data used in my study were collected and
analyzed from motion-triggered cameras deployed around underpasses, collision
records from Washington State Patrol Officers, the WSDOT carcass removal
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database, GPS locations from elk collared by the Upper Snoqualmie Valley Elk
Management Group (USVEMG), and traffic volumes from WSDOT permanent
traffic recorders.
Identifying Underpasses for Analysis and Camera Set Up
Highway segments that had high to medium amounts of road kill were initially
used to identify problem areas in terms of WVC rates. With a general awareness
of wildlife-vehicle issues and known crossing structures, bridges were then
further chosen within a 320km distance of office headquarters to ensure that
cameras could be checked regularly. Bridge selection was also based on:
appropriate dimensions sizeable enough to allow large mammal passage,
occurrence within a riparian area, and known elk presence. Bridges were then
outfitted with 3-4 motion-triggered cameras. According to Kintsch and Cramer
(2011) cameras were initially positioned at each end of the structure so that
animal approaches and passes could be recorded. Since each site is different,
modifications were made to the camera deployment. In North Bend along I-90 at
milepost 31.6 two cameras were placed in the median facing east and west. One
monitored a pedestrian trail and the other monitored the river. Under the
eastbound lanes of traffic two cameras were positioned to capture animals moving
past the abutment and along a dike that flanks the river. Along I-90 at milepost
38 cameras were located under the off-ramp and westbound lanes of traffic,
monitoring the area adjacent to the river and the abutment. Due to large areas
beneath bridges, cameras could not always be safely deployed at the ends of each
structure. Therefore cameras were placed in positions under bridges that allowed
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researchers to assess whether or not an animal passed through the structure.
The motion-triggered trail cameras (ReconyxPC85, ReconyxHC600,
ReconyxPC900, and Bushnell) were either disguised in steel utility boxes or
bolted to trees. Utility boxes were set into a concrete foundation of about 1827kg with a protruding bike cable that attached, by padlock, to the camera. The
front portion of the box was also secured with a combination padlock. Cameras
in trees were enclosed in metal boxes bolted from the inside to the tree. The
camouflaged faceplate on the box was secured using a combination padlock.
Cameras were also equipped with a passcode. Despite safety precautions taken,
theft and vandalism occurred periodically at other camera locations in the area,
which made us particularly cautious with security measures. Fortunately, none of
the 12 cameras used in this study were destroyed or stolen during the length of
this study.
Camera Data and Elk Activity at Underpasses
Data Collection
Cameras were serviced every four weeks. Servicing included changing all
batteries (either 6C batteries or 12AA batteries) and data cards (holding 2 to 16
GB). Cameras were also checked for correct settings: date and time, 3-5 pictures
were taken when motion was detected, with no delay in between pictures, pictures
were taken day and night, and trigger speed was high. Cameras were also
changed and swapped if poor performance or malfunctions occurred.
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I downloaded and processed data from memory cards in the office. Each
series of images was reviewed and recorded. Information recorded included:
date, temperature, time (in Pacific Standard Time) the animal was first observed,
time (in Pacific Standard Time) the animal was last observed, species, age, gender
if identifiable, total number of animals, whether the animal went through the
structure or not, as well as anything of note such as number of antler points or if a
cow wore a collar. Detections were recorded in 30min intervals. Therefore, if an
animal was seen grazing in front of the camera for one hour, it constituted 2
separate detections unless the individual animal was clearly identifiable, in which
case it counted as a single detection. Individuals were recognized when possible,
and total numbers within 30min increments were enumerated. Any species
identification challenges were brought to several biologists for discussion. Data
were kept for every image on each camera in extensive spreadsheets.
Data Organization
Data from Microsoft Excel spreadsheets were then compiled until a full annual
cycle of monitoring was obtained. I then selected elk records for further analysis.
Furthermore, only records indicating that an elk actually passed through a
structure were used. If an animal was detected by the camera, but did not actually
cross through the structure, it was not considered a passage, and was not included
in the further analysis.
According to the Transportation Research Board of the National Academies
A crossing structure [is] defined as a new or retrofit passage over or below
roadway or railroad that was designed specifically or in part, to assist in
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wildlife movement. Culverts and bridges already in place when fencing was
installed to lead animals to these pre-existing structures were not considered
crossings.
Based on this definition, the structures analyzed in this study are not considered
crossing structures because they were not built specifically for wildlife use, nor
have they been retrofitted to accommodate them. However, because elk do use
them to cross under roads and provide safe passage, they will be referred to as
“underpasses,” “safe crossing opportunities,” or “crossing structures” in this study
because of their ability to facilitate wildlife movement, albeit unintentionally.
At each underpass, several cameras monitored the structure, so duplicate
occurrences were identified and deleted to avoid over-estimates of elk occurrence
at each structure. To do this, I compared MS Excel records of images on each
camera. If an elk was detected by both a camera and its partner camera (or the
camera located at the opposite end of the structure) within a 15min time frame,
the occurrence was deleted from one camera’s data. Therefore, when data were
compiled in total for each site from the individual cameras, elk detections were
not over-enumerated. Though total elk numbers were recorded, a detection was
considered a single occurrence regardless of the total number of individuals.
Since elk are often found in herds, to ensure independence due to herd mentality
only single occurrences were analyzed. In the context of this study, an
“occurrence” or “detection” refers to a single event in which the camera detected
elk movement regardless of the total number of individuals. In reality, the
number of actual crossings made by individual elk is a much larger number
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(Figure 5).
Categorizing Detections
These occurrences were then categorized according to light level. It is widely
reported that elk are crepuscular, meaning they are most active at dusk and dawn,
or rather during twilight (Green and Bear 1990, Wichrowski et al. 2005).
According to the National Oceanic Atmospheric Administration and the US
Navy, twilight occurs, “Before sunrise and again after sunset… during which
there is natural light provided by the upper atmosphere, which does receive direct
sunlight and reflects part of it toward the Earth's surface (NOAA 2013)”. Many
factors, including atmospheric state and weather conditions, affect duration of
twilight. Furthermore, twilight can be broken down into three categories: civil,
nautical and astronomical. According to NOAA, civil twilight occurs when the
sun is 6° below the horizon. This state usually allows sufficient light to see, and
is commonly referred to as twilight. There are two other stages of twilight,
nautical and astronomical. Nautical twilight, according to NOAA, occurs when
the sun’s center is 12° below the horizon (Figure 6). Finally, astronomical
twilight occurs when the sun is 18° below the horizon. Before astronomical
twilight in the morning and after astronomical twilight in the evening, the sky is
completely dark. Since twilight depends upon sunrise and sunset, sunrise
according to NOAA is defined as, “The time at which the first part of the sun
appears above the horizon in the morning,” and sunset is defined as, “The time at
this the last part of the sun disappears below the horizon in the evening (NOAA
2013).” Dawn and dusk refer to these periods of twilight as well. Therefore the
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term “dawn” shall reference the entire time from when the sun is 18° below the
horizon, to sunrise. The term “dusk” references the time from sunset to when the
sun is 18° below the horizon. The term “twilight” will refer to these periods of
dawn and dusk.
Using these definitions of light, a formula was created in MS Excel to
analyze each elk crossing in terms of relationship to sunrise, sunset, and twilight
time periods. Data on sunrise, sunset, and twilight were obtained from NOAA for
North Bend and Morton (data were not available for Randle or Packwood
specifically, so the nearest location at a similar latitude was chosen) (NOAA
2013). Data were then converted, consisting of every day’s times for sunrise,
sunset and twilight periods from 2008-2013. As amount of visible light differs
slightly each day due to Earth’s rotation around the sun and axis tilt, sunrise,
sunset and twilight were calculated to the minute for each day and categorized as:
dawn (includes time beginning at astronomical twilight up to sunrise), day
(includes time from sunrise to sunset), dusk (includes time from sunset to
astronomical twilight), and night (includes time from the end of astronomical
twilight after sunset to the beginning of astronomical twilight before sunrise). A
multiple step IF/THEN, VLOOKUP, and INDEX:MATCH function was written
to categorize each camera detection of elk into one of the previous light categories
to the specific day and minute of each light level. Seasons were categorized into
sub-equal three-month intervals as Fall (September, October, and November),
Winter (December, January, and February), Spring (March, April, and May), and
Summer (June, July, and August) using a MS Excel function.
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Light Level
Data were first summarized to detect patterns and frequencies of elk movement at
all three underpasses. A nonparametric analysis of variance using a
Wilcoxon/Kruskal-Wallis Rank Sum Test and Post-hoc analyses using the
Wilcoxon Method were used to understand if underpass use differed according to
light level. A chi-square goodness-of-fit test with a William’s correction for
small sample sizes was used to determine if elk crossed under bridges more or
less than expected during a certain light level. Due to differences between sites, I90 and US-12 were separately analyzed with chi-square tests and non-parametric
analysis of variance tests using a Wilcoxon/Kruskal-Wallis Rank Sum Test and
Post-hoc analyses using the Wilcoxon Method.
Light Level and Traffic Volume
A parametric analysis of variance using log-transformed traffic values was used to
analyze differences in traffic volumes at different light levels in addition to
analyses using the Wilcoxon/Kruskal Wallis Rank Sum Test and a Post-hoc
analysis using the Wilcoxon Method. Further analyses of underpasses along I-90
and US-12 were conducted separately using Wilcoxon/Kruskal Wallis Rank Sum
Tests and a Post-hoc analysis using the Wilcoxon Method to examine the
differences in mean ranks of traffic volume during elk detections according to
light level.
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Seasonality
MS Excel functions were used to identify each underpass use into different
seasons, and then these data were summarized. A chi-square goodness-of-fit test
with a William’s correction for small sample sizes was used to determine if elk
crossed under bridges more or less than expected during a certain season. Further
analyses using chi-square goodness-of-fit tests revealed differences between use
of underpasses by elk during various seasons. Therefore, a nonparametric
analysis of variance using a Wilcoxon/Kruskal-Wallis Rank Sum Test and Posthoc analyses using the Wilcoxon Method were used to understand which mean
ranks according to seasons differed from one another combining sites when
appropriate.
Elk-Vehicle Collision Data
Data Collection
Data were obtained from the WSDOT Collision Database by performing a query,
and selecting for “elk” and the years “2008-2013”. Accidents involving animals
and vehicles were recorded and reported by Washington State Patrol if the
damage was at least $700. Therefore, collisions resulting in minor damage or
injury were not included. Reports included time of collision, type of animal, any
evident human injury, surface conditions, and other facts regarding the collision.
These reports were then narrowed down from the state level to an appropriate
proximity to both study areas within 32km east or west of camera placement
under bridges along either I-90 or US-12.
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Light Level
Formulas in MS Excel were applied to collision records, categorizing each into
the appropriate light level. A nonparametric analysis of variance using a
Wilcoxon/Kruskal-Wallis Rank Sum Test and a Post-hoc analysis using the
Wilcoxon Method were used to understand which light levels differed from one
another.
Light Level and Traffic Volume
Effects of traffic volume at each site were analyzed using a Wilcoxon/KruskalWallis Rank Sum Test followed by a Post-hoc analysis using the Wilcoxon
Method. Collisions along I-90 and US-12 were further analyzed according to
light level separately using a Wilcoxon/Kruskal-Wallis Rank Sum Test followed
by a Post-hoc analysis using the Wilcoxon Method. After failing to meet
assumptions of normality, traffic data was log-transformed and a two-way
ANOVA using site and light as independent variables and log-transformed traffic
volumes during times of elk collisions as the dependent variable was performed
(Table 4). This was followed by a Student-Newman-Keuls mean separation test
to determine differences between traffic volumes at each site.
Seasonality
MS Excel functions were used to identify each elk collision into different seasons,
and these data were summarized. A chi-square goodness-of-fit test with a
William’s correction for small sample size was used to determine if EVCs
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occurred more or less than expected during a certain season at each site. Then a
nonparametric analysis of variance using a Wilcoxon/Kruskal-Wallis Rank Sum
Test and Post-hoc analyses using the Wilcoxon Method were used to understand
which seasons differed from one another along I-90 and US-12 separately.
Carcass Removal Data
Data Collection
I also obtained data from the WSDOT Carcass Removal Database. Road
maintenance crews around the state often remove animals from the road, and
record their findings on a PDA device selecting for species, age, and highway
milepost number. Some maintenance crew members record road-kill by hand and
these are sent to WSDOT headquarters and entered into the database manually.
These reports likely represent a portion of the total number of actual collisions
between animals and vehicles because not all carcasses are reported, some
animals do not die directly on the road, and not all maintenance crew members
record road-kill pickups. The reports that are generated are compiled and stored
in the WSDOT carcass removal database. Records were obtained using a query
for “elk” and years “2008-2013”. The database is updated periodically. Data was
gathered and summarized for descriptive statistical purposes, especially for
comparison to EVCs.
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Telemetry Data
Data Collection
Data were also obtained from GPS collars on elk in the Upper Snoqualmie Valley.
The USVEMG has deployed 13 GPS collars on female elk to understand their
movements. Due to a partnership with WSDOT, GPS locations are made
available for analysis in the form of MS Excel spreadsheets that include: date,
time, number of fixes, GPS locations in latitude/longitude form, and positional
accuracy. Female elk were captured and fitted with global positioning system
(GPS) telemetry collars, LOTEK 4400S and 4400M (Lotek Wireless, Newmarket,
Ontario, Canada) between 2010 and 2012. Clover traps were used to capture elk,
with immobilization used as necessary with telazol/xylazine HCL with the
reversal Yohimbine on hand. Biologists from the state and Muckleshoot Tribe,
and a veterinarian handled captured elk (USVEMG 2010) (Starr 2013). Elk collar
data ranges according to when elk were captured, how long their collars collected
data for, how many fixes were scheduled, and survival of individual elk. I chose
three elk from this dataset for this study expressly because each had a full year
worth of fixes (from March 2011 to March 2012).
Geographic Information Systems and Spatial Statistics
Elk location data, in the form of latitude and longitude positions, were imported
into ArcMap, and converted into North American Datum (NAD) 1983. I created
a projection using the middle of the study area to minimize visual distortion since
this area is close to the boundary between UTM Zone 10 and 11 (ESRI 2013).
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Elk distance to the road was calculated using ArcMap’s “near” tool. I then
exported these distances, calculated in meters, to MS Excel. Each fix was
categorized using a MS Excel function into light level, and traffic volume
according to day and hour was assigned. As a precursor to analysis, I then
compiled summary information including average distance to the road and
number of fixes.
Light Level and Distance to I-90
A chi-square goodness-of-fit test with a William’s correction for small sample
size was used to see if the observed distances that elk were found to the road were
different than the expected distances of elk to the road.
Distance to I-90 and Traffic Volume
A correlation function using a Pearson Product-Moment Correlation Coefficient
was used to see if any correlation existed between average distance of elk to the
road and corresponding traffic levels. Traffic volumes were log-transformed to
normalize the data.
Traffic Data
Data Collection
WSDOT monitors traffic levels through use of permanent traffic recorders around
the state. Traffic volume data was obtained for I-90 near North Bend and US-12
near Randle and Packwood by performing a query in WSDOT’s internal database.
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A permanent traffic recorder is located within 16km of North Bend. Since no
permanent traffic recorder exists close to Randle or Packwood, I used the closest
permanent traffic recorder that showed the same level of traffic to the
Randle/Packwood area. Therefore, I performed a query using the years “20082013” to obtain annual average hourly traffic (AAHT) at the two study sites. Due
to malfunctions or missing data from these permanent traffic recorders, some days
lacked traffic data. Using an INDEX:MATCH function in MS Excel, traffic data
was then linked to each camera detection, collision and GPS fix according to
specific date and hour. Data were analyzed using Microsoft Excel, JMP, JMP
Pro, and CoStats. Analyses assumed data were independent. However, efficiency
of reporting collisions and carcasses varied, and was a source of uncertainty of
unknown dimension. Data were screened for outliers and errors before analyses.
Sample sizes differed between record strings, and those too small for adequate
analysis were excluded.
Results
Camera Data and Elk Activity at Underpasses
Using data collected from cameras at one underpass along US-12 during 2012 and
2013, and two underpasses along I-90 during 2013, I found that elk used these
structures 183 times with the highest levels of use documented at underpasses in
the Upper Snoqualmie Valley (Figure 7).
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Light Levels
Of the 183 safe crossing detections, most occurred during two of the four light
categories, night and day (Figure 8). Elk use of underpasses significantly differed
among light levels (p<0.0001). Post-hoc analyses revealed that the mean ranks of
elk underpass use differed among all possible pairs of light level categories:
between night and dawn, night and dusk, night and day, day and dawn
(p<0.0001), dusk and dawn (p=0.0036), and between dusk and day (p=0.0414).
Results also indicate that the observed values of elk use of underpasses differed
from the expected values of elk underpass use during different light levels at the
three study sites (g-adjusted p=0.0032, df=6) (Table 1).
The two underpasses in North Bend did not show any significant
difference between expected and observed values of elk underpass use based on
light levels (!2=0.2408, df=3). Therefore, the data were combined. Since there
was a difference between the three study sites, further analyses using the
combined data from the two underpasses along I-90 were performed separately
from the underpass at US-12 to understand the differences between sites on a
finer scale.
Based on its mean rank, elk underpass use along I-90 underpasses differed
among light levels (p<0.0001, df=3); use further differed between all light level
category combinations (p<0.0001). The observed versus expected detections of
elk underpass use at US-12 did not differ between years (!2=0.1401, df=3). Elk
underpass use along the US-12 underpass also differed based on light levels for
both years of camera data combined (p<0.0001) differing between night and
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dawn, night and dusk, night and day (p<0.0001), day and dawn, dusk and dawn
(p=0.0040), but not between dusk and day.
Light Level and Traffic Volume
Since the data failed to meet parametric assumptions, a 2-way ANOVA used logtransformed values of the dependent variable traffic volume to examine the
relationship between light level and site according to traffic volume (Table 2).
Results reveal that the interaction between light and site was significant
(p<0.0001, df=6). Additionally elk underpass use differed according to traffic
levels during different light levels (p<0.0001, df=3) with the mean ranks of dusk
and day, dusk and dawn, day and dawn (p<0.0001), and night and dusk
(p=0.0441) differing from one another, though night and dawn (p=0.6456) and
night and day (p=0.4592) did not differ. Since the underpass structures differ
along I-90 and US-12, each was analyzed separately to understand finer-scale
differences in response to traffic levels.
At the underpasses along I-90 in North Bend, traffic volume during the
periods of elk detections differed significantly based on light level (p<0.0001,
df=3). Specifically, the light categories of day and dawn, night and dusk, and
night and day (p<0.0001) differed, but night and dawn (p=0.9678) and dusk and
day (p=0.5030) did not.
Along US-12, the mean ranks of traffic volume during elk detections
significantly differed according to light level (p=0.0002, df=3). Day and dawn
(p=0.0136), dusk and dawn (p=.0139), night and day (p=0.0016) and night and
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dusk (p=0.0016) differed but night and dawn (p=0.3313) did not, nor did dusk and
day since values were the same.
Seasonality
Using 183 detections, our research found that elk use of underpasses in summer
and fall was twice the rate of spring and winter (Figure 9). Our results analyzing
seasonal differences of elk underpass use revealed that elk used underpasses
differently than expected according to season at the study sites (g-adjusted
p=0.0013, df=6) (Table 3).
Analyzing the seasonal differences between the two underpasses in North
Bend, I found that observed elk movement according to season differed from
expected values at these structures (!2=0.0264, df=3). Observed values of elk use
of underpasses according to season between the structure along I-90 at milepost
31.6 and the underpass along US-12 also significantly differed from expected
values (!2=0.0006, df=3). Observed values of underpass use between the
structure along I-90 at milepost 38 and the underpass along US-12 did not differ
from expected values between seasons (!2=0.2802, df=3).
I found that elk use was different among seasons at the I-90 underpasses
located at mileposts 31.6 and 38 (p<0.0001, df=3) between all possible season
comparisons (p<0.0001), except for winter and spring because both had the same
number of detections.
I also found that elk use differed among seasons using camera detections
from the I-90 underpass located at milepost 38 and the US-12 underpass
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combined (p<0.0001, df=3). At both underpasses, elk use differed between all
possible season comparisons (p<0.0001).
Elk-Vehicle Collisions
From 2009-2013, 540 elk collisions were recorded in Washington State, which
averages to 108 elk collisions/year. Of these collisions, an average of 18%
occurred within my I-90 and US-12 study areas, with some years accounting for
fully over 25% of state-wide collisions (Figure 10). During the same 5-year time
period, 695 elk carcasses were removed from state-maintained roads by WSDOT
maintenance staff (Figure 11).
Light Level
Of the 99 collisions reported along I-90 and US-12 near underpasses, twice as
many collisions occurred during night than any other light level category (Figure
12). On average, more collisions occurred along US-12 with 11 collisions/year in
comparison to I-90, which averaged 8 collisions/year. Elk collisions differed
among light level categories along the interstate highways in my study area
(p<0.0001) with differences occurring between all possible comparisons of light
level categories (p<0.0001).
Light Level and Traffic Volume
Traffic volumes associated with elk collisions differed among light level
categories over the last five years at both study sites (p<0.0001, df=3).
Specifically, I found differences between day and dawn (p=0.0014), dusk and day
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(p=0.0084), night and dusk (p=0.0228), and night and day (p<0.0001), but not
between dusk and dawn (p=0.3122) or night and dawn (p=0.8951).
Because I-90 and US-12 vary in number of lanes and division of lanes by
medians, they were further analyzed separately. Results indicate that collisions
along I-90 occurred during different levels of traffic (p=0.0057, df=3) and
between the light level categories of day and dawn (p=0.0057), dusk and dawn
(p=0.0450), and night and day (p=0.0083), but there was no difference between
night and dawn (p=0.2928), dusk and day (p=0.7104), or night and dusk
(p=0.0937). Collisions with elk occurred during different levels of traffic along
US-12 as well (p=0.0001, df=3) and between the light level categories of day and
dawn (p=0.0139), dusk and dawn (p=0.0014), and night and dusk (p=0.0002), but
not between night and dawn (p=0.0779), dusk and day (p=0.7261) or night and
day (p=0.1126).
Light level and site were significantly related to traffic volume at the time
that elk were struck (p=0.0000). However, the interaction term light ! site was
not significant (p=0.4874). Moreover, traffic volume when elk are struck on I-90
is significantly greater than traffic volume when elk are struck on US-12 and that
traffic volume during the day when elk are struck is significantly greater than
traffic volume when elk are struck at dusk, at dawn and at night (Table 5-6).
Further, traffic volume at dawn does not differ significantly from traffic volume at
night.
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Seasonality
Of the 99 collisions reported along I-90 and US-12 near monitored underpasses,
collisions were highest in the spring and fall, but occurred in all seasons (Figure
13).
An analysis of seasonal differences revealed that elk collisions differed
from expected values of elk collisions according to specific seasons at the study
sites (g-adjusted p=0.0110, df=3) (Table 7).
Elk collision levels along I-90 differed among seasons (p<0.0001)
between all season pair combinations (p=0.0001). Elk collision levels along US12 also differed significantly among seasons (p<0.0001) between all season pair
combinations (p=0.0001) except between winter and summer since values were
the same.
Telemetry
Using three elk, I found that each remained relatively close to I-90, though elk
1550 traveled farther on average (Table 8).
Light Level and Distance to I-90
Distances that elk were found from the road were different than expected
distances according to light level (g-adjusted p=0.0012, df=6) (Table 9).
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Distance to I-90 and Traffic Volume
Using elk distances to I-90 and corresponding traffic volumes, I observed only a
weak (r=0.0096), non-significant (p=0.6294) relationship. Using the logtransformed values revealed a high degree of correlation between elk distance to
road and traffic volume (r=0.9942). Though the two are correlated, the
relationship is non-linear.
Discussion
This study showed that underpasses are a mitigation tool that can potentially
redirect above-grade movement of elk and perhaps decrease the incidence of
collisions between wildlife and vehicles. Light levels, seasons, and traffic
volumes affected when elk used underpasses, when vehicles hit elk, and how
close elk were found to at least one interstate highway. Based on diel light level
categories, elk used three underpasses in Washington differently, though the most
frequent usage occurred at night when traffic volumes were typically lower. This
research also revealed that elk used underpasses in all seasons, but increased their
use during seasons that typically correspond with increased movement, such as
fall. Unsuccessful elk attempts to cross at grade resulted in several WVCs along
I-90 and US-12 during the past five years. Though elk collisions occurred during
all light levels, twice as many collisions occurred at night than any other light
level category, when traffic levels were usually lowest. Collisions were more
frequent during the spring and fall, when nutritional requirements and activity
levels are typically heightened (Green and Bear 1990). Lastly, three elk with
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home ranges close to I-90 were shown to remain close to the highway. They were
not found near the highway equally between all light levels, revealing a non-linear
relationship to corresponding traffic volumes.
Therefore, this research demonstrates that I-90 in the Upper Snoqualmie
River Valley and US-12 in the Cowlitz Valley pose a significant barrier to elk
movement. Elk still cross at grade, as shown from collision and carcass removal
data, but the few underpasses in these areas do serve to offset some local
movement at all times of the day, year round. This suggests that the underpasses
may serve as effective mitigation measures for the high amount of collisions
occurring in these areas. However, additional fencing, jumpouts and more
underpasses and overpasses in this area could serve to reduce collisions and
further promote habitat connectivity in these hot spots.
Camera Data and Elk Activity at Underpasses
Light Level
I expected to observe more elk movement through underpasses during crepuscular
time intervals since studies show that elk spend a majority of their time feeding,
with peak activity levels during dawn and dusk (Green and Bear 1990;
Wichrowski et al. 2005). I found that elk used underpasses during all light level
categories differently, with most usage occurring at night (Figure 8). Elk also
moved through these underpasses at various light levels differently than expected,
with most use not occurring during dusk and dawn (Table 1). In this study elk do
not use underpasses more frequently during their normal peak hours of activity at
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dusk and dawn like other studies have shown (Green and Bear 1990, Gates and
Hudson 1983, Servheen et al. 2003). This suggests that some factor other than
their normal behavior is driving their temporal use of these structures. Possible
explanations for this nighttime-dominated use may be explained by human
disturbance or temperature. Similar to this study, Barrueto et al. (2014) found that
elk were sensitive to human activities, and altered their activity patterns in
response. Elk may use habitat closer to the highway, including underpasses at
night because of reduced human disturbance. Both study sites are located along
rivers that have typically high human activity. Additionally, hunting occurs in
both study sites, accounting for a large source of mortality especially in the South
Rainier Herd. Elk may be more active near roadways at night in response to
either human presence or hunting pressure.
Moreover, elk may use structures more during nighttime to conserve
energy when daytime temperatures are elevated. Elk, like most species, attempt
to gain the greatest caloric intake while minimizing energy expenditure (Charnov
1976). Elk are typically active when temperatures <15°C (Bleich et al. 2001), but
bed down possibly to avoid higher temperatures during the day during warmer
months. Because of this, elk may in fact alter their normal patterns of movement
to take advantage of cooler temperatures at night and expend less energy while
grazing, thus attributing to the high presence of elk at night.
Though the nighttime-dominated use of underpasses was unexpected,
other factors including human disturbance or temperatures might be contributing
to this behavior. Another factor, traffic volume, has also been studied recently
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and could be influential in explaining the high frequency of underpass use at
night.
Light Level and Traffic Volume
Light level and underpass site had an interactive effect when examining when elk
used underpasses at different light levels according to traffic volumes. More
underpass use occurred at night, when traffic volumes were typically lower.
Other studies have similarly hypothesized that low nighttime traffic volumes may
be an important influence on elk underpass use (Servheen et al. 2003). The
greater traffic volumes may inhibit elk crossings at grade, though this may be less
of a deterrent for elk that use underpasses (Gagnon et al. 2007b, Dodd et al.
2009). In some cases, traffic volume appeared unrelated to underpass use
(Gagnon et al. 2011), especially since some studies have showed that elk herds
will follow a leader through an underpass, increasing the frequency of underpass
use regardless of disturbances (Gagnon et al. 2007b). While elk detections at
underpasses in this study occurred during all light level categories, elk were seen
to use underpasses more frequently at night. Naylor et al. (2009) found that elk
altered their normal activities in response to off-road recreation treatments,
revealing that even recreational activities like mountain biking disturb elk.
Therefore, elk in these study areas may be influenced by traffic volumes and
associated visual and auditory annoyances. The results of this study suggest that
elk in these study areas might still be shifting their normal patterns of behavior in
response to some disturbance.
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In summary, this study revealed much higher use of underpasses at night,
in contrast to other studies showing elk use predominately during crepuscular
periods. This dissimilarity might be attributed to other factors including human
disturbance, temperature or traffic volume.
Seasonality
Ungulates perform different life functions according to season, and
correspondingly change their behavior to meet their energy requirements (Fancy
and White 1985, Jones and Hudson 2002). Since the rut, or mating season, for elk
takes place during the fall, I expected to see more elk use of underpasses during
the fall. While I did observe substantial use during the fall, elk used underpasses
more during the summer in contrast to other studies (Montgomery et al. 2013)
(Figure 9). However, elk were observed using underpasses during all seasons
similar to previous studies (Gagnon et al. 2011) but several factors might explain
the increases in summer and fall observed in this study including foraging to meet
energy requirements, anthropogenic influences, migration or gender.
Ungulates synchronize activities with foraging opportunities to meet basic
nutritional requirements (Gedir and Hudson 1999) which are often influential in
establishment of home ranges (Anderson et al. 2005). Since forage changes in
response to seasons, ungulate activity responds accordingly. Similar to my
findings, other studies have also showed that elk forage more at night during the
summer (Gates and Hudson 1983). During the summer, ample vegetation exists
for elk to graze on, getting ready for the rut in the fall when elk seek mates and
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will travel long distances to find suitable ones (Craighead et al. 1973). Similarly,
Gagnon et al. (2007b) found higher passage rates of elk during the summer and
spring likely due to the quality forage found in riparian areas. Manzo (2006) also
found that elk use riparian habitats near roads. Since the study sites used in this
thesis are found in riparian habitats, high peaks of underpass use observed during
the summer reflect this factor as well, whereas high usage during the fall may be
attributed to the increased movement during the rut.
Anthropogenic disturbances are important in determining when elk are
most likely to use underpasses. Ungulates have been shown to be sensitive to
human-caused disturbances (Perry and Overly 1977, Lyon 1979, Rowland et al.
2000) and will increase their movements, which decreases their likelihood of
survival (Cole et al. 1997). Hunting is a real pressure felt by both herds in these
study areas (WDFW) and represents a large source of mortality for populations
that may be legally harvested (Raedeke et al. 2002, McCorquodale et al. 2003).
Since roads increase human access into areas, with Lyon and Burcham (1998)
finding that fully one quarter of hunters’ time is spent within less than 300m of a
road, it is possible that elk respond accordingly to this pressure and increase their
movement, causing a spike in underpass use during the fall.
Migration is also an important factor in seasonal use of underpasses.
Levels of migration differ in both the North and South Rainier Elk Herds, ranging
from individuals that migrate long distances to those that remain residents of an
area. According to Moeller (2010,) many forms of elk migration exist, ranging
from individuals that do not migrate, to those that migrate in response to forage,
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to those that move to entirely new habitats. Since many elk in the Cascades were
transplanted in the 1900s from Montana, their seasonal movement patterns are
extremely varied. Similar to other studies, the fewer numbers of elk that are seen
during the spring and winter might be attributed to migratory elk (Dodd et al.
2007). Despite the fact that most elk observed in this study are thought to be
resident herds, it is possible that some may migrate during different parts of the
year.
Gender may also play a role in seasonal underpass use. Montgomery et al.
(2013) found that elk were closer to roads during different seasons, with females
avoiding busy roads during spring and autumn when their calves are typically
born. Males on the other hand avoided busy roads during the summer time, when
traffic levels are typically the highest. Though the data in this study are not
gender specific it is possible that low levels of elk underpass use during the spring
may be attributed to female calving, and their avoidance of roads.
Ultimately, season remains an important driving force of elk activity. In
this study, we saw high frequency of elk use during the summer and fall. Though
not consistent with all other studies, factors like forage quality, anthropogenic
disturbances, migration and gender may be influential in seasonal use of
underpasses by elk.
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Elk-Vehicle Collisions (EVC)
Light Level
Using Washington State Patrol’s records of vehicle collisions with elk, I found
approximately 11 collisions with elk occurred along the US-12 site per year, as
opposed to 8 per year along I-90 in North Bend (Figure 10). These collision
records represent the absolute minimum number of road caused elk mortalities.
Looking at carcass removal records for the same stretch of highway during the
same years, there were more elk removed from roads that reported by collisions
(Figure 11). Because elk are generally considered crepuscular in their activity, I
hypothesized that collisions between elk and vehicles would occur more
frequently during dusk and dawn. However, of the collisions in these study areas,
twice as many occurred at night than any other light level (Figure 12) similar to
other studies (Carbaugh et al. 1975). However, EVCs did occur differently at all
light levels in contrast to other studies that showed collisions occurred more
frequently during dawn and dusk (Dodd et al. 2006a, Haikonen and Summala
2001, Gunson et al. 2003). Therefore other factors such as driver reactions,
habitat type and surrounding vegetation may influence patterns of EVCs in
relation to light level.
Drivers are an important component of EVCs. Lao et al. (2011) found that
speed limit and surrounding habitat type increased effects of animal-vehicle
collisions (AVC). Using regression models to predict AVCs, Lao et al. (2011)
found that drivers’ responses became less effective with higher speeds, increasing
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significantly at speeds over 50mph. Since both I-90 and US-12 speed limits
exceed 50mph, more collisions might occur due to high speeds either because of a
driver’s inability to react effectively or because elk cannot judge distance and
speed accurately. Exacerbating the problem, drivers also tend to increase their
speed at night (Barrientos and Bolonio 2009), especially if the road is straight
(Gunson et al. 2011). Since most collisions along I-90 and US-12 occurred at
night when speeds were likely faster, reduced light level might further prohibit
drivers’ response abilities resulting in more frequent collisions at night.
Surrounding habitat is likely an important factor in temporal factors
associated with EVCs. Lao et al. (2011) found that collisions were more likely to
occur in rural areas. This might explain why there were, on average, more
collisions along US-12 than I-90, for the past five years since the underpass is
located in a relatively rural area with few urban settings and low human
population numbers. Lao et al. (2011) attributed this to animal populations likely
being different in urban and rural settings. Additionally there is only one
underpass in my study area along US-12, as opposed to several along I-90. The
lack of safe crossing opportunities along US-12 could also exacerbate the
frequency of EVCs. Both study sites in Washington have large elk populations,
but it is possible that more elk are hit in the Cowlitz River Valley due to its rural
setting especially since roads are not lit, and visibility is greatly reduced at night
in conjunction with a lack of safe crossing opportunities altogether.
Regardless of an urban or rural setting, surrounding vegetation and overall
habitat type adjacent to roads has been shown to result in more AVCs (Forman et
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al. 2003, Keller and Largiader 2003, Kramer-Schadt et al. 2004, Litvaitis and
Tash 2008). Many studies have found that ungulate collisions occur on roads
surrounded by forest-open habitat (Finder et al. 1999, Malo et al. 2004, Gunson et
al. 2009) while others document ungulate association with riparian areas (Bellis
and Graves 1971, Feldhammer et al. 1986, Finder et al. 1999, Malo et al. 2004,
Gunson et al. 2009). Overall, wildlife show attraction to roads near areas with
adequate foraging opportunities, thus increasing their risk of collisions (Gunson et
al. 2011). In this study, roads bisect landscapes that provide quality habitat, and
elk are often seen grazing in or around underpasses. Therefore, areas where road
curvature reduces visibility (Bashore et al. 1985) but provides quality grazing
habitat could be especially dangerous, especially at night when motorists see less
clearly. Ultimately, both I-90 and US-12 were built to follow the grade of least
resistance, where animals also typically travel (Boone et al. 1996, Schippers et al.
1996, Larkin et al. 2004). This factor may influence the frequent occurrence of
EVCs in these study areas.
Overall, many factors exist that might contribute to elk collisions during
different light levels, especially driver reactions and surrounding habitat. Traffic
volume, another likely influential factor, will be explained further below.
Light Level and Traffic Volume
Collisions with elk can be especially detrimental because of their large
size and mobility, often resulting in property damage to vehicles, injury or death.
Though no data exist on the amount of successful crossing attempts of elk at
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grade in Washington State, collisions may be used as an indicator if we assume
that the number of collisions is proportional to safe crossings made at grade.
Previous studies have shown that traffic volumes may indicate when an elk will
cross at grade (Gagnon et al. 2007a, Gagnon et al. 2007b). Therefore, using all
EVCs that occurred within the study area sites of the motion-triggered cameras
from years 2009-2013, I found that traffic volumes associated with EVCs
significantly differed between light categories except between dusk and dawn,
and night and dawn. Furthermore, both light level and site demonstrated a nonlinear relationship to the log-transformed values of traffic volumes during times
when elk were struck by vehicles. Overall, I-90 had significantly higher traffic
volumes than US-12, and traffic volumes were highest during the day (Table 5).
Previous studies also observed a high frequency of collisions at night
when traffic was typically lower. Millspaugh (1999) found that elk move closer
to roads at night when traffic volume is lower (Millspaugh 1999). Dodd et al.
(2007) found that elk crossed at grade during lower volumes of traffic and moved
away during periods of high traffic. Therefore, similar to these studies, the high
frequency of collisions at night might indicate that some elk avoid roads during
periods of high traffic volume during the day, and move closer to roads when
there are fewer disturbances from traffic, which is at night.
Other studies have showed the opposite effect, with negative effects of
roads increasing with higher amounts of road traffic (Gagnon et al. 2007b), or
more WVCs occurring during periods of high traffic (Gunson et al. 2003). Elk,
though highly mobile, may not be able to judge length and speed of oncoming
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vehicles making a collision with a crossing elk more likely when more vehicles
are on the road.
Interestingly, some studies have shown that despite high traffic volumes,
elk will use habitat closer to roads if a visual barrier exists (Montgomery et al.
2013) or if suitable grazing habitat is nearby. This could contribute to explaining
the differences in EVCs found along I-90 and US-12 in the study areas. In North
Bend, along I-90 elk are found close to the road, but rarely cross it. According to
Montgomery et al. (2013), the dense vegetation obscuring the road may cause elk
to feel more protected from humans, anthropogenic disturbances like noise, or
hunting pressures. Along US-12 in the Cowlitz River Valley, elk may use the
underpass for access to the riparian area adjacent to the Cowlitz River but cross
the highway otherwise since there is little vegetation along the roadside to
prohibit movement.
Despite higher levels of traffic along I-90, fewer collisions occur annually
in this study area per year in comparison to the US-12 study area. Similar to Lao
et al. (2011) who found that number of lanes has a negative effect on animal
presence, the larger number of lanes along I-90 might create more of barrier effect
for elk. This agrees with the barrier effects observed by Dodd et al. (2007) and
Jaeger et al. (2005).
Other studies have showed that elk as well as other species might shift
their temporal behaviors because of anthropogenic disturbances (Neumann et al.
2013, Northrup et al. 2012). Since collisions with elk in my two study areas
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occurred more frequently at night during lower traffic volumes, it is possible that
these normally crepuscular species are temporally adjusting their behavior in
response to traffic, or another disturbance attributed to the road.
Overall, Gunson et al. (2011) noted that temporal analyses of traffic
volume effects are complicated. Some studies did not reveal a temporal pattern
between traffic and AVCs (Shepard et al. 2008), others reported mixed results
(Bissonette and Kassar 2008), and others were confounded due to the barrier
effect (Jaarsma et al. 2006). Analyses of traffic volumes are difficult to compare
especially since road types differ dramatically, as do wildlife responses to them.
Ultimately, more crossing data needs to be collected to define a pattern of use for
elk in response to traffic volume, though a relationship seems to exist. As of now,
too many entangled factors appear to contribute to elk crossings to make succinct
conclusions.
Seasonality
Due to high elk activity levels during the rut when elk try to find suitable mates
and resources, I expected to see more collisions occur during the fall. EVCs were
observed to be different along I-90 and US-12 study sites according to season,
with most overall collisions occurring during the spring and fall (Table 7, Figure
13). However, collisions differed substantially by season along I-90 and US-12.
Along I-90 most collisions occurred during the spring and fall, whereas most
collisions along US-12 occurred during the winter and summer, similar to other
studies that did not show seasonal differences in elk activities (Wichrowski et al.
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2005). Regardless, small sample sizes made resulting seasonal analyses difficult,
but several factors may explain the high frequency of EVCs during different
months. Besides the factors that influence seasonal underpass use such as
foraging to meet energy requirements, anthropogenic influences, migration and
gender, other factors like age might play a role.
As other studies have revealed the importance of animal habituation to
roads and crossing structures (Barrueto et al. 2014), younger, or migratory elk
unfamiliar with underpasses may attempt to cross at grade and are more likely to
be struck by a vehicle. Though there is no data on gender or age reported by
Washington State Patrol, future work may want to consider age and gender in
predicting EVCs.
Telemetry
Other studies have shown that elk move towards highways during levels
of lower traffic, similar to my findings using elk fitted with GPS collars in North
Bend (Gagnon et al. 2007b). GPS telemetry points clearly show that three
collared elk used for this study in the North Bend vicinity appear to rarely cross I90, though they often come close (Table 8). According to Starr (2012), few elk
had home ranges on both sides of the highway despite suitable habitat. The
observed distances of elk in relation to I-90 were not the same as expected values
based on light level. This suggests that I-90 poses a serious barrier to movement
not only during certain times of the day, but altogether, which is similar to
findings from other studies (Mueller and Berthound 1997). Like Gagnon et al.
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(2007a), who found that elk moved farther from roads during periods of high
traffic, elk in North Bend demonstrated a similar pattern. When traffic volumes
were log-transformed, a strong non-linear relationship between traffic volume and
distance to the road was observed, suggesting that elk are found further from the
road during peak traffic volumes.
Adjacent foraging opportunities and habituation likely influence how close
elk are found to I-90 in North Bend. Ample riparian habitat along the highway in
North Bend may influence elk to rank their nutritional requirements higher than
disturbance brought by roads. However, since these elk are often found within
the city limits, they might also be habituated to the noise from the highway,
similar to other observed elk (Barrueto et al. 2014). Overall, elk demonstrated a
non-linear relationship to traffic volume suggesting that elk moved farther from
the road as traffic volume increased. More data needs to be collected to tease
apart influential factors like vegetation and habituation so that a clearer pattern
may emerge.
Conclusion
In conclusion, this study’s efforts and findings highlight the importance of
collaborative science, research and adaptive management between government
agencies, academia and non-governmental entities. By working within a strong
collaboration nexus, I was able to evaluate the ability of existing underpasses’
abilities to provide safe passage for elk below grade. Ultimately I discovered that
elk most frequently used underpass structures and were struck by vehicles most
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often at night when traffic volumes are usually lower. Because most elk activity
near the underpasses and roads in the study area occurs outside of crepuscular
time periods when elk have been reported to be most active, the findings of this
research suggest that elk are shifting their normal behavior patterns. Additionally,
elk most frequently used underpasses during the summer and fall, likely in
conjunction with forage needs and life cycle habits. Elk collisions did not show a
clear pattern between seasons; however a small sample size may be to blame.
Collared elk in the Upper Snoqualmie River Valley were observed to stay close to
I-90, but rarely crossed it, suggesting an affinity with riparian areas adjacent to
the highway, and habituation to the road. Overall, underpass structures were
shown to effectively allow elk passage safely below grade. Despite the fact that
the three bridges in these study areas were not built intentionally for wildlife, the
structures contribute to promoting safe movement opportunities for elk. With
some retrofitting including making passages longer, wider and taller and fencing
in these areas, as well as vegetation management, these structures have the
potential to mitigate for even more of the WVCs that occur in these areas.
Additional underpasses and overpasses designed specifically for wildlife in areas
with high rates of WVCs would be especially useful for habitat connectivity
measures and safety.
Overall, I found that elk use of underpasses and collisions with vehicles
differed according to light level, season, and traffic volume. While many
explanations exist for these patterns, and each should be addressed and further
studied, this is an important initial step in synthesizing many forms of data toward
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understanding elk-road interactions in Washington State. With a baseline
understanding of when elk will use structures, further research into specific ways
to decrease elk-vehicle interactions can be more effectively addressed.
Road ecology is still very much a new field of study, and basic questions
including when elk will use underpasses and when they are likely to be hit by
vehicles are important to study and understand. With this information we can
further build upon and gain better insight into different factors influencing elk
movement in relation to transportation infrastructure, keeping in mind that habitat
connectivity measures are not a one-size-fits-all solution. Each species,
population, and even individual responds to external anthropogenic influences
differently. Roads however will likely only continue to increase in their extent
across global landscapes. As these roads intersect wildlife populations and
habitats, understanding the effects and being able to mitigate for them is vital to
reducing WVCs, keeping both animals and humans safe on the road.
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Chapter 3: Conclusions and Management Implications
Conclusions
As transportation networks continue to expand, conflicts between wildlife
populations and humans will necessitate greater recognition and further study of
these conflicts. More people and agencies are becoming aware of the negative
effects that roads have on habitat connectivity and wildlife populations through
direct mortality, resulting in increasing efforts to study and mitigate for some of
these issues. Moving forward, transportation agencies must be especially
proactive in addressing the intersection between modes of human transport and
wildlife movement. Though this is no easy task, gaining an understanding of how
different factors affect wildlife movement in relation to roads is critical to
addressing current concerns and preventing future ones.
A fine-scale understanding of elk underpass use at three structures in
Washington had never before been synthesized. Using a multidisciplinary
approach to gathering data, this study used records from the WSDOT Carcass
Removal Database, WSDOT Traffic Information, Washington State Patrol
Collision Database, and GPS collar locations from the Upper Snoqualmie Valley
Elk Management Group to understand when elk most frequently move through
underpasses or when they are struck by vehicles according to light level, season
and traffic volume. Though the literature has documented regular activity of elk
(Cervus elaphus), less is known about their activity levels at these underpasses. It
is important to understand whether or not elk incorporate underpass use directly
!
82!
into their movement, and whether other factors are still prohibiting elk from using
them.
I discovered that three underpasses in Washington provided adequate safe
crossing opportunities for elk. However, a majority of these crossings occurred
during the nighttime, when elk are generally not thought to be most active. This
suggests that some other, possibly anthropogenic influence, may be influencing
elk behavioral patterns with relation to underpass use. Many of these crossings
occurred during summer and fall, in association with the growing season and rut,
when elk would normally be active. This suggests that forage quality and typical
life cycle behaviors may influence crossing rates accordingly. Most crossings
also occurred at night, when traffic volumes were typically lower. This suggests
that traffic volume may influence crossing rates of elk. Overall the factors studied
here suggest that light level, season and traffic volume are influential factors in
elk use of underpasses and collisions. This was an initial synthesis of several data
sets, but more research over a longer period of time would provide a better
understanding of elk movement in relation to transportation infrastructure.
However, the underpasses in these locations do in fact show their ability to
facilitate safe wildlife movement under busy highways. This is important in
mitigating for barriers to wildlife movement and connecting habitats, though more
research is necessary to gain a better understanding of the temporal and seasonal
effects that roads and traffic volumes may have on elk use of underpasses.
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83!
Management Implications
Many management implications associated with elk research have been
suggested to date. Previous studies have focused on the influences of traffic
volume, vegetation, road size, adjacent habitat, surrounding vegetation, gender,
age, or seasonal and temporal variations (Dodd and Gagnon 2011, Webb et al.
2011, Barrueto et al. 2014, Clevenger and Waltho 2003). Each of these may be
an important factor in addressing the effects of roads and the ability of safe
crossing structures to provide permeability and passage for wildlife. As scientists
learn more, it paves the way for more research and a finer understanding of elk
responses to anthropogenic disturbances and how humans can use infrastructure
to help alleviate some of these pressures.
Indeed, many other researchers have also brought attention to areas in
need of research. More specific parameters should also be evaluated including: 1)
Most effective location of structures, 2) Proper dimensions for species of interest,
3) Approaches in terms of migration effectiveness, 4) Surrounding vegetation, 5)
Amount of residual cover, 6) Fencing funneling animals towards structures, and
7) Conditions including noise levels all need to be monitored to better understand
wildlife use of crossings (Glista et al. 2008). If road mitigation measures
including underpasses are not adequately evaluated, results could lead to
mismanaged wildlife populations and wasted physical and fiscal resources (Grift
et al. 2013). Indeed, the ability of corridors to effectively connect landscapes has
long been questioned. Reviews of the literature acknowledge the difficulty in
quantifying this because each corridor is likely unique with respect to: 1) Target
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84!
species, 2) Surrounding landscape, and 3) Infrastructure and study design (Beier
and Noss 1998). Since the field of road ecology is relatively young, there are
many aspects of road ecology where more research is warranted.
Recommendations for Future Research
1.) Continue and increase monitoring efforts of existing bridges and culverts
a. Increase the number of monitored structures
b. Continue collecting data so that yearly differences may be
accounted for
c. Identify additional high priority areas using carcass removal
information and WVC information
This study was the first attempt to use data gathered from motiontriggered cameras, carcass removal reports, collision reports and GPS
collars to understand temporal factors of elk movement at or below grade.
Therefore, I used structures that had been part of existing monitoring
efforts. With a better understanding of elk movement and affinity for
structure types, a greater sample size using more structures would improve
future analyses. Possibly using carcass and collision information to
identify high priority areas could be used (Teixera et al. 2013).
Furthermore, collecting data for multiple years at structures would help
reveal trends over time and increase the number of replicates, making
analyses with ANOVA possible. Clevenger and Waltho (2003) state that
lack of information about the effectiveness of wildlife crossings is partly
due to lack of experimental design, resulting in mostly observational data.
!
85!
Now that WSDOT has synthesized preliminary data, hopefully the agency
can move more towards an experimental design and develop studies to ask
and answer more specific questions. Monitoring structures in these areas
that could be used to facilitate wildlife movement would be warranted,
helping WSDOT scientists understand wildlife movement in relation to
roads and implement mitigation measures. Measuring success of at grade
crossings is problematic, and rarely accomplished. However without this
metric, a comparison to safe crossings below grade is difficult. In the
future, WSDOT may consider deploying cameras over stretches of roads
at grade so that a comparison can be made between crossings at grade and
below grade. This would enable the department to ascertain the actual
success rate of underpasses for providing safe crossing alternatives for
wildlife.
2.) Improve/reconcile differences in reporting methods between collision
records and carcass removal records
In Washington State, WSDOT records indicate an average of 139 elk
carcasses removed from state-maintained roads each year according to the
past 5 years’ worth of data. For that same time period, Washington State
Patrol records indicate an average of 108 collisions between elk and
vehicles (Figure 11). These records represent a minimum count of elk that
are injured/killed on state maintained roads each year. It is important to
note that these are absolute minimum numbers and that the negative
effects of roads on elk are likely much larger. Though carcass removals
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86!
and collision data are some of the only available data for use in
quantifying direct effects of roads on elk, studies have shown the two
datasets differ significantly (Huijser et al. 2008). In fact, according to a
project completed for WSDOT using both carcass removal and collision
data, Wang et al. (2010) found that only 27 percent to 37 percent of
Collision Report data matched Carcass Removal data. Researchers found
that a combination of the two datasets increased total records by 13
percent to 22 percent (Wang et al. 2010) indicating that either dataset
alone may be insufficient. Ultimately a well-rounded understanding of the
conflicts between roads and wildlife requires various datasets, and a multiperspective view to analyze properly. Therefore, despite limitations in
both datasets, both contribute to researchers’ understanding of elk-vehicle
collisions and should be used.
3.) Improve terminology and methodology between similar studies
Though some of the results in this study are similar to previous studies
found in the literature, it was often difficult to make comparisons between
studies. According to Gunson et al. (2011) when completing a review of
24 published manuscripts, comparisons were difficult even between
studies focusing on the same species. Most studies, especially temporal
studies, use different categorizations of time or define light levels
differently. This study attempted to use light based on stringent
calculations of sunrise, sunset and twilight periods based on the Sun’s
position to Earth and corresponding amount of visible light. Therefore,
!
87!
the dissimilarities between this study and other studies may be explained
by a lack of comparable factors.
4.) Conduct a DNA Hair Snag Analysis
Cameras do not allow us to identify individual animals unless they have
some unique feature. A DNA hair snag analysis would inform us about
the number of individuals that use crossing structures, their gender, and if
they continue to use structures over a long period of time.
5.) Include gender information in collision reports
There may be a gender bias in animals that are struck by vehicles.
Previous studies have shown that male sub-adults are killed on roads most
frequently (Gunson et al. 2003). This might indicate that animals
traveling longer distances are more likely to get hit on roads, especially
during the rut when male elk travel long distances to find mates, thus
accounting for seasonal increases in EVCs. It is also possible that
elevated hormonal levels of males in the fall during the rut might make
males less aware of their surroundings and more likely to be hit be a
vehicle.
6.) Continue monitoring elk with GPS collars
a. Additional collaring of elk
b. Standardize collar schedule
c. Collar females and males
d. Collar elk in more geographically diverse locations
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Locations of elk in relation to I-90 were gathered from a partnership with
the USVEMG. Though more than 10 cow elk have been collared over the
past few years, only three were used in this study because they were
collared for at least one during the same time. All other collars were
deployed at different dates, even years and the collar schedules were not
succinct, some missing fixes for days. Increasing the number of collared
elk would increase the sample size and possibly help make a more
conclusive analysis. Collaring bulls in addition to cows would help us
understand differences in gender, and movement patterns throughout the
year. Finally, collaring elk in geographically diverse locations would help
the DOT understand how different types of roads may affect different
populations of elk. Since several hot spots exist around the state where elk
are frequently hit on highways, understanding how elk move at one
location alone is inadequate. Elk need to be collared in multiple problem
areas so that the most effective mitigation techniques may be applied.
7.) Implement studies addressing effects of road and human noise on elk
behavior
Highways and vehicles are sources of anthropogenic disturbances,
especially noise. If noise acts as a repellent during underpass use,
attempts to reduce noise are important (Gagnon et al. 2007b). If noise
from highways causes animals to flee and instead cross over highways,
then additional fencing acting as a funnel into such crossings may be
necessary (Gagnon et al. 2007b) or attempts to reduce noise may be
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89!
important. Though elk in the Upper Snoqualmie Valley may already be
habituated to noise, those in the Cowlitz River Valley may be greatly
influenced by vehicles and associated noise. WSDOT might consider
studying flight behavior and effects of noise from video and camera
images in addition to devices deployed around the same underpasses to
record types and intensities of noise.
8.) Manage riparian areas
a. Increase vegetation at some underpasses to entice elk to use
underpasses or dissuade them from crossing the road
Effective vegetation management could be used to attract elk to
underpasses or dissuade their ability to cross roads. Both study areas have
underpasses surrounded by riparian areas due to the proximity of the
South Fork Snoqualmie River and the Cowlitz River. Elk are naturally
attracted to riparian areas due to the ample forage they provide.
Therefore, because elk may move closer to roads that are surrounded by
high quality habitat, future wildlife crossings should be located in such
areas to increase habitat effectiveness (Gagnon et al. 2007b).
Furthermore, vegetation can also be used in areas without safe crossing
opportunities. In areas where roads intersect critical habitat, barriers like
dense vegetation may disguise the visual and auditory disturbances caused
by roads (Montgomery et al. 2013). Such screening vegetation could be
used to help researchers better understand elk habitat selection near roads
(Montgomery et al. 2012).
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90!
9.) Limit human access to some underpasses
Human presence at underpasses may discourage elk use. Therefore,
continuing to document and gain more evidence of this negative
relationship is necessary. Limiting human access to underpasses,
especially during times of peak elk activity, is also important. If elk are
shifting their normal behavior patterns in an effort to avoid humans, this
could have long-term consequences on their survival and health (Webb et
al 2011). Limiting human exposure may help decrease that negative
effect.
10.) Continue with an interdisciplinary approach to understanding effects of
roads and how existing or future underpass design can mitigate for effects
The increasing needs of humans often conflict with efforts to protect the
environment, and we are now tasked with trying to balance these needs.
Roads have been an increasing part of the landscape for 100s of years
now, but only recently have humans become aware of the immense
impacts these roads have had on wildlife. The nascent field of road
ecology has only begun to address these issues. Understanding issues in
road ecology requires much knowledge including expertise in roads and
transportation infrastructure, wildlife ecology and biology, engineering,
design, hydrology, and chemistry. Since the issues contributing to habitat
fragmentation are so complex, their solutions must be equally innovative.
This often requires an interdisciplinary approach to be successful.
!
91!
In Washington State, problems with WVCs are addressed using
dynamic efforts between state agencies, non-governmental groups and
academia. The interdisciplinary approach taken in this thesis research
involved the sharing and analysis of multiple data sets to understand the
temporal and seasonal movement patterns of elk in relation to
transportation infrastructure above and below grade. With additional
research and management efforts, we can continue collecting better data to
analyze this issue more succinctly and create a safer highway system for
drivers, while protecting wildlife populations and increasing habitat
connectivity.
!
92!
Figure 1: Total Collisions versus Wildlife-Vehicle Collisions
NOTE- Total vehicle collisions have remained ~ 6-7,000,000 from 1990-2004
while wildlife-vehicle collisions have increased from ~ 200,000-300,000 (Huijser
et al. 2008).
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93!
Figure 2: Study Areas in the Upper Snoqualmie Valley and Cowlitz River Valley
!
NOTE- Motion-triggered cameras were deployed around three underpasses, and
corresponding stretches of highway were analyzed.
!
!
94!
Figure 3: Study Area in the Upper Snoqualmie Valley
NOTE- Eight motion-triggered cameras were deployed around these two
underpasses, and a portion of I-90 was analyzed.
!
95!
Figure 4: Study Area in the Cowlitz River Valley
NOTE- Three motion-triggered cameras were deployed around one underpass,
and a portion of US-12 was analyzed.
!
!
96!
Figure 5: Number of Detections versus Individual Elk Using Underpasses
7+89)(!#0!:)$)-;#,/!<)(/+/!=,4.<.4+%&!
1&2!3/.,>!3,4)(5%//)/!
"#$%&!'()*+),-.)/!#0!1&2!
&#"!
&""!
%#"!
%""!
$#"!
(.89:/;<!=040>?283!
$""!
@:AB0<!2C!*8D.E.D:;/3!
#"!
"!
'()$%!
*)+",!-./01234! *)+",!-./01234!&7!
&$56!
3,4)(5%//!6$(+-$+()/!
NOTE- This graph represents the difference between numbers of total individuals
of elk who crossed through underpasses and singular detections that did not take
multiple individuals into account.!!!
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Figure 6: Categories of Twilight
NOTE- This graph represents the designation of twilight according to the angle of
the sun on Earth’s horizon (Reid 2014).
!
!
!
98!
!"#$%#&'()*+),-.)/#0#'01*&2)
Figure 7: Frequency of Elk Detections using Three Different Underpasses
!"#$%#&'()*+),-.)/#0#'01*&2)60)8)
3&4#"5622#2)1&)962:1&;0*&)
'!!"
&!"
%!"
$!"
#!"
!"
()*+,-.//".0"123+-4/0" ()*+,-.//".0"123+-4/0" ()*+,-.//".0"123+-4/0"
'#5".34)6"(78'#"
5'9%".34)6":8;!"
5&".34)6":8;!"
3&4#"5622)70"%'0%"#2)
NOTE- Total detections for the underpass along US-12 were collected from
2012-2013. Total detections for underpasses along I-90 were collected during
2013.!
!
99!
Figure 8: Total Frequencies of Detections of Elk Movement through Underpasses
!"#$%#&'()*+),-.)/#0#'01*&2)
!"#$%#&'1#2)*+),-.)3&4#"5622)32#)
>()<1;:0)<#=#-)
&!"
=!"
%!"
<!"
$!"
5!"
#!"
'!"
!"
>.?)"
>.@"
>A/B"
C26D0"
<1;:0)<#=#-)
NOTE- Data was collected during 2012 and 2013 and combined according to
light level for all three underpasses.!
!
100!
Table 1: Goodness-of-fit test Using Light Level and Site
Light
Site
Observed
frequency
Expected
frequency
Dawn
CB
6
5.8251366
Day
CB
4
10.754098
Dusk
CB
4
7.3934426
Night
CB
27
17.027322
Dawn
NB31.6
12
12.502732
Day
NB31.6
23
23.081967
Dusk
NB31.6
22
15.868852
Night
NB31.6
31
36.546448
Dawn
NB38
8
7.6721311
Day
NB38
21
14.163934
Dusk
NB38
7
9.7377049
Night
NB38
18
22.42623
Note- Light level categories (dawn, day, dusk, and night) and site (underpasses in
Cowlitz River Valley and Snoqualmie Valley) were used as categorical variables
revealing the differences in observed values from expected values.
!
101!
Table 2: Results of a 2-Way ANOVA using Light Level and Site
df
Type III SS
Mean
Square
F
Light
3
14.0331206
4.6777069
56.980373 .0000*
Site
2
35.7288122
17.864406
217.61101 .0000*
Light x Site
6
2.768169938
0.4613617
5.6199672 .0000*
Error
149
12.23190169
0.820933
Source
Variation
p
Main Effects
Interaction
*Indicates a significant result at a = 0.5
NOTE-Light level categories (dawn, day, dusk and night) and site (underpasses
in Cowlitz River Valley and Snoqualmie Valley) were used as independent
variables and traffic volume was used as the dependent variable of elk detections
by motion-triggered cameras at underpasses, 2012 and 2013.
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
102!
Figure 9: Total Frequencies of Elk Movement through Underpasses by Season
'()*+),-?!#0!1&2!:)$)-;#,/!
'()*+),-?!#0!1&2!3,4)(5%//!3/)!9?!
6)%/#,!
7"!
G"!
6"!
#"!
F"!
&"!
%"!
$"!
"!
H;//!
I.840<!
(1<.89!
(:AA0<!
H;//!
I.840<!
(1<.89!
(:AA0<!
6)%/#,!
NOTE- Seasons are designated as fall, winter, spring and summer.
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
103!
Table 3: Results of a Chi-square Goodness-of-fit Test using Season and Site
Season
Site
Fall
CB
13
12.54648
Spring
CB
1
6.0491803
Summer
CB
23
15.68306
Winter
CB
4
6.7213115
Fall
NB31.6
29
26.928962
Spring
NB31.6
19
12.983607
Summer
NB31.6
21
33.661202
Winter
NB31.6
19
14.42623
Fall
NB38
14
16.52459
Spring
NB38
7
7.9672131
Summer
NB38
26
20.655738
Winter
NB38
7
8.852459
Observed
frequency
Expected
frequency
NOTE- Categorical variables of season (fall, winter, spring and summer) versus
site (underpasses in the Cowlitz River Valley and Snoqualmie Valley) were used.
!
!
!
!
!
!
!
!
!
!
!
104!
Figure 10: Total Elk-Vehicle Collisions in Study Areas
7+89)(!#0!1&2@A)B.-&)!C#&&./.#,/!
$6"!
1&2@A)B.-&)!C#&&./.#,/!6$%$)E.4)!%,4!.,!6$+4?!
F()%!
$F"!
$%"!
(4;40J.D0!
K2//.3.283!
$""!
K2//.3.283!J.4L.8!
(4:DM!N<0;3!
7"!
K2//.3.283!N/289!
*+"!(4:DM!(.40!
6"!
F"!
K2//.3.283!N/289!
'($%!(4:DM!(.40!
%"!
"!
%""+!
%"$"!
%"$$!
D)%(!
%"$%!
%"$&!
NOTE- Total collisions with elk along I-90 and US-12 within 32km of
underpasses outfitted with motion-triggered cameras from 2009-2013.
!
!
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!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
105!
7+89)(!#0!C#&&./.#,/!#(!C%(-%//!G)8#<%&/!
Figure 11: Statewide Records of Elk-Vehicle Collisions and Carcass Removals
%#"!
6$%$)E.4)!1&2@A)B.-&)!C#&&./.#,/!H!C%(-%//!
G)8#<%&/!
%""!
$#"!
(4;40J.D0!
K2//.3.283!
(4;40J.D0!K;<>;33!
O0A2E;/3!
$""!
#"!
"!
%""+!
%"$"!
%"$$!
%"$%!
%"$&!
D)%(!
NOTE- Elk collisions and carcasses were recorded within 32km east or west of
underpasses along I-90 and US-12.
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
106!
Figure 12: Elk-Vehicle Collisions in Study Areas
"#$%&!1&2@A)B.-&)!C#&&./.#,/!%&#,>!=@JK!
%,4!36@LM!6$+4?!F()%/!0(#8!MKKJ@MKLN!
'()*+),-?!#0!1&2@A)B.-&)!C#&&./.#,/!
6"!
#"!
F"!
&"!
%"!
$"!
"!
=;J8!
=;M!
=:3P!
@.9L4!
I.>B$!I)<)&!
NOTE- Collisions were used that were found within in 32km of underpasses
according to light level.!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
107!
Table 4: Results of a 2-Way ANOVA using Light Levels and Collisions
df
Type III SS
Mean
Square
F
Light
3
3.704417212
1.2348057
11.658879 .0000*
Site
1
43.02024955
43.02025
406.19175 .0000*
Light x Site
3
0.0259860934 0.0866203
Error
89
9.426095539
Total
96
78.61487707
Source
Variation
p
Main Effects
Interaction
0.817858
0.4874
not sig
0.1059112
*Indicates a significant result at a = 0.5
NOTE- Light level categories (dawn, day, dusk and night) and site (portions of I90 and US-12 related to underpasses in Cowlitz River Valley and Snoqualmie
Valley) were used as dependent variables versus the independent variable of logtransformed traffic volume when elk were struck by vehicles, 2009-2013.
!
108!
Table 5: Student-Newman-Keuls Mean Separation Test of Collisions
Site Name
Mean (log traffic
volume)
N (# samples)
Non-significant
ranges
I-90
2.98641320017
41
a
US 12
1.33789764955
56
b
NOTE- Higher traffic volumes occurred along I-90.
!
109!
Table 6: Student-Newman-Keuls Mean Separation Test of Traffic Volumes
during Elk-Vehicle Collisions
Light level
Mean (log traffic
volume)
N (# samples)
Non-significant
ranges
Day
2.84632133548
18
a
Dusk
2.0714058502
21
b
Dawn
1.79525493841
12
C
Night
1.76280094409
46
C
NOTE- Traffic levels are highest during the day.
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
110!
Figure 13: Frequency of Elk-Vehicle Collisions
'()*+),-?!#0!1&2!C#&&./.#,/!9?!6)%/#,!
'()*+),-?!#0!1&2@A)B.-&)!C#&&./.#,/!
&#!
&"!
%#!
%"!
H;//!
I.840<!
$#!
(1<.89!
$"!
(:AA0<!
#!
"!
H;//!
I.840<!
(1<.89!
(:AA0<!
6)%/#,!
NOTE- Collisions recorded within 32km of underpasses equipped with motiontriggered cameras along I-90 and US-12 by season were used.!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
111!
Table 7: A Chi-square Goodness-of-fit Test using Season and Site
Site
Season
I-90
Fall
13
11.030303
I-90
Spring
18
12.30303
I-90
Summer
4
8.4848485
I-90
winter
7
10.181818
US 12
Fall
13
14.969697
US 12
Spring
11
16.69697
US 12
Summer
16
11.515152
US 12
winter
17
13.818182
Observed
frequency
Expected
frequency
NOTE- Seasons (fall, winter, spring and summer) versus site (underpasses in the
Cowlitz River Valley and Snoqualmie River Valley) resulted in seasonal
frequencies of collisions being different than expected.
!
!
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!
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!
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!
!
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112!
Table 8: Elk Distances to the Highway
Animal
Mean
distance
to road in
meters
Standard
deviation
339
1030.40
341
1550
!
Minimum
Maximum
N
(number
of fixes)
621.802644212 65.60
2366.82
633
1031.07
565.024682796 14.08
2410.58
1118
1646.01
1084.19059117 49.07
5158.45
778
113!
Table 9: A Chi-square Goodness-of-fit Test using Light Level and GPS Points
Animal
Light
Observed
frequency
Expected
frequency
339
Dawn
74
58.819692
339
Day
329
329.89087
339
Dusk
25
26.781732
339
Night
205
217.50771
341
Dawn
93
103.88691
341
Day
544
582.65085
341
Dusk
53
47.3017
341
Night
428
384.16054
1550
Dawn
68
72.293397
1550
Day
445
405.45828
1550
Dusk
29
32.916568
1550
Night
236
267.33175
NOTE- Elk distances to the road were different than expected according to light
level.
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114!
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