behavioral and population ecology of swift foxes in
TRANSCRIPT
Clemson UniversityTigerPrints
All Theses Theses
5-2019
Behavioral and Population Ecology of Swift Foxesin Northeastern MontanaAndrew Russell ButlerClemson University, [email protected]
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Recommended CitationButler, Andrew Russell, "Behavioral and Population Ecology of Swift Foxes in Northeastern Montana" (2019). All Theses. 3133.https://tigerprints.clemson.edu/all_theses/3133
BEHAVIORAL AND POPULATION ECOLOGY OF SWIFT FOXESIN NORTHEASTERN MONTANA
A Thesis
Presented to
the Graduate School of
Clemson University
In Partial Fulfillment
of the Requirements for the Degree
Master of Science
Wildlife and Fisheries Biology
by
Andrew Russell Butler
May 2019
Accepted by:
David Jachowski, Committee Chair
Cathy Bodinof Jachowski
Michael Childress
Axel Moehrenschlager
ii
ABSTRACT
Swift foxes are endemic to the Great Plains of North America, but they were extirpated
from the northern portion of their range by the mid-1900s. Despite several
reintroductions to the Northern Great Plains, there is still a large range gap between the
swift fox population along the Montana and Canada border, and the population in
northeastern Wyoming and northwestern South Dakota. A better understanding of the
resources swift foxes use and demography of the population at the edge of this range gap
in northern Montana might help to managers to facilitate connectivity among populations.
In Chapter 1, we collected fine-scale locational data from swift foxes fitted with Global
Positioning System collars to examine movement and resource use patterns during winter
of 2016-2017 in northeastern Montana. Our results suggest that swift foxes displayed
three distinct movement patterns (i.e., resting, foraging, and travelling) during the winter.
Distance to road decreased relative probability of use by 39-46% per kilometer across all
movement states and individuals, whereas the influence of topographic roughness and
distance to crop field varied among movement states and individuals. Overall, while our
findings are based on data from three individuals, our study suggests that across
movement states during the critical winter season, swift foxes are likely using topography
and areas near roads to increase their ability to detect predators.
In Chapter 2, we estimated the home range size and evaluated third order resource
selection of 22 swift foxes equipped with Global Positioning System tracking collars in
northeastern Montana. Swift fox home ranges in our study were some of the largest ever
recorded averaging (+/-SE) 42.0 km2 +/- 4.7. Our results indicate that both
iii
environmental and anthropogenic factors influenced resource use. At the population
level, relative probability of use increased by 3.3% for every 5.0% increase in percent
grasslands. Relative probability of use decreased by 7.9% and 7.4% for every kilometer
away from nonpaved roads and gas well sites, respectively, and decreased by 2.9% and
11.3% for every one-unit increase in topographic roughness and every 0.05 increase in
normalized difference vegetation index (NDVI). Overall, to reestablish connectivity
among swift fox populations in Montana, our study suggests that managers should aim to
maintain large corridors of contiguous grasslands a landscape-scale, a process that will
likely require having to work with multiple property owners.
In the Northern Great Plains, a suite of carnivores has experienced a large decline
in distribution and abundance since the 1800s. In Chapter 3, our objective was to
estimate survival and reproductive rates of swift foxes in Montana and assess population
viability. In addition, we evaluated support for nine different hypotheses of how several
demographic and environmental factors influence survival. We found that adult and
juvenile annual survival rates were 54% and 74%, respectively, and fecundity was 0.85.
We found the most support for the hypothesis that the percentage of native grassland at
the 1 km scale influenced survival and found that survival increased, on average, 2.1%
for every 5% increase in grassland. The estimated population growth rate of this
population was estimated to be 1.002, indicating that the population was likely to be
stable. Our results suggest that this population is currently not likely acting as a source
population (i.e. not producing a sufficient number of dispersers), which might be
contributing to the lack of range expansion. The long-term success of swift fox recovery
iv
will likely be dependent on maintaining large tracts of contiguous grassland with
abundant prey, which would be benefit not only the swift fox, but a suite of recovering
carnivores.
v
DEDICATION
I dedicate my thesis to my fiancé Sarah, my Mom and Dad, and my brothers,
Samuel and Daniel, for their never-ending support and love throughout my life and
during this research project. I also dedicate my thesis to Jennie, for all her friendship,
reassurance, and without whom I could not have completed graduate school.
vi
ACKNOWLEDGMENTS
I would like to thank my advisor David Jachowski for taking me on as his
graduate student, for his full support of this project from the very beginning, and his
patience while I learned how to do my analysis and improve my scientific writing. Thank
you to my committee: Cathy Jachowski, Michael Childress, and Axel Moehrenschlager
for their assistance in project design and support throughout the project. Thank you to
Kristy Bly, Doni Schwalm, and Axel for welcoming me into the world of swift fox
research and conservation, writing the grant to get this project off the ground, and
allowing me to pick your brains on swift fox ecology. Thank you, Jessica Swift, for
working above and beyond expectations through the first year of this project to help get it
off the ground and make it successful. Also, thank you Keifer Titus, Maddie Jackson,
Jordan Holmes, and Thoneta Bond, for being amazing technicians and working through
frigid winters and scorching summers to collect data. Thank you, Heather Harris, for all
your hard work to trap and collar foxes and providing logistical assistance a drop of a hat.
Thank you, Craig Miller and Kathy Tribby, for providing so much logistical help during
the entirety of the study. Thank you, Steve and Patty Young, for allowing us to run this
project out of your basement for two years and always being so kind and welcoming.
Thanks to all my Jachowski lab mates for your laughs, support, and critiques of drafts,
presentations, and posters. You’ve made this a wonderful experience. Lastly, I’d like to
thank Ken Logan, Shannon Barber-Meyer, and Sean Flint for being incredible mentors
early on in my career, and who continue to be amazing mentors and friends.
vii
DISCLAIMER
Chapters are formatted in manuscript format for journal submission. Chapter 1
was submitted to and accepted for publication by the Canadian Journal of Zoology.
Chapter 2 is formatted for submission to the Journal of Mammalogy. Chapter 3 is
formatted for submission to Animal Conservation.
viii
TABLE OF CONTENTS
Page
TITLE PAGE .................................................................................................................... i
ABSTRACT ..................................................................................................................... ii
DEDICATION ................................................................................................................. v
ACKNOWLEDGMENTS .............................................................................................. vi
DISCLAIMER ............................................................................................................... vii
LIST OF TABLES ........................................................................................................... x
LIST OF FIGURES ....................................................................................................... xii
CHAPTER
I. WINTER MOVEMENT BEHAVIOR OF SWIFT
FOXES (VULPES VULPES) AT THE
NORTHERN EDGE OF THEIR RANGE .............................................. 1
Introduction .............................................................................................. 1
Methods.................................................................................................... 4
Results ...................................................................................................... 9
Discussion .............................................................................................. 11
II. HOME RANGE SIZE AND RESOURCE USE BY
SWIFT FOXES IN NORTHEASTERN
MONTANA ........................................................................................... 22
Introduction ............................................................................................ 22
Methods.................................................................................................. 24
Results .................................................................................................... 31
Discussion .............................................................................................. 33
III. SWIFT FOX SURVIVAL AND REPRODUCTIVE
RATES IN NORTHEASTERN MONTANA:
IMPLICATIONS FOR POPULATION
VIABILITY AND EXPANSION .......................................................... 46
ix
Table of Contents (Continued)
Page
Introduction ............................................................................................ 46
Methods.................................................................................................. 49
Results .................................................................................................... 54
Discussion .............................................................................................. 56
APPENDICES ............................................................................................................... 71
A: Supplemental Materials .............................................................................. 71
REFERENCES .............................................................................................................. 75
x
LIST OF TABLES
Table Page
1.1 A priori models of resource use, developed from a
literature review, predicted to influence swift fox
(Vulpes velox) resource use in northeastern Montana,
October 2016- January 2017. Null model does not
include any of the hypothesized variables and the
Global model includes all variables. ....................................................... 17
1.2 Summary of GPS data used in analysis of adult swift
fox (Vulpes velox) movement states in northeastern
Montana, October 2016- January 2017. .................................................. 17
1.3 Model selection results for swift fox 2-state and 3-state
HMMs for adult swift fox (Vulpes velox) in northeastern
Montana, October 2016- January 2017 ................................................... 17
1.4 Estimates of model parameters for the 3-state Hidden
Markov model averaged across three adult swift
foxes (Vulpes velox) in northeastern Montana,
October 2016- January 2017 .................................................................. 17
1.5 Multiple regression resource use models and number of
times each model of resource use received the most
support and the average AIC weight developed for
adult swift fox (Vulpes velox) in northeastern
Montana, October 2016- January 2017 .................................................. 18
1.6 Population-level resource utilization function
coefficients (β) and standard error (SE) for adult
swift fox (Vulpes velox) habitat use by movement
state in northeastern Montana, October 2016-
January 2017, and the number of individuals with
positive (+) or negative (-) coefficients. Coefficients
and standard errors were obtained by averaging
each covariant coefficient across all individuals ................................... 18
2.1 Geographic location, home range estimator used in
study, temporal scale of home range estimates,
sample size used to estimate home range size, and
average home range size (km2) and SE of swift
foxes in North America. Locations in the top part
of the table are from the northern portion of the
range and locations in the bottom part of the table
are from the southern portion. ................................................................ 40
2.2 Variables predicted to influence resource use by swift
foxes in northeastern Montana during 2016-2018
xi
List of Tables (Continued)
Table Page
with their abbreviation, description, units,
prediction, range, and supporting citation.............................................. 41
2.3 A priori models developed from competing hypotheses
for swift fox resource use in northeastern Montana
during 2016-2018. .................................................................................. 42
2.4 Population-level resource use coefficients, variance,
and 95% confidence intervals for the top model of
swift foxes in northeastern Montana during 2016-
2018. We counted the number of swift foxes with
positive or negative values for each coefficient ..................................... 43
3.1 Swift fox population size estimates for Canada,
Montana, and both areas combined (Total) from the
population census from Moehrenschlager &
Moehrenschlager (2001, 2006, 2018). ................................................... 62
3.2 Model selection results for swift fox survival known-
fate survival models in northeastern Montana, 2016-
2018........................................................................................................ 62
3.3 Vital rate values with 95% lower confidence interval
(LCI) and upper confidence interval (UCI) used to
parameterize projection matrix and sensitivity and
elasticity values for swift foxes in northeastern
Montana, 2016-2018.. ............................................................................ 63
3.4 Geographic location, survival rates of all individuals,
adult, or juvenile swift foxes, number of foxes
tracked in the study, and study length of swift foxes
in North America. Average values exclude this
study. ...................................................................................................... 64
3.5 Geographic location, percent of tracked females
reproducing, average litter size, number of social
units (male-female or trios) monitored, and study
length of swift foxes in North America. Average
values exclude this study. ...................................................................... 65
xii
1.1 Example movement trajectory of a single adult swift
fox (Vulpes velox) during one nightly four-hour
movement bout in January 2017. Movement states
were determined using the Viterbi algorithm and
different colors indicate the most likely state that
was assigned to each location: X = sedentary state,
square = restricted state, and circle = travelling
state. ....................................................................................................... 19
1.2 Influence of topographic roughness (A), distance to
road (B), and distance to crop (C) on the relative
probability of use by three swift foxes (Vulpes
velox) in northeastern Montana, October 2016-
January 2017, during the resting, foraging, and the
travelling states. .................................................................................... 20
2.1 Swift fox distribution and study area in Montana where
we estimated swift fox home range size and
resource use during 2016-2018, and B) Areas
within the study area where we trapped six swift
foxes (a), four swift foxes (b), three swift foxes (c),
twenty-two swift foxes (d), and thirteen swift foxes
(e) in 2016-2018. .................................................................................. 44
2.2 Relative probability of use curves for significant
resource variables in resource utilization functions
including a) percent grassland, b) distance from
natural gas well, c) distance from nonpaved road,
d) topographic roughness, and e) NDVI for swift
foxes in northeastern Montana, 2016-2018. ......................................... 45
3.1 A) Distribution of swift foxes and study area in
Montana where we estimated vital rates during
2016-2018; B) Focal areas within the study area
where we trapped six swift foxes (a), four swift
foxes (b), three swift foxes (c), twenty-two swift
foxes (d), and thirteen swift foxes (e) in 2016-
2018. ..................................................................................................... 66
LIST OF FIGURES
Figure Page
xiii
List of Figures (Continued)
Figure Page
3.2 Population projection of the swift fox population in
northeastern Montana based on a stage-based
matrix incorporating demographic stochasticity.
Gray region is the confidence interval of the
population size at each year. ................................................................. 67
3.3 Predictive plot showing the effects of percent grassland
on monthly survival of swift foxes in northeastern
Montana during 2016-2018. ................................................................. 68
1
CHAPTER ONE
WINTER MOVEMENT BEHAVIOR BY SWIFT FOXES (VULPES VELOX) AT THE
NORTHERN EDGE OF THEIR RANGE
Winter can be a pivotal time of the year for many temperate species as individuals
simultaneously balance increased energetic demands and decreased prey resources
(Marchand 2013). Many mammals do not hibernate or undergo torpor, and must
morphologically, physiologically, or behaviorally adapt to harsh winter conditions in
northern latitudes. In particular, individuals face increased energetic demands due to
increased metabolism to counteract heat loss due to cold temperatures (Prestrud 1991) and
increased costs of locomotion due to snow depth (Crête and Larivière 2003). At the same
time, winter is typically associated with decreased prey availability (Michener 1998) and
accessibility (Halpin and Bissonette 1988) which can negatively impact energy budgets for
carnivores, particularly for smaller carnivores that must balance energetic needs with
predation risk from larger carnivores (Thompson and Gese 2012). Collectively, these
stressors can lead to decreased overwinter survival. For example, winter severity had the
largest influence on red fox (Vulpes Vulpes Linnaeus 1758) density across Europe and Asia
(Bartoń and Zalewski 2007), and winter starvation accounted for 20% of mortalities in
several bobcat (Lynx rufus Schreber, 1777) studies in the United States (Major and Sherburne
1987; Fuller et al. 1995). Moreover, harsh winters can decrease the body condition of female
red foxes and reduce the number of breeding females (Kjellander and Nordström 2003).
Swift foxes (Vulpes velox Say, 1823) are a small canid endemic to the short and
mixed-grass prairies of North America from southern Canada to New Mexico and Texas and
from the Rockies to the western Dakotas and central Kansas. However, due to conversion of
2
native prairie habitat to agriculture and predator control programs (Carbyn 1998), the swift
fox is currently a species of conservation concern over much of its range (Dowd Stukel
2011). Swift foxes were extirpated from Canada in 1938 (Moehrenschlager and Lloyd 2016)
and in Montana in 1969 (Hoffmann et al. 1969). Between 1983 – 1997, Canada initiated a
large-scale reintroduction effort in Alberta and Saskatchewan, releasing 942 swift foxes
during this time period (Moehrenschlager and Lloyd 2016), which led to the establishment of
a population in northeastern Montana. Reintroduced swift foxes were first documented
dispersing south into northeastern Montana during the late 1980s and early 1990s and
reproduction in Montana was first documented in 1997 (Zimmerman 1998). Since then, the
distribution of swift foxes has expanded 56 km into northeastern Montana. However, recent
census data suggest a sharp decline in swift fox abundance in northeastern Montana between
2005 and 2015 (Moehrenschlager and Moehrenschlager 2018), potentially due to the above
average snowfall and below average temperatures during the winter of 2010-2011 (NOAA
2011).
Swift foxes in northeastern Montana and southern Canada are at the northern edge of
their current range where winters can be harsh, and their winter ecology is not well
understood. In northern Montana, the average (1981-2010) minimum temperature during
winter (December-February) is 6.7oC and the average number of days with a snow depth of
at least 12.7cm is 25.3 days per year (Glasgow, MT) compared to 24.5oC and 2.5 days per
year in the southern portion of their range (Amarillo, TX; National Centers for
Environmental Information 2018; https://www.ncdc.noaa.gov/cdo-web/datatools/normals).
The average swift fox home range size in the region is 32.9 km2 (Moehrenschlager et al.
2007), which is substantially larger than home ranges across the southern portion of their
3
range (Kitchen et al. 1999; Olson and Lindzey 2002; Harrison 2003; Kamler et al. 2003a).
Moehrenschlager et al. (2007) hypothesized that exceptionally large home ranges observed in
Canada are due to low rodent abundance, which along with insects and birds, are the most
frequently consumed food item for swift foxes in this region. Indeed low prey abundance in
this region during winter has led to an increase in winter mortality, with several swift foxes
dying from starvation (Klausz 1997). In addition, low prey abundance during the winter
might cause swift foxes to increase movement and foraging effort (Covell et al. 1996),
potentially causing them to encounter more predators, such as coyotes (Canis latrans Say,
1823), their primary predator (Matlack et al. 2000; Kamler et al. 2003a; Moehrenschlager et
al. 2007). However, it is not known how swift foxes move on the landscape and how they
move to minimize predation risk. A better understanding of swift fox winter movement
ecology would come from knowing how swift foxes select habitat resources that minimize
predation risk, especially during different movement states.
Swift fox behavior-specific habitat use has been difficult to study in the past due to
the swift fox’ small size and nocturnal nature but could provide key insights into the
mechanisms underlying adult habitat use patterns. Previously, only one study has
investigated how fine scale habitat resources (i.e., vegetation structure) differ between
behaviors. Uresk et al. (2003) compared vegetation structure between foraging sites and
denning sites and found that swift fox den sites had lower grass canopy cover than foraging
sites. Both denning and foraging sites had greater horizontal visual obstructions than that of
random sites. The lack of studies on difference is behavior-specific habitat use might be
because all other studies of swift fox ecology have used very high frequency (VHF) radio
telemetry, which requires the researcher to actively locate foxes and can be logistically
4
difficult, leading to long periods between locations (Kitchen et al. 1999; Kamler et al. 2003a;
Lebsock et al. 2012). Now, Global Positioning System (GPS) collars are available in the
appropriate size for use on swift foxes, and this could allow for the collection of fine scale
temporal and spatial location data needed to detect different behaviors. This in turn provides
opportunities to gain new knowledge regarding how swift foxes use resources (i.e. vegetation
structure, topography) and perceive predation risk based during different behaviors.
The objective of this study was to better understand within home range movement
patterns and resource selection of swift foxes when resources (i.e., prey) are seasonally
limited in northeastern Montana. To achieve this objective, we placed GPS tracking collars
on swift foxes and tracked them during the winter, then used hidden Markov models
(HMMs) to evaluate support for the hypothesis that swift fox movement patterns could be
categorized into three different movement states (i.e., resting, foraging, and travelling).
Then, we developed resource utilization functions (RUFs) to evaluate support for the
hypothesis that predation risk would drive resource use, and that resource use patterns would
differ among movement states. The results of our field study, despite the small sample size,
provide novel insights into the behavioral ecology of swift fox during a critical time of year.
METHODS
Study Area
We conducted research on federal and private lands in an 8500 ha region of northern
Phillips County, Montana near the Canadian border. The dominant vegetation types in the
study area are native short-grass and mixed-grass prairie intermixed with dryland agriculture
on gently rolling terrain and areas of shrubland consisting mostly of sagebrush. There are
few paved roads in the study area; most are gravel or unimproved two-track trails through
5
pastures. The average minimum temperature during the study was -11.7oC, the average snow
depth was 5.8 cm, and elevations ranged from 708 m to 762 m.
Field Methods
We trapped adult swift foxes from October to December 2016 using modified
Tomahawk single door and double door box traps (Tomahawk Live Trap Co., Tomahawk,
WI; Moehrenschlager et al. 2003) baited with roadkill white-tailed jack rabbit (Lepus
townsendii Bachman, 1839) and commercially available beef steak, as well as a
commercially available trapping bait (O’Gorman’s Powder River Paste Bait, Broadus, MT).
We set traps set at sunset, checked them at sunrise, and checked them at midnight as well if
the temperature was <6oC. We did not open traps during inclement weather (rain, sleet,
snow, high wind) and when temperatures were below -20oC. We manually restrained,
weighed, measured, aged, and sexed all swift foxes. We determined age based on tooth wear
and color (Kamler et al. 2003a; Moehrenschlager et al. 2007; Thompson and Gese 2007).
Swift foxes weighing greater than 2kg were ear-tagged and fitted with ~35g GPS collars
(LiteTrack30, Sirtrack, Havelock, New Zealand) representing less than 1.75% of the body
weight. Handling procedures were approved by the Clemson University Institutional Animal
Care and Use Committee (AUP2016-036) and Montana Department of Fish, Wildlife, and
Parks Scientific Collector’s Permit (2016-107).
We programmed collars to attempt a fix every five minutes from 19:00-23:00 every
other night. We chose this time of day because swift foxes have a peak in activity for several
hours after sunset (Brian Cypher, California State University-Stanislaus, personal
communication; Kitchen et al. 1999) and as an attempt to extend battery length due to the
small size of the collars. We selected a five minute fix interval because swift foxes primarily
6
eat small mammals (Moehrenschlager et al. 2007; Thompson and Gese 2007), and foraging
most likely consists of short steps, high turning angles, and short handling time. Therefore, if
the fix interval was too large, this behavior might be missed.
We evaluated the accuracy of GPS collars by placing a test collar on low hanging
barbed wire fences at two locations during the winter. At each test location, we recorded the
location of the test collar with a handheld GPS (Garmin GPSMAP 64), and averaged the
distance between the test collar location and the locations (n=460) from the test collar that
had greater than three satellites, which provides higher accuracy than two dimensional fixes
(Moen et al. 2016). The results of our collar test indicated that the average GPS error for
locations with three or more satellites was 6.7 m (range=0.31-33.4 m).
Analytical Methods
We used Hidden Markov models (HMMs) to evaluate and identify movement states
of swift fox movement. Our HMMs used bivariate data to decompose movement processes
into distinct underlying states based on turning angles and step lengths (Patterson et al. 2008;
Langrock et al. 2012). The sequence of states, identified by distinct random walk pattern, is
assumed to be a Markov chain with a tendency to stay in one state before transitioning to
another. We fit 2-state and 3-state HMMs to swift fox movement data to identify different
movement states. The 2-state model represents the hypothesis that foxes will either be
resting or travelling, whereas the 3-state model represents the hypothesis that foxes will be
resting, foraging, and travelling. Because data were collected every other night, we analyzed
data by nightly movement bursts so that steps between the last location of one night and the
first location of the next night were not included when estimating parameters. We assumed
7
that individuals of either sex had identical movement parameters because data was collected
during the winter, prior to the onset of pup rearing.
We used the following steps to fit the HMMs. First, we calculated the step length and
turning angle for each step, between two consecutive locations. Second, we chose initial
parameters for step length from a zero-inflated gamma distribution, to account for step
lengths of zero, and turning angles from a von Mises distribution to fit the 2-state and 3-state
models (Michelot et al. 2016). In the 2-state model, one state was fit with small step lengths
and large turning angles (state 1), and the second state was fit with large step lengths and
small turning angles (state 2). In the 3-state model, we fit the same states as the 2-state
model, as well as an additional state with intermediate turning angle and shorter movement
steps than the other two models (state 3). State 1 represents when a fox is potentially resting,
state 2 represents when a fox might be travelling, and state 3 represents when a fox might be
foraging. Third, we evaluated support for each model using Akaike’s Information Criteria
(AIC) to identify the top ranked model based on model weights (Burnham and Anderson
2002) and examination of pseudo-residuals (Pohle et al. 2017). Lastly, for each model, we
estimated the most likely movement state of each observed location for each swift fox using
the Viterbi algorithm (Langrock et al. 2012). We used the moveHMM package (Michelot et
al. 2016) for program R (R Core Team 2013) to perform the analysis.
To determine if resource (i.e., topography, distance to roads, and distance to crop
fields) use patterns differed by movement state, we first created 99% utilization distributions
(UDs) using the fixed kernel density estimator (Worton 1989; Seaman and Powell 1996) with
plug-in bandwidth (Gitzen et al. 2006) based on all locations from an individual separately
for each movements state. To estimate UDs, we did not subsample location data because 1)
8
kernel smoothing incorporates spatial autocorrelation (Fieberg 2007; Kie et al. 2010) and 2)
we did not want to decrease the biological relevance of the UDs by removing data points that
might be important to swift foxes (De Solla et al. 1999).
To understand the relationship between swift foxes in differing movement states and
their environment, we developed a priori models based on the hypothesis that predation risk
is the main driver of swift fox resource use (Table 1.1). Thompson and Gese (2007) found
that swift fox density in Colorado decreased with shrub density and prey abundance, whereas
coyote abundance increased in these areas, suggesting that swift foxes use areas that
minimize predation risk at the expense of access to higher prey abundance. The variables we
predicted to be important to how swift fox perceive predation risk were distance to cultivated
crop field (DistCrop), distance to gravel road (DistRoad), and topographic roughness (TRI).
Specifically, we predicted that 1) during the short step length and high turning angle (resting
state), and the intermediate step length and turning angle state (foraging state), that DistRoad
and TRI would have a negative effect whereas DistCrop would have a positive effect, and 2)
during the long step length and small turning angle state (travelling state) that all variables
would have a negative effect. We predicted that DistCrop would have a negative effect
during the travelling state because during the time of the study, crop fields were all harvested
and would be selected for due to high visibility and low vegetative cover. Sovada et al.
(2003) found that during the growing season, wheat fields grow taller than the average height
of a swift fox (30 cm, Kamler et al. 2003a) and inhibit swift fox predator detection, but early
season, fallow or harvested fields were used by swift foxes due to the short vegetation height
(Sovada et al. 2003), which enhances visual predator detection. We predicted that DistRoad
would have a negative effect across all states because areas closer to roads have a lower
9
coyote predation risk (Sasmal et al. 2011). We predicted that TRI would have a negative
effect across all states because more level terrain enhances the viewshed for which foxes to
detect predators (Cameron 1984). The observation that swift fox coyote-specific mortalities
occurred in areas where viewsheds were less than 50m (Russell 2006) supports this
prediction. We calculated distance to cultivated crop and DistRoad in ArcGIS 10.3.1 (ESRI,
Redlands, CA) as the Euclidean distance from each grid cell in the utilization distribution to
the nearest edge of cultivated crop polygon from the 2011 National Landcover Database
(Homer et al. 2015) and primary road line shapefile (Montana State Library 2017). In
program R, we calculated the Terrain Roughness Index across a 30m digital elevation model
for the study area, which is calculated by comparing the height of a cell to the eight
surrounding cells. Values close to zero indicate level terrain and values closer to one indicate
more rugged terrain (Shawn Riley et al. 1999). We screened all covariates for
multicollinearity (r>0.7) and then scaled and centered them to mean=0 and variance=1 prior
to analysis.
We used resource utilization functions (RUFs) to relate intensity of use throughout
each UD to our habitat covariates described above (Marzluff et al. 2004). We used the height
of the UD of each grid cell, standardized to a scale of 0 (no probability of use) to 100
(highest probability of use), then log-transformed the standardized UD values to meet
assumptions of normality (Hooten et al. 2013). We evaluated support for each model (Table
1.1) in a multiple regression modeling framework to identify the top ranked model based on
the evidence ratio and model composition, and did not model average (Cade 2015). We
estimated population-level models for each state by averaging the top model for each
10
individual (Equation 3, Marzluff et al. 2004). We used the ruf package (Handcock 2012) in
program R to perform the analysis.
RESULTS
Between October and December 2016, we captured and collared 11 adult swift foxes
and monitored them between October 2016 and January 2017. Of those 11 swift foxes, due
to collar failure and logistical issues, we were only able to collect data from three adults (one
male and two females) to use in our analysis (Table 1.2). We monitored these three swift
foxes for an average of 53.67 days (range: 31-71), collecting an average of 836.33 locations
per individual (range: 479-1327) from an average of 27.33 nightly movement bouts (range:
16-36).
We found the most support for the 3-state model based on AICc and the fit of pseudo-
residuals, termed as resting, foraging, and travelling movement states (Table 1.3, Figure 1.1).
When using the Viterbi algorithm to identify the most likely state of each location under the
3-state model, most locations were identified as the travelling movement state (43.90%),
followed by foraging state (30.66%), and resting state (25.44%). The average step length for
the travelling state was approximately four times larger than the foraging state, and 40 times
larger than the resting state (Table 1.4). The average turning angle for the travelling and
foraging states were oriented in the forward direction whereas the average turning angle for
the resting state was in the reverse direction of the previous direction of travel (Table 1.4).
The global model (includes all 3 variables) was the most supported model across all
individuals and states in our resource utilization function analysis (Table 1.5). In support of
our predation risk hypothesis, we observed that DistRoad had a negative effect on swift fox
space use among all states and TRI had a negative effect on space use during the travelling
11
state (Table 1.6, Figure 1.2). Contrary to our predictions, TRI had a positive effect on space
use during resting and foraging states. The probability of use increased 7% and 5% for every
one unit increase in topographic roughness during the resting and foraging states,
respectively, and decreased by 5% during the travelling state. Distance to roads caused a
46%, 40%, and 45% decrease in the probability of use during the resting, foraging, and
travelling states, respectively, for every kilometer from road. The probability of use
decreased by 15% and 10% during the resting and foraging states, respectively, and increased
by 10% during the travelling state, for every one kilometer away from crop fields. However,
the standard error for DistCrop parameter estimates overlapped zero and we observed
conflicting support across our three foxes for a positive effect of among states, limiting our
ability to make inference. (Table 1.6, Figure 1.2).
DISCUSSION
Our pioneering use of fine-scale GPS tracking collars on swift foxes, while based on
a small sample size, suggest they switch between three distinct movement behaviors during
winter. While we cannot say with certainty which behaviors swift foxes were displaying
during each movement state, given the difficulties in making direct behavioral observations
of nocturnal movements, we can cautiously interpret the behavior for which each state might
serve as a proxy. The resting state was characterized by short movement distances with high
turning angles (Table 4). In other studies of swift foxes, they have been documented near
dens during the night (Andersen et al. 2003; Lemons et al. 2003), potentially resting between
foraging bouts. The foraging state had a higher average turning angle and shorter average
movement steps than the travelling state. Swift foxes feed primarily on small mammals
during the winter (Kitchen et al. 1999), we would expect foraging movement patterns to be
12
more winding than traveling behavior as swift foxes chase their prey. The travelling state
was characterized by long movement distances and small turning angles, which could be
interpreted as inter-foraging patch or inter-den movement, or traveling the edge of the home
range scent marking (Darden et al. 2008). Indeed swift foxes often follow linear routes while
traveling, such as cattle trails and fence lines (Carbyn 1998), and this could lead to more
directional movement. In additions, by spending most of their time in this long movement
state, they potentially expose themselves to higher predation risk.
Similar to previous studies, we observed that swift foxes used areas close to roads.
Although vehicle collisions can be a significant source of swift fox mortality (Sovada et al.
1998; Kamler et al. 2003a), previous studies have found swift foxes often den close to and
are located closer to roads than expected by random chance (Hines and Case 1991; Pruss
1999; Olson 2000; Harrison 2003; Russell 2006). Proposed mechanisms for this relationship
include availability of carrion and small mammals (Hines and Case 1991; Klausz 1997),
avoidance of coyotes (Russell 2006), and use as a travel corridor (Hines and Case 1991;
Pruss 1999). These mechanisms might explain why swift foxes used areas closer to roads in
each state. During the resting state when a swift fox might have been resting near a den, it
might use areas closer to roads because hunters shoot coyotes from or near roads causing
coyotes to avoid them (Kamler et al. 2003b) or coyotes might be avoiding roads to reduce
mortality (Murray and St Clair 2015); thus, creating an area of lower predation risk for swift
foxes, especially during the winter when coyote pelts are at their highest quality and hunting
pressure mightbe heaviest. During the foraging state, when swift foxes might be foraging,
they might forage near roads because the taller vegetation in roadside right-of-way can
support a higher abundance of small mammals (Klausz 1997) while minimizing predation
13
risk. We observed road killed rabbits and songbirds in roadside ditches that would be
available to swift foxes. We also observed swift foxes using roads during the travelling state,
potentially for easier travel within their home range and at lower predation risk. Finally,
swift fox use of roads in our study area could be particularly important during winter when
vehicle traffic due to natural gas well maintenance, and other needs, kept snow depth
amounts lower on the road than in surrounding pastures, which potentially decreased the
energetic cost of locomotion for swift foxes while travelling.
Consistent with other large-scale studies of swift fox habitat selection, distance to
cultivated crop had both positive and negative influences on space use, but this was the first
study to find differences based on fine scale movement states. Several previous studies on
swift fox habitat use have found that swift foxes avoid agricultural areas and crop fields due
to the fact that growing vegetation was taller than foxes inhibiting their ability to detect
predators (Kamler et al. 2003a; Sasmal et al. 2011). In contrast, Matlack et al. (2000) and
Sovada et al. (2003) compared swift fox ecology and habitat selection in cropland dominated
and rangeland dominated areas between spring/summer and fall/winter and found that in the
cropland areas, swift foxes selected for fallow fields and row crop fields year round. By
contrast, they observed swift foxes in the rangeland area avoided these habitats year-round.
Our study area was rangeland dominated, which makes negative influence of distance to crop
during the resting and foraging states somewhat surprising. However, we found that the
influence of distance to crop was variable at the individual level (Table 1.6), but, this might
be an artifact of our small sample size. The swift fox that had its home range closet to the
crop fields had a negative influence of distance to crop during all states and had 12 GPS
locations in crop fields whereas the other two swift foxes were located farther away from the
14
crop field. One of the swift foxes located further away had a positive influence of distance to
crop on space use during all states, whereas the other swift fox only had a positive influence
during the travelling state. Therefore, there might be a functional response, a change in
relative use of a habitat type relative to its availability (Mysterud and Ims 1998), in swift
foxes use of crop fields. Swift foxes that established home ranges with minimal overlapping
of crop fields, had a positive influence of distance to crop field, whereas swift foxes that
established home ranges with a high percentage of cultivated crop fields within their home
ranges had a negative effect of distance to crop fields. However, we would need data from
the summer to support this hypothesis in our study area. Therefore, future studies should
monitor an increased number of swift foxes across a gradient of cropland abundance to better
illuminate the influence of distance to crop fields on resource use during different movement
states and seasons.
Our study is the first to find differential selection of topographic roughness by swift
foxes dependent on their current movement state. In Wyoming swift foxes have been
observed to prefer areas with slope less than 3% and avoided areas with slopes between 3-9%
(Olson 2000), and in South Dakota, swift foxes preferred level terrain over rugged terrain
(Russell 2006). The hypothesis behind this pattern is that the level terrain provides swift
foxes with a greater viewshed to detect predators, which is why they avoid steep terrain such
as creek drainages and badlands (Cameron 1984). Similarly, in our study, swift foxes likely
used flatter areas during the travelling state to increase predator detection while travelling
between foraging patches or along the perimeter of their home range. Unlike during the
travelling phase, the effect of topographic roughness on swift fox space use was not
consistent across all individuals during the resting and foraging phases; two of three foxes
15
had a positive coefficient. However, the small sample size of our study makes inferring
conclusions across the population difficult. It is unclear why swift foxes might be using
rougher areas during the foraging state, as rougher terrain might inhibit vigilance during
foraging. One possible explanation could be that because swift fox dens are often located
within the core of an individual’s home range, individuals might have a more updated
cognitive map (Powell and Mitchell 2012; Spencer 2012) of the resources and threats in
proximity to dens, where foraging occurred, and therefore can utilize potentially risky areas.
It is important to note that swift foxes in this study rarely traveled into any areas of extremely
steep terrain (TRI >14). Thus, at a larger, landscape scale foxes are likely avoiding rougher
terrain, and future studies should monitor swift foxes across a range of TRI values or
adjacent to steep and rugged terrain to better understand the influence of TRI on space use.
Our study was the first to use GPS collars to study movement ecology of swift foxes
and we encountered several difficulties that should be considered by others considering using
GPS collars on this and similarly sized species. First, at least three of the VHF antennas
broke off the collars making it impossible to locate foxes and remotely download data. We
recovered one collar that was missing its VHF antennas and it appeared that the GPS antenna
was chewed off as well. Second, our objective was to obtain GPS data from foxes for one
year, and to achieve that objective, we had to make a trade off in how long the collars could
be on each night and how many days a week they could collect fixes. Despite selecting a
programing schedule that was believed to last greater than six months, we believe that
possibly three of the collar batteries died before the three-month sampling period. One
possible explanation for this could be that when conditions were extremely harsh (i.e., <-
25oC wind-chill and snowing), swift foxes did not come out of their dens during the time that
16
the collars were programmed to collect fixes. Therefore, collars spent the maximum time
attempting to communicate with satellites for 48 consecutive fixes, which drained the battery.
A second possible explanation could be that in milder conditions, swift foxes did not
consistently leave their dens to begin nightly activity bouts during the time that the collars
were programmed to collect fixes because they might be less restricted to foraging during a
certain period because there is a larger period in which foraging conditions are favorable.
This disconnect between activity and fix schedule also likely contributed to missing nightly
bouts of data that prevented inclusion of an additional fox into the analysis. Indeed, we
obtained data from an additional adult swift fox, but the fix success of that individual was
only 22.88% and thus we did not include it in our analysis. Future studies of fine-scale swift
fox movement ecology using GPS collars might benefit from programming collars to collect
fixes later at night to increase the probability of foxes being above ground, having collars
manufactured with VHF antennas built internally into the collar to avoid losing
communication with the collar, and from more time between GPS fixes to improve the
battery length of the collar.
Overall, this study provided new insights into the behavioral mechanisms that
influence swift foxes resource use, which can help improve our understanding of the ecology
of this species in the northern portion of their range. Although our sample size was small,
this study demonstrated the utility of fine-scale movement data in identifying different
movement states and quantifying how resource selection patterns differ among these states.
The travelling state was the predominant and most wide-ranging state and might reflect the
appropriate spatial scale for land managers to use for habitat conservation as this state was
likely critical to fulfilling energetic needs and avoiding predators during a critical time. For
17
example, conserving pastures and fallow agricultural fields with level to rolling topography
might help foxes meet their foraging needs. In addition, based on their association with
resting and foraging movement states, conserving open pastures and fallow fields with these
characteristics will also provide denning for swift foxes, which is important for enhancing
recruitment and survival. In addition to managers maintaining open pastures on level
topography to conserve swift fox populations, conserving swift foxes and these resources
might benefit farmers as well through reduction in small mammal populations due to swift
fox predation. While technological improvements in GPS collars are needed, we recommend
that future studies of swift fox resource use continue the use of GPS collars to assess if the
resource use patterns illustrated here are a phenomenon unique to winter, or if they occur
through the year.
18
TABLES
Table 1.1. A priori models of resource use, developed from a literature review, predicted to
influence swift fox (Vulpes velox) resource use in northeastern Montana, October 2016-
January 2017. Null model does not include any of the hypothesized variables and the Global
model includes all variables.
Model Number Model
1 Null
2 TRI
3 DistRoad
4 DistCrop
5 DistRoad + DistCrop
6 Global
Note: TRI = topographic roughness index, DistRoad = distance to gravel road, DistCrop =
distance to cultivated crop field.
Table 1.2. Summary of GPS data used in analysis of adult swift fox (Vulpes velox) movement
states in northeastern Montana, October 2016- January 2017.
Fox ID Days Monitored Number of Locations Number of Nightly Bursts
M2 71 1327 36
F9 31 479 16
F27 59 703 30
Average 53.67 836.33 27.33
Table 1.3. Model selection results for swift fox 2-state and 3-state HMMs for adult swift fox
(Vulpes velox) in northeastern Montana, October 2016- January 2017.
Model K Log-L AIC AIC wi
3-states 1 -16069.62 32185.25 0 1.00
2-states 1 -16377.81 32781.61 596.36 0.00
Note: K = number of parameters, Log-L = Log-Likelihood, AIC = difference in AIC value
between top model and other model, wi= Akaike weights
Table 1.4. Estimates of model parameters for the 3-state Hidden Markov model averaged
across three adult swift foxes (Vulpes velox) in northeastern Montana, October 2016- January
2017.
Parameter Sedentary Restricted Travelling
Average Step Length 5.58 57.58 221.91
Average Turning Angle 175.32 -7.45 -1.72
Note: Step length is reported in meters and turning angel is reported in degrees.
19
Table 1.5. Multiple regression resource use models and number of times each model of resource use received the most support
and the average AIC weight developed for adult swift fox (Vulpes velox) in northeastern Montana, October 2016- January
2017.
State Sedentary Restricted Travelling
Model
Number of
Times
Average Akaike
Weight
Number
of Times
Average Akaike
Weight
Number
of Times
Average Akaike
Weight
Global 3 0.537 3 0.575 3 0.772
DistRoad + DistCrop 0 0.374 0 0.348 0 0.172
DistCrop 0 0.086 0 0.077 0 0.032
DistRoad 0 0.001 0 0.000 0 0.013
TRI 0 0.001 0 0.000 0 0.007
Null 0 0.001 0 0.000 0 0.003
Table 1.6. Population-level resource utilization function coefficients (β) and standard error (SE) for adult swift fox (Vulpes
velox) habitat use by movement state in northeastern Montana, October 2016- January 2017, and the number of individuals
with positive (+) or negative (-) coefficients. Coefficients and standard errors were obtained by averaging each covariant
coefficient across all individuals.
Sedentary Restricted Travelling
Number of foxes
Number of foxes
Number of foxes
Resource
Variable β SE + - β SE + - β SE + -
TRI 0.070 0.039 2 1 0.048 0.046 2 1 -0.064 0.008 0 3
Dist Road -0.578 0.258 0 3 -0.495 0.331 1 2 -0.608 0.283 0 3
Dist Crop -0.248 0.362 1 2 -0.153 0.398 1 2 0.131 0.313 2 1
20
FIGURES
Figure 1.1. Example movement trajectory of a single adult swift fox (Vulpes velox)
during one nightly four-hour movement bout in January 2017. Movement states were
determined using the Viterbi algorithm and different symbols indicate the most likely
state that was assigned to each location: X = sedentary state, square = restricted state, and
circle = travelling state.
21
A B
C
Figure 2. 1.Influence of topographic roughness (A), distance to road (B), and distance to crop (C) on the relative probability of
use by three swift foxes (Vulpes velox) in northeastern Montana, October 2016- January 2017, during the resting, foraging, and
the travelling states.
22
CHAPTER TWO
HOME RANGE SIZE AND RESOURCE USE BY SWIFT FOXES IN
NORTHEASTERN MONTANA
The swift fox (Vulpes velox) is a small canid, endemic to the short and mixed-
grass prairies of North America. Once abundant throughout the Great Plains, populations
began to decline in the late 1800s due to rodent and predator control programs and the
conversion of prairie to cultivated crop fields (Carbyn 1998). As a result, the species is
currently recognized as a species of conservation concern over much of its range (Dowd
Stukel 2011). In the Northern Great Plains, swift foxes were extirpated from the region
by the mid-1900s (Sovada et al. 2009) and the swift fox was listed as endangered in
Canada in 1978. There have been three reintroductions in Montana and Canada and four
in South Dakota since the 1980s that have established regional populations (Sasmal et al.
2015). Despite over 30 years elapsing since these reintroductions, there is still a large
range gap between the swift fox population along the Montana and Canada border and
the population in northeastern Wyoming and northwestern South Dakota (Alexander et
al. 2016).
Determining how much space a species needs to meet its life history requirements
and where suitable habitat is located are essential aspects of creating sound strategies for
enhancing population connectivity (Güthlin et al. 2011; Magg et al. 2016). Many animals
restrict their movement to certain areas instead of wandering nomadically (Burt 1943).
These home ranges, defined by Powell and Mitchell (2012) as “that part of an animal’s
cognitive map that it chooses to keep up-to-date with the status of resources (including
23
food, potential mates, safe sites, and so forth) and where it is willing to go to meet its
requirements (even though it may not go to all such places),” are important areas to
delineate to better understand a species’ ecology. Previous estimates of swift fox home
range size show that home range sizes can be quite variable depending on geographic
location. The average home range sizes in Colorado were estimated to be 4.2-7.6 km2
(Kitchen et al. 1999; Lebsock et al. 2012) whereas home ranges in Nebraska and Canada
were estimated to be approximately 32.0 km2 (Hines and Case 1991; Moehrenschlager et
al. 2007), suggesting that home range size might vary by latitude (Table 2.1). However,
there are only a few studies of home range size from the northern range of swift foxes
(Hines and Case 1991; Moehrenschlager et al. 2007; Mitchell 2018). Additional studies
on swift fox home range size in the northern Great Plains will provide managers with a
better understanding of the scale of swift fox resource use in this portion of its range.
Contrasting patterns have also been observed in the types of habitats swift fox
select for across their range. Most of the past studies of second, third, and fourth order
habitat selection have found that swift foxes are grassland specialists that prefer short and
mixed-grass prairie habitats where grass is less than 30 cm tall, small mammals are
abundant, terrain is flat, and shrub densities are low (Hines and Case 1991; Kitchen et al.
1999; Kamler et al. 2003; Sovada et al. 2003; Russell 2006; Thompson and Gese 2007;
Sasmal et al. 2011). These studies also found that they generally avoid agricultural
fields, areas of grass greater than 30 cm tall, steep terrain, and areas of high shrub density
(Harrison and Schmitt 2003; Kamler al. 2003; Russell 2006; Thompson and Gese 2007;
Sasmal et al. 2011). However, research in Kansas, which compared swift fox ecology in
24
agricultural versus rangeland dominated areas, suggests that swift foxes might be tolerant
of agricultural fields and utilize them in some conditions (Sovada et al. 2003). There is
an increasing anthropogenic footprint on the landscape in the northern portion of their
range in the form of cultivated crop fields and oil and gas development that might
provide challenges for swift fox conservation, especially connecting the population on the
Montana-Canada border and those in South Dakota and Wyoming (MTFWP 2017).
Providing managers with more information on swift fox resource use in this region will
help to facilitate connectivity among disjunct populations.
In this study we addressed two main objectives: 1) to estimate the home range
size of swift foxes in the Great Plains of northeastern Montana, and 2) to evaluate
multiple competing hypotheses of swift fox resource use (environmental versus
anthropogenic factors). Further, this paper presents the first analysis of swift fox home
range size and resource selection based on data from Global Positioning System (GPS)
collars. Resulting data allow for finer-scale investigations into animal movement
behavior and resource utilization, a potentially important advancement in the
understanding of the spatial ecology of swift foxes and other small carnivores.
MATERIALS AND METHODS
Study area-We selected our study area to overlap the current southern edge of
known swift fox distribution in northeastern Montana (Figure 2.1A). At least 900 swift
foxes were reintroduced into Canada, just north of this region in between 1983 and 1997
(Moehrenschlager and Moehrenschlager 2018), and based on subsequent monitoring
through 2015, have largely not expanded south into the US beyond US Route 2 and the
25
Milk River (Schwalm and Bly 2017). This lack of range expansion is a concern of
regional managers and conservation groups. Specifically, our study area included
northern Blaine, Phillips, and Valley counties (Figure 2.1B), totaling 17,991 km2. A
recent survey of the northern swift fox population determined that there were 3.63 swift
foxes per 100 km2 in 2014-2015 (Moehrenschlager and Moehrenschlager 2018). The
dominant vegetation types in the study area were native short-grass and mixed-grass
prairie with areas of dryland agriculture, consisting mostly of wheat fields, and shrubland
consisting mostly of sagebrush (Artemisia spp.). Irrigated agricultural fields were
predominant along the southern boundary of the study area adjacent to US route 2 and the
Milk River. There were few paved roads in the study area; most roads are gravel and
unimproved two-track trails through pastures. Topography consisted mostly of level to
rolling terrain with some steeper coulees and elevations ranged from 629 m to 1068 m.
The climate of the study area was arid with the average annual precipitation ranging from
19 to 52 cm and average monthly temperature ranging from -1.8oC to 13.9oC
(Zimmerman 1998).
Capture and monitoring-We trapped swift foxes from October to December in
2016 and 2017 using Tomahawk (Tomahawk Live Trap Co., Tomahawk, Wisconsin,
USA) single door and double door box traps modified following Moehrenschlager et al.
(2003). We baited traps with roadkill white-tailed jack rabbit (Lepus townsendii), mule
deer (Odocoileus hemionus), commercially available beef steak, as well as a
commercially available trapping bait (Powder River, Minnesota Trapline Products,
Pennock, Minnesota, USA). We opened traps at sunset and checked and closed them at
26
sunrise, and when night time temperatures were less than 6oC, we checked traps at
midnight as well. We weighed, measured, aged, and sexed swift foxes without the use of
chemical immobilization (Kamler et al. 2003a; Moehrenschlager et al. 2007; Thompson
and Gese 2007). We classified swift foxes as adult or juvenile based on tooth wear and
color (Ausband and Foresman 2007a). We collared swift foxes weighing greater than
2kg with ~35g GPS collars (LiteTrack30, Sirtrack, Havelock, New Zealand), which
weighed 1.75% or less of a swift fox’ body weight. Handling procedures followed
American Society of Mammalogists guidelines (Sikes 2016) and were approved by the
Clemson University Institutional Animal Care and Use Committee (AUP2016-036) and
Montana Department of Fish, Wildlife, and Parks Scientific Collector’s Permit (2016-
107).
We programmed collars to attempt a GPS fix every two hours in October 2016-
March 2017. In our second field season of October 2017-May 2018, to extend the battery
life of the collars, we programmed collars to attempt a GPS fix every five hours for all
individuals. Given the differences in fix rate between years of the study, we conducted t-
tests to determine if there was a difference in the average number of days that swift foxes
were monitored, and the average number of locations collected per swift fox between
2016-2017 and 2017-2018. We tested the accuracy of GPS collars by hanging two
collars on strands of barbed wire approximately 45 cm off the ground. We marked the
location of the collar with a handheld GPS (Garmin GPSMAP 64, Olathe, Kansas) and
averaged the distance between the test collar location and the GPS locations from the
collar. We used locations that had greater than three satellites, which provides higher
27
accuracy than two dimensional fixes from three or fewer satellites (Moen et al. 2016).
The average GPS error for collar locations with three or more satellites (n=460) was 6.7
m (range=0.31-33.4 m).
Home range size- We monitored swift foxes for an average of 110 days (range=
31-225) and estimated the home range size for each swift fox in which we collected at
least 30 locations (Seaman et al. 1999), which we considered to be representative of each
individual annual home range. We generated 99% utilization distributions (UDs) using
the fixed kernel density estimator (Worton 1989; Seaman & Powell 1996) with plug-in
bandwidth (Gitzen et al. 2006) for each swift fox with package ks in program R (R Core
Team 2018). We estimated the home range size of each swift fox by calculating the area
within the 99% volume contour, to be comparable with home range estimates from
nearby study by Moehrenschlager et al. (2007). We used the Shapiro-Wilk test to test the
hypothesis that home range sizes were normally distributed. Home range sizes were
skewed to the left and therefore we rejected the normality hypothesis (W=0.88, p=0.009).
Consequently, we log-transformed the home range sizes and log-transformed sizes were
considered normally distributed (W=0.98, p=0.95), and we used log-transformed sizes in
further analysis. We conducted three-way ANOVA to determine if there was a difference
in the average 99% home range size due to field season, age class, and sex. Significant
effects were further investigated with Tukey’s honestly significant difference (HSD)
procedure. We also estimated the home range size of each swift fox by calculating area
within the 95% volume contour to be comparable with other studies (Table 2.1).
28
Creating resource layers-We identified nine environmental resources (Table 2.2)
from the literature that we hypothesized would influence how swift foxes used the
landscape. We predicted that loam soils would have a positive influence on space use
because they are soft soils for digging dens and that others soil types would have a
negative influence (Hines 1980; Olson 2000), with clay loam serving as the reference
category because it was the most widespread soil type. We created a map of soil types
from the U.S. Department of Agriculture (USDA) Natural Resources Conservation
Service, Soil Survey Geographic soils database
(https://websoilsurvey.sc.egov.usda.gov/App/WebSoilSurvey.aspx). We classified soil
vector data into six types (clay, clay loam, loam, sand, silt, and other [a combination of
plant material, peat, and bedrock]) based on the USDA soil texture classification (Soil
Science Division Staff 2017) following the methods outlined in Lahatte and Pradhan
(2016). We predicted that the proportion of shrub cover would negatively influence swift
fox space use because they would avoid these areas due to high predation risk from
coyotes (Canis latrans) and decreased visual predator detection (Harrison and Schmitt
2003; Thompson and Gese 2007). We used raster data of shrub cover, quantified as the
proportion of shrub canopy in a 30 x 30m cell using the National Land Cover Database
(NLCD; Xian et al. 2015). We predicted that proportion of grassland would have a
positive influence on resource use as proportion of grassland has been found to be
important in a past study of swift fox occupancy (Martin et al. 2007). We calculated the
proportion of grassland landcover type from the NLCD within a 1 km radius circular
moving window in ArcGIS 10.3.1 (ESRI, Redlands, California). We predicted that
29
topographic roughness would have a negative influence on space use because rough
topography can inhibit swift fox visual predator detection (Russell 2006). We estimated
topographic roughness of the study area by calculating the Terrain Roughness Index,
across a 30m digital elevation model, which compares the differences between the
altitude of a cell and the eight surrounding cells. Values close to zero indicate level
terrain and larger values indicate more rugged terrain (Shawn Riley et al. 1999). We
predicted that vegetation productivity would have a negative effect on space use because
larger canids use these areas (such as shrublands and crop fields) and outcompete swift
foxes in them (Phillips et al. 2003; Nelson et al. 2007; Thompson and Gese 2007). To
account for productivity, and indirectly large canid distribution, in our analysis, we used
raster data of NDVI, which is a measure of the amount of visible and infrared light
reflected into space by vegetation such that high values indicate more vegetative growth
and lower values indicate sparse vegetation or senescence. We obtained data from
NASA’s Land Process Distributed Active Archive Center and calculated NDVI as the
average of the maximum value between May and September during 2017 and 2018.
We also identified four anthropogenic features from our literature review that we
hypothesized would influence swift fox resource use (Table 2.1). We predicted that
distance to cultivated crop fields would have a negative effect because crops were
harvested or fallow during our study and thus available to swift foxes (Sovada et al.
2003). We predicted that distance to paved and nonpaved roads would negatively
influence space use because swift foxes might use these areas as travel corridors and to
avoid coyotes (Hines and Case 1991, Clevenger et al. 2010). We predicted that distance
30
to active gas wells would have a negative influence on swift fox space use because the
human activity associated with their maintenance would deter coyotes and the soil around
the wells might be softer than the surrounding landscape. We calculated distance to
cultivated crop fields, paved roads, nonpaved roads, and gas wells in ArcGIS 10.3.1 as
the Euclidean distance from the edge of cultivated crop field from the 2011 NLCD
(Homer et al. 2015), distance from paved and nonpaved roads (Montana State Library,
Downloaded April 2017, http://geoinfo.msl.mt.gov/data), and active gas well sites
(Montana Department of Natural Resources and Conservation Board of Oil and Gas
Conservation, Downloaded September 2018, http://dnrc.mt.gov/divisions/board-of-oil-
and-gas-conservation).
Resource Use-We used resource utilization functions (RUFs; Marzluff et al.
2004) to evaluate resource use of each swift fox within the home range (third-order scale
sensu Johnson 1980). Resource utilization functions treat resource use as a continuous
process rather than a binary process (i.e., used or not used), and use a multiple regression
framework to compare differential space use to environmental features while accounting
for spatial autocorrelation (Marzluff et al. 2004; Kertson and Marzluff 2011). For each
swift fox, we created a grid of points for each UD and rescaled values to a scale of 0
(lowest probability of use) to 100 (highest probability of use), and then log-transformed
the UD values to meet assumptions of normality (Hooten et al. 2013). At each point, we
extracted the values of the nine underlying covariate layers for each swift fox. We used
the log-transformed UD values as the response variable in the multiple regression
analysis (Marzluff et al. 2004). Prior to analysis, we screened all covariates for
31
multicollinearity (r>0.7) and scaled them to mean=0 and centered them to variance=1.
We used the ruf package (Handcock 2012) in program R (R Core Team 2013) to perform
the analysis.
We developed 16 a priori models based on competing hypothesis of how swift
foxes use resources on the landscape (Table 2.3). Following from our predictions above,
we hypothesized that swift fox resource use would be determined by environmental
resources, anthropogenic resources, both or none of our covariates (i.e. null model). We
evaluated support for each model using Akaike’s Information Criteria adjusted for small
sample size (AICc; Burnham & Anderson 2002) to identify the top ranked model based
on Akaike weights (wi), with a top model having >70% of the model weight. Based on
the top ranked model, we used standardized beta-coefficients to assess inter-individual
variability in resource use patterns. In addition, we developed a population-level RUF by
averaging beta-coefficients from top models across all individuals and calculating the
associated variance (Marzluff et al. 2004). We considered variables with 95% confidence
intervals that did not overlap zero to influence to resource use.
We evaluated the predictive performance of the population-level model using k-
fold cross-validation (Boyce et al. 2002) and data we collected using trail cameras. We
randomly designated 20% of the UD cells of an individual swift fox as the testing set and
estimated the RUF coefficients again using the remaining 80% of UD cells (training set).
We repeated this process 10 times to create 10 sets of testing and training data sets. We
then used the RUF coefficients from the training data to estimate the UD values of the
testing data set. We calculated the Pearson’s correlation value among all iterations of the
32
actual UD values of the testing set with the predicted UD values of the training sets. We
then averaged the individual correlation value across all swift foxes to create a population
level correlation value. We expected the models with a strong predictive ability to have a
high correlation value. In addition, we deployed trail cameras across the study area based
on the proportion of cropland on the landscape (Appendix A). We then determined the
number of cameras in 0.10 incremental probability of use bins and compared the
proportion of swift fox detections in each bin to the expected proportion of detections.
We performed a chi-squared goodness of fit test to determine if observed proportions
differed from expected proportions.
RESULTS
Capture and Monitoring-We captured 46 swift foxes in northeastern Montana during
2016 and 2017 at five locations throughout the study area (Figure 2.1). We obtained at
least 30 locations from 22 individuals (13 males, and 9 females) during October through
March 2016-2017 and October 2017-May 2018 for use in our analysis. One male was
captured in both 2016 as a juvenile and 2017 as an adult. We treated data from each year
independently as 183 days elapsed between the last location from 2016-2017 and the first
location of 2017-2018; additionally, environmental conditions and areas of use varied
between years. On average, we collected 267 locations (range= 35-550) per swift fox and
there was no statistical difference between the number of locations collected per swift fox
between 2016-2017 and 2017-2018 (t(21) =1.48, p=0.16) despite the fact that swift foxes
in 2017-2018 were monitored for a statistically significant greater number of days (t(21) =-
2.10, p=0.048) . The average GPS fix success was 50% (range= 26-80%), and we
33
attribute most of the GPS fix failure to swift foxes being in dens when collars were
attempting fixes as test collars had >93% fix success.
Home Range Size- We observed a significant effect of year (F(1,19)=4.51,
p=0.047) on home range size, and that sex (F(1,19)=1.45, p=0.24) and stage
(F(1,19)=0.36, p=0.56) were insignificant. Results from Tukey’s HSD procedure
indicated that home range sizes were larger in 2017-2018, but not significantly so
(p=0.06). Therefore, we pooled sexes, age classes, and years together to generate an
average 99% fixed kernel home range size of 42.0 km2 (SE=4.7) and average 95% fixed
kernel home range size of 29.4 km2 (SE=3.1).
Resource Use-The global model received the most support (average wi=0.985)
across all swift foxes and therefore our population level model contained all covariates.
Of the nine variables included in the population level RUF, four were important to
resource use: topographic roughness index, proportion grassland, distance from nonpaved
roads, and distance to well. In addition, although the 95% confidence intervals of NDVI
overlapped zero (-0.110, 0.005), we believe it to be ecologically influential. These five
variables also had less interindividual variation (>70% of swift foxes) in the direction of
influence than the less important variables (~50%, Table 4). Proportion grassland had the
largest influence on resource use (βPG= 0.154) and the relative probability of use
increased by 3.3% for every one percent increase in percent grasslands (Figure 2.2a).
The relative probability of use decreased by 7.9% and 7.4% for every kilometer away
from nonpaved roads (Figure 2.2b) and gas well sites (Figure 2.2c), respectively. The
relative probability of use decreased by 3.0% and 11.3% for every one unit increase in
34
topographic roughness (Figure 2.2d) and for every 0.05 unit increase in NDVI,
respectively (Figure 2.2e).
The model cross-validation results suggest that our population-level global model
had weak predictive ability (r=0.33). However, our camera trapping results suggest that
the population model was accurate in predicting where swift foxes might occur. Swift
fox detections were observed in the frequency expected by the distribution of cameras (X2
(4, N=17)=3.42 , p>0.05) and we only detected foxes at cameras were probability of use
was greater than 0.7, with 88% occurring in areas greater than 0.8 (Table A1). Therefore,
we consider areas of Montana with a predicted use value greater than 0.8, to be the most
suitable for swift foxes (Figure 3A.1)
DISCUSSION
We found that the average home range size of swift fox in northeastern Montana
is one of the largest recorded across their entire range. Our results are consistent with
other swift fox studies that have found that home ranges are larger in the northern portion
of the swift fox range than in the southern portion (Table 2.1). One possible explanation
for why home range size is larger in the Northern Great Plains is that prey abundance is
lower than in the southern portion of their range. Prey abundance is believed to be the
primary driver of intraspecific variation in animal home range size (reviewed by
Mcloughlin et al. 2000), and this was the hypothesis proposed by Moehrenschlager et al.
(2007) for why swift fox home ranges were larger in Canada. This hypothesis is
supported by White & Garrott (1997), who in review, found an inverse relationship
between swift fox and kit fox (Vulpes macrotis) home range size with lagomorph density.
35
Given that the majority of our data came from the dispersal and breeding seasons
(December-April), when swift fox home ranges have been observed to be largest (Hines
1980; Kitchen et al. 1999; Lebsock et al. 2012), it is possible that our results are biased
towards a high estimate of annual home range size. Regardless, such large spatial
requirements for swift foxes in this region have important implications for the species
recovery. First, it suggests that managers need to ensure that large tracts of preferred
habitat are available to swift foxes. Second, while more research is needed on the degree
of territoriality among swift foxes, it suggests that carrying capacity of the region might
be lower than in southern populations.
Our results indicate that the proportion of grassland is the most important factor
driving swift fox resource use, which might have important implications as the Northern
Great Plains is becoming increasingly fragmented (Comer et al. 2018). Swift foxes used
areas primarily made up of grasslands, which in our study area were mostly used for
cattle ranching, rather than in areas dominated by row crop agriculture. In our study area
of northeastern Montana, the size and distribution of landowner property is primarily
based on one square mile sections (2.56 km2), and ownership activities might include
multiple sections of open range for cattle, irrigated and dryland cultivated crop fields.
The Conservation Reserve Program (CRP) in particular has been essential for the
conservation of native grasslands, providing habitat for grassland birds and other native
prairie species in areas dominated by cultivated crops (Niemuth et al. 2007). There is a
growing concern among managers and environmental organizations over the large
amount of CRP contracts that will be ending because of fear that farmers and ranchers
36
might convert their CRP fields to row-crop agriculture (Lark et al. 2016; Hendricks and
Er 2018) and the impact that might have on grassland birds (Niemuth et al. 2007).
Similarly, our findings suggest that swift foxes might be negatively impacted by a large
conversion of CRP fields to row-crop agriculture. However, at a fine spatiotemporal
scale, swift foxes in this region might at least temporarily use harvested row crop fields
(Chapter 1). Thus, the impact of conversion of native prairie to row-crop agriculture
might not only be felt at a local scale, but at the landscape scale given the large average
home range size of swift foxes in this area.
This is the first analysis using telemetry data of assessing how known swift foxes
move relative to natural gas wells, and our results suggest that swift foxes might not
actively avoid natural gas development. This is in contrast to the effects of natural gas
development on other species in the region such as greater sage-grouse (Centrocercus
urophasianus; Holloran et al. 2015; Green et al. 2017), elk (Cervus elaphus; Buchanan et
al. 2014), mule deer (Odocoileus hemionus; Sawyer et al. 2006), and pronghorn
(Antilocapra americana; Beckmann et al. 2012). While we did not collect data on the
distribution of coyotes in the study area, we hypothesize that the human activity around
gas wells could have acted as a “human shield” (Berger 2007; Kuijper et al. 2015; Moll et
al. 2018), where coyotes avoid these areas creating areas of low predation risk for swift
foxes. A potentially confounding, but not mutually exclusive, hypothesis is that swift
foxes used areas closer to wells because wells were built on areas of flat topography, and
therefore, swift foxes might be selecting for this level topography rather than the gas
wells themselves. However, we did not find a high correlation between topographic
37
roughness and distance to well, and if topographic roughness was the main driver, we
would expect to find no relationship between distance to gas well and use. Finally, it
could be that gas wells in our study area are not at a high enough density to have a
negative impact on swift fox resource use. Future research should investigate the effects
of human activity near gas wells on swift fox and coyote movements.
Consistent with previous swift fox studies, we found that swift foxes used areas
closer to nonpaved roads, which suggests that nonpaved roads do not act as barriers for
movement. Swift foxes might use areas on and adjacent to roads for three reasons: 1) the
availability of roadkill and small mammals (Hines & Case 1991; Klausz 1997); 2) use as
a travel corridor (Hines & Case 1991; Pruss 1999; Nevison 2017); and 3) avoidance of
coyotes (Kamler et al. 2003; Nevison 2017). While we did not quantify carrion amounts
on roads, we frequently observed roadkill birds, snakes, lagomorphs, and Richardson’s
ground squirrels (Urococitellus richardsonii) that would be available for swift foxes to
scavenge. We occasionally observed foxes running and trotting on roads while
conducting radio telemetry monitoring, potentially seeking out carrion or using the
elevated roads to enhance visual detection of predators. We did not quantify coyote
space use in this study, although similar to the gas well development discussed above,
roads could act as a “human shield” where coyotes avoid these areas (Kamler et al. 2003)
due to potential killing by humans. By contrast, swift foxes collared in our study did not
select for areas adjacent to or overlapping paved roads, which supports our original
prediction that US Route 2 could be acting as a barrier to swift fox movement and
dispersal. However, this could have been an artifact of the low paved road density in our
38
study area and our failure to collar foxes directly adjacent to paved roads, as research
from Badlands National Park indicated that swift foxes selected dens closer to roads and
were observed traveling on paved roads and crossing interstate highways (Clevenger et
al. 2010; Nevison 2017). Future studies should attempt to collar foxes adjacent to paved
roads to better understand how roads and traffic volume influence space use, dispersal,
and connectivity of foxes across the landscape.
Topographic roughness has been found to be a strong predictor of swift fox
occurrence and resource use in previous studies, however, it only had marginal influence
in this study. Swift foxes prefer to use areas of level to rolling topography and avoid
steep areas (Loy 1981; Olson 2000; Russell 2006), likely in an attempt to enhance
predator detection (Cameron 1984). In accordance with previous studies, we found that
topographic roughness had a negative influence on space use. However, the effect of
topographic roughness was small (βTRI= -0.055). We believe that there might be two,
non-mutually exclusive explanations for why the effect of topographic roughness was
small. First, to enhance our ability to catch a sufficient number of individuals, we
trapped areas that we believed to be good swift fox habitat that were not near steep
coulees or badlands areas. Secondly, it is possible that because we only investigated
resource use within the home range (i.e, third-order selection), swift foxes might have
selected the location of their home ranges away from the roughest topography of the area
(e.g. second-order selection).
In accordance with our predictions, swift fox space use was negatively influenced
by areas with higher primary productivity where inter-specific competition and predation
39
pressure are likely highest. In our study area, high NDVI values primarily were
associated with plains cottonwood (Populus deltoides) forests and crop fields. Past
studies have found that swift foxes avoid forests as well as crop fields potentially because
these areas inhibit predator detection (Kamler et al. 2003; Sasmal et al. 2011). Swift
foxes might also avoid crop fields because of competition with red foxes (Vulpes vulpes)
and coyotes. Red foxes in the region are the result of eastward range expansion by
nonnative red foxes, which arrived during the 1960s (Kamler and Ballard 2002). These
red foxes, being of European origin, are thought to be better adapted to anthropogenically
impacted environments such as agricultural areas (Kamler and Ballard 2002). Indeed, in
a concurrent camera trapping study (Appendix A), red foxes in our study area were most
frequently detected near areas with a high proportion of cultivated crop fields
(unpublished data). Moreover, previous research found that resident coyotes selected
farmlands in the summer, but native prairie in the winter (Kamler et al. 2005). Future
studies should focus on determining how the spatial overlap among swift foxes, red
foxes, and coyotes changes annually relative to the crop growing and harvest cycle.
Our research improved the understanding of the spatial and resource requirements
of swift foxes and provides important information for long-term management strategies
to improve population connectivity of this species. Our predictive map indicates that
there is likely suitable swift fox habitat between the northern population and the
population in southeastern Montana (Figure A3.1). Swift fox conservation in the
Northern Great Plains might be particularly difficult because of the large spatial
requirements of swift foxes in this region, likely requiring wildlife managers to work
40
across individual property boundaries. Therefore, we encourage wildlife managers and
conservation groups to work with local ranchers to maintain their pastures as native
prairie toward the goal of maintaining large tracts of intact grassland that is likely to
support natural range expansion. In this vein, swift foxes can be added to a list of species
in the Northern Great Plains, along with pronghorn (Jakes et al. 2018) and sage grouse
(Tack et al. 2012), where conservation success will likely require the creation and
maintenance of large native grassland north-south corridors that allow these species to
migrate and disperse.
41
TABLE
Table 2.1. Geographic location, home range estimator used in study, temporal scale of home range estimates, sample size used
to estimate home range size, and average home range size (km2) and SE of swift foxes in North America. Locations in the top
part of the table are from the northern portion (>42oN) of the range and locations in the bottom part of the table are from the
southern portion (<42oN) and ordered from north to south.
Location Home range estimator Temporal Scale Sample
Size HR Size Citation
Canada 99% fixed kernel density Annual 47 31.9 +/- 4.8 Moehrenschlager et al. 2003
Montana 99% fixed kernel density Annual 23 42.0 +/- 4.7 This Study
Montana 95% fixed kernel density Annual 23 29.4 +/- 3.1 This Study
South Dakota 95% kernel density Annual 24 55.4 +/- 5.8 Mitchell 2018
Nebraska 100% minimum convex polygon Annual 7 32.3 +/- 9.8 Hines and Case 1991
Wyoming 95% adaptive kernel density Annual 10 11.7 +/- 1.7 Pechacek et al. 2000
NE Colorado 95% fixed kernel density Annual 13 4.2 +/- 0.8 Lebsock et al. 2012
Kansas 95% adaptive kernel density Breeding/Pup-rearing 21 15.9 +/- 1.6 Sovada et al. 2003
SE Colorado 95% adaptive kernel density Annual 73 7.6 +/- 0.5 Kitchen et al. 1999
Texas 95% adaptive kernel density Annual 17 11.7 +/- 1.0 Kamler et al. 2003
New Mexico 95% adaptive kernel density Annual 6 21.9* Harrison 2003
*No SE reported.
42
Table 2.2. Variables predicted to influence resource use by swift foxes in northeastern Montana during 2016-2018 with their
abbreviation, description, units, prediction, range, and supporting citation.
Resource
Variable Description Prediction Range Citation
Soil Type* Soil types based on the USDA Texture Triangle Hines 1980
Clay B<0
Loam B>0
Sand B<0
Silt B<0
Other Combination of plat material, peat, bedrock B<0
Shrub Percent of shrub canopy in each 30 x 30m raster cell (%) B<0 0-72 Thompson and Gese 2007
TRI Surface roughness from level - rough B<0 0-40 Russell 2006
NDVI Normalized difference vegetation index B<0 0.2-0.74 Thompson and Gese 2007
PG Percent of cells as grassland in a 1 km circular moving window (%) B>0 0-100 Martin et al. 2007
Paved Distance to nearest paved road (m) B<0 0-6,825 Nevison 2017
NonPaved Distance to nearest gravel road (m) B<0 0-6,826 Hines and Case 1991
DistCrop Distance to nearest cultivated crop edge (m) B<0 0-5,544 Sovada et al. 2003
DistWell Distance to nearest active natural gas well (m) B<0 0-33,985 Moll et al. 2018
*Clay loam was the reference category
43
Table 2.3. A priori models developed from competing hypotheses for swift fox resource
use in northeastern Montana during 2016-2018.
Hypothesis Model
No factors Null
Environmental TRI
NDVI
PG
TRI + NDVI
TRI + PG
Shrub + TRI + NDVI
Soil Type + Shrub + TRI + NDVI + PG
Anthropogenic Paved + NonPaved
DistCrop
Paved + NonPaved + DistCrop
DistWell + DistCrop
Paved + NonPaved + DistCrop + DistWell
Sub global TRI + PG + Paved + NonPaved + DistCrop
Soil Type+ Shrub + NDVI + DistWell
Global Soil Type + Shrub + TRI + NDVI + PG + Paved + NonPaved +
DistCrop + DistWell
44
Table 2.4. Population-level resource use coefficients, variance, and 95% confidence
intervals for the top model of swift foxes in northeastern Montana during 2016-2018. We
counted the number of swift foxes with positive or negative values for each coefficient.
Number of foxes
Variable β SE Lower
CI
Upper
CI + -
Intercept 1.062 0.339 0.723 1.401 20 3
Clay -0.022 0.044 -0.066 0.022 9 14
Loam -0.053 0.061 -0.114 0.008 5 16
Sand -0.030 0.038 -0.067 0.008 7 14
Silt -0.046 0.064 -0.110 0.018 9 14
Other -0.033 0.036 -0.069 0.003 10 13
Shrub -0.018 0.050 -0.068 0.031 11 12
TRI -0.055 0.037 -0.092 -0.019 7 16
NDVI -0.053 0.058 -0.110 0.005 4 19
PG 0.154 0.075 0.079 0.229 20 3
Paved -0.023 0.187 -0.209 0.164 7 7
NonPaved -0.104 0.092 -0.196 -0.011 9 12
DistCrop -0.045 0.248 -0.293 0.203 13 10
DistWell -0.108 0.082 -0.190 -0.026 5 17
45
FIGURES
Figure 2.1. A) Swift fox distribution and study area in Montana where we estimated swift
fox home range size and resource use during 2016-2018, and B) Areas within the study
area where we trapped six swift foxes (a), four swift foxes (b), three swift foxes (c),
twenty-two swift foxes (d), and thirteen swift foxes (e) in 2016-2018.
46
Figure 2.2. Relative probability of use curves for significant resource variables in
resource utilization functions including a) percent grassland, b) distance from natural gas
well, c) distance from nonpaved road, d) topographic roughness, and e) NDVI for swift
foxes in northeastern Montana, 2016-2018.
47
CHAPTER THREE
SWIFT FOX SURVIVAL AND REPRODUCTIVE RATES IN NORTHEASTERN
MONTANA: IMPLICATIONS FOR POPULATION VIABILITY AND EXPANSION
INTRODUCTION
Understanding how survival and reproduction influence population growth and
expansion is critical for the management of recovering populations. Initially, the
objective of a reintroduction or recovery program is to maintain a positive population
growth rate during the establishment and growth phases (Seddon and Armstrong 2016).
Once the population approaches carrying capacity, management focus might turn to
regulation and persistence (Seddon and Armstrong 2016). The manner in which a
population persists and then begins to recolonize its range is mediated by the
environmental factors that influence survival, reproduction, and dispersal (Lubina and
Levin 1988; Swenson et al. 1998). Studying individuals at the expansion front can
illuminate the factors that enhance or inhibit range expansion (Swenson et al. 1998;
Jerina and Adamic 2008; Urban et al. 2008).
In the Northern Great Plains (NGP), several carnivores including the swift fox
(Vulpes velox), black-footed ferret (Mustela nigripes), mountain lion (Puma concolor),
gray wolf (Canis lupus), and grizzly bear (Ursus arctos) have gone through severe
population declines since the 1800s (Flores 2017). Recovery of these carnivores has
come from natural recolonization as well as reintroduction programs. Mountain lions
(Kunkel et al. 2012), gray wolves (AP 2019), and grizzly bears (Puckett 2013) have
slowly begun to expand into the NGP. Black-footed ferrets were first reintroduced in
Montana and South Dakota in 1994 (Jachowski and Lockhart 2009), and swift foxes were
48
reintroduced to southern Canada in 1983 (Moehrenschlager and Lloyd 2016). However,
the recovery of these species is still limited in the NGP of Montana where one of the
largest intact native grasslands persists (Flores, 2017). In this region, grizzly bear and
wolf expansion from the Rocky Mountains is still limited to isolated events, and
mountain lions, while increasingly encountered, are likely harvested at an unsustainably
high rate creating a population sink (Kunkel et al. 2012). Black-footed ferrets have
recently been extirpated from their core reintroduction site despite the release of over 255
individuals over 25 years of restoration efforts (R. Matchett, US Fish and Wildlife
Service, personal comm.). Swift fox distribution in this region is restricted to areas
adjacent to release (Moehrenschlager and Moehrenschlager 2018) sites in southern
Canada, despite 36 years elapsing since the initial reintroductions and the species known
ability for long distance dispersal (Ausband and Moehrenschlager 2009).
The reintroduction of swift foxes to Canada is considered the largest canid
reintroduction effort to date (Boitani et al. 2004). Nine hundred and forty two foxes were
released in southern Alberta and Saskatchewan between 1983 and 1997, after
approximately 45 years of extirpation (Moehrenschlager and Lloyd 2016). Soon after,
swift foxes were documented dispersing into Montana (USA), where swift foxes were
previously extirpated, and the first documentation of reproduction Montana occurred in
1997 (Zimmerman 1998). To monitor the status of swift fox along the Canada-Montana
boundary, mark-recapture efforts (hereafter referred to as population census) have been
conducted in 1996, 2001, 2006, and 2015 (Moehrenschlager and Moehrenschlager 2018).
Estimates from the population census indicated growth between 1996 and 2006, but then
49
a decrease between 2006 and 2015 (Table 3.1). Although no population census was
conducted between 2006 and 2015, making it difficult to assess the cause of the decrease
in population size, it is believed that the harsh winter of 2010-2011 (NOAA 2011) might
have led to the decline (Moehrenschlager and Moehrenschlager 2018). In addition, while
the distribution of foxes has spread approximately 56 km south into Montana since the
initial reintroduction, population expansion appears to have stalled and there is still a
large gap in distribution to the northeastern Wyoming and northwestern South Dakota
(Alexander et al. 2016). Studying the demographic rates of foxes at the expansion front
of this population could provide information on possible mechanisms of population
decline and lack of expansion.
Given that the last estimates of survival and reproduction in the Canada-Montana
region occurred when the population was in the establishment and growth phase of
restoration (Zimmerman 1998; Moehrenschlager 2000), it would be beneficial to estimate
these rates again during the current persistence phase to determine if they have changed
and determine how the current estimates might influence population expansion.
Therefore, the objectives of this study were to: 1) estimate sex- and stage-specific
survival and fecundity rates; 2) evaluate support for factors hypothesized to influence
survival; 3) create a predictive model to assess viability of swift fox in this region 100
years into the future; and 4) evaluate sensitivity and elasticity of vital rates.
METHODS
STUDY AREA
We conducted our study in the 17,991 km2 area of northern Blaine, Phillips, and
Valley counties of northeastern Montana from US route 2, north to the border with
50
Canada. The dominant vegetation type in the study area was short and mixed-grass
prairie interspersed with wheat fields and patches of sagebrush (Artemisia spp.). Most of
the roads were gravel or unimproved two tracks in pastures, with only a few paved roads
in the study area. At the southern boundary of the study area, irrigated agricultural fields
were common adjacent to the Milk River, which largely runs west to east along US route
2. Most of the area was level or rolling terrain with some steep coulees along drainages,
and elevation ranged from 629 to 1068 m. The average monthly temperature ranges from
-1.8oC in the winter to 13.9oC in the summer and the average annual precipitation ranges
from 19 to 52 cm (Zimmerman 1998).
CAPTURE AND MONITORING
To sample foxes across a range of conditions at the periphery of the population,
we attempted to capture foxes within five focal areas across the entire study area (Figure
3.1). We captured foxes in lined box traps (Tomahawk Live Trap Co., Tomahawk, WI;
Moehrenschlager et al. 2003) and fitted captured foxes with LiteTrack30 store on board
Global Positioning System (GPS; Sirtrack, Havelock, New Zealand) collars that also
emitted a VHF signal, and uniquely numbered ear tags. We aged foxes based on tooth
wear and color (Ausband and Foresman 2007a) and classified foxes as juvenile or adult.
We assumed that swift foxes were born in April and we considered juveniles to only
remain juveniles from October until March following their birth, and adults thereafter
Handling procedures were approved by the Clemson University Institutional Animal Care
and Use Committee (AUP2016-036) and Montana Department of Fish, Wildlife, and
Parks Scientific Collector’s Permit (2016-107).
51
We attempted to locate collared foxes twice a week to monitor their survival
status. When a mortality signal was detected, we attempted to determine the cause of
mortality by examining carcass puncture wounds, skeletal injuries and other evidence at
the mortality site such as tracks of other animals and carcass location. We classified
mortalities as coyote (Canis latrans), other predation, vehicle, or unknown.
During the study, we had issues with some collar batteries dying prematurely
(Chapter 1). We expected collars to last at least six months, but several collars had
batteries that died or lost their VHF antennas in the first three months. In an effort to re-
sight collared individuals that might have had collar failure and still be alive, when we
had not located a fox after a month, we deployed three baited, heat and motion-activated
camera traps (TrophyCam, Bushnell, Overland Park, KS) within a fox’ known primary
area of use or suspected area of use for two weeks. If we detected the fox, then we kept
cameras active until the fox was no longer detected. If a collared fox was not detected
after two weeks, then we moved cameras to a new location the individual historically
frequented (i.e., secondary area) for an additional two weeks. If the targeted fox was still
not located, then we removed all cameras. Collared foxes were able to be identified from
camera trap photographs based on the number on their ear tags.
Each spring, we searched clusters of GPS points to look for natal dens. When a
den was located, we placed a camera trap 3-4 m away from the den. Cameras were
programmed to take a burst of three pictures every time they were triggered, and cameras
were kept at the same den sites until foxes moved to a new den site. We estimated litter
size as the maximum number of pups counted in a single picture during our monitoring.
52
DATA ANALYSIS
We estimated survival probabilities using the known-fate model in program R (R
Core Team 2013) using the ‘RMark’ package (Laake and Rexstad 2008). We
summarized data into monthly (i.e., calendar month) encounter histories for individuals.
If a fox was not located during a month, then they were censored during that interval.
We estimated 12-month survival rates for adults and 6-month survival rates for juveniles,
as has been done in other swift fox studies (Sovada et al. 1998; Kamler et al. 2003a). To
test for effects of sex, age class, year captured, and several environmental variables on
survival, we fit 9 a-priori monthly survival models (Table 3.2). Previous theoretical and
field studies have shown that individuals use and select resources in an attempt to
maximize survival and reproduction (Rosenzweig 1981; Morris 2003; McLoughlin et al.
2006; Mcloughlin et al. 2007). Therefore, hypothesizing that foxes selected resources to
maximize survival, we modeled survival as a function of several resources that we found
swift foxes selected for in concurrent fine- and coarse-scale evaluations of swift fox
movement behavior in our study area (Chapters 1 and 2). We evaluated the influence of
the percentage of native grassland within a 1 km radius moving window (PG),
topographic roughness (TRI), normalized difference vegetation index (NDVI), distance
to nonpaved roads (NonPaved), and distance to natural gas well (DistWell). Methods of
how these variables were delineated in Chapter 2. We intended to extract these
environmental variables from within the home range of each individual fox. However,
due to GPS collar failure, we were never able to download GPS data from 10 individuals.
For seven of these individuals, encounter histories were entirely from the VHF signal
detection alone, and three of the encounter histories came from camera re-sights.
53
Therefore, instead of excluding these individuals from the analysis, we decided to extract
and average environmental data from a 3.66 km buffer around the initial trap location of
each fox, which is equal to the average home range size of foxes in our study (Chapter 2).
Due to our small sample size and model convergence issues when trying to fit models
with multiple predictor variables, we only evaluated univariate models. We evaluated
support for each model with Akaike's Information Criterion adjusted for sample size
(AICc) and used AICc weights (wi) to determine strength of support for each model
(Burnham and Anderson 2002). For the covariate present in the top model(s), we
calculated the 85% confidence intervals (Arnold 2010) and considered the variable to be
important to influencing survival if confidence intervals did not overlap zero. We then
estimated the 12-month adult and 6-month juvenile survival rates using the top model.
To estimate the population growth rate (the change in population size from the
current time step to the next ()) and assess population viability, we created a pre-birth-
pulse female-based stochastic Lefkovitch matrix model consisting of three stages: pup (0-
5 months old), juveniles (6-12 months), and adults (>1-year-old):
0 0 F adult
A= S pup 0 0
0 S juvenile S adult
where F represents fecundity and S represents survival. We projected the population at a
yearly interval t using the equation:
N(t+1) = A * N(t)
where N(t) was the abundance vector for each stage at time t and A was the projection
matrix.
54
We parametrized our matrix with demographic rates determined from the above
methods, with the exception of pup survival which we gathered from the literature. We
calculated annual fecundity as the number of female offspring divided by the number of
denning females, assuming a 1:1 sex ratio for pups observed. We used estimates of pup
survival from another reintroduced population of swift foxes in Montana, which was
approximately 385 km west our study area (Ausband & Foresman, 2007). To determine
the composition of our initial abundance vector, we used population estimates from an
international census conducted in winter 2014-2015 that largely overlapped with our
study area (Moehrenschlager and Moehrenschlager 2018). This census estimated that
there were 346.9 +/- 79.5 foxes within our study area, and we used 174 female foxes as
our starting abundance as past studies have found 50:50 sex ratios (Cameron 1984;
FaunaWest 1991; Schauster et al. 2002). In addition, several past studies and reviews
have found that approximately 50% of populations were made up of adults (>1 year old)
and 50% were juveniles, during the winter (Rongstad et al. 1989; FaunaWest 1991;
Covell 1992). Therefore, our starting vector was composed of 0 pups, 87 juveniles, and
87 adults. We did not include any pups as we assumed our model represented the
population during the winter when all pups had already transitioned to juveniles. To
incorporate stochasticity, for each simulation we varied the survival values based on the
normal distribution between the low and high 95% confidence intervals.
We determined for the population by calculating the mean for 1,000 simulations
projected out to 100 years in program R. Lastly, we performed sensitivity and elasticity
analysis using the package ‘popbio’ (Stubben and Milligan 2007) to determine the
55
importance of each life history stage and demographic rate following the methods of
Caswell (1989). Low sensitivity values indicate that a change in that rate, survival or
fecundity, has a small influence on the population growth rate where as a high value
indicates that a change in the rate has a large influence on the population growth rate. In
this case, sensitivity values cannot be compared between survival and fecundity rates
because they are measured on difference scales. A low elasticity value indicates that a
proportional change in one rate, survival and fecundity, has a small influence on the
population growth rate and a large proportional change has a large influence. In this
instance, elasticities can be compared because they are based on a proportional rate.
RESULTS
We captured and collared 46 swift foxes in northeastern Montana during October-
December in 2016 and 2017. We collected survival data between October-April 2016-
2017 and October 2017-May 2018. While most re-sightings were based on VHF
telemetry (29 of 46 collared foxes), five additional encounters were recorded using
camera traps for individuals we had monitored for several months via telemetry but had
not been able to locate due to collar malfunction. In addition, camera traps provided a
single re-sighting encounter for three collared individuals that we had not been able to
locat since their capture due to collar malfunction. In total our analysis was based on
encounter histories for 32 individual collared swift foxes (10 adult males, 8 juvenile
males, 9 adult females, and 5 juvenile females). On average, foxes were monitored for an
average of 4 months (range: 2-8 months). We documented eight mortalities during the
56
study including five due to coyote predation, one hit by a car, one by fur trapping, and
one unknown cause.
We monitored three dens of radio collared females and an additional den of an
uncollared female in May-July2017, and three dens of radio collared females and three
dens of uncollared females in May-July 2018. Litter counts ranged from 2-4 pups. Five
dens did not produce any pups, one produced two pups, one produced three pups, and
three produced four pups. In 2017, the fecundity rate was 1.5 and in 2018 it was 0.42
with a combined overall average fecundity rate of 0.85 female pups per female.
Our model selection analysis indicated that the percentage of grassland was the most
supported model (Table 3.2) and had a positive influence on survival (β=0.57, 85%
Confidence Interval (CI): 0.15,0.99). Monthly survival increased, on average, 2.1% for
every 5% increase in the percentage grassland (Figure 3.3). Topographic roughness was
the second most supported model but had confidence intervals overlapping zero and
therefore there might be no true effect on swift fox survival. The average percentage of
grassland for the study area was 62% and the estimated monthly survival rate at that
percentage of grassland was 95%. When we extrapolated this survival rate to 6-months
and 12-months, the average annual adult survival rate during our study was 0.54 (95%
CI= 0.30,0.84) and the average juvenile survival rate was 0.74 (95% CI=0.56, 0.92). We
parameterized our population matrix using the estimates of adult and juvenile
survivorship from this study (Table 3.3) and we used the average pup survival rate of
0.73 (95% CI= 0.55,0.89) from Ausband and Foresman (2007b):
57
0 0 0.85
A= 0.55-0.89 0 0
0 0.56-0.92 0.30-0.84
We estimated the average to be 1.002 (95% CI= 0.996,1.008) and that the population
size remained stable over 100 years (Figure 3.2). Adult survival was the most sensitive
and elastic vital rate (Table 3.3).
DISCUSSION
Our study indicates that the population of swift foxes in northeastern Montana is
currently stable. In source-sink theory, source populations occur in areas where
reproduction is greater than mortality, and there is a surplus of juveniles in the population
that disperse to other areas (Pulliam 1988). From a species restoration or recovery
perspective, in order for a population to expand its range, the current population needs to
produce a sufficient number of “surplus” dispersing individuals (Lubina and Levin 1988).
Indeed, similar to Moehrenschlager (2000) who found 64% of juveniles did not disperse,
all the juvenile foxes we were able to monitor into spring remained in the area that we
caught them indicating philopatric behavior. Thus, the stable growth rate we observed
suggests that the population is likely not acting as a source population for the region; and
could explain why despite there being suitable habitat in the gap between populations
(Figure A3.1), areas south of our study area remain unoccupied.
Our estimate of adult survival rate suggests that the swift fox population in
northeastern Montana is likely in the persistence phase of population recovery and
occupying fringe habitat. The adult survival rate is lower than two previous estimates
from the Canada-Montana population that were calculated during the growth phase of the
58
population following reintroduction (Zimmerman 1998; Moehrenschlager 2000), and that
of a recently established population of foxes on the Blackfeet Reservation in western
Montana (Table 3.4; Ausband & Foresman, 2007b). This is similar to the results of
Devineau et al., (2010), who found that Canada lynx (Lynx canadensis) that moved
outside of the core reintroduction area in Colorado, had lower survival than those inside
it. We might expect this to occur because often release areas are chosen to maximize the
long-term survival and reproduction of the restored population (Moehrenschlager and
Lloyd 2016) and populations might expand into less suitable habitat over time. In
addition, we might expect a restored population to have decreased survival and
reproductive rates as the population reaches carrying capacity of the area and experience
density-dependent effects during the persistence phase (Armstrong et al. 2005; Trinkel et
al. 2010). This is supported by the fact that the estimated population growth rate
indicates a stable population as would be expected by a population approaching or at
carrying capacity.
Swift foxes in our study had an adult survival rate compared to other studies of
the species (Table 3.4). Our elasticity and sensitivity analyses found adult survival to be
the most important demographic rate to population growth (Table 3.4. Therefore, it is
important to understand the mechanism(s) that drive survival rate. In our study area,
survival was best explained by the amount of grassland within a fox’ area of use (Figure
3.3), which was also the strongest predictor of resource use in our study (Chapter 2).
Survival might have increased in more grassland dominated areas because swift foxes
were better able to avoid coyotes and red foxes (Vulpes vulpes) there. Cultivated crop
59
fields were the second most dominant land cover type in the region, and red foxes and
coyotes have been shown to be associated with these cover types (Kamler and Ballard
2002; Kamler et al. 2005). Consistent with other studies of North American foxes,
coyote predation was the primary cause of mortality of swift foxes (Sargeant et al. 1987;
Sovada et al. 1998; Kitchen et al. 1999; Cypher et al. 2000; Farias et al. 2005). In our
study area, swift foxes had some of the largest home ranges recorded for the species
(Moehrenschlager et al. 2007; Chapter 2). We hypothesize that because of the large
spatial requirements, foxes in this region were more at risk for encountering a coyote than
in other populations. Further, two winters of above average snowfall and a summer of
well below average precipitation (NWS Glasglow MT 2018, 2019) occurred during our
study, which might have further increased movement to meet foraging needs and exposed
swift foxes to increased predation risk. However, we must note that caution must be
taken when interpreting our survival estimates as we extrapolated survival across almost
double the time that we collected survival data. It is possible that our estimate may be
biased higher than the actual annual survival as several studies have found lower survival
in the spring and summer compared to fall and winter (Covell 1992; Sovada et al. 1998;
Moehrenschlager and Macdonald 2003).
Lack of population growth in our study population could also be due to reduced
reproductive output. Our elasticity analyses indicated fecundity was only had a slightly
smaller influence on population growth than adult survival (Table 3.4). While the
average litter size of females in our study was similar to those of other studies (Table
3.5), the number of females that produced pups that survived to come above ground was
60
lower than in other studies. Although our sample size was small, we believe that the
cause of the low number of females reproducing might be due to low prey abundance. In
a review of the factors affecting kit fox and swift fox demographics, White and Garrott
(1997) found that reproductive rate was positively influenced by leoprid abundance and
precipitation levels. Moreover, the lower reproductive rate during the second field season
of our study followed a year of exceptionally low precipitation (NWS Glasglow MT
2018, 2019) which has been found to influence small mammal abundance, and kit fox
reproduction (White and Garrott 1999; Cypher et al. 2000). In addition to investigating
the influence of prey abundance on swift fox reproduction, future research should
investigate whether low reproduction is a consequence of the population occurring in
fringe habitat.
Our study on the status of swift foxes in northeastern Montana provides a
snapshot of the population dynamics of this population, but similar to other small canids,
population fluctuations could be highly stochastic depending on environmental
conditions. Previous long-term studies of the arctic fox and kit fox have found that these
species exhibit large fluctuations in abundance, survival, and reproduction (Angerbjörn et
al. 1994; Angerbjorn et al. 1999; White and Garrott 1999; Cypher et al. 2000). In a 15
year study of San Joaquin kit foxes, Cypher et al. (2000) found that San Joaquin kit foxes
can experience rapid booms and busts in population size, density, and growth rate due to
changes in the current and previous year’s rodent abundance and precipitation from the
previous three years. Precipitation has also been found to be important in determining
the abundance prairie rodents, which can similarly undergo large annual changes in
61
population abundance (Heisler et al. 2014). Therefore, while we currently estimate that
this population of swift foxes is stable, we hypothesize that it might behave similarly to
those of arctic and kit foxes, and we should assume that demographic rates will fluctuate
due stochastic environmental events. It would be beneficial to evaluate how fluctuating
precipitation and prey populations could explain observed variation in swift fox
demography.
Overall, our study highlights some of the broader difficulties carnivores face in
recolonizing the Northern Great Plains. Successful expansion of recovering and
reintroduced populations also requires vacant suitable habitat to recolonize and produce
additional dispersers (Wolf et al. 1998; Dinerstein et al. 2007). Our finding that survival
was positively associated with the percentage of grassland on the landscape, coupled with
the large spatial requirements of swift foxes (Chapter 2), highlights the need for
conservation actions to occur at large scales, likely across multiple landowners. The
importance of contiguous grassland was further supported by our fine scale movement
data, where we were able to document potential dispersal movements by two adult foxes
that moved 25 km south towards the Milk River but then moved back to their area of
origin (Figure A3.2). Suggesting that the Milk River might be a barrier to recolonization.
In addition, our habitat suitability map predicts that there is suitable habitat in the
southeastern part of the state that may aid in restoring connectivity between populations
(Figure A3.1). Similar large contiguous of parcels with native grasslands are necessary
for another recovering prairie carnivore, the black-footed ferret. Jachowski et al. (2011)
found that to sustain a population of at least 30 adult ferrets, prairie dog (Cynomys sp.)
62
colonies had to be least 4300 ha (43 km2) with a high density of prairie dogs. Thus,
recovery of swift fox and ferrets in this region, and throughout the Great Plains, might
become increasingly difficult as the amount of intact grasslands continues to decline
(Sohl et al. 2012). Therefore, maintaining or restoring large tracts of native grasslands
has the potential to benefit recovery of swift fox and other carnivores in the Northern
Great Plains.
63
TABLES
Table 3.1. Swift fox population size estimates for Canada, Montana, and both areas
combined (Total) from the population census from Moehrenschlager & Moehrenschlager
(2001, 2006, 2018).
Region
Year Canada Montana Total
1996 281 NA 281
2001 656 221 877
2006 647 515 1,162
2015 523 347 870
Table 3.2. Model selection results for swift fox survival known-fate survival models in
northeastern Montana, 2016-2018.
Model k AICc AICc wi
S(~PG) 2 54.2 0.00 0.26
S(~TRI) 2 55.2 1.01 0.16
S(~1) 1 55.3 1.10 0.15
S(~NDVI) 2 56.2 2.05 0.09
S(~Year) 2 56.4 2.20 0.09
S(~Stage) 2 56.5 2.37 0.08
S(~Sex) 2 56.9 2.73 0.07
S(~NonPaved) 2 57.2 2.98 0.06
S(~DistWell) 2 57.3 3.17 0.05
Table 3.3. Average vital rate values with 95% lower confidence interval (LCI) and upper
confidence interval (UCI) used to parameterize projection matrix and sensitivity and
elasticity values for swift foxes in northeastern Montana, 2016-2018. Sensitivity and
elasticity values were calculated from average rates
Parameter Value (LCI, UCI) Sensitivity Elasticity Source
Spup 0.73 (0.55, 0.89) 0.33 0.24 Ausband and Foresman, 2007b
64
Sjuvenile 0.74 (0.56, 0.92) 0.32 0.24 This study
Sadult 0.54 (0.30, 0.84) 0.52 0.28 This study
Fadult 0.85 (NA, NA) 0.28 0.24 This study
65
Table 3.4. Geographic location, survival rates of all individuals, adult, or juvenile swift foxes, number of foxes tracked in the
study, and study length of swift foxes in North America. Average values exclude this study.
Location
All
individuals
Resident Adult Survival
Rate
Juvenile Survival
Rate
Number of
foxes
Study Length
(years) Citation
Canada 46% 40%* 73 3 Moehrenschlager, 2000
Montana 46% 11 2 Zimmerman, 1998
Montana 54% 74%** 32 2 This Study
Montana 67% 52%*** 73 2 Ausband & Foresman, 2007a
South Dakota 27% 27%* 98 6 Sasmal et al., 2016
Wyoming 58% 56 3 Olson & Lindzey, 2002
Colorado 65% 133 2 Schauster, Gese, & Kitchen, 2002
Colorado 45% 13%**** 23 1.5 Andersen et al., 2003
Kansas 45% 33%** 65 0.92 Sovada et al., 1998
Texas 53% 60%** 46 2.5 Kamler et al., 2003
New Mexico 53% 27 2.67 Harrison, 2003
Average 56% 49% 38% 17.3 2.1
* = 12-month estimate
** = 6-month estimate
*** = 9-month estimate
**** = 11-month estimate
66
Table 3.5. Geographic location, percent of tracked females reproducing, average litter size, number of social units (male-
female or trios) monitored, and study length of swift foxes in North America. Average values exclude this study.
Location
% Females with
Pups
Average Litter
Size
Sample Size (social
units)
Study Length
(years) Citation
Canada 85% 3.8 29 3 Moehrenschlager, 2000
Montana 5.0 3 2 Zimmerman, 1998
Montana 50% 3.4 10 2 This Study
Montana 67% 4.0 27 2 Ausband & Foresman, 2007a
Wyoming 79% 4.6 25 3 Olson & Lindzey, 2002
Colorado 60% 2.3 42 2 Schauster, Gese, & Kitchen, 2002
Colorado 63% 3.4 5 1.5 Andersen et al., 2003
Kansas 53% 3.1 11 0.92 Sovada et al., 1998
New Mexico 2.3 4 2.67 Kamler et al., 2003
Average 68% 3.6 58 3
67
Figures
Figure 3.1. A) Distribution of swift foxes and study area in Montana where we estimated
vital rates during 2016-2018; B) Focal areas within the study area where we trapped six
swift foxes (a), four swift foxes (b), three swift foxes (c), twenty-two swift foxes (d), and
thirteen swift foxes (e) in 2016-2018.
68
Figure 3.2. Population projection of the swift fox population in northeastern Montana
based on a stage-based matrix incorporating demographic stochasticity. Gray region is
the 95% confidence interval of the population size at each year.
69
Figure 3.3. Predictive plot showing the effects of percent grassland on monthly survival
of swift foxes in northeastern Montana during 2016-2018.
70
Appendix A
Supplemental Materials
Figure A3.1. A) Relative probability of use map for swift foxes in the Great Plains of Montana
determined by the most supported resource utilization function model (Chapter 2) during 2016-
2018. B) Areas of high suitability (black) were delineated based on having a probability of use
value greater than 0.8 and areas of low suitability (gray; <0.8 probability of use) for swift foxes
based on the results of camera detections (Chapter 2).
71
Figure A3.2. Long distance movements by two adult swift fox during the winter of 2017-2018 in
northeastern Montana. Both individuals moved south towards the Milk River and then moved
north back to their core areas of use. Milk River is highlighted as a potential dispersal barrier.
72
Camera Trap Methods
We deployed trail cameras using a stratified random sampling design across the
study area during the summer of 2017 and 2018 (Figure A3.3). To create the
stratification design, we first created a grid of 22km2 square cells across the study area,
which is based on the maximum home range size of prairie red foxes (Sargeant et al.
1987). We then calculated the proportion of cropland within each cell to create four
strata (Strata 1 = 0-0.10 grassland, Strata 2 = 0.10-0.25, Strata 3 = 0.25-0.50, Strata 4 =
0.50-1.0). We randomly picked 40 sites for sampling within each stratum that are
spatially balanced across our study area in order to sample 25 sites in each strata during
the summer of 2018 and 2018 (Figure A3.3). Within each chosen grid cell, a camera was
placed on a fence post for 10 days as close to the center of the cell as possible.
In the summer of 2017 we deployed cameras at 100 sites and 93 in the summer of
2018. We tallied 17 detections between years. Two detections in the 0.7-0.8 bin and 0.9-
1.0 bin, and 13 detections in the 0.8-0.9 bin (Table A1.1).
73
Figure A3.3. Location of sampling points in northeastern Montana used to survey for
swift foxes during the summers of 2017 and 2018. Grid cells are based on maximum red
fox home range size and stratified by proportion of cropland within a cell.
Table A1.1. Probability of use bins (based on predicted use from Chapter 2) and the
number of swift fox detections and cameras within each been. Observed and expected
proportion of swift fox detections in each bin.
Probability of Use
Bins
#
Detections
# Camera
Sites Observed Expected
0.0-0.1 0 0 0 0
0.1-0.2 0 0 0 0
0.2-0.3 0 0 0 0
0.3-0.4 0 0 0 0
0.4-0.5 0 0 0 0
0.5-0.6 0 1 0 0.01
0.6-0.7 0 7 0 0.07
0.7-0.8 2 23 0.12 0.23
0.8-0.9 13 62 0.76 0.62
0.9-1.0 2 7 0.12 0.07
74
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