BLACK BEAR (URSUS AMERICANUS) ECOLOGY IN SOUTHERN GRAND TETON
NATIONAL PARK
by
Leslie Marie Frattaroli
A thesis submitted in partial fulfillment
of the requirements for the degree
of
Master of Science
in
Fish and Wildlife Management
MONTANA STATE UNIVERSITY
Bozeman, Montana
July 2011
©COPYRIGHT
by
Leslie Marie Frattaroli
2011
All Rights Reserved
ii
APPROVAL
of a thesis submitted by
Leslie Marie Frattaroli
This thesis has been read by each member of the thesis committee and has been
found to be satisfactory regarding content, English usage, format, citation, bibliographic
style, and consistency and is ready for submission to The Graduate School.
Dr. Scott Creel
Approved for the Department of Ecology
Dr. Dave Roberts
Approved for The Graduate School
Dr. Carl A. Fox
iii
STATEMENT OF PERMISSION TO USE
In presenting this thesis in partial fulfillment of the requirements for a master’s
degree at Montana State University, I agree that the Library shall make it available to
borrowers under rules of the Library.
If I have indicated my intention to copyright this thesis by including a copyright
notice page, copying is allowable only for scholarly purposes, consistent with “fair use”
as prescribed in the U.S. Copyright Law. Requests for permission for extended quotation
from or reproduction of this thesis in whole or in parts may be granted only by the
copyright holder.
Leslie Marie Frattaroli
July 2011
iv
ACKNOWLEDGEMENTS
I would like to thank Shannon Podruzny and Craig Whitman for taking the time to
teach me the field methods that were essential to this project. I would also like to thank
Chelsea Rector, Andrea Drayer, Marissa Rodero, Tim Lyons, Aly Courtemanche, and
Mary Greenblatt for helping me conduct field work. I would like to express my sincerest
thanks to Megan Ruehmann, Jessica Chitwood, Josh Brown, and Kim Wells for their
moral support. I would like to thank my committee members, Dr. Scott Creel, Dr. Steve
Cherry, and Dr. Andy Hansen, for their technical support and patience. I would like to
extend my thanks to my supervisor, Steve Cain, for giving me the opportunity to conduct
this research, continual guidance, and constructive criticism. I would also like to express
my gratitude to my co-advisor, Chuck Schwartz, for his encouragement, guidance,
refusing to give up on me, and most of all for being the researcher/scientist I strive to be.
I would like to thank my family for support and encouragement. A special thanks to
David Moeny for edits, data analysis, discussions, encouragement, and friendship. His
unwavering support and guidance were instrumental in the final stages of my thesis. A
final thank you to the black bears of southern Grand Teton National Park, I am honored I
had the chance to work with such magnificent animals. Funding for this project was
provided by the Charles Engelhard Foundation, Earth Friends Wildlife Foundation,
Greater Yellowstone Coordinating Committee, University of Wyoming/National Park
Service Research Station, John and Karin McQuillan, The Wildlife Society/Wyoming
Chapter Memorial Bear Fund, Grand Teton National Park Foundation, Grand Teton
National Park, and the USGS Interagency Grizzly Bear Study Team.
v
TABLE OF CONTENTS
1. BLACK BEAR (URSUS AMERICANUS) ACTIVITY AND FOOD
PATTERNS IN SOUTHERN GRAND TETON NATIONAL
PARK……………………………………………………………………………..…….1
Abstract............................................................................................................................1
Introduction ......................................................................................................................2
Activity Level ..........................................................................................................3
Determining the Cause of Missed Fixes ..................................................................5
Food Habits ..............................................................................................................6
Study Area .............................................................................................................10
Methods...........................................................................................................................11
Missed Fix Analysis ...............................................................................................13
Activity Level Analysis .........................................................................................14
Activity Patterns.....................................................................................................15
Site Visit Analysis..................................................................................................16
Scat Analysis ..........................................................................................................18
Results .............................................................................................................................20
Missed Fix Analysis ...............................................................................................20
Activity Level Analysis .........................................................................................20
Activity Patterns.....................................................................................................21
Scat Analysis ..........................................................................................................22
Site Visit Analysis..................................................................................................23
Discussion .......................................................................................................................25
Literature Cited ...............................................................................................................54
2. BLACK BEAR (URSUS AMERICANUS) HABITAT USE IN
SOUTHERN GRAND TETON NATIONAL PARK....................................................63
Abstract..........................................................................................................................63
Introduction ....................................................................................................................64
Home Range and Habitat Use ................................................................................65
Response to Human Influence ...............................................................................66
Study Area .............................................................................................................71
Methods...........................................................................................................................72
Home range Analysis .............................................................................................73
Habitat Use Analysis..............................................................................................74
Results .............................................................................................................................77
Home Range Analysis............................................................................................77
Habitat Use Analysis..............................................................................................78
Discussion .......................................................................................................................82
Literature Cited .............................................................................................................110
vi
TABLE OF CONTENTS CONTINUED
3. SYNOPSIS……………...............................................................................................119
Introduction ..................................................................................................................119
Activity Patterns...................................................................................................120
Food Habits ..........................................................................................................123
Habitat Use...........................................................................................................126
Post hoc ................................................................................................................131
Literature Cited............................................................................................................134
APPENDIX A: Food items identified at black bear site visits and in scats ....................140
vii
LIST OF TABLES
Table
Page
1.1 Results of AICc for missed GPS fix models. ...................................................38
1.2. Regression of activity score and ambient temperature. ..................................39
1.3. Proportion black bears were active by month. ................................................40
1.4. Summary results of scat contents ....................................................................47
1.5. Summary results of scat contents by season. ..................................................48
1.6. Pianka’s index per season, sexes separate for scat analysis............................50
1.7. Pianka’s index per season, sexes combined for scat analysis. ........................50
1.8. Activities by forested areas .............................................................................52
1.9. Ungulate use by month ...................................................................................52
1.10. Pianka’s index per season, sexes separate for site visit analysis ..................53
1.11. Pianka’s index per season, sexes combined for scat analysis .......................53
2.1. Black bear home range and distances moved .................................................96
2.2 Habitat covariate results for individual black bears .........................................99
2.3. Human use covariate results for individual black bears ...............................100
2.4. Correlation coefficients for MADIFA analysis. ...........................................109
viii
LIST OF FIGURES
Figure
Page
1.1. Study Area ......................................................................................................35
1.2. Frequency of missed GPS fixes ......................................................................36
1.3. Activity scores and temperature by hour ........................................................37
1.4. Activity levels by month .................................................................................39
1.5. Activity counts from roads..............................................................................41
1.6. Activity counts from developed areas.............................................................42
1.7. Activity counts from trails ..............................................................................43
1.8. Mean activity counts from roads.....................................................................44
1.9. Mean activity counts from developed areas....................................................45
1.10. Mean activity counts from trails ...................................................................46
1.11. Relative scat composition .............................................................................49
1.12. Food items used at site visits ........................................................................51
2.1. Study Area ......................................................................................................93
2.2. Female black bear home ranges ......................................................................94
2.3. Male black bear home ranges .........................................................................95
2.4. Black bear distance moved/season..................................................................97
2.5. Black bear mean distance to Whitebark pine ..................................................98
2.6. Mahalanobis distance model for the study area,
habitat covariates ..........................................................................................101
2.7. Mahalanobis distance model for the study area,
human use covariates ....................................................................................102
ix
LIST OF FIGURES – CONTINUED
Figure
Page
2.8. Mahalanobis distance global model for the study area,
all covariates .................................................................................................103
2.9. Mahalanobis distance model for GRTE, habitat covariates..........................104
2.10. Mahalanobis distance model for GRTE, human use covariates .................105
2.11. Mahalanobis distance global model for the study area,
all covariates ...............................................................................................106
2.12. Cumulative Distribution of scaled Mahalanobis distance values ...............107
2.13. Barplots of eigenvalues of factorial axes for study area
and GRTE ...................................................................................................108
x
ABSTRACT
Black bears (Ursus americanus) in Grand Teton National Park (GRTE), Wyoming
occupy an environment that is changing due to human pressure and environmental
variability. I analyzed activity patterns, food habits, and habitat use of black bears in
southern GRTE, trying to identify if human recreation impacted these patterns. I studied
9 black bears equipped with spread spectrum technology (SS) collars from June, 2005 to
October, 2006. Each collar contained -15° head to tail activity switch, a GPS radio
receiver, and an independent very high frequency (VHF) transmitter. I used logistic
regression on a sample of bear locations that were field-verified as active or resting to
determine a break point to classify all locations as resting or active based upon activity
counts. My discriminant analysis indicated that bears were likely resting if their recorded
activity count was <16.5. I used logistic regression to determine which factors were most
responsible for missed fixes. Overall, bear activity levels were consistent regardless of
their distance from roads, human developments, and trails. Bears fed on a wide range of
foods including vegetation, insects, and mammals that were seasonally abundant.
Graminoids and ants were important food sources for black bears in the spring and
summer. I used the Mahalanobis Distances Factor Analysis (MADIFA) to quantitatively
break down the D2 statistic into linear combinations to determine the impact of each
variable on D2. My models displayed areas of high use (i.e. larger D2 values), in forested
regions adjacent to trails and roads. Several axes in different linear combinations,
including habitat and human use covariates, are present in the analysis. This suggests
that a complexity exists for black bear habitat use, beyond proximity to human use
activities. Therefore it would be an oversimplification to conclude that black bears only
utilize areas close to trails and/or roads in southern GRTE.
1
CHAPTER 1
BLACK BEAR (URSUS AMERICANUS) ACTIVITY PATTERNS AND FOOD
HABITS IN SOUTHERN GRAND TETON NATIONAL PARK
Abstract
Black bears (Ursus americanus) in Grand Teton National Park (GRTE),
Wyoming face a dynamic environment. GRTE is an ideal place to observe black bears
utilizing habitat that is managed by natural processes, but is dominated by human
recreation. I analyzed activity patterns and food habits of black bears in southern GRTE,
trying to identify if human recreation impacted these patterns. I studied 9 black bears
equipped with spread spectrum technology (SS) collars from June, 2005 to October,
2006. Each collar contained -15° head to tail activity switch, a GPS radio receiver, and
an independent very high frequency (VHF) transmitter. Collars recorded date, time,
activity level and a GPS location. This technology enabled me to retrieve the GPS
locations weekly by aircraft and visit site fixes for each bear from a randomly selected
24-hour period. I visited sites and obtained data on foods used at sites and scats for
analysis. I used logistic regression on a sample of bear locations that were field-verified
as active or resting to determine a break point to classify all locations as resting or active
based upon activity counts. My discriminant analysis indicated that bears were likely
resting if their recorded activity count was <16.5. I also used logistic regression to
determine which factors were most responsible for missed GPS fixes. Collars were more
likely to have unsuccessful fix attempts if bears were resting than if the bear was active.
2
Black bears in southern GRTE were diurnal and exhibited bimodal activity peaks around
sunrise and before sunset from June through August. During nocturnal hours, black
bears’ activity levels were consistent with resting. Overall, bear activity levels were
consistent regardless of their distance from roads, human developments, and trails. Bears
fed on a wide range of foods including vegetation, insects, and mammals that were
seasonally abundant. Graminoids and ants were important food sources for black bears in
the spring and summer. Future research should include a time scale that embodies the
full fluctuation of whitebark pine cone crops and soft mast surveys to determine how
activity levels and food habits would be affected.
Introduction
Black bears (Ursus americanus) that live in Grand Teton National Park (GRTE)
face a dynamic environment. Although the landscape contains rich habitat it also
includes many factors that may limit or alter how bears use the landscape. Several
studies have detailed food habits of black bears and how they interact with their habitat
on a fine scale (Raine and Kansas 1990, Holcroft and Herrero 1991, Benson and
Chamberlain 2006). However, activity patterns and food habits have not been studied in
southern GRTE. The success of bear management strategies in GRTE depends on
detailed information about the black bear population. In southern GRTE, where pressure
from humans is pervasive, potential food shortages are at play, and competition with
grizzly bears (Ursus arctos) is imminent, it is important to detail black bear activity
pattern and food habits. Their reaction to these variables could result in a change in
3
temporal or spatial use of the area, or an increase in bear-human conflict.
Activity Patterns
Black bears have been documented as crepuscular (Beecham and Rohlman 1994,
Gaines and Lyons 2003) throughout their range. Amstrup and Beecham (1976) also
reported that bears were diurnal and crepuscular, but displayed nocturnal patterns in the
early spring and late fall. The difference in activity patterns has been attributed to human
activity (Lariviere et al. 1994, Beckmann and Berger 2003, Lyons 2005), human
habituation (Ayers et al. 1986), temperature (Garshelis and Pelton 1980), or in some
locations grizzly bear activity (Holm et al. 1999, Schwartz et al. 2010). Sex, age, and
reproductive status have also been shown to influence an animal’s activity pattern
(Amstrup and Beecham 1976, Gaines and Lyons 2003, Kolbe and Squires 2007). The
fluctuations of food resources may also impact bear activity patterns. Black bears in
Idaho were more active when food was less abundant (Amstrup and Beecham 1976).
The phenological state of the vegetation and food quantity also play an important role in
bear activity (Jonkel and Cowan 1971).
Road corridors are often located in prime habitat and occupied by subadult bears
(Mattson 1990, Mueller et al. 2004, Nellemann et al. 2007). Roads are not only barriers
for many wildlife species (Forman and Alexander 1998), but can be a source of mortality
(Forman and Alexander 1998, Gunther and Biel 1999, Gunther et al. 2000, McCown et
al. 2009) and habitat fragmentation (Servheen et al. 1998). Kasworm and Manley (1990)
found that roads had a greater influence than trails on use of habitats by both black and
4
grizzly bears. The literature regarding the impact of roads on bears has provided both
positive and negative results. Grizzly and black bears utilized habitat near roads less than
expected, but the distance from roads over which habitat use was affected was greater for
grizzly bears than black bears (Kasworm and Manley 1990). GRTE and the John D.
Rockefeller, Jr. Memorial Parkway have 245 km of paved roadways. The main road that
intersects the study area is the Teton Park road. During the course of this study (2005
and 2006) there was an average of 1,433,517 vehicles traveling the Teton Park road from
June to August (National Park Service Statistics http://www.nature.nps.gov/stats/).
Roads may or may not be utilized by bears due to traffic volume, habitat type, habitat
quality adjacent to roads, or the age, sex, or reproductive status of the bear.
Black bears in GRTE face many human pressures. The south border of the park
abuts a ski resort and outlying subdivisions from the town of Jackson, Wyoming. The
south part of GRTE contains several developments in the form of visitor centers, the
park’s headquarters, and housing for park and concession employees. Development
inside park boundaries increases the bear-human interaction interface. There are 383 km
of trails (National Park Service Statistics http://www.nature.nps.gov/stats/) that bisect the
park; approximately 215 km of trails are in southern GRTE. The trail system is extensive
and maintained trails run through all but one canyon in southern GRTE.
Due to road and trail access, as well as geography, the southern part of GRTE
receives more backcountry visitors than the north. In 2006, 80.6% (n = 21,393) of
backcountry camper nights (one person spending one night in the backcountry) occurred
in the south district of GRTE, compared to 19.4% (n = 5,148) in the north district. The
5
higher level of backcountry use in the southern GRTE may impact bear activity. Many
backcountry trails and camping sites in GRTE are located in important habitat that is rich
in berry producing shrubs and whitebark pine. Gunther (1990) reported that areas within
400 m of occupied backcountry campsites were used less by grizzly bears than
unoccupied sites in Yellowstone National Park. Beckmann and Berger (2003) and Lyons
(2005) documented a shift in black bear temporal patterns to avoid human activity.
Human occupation in the backcountry may lead to temporal shifts of bears.
As part of my project, I studied black bear activity patterns in the southern GRTE
to determine if black bear activity patterns were affected by a bear’s proximity to trails in
GRTE. I also contrasted activity patterns at specific distances from roads and human
development to determine if black bears in southern GRTE adjusted their activity in
response to either human development or roads. One objective of my research was to
identify if human development alters bear activity patterns. In order to determine if any
activity differences occurred I investigated the hypothesis:
H1: Black bears will be more day active when distant from human developments, roads,
and trails.
Determining the Cause of Missed Fixes
Global Positioning System (GPS) collar technology has enabled researchers to
monitor wildlife during 24-hour periods in remote locations (Girard et al. 2006).
Although there are many advantages, one main disadvantage of GPS collars is missed
GPS fixes. Missed GPS fixes result when an inadequate number of satellites are
available and a geographic location cannot be obtained (Obbard et al. 1998, Graves and
6
Waller 2006). Missed fixes represent lost information and may result in systematically
under sampling certain types of habitat or terrain (Johnson et al. 1998, Dussault et al.
1999, D’Eon 2003, Friar et al. 2004). D’Eon (2003) and Friar et al. (2004) showed that
this loss of information can bias resource selection analyses.
Before home range estimates were created, it was important to identify the source
of missed fixes from the collar data that I collected. Although terrain and habitat type
have been shown to cause missed fixes, research suggests that animal behavior may also
have a role (Moen et al. 1996, Bowman et al. 2000, Graves and Waller 2006, Heard et al.
2008, Schwartz et al. 2009). The overall position and angle of the GPS unit and antenna
may be associated with the behavior of the animal (D’Eon and Delparte 2005), and
bedded animals may angle the antenna towards the ground and inhibit fix success
(Bowman et al. 2000, Graves and Waller 2006, Belant 2009). Graves and Waller (2006)
and Schwartz et al (2009) also reported that bears with increased movement had a
corresponding increase in the GPS fixes that were obtained. My objective was to
determine if activity, time of day, and age of collar impact the collar’s ability to obtain a
successful GPS location. I assessed the cause of missed fixes by investigating the
following hypothesis:
H2: GPS collars deployed on black bears will obtain more GPS locations when bears are
active.
Food Habits
Black bears are omnivores (Hererro 1978) and consume a variety of plant and
animal species (Raine and Kansas 1990, Holcroft and Herrero 1991, Aune 1994, Bull et
7
al. 2001). Throughout North America graminoids are an important food source to black
bears after den emergence, when other food sources are unavailable (Irwin and
Hammond 1985, Raine and Kansas 1990, Boileau et al. 1994, Bull et al. 2001). Ants
have been documented as another significant food source in spring and summer (Irwin
and Hammond 1985, Boileau et al. 1994, Bull et al. 2001). As summer advances, black
bears shift from grazing on ants, forbs, and graminoids to browsing on berries (Irwin and
Hammond 1985, Raine and Kansas 1990). Whitebark pine (Pinus albicaulis) is a critical
bear food in the Greater Yellowstone Ecosystem (GYE) (Kendall 1983, Mattson and
Jonkel 1990, Mattson et al. 1991). Whitebark pine nuts have a high caloric value (3.99
kcal/g) (Craighead et al. 1995). Many bears depend on this last surge of high quality
food before denning (Mattson and Jonkel 1990). Gunther et al. (2004) also reported that
when natural food crops are poor, bear human conflicts increase in the GYE. Whitebark
pine habitat covers approximately 10,772 hectares in GRTE. In 2005, mean cone
production per tree was 16.8 (SD = 25.9) in the GYE (Haroldson and Podruzny 2005).
Cone production was over twice as high in 2006, with a mean cone production of 34.4
cones/tree (SD = 40.7) (Haroldson and Podruzny 2006).
My focus here is to provide managers with information needed to efficiently
manage black bears and predict any changes that may occur should human impacts
change that are concurrent with food fluctuations. The data I collect will also enable
researchers to compare food habits of my study animals which are allopatric with grizzly
bears to a similar population of black bears which are sympatric with grizzly bears.
Although fecal analysis gives a depiction of the food habits of bears, not all foods are
8
represented in fecal samples due to the digestibility of different food items (Hewitt and
Robbins 1996). My objective was to obtain comprehensive food habits of black bears in
southern GRTE, by analyzing fecal samples and identifying foods bears consume in the
field. Due to differences in food digestibility I will tested the following hypothesis:
H3: Data on food habits collected at site visits will be different from foods identified in
scat contents, because some foods are more digestible than others.
Throughout their history, black bears have depended on the forest not only for
food but also for protection from predators (Herrero 1978). Aune (1994) and Holm et al.
(1999) documented that black bears living sympatrically with grizzly bears were more
restricted to forested areas than their grizzly counterparts. Although black bear home
range size depended on food quality and climate (Amstrup and Beecham 1976), black
bears had larger home ranges when in the presence of grizzly bears when compared to
allopatric populations (Aune 1994, Holm 1998). In addition to different habitat selection,
the two species may utilize the same habitat in different capacities, a form of niche
differentiation, by shifting temporal patterns or utilizing different foods (Holm et al.
1999). Different temporal activity patterns (Holm et al. 1999) and complete displacement
from an area may be another effect of interspecific competition between the species.
Differences in spatial distribution and resource use between the species may be a
consequence of displacement of the subordinate competitor, or they may simply reflect
different responses to different ecological requirements. Distinguishing between these
hypotheses is difficult, but studies of many species suggest that competitive displacement
is frequently a strong force within carnivore guilds (Johnson and Franklin 1994, Creel
9
and Creel 1996, Mills and Gorman 1997, Palomares and Caro 1999). Temporal and
spatial differences between the species may be a component of this sympatric
coexistence.
To date studies have focused on black bears in areas where black and grizzly
bears (Ursus arctos) live sympatrically (Jonkel and Cowan 1971, Aune 1994, Holm et al.
1999, Belant and Follmann 2002) and areas historically allopatric from grizzly bears
(Wagner et al. 2001, Benson and Chamberlain 2006). Few studies have investigated
allopatric black bear populations that were historically sympatric with grizzly bears.
Although black bear distribution has been relatively constant for decades, grizzly bear
distribution has fluctuated over time in GRTE. Historically, black bears coexisted with
grizzly bears in GRTE, until the early 1900s. By the 1940s, grizzly bear range was
substantially retracted and black bears were the only ursid species residing in the
southern GRTE (Pyare et al. 2004). As the population of grizzly bears in the
Yellowstone Ecosystem increased in the 1970s, their range expanded southward, and
numbers increased in the northern part of the GRTE (Pyare et al 2004). As of 2006,
grizzly bears had a well established distribution in the north part of the park, with a few
collared grizzly bears traveling through the southern GRTE. Several generations of black
bears have lived in the southern GRTE without the associated pressures that grizzlies
place upon black bears living in sympatric habitats.
With different habitat resources, human pressures, and development influencing
the southern GRTE black bear population, it is critical to identify their interactions with
each. The information collected from this study will provide base-line data for black
10
bears in the southern GRTE prior to grizzly recolonization and provide comparative
information for a project that collected similar information on both black and grizzly
bears living sympatrically in the northern GRTE.
Natural processes are an important component influencing black bear habitat.
Schoen (1990) highlighted the need for bear habitat analysis to include the human
component. GRTE is an ideal place to observe black bears utilizing habitat that is
managed by natural processes, but is dominated by human recreation. Additionally, the
information collected will assist managers in identifying possible areas where human
recreation practices should be modified due to bear activity.
Study Area
The study area encompassed approximately 111,196 hectares; 46% (51,150
hectares) was within the boundaries of southern GRTE, Wyoming (Figure 1.1). I
delineated the study area by creating minimum convex polygons (MCP) for each bearyear and combining them to create study area boundaries. I defined a bear-year as data
from one bear collected during one field season. The area extended from Moran Canyon
at the north past the southern border of the park to Wilson, Wyoming. The south border
of the park was adjacent to a ski resort and additional developments on the outskirts of
Jackson Wyoming. The east and west borders of the park were contiguous with the
borders of Bridger Teton and Caribou-Targhee National Forests, respectively.
Elevations ranged from 1,959 m to 4,197 m. The study area contained six glacial
lakes at the base of the Teton Mountains, with stream drainages through each canyon.
11
Average high and low temperatures for a 30 year period (1971 – 2000) at Moose
Wyoming in January and July were -3.4, -17.6, and 26.0, 4.9°C, respectively (Western
Regional Climate Center http://www.wrcc.dri.edu). Mean annual precipitation for the
same 30 year period, was 56.2 cm, with most occurring in the Teton Mountains as snow
(Regional Climate Center http://www.wrcc.dri.edu).
Vegetation communities varied from sagebrush (Artemesia tridentata) on the
valley floor to subalpine fir (Abies lasiocarpa) and whitebark pine (Pinus albicaulis)
stands just below treeline (Shaw 1976). Most low elevation forested areas within the
park were dominated by lodgepole pine (Pinus contorta). Many climax stands of
Engleman spruce (Picea englemannii), Douglas fir (Pseudotsuga menziesii), and
subalpine fir occurred throughout the area. Huckleberry (Vaccinium spp.), buffaloberry
(Sheperdia canadensis), chokecherry (Prunus virginiana), and hawthorn (Crataegus
douglasii) were common in the forest communities. Riparian areas were dominated by
cottonwood (Populus spp.) and aspen (Populus tremuloides) with a variety of willow
(Salix spp.) and graminoid species in the understory.
Methods
I captured black bears using culvert traps during summers of 2005 and 2006.
Bears were immobilized with tiletamine hydrochloride and zolazepam hydrochloride
(Telazol, Fort Dodge Animal Health, Fort Dodge, Iowa) following the approved animal
welfare policy (United States Geological Survey Midcontinent Ecological Science Center
Policy for Utilization of Animals in Research). All captured bears, except dependent
12
offspring or very small subadult black bears, were fitted with Telonics Spread Spectrum
(SS) GPS collars (Telonics, Inc., Mesa, AZ), a CR-2a programmable breakaway collar
release (Telonics, Inc., Mesa, AZ), a biodegradable canvas spacer, and a motion sensor
that reduced transmitter pulse rate if stationary for 4–5 hours. Collars attempted to
acquire GPS fixes every 180 minutes, a single male collar attempted a fix every 90
minutes. Each transmitter also contained a -15° head to tail activity switch that tallied
seconds of switch closure accumulated during a 15-minute interval just prior to the GPS
fix attempt. The number returned was a percentage of total seconds of switch closure
during the collection interval at 0.5% resolution. These activity counts were reflective of
the bear’s head-up head-down movement just prior to each GPS fix or attempted fix and
have been shown to be useful in discriminating between active and resting bears based
upon activity count (Schwartz et al. 2009). A sensor recorded temperature inside the unit
just prior to each fix attempt over a temperature range of -55º to +125º C, at a resolution
of 1º C. Date, time and activity level were recorded even if the collar was not able to
obtain a GPS location. Spread spectrum collars were programmed to be available for
data uploads on Tuesdays and Thursdays from 0800–1200 hours MDT (Schwartz et al.
2009). This technology enabled me to retrieve GPS locations weekly by aircraft and visit
GPS coordinates for each bear from a randomly selected 24-hour period while the signs
of bear use at each site were still fresh.
I assessed each site for black bear use by physical disturbance (i.e. log rips,
grazing, day beds, scats etc) and foods used (Mattson et al. 1990, Podruzny and Schwartz
2002). I examined every plot to determine if the bear was feeding, resting, or traveling.
13
For purposes here, I considered bears active if determined to be feeding or traveling. I
considered them inactive if I could determine they were resting based upon the presence
of a day bed. If a site contained mixed sign (both active and passive), I recorded all
activity types.
I calculated 100% minimum convex polygon (MCP) home ranges for each bearyear using Hawth’s Tools (Beyer, H. L. 2004. Hawth's Analysis Tools for ArcGIS.
Available at http://www.spatialecology.com/htools). I combined MCP home ranges
using Hawth’s Tool’s, to produce a polygon that created boundaries for the study area.
Missed Fix Analysis
With the activity values obtained from the SS collars, I determined activity break
points, diurnal activity patterns, and the cause of missed GPS fixes. The sampling unit
for activity was bear-year. I utilized logistic regression to evaluate variables related to
missed GPS fixes. The dependent variable was fix success, coded 0 for missed fix or 1
for a successful fix. Independent variables I evaluated included activity level, collar age
(days collar had been deployed), and hour of the day. Because hour is a circular variable,
I transformed hours to radians and used the sine and cosine in models (Flury and Levri,
1999). I used small sample size corrected Akaike’s Information Criterion (AICc) to rank
different a priori models with the above independent variables (Burnham and Anderson
2002). I considered models within 2 AICc units of the top model equivalent (Burnham
and Anderson 2002). I conducted all statistical analysis using the R statistical
programming language (R Development Core Team 2009).
14
Activity Level Analysis
I examined monthly activity patterns hourly from April to October. I contrasted
activity level against monthly average temperatures in 2005 and 2006
(www.met.utah.edu/cgi-bin/droman/meso_base.cg?stn=GTGW4) using Pearson’s
correlation coefficient (Kolbe and Squires 2007). Since black bears were inactive at
night, I selected locations between sunrise and sunset. The activity values that were
logged from the collars ranged from 0 to 90. In order to determine a possible threshold
among these values if a bear was “resting” or “active”, I utilized the logistic discriminant
analysis function. The discriminant analysis function is used to determine if an object
belongs to a certain group (Seber 1984). I used activity values logged by collars at fieldverified bear locations to classify GPS fix attempts as belonging to an “active” or
“resting” bear. I used logistic regression with generalized estimating equations to
evaluate activity levels at site visits where there was physical evidence of resting (e.g.,
daybeds). I omitted site visits that exhibited both resting and activity signs from the
analysis (n = 46). I set the dependent variable as the recorded activity observed at each
visited site (0 = resting, 1 = active) and the independent variable as activity count
recorded in the collar. I treated individual bears as clusters since clustered data may be
positively correlated (Agresti 2007), and utilized generalized estimating equations to
avoid bias among standard errors. I used the logistic discriminant function to classify
GPS fixes as belonging to either an active or resting bear, where the threshold for
classifying a bear as active was given by Seber (1984):
 n1 
 ,
 n2 
β 0 + β1 × activity count > ln
15
where n1 and n2 were the sample sizes from feed site visits where activity was determined
for active and resting bears, respectively (Schwartz et al 2009). βo and β1 were estimated
from the above logistic regression which accounted for bears with multiple measurements
(Schwartz et al. 2009). I derived the threshold value by solving the above equation
algebraically in terms of activity count. Values less than or equal to the break point were
consistent with a resting bear whereas values greater than the break point corresponded
with an active bear (Gervasi et al. 2006). I then applied this active/resting break point to
all GPS locations.
Activity Patterns
I graphed average activity levels (mean ± standard error) against hour of the day
with twilight, sunrise, and sunset times to establish diurnal activity patterns. I used bearyear as the sample unit. I obtained twilight, sunrise and sunset times from
http:/aa.usno.navy.mil.data/docs/RS_OneYear.php. I defined diurnal as the time between
sunrise to sunset and nocturnal as the time between evening astronomical twilight and
morning astronomical twilight. I defined crepuscular as the time from morning
astronomical twilight to sunrise and sunset to evening astronomical twilight.
Astronomical twilight is defined as the time period when the sun is 12º to 18º below the
horizon and used to describe activity patterns for bears (Schwartz et al. 2009).
I analyzed activity levels in relation to the bears’ distances from roads, trails, and
developed areas within the park by hour of day. I used bear locations from June–August,
since most bears were collared during this time. I grouped fix locations by hour of the
day and then binned them into one of three ordinal distance groups for roads and
16
developed areas; < 1 km, 1–2 km, and >2 km. Because black bear locations were close to
trails, I adjusted ordinal distance groups to <0.5 km, 0.5–1.5 km, and >1.5 km. Roads
were defined as gravel and paved roads open to public vehicle travel. Developments
within the park consisted of administrative buildings, housing, picnic areas, campground
and other buildings utilized by staff and visitors (Schwartz et al. 2010).
I calculated distance from each fix location to the closest road, trail, or
development using ArcMap 9.3. I fit no-intercept regression models for each bear-year
for each hour of the day according to Murtaugh (2007). Each model returned the mean as
the beta coefficient with a corresponding standard error (Schwartz et al. 2010). I
summarized results using the weighted average of the bear-year specific regression
coefficients, with weights proportional to reciprocals of squared standard errors for
individual fits (Murtaugh 2007). I considered differences statistically significant if the
95% confidence intervals did not overlap (at each hour). I conducted the above analysis
to determine if differences existed for the mean activity level from 0600 – 1900 hrs
(when bears were most active) at the respective distance groups to roads, development,
and trails.
Site Visit Analysis
I examined every plot in up to three levels of detail. Level 1 information
identified and measured at all plots included: (1) slope, (2) aspect, (3) elevation (Mattson
and Blasche 1990), (4) habitat type (Steele et al. 1984), and (5) forest cover type
(Despain 1986). If there was no evidence of activity, the site was deemed a travel site
and data collection ceased. If I observed log rips, cambium tears, overturned rocks,
17
and/or daybeds, I evaluated the site at the next level. At Level 2, I determined the center
of activity and established a 10-meter habitat plot where I measured several parameters.
If the site contained a day bed, I measured its length, width, and depth, the closest
standing tree (tree species, DBH [diameter breast height]), the distance from this tree to
the daybed, and closest log was recorded (distance to the daybed and width). A 10 m
tape measure was laid out in each cardinal direction from plot center, and the ground
cover type was determined at 10 cm intervals along the transect lines. Ground cover was
classified into five categories: (1) bare ground, (2) graminoid, (3) forb, (4) shrub, and (5)
log. This information was used to give an overall estimate of the understory cover.
When the tape measure intersected with a log at the 10 cm interval, the log would be
measured (length and width) and then I determined if a bear could rip into the log
(Mattson and Blasche 1990, Podruzny and Schwartz 2002). All cambium tears within the
10 m plot were measured (tree species, length of tear, width of tear and DBH of the tree).
When evidence of plant grazing or browsing for berries was observed, the site
would be investigated at a finer scale. At Level 3 activity, four 10 meter transects (North,
South, East and West) were established that branched out from the center of grazing
activity. A Daubenmire frame was utilized along the transects to detail possible use that
was overlooked. Along the north transect, a Daubenmire frame was investigated at 1, 5,
and 9 meters. The south transect was inspected at 3 and 7 meters. The East and West
transects were examined at 8 and 4 meters, and 2, 6, and 10 meters respectively. For
each bear food within the Daubenmire frame I identified: (1) plant species, (2) use of the
plant, (3) degree of use [low, medium, high], (4) phenological stage [preflower
18
vegetative, flower initiation, full flower, post-flower/fruit, partially desiccated/browned,
cured or fall re-growth present] if it was a graminoid or forb, and (5) number of berries
on shrubs when appropriate. The mean height of plants and percent cover of each plant
category (graminoid, forb, shrub) and bare ground were recorded. I used radio telemetry
to determine if collared bears were near GPS locations from the week prior. If collared
bears were near these areas, I avoided them so I would not disturb the bears and possibly
bias their use in the area.
I collected scats by searching a circle 15-m radius around all sites. Scats were
frozen then air-dried before being analyzed. I analyzed scats according to Mattson et al.
(1991) as outlined below.
Scat Analysis
To determine volume, I air-dried each scat and measured the displacement of 600
ml of water in a 2 liter graduated cylinder. I then washed scats through two screens
following Mattson et al. (1991), to identify scat contents. I took 1 random subsample
from the coarse grain screen (0.3175 cm holes) and 1 from the fine grain screen (0.0833
cm holes). I visually estimated each food item from these subsamples to determine
percent volume (Mattson et al. 1991). I identified plants (graminoids, forbs, and shrubs)
to species when possible. I identified mammal hair to species when possible using
Moore et al. (1974); and insects were classified into broad groups. I determined
frequency, percent frequency, percent volume, importance value percent, and importance
value for each item identified in scat (Raine and Kansas 1990, Aune 1994). These were
defined as:
19
Frequency = Number of scats having the same item
% Frequency = Frequency of item/Total # of scats X 100
% Volume = Total percent volume of item/Total # of scats
Importance Value = (% volume)(% frequency)/100
% Importance Value = Importance value of item/Σ all importance values X 100
I adjusted percent volume using a scat correction factor for each food item to
reduce bias for easily digested items (Hewitt and Robbins 1996). I combined scat data
were combined from 2005 and 2006 due to small sample sizes. I lumped scats into 3
seasons for analysis spring (den emergence – July 15th), summer (July 16th – August 21st)
and autumn (August 22nd – den entrance).
I utilized Pianka’s index to determine dietary niche overlap for males and females
during spring, summer, and autumn (Pianka 1973, Pianka and Pianka 1976, Kauhala et al.
1998). Pianka’s index, Ojk, ranges from 0 (no dietary niche overlap) to 1 (total dietary
niche overlap) (Pianka and Pianka 1976). The formula is:
= ∑ ∑
∑
Where p is the proportion of each food item i, detected in each scat for sex j, during a
particular season k.
I combined sexes and calculated Pianka’s index to determine dietary niche
overlap that may occur amongst seasons; spring to summer, summer to autumn, and
spring to autumn. I bootstrapped the indices for the above comparisons using 20,000
permutations, with replacement at the 95% confidence interval.
20
Results
Missed Fixes
I captured 9 bears (4 males and 5 females) resulting in 13 bear-years for analysis.
The collars attempted to collect 12,794 fixes while deployed on black bears and
successfully obtained 9,507 fixes (74%). One female collar began to malfunction at den
emergence in 2006, and I omitted these data (n = 1,236). My discriminate analysis
suggested collars were more likely to obtain a successful GPS location if the bear was
predicted to be active. Activity level and hour were significant variables (p < 0.0001),
while collar age (p > 0.71) was not. Seventy one percent (n = 2,338) of the 3,287 missed
fixes (Figure 1.2) were associated with an activity level of ≤16.5, indicating that the
individual bear was resting. The most parsimonious top model, that had variables
activity level and hour of day, had the lowest AICc score (Table 1.1). The other two top
AICc models contained activity level, hour of the day, and age of collar (Table 1.1).
Activity Level Analysis
From June 2005 to October 2006, collars deployed on my black bears recorded
14,030 activity counts. Because temperature records were missing for some days/hours, I
and because I only used activity counts collected during the day, I sampled 8,130 counts
to contrast recorded activity level against temperature from sunrise to sunset. My
analysis showed that temperature and black bear activity were negatively correlated for
months with pronounced bimodal activity (June – August) (Table 1.2). Black bears had
21
lower activity levels during mid-day diurnal periods that corresponded with higher
ambient temperatures. Mid-day resting usually occurred from 1000 – 1200.
I visited 501 sites and determined activity based on physical evidence. I recorded
bears as active 79% (n = 397), resting 12% (n = 58) or both 9% (n = 46). I omitted the 46
sites with mixed activity (feeding, traveling, etc). Using the Seber (1984) equation with
the activity estimates (1.043256 + 0.053012 x activity count > ln (397/58)), I calculated
the activity break point as 16.6. Based on this, I designated all activity levels ≤16.5 as
resting, whereas activity levels >16.5, were designated active bears. Fifty two of 58
(89%) sites that exhibited resting had an activity level <16.5, whereas 231 of 397 (58%)
sites visited that displayed signs of activity had an activity level ≥16.5.
Activity Patterns
Black bears in southern GRTE exhibited bimodal activity peaks at the crepuscular
time periods (0500 and 2000) from June through August. The increase in activity prior to
crepuscular times occurred near the beginning and end of astronomical twilight. During
nocturnal hours, black bears’ activity levels were consistent with resting (Figure 1.3).
Bears rested nocturnally from 2100 – 0200. Bears were least active after den emergence
(April) and before den entrance (October), and had peak activity in July (Figure 1.4,
Table 1.3). Bears were more active in July, than in any other month (Table 1.3). Bears
spent more time resting in April than May – October (Table 1.3).
I analyzed 5,462 fixes in relation to their distance from roads, developed areas,
and trails within the park. Black bears that remained close to roads appeared to have
slightly higher activity levels than bears farther away from roads (Figure 1.5), but there
22
was not a statistical difference at any hour from < 1 km and > 2 km away from roads. I
did detect statistical differences in a few instances, but concluded these were type II
errors associated with multiple t-tests. For example, bears were more active at a middle
distance of 1–2 km away from roads than >2 km at 0200 (Figure 1.5). Likewise at 0200,
bears were more active when they were < 1 km away from roads than when 1–2 km from
roads. Activity patters were similar for distances to developed areas (Figure 1.6) and
trails (Figure 1.7). I did not detect a difference in the mean activity levels, from 0600 to
1900 hrs, at the varying distances to roads, developed areas, and trails (Figure 1.8 – 1.10).
Scat Analysis
I collected 151 scats from May to October. Scat composition varied as foods
became available during the seasons. I identified 22 different food items in the scats
collected (Appendix A). I did not find any anthropogenic foods or debris associated with
anthropogenic foods in any of these scats. Scat composition was dominated by three
food items; graminoids, berries, and ants. Overall graminoids had the highest frequency
(n = 103) among food items (Table 1.4). Ants (n = 55) had a similar frequency to berries
(n = 57) in the scats sampled, but due to the smaller volume of ants, had a lower percent
importance value of 17.4% (Table 1.4) when compared to berries. Berries had the
highest percent importance value (51.2%). Small mammals and rare items had the lowest
percent importance value (0.1%) and lowest frequency (n = 6 and 5 respectively).
Maggots, ungulates, whitebark pine, and forbs, each had a percent importance value <
5% (Table 1.4).
23
The three dominant food items, ants, graminoids, and berries, varied inversely
through the seasons. Graminoids remained a significant food item from spring to
autumn. They were the most significant food item in spring (50.5%). There was a
gradual decrease in the percent importance value from spring to autumn (Table 1.5). Ant
use also declined from spring to autumn, while berry use increased during the same time
period (Figure 1.11). Berries were the most important food source in the summer
(68.4%), autumn (86.5%), and overall when the seasons were combined (51.2%).
Although ungulate use remained constant, it was most frequently observed in scats in the
spring (Figure 1.11, Table 1.5). I did not detect whitebark pine in spring, but it became
more important during autumn (6.8%).
The Pianka Index niche overlap between sexes varied among seasons (Table 1.6).
I observed the greatest dietary niche overlap between males and females in the spring (O
= 0.98), followed by autumn then summer. When I combined sexes, the greatest dietary
niche overlap was between summer and autumn, and the least overlap between spring and
autumn (Table 1.7).
Site Visit Analysis
Out of 9,507 GPS locations that were downloaded from the 9 SS collars, 501 bear
sites were visited between June to October, 2005 (n = 146) and May to September, 2006
(n = 355). A total of 113 sites visited were determined to be travel sites (Table 1.8),
where no feeding or bedding activity was observed. I found evidence of feeding at 284
sites. I did not detect anthropogenic foods at any of the site visits. Four hundred twenty
(84%) of the 501 field-verified sites were in forested areas (Table 1.8). I recorded that
24
bears utilized non-forested areas at 81 (16%) field-verified sites. I documented a
majority of daybeds in forested areas (n = 56) as opposed to non-forested areas (n = 2)
(Table 1.8). Most daybeds that I observed were found at the base of trees with a mean
DBH of 55.86 cm (SE ± 2.92 cm). On average, field verified sites located in non-forested
areas were 49.4 m (SE ± 8.5 m) from the forest edge.
I recorded 32 different animal and plant species at site visits (Appendix A). Ants
were fed on at 49% (n =165) of the sites (Figure 1.12). I observed foraging for most
frequently during July with moderate consumption in June and August. I recorded bears
feeding on graminoids most frequently in June followed by July then May (Figure 1.9).
Bears grazed most frequently on brome (Bromus spp.) (n = 70), followed by pinegrass
(Calamagrostis rubescens) (n = 39), sedge (Carex spp.) (n = 29), bluegrass (Poa spp.) (n
= 14), and wildrye (Elymus spp.) (n = 12). Berry browsing occurred mainly in August.
Bears browsed most commonly on huckleberries (n = 46), serviceberry (Amelanchier
alnifolia) (n = 17), hawthorn (n = 16), chokecherry (n = 11), and whortleberry
(Vaccinium spp.) (n = 9). Overall forb use was low at site visits. Grazing on dandelion
(Taraxacum officinale) (n = 6) was observed most frequently, followed by angelica
(Angelica arguta) (n =5), cow parsnip (Heracleum maximum) (n = 5), and geranium
(Geranium viscosissimum) (n = 4). Ungulate use was higher in the fall than during the
spring (Figure 1.9). I visited 47 sites that had either sign of calf or adult ungulate carcass
consumption. I recorded bears feeding on ungulates 8 different times between May and
October (Table 1.9). Several bears would stay at an ungulate carcass for over 24 hours
resulting in several site visits at a single carcass (Table 1.9). Deer and elk calves were
25
taken in May and June (n = 3), whereas adult elk were consumed from August to October
(n = 5) (Table 1.5).
The Pianka Index of niche overlap between sexes varied among seasons (Table
1.10). I observed the greatest dietary niche overlap between males and females in the
spring (O = 0.98), followed by summer then autumn. After combining sexes, the greatest
dietary niche overlap I observed was between spring and summer, and the least overlap
between summer and autumn (Table 1.11).
Some foods I classified to species at site visits, were not seen in the scats
collected (Appendix A), whereas others were observed in scats (eg., moose [Alces alces]
and small rodents) were not detected at site visits (Appendix A). Oregon grape (Mahonia
repens) and Twinberry (Lonicera spp.) were the only shrubs that I identified in scats, but
feeding was not observed at site visits (Appendix A). Tree cambium (n = 5) was eaten at
sites visited, but was not detected in scats (Appendix A, Figure 1.12).
Discussion
I found that GPS collars were more likely to miss a fix when the bear was
predicted to be resting, failing to reject my second hypothesis. My results were
consistent with previous studies that concluded that bear behavior affected fix success in
GPS collars (Bowman et al. 2000, Graves and Waller 2006, Heard et al. 2008, Schwartz
et al. 2009). Bears were often bedded at the base of larger trees. This coupled with the
potential change in angle of the antenna likely obscured visibility to an adequate number
of satellites required for a successful GPS fix (Belant 2009). Since GPS fixes were
26
missed due to activity, I used the GPS locations from this study to create home ranges for
habitat use analysis. At least two of the top 3 AICc models contained all three variables,
activity, hour of the day, and age of collar. Not surprisingly, the most parsimonious
model contained the variables activity level and hour of the day. The second top model
had an interaction term between activity and hour of the day. Bears were commonly,
most and least active, during the same times of the day.
Overall black bear activity increased through spring (Zytaruk and Cartwright
1978), corresponding with intense foraging during berry season. This peak activity could
be attributed to the phenological state of shrubs in GRTE, with an increase in head
movement among berry patches. Lariviere et al. (1994) suggested that activity during the
day concurrent with berry season may increase foraging efficiency. Black bears in
southern GRTE also utilized ants the most frequently in July. Bears consuming ants
usually feed intensively at ant colonies in brief bouts and move on (Noyce et al. 1997,
Swenson et al. 1999), corresponding with increased activity levels at that time. In 2005,
the whitebark pine crop was lower than 2006. If natural food shortages occur, bears may
travel more extensively (Schoen 1990), in turn increasing their levels of activity
(Amstrup and Beecham 1976, Knight et al. 1988). This natural food fluctuation may
have caused an increase in activity levels in the late summer of 2005.
Activity levels that dropped before denning were similar to what Lariviere et al.
(1994) found in black bears in Quebec. Monthly activity patterns indicated a unimodal
activity pre and post-denning (April, October), consistent with other studies that have
documented a lack of bimodal activity patterns shortly after den emergence (MacHutchon
27
2001). Ambient temperatures did not exceed 20 °C (Figure 1.3) during April and
October, when activity patterns were unimodal. The bimodal activity patterns I observed
in summer (June – August) likely reflect a response to ambient temperature highs
(Schwartz et al. 2010).
The peaks in crepuscular activity I observed were consistent with naturally
foraging (Ayers et al. 1986) and non-human habituated bears (Olson et al. 1998,
Beckman and Berger 2003, Lyons 2005). Bears consuming human-foods may maintain
nocturnal time periods to avoid detection. Olson et al. (1998) demonstrated that brown
bears with little associated human activity had relatively constant levels of activity
through the day. I rejected my first hypothesis that bears are less active when closer to
roads, trails and human developments. Overall, bear activity levels were consistent
regardless of their distance from roads, human developments, and trails. For naturally
foraging bears, crepuscular activity patterns may minimize encounters with humans
during peak recreation times in high human use areas (Ayers et al. 1986, Gunther 1990,
Schwartz et al. 2010). Although the data were not statistically significant, black bears
tended to be more active when > 2 km away from human developments than closer
distances during midday (1100 – 1300) (Figure 1.6). Lower activity levels closer to
human developments at peak times may be a response by black bears to avoid human
activities. Responding temporally may allow bears to utilize prime habitat in different
capacities. I found that bears had a minimal response to roads or trails in regards to
activity. The black bears that I sampled remained diurnal regardless of distance to roads,
unlike previous research on Malayan sun bears (Helarctos malayanus) (Griffiths and Van
28
Schaik 1993) or grizzly bears (Waller and Servheen 2005). These studies suggested that
bears that utilized roads more frequently were nocturnal when the volume of traffic was
lower. The similarity in activity levels with varying distances from the road, may also be
a function of habitat. Graham et al. (2010) found that bears utilized areas close to roads
that provided forage in the spring. In my study area, a majority of the Teton Park road
runs through sagebrush habitat that is close to forests or timbered islands. The distance to
cover from a road may increase the likelihood of a bear crossing the road (Graves et al.
2006). Although I evaluated activity levels in proximity to roads, I did not investigate
activity levels at different distances to forested habitat. Differences in activity level may
depend on a bear’s distance to forest cover in addition to distance to human activities.
Examining activity levels at varying distances to forested habitat may also help identify
key road crossing corridors.
Activity patterns were similar throughout the day regardless of a bear’s distance
from trails. From June through August, 87% (4,763) of bear locations were < 1.5 km
away from a trail. Trail density is higher in southern GRTE compared to the northern
part of the park. The activity patterns displayed by black bears in the southern GRTE
document that black bears forage naturally, but may alter their patterns slightly when
human activity is highest. Bears may have activity patterns that reflect a certain tolerance
and habituation to human disturbance. Although activity levels of the bears sampled did
not differ at different distances from trails, I am not suggesting that bears were not
affected. Bears may flee an area and be displaced from the habitat along a trail when
they encounter a hiker (Jope 1985). Similar to bears that flee humans more easily when
29
in open habitats (McLellan and Shackleton 1989), bears may have different activity
patterns in open versus closed habitat.
Activity highs that I observed, during crepuscular times were also similar to
activity peaks where black bears were sympatric with grizzly bears (Holm et al. 1999,
Schwartz et al. 2010). Although black bears in southern GRTE were mainly diurnal, they
had lower activity levels midday compared to black bears in the northern GRTE
(Schwartz et al. 2010). Remaining active during the day and resting at night may be a
strategy to avoid contact with humans and male grizzly bears that are nocturnal (Holm et
al. 1999, Schwartz et al. 2010). Black bears utilized berry fields at different times than
grizzly bears (Schaffer 1971), allowing access to prime habitat by possible avoidance.
Alternatively, MacHutchon et al. (1998) suggested that black bears responded more to
grizzly bear presence than human activity. Black bears may shift their temporal activity
patterns again to avoid grizzly bears (Belant et al. 2010) increasing their activity during
the day and encounters with humans.
Bears fed on a wide range of foods including vegetation, insects, and mammals
that were seasonally abundant. Food habits of black bears observed in southern GRTE
were consistent with what has been widely reported across North America (Hererro 1978,
Raine and Kansas 1990, Aune 1994, Bull et al. 2001, Baldwin and Bender 2009). My
study supports previous research findings (Irwin and Hammond 1985, Raine and Kansas
1990, Boileau et al. 1994, Bull et al. 2001, Baldwin and Bender 2009) that graminoids
and ants are important food sources for black bears. Although ants were a small
proportion of the total volume of scats, they are positively related to an increased level in
30
gross energy (calories/g) in black bear diets (Baldwin and Bender 2009). Accessibility of
ants when other food items are unavailable, may explain the increase in ant use during the
spring into summer (Noyce et al. 1997, Swenson et al. 1999). Ants may also be an
important food source when others fluctuate (Swenson et al. 1999). I observed black
bears consuming neonate ungulates during the spring, which coincides with fawning of
mule deer and elk. Bull et al. (2001) also reported neonate use in spring then scavenging
during September and October. It was unclear at the sites I visited where adult elk were
consumed, if the elk were simply scavenged or predated upon by that specific black bear.
Bears utilized whitebark pine during the late summer and autumn, which is
important before denning (Mattson and Jonkel 1990). Whitebark pine consumption
increased in mid-August when the cones matured and seeds became accessible.
Although whitebark pine was detected in scats from July – October, I only observed
feeding on whitebark pine cones in August and September. In contrast to Kendall (1983)
and Raine and Kansas (1990), I did not find evidence of black bears utilizing squirrel
middens at site visits, but that they climbed trees and broke down branches to obtain
whitebark pine cones instead. Utilizing a different foraging strategy on whitebark pine,
may be beneficial to black bears when they occupy the same area as grizzly bears. This
strategy may enable them to obtain whitebark pine cones that are otherwise inaccessible
to grizzly bears or allow them to forage safely in the presence of grizzly bears.
Male and female bears had the greatest niche overlap in spring based on both scat
and site visit analyses. Males did not have every food item represented in their samples
for summer scat analysis and autumn site visit analysis. Many food items lacked a
31
representative sample size between sexes, which may explain the low value for Pianka’s
index. When sexes were combined for scat analysis, summer and autumn had the
greatest overlap. Berry use was higher during the summer and autumn and was minimal
during the spring. In addition, graminoids and ants had greater frequencies in scats, in
the spring compared to summer or fall. Pianka’s index values for site analysis varied
from the niche overlap documented in the scat analysis. Spring and summer had the
greatest overlap since most sites visited during those seasons had signs of graminoid and
ant consumption. During the autumn many sites visited had observations of berry
browsing and a large number of sites were associated with an ungulate carcass. The
major discrepancies which were documented between niche overlap may be a result of
small sample sizes in the number of scats analyzed and GPS locations visited.
Roots were not used at site visits or identified in the scats collected. Aune (1994)
did not observe root consumption by black bears but did observe root use in the grizzly
bears in his study area. Hererro (1978) suggested that black bears lack of root use is due
to their evolutionary adaptations. Roots are not a food item utilized by black bears in
southern GRTE, which is consistent with the findings of black bears in southwestern
Alberta and northwestern Montana (Holcroft and Herrero 1991, Aune 1994).
The data supported my third hypothesis that some food items identified at site
visits were not observed in scats. Most notably, cambium feeding was observed at site
visits, but was not detected in the scats collected. Tree cambium is highly digestible
(Raine and Kansas 1990), unlike most vegetation with high cellulose content, which is
not easily digestible and have low scat correction factors (Hewitt and Robbins 1996).
32
Tree cambium that passes through the digestive system may be sparse and mixed with
tree bark debris in scats. Since tree cambium is not readily identifiable in scats, site visits
provide the opportunity to observe this food use. Many graminoids and forbs were not
identified to species in scats, therefore these were not compared to observations at site
visits. While at site visits, graminoids and forbs were easily identified to species. Level
3 site analysis enabled close inspection of the species used that may have been
overlooked. Raine and Kansas (1990) identified 8 food items at site visits that were not
identified in scats. Scats provided several food items that were not identified at site
visits, including moose, small mammals, and maggots. Maggots may be a secondary
food source when a bear is scavenging on a carcass. Shrubs that were not commonly
used by black bears were identified in scats. Scat analysis and GPS site visits highlighted
different species being used by bears that weren’t observed in the other technique.
Utilizing both techniques, i.e. analyzing scats and visiting GPS site locations, provided
me with a more accurate picture of the diet of black bears in southern GRTE.
The data from this study add to our understanding of black bear activity and food
patterns in the southern part of GRTE. Although previous literature suggests black bears
can adapt their activity patterns in response to disturbance or threats (i.e., humans, grizzly
bears), park managers can cluster backcountry campsites to minimize conflicts between
bears and humans. During the course of my study the whitebark pine crop did not
fluctuate to low levels. I recommend that a longer study be considered that encompasses
whitebark pine crop highs and lows coupled with soft mast surveys to provide additional
information regarding black bear activity and food patterns in GRTE during autumn.
33
This additional research should provide insight into the variability of the soft mast crop in
GRTE, and if soft mast coupled with hard mast might be a limiting resource to bears
(Reynolds-Hogland et al. 2007). Mattson et al. (1992) demonstrated that grizzly bears in
Yellowstone utilize locations closer to developments and roads when whitebark pine
crops are low. And Gunther et al. (2004) documented an increase in property damage,
anthropogenic foods obtained, and human injury when natural bear foods were in short
supply in the GYE. It is currently unclear if this relationship exists in GRTE, especially
with black bears. However, the continued research I recommend should provide
additional insight into this issue.
The activity levels close to human developments and overall food habits that I
observed may be a subset of their patterns within the southern GRTE. Inman and Pelton
(2002) demonstrated the importance of soft mast production when hard mast crops fail.
Although it has not been documented in southern GRTE, abundant soft mast crops may
dampen the effect of a diminished whitebark pine cone crop, resulting in similar activity
patterns from year to year. Due to the limitations of data identifying exactly where
backcountry campers were in a camping zone, I could not analyze this aspect of
recreation within the park. Park visitation has fluctuated since 1980, but has also
increased over time, along with backcountry overnight visits (National Park Service
Statistics http://www.nature.nps.gov/stats/). Marinka (1982) found that the increase in
visitation coincides with a consistent encroachment on bear habitat, increasing bears
exposure to humans and increasing bear-human conflicts near Glacier National Park.
Singer and Bratton (1980) suggested that bear-human conflicts in the backcountry were
34
due to the high quality of the habitat where campsites were located. In areas that have
high levels of backcountry use, I suggest that the park regulate these areas by eliminating
camping zones and creating designated campsites. Designated campsites may disturb
bears less frequently by producing an environment where humans are more predictable in
space and time (MacHutchon and Wellwood 2002). The campsites can also be
strategically placed in areas that contain minimal bear forage (soft and hard mast), further
decreasing the bear-human interface.
My study suggests that soft mast production is important during the late summer
and autumn in GRTE. If designated campsites are located in prime bear foraging areas,
these sites can be temporarily closed when bear use increases, minimizing disturbance to
foraging bears. Ultimately campsites that are located in prime bear foraging areas could
be moved to locations that are utilized less frequently by bears. The significance of bear
food should be considered when new development or redevelopment occurs in the park.
Bear foods should be considered when trails within the park are being rerouted. In order
to decrease the amount of bear-human interactions, trails should be routed through
habitats that contain less preferable food items. Where development, transportation and
recreation corridors are not optional, the activity pattern and food habit data will be
useful in educating the public to reduce encounters.
35
Figure 1.1 Study area, Grand Teton National Park, Wyoming.
36
Figure 1.2. Frequency of missed GPS fixes by activity level for black bears, southern
Grand Teton National Park, Wyoming, 2005–2006. The vertical dashed line represents a
cutoff value for resting versus active bears as determined by logistic discriminate analysis
and the equation of Seber (1984).
37
37
Figure 1.3. Means of black bear activity scores and temperatures by hour, southern Grand Teton National Park,
Wyoming, 2005–2006. Data are displayed in individual panels by month April – October. Vertical lines
designate monthly twilight and gray shading represents time between sunset and sunrise.
38
Table 1.1. AICc score, ∆AICc, AICc weight, log likelihood, and number of parameters for models constructed to
evaluate what variables are responsible for missed GPS fixes for collars deployed on black bears in the southern Grand
Teton National Park, Wyoming, 2005–2006.
k
Log
Likelihood
AICc
ΔAICc
wi
1. Activity level + MST Hour
2. Age of Collar + Activity level:MST
hour
3. Activity level + MST Hour + Age of
Collar
4
-7022.7
14053.4
0.0
0.5
8
-7020.2
14054.5
1.1
0.3
5
-7022.6
14055.2
1.9
0.2
4. Activity level
3
-7092.0
14188.0
134.6
0.0
5. Activity level + Age of Collar
4
-7091.9
14189.8
136.4
0.0
6. MST Hour
3
-7219.7
14445.4
392.1
0.0
7. Age of Collar + MST Hour
4
-7219.5
14447.0
393.7
0.0
8. Age of Collar
3
-7377.5
14759.1
705.8
0.0
38
Model
39
Table 1.2. Pearson’s correlation (r) of activity score and ambient temperature by month
for GPS-radio collared black bears in southern Grand Teton National Park, Wyoming,
2005–2006.
Month
April
May
June
July
August
September
October
n
Pearson’s r
p
257
1043
1418
1699
1575
1349
789
0.12
0.04
- 0.14
- 0.19
- 0.06
0.02
0.15
0.06
0.18
<0.001
<0.001
0.03
0.42
<0.001
Figure 1.4. Mean and standard error of black bear activity count by month, southern
Grand Teton National Park, Wyoming, 2005–2006.
40
Table 1.3. Proportion of time black bears were active by month in southern Grand Teton
National Park, Wyoming, 2005–2006.
Month
April
May
June
July
August
September
October
Proportion Active
Proportion Resting
SE p
0.14
0.40
0.50
0.62
0.54
0.47
0.27
0.86
0.60
0.50
0.38
0.46
0.53
0.73
0.02
0.01
0.01
0.01
0.01
0.01
0.01
41
41
Figure 1.5. Black bear activity counts by distance categories (< 1 km, 1 km – 2 km, and > 2 km) from roads, southern
Grand Teton National Park, Wyoming, 2005–2006. Numbers below the graph represent sample sizes used to generate
mean values displayed for each hour for each category. Confidence intervals are not shown, but values that are
statistically different (P<0.05) are shown as solid symbols.
42
42
Figure 1.6. Black bear activity counts by distance categories (< 1 km, 1 km – 2 km, and > 2 km) from developed areas,
southern Grand Teton National Park, Wyoming, 2005–2006. Numbers below the graph represent sample sizes used to
generate mean values displayed for each hour for each category. Confidence intervals are not shown, but values that
are statistically different (P<0.05) are shown as solid symbols.
43
43
Figure 1.7. Black bear activity counts by distance categories (< 0.5 km, 0.5 km – 1.5 km, and > 1.5 km) from trails,
southern Grand Teton National Park, Wyoming, 2005–2006. Numbers below the graph represent sample sizes
used to generate mean values displayed for each hour for each category. Confidence intervals are not shown,
but values that are statistically different (P<0.05) are shown as solid symbols.
44
44
Figure 1.8. Mean black bear activity counts (± 95% confidence intervals), during daylight hours (0600 – 1900), versus
distance (km) from roads, southern Grand Teton National Park, Wyoming, 2005–2006.
45
45
Figure 1.9. Mean black bear activity counts (± 95% confidence intervals), during daylight hours (0600 – 1900), versus
distance (km) from development, southern Grand Teton National Park, Wyoming, 2005–2006.
46
46
Figure 1.10. Mean black bear activity counts (± 95% confidence intervals), during daylight hours (0600 – 1900),
versus distance (km) from trails, southern Grand Teton National Park, Wyoming, 2005–2006.
47
Table 1.4. Frequency, percent frequency, total volume, percent volume, importance value and percent importance value of
food items identified in scats collected from black bears, southern Grand Teton National Park, Wyoming, 2005–2006.
Frequency
Percent
Frequency
Total
Volume
Percent
Volume
Importance
Value
Percent Importance
Value
Ants
55
36.4
1589
11.6
4.2
17.4
Berries
57
37.8
4140
32.9
12.4
51.2
Forbs
30
19.9
885
1.5
0.3
1.3
Graminoids
103
68.2
5040
8.0
5.5
22.5
Maggots
8
5.3
343
2.5
0.1
0.6
Rare*
6
4.0
112
0.8
0.0
0.1
Small Mammals
5
3.3
31
0.8
0.0
0.1
Ungulates
16
10.6
543
10.8
1.1
4.7
Whitebark
10
6.6
821
8.2
0.5
2.2
*Earthworms and unknown insects, n ≤ 5 for each
47
Item
48
Table 1.5. Frequency, percent frequency, total volume, percent volume, importance value and percent importance value for
food items identified in black bear scats by season, southern Grand Teton National Park, Wyoming, 2005–2006.
48
* Earthworms and unknown insects, n ≤ 5 for each
49
100%
90%
70%
Whitebark
Ungulates
60%
Small Mammals
Rare
50%
Maggots
40%
Gramminoids
Forbs
30%
Berries
20%
Ants
10%
0%
May
June
July
August
September
October
Month
Figure 1.11. Relative scat composition by month for black bears, southern Grand Teton National Park, Wyoming, 2005–2006.
49
Relative Percent Composition
80%
50
Table 1.6. Pianka’s Index values of dietary niche overlap between female and male black
bears by season for scat analysis, southern Grand Teton National Park, Wyoming, 2005–
2006.
Season
Piank'a
Index
Bootstrap
Index
95% C. I.
Spring
Summer
Fall
0.98
0.73
0.94
0.96
0.66
0.82
0.88 – 1.0
0.21 – 0.94
0.37 – 0.98
Table 1.7. Pianka’s Index values of dietary niche overlap among seasons, sexes
combined, for scat analysis, southern Grand Teton National Park, Wyoming, 2005–2006.
Seasons
Compared
Pianka's
Index
Bootstrap
Index
95% C. I.
Spring to
Summer
0.41
0.41
0.22 – 0.63
Spring to Fall
0.27
0.28
0.15 – 0.42
Summer to Fall
0.93
0.90
0.77 – 0.97
51
100%
90%
70%
Whitebark Pine
60%
Ungulates
Graminoids
50%
Forbs
40%
51
Food Item Percent Frequency
80%
Cambium
Berries
30%
Ants
20%
10%
0%
May
June
July
August
September
October
Month
Figure 1.12. Food items used by black bears identified at site visits, southern Grand Teton National Park, Wyoming, 2005–
2006.
52
Table 1.8. Activities by forested and non-forested areas at site visits for black bears,
southern Grand Teton National Park, Wyoming, 2005–2006.
Activity
Forested
NonForested
Feeding
241
43
284
Travel
78
35
113
Resting
56
2
58
Feeding/Resting
45
1
46
Total
Table 1.9. Ungulate use by black bears, southern Grand Teton National Park, Wyoming
2005–2006.
Bear
Sex
Month
Site Visits
Ungulate Age
22046
Female
May
1
Deer Fawn
22226
Female
June
3
Elk Calf
22046
Female
June
2
Elk Calf
22207
Female
August
5
Adult Elk
22046
Female
August
1
Adult Elk
22207
Female
August/September
11
Adult Elk
22228
Male
October
10
Adult Elk
22228
Male
October
14
Adult Elk
53
Table 1.10. Pianka’s Index values of dietary niche overlap between female and male
black bears by season for site visits, southern Grand Teton National Park, Wyoming,
2005–2006.
Season
Pianka's
Index
Bootstrap
Index
95% C. I.
Spring
0.98
0.96
0.88 – 0.99
Summer
0.97
0.93
0.82 – 0.98
Fall
0.63
0.60
0.37 – 0.82
Table 1.11. Pianka’s Index values of dietary niche overlap among seasons, sexes
combined, for site visits, southern Grand Teton National Park, Wyoming, 2005–2006.
Seasons
Compared
Pianka's
Index
Bootstrap
Index
95% C. I.
Spring to
Summer
0.81
0.80
0.70 – 0.88
Spring to Fall
0.40
0.39
0.25 – 0.55
Summer to Fall
0.39
0.39
0.25 – 0.54
54
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CHAPTER 2
BLACK BEAR (URSUS AMERICANUS) HABITAT USE IN SOUTHERN GRAND
TETON NATIONAL PARK
Abstract
Black bears (Ursus americanus) in Grand Teton National Park (GRTE),
Wyoming occupy an environment that is changing due to human pressure and
environmental variability. GRTE is an ideal place to observe black bears utilizing habitat
that is largely affected by natural processes, but also dominated by human recreation. I
investigated habitat use of black bears and how human activities influenced it in southern
GRTE. I studied 9 black bears equipped with spread spectrum technology (SS) radio
collars from June, 2005 to October, 2006. Collars recorded date, time, activity level, and
a GPS location. I used the local convex hull to establish home ranges and ArcMAP 9.3
to measure distances traveled in a 24 hour period. I used the Mahalanobis distance (D2)
to create models of bear use in the study area and for the entire park. Models
incorporated human use and natural habitat variables. I used the Mahalanobis Distances
Factor Analysis (MADIFA) to quantitatively break down the D2 statistic into linear
combinations to determine the impact of each variable on D2. My models displayed areas
of high use (i.e. larger D2 values), in forested regions adjacent to trails and road. Several
axes in different linear combinations, including habitat and human use covariates, are
present in the analysis. This suggests that a complexity exists for black bear habitat use,
beyond proximity to human use activities. Although my results suggested that bears did
64
not avoid areas close to trails and roads, I lacked the fine scale human use information
and therefore could not determine if human activity might have affected their use.
Therefore it would be an oversimplification to conclude that black bears only utilize areas
close to trails and/or roads in southern GRTE. Additional studies should be carried out to
analyze bear habitat use in response to high and low use trails in GRTE.
Introduction
Black bears (Ursus americanus) in Grand Teton National Park (GRTE) occupy an
environment that is changing due to human pressure and natural processes. Although the
landscape contains rich habitat it also includes many factors that potentially limit or alter
how bears use the landscape. Black bears depend on the forest not only for food but also
for protection from predators (Herrero 1978). Black bears have been documented
utilizing forested habitats more frequently than non-forested habitats (Aune 1994, Holm
et al. 1999, Fecske et al. 2002, Lyons et al. 2002), and several studies have detailed on a
fine scale how they interact with their habitat (Reynolds and Beecham 1980, Unsworth et
al. 1989, Fecske et al. 2002, Koehler and Pierce 2003). However, habitat use has not
been studied in southern GRTE, where pressure from humans, imminent competition
with grizzly bears, and potential food shortages are at play. Their reactions to these
variables could include changes in spatial use of the area or increases in human conflict.
In order to enhance black bear management practices in high human use areas, like
southern GRTE, it is important to fully understand how they utilize the landscape and
how human activities may potentially influence this use.
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Home Range and Habitat Use
Home ranges for black bears in North America differ in size due to a number of
environmental factors (Lindzey and Meslow 1977, Smith and Pelton 1990, Aune 1994,
Fecske et al. 2002). A black bear’s home range consists of several habitat types that
encompass adequate protection (Herrero 1978) and food resources (Smith and Pelton
1990). The average size of a home range is usually a function of the age, sex,
reproductive status of the bear (Young and Ruff 1982), and quality and distribution of
food resources (Cowan and Jonkel 1971, Amstrup and Beechman 1976). Males typically
have larger home ranges than females, to ensure they overlap with home ranges of
several breeding females (Orians 1969, Lindzey and Meslow 1977, Dahle and Swenson
2003). Due to the size and habitat requirements of a black bear, home ranges may extend
outside of boundaries of parks and preserves into unprotected areas (Samson and Huot
1998), increasing contact with humans under less controlled and potentially more lethal
conditions.
Habitat quality affects the distance a bear moves. It has been documented that
bears increase distance traveled when making use of higher elevations as they follow the
phenological changes of vegetation during the growing season (Amstrup and Beecham
1976, Reynolds and Beecham 1980, Garshelis and Pelton 1981, Unsworth et al. 1998).
Likewise, Schoen (1990) suggested that bears increased distance moved when there were
food shortages. Garshelis and Pelton (1981) observed that black bears traveled more
extensively when the hard mast crop was low and Amstrup and Beecham (1976) reported
increased movement when food resources were scarce. Gunther et al. (2004) reported
66
that when natural food crops were poor, bear human conflicts increased in the Greater
Yellowstone Ecosystem (GYE).
Bears in the GYE utilize whitebark pine (Pinus albicaulis) before hibernation
(Mattson and Jonkel 1990). In western Wyoming, whitebark pine is usually found
between 2,590 m and 3,200 m (Arno and Hoff 1990). Whitebark pine, like other fall
food resources, may play an important role in reproductive rates of black bears in GRTE.
The amount of fat stored and body condition affect the reproductive success of individual
females (Jonkel and Cowan 1971, Young and Ruff 1982, Elowe and Dodge 1989,
Schwartz and Franzmann 1991). My objective was to understand how bears utilize the
landscape. I investigated distances moved with the following hypothesis:
H1: The distance moved in a fixed time interval will differ seasonally, because of an
increased demand for food as seasons progress towards denning. A shift in elevation will
correspond with phenological changes of vegetation, in particular, when whitebark pine
cones become mature from mid-August to mid-September.
Response to Human Influence
Black bears in GRTE face many human pressures. The south border of the park
abuts a ski resort and outlying subdivisions of the town of Jackson, Wyoming. The south
part of GRTE contains several developments including visitor centers, park headquarters,
and housing for park and concession employees. Development inside park boundaries
increases the bear-human interaction interface. There are 383 km of trails (National Park
Service Statistics http://www.nature.nps.gov/stats/) that bisect the park, and
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approximately 215 km of trails in southern GRTE. The trail system is extensive and
maintained trails run through all but one canyon in southern GRTE.
Due to road and trail access, as well as geography, the southern part of GRTE
receives more backcountry visitation than the north. In 2006, 80.6% (n = 21,393) of
backcountry camper nights (one person spending one night in the backcountry) occurred
in the south district of GRTE, compared to 19.4% (n = 5,148) in the north district. The
higher level of backcountry use in the southern GRTE may impact bear activity. Many
backcountry trails and camping sites in GRTE are located in habitat that is rich in berry
producing shrubs and whitebark pine. Gunther (1990) reported that in Yellowstone
National Park, areas within 400 m of occupied backcountry campsites were used less by
grizzly bears than unoccupied sites. Human occupation in the backcountry may lead to
bears moving to less favorable habitat becoming more habituated to humans.
Road corridors are often located in prime habitat, and are occupied by subadult
bears (Mattson 1990, Mueller et al. 2004, Nellemann et al. 2007). Roads are not only
barriers for many wildlife species (Forman and Alexander 1998), but can be a source of
mortality (Forman and Alexander 1998, Gunther and Biel 1999, Gunther et al. 2000,
McCown et al. 2009) and habitat fragmentation (Servheen et al. 1998). Several state
agencies view habitat loss and habitat fragmentation as a problem for black bear
populations (Pelton et al. 1999). Kasworm and Manley (1990) found that roads had a
greater influence than trails on use of habitats by both black and grizzly bears. The
literature suggests roads can have either a positive or negative impact to bears. Kasworm
and Manley (1990) found that both grizzly and black bears utilized habitat near roads less
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than expected, but the distance from roads over which habitat use was affected was
greater for grizzly bears than black bears.
GRTE and the John D. Rockefeller, Jr. Memorial Parkway have 245 km of paved
roadways. The main road that intersects the study area is the Teton Park road. During
the course of this study (2005 and 2006) there was an average of 1,433,517 vehicles
traveling the Teton Park road from June to August (National Park Service Statistics
http://www.nature.nps.gov/stats/). Habitat use adjacent to roads is likely influenced by
traffic volume, habitat type, habitat quality, the age, sex and reproductive status of the
bear.
Black bear human conflicts are an increasing problem in Wyoming (Hristienko
and McDonald 2007). Bear-human conflicts not only impact the bear population, but can
cause economic losses to a community and increase agency costs associated with
managing conflicts (Baruch-Mordo et al. 2008). Many bear-human conflicts are
clustered spatially (Singer and Bratton 1980, Baruch-Mordo et al. 2008) and identifying
bear habitat use patterns in an area can help narrow management efforts. The human
population in Teton County has more than doubled since 1980 (Wyoming Economic
Analysis Division http://eadiv.state.wy.us/pop/poplhtml). With human development
increasing outside of park boundaries, and visitation to GRTE on the rise, it is important
to understand current factors influencing black bear habitat use.
To date studies have focused on black bears in areas where black and grizzly
bears (Ursus arctos) live sympatrically (Jonkel and Cowan 1971, Aune 1994, Holm et al.
1999, Belant and Follmann 2002) and areas historically allopatric from grizzly bears
69
(Wagner et al. 2001, Benson and Chamberlain 2006). Few studies have investigated
allopatric black bear populations that were historically sympatric with grizzly bears.
Black bears occupy all of GRTE. Although black bear distribution has been relatively
constant for decades, grizzly bear distribution has changed over time. Historically, black
bears were sympatric with grizzly bears in GRTE, until the early 1900s. By the 1940s,
grizzly bear range was substantially retracted and black bears were the only ursid species
residing in the southern GRTE (Pyare et al. 2004). As the population of grizzly bears in
the Yellowstone Ecosystem increased in the 1970s, their range expanded southward, and
numbers increased in the northern part of the GRTE (Pyare et al 2004). As of 2006,
grizzly bears had a well established distribution in the north part of the park, with a few
collared grizzly bears traveling through the southern GRTE. Several generations of black
bears have lived in the southern GRTE without the associated pressures that grizzlies
place upon black bears living in sympatric habitats.
Aune (1994) and Holm et al. (1999) documented that black bears living
sympatrically with grizzly bears were more restricted to forested areas than their grizzly
counterparts. Although black bear home range size depended on food quality and climate
(Amstrup and Beecham 1976), black bears had larger home ranges when in the presence
of grizzly bears compared to allopatric populations (Aune 1994, Holm 1998). In addition
to different habitat selection, the two species may utilize the same habitat in different
capacities, a form of niche differentiation, by shifting temporal patterns or utilizing
different foods (Holm et al. 1999). Different temporal activity patterns (Holm et al.
1999) or complete displacement from an area may be another effect of interspecific
70
competition between the species. Differences in spatial distribution and resource use
between species may be a consequence of displacement of the subordinate competitor, or
they may simply reflect different responses to different ecological requirements.
Distinguishing between these hypotheses is difficult, but studies of many species suggest
that competitive displacement is frequently a strong force within carnivore guilds
(Johnson and Franklin 1994, Creel and Creel 1996, Mills and Gorman 1997, Palomares
and Caro 1999). Temporal and spatial differences between species may be a component
of this sympatric coexistence.
The information collected from this study will provide data on black bears in the
southern GRTE prior to grizzly recolonization and provide comparative information for a
project that collected similar information on both black and grizzly bears living
sympatrically in the northern GRTE. My objective here was to provide managers with a
spatially explicit model of habitat use to help efficiently manage black bears. Results
should also improve our understanding of the potential impacts of human developments
on black bear habitat selection. The data I collected and models can also serve as a
baseline for comparisons when grizzly bears recolonize the study area. Using the data I
collected, I modeled high bear use areas in relation to human activity in the southern part
of GRTE. I will also build a model of frequently used habitat for the entire park. To
determine if human use has an effect on bears, I first analyzed black bear habitat use and
tested the following hypothesis:
H2: Bears will use forested habitat types more often than non-forested habitats.
It is essential in high recreation areas to understand human activities when
71
analyzing wildlife habitat use. Black bears in southern GRTE live with fluctuating food
resources, human pressures and development, and understanding how these factors
influence bear habitat use is important. My objective is to study black bear habitat use in
proximity to trails, roads, and developments in and around southern GRTE. I tested the
following hypothesis:
H3: Black bears will use areas distant from human developments, roads, and trails more
than expected when compared to random.
Study Area
The study area encompassed approximately 111,196 hectares; 46% (51,150
hectares) was within the boundaries of southern GRTE, Wyoming (Figure 2.1). I
delineated the study area by creating minimum convex polygons (MCP) for each bearyear. I defined a bear-year as data from one bear collected during one field season. The
MCP’s for each bear-year were combined to create the boundaries of the study area. The
area extended from Moran Canyon at the north past the southern border of the park to
Wilson, Wyoming. The south border of the park was adjacent to a ski resort and
additional developments on the outskirts of Jackson Wyoming. The east and west
borders of the park were contiguous with Bridger Teton and Caribou-Targhee National
Forests, respectively.
Elevations ranged from 1,959 m to 4,197 m. The study area contained six glacial
lakes at the base of the Teton Mountains, with stream drainages through each canyon.
Average high and low temperatures for a 30 year period (1971 – 2000) at Moose
Wyoming in January and July were -3.4, -17.6, and 26.0, 4.9°C, respectively (Western
72
Regional Climate Center http://www.wrcc.dri.edu). Mean annual precipitation for the
same 30 year period, was 56.2 cm, with most occurring in the Teton Mountains as snow
(Regional Climate Center http://www.wrcc.dri.edu).
Vegetation communities varied from sagebrush (Artemesia tridentata) on the
valley floor to subalpine fir (Abies lasiocarpa) and whitebark pine (Pinus albicaulis)
stands just below treeline (Shaw 1976). Most low elevation forested areas within the
park were dominated by lodgepole pine (Pinus contorta). Many climax stands of
Engleman spruce (Picea englemannii), Douglas fir (Pseudotsuga menziesii), and
subalpine fir occurred throughout the area. Huckleberry (Vaccinium spp.), buffaloberry
(Sheperdia canadensis), chokecherry (Prunus virginiana), and hawthorn (Crataegus
douglasii) were common in the forest communities. Riparian areas were dominated by
cottonwood (Populus spp.) and aspen (Populus tremuloides) with a variety of willow
(Salix spp.) and graminoid species in the understory.
Methods
I captured black bears using culvert traps during summers of 2005 and 2006. I
immobilized bears with tiletamine hydrochloride and zolazepam hydrochloride (Telazol,
Fort Dodge Animal Health, Fort Dodge, Iowa) following the approved animal welfare
policy (United States Geological Survey Midcontinent Ecological Science Center Policy
for Utilization of Animals in Research). All captured bears, except dependent offspring
or very small subadult black bears, were fitted with Telonics Spread Spectrum (SS) GPS
collars (Telonics, Inc., Mesa, AZ), a CR-2a programmable breakaway collar release
73
(Telonics, Inc., Mesa, AZ), a biodegradable canvas spacer, and a motion sensor that
reduced transmitter pulse rate if stationary for 4–5 hours. Collars attempted to acquire
GPS fixes every 180 minutes, a single male collar attempted a fix every 90 minutes.
Spread spectrum collars were programmed to be available for data uploads on Tuesdays
and Thursdays from 0800–1200 hours MDT (Schwartz et al. 2009). This technology
enabled me to retrieve GPS locations weekly by aircraft and visit GPS coordinates for
each bear from a randomly selected 24-hour period.
Home Range Analysis
I estimated home ranges using the 95% isopleth of the k-local convex hull (Getz
and Wilmers 2004, Getz et al. 2007) for each bear-year. I used the k-local convex hull
due to its low error and sensitivity to multiple centers of activity (Getz et al. 2007). I
used the equation k = √n (Getz et al. 2007), where n is the number of GPS locations for
the bear-year to determine k, the number of nearest neighbors. I created local convex hull
(LoCoH) home ranges using the Local Convex Hull Homerange Generator (FortmanRoe, S., and W. Getz. 2005. Local Convex Hull (LoCoH) Homerange Generator.
Available at http://www.locoh.cnr.berkeley.edu) in ArcMAP 9.3 (Environmental Systems
Research Institute, ArcMAP software). I created 100% minimum convex polygon
(MCP) home ranges for each bear-year using Hawth’s Tools (Beyer, H. L. 2004.
Hawth's Analysis Tools for ArcGIS. Available at
http://www.spatialecology.com/htools). I created MCPs to compare home range sizes
with other studies. I then combined MCP home ranges using Hawth’s Tool’s, to produce
a polygon that created boundaries for the study area. I measured the distance each bear
74
traveled within a 24 hour period in ArcMAP 9.3. I calculated the mean and standard
error distance moved for each season. I analyzed three seasons; spring (den emergence –
July 15th), summer (July 16th – August 21st) and autumn (August 22nd – den entrance). I
summarized results using the weighted average of the bear-year specific regression
coefficients, with weights proportional to reciprocals of squared standard errors for
individual fits (Murtaugh 2007). I considered differences statistically significant if the
95% confidence intervals did not overlap (for each season). I utilized the same methods
to calculate the distance to whitebark pine for each month.
Habitat Use Analysis
I determined habitat use using the Mahalanobis distance statistic (D2) (Clark et al.
1993):
–1 (x – û)
D2 = (x – û)' ∑
where x is a vector for the habitat characteristic, û is the estimated mean habitat
–1 is the inverse of the covariance matrix from
characteristics at bear GPS locations, and ∑
the GPS locations (Clark et al. 1993). I used the Mahalanobis distance statistic since it
analyzes GPS locations that were occupied by individuals (Clark et al. 1993, Alldredge et
al. 1998). The Mahalanobis distance statistic has been shown to have greater predictive
accuracy than resource selection functions (Johnson and Gillingham 2005). It does not
require absence data or multivariate normality (Knick and Dyer 1997), unless utilizing
the χ2 distribution to obtain P-values (Clark et al. 1993, Hellgren et al 2006). Regardless
of multivariate normality, the D2can be scaled using the χ2 distribution. Rescaling the
75
Mahalanobis statistic to the χ2distribution from 0 – 1, gives limits to the statistic and
allows for easier interpretation (Clark et al. 1993, Farber and Kadmon 2002). The
Mahalanobis statistic has n degrees of freedom, where n is the number of variables
(Hellgren et al. 2006). The Mahalanobis distance statistic also handles issues of
covariance among variables utilizing the covariance matrix (Clark et al. 1993, Knick and
Dyer 1997).
I used the individual bears as the sampling unit and did not pool across years
because pooling across years may mask changes in habitat use (Belant and Follman
2002). By utilizing individual bears as opposed to GPS fixes, I avoided autocorrelation
and pseudoreplication problems (Aebischer et al. 1993, Otis and White 1999). I created a
model for each bear year then combined these models to create two global models. I
partitioned the analysis into two regions, creating habitat models for the study area and
the entire GRTE. I subdivided these models into three additional categories; natural
habitat, human use, and natural habitat and human use covariates combined. This
enabled me to examine how black bears use the landscape in regard to two different
factors and how these factors interacted. I established the habitat covariates elevation and
solar radiation (Keating et al. 2006) from a 30 m X 30 m Digital elevation model (DEM)
raster image in ArcMAP 9.3 for each GPS location. I utilized Greater Yellowstone
Ecosystem 1999 Landsat reflectance bands 1 – 4 as covariates for the natural habitat
analysis. Reflectance bands 1 – 3 correspond with blue, green, and red on the visible
spectrum (Lillesand et al. 2008). Reflectance band 4 corresponds with the near-infrared
of the electromagnetic spectrum (Lillesand et al 2008). Vegetation type and abundance
76
influence Landsat data (Kenk et al. 1988). Different vegetation types have been shown to
emit distinct spectral values in the Landsat reflectance bands (Ferguson 1990). Bare rock,
soil, and vegetation emit different wavelengths and have different values for reflectance
bands 1 – 4 (Lillesand et al. 2008), this information will help me determine what habitat
areas bears are using. I utilized 30 m X 30 m rasters that had computed distance in
kilometers from each pixel to trails, development, and major roads. I rescaled the
Mahalanobis statistic to a χ2 distribution (0 – 1). Once rescaled, the closer the pixel
values were to 1, the closer they represented the ideal value of covariates for the bear at
that pixel (Clark et al. 1993). I created all Mahalanobis distance maps, rescaled from 0 –
1, using the Land Facet Corridor Application (Majka, D., J. Jenness, and P. Beier. 2007.
CorridorDesigner: ArcGIS tools for designing and evaluating corridors, available at
http://corridordesign.org) in ArcMAP 9.3. I obtained scaled D2 values for the 9,507 bear
locations.
I generated 9,507 random points and calculated D2 for those values with the above
covariates within the study area and GRTE. I also produced 9,193 random points within
the 95% isopleth LoCoH home ranges and calculated the D2 values for those points. I
graphed the D2 cumulative distributions for bear locations and random locations within
the 95% isopleths LoCoH home ranges (Johnson’s third-order selection; Johnson 1980),
study area (Johnson’s second-order selection; Johnson 1980), and GRTE (Hellgren et al.
2006, Thatcher et al. 2006, Feng et al. 2009).
In order to understand which covariates were influencing black bear habitat use in
the southern part of GRTE, I computed the factorial decomposition of the Mahalanobis
77
distances. The Mahalanobis Distances Factor Analysis (MADIFA) allows the
investigator to quantitatively break down the D2 statistic into components or axes to
determine the impact of each on the proportion of the mean of the squared D2 (Calenge et
al. 2008). I used the MADIFA function in the adehabitat package (Calenge 2006) for the
R statistical programming software, to first calculate the eigenvalues of “factorial axes”
(Odden et al. 2005, Calenge 2006, Calenge et al. 2008). The axes’ correspond to the
niche space for that linear combination of variables (Calenge et al. 2008). Each axes’
eigenvalue represents the proportion of the mean of the squared Mahalanobis distance
(Calenge et al. 2008). The larger the eigenvalue, the more that axis explained the mean
of the squared Mahalanobis distance (Calenge et al. 2008). I chose the components that
when combined, were approximately 70% of the mean of the squared Mahalanobis
distance. Utilizing the MADIFA function, I then calculated the correlation coefficient for
each covariate for the components with the greatest eigenvalues. A positive correlation
coefficient represents a direct correlation between that particular variable and the axis
(Calenge et al. 2008). A negative correlation coefficient reflects an inverse relationship
between that variable and the axis (Calenge et al. 2008). I created full and reduced rank
models for the study area and entire park. The full rank models contained all the
associated axes. The reduced rank model contained the axes that were 70% of the mean
of the squared Mahalanobis distance.
Results
I analyzed 13 bear years (7 females and 6 males) home range size and distance
78
moved/24 hour period. I found that on average, male black bear home ranges ( x = 65.9
± 12.5 km2 SE) were 3 times larger than female black bear home ranges ( x = 21.2 ± 4.2
km2 SE) in the southern part of GRTE (Table 1.1). I also found that 62% (n = 8) of bears
sampled had part of their home ranges’ outside of park boundary (Figures 2.2 – 2.4,
Table 2.1) (range 0.7% – 91.0%). MCP home ranges were on average 3 times larger than
the LoCoH home ranges (range 1.2 – 14.3 times larger).
I measured mean distances traveled/24 hours for 13 bear-years with a total of
1,462 24-hour periods. Overall, black bears traveled a mean distance of 3.2 km/day.
Bears traveled the greatest distances in summer ( x = 3.7 ± 0.3 km SE, n = 12), followed
by spring ( x = 3.3 ± 0.3 km SE, n = 9), then fall ( x = 2.8 ± 0.3 km SE, n = 12) (Figure
2.4). The 95% confidence intervals overlapped for the distance moved among seasons,
indicating a lack of statistical difference. On average male black bears traveled greater
distances per day ( x = 3.7 ± 0.3 km SE) than females ( x = 2.9 ± 0.3 km SE), but the 2
were not statistically different. Distance to whitebark pine stands varied from April to
October (Figure 2.5) with bears closest to whitebark pine stands in April ( x = 1.0 ± 0.4
km SE) and October ( x = 1.0 ± 0.3 km SE). Distances to whitebark pine stands were
greatest during July ( x = 1.6 ± 0.2 km SE) followed by September ( x = 1.5 ± 0.4 km
SE) (Figure 2.5). The 95% confidence intervals overlapped among months indicating a
lack of statistical difference.
I found that the habitat characteristics used by black bears were similar among
individuals for elevation ( x = 2.2 ± 0.0 km SE), solar radiation ( x = 0.7 ± 0.0 km SE),
reflectance band 1 ( x = 32.5 ± 0.4 km SE), reflectance band 2 ( x = 27.8 ± 0.6 km SE),
79
and reflectance band 3 ( x = 24.0 ± 0.7 km SE) (Table 2.2). Mean reflectance band
values for bear locations paralleled reflectance band values for forested regions in GRTE.
Male and female black bears utilized areas with similar distances from trails, major roads,
and developments. Black bears used areas that were closest to trails ( x = 0.8 ± 0.1 km
SE), followed by major roads ( x = 1.6 ± 0.3 km SE), and developments ( x = 8.8 ± 0.7
km SE) (Table 2.3).
I analyzed 13 bear-years to calculate the Mahalanobis distance statistic.
I observed major differences between models that contained habitat variables and those
with human use variables (Figures 2.6 – 2.11). Final models with combined covariates
(habitat and human disturbance) for the study area and GRTE had D2 values ranging from
0.0 – 0.99. Zero represented the least used habitat and 0.99 the habitat most frequently
used by the black bears that I sampled. Overall, black bears in southern GRTE utilized
timbered areas that were on the east side of the Teton Range. The bears utilized habitats
that were low in elevation and in valley bottoms, typically where trails and roads are
built.
My results indicated that black bears in southern GRTE selected resources at the
landscape level (Figure 2.12). Mahalanobis distance values at bear locations were similar
to random locations within the 95% LoCoH home ranges (Figure 2.12), whereas D2
values for bear locations were dissimilar to random locations within the study area
(Johnson’s second order selection) and GRTE (Figure 2.12).
The number of axes that contributed to the reduced rank model for the study area
and GRTE varied (Figure 2.13). The 3 eigenvalues that contributed most to the habitat
80
reduced rank model for the study area were 36%, 25%, and 14% of the mean squared
Mahalanobis distances. The first component for the human use reduced rank model for
the study area was 75% (Figure 2.13). The comprehensive reduced rank model for the
study area had 4 eigenvalues that contributed the most; 38%, 19%, 12%, and 10%. The
GRTE models had similar results. The habitat reduced rank model for the GRTE had 3
top components that had eigenvalues of 25%, 30%, and 32%. The human use reduced
rank model had 2 components with eigenvalues of 56% and 32%. GRTE had similar
eigenvalues for the first axis. My reduced model showed that 38%, 53%, and 33% of the
mean squared Mahalanobis distances for habitat, human use, and combined covariates
respectively, was explained by the first 4 axes (Figure 2.13).
My models showed that the study area reduced rank habitat model was most
impacted by reflectance bands 1 – 3 (R = -0.94, R = -0.87, and R = -0.83) respectively
(Table 2.4). Black bears utilized areas with lower reflectance band values more
frequently than random locations in the study area. The second axes had weak
correlations with elevation, reflectance band 4, solar radiation, and reflectance band 3 (R
= 0.41, R = -0.35, R = -0.33, and R = -0.33), respectively (Table 2.4). The third axes had
stronger correlations from solar radiation (R = -0.67) and elevation (R = 0.63). The
human use model for the study area had one axis that was over 70% of the proportion of
the mean squared Mahalanobis distance. This axis was most influenced by distance to
trails (R = -1.00), distance to roads (R = -0.96), and distance to development (R = -0.91)
(Table 2.4). Bears used habitats in lower elevation in forested areas (where trails, roads,
and development) more than random locations in the study area. The final reduced rank
81
model that incorporated both habitat and human use had four important axes. Distance to
trails (R = -1.00), distance to roads (R = -0.97), and distance to development (R = -0.91)
were most influential in the combined reduced rank model for the first axes (Table 2.4).
The second axis was influenced by reflectance bands 1 – 3 (R = 0.88, R = 0.82, and R =
0.78).
The GRTE reduced rank model for habitat had three axes that were influential.
The first axis was most influenced by reflectance bands 1 – 3 (R = 0.84, R = 0.75, and R
= 0.67, respectively) (Table 2.4). The second axis was influenced by reflectance bands 1
– 4 with slightly weaker correlation coefficients (Table 2.4). Lastly, the third axis was
influenced by solar radiation (R = 0.69) and elevation (R = -0.59). Bears used areas with
higher reflectance band values more frequently than random in the entire GRTE. The
GRTE human use model was driven by distance to trails (R = -0.88). The second axis
was influenced by distance to roads (R = 0.85) and trails (R = -0.61). Bears used areas
farther away from development and closer to trails than random within the GRTE. The
global reduced rank model for GRTE was also affected by human use variables. Bears
utilized areas closer to roads (R = 0.75). The second and third axes were influenced by
human use and habitat variables (Table 2.4). Reflectance bands 1 – 4 were important in
the fourth axis for the GRTE global reduced rank model (Table 2.4).
I found that my reduced rank models overall had higher D2 values across the
landscape compared to the full rank Mahalanobis distance models (Figures 2.6 – 2.11).
Reduced rank models that included habitat variables (habitat only models and mixed
habitat and human use models) included areas that were at lower elevations and forested
82
regions than the Mahalanobis full rank models (Figures 2.6, 2.8, 2.9, and 2.11). Reduced
rank models of the study area and park that included human use variables were more
constricted to areas that were closer to roads than the full rank models (Figures 2.7, 2.8,
2.10, and 2.11).
Discussion
This study was the first to examine black bear ecology in southern Grand Teton
National Park. It provides important information regarding home ranges and habitat use
that could guide management decisions within the park. While black bears face high
levels of human pressures, their home ranges were similar to bears from Washington
(Koehler and Pierce 2003). I utilized the k-LoCoH to estimate bear home ranges, and the
calculated home ranges were smaller than many studies that used less sensitive methods
(Amstrup and Beecham 1976, Manville 1983, Aune 1994, Holm et al. 1999). The MCP
home range estimates that I calculated were similar in size to black bear home ranges’
calculated with the MCP (Amstrup and Beecham 1976, Alt et al. 1980, Aune 1994).
Bears that traveled extensively to different core areas had large MCP home ranges
compared to LoCoH estimates. Unlike Amstrup and Beecham (1976), my data did not
support the finding that older females had smaller home ranges. The oldest females
22207 and 22212 had the largest and smallest home ranges, respectively, among females
(Table 1.1). Overall, female black bears had smaller home ranges than male black bears,
which is similar to black bears across the United States (Jonkel and Cowan 1971,
Lindzey and Meslow 1977, Aune 1994, Koehler and Pierce 2003). It has been
83
hypothesized that female black and grizzly bears with cubs have smaller home ranges
because of nutritional demands of family groups (Herrero 1978, Dahle and Swenson
2003). Females with cubs of the year have decreased movement that may be due to
physical constraints of the cubs (Alt et al. 1980). There was extensive home range
overlap among bears, which is similar to the findings of other studies (Amstrup and
Beecham 1976, Garshelis and Pelton 1981, Young and Ruff 1982, Horner and Powell
1990, Aune 1994).
I rejected my first hypothesis that bears would move greater distances as the
seasons progressed. Bears moved the most in the summer when many berries were ripe.
Bears moved the least in the autumn, closer to the time when they would enter
hibernation. The increase in movement in mid-summer corresponds with phenological
stages of vegetation at higher elevations. Many bear populations across their range have
a shift in elevation that corresponded to vegetation availability (Reynolds and Beecham
1980, Garshelis and Pelton 1981, Unsworth et al. 1998). Bears have also been
documented as constantly moving when they are browsing in berry patches (Welch et al.
1997), which would increase the distance moved. The distance black bears traveled in
southern Grand Teton National Park was consistent with Garshelis et al. (1981), but
greater than Amstrup and Beecham (1976) and Young and Ruff (1982).
Although the distance moved may be affected by vegetation availability and
quality, it can also be affected by sampling (number of times the bear was located in a
specified time period) and home range size (Alt et al. 1980). Not surprisingly, black
bears in southern GRTE that had larger home ranges, moved greater distances over the
84
landscape (Table 2.1). Male black bears with larger home ranges tended to move greater
distances in a 24-hour period compared to females in the study area. Similar to the
results of Reynolds and Laundre’ (1990), I found that the actual distance moved in a 24hour period also increased as the GPS sampling frequency increased. Bear 22228 was fit
with a collar that downloaded GPS locations every 90 minutes whereas the other bears
sampled were fit with GPS collars that downloaded locations every 180 minutes.
Although 22228’s home ranges were not the largest of males or females sampled, he
traveled the most extensively in a 24-hour period compared to the other bears. The
overall sampling frequency may also explain the greater distances traveled that I
observed, in comparison to distances moved by black bear in Idaho (Amstrup and
Beecham 1976) since they sampled bears less frequently.
I also rejected the second part of my first hypothesis that bears would move closer
to whitebark pine stands as the cones matured. Although bears were closer to whitebark
pine stands in October, they were also close when they emerged from the den in April.
Elevation and whitebark pine stands may be highly correlated; therefore when bears
utilize higher elevations pre and post-hibernation, it may be difficult to distinguish the
cause. Reynolds and Beecham (1980) saw a shift of bears to higher elevations when soft
mast and other vegetation became available after the snowmelt. Bears in my study were
farthest from whitebark pine stands in July and September. Although bears were a
substantial distance from whitebark pine stands in August and September, when the pine
cones become mature, the distance was within the range they would travel within a 24hour period. Additional research may be necessary to discern if elevation shifts occur
85
regardless of whitebark pine seeds and consequently may be associated with the
availability of other vegetation.
My reduced rank models had higher D2 values overall when compared to their full
rank counterparts (the full rank models contained all the associated axes while the
reduced rank model contained axes that were 70% of the mean of the squared
Mahalanobis distance). The reduced rank models decreased a component of the noise in
the full rank models. This was evident in the higher amount of D2 values near 1 in the
reduced rank models. The bear locations on average had higher D2 values in the reduced
rank model compared to the full rank model. I failed to reject my second hypothesis that
bears used forested habitat types more often than non-forested habitats. Black bears
mainly used forested areas in southern GRTE, which was similar to black bears across
the United States (Garshelis and Pelton 1981, Aune 1994, Lyons et al. 2002, Koehler and
Pierce 2003). This use pattern was shown by the Mahalanobis distance full rank and
reduced rank models for habitat variables for the study area and GRTE. Quantitatively,
timbered islands on the valley floor had larger D2 values (indicating areas that the bears
sampled used typically) than the sagebrush communities that surround them. The Snake
River, with the surrounding cottonwoods and assorted coniferous trees, created a possible
corridor in the middle of the valley (Figures 2.6 – 2.7). Forested riparian areas, similar to
those adjacent to the Snake River, have been documented as travel corridors in Yosemite
National Park (Matthews et al. 2006). Protecting riparian corridors for bear use was a
major aspect of campground development in the Shoshone National Forest (Creachbaum
86
et al. 1998). The importance of riparian corridors to bears should be considered in
management strategies and development in GRTE.
The MADIFA analysis and reduced rank models highlight the importance of the
linear combination of variables (Calenge et al. 2008). Reflectance band 4, solar
radiation, and elevation were important on the second and third axes for the study area
and park reduced rank habitat models. These three variables, although indirect,
influenced habitat use when in a specific combination with the first three reflectance
bands in the second and third axes. Ecologically, black bears that I sampled rarely used
low elevations that were in non-forested regions. Solar radiation and reflectance band 4
values corresponded with forested regions in the park. A specific elevation, solar
radiation, or reflectance band 4 value was not overtly utilized without being located in
forested habitat.
The MADIFA results for the human use reduce rank models were similar to the
qualitative assessment of the Mahalanobis full rank models for the study area and GRTE.
My data suggest that the black bears that I sampled in southern GRTE utilized areas close
to trails and roads. All three variables (distance to development, roads, and trails) were
important in the first axis for the study area. In contrast, the park reduced rank model had
two influential axes. The first axis highlights areas close to trails that bears were utilizing
disproportionately in the park. Although, proximity to roads was influential in the second
axis, it was correlated with distance to trails. Bears utilized areas that were low in
elevation and moderately sloped, these areas are typically where trails and roads are
constructed in the park. Depending on the bear’s location, it could utilize areas close to
87
trails but distant to roads, such as in remote backcountry areas. Locations in remote areas
of the backcountry that were distant from roads and close to trails had lower Mahalanobis
distance values than areas close to trails and roads. This held true for full rank and
reduced rank Mahalanobis distance models.
The reduced rank model that explored the covariates for natural habitat and
human use provided a comprehensive habitat use model. Distances to trails, roads, and
development were the best predictors of habitat use. Reflectance bands 1 – 3 were
influential in the second axis, and were related to the first axis. Bears sampled typically
utilized areas close to trails and roads that were in forested regions. The bears sampled
did not use areas that were close to trails and far from roads, which is indicative of many
high alpine meadows in the park. The habitat that bears utilized that was close to roads
occurred primarily along the Moose-Wilson road, in forested regions. Highway 191/287
and other roads in the eastern part of the study area are in sagebrush habitat and had low
Mahalanobis distance values in the full rank and reduced rank models. The remaining
variables were influential in the third and fourth axis, but had weaker correlation
coefficients in the third axis. The influence of solar radiation, elevation, and reflectance
band 4 was less direct than the human use variables and reflectance bands 1 – 3. The
importance of reflectance bands 1 – 3 appeared again with moderate correlation
coefficients in the fourth axis. The reduced rank model for the study area reflected
associations observed in the habitat only reduced rank model. Bears utilize areas that
were coupled with forested regions. Based on the reduced rank model axes, bears did not
choose areas with a certain elevation level or solar radiation value alone. Bears chose
88
locations closer in forested regions that were at low to moderate elevations and had
minimal slopes compared to random locations within the study area.
The GRTE comprehensive reduced rank model was unlike the park reduced rank
model for human use. The most influential variable on the first axis was distance to
roads. The west side of the park that extends from the northern to southern tip of Jackson
Lake did not have a road bisecting it. The lack of roads in a large section of the park may
explain why proximity to roads was the most influential on the first axis. The second
axis was influenced by distance to roads, trails, and reflectance bands 1 – 3. Like the
study area, bears utilized forested habitats close to trails and roads compared to random
locations within the park. The reduced rank park model was similar to the study area
model, in that the third axis had weaker correlations, but the fourth axis was influenced
by reflectance bands 1 – 4. Proximity to roads and trails was hinged on the habitat being
forested. Bears infrequently used non-forested areas that were close to trails and roads.
Graves et al. (2006) found that the distance to cover from a road may increase the
likelihood of a bear crossing the road. The trail system in GRTE intersected mostly
forested areas containing suitable habitat for black bears. Trails in forested regions may
provide more security for bears than trails in open areas (McLellan and Shackleton 1989).
Forested regions may allow bears to be passed by humans at close ranges and go virtually
unnoticed.
Bear habitat use in southern GRTE is influenced by many factors. Although
proximity to trails (in the study area) and proximity to roads (for GRTE) are on the first
axis with the largest correlation coefficients for the comprehensive maps, they were not
89
the only variables and/or axes that explained habitat use by bears. The comprehensive
models for the study area and park, if only influenced by proximity to trails and roads
respectively, would only have human use covariates in the axes of the MADIFA analysis.
Several axes in different linear combinations, including habitat and human use covariates,
are present in the analysis of both the study area and GRTE. This suggests that a
complexity exists for black bear habitat use, beyond proximity to human use activities.
Therefore it would be an oversimplification to conclude that black bears only utilize areas
close to trails and/or roads in southern GRTE.
The different correlation coefficient values between the study area and GRTE
model underscores the importance of scale in this study. These differences also
underscore the need to be cautious when extrapolating data collected on a small sample
of bears to an area as large as GRTE. The Mahalanobis distance equation is related to
scale, since x is the vector of a habitat characteristic at all pixels analyzed. Erickson et
al. (2001) suggested that when utilizing the Mahalanobis distance a researcher must
“define a boundary” that the overall results are influenced by the boundaries drawn.
Inferences drawn from habitat use studies may fail to fully explain the interactions if the
scale is not correctly addressed (Morris 1987). At both local and landscape scales, both
reduced and full models, predicted the bears I sampled to use areas close to trails in
forested areas. Further exploration also indicated that habitat use was explained by
different variables at the 2 scales. At a more local level bears used areas close to trails
and roads while at a landscape level they used areas close to roads suggesting that habitat
use patterns may depend on variables at the local and landscape scale (McClean et al.
90
1998, Erickson et al 2001). The influence of scale was also pronounced in the
cumulative distribution figure (Figure 2.12). The fact that my models predict bears that
utilized habitats near trails may have been a result of where I sampled bears and where
trails are typically located. Trail density was much higher in southern GRTE than the rest
of the park. In addition, most trails occur in areas that make logical travel corridors for
bears based on cover and terrain. The habitat utilized by black bears in southern GRTE
was more similar to random locations within their home ranges than locations in the
study area and the entire GRTE suggesting bears selected resources at the landscape
level, and not at a local level.
Black bears that I sampled typically used forested areas in southern GRTE that
were close to trails. Most notable was the difference in their use of landscape when
analyzing natural habitat and human use areas. The patterns outlined in the global
Mahalanobis full rank models and reduced rank models provide park managers with data
on black bear habitat use in the park. Although black bears that I sampled were not
avoiding areas close to trails, this does not mean they were not affected by humans on
trails. Bears may become more habituated to humans (Jope 1983) or may have negative
physiological responses (Herrero et al. 2005). Bear habituation to people can be positive
(i.e. an increase in amount of available habitat, security for less dominant bears) or
negative (i.e. more likely to obtain human food, more likely to be killed by an
automobile) (Herrero et al. 2005). Bears that live in a more urban environment have
higher levels of mortality than bears in a wildland setting (Beckman and Lackey 2008).
Due to the limitations of my study, I was unable to determine if habituation of studied
91
bears occurred on the trail or in other developed areas. Additional studies could analyze
bear habitat use in response to high and low use trails in GRTE. This might help
determine if there is a threshold of human use that bears can tolerate in GRTE. If high
use trails and backcountry areas have correlations to the number of bear human conflicts
(Singer and Bratton 1980) it is important to identify these locations within the park.
Hristienko and McDonald (2007) suggested that “proactive education” is an
important aspect of bear management. The information provided in my study can help
park managers develop effective and cost conscious educational programs by targeting
and prioritizing high use black bear areas as highlighted in my study area for interpretive
programs. In addition to the current bear awareness messages posted at each trail head in
GRTE, additional trail specific education information for high use bear areas should be
provided and tailored to the specific trail area. Along with education, it is important to
monitor and enforce regulations in the backcountry to ensure hikers and backcountry
campers in high use black bear areas are in compliance. High use black bear areas
mapped in my study can help rangers effectively focus their patrols for food and camper
compliance as bears move through the park in response to seasonal food availability.
Education although very important, is more effective when accompanied by enforcement
of food containment regulations (McCarthy and Seavoy 1994).
The data I’ve collected can help guide placement of new or realigned trails, roads
and/or buildings being planned in southern GRTE. Avoiding forested areas and situating
these types of developments in lower use non-forested areas could help reduce human
impacts in the future. The black bears I sampled were only a subset of the population
92
consequently my data and analyses were most representative of the habitat sampled.
Caution should be taken when extrapolating from my models beyond the study area.
Clark et al. (1993) and Erickson et al. (1998) underscored that although Mahalanobis
distance models highlight high use areas; D2 does not equate to the most favorable habitat
of that species. Interpretation of my study’s results is complicated by the fact that the
trail system is extensive throughout southern GRTE, and there were few places in my
study area that were distant from a trail. The black bears that I sampled may not be
representative of all black bears in southern GRTE. My sample of bears did not include
individuals that utilized areas distant from trails or on the west side of the Teton Range.
However, many black bears in southern GRTE did utilize areas distant from human use
areas on the west side. The data I collected when compiled with a sample from the
northern part of GRTE should provide a more comprehensive depiction of bear habitat
use throughout the GRTE. However, my study outlines habitats that are important for the
park to conserve as bear management strategies continue to evolve.
93
93
Figure 2.1. Study area, Grand Teton National Park, Wyoming, USA.
94
94
Figure 2.2. LoCoH home ranges (95%) for female black bears, southern Grand Teton National Park, Wyoming, USA, 2005–
2006.
95
Figure 2.3. LoCoH home ranges for male black bears, southern Grand
Teton National Park, Wyoming, USA, 2005–2006.
Table 2.1. Bear year, age, sex, LoCoH home range areas (km2), distance moved/24 hours, and MCP home range (km2) for
individual female and male black bears, southern Grand Teton National Park, Wyoming, USA, 2005–2006.
Home range
area (km2)
Percent of home range
spent outside of Park
boundary
Distance moved in a 24
hour period (km/day)
MCP home
range area
(km2)
Year
Sex
Age
22046
2005
F
5
13.2
0.0
3.2 ± 0.3
36.3
22046
2006
F
6
25.9
14.5
3.6 ± 0.2
58.6
22207
2006
F
13
43.3
1.7
3.9 ± 0.2
169.0
22212a
2006
F
+15
13.1
19.0
1.9 ± 0.2
25.9
22220
2005
F
7
15.8
0.0
2.7 ± 0.2
41.5
22226
2005
F
7
23.2
0.0
2.1 ± 0.4
27.6
22226
2006
F
8
13.7
0.0
2.6 ± 0.2
42.0
22211
2006
M
6
116.0
30.6
3.7 ± 0.2
434.0
22224
2005
M
3
54.9
91.0
3.3 ± 0.3
786.0
22224
2006
M
4
39.9
81.0
3.0 ± 0.2
61.2
22228
2005
M
4
59.1
18.4
5.1 ± 0.5
158.3
22228
2006
M
5
36.7
9.2
4.7 ± 0.3
95.4
88.5
0.0
3.4 ± 0.2
179
22229
2006
M
7
a
Estimated age from canine wear
96
Bear
97
Figure 2.4. Mean distance traveled/24 hours/season with a 95% confidence interval for
black bears in southern Grand Teton National Park, Wyoming, USA, 2005–2006.
98
Figure 2.5. Monthly mean distance to whitebark pine stands with a 95% confidence
interval for black bears in southern Grand Teton National Park, Wyoming, USA, 2005–
2006.
99
Table 2.2. Mean and standard error for habitat covariates for individual bear years, southern Grand
Teton National Park, Wyoming, USA, 2005–2006.
Elevation (km)
Solar radiation
Reflectance
bandwith 1
Reflectance
bandwith 2
Reflectance
bandwith 3
Reflectance
bandwith 4
SE
x
SE
x
SE
x
SE
x
SE
x
SE
22046 2005
2.1
0.0
0.8
0.0
32.4
0.2
28.0
0.3
23.9
0.4
80.1
1.0
22046 2006
2.1
0.0
0.8
0.0
31.4
0.2
26.0
0.2
21.8
0.3
69.5
0.7
22207 2006
2.2
0.0
0.7
0.0
33.4
0.2
28.5
0.3
25.0
0.3
75.8
0.8
22211 2006
2.2
0.0
0.7
0.0
32.1
0.2
27.1
0.3
23.4
0.2
73.6
0.9
22212 2006
2.4
0.0
0.8
0.0
34.6
0.2
31.2
0.2
27.9
0.3
88.4
22220 2005
2.2
0.0
0.7
0.0
30.5
0.2
24.6
0.2
20.0
0.3
64.1
0.7
0.8
22224 2005
2.4
0.0
0.7
0.0
30.3
0.2
25.3
0.3
21.3
0.4
71.8
1.0
22224 2006
2.2
0.0
0.7
0.0
32.4
0.2
28.2
0.3
24.6
0.4
84.0
1.0
22226 2005
2.2
0.0
0.8
0.0
33.3
0.3
28.7
0.4
25.2
0.5
77.8
1.2
22226 2006
2.3
0.0
0.8
0.0
32.0
0.2
26.9
0.2
23.4
0.3
69.7
0.7
22228 2005
2.2
0.0
0.8
0.0
31.0
0.2
25.8
0.4
21.3
0.5
74.9
1.1
22228 2006
2.2
0.0
0.8
0.0
32.0
0.1
27.1
0.2
23.6
0.3
77.5
0.6
22229 2006
2.5
0.0
0.7
0.0
35.8
0.2
32.3
0.2
30.0
0.3
84.4
0.8
All Bear
Years
2.2
0.0
0.7
0.0
32.5
0.4
27.8
0.6
24.0
0.7
76.2
2.0
99
x
Bear
100
Table 2.3. Mean and standard deviation for human use covariates for
individual bear years, southern Grand Teton National Park, Wyoming,
USA, 2005–2006.
Distance to
development (km)
Distance to major
roads (km)
Distance to trails
(km)
Bear
Year
x
SE
x
SE
x
SE
22046
2005
9.2
0.1
0.9
0.1
0.8
0.0
22046
2006
9.0
0.1
0.8
0.0
1.0
0.0
22207
2006
3.3
0.1
1.1
0.1
0.8
0.0
22211
2006
8.4
0.1
1.2
0.1
0.9
0.0
22212
2006
10.7
0.1
3.3
0.0
0.3
0.0
22220
2005
10.1
0.1
1.3
0.1
0.5
0.0
22224
2005
10.8
0.1
2.4
0.1
1.1
0.0
22224
2006
11.0
0.1
0.7
0.1
1.7
0.0
22226
2005
8.9
0.1
1.0
0.1
0.5
0.0
22226
2006
9.9
0.1
1.4
0.1
0.8
0.0
22228
2005
8.8
0.1
1.1
0.1
0.8
0.0
22228
2006
10.0
0.1
1.2
0.0
0.6
0.0
22229
2006
3.9
0.1
3.8
0.1
0.9
0.0
8.8
0.7
1.6
0.3
0.8
0.1
All Bear
Years
101
Figure 2.6. Mahalanobis distance for natural habitat, full rank model (top)
and reduced rank model for black bears (bottom) for the study area, Grand
Teton National Park, Wyoming, USA, 2005–2006. Dark gray denotes
predicted high black bear use areas by the Mahalanobis distance model.
102
Figure 2.7. Mahalanobis distance for human use, full rank model (top)
and reduced rank model for black bears (bottom) for the study area, Grand
Teton National Park, Wyoming, USA, 2005–2006. Dark gray denotes
predicted high black bear use areas by the Mahalanobis distance model.
103
Figure 2.8. Mahalanobis distance for natural habitat and human use
variables combined, full rank model (top) and reduced rank model for
black bears (bottom) for the study area, Grand Teton National Park,
Wyoming, USA, 2005–2006. Dark gray denotes predicted high black bear
use areas by the Mahalanobis distance model.
104
Figure 2.9. Mahalanobis distance for natural habitat, full rank model (left) and reduced rank model (right) for
black bears, Grand Teton National Park, Wyoming, USA, 2005–2006. Dark gray denotes predicted high black
bear use areas by the Mahalanobis distance model.
105
Figure 2.10. Mahalanobis distance for human use, full rank model (left) and reduced rank model (right) for
black bears, Grand Teton National Park, Wyoming, USA, 2005–2006. Dark gray denotes predicted high black
bear use areas by the Mahalanobis distance model.
106
Figure 2.11. Mahalanobis distance for natural habitat and human use variables, full rank model (left) and
reduced rank model (right) for black bears, Grand Teton National Park, Wyoming, USA, 2005–2006. Dark
gray denotes predicted high black bear use areas by the Mahalanobis distance model.
107
Figure 2.12. Cumulative distribution of scaled Mahalanobis distance values (0 – 1) for bear locations, random
locations within 95% isopleth LoCoH home ranges, random locations in the study area, and random locations in
Grand Teton National Park, Wyoming, USA, 2005–2006.
108
Figure 2.13. Barplots of eigenvalues (y-axis) of factorial axes (x-axis) for
the study area (left side) and GRTE (right side) from the MADIFA. (A)
and (D) are the habitat covariates (Reflectance bands 1–4, elevation (in
km), and solar radiation). (B) and (E) are the human use covariates
(distance in km to developments, roads, and trails). (C) and (F) are habitat
and human use covariates combined.
Table 2.4. Correlation coefficients from MADIFA analysis of habitat and human use covariates for the study
area and Grand Teton National Park, Wyoming, USA, 2005–2006.
Correlation coefficient
Model
Habitat
Habitat &
human use
a
-0.12
-0.94
-0.87
-0.84
-0.27
-0.01
-0.91
-0.97
-1.00
-0.28
0.05
0.05
0.05
0.02
0.14
0.91
0.97
1.00
Study Area
0.41
0.63
-0.10
-0.27
-0.20
-0.30
-0.33
-0.21
-0.35
-0.43
-0.33
-0.67
0.13
0.88
0.82
0.78
0.25
-0.02
-0.17
-0.12
-0.08
-0.36
-0.09
0.01
0.15
0.25
0.25
-0.14
-0.16
-0.03
-0.18
0.61
0.64
0.65
0.48
0.34
0.12
0.07
-0.01
Human use variable values are the distance to that specific variable measured in km.
Grand Teton National Park
0.28
-0.19
-0.59
0.84
0.43
0.19
0.75
0.51
0.20
0.67
0.62
0.11
0.09
0.57
0.16
-0.12
0.30
0.69
0.10
0.19
-0.35
0.85
-0.88
-0.61
0.08
0.09
-0.45
0.07
-0.55
-0.54
0.05
-0.49
-0.47
0.06
-0.44
-0.38
-0.04
-0.06
-0.01
-0.04
-0.02
0.20
-0.02
0.12
-0.14
0.75
0.52
-0.41
0.48
-0.64
0.48
109
Human usea
Covariates
Elevation (km)
Reflectance band 1
Reflectance band 2
Reflectance band 3
Reflectance band 4
Solar radiation
Development (km)
Roads (km)
Trails (km)
Elevation (km)
Reflectance band 1
Reflectance band 2
Reflectance band 3
Reflectance band 4
Solar radiation
Development (km)
Roads (km)
Trails (km)
-0.01
-0.58
-0.65
-0.74
-0.65
-0.23
-0.23
-0.05
0.05
110
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119
CHAPTER 3
SYNOPSIS
Introduction
The activity patterns, habitat use, and food habits of black bears in southern
Grand Teton National Park (GRTE) have not been adequately studied. Food habits and
activity patterns of black bears and how they interact with their habitat on a fine scale
have been detailed by several studies (Raine and Kansas 1990, Holcroft and Herrero
1991, Benson and Chamberlain 2006). The southern GRTE black bear population lives
with fluctuating food resources, high levels of human activity, and encroaching
development. With human development increasing outside of park boundaries, and
visitation to GRTE on the rise, it is important to understand current factors influencing
black bear habitat use.
The success of bear management strategies in GRTE depends on detailed
information about the black bear population. I studied black bear activity patterns and
habitat use in the southern GRTE in proximity to trails, roads, and developments in and
around GRTE. I studied 9 black bears equipped with spread spectrum technology (SS)
collars from June, 2005 to October, 2006. Collars recorded date, time, activity level, and
a GPS location. I analyzed activity and habitat use patterns trying to identify if human
recreation impacted them. My research also focused on development of a spatially
explicit model of habitat use that can assist managers when making decisions that
potentially impact black bears in GRTE. Results of my study also improve our
120
understanding of the potential impacts of human developments on black bear habitat
selection in GRTE. When compiled, the activity patterns, food habits, and habitat use
data, give a comprehensive picture of how black bears currently utilize this diverse
landscape.
I found that black bears in southern GRTE utilized forested habitats most
frequently, which was similar to habitat use by black bear populations across the United
States (Garshelis and Pelton 1981, Aune 1994, Lyons et al. 2002, Koehler and Pierce
2003). Bears used areas in forested habitats that are typical of areas where roads and
trails are constructed. Black bears were diurnal and exhibited bimodal activity peaks
around sunrise and before sunset from June through August. The food patterns revealed
by the data I collected supported the previous research findings (Irwin and Hammond
1985, Raine and Kansas 1990, Boileau et al. 1994, Bull et al. 2001, Baldwin and Bender
2009) that graminoids and ants were important food sources for black bears. Berry
producing shrubs were an important food item for black bears. Whereas ungulate
neonates were a minor food source for black bears in southern GRTE; this finding was
analogous to results from other studies (Beeman and Pelton 1980, Irwin and Hammond
1985, Raine and Kansas 1990). This information should help guide management
strategies of the park and focus resources and attention to specific high use areas.
Activity Patterns
I found that black bears in southern GRTE were diurnal with crepuscular activity
peaks. Crepuscular activity peaks have been documented in naturally foraging (Ayers et
al. 1986) and non-human habituated bears (Olson et al. 1998, Beckman and Berger 2003,
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Lyons 2005). For naturally foraging bears, crepuscular activity patterns may minimize
encounters with humans during peak recreation times in high human use areas (Ayers et
al. 1986, Gunther 1990, Schwartz et al. 2010). Although some researchers have found
that bears consuming human-foods may be active during nocturnal time periods to avoid
detection, I did not observe this. This difference was likely a product of the fact that I did
not detect any anthropogenic foods or debris associated with anthropogenic foods at site
visits or in any of the scats I collected, suggesting bears in my study were not conditioned
to human food. However, I was unable to determine if the bears I studied were
habituated to humans. The bears in my study used areas close to roads more often than if
their habitat use was random. These results were similar to black bears in the Great
Dismal Swamp in Virginia and North Carolina, which used roads and areas closer to
roads more than random (Hellgren et al. 1991). According to Kasworm and Manley
(1990) grizzly bears (Ursus arctos) are known to avoid areas near human developments
whereas black bears appear to be less affected by similar activities. Regardless of the
distance the bears were from trails, roads, or human development their overall activity
patterns throughout a 24 period remained similar.
Unlike previous research on Malayan sun bears (Helarctos malayanus) and
grizzly bears (Griffiths and Van Schaik 1993, Waller and Servheen 2005), the black bears
that I sampled remained diurnal regardless of distance to roads. This pattern may have
been observed since the trails and roads that bears were close to occurred mostly in
forested habitat. Forested habitat provided a certain amount of cover, protection, and
forage for bears. Graham et al. (2010) found that bears utilized areas close to roads that
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provided forage in the spring. Although the objectives of this study did not address shifts
in diurnal activity patterns with distance to forests, this is an area that should be
investigated. Differences in activity level may depend on a bear’s distance to forest
cover in addition to distance to human activities. Examining activity levels at varying
distances to forested habitat may also help identify key road crossing corridors. Graves
et al. (2006) found that the as the distance to cover from a road decreases it may increase
the likelihood of a bear crossing the road. Chi and Gilbert (1999) suggested that black
bears may have a threshold in regards to human recreation. Additional studies could
analyze bear activity patterns in response to high and low use trails/areas and recreational
patterns in GRTE. This might help determine if bears are adjusting their activity levels to
human density patterns. If high use trails and backcountry areas have correlations to the
number of bear human conflicts (Singer and Bratton 1980) it is important to identify
these locations within GRTE.
Black bears in southern GRTE had a recorded activity peak that occurred in July.
Several components of my research align with this finding. I observed that the greatest
distances bears traveled during a 24 hour period occurred in July. In addition, black
bears in southern GRTE utilized ants most frequently in July. Bears consuming ants
usually feed intensively at ant colonies in brief bouts and move on (Noyce et al. 1997,
Swenson et al. 1999), corresponding with increased activity levels at that time. Feeding
at ant colonies and traveling among colonies may be reflected in the activity peak and
distance moved that I observed July. Activity may also increase as bears travel greater
distances to utilize high quality vegetation as it becomes available, and as cubs of the
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year become more mobile. I recorded that berry use increased at site visits in July. Bears
have also been documented as constantly moving when they are browsing in berry
patches (Welch et al. 1997), which would increase the overall activity pattern and
distance moved. Black bears’ peak in activity coincided with increased human activity
patterns in July. For 18 years of the past 20 years (1989 – 2009), GRTE had the highest
recorded peak visitation in July (National Park Service Statistics
http://www.nature.nps.gov/stats/), suggesting that July may be an essential month to
focus educational and enforcement efforts on food-containment in the front and
backcountry.
Food Habits
I found that black bears of southern GRTE had food habits which were highly
variable according to the time of year. This information may be valuable in the
development and execution of bear management practices in GRTE. Berries were a
common food source for black bears in GRTE which is analogous to studies across North
America (Garshelis and Pelton 1980, Irwin and Hammond 1985, Raine and Kansas 1990,
Hellgren et al. 1991). Utilizing a variety of hard and soft mast species may be a benefit
to bears if a plant species is cyclic in mast production or fails (Inman and Pelton 2002).
Huckleberries (Vaccinium spp.) were the most common berry consumed by black bears
that I sampled. Vaccinium spp. use that I reported has been seen in other bear
populations across their range (Beeman and Pelton 1980, Irwin and Hammond 1985,
Clark et al. 1994). Serviceberry (Amelanchier alnifolia) and hawthorn (Crataegus
douglasii) were the second most consumed shrub species. Eighty six percent (n = 86) of
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100 field verified site visits where I observed berry browsing were located in forested
habitats. Hawthorn is mainly found in the southern GRTE in cottonwood and aspen
stands. One success of this study was my ability to locate areas of hawthorn in the park
and demonstrate use by bears in southern GRTE. A visitor center was built in an area
where there are dense hawthorn stands, shortly after my study was conducted, and several
trails were built bisecting many hawthorn stands in this area. Interpretive programs
during the fall, in this area, should focus on hawthorn as a bear food and also ensure that
people are keeping a distance while watching bears in the area. The information on
hawthorn and other soft mast food sources can be used when future development is being
planned in GRTE.
Unlike black bears in the eastern United States that consume a considerate amount
of hard mast (i.e. oak (Quercus spp.), beechnuts (Fagus spp.), and hickory (Carya spp.))
(Elowe and Dodge 1989, Hellgren et al. 1991, Schooley et al. 1994) bears that I sampled
minimally used hard mast food sources such as whitebark pine (Pinus albicaulis), even
though it was available throughout the high elevation areas of my study area. Although
limber pine (Pinus flexilis) was an available hard mast food source in GRTE, I did not
detect any use at site visits or in scats collected, which was similar to an earlier study in
the GYE (Kendall 1983). McCutchen (1996) suggested that bears use limber pine if it is
the only hard mast food source in an area. Black bears were located in whitebark pine
stands at 1,110 of the total 9,507 GPS locations. Sixty four percent (n = 710) of these
locations were during August and September, when whitebark pine cones were ripe.
Unlike Kendall (1983) and Mattson and Jonkel (1990), I did not document whitebark
125
pine consumption in the spring, even though 200 out of 1,110 GPS locations were located
in whitebark pine stands in the spring (den emergence – July 15th). However, of the 154
field-verified sites that I visited between August and September, I documented 8 site
visits where bears utilized whitebark pine. These 8 locations were from 2 different bears.
According to the scat analysis, only 3 bears utilized whitebark pine, and 2 of these
individuals were previously identified from site visit data. Although, I documented
minimal use of whitebark pine as a food source in southern GRTE by black bears, my
data were equivocal, and additional studies should be carried out to determine if it is
more widely used, and how it is used during low whitebark pine cone years. Black bears
may use soft mast more than whitebark pine cones since soft mast is more widely spread
throughout the southern GRTE. Welch et al. (1997) suggested that bear food habits are
reflective of plant species that are common in a location.
Grizzly bears and black bears in the GYE rely on whitebark pine (Kendall 1983,
Mattson and Jonkel 1990, Mattson et al. 1991). Black bears have also been documented
utilizing the same red squirrel (Tamiasciurus hudsonicus) middens as grizzly bears
(Kendall 1983). As grizzly bears occupy the south, continued research could determine if
black bears are utilizing whitebark pine stands less than before their occupation. Grizzly
bears’ aggressive nature, claws, and musculature are well suited for digging (Herrero
1978), may make them a superior competitor at whitebark pine stands. Black bears may
also use whitebark pine stands at different times or continue to climb trees to obtain pine
cones that might be inaccessible to grizzlies. Using a different strategy to obtain
whitebark pine cones may also provide protection to black bears that forage in the
126
presence of grizzlies.
Habitat Use
Black bears frequently used forested areas in the southern part of GRTE, which is
was reflected in the Mahalanobis full rank and reduced rank models that I constructed.
Most home ranges overlapped heavily with forested parts of the park. The relatively high
utilization of forested habitat was also verified at field sites that I visited. I found that
420 of 501 field-verified sites were in forested areas. I recorded that bears utilized nonforested areas at only 81 field-verified sites and 78 of these were in meadows. On
average, field verified sites located in non-forested areas were 49.4 m (SE ± 8.5 m) from
the forest edge. I also found that 81% (n = 379) of the 421 field verified forested sites,
were dominated by subalpine fir (Abies lasiocarpa) and lodgepole pine (Pinus contorta)
in the overstory. My site visits suggested that black bears mainly fed while in nonforested habitats, followed by traveling. Although I found that graminoids were
consumed in both forested and non-forested habitats, I recorded use of grasses at all of
the feeding sites in non-forested habitat. In contrast, I found that berries and ants were
the most frequently consumed foods in forested sites.
The bears that I sampled used areas closer to trails more often than would be
expected if their movements were random. This finding differs from Kasworm and
Manley (1990), who found that black bears used areas close to trails less than expected.
Distances to trails, roads, and development were the best predictors of habitat use, and
this finding is likely explained by the placement of trails in forested areas and in areas
that make likely travel corridors for bears. However, this finding highlights an important
127
issue for managers of the park. Mazur and Seher (2008) found that food conditioning of
bears was more dependent on where a bear was raised (e.g., where it lived on the
landscape), and less dependent on if its mother was food conditioned. Singer and Bratton
(1980) found that black bear-human conflicts were clustered within backcountry areas
that were used most frequently by humans in Great Smoky Mountains National Park.
Since my data suggest that black bears were more likely to use areas near hiking trails
frequented by visitors managers within GRTE should consider effective education
programs that focus on minimizing or preventing food conditioning of bears by
uniformed hikers. Creachbaum et al. (1998) highlighted the importance of educating the
public foremost. For backpackers, this would involve in-depth education on food storage
and bear country safety as soon as they apply for a backcountry permit. At each trail
head an information board could emphasize bear ecology and safe hiking practices in
bear country. Decker and Chase (1997) emphasized the importance of managing people
as well as wildlife in human-wildlife interactions. Backcountry campers must continue to
utilize bear canisters and take every necessary precaution, since GRTE has a population
of bears that live in high recreation areas.
The human-wildlife interface is becoming an increasing problem not only in the
protected lands in the Greater Yellowstone Ecosystem (GYE) (Rasker and Hansen 2000),
but areas across the U.S. (Radeloff et al. 2005). With an increase in habitat
fragmentation and habitat loss in the GYE as a whole (Rasker and Hansen 2000), it is
important to understand what is affecting black bears within park boundaries. Many
wildlife managers around the country feel that habitat fragmentation and loss are a
128
problem for black bears (Pelton et al. 1999). Recognizing detailed aspects of wildlife
ecology is vital when land managers manage for unpredictable visitors with a varying
degree of wildlife knowledge (Decker and Chase 1997).
Bears have been known to follow the phenological stages of vegetation as it
becomes available (Reynolds and Beecham 1980, Garshelis and Pelton 1981, Unsworth
et al. 1998). When food shortages occur, bears increase their activity in search of food
(Amstrup and Beecham 1976, Knight et al. 1988). There has been a documented
increase in property damage, anthropogenic foods obtained, and human injury when
natural bear foods are in short supply in the GYE (Gunther et al. 2004). The trail and
backcountry camping systems increase opportunities for bears to obtain human foods,
especially when natural food shortages occur. Beginning in 2008, the park required bearproof food canisters in the backcountry below 10,000 ft (National Park Service 2008) to
help eliminate bears obtaining anthropogenic food. Food storage regulation in the
backcountry are often more difficult to enforce than in front country developments.
Backcountry campsites are located in remote areas that are difficult to access. In
addition, backcountry campers have the option to camp in a zone opposed to designated
sites that would be difficult to locate and monitor.
During years of food shortages and increased bear activity and bear-human
interactions, I suggest that the park limit or place a temporary closure on backcountry
campsites located in critical bear food habitats. Placing this limit/temporary closure in an
area may be beneficial when all proactive measures of food storage have failed. Several
agencies have assessed bear food items, and bear use patterns in an area prior to
129
development (Creachbaum et al. 1998). The information I collected during this study, on
food and activity patterns can be used to help inform spatial placements of future
developments. Where development, transportation, and recreation corridors are not
optional, the activity pattern data will be useful in educating the public to reduce
encounters and create temporal closures when necessary. Similar information was
critical for the planning and redevelopment of the Three Mile Campground, Wyoming
(Creachbaum et al. 1998). Coordinating developments in prime bear habitat allows
managers to emphasize planning designs that incorporate bear ecology in the proposal
and use the layout of the area for interpretive opportunities (Creachbaum et al. 1998).
Since many bear-human conflicts are spatially clustered (Singer and Bratton 1980,
Baruch-Mordo et al. 2008), the activity data collected from my study when compiled
with bear-human conflict information could further guide management strategies. This
information will assist park managers as they prioritize campsite designation in the
backcountry and focus enforcement on bear proof food containment.
According to my data, 62% of sampled bears had part of their home ranges
outside of park boundaries. Due to the size and habitat requirements of a black bear, their
home ranges may extend beyond park boundaries and into unprotected areas (Samson
and Huot 1998). During the summer of 2007, 3 bears (22046, 22211, and 22224) that
were a part of this study were euthanized by Wyoming Game and Fish personnel after
obtaining human food outside the park in subdivisions and developed areas. When
carnivores leave protected areas there is an increase in mortality (Woodroffe and
Ginsberg 1998). Leaving parks and preserves also increases the probability for bears to
130
come in contact with humans under less controlled conditions. Bears have increased
opportunity to obtain food in less secure areas and are potentially more vulnerable to
hunters if they have been are habituated to humans while residing inside park areas.
Brody and Pelton (1989) suggested that bears accustomed to roads were more likely to
come in contact with hunters. A protected area such as a park, allows bears to utilize
road corridors that may be rich in food and/or protection, with no hunting or poaching
pressures (Hellgren and Vaughnn 1988, Hellgren et al. 1991). Bears may move to the
outskirts of their home range (Beeman and Pelton 1980) and leave park boundaries more
frequently in search of food during poor soft or hard mast years. Agencies and exurban
development that surround GRTE need to consider programs that focus on bear-aware
education and waste management since what happens outside of the park is not
independent from the park itself.
I found that the Mahalanobis distance statistic (D2) correctly modeled areas that
were indicative of the bears that I sampled. These black bears primarily utilized forested
regions that were close to trails and roads, and relatively distant from development in the
park. When I modeled the entire park, a shortcoming of the model became apparent,
when a majority of the northern part of the park, had low D2 values that were indicative
of areas of low use. Although these areas modeled as low use this does not mean that
they are poor habitat, they represented areas that were atypical of what the bears I
sampled would use. Clark et al. (1993) and Erickson et al. (1998) highlighted that
although Mahalanobis distance models highlight high use areas, the D2 statistic does not
necessarily equate to the most favorable habitat of that species.
131
The Mahalanobis distance model was accurate within my study area, but was of
limited value when extrapolated to all areas of the park because a majority of the
northern part of GRTE lacked an extensive trail system and was distant from roads and
trails. Since both of these covariates were important in my study area, the model
predicted the areas in the northern part of the park to be poor habitat. In order to account
for the limitations of the D2 model in GRTE, a more extensive sample of black bears
throughout the region should be undertaken, allowing for the development of a more
comprehensive model. This revised model, including bears in the north, would then be
useful for planning throughout the park. Utilizing the Mahalanobis Distances Factor
Analysis (MADIFA) proved to be a useful tool in interpretation of the D2. I was able to
break down important components of linear combinations of the D2. This information
was critical when identifying what factors were most predictive of typical use in an area.
I was able to identify that the combination of factors leading to high use of an area was
forested habitat where trails and roads are usually constructed. In turn I was able to
determine the probable causes of low use that was modeled by the D2.
Post hoc
As documented by (Woodroffe and Ginsberg 1998) carnivores leaving protected
areas are more likely to have negative outcomes due to human interactions. During the
summer of 2007, 3 bears (22046, 22211, and 22224) that were a part of this study were
euthanized by Wyoming Game and Fish personnel after obtaining human food outside
the park in subdivisions and developed areas. The 2 males that were euthanized had
several locations outside of the park from June to October. Female 22046, euthanized in
132
2007, had locations that ranged outside of the park from June to August. Several bears
that left the park less frequently in 2005 and 2006 survived. Two bears that were not
euthanized, only left the park in September and October. Male 22228, which was not
euthanized, denned outside of the park. He exited the park in October 2005 and entered
again in April 2006, spending the rest of the time within park boundaries. Although
limited in scope, these data suggested that bears that spent more time outside of the park
were more likely to get into management conflicts (Gunther et al. 1994).
Bears that leave the park encounter an ever growing human population that
surrounds GRTE. The population in Teton County has more than doubled since 1980
(Wyoming Economic Analysis Division http://eadiv.state.wy.us/pop/poplhtml). Several
of the communities that are near the park did not have secure garbage regulations in place
during the study. In 2007, Teton County, Wyoming enacted the Bear Conflict Mitigation
and Prevention Land Development Regulation, to increase the use of bear proof garbage
canisters in the county. As of 1 July, 2009, the towns of Jackson and Wilson Wyoming
made it mandatory for citizens in certain zones to use bear resistant garbage containers to
decrease bear-human conflict. GRTE, by the mid 1990’s, had converted all garbage
receptacles to bear-proof containers (S. Cain, National Park Service personal
communication). GRTE may act as a source of black bears to adjacent areas where
resident black bears have extensive overlap into human developed areas. The adjacent
area around the park may act as a sink. Beckmann and Lackey (2008) suggested that
bears in a wildland setting were drawn to urban areas due to the clusters of anthropogenic
133
foods. If removal of a species is extensive outside of the source area, it may have
impacts on the source population (Woodroffe and Ginsberg 1998).
I recommend the park consider conducting additional studies addressing bear
activity and bear-human interactions after bear/garbage ordinances have been in place for
several years. For managers of park and public wildlife areas which abut private
property, it will be important to determine whether these ordinances have a positive
impact on the reduction of bear-human conflicts. Spencer et al. (2007) found that most
agencies in the United States that had bear-human problems, wanted garbage
management to reduce these interactions. A key to adaptive management techniques is to
continue analyzing management that is in place and revise strategies accordingly (Haney
and Power 1996).
The data that I have collected can serve as a useful and detailed baseline upon
which additional studies can build upon in GRTE. Together such information can guide
future development and implementation of management strategies. The Mahalanobis
distance models highlight typical use bear areas in southern GRTE and can guide future
placement of trails, roads, and other human development. The food-use data emphasizes
specific foods in these high use areas, and should help guide backcountry campsite
development to avoid bear-human conflicts. The food use information coupled with
habitat use, detailed in this study, highlights areas of high priority for conservation within
park boundaries.
134
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APPENDIX A
FOOD ITEMS IDENTIFIED AT BLACK BEAR SITE VISITS AND SCATS,
SOUTHERN GRAND TETON NATIONAL PARK, 2005–2006.
Food items identified at black bear site visits and scats, southern Grand Teton National
Park, 2005–2006.
Food item
Site Visit
Scat content
Food type
Bluegrass (Poa spp.)
Bluejoint reedgrass (Calamagrostis
canadensis)
x
x
Graminoid
Brome (Bromus spp.)
x
Pinegrass (Calamagrostis rubescens)
x
Graminoid
Sedge (Carex spp.)
x
Graminoid
Wheatgrass (Agropyron spp.)
x
Graminoid
Wildrye (Elymus spp.)
x
Graminoid
Angelica (Angelica arguta)
x
Forb
Clover (Trifolium spp.)
x
Forb
Cow-parsnip (Heracleum maximum)
x
Dandelion (Taraxacum officinale)
x
Fern-leaved lovage (Ligusticum filicinum)
x
Fireweed (Epilobium angustifolium)
x
Forb
Geranium (Geranium viscosissimum)
x
Forb
Harebell (Campanula rotundifolia)
x
Forb
Horsetail (Equisetum spp.)
x
Lousewort (Pedicularis bracteosa)
x
Forb
Strawberry (Fragaria virginiana)
x
Forb
Sweet-cicely (Osmorhiza occidentalis)
x
Forb
Buffaloberry (Shepherdia canadensis)
x
Shrub
Chokecherry (Prunus virginiana)
x
x
Shrub
Currant (Ribes spp.)
x
x
Shrub
Hawthorn (Crataegus douglasii)
x
x
Shrub
Huckleberry (Vaccinium spp.)
x
x
Shrub
x
Graminoid
x
Forb
Forb
x
x
Forb
Forb
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x
Graminoid
Food item
Mountain Ash (Sorbus scopulina)
Site Visit
Scat content
Food type
x
x
Shrub
x
Shrub
x
Shrub
x
Shrub
Oregon Grape (Mahonia repens)
Serviceberry (Amelanchier alnifolia)
x
Twinberry (Lonicera spp.)
Whortleberry (Vaccinium spp.)
x
Whitebark Pine (Pinus albicaulis)
Rocky Mountain Juniper (Juniperus
scopulorum)
x
Lodgepole pine (Pinus contorta)*
x
Tree
Engelmann spruce (Picea engelmannii)*
x
Tree
Ant (Formicidae)
x
Elk (Cervus elaphus)
x
Moose (Alces alces)
Mule deer (Odocoileus hemionus)
Small mammals (Rodentia)
x
x
Tree
x
Tree
x
Insect
x
Insect
x
Mammal
x
Mammal
x
Mammal
x
Mammal
* Black bears stripped Lodgepole pine and Engelmann spruce trees to obtain cambium as a food source.
142
Maggot (Diptera)
Shrub