SPATIAL MOVEMENTS AND ECOLOGY OF MOUNTAIN LIONS IN ... … · spatial movements of mountain lions...
Transcript of SPATIAL MOVEMENTS AND ECOLOGY OF MOUNTAIN LIONS IN ... … · spatial movements of mountain lions...
SPATIAL MOVEMENTS AND ECOLOGY OF MOUNTAIN LIONS IN SOUTHERN
ARIZONA
by
Kerry Lynn Nicholson
_______________________________ Copyright © Kerry Lynn Nicholson 2009
A Dissertation Submitted to the Faculty of the
SCHOOL OF NATURAL RESOURCES
In Partial Fulfillment of the Requirements For the Degree of
DOCTOR OF PHILOSOPHY
In the Graduate College
THE UNIVERSITY OF ARIZONA
2009
2
THE UNIVERSITY OF ARIZONA
GRADUATE COLLEGE
As members of the Dissertation Committee, we certify that we have read the dissertation prepared by Kerry Lynn Nicholson entitled Spatial ecology of mountain lions in southern Arizona and recommend that it be accepted as fulfilling the dissertation requirement for the Degree of Doctor of Philosophy. _______________________________________________________________________ Date: 08/31/2009 Paul R. Krausman _______________________________________________________________________ Date: 08/31/2009 Warren B. Ballard _______________________________________________________________________ Date: 08/31/2009 John L. Koprowski _______________________________________________________________________ Date: 08/31/2009 William W. Shaw Final approval and acceptance of this dissertation is contingent upon the candidate’s submission of the final copies of the dissertation to the Graduate College. I hereby certify that I have read this dissertation prepared under my direction and recommend that it be accepted as fulfilling the dissertation requirement. Date: 08/31/2009 Dissertation Director: Paul R. Krausman Date: 08/31/2009 Dissertation Director: Warren B. Ballard
3
STATEMENT BY THE AUTHOR
This dissertation has been submitted in partial fulfillment of requirements for an advanced degree at the University of Arizona and is deposited in the University Library to be made available to borrowers under rules of the Library. Brief quotations from this dissertation are allowable without special permission, provided that accurate acknowledgement of source is made. Requests for permission for extended quotation from or reproduction of this manuscript in whole or in part may be granted by the head of the major department or the Dean of the Graduate College when in his or her judgment the proposed use of the material is in the interests of scholarship. In all other instances, however, permission must be obtained from the copyright holder.
SIGNED: Kerry L. Nicholson
4
ACKNOWLEDGEMENTS Because science is not conducted in a vacuum, I thank a number of agencies,
organizations, and people who made this research possible. I thank the personnel of the Arizona Game and Fish Department (AGFD): Jim Heffelfinger, Robert Fink, and Dean Treadwell, Richard Ockenfels, John McGehee, Gerry Perry and all the other wildlife managers in Region V. I thank the capture and telemetry crew in Payson, Thorry Smith, Bob Waddell, Scott Sprague, and the pilots. I thank Chasa O’Brian chief of the Research Branch (AGFD) for her support of this project. I thank Jim deVos and the Arizona Game and Fish Commissioners for initiating the study. Without their support nothing would have happened for this degree. Ron Thompson was a sage purveyor of all things mountain lion and a steady, supportive force. I thank the excellent hounds’ men Tim and Andy Salazar, and Nick Smith. I thank Josh Taiz at the U.S. Forest Service and Kerry Baldwin with Pima Park and Recreation for encouraging my work. The Berryman Institute, Mississippi State University; College of Agriculture and Life Sciences, Agricultural and School of Natural Resources, University of Arizona and provided scholarships to support my studies at the University of Arizona. I’d like to thank the Faculty and Staff of the School of Natural Resources for their support and assistance and Val Catt, Cheryl Craddock, Anne Hartley, Dee Simmons, Cecily Westphal, Shri Ramakrishnan and Carol Yde for their help with all the administrative issues. Andy Honoman, Phil Guertin, Mickey Reed, and Craig Wissler were an indispensible source of geospatial knowledge. I thank Paul R. Krausman for serving as my major professor, I couldn’t have asked for a better advisor and mentor. I am a more critical thinker and better writer for having worked under his tutelage. I appreciate all the confidence placed in my abilities and the many learning opportunities he provided. His dedication to wildlife and the profession, his productivity throughout his career, and the professionalism he maintains provide an example for us all. I have learned a lot about how to be a professional from him. I thank Warren B. Ballard who encouraged Paul to take me as a student. Warren was my Masters advisor and knew what kind of a challenge I would be. Warren’s unwavering faith in my abilities has been a substantial motivator – I would not want to let him down. I thank my other committee members John Koprowski and William W. Shaw, for their support and guidance during my time at the University of Arizona. In addition, I thank Randy Gimblet for making me a pseudo advisee and just being a friendly face. I am grateful for graduate student community that supported me through this project, Cori Dolan, Bryan Kluever, Sonja Smith, Beth Williams, Karla Peltz, Meghan Maloney, Jerod Merkel, Nichole Cudworth, Nate Gwinn, Geoff Palmer, Anne Kretschmann, Adrian Munguia Vega and especially towards the end - my dissertation buddy Karen Munroe. I thank my good friends from afar, especially Kim Van Dalsem and Holly Costa and my brother Shawn who at the last minute saved me in a time of need. Lastly, I thank my family and Byron Buckley who supported me throughout this chapter in my life. They supported me when times were tough and helped me stay focused through to the end.
5
DEDICATION
To my father and mother, Cathy and Steen Nicholson, who instilled in me strength and
determination to always do what makes me happy.
and
Dr. Ted McKinney and Tim Salazar. Their inspiration of hard work, determination, and
unique perspectives touched so many people. You are both missed.
“The land ethic simply enlarges the boundaries of the community to include soils, waters, plants, and animals, or collectively: the land. This sounds simple: do we not already sing our love for and obligation to the land of the free and the home of the brave? Yes, but just what and whom do we love? Certainly not the soil, which we are sending helter-skelter downriver. Certainly not the waters, which we assume have no function except to turn turbines, float barges, and carry off sewage. Certainly not the plants, of which we exterminate whole communities without batting an eye. Certainly not the animals, of which we have already extirpated many of the largest and most beautiful species. A land ethic of course cannot prevent the alteration, management, and use of these 'resources,' but it does affirm their right to continued existence, and, at least in spots, their continued existence in a natural state.”
Aldo Leopold (1948) A Sand County Almanac and Sketches Here and There
6
TABLE OF CONTENTS
ABSTRACT……………………………………………………………………………. 7 INTRODUCTION…………………………………………………………………...... 8
Explanation of the Problem……………………….….…………………….... 8 Literature Review…………………………………………………………….. 12 Explanation of Dissertation Format……………….……………………….... 16
PRESENT STUDY………………………………………….………………….……... 17
Study Area …………………………………………..…………………….…... 17
Mountain Lion Habitat Selection in Arizona…….….………………….…….. 18 Mountain Lion Use of Urban Landscapes in Arizona…………...….……….. 19
Spatial and Temporal Interactions of Sympatric Mountain Lions in Arizona.. 20 Serosurvey of Mountain Lions in Southern Arizona………………………… 21 Flea and Tick s of Mountain Lions in Southwestern Arizona……………….. 21
Management Implications…………………………………..……….…….…. 21 REFERENCES…………………………………………………………………..…..... 27 APPENDIX A. MOUNTAIN LION HABITAT SELECTION IN
ARIZONA………………………………………………………………..…… 38 APPENDIX B. MOUNTAIN LION USE OF URBAN LANDSCAPES IN
ARIZONA…………..………………………………..………………..……… 71
APPENDIX C. SPATIAL AND TEMPORAL INTERACTIONS OF SYMPATRIC MOUNTAIN LIONS IN ARIZONA………………………….……………… 101
APPENDIX D. SEROSURVEY OF MOUNTAIN LIONS IN SOUTHERN
ARIZONA…………...……………………………………………………….. 147 APPENDIX E. NEW FLEA AND TICK RECORDS FOR MOUNTAIN LIONS IN
SOUTHWESTERN ARIZONA ………………………….………………….. 165
7
ABSTRACT Managing wildlife in urban areas is increasingly necessary for wildlife
conservation. Large carnivores like mountain lions (Puma concolor) present a particular
challenge to managers because of public safety and the polarizing emotional reactions to
human-lion encounters. Intensive development and conversion of large open spaces to
small properties and subdivisions has caused increased habitat loss, fragmentation and
encroachment. Preserving movement corridors for access to habitat patches is important
in maintaining landscape connectivity to ensure viable populations adjacent to urban
areas. Because mountain lion habitat is often adjacent to urbanization in Arizona and
lions traverse large landscapes, mountain lions are ideal models to examine how human
alteration of habitats influences their life history characteristics and ability to adapt to a
variety of environments. The objective of this study was to examine the ecology and
spatial movements of mountain lions surrounding urban areas. We studied habitat
selection, urban use by mountain lions, spatial movements and overlap, genetic
relatedness, feline disease, and ectoparasites of mountain lions in southern Arizona.
8
INTRODUCTION
Explanation of the Problem
Humans and predators have a delicate relationship. Depending on the web of
societal myths, practices, stories, and perceptions, forests are cut, wildlife conserved,
grasslands plowed, wilderness established, or predators eradicated. As societal values
change, so does natural resource management and practices implemented by biologists
and managers. In some cultures historically, carnivores were revered (Young and
Goldman 1946, Hungry Wolf 1972, Erdoes and Ortiz); yet in other situations, they were
persecuted for livestock losses and competition with man. Historical management
actions have focused on agriculture and game resources, which were influenced by and
biased towards stakeholder values that, in many cases, have negative consequences for
predators (Estes 1996, Weber and Rabinowitz 1996).
While humans and predators have co-existed for millennia, the frequency of
conflicts has grown in recent decades, largely because of the exponential increase in
human populations and resultant expansion of human activities into predator habitat
(Beier 1991, Woodroffe 2000, Conover 2002). Conservation of a group of animals that
has such an inconsistent relationship with man is a difficult task (Mech 1995).
Interactions between carnivores and humans are not restricted to rural wilderness areas.
Conflicts that were once focused on agricultural borders and remote areas are expanding
beyond human created edges (Manfredo and Dayer 2004) and are encroaching in local
parks, neighborhoods, and back yards. The increasing occurrence of conflicts with
carnivores is increasing the necessity for conflict resolutions to go beyond traditional
9
methods of establishing reserves or defining the legal status for carnivore survival. These
mitigation measures may not be sufficient for long term survival for carnivores (Shivik
2006).
Urbanization favors some species, while others are stressed or eliminated
(Ferguson et al. 2001). Urban areas are often a mix of natural areas, human made-
disturbances, and activities, exotic and native species, and natural and human made
structures (Van Druff and Rose 1986). Urbanization generally leads to fire suppression,
an influx of exotic species, intensive maintenance of some areas, and fragmentation of
landscapes creating patches of differing successional stages, shapes, sizes, composition,
and age (Ferguson et al., 2001). In 2006, 79% of the U.S. population (299.1 million
people) lived in urban areas (Population Reference Bureau 2009). In Arizona alone, the
state population is projected to double within the next 30 years (U. S. Census Bureau
Population Division Projections Branch 2007).
Increasing human populations create new environments that can be detrimental to
some wildlife (Woodroffe 2000), yet others such as the red fox (Vulpes vulpes [Harris
1981]), gray fox (Urocyon cinereoargenteus [Crooks 2002]0, skunk (Mephitis mephitis
[Mech 1996], raccoon (Procyon lotor [Riley et al. 1998]), and coyote (Canis latrans
[Grinder and Krausman 2001]) seem to flourish. However, mammals that are wide
ranging and exist at low densities are particularly vulnerable to habitat loss and
fragmentation (Wilcox and Murphy 1985, Noss et al. 1996, Gittleman et al. 2001)
especially wilderness species like the mountain lion (Puma concolor [Leopold 1933]).
10
In Arizona, there are urban raccoons, gray foxes, skunks, collared peccaries
(Tayassu tajacu), bobcats (Lynx rufus), coyotes, black bears (Ursus americanus) and an
encroaching population of mountain lions. With larger carnivores such as mountain
lions, it is not the animal invading established human landscapes; rather, human
development has infringed on the animal’s territory and has left no room for normal
movements. The full impact of urbanization on large carnivores is unknown.
With the presence of carnivores in urban settings, public opinion has polarized,
particularly in relation to mountain lions. As a result, the Arizona Game and Fish
Department (AGFD) initiated a lion awareness campaign in conjunction with the
initiation of urban carnivore programs focusing on lions. Our objective was to monitor
mountain lions in Tucson, Payson, and Prescott’s urban landscapes. We selected these
areas due to their gradient of urbanization and mountain lion presence because
occurrences and interactions between the 2 are increasing.
Public and Lions in Arizona
Mountain lions are one of the most difficult large-terrestrial mammals to study in
the wild (Currier 1976, Logan and Sweanor 2001). Little information is available to
managers on daily lion movements within human environments. Thus, managers rely on
reports from the public that typically reflect 1 of 2 philosophies; the lion is appreciated as
a symbol of wilderness and is to be conserved, or the lion is a threat. A single glimpse of
a mountain lion can initiate a flurry of reports, panic, and more sightings that typically
are benign because they are misidentified bobcats, house cats, or even white-tailed deer
(E. Ostergaard, Urban Wildlife Specialist, AGFD, personal communication). Because of
11
the opposing opinions about mountain lions, actions taken when dealing with lions by
government officials are scrutinized heavily. In Arizona, 5 documented mountain lion
attacks occurred on humans between 1988 and 2006 (K. Bergersen, AGFD, personal
communication). Arizona has a depredation statute (Arizona Revised Statues 17-302)
that allows livestock producers to harvest mountain lions that kill livestock. Since 2001,
an average of 50 mountain lions were harvested annually under this statute (Arizona
Game and Fish Department 2006).
In 2003, AGFD began receiving reports of lion activity near Sabino Canyon
Recreation Area that receives >160,000 visitors a year in Tucson, Arizona. In 2004,
officials took action to remove nuisance lions. A strong public outcry, the controversy
escalated to the Arizona Legislature and Governor, and mountain lion removal attempts
were suspended (Perry and deVos 2005). Until 2004, Arizona had no mitigation
protocols for nuisance mountain lions. The public criticized AGFD and accused the
agency of having no data to support their actions, no statewide protocol specifically for
nuisance lions, no comprehensive incident file, lack of a rigorous policy for dealing with
incidents involving lions, and inconsistent classification of reports. Guidelines were
needed on: 1) how to verify reports; 2) how to deal with nuisance lions; and most
importantly 3) classifications of mountain lion behavior to include what is dangerous or
abnormal.
There were many proposals as to how to deal with nuisance mountain lions and
how to decrease the potential for human interactions. Considered proposals included
bans on wildlife feeding (which subsequently passed in 2006; Arizona Revised Statues
12
13-2927); aversive conditioning; development of a statewide conservation plan for urban
mountain lion habitat; an open space bond to slow human encroachment into mountain
lion habitat; and removal of non-native grasses, shrubs and ornamentals from landscaped
yards because they attract prey which in turn attracts mountain lions. The eventual
removal attempts of habituated mountain lions resulted in 1 mountain lion being sent to a
rehabilitation facility in Scottsdale, Arizona, ≥ 4 lions removed after confronting hikers,
and some lions legally shot by hunters in the vicinity of the Sabino Canyon Recreation
Area. Also among the actions taken to mitigate human/lion interactions, AGFD teamed
with the University of Arizona to answer some basic questions about lion behavior and
movement in urban areas.
Literature Review
Home Range and Habitat Use of Mountain Lions – Mountain lion use of habitat
has received limited attention in the West (Logan and Irwin 1985, Koehler and
Hornocker 1991, Laing and Lindzey 1993a, Williams et al. 1995, Dickson and Beier
2002) and has not been addressed in Arizona (Beier and Cunningham 1996, Cunningham
et al. 1999, Cunningham 2001, McRae et al. 2005, McKinney et al. 2006) with 1
exception (Arundel et al. 2007). Mountain lions select forested and woody cover and
avoid flat open areas (Logan and Irwin 1985, Koehler and Hornocker 1991, Williams et
al. 1995, Riley and Malecki 2001). The aversion to flat, open areas could potentially
limit lion movement in southern Arizona’s landscape, because it is dominated by large
insular mountains, separated by desert and grassland plains. Avoidance of flat areas
13
could cause lions to cluster use of landscapes or incorporate urban areas into their home
ranges.
Mountain Lion Movement and Urban Use. – Radio-collars and telemetry have
been used to document mountain lion biology with varying success (Beier 1995, Beier et
al. 1995, Pierce et al. 1998, Meegan and Maehr 2002, Dickson et al. 2005). Most studies
of mountain lions described patterns over weeks or months, based on ≤1 location/day,
usually during daylight hours (Hemker et al. 1984, Anderson et al. 1992, Beier et al.
1995, Ruth et al. 1998, Sweanor et al. 2000) using Minimum Convex Polygon (MCP)
estimator of home range. Little information is available to managers on daily mountain
lion movements within human environments.
Interactions of Mountain Lions. – Felids are characterized as solitary with
exclusive territories within the sexes (Sunquist and Sunquist 2002). Exceptions to this
standard have been documented in several species of cats including the mountain lion
(Seidensticker et al. 1973, Hopkins et al. 1986). Generally, female mountain lions are not
territorial, whereas territoriality among males regulates their density and distribution
(Hornocker 1969, Seidensticker et al. 1973). Females select vegetation, topography, and
prey availability sometimes responding to migratory movement of deer (Odocoileus spp.)
between seasons (Seidensticker et al. 1973) whereas males compete for access to females,
and have distinct territories without overlap (Hornocker 1969). Mountain lion
populations regulate their populations based on a territorial system, involving mutual
avoidance (Hornocker 1969, 1970, Seidensticker et al. 1973). Overall, general
associations between individual mountain lions of the same gender are considered rare
14
(Hemker et al. 1984, Hornocker 1969, Seidensticker et al. 1973, Ashman et al. 1983).
Typically mountain lions demonstrate a mutual avoidance (Hornocker 1969,
Seidensticker et al. 1973) facilitated by urine, scrapes, or scratches in suitable substrates
(Anderson 1983a). Home range overlap and dynamic interactions between mountain
lions has not been examined in detail; consequently, comparisons are limited. Anderson
et al. (1992) provided a table of associations among radio-collared pairs of mountain
lions, where of the 345 simultaneous locations, only 19 were male-male interactions.
Five additional studies document male-male overlap, but none examined dynamic
interaction and sample sizes were ≤7 dyads (Anderson et al. 1992). Studies that have
documented male-male interactions have done so typically by following tracks of
individuals in the snow (Hornocker 1969) or by aerial telemetry with ≤1 locations/day
(McBride 1976, Padley 1990, Laing and Lindzey 1993b). Data on how often overlapping
male individuals interact with each other is limited and is generally unknown if the
interacting individuals are related. No mountain lion social organization study thus far
has had supporting evidence of genetic relatedness to explain overlap and interaction
dynamics.
Mountain Lion Diseases. – The prevalence of disease in wild populations of
mountain lions in the southwestern USA has not been thoroughly examined, especially
related to infection and transmission between mountain lions surrounding urban areas. In
Arizona, little information is available on prevalence of diseases in mountain lions.
Mountain lions potentially are susceptible to many infectious agents that affect domestic
cats (Paul-Murphy et al. 1994). The prevalence of infectious agents varies by location.
15
For example, Anderson (1983b) reported the occurrence of feline panleukopenia virus as
low, yet Paul-Murphy (1994) reported titers in nearly 100% of the animals screened.
Some of these agents have the ability to limit populations and have implications for the
conservation and persistence of the endangered Florida panther (Puma concolor coryi
[Roelke et al. 1993]). Feline panleukopenia virus has the potential to limit mortality of
populations and has been reported in California, Florida, and the Rocky Mountains
(Roelke et al. 1993, Paul-Murphy et al. 1994, Biek et al. 2006). Mountain lions have
been sereopositive for feline immunodeficiency virus (Olmsted et al. 1992, Roelke et al.
1993, Evermann et al. 1997, Biek et al. 2006). Feline herpes virus has not been
documented as prevalent; however it does occur (Paul-Murphy et al. 1994, Batista et al.
2005). Jessup et al. (1993) was the first to document feline leukemia virus, Adaska
(1999) reported the first case of coccidioidomycosis, and Yamamoto et al. (1998) was
the first to report bartonellosis in lions from California. Epizootic diseases are likely not
a primary threat to mountain lion populations in the western US (Foley 1997). However,
it is important to understand disease ecology of wild carnivores to enhance their
management.
Ectoparasites of Mountain Lions. – Parasitic infections in mountain lions have
been documented (Anderson 1983b; Forrester et al. 1985; Waid 1990). Yet, few data are
available on ectoparasites associated with this widely distributed large carnivore. In
general mountain lions have been described as free of external parasites (Currier 1983),
but others report detection of ticks, fleas, and lice (Young and Goldman 1946, Forrester
et al. 1985, Wehinger et al. 1995). The most comprehensive studies involved
16
ectoparasites of the endangered Florida panther (Forrester et al. 1985, Wehinger et al.
1995). Pulex simulans were reported on Paraguayan (South America) mountain lions and
jaguars (Panthera onca; Durden et al. 2006); Arctopsylla setose (Young and Goldman
1946), and Pulex porcinus in Mexico (Eckerlin 2004); and Polygenis tripopsis in Brazil
(Linardi and Guimaraes 2000). In southern California, severe notoedric mange
(Notoedres cati) infestation was reported in mountain lions and bobcats (Riley et al.
2007, Uzal et al. 2007). Additionally, in southern California, 2 mountain lion mortalities
from anticoagulant toxicity showed signs of severe mange infestation (Uzal et al. 2007), a
disease usually reported in isolated cases (Riley et al. 2007). There are no data on
prevalence of titers to many of these infectious agents for mountain lions in Arizona.
Explanation of Dissertation Format
Manuscripts in the appendices of this dissertation are the result of research on the
spatial movements of mountain lions in Arizona. The primary objectives of this research
were to determine and describe habitat and home range selection of mountain lions,
mountain lion use of urban areas, dynamic interactions of individuals, and baseline
disease and ectoparasite ecology from lions using urban landscapes.
All manuscripts in this dissertation are the result of research I conducted as a
Ph.D. student at the University of Arizona. My major professors and committee members
provided advice and guidance however, I was responsible for study design, data
collection and analysis, and the presentation of results in this dissertation. I am the senior
author on all manuscripts resulting from my dissertation research; coauthors include
others, including committee members, who contributed to this research.
17
PRESENT STUDY
Descriptions of the methodology, results, and conclusions are contained in the
manuscripts in the appendices. The following is a summary of the major results of these
manuscripts.
Study Area
We studied mountain lions in north-central Arizona near Payson (including the
cities of Star Valley, Pine, and Strawberry; 34.2 ˚N 111.3˚W), Prescott (including
Prescott Valley, Paulden, Williams, Cottonwood, Clarkdale, and Chino Valley; 34.6˚N
112.5˚W), and in southern Arizona near Tucson (including Oracle, Marana, Catalina, and
Saddle Brook; 32.2˚N 111.0˚W) during 2005-2008. Payson and Prescott were ≥ 130 km
apart, and ranged between 1,280 and 1,860 m in elevation. Annual precipitation
averaged 57 cm in Payson and 48 cm in Prescott, with annual snowfall for both areas of
62 cm with average temperatures of 3-23 ˚C. Tucson was 307 km south of Payson with
an average annual rainfall of 30 cm and snowfall of 3.0 cm. Maximum and minimum
temperatures were 28 and 12.6˚C, respectively. Located in the Sonoran Desert, Tucson
sits within a valley, circumscribed by the Santa Catalina, Tucson, Tortolita, Rincon, and
the Santa Rita mountains. Elevation ranged from approximately 640 to ≥2,700 m in ≤60
km. Human population of Payson in July 2007 was 16,742, Prescott was 43,217, and
Tucson was 541,132 (Department of Urban Planning and Design 2009). Land jurisdiction
and management of the surrounding areas included Arizona Wildlife Management,
Bureau of Land Management, Bureau of Reclamation, County Land, Department of
Defense, Indian Reservations, National Forest, National Monuments, National Park
18
Service, National Wildlife Reserves, State Trust Land, Parks and Recreation, and private
lands.
Vegetation associations were similar for Payson and Prescott including interior
chaparral, pinyon (Pinus edulis) - juniper (Juniperus spp.) woodlands, grasslands and
mixed ponderosa pine (Pinus ponderosa) forests (Brown 1994). Tucson contained
Arizona upland subdivision of Sonoran Desert vegetation and riparian and xeroriparian
vegetation (mixed riparian desert scrub series; Brown 1994). Mountain ranges that
surround Tucson ascend from the Sonoran desert scrub (e.g., mesquite [Prosopis
juliflora], paloverde [Cercidium spp.], cactus [Opuntia spp.] and various grasses) to
Chihuahuan semi-desert to grassland to oak (Quercus)-alligator juniper (Juniperus
deppeana) woodland to Petran-montane and mixed conifer forest (Whittaker and Niering
1965, Brown 1994). On all study sites, mule deer (Odocoileus hemionus), white-tailed
deer (O. virginianus), collared peccary, black bear, bobcats, and coyotes were common.
Cattle and pronghorn (Antilocapra americana) inhabited areas near Tucson and Prescott,
and elk (Cervus elephus) were common in Payson and Prescott and bighorn sheep (Ovis
canadensis) inhabited the Silverbell Mountains near Tucson.
Mountain Lion Habitat Selection in Arizona
We quantified mountain lion home range characteristics and selection of
vegetation associations in central and southern Arizona. We calculated 95% and 50%
fixed kernel home ranges for 8 female and 21 male mountain lions that were radio-
collared in Payson, Prescott, and Tucson, Arizona from August 2005 through August
2008. Using compositional analysis, we assessed use of vegetation associations and
19
urban areas within the home range (3rd order) and within the study area (2nd order). At
both levels of selection, mountain lions at all study sites avoided human dominated
landscapes. At the 2nd order selection, mountain lions preferred woodland habitat in
Tucson and Prescott and chaparral in Payson. Whereas at the 3rd order, riparian was
selected in Tucson and Payson, and chaparral was selected in Prescott. Season, lion
mass, and ungulate density had no effect on the size of home ranges. Home range sizes
for resident males ranged from 5,286 to 83,859 ha; transient males covered up to 409,195
ha. Home ranges for females ranged from 2,860 to 21,772 ha. Intensive development
and conversion of large open spaces to small properties and subdivisions has caused
increased habitat loss, fragmentation and encroachment. Preserving biological linkages
for access to habitat patches is important in maintaining landscape connectivity to ensure
viable populations adjacent to urban areas.
Mountain Lion Use of Urban Landscapes in Arizona
We evaluated movements of mountain lions that interacted with urban
development in Arizona. We collared and monitored 29 mountain lions between August
2005 and August 2008 near Payson, Prescott, and Tucson, Arizona. We obtained satellite
locations every 4.15 for lions near Tucson, and 7 h in Payson, and Prescott. Nine
mountain lions used urban landscapes (7 M, 2 F). Mountain lions avoided urban areas (n
= 18, χ2 = 1219.49, P < 0.0001), had shorter step-lengths (i.e., distances between
locations) within an urban environment (one way ANOVA F1, 2053 = 14.11, P < 0.0002)
than non-urban lions. When in urban areas, lions moved at a rate of 0.16 m/min (0.12-
0.21 95% C.I.) versus 1.54 m/min (1.43-1.65 95% C.I.) when outside urban landscapes.
20
Lions occupied urban areas mostly nocturnally (72% of urban locations). There were 143
forays into urban areas and 63% were single occurrences where the next mountain lion
location was outside of the urban boundary.
Spatial and temporal interactions of sympatric mountain lions in Arizona
We evaluated home range overlap and spatial/temporal use of overlap zones (OZ)
of mountain lions in Arizona. We incorporated spatial data with genetic analyses to
assess the influence of relatedness on distribution among pairs of mountain lions. We
recorded space use patterns of 29 radio-collared mountain lions in Arizona from August
2005 to August 2008. We genotyped 28 lions at 12 microsatellite DNA loci and
estimated the degree of relatedness among individuals. There were 26 pairs of
temporally overlapping mountain lions, 18 of which overlapped spatially and temporally
and 8 of those had corresponding genetic information. Home range overlap ranged from
1.18-46.38% (�� = 24.43, SE = 2.96). There was no significant difference in size of
overlap between male-male pairs and male-female pairs (t1,16 = 1.04, P = 0.84). Male-
male and male-female pairs were located within 1 km on average, 0.63% and 4.90% of
the time, respectively. Two male-male pairs exhibited symmetrical spatial avoidance and
2 symmetrical spatial attractions to the OZ. The remaining 5 pairs had asymmetrical or
singular spatial attraction to the OZ. We observed simultaneous temporal attraction in 3
male-male pairs and 4 male-female pairs. Overall, individuals were not related (n = 28,
mean R = - 0.0037). Individuals from Tucson were slightly related to one another within
the population (n = 13, mean R = 0.0373 ± 0.0151) whereas lions from Payson (n = 6,
mean R = -0.0079 ± 0.0356) and Prescott (n = 9, mean R = -0.0242 ± 0.0452) were not as
21
related. Overall, males were less related to other males (n = 20, mean R = -0.0495 ±
0.0161) than females were related to other females (n = 8, mean R = 0.0015 ± 0.0839).
Genetic distance was positively correlated with geographic distance (r = 0.22, P = 0.001).
None of the 8 pairs of overlapping lions was identified as > 25% related.
Serosurvey of Mountain Lions in Southern Arizona
We collected serum samples from 9 radiocollared mountain lions in the
mountains surrounding Tucson, Arizona (32.189N -110.881E) from January 2006 to
March 2007. We tested serum samples for evidence of exposure to 8 common feline
viruses, Canine Distemper Virus (CDV), and Toxoplasma gondii. The highest
incidences of exposure were: T. gondii (8/9), Feline Panleukopenia Virus (FPLV [7/9])
and Feline Calicivirus (FCV [6/9]). One male was seropositive for CDV, T. gondii and
FPLV. Tissue samples from this male were collected post-mortem and formalin-fixed,
de-paraffinized specimens were tested for antigens of CDV using immunohistochemistry
(IHC) and for amplicons of T. gondii , and FPLV using polymerase chain reaction (PCR).
Results were negative for CDV, T. gondii, and FPLV.
Flea and Tick s of Mountain Lions in Southwestern Arizona
We collected ectoparasites from 4 radio-collared mountain lions in Tucson,
Arizona (32.189N -110.881E) between January 2006 and December 2007. Ectoparasites
were identified as Pulex, a species of flea not commonly reported on mountain lions. The
tick was a nymph from the genus Argas (Alveonasus), a species with little known
information.
Management Implications
22
Despite their wide-ranging ability, mountain lions are a low density species, and
thus, are vulnerable in isolated habitats and to changes that will impede movement among
suitable habitats (Beier 1993, Dickson et al. 2005). Spatial requirements and interactions
address social behavior (Powell 2000, Millspaugh and Marzluff 2001), and ultimately
population density (Hornocker 1970, Pierce et al. 2000). Maintaining landscape linkages
and opportunities for animals to find resources (e.g., mates) are important wildlife
considerations (Noss and Cooperrider 1994). As human development expands in Arizona,
available habitat for wildlife is changing. Arizona’s population is expected to double to
≥12 million people by 2040, and urban areas occupied within the state will overlap with
suitable mountain lion habitat. Development that limits resource opportunities for the
survival of a species can be reduced with knowledge about basic behavioral responses
and use of novel environments. Additionally, understanding the spatial organization of
mountain lions in Arizona should help managers frame realistic population management
goals based on habitat condition and ecosystem size. Arizona has the ability to integrate
natural resource protection and land use planning to identify potential corridors, and
develop detailed plans for landscapes of high importance and at high risk of impairment
by highways, urbanization, and other threats.
Preservation of woodland, chaparral, and riparian areas as connective habitats
from development and alteration are necessary for mountain lion conservation, especially
around urban areas. We suggest that habitat conservation efforts should prioritize these
areas. We recommend that additional GPS telemetry data should be collected and
analyzed from across Arizona, Mexico, and New Mexico, to allow for a more
23
comprehensive assessment of mountain lion habitat selection in southwestern North
America.
Global Positioning System telemetry may eventually be effective in completely
replacing VHF aerial telemetry in mountain lion field research. Until acquisition rates
and battery life of GPS improve, using VHF locations in conjunction may decrease any
habitat biases. The trade off is although there is additional supporting data from VHF
aerial flights, with most GPS systems aerial flights are not necessary, thus potentially
reducing man hours and costs associated with monitoring. Even with failure in
acquisition, the amount of data collected was unique for this type of study.
In southern Arizona, particularly in the Sky Island region, maintaining biological
linkages between habitat types that support native prey is important for maintaining
mountain lion populations. Therefore, managers should be involved with planning
commissions from the onset to represent wildlife interests. Future planning projects in
the region must consider habitats directly lost to development and if possible, direct
development away from highly suitable lion areas. Collaboration between agencies is
necessary because lions traverse large landscapes that are managed by multiple interests.
For this study, suitable habitat for mountain lions fell under the jurisdiction of >30
interests groups including several different tribal lands, national parks, and national
forests. Each one of these interest groups potentially has different goals in regards to
mountain lion management and conservation. Maintaining habitat linkages will require
collaboration between landowners; therefore, it is necessary to initiate partnerships early
and communicate often.
24
Conservation of wildlife has implications for urban development. Often, setting
aside land for habitat of an animal is not an available option for managers because of
political and economical pressures or lack of ecological knowledge. In discovering
habitat for animals, biologists can assess the gradient or flexibility animals may have
under various scenarios. Understanding how mountain lions use areas where human
development occurs or is expanding may offer several tools for managers to maintain
mountain lion populations. Managers have the ability to potentially minimize human-
mountain lion conflict and determine permeability of the landscape for mountain lion
movement. Maintaining opportunities for animals to move across landscapes is an
important wildlife conservation consideration (Noss and Cooperrider 1994). Developers
and conservationists can use this knowledge of mountain lions ability to navigate through
various urban matrixes when designating critical biological linkages for mountain lions.
Managers may need conservation strategies that go beyond traditional land
acquisition by government and include economic programs to preserve critical landscapes
on private land. Arizona has the ability to plan and Pima County has initiated the
Sonoran Desert Conservation Plan that will integrate natural resource protection and land
use planning. Also, the Arizona Wildlife Linkage Workgroup is a collaboration of
agencies and others to identify potential landscape corridors around Arizona, and to
develop detailed plans for corridors of high importance and at high risk of impairment by
highways, urbanization, and other threats. We advise caution when designating reserved
land and to incorporate data from multiple individuals within the species of concern in
the decision process. Maintaining biological linkages for mountain lions potentially will
25
benefit multiple species and design efforts benefit from empirical data on how mountain
lions respond to habitat features in their activity and travel in Arizona landscapes.
Collaboration between agencies is necessary for successful management and
studies of mountain lions. Mountain lions range over large expanses of land managed by
multiple agencies. This study was collaboration between the University of Arizona and
Arizona Game and Fish Department Research Branch. In one instance, we had a unique
opportunity to study urban use by mountain lions where of 1 mountain range enveloped
by urbanization was supporting a documented female resident with cubs. The mountain
range fell under the jurisdiction of 2 other agencies, yet collaboration and relevant goals
between all interested parties was lacking. Compounding the issue, one of the agencies
had a different agenda regarding mountain lion management and had historical political
problems with another agency. For successful management of any wildlife species,
cooperation is critical within and between agencies. We the managers request that the
public learn to compromise and understand another’s perspective, yet when the experts
cannot agree it is the wildlife that looses.
A proactive approach by agencies involving education and predetermined
protocols for dealing with mountain lion–human encounters may enhance human safety
in lion habitat and improve mountain lion conservation (Sweanor et al. 2008) and
Arizona has recently just initiated. Most collared mountain lions in southern and central
Arizona are not using urban landscapes. However, even lions that appear to avoid areas
of human use will likely be in proximity to humans at some time. Development that
limits resource opportunities for the survival of a species can be reduced with knowledge
26
about basic behavioral responses and use of novel environments. Mountain lions are
apex predators that are adaptable to most environments. Urban landscapes are not ideal
environments, but lions do use them. Consequently, educational materials on mountain
lion behavior and correct human responses during a mountain lion encounter should be
provided and targeted at communities that have been established in prime mountain lion
habitat. In areas frequented by mountain lions, more active management could include
limitations on time of day when human activity is permitted (e.g., closing trails between
dusk and dawn) or the removal of individual mountain lions deemed to be a threat to
human safety (Cougar Management Guidelines Working Group 2005, Arundel et al.
2007). The social ramifications of removing potential threats are difficult to manage with
such a controversial species like the mountain lion. Thus, additional education about the
impact on mountain lion populations from removing one problem individual is extremely
important.
27
REFERENCES
Anderson, A. E. 1983a. A critical review of literature on puma (Felis concolor).
Colorado Division of Wildlife. Special Report 54. Denver, CO, USA.
Anderson, A. E. 1983b. A critical review of the literature on puma (Felis concolor).
Colorado Division of Wildlife. Special Report No. 54. Fort Collins, CO, USA.
Anderson, A. E., D. C. Bowden, and M. M. Kattner. 1992. The puma on the
Uncompahgre Plateau, Colorado. Colorado Division of Wildlife Technical
Publication 40, Fort Collins, CO, USA.
Arizona Game and Fish Department. 2006. Hunt Arizona. Arizona Game and Fish
Department. Phoenix, AZ, USA.
Arundel, T., D. Mattson, and J. Hart. 2007. Movements and habitat selection by mountain
lions in the Flagstaff uplands. Pages 68 in D. Mattson, editor. Mountain lions of
the Flagstaff uplands 2003-2006 progress report. USGS Open-File Report 2007-
1062, Flagstaff, AZ, USA.
Ashman, D. L., G. C. Christensen, M. L. Hess, G. K. Tsukamoto, and M. S. Wickersham.
1983. The mountain lion in Nevada. Nevada Department of Wildlife, Carson City,
NV, USA.
Batista, H. B. C. R., F. K. Vicentini, A. C. Franco, F. R. Spiliki, J. C. R. Silva, C. H.
Adania, and P. M. Roehe. 2005. Neutralizing antibodies against feline herpesvirus
type 1 in captive wild felids of Brazil. Journal of Zoo and Wildlife Medicine
36:447-450.
28
Beier, P. 1991. Cougar attacks on humans in the United-States and Canada. Wildlife
Society Bulletin 19:403-412.
Beier, P. 1993. Determining minimum habitat areas and habitat corridors for cougars.
Conservation Biology 7:94-108.
Beier, P. 1995. Dispersal of juvenile cougars in fragmented habitat. Journal of Wildlife
Management 59:228-237.
Beier, P., D. Choate, and R. H. Barrett. 1995. Movement patterns of mountain lions
during different behaviors. Journal of Mammalogy 76:1056-1070.
Beier, P., and S. C. Cunningham. 1996. Power of track surveys to detect changes in
cougar populations. Wildlife Society Bulletin 24:540-546.
Biek, R., T. K. Ruth, K. M. Murphy, C. R. Anderson, Jr., M. Johnson, R. DeSimone, R.
Gray, M. G. Hornocker, C. M. Gillin, and M. Poss. 2006. Factors associated with
pathogen seroprevalence and infection in Rocky Mountain cougars. Journal of
Wildlife Diseases 42:606-615.
Brown, D. E., editor. 1994. Biotic communities southwestern United States and
northwestern Mexico. University of Utah Press, Salt Lake City, USA.
Conover, M. 2002. Resolving Human-Wildlife Conflicts: The Science of Wildlife
Damage Management. CRC Press, Boca Raton, FL, USA.
Cougar Management Guidelines Working Group. 2005. Cougar management guidelines,
First edition. Opal Creek Press, LLC, Salem, OR, USA.
Crooks, K. R. 2002. Relative sensitivities of mammalian carnivores to habitat
fragmentation. Conservation Biology 16:488-502.
29
Cunningham, S. C., C. R. Gustavson, and W. B. Ballard. 1999. Diet selection of
mountain lions in southeastern Arizona. Journal of Range Management 52:202-
207.
Cunningham, S. C., W. B. Ballard, and H. A. Whitlaw. 2001. Age structure, survival, and
mortality of mountain lions in southeastern Arizona. The Southwestern Naturalist
46:76-80.
Currier, M. J. P. 1976. Characteristics of the mountain lion population near Canon City,
Colorado. Thesis, Colorado State University, Fort Collins, CO, USA.
Currier, M. J. P. 1983. Felis concolor. Mammalian Species 200:1-7.
Department of Urban Planning and Design. 2009. Department of urban planning and
design. <http://www.tucsonaz.gov/planning/data/demographic>. Accessed 3 Feb
2009.
Dickson, B. G., and P. Beier. 2002. Home-range and habitat selection by adult cougars in
southern California. Journal of Wildlife Management 66:1235-1245.
Dickson, B. G., J. S. Jenness, and P. Beier. 2005. Influence of vegetation, topography,
and roads on cougar movement in southern California. Journal of Wildlife
Management 69:264-276.
Durden, L. A., M. W. Cunningham, R. McBride, and B. Ferree. 2006. Ectoparasites of
free-ranging pumas and jaguars in the Paraguayan Chaco. Veterinary Parasitology
137:189-193.
Eckerlin, R. P. 2004. Flea (Siphonaptera) of the Yucatan Peninsula (Campeche, Quintana
Roo, and Yucatan), Mexico. Caribbean Journal of Science 41:152-157.
30
Erdoes, R., and A. Ortiz. 1984. American Indian myths and legends. Pantheon Books,
New York, NY, USA.
Estes, J. A. 1996. Predators and ecosystem management. Wildlife Society Bulletin
24:390-396.
Evermann, J. F., W. J. Foreyt, B. Hall, and A. J. McKeirnan. 1997. Occurrence of puma
lentivirus infection in cougars from Washington. Journal of Wildlife Diseases
33:316-320.
Ferguson, H. L., K. Robinette, and K. Stenberg. 2001. Wildlife of urban habitats. Pages
317-341 in D. H. Johnson, and T. A. O'Neil, editors. Wildlife-habitat relationships
in Oregon and Washington. Oregon State University Press, Corvallis, OR, USA.
Foley, J. E. 1997. The potential for catastrophic infectious disease outbreaks in
populations of mountain lions in the western United States. Mountain lion
workshop 5:29-36
Forrester, D. J., A. C. Joseph, C. B. Robert, J. A. Conti, and R. C. Belden. 1985. Parasites
of the Florida panther (Felis concolor coryi). Proceedings of the Helminthological
Society of Washington 52:95-97.
Gittleman, J. L., S. M. Funk, D. MacDonald, and R. K. Wayne. 2001. Carnivore
conservation. Cambridge University Press, Cambridge, United Kingdom.
Grinder, M. I., and P. R. Krausman. 2001. Home range, habitat use, and nocturnal
activity of coyotes in an urban environment. Journal of Wildlife Management
65:887-898.
31
Harris, S. 1981. An estimation of the number of foxes (Vulpes vulpes) in the city of
Bristol, and some possible factors affecting their distribution. Journal of Applied
Ecology 18:455-465.
Hemker, T. P., F. G. Lindzey, and B. B. Ackerman. 1984. Population characteristics and
movement patterns of cougars in southern Utah. Journal of Wildlife Management
48:1275-1284.
Hopkins, R. A., M. J. Kutilek, and G. L. Shreve. 1986. Density and home range
characteristics of mountain lions in the Diablo Range of California. Pages 223-
235 in S. D. Miller, and D. D. Everett, editors. Cats of the world. National
Wildlife Federation, Washington, D.C., USA.
Hornocker, M. G. 1969. Winter territoriality in mountain lions. Journal of Wildlife
Management 33:457-464.
Hornocker, M. G. 1970. An analysis of mountain lion predation upon mule deer and elk
in the Idaho Primitive Area. Wildlife Monographs: 21.
Hungry Wolf, A. 1972. Legends told by the old people. Book Publishing Company,
Summertown, TN.
Jessup, D. A., K. C. Pettan, L. J. Lowenstine, and N. C. Pedersen. 1993. Feline leukemia-
virus infection and renal spirochetosis in a free-ranging cougar (Felis concolor).
Journal of Zoo and Wildlife Medicine 24:73-79.
Koehler, G. M., and M. G. Hornocker. 1991. Seasonal resource use among mountain
lions, bobcats, and coyotes. Journal of Mammalogy 72:391-396.
32
Laing, S. P., and F. G. Lindzey. 1993a. Cougar habitat selection in south-central Utah.
Pages 27-37 in C. S. Braun, editor. Mountain lion-human interaction symposium
and workshop. Colorado Division of Wildlife, Denver, CO, USA.
Laing, S. P., and F. G. Lindzey. 1993b. Patterns of replacement of resident cougars in
southern Utah. Journal of Mammalogy 74:1056-1058.
Leopold, A. 1933. Game Management. Charles Scribner's Sons, New York, NY, USA.
Linardi, P. M., and L. R. Guimaraes. 2000. Sifonapteros do Brasil. Fundação de Amparo
a Pesquisa do Estado de São, Sao Paulo.
Logan, K. A., and L. L. Irwin. 1985. Mountain lion habitats in the big horn mountains,
Wyoming. Wildlife Society Bulletin 13:257-262.
Logan, K. A., and L. L. Sweanor. 2001. Desert puma: evolutionary ecology and
conservation of an endangered carnivore. Island Press, Washington, D.C., USA.
Manfredo, M. J., and A. A. Dayer. 2004. Concepts for exploring the social aspects of
human-wildlife conflict in a global context. Human Dimensions of Wildlife 9:17-
328.
McBride, R. T. 1976. The status and ecology of the mountain lion (Felis concolor
stanleyana) of the Texas-Mexico border. Thesis, Sul Ross State University,
Alpine, TX, USA.
McKinney, T., J. C. Devos, W. B. Ballard, and S. R. Boe. 2006. Mountain lion predation
of translocated desert bighorn sheep in Arizona. Wildlife Society Bulletin
34:1255-1263.
33
McRae, B. H., P. Beier, L. E. Dewald, L. Y. Huynh, and P. Keim. 2005. Habitat barriers
limit gene flow and illuminate historical events in a wide-ranging carnivore, the
American puma. Molecular Ecology 14:1965-1977.
Mech, L. D. 1995. The challenge and opportunity of recovering wolf populations.
Conservation Biology 9:270-278.
Mech, L. D. 1996. A new era for carnivore conservation. Wildlife Society Bulletin
24:397-401.
Meegan, R. P., and D. S. Maehr. 2002. Landscape conservation and regional planning for
the Florida panther. Southeastern Naturalist 1:217-232.
Millspaugh, J. J., and J. M. Marzluff, editors. 2001. Radio tracking and animal
populations. Academic Press, San Diego, CA, USA.
Noss, R. F., and A. Y. Cooperrider. 1994. Saving nature's legacy: protecting and restoring
biodiversity. Island Press, Covelo, California, USA.
Noss, R. F., H. B. Quigley, M. G. Hornocker, T. Merrill, and P. C. Paquet. 1996.
Conservation biology and carnivore conservation in the Rocky Mountains.
Conservation Biology 10:949-963.
Olmsted, R. A., R. Langley, M. E. Roelke, R. M. Goeken, D. Adger-Johnson, J. P. Goff,
J. P. Albert, C. Packer, M. K. Laurenson, and T. M. Caro. 1992. Worldwide
prevalence of lentivirus infection in wild feline species: epidemiologic and
phylogenetic aspects. Journal of Virology 66:6008-6018.
34
Padley, W. D. 1990. Home ranges and social interactions of mountain lion (Felis
concolor) in the Santa Ana Mountains, California. Thesis. California State
Polytechnic University, Pomona, CA, USA.
Paul-Murphy, J., T. Work, D. Hunter, E. McFie, and D. Fjelline. 1994. Serologic survey
and serum biochemical reference ranges of the free-ranging mountain lion (Felis
concolor) in California. Journal of Wildlife Diseases 30:205-215.
Perry, G. L., and J. C. deVos, Jr. 2005. A case study of mountain lion-human interaction
in southeastern Arizona. Mountain Lion Workshop 8:104-113.
Pierce, B. M., V. C. Bleich, C. L. B. Chetkiewicz, and J. D. Wehausen. 1998. Timing of
feeding bouts of mountain lions. Journal of Mammalogy 79:222-226.
Pierce, B. M., C. B. Vernon, and R. T. Bowyer. 2000. Social organization of mountain
lions: Does a land-tenure system regulate population size? Ecology 81:1533-1543.
Population Reference Bureau. 2009. Population Reference Bureau.
<http://www.census.gov/population/projections/SummaryTabA1.pdf>. Accessed
10 January 2007.
Powell, R. A. 2000. Animal home ranges and territories and home range estimators.
Pages 65-110 in L. Boitani, and T. K. Fuller, editors. Research Techniques in
Animal Ecology Controversies and Consequences. Columbia University Press,
New York, NY, USA.
Riley, S. J., and R. A. Malecki. 2001. A landscape analysis of cougar distribution and
abundance in Montana, USA. Environmental Management 28:317-323.
35
Riley, S. P. D., C. Bromley, R. H. Poppenga, F. A. Uzal, L. Whited, R. M. Sauvajot, and
S. P. Riley. 2007. Anticoagulant exposure and notoedric mange in bobcats and
mountain lions in urban southern California. Journal of Wildlife Management
71:1874-1884.
Riley, S. P. D., J. Hadidian, and D. M. Manski. 1998. Population density, survival, and
rabies in raccoons in an urban national park. Canadian Journal of Zoology
76:1153-1164.
Roelke, M. E., J. F. Donald, R. J. Elliott, V. K. George, W. S. Fred, C. B. Margaret, F. E.
James, and C. P. Eugene. 1993. Seroprevalence of infectious disease agents in
free-ranging Florida panthers (Felis concolor coryi). Journal of Wildlife Diseases
29:36-49.
Ruth, T. K., K. A. Logan, L. L. Sweanor, M. G. Hornocker, and L. J. Temple. 1998.
Evaluating cougar translocation in New Mexico. Journal of Wildlife Management
62:1264-1275.
Seidensticker, J. C. J., M. G. Hornocker, W. V. Wiles, and J. P. Messick. 1973. Mountain
lion social organization in the Idaho primitive area. Wildlife Monographs 35:1-60.
Shivik, J. A. 2006. Tools for the edge: what's new for conserving carnivores. BioScience
56:253-259.
Sunquist, M. E., and F. Sunquist. 2002. The essence of cats. Pages 11-13 in M. E.
Sunquist, and F. Sunquist, editors. Wild cats of the world. University of Chicago
Press, Chicago, IL, USA.
36
Sweanor, L. L., K. A. Logan, J. W. Bauer, B. Millsap, and W. M. Boyce. 2008. Puma and
human spatial and temporal use of a popular California State Park. Journal of
Wildlife Management 72:1076-1084.
Sweanor, L. L., K. A. Logan, and M. G. Hornocker. 2000. Cougar dispersal patterns,
metapopulation dynamics, and conservation. Conservation Biology 14:798-808.
U. S. Census Bureau Population Division Projections Branch. 2007. State interim
population projections by age and sex: 2004 - 2030.
<http://www.census.gov/population/projections/SummaryTabA1.pdf>. Accessed
10 January 2007.
Uzal, F. A., R. S. Houston, S. P. D. Riley, R. Poppenga, J. Odani, and W. Boyce. 2007.
Notoedric mange in two free-ranging mountain lions (Puma concolor). Journal of
Wildlife Diseases 43:274-278.
Van Druff, L. W., and R. N. Rose. 1986. Habitat association of mammals in Syracuse,
New York. Urban Ecology 9:431-434.
Waid, D. D. 1990. Movements, food habits, and helminth parasites of mountain lions in
southwestern Texas. Dissertation. Texas Tech University, Lubbock, TX, USA.
Weber, W., and A. Rabinowitz. 1996. A global perspective on large carnivore
management. Conservation Biology 10:1046-1054.
Wehinger, K. A., M. E. Roelke, and E. C. Greiner. 1995. Ixodid ticks from panthers and
bobcats in Florida. Journal of Wildlife Diseases 31:480-485.
Whittaker, R. H., and W. A. Niering. 1965. Vegetation of the Santa Catalina Mountains,
Arizona: a gradient analysis of the south slope. Ecology 46:429-452.
37
Wilcox, B. A., and D. D. Murphy. 1985. Conservation strategy: the effects of
fragmentation on extinction. American Naturalist 125:879-887.
Williams, J. S., J. J. McCarthy, and H. D. Picton. 1995. Cougar habitat use and food
habits on the Montana Rocky Mountain Front. Intermountain Journal of Sciences
1:16-28.
Woodroffe, R. 2000. Predators and people: using human densities to interpret declines of
large carnivores. Animal Conservation 3:165-173.
Yamamoto, K., B. B. Chomel, L. J. Lowenstine, Y. Kikuchi, L. G. Phillips, B. C. Barr, P.
K. Swift, K. R. Jones, S. P. Riley, R. W. Kasten, J. E. Foley, and N. C. Pedersen.
1998. Bartonella henselae antibody prevalence in free-ranging and captive wild
felids from California. Journal of Wildlife Disease 34:56-63.
Young, S. P., and E. A. Goldman. 1946. The puma: mysterious American cat. American
Wildlife Institute, Washington, D.C., USA.
38
APPENDIX A. MOUNTAIN LION HABITAT SELECTION IN ARIZONA. To be
submitted to Journal of Wildlife Management: Nicholson, K. L., P.R. Krausman, T.
Smith, W. B. Ballard, and T. McKinney.
39
11 September 2009 Kerry L. Nicholson University of Arizona 325 Biological Sciences East Tucson, AZ 85721 520-204-7830 [email protected]
RH: Nicholson et al. • Mountain lion habitat selection
Mountain Lion Habitat Selection in Arizona
Kerry L. Nicholson, Wildlife Conservation and Management, School of Natural
Resources and the Environment, University of Arizona, Tucson, AZ 85721, USA
Paul R. Krausman, Boone and Crockett Program in Wildlife Conservation, University of
Montana, Missoula, MT 59812, USA
Thorry Smith, Arizona Game and Fish Department, Research Branch, 5000 W. Carefree
Highway, Phoenix, AZ 85086, USA
Warren B. Ballard, Department of Natural Resource Management, Texas Tech
University, Lubbock, TX 79409, USA
Ted McKinney1, Arizona Game and Fish Department, Research Branch, 5000 W.
Carefree Highway, Phoenix, AZ 85086, USA
ABSTRACT One challenge for wildlife managers in the 21st century is to maintain a 1
balance for wildlife and human use of the landscape. Because mountain lion (Puma 2
concolor) habitat is often adjacent to urbanization in Arizona, mountain lions are ideal 3
models to examine how human alteration of habitats influences their life history 4
characteristics and ability to adapt to a variety of environments. We quantified mountain 5
1 Deceased.
40
lion home range characteristics and selection of vegetation associations in central and 6
southern Arizona. We calculated 95% and 50% fixed kernel home ranges for 8 female 7
and 21 male mountain lions that were radiocollared in Payson, Prescott, and Tucson, 8
Arizona from August 2005 through August 2008. Using compositional analysis, we 9
assessed use of vegetation associations and urban areas within the study area (2nd order) 10
and within the home range (3rd order). At both levels of selection, mountain lions at all 11
study sites avoided human-dominated landscapes. At the 2nd order selection, mountain 12
lions preferred woodland habitat in Tucson and Prescott and chaparral in Payson. At the 13
3rd order, lions in Tucson and Payson selected riparian and in Prescott lions selected 14
chaparral. Season, mountain lion mass, and ungulate density had no effect on the size of 15
home ranges. Home range sizes for resident males ranged from 5,286 to 83,859 ha; 16
transient males covered up to 409,195 ha. Home ranges for females ranged from 2,860 to 17
21,772 ha. Intensive development and conversion of large open spaces to small 18
properties and subdivisions has caused increased loss, fragmentation, and encroachment, 19
of mountain lion habitat. Preserving biological linkages for access to habitat patches is 20
important in maintaining landscape connectivity to ensure viable populations adjacent to 21
urban areas. 22
KEY WORDS Arizona, compositional analysis, home range, habitat selection, mountain 23
lion, Puma concolor, urban. 24
The Journal of Wildlife Management: 00(0): 00-000, 200X 25
Models built for terrestrial carnivores can be used as tools for conservation 26
planning and also assess, maintain, or improve connectivity between habitat patches in 27
41
human-dominated landscapes (Schadt et al. 2002). Managers can also identify high-risk 28
areas for lions and humans and begin to understand impacts of humans on individuals and 29
the population leading to incorporation of natural population dynamics and behaviors into 30
mitigation design. Mountain lions (Puma concolor) are of particular interest because 31
they traverse large landscapes and modeling habitat use of umbrella species is an efficient 32
way to address the viability of an ecosystem (Shaffer 1983, Soulé 1987, Noss 1991, Beier 33
1993). 34
Radiocollars and telemetry have been used to document mountain lion biology 35
with varying success (Beier 1995, Beier et al. 1995, Pierce et al. 1998, Meegan and 36
Maehr 2002, Dickson et al. 2005) Most studies of mountain lions described patterns over 37
weeks or months, based on ≤1 location/day, usually during daylight hours (Hemker et al. 38
1984, Anderson et al. 1992, Beier et al. 1995, Ruth et al. 1998, Sweanor et al. 2000) 39
using a Minimum Convex Polygon (MCP) estimator of home range. Using fine-scaled 40
movement patterns to describe broad-scale distributions can provide a mechanistic link to 41
many ecological processes (Wiens et al. 1993). Satellite collar technology is becoming a 42
preferred method of determining mountain lion movements (Koehler and Maletzke 2005, 43
Mattson et al. 2005). Global Positioning System (GPS) telemetry offers the possibility to 44
study habitat selection at temporal and spatial scales unachievable with conventional very 45
high frequency (VHF) telemetry (Dussault et al. 2001). 46
Mountain lion use of habitat has received limited attention in the West (Logan 47
and Irwin 1985, Koehler and Hornocker 1991, Laing and Lindzey 1993, Williams et al. 48
1995, Dickson and Beier 2002) and has not been addressed in Arizona (Beier and 49
42
Cunningham 1996, Cunningham et al. 1999, Cunningham 2001, McRae et al. 2005, 50
McKinney et al. 2006) except in 1 study (Arundel et al. 2007). Mountain lions select 51
forested and woody cover and avoid flat open areas (Logan and Irwin 1985, Koehler and 52
Hornocker 1991, Williams et al. 1995, Riley and Malecki 2001). The aversion to flat, 53
open areas could potentially limit lion movement in southern Arizona’s landscape 54
because it is dominated by large insular mountains and separated by desert and grassland 55
plains. The landscapes that surrounds Tucson can change from 1,000 to >3,000 m with 56
valley floors between each range that are <25 km wide, whereas Payson and Prescott are 57
similar in elevation. 58
Mountain lions appear to avoid use of flat areas and this could result in 59
concentrations of locations and incorporating urban areas into their home range. For 60
example, mountain lions in Tucson were thought to use urban landscapes more than 61
expected by chance and to avoid flat open areas (Nicholson 2009). Since 2003, the 62
Arizona Game and Fish Department (AGFD) received multiple reports of mountain lion 63
presence on elementary school grounds and approaching and not yielding to groups of 64
people or vehicles. These reports were near Sabino Canyon Recreation Area located in 65
the foothills of the Santa Catalina Mountains, Pima County Arizona. In 2004, officials 66
took action in the interest of public safety and closed the Sabino Canyon recreation area 67
to attempt removal of the nuisance lions. This situation was the impetus to begin 68
mountain lion studies throughout Arizona. 69
Johnson (1980) describes 4 orders of habitat selection and the hierarchical 70
decisions animals make about resource section. We quantified habitat selection of home 71
43
ranges within the study area (2nd order selection) and selection of patches within a home 72
range (3rd order selection; Johnson 1980, Laing and Lindzey 1993). First order selection 73
(i.e., species distribution) has been determined and 4th order selection (i.e., use of specific 74
patches) is beyond the scope of this study. Defining selection at only 1 of these orders 75
could hide some aspects of selection that can be defined at a different scale (Dickson and 76
Beier 2002). To assess habitat selection at the 3rd order, it is necessary to delineate home 77
ranges. Therefore, in addition to describing a home range boundary we also identify 78
ecological factors that influence home range size. 79
Home range sizes and distribution can be driven by ecosystem-level influences 80
(e.g., prey abundance, climate, location) and by individual specific factors (e.g., sex, age, 81
body mass), which are independent of ecosystems (Anderson 1983, Grigione et al. 2002). 82
Our objectives were to investigate: home range size and distribution; the relationship 83
between home range size and age, sex, body mass, seasonality, and relative prey 84
abundances; and quantify habitat use at 2 orders of selection (within the home range and 85
within the study site). Due to the lack of knowledge of habitat selection by mountain 86
lions in the arid urbanlands of Arizona and numerous reports of mountain lion 87
occurrences in Sabino Canyon, we hypothesized that mountain lions were frequenting 88
urban areas. 89
STUDY AREA 90
We studied mountain lions in north-central Arizona near Payson (including the 91
cities of Star Valley, Pine, and Strawberry; 34.2 ˚N 111.3˚W), Prescott (including 92
Prescott Valley, Paulden, Williams, Cottonwood, Clarkdale, and Chino Valley; 34.6˚N 93
44
112.5˚W), and in southern Arizona near Tucson (including Oracle, Marana, Catalina, and 94
Saddle Brook; 32.2˚N 111.0˚W). Payson and Prescott were ≥ 130 km apart, and ranged 95
between 1,280 and 1,860 m in elevation. Annual precipitation averaged 57 cm in Payson 96
and 48 cm in Prescott, with total annual snowfall for both areas of 62 cm with average 97
temperatures of 3-23 ˚C. Tucson was 307 km south of Payson with an average annual 98
rainfall of 30 cm and snowfall of 3.0 cm. Maximum and minimum temperatures were 28 99
and 12.6˚C, respectively. Located in the Sonoran Desert, Tucson sits within a valley, 100
circumscribed by the Santa Catalina, Tucson, Tortolita, Rincon, and the Santa Rita 101
mountains. Elevation ranged from approximately 640 to ≥2,700 m in ≤60 km. Human 102
population of Payson in July 2007 was 16,742, Prescott was 43,217, and Tucson was 103
541,132 (Department of Urban Planning and Design 2009). Land jurisdiction and 104
management of the study areas included Arizona Wildlife Management, Bureau of Land 105
Management, Bureau of Reclamation, County Land, Department of Defense, Indian 106
Reservations, National Forest, National Monuments, National Park Service, National 107
Wildlife Reserves, State Trust Land, Parks and Recreation, and private lands. 108
Vegetation associations were similar for Payson and Prescott including interior 109
chaparral, pinyon (Pinus edulis) - juniper (Juniperus spp.) woodlands, grasslands and 110
mixed ponderosa pine (Pinus ponderosa) forests (Brown 1994). Tucson contained 111
Arizona upland subdivision of Sonoran Desert vegetation and riparian and xeroriparian 112
vegetation (mixed riparian desert scrub series; Brown 1994). Mountain ranges that 113
surround Tucson ascend from the Sonoran desert scrub (e.g., mesquite [Prosopis 114
juliflora], paloverde [Cercidium spp.], cactus [Opuntia spp.] and various grasses) to 115
45
Chihuahuan semi-desert to grassland to oak (Quercus)-alligator juniper (Juniperus 116
deppeana) woodland to Petran-montane and mixed conifer forest (Whittaker and Niering 117
1965, Brown 1994). On all study sites, mule deer (Odocoileus hemionus), white-tailed 118
deer (O. virginianus), collared peccary (Tayassu tajacu), black bear (Ursus americanus), 119
bobcats (Lynx rufus), and coyotes (Canis latrans) were common. Cattle and pronghorn 120
(Antilocapra americana) inhabited areas near Tucson and Prescott, and elk (Cervus 121
elephus) were common in Payson and Prescott and bighorn sheep (Ovis canadensis) 122
inhabited the Silverbell Mountains near Tucson. 123
METHODS 124
Capture 125
Between August 2005 and February 2008 mountain lions were captured by 126
AGFD personnel using snare and hound techniques (Shaw 1983, Logan et al. 1999, 127
Cougar Management Guidelines Working Group 2005). Mountain lions were 128
immobilized using Ketamine (Ketamine HCL, Wildlife Pharmaceutical, Ft. Collins, CO, 129
USA) and medetomidine hydrochloride (Domitor, Wildlife Pharmaceutical, Ft. Collins, 130
CO, USA). Medetomidine was reversed using antisedan (Atipamezole hydrochloride, 131
Pfizer Inc., New York, NY, USA) at a dose of 3mg of antisedan for every 1mg of 132
medetomidine. We determined age based on tooth wear and condition and obtained mass 133
(kg) and sex for each individual (Anderson and Lindzey 2000). 134
Upon capture, lions were equipped with Spread Spectrum Satellite collars 135
(Telonics, Mesa, AZ, USA). We obtained satellite locations every 4.15 for lions near 136
Tucson, and 7 h in Payson, and Prescott. We downloaded and processed location data at 137
46
the end of the study in August 2008. We incorporated all locations into an ArcGIS 9.x 138
(1995-2005 Environmental Systems Research Institute, Redlands, CA, USA) database for 139
analysis of habitat use. 140
Home range 141
Within ArcGIS, we created a 95% and 50% fixed kernel home range (KHR; 142
Worton 1989) using Home Range Tools (HRT) v 1.1 extension (Rodgers et al. 2007) and 143
a 95% MCP for comparison with other studies using Hawths Analysis Tools (Beyer 144
2004). Within HRT, we used a bivariate normal distribution, rescaled to unit variance, 145
and selected a 0.6 proportion of the reference bandwidth to create the fixed kernel home 146
ranges. We calculated home ranges separately for winter (October – March), 147
spring/summer (April – September), and annual periods (all months). Seasonal home 148
ranges were calculated for individuals monitored >90 days that yielded >30 149
locations/season. To normalize home range size data, we used a natural log 150
transformation and a two-tailed paired t-test to determine whether there was a significant 151
effect of seasonality on mountain lion home range size. 152
We used multivariate analysis for all study sites during each season to explain 153
variation associated with home range size. The regression model included body mass, 154
sex, age (>4 years, 2-4 years), study sites, and relative-abundance estimates of selected 155
ungulates (i.e., elk, bighorn sheep, pronghorn, mule deer, and white-tailed deer) within 156
each study area. We obtained relative abundance of ungulates from AGFD annual 157
surveys of wildlife game management units (GMU) for the units that had collared 158
mountain lions. We used the 3 year average of ungulates surveyed in each GMU and 159
47
calculated a total number of ungulates/management unit in which lions occurred as an 160
index of ungulate abundance. The AGFD survey provided ungulate/km2 for the units 161
surrounding Payson, Prescott, and Tucson and then we ranked each location relative to 162
the others. 163
Habitat analysis 164
We reclassified the Southwest Regional Gap Analysis Project map (USGS 165
National Gap Analysis Program 2004) into 9 categories: woodland, shrub land, chaparral, 166
forest, grassland, riparian, rock, agriculture and urban. We buffered all GPS locations by 167
60 m and calculated the average area for each category for each individual. We assumed 168
that vegetation associations ≤60 m radius were used in proportion to availability (Rettie 169
and McLoughlin 1999). We chose a buffer of 60 m because of our high number of 170
locations, the 10-15 m error associated with GPS collars, and the 30x30 m resolution of 171
the vegetation layer. Buffering locations increased our ability to detect potentially 172
important habitat components compared with using single points (Rettie and McLoughlin 173
1999). Buffer composition data accounted for varying patch sizes, selection of habitat 174
mosaics, and spatial associations among habitats (Rettie and McLoughlin 1999). To 175
delineate the study area we pooled all locations for all individuals within each study site 176
and created a 100% MCP (Fig. 1). We buffered the study area boundary by 1,000 m to 177
minimize potential edge effects. 178
We conducted a habitat analysis using the resource selection framework set by 179
Aebischer et al. (1993) and Johnson (1980). Second order selection compared the 180
available habitat composition of the study area to the averaged habitat composition of the 181
48
buffered radiolocations (a broad view of an animal’s requirements). Third order selection 182
(detailed view of resource use) compared the habitat composition of an individual’s 95% 183
KHR to averaged composition of buffered locations and as a comparison to the 50% 184
KHR (Porter and Church 1987). We used the Compos Analysis Excel Add-In tool v6.2 185
(Smith 2005) to develop a ranking of habitat preference (Aebischer et al. 1993). 186
RESULTS 187
Between August 2005 and March 2008 we captured and radiocollared 30 188
mountain lions near Tucson (4 F, 10 M), Payson (1 F, 5 M), and Prescott (3 F, 7 M) 189
areas. We retrieved all but 1 collar near Tucson and obtained 30,282 relocations from 29 190
lions. For the compositional analysis of habitat use, we used all data available from 29 191
lions. 192
The 95% KHR size did not change by season (paired t-test: t26 = 1.47 P = 0.15, n 193
= 27) therefore, we pooled data for annual home ranges. Ungulate availability differed 194
slightly by location. Payson had high prey availability (2.1 ungulates/km2), Tucson 195
medium availability (0.22 ungulates/km2) and Prescott low availability (0.12 196
ungulates/km2) prey availability. Home range size did not change due to mass of 197
mountain lion or ungulate availability, however sex and age class did affect home range 198
size (simple linear regression F5,19 = 4.63, P = 0.006). On average, female home ranges 199
were 0.40 times the size of males (Table 1) and older lion home ranges were about 0.39 200
times the size of younger lions (Table 1). 201
Habitat selection 202
49
After incorporating all variables into our model (i.e., sex, season, location, home 203
range size, and interactions) the important variables in the habitat selection model were 204
location and 95% and 50% KHR (-NlnΛdf=26 = 4.58 P < 0.001). Therefore, when running 205
compositional analysis we separated the 3 study sites. In doing so, we combined 206
woodland and forest, and agriculture and urban, and removed grassland in Payson 207
because the availability was 0.17% leaving chaparral, riparian, rock, shrub land, 208
woodland, and urban. The vegetation categories for Tucson and Prescott were not 209
changed. 210
Within study areas, all mountain lions avoided urban areas. Mountain lions in 211
Tucson used woodland and avoided urban and agriculture landscapes (-NlnΛdf=8 = 0.028, 212
χ2 = 46.72, P < 0.0001 [Table 2]). Similarly, lions in Prescott selected woodland more 213
often, and avoided urban and agriculture (-NlnΛdf=8 = 0.105, χ2=29.32, P < 0.001 [Table 214
3]). Lions in Payson selected chaparral more often and avoided urban (-NlnΛdf=5 = 0.013, 215
χ2=26.22, P < 0.0001[Table 4]). 216
For habitat selection within home ranges, we combined agriculture with urban for 217
Tucson and Prescott because not all lions had agriculture available. Additionally for 218
Tucson, forest was only available to 5 individuals and used by 2; therefore, we combined 219
forest with woodland. No changes were made for habitats in Prescott. Again, all 220
mountain lions avoided urban landscapes. Mountain lions in Tucson, selected riparian 221
habitats and avoided urban (-NlnΛdf=6 = 0.161, χ2 = 23.77, P < 0.001[Table 4]). Slightly 222
different from 2nd order selection, lions in Prescott selected chaparral over woodland, 223
however, this was not significant. They avoided urban and agriculture (-NlnΛdf=7 = 224
50
0.092, χ2 = 23.83, P < 0.01). Similar to Tucson, lions in Payson selected for riparian 225
habitat and avoided urban (-NlnΛdf=5 = 0.036, χ2 = 20.03, P < 0.01). 226
DISCUSSION 227
Thus far, mountain lion home range and habitat studies in the Southwest have 228
been conducted with aerial or ground telemetry, camera traps, or scat collection 229
techniques with limited frequency of relocations. As satellite technology became 230
available, the intensity of monitoring changed. In our study the minimum number of 231
locations we obtained for lions was adequate to determine home range size (n = 188; 232
Seaman et al. 1999, Garton et al. 2001), with the majority (n = 26) of individuals 233
attaining >475 locations. Our home range sizes for mountain lions in the Southwest were 234
comparable to those reported by Dickson and Beier (2002) and Logan and Sweanor 235
(2001), but were larger than reported by Shaw (1973). 236
Our study obtained on average 3.6 fixes/day for Tucson and 2.9 for Payson and 237
Prescott. Due to a 26% failure rate of fix acquisitions for Tucson and a 21% failure rate 238
for Payson and Prescott, there was a potential for bias in our habitat selection results (i.e., 239
collar performance varying between vegetation associations). One collar, put on a male 240
lion, attempted 2,971 locations yet was only successful at obtaining 523 fixes. However, 241
the likelihood of successful acquisition is lower in forested steep terrain compared to flat 242
open areas (D’Eon et al. 2002, Frair et al. 2004, Cain et al. 2005) and this probably 243
affected our fix success. Yet, in our study, we found that lions preferred woodland, 244
riparian, and chaparral vegetation types. Additionally, agriculture, grassland, and shrub 245
51
land all with potentially higher likelihoods of success were avoided, or avoided relative to 246
other vegetation types. 247
Mountain lions in our study selected for woodland, chaparral, or riparian 248
vegetation and avoided human dominated landscapes. Our results are consistent with 249
previous observed affinities of mountain lions for woody vegetation in the western 250
Untied States (Logan and Irwin 1985, Koehler and Hornocker 1991, Williams et al. 1995, 251
Riley and Malecki 2001, Arundel et al. 2007). The variability of preference within the 252
remaining categories and between the orders of selection suggests mountain lions can 253
exist in a variety of environments. Mountain lions have broad geographic distribution 254
from elevations at sea level to > 4,500 m (Logan and Sweanor 2001). Lions use almost 255
every vegetation association in the United States, including coniferous and deciduous 256
forests, swamps, savannahs, woodlands, riparian forests, desert canyons, chaparral, desert 257
mountains, and semi-arid shrub lands (Hansen 1992). Mountain lions have the ability to 258
persist in almost any habitat that offers adequate prey and cover (Mountain Lion 259
Management Guidelines Working Group 2005). It is clear lions have established their 260
viability throughout distinctly different landscapes. Due to the variability in habitat 261
ranking between our study sites in Arizona, vegetation is not the driving factor in home 262
range establishment. 263
We found no seasonal shift in home range size or in habitat selection. Though 264
this is not uncommon (Shaw 1977, 1979, Beier and Barrett 1993, Logan and Sweanor 265
2001), other researchers have reported significant changes in both parameters (Arundel et 266
al. 2007, Padley 1990). Lack of seasonal shifts in home range and habitat use could be 267
52
because mountain lion prey does not seasonally migrate in our study areas. Food 268
availability is often cited as important in determining size of home ranges (McLoughlin 269
and Ferguson 2000). Obtaining accurate large scale densities of ungulates is difficult and 270
our measure may not adequately represent the influence of prey on home range size, 271
however we suggest that prey availability was adequate across our study site allowing 272
mountain lions to use the variety of habitats available except those that were dominated 273
by humans. Inspite of some degree of aversion by mountain lions to most human-related 274
landscape features, lion populations nonetheless have been relatively resilient to humans 275
and their activities, especially compared to other large carnivores such as grizzly bears 276
(Ursus arctos) and wolves (Canis lupus; Laliberte and Ripple 2004, Riley et al. 2004). 277
MANAGEMENT IMPLICATIONS 278
As human development expands in Arizona, available habitat for wildlife is 279
changing. Arizona’s population is expected to double to ≥12 million people by 2050, and 280
urban areas occupied within the state will overlap with suitable mountain lion habitat. 281
Despite their wide-ranging ability, mountain lions are a low density species, and thus, are 282
vulnerable in isolated habitats and to changes that will impede movement among patches 283
(Dickson et al. 2005, Beier 1993). Preservation of woodland, chaparral, and riparian 284
areas as connective habitats from development and alteration are necessary for mountain 285
lion conservation, especially around urban areas. We suggest that habitat conservation 286
efforts should prioritize these areas. We recommend that additional GPS telemetry data 287
should be collected and analyzed from across Arizona, Mexico, and New Mexico, to 288
53
allow for a more comprehensive assessment of mountain lion habitat selection in 289
southwestern North America. 290
Global Positioning System telemetry may eventually be effective in completely 291
replacing VHF aerial telemetry in mountain lion field research. Until acquisition rates 292
and battery life of GPS improve, using VHF locations in conjunction may decrease any 293
habitat biases. The trade off is although there is additional supporting data from VHF 294
aerial flights, with most GPS systems aerial flights are not necessary, thus potentially 295
reducing man hours and costs associated with monitoring. Even with failure in 296
acquisition, the amount of data collected was unique for this type of study. 297
In southern Arizona, particularly in the Sky Island region, maintaining biological 298
linkages between habitat types that support native prey is important for maintaining 299
mountain lion populations. Therefore, managers should be involved with planning 300
commissions from the onset to represent wildlife interests. Future planning projects in 301
the region must consider habitats directly lost to development and if possible, direct 302
development away from highly suitable lion areas. Collaboration between agencies is 303
necessary because lions traverse large landscapes that are managed by multiple interests. 304
For this study, suitable habitat for mountain lions fell under the jurisdiction of >30 305
interests groups including several different tribal lands, national parks, and national 306
forests. Each one of these interest groups potentially has different goals in regards to 307
mountain lion management and conservation. Maintaining habitat linkages will require 308
collaboration between landowners; therefore, it is necessary to initiate partnerships early 309
and communicate often. 310
54
ACKNOWLEDGEMENTS 311
We thank all of the hounds men, hounds, trappers, and volunteers on this project 312
including R. Thompson, T. Salazar, B. Buckley, N. Smith, A. Salazar, K. Munroe, B. 313
Jansen, L. Haynes, C. Dolan and B. Kluver. We thank AGFD personnel B. Waddell, and 314
all of the aerial surveyors and pilots. We thank J. deVos for initial consultation and 315
funding, and C. O’Brien (AGFD) and C. Yde for administration of the contract. We 316
thank the Advanced Resource Technologies lab at the University of Arizona especially 317
M. Reed, P. Guertin, and A. Honaman. Funding was provided by AGFD and the 318
University of Arizona. Capture and handling procedures were approved by the Animal 319
Care and Use Committee at the University of Arizona (protocol #05-184). 320
LITERATURE CITED 321
Aebischer, N. J., P. A. Robertson, and R. E. Kenward. 1993. Compositional analysis of 322
habitat use from animal radio-tracking data. Ecology 74:1313-1325. 323
Anderson, A. E. 1983. A critical review of literature on puma (Felis concolor). Colorado 324
Division of Wildlife. Special Report 54. Denver, CO, USA. 325
Anderson, A. E., D. C. Bowden, and M. M. Kattner. 1992. The puma on the 326
Uncompahgre Plateau, Colorado. Colorado Division of Wildlife Technical 327
Publication 40. Fort Collins, CO, USA. 328
Anderson, C. R., Jr., and F. G. Lindzey. 2000. A photographic guide to estimating 329
mountain lion age classes. Wyoming Cooperative Fish and Wildlife Research 330
Unit. Laramie, WY. 331
55
Arundel, T., D. Mattson, and J. Hart. 2007. Movements and habitat selection by mountain 332
lions in the Flagstaff uplands. Pages 68 in D. Mattson, editor. Mountain lions of 333
the Flagstaff uplands 2003-2006 progress report. USGS Open-File Report 2007-334
1062, Flagstaff, AZ, USA. 335
Beier, P. 1993. Determining minimum habitat areas and habitat corridors for cougars. 336
Conservation Biology 7:94-108. 337
Beier, P. 1995. Dispersal of juvenile cougars in fragmented habitat. Journal of Wildlife 338
Management 59:228-237. 339
Beier, P., and R. H. Barrett. 1993. The cougar in the Santa Ana Mountain range, 340
California. Department of Forestry and Resource Management, University of 341
California. Berkeley, USA. 342
Beier, P., D. Choate, and R. H. Barrett. 1995. Movement patterns of mountain lions 343
during different behaviors. Journal of Mammalogy 76:1056-1070. 344
Beier, P., and S. C. Cunningham. 1996. Power of track surveys to detect changes in 345
cougar populations. Wildlife Society Bulletin 24:540-546. 346
Beyer, H. L. 2004. Hawth's Analysis Tools for ArcGIS. 347
<http://www.spatialecology.com/htools>. Accessed 12 December 2008. 348
Brown, D. E., editor. 1994. Biotic communities southwestern United States and 349
northwestern Mexico. University of Utah Press, Salt Lake City, USA. 350
Cain, J. W., III , P. R. Krausman, B. D. Jansen, and J. R. Morgart. 2005. Influence of 351
topography and GPS fix interval on GPS collar performance. Wildlife Society 352
Bulletin 33:926-934. 353
56
Cougar Management Guidelines Working Group. 2005. Cougar management guidelines. 354
First edition. Opal Creek Press, LLC, Salem, OR, USA. 355
Cunningham, S. C., C. R. Gustavson, and W. B. Ballard. 1999. Diet selection of 356
mountain lions in southeastern Arizona. Journal of Range Management 52:202-357
207. 358
Cunningham, S. C., W. B. Ballard, and H. A. Whitlaw. 2001. Age structure, survival, and 359
mortality of mountain lions in southeastern Arizona. The Southwestern Naturalist 360
46:76-80. 361
D’Eon, R. G., R. Serrouya, G. Smith, and C. O. Kochanny. 2002. GPS error and bias in 362
mountainous terrain. Wildlife Society Bulletin 30:430-439. 363
Department of Urban Planning and Design. 2009. Department of urban planning and 364
design. <http://www.tucsonaz.gov/planning/data/demographic>. Accessed 3 Feb 365
2009. 366
Dickson, B. G., and P. Beier. 2002. Home-range and habitat selection by adult cougars in 367
southern California. Journal of Wildlife Management 66:1235-1245. 368
Dickson, B. G., J. S. Jenness, and P. Beier. 2005. Influence of vegetation, topography, 369
and roads on cougar movement in southern California. Journal of Wildlife 370
Management 69:264-276. 371
Dussault, C., R. Courtois, J.-P. Ouellet, and J. Huot. 2001. Influence of satellite geometry 372
and differential correction on GPS location accuracy. Wildlife Society Bulletin 373
29:171-179. 374
57
Frair, J., S. E. Nielsen, E. H. Merrill, S. R. Lele, M. S. Boyce, R. H. M. Munro, G. B. 375
Stenhouse, and H. L. Beyer. 2004. Removing GPS collar bias in habitat selection 376
studies. Journal of Applied Ecology 41:201-212. 377
Garton, E. O., M. J. Wisdom, F. A. Leban, and B. K. Johnson. 2001. Experimental design 378
for radiotelemtry studies. Pages 15-42 in J. J. Millspaugh, and J. M. Marzluff, 379
editors. Radio tracking and animal populations. Academic Press, San Diego. 380
Grigione, M. M., P. Beier, R. A. Hopkins, D. Neal, W. D. Padley, C. M. Schonewald, and 381
M. L. Johnson. 2002. Ecological and allometric determinants of home-range size 382
for mountain lions (Puma concolor). Animal Conservation 5:317-324. 383
Hansen, K. 1992. Mountain lion, the American lion. Northland Publishing, Flagstaff, 384
Arizona, USA. 385
Hemker, T. P., F. G. Lindzey, and B. B. Ackerman. 1984. Population characteristics and 386
movement patterns of cougars in southern Utah. Journal of Wildlife Management 387
48:1275-1284. 388
Johnson, D. H. 1980. The comparison of usage and availability measurements for 389
evaluating resource preference. Ecology 61:65-71. 390
Koehler, G. M., and M. G. Hornocker. 1991. Seasonal resource use among mountain 391
lions, bobcats, and coyotes. Journal of Mammalogy 72:391-396. 392
Koehler, G. M., and B. T. Maletzke. 2005. Movement patterns of male and female 393
cougars (Puma concolor): implications for harvest vulnerability. in Proceedings 394
of 8th Mountain lion workshop. Leavenworth, Washington. 395
58
Laing, S. P., and F. G. Lindzey. 1993. Cougar habitat selection in south-central Utah. 396
Pages 27-37 in C. S. Braun, editor. Mountain lion-human interaction symposium 397
and workshop. Colorado Division of Wildlife, Denver, CO, USA. 398
Laliberte, A. S., and W. J. Ripple. 2004. Range contractions in North American 399
carnivores and ungulates. BioScience 54:123-137. 400
Logan, K. A., and L. L. Irwin. 1985. Mountain Lion Habitats in the Big Horn Mountains, 401
Wyoming. Wildlife Society Bulletin 13:257-262. 402
Logan, K. A., and L. L. Sweanor. 2001. Desert puma: evolutionary ecology and 403
conservation of an endangered carnivore. Island Press, Washington, D.C., USA. 404
Logan, K. A., L. L. Sweanor, J. F. Smith, and M. G. Hornocker. 1999. Capturing pumas 405
with foot-hold snares. Wildlife Society Bulletin 27:201-208. 406
Mattson, D., J. Hart, and T. Arundel. 2005. Cougars of the Flagstaff uplands: results of 407
2003-2004 predation studies. in Proceedings of 8th Mountain lion workshop. 408
Leavenworth, WA, USA. 409
McKinney, T., J. C. Devos, W. B. Ballard, and S. R. Boe. 2006. Mountain lion predation 410
of translocated desert bighorn sheep in Arizona. Wildlife Society Bulletin 411
34:1255-1263. 412
McLoughlin, P. D., and S. H. Ferguson. 2000. A hierarchical sequence of limiting factors 413
may help explain variation in home range size. Ecoscience 7:123-130. 414
McRae, B. H., P. Beier, L. E. Dewald, L. Y. Huynh, and P. Keim. 2005. Habitat barriers 415
limit gene flow and illuminate historical events in a wide-ranging carnivore, the 416
American puma. Molecular Ecology 14:1965-1977. 417
59
Meegan, R. P., and D. S. Maehr. 2002. Landscape conservation and regional planning for 418
the Florida panther. Southeastern Naturalist 1:217-232. 419
Mountain Lion Management Guidelines Working Group. 2005. Cougar Management 420
Guidelines. WildFutures, Bainbridge Island, WA. 421
Nicholson, K. L. 2009. Spatial movements and ecology of mountain lions in Arizona. 422
Dissertation, University of Arizona, Tucson, AZ, USA. 423
Noss, R. F. 1991. From endangered species to biodiversity. Pages 227–246 in K. A. 424
Kohm, editor. Balancing on the brink of extinction. Island Press, Washington, 425
D.C., USA. 426
Padley, W. D. 1990. Home ranges and social interactions of mountain lion (Felis 427
concolor) in the Santa Ana Mountains, California. Thesis, California State 428
Polytechnic University, Pomona, CA, USA. 429
Pierce, B. M., V. C. Bleich, C. L. B. Chetkiewicz, and J. D. Wehausen. 1998. Timing of 430
feeding bouts of mountain lions. Journal of Mammalogy 79:222-226. 431
Porter, W. F., and K. E. Church. 1987. Effects of environmental pattern on habitat 432
preference analysis. Journal of Wildlife Management 51:681-685. 433
Rettie, J. W., and P. D. McLoughlin. 1999. Overcoming radiotelemetry bias in habitat-434
selection studies. Canadian Journal of Zoology 77:1175-1184. 435
Riley, S. J., and R. A. Malecki. 2001. A landscape analysis of cougar distribution and 436
abundance in Montana, USA. Environmental Management 28:317-323. 437
60
Riley, S. J., G. M. Nesslage, and B. A. Maurer. 2004. Dynamics of early wolf and cougar 438
eradication efforts in Montana: implications for conservation. Biological 439
Conservation 119:575-579. 440
Rodgers, A. R., A. P. Carr, H. L. Beyer, L. Smith, and J. G. Kie. 2007. HRT: Home 441
Range Tools for ArcGIS. Ontario Ministry of Natural Resources, Centre for 442
Northern Forest Ecosystem Research. Thunder Bay, Ontario, Canada. 443
Ruth, T. K., K. A. Logan, L. L. Sweanor, M. G. Hornocker, and L. J. Temple. 1998. 444
Evaluating cougar translocation in New Mexico. Journal of Wildlife Management 445
62:1264-1275. 446
Schadt, S., F. Knauer, P. Kaczensky, E. Revilla, T. Wiegand, and L. Trepl. 2002. Rule-447
based assessment of suitable habitat and patch connectivity for the Eurasian lynx. 448
Ecological Applications 12:1469–1483. 449
Seaman, D. E., J. J. Millspaugh, B. J. Kernohan, G. C. Brundige, K. J. Raedeke, and R. 450
A. Gitzen. 1999. Effects of sample size on kernel home range estimates. Journal 451
of Wildlife Management 63:739-747. 452
Shaffer, M. 1983. Determining minimum population sizes for the grizzly bear. 453
International Conference on Bear Research and Management 5:133–139. 454
Shaw, H. G. 1973. Ecology of the mountain lion in Arizona. Pages 77-107 in Wildlife 455
Resources in Arizona. Arizona Game and Fish Department. 456
Shaw, H. G. 1977. Impact of mountain lions on mule deer and cattle. Pages 17-32 in 457
Proceedings of 1975 predator symposium. Montana Forest and Conservation 458
Experiment Station. University of Montana, Missoula, USA. 459
61
Shaw, H. G. 1979. A mountain lion field guide. Arizona Game and Fish Department. 460
Special Report 9. Phoenix, AZ, USA. 461
Shaw, H. G. 1983. Mountain lion field guide. Arizona Game and Fish Department. 462
Special Report Number 9. Phoenix, AZ, USA. 463
Smith, P. G. 2005. Compos Analysis, version 6.2 standard [software]. Smith Ecology 464
Ltd. Abergavenny, UK 465
Soulé, M. E. 1987. Introduction. Pages 1–10 in M. E. Soulé, editor. Viable populations 466
for conservation. Cambridge University Press, New York, USA. 467
Sweanor, L. L., K. A. Logan, and M. G. Hornocker. 2000. Cougar dispersal patterns, 468
metapopulation dynamics, and conservation. Conservation Biology 14:798-808. 469
USGS National Gap Analysis Program. 2004. Provisional Digital Land Cover Map for 470
the Southwestern United States. College of Natural Resources, Utah State 471
University. Logan, UT, USA. 472
Whittaker, R. H., and W. A. Niering. 1965. Vegetation of the Santa Catalina Mountains, 473
Arizona: a gradient analysis of the south slope. Ecology 46:429-452. 474
Wiens, J. A., N.C. Stenseth, B. Van Horne, and R. A. Ims. 1993. Ecological mechanisms 475
and landscape ecology. Oikos 66:369-380. 476
Williams, J. S., J. J. McCarthy, and H. D. Picton. 1995. Cougar habitat use and food 477
habits on the Montana Rocky Mountain Front. Intermountain Journal of Sciences 478
1:16-28. 479
Worton, B. J. 1989. Kernel methods for estimating the utilization distribution in home 480
range studies. Ecology 70:164-168.481
Table 1. Mean annual home range sizes (km2) of 95%, 50%, 85%, and 90% fixed kernel density home ranges (KHR) and 95% 482
Minimum Convex Polygon (MCP) with standard error (SE) by sex and age classes of mountain lions captured near Payson, 483
Prescott, and Tucson Arizona 2005-2008. Other kernel sizes and minimum convex polygon (MCP) estimation methods are 484
provided for comparison. 485
486
Category N 95% KHR (±SE) 50% 85% 90% 95% MCP 487
Age >4 16 825.4 (266.9) 162.5 (44.2) 510.9 (152.7) 630.3 (194.5) 984.1 (364.1) 488
Age 2-4 13 233.9 (51.9) 58.2 (11.4) 162.2 (35.6) 191.2 (43.2) 244.4 (55.9) 489
Females 8 116.2 (23.6) 31.7 (6.3) 79.8 (16.2) 94.0 (19.2) 114.0 (20.9) 490
Males 21 729.5 (205.9) 147.7 (34.1) 459.3 (117.7) 562.7 (150.0) 857.6 (280.3) 491
Male (Age<4) 13 987.3 (312.89) 192.9 (50.9) 609.8 (177.5) 753.0 (226.9) 1184.6 (431.2) 492
Male (Age>4) 8 31.37 (70.6) 74.4 (15.5) 214.7 (48.4) 253.5 (57.5) 326.2 (76.9) 493
Female (Age<4) 3 123.7 (42.7) 30.95 (11.8) 82.5 (30.1) 98.5 (35.5) 114.7 (35.9) 494
Female (Age>4) 5 111.7 (31.7) 32.3 (8.4) 78.1 (21.5) 91.4 (25.5) 113.5 (29.0) 495
496
62
Table 2. Simplified ranking matrices for mountain lions based on (a) comparing proportional habitat use within 95% kernel 497
home ranges with proportions of total available vegetation associations (2nd order selection), and (b) comparing the proportions 498
of satellite locations (GPS) for each animal in each habitat with the proportion of each habitat within the animals 95% KHR 499
(3rd order selection) for the Tucson, Arizona study, 2005-2008. Ranked preference for each association ranges from high to 500
low. Each mean in the matrix was replaced by its positive (+) or negative (-) sign; a triple sign (--- or +++) represents 501
significant deviation from random at P < 0.05, those in parentheses indicate significant difference from standard t-tests. 502
a) Tucson 95% KHR vs. study area (2nd
Order) 503
Habitat 504
Habitat Agriculture Chaparral Forest Grassland Riparian Rock Shrubland Woodland Urban Rank 505
Agriculture --- --- --- --- --- --- --- + 1 506
Chaparral +++ +++ - + - + --- +++ 5 507
Forest +++ --- --- - --- --- --- +++ 2 508
Grassland +++ + +++ + - + --- +++ 6 509
Riparian +++ - + - - - --- +++ 3 510
Rock +++ + +++ + + + - +++ 7 511
Shrubland +++ - +++ - + - - +++ 4 512
63
Table 2. continued. 513
Woodland +++ +++ +++ +++ +++ + + +++ 8 514
Urban - --- --- --- --- --- --- --- 0 515
b) Tucson GPS locations vs. 95% KHR (3rd
Order) 516
Habitat 517
Habitat Chaparral Grassland Riparian Rock Shrubland Urban Woodland Rank 518
Chaparral - - - - + - 1 519
Grassland + - + + (+++) - 4 520
Riparian + + + +++ +++ + 6 521
Rock + - - + (+++) + 4 522
Shrubland + - --- - +++ - 2 523
Urban - (---) --- (---) --- --- 0 524
Woodland + + - - + +++ 4 525
526
64
Table 3. Simplified ranking matrices for mountain lions based on (c) comparing proportional habitat use within 95% kernel 527
home ranges with proportions of total available habitat (2nd order selection), and (d) comparing the proportions of satellite 528
locations (GPS) for each animal in each habitat with the proportion of each habitat within the animals 95% KHR (3rd order 529
selection) for the Prescott, Arizona study, 2005-2008. Ranked preference is given for each habitat from high to low. Each 530
mean in the matrix was replaced by its positive (+) or negative (-) sign; a triple sign (--- or +++) represents significant 531
deviation from random at P < 0.05, those in parentheses indicate significant difference from standard t-tests. 532
c) Prescott 95% KHR vs. study area (2nd
Order) 533
Habitat 534
Habitat Agriculture Chaparral Forest Grassland Riparian Rock Shrubland Woodland Urban Rank 535
Agriculture --- - --- --- --- --- --- - 0 536
Chaparral +++ +++ - + - - --- +++ 4 537
Forest + --- --- (---) --- --- --- + 2 538
Grassland +++ + +++ + - - --- +++ 5 539
Riparian +++ - (+++) - - - --- +++ 3 540
Rock +++ + +++ + + + - +++ 7 541
Shrubland +++ + +++ + + - --- +++ 6 542
65
Table 3. continued. 543
Woodland +++ +++ +++ +++ +++ + +++ +++ 8 544
Urban + --- - --- --- --- --- --- 1 545
d) Prescott GPS locations vs. 95% KHR (3rd
Order) 546
Habitat 547
Habitat Chaparral Forest Grassland Riparian Rock Shrubland Urban Woodland Rank 548
Chaparral + +++ +++ + (+++) + + 7 549
Forest - +++ + + (+++) + - 5 550
Grassland --- --- + - + + --- 3 551
Riparian --- - - - + + --- 2 552
Rock - - + + + + - 4 553
Shrubland (---) (---) - - - + (---) 1 554
Urban - - - - - - - 0 555
Woodland - + +++ +++ + (+++) + 6556
66
67
Table 4. Simplified ranking matrices for mountain lions based on (e) comparing 557
proportional habitat use within 95% kernel home ranges with proportions of total 558
available habitat (2nd order selection), and (f) comparing the proportions of satellite 559
locations (GPS) for each animal in each habitat with the proportion of each habitat within 560
the animals 95% KHR (3rd order selection) for the Payson, Arizona study site, 2005-561
2008. Ranked preference is given for each habitat from high to low. Each mean in the 562
matrix was replaced by its positive (+) or negative (-) sign; a triple sign (--- or +++) 563
represents significant deviation from random at P < 0.05, those in parentheses indicate 564
significant difference from standard t-tests. 565
566
e) Payson 95% KHR vs. study area (2nd
Order) 567
Habitat 568
Habitat Chaparral Riparian Rock Shrubland Woodland Urban Rank 569
Chaparral + + + + + 5 570
Riparian - + - - + 2 571
Rock - - - - + 1 572
Shrubland - + + - + 3 573
Woodland - + + + + 4 574
Urban - - - - - 0 575
f) Payson GPS locations vs. 95% KHR (3rd
Order) 576
Habitat 577
Habitat Chaparral Riparian Rock Shrubland Woodland Urban Rank 578
68
Table 4. continued. 579
Chaparral - + + (+++) - 3 580
Riparian + + + (+++) + 5 581
Rock - - - (+++) - 1 582
Shrubland - - + (+++) - 2 583
Urban (---) (---) (---) (---) (---) 0 584
Woodland + - + + (+++) 4 585
586
69
587 Figure 1. 588
589
70
Figure 1. Minimum convex polygon (MCP) that encompass all satellite locations of 590
mountain lions that we captured near Payson, Prescott, and Tucson, Arizona, 591
2005-2008. 592
71
APPENDIX B. MOUNTAIN LION USE OF URBAN LANDSCAPES IN ARIZONA.
To be submitted to Ecological Applications: Nicholson, K. L., P.R. Krausman, T. Smith,
W. B. Ballard, and T. McKinney.
72
31 August 2009 Kerry L. Nicholson University of Arizona 325 Biological Sciences East Tucson, AZ 85721 520-204-7830 [email protected]
Mountain Lion Use of Urban Landscapes in Arizona
Kerry L. Nicholson, School of Natural Resources, University of Arizona, Tucson, AZ
85721, USA, [email protected]
Paul R. Krausman, Boone and Crockett Program in Wildlife Conservation, University of
Montana, Missoula, MT 59812, USA
Thorry Smith, Arizona Game and Fish Department, Research Branch, 5000 W. Carefree
Highway, Phoenix, AZ 85086, USA
Warren B. Ballard, Department of Natural Resource Management, Texas Tech
University, Lubbock, TX 79409, USA
Ted McKinney2, Arizona Game and Fish Department, Research Branch, 5000 W.
Carefree Highway, Phoenix, AZ 85086, USA
Abstract: Managing wildlife in urban areas is necessary for wildlife conservation. Large 1
carnivores like mountain lions (Puma concolor) present a particular challenge to 2
managers because of public safety and the polarizing emotional reactions to human-lion 3
encounters. We evaluated movements of mountain lions that interacted with urban 4
development in Arizona. We collared and monitored 29 mountain lions between August 5
2 Deceased.
73
2005 and August 2008 near Payson, Prescott, and Tucson, Arizona. Nine mountain lions 6
used urban landscapes (7 M, 2 F). Mountain lions avoided urban areas (n = 18, χ2 = 7
1219.49, P <0.0001), and had shorter step-lengths (i.e., distances between locations) 8
within an urban environment (one way ANOVA F1, 2053 = 14.11, P < 0.0002) than non-9
urban lions. When in urban areas, lions moved at a rate of 0.16 m/min (0.12-0.21 95% 10
C.I.) versus 1.54 m/min (1.43-1.65 95% C.I.) when outside urban landscapes. Lions 11
occupied urban areas mostly during the night (72% of urban locations). There were 143 12
forays into urban areas and 63% were single occurrences where the next mountain lion 13
location was outside of the urban boundary. Use of urban areas by lions was rare and 14
lions that continuously or repeatedly use urban areas likely are those that pose a threat to 15
humans. 16
Key Words: carnivore, cougar, fragmentation, human-wildlife conflict, management, 17
Puma concolor, urban wildlife, urbanization 18
Introduction 19
Over the last century, the United States (US) has transformed from an agrarian to 20
an urban society that has changed the way in which people interact with wildlife. This 21
has resulted in changes of perceptions on hunting, nonconsumptive use (Shaw and 22
Mangun 1984), wildlife education (Adams et al. 1987), and conservation (Hunter 1989). 23
How the urban population perceives wildlife and the interactions that they have with 24
wildlife translate through political, regulatory, and legislative processes that result in 25
guiding wildlife management decisions (Hadidian 1991). Maintenance of connectivity 26
and habitat conservation are issues of concern. Large, continuous tracts of habitat are the 27
74
biological ideal of maintaining genetic and demographic connectivity (Graves et al. 2007) 28
particularly for large carnivores and species that do not flourish in urban environments. 29
It is important for wildlife managers to understand how humans and wildlife interact. 30
This understanding will improve with knowledge of how urban centers connect to 31
wildlife habitat. 32
The human population is growing and natural habitats are declining in extent and 33
diversity. Combined with global political and economic adversity, causes for worldwide 34
decline in native megafauna are apparent (Rubenstein et al. 2006). In 2006, 79% of the 35
USA population (299.1 million people) lived in urban areas 36
(http://www.census.gov/population/projections/SummaryTabA1.pdf). In Arizona, the 37
population will double by 2040 (U. S. Census Bureau Population Division Projections 38
Branch 2007) and the urban areas occupied in the state will overlap or destroy suitable 39
wildlife habitat for many species. Small carnivores (≤10 kg) can coexist sympatrically 40
with humans and are usually not perceived as an imminent threat to humans (Nicholson 41
et al. 2007). Whereas mammals that are wide ranging and exist at low densities such as 42
wilderness species like the mountain lion (Puma concolor; Leopold 1933) are vulnerable 43
to habitat loss and fragmentation (Wilcox and Murphy 1985, Noss et al. 1996, Gittleman 44
et al. 2001). Large carnivores come into conflict with humans and their domestic animals, 45
and although controversial (Kellert et al. 1996, Riley and Decker 2000,Teel et al. 2002), 46
public interest is still often focused on conservation (Riley et al. 2003). As top predators 47
in terrestrial ecosystems, carnivores may also affect other populations in lower trophic 48
levels (Crooks and Soule 1999). How mountain lions interact with urban landscapes has 49
75
gained public attention. Learning about the influence of urbanization on wildlife and 50
determining the influence of wildlife on community structure within urban areas is 51
important. 52
Little information is available to managers on daily mountain lion movements 53
within urban environments. Thus, managers rely on reports from the public that 54
typically reflect one of two philosophies; either the lion is appreciated as a symbol of 55
wilderness and is to be conserved, or the lion is a threat. A single glimpse of a mountain 56
lion can initiate a flurry of reports, panic, and even more sightings that are typically 57
benign because they are misidentified (E. Ostergaard, Urban Wildlife Specialist, Arizona 58
Game and Fish Department [AGFD], personal communication). 59
In Arizona, there have been 5 documented mountain lion attacks on humans 60
between 1988 and 2006 (K. Bergersen, AGFD, personal communication). In 2003, the 61
AGFD began receiving reports of lion activity near Sabino Canyon Recreation Area that 62
receives >160,000 visitors a year in Tucson, Arizona. In 2004, officials took action to 63
remove nuisance lions. There was a strong public outcry, and the controversy escalated 64
to the Arizona Legislature and Governor, and mountain lion removal attempts were 65
suspended (Perry and deVos 2005). Until 2004, Arizona had no mitigation protocols for 66
nuisance mountain lions. The public criticized AGFD and accused the agency of having 67
no data to support their actions, no statewide protocol specifically for nuisance lions, no 68
comprehensive incident file, lack of a rigorous policy for dealing with incidents involving 69
lions, and inconsistent classification of reports. There was a need for guidelines on: 1) 70
76
how to verify reports; 2) how to deal with nuisance lions; and most importantly 3) 71
classifications of mountain lion behavior to include what is dangerous or abnormal. 72
We investigated use of urban landscapes by mountain lions to understand the 73
ecology of carnivores in urban areas. We expected mountain lions to use urban 74
landscapes more than available due to the problems arising from the mountain lions in 75
Sabino Canyon. We expected that use of urban areas would differ by location with 76
greater use in less densely populated and developed areas such as Payson versus Tucson. 77
We expected that mountain lions that overlapped urban areas would have larger home 78
ranges because urban areas would not provide adequate resources. We also predicted that 79
mountain lion movement within urban landscapes would be faster than movement outside 80
urban boundaries because lions would not linger within urban areas. 81
Study area 82
We studied mountain lions in north-central Arizona near Payson (including the 83
cities of Star Valley, Pine, and Strawberry; 34.2 ˚N 111.3˚W), Prescott (including 84
Prescott Valley, Paulden, Williams, Cottonwood, Clarkdale, and Chino Valley; 34.6˚N 85
112.5˚W), and in southern Arizona near Tucson (including Oracle, Marana, Catalina, and 86
Saddle Brook; 32.2˚N 111.0˚W) from 2005 to 2008. Payson and Prescott were ≥ 130 km 87
apart, and ranged between 1,280 and 1,860 m in elevation, respectively. Annual 88
precipitation averaged 57 cm in Payson and 48cm in Prescott, with total annual snowfall 89
for both areas of 62 cm with average temperatures of 3-23 ˚C. Tucson was 307 km south 90
of Payson with an average annual rainfall of 30 cm and snowfall of 3.0 cm. Maximum 91
and minimum temperatures were 28 and 12.6˚C, respectively. Located in the Sonoran 92
77
Desert, Tucson sits within a valley, circumscribed by the Santa Catalina, Tucson, 93
Tortolita, Rincon, and the Santa Rita mountains. Elevation ranged from approximately 94
640 to ≥2,700 m in ≤60 km. Human population of Payson in July 2007 was 16,742, 95
Prescott was 43,217, and Tucson was 541,132 (Department of Urban Planning and 96
Design 2009). 97
Vegetation associations were similar for Payson and Prescott including interior 98
chaparral, pinyon (Pinus edulis) - juniper (Juniperus spp.) woodlands, grasslands and 99
mixed ponderosa pine (Pinus ponderosa) forests (Brown 1994). Tucson contained 100
Arizona upland subdivision of Sonoran Desert vegetation and riparian and xeroriparian 101
vegetation (mixed riparian desert scrub series; Brown 1994). Mountain ranges that 102
surround Tucson ascend from the Sonoran desert scrub (e.g., mesquite [Prosopis 103
juliflora], paloverde [Cercidium spp.], cactus [Opuntia spp.] and various grasses) to 104
Chihuahuan semi-desert to grassland to oak (Quercus)-alligator juniper (Juniperus 105
deppeana) woodland to Petran-montane and mixed conifer forest (Whittaker and Niering 106
1965, Brown 1994). On all study sites, mule deer (Odocoileus hemionus), white-tailed 107
deer (O. virginianus), collared peccary (Tayassu tajacu), black bear (Ursus americanus), 108
bobcats (Lynx rufus), and coyotes (Canis latrans) were common. Cattle and pronghorn 109
(Antilocapra americana) inhabited areas near Tucson and Prescott, and elk (Cervus 110
elephus) were common in Payson and Prescott and bighorn sheep (Ovis canadensis) 111
inhabited the Silverbell Mountains near Tucson. 112
Methods 113
78
Between August 2005 and February 2008 mountain lions were captured by 114
AGFD personnel using snare and hound techniques (Shaw 1983, Logan et al. 1999, 115
Logan and Sweanor 2001, Cougar Management Guidelines Working Group 2005). Upon 116
capture, lions were equipped with Spread Spectrum Satellite collars (Telonics, Inc., 117
Mesa, AZ, USA). We obtained satellite locations every 4.15 for lions near Tucson, and 7 118
h in Payson, and Prescott. We downloaded and processed location data at the end of the 119
study in August 2008. We incorporated all locations into an ArcGIS 9.x (1995-2005 120
Environmental Systems Research Institute, Inc., Redlands, CA, USA) database for 121
analysis of habitat use. 122
We used Hawths Tools Animal Movements (Beyer 2004) and converted locations 123
to paths. We assumed a straight line distance and a constant rate of movement between 124
points. We calculated the length (step length) and duration between locations and then 125
obtained a rate of movement (m/min). We assigned each segment an identifier that 126
indicated consecutive locations and unique identifier of all paths and points that entered 127
our urban areas. We used the density function in the Spatial Analyst Extension to 128
identify mountain lion location clusters for lions with >30 locations in urban areas. We 129
used the kernel density estimator and a 200 m search radius based upon identification of 130
predation clusters described by Anderson and Lindzey (2003). We designated day light 131
hours based upon the yearly sunrise and sunset times obtained from the U.S. Naval 132
Observatory (http://aa.usno.navy.mil/data/docs/RS_OneYear.php). We obtained 133
population density from the U.S. Census Bureau population finder 134
(http://factfinder.census.gov/home/saff/main.html?_lang=en) for incorporated cities and 135
79
www.city-data.com for unincorporated. We used population estimates from 2007 for all 136
towns. 137
As census data are collected every 10 yrs, digital human population or housing 138
density data that covered our study areas were based upon the 2000 census. Therefore, 139
we created a surrogate digital layer to represent current human density and distribution 140
based upon roads. Arizona Department of Transportation (ADOT) continually maintains 141
road information and therefore, incorporates new roads and new areas of development. 142
We considered roads as an indicator of human inhabitance. We created a 0.5 km 2 grid 143
across our study areas and standardized our measured area and calculated the density of 144
roads (i.e., total length of all roads) within a 0.5 km2 grid cell. We removed all primitive 145
roads, trails, and alleys from the road layer provided by ADOT before our analysis. 146
Within a 0.5 km2 cell, the maximum distance of roads was 5.5 km and we classified 147
density of roads into 8 groups: 0 = 0 m, 1 = 1 – 250 m, 2 = 251 – 500 m, 3 = 501–1,000 148
m, 4 = 1,001–1,500 m, 5 = 1,501 – 2,000 m, 6 = 2,001 – 3,000 m, and 7 = >3,001 m. We 149
calculated the density of roads for each class within each individual’s 95% kernel home 150
range. We used a chi-square use versus availability to determine if lions avoided travel in 151
areas with higher road densities. 152
We created an urban boundary layer by combining high resolution satellite 153
imagery (1 m pixel resolution) with an ADOT urban boundary layer and a road density 154
layer. The ADOT city layer already accounted for incorporated cities in Arizona and we 155
used this layer as the base map. To find towns not accounted for or to modify the 156
existing boundary of an ADOT city boundary, we used the density of roads layer to 157
80
locate high density areas (>3km roads within 0.5 km2) and overlaid them on satellite 158
imagery and then used heads up digitizing to outline urban areas found. We also used the 159
Pima County Economic Analysis Section 10 Permit MSCP projected urban growth model 160
for Tucson to determine additional use of areas projected to become urban by 2030 (ESI 161
Corporation Study Team 2003). 162
Within ArcGIS, we created a 50% and 95% fixed kernel home range (KHR; 163
Worton 1989) using Home Range Tools (HRT) v 1.1 extension (Rodgers et al. 2007). 164
Within the HRT environment, we used a bivariate normal distribution, rescaled to unit 165
variance, and selected a 0.6 proportion of the reference bandwidth to create the fixed 166
KHR. We generated a polygon centroid point that finds the center of gravity of an 167
animal’s home range. We buffered the centroid points of the same area as the 95% KHR 168
to create circular home ranges (Figure 1). We calculated percent urban area of the Kernel 169
and circular home ranges. We used a chi-square test to determine if use of urban areas 170
differed from the satellite locations and a paired t-test between percentage of urban area 171
in the KHR and circular range (Zar 1996). We also used simple linear regression to 172
determine if population density predicted the differences found between observed 173
locations in urban areas versus expected (Zar 1996). 174
Results 175
Nine of 29 mountain lions used urban landscapes (7 M, 2 F) but only 3 mountain 176
lions used urban areas as expected or more than expected (Table 1). Less than 0.01% of 177
locations (i.e., 338 of 30,282) were within urban boundaries (Table 1). The majority of 178
the mountain lions (n = 18) avoided urban areas (χ2 = 1219.49, P < 0.0001). Percent area 179
81
of urban landscapes differed between the circular (random) and KHR (paired t-test 2-180
tailed probability, t17, = 2.28, P = 0.03). Mountain lion home ranges were similar in size 181
between lions that overlapped and did not overlap urban landscapes (one way ANOVA 182
F1,28 = 0.51, P = 0.48). 183
Based on 9 lions that overlapped with urban areas, mountain lions had shorter step 184
lengths within an urban environment (one way ANOVA F1, 2053 = 14.11, P < 0.0002), and 185
moved at a slower rate (one way ANOVA F1, 2053 = 258.65, P < 0.0001) than lions 186
outside urban areas. On average, lions in an urban area moved at a rate of 0.16 m/min 187
(0.12-0.21 95% C.I.) versus 1.54 m/min (1.43-1.65 95% C.I.) outside the urban area. 188
There was a difference in the average duration lions occupied Payson and Prescott 189
versus Tucson (one way ANOVA F2,335 = 11.57, P < 0.0001; Student’s t-test t2,335 = 1.97, 190
P < 0.05). Also, mountain lions in Tucson had shorter step lengths than either Payson or 191
Prescott (one way ANOVA F2,335 = 27.46, P < 0.0001). Rate of movement within urban 192
areas differed between Tucson and Prescott (Students t-test t2,335 = 1.97, P < 0.05). Lions 193
in Tucson moved at a rate of 181 m/min (159 - 207 95% C.I.) versus lions in Prescott that 194
moved 272 m/min (232 – 318 95% C.I.). 195
Of the 338 locations in urban areas, 72% were during nocturnal hours (Table 1). 196
Incorporating the projected expansion of Tucson added an additional 95 locations that 197
would be within an urban environment in 2030. The majority of urban locations (68%) 198
were from a 3 yr old male lion (M310) in Oracle and an 8 yr old female (F104) in 199
Prescott (Table 2). One-hundred-forty-three consecutive groupings of locations were 200
considered forays into urban areas; 90 were single locations where the next location of 201
82
the mountain lion was outside of the urban boundary. Duration of forays did not differ 202
by study location (one way ANOVA F2,141 = 2.31, P < 0.103). On average, forays into 203
urban areas occurred 5.9 days apart and would last 12.6 hrs and cover 1.1 km. The 204
longest any individual stayed within urban areas was M310 with 15 consecutive locations 205
and moved at a rate of 4.2 m/min over 1.1 km. The maximum distance covered by a lion 206
during 1 foray within an urban boundary was 8.8 km. 207
Three mountain lions had >30 locations within the urban boundary. Clusters of 208
locations within an urban area consisted of 2 to 23 locations. Female F104 had 18 209
unique clusters in Prescott, 4 had >5 locations within the 200 m buffer criteria. The 210
average distance of clusters for F104 from the urban boundary was 0.46 km. Male M310 211
in Oracle, had 13 unique clusters, but unlike F104, averaged 8 locations/cluster. F411 212
had 7 unique clusters averaging 4 locations/cluster. Female F411 in Marana was the only 213
lion with a cluster >1 km in the urban matrix. Clusters did not always consist of 214
consecutive locations. All lions averaged 3 unique forays within 1 cluster. 215
We used the gridded density of roads as a surrogate for human population density 216
(i.e., more roads indicated more human-use) but not all classes were available to all 217
mountain lions. Eight mountain lions had all 8 road density classes available to them and 218
they were not used in proportion to availability (χ2α = 0.05, 7 = 326.67, P < 0.0001). By 219
combining the 3 highest categories (5 = 1,500 – 2,000, 6 = 2001 – 3,000m, 7 = >3,001m) 220
we increased the number of individuals with all available road density classes within their 221
home range to 29 without violating assumptions regarding expected values being >0. 222
Mountain lions avoided higher density road areas (χ2α = 0.05, 5 = 472.51, P < 0.0001). 223
83
Seventy four percent of all locations were found in areas with no roads and <1% were 224
found in areas that had >1,500 m of roads per 0.5 km2. 225
Human population density provided a viable predictor of urban use by lions 226
(simple linear regression t17, = -1.93, P = 0.07). As human population increased, the 227
likelihood of use by mountain lions decreased (Figure 2). The distance that a mountain 228
lion penetrated the urban boundary differed between urban locations (one way ANOVA 229
F337, = 25.82, P < 0.0001). Lions traversed further into the urban matrix in lower human 230
populated areas such as Pine, Oracle, Williams and Summer Haven (Table 2). For 231
instance, mountain lions entering Tucson were able to move into the urban matrix 0.23 232
km infiltrating >0.1% into the city whereas those that moved into Pine were able to 233
infiltrate 1.8 km or 64% of the way into the center of town (Table 2). The maximum 234
distance of any mountain lion into the urban matrix from the edge of town was in 235
Williams (2.9 km; Table 2). 236
Discussion 237
Individual mountain lions were variable in their use of urbanized areas, but 238
overall lions did not use the urban landscape as would be expected from either area 239
covered by home range or by individual satellite locations. Even in low human populated 240
areas, most (n = 20) mountain lions did not overlap home ranges with urban landscapes. 241
Lion M308’s home range distribution (Figure 1) was typical of mountain lion home 242
ranges near urbanization. When the home range of mountain lions incorporated 243
urbanization, the locations were rarely in an urban landscape and the shape of the home 244
range skirted that of city boundaries. 245
84
Mountain lions used urban areas differently, an artifact likely due to duration and 246
step length caused by the difference in satellite acquisition rate of locations. However, 247
when calculating rate of movement in urban areas, which incorporated length and time of 248
presence, there was only a difference between Tucson and Prescott. This difference was 249
likely due to a difference in the composition of the landscape. It is difficult to specify 250
housing density where mountain lions will cease to use an area. In California, mountain 251
lions tolerated 1 dwelling per 16.2 ha, if the area was adjacent to unpopulated areas 252
(Beier and Barrett 1993). A transition from suitable mountain lion habitat to nonhabitat 253
appeared to be about 1 dwelling per 8.1 ha (Beier and Barrett 1993). This available zone 254
may increase if other ideal conditions were to exist (e.g., minimal loss of vegetation, no 255
free-roaming pets; Beier and Barrett 1993). 256
Overall, mountain lions moved slowly through urban areas compared to 257
movement outside, but relative to each study location, lions moved at a greater rate 258
through Tucson and the surrounding suburbs. Although mountain lions avoid human-259
dominated places, they were capable of maneuvering through lower density landscapes. 260
This could explain why lions moved slower through Payson and Prescott, which have 261
lower human densities and development. Due to duration and frequency of use, 262
mountain lions appeared to find temporary cover and other resources within these urban 263
areas. 264
Mountain lions in Arizona avoided high density urban areas. One problem with 265
using road’s as a surrogate to human density is the lack of distinction of types of roads 266
(i.e., paved or unpaved, high use versus low use). Because of this, we may be over-267
85
estimating the impact or presence of humans within mountain lion territories. 268
Additionally, historical roads or forest service roads that were once primary travel routes 269
were incorporated into the database and may not be maintained or even still available for 270
traffic. Regardless of these potential biases, increasing human population densities 271
resulted in decreased habitat use by mountain lions. 272
The 2 mountain lions that had the most locations within urban environments were 273
the only lions observed by residents, but not classified as a nuisance or threat. Mountain 274
lion M310 was initially captured in Oracle and moved >100 km from the initial capture 275
site before data collection ended. Residents observed F104, captured in Prescott, several 276
times during the duration of the study with 2 kittens, and once when she had a deer 277
(Odocoileus spp.) kill in a neighborhood (B. Waddell, Arizona Game and Fish Research 278
Branch, personal communication). Lion F411 was a third individual that, though never 279
reported as observed, managed to infiltrate into a newly (<4 yrs) developed neighborhood 280
at the base of the Tortalita Mountains. Lion M313 walked repeatedly through the center 281
of Summer Haven at night and M208 was located multiple times on surface streets in 282
Pine, neither was reported. There were no other reports to AGFD of the other 25 lions as 283
observed or causing problems. Between January 2006 and December 2008, AGFD 284
received 1,237 reports of potential mountain lion encounters and 21 were classified as 285
potential attacks. None of those attacks were on humans, rather potential attacks on 286
domestic animals and livestock, 6 had verified evidence of mountain lions causing 287
problems (Human-Wildlife Interaction Database, AGFD, 2009). 288
86
Mountain lions entered urbanized areas, explored briefly and then left, or moved 289
through, and others used the urban landscape as part of their normal habits. Those lions 290
using the urban landscape moved through the area more during night than day. Because 291
mountain lions are primarily active during crepuscular hours and considered a nocturnal 292
predator (Currier 1983), it was not surprising that the majority of the movements through 293
urban areas were at night. Mountain lions did not linger in urban areas, but passed 294
through them quickly. Those that do linger and create problems are anomalies and 295
should be removed for human safety. 296
We speculate that urban areas did not provide adequate resources for sustained 297
constant use. Using techniques to describe classified clusters we can begin to formulate 298
ideas of how urban areas were used. Clusters of locations indicated repeated visits and 299
with duration or time between visits to the same area can indicate a possible kill site (long 300
duration, but not revisited), bed sites (long duration and repeated visits), travel corridors 301
(short duration but repeated visits) or some other available resource (variable visits; 302
Anderson and Lindzey 2003, Webb et al. 2008). Mountain lion F104 had multiple 303
clusters within Prescott possibly indicating familiarity with the landscape and a level of 304
comfort to continually use the same areas. Most (n = 14) of F104’s clusters consisted of 305
2 - 4 locations with several days or weeks between visits. Mountain lion M310 had more 306
locations/cluster than F104 and F411 and consisted of fewer unique forays. These were 307
long in duration determined from ≤ 15 consecutive locations with no return. Cluster 308
analysis is useful to locate repeatedly used areas and coupled with date/time information 309
can allow managers to determine duration and fidelity to particular locations. 310
87
Determining behavior at each cluster site would be useful for managers; unfortunately, 311
this did not occur in present study due to inconsistencies in data download. 312
Conservation of wildlife has implications for urban development. Often, setting 313
aside land for habitat of an animal is not an available option for managers because of 314
political and economical pressures or lack of ecological knowledge. In discovering 315
habitat for animals, biologists can assess the gradient or flexibility animals may have 316
under various scenarios. Understanding how mountain lions use areas where human 317
development occurs or is expanding may offer several tools for managers to maintain 318
mountain lion populations. Managers have the ability to potentially minimize human-319
mountain lion conflict and determine permeability of the landscape for mountain lion 320
movement. Maintaining opportunities for animals to move across landscapes is an 321
important wildlife conservation consideration (Noss and Cooperrider 1994 Developers 322
and conservationists can use this knowledge of mountain lions ability to navigate through 323
various urban matrixes when designating biological linkages for mountain lions. 324
Managers may need conservation strategies that go beyond traditional land 325
acquisition by government and include economic programs to preserve critical landscapes 326
on private land. Arizona has the ability to plan and Pima County has initiated the 327
Sonoran Desert Conservation Plan that will integrate natural resource protection and land 328
use planning. Also, the Arizona Wildlife Linkage Workgroup is a collaboration of 329
agencies and others to identify potential landscape corridors around Arizona, and to 330
develop detailed plans for corridors of high importance and at high risk of impairment by 331
highways, urbanization, and other threats. We advise caution when designating reserved 332
88
land and to incorporate data from multiple individuals within the species of concern in 333
the decision process. Maintaining biological linkages for mountain lions potentially will 334
benefit multiple species and design efforts benefit from empirical data on how mountain 335
lions respond to habitat features in their activity and travel in Arizona landscapes. 336
Collaboration between agencies is necessary for successful management and 337
studies of mountain lions. Mountain lions range over large expanses of land managed by 338
multiple agencies. This study was collaboration between the University of Arizona and 339
Arizona Game and Fish Department Research Branch. In one instance, we had a unique 340
opportunity to study urban use by mountain lions where of 1 mountain range enveloped 341
by urbanization was supporting a documented female resident with cubs. The mountain 342
range fell under the jurisdiction of 2 other agencies, yet collaboration and relevant goals 343
between all interested parties was lacking. Compounding the issue, one of the agencies 344
had a different agenda regarding mountain lion management and had historical political 345
problems with another agency. For successful management of any wildlife species, 346
cooperation is critical within and between agencies. We the managers request that the 347
public learn to compromise and understand another’s perspective, yet when the experts 348
cannot agree it is the wildlife that looses. 349
A proactive approach by agencies involving education and predetermined 350
protocols for dealing with mountain lion–human encounters may enhance human safety 351
in lion habitat and improve mountain lion conservation (Sweanor et al. 2008) and 352
Arizona has recently just initiated. Most collared mountain lions in southern and central 353
Arizona are not using urban landscapes. However, even lions that appear to avoid areas 354
89
of human use will likely be in proximity to humans at some time. Development that 355
limits resource opportunities for the survival of a species can be reduced with knowledge 356
about basic behavioral responses and use of novel environments. Mountain lions are 357
apex predators that are adaptable to most environments. Urban landscapes are not ideal 358
environments, but lions do use them. Consequently, educational materials on mountain 359
lion behavior and correct human responses during a mountain lion encounter should be 360
provided and targeted at communities that have been established in prime mountain lion 361
habitat. In areas frequented by mountain lions, more active management could include 362
limitations on time of day when human activity is permitted (e.g., closing trails between 363
dusk and dawn) or the removal of individual mountain lions deemed to be a threat to 364
human safety (Cougar Management Guidelines Working Group 2005, Mattson 2007). 365
The social ramifications of removing potential threats are difficult to manage with such a 366
controversial species like the mountain lion. However, additional education about the 367
impact on mountain lion populations from removing one problem individual is extremely 368
important. 369
Acknowledgements 370
We thank all of the hounds men, hounds, trappers, and volunteers on this project 371
including R. Thompson, T. and A. Salazar, B. Buckley, T and A. Anderson, R. Murphy, 372
T. McNealy, L. Haynes, N. Smith, B. Jansen, C. Dolan and B. Kluver. We thank AGFD 373
personnel, B. Waddell, and all of the aerial surveyors and pilots. We thank J. deVos for 374
initial consultation and funding and C. O’Brien with AGFD and C. Yde for 375
administration of the contract. We thank the Advanced Resource Technologies lab at the 376
90
University of Arizona especially M. Reed, P. Guertin, and A. Honaman. Funding was 377
provided by Arizona Game and Fish Department and the University of Arizona. Capture 378
and handling procedures were approved by the Animal Care and Use Committee at the 379
University of Arizona (protocol #05-184). 380
Literature cited 381
Adams, L. W., D. L. Leedy, and W. C. McComb. 1987. Urban wildlife research and 382
education in North American colleges and universities. Wildlife Society Bulletin 383
15:591-595. 384
Anderson, C. R., Jr., and F. G. Lindzey. 2003. Estimating cougar predation rates from 385
GPS location clusters. Journal of Wildlife Management 67:307-316. 386
Arundel, T., D. Mattson, and J. Hart. 2007. Movements and habitat selection by mountain 387
lions in the Flagstaff uplands. Pages 68 in D. Mattson, editor. Mountain lions of 388
the Flagstaff uplands 2003-2006 progress report. USGS Open-File Report 2007-389
1062, Flagstaff, AZ, USA. 390
Beier, P., and R. H. Barrett. 1993. The cougar in the Santa Ana Mountain range, 391
California. Department of Forestry and Resource Management, University of 392
California, Berkeley, USA. 393
Beyer, H. L. 2004. Hawth's Analysis Tools for ArcGIS. 394
http://www.spatialecology.com/htools. Accessed 12 December 2008. 395
Brown, D. E., editor. 1994. Biotic communities southwestern United States and 396
northwestern Mexico. University of Utah Press, Salt Lake City, USA. 397
91
Cougar Management Guidelines Working Group. 2005. Cougar management guidelines, 398
First edition. Opal Creek Press, LLC, Salem, OR, USA. 399
Crooks, K. R., and M. E. Soule. 1999. Mesopredator release and avifaunal extinctions in 400
a fragmented system. Nature 400:563-566. 401
Currier, M. J. P. 1983. Felis concolor. Mammalian Species 200:1-7. 402
Department of Urban Planning and Design. 2009. Department of urban planning and 403
design. http://www.tucsonaz.gov/planning/data/demographic. Accessed 3 Feb 404
2009. 405
ESI Corporation Study Team. 2003. Pima County Economic Analysis Section 10 Permit 406
MSCP http://www.pima.gov/cmo/sdcp/reports/d28/ECONOM.PDF. Accessed 9 407
June 2009. 408
Gittleman, J. L., S. M. Funk, D. MacDonald, and R. K. Wayne. 2001. Carnivore 409
conservation. Cambridge University Press, Cambridge, United Kingdom. 410
Graves, T., S. Farley, M. Goldstein, and C. Servheen. 2007. Identification of functional 411
corridors with movement characteristics of brown bears on the Kenai Peninsula, 412
Alaska. Landscape Ecology 22:765-772. 413
Hadidian, J. 1991. Interactions between people and wildlife in urbanizing landscapes. 414
Eastern Wildlife Damage Control Conference 5:8-11. 415
Hunter, M. L. 1989. Conservation biology, wildlife management, and spaceship earth. 416
Wildlife Society Bulletin 17:351-354. 417
Kellert, S. R., M. Black, C. R. Rush, and A. J. Bath. 1996. Human culture and large 418
carnivore conservation in North America. Conservation Biology 10:977-990. 419
92
Leopold, A. 1933. Game Management. Charles Scribner's Sons, New York, NY, USA. 420
Logan, K. A., and L. L. Sweanor. 2001. Desert puma: evolutionary ecology and 421
conservation of an endangered carnivore. Island Press, Washington, D.C., USA. 422
Logan, K. A., L. L. Sweanor, J. F. Smith, and M. G. Hornocker. 1999. Capturing pumas 423
with foot-hold snares. Wildlife Society Bulletin 27:201-208. 424
Nicholson, K. L., L. Haynes, and P. R. Krausman. 2007. Current status of mountain lions 425
and urban issues in Tucson, Arizona. Southwest Desert Resources: in press. 426
Noss, R. F., and A. Y. Cooperrider. 1994. Saving nature's legacy: protecting and restoring 427
biodiversity. Island Press, Covelo, California, USA. 428
Noss, R. F., H. B. Quigley, M. G. Hornocker, T. Merrill, and P. C. Paquet. 1996. 429
Conservation biology and carnivore conservation in the Rocky Mountains. 430
Conservation Biology 10:949-963. 431
Perry, G. L., and J. C. deVos, Jr. 2005. A case study of mountain lion-human interaction 432
in southeastern Arizona. Mountain Lion Workshop 8:104-113. 433
Riley, S. J., and D. J. Decker. 2000. Wildlife stakeholder acceptance capacity for cougars 434
in Montana. Wildlife Society Bulletin 28:931-939. 435
Riley, S. P. D., R. M. Sauvajot, T. K. Fuller, E. C. York, D. A. Kamradt, C. Bromley, and 436
R. K. Wayne. 2003. Effects of urbanization and habitat fragmentation on bobcats 437
and coyotes in southern California. Conservation Biology 17:566-576. 438
Rodgers, A. R., A. P. Carr, H. L. Beyer, L. Smith, and J. G. Kie. 2007. HRT: Home 439
Range Tools for ArcGIS. in. Ontario Ministry of Natural Resources, Centre for 440
Northern Forest Ecosystem Research, Thunder Bay, Ontario, Canada. 441
93
Rubenstein, D. R., D. I. Rubenstein, P. W. Sherman, and T. A. Gavin. 2006. Pleistocene 442
Park: Does re-wilding North America represent sound conservation for the 21st 443
century? Biological Conservation 132:232-238. 444
Shaw, H. G. 1983. Mountain lion field guide. Arizona Game and Fish Department. 445
Special Report Number 9, Phoenix, AZ, USA. 446
Shaw, W. W., and W. R. Mangun. 1984. Nonconsumptive use of wildlife in the United 447
States. Pages 1-20 in U.S. Fish and Wildlife Service Research Publication, editor. 448
Teel, T. L., R. S. Krannich, and R. H. Schmidt. 2002. Utah stakeholders' attitudes toward 449
selected cougar and black bear management practices. Wildlife Society Bulletin 450
30:2-15. 451
U. S. Census Bureau Population Division Projections Branch. 2007. State interim 452
population projections by age and sex: 2004 - 2030. 453
http://www.census.gov/population/projections/SummaryTabA1.pdf. Accessed 10 454
January 2007. 455
Webb, N. F., M. Hebblewhite, and E. H. Merrill. 2008. Statistical methods for identifying 456
wolf kill sites using global positioning system locations. Journal of Wildlife 457
Management 72:798-807. 458
Whittaker, R. H., and W. A. Niering. 1965. Vegetation of the Santa Catalina Mountains, 459
Arizona: a gradient analysis of the south slope. Ecology 46:429-452. 460
Wilcox, B. A., and D. D. Murphy. 1985. Conservation strategy: the effects of 461
fragmentation on extinction. American Naturalist 125:879-887. 462
94
Worton, B. J. 1989. Kernel methods for estimating the utilization distribution in home 463
range studies. Ecology 70:164-168. 464
Zar, J. H. 1996. Biostatistical analysis. Prentice Hall, Upper Saddle River, NJ, USA. 465
95
Table 1. Number of satellite telemetry locations for each mountain lion (Id) by nocturnal 466
or diurnal hours, in urban areas and the expected and chi square value of use of urban 467
areas in Arizona 2005-2008. 468
No. No. in town χ2 469
Id Sex Age locations Day Night Used Expected 470
101 Female <4 869 0 0 471
103 Female >4 1,070 0 0 472
104 Female >4 1,733 24 76 246 87 473
105 Female <4 73 0 0 474
201 Male <4 510 0 0 1 1 475
202 Male <4 916 0 0 476
203 Male >4 477 0 0 477
204 Male <4 976 0 0 478
207 Male >4 982 0 0 73 73 479
208 Male <4 188 1 28 44 5 480
209 Male <4 870 6 4 2 47 481
210 Male >4 497 0 0 6 6 482
211 Male <4 279 0 1 76 73 483
212 Male >4 1,153 0 0 484
214 Male <4 727 0 0 14 14 485
215 Male <4 1,025 0 0 18 18 486
301 Male <4 1,051 5 6 136 115 487
96
Table 1. continued. 488
302 Male <4 690 0 0 489
303 Male >4 725 0 0 80 80 490
304 Male >4 882 0 0 0 1 491
305 Male <4 522 0 0 492
306 Male <4 1,388 0 0 15 15 493
308 Male >4 1,849 0 7 554 139 494
310 Male <4 1,629 49 80 54 104 495
313 Male >4 2,030 0 11 36 18 496
402 Female <4 2,499 0 0 497
407 Female >4 1,723 0 0 1 1 498
409 Female >4 1,392 0 0 499
411 Female >4 1,557 11 29 19 23 500
501
97
Table 2. Number of satellite telemetry locations of mountain lions (Id) within urban 502
areas, human population, percent distance from the city center, and the maximum 503
distance (km) from the urban boundary mountain lions were able to infiltrate in Arizona 504
2005-2008. 505
No. locations Human % distance Maximum distance 506
Id in town Town population from centroid from boundary (km) 507
104 100 Prescott 42,265 6.93 1.08 508
208 29 Pine 1,954 64.38 1.08 509
209 10 Williams 3,270 29.13 2.48 510
211 1 Payson 15,407 2.91 0.26 511
301 2 Catalina 8,005 6.15 0.73 512
301 9 Marana 31,860 0.31 0.08 513
308 7 Tucson 525,529 0.43 0.25 514
310 129 Oracle 5,878 22.25 0.75 515
313 11 Summer Haven 100 40.30 0.18 516
411 36 OroValley 40,195 4.93 0.87 517
411 4 Marana 31,860 0.01 0.44 518
519
98
520 Figure 1. 521
99
522
Figure 2. 523
1 2 3 4 5 6
Log(human population)
-700
-600
-500
-400
-300
-200
-100
0
100O
bser
ved
- ex
pect
ed
100
Figure 1. Example of mountain lion locations in respect to urban areas. Black circles 524
were satellite locations from M308 outlined by the 95% Kernel home range, 525
which occupied the Rincon Mountains on the east side of Tucson, AZ from 2006-526
2008. 527
Figure 2. Regression of the log of the human population density of towns in Arizona 528
entered by mountain lions versus the expected use for each town by mountain 529
lions in Arizona, 2005-2008.530
101
APPENDIX C. SPATIAL AND TEMPORAL INTERACTIONS OF SYMPATRIC
MOUNTAIN LIONS IN ARIZONA. To be submitted to Biological Conservation:
Nicholson, K. L., and P.R. Krausman.
102
31 August 2009 Kerry L. Nicholson University of Arizona 325 Biological Sciences East Tucson, AZ 85721 520-204-7830 [email protected]
RH: Nicholson et al. • Mountain lion interactions
Spatial and temporal interactions of sympatric mountain lions in Arizona
Kerry L. Nicholson, School of Natural Resources, University of Arizona, Tucson, AZ,
85721, USA
Paul R. Krausman, Boone and Crockett Program in Wildlife Conservation, University of
Montana, Missoula, MT, 59812, USA
Adrian Munguia-Vega, School of Natural Resources and the Environment, University of
Arizona, Tucson, AZ, 85721, USA
Melanie Culver, School of Natural Resources and the Environment, University of
Arizona, Tucson, AZ, 85721, USA
Abstract Spatial and temporal interactions among individual members of populations can 1
have direct applications to habitat management of mountain lions. Spatial requirements 2
and interactions influence social behavior and ultimately population density. Information 3
on mountain lion interactions, particularly male-male interactions is limited. Our 4
objectives were to evaluate home range overlap and spatial/temporal use of overlap zones 5
(OZ) of mountain lions (Puma concolor) in Arizona. We incorporated spatial data with 6
genetic analyses to assess the influence of relatedness on distribution between individual 7
mountain lions. We recorded the space use patterns of 29 radio-collared mountain lions 8
103
in Arizona from August 2005 to August 2008. We genotyped 28 mountain lions at 12 9
felid microsatellite DNA loci and estimated the degree of relatedness among individuals. 10
For 26 pairs of temporally overlapping mountain lions, 18 overlapped spatially and 11
temporally and 8 had corresponding genetic information. Home range overlap ranged 12
from 1.18-46.38% (�� = 24.43, SE = 2.96). There was no difference in size of overlap 13
among male-male pairs and male-female pairs (t1,16 = 1.04, P = 0.84). Male-male pairs 14
were located within 1 km on average, 0.63% of the time, whereas male-female pairs, on 15
average were 4.90%. Two male-male pairs exhibited symmetrical spatial avoidance and 2 16
symmetrical spatial attractions to the OZ. The remaining 5 pairs had asymmetrical or 17
singular spatial attraction to the OZ. We observed simultaneous temporal attraction in 3 18
male-male pairs and 4 male-female pairs. Overall, individuals were not related (n = 28, 19
mean R = - 0.0037). Individuals from Tucson were slightly related to one another within 20
the population (n = 13, mean R = 0.0373 ± 0.0151) whereas lions from Payson (n = 6, 21
mean R = -0.0079 ± 0.0356) and Prescott (n = 9, mean R = -0.0242 ± 0.0452) were not as 22
related. Overall, males were less related to other males (n = 20, mean R = -0.0495 ± 23
0.0161) than females were related to other females (n = 8, mean R = 0.0015 ± 0.0839). 24
Genetic distance was positively correlated with geographic distance (r = 0.22, P = 0.001). 25
None of the 8 pairs of overlapping lions were identified as 1st or 2nd order relatives. 26
Keywords: home range overlap, Puma concolor, social organization, spatial distribution, 27
temporal distribution, territoriality. 28
1. Introduction 29
104
Spatial organization of mammalian populations are influenced by environmental 30
and ecological factors and social behaviors (Emlen and Oring, 1977). Social organization 31
of carnivores can be highly variable due to distribution of key resources (Lott, 1984; 32
McLoughlin, et al., 2000). As in most carnivores, solitary existence is maintained with 33
little overt aggression mostly by mutual avoidance and scent-marking (Ewer, 1973; 34
Hornocker, et al., 1983). Felids are characterized as solitary with exclusive territories 35
within the sexes (Sunquist and Sunquist, 2002). Exceptions to this standard have been 36
documented in several felid species including the mountain lion (Puma concolor; 37
Hopkins, et al., 1986; Seidensticker, et al., 1973). Generally, female mountain lions are 38
not territorial, whereas territoriality among males (Hornocker, 1969; Seidensticker, et al., 39
1973) or prey availability (Pierce et al 2000) have been suggested to regulate density and 40
distribution. Differences in territoriality between the sexes correspond to differences in 41
limiting resources; females dedicate more to parental investment and are therefore 42
primarily limited by food availability, whereas males dedicate little to parental 43
investment and instead are primarily limited by access to mates (Clutton-Brock and 44
Harvey, 1978). Females select for vegetation, topography, and prey availability 45
sometimes responding to migratory movement of deer (Odocoileus spp.) between seasons 46
(Seidensticker, et al., 1973) whereas males compete for access to females, and were 47
thought to have distinct territories without overlap (Hornocker, 1969). Historically, 48
mountain lions were thought to regulate their population size based on a territorial 49
system, involving mutual avoidance (Hornocker, 1969, 1970; Seidensticker, et al., 1973). 50
105
Recently in California, Pierce et al. (2000), suggested populations were not primarily 51
limited by territoriality rather food availability. 52
Overall, general associations between individual same sex mountain lions are 53
considered rare (Hemker, et al., 1984, Ashman, et al., 1983; Hornocker, 1969; 54
Seidensticker, et al., 1973). Data on how often overlapping male individuals interact 55
with each other is limited and it is generally unknown if the interacting individuals are 56
related. Home ranges are used to understand how animals use landscapes but use of 57
home ranges is not uniform and subsets of populations may exhibit different spatial 58
patterns of simultaneous use (Horner and Powell, 1990, Mohr, 1947; Powell, 2000; 59
Sanderson, 1966). Relatives, for example, may use areas of overlap more than expected 60
at random, whereas animals that are not related may intentionally avoid each other and 61
use areas of overlap less than expected (Powell, 2000). Kinship can play a role in 62
determining resource allocation and spatial organization of individuals, therefore 63
relatedness can explain patterns of overlap and interactions. In the mating system of 64
mountain lions, parental investment by males is minimal and reproductive success is 65
limited by the number of females encountered (Emlen and Oring, 1977). Dispersal by 66
subadult males reduces competition with male relatives for mating opportunities and 67
increases the probability of mating with unrelated females (Costello, et al., 2008; Logan 68
and Sweanor 2001, Waser and Jones, 1983). Traditional methods of assessing the degree 69
of philopatry involves tracking subjects from birth to adulthood or examining age 70
differences in neighboring individuals (Waser and Jones, 1983). Among mountain lions, 71
female-offspring relationships are obtainable prior to dispersal of offspring or from 72
106
female home ranges, otherwise relatedness is generally inferred from tracking as many 73
individuals as possible (Logan and Sweanor, 2001). The recent application of molecular 74
genetics to behavioral and ecological postulates provides an opportunity to examine 75
social relationships within a reduced time frame (Schenk, et al., 1998). 76
Tests require estimating densities of males and females, and evaluating their land 77
tenure system in relation to each other and to prey availability and distribution (Pierce, et 78
al., 1999; Pierce, et al., 2000). Information on land tenure is further complicated by male 79
dominance status (Harmsen, et al., 2009). Territorial behavior results from the difference 80
in how space is shared with competitors (McLoughlin, et al., 2000). Hypotheses for 81
explaining spacing behavior as a function of investment in resource and mate acquisition 82
are difficult to test on large felids in field situations because of the need for adequate 83
sample sizes. 84
Typically mountain lions demonstrate a mutual avoidance (Hornocker, 1969; 85
Seidensticker, et al., 1973) facilitated by urine, scrapes, or scratches in suitable substrates 86
(Anderson, 1983). Scrapes are a visual and olfactory marker (identifies sex), and 87
indicates direction of travel (McBride, 1976), and reflect the relative level of population 88
stability (Anderson, 1983). Scrapes are made during travel and prowling behavior rather 89
than on hunting routes (McBride, 1976) and are more frequent along edges of home 90
ranges or in regions of overlap between home ranges (Seidensticker, et al., 1973). 91
Contradictory reports of mountain lions revisiting scent marks occur in the literature, 92
McBride (1976:73) indicated repeated visits by one male all within <1 m of the last mark, 93
whereas Seidensticker et al. (1973) observed 11 of 86 sites revisited; resident males did 94
107
not revisit the scrape of another male and females would reuse scrape sites. Logan and 95
Sweanor (2001) indicated scraping occurred in areas with the highest likelyhood of being 96
ncountered and were found throughout the home range. For many carnivores, like 97
mountain lions, territory defense and responses to scent marks are difficult to document 98
(Powell, 2000). Mountain lions leave communication markers, but without monitoring 99
each mark, it is unknown when they affect other individuals. Simple measures of home 100
range overlap are not sufficient to provide insight into the extent of interaction between 101
individuals because it provides no information as to the intensity of interaction within an 102
overlap zone (OZ [Atwood and Weeks, 2003]). 103
Animal interaction analysis can be either static or dynamic (Kernohan, et al., 104
2001). Static analyses measure animal interaction throughout a time interval of interest, 105
whereas dynamic interactions compare the relationship among animals at a particular 106
point in time, thus require simultaneous or near simultaneous locations for each animal 107
(Minta, 1992). Anderson et al. (1992) compared 16 mountain lion studies and 8 108
documented home range overlap in males; very few studies attempted to quantify the 109
overlap (Anderson, et al., 1992; Hopkins, 1989; Hopkins, et al., 1986; and Laing, 1988), 110
although they occasionally provided information on dynamic interactions; male-male 111
interactions were limited in occurrences and were typically treated as anecdotal or simply 112
as exploratory in nature. Logan and Sweanor (2001), examined overlap between 4-19 113
pairs of adult males including direct associations between males. Conversely, 114
overlapping home ranges of females is common, and explanations for occurrence tend to 115
revolve around food resources (Sandell, 1989). 116
108
We quantified home range overlap and individual interactions between mountain 117
lions in Arizona. We hypothesize that temporal use of the overlap zone by either 118
individual is not significantly different from random. We also expected spatial use of the 119
OZ to differ in relation to use of the respective home range and simultaneous use and non 120
use of the OZ would differ from solitary use. We tested the hypothesis that male 121
mountain lions with high home range overlap would be more related than those that had 122
reduced overlap and that geographical distance between home range centroids was 123
correlated with the amount of genetic distance between individuals. 124
2. Study site 125
We studied mountain lions in north-central Arizona near Payson (including the 126
cities of Star Valley, Pine, and Strawberry; 34.2 ˚N 111.3˚W), Prescott (including 127
Prescott Valley, Paulden, Williams, Cottonwood, Clarkdale, and Chino Valley; 34.6˚N 128
112.5˚W), and in southern Arizona near Tucson (including Oracle, Marana, Catalina, and 129
Saddle Brook; 32.2˚N 111.0˚W). Payson and Prescott were ≥ 130 km apart, and ranged 130
between 1,280 and 1,860 m in elevation, respectively. Annual precipitation averaged 57 131
cm in Payson and 48cm in Prescott, with total annual snowfall for both areas of 62 cm 132
with average temperatures of 3-23 ˚C. Tucson was 307 km south of Payson with an 133
average annual rainfall of 30 cm and snowfall of 3.0 cm. Maximum and minimum 134
temperatures were 28 and 12.6˚C, respectively. Located in the Sonoran Desert, Tucson 135
sits within a valley, circumscribed by the Santa Catalina, Tucson, Tortolita, Rincon, and 136
the Santa Rita mountains. Elevation ranged from approximately 640 to ≥2,700 m in ≤60 137
109
km. Human population of Payson in July 2007 was 16,742, Prescott was 43,217, and 138
Tucson was 541,132 (Department of Urban Planning and Design, 2009). 139
Vegetation associations were similar for Payson and Prescott including interior 140
chaparral, pinyon (Pinus edulis)- juniper (Juniperus spp.) woodlands, grasslands and 141
mixed ponderosa pine (Pinus ponderosa) forests (Brown, 1994). Tucson contained 142
Arizona upland subdivision of Sonoran Desert vegetation and riparian and xeroriparian 143
vegetation (mixed riparian desert scrub series [Brown 1994]). The mountain ranges that 144
surrounded Tucson ascend from Sonoran desertscrub (e.g., mesquite [Prosopis juliflora], 145
paloverde [Cercidium spp.], cactus [Opuntia spp.] and various grasses) to Chihuahuan 146
semi-desert to grassland to oak (Quercus)-alligator juniper (Juniperus deppeana) 147
woodland to Petran-montane and mixed conifer forest (Brown, 1994; Whittaker and 148
Niering, 1965). On all study sites, mule deer (Odocoileus hemionus), white-tailed deer 149
(O. virginianus), collared peccary (Tayassu tajacu), black bear (Ursus americanus), 150
bobcats (Lynx rufus), and coyotes (Canis latrans) were common. Cattle and pronghorn 151
(Antilocapra americana) inhabited areas near Tucson and Prescott, elk (Cervus elephus) 152
were common in Payson and Prescott, and bighorn sheep (Ovis canadensis) inhabited the 153
Silverbell Mountains near Tucson. 154
3. Material and methods 155
Between August 2005 and February 2008, mountain lions were captured by 156
AGFD personnel using snare and hound techniques (Cougar Management Guidelines 157
Working Group, 2005; Logan, et al., 1999; Shaw, 1983). Mountain lions were 158
immobilized using Ketamine (Ketamine HCL, Wildlife Pharmaceutical, Ft. Collins, CO, 159
110
USA) and medetomidine hydrochloride (Domitor, Wildlife Pharmaceutical, Ft. Collins, 160
CO, USA). Medetomidine was reversed using antisedan (Atipamezole hydrochloride, 161
Pfizer Inc, New York, NY, USA) at a dose of 3 mg of antisedan for every 1 mg of 162
medetomidine. We determined age based on tooth wear and condition and sex for each 163
individual (Anderson and Lindzey, 2000). 164
Upon capture, lions were equipped with Spread Spectrum Satellite collars 165
(Telonics, Inc., Mesa, AZ, USA). We obtained satellite locations every 4.15 for lions 166
near Tucson, and 7 h in Payson, and Prescott. We downloaded and processed location 167
data at the end of the study in August 2008. We incorporated all locations into an 168
ArcGIS 9.x (1995-2005 Environmental Systems Research Institute, Inc., Redlands, CA, 169
USA) database for analysis of home range overlap. 170
For each unique pair (α, β) of mountain lions with overlapping home ranges, we 171
constrained data analysis to the temporal period both individuals were active. Therefore, 172
each overlapping dyad had a unique home range for each unique pairing. For each 173
matched pair, we created a 95% fixed kernel home range, (KHR; Worton, 1989) within 174
ArcGIS using Home Range Tools (HRT) v 1.1 extension (Rodgers, et al., 2007). Within 175
the HRT environment, we used a bivariate normal distribution, rescaled to unit variance, 176
and selected a 0.6 proportion of reference bandwidth to create the fixed KHR. We used 177
all locations to increase accuracy and precision of home range estimates (de Solla, et al., 178
1999). We used 95% isolpleths to assess spatial and temporal interactions and 179
simultaneous locations for each member of a pair (Minta, 1992). This method reduces 180
observations over space and time to a binomial distribution incorporating used and 181
111
expected frequencies in different areas of a home range. Zero values are replaced with 182
pseudo-Bayse estimates (Bishop, 1975) that improve the stability of the χ2 inference from 183
probability values. 184
Home range overlap could occur in 2 ways. The entire home range of β is 185
encompassed within the range of α, or the overlap could only be a portion of each lions’ 186
range. For each pair, we mapped 3 areas of the combined ranges: home range area 187
unique to α (areaA), unique to β (areaB), and area of overlap shared by both (areaAB) and 188
pairs with encompassed ranges areaAB = areaB. Four possibilities existed for each pair of 189
overlapping ranges with simultaneous locations: (1) both lions were in the overlap 190
simultaneously, (2) α alone was in the overlap while β was outside, (3) β alone was in the 191
overlap while α was outside, or (4) neither lion was in the overlap simultaneously. For 192
enclosed ranges, we determined only the number of times α occurred in areaB. Percent 193
overlap in home ranges was calculated as [(areaAB/ home range areaA) * (areaAB/ home 194
range areaB)]0.05. 195
Our sampling unit was groups of pairs that we identified using gender and age. 196
We delineated 3 groups when both individuals were male: pairs consisting of 2 solitary 197
adults, 1 adult and 1 sub-adult, or 2 sub-adults. We had no overlapping female home 198
ranges which is likely due to capture effort. Male-female pairs were grouped into adult-199
adult, female adult-male sub-adult, male adult-female sub-adult, male sub-adult-female 200
sub-adult. Due to mortality and function of collars, we could not ascertain changes in 201
spatial and temporal interactions of each unique pair over the course of the study. 202
112
We used 2 methods to determine whether the intensity and fidelity of associations 203
varied over time and to characterize the simultaneous use of shared areas. At a fine scale, 204
to measure proportion of locations in which space-sharing mountain lions were 205
associated with each other in the OZ, we calculated half-weighted association indices 206
(HAI; [Atwood and Weeks, 2003; Bromley and Gese, 2001; Brotherton, et al., 1997]) 207
with n/[n = 0.05(x +y)] where n is the number of times animals were located together 208
(within 1 km of each other) and x and y are the number of times each animal was located 209
in the OZ without the other. For animals that are always together HAI = 1, whereas those 210
never together HAI = 0. 211
Our second method used Minta’s (1992) methods that allow for testing multiple 212
hypotheses. For overlapping home ranges we tested whether α and β influenced each 213
other’s spatial use of the OZ. We defined simultaneous locations when obtained <1 hour 214
buffer from satellite acquisition. Did α and β use their respective areas as expected in 215
relation to size of OZ? Spatial relation to OZ by each individual was categorized as 216
either random, attraction, or avoidance with coefficients of interaction (LA:A for α and 217
LB:B for β [Minta, 1992]). Attraction is suggested by coefficients >0, spatial avoidance 218
by coefficients <0, and random use as coefficients approach zero (Minta, 1992). We 219
calculated coefficient probabilities (PA:A and PB:B) at α = 0.1(Minta, 1992, 1993). Spatial 220
responses were classified as symmetrical (same response by the pair), asymmetrical 221
(opposite response), or singular (only 1 individual showing significant departure from 222
use; Mace and Waller, 1997). In cases were β’s range was enclosed within α’s, we tested 223
113
the hypothesis that α used OZ in a nonrandom fashion. Symmetrical avoidance of α and 224
β was evidence of territoriality, or defense of an area (Minta, 1992). 225
We hypothesized temporal use of OZ by α or β is not significantly different from 226
random. Alpha and β’s simultaneous use and non use of OZ equal the solitary use of OZ 227
by each member of the pair (Mace and Waller, 1997). We used Minta’s (1992) 228
coefficient of temporal interaction (Lixn) which indicated whether significant interaction 229
is due to temporal attraction or temporal avoidance. When Lixn > 0, both individuals use 230
OZ simultaneously, when Lixn <0, only solitary use, when temporal use is random Lixn 231
approaches 0. We calculated departures from random expectation (odds for each of the 4 232
location possibilities). We used age difference to determine if seniority could predict the 233
spatial or temporal avoidance of overlapping areas. 234
3.2 Genetic Analysis 235
We assessed genetic relatedness among individual mountain lions using 12 felid 236
microsatellite loci. During capture, we collected 4 cc of blood or a cheek (buccal) swab 237
from mountain lions for DNA analysis. All samples were placed into lysis buffer at a 1:5 238
ratio of sample:lysis buffer (TES buffer; 100mM Tris, 100mM EDTA, 2%SDS). 239
Samples were transported to The University of Arizona where they were stored frozen at 240
-20˚ C. 241
3.3 DNA purification 242
Genomic DNA was extracted from all 30 samples using a QIAamp DNA kit, 243
following the standard blood or tissue protocol developed by Qiagen (Qiagen Inc., 244
114
Valencia, CA, USA). DNA yield from samples ranged from 0.30 to 36.67 ng/ul as 245
quantified by a fluorometer. 246
3.4 PCR amplification and genotyping 247
Samples were amplified by polymerase chain reaction (PCR; Saiki, et al., 1985) 248
using 12 felid microsatellite DNA primers. We optimized conditions for microsatellite 249
DNA primers, 6 of which were developed from the cat (Felis catus) genome (Menotti-250
Raymond et al. 2005) and 6 developed from the mountain lion genome (Culver, et al., 251
2000; Kurushima, et al., 2006; Rodzen, et al., 2007). The 6 F. catus loci used were Fca-252
43, -57, -82, -90, -96, and -166. The 6 P. concolor loci used were Pco-A2, -B105, -B010, 253
-D8, -D301, and -D329. The universal M13 primer was added at the 5’ end of the 254
forward primers to allow fluorescent labeling of the amplicons (Kurushima, et al., 2006; 255
Schuelke, 2000) and reverse primers were designed with a “pig-tail” at the 5’ end to 256
reduce varability in adenylation of amplification products (Brownstein, et al., 1996). 257
Conditions for PCR amplification were: 1X PCR buffer, 0.2microMolar each dNTP, 258
0.05% BSA (Bovine Serum Albumin), 0.1 microMolar M13 universal labeled primer, 259
0.01 microMolar M13-tailed forward primer, 0.1 microMolar reverse primer, and 0.25 260
Units of Taq Polymerase enzyme (Qiagen), in a total volume of 10 microliters. PCR 261
cycling conditions for the Fca microsatellite loci were 35 cycles of denaturation at 94˚C 262
for 30 s, annealing at 51 or 55˚ C, (depending on the primer pair), for 30 s, and extension 263
at 72˚ C for 30 s. The Pco microsatellite loci were amplified with a touchdown protocol 264
that included an initial denaturation at 95˚C for 5 min; then 2 cycles of 94˚C for 45 s, 265
62˚C for 1 min, 72˚C for 30 s; then 2 cycles of 94˚C for 45 s, 60˚C for 1 min, 72˚C for 30 266
115
s; then 2 cycles of 94˚C for 45 s, 58˚C for 1 min, 72˚C for 30 s; then 2 cycles of 94˚C for 267
45 s, 56˚C for 1 min, 72˚C for 30 s; then 27 cycles of 94˚C for 45 s, 55˚C for 1 min, 72˚C 268
for 30 s; and a 20 minute final extension at 72˚C. The M13 forward universal primer was 269
labeled with a fluorochrome (FAM, HEX, or TET). 270
We obtained microsatellite fragment sizes using an ABI 3730 Genetic Analyzer 271
(Applied Biosystems, Foster City, CA) and a GENSCAN 500-Tamra size standard. 272
Alleles were scored using software programs GENESCAN ANALYSIS (3.7) and 273
GENOTYPER version 2.1 (Applied Biosystems, Foster City, CA). Allele sizes were 274
classified into bins with FLEXIBIN (Amos, et al., 2007). We only used samples that 275
were successfully scored at ≥9 loci. We used program GenAlex 6.1 to calculate 276
observed and unbiased (corrected for small sample size) expected heterozygosities 277
(Peakall and Smouse, 2006). We used program POPGENE 1.32 to assess Hardy-278
Weinberg disequilibrium according to a probability test using the Markov chain method 279
(Yeh and Boyle, 1997). We used program Relatedness 5.0.8 to calculate relatedness 280
coefficients among and between demographically-defined groups of individuals and 281
jackknifing over loci to obtain 95% confidence intervals (Queller and Goodnight, 1989). 282
We tested whether pairs of individuals (dyads) belonged to a particular relationship 283
category by simulating 1000 pairs using the observed allele frequencies with the program 284
KINSHIP 1.12 (Goodnight and Queller, 1999). Kinship estimates were obtained using 285
Grafen’s relatedness coefficient (Grafen, 1985) between all possible pairs of individuals. 286
This coefficient measures the degree to which two individuals share identical alleles, 287
taking into account allele frequencies in the population and each individual’s genotype 288
116
(Goodnight and Queller, 1999). Loci exhibiting lower than expected heterozygosity 289
levels contribute less to the calculation of R than loci with higher than expected 290
herterozygosity. R-values range between -1 to 1. A positive R-value indicates greater 291
relatedness (i.e., they share more alleles that are identical by descent) than expected by 292
chance, and a negative value indicates lower relatedness than expected by chance. 293
Relatedness coefficients for some common relationship categories include: R = 1 294
(monozygotic twins or self parent-offspring), R = 0.5 (1st degree relatives such as parent-295
offspring or full siblings), R = 0.25 (2nd degree relatives, such as half siblings or 296
avuncular), R = 0.125 (3rd degree relatives, such as first cousins), and R = 0 (unrelated 297
[Blouin, 2003]). We compared the relatedness within study sites, between study sites, 298
and between overlapping individuals. We expected male-female overlapping ranges to be 299
less related to avoid inbreeding. We expected male-male dyads to be more related than 300
those with little to no overlap. 301
We hypothesized that the geographical distance between home range centroids 302
were correlated with genetic distance between individuals. That is, the more closely 303
related mountain lions were, the closer their home ranges would be. We used Hawth’s 304
Tools Extension (Beyer, 2004) in ArcMap to calculate the geometric center of the 95% 305
kernel home range and for those individuals that we did not collar we used initial capture 306
location. We used Alleles in Space v. 1.0 (AIS [Miller, 2005]) to analyze inter-individual 307
patterns of genetic and geographical variation. Using AIS, we performed analyses to 308
determine the correlation between genetic and geographical distances (i.e., pattern of 309
Isolation by Distance [IBD]) of observations across a landscape using Mantels test 310
117
(1967). We also used AIS to evaluate spatial autocorrelation, which is a finer resolution 311
than IBD, and determine at what dispersal distance individuals become less related. We 312
used 5 equal distance classes and unequal number of observations within each distance 313
class. For both IBD and autocorrelation analyses we used 1,000 permutations. 314
4. Results 315
Between August 2005 and March 2008, we captured 39 mountain lions. We 316
obtained genetic material from 28 individuals and radio-collared 30 individuals; 14 from 317
Tucson (4 F, 10 M), 6 from Payson (1 F, 5 M), and 10 from Prescott (3 F, 7 M). We 318
retrieved all but 1 collar (in Tucson) successfully, obtaining 30,282 relocations for 29 319
lions. We identified 26 pairs of spatially overlapping mountain lions, 18 of which 320
overlapped spatially and temporally, 8 of which had corresponding genetic information 321
(Table 1). We combined the age/sex groupings due to small samples into male-male and 322
male-female pairs regardless of age. 323
4.2 Spatial and temporal overlap 324
Home ranges overlapped from 1.18 to 46.38% (�� = 24.43, SE = 2.96: Table 1). 325
We detected no difference in amount of overlap between male-male pairs and male-326
female pairs (t1,16 = 1.04, P = 0.84). The HAI scores for the 18 pairs ranged between 327
0.002 and 0.25; male-female pair index scores were greater than those of male-male pairs 328
(U1,10 = 2.28, P = 0.022: Table 2). Male-male pairs were located within 1 km on average, 329
0.63% of the time, whereas male-female pairs, on average were 4.90% (Table 2). We 330
observed suspected breeding male-female pairs 11, 14, and 49 times within 1 km of each 331
other (Table 2). 332
118
Nine of the 18 pairs were male-male that overlapped spatially; with 1 instance of 333
an encompassed range for males (209/202) and percent overlap for all male dyads 334
averaged 23% (1.18 to 41.07: Table 1). Two male-male pairs exhibited symmetrical 335
spatial avoidance and 2 symmetrical spatial attraction to the OZ (Table 3). The 336
remaining 5 pairs had asymmetrical or singular attraction to the OZ, with 1 individual 337
having greater odds of using the OZ than the other (Table 3). Singular attraction was 338
because of the insignificance of Lixn. Three male-female pairs exhibited symmetrical 339
attraction and 2 exhibited symmetrical avoidance (Table 3). The remaining 4 pairs were 340
asymmetrical or singular attraction or avoidance to the OZ (Table 3). Seven pairs were 341
not temporally coincident in their attraction or avoidance of the OZ (Table 3). However, 342
we observed simultaneous temporal attraction in 3 male-male pairs and 4 male-female 343
pairs (Table 3). Coefficients of temporal interaction did not differ by pair types (U1,9 = -344
0.79, P = 0.42). Age difference was not useful in predicting the spatial and temporal use 345
patterns (simple linear regression χ1,17 = 1.71, P = 0.19). 346
4.3 Genetic relatedness 347
Mean proportion of individuals genotyped at each locus was 0.923. Mean number 348
of alleles/locus for all lions was 7.50 with average observed and expected 349
heterozygosities of 0.603 and 0.768 respectively. Observed heterozygosities per locus 350
ranged from 0.393-0.769. The mean number of alleles/locus differed by study site with 351
the greatest allele diversity in Prescott and the lowest in Payson (likely due to sample 352
size). The 3 localities showed a deficit of heterozygotes compared to the expected 353
values, with Tucson (the locality with the largest sample size) showing the largest deficit 354
119
(Table 4). There were no significant deviations from Hardy-Weinberg disequilibrium 355
within each study site (Tucson P = 0.92; Payson P = 0.44; Prescott P = 0.77). 356
Overall, individuals were not related (n = 28, mean R = - 0.0037). Individuals 357
from Tucson were slightly related to one another within the population (n = 13, mean R = 358
0.0373 ± 0.0151) whereas lions from Payson (n = 6, mean R = -0.0079 ± 0.0356) and 359
Prescott (n = 9, mean R = -0.0242 ± 0.0452) were not as related. Overall, males were less 360
related to other males (n = 20, mean R = -0.0495 ± 0.0161) than females were related to 361
other females (n = 8, mean R = 0.0015 ± 0.0839). Genetic distance was positively 362
correlated with geographic distance (r = 0.22, P = 0.001). As geographic distance 363
increased so did genetic distance (Fig. 1). Male and females were more related than 364
average until approximately 250 km, at which point, mountain lions were less related 365
(Fig. 2). 366
Genetic data from 28 lions produced 378 dyads of related individuals. Of those, 367
151 dyads were at least 3rd order relatives (e.g., cousins; R ≥ 0.125). Two dyads were 1st 368
order relatives (e.g., siblings or parent offspring; R ≥ 0.5< 0.25) and 19 dyads were ≤2nd 369
order relatives (step-siblings, or grandparent-grandchild; R ≥ 0.25 [Table 5]). There were 370
18 dyads of overlapping lions in space and time and we obtained genetic material to 371
determine the genetic relationships of 8 dyads (Fig. 1). However, from these 8 dyads of 372
overlapping lions none were identified as 1st or 2nd order relatives (Table 5). Genetic 373
relatedness tended to increase with percent overlap, however this was not significant (n = 374
8 dyads, r2 = 0.09, P = 0.46 [Fig. 3]). Mountain lions that did not overlap in their home 375
ranges were on average not related (mean R = -0.0375±0.0330 [Fig. 3]). 376
120
Discussion 377
Our data supported the hypothesis that geographical distance between home range 378
centroids correlated with the amount of genetic distance between individuals. Individuals 379
that were in proximity to each other were more related. Our data did not support the 380
hypothesis that mountain lions with overlapping home ranges would be more related to 381
each other. From the 8 dyads, 7 were <3rd order related and 1 at the 2nd order but with 382
insufficient confidence. We rejected the hypothesis that simultaneous use and nonuse of 383
the OZ equaled the solitary use of the zone by each individual. 384
Home range overlap and dynamic interactions between mountain lions has not 385
been previously examined in such detail; consequently, comparisons are limited. 386
Anderson, et al., (1992) provided a table of associations among radio-collared pairs of 387
mountain lions, where of the 345 simultaneous locations 19 were male-male interactions. 388
Five additional studies document male-male overlap, but none examined dynamic 389
interaction and sample sizes were ≤7 dyads (Anderson, et al., 1992). Logan and Sweanor 390
(2001) provide a table documenting 6 of 11 studies documented male-male overlap, with 391
only 3 studies using similar methods of home range delineation. Documenting male-392
male interactions was done typically by following tracks of individuals in the snow 393
(Hornocker, 1969) or by aerial telemetry with ≤1 locations/day (Laing and Lindzey, 394
1993; McBride, 1976; Padley, 1990). Males use the same areas but rarely at the same 395
time. Male lions in Idaho avoided all other lions, and social tolerance was exhibited only 396
by males and females during breeding seasons (Hornocker, 1969). Yet, other studies 397
since then have documented fighting and male-male interactions (Logan and Sweanor 398
121
2001). No mountain lion social organization study thus far has had supporting evidence 399
of genetic relatedness to explain overlap and interaction dynamics. 400
We were able to detect close (<1 km) interactions between 11 lions (Table 2). We 401
assume that dyads 304/402 and 214/103 were mated because of the high number of close 402
encounters, the HAI value, and Minta’s indication of both spatial and temporal attraction. 403
The spatial avoidance indicated by Minta’s (1992) method by 103, yet temporal attraction 404
by both, could indicate we were observing the lions over a sufficient time span where the 405
female came into estrus, mated with the male and then moved back into her home range. 406
If we were to only use the HAI to interpret dyad 308/407 we could also conclude a mated 407
pair, however Minta’s (1992) methods indicate avoidance where 407 is more likely to use 408
the overlapping area. This pair is not highly related (R = 0.02) and they are 409
approximately the same age (308 = 7; 407 >8). Individuals in this pair were captured 410
within 1 day of each other and monitored for >1.5 years. At capture, she did not have 411
visible signs of lactation or estrus and her canines were worn to nubs. We do not know if 412
she had offspring in the area or if she was unable to continue to produce offspring. 413
Therefore, we suggest using the HAI in conjunction with additional analyses. The HAI, 414
though a finer index than Minta’s methods, does not account for other forms of 415
communication. The HAI typically is calculated with distances visible between the 416
individuals (Atwood and Weeks, 2003; Bromley and Gese, 2001; Brotherton, et al., 417
1997). We chose 1 km because mountain lions can easily traverse 1 km in <4 hours, yet 418
is still within a potential visual range. Additionally, if we had increased the time frame of 419
simultaneous locations from the 1 hr buffer to how often, individuals came within 1 km 420
122
in 24 hrs; the number of interactions would increase changing the HAI scores. Increasing 421
the distance radius or time frame can begin to assess scent or auditory communication; 422
however, we still need a better understanding of scent communication (i.e., duration, 423
penetration, and distance of scents). Minta’s (1992) method attempts to account for this 424
by using the area of overlap, but you lose the finer interactions which can be valuable for 425
elusive species like mountain lions is lost. 426
Spatial avoidance or attraction varied across dyads of mountain lions. Minta’s 427
(1992) coefficients of spatial and temporal interactions allowed us to examine lion use of 428
space, specifically in relation to male-male interactions. However, we were unable to 429
assess and quantify simultaneous interactions among >2 individuals even though such 430
overlap occurred. This could explain the lack of overall pattern of avoidance or attraction 431
between lions. For instance, female 402 avoided the overlap area with male 310. There 432
are several possibilities for this to occur, such as evidence 402 was attracted to male 304 433
(Table 3) and was found most often with 304 (Table 2). Female 402 also overlapped 434
with male 303, and both 303 and 304 were adult established males, whereas 310 was a 435
younger male that could have had a lower status. Another possibility is female 402 was 436
related to 310, as they were both approximately 4 years old, and they were avoiding 437
inbreeding. We expect male-female pairs to display all potential interactions. An 438
unexpected finding was the male-male interactions where males were attracted to each 439
other both temporally and spatially. 440
We observed 2 male-male dyads where individuals were attracted to each other, 3 441
instances of asymmetrical use, and 1 instance of mutual avoidance (Table 3). In male 442
123
dyads with overlap and attraction, or mutual use, we would expect relatedness to be high. 443
In these incidents, 306/310 are related, however we only have certainty at 3rd order (R = 444
0.33; P = 0.01). We aged male 306 at approximately 5 years and 310 at 3 to 4. Due to 445
age, we could conclude they are potentially cousins, 1/2-siblings, or a grandparend-446
grandoffspring pair. Another explanation for the attraction interaction is a product of 447
data collection. With close inspection of locations, the collar function of 306 was 448
inconsistent combined with a relatively small number of interactions during a short time 449
period (≤3 months). Statistically this can still be significant, however biologically it may 450
prove incorrect. Without genetic information from 209, we do not know the relatedness 451
with 202, but both were 3 to 5 years old. When we examine their locations and complete 452
movements through the system, 209 remained in the same area as 202 for ≤2 months and 453
then proceeded to move northeast without return to the area of overlap. We continued to 454
monitor both individuals another 4 months. During that overlap time, we recorded 1 455
occurrence where they were within 245 m of each other at the same time (Table 2). After 456
this encounter, they continued to occupy the same area. The males staying in proximity 457
could be due to competition for a female in estrus or territory. Dyads 204/202, 303/304, 458
303/306 all had asymmetrical attraction or interactions. This is 1 individual using the 459
area more than the other, which could be interpreted as a territorial individual. None of 460
these individuals were highly related (R = 0.12, 0.02, -0.02 respectively) and mutual 461
avoidance occurred between 1 dyad (210/211) who were not related at all (R = 0.08). 462
Mountain lions appear to be mating randomly as they conform to Hardy-463
Weinberg equilibrium. We postulated that landscape features of large expanses of flat 464
124
open spaces were impeding emigration and immigration. Mountain lions select for 465
forested and woody cover with aversion to flat open areas (Koehler and Hornocker, 1991; 466
Logan and Irwin, 1985; McRae, et al., 2005; Nicholson, et al., 2009; Riley and Malecki, 467
2001). Tucson is surrounded by large expanses of flat open areas that could be a 468
deterrent to movement; however our data indicates that genetic material is moving 469
between all locations (e.g., 304 from Tucson is related to 211 Payson [Table 5]). 470
Additionally, we tracked male 310 from the Tucson area ≥125 km north beyond Globe, 471
Arizona. There is 1 link from our data of a Tucson lion as 2nd order related to an 472
individual in Prescott (301/104 [Table 5]). 473
The extent of overlap and interactions can be influenced by demography, habitat 474
condition, and population size. Pierce et al. (2000) suggested mountain lions are 475
regulated spatially by supply of food rather than territoriality like Hornocker (1969:464), 476
who stated, “territoriality appears to be extremely important in regulating numbers”. 477
Overall, lions in Arizona are solitary and there is evidence to support mutual avoidance in 478
their distribution of home ranges (Hornocker, 1969). Mountain lions occupied territories 479
that were relatively exclusive and were able to distribute themselves in both space and 480
time. 481
Home range overlap does not account for the temporal order of movements and 482
therefore reveal little about animal interactions. Tolerance shown between overlapping 483
males could indicate that mountain lions can identify related individuals. The low 484
genetic variability in mountain lions in Tucson could indicate a lower territoriality social 485
structure because of clustered or patchy resources and therefore increase the likelihood of 486
125
overlap of related and non-related individuals. Territoriality does not appear to be as rigid 487
or limited in occurrences for lions in Arizona. 488
Spatial and temporal interactions among individuals have direct application to 489
habitat management of mountain lions. Spatial requirements and interactions address 490
social behavior (Millspaugh and Marzluff, 2001; Powell, 2000; White and Garrott, 1990), 491
and ultimately population density (Hornocker, 1970; Pierce, et al., 2000). The 492
demography, habitat condition, and population size can also influence the extent of 493
overlap and interactions (Sanderson, 1966, Mace and Waller, 1997). Understanding the 494
spatial organization of mountain lions in Arizona should help managers frame realistic 495
population management goals based on habitat condition and ecosystem size. As 496
technology to monitor animals improves and biologists are able to monitor mountain lion 497
movements at greater frequencies than 1 location/day or week, the definitive line of strict 498
territories is changing (Harmsen, et al., 2009; Pierce, et al., 2000). Males and females are 499
possibly responding to clumped distributions of prey, and dominance hierarchies may be 500
expressed in other ways than exclusive territories (Ashman, et al., 1983; Harmsen, et al., 501
2009). 502
Acknowledgments 503
We thank the hounds men, trappers, and volunteers that helped track mountain 504
lions including R. Thompson, T. and A. Salazar, B. Buckley, N. Smith, T. and A. 505
Anderson, R. Murphy, T. McNealy, B. Jansen, L. Haynes, C. Dolan and B. Kluver. We 506
thank Arizona Game and Fish personnel T. Smith, B. Waddell, and all of the aerial 507
surveyors and pilots. We thank J. deVos and C. O’Brien with Arizona Game and Fish 508
126
Department and C. Yde for administration of the contract. We thank M. Borgstrom for 509
statistical consulting and S. Minta for instructions and long discussions of his technique. 510
We thank M. Culver, T. Edwards, A. Naidu, A. Munguia-Vega, and K. Munroe for 511
genetic analysis and consultation. We thank the Advanced Resource Technologies lab at 512
the University of Arizona especially R. Gimblett, C. Wissler, P. Guertin, and A. 513
Honaman. We thank S. Nicholson for some data analysis and programming. Funding 514
was provided by Arizona Game and Fish Department and the University of Arizona. 515
Capture and handling procedures were approved by the Animal Care and Use Committee 516
at the University of Arizona (protocol #05-184). 517
Literature Cited 518
Amos, W., Hoffman, J.I., Frodsham, A., Zhang, L., Best, S., Hill, A.V.S., 2007. 519
Automated binning of microsatellite alleles: problems and solutions. Molecular 520
Ecology Notes 7, 10–14. 521
Anderson, A.E., 1983. A critical review of literature on puma (Felis concolor). Colorado 522
Division of Wildlife. Report Special Report 54. Denver, CO, USA. 523
Anderson, A.E., Bowden, D.C., Kattner, M.M., 1992. The puma on the Uncompahgre 524
Plateau, Colorado. Colorado Division of Wildlife Technical Publication 40. Fort 525
Collins, CO, USA. 526
Anderson, C.R., Jr., Lindzey, F.G., 2000. A photographic guide to estimating mountain 527
lion age classes. Wyoming Cooperative Fish and Wildlife Research Unit. 528
Laramie, WY. 529
127
Ashman, D.L., Christensen, G.C., Hess, M.L., Tsukamoto, G.K., Wickersham, M.S., 530
1983. The mountain lion in Nevada. Nevada Department of Wildlife, Carson City, 531
NV, USA. 532
Atwood, T.C., Weeks, H.P.J., 2003. Spatial home-range overlap and temporal interaction 533
in eastern coyotes: the influence of pair types and fragmentation. Can J Zool 81, 534
1589-1597. 535
Beyer, H.L., 2004. Hawth's Analysis Tools for ArcGIS. 536
<http://www.spatialecology.com/htools>. Accessed 12 December 2008. 537
Bishop, Y.M., S. E. Fienberg, and P. W. Holland, 1975. Discrete multivariate analysis: 538
theory and practice. Massachusetts Institute of Technology Press, Cambridge, 539
MA, USA. 540
Blouin, M.S., 2003. DNA-based methods for pedigree reconstruction and kinship 541
analysis in natural populations. Trends in Ecology and Evolution 18, 503-511. 542
Bromley, C., Gese, E.M., 2001. Effects of sterilization on territory fidelity and 543
maintenance, pair bonds, and survival rates of free-ranging coyotes. Can J Zool 544
79, 386-392. 545
Brotherton, P.N.M., Pemberton, J.M., Komers, P.E., Malarky, G., 1997. Genetic and 546
behavioural evidence of monogamy in a mammal, Kirk's dik-dik (Madoqua 547
kirkii). Proceedings of the Royal Society of London Biological Sciences 264, 675-548
681. 549
Brown, D.E., (ed) (1994) Biotic communities southwestern United States and 550
northwestern Mexico. University of Utah Press, Salt Lake City, USA. 551
128
Brownstein, M.J., Carpten, D., Smith, J.R., 1996. Modulation of non-templated 552
nucleotide addition by Taq DNA polymerase: primer modifications that facilitate 553
genotyping. Biotechniques 20, 1004–1010. 554
Clutton-Brock, T.H., Harvey, P.H., 1978. Mammals, resources and reproductive 555
strategies. Nature 273. 556
Costello, C.M., Creel, S.R., Kalinowski, S.T., Vu, N.V., Quigley, H.B., 2008. Sex-biased 557
natal dispersal and inbreeding avoidance in American black bears as revealed by 558
spatial genetic analyses. Molecular Ecology 17, 4713-4723. 559
Cougar Management Guidelines Working Group, 2005. Cougar management guidelines. 560
First edition. Opal Creek Press, LLC, Salem, OR, USA. 561
Culver, M., Johnson, W.E., Pecon-Slattery, J., O'Brien, S.J., 2000. Genomic ancestry of 562
the American puma (Puma concolor). J Hered 91, 186-197. 563
de Solla, S.R., Bonduriansky, R., Brooks, R.J., 1999. Eliminating autocorrelation reduces 564
biological relevance of home range estimates. Journal of Animal Ecology 68, 565
221-234. 566
Department of Urban Planning and Design, 2009. Department of urban planning and 567
design. <http://www.tucsonaz.gov/planning/data/demographic>. Accessed 3 Feb 568
2009. 569
Emlen, S.T., Oring, L.W., 1977. Ecology, sexual selection, and the evolution of mating 570
systems. Science 197, 215-223. 571
Ewer, R.F., 1973. The Carnivores. Cornell University Press, Ithaca, N.Y. USA. 572
129
Goodnight, K.F., Queller, D.C., 1999. Computer software for performing likelihood tests 573
of pedigree relationship using genetic markers. Molecular Ecology 8, 1231-1234. 574
Grafen, A., 1985. A geometric review of relatedness. Oxford Surveys in Evolutionary 575
Biology 2, 28-89. 576
Harmsen, B.J., Foster, R.J., Silver, S.C., Ostro, L.E.T., Doncaster, C.P., 2009. Spatial and 577
temporal interactions of sympatric jaguars (Panthera onca) and pumas (Puma 578
concolor) in a neotropical forest. J Mammal 90, 612-620. 579
Hemker, T.P., Lindzey, F.G., Ackerman, B.B., 1984. Population characteristics and 580
movement patterns of cougars in southern Utah. J. Wildl Manage 48, 1275-1284. 581
Hopkins, R.A., 1989. Ecology of the puma in the Diablo Range, California. Ph.D. 582
Dissertation, University of California at Berkeley, Berkeley, CA, USA. 583
Hopkins, R.A., Kutilek, M.J., Shreve, G.L., 1986. Density and home range characteristics 584
of mountain lions in the Diablo Range of California. In: Miller, S.D., Everett, 585
D.D., (Eds). Cats of the world. National Wildlife Federation, Washington, D.C., 586
USA, pp 223-235. 587
Horner, M.A., Powell, R.A., 1990. Internal structure of home ranges of black bears and 588
analyses of home-range overlap. J Mammal 71, 402-410. 589
Hornocker, M.G., 1969. Winter territoriality in mountain lions. J. Wildl Manage 33, 457-590
464. 591
Hornocker, M.G., 1970. An analysis of mountain lion predation upon mule deer and elk 592
in the Idaho Primitive Area. Wildl Monogr 21. 593
130
Hornocker, M.G., Messick, J.P., Melquist, W.E., 1983. Spatial strategies in three species 594
of Mustelidae. Acta Zool Fenn 174, 185-188. 595
Kernohan, B.J., R. A. Gitzen, Millspaugh, J.J., 2001. Analysis of animal space use and 596
movements. In: Millspaugh, J.J., Marzluff, J.M., (Eds). Radio tracking and animal 597
populations. Academic Press, San Diego, pp 125-166. 598
Koehler, G.M., Hornocker, M.G., 1991. Seasonal resource use among mountain lions, 599
bobcats, and coyotes. J Mammal 72, 391-396. 600
Kurushima, J.D., Collins, J.A., Well, J.A., Ernest, H.B., 2006. Development of 21 601
microsatellite loci for puma (Puma concolor) ecology and forensics. Molecular 602
Ecology Notes 6, 1260-1262. 603
Laing, S.P., 1988. Cougar habitat selection and spatial use patterns in southern Utah. 604
M.S. Thesis, University of Wyoming, Laramie, WY, USA. 605
Laing, S.P., Lindzey, F.G., 1993. Patterns of replacement of resident cougars in southern 606
Utah. J Mammal 74, 1056-1058. 607
Logan, K.A., Irwin, L.L., 1985. Mountain lion habitats in the big horn mountains, 608
Wyoming. Wildl Soc Bull 13, 257-262. 609
Logan, K.A., Sweanor, L.L., 2001. Desert puma: evolutionary ecology and conservation 610
of an endangered carnivore. Island Press, Washington, D.C., USA. 611
Logan, K.A., Sweanor, L.L., Smith, J.F., Hornocker, M.G., 1999. Capturing pumas with 612
foot-hold snares. Wildl Soc Bull 27, 201-208. 613
Lott, D.F., 1984. Intraspecific variation in the social systems of wild vertebrates. 614
Behaviour 88, 266-325. 615
131
Mace, R.D., Waller, J.S., 1997. Spatial and temporal interaction of male and female 616
grizzly bears in northwestern Montana. J. Wildl Manage 61, 39-52. 617
Mantel, N., 1967. The detection of disease clustering and a generalized regression 618
approach. Cancer Res 27, 209-220. 619
McBride, R.T., 1976. The status and ecology of the mountain lion (Felis concolor 620
stanleyana) of the Texas-Mexico border. Thesis, Sul Ross State University, 621
Alpine, TX, USA. 622
McLoughlin, P.D., Ferguson, S.H., Messier, F., 2000. Intraspecific variation in home 623
range overlap with habitat quality: A comparison among brown bear populations. 624
Evolutionary Ecology 14, 39-60. 625
McRae, B.H., Beier, P., Dewald, L.E., Huynh, L.Y., Keim, P., 2005. Habitat barriers 626
limit gene flow and illuminate historical events in a wide-ranging carnivore, the 627
American puma. Molecular Ecology 14, 1965-1977. 628
Miller, M.P., 2005. Alleles In Space (AIS): Computer software for the joint analysis of 629
interindividual spatial and genetic information. J Hered 96, 722-724. 630
Millspaugh, J.J., Marzluff, J.M., (eds) (2001) Radio tracking and animal populations. 631
Academic Press, San Diego, CA, USA. 632
Minta, S.C., 1992. Tests of spatial and temporal interaction among animals. Ecol Appl 2, 633
178-188. 634
Minta, S.C., 1993. Sexual differences in spatio-temporal interaction among badgers. 635
Oecologia 96, 402-409. 636
132
Mohr, C.O., 1947. Table of equivalent populations of North American small mammals. 637
Am Midl Nat 37, 223-249. 638
Nicholson, K.L., Krausman, P.R., Smith, T., Ballard, W.B., McKinney, T., 2009. 639
Mountain lion habitat selection in Arizona. J. Wildl Manage, submitted. 640
Padley, W.D., 1990. Home ranges and social interactions of mountain lion (Felis 641
concolor) in the Santa Ana Mountains, California. Thesis, California State 642
Polytechnic University, Pomona, CA, USA. 643
Peakall, R., Smouse, P.E., 2006. GENALEX 6: genetic analysis in Excel. Population 644
genetic software for teaching and research. Molecular Ecology Notes 6, 288-295. 645
Pierce, B.M., Bleich, V.C., Wehausen, J.D., Bowyer, R.T., 1999. Migratory patterns of 646
mountain lions: Implications for social regulation and conservation. J Mammal 647
80, 986-992. 648
Pierce, B.M., Vernon, C.B., Bowyer, R.T., 2000. Social organization of mountain lions: 649
Does a land-tenure system regulate population size? Ecology 81, 1533-1543. 650
Powell, R.A., 2000. Animal home ranges and territories and home range estimators. In: 651
Boitani, L., Fuller, T.K., (Eds). Research Techniques in Animal Ecology 652
Controversies and Consequences. Columbia University Press, New York, NY, 653
USA, pp 65-110. 654
Queller, D.C., Goodnight, K.F., 1989. Estimating relatedness using genetic markers. 655
Evolution 43, 258-275. 656
Riley, S.J., Malecki, R.A., 2001. A landscape analysis of cougar distribution and 657
abundance in Montana, USA. Environmental Management 28, 317-323. 658
133
Rodgers, A.R., Carr, A.P., Beyer, H.L., Smith, L., Kie, J.G. 2007. HRT: Home Range 659
Tools for ArcGIS. Ontario Ministry of Natural Resources, Centre for Northern 660
Forest Ecosystem Research. Thunder Bay, Ontario, Canada. 661
Rodzen, J., Banks, J., Meredith, E., Jones, K., 2007. Characterization of 37 microsatellite 662
loci in mountain lions (Puma concolor) for use in forensic and population 663
applications. Conservation Genetics 8, 1239-1241. 664
Saiki, R.K., Scharf, S., Faloona, F., Mullis, K.B., Horn, G.T., Erlich, H.A., Arnheim, N., 665
1985. Enzymatic amplification of β-globin genomic sequences and restriction site 666
analysis for diagnosis of sickle cell anemia. Science 230, 1350-1354. 667
Sandell, M., 1989. The mating tactics and spacing patterns of solitary carnivores. In: 668
Gittleman, J., (Ed). Carnivore behavior, ecology and evolution. Comstock 669
Publishing Associates, Ithaca, NY, USA, pp 164-182. 670
Sanderson, G.C., 1966. The study of mammal movements: a review. The Journal of 671
Wildlife Management 30, 215-235. 672
Schenk, A., M. E. Obbard, Kovacs, K.M., 1998. Genetic relatedness and home-range 673
overlap among female black bears (Ursus americanus) in northern Ontario, 674
Canada. Can J Zool 76, 1511-1519. 675
Schuelke, M., 2000. An economic method for the fluorescent labeling of PCR fragments. 676
Nat Biotechnol 18, 233-234. 677
Seidensticker, J.C.J., Hornocker, M.G., Wiles, W.V., Messick, J.P., 1973. Mountain lion 678
social organization in the Idaho primitive area. Wildl Monogr 35, 1-60. 679
134
Shaw, H.G., 1983. Mountain lion field guide. Arizona Game and Fish Department. 680
Special Report Number 9. Phoenix, AZ, USA. 681
Sunquist, M.E., Sunquist, F., 2002. The essence of cats. In: Sunquist, M.E., Sunquist, F., 682
(Eds). Wild cats of the world. University of Chicago Press, Chicago, IL, USA, pp 683
11-13. 684
Waser, P.M., Jones, W.T., 1983. Natal philopatry among solitary mammals. The 685
Quarterly Review of Biology 58, 355-390. 686
White, G.C., Garrott, R.A., 1990. Analysis of wildlife radio-tracking data. Academic 687
Press, Inc., San Diego, CA, USA. 688
Whittaker, R.H., Niering, W.A., 1965. Vegetation of the Santa Catalina Mountains, 689
Arizona: a gradient analysis of the south slope. Ecology 46, 429-452. 690
Worton, B.J., 1989. Kernel methods for estimating the utilization distribution in home 691
range studies. Ecology 70, 164-168. 692
Yeh, F.C., Boyle, T.J.B., 1997. Population genetic analysis of co-dominant and dominant 693
markers and quantitative traits. Belgian Journal of Botany 129, 157.694
135
Table 1. Dyads of individuals (α and β) with corresponding gender and age, number of 695
simultaneous locations, 95% kernel home ranges (areaα and areaβ), area of overlap 696
(areaαβ), and percent overlap for mountain lions in Payson, Prescott, and Tucson Arizona 697
2005-2008. 698
# simultaneous 95% home range (km) % overlap in home 699
Dyad typea α β locations areaα areaβ areaαβ range 700
Male-male 701
SA/SA 204b 202 1,562 256.29 897.362 109.07 22.74 702
SA/SA 204 209 1,824 522 2,372 150.433 13.52 703
SA/SA 209 202 1,528 2440 200.45 200.45 28.66 704
SA/SA 306b 310 110 164.778 35.87 24.92 32.41 705
SA/A 209 212 699 1392.61 550.347 10.36 1.18 706
SA/A 211b 210 436 575.084 317.185 163.78 38.35 707
A/SA 303b 306 528 149.68 590.58 22.74 7.65 708
A/A 207 203 246 527.37 896.85 282.48 41.07 709
A/A 303b 304 386 147.985 156.77 33.264 21.84 710
Male-female 711
SA/FSA 310 402 1,312 4925.19 45.045 44.77 9.51 712
A/FSA 303 402 2,486 153.411 52.88 12.52 13.90 713
A/FSA 304 402 1,404 156.79 33.733 33.733 46.38 714
SA/FA 204 103 1,546 870 283.26 94.96 19.13 715
SA/FA 214 103 334 1,019.457 147.14 89.019 22.98 716
136
Table 1. continued 717
SA/FA 214b 104 1,150 1,049 95.114 89.133 28.22 718
SA/FA 302b 409 56 206.11 26.069 26.069 35.56 719
SA/FA 306 411 548 1,449.56 121.92 81.66 19.42 720
A/FA 308b 407 2,540 190.4438 82.73 46.855 37.33 721
aSA = male sub adult, A = male adult, FSA = female sub adult, FA = female adult. 722
bMountain lion dyads with corresponding genetic information. 723
724
137
Table 2. Half-weight association index scores for mountain lion dyads with the minimum 725
distance and count of how often mountain lion locations were within 1 km of each other 726
in Payson, Prescott, and Tucson Arizona, 2005-2008. 727
Count Minimum 728
Sex/age α β ≤ 1 km distance (m) HAI HAI (average) 729
Male-male 0.006 730
SA/SAa 204 202 1 288.61 0.002 731
SA/SA 209 202 1 245.69 0.001 732
SA/SA 204 209 3 137.85 0.006 733
SA/SA 306 310 2 437.13 0.008 734
A/A 207 203 1 775.38 0.019 735
A/A 303 304 1 575.71 0.002 736
Male-female 0.049 737
SA/FAb 214 104 5 101.98 0.009 738
SA/FA 214 103 11 6.17 0.093 739
SA/FA 302 409 1 842.17 0.043 740
A/FA 308 407 14 21.49 0.014 741
A/FSA 304 402 49 6.92 0.086 742
aA = adult, SA = Sub-adult 743
bFA = female adult, FSA = female sub-adult 744
138
Table 3. Coefficients of spatial (LA:A and LB:B) and temporal association (Lixn) and type 745
of interaction of mountain lions in Tucson, Payson, and Prescott Arizona, 2005-2008. 746
Dyada α β LA:A LB:B Lixn Spatial response Temporal 747
M/M 204 202 -2.54 1.55 -1.49 Attraction by 202 202 more likely to use 748
204 209 -1.16 1.11 0.09b Attraction by 209 Both attracted 749
207 203 0.17b 0.47 -0.38 Attraction by 203 203 more likely to use 750
303 304 0.37 -0.09b -0.21b Attraction by 304 751
303 306 0.44 -0.96 -0.73b Attraction by 303 752
306 310 2.02 4.38 2.01 Attraction Both attracted 753
209 202 1.06 5.56 1.04 Attraction Both attracted 754
209 212 -6.56 -6.57 5.89b Avoidance 755
210 211 0.36 0.08b -0.04b Avoidance 756
M/F 303 402 0.46 -0.54 -0.19 Attraction by 303 303 more likely to use 757
214 104 -0.02b 1.06 0.32b Attraction by 104 758
302 409 0.62 5.99 0.63 Attraction Both attracted 759
304 402 1.24 4.27 1.27 Attraction Both attracted 760
306 411 1.65 4.62 1.65 Attraction Both attracted 761
308 407 -0.6 0.36 -0.23 Avoidance 407 more likely to use 762
214 103 0.10b -0.9 1.07 Avoidance by 103 Both attracted 763
310 402 0.30b -0.82 1.29b Avoidance by 402 764
204 103 -0.49 -0.4 -0.25b Avoidance 765
a M = Male, F = Female. 766
139
bNot significant at 0.1. 767
140
Table 4. Mean observed and expected heterozygosities for microsatellite loci of 768
mountain lions from Prescott, Payson, and Tucson Arizona, 2005-2008. 769
Location N Na Ho UHe 770
Prescott 9 Mean 6.000 0.651 0.786 771
SE 0.603 0.040 0.028 772
Payson 6 Mean 4.250 0.624 0.740 773
SE 0.279 0.073 0.038 774
Tucson 13 Mean 5.250 0.561 0.704 775
SE 0.351 0.035 0.023 776
Na = No. different alleles 777
Ho = Observed heterozygosity = No. hets/N. 778
UHe = Unbiased Expected Heterozygosity = (2N / (2N-1)) * He. 779
141
Table 5. First and 2nd order related mountain lion dyads with age and significance from 780
Payson, Prescott, and Tucson, Arizona, 2005-2008. 781
Lion Lion Order of P 782
Id Location Age Id Location Age Relatedness relatedness significance 783
304 Tucson 6 to7 308 Tucson 6 to 7 0.63 1st 0.05 784
304 Tucson 6 to7 211 Payson 4 0.51 1st 0.01 785
306 Tucson 3 to 5 302 Tucson 3 to 5 0.51 2nd 0.01 786
301 Tucson 3 to 5 407 Tucson >8 0.42 2nd 0.05 787
301 Tucson 3 to 5 403 Tucson ≤2 0.39 2nd 0.05 788
407 Tucson >8 102 Payson ≤2 0.38 2nd 0.01 789
407 Tucson >8 302 Tucson 3 to 5 0.38 2nd 0.05 790
101 Payson 3 to 5 201 Payson 3 to 5 0.37 2nd 0.05 791
301 Tucson 3 to 5 104 Prescott 6 to 7 0.36 2nd 0.01 792
101 Payson 3 to 5 204 Prescott 2 0.36 2nd 0.05 793
301 Tucson 3 to 5 306 Tucson 3 to 5 0.35 2nd 0.01 794
102 Payson ≤2 101 Payson 3 to 5 0.35 2nd 0.05 795
409 Tucson >9 210 Payson >9 0.34 2nd 0.05 796
303 Tucson 5 to 6 101 Payson 3 to 5 0.34 2nd 0.01 797
101 Payson 3 to 5 104 Prescott 6 to 7 0.34 2nd 0.05 798
102 Payson ≤2 201 Payson 3 to 5 0.33 2nd 0.05 799
313 Tucson 6 to 7 306 Tucson 3 to 5 0.31 2nd 0.05 800
999 Payson ≤2 207 Prescott 6 to 7 0.3 2nd 0.01 801
142
Table 5. continued 802
205 Prescott ≤2 201 Payson 3 to 5 0.3 2nd 0.05 803
306 Tucson 3 to 5 210 Payson >9 0.24 2nd 0.05 804
302 Tucson 3 to 5 210 Payson >9 0.24 2nd 0.05 805
999 = unmarked yearling near Cottonwood AZ 806
807
143
r2 = 0.21, P = 0.0003
Geographic distance (km)
0 100 200 300 400 500
Gen
etic
dis
tanc
e
0.2
0.4
0.6
0.8
1.0214, 104
202, 204
303, 306
303, 304
302, 409210, 211308, 407
306, 310
808 Figure 1. 809
144
r2 = 0.09, P = 0.46
% home ranges overlap
10 20 30 40
Rel
aten
ess
(R)
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
810 811 Figure 2. 812
145
Mean = 0.67
Geographical distance (km)
50 100 150 200 250 300 350 400 450
Ay
0.45
0.50
0.55
0.60
0.65
0.70
0.75
813 Figure 3. 814
146
Figure 1. Geographical versus genetic distance of mountain lions in Arizona, 2005-2008. 815
Large dark grey circles are labeled from the 8 dyads with overlapping home 816
ranges. 817
Figure 2. Spatial autocorrelation analysis of genetic distance calculated from Alleles in 818
Space (Ay) versus geographical distance (km) between mountain lions from 819
Payson, Prescott, and Tucson, Arizona. Dashed line indicates mean. 820
Figure 3. Genetic relatedness calculated from program Relatedness of 8 mountain lion 821
dyads with overlapping home ranges from Payson, Prescott and Tucson, Arizona 822
2005-2008. Mean relatedness of lions that did not overlap in home range 823
distribution is the dashed line with 95% confidence interval. 824
147
APPENDIX D. SEROSURVEY OF MOUNTAIN LIONS IN SOUTHERN ARIZONA.
Submitted to the Journal of Wildlife Diseases: Nicholson, K. L., P.R. Krausman, and T.
Noon.
148
Short Communications
Serosurvey of Mountain Lions in Southern Arizona.
KERRY L. NICHOLSON1, School of Natural Resources, University of Arizona, Tucson,
AZ 85721, USA
TED H. NOON2, Arizona Veterinary Diagnostic Laboratory, 2831 N. Freeway, Tucson,
Arizona 85705, USA (retired).
PAUL R. KRAUSMAN, Wildlife Biology Program, University of Montana, Missoula,
MT, 59812, USA
1Contact email: [email protected]
2Current address: Arizona Department of Agriculture, Animal Services Division, Office
of the State Veterinarian, 1688 West Adams, Phoenix, AZ 85007.
149
ABSTRACT: Our understanding of disease ecology in felid populations is expanding;
but is still limited in free-ranging species and in regions such as Arizona. We collected
serum samples from nine radio-collared mountain lions (Puma concolor) in the
mountains surrounding Tucson, Arizona (32.189N -110.881E) from January 2006 to
March 2007. We tested serum samples for evidence of exposure to eight common feline
viruses, Canine Distemper Virus (CDV), and Toxoplasma gondii. The highest
incidences of exposure were: T. gondii (8/9), Feline Panleukopenia Virus (FPLV [7/9])
and Feline Calicivirus (FCV [6/9]). One male was seropositive for CDV, T. gondii and
FPLV.
Key words: canine distemper virus, carnivore, feline enteric corona virus, feline
calicivirus, feline panleukopenia virus, Puma concolor, Toxoplasma gondii.
When populations become isolated because of increased fragmentation and loss of
suitable habitat, chances of disease epizootics increase (Roelke et al., 1993).
Additionally, human-induced effects can be a source of mortality for carnivores or a
transmission zone for disease from domestic animals into the carnivore population (Deem
et al., 2001). Several studies examined strictly urban carnivores, particularly those that
are hosts for zoonotic diseases: (e.g. coyote [Canis latrans] Grinder and Krausman,
2001); raccoons ([Procyon lotor], Junge et al., 2007), and hooded skunk ([Mephitis
macroura], Hass and Dragoo, 2006). A few researchers have examined disease exposure
in an urban-rural gradient (e.g., red foxes [Vulpes vulpes], Truyen et al., 1998; bobcats
[Lynx rufus], and gray foxes [Urocyon cinereoargenteus] Riley et al. 2004). Studies of
the urban effects of disease transmission are lacking for mountain lions. This is of
150
particular concern for areas that have high rates of urban expansion in Arizona. Arizona
is the fifth fastest growing state in the USA (U.S. census bureau http://www.census.gov).
Mountain lions inhabiting fragmented landscapes have an increased likelihood of contact
with other species and domesticated animals, and therefore an increased prevalence of
exposure (Riley et al., 2004). As a solitary carnivore, mountain lion intra-species
interactions are typically limited to territorial fights, mating, and family groups (Logan
and Sweanor, 2001). The long distance dispersal capabilities of this species (Launder,
2007) increase opportunities for contact between domesticated and wild animals and can
lead to political and sociological issues outside the realm of biology (Krausman, 2002).
As part of a larger study (Nicholson 2009), serologic data were collected from nine
mountain lions in the mountain ranges surrounding Tucson, Arizona (32.189N -
110.881E). The prevalence of disease in wild populations of mountain lions in the
southwestern USA has not been thoroughly examined, especially related to infection and
transmission between mountain lions surrounding urban areas. Our serosurvey of the
lion population was conducted to establish baseline knowledge of enzootic pathogens at
the urban-wild land interface in southern Arizona.
During 2006-2007, we captured 15 lions using hounds or leg-hold snares and
immobilized using Ketamine (Ketamine HCL, Wildlife Pharmaceutical, Ft. Collins,
Colorado, USA) and medetomidine hydrochloride (Domitor, Wildlife Pharmaceutical, Ft.
Collins, Colorado, USA). Medetomidine was reversed using antisedan (Atipamezole
hydrochloride, Pfizer Inc., New York, New York, USA) at a dose of 3mg of atipamezole
for every 1mg of medetomidine. Immediately after sedation, we fitted each mountain
151
lion with a Spread Spectrum satellite collar (Telonics, Mesa, AZ, USA). We collected
blood from nine individuals via saphenous venipuncture into serum separator tubes and
centrifuged (15min @ approximately 3,000 rpm). We froze the serum samples until
tested.
We separated serum from the clotted, centrifuged blood and tested for antibody or
antigens to eight feline viruses, Canine Distemper Virus (CDV), and T. gondii. The
Animal Health Diagnostic Center, College of Veterinary Medicine, Cornell University,
Ithaca, New York, USA performed the serology (accessions #93055-06 and #88136-07).
The following testing procedures were used: Serum Neutralization (SN; [Canine
Distemper Virus - CDV, Feline Calicivirus - FCV, Feline Herpesvirus - FHV, Feline
Enteric Coronavirus-FECV, FSyV/FFV-Feline Syncytial Virus/Feline Foamy Virus]);
kinetic Enzyme –linked Immunosorbent Assay (kELA; [Toxoplasma gondii], Feline
Infectious Peritonitis-FIP, and Feline Immunodeficiency Virus-FIV using PetChek (Idexx
Laboratories, Westbrook, Maine, USA)]); Hemagglutiation Inhibition (HAI; [Feline
Panleukopenia Virus- FPLV; aka Feline Parvovirus]); p27 antigen by enzyme-linked
immunosorbent assay (ELISA; [Feline Leukemia Virus – FeLV; ViraChek, Synbiotics
Corporation, Kansas City, Missouri, USA]).
One mountain lion (M301) was seropositive to CDV, T. gondii, FPLV, and FECV
and was subsequently euthanized due to recapture-related injuries. The carcass was
frozen and then necropsied at the Arizona Veterinary Diagnostic Lab (accession #06-
6615; AZVDL, Tucson, Arizona, USA). We collected tissue samples at necropsy, fixed
the tissues in formalin, and mounted them in paraffin blocks for sectioning for histology.
152
Microscopic examination of brain, liver, spleen, heart, lungs, kidney, adrenal glands,
trachea, stomach, intestine, pancreas, urinary bladder, and foot pad was not revealing
although freezing artifact severely degraded tissue morphology. Polymerase chain
reaction (PCR) testing of unfixed brain tissue reported negative for CDV. Fluorescent
antibody testing of brain tissue for rabies virus also reported negative.
Retrospectively, de-paraffinized formalin-fixed sections of stomach, urinary
bladder, lung, and spleen were used for testing for antigens of CDV using
immunohistochemistry (IHC [accession #09-1973; Washington Animal Disease
Diagnostic Laboratory Pullman, WA, USA]) as unfixed tissue was not available. In
addition, tissue from paraffin blocks containing spleen and small intestine was tested for
amplicons of FPLV and tissue from paraffin blocks containing skeletal muscle and small
intestine was tested for amplicons of T. gondii using PCR (accession #089-51144;
Veterinary Diagnostic Laboratory at Colorado State University Ft. Collins, CO, USA).
Tests were negative for CDV, FPLV, and T. gondii.
The disease agent detected at the highest prevalence in our study was T. gondii
(Table 1), which is enzootic in felidae. After being shed in feces, the sporulated oocysts
can persist in the environment for up to a year (Aiello, 1998). Members of the cat family
are the only known definitive hosts for T. gondii (Aiello, 1998) and therefore serve as the
main reservoir. Antibodies to T.gondii were detected in 51% bobcats and 22% in
mountain lions sampled in North, Central, and South America (Table 2; Kikuchi et al.,
2004). Mountain lions in the southwestern states (Arizona, California, New Mexico,
153
USA) were reported to be more likely to be seropositive for T.gondii than those from
northwestern and mountain states (Kikuchi et al., 2004).
Prevalence of antibody to FPLV in Arizona was similar to other studies (Table 2),
and is a potentially population limiting viral disease of felidae (Anderson, 1983). We
also had higher prevalence of antibody to FCV (n = 6/9) than found elsewhere (Table 2).
Incidence of antibody to FCV was higher in bobcats in California in rural zones that
potentially contacted domestic cats (Felis catus; Riley et al., 2004). For antibody to FIV,
our (n = 3/9) seropositive results are reasonable as FIV is not uncommon in mountain
lions and chronic infection is asymptomatic in its natural lion host due to a long
evolutionary association between virus and host (Biek et al., 2006a). Antibodies to FIV
have been detedted in Brazilian felids (Filoni et al., 2006), African lions ([Panthera leo],
Roelke et al., 2006), and in mountain lions in Montana, Washington, Texas, and Florida
in the USA (Evermann et al., 1997; Biek and Poss, 2002; Miller et al., 2006).
Closely related to FIV is the puma-lentivirus (PLV; Olmsted et al., 1992).
Commercially available ELISA kits based on domestic cat FIV have a reasonable ability
to recognize seropositive samples from bobcats and ocelots (Leopardus pardalis), but not
from mountain lions (Franklin et al., 2007). When compared to the PLV immunoblot,
31% of positive lions (by PLV Western Blot) tested positive with the FIV-based kit
(Franklin et al., 2007). In contrast, sera from bobcats and ocelots showed a 74% and 80%
congruency between the tests. Serology for FIV from mountain lion disease surveys
using the ELISA kit could be inaccurate and would suggest that more specific testing for
PLV be implemented in future studies of mountain lions (Franklin et al., 2007). The high
154
prevalence of antibody to PLV in felids suggests that this lentivirus is common in wild
populations (Olmsted et al., 1992). A PLV-specific ELISA test was evaluated by van
Vuuren et al., (2003) and reported to have a sensitivity of 78.6% and a specificity of
100% when compared with the “gold standard” Western Blot test. This suggests that this
test would be suited to screening wild felid populations for lentivirus exposure.
Speculatively, the presence of antibody to CDV, FPLV, FECV and T. gondii in
serum from mountain lion M301 may indicate prior infection with those agents.
Infection by CDV might have resulted from contact with an actively infected wild or
domestic canid, collared peccary (Tayassu tajacu), or raccoon. The collared peccary is a
common prey of mountain lions in the Southwest and has been infected with or
seropositive to CDV (Appel et al., 1991; Noon et al., 2003). Contact between mountain
lions and infected feral or pet domesticated felids or other infected free-ranging wild
felids present in their habitat might explain some of our findings.
Another explanation would be intra-species transmission among mountain lions
of some of the viral agents. Feline herpes and FIV are endemic in African lions in the
Serengeti and over a 30 yr study, FCV, FPLV, FECV, and CDV show patterns of disease
prevalence indicative of discrete disease epidemics (Packer et al., 1999). Continuation of
long-term serologic studies over several years in mountain lions would allow a better
evaluation of this possibility. As humans encroach into habitats of large predators
managers and biologists should take advantage of opportunities to develop more
complete databases and better understand the disease ecology of wild carnivores to
enhance their management.
155
We thank the hounds-men and R. Thompson who participated in the acquisition
of blood samples. We thank S. L. Wesche and J. Heffelfinger for comments on earlier
drafts of this manuscript well as B. Rickert and E. Kerr of Arizona Veterinary Diagnostic
Laboratory for sample handling and preparation. Funding was provided by Arizona
Game and Fish Department and the University of Arizona. Capture and handling
procedures were approved by the Animal Care and Use Committee at the University of
Arizona (protocol #05-184).
LITERATURE CITED
AIELLO, S. E. 1998. The Merck Veterinary Manual. Merck & Co., Inc., Whitehouse
Station, NJ, USA. 2305 pp.
ANDERSON, A. E. 1983. A critical review of the literature on puma (Felis concolor).
Colorado Division of Wildlife, Research Paper Special Report No. 54: 92.
APPEL, M. J. G., C. REGGIARDO, B. A. SUMMERS, S. PEARCE-KELLING, C. J.
MARE, T. H. NOON, R. E. REED, J. N. SHIVELY and C. ÖRVELL. 1991.
Canine distemper virus infection and encephalitis in javelinas (collared peccaries).
Archives of Virology 119: 147-152.
BATISTA, H. B. D. C. R., A. C. L. U. FRANCO, C. H. ADANIA, P. M. ROEHE, H. B.
D. E. C. RUTHNER BATISTA, F. KINDLEIN VICENTINI, F. ROSADO
SPILKI and J. C. RAMOS SILVA. 2005. Neutralizing antibodies against feline
herpesvirus type 1 in captive wild felids of Brazil. Journal of Zoo and Wildlife
Medicine 36: 447-450.
156
BIEK, R. and M. POSS. 2002. Large-scale sampling of cougar populations and one of
their pathogens: working with cougar hunters in Montana. Intermountain Journal
of Sciences 8: 247.
BIEK, R., T. K. RUTH, K. M. MURPHY, C. R. ANDERSON, JR., M. JOHNSON, R.
DESIMONE, R. GRAY, M. G. HORNOCKER, C. M. GILLIN and M. POSS.
2006a. Factors associated with pathogen seroprevalence and infection in Rocky
Mountain cougars. Journal of Wildlife Diseases 42: 606-615.
BIEK, R., T. K. RUTH, K. M. MURPHY, C. R. ANDERSON, JR. and M. POSS. 2006b.
Examining effects of persistent retroviral infection on fitness and pathogen
susceptibility in a natural feline host. Canadian Journal of Zoology 84: 365-373.
DANIELS, M. J., M. C. GOLDER, O. JARRETT and D. W. MACDONALD. 1999.
Feline viruses in wildcats from Scotland. Journal of Wildlife Diseases 35: 121-
124.
DEEM, S. L., W. B. KARESH and W. WEISMAN. 2001. Putting theory into practice:
wildlife health in conservation. Conservation Biology 15: 1224-1233.
ENDO, Y., M. UEMA, R. MIURA, K. TSUKIYAMA-KOHARA, H. TSUJIMOTO, K.
YONEDA and C. KAI. 2004. Prevalence of canine distemper virus, feline
immunodeficiency virus and feline leukemia virus in captive African lions
(Panthera leo) in Japan. Journal of Veterinary Medical Science 66: 1587-1589.
EVERMANN, J. F., W. J. FOREYT, B. HALL and A. J. MCKEIRNAN. 1997.
Occurrence of puma lentivirus infection in cougars from Washington. Journal of
Wildlife Diseases 33: 316-320.
157
FILONI, C., J. L. CATAO-DIAS, G. BAY, E. L. DURIGON, R. S. P. JORGE, H. LUTZ
and R. HOFMANN-LEHMANN. 2006. First evidence of feline herpesvirus,
calicivirus, parvovirus, and Ehrlichia exposure in Brazilian free-ranging felids.
Journal of Wildlife Diseases 42: 470-477.
FRANKLIN, S. P., J. L. TROYER, J. A. TERWEE, L. M. LYREN, R. W. KAYS, S. P.
D. RILEY, W. M. BOYCE, K. R. CROOKS and S. VANDEWOUDE. 2007.
Variability in assays used for detection of lentiviral infection in bobcats (Lynx
rufus), pumas (Puma concolor) and ocelots (Leopardus pardalis). Journal of
Wildlife Diseases 43: 700-710.
GRINDER, M. and P. R. KRAUSMAN. 2001. Morbidity-mortality factors and survival
of an urban coyote population in Arizona. Journal of Wildlife Diseases 37: 312-
317.
HASS, C. C. and J. W. DRAGOO. 2006. Rabies in hooded and striped skunks in
Arizona. Journal of Wildlife Diseases 42: 825-829.
JUNGE, R. E., K. BAUMAN, M. KING and M. E. GOMPPER. 2007. A serologic
assessment of exposure to viral pathogens and leptospira in an urban raccoon
(Procyon lotor) population inhabiting a large zoological park. Journal of Zoo and
Wildlife Medicine 38: 18-26.
KIKUCHI, Y., B. B. CHOMEL, R. W. KASTEN, J. S. MARTENSON, P. K. SWIFT and
S. J. O'BRIEN. 2004. Seroprevalence of Toxoplasma gondii in American free-
ranging or captive pumas (Felis concolor) and bobcats (Lynx rufus). Veterinary
Parasitology 120: 1-9.
158
KOCK, R., W. S. K. CHALMERS, J. MWANZIA, C. CHILLINGWORTH, J.
WAMBUA, P. G. COLEMAN and W. BAXENDALE. 1998. Canine distemper
antibodies in lions of the Masai Mara. Veterinary Record 142: 662-665.
KRAUSMAN, P. R. 2002. Introduction to wildlife management; the basics. Prentice
Hall, Upper Saddle River, New Jersey, USA, 478 pp.
LAUNDER, J. W. 2007. Use of dispersal distance to assess the long term conservation of
mountain lions. In Sixth Mountain Lion Workshop, San Antonio, TX, USA, pp.
69.
LOGAN, K. A. and L. L. SWEANOR. 2001. Desert Puma: Evolutionary ecology and
conservation of an endangered carnivore. Island Press, Washington, D.C., 463 pp.
MILLER, D. L., S. K. TAYLOR, D. S. ROTSTEIN, M. B. POUGH, M. C. BARR, C. A.
BALDWIN, M. CUNNINGHAM, M. ROELKE and D. INGRAM. 2006. Feline
immunodeficiency virus and puma lentivirus in Florida panthers (Puma concolor
coryi): Epidemiology and diagnostic issues. Veterinary Research
Communications 30: 307-317.
NOON, T. H., J. R. HEFFELFINGER, R. J. OLDING, S. L. WESCHE and C.
REGGIARDO. 2003. Serologic survey for antibodies to canine distemper virus in
collared peccary (Tayassu tajacu) populations in Arizona. Journal of Wildlife
Diseases 39: 221-223.
OLMSTED, R. A., R. LANGLEY, M. E. ROELKE, R. M. GOEKEN, D. ADGER-
JOHNSON, J. P. GOFF, J. P. ALBERT, C. PACKER, M. K. LAURENSON and
T. M. CARO. 1992. Worldwide prevalence of lentivirus infection in wild feline
159
species: epidemiologic and phylogenetic aspects. Journal of Virology 66: 6008-
6018.
PACKER, C., S. ALTIZER, M. APPEL, E. BROWN, J. MARTENSON, S. J. O'BRIEN,
M. ROELKE-PARKER, R. HOFMANN-LEHMANN and H. LUTZ. 1999.
Viruses of the Serengeti: patterns of infection and mortality in African lions.
Journal of Animal Ecology 68: 1161-1178.
RAMOS SILVA, J. C., C. HARUMI ADANIA, F. FERREIRA, S. M. GENNARI, J. P.
DUBEY, J. S. FERREIRA-NETO, J. C. R. SILVA, S. OGASSAWARA and C.
H. ADANIA. 2001. Seroprevalence of Toxoplasma gondii in captive neotropical
felids from Brazil. Veterinary Parasitology 102: 217-224.
RILEY, S. P. D., J. FOLEY and B. CHOMEL. 2004. Exposure to feline and canine
pathogens in bobcats and gray foxes in urban and rural zones of a National Park
in California. Journal of Wildlife Diseases 40: 11-22.
ROELKE, M. E., J. F. DONALD, R. J. ELLIOTT, V. K. GEORGE, W. S. FRED, C. B.
MARGARET, F. E. JAMES and C. P. EUGENE. 1993. Seroprevalence of
infectious disease agents in free-ranging Florida panthers (Felis concolor coryi).
Journal of Wildlife Diseases 29: 36-49.
ROELKE, M. E., J. PECON-SLATTERY, S. TAYLOR, S. CITINO, E. BROWN, C.
PACKER, S. VANDEWOUDE and S. J. O'BRIEN. 2006. T-lymphocyte profiles
in FIV-infected wild lions and pumas reveal CD4 depletion. Journal of Wildlife
Diseases 42: 234-248.
160
TRUYEN, U., T. MULLER, R. HEIDRICH, K. TACKMANN and L. E.
CARMICHAEL. 1998. Survey on viral pathogens in wild red foxes (Vulpes
vulpes) in Germany with emphasis on parvoviruses and analysis of a DNA
sequence from a red fox parvovirus. Epidemiology and Infection 121: 433-440.
VAN VUUREN, M., S. A. KANIA, E. E. ZUCKERMAN, E. STYLIANIDES and W. D.
HARDY, JR. 2003. Evaluation of an indirect enzyme-linked immunosorbent
assay for the detection of feline lentivirus-reactive antibodies in wild felids,
employing a puma lentivirus-derived synthetic peptide antigen. Onderstepoort
Journal of Veterinary Research 70: 1-6.
Table 1. Serosurvey results from mountain lions from mountain ranges surrounding Tucson, Arizona, 2006-2008.
Diseasea
FCV FHV FPLV FECV FIP FeLV FIV FIV FSyV T. gondii CDV
Testb
SN SN HAI SN kELA ELISA kELA WBlot SN kELA SN
Animal No.
301 Nc N 1:1280 1:64 N N Ed N N 1:127 1:16
302 1:12 N 1:320 N N N POSe POS 1:24 1:98 N
303 1:24 N 1:160 N N N E N N 1:78 N
304 1:64 N N N N N N N N N N
306 1:24 N 1:640 N N N N N N 1:59 N
312 N (1:4) N 1:2560 1:16 N N POS POS N (<1:16) 1:65 N (<1:16)
313 1:12 N 1:160 N N N E N N 1:159 N
405 N (1:6) N N N N N POS POS N 1:92 N
409 1:12 N 1:160 N N N N N N 1:85 N
161
Table 1. continued
% Positive 67 0 78 22 0 0 -- 33 11 89 11
aDiseases: FCV = Feline Calcivirus, FHV = Feline Herpes, FPLV = Feline Panleukopenia Virus, FECV = Feline Enteric
Corona Virus, FIP = Feline Infectious Peritonitis, FeLV = Feline Leukemia Virus, FIV = Feline Immunodeficiency Virus,
FSyV = Feline Syncytial Virus, T. gondii = Feline Toxoplasmosis, CDV = Canine Distemper Virus
bTests: SN = Serum neutralization, HAI = hemagglutinin inhibition, kELA = kinetic enzyme-linked immunosorbent assay,
ELISA = enzyme-linked immunosorbent assay, WBlot = Western Blot
cN = Negative results
dE* = Equivocal antibody activity present but is below positive cut-off levels. Therefore a Western Blot is run as a
confirmatory test.
ePOS = Positive results
162
Table 2. Comparison of seropositive individuals from various studies conducted on wild and captive felids for 11 diseases.
Sample size (n) and percent positive.
Study Location Speciesa Diseasesb
n FCV FHV FPLV FECV FIP FeLV FIV FSyV T. gondii CDV PLV
Nicholson et al., 2009 SW Arizona P. c. 9 67 0 78 22 0 0 33 11 89 11
Biek et al., 2006 Rocky Mountain P. c. 207 18 0 69 28 50 11
Paul-Murphy et al., 1994 California P. c. 58 17 19 93 28 4 0 58
Riley et al., 2004 (urban) California L. r. 12 17 0 0 0 0 0 100
Riley et al., 2004 (rural) California L. r. 13 67 0 0 8 0 0 77
Roelke et al., 1993 Florida P. c. 38 56 0 78 19 0 37 9
Kock et al., 1998 Masai Mara P. l. 55 55
Endo et al., 2004c Japan P. l. 20 0 40 65
Bekoff et al., 1984 Scotland F. s. 50 25 6 10 0 33
Filoni et al., 2006d Brazil 21 29 29 48 5 10 5 10
Batista et al., 2005c Brazil P. c. 67 18
Ramos Silva et al., 2001c Brazil P. c. 172 48
163
Table 2. continued.
Kikuchi et al., 2004 Americas P.c. 438 22.4
Kikuchi et al., 2004 Americas L. r. 58 51.7
Biek et al., 2006 Yellowstone P. c. 110 48
Biek et al., 2006 Wyoming P. c. 58 16
Evermann et al., 1997 Washington P. c. 52 25
Olmsted et al., 1992 Arizona P. c. 10 80
Olmsted et al., 1992 California P. c. 16 56
Olmsted et al., 1992 Colorado P. c. 6 67
Olmsted et al., 1992 New Mexico P. c. 2 50
aSpecies: P.c. = Puma concolor, P. l. = Panthera leo, L. r. = Lynx rufus, F. s. = Felis sylvestris
bDiseases: FCV = Feline Calcivirus, FHV = Feline Herpes, FPLV = Feline Panleukopenia Virus, FECV = Feline Enteric
Corona Virus, FIP = Feline infectious Peritonitis, FeLV = Feline Leukemia Virus, FIV = Feline Immunodeficiency Virus,
FSyV = Feline Syncytial Virus, T. gondii = Feline Toxoplasmosis, CDV = Canine Distemper Virus, PLV = Puma Lentivirus
c Captive individuals
d Multiple species including 18 pumas, 1 ocelot (Leopardus pardalis), 2 spotted cats (Leopardus tigrinus)
164
165
APPENDIX E. NEW FLEA AND TICK RECORDS FOR MOUNTAIN LIONS IN
SOUTHWESTERN ARIZONA. Submitted to the Southwestern Naturalist: Nicholson,
K. L., and P.R. Krausman.
166
Short Communications
NEW FLEA AND TICK RECORDS FOR MOUNTAIN LIONS IN SOUTHWESTERN
ARIZONA.
KERRY L. NICHOLSON* AND PAUL R. KRAUSMAN,
Wildlife Conservation and Management, School of Natural Resources and the
Environment, University of Arizona, Tucson, AZ 85721, USA
Wildlife Biology Program, University of Montana, Missoula, MT, 59812, USA
*Correspondent: [email protected]
ABSTRACT-- Our understanding of ectoparasite ecology in wild felid populations is
limited in free-ranging species and in regions such as Arizona. As part of a larger study,
we collected ectoparasites from 4 radio-collared mountain lions (Puma concolor) in
Tucson, Arizona (32.189N -110.881E) between January 2006 and December 2007.
Ectoparasites were identified as Pulex, a genus of flea not commonly reported on
mountain lions. The tick was a nymph from the genus Argas (Alveonasus) cooleyi, a
species with little known information.
Key words: Flea, Pulex irritans, Pulex simulans, Puma concolor
Mountain lions are widely distributed throughout western North America and
South America. Parasitic infections in mountain lions have been documented (Anderson,
167
1983; Forrester et al., 1985; Waid, 1990) but, few data are available on ectoparasites
associated with this widely distributed large carnivore. In general mountain lions have
been described as free of external parasites (Currier, 1983), but others report detection of
ticks, fleas, and lice (Young and Goldman, 1946; Forrester et al., 1985; Wehinger et al.,
1995). The most comprehensive studies have involved ectoparasites of the endangered
Florida panther (Puma concolor coryi; Forrester et al., 1985; Wehinger et al., 1995). As
part of a larger urban mountain lion study, ectoparasite data were collected from 15
individuals in the mountain ranges surrounding Tucson, Arizona (32.189N -110.881E).
During 2006-2007, we captured 15 mountain lions using hounds or leg-hold
snares and immobilized them using Ketamine (Ketamine HCL, Wildlife Pharmaceutical,
Ft. Collins, Colorado, USA) and medetomidine hydrochloride (Domitor, Wildlife
Pharmaceutical, Ft. Collins, Colorado, USA). Medetomidine was reversed using
antisedan (Atipamezole hydrochloride, Pfizer Inc, New York, New York, USA) at a dose
of 3mg of atipamezole for every 1mg of medetomidine. After sedation, we sprayed each
mountain lion with over-the-counter flea and tick spray (Hartz® Ultra Guard Plus Flea&
Tick) and fitted each lion with a Spread Spectrum satellite collar (Telonics, Mesa, AZ).
We inspected each mountain lion for ectoparasites focusing on the neck, ears,
peri-anal area, and axillae. We collected fleas and ticks by hand and/or with a flea comb,
and stored them in 70% isopropyl alcohol. We cleared fleas in 10% potassium
hydroxide, dehydrated through a ethanol series, further cleared in xylene, mounted on
slides in Canada balsam, and identified according to Hopla (1980) and Hubbard (1947).
The tick specimen was immersed in lactophenol identified and stored in 75% EtOH.
168
Voucher flea specimens from this study were deposited in the Insect Research Collection,
Department of Entomology, University of Arizona, the tick specimen is in the Acarology
Collection, Department of Entomology, Ohio State University (voucher
#OSAL0084966).
Only female Pulex were found on 3 lions in our study (M301, M303, F409). One
mountain lion in our study (M301) had a ‘severe’ flea infestation upon initial capture,
where fleas were visible without close inspection. Initial capture was in a residential area
at the base of the Santa Catalina Mountains near Catalina State Park just north of Tucson
Arizona in August 2005. Fleas were not sampled at this time; however during
subsequent captures we collected fleas. This particular individual also was seropositive
for several diseases including canine distemper, Toxoplasma gondii, and feline
panleukopenia virus (FPLV; Nicholson 2009). Male 303 was seropositive for feline
calcivirus (FCV), FPLV, and T. gondii. Both M303 and M301 had equivocial antibody
activity present to feline immunodeficiency virus but were below positive cut-off levels
(Nicholson 2009). Male 305 who was captured in the Silver Bell Mountains west of
Tucson, was the only lion to have a nymphal tick which was identified as Ornithodoros
(Alvenonasus) cooleyi McIvor, (1941). This soft tick species was reclassified into Argas
(Alvenonasus) cooleyi (Klompen and Oliver, 1993) a homonym of Argas cooleyi Kohls
and Hoogstraal (1960). This is a rare tick, with very few specimens in collections and
was previously recorded from Nevada (type specimen) from a shipment of skins of
striped skunk, swift foxes, and coyotes (H. Klompen, Department of Entomology, The
Ohio State University, personal communication). It is the only species of the basal Argas
169
that occurs in North America. We captured female 409 near Box Canyon in the Santa
Rita Mountains south of Tucson. She was seropositive for FCV, FPLV, and T. gondii.
We captured male 303 in the north end of the Santa Catalina Mountains and in addition to
fleas, had 1 jumping flea beetle (Glyptina spp.). None of the lions had >5 fleas found on
each individual, reconfirming the description of mountain lions are remarkably free of
external parasites (Currier, 1983).
Pulex irritans has the potential to be confused with P. simulans in areas of
sympatry as male genitalia structures are the only reliable characteristic that can be used
to separate the species (Pence et al., 2004). Because we only found female fleas on our
mountain lions, it will remain unclear as to which species was found. Pulex irritans is a
cosmopolitan species that is usually found on various large, coarse-coated mammals such
as pigs, canids, mustelids, deer, tapirs (Tapirus spp.), collared peccaries (Tayassu tajacu)
and humans (Smit, 1958; Lewis, 1993). Pulex simulans occurs mostly on colonial
rodents such as prairie dogs (Cynomys spp.; Smit, 1958). Both species of flea are
considered as vectors for bacilli plague in the USA, but P. iritans is not as significant in
maintaining the sylvatic cycle as Pulex simulans (Lewis, 1993). Ultimately, however,
Pulex spp. are a poor vectors of the plague (Burroughs, 1947). In 2007, in northern
Arizona, a mountain lion died due to contracting the plague, which is usually transmitted
between rodents and fleas, though it is unknown how the mountain lion became infected
(Wong et al., 2009).
Pulex simulans has been reported on Paraguayan (South America) mountain lions
and jaguars (Panthera onca; Durden et al., 2006), Pulex porcinus in Mexico (Eckerlin,
170
2004), Polygenis tripopsis in Brazil (Linardi and Guimaraes, 2000) and Chaetopsylla
setosa in North America (Young and Goldman, 1946). In southern California, severe
notoedric mange (Notoedres cati) infestation was reported in mountain lions and bobcats
(Riley et al., 2007; Uzal et al., 2007). Additionally, in southern California, 2 mountain
lion mortalities from anticoagulant toxicity showed signs of severe mange infestation
(Uzal et al., 2007), a disease usually reported in isolated cases (Riley et al., 2007).
Confusion can still occur due to the unclear taxonomic relationship between P.
simulans and P. irritans because of overlapping geographic ranges and because of
morphologically identical females (Dittmar et al., 2003; Pence et al., 2004). Until doubts
about the species validity are cleared, it is important to document the occurrence and
distribution of Pulex spp. It is also important to document ectoparasite fauna associated
with carnivores, particularly for those with opportunities of human-wildlife interaction,
those in close proximity to domestic animals or urban areas.
We thank the hounds-men and R. Thompson, B. Buckley, B. Jansen, and T.
Salazar who participated in the acquisition of ectoparasite samples. We thank F.
Ramberg and C. Olson at the University of Arizona and H. Klompen from Ohio State
University for identifying samples. Funding was provided by Arizona Game and Fish
Department and the University of Arizona. Capture and handling procedures were
approved by the Animal Care and Use Committee at the University of Arizona (protocol
#05-184).
LITERATURE CITED
171
ANDERSON, A. E. 1983. A critical review of the literature on puma (Felis concolor).
Colorado Division of Wildlife, Research Paper Special Report No. 54: 92.
Burroughs, A. L. 1947. Sylvatic plague studies: The vector efficiency of nine species of
fleas compared with Xenopsylla cheopis. The Journal of Hygiene 45: 371-396.
CURRIER, M. J. P. 1983. Felis concolor. Mammalian Species 200: 1-7.
DITTIMAR, K., U. MAMAT, M. WHITING, T. GOULDMANN, R. REINHARD AND S. GUILLEN.
2003. Techniques of DNA-studies on prehispanic ectoparasites (Pulex sp.,
Pulicidae, Siphonaptera) from animal mummies of the Chiribaya Culture,
southern Peru. Memorias do Institudo Oswaldo Cruz Rio de Janeiro 98: 53-58.
DURDEN, L. A., M. W. CUNNINGHAM, R. MCBRIDE AND B. FERREE. 2006. Ectoparasites of
free-ranging pumas and jaguars in the Paraguayan Chaco. Veterinary Parasitology
137: 189-193.
ECKERLIN, R. P. 2004. Flea (Siphonaptera) of the Yucatan Peninsula (Campeche,
Quintana Roo, and Yucatan), Mexico. Caribbean Journal of Science 41: 152-157.
FORRESTER, D. J., A. C. JOSEPH, C. B. ROBERT, J. A. CONTI AND R. C. BELDEN. 1985.
Parasites of the Florida panther (Felis concolor coryi). Proceedings of the
Helminthhological Society of Washington 52: 95-97.
HOPLA, C. E. 1980. A study of the host associations and zoogography of Pulex In Fleas,
R. Traub and H. Starcke (eds.). Balkema, Rotterdam, pp. 185-207.
HUBBARD, C. A. 1947. Fleas of western North America. Their relation to public health.
Iowa State College Press, Ames, IA, USA., 533 pp.
172
KLOMPEN, J. S. H. AND J. H. OLIVER. 1993. Systematic relationships in the soft ticks
(Acari, Ixodida, Argasidae). Systematic Entomology 18: 313-331.
KOHLS, G. M. AND H. HOOGSTRAAL. 1960. Observations on the subgenus Argas
(Ixodoidea, Argasidae, Argas) 2. A. cooleyi, new species from western North
American birds. Annals of the Entomological Society of America 53: 625-631.
LEWIS, R. E. 1993. Fleas (Siphonaptera). In Medical insects and arachnids, R. P. Lane
and R. W. Crosskey (eds.). Chapman and Hall, London, UK, pp. 529–575.
LINARDI, P. M. AND L. R. GUIMARAES. 2000. Sifonapteros do Brasil. FAPESP, Sao
Paulo, 291 pp.
MCIVOR, B. C. 1941. A new species of Ornithodoros tick from Nevada (Acarina:
Ixodoidea). The Journal of Parasitology 27: 435-436.
NICHOLSON, K. L. 2009. Spatial movements and ecology of mountain lions in southern
Arizona. Dissertation, University of Arizona, Tucson, AZ, USA.
PENCE, D. B., J. F. KAMLER AND W. B. BALLARD. 2004. Ectoparasites of the swift fox in
northwestern Texas. Journal of Wildlife Diseases 40: 543-547.
RILEY, S. P. D., C. BROMLEY, R. H. POPPENGA, F. A. UZAL, L. WHITED, R. M.
SAUVAJOT AND S. P. RILEY. 2007. Anticoagulant exposure and notoedric mange
in bobcats and mountain lions in urban southern California. Journal of Wildlife
Management 71: 1874-1884.
SMIT, F. G. A. M. 1958. A preliminary note on the occurrence of Pulex irritans L. and
Pulex simulans Baker in North America. The Journal of Parasitology 44: 523-
526.
173
UZAL, F. A., R. S. HOUSTON, S. P. D. RILEY, R. POPPENGA, J. ODANI AND W. BOYCE.
2007. Notoedric mange in two free-ranging mountain lions (Puma concolor).
Journal of Wildlife Diseases 43: 274-278.
WAID, D. D. 1990. Movements, food habits, and helminth parasites of mountain lions in
southwestern Texas, Texas Tech University, Lubbock, TX, USA.
WEHINGER, K. A., M. E. ROELKE AND E. C. GREINER. 1995. Ixodid ticks from panthers
and bobcats in Florida. Journal of Wildlife Diseases 31: 480-485.
WONG, D., M. A. WILD, M. A. WALBURGER, C. L. HIGGINS, M. CALLAHAN, L. A.
CZARNECKI, E. W. LAWACZECK, C. E. LEVY, J. G. PATTERSON, R. SUNENSHINE,
P. ADEM, C. D. PADDOCK, S. R. ZAKI, J. M. PETERSEN, M. E. SCHRIEFER, R. J.
EISEN, K. L. GAGE, K. S. GRIFFITH, I. B. WEBER, T. R. SPRAKER AND P. S.
MEAD. 2009. Primary pneumonic plague contracted from a mountain lion carcass.
Clinical Infectious Diseases 49: e33-e38.
YOUNG, S. P. and E. A. GOLDMAN. 1946. The puma, mysterious American cat. Dover
Publications, Nueva York, 358 pp.