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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

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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

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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

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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.

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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

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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

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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.

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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

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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]).

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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

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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

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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

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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

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(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.

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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

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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.

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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

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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

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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.

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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

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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

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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

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62:1264-1275. 446

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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

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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

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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


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


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]


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





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


chaparral, pinyon (Pinus edulis) - juniper (Juniperus spp.) woodlands, grasslands and 99

mixed ponderosa pine (Pinus ponderosa) forests (Brown 1994). Tucson contained 100


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


(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

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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


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


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





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


chaparral, pinyon (Pinus edulis)- juniper (Juniperus spp.) woodlands, grasslands and 141

mixed ponderosa pine (Pinus ponderosa) forests (Brown, 1994). Tucson contained 142


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


(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

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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

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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


209 212 -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




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


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


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).

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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).

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