Southwest Biological Science Center 

Mountain Lions of the Flagstaff Uplands 

2003–2006 Progress Report

Open-File Report 2007-1062 

2007 

U.S. Department of the Interior 

U.S. Geological Survey 
Mountain Lions of the Flagstaff Uplands 

2003–2006 Progress Report

Edited by David J. Mattson 

2007 

Open-File Report 2007-1062 

U.S. Department of the Interior 

U.S. Geological Survey 

3 
Contents 

Executive Summary......................................................................................................Page 5 

Chapter 1. Background and Methods for Investigating Mountain Lions in the Flagstaff Uplands

Jan Hart, David Mattson, and Terence Arundel.....................................................................Page 6 

Chapter 2. Movements and Habitat Selection by Mountain Lions in the Flagstaff Uplands

Terence Arundel, David Mattson, and Jan Hart.....................................................................Page 17 

Chapter 3. Predation and Other Behaviors of Mountain Lions in the Flagstaff Uplands 

David Mattson, Jan Hart, Mike Miller, and Diane Miller............................................................Page 31 

Chapter 4. Managing for Human Safety in Mountain Lion Range 

David Mattson................................................................................................................Page 43 

References Cited...........................................................................................................Page 57 

Acknowledgments.........................................................................................................Page 63 

Appendix 1. Summary of Outreach.................................................................................Page 64

4 
U.S. Department of the Interior 

DIRK KEMPTHORNE, Secretary 

U.S. Geological Survey 

Mark D. Myers, Director 

U.S. Geological Survey, Reston, Virginia 2007 

For product and ordering information: 

World Wide Web: http://www.usgs.gov/pubprod

Telephone: 1-888-ASK-USGS 

For more information on the USGS—the Federal source for science about the Earth, 

its natural and living resources, natural hazards, and the environment: 

World Wide Web: http://www.usgs.gov

Telephone: 1-888-ASK-USGS 

Suggested citation: 

Mattson, David J., ed., 2007, Mountain Lions of the Flagstaff Uplands; 2003–2006 

Progress Report: U.S. Geological Survey Open-File Report 2007-1050 

[available on the World Wide Web at URL http://pubs.usgs.gov/of/2007/1062/ ]. 

Cover: Background photo, San Francisco Peaks behind downtown Flagstaff, AZ, David Mattson, USGS 

Southwest Biological Science Center; inset photo, adult male mountain lion N6, Anderson Mesa, Jan Hart, 

USGS Colorado Plateau Research Station. 

2 
Executive Summary 

Stakeholders in management of mountain 

lions in the Flagstaff Uplands of northern Arizona 

have expressed increasing concern about both 

potential impacts of humans on lions and potential 

risks posed by lions to humans. A series of 

human-mountain lion encounters during 2000-

2001 on Mt. Elden, immediately adjacent to 

Flagstaff, and similar incidents during 2004 near 

Tucson brought increased attention to management 

of human safety in mountain lion range. These 

human-centered concerns, together with longstanding questions about how the human 

infrastructure centered on Flagstaff might be 

affecting lion movements led us to initiate a 

mountain lion study in 2003 which we plan to 

continue through 2009. Our study focuses on 

movements and other behaviors of mountain lions, 

with the goal of providing information that can be 

used to increase human safety, decrease human 

impacts, and, overall, provide insight into the 

ecology of lions in this region. To serve this goal, 

we have focused on collecting data that will be the 

basis of explanatory models that can provide 

spatially-explicit predictions of mountain lion 

activity, specify the effects of human facilities, 

such as highways and urban areas, and provide 

insight into when, where, and how often different 

kinds of lions kill different kinds of prey. 

During 2003-2006, we captured six female 

and five male mountain lions in the Flagstaff 

Uplands, 10 of which we fitted with collars that 

collected up to six high-precision GPS fixes per 

day, transmitted daily to our offices via Argos 

satellites. This timely delivery of data allowed us 

to visit kill sites and other foci of localized activity 

to collect detailed information on lion behavior. 

By June 2006 we had obtained 9357 GPS locations 

and visited 394 sites, at which we documented 218 

kills, 165 of which were by five females and 53 by 

five males. These data were the basis for 

preliminary analyses presented in this report. All 

lions during all seasons exhibited a strong selection 

for rough terrain and forest or woodland cover. 

Females differed from males by selecting more 

strongly for intermediate, rather than extreme, 

levels of terrain roughness, by selecting more 

strongly for chaparral vegetation and related rocky 

areas during winter, and by not selecting as 

strongly for areas near water sources. Overall, 

lions collared during this study strongly avoided 

flat open areas in private ownership. Male but not 

female lions exhibited pronounced selection for 

National Park Service jurisdictions. Both males 

and females year-round avoided residential areas 

and a zone outward to about 1-3 km and, when 

within this zone, moved more slowly and with less 

change in direction compared to when farther 

away. Collared lions have so far rarely crossed 

paved highways of any description – orders of 

magnitude less often than expected by chance. We 

observed only 3 crossings of an interstate highway, 

all on I17 and none on I40. 

Elk comprised the majority (52%) of kills by 

lions in our study, followed by mule deer (46%), 

and small mammals (15%). Adults comprised 

most of the mule deer kills (68%) and 

mesocarnivores, primarily coyotes (n = 21), 

comprised 73% of smaller prey. Calf and shortyearling elk comprised the largest single category 

of kills (29%). In addition to kills, we documented 

seven instances of scavenging, involving four 

different lions. Females differed from males by 

killing more mule deer and virtually all of the 

mesocarnivores, and by killing fewer elk of all 

ages. Intervals between kills averaged between 

144 hrs (young females) to 221 hours (adult 

females), whereas average time spent on a kill 

ranged from 19 hrs (adult males) to 40 hrs (young 

males). Carcass mass had a strong effect on 

likelihood that a lion would bury or relocate a kill, 

the percentage of edibles consumed, and overall 

time spent feeding. Time spent feeding and 

likelihoods of carcass burial and relocation all 

peaked at intermediate carcass masses, suggesting 

an optimal mass in the range of 50-150 kg, likely 

dictated as much by handling efficiencies and 

competition from other scavengers as by a lion’s 

shear ability to kill prey. Adult male lions 

exhibited a life strategy distinctly different from all 

other sex-age classes that entailed moving more 

rapidly over larger areas, and spending less time on 

kills in which they invested less energy handling, 

but from which they consumed tissue at a higher 

sustained rate. 

5 
Chapter 1 

Background and Methods for Investigating Mountain 

Lions in the Flagstaff Uplands 

Jan Hart, David Mattson, and Terence Arundel 

Background

Trends and Conditions in the West 

Mountain lions (Puma concolor) are the most 

widespread large predator in the western United 

States, creating both opportunities and challenges 

for wildlife and public lands managers. Mountain 

lions are sufficiently abundant not only to directly 

and indirectly affect ecosystems, but also to pose a 

threat to humans. Human populations and related 

infrastructures in and near mountain lion range 

have, moreover, increased rapidly enough during 

the last three decades to exacerbate risks to humans 

and to impact mountain lion behaviors and 

populations (Torres et al. 1996). Wildlife and 

public land managers are confronted with 

somehow harmonizing the dual mandate of 

ensuring human safety while conserving 

ecologically functional mountain lion populations, 

often with little or no control over human 

behaviors and the pace and nature of infrastructure 

growth. Under such circumstances, detailed 

information about mountain lions and the effects of 

humans and human facilities is becoming ever 

more important to crafting integrated management 

strategies that create “win-win” solutions. 

Threats posed to humans by mountain lions 

have commanded the attention of managers and the 

public since the 1980s. In large part this reflects a 

marked increase in well-publicized and welldocumented attacks on humans by mountain lions 

during the last 25 years (Etling 2001; see Chapter 

4), mirrored in most western states by increases in 

documented non-injurious encounters with 

mountain lions that were deemed problematic by 

the involved humans. These trends have 

dominated public discourses, despite the fact that 

only 19 people have been killed by mountain lions 

in all of Canada and the U.S. since the 1890s, and 

despite attack rates that are small compared to rates 

of human injury and death caused by numerous 

other factors such as automobiles or even lightning 

(Beier 1991; but see Chapter 4). The gruesome 

details of mountain lion attacks seem to fascinate 

and horrify many people who might not give a 

second thought to the equally gruesome prospects 

of a traffic accident. 

At the same time that increasing numbers of 

people have felt threatened by mountain lions, 

increasing numbers have also valued lions for their 

aesthetics, naturalness, and role in ecosystems. 

This trend towards intrinsic valuation of mountain 

lions parallels a rapid emergence since the 1960s 

of ecologistic and scientific world views or value 

systems (Kellert 1996). In fact, a recent survey of 

Arizona residents revealed not only that a large 

majority valued having healthy populations of 

mountain lions, but also that the primary reason for 

this valuation was the belief that lions play an 

important ecological role – the essence of Kellert’s 

ecologistic world view (Decision Research 2004). 

Sometimes heated exchanges between those who 

value the beauty and ecological role of mountain 

lions and those who either fear lions or value them 

for instrumental reasons (Kellert’s dominionistic 

and utilitarian views) have increasingly typified 

public discourses that end up placing contradictory 

demands on management (Clark and Munno 

1995). 

Those who value mountain lions – including 

wildlife and public land managers with a 

conservation mandate – have been an increasingly 

vocal constituency for documenting and mitigating 

the impacts on lion populations of growing human 

populations and spreading human infrastructures. 

Residential areas and major highways potentially 

fragment and reduce mountain lion populations, 

not only by preventing lion movements, but also 

by increasing lion death rates resulting from 

collision with vehicles or conflict with humans. 

These kinds of impacts are particularly evident in 

places such as southern Florida, where vehicular 

collisions are a major cause of lion deaths (Maehr 

and Caddick 1995), and southern California, where 

lion range has shrunk to islands surrounded by a 

6 
veritable sea of humanity (Beier 1993). Even in 

less urbanized regions of the interior West, heavily 

used interstate highways and spreading cities 

threaten to impact of mountain lion populations. 

Wildlife managers have perhaps most 

consistently given mountain lions their attention 

because of long-lasting concerns about impacts of 

lion predation on vulnerable ungulate populations 

or game species. In recent decades this concern 

has crystallized around widespread declines of 

mule deer (Odocoileus hemionus) and the 

precarious status or even extirpation of small and 

isolated bighorn sheep (Ovis canadensis) 

populations. Although a recent review of research 

regarding mule deer population declines concluded 

that mountain lions probably have had major 

impacts only where deer populations were severely 

reduced for other reasons (Ballard et al. 2001), and 

where lions have had significant numbers of 

alternate prey, an influential constituency still 

exists for widespread reduction of lion populations 

to increase densities of mule deer. Even greater 

support exists among hunters and wildlife 

managers for reducing or eliminating mountain 

lions in limited areas to help recover or re-establish 

bighorn sheep populations, supported by recent 

research showing that mountain lions were a 

significant factor in the loss or near loss of several 

populations of desert bighorn sheep (Hayes et al. 

2000, Rominger et al. 2004). 

Arizona and the Flagstaff Uplands 

Even though mountain lions have injured 

only four people and killed none in Arizona since 

1900, recorded conflict between humans and lions 

has increased especially since the early 1990s 

(Barber 2005). This conflict was epitomized by a 

series of incidents during the winter of 2000-2001 

on Mt. Elden, immediately adjacent to Flagstaff, 

and a similar series of incidents during the winter 

of 2004 in Sabino Canyon, immediately adjacent to 

Tucson (Perry and DeVos 2005). In both cases, 

people reported what to them were threatening 

encounters with lions in popular hiking areas. On 

Mt. Elden, the encounters culminated in an attack 

by a lion on a dog accompanying a human. 

Mountain lions were killed or otherwise removed 

in both areas out of concern for human safety, but 

the publicity surrounding both cases was prolific, 

sometimes strongly expressed, and often focused 

on the performance of management agencies. 

Many participants emerged from these cases with a 

desire for standardized response protocols and 

more information about mountain lion behavior, 

especially pertaining to human safety. 

Like other wildlife and public lands 

managers, National Park Service (NPS) managers 

on the Colorado Plateau have expressed increasing 

concern about mountain lions, focused on not only 

on human safety, but also on human impacts on 

lions and the ecological role of lions in park 

ecosystems. A 1999 survey of NPS units on the 

Colorado Plateau revealed that 11 parks (NPs) and 

monuments (NMs) considered mountain lions to be 

a management priority. Among these NPS units 

were Walnut Canyon and Wupatki NMs in the 

Flagstaff Uplands. These monuments, in 

particular, were concerned not only about human 

safety, but also about impacts of nearby Flagstaff 

residential areas and adjacent highways and 

railroads on mountain lion movements. Interstate 

Highway 40 (I40) and the paralleling Burlington 

Northern-Santa Fe (BNSF) railroad separate 

Walnut Canyon NM to the north from the two 

other Flagstaff area NMs (Sunset Crater Volcano 

and Wupatki), and I17 and the Flagstaff urban area 

lie astride potential wildlife movements routes to 

the west. Nearly 100 trains travel the BNSF 

railroad daily; I17 and I40 carry the bulk of traffic 

destined for Phoenix and the Los Angeles basin. 

Analysis of DNA from hunter-harvested mountain 

lions north and south of I40 and the railroad has 

shown a degree of genetic differentiation 

consistent with complete blockage of mountain 

lion movements by the interstate and railroad 

(McRae et al. 2005). 

Arizona Game and Fish Department (AZGF) 

has had two priorities for mountain lion 

management in the Flagstaff Uplands: (1) insuring 

human safety; and (2) providing a quality 

sustainable hunt of mountain lions (AZGF 2001, 

2005a). Concerns have also been expressed about 

potential impacts of lion predation on vulnerable 

pronghorn (Antilocapra americana) populations. 

The importance of human safety came into focus 

especially with the Mt. Elden incidents of winter 

2000-2001, and was reason for AZGF holding one 

of three statewide workshops on management of 

mountain lion incidents in Flagstaff during the 

winter of 2004-2005. Concern about human safety 

has also precipitated a considerable increase in 

public outreach and education by AZGF, including 

presentations and the distribution of flyers and 

brochures (AZGF 2005a). Mountain lion hunting 

has always been a priority outcome for AZGF, but 

7 
an increasingly controversial one in light of 

regulations that, until recently, did not expressly 

provide for area limits on number of kills or 

prohibit the killing of cubs or females with cubs 

(Schubert 2004). Concern on the part of animal 

rights advocates over these non-restrictive 

regulations has spilled over into debates about how 

to manage for human safety, and the relative merits 

of lethal versus non-lethal measures (Clark and 

Munno 2005, Perry and DeVos 2005). 

Although pronghorn are generally not 

considered to be mountain lion prey, primarily 

because of their fleetness and open habitats, some 

pronghorn kills by lions have been documented 

(Ockenfels 1994). Several comparatively small 

and vulnerable populations of pronghorn in the 

Flagstaff Uplands spend much of their time in 

semi-isolated grasslands surrounded and 

encroached upon by ponderosa pine (Pinus 

ponderosa) forests and pinyon (Pinus edulis) and 

juniper (Juniperus utahensis and J. monosperma) 

woodlands. This nearness and comparative 

commonness of potential ambush cover has been 

thought to increase mountain lion predation on 

these pronghorn populations, compounding the 

impacts of cattle grazing on pronghorn habitat and 

coyote (Canis latrans) predation on fawns (Barrett 

et al. 1984). Even so, little is known about the 

extent of lion predation on pronghorns, factors 

driving it, and whether many lions or few are 

responsible. 

This Study 

This study was designed to address a broad 

range of questions arising from issues articulated 

in the preceding section, focused on mountain lion 

behavior in areas surrounding Flagstaff and nearby 

NPS NMs. Our focal questions pertain to 

mountain lions movements, habitat selection, and 

predatory behaviors, as follow: 

(1) What are seasonal mountain lion range 

sizes and rates of movement, by gender and ageclass, and the locations of ranges relative to 

Flagstaff residential areas, NPS NMs, and heavilytrafficked highways or railroads? 

(2) How often do lions cross roads of 

different types, and is this rate different from what 

would be expected at random? 

(3) What are diagnostic features of places 

where mountain lions do cross heavily-trafficked 

highways or railroads? 

(4) What are seasonally diagnostic features of 

habitats heavily used by mountain lions, by gender 

and age-class, and where are these heavily-used 

seasonal habitats located relative to Flagstaff 

residential areas, NPS NMs, heavily-trafficked 

highways or railroads, and areas regularly used by 

pronghorn? 

(5) What are the diel patterns of kills and 

movements by mountain lions when near humans 

versus far away? 

(6) How often and what do mountain lions 

kill, by season, by species and estimated mass of 

prey, and by gender and age-class of lion? 

(7) How long do mountain lions stay with 

kills and what behaviors do they exhibit while 

there, by species and estimated mass of prey, and 

by gender and age-class of lion? 

(8) What are seasonally diagnostic features of 

sites where mountain lions make kills, and where 

are these types of sites located relative to Flagstaff 

residential areas, NPS NMs, and areas regularly 

used by pronghorn? 

In more concrete terms, this study is designed 

to produce information that will allow managers to 

assess temporal and spatial dimensions of risk to 

humans, impacts of humans on mountain lions, and 

impacts of mountain lion predation on prey 

species. To serve this end, key products will be: 

(1) Explanatory models of mountain lion 

habitat selection that can be used to generate maps 

of high versus low levels of predicted mountain 

lion activity; 

(2) Explanatory models of kill site selection 

by mountain lions that can be used to generate 

maps of high versus low predicted levels of 

predatory activity; and 

8 
(3) Explanatory models of predatory behavior 

that will provide insight into when, where, and 

how often different kinds of mountain lions kill 

different kinds of prey. 

 

Proposals to initiate this study were written in 

2002, at a time when there were no investigations 

in Arizona or neighboring interior western states 

focused on human safety or mountain lions using 

the urban-wildland interface, and after other 

management agencies expressed little interest in 

pursuing such a study after being approached by 

this study’s investigators. The impetus for our 

work arose from NPS concerns and initial funding 

came from the USGS Park-Oriented Biological 

Support (POBS) Program. Subsequent funded 

proposals broadened the topical and geographic 

scope of our investigations and were supported by 

the USGS Southwest Biological Science Center, 

the Summerlee Foundation, the NPS Cooperative 

Conservation Initiative, the USGS Fire Research 

Program, the USGS Colorado Plateau Research 

Station, the Wilburforce Foundation, and the 

Johnson Family Foundation. 

Study Area 

Figure 1.1. The Flagstaff Uplands with the Flagstaff incorporated area and major highways in red, and National 

Monuments in yellow. (a) Capture areas are delineated by dashed black lines and locations of mountain lion captures are 

denoted by stars of different colors corresponding to different years, as indicated by years given on the map. (b) The study 

area, as denoted by the orange area bounded by a dashed black line is defined by the extent of current movements by 

captured lions. 

Our study area is centered on Flagstaff, 

Arizona, at approximately 35° 10’N latitude and 

111° 35’ W longitude. The study area boundary 

continues to evolve with documented movements 

of marked animals, and was initially determined by 

capture areas delineated to radio-mark animals that 

would be exposed to NPS units and important 

human features of the Flagstaff Uplands (Figures 

1.1a and 1.1b). We established capture areas north 

and south of I40, containing Walnut Canyon 

(south) and Sunset Crater Volcano (north) NMs, 

with the intention of marking animals that 

potentially used these NPS units and also had 

opportunity to cross I40 and, to the north, U.S. 

Highway 89. We also established a capture area 

immediately north of Flagstaff, on Mt. Elden and 

the San Francisco Peaks, to mark animals that 

would likely use the urban-wildlife interface, 

including the problematic south-face of Mt. Elden 

(see Background), and also have the opportunity to 

cross U.S. Highway 89 in areas farther from 

Flagstaff. 

9 
The current study area ranges from grassland 

and juniper woodland at 1,600 m elevation up to 

alpine at 3,850 m on the top of Mt. Humphreys, the 

highest point in Arizona. Most of the study area is 

between 1,980 and 2,290 m elevation and consists 

of plateaus and other comparatively flat 

topography incised by shallow canyons. Aside 

from the San Francisco Peaks massif, most 

mountainous topography consists of cinder cones 

and other volcanic features, primarily to the north 

of Flagstaff, and the scarps of plateaus, primarily 

to the south. Natural surface water is uniformly 

scarce because typically porous substrates allow 

for rapid drainage of precipitation, even in 

comparatively well-watered areas. Humans have 

compensated by building a network of numerous 

well-distributed artificial devices to provision 

livestock and water-dependent wildlife with water. 

The climate is sunny and temperate to warm 

and semi-arid. On average, Flagstaff has 209 days 

with minimum temperatures below 0°C, three days 

with maximum temperatures above 32°C, a 

January mean of –1.8°C, and a July mean of 

19.1°C. Flagstaff’s 58 cm of precipitation is moreor-less uniformly distributed throughout the year, 

with the exception of a strong spring drought that 

lasts roughly from mid-April to the first week in 

July. Monsoonal precipitation, primarily from 

thunderstorms, breaks the drought, and typically 

lasts until mid-September. Snowfall around 

Flagstaff averages 245 cm. To the east, and lower 

in elevation, annual precipitation declines to about 

20 cm and mean summer temperatures increase by 

about 6°C. To the south temperatures increase, 

although monsoonal precipitation also increases, 

resulting in an elevational depression of vegetation 

zones. 

Pinyon-juniper woodlands and ponderosa 

pine forests cover most of the study area, grading 

to sparse grass and low shrubs at lower elevations 

to the north and east, and dense chaparral-like 

shrublands on south-facing slopes at mid- and 

lower-elevations near Flagstaff and south. This 

chaparral is typified by oaks (Quercus spp.), 

mountain mahogany (Cercocarpus montanus), 

cliffrose (Purshia stansburiana), and Fremont 

mahonia (Mahonia fremontii). At higher 

elevations of the San Francisco Peaks ponderosa 

pine forests are progressively replaced upward in 

elevation by aspen (Populus tremuloides) and 

mixed conifers (typified by white fir [Abies 

concolor] and southwestern white pine [Pinus 

strobiformis]), by subalpine fir (Abies lasiocarpa) 

and bristlecone pine (Pinus aristata), and finally 

by alpine fellfields and tundra. 

There are no reliable estimates for populations 

of potential mountain lion prey in our study area, 

although there are substantial numbers of mule 

deer and elk (Cervus elaphus), blacktail jackrabbit 

(Lepus californicus) and desert cottontail 

(Sylvilagus auduboni), and coyotes, bobcats (Lynx 

rufus), and badgers (Taxidea taxus). Whitetail 

deer (O. virginianus) are rare and javelina (Pecari 

angulatus) are present but not abundant. 

Pronghorn are present and seasonally semi-isolated 

in grasslands scattered throughout the 

predominantly forested study area. At mid- to 

high-elevations, mule deer tend to concentrate on 

southerly slopes during the winter, especially 

where oaks, cliffrose, and mountain mahogany are 

abundant. Elk tend to be more widely dispersed, 

especially during milder winters. 

Humans in our study area are concentrated in 

and near Flagstaff, primarily because most lands 

outside the Flagstaff corporate boundary are 

publicly owned and managed by the U.S. Forest 

Service. Current populations of Flagstaff and the 

broader study area are roughly 60,000 and 

100,000, respectively, with Flagstaff’s population 

roughly six-times larger now compared to in the 

1950s. Interstate Highways 17 and 40 (I17 and 

I40) were constructed through the study area 

during the 1970s and 1980s, and both currently are 

traveled by >20,000 vehicle per day. The 

Burlington Northern Santa-Fe railroad parallels I40 

and is traveled by roughly 100 trains per day. 

Other paved roads in the study area include U.S. 

Highways 180 and 89, which connect Flagstaff to 

Grand Canyon NP and Page, AZ, respectively, and 

Lake Mary Road, which is a comparatively littleused county road. 

Methods 

Field Methods 

We contracted with USDA Wildlife Services 

for pursuit and capture of mountain lions for our 

study. We used snares and hounds to restrain or 

bay lions and either Telazol® or KetasetRompun® (with Yohimbine antagonist) for 

subsequent sedation and anesthetization. In all but 

one instance we fitted cougars with 880g-weight 

Telonics® TGW-3580 collars fitted with a VHF 

beacon, GPS locational device, and Argos satellite 

transmitter (Figure 2.1). We fitted one mountain 

10 
lion with a VHF-only collar, and do not include 

data from this animal in our analyses. Our collars 

were programmed to attempt a 2–20-m accuracy 

GPS location once every four hours. Collars were 

also programmed to attempt transmission of the 

most recent six GPS fixes via Argos satellites once 

daily, for most collars during a four-hour evening 

window. Successfully transmitted data were 

relayed through Argos Inc. to us within 12 hours, 

embedded in an email message. We converted 

these data into decimal degree fixes and plotted 

them on USGS 1:24,000 topographic maps for 

assistance in field investigations. 

We identified all clusters of ≥2 GPS locations 

<200 m apart as candidate for investigation 

(Anderson and Lindzey 2003). These clusters 

represented, at a minimum, four hours of mountain 

lion activity within a localized area. We attempted 

to visit clusters within 10 days of when lions had 

departed. We were not able to visit all such 

clusters because data transmission failures did not 

allow us to identify some in a timely manner and, 

more commonly, because of lack of time and 

personnel. Under such circumstances we 

prioritized visiting clusters of ≥4 locations, 

representing a minimum of 12 hours of localized 

activity. We did not visit even some of these 

longer-duration clusters within 10 days of a focal 

animal’s departure, primarily because of data 

transmission failures. However, we did visit all 

such clusters once a collar had been retrieved, the 

complete collection of data downloaded, and 

clusters comprehensively identified. Under these 

circumstances field visits occurred between one 

and 18 months after they had occurred. 

We investigated the vicinity of GPS location 

clusters to document site characteristics, mountain 

lion activity, and characteristics of prey, if present. 

We used a Magellan handheld GPS unit with up to 

3-m accuracy to navigate to clusters. Of relevance 

to this analysis, we determined a kill based on the 

presence of animal remains, diagnostic physical 

trauma, and/or evidence of a struggle (e.g., blood, 

torn ground, broken branches, snagged hair). We 

determined scavenging in instances where we 

found animal remains, but together with often 

substantial sign of especially avian scavengers and 

no evidence of a struggle. We mapped and 

measured distances (in m) between activity 

features, including kill site, burials, latrines, and 

bed sites. We paced drag trails to estimate total 

distances and measured length and width of burials 

(in dm). Where possible, we identified prey to 

species and, for ungulates, aged animals on the 

basis of tooth wear and eruption (Anderson 1981, 

Bubenik 1982). We determined gender on the 

basis of remaining genitalia and, for ungulates, 

presence and development of antlers or horns. We 

could rarely determine species of Leporids, much 

less age and gender, because remains almost 

always consisted solely of hair. Remains of 

animals as large as adult deer also on rare 

occasions consisted of no more than hair and bone 

chips, even when sites were visited within 24 hours 

of abandonment by a mountain lion. Under such 

circumstances we judged age-class (fawn or 

yearling versus adult) on the basis of volumes of 

hair and sizes of burials. 

Figure 2.1. Telonics GPS/Argos collar with 

automatic release mechanism. The VHF and Argos 

antennas can be seen on the top right, and the 

release mechanism on the lower left. 

 

Analysis Methods 

Predation 

We comprehensively documented clusters for 

individual animals using locational data 

downloaded from retrieved collars. All clusters of 

≥2 points <200 m apart were distinguished, 

assigned a unique identifier, and ascribed a starting 

time and duration (in hours). Where field data 

were available, each cluster was also identified as 

being with or without a kill, and where a kill was 

present, identified with the prey type (by species, 

age class, and gender). Starting time was defined 

as that of the GPS fix initiating the sequence minus 

half the interval between the initiating fix and the 

fix immediately prior. Duration was defined as 

starting with the temporal midpoint between the 

11 
initiating GPS fix and the fix immediately prior 

and ending with the temporal midpoint between 

the terminating GPS fix and the fix immediately 

following. We excluded long-distance (>400 m) 

excursions of ≥4 hours duration. 

Because we did not visit all clusters to 

determine presence of a kill and prey type, we 

were faced with probabilistically assigning this 

information to unvisited clusters, especially where 

we were interested in determining kill rates. For 

purposes of this analysis we differentiated three 

states: no kill, kill of small-bodied prey, and kill of 

large-bodied prey. We defined small-bodied prey 

as being between 2 and 30 kg wet weight, 

including fawns, porcupines (Erethizon dorsatum), 

and all mesocarnivores. Very small prey, <2 kg in 

size, largely went undetected by our protocols for 

investigating GPS clusters at our employed 

temporal resolution. Large prey included all adult 

ungulates plus elk calves. We used logistic 

regression to specify relations between the log 

odds of each state and cluster duration (Anderson 

and Lindzey 2003). Of relevance to the 

explanatory power of cluster duration, we found 

little difference in the duration of time spent on 

kills of larger prey, including both deer kills (45-80 

kg wet weight) and elk kills (100-400 kg; see 

Chapter 3). 

We used our logistic regression models and 

data from visited clusters to assign kill states to 

unvisited clusters. Each unvisited cluster was 

assigned a probability of being associated with no 

kill, a small kill, and a large kill based on logistic 

regression models developed from visited clusters 

for each state. Models for probability of no kill 

and a small kill both included time-of-day as a 

predictor, in addition to cluster duration (see 

Chapter 3). Unvisited clusters were then assigned 

a state based on which had the highest predicted 

probability. Once all clusters were 

comprehensively assigned, we then calculated for 

each animal hourly intervals between all kills, 

large kills only, and between time of departure 

from a kill site and next kill (basically, duration of 

time without feeding). 

In addition to calculating kill rates, time spent 

on kills, and intervals without feeding, we also 

estimated biomass of kills, and from that, total 

amount of biomass consumed per kill and hourly 

rates of consumption. For animals <30 kg wet 

weight, we assigned a mass based on weights 

given in Burt and Grossenheider (1976). For mule 

deer and elk, we assigned weights given by 

Anderson (1981) and Bubenik (1982), 

respectively, differentiating adults from calves or 

fawns and adult males from adult females and, 

additionally for elk, yearlings from older animals. 

These assigned masses were obviously only 

approximations, but nonetheless usefully 

partitioned a range of masses spanning <1 kg to 

400 kg for insight through analysis. When 

multiplied by field estimates of the percentage of 

total edibles consumed by lions from a kill, we 

were able to estimate total biomass consumed 

which, when divided by hours spent on a kill, also 

allowed us to estimate hourly rates of consumption 

reckoned for the duration of a cluster. 

For purposes of this report, we undertook 

preliminary explanatory analyses that considered a 

range of potential causal factors. We treated 

mountain lion sex and age-class and seasons 

defined by climate and mating activity as class 

variables. We considered pre-reproductive lions to 

be “young” and reproductive lions to be “mature.” 

We defined three climate-based seasons: (1) fallwinter, lasting from mid-September through the 

end of April; (2) spring drought, lasting from the 

beginning of May through mid-July; and (3) 

monsoons, lasting from mid-July through midSeptember. We defined two mating-related 

seasons based on observed births in two previous 

comprehensive studies; one in New Mexico 

(Logan and Sweanor 2001) and the other in 

Alberta (Ross and Jalkotzy 1992). We back-dated 

births by a 90-dy gestation period (Anderson 1983, 

Beier and Barrett 1993, Logan and Sweanor 2001) 

to establish probable months of conception, and 

from that defined a season of peak mating activity 

(February-August) and a season of ebb mating 

activity (the remainder of the year). We also 

considered continuous variables such as prey mass, 

precipitation, maximum and minimum ambient 

temperatures, site slope, aspect (degrees from due 

south), and elevation, and percent coverages of 

trees, shrubs, herbaceous vegetation, and rocks. 

We determined temperatures and precipitation 

from daily records at Flagstaff’s Pulliam Airport 

and adjusted temperatures for effects of elevation 

by applying a standard adiabatic lapse rate 

(9.8°C/1000 m) to the elevational difference 

between the kill site of interest and Pulliam 

Airport. 

For continuous response measures such as time 

spent on kills and kill intervals, we developed 

general linear models (GLM). For binary 

categorical responses, such as likelihood of a kill 

12 
being buried or drug, we used logistic regression 

analysis and related categorical models. We 

judged the explanatory performance of GLMs by 

R2

 and P values, but, for lack of experimental 

control, without taking p-values as literally 

representing the probability of committing type I 

errors. We judged the explanatory and predictive 

power of our logistic regression models by area 

under the Receiver Operating Characteristic (ROC) 

curve and RL

2

 (Hosmer and Lemeshow 2000). We 

determined probabilities (p values) by backtransforming the modeled logits. 

Movements 

We high-graded locational data down-loaded 

from retrieved collars for use in analysis of 

movements and habitat selection, retaining only 

those locations rated as “good” by the on-collar 

GPS software and eliminating obvious erroneous 

locations identified by visually examining x-y 

plots. We calculated straight-line distance (m) and 

turn angle (°) between subsequent locations, which 

were also attributed with time interval (hrs). We 

used the HRT home range analysis extension for 

ArcGIS (Rodgers et al. 2005) to estimate 95% 

home ranges for each animal using fixed kernels 

determined by least-squares cross validation 

(LSCV). 

We used logistic regression to develop 

seasonal models of habitat selection for each 

animal. Conceptually, we discriminated between 

observed locations and locations expected if 

animals were moving within their ranges randomly 

(Keating and Cherry 2004). We paired random 

locations with observed locations, constraining 

random points to fall within a buffer centered on 

the paired lion location (discrete choice; (Cooper 

and Millspaugh 1999). Radii for buffers were 

specific to each animal, and were equal to the 99th

percentile of observed straight-line distances 

between sequential locations obtained at 4-hr 

intervals. We adopted the 99th percentile after 

determining that random points generated within a 

buffer of this radius provided consistently better 

discrimination compared to using buffer radii 

based on 50th, 67th, 75th or 95th distance percentiles. 

We stratified our analyses of habitat selection 

by sex and by season and considered a number of 

continuous and categorical explanatory variables. 

We used three seasons based on climate (fallwinter, spring drought, and monsoons), described 

under methods pertaining to predation. As 

explanatory variables, we considered an index of 

terrain roughness (TRI), elevation, distance to 

nearest paved highway (differentiated by highway 

ID), distance to nearest human residential area, 

distance to nearest known source of free water, 

jurisdiction (private and leased state lands, NPS, 

public but non-NPS), and habitat type. We 

calculated TRI as the sum change in elevation 

between focal 10-m resolution grid cells and all 8 

neighboring grid cells, using standard USGS 

digital elevation maps (DEMs; Riley et al. 1999). 

We specified habitat types based on GAP maps 

available for our study area (http://gapanalysis. 

nbii.gov/xml/th_landcover_az_1.htm), 

consolidating the more numerous GAP types into 

the following nine general types: urban, desert 

grass and low shrubland, montane grassland and 

open areas, rock and cliff, chaparral, pinyonjuniper forest and woodland, ponderosa pine forest, 

mesic high-elevation forest, and subalpine forest. 

We also considered effects of roads in terms 

of whether observed road crossings were different 

from those expected with random movement. To 

achieve this, we selected mountain lion locations 

that were within range of potential movement 

across a paved highway based on the 99th

percentile of observed 4-hr straight-line 

movements. We connected the selected lion 

locations by vectors with observed locations next 

in time sequence and with random points generated 

within the 99th percentile buffer. We then counted 

how many times mountain lions actually crossed a 

highway compared to the times they would have 

crossed if connecting to a random point, and tested 

to determine whether observed frequencies 

differed from frequencies expected with random 

movements. 

Mountain Lion Captures and 

Collar Performance

Captures and Collaring 

 Between March 2003 and June 2005 we 

conducted 12 captures of 11 mountain lions (six 

females and five males) in the Flagstaff Uplands 

and fitted them with radio-telemetry collars. We 

fitted the first lion captured (C1) with a standard 

Telonics® VHF-only radio collar. We fitted all 

other captured lions (C2 through C11) with 

Telonics® TGW-3580 GPS-Argos collars. We 

captured 9 of the lions (five females, four males) 

using hounds and we snared two others (one male, 

13 
one female). We captured four lions in each year 

(2003-2005), although one in 2005 was a 

recapture. At the time of this report (June 2006) 

our study lions had accumulated a total of 2365 

collar-days (one collar day = one lion wearing a 

collar for one day). 

 We used two sizes of collars: small (with an 

inner circumference of 30.5-40.5 cm [12-16 

inches]), intended for females, and large (33.0-43.0 

cm [13-17 inches]), intended for males. Proper fit 

is important when sizing these collars to ensure 

that the GPS antenna block is in the correct 

position on the back of the neck. However, the 

correct collar size was sometimes unavailable 

when a capture occurred, requiring modification of 

the available collar though shortening (cutting) or 

lengthening (adding a spacer). 

The approximate life expectancy for the 

Argos/GPS battery system is one year given our 

programmed parameters (six fixes attempted/day 

with four hours of attempted satellite uplink each 

evening). We typically programmed collar release 

for one year after deployment, or slightly less if the 

collar had been active for some time prior to 

deployment. To save power, the GPS system was 

set to time-out if a successful fix had not been 

obtained within three minutes. The VHF beacon in 

each collar had a separate battery and was 

programmed to function somewhat longer than the 

Argos-GPS systems to aid in collar recovery after 

release from collared animals. All data collected 

during deployment were stored in non-volatile 

memory that did not require battery power. 

Collar Performance 

To date, ten lions fitted with GPS-Argos 

collars averaged 237 collar-days each and collected 

a total of 9357 high-precision GPS fixes, for an 

average of 4.3 fixes per day out of six possible. Of 

11 collars deployed, six performed as expected in 

theory, one experienced a failure of the GPS 

system, two experienced failures of the Argos 

system, and two experienced failures of the VHF 

beacon. User errors caused one collar to release 

prior to the expected date and one collar to 

experience poor Argos uplink success (see below). 

 We assessed performance of collar 

components in two ways: 1) the percentage of all 

possible GPS fixes that were successfully obtained, 

and 2) the percentage of all possible uplinks to 

Argos satellites that delivered data of any quality. 

The first of these measures can only be determined 

from stored data once a collar has been recovered. 

By contrast, successful uplinks to Argos satellites 

can provide an ongoing measure of performance 

for the Argos feature, in addition to providing near 

real-time locational data. 

GPS Fix Success Rates 

We found both similarities and differences 

between sexes in performance of GPS systems on 

deployed collars (Table 1a). Collars on female 

lions collected successful GPS fixes at a higher 

rate than did collars on male lions (76% and 64%, 

respectively), with the proviso that data shown in 

Table 1a are not corrected for collar malfunctions, 

of which there were several (see Table 1b for 

comments). At the high-performance extreme, 

collars on two females with >100 collar-days each 

(C4 and C7) had GPS fix rates around 90%, as did 

one collar on a male (C5). At the low performance 

end of the spectrum, female C2’s collar acquired 

successful GPS fixes at the lowest rate of all lions 

(53%), followed closely by male C10’s 56%. It is 

worth noting, however, that the GPS in C10’s 

collar, which accounted for 42% of all male collardays, began to malfunction at 255 days, and 

provided little additional data before failing 

completely at 302 days. Considering only the first 

254 days, C10’s collar successfully collected GPS 

locations at a rate of 77%. If we do not consider 

data from after when C10’s collar began to 

malfunction, then overall rate of successful GPS 

fixes on male-deployed collars increased to 76%, 

which is virtually identical to the rate we observed 

for collars on females. 

Argos Satellite Uplink Rates 

Percentages of successful Argos uplinks also 

differed between collars deployed on females 

versus males. On average, collars worn by females 

successfully uplinked 70% of the time, whereas 

collars worn by males successfully uplinked only 

48% of the time. However, malfunctions of the 

Argos system on two collars (one for each sex), 

and an unsuccessful user attempt to “refine” the 

Argos uplink window on a second male collar, 

affected these rates as well. Three females with 

>100 collar-days each achieved well-above 

average uplink success (around 80%), whereas the 

most successful male collar (C11’s) managed only 

58% of possible uplinks. 

The Argos system on female C8’s collar 

functioned with some regularity for just 64 days, 

14 
during which time the uplink rate was only 27%. 

During the next 106 days, successful uplinks 

declined to only 10% of all possible (Table 1a, see 

C8-A). On Day 171, C8 was recaptured and fitted 

with a replacement collar that she wore for an 

additional 362 days (Table 1, C8-B). If we ignore 

the 106 potential uplinks to C8-A’s collar that 

occurred after the apparent malfunction of the 

Argos feature, overall success rate for Argos 

uplinks from female collars increased to 76%. 

We experienced two different problems that 

affected Argos uplinks from male collars. C6’s 

Argos system failed after 217 days, and we lost all 

ability to track the collar at approximately Day 310 

when the VHF beacon also failed. For two weeks 

before the collar was programmed to release we 

conducted an intensive hunt to capture C6 and 

retrieve his collar, but these efforts proved 

unsuccessful and the collar and its data were 

temporarily lost. However, the intensive effort to 

locate C6 led to the capture and collaring of C9, 

C10, and C11. Because we do not yet have the 

comprehensive data set from C6’s collar, we only 

included potential uplinks in our analysis from 

before Argos failure. Analysis of GPS 

performance for this collar will need to await 

retrieval of the data. 

The second problem we experienced with 

Argos uplink on male collars was one of our own 

making. In an attempt to improve uplink success 

after the first year of data collection, we decided to 

split the usual 4-hour uplink window (1700 to 2100 

MST) into two 2-hour segments (1630 to 1830 and 

0330 to 0530 MST). Our intent was to increase 

odds that a lion would be out in the open, where its 

collar would more likely uplink with an Argos 

satellite during two periods of good satellite 

coverage instead of one. We modified the uplink 

programming on two collars and deployed one of 

these on male C5. As male uplink success was 

already lagging behind that for females, we were 

optimistic about prospects for this modification. 

Unfortunately, the success rate for C5 (26%) was 

half that of other males. We returned the second 

collar to its original uplink schedule before 

deployment. However, even if we were to omit 

C5’s uplink data from our assessment, the overall 

success rate for uplinks from collars deployed on 

males would only improve slightly (from 48% to 

51%) and would still be substantially less than 

what we observed for females. 

Discussion of Collar Performance 

We suspect that differences in rates of 

successful GPS fixes between collars deployed on 

the two sexes were partly because males spent 

more time in rugged terrain than did females (see 

Chapter 2). Rugged terrain is known to reduce 

rates at which GPS locations are successfully 

acquired (D’Eon et al. 2002, Frair et al. 2004, Cain 

et al. 2005), and also very likely reduces the odds 

of successfully uplinking to Argos satellites. If 

true, then one would expect that differences 

between the sexes in successful uplinks to Argos 

satellites would be greater than differences in 

successful acquisitions of GPS fixes, primarily 

because south-north transiting Argos satellites 

should be more difficult to contact than geostationary GPS satellites. In fact, this is the effect 

observed, which adds weight to our speculation 

that selection by males for rough terrain, at least 

during the daily period when uplinks to Argos 

satellites were attempted, hindered communication 

with Argos satellites. 

Another factor that could have contributed to 

observed differences in collar performance was 

collar fit and related position of GPS and Argos 

elements on the animal (D’Eon and Delparte 

2005). As previously mentioned, the correct collar 

size was sometimes not available at captures, 

requiring that we modify length of the available 

collar. This happened with five of 11 captures. 

Three females received large collars that had to be 

cut down and two males received small collars that 

had to be extended (Table 1b). We observed no 

consistent effect of modifying length for the large 

collars shortened for deployment on females. On 

one collar the VHF battery failed and GPS 

performance was poor – but performance of the 

Argos system was good. A second collar’s Argos 

system failed, but GPS performance was average. 

The third cut-down collar had the highest GPS 

success rate and second highest Argos success rate 

of any collar. Results from the two small collars 

extended to fit on males were similarly ambiguous. 

One collar had average GPS performance and 

above average Argos performance, while the other 

collar experienced Argos and VHF failures. It 

does not appear that collar fit, at least to date, had 

any major effect on collar performance. 

15 
Table 1a. Summary of capture and collaring information for 10 mountain lions caught near Flagstaff, Arizona in 2003-2005 and fitted w

Collar performance, including GPS position fix and Argos uplink success rates is shown for each collar. Color-shaded cells indicate note

Animal ID Sex Age 

Date Collared Collar Released 

Days On 

Possible Fixes (#) 

Successful Fixes (#) 

Successful Fixes (%) 

Possible Uplinks (#

C2 F A 14-Jul-03 15-Jun-04 338 2022 1075 53 337 C4 F J-A 4-Aug-03 15-Jun-04 317 1902 1682 88 316 C7 F A 14-Dec-04 died with collar 204 1224 1122 92 203 C8-A F A 15-Dec-04 4-Jun-05 170 1020 715 70 169 C8-B F A 4-Jun-05 1-Jun-06 362 2172 1681 77 361 C9 F J 16-May-05 died with collar 138 828 706 85 137 Totals for females 1529 9168 6981 76.1 1523 C3 M A 3-Aug-03 15-Dec-03 134 804 504 63 133 C5 M J-A 19-Aug-04 died with collar 110 660 571 87 109 C6 M A 17-Aug-04 not recovered >217 unknown unknown unknown 217 C10 M A 25-May-05 15-May-06 355 2130 1196 56 354 C11 M J 13-Jun-05 died with collar 20 120 105 88 19 Totals for males >836 3714 2376 64.0 832 Overall totals for both sexes >2365 12,882 9357 72.6 2249 

Table 1b. Additional information regarding collar fit and performance for 10 mountain lions caught near Flagstaff, Arizona in 2003-2005 

telemetry collars. Collar modifications are noted and the fate of each lion/collar is briefly described. Color-shaded cells indicate notewo

Animal ID Sex Age 

Correct collar size/

size used 

Collar 

Modified? Comments 

C2 F A small/large cut VHF battery failed after 330 days; Argos continued to relay coC4 F J-A small/small no Greatest number of successful GPS-fixes; wildfire consumed C7 F A small/large cut Lion hunted-collar returned; C7 had highest percentage of sucC8-A F A small/large cut Argos failed after 64 days--uplink rate only 27%; recapture on C8-B F A small/small no Replacement collar (C8-B) worked very well; highest percentaC9 F J small/small no Lion hunted-collar returned; C9 migrated soon after collaring tC3 M A large/small spacer Collar released prematurely (user error); C3 was often located C5 M J-A large/large no Lion hunted-collar returned; came from Grand Canyon NP; ineC6 M A large/small spacer Argos failed after 217 days; VHF also failed before scheduled C10 M A large/large no GPS began failing at 255 days and failed at 302 days; Argos cC11 M J small/small no Lion killed—head trauma, apparently caused by an elk; collar 

16 
Chapter 2 

Movements and Habitat Selection by Mountain Lions in 

the Flagstaff Uplands 

Terence Arundel, David Mattson, and Jan Hart 

Introduction 

Movements and habitat selection by 

mountain lions (Puma concolor) are of interest to 

managers and the public for several reasons. 

Insight into habitat selection is germane to 

assessing and managing risks posed to humans by 

lions – by allowing researchers and managers to, 

ideally, explain and map distributions of lion 

activity, potentially at the level of different 

behaviors, of which predation is the most relevant 

to human safety. Such information allows 

managers to identify high-risk areas for 

ameliorative management, and to better understand 

what measures, when, will most likely yield 

desired results. Insights into movements and 

habitat selection also provide a basis for assessing 

impacts of humans on mountain lion populations 

and individuals, both to judge impairments of 

natural dynamics and behaviors and to design 

mitigations. Finally, information on movements 

and habitat use are a basis for insight into the 

comparative ecology of this species, and 

potentially a compelling part of public education 

about mountain lions. 

Other than standard calculations of home 

range size, which date back to the onset of radiotelemetry investigations in the 1960s and early 

1970s (e.g., Seidensticker et al. 1973), 

investigations of especially habitat selection have a 

comparatively recent pedigree in mountain lion 

research. Only within the last decade have 

analyses of habitat use and selection included 

multi-variable explanatory models employing 

methods such as logistic regression (Dickson and 

Beier 2002, Dickson et al. 2005), allowed 

principally by recent developments in modeling 

methods (Burnham and Anderson 1998, Hosmer 

and Lemeshow 2000). More commonly, habitat 

selection has focused on rejection of null 

hypotheses pertaining to landscape classifications 

that do not encompass questions of nearness, as to 

water or human facilities, or questions pertaining 

to continuous landscape measures (e.g., Logan and 

Irwin 1985, Williams et al. 1995, Katnick and 

Wielgus 2005). With the development of genetic 

methods, mountain lion research has also only 

recently broached very broad-scale, populationlevel questions of movements, signaled by patterns 

of genetic similarity (Sinclair et al. 2001, McRae et 

al. 2005). By contrast, the tradition of 

investigating fine-scale movements, associated 

with behaviors such as predation, bedding, or roadcrossing, is long-standing and based on diverse 

methods that include intensive ground-telemetry 

(e.g., Hornocker 1970, Laundré and Hernández 

2003, Dickson et al. 2005) and fixed track or 

camera stations (Clevenger and Waltho 2000, 

2005; Gloyne and Clevenger 2001). 

Regardless of the scale, previous research on 

habitat use has shown that mountain lions select 

for rugged terrain with forested or woody cover 

and are averse to crossing or otherwise using flat 

open areas (e.g., Logan and Irwin 1985, Koehler 

and Hornocker 1991, Laing and Lindzey 1993, 

Williams et al. 1995, Riley and Malecki 2001). 

McRae et al. (2005) speculated that expansive 

open areas impeded lion movements to such an 

extent that it explained genetic differentiation 

among lion populations at the regional scale. Even 

at a micro-scale, lions select for cover, whether 

greater forest cover, nearby rockiness, and even 

more confined highway underpasses (Akenson et 

al. 2003; Laundré and Hernández 2003; Clevenger 

and Waltho 2000, 2005). Mountain lions also tend 

to select for ecotone edges (Laing and Lindzey 

1993, Akenson et al. 2003, Laundré and Hernández 

2003), and areas with abundant browse of their 

favored prey (Logan and Irwin 1985, Laing and 

Lindzey 1993). 

Insofar as human features are concerned, 

mountain lions tend to avoid residential areas 

(Beier 1995, Dickson et al. 2005) and areas with 

on-going or recent timber harvest (Van Dyke et al. 

1986, Meinke et al. 2004). Ernest et al. (2003) 

associated broad-scale genetic differentiation 

17 
among mountain lions in California with limited 

movements not only across open areas, as 

speculated by McRae et al. (2005) for lions in the 

interior Southwest, but also population-level 

impediments posed by broad areas of human 

developments. At a finer scale, lions avoid 

crossing heavily trafficked paved highways 

(Alexander and Waters 2000, Dickson et al. 2005) 

and areas with high densities of secondary roads 

(Van Dyck et al. 1986), but show little aversion to, 

or even potentially favor traveling on, lightly 

trafficked unpaved roads (Dickson et al. 2005). In 

spite of some degree of aversion by lions to most 

human-related landscape features, mountain lion 

populations nonetheless have been relatively 

resilient to humans and their activities, especially 

compared to other large carnivores such as grizzly 

bears (Ursus arctos) and wolves (Canis lupus; 

Laliberte and Ripple 2004, Riley et al. 2004). 

Of the analyses of habitat selection done to 

date, comparatively few have produced robust 

models with wide applicability and non-monotonic 

parameters that have been used to address 

spatially-explicit management questions. The 

coarse-grained broad-scale model developed by 

Riley and Makecki (2001) has been given 

spatially-explicit application to the state of 

Montana, as has a similar model to Utah. At a 

much finer scale, Dickson et al. (2005) used an 

analysis of movements at 15-minute intervals to 

parameterize an agent-based model for identifying 

likely movement corridors. At a similar resolution, 

Clevenger and Waltho (2005) applied models to 

identify locales and other features of road-crossing 

structures that favored movements of lions. To our 

knowledge, no models have been used to map 

probability of mountain lion activity as an aid to 

managing human exposure to lions (see Chapter 4). 

Other models have merely specified directions of 

relations, without elucidating important features 

such as asymptotes or optima that can only be 

captured by gathering data over a broad range of 

landscape conditions. Without such critical 

nuances, models can suggest, for example, an 

unconditional affinity for rugged terrain which 

produces improbable results such as shear canyon 

walls in Zion National Park, Utah, being rated as 

prime mountain lion habitat. Clearly, opportunities 

exist to improve habitat selection models for 

mountain lions, and find applications that provide 

insight and highlight opportunities for managers. 

The Flagstaff Uplands 

Human safety, human impacts, and public 

education are all issues for stakeholders of 

mountain lion management in the Flagstaff 

Uplands. A series of high-profile incidents during 

the winter of 2000-2001 on Mt. Elden, a mountain 

massif popular for hiking and adjacent to 

residential areas, brought the potential for lion 

attacks on humans sharply into focus, along with 

the related need for additional information to 

manage human risk. There has also been on-going 

concern among local wildlife and public lands 

managers about potential impacts of the 

considerable human infrastructure in the Flagstaff 

Uplands on mountain lion movements and 

populations. Two major interstate highways 

(Interstate Highways 40 and 17 [I40 and I17]), one 

twined federal highway (US 89), and a major 

railroad (Burlington Northern-Santa Fe [BNSF]) 

transect the region and converge on the city of 

Flagstaff’s urbanized areas. Although localized 

impacts have not been documented, McRae et al. 

(2005) speculated on the basis of broad-scale 

north-south genetic differences that the BNSF 

railroad, since perhaps it construction in 1884, 

together with the paralleling U.S. 66, then I40, 

were major filters or even barriers to lion 

movements. Information on mountain lion 

movements, analyses of effects of especially 

transportation routes, and related products such as 

maps of predicted lion activity are clearly germane 

to concerns regarding mountain lions in the 

Flagstaff Uplands. 

We initiated a study in 2003 that included 

investigations of mountain lion movements and 

habitat selection in the Flagstaff Uplands. Our 

goal was to provide information that would help 

managers and other stakeholders assess impacts of 

human facilities on mountain lions and manage 

exposure of humans to risk of mountain lion 

attacks (Chapter 1). The primary products in 

service of such goals are seasonal maps of 

predicted mountain lion activities and analyses of 

movements and habitat selection that include 

candidate effects of human facilities. To achieve 

these ends we continue to deploy GPS/satellite 

collars (see Chapter 1) that collect highly accurate 

locations at four-hour intervals round-the-clock. 

We used these time-specific locational data to 

determine the effects of highways on movements, 

including turn angles and crossings. We also used 

these data to parameterize preliminary multi18 
variable models of habitat selection for radiocollared mountain lions, which we then used to 

develop preliminary seasonal maps of predicted 

lion activity, which we present in this chapter. 

Like previous researchers, we considered the 

effects of terrain roughness, woody cover, and 

nearness to roads and human residential areas on 

selection. Unlike previous studies, we also 

considered effects of jurisdiction, nearness to open 

water, and distance to the centroid of the lion’s 

GPS locations, as surrogate for an individual lion’s 

attraction to familiar areas. We considered terrain 

roughness and woody cover to be surrogates for 

ambush advantage and also for concentration of 

prey, terrain roughness especially for mule deer 

during winter and woody cover for elk year-round. 

We also considered elevation and nearness to 

water as surrogates for prey distribution, water 

especially for elk. 

This preliminary analysis is for data collected 

during July 2003-June 2006. We expect to present 

a final analysis after 2009, when field work will be 

completed. At that time we will consider 

additional explanatory variables, including local 

monthly temperatures and precipitation, incident 

radiation, satellite-derived wetness and greenness, 

and, where available, forest cover and stem 

density. These additional variables will provide 

additional surrogate measures for prey distribution 

and, in the case of forest measures, direct links to 

management for forest health. 

Notes on Methods 

We describe methods for the analysis 

presented here in Chapter 1. However, a few 

issues warrant discussion; in particular, 

autocorrelation and bias in GPS locations, and our 

future plans for addressing these potential 

problems. The likelihood that a collar-mounted 

GPS unit will obtain a location has been shown to 

be lower in forested steep terrain compared to flat 

open areas (D’Eon et al. 2002, Frair et al. 2004, 

Cain et al. 2005); a bias which can be mitigated by 

applying correction factors (Frair et al. 2004). We 

have designed but not yet implemented a study to 

develop correction factors which we plan to use in 

future analyses of habitat selection. For lack of 

such correction factors now, results presented here 

likely under-estimate the degree of selections both 

for rough forested terrain and against grasslands 

and low shrub-lands. 

Spatial and temporal autocorrelation can bias 

especially estimates of parameter variances, and 

taint judgments regarding the performance of 

related models. In future analyses of movements 

using multiple regression, we plan to assess 

autocorrelation bias in estimates of coefficients and 

variances by incorporating lag terms, not only for 

dependent variables (previous turn angle or 

distance moved), but also for independent 

variables (e.g., terrain roughness at prior locations; 

Weisberg 1985). We will also account in some 

measure for non-independence and related 

autocorrelation by eliminating locations that are 

<100 m away from the prior location, which 

signify instances where a lion effectively exercised 

no choice independent of prior choices regarding 

use of a site. Even with these measures, estimates 

of coefficient variance will likely be underestimated, leading to inflated judgments of model 

performance (Nielson et al. 2002), which we plan 

to address by using measures accepted at the time 

for adjusting variances and related judgments of 

“significance” to additionally account for 

autocorrelation. 

Results 

Natural and human features, as well as the 

identities of monitored animals, had major 

apparent effects on habitat use and movements by 

mountain lions in the Flagstaff Uplands during 

2003-2006 (Table 2.1). Of the independent 

variables we considered, all were in at least one 

explanatory model. The models themselves 

exhibited varying degrees of sufficiency reckoned 

in terms of predictive capabilities (e.g., area under 

the Receiver Operating Characteristic [ROC] 

curve) or explanation of data variance (e.g., R2

 or 

R2

L), but none were likely to have been specified 

by chance alone (i.e., p-values were all <0.0001; 

Table 2.1). We specified models for only two 

seasons, fall-winter and spring drought-monsoons 

(or “summer”), after preliminary analyses showed 

surprisingly little difference between models for 

the summer months. We present the following 

results by whether they pertain to natural features, 

the animals themselves, or human-made features, 

integrating among models specific to different 

sexes, seasons, and response variables. We include 

water sources in “natural” features, realizing that 

most in our study area were created and are 

maintained by humans to provision livestock and 

wildlife species such as elk with water. 

19 
Table 2.1. Parameters for models of habitat selection and movement (turn angle and distance between 

successive locations) for mountain lions in the Flagstaff Uplands, 2003-2006, including score χ2

 or parametric 

F values, degrees of freedom , and p-values for tests of model type I errors, R2

 or R2

L values for explained 

variance, and area under the ROC curve for logistic regression models. Values in the table body are, to the 

right of the slash, ∆AIC (Akaike Information Criterion), which signifies the comparative weight of the 

corresponding variable in the model (Burnham and Anderson 1998) and, to the left of the slash, the direction 

of the relation between continuous explanatory variables and likelihood of a mountain lion location versus a 

paired random point; ‘na’ signifies “not applicable” for class variables. Comments in parentheses identify an 

interaction effect.

Model 

Explanatory variable 

Male 

habitat 

selection 

(Fallwinter) 

Male 

habitat 

selection 

(Spring 

droughtmonsoon) 

Female 

habitat 

selection 

(Fallwinter) 

Female 

habitat 

selection 

(Spring 

droughtmonsoon) 

Distance 

moved 

between 

successive 

locations 

(Step 

length, m) 

Turn angle 

between 

successive 

movements 

(°) 

Terrain roughness (index) + / 325.0 + / 166.0 + / 269.1 + / 73.6 – / 10.7 ―

Terrain roughness polynomial ― ― – / 9.0 – / 70.1 ― ―

Vegetation cover type (consolidated 

 GAP types) 

na / 74.9 na / 31.2 na / 223.2 na / 62.1 na / 18.8 ―

Distance to nearest free water (m) – / 4.0 – / 12.1 ― – / 5.1 ― ―

Season (winter-fall versus spring 

 drought-monsoons) 

― ― ― ― na / 47.0 ―

Diel time period ― ― ― ― na / 436.8 ―

Distance from centroid of an individual 

 animal’s locations (m) 

― – / 4.1 – / 36.5 – / 4.2 ― + / 22.5 

(by sex) 

Time interval between successive 

 locations (hr) 

― ― ― ― + / 23.2 ―

Modeled probability (p) of a male 

 mountain lion being present 

― ― ― – / 10.6 ― ―

Ownership/jurisdiction na / 8.7 na / 27.6 na / 62.3 na / 43.5 na / 8.8 ―

Distance to nearest paved highway (m) ― ― + / 64.0 

(by hiway 

type) 

+ / 29.9 

(by hiway 

type) 

+ / 9.3 

(by sex) 

―

Distance to nearest urban area (m) + / 23.8 + / 25.8 + / 31.0 + / 33.3 + / 37.4 

(by sex) 

+ / 44.0 

(by sex) 

Score χ

2

 or F statistic 558.3 288.3 976.3 424.8 62.1 12.3 

df (numerator/denominator) 15 14 17 19 28/9274 11/9292 

P-value <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 

R2

 or R2

L 0.249 0.233 0.156 0.115 0.158 0.014 

Area under ROC curve 0.751 0.744 0.682 0.656 ― ―

Effects of Natural Features 

 Terrain roughness had a major, if not 

dominant, effect in all models of habitat selection, 

regardless of season or sex, and was also included 

in the model explaining distance moved between 

successive locations (i.e., step length; Table 1). It 

was excluded only from the model explaining turn 

angle. Lions strongly positively selected for 

terrain roughness, males more-so than females. 

Unlike males, female selection exhibited 

inflections during both fall-winter and spring 

drought-monsoons that denoted optima at 

intermediate levels of roughness (Figure 2.1). In 

general, both males and females more strongly 

selected for rough terrain during fall-winter 

compared to summer. They also tended to travel 

20 
shorter distances between successive locations as 

terrain roughness increased (Table 2.1). 

Mountain lions in our study have so far 

exhibited pronounced patterns of selection for 

different vegetation cover types during both 

summer and fall-winter (Figure 2.2). Overall, they 

used urban areas and desert grass- and shrublands 

much less than expected by chance. They also 

tended to under-use montane grasslands and other 

higher-elevation open areas. By contrast, piñonjuniper woodlands and ponderosa pine forest were 

consistently used more than expected, although not 

by much. Male and female mountain lions differed 

most in their use of chaparral and rock and cliff, 

with males registering ambivalence and females 

strongly selecting for both types, especially during 

fall-winter. In large measure consistent with 

patterns of selection, monitored lions tended to 

move most rapidly through urban areas, subalpine 

forest, rock and cliff, and desert grass- and 

shrubland, and slowest through chaparral 

(Figure2.2). 

Insofar as other natural factors were 

concerned, mountain lions of both sexes selected 

for areas near water sources compared to farther 

away, more-so during summer compared to fallwinter, and more consistently for males than for 

females (Table 2.1). Males exhibited an affinity 

for water sources during both fall-winter and 

summer whereas females did so only during 

summer. We also introduced the distribution of 

males as a candidate for explaining, in part, the 

distribution of females. We did this by first 

developing models of habitat selection for males, 

which we then used to assign probabilities of 

habitat use by this sex to locations and paired 

random points for females. On this basis, females 

Figure 2.2. Relations between vegetation cover 

types and probability of a mountain lion location 

versus a random point, for males (top) and females 

(middle), as well as mean distance moved between 

successive locations (step length, bottom), for 

mountain lions in the Flagstaff Uplands, 2003-2006. 

Error bars show 95% confidence intervals. Light 

gray bars, top and middle, are for winter-fall, 

whereas dark gray bars are for summer.

Figure 2.1. Relations between probability of a 

mountain lion location versus a random point and 

index of terrain roughness, for mountain lions in the 

Flagstaff Uplands, 2003-2006. Lines show 

modeled relations and circles are proportions and 

associated 95% confidence intervals for septiles of 

the data, shown to illustrate goodness-of-fit. Open 

circles and dashed lines are for fall-winter; filled 

circles and solid lines are for summer, for males at 

top and females at bottom. 

21 
22 

 Figure 2.3. Home ranges of female (left) and male (right) mountain lions in the Flagstaff Uplands, 2003-2006.
Figure 2.5. Relations between sex-age class and mean distance moved (step length; dark bars behind) and turn angle 

(cross-hatched bars in front) between successive locations, by ebb (left) versus peak (right) mating seasons, for 

mountain lions in the Flagstaff Uplands, 2003-2006. Means were calculated by marginal least squares and error bars 

show 95% confidence intervals. Letters above bars denote means not equal at α = 0.05 using the Tukey-Kramer test for 

multiple comparisons.

appeared to select for areas not likely used by 

males during summer (Table 2.1). 

Movements differed by several time-related 

factors, in addition to sex and age of the animal. 

Annual ranges of males averaged 488 km2

 (Range 

= 281-606 km2

), much larger than the average 165 

km2

 (111-262 km2) observed for females (see 

Figure 2.3 for range locales). Outliers include 47 

km2

 for the one-month range of a young male, 30 

km2

 for the two-month range of an adult female, 

and 432 km2

 for a dispersing young female. On 

average, lions moved longest distances during 

dusk, shortest distances during daylight, and 

intermediate distances at night and dawn (Figure 

2.4). They also averaged longer movements 

during fall-winter (least squares marginal mean = 

1895 m, 95% CI = 1657-2133 m) compared to 

summer (1642 m [1408-1877]). Adult males 

consistently averaged longer movements and 

greater turn angles compared to any other sex or 

age class, regardless of climatic or mating season 

(Figure 2.5). By contrast, young males averaged 

movements longer than those of both young and 

mature females during ebb mating season, but not 

during peak mating season, when length of 

Figure 2.4. movements by young males lessened (Figure 2.5). Mean distance moved between 

successive locations (step length ) by mountain 

lions in the Flagstaff Uplands, 2003-2006, relative 

to diel period. Means were calculated by marginal 

least squares and error bars show 95% confidence 

intervals. Letters above bars denote means not 

equal at α = 0.05 using the Tukey-Kramer test for 

multiple comparisons. 

23 

Effects of Human Features 

Locations of mountain lions in our study area 

were affected by several human-related features, 

including nearness to paved highways and urban 

areas and occurrence in different jurisdictions or 

land ownerships. Controlling for the effects of 

temporal factors and other natural and human 

features, males and females consistently selected 

against areas in private ownership, including 

leased Arizona state lands (Figure 2.6). Males 

differed from females in use of National Park 

Service (NPS) jurisdictions by selecting for these 

areas, especially during fall-winter, in contrast to a 

tendency towards under-use by females yearround. Lions of both sexes moved longer average 

distances when in Park Service or Arizona Wildlife 

Management areas, in contrast to shorter distances 
when on U.S. Forest Service lands or private and 

leased state lands (Figure 2.6). Of all humanrelated features, mountain lions of both sexes 

exhibited consistently strongest responses to 

nearness to urban areas, principally Flagstaff’s 

incorporated jurisdiction (Table 2.1). Males 

exhibited a broader zone of avoidance compared to 

females, pronounced out to roughly 1.5 to 3 km, 

depending on season, in contrast to roughly 1 km 

for females year-round (Figure 2.7). Males also 

differed somewhat from females in their 

movements when near urban areas. Both turn 

angles and movement distances declined for males 

and females when near urban areas, but more-so 

for females, evident out to a greater distance 

(Figure 2.8). Females moved shorter distances, 

with less angular deviation, out to as much as 3-5 

km away from urban areas, whereas the same was 

true for males out only to 1-2 km, depending on 

whether the measure was step length or turn angle. 

Figure 2.7. Relations between probability of a 

mountain lion location versus a random point and 

distance to urban area, for mountain lions in the 

Flagstaff Uplands, 2003-2006. Lines show 

modeled relations and circles are proportions and 

associated 95% confidence intervals for septiles of 

the data, shown to illustrate goodness-of-fit. Open 

circles and dashed lines are for fall-winter; filled 

circles and solid lines are for summer, for males at 

top and females at bottom. 

Figure 2.6. Relations between land ownership or 

jurisdiction and proportions of mountain lion 

locations versus a random points, by jurisdiction, for 

males (top) and females (middle), as well as mean 

distance moved between successive locations (step 

length, bottom), for mountain lions in the Flagstaff 

Uplands, 2003-2006. Light gray bars, top and 

middle, are for winter-fall, whereas dark gray bars 

are for summer. Mean step length was calculated 

by marginal least squares and error bars show 95% 

confidence intervals. The dashed line, top and 

middle, denotes non-selective use (prop. = 0.5). Females exhibited comparatively strong 

responses to nearness of paved highways, in 

contrast to males, which did not (Table 2.1). 

Females consistently selected against areas near 

highways, but to varying degrees depending on 

season and type of highway (Figure 2.8). Female 

response was greatest during fall-winter, and least 

reactive to highways with low traffic volumes 

(<4,000 vehicles/day) and most reactive to 

interstate highways and highways with high traffic 

volumes (>6,000 vehicles per day; Figure 2.8). By 

contrast, during summer, when responses were 

somewhat weaker, females tended to be least 

reactive to nearness of interstates and most reactive 

24 
to nearness of highways with low traffic volumes. 

When near highways, females moved shorter 

average distances compared to when farther away, 

and to a greater extent compared to males (Table 

2.1; Figure 2.8). 

To date, perhaps the most dramatic effect of a 

human feature on mountain lion behavior was 

evident in the probability that a lion would cross a 

paved highway. Mountain lions tracked so far in 

our study were much less likely to cross a paved 

road of any type compared to crossings expected 

with random movements (Likelihood Ratio χ

2

 = 

713.8, df = 3, p < 0.0001). Between 16 and 20% 

of random movements originating at a lion location 

within range of a paved highway crossed the 

highway (Figure 2.9). By contrast, only 0.4 to 2% 

of observed mountain lion movements did so. 

Even so, lions were more likely to cross a paved 

road with low traffic volumes compared to 

especially an interstate highway (Figure 2.10). To 

date, we have observed 20 crossings of little-used 

paved highways, primarily Lake Mary Road, only 

three crossings of I17, and none of I40. 

Figure 2.8. Relations between distance to urban area 

or paved highway and distance moved (solid lines and 

filled circles or squares) and turn angle (dashed lines 

and open circles) between successive locations, for 

mountain lions in the Flagstaff Uplands, 2003-2006, by 

sex. Lines show modeled relations and circles are 

means and associated 95% confidence intervals for 

septiles of the data, shown to illustrate goodness-of-fit. 

Squares denote highways and circles denote urban 

Figure 2.9. Relations between probability of a 

mountain lion location versus a random point and 

distance to paved highway, for mountain lions in the 

Flagstaff Uplands, 2003-2006, by season. Lines 

show modeled relations and circles are proportions 

and associated 95% confidence intervals for 

septiles of the data, shown to illustrate goodnessof-fit. Black circles and lines are for paved roads 

with low traffic volumes; dark gray diamonds and 

lines are for paved roads with high traffic volumes; 

and light gray boxes and lines are interstate 

highways. 

Mapped Application to the Flagstaff 

Uplands 

We used preliminary models of habitat 

selection presented here for male and female 

mountain lions to map seasonal probability of lion 

activity in the Flagstaff Uplands (Figures 2.11-

2.12, following pages). Our intent was not to 

produce a definitive product, but rather to illustrate 

potential application of this information to 

management and planning, and to also provide a 

spatially explicit picture of what seemingly 

abstract relations look like applied to real and 

potentially familiar places. We included the 

spatially-explicit effects of all explanatory 

variables, with the exception of distance to 

centroid of locations for individual animals. This 

25 
measure is specific to a given animal and period of 

time, and so has no stable spatial frame. We 

controlled for this effect by subsuming into the 

intercepts of relevant models. 

Hotspots of potential mountain lion activity 

stand out in the Flagstaff Uplands, denoted in our 

maps as shades of red. Throughout the year, areas 

of greater topographic relief or terrain roughness 

stand out, specifically Mt. Elden and slopes of the 

San Francisco Peaks, Walnut Canyon, cinder cones 

north and east of Flagstaff and Doney Park, and 

scarps of Observatory Mesa. Predicted activity is 

decidedly low (shades of blue) in flatter areas 

without forest cover, especially to the north and 

east where piñon-juniper woodland grades to 

desert grass- and low shrubland, largely on private 

and leased state lands. 

Discussion 

We are not yet in a position to reach 

definitive conclusions regarding movements and 

habitat selection by mountain lions in the Flagstaff 

Uplands. Although our sample of telemetry 

locations is large by any standard, our basis for 

inference is compromised by, as yet, the small 

sample of individuals and the study’s relatively 

short duration. Even so, we found comparatively 

strong signals from the data revealing not only 

expected patterns, but also suggestive patterns that 

raise questions rather than provide answers. In 

what follows, we note similarities with previous 

research, offer what seem to be plausible 

explanations for novel patterns, and identify results 

that, as yet, defy ready explanation. 

Figure 2.10. Proportion of instances when a 

mountain lion location was within range of potential 

movement across a highway that a random vector 

crossed the road (dark bars behind) versus an 

observed lion movement (open bar with crosshatching in front), for mountain lions in the Flagstaff 

Uplands, 2003-2006, by highway type and traffic 

volume. 

Selection of Natural Features 

Our results are consistent with previous 

more-or-less universally observed affinities of 

mountain lions for rugged terrain and woody 

vegetation in the western United States (Logan and 

Irwin 1985, Koehler and Hornocker 1991, Laing 

and Lindzey 1993, Williams et al. 1995, Riley and 

Malecki 2001). As others have previously 

remarked, anyone who has spent time tracking 

mountain lions already knows that lions prefer 

wooded rough terrain. However, results such as 

the ones presented here allow for specific 

quantitative comparisons, as well as explicit 

application to landscapes to produce maps of 

mountain lion activity. The main argument for 

spatially and temporally explicit quantitative 

representations is that they provide stakeholders 

with an inter-subjective basis for communication, 

management, and planning. Of course, a key 

requirement for these kinds of applications is that 

spatial frames and related landscape metrics be 

stable for the areas of intended application. 

Although broadly consistent with previous 

research elsewhere, our results did reveal novel 

patterns relevant to understanding and representing 

mountain lion behavior. For one, female lions 

clearly expressed selection for an intermediate 

range of terrain roughness, most evident during 

summer. Such a pattern is consistent with our 

prior expectation that lions probably do not prefer, 

despite occasionally using, extreme topography 

such as shear cliffs. Even so, unlike the females, 

male lions did not express selection for a midrange of terrain roughness and, instead, exhibited 

monotonic relations to this variable. We suspect 

that this failure to detect a mid-range optimum for 

males was related to at least two factors. For one, 

the males were not as exposed as the females were 

to the roughest terrain in our study area, 

concentrated primarily around the San Francisco 

Peaks and cinder cones north of I40. For another, 

the males did, in fact, orient more strongly towards 

rough terrain compared to females. 

We observed other differences in habitat 

selection by males and females, with potential 

26 
Figure 2.11. Predicted probabilities of female (left) and male (right) mountain lion activity in the Flagstaff Uplands during Spring drough

preliminary models of habitat selection from data collected during 2003-2006. 

27 
Figure 2.12. Predicted probabilities of female (left) and male (right) mountain lion activity in the Flagstaff Uplands during Fall-Winter, ba

habitat selection from data collected during 2003-2006. 

28 
implications to explaining composition of kills. 

Especially during fall-winter, females more 

strongly selected for chaparral and bare rock, 

which roughly equated with south-facing slopes of 

Mt. Elden and the Dry Lake Hills near Flagstaff, 

and with south-facing scarps of the Mogollon Rim 

at the southern end of our current study area. 

These areas of chaparral often contained abundant 

oaks, cliffrose, and mountain mahogany, and were 

heavily used by mule deer, especially during 

winter. This potential juxtaposition of female 

lions, deer, and chaparral plausibly explains both 

the much greater incidence of mule deer killing by 

females compared to males, as well as the 

association of mule deer kill sites with presence of 

cliffrose and mountain mahogany (Chapter 3). In 

contrast to the pattern with chaparral, males yearround more strongly oriented toward water sources 

than did females. Again, this sex-based difference 

in habitat selection was plausibly associated with 

sex-based differences in kills. Unlike mule deer, 

elk are among the heaviest users of free water in 

our study area (B. Holton, personal 

communication). Thus, the much higher incidence 

of elk kills by male lions (Chapter 3) is plausibly 

associated with male lions having spent more time 

near water sources. 

Movements by Time Period 

Mountain lions tracked so far in the Flagstaff 

Uplands moved at different rates depending on 

season and time of day. Movements were much 

longer during fall-winter, which is consistent with 

longer intervals between kills and meals also 

evident during this season, especially in contrast to 

during the spring drought (Chapter 3). Together 

with the results of our analysis of foraging 

behavior, this result suggests that fall-winter was 

energetically perhaps the most stressful season for 

lions in our study area, a result of expending 

greater energy for movement, while obtaining less 

energy from fewer kills. We were surprised by this 

pattern because we expected fall-winter to be a 

season that favored lions, primarily because their 

prey tended to be concentrated on winter ranges, in 

somewhat poorer condition, especially during late 

winter and early spring. It may be that sport 

hunting of mountain lions by humans provides 

some explanation for this pattern, given that most 

successful sport hunts in Arizona occur during 

winter, when snowfall facilitates tracking 

(Schubert 2004). A plausible hypothesis is that 

lions accelerate their movements to avoid or 

otherwise in response to hunters. 

The relation between diel pattern of 

movements and kills by mountain lions in our 

study area contrasted with relations between 

seasonal movements and kill rates. Whereas lower 

kill rates were associated with more extensive 

movements seasonally, the opposite was true on a 

daily basis. The dusk peak in movements by lions 

corresponded with a dusk peak in numbers of kills 

of large prey (Chapter 3). Likewise, the dawn 

peak in initiation of 8-12 hr inactive periods 

(Chapter 3), not surprisingly, corresponded with a 

nadir in movements. This diel pattern suggests 

that the higher incidence of kills at dusk was not 

fortuitous, or driven strictly by prey vulnerability, 

but, rather, partly the result of lions employing a 

strategy that entailed greater activity. 

Effects of Roads, Urban Areas, and 

Jurisdiction 

Consistent with results of previous research 

elsewhere (Beier 1995, Alexander and Waters 

2000, Dickson et al. 2005), mountain lions in our 

study area were clearly affected by paved 

highways and human residential areas or, more 

plausibly, by human activities associated with 

these features. These effects were evident in 

under-use of areas near roads and urban areas, 

dramatic under-use of urban areas themselves, 

changes in lion movements when near human 

features, and failure to cross paved roads, even 

when nearby. With one proviso, movements by 

lions decelerated and became less angular when 

they were in areas that they otherwise avoided near 

highways and urban areas. This raises the 

intriguing possibility that slower and less angular 

movements typify cautious behavior by mountain 

lions. That said, once a lion was in a mapped 

residential area, movements tended to accelerate, 

which could signify flight behavior. 

Effects of one sort or another extended out 

between 0.5 and 5 km from highways and urban 

areas, but with intriguing differences between male 

and female mountain lions. In contrast to females, 

males more evidently avoided urban areas, but 

modified their movements less when nearby. On 

the other hand, females avoided areas near paved 

highways, which had no apparent effect on males. 

We have no highly plausible explanation for these 

differences except that the intrinsically longer 

movements of males could explain, in part, the 

29 
greater spatial extent of their avoidance of urban 

areas. The 75th percentile for movements during 

four hour intervals was around 2-4 km for males, 

in contrast to 0.5-2 km for females, which would 

be consistent with observed differences in the 

spatial extent of avoidance. 

Paved highways of all sorts were clearly a 

barrier to movements of mountain lions tracked so 

far in the Flagstaff Uplands. Numbers of observed 

highway crossings were orders of magnitude less 

than expected if highways were not a “presence” 

for lions. This applied not only to the interstate 

highways, which we expected, but also to 

comparatively little-used highways such as Lake 

Mary Road and U.S. Highway 180, which 

surprised us. Lions have so far crossed minor 

highways more often than interstates, but this 

difference among types of highways pales in 

comparison to the rate at which lions failed to 

cross highways of all sorts. Our failure to detect 

any crossings of I40 and the paralleling BNSF 

railroad tracks is consistent with speculation by 

McRae et al. (2005) that these transportation 

features have functioned as a near impermeable 

barrier to lions for a non-trivial period of time, for 

the BNSF since 1884. Overall, our results suggest 

the potential extent to which major transportation 

features can function as strong filters or even 

barriers in study areas, such as ours, comprised 

largely of wildlands. 

Controlling for other human and natural 

features, jurisdiction, per se, had a surprising 

degree of influence on habitat selection by 

mountain lions monitored to date. All lions 

avoided privately owned and associated 

intermingled state lands. Males, on the other hand, 

tended to select for National Park Service (NPS) 

jurisdictions, which did not allow hunting. In this 

light, the exhibited female tendency towards 

ambivalence or even avoidance of NPS lands is 

intriguing. However, if females seasonally tended 

to avoid adult males (see the next section), then 

they may have avoided the quite small NPS units 

typical of the Flagstaff Uplands more as an 

expression of avoiding males than avoiding this 

jurisdiction, as such. 

Effects of Lions on Each Other 

We found evidence that especially adult male 

mountain lions were affecting habitat selection and 

movements by other lions. As noted previously, 

selection of NPS jurisdictions by males could 

explain ambivalence towards or even avoidance of 

these areas by females. More directly, modeled 

probability of male activity explained some 

measure of habitat selection by females during 

summer (May through mid-September). Assuming 

that patterns of parturition were the same in our 

study area as in southwestern Alberta (Ross and 

Jalkotzy 1992) and the San Andres Mountains of 

New Mexico (Logan and Sweanor 2001), 

“summer,” as defined for this analysis, would also 

have been time of peak parturition and denning. 

Females were presumably most risk averse during 

this period, especially when venturing out with 

cubs (Logan and Sweanor 2001). Male lions, 

which do kill cubs, would have probably 

constituted a non-trivial part of this risk (e.g., 

Logan and Sweanor 2001). We can only speculate 

whether females in our study area were avoiding 

males as a strategy for reducing risk to cubs. 

However, such a strategy would be consistent with 

what we know of mountain lion biology (Logan 

and Sweanor 2000). 

As in every other study area where mountain 

lion movements have been studied (e.g., 

Seidensticker et al. 1973, Logan and Sweanor 

2001, Dickson and Beier 2002), adult males in our 

study area moved more extensively over larger 

areas compared to non-dispersal movements of all 

other sex and age classes. This held for both peak 

and ebb mating seasons, which suggests that the 

drop in kill rate by adult males during peak mating 

season (Chapter 3) was not a function of lessened 

movements but rather a reflection of adult males 

shifting their focus from predation to reproductive 

opportunities. Interestingly, young males were the 

only sex-age class to exhibit a difference in 

movements between peak and ebb mating seasons, 

with shorter movements and less acute changes in 

direction evident during the peak season. This 

pattern is similar to that of movements by all lions 

when near highways or urban areas. If our earlier 

speculation is correct, that shorter more linear 

movements betoken more cautious behavior, then 

young males may have likewise been more 

cautious during peak mating season, but 

generalized to their entire range. This would be 

consistent with heightened intolerance of young 

males by adult males during peak mating activity 

(Logan and Sweanor 2001). 

30 
Chapter 3 

Predation and Other Behaviors of Mountain Lions in the 

Flagstaff Uplands 

David Mattson, Jan Hart, Mike Miller, and Diane Miller 

Introduction 

Information on mountain lion (Puma concolor) 

predation is potentially germane to a number of 

management issues. Times and locales preferred 

for hunting are clearly relevant to assessing and 

managing the risks to humans that are increasingly 

of concern to managers. Under certain 

circumstances, mountain lion predation can limit 

bighorn sheep (Ovis canadensis), have potentially 

deleterious impacts on vulnerable pronghorn 

(Antilocapra americana), and potentially regulate 

mule deer (Odocoileus hemionus) and other 

ungulate populations (Ockenfels 1994, Hayes et al. 

2000, Logan and Sweanor 2001, Robinson et al. 

2002, Rominger et al. 2004). These direct effects 

on populations of herbivores potentially translate 

into indirect effects on vegetation structure and 

composition (Ripple and Beschta 2006). Indirect 

effects could also be engendered by mountain lion 

predation on mesocarnivores such as coyotes 

(Canis latrans; Boyd and O’Gara 1985). 

However, despite this multitude of potential effects 

and the fact that mountain lions are the most 

abundant of large predators in the western United 

States, we know comparatively little about 

mountain lion predation and its impacts on 

ecosystems (Logan and Sweanor 2000). Some of 

this dearth is attributable to lack of attention and 

resources, but most is attributable to the difficulty 

of studying this cryptic low-density species. 

Methods for studying mountain lion diet and 

predation have steadily improved since the 1940s. 

Early studies relied primarily on anecdote, the 

analysis of opportunistically collected feces, or 

contents of stomachs from hunter-killed animals 

(e.g., Robinette et al. 1959, Spalding and Lesowski 

1971). Beginning in the 1960s, researchers began 

using ground-based radiotelemetry, often in 

combination with snow tracking, to systematically 

study kills and kill sites (e.g., Hornocker 1970). 

These methods were pushed to their limits by 

researchers in southern California, southern New 

Mexico, Alberta, northwestern Montana and the 

Yellowstone ecosystem (Murphy et al. 1992, Beier 

et al. 1995, Ross and Jalkotzy 1996, Kunkel et al. 

1999, Logan and Sweanor 2001, Ruth 2004). Even 

so, these more recent intensive and often grueling 

studies yielded only partial pictures of predation 

behavior. Kill rates, diel timing of kills, time spent 

consuming kills, and differences among sex and 

age classes remained only sparsely sampled or 

reliably known from only one or two study areas. 

The recent incorporation of GPS technology into 

telemetry collars introduced the potential for 

comprehensive year-long round-the-clock records 

of mountain lion movements which have so far 

been demonstrated in one study reported from 

Wyoming (Anderson and Lindzey 2003). 

There are a number of issues involving 

mountain lions in the Flagstaff Uplands of northcentral Arizona, including threats to human safety, 

impacts of humans and human infra-structure on 

movements, effects of different management 

regimes (e.g., National Park versus non-park) on 

populations, and effects of predation, especially on 

several small semi-isolated populations of 

pronghorn. Information on predation is obviously 

germane to understanding impacts on prey in this 

region, and also relevant to managing for human 

safety. Moreover, the natural history of mountain 

lion predation is a potentially compelling centerpiece for public outreach designed to educate and 

raise awareness. We deployed newly-developed 

technology on mountain lions in the Flagstaff 

Uplands that coupled satellite delivery of data with 

GPS-based telemetry, allowing us to document 

round-the-clock movements and collect detailed 

information on predation (see Chapter 1). Our 

primary goals for investigation of predation were 

to determine: (1) diel timing of kills; (2) kill rates, 

durations of consumption, and prey composition 

31 
by mountain lion sex and age class; (3) the 

likelihood that kills were of small (2-30 kg wet 

weight) and large (>30 kg) prey as a function of 

durations of localized movements; (4) diagnostic 

features of kill sites; and (5) other details of 

mountain lion predatory behavior. In this chapter 

we present a preliminary analysis of predation in 

our study area using data collected during July 

2003-May 2006. A complete analysis will be 

presented after conclusion of scheduled field work 

in late 2009. We describe methods for this 

analysis in Chapter 1. 

Results 

Activities by Type and Time of Day 

Between July 2003 and May 2006 we visited 394 

clusters of GPS locations (see Methods in Chapter 

1), at which we documented 218 kills, 165 of 

which were attributable to five female mountain 

lions and 53 attributable to five males. A kill of 

large prey (logit[plarge prey]) was strongly positively 

related to duration (Dura; in hrs) of the associated 

cluster of GPS locations (Figure 3.1; area under the 

ROC curve [c] = 0.90; Goodness-of-fit [Hosmer 

and Lemeshow] X2

 = 1.77, df = 7, p = 0.98): 

logit[plarge prey] = –8.30 + 2.38ln(Dura + 1). 

A cluster without any kill (logit[pno kill]) was 

strongly negative related to duration and associated 

with time-of-day partitioned into six equal periods 

(Timex) (c = 0.92, Goodness-of-fit X2

 = 11.56, df = 

8, p = 0.17): 

logit[pno kill] = 3.54 – 0.42(ln[Dura + 1])2

 + 

Timex; 

where Time2 = –0.36, Time6 = 1.21, Time10 = 

0.06, Time14 = 0.56, Time18 = –0.87, and Time22 = 

–0.61. A kill of small (<30 kg wet weight) prey 

was also related to time-of-day, as well as to a 

polynomial of cluster duration (c = 0.75, 

Goodness-of-fit X2

 = 11.21, df = 8, p = 0.19): 

logit[psmall prey] = 0.48 + 1.42(ln[Dura + 1])2

 – 

3.34 sqrt(Dura + 0.5)+ Timex; 

where Time2 = –0.06, Time6 = –0.99, Time10 = 

0.03, Time14 = 0.07, Time18 = 0.07, and Time22 = 

0.87. 

 

 

Figure 3.1. Relations between duration of GPS clusters 

(in hrs) and the probability that (top) a kill of large prey 

had occurred, (middle) a kill of small (<30 kg wet weight) 

prey had occurred, and (bottom) no kill had occurred. 

Probability of a small kill peaked at roughly 40-hr duration, 

which is about the same duration when p exceeded 0.5 

for a kill of large prey. Dots and associated 95% CIs are 

for septiles of the data and shown to illustrate goodnessof-fit. 

Of the visited clusters, we found kills of large 

prey at 146, kills of small prey (2-30 kg wet 

weight) at 49, and no kills at 150. Applying our 

predictive equations to unvisited clusters, kills of 

large prey totaled 207, kills of small prey totaled 

54, and clusters with no kills totaled 568, 

suggesting that we had documented 70% of the 

large kills, 91% of the small kills, and 26% of the 

sites where marked animals were inactive, but not 

because of a kill. 

When partitioned among diel time periods, 

kills of large prey, kills of small prey, and periods 

of inactivity for other reasons exhibited markedly 

different distributions (Figure 3.2). Most kills of 

32 
large prey (62-64%, the range depending upon 

whether predicted events were included) occurred 

during night-time or crepuscular hours, with a peak 

(26%) at dusk. A similar majority of kills of small 

prey (67-70%) occurred during dark or near-dark, 

but with a peak (32-39%) at night. By contrast, 

periods of inactivity not associated with kills, and 

typically of 10-12 hr duration, were commonly 

initiated at dawn (45-49%), and a majority (65-

71%) during dawn or daylight hours. 

Interval between Kills 

Figure 3.3. Mean intervals (with 95% CIs) between 

kills (top) and without eating (bottom), in hours, 

relative to three climatic seasons. Letters in dark gray 

are for results of multiple comparisons of means. 

Means with the same letter are not different at α = 

0.05. Darker bars behind are for kills of large prey 

only. 

Figure 3.2. Proportional distributions of kills of large 

prey (top), kills of small prey (middle), and initiation of 

periods of inactivity (bottom), by six diel periods 

indicated by the median hour in military time. Lighter 

open or diagonally striped bars are for visited clusters 

only; dark bars behind include predicted activities; error 

bars are for 95% CIs. 

Intervals between observed and predicted 

kills of large prey varied among sex-age classes (F

= 3.5, df = 3/269, p = 0.016) and averaged 221 

hours (95% CI = 191-252) for mature females, 177 

hrs (143-211) for mature males, 144 hrs (106-181) 

for young females, and 191 hrs (104-277) for 

young males. Intervals between kills and intervals 

between departure from a kill site and the next kill 

(roughly, time between feedings) were strongly 

related to climatic season (fall-winter, spring 

drought and monsoons; Figure 3.3) and an 

interaction between mating season (peak versus 

ebb) and sex-age class (male or female by prereproductive [young] or mature; Figure 3.4). 

Model type I error probabilities were quite low and 

R2

 values modest, considering kill intervals for 

large prey only (F = 5.59, df = 9/263, p < 0.0001; 

R2

 = 0.16) and for prey of all sizes (F = 5.73, df = 

9/312, p < 0.0001; R2

 = 0.14); or time without 

33 
feeding for large prey only (F = 7.09, df = 9/263, p

< 0.0001; R2

 = 0.20) and for prey of all sizes (F = 

7.14, df = 9/312, p < 0.0001; R2

 = 0.17). Type III 

sums of squares tended to be higher for the mating 

season by sex-age class interaction in all models, 

although climatic season also was consistently a 

strong effect. 

We used least squares marginal means and 

the Tukey-Kramer method of controlling for type I 

error rate (α = 0.05) to test for differences in mean 

kill intervals and mean intervals without feeding 

among climatic seasons and mating season × sexage class categories. Intervals of both types were 

longer during fall-winter compared to spring 

drought (Figure 3.3). Kill intervals, but not 

intervals without feeding, were also longer during 

the monsoons compared to the spring drought. 

Kill intervals and intervals without feeding did not 

differ among sex-age classes for the ebb mating 

season (Figure 3.4). However, during the peak 

mating season, kill intervals and intervals between 

feeding were longest for mature males, especially 

compared to pre-reproductive females. Both types 

of intervals also tended to be longer for mature 

females, again, especially when compared to 

young animals of the same sex. 

Figure 3.4. Mean Intervals (with 95% CIs) between kills (top) and without eating (bottom), in hours, for sexage classes relative to ebb mating season (left) and peak mating season (right). Letters in dark gray are for 

results of multiple comparisons of means. Means with the same letter are not different at α = 0.05. Dark bars 

behind are for kills of large prey only. 

 

Kill Composition 

Of the 218 kills or instances of scavenging 

documented at the time of this report, the majority 

(52%) were elk (Table 3.1). Small prey comprised 

15% and mule deer 46% of all kills. Total percent 

is >100 because >1 kill was recorded at some sites. 

Reckoned in terms of finer categories, females 

34 
Table 3.1. Numbers of kills or carcasses scavenged by mountain lions in the Flagstaff Uplands during 2003—

2006, total (n = 218) and differentiating female from male mountain lions. 

Type of kill or scavenging Total Females Males 

Mule deer (Odocoileus hemionus) 

 Sex and age not specified 3 3 0 

 Adult (sex not specified) 20 19 1 

 Adult female 25 24 1 

 Adult male 12 7 5 

 Yearling 10 8 2 

 Fawn (<1 yr old) 14 12 2 

Whitetail deer (Odocoileus virginianus) 

 Adult male 1 0 1 

Elk (Cervus elaphus) 

 Sex and age not specified 1 1 0 

 Adult (sex not specified) 3 2 1 

 Adult female 12 4 8 

 Adult male 5 5 0 

 Yearling male 3 1 2 

 Calf 6-12 months old 11 6 5 

 Calf <6 months old 53 34 19 

 Scavenged adult female 5 3 2 

 Scavenged adult male 2 1 1 

Pronghorn (Antilocapra americana) 

 Adult male 1 1 0 

Small (c. 2-30 kg wet weight) prey 

 Mesocarnivores 

 Coyote (Canis latrans) 21 21 0 

 Bobcat (Lynx rufus) 2 2 0 

 Badger (Taxidea taxus) 2 2 0 

 Grey fox (Urocyon cinereoargenteus) 1 1 0 

 Raccoon (Procyon lotor) 1 0 1 

 Other 

 Rabbit (Leporidae) 7 5 2 

 Porcupine (Erithizon dorsatum) 1 1 0 

 Owl 2 2 0 

comprised 68% of all sexed adult mule deer kills. 

Adult mule deer, in turn, comprised 26% of all 

kills, and 68% of all deer kills. Among 

documented kills of small prey, 73% were of 

mesocarnivores, of which 78% (n = 21) were 

coyotes. Seven of the remaining 10 documented 

small kills were lagomorphs. Of the elk, 21% were 

adults that had been killed and 7% (n = 7) were 

adults that had been scavenged. Elk <1-yr old 

comprised the largest single category of fully 

identified kills, accounting for 67% of all elk 

carcasses, and 29% of all kills or scavenging. 

Male and female mountain lions tended to 

kill different prey (Table 3.1). Elk comprised 72% 

of all documented male kills or instances of 

scavenging compared to 34% for females. Of elk 

prey (not including scavenging), 31% of male kills 

and 21% of female kills were adults. Females 

killed virtually all (96%) of the mesocarnivores 

and the large majority (87%) of mule deer. 

Logistic regression models discriminating 

mesocarnivores, mule deer, and elk <1-yr-old from 

other kills all included an effect of mountain lion 

sex-age class. The model for mesocarnivores 

included sex-age class and elevation (negative 

effect) as explanatory variables and provided good 

discrimination (c = 0.80) and adequate fit 

(Hosemer and Lemeshow goodness-of-fit, X2

 = 

10.48, df = 7, p = 0.16). Differences among sexage classes were wholly attributable to a lack of 

mesocarnivore kills by mature males (Figure 3.5). 

The model for mule deer kills included mountain 

lion sex-age class, climatic season, elevation (a 

positive effect), and kill site abundances of 

35 
Figure 3.5. Proportional kills (with 95% CIs) of elk <1-

yr-old (top), mule deer of all classes (middle), and 

mesocarnivores (bottom) by different sex-age classes 

of mountain lions in the Flagstaff Uplands. 

cliffrose and mountain mahogany (both positive), 

and also provided good discrimination (c = 0.82) 

and good fit (X2

 = 6.04, df = 8, p = 0.64). Mule 

deer kills were much more common during fallwinter (Figure 3.6) and more common by mature 

females and young males compared to mature 

males and young females (Figure 3.5). Finally, the 

model for kills of elk <1-yr-old included sex-age 

class, a polynomial of days since onset of elk 

calving season (mid-May), and elk calf crop year 

(3 years treated categorically), and provided good 

discrimination (c = 0.83) but poor fit (X2

 = 44.2, df 

= 8, p < 0.0001). Differences among sex-age 

classes were largely attributable to proportionately 

more kills by mature males (Figure 3.5). 

Otherwise, kills of elk calves peaked at roughly 70 

dys after mid-May and in association with the calf 

crop produced during May and June of 2005 

(Figure 3.6). 

Figure 3.6. Proportion of mountain lion kills in the 

Flagstaff Uplands (with 95% CIs) of mule deer by 

season (top), and of elk <1-yr-old by days after the 

advent of calving season (middle) and by elk calf crop 

year (bottom). 

Behavior at Kills 

Time Spent at Kills and Percent of Kills 

Consumed 

Time spent at documented kills (in hrs) was related 

to nine independent variables that explained 41% 

of total variance and were part of a model that was 

almost certainly not attributable to chance (F = 4.5, 

df = 11/193, p < 0.0001). Time spent increased 

with amount of precipitation at the time of feeding 

and with length of time spent at the previous kill, 

and decreased with increasing elevation and when 

a lion was scavenging. Otherwise, time spent at a 

36 
kill was related to sex-age class and polynomials of 

maximum ambient temperature and carcass mass. 

Duration peaked at prey mass ≈ 114 kg (251 lbs.; 

Figure 3.7) and at ambient temperature ≈ 15°C 

(59°F). Strongest effects were attributable to 

precipitation, prey mass, and sex-age class. 

Compared to all other classes, mature males 

averaged the least time at a kill (19.1 [95% CI = 

13.4-28.1] hrs, compared to 33.7 [25.3-44.7] hrs 

for mature females, 37.7 [27.4-51.6] hrs for young 

females, and 39.6 [25.7-60.8] hrs for young males; 

Figure 3.8). 

Estimated percentage of total edibles 

consumed from a carcass was related to seven 

independent variables that explained 40% of total 

variance and were also part of a model that was 

almost certainly not attributable to chance (F = 

11.26, df = 9/150, p < 0.0001). Percentage 

consumed was positively related to elapsed time 

(in days) between the kill and our site 

investigation, elevation, and whether the lion drug 

the carcass >3 m or not, and negatively related to 

prey mass (Figure 3.7) and percent forest and 

shrub cover at the site. Percentages consumed 

were also related to lion sex-age class, with 

percentages averaging least for mature males and 

greatest for young females (Figure 3.8). Overall, 

strongest effects were attributable to time interval 

between the kill and site investigation, elevation, 

prey mass, and sex-age class. 

Figure 3.7. Effects of prey mass on mountain lion behaviors at kills, including total time spent at the kill site (top 

left), percent of edibles consumed from the carcass (bottom left), proportion of kills that a lion drug >3 m (top 

right), and proportion of kills that a lion buried (bottom right). Univariate relations are shown by solid lines and 

dots with 95% CIs are for means of quintiles of the data, shown to illustrate goodness-of-fit. 

Handling of Kills 

The probability that a lion had drug a carcass 

>3 m, versus not, was related to five variables that 

were part of a logistic regression model that 

provided adequate discrimination (area under the 

ROC curve [c] = 0.75) and good fit to the data 

37 
(Hosmer-Lemeshow goodness-of-fit, X2

 = 10.63, 

df = 8, p = 0.22). Probability of dragging 

increased with increasing cover of herbaceous 

vegetation and with increasing southerly 

orientation at the site, and decreased with 

increasing elevation. Probability of dragging was 

also related to a polynomial of prey mass (Figure 

3.7) and peaked at a mass ≈ 131 kg (289 lbs.). 

Overall, prey mass had the strongest effect on 

probability of dragging in this model. 

The probability that a lion had buried a 

carcass, versus not, was related to six variables that 

were part of a model that also provided adequate 

discrimination (c = 0.76) and good fit to the data 

(X2

 = 7.94, df = 8, p = 0.44). Probability of burial 

increased with increasing forest cover and ambient 

temperature and, like probability of dragging, 

decreased with increasing elevation. Also like 

dragging, probability of burial was related to a 

polynomial of prey mass (Figure 3.7), but peaked 

at a much lesser mass, ≈ 47 kg (104 lbs.). 

Probability of burial was related to sex-age class, 

and was lowest for mature males compared to all 

other classes of mountain lion (Figure 3.8). As 

with probability of dragging, carcass mass had the 

strongest effect of any explanatory variable on 

probability of burial. 

Figure 3.9. Effects of sex-age class on estimated total 

biomass consumed (top) and rate of consumption 

(bottom) by mountain lions at kills in the Flagstaff 

Uplands. Error bars are 95% CIs and bars topped by 

the same letters denote means that are not 

significantly different. 

Figure 3.8. Effects of sex-age class on behavior of 

mountain lions at kills, including time spent at the kill 

site (top), percent of edibles consumed from the 

carcass (middle), and proportion of kills that a lion 

buried (bottom). Error bars are 95% CIs and bars 

topped by the same letters denote means that are not 

significantly different. 

Biomass Consumed at Kills 

Not considering the effect of prey mass, total 

biomass estimated to have been consumed by a 

mountain lion from a documented kill was related 

to four variables in a model that explained 15% of 

total variance and almost certainly was not by 

chance alone (F = 4.87, df = 7/196, p < 0.0001). 

Consumed mass increased with elevation and with 

a kill versus an instance of scavenging, and 

averaged greatest during fall-winter (96 kg [95% 

38 
CI = 64-145]), especially compared to during the 

monsoons (56 kg [34-92]). Consumed mass was 

also related to sex-age class (Figure 3.9), and was 

greatest for mature males (95 kg [57-159]) and 

young females (96 kg [60-154]), especially 

compared to mature females (44 kg [28-70]). Sexage class and scavenging had the greatest effects in 

this model. 

Estimated rate of consumption (kg/hr) was 

also related to four explanatory variables in a 

model that explained 19% of total variance, almost 

certainly not by chance alone (F = 7.35, df = 

6/184, p < 0.0001). Rate of consumption increased 

with increasing elevation and decreased with 

increasing forest cover and when a carcass was 

buried versus not. As with total consumed mass, 

rate of consumption was related to sex-age class 

(Figure 3.9), and greatest for mature males (2.1 

kg/hr [1.6-2.8]), especially compared to mature 

females (0.9 kg/hr [0.7-1.2]). Elevation, sex-age 

class, and forest cover all had strong effects in this 

model. 

Discussion 

Although the number of activity sites we 

sampled is already comparatively large, the small 

number of sampled individuals precludes our 

reaching definitive conclusions at this time about 

mountain lions in the Flagstaff Uplands. Even so, 

some intriguing patterns emerged from this 

preliminary analysis, well supported by the data. 

These patterns pertained to the effects of season 

and time of day, weather, site vegetation and 

physical features, type and mass of prey, and 

mountain lion sex-age class. Moreover, these 

effects pertained to a number of mountain lion 

behaviors, many of which have not previously 

been rigorously investigated, including kill 

intervals, timing of kills, time spent at kills, 

handling of kills, and estimated rates of 

consumption. Many of these patterns are germane 

to managing for human safety, as elaborated in 

Chapter 4. 

Kill Rates and Timing of Kills 

As previously reported by Mattson et al. 

(2005), dusk and to a lesser extent night were times 

of peak success for mountain lions preying on 

larger ungulates in our study area. Extensive 

movements by our marked lions during dusk (see 

Chapter 2) suggest that this greater success was at 

least partly intentional rather than simply 

fortuitous. Beier et al. (1995) and Anderson and 

Lindzey (2003) also observed that most kills in 

their study areas occurred at night, between 1800 

and 0200 hours. This begs the question, though, 

why predatory success in our study area was 

appreciably greater or, similarly, why lions were 

apparently making a greater effort at dusk 

compared to dawn? More straight-forward, 

initiation of inactive periods during early morning 

by lions in our study area, typically sustained 

through most of the day (see Chapter 2), is 

consistent with our expectation that mountain lions 

will avoid daytime heat and favor activity during 

darker times of day when prospective prey are 

presumably more vulnerable. 

Considering both small and large prey, kill 

rates documented so far for mountain lions in our 

study area (5.4-7.9 dys, depending on sex-age 

class) are slightly lower than kill rates of large prey 

documented for mountain lions elsewhere. 

Murphy et al. (1992) and Ruth (2004) reported kill 

rates from the Yellowstone ecosystem mostly in 

the range of once every six to nine or 11 days, with 

primary differences between maternal females and 

other lions. Shaw (1977), Beier et al. (1995), and 

Anderson and Lindzey (2003) reported similar 

results from northern Arizona, southern California, 

and southeastern Wyoming. However, small (2-30 

kg wet weight) prey were apparently not a 

significant factor in these other studies, or at least 

not overtly considered in calculation of kill rates. 

Considering only large prey, our results (one kill 

every 6.0-9.2 dys) align remarkably well with 

previous research, and are lower than rates 

predicted by Ackerman et al. (1986) on the basis of 

energetics – around once every eight-nine days for 

adult males and once every 16 days for adult 

females. 

Seasonal differences in predatory activity and 

apparent predatory success were only partly 

consistent with our prior expectations. The short 

average interval between kills during the spring 

drought is consistent with prey comparatively 

concentrated, presumably near water, and in poor 

physical condition as a result of prolonged poor 

forage conditions (Logan and Sweanor 2001). 

Longer kill intervals during the monsoons are 

consistent with expected reductions in predatory 

success plausibly arising from greater dispersal and 

improved condition of prey. However, the long 

intervals between kills during fall-winter were 

opposite of what we had expected, with prey 

39 
presumably concentrated on spatially-restricted 

winter ranges and in increasingly poor condition. 

And, indeed, the increased frequency of mule deer 

kills during winter, in association with sites 

offering abundant winter deer browse such as 

cliffrose and mountain mahogany, is consistent 

with this expectation. In any case, the lower fallwinter kill rates were somewhat offset for lions by 

their consumption of greater mass, on average, 

from kills that did occur. 

Kill Composition and Scavenging 

The composition of mountain lion kills 

reported here is consistent with the composition 

reported by Mattson et al. (2005) and much like the 

composition of mountain lion kills documented in 

central Idaho, the Montana Rocky Mountain East 

Front, and northern Yellowstone – other areas with 

substantial numbers of both elk and deer 

(Hornocker 1970, Williams et al. 1995, Husseman 

et al. 2003, Ruth 2004). Elk comprised 50 to 73% 

of all kills in these more northern study areas, 

which overlaps the 52% documented in our study 

area. So far, mountain lions in our study have 

apparently strongly selected for elk <1-yr-old, 

which comprised 67% of all elk kills, 

proportionately more than were documented in 

central Idaho (51%; Husseman et al. 2003) and 

Yellowstone (50-53%; Ruth 2004). This apparent 

tendency to select for smaller age classes among 

intrinsically larger-bodied prey, such as elk, is 

consistent with the near exclusive focus of 

mountain lion predation on moose (Alces alces) 

calves and juveniles observed by Ross and 

Jolkotzy (1996) in Alberta. 

Kills of elk <1-yr-old by mountain lions in our 

study area were consistent with what we expected 

both by probable vulnerability and abundance of 

elk calves. Calving among elk in temperate 

regions of North America most commonly begins 

near mid-May and peaks near the beginning of 

June (Taber et al. 1982). Predation on elk calves 

by mountain lions in our study area peaked during 

mid- to late-July, presumably at a time when elk 

calves had reached maximum numbers and were 

still quite vulnerable (Geist 1982). The steady 

decline in proportional frequency of elk calves 

among lion kills thereafter presumably reflected 

reduced vulnerability of calves with increasing 

mobility and vigor. Given that elk calves were the 

single largest category of lion kills, this presumed 

decline in vulnerability and more certain decline in 

frequency among lion kills may partly explain 

reduced kill rates and increased movements of 

lions that we observed during fall and winter. The 

large number of elk calf kills following the 2005 

elk calving season probably reflected the 

exceptionally large calf crop (Arizona Game and 

Fish Department 2005b) that followed an 

exceptionally wet winter (187% of average at 

Flagstaff’s Pulliam Airport, September through 

mid-April), in what has otherwise been an 

extended multi-year drought. 

Differences in prey selection between 

mountain lion sexes have only rarely been 

documented. Anderson and Lindzey (2003) 

observed a tendency for adult males to kill more 

bull elk and for adult females to kill more does, 

whereas Pierce et al. (2000) observed that, 

compared to male mountain lions, females killed 

more young deer. We found that, compared to 

females, males killed virtually no small prey and 

many more elk. This result is consistent with the 

expectation that larger-bodied mountain lions of 

whatever sex would be killing larger-bodied prey, 

and fits similar range-wide population-level 

patterns (Iriarte et al. 1990). Our results clearly 

point to the potential importance of smaller (2-30 

kg) prey to especially female mountain lions in our 

study area, and highlight potential implications of 

the widespread bias against documenting small 

prey to our understanding of mountain lion 

ecology (Ackerman et al. 1984). 

Most of the documented kills of small (2-30 

kg) prey in our study area were mesocarnivores, 

particularly coyotes. In fact, we have so far 

documented more kills of mescocarnivores (n = 

27) than have been reported during other studies 

regardless of duration, intensity and number of 

collared animals, including studies with >200 

investigated kills (Murphy et al. 1992, Ross and 

Jalkotzy 1996, Logan and Sweanor 2001, Ruth 

2004). In contrast to speculations by Boyd and 

O’Gara (1985), we found little indication that 

mountain lions in our study area were killing 

coyotes to protect kills of ungulates. All but three 

of the mesocarnivores were mostly consumed, and 

only three were associated in time and space with 

another kill. Our results are more consistent with 

Logan and Sweanor (2001), who speculated that 

mountain lions killed coyotes to protect kittens and 

more broadly to reduce competition for food. We 

also speculate that female lions were killing 

mesocarnivores simply as an immediate source of 

energy. 

40 
We documented seven instances of 

scavenging, which lends weight to a growing body 

of evidence that mountain lions are not exclusively 

predators. Bauer et al. (2005), Logan and Sweanor 

(2001) and Ross and Jalkotzy (1996) previously 

recorded 20, 16, and four instances of scavenging, 

respectively, involving primarily mule deer and 

moose. All of our cases involved adult elk, which 

were the largest-bodied ungulates in our study 

area, consistent with the likelihood that, if 

scavenging were to happen, it would involve a 

carcass with greater edible biomass (Mattson 

1997). This documentation of scavenging raises 

the possibility that we and others have 

misclassified incidents of scavenging as predations 

(Bauer et al. 2005). Although this may be true, we 

suspect our error rate has been small primarily 

because of our often close follow-up on mountain 

lion activity and our reliance on confirmatory 

evidence such as signs of struggle to identify 

predations. 

Effects of Prey Mass and Competing 

Scavengers 

Our results suggest that prey mass may affect 

mountain lion behaviors in complex ways, and that 

optimal prey mass may not be a simple function of 

what lions are capable of killing. Estimated 

percentages of edibles consumed by lions declined 

as prey mass increased, whereas relocation and 

burial of kills reached maximum probabilities at 

intermediate prey masses, in the range of 50-130 

kg (110-290 lbs.). Moreover, absolute time spent 

on a kill did not increase monotonically with prey 

mass, as might be expected, but rather also peaked 

at an intermediate mass, around 115 kg (250 lbs.). 

Our results suggest that mountain lions in our 

study area were perhaps both less able and less 

motivated to secure large carcasses by relocating 

and burying them, and that they often profited little 

from the added mass obtained by risking the killing 

of large prey. Taken together, our results suggest 

that, although lions are capable of killing large 

ungulates, including fully adult male elk in the 

range of 270-360 kg, optimal prey size is in the 

range of 50-150 kg, the size of adult deer and calf 

elk, as much because of post-kill foraging 

efficiencies as risks of injury. 

These speculations are consistent with the 

observed effect of ambient temperature on time 

spent at a kill and likelihood of burial. Burial 

reduced rates of consumption, which, as might be 

expected, suggests that burial served to preserve 

edible tissue (Goff 1992) and perhaps also 

engender a greater sense of security for the 

mountain lion. Probability of burial increased with 

increased ambient temperature. Again, this makes 

sense if burial is a strategy to both inhibit 

dissemination of odor and reduce loss of edibles to 

microbes and arthropods such as adult wasps 

(Vespidae), maggots (larvae of the family 

Calliphoridae), and carrion beetles (Silphidae), 

which would otherwise be accelerated with 

increased carcass temperature (Payne 1965, 

Tantawi et al. 1996). Controlling for the effect of 

carcass mass, the peak tenure of lions on kills at 

middling ambient temperatures (15°C, roughly 

60°F) is consistent with accelerated loss of mass to 

especially arthropods and increased displacement 

or fear of displacement by large scavengers such as 

black bears (Ursus americanus) with warmer 

temperatures, as well as with potentially greater 

difficulty consuming frozen tissues at sub-freezing 

temperatures, which would be a factor during 

winter in our study area.

Our interpretation of the tendency for 

duration of consumption to decline with greater 

prey size and warmer ambient temperatures is 

consistent with previous theorizing by Hornocker 

(1970) and Ruth (2004). Hornocker (1970) 

speculated that warm weather would accelerate 

decomposition and thereby reduce efficiency of 

carcass use, whereas Ruth (2004) noted the 

potential for greater competition with scavengers at 

large kills, if for no other reasons than the 

difficulty of moving a large carcass to a more 

secure location and adequately burying it. Murphy 

et al. (1998) documented the loss of substantial 

prey biomass by mountain lions in Glacier and 

Yellowstone National Parks to bears that had 

displaced them from their kills. 

The strong positive relation between time 

spent at a kill and precipitation, controlling for the 

effect of carcass mass, has not been previously 

observed nor previously subject to speculation. 

This relation is, again, consistent with a potentially 

major effect of competition with microbes and 

arthropods on behavior of mountain lions at kills. 

Precipitation has been shown to retard 

decomposition and other carcass depletion from 

microbes and arthropods (Payne 1965, Tantawi et 

al. 1996), which would logically prolong 

consumption of a kill by a lion. The effects of 

precipitation on decomposition can be neutralized 

by forest cover, although forest cover can at the 

41 
same time also retard decomposition by reducing 

carcass temperature (Isiche et al. 1992, Tessmer 

and Meek 1996). Indeed, this latter effect is 

consistent with the reduced rates of consumption 

observed for lions in areas with greater forest 

cover, potentially also linked to the greater odds of 

carcass burial that we observed at forested kill 

sites, which would, alone, prolong lion use of a 

carcass. 

Elevation was correlated with numerous 

mountain lion behaviors in our study area, 

including positive relations with percentage of 

edibles consumed, total biomass consumed, and 

rate of consumption, and negative relations with 

total time spent at a kill, probabilities of both 

relocating and burying a carcass, and likelihood of 

killing a mesocarnivore. In other words, mountain 

lions in our study area were apparently much more 

efficient at appropriating edibles from a kill at high 

compared to low elevations, and without resorting 

to extensive securing of the carcass. This suggests, 

in turn, that competition from microbes and 

arthropods may have been a lesser factor at high 

compared to low elevations, consistent with 

observations of diminished arthropod activity on 

carrion with increasing elevation in Colorado 

(DeJong and Chadwick 1999). We were somewhat 

surprised that elevation emerged as a strong 

correlate in so many models given that we had 

controlled for proximal ambient temperatures 

through application of an elevational lapse rate. If 

elevation effects were, indeed, a surrogate for 

arthropod activity on carrion, then proximal 

temperature would not logically serve as a control 

because arthropod activity is typically keyed more 

to the abundance and diversity of local arthropod 

communities than to site and carcass temperatures 

(Gill 2005). 

The Strategy of Mature Males 

We close this chapter by commenting on 

what is probably one of the most compelling 

results from this research so far – the marked 

differences in predatory and feeding behaviors by 

mature males compared to all other sex-age 

classes. All else equal, mature males spent the 

least time on a kill, consumed the least percentage 

of edibles, and were least likely to bury a carcass, 

yet, despite all this, consumed, on average, the 

greatest biomass at the greatest rate. As we noted 

before, they were also more likely than any other 

sex-age class to kill elk, especially elk calves, and 

least likely to kill small prey, especially 

mesocarnivores. Overall, these behaviors are 

logically interpreted as a time minimizing strategy; 

in other words, maximizing intake of energy in the 

minimum time possible. 

As a corollary, mature males were likely 

adopting a time-minimizing approach to feeding to 

maximize reproductive opportunities. What they 

likely gained was the opportunity to travel through 

and monitor much larger areas than would 

otherwise be the case. Such movements would 

allow not only monitoring of potentially 

reproductive females, but also defense against 

incursions by competing males (Logan and 

Sweanor 2001). This interpretation is consistent 

with the greater movements of mature males 

documented here (Chapter 2) and in many other 

studies (cf., Logan and Sweanor 2001). This 

interpretation is also consistent with the observed 

decline in kill rate by mature males during the time 

of hypothesized greatest mating activity (FebruaryJuly), at the same time that kill rates of all other 

sex-age classes did not appreciably change. This 

decrease in kill rate raises the interesting 

possibility that males have a seasonal mating 

strategy cued to female availability, despite having 

the physiological capacity to reproduce year-round. 

42 
Chapter 4 

Managing for Human Safety in Mountain Lion Range 

David Mattson 

Introduction 

Mountain lions (Puma concolor) are the 

smallest of only four felids that have a welldocumented history of killing people – the other 

cats being African and Asiatic lions (Panthera 

leo), leopards (Panthera pardus), and tigers 

(Panthera tigris). Of these species, mountain lions 

are responsible for the fewest recorded deaths, not 

anywhere near the numbers of people killed by 

tigers in India, which have been between 30 and 

1300 per year since the 1920s (Løe 2002). Even 

so, any predation on a human by a large carnivore 

commands the attention of people in Canada and 

the U.S., and is sometimes cause for stronglyexpressed demands on managers to provide safety 

and exact retribution (Deurbrock and Miller 2001, 

Etling 2001). Concerns about threats posed by 

mountain lions have been fueled by a marked 

increase in numbers of attacks on people especially 

since the 1970s, to around 3-4/yr during the last 

two decades (Beier 1991, Fitzhugh et al. 2003; 

Figure 4.1). Numbers of attacks have stabilized 

since the early 1990s and declined during the last 

six years (Figure 4.1). Although there have been 

comparatively few attacks by lions on humans in 

Utah, New Mexico, Arizona, and Nevada, 

especially in contrast to California, Colorado, and 

British Columbia (Figure 4.2), encounters with 

lions that were perceived to be threatening by the 

involved humans have increased even in interior 

southwestern states, especially since the 1990s 

(e.g., Wakeling 2003, Barber 2005). 

In this chapter I review what we know, 

deduce, and speculate about factors that govern 

risk of mountain lions attacking humans, to 

provide a basis for design of management to 

reduce risk to socially acceptable levels. I begin 

by describing a way to usefully frame risk and 

partition factors governing its’ different facets, and 

then analyze existing relevant literature and 

available data to provide insight into the nature and 

relative importance of landscape features and 

human and mountain lion behaviors and physical 

attributes associated with encounters and attacks. I 

used data that were previously published by Beier 

(1991), Etling (2001), and Fitzhugh et al. (2003), 

or were publicly available online in a compilation 

of attacks and other close encounters at 

http://www.cougarinfo.com. I emphasized 

empirical results, but also gave due consideration 

to published recommendations grounded in 

experience, anecdote, and general knowledge of 

feline behavior. I employed weight-of-evidence to 

reach conclusions rather than emphasizing either 

Figure 4.1. Annual number of attacks on humans 

by mountain lions (light bar, top) and number of 

resulting human fatalities (dark bar, top), averaged 

per decade, and a 3-yr running average of number 

of attacks, by year (bottom), for Canada and the 

U.S. Data are from Beier (1991), Etling (2001) and 

online from http://www.cougarinfo.com. 

43 
proof or disproof of a particular effect (i.e., I did 

not minimize either type I or type II errors). This 

approach to uncertainty is consistent with 

likelihood theory and well-suited to identifying 

models of how the world works that are, of all 

feasible candidates, best supported by the available 

information (Smith et al. 2002). 

Partitioning Risk of Mountain 

Lion Attack 

Of all possible outcomes of a humanmountain lion encounter, wildlife and public lands 

managers are most concerned about human death. 

Human injury, almost always a consequence of any 

physical contact with a lion, is of second greatest 

concern. Of third greatest concern are encounters 

perceived to be threatening by the involved 

humans, and also possible precursors to later 

attacks. Of least concern are benign encounters 

(Arizona Game and Fish Department 2005a). 

These outcomes are not only given different 

management priority, they are also associated with 

different probabilistic transitions governed by 

different causal factors. More specifically, human 

death is the logical outcome of: 

1) the probability that a mountain lion will 

encounter a human (probability of an 

encounter); 

2) the probability that an encounter will trigger 

curiosity and other similar lion behaviors 

that will be perceived as threatening by the 

involved human or, indeed, be aggressive 

(probability of an aggressive response 

given an encounter); 

3) the probability that initial responses by the 

lion will result in an attack (probability of 

an attack given an aggressive response); 

and 

4) the probability that an attack will be fatal 

(probability of death given an attack). 

Managers or people involved in an encounter 

can affect the odds of a fatal or injurious outcome 

at each one of these transitions. Managers 

plausibly have greatest influence over risk at 

transition (1), through area-specific management of 

mountain lion populations or human activity, and 

some degree of influence over all other transitions, 

through activities such as education and 

interventions to remove or otherwise modify the 

behaviors of known lions that are perceived to pose 

an unacceptable threat. Humans involved in 

encounters also have influence over all transitions, 

but most importantly transitions (2)-(4), associated 

with an unfolding interaction with a lion. Given 

information, involved humans can also affect their 

baseline exposure (transition [1]) by deciding 

whether or not to use areas known to be frequented 

by mountain lions. I have structured the main part 

of this chapter that follows around these 

transitions, beginning each related section with 

conceptual considerations, followed by relevant 

information, empirical where possible, focused on 

the Flagstaff Uplands and other similar areas. 

Figure 4.2. Number of attacks on humans by 

mountain lions and number of resulting human 

fatalities (in parentheses to right) since 1890, by 

state and province for Canada and the U.S. Data 

are from Beier (1991), Etling (2001) and online 

Probability of an Encounter 

Encounters between humans and mountain 

lions, benign or otherwise, more-or-less equate to 

the notion of exposure used in risk management 

(Pritchard 2000). Without exposure to mountain 

lions, there would be no risk of human injury or 

death. This proviso is important to making 

meaning of oft-quoted statistics regarding 

comparative risks posed by different factors in the 

human environment. For example, coyotes (Canis 

lupus) and venomous snakes have killed 

substantially more people than have mountain lions 

(Conover et al. 1995), yet people are undoubtedly 

44 
exposed much more often to these comparatively 

numerous and widespread animals. Put simply, 

risk of attack by lions is most usefully judged in 

terms of odds for those humans who are out and 

about in areas occupied by mountain lions 

(Fitzhugh 1988). Those who hold a negativistic 

worldview (in the parlance of Kellert [1996]) are 

overtly fearful of wildlife, and sometimes advocate 

for managing risk by eliminating exposure through 

eradication of animals such as mountain lions 

(Kellert 1996). This is an extreme view, but 

highlights the potential importance of managing 

exposure as part of reducing risk of human injury 

or death, more plausibly through affecting human 

activities. Such a strategy would be most feasible 

where managers and the public had access to 

detailed information about spatial and temporal 

distributions of especially predatory mountain lion 

activities. Local mountain lion densities and 

landscape features that predict distributions of 

mountain lion activity would be important factors 

for assessing likely human exposure. Such 

parameters are amongst the easiest to specify in 

management of risk given that they are grounded 

in data typically collected from radio-telemetry 

studies, the core of most wildlife research. 

Despite the dramatic increase in numbers of 

mountain lion attacks on humans since 1970 

(Figure 4.1), it remains unclear whether per capita 

risk is any higher now compared to in the past for a 

human who ventures into lion range. Absolute 

numbers of lion attacks could increase for several 

reasons, including increased numbers of lions and 

humans in lion range, and increases in 

predisposing behaviors by both species. Increased 

numbers of mountain lions would lead to increased 

exposure of humans to risk, unless modified by 

factors such as lions increasingly avoiding people. 

A number of wildlife managers have concluded 

that mountain lion populations increased in most of 

the western U.S. since the 1970s (for example, see 

the State Reports in Proceedings of Mountain Lion 

Workshops and DeVos and McKinney [2005]). 

Increases in lion range and associated numbers are 

most certain to the east, especially in the Great 

Plains (Riley and Malecki 2001, Tischendorff 

2003, Kintigh 2005). Otherwise, the basis for 

judgments about state-wide lion population trends 

is generally uncertain evidence such as harvest and 

depredation trends and sex-age composition of 

harvested lions (Logan and Sweanor 2001, Cougar 

Management Guidelines Working Group 2005, 

DeVos and McKinney 2005). More certainly, 

decadinal U.S. censuses show that human numbers 

have increased dramatically in states occupied by 

mountain lions, two to five-fold since the 1950s in 

Arizona, New Mexico, and Utah. There has also 

been a similar, less dramatic, increase in numbers 

of people recreating on U.S. Forest Service lands 

(http://www.srs.fs.usda.gov/trends/Nsre). Torres et 

al. (1996) concluded that increased human safety 

incidents in California, principally depredation on 

pets, was attributable more to the spread of humans 

into lion habitat than to increases in lion 

populations. Whatever these broad-scale patterns, 

area-specific drivers of exposure, and of risk, are 

likely to be unique, requiring management based 

on case-specific details. 

T

 

emporal Dimensions of Exposure 

Managers of risk are assisted by natural 

temporal partitioning of humans and mountain 

lions. Each species favors different diel periods 

for daily activity. Mountain lions tend to be active 

during night (see Chapters 2 and 3) and humans 

during day, especially when recreating or 

otherwise active in wildlands. A priori, the 

greatest likely period of overlap between lions and 

people is at dusk, which is also the period of 

greatest predation by lions on large prey in the 

Flagstaff Uplands (Figure 4.3). With this context, 

it is interesting to note that historical attacks on 

people by mountain lions peaked during daylight 

and dusk hours (Figure 4.3). The daylight peak no 

doubt reflected much greater activity by humans in 

lion habitat during this time of day, especially 

compared to dawn and night. The large number of 

attacks at dusk is plausibly interpreted as arising 

from overlap between continued human activities 

initiated during daytime and predatory activities 

initiated by lions with fading daylight. We don’t 

have comprehensive information on diel activity of 

humans in mountain lion range, but the patterns 

documented here are consistent with per capita risk 

being greatest for humans at dusk. 

Levels of human activity also potentially 

affect seasonal distributions of mountain lion 

attacks. Most attacks have occurred during spring 

and summer, and the fewest during fall and winter 

(Figure 4.4). Interestingly, the attacks that did 

occur during winter were more likely to be fatal 

compared to attacks during any other time of year. 

The summertime part of peak attacks matches 

spring through fall peaks in numbers of mountain 

lion sightings, or “encounters,” observed in 

45 
Montana and in state and national parks of the 

Redwood Basin of California (Riley and Aune 

1997, Holm 2003), and is consistent with the 

notion that more people see and are attacked by 

lions during summer simply because more people 

are out in lion habitat (Beier 1991). However, this 

explanation does not adequately address the 

pronounced ebb in attacks during fall, when 

substantial numbers of humans are still likely out 

in mountain lion range, or the dearth of mountain 

lion observations during winter among year-round 

residents of a Colorado community near good lion 

habitat (Anderson 1993). Nor is the invocation of 

human activity to explain more frequent springsummer attacks entirely consistent with the peak in 

mountain lions sighting observed along the 

Colorado Rocky Mountain Front during winter, in 

presumed association with winter concentrations of 

mule deer (Odocoileus hemionus) near human 

residential areas (Halfpenny et al. 1993). As we 

learn more about mountain lion ecology and 

behaviors near people, I suspect that the 

explanation for seasonal patterns of encounters 

with humans will become more complex. 

Figure 4.4. Proportional distribution of attacks by 

mountain lions on humans in Canada and the U.S. 

during 1890-2006, by season (with 95% CIs). Open 

bars with cross-hatches are attacks and dark bars 

behind are proportion of attacks that are fatal, by 

season. 

Figure 4.3. Proportional distribution (with 95% 

CIs) of attacks by mountain lions on humans in 

Canada and the U.S. during 1890-2006, by time of 

day (top), and proportional distribution of kills of 

large prey by mountain lions in the Flagstaff 

Uplands during 2003-2006 by 4-hour intervals 

denoted by modal military time (bottom, see 

Chapter 3). 

Spatial Dimensions of Exposure 

Little has been done to systematically 

investigate the spatial dimensions of human 

exposure to mountain lions and related incidences 

of lion attacks. Of the few investigations, 

Halfpenny et al. (1993) and Shuey (2005) not 

surprisingly observed that encounters between 

humans and lions along the east front of the 

Colorado Rockies were concentrated during winter 

along the urban-wildland fringe facing the 

mountain front, which constituted high-quality 

mule deer winter range. In general, the plausible 

expectation is for human exposure to be greatest in 

areas where mountain lion populations are near 

carrying capacity and, at a finer grain, where 

habitat features attract lions. As discussed in more 

detail in Chapter 2, mountain lions apparently use 

landscapes so as to optimize prey density and 

prevalence of features that provide ambush cover 

(Riley and Malecki 2001). Mountain lions have 

been consistently shown to select for shrubby or 

timbered habitat, rugged terrain, and areas with 

favored mule deer browse species (Chapter 2; 

Logan and Irwin 1985, Koehler and Hornocker 

1991, Laing and Lindzey 1993, Williams et al. 

1995, Riley and Malecki 2001, Husseman et al. 

2003). Throughout the West, as in the Flagstaff 

Uplands of Arizona (Chapter 2), the Rocky 

46 
Mountain Front of Colorado, and the Wasatch 

Front of Utah, humans have favored settling in 

valleys or flatlands immediately adjacent to 

mountain massifs or other terrains that constitute 

prime mountain lion habitats. Regardless of trends 

in lion populations, habitat preferences of humans 

alone have almost certainly increased human 

exposure to mountain lions. 

Probability of an Aggressive 

Response Given an Encounter

We probably know the least about factors that 

turn a benign encounter into one of intent curiosity 

or non-contact aggression by the involved 

mountain lion. Other than the work of Sweanor et 

al. (2005), we have virtually no data on benign 

encounters, in contrast to those deemed 

problematic by the involved human, including no 

information on conditioning factors that might be 

at play. Nor has any systematic research been 

conducted under non-field controlled conditions 

covering lion responses to humans. Insight is 

further muddied by the fact that judgments 

regarding curiosity or aggression of the involved 

lions are colored by the fear, knowledge and other 

vagaries of involved people. We are largely left 

with often limited anecdote and generalized 

notions about feline behavior. 

Fitzhugh (1988), citing Leyhausen (1979), 

speculated that mountain lions treat other animals 

as either conspecifics or prey, and that, when 

engaged with an unknown type, will gather 

information to make a provisional determination 

one way or the other. Fitzhugh (1988) did not 

make an obvious allowance for lions treating 

humans uniquely. If so, by deduction, lions not 

being pursued by a human will react to an 

encounter one of five modal ways: (1) depart 

without any obvious engagement because a human 

is readily judged to be a “conspecific” or because 

of paramount distractions, in which case the 

involved human may or may not be aware of the 

lion, but, if aware, likely not feel threatened; (2) 

follow or otherwise observe the human, but while 

purposefully remaining hidden, in which case the 

human will not likely know the lion is present, but 

if aware, likely feel threatened; (3) remain in place 

or approach the involved human fully visible while 

exhibiting intense curiosity (e.g., staring; Fitzhugh 

1988, Fitzhugh and Feljine 1997), in which case 

the involved human will very likely feel 

threatened, (4) remain in place or approach the 

involved human exhibiting defensive aggressive 

behavior (e.g., hissing, growling, or charging; 

Sweanor et al. 2005), in which case the involved 

human will almost certainly feel threatened, or (5) 

approach in a predatory manner (e.g., crouched, 

with tail sweeping), either hidden or in view, in 

which case virtually anyone involved would feel 

endangered if aware of the lion’s approach. 

Fitzhugh (1988) and Fitzhugh and Feljine (1997), 

again referencing Leyhausen (1979) and 

individuals who train mountain lions, judged 

modalities 1 and 2 to be a minimal threat to human 

safety, modalities 3 and 4 to be a “substantial” 

threat, and modality 5 to be a “serious” to 

“extreme” threat. Sweanor et al. (2005) found that 

defensive aggression against humans (modality 4) 

was almost exclusively exhibited at ranges of <50 

m and by female lions in apparent defense of 

kittens. That said, much can be speculated and 

little is reliably known regarding factors that 

govern transitions between each of these 

modalities, other than what we can back-cast from 

factors associated with actual attacks (see 

following sections). 

Again, referencing Leyhausen (1979) and 

mountain lion trainers, Fitzhugh (1998) and 

Fitzhugh and Feljine (1997) speculated that the 

transition from uncertainty regarding a novel 

animal to responding as if it were prey could be 

virtually instantaneous if the otherwise unknown 

creature were about the size of normal prey and 

moving rapidly away. As we speculate in Chapter 

3, optimal prey for most mountain lions, at least in 

the Flagstaff Uplands, is likely in the range of 100-

300 lbs., the size of most adult humans. If 

Fitzhugh and Feljine are correct, then, a priori, a 

jogging, running, or skiing human, not 

immediately typed as “non-prey” by a nearby lion, 

may have little chance to respond to an uncertain 

or otherwise curious lion but, rather, would be 

dealing with an overt predator at the virtual onset 

of an encounter (see following sections). 

We know very little about just how 

intrinsically curious mountain lions are, or how 

fixed their prey images might be. If mountain 

lions were prone to curiosity about anything a bit 

out of the ordinary, and employed highly malleable 

prey images that readily encompassed anything 

novel, then one might expect modalities (1) and (2) 

to be relatively uncommon or fleeting, and 

modalities (3) and (4) to be commonplace, 

followed by an attack. However, the scarcity of 

well-documented attacks by lions on humans (only 

47 
Figure 4.5. Proportional distribution of human 

victims of mountain lion attacks, by age (with 95% 

CI, top, cross-hatched bars), and proportion of 

those attacks that were fatal (top, dark bars 

behind), for Canada and the U.S., 1890-2006. 

Proportional distribution of visitors to USFS 

jurisdictions in the U.S., by age (bottom). 

about 90 since 1890 in Canada and the U.S.), 

despite what must be considerable opportunity, 

argues strongly against an exceptionally high 

degree of curiosity or highly flexible prey images. 

Another possibly complementary explanation 

might be that mountain lions do, indeed, 

differentiate humans from other creatures and 

transmit this distinction among generations, 

presumably matrilineally. 

To my knowledge, there are only three 

published estimates of rates at which sightings of 

lions or other encounters known to the involved 

human transitioned into something more 

aggressive or threatening, including 11% from the 

Redwoods Basin of California (Holm 2003) and 

3% from a residential area along the Front Range 

of Colorado (Anderson 1993). The most reliable 

estimate is 6% reported by Sweanor et al. (2005) 

based on approaches to radio-marked lions 

initiated by researchers in a wilderness study area. 

These rates are almost certainly high when 

considering all types of human-lion encounters, 

including encounters in urban-wildland contact 

zones where the lion was aware of the human, but 

the human unaware of the lion. If we ever develop 

means of documenting all types of encounters, I 

suspect that we will find there are numerous cases 

where the lion chose to immediately depart, was 

merely curious, or otherwise behaviorally neutral, 

consistent with the results of Sweanor et al. (2005). 

Probability of an Attack Given an 

Aggressive Response

Much of what can be said here is germane to 

modalities of encounters discussed in the preceding 

section. Factors affecting the transition from noncontact aggression to an attack probably similarly 

affect whether a mountain lion transitions from 

being curious to aggressive (i.e., predatory). And, 

although data on transitions from aggressive 

approaches to attacks are comparatively scant, they 

are more numerous than data on transitions from 

awareness or curiosity to aggression, if for no other 

reason than the involved people are more likely to 

report or otherwise document the former types of 

incidents compared to the latter. In general, the 

basic notion is that if you encounter a mountain 

lion, you do not want to superficially look and act 

like prey, or if engaged with a lion that is 

deliberating the situation, you want to dominate the 

lion or otherwise convince it that you pose a 

potential risk (Fitzhugh and Feljine 1997, Sweanor 

et al. 2005). 

There is good evidence to suggest that 

physical attributes and behaviors of humans 

involved in threatening encounters with mountain 

lions strongly influence whether the encounter 

turns into an attack. A large percent (nearly 60%) 

of human victims during 1890-2006 were ≤10-yrsold (Figure 4.5). Whether this can be interpreted 

as evidence of high risk for young children 

depends on the relative commonness of differentaged people active in mountain lion range. 

Although these specific data do not exist, the U.S. 

Forest Service (USFS) does provide statistics on 

ages of visitors to USFS jurisdictions 

(http://www.srs.fs.usda.gov/trends/Nsre), which, in 

a broad sense, can be applied to the question. 

These USFS statistics show a peak in visitors <16 

yrs old (Figure 4.5), but this peak is 

proportionately much smaller relative to numbers 

of visitors between 20 and 70 yrs old, compared to 

numbers of victims of mountain lion attacks who 

were ≤10-yrs-old relative to numbers of older 

victims (Figure 4.5). This result in consistent with 

conclusions reached earlier by Beier (1991), Etling 

48 
(2001), and Fitzhugh et al. (2003) – that children 

are, indeed, at much greater risk of being attacked

with any given level of exposure to mountain lions.

As might be expected by predator body sizes, this 

proclivity of mountain lions to attack children 

versus adults is similar to that of leopards and 

intermediate between higher proportional rates 

coyotes and lower proportional rates for tigers and 

Asiatic and African lions (Løe 2002). 

The presence of children plausibl

 

for 

y interacts 

with 

nd 

d 

 not 

oted 

ttack, 

ilable data suggest that human 

beha tantial 

group size to determine whether a menacing 

mountain lion attacks or not. Proportionately, the 

largest number of mountain lion attacks has been 

on lone juvenile and adult humans (≥10 yrs old; 

Figure 4.6). However, for groups of two or more

people, very few attacks occurred when these 

groups were exclusively comprised of adults, a

when groups were attacked, virtually all victims 

were children. For lack of information on the 

relative frequency of groups of different size an

age composition in mountain lion range, it is not 

possible to reach definitive conclusions regarding

the risk incurred by each group type. Even so, lone

adults are probably much more common than lone 

children in mountain lion range, often involved in 

some activity such as jogging (see below). 

Similarly, groups with children are probably

that much more numerous compared to groups 

comprised exclusively of adults, certainly not 

enough to explain the disparity in numbers of 

attacks on children in groups. As previously n

by Kadesky et al. (1998) and Fitzhugh et al. 

(2003), groups probably do not afford much 

protection for children from a mountain lion a

especially when the group is comprised wholly or 

mostly of children. The only advantages are 

apparently in preventing fatalities (see next 

section). 

The ava

viors at the onset of encounters had subs

effects on the likelihood that encounters known to 

the involved humans turned into an attack. People 

who were either running (including jogging), 

Figure 4.7. Proportions (with 95% CIs) of mountain 

lion attacks on humans that were fatal (top) and 

proportions of known & reported encounters with 

mountain lions that turned into an attack (bottom), 

as a function of human activities, for Canada and 

the U.S., 1890-2006. Open bars with cross-hatches 

(top) are proportions of encounters, including nonattacks, that turned fatal whereas the dark bars 

behind are proportions of attacks, only, that were 

fatal.

Figure 4.6. Proportional distribution of attacks 

(open cross-hatched bars in front) and fatalities as 

a fraction of attacks (dark bars behind), with 95% 

CIs, by group size, for groups including children

(top) and for groups not including children 

(bottom), for Canada and the U.S., 1890-2006. 

49 
“playing,” or skiing or snowshoeing were much 

more likely to be attacked during an encounter 

compared to people who encountered a lion whi

around camp or their home, or while working or 

hunting (Figure 4.7). People on horseback, 

bicycling, or hiking apparently experienced 

intermediate risk of attack. These patterns ar

the most part, consistent with predictions by 

Fitzhugh (1988) and Fitzhugh and Feljine (19

(see previous section) that, compared to sedentary

humans, individuals moving rapidly away from or 

laterally to mountain lions would more likely 

trigger a predatory response. 

Given a threatening enco

le 

e, for 

97) 

unter, there is 

evide and 

), 

s 

, 

ted 

e efficacy of sustained noise is perhaps 

best s

.8). The 

ast, 

 the 

ns 

ay 

 in 

r 

t all lions are apparently equally likely to 

confr

at, although 

ld) 

of 

als 

91) 

ese 

h 

Figure 4.8. Reactions of mountain lions to different 

kinds of noise created by humans during close or 

otherwise threatening encounters (proportions with 

95% CI), differentiating departure of the mountain 

lion (open hatched bars behind) versus continued 

threatening approach or attack (dark bars in front). 

Results are regraphed from Fitzhugh et al. (2003), n

= 76. 

nce from general observations of felines

from mountain lion attacks that human responses 

can influence outcomes. Fitzhugh (1988), Beier 

(1991), Fitzhugh and Feljine (1997), Etling (2001

Fitzhugh et al. (2003), and Sweanor et al. (2005) 

all recommend that humans involved in encounter

not run away, which would presumably stimulate a 

predatory response, and, instead, respond in an 

assertive, even aggressive manner. In particular

they recommend that the involved people stand 

their ground and attempt to look large, especially

by holding something over their head. These 

recommendations are partly based on documen

outcomes of encounters (e.g., Sweanor et al. 2005),

and partly on observations by Leyhausen (1979) 

and others regarding cats, which appear fearful 

when approached by large objects from above. 

There are mixed recommendations regarding eye 

contact, an aggressive approach, and throwing 

things. Conrad (1992) recommends not attempting 

to make eye contact, but then maintaining eye 

contact once made. Etling (2001), by contrast, 

recommends initiating eye contact as a means of

asserting dominance. Throwing things at the lion

is generally recommended, but only if objects can 

be obtained without bending down; that is, without

appearing prey-like (Beier 1991, Fitzhugh et al. 

2003). 

Th

upported by the available data. Fitzhugh et

al. (2003) found that loud sustained noise 

(typically yelling), of all noises, most often 

precipitated the departure of a potentially 

threatening lion (61% of the time; Figure 4

field observations reported by Sweanor et al. 

(2005) support the efficacy of noise. By contr

the single sharp report of a warning gunshot had 

little apparent effect (only 17% of animals 

departed). These results are consistent with

finding by Sanders and Halfpenny (1993) that lio

were averted from attacking in 43% of cases by the 

involved people yelling and throwing objects. 

Given these results, devices such as air-horns m

be amongst the best non-lethal deterrents for 

people to carry in mountain lion range, useful

situations where there is time to affect the behavio

of a curious or otherwise deliberating lion (Etling 

2001). 

No

ont a human or, having done so, attack. Data 

from Beier (1991), Etling (2001), and 

fttp:\\www.cougarinfo.com, suggest th

there is no great disparity in sex of lions involved 

in encounters with humans (see also Aune 1993), 

females are more likely to attack if involved 

(Figure 4.9). Otherwise, of animals that were

aged, young mountain lions (generally ≤2-yrs o

were responsible for 73% of attacks, and, where 

body condition was reported, the majority (65%) 

involved animals were considered to be “poor,” 

which is probably in excess of the norm for a 

mountain lion population. In short, young anim

in poor condition were probably the most likely to 

attack, although not necessarily kill, a human. 

This is consistent with conclusions by Beier (19

and Løe (2002), but somewhat at variance with 

observations by Aune (1993), Riley and Aune 

(1997), and Mansfield and Charlton (1998). Th

last three authors reported that depredating lions 

tended to be in fair to excellent condition, althoug

the animals that they considered were involved in 

50 
livestock depredation as well as public safety 

incidents. 

The apparent selection of children, rather 

than 

t 

y 

ss 

expre to 

s 

) 

g 

We 

reat 

bility of Death Given an 

though only 19 people are known to 

have 0 

with a 

adults, for attack by mountain lions is at 

variance with our speculations in Chapter 3 tha

lions select for prey in the range of 50-130 kg 

(110-290 lbs), the mass of most human adults. 

This discrepancy can be explained in two ways. 

First, small mountain lions tend to kill smaller pre

(see Chapter 3), and given that young lions are 

involved in most attacks, it is not surprising that

they would be selecting for children rather than 

adults. The second complementary explanation 

invokes the possibility that stature rather than ma

is a primary trigger of lion attacks. Humans are 

unique in their bipedal stance amongst potential 

prey in mountain lion range, and human children

ages 8-10 average 130-140 cm (51-55inches) tall, 

which is in the range of heights for adult mule deer

and elk calves – preferred prey of mountain lions, 

at least in the Flagstaff Uplands of Arizona. 

Several researchers and managers have 

Figure 4.9. Proportional characteristics (with 95% 

CIs) of mountain lions involved in encounters, 

attacks, or human fatalities by sex (top), age-class 

(middle), and physical condition (bottom), for 

Canada and the U.S., 1890-2006. Open bars with 

cross-hatches at top are for lions involved in 

encounters, with and without attacks, and dark bars 

behind are for proportions of encounters that 

resulted in injury or death. Open bars with crosshatches at middle are for lions involved in attacks 

and dark bars behind are for proportion of those 

attacks that were fatal. 

ssed the opinion that habituation of lions

humans increases odds of attack (Fitzhugh 1988, 

Halfpenny et al. 1993, Løe 2002). Habituation ha

been confirmed as a major risk factor in managing 

for human safety around bears (Herrero 1985). 

Habitation is the conditioning of an animal to the

presence of humans, almost always entailing loss 

of fear. This notion is relevant to judging risks in 

situations where a lion has been repeatedly sighted

not exhibiting obvious fear of nearby humans. 

Under such circumstances, judgment of risks 

intersects with speculations by Fitzhugh (1988

and Fitzhugh and Feljine (1997), that lions treat 

humans as either prey or virtual conspecifics. 

Framed this way, loss of fear could signal that a

lion is, indeed, viewing humans as non-threatenin

conspecifics rather than as prey. However, the 

implicit concern is that increased exposure of a 

lion to people increases the odds that, at some 

point, the lion switches its’ typing of humans. 

currently know little that is reliable about risks 

entailed by habituation, and future research may

uncover nuances in how habituation affects 

responses of lions to humans. That said, a 

precautionary approach would meanwhile t

obviously habituated mountain lions as a threat. 

 

Proba

Attack 

Even 

 been killed by mountain lions between 189

and 2006, of all transitions, we have perhaps the 

best understanding of factors associated with 

human death. Virtually all serious attacks and

related human fatalities have been documented,

recently in great detail. In most cases the 

perpetrating lion has also been killed and 

characterized. Under such circumstances, 

near census of events, the role of statistical 

51 
inference is minor, except as it relates to the 

“population” of attacks yet to happen in the future. 

The proportion of mountain lion attacks on 

humans that were fatal exhibited an interesting 

pattern as a function of victim age, realizing that 

all conclusions here are compromised by a 

(fortunately) very small sample size. Fatalities 

were proportionally highest for victims 31-70 yrs 

old (35%), and progressively lower for victims 11-

20 yrs old (30%), 1-10 yrs old (22%), and, finally, 

21-30 yrs old (8%; Figure 4.5). Victims 21-30 yrs 

old, at the usual physical prime for humans, were 

able to fight off most attacks, typically with greater 

success than juvenile humans 11-20 yrs old (Etling 

2001). The comparatively low death rate of young 

children in contrast to juveniles was attributable to 

interventions by adults (Kadesky et al. 1998, Etling 

2001). Most attacks on children occurred while 

they were in groups that included adults, who often 

drove off the attacking lion. However, when 

alone, the death rate for children, given an attack, 

was an alarming 80% (Figure 4.6). These results 

highlight the importance of fighting back when 

attacked by a mountain lion, and the related 

importance of physical prowess under such 

circumstances (Beier 1991, Conrad 1992, Etling 

2001). These results similarly emphasize that, 

whereas group size and the presence of adults may 

not prevent a lion from attacking a child, 

subsequent speedy intervention by the attendant 

adults can prevent death. 

The presence of a dog seems to reduce the 

odds of death when a human is attacked. During 

1890-2006, only 7% (95% CIs = 2.1-33.9) of 

people who were attacked when accompanied by a 

dog died compared to 28% (19.9-38.0) of people 

who were unaccompanied. This is consistent with 

case-specific reports of dogs harassing an attacking 

lion sufficient to drive the lion away or to allow for 

arrival of human rescuers (Etling 2001). Aside 

from this apparent benefit of dogs, much has been 

speculated about the role of dogs in precipitating 

aggression by lions that would have not otherwise 

happened. There is no doubt that mountain lions 

will kill free-ranging dogs, as well as dogs in their 

owner’s yard (Aune 1993, Davies 1993, Torres et 

al. 1996). Killings of domesticated canids are 

consistent with our finding in the Flagstaff 

Uplands that mountain lions can kill substantial 

numbers of coyotes (Canis latrans), most likely for 

food (Chapter 3). However, looking at the data, 

there was virtually no difference in percentages of 

encounters where a mountain lion attacked a 

human with (70%) or without (72%) a dog present, 

suggesting that dogs were not a precipitating 

factor, while potentially providing a benefit for 

those people who were attacked. 

This still begs the question of whether an 

attack by a lion on a dog automatically poses a 

threat to a nearby human. Typical of scenarios 

reported to me, a dog running free on a trail is 

chased back to its accompanying owner by a lion 

hot on its tail. Not unnaturally, owners feel 

concern for the welfare of their pets and a certain 

amount of threat to themselves. People have 

intervened to protect a pet and been injured in the 

process (Etling 2001); but none have died. I 

speculate that, in most cases, an attack by a lion on 

a dog is simply that, and not an attack on an 

accompanying human. The apparent ability of a 

lion to focus on single prey in the midst of other 

animals is consistent with attacks on children in 

groups of adults and fawns or calves in herds of 

deer or elk. 

Not surprisingly, human activity at the time 

of an attack seems to strongly influence whether 

the outcome is deadly. During 1890-2006, not 

only were people involved in “playing,” running, 

or active snow sports more likely to be attacked 

without warning, the outcome was more often fatal 

(Figure 4.7). By contrast, people moving more 

slowly and often deliberately, such as when 

hunting, working, or around camp or a residence, 

were rarely killed when attacked. For hunters, this 

was often because they shot and killed the 

attacking lion (Fitzhugh et al. 2003). Otherwise, 

these results are consistent with the expectation 

that prey-like movements by humans will trigger 

more aggressive and perhaps instantaneously 

predatory responses from an observing mountain 

lion (Fitzhugh 1988, Fitzhugh and Feljine 1997). 

Given an attack, some mountain lions are 

more deadly to the victim. In particular, 43% of 

attacks by adult lions resulted in the human 

victim’s death, compared to 11% of attacks by 

young lions (Figure 4.9). This range of lethalities 

is comparable to that of leopards, but much lower 

than the 50-90% range observed for attacks on 

humans by African lions (Treves and NaughtonTreves 1999). Not surprisingly, larger and more 

experienced felids seem to be more effective at 

killing humans once they’ve judged them to be 

prey. In contrast to young mountain lions, adults 

rarely attack humans, but the attacks that do occur 

are often on lone humans that are jogging or skiing 

– moving in ways that resemble movements of 

52 
native prey (Etling 2001). These sorts of 

presumably spontaneous attacks by adult lions are 

intrinsically hard to anticipate or manage, except at 

the level of people’s overall exposure to high 

quality lion habitat. On the other hand, these 

results suggest that, where time is available for a 

considered management response, adult lions that 

exhibit threatening behaviors should be taken very 

seriously. 

Conclusions 

I focus here on conclusions relevant to 

managing for human safety on recreational trails or 

around human facilities in backcountry areas 

typical of USFS and U.S. National Park Service 

jurisdictions on the Colorado Plateau, and along 

the urban-wildland interface typical of cities such 

as Payson, Sedona, and Flagstaff, Arizona. Most 

conclusions regarding risks of mountain lions 

attacking humans are necessarily tentative, simply 

because we have so few data. Not only have 

attacks been rare, record-keeping has also been 

sparse, especially regarding incidents or encounters 

not resulting in human injury or death. Even so, 

some conclusions are well-supported by not only 

the available data, but also by what we otherwise 

know of mountain lion, feline, and human 

behaviors. For example, I can say with a fair 

degree of confidence that a small adult or child 

running or jogging alone at dusk in wooded rugged 

terrain, in an area used by a young mountain lion 

in poor condition, will maximize what are 

otherwise the slim odds of being attacked by a lion. 

As others have done before me, I present here 

a laundry list of tactics and strategies that 

managers could use to increase human safety in 

mountain lion range. I emphasize non-lethal 

approaches primarily because lethal ones, 

involving removal of lions, are common-place and 

comparatively rote. I also do not stray far from the 

empirical and conceptual basis I present in this 

chapter. I have noticed that a number of 

educational materials seem to reiterate the exact 

same safety recommendations, several of which 

have only a very tenuous basis, apparently 

validated primarily through frequent repetition. 

That said, some recommendations with a weak 

basis in fact can be legitimized by the 

precautionary principle, applied to provision 

human safety. 

I do not cover here diagnostics regarding 

level of risk implicit to different mountain lion 

behaviors during an encounter with a human. The 

basis for these diagnostics is almost wholly 

anecdote or generalized conclusions from 

observing other felines. This is not to say that 

making judgments regarding the implications of 

lion behaviors is unimportant to management. In 

some ways it is critically important. Rather, I have 

little basis in published or other inter-subjective 

information for assessing the merits of existing 

assertions. Sources such as Fitzhugh and Feljine 

(1997) and Cougar Management Guidelines 

Working Group (2005) do a good job of outlining 

current understandings of risks associated with 

different mountain lion behaviors. 

I structure my following recommendations 

according to whether the focus is on planning 

spatial aspects of visitor facilities or residential 

developments, managing exposure of humans who 

are at high risk of lion attacks, or educating people 

to successfully manage threatening encounters 

with lions: 

Planning for Safe Development 

Urban and federal lands planners have the 

opportunity to build human facilities, especially 

those providing over-night or permanent residence, 

in areas where likelihoods of mountain lion 

activity are low and quality lion habitat far 

removed. Such an accommodation is to some 

degree natural given that mountain lions select for 

rugged terrain, which is not well-suited for 

building most human facilities. Given that highquality habitat for mountain lion prey often also 

constitutes high-quality habitat for mountain lions, 

avoidance of areas where predicted levels of lion 

activity are high typically has the added benefit of 

preserving quality habitat for species such as mule 

deer and elk (Cervis elaphus). Of course, planning 

for such an outcome is in large measure contingent 

on having a reliable map of predicted mountain 

lion activity, such as we present in Chapter 2 for 

the Flagstaff Uplands. 

Unfortunately, managers and planners are 

confronted with a legacy of choices that has placed 

human facilities and resident or visiting humans in 

potential harms way. Such is the case for most 

Colorado Plateau towns and cities, where a number 

of residential areas are located immediately 

adjacent to prime mule deer or elk winter range 

associated with rugged terrain. Moreover, there is 

a speculated dynamic where profuse planting of 

shrubs and liberal watering of private and public 

53 
lawns creates habitat that is attractive to mountain 

lion prey, and hence mountain lions, within 

peripheral urban or visitor areas (Halfpenny et al. 

1993, Baron 2004). 

Under such circumstances, the common 

recommendation is to reduce plantings of palatable 

shrubs, reduce the extent and watering of grassy 

areas, and directly manage deer and elk to reduce 

their numbers in and near people (Baron 2004, 

Cougar Management Guidelines Working Group 

2005). The basic idea is to manage attractants of 

mountain lion prey, which, in turn, attract 

mountain lions. This all seems to make selfevident sense. Interestingly, though, a strong 

empirical basis for these phenomena and for the 

purported efficacy of ameliorative management 

does not exist. Managers must invoke first 

principles to justify following such 

recommendations for managing residential areas, 

which is often a reasonable basis for management 

in the face of uncertainty coupled with cause to act. 

Proximal Management of High-Risk 

Exposure 

There is little doubt that children and people 

of all sizes when alone and moving like native prey 

are at greatest risk of being peremptorily attacked 

by mountain lions. It is also quite likely that dusk 

constitutes a high-risk time of day. Given these 

tenets, there are several tactics that wildland and 

wildlife managers could use to potentially 

substantially reduce risk in areas known to be 

frequented by mountain lions. 

a) Trails (and other facilities) could be closed 

during seasons of peak lion activity; 

b) trails could be closed before dusk and 

reopened at dawn; 

c) children could be prohibited from using the 

area, even in the company of adults (i.e., 

adult use areas only); 

d) adults could be required to keep children in 

their immediate vicinity; 

e) non-motorized users could be required to 

travel in groups of at least two; and 

f) jogging and trail running could be 

prohibited. 

Managers could, to the extent deemed desirable, 

mix and match any of these recommendations, for 

example, prohibiting trail running and jogging at 

dusk during times of year when mountain lion 

activity was most common. Alternatively, a nonprohibitive approach could be taken, emphasizing 

information and education about risks and options 

that would provide users of high-risk areas a more 

informed basis for their choices. 

Contrary to recommendations made 

elsewhere, I found little justification for being 

particularly concerned about dogs near people in 

the backcountry of lion range, or risks of having 

dogs off leash. There are, perhaps, many good 

reasons relevant to other values for prohibiting 

dogs or requiring that they be restrained, but safety 

from lions does not provide a compelling 

justification at this point in time. People with dogs 

were not more likely to be attacked than people 

without, and dogs possibly helped prevent some 

attacks from being fatal. I also found little to 

convince me that attacks on dogs by lions were 

necessarily an immediate threat to nearby humans, 

except to the extent that the humans tried to 

intervene. An attack on a dog could signal a 

progression of mountain lion behavior that might 

ultimately put a human at risk, but this is a 

different consideration from whether dogs, as such, 

increase risks for nearby humans. 

Regarding tactical management of lions 

themselves, there seem to be limited options, or the 

justifications for intervention are highly uncertain. 

The clearest case for intervention and possible 

removal is when young lions in poor condition are 

observed being aggressive or intently curious about 

humans. Likewise, an adult mountain lion known 

to have closely followed or stalked a human, or 

exhibit a predatory stance near people, should not 

be treated lightly given the high death rate 

attributable to attacks by larger older lions. The 

risk posed by an adult mountain lion is classically 

difficult to judge and manage, in that the odds are 

likely quite low, although largely unknown, 

whereas the outcome is potentially severe. 

Moreover, cogent management response is 

complicated in most real-life situations because 

reliable determinations of lion age and condition 

are not possible from information provided by 

most people involved in encounters (Beier 1991). 

Perhaps the most difficult type of attack to 

prevent is one triggered by a human acting like 

prey under circumstances that trigger an 

instantaneous, perhaps instinctive, predatory 

response by a lion. As noted before, these types of 

attacks are likely to occur at a very low rate 

wherever numerous humans are active for 

sustained periods near mountain lions. The only 

practical tactics for lowering odds of this kind of 

54 
attack are those designed to minimize exposure. 

This entails managing to minimize temporal and 

spatial overlap, including the means outlined at the 

beginning of this section. 

Non-lethal options for managing known 

potentially problematic lions are largely of 

unknown efficacy. Limited results from capturing 

and relocating lions suggest that many die 

afterwards, and a few simply return (Ross and 

Jalkotzy 1995, Ruth et al. 1998). Even so, 

relocation does allow for the possibility of at least 

a few animals surviving elsewhere (ideally >480 

km away to prevent return; Ruth et al. 1998), as 

opposed to the unequivocally certain verdicts of 

lethal control or disposal in a zoo. Otherwise, to 

my knowledge, aversive conditioning has not been 

systematically attempted with mountain lions. 

Such a tactic may work under certain 

circumstances, but the current state of knowledge 

makes employing this method in management 

risky for any humans subsequently exposed to a 

“treated” animal (Beier 1991, Cougar Management 

Guidelines Working Group 2005). Hazing, that is, 

simply trying to scare a lion away without 

necessarily changing fundamental behavioral traits, 

is another tactic that has only anecdotal 

justification and unclear results (Cougar 

Management Guidelines Working Group 2005). 

 

Management of Threatening 

Encounters 

It is at this level of managing encounters that 

risk of falling prey to oft-repeated truisms is 

perhaps greatest, if for no other reason than there 

are so many in circulation. One can go to the 

literature and track lineages of recommendations 

that, at root, have justification in only one 

reference or in anecdote, generalizations, and 

deduction. This does not axiomatically make such 

recommendations unhelpful or inefficacious. 

Rather, these circumstances merely recommend 

that managers be judicious and clear about 

uncertainties that entail risk of being wrong. 

Under such circumstances it is also often 

appropriate to adopt a precautionary approach – 

acting so that, if proven wrong, additional risk is 

not placed on people in a crisis situation with a 

mountain lion. 

Of all responses to a potentially threatening 

mountain lion, the efficacy of standing ground and 

making loud sustained noise is best supported by 

currently available information. Although not 

tested, this result suggests that an air horn might be 

the best non-lethal device for backcountry users to 

carry for deterring mountain lions. By contrast, 

anecdotal results from use of pepper spray on lions 

are mixed and not very reassuring, despite the 

popularity of this deterrent for use on bears. 

Otherwise, recommendations regarding the 

desirability of making eye contact, of looming 

large by spreading a coat or holding an object over 

one’s head, or even of throwing objects at a 

threatening lion, are all highly plausible, but more 

in the nature of oft-repeated truisms rather than 

well-substantiated tactics. 

Insofar as children are concerned, certainly 

they should not be allowed to wander away, even a 

few yards, from a group of any sort while in 

mountain lion habitat, realizing that in a few 

attacks the lion literally grabbed a child away from 

the hand of an adult (Etling 2001). It has often 

been recommended that, when confronting a lion, 

children be put behind an adult, or even lifted off 

the ground (Etling 2001, Cougar Management 

Guideline Working Group 2005). Again, the 

efficacy here is more surmised than verified, but 

such an action would more likely be precautionary 

than not. 

Analysis and Research Opportunities 

Additional research is needed for managing 

human safety in mountain lion range to the extent 

that managers are willing, or not, to make 

decisions based on the current state of knowledge. 

Much of this knowledge is derived from anecdote, 

informed speculation, and scant analyses of scant 

data. This is not said with the intent of belittling 

historical efforts to understand lions and the threats 

they pose to humans. Rather, the current weak 

state of knowledge is very likely the result of 

overall very few mountain lion attacks and related 

human fatalities, a comparatively only recent 

burgeoning of attacks, an historical proclivity to 

deal with perceived risks simply by killing or 

otherwise removing implicated lions (Papouchis 

2004, Clark and Munno 2005), and related underattention to comprehensive record-keeping and 

analyses of encounters between humans and lions. 

Researchers have been left to do what they can 

with minimal time, resources, and data. 

As suggested many places in this chapter, 

there are a number of measures that could be taken 

and topics that could be investigated to 

substantially increase the richness and reliability of 

55 
our collective basis for managing human safety in 

mountain lion range. Opportunities potentially 

reside in analysis of existing data, better record 

keeping, field studies, and controlled experimental 

investigations: 

Analysis of Existing Data: There are 

potentially ample opportunities to analyze existing 

data on human-mountain lion conflicts and 

mountain lion movements and habitat selection to 

improve the current basis of management. Shuey 

(2005) made a laudable first cut at explaining the 

spatial distribution of human-mountain lion 

conflict along the Front Range of Colorado. 

However, much more could be done with this data 

set and with others in other states to more 

comprehensively diagnose landscape features that 

drive conflict. Even more insight could be gained 

by coupling information on distributions of 

humans, models that predict distributions of lions, 

and spatial data on human-mountain lion 

encounters to specify and explain spatial elements 

of exposure and other facets of risk. More 

generally, much insight could be gained by pooling 

the considerable data collected any more by each 

state on mountain lion encounters or “incidents” 

and subjecting these data to rigorous analysis. 

Even a state-by-state effort of this sort would no 

doubt yield considerable insight. 

Record Keeping: Historically, little 

attention has been given to keeping standardized 

records on encounters of all sorts between humans 

and mountain lions, detailed enough to control for 

biases that potentially plague any data based on 

self-reporting by the general public (cf., Green 

1991). Such record keeping is a non-trivial task, 

but virtually essential to gaining reliable insight 

into factors that drive the probability of an 

otherwise benign encounter turning into one of 

aggression, or of an encounter typified by noncontact aggression turning into an attack. Ideally, 

such record-keeping would be done in a way that is 

standardized among management jurisdictions, and 

constructed in such a way so as to anticipate 

relevant questions and control as much as possible 

for bias. 

Field Studies: Given the current availability 

of GPS and satellite technologies, it is possible 

now more than ever to design field studies 

organized around deliberate approaches to or other 

placements of people nearby radio-marked 

mountain lions. Both near- and long-term 

responses of lions could be readily monitored. 

Even with current technologies, such studies are 

costly and logistically difficult. However, they are 

doable and potentially compatible with studies 

undertaken to address more general ecological 

questions. 

Controlled Experimental Studies: This is 

perhaps where the greatest opportunities can be 

found for gaining rapid insight into issues of 

import to managing for human safety. As has been 

done with bears, the efficacy of deterrents such as 

pepper spray and air-horns could be efficiently 

tested. A surer assessment of the risks implicit to 

different mountain lion behaviors, postures, or 

expressions could be produced comparatively 

rapidly. Responses of lions to different human 

behaviors, both at the initiation of an encounter 

and with an encounter’s progression, could also be 

efficiently determined. Coupled with results of 

field studies, as described above, results of 

experimental studies such as these could provide a 

reliable and robust basis for education outreach 

and for judgment of risk and appropriate reaction 

to management situations. 

 

56 
References Cited

Ackerman, B.B., Lindzey, F.G., and Hemker, T.P. 1984. 

Cougar food habits in southern Utah. Journal of 

Wildlife Management 48: 147-155. 

Ackerman, B.B., Lindzey, F.G., and Hemker, T.P. 1986. 

Predictive energetics model for cougar. Pages 333-

352 in S.D. Miller and D.D. Everett, editors. Cats of 

the world: Biology, conservation and management. 

National Wildlife Federation, Washington, D.C. 

Akenson, J.J., Nowak, M.C., Henjum, M.G., and 

Witmer, G.W. 2003. Characteristics of mountain 

lion bed, cache and kill sites in northeastern Oregon. 

Pages 111-118 in S.A. Becker, D.D. Bjornlie, F.G. 

Lindzey, and D.S. Moody, editors. Proceedings of 

the seventh mountain lion workshop. Wyoming 

Game and Fish Department, Lander, Wyoming. 

Alexander, S.M., and Waters, N.M. 2000. The effect of 

highway transportation corridors on wildlife: A case 

study of Banff National Park. Transportation 

Research Part C 8: 307-320. 

Anderson, A.E. 1981. Morphological and 

physiological characteristics. Pages 27-97 in O.C. 

Wallmo, editor. Mule and black-tailed deer of 

North America. University of Nebraska Press, 

Lincoln. 

Anderson, A.E. 1983. A critical review of literature on 

puma (Felis concolor). Colorado Division of 

Wildlife Special Report 54, Ft. Collins, Colorado. 

Anderson, A.E. 1993. Frequency of mountain lion 

sightings by residents and employees of a housing 

development. Page 19 in C.S. Braun, editor. 

Mountain lion-human interaction symposium and 

workshop. Colorado Division of Wildlife, Denver, 

Colorado. 

Anderson, C.R., Jr., and Lindzey, F.G. 2003. Estimating 

cougar predation rates from GPS location clusters, 

Journal of Wildlife Management 67: 307-316. 

Arizona Game and Fish Department. 2001. Wildlife 

2006: The Arizona Game and Fish Department 

wildlife management strategic plan for the years 

2001-2006. Arizona Game and Fish Department, 

Phoenix, Arizona. 

Arizona Game and Fish Department. 2005a. Action plan 

for minimizing and responding to lion/human 

interaction. Arizona Game and Fish Department, 

Phoenix, Arizona. 

Arizona Game and Fish Department. 2005b. Regional 

elk management operational plans. Arizona Game 

and Fish Department, Phoenix, Arizona. 

Aune, K.E. 1993. Increasing mountain lion populations 

and human-mountain lion interactions in Montana. 

Pages 86-94 in C.S. Braun, editor. Mountain lionhuman interaction symposium and workshop. 

Colorado Division of Wildlife, Denver, Colorado. 

Ballard, W.B., Lutz, D., Keegan, T.W., Carpenter, L.H., 

and DeVos, J.C., Jr. 2001. Deer-predator 

relationships: A review of recent North American 

studies with emphasis on mule and black-tailed deer. 

Wildlife Society Bulletin 29: 99-115. 

Barber, S.P. 2005. Arizona mountain lion status report. 

Mountain Lion Workshop 8: 65-69. 

Baron, D. 2004. The beast in the garden: A modern 

parable of man and nature. W.W. Norton, New 

York, New York. 

Barrett, M.W. 1984. Movements, habitat use, and 

predation on pronghorn fawns in Alberta. Journal of 

Wildlife Management 48: 542–550. 

Bauer, J.W., Logan, K.A., Sweanor, L.L., and Boyce, 

W.M. 2005. Scavenging behavior in puma. The 

Southwestern Naturalist 50: 466-471. 

Beier, P. 1991. Cougar attacks on humans in the United 

States and Canada. Wildlife Society Bulletin 19: 403-

412. 

Beier, P. 1993. Determining minimum habitat areas and 

habitat corridors for cougars. Conservation Biology 

7: 94-108. 

Beier, P. 1995. Dispersal of juvenile cougars in 

fragmented habitat. Journal of Wildlife Management 

59: 228-237. 

Beier, P., and Barrett, R.H. 1993. The cougar in the 

Santa Ana Mountain Range, California. Orange 

County Cooperative Mountain Lion Study Final 

Report. 

Beier, P., Choate, D., and Barrett, R.H. 1995. 

Movement patterns of mountain lions during 

different behaviors. Journal of Mammalogy 76: 

1056-1070. 

57 
Boyd, D., and O’Gara, B. 1985. Cougar predation on 

coyotes. The Murrelet 66:17. 

Bubenik, A.B. 1982. Physiology. Pages 125-179 in

J.W. Thomas and D.E. Toweill, editors. Elk of 

North America: ecology and management. 

Stackpole Books, Harrisburg, PA. 

Burnham, K.P., and Anderson, D.R. 1998. Model 

selection and inference: A practical informationtheoretic approach. Springer, New York, New York. 

Burt, W.H., and Grossenheider, R.P. 1976. A field 

guide to the mammals: North America north of 

Mexico. Third edition. Houghton Mifflin, Boston, 

MA. 

Cain, J.W., III, Krausman, T.R., Jansen, B.D., Morgart, 

J.R. 2005. Influence of topography and GPS fix 

interval on GPS collar performance. Wildlife Society 

Bulletin 33: 926-934. 

Clark, T.W., and Munno, L. 2005. Mountain lion 

management: Resolving public conflict. Pages 71-98 

in T.W. Clark, M.B. Rutherford, and D. Casey, 

editors. Coexisting with large carnivores: Lessons 

from Greater Yellowstone. Island Press, 

Washington, D.C. 

Clevenger, A.P., and Waltho, N. 2000. Factors 

influencing the effectiveness of wildlife underpasses 

in Banff National Park, Alberta, Canada. 

Conservation Biology 14: 47-56. 

Clevenger, A.P., and Waltho, N. 2005. Performance 

indices to identify attributes of highway crossing 

structures facilitating movement of large mammals. 

Biological Conservation 121: 453-464. 

Conover, M.R., Pitt, W.C., Kessler, K.K., DuBow, T.J., 

and Sanborn, W.A. 1995. Review of human injuries, 

illnesses and economic losses caused by wildlife in 

the United States. Wildlife Society Bulletin 23: 407-

414. 

Conrad, L. 1992. Cougar attack: A case report of a 

fatality. Journal of Wilderness Medicine 3: 387-396. 

Cooper, A.B., and Millspaugh, J.J. 1999. The 

application of discrete choice models to wildlife 

resource selection studies. Ecology 80: 566-575. 

Cougar Management Guidelines Working Group. 2005. 

Cougar management guidelines. 1st edition. 

WildFutures, Bainbridge Island, Washington. 

Davies, R.B. 1993. Lion damage to pets in urban 

Colorado Springs, Colorado. Pages 79-80 in C.S. 

Braun, editor. Mountain lion-human interaction 

symposium and workshop. Colorado Division of 

Wildlife, Denver, Colorado. 

Decision Research. 2004. Arizona statewide polling 

results. Unpublished memorandum. Decision 

Research, San Diego, California. 

DeJong, G.D., and Chadwick, J.W. 1999. 

Decomposition and arthropod succession on 

exposed rabbit carrion during summer at high 

altitudes in Colorado, USA. Journal of Medical 

Entomology 36: 833-845. 

D’Eon, R.G., Serrouya, R., Smith, G., and Kochanny, 

C.O. 2002. GPS error and bias in mountainous 

terrain. Wildlife Society Bulletin 30: 430-439. 

D’Eon, R.G., and Delparte, D. 2005. Effects of radiocollar position and orientation on GPS radio-collar 

performance, and the implications of PDOP for data 

screening. Journal of Applied Ecology 42: 383-388. 

Ernest, H.B., Boyce, W.M., Bleich, V.C., May, B., 

Stiver, S.J., and Torres, S.G. 2003. Genetic structure 

of mountain lion (Puma concolor) populations n 

California. Conservation Genetics 4: 353-366. 

Deurbrock, J., and Miller, D. 2001. Cat attacks: True 

stories and hard lessons from cougar country. 

Sasquatch Books, Seattle, Washington. 

DeVos, J.C., Jr., and McKinney, T. 2005. Recent trends 

in North American mountain lion populations: A 

hypothesis. Pages 297-307 in C. van Riper III and 

D.J. Mattson, editors. The Colorado Plateau II: 

Biophysical, socioeconomic, and cultural research. 

University of Arizona Press, Tucson, Arizona. 

Dickson, B.G., and P. Beier. 2002. Home-range and 

habitat selection by adult cougars in southern 

California. Journal of Wildlife Management 66: 

1235-1245. 

Dickson, B.G., J.S. Jenness, and P. Beier. 2005. 

Influence of vegetation, topography, and roads on 

cougar movement in southern California. Journal of 

Wildlife Management 69: 264-276. 

Etling, K. 2001. Cougar attacks: Encounters of the 

worst kind. The Lyons Press, Guilford, Connecticut. 

58 
Fitzhugh, E.L. 1988. Managing with potential for lion 

attacks against humans. Pages 74-77 in R.H. Smith. 

editor. Proceedings of the third mountain lion 

workshop. Arizona Game and Fish Department, 

Phoenix, Arizona. 

Fitzhugh, E.L., and Fjelline, D.P. 1997. Puma behaviors 

during encounters with humans and appropriate 

human responses. Pages 26-28 in W.D. Padley, 

editor. Proceedings of the fifth mountain lion 

workshop. Southern California Chapter of The 

Wildlife Society, San Diego, California. 

Fitzhugh, E.L., Schmid-Holmes, S., Kenyon, M.W., and 

Etling, K. 2003. Lessening the impact of a puma 

attack on a human. Pages 89-103 in S.A. Becker, 

D.D. Bjornlie, F.G. Lindzey, and D.S. Moody, 

editors. Proceedings of the seventh mountain lion 

workshop. Wyoming Game and Fish Department, 

Lander, Wyoming. 

Frair, J., Nielsen, S.E., Merrill, E.H., Lele, S.R., Boyce, 

M.S., Munro, R.H.M., Stenhouse, G.B., and Beyer, 

H.L. 2004. Removing GPS collar bias in habitat 

selection studies. Journal of Applied Ecology 41: 

201-212. 

Geist, V. 1982. Adaptive behavioral strategies. Pages 

219-277 in J.W. Thomas and D.E. Toweill, editors. 

Elk of North America: Ecology and management. 

Stackpole Books, Harrisburg, PA. 

Gill, G.J. 2005. Decomposition and arthropod 

succession on above ground pig carrion in rural 

Manitoba. Canadian Police Research Centre 

Technical Report TR-06-2006, Ottawa, Ontario. 

Gloyne, C.C., and Clevenger, A.P. 2001. Cougar Puma 

concolor use of wildlife crossing structures on the 

Trans-Canada highway in Banff National Park, 

Alberta. Wildlife Biology 7: 117-124. 

Goff, M.L. 1992. Problems in estimation of postmortem 

interval resulting from wrapping of the corpse: a 

case study from Hawaii. Journal of Agricultural 

Entomology 9: 237-243. 

Green, K.A. 1991. Development of a data base for 

analysis of information about lion sightings and 

lion-human interactions. Page 18 in C.S. Braun, 

editor. Mountain lion-human interaction symposium 

and workshop. Colorado Division of Wildlife, 

Denver, Colorado. 

Halfpenny, J.C., Sanders, M.R., and McGrath, K.A. 

1993. Human-lion interactions in Boulder County, 

Colorado: Past, present, and future. Pages 10-16 in

C.S. Braun, editor. Mountain lion-human interaction 

symposium and workshop. Colorado Division of 

Wildlife, Denver, Colorado. 

Hayes, C.L., Rubin, E.S., Jorgensen, M.C., Botta, R.A., 

and Boyce, W.M. 2000. Mountain lion predation of 

bighorn sheep in the Peninsular Ranges, California. 

Journal of Wildlife Management 64: 954-959. 

Holm, G.W. 2003. What does ten years (1993-2002) of 

mountain lion observation data reveal about 

mountain lion-human interactions within Redwood 

National and State Parks? Page 165 in S.A. Becker, 

D.D. Bjornlie, F.G. Lindzey, and D.S. Moody, 

editors. Proceedings of the seventh mountain lion 

workshop. Wyoming Game and Fish Department, 

Lander, Wyoming. 

Herrero, S. 1985. Bear attacks: Their causes and 

avoidance. Nick Lyons Books, New York, New 

York. 

Hornocker, M.G. 1970. An analysis of mountain lion 

predation upon mule deer and elk in the Idaho 

Primitive Area. Wildlife Monograph 21: 1-39. 

Hosmer, D.W. and Lemeshow, S. 2000. Applied logistic 

regression. Second edition. Wiley and Sons, New 

York. 

Husseman, J.S., Murray, D.L., Power, G., Mack, C., 

Wenger, C.R., and Quigley, H. 2003. Assessing 

differential prey selection patterns between two 

sympatric large carnivores. Oikos 101: 591-601. 

Iriarte, J.A., Franklin, W.L., Johnson, W.E., and 

Redford, K.H. 1990. Biogeographic variation of 

food habits and body size of the American puma. 

Oecologia 85: 185-190. 

Isiche, J., Hillerton, J.E., and Nowell, F. 1992. 

Colonization of the mouse cadaver by flies in 

southern England. Medical and Veterinary 

Entomology 6: 168-170. 

Kadesky, K.M., Manarey, C., Blair, G.K., Murphy, J.J., 

III, Verchere, C., and Atkinson, K. 1998. Cougar 

attacks on children: Injury patterns and treatments. 

Journal of Pediatric Surgery 33: 863-865. 

Katnik, D.D., and Wielgus, R.B. 2005. Landscape 

proportions versus monte carlo simulated home 

ranges for estimating habitat availability. Journal of 

Wildlife Management 69: 20-32. 

59 
Keating, K.A., and Cherry, S. 2004. Use and 

interpretation of logistic regression in habitat 

selection studies. Journal of Wildlife Management

68: 774-789. 

Kellert, S.R. 1996. The value of life: Biological diversity 

and human society. Island Press, Washington, D.C. 

Kintigh, M. 2005. South Dakota mountain lion status 

report. Mountain Lion Workshop 8: 34-37. 

Koehler, G.M., and Hornocker, M.G. 1991. Seasonal 

resource use among mountain lions, bobcats, and 

coyotes. Journal of Mammalogy 72: 391-396. 

Kunkel, K.E., Ruth, T.K., Pletscher, D.H., and 

Hornocker, M.G. 1999. Winter prey selection by 

wolves and cougars in and near Glacier National 

Park, Montana. Journal of Wildlife Management 63: 

901-910. 

Laing, S.P., and Lindzey, F.G. 1993. Cougar habitat 

selection in south-central Utah. Pages 27-37 in C.S. 

Braun, editor. Mountain lion-human interaction 

symposium and workshop. Colorado Division of 

Wildlife, Denver, Colorado. 

Laliberte, A.S., and Ripple, W.J. 2004. Range 

contractions in North American carnivores and 

ungulates, BioScience 54: 123-137. 

Laundré, J.W., and Hernández, L. 2003. Winter hunting 

habitat of puma Puma concolor in northwestern 

Utah and southern Idaho, USA. Wildlife Biology 9: 

123-129. 

Leyhausen, P. 1979. Cat behaviour: The predatory and 

social behaviour of domestic and wild cats. Garland 

Press, New York, New York. 

Løe, J. 2002. Large carnivore related deaths: A 

conservation issue. M.Sc. Thesis, Norwegian 

University of Science and Technology, Trondheim, 

Norway. 

Logan, K.A., and Irwin, L.L. 1985. Mountain lion 

habitats in the Bighorn Mountains, Wyoming. 

Wildlife Society Bulletin 13: 257-262. 

Logan, K.A., and L.L. Sweanor. 2000. Puma. Pages 

347-377 in S. Demaris and P.R. Krausman, editors. 

Ecology and management of large mammals in 

North America. Prentice Hall, Upper Saddle River, 

NJ. 

Logan, K.A., and Sweanor, L.L. 2001. Desert puma: 

Evolutionary ecology and conservation of an 

enduring carnivore. Island Press, Washington, D.C. 

Maehr, D.S., and Caddick, G.B. 1995. Demographics 

and genetic introgression in the Florida panther. 

Conservation Biology 9: 1295-1298. 

Mansfield, T.M., and Charlton, K.G. 1998. Trends in 

mountain lion depredation and public safety 

incidents in California. Pages 118-120 in R.O. Baker 

and A.C. Crabb, editors. Proceedings of the 18th

Vertebrate Pest Conference. 

Mattson, D.J. 1997. Use of ungulates by Yellowstone 

grizzly bears Ursus arctos. Biological Conservation

81: 161-177. 

Mattson, D., Hart, J., and Arundel, T. 2005. Cougar 

predation in the Flagstaff Uplands: preliminary 

results from July 2003-May 2005. Mountain Lion 

Workshop 8: 158-169. 

McRae, B.H., Beier, P., DeWald, L.E., Huynh, L.Y., 

and Keim, P. 2005. Habitat barriers limit gene flow 

and illuminate historical events in a wide-ranging 

carnivore, the American puma. Molecular Ecology 

114: 1965-1977. 

Meinke, C.W., Golightly, R.T., Quigley, H.B., 

Ellingson, J., and Hofstra, T. 2004. Mountain lion 

habitat use relative to human activities in the 

Redwood Basin of northwest California. Abstract. 

American Society of Mammalogists Annual 

Meeting, 2004. 

Murphy, K.M., G.S. Felzien, and S.E. Relyea. 1992. 

The ecology of the mountain lion (Felis concolor 

missoulensis) in the northern Yellowstone 

ecosystem. Cumulative Progress Report No. 5. 

Wildlife Research Institute, Moscow, ID. 

Murphy, K.M., Felzien, G.S., Hornocker, M.G., and 

Ruth, T.K. 1998. Encounter competition between 

bears and cougars: some ecological implications. 

Ursus 10: 55-60. 

Nielsen, S.E., Boyce, M.S., Stenhouse, G.B., and 

Munro, R.H.M. 2002. Modeling grizzly bear 

habitats in the Yellowhead Ecosystem of Alberta: 

Taking autocorrelation seriously. Ursus 13: 45-56. 

Ockenfels, R.A. 1994. Mountain lion predation on 

pronghorn in central Arizona. Southwestern 

Naturalist 39: 305-306. 

Papouchis, C.M. 2004. Conserving mountain lions in a 

changing landscape. Pages 219-239 in N. Fascione, 

A. Delach, and M.E. Smith, editors. People and 

predators: From conflict to coexistence. Island 

Press, Washington, D.C. 

60 
Payne, J.A. 1965. A summer carrion study on the baby 

pig Sus scrofa L. Ecology 46: 592-602. 

Perry, G.L., and DeVos, J.C., Jr. 2005. A case study of 

mountain lion-human interaction in southeastern 

Arizona. Mountain Lion Workshop 8: 104-113. 

Pierce, B.M., Bleich, V.C., and Bowyer, R.T. 2000. 

Selection of mule deer by mountain lions and 

coyotes: effects of hunting style, body size, and 

reproductive status. Journal of Mammalogy 81: 462-

472. 

Pritchard, P. 2000. Environmental risk management. 

Earthscan Publications, London, United Kingdom. 

Riley, S.J., and Aune, K.E. 1997. Mountain lion-human 

and mountain lion-livestock incidents in Montana. 

Page 91 in W.D. Padley, editor. Proceedings of the 

fifth mountain lion workshop. Southern California 

Chapter of The Wildlife Society, San Diego, 

California. 

Riley, S.J., and Malecki, R.A. 2001. A landscape 

analysis of cougar distribution and abundance in 

Montana, USA. Environmental Management 28: 

317-323. 

Riley, S.J., DeGloria, S.D., and Elliot, R. 1999. A 

terrain ruggedness index that quantifies topographic 

heterogeneity. Intermountain Journal of Sciences 5: 

23-27. 

Riley, S.J., Nesslage, G.M., and Maurer, B.A. 2004. 

Dynamics of early wolf and cougar eradication 

efforts in Montana: Implications for conservation. 

Biological Conservation 119: 575-579. 

Ripple, W.J., and Beschta, R.L. 2006. Linking a cougar 

decline, trophic cascade, and catastrophic regime 

shift in Zion National Park. Biological Conservation 

133: 397-408. 

Robinnette, W.L., Gashwiler, J.S., and Morris, O.W. 

1959. Food habits of the cougar in Utah and Nevada. 

Journal of Wildlife Management 23: 261-273. 

Robinson, H.S., Wielgus, R.B., and Gwilliam, J.C. 

2002. Cougar predation and population growth of 

sympatric mule deer and white-tailed deer. 

Canadian Journal of Zoology 80: 556-568. 

Rodgers, A.R., Carr, A.P., Smith, L., and Kie, J.G. 

2005. HRT: Home range tools for ArcGIS. Ontario 

Ministry of Natural Resources, Centre for Northern 

Forest Ecosystem Research, Thunder Bay, Ontario, 

Canada. 

Rominger, E.M., Whitlaw, H.A., Weybright, D.L., 

Dunn, W.C., and Ballard, W.B. 2004. The influence 

of mountain lion predation on bighorn sheep 

translocations. Journal of Wildlife Management 68: 

412-426. 

Ross, P.I., and Jalkotzy, M.G. 1992. Characteristics of a 

hunted population of cougars in southwestern 

Alberta. Journal of Wildlife Management 56: 417-

426. 

Ross, P.I., and Jalkotzy, M.G. 1995. Fates of 

translocated cougars, Felis concolor, in Alberta. 

Canadian Field-Naturalist 109: 475-476. 

Ross, P.I., and Jalkotzy, M.G. 1996. Cougar predation 

on moose in southwestern Alberta. Alces 32: 1-8. 

Ruth, T.K. 2004. Ghost of the Rockies: the Yellowstone 

cougar project. Pages 13-24 in R.J. Anderson, editor. 

Yellowstone Science 12. Yellowstone National Park, 

Wyoming. 

Ruth, T.K., Logan, K.A., Sweanor, L.L., Hornocker, 

M.G., and Temple, L.J. 1998. Evaluating cougar 

translocation in New Mexico. Journal of Wildlife 

Management 62: 1264-1275. 

Sanders, M.R., and Halfpenny, J.C. 1993. Human-lion 

interactions in Boulder County, Colorado. Page 17 

in C.S. Braun, editor. Mountain lion-human 

interaction symposium and workshop. Colorado 

Division of Wildlife, Denver, Colorado. 

Schubert, D.J. 2004. Arizona mountain lion 

information. http://lions.adlaz.org/azsum1.html. 

Seidensticker, J.C., IV, Hornocker, M.G., Wiles, W.V., 

and Messick, J.P. 1973. Mountain lion social 

organization in the Idaho Primitive Area. Wildlife 

Monographs 35: 1-60. 

Shaw, H.G. 1977. Impact of mountain lion on mule deer 

and cattle in northwestern Arizona. Pages 17-32 in

R.C. Phillips and C. Jonkel, editors. Proceedings of 

the 1975 predator symposium. Montana Forest and 

Range Conservation Experiment Station, University 

of Montana, Missoula. 

Shuey, M.L. 2005. Land-cover characteristics of 

cougar/human interactions in and around an urban 

landscape. Mountain Lion Workshop 8: 117-126. 

Sinclair, E.A., Swenson, E.L., Wolfe, M.L., Choate, 

D.C., Bates, B., and Crandall, K.A. 2001. Gene flow 

estimates in Utah imply management beyond Utah. 

Animal Conservation 4: 257-264. 

61 
Smith, E.P., Lipkovich, I., and Ye, K. 2002. Weight-ofevidence (WOE): Quantitative estimation of 

probability of impairment for individual and 

multiple lines of evidence. Human and Ecological 

Risk Assessment 8: 1585-1596. 

Spalding, D.J., and Lesowski, J. 1971. Winter food of 

the cougar in south-central British Columbia. 

Journal of Wildlife Management 35: 378-381. 

Sweanor. L.L., Logan, K.A., and Hornocker, M.G. 

2005. Puma responses to close approaches by 

researchers. Wildlife Society Bulletin 33: 905-913. 

Taber, R.D., Raedeke, K., and McCaughran, D.A. 1982. 

Population characteristics. Pages 279-298 in J.W. 

Thomas and D.E. Toweill, editors. Elk of North 

America: ecology and management. Stackpole 

Books, Harrisburg, PA. 

Tantawi, T.I., El-Kady, E.M., Greenberg, B., and ElGhaffar, H.A. 1996. Arthropod succession on 

exposed rabbit carrion in Alexandria, Egypt. Journal 

of Medical Entomology 33: 566-580. 

Tessmer, J.W., and Meek, C.L. 1996. Dispersal and 

distribution of Calliphoridae (Diptera) immatures 

from animal carcasses in southern Louisiana. 

Journal of Medical Entomology 33: 665-669. 

Tischendorf, J.W. 2003. Cryptic cougars – perspectives 

on the puma in the eastern, midwestern, and Great 

Plains regions of North America. Pages 71-86 in

S.A. Becker, D.D. Bjornlie, F.G. Lindzey, and D.S. 

Moody, editors. Proceedings of the seventh 

mountain lion workshop. Wyoming Game and Fish 

Department, Lander, Wyoming. 

Torres, S.G., Mansfield, T.M., Foley, J.E., Lupo, T., and 

Brinkhaus, A. 1996. Mountain lion and human 

activity in California: Testing speculations. Wildlife 

Society Bulletin 24: 451-460. 

Treves, A., Naughton-Treves, L. 1999. Risk and 

opportunity for humans coexisting with large 

carnivores. Journal of Human Evolution 36: 275-

282. 

Van Dyke, F.G., Brocke, R.H., Shaw, H.G., Ackerman, 

B.B., Hemker, T.P., and Lindzey, F.G. 1986. 

Reactions of mountain lions to logging and human 

activity. Journal of Wildlife Management 50: 95-

102. 

Wakeling, B.F. 2003. Status of mountain lions in 

Arizona. Pages 1-5 in S.A. Becker, D.D. Bjornlie, 

F.G. Lindzey, and D.S. Moody, editors. 

Proceedings of the seventh mountain lion workshop. 

Wyoming Game and Fish Department, Lander, 

Wyoming. 

Weisberg, S. 1985. Applied linear regression. Second 

edition. John Wiley, New York, New York. 

Williams, J.S., McCarthy, J.J., and Picton, H.D. 1995. 

Cougar habitat use and food habits on the Montana 

Rocky Mountain Front. Intermountain Journal of 

Sciences 1: 16-28. 

62 
Acknowledgments 

Paul Whitefield (NPS), Mark Sogge (USGS), Denny Fenn (USGS, retired), Ron Hiebert (NPS) and Melanie 

Lambert (Summerlee Foundation) provided critically important support for our study. Paul Beier (NAU), 

Stephanie Nichols-Young (Wiburforce Foundation), Mary and Jesse Johnson (Johnson Family Foundation), 

Rick Miller (AZGF), Karen Murray (Grand Canyon Trust), Dave Bergman (USDA Wildlife Services), Steve 

Mitchelson (NPS), Richard Ockenfels (AZGF), and Ron Sieg (AZGF) offered much appreciated support and 

encouragement. Sam Dieringer (USDA Wildlife Services) and his hounds provided extraordinary and 

otherwise much appreciated capture services. Scott Sprague (AZGF) located lost mountain lions for us. Mike 

and Diane Miller were volunteers extraordinaire. We greatly valued all of our volunteers, including Zachary 

and Sky Bischoff-Mattson, Susan Bischoff, Brandon Holton, Trevor Streng, Jesse Millen-Johnson, David 

Allen, Margaret Carr, Samantha McClure, John Broecher, Bill Martin, Jamie and Dick McNeil, Chris and 

Michael Finney, Tim Melnick, and Adam Glover. Financial support for this study has been provided by the 

USGS Park-Oriented Biological Support program, the USGS Colorado Plateau Research Station, the NPS 

Cooperative Conservation Initiative, the Summerlee Foundation, the Wilburforce Foundation, the USGS 

Southwest Biological Science Center, the USGS Fire Research Program, and the Johnson Family Foundation. 

Material and other in-kind support has been provided by USDA Wildlife Services, The Grand Canyon Trust, 

Northern Arizona University, Arizona Game and Fish Department, and the NPS Flagstaff Area National 

Monuments. The use of trade, product, or firm names in this publication is for descriptive purposes only and 

does not imply endorsement by the U.S. Government.

63 
Appendix 1. Summary of Outreach 

Public presentations, informational meetings, and other public outreach or academic 

service accomplished during January 2003-July 2006 as part of this project. 

Presentations 

1. “A conceptual model and appraisal of existing research related to interactions between humans and pumas,” 

by D. Mattson, J. Hart, P. Beier, & J. Millen-Johnson at 7th Mountain Lion Workshop, sponsored by Wyoming 

Game & Fish Department and The Wildlife Society, Jackson, WY, May 2003. 

2. “Cougars of the Flagstaff Uplands,” by J. Hart and D. Mattson for Science in the Park, sponsored by 

Flagstaff Festival of Science, Flagstaff, AZ, September 2003. 

3. “A conceptual model and appraisal of research related to interactions between humans and pumas,” by D. 

Mattson, J. Hart & P. Beier for 7th Biennial Conference of Research on the Colorado Plateau, sponsored by 

the 7th Biennial Conference Committee, Flagstaff, AZ, November 2003. 

4. “Cougars of the Flagstaff Uplands research project,” by D. Mattson, J. Hart & T. Arundel for 2004 Western 

Region Center Director’s Meeting, sponsored by USGS BRD Western Region, Flagstaff, AZ, June 2004. 

5. “Cougars of the Flagstaff Uplands: an introduction and results of the 2003-2004 field season,” by J. Hart, 

D. Mattson & T. Arundel for 2004 State Meeting of the Arizona Wildlife Services Program, sponsored by 

USDA Wildlife Services, Hawley Lake, AZ, July 2004. 

6. “Cougars of the Flagstaff Uplands: an introduction and results of the 2003-2004 field season,” by D. 

Mattson, J. Hart & T. Arundel for 2004 Flagstaff Field Center Open House, sponsored by the USGS Flagstaff 

Field Center, Flagstaff, AZ, July 2004. 

7. “Cougars of the Flagstaff Uplands: an introduction and results of the 2003-2004 field season,” by D. 

Mattson, J. Hart & T. Arundel for August Staff Meeting, Region 2 Arizona Game & Fish Department, 

sponsored by Region 1, Arizona Game & Fish Department, Flagstaff, AZ, August 2004. 

8. “Cougars on the edge...of Flagstaff,” by D. Mattson, J. Hart and T. Arundel for Northern Arizona 

University Forestry Seminar Series, sponsored by NAU School of Forestry, Flagstaff, AZ, September 2004. 

9. “Cougars of the Flagstaff Uplands: an introduction and results of the 2003-2004 field season,” by D. 

Mattson, J. Hart & T. Arundel for Flagstaff Science Center September All Hands Meeting, sponsored by 

USGS Flagstaff Science Center, Flagstaff, AZ, September 2004. 

10. “Cougars of the Flagstaff Uplands,” by J. Hart & D. Mattson for Science in the Park, sponsored by 

Flagstaff Festival of Science, Flagstaff, AZ, September 2004. 

11. “Cougars of the Flagstaff Uplands,” by J. Hart, D. Mattson & T. Arundel for Community Forest Forum, 

Flagstaff City Hall, sponsored by the Greater Flagstaff Forests Partnership, Flagstaff, AZ, October 2004. 

12. “Cougars of the Flagstaff Uplands: an introduction and results of the 2003-2004 field season,” by J. Hart, 

D. Mattson & T. Arundel for Forest Management (FOR323W), Northern Arizona University School of 

Forestry, Flagstaff, AZ, February 2005. 

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13. “A multi-park design for investigating cougar-related risks to humans in the Southwest,” by D.J. Mattson, 

J. Hart, T. Arundel, E. Garding, H.S. Kim, and E. Leslie, for 2005 George Wright Society Biennial Conference 

on Parks, Protected Areas and Cultural Sites, sponsored by the George Wright Society, Philadelphia, PA, 

March 2005. 

14. “Cougars of the Flagstaff uplands: cougar-informed spatial frames for analyzing habitat selection,” by D.J. 

Mattson, T. Arundel and J. Hart, for 8th Mountain Lion Workshop, sponsored by the Washington Department 

of Fish & Wildlife, Leavenworth, WA, May 2005. 

15. “Cougars of the Flagstaff uplands: results of 2003-2004 predation studies,” by D.J. Mattson, J. Hart and T. 

Arundel, for 8th Mountain Lion Workshop, sponsored by the Washington Department of Fish & Wildlife, 

Leavenworth, WA, May 2005. 

16. “Development of an agent-based model for cougars in Arizona,” by T.R. Arundel, D.J. Mattson, and J. 

Hart, for ESRI 2005 User Conference, San Diego, CA, June 2005. 

17. “Conflict over carnivores: a window on natural resources governance,” by D.J. Mattson for conference on 

Governance and Decision-Making in Mountain Areas, sponsored by Parks Canada and The Banff Centre, 

Banff, AB, Canada, June 2005. 

18. “Cougars of the Flagstaff Uplands,” by J. Hart for Science in the Park, sponsored by The Flagstaff Festival 

of Science, Flagstaff, AZ, September 2005. 

19. “(We are) living with cougars in the Flagstaff Uplands,” by D.J. Mattson, sponsored by the Phoenix Zoo 

and Sierra Club, Flagstaff, AZ, September 2005. 

20. “Safety in Red Rock’s Lion Country,” by D.J. Mattson for the USFS Red Rocks Ranger District Monthly 

Safety Meeting, sponsored by the USFS Red Rocks Ranger District, Sedona, AZ, October 2005. 

21. “Conflict over cougars: A window on the institution of wildlife management,” by D. Mattson for 

Foundations of Natural Resources Policy and Management (F&ES 891b), Yale School of Forestry and 

Environmental Studies, New Haven, CT, October 2005. 

22. “Human dimensions of mountain lion management: Value orientations and policy preferences of Northern 

Arizona residents,” by E.J. Ruther, D.M. Ostergren, and D.J. Mattson for 8th Biennial Conference of Research 

on the Colorado Plateau, sponsored by the 8th Biennial Conference Committee, Flagstaff, AZ, November 

2005. 

23. “Predation by cougars in the Flagstaff Uplands 2003-2005,” by D. Mattson, J. Hart and T. Arundel for 8th

Biennial Conference of Research on the Colorado Plateau, sponsored by the 8th Biennial Conference 

Committee, Flagstaff, AZ, November 2005. 

24. “Cougars of the Colorado Plateau: A multi-park investigation,” by D. Mattson, J. Hart, T. Arundel, R. 

Stevens, E. Garding, RV Ward, J. Bradybaugh, and E. Leslie, for 4th Annual Southwest Biological Science 

Center All-Employees Meeting, sponsored by the USGS Southwest Biological Science Center, Flagstaff, AZ, 

November 2005. 

25. “Cougars of the Colorado Plateau: A multi-park investigation,” by D. Mattson, J. Hart, T. Arundel, R. 

Stevens, and J. Bradybaugh, for Zion National Park staff, sponsored by Zion National Park, UT, December 

2005. 

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26. “Cougars of the Colorado Plateau: A multi-park investigation,” by D. Mattson, J. Hart, T. Arundel, R. 

Stevens, and J. Bradybaugh, for public venue, sponsored by Zion National Park, Springdale, UT, December 

2005. 

27. “Cougars of the Colorado Plateau: A multi-park investigation,” by D. Mattson, J. Hart, T. Arundel, R. 

Stevens, E. Garding, RV Ward, J. Bradybaugh, and E. Leslie, for 1st Meeting of the Colorado Plateau 

Mountain Lion Working Group, sponsored by the USGS Southwest Biological Science Center, Flagstaff, AZ, 

January 2006. 

28. “Cougars of the Flagstaff Uplands: Preliminary results 2003-2005,” by D. Mattson, J. Hart, and T. 

Arundel, for 1st Meeting of the Colorado Plateau Mountain Lion Working Group, sponsored by the USGS 

Southwest Biological Science Center, Flagstaff, AZ, January 2006. 

29. “Living with large fierce creatures: Cougars at the Arizona urban-wildland interface,” by D. Mattson for 

the Environmental Studies Colloquium, sponsored by Prescott College, Prescott, AZ, April 2006. 

30. “Cougar studies at the Southwest Biological Science Center,” by D. Mattson, J. Hart and T. Arundel, for 

the Flagstaff Leadership Program, sponsored by the USGS Flagstaff Science Center and Flagstaff Leadership 

Program, Flagstaff, AZ, May 2006. 

31. “Cougars of the Flagstaff Uplands,” by D. Mattson, J. Hart and T. Arundel, for Flagstaff Area National 

Monuments staff, sponsored by the NPS Flagstaff Area National Monuments, Flagstaff, AZ, May 2006. 

32. “Lions in the mountains: Co-existence and the nature of pumas,” by D. Mattson for the First Sunday 

Program, sponsored by Red Rock State Park, Sedona, AZ, June 2006. 

33. “Lions in the mountains: Co-existence and the nature of pumas,” by D. Mattson for the Willow Bend 

Environmental Center Summer Speaker Series, sponsored by the Willow Bend Environmental Center, 

Flagstaff, AZ, July 2006. 

Posters and Other Printed Informational Material 

1. “Monitoring cougar movements near the Flagstaff urban interface,” POSTER and presentation by J. Hart 

and D. Mattson for Cougars and Human Safety Trailhead Workshop, sponsored by the US Forest Service and 

Arizona Department of Game & Fish, Flagstaff, AZ, December 2002. 

2. Mattson, D.J., J. Hart & T. Arundel. (2002). Cougars of the Flagstaff uplands. USGS Southwest 

Biological Science Center, Flagstaff, AZ. 2pp. (RESEARCH BRIEFING). 

3. “Monitoring cougar movements near the Flagstaff urban interface,” POSTER by J. Hart and D. Mattson for 

Cougars and Human Safety Trailhead Workshop, sponsored by the US Forest Service and Arizona 

Department of Game & Fish, Flagstaff, AZ, December 2002. 

4. “Spatial analysis of puma (Puma concolor) habitat use relative to topographic roughness in northern 

Arizona,” POSTER by T.R. Arundel, S.T. Arundel, D. Mattson & J Hart for 7 th Biennial Conference of 

Research on the Colorado Plateau, sponsored by the 7th Biennial Conference Committee, Flagstaff, AZ, 

November 2003. 

5. “Kills by cougars in the Flagstaff uplands of northern Arizona” POSTER by D. Mattson, J. Hart, & T. 

Arundel for 2004 Western Region Center Director’s Meeting, sponsored by USGS BRD Western Region, 

Flagstaff, AZ, June 2004. 

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6. Mattson, D.J., J. Hart & T. Arundel. (2004). Kills by cougars in the Flagstaff Uplands of northern Arizona 

July2003-May 2004. USGS Southwest Biological Science Center, Flagstaff, AZ. 1pp. (FACT SHEET) 

7. “Cougar-informed spatial frames and control for autocorrelation in analyses of habitat selection,” POSTER 

by D. Mattson, T. Arundel, & J. Hart for 8th Mountain Lion Workshop, sponsored by Washington Department 

of Fish & Wildlife, Leavenworth, WA, May 2005. 

8. Mattson, D.J., T. Arundel & J. Hart. (2005). Preliminary analysis of habitat selection by cougars in the 

Flagstaff uplands. USGS Southwest Biological Science Center, Flagstaff, AZ. 1pp. (FACT SHEET) 

 

9. Mattson, D., Hart, J. and Arundel, T. (2005). Kills by cougars in the Flagstaff uplands of northern Arizona, 

July 2003-February 2005. USGS Southwest Biological Science Center, Flagstaff, AZ. 1pp. (RESEARCH 

BRIEFING). 

10. Mattson, D., Arundel, T., and Hart, J. (2005). Preliminary analysis of habitat selection by cougars in the 

Flagstaff uplands of northern Arizona. USGS Southwest Biological Science Center, Flagstaff, AZ. 1pp. 

(RESEARCH BRIEFING). 

Reviews of Management Materials 

1. Review of the draft report Analysis of Scientific Publications Related to the Florida Panther for U.S. Fish & 

Wildlife Service and Florida Fish & Wildlife Commission, 2003. 

2. Review of Cougar Management Guidelines for North America, for the authors and for Wild Futures, Earth 

Island Institute, Bainbridge Island, WA, 2004-2005. 

Published Abstracts and Papers 

1. Mattson, D.J., J.V. Hart, P. Beier & J. Millen-Johnson. (2003). A conceptual model and appraisal of 

existing research related to interactions between humans and pumas. Page 104 in S.A. Becker, D.D. Bjornlie, 

F.G. Lindzey & D.S. Moody, editors. Proceedings of the Seventh Mountain Lion Workshop. Wyoming Game 

& Fish Department, Lander, WY. (ABSTRACT) 

2. Mattson, D., J. Hart, & T. Arundel. (2005). Cougar predation in the Flagstaff Uplands: preliminary results 

from July 2003-May 2005. Pages 158-169 in R. Beausoleil & D. Martorello, editors. Proceedings of the 8th

Mountain Lion Workshop. Washington Department of Fish & Game Department, Olympia, WA. 

Mentoring and Training 

Volunteers (listing those who made substantial contributions) 

Grand Canyon Trust: Karen Murray, Bill Martin, Jamie and Dick McNeil, Chris and Michael Finney, and 

Adam Glover. 

Northern Arizona University: Samantha McClure, John Broecher, Brandon Holton, Trevor Streng, and Tim 

Melnick. 

USGS: Mike and Diane Miller, Zachary and Sky Bischoff-Mattson, Susan Bischoff, Jesse Millen-Johnson, 

David Allen, and Margaret Carr. 

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68 

Preceptorships and Graduate Committees 

1. D. Mattson, Preceptor, 2003, Intern program for Jesse Millen-Johnson involving field work for the 

Flagstaff area cougar project, Bates College, Lewiston, ME. 

2. D. Mattson, Committee Member, 2002-2005, Master’s program for Elizabeth Ruther, Conflict and 

cohabitation: a survey of northern Arizona ponderosa pine ecosystem residents assessing nature views and 

cougar perceptions, Environmental Sciences & Policy, Northern Arizona University, Flagstaff, AZ. 

3. D. Mattson, Preceptor, 2004, Winter Study Project for Margaret Carr and David Allen, Where the wild 

things are: a study of cougar response to the presence of humans, Winter Studies Program (SPEC 99), 

Williams College, Williamsburg, MA. 

4. D. Mattson, Faculty Advisor, 2004-2005, Senior Project for Trevor Streng, Cougar biology in northern 

Arizona, Center for Environmental Studies & Education, Northern Arizona University, Flagstaff, AZ.