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Exciting New Findings about the Species Ecology!

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Exciting New Findings about the Species Ecology!

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A new study finds that mountain lions in the western United States change their surroundings and as a result are “ecosystem engineers.” A team of scientists tracked 18 lion kills in the Greater Yellowstone Ecosystem in Wyoming and identified 215 species of beetles living in, on and off the carcasses — that is, the kills provided habitat as well as food for scavengers.
The work demonstrates the critical role mountain lions play in providing resources to other species in the ecosystems in which they live.

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References

1. DiZinno JA, Lord WD, Collins-Morton MB, Wilson MR, Goff ML (2002) Mitochondrial DNA sequencing of beetle larvae (Nitidulidae: omosita) recovered from human bone. J Forensic Sci 47:1337-1339. https://doi.org/10.1520/JFS15571J

2. Hoermann CV, Ruther J, Reibe S, Madea B, Ayasse M (2011) The importance of carcass volatiles as attractants for the hide beetle Dermestes maculatus (De Geer). Forensic Sci Int. 212:173-179. https://doi.org/10.1016/j.forsciint.2011.06.009

3. Midgley JM, Collett IJ, Villet MH (2012) The distribution, habitat, diet and forensic significance of the scarab Frankenbergerius forcipatus (Harold, 1881) (Coleoptera: Scarabaeidae). Afr Invertebr. 53:745-749. https://doi.org/10.5733/afin.053.0214

4. Trumbo ST (1992) Monogamy to communal breeding: exploitation of a broad resource base by burying beetles ( Nicrophorus). Ecol Entomol 17:289-298. https://doi.org/10.1111/j.1365-2311.1992.tb01060.x

5. Vaurie P (1955) A revision of the genus Trox in North America (Coleoptera: Scarabaeidae). Bull Am Mus Nat Hist 106:1-89

6. Wang Y, Yang JB, Wang JF, Li LL, Wang M, Yang LJ, Tao LY, Chu J, Hou YD (2017) Development of the forensically important beetle Creophilus maxillosus (Coleoptera: Staphylinidae) at constant temperatures. J Med Entomol 54:281-289. https://doi.org/10.1093/jme/tjw193

7. Warnes GR, Bolker B, Lumley T (2018) gtools: Various R Programming Tools. R package version 3.8.1. https://CRAN.R-project.org/package=gtools

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Acknowledgements

We thank the Summerlee Foundation, Pace University, Hofstra University, Charles Engelhard Foundation, Eugene V. & Clare E. Thaw Charitable Trust, National Geographic Society (Grant C236-13), Connemara Fund, Tapeats Foundation, Community Foundation of Jackson Hole, Hogan Films, Ecotour Adventures, Mr. and Mrs. Folger, L. Westbrook, The Scully family, Mr. and Mrs. Haberfeld, Mr. and Mrs. Robertson, Mr. and Mrs. Heskett, Mr. and Mrs. Burgesses, D. Bainbridge, T. Thomas, and A. Smith for financial support of the project. We thank M. Ivie for his assistance to beetle identification. We also thank M. Peziol, C. O'Malley, A. Kusler, J. Williams, B. Smith and S. Smith for contributions to fieldwork. We are grateful for the feedback from the handling editor, two anonymous reviewers, and R. Brandl, Editor in Chief, Terrestrial Invertebrate.

Author information

Affiliations

1. Department of Environmental Studies and Science, Pace University, New York, USA

Joshua M. Barry

, Matthew E. Aiello-Lammens

& Anna Kusler

2. Panthera, New York, USA

L. Mark Elbroch

& Howard B. Quigley

3. Department of Biology, Hofstra University, New York, USA

4. Montana Entomology Collection, Montana State University, Montana, USA

5. Department of Biology, Pace University, New York, USA

Contributions

JMB and LME conceived and designed the experiment. JMB and LME conducted the field work. JMB and MEAL conceived and performed statistical analyses. LS and AK conducted laboratory work on the beetle dataset. JMB and LME wrote the manuscript, with constructive feedback and additions from all authors. HBQ, MMG, and RJS provided institutional support for the work and graduate student training.

Corresponding author

Correspondence to Joshua M. Barry.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in this study involving animals were in accordance with the ethical standards of the American Society of Mammals and were approved by two independent Institutional Animal Care and Use Committees (IACUC): the Jackson IACUC (Protocol 027-10EGDBS-060210) and National Park Service IACUC (IMR_GRTE_Elbroch_Cougar_2013-2015).

Additional information

We offer a new predator as an ecosystem engineer. Our study design can reveal the positive role predators have in supporting insect communities. Our design can be applied to predators globally.

Communicated by Liliane Ruess.

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Cite this article

Barry, J.M., Elbroch, L.M., Aiello-Lammens, M.E. et al. Pumas as ecosystem engineers: ungulate carcasses support beetle assemblages in the Greater Yellowstone Ecosystem. Oecologia 189, 577-586 (2019). https://doi.org/10.1007/s00442-018-4315-z
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Keywords

Biodiversity

Carrion

Invertebrates

Pumas

Scavenging

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Pumas as ecosystem engineers: ungulate carcasses support beetle assemblages in the Greater Yellowstone Ecosystem

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Ecosystem engineers create physical changes in abiotic and biotic material, and through this process control the availability of resources for other species. Predators that abandon large portions of their prey may be ecosystem engineers that create habitat for carrion-dependent invertebrates that utilize carcasses during critical life-history periods.

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The cougar (Puma concolor) is a large felid of the subfamily Felinae. It is native to the Americas. Its range spans from the Canadian Yukon to the southern Andes in South America, and is the widest of any large wild terrestrial mammal in the Western Hemisphere. It is an adaptable, generalist species, occurring in most American habitat types. Due to its wide range, it has many names including puma, mountain lion, red tiger, panther, and catamount.

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Controversy that needs to be addressed for the species

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Controversy that needs to be addressed for the species

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Texas is the only U.S. state with a viable cougar population that is not protected. In Texas, the cougar is considered as nuisance wildlife and any person holding a hunting or a trapping permit can kill a cougar regardless of the season, number killed, sex ,or age of the animal.

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Like most wildlife, he says mountain lions in general will avoid areas of human disturbance. As urban areas are quiet amid orders related to COVID-19, it's possible they may move into new territory.

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As coronavirus restrictions linger on and people staying at home have less and less contact with wildlife, officials are unsure if recent activity, like several mountain lion group sightings, is related to people self-quarantining.

Pictures of three mountain lions wandering through the north Boulder area began popping up on social media over the weekend, with Boulder police confirming they have also received calls about the animals.

Short straw gets to go tell the mountain lions they are not social distancing properly... Taken by reader KB in north #Boulder over the weekend pic.twitter.com/dcl1XzcdJY

- Mitchell Byars (@mitchellbyars) March 23, 2020

But wildlife officials said while it might seem like a strange sight related to the emptier streets, increased mountain lion activity in urban Boulder is not unusual for this time of year.

Experts are still unsure how the quarantine will affect wildlife patterns as a whole, since it is still at its early stages.

Fewer opportunities for wildlife and human interaction may not change any general trends, but Jason Clay, public information officer for Colorado Parks and Wildlife, noted animals usually avoid areas with high human activity.

Read the rest of the story on our partner site The Daily Camera.
Subscribe to our weekly newsletter, The Adventurist, to get outdoors news sent straight to your inbox.

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After mountain lions wandered through Boulder, officials unsure how sheltering will impact wildlife

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As coronavirus restrictions linger on and people staying at home have less and less contact with wildlife, officials are unsure if recent activity, like several mountain lion group sightings, is related to people self-quarantining. Pictures of three mountain lions wandering through the north Boulder area began popping up on social media over the weekend, with Boulder police confirming they have also received calls about the animals.

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Many management plans include the following:
1. Maintain genetically diverse and demographically viable populations;
2. Minimize conflicts between mountain lions and humans (e.g. public safety events, property damage);
3. Identify and protect important habitats; and
4. Improve public awareness of mountain lions; and
5. Identify and research emerging issues that threaten mountain lion populations or the habitats upon which they depend.

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Due to the expanding human population, cougar ranges increasingly overlap with areas inhabited by humans. Attacks on humans are very rare, as cougar prey recognition is a learned behavior and they do not generally recognize humans as prey. In a 10-year study in New Mexico of wild cougars who were not habituated to humans, the animals did not exhibit threatening behavior to researchers who approached closely (median distance=18.5 m; 61 feet) except in 6% of cases; 14/16 of those were females with cubs. Attacks on people, livestock, and pets may occur when a puma habituates to humans or is in a condition of severe starvation. Attacks are most frequent during late spring and summer, when juvenile cougars leave their mothers and search for new territory.

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mountain lion attack: a colorado deputy escaped the jaws of a big cat - cnn

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A witness captured the moment when a Colorado deputy escaped the jaws of a mountain lion

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When a group of officers arrived at a neighborhood in Loveland, Colorado, after a call of a mountain lion attack, they tried to contain the big cat. But instead, it attacked a deputy and took her to the ground.

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The grace and power of the cougar have been widely admired in the cultures of the indigenous peoples of the Americas. The Inca city of Cusco is reported to have been designed in the shape of a cougar. The Moche people represented the puma often in their ceramics. The sky and thunder god of the Inca, Viracocha, has been associated with the animal. In North America, mythological descriptions of the cougar have appeared in the stories of the Hocąk language of Wisconsin and Illinois and the Cheyenne, amongst others. To the Apache and Walapai of Arizona, the wail of the cougar was a harbinger of death. The Algonquins and Ojibwe believe that the cougar lived in the underworld and was wicked, whereas it was a sacred animal among the Cherokee. In sports, Several sports teams currently or in the past have used the cougar as the mascot/nickname for their team, although the name actually used has depended on the most popular regional name for the species.

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See Why the Mysterious Mountain Lion Is the ‘Bigfoot’ of Big Cats | Short Film Showcase

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As a top predator, Mountain Lions play a pivotal and critical role in maintaining biodiversity and stability in complex communities. By preying on deer and elk, for instance, cougars help prevent their populations from becoming larger than their habitat can support.

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Mountain Lion (Puma concolor) Population Characteristics in the Little Missouri Badlands of North Dakota

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Mountain lions (Puma concolor) have many impacts on the ecosystems they inhabit, leading to both biological and social ramifications. Yet, due to the relatively recent natural recolonization by mountain lions of the Little Missouri Badlands Region of western North Dakota, detailed data regarding many aspects of this population have been lacking.

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The cougar is a mostly solitary animal. Only mothers and kittens live in groups, with adults meeting rarely. While generally loners, cougars will reciprocally share kills with one another and seem to organize themselves into small communities defined by the territories of dominant males.

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Cameras Reveal the Secret Lives of a Mountain Lion Family | Short Film Showcase

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Mountain lions were once thought to be solitary animals, feared and hunted by humans. New footage captured by motion-triggered cameras in secluded areas deep in the Wyoming Wind River range shows a mountain lion family and the animals' unknown social bonds.

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A successful generalist predator, the cougar will eat any animal it can catch, from insects to large ungulates (over 500 kg. Like other cats, it is an obligate carnivore, meaning it must feed on meat to survive. Its most important prey species are various deer species, particularly in North America; mule deer, white-tailed deer, elk, and even bull moose. A survey of North America research found 68% of prey items were ungulates, especially deer. Only the Florida panther showed variation, often preferring feral hogs and armadillos.

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Abstract

Recent recolonization of mountain lions ( Puma concolor ) into the Little Missouri Badlands of North Dakota has led to questions regarding the potential impacts of predation on prey populations in the region. From 2012 to 2013, we deployed 9 real-time GPS collars to investigate mountain lion feeding habits. We monitored mountain lions for 1,845 telemetry-days, investigated 506 GPS clusters, and identified 292 feeding events. Deer ( Odocoileus spp.) were the most prevalent item in mountain lion diets (76.9%). We used logistic regression to predict feeding events and size of prey consumed at an additional 535 clusters. Our top model for predicting presence of prey items produced a receiver operating characteristic score of 0.90 and an overall accuracy of 81.4%. Application of our models to all GPS clusters resulted in an estimated ungulate kill rate of 1.09 ungulates/week (95% confidence interval [ CI ] = 0.83-1.36) in summer (15 May‒15 November) and 0.90 ungulates/week (95% CI = 0.69-1.12) in winter (16 November‒14 May). Estimates of total biomass consumed were 5.8kg/day (95% CI = 4.7-6.9) in summer and 7.2kg/day (95% CI = 5.3-9.2) in winter. Overall scavenge rates were 3.7% in summer and 11.9% in winter. Prey composition included higher proportions of nonungulates in summer (female = 21.5%; male = 24.8%) than in winter (female = 4.8%; male = 7.5%). Proportion of juvenile ungulates in mountain lion diets increased during the fawning season (June‒August) following the ungulate birth pulse in June (June-August = 60.7%, 95% CI = 43.0-78.3; September-May = 37.2%, 95% CI = 30.8-43.7), resulting in an ungulate kill rate 1.61 times higher (1.41 ungulates/week, 95% CI = 1.12-1.71) than during the remainder of the year (0.88 ungulates/week, 95% CI = 0.62-1.13). Quantifying these feeding characteristics is essential to assessing the potential impacts of mountain lions on prey populations in the North Dakota Badlands, where deer dominate the available prey base and mountain lions represent the lone apex predator.

Quantifying kill rates, consumption rates, and composition of prey (e.g., species, age, sex), and understanding the ecological factors (e.g., competing predators, available prey guilds, population structure, season) that may cause them to vary, is vital in assessing potential impacts of mountain lions ( Puma concolor ) on prey populations ( Knopff and Boyce 2007 ; Sand et al. 2008 ; Knopff et al. 2009 ). Although numerous studies have been published on mountain lion feeding habits in North America, reported predation rates have varied based upon study area and methods used (see Knopff et al. 2010 , Table 1 ). Prior to the introduction of GPS collars, estimates of predation rates were obtained primarily via intensive snow-tracking ( Hornocker 1970 ), radio-tracking ( Murphy et al. 1998 ; Nowak 1999 ; Cooley et al. 2008 ), or energetic models ( Hornocker 1970 ; Ackerman et al. 1986 ; Laundré 2005 ). However, these methods have distinct limitations as they tend to yield small sample sizes and are often restricted by season or weather (e.g., snow, flying conditions), which requires extrapolation of findings to other seasons ( Sand et al. 2008 ). These techniques also potentially underestimate the importance of smaller prey items in the diet due to shorter handling times ( Webb et al. 2008 ). All of these factors have the potential to increase bias and decrease precision when estimating basic parameters such as kill rates ( Sand et al. 2008 ; Knopff et al. 2010 ). More recent studies have employed GPS collars to aid in monitoring large carnivore predation ( Anderson and Lindzey 2003 ; Webb et al. 2008 ; Knopff et al. 2010 ; Miller et al. 2013 ), allowing efficient monitoring of more individuals over longer, continuous periods, across all seasons, leading to increased precision in predation estimates.

Previous studies have demonstrated that the presence of other large carnivores can affect feeding habits of mountain lions ( Murphy et al. 1998 ; Kortello et al. 2007 ; Bartnick et al. 2013 ). Mountain lions represent the only large carnivore in the Little Missouri Badlands of North Dakota, a factor that distinguishes this system from other predation studies conducted in multipredator systems (e.g., Knopff et al. 2010 ; Ruth et al. 2010 ). Additionally, variation in available prey types, habitat conditions, and anthropogenic influences among study areas further limits the extrapolation of mountain lion feeding rates to other populations or regions, even when the most rigorous methods are used (e.g., Knopff et al. 2010 ).

Historically, mountain lions were found throughout North Dakota but dropped below detectable numbers by the early 1900s with the last confirmation occurring in 1902 ( Bailey 1926 ; Young and Goldman 1946 ). It was not until the early 2000s that reports of mountain lions in western North Dakota once again confirmed residency and a breeding population, indicating a comparatively recent recolonization ( North Dakota Game and Fish Department 2006 ). Mountain lions in North Dakota persist in a small population separated from neighboring breeding populations by vast expanses of grasslands that comprise the Northern Great Plains, likely limiting immigration ( Thompson and Jenks 2005 ), and resulting in a genetically distinct population ( Juarez 2014 ). We documented mountain lion predation patterns in the Little Missouri Badlands of North Dakota using GPS collars to evaluate 5 research questions: 1) what is the prey composition of mountain lion diets, 2) how many ungulates do mountain lions kill per week, 3) what effect does scavenging have on ungulate consumption rates, 4) what effect does season have on mountain lion feeding habits, and 5) what effect does demographic status have on mountain lion feeding habits?

Study area.

We studied mountain lion predation in western North Dakota, within Billings, Dunn, and McKenzie counties. Our 2,050-km ² study area comprised approximately one-third of the Little Missouri Badlands and was contained within "Zone 1" (limited harvest region) of the mountain lion hunting area, as defined by the North Dakota Game and Fish Department ( Fig. 1 ). The Badlands Region is characterized by highly eroded, steep clay canyons and buttes, distributed along the Little Missouri River and ranging in elevation from approximately 570-710 m above mean sea level ( Hagen et al. 2005 ). Vegetation occurring in draws of the northern and eastern slopes was predominately Rocky Mountain juniper ( Juniperus scopulorum ) and green ash ( Fraxinus pennsylvanica ), while riparian areas contained cottonwood ( Populus deltoides ) stands. Southern and western slopes, plateaus, and bottomlands were often barren or contained short-grass prairie ( Hagen et al. 2005 ). Grass species within the region included blue grama ( Bouteloua gracilis ), bluebunch wheatgrass ( Pseudoroegneria spicata ), Indian ricegrass ( Achnatherum hymenoides ), Western wheatgrass ( Pascopyrum smithii ), and little bluestem ( Schizachyrium scoparium - Hagen et al. 2005 ). The Killdeer Mountains Region also was located within the study area and represented a 60-km ² island of elevated habitat connected to the Badlands Region by a few small drainages. This area rises 300 m above the surrounding prairie to 1,010 m and is comprised of a mosaic of open grassland and deciduous species, including green ash, quaking aspen ( Populus tremuloides ), burr oak ( Quercus macrocarpa ), paper birch ( Betula papyrifera ), western black birch ( Betula nigra ), and American elm ( Ulmus americana ), with a dense undergrowth of beaked hazelnut ( Corylus cornuta - Hagen et al. 2005 ).

The climate in North Dakota is continental; a relatively dry climate (42.7cm mean annual precipitation) characterized by hot summers (record high 49°C) and cold winters (record low −51°C- Seabloom 2011 ). Cattle grazing was the most common land use in the Little Missouri Badlands; however, oil and gas development was rapidly increasing within the region ( Hagen et al. 2005 ). Our study area was a mosaic of public (49%) and private land (51%), with the western portion predominately public and the eastern portion predominately private ( Fig. 1 ). Public lands within the study area included Theodore Roosevelt National Park (TRNP), the Little Missouri National Grasslands, Bureau of Land Management properties, North Dakota State Trust lands, and the North Dakota Game and Fish Department Killdeer Wildlife Management Area.

Potential ungulate prey for mountain lions included mule deer ( Odocoileus hemionus ), white-tailed deer ( O. virginianus ), elk ( Cervus elaphus ), bighorn sheep ( Ovis canadensis ), pronghorn ( Antilocapra americana ), and bison ( Bison bison ). Potential nonungulate prey included beaver ( Castor canadensis ), porcupine ( Erethizon dorsatum ), turkey ( Meleagris gallopavo ), and raccoon ( Procyon lotor ). Domestic livestock (e.g., cattle, horses, goats) also were available as prey. Other carnivores including coyote ( Canis latrans ), bobcat ( Lynx rufus ), and red fox ( Vulpes vulpes ) were present as both potential competitors of and prey items for mountain lions.

Capture and monitoring.

From 2011 to 2013, we captured mountain lions from established bait (e.g., roadkilled ungulates) sites with the use of foothold traps and foothold snares ( Logan et al. 1999 ). We immobilized mountain lions with a mixture of tiletamine and zolazepam (Telazol, Zoetis Inc., Kalamazoo, Michigan; 5.0mg/kg) and xylazine (Anased, LLOYD Inc., Shenandoah, Iowa; 1.0mg/kg- Kreeger and Arnemo 2007 ) based on estimated live animal body weight via dart rifle (Dan-Inject, Børkop, Denmark, EU). We weighed, measured, determined sex, and estimated age (using tooth wear and pelage characteristics- Anderson and Lindzey 2000 ) of captured mountain lions. We classified mountain lions as kittens (dependent on mother), subadults (dispersal until 2.5-3 years), or adults (> 3 years) and fitted subadult and adult mountain lions with real-time GPS radio-collars (Advanced Telemetry Systems [ATS] G2110E, Isanti, Minnesota). Upon completion of handling, we administered 0.125mg/kg yohimbine to reverse xylazine, released mountain lions on site, and monitored them from a distance to ensure safe recovery ( Kreeger and Arnemo 2007 ). All procedures were approved by the South Dakota State University Animal Care and Use Committee (Approval number 11-080A) and followed recommendations of the American Society of Mammalogists ( Sikes et al. 2011 ).

We programmed GPS collars to collect 8 locations/day (0000, 0300, 0600, 0900, 1200, 1500, 1800, and 2100h). Collars were set to attempt a GPS fix for 120s at each scheduled fix time (ATS "forest" setting), then transmit those coordinates via satellite every 24h to an automated email system. We used a Python script (Python Software Foundation, Hampton, New Hampshire) developed by Knopff et al. (2009) to identify potential feeding sites from independent subadult and adult mountain lions. We considered a cluster (i.e., a potential feeding site) as ≥ 2 GPS locations within 200 m over a 6-day period ( Knopff et al. 2009 ). Once clusters were identified, we downloaded all GPS locations associated with the cluster into a hand-held GPS unit (Garmin International, Olathe, Kansas) and visited clusters as soon as possible to determine possible predation events. We conducted a systematic search at each cluster by walking 5−10 m wide (depending on visibility) zig-zag patterns out to a 30 m radius around each cluster location and cluster centroid. A concurrent study ( Smith 2014 ) found a low probability of 2-location clusters containing kills (1 probable ungulate kill out of 237 clusters investigated; 0.4%) from September to May (i.e., outside of the ungulate birth pulse). Therefore, we visited clusters containing ≥ 2 locations from May through August and those containing ≥ 3 locations from September to May.

Characteristics of prey.

We categorized prey remains found at cluster sites as either a predation or scavenging event provided there was evidence the mountain lion had killed or fed on the carcass (e.g., caching, sheared hair from prey). Prey remains were classified as a predation event if evidence suggested that a mountain lion likely killed the prey item (e.g., bite marks, hemorrhaging, or subcutaneous bruising on the neck). If we were unable to locate such evidence, but the decomposition of prey remains indicated the animal died around the time the cluster was created, and there was no evidence to contradict the occurrence of mountain lion predation, we assigned that cluster as a predation event. We classified clusters as scavenging events in cases where it was clear the cause of death was not due to the mountain lion assigned to that cluster (e.g., another collared mountain lion had made the kill, hunter-wounding loss [e.g., bullet wounds or arrow present], roadkill [e.g., broken bones and close to a road], or carcass dump site) or if the amount of decomposition indicated the animal died before the cluster dates ( Knopff et al. 2010 ).

We identified prey species and sex by anatomical characteristics (e.g., tail, metatarsal glands, antlers) and determined age from tooth eruption and wear ( Severinghaus and Cheatum 1956 ). In cases where we could not determine species, sex, or age, carcasses were classed as "undetermined." We assigned each predation event to either summer (15 May-15 November) or winter (16 November-14 May) based on time of the 1st GPS location in the cluster. Seasonal cutoffs were chosen to ensure we encapsulated the ungulate birth pulse within one season ( Knopff et al. 2010 ).

We calculated species composition by percent frequency seasonally for male and female mountain lions. We compared proportions of ungulate and nonungulate prey in diets and percentage of prey scavenged, by mountain lion sex and season (winter or summer) using chi-square tests. We compared proportions of juvenile ungulates in diets of all mountain lions during fawning (June-August) and nonfawning periods (September-May) using a single-factor analysis of variance. We performed these analyses using SYSTAT 13 ( Systat Software, Inc. 2009 ).

To estimate biomass (kg) consumed, we assigned approximate live weights to adult (≥ 2 years) ungulate prey based on published estimates ( Table 1 ). For elk and deer (white-tailed and mule deer combined), we assigned weights to adults based on those from Jensen (2000) . For bighorn sheep, we assigned values based on known weights from the Black Hills, South Dakota ( Smith et al. 2015 ). To estimate yearling and young of the year ungulate biomass, we used a von Bertalanffy growth equation modified from Knopff et al. (2010) . For example, we used an inflection point ( I ) of 140 days ( Knopff et al. 2010 ) and adjusted the growth rate ( K ) until birth weights approximated known ungulate birth weights for the region (e.g., Smith et al. 2014 ). We then assigned median weights for each of 4 juvenile age classes (0-3 months, 3-6 months, 6-12 months, and 12-24 months; Table 1 ). When we could not determine age class, we assigned yearling weight to the kill ( Knopff et al. 2010 ). When we could determine that the prey was an adult but were unable to determine sex, we assigned the average adult weight (i.e., [ad M + ad F]/2- Knopff et al. 2010 ). For nonungulate prey, we did not differentiate between sex or age class; instead, we assigned mean weights provided in Seabloom (2011) for mammals and Dunn and Alderfer (2006) for birds. As it was often difficult to ascertain the amount of biomass consumed at scavenge sites, we used predictions from our large- versus small-prey model (see below) to assign scavenge events to one of the 2 categories. We then applied the average biomass from all known small-prey kills or large-prey kills to each scavenged item within the same category.

Model development.

We attempted to census mountain lion predation of ungulates throughout the monitoring period for each marked mountain lion by visiting all generated GPS clusters. However, not all clusters could be investigated (e.g., limited private property access); therefore, we used multivariate logistic regression ( Hosmer and Lemeshow 2000 ) to predict predation events at noninvestigated clusters. First, we developed a model using the lme4 package ( Bates et al. 2014 ) in R ( R Development Core Team 2014 ) to predict presence or absence of prey items ≥ 4kg using data from investigated clusters where collar fix success was ≥ 45% ( Knopff et al. 2009 ). We removed clusters associated with the initial radio-collaring, collar removals, and den sites prior to modeling. We used season (summer and winter) and 8 variables generated from our Python script for model development: 1) number of locations in clusters, 2) fidelity (number of locations in the cluster minus number of locations away from cluster during cluster duration), 3) hours (number of hours between the first and last location in the cluster), 4) days (number of 24-h periods in which at least one location was collected within the cluster), 5) night points (total number of locations within cluster that were obtained between 1800 and 0600h), 6) night proportion (number of night locations in cluster divided by total number of locations in cluster), 7) average distance (average distance that each cluster location was from cluster centroid), and 8) cluster radius (difference between cluster centroid and the farthest location within the cluster).

After testing for correlation between variables ( r ≥ 0.7; SYSTAT 11.0), we developed a priori models using uncorrelated variables and fit them to our investigated GPS clusters. We assessed model fit using Akaike's Information Criteria (AIC- Burnham and Anderson 2002 ). We applied coefficients from our top regression model to our investigated cluster data to calculate a prediction value for each cluster. We then used a receiver operating characteristic (ROC) curve generated in package ROCR ( Sing et al. 2005 ) in R to determine an optimal cutoff value for our model predictions ( Boyce et al. 2002 ; Knopff et al. 2009 ; Miller et al. 2013 ). Selection of cutoff values was imperative for optimal prediction performance of the model ( Webb et al. 2008 ; Knopff et al. 2009 ; Merrill et al. 2010 ; Miller et al. 2013 ). Selecting a cutoff that maximizes the true positive rate, or sensitivity, may lead to an overestimation of kill rate due to an increased number of false positives generated by the model. However, maximizing the true negative rate, or specificity, may lead to an underestimate of kill rate by misclassifying many sites as not having a ≥ 4kg prey item present. We chose our cutoff values by maximizing the overall model accuracy, defined as the sum of the true positive predictions and true negative predictions divided by the sum of actual positive and negative clusters ( Sing et al. 2005 ).

We developed a 2nd logistic model using this same methodology to further predict if clusters contained large (≥ 32kg) or small (< 32kg) prey items. We used 32kg as a cut point to separate classes, allowing deer fawns ≥ 6 months of age to be incorporated as large prey items. We applied our top regression models to noninvestigated GPS clusters and categorized them as no-prey, large prey, or small prey. We assigned a consumption category (ungulate kill, nonungulate kill, scavenge) to noninvestigated clusters based upon percentage of each category found at investigated clusters within the same prey-size class and season (e.g., large prey, winter) as the predicted cluster. We estimated a predicted biomass value for each noninvestigated cluster based upon the average biomass from investigated clusters within the same prey-size class and season.

Feeding rates.

For mountain lions fitted with GPS collars that had ≥ 45 % fix success and were monitored for ≥ 28 days within a given season and demographic class ( Knopff et al. 2010 ), we calculated 3 feeding rates: 1) ungulate kill rate, using feeding events where we determined that the ungulate prey was killed by the mountain lion that generated the cluster, 2) ungulate consumption rate, using both mountain lion-killed ungulates and scavenged ungulates, and 3) biomass consumed, including all ungulate and nonungulate prey items consumed. We used a ratio estimator to calculate feeding rates using total number of weeks (or days for biomass) monitored as the denominator and total number (actual + predicted) of ungulate kills, ungulate consumptions, or prey biomass as the numerator ( Knopff et al. 2010 ).

We used all 3 rates to estimate annual feeding rates of mountain lions by using individuals as the unit of analysis and pooling data across season and demographic class. We also calculated seasonal feeding rates for 3 demographic classes: female (F), male (M), and adult female with young (family group [FG]). Previous studies (e.g., Anderson and Lindzey 2003 ; Knopff et al. 2010 ; Elbroch et al. 2014 ) found kill rates to vary between lone females and those with young; therefore, we calculated multiple feeding rates for individual mountain lions if they transitioned between these classes during our study. We assessed the effects of demographic class on feeding rates using a single-factor analysis of variance for each season (SYSTAT 11.0). Demographic and season-specific feeding rates were the unit of analysis.

We monitored predation of 9 GPS-collared mountain lions for 1,845 telemetry-days ( = 205 days/mountain lion, SD = 116.55) across summer (1,029 days) and winter (816 days) seasons from July 2012 to July 2013. Our sample of GPS-collared mountain lions included 4 adult females (3 of which transitioned to FG status during the study), 1 subadult male, and 5 adult males (1 of which was collared as a subadult but transitioned to adult during the study). Our sample of FGs consisted of 1 female with 3 kittens > 9 months, 1 female with 3 kittens 3‒6 months, and 1 female with kittens < 3 months. GPS collars averaged 77% fix success during our monitoring period (range = 67-90%). We visited 506 GPS clusters and documented 292 feeding events. We visited cluster sites an average of 14.9 days ( SD = 13.1) after the 1st date of the cluster. We were able to identify species at 251 (86.0%) clusters (100.0% positively identified when combining mule deer and white-tailed deer), age of carcass at 252 (86.3%) clusters, and sex of carcass at 117 (40.1%) clusters. Due to nearly complete consumption (e.g., only hooves, leg bones, or jaws remaining) prior to investigation of the cluster, differentiation of deer species and sex was often difficult for juvenile ungulates within the first few months postpartum.

Prey composition.

We documented 12 unique species of prey items at mountain lion feeding sites. Ungulates (mule deer, white-tailed deer, elk, and bighorn sheep) comprised the majority of identified prey items found based on relative frequency (80.9%). Deer ( Odocoileus spp.) were the most prevalent (76.9%) prey and at clusters where we could identify deer species ( n = 189), mule deer were most common (85.7%), accounting for 54.2% of our sample. White-tailed deer (9.0%), bighorn sheep (2.7%), and elk (1.3%) occurred at lower frequencies. Other prey items included beaver (6.7%), porcupine (5.0%), coyote (1.7%), raccoon (1.0%), turkey (1.0%), and domestic cattle (1.0%) with the remainder of species (mountain lion, cottontail [ Sylvilagus spp.]) occurring at < 1.0% ( Table 2 ). Scavenged prey accounted for 6.7% based on relative frequency of prey items.

We observed significant seasonal variation in prey composition (ungulate or nonungulate distribution) for both sexes (female, χ ² ₁ = 14.347, P < 0.001; male, χ ² ₁ = 11.591, P = 0.001); however, prey composition was similar between sexes (χ ² ₃ = 1.136, P = 0.768) throughout both seasons. Mountain lions consumed higher proportions of nonungulate prey in summer (females = 21.5%; males = 24.8%) than winter (females = 4.8%; males = 7.5%). Scavenge frequencies for both sexes were higher in winter (females = 11.9%; males = 11.9%) than in summer (females = 3.1%; males = 4.0%; Table 2 ).

Model performance.

We used logistic regression to predict predation events for 535 clusters. The top model for predicting consumption of prey ≥ 4kg at GPS clusters included hours, average distance, night proportion, season, and fidelity as covariates. This model had an AIC score that was 2.34 AIC units less than the 2nd-ranked model and carried 56.1% of the AIC weight (3.2 times more AIC weight than the 2nd-ranked model; Table 3 ). Prey carcasses were more likely to be present at clusters in summer with a higher number of hours between first and last locations at the cluster, smaller average distance from cluster locations to cluster centroid, higher proportion of night time locations within the cluster, and higher site fidelity ( Table 4 ). This model fit the data well ( Hosmer and Lemeshow 2000 ), producing a ROC area under the curve (AUC) of 0.90. We used a cutoff value of 0.53 resulting in a maximized overall model accuracy of 81.4%. The resulting model predicted consumption of prey items at 62 of 535 (11.6%) noninvestigated clusters.

Our top model for differentiating large and small prey items included days, season, and fidelity as covariates. This model had an AIC score that was 2.73 ΔAIC less than the 2nd-ranked model and carried 60.9% of the AIC weight (3.9 times greater than the 2nd-ranked model; Table 5 ). Large carcasses were more likely to be present at clusters in winter when fidelity and number of days a mountain lion was present at the cluster increased ( Table 4 ). This model fit the data well (Hosmer and Lemeshow 2000), producing a ROC curve with an AUC value of 0.89. We used a cutoff value of 0.55 resulting in a maximized overall model accuracy of 84.6%. Of the 62 noninvestigated clusters that were previously predicted to have a prey item consumed, our 2nd model predicted 24 (38.7%) clusters contained large carcasses and 38 (61.3%) contained small carcasses.

Feeding rates.

Overall ungulate kill rate, not accounting for seasonal or demographic effects, was 1.01 ungulates/week (95% confidence interval [ CI ] = 0.76-1.27), total ungulate consumption rate was 1.11 ungulates/week (95% CI = 0.87-1.35), and total biomass consumed was 6.55kg/day (95% CI = 5.07-8.20). Summer ungulate kill rates (1.09 ungulates/week, 95% CI = 0.83-1.36) were similar ( F 1,15 = 1.326, P = 0.268) to winter rates (0.90 ungulates/week, 95% CI = 0.69-1.12). Ungulate consumption rates also were similar ( F 1,15 = 0.742, P = 0.403) for summer (1.15 ungulates/week, 95% CI = 0.91-1.40) and winter (1.03 ungulates/week, 95% CI = 0.83-1.24). Total biomass consumed/day was similar ( F 1,15 = 2.053, P = 0.172) for summer (5.81kg/day, 95% CI = 4.71-6.90) and winter (7.24kg/day, 95% CI = 5.25-9.22).

We documented a difference ( F 1,14 = 4.237, P = 0.059) in ungulate kill rates during the fawning period (June-August) compared to the remainder of the year. Ungulate kill rate was 1.61 times higher during the fawning period (1.41 ungulates/week, 95% CI = 1.12-1.71) than during the remainder of the year (0.88 ungulates/week, 95% CI = 0.62-1.13). Proportion of juvenile prey consumed by mountain lions also increased during this period ( F 1,14 = 4.959, P = 0.043; fawning period = 60.7%, 95% CI = 43.1-78.3; nonfawning period = 37.2%, 95% CI = 30.8-43.7). Despite increase in ungulate kill rate, total biomass consumed did not vary ( F 1,14 = 0.026, P = 0.873; fawning period = 6.73kg/week, 95% CI = 4.20-9.25; nonfawning period = 6.90kg/week, 95% CI = 4.41-9.39).

Our sample of subadults was limited to one male that was monitored for 5 months prior to transitioning to an adult. As this individual's kill rate (0.92 ungulates/week) did not differ from adult male kill rates during this time period ( = 0.97 ungulates/week, 95% CI = 0.56‒1.38), we included all males as one demographic group for our analysis. When comparing seasonal (summer versus winter) effects across demographic classes, we found significant differences in ungulate kill ( F 5,11 = 2.934, P = 0.064) and ungulate consumption ( F 5,11 = 2.916, P = 0.065) rates; however, biomass consumed per week did not differ ( F 5,11 = 0.870, P = 0.531; Fig. 2 ). Our results were limited to one adult female without kittens for the summer season; this female was only collared for June and July (time of highest ungulate kill rate). Excluding this female, ungulate kill ( F 5,10 = 0.881, P = 0.506) and consumption rates ( F 5,10 = 0.919, P = 0.487) were similar for seasonal demographic classes.

D iscussion

Our study represents the 1st research conducted on mountain lions in the Little Missouri Badlands of North Dakota. This population occurs on the eastern edge of mountain lion range ( Larue et al. 2012 ) where environmental conditions (e.g., habitat, prey guilds, anthropogenic influences, land use) vary from many previously studied western populations and where mountain lions represent the lone large carnivore. Understanding these regional differences in populations of mountain lions is important for management because mountain lions inhabit a wide range of habitats, thus are affected by varying social and political pressures ( Jenks 2011 ). Additionally, mountain lions persist within a relatively small region of North Dakota where breeding range is estimated at 2,671 km ² ( Tucker 2013 ), less than one-third the area of another recently studied Midwestern population (8,400 km ² Black Hills, South Dakota- Fecske 2003 ). Estimated population size within the region also was low ( Tucker 2013 ); thus, our sample of radio-collared individuals likely represents a considerable proportion of the mountain lion population in North Dakota.

We documented higher overall ungulate kill rates for mountain lions in our study compared to most previous studies (see Table 1 in Knopff et al. 2010 ), including recent studies using similar methodology ( Knopff et al. 2010 ; Smith 2014 ). However, our ungulate kill rates were similar to some ( Allen et al. 2014 ; Elbroch et al. 2014 [Colorado]) and lower than others ( Ruth et al. 2010 ; Elbroch et al. 2014 [California, Patagonia]). Lower kill rates reported in early studies may have been influenced by monitoring technique, with very high frequency monitoring likely missing some ungulate kills; Ruth et al. (2010) reported VHF monitoring underestimated kill rates an average of 5.2 days/kill compared to GPS monitoring. Variation in reported ungulate kill rates between our study and others using similar techniques was likely due to differences in body size of available ungulate prey between study areas, as previously suggested by Elbroch et al. (2014) . For example, deer were the primary ungulate prey (76.9% of diet) for mountain lions in North Dakota, and although bison were available, elk were the only large ungulate prey consumed by mountain lions but accounted for a minimal portion of annual diets (2.1% males; 0% ad females); additionally, 0‒3 month calves comprised 100% of elk killed by mountain lions in our study. In contrast, Knopff et al. (2010) found that large ungulates (elk, moose [ Alces americanus ], feral horse [ Equus caballus ]) played a much greater role in mountain lion diets in Alberta (48.4% ad males; 8.6% ad females); similarly, Smith (2014) found high proportions of large ungulates (e.g., elk) in adult male mountain lion diets (20.1%) in Black Hills, South Dakota. Influence of scavenged prey may additionally alter ungulate kill rates; Smith (2014) saw much greater reliance on scavenged prey items (17.3%) by mountain lions in the Black Hills compared to those in the Little Missouri Badlands (6.7%), which allow mountain lions to persist in the Black Hills without making as many kills. Use of larger prey items and higher scavenge rates are likely drivers of a lower ungulate kill rate for mountain lions. Previous research has suggested that subadult females tend to have the lowest kill rates among demographic groups ( Knopff et al. 2010 ; Smith 2014 ), while FGs generally have the highest ( Anderson and Lindzey 2003 ; Cooley et al. 2008 ; Knopff et al. 2010 ; Elbroch et al. 2014 ; Smith 2014 ). Our study did not include any subadult females and had a high proportion of FGs, which likely contributed to our higher estimates of overall ungulate kill rate.

We concur with Elbroch et al. (2014) that although ungulate kill rates (ungulates/week) are needed to assess impacts of mountain lions on local prey populations, kill rates reported in kg/day rather than ungulates/week may be more useful in comparing prey consumption by mountain lions across study sites given regional variation in body size of available ungulate prey and differing ungulate guilds. Our estimates of live weight prey biomass killed by mountain lions (6.55kg/day) were much lower than that reported by Elbroch et al. (2014 ; 10.10kg/day), Ruth et al. (2010 ; 14.9‒21.8kg/day), and Knopff et al. (2010 ; 8.28kg/day), however, were comparable to Allen et al. (2014 ; 5.63‒6.74kg/day) and Smith (2014 ; 6.61kg/day). Differing techniques in calculating biomass likely has some influence on these varying rates. Higher ungulate kill rates observed in some studies, both in terms of ungulates/week and kg/day, may be due to premature carcass abandonment by mountain lions in the presence of potential scavengers such as bears ( Ursus sp.- Murphy et al. 1998 ) and condors ( Vultur sp.- Elbroch and Wittmer 2013 ), whereas competition is limited to mesocarnivores in the Little Missouri Badlands.

Although our seasonal comparisons may be influenced by variation among individuals due to the small number of radio-collared mountain lions ( n = 2) that maintained demographic class through both seasons, we did see similar dietary shifts in both males and females. Mountain lion diets contained much higher proportions of juvenile ungulates (60.7%) during the fawning season than during the remainder of the year, suggesting selection for these ungulates during this time ( Knopff et al. 2010 ). The ungulate birth pulse represents a time of high ungulate densities, and high vulnerability for neonates, resulting in an increased encounter rate and increased risk of predation at each encounter ( Lingle et al. 2008 ). This shift to juveniles after the ungulate birth pulse resulted in a substantial increase in ungulate kill rate (1.61 times greater in June-August). Such shifts also have been found in previous studies ( Knopff et al. 2010 ; Allen et al. 2014 ; Smith 2014 ) and should be considered when evaluating potential impacts of mountain lion predation on ungulate populations. Additionally, we found considerable increases in nonungulate consumption during summer by males and females, which was contrary to findings of Knopff et al. (2010) . Beaver and porcupine were the primary nonungulate prey items in mountain lion diets in North Dakota. Increase in beaver consumption was likely attributed to their increased availability in summer after ice out. Increase in porcupine consumption in summer was mainly due to the propensity of one young adult male to kill porcupines, which accounted for 75% of porcupines killed in summer, and 67% of all porcupines killed.

We did not find significant seasonal differences in biomass consumed. This may be due to mountain lions consuming enough biomass to meet some energetic threshold, although, it also could be partially the result of our biomass estimates for scavenge sites. Our assessment of scavenged biomass likely represents an overestimation due to uncertainty in true consumption and thus, potentially inflated estimates of total biomass consumed in winter when scavenge events were more prevalent.

Due to small sample size, we separated our mountain lion sample into 3 groups: males (1 subadult, 5 adults), independent females (3 adults), and females with kittens (3 adults), unlike the 6 demographic groups compared by Knopff et al. (2010) and Smith (2014) . Our findings are similar to those of previous studies (e.g., Anderson and Lindzey 2003 ; Knopff et al. 2010 ; Smith 2014 ), with FGs having the highest ungulate kill and consumption rates, followed by independent females, and males. Higher energetic needs of FGs ( Laundré 2005 ) likely explain their higher feeding rates; variation within our winter FG class was likely due to differences in age of kittens not accounted for in our grouping. Intuitively, one might expect male mountain lions to have higher kill rates than independent females due to their larger size ( Laundré 2005 ); however, tendency of males to take more larger bodied ungulate prey in winter ( = 59.11kg) than females ( = 47.67kg) likely explains the similar kill rates we observed.

Estimated overall scavenge rates (6.7%) in our study area were comparable to those for mountain lions in Alberta, Canada (5.8%- Knopff et al. 2010 ), but much lower than rates reported for Black Hills, South Dakota (17.3%- Smith 2014 ), where anthropogenic influences (e.g., carcass dumpsites and carcasses disposed of by hunters) were much greater. Sources of scavenged prey included vehicle collisions, hunter-wounding loss, winter-kill, and mountain lions sharing prey items. Although scavenging occurred throughout the year, higher rates were observed in winter ( = 11.9%) than summer ( = 3.7%), consistent with patterns found in previous studies ( Bauer et al. 2005 ; Knopff et al. 2010 ; Smith 2014 ). Colder winter conditions likely influenced scavenge rates by slowing or halting decomposition of potential forage items, which allowed greater time for mountain lions to encounter them. Additionally, higher energetic needs in winter for thermoregulation may lead to an increased tendency of mountain lions to conserve energy by scavenging items they may normally ignore in summer.

There are several potential biases that could have influenced our assessment of scavenging rates. First, if there was insufficient evidence at a site to suggest otherwise, we classified prey items as probable mountain lion predation; the nearly complete consumption of most carcasses prior to investigation may have resulted in misclassification of scavenged prey items as kills. Second, prey killed by unmarked mountain lions and subsequently scavenged by marked individuals, or the sharing of prey items by mountain lions, may have caused similar misclassifications. Third, information from our bait sites indicated that mountain lions exhibiting scavenging behavior may not stay at a scavenge site long enough for the formation of a GPS cluster, resulting in an underestimation of the influence of scavenging on mountain lion diets. The number of individuals that fed at our bait sites (minimum 24 individuals, 76 events), but were not captured, indicated that scavenging may occur more in this system than demonstrated by our marked individuals. Conversely, by capturing mountain lions using bait sites, we may have potentially selected mountain lions with an increased propensity toward scavenging. Additional work, however, is needed to fully assess the impacts of scavenging on mountain lion predation in this region.

Our assessment of small prey consumed also may represent an underestimation as mountain lions would likely exhibit shorter handling times with these items, which could result in clusters not generated at these sites. Additionally, the complete consumption of some small prey items could have resulted in our inability to detect them even if clusters were generated. Increasing the number of locations collected per day from GPS collars may allow researchers to identify these small prey items more readily; however, this would substantially increase intensity of field work and decrease efficiency by incorporating more no-prey sites that would need to be investigated.

Adjusting our criteria for cluster formation may have led to increased efficiency in our cluster investigations. Had we required each cluster to contain one night location (GPS locations within cluster obtained between 1800 and 0600h), we would have reduced the number of no-prey clusters that we investigated by 12% while still visiting 100% of clusters containing prey remains. Furthermore, requiring 2 night points would have reduced no-prey cluster investigations by 41%, while maintaining 96% of all clusters with prey, including 88% of 0‒3 month ungulates and 98% of > 3 month prey items (including nonungulates; Fig. 3 ). Requiring a minimum of 3 night points reduced no-prey clusters dramatically (74%), however, the percentage of 0-3 month ungulate clusters investigated would have dropped to 66%, thus, underestimating mountain lion predation on juvenile ungulates. We recommend that researchers consider using a minimum number of night points while determining criteria for cluster formation based upon their research goals.

High proportions of private land within Midwestern mountain lion populations provide an additional challenge for researchers. Although our study area was focused mainly within the Little Missouri National Grassland, eastern portions were comprised almost entirely of private lands ( Fig. 1 ). Cooperation with private landowners was generally amicable; however, we were denied access to a few large tracts resulting in a considerable number of noninvestigated clusters. Use of logistic regression modeling allowed us to predict potential predation for clusters we were not able to investigate. Despite having to predict feeding events for a large number of clusters (51.4%), our model estimated that 11.6% of these clusters contained prey items, representing 17.3% of the total number of carcasses (investigated + predicted) used to calculate feeding rates. To verify the efficacy of our predictions, we applied Smith's (2014) top logistic regression model for predicting mountain lion predation events in the Black Hills, South Dakota to our noninvestigated clusters. Using the provided beta values and prediction cutoff value, that model predicted similar results (9.5% predicted to contain prey items) for our noninvestigated clusters. We believe that the low proportion of predicted feeding events at noninvestigated clusters was likely due to the behavior of mountain lions within areas where we were denied access and not from model bias. For example, the Killdeer Mountain region was connected to the nearest Badlands habitat by a few small privately owned drainages surrounded by agricultural lands and oil and gas development. We routinely saw individuals using one of these drainages (~5 km long) as a corridor between the 2 habitats; however, rarely did they stay within it for extended time periods. Smaller clusters within this corridor, and other similar travel ways across "nontraditional" habitats (e.g., short-grass prairie), were likely generated as a result of movement patterns, not feeding events. This behavior resulted in a much larger proportion of noninvestigated clusters having a low number of cluster hours compared to our investigated clusters. The average duration (1st coordinate minus last coordinate) for all noninvestigated clusters was 25.3h ( SE = 1.7), 38.8h ( SE = 1.9) for investigated clusters, and 53.3h ( SE = 2.5) for known consumption sites. The shorter period of time spent at noninvestigated clusters is a probable explanation for the lower proportion of predicted feeding events generated by our model for these sites. Additionally, noninvestigated clusters had a much lower number of night locations ( = 1.9 night locations/cluster) when compared to investigated clusters ( = 5.1 night locations/cluster); investigated sites with prey items present contained an average of 7.2 night locations/cluster. This lower number of night locations is likely due to mountain lions creating clusters at day bed locations while traversing "nontraditional" habitats; Ruth et al. (2010) found that the proportion of clusters containing bed sites or mountain lion sign, but no prey items, was highest when the clusters were generated between 0800 and 1700. Over- or underestimation of prey consumption by our model has the potential to alter our estimated feeding rates; however, it is probable that they approximate true rates for mountain lions inhabiting the Little Missouri Badlands of North Dakota.

Use of GPS collars allows for increased length of monitoring periods, providing improved precision in predation estimates ( Knopff et al. 2009 ). Additionally, real-time GPS collars have allowed researchers to reduce time between cluster formation and investigation, leading to easier identification of prey remains and cause of death ( Anderson and Lindzey 2003 ; Knopff et al. 2009 ; Smith 2014 ). Although care should be taken to ensure mountain lions are not prematurely displaced from carcasses, potentially altering feeding habits ( Knopff et al. 2010 ; Miller et al. 2013 ), we do not suspect that these displacements would introduce excessive bias; we routinely found mountain lions returning to bait sites where they had been captured, given that edible bait was still present at the site.

Application of our feeding rates to other study areas should be performed with caution. Variation in ecological conditions (e.g., predator and prey guilds, mountain lion population structure, habitat) has the potential to alter predation characteristics. A larger sample of mountain lions would increase the precision of our estimates and more data are needed to differentiate potential differences in mountain lion predation rates between seasons and among demographic groups. The use of GPS collar technology and modern modeling techniques allowed us to estimate predation rates in a landscape interspersed with private lands that does not allow for the investigation of all potential feeding sites. Use of these techniques will prove valuable as mountain lions continue range expansion into regions with high proportions of private lands (e.g., Midwestern states).

A cknowledgments

We thank W. Schmaltz, A. Powers, and C. Anderson for help with captures and cluster investigations. Wildlife veterinarian D. Grove provided extensive knowledge for capture immobilizations. B. Hosek was integral in creating and maintaining our automated GPS location retrieval system and database. J. Faught assisted with aerial telemetry support. USDA Wildlife Services provided both equipment and knowledge that allowed our project to be successful. TRNP-NU furnished researchers with field housing during the study. Finally, we thank cooperating landowners for granting us access to their land for cluster investigations. Funding for this project was provided by Federal Aid to Wildlife Restoration administered through North Dakota Game and Fish Department.

Author notes

# data[].attributes.attachment_link.title
Mountain lion ( Puma concolor ) feeding behavior in the Little Missouri Badlands of North Dakota

# data[].attributes.attachment_link.description
Recent recolonization of mountain lions ( Puma concolor ) into the Little Missouri Badlands of North Dakota has led to questions regarding the potential impacts of predation on prey populations in the region. From 2012 to 2013, we deployed 9 real-time GPS collars to investigate mountain lion feeding habits.

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The cougar is listed as least concern on the IUCN Red List since 2008.

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The cougar's total breeding population is estimated at less than 50,000 by the IUCN, with a declining trend.