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Frontiers in Conservation Science ·

Volume 7 - 2026 | https://doi.org/10.3389/fcosc.2026.1779101

Mountain lion prey-selection in a community comprised of native and introduced ungulate species

  • Peter C. Iacono1
  • Kathryn A. Schoenecker2*corresponding author
  • Jessie M. Dodge2
  • Patrick J. Jackson3
  • Kezia R. Manlove1
  • David C. Stoner1
  1. 1Department of Wildland Resources and Ecology Center, Quinney College of Natural Resources, Utah State University, Logan, UT, United States
  2. 2U.S. Geological Survey, Fort Collins Science Center, Fort Collins, CO, United States
  3. 3Nevada Department of Wildlife, Reno, Nevada, NV, United States
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Abstract

Species introductions are a hallmark of human influence on ecosystems worldwide. In communities where native and introduced fauna coexist, predators may select prey based on availability or vulnerability. Free-roaming feral horses (Equus caballus) are a recent addition to North America and have become widespread, abundant, and in some areas serve as major prey for mountain lions (Puma concolor), a native carnivore. The proliferation of horses as well as potential effects of mountain lion predation on native ungulates create ecological and social management challenges; our goal was to assess mountain lion prey selection in a system composed of native mule deer (Odocoileus hemionus) and introduced feral horses. Our objectives were to: (1) describe seasonal patterns of prey use within individual mountain lion home ranges; (2) quantify prey use relative to availability; and (3) determine seasonal kill rates on the two primary ungulates in the study area. Fieldwork occurred in southeastern Nevada, 2021–2022. We identified prey use by investigating cache sites from 6 GPS-collared mountain lions. Prey availability and relative abundance were estimated with 49 camera traps distributed across the 3,158-km² study area. During the growing season, weekly kill rates averaged 0.22 deer and 0.55 horses, and in winter, 1.43 deer and 0.33 horses. Thus, lions killed horses at 2.54 times the rate of deer in the growing season but deer at 4.33 times the rate of horses in winter. Model selection indicated mule deer density and prey age class were the strongest predictors of prey species. Lions selected adult deer over fawns, and foals over adult horses, suggesting choices may be driven by optimal prey size rather than species. Mountain lions prey on foals in areas with relatively high mule deer densities, suggesting deer presence may increase foal vulnerability. We hypothesize that lions select foals when available, potentially reducing pressure on mule deer and/or supporting larger lion populations than would occur without this additional prey source. Although individual-level data on mountain lions are challenging to obtain, such information could help managers identify individuals for removal or conservation, improving predator-management outcomes in mixed-prey systems.

Keywords

1 Introduction

Introduced non-native species have modified and influenced ecosystems globally. Management of these species has typically focused on assessing economic damage to agriculture or industry, or their potential for competition with native wildlife (Butchart et al., 2010, Seebens et al., 2020). However, an important but poorly understood impact of species introductions is the potential for exotic species to serve as an alternative prey resource for native predators (Pintor and Byers, 2015). In faunal communities with established exotic species, native predators may select one species over another due to differences in relative availability (Johnson, 1980, Robinson et al., 2002, Sanderson et al., 2016), or vulnerability (Knopff et al., 2010, Krumm et al., 2010). Under these conditions, predation on an introduced species by a native predator could lead to the decoupling of population dynamics between that predator and its primary prey species (Johnson et al., 2012) through prey switching (Murdoch 1969), apparent competition (Holt, 1977, Johnson et al., 2013), or predator pits (Messier, 1994, Clark et al., 2021). As such, the direct or ancillary effects of species introductions are of interest to natural resource managers, especially in systems undergoing climatic or ecosystem shifts (Seebens et al., 2020, Zhang et al., 2025).

Many models of predator-prey relationships assume a single-predator focused on a single prey species. For example, the lynx-hare cycle (Krebs et al., 1995), or wolf-caribou relationships (Michelot et al., 2024), however, these conditions are relatively rare in vertebrates. More typically, predator-prey relationships include multiple, alternative prey species in a given community and/or individual foraging behavior. Managers are concerned that introduced species can buoy predator numbers following declines in their primary prey (Jansen, 2001) or support higher densities than might be found in the absence of the secondary species (Hobbs et al., 2006). Investigations of predation on free-ranging livestock approximate this ecological problem, but the intensive management that surrounds the response to livestock depredations limits the utility of this scenario as a model. Studies examining the effects of multiple predators on a single prey species have been conducted (Husseman et al., 2002, Kohl et al., 2019), as have examinations of a single predator acting on multiple alternative prey species (Garneau et al., 2007). However, the question of how native predators act in a mixed-prey community that includes introduced species remains poorly studied, and species invasions are considered one of the most pressing conservation problems facing wildlife communities, globally. Thus, understanding how prey availability drives selective or opportunistic foraging strategies of predators is critical for predicting how species introductions may affect community dynamics.

In its simplest terms, prey-selection is a population-level expression of the caloric benefits of pursuing a specific resource relative to its energetic costs (Pettorelli et al., 2015). Prey-selection occurs when a given species is used disproportionate to its availability (Johnson, 1980, Walker et al., 2023). Conversely, opportunistic predation is the absence of selection, and can be defined as the procurement of a prey species in proportion to its respective availability (Johnson, 1980). Prey-specialization on the other hand, is an individual-level characteristic, defined simply as “a prey resource comprising the primary component of an individual’s diet” (Bolnick et al., 2003, Knopff and Boyce 2007). The proportion of the prey resource in the diet of the individual should be greater than the proportion of that same resource at the population level (Knopff and Boyce, 2007). Individuals within a predator population may exhibit specialization or opportunistic hunting based on sex, age, reproductive status, or other factors that are difficult to measure, such as individual experience (Pierce et al., 2000).

Free-roaming feral horses (Equus caballus), hereafter ‘horses’, are descended from European domestic stock introduced during the 16th century (Curry, 2023). These animals are now found throughout western North America, yet their interactions with predators are little studied other than documenting that predation does occur under certain conditions (Andreasen et al., 2021). Exceptional growth rates (Garrott et al., 1991) coupled with a broad dietary spectrum mean that horses have both the potential to compete with native ungulates (Stoner et al., 2021), and act as a subsidy for native predators in areas of substantial geographic overlap (Andreasen et al., 2021). Although prey-selection patterns have been described for a range of taxa, minimal research has explicitly addressed the role of introduced or exotic species as an alternative prey resource for widely distributed, generalist native predators (Pintor and Byers, 2015). Thus, the potential for horses to facilitate— or be subjected to— apparent competition with small, isolated populations of other prey species remains a management issue stemming from increasing horse populations.

Mountain lions (Puma concolor) exhibit the greatest latitudinal distribution of any terrestrial carnivore in the western hemisphere, occurring across a wide range of climatic zones, habitat types, and associated prey assemblages (Hornocker and Negri 2010). Within their global distribution, mountain lions exhibit extraordinary dietary diversity (Murphy and Ruth 2009, Karandikar et al., 2022), but in western North America they are generally associated with Odocoileus spp., especially mule deer (O. hemionus; Pierce et al., 2000, Cooley et al., 2008). Mountain lions have been documented exhibiting prey-selection or specialization on several other species, including elk (Cervus canadensis; Clark et al., 2014), moose (Alces alces; Ross and Jalkotzy 1996), guanacos (Lama guanicoe) and vicunas (Vicugna vicugna; Donadio et al., 2010), or species with specialized habitat requirements, such as bighorn sheep (Ovis canadensis; Festa-Bianchet et al., 2006). Introduced species have been shown to be locally important in mountain lion diets. Examples include red deer (Cervus elaphus; Novaro et al., 2000, Zanón Martínez et al., 2016), feral burros (Equus asinus; Mesler and Jones, 2022), feral pigs (Sus scrofa domesticus; Parsons et al., 2025), cattle (Bos taurus; Mazzolli et al., 2002, Guerisoli et al., 2021), domestic sheep (Ovis aries; Mazzolli et al., 2002, Guerisoli et al., 2021), and goats (Capra hircus; Mazzolli et al., 2002). More specifically, consumption of horses has been documented in both North and South America (Turner et al., 1992; Andreasen et al., 2021; Bostal et al., 2025), yet the question of whether mountain lion predation on horses is a result of selection or opportunism remains unclear.

Since 2004, socially unpopular methods for reducing horse populations have been curtailed by Congressional actions (in the form of “funding riders”) to the Wild Free Roaming Horses and Burros Act of 1971 (PL 92-195). These riders are aimed at limiting the use of certain management tools allowed in the original law. Consequently, populations have increased beyond prescribed levels on > 80% of units designated for persistence of free-roaming horse populations (i.e. Herd Management Units, or HMAs; Hennig et al., 2023), with land-use conflicts increasing in tandem with these trends. In response, the public has called for natural solutions to help regulate horse populations, with mountain lion predation put forth as one potential option (Philipps 2018). Despite the intuitive appeal of this proposition, limited habitat overlap between the species, and discrepancies in body mass in which adult horses are much larger than typical mountain lion prey, have led some in the scientific community to consider predation inconsequential for limiting horse populations (National Research Council 2013). Nevertheless, previous studies have implicated mountain lions in hastening the decline and/or slowing recovery of several native ungulate species, including mule deer (Johnson et al., 2012), woodland caribou (Rangifer tarandus caribou; Kinley and Apps 2001), and bighorn sheep (Johnson et al., 2013). As a small, but growing body of research indicates they also have the potential to reduce foal survival in small horse populations (< 300 individuals; Greger and Romney 1999, Turner and Morrison 2001, Roelle et al., 2010, Bostal et al., 2025). Thus, if mountain lions do exhibit selection for horses, it is possible they could influence horse population growth rates through sustained predation on younger age classes (Stoner et al., 2026).

An important step in predicting the effects of predation on horse populations is to determine whether selection is occurring, and under what conditions. Here, our goal was to identify the factors that influence prey-use of mountain lions in a multi-prey species community. Our objectives were: (1) to describe seasonal patterns in prey-use within individual mountain lion home ranges; (2) calculate mountain lion prey-selection as a function of prey-use relative to availability; and (3) determine mountain lion kill rates on ungulates, regardless of native status. To address these objectives, we tested for effects of prey abundance for the two most common ungulates species, horses and mule deer (Iacono et al., 2024), season of predation event, age class of prey species, and kill rate for both prey species.

2 Methods

2.1 Study area

We assessed mountain lion diet and behavior in the Delamar and Clover Mountains of southeast Nevada, United States (Figure 1). The study area encompassed 3,158 km2, comprising parts of three major plant communities, including semi-arid pinyon-juniper woodlands (Pinus monophylla, Juniperus osteosperma), Great Basin sagebrush steppe (Artemisia tridentata), and Joshua tree foothill (Yucca brevifolia) communities. The Clover Mountains also include ponderosa pine (Pinus ponderosa), cheatgrass-dominated meadows (Bromus tectorum), Gambel oak (Quercus gambelii), and shrub thickets. Elevations ranged from 980 to 2,267 m above sea level. Mean annual precipitation ranged from 106.6 mm to 403.1 mm, and mean daily temperatures ranged from -7 to 8.3 °C in January, and 15 to 35 °C in July (https://www.usclimatedata.com). Water resources were available year-round from springs, guzzlers, cattle troughs, and perennial creeks throughout Rainbow Canyon, which divides the study area (Figure 1). Approximately 99% of the study area is managed by the Bureau of Land Management (BLM) for multiple uses, including mining, livestock grazing, and outdoor recreation. Free-roaming horses within the study area are managed by the BLM across portions of 9 Herd Areas (HA) in accordance with the Wild Free-Roaming Horse and Burros Act of 1971 (PL 92-195). Native wildlife in the area are managed by the Nevada Department of Wildlife (NDOW). Major ungulate species reported in North American mountain lion diet studies include mule deer, elk, desert bighorn sheep (O. c. mexicana), and pronghorn (Antilocapra americana). Mountain lions are the only large predator recorded in the study area.

**(A)** The study area (black polygon) displaying camera locations (burgundy points) intersecting with Bureau of Land Management designated herd management areas (HMAs) and herd areas (HAs; red polygon) and two (of 7) mountain lion home ranges (95% minimum convex polygon (MCP); green and yellow polygons), for a study of mountain lion predation in southeast Nevada, USA (November 2021 to October 2022). The map insert depicts current feral horse distributions within HMAS and HAs (solid red polygons) throughout Nevada. **(B)** Diagram depicting camera angle placement standardization, with the red circle delineating the ideal placement of the 10-m marker within the picture frame; photo by (P) Iacono. **(C)** Two (of 7) mountain lion home ranges (95% MCP) intersecting camera locations within the study area. **(D)** Conceptual model depicting varying prey availabilities within each mountain lion home-range based on camera trap data (i.e., mountain lion #1 has 33% horse availability whereas mountain lion #2 has 66% horse availability). Base map is derived from NASA Digital Elevation Models, [https://www.earthdata.nasa.gov/](https://www.earthdata.nasa.gov/).
Figure 1. (A) The study area (black polygon) displaying camera locations (burgundy points) intersecting with Bureau of Land Management designated herd management areas (HMAs) and herd areas (HAs; red polygon) and two (of 7) mountain lion home ranges (95% minimum convex polygon (MCP); green and yellow polygons), for a study of mountain lion predation in southeast Nevada, USA (November 2021 to October 2022). The map insert depicts current feral horse distributions within HMAS and HAs (solid red polygons) throughout Nevada. (B) Diagram depicting camera angle placement standardization, with the red circle delineating the ideal placement of the 10-m marker within the picture frame; photo by (P) Iacono. (C) Two (of 7) mountain lion home ranges (95% MCP) intersecting camera locations within the study area. (D) Conceptual model depicting varying prey availabilities within each mountain lion home-range based on camera trap data (i.e., mountain lion #1 has 33% horse availability whereas mountain lion #2 has 66% horse availability). Base map is derived from NASA Digital Elevation Models, https://www.earthdata.nasa.gov/.

2.2 Data collection

2.2.1 Mountain lion capture and marking

In January 2018, NDOW initiated management in which they captured mountain lions in the Delamar Mountains to monitor predation on reintroduced bighorn sheep. Mountain lion captures were expanded into the Clover Mountains in November 2020. All captures were conducted using foot snares or hounds following methods detailed in Jansen and Jenks (2012) and Mesler and Jones (2022) during 2020 through 2022. Upon capture, mountain lions were identified as adult or subadult based on tooth wear (Anderson and Lindzey, 2009) and fitted with VERTEX PLUS model GPS collars programmed to record 6 fixes/day at 4-hour intervals (Vectronic Aerospace, LLC, Berlin, Germany). Capture methods and handling procedures followed standard NDOW protocols (detailed in Iacono et al., 2024).

2.2.2 Study design

We employed a use-availability study design, in which we compared prey species that mountain lions used to what was available within their respective home ranges.

2.2.3 Prey-use; mountain lion diet

To determine mountain lion prey-use, we identified and described cache sites following methods described in Anderson Jr. and Lindzey (2009), and Knopff et al. (2010) starting in 2018 and continuing through 2022. Potential cache sites were determined using mountain lion GPS cluster locations averaged to a single point. Location clusters were defined as ≥ 3 locations within a 150 m radius of each other, collected within a minimum 20-hour time frame (Mahoney and Young, 2017). Each cluster was derived from an individual lion and included a center point location (“centroid”) recorded in Universal Transverse Mercator coordinate system (WGS 84 Zone 11), start and end dates, duration of use (hours), and an error radius (meters). Cluster locations were investigated on the ground by 1–3 trained observer(s), starting at the centroid and searching within 75 m of the estimated error radius for ≥ 35 minutes, or until evidence of prey was found. Kill sites were investigated within 12 weeks of the first date of a cluster. We estimated the age class of prey specimens from tooth eruption and wear, and/or bone length (Schroeder and Robb, 2005; Hoopes et al., 2020). For mule deer and horses, prey items were categorized as juvenile (<1 yr), subadult (1–2 yrs), or adult (>2 yrs). We fit a generalized linear mixed model (GLMM) for deer and horses, with prey species treated as a Poisson-distributed response variable to determine the kill rate of each lion as a function of season and prey species. The model included an offset to adjust the number of detections according to the duration over which an individual lion’s clusters were investigated, and a random intercept for systematic differences in overall kill rates among lions. The intercept associated with each prey species was exponentiated to obtain average weekly kill rate of each prey species by a single lion.

2.2.4 Prey-availability; remote camera traps

To estimate prey availability, we developed a systematic camera grid to cover the study area (Kafley et al., 2019). We used Bushnell Core Trail Cameras (model # 119938C, manufactured by Bushnell Outdoor Products©, Kansas, U.S.A), deployed from May 2021 to April 2023. Cameras were spaced by 7 km (1 camera/49 km2; Figure 1) on a grid to ensure spatial independence (Sultaire et al., 2023) and suitable replication within individual mountain lion home ranges (Logan et al., 1986; Stoner et al., 2018), resulting in 49 camera locations (Figure 1A). Cameras were placed in proximity to grid point centroids (0–622 m) while accommodating constraints based on terrain, accessibility, visibility from road (to reduce potential theft), ungulate sign, and vegetation height/density. We mounted cameras on woody vegetation or metal fence posts 0.75 m above ground and set facing NW to NE (330° to 30°) to reduce sun glare (Apps and McNutt, 2018). We then placed a marker 10 m from each camera to standardize the detection area by framing the marker using the “rule of thirds” (Figure 1B; Mai et al., 2011). Cameras were operational 24 hours a day and set to take 2 pictures per trigger at 5 second intervals. Animal detections and group size were determined by summing all individuals captured within 30-minute intervals between detections. Any detections after a 30-minute span were considered an independent “capture” event (Iannarilli et al., 2021; O’Brien et al., 2003).

2.2.5 Predation rates

To determine the kill rate for each mountain lion in each season, we divided the number of kills by the number of weeks for consecutively surveyed clusters. We did not calculate kill rate for any time span less than 3 weeks. We fit a generalized linear mixed model for deer and horses, with prey species treated as a Poisson-distributed response variable to determine the kill rate of each lion as a function of season and prey species. The model included an offset that adjusted the number of detections according to the duration over which an individual lion’s clusters were investigated, and a random intercept adjusting for systematic differences in overall kill rates among lions. We then exponentiated the intercept associated with each prey species to obtain the weekly kill rate of each prey species by a single “average” lion.

2.3 Analysis

2.3.1 Mountain lion seasonal home ranges

To estimate prey availability, we calculated seasonal home ranges for each mountain lion using 95% minimum convex polygons (MCP) based on global positioning system (GPS) collar tracking points (Mohr, 1947; Worton, 1987). This way, the home range was used to sample the cameras within the grid that best approximated the frequency of each prey species available to each mountain lion sampled from the population (Figures 1C, D). GPS tracking points were divided into the two seasons: growing (March 15th, 2022, to October 31st, 2022) and winter (November 1st, 2021, to March 14th, 2022). Seasons were based on the mean peak of Normalized Difference Vegetation Index (NDVI) for the study area for the decade 2010–2020 (Pettorelli et al., 2011; Stoner et al., 2016). The Animal Movement Tools (“amt”) package (Signer et al., 2022) in R Studio (R Core Team, 2022) was then used to calculate each lion’s seasonal MCP home range from their GPS telemetry locations.

2.3.2 Prey-selection

Although mountain lions commonly prey upon smaller-bodied animals (< 10 kg), we focused solely on ungulate species because of the importance of these animals in supporting mountain lion populations, and the imprecise nature of identifying small-bodied prey species within cache sites. Here, we determined prey-use by dividing the number of ungulate kills by species by the total number of ungulate kills within a season for each individual mountain lion. To determine prey-availability, we calculated a relative abundance index (RAI) for each ungulate species by camera. We divided the total number of photo detections of each ungulate species by the total number of days the camera was operational and multiplied that value by 100 (Jenks et al., 2011; Palmer et al., 2018; Martin-Garcia et al., 2022; Kinnaird and O’Brien, 2012). The RAI does not account for the identity of each individual detected by the camera but simply treats each observation as a “predation opportunity”, rather than as a count of the number of individuals passing the camera. To determine which cameras were located within an individual mountain lion home range, we used the “selection by location” tool in QGIS (QGIS Development Team, 2022). We then calculated the mean RAI for each camera by month and season (growing or winter; Figure 1D).

Additionally, we estimated prey density using horse population estimates for the Delamar and Clover Mountains from BLM aerial surveys in 2022 (Griffin et al., 2020) and NDOW mule deer population estimates from 2021 and 2022 for game management units (GMUs) within the study area (Nevada Department of Wildlife, 2022). Population estimates for each species and year were divided by game management unit (GMU) to get population estimates per hectare. Finally, population estimates per hectare (estimated density) for each species and year were averaged across GMUs that intersected each mountain lion’s seasonal home range.

To test whether mountain lions exhibited selection (i.e., use disproportionate to availability), we employed a binary, logistical generalized linear model (GLM) with mountain lion prey-use being either a horse (1) or a native ungulate (0) as the response variable. Prior to model runs, we conducted single-variable binary GLMs using the predictor variables: deer RAI, horse RAI, estimated deer density per GMU, estimated horse density per GMU, season, mountain range (either Delamar or Clover), prey age-class, mountain lion sex, tree canopy type (open, sparse, or dense), and elevation to determine which variables to include in the full model. Predictor variables that were significant, marginally significant (p <0.1), or deemed ecologically important (i.e., deer density) were included in a full model (Table 1). Additionally, collinearity tests using Spearman’s correlations with an α = 0.1 to remove correlated variables from the model (Figure 1). Estimated deer density was found to be correlated with estimated horse density and horse RAI, so horse density and horse RAI were dropped from the model. All continuous variables were scaled and centered. Thus, our full GLM included the binary prey-use being either a horse or an ungulate as our response variable deer RAI, estimated deer density, season, prey age-class, mountain lion sex, tree canopy type, and elevation were used as our predictor variables. Multiple full-model variations were then run to determine which combination of predictor variables had the best fit. The three models with the lowest Akaike information criteria (AIC) scores were then bootstrapped using the “boot” package (Ripley 2025) in R Studio (R Core Team, 2022) with 10,000 simulations to account for our small sample sizes. If binomial model assumptions were not met (i.e., overdispersion), models were re-run using a quasi-binomial logistic regression fit and then bootstrapped using quasi-binomial results. Similarly, because of the small number of cache sites examined/individual, a Fisher’s exact test in R Studio was used to compare age classes (“Adult,” “Subadult,” “Juvenile,” or “Unknown”) between horse and ungulate kills (mule deer, bighorn sheep, or elk).

Table 1. Model selection results of all models used to predict mountain lion predation on either feral horses or native ungulates in southeast Nevada, 2021-2022.

Predictor variablesAICBIClogLik
Deer RAI + Prey age class + Season + Sex + Elevation + Tree canopy109.9277138.6797-44.9639
Deer density + Prey age class + Season + Sex + Elevation + Tree canopy107.1579135.9098-43.5789
Deer density + Prey age class + Season + Sex + Elevation104.5698127.5714-44.2849
Deer RAI + Prey age class + Season + Sex + Elevation107.3802130.3818-45.6901
Deer RAI + Prey age class + Season + Sex105.3925125.5189-45.6963
Deer density + Prey age class + Season + Sex102.8344122.9607-44.4172
Deer RAI + Prey age class + Season + Elevation111.0759131.2023-48.538
Deer density + Prey age class + Season + Elevation104.4968124.6232-45.2484
Deer RAI + Prey age class + Sex + Elevation105.844125.9704-45.922
Deer density + Prey age class + Sex + Elevation103.9893124.1157-44.9946
Deer RAI + Prey age class + Sex103.9518121.203-45.9759
Deer density + Prey age class + Sex102.0024119.2536-45.0012
Deer RAI + Prey age class + Elevation110.4845127.7357-49.2423
Deer density + Prey age class + Elevation105.4249122.6761-46.7125
Deer density + Prey age class + Season + Sex + Tree canopy105.5009131.3776-43.7504
Deer density + Prey age class + Sex + Tree canopy104.4602127.4618-44.2301
Deer density + Prey age class + Season + Tree canopy105.8605128.8621-44.9303
Deer density + Prey age class + Season103.0754120.3266-45.5377
Deer density + Prey age class + Tree canopy106.0151126.1415-46.0076
Deer density x Prey age class + Sex + Season107.9378136.6898-43.9689
Deer RAI x Prey age class + Sex + Season107.5363136.2883-43.7682
Deer density x Season + Sex + Prey age class104.621127.6226-44.3105
Deer RAI x Season + Sex + Prey age class107.2305130.2321-45.6153
Deer density + Prey age class103.4821117.8581-46.74106
Deer RAI + Prey age class108.5024131.504-46.2512
Deer density x Prey age class108.6552123.0312-49.32762
Deer RAI x Prey age class110.8343133.8359-47.41714

Predictor variables shown include estimated mule deer density per hectare (deer density), prey age class (either young, subadult, adult, or unknown), season (winter or growing), elevation, and mule deer relative abundance index (deer RAI). Akaike's information criterion (AIC), Bayesian information criterion (BIC), and log likelihood (logLik) are reported. Boldface denotes the three models with the lowest AIC and BIC scores.

3 Results

3.1 Prey use and availability

Between November 2021 and October 2022, we monitored 10 mountain lions: 5 adult females, 3 subadult females, and 2 adult males (Table 2; Iacono et al., 2024; Schoenecker et al., 2024). During this time, 3 GPS-collars failed and one left the study area. Due to those 4 mountain lions producing < 5 kills within the study area, they were not used in our analysis. Additionally, one lion was removed during the study period by NDOW, but enough data were collected from that individual for analysis. The remaining dataset consisted of 6 adult mountain lions, with 4 females spanning both seasons, and one male and one female captured and monitored during the growing season.

Table 2. Results of the top three GLM models with the lowest AIC scores predicting mountain lion prey kill being either wild horse or mule deer.

Percentile confidence intervalBias-corrected and accelerated interval
ModelCoefficientsEstimate (β)Standard errorz-valueP-valuelowerUpperLowerUpper
Deer density + Prey age class + SexIntercept-2.4510.591-4.149< 0.001-4.729-1.536-3.951-1.271
Deer density0.4480.3161.4160.157-0.2071.305-0.3611.143
Juvenile4.4400.7995.555< 0.0013.2317.9212.6966.173
Subadult3.6560.9803.730< 0.0012.01321.8571.2546.132
Unknown0.7370.8190.9000.368-15.9963.079-1.8843.295
Male2.1421.2771.6780.0930.48819.552-47.91517.815
Deer density + Prey age class + Season + SexIntercept-2.0180.692-2.9170.004-4.987-0.989-3.706-0.745
Deer density0.5210.3331.5640.118-0.2021.596-0.3071.418
Juvenile4.0940.8464.838< 0.0012.8059.4632.3646.010
Subadult3.6740.9953.692< 0.0011.99022.5061.3726.448
Unknown0.6260.8310.7530.451-15.8392.926-1.7033.333
Winter-0.7570.702-1.0790.281-2.6360.733-2.3771.032
Male1.7711.2951.3680.171-0.11619.232-709.61317.427
Deer density + Prey age class + SeasonIntercept-1.8870.695-2.7170.008-4.499-0.935-3.328-0.630
Deer density0.7130.3152.2640.0250.1591.7190.0271.437
Juvenile4.2300.8544.955< 0.0013.0748.6482.6085.931
Subadult3.8961.0153.839< 0.0012.34221.7301.7066.403
Unknown0.6870.8420.8160.416-1.4463.002-1.8022.778
Winter-1.0470.697-1.5020.136-2.9070.257-2.5500.540

The first two models were binomial logistic GLMs, whereas the last one was a quasi-binomial logistic GLM to correct for overdispersion. Bold font denotes significance at α = 0.05, and italic font denotes marginal significance at α = 0.1.

From these individuals, we identified 326 clusters, of which 265 were located in the study area. Based on accessibility, 201 clusters were investigated, and evidence of a kill was found at 147 (73%). Scavenging by mountain lions was documented on one occasion but may be under-reported due to carcass condition and loss of prey remains with time. Evidence of sharing at cache sites was observed 8 times, with all events occurring between a collared mother and female offspring pair. Only one elk and one bighorn sheep kill were identified and, therefore, removed from analysis due to their low sample size. Thus, after removing individuals with < 5 ungulate kills, scavenging events, and shared kills, the analysis consisted of 131 ungulate kills from 6 mountain lions (Figure 2).

Ungulate prey composition (i.e. prey-use) of mountains lions tracked between the growing season (15 March 2022, to 31 October 2022) and the winter season (1 November 2021 to 14 March 2022) in southeast Nevada, USA (shaded by species: "Deer" denotes mule deer, "Horse" for feral horse, and "Other" for either bighorn sheep or elk). Mountain lion sex is denoted by an (F) or (M).
Figure 2. Ungulate prey composition (i.e. prey-use) of mountains lions tracked between the growing season (15 March 2022, to 31 October 2022) and the winter season (1 November 2021 to 14 March 2022) in southeast Nevada, USA (shaded by species: "Deer" denotes mule deer, "Horse" for feral horse, and "Other" for either bighorn sheep or elk). Mountain lion sex is denoted by an (F) or (M).

Overall, 69 of 131 kills were horses which included 52 foals, 9 sub-adults, 4 adults, and 4 of unknown age (Figure 3). Sixty of the 131 kills were mule deer, comprised of 31 adults, 6 juvenile, 2 subadult, and 21 unknown age (Figure 3), and 2 were other species (1 elk, 1 bighorn sheep). Prey age class between horse and deer kills showed a significant difference (Fisher’s exact test p-value < 0.001), with only 10% of adult prey kills being feral horses, and 90% of adult prey kills being mule deer. In contrast, 90% of juvenile and 75% of subadult prey kills were horses, whereas only 25% and 10%, respectively, were mule deer or other ungulates (Figure 3).

The proportion of mountain lion kills among age classes (Adults > 2 yrs, Subadults 1-2 yrs, Juveniles < 1 yr, and Unknown age) between feral horses and mule deer in southeast Nevada, USA (November 2021 to November 2022).
Figure 3. The proportion of mountain lion kills among age classes (Adults > 2 yrs, Subadults 1-2 yrs, Juveniles < 1 yr, and Unknown age) between feral horses and mule deer in southeast Nevada, USA (November 2021 to November 2022).

From May 2021 to December 2022, camera traps recorded a total of 343,243 photographs, equating to 22,168 camera trap-nights. Twenty-nine unique species were observed, comprising large mammals (n = 5), mesocarnivores (n = 8), small mammals (n = 5), and birds (n = 11). When narrowing the dataset to the focal seasons, there were 168,560 images from 16,080 camera trap nights. Mule deer and horses were detected on 92% and 55% of cameras, respectively (Figure 4). During winter, the number of mule deer detections ranged from 1 to 27 per month, in attendant group sizes of 1 to 17 individuals (mean ± SE = 2.29 ± 0.28 RAI); and from 1 to 36 deer per month with group sizes of 1 to 7 individuals during the growing season (1.03 ± 0.12 RAI; Figure 4). In contrast, winter horse detections ranged from 1 to 584 per month in groups ranging from 1 to 14 (12.08 ± 4.86 RAI) and from 1 to 569 per month, with group sizes ranging from 1 to 28 during the growing season (2.99 ± 0.51 RAI; Figure 4).

Prey-availability defined as the number of feral horse (left) or mule deer (right) detections within camera trap locations by season in southeast Nevada, USA (growing season: 15 March 2022, to 31 October 2022, and winter: 1 November 2021 to 14 March 2022). Base map is derived from NASA Digital Elevation Models; [https://www.earthdata.nasa.gov/](https://www.earthdata.nasa.gov/).
Figure 4. Prey-availability defined as the number of feral horse (left) or mule deer (right) detections within camera trap locations by season in southeast Nevada, USA (growing season: 15 March 2022, to 31 October 2022, and winter: 1 November 2021 to 14 March 2022). Base map is derived from NASA Digital Elevation Models; https://www.earthdata.nasa.gov/.

3.2 Mountain lion prey-selection

Model selection showed that the top-fitting predictors of mountain lion prey being either a horse or mule deer were mule deer density and prey age class (Table 1). Mountain lion prey was more likely to be a horse if the prey was within the young or subadult age classes (both p-values < 0.001) and within areas of higher mule deer density (p-value = 0.020). Season and mountain lion sex variables were also included in the top three fitting models; however, they were not found to be significant predictors (Table 2).

3.3 Mountain lion predation rates

We estimated 15 kill rates in the growing season and 7 kill rates during winter from 6 mountain lions. Time periods in which kill rates were calculated spanned 6.6 to 17.2 consecutive weeks in the growing season and 3.9 to 13.4 weeks during winter. In the growing season, the deer kill rate was 0.22 deer/week/lion, and the horse kill rate was 0.55 horses/week/lion, indicating that mountain lions killed horses during the growing season at 2.54 times the rate at which they killed deer (p < 0.001). During winter, the deer kill rate was 1.43 deer/week/lion, and the horse kill rate 0.33 horses/week/lion, indicating mountain lions killed mule deer at 4.33 times the rate at which they killed horses. Reported rates account for differences in specific kill rates among lions.

4 Discussion

We predicted that mountain lions would select mule deer, a native species with which they share a long evolutionary history (Cooley et al., 2008, Clark et al., 2014), over horses; a comparatively recent arrival to western ecosystems (Guerisoli et al., 2021; Luque-Machaca et al., 2022). Counter to expectations, results showed high heterogeneity in prey selection among individual mountain lions. During the growing season we found evidence that 2 of 5 individual mountain lions (1 adult male and 1 adult female) selected for horses and avoided mule deer. Conversely, individual selection for mule deer and avoidance of horses was observed only in the winter for 2 adult female mountain lions; the remaining 3 mountain lions demonstrated an opportunistic foraging strategy by consuming the two prey species in proportion to their relative availability. The wide variation across our 6 focal animals provided evidence for all three feeding strategies in this population: selection, avoidance, and opportunism. Our results corroborate findings that show a range of feeding strategies across individuals and varying demographics of mountain lions (Clark et al., 2014, Lowrey et al., 2016; Andreasen et al., 2021) and further demonstrate the dietary plasticity of mountain lions when exposed to a novel, abundant, and vulnerable prey species.

The question remains as to whether intrinsic or extrinsic factors prompt high individual variability. To better understand heterogeneity in predation of non-native species, the impacts of extrinsic factors on overall prey availability (Pintor and Byers, 2015; Laurent et al., 2021), demography and prey species assemblages (Forbes and Theberge, 1996), prey group size (Fryxell et al., 2007), and anti-predator behaviors employed by prey, need to be considered. Additionally, understanding the magnitude of intrinsic factors such as lion sex, body condition, maternal-offspring group composition, and learned feeding behaviors (Tinker et al., 2009; Andreasen, 2014; Coon et al., 2019) may provide insights on the causes of individual variability.

Population-level prey-selection varied across seasons. In general, mountain lions consumed horses opportunistically during the growing season but avoided them in winter. Mule deer on the other hand, were consumed in proportion to estimated availability in both seasons. Avoidance of horses during winter is consistent with the results of Turner et al. (1992), who found mountain lions on Montgomery Pass in eastern California largely consumed juvenile horses in summer, relying more heavily on deer during winter. The authors presumed this was related to horse habitat use in winter; in that ecosystem horses foaled in high elevation pinion-juniper woodlands, but migrated into open, flat habitats during winter. In conjunction with changes in habitat use, Turner et al. (1992) further hypothesized that by autumn foals reached a body size that made them more difficult to capture, which accounted for seasonal differences in the use of horses as a prey item. Conversely, Andreasen et al. (2021) found that in the Virginia Range of western Nevada, where mule deer are relatively rare, female mountain lions consumed horses year-round. When put in the context of the literature, our population-level selection results illustrate the risk of over-generalizing behavior of predator populations that exhibit a high degree of individual diet heterogeneity. If we had focused solely on general selection patterns, we would have negated any findings at the individual level. This is particularly important in small ungulate populations in which individual predator proclivities can have large proportional differences in terms of influencing prey composition (Johnson et al., 2013).

Total kill rates were similar to those measured in other mountain lion populations (Knopff et al., 2010, Clark et al., 2014), however, we found that during the growing season horses were killed at a rate 2.5x greater than mule deer, suggesting the availability of juvenile horses across the growing season was high and consistent. Horses give birth from March to September (Roelle et al., 2020), whereas mule deer exhibit greater birth synchrony and consequently a narrower birth pulse (Stoner et al., 2016), making foals available for a longer time.

Most prey-selection analyses use either absolute or relative prey abundance to quantify availability (Clark et al., 2014). Others have used different indices of availability such as biomass (Elbroch and Wittmer, 2013), which may be indicative of preference for body size rather than selection for a particular species (Bates-Mundell et al., 2024). Here, we assumed that all individuals of a given species were equally “available”. Yet individuals within a prey population are not equally vulnerable (Dammhahn et al., 2022). Quantifying availability is an essential component for determining selection (Johnson, 1980) and therefore evaluating predator effects on ungulate populations. Future efforts to estimate these parameters could consider developing an integrated index of availability; i.e. one that is represented as a sliding score or a gradient of vulnerability to predation. In addition to the more readily quantifiable demographic attributes noted above, prey-availability could also be assessed as a function of prey behavior (vigilance, group size, temporal activity, landscape use), and physiology (defensive ornaments, body size, body condition), because these factors might represent a subsection of the prey population that is actually available rather than the population as a whole.

A report by the National Academy of Sciences speculated that mountain lion predation was inconsequential in the population dynamics of horses in western ecosystems (National Research Council, 2013). These arguments stem from notable discrepancies in body size and habitat affiliations between the two species. Horse availability may be less than what is derived from abundance estimates due to their preference for gentle slopes and open habitats (Schoenecker et al., 2023). These qualities, when combined with their large adult body size and agile back legs, collectively make horses a potentially dangerous prey species (sensu Tallian et al., 2017). Although most mountain lions did not actively select horses, horses comprised 49% of the measured kill sites in a community where deer were widely distributed, all of which begs the question of what factors make horses vulnerable to predation.

We hypothesize that the vulnerability of horses stems from several factors. First, horses in many parts of the Great Basin, including most U.S. Forest Service Wild Horse and Burro Territories, exhibit extensive use of steep, densely wooded terrain, making them sympatric with mule deer, the mountain lions most common prey species (Pierce and Bleich 2003). Thus, the presence of mule deer may render horses susceptible to predation in areas where they overlap (Stoner et al., 2021). We did not find a significant interaction in our model between deer density and horse kills that would suggest horses are more vulnerable to opportunistic predation in the presence of deer, but Fisher’s exact test results indicated high predation on juvenile and subadult horses in areas with higher mule deer densities. It may be that our model lacked power to capture an interaction due to small sample sizes, but our results suggest that deer presence may play a role in feral horse vulnerability to predation.

Second, the history of domestication has likely dulled anti-predator behaviors that remain highly conserved in wild, non-domesticated species (Olsen, 2016; Sanchez-Villagra, 2022). When comparing feral horses to other wild ungulates, Berger (1986) noted that typical vigilance patterns of feral horses were 740% and 850% times lower than sympatric bighorn sheep and pronghorn, respectively. When compared to wild equids, such as zebras (subgenus Hippotigris), behaviors including flight response, grouping, and male defense were all lower in feral horses (Berger, 1986). Moreover, strong maternal care is linked to high neonate survival (Gaillard et al., 2000). Foals remain close to mares for the first month after birth but then begin conducting independent forays and frolicking farther from social groups. Feral horses in the Great Basin exhibit a wide foaling season with little to no apparent birth synchrony (Berger, 1986), making horses a reliable prey source for approximately six months out of the year.

Third, naïveté of prey may be a strong factor as observed in mountain lion affinity for juveniles (Ross and Jalkotzy 1996), and prey that are new to their surroundings (e.g. translocated animals; McKinney et al., 2006; Leech et al., 2017). Therefore, we hypothesize that feral horses represent an energetically profitable prey species, as they are (a) comparatively naïve, (b) large relative to their anti-predator behavior, (c) regularly inhabit forested, steep and rugged terrain, and (d) foals begin exploring farther from social groups within 2 months after birth, making them less guarded by maternal oversight. As such, feral horses may be widely vulnerable to mountain lion predation where they occur in optimal mountain lion hunting habitat, which describes much of their distribution in the Great Basin.

Lastly, we propose that mountain lion prey preferences may be influenced as much by prey body size as by species. Body mass has been identified as an important predictor of vulnerability to predation (Naisbit et al., 2011; Green and Cote, 2014) and this factor plays a central role among factors affecting the strength of predator–prey relationships (Klecka and Boukal, 2013). Studies from western North America have found that mountain lions regularly take both sexes and all age classes of mule deer (Pierce and Bleich 2003, Ruth and Murphy 2010). However, results from our study indicate adult mule deer were predated at a proportionally higher rate than fawns, but horse foals were predated at a proportionally higher rate than adults. Plotting the body weight of feral horses, mule deer, and domestic horses at various ages indicates that adult mule deer are similar in size to feral horse foals at 0 to 2 months old (Figure 1). This suggests that mountain lions in the semi-arid Great Basin system we studied may be using prey that fits a select body size rather than species, per se, representing an optimum between caloric gains relative to physical risks.

4.1 Management implications

Feral horse management, including removals and fertility control applications, are expensive, logistically challenging, and often controversial (Hennig et al., 2023). Given these constraints, four important implications stem from our work. The effects of predator control are difficult to predict in mixed-prey communities, yet it is especially important to consider when certain individuals may be providing ecosystem services through selective predation. Although acquiring individual-level information on mountain lion predation can be expensive and time-consuming, the approach described herein could benefit natural resource managers charged with feral horse population control. Targeting individual lions for removal (bighorn sheep) or conservation (feral horses) could provide more strategic results from predator control. Second, it appears that in this ecosystem with the densities of horses and mule deer that were present, mountain lions prey on young horses in areas with relatively high mule deer densities suggesting that the presence mule deer may make juvenile horses more vulnerable to predation. It may be possible that mountain lions switch to a diet comprised of more naïve foals when they are available, which could relax predation pressure on mule deer, and/or it may help support a higher mountain lion population than would be found in the absence of this resource— this question can benefit from further research to clarify. Our study results are relevant to the ecosystem we studied and may differ in other ecosystems with different prey community composition, terrain, and climate. Third, a robust monitoring program to track annual deer production and population trends on ranges occupied by horses could provide additional insights. This is particularly appropriate given the highly individualistic behavior expressed in our sample of mountain lions, such that a wide range of possible interpretations could easily translate into different potential management outcomes. Fourth, we did not identify the ideal horse population size that could remain susceptible to the effects of predation, due to small populations being predisposed to predator-pits. However, based on extant literature (e.g. Turner et al, 2001, Bostal et al., 2025, Clark et al., 2021) this may be < 300 adults.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcosc.2026.1779101/full#supplementary-material

Supplementary material Supplementary material at the source

Supplementary material Supplementary material at the source

Footnotes

Edited by: João Pedro Barreiros, University of the Azores, Portugal

Reviewed by: Charles Doncaster, University of Southampton, United Kingdom

Brayan Morera, University of Valencia, Spain

Statements

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

The author(s) declared that financial support was received for this work and/or its publication. Funding was provided by the U.S. Geological Survey Ecosystems Mission Area through direct purchases of remote cameras, mountain lion GPS collars, lodging for field crew, and salary for 1 field technician. and additionally via financial support to Utah State University in Cooperative Ecosystem Study Unit (CESU) Agreement No. G20AC00407. Funding from the Nevada Department of Wildlife was provided through direct purchases of mountain lion GPS collars, funding for all captures and collaring of mountain lions, lodging for field crew, multiple field technician salaries, and field equipment.

Data availability

The datasets presented in this study can be found in online repositories. The data presented in this study can be found in an online repository at the link below: https://doi.org/10.5066/P13IGZUV.

Ethics approval

The animal study was approved by the Nevada Department of Wildlife and all capture and handling was conducted by the Nevada Department of Wildlife, following guidelines of the American Society of Mammalogists (Sikes and Gannon, 2011). The study was conducted in accordance with local legislation and institutional requirements.

Author contributions

PI: Resources, Validation, Conceptualization, Project administration, Data curation, Visualization, Methodology, Formal analysis, Investigation, Writing – review & editing, Writing – original draft. KS: Conceptualization, Funding acquisition, Investigation, Writing – original draft, Writing – review & editing, Project administration, Data curation, Supervision, Validation, Formal analysis, Methodology. JD: Writing – original draft, Visualization, Methodology, Formal analysis, Data curation, Validation. PJ: Investigation, Conceptualization, Supervision, Funding acquisition, Resources, Data curation, Writing – review & editing, Project administration, Methodology. KM: Validation, Conceptualization, Project administration, Writing – review & editing, Methodology, Supervision, Investigation, Formal analysis. DS: Formal analysis, Supervision, Project administration, Resources, Writing – original draft, Writing – review & editing, Data curation, Investigation, Conceptualization, Funding acquisition, Validation, Methodology.

Acknowledgements

Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. We thank field crew members: H. Klugman, C. Devine, J. Davis, M. MacPhail and R. Passernig, and support from the BLM Caliente Field Office: A. Delcalzo, C. Brooks, and C. Boyce. We are grateful to staff from Nevada Department of Wildlife (NDOW), including D. Sallee, J. Anderson, C. Schroeder, and S. Espinosa. Thank you to J. Dye, Utah State University for data collating, and K. Hersey, Utah Division of Wildlife Resources, for mule deer body weight data. We also thank B. Kelly for spatial data assistance and cluster identification using Sengi Software’s ClusterFudge. Finally, we thank B. Jansen and R. Passernig for conducting mountain lion captures, collaring, and providing early insights on mountain lions in the area. This research was funded by the Species Management Research Program, United States Geological Survey, and the Nevada Department of Wildlife ($3 Predator Fee).

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Citation

Iacono, P. C., et al. (2026). Mountain lion prey-selection in a community comprised of native and introduced ungulate species. Frontiers in Conservation Science, Volume 7. https://doi.org/
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7 - 2026

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Copyright © 2026 Iacono, Schoenecker, Dodge, Jackson, Manlove and Stoner.

License: https://creativecommons.org/licenses/by/4.0/

*Correspondence: Kathryn A. Schoenecker

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