Skip to main content
NCBI home page
Wiley Open Access Collection logoLink to Wiley Open Access Collection
. 2025 Feb 19;106(2):e70038. doi: 10.1002/ecy.70038

Residential development reduces black bear (Ursus americanus) opportunity to scavenge cougar (Puma concolor) killed prey

Clint W Robins 1,2,✉, Brian N Kertson 3, Shannon M Kachel 4, Aaron J Wirsing 1
PMCID: PMC11836637  PMID: 39967573

Abstract

Large carnivores commonly scavenge on kills made by other species, but if and how this phenomenon is influenced by urbanization remains unclear. To address this knowledge deficit, we investigated whether housing density, along with demographic and environmental covariates, impacted the probability of American black bear (Ursus americanus) occurrence at cougar (Puma concolor) killed prey along the wildland–urban gradient of western Washington, USA. Under the refuge hypothesis, which stipulates that residential development reduces opportunities for black bears to visit cougar prey carcasses by (1) altering cougar kill composition and/or (2) drawing black bears to human subsidies, we expected the probability of bear presence at cougar kills to decline as housing density increased. Alternatively, under the pileup hypothesis whereby reduced green space drives a greater overlap and thus more frequent interactions among carnivores, we predicted that bear presence at cougar kills would increase with housing density. Occupancy models derived from forensic and remote camera evidence of bear visitation to carcasses at kill sites identified from 12 GPS‐collared cougars indicated that the probability of bear presence at kill sites decreased when cougars foraged on small‐bodied prey, increased in summer compared with autumn, and declined with increasing housing density. Indeed, the top model indicated a multiplicative decrease of 500 in the odds of black bear carcass visitation for every additional house per hectare on the landscape, supporting the refuge hypothesis. These results suggest that residential development has the potential to alter intraguild relationships among large carnivores, even at modest levels where robust carnivore populations persist on the landscape, and may alter scavenger dynamics at carcasses where black bear presence is virtually eliminated.

Keywords: intraguild competition, large carnivore interactions, prey carcass visitation, Puma concolor, urbanization, Ursus americanus, wildland‐urban gradient

INTRODUCTION

Large carnivores often play pivotal ecological roles from their apex positions in food webs (Estes et al., 2011; Ripple et al., 2014). Namely, as direct predators or threats, they can exert top‐down control of ecosystems by reducing prey abundance (Holt et al., 2008), modifying intraguild competition (Pringle et al., 2019; Vanak et al., 2013), altering prey traits such as behavior (Preisser et al., 2005; Say‐Sallaz et al., 2019), redistributing herbivory (Hebblewhite et al., 2005), and subsidizing other species via carrion (Barry et al., 2019; Elbroch et al., 2017). Interspecific interactions among large carnivores, which can affect their abundance and distribution, thus having the potential to alter the strength and nature of these top‐down effects and, ultimately, ecosystem properties (Grassel et al., 2015; Hubbard et al., 2022). Accordingly, studies of factors that shape intraguild relationships among large carnivores can improve our capacity to predict how ecosystem dynamics are likely to respond to perturbation.

Large carnivores compete for food both indirectly (via resource exploitation) and directly (via interference and interspecific killing; Creel et al., 2001; Donadio & Burskirk, 2006). Carnivores may also scavenge on kills made by other guild members (Pereira et al., 2014; Walker et al., 2021). In so doing, they may affect the amount of carcass tissue available for cycling through the rest of the food web (Allen et al., 2014) and, in the case of kleptoparasitism (i.e., the stealing of already acquired food, also described as food parasitism through theft; Allen et al., 2021), resource acquisition and potentially the fitness of the carnivore that made the kill (Balme et al., 2017; Elbroch et al., 2015). Thus, studies of scavenging interactions or opportunities among large carnivores are key to a comprehensive understanding of their ecological effects.

Most research on scavenging interactions among large carnivores at carcasses has occurred in wildland landscapes and protected areas (e.g., Ballard et al., 2003; Krofel et al., 2019; Tallian et al., 2021). These studies have shown that carnivores like the Eurasian lynx (Lynx lynx) and wolves (Canis lupus) can lose a substantial amount of biomass to sympatric guild members, particularly bears (Krofel et al., 2022; MacNulty et al., 2001; Smith et al., 2003). Yet, many landscapes occupied by large carnivores are subject to anthropogenic disturbance, which can modify ecological relationships among carnivores (Gaynor et al., 2019; Kuijper et al., 2016; Ordiz et al., 2021). For example, competition and interspecific killing among large carnivores may be heightened when they concentrate around, and/or shift their diets to include more, human resource subsidies (Newsome et al., 2014), or when human disturbance drives them into shared spatiotemporal refugia (Ordiz et al., 2015). By contrast, species‐specific sensitivity to humans or patterns of resource redistribution in anthropogenic landscapes may cause large carnivores to segregate spatiotemporally as human disturbance increases (Foster et al., 2010). No study to date has explored scavenging interactions among large carnivores along gradients of human disturbance. Hence, there remains a need for a better understanding of whether and how the scavenging relationships among large carnivores that have been documented in wildland ecosystems are affected by human activity and landscape modification.

Cougars (Puma concolor) are solitary, far‐ranging felid predators that often target ungulate prey (Kertson, Spencer, & Grue, 2011; Murphy & Ruth, 2010) and occupy a broad range of habitats throughout the Americas (Sunquist & Sunquist, 2002). Like many solitary predators, cougars suffer energetic loss due to kleptoparasitism when foraging on large ungulate prey (Allen et al., 2021; Krofel et al., 2012). Hence, cougar‐provided carrion subsidies support high vertebrate scavenger diversity, including several carnivore species (Elbroch et al., 2017). The American black bear (Ursus americanus) is a generalist, opportunistic omnivore, and although meat usually accounts for a small proportion of their diet (Maser, 1998), bears are known to scavenge large‐bodied cougar‐killed prey (Murphy et al., 1998). In wildland environments where these species overlap, black bears have been documented visiting the carcasses of cougar‐killed ungulates (Allen et al., 2014; Elbroch et al., 2015, 2017; Kertson, Spencer, & Grue, 2011). Prior research on black bear use of cougar kills has demonstrated that partial prey consumption by cougars is more strongly explained by bear visitation than by patterns of optimal foraging (Elbroch et al., 2015) and that cougars spend less time feeding on ungulate carcasses where bear scavenging occurs (Allen et al., 2021), suggesting that bear scavenging often involves kleptoparasitism. In the western foothills of the Cascade Mountains, cougars and black bears occur together across a well‐defined wildland–urban gradient (0–>10 residences/ha; Kertson et al., 2013; Kertson, Spencer, Marzluff, et al., 2011; Robinson et al., 2005; Welfelt et al., 2019). Bear scavenging of cougar‐killed prey is likely important to both species there, but how urbanization impacts this interaction has yet to be elucidated.

To better understand how humans influence large carnivore interactions, we explored how residential development impacts black bear opportunities to scavenge cougar‐killed prey remains in western Washington, USA. We leveraged data from cougar kill sites to evaluate two hypotheses. The refuge hypothesis, whereby residential development reduces the incentive and opportunity for bears to scavenge carrion by increasing cougar use of small‐bodied (<20 kg; Smith et al., 2016) prey and by providing anthropogenic food resources for bears (Elbroch et al., 2015; Robins et al., 2019), predicts that the probability of black bear occurrence at cougar kill sites decreases with increasing housing density. Alternatively, the pile‐up hypothesis, whereby the intensity of carnivore competition peaks at moderate levels of anthropogenic disturbance because top predators are still capable of persisting, but habitat and resource constraints heighten antagonistic encounters (Gehrt et al., 2010; Riley, 2006), predicts that the probability of black bear visitation to cougar kills increases with increasing housing density. Under both hypotheses, we expected bear presence to be positively correlated with warmer daily temperatures and the summer season, as hotter weather and warmer seasons may contribute to spoilage, thereby increasing carcass detection. DeVault et al. (2004) and Walker et al. (2021) provide a basis for this hypothesis as they argue that increased microbe activity at hotter temperatures allows for stronger scavenger olfactory cues, which can attract more vertebrates to carcasses. Alternatively, it is possible that berry production produces adequate forage during summer for black bears (Baldwin & Bender, 2009) thereby reducing bear presence at prey carcasses, but assessing berry production as a possible driver of bear access to carrion was beyond the scope of this investigation. Finally, we expected cougar handling time to decrease with prey size but not housing density under the refuge hypothesis and to decrease with both prey size and rising housing density under the pile‐up hypothesis. In weighing support for these two hypotheses, this study expands our understanding of not only the cougar–black bear relationship along gradients of anthropogenic development but also how human disturbance shapes the interplay among large carnivores.

METHODS

Study site

Our study site was defined as a 993‐km2 landscape encompassing portions of King and Snohomish Counties, Washington, USA (Decimal Degrees WGS 84, Zone 10; Appendix S1: Figure S1). The topographically complex study site encompassed state, federal, municipal, and private property across an east‐to‐west gradient spanning wildland (0 residences/ha), exurban (0 < 2.5 residences/ha), suburban (2.5–10 residences/ha), and urban (>10 residences/ha) environments (Kertson, Spencer, Marzluff, et al., 2011; Robinson et al., 2005). Most wildland spaces consisted of temperate coniferous forests typical of the North Cascades eco‐region (Franklin & Dyrness, 1973). Cougars within the study area primarily prey on black‐tailed deer (Odocoileus hemionus columbianus), elk (Cervus elaphus), beaver (Castor canadensis), raccoon (Procyon lotor), and mountain beaver (Aplodontia rufa; Kertson, Spencer, Marzluff, et al., 2011; Robins et al., 2019). We considered prey <20 kg to be small‐bodied, which included beaver, raccoon, and mountain beaver as well as black‐tailed deer <6 months of age and elk <3 months of age. Black bears in western Washington are a dominant scavenger that subsists primarily on native vegetation but may also prey upon deer fawns, elk calves, and fish when available (Partridge et al., 2001). The topographic and residential characteristics of the study site are described at greater length in Kertson et al. (2013) and Kertson, Spencer, Marzluff, et al. (2011). Carnivore densities within our study area included 2.34 independent‐aged cougars/100 km2 (i.e., >18 months of age; Beausoleil et al., 2021), 27.8 bears/100 km2 in wildland areas, and 13.5 bears/100 km2 in developed landscapes (Welfelt et al., 2019).

Radio‐tagging and GPS cluster analysis

We used trained dogs and cage traps to capture cougars (n = 37) from 2013 to 2017. Captured cougars >12 months of age (both male and female) were chemically immobilized, given a physical examination, and outfitted with a GPS radio‐collar equipped with Globalstar satellite uplinks (GPS Plus‐2, Vectronic Aerospace, Berlin, Germany) as described in detail elsewhere (Kertson et al., 2013; Kertson, Spencer, & Grue, 2011; Robins et al., 2019). We chose not to collar cougars <30 kg to avoid potential fit issues with radio collars as animals grew. All capture and handling activities were performed in accordance with the University of Washington Institutional Animal Care and Use Committee protocol No. 3077 and the American Society of Mammalogists' guidelines for the use of wild mammals in research (Sikes et al., 2016).

Cougar GPS radio‐collars were programmed to attempt a satellite fix for 180 s every 4 h at 2:00, 6:00, 10:00, 14:00, 18:00, and 22:00 h. The 4‐h fix interval was chosen to maximize data acquisition and battery life (Cain et al., 2005; Kertson, Spencer, Marzluff, et al., 2011). We identified potential cougar kill site locations throughout the year in accordance with the methodology used by Robins et al. (2019). We defined location clusters as ≥3 GPS fixes occurring within an area ≤100 m2 over an 8‐h period (methods by Anderson & Lindzey, 2003, adapted slightly to account for small prey items). We then navigated to the geometric center of the cluster and searched in concentric circles varying between 5 and 10 m apart (depending on visibility) out to the extent of the cluster radius or until prey remains were discovered. We recorded a GPS location at the kill site if prey remains were found in a state of decay that closely matched the dates during which the cluster was created and if we also found definitive evidence of cougar feeding behavior (e.g., carcass caching, drag marks, hemorrhaging, and cougar scat; Knopff et al., 2009; Wilckens et al., 2016). We attempted to visit clusters after the cougar left, but within 2–4 weeks of the final GPS fix in order to obtain as much data on each prey item as possible (e.g., sex, age, and relative condition, Ballard, 1995).

Kill site assessment and scavenging behavior documentation

After confirming cougar kill site locations, we adjusted kill site coordinates to correspond to the location of the rumen (or intestines in the case of non‐ungulate prey). Cougars may cache a carcass 0–80 m from the initial kill location (Beier et al., 1995), so designating the position of the rumen as the kill location allowed for more consistent assessment of kill site features. Whenever possible, we documented prey species, sex, age, condition, and relative carcass consumption. We determined ungulate prey age using dentition and patterns of tooth wear and replacement (Severinghaus, 1949). Similar to other studies, we documented black bear visitation and assumed scavenging behavior at cougar kill sites, using forensic evidence of bear visitation on the ground determined by the presence of black bear feeding patterns, footprints, hair, and scat near carcasses (Elbroch et al., 2015; Murphy et al., 1998). Our forensic investigations thus indicated black bear presence at a carcass but could not distinguish between carcass sharing or alternating feeding bouts between cougars and bears nor instances where bears scavenged or kleptoparasitized cougar‐killed prey items. Forensic evidence is effective at documenting bear visitation, whereas cameras are often used to quantify carcass consumption or scavenger diversity (Allen et al., 2014, 2021). Nonetheless, our forensic methods were potentially prone to false negatives (i.e., we may have failed to detect bears at sites that they had truly visited). To address this possibility, we leveraged data from motion‐activated cameras (Reconyx Hyperfire Semi‐Covert IR HC500) placed at cougar kill sites 1–2 days after the predation event occurred to monitor cougar activity or document kitten survival to model bear detection probability. Cameras were deployed at 6% of kill sites for multiple days (often 1–4 weeks) prior to the time of forensic investigations with motion sensors set at “High” sensitivity, 5 “Rapidfire” pictures per trigger, and no delay between triggers. Twelve cougars wore GPS collars long enough to produce adequate kill data for this investigation, and we only included kills made outside of the black bear denning period (November–February) when considering black bear visitation.

Statistical analysis

We quantified housing density by using the most temporally relevant county parcel data to identify residence structures, calculated the density of these structures at a 30 × 30 m resolution, and sampled the resulting raster at kill site locations. This housing layer is described in detail in Robins et al. (2019). We tested for differences in bear visitation to cougar‐killed prey carcasses as a function of housing density, measured in houses per hectare, prey type, temperature, season, cougar sex, and handling time using single‐season occupancy models (MacKenzie et al., 2002) implemented in the “unmarked” package in R (R Core Team, 2022). This approach allowed us to estimate the probability that bears visited kill sites jointly with the probability that we detected those visits. Thus, we treated each forensic search (limited to one per kill site) and each calendar date of camera activity as an independent sampling occasion or opportunity to detect bear presence. This allowed us to use camera data to cross‐check our forensic determinations of black bear visitation. We assumed that our binary data, y ij , reflecting bear detection/non‐detection at the ith carcass during the jth occasion, were y ij |z i  ~ Bernoulli(p ij z i ), where z i , the partially observed true occurrence of bears at the site, was z i  ~ Bernoulli(ψ i ). We used logit links to model linear covariate effects on p and ψ, respectively, detection probability and the probability of black bear presence at a carcass. Given that the repeated sampling necessary to estimate detection probability occurred at only a fraction of sites, we restricted candidate submodels for detection probability to consider the linear effects of sampling method (“forensic” or “camera”) and the number of days elapsed between the first cluster fix and kill site sampling. The aforementioned covariates, other than housing density, were structured as follows: a categorical variable for prey type (small‐bodied < 20 kg ≤ large‐bodied; with all prey other than older fawns, yearlings, and adult ungulates falling into the small‐bodied category), cougar sex, the seven‐day temperature high and low following the first cougar GPS location at the kill site, season (spring: March–May, Summer: June–August, Autumn: September–November) to test whether bear visitation changed throughout the non‐denning portion of the year, and handling time in hours were included in occupancy submodels. The reference categories for categorical predictors were large‐bodied prey, male cougars, and the autumn season, respectively. We considered the refuge hypothesis supported if housing density was a significant negative predictor of the probability of bear occurrence at kill sites, and the pile‐up hypothesis supported if housing density was a significant positive predictor. We expected higher temperatures, the summer season, and handling time to be significant positive predictors of bear presence at carcasses for both the refuge and pileup hypotheses. We followed a two‐step procedure for model selection (Morin et al., 2020). First, we selected the top detection submodel under an intercept‐only occupancy submodel. Then, for occupancy submodels, we ran all predictor combinations that included housing density (which was present in all models) in combination with the best detection submodel identified in the first step. We ranked models according to differences in the Akaike information criterion (AIC) and used the top model to make inferences on the effect of housing density on black bear carcass visitation behavior (Burnham & Anderson, 2002). In our final models, we did not consider random effects for individual cougars; preliminary models built with the “ubms” package (Kellner et al., 2021) did include them, but 95% posterior credible intervals encompassed 0 for all individuals (Appendix S1: Table S3), suggesting bear presence was not affected by cougar identity.

We separately tested for differences in cougar handling time as a function of housing density, cougar sex, season, and prey type using general linear models (GLMs) with a negative binomial distribution and log link in the “MASS” package in R (Venables & Ripley, 2002). Handling time was measured as the number of 4‐h fix intervals, with the number of GPS fixes determined according to the first and last cougar GPS location at a given kill site (Elbroch et al., 2015). A likelihood ratio test comparing models with and without individual random effects structure indicated that cougar identity was not a significant predictor and therefore did not need to be included in the model (Appendix S1: Figure S2). We considered the refuge hypothesis supported by a significant negative coefficient for the small‐bodied prey type only, and the pile‐up hypothesis supported by a significant negative coefficient for the small‐bodied prey type and for housing density. Under both scenarios, we expected cougars to spend less time at kills during the summer relative to the spring and fall (GLM reference category) seasons. We again based inference on the top handling time model and used ∆AIC for model ranking, considering models within <2 AIC units of the top model to be equally valid (Burnham & Anderson, 2002). Covariate significance was assessed using 95% CIs.

RESULTS

We visited 306 cougar kill site locations in spring (n = 100), summer (n = 142), and autumn (n = 64) from 2013 to 2017 and documented 59 instances of black bear presence. Of 12 cougars, only one had zero cases of bear carcass visitation during this investigation (Appendix S1: Table S1). All cougar kills occurred between wildland (0 residences/ha) and exurban (maximum 2.5 residences/ha) landscapes in this study, and both small‐bodied and large‐bodied prey carcasses were represented across this development gradient (Appendix S1: Figure S3). In the occupancy models used to investigate drivers of bear presence at kill sites, the only covariate that affected detection probability was detection method; the daily camera‐based bear detection probability was 0.327 compared with an overall forensic detection probability of 0.748. Thus, the top occupancy model (Appendix S1: Table S4) included detection method along with housing density, prey type, and season. The probability of black bear presence at cougar‐killed prey carcasses decreased substantially as housing density increased (β = −6.458, CI = −12.465, −0.451; Table 1; Figure 1), equivalent to an odds ratio of 0.002 (CI = < 0.001, 0.637)—a multiplicative decrease in the odds of black bear occurrence at cougar kills of 500 for every additional house per ha (Figure 2). Bears were also significantly less likely to be present at small‐ relative to large‐bodied carcasses (βSmall‐bodied = −2.595, CI = −3.916, −1.274). Lastly, the probability of black bear presence at cougar‐killed prey carcasses increased significantly in summer relative to the fall season (β = 1. 413, CI = 0.326, 2.500; Table 1).

TABLE 1.

Covariate estimates for the best fit single‐season, single‐species occupancy model.

Submodel Predictor Estimate 95% CI
Occupancy Prey type (small‐bodied) −2.595 −3.916, −1.274
Housing density −6.458 −12.465, −0.451
Season (spring) 0.794 −0.225, 1.812
Season (summer) 1.413 0.326, 2.500
Detection Intercept (detection) −0.724 −1.914, 0.466
Forensic (method) 1.811 −0.190, 3.812
Open in a new tab

Note: Bold fonts indicate significant covariates, defined by a 95% CI not overlapping zero.

FIGURE 1.

FIGURE 1

Open in a new tab

Probability of black bear scavenging in each season as housing density increases (in residences per hectare) based on the top single‐season occupancy model. Solid lines indicate probability in each season (purple = spring, red = summer, blue = fall) and the boundaries of the same‐colored polygons indicate the 95% confidence bands.

FIGURE 2.

FIGURE 2

Open in a new tab

Odds ratios and 95% CIs for all covariates included in the top bear presence occupancy model. Odds ratios are relative to 1, with values greater than 1 signifying increased chances of black bear carcass visitation and values less than 1 indicating decreasing chances of bear occurrence. The fall season and large‐bodied prey were the reference categories used, indicated by parentheses on the right‐hand side of the plot.

The top handling time model included season and prey type (Appendix S1: Table S5). In this model, the spring season was a significant positive predictor (β = 0.313, CI = 0.117, 0.508; Table 2), and small‐bodied prey was a significant negative predictor (β = −0.499, CI = −0.664, −0.333) of cougar handling time. Based on the associated incidence rate ratios (Appendix S1: Figure S4), handling times were approximately 1.5 times greater in spring than in autumn and approximately doubled for cougars foraging on large‐bodied prey.

TABLE 2.

Covariate estimates for the best fit handling time negative binomial model of cougar handling time.

Predictor Estimate 95% confidence interval
Season (spring) 0.313 0.117, 0.508
Season (summer) −0.003 −0.192, 0.184
Prey type (small‐bodied) −0.499 −0.664, −0.333
Intercept 2.653 2.497, 2.814
Open in a new tab

Note: Bold fonts indicate significant covariates, defined by a 95% CI not overlapping zero.

DISCUSSION

Urbanization is a pervasive driver of large carnivore ecology globally (Bateman & Fleming, 2012; Cardillo et al., 2004; Woodroffe, 2000). However, previous research on large carnivore responses to urbanization has largely focused on individual species and less so on changes to interspecific relationships (Lewis et al., 2017) like scavenging interactions (Luna et al., 2021). To address this knowledge gap, we asked whether black bear presence at cougar‐killed prey carcasses would either decrease (refuge hypothesis) or increase (pile‐up hypothesis) with housing density. We found that the probability of a black bear visiting a prey carcass decreased significantly as housing density increased, supporting the refuge hypothesis. Furthermore, cougar prey handling time increased significantly during the spring season and decreased significantly when cougars were foraging on small‐bodied prey (<20 kg), partially supporting the refuge hypothesis as most synanthropic prey in this system is small‐bodied (Robins et al., 2019), similar to findings in Smith et al. (2016) from southern California. To our knowledge, this is the first study to demonstrate that anthropogenic development can be a driver of reduced bear presence at cougar kill sites, which suggests urbanization can alter interspecific carnivore dynamics.

There is growing recognition that anthropogenic presence and landscape modification can alter interactions involving predators (Kuijper et al., 2016; Newsome et al., 2015; Ordiz et al., 2021). Most research to date has addressed the possibility of humans modifying large predator interactions with smaller predators and prey species (e.g., Ciucci et al., 2020; Haswell et al., 2020). Our results add a new pathway by which human activity can influence predator ecology: disruption of scavenging relationships among large carnivores. The effect of residential development on black bear occurrence at cougar kill sites was immediate and pronounced: Each additional residence per hectare decreased the odds of black bear presence at kill sites by a factor of 500. Moreover, this effect was not plausibly attributable to lower black bear density in residential landscapes. Although Welfelt et al. (2019) found that black bear density in our study area decreased with human development (from 27.8 bears/100 km2 in wildland areas to 13.5 bears/100 km2 in developed areas), the odds of bear presence at cougar kills decreased by multiple orders of magnitude across the same gradient—a far greater effect size than differences in bear density alone can explain. Cougars have a consistent, albeit nuanced, response to human development in Washington state: They are consistently able to use exurban residential areas, especially those that maintain forest connectivity and are characterized by dense understory vegetation (Maletzke et al., 2017). By implication, residential development has the potential to alter intraguild relationships among large carnivores even at levels that allow for the affected carnivores to persist on the landscape. The extents to which this effect of residential development on large carnivore scavenging relationships triggers population responses and is mirrored by other forms of anthropogenic disturbance are beyond the scope of the present study and remain as important research frontiers.

Previous research on carnivore–carnivore interactions in urbanizing environments has largely focused on competition involving mesocarnivores (e.g., Lewis et al., 2015; Malhotra et al., 2022) and has often indicated that urbanization increases the opportunity for interspecific interactions by constraining wildlife to limited greenspaces, that is, the pile‐up hypothesis. For example, forest fragmentation was the most important determinant of interspecific interactions between foxes, bobcats (Lynx rufus), and coyotes in Washington, DC, USA, and Raleigh, NC, USA, especially as urbanization increased (Parsons et al., 2019). Our results show, by contrast, that black bear visitation to cougar prey remains declines with residential development, supporting the refuge hypothesis. Thus, the impacts of urbanization on interspecific relationships in carnivore communities appear to differ as a function of the species involved and the type of interaction in question. Similarly, temporal avoidance of humans and dominant competitors by subordinate carnivores in anthropogenic ecosystems does not follow a consistent pattern (Seveque et al., 2021), suggesting more broadly that human mediation of intraguild interactions involving carnivores is context dependent. Accordingly, there is a need for studies aimed at identifying the factors that determine whether and how anthropogenic perturbation shapes carnivore interactions and coexistence.

Why did the incidence of black bear presence at cougar‐killed prey carcasses decrease with increasing residential development? Previous research on cougar foraging behavior in western Washington and elsewhere revealed increased usage of smaller, synanthropic prey as urbanization intensified (Kertson, Spencer, & Grue, 2011; Moss et al., 2016; Robins et al., 2019; Smith et al., 2016), suggesting that cougars adjust their diets when exposed to more opportunities to exploit urban prey (Moss et al., 2016; Smith et al., 2016). In accord with this pattern, use of large‐bodied prey by cougars in our study dropped as housing density increased (Appendix S1: Figure S3), with the only kills beyond 1.0 residences/ha being of small‐bodied prey (n = 2). Moreover, whereas the housing density covariate was not included in the top handling time GLM, our prey type covariate indicated that cougar foraging on small‐bodied prey translated to a significant reduction in cougar handling time and therefore reduced opportunity for bears to find a cougar‐killed carcass. By inference, increased use of smaller prey taxa by cougars appears to have been at least partly responsible for limiting scavenging opportunities for black bears near residential development. Notably, however, the effect of residential development on black bear visits to cougar‐killed prey carcasses was not simply an artifact of cougars no longer killing large‐bodied prey beyond the 1.0 residences/ha threshold but rather a continuous phenomenon across the entire residential density spectrum, as a separate analysis of bear visitation excluding all observations at or above this housing threshold yielded the same outcome (Appendix S1: Section S2: Results).

A second and non‐mutually exclusive reason for reduced bear visitation to cougar kills as urbanization intensifies is that black bears in developed landscapes have greater access to alternative, calorie‐rich food sources than their wildland counterparts. Indeed, black bears in western Washington have been shown to be larger and rely more heavily on anthropogenic food sources than conspecifics navigating much less developed eastern portions of the state (Welfelt et al., 2019). Elsewhere, black bears have been shown to forage on anthropogenic foods near residences even when natural, wildland foods are available (Merkle et al., 2013), and rely more heavily on human food resources when natural foods are scarce (Johnson et al., 2015), suggesting that bears may use human foods as a subsidy or alternate food source when natural foods are less available (i.e., less carrion or years with poor mast production). Answering whether black bears in our study system were drawn away from cougar kills in areas with higher residential density by more attractive anthropogenic food sources was beyond the scope of the present analysis, given the absence of bear GPS data. We recommend bear‐centric movement data be made a priority for future research seeking to elucidate bear‐cougar interactions at carcasses.

Approximately 50% of black bear carcass visits occurred during the months of June through August, consistent with our carcass spoilage prediction. While temperature was not included in our most parsimonious best fit occupancy model, the summer season was retained as a significant positive predictor of bear visitation to cougar‐killed prey carcasses. Black bears are widely documented as a source of mortality for neonate ungulates in spring via hunting (Bowersock et al., 2021; Ruprecht et al., 2022), but the timing of their use of animal food resources, and carrion specifically, may be system specific. Baldwin and Bender (2009) found that the use of animal matter in black bear diets increased following the spring season, consistent with our finding of increased visitation to cougar‐provided carrion during the summer months. In this light, our findings suggest warmer weather, at least on a seasonal basis, aids bears in detecting carcasses; however, the extent to which carcass detection rates are actually shaped by daily temperature variation as well as other likely drivers such as bear physiological state requires further investigation.

There is significant individual variation in cougar diets across western Washington's wildland–urban gradient (Robins et al., 2019). Our focal cougars, however, did not differ significantly with respect to prey handling time (Appendix S1: Figure S2), with disparities among cougars in time spent at carcasses predominantly being tied to prey size. Similarly, cougar identity was not retained in the top occupancy model of black bear presence at cougar kills. Collectively, these patterns suggest that susceptibility to black bear carcass visitation and subsequent scavenging may be relatively consistent across individual cougars in wildland landscapes. However, our analysis did not discriminate between cases where black bears kleptoparasitized cougar kills from those where bears merely visited carcasses after the cougar had departed. Therefore, it remains possible that rates of kleptoparasitism vary among individual cougars. Accordingly, future research should isolate cases of carcass usurpation by bears and then test for individual variability and demographic differences in the frequency with which cougars experience this phenomenon.

It is possible that some instances of carcass visitation by black bears went undetected. Yet, unlike previous investigations relying on similar methods (e.g., Allen et al., 2021; Elbroch et al., 2015; Kertson, Spencer, & Grue, 2011), we accounted for this uncertainty formally through our use of an alternative data stream (remote cameras) and occupancy modeling. The high daily detection probability at cameras (0.33) and overall forensic detection probability (0.75) suggest a high likelihood of bear detection during data collection for this study. Even so, by estimating the probability of both bear detection and bear presence, we ensured that our inferences reflected the possibility that we missed bear visits. However, other aspects of urban development that we did not assess (e.g., roads and trails, time of day, human presence) may have affected both cougar handling time and bear foraging behavior and thus should be the focus of further investigation.

There is ample evidence that human landscape modification can alter the ecology of large terrestrial predators (Newsome et al., 2014), but this study is the first to show that residential development can reduce black bear presence at prey carcasses provided by a sympatric large carnivore, suggesting urban development has the potential to alter interspecific dynamics between large carnivores. Our findings reveal that the impacts of urbanization on interspecific relationships among large carnivores can be dramatic: Even modest levels of residential development associated with the transition from wildland to exurban environments (<2.5 house/ha) all but eliminated instances of black bear carcass visitation despite cougars continuing to kill adult ungulates in these landscapes, effectively disassociating this aspect of the bear‐cougar relationship. Our results imply that the negative effects of bears on cougars may relax in residential landscapes despite reduced green space and natural food sources compared with wildland environments. Elsewhere, black bear kleptoparasitism (Elbroch et al., 2015) and urbanization (Smith et al., 2015) are associated with increasing cougar kill rates, suggesting that bears and humans alike might promote more frequent carcass abandonment. Accordingly, there is a need for research examining how urbanizing landscapes alter prey consumption by cougars, and whether their consumption patterns alter the availability of cougar‐provided carrion for scavengers. Our findings suggest that residential development may promote cougar provisioning of carrion for non‐bear scavengers by virtually eliminating bear scavenging. On the other hand, urbanization also appears to correspond with cougars using smaller, synanthropic prey species, which may mean less biomass for scavengers. Future studies should explore the use of cougar‐provided carcasses by the wider scavenging community along wildland‐urban gradients.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.

Supporting information

Appendix S1.

ECY-106-e70038-s001.pdf (725KB, pdf)

ACKNOWLEDGMENTS

Special thanks to S. Converse, J. Marzluff, and T. Essington for editorial feedback and to two anonymous reviewers for helpful suggestions. We are grateful to houndsman M. White, and WDFW officers and biologists J. Capelli, C. Chandler, C. Moszeter, B. Richards, and M. Smith for assistance with cougar captures and trapping efforts and to R. Beausoleil and L. Welfelt for their insights on western Washington black bear population dynamics and densities. We thank the Doris Duke Conservation Scholars Program (DDCSP) and multiple volunteers for assistance with data collection. We also thank Campbell Global Timber Resources, Hancock Timber Company, the Washington Department of Natural Resources, and myriad private landowners for kindly providing access to their lands. Funding and logistical support for this research were provided by the Washington Department of Fish and Wildlife, the University of Washington, and the National Science Foundation Graduate Research Fellowships Program.

Robins, Clint W. , Kertson Brian N., Kachel Shannon M., and Wirsing Aaron J.. 2025. “Residential Development Reduces Black Bear (Ursus Americanus) Opportunity to Scavenge Cougar (Puma Concolor) Killed Prey.” Ecology 106(2): e70038. 10.1002/ecy.70038

Handling Editor: Lin Meng

DATA AVAILABILITY STATEMENT

Data and code (Robins et al., 2024) are available in Dryad at https://doi.org/10.5061/dryad.dv41ns249, with the exception of cougar telemetry relocation data that are sensitive data. The cougar telemetry relocation data are available to qualified researchers by contacting the Washington Department of Fish and Wildlife's Chief Wildlife Scientist and requesting cougar telemetry data from the West Cascades Cougar Project (2013–2017); a Washington Department of Fish and Wildlife data‐sharing agreement is required.

REFERENCES

  1. Allen, M. L. , Elbroch L. M., Wilmers C. C., and Wittmer H. U.. 2014. “Trophic Facilitation or Limitation? Comparative Effects of Pumas and Black Bears on the Scavenger Community.” PLoS One 9(7): e102257. 10.1371/journal.pone.0102257. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Allen, M. L. , Elbroch L. M., Wilmers C. C., and Wittmer H. U.. 2021. “Can't Bear the Competition: Energetic Losses from Kelptoparastism by a Dominant Scavenger May Alter Foraging Behaviors of an Apex Predator.” Basic and Applied Ecology 51: 1–10. [Google Scholar]
  3. Anderson, C. R. , and Lindzey F. G.. 2003. “Estimating Cougar Predation Rates from GPS Location Clusters.” The Journal of Wildlife Management 67(2): 307–316. [Google Scholar]
  4. Baldwin, R. A. , and Bender L. C.. 2009. “Foods and Nutritional Components of Diets of Black Bear in Rocky Mountain National Park, Colorado.” Canadian Journal of Zoology 87: 1000–1008. [Google Scholar]
  5. Ballard, W. B. 1995. “Bone Marrow Fat as an Indicator of Ungulate Condition–How Good Is It?” Alces 31: 105–109. [Google Scholar]
  6. Ballard, W. B. , Carbyn L. N., and Smith D. W.. 2003. “Wolf Interactions with Non‐Prey.” In Wolves: Behavior, Ecology, and Conservation, edited by Mech D. and Boitani L., 259–271. Chicago, IL: University of Chicago Press. [Google Scholar]
  7. Balme, G. A. , Miller J. R. B., Pitman R. T., and Hunter L. T.. 2017. “Caching Reduces Kleptoparasitism in a Solitary, Large Felid.” Journal of Animal Ecology 86(3): 634–644. [DOI] [PubMed] [Google Scholar]
  8. Barry, J. M. , Elbroch L. M., Aiello‐Lammens M. E., Sarno R. J., Seelye L., Kusler A., Quigley H. B., and Grigione M. M.. 2019. “Pumas as Ecosystem Engineers: Ungulate Carcasses Support Beetle Assemblages in the Greater Yellowstone Ecosystem.” Oecologia 189: 577–586. [DOI] [PubMed] [Google Scholar]
  9. Bateman, P. W. , and Fleming P. A.. 2012. “Big City Life: Carnivores in Urban Environments: Urban Carnivores.” Journal of Zoology 287: 1–23. [Google Scholar]
  10. Beausoleil, R. A. , Welfelt L. S., Keren I. N., Kertson B. N., Maletzke B. T., and Koehler G. M.. 2021. “Long‐Term Evaluation of Cougar Density and Application of Risk Analysis for Harvest Management.” The Journal of Wildlife Management 85: 462–473. [Google Scholar]
  11. Beier, P. , Choate D., and Barrett R. H.. 1995. “Movement Patterns of Mountain Lions during Different Behaviors.” Journal of Mammalogy 76: 1056–1070. [Google Scholar]
  12. Bowersock, N. R. , Litt A. R., Merkle J. A., Gunther K. A., and van Manen F. T.. 2021. “Responses of American Black Bears to Spring Resources.” Ecosphere 12(11): e03773. [Google Scholar]
  13. Burnham, K. P. , and Anderson D. R.. 2002. Model Selection and Multimodel Inference: A Practical Information‐Theoretic Approach, 2nd ed. New York, NY, USA: Springer‐Verlag. [Google Scholar]
  14. Cain, J. W. , Krausman P. R., Jansen B. D., and Morgart J. R.. 2005. “Influence of Topography and GPS Fix Interval on GPS Collar Performance.” Wildlife Society Bulletin 33: 926–934. [Google Scholar]
  15. Cardillo, M. , Purvis A., Sechrest W., Gittleman J. L., Bielby J., and Macel G. M.. 2004. “Human Population Density and Extinction Risk in the World's Carnivores.” PLoS Biology 2: 909–914. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Ciucci, P. , Mancinelli S., Boitani L., Gallo O., and Grottoli L.. 2020. “Anthropogenic Food Subsidies Hinder the Ecological Role of Wolves: Insights for Conservation of Apex Predators in Human‐Modified Landscapes.” Global Ecology and Conservation 21: e00841. [Google Scholar]
  17. Creel, S. , Spong G., and Creel N. M.. 2001. “Interspecific Competition and the Population Biology of Extinction‐Prone Carnivores.” In Conservation of Carnivores, edited by Macdonald D., Gittleman J., Funk S., and Wayne R., 35–60. New York, NY: Cambridge University Press. [Google Scholar]
  18. DeVault, T. L. , Brisbin J., Lehr I., Rhodes J., and Olin E.. 2004. “Factors Influencing the Acquisition of Rodent Carrion by Vertebrate Scavengers and Decomposers.” Canadian Journal of Zoology 82: 502–509. [Google Scholar]
  19. Donadio, E. , and Burskirk S. W.. 2006. “Diet, Morphology, and Interspecific Killing in Carnivora.” The American Naturalist 167(4): 524–536. [DOI] [PubMed] [Google Scholar]
  20. Elbroch, L. M. , O'Malley C., Peziol M., and Quigley H. B.. 2017. “Vertebrate Diversity Benefiting from Carrion Provided by Pumas and Other Subordinate, Apex Felids.” Biological Conservation 215: 123–131. [Google Scholar]
  21. Elbroch, M. L. , Lendrum P. E., Allen M. L., and Wittmer H. U.. 2015. “Nowhere to Hide: Pumas, Black Bears, and Competition Refuges.” Behavioral Ecology 26(1): 247–254. [Google Scholar]
  22. Estes, J. A. , Terborgh J., Brashares J. S., Power M. E., Berger J., Bond W. J., Carpenter S. R., et al. 2011. “Trophic Downgrading of Planet Earth.” Science 333(6040): 301–306. [DOI] [PubMed] [Google Scholar]
  23. Foster, R. J. , Harmsen B. J., and Doncaster C. P.. 2010. “Habitat Use by Sympatric Jaguars and Pumas across a Gradient of Human Disturbance in Belize.” Biotropica 42: 724–731. [Google Scholar]
  24. Franklin, J. F. , and Dyrness C. T.. 1973. Natural Vegetation of Oregon and Washington. General Technical Report PNW‐8. Portland, OR: Pacific Northwest Forest and Range Experimental Station, USDA Forest Service. [Google Scholar]
  25. Gaynor, K. M. , Brown J. S., Middleton A. D., Power M. E., and Brashares J. S.. 2019. “Landscapes of Fear: Spatial Patterns of Risk Perception and Response.” Trends in Ecology and Evolution 34(4): 355–368. [DOI] [PubMed] [Google Scholar]
  26. Gehrt, S. D. , Riley S. P. D., and Cypher B. L.. 2010. Urban Carnivores: Ecology, Conflict, and Conservation. Baltimore, MD: John Hopkins University Press. [Google Scholar]
  27. Grassel, S. M. , Rachlow J. L., and Williams C. J.. 2015. “Spatial Interactions Between Sympatric Carnivores: Asymmetric Avoidance of an Intraguild Predator.” Ecology and Evolution 5(14): 2745–2989. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Haswell, P. M. , Kusak J., Jones K. A., and Hayward M. W.. 2020. “Fear of the Dark? A Mesopredator Mitigates Large Carnivore Risk through Nocturnality, but Humans Moderate the Interaction.” Behavioral Ecology and Sociobiology 74(5): 62. 10.1007/s00265-020-02831-2. [DOI] [Google Scholar]
  29. Hebblewhite, M. , White C. A., Nietvelt C. G., Mckenzie J. A., Hurd T. E., Fryxell J. M., Bayley S. E., and Paquet P. C.. 2005. “Human Activity Mediates a Trophic Cascade Caused by Wolves.” Ecology 86(8): 2135–2144. [Google Scholar]
  30. Holt, A. R. , Davies Z. G., Tyler C., and Staddon S.. 2008. “Meta‐Analysis of the Effects of Predation on Animal Prey Abundance: Evidence from UK Vertebrates.” PLoS One 3(6): e2400. 10.1371/journal.pone.0002400. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Hubbard, T. , Cove M. V., Green A. M., Iannarilli F., Allen M. L., LaRose S. H., Nagy C., Compton J. A., and Lafferty D. J. R.. 2022. “Human Presence Drives Bobcat Interactions among the U.S. Carnivore Guild.” Biodiversity and Conservation 31: 2607–2624. [Google Scholar]
  32. Johnson, H. E. , Breck S. W., Baruch‐Mordo S., Lewis D. L., Lackey C. W., Wilson K. R., Broderick J., Mao J. S., and Beckmann J. P.. 2015. “Shifting Perceptions of Risk and Reward: Dynamic Selection for Human Development by Black Bears in the Western United States.” Conservation Biology 187: 164–172. [Google Scholar]
  33. Kellner, K. , Fowler N., Petroelje T., Kautz T., Beyer D., and Belant J.. 2021. “Ubms: An R Package for Fitting Heirarchical Occupancy and N‐Mixture Abundance Models in a Bayesian Framework.” Methods in Ecology and Evolution 13: 577–584. [Google Scholar]
  34. Kertson, B. N. , Spencer R. D., and Grue C. E.. 2011. “Cougar Prey Use in a Wildland‐Urban Environment in Western Washington.” Northwestern Naturalist 92: 175–185. [Google Scholar]
  35. Kertson, B. N. , Spencer R. D., and Grue C. E.. 2013. “Demographic Influences on Cougar Residential Use and Interactions with People in Western Washington.” Journal of Mammalogy 94(2): 269–281. [Google Scholar]
  36. Kertson, B. N. , Spencer R. D., Marzluff J. M., Hepinstall‐Cymerman J., and Grue C. E.. 2011. “Cougar Space Use and Movements in the Wildland‐Urban Landscape of Western Washington.” Ecological Applications 21(8): 2866–2881. [Google Scholar]
  37. Knopff, K. H. , Knopff A. A., Warren M. B., and Boyce M. S.. 2009. “Evaluating Global Positioning System Telemetry Techniques for Estimating Cougar Predation Parameters.” The Journal of Wildlife Management 73: 586–597. [Google Scholar]
  38. Krofel, M. , Hočevar L., Fležar U., Topličanec I., and Oliviera T.. 2022. “Golden Jackal as New Kleptoparasite for Eurasian Lynx in Europe.” Global Ecology and Conservation 36: e02116. [Google Scholar]
  39. Krofel, M. , Kos I., and Jerina K.. 2012. “The Noble Cats and the Big Bad Scavengers: Effects of Dominant Scavengers on Solitary Predators.” Behavioral Ecology and Sociobiology 66: 1297–1304. [Google Scholar]
  40. Krofel, M. , Skrbinšek T., and Mohorović M.. 2019. “Using Video Surveillance to Monitor Feeding Behaviour and Kleptoparasitism at Eurasian Lynx Kill Sites.” Folia Zoologica 68(4): 274–284. [Google Scholar]
  41. Kuijper, D. P. J. , Sahlén E., Elmhagen B., Chamaillé‐Jammes S., Sand H., Lone K., and Cromsigt J. P. G. M.. 2016. “Paws without Claws? Ecological Effects of Large Carnivores in Anthropogenic Landscapes.” Proceedings of the Royal Society B: Biological Sciences 283: 20161625. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Lewis, J. S. , Bailey L. L., VandeWoude S., and Crooks K. R.. 2015. “Interspecific Interactions between Wild Felids Vary across Scales and Levels of Urbanization.” Ecology and Evolution 5(24): 5946–5961. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Lewis, J. S. , Logan K. A., Alldredge M. W., Theobald D. M., VandeWoude S., and Crooks K. R.. 2017. “Contact Networks Reveal Potential for Interspecific Interactions of Sympatric Wild Felids Driven by Space Use.” Ecosphere 8(3): e01707. 10.1002/ecs2.1707. [DOI] [Google Scholar]
  44. Luna, Á. , Romero‐Vidal P., and Arrondo E.. 2021. “Scavenging in the City: A Review of Spatio‐Temporal Trends in Research.” Diversity 13(2): 46. 10.3390/d13020046. [DOI] [Google Scholar]
  45. MacKenzie, D. I. , Nichols J. D., Lachman G. B., Droege S., Royle J. A., and Langtimm C. A.. 2002. “Estimating Site Occupancy Rates When Detection Probabilities Are Less than One.” Ecology 83: 2248–2255. [Google Scholar]
  46. MacNulty, D. R. , Varley N., and Smith D. W.. 2001. “Grizzly Bear, Ursos Acrtos, Usurps Bison Calf, Bison Bison, Captured by Wolves, Canis Lupis, in Yellowstone National Park, Wyoming.” Canadian Field‐Naturalist 115(3): 495–498. [Google Scholar]
  47. Maletzke, B. , Kertson B., Swanson M., Koehler G., Beausoleil R., Wielgus R., and Cooley H.. 2017. “Cougar Response to a Gradient of Human Development.” Ecosphere 8(7): e01828. [Google Scholar]
  48. Malhotra, R. , Lima S., and Harris N. C.. 2022. “Temporal Refuges of a Subordinate Carnivore Vary Across Rural‐Urban Gradient.” Ecology and Evolution 12(9): e9310. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Maser, C. 1998. Mammals of the Pacific Northwest from the Coast to the High Cascades. Corvallis, OR: Oregon State University Press. [Google Scholar]
  50. Merkle, J. A. , Robinson H. S., Krausman P. R., and Alaback P.. 2013. “Food Availability and Foraging near Human Developments by Black Bears.” Journal of Mammalogy 94(2): 378–385. [Google Scholar]
  51. Morin, D. J. , Yackulic C. B., Diffendorfer J. E., Lesmeister D. B., Nielsen C. K., Reid J., and Schauber E. M.. 2020. “Is your Ad Hoc Model Selection Strategy Affecting your Multimodel Inference?” Ecosphere 11(1): e02997. [Google Scholar]
  52. Moss, W. E. , Alldredge M. W., and Pauli J. N.. 2016. “Quantifying Risk and Resource Use for a Large Carnivore in an Expanding Urban‐Wildland Interface.” Journal of Applied Ecology 53: 371–378. [Google Scholar]
  53. Murphy, K. M. , Felzien G. S., Hornocker M. G., and Ruth T.. 1998. “Encounter Competition between Bears and Cougars: Some Ecological Implications.” Ursus 10: 55–60. [Google Scholar]
  54. Murphy, K. M. , and Ruth T.. 2010. “Diet and Prey Selection of a Perfect Predator.” In Cougar: Ecology and Conservation, edited by Hornocker M. and Negri S., 118–137. Chicago: University of Chicago Press. [Google Scholar]
  55. Newsome, S. D. , Garbe H. M., Wilson E. C., and Gehrt S. D.. 2015. “Individual Variation in Anthropogenic Resource Use in an Urban Carnivore.” Oecologia 178: 115–128. [DOI] [PubMed] [Google Scholar]
  56. Newsome, T. M. , Dellinger J. A., Pavey C. R., Ripple W. J., Shores C. R., Wirsing A. J., and Dickman C. R.. 2014. “The Ecological Effects of Providing Resource Subsidies to Predators.” Global Ecology and Biogeography 24: 1–11. [Google Scholar]
  57. Ordiz, A. , Aronsson M., Persson J., Støen O., Swenson J. E., and Kindberg J.. 2021. “Effects of Human Disturbance on Terrestrial Apex Predators.” Diversity 13: 68. [Google Scholar]
  58. Ordiz, A. , Milleret C., Kindberg J., Mansson J., Wabakken P., Swenson J. E., and Sand H.. 2015. “Wolves, People, and Brown Bears Influence the Expansion of the Recolonizing Wolf Population in Scandinavia.” Ecosphere 6(12): 1–14. [Google Scholar]
  59. Parsons, A. W. , Rota C. T., Forrester T., Baker‐Whatton M. C., McShea W. J., Schuttler S. G., Millspaugh J. J., and Kays R.. 2019. “Urbanization Focuses Carnivore Activity in Remaining Natural Habitats, Increasing Species Interactions.” Journal of Applied Ecology 56: 1894–1904. [Google Scholar]
  60. Partridge, S. T. , Nolte D. L., Ziegltrum G. J., and Robbins C. T.. 2001. “Impacts of Supplemental Feeding on the Nutritional Ecology of Black Bears.” Journal of Wildlife Management 65: 191–199. [Google Scholar]
  61. Pereira, L. M. , Owen‐Smith N., and Moleón M.. 2014. “Facultative Predation and Scavenging by Mammalian Carnivores: Seasonal, Regional and Intra‐Guild Comparisons.” Mammal Review 44: 44–55. [Google Scholar]
  62. Priesser, E. L. , Bolnick D. I., and Benard M. E.. 2005. “Scared to Death? The Effects of Intimidation and Consumption in Predator‐Prey Interactions.” Ecology 86(2): 501–509. [Google Scholar]
  63. Pringle, R. M. , Kartzinel T. R., Palmer T. M., Thurman T. J., Fox‐Dobbs K., Xu C. C. Y., Hutchinson M. C., et al. 2019. “Predator‐induced collapse of niche structure and species coexistence.” Nature 570(7759): 58–64. [DOI] [PubMed] [Google Scholar]
  64. R Core Team . 2022. R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing. [Google Scholar]
  65. Riley, S. P. D. 2006. “Spatial Ecology of Bobcats and Gray Foxes in Urban and Rural Zones of a National Park.” Journal of Wildlife Management 70: 1425–1435. [DOI] [PubMed] [Google Scholar]
  66. Ripple, W. J. , Estes J. A., Beschta R. L., Wilmers C. C., Ritchie E. G., Hebblewhite M., Berger J., et al. 2014. “Status and Ecological Effects of the World's Largest Carnivores.” Science 343: 1241484. 10.1126/science.1241484. [DOI] [PubMed] [Google Scholar]
  67. Robins, C. W. , Kertson B. K., Faulkner J. R., and Wirsing A. J.. 2019. “Effects of Urbanization on Cougar Foraging Ecology along the Wildland‐Urban Gradient of Western Washington.” Ecosphere 10(3): e02605. [Google Scholar]
  68. Robins, C. W. , Kertson B. K., and Kachel S.. 2024. “Data from: Residential Development Reduces Black Bear (Ursus americanus) Opportunity to Scavenge Cougar (Puma concolor) Killed Prey.” Dataset. Dryad. 10.5061/dryad.dv41ns249. [DOI] [PMC free article] [PubMed]
  69. Robinson, L. , Newell J. P., and Marzluff J. M.. 2005. “Twenty‐Five Years of Sprawl in the Seattle Region: Growth Management Responses and Implications for Conservation.” Landscape and Urban Planning 71: 51–72. [Google Scholar]
  70. Ruprecht, J. , Forrester T. D., Jackson N. J., Clark D. A., Wisdom M. J., Rowland M. M., Smith J. B., Steward K. M., and Levi T.. 2022. “A Seasonal Pulse of Ungulate Neonates Influences Space Use by Carnivores in a Multi‐Predatory, Multi‐Prey System.” Ecology and Evolution 12(10): e9389. [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Say‐Sallaz, E. , Chamaille‐Jammes S., Fritz H., and Valeix M.. 2019. “Non‐Consumptive Effects of Predation in Large Terrestrial Mammals: Mapping our Knowledge and Revealing the Tip of the Iceberg.” Biological Conservation 235: 36–52. [Google Scholar]
  72. Seveque, A. , Gentle L. K., Lopez‐Bao J. V., Yarnell R. W., and Uzal A.. 2021. “Impact of Human Disturbance on Temporal Partitioning within Carnivore Communities.” Mammal Review 52: 67–81. [DOI] [PubMed] [Google Scholar]
  73. Severinghaus, C. W. 1949. “Tooth Development and Wear as Criteria of Age in White‐Tailed Deer.” The Journal of Wildlife Management 13(2): 195–216. [Google Scholar]
  74. Sikes, R. S. , and The Animal Care and Use Committee of the American Society of Mammalogists . 2016. “2016 Guidelines of the American Society of Mammalogists for the Use of Wild Mammals in Research and Education.” Journal of Mammalogy 97(3): 663–688. [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Smith, D. W. , Peterson R. O., and Houston D. B.. 2003. “Yellowstone after Wolves.” BioScience 53(4): 330–340. [Google Scholar]
  76. Smith, J. A. , Wang Y., and Wilmers C. C.. 2016. “Spatial Characteristics of Residential Development Shifts Large Carnivore Prey Habits.” Journal of Wildlife Management 80(6): 1040–1048. [Google Scholar]
  77. Smith, J. A. , Wany Y., and Wilmers C. C.. 2015. “Top Carnivores Increase Their Kill Rates on Prey as a Response to Human‐Induced Fear.” Proceedings of the Royal Society B: Biological Sciences 282(1802): 20142711. 10.1098/rspb.2014.2711. [DOI] [PMC free article] [PubMed] [Google Scholar]
  78. Sunquist, M. , and Sunquist F.. 2002. Wild Cats of the World 1–452. Chicago: University of Chicago Press. [Google Scholar]
  79. Tallian, A. , Ordiz A., Metz M. C., Zimmermann B., Wikenros C., Smith D. W., Stahler D. R., et al. 2021. “Of Wolves and Bears: Seasonal Drivers of Interference Exploitation Competition between Apex Predators.” Ecological Monographs 92: e1498. 10.1002/ecm.1498. [DOI] [Google Scholar]
  80. Vanak, A. T. , Fortin D., Thaker M., Ogden M., Owen C., Greatwood S., and Slotow R.. 2013. “Moving to Stay in Place: Behavioral Mechanisms for Coexistence of African Large Carnivores.” Ecology 94(11): 2619–2631. [DOI] [PubMed] [Google Scholar]
  81. Venables, W. N. , and Ripley B. D.. 2002. Modern Applied Statistics with S, Fourth ed. New York: Springer. [Google Scholar]
  82. Walker, M. A. , Uribasterra M., Asher V., Getz W. M., Ryan S. J., Ponciano J. M., and Blackburn J. K.. 2021. “Factors Influencing Scavenger Guilds and Scavenging Efficiency in Southwestern Montana.” Scientific Reports 11: 4254. [DOI] [PMC free article] [PubMed] [Google Scholar]
  83. Welfelt, L. S. , Beausoleil R. A., and Wielgus R. B.. 2019. “Factors Associated with Black Bear Density and Implications for Management.” Journal of Wildlife Management 83(7): 1527–1539. [Google Scholar]
  84. Wilckens, D. T. , Smith J. B., Tucker S. A., Thompson D. J., and Jenks J. A.. 2016. “Mountain Lion (Puma concolor) Feeding Behavior in the Little Missouri Badlands of North Dakota.” Journal of Mammalogy 97(2): 373–385. [Google Scholar]
  85. Woodroffe, R. 2000. “Predators and People: Using Human Densities to Interpret Declines of Large Carnivores.” Animal Conservation 3: 165–173. [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Appendix S1.

ECY-106-e70038-s001.pdf (725KB, pdf)

Data Availability Statement

Data and code (Robins et al., 2024) are available in Dryad at https://doi.org/10.5061/dryad.dv41ns249, with the exception of cougar telemetry relocation data that are sensitive data. The cougar telemetry relocation data are available to qualified researchers by contacting the Washington Department of Fish and Wildlife's Chief Wildlife Scientist and requesting cougar telemetry data from the West Cascades Cougar Project (2013–2017); a Washington Department of Fish and Wildlife data‐sharing agreement is required.

Articles from Ecology are provided here courtesy of Wiley

RESOURCES