
==== Front
PNAS Nexus
PNAS Nexus
pnasnexus
PNAS Nexus
2752-6542
Oxford University Press US

10.1093/pnasnexus/pgae382
pgae382
Review
AcademicSubjects/MED00010
AcademicSubjects/SCI00010
AcademicSubjects/SOC00010
PNAS_Nexus/eco
Large mammal behavioral defenses induced by the cues of human predation
https://orcid.org/0000-0001-9177-4420
Slovikosky Sandy A Department of Biology, University of Oxford, 11a Mansfield Road, Oxford OX1 3SZ, United Kingdom

https://orcid.org/0000-0001-5894-0589
Montgomery Robert A Department of Biology, University of Oxford, 11a Mansfield Road, Oxford OX1 3SZ, United Kingdom

O'Connell James Editor
To whom correspondence should be addressed: Email: sandy.slovikosky@jesus.ox.ac.uk
Competing Interest: The authors declare no competing interests.

9 2024
03 9 2024
03 9 2024
3 9 pgae38213 9 2024
© The Author(s) 2024. Published by Oxford University Press on behalf of National Academy of Sciences.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.

Abstract

Large mammals respond to human hunting via proactive and reactive responses, which can induce subsequent nonconsumptive effects (NCEs). Thus, there is evidence that large mammals exhibit considerable behavioral plasticity in response to human hunting risk. Currently, however, it is unclear which cues of human hunting large mammals may be responding to. We conducted a literature review to quantify the large mammal behavioral responses induced by the cues of human hunting. We detected 106 studies published between 1978 and 2022 of which 34 (32%) included at least one measure of cue, typically visual (n = 26 of 106, 25%) or auditory (n = 11 of 106, 10%). Space use (n = 37 of 106, 35%) and flight (n = 31 of 106, 29%) were the most common behavioral responses studied. Among the 34 studies that assessed at least one cue, six (18%) measured large mammal behavioral responses in relation to proxies of human hunting (e.g. hunting site or season). Only 14% (n = 15 of 106) of the studies quantified an NCE associated with an animal's response to human hunting. Moreover, the association between cues measured and antipredator behaviors is unclear due to a consistent lack of controls. Thus, while human hunting can shape animal populations via consumptive effects, the cues triggering these responses are poorly understood. There hence remains a need to link cues, responses, NCEs, and the dynamics of large mammal populations. Human activities can then be adjusted accordingly to prevent both overexploitation and unintended NCEs in animal populations.

behavioral response
cue
human hunting
large mammals
nonconsumptive effect
UK Research and Innovation 10.13039/100014013 EP/Y03614X/1
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pmcIntroduction

Predators control prey populations via both consumptive and predation-risk, or nonconsumptive, effects (1–6). Consumptive effects (CEs) refer to direct lethal offtake that occurs when predators kill and consume prey individuals (7–10). In contrast, predation-risk effects encompass all behavioral, morphological, and life-history responses to predators (3, 4, 11). They also include the resulting fitness consequences experienced by prey, and the impact these changes have across species and trophic levels (4, 12–14). There are four broad classes of predation-risk effects: behavioral responses to reduce likelihood of encountering a predator or depredation once encountered (risk-induced trait response), fitness costs resulting from these responses (nonconsumptive effect; hereafter “NCE”), changes in interactions between the prey and another species (interaction modification), and resultant cascading impacts on that third species or the broader community (trait-mediated indirect effect) (4, 11, 13–19). Risk-induced trait responses (hereafter “antipredator response”) are the most common and evident effects of predators in large mammals (6). These responses are triggered upon detection of cues of predation risk whether they are visual, auditory, or olfactory (20–23). The antipredator responses of prey are influenced by the distribution and intensity of these cues, which in turn vary by predator hunting mode (21, 23, 24).

Throughout the animal kingdom, there are generally three predator hunting modes including active, sit-and-pursue, and sit-and-wait (23, 25, 26). Active hunting is employed by predators that are constantly moving in search of prey, sit-and-pursue predators wait for prey to pass and subsequently follow over short distances, and sit-and-wait predators capture prey only once they are within striking distance (21, 23, 26, 27). These cues are most diffused in the active hunting mode given that these predators seldom remain in one place for extended periods of time (21, 23, 27). Conversely, cues from sit-and-pursue and sit-and-wait predators are more concentrated because the predator stays in one location waiting for a target to pass (23, 27, 28). Consequently, the more sedentary hunting modes (i.e. sit-and-wait, sit-and-pursue) are most likely to elicit “reactive” responses (e.g. fleeing, freezing, and fighting) in prey given that the cues are indicative of imminent risk. In contrast, prey is expected to respond to active predators via “proactive” responses (e.g. changes in space use, movement, and group size) as cues provide less reliable indicators of immediate risk (6, 23, 26, 29, 30). According to the control of risk framework, the risk of an active predator would be expected to induce nutritional and energetic costs as prey forgo foraging opportunities to decrease their likelihood of encountering a predator (31–34). Sit-and-pursue and sit-and-wait predators would impose mostly stress-mediated costs in prey due to a rapid response upon detection of a cue (31, 35–37). Combined with CEs, the NCEs emerging from these antipredator decisions could shape animal populations by lowering growth rate, recruitment, and survival (16, 19, 38–40). While much attention has been given to how nonhuman predators affect prey populations via both CEs and NCEs, less is known about how humans may impact animal populations via NCEs (6, 18).

Human cues come in different forms and vary in the degree to which they might induce antipredator responses within an animal population. Traits evolve in populations when they vary across individuals, are heritable, and result in fitness differences (41). The evolution of inducible defenses, in turn, requires four additional conditions (42): (ⅰ) The threat must vary in strength across space and time, (ⅱ) The sensory cue must accurately reflect the threat, hence triggering a response, (ⅲ) The prey response must reduce likelihood of predation, and (ⅳ) That response must carry a cost that would otherwise have been avoided. There is evidence that certain animals have stronger behavioral responses to human hunting than natural predators (43, 44). Certain forms of human hunting are also usually predictable in time and space where laws are enforced based on hunting proxies, causing animals to quickly learn to associate cues with predation risk (45). Animals likely escalate their antipredator strategies in the face of human expansion into natural areas because anthropogenic cues signaling risk become more prevalent (46–49). This increase in antipredator strategies could manifest in heightened costs and subsequent consequences at the ecosystem-scale (6, 20, 23, 40, 47, 50).

Large mammals, specifically Ungulata, Carnivora, and Proboscidea, respond to nonhuman predators based on visual, auditory, and olfactory cues (28, 51–56). Scent however is likely the most important sensory modality for these taxa as predators often conceal themselves before approaching prey (57–59). Ungulates, carnivores, and proboscides are also heavily hunted by humans, comparatively intelligent, and long-lived (60–62). Therefore, there is reason to expect that they should demonstrate behavioral plasticity in response to hunting (18, 63). Yet with regard to human predators, a species with the highest exploitation rates on the planet (64), it is unknown which cues induce proactive and reactive responses and associated nutritional, energetic, reproductive, or survival costs. Prey frequently uses multiple senses to detect predators, further complicating linking antipredator behaviors to a given visual, auditory, or olfactory cue (65, 66). If the nature, strength, and cause of an NCE are unknown, reductions in reproduction and survival will be attributed to other causes, such as food supply (16). There is good reason to believe that animal responses to humans may also carry costs given the rates at which humans predate and disturb nonhuman species (sensu Frid and Dill (63)). Human hunting modes parallel those used by natural predators, albeit with more sophisticated tools (64, 67, 68). Hunting dogs are a characteristic example of the active hunting mode, whereas the two sedentary modes are characterized by traps or waiting in ambush for a prey item to pass (67). Guns can be employed across all three modes. Although human hunting is associated with novel cues to which animals were not exposed throughout their evolutionary history (e.g. traps, spears, and guns), antipredator responses and associated costs are likely similar as those used in response to natural predators based on the degree to which cues are diffused and represent imminent risk (20, 47, 63). Yet these questions cannot be adequately addressed because the cues that induce large mammal behavioral plasticity to human hunting are unclear.

Awareness of what cues trigger an antipredator response is vital to quantifying the degree to which humans shape animal populations nonconsumptively as well as consumptively (46, 47, 64, 69, 70). Thus, we conducted a literature review to document cues of human hunting, associated proactive and reactive responses of large mammals, and affiliated costs of these responses. Based on our findings, we emphasize the need to link animal behavioral responses with sensory cues, as this knowledge will help clarify how nonhuman species perceive anthropogenic disturbances. Human activities can then be adjusted accordingly to prevent both overexploitation and unintended NCEs in animal populations where desired.

Results

Taxa and geographic distribution

Ungulata species were the most common research subjects among this literature occurring in 72% (n = 76 of 106) of the studies. The next most common were Carnivora species (n = 26 of 106, 25%) followed by Proboscidea (n = 9 of 106, ∼8%). Cervids, notably various deer species, moose (Alces alces), and elk (Cervus elaphus), were assessed in 44% (n = 47 of 106) of studies, whereas bovids were included in 25 (24%). Bears were the most common carnivorans, occurring in nine studies (∼8%). Most research was positioned in Europe (n = 36 of 106, 34%), Africa (n = 24 of 106, 23%), and North America (n = 31 of 106, 29%), composing 86% of the literature (Fig. 1). Asia (n = 10 of 106, 9%) and South America (n = 5 of 106, ∼5%) were less represented, and no studies among this literature originated from Australia.

Fig. 1. The geographic distribution of studies extracted from a literature review of 106 peer-reviewed studies measuring proactive and reactive responses of large mammals to cues of human hunting from 1978 to 2022. Basemap: Esri, GEBCO, Garmin, TomTom, FAO, NOAA, USGS (71).

Cues measured and antipredator responses

Via our literature review, we retained 106 studies that met our search criteria (Dataset S1), of which 32% (n = 34 of 106) measured at least one cue of human predation on large mammals (Fig. 2). The most common cues measured among this literature were visual occurring among 25% (n = 26 of the 106 studies). These cues were most notably represented by an observer approaching the focal animal or herd (n = 22 of 106, 21%). The next most common cues were acoustic (n = 11 of 106, 10%) and measured via playbacks of humans speaking (n = 10 of 106, 9%) and dogs barking (n = 10 of 106, 9%). There were two studies (∼2%) that considered olfactory cues via scent from a motionless human. Nine studies (∼8%) used vehicle presence as a cue test (visual), and two of these (∼2%) included another measure of cue in assessing reactions to a vehicle engine (auditory). However, of the 34 studies that measured a cue, only six (n = 6 of 106, ∼6%) also drew comparisons between proxies representing different degrees of hunting presence or intensity (Table 1). The remaining studies that included a measure of cue only provided descriptive statistics of behaviors in the region overall, or modeled behavioral differences based on variables that were unaffiliated with hunting (e.g. sex, age, and group size). Space use, flight, movement, and diel patterns were the behaviors that were most often quantified in relation to human hunting risk (Fig. 2). Fifteen studies (14%) measured some form of cost associated with the response, most notably nutritional (n = 13 of 106, 12%), followed by survival (n = 2 of 106, ∼2%) and reproductive (n = 1 of 106, ∼1%) and energetic (n = 1 of 106, ∼1%). No study measured costs resulting from responses to a cue between hunting proxies.

Fig. 2. The count of studies measuring a given behavioral response from a literature review of 106 peer-reviewed studies assessing proactive and reactive responses of large mammals to cues of human hunting from 1978 to 2022. The sizes of the circles are based on numbers of studies: 1–10 (small), 11–20 (medium), and greater than 20 (large). The fraction above each icon represents the proportion of studies that included at least one measure of cue, also indicated as shading in the circle. All icons obtained from Pixabay.com.

Table 1. Summary of six studies that assessed differences in large mammal responses to a cue between hunting proxies.

Hunting proxy	Cue	Response measured	Response significant?	Description	Reference	
Time spent in protected area, time since entering protected area	Visual	Flight	Mixed	Elephants were less likely to react to a vehicle with increased poaching pressure. Time spent in the protected area was negatively associated with reaction index, and time since entering the protected area had no effect.	Goldenberg et al. (72)	
Hunting area, hunting season	Visual	Flight	Yes	Three species of African ungulates fled more quickly upon encountering a human on foot in hunting vs. a no-hunting area. Flight initiation distance was longer in the hunting season.	Muposhi et al. (73)	
Hunting area	Visual	Flight	Yes	Two species of African ungulates were more likely to exhibit extreme flight responses upon encountering a vehicle in a hunting vs. a no-hunting area.	Ndiweni et al. (74)	
Distance to protected area, areas differing in protection/conservation status	Visual	Flight, vigilance	Mixed	For guanacos, distance to protected area had no effect on likelihood of vigilance or flight upon sighting a vehicle. Flight and vigilance behaviors were amplified in less-protected areas.	Puig et al. (75)	
Areas differing in protection/conservation status	Visual	Flight, vigilance	Yes	Impala displayed longer flight initiation distances and heightened vigilance in a partially protected vs. a protected area. This occurred in the presence of both a vehicle and an approaching human.	Setsaas et al. (76)	
Areas differing in protection/conservation status	Visual	Flight, deterrence signals	Mixed	Four species of African mammals displayed heightened flight and deterrence signal reactions to humans sitting in a vehicle within nonprotected compared to fully protected areas. Three other species demonstrated no effect.	Kiffner et al. (77)	
Results are from a literature review of 106 peer-reviewed studies measuring proactive and reactive responses of large mammals to cues of human hunting from 1978 to 2022.

Hunting proxies and study methodologies

There were 76 studies (72%) that compared responses between hunting proxies, of which hunting season and hunting intensity were the most common (Fig. 3). Tracking large mammals via GPS or radio telemetry was most implemented (n = 43 of 106, 41%), followed by observation (n = 34 of 106, 32%), camera traps (n = 23 of 106, 22%), and large mammal signs (n = 7 of 106, ∼7%). Three studies (∼3%) used fecal samples to measure physiological responses, and one (∼1%) used a biologger.

Fig. 3. The count of studies using a given cue or proxy of human hunting from a literature review of 106 peer-reviewed studies measuring proactive and reactive responses of large mammals to cues of human hunting from 1978 to 2022. Hunting intensity is typically measured in number of hunter detections or harvest rates. The distance metric indicates distances to either safety (e.g. prohibited hunting areas) or danger (e.g. hunter access points). Protected area metrics largely refer to degree of protection. Some studies included multiple measures of cue or hunting proxy, hence the total count listed here is >106.

Discussion

Large mammals respond to human hunting in a diversity of ways. Proactive and reactive responses reduce the likelihood of either encountering a predator or avoiding capture once detected (3, 11). These behaviors come at a cost, although not all are substantial enough to reduce an individual's health or alter a population's long-term dynamics (78–80). The intensity of these behaviors and resultant NCEs likely depend on the nature and concentration of predatory cues, including those of human predators (23, 67). We reviewed 106 studies that assessed large mammal behavioral plasticity in relation to human hunting and found that very few measured the cue that triggered responses. Although 76 studies (72%) compared responses between hunting proxies, few measured the sensory cue to which animals actually responded. Of the six studies that did include a measure of cue between proxies, each one detected a significant effect of hunting on the behavioral plasticity of at least one species. Hunting, therefore, likely varied spatiotemporally in presence or degree (i.e. the proxies used indeed represented different levels of threat), and the visual cues approximated that threat (20, 42). The behaviors measured in these six studies were all reactive, meaning that they occurred in response to encountering a human rather than beforehand. Thus, there is a need to link cues with changes in proactive responses. None of the six studies determined whether these reactive responses resulted in nutritional or energetic consequences substantial enough to impact individual health or broader population dynamics (6, 40).

A visual, auditory, or olfactory hunting cue may evoke an antipredator response and associated NCE in a prey individual (24, 38, 52, 81). However, it is difficult to quantify the strength of these behaviors without drawing comparisons across times or places that differ in hunting pressure. Variations in proactive and reactive responses can only be linked to measured cues when those cues represent different levels of risk depending on the context (47). There were six studies that included both a measure of cue and compared animal responses to that cue between hunting proxies. We refer to a “proxy” as a measure of hunting presence or intensity (e.g. hunting vs. no-hunting season or site; Montgomery et al. (18)), across which cues might differ in the degree to which they represent risk. Of these, three detected no reactions to human hunting. For instance, some mammals demonstrated no difference in vigilance, flight, or deterrence signals in relation to conservation status or distance to protected area (75, 77). This result could be due to illegal poaching occurring within protected areas as well as outside (82). Thus, in these cases the threat might not vary sufficiently in time and space (40, 83), failing to meet one of the conditions required for inducible defenses to evolve (40, 42, 83). Alternatively, animals might not detect a difference in threat because their perception does not match reality (20, 47, 84). This mismatch can occur when anthropogenic cues of risk are unreliable, representing both benign and lethal activities (47, 70, 85, 86). Hence, animals might perceive protected and unprotected areas as equally risky even though lethal offtake differs between them, resulting in no detectable differences in response. Future work should quantify variation in antipredator strategies based on perceived versus actual risk (sensu Goumas et al. (20)). These experimental designs could assess animal responses to various cues (e.g. one human acting as a poacher and another as a tourist) between hunting proxies (see Papworth et al. (87)). Animals might also accurately judge the level of threat but choose not to respond to mitigate potential costs or obtain certain benefits (47, 88, 89).

There is evidence that animals can distinguish among threats, an ability which is often learned over time (20, 47). Variations in responses to cues representing different levels of risk support this conclusion. Auditory playbacks provide an ideal setup to test this assumption, as animals frequently responded to hearing a human voice but were less responsive upon hearing a dog barking or a natural sound (13, 90–92). Future work could consider other auditory playbacks more representative of human lethality (e.g. gunshots) to further assess which factors trigger a reaction. Of those studies included in our review, species were also generally more reactive to a human approaching than the presence of a vehicle, suggesting that animals perceived the presence of the former as a greater threat (93–95). A person on foot could resemble an approaching predator (95, 96), although humans in these studies did not employ behaviors specific to hunters (e.g. carrying a weapon and approaching stealthily, see Papworth et al. (87)). Vehicles might not be as threatening given that humans don’t often hunt from within a truck or car, obscuring the association between threat and cue (95, 97, 98). Hence the ability of an animal to associate a cue with danger, and respond accordingly, will depend on whether a perceived threat followed detection of the cue in previous encounters (47). However, in many studies it is unclear to which degree wildlife responded to a visual stimulus as opposed to scent. Olfaction is one of the most common senses used by vertebrate prey to initially detect and avoid a threat (57, 99), and thus responses to humans or vehicles could very well be attributed to scent rather than sight or sound. Nevertheless, the influence of olfaction could also be minimal given that humans were already within sighting distance of the animals when beginning their approach. Future experimental designs should thus link cues and behaviors, as well as assess additive effects, by comparing responses to single (e.g. only visual or auditory) vs. paired cues (visual and auditory together). These responses should be captured by remote video to minimize the influence of confounding factors, most notably scent (91, 92).

Antipredator responses may be accompanied by nutritional, energetic, reproductive, or survival costs (6, 31). However these are challenging to quantify, especially over long periods of time (78, 100). Thus the impact of NCEs at the population level is still largely unknown (78). Of the 106 studies in our review, 91 (86%) did not measure the cost of large mammal behavioral responses to human hunting, although those that did primarily focused on nutritional costs. Developing methods for quantifying NCEs is an important area of future research, and depends on disentangling the impact of NCEs from those of CEs in systems with natural feedbacks (18, 67, 78, 101). Consequently, it is unclear whether these prospective costs are substantial enough to influence recruitment and survival, which age groups are most affected, how the implications compare to those resulting from lethal offtake, and subsequently whether these NCEs must be considered in management schemes (6, 16, 46, 78). Data on energetic, reproductive, and survival costs are too sparse to draw conclusions at this point, and the findings on nutritional costs are inconclusive. For instance, 13 studies in our review assessed foraging costs in response to auditory cues. Badgers (Meles meles), white-tailed deer (Odocoileus virginianus), and pumas (Puma concolor) demonstrated lower feeding time, heightened latency, and fewer visits within controlled plots in response to playbacks of a human voice (13, 91, 102, 103), although human scent in the vicinity and on the equipment might also have influenced the responses. These cues also solely depicted a human speaking, which could be a source of disturbance alone (i.e. indicative of nonlethal human activity). An individual might also simply choose an alternative location to forage, with no subsequent health consequences. One study did quantify variation in elk body fat resulting from differential space use across phases of the hunting season, finding that individuals who avoided high-risk roads had lower fat reserves at the onset of winter (104). Other times a cost is evident although indirectly tied to human activity, e.g. moose (Alces alces) did not alter their space use between the hunting and no-hunting seasons, although calf growth was higher in grasslands where there was also heightened vulnerability of being killed by hunters (105). Moreover, costs might only be detected at one spatiotemporal scale (106). Elk adjusted their migration patterns in response to the hunting season, resulting in decreased access to ideal forage (107), and white-tailed deer compensated for decreased selection of anthropogenic food sources during the day, when hunting intensity was strongest, by increasing selection at night (108). Moreover, the degree to which an NCE influences an individual's health depends on the strength of the antipredator response, and by extension the concentration of cues evoking the response (23). Olfactory cues might elicit a stronger reaction than visual or auditory cues based on their ability to disperse over wide distances and remain in one place for extended periods of time (109–111). However no study assessed whether olfactory cues induced an NCE, a feat that could be accomplished via long-term monitoring of prey exposed to predator scent in predator-free enclosures (54, 57). These results all demonstrate the complexity of linking cues, responses, costs, and population dynamics, with implications for sustainable management (46, 78).

Frid and Dill (63) postulated that animal responses to human hunting and disturbance should be analogous to those of natural predators. Future research should assess behavioral responses of large mammals in relation to specific human hunting modes, which parallel those used by nonhuman species (67). While it is evident that human hunting induces fear in animals, sometimes to a greater degree than natural predators, there is still an open question regarding whether responses of animals to anthropogenic pressures are adaptive changes that have evolved over time, or mere behavioral plasticity (18, 112, 113). Of the four conditions required for inducible defenses to evolve, perhaps the most uncertain factors relate to whether the sensory cue accurately depict the threat and if the animal responses to that risk are costly (42). The threats of human hunting are expected to vary across space and time when restrictions surrounding lethal human activities are enforced (e.g. hunting vs. no-hunting site or season) (45, 114–116). However, it is difficult to assess whether and how animals perceive these cues (20, 47, 115). The challenge of linking cues to responses will become more substantive in a changing environment, where novel cues are continuously introduced into animal habitats and not always clearly tied to the activity from an animal's perspective (47). Moreover, while responses to a threat might be effective via broad- or fine-scale spatiotemporal avoidance of lethal activity, the magnitude of prospective costs is unclear (43, 68, 78, 104, 117). Although every change in behavior comes with a tradeoff, the question is rather whether such costs are substantive enough to impact long-term growth, recruitment, and survival (6, 78). We recommend that future research seek to quantify the specific cues that animals respond to, and implement experimental studies that integrate long-term monitoring of individual health, population demography, and environmental factors so as to quantify the NCEs that might emerge from these responses.

Attributing observed behaviors to measured cues is challenging due to a consistent lack of controls in experimental designs. Most studies in our review assessed responses to either a human approaching or auditory playbacks. Human approaches typically occurred after spotting the target species from a vehicle or walking transects, whereas auditory playbacks were paired with video recording devices (13, 90, 92, 118, 119). Reactions to auditory playbacks can therefore reasonably be linked to the cue measured because the influence of other cues, namely visual or olfactory, caused by direct human presence is minimal assuming measures are taken to reduce anthropogenic scent on the recording devices. Moreover, natural sounds are commonly used as a standard of comparison (90, 103). The use of approaching or motionless humans as visual cues, however, does not rule out the influence of scent, one of the most important sensory modalities for vertebrates (99). Therefore, within our review there is a wide range of confidence regarding whether mammals were indeed responding to the cue measured. Future experimental trials could use motionless human dummies lacking scent (visual), or alternatively assess wildlife responses to various odors (olfactory). Implementing controls such as dummy prey or natural scents would solidify the link between any observed behaviors and the cue measured.

Evaluating how animals respond to lethal human cues carries important implications for conservation (46, 55). Visual and auditory cues may be used to deter animal within contexts leading to conflict, although any tactics should be used sparingly, or in conjunction with a painful physical cue, to avoid habituation (96, 120–122). Olfactory cues could also be used for effective management given the variety of information they convey, as well as the range at which vertebrates detect them and respond accordingly (99, 123, 124). Fear that results from spatial and temporal variation in risk can condition animals to avoid areas where their presence is undesired (45, 83, 125–127). Moreover, for threatened species, NCEs could be prevented by reducing the prevalence or concentration of human cues that trigger an antipredator response. Future work will then need to consider whether the intended results show at the population level (40, 45, 67). Behaviors and associated fitness costs can also be insignificant, with trivial impacts on an individual, emphasizing the need to determine which cues and conditions evoke a response substantial enough to reduce an animal's long-term health (3, 128, 129). Predators, specifically humans, can have more substantial impacts on prey behavior and abundance than abiotic features, with ecosystem-scale consequences (13, 30, 68, 128, 130). Hence exploring how antipredator responses and NCEs vary by type and strength of cue will provide a more complete picture of how human hunting shapes animal populations (23, 67, 131, 132).

Materials and methods

Literature review

We conducted a literature review (completed in November 2023) to assess the extent to which cues are measured in studies quantifying proactive and reactive responses of large mammals to human hunting. To execute this search, we interrogated the Web of Science Core Collection using the following terms: (large carnivore* OR carnivor* OR ungulat* OR large herbivore* OR elephant*) AND (human* OR anthropogenic) AND (predat* OR hunt* OR poach* OR kill* OR cull* OR harvest* OR super predat*) AND (risk effect* OR predation risk OR risk of predation OR nonlethal OR nonlethal OR nonconsumptive OR trait-mediated OR behaviorally-mediated OR landscape of fear OR ecology of fear OR antipredator OR antipredator OR inducible defense*). Our initial search returned 929 peer-reviewed results, including those classified as “article” and “early access.” We read all papers and excluded those that assessed: (ⅰ) species other than large mammals (i.e. those outside of the orders Ungulata, Carnivora, or Proboscidea), (ⅱ) responses to livestock depredation or crop-raiding deterrents, (ⅲ) human disturbance (e.g. roads, human settlements, nonlethal wildlife recreation) without any clear indication of hunting, and (ⅳ) cue tests (e.g. observer approaching a herd) in study areas without any evidence of legal or illegal hunting or where observers intentionally imitated tourists. We then read each remaining study and quantified the: (ⅰ) cue used to represent human hunting risk, (ⅱ) whether a proxy of human hunting was measured, and (ⅲ) ways in which animals responded to that risk (Table 2). We identified cues measured based on descriptions provided by the authors. Cues were recorded as visual when wildlife behaviors were observed in response to stationary or moving humans or vehicles. Auditory cues were measured using playbacks caught on remote video, and twice by assessing responses to vehicle engines. We recorded olfaction as a cue measured on two occasions based on notes by the author that wildlife responded to scent from stationary humans.

Table 2. Data extracted from a literature review of 106 peer-reviewed studies, published between 1978 and 2022, measuring proactive and reactive responses of large mammals to cues of human hunting.

Data	Definition	Examples (not exhaustive)	
Explanatory variable	Proxy of hunting	Hunting site, hunting season, hunting intensity, distance to protected area	
Measure of cue (1/0)	Was the factor that elicited the response measured?	1,0	
Cue	Nature of the cue	Dog barking, human speaking, vehicle moving	
Cue type	Classification of the cue	Auditory, visual, olfactory	
Response variable	Behavioral or physiological response of prey	Occupancy, space use, vigilance, foraging	
Technique	Method to measure response	Camera traps, GPS collars, observation, wildlife signs	
Cost	Cost of response	Diet quality, feeding time, energy expenditure	
Cost type	Classification of the cost	Nutritional, energetic	

Supplementary Material

pgae382_Supplementary_Data

Acknowledgments

We thank H. Kasozi for his inputs during the data collection phase of this work. We also thank C. Darimont and one anonymous reviewer for their feedback on this manuscript.

Supplementary Material

Supplementary material is available at PNAS Nexus online.

Funding

This research was supported by the UK Research and Innovation Grant EP/Y03614X/1.

Author Contributions

S.A.S. and R.A.M. conceived and designed the study. S.A.S. conducted the literature review and drafted the initial manuscript. S.A.S. and R.A.M. edited the manuscript. Both authors read and approved the final manuscript.
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References

1 Abrams  PA . 2000. The evolution of predator-prey interactions: theory and evidence. Annu Rev Ecol Syst. 31 :79–105.
2 Grange  S, Duncan  P. 2006. Bottom-up and top-down processes in African ungulate communities: resources and predation acting on the relative abundance of zebra and grazing bovids. Ecography. 29 :899–907.
3 Lima  SL . 1998. Nonlethal effects in the ecology of predator-prey interactions. Bioscience. 48 :25–34.
4 Peacor  SD, Barton  BT, Kimbro  DL, Sih  A, Sheriff  MJ. 2020. A framework and standardized terminology to facilitate the study of predation-risk effects. Ecology. 101 :e03152.32736416
5 Proffitt  KM, Cunningham  JA, Hamlin  KL, Garrott  RA. 2014. Bottom-up and top-down influences on pregnancy rates and recruitment of northern Yellowstone elk. J Wildl Manage. 78 :1383–1393.
6 Say-Sallaz  E, Chamaillé-Jammes  S, Fritz  H, Valeix  M. 2019. Non-consumptive effects of predation in large terrestrial mammals: mapping our knowledge and revealing the tip of the iceberg. Biol Conserv. 235 :36–52.
7 Estes  JA, et al  2011. Trophic downgrading of planet earth. Science. 333 :301–306.21764740
8 Hairston  NG, Smith  FE, Slobodkin  LB. 1960. Community structure, population control, and competition. Am Nat. 94 :421–425.
9 Ling  SD, et al  2015. Global regime shift dynamics of catastrophic sea urchin overgrazing. Philos Trans R Soc Lond B Biol Sci. 370 :20130269.
10 O’Donoghue  M, Boutin  S, Krebs  CJ, Hofer  EJ. 1997. Numerical responses of coyotes and lynx to the snowshoe hare cycle. Oikos. 80 :150.
11 Lima  SL, Dill  LM. 1990. Behavioral decisions made under the risk of predation: a review and prospectus. Can J Zool. 68 :619–640.
12 Anholt  BR, Werner  E, Skelly  DK. 2000. Effect of food and predators on the activity of four larval ranid frogs. Ecology. 81 :3509–3521.
13 Suraci  JP, Clinchy  M, Zanette  LY, Wilmers  CC. 2019. Fear of humans as apex predators has landscape-scale impacts from mountain lions to mice. Ecol Lett. 22 :1578–1586.31313436
14 Werner  EE, Peacor  SD. 2003. A review of trait-mediated indirect interactions in ecological communities. Ecology. 84 :1083–1100.
15 Abrams  PA . 1983. Arguments in favor of higher order interactions. Am Nat. 121 :887–891.
16 Creel  S, Christianson  D. 2008. Relationships between direct predation and risk effects. Trends Ecol Evol. 23 :194–201.18308423
17 Matassa  CM, Trussell  GC. 2011. Landscape of fear influences the relative importance of consumptive and nonconsumptive predator effects. Ecology. 92 :2258–2266.22352165
18 Montgomery  RA, Macdonald  DW, Hayward  MW. 2020. The inducible defences of large mammals to human lethality. Funct Ecol. 34 :2426–2441.
19 Preisser  EL, Bolnick  DI, Benard  MF. 2005. Scared to death? The effects of intimidation and consumption in predator-prey interactions. Ecology. 86 :501–509.
20 Goumas  M, Lee  VE, Boogert  NJ, Kelley  LA, Thornton  A. 2020. The role of animal cognition in human-wildlife interactions. Front Psychol. 11 :589978.33250826
21 Miller  JRB, Ament  JM, Schmitz  OJ. 2014. Fear on the move: predator hunting mode predicts variation in prey mortality and plasticity in prey spatial response. J Anim Ecol. 83 :214–222.24028410
22 Petranka  JW, Kats  LB, Sih  A. 1987. Predator-prey interactions among fish and larval amphibians: use of chemical cues to detect predatory fish. Anim Behav. 35 :420–425.
23 Preisser  EL, Orrock  JL, Schmitz  OJ. 2007. Predator hunting mode and habitat domain alter nonconsumptive effects in predator–prey interactions. Ecology. 88 :2744–2751.18051642
24 Palmer  MS, Packer  C. 2021. Reactive anti-predator behavioral strategy shaped by predator characteristics. PLoS One. 16 :e0256147.34407141
25 Huey  RB, Pianka  ER. 1981. Ecological consequences of foraging mode. Ecology. 62 :991–999.
26 Schoener  TW . 1971. Theory of feeding strategies. Annu Rev Ecol Syst. 2 :369–404.
27 Schmitz  OJ . 2008. Effects of predator hunting mode on grassland ecosystem function. Science. 319 :952–954.18276890
28 Wikenros  C, Kuijper  DPJ, Behnke  R, Schmidt  K. 2015. Behavioural responses of ungulates to indirect cues of an ambush predator. Behaviour. 152 :1019–1040.
29 Schmitz  OJ . 2007. Predator diversity and trophic interactions. Ecology. 88 :2415–2426.18027743
30 Thaker  M, et al  2011. Minimizing predation risk in a landscape of multiple predators: effects on the spatial distribution of African ungulates. Ecology. 92 :398–407.21618919
31 Creel  S . 2018. The control of risk hypothesis: reactive vs. proactive antipredator responses and stress-mediated vs. food-mediated costs of response. Ecol Lett. 21 :947–956.29744982
32 Dröge  E, Creel  S, Becker  MS, M'soka  J. 2017. Risky times and risky places interact to affect prey behaviour. Nat Ecol Evol. 1 :1123–1128.29046564
33 Hernández  L, Laundré  JW. 2005. Foraging in the ‘landscape of fear’ and its implications for habitat use and diet quality of elk Cervus elaphus and bison Bison bison. Wild Biol. 11 :215–220.
34 Sinclair  ARE, Arcese  P. 1995. Population consequences of predation-sensitive foraging: the Serengeti wildebeest. Ecology. 76 :882–891.
35 Boonstra  R, Hik  D, Singleton  GR, Tinnikov  A. 1998. The impact of predator-induced stress on the snowshoe hare cycle. Ecol Monogr. 68 :371–394.
36 Hammerschlag  N, et al  2017. Physiological stress responses to natural variation in predation risk: evidence from white sharks and seals. Ecology. 98 :3199–3210.29193090
37 Dulude-de Broin  F, Hamel  S, Mastromonaco  GF, Côté  SD. 2020. Predation risk and mountain goat reproduction: evidence for stress-induced breeding suppression in a wild ungulate. Funct Ecol. 34 :1003–1014.
38 Bourbeau-Lemieux  A, Festa-Bianchet  M, Gaillard  J-M, Pelletier  F. 2011. Predator-driven component Allee effects in a wild ungulate. Ecol Lett. 14 :358–363.21320261
39 Creel  S, Christianson  D, Liley  S, Winnie  JA. 2007. Predation risk affects reproductive physiology and demography of elk. Science. 315 :960.17303746
40 Gaynor  KM, Brown  JS, Middleton  AD, Power  ME, Brashares  JS. 2019. Landscapes of fear: spatial patterns of risk perception and response. Trends Ecol Evol. 34 :355–368.30745252
41 Endler  J . 1986. Natural selection in the wild. Princeton University Press.
42 Tollrian  R, Harvell  CD. 1999. The ecology and evolution of inducible defenses. Princeton University Press.
43 Ciuti  S, et al  2012. Effects of humans on behaviour of wildlife exceed those of natural predators in a landscape of fear. PLoS One. 7 :e50611.23226330
44 Proffitt  KM, Grigg  JL, Hamlin  KL, Garrott  RA. 2009. Contrasting effects of wolves and human hunters on elk behavioral responses to predation risk. J Wildl Manage. 73 :345–356.
45 Cromsigt  JPGM, et al  2013. Hunting for fear: innovating management of human–wildlife conflicts. J Appl Ecol. 50 :544–549.
46 Gaynor  KM, et al  2021. An applied ecology of fear framework: linking theory to conservation practice. Anim Conserv. 24 :308–321.
47 Smith  JA, Gaynor  KM, Suraci  JP. 2021. Mismatch between risk and response may amplify lethal and non-lethal effects of humans on wild animal populations. Front Ecol Evol. 9 :604973.
48 Tablado  Z, Jenni  L. 2017. Determinants of uncertainty in wildlife responses to human disturbance. Biol Rev Camb Philos Soc. 92 :216–233.26467755
49 Wevers  J, Fattebert  J, Casaer  J, Artois  T, Beenaerts  N. 2020. Trading fear for food in the Anthropocene: how ungulates cope with human disturbance in a multi-use, suburban ecosystem. Sci Total Environ. 741 :140369.32610236
50 Symes  LB, Martinson  SJ, Kernan  CE, Ter Hofstede  HM. 2020. Sheep in wolves’ clothing: prey rely on proactive defences when predator and non-predator cues are similar. Proc Biol Sci. 287 :20201212.32842929
51 Atkins  R, et al  2016. Deep evolutionary experience explains mammalian responses to predators. Behav Ecol Sociobiol. 70 :1755–1763.
52 Cremona  T, Crowther  MS, Webb  JK. 2014. Variation of prey responses to cues from a mesopredator and an apex predator. Austral Ecol. 39 :749–754.
53 Fletcher  RJ, et al  2023. Frightened of giants: fear responses to elephants approach that of predators. Biol Lett. 19 :20230202.37817576
54 Fležar  U, et al  2019. Simulated elephant-induced habitat changes can create dynamic landscapes of fear. Biol Conserv. 237 :267–279.
55 Harrison  ND, et al  2023. Identifying the most effective behavioural assays and predator cues for quantifying anti-predator responses in mammals: a systematic review. Environ Evid. 12 :5.
56 Sunde  P, et al  2022. Mammal responses to predator scents across multiple study areas. Ecosphere. 13 :e4215.
57 Apfelbach  R, Blanchard  CD, Blanchard  RJ, Hayes  RA, McGregor  IS. 2005. The effects of predator odors in mammalian prey species: a review of field and laboratory studies. Neurosci Biobehav Rev. 29 :1123–1144.16085312
58 Pembury Smith  MQR, Ruxton  GD. 2020. Camouflage in predators. Biol Rev Camb Philos Soc. 95 :1325–1340.32410297
59 Parsons  MH, et al  2018. Biologically meaningful scents: a framework for understanding predator–prey research across disciplines. Biol Rev Camb Philos Soc. 93 :98–114.28444848
60 Benítez-López  A, Santini  L, Schipper  AM, Busana  M, Huijbregts  MAJ. 2019. Intact but empty forests? Patterns of hunting-induced mammal defaunation in the tropics. PLoS Biol. 17 :e3000247.31086365
61 Collins  C, Kays  R. 2011. Causes of mortality in North American populations of large and medium-sized mammals: causes of mortality in mammals. Anim Conserv. 14 :474–483.
62 Fa  JE, Brown  D. 2009. Impacts of hunting on mammals in African tropical moist forests: a review and synthesis. Mamm Rev. 39 :231–264.
63 Frid  A, Dill  LM. 2002. Human-caused disturbance stimuli as a form of predation risk. Conserv Ecol. 6 :11.
64 Darimont  CT, Fox  CH, Bryan  HM, Reimchen  TE. 2015. The unique ecology of human predators. Science. 349 :858–860.26293961
65 Weissburg  M, Smee  DL, Ferner  MC. 2014. The sensory ecology of nonconsumptive predator effects. Am Nat. 184 :141–157.25058276
66 Fischer  S, Oberhummer  E, Cunha-Saraiva  F, Gerber  N, Taborsky  B. 2017. Smell or vision? The use of different sensory modalities in predator discrimination. Behav Ecol Sociobiol. 71 :143.28989227
67 Montgomery  RA, et al  2022. The hunting modes of human predation and potential nonconsumptive effects on animal populations. Biol Conserv. 265 :109398.
68 Oriol-Cotterill  A, Valeix  M, Frank  LG, Riginos  C, Macdonald  DW. 2015. Landscapes of coexistence for terrestrial carnivores: the ecological consequences of being downgraded from ultimate to penultimate predator by humans. Oikos. 124 :1263–1273.
69 Moll  RJ, et al  2017. The many faces of fear: a synthesis of the methodological variation in characterizing predation risk. J Anim Ecol. 86 :749–765.28390066
70 Nisi  AC, Benson  JF, Wilmers  CC. 2022. Puma responses to unreliable human cues suggest an ecological trap in a fragmented landscape. Oikos. 2022 :e09051.
71 Esri, GEBCO, Garmin, TomTom, FAO, NOAA, USGS . 2023. Ocean Basemap. Esri. https://www.arcgis.com
72 Goldenberg  SZ, Douglas-Hamilton  I, Daballen  D, Wittemyer  G. 2017. Challenges of using behavior to monitor anthropogenic impacts on wildlife: a case study on illegal killing of African elephants. Anim Conserv. 20 :215–224.
73 Muposhi  VK, Gandiwa  E, Makuza  SM, Bartels  P. 2016. Trophy hunting and perceived risk in closed ecosystems: flight behaviour of three gregarious African ungulates in a semi-arid tropical savanna. Austral Ecol. 41 :809–818.
74 Ndiweni  T, Zisadza-Gandiwa  P, Ncube  H, Mashapa  C, Gandiwa  E. 2015. Vigilance behavior and population density of common large herbivores in a southern African savanna. JAPS. 25 :876–883.
75 Puig  S, Videla  F, Rosi  MI. 2023. Influence of human activities, social and environmental variables on the behavior of guanacos in Southern Andean Precordillera (Argentina). Stud Neotrop Fauna Environ. 58 :462–475.
76 Setsaas  TH, Holmern  T, Mwakalebe  G, Stokke  S, Røskaft  E. 2007. How does human exploitation affect impala populations in protected and partially protected areas? A case study from the Serengeti Ecosystem, Tanzania. Biol Conserv. 136 :563–570.
77 Kiffner  C, et al  2014. Interspecific variation in large mammal responses to human observers along a conservation gradient with variable hunting pressure. Anim Conserv. 17 :603–612.
78 Sheriff  MJ, Peacor  SD, Hawlena  D, Thaker  M. 2020. Non-consumptive predator effects on prey population size: a dearth of evidence. J Anim Ecol. 89 :1302–1316.32215909
79 Middleton  AD, et al  2013. Linking anti-predator behaviour to prey demography reveals limited risk effects of an actively hunting large carnivore. Ecol Lett. 16 :1023–1030.23750905
80 Peacor  SD, et al  2022. A skewed literature: few studies evaluate the contribution of predation-risk effects to natural field patterns. Ecol Lett. 25 :2048–2061.35925978
81 McComb  K, Shannon  G, Sayialel  KN, Moss  C. 2014. Elephants can determine ethnicity, gender, and age from acoustic cues in human voices. Proc Natl Acad Sci U S A. 111 :5433–5438.24616492
82 Hilborn  R, et al  2006. Effective enforcement in a conservation area. Science. 314 :1266–1266.17124316
83 Palmer  MS, et al  2022. Dynamic landscapes of fear: understanding spatiotemporal risk. Trends Ecol Evol. 37 :911–925.35817684
84 Sih  A, Ferrari  MCO, Harris  DJ. 2011. Evolution and behavioural responses to human-induced rapid environmental change. Evol Appl. 4 :367–387.25567979
85 Crane  AL, Feyten  LEA, Preagola  AA, Ferrari  MCO, Brown  GE. 2024. Uncertainty about predation risk: a conceptual review. Biol Rev Camb Philos Soc. 99 :238–252.37839808
86 Kays  R, et al  2017. Does hunting or hiking affect wildlife communities in protected areas?  J Appl Ecol. 54 :242–252.
87 Papworth  S, Milner-Gulland  EJ, Slocombe  K. 2013. Hunted woolly monkeys (Lagothrix poeppigii) show threat-sensitive responses to human presence. PLoS One. 8 :e62000.23614003
88 Berger  J . 2007. Fear, human shields and the redistribution of prey and predators in protected areas. Biol Lett. 3 :620–623.17925272
89 St Clair  CC, et al  2019. Animal learning may contribute to both problems and solutions for wildlife–train collisions. Philos Trans R Soc Lond B Biol Sci. 374 :20180050.31352891
90 Bhardwaj  M, et al  2022. Inducing fear using acoustic stimuli—a behavioral experiment on moose (Alces alces) in Sweden. Ecol Evol. 12 :e9492.36407905
91 Clinchy  M, et al  2016. Fear of the human “super predator” far exceeds the fear of large carnivores in a model mesocarnivore. Behav Ecol. 27 :1826–1832.
92 Suraci  JP, Smith  JA, Clinchy  M, Zanette  LY, Wilmers  CC. 2019. Humans, but not their dogs, displace pumas from their kills: an experimental approach. Sci Rep. 9 :12214.31434976
93 Blank  DA . 2018. The use of tail-flagging and white rump-patch in alarm behavior of goitered gazelles. Behav Processes. 151 :44–53.29526811
94 Brown  CL, et al  2012. The effect of human activities and their associated noise on ungulate behavior. PLoS One. 7 :e40505.22808175
95 Stankowich  T . 2008. Ungulate flight responses to human disturbance: a review and meta-analysis. Biol Conserv. 141 :2159–2173.
96 Kloppers  EL, St Clair  CC, Hurd  TE. 2005. Predator-resembling aversive conditioning for managing habituated wildlife. Ecol Soc. 10 :art31.
97 Colman  JE, Jacobsen  BW, Reimers  E. 2001. Summer response distances of Svalbard reindeer Rangifer tarandus platyrhynchus to provocation by humans on foot. Wildl Biol. 7 :275–283.
98 Marino  A, Johnson  A. 2012. Behavioural response of free-ranging guanacos (Lama guanicoe) to land-use change: habituation to motorised vehicles in a recently created reserve. Wildl Res. 39 :503.
99 Stoddart  DM . 1980. The ecology of vertebrate olfaction. Springer Dordrecht.
100 Preisser  EL, Bolnick  DI, Grabowski  JH. 2009. Resource dynamics influence the strength of non-consumptive predator effects on prey. Ecol Lett. 12 :315–323.19243407
101 Gill  JA, Norris  K, Sutherland  WJ. 2001. Why behavioural responses may not reflect the population consequences of human disturbance. Biol Conserv. 97 :265–268.
102 Crawford  DA, Conner  LM, Clinchy  M, Zanette  LY, Cherry  MJ. 2022. Prey tells, large herbivores fear the human ‘super predator’. Oecologia. 198 :91–98.34981219
103 Smith  JA, et al  2017. Fear of the human ‘super predator’ reduces feeding time in large carnivores. Proc Biol Sci. 284 :20170433.28637855
104 Spitz  DB, et al  2019. Behavioral changes and nutritional consequences to elk (Cervus canadensis) avoiding perceived risk from human hunters. Ecosphere. 10 :e02864.
105 Ofstad  EG, et al  2020. Opposing fitness consequences of habitat use in a harvested moose population. J Anim Ecol. 89 :1701–1710.32220065
106 Dröge  E, et al  2019. Response of wildebeest (Connochaetes taurinus) movements to spatial variation in long term risks from a complete predator guild. Biol Conserv. 233 :139–151.
107 Mikle  NL, Graves  TA, Olexa  EM. 2019. To forage or flee: lessons from an elk migration near a protected area. Ecosphere. 10 :e02693.
108 Henderson  CB, Demarais  S, Strickland  BK, McKinley  WT, Street  GM. 2023. Temporal effects of relative hunter activity on adult male white-tailed deer habitat use. Wildlife Res. 51 :WR22145.
109 Marin  AC, Schaefer  AT, Ackels  T. 2021. Spatial information from the odour environment in mammalian olfaction. Cell Tissue Res. 383 :473–483.33515294
110 Bytheway  JP, Carthey  AJR, Banks  PB. 2013. Risk vs. reward: how predators and prey respond to aging olfactory cues. Behav Ecol Sociobiol. 67 :715–725.
111 Cablk  ME, Sagebiel  JC, Heaton  JS, Valentin  C. 2008. Olfaction-based detection distance: a quantitative analysis of how far away dogs recognize tortoise odor and follow it to source. Sensors (Basel). 8 :2208–2222.27879818
112 Fox  RJ, Donelson  JM, Schunter  C, Ravasi  T, Gaitán-Espitia  JD. 2019. Beyond buying time: the role of plasticity in phenotypic adaptation to rapid environmental change. Philos Trans R Soc Lond B Biol Sci. 374 :20180174.30966962
113 Gotthard  K, Nylin  S, Nylin  S. 1995. Adaptive plasticity and plasticity as an adaptation: a selective review of plasticity in animal morphology and life history. Oikos. 74 :3.
114 Ausilio  G, et al  2022. Environmental and anthropogenic features mediate risk from human hunters and wolves for moose. Ecosphere. 13 :e4323.
115 Palmer  MS, Gaynor  KM, Abraham  JO, Pringle  RM. 2023. The role of humans in dynamic landscapes of fear. Trends Ecol Evol. 38 :217–218.36586766
116 Parsons  AW, Wikelski  M, Keeves Von Wolff  B, Dodel  J, Kays  R. 2022. Intensive hunting changes human-wildlife relationships. PeerJ. 10 :e14159.36248718
117 Bonnot  N, et al  2013. Habitat use under predation risk: hunting, roads and human dwellings influence the spatial behaviour of roe deer. Eur J Wildl Res. 59 :185–193.
118 Ciuti  S, Pipia  A, Ghiandai  F, Grignolio  S, Apollonio  M. 2008. The key role of lamb presence in affecting flight response in Sardinian mouflon (Ovis orientalis musimon). Behav Processes. 77 :408–412.18029113
119 Caro  TM . 1994. Ungulate antipredator behaviour: preliminary and comparative data from African bovids. Behaviour. 128 :189–228.
120 Found  R, Kloppers  EL, Hurd  TE, St Clair  CC. 2018. Intermediate frequency of aversive conditioning best restores wariness in habituated elk (Cervus canadensis). PLoS One. 13 :e0199216.29940021
121 Smith  ME, Linnell  JDC, Odden  J, Swenson  JE. 2000. Review of methods to reduce livestock depredation II. Aversive conditioning, deterrents and repellents. Acta Agric Scand A Anim Sci. 50 :304–315.
122 Walter  WD, et al  2010. Management of damage by elk (Cervus elaphus) in North America: a review. Wildl Res. 37 :630.
123 Price  C, McArthur  C, Norbury  G, Banks  P. 2022. Olfactory misinformation: creating “fake news” to reduce problem foraging by wildlife. Front Ecol Environ. 20 :531–538.
124 Elmer  LK, et al  2021. Exploiting common senses: sensory ecology meets wildlife conservation and management. Conserv Physiol. 9 :coab002.33815799
125 Creel  S, Winnie  JA, Christianson  D, Liley  S. 2008. Time and space in general models of antipredator response: tests with wolves and elk. Anim Behav. 76 :1139–1146.
126 Creel  S, Winnie  J, Maxwell  B, Hamlin  K, Creel  M. 2005. Elk alter habitat selection as an antipredator response to wolves. Ecology. 86 :3387–3397.
127 Ferrari  MCO, Sih  A, Chivers  DP. 2009. The paradox of risk allocation: a review and prospectus. Anim Behav. 78 :579–585.
128 Owen-Smith  N . 2019. Ramifying effects of the risk of predation on African multi-predator, multi-prey large-mammal assemblages and the conservation implications. Biol Conserv. 232 :51–58.
129 Tyack  PL, et al  2022. Managing the effects of multiple stressors on wildlife populations in their ecosystems: developing a cumulative risk approach. Proc Biol Sci. 289 :20222058.36448280
130 Burgos  T, et al  2023. Top-down and bottom-up effects modulate species co-existence in a context of top predator restoration. Sci Rep. 13 :4170.36914804
131 Peers  MJL, et al  2018. Quantifying fear effects on prey demography in nature. Ecology. 99 :1716–1723.29897623
132 Prugh  LR, et al  2019. Designing studies of predation risk for improved inference in carnivore-ungulate systems. Biol Conserv. 232 :194–207.
