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. 2026 Oct 5;88(10):e70212. doi: 10.1002/ajp.70212

Context‐Dependent Reactive Antipredator Behavior of Chacma Baboons (Papio ursinus griseipes) Amidst Predator Recovery

Sophia Van Cuylenborg 1,✉, Nicholas S Wright 1, Meredith S Palmer 2, Susana Carvalho 3,4, Kaitlyn M Gaynor 1,5,✉
PMCID: PMC13636610  PMID: 42831488

ABSTRACT

Predation is a driving force in prey ecology and evolution, and primates exhibit diverse antipredator strategies for minimizing risk. Because these behaviors can be costly, individuals balance costs and benefits when responding to perceived threats. The cognitive capacity and behavioral plasticity of baboons make them an ideal taxon for studying the context‐dependent variation in antipredator strategies. We used an autonomous, motion‐activated playback experiment to study the behavioral responses of chacma baboons (Papio ursinus griseipes) to simulated predator encounters in Gorongosa National Park, Mozambique. We compared responses in 2021, when predator densities were relatively low, to 2024, after predation increased due to lion (Panthera leo) population recovery and African wild dog (Lycaon pictus) reintroduction. We compared flight and vigilance responses to vocalizations of these common predators with responses to leopard (Panthera pardus), historically a key predator; spotted hyena (Crocuta crocuta), a rare predator; and cheetah (Acinonyx jubatus), absent historically and currently. We assessed how responses varied with habitat, age‐sex class, presence of offspring, and number of baboons in frame. Across 916 predator playbacks, baboons fled in 19% and displayed vigilance in 71%. When predator density was higher in 2024, baboons displayed weakened antipredator responses, possibly because baboons were more experienced with assessing risk, including risk associated with playback cues. Baboons were more likely to flee and more vigilant in response to leopard and wild dog cues. Juveniles fled more frequently than other demographics, while adult females with offspring were more vigilant. Responses were highly heterogeneous, reflecting behavioral flexibility characteristic of baboons.

Keywords: ABR, flight, landscape of fear, playback, predator reintroduction, vigilance

Chacma baboons exhibited context‐dependent flight and vigilance behavior in response to experimental predator vocalizations in Mozambique's Gorongosa National Park. Reactions were strongest in juveniles and in individuals with fewer otherbaboons in frame and in response to leopard and wild dog cues, and weakened with increased exposure to predators, demonstrating flexible risk assessment in a dynamic environment.

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Summary

  • We conducted a playback experiment using audio recordings of predator vocalizations, and evaluated chacma baboon flight and vigilance responses from videos recorded by a camera trap‐based Automated Behavioral Response (ABR) system.

  • Baboons displayed context‐dependent antipredator behavior, with greater flight and vigilance in response to leopard and wild dog cues. Flight and vigilance responses were stronger in the year with lower predator density, and stronger in juveniles and individuals with fewer neighbors.

  • Our study highlights the behavioral plasticity of these socially and cognitively complex primates in dynamic environments.

Abbreviations

ABR System

Automated Behavioral Response system

1. Introduction

Predation is the leading cause of mortality in many primate species and therefore plays a key role in regulating their populations (Hill et al. 2019; Preisser et al. 2005) and shaping their behavior across ecological and evolutionary time scales (Shultz et al. 2011). Primates display a wide range of antipredator strategies, and their advanced cognitive capacity confers a high degree of behavioral plasticity, allowing them to fine‐tune their responses in dynamic environments (Dall et al. 2005). These strategies can be divided into proactive behaviors that reduce the likelihood of encountering a predator and reactive behaviors utilized once predators are detected (Creel 2018). Reactive behaviors, which are performed later in the predation sequence, have particularly high stakes given the imminent threat of death. The nature and intensity of reactive antipredator responses performed can vary with many factors, including habitat structure, predator density and identity, and individual prey traits (Lemasson et al. 2009; Fornof et al. 2023). Reactive antipredator behaviors entail complex trade‐offs between lowering predation risk and incurring energetic or opportunity costs, such as reduced foraging and mating. Flight is a common but energetically demanding reactive response, involving rapid movement away from a perceived threat (Ydenberg and Dill 1986). Alternatively, prey can remain in place and engage in reactive vigilance, surveying their environment to assess relative danger (Lima 1992). Vigilance is less energetically costly than flight, but is associated with an opportunity cost, given trade‐offs with foraging (Brown 1999). Although both behaviors lower the probability of predation, their use can be prohibitively costly if predator encounters are frequent (Ferrari et al. 2009).

For prey facing risk from multiple predators, the strength of antipredator behavior towards a given species should vary with predator identity and hunting strategy, as well as experience with that predator over both evolutionary and ecological time scales (Preisser et al. 2007; Sih et al. 1998). Ambush predators typically elicit vigilance and alerting responses as prey try to eliminate the element of surprise, while cursorial (pursuit) predators usually provoke immediate flight responses (Palmer and Packer 2021). Prey that have coevolved with a particular predator often exhibit finely tuned responses to their specific hunting strategies. Given that predation imposes strong selective pressure on prey, prey should exhibit stronger perception of and response to risk from predators that historically accounted for a greater share of mortality. When predation risk relaxes on ecological time scales (e.g., following local predator extirpation), prey may nonetheless retain risk perception of their “ghosts of predators past” (Blumstein 2006). However, as maintaining unnecessary defenses is costly, antipredator behavior may weaken over time (Blumstein and Daniel 2005; Jolly et al. 2018). If re‐exposed to predators, naïve prey can rapidly learn to associate novel stimuli with risk, especially when the cues are paired with predation attempts or alarm calls of conspecifics or heterospecifics (Griffin et al. 2000). In social primates, individual learned responses can be transmitted within social groups or populations (Griffin and Evans 2003). Consequently, the expression of anti‐predator behavior reflects the interaction between evolutionary history and individual and social learning, producing variable responses across dynamic predation landscapes.

Demographic, social, and environmental characteristics also strongly mediate antipredator responses in primates. Juveniles are typically the most vulnerable age class because of their small size and inexperience (Hill et al. 2019). As a result, juveniles often show more intense antipredator responses or rely heavily on parental protection (Mohr et al. 2023). In sexually dimorphic species, sex differences in body size and physical condition further influence vulnerability to predation. Larger males are generally more conspicuous but better able to deter or escape predators, while smaller or reproductively encumbered females may face greater risk due to reduced mobility or energy reserves (Caro 2005; Pettorelli et al. 2011). Additionally, the number of nearby conspecifics can influence individual risk assessment: primates often increase vigilance when alone or in smaller groups, whereas in larger groups, the dilution and “many‐eyes” effects (shared vigilance) can reduce individual vigilance (Dehn 1990; Lehtonen and Jaatinen 2016; Pulliam 1973). However, the group size effect is often weak in primates (Allan and Hill 2018). Finally, habitat structure influences how primates respond to predators: the utility of vigilance may be greater in open habitats, where detection is easier, but risk may be higher in closed habitats where visibility is limited, thus necessitating greater vigilance (Lima and Dill 1990; Fornof et al. 2023). However, primates are often more adapted to arboreal environments than their predators (Lemasson et al. 2009), and trees may therefore provide a spatial refuge from terrestrial predators that are not available in open habitats.

Baboons offer a useful model for studying primate antipredator behavior. Baboons are highly terrestrial and are therefore vulnerable to a large suite of mammalian carnivores, including African wild dogs (Lycaon pictus), lions (Panthera leo), and spotted hyenas (Crocuta crocuta; Galán‐Acedo et al. 2019; Dzingwena et al. 2025; Hammond et al. 2025; van der Meer et al. 2019). Their primary predators, however, are leopards (Panthera pardus), which are unique among mammalian carnivores in their ability to hunt baboons both terrestrially and arboreally (Bidner et al. 2018). Within baboon groups, predation risk varies among individuals, with males often occupying riskier peripheral positions (Lehmann and Ross 2011; van Schaik et al. 2022). Further, baboons exhibit significant behavioral plasticity (Fehlmann et al. 2017) and cognitive capacity (Fagot and Cook 2006), traits that may aid them in responding quickly to shifts in predation risk via individual or group learning mechanisms (Hammond et al. 2022). Recent evidence suggests that baboons do not adjust their space use in response to predator activity at both coarse (van Rooyen et al. 2026a; Hammond et al. 2025) and fine (van Rooyen et al. 2026b) spatiotemporal scales, nor do they adjust diel activity patterns to minimize overlap with predators (Dzingwena et al. 2025). These authors suggest that baboons may rely more strongly on reactive behavior to deter attacks, including alarm calls, aggression, and mobbing (Kiffner et al. 2012).

Here, we investigated how predation risk, predator characteristics, and social and environmental context shape the flight and vigilance behavior of chacma baboons (Papio ursinus griseipes) in Gorongosa National Park, Mozambique. We conducted a playback experiment using Automated Behavioral Response (ABR) systems, which employ a camera trap to record baboon behavioral responses to automated audio playbacks of predator vocalizations and control sounds (Palmer et al. 2022; Smith et al. 2020; Suraci et al. 2017). ABRs have been used to study fear responses of ungulates to apex predators and megaherbivores (Fletcher et al. 2023; Rigoudy et al. 2022) and the impacts of humans on wildlife (Zeller et al. 2024). Further, they can address some of the major challenges in studying primate antipredator behavior, including the rarity of observing primate predation events (Lin et al. 2020) and the influence of human observers on the behavior of both primates and their predators (Nowak et al. 2014; Allan et al. 2020; Iredale et al. 2010). ABRs offer a promising answer to the call for an increased use of indirect methods to study primate‐predator interactions (LaBarge et al. 2020).

Gorongosa National Park provides a natural experiment for understanding the effects of predation on baboon behavior. Gorongosa's large carnivores historically included lions, wild dogs, leopards, and spotted hyenas. During Mozambique's civil conflict (1977–1992), all large carnivores except for lions were extirpated (Pringle and Gonçalves 2022), and the population of lions was greatly reduced (Bouley et al. 2018). The park has since reintroduced all historically present large carnivore species, and during the period of our study (2021 to 2024), populations of lions and wild dogs rose, and leopards and spotted hyenas were first reintroduced (Table S1). We evaluated responses of baboons to cues from each of Gorongosa's large carnivore species, as well as cheetah (Acinonyx jubatus), a novel large carnivore species that was never present in Gorongosa and rarely preys on baboons (Mutoro et al. 2022), as a control. Prior research suggests that predator activity does not affect baboon space use in Gorongosa (Hammond et al. 2025), and we were therefore interested in the role that reactive antipredator behavior might play in antipredator defense.

We hypothesize that baboon reactive antipredator behavior is influenced by several factors: (a) predator exposure (year as a proxy for relative predator density), (b) predator identity, (c) prey demographics, (d) number of baboons in frame, and e) habitat type. Baboons may exhibit stronger antipredator responses as exposure to predators increases between 2021 and 2024, if repeated experiences with predators have led to a heightened perception of risk in baboons. Alternatively, baboons may exhibit weaker antipredator responses in 2024, if risk reached chronically high levels such that frequent anti‐predator responses were too costly, or if baboons learned to more accurately assess risk cues (potentially, in the process, becoming more discerning in their response to simulated cues). We predicted that baboons would respond most strongly to cues from leopards, their primary predator over evolutionary history, given greater perceived risk, whereas they would respond less strongly to cues from cheetahs, as they are not a historically important predator of baboons and have been, and remain, absent from the study area.

Given that vulnerability should shape antipredator responses, we predicted that adult female baboons with offspring would exhibit stronger antipredator responses than other demographics, and that responses should be weaker when individuals are in larger groups. Finally, given that closed habitats offer more arboreal refugia from predators but also obscure visibility, we predicted stronger flight and vigilance in closed habitats relative to open habitats. While we were also interested in understanding interactions among these mediators of behavior (e.g., how the effects of habitat and year varied with predator identity), sample size constraints precluded these analyses, so we instead focused on evaluating the independent effects of ecological and social factors on reactive baboon antipredator responses.

2. Methods

2.1. Ethical Note

This study was approved by The University of British Columbia Animal Care Committee (A24‐0038) and by Princeton University's Institutional Animal Care and Use Committee (IACUC #2119F‐18). The research complied with the legal requirements of Mozambique, and a research permit was granted by Gorongosa National Park's Department of Scientific Services. All procedures adhered to the American Society of Primatologists' Principles for the Ethical Treatment of Non‐Human Primates.

2.2. Study System

Gorongosa National Park is a 4000 km2 protected area in central Mozambique, established in 1960 (Figure 1). The park's wildlife population was severely depressed during Mozambique's civil conflict, amidst rampant bushmeat hunting, with large carnivore populations particularly affected by both snaring and prey loss (Stalmans et al. 2019). Following the end of the conflict, and facing relaxed predation pressure, many non‐carnivore large mammal species recovered quickly (Stalmans et al. 2019). Currently, chacma baboons are among the most abundant mammal species in the park (Gaynor et al. 2021; Martinez et al. 2019; Carvalho et al. 2025), and there are now over 200 troops (Stalmans and Peel 2024). In recent years, the park has reintroduced the extirpated carnivore species (Table S1), beginning with the translocation of 45 African wild dogs starting in 2018 (Bouley et al. 2021; Pringle and Gonçalves 2022). Wild dogs had a minimum estimated population size of 130 in 2021, which increased to over 250 in 2024 (Table S1). In 2021, four leopards were introduced to the park (numbering seven in 2024), although their density was low enough that the baboons in our study may not have encountered them in either study year. In 2022, spotted hyena reintroduction began, with an estimated 23 individuals in the park as of 2024 (Table S1). Lion populations, which were extremely depressed post‐conflict (Bouley et al. 2018), were estimated to have a minimum population size of 130 in 2021, which increased to 245 in 2024 (Table S1).

Figure 1.

Figure 1

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Location of Gorongosa National Park in Mozambique (left) and location of Automated Behavioral Response (ABR) system deployments within the park (right). Each circle represents the location of an ABR site, colored by habitat. The size of the circle represents the total number of baboon videos captured at the site in both 2021 and 2024, with the shading representing the portion of detections from each study year.

2.3. Study Design

We set up the ABRs following the methodology in Palmer et al. (2022). Each system incorporated a Browning Strike Force Pro camera trap and a Freaklabs BoomBox (audio system; https://boomboxproject.org/). We programmed the soundboard with predator vocalizations from five mammalian species (leopard, lion, spotted hyena, cheetah, and wild dog) and control sounds from five avian species (the southern ground hornbill, Bucorvus leadbeateri; the African hoopoe, Upupa africana; laughing dove, Spilopelia senegalensis; African Scops owl, Otus senegalensis; and African barred owlet, Glaucidium capense). We generated a looping playlist of all sounds in random order, consisting of 30 predator cues and 8 avian cues. For each predator species, we included six unique representative vocalizations, such as growls and snarls, to capture acoustic variability. The chosen species represent the largest mammalian carnivores present in the park at the time of the study, as well as cheetahs, which represent a non‐native species never present in the study system. The avian control sounds allowed us to evaluate how much of the behavioral response was driven by the sudden loud sound, versus the predator cue specifically.

We edited all sounds for consistent amplitude and quality using Audacity (v. 2.1.0; Audacity Team 2014). Following Suraci et al. (2017), we standardized temporal properties by visually inspecting spectrograms and waveforms, and we used t‐tests to confirm that frequency characteristics (peak, minimum, maximum, range) did not differ significantly between predator and control groups. We adjusted the playbacks to a consistent peak sound pressure level of 78 dB at 1 m, measured with a Radioshack 33‐2055 Digital Sound Level Meter set to fast response and C‐weighting. Each video lasted 30 s, followed by a 30‐second rest period before another trigger could re‐activate the ABR system. Each device played a unique randomized playlist of all auditory cues, which were between 9 and 10 s in duration.

2.4. Data Collection

We deployed ABRs at 19 sites in Gorongosa National Park, with 10 in open habitats (floodplains, grasslands, or open savanna) and nine in closed habitats (woodlands or dense shrublands) (Figure 1). Carnivore GPS collars and regular monitoring by the Gorongosa Carnivore Project confirm that predators are active within the study area (Bouley et al. 2021), and all predator reintroductions took place within the study grid. The ABRs were each ~100 m from established camera trap sites that were part of a long‐term grid, and the sites were chosen based on prior animal detection rates to ensure adequate sample sizes (Gaynor et al. 2021). We deployed the ABRs in the same locations in two dry seasons: June–August 2021, when predator density was still low, and July–August 2024, when predator density had increased. Small sample sizes precluded formal assessment of spatial variation in predator activity (n = 9 lion detections and n = 2 wild dog detections throughout the study). Two of the sites were excluded in 2024 due to inaccessibility. To minimize habituation to audio cues from repeated exposure, each sampling period lasted 2–3 months.

There were a total of 1,462 ABR videos of chacma baboons (n = 967 from 2021 and n = 495 from 2024). Two ABR sites had far greater baboon detections (one open habitat site had 485 detections in 2021, and 120 in 2024; one closed habitat site had 109 detections in 2024) compared to the other sites (mean = 19 detections per study year). The large number of detections made manual annotation of all videos infeasible, so for these two sites, we selected a random subset of 88 videos, as this was the number of videos for the site with the third‐highest detections. We also excluded videos in which the auditory cue did not play properly. In total, we analyzed 914 ABR videos (n = 516 in 2021; n = 398 in 2024).

After developing an ethogram (Table S2; Figure 2), we classified behaviors frame‐by‐frame using the Computer Vision Analysis Tool (CVAT; Cvat.ai Corporation 2025). A single observer (S.V.C.) coded all videos. To minimize bias, we annotated videos without audio so that the observer did not know whether the video contained a control or predator sound. For videos featuring multiple individuals, we identified the focal subject as the individual closest to the camera, at the center of the frame—presumably the one who triggered the camera. We reviewed each video a second time with sound to record the species of predator or control audio cue, number of baboons (number of unique individuals appearing in the 30‐second video, including the focal individual), and prey demographic characteristics, including age (adult or juvenile), sex (male or female, for adults only), and presence of offspring (young juveniles that were carried by their mother).

Figure 2.

Figure 2

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Chacma baboon behavior exemplars in response to simulated predator audio cues from our ABR study in Gorongosa National Park. (A) vigilance (looking at ABR), (B) flight, and (C) non‐vigilant walking.

2.5. Data Analysis

Our two response variables of interest were (1) flight, a binary variable representing whether or not the baboon fled following the predator cue playback (at any point in the 30‐second video), and (2) vigilance, the proportion of non‐occluded frames in which the focal baboon was vigilant. Definitions of vigilance in the literature vary (Allan and Hill 2018), and for the purposes of this study we were interested in reactive vigilance elicited by the auditory playback, rather than any “looking” behavior. We thus coded vigilance into two categories: (1) looking at the ABR and (2) scanning (with movement of the head > 45°). Sensitivity analyses confirmed that both types of vigilance were explained by similar covariates (Figure S4), and we therefore chose to pool them to reduce sampling error and improve inference.

We modeled flight using generalized linear mixed models (GLMMs) with a binomial distribution, using all 914 videos (n = 776 with predator cues, and n = 138 with control cues). For vigilance, we used GLMMs with a beta distribution after transforming the proportion of vigilance using the Smithson & Verkuilen transformation (x′=x(n−1)+0.5n) to accommodate values of 0 and 1 (Smithson and Verkuilen 2006). For the vigilance analysis, we excluded videos in which the baboons were non‐occluded for < 2 s, including those in which they fled immediately (latency to flee = 0). The vigilance analysis, therefore, included a total of 568 videos (n = 475 with predator cues, and n = 93 with control cues).

To confirm that baboons were responding to information in the predator cue rather than only the sudden auditory stimulus itself, we first ran a single model for each response variable with a binary covariate representing whether the auditory cue was a control (avian vocalization) or treatment (predator vocalization) and included the ABR site as a random effect.

To test our hypotheses about the factors mediating variation in flight and vigilance, we ran models using only the predator cue videos. We specified predictors a priori based on our hypotheses, and made inference on a single global model. Fixed effects included predator species (cheetah, hyena, leopard, lion, or wild dog), year (2020 or 2024), habitat (open or closed), number of baboons in a video, and day of study. We included the day of study (number of days since the ABR was deployed in a given year) to account for potential effects of habituation to the cues over the course of the study. We centered and scaled the numerical covariates (number of baboons, day of study) prior to modeling. We also included the ABR site as a random effect in all models to account for site‐level variation and potential pseudoreplication arising from repeated sampling of the same individual or troop. We performed all analyses using R version 4.4.3. To determine the marginal effects of year, predator characteristics, prey demographics, and habitat on vigilance and flight probability, we used the ‘predict()’ function on the averaged model excluding random effects, holding other variables constant while varying one predictor of interest.

To explore the cumulative probability of flight as a function of time since cue playback, we used a Kaplan–Meier survival analysis and constructed survival curves. This approach estimates latency to flee while also accounting for censored observations (in which there was no flight observed in the video timeframe).

3. Results

Baboons fled from the predator auditory cues in 18% of trials (in contrast to 10% for the control cues), with a mean latency to flee of 1.14 s for the predator cues (standard deviation = 1.62 s) after the cue began to play (mean = 1.54 s, SD = 1.92 for control cues). Baboons were significantly more likely to flee in response to predator cues than to the avian control cues (beta = 0.707, SE = 0.300, p = 0.019).

Of the baboons that did not immediately flee from the predator cues and that remained in the field of view for > 2 s, 91% of them exhibited some amount of vigilance (vigilance was displayed in 71% of the predator cue trials overall). For the control cues, 78% of baboons that did not immediately flee were vigilant. In response to the predator playbacks, baboons were vigilant for a mean of 45% of the time that they were in frame (standard deviation = 30%). In contrast, baboons were vigilant for a mean of 36% of the time in response to control cues (standard deviation = 30%). Baboons were significantly more vigilant in response to predator cues relative to the control (beta = 0.508, SE = 0.133, p < 0.001).

For all of the videos in which baboons exhibited vigilance, 48% included the behavior “looking at ABR (stationary)” (with a mean of 12.7% time spent in this behavior across all videos included in vigilance analysis), 23% included “looking at ABR (rapid movement)” (mean = 5.3% time in this behavior), 0.9% included “looking at ABR (standing)” (mean = 0.3% time in this behavior), 28% included “scanning” (mean = 7% of time in this behavior), and 75% included “walking (vigilant)” (mean = 20% of time in this behavior), as described in the ethogram (Table S2; many videos included multiple types of vigilance).

3.1. Flight Predictors

The probability of flight in response to predator cues varied with environmental and social context (Figures 3A and 4, Figure S1). Juveniles were the most likely demographic to flee, and adult males and females with offspring were also slightly more likely to flee than adult females without offspring. Year was also an important predictor of flight: baboons were less likely to flee in 2024 than in 2021. There was also support for predator species as a predictor of flight, with the greatest flight responses elicited by wild dogs, followed by leopards, lions, hyenas, and cheetahs. More baboons in frame were associated with lower flight probability, although the effect was weak. There was weak evidence that baboons are more likely to flee in closed relative to open habitats. The day of study did not have an effect on flight. Overall, our model explained a low proportion of the variation in flight behavior, with a conditional R 2 value of 0.146.

Figure 3.

Figure 3

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Effects of the environmental, social, and demographic context on (A) flight and (B) vigilance behavior of baboons in response to simulated auditory predator cues in Gorongosa National Park, Mozambique. Each covariate is represented by a unique color. Error bars correspond to 95% CIs. For the categorical variables, the reference categories are Year = 2021, Species = Cheetah, Habitat = Open, Class = Adult female (without offspring). Positive beta coefficients correspond to a positive effect of that covariate (relative to the reference category, for categorical variables) on (A) probability of flight or (B) percent time vigilant, while negative coefficients correspond to a negative effect.

Figure 4.

Figure 4

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Marginal effects of the environmental, social, and demographic context on flight behavior of baboons in response to simulated auditory predator cues in Gorongosa National Park, Mozambique. For each covariate, all other covariates are kept at their reference levels (for categorical variables: Predator cue = Cheetah, Year = 2021, Habitat = Open, Age‐sex class = Adult F) or means (for continuous variables, Number of baboons and Day of study). Predictions are based on fixed effects only, with random (site) effects set to their mean. Gray bars and ribbons represent 95% confidence intervals.

Kaplan–Meier survival analysis indicated that flight is an immediate response when it occurs. Of the baboons that fled from predator cues, 89% did so within 2 s of cue initiation, with 66% fleeing within 1 s. The subsequent flattening of the curve suggests that the probability of flight declined as the elapsed time from the cue increased (Figure 5).

Figure 5.

Figure 5

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Kaplan–Meier survival curve showing the probability that baboons in Gorongosa National Park, Mozambique, fled with elapsed time since experimental predator audio cues. Shaded areas indicate 95% CIs.

3.2. Vigilance Predictors

The proportion of time spent vigilant in response to predator cues was also context‐dependent (Figures 3B and 6, Figure S2). The day of study was an important predictor of vigilance, suggesting that vigilance decreased over the study period each year. Vigilance reduced with the number of baboons in the frame. There was weak evidence that baboons were less vigilant in 2024 than in 2021. Age‐sex class explained some variation in vigilance, with female adults with offspring showing greater vigilance relative to the other classes. Habitat had a near‐zero effect on vigilance. Baboons exhibited slightly more vigilance in response to leopards and wild dogs than to cheetah, hyena, and lion. Overall, our model explained a moderate proportion of the variation in vigilance, with a conditional R 2 value of 0.364.

Figure 6.

Figure 6

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Marginal effects of the environmental, social, and demographic context on vigilance behavior of baboons in response to simulated auditory predator cues in Gorongosa National Park, Mozambique. For each covariate, all other covariates are kept at their reference levels (for categorical variables: Predator cue = Cheetah, Year = 2021, Habitat = Open, Age‐sex class = Adult F) or means (for continuous variables, Number of baboons and Day of study). Predictions are based on fixed effects only, with random (site) effects set to their mean. Gray bars and ribbons represent 95% confidence intervals.

4. Discussion

In response to simulated predator vocalizations, baboons in Gorongosa National Park exhibited heterogeneous antipredator responses that varied by predator identity, year, and environmental and social context. Overall, baboons were far more likely to exhibit vigilance than flight: 71% of baboons were vigilant after the onset of the cue, while 19% fled. Vigilance enables ongoing assessment of a threat under uncertainty, and such information‐gathering may be especially necessary when predator cues are novel, ambiguous, or rare (or in this case, when an auditory cue is decoupled from any visual or olfactory cues (Jones et al. 2024)). In contrast, flight is a decisive response to perceived immediacy or certainty of danger, and when baboons fled, they typically did so immediately.

Predator‐dependent antipredator responses suggest that predators are not a functionally interchangeable trophic level, but instill varying levels of perceived risk to which prey respond accordingly. This idea of a “hierarchy of fear” echoes findings of a recent ungulate ABR study (Rigoudy et al. 2022). Baboons were most likely to flee and exhibited the greatest vigilance in response to cues from wild dogs, which comprise the fastest‐growing predator population in Gorongosa (Bouley et al. 2021; Table S1). The heightened response to wild dogs may be caused by their recent and rapid increase in abundance, given that their cursorial hunting mode and infrequent predation on baboons would suggest a weaker antipredator response. While there have been no confirmed predation events of wild dogs on baboons in Gorongosa, baboon DNA has been detected in multiple wild dog fecal samples (M. Hutchinson, personal communication), suggesting the possibility that they feed on baboons. Baboons also showed relatively strong flight responses and were more vigilant in response to leopards, the primary predators of baboons across ecosystems (Cowlishaw 1994). Given the low leopard density during the study, this antipredator behavior may reflect a hardwired evolutionary response. Baboons also fled more in response to lions, the other common predator in the park and the only large carnivore that was never locally extirpated (i.e., continuous experience with this predator species; Bouley et al. 2018). As predicted, baboons responded most weakly to cheetah cues, likely because this population of baboons lacks both recent and long‐term experience with cheetahs as a predator, and cheetahs do not pose a significant threat to baboons (Anderson et al. 2006; Tinley 1977). Thus, the pattern of responses observed across predator species supports the idea that both experience and evolutionary history may contribute to variation in perceived predation risk and subsequent behavioral responses.

Baboons fled less frequently in 2024 than in 2021, despite increases in predator densities in the intervening years. They were also slightly less vigilant, although this effect size was very small. Reduced antipredator behavior in 2024 may reflect a shift towards strategic risk tolerance, where baboons weigh the relative value of immediate safety against the long‐term needs of energy acquisition and rest (Lima 1998). These findings echo previous studies on the ability of baboons to adjust to the dynamic and rapidly changing environment of Gorongosa, including evidence of plasticity in spatial behavior in response to both seasonal flooding (Lewis‐Bevan et al. 2025) and extreme weather events (Beardmore‐Herd et al. 2025). Given that the experiment was deployed at the same sites in both years, it is also possible that baboons habituated to the playback cues in 2021 and were therefore less likely to associate them with risk in 2024. Thus, the weaker responses observed in 2024 may reflect a combination of changes in perceived predation risk and learning about the experimental cues, which our experiment design cannot distinguish. This finding highlights an important consideration for autonomous playback experiments, as repeated exposure to experimental cues may alter patterns of risk perception and response, especially in cognitively advanced species like primates.

Individual demographic characteristics influenced the strength of baboon antipredator behaviors. Juveniles were most likely to flee and were the most vigilant, consistent with their disproportionately high predation risk given their size and lack of experience recognizing and assessing risk (Pettorelli et al. 2011). Adult females with offspring, which face a higher risk and cost of predation, were also more vigilant, consistent with patterns observed in other primates (Liu et al. 2024). The least likely demographic to flee was adult females, who have the most experience with assessing predation risk in the troop's home range, and may be less likely to equate auditory cues with risk in the absence of other cues (e.g., visual and olfactory cues, alarm calls).

Both flight and vigilance decreased when there were more baboons in the field of view of the video (suggesting larger troop size). With more conspecifics nearby, baboons may be less likely to flee and more likely to collectively mob a predator, given that mobbing is an effective defense strategy (Crofoot 2013). Although the “many eyes” effect is common among prey species (Beauchamp et al. 2021), its evidence in primates is mixed, as vigilance is also heavily shaped by social monitoring and group dynamics (Gaynor and Cords 2012; Allan et al. 2024). Notably, we were limited by our ability to count only the number of individuals that were in frame, which may not accurately reflect the actual group size or number of neighbors, as chacma baboon troops can range from < 10 to > 150 individuals (Hammond et al. 2025). We also found weak evidence that baboons were more likely to flee in closed habitats than in open habitats as predicted, and field observations suggest that baboons often take advantage of refuge in trees to flee vertically. However, there was no effect of habitat type on vigilance, and trade‐offs between visibility and concealment may minimize the effect of habitat on vigilance (Camp et al. 2013).

Behavioral responses are complex and difficult to predict (Crane et al. 2024), and much of the variation in baboon reactive antipredator behavior remains unexplained by the variables we examined. Vigilance was moderately explained by the covariates in our model, and flight was only weakly explained, with relatively small effect sizes for each of the covariates that we investigated. Our findings demonstrate a high degree of variation in antipredator responses, likely arising from differences across individual animals and environmental contexts (Cooper et al. 2015). While we were unable to identify individual animals in our study, individual variation in risk responses may be shaped by prior experience, social dynamics, or behavioral syndromes, including boldness (Sih et al. 2004). Future research should assess the role of individual variation alongside environmental context in shaping antipredator behavior.

Our study used recordings of predator vocalizations to simulate predator presence, which, while effective in eliciting behavioral responses from prey, is not a perfect proxy for an actual encounter. While baboons responded more strongly to predator cues, they also exhibited flight and vigilance in response to avian control sounds, suggesting that they may be reacting to the sudden and unexpected sound rather than to the perceived presence of a predator. Furthermore, baboons exhibited neither flight nor vigilance in 9% of predator simulation trials, suggesting that these individuals did not associate the cues with a high level of risk. Habituation is also a potential concern with studies involving simulated predator cues, such as playback experiments, given that the cues are not reinforced by actual predator encounters. We found that vigilance decreased over the course of the study, although there was no effect of day on flight probability. However, this finding highlights that habituation may weaken effect sizes in experimental studies of antipredator behavior in response to artificial predator cues. We encourage future researchers to explicitly account for habituation in their analyses and minimize the duration of the experiment to the extent possible.

Furthermore, acoustic cues alone do not capture the full range of sensory information prey use to assess risk, including visual or olfactory signals (Munoz and Blumstein 2012). Additionally, ambush predators like lions and leopards rely on stealth, and thus rarely vocalize when hunting. However, carnivore vocalizations for territorial advertisement, social communication, or intimidation still signal the presence of a nearby predator (Hettena et al. 2014). Future studies could incorporate multimodal cues (e.g., visual, olfactory, auditory) to better capture the range of sensory information prey use to assess predation risk (Zuberbühler 2007; Jordan and Ryan 2015). While observations of real predator‐prey encounters are necessary to fully understand the nature of reactive antipredator behavior in the wild, simulations remain useful for elucidating context‐dependent animal reactions to sensory stimuli at greater scale.

This work contributes to an understanding of how baboons, as socially and cognitively complex prey species, balance competing ecological pressures and adapt to changing predator landscapes. Antipredator behavior is modulated by the factors explored in this study—learned responses, predator identity, prey demographics, and habitat—and other unexplored factors (Lemasson et al. 2009). This work moves us along the path towards a better understanding of the complex drivers of antipredator behavior. Studying these behaviors provides critical insights into the role predators play in shaping the evolution of cognition, sociality, and life history strategies in primates.

Supporting information

Supporting File

AJP-88-e70212-s001.docx (5.1MB, docx)

Acknowledgments

This research was made possible by the permission of the Mozambican government and the support of Gorongosa National Park, particularly P. Muagura, M. Stalmans, and M. Lajas. We are grateful to the many rangers who offered their protection and expertise in the field. C.A. Wang and J. Plucinski at Freaklabs built the BoomBox Automated Behavioral Response system and helped us troubleshoot technical difficulties. S. Sun helped assemble and program the ABRs, and A. Annersten assisted with data quality control. M. Angela provided data on carnivore populations in the park. Funding for this work came from an NSERC Discovery Grant (RPGIN‐2022‐03096) to KMG, a Sloan Research Fellowship (FG‐2024‐21781) to KMG, an NSF Postdoctoral Research Fellowship in Biology (Award No. 1810586) to MSP, and an Animal Behavior Society Student Research Grant to NSW. This work is part of the AI and Biodiversity Change (ABC) Global Climate Center, which is supported by the US National Science Foundation award No. 2330423 and Natural Sciences and Engineering Research Council of Canada award No. 585136. We thank the Gaynor Lab (UBC) and Pringle Lab (Princeton) for their feedback and support, particularly R. Pringle. We would like to acknowledge that the University of British Columbia, where much of this work took place, is located on the unceded traditional territories of the xʷməθkʷəy̓əm (Musqueam), Sḵwx̱wú7mesh (Squamish), and səlilwətaɬ (Tsleil‐Waututh) Nations.

Contributor Information

Sophia Van Cuylenborg, Email: sophievc@student.ubc.ca, Email: sophievc@shaw.ca.

Kaitlyn M. Gaynor, Email: kaitlyn.gaynor@ubc.ca.

Data Availability Statement

Data and code used for analyses can be found on Zenodo: https://doi.org/10.5281/zenodo.23021446.

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Associated Data

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

Supplementary Materials

Supporting File

AJP-88-e70212-s001.docx (5.1MB, docx)

Data Availability Statement

Data and code used for analyses can be found on Zenodo: https://doi.org/10.5281/zenodo.23021446.

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