Sleep is an important and understudied facet of animal lives, with every species, from honey bees to humans, allocating a portion of each day to this period of rest (Cirelli and Tononi, 2008). The universality of sleep reflects its central role in important physiological processes, including memory consolidation, support of the central nervous system, energy conservation, and physical restoration (Chowdhury and Shafer, 2020; Gangwisch, 2014; Stickgold, 2005; Vyazovskiy, 2015). Accordingly, failure to meet daily sleep demand has health consequences (Basner et al., 2013), with potentially fatal repercussions of extreme sleep deprivation (Rechtschaffen and Bergmann, 2002). The physiological need for sleep is emphasized by its homeostatic control – after periods of insufficient sleep or extreme physical exertion, individuals experience particularly long and intense bouts of sleep (Kitamura et al., 2016). Decades of sleep research have consistently implicated homeostasis as a primary determinant of sleep patterns, such that homeostatic regulation has become an important criterion in the very definition of sleep (Siegel, 2008).

A strong focus on studying sleep in the laboratory or at the bedside, although revealing much about the physiology of sleep, has inherently overlooked the ecological pressures that drive the regulation and evolution of sleep (Rattenborg et al., 2017; Reinhardt, 2020). In the natural world, the significance of sleep extends beyond its direct physiological impacts. Sleeping animals typically cannot engage in other behaviors that are important to their survival (but see Lyamin et al., 2008; Lyamin et al., 2018; Rattenborg et al., 1999; Rattenborg et al., 2016), and investing in sleep when environmental forces render vigilance and activity particularly important may impose substantial costs to wild animals. In addition to preventing animals from foraging, searching for mating opportunities, defending territories, and caring for young, sleep leaves animals in a state of extreme inattention, and thus highly vulnerable to their predators (Lima et al., 2005). The evolution of sleep and its manifestation in the wild may therefore be driven by a complex balance between the physiological need for sleep and ecological costs imposed on sleeping animals.

For gregarious animals, the balance between the costs and benefits of sleep may be further modulated by the social environment. However, even the most basic aspects of sleeping with conspecifics, such as whether the social context facilitates or constrains sleep, remain unknown (Karamihalev et al., 2019). Sleeping in a social context could alter the costs of sleep – the sentinel hypothesis suggests that staggering the timing of sleep bouts in a group can collectively maintain both high-quality sleep and high levels of antipredator vigilance (Samson et al., 2017; Snyder, 1966). Sleeping in a group may therefore facilitate an individual’s ability to fulfill its physiological sleep requirement by reducing the risk of doing so. Alternatively, social dynamics may actually inhibit investment in sleep. Sleep may present social opportunity costs, causing individuals to sacrifice sleep in order to spend more time actively engaging with group-mates. Additionally, proximity to group-mates may cause sleep disruptions initiated by short periods of wakeful activity of neighboring individuals. Cascading disruptions could then lead to collective dynamics of sleep, such as the waves of wakefulness that have been documented in flocks of gulls (Beauchamp, 2009; Beauchamp, 2011). Thus, sleeping in close proximity to conspecifics may potentially be accompanied by both costs and benefits for an individual’s ability to obtain sufficient sleep, and discovering how these potential costs and benefits are actually realized will shed light on the forces that have guided sleep adaptations in social animals.

To understand how group-living animals navigate trade-offs between their physiological need for sleep and the ecological and social pressures that shape the costs and benefits associated with this biological imperative, we investigated the factors shaping sleep patterns of wild olive baboons (Papio anubis). Baboons live in stable multi-male, multi-female groups of up to 100 individuals (Cheney and Seyfarth, 2008), and during the night, they seek safety in trees and rock outcroppings (Altmann and Altmann, 1970; Busse, 1980). Despite seeking refuge in these sleep sites, baboons remain particularly vulnerable to nighttime predation from leopards, which represents the single largest source of mortality for adult baboons (Cheney et al., 2004; Cowlishaw, 1994; Isbell et al., 2018). Baboons must therefore navigate the trade-off between investing in sleep and maintaining antipredator vigilance. As a highly gregarious animal whose fitness depends heavily on their social relationships (Silk et al., 2009), baboons must also balance their time spent sleeping with their investment in social interactions as time constraints during the day limit their ability to build and maintain their relationships (Dunbar, 1992).

We simultaneously tracked the activity of 26 wild olive baboons from the same group using collars fitted with GPS sensors and triaxial accelerometers to understand how baboons manage their competing nighttime priorities. Accelerometer-based sleep classification has shown an impressive ability to detect and monitor sleep behavior across taxa (Ancoli-Israel et al., 2003; de Souza et al., 2003; Hoffmann et al., 2012; Ladha and Hoffman, 2018; Malungo et al., 2021; Qin et al., 2020), and is now commonly used to assess sleep in both humans (e.g., Jones et al., 2019; Patel et al., 2017) and nonhuman animals (e.g., Gravett et al., 2017; Reinhardt, 2020; Samson et al., 2018). Validation studies comparing performance of this noninvasive method to polysomnography – the gold standard in sleep research – generally show high accuracy (78–90%; Ancoli-Israel et al., 2003; Kanady et al., 2011; Malungo et al., 2021; Shambroom et al., 2012), although concerns remain about the ability of movement-based methods to distinguish sleep from resting wakefulness (Ancoli-Israel et al., 2003; de Souza et al., 2003), and results must be evaluated with these caveats in mind. For this study, we adapted a well-validated sleep classification algorithm used in human research (van Hees et al., 2015; van Hees et al., 2018) and validated its ability to detect sleep in wild baboons. We then used this algorithm to describe the sleep patterns of members of our study group over a period of a month (Supplementary file 1a). We used these data to assess the influence of homeostatic regulation on the pattern of sleep and wake bouts within nights, as well as the duration and fragmentation of sleep across nights. We also leveraged naturally occurring sleep disturbances to test how recent sleep history influenced arousal threshold during sleep. We compared the influence of homeostatic regulation to that of the location in which individuals slept (both within the sleep site as well as between distinct sleep sites) and their local social environment, both of which may exert pressures on sleep behavior in the wild that conflict with the maintenance of homeostasis.

The diel pattern of activity in wild baboons, as reflected by accelerometry data, reveals a clear monophasic sleep pattern, with individuals active during the day and inactive at night (Figure 1B). To derive metrics of sleep (sleep onset time, awakening time, total sleep time, sleep period duration, sleep efficiency, sleep fragmentation), we calculated the log of the vectorial dynamic body acceleration (VeDBA), a widely used measure of overall movement activity (Qasem et al., 2012), from 36 calendar days or 354 baboon-nights. Sleep onset occurred 53.0 ± 1.7 (mean ± SE) min prior to the end of evening astronomical twilight, and baboons awoke 35.9 ± 1.7 min after the beginning of morning astronomical twilight (Figure 1A and C). The duration of the sleep period – the period between sleep onset and awakening – was 11.0 ± 0.04 hr on average. Within the sleep period, baboons slept for an average of 9.2 ± 0.04 hr (total sleep time), displaying an average sleep efficiency of 85.0% ± 0.2%. Baboons exhibited 1.8 ± 0.03 distinct wake bouts per hour of sleep during the sleep period (sleep fragmentation).

Figure 1

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Due to high correlation of total sleep time with onset time, awakening time, sleep period duration, and sleep efficiency, as well as a strong relationship between sleep fragmentation and sleep efficiency (Supplementary file 1b), we focused the majority of our analyses on total sleep time and sleep fragmentation. Individuals differed in their total sleep time and sleep fragmentation, and much of this variation reflected differences between the sexes and variation across age categories. Males slept an average of 20 min longer than females and experienced 0.23 fewer wake bouts per hour of sleep (total sleep time linear mixed model [LMM]: standardized estimate [95% credible interval lower bound, 95% credible interval upper bound]: 0.44 [–0.05, 0.94]; sleep fragmentation LMM: –0.44 [–1.04, 0.17]). Juveniles and subadults slept, on average, 16 min less and woke on 0.38 and 0.28 more occasions per hour of sleep, respectively, than adults (total sleep time LMM: juveniles: –0.34 [–1.15, 0.50], subadults: –0.34 [–0.81, 0.14]; sleep fragmentation LMM: juveniles: 0.72 [–0.28, 1.75], subadults: 0.52 [–0.06, 1.10]).

An individual’s recent history of sleep and activity was not a strong driver of sleep patterns (Figure 2, Figure 2—figure supplement 1, Figure 2—figure supplement 2). Neither total sleep time nor sleep fragmentation the previous night affected time spent napping the following day, time spent sleeping the following night, or the fragmentation of sleep the following night (Supplementary file 1f; time spent napping LMM: previous night total sleep time: 0.03 [–0.10, 0.17], previous night sleep fragmentation: 0.00 [–0.11, 0.12]; Figure 2A i-ii, total sleep time LMM: previous night relative total sleep time: –0.04 [–0.19, 0.12], previous night relative sleep fragmentation: –0.06 [–0.19, 0.06]; Figure 2B i-ii, sleep fragmentation LMM: previous night relative total sleep time: 0.09 [–0.10, 0.27], previous night relative sleep fragmentation: –0.03 [–0.18, 0.12]). However, after spending more time napping during the day, baboons did experience shorter total sleep time and more fragmented sleep during the night (Figure 2Ai and Bi; total sleep time LMM: –0.19 [–0.36, –0.01], sleep fragmentation LMM: 0.22 [0.00, 0.45]). For every 10 min spent napping, baboons spent six fewer minutes sleeping the following night and woke 0.10 times more per hour of sleep. Neither sleep duration nor fragmentation was influenced by the distance baboons traveled (Figure 2Aiv and Biv; total sleep time LMM: –0.03 [–0.19, 0.14]; sleep fragmentation LMM: –0.04 [–0.13, 0.05]) or the VeDBA they accumulated during the day (Supplementary file 1e; Supplementary file 1l total sleep time LMM: –0.12 [–0.34, 0.12]; sleep fragmentation LMM: –0.05 [–0.33, 0.23]).

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In humans, homeostatic regulation of sleep manifests within as well as between nights: sleep wanes and wakeful activity increases over the course of the sleep period as individuals gradually fulfill their sleep requirements (Winnebeck et al., 2018). Baboons, in contrast, did not demonstrate this pattern: their probability of being asleep did not decrease as the night progressed despite exhibiting cyclic sleep patterns that are otherwise similar to patterns of human sleep (Figure 2C; generalized additive mixed model [GAMM]: r²_adj = 0.008, F_(8.741) = 89.16, p<1 × 10^–15).

The location where baboons slept had a strong influence on sleep duration, with individuals experiencing reduced sleep quality in less familiar locations. For the first 21 nights of the study, group members slept at the same site along the Ewaso Ng’iro river, distributed across 10 adjacent yellow fever (Vachellia xanthophloea) trees (Figure 3A). Individuals showed high fidelity to particular sleep trees (Figure 3—figure supplement 1; one-tailed two-sample Kolmogorov–Smirnov test: p<1.0 × 10^–9), returning each night to one of the small set of available trees populated by the group. Not only did the choice of tree itself influence sleep duration (Supplementary file 1c, d, Figure 3—figure supplement 2), but the individual’s familiarity with their selected tree impacted how much they slept. Baboons slept longer in trees to which they showed higher fidelity (Figure 3C; LMM: 0.21 [0.05, 0.36]), with individuals sleeping up to 33.4 min longer in the tree to which they showed highest fidelity than in the tree to which they showed lowest fidelity. Baboons did not compensate for shorter sleep duration with less fragmented sleep when sleeping in nonpreferred trees (LMM: –0.05 [–0.24, 0.14]).

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During the 21st night of the study, we heard the snarls and growls of a large felid, followed by sustained baboon screams and alarm calls that were emitted from the study group’s sleep trees at 20:55. After listening to recordings of the vocalizations of the large cats present at the study site, and because leopards are the only predators that readily attack baboons in their sleep site during the night (Busse, 1980; Cheney et al., 2004), we concluded that the commotion reflected a leopard attack, and that the leopard attack had failed, as all members of the group were present the next morning. On the day following this attack, the group moved to a less commonly used sleep site 1.5 km away (Figure 3B). They remained at this sleep site for three nights before returning to sleep at their main sleep site. While the baboons showed no substantial change in their sleep duration or fragmentation on the night of the leopard attack (Figure 3D, Figure 3—figure supplement 3, Figure 3—figure supplement 4; total sleep time LMM: –0.25 [–0.87, 0.39]; sleep fragmentation LMM: –0.32 [–0.96, 0.33]), they slept 72 min less and exhibited 0.50 more wake bouts per hour of sleep, on average, upon moving to the less familiar sleep site (Figure 3D, total sleep time LMM: –1.52 [–2.15, –0.86]; sleep fragmentation LMM 0.96 [0.33, 1.60]). This decrease in total sleep time and increase in fragmentation following the change in sites was limited to the first night in the new sleep site, after which sleep duration and fragmentation returned to normal (Figure 3D, Figure 3—figure supplement 3, Figure 3—figure supplement 4).

Sleeping in a social context also impacted sleep patterns. Contrary to predictions of the sentinel hypothesis, the proportion of the night in which at least one individual was awake was significantly less than expected by chance (Figure 4A, Figure 4—figure supplement 1, Fisher’s exact test: p<0.0001; Figure 4—figure supplement 2B, Fisher’s exact test: p<0.0001), suggesting that, rather than staggering periods of nocturnal wakefulness, group-mates were actually synchronized in their sleep-wake patterns throughout the night. Confirming this synchronization, we found that a significantly greater proportion of the group exhibited the same simultaneous behavior, either being asleep or awake, than expected (Figure 4B, Figure 4—figure supplement 1, Fisher’s exact test: p<0.0001; Figure 4—figure supplement 2C, Fisher’s exact test: p<0.0001). Moreover, pairs of baboons showed more synchronization when sleeping in the same tree than when sleeping in different trees (Figure 4C; LMM: 0.56 [0.47, 0.64]), which suggested that sleeping individuals may awaken in response to the activity of group-mates in their local environment, or that external disruptions in the local environment may simultaneously waken all group members in the vicinity. To distinguish between these potential explanations, we tested the influence of the number of group-mates in an individual’s local environment on their total sleep time and found that individuals slept less when sharing their sleeping tree with more group-mates (Figure 4D; LMM: –0.53 [–0.87, –0.19]). Each additional tracked group-mate in a tree resulted in a 4.0 min decrease in total sleep time. Individuals also appeared to experience more fragmented sleep when sleeping in the proximity of a greater number of group-mates, but large uncertainty in the model estimate prevents a definitive conclusion about the influence of the social environment on sleep fragmentation (LMM: 0.26 [–0.15, 0.67]).

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Building on the evidence that co-sleeping baboons disrupt each other’s sleep, we tested whether baboons maintain sleep homeostasis by sleeping deeper, rather than longer, following nights of poor sleep. Because sleep depth is measured by the amount of stimulation needed to awaken an animal (i.e., arousal threshold), we assessed sleep depth in situ by evaluating the probability of an individual waking in response to the activity of a neighboring group member. We found that although individuals were indeed substantially more likely to wake following the awakening of others in their sleep tree, neither shorter duration nor more fragmented sleep the previous night dampened their responsiveness to group-mates (Bernoulli generalized linear mixed model [GLMM]: influence of group-mate activity: 0.25 [0.16, 0.34], interaction between influence of group-mate activity and previous night relative total sleep time: –0.01 [ –0.13, 0.10], interaction between influence of group-mate activity and previous night relative sleep fragmentation: –0.01 [ –0.11, 0.09]).

We found no influence of moon phase or the minimum ambient temperature during the night on baboon sleep duration or fragmentation (total sleep time LMM: moon phase: 0.05 [–0.12, 0.22], temperature: –0.04 [–0.20, 0.13]; sleep fragmentation LMM: moon phase: –0.08 [–0.30, 0.13], temperature: 0.04 [–0.17, 0.26]).

In this study, we demonstrate that the ecological and social demands that animals experience in the natural world can take precedence over the maintenance of sleep homeostasis. We show that, while baboons sleep less in unfamiliar environments, and while their sleep is disrupted by the activity of group-mates, their recent history of sleep and physical exertion has only a limited role in influencing sleep behavior. Because baboons are highly vulnerable to nocturnal predation (Busse, 1980; Cheney et al., 2004; Isbell et al., 2018) and because they experience fitness benefits from maintaining strong social bonds (Silk et al., 2009), sacrificing sleep to maintain alertness in novel environments and to remain close to group-mates may represent critical adaptations. Our results highlight the trade-offs that group-living animals navigate when investing in sleep in the wild, and in doing so, challenge the centrality of the role that homeostasis has played in shaping sleep patterns in the environment in which sleep evolved. Decades of research in the laboratory and at the bedside have implicated homeostatic regulation as a key driver of sleep patterns, with the sleep rebound that follows periods of deficit facilitating the maintenance of a physiologically required amount of sleep (Amlaner et al., 2009). However, sleep studies have traditionally investigated sleep in highly controlled environments, where the costs of investing in sleep are largely absent. Our findings suggest that, in the natural world, ‘sleep need’ may be a relatively flexible concept, with variation in sleep investment driven as much by the opportunity costs of sleep as by its physiological benefits.

There are substantial opportunity costs of devoting a significant portion of every day to sleeping. Sleeping animals are highly vulnerable to predation (Lima et al., 2005), and our results suggest that individuals sleep less when the risk of predation is particularly high. Baboon group members showed high fidelity to particular locations within their main sleep site, and individuals sacrificed sleep both when sleeping in nonpreferred trees as well as upon moving to a new, less familiar sleep site following a leopard attack. Given that predation risk tends to be greater in unfamiliar locations (Forrester et al., 2015; Gehr et al., 2020; Yoder et al., 2004), baboons appear to trade sleep for vigilance according to the current risk of predation. Notably, however, we did not find that baboons decreased their investment in sleep on the night of the leopard attack. This surprising result may reflect leopards’ disinclination, as stealth hunters, to launch repeated attacks (Hayward et al., 2006; Lin et al., 2020), or indicate that baboons perceive uncertainty in the level of risk as potentially more dangerous than a confirmed threat. It is also possible that baboons did, in fact, sleep less following the attack, but remained exceptionally still, in a state of highly elevated vigilance. Because accelerometer-based sleep classification performs poorly in distinguishing motionless wakefulness from sleep, this state would likely have been falsely classified as sleep.

Engaging in sleep precludes investment in a variety of behaviors, in addition to antipredator vigilance, that are important to fitness (Aulsebrook et al., 2016; Lesku et al., 2012; Lima et al., 2005; Roth et al., 2010). Consistent with our results, recent studies in ecologically relevant contexts have revealed that animals forego sleep when ecological demands increase the associated opportunity costs. For example, while engaging in long, nonstop flights, great frigatebirds reduced the amount they slept by 92.7%, without apparent physiological consequences (Rattenborg et al., 2016). Similarly, male pectoral sandpipers greatly reduce their time spent sleeping during their short and intense mating season, and males that slept less actually experienced higher reproductive success (Lesku et al., 2012). Thus, across contexts and taxa, ecological pressures appear to supersede investment in sleep in the wild.

Animals might maintain homeostasis in the face of significant opportunity costs of sleep by sleeping more intensely, rather than longer, in response to elevated sleep need. Baboons in this study, however, did not appear to compensate in this way. We reached this conclusion using two distinct approaches of inferring sleep depth from accelerometry data. First, we measured the fragmentation of sleep, which has been shown to correlate inversely with sleep depth (Bastuji and García-Larrea, 1999). Second, we directly assessed changes in arousal thresholds by tracking the propensity to wake in response to the nocturnal activity of neighboring group-mates. These approaches offer potentially promising avenues to expand our ability to noninvasively quantify the sleep behavior of animals in their natural habitats using accelerometry. However, validation against polysomnography – the gold standard for recording sleep – should be a priority for future work.

Recent technological advances allowing for the use of polysomnography in field conditions have played an important role in revealing the ecological trade-offs that wild animals face when navigating decisions about when, where, and how to sleep (Davimes et al., 2018; Lesku et al., 2011; Lesku et al., 2012; Rattenborg et al., 2008; Rattenborg et al., 2016; Scriba et al., 2013; Voirin et al., 2014). Although these advances hold great promise for wider application in the future, the invasive nature of polysomnography unfortunately limits its current use to taxa whose daily activities do not interfere with electrodes implanted either subdermally or intercranially. Because baboons are highly dexterous and engage in frequent allogrooming, we were unable to apply this gold standard, and instead, resorted to an alternate method to ask and answer important questions about the ecology of sleep in a wild social primate. Accelerometer-based sleep classification – a tool already used to investigate sleep across terrestrial (human: Jones et al., 2019; Patel et al., 2017; nonhuman: Bäckman et al., 2017; Davimes et al., 2018; Gravett et al., 2017; Lesku et al., 2011; Malungo et al., 2021; Qin et al., 2020; Reinhardt et al., 2019; Reyes et al., 2021; Samson et al., 2018; Sellers and Crompton, 2004; Sri Kantha and Suzuki, 2006; Suzuki et al., 2018) and marine taxa (Miller et al., 2008; Mitani et al., 2010; Wright et al., 2017) – offered a valid alternative to polysomnography. We note that the use of accelerometry can introduce biases in sleep monitoring, typically by overestimating total sleep time as a result of an inability to distinguish resting wakefulness from sleep (Ancoli-Israel et al., 2003; de Souza et al., 2003). Accelerometry is also limited in its ability to differentiate the stages of sleep (Conradt et al., 1997, but see Devine et al., 2020; Winnebeck et al., 2018). However, if these biases and limitations are considered during the interpretation of results, the use of accelerometry to investigate sleep provides an immediate opportunity to shed light on how diverse species balance their physiological sleep requirements with ecological opportunity costs that vary according to natural history, trophic level, community composition, climate, and local environment. Further, the relative ease of accelerometer deployment, and its prevalence in ecological research, changes the scale at which sleep behavior can be studied, enabling the simultaneous and long-term monitoring of sleep at the population level. This rescaling of sleep research creates many new opportunities, one of which is the ability to record sleep in the majority of social group members and thus explore an exciting new scientific frontier: the collective dynamics of sleep.

Using accelerometry to track the sleep patterns of nearly an entire group of wild baboons, we demonstrated the importance of the social environment in shaping the sleep patterns of group-living animals. Contrary to the predictions of the sentinel hypothesis (Samson et al., 2017; Snyder, 1966), periods of nocturnal wakefulness of group members were not staggered, but rather synchronized, particularly with nearby group-mates. Baboons also slept less when in close proximity to a greater number of group-mates. Taken together, these results suggest that group-mates disrupt each other’s sleep. Social disruptions may result from group-mates actively interacting with each other during the night. Gregarious animals often invest substantially in building and maintaining social relationships with their group-mates (Ward and Webster, 2016), and these bonds can prove essential to their fitness (Cameron et al., 2009; Campos et al., 2020; Frère et al., 2010; Riehl and Strong, 2018; Silk et al., 2009). Because animals have limited time to devote to maintaining their social bonds during the day (Dunbar, 1992), they may actively sacrifice sleep in order to invest in these relationships at night. Alternatively, social animals may wake in response to the periodic waking and repositioning of their group-mates during the night, and thus, socially disrupted sleep is, perhaps, an inherent by-product of sleeping in a group. Simply remaining in a cohesive group may therefore present a challenge to obtaining sufficient sleep.

Social animals may jeopardize sleep homeostasis to maintain cohesion with their conspecifics because remaining in close proximity to their group-mates during the sleep period could prove essential to their fitness. Individuals likely benefit from the dilution of predation risk that is achieved through group cohesion, particularly when they are sleeping and thus highly vulnerable to predators (Lehtonen and Jaatinen, 2016). Collective vigilance may also reduce the risk of predation for group members. Even in the absence of collective vigilance optimization via nonrandomly staggered wakefulness, the proportion of the night with at least one group member awake is still likely to be substantially greater than any particular individual’s investment in vigilance. In our study, at least one individual in the group was awake for 394 ± 11 min (82% ± 2%) from 21:00 to 05:00, although each individual was only awake for 79 ± 1 min (16% ± 0.2%) of the same period. Samson et al., 2017 found high levels of collective vigilance during the night in a group of Hadza hunter-gatherers, and they suggest that this collective vigilance may facilitate higher-intensity sleep (Samson and Nunn, 2015). Future studies leveraging the indices of sleep depth that we applied in this study or advances in polysomnography (i.e., EEG) that may eventually allow its application in wild social animals could enable a test of this possibility.

Unexpectedly, we found that adult baboons slept longer than subadults and juveniles, and males slept longer than females. This contrasts with previous research that found age differences in sleep patterns linked to physiological demands during the development of the central nervous system (Amlaner et al., 2009) and sex differences in sleep tied to the influence of sex steroids (Mong and Cusmano, 2016), with younger individuals sleeping more than older individuals (Knutson, 2014; Ohayon et al., 2004; Olds et al., 2010; Steinmeyer et al., 2010; Stuber et al., 2015) and females sleeping more than males in birds and humans (Lendrem, 1983; Mong and Cusmano, 2016; Roehrs et al., 2006; Steinmeyer et al., 2010; Stuber et al., 2015). Our surprising results here may have been caused by biases in our study sample. The bio-logging collars were too heavy to mount on young juveniles and infants – the individuals that we would have predicted to sleep the most due to their rapidly developing central nervous system (Frank, 2020; Landolt and Dijk, 2019). Thus, there may have been an overall decrease in time spent sleeping with age that we were unable to observe because we did not collect data on the youngest individuals. This result may also be an artifact of the tendency of accelerometer-based sleep monitoring to classify resting wakefulness as sleep (Supplementary file 1r; Ancoli-Israel et al., 2003; de Souza et al., 2003). Older individuals may rest quietly when waking during the night, thus falsely determined to be asleep according to their accelerometry, whereas younger individuals may be more likely to resume activity upon waking. However, if these findings are not the result of a bias in our study subject inclusion or sleep recording technique, they may reflect variation in the vulnerability to predation among the age-sex classes in this highly sexually dimorphic species (Cheney et al., 2004), with young and female baboons likely realizing a higher cost of sleep than adult males. Individuals may also differ in their sleep patterns due to their ability to gain access to a high-quality sleep location within the group’s sleep site. Our results have demonstrated the importance of location to sleep. However, group-mates may differ in their access to preferred sleep locations, particularly if preferred locations are limited. Baboon groups are structured by linear dominance hierarchies that shape the priority of access to resources (Cheney and Seyfarth, 2008; King et al., 2009; Marshall et al., 2015), and individuals can leverage their affiliative and kinship relationships to obtain resources that they would not be able to access based on social rank alone (Sick et al., 2014). Further research is needed to investigate the extent to which these complex social dynamics influence an individual’s ability to obtain a preferred sleep location and, thus, a good night’s sleep.

In addition to highlighting social dynamics as a key driver of sleep patterns in group-living species, our study provides important insights into selective pressures that may have shaped the evolution of human sleep. The physiological requirements for sleep and the homeostatic mechanisms that ensure this requirement is fulfilled have long been assumed to be the key drivers influencing the way that our sleep has evolved and the characteristics of our sleep today. However, we suggest that the criticality of homeostatic control in shaping our sleep patterns could be an artefact of sleeping in an environment devoid of the ecological and social costs that sleep would have presented our ancestors. Evidence suggests that, like baboons, early hominins were extremely vulnerable to nighttime predation in their dry savannah habitats (Brain, 1983; Treves and Palmqvist, 2007; Wrangham and Carmody, 2010). Hominins likely remained vulnerable to nocturnal predation until they began to manipulate fire, around which they could sleep to reduce the risk of predation (Samson and Nunn, 2015), and some characteristics of our sleep today may be best explained in light of the vulnerability that sleep imparted on our ancestors. For example, modern humans exhibit decreased sleep quality when sleeping in an unfamiliar environment (Tamaki et al., 2016), similar to the baboons in our study. The lower quality sleep resulting from this aptly named ‘first night effect’ is limited to the first night in a new location (Tamaki et al., 2016), and our findings suggest that the first night effect may be conserved from an environment where this first night would have been accompanied by poor information about risk, and thus, a higher likelihood of predation. Early hominins would have also experienced a social opportunity cost of sleep as they likely slept in groups (Samson and Nunn, 2015; Willems and van Schaik, 2017) and would have experienced constraints on the time available to maintain their social network during the day until developing the advanced cognition that enabled a more efficient use of time (Nunn and Samson, 2018; Samson and Nunn, 2015). While our sleep has likely evolved substantially from that of our earliest ancestors, with modern human sleep being extremely short and intense compared to that of other primates (Nunn et al., 2016; Nunn and Samson, 2018), a full understanding of the way we sleep involves considering not only the physiological benefits of sleep, but also its ecological and social costs in the environment in which it evolved.

GPS and accelerometry data generated during this study are published and available in the Movebank repository (https://doi.org/10.5441/001/1.3q2131q5). Drone imagery is publicly available for download from Dryad (https://doi.org/10.5061/dryad.6h5b7). Accelerometry data and behavioral scoring data from the 2019 sleep validation study is also publicly available for download from Dryad (https://doi.org/10.5061/dryad.p5hqbzkqf). All code used to produce the final results from the raw data is on GitHub (https://github.com/CarterLoftus/baboon_sleep/) and archived on Zenodo (https://doi.org/10.5281/zenodo.5905742).

1. 1. Loftus JC
   2. Harel R
   3. Nuñez CL
   4. Crofoot M

   (2021) Dryad Digital Repository

   Data from: Ecological and social pressures interfere with homeostatic sleep regulation in the wild.

   https://doi.org/10.5061/dryad.p5hqbzkqf

1. 1. Strandburg-Peshkin A
   2. Farine D
   3. Crofoot M
   4. Couzin I

   (2020) Dryad Digital Repository

   Data from: Habitat and social factors shape individual decisions and emergent group structure during baboon collective movement.

   https://doi.org/10.5061/dryad.6h5b7
2. 1. Crofoot MC
   2. Kays RW
   3. Wikelski M

   (2021) Movebank Data Repository

   Collective movement in wild baboons.

   https://doi.org/10.5441/001/1.3q2131q5

1. Book

   1. Altmann SA
   2. Altmann J

   (1970) Baboon Ecology: African Field Research

   University of Chicago Press.

   https://doi.org/10.1002/ajpa.1330390116

   • Google Scholar
2. Book

   1. Amlaner CJ
   2. Phil D
   3. Fuller PM

   (2009)

   Basics of Sleep Guide

   Sleep Research Society.

   • Google Scholar
3. 1. Ancoli-Israel S
   2. Cole R
   3. Alessi C
   4. Chambers M
   5. Moorcroft W
   6. Pollak CP

   (2003) The role of actigraphy in the study of sleep and circadian rhythms

   Sleep 26:342–392.

   https://doi.org/10.1093/sleep/26.3.342

   • PubMed
   • Google Scholar
4. 1. Aulsebrook AE
   2. Jones TM
   3. Rattenborg NC
   4. Roth TC
   5. Lesku JA

   (2016) Sleep Ecophysiology: Integrating Neuroscience and Ecology

   Trends in Ecology & Evolution 31:590–599.

   https://doi.org/10.1016/j.tree.2016.05.004

   • PubMed
   • Google Scholar
5. 1. Bäckman J
   2. Andersson A
   3. Pedersen L
   4. Sjöberg S
   5. Tøttrup AP
   6. Alerstam T

   (2017) Actogram analysis of free-flying migratory birds: new perspectives based on acceleration logging

   Journal of Comparative Physiology. A, Neuroethology, Sensory, Neural, and Behavioral Physiology 203:543–564.

   https://doi.org/10.1007/s00359-017-1165-9

   • PubMed
   • Google Scholar
6. 1. Barrett CE
   2. Noble P
   3. Hanson E
   4. Pine DS
   5. Winslow JT
   6. Nelson EE

   (2009) Early adverse rearing experiences alter sleep-wake patterns and plasma cortisol levels in juvenile rhesus monkeys

   Psychoneuroendocrinology 34:1029–1040.

   https://doi.org/10.1016/j.psyneuen.2009.02.002

   • PubMed
   • Google Scholar
7. 1. Barton RA
   2. Byrne RW
   3. Whiten A

   (1996) Ecology, feeding competition and social structure in baboons

   Behavioral Ecology and Sociobiology 38:321–329.

   https://doi.org/10.1007/s002650050248

   • Google Scholar
8. 1. Basner M
   2. Rao H
   3. Goel N
   4. Dinges DF

   (2013) Sleep deprivation and neurobehavioral dynamics

   Current Opinion in Neurobiology 23:854–863.

   https://doi.org/10.1016/j.conb.2013.02.008

   • PubMed
   • Google Scholar
9. 1. Bastuji H
   2. García-Larrea L

   (1999) Sleep/wake abnormalities in patients with periodic leg movements during sleep: factor analysis on data from 24-h ambulatory polygraphy

   Journal of Sleep Research 8:217–223.

   https://doi.org/10.1046/j.1365-2869.1999.00157.x

   • PubMed
   • Google Scholar
10. 1. Beauchamp G

    (2009) Sleeping gulls monitor the vigilance behaviour of their neighbours

    Biology Letters 5:9–11.

    https://doi.org/10.1098/rsbl.2008.0490

    • PubMed
    • Google Scholar
11. 1. Beauchamp G

    (2011) Collective Waves of Sleep in Gulls (Larus spp.)

    Ethology : Formerly Zeitschrift Fur Tierpsychologie 117:326–331.

    https://doi.org/10.1111/j.1439-0310.2011.01875.x

    • Google Scholar
12. Software

    1. Benoit T
    2. Elmarhraoui A

    (2019) suncalc: Compute Sun Position, Sunlight Phases, Moon Position and Lunar Phase, version 0.5.0

    Computer Software.

    https://CRAN.R-project.org/package=suncalc
13. 1. Biro PA
    2. Dingemanse NJ

    (2009) Sampling bias resulting from animal personality

    Trends in Ecology & Evolution 24:66–67.

    https://doi.org/10.1016/j.tree.2008.11.001

    • PubMed
    • Google Scholar
14. Book

    1. Brain CK

    (1983)

    The Hunters or the Hunted?: An Introduction to African Cave Taphonomy

    University of Chicago Press.

    • Google Scholar
15. 1. Brutcher RE
    2. Nader MA

    (2013) The relationship between cocaine self-administration and actigraphy-based measures of sleep in adult rhesus monkeys

    Psychopharmacology 229:267–274.

    https://doi.org/10.1007/s00213-013-3101-1

    • PubMed
    • Google Scholar
16. 1. Bürkner PC

    (2017) brms: An R Package for Bayesian Multilevel Models Using Stan

    Journal of Statistical Software 80:1–28.

    https://doi.org/10.18637/jss.v080.i01

    • Google Scholar
17. 1. Busse C

    (1980)

    Leopard and lion predation upon chacma baboons living in the Moremi Wildlife Reserve

    Botswana Notes and Records 1:15–21.

    • Google Scholar
18. 1. Cameron EZ
    2. Setsaas TH
    3. Linklater WL

    (2009) Social bonds between unrelated females increase reproductive success in feral horses

    PNAS 106:13850–13853.

    https://doi.org/10.1073/pnas.0900639106

    • PubMed
    • Google Scholar
19. 1. Campos FA
    2. Villavicencio F
    3. Archie EA
    4. Colchero F
    5. Alberts SC

    (2020) Social bonds, social status and survival in wild baboons: a tale of two sexes

    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 375:20190621.

    https://doi.org/10.1098/rstb.2019.0621

    • PubMed
    • Google Scholar
20. 1. Cheney DL
    2. Seyfarth RM
    3. Fischer J
    4. Beehner J
    5. Bergman T
    6. Johnson SE
    7. Kitchen DM
    8. Palombit RA
    9. Rendall D
    10. Silk JB

    (2004) Factors Affecting Reproduction and Mortality Among Baboons in the Okavango Delta, Botswana

    International Journal of Primatology 25:401–428.

    https://doi.org/10.1023/B:IJOP.0000019159.75573.13

    • Google Scholar
21. Book

    1. Cheney DL
    2. Seyfarth RM

    (2008) Baboon Metaphysics: The Evolution of a Social Mind

    University of Chicago Press.

    https://doi.org/10.7208/chicago/9780226102429.001.0001

    • Google Scholar
22. 1. Chowdhury B
    2. Shafer OT

    (2020) Why the young sleep longer

    eLife 9:e56833.

    https://doi.org/10.7554/eLife.56833

    • PubMed
    • Google Scholar
23. 1. Cirelli C
    2. Tononi G

    (2008) Is sleep essential?

    PLOS Biology 6:e216.

    https://doi.org/10.1371/journal.pbio.0060216

    • PubMed
    • Google Scholar
24. 1. Conradt R
    2. Brandenburg U
    3. Ploch T
    4. Peter JH

    (1997)

    Actigraphy: methodological limits for evaluation of sleep stages and sleep structure of healthy probands

    Pneumologie (Stuttgart, Germany) 51 Suppl 3:721–724.

    • PubMed
    • Google Scholar
25. 1. Cowlishaw G

    (1994) Vulnerability To Predation in Baboon Populations

    Behaviour 131:293–304.

    https://doi.org/10.1163/156853994X00488

    • Google Scholar
26. 1. Davimes JG
    2. Alagaili AN
    3. Bhagwandin A
    4. Bertelsen MF
    5. Mohammed OB
    6. Bennett NC
    7. Manger PR
    8. Gravett N

    (2018) Seasonal variations in sleep of free-ranging Arabian oryx (Oryx leucoryx) under natural hyperarid conditions

    Sleep 41:zsy038.

    https://doi.org/10.1093/sleep/zsy038

    • PubMed
    • Google Scholar
27. 1. de Souza L
    2. Benedito-Silva AA
    3. Pires MLN
    4. Poyares D
    5. Tufik S
    6. Calil HM

    (2003) Further Validation of Actigraphy for Sleep Studies

    Sleep 26:81–85.

    https://doi.org/10.1093/sleep/26.1.81

    • Google Scholar
28. 1. Devine JK
    2. Chinoy ED
    3. Markwald RR
    4. Schwartz LP
    5. Hursh SR

    (2020) Validation of Zulu Watch against Polysomnography and Actigraphy for On-Wrist Sleep-Wake Determination and Sleep-Depth Estimation

    Sensors 21:E76.

    https://doi.org/10.3390/s21010076

    • PubMed
    • Google Scholar
29. 1. Dodge S
    2. Bohrer G
    3. Weinzierl R
    4. Davidson SC
    5. Kays R
    6. Douglas D
    7. Cruz S
    8. Han J
    9. Brandes D
    10. Wikelski M

    (2013) The environmental-data automated track annotation (Env-DATA) system: linking animal tracks with environmental data

    Movement Ecology 1:3.

    https://doi.org/10.1186/2051-3933-1-3

    • PubMed
    • Google Scholar
30. 1. Dunbar RIM

    (1992) Time: a hidden constraint on the behavioural ecology of baboons

    Behavioral Ecology and Sociobiology 31:35–49.

    https://doi.org/10.1007/BF00167814

    • Google Scholar
31. 1. Forrester TD
    2. Casady DS
    3. Wittmer HU

    (2015) Home sweet home: fitness consequences of site familiarity in female black-tailed deer

    Behavioral Ecology and Sociobiology 69:603–612.

    https://doi.org/10.1007/s00265-014-1871-z

    • Google Scholar
32. 1. Frank MG

    (2020) The Ontogenesis of Mammalian Sleep: Form and Function

    Current Sleep Medicine Reports 6:267–279.

    https://doi.org/10.1007/s40675-020-00190-y

    • PubMed
    • Google Scholar
33. 1. Frère CH
    2. Krützen M
    3. Mann J
    4. Connor RC
    5. Bejder L
    6. Sherwin WB

    (2010) Social and genetic interactions drive fitness variation in a free-living dolphin population

    PNAS 107:19949–19954.

    https://doi.org/10.1073/pnas.1007997107

    • PubMed
    • Google Scholar
34. 1. Gangwisch JE

    (2014) A review of evidence for the link between sleep duration and hypertension

    American Journal of Hypertension 27:1235–1242.

    https://doi.org/10.1093/ajh/hpu071

    • PubMed
    • Google Scholar
35. 1. Gehr B
    2. Bonnot NC
    3. Heurich M
    4. Cagnacci F
    5. Ciuti S
    6. Hewison AJM
    7. Gaillard J-M
    8. Ranc N
    9. Premier J
    10. Vogt K
    11. Hofer E
    12. Ryser A
    13. Vimercati E
    14. Keller L

    (2020) Stay home, stay safe-Site familiarity reduces predation risk in a large herbivore in two contrasting study sites

    The Journal of Animal Ecology 89:1329–1339.

    https://doi.org/10.1111/1365-2656.13202

    • PubMed
    • Google Scholar
36. 1. Gravett N
    2. Bhagwandin A
    3. Sutcliffe R
    4. Landen K
    5. Chase MJ
    6. Lyamin OI
    7. Siegel JM
    8. Manger PR
    9. Vyazovskiy V

    (2017) Inactivity/sleep in two wild free-roaming African elephant matriarchs - Does large body size make elephants the shortest mammalian sleepers?

    PLOS ONE 12:e0171903.

    https://doi.org/10.1371/journal.pone.0171903

    • PubMed
    • Google Scholar
37. 1. Han KS
    2. Kim L
    3. Shim I

    (2012) Stress and sleep disorder

    Experimental Neurobiology 21:141–150.

    https://doi.org/10.5607/en.2012.21.4.141

    • PubMed
    • Google Scholar
38. 1. Hayward MW
    2. Henschel P
    3. O’Brien J
    4. Hofmeyr M
    5. Balme G
    6. Kerley GIH

    (2006) Prey preferences of the leopard (Panthera pardus)

    Journal of Zoology 270:298–313.

    https://doi.org/10.1111/j.1469-7998.2006.00139.x

    • Google Scholar
39. 1. Hoffmann K
    2. Coolen A
    3. Schlumbohm C
    4. Meerlo P
    5. Fuchs E

    (2012) Remote long-term registrations of sleep-wake rhythms, core body temperature and activity in marmoset monkeys

    Behavioural Brain Research 235:113–123.

    https://doi.org/10.1016/j.bbr.2012.07.033

    • PubMed
    • Google Scholar
40. 1. Isbell LA
    2. Bidner LR
    3. Crofoot MC
    4. Matsumoto-Oda A
    5. Farine DR

    (2017) GPS-identified, low-level nocturnal activity of vervets (Chlorocebus pygerythrus) and olive baboons (Papio anubis) in Laikipia, Kenya

    American Journal of Physical Anthropology 164:203–211.

    https://doi.org/10.1002/ajpa.23259

    • PubMed
    • Google Scholar
41. 1. Isbell LA
    2. Bidner LR
    3. Van Cleave EK
    4. Matsumoto-Oda A
    5. Crofoot MC

    (2018) GPS-identified vulnerabilities of savannah-woodland primates to leopard predation and their implications for early hominins

    Journal of Human Evolution 118:1–13.

    https://doi.org/10.1016/j.jhevol.2018.02.003

    • PubMed
    • Google Scholar
42. 1. Jones SE
    2. van Hees VT
    3. Mazzotti DR
    4. Marques-Vidal P
    5. Sabia S
    6. van der Spek A
    7. Dashti HS
    8. Engmann J
    9. Kocevska D
    10. Tyrrell J
    11. Beaumont RN
    12. Hillsdon M
    13. Ruth KS
    14. Tuke MA
    15. Yaghootkar H
    16. Sharp SA
    17. Ji Y
    18. Harrison JW
    19. Freathy RM
    20. Murray A
    21. Luik AI
    22. Amin N
    23. Lane JM
    24. Saxena R
    25. Rutter MK
    26. Tiemeier H
    27. Kutalik Z
    28. Kumari M
    29. Frayling TM
    30. Weedon MN
    31. Gehrman PR
    32. Wood AR

    (2019) Genetic studies of accelerometer-based sleep measures yield new insights into human sleep behaviour

    Nature Communications 10:1585.

    https://doi.org/10.1038/s41467-019-09576-1

    • PubMed
    • Google Scholar
43. 1. Kanady JC
    2. Drummond SPA
    3. Mednick SC

    (2011) Actigraphic assessment of a polysomnographic-recorded nap: a validation study

    Journal of Sleep Research 20:214–222.

    https://doi.org/10.1111/j.1365-2869.2010.00858.x

    • PubMed
    • Google Scholar
44. 1. Karamihalev S
    2. Flachskamm C
    3. Eren N
    4. Kimura M
    5. Chen A

    (2019) Social context and dominance status contribute to sleep patterns and quality in groups of freely-moving mice

    Scientific Reports 9:15190.

    https://doi.org/10.1038/s41598-019-51375-7

    • PubMed
    • Google Scholar
45. 1. King AJ
    2. Isaac NJB
    3. Cowlishaw G

    (2009) Ecological, social, and reproductive factors shape producer–scrounger dynamics in baboons

    Behavioral Ecology 20:1039–1049.

    https://doi.org/10.1093/beheco/arp095

    • Google Scholar
46. 1. Kitamura S
    2. Katayose Y
    3. Nakazaki K
    4. Motomura Y
    5. Oba K
    6. Katsunuma R
    7. Terasawa Y
    8. Enomoto M
    9. Moriguchi Y
    10. Hida A
    11. Mishima K

    (2016) Estimating individual optimal sleep duration and potential sleep debt

    Scientific Reports 6:35812.

    https://doi.org/10.1038/srep35812

    • PubMed
    • Google Scholar
47. 1. Knutson KL

    (2014) Sleep duration, quality, and timing and their associations with age in a community without electricity in Haiti

    American Journal of Human Biology 26:80–86.

    https://doi.org/10.1002/ajhb.22481

    • PubMed
    • Google Scholar
48. 1. Ladha C
    2. Hoffman CL

    (2018) A Combined Approach to Predicting Rest in Dogs Using Accelerometers

    Sensors 18:E2649.

    https://doi.org/10.3390/s18082649

    • PubMed
    • Google Scholar
49. Book

    1. Landolt HP
    2. Dijk DJ

    (2019) Sleep-Wake Neurobiology and Pharmacology

    In: Landolt HP, Dijk DJ, editors. Handbook of Experimental Pharmacology. Cham: Springer International Publishing. pp. 3–34.

    https://doi.org/10.1007/978-3-030-11272-1

    • Google Scholar
50. 1. Lehtonen J
    2. Jaatinen K

    (2016) Safety in numbers: the dilution effect and other drivers of group life in the face of danger

    Behavioral Ecology and Sociobiology 70:449–458.

    https://doi.org/10.1007/s00265-016-2075-5

    • Google Scholar
51. 1. Lendrem DW

    (1983) Sleeping and vigilance in birds. I. Field observations of the mallard (Anas platyrhynchos)

    Animal Behaviour 31:532–538.

    https://doi.org/10.1016/S0003-3472(83)80076-1

    • Google Scholar
52. 1. Lesku JA
    2. Meyer LCR
    3. Fuller A
    4. Maloney SK
    5. Dell’Omo G
    6. Vyssotski AL
    7. Rattenborg NC

    (2011) Ostriches sleep like platypuses

    PLOS ONE 6:e23203.

    https://doi.org/10.1371/journal.pone.0023203

    • PubMed
    • Google Scholar
53. 1. Lesku JA
    2. Rattenborg NC
    3. Valcu M
    4. Vyssotski AL
    5. Kuhn S
    6. Kuemmeth F
    7. Heidrich W
    8. Kempenaers B

    (2012) Adaptive sleep loss in polygynous pectoral sandpipers

    Science 337:1654–1658.

    https://doi.org/10.1126/science.1220939

    • PubMed
    • Google Scholar
54. 1. Lima SL
    2. Rattenborg NC
    3. Lesku JA
    4. Amlaner CJ

    (2005) Sleeping under the risk of predation

    Animal Behaviour 70:723–736.

    https://doi.org/10.1016/j.anbehav.2005.01.008

    • Google Scholar
55. 1. Lin B
    2. Foxfoot IR
    3. Miller CM
    4. Venkatamaran VV
    5. Kerby JT
    6. Bechtold EK
    7. Kellogg BS
    8. Nguyen N
    9. Fashing PJ

    (2020) Leopard predation on gelada monkeys at Guassa, Ethiopia

    American Journal of Primatology 82:e23098.

    https://doi.org/10.1002/ajp.23098

    • PubMed
    • Google Scholar
56. 1. Lyamin OI
    2. Manger PR
    3. Ridgway SH
    4. Mukhametov LM
    5. Siegel JM

    (2008) Cetacean sleep: an unusual form of mammalian sleep

    Neuroscience and Biobehavioral Reviews 32:1451–1484.

    https://doi.org/10.1016/j.neubiorev.2008.05.023

    • PubMed
    • Google Scholar
57. 1. Lyamin OI
    2. Kosenko PO
    3. Korneva SM
    4. Vyssotski AL
    5. Mukhametov LM
    6. Siegel JM

    (2018) Fur Seals Suppress REM Sleep for Very Long Periods without Subsequent Rebound

    Current Biology 28:2000–2005.

    https://doi.org/10.1016/j.cub.2018.05.022

    • PubMed
    • Google Scholar
58. 1. Malungo IB
    2. Gravett N
    3. Bhagwandin A
    4. Davimes JG
    5. Manger PR

    (2021) Sleep in two free-roaming blue wildebeest (Connochaetes taurinus), with observations on the agreement of polysomnographic and actigraphic techniques

    IBRO Neuroscience Reports 10:142–152.

    https://doi.org/10.1016/j.ibneur.2021.02.005

    • PubMed
    • Google Scholar
59. 1. Marshall HH
    2. Carter AJ
    3. Ashford A
    4. Rowcliffe JM
    5. Cowlishaw G

    (2015) Social effects on foraging behavior and success depend on local environmental conditions

    Ecology and Evolution 5:475–492.

    https://doi.org/10.1002/ece3.1377

    • PubMed
    • Google Scholar
60. 1. Miller PJO
    2. Aoki K
    3. Rendell LE
    4. Amano M

    (2008) Stereotypical resting behavior of the sperm whale

    Current Biology 18:R21–R23.

    https://doi.org/10.1016/j.cub.2007.11.003

    • PubMed
    • Google Scholar
61. 1. Mitani Y
    2. Andrews RD
    3. Sato K
    4. Kato A
    5. Naito Y
    6. Costa DP

    (2010) Three-dimensional resting behaviour of northern elephant seals: drifting like a falling leaf

    Biology Letters 6:163–166.

    https://doi.org/10.1098/rsbl.2009.0719

    • PubMed
    • Google Scholar
62. 1. Mong JA
    2. Cusmano DM

    (2016) Sex differences in sleep: impact of biological sex and sex steroids

    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 371:20150110.

    https://doi.org/10.1098/rstb.2015.0110

    • PubMed
    • Google Scholar
63. 1. Nunn CL
    2. Samson DR
    3. Krystal AD

    (2016) Shining evolutionary light on human sleep and sleep disorders

    Evolution, Medicine, and Public Health 2016:227–243.

    https://doi.org/10.1093/emph/eow018

    • PubMed
    • Google Scholar
64. 1. Nunn CL
    2. Samson DR

    (2018) Sleep in a comparative context: Investigating how human sleep differs from sleep in other primates

    American Journal of Physical Anthropology 166:601–612.

    https://doi.org/10.1002/ajpa.23427

    • PubMed
    • Google Scholar
65. 1. Ohayon MM
    2. Carskadon MA
    3. Guilleminault C
    4. Vitiello MV

    (2004) Meta-analysis of quantitative sleep parameters from childhood to old age in healthy individuals: developing normative sleep values across the human lifespan

    Sleep 27:1255–1273.

    https://doi.org/10.1093/sleep/27.7.1255

    • PubMed
    • Google Scholar
66. 1. Olds T
    2. Blunden S
    3. Petkov J
    4. Forchino F

    (2010) The relationships between sex, age, geography and time in bed in adolescents: a meta-analysis of data from 23 countries

    Sleep Medicine Reviews 14:371–378.

    https://doi.org/10.1016/j.smrv.2009.12.002

    • PubMed
    • Google Scholar
67. 1. Patel SI
    2. Miller BW
    3. Kosiorek HE
    4. Parish JM
    5. Lyng PJ
    6. Krahn LE

    (2017) The Effect of Dogs on Human Sleep in the Home Sleep Environment

    Mayo Clinic Proceedings 92:1368–1372.

    https://doi.org/10.1016/j.mayocp.2017.06.014

    • PubMed
    • Google Scholar
68. 1. Qasem L
    2. Cardew A
    3. Wilson A
    4. Griffiths I
    5. Halsey LG
    6. Shepard ELC
    7. Gleiss AC
    8. Wilson R

    (2012) Tri-axial dynamic acceleration as a proxy for animal energy expenditure; should we be summing values or calculating the vector?

    PLOS ONE 7:e31187.

    https://doi.org/10.1371/journal.pone.0031187

    • PubMed
    • Google Scholar
69. 1. Qin DD
    2. Feng SF
    3. Zhang FY
    4. Wang N
    5. Sun WJ
    6. Zhou Y
    7. Xiong TF
    8. Xu XL
    9. Yang XT
    10. Zhang X
    11. Zhu X
    12. Hu XT
    13. Xiong L
    14. Liu Y
    15. Chen YC

    (2020) Potential use of actigraphy to measure sleep in monkeys: comparison with behavioral analysis from videography

    Zoological Research 41:437–443.

    https://doi.org/10.24272/j.issn.2095-8137.2020.056

    • PubMed
    • Google Scholar
70. Software

    1. R Development Core Team

    (2021) R: A language and environment for statistical computing

    R Foundation for Statistical Computing, Vienna, Austria.

    https://www.R-project.org/
71. 1. Rattenborg NC
    2. Lima SL
    3. Amlaner CJ

    (1999) Facultative control of avian unihemispheric sleep under the risk of predation

    Behavioural Brain Research 105:163–172.

    https://doi.org/10.1016/s0166-4328(99)00070-4

    • PubMed
    • Google Scholar
72. 1. Rattenborg NC
    2. Voirin B
    3. Vyssotski AL
    4. Kays RW
    5. Spoelstra K
    6. Kuemmeth F
    7. Heidrich W
    8. Wikelski M

    (2008) Sleeping outside the box: electroencephalographic measures of sleep in sloths inhabiting a rainforest

    Biology Letters 4:402–405.

    https://doi.org/10.1098/rsbl.2008.0203

    • PubMed
    • Google Scholar
73. 1. Rattenborg NC
    2. Voirin B
    3. Cruz SM
    4. Tisdale R
    5. Dell’Omo G
    6. Lipp H-P
    7. Wikelski M
    8. Vyssotski AL

    (2016) Evidence that birds sleep in mid-flight

    Nature Communications 7:12468.

    https://doi.org/10.1038/ncomms12468

    • PubMed
    • Google Scholar
74. 1. Rattenborg NC
    2. de la Iglesia HO
    3. Kempenaers B
    4. Lesku JA
    5. Meerlo P
    6. Scriba MF

    (2017) Sleep research goes wild: new methods and approaches to investigate the ecology, evolution and functions of sleep

    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 372:20160251.

    https://doi.org/10.1098/rstb.2016.0251

    • PubMed
    • Google Scholar
75. 1. Ray JC
    2. Sapolsky RM

    (1992) Styles of male social behavior and their endocrine correlates among high-ranking wild baboons

    American Journal of Primatology 28:231–250.

    https://doi.org/10.1002/ajp.1350280402

    • PubMed
    • Google Scholar
76. 1. Réale D
    2. Garant D
    3. Humphries MM
    4. Bergeron P
    5. Careau V
    6. Montiglio PO

    (2010) Personality and the emergence of the pace-of-life syndrome concept at the population level

    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 365:4051–4063.

    https://doi.org/10.1098/rstb.2010.0208

    • PubMed
    • Google Scholar
77. 1. Rechtschaffen A
    2. Bergmann BM

    (2002) Sleep Deprivation in the Rat: An Update of the 1989 Paper

    Sleep 25:18–24.

    https://doi.org/10.1093/sleep/25.1.18

    • Google Scholar
78. 1. Reinhardt KD
    2. Vyazovskiy VV
    3. Hernandez-Aguilar RA
    4. Imron MA
    5. Nekaris KAI

    (2019) Environment shapes sleep patterns in a wild nocturnal primate

    Scientific Reports 9:9939.

    https://doi.org/10.1038/s41598-019-45852-2

    • PubMed
    • Google Scholar
79. 1. Reinhardt KD

    (2020) Wild primate sleep: understanding sleep in an ecological context

    Current Opinion in Physiology 15:238–244.

    https://doi.org/10.1016/j.cophys.2020.04.002

    • Google Scholar
80. 1. Reyes KR
    2. Patel UA
    3. Nunn CL
    4. Samson DR

    (2021) Gibbon sleep quantified: the influence of lunar phase and meteorological variables on activity in Hylobates moloch and Hylobates pileatus

    Primates; Journal of Primatology 62:749–759.

    https://doi.org/10.1007/s10329-021-00920-y

    • PubMed
    • Google Scholar
81. 1. Riehl C
    2. Strong MJ

    (2018) Stable social relationships between unrelated females increase individual fitness in a cooperative bird

    Proceedings. Biological Sciences 285:20180130.

    https://doi.org/10.1098/rspb.2018.0130

    • PubMed
    • Google Scholar
82. 1. Roehrs T
    2. Kapke A
    3. Roth T
    4. Breslau N

    (2006) Sex differences in the polysomnographic sleep of young adults: a community-based study

    Sleep Medicine 7:49–53.

    https://doi.org/10.1016/j.sleep.2005.05.008

    • PubMed
    • Google Scholar
83. 1. Roth TC
    2. Rattenborg NC
    3. Pravosudov VV

    (2010) The ecological relevance of sleep: the trade-off between sleep, memory and energy conservation

    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 365:945–959.

    https://doi.org/10.1098/rstb.2009.0209

    • PubMed
    • Google Scholar
84. 1. Samson DR
    2. Nunn CL

    (2015) Sleep intensity and the evolution of human cognition

    Evolutionary Anthropology 24:225–237.

    https://doi.org/10.1002/evan.21464

    • PubMed
    • Google Scholar
85. 1. Samson DR
    2. Crittenden AN
    3. Mabulla IA
    4. Mabulla AZP
    5. Nunn CL

    (2017) Chronotype variation drives night-time sentinel-like behaviour in hunter-gatherers

    Proceedings. Biological Sciences 284:20170967.

    https://doi.org/10.1098/rspb.2017.0967

    • PubMed
    • Google Scholar
86. 1. Samson DR
    2. Bray J
    3. Nunn CL

    (2018) The cost of deep sleep: Environmental influences on sleep regulation are greater for diurnal lemurs

    American Journal of Physical Anthropology 166:578–589.

    https://doi.org/10.1002/ajpa.23455

    • PubMed
    • Google Scholar
87. 1. Sanford LD
    2. Suchecki D
    3. Meerlo P

    (2015) Stress, arousal, and sleep

    Current Topics in Behavioral Neurosciences 25:379–410.

    https://doi.org/10.1007/7854_2014_314

    • PubMed
    • Google Scholar
88. 1. Scriba MF
    2. Ducrest AL
    3. Henry I
    4. Vyssotski AL
    5. Rattenborg NC
    6. Roulin A

    (2013) Linking melanism to brain development: expression of a melanism-related gene in barn owl feather follicles covaries with sleep ontogeny

    Frontiers in Zoology 10:42.

    https://doi.org/10.1186/1742-9994-10-42

    • PubMed
    • Google Scholar
89. 1. Sellers WI
    2. Crompton RH

    (2004) Automatic monitoring of primate locomotor behaviour using accelerometers

    Folia Primatologica; International Journal of Primatology 75:279–293.

    https://doi.org/10.1159/000078939

    • PubMed
    • Google Scholar
90. 1. Shambroom JR
    2. Fábregas SE
    3. Johnstone J

    (2012) Validation of an automated wireless system to monitor sleep in healthy adults

    Journal of Sleep Research 21:221–230.

    https://doi.org/10.1111/j.1365-2869.2011.00944.x

    • PubMed
    • Google Scholar
91. 1. Sick C
    2. Carter AJ
    3. Marshall HH
    4. Knapp LA
    5. Dabelsteen T
    6. Cowlishaw G

    (2014) Evidence for varying social strategies across the day in chacma baboons

    Biology Letters 10:20140249.

    https://doi.org/10.1098/rsbl.2014.0249

    • PubMed
    • Google Scholar
92. 1. Siegel JM

    (2008) Do all animals sleep?

    Trends in Neurosciences 31:208–213.

    https://doi.org/10.1016/j.tins.2008.02.001

    • PubMed
    • Google Scholar
93. 1. Silk JB
    2. Beehner JC
    3. Bergman TJ
    4. Crockford C
    5. Engh AL
    6. Moscovice LR
    7. Wittig RM
    8. Seyfarth RM
    9. Cheney DL

    (2009) The benefits of social capital: close social bonds among female baboons enhance offspring survival

    Proceedings. Biological Sciences 276:3099–3104.

    https://doi.org/10.1098/rspb.2009.0681

    • PubMed
    • Google Scholar
94. 1. Snyder F

    (1966) Toward an evolutionary theory of dreaming

    The American Journal of Psychiatry 123:121–142.

    https://doi.org/10.1176/ajp.123.2.121

    • PubMed
    • Google Scholar
95. 1. Sri Kantha S
    2. Suzuki J

    (2006) Sleep quantitation in common marmoset, cotton top tamarin and squirrel monkey by non-invasive actigraphy

    Comparative Biochemistry and Physiology. Part A, Molecular & Integrative Physiology 144:203–210.

    https://doi.org/10.1016/j.cbpa.2006.02.043

    • PubMed
    • Google Scholar
96. 1. Steinmeyer C
    2. Schielzeth H
    3. Mueller JC
    4. Kempenaers B

    (2010) Variation in sleep behaviour in free-living blue tits, Cyanistes caeruleus: effects of sex, age and environment

    Animal Behaviour 80:853–864.

    https://doi.org/10.1016/j.anbehav.2010.08.005

    • Google Scholar
97. 1. Stickgold R

    (2005) Sleep-dependent memory consolidation

    Nature 437:1272–1278.

    https://doi.org/10.1038/nature04286

    • PubMed
    • Google Scholar
98. 1. Strandburg-Peshkin A
    2. Farine DR
    3. Couzin ID
    4. Crofoot MC

    (2015) GROUP DECISIONS shared decision-making drives collective movement in wild baboons

    Science (New York, N.Y.) 348:1358–1361.

    https://doi.org/10.1126/science.aaa5099

    • PubMed
    • Google Scholar
99. 1. Strandburg-Peshkin A
    2. Farine DR
    3. Crofoot MC
    4. Couzin ID

    (2017) Habitat and social factors shape individual decisions and emergent group structure during baboon collective movement

    eLife 6:e19505.

    https://doi.org/10.7554/eLife.19505

    • PubMed
    • Google Scholar
100. 1. Stuber EF
     2. Dingemanse NJ
     3. Kempenaers B
     4. Mueller JC

     (2015) Sources of intraspecific variation in sleep behaviour of wild great tits

     Animal Behaviour 106:201–221.

     https://doi.org/10.1016/j.anbehav.2015.05.025

     • Google Scholar
101. 1. Suzuki M
     2. Nakayama M
     3. Ando KB
     4. Arima S
     5. Nakamura Y
     6. Yokota M
     7. Murakami S

     (2018) Sleep Disturbance and Hyperactivity Detected by Actigraphy in Rats with Allergic Rhinitis or Attention-Deficit Hyperactivity Disorder

     The Tohoku Journal of Experimental Medicine 246:65–71.

     https://doi.org/10.1620/tjem.246.65

     • PubMed
     • Google Scholar
102. 1. Tamaki M
     2. Bang JW
     3. Watanabe T
     4. Sasaki Y

     (2016) Night Watch in One Brain Hemisphere during Sleep Associated with the First-Night Effect in Humans

     Current Biology 26:1190–1194.

     https://doi.org/10.1016/j.cub.2016.02.063

     • PubMed
     • Google Scholar
103. Book

     1. Treves A
     2. Palmqvist P

     (2007) Reconstructing Hominin Interactions with Mammalian Carnivores (6.0–1.8 Ma)

     In: Gursky SL, Nekaris KAI, editors. Primate Anti-Predator Strategies. Boston, MA: Springer. pp. 355–381.

     https://doi.org/10.1007/978-0-387-34810-0

     • Google Scholar
104. 1. van Hees VT
     2. Sabia S
     3. Anderson KN
     4. Denton SJ
     5. Oliver J
     6. Catt M
     7. Abell JG
     8. Kivimäki M
     9. Trenell MI
     10. Singh-Manoux A

     (2015) A Novel, Open Access Method to Assess Sleep Duration Using a Wrist-Worn Accelerometer

     PLOS ONE 10:e0142533.

     https://doi.org/10.1371/journal.pone.0142533

     • PubMed
     • Google Scholar
105. 1. van Hees VT
     2. Sabia S
     3. Jones SE
     4. Wood AR
     5. Anderson KN
     6. Kivimäki M
     7. Frayling TM
     8. Pack AI
     9. Bucan M
     10. Trenell MI
     11. Mazzotti DR
     12. Gehrman PR
     13. Singh-Manoux BA
     14. Weedon MN

     (2018) Estimating sleep parameters using an accelerometer without sleep diary

     Scientific Reports 8:12975.

     https://doi.org/10.1038/s41598-018-31266-z

     • PubMed
     • Google Scholar
106. 1. Voirin B
     2. Scriba MF
     3. Martinez-Gonzalez D
     4. Vyssotski AL
     5. Wikelski M
     6. Rattenborg NC

     (2014) Ecology and neurophysiology of sleep in two wild sloth species

     Sleep 37:753–761.

     https://doi.org/10.5665/sleep.3584

     • PubMed
     • Google Scholar
107. 1. Vyazovskiy VV

     (2015) Sleep, recovery, and metaregulation: explaining the benefits of sleep

     Nature and Science of Sleep 7:171–184.

     https://doi.org/10.2147/NSS.S54036

     • PubMed
     • Google Scholar
108. Book

     1. Ward A
     2. Webster M

     (2016) Sociality: The Behaviour of Group-Living Animals

     Springer.

     https://doi.org/10.1007/978-3-319-28585-6

     • Google Scholar
109. 1. Webster MM
     2. Rutz C

     (2020) How STRANGE are your study animals?

     Nature 582:337–340.

     https://doi.org/10.1038/d41586-020-01751-5

     • PubMed
     • Google Scholar
110. 1. Willems EP
     2. van Schaik CP

     (2017) The social organization of Homo ergaster: Inferences from anti-predator responses in extant primates

     Journal of Human Evolution 109:11–21.

     https://doi.org/10.1016/j.jhevol.2017.05.003

     • PubMed
     • Google Scholar
111. 1. Winnebeck EC
     2. Fischer D
     3. Leise T
     4. Roenneberg T

     (2018) Dynamics and Ultradian Structure of Human Sleep in Real Life

     Current Biology 28:49–59.

     https://doi.org/10.1016/j.cub.2017.11.063

     • PubMed
     • Google Scholar
112. 1. Wood SN

     (2011) Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models

     Journal of the Royal Statistical Society 73:3–36.

     https://doi.org/10.1111/j.1467-9868.2010.00749.x

     • Google Scholar
113. 1. Wrangham R
     2. Carmody R

     (2010) Human adaptation to the control of fire

     Evolutionary Anthropology 19:187–199.

     https://doi.org/10.1002/evan.20275

     • Google Scholar
114. 1. Wright AJ
     2. Akamatsu T
     3. Mouritsen KN
     4. Sveegaard S
     5. Dietz R
     6. Teilmann J

     (2017) Silent porpoise: potential sleeping behaviour identified in wild harbour porpoises

     Animal Behaviour 133:211–222.

     https://doi.org/10.1016/j.anbehav.2017.09.015

     • Google Scholar
115. 1. Yoder JM
     2. Marschall EA
     3. Swanson DA

     (2004) The cost of dispersal: predation as a function of movement and site familiarity in ruffed grouse

     Behavioral Ecology 15:469–476.

     https://doi.org/10.1093/beheco/arh037

     • Google Scholar
116. 1. Zhdanova IV
     2. Geiger DA
     3. Schwagerl AL
     4. Leclair OU
     5. Killiany R
     6. Taylor JA
     7. Rosene DL
     8. Moss MB
     9. Madras BK

     (2002) Melatonin promotes sleep in three species of diurnal nonhuman primates

     Physiology & Behavior 75:523–529.

     https://doi.org/10.1016/s0031-9384(02)00654-6

     • PubMed
     • Google Scholar

Author details

1. J Carter Loftus

   1. Department of Anthropology, University of California, Davis, Davis, United States
   2. Department for the Ecology of Animal Societies, Max Planck Institute of Animal Behavior, Konstanz, Germany
   3. Department of Biology, University of Konstanz, Konstanz, Germany
   4. Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany
   5. Mpala Research Centre, Nanyuki, Kenya
   6. Animal Behavior Graduate Group, University of California, Davis, Davis, United States

   Contribution

   Conceptualization, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review and editing

   For correspondence

   jcloftus@ucdavis.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0723-9159

2. Roi Harel

   1. Department for the Ecology of Animal Societies, Max Planck Institute of Animal Behavior, Konstanz, Germany
   2. Mpala Research Centre, Nanyuki, Kenya

   Contribution

   Conceptualization, Formal analysis, Investigation, Methodology, Supervision, Validation, Visualization, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9733-8643

3. Chase L Núñez

   1. Department for the Ecology of Animal Societies, Max Planck Institute of Animal Behavior, Konstanz, Germany
   2. Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany
   3. Mpala Research Centre, Nanyuki, Kenya

   Contribution

   Investigation, Methodology, Supervision, Validation, Visualization, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3273-6966

4. Margaret C Crofoot

   1. Department of Anthropology, University of California, Davis, Davis, United States
   2. Department for the Ecology of Animal Societies, Max Planck Institute of Animal Behavior, Konstanz, Germany
   3. Department of Biology, University of Konstanz, Konstanz, Germany
   4. Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany
   5. Mpala Research Centre, Nanyuki, Kenya
   6. Animal Behavior Graduate Group, University of California, Davis, Davis, United States

   Contribution

   Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing – review and editing

   For correspondence

   mcrofoot@ab.mpg.de

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0056-7950

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