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


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Objectives Primates spend almost half their lives asleep, yet we know little about how evolution has shaped variation in the duration or intensity of sleep (i.e., sleep regulation) across primate species. Our objective was to test hypotheses related to how sleeping site security influences sleep intensity in different lemur species. Methods We used actigraphy and infrared videography to generate sleep measures in 100 individuals (males = 51, females = 49) of seven lemur species (genera: Eulemur, Lemur, Propithecus, and Varecia) at the Duke Lemur Center in Durham, NC. We also generated experimental data using sleep deprivation for 16 individuals. This experiment used a pair‐wise design for two sets of paired lemurs from each genus, where the experimental pair experienced a sleep deprivation protocol while the control experienced normal sleeping conditions. We calculated a sleep depth composite metric from weighted z scores of three sleep intensity variables. Results We found that, relative to cathemeral lemurs, diurnal Propithecus was characterized by the deepest sleep and exhibited the most disruptions to normal sleep‐wake regulation when sleep deprived. In contrast, Eulemur mongoz was characterized by significantly lighter sleep than Propithecus, and E. mongoz showed the fewest disruptions to normal sleep‐wake regulation when sleep deprived. Security of the sleeping site led to greater sleep depth, with access to outdoor housing linked to lighter sleep in all lemurs that were studied. Conclusions We propose that sleeping site security was an essential component of sleep regulation throughout primate evolution. This work suggests that sleeping site security may have been an important factor associated with the evolution of sleep in early and later hominins.
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The cost of deep sleep: Environmental influences on sleep
regulation are greater for diurnal lemurs
David R. Samson
Joel Bray
Charles L. Nunn
Department of Anthropology, University of
Toronto, Mississauga
Evolutionary Anthropology, Duke
University, Tempe, AZ
School of Human Evolution and Social
Change, Arizona State University
Duke Global Health Institute, Duke
David R. Samson, Department of
Anthropology, 19 Russell St, M5S 2S2,
University of Toronto, Mississauga.
Objectives: Primates spend almost half their lives asleep, yet we know little about how evolution
has shaped variation in the duration or intensity of sleep (i.e., sleep regulation) across primate spe-
cies. Our objective was to test hypotheses related to how sleeping site security influences sleep
intensity in different lemur species.
Methods: We used actigraphy and infrared videography to generate sleep measures in 100 indi-
viduals (males 551, females 549) of seven lemur species (genera: Eulemur,Lemur,Propithecus,and
Varecia) at the Duke Lemur Center in Durham, NC. We also generated experimental data using
sleep deprivation for 16 individuals. This experiment used a pair-wise design for two sets of paired
lemurs from each genus, where the experimental pair experienced a sleep deprivation protocol
while the control experienced normal sleeping conditions. We calculated a sleep depth composite
metric from weighted zscores of three sleep intensity variables.
Results: We found that, relative to cathemeral lemurs, diurnal Propithecus was characterized by
the deepest sleep and exhibited the most disruptions to normal sleep-wake regulation when sleep
deprived. In contrast, Eulemur mongoz was characterized by significantly lighter sleep than Propithe-
cus,andE. mongoz showed the fewest disruptions to normal sleep-wake regulation when sleep
deprived. Security of the sleeping site led to greater sleep depth, with access to outdoor housing
linked to lighter sleep in all lemurs that were studied.
Conclusions: We propose that sleeping site security was an essential component of sleep regula-
tion throughout primate evolution. This work suggests that sleeping site security may have been
an important factor associated with the evolution of sleep in early and later hominins.
activity, lemur, primate evolution sleep intensity, sleep regulation
The function of sleep remains a mystery. Sleep is a complex behavior
(Vyazovskiy & Delogu, 2014; Webb, 1988) and several functions
have been hypothesized, including energy restoration, immunocom-
petence, brain metabolic homeostasis, neural ontogenesis, and cog-
nitive and emotional processing (McNamara & Auerbach, 2010;
Preston, Capellini, McNamara, Barton, & Nunn, 2009; Walker, 2009;
Xie et al., 2013). One dimension of sleep involves its architecture,
such as the durations of REM and NREM (and the rate of cycling
between these states). Numerous studies have investigated how
ecological factors influence these dimensions of sleep among indi-
viduals and across species (Campbell & Tobler, 1984; Capellini, Bar-
ton, McNamara, Preston, & Nunn, 2008; Lesku et al., 2012; Lesku,
Roth, Amlaner, & Lima, 2006; Zepelin, Siegel, & Tobler, 2005). More-
over, recent work has suggested that, relative to nonhuman prima-
tes, sleep in humans is evolutionarily exceptional, departing from
patterns expected in other primates (Samson & Nunn, 2015; Nunn &
Samson, 2018, in this volume). These studies revealed, for example,
that humans have the shortest sleep duration but the greatest pro-
portion of that duration dedicated to REM (rapid eye movement)
C2018 Wiley Periodicals, Inc. Am J Phys Anthropol. 2018;166:578589.
Received: 5 July 2017
Revised: 21 February 2018
Accepted: 26 February 2018
DOI: 10.1002/ajpa.23455
Sleep intensity, defined here as compensatory process for too
much or too little sleep, is another dimension of sleep that is critical to
the homeostatic sleep drive (Borbely, 1982; Borbely & Neuhaus, 1979).
The primary measure of sleep intensity is the relative proportion of
EEG slow wave activity (SWA; defined as EEG delta waves between
0.5 and 4 Hz) within nonrapid eye movement (NREM). An individual
can maintain a relatively constant quota of sleep by having either lon-
ger duration sleep or more intense sleep, as found in responses to sleep
deprivation in humans (Dijk, Beersma, & Daan, 1987; Feinberg et al.,
1985; Werth, Dijk, Achermann, & Borbely, 1996) and nonhuman ani-
mals such as mice, hamsters, rats, squirrels and cats (Franken, Dijk,
Tobler, & Borbely, 1991; Tobler, 2011), including unihemispheric
sleepers such as dolphins (Oleksenko, Mukhametov, Polyakova, Supin,
& Kovalzon, 1992). This process of sleep regulation is a homeostatic
balance between sleep duration and sleep intensity that aims to main-
tain a constant species-typical amount of daily sleep necessary for nor-
mal, healthy function.
Tobler (2011) notes that definitions of sleep should consider regu-
latory processes, including the interaction between sleep duration and
intensity. Sleep intensity is relevant for understanding sleep in wild ani-
mals, where individuals are faced with many risks when sleeping,
including increased predation, dangers from inclement weather, social
competition, and opportunity costs of foraging, searching for mates, or
caring for offspring.
Sleep durations have been observed in most primate studies, yet
due to the challenges of measuring intensity (traditionally measured
using invasive EEG), few studies have reported variables that target
sleep intensity or enable its comparison among species (Nunn, McNa-
mara, Capellini, Preston, & Barton, 2010). When EEG data are lacking,
secondary measures rely on behavioral coding of activity threshold,
sleep continuity (defined as the frequency of short wake episodes), and
motor activity (Tobler, 2011). When compared to short, fragmented
sleep epochs, long, consolidated sleep epochs have been demonstrated
to exhibit increased recovery power (Vyazovskiy, Achermann, & Tobler,
2007). In response to sleep deprivation, measures of sleep intensity are
altered, involving increases in sleep depth that are characterized by
decreased frequency of motor movements, and less fragmented sleep
(Franken et al., 1991). Although EEG-based measures of sleep intensity
are preferred, experimental studies have identified behavioral correlates
of sleep intensity. For example, in rodents, the reduction in the number
of brief awakenings correlates with increased SWA (Franken et al.,
1991; Tobler, Franken, & Jaggi, 1993; Tobler et al., 1996). In sleep-
deprived dogs, motor activity measured continuously using actigraphy
was reduced up to 40% during recovery (Tobler & Sigg, 1986). Addition-
ally, research has demonstrated a similar reduction in motor activity in
sleep-deprived humans (Naitoh, Muzet, Johnson, & Moses, 1973).
Cathemerality (activity throughout the 24-hr circadian cycle) is
common in several lemur species, despite being rare in anthropoid pri-
mates (Curtis & Rasmussen, 2006; Halle, 2006; Tattersall, 1987).
Lemurs are endemic to Madagascar, an island that is characterized by a
hyper-variable environment (Dewar & Richard, 2007). Climactic unpre-
dictability, which can influence the distribution of light, temperature,
and circadian variation in predator activity, has been suggested to
influence variation in lemur activity patterns (Donati & Borgognini-
Tarli, 2006; Wright, 1999). This environmental variation makes lemurs
a valuable sytsem in which to investigate sleep regulation in primates.
However, seasonal variation in environmental factors masks endoge-
nous circadian rhythms, making it difficult to identify species-typical
activity patterns. Masking factors in lemurs include temperature, moon-
light, availability of food, and day length (Curtis, Zaramody, & Martin,
1999; Donati, Baldi, Morelli, Ganzhorn, & Borgognini-Tarli, 2009; Don-
ati & Borgognini-Tarli 2006; Eppley, Ganzhorn, & Donati, 2015). Impor-
tantly, captive studies provide a method to overcome the challenges of
categorizing activity pattern by controlling for environmental variables
that influences sleep-wake regulationthereby providing a comple-
mentary approach to determining endogenous activity patterns (Rat-
tenborg et al., 2017).
Recent studies have challenged the historical classification of activ-
ity patterns in the Lemuridae. For example, traditionally, cathemeral
species have included Eulemur, whereas species such as Varecia rubra,
V. variegata,andL. catta have been classified as diurnal. At a number of
different sites, however, notable variation has been reported in the
degree of nocturnal activity in L.catta. L. catta has been documented
to have shown some nocturnal activity at some sites (Donati, Santini,
Razafindramanana, Boitani, & Borgognini-Tarli, 2013; LaFleur et al.,
2014), while they were more strictly diurnal at several other sites
(Sauther et al., 1999; Sussman et al., 2012). Reports of cathemeral
behavior in wild V. variegata have also been published (Donati &
Borgognini-Tarli, 2006). In previous captive work, Bray, Samson, and
Nunn (2017) used actigraphy at the Duke Lemur Center (DLC) to gen-
erate data on seven lemur species and showed that Propithecus coquer-
eli engaged in the least amount of nocturnal activity and that Varecia
and Lemur deviated from the diurnal Propithecus pattern. Previous cap-
tive work performed on five lemur species at the DLC revealed similar
findings (Rea, Figueiro, Jones, & Glander, 2014). Thus, based on these
recent findings in this captive environment, we classify Lemur and Vare-
cia as cathemeral, and compare them specifically to an unequivocally
categorized diurnal speciesPropithecus.
Studying sleep in primates presents several challenges. For exam-
ple, polysomnography (PSG), a multiparametric test that records both
brain and body functions and serves as the standard method for study-
ing sleep in captive mammals, is impractical due to invasive surgical
procedures that involve fitting electrodes on the brains surface (Sri
Kantha & Suzuki, 2006). Primary measures in PSG are electroencepha-
lography (EEG) and electromyography (EMG), and their application to
captive animals typically involves resource intensive surgery, a signifi-
cant recovery period, and risk of infection. Moreover, most primates
have strong grooming instincts that would result in removal of these
devices, especially when animals are housed socially. These negative
consequences eliminate the use of EEG in non-research institutions
(i.e., zoos and sanctuaries) that have strict guidelines for animal welfare
and maintain animals in species-typical social groups.
The limitations of EEG have recently been overcome through tech-
nological advances involving cost-effective actigraphy and infrared vid-
eography (Andersen, Diaz, Murnane, & Howell, 2013; Barrett et al.,
2009; Kantha & Suzuki, 2006; Zhdanova et al., 2002). Here, we used
actigraphic data and videography to investigate the factors that influ-
ence proxies for sleep intensity in seven different species of lemurs at
the DLC, including through experimental sleep deprivation in 16 indi-
vidual of four species. To investigate the links between environmental
parameters and sleep, we tested two hypotheses: (1) lemur sleep inten-
sity is influenced by the security of sleeping sites, and (2) more flexibly
active cathemeral lemurs show less strict sleep regulation, as compared
to more strictly diurnal lemurs. On the basis of the first hypothesis, we
predicted that lemurs would exhibit less fragmentation, arousal, and
short sleep bouts when they are housed in the safety of less dynamic,
indoor enclosures. Based on the second hypothesis, which assumes
that sleep flexibility is achieved through a weaker homeostatic drive,
we predicted that diurnal lemurs (Propithecus sp.) would exhibit less
fragmentation, reduced number of arousals, and shorter sleep bouts
when compared to cathemeral genera (Eulemur, Lemur,andVarecia).
We further predicted that diurnal lemurs would show more deviations
from normal (control) activity patterns in response to experimentally
induced sleep deprivation.
Study subjects
We generated actigraphic data from seven lemur species totaling 100
individuals with a nearly equal sex ratio (male n551, female n549;
see Table 1). Complete biographic information is available in Bray et al.
(2017). Subjects were housed at the DLC in Durham, NC. Eulemur spe-
cies were generally housed in adult pairs along with any dependent
offspring (Colquhoun, 2006; Tattersall, 1975), while Lemur catta (Jolly,
1966; Sauther et al., 1999), Propithecus coquereli (Richard et al., 1991),
and Varecia species (Britt, 2000; Vasey, 2007) were typically housed in
multimale-multifemale groups. All animals had unlimited access to
water and received fresh fruit, vegetables, and Purina monkey chow
daily. All animal use and methods were approved by the Duke Univer-
sity Institutional Animal Care and Use committee (Protocol #: A236-
13-09) and the DLC Research Committee.
Data collection
The baseline study was conducted over 11 months from January 2014
to November 2014. Daily activity was continuously recorded using
MotionWatch 8 (CamNtech) tri-axial accelerometers generating a data-
set totaling 596 days. These actigraphic sensors are lightweight (7g),
and attached to standard nylon pet collars. Animals were monitored to
ensure no adverse reactions to the collar; subjects acclimated to the
collars within 2 hr. Most subjects wore the collars between 6 and 8
days, although a small subset of L. catta subjects were collared for 68
73 days to generate longitudinal data. Housing (i.e., the sleeping site
environment) was recorded for each night of sampling (indoor only,
indoor and outdoor enclosure access, and free-range forest access).
Each days recording was indexed by several independent variables:
day length (the difference between sunrise and sunset times), moon-
phase (continuously between 1 5full moon and 0 5new moon), and
mean nighttime temperature (8C).
Dependent variables were generated from processed activity logs
recorded at one-minute epochs. The sensor sampled movement once a
TABLE 1 Sleep duration (total sleep time), sample size, and activity pattern classification summary information for the lemur species in this
Species Common name Mean TST (hr) NSample Activity pattern and references
Eulemur coronatus Crowned lemur 8.96 61.58 56 9 Cathemeral (Freed, 1996)
(Bray et al., 2017)
Eulemur flavifron Blue-eyed black lemur 8.84 61.72 61 12 Cathemeral (Schwitzer et al., 2007)
(Bray et al., 2017)
Eulemur mongoz Mongoose lemur 13.68 62.40 79 11 Cathemeral (Andriatsarafara, 1998)
(Rea et al., 2014)
(Bray et al., 2017)
Eulemur spp. 9.96 61.65 196 32
Lemur catta Ring-tailed lemur 11.05 61.68 168 29 Moderate cathemerality
(Donati et al., 2013)
(LaFleur et al., 2014)
(Rea et al., 2014)
(Bray et al., 2017)
Propithecus coquereli Coquerels sifaka 10.63 61.92 128 22 Diurnal (Erkert & Kappeler, 2004)
(Rea et al., 2014)
(Bray et al., 2017)
Varecia rubra Red ruffed lemur 9.81 61.85 79 13 Moderate cathemerality (Rea et al., 2014)
(Bray et al., 2017)
Varecia variegata Black-and-white
ruffed lemur
10.90 62.15 25 4 Moderate cathemerality (Rea et al., 2014)
(Balko in Wright, 1999)
(Bray et al., 2017)
Varecia spp. 10.36 h 62.00 104 17
Wild study.
Captive study.
N5the number of 24-hr periods where values were derived for TST. Sample 5the number of individuals that contributed to the sample to produce
the mean TST values.
second at 50 Hz and accumulated data (which outputs on a ratio scale),
ultimately assigning an activity value per 1-min epoch. Recent advances
in scoring algorithms have increased accuracy in detecting wake-sleep
states and total sleep times (Stone & Ancoli-Israel, 2011). Using actigra-
phy data, we generated total average sleep times for each species. As
in previous studies quantifying sleep in primates (Andersen et al., 2013;
Barrett et al., 2009; Kantha & Suzuki, 2006; Zhdanova et al., 2002), we
used the definition of sleep in actigraphy as the absence of any force in
any direction during the measuring period (i.e., one minute epoch)
(Campbell & Tobler, 1984).
Kawada (2013) notes that actigraphy is not a substitute for sleep
measures generated by polysomnography, which directly quantifies brain
activity, and cautions that actigraphy can overestimate sleep given the
lack of sensitivity for arriving at sleep-wake differentiation. In addition to
these general limitations of actigraphy, sleep-wake algorithms have been
developed and validated for humans, but not for nonhuman primates. We
arrived at a cutoff value for sleep-wake determination based on ground-
truthed validation that used infrared videography (AXIS P3364-LVE Net-
work Camera) to determine that animals were consistently at rest (i.e.,
sustained quiescence in a species-specific posture) when actigraphy val-
ues were less than four. We performed this videographic analysis ran-
domly throughout the night and for each species. Observing the range of
values from all epochs in our dataset, we noted a clear break, with values
from one to three being absent. Confirming the validity of this break,
video recordings of epochs with values of zero were clearly inactive,
whereas values of four or more showed small-scale behavior such as sub-
jects visually scanning their environment.
To assess measures of inferred sleep intensity, the following varia-
bles were derived from nighttime recordings: sleep motor activity is the
number of motor activity bouts per hour; this value was derived by
assigning each epoch either a 0or 1based on whether there was
activity (raw activity counts >4) scored during the epoch (assigning a 1)
or not (assigning a 0), and was assigned to only single epochs of activity
preceded and followed by inactivity. Sleep fragmentation is the number
of awakenings greater or equal to two minutes of consecutive activity
per hour. Short sleep bouts are the number of brief inactive episodes
per hour, lasting only one epoch and preceded and followed by activity.
To provide a measure of underlying inferred sleep intensity, a sleep
depth composite (SDC) score was calculated (by first transforming raw
scores into zscores and then generating a sum each categorical zscore)
using the unit-weighted zscores (Ackerman & Cianciolo, 2000) of the
three sleep intensity variables. For sleep intensity measures, we used
previous methods for studying sleep in primates (Barrett et al., 2009;
Zhdanova et al., 2002). We analyzed recorded variables from 12-hr
periods between 18:00 and 06:00 (following the DLC lights-off/staff
away time period). Definitions for sleep intensity variables follow those
outlined in previous work (Samson & Shumaker, 2015).
Experimental procedure
The experimental procedure was conducted over 2 months from
September to October 2015. In a pair-wise experimental design (focus-
ing on cathemeral Eulemur and diurnal Propithecus) two sets of paired
lemurs (total n54 from each species) underwent 2 weeks of simulta-
neous testing. During the same night, the experimental pair experi-
enced a sleep deprivation procedure while the other pair (housed in a
different wing) experienced normal sleeping conditions. To achieve
sleep deprivation, the lemurs experienced 10 hr (from 18:00 to 04:00)
of audio playbacks of <30-s duration every five minutes; the following
day, the pairs were switched and the experimental pair became the
control pair and vice versa. The audio stimuli included the following
noises randomly emitted playbacks: cage doors closing, dishes falling,
general daytime DLC ambient noise, and inclement weather. We used
four different sound sequences per category for 16 total possible play-
backs. The playbacks dB level ranged from 60 to 100 dB. In addition to
actigraphy data, we used infrared videography to determine whether
animals were awakened by sounds. Using videography, we also deter-
mined species-specific responses to playback to ensure animal welfare.
The typical response to playback was an opening of the eyes and a
more upright body posture. We monitored post nighttime period
behavior for increased aggression or signs of distress, which were not
observed by us or DLC staff that also monitored the animals.
Data analysis
We generated descriptive statistics characterizing the nightly distribu-
tion of total sleep time and sleep intensity among lemurs by individual,
species, sex, and activity pattern. Activity patterns were assessed in a
companion study (Bray et al., 2017), which corroborates recent studies
showing that Propithecus is diurnal (Erkert & Kappeler, 2004) and Eule-
mur spp. cathemeral (Donati et al., 2013; LaFleur et al., 2014), and fur-
ther suggesting that Varecia spp. And L. catta demonstrate moderate
expression of cathemerality (see above, and also Rea et al., 2014).
Statistical analyses were conducted using Rversion 3.1.3 (R Core
Team, 2016) and IBM SPSS 22. To assess total sleep times, we used
the accelerometry package (Van Domelen, 2015) to process 24-hr peri-
ods of actigraphy. Averaged nightly sleep intensity variables were
checked for normality with Kolmogorov-Smirnov tests. Because of
non-normal distributions of data, we used Spearmans rank correlation
coefficients to examine relationships among activity patterns and sleep
To assess the predictors of sleep intensity, we built a linear mixed
effects model for the SDC using the lme4 package (Bates, Mächler,
Bolker, & Walker, 2015). Species was used as a fixed-effect as a proxy
for activity pattern, and comparisons were made to Propithecus (the
only unambiguously diurnal species) as the reference taxon. Other fixed
effects in the model were sex as well as nighttime temperature, day
length, and housing access. Two interactions were assumed in the
model: (1) temperature and housing access and (2) temperature and
daylength. To control for repeated measures, we included subjectas
random effects. We obtained parameter estimates using optimization
of the log-likelihood. We averaged statistical models with DAIC <10,
and we used the MuMIn package (Barto
n, 2015). Statistical inferences
were made using standardized coefficient estimates with shrinkage and
95% confidence intervals.
TABLE 2 Descriptive statistics characterizing baseline lemur sleep intensity by species.
Variable Genus NMean SE Range
Sleep motor activity (per hour) E. coronatus 19 23.0 0.73 9.718.0
E. flavifron 49 20.4 0.69 11.431.8
E. mongoz 55 16.5 0.62 9.627.3
Eulemur spp. 123 20.0 0.68
L. catta 145 18.3 0.36 0.026.7
P. coquereli 81 14.3 0.50 6.824.3
V. rubra 16 23.5 1.98 14.141.3
V. variegata 9 20.2 1.23 12.425.4
Varecia spp. 25 21.9 1.6
Sleep fragmentation (per hour) E. coronatus 19 2.8 0.17 1.33.8
E. flavifron 49 2.6 0.09 1.54.3
E. mongoz 55 2.2 0.06 1.33.3
Eulemur spp. 123 2.5 0.11
L. catta 145 2.9 0.07 0.05.3
P. coquereli 81 2.5 0.12 1.05.6
V. rubra 16 3.1 0.32 1.86.2
V. variegata 9 3.8 0.23 2.54.5
Varecia spp. 25 3.5 0.28
Short sleep bout (per hour) E. coronatus 19 1.6 0.12 0.62.6
E. flavifron 49 1.2 0.07 0.53.2
E. mongoz 55 1.1 0.04 0.62.3
Eulemur spp. 123 1.3 0.08
L. catta 145 1.2 0.05 03.1
P. coquereli 81 1.3 0.07 0.23.0
V. rubra 16 1.6 0.30 0.44.4
V. variegata 9 1.8 0.10 1.52.45
Varecia spp. 25 1.7 0.20
Sleep depth composite E. coronatus 19 20.88 0.30 23.12.8
E. flavifron 49 20.07 0.18 23.15.7
E. mongoz 55 0.79 0.13 23.54.3
Eulemur spp. 123 20.58 0.20
L. catta 145 20.05 0.11 25.44.3
P. coquereli 81 0.57 0.21 24.812.7
V. rubra 16 21.16 0.72 24.09.3
V. variegata 921.56 0.34 23.32.8
Varecia spp. 25 21.36 0.53
Higher sleep depth composite (SDC) values indicate deeper sleep. To remove the confounds of temperature and dynamic sleep environments on sleep
intensity, free ranging sleep environments and extreme nighttime temperatures >208C were removed from this sample. N5the number of 24-hr peri-
ods where values were derived for sleep intensity variables.
Finally, to experimentally assess the influence of security of sleep-
ing site on sleep intensity, we performed a within species (L. catta)lin-
ear mixed effects model for SDC (see above protocol) on one male and
female for a total of 144 nights. The fixed effect was housing access
and we include subjectas a random effect. The sample was balanced
for indoor/outdoor vs. free-range sleep environments (i.e., Monday to
Thursday, subjects slept indoor/outdoor; Friday to Sunday they slept in
the free ranging environment).
Functional linear modeling (FLM) was used to assess deviations
from normal (control) activity patterns. The FLM approach, specifically
designed for actigraphy time-series data analysis, measures raw, activ-
ity counts within and between samples, and can overcome problems
when summary statistics mask differences across groups (Wang et al.,
2011). FLM was used to compare activity patterns, on the 24-hr cycle
(with Fourier smoothed averages), within species to assess the differen-
ces in sleep-wake activity between normal sleep and sleep-deprived
TABLE 3 The effect of predictor variables on the sleep depth composite (SDC)
Predictor bSE Confidence interval z Importance
Day length 0.18 0.14 (20.098, 0.449) 1.26 0.93
Outdoor access 20.20 0.10 (20.396, 20.001) 1.96 0.92
Temperature 20.38 0.56 (21.467, 0.716) 0.68 0.96
Male 20.18 0.08 (20.341, 20.025) 2.28 0.82
Temperature 3housing 0.30 0.15 (20.001, 0.603) 1.95 0.62
Temperature 3day length 0.99 0.70 (20.395, 2.374) 1.40 0.47
Eulemur coronatus 20.02 0.09 (20.195, 0.150) 0.25 0.20
Eulemur flavifron 0.03 0.09 (20.137, 0.201) 0.41 0.20
Eulemur mongoz 20.18 0.09 (20.364, 20.014) 2.11 0.20
Lemur catta 20.17 0.10 (20.362, 0.021) 1.73 0.20
Varecia rubra 0.05 0.10 (20.136, 0.246) 0.56 0.20
Varecia variagata 20.02 0.08 (20.176, 0.134) 0.27 0.20
Female is the reference category for sex, indoor access is the reference category for housing, and outgroup diurnal Propithecus is the reference category
for species. Positive coefficients indicate deeper sleep, while negative coefficients indicate lighter sleep. After correcting for fixed effects, outdoors
access negatively influenced sleep depth.
FIGURE 1 A longitudinal experiment to assess sleep security and sleep intensity in L. catta. Individuals (one male and one female) slept
more deeply when within secure indoor/outdoor enclosure compared to when they slept in dynamic free range environments. The effect
was similar for both the male and female, with the male characterized by greater sleep
(experimental) groups. All reported errors are standard deviations and
all significance tests were set at the level of P0.05.
Table 1 provides average total sleep times for seven lemur species,
based on 596 total days of actigraphy. Six of these are new reports for
species that had not previously been studied. Averaged or summed by
genus, total sleep durations (within a 24-hr period) were longest in
Lemur (11.05 hr 61.68), second longest in Propithecus (10.63 hr 6
1.92), third longest in Varecia (10.36 hr 62.00) and shortest in Eulemur
(9.96 61.65). A correlation matrix revealed that sleep intensity varia-
bles show significant positive linear relationships with one another
(range of correlation matrix: r50.420.89, N5100, p<0.01),
revealing that they make suitable variables with which to calculate a
sleep depth composite score (Ackerman & Cianciolo, 2000). SDC was
averaged for each genus to provide a baseline genus-specific measure
of sleep intensity. Varecia showed the least sleep intensity (1.36),
whereas Propithecus showed the greatest sleep intensity. Eulemur
(0.05) and Lemur (0.05) were characterized by moderate sleep intensity
(Table 2).
Lemur sleep intensity was influenced by security of sleeping sites
(Table 3). Based on the confidence intervals that excluded zero in the
model, lemurs were characterized by greater SDC when sleeping
indoors (Figure 1). Male lemurs were characterized by lower SDC. Of
all the species compared to the Propithecus reference taxon, Eulemur
FIGURE 3 Functional linear modeling comparison between normal
sleep and sleep-deprived lemurs. Propithecus (a diurnal lemur)
showed greater deviations from normal activity patterns than cath-
emeral lemurs when exposed to the experimental sleep deprivation
condition. When exposed to sleep deprivation, Propithecus is
characterized by depressed daytime activity and lower amplitude
activity at night. The panel illustrates both the maximum critical
value (a conservative pvalue threshold) and point-wise critical
value (less conservative pvalue); the blue hashed and dotted lines
are the proportion of all permutation Fvalues at each time point at
the significance level of 0.05. When the observed F-statistic (solid
line) is above the hashed or dotted line, it is concluded that the
two groups have significantly different mean circadian activity
patterns at those time points
FIGURE 2 Activity pattern and sleep intensity. Propithecus
characterized by a dirunal activity pattern (green) are more
sensitive to fluctuations in the environment than cathemerals
(blue). Specifically, diurnals are more sensitive (exhibiting lighter
sleep) to temperature fluctuation (left: diurnal slope,
y522.62 10.26*x, R
50.38; cathemeral slope,
y522.06 10.13*x, R
50.27) and environmental security (top
panel). Housing status influenced lemur SDC, but more so for
diurnal lemurs (ANOVA F54.64, df 5188, p50.032; bottom
mongoz was characterized by lower SDC. The confidence interval on
the estimates for the other variables overlapped with zero, suggesting
that these factors have weaker or less consistent effects on sleep
intensity. The experimental intraspecies (L. catta) mixed model that
controlled for repeated measures of subjects showed that nights spent
in the indoor/outdoor enclosures were characterized by deeper sleep
compared to nights when they had access to forest enclosures (SDC:
b6SE 520.19 60.09, p50.04, C.I.50.054, 0.358), where security
of sleeping site is expected to be lower.
Diurnal Propithecusnormal sleep-wake patterns were more
sensitive to fluctuations in the environment than the other
lemurs. Relative to other lemurs, Propithecus sleep was more dis-
turbed (i.e., a lower SDC value) on nights when they had outside
access and when temperatures were higher (see Figure 2). Addi-
tionally, the sleep deprivation experiment revealed that sleep-
deprived diurnal Propithecus was characterized by the greatest
number of significant deviations from normal sleep conditions.
Moreover, daytime periods after sleep deprivation show a recov-
ery period of less overall activity in diurnal Propithecus; but show
no such recovery period in Lemur and Varecia and Eulemur species
that deviate from traditional diurnality. By the conservative maxi-
mum critical value threshold, FLM analysis showed that Eulemur
experienced one significant alteration to their normal pattern,
whereas Propithecus experienced three significant alterations (see
Figure 3). Lemur experienced one significant alteration and Varecia
experienced no significant alterations from the normal sleep
This study investigated sleep intensity in lemurs in relation to the secu-
rity of sleeping sites. We found two lines of evidence supporting the
hypothesis that lemur sleep intensity is influenced by the security of
sleeping sites. First, our linear mixed model (Table 3 and Figure 1)
revealed that a strong predictor for SDC was housing conditions. That
is, subjects that had access to the outside enclosure exhibited lighter
sleep than subjects with indoor access only; additionally, the interaction
between housing and temperature indicated that sleep was lighter on
nights when subjects were outside and temperature was greater, as
compared to nights when subjects had indoor access only. Second, L.
catta in the longitudinal condition followed a similar trend, with lighter
sleep (lower SDC) on nights without access to secure indoor environ-
ments. Sleeping indoors provides an environmental buffer from noise,
rainfall, temperature extremes, exposure to moonlight, and perceived
predation threats, and thus may serve as a mediating factor that
increases depth of sleep. Collectively, these findings show that deep
sleep in lemurs is significantly influenced by the perceived security of
local sleep environments.
In support of the hypothesis that more flexibly active cathemeral
lemurs show less strict sleep regulation, as compared to more strictly
diurnal Propithecus, we found that diurnal lemurs are characterized by
deeper sleep and greater activity pattern disruption following exposure
to dynamic or stimulating environments. Not only did diurnal Propithe-
cus show marked differences (compared to cathemeral lemurs) in sleep
intensity in response to outdoor environments (Figure 2), but experi-
mental evidence showed that sleep deprivation alters diurnal more
than cathemeral activity patterns (Figure 3). Hence, cathemeral lemur
activity patterns may be less vulnerable to environmental fluctuation,
or it may be that transitions to and from a sleep state are less costly
given they can reboundanytime throughout the circadian cycle.
Until recently, sleep quotasthe basic parameters of sleep expres-
sionwere available for only 20 of the 350 or so recognized extant pri-
mate species (McNamara et al., 2008). The sleep intensity data
presented in this study also augment the data on this variable for non-
human primates (Table 2). The only other primate species with
recorded values for sleep intensity are Papio and Pongo (Samson &
Shumaker, 2015), Macaca (Kaemingk & Reite, 1987), Saimiri (Erny,
Wexler, & Moore-Ede, 1985) and humans (Naitoh et al., 1973). Some
hints of potentially interesting patterns emerge from this small sleep
intensity dataset. For example, it appears that one measure of sleep
intensitymotor activitymay show a phylogenetic signal, with Homo
sapiens being characterized by the least nighttime motor activity and
Eulemur coronatus (in our study) being characterized by the most (see
Figure 4). This hypothesis awaits sample sizes large enough to perform
formal phylogenetic tests.
As another example of general patterns to investigate, our analyses
of lemurs suggest that body mass may explain variation in sleep
FIGURE 4 Sleep motor activity as a measure of sleep intensity
across primates. Few studies have generated sleep intensity
values in primates, thus the sample size is not yet large enough
for a formal statistical analysis. Sleep motor activity is a measure
of sleep intensity and can be recorded noninvasively using
infrared videography. Descriptive statistics shown here suggest
that sleep motor activity, and thus sleep depth, may be a derived
trait in humans, with a trend of more light sleep being
characteristic of phylogenetically distant primates. Sleep intensity
values derived from unpublished data and integrated with data
from this study
intensity for other primates. One factor may be the ability to sleep in a
concealed and safe sleep site. For example, although wild Propithecus
has a substantial range of variation in body massthe smallest being
P. verreauxi at 2.8 kg (Richard et al., 2002) to largest P. diadema at 6.5
6.9 kg (Powzyk, 1997)the species in this study (P. coquereli)exhibitsa
body mass of 3.34.6 kg (Hartstone-Rose & Perry, 2011). Although
comparable to Varecia at 3.04.5 kg (Vasey, 2002), this was larger than
Eulemur at 1.482.47 kg (Terranova & Coffman, 1997) and Lemur at
2.2 kg (Sussman, 1991). Therefore, Propithecus may generally find it
more difficult to locate cryptic sleeping sites, such as in lianas, suggest-
ing the existence of a tradeoff between body mass and flexibility
in sleep timing and continuity. This interpretation would explain
the increased environmental sensitivity that we documented in
Another aspect of primate sleep evolution involves use of arboreal
sleeping platforms, which are often called nests.Phylogenetic recon-
struction estimates the innovation of ape nest construction sometime
between 18 and 14 million years ago (Duda & Zrzavy, 2013). Nest
building, coinciding with the evolution of increased body mass over the
30 kg threshold, suggests that larger body mass made sleeping on
branches less viable for these large-bodied apes (Samson, 2012;
Samson & Nunn, 2015). Arboreal sleeping platforms likely served multi-
ple functions (McGrew, 2004), including predation avoidance (Stewart
& Pruetz, 2013), thermoregulatory buffering (Stewart, 2011), reduced
insect and disease vector exposure (Samson, Muehlenbein, & Hunt,
2013; Stewart, 2011), and improved sleep quality (Samson &
Shumaker, 2015) and comfort (Stewart, Pruetz, & Hansell, 2007). The
transition from tree-branch to arboreal sleeping platform would have
been a stepwise improvement in the overall quality of sleeping sites
(Fruth & Hohmann, 1996). The next significant improvement in sleep-
ing site could have been the tree-to-ground transition, which likely
occurred with early Homo given the dramatic morphological changes
that took place during the Australopithecus-Homo transition (Coolidge &
Wynn, 2009). This evolutionary event could have then established the
prerequisite adaptations to alter early hominin sleep architecture,
where hominins would have benefited from more stable and less ther-
modynamically stressful sleeping sites (Samson & Hunt, 2012), and
could have combined shelter and bedding technology (Samson, Critten-
den, Mabulla, Mabulla, & Nunn, 2017b) and group level social cohesion,
promoting sentinel-like behavior (Samson, Crittenden, Mabulla, &
Mabulla, 2017c) to improve sleep intensity as a result of greater com-
fort and security at sleeping sites.
Greater quality sleeping environments may have been linked to
changes to cognitive ability (Fruth & Hohmann, 1996; Samson & Nunn,
2015). This hypothesis has garnered recent support through research
that investigated the link between sleep environment and cognitive
performance in nonhuman great apes. For example, captive orangutan
sleeping platform complexity, measured as an index of the number of
material items available to construct a bed, covaried positively with
reduced nighttime motor activity, less fragmentation, and greater sleep
efficiency (Samson & Shumaker, 2013). In another study of captive
apes undergoing experimental cognitive testing, sleep was shown to
stabilize and protect memories from interference (Martin-Ordas & Call,
2011). Future research should investigate the relationship between
cognition and sleep intensity and quality in more phylogenetically dis-
tant primates. If a link was established between cognition and sleep
intensity in lemurs, for example, and not just humans and apes, it would
suggest that the importance of sleep to cognition was an evolutionarily
conserved trait within primates.
Sleep is a time of great risk for animals, potentially resulting in
selection of safe sleep sites and greater vigilance when a safe site is
unavailable (Nunn et al., 2010). We see signatures of this risk in our
data, with lower sleep intensity when animals sleep outside, as com-
pared to greater sleep intensity when sleeping indoors where it is safer.
Lower sleep intensity in outdoor-sleeping lemurs may have been a
result of abiotic (e.g., inclement weather, variation in temperature, and
lunar phase) and biotic stimuli (e.g., calls from predatory animals). Our
data suggest that wild lemurs would benefit from deeper, more intense
would be more protected from these threats. Therefore, we propose
that sleeping site security is an essential component for regulation of
sleep in lemuriformes Evidence for the importance of sleeping sites for
sleep quality has been investigated in hominoids (Koops, McGrew, de
Vries, & Matsuzawa, 2012; Samson & Hunt, 2012, 2014; Stewart,
2011; Stewart & Pruetz, 2013; Stewart et al., 2007) and cercopithe-
coids (Bert, Balzamo, Chase, & Pegram, 1975). This conclusion suggests
that behaviors that influence sleeping site selection, thereby augment-
ing sleep quality, are evolutionarily conserved in primates and may be
critically important for primates with diurnal activity patterns.
Humans appear to be characterized by deeper sleep than phyloge-
netically distant primates, but they may share with lemurs the flexibility
in sleep phase. For example, controlled laboratory studies revealed
that, when exposed to a short photoperiod, human sleep becomes
unconsolidated (Wehr, 1999). Ethnographic work has demonstrated
that a variety of cultures (across subsistence regimes) often exhibit
nighttime activity and daytime napping (Worthman & Melby, 2002).
Historical records document a segmented sleep pattern associated
with European and equatorial preindustrial populations (Ekirch, 2016).
Sleep measured in a small scale traditional equatorial agricultural soci-
ety in Madagascar, without access to electricity, has been described as
segmentedor nocturnally biphasic with common noon-time napping
(Samson et al., 2017d) and Hadza hunter-gatherer sleep has been dem-
onstrated to be flexibly expressed in different social and ecological con-
texts (Samson, Crittenden, Mabulla, Mabulla, & Nunn, 2017a). These
studies support the notion that ancestral human sleep was more flexi-
ble than typically experienced today by Western populations, suggest-
ing perhaps even a biphasic, or polyphasic, pattern. This suggests that
as sleeping site security increased, early hominins may have been
permitted greater sleep intensity and flexibility in timing of sleep
periodswhich could have been a critical event marked by changes in
sleep architecture, cognition, and waking performance.
The authors are grateful to the staff at the Duke Lemur Center and
offer thanks to Erin Ehmke and David Brewer for continuous
support through all aspects of this research. They thank Randi Grif-
fin for feedback on statistical approaches used in this study. Finally,
they thank the Associate Editor and two anonymous reviewers for
constructive comments that significantly improved the quality of the
paper. This research was supported by Duke University.
David R. Samson
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... 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. ...
... 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). ...
... The algorithm from which the sleep classification technique is adapted is well-validated using polysomnography (C-statistic = 0.83-0.86) to both classify sleep behavior and determine the sleep period in humans (van Hees et al., 2015;van Hees et al., 2018). Although the classification of sleep in nonhuman primates using devices and algorithms that were validated with polysomnography only in humans has become a common practice in sleep research (Barrett et al., 2009;Brutcher and Nader, 2013;Reinhardt et al., 2019;Reyes et al., 2021;Samson et al., 2018;Sri Kantha and Suzuki, 2006;Zhdanova et al., 2002), we returned to the study site in July 2019 to validate the accelerometer-based sleep classification. Because logistical and ethical limitations prevent the use of polysomnography in free-ranging, highly dexterous animals, we compared the accelerometer-based sleep classification to direct observations of wakeful and sleeping baboons fit with accelerometer collars for validation ( Figure 5-figure supplement 1), as suggested by Rattenborg et al., 2017. ...
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Sleep is fundamental to the health and fitness of all animals. The physiological importance of sleep is underscored by the central role of homeostasis in determining sleep investment - following periods of sleep deprivation, individuals experience longer and more intense sleep bouts. Yet, most sleep research has been conducted in highly controlled settings, removed from evolutionarily relevant contexts that may hinder the maintenance of sleep homeostasis. Using triaxial accelerometry and GPS to track the sleep patterns of a group of wild baboons (Papio anubis), we found that ecological and social pressures indeed interfere with homeostatic sleep regulation. Baboons sacrificed time spent sleeping when in less familiar locations and when sleeping in proximity to more group-mates, regardless of how long they had slept the prior night or how much they had physically exerted themselves the preceding day. Further, they did not appear to compensate for lost sleep via more intense sleep bouts. We found that the collective dynamics characteristic of social animal groups persist into the sleep period, as baboons exhibited synchronized patterns of waking throughout the night, particularly with nearby group-mates. Thus, for animals whose fitness depends critically on avoiding predation and developing social relationships, maintaining sleep homeostasis may be only secondary to remaining vigilant when sleeping in risky habitats and interacting with group-mates during the night. Our results highlight the importance of studying sleep in ecologically relevant contexts, where the adaptive function of sleep patterns directly reflects the complex trade-offs that have guided its evolution.
... We generated twenty-four-hour total sleep times (TST) for individuals in each species. We followed protocols used in previous primate sleep studies [22][23][24][25][26] and in prior work by our group performed at the DLC [20,27]. The sensor sampled movement once per second at 50 Hz and assigned an activity value, referred to as "counts", per one-minute epoch. ...
... Following recommendations for validating actigraphy-based inferences of sleep state [30], a previous study compared infrared videography of sleeping lemurs to actigraphy counts from the same model of collar used in the current study. Lemurs could be clearly seen in the videos to make minor movements such as looking around or adjusting body position during 1 minute epochs with four or more activity counts, but such motions were not detected during epochs with fewer than four activity counts [27]. Nonetheless, using motion as a proxy for sleep leaves open the possibility that we may detect periods of relaxed wakefulness as sleep, and thus requires appropriate care when interpreting the results. ...
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Characteristics of the sleep-site are thought to influence the quality and duration of primate sleep, yet only a handful of studies have investigated these links experimentally. Using actigraphy and infrared videography, we quantified sleep in four lemur species ( Eulemur coronatus , Lemur catta , Propithecus coquereli , and Varecia rubra ) under two different experimental conditions at the Duke Lemur Center (DLC) in Durham, NC, USA. Individuals from each species underwent three weeks of simultaneous testing to investigate the hypothesis that comfort level of the sleep-site influences sleep. We obtained baseline data on normal sleep, and then, in a pair-wise study design, we compared the daily sleep times, inter-daily activity stability, and intra-daily activity variability of individuals in simultaneous experiments of sleep-site enrichment and sleep-site impoverishment. Over 164 24-hour periods from 8 individuals (2 of each species), we found evidence that enriched sleep-sites increased daily sleep times of lemurs, with an average increase of thirty-two minutes. The effect of sleep-site impoverishment was small and not statistically significant. Though our experimental manipulations altered inter-daily stability and intra-daily variability in activity patterns relative to baseline, the changes did not differ significantly between enriched and impoverished conditions. We conclude that properties of a sleep-site enhancing softness or insulation, more than the factors of surface area or stability, influence lemur sleep, with implications regarding the importance of nest building in primate evolution and the welfare and management of captive lemurs.
... With the development of non-invasive methods of observing nocturnal behavior among apes (Morimura et al. 2012;Mizuno et al. 2006;Videan 2006) and monkeys (Zhdanova et al. 2002;Ancoli-Israel et al. 2003), EEG has given way to a multifaceted approach including actigraphy (Bray et al. 2017;Samson et al. 2018) and high-sensitivity video recordings as the preferred method of measuring primate sleep (Muñoz-Delgado et al. 1995;Balzamo et al. 1998). Actigraphy can accurately detect sleep vs. wake states when compared with EEG (Ancoli-Israel et al. 2003;Johnson et al. 2007;Samson et al. 2016) and overall has the advantage of being less invasive and more cost-efficient than EEG. ...
... Recent advances in scoring algorithms have increased accuracy in detecting wake-sleep states and total sleep times (Stone and Ancoli-Israel 2017). We followed protocols used in previous primate sleep studies (Andersen et al. 2013;Barrett et al. 2009;Kantha and Suzuki 2006a, b;Zhdanova et al. 2002), including previous research using these devices on the multiple species included in this study (Eulemur coronatus, Eulemur flavifrons, Eulemur mongo, Lemur catta, Propithecus coquereli, Varecia rubra, Varecia variegata) at the Duke Lemur Center (Bray et al. 2017;Samson et al. 2018;Samson et al. 2019). We used the operational definition of sleep in actigraphy as the absence of any force in any direction during the measuring period (Campbell and Tobler 1984). ...
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Sleep is a critically important dimension of primate behavior, ecology, and evolution, yet primate sleep is under-studied because current methods of analyzing sleep are expensive, invasive, and time-consuming. In contrast to electroencephalography (EEG) and actigraphy, videography is a cost-effective and non-invasive method to study sleep architecture in animals. With video data, however, it is challenging to score subtle changes that occur in different sleep states, and technology has lagged behind innovations in EEG and actigraphy. Here, we applied Eulerian videography to magnify pixels relevant to scoring sleep from video, and then compared these results to analyses based on actigraphy and standard infrared videography. We studied four species of lemurs (Eulemur coronatus, Lemur catta, Propithecus coquereli, Varecia rubra) for 12-h periods per night, resulting in 6480 1-min epochs for analysis. Cramer’s V correlation between actigraphy-classified sleep and infrared videography-classified sleep revealed consistent results in eight of the nine 12-h videos scored. A sample of the infrared videography was then processed by Eulerian videography for movement magnification and re-coded. A second Cramer’s V correlation analysis, between two independent scorers coding the same Eulerian-processed video, found that interobserver agreement among Eulerian videography increased sleep vs. awake, NREM, and REM classifications by 7.1%, 46.7%, and 34.3%, respectively. Furthermore, Eulerian videography was more strongly correlated with actigraphy data when compared to results from standard infrared videography. The increase in agreement between the two scorers indicates that Eulerian videography has the potential to improve the identification of sleep states in lemurs and other primates, and thus to expand our understanding of sleep architecture without the need for EEG.
... Amongst other applications, they are being used to chart activity profiles, to estimate energy expenditure, and to detect difficult-to-observe behaviours (Yoda et al., 2001;Wilson et al., 2006;Watanabe and Takahashi, 2013). Yet, despite the success of a first wave of pioneering studies, the potential of accelerometers as 'sleep detectors' remains to be fully exploited (e.g., Miller et al., 2008;Samson et al., 2018; for additional references, see Loftus et al., 2022). ...
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Body-motion sensors can be used to study non-invasively how animals sleep in the wild, opening up exciting opportunities for comparative analyses across species.
... Creating an appropriate sleeping environment has a number of aspects, and amongst the most important are: • a suitable sleeping surface, for example, deep sand for elephants (Williams et al., 2015); • suitable noise levels for the species, taking into account hearing ranges outside human limits, such as infrasound (Orban et al., 2017); • light provision of both natural and artificial sources -for example, the importance of natural light to maintain health circadian rhythms and avoidance of artificial light overnight (Raap et al., 2015;Samson et al., 2017). Therefore, whilst we cannot make the assumption that animals' sleep patterns in the wild are the same as their requirement in captivity (due to unknown impact of lack of predators, readily available food etc.), providing a similar environment and opportunity for good-quality sleep for animals in human care, which mimics that of their wild behaviours, is a reasonable approach to take in promoting positive welfare. ...
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This article summarises highlights from an Animal Welfare session presented at the EAZA Annual Conference in 2018 on the importance of monitoring nocturnal behaviour and sleep in zoo animals. Recently published research from Asian elephants is included to provide an insight into the use of these types of data for welfare monitoring.
... Recumbent rest is thought to only occur if elephants are comfortable in their environment (Abou-Ismail et al., 2007;Koyama, Ueno, Eguchi, Uetake, & Tanaka, 2012;Siegel, 2009). Creating an appropriate sleep environment with acceptable noise levels (Orban, Soltis, Perkins, & Mellen, 2017), natural and artificial light provision (Raap, Pinxten, & Eens, 2015;Samson, Bray, & Nunn, 2017) and suitable sleeping surfaces (Walter, 2010;Williams et al., 2015) increases the potential for quality sleep and therefore caregivers have an important responsibility to not only provide an appropriate environment but to monitor nocturnal behavior to assess wellbeing. ...
Modern zoos strive to improve standards of animal management, husbandry and welfare of their animals as part of a continual evaluation process. Elephants (Elephantidae) have received particular attention in recent years due to the challenge of providing environments which promote natural behavior and opportunities for social interaction. A number of measures have been proposed to measure wellbeing, with sleep quality increasingly being used. Sleep is a vital aspect of life for cell replenishment as well as optimal development of young. Sleep deprivation can lead to immunosuppression and illness; therefore animal managers have a responsibility to ensure they reduce the potential for disturbance through noise, light, or other environmental factors. The social environment also plays an essential role in wellbeing, particularly for species that live in multi-generational family units. In this study the nocturnal behavior of a multi-generational captive herd was observed to determine impacts of husbandry changes on sleep duration and bout length (measured as recumbent rest). As expected, average total duration of sleep was higher in younger elephants and rates were comparable to those reported in other studies of Asian elephants. Overnight access to an outdoor paddock in warmer weather increased overall average bout length of sleep in the herd. Average total duration of sleep also increased for the herd following the movement of an unrelated adult female who had previously shown weak bonds with other herd members. This indicates that social compatibility is a vital component of elephant welfare, impacting not only behavioral interactions but sleep quality and duration.
Sleeping ecology plays a key integrative role in the primates' lives. Selecting an adequate sleeping site is therefore critical, but both extrinsic (e.g., predation, thermoregulation) and intrinsic factors (e.g., body size, circadian activity) need to be considered simultaneously. There is, however, a notable lack of comprehensive comparative analyses of sleeping ecology within primates. We explored the links among body size, circadian activity, and sleeping site choice in phylogenetically controlled comparative analyses across all major primate lineages. We compiled published information on body size, circadian activity, and sleeping site choice of extant primates. We performed ancestral state reconstructions for the different sleeping sites and used comparative phylogenetic analyses to quantify associations between sleeping site preference, body size and circadian pattern. Comparative analyses across 390 species revealed that primate sleeping site usage was best predicted by circadian activity patterns. Nocturnal species were more likely to use tree holes and nests, whereas diurnal primates were more likely to sleep on trees. Ancestral reconstructions indicated a relative ambiguity among states for several nodes, especially within strepsirrhines, whereas “on trees” was the most likely sleeping site type within haplorrhines. The main intrinsic predictor of sleeping site choice in primates has links to two extrinsic factors: predation risk and thermoregulation. Thermoregulatory and anti‐predator benefits accrue for nocturnal species that rest in nests or tree holes. Body size only poses a constraint for shelter use in larger species. Comparative studies of resting ecology are integral for reconstructing primate evolution and for revealing complex adaptations in arboreal mammals.
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Characteristics of the sleep-site are thought to influence the quality and duration of primate sleep, yet only a handful of studies have investigated these links experimentally. Using actigraphy and infrared videography, we quantified sleep in four lemur species ( Eulemur coronatus, Lemur catta, Propithecus coquereli, and Varecia rubra ) under two different experimental conditions at the Duke Lemur Center (DLC) in Durham, NC, USA. Individuals from each species underwent three weeks of simultaneous testing to investigate the hypothesis that comfort level of the sleep-site influences sleep. We obtained baseline data on normal sleep, and then, in a pair-wise study design, we compared the daily sleep times of individuals in simultaneous experiments of sleep-site enrichment and sleep-site impoverishment. Over 163 24-hour periods from 8 individuals (2 of each species), we found strong evidence that enriched sleep-sites increased daily sleep times of lemurs, with an average increase of thirty-one minutes. The effect of sleep-site impoverishment was small and not statistically significant. We conclude that properties of a sleep-site enhancing softness or insulation, more than the factors of surface area or stability, influence lemur sleep, with implications regarding the importance of nest building in primate evolution and the welfare and management of captive lemurs.
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Sleep in the primate order remains understudied, with quantitative estimates of sleep duration available for less than 10% of primate species. Even fewer species have had their sleep synchronously quantified with meteorological data, which have been shown to influence sleep-wake regulatory behaviors. We report the first sleep duration estimates in two captive gibbon species, the Javan gibbon (Hylobates moloch) and the pileated gibbon (Hylobates pileatus) (N = 52 nights). We also investigated how wind speed, humidity, temperature, lunar phase, and illumination from moonlight influence sleep-wake regulation, including sleep duration, sleep fragmentation, and sleep efficiency. Gibbons exhibited strict diurnal behavior with little nighttime activity and mean total average sleep duration of 11 h and 53 min for Hylobates moloch and 12 h and 29 min for Hylobates pileatus. Gibbons had notably high sleep efficiency (i.e., time score asleep divided by the time they spent in their sleeping site, mean of 98.3%). We found illumination from moonlight in relation to lunar phase and amount of wind speed to be the strongest predictors of sleep duration and high-quality sleep, with increased moonlight and increased wind causing more fragmentation and less sleep efficiency. We conclude that arousal threshold is sensitive to nighttime illumination and wind speed. Sensitivity to wind speed may reflect adaptations to counter the risk of falling during arboreal sleep.
Environmental variables have a major influence on the sleep patterns of animals, and can be presumed to have a strong role in the evolutionary paths of sleep in humans. Despite this understanding, only a few primate species have been systematically studied in their natural habitat, with research lacking on sleep characteristics in wild primates. Due to the difficulties of measuring sleep in the wild, primatology has largely focused on sleep through measurements of activity patterns, sleep ecology and habitat, which are assumed to reflect sleep patterns. I propose that advances in non-invasive technologies provide new opportunities for expanding sleep research in wild settings, and that well-known phenotypic variability in sleep across species represents an adaptation to the environment. The most important advances needed to understand the evolutionary pathways of sleep include wild comparisons to those already conducted in captivity, as well as examining sleep homeostasis in the wild.
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Objectives: Primates vary in their sleep durations and, remarkably, humans sleep the least per 24-hr period of the 30 primates that have been studied. Using phylogenetic methods that quantitatively situate human phenotypes within a broader primate comparative context, we investigated the evolution of human sleep architecture, focusing on: total sleep duration, rapid eye movement (REM) sleep duration, non-rapid eye movement (NREM) sleep duration, and proportion of sleep in REM. Materials and methods: We used two different Bayesian methods: phylogenetic prediction based on phylogenetic generalized least squares and a multistate Onrstein-Uhlenbeck (OU) evolutionary model of random drift and stabilizing selection. Results: Phylogenetic prediction confirmed that humans sleep less than predicted for a primate of our body mass, predation risk, brain size, foraging needs, sexual selection, and diet. These analyses further revealed that humans pack an unexpectedly higher proportion of REM sleep within a shorter overall sleep duration, and do so by reducing NREM sleep (rather than increasing REM). The OU model generally confirmed these findings, with shifts along the human lineage inferred for TST, NREM, and proportion of REM, but not for REM. Discussion: We propose that the risks and opportunity costs of sleep are responsible for shorter sleep durations in humans, with risks arising from terrestrial sleep involving threats from predators and conspecifics, and opportunity costs because time spent sleeping could be used for learning, creating material objects, and socializing.
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Despite being a prominent aspect of animal life, sleep and its functions remain poorly understood. As with any biological process, the functions of sleep can only be fully understood when examined in the ecological context in which they evolved. Owing to technological constraints, until recently, sleep has primarily been examined in the artificial laboratory environment. However, new tools are enabling researchers to study sleep behaviour and neurophysiology in the wild. Here, we summarize the various methods that have enabled sleep researchers to go wild, their strengths and weaknesses, and the discoveries resulting from these first steps outside the laboratory. The initial studies to ‘go wild’ have revealed a wealth of interindividual variation in sleep, and shown that sleep duration is not even fixed within an individual, but instead varies in response to an assortment of ecological demands. Determining the costs and benefits of this inter- and intraindividual variation in sleep may reveal clues to the functions of sleep. Perhaps the greatest surprise from these initial studies is that the reduction in neurobehavioural performance resulting from sleep loss demonstrated in the laboratory is not an obligatory outcome of reduced sleep in the wild. This article is part of the themed issue ‘Wild clocks: integrating chronobiology and ecology to understand timekeeping in free-living animals’.
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Sleep is essential for survival, yet it also represents a time of extreme vulnerability to predation, hostile conspecifics and environmental dangers. To reduce the risks of sleeping, the sentinel hypothesis proposes that group-living animals share the task of vigilance during sleep, with some individuals sleeping while others are awake. To investigate sentinel-like behaviour in sleeping humans, we investigated activity patterns at night among Hadza hunter–gatherersof Tanzania. Using actigraphy, we discoveredthat all subjects were simultaneously scored as asleep for only 18 min in total over 20 days of observation, with a median of eight individuals awake throughout the nighttime period; thus, one or more individuals was awake (or in light stages of sleep) during 99.8% of sampled epochs between when the first person went to sleep and the last person awoke. We show that this asynchrony in activity levels is produced by chronotype variation, and that chronotype covaries with age. Thus, asynchronous periods of wakefulness provide an opportunity for vigilance when sleeping in groups. We propose that throughout human evolution, sleeping groups composed of mixed age classes provided a form of vigilance. Chronotype variation and human sleep architecture (including nocturnal awakenings) in modern populations may therefore represent a legacy of natural selection acting in the past to reduce the dangers of sleep. © 2017 The Author(s) Published by the Royal Society. All rights reserved.
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Objectives: We studied sleep in a rural population in Madagascar to (i) characterize sleep in an equatorial small-scale agricultural population without electricity, (ii) assess whether sleep is linked to noise levels in a dense population, and (iii) examine the effects of experimentally introduced artificial light on sleep timing. Methods: Using actigraphy, sleep-wake patterns were analyzed for both daytime napping and nighttime wakefulness in 21 participants for a sum total of 292 days. Functional linear modeling was used to characterize 24-h time-averaged circadian patterns and to investigate the effect of experimentally introduced mobile field lights on sleep timing. We also obtained the first polysomnography (PSG) recordings of sleep in a traditional population. Results: In every measure of sleep duration and quality, the Malagasy population experienced shorter and lower quality sleep when compared to similarly measured postindustrial values. The population slept for a total of 6.5 h per night and napped during 89% of recorded days. We observed a peak in activity after midnight for both sexes on 49% of nights, consistent with segmented sleep. Access to mobile field lights had no statistical effect on nighttime sleep timing. From PSG, we documented relatively short rapid eye movement (14%), poor sleep efficiency (66%), and high wake after sleep onset (162 min). Conclusions: Sleep in this population is segmented, similar to the "first" sleep and "second" sleep reported in the historical record. Moreover, although average sleep duration and quality were lower than documented in Western populations, circadian rhythms were more stable across days.
The great apes (chimpanzees, bonobos, gorillas and orangutans) are our closest living relatives, sharing a common ancestor only five million years ago. We also share key features such as high intelligence, omnivorous diets, prolonged child-rearing and rich social lives. The great apes show a surprising diversity of adaptations, particularly in social life, ranging from the solitary life of orangutans, through patriarchy in gorillas to complex but different social organisations in bonobos and chimpanzees. As great apes are so close to humans, comparisons yield essential knowledge for modelling human evolutionary origins. Great Ape Societies provides comprehensive up-to-date syntheses of work on all four species, drawing on decades of international field work, zoo and laboratory studies. It will be essential reading for students and researchers in primatology, anthropology, psychology and human evolution.
The sleep-wake regulatory system and the circadian rhythm generating system are two brain networks integrated in the classical two-process model. These processes interact continuously, and to obtain an optimal sleep they must be correctly aligned. The suprachiasmatic nucleus located in the hypothalamus is crucial in the regulation of circadian rhythms, and the hypothalamus is a key component of the sleep-wake regulatory system in association with the thalamus, basal forebrain and brainstem. Alterations of these processes can result in a variety of sleep disturbances or sleep disorders.
Sleep is necessary for the survival of all mammalian life. In humans, recent investigations have generated critical data on the relationship between sleep and ecology in small-scale societies. Here, we report the technological and social strategies used to alter sleep environments and influence sleep duration and quality among a population of hunter-gatherers, the Hadza of Tanzania. Specifically, we investigated the effects that grass huts, sound levels, and fire had on sleep. We quantitatively compared thermal stress in outdoor environments to that found inside grass hut domiciles to test whether the huts function as thermoregulated microhabitats during the rainy season. Using physiological equivalent temperature (PET), we found that the grass huts provide sleep sites with less overall variation in thermal stress relative to outside baseline environments. We also investigated ambient acoustic measures of nighttime environments and found that sound significantly covaried with sleep-wake activity, with greater sound levels associating with less sleep. Finally, after controlling for ecological variables previously shown to influence sleep in this population, fire was shown to neither facilitate nor discourage sleep expression. Insofar as data among contemporary subtropical foragers can inform our understanding of past lifeways, we interpret our findings as suggesting that after the transition to full time terrestriality, it is likely that early Homo would have had novel opportunities to manipulate its environments in ways that could have significantly improved sleep quality. We further conclude that control over sleep environment would have been essential for migration to higher latitudes away from equatorial Africa.
Cathemerality, or activity throughout the 24-hr cycle, is rare in primates yet relatively common among lemurs. However, the diverse ecological conditions under which cathemerality is expressed complicates attempts to identify species-typical behavior. For example, Lemur catta and Varecia have historically been described as diurnal, yet recent studies suggest that they might exhibit cathemeral behavior under some conditions. To investigate this variation, we monitored activity patterns among lemurs that are exposed to similar captive environments. Using MotionWatch 8 ® actigraphy data loggers, we studied 88 lemurs across seven species at the Duke Lemur Center (DLC). Six species were members of the family Lemuridae (Eulemur coronatus, E. flavifrons, E. mongoz, L. catta, V. rubra, V. variegata), while a seventh was strictly diurnal and included as an out-group (Propithecus coquereli). For each 24-hr cycle (N = 503), we generated two estimates of cathemerality: mean night (MN) activity and day/night (DN) activity ratio (day and night cutoffs were based on astronomical twilights). As expected, P. coquereli engaged in the least amount of nocturnal activity according to both measures; their activity was also outside the 95% confidence intervals of all three cathemeral Eulemur species, which exhibited the greatest evidence of cathemerality. By these estimates, Varecia activity was most similar to Eulemur and exhibited substantial deviations from P. coquereli (β (MN) = 0.22 ± SE 0.12; β (DN) = -0.21 ± SE 0.12). L. catta activity patterns also deviated from P. coquereli (β (MN) = 0.12 ± SE 0.11; β (DN) = -0.15 ± SE 0.12) but to a lesser degree than either Varecia or Eulemur. Overall, L. catta displayed an intermediate activity pattern between Eulemur and P. coquereli, which is somewhat consistent with wild studies. Regarding Varecia, although additional observations in more diverse wild habitats are needed, our findings support the existence of cathemeral behavior in this genus.
Objectives: Cross-cultural sleep research is critical to deciphering whether modern sleep expression is the product of recent selective pressures, or an example of evolutionary mismatch to ancestral sleep ecology. We worked with the Hadza, an equatorial, hunter-gatherer community in Tanzania, to better understand ancestral sleep patterns and to test hypotheses related to sleep segmentation. Methods: We used actigraphy to analyze sleep-wake patterns in thirty-three volunteers for a total of 393 days. Linear mixed effects modeling was performed to assess ecological predictors of sleep duration and quality. Additionally, functional linear modeling (FLM) was used to characterize 24-hr time averaged circadian patterns. Results: Compared with post-industrialized western populations, the Hadza were characterized by shorter (6.25 hr), poorer quality sleep (sleep efficiency = 68.9%), yet had stronger circadian rhythms. Sleep duration time was negatively influenced by greater activity, age, light (lux) exposure, and moon phase, and positively influenced by increased day length and mean nighttime temperature. The average daily nap ratio (i.e., the proportion of days where a nap was present) was 0.54 (SE = 0.05), with an average nap duration of 47.5 min (SE = 2.71; n = 139). Discussion: This study showed that circadian rhythms in small-scale foraging populations are more entrained to their ecological environments than Western populations. Additionally, Hadza sleep is characterized as flexible, with a consistent early morning sleep period yet reliance upon opportunistic daytime napping. We propose that plasticity in sleep-wake patterns has been a target of natural selection in human evolution.