The cost of deep sleep: Environmental influences on sleep
regulation are greater for diurnal lemurs
David R. Samson
Charles L. Nunn
Department of Anthropology, University of
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. wileyonlinelibrary.com/journal/ajpa Am J Phys Anthropol. 2018;166:578–589.
Received: 5 July 2017
Revised: 21 February 2018
Accepted: 26 February 2018
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 regulation—thereby 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 species—Propithecus.
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 brain’s 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
SAMSON ET AL.
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.
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.
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 day’s 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 Coquerel’s 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
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
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.
SAMSON ET AL.
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 “0”or “1”based 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).
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.
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 Spearman’s 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 “subject”as
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.
SAMSON ET AL.
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 9.7–18.0
E. flavifron 49 20.4 0.69 11.4–31.8
E. mongoz 55 16.5 0.62 9.6–27.3
Eulemur spp. 123 20.0 0.68
L. catta 145 18.3 0.36 0.0–26.7
P. coquereli 81 14.3 0.50 6.8–24.3
V. rubra 16 23.5 1.98 14.1–41.3
V. variegata 9 20.2 1.23 12.4–25.4
Varecia spp. 25 21.9 1.6
Sleep fragmentation (per hour) E. coronatus 19 2.8 0.17 1.3–3.8
E. flavifron 49 2.6 0.09 1.5–4.3
E. mongoz 55 2.2 0.06 1.3–3.3
Eulemur spp. 123 2.5 0.11
L. catta 145 2.9 0.07 0.0–5.3
P. coquereli 81 2.5 0.12 1.0–5.6
V. rubra 16 3.1 0.32 1.8–6.2
V. variegata 9 3.8 0.23 2.5–4.5
Varecia spp. 25 3.5 0.28
Short sleep bout (per hour) E. coronatus 19 1.6 0.12 0.6–2.6
E. flavifron 49 1.2 0.07 0.5–3.2
E. mongoz 55 1.1 0.04 0.6–2.3
Eulemur spp. 123 1.3 0.08
L. catta 145 1.2 0.05 0–3.1
P. coquereli 81 1.3 0.07 0.2–3.0
V. rubra 16 1.6 0.30 0.4–4.4
V. variegata 9 1.8 0.10 1.5–2.45
Varecia spp. 25 1.7 0.20
Sleep depth composite E. coronatus 19 20.88 0.30 23.1–2.8
E. flavifron 49 20.07 0.18 23.1–5.7
E. mongoz 55 0.79 0.13 23.5–4.3
Eulemur spp. 123 20.58 0.20
L. catta 145 20.05 0.11 25.4–4.3
P. coquereli 81 0.57 0.21 24.8–12.7
V. rubra 16 21.16 0.72 24.0–9.3
V. variegata 921.56 0.34 23.3–2.8
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.
SAMSON ET AL.
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 “subject”as 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
SAMSON ET AL.
(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: r50.42–0.89, 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
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
SAMSON ET AL.
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 Propithecus’normal 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 “rebound”anytime throughout the circadian cycle.
Until recently, sleep quotas—the basic parameters of sleep expres-
sion—were 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
intensity—motor activity—may 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
SAMSON ET AL.
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 mass—the 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 3.3–4.6 kg (Hartstone-Rose & Perry, 2011). Although
comparable to Varecia at 3.0–4.5 kg (Vasey, 2002), this was larger than
Eulemur at 1.48–2.47 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
“segmented”or 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
periods—which 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
SAMSON ET AL.
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 http://orcid.org/0000-0003-3318-7652
Joel Bray http://orcid.org/0000-0001-8417-7492
Charles L. Nunn http://orcid.org/0000-0001-9330-2873
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How to cite this article: Samson DR, Bray J, Nunn CL. The cost
of deep sleep: Environmental influences on sleep regulation are
greater for diurnal lemurs. Am J Phys Anthropol. 2018;166:578–
SAMSON ET AL.