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Differences in the sleep architecture of forager and young honeybees (Apis mellifera)

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Differences in the sleep architecture of forager and young honeybees (Apis mellifera)

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Honeybee (Apis mellifera) foragers are among the first invertebrates for which sleep behavior has been described. Foragers (typically older than 21 days) have strong circadian rhythms; they are active during the day, and sleep during the night. We explored whether young bees (approximately 3 days of age), which are typically active around-the-clock with no circadian rhythms, also exhibit sleep behavior. We combined 24-hour video recordings, detailed behavioral observations, and analyses of response thresholds to a light pulse for individually housed bees in various arousal states. We characterized three sleep stages in foragers on the basis of differences in body posture, bout duration, antennae movements and response threshold. Young bees exhibited sleep behavior consisting of the same three stages as observed in foragers. Sleep was interrupted by brief awakenings, which were as frequent in young bees as in foragers. Beyond these similarities, we found differences in the sleep architecture of young bees and foragers. Young bees passed more frequently between the three sleep stages, and stayed longer in the lightest sleep stage than foragers. These differences in sleep architecture may represent developmental and/or environmentally induced variations in the neuronal network underlying sleep in honeybees. To the best of our knowledge, this is the first evidence for plasticity in sleep behavior in insects.
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2408
INTRODUCTION
Sleep is a behavioral state that is regulated by two main mechanisms:
the circadian clock and sleep homeostasis. The circadian clock plays
a crucial role in the timing and consolidation of wakefulness and
sleep, whereas the homeostatic mechanism reflects the need for sleep
that accumulates during periods of wakefulness and dissipates during
sleep (Dijk et al., 1999; Tobler, 2005).
Accumulating evidence suggests that rest behavior in many
invertebrates meets the criteria for defining it as ‘sleep’ (Tobler,
1983; Tobler and Stalder, 1988; Hendricks et al., 2000a; Shaw et
al., 2000; Ramón et al., 2004; Stephenson et al., 2007). The best
studied invertebrate model is the fruit fly Drosophila melanogaster,
in which a combination of behavioral, neurophysiological and
genetic analyses have linked molecular and neuronal processes to
sleep behavior, demonstrating the usefulness of invertebrate models
in the study of sleep biology (Greenspan et al., 2001; Hendricks
and Sehgal, 2004; Shaw, 2003). Sleep in flies is similar to mammals
in the following ways: (1) consolidated periods of immobility are
homeostatically regulated, (2) the presence of an elevated arousal
threshold (Hendricks et al., 2000b; Shaw et al., 2000; Huber et al.,
2004), (3) characteristic brain electrical activity (Nitz et al., 2002;
Andretic et al., 2005; van Swinderen et al., 2004), (4) a characteristic
brain gene expression signature (Cirelli and Tononi, 1999; Cirelli
et al., 2004; Cirelli et al., 2005; Zimmerman et al., 2006), and (5)
sleep is increased by antihistamines and reduced by caffeine and
other stimulants (Shaw et al., 2000; Andretic et al., 2005). In both
mammals and flies, sleep persists in the absence of a functioning
circadian clock, demonstrating the importance of non-circadian
mechanisms in the homeostatic regulation of sleep (Mistlberger et
al., 1983; Shaw et al., 2000). Furthermore, as in mammals (Tobler,
2005), sleep rebound in insects is not affected by levels of activity
during sleep deprivation (Shaw et al., 2000; Sauer et al., 2004).
Honeybees (Apis mellifera) are among the first invertebrates for
which sleep behavior has been described (Kaiser and Steiner-Kaiser,
1983). Honeybee foragers exhibit sleep, both in their natural hive
environment, and when isolated individually in the lab. Foragers
sleep in a posture characterized by a relaxation of the thorax, head
and antennae. This characteristic posture is associated with a
decrease in muscle tonus and body temperature, and an increase in
response threshold, measured both neurophysiologically and
behaviorally (Kaiser and Steiner-Kaiser, 1983; Kaiser, 1988). It was
further suggested that deep sleep in foragers (determined as periods
lacking antennal movements) is correlated with rhythmic
electrophysiological activity in the brain, including the mushroom
bodies (Schuppe, 1995). Foragers deprived of sleep for 12 h showed
a rebound the next day; they increased the duration of antennal
immobility, one of the characteristics of sleep in bees (Sauer et al.,
2004). This suggests that sleep in honeybee foragers is
homeostatically regulated, similar to sleep in mammals (Tobler,
2005), birds (Martinez-Gonzalez et al., 2008) and flies (Hendricks
et al., 2000a; Shaw et al., 2000).
Foragers are relatively old workers, have strong circadian
rhythms, and sleep during the night. However, circadian rhythms
are not typical to all worker bees; young bees typically perform
various in-hive activities around-the-clock, with no circadian
rhythms (Crailsheim et al., 1996; Moore et al., 1998). Young bees
that are isolated individually, or kept in small groups in constant
conditions, have no circadian rhythms in locomotor activity during
their first 3–14 days (Moore, 2001; Meshi and Bloch, 2007; Bloch,
2008). Their around-the-clock pattern of activity raises the question
The Journal of Experimental Biology 211, 2408-2416
Published by The Company of Biologists 2008
doi:10.1242/jeb.016915
Differences in the sleep architecture of forager and young honeybees (
Apis mellifera
)
Ada D. Eban-Rothschild and Guy Bloch*
Department of Evolution, Systematics, and Ecology, The Alexander Silberman Institute of Life Sciences, The Hebrew University of
Jerusalem, Jerusalem, 91904, Israel
*Author for correspondence (e-mail: bloch@vms.huji.ac.il)
Accepted 19 May 2008
SUMMARY
Honeybee (
Apis mellifera
) foragers are among the first invertebrates for which sleep behavior has been described. Foragers
(typically older than 21 days) have strong circadian rhythms; they are active during the day, and sleep during the night. We
explored whether young bees (~3 days of age), which are typically active around-the-clock with no circadian rhythms, also exhibit
sleep behavior. We combined 24-hour video recordings, detailed behavioral observations, and analyses of response thresholds to
a light pulse for individually housed bees in various arousal states. We characterized three sleep stages in foragers on the basis
of differences in body posture, bout duration, antennae movements and response threshold. Young bees exhibited sleep behavior
consisting of the same three stages as observed in foragers. Sleep was interrupted by brief awakenings, which were as frequent
in young bees as in foragers. Beyond these similarities, we found differences in the sleep architecture of young bees and
foragers. Young bees passed more frequently between the three sleep stages, and stayed longer in the lightest sleep stage than
foragers. These differences in sleep architecture may represent developmental and/or environmentally induced variations in the
neuronal network underlying sleep in honeybees. To the best of our knowledge, this is the first evidence for plasticity in sleep
behavior in insects.
Supplementary material available online at http://jeb.biologists.org/cgi/content/full/211/15/2408/DC1
Key words:
Apis mellifera
, sleep, response threshold, behavioral development, insect.
THE JOURNAL OF EXPERIMENTAL BIOLOGY
2409Sleep in young bees and foragers
of whether young bees sleep as foragers do. It is possible that young
honey bees do not sleep at all, which would make them an exception
in the animal kingdom (Lyamin et al., 2005; Rattenborg et al., 2004).
An alternative hypothesis is that young bees do sleep like foragers,
but distribute their sleep throughout the day. A third hypothesis is
that young bees sleep, but their sleep is essentially different from
that of foragers.
In order to distinguish between these hypotheses, we characterized
the sleep behavior of individually isolated young bees, and compared
it to that of sister foragers. Our detailed behavioral observations and
analyses of response thresholds lend weight to the third hypothesis.
We show that young honeybees exhibit sleep behavior which is
composed of the same stages observed in foragers, but that their
sleep dynamics differ.
MATERIALS AND METHODS
Bees
We kept honeybee colonies according to standard beekeeping
techniques in a bee research facility at the Edmond J. Safra campus
of the Hebrew University of Jerusalem, Givat-Ram, Jerusalem,
Israel. The bees were derived from a mixture of European races of
Apis mellifera L. typical to this region. Two of the source colonies
(colonies S23 and S25) were headed by a queen instrumentally
inseminated with semen from a single (different) drone. Single-drone
insemination helps reduce genetic variability between bees within
each experiment [average coefficient of relatedness between
workers=0.75 because of haplodiploidy (Page and Laidlaw, 1988)].
Colonies H3 and H12 were headed by a naturally mated queen
(queens typically mate with 10–20 drones).
We identified foragers by the presence of pollen loads in their
corbiculate. We only collected foragers with undamaged wings. To
obtain 1-day-old bees, we removed honeycomb frames containing
pupae (sealed in cells) from source colonies in the field. We
transferred the frames immediately to a light-proof container, which
we placed inside a dark incubator [32±0.5°C; relative humidity
(RH)=55±5%; monitored with an Onset HOBO (Contoocook, NH,
USA) H01-001-01 data logger]. We collected the newly emerging
bees the next day, when they were 0–24 h old.
Video recording
We video recorded bees from three different source colonies. In
the experiments with bees from colonies H3 and H12, we marked
newly emerged bees with a paint-dot on their thorax, and
introduced them to a foster colony that was housed in a two-frame
observation hive (with transparent glass walls), placed in a
constantly dark environmental chamber (29±1°C; RH 50±5%). We
connected the observation hive to the outside by a clear plastic
tube (length 60 cm, diameter 3 cm). After 48 h in the observation
hive, we collected two marked callow bees, as well as two foragers
from the same source colony (‘genotype’). In the experiment with
colony H3, we collected the focal bees between 15:00 h and
17:00 h, whereas in the experiment with colony H12, we collected
them between 7:30 h and 8:00 h. These time variations did not
appear to influence the observed behavior, since the results from
the two colonies were essentially similar. Each of the four bees
was placed in an individual small cage (7.52.52.5 cm). The
cages were made of transparent glass, and were padded on one
wall with a panel of Palziv substrate. We provided each cage with
a tube of sugar syrup (50%, w/w). We placed the cages in a dark
environmental chamber (28±1°C; RH 55±5%), which was
illuminated by dim red light that bees cannot see (von Frisch,
1967). Since some of the callows from colonies H3 and H12
atypically appeared to have a circadian rhythm, we monitored
circadian rhythms in locomotor activity (see below) before
performing sleep observations, in the last experiment with colony
S25. Importantly, the callow bees from the three colonies were
similar in age (3 days old). After monitoring the bees for 48 h, we
transferred two foragers (with robust circadian rhythms), and two
callows (that were active around-the-clock with no circadian
rhythms) to a dark environmental chamber for video recording
and sleep analysis. For the sleep analysis, we video recorded the
bees using an infrared-sensitive camera (Sony TRV 75E), over
successive 24 h periods. We started recording after the bees had
acclimatized to the lab for 2 h. We video recorded 64 bees, eight
groups of four bees (N=32 bees) from colony H12, and four groups
of four bees (N=16 bees) from colonies H3 and S25, each.
Analysis of video records
We used Pinnacle Studio (version 9.1; Pinnacle Systems Inc.,
Mountain View, CA, USA) software to sample the video records
to a computer. We omitted from our analysis records of bees that
died during the experiment (N=2), were not visible throughout most
of the experiment (N=4), were continually active (N=2), or
repeatedly slipped along the glass wall during their rest period (N=8).
Table 1. Behavioral categories defining arousal/sleep stages in honeybees
Category Abbreviation Description
Active A The bee walked over a distance greater than twice her body size, during a 1 min period.
Immobile–active IA The bee moved her legs, made >20 antenna movements/min, or >5 head movements/min, but did not walk
over a distance more than twice her body size (Fig. 1A).
Grooming G The bee cleaned her body parts or proboscis, by rubbing her legs over them, but did not walk over a distance
more than twice her body size.
First sleep stage FS The bee stayed in the same location, without moving her legs. The abdomen and thorax were clearly raised
above the substrate, and the antennae were extended at an angle of ~180° between the pedicle and the
scape (Fig. 1B).
Second sleep stage SS Same as FS, but body posture was more relaxed, and the angle of the antennae was ~90° (Fig. 1C).
Third sleep stage TS Same as SS, but the abdomen and thorax were adjacent to the substrate (reduced muscle tonus), and the
angle of the antennae was <90° (Fig. 1D).
Unknown sleep stage US The bee stayed in the same location, and was clearly in a sleep stage, but it was not possible to assign her to
a specific stage (for example, when bees faced the camera with their ventral side, it was impossible to
determine whether their abdomen and thorax were raised above the substrate).
THE JOURNAL OF EXPERIMENTAL BIOLOGY
2410
We defined seven behavioral states that we used for analyzing the
remaining 48 records. Three characterized awake bees, and the other
four sleeping bees. We assigned a single prevailing behavioral
category (see Table 1 and Fig. 1 for definitions of behavioral states)
for every minute using the following heuristic. If the bee showed
‘active’ (A) behavior during any part of the minute, we labeled the
entire minute as ‘A’. Otherwise, if the bee showed ‘immobile–active’
(IA) and/or ‘grooming’ (G) behavior, we labeled the minute as ‘IA’
or ‘G’ respectively, according to the predominant behavior in that
minute (even if the bee also exhibited sleep behavior during this
minute). In minutes in which the bees did not show any of the awake
categories, we assigned the most prevailing sleep stage. In addition,
we counted the number of antenna movements for each minute for
sleeping bees. We defined a ‘bout’ as a continuous episode in the
same behavioral state.
Analysis of response threshold
We determined the response threshold of bees to light. We placed
each focal bee in a small cage that was placed in a separate dark
chamber (23620 cm), in an experimental room (28.5±0.5°C;
RH=50±5%). This enabled us to expose the focal bees to light
without disturbing bees in neighboring chambers. We started each
experiment by calibrating the light intensity. We placed the light
source (an optic glass fiber; Schott-Fostec, LLC, Elmsford, NY,
USA) 2 cm away from a light meter (LI-185A, Li-Cor, Lincoln,
NE, USA), measured the light intensity of each illumination level
three times, and calculated the mean value. After this calibration,
we tested the response to light of the focal bees at various arousal
states. We illuminated the lateral part of the bee’s head from a
distance of 2 cm (as in the calibration of the light intensities), for
a period of exactly 10 s. We increased the light intensity at
intervals of 5 s between light stimuli, and video recorded the bee
throughout the entire procedure. We used 20 discrete levels of
light intensity. We defined a response as the bee turned toward
the light source, and/or moved her head more than twice during,
or immediately after (<1 s) the stimulus. The response threshold
for each bee was the lowest light intensity that triggered a
response.
We limited our analysis of response threshold to 3-day-old bees
with no circadian rhythms, and foragers with robust circadian
rhythms. In order to determine circadian rhythms, we monitored
A. D. Eban-Rothschild and G. Bloch
bee locomotor activity during the 2 days preceding the analysis
(see below). In each experiment, we tested 20 foragers and 20
callows, out of 30 bees for which we monitored locomotor
activity. We conducted seven trials with bees from colony S23
(N=58 bees tested), and 12 trials with bees from colony S25
(N=107 bees tested). Each trial started approximately 4 h after
sunset, and lasted about 6 h. The response threshold analysis for
foragers and callows at the different arousal states was carried
out at approximately the same time of day. Thus, variation in
circadian time cannot account for the observed variation in
response threshold.
Locomotor activity
We placed each bee in a separate glass cage (as described above)
in an environmental chamber (28±1°C; RH=45±5%), and monitored
locomotor activity with the ClockLab data acquisition system
(Actimetrics Co., Wilmette, IL, USA). We used a high-quality
monochrome image acquisition board (IMAQ 1409, National
Instruments Co., Austin, TX, USA), and a light-sensitive black and
white Panasonic WV-BP334, 0.08 lux CCD camera. The system
collected the data continuously, at a frequency of 1 Hz, as described
by Yerushalmi et al. (Yerushalmi et al., 2006). Circadian rhythms
in activity were assessed with the ClockLab software.
Statistical analyses
In order to test whether the sleep stages differed in bout duration
and amount of antenna movement, we carried out a separate
statistical test on the data set of each individual bee (we included
only bees with N>10 samples for each sleep stage; foragers, N=17;
callows, N=24). We used non-parametric tests, since these
variables were not normally distributed [Kruskal–Wallis analysis
with a correction for ties, followed by multiple comparisons
(Siegel and Castellan, 1988)]. In addition to the individual
analyses, we ran three-way ANOVAs to determine the influence
of colony, age (callow vs foragers) and sleep stage on bout
duration and antenna movement. For these analyses we used the
average values calculated for each individual bee, and used a data
set that included the values of all individuals. We carried out
complementary t-tests for each sleep stage to determine whether
antenna movement and bout duration differed between callows
and foragers. We used non-parametric analyses to determine
Fig. 1. Body posture of honeybee workers in various arousal states. Each photograph is a single frame taken from continuous 24 h video recordings.
(A) Immobile–active state (IA) – the bee stays in the same place, the thorax, abdomen and head are clearly raised above the substrate. This bee is moving
her wings. (B) First sleep stage (FS) – the abdomen and thorax are clearly raised above the substrate, and the antennae are extended at an angle of
90–180° between the pedicle and the scape. (C) Second sleep stage (SS) – the body is typically more adjacent to the substrate, and the antennae are
extended at an angle of ~90° between the pedicle and the scape. (D) Third sleep stage (TS) – the muscle tonus is reduced, and the body is adjacent to the
substrate. The angle between the pedicle and scape <90°, with the antennae tips typically touching the substrate. For more details, see Table 1.
THE JOURNAL OF EXPERIMENTAL BIOLOGY
2411Sleep in young bees and foragers
whether the response thresholds differed between arousal states
(Kruskal–Wallis test), and between foragers and callows for each
arousal state (Mann–Whitney test).
We used a first-order Markov chain to model the likelihood of
transitions between behavioral states. A behavioral transition was
defined as a change in the behavioral state displayed between two
consecutive minutes. We constructed a separate transition matrix
for each bee, in which each row represents transitions originating
from one behavioral state (X) to all other states. Each cell represents
the proportion of transitions to behavior Y, out of all transitions
originating from behavior X. In order to examine whether the
transition pattern of callows and foragers differed, we conducted a
‘leave-one-out cross-validation’ (LOOCV) analysis. We removed
the data of one bee, and computed two separate transition matrices
(TX,Y) for the remaining foragers and callows (denoted as the
‘foragers’ transition model’, and the ‘callows’ transition model’,
respectively). These models were based on the average transition
matrices of each group member. We calculated the likelihood that
the transition pattern of the removed bee originated from each model
using the following formula:
where TX,Y is the transition model (of callows or foragers), and BX,Y
is the actual number of transitions from X to Y observed in the bee
we removed. The removed bee was assigned to the group that yielded
the higher likelihood value (L). We repeated this procedure for each
bee, and summarized the number of individuals correctly assigned
to their group (e.g. foragers assigned to the group of foragers). We
determined the statistical significance of this analysis by calculating
the probability distribution of correct assignments based on randomly
divided groups (similar in size to the groups of callows and foragers
in the analysis above). We repeated the LOOCV procedure 100 000
times, and recorded, for each trial, the number of correctly assigned
bees (see Fig. S1 in supplementary material). The P-value is the
L=log(TX,Y)BX,Y
X,Y
,
A
Bout duration (min)
0
5
10
15
SS
B
FS
A
Sleep stage
TS
C
B
0
2
4
6
8
10
Antennae movements min–1
Sleep stage
SS
B
FS
A
TS
C
FSFSSS TSFSSSSS TS
FSSSSS TS
0
2
4
6
8
10
Antennae movements min–1
Sleep stage
FSSSTSFSSSTS
Colony H12
D
Bout duration (min)
0
5
10
15
FSSSTS
Colony H3 Colony S25
C
403
6
666661477 7 7 7 714 14 4 4 4
66 6 141414 4 4 4777777666
70 84 40 31 255
Fig. 2. Ethological characterization of sleep stages in honeybees. (A) Bout duration at different sleep stages (mean ± s.e.m.), for a representative forager.
Similar results were obtained for 15 additional foragers (
N
=21–194 bouts/bee). (B) Antennae movements at different sleep stages (mean ± s.e.m.), for a
representative forager. Similar results were obtained for 16 additional foragers (
N
=131–906 min/bee). Different capital letters indicate statistically significant
differences. (C) Group summary of antennae movement data for all foragers and callows. There was no significant difference between foragers and callows
(see supplementary material Table S1). (D) Summary of bout duration data for all foragers and callows. Bout duration differed between foragers and callows
(see supplementary material Table S2). Numbers within or above bars are the sample sizes. Filled bars, foragers; open bars, callows; left panels, colony H3;
middle panels, colony H12; right panels, colony S25. FS, first sleep stage; SS, second sleep stage; TS, third sleep stage.
THE JOURNAL OF EXPERIMENTAL BIOLOGY
2412
probability of having at least the number of correct assignments
obtained in our analysis of callows and foragers.
In order to find which transitions contributed most to the observed
differences between foragers and callows (see Results), we
performed six separate additional LOOCV analyses, each one based
on transitions originating from one behavioral state (one row in the
transition matrix; N=6). We used a Bonferroni correction for
multiple comparisons to correct our P-values.
RESULTS
Sleep behavior of forager and callow bees
We carried out detailed video analyses of the sleep behavior of 20
foragers and 24 callows from three source colonies. We found that
during consolidated periods of immobility, foragers exhibited sleep
behavior which was similar to that described in previous studies
(Kaiser, 1988; Sauer et al., 2003; Sauer et al., 2004). Our
experimental protocol, in which the bees could move freely inside
their cages, allowed us to identify and characterize three distinct
sleep stages differing in body posture and antennae position (Fig. 1;
see Table 1 for details). These three sleep stages were termed ‘first
sleep stage’ (FS), ‘second sleep stage’ (SS) and ‘third sleep stage’
(TS). Bout duration differed between the three sleep stages, and
was typically shortest for the first sleep stage, and longest for the
third sleep stage (Kruskal–Wallis tests, N=31–194 bouts/bee; P<0.05
in 16 out of 20 foragers from three different colonies; Fig. 2A). The
three sleep stages also differed in the number of antenna movements
per minute. The highest level of antenna activity was observed in
first sleep stage, and the lowest in third sleep stage (Kruskal–Wallis
tests, N=131–906 min/bee, P<0.05 in all 20 foragers; Fig.2B).
Callow bees exhibited the same three sleep stages as described
above for foragers. Again as in foragers, the three sleep stages
differed in their bout duration (N=36–237 bouts, P<0.05 in 19 out
of 24 bees, the P-value was 0.052 for an additional bee; a similar
trend was observed for the remaining four bees; Fig.2C), and number
A. D. Eban-Rothschild and G. Bloch
of antenna movements per minute (Kruskal–Wallis tests,
N=95–1094min/bee, P<0.05 in all 24 callows; Fig. 2D).
Foragers and callows in the same sleep stage did not differ in
the number of antenna movements [three-way ANOVA, P=0.5
for the comparison of foragers and callows (‘age’); supplementary
material Table S1; Fig. 2C]. In the analysis of bout duration we
found significant differences between foragers and callows, and
a significant interaction between age and sleep stage (three-way
ANOVA, age effect: P=0.037; ‘age sleep stage’ effect:
P=0.035; supplementary material Table S2; Fig. 2D). In order
to identify which of the sleep stages differed between callows
and foragers, we ran complementary t-tests and found that in
colony H3 the bout duration of the first sleep stage was longer
in callows than in foragers, whereas in colony S25 in the second
sleep stage the bout duration was shorter in callows (t-test,
P<0.05; Fig. 2D).
We found no consistent differences in the percentage of time that
foragers and callows spent sleeping (supplementary material
Fig. S2). In the experiment with bees from colony H3, callows slept
more than foragers (t-test, P<0.05), whereas in colony S25 callows
slept less (P<0.05). It is not clear whether this variation across trials
reflects genetic differences between colonies, or stems from
variability in experimental procedures (lab vs hive environment
before monitoring sleep; see Materials and methods).
In an analysis of all motionless bees (including all behavioral
states besides active), we found that bees slept in 80% of all bouts
in which they did not move for 5 min. This suggests that lack of
movement for >5 min can serve as an indirect measure of sleep in
studies of locomotor activity.
Response threshold
The response threshold varied with arousal states for both forager
and callow bees (Kruskal–Wallis tests, P<0.0001, followed by one-
tailed multiple comparisons, P0.05 for both foragers and callows;
log (X+0.001) light intensity (µmol photons m–2 s–1)
CBC
B
A
BC
AB
A
B
AColony S25 Colony S23
17
16
27
2
11
217
BB
B
A
BC
BC
AB
A
18
352
18
8
4
15
4
2
0
–2
4
2
0
–2
TSSSFS
Arousal state
IA TSSSFSIA
52
8
4
15
*
*
*
*
*
*
*
*
Fig. 3. Response threshold of bees in
different arousal states. (A) Foragers.
(B) Callows. Right panels: colony H23; left
panels: colony S25. The response threshold
to a light pulse differed significantly across
arousal states, but not between callows and
foragers. The central horizontal line in each
box indicates the median, the box borders
indicate the 75th and 25th percentile, and
the error bars outline the range. Circles
indicate outliers; values in a range spanning
between 1.5 and 3 box lengths. Asterisks
indicate extreme values: values in a range
spanning more than three box lengths.
Sample size (number of bees) is shown
within or above each box. Different capital
letters indicate arousal states that are
statistically different (Kruskal–Wallis tests
followed by multiple comparisons,
P
<0.0001
for both foragers and callows). FS, first
sleep stage; SS, second sleep stage; TS,
third sleep stage. Response threshold, at
TS, was not determined for foragers in
colony S23.
THE JOURNAL OF EXPERIMENTAL BIOLOGY
2413Sleep in young bees and foragers
Fig. 3). Awake, immobile–active bees responded to very low light
intensities (<0.05 μmol l photons m–2 s–1), whereas bees in the third
sleep stage typically responded only to intense light
(>1000 μmol l photons m–2 s–1). The responses of bees in the first and
second sleep stages were between these two extremes (Fig. 3). There
was no significant difference in the response threshold of foragers
and callows in the same arousal state (Mann–Whitney tests, P>0.085
for all behavioral states).
The dynamics of sleep behavior
Foragers were typically active throughout the subjective day, and
limited their sleep to the subjective night (Fig. 4A). The temporal
pattern of activity was more variable in callow bees. Callows from
colony S25 were typically active around-the-clock, with periods
of sleep behavior distributed throughout the day (N=6). A similar
pattern of activity was also observed in 45% (N=9) of the callows
from the two other source colonies (Fig. 4B). By contrast, in 55%
(N=11) of the callows from these two colonies, sleep behavior
tended to be more common during the subjective night,
reminiscent of the pattern in foragers (Fig. 4C). All sleep bouts,
in both foragers and callows, were interrupted by brief episodes
of awakening (transitions from sleep stages to immobile–active
or grooming; Fig. 4A–C). We could not determine clear sleep
cycles as those commonly reported for mammals. The average
sleep bout duration was shorter in foragers (two-way ANOVA,
age effect: P=0.04; colony effect: P<0.001; ‘age colony’ effect:
P=0.04; Fig. 4D). Consistent with this trend, the average number
of bouts per day was higher in foragers than in callows (two-way
ANOVA, age effect: P=0.016; colony effect: P=0.4; ‘age
colony’ effect: P=0.002; Fig. 4E).
We further characterized the likelihood of transitions between
behavioral states, using first-order Markov chain analysis (see
Materials and methods). Both foragers and callows typically passed
from the active state to either the grooming or immobile–active state
(Fig. 5A,B). The transition to sleep was gradual, typically through
the first sleep stage, less frequently through SS and hardly ever
directly to the third sleep stage. When bees returned from sleep to
wakefulness, they almost always did so by passing through the
immobile–active or grooming state, rather than by passing directly
to the active state (Fig. 5A,B). However, we found that the transition
matrices of foragers and callows differed significantly (LOOCV
analysis, P=0.002; see Materials and methods). When examining
the overall differences between the transition patterns of foragers
and callows (Fig. 5C), we found that the largest differences were in
the transitions from the second and third sleep stages to the other
behavioral states. The likelihood of transitions from the second and
third sleep stages, but not from activity (A, IA and G) and the first
sleep stage, to the other states differed between foragers and callows
(P=0.009 for SS; P=0.006 for TS, LOOCV analysis with a
Bonferroni correction; Fig. 5C). Callow bees reverted from the
second and third sleep stages to the first stage more commonly,
whereas foragers typically exited sleep from these stages and entered
either the immobile–active or grooming states (Fig. 5A–C). Callows
and foragers did not differ in the number of brief awakenings (with
the exception of colony S25; Fig.6A), however, callows passed more
often between the sleep stages than foragers (two-way ANOVA,
age effect: P=0.001; colony effect: P=0.81, interaction: P=0.89;
Fig. 6B, see also Fig. 5).
DISCUSSION
We characterized three distinct sleep stages in honeybees that differ
in body and antennae posture, bout duration, antenna movements,
and response threshold. We further provided the first analysis of
sleep in young bees, which we found to include the same sleep stages
observed in foragers, even in individuals that were active around-
Time of day (h:min)
TS
SS
FS
IA/G
A
TS
SS
FS
IA/G
A
17:00
TS
SS
FS
IA/G
A
21:00 01:00 05:00 09:00 13:00 17:00
19:30 23:30 03:30 07:30 11:30 15:30 19:30
19:30 23:30 03:30 07:30 11:30 15:30 19:30
Behavioral states
A
B
C
0
5
10
15
20
25
Foragers Callows
Sleep bout duration (min)
22 26
D
0
20
40
60
Foragers Callows
22
Number of sleep
bouts per day
22 26
E
**
Fig. 4. Transitions between behavioral states throughout the day. (A) An
example of a forager. (B) An example of a callow with apparent around-
the-clock activity. (C) An example of a callow with apparent circadian
rhythm in activity. The three bees are from colony H3. Note that the two
callow bees manifested all three sleep stages. A, active; IA/G,
immobile–active or grooming; FS, first sleep stage; SS, second sleep
stage; TS, third sleep stage. For details on behavioral states see Fig. 1 and
Table 1. Gray background indicates sleep stages; white background
indicates awake states. The horizontal bars at the bottom of the plots
depict the subjective time: black bars, subjective night; hatched bars,
subjective day. (D) Sleep bout duration (mean ± s.e.m.). Bout duration
differed between foragers and callows (two-way ANOVA, age effect,
P
=0.04) (E) Number of sleep bouts per day (mean ± s.e.m.). The number
of sleep bouts differed between foragers and callows (two-way ANOVA,
P
=0.016). Numbers within boxes indicate the sample size (pooled from the
three colonies). Filled bars, foragers; open bars, callows. Asterisks indicate
a statistically significant difference between foragers and callows.
THE JOURNAL OF EXPERIMENTAL BIOLOGY
2414
the-clock. However, the sleep architecture of young bees differed
from that of foragers, which may suggest variation in the underlying
sleep neuronal network.
Our detailed characterization of sleep behavior confirms and
extends earlier studies that focused on sleep in forager bees
(Kaiser and Steiner-Kaiser, 1983; Kaiser, 1988; Schmolz, 2002;
Sauer et al., 2003; Sauer et al., 2004). An important aspect of the
current work is that the bees were free to move in their cages,
and were not tethered, as in most previous studies on sleep in
bees. Our experimental procedure allowed bees to choose their
resting place, and change their body posture freely. Although the
experimental setup of the current study differs from previous ones,
we also found that sleep in honeybees is a dynamic process, and
that deep sleep is associated with an increased response threshold,
relaxation of the antennae and body, and reduced antennal
movements.
The description of three sleep stages in bees is reminiscent of
the classification of sleep into distinct stages in mammals. For
example, human sleep is divided into five stages: NREM (non
rapid eye movement) stages 1–4 and REM (rapid eye movement)
sleep. These sleep stages are categorized mainly by their
electroencephalographic (EEG) pattern, but they also differ in other
behavioral and physiological parameters such as response
threshold, muscle tonus and activity level (e.g. Grahnstedt and
A. D. Eban-Rothschild and G. Bloch
Ursin, 1980; Thoman and Glazier, 1987; Wilde-Frenz and Schulz,
1983; Keenan et al., 1993). NREM1 and NREM2 are characterized
by a relatively low arousal threshold and high muscle tonus and
body movements, and are therefore considered ‘light sleep’;
NREM3 and NREM4 have higher arousal thresholds and reduced
muscle tonus and body movements, and are considered ‘deep
sleep’. REM sleep is accompanied by a near-to-complete loss of
muscle tonus (Keenan et al., 1993). As in mammals, sleep depth
in honeybees varies with stage. The first sleep stage seems to be
the lightest one, and appears as a transitory stage between
wakefulness and deep sleep. Bees in the first sleep stage exhibit
the most frequent antennae movements, are most sensitive to light
stimuli, and have the shortest bout duration. Nevertheless, the
behavior and response threshold of bees in the first sleep stage
still differ significantly from those of bees that are inactive but
awake. The third sleep stage of honeybees appears to be the
deepest. Bees in the third sleep stage show the lowest number of
antennae movements, have the highest response threshold, the most
reduced muscle tonus, and the longest bout duration. Deep sleep
in bees is also associated with an increase in ventilatory cycle
duration (Sauer et al., 2003), and reduced body temperature
(Kaiser, 1988). In both bees and mammals, the transitions from
arousal to deep sleep and from deep sleep to awake states are
typically gradual [for mammals, see Feinberg and Ucbida
C
stage
AB
10%
30%
60%
40%
20%
50%
ΔPr (callows–foragers)
0
0.1
0.2
0.3
0.4
–0.4
–0.3
–0.2
–0.1
TS
SS
FS
G
IA
A
AIAGFSSSTS
Third stageThird stage
Second
stage
First stage First stage
Immobile–active Immobile–active
Grooming
Active
Grooming
Active
Second
stage
Fig. 5. The dynamics of transitions between
behavioral states. Schematic representation
of average transition matrix for foragers (A),
and callows (B). The width of arrows is
proportional to the average probabilities of
transitions from each behavioral state to all
other states. The patterns are analyzed
from a first-order Markov chain (see
Materials and methods), by using 3127 and
3928 behavioral transitions, from 16
foragers and 17 callows, respectively
(pooled from colonies H3, H12 and S25).
The overall matrices of callows and
foragers differ from each other (LOOCV
analysis,
P
=0.002; supplementary material
Fig. S1). Transitions that are statistically
significantly different between callows and
foragers are highlighted in black. For clarity,
we show only transitions with average
probability >0.1. See Fig. 1 and Table 1 for
more details on behavioral states. (C) The
difference between average transition
probability of foragers and callows. ΔPr=the
probability matrix of foragers subtracted
from that of callows. Blue colors represent
transitions that are more frequent in
foragers; red colors represent transitions
that are more frequent in callows (see
scale).
THE JOURNAL OF EXPERIMENTAL BIOLOGY
2415Sleep in young bees and foragers
(Feinberg and Ucbida, 1993)]. However, although we did observe
a general tendency of movement toward and away from deep sleep,
we did not recognize clear sleep cycles as reported for humans.
We noted that sleeping bees occasionally showed bursts of rapid
small-amplitude antenna movements, which were associated with
a specific body posture. This behavior, which was observed for
all bees and may correspond to the bursts of antennal activity
described in Kaiser (Kaiser, 1988) and Sauer et al. (Sauer et al.,
2004), was not analyzed systematically in the current report. It
should be noted that the classification into distinct sleep stages
was useful for sleep characterization and quantification, and
enabled us to rigorously compare young bees and foragers, but
does not imply a step-like transition between consecutive sleep
stages or their underlying neuronal mechanisms.
An additional similarity to mammalian sleep is the interruption
of all three sleep stages by brief awakenings, in both young bees
and foragers. In mammals similar sleep–wake transitions are
observed across different species, and the distribution of their
episode durations follows a common scale-invariant pattern, leading
to the hypothesis that brief awakenings have some yet unknown
essential function in the process of sleep regulation (Halasz et al.,
2004; Lo et al., 2004; Diniz Behn et al., 2007).
Prior to our study, it was not clear whether young bees sleep at
all, since they are typically active around-the-clock with no circadian
rhythms (reviewed by Moore, 2001; Bloch, 2008). Our findings
show that young bees, even those that are active around-the-clock,
exhibit sleep behavior. Moreover, body and antenna postures,
antenna movements and response thresholds are similar to those of
foragers in the same sleep stage. Both young bees and foragers
progressed gradually from light sleep (FS) to deeper sleep (TS),
and passed from sleep to awake states a similar number of times.
However, their sleep architecture appears different. Overall, foragers
had more sleep bouts during the day that were on average shorter
than in young bees. They also tended to progress mainly from light
to deep sleep, and from there tended to pass directly to awake states,
switching less often between sleep stages. Young bees tended to
pass more frequently between the three sleep stages, and had longer
bouts in the first sleep stage and shorter bouts in the second and
third stages.
The differences in sleep dynamics between young bees and
foragers may represent variability in the neuronal network
underlying sleep behavior. In mammals, the transitions between
wake and sleep, and between sleep stages, stem from complex
interactions between sleep and wake-promoting centers (reviewed
by Merica and Fortune, 2004; Saper et al., 2001; Fuller et al., 2006;
Lu et al., 2006). The differences between callows and foragers could
represent developmental changes in the organization or function
of the sleep neuronal network, since callows are younger than
foragers. In humans, there is evidence for changes (‘maturation’)
of sleep during early infant development (Jenni et al., 2004;
Mirmiran et al., 2003). Young bees and foragers also differ in the
environment they experience, which may contribute as well to the
observed variation in sleep architecture (Ribeiro et al., 1999;
Miyamoto et al., 2003; Ganguly-Fitzgerald et al., 2006). In this
regard, it is interesting to note that electrophysiological recordings
suggest that sleep in honeybee foragers is associated with distinct
rhythmic activity in their mushroom bodies (Schuppe, 1995). The
mushroom bodies, which differ in their neuroanatomy between
young bees and foragers (Withers et al., 1993; Withers et al., 1995;
Farris et al., 2001), have recently been implicated as the main brain
region regulating sleep in Drosophila (Joiner et al., 2006; Pitman
et al., 2006).
To the best of our knowledge, this study is the first to show
plasticity in sleep behavior in insects. Even though both young bees
and foragers have a characteristic sleep state, there appear to be
notable differences in their sleep architecture. Since the behavior
of bees is strongly influenced by the social environment in the hive
(Shemesh et al., 2007), an important question for future research is
whether similar plasticity in sleep behavior also occurs in field
colonies, in which young bees typically care for the brood around-
the-clock (Moore et al., 1998).
LIST OF ABBREVIATIONS
A active state
FS first sleep stage
G grooming state
IA immobile–active state
LOOCV leave-one-out cross-validation
SS second sleep stage
TS third sleep stage
We thank Gideon Rothschild, Elad Eban, and Uzi Motro for valuable advice on
statistical analyses; Aaron Kaplan’s group at the A. Silberman Institute of Life
Sciences for making their light meter available to us; Gideon Rothschild, Yair
Shemesh and Noa B. Kahana for helpful comments on earlier versions of this
manuscript. This work was supported by grants, number 1-822-73.1/2004 (to
Colony
0
0.04
0.08
0.12
0.16
H3 H12 S25
***
Sleep–wake transitions day–1
Sleep–stage transitions min–1
*
0
40
80
120
6148866
A
B
6148866
Fig. 6. Transitions between arousal states. (A) Number of sleep–wake
transitions during the day. (B) Number of transitions between sleep stages
during a single sleep bout. Numbers within boxes indicate the sample size.
Filled bars, foragers; open bars, callows. Asterisks indicate a statistically
significant difference between foragers and callows.
THE JOURNAL OF EXPERIMENTAL BIOLOGY
G.B.), and 1-2064-1205.13/2002 (to G.B.) from the German-Israeli Foundation for
Scientific Research and Development (GIF).
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A. D. Eban-Rothschild and G. Bloch2416
THE JOURNAL OF EXPERIMENTAL BIOLOGY
... 5,11 Even honeybees settle in a safe location, groom, and adopt a stereotypic posture before falling asleep. 12,13 These pre-sleep behaviors may not only provide direct fitness benefits, such as thermal advantages and protection from predation during sleep, 14,15 but have also been suggested to promote tranquilization and de-arousal of the brain, facilitating the transition from wakefulness to sleep. 3,4,7,8 For instance, adopting a regular behavioral pre-sleep routine facilitates and consolidates sleep in humans, 9,16 and nest complexity and comfort positively correlate with the efficiency and depth of sleep in several species of great apes. ...
... Prior to sleep onset, animals ranging from bees to humans display a stereotypic repertoire of behaviors, including finding a safe location, performing hygiene-related behaviors, preparing a space for sleep, and adopting a sleeping posture. [2][3][4][5][6][7][8][9][10][11][12]33,56 Here, we provide the first systematic description of the transition from wakefulness to sleep at the behavioral and electrocortical levels in mice and show that the 20-min-long period prior to sleep initiation represents a time frame during which mice shift from engaging in wake-related to sleep-related behaviors. ...
Article
The transition from wakefulness to sleep requires striking alterations in brain activity, physiology, and behavior, yet the precise neuronal circuit elements facilitating this transition remain unclear. Prior to sleep onset, many animal species display characteristic behaviors, including finding a safe location, performing hygiene-related behaviors, and preparing a space for sleep. It has been proposed that the pre-sleep period is a transitional phase in which engaging in a specific behavioral repertoire de-arouses the brain and facilitates the wake-to-sleep transition, yet both causal evidence for this premise and an understanding of the neuronal circuit elements involved are lacking. Here, we combine detailed behavioral observations, EEG-EMG recordings, selective targeting, and activity modulation of pre-sleep-active neurons to reveal the behaviors preceding sleep initiation and their underlying neurobiological mechanisms. We show that mice engage in temporally structured behaviors with stereotypic EEG signatures prior to sleep and that nest-building and grooming become significantly more prevalent with sleep proximity. We next demonstrate that the ability to build a nest promotes the initiation and consolidation of sleep and that the lack of nesting material chronically fragments sleep. Lastly, we identify broadly projecting and predominantly glutamatergic neuronal ensembles in the lateral hypothalamus that regulate the motivation to engage in pre-sleep nest-building behavior and gate sleep initiation and intensity. Our study provides causal evidence for the facilitatory role of pre-sleep behaviors in sleep initiation and consolidation and a functional characterization of the neuronal underpinnings regulating a sleep-related and goal-directed complex behavior.
... Distinct postural differences exist between putative sleeplike and active (awake) states in multiple mosquito species Sleep states induce a behavioral quiescence typically associated with an animal-specific stereotypical posture (Eban-Rothschild and Bloch, 2008;Raccuglia et al., 2019;Ramón et al., 2004;van Alphen et al., 2021). In Ae. aegypti, we previously showed that prolonged immobilization was associated with a prostrate state where the hind legs are lowered, and the thorax and abdomen brought closer to the substrate (Haufe, 1963). ...
Article
Sleep is an evolutionarily conserved process that has been described in different animal systems. For insects, sleep characterization has been primarily achieved using behavioral and electrophysiological correlates in a few systems. Sleep in mosquitoes, which are important vectors of disease-causing pathogens, has not been directly examined. This is surprising as circadian rhythms, which have been well studied in mosquitoes, influence sleep in other systems. In this study, we characterized sleep in mosquitoes using body posture analysis and behavioral correlates and quantified the effect of sleep deprivation on sleep rebound, host landing and blood-feeding propensity. Body and appendage position metrics revealed a clear distinction between the posture of mosquitoes in their putative sleep and awake states for multiple species, which correlate with a reduction in responsiveness to host cues. Sleep assessment informed by these posture analyses indicated significantly more sleep during periods of low activity. Nighttime and daytime sleep deprivation resulting from the delivery of vibration stimuli induced sleep rebound in the subsequent phase in day and night active mosquitoes, respectively. Lastly, sleep deprivation suppressed host landing in both laboratory and field settings, and impaired blood feeding of a human host when mosquitoes would normally be active. These results suggest that quantifiable sleep states occur in mosquitoes and highlight the potential epidemiological importance of mosquito sleep.
... Distinct postural differences exist between putative sleep-like and active (awake) states in multiple mosquito species Sleep states induce a behavioral quiescence typically associated with an animal-specific stereotypical posture. [31][32][33][34] In Ae. aegypti we previously showed that prolonged immobilization was associated with a prostrate state where the hind legs are lowered and the thorax and abdomen (which was not certified by peer review) is the author/funder. All rights reserved. ...
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Sleep is an evolutionarily conserved process that has been described in different animal systems. For insects, sleep characterization has been primarily achieved using behavioral and electrophysiological correlates in a few systems. Sleep in mosquitoes, which are important vectors of disease-causing pathogens, has not been directly examined. This is surprising as circadian rhythms, which have been well studied in mosquitoes, influence sleep in other systems. In this study, we characterized sleep in mosquitoes using body posture analysis and behavioral correlates, and quantified the effect of sleep deprivation on sleep rebound, host landing and blood-feeding propensity. Body and appendage position metrics revealed a clear distinction between the posture of mosquitoes in their putative sleep and awake states for multiple species, which correlate with a reduction in responsiveness to host cues. Sleep assessment informed by these posture analyses indicated significantly more sleep during periods of low activity. Nighttime and daytime sleep deprivation resulting from the delivery of vibration stimuli induced sleep rebound in the subsequent phase in day and night active mosquitoes, respectively. Lastly, sleep deprivation suppressed host landing in both laboratory and field settings and also impaired blood feeding of a human host when mosquitoes would normally be active. These results suggest that quantifiable sleep states occur in mosquitoes, and highlight the potential epidemiological importance of mosquito sleep.
... Within the healthy audio samples, we observed changes in magnitude and broadening of spectral peaks across the day, which could be associated Test Figure 5: Sampled test spectrograms (top) and reconstructions (bottom). with honey bee circadian rhythms, and similar changes are observed in the corresponding latent variables (Eban-Rothschild & Bloch, 2008;Ramsey et al., 2018). The low disease severity class, which shows the most separation in PCA space, has the most distinct spectrogram signatures. ...
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Honey bees are critical to our ecosystem and food security as a pollinator, contributing 35% of our global agriculture yield. In spite of their importance, beekeeping is exclusively dependent on human labor and experience-derived heuristics, while requiring frequent human checkups to ensure the colony is healthy, which can disrupt the colony. Increasingly, pollinator populations are declining due to threats from climate change, pests, environmental toxicity, making their management even more critical than ever before in order to ensure sustained global food security. To start addressing this pressing challenge, we developed an integrated hardware sensing system for beehive monitoring through audio and environment measurements, and a hierarchical semi-supervised deep learning model, composed of an audio modeling module and a predictor, to model the strength of beehives. The model is trained jointly on audio reconstruction and prediction losses based on human inspections, in order to model both low-level audio features and circadian temporal dynamics. We show that this model performs well despite limited labels, and can learn an audio embedding that is useful for characterizing different sound profiles of beehives. This is the first instance to our knowledge of applying audio-based deep learning to model beehives and population size in an observational setting across a large number of hives.
... Slow oscillations (~8 Hz), accompanied by a specific posture and increased arousal thresholds, were identified in crayfish (16), and 1-Hz oscillations in response to increased sleep pressure were observed locally in Drosophila R5 neurons (17). Bees that fall asleep progress through different postures that correlate with increased arousal thresholds (18) and fruitflies cycle through stages of lighter and deeper sleep within a sleep bout, as indicated by changes in arousal thresholds (19). Sleep initiation is a discrete brain process in Drosophila that is characterized by 7-to 10-Hz oscillations as the fly transitions into sleep (20). ...
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Sleep is a highly conserved state, suggesting that sleep’s benefits outweigh the increased vulnerability it brings. Yet, little is known about how sleep fulfills its functions. Here, we used video tracking in tethered flies to identify a discrete deep sleep stage in Drosophila, termed proboscis extension sleep, that is defined by repeated stereotyped proboscis extensions and retractions. Proboscis extension sleep is accompanied by highly elevated arousal thresholds and decreased brain activity, indicative of a deep sleep state. Preventing proboscis extensions increases injury-related mortality and reduces waste clearance. Sleep deprivation reduces waste clearance and during subsequent rebound sleep, sleep, proboscis extensions, and waste clearance are increased. Together, these results provide evidence of a discrete deep sleep stage that is linked to a specific function and suggest that waste clearance is a core and ancient function of deep sleep.
... Typically, young bees tend the brood without a rhythm in locomotion, which is supposed to be beneficial for optimizing brood care and colony growth. They also display more and less pronounced sleep-bouts scattered over the day (Eban-Rothschild and Bloch, 2008;Klein et al., 2008). Older foraging bees on the other hand, display robust day-night rhythms of activity and sleep. ...
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The fruit fly Drosophila melanogaster is an established model organism in chronobiology, because genetic manipulation and breeding in the laboratory are easy. The circadian clock neuroanatomy in D. melanogaster is one of the best-known clock networks in insects and basic circadian behavior has been characterized in detail in this insect. Another model in chronobiology is the honey bee Apis mellifera, of which diurnal foraging behavior has been described already in the early twentieth century. A. mellifera hallmarks the research on the interplay between the clock and sociality and complex behaviors like sun compass navigation and time-place-learning. Nevertheless, there are aspects of clock structure and function, like for example the role of the clock in photoperiodism and diapause, which can be only insufficiently investigated in these two models. Unlike high-latitude flies such as Chymomyza costata or D. ezoana, cosmopolitan D. melanogaster flies do not display a photoperiodic diapause. Similarly, A. mellifera bees do not go into “real” diapause, but most solitary bee species exhibit an obligatory diapause. Furthermore, sociality evolved in different Hymenoptera independently, wherefore it might be misleading to study the social clock only in one social insect. Consequently, additional research on non-model insects is required to understand the circadian clock in Diptera and Hymenoptera. In this review, we introduce the two chronobiology model insects D. melanogaster and A. mellifera, compare them with other insects and show their advantages and limitations as general models for insect circadian clocks.
... However, most comparative sleep data exist for terrestrial vertebrates, with much less known about sleep in invertebrates [1]. Though, recently the scientific community has sought to characteristic sleep in non-mammalian species like the fruit fly (Drosophila melanogaster) [2][3][4], the zebrafish (Danio rerio) [5][6][7], the nematode (Caenorhabditis elegans) [8], and bees (Apis mellifera, and Bombus terrestris) [9][10][11][12]. Prolonged sleep deprivation is fatal in many of the animals studied, except for pigeons, and several studies have sought to address how sleep promotes survival in rodents and primates [13][14][15][16]. ...
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Sleep is essential for the survival of most living beings. Numerous researchers have identified a series of genes that are thought to regulate "sleep-state" or the "deprived state". As sleep has a significant effect on physiology, we believe that lack of total sleep, or particularly rapid eye movement (REM) sleep, for a prolonged period would have a profound impact on various body tissues. Therefore, using the microarray method, we sought to determine which genes and processes are affected in the brain and liver of rats following nine days of REM sleep deprivation. Our findings showed that REM sleep deprivation affected a total of 652 genes in the brain and 426 genes in the liver. Only 23 genes were affected commonly, 10 oppositely, and 13 similarly across brain and liver tissue. Our results suggest that nine-day REM sleep deprivation differentially affects genes and processes in the brain and liver of rats.
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Pollen foraging efficiency provides vital information for the behavioral research on honey bees. The pollen production of beehives can be measured by manually weighing the pollen collected from pollen traps. For long-term pollen foraging monitoring, this approach is both inefficient and laborious. This study presents an efficient method for automatically monitoring the pollen foraging behavior and environmental conditions through an embedded imaging system. The imaging system uses an off-the-shelf camera installed at the beehive entrance to acquire video streams that are processed using the developed image processing algorithm. A lightweight real-time object detection and deep learning-based classification model, supported by an object tracking algorithm , was trained for counting and recognizing honey bee into pollen or non-pollen bearing class. The F1-score was 0.94 for pollen and non-pollen bearing honey bee recognition, and the precision and recall values were 0.91 and 0.99, respectively. For foraging trip counting algorithm, the mean average percent errors of the pollen bearing honey bee count and the total incoming honey bee count were 8.45 ± 2.72% and 10.55 ± 2.10%, respectively. An experiment was performed to test the performance of the imaging system in continuous monitoring of honey bee pollen foraging behavior as well as to investigate the effect caused by weather factors. The incoming and outgoing honey bee count were recorded and used to calculate indices based on the hourly and daily recorded counts for further analyses. The experimental results and analyses revealed that the daily pollen foraging trip ratio was 24.5 ± 3.5%; a single beehive collected about 49.1 ± 11.0 g of pollen per day. The pollen foraging trip count increased with increasing temperature and light intensity, and decreased with increasing relative humidity, rain level and wind speed. A significant reduction of pollen foraging activities was observed in heavy rainfall or gentle breeze conditions. This study not only quantitatively presents the effect of environmental factors on pollen foraging behavior, but also demonstrates the efficacy of the proposed imaging system. The automated imaging system can be applied as an efficient and reliable tool for researchers to gain deeper insights into honey bee foraging behavior, and help beekeepers achieve beehive management.
Chapter
This review summarizes our knowledge of sleep in insects, from the first description in the 19th century to the most recent results on sleep function. It comprises behavioral studies on insect sleep in different species, electrophysiological investigations on selected species and neurogenetic analyses in the fruit fly, Drosophila melanogaster, which nowadays can be regarded as insect model of sleep research. Similar to vertebrate sleep, sleep in insects has a homeostatic and a circadian component, consists of defined sleep stages and is controlled in a sophisticated way by several regions in the brain.
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Neonicotinoids have been implicated in the large declines observed in insects such as bumblebees, an important group of pollinators. Neonicotinoids are agonists of nicotinic acetylcholine receptors that are found throughout the insect central nervous system and are the main mediators of synaptic neurotransmission. These receptors are important for the function of the insect central clock and circadian rhythms. The clock allows pollinators to coincide their activity with the availability of floral resources and favorable flight temperatures, as well as impact learning, navigation, and communication. Here we show that exposure to the field-relevant concentration of 10 μg/L imidacloprid caused a reduction in bumblebee foraging activity, locomotion, and foraging rhythmicity. Foragers showed an increase in daytime sleep and an increase in the proportion of activity occurring at night. This could reduce foraging and pollination opportunities, reducing the ability of the colony to grow and reproduce, endangering bee populations and crop yields.
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In most mammalian species studied, two distinct and successive phases of sleep, slow wave (SW), and rapid eye movement (REM), can be recognized on the basis of their EEG profiles and associated behaviors. Both phases have been implicated in the offline sensorimotor processing of daytime events, but the molecular mechanisms remain elusive. We studied brain expression of the plasticity-associated immediate-early gene (IEG) zif-268 during SW and REM sleep in rats exposed to rich sensorimotor experience in the preceding waking period. Whereas nonexposed controls show generalized zif-268 down-regulation during SW and REM sleep, zif-268 is upregulated during REM sleep in the cerebral cortex and the hippocampus of exposed animals. We suggest that this phenomenon represents a window of increased neuronal plasticity during REM sleep that follows enriched waking experience.
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Bees defined as nurses by age (7-12 days), or as foragers by behaviour, were observed for 1.5 h around noon during days with good weather conditions and during the following night around midnight. Nurse bees spent more than half of their time in the broodnest and their average periods of activity and inactivity were rather similar during day and night, except that the feeding of adults was more frequent during daytime. Foragers had a more cyclic lifestyle, spending most of their time outside the broodnest. During daytime they flew and had shorter periods of inactivity compared to nighttime and compared to nurse bees. Trophallactic interactions of foragers were much more frequent during daytime and they were more often fed than nurses. In contrast to nurses, we never saw foragers taking food from honey cells, and seldom visiting pollen cells. That foragers seldom eat honey and frequently receive food during daytime demonstrates the important role of passive trophallaxis for the foragers.
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Heat production rates of sleeping honeybees were determined at ambient temperatures between 20 and 35 8C. They increased with temperature from 4.7 mW g À1 (S.D. 2.2 mW g À1) at 20 8C (n ¼ 18) up to a value of 12.3 mW g À1 (S.D. 7.6 mW g À1 n ¼ 12) at 35 8C. This indicates that honeybees behave ectothermicly during sleep. The preferred ambient temperatures for sleep were investigated in a temperature choice experiment. The highest number of sleeping bees were found at 28 8C. Evaluation of sleep behaviour in an observation hive revealed that bees prefer the same ambient temperature of about 28 8C under natural conditions. Honeybees save energy during sleep with an ectothermic behaviour, but do not reduce their metabolic rates as much as possible by choosing places in the beehive with the lowest temperature. Instead, they prefer places with moderate intermediate temperatures, probably in order to promote regenerative processes during sleep. # 2002 Elsevier Science B.V. All rights reserved.
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Forest-dwelling scorpions (Heterometrus, Pandinus) were continuously observed by time-lapse video recording and their behavior and body position were analyzed. Activity recorded on the tapes was scored into three states: (1) activity, (2) alert immobility, and (3) relaxed immobility. Arousal thresholds were determined by mechanical stimulation. Responsiveness was highest during activity and alert immobility, and significantly lower during relaxed immobility. Heart rate was continuously measured by chronically implanted electrodes and related to the behavioral state. Heart rate was highest during activity, intermediate in alert immobility, and lowest during relaxed immobility. Activity bouts were associated with sudden increases in heart rate. ‘Settling down’, however, was associated with a progressive decline in heart rate. The presence of rest regulation was investigated by 12 h rest deprivation by mechanical stimulation. During recovery, after initial activation, alert immobility and relaxed immobility were decreased. It can be concluded that rest in the scorpion is not a homogeneous state. The subdivision into alert and relaxed immobility on the basis of body posture revealed differences in arousal threshold and heart rate between the two states. The compensation of rest after rest deprivation indicates the presence of regulatory mechanisms comparable with those present in mammals and several nonmammalian vertebrate species, thus providing evidence for a ‘sleep-like’ state in scorpions.
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Recovery sleep was studied for 3-5 days following 24 h of sleep deprivation (TSD) in normal rats and in rats lacking circadian rhythms (CRs) of sleep because of prior lesioning of the suprachiasmatic nuclei (SCN). One group of lesioned rats was run in constant dim light. Another lesioned group and an intact group were run on a 12:12 dark-light schedule with TSD and recovery beginning at lights-off. All groups showed immediate rebounds of high-amplitude NREM sleep and paradoxical sleep, confined mostly to the first 12-18 h of recovery, and decreases in moderate and low-amplitude NREM sleep during the first 6-12 h of recovery. Thus, sleep stage rebound priorities were little affected by CRs. Total sleep rebound was initially greatest in intact rats, but limited mostly to the first 12 h of recovery. Total sleep rebound was distributed over a longer period in SCN rats, but total accumulated rebound was similar in all groups. Thus, CRs appear to modulate the timing but not the amount of accumulated total sleep rebound. Results were interpreted in terms of ceiling effects on total sleep, delayed rebounds, and competition between CRs and homeostatic recovery processes. Recovery sleep of lesioned rats on the dark-light schedule was marked by a transient diurnal rhythm.
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A summary of 50 years' work on the biology and behavior of honeybees. Liberally illustrated. Harvard Book List (edited) 1971 #215 (PsycINFO Database Record (c) 2012 APA, all rights reserved)
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• The circadian timing system has been implicated in age-related changes in sleep structure, timing and consolidation in humans. • We investigated the circadian regulation of sleep in 13 older men and women and 11 young men by forced desynchrony of polysomnographically recorded sleep episodes (total, 482; 9 h 20 min each) and the circadian rhythms of plasma melatonin and core body temperature. • Stage 4 sleep was reduced in older people. Overall levels of rapid eye movement (REM) sleep were not significantly affected by age. The latencies to REM sleep were shorter in older people when sleep coincided with the melatonin rhythm. REM sleep was increased in the first quarter of the sleep episode and the increase of REM sleep in the course of sleep was diminished in older people. • Sleep propensity co-varied with the circadian rhythms of body temperature and plasma melatonin in both age groups. Sleep latencies were longest just before the onset of melatonin secretion and short sleep latencies were observed close to the temperature nadir. In older people sleep latencies were longer close to the crest of the melatonin rhythm. • In older people sleep duration was reduced at all circadian phases and sleep consolidation deteriorated more rapidly during the course of sleep, especially when the second half of the sleep episode occurred after the crest of the melatonin rhythm. • The data demonstrate age-related decrements in sleep consolidation and increased susceptibility to circadian phase misalignment in older people. These changes, and the associated internal phase advance of the propensity to awaken from sleep, appear to be related to the interaction between a reduction in the homeostatic drive for sleep and a reduced strength of the circadian signal promoting sleep in the early morning.