DOI: 10.5061/dryad.p5hqbzkqf
title: Data from: Ecological and social pressures interfere with homeostatic sleep regulation in the wild

description: Sleep is fundamental to the health and fitness of all animals. The
 physiological importance of sleep is underscored by the central role of
 homeostasis in determining sleep investment – following periods of sleep
 deprivation, individuals experience longer and more intense sleep bouts.
 Yet, most sleep research has been conducted in highly controlled settings,
 removed from evolutionarily-relevant contexts that may hinder the
 maintenance of sleep homeostasis. Using tri-axial accelerometry and GPS to
 track the sleep patterns of a group of wild baboons (Papio anubis), we
 found that ecological and social pressures indeed interfere with
 homeostatic sleep regulation. Baboons sacrificed time spent sleeping when
 in less familiar locations and when sleeping in proximity to more
 group-mates, regardless of how long they had slept the prior night or how
 much they had physically exerted themselves the preceding day. Further,
 they did not appear to compensate for lost sleep via more intense sleep
 bouts. We found that the collective dynamics characteristic of social
 animal groups persist into the sleep period, as baboons exhibited
 synchronized patterns of waking throughout the night, particularly with
 nearby group-mates. Thus, for animals whose fitness depends critically on
 avoiding predation and developing social relationships, maintaining sleep
 homeostasis may be only secondary to remaining vigilant when sleeping in
 risky habitats and interacting with group-mates during the night. Our
 results highlight the importance of studying sleep in ecologically
 relevant contexts, where the adaptive function of sleep patterns directly
 reflects the complex trade-offs that have guided its evolution.
description: <strong>Sleep location characterization and
 fidelity</strong> Visualization of the GPS data
 indicated that individuals remained reliably stationary until at least
 06:15 every day, and thus we determined the location in which each baboon
 slept from the median of the first 10 GPS locations that occurred before
 06:15. If an individual’s GPS collar did not successfully collect 10
 locations before 06:15, its data on this day were excluded from analyses
 involving sleep location. This resulted in the removal of 9/483
 baboon-days of data. In ArcGIS, drone imagery was used to trace the crowns
 of distinct sleep trees within the group’s main sleep site. We determined
 that an individual slept in a particular tree if its sleep location was
 within the traced polygon of that tree crown. Sleep locations that fell
 outside the crown of a tree, likely reflecting minor error in the GPS
 location estimates, were assigned to the closest sleeping tree. Only
 32/469 sleep locations (6.8%) had to be assigned to a sleep tree in this
 manner. In rare cases where an individual’s sleep location was greater
 than 10 m from the crown of the closest sleep tree (5/474 cases – 1.1% of
 baboon-days), its data on this day were excluded from analysis.
 The following files in this Dyad dataset resulted from the
 tracing of the tree crowns in ArcGIS: "sleep_trees.cpg",
 "sleep_trees.dbf", "sleep_trees.prj",
 "sleep_trees.shp", "sleep_trees.shx"
 <strong>Physical activity</strong>
 We also calculated cumulative activity during the day from the
 accelerometry data. Using the continuous 12 Hz accelerometry data, we
 calculated VeDBA from 06:00 to 18:00 using a 0.5 second time window,
 averaged VeDBA over each minute, and then summed these values to generate
 a cumulative measure of activity during the day. The
 average VeDBA per minute produced by this method is stored in this dataset
 in the file: "vedba_mean_2012.csv"
 <strong>Environmental data</strong>
 The minimum ambient temperature represented the minimum
 temperature at the sleep site during the night, determined using
 interpolated ECMWF air temperature (2 m above ground) data obtained with
 the Env-DATA functionality (Dodge et al., 2013) provided on Movebank data
 repository (www.movebank.org). The file containing the
 ambient temperatures that was produced by the Env-DATA functionality in
 Movebank can be found in this Dryad dataset:
 "env_data.csv-6150899038464587825.csv"
 <strong>Sleep validation study</strong>
 To evaluate whether the accelerometer-based sleep classification
 technique was accurately monitoring sleep in baboons, we returned to Mpala
 Research Centre in July 2019 to perform a validation study in which we
 compared the results of the accelerometer-based sleep classification to
 direct observations of awake and sleeping baboons. Using the procedures
 described in Strandburg-Peshkin et al., 2015, we trapped and anesthetized
 27 members of a group of habituated olive baboons, fitting each with a GPS
 and accelerometry collar. Eleven of the 27 collars deployed recorded
 continuous tri-axial accelerations at 12 Hz/axis from 06:30 to 18:00 and
 0.71-second bursts of accelerations at 56.2 Hz/axis at the beginning of
 every minute from 18:00 to 06:30. Accelerometry data was collected by each
 of these 11 collars for up to 31 days. The remaining 16 collars did not
 collect accelerometry data from 06:30 to 18:00, and thus we excluded data
 from these collars from the validation study. We
 down-sampled and interpolated the accelerometry data such that it matched
 the sampling frequency and schedule of the data collected in 2012 (i.e.
 the data analyzed for this manuscript). We then applied the sleep
 classification algorithm described in the Materials and Methods to this
 validation dataset. To validate the sleep
 classification algorithm, we performed direct behavioral observations of
 the baboons at their primary sleep site. We recorded the behavior of the
 study baboons starting when they approached their sleep site using a FLIR
 T1020 high-resolution infrared camera (FLIR Systems Inc., Wilsonville, OR,
 USA). Recordings continued into the night for as long as the camera
 battery allowed (average recording duration (range of recording
 durations): 7.4 hours (1.7 – 14.9 hours)), and we collected thermal
 imaging data on 21 nights. We identified individuals in the thermal
 imagery both in real-time, via observer narration of the recorded imagery,
 and post-recording, by matching movements of individuals in the thermal
 imagery to the GPS tracks of collared individuals.
 Following initial data collection, we used the commercial
 software Loopy (Loopbio GmbH, Austria) to score the behavior of identified
 individuals in the thermal imagery. Individuals’ behavior was scored as
 “wakefulness”, “resting wakefulness”, or “sleep”. Wakefulness refers to
 any behavior involving active movement (i.e. walking, running) or engaged
 activity (i.e. allogrooming), whereas resting wakefulness refers to
 behaviors that are dormant (i.e. sitting), but not in the typical sleeping
 posture of a baboon (sitting or lying with neck relaxed and head hung).
 Sustained dormant behavior in the typical sleep posture was considered
 sleep. Video scoring resulted in a total of 8.0 hours of behavioral
 observation across a total of 16 individual baboons.
 Synchronizing the thermal imagery data with the accelerometry
 data produced a validation dataset of 294 minute-epochs across six baboons
 that were both classified as either sleep or wakeful behavior from
 accelerometry, and scored as wakefulness, resting wakefulness, or sleep
 from direct observation. With both wakefulness and resting wakefulness
 representing wakeful behavior, the accelerometer-based sleep
 classification exhibited an accuracy of 79.9% (Table S12). Consistent with
 previous validation studies of the use of accelerometry in measuring sleep
 (Ancoli-Israel et al., 2003; de Souza et al., 2003), we found that
 accelerometer-based sleep classification has difficulty distinguishing
 resting wakefulness from sleep, and we consider this limitation in our
 interpretation of the results. The accelerometry data
 and the behavioral observation data used for this validation study
 correspond to the following files in this dataset, respectively:
 "2019_Papio_anubis_acc_Loftus_et_al_Dryad.csv",
 "2019_Papio_anubis_behavioral_scoring_Loftus_et_al_Dryad.csv"
description: There is a 16 second delay between the timestamps of the
 accelerometry and the timestamps of the behavioral scoring data. This 16
 second delay is typical of GPS clocks. Thus, to synchronize the timestamps
 in the accelerometry and behavioral scoring datasets, 16 seconds must be
 added to the timestamps of the behavioral scoring. The
 GitHub repository (CarterLoftus/baboon_sleep) archived in the Zenodo link
 in this Dryad repository contains a README.md file that provides detailed
 instructions on how to replicate the final results of this study using the
 data available in this Dyrad repository and on Movebank
 (http://www.movebank.org/).