Ecological and social pressures interfere with homeostatic sleep regulation in the wild

Abstract

Sleep is fundamental to the health and fitness of all animals. The physiological importance of sleep is underscored by the central role of homeostasis in determining sleep investment – following periods of sleep deprivation, individuals experience longer and more intense sleep bouts. Yet, most sleep research has been conducted in highly controlled settings, removed from evolutionarily relevant contexts that may hinder the maintenance of sleep homeostasis. Using triaxial accelerometry and GPS to track the sleep patterns of a group of wild baboons (Papio anubis), we found that ecological and social pressures indeed interfere with homeostatic sleep regulation. Baboons sacrificed time spent sleeping when in less familiar locations and when sleeping in proximity to more group-mates, regardless of how long they had slept the prior night or how much they had physically exerted themselves the preceding day. Further, they did not appear to compensate for lost sleep via more intense sleep bouts. We found that the collective dynamics characteristic of social animal groups persist into the sleep period, as baboons exhibited synchronized patterns of waking throughout the night, particularly with nearby group-mates. Thus, for animals whose fitness depends critically on avoiding predation and developing social relationships, maintaining sleep homeostasis may be only secondary to remaining vigilant when sleeping in risky habitats and interacting with group-mates during the night. Our results highlight the importance of studying sleep in ecologically relevant contexts, where the adaptive function of sleep patterns directly reflects the complex trade-offs that have guided its evolution.

Accelerometer-based analyses of animal sleep patterns

“…Now, in eLife, Carter Loftus, Roi Harel, Chase Núñez and Margaret Crofoot report on the use of animal-borne accelerometers to map the sleep patterns of a free-ranging group of baboons in Kenya ( Loftus et al, 2022 ). Collars, which also housed GPS loggers for high-resolution movement tracking, were fitted to 26 adults, yielding data for more than 500 nights.…”

“…Amongst other applications, they are being used to chart activity profiles, to estimate energy expenditure, and to detect difficult-to-observe behaviours ( Yoda et al, 2001 ; Wilson et al, 2006 ; Watanabe and Takahashi, 2013 ). Yet, despite the success of a first wave of pioneering studies, the potential of accelerometers as ‘sleep detectors’ remains to be fully exploited (e.g., Miller et al, 2008 ; Samson et al, 2018 ; for additional references, see Loftus et al, 2022 ).…”

A guide to sampling design for GPS-based studies of animal societies

“…For example, diurnal species are largely inactive at night (Isbell et al . 2017; but see Loftus et al . 2021), meaning that GPS devices are often switched off at night to conserve battery.…”

“…It can be useful to vary GPS sampling across time to focus data collection on certain time windows over others. For example, diurnal species are largely inactive at night (Isbell et al 2017; but see Loftus et al 2021), meaning that GPS devices are often switched off at night to conserve battery. It can also be worthwhile to program devices to increase sampling frequency during periods when animals are more active (e.g., morning and/or evening), or decrease sampling frequency during seasons when conditions are less optimal for data collection (e.g., during breeding seasons, Box 2).…”

Quantifying the movement, behavior, and environmental context of group-living animals using drones and computer vision

“…Bio-logging has become a powerful approach for studying behavior, allowing scientists to collect data over expansive spatial and temporal scales and from inaccessible environments (Brown et al, 2013; Kays et al, 2015). This approach has generated important insights into behaviors that were previously difficult or impossible to study effectively, including migration (Jesmer et al, 2018), dispersal (Klarevas-Irby et al, 2021), energetics (Flack et al, 2020; Williams et al, 2014), sleep (Loftus et al, 2022; Rattenborg et al, 2016), and individual and collective decision-making (Flack et al, 2018; Strandburg-Peshkin et al, 2015, 2017).…”