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In modern society the night sky is lit up not only by the moon but also by artificial light devices. Both of these light sources can have a major impact on wildlife physiology and behaviour. For example, a number of bird species were found to sleep several hours less under full moon compared to new moon and a similar sleep-suppressing effect has been reported for artificial light at night (ALAN). Cloud cover at night can modulate the light levels perceived by wildlife, yet, in opposite directions for ALAN and moon. While clouds will block moon light, it may reflect and amplify ALAN levels and increases the night glow in urbanized areas. As a consequence, cloud cover may also modulate the sleep-suppressing effects of moon and ALAN in different directions. In this study we therefore measured sleep in barnacle geese (Branta leucopsis) under semi-natural conditions in relation to moon phase, ALAN and cloud cover. Our analysis shows that, during new moon nights stronger cloud cover was indeed associated with increased ALAN levels at our study site. In contrast, light levels during full moon nights were fairly constant, presumably because of moonlight on clear nights or because of reflected artificial light on cloudy nights. Importantly, cloud cover caused an estimated 24.8% reduction in the amount of night-time NREM sleep from nights with medium to full cloud cover, particularly during new moon when sleep was unaffected by moon light. In conclusion, our findings suggest that cloud cover can, in a rather dramatic way, amplify the immediate effects of ALAN on wildlife. Sleep appears to be highly sensitive to ALAN and may therefore be a good indicator of its biological effects.

Sleep is a behavioral and physiological state that is thought to serve important functions. Many animals go through phases in the annual cycle where sleep time might be limited, for example, during the migration and breeding phases. This leads to the question whether there are seasonal changes in sleep homeostasis. Using electroencephalogram (EEG) data loggers, we measured sleep in summer and winter in 13 barnacle geese (Branta leucopsis) under semi-natural conditions. During both seasons, we examined the homeostatic regulation of sleep by depriving the birds of sleep for 4 and 8 h after sunset. In winter, barnacle geese showed a clear diurnal rhythm in sleep and wakefulness. In summer, this rhythm was less pronounced, with sleep being spread out over the 24-h cycle. On average, the geese slept 1.5 h less per day in summer compared with winter. In both seasons, the amount of NREM sleep was additionally affected by the lunar cycle, with 2 h NREM sleep less during full moon compared to new moon. During summer, the geese responded to 4 and 8 h of sleep deprivation with a compensatory increase in NREM sleep time. In winter, this homeostatic response was absent. Overall, sleep deprivation only resulted in minor changes in the spectral composition of the sleep EEG. In conclusion, barnacle geese display season-dependent homeostatic regulation of sleep. These results demonstrate that sleep homeostasis is not a rigid phenomenon and suggest that some species may tolerate sleep loss under certain conditions or during certain periods of the year.

Manual scoring of polysomnography data is labor-intensive and time-consuming, and most existing software does not account for subjective differences and user variability. Therefore, we evaluated a supervised machine learning algorithm, Somnivore (TM), for automated wake-sleep stage classification. We designed an algorithm that extracts features from various input channels, following a brief session of manual scoring, and provides automated wake-sleep stage classification for each recording. For algorithm validation, polysomnography data was obtained from independent laboratories, and include normal, cognitively-impaired, and alcohol-treated human subjects (total n = 52), narcoleptic mice and drug-treated rats (total n = 56), and pigeons (n = 5). Training and testing sets for validation were previously scored manually by 1-2 trained sleep technologists from each laboratory. F-measure was used to assess precision and sensitivity for statistical analysis of classifier output and human scorer agreement. The algorithm gave high concordance with manual visual scoring across all human data (wake 0.91 +/- 0.01; N1 0.57 +/- 0.01; N2 0.81 +/- 0.01; N3 0.86 +/- 0.01; REM 0.87 +/- 0.01), which was comparable to manual inter-scorer agreement on all stages. Similarly, high concordance was observed across all rodent (wake 0.95 +/- 0.01; NREM 0.94 +/- 0.01; REM 0.91 +/- 0.01) and pigeon (wake 0.96 +/- 0.006; NREM 0.97 +/- 0.01; REM 0.86 +/- 0.02) data. Effects of classifier learning from single signal inputs, simple stage reclassification, automated removal of transition epochs, and training set size were also examined. In summary, we have developed a polysomnography analysis program for automated sleep-stage classification of data from diverse species. Somnivore enables flexible, accurate, and high-throughput analysis of experimental and clinical sleep studies.

The low-throughput nature of manual scoring of polysomnography (sleep) data, both in terms of speed and consistency, is a major factor preventing sleep research from reaching its full efficiency and potential. Automated approaches developed previously have generally failed to provide sufficient accuracy or 'usability' for sleep scientists lacking specialist-engineering expertise. Moreover, all earlier approaches have only been validated using baseline data, suggesting a failure to embed in the algorithm the robustness to remain effective when used to analyse the effect on sleep of treatment or disease. Finally, no single approach has been validated for mouse, rat and human data. Therefore, the aim of my research was to develop a user-friendly platform for real-time automated scoring and analysis of polysomnography data. The program is known as ‘Somnivore’ (from Latin somnus, ‘sleep’, and vorare, ‘to devour’), and was developed using state of the art supervised machine learning technology, with support vector machine (SVM) at its core, and coded as a graphical user interface (GUI)-based solution in the Matlab™ ambient. Somnivore learns, in parallel, by surveying features from a variety of different inputs (including EEG, EMG, EOG and ECG) and outputs data into the various sleep stages (wake, NREM, N1, N2, N3, REM). The classifier is trained for each subject via a brief session of manual scoring. Design and development strategies were built around both theoretical and heuristic approaches. This led to a multi-layered system capable of learning from extremely limited training sets, using large input space dimensionalities from a rich variety of polysomnography inputs, and with rapid computational times. Validation was pursued to approach the numerous contentious dynamics that have led to the demise of previous solutions. Somnivore generalisation was evaluated at the level of canonical classifier evaluation metrics such as F-measure, as well as experimental end-measures