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Biological rhythms have a crucial role in shaping the biology and ecology of organisms. Light pollution is known to disrupt these rhythms, and evidence is emerging that chemical pollutants can cause similar disruption. Conversely, biological rhythms can influence the effects and toxicity of chemicals. Thus, by drawing insights from the extensive study of biological rhythms in biomedical and light pollution research, we can greatly improve our understanding of chemical pollution. This Essay advocates for the integration of biological rhythmicity into chemical pollution research to gain a more comprehensive understanding of how chemical pollutants affect wildlife and ecosystems. Despite historical barriers, recent experimental and technological advancements now facilitate the integration of biological rhythms into ecotoxicology, offering unprecedented, high-resolution data across spatiotemporal scales. Recognizing the importance of biological rhythms will be essential for understanding, predicting, and mitigating the complex ecological repercussions of chemical pollution.

Sleep has a multitude of benefits and is generally considered necessary for optimal performance. Disruption of sleep by extended photoperiods, moonlight and artificial light could therefore impair performance in humans and non-human animals alike. Here, we review the evidence for effects of light on sleep and subsequent performance in birds. There is accumulating evidence that exposure to natural and artificial sources of light regulates and suppresses sleep in diurnal birds. Sleep also benefits avian cognitive performance, including during early development. Nevertheless, multiple studies suggest that light can prolong wakefulness in birds without impairing performance. Although there is still limited research on this topic, these results raise intriguing questions about the adaptive value of sleep. Further research into the links between light, sleep and performance, including the underlying mechanisms and consequences for fitness, could shed new light on sleep evolution and urban ecology.

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 , 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 = 52), narcoleptic mice and drug-treated rats (total = 56), and pigeons ( = 5). Training and testing sets for validation were previously scored manually by 1-2 trained sleep technologists from each laboratory. -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.

Investment in immune function can be costly, and life-history theory predicts trade-offs between immune function and other physiological demands. Environmental heterogeneity may constrain or change the optimal strategy and thereby alter baseline immune function (possibly mediated by stress responses). We tested several hypotheses relating variation in climatic, ecological, and social environments to chronic stress and levels of baseline innate immunity in a wild, cooperatively breeding bird, the purple-crowned fairy-wren (Malurus coronatus coronatus). From samples collected biannually over 5 yr, we quantified three indexes of constitutive innate immune function (haptoglobin/PIT54, natural antibodies, complement activity) and one index of chronic stress (heterophil-lymphocyte ratio; n = 513–647). Using an information-theoretic and multimodel inference statistical approach, we found that habitat quality and social group size did not affect any immune index, despite hypothesized links to resource abundance and parasite pressure. Rather, short-term variation in temperature and rainfall was related to immune function, while overall differences between seasons were small or absent, despite substantial seasonal variation in climate. Contrary to our expectation, we found no evidence that physiological stress mediated any effects of short-term climatic variables on immune indexes, and alternative mechanisms may be involved. Our results may be interpreted from the perspective of reactive scope models, whereby predictive homeostasis maintains standing immune function relative to long-term demands, while short-term environmental change, being less predictable, has a greater influence on baseline immune function.