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Abstract Lithium has been considered a potential acaricidal agent against the honey bee (Apis mellifera) parasite Varroa. It is known that lithium suppresses elevated activity and regulates circadian rhythms and light response when administered to humans as a primary therapeutic chemical for bipolar disorder and to other bipolar syndrome model organisms, given the crucial role of timing in the bee's foraging activity and the alternating sunlight vs dark colony environment bees are exposed, we explored the influence of lithium on locomotor activity (LMA) and circadian rhythm of honey bees. We conducted acute and chronic lithium administration experiments, altering light conditions and lithium doses to assess LMA and circadian rhythm changes. We fed bees one time 10 μl sucrose solution with 0, 50, 150, and 450 mM LiCl in the acute application experiment and 0, 1, 5, and 10 mmol/kg LiCl ad libitum in bee candy in the chronic application experiment. Both acute and chronic lithium treatments significantly decreased the induced LMA under constant light. Chronic lithium treatment disrupted circadian rhythmicity in constant darkness. The circadian period was lengthened by lithium treatment under constant light. We discuss the results in the context ofVarroacontrol and lithium's effect on bipolar disorder. 

Circadian rhythms in honey bees are involved in various processes that impact colony survival. For example, young nurses take care of the brood constantly throughout the day and lack circadian rhythms. At the same time, foragers use the circadian clock to remember and predict food availability in subsequent days. Previous studies exploring the ontogeny of circadian rhythms of workers showed that the onset of rhythms is faster in the colony environment (~2 days) than if workers were immediately isolated after eclosion (7–9 days). However, which specific environmental factors influenced the early development of worker circadian rhythms remained unknown. We hypothesized that brood nest temperature plays a key role in the development of circadian rhythmicity in young workers. Our results show that young workers kept at brood nest-like temperatures (33–35 °C) in the laboratory develop circadian rhythms faster and in greater proportion than bees kept at lower temperatures (24–26 °C). In addition, we examined if the effect of colony temperature during the first 48 h after emergence is sufficient to increase the rate and proportion of development of circadian rhythmicity. We observed that twice as many individuals exposed to 35 °C during the first 48 h developed circadian rhythms compared to individuals kept at 25 °C, suggesting a critical developmental period where brood nest temperatures are important for the development of the circadian system. Together, our findings show that temperature, which is socially regulated inside the hive, is a key factor that influences the ontogeny of circadian rhythmicity of workers. 

Abstract Honey bees utilize their circadian rhythms to accurately predict the time of day. This ability allows foragers to remember the specific timing of food availability and its location for several days. Previous studies have provided strong evidence toward light/dark cycles being the primary Zeitgeber for honey bees. Work in our laboratory described large individual variation in the endogenous period length of honey bee foragers from the same colony and differences in the endogenous rhythms under different constant temperatures. In this study, we further this work by examining the temperature inside the honey bee colony. By placing temperature and light data loggers at different locations inside the colony we measured temperature at various locations within the colony. We observed significant oscillations of the temperature inside the hive, that show seasonal patterns. We then simulated the observed temperature oscillations in the laboratory and found that using the temperature cycle as a Zeitgeber, foragers present large individual differences in the phase of locomotor rhythms for temperature. Moreover, foragers successfully synchronize their locomotor rhythms to these simulated temperature cycles. Advancing the cycle by six hours, resulting in changes in the phase of activity in some foragers in the assay. The results are shown in this study highlight the importance of temperature as a potential Zeitgeber in the field. Future studies will examine the possible functional and evolutionary role of the observed phase differences of circadian rhythms. 

The LabelBee system is a web application designed to facilitate the collection, annotation and analysis of large amounts of honeybee behavior data from video monitoring. It is developed as part of NSF BIGDATA project “Large-scale multi-parameter analysis of honeybee behavior in their natural habitat”, where we analyze continuous video of the entrance of bee colonies. Due to the large volume of data and its complexity, LabelBee provides advanced Artificial Intelligence and visualization capabilities to enable the construction of good quality datasets necessary for the discovery of complex behavior patterns. It integrates several levels of information: raw video, honeybee positions, decoded tags, individual trajectories and behavior events (entrance/exit, presence of pollen, fanning, etc.). This integration enables the combination of manual and automatic processing by the biologist end-users, who also share and correct their annotation through a centralized server. These annotations are used by the Computer Scientists to create new automatic models, and improve the quality of the automatic modules. The data constructed by this semi-automatized approach can then be exported for the analytic part, which is taking place on the same server using Jupyter notebooks for the extraction and exploration of behavior patterns. 

In this paper, the recognition of pollen bearing honey bees from videos of the entrance of the hive is presented. This computer vision task is a key component for the automatic monitoring of honeybees in order to obtain large scale data of their foraging behavior and task specialization. Several approaches are considered for this task, including baseline classifiers, shallow Convolutional Neural Networks, and deeper networks from the literature. The experimental comparison is based on a new dataset of images of honeybees that was manually annotated for the presence of pollen. The proposed approach, based on Convolutional Neural Networks is shown to outperform the other approaches in terms of accuracy. Detailed analysis of the results and the influence of the architectural parameters, such as the impact of dedicated color based data augmentation, provide insights into how to apply the approach to the target application.