RESUMO
Honey bee colonies have great societal and economic importance. The main challenge that beekeepers face is keeping bee colonies healthy under ever-changing environmental conditions. In the past two decades, beekeepers that manage colonies of Western honey bees (Apis mellifera) have become increasingly concerned by the presence of parasites and pathogens affecting the bees, the reduction in pollen and nectar availability, and the colonies' exposure to pesticides, among others. Hence, beekeepers need to know the health condition of their colonies and how to keep them alive and thriving, which creates a need for a new holistic data collection method to harmonize the flow of information from various sources that can be linked at the colony level for different health determinants, such as bee colony, environmental, socioeconomic, and genetic statuses. For this purpose, we have developed and implemented the B-GOOD (Giving Beekeeping Guidance by computational-assisted Decision Making) project as a case study to categorize the colony's health condition and find a Health Status Index (HSI). Using a 3-tier setup guided by work plans and standardized protocols, we have collected data from inside the colonies (amount of brood, disease load, honey harvest, etc.) and from their environment (floral resource availability). Most of the project's data was automatically collected by the BEEP Base Sensor System. This continuous stream of data served as the basis to determine and validate an algorithm to calculate the HSI using machine learning. In this article, we share our insights on this holistic methodology and also highlight the importance of using a standardized data language to increase the compatibility between different current and future studies. We argue that the combined management of big data will be an essential building block in the development of targeted guidance for beekeepers and for the future of sustainable beekeeping.
RESUMO
Artificial light at night (ALAN) is an increasing phenomenon associated with worldwide urbanization. In birds, broad-spectrum white ALAN can have disruptive effects on activity patterns, metabolism, stress response and immune function. There has been growing research on whether the use of alternative light spectra can reduce these negative effects, but surprisingly, there has been no study to determine which light spectrum birds prefer. To test such a preference, we gave urban and forest great tits (Parus major) the choice where to roost using pairwise combinations of darkness, white light or green dim light at night (1.5 lux). Birds preferred to sleep under artificial light instead of darkness, and green was preferred over white light. In a subsequent experiment, we investigated the consequence of sleeping under a particular light condition, and measured birds' daily activity levels, daily energy expenditure (DEE), oxalic acid as a biomarker for sleep debt and cognitive abilities. White light affected activity patterns more than green light. Moreover, there was an origin-dependent response to spectral composition: in urban birds, the total daily activity and night activity did not differ between white and green light, while forest birds were more active under white than green light. We also found that individuals who slept under white and green light had higher DEE. However, there were no differences in oxalic acid levels or cognitive abilities between light treatments. Thus, we argue that in naive birds that had never encountered light at night, white light might disrupt circadian rhythms more than green light. However, it is possible that the negative effects of ALAN on sleep and cognition might be observed only under intensities higher than 1.5 lux. These results suggest that reducing the intensity of light pollution as well as tuning the spectrum towards long wavelengths may considerably reduce its impact.
