The colonies of honey bees are mostly sessile organisms. Consequently, the type of nest boxes that beekeepers provide to their bees should impact a colony’s ability to maintain homeostasis, which is a key determinant of performance and fitness. Here, we used European honey bees (Apis mellifera) and provided them with two hive setups widely used and known as Langstroth and Warré. We compared colony performance in a Mediterranean climate for five months from late spring to early autumn, which covered the most active time of bees and included periods of heat and drought. We found that irrespective of hive type or season, honey bees kept hive temperature and humidity within a remarkably narrow range. Nevertheless, the hive type impacted the daily fluctuations in temperature and humidity. In Warré hives, where bees have more autonomy to build and maintain their combs, we found that bees were able to reduce daily fluctuations in temperature and humidity and kept both measures closer to the overall average. This increase in colony homeostasis found in Warré hives negatively correlated with other hive performance indicators, such as immunocompetence. We conclude that different hive types affect key areas, such as the central part of the colony with frames of developing brood or the queen, which are the most susceptible individuals. This implies that climatic changes resulting in extreme weather events are expected to impact colony performance and fitness, especially in non-managed honey bees that are limited by available nesting sites. For managed bees, adaptations to existing hive setups could be provided to help bees minimize the effects of abiotic stress.

In the leaf-cutting ant Atta colombica, queens receive ejaculates from multiple males during one single mating event early in their lives. A queen’s fertility and fitness therefore depend on maximizing the number of sperm cells she can store and maintain inside her spermatheca. Previous studies implied significant physiological mating costs, either originating from energetic investments maximizing sperm survival, or from resolving sexual conflicts to terminate male-driven incapacitation of rival sperm via serine proteases found in seminal fluid. Here we conducted an artificial insemination experiment, which allowed us to distinguish between the effects of sperm and seminal fluid within the queen’s sexual tract on her survival and immunocompetence. We found significantly higher mortality in queens that we had inseminated with sperm, independently of whether seminal fluid was present or not. Additionally, after receiving sperm, heavier queens had a higher probability of survival compared to lightweight queens, and immunocompetence decreased disproportionally for queens that had lost weight during the experiment. These findings indicate that queens pay significant physiological costs for maintaining and storing sperm shortly after mating. On the other hand, the presence of seminal fluid within the queens’ sexual tract neither affected their survival nor their immunocompetence. This suggests that the energetic costs that queens incur shortly after mating are primarily due to investments in sperm maintenance and not costs of terminating conflicts between competing ejaculates. This outcome is consistent with the idea that sexually selected traits in social insects with permanent castes can evolve only when they do not affect survival or life-time fitness of queens in any significant way.

Preventing beneficial insects like honey bees ( Apis mellifera ) from contacting pesticides on crops using odorants could counter current pollinator declines. However, the discovery of behaviorally aversive odorants is impeded by the complexity of the honey bee olfactory system where >170 olfactory receptors detect volatiles and generate valence. To solve this systems-level challenge, we generated a machine-learning model to predict aversive valence from chemical structure using published olfactory behavior data in honey bees. We refine the predictive model by generating species-level behavioral data for honey bees and Drosophila on an initial set of novel predicted repellents. The improved second computational model was then used to screen a chemical space of >50 million compounds and identify >130 repellent candidates. Behavioral validation using honey bees in the laboratory shows a high predictive success. Additional testing of the top seven candidates using freely foraging honey bees in a field assay confirmed strong repellency, thus predicting a high probability to repel foraging bees from pesticide-treated crops. Machine learning, with iterative testing and modeling, therefore provides a powerful approach for rational discovery of aversive volatiles for control of insects for which limited data is available.