Declines in pollinator colonies represent a worldwide concern. The widespread use of agricultural pesticides is recognized as a potential cause of these declines. Previous studies have examined the effects of neonicotinoid insecticides such as imidacloprid on pollinator colonies, but these investigations have mainly focused on adult honey bees. Native stingless bees (Hymenoptera: Apidae: Meliponinae) are key pollinators in neotropical areas and are threatened with extinction due to deforestation and pesticide use. Few studies have directly investigated the effects of pesticides on these pollinators. Furthermore, the existing impact studies did not address the issue of larval ingestion of contaminated pollen and nectar, which could potentially have dire consequences for the colony. Here, we assessed the effects of imidacloprid ingestion by stingless bee larvae on their survival, development, neuromorphology and adult walking behavior. Increasing doses of imidacloprid were added to the diet provided to individual worker larvae of the stingless bee Melipona quadrifasciata anthidioides throughout their development. Survival rates above 50% were only observed at insecticide doses lower than 0.0056 µg active ingredient (a.i.)/bee. No sublethal effect on body mass or developmental time was observed in the surviving insects, but the pesticide treatment negatively affected the development of mushroom bodies in the brain and impaired the walking behavior of newly emerged adult workers. Therefore, stingless bee larvae are particularly susceptible to imidacloprid, as it caused both high mortality and sublethal effects that impaired brain development and compromised mobility at the young adult stage. These findings demonstrate the lethal effects of imidacloprid on native stingless bees and provide evidence of novel serious sublethal effects that may compromise colony survival. The ecological and economic importance of neotropical stingless bees as pollinators, their susceptibility to insecticides and the vulnerability of their larvae to insecticide exposure emphasize the importance of studying these species.

Although the importance of bees as the pollinators responsible for maintaining gene flow for many native and cultivated plants in ecosystems around the world is recognized, much of their biodiversity and behavior remains to be discovered. Stingless bees are considered key pollinators for several plant species in tropical and subtropical ecosystems and they also provide pollination services for economically important agricultural crops. Many countries are using the honey bee (Apis mellifera Linnaeus, 1758, Hymenoptera: Apidae) as a surrogate to evaluate the risk of pesticides to all species of bees. However, there is uncertainty regarding the extent to which honey bees can serve as surrogates for non-Apis bee species in the risk assessment for pesticides. This paper provides a short overview of the life history traits relevant in risk assessment of stingless bees. It summarizes what is known about stingless bee exposure to pesticides compared to that of honey bees and presents criteria for potential candidate species from Brazil for use in pesticide risk assessment in tropical environments. This paper also identifies gaps in knowledge of bee biology and pesticide exposure routes not covered by the current honey bee exposure assessment paradigm. Based on these gaps, research is needed on life history traits, estimates of nectar and pollen consumption, mud, resin, and water collection and available protocols to adequately assess toxic effects of pesticides to stingless bees. This review is part of a series of papers on the risk of exposure of non-Apis bees to pesticides.

Manual monitoring of animal behavior is time-consuming and prone to bias. An alternative to such limitations is using computational resources in behavioral assessments, such as tracking systems, to facilitate accurate and long-term evaluations. There is a demand for robust software that addresses analysis in heterogeneous environments (such as in field conditions) and evaluates multiple individuals in groups while maintaining their identities. The Ethoflow software was developed using computer vision and artificial intelligence (AI) tools to monitor various behavioral parameters automatically. An object detection algorithm based on instance segmentation was implemented, allowing behavior monitoring in the field under heterogeneous environments. Moreover, a convolutional neural network was implemented to assess complex behaviors expanding behavior analyses’ possibilities. The heuristics used to generate training data for the AI models automatically are described, and the models trained with these datasets exhibited high accuracy in detecting individuals in heterogeneous environments and assessing complex behavior. Ethoflow was employed for kinematic assessments and to detect trophallaxis in social bees. The software was developed in desktop applications and had a graphical user interface. In the Ethoflow algorithm, the processing with AI is separate from the other modules, facilitating measurements on an ordinary computer and complex behavior assessing on machines with graphics processing units. Ethoflow is a useful support tool for applications in biology and related fields.