Pirimicarb exposure disrupts gut microbiome composition in honey bees (Apis mellifera L.): Seasonal differences and impaired colony recovery.

Pesticide exposure poses a significant threat to the honey bee's health, yet the effects of chemicals like pirimicarb on honey bee gut microbiomes remain poorly understood. This study investigates the impact of pirimicarb on honey bee microbiota, with a particular focus on how host physiology (spring vs. winter bees) modulates microbial responses to pesticide exposure. Through a series of controlled experiments, we demonstrate that pirimicarb is associated with selective dysbiosis resulting in taxon-specific microbial shifts without altering total bacterial load. Key bacterial taxa such as Snodgrassella alvi were consistently depleted across both spring and winter cohorts, while stress-tolerant lactic acid bacteria (LAB) proliferated. Winter bees exhibited more pronounced microbiome instability, characterized by an unregulated expansion of multiple microbial taxa, which strongly correlated with suppressed detoxification and antioxidant enzyme profiles under pesticide stress. Enzymatic assays further revealed seasonal differences in detoxification and immune activation, with spring bees mounting robust responses, while winter bees exhibited compromised defense mechanisms. Behavioral changes, including prolonged feeding inhibition in winter bees, indicated exacerbated vulnerability that is likely linked to a reduced metabolic capacity. Hive reintegration experiments showed that pirimicarb exposure severely impairs microbiome resilience, with pesticide-compromised bees failing to recover despite access to the colony's social microbiome transfer. These results underscore the need for risk assessments that incorporate both physiological states and ecological contexts, as pesticide exposure may have more severe, long-lasting effects during overwintering periods. Our findings suggest that integrating absolute core microbiome health metrics, seasonal phenotypes, and social recovery mechanisms into evaluation frameworks is crucial for improving the ecological relevance and predictive accuracy of pollinator risk assessments.