What's all the buzz about? Passive acoustic monitoring of bees

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Abstract
There has been a growing need for non‐lethal methods of monitoring pollinators. Despite advances in passive acoustic monitoring (PAM) transforming other fields of wildlife research, its application to pollinator monitoring has remained limited. However, bees produce distinctive flight and sonication sounds, making them promising candidates for PAM. We developed two convolutional neural network models to detect bee acoustic signals from field recordings. Our dataset comprised 594 recordings collected over five years from 43 sites and encompassed a broad range of habitat types and background noise conditions. The models primarily target honey bees ( Apis mellifera ) and bumble bees ( Bombus spp.). Model 1 detects overall bee activity, including honey bee and bumble bee flight and sonication sounds as well as foraging sounds produced by other species such as the eastern carpenter bee ( Xylocopa virginica ). Model 2 distinguishes honey bee flight, bumble bee flight and bumble bee sonication. Both models were validated against independent field observations made by trained observers. At a confidence threshold of 0.9, both models achieved high precision (93%–97%), with lower recall (13%–44%), prioritizing reliable detections over detection completeness. Model detections of overall bee activity and honey bee and bumble bee activity were strongly correlated with observer counts in the field. Model detections occurred primarily within 50 cm of the recorder. Both models are available within the Raven Pro platform, a user‐friendly acoustic analysis tool. This approach provides a standardized, non‐lethal method for monitoring pollinator activity in real‐world soundscapes, complementing traditional survey techniques and supporting long‐term ecological research and conservation.
Funding Information
  • Office of Energy Efficiency and Renewable Energy (DE‐EE0010382)

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