Comparing diel pollinator foraging patterns between patches using passive acoustic monitoring

The timing of pollinator foraging has been well studied at large phenological scales. Growing degree day models are a powerful predictor of the onset and offset of flowering, many pollinators have well established seasonal patterns of activity, and trapping efforts such as pan trapping and sweep netting afford day-by-day insights into pollinator prevalence. Within-day dynamics, however, require much more intensive sampling to observe. Diel patterns of nectar production in flowers are often unknown and while activity of some social pollinators can be easily monitored from the hive, monitoring at the patch is more difficult. In this work, we leverage passive acoustic monitoring to obtain second-by-second sampling of insect activity at various floral patches in the spring and summer in central Ohio. Recorders were placed near flowers of different species over the course of several days. Audio data was processed with our lab’s open source bioacoustic analysis tool “buzzdetect”. Using this tool, we applied a machine learning model capable of detecting the flight buzzes of foraging bees and other flying insects to produce phenological curves of activity across the course of the day. We will present an exploration of these data, comparing the temporal niches between species and hypothesizing drivers of the observed patterns. Preliminary results show a consistent general trend of activity between 10:00AM and 6:00PM, but consistently differing times of peak and total foraging.