Leveraging automated monitoring of solitary bee foraging and nesting behavior to inform population models

Osmia cornifrons bees are important pollinators for spring-blooming fruit crops like apples. However, there can be considerable fluctuations in spring weather conditions, including temperature and precipitation, which can limit O. cornifrons’ ability to forage to collect pollen to feed their brood, which can lead to high variation in abundance across locations and years. Here, we investigate the influence of temperature, precipitation, and landscape on O. cornifrons foraging activity, foraging preferences, and reproductive success by developing an automated AI-enabled monitoring system. The automated monitoring system consists of an IoT-based camera module that records bee activity at nesting hotels and a computer vision system that analyzes the videos to extract information on when individual bees enter/exit a particular nesting tube. After nesting was completed, the bee hotels were deconstructed. The number of cells per nest was evaluated to calibrate the behavioral data and determine how weather and land use affected brood production. The data from these studies were integrated into a model that calculates the predicted emergence data and number of brood cells produced by O. cornifrons bees across Pennsylvania over the last ten years. This automated monitoring system will allow for precise behavioral data to be collected for O. cornifrons and other solitary, cavity-nesting bees and enable the development of precise population models that can help growers and conservationists predict the annual abundance of wild bees across diverse climates and habitats.