Leveraging automated monitoring of solitary bee foraging and nesting behavior to inform population models
Research Poster Health & Life Sciences 2025 Graduate ExhibitionPresentation by Edward Amoah
Exhibition Number 3
Abstract
The Osmia cornifrons 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. The results of the study reveal that spring pollinators that emerge in the spring experience more unfavorable foraging conditions and have lower reproductive success than pollinators that emerge later in spring. The empirical results from this study will 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.
Importance
Pollinators, particularly bees, sustain biodiversity and enhance agricultural output. However, their populations are rapidly declining due to habitat destruction, pesticide use, and climate change. Although there is increasing awareness of the impact of weather on bee populations, the primary environmental factor causing these changes remains uncertain. This research aims to fill this gap by introducing AppleBee, a mechanistic model designed to simulate the reproductive success of Osmia cornifrons, an economically crucial solitary bee for spring-blooming crops like apples. The AppleBee model was parameterized with data from an AI-driven automated monitoring system used to observe populations of Osmia cornifrons. This study is essential for addressing the decline of pollinators and maintaining reliable pollination services in a changing environment.