BIGDATA: Collaborative Research: IA: Large-Scale Multi-Parameter Analysis of Honeybee Behavior in their Natural Habitat

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Honey bees exhibit highly complex behavior and are vital for our agriculture. Due to the rich social organization of bees, the overall performance and health of a bee colony depends both on a successful division of labor among the bees and on adequate reaction to the environment, which involves complex behavioral patterns and biological mechanisms. Much remains to be discovered on these matters as research is currently limited by our ability to effectively collect and analyze individual's behavior at large scale, out of the laboratory. The technology developed in this project will enable biologists to study the individual behavior of thousands of bees over extended periods of time. It builds on innovative algorithms and software to analyze big data collected from colonies in the field. Study of behavioral patterns at such scale will provide unique information to advance knowledge on biological processes such as circadian rhythms that influence bee behavior in addition to playing an important role in animals and humans. The models developed will help better understand factors involved in colony collapse disorder, thus guiding future research on threats to such an important pollinator. This work will be performed through the tight collaboration of a multi-disciplinary team of researchers to combine the latest advances in computer science and data science with expertise in biology. It will provide the opportunity to train students from underrepresented minority on research at the intersection of these fields and to reach more than 600 undergraduate students, high school students, and the general public about how the Big Data approach can contribute to current scientific and ecological challenges.The project will develop a platform for the high-throughput analysis of individual insect behaviors and gain new insights into the role of individual variations of behavior on bee colony performance. Joint video and sensor data acquisition will monitor marked individuals at multiple colonies over large continuous periods, generating the first datasets of bee activities of this kind on such a scale. Algorithms and software will be developed to take advantage of a High Performance Computing facility to perform the analysis of these massive datasets. Semi-supervised machine learning will leverage the large amount of data available to facilitate the creation of new detectors for parameters such as pollen carrying bees or fanning behavior, currently annotated manually. Predictive models and functional data analysis methods will be developed to find patterns in individual behavior based on multiple parameters and over large temporal scales. These advances are expected to help uncover mechanisms of individual variations previously unobservable. They will enable the first large scale biological study on the circadian rhythms of the bee based on the variations in behavior of individuals in multiple activities instead of reasoning on single activities or averages. Progress, datasets and software will be shared with the community on the project website (sites.google.com/a/upr.edu/bigdbee).