This workshop, sponsored by AIM and the NSF, will focus on advancing mathematical modeling frameworks and theory to address the urgent challenges posed by declining pollinator populations.

Insect pollination is vital to terrestrial ecosystems and agriculture, with honeybee pollination in the United States alone valued at over $12 billion annually. Yet, substantial evidence shows that pollinator populations are in sharp decline, with honeybee colony losses reaching unprecedented levels — threatening agricultural sustainability and food security. These declines result from complex interactions among environmental factors (e.g., shifting climate patterns) and biological processes (e.g., exposure to agrochemicals such as insecticides and fungicides, parasitic infections, diseases, and habitat degradation).

Traditional models often struggle to represent the multiple, interacting factors operating across diverse spatial and temporal scales, and they frequently lack integration with empirical data for robust validation and parameterization. Addressing this complexity requires new mathematical approaches — particularly in dynamical systems, optimal control, reinforcement learning, and hybrid symbolic–data-driven modeling — capable of capturing intricate feedbacks and guiding actionable solutions.

This workshop will bring together mathematicians, biologists, environmental scientists, and beekeepers to develop biologically realistic, predictive models that reflect the intricacies of pollinator health, especially honeybees. The program will focus on three main themes:

1. Mathematical Modeling of Complex Pollinator–Environment Dynamics
2. Data-Driven Optimization and AI-Integrated Approaches
3. Decision Support and Sustainable Management Strategies