Collaborative Research: GCR: A new scientific framework for understanding and valuing ecosystem services at scale

Wild pollinators such as bees, butterflies, and other insects play a vital role in producing many of the fruits, vegetables, and nuts we rely on every day. Effective investment in protecting these species requires knowing where conservation efforts will have the greatest impact, but current information is limited because traditional field studies are time-consuming, labor-intensive, and conducted at relatively small scales. This project proposes a new scientific framework that combines artificial intelligence (AI), ecological field research, and economics to overcome these limitations. The project uses AI and remote sensing to identify bee hives, flowering habitats, and crop fields across entire landscapes, then verifies those observations through field research before linking them to managed pollinator populations, crop production, and economic outcomes. This provides a new way to measure the value of pollination services and identify where conservation investments can have the greatest environmental and economic impact. Although this project focuses on pollinators, the results provide a scalable framework for understanding and valuing many other ecosystem services, creating new opportunities to guide conservation and land management decisions. Declining populations of wild and managed pollinators threaten humanity’s ability to sustain future food and fiber needs. Habitat destruction caused by factors such as agricultural expansion, pesticide exposure, and disease is drastically reducing the abundance and biodiversity of wild pollinators, which in turn can affect agricultural productivity. Maintaining healthy pollinator populations and the ecosystem services they provide requires understanding the complex relationships among pollinators, their habitats, managed pollinator availability (e.g., honey bee stocking rates), agricultural outputs, land use management decisions, and economic factors. Current approaches to mitigate pollinator declines are constrained by limited data and disciplinary boundaries. Deep integration of disciplines including computer science, ecology, geography, and economics is needed to understand this complex human-mediated ecological system. This project will create a transdisciplinary paradigm for ecological-economic analysis of ecosystem services, designed and implemented in the context of pollinator preservation. It will develop artificial intelligence technologies for detecting small objects like bee boxes and floral abundance to create novel, landscape-scale datasets that will be validated with on-the-ground ecological data. It will also use contemporary econometric techniques for causal identification guided by ecological and economic theory to determine the integrated relationships among land use, habitat, pollination, and crop yields. The results will be inputs into economic techniques for valuing nonmarket goods and services. This combination of AI, ecology, and economics will be used to develop new methods to identify land use practices, habitat quality, yield, and use of managed pollinators from a combination of remotely sensed, survey, and government data at scales that transcend standard ecological data. Results will include identification of land use practices that benefit pollinators and quantification of the economic value of those benefits to farmers and our wider society. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.