NSFGEO-NERC: Investigating phytoplankton biogeography and nutrient limitation using trace metals, genomics, and artificial intelligence

Phytoplankton drive the ocean’s biological carbon pump by absorbing atmospheric carbon dioxide and converting it to organic carbon through photosynthesis, then sinking to the deep ocean. Because phytoplankton metabolism depends on inorganic nutrients, the availability of these limiting nutrients controls the biological carbon pump. This project advances understanding of the relationship between phytoplankton species distributions and patterns of nutrient limitation by applying advanced molecular genetic tools and machine learning techniques to phytoplankton samples and biogeochemical data collected across the Pacific Ocean. The resulting model advances capacity to predict marine productivity and carbon export under future ocean conditions. The project also supports research training for postdoctoral scientists and a graduate student and K-12 outreach through science festivals, a summer oceanography camp, STEM competitions, classroom videoconferencing, and development of educational videos on building machine learning models with associated coding lessons. This project advances understanding of phytoplankton niches and nutrient limitation status across the latitudinal range of the Pacific Ocean, including the undersampled South Pacific Ocean. The team is leveraging a unique set of phytoplankton samples collected concurrently with trace element and isotope data and using a combination of amplicon sequencing and metagenomic and metatranscriptomic analyses to 1) build species distribution models for oceanic phytoplankton functional types (PFTs) with a focus on micronutrients, 2) develop nutrient limitation indices (NLIs) for key phytoplankton functional types, then 3) combining PFT-specific NLIs and environmental data and building machine learning models to infer ocean basin-wide patterns of nutrient limitation status for PFTs. The resulting model improves our capacity to predict phytoplankton biogeography and metabolism using independent data sources, such as remote sensing and ocean observing data. 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.