Incorporating phytoplankton genomic traits into cellular resource allocation models

Catherine Garcia1, George Hagstrom2, Alyse Larkin3, Lucas Ustick4, Simon Levin2, Michael W Lomas5 and Adam Martiny1,4, (1)University of California Irvine, Earth System Science, Irvine, CA, United States, (2)Princeton University, Ecology and Evolutionary Biology, Princeton, NJ, United States, (3)University of California Irvine, Earth System Science, Irvine, United States, (4)University of California Irvine, Ecology and Evolutionary Biology, Irvine, CA, United States, (5)Bigelow Lab for Ocean Sciences, East Boothbay, ME, United States
Regulation of the Carbon:Nitrogen:Phosphorus (C:N:P) ratios of marine phytoplankton communities plays a key role in linking the nutrient and carbon cycles in the ocean. Nutrient limitation is a major driver of C:N:P variation in oligotrophic biomes, but nutrient concentrations are often below assay detection limits. In addition, current models poorly constrain C:N:P ratios in many regions. Here we propose that regional genomic and physiological shifts allow us to identify the regulation of cellular elemental composition and nutrient uptake among marine phytoplankton. We quantified the in situ nutrient assimilation genes, taxonomic variation, nutrient uptake rates, and cellular elemental composition across 54 marine phytoplankton communities from three major ocean basins (Indian, Pacific, and Atlantic). As expected, we found indications of iron (Fe) limitation and not macronutrient limitation in the Equatorial Pacific, and P limitation in the North Atlantic Ocean. Furthermore, we found widespread evidence of N limitation, and possibly a northward shift from Fe to P limitation in the Indian Ocean. The observations were then integrated using a newly developed adaptive trait optimization model (ATOM). The trait model described cellular resource allocation strategies optimizing biosynthesis, nutrient uptake, photosynthesis, structural modifications, and storage. By combining metagenomics and in situ physiological observations with a trait model, we are able to incorporate the relative role of microbial adaptation into ocean biogeochemical models. As such, the work aims to demonstrate how one can cross the gap between the genomic diversity of microbial communities and basin-wide variation in ocean biogeochemistry.