NSF Award
2219794
This project investigates the factors determining where and why microbes are the most efficient at turning carbon into biomass within the upper several hundred meters of the ocean. Microbial growth efficiency is a critical ecological parameter that describes the energy required by microbes for biomass production and defines the proportion of carbon lost from marine microbial food webs through respiration—conversion of organic carbon into carbon dioxide. Despite its significance, there is a limited understanding of the drivers of microbial efficiency. Assessing microbial efficiency variation across different marine microbes is hindered by the difficulty of directly measuring the energy used for biomass production. This project uses new methods to estimate microbial energy conversion within cells and compare it to measurements of other metabolic processes such as respiration and primary production. Additionally, the researchers measure environmental nutrient concentration, oxygen concentration, temperature, pH, and microbial community structure from diverse oceanographic environments in the California Current ecosystem to include within mathematical models for interpreting and predicting microbial carbon flow. In addition, this project provides education and at-sea research training opportunities for new scientists, including graduate students, postdoctoral scholars, and a cohort of undergraduate students from groups historically underrepresented in the marine sciences. <br/><br/>This project aims to identify the ecological conditions and microbial taxa that account for the variance in microbial growth efficiency along light and nutrient gradients in the ocean. The project uses data collected on an oceanographic research expedition in the California Current ecosystem along the central and southern California coast, a well-characterized and heterogenous region broadly representative of key ecosystems in the global ocean. Microbial growth efficiency measurements are being made using an innovative combination of two new radioisotope tracer-based techniques, flow cytometry, and microbial community structure analysis. In addition, the researchers use machine learning techniques to provide predictive analytics and link microbial community structure, abundance, efficiency, and environmental conditions.<br/><br/>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.
