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Ocean microbial communities are made up of thousands of diverse taxa whose metabolic demands set the rates of both biomass production and degradation. Thus, these microscopic organisms play a critical role in ecosystem dynamics, global carbon cycling, and climate. While we have frameworks for relating phytoplankton diversity to rates of carbon fixation, our knowledge of how variations in heterotrophic microbial populations drive changes in carbon cycling is in its infancy. Here, we leverage global metagenomic datasets and metabolic models to identify a set of metabolic niches with distinct growth strategies. These groupings provide a simplifying framework for describing microbial communities in different oceanographic regions and for understanding how heterotrophic microbial populations function. This framework, predicated directly on metabolic capability rather than taxonomy, enables us to tractably link heterotrophic diversity directly to biogeochemical rates in large scale ecosystem models. 

Abstract The systematic substitution of direct observational data with synthesized data derived from models during the stock assessment process has emerged as a low-cost alternative to direct data collection efforts. What is not widely appreciated, however, is how the use of such synthesized data can overestimate predictive skill when forecasting recruitment is part of the assessment process. Using a global database of stock assessments, we show that Standard Fisheries Models (SFMs) can successfully predict synthesized data based on presumed stock-recruitment relationships, however, they are generally less skillful at predicting observational data that are either raw or minimally filtered (denoised without using explicit stock-recruitment models). Additionally, we find that an equation-free approach that does not presume a specific stock-recruitment relationship is better than SFMs at predicting synthesized data, and moreover it can also predict observational recruitment data very well. Thus, while synthesized datasets are cheaper in the short term, they carry costs that can limit their utility in predicting real world recruitment.