Simons Collaboration on Computational BIOgeochemical Modeling of marine EcosystemS
Bacteria, archaea, and viruses play critical roles in all marine systems, but most of these organisms have not been incorporated into trait-based global biogeochemical models, such as Darwin, for a variety of reasons. As part of the Simons Collaboration on Computational Biogeochemical Modeling of Marine Ecosystems (CBIOMES), the Fuhrman Lab is working to develop information and tools to integrate diverse microorganisms into such models. This includes assessing the functional characteristics, community composition, and global distributions of most such microbes. In particular, we are using time-series analyses, laboratory experiments, and data mining to better describe the fundamental traits and worldwide distributions of microorganisms and the factors controlling them. We also are working with modelers to optimize how these microbes are incorporated into the models.
Ongoing projects as part of CBIOMES include:
- · Using modern denoising algorithms and basin-scale meta-‘omics datasets to infer the spatial and temporal distribution of microbial taxa as exact amplicon sequence variants. This work will create a stable biogeographic database of global organismal distribution and abundance patterns, which will contribute essential data for modelling work (including “ground truthing”).
- · Using genomics and other information to develop a database of traits of a broad variety of marine microorganisms, including fundamental lifestyle, preferred nutrients and conditions, geographic distributions, interactions with other microbes, etc.
- · Using metatranscriptomics and metagenomics to infer taxon-specific indicators of growth and maximum growth rates. Resulting data will allow use of increasingly-available ‘omics data to assign growth traits of varied marine prokaryotes at different ocean locations and times, and integrate them into global biogeochemical models.
- · Conducting controlled experiments with natural seawater microbial communities to directly observe taxon-specific growth traits and to validate ‘omics-based growth metrics. This work will allow us to better use static meta-‘omics datasets to infer dynamic growth characteristics of natural microbial communities.