Table of Contents

• Dataset Description
  • Methods & Sampling
  • Data Processing Description
• Data Files
• Parameters
• Deployments
• Project Information
• Funding

Virus and microbial cell abundances from 25 marine surveys.

Data is also available via: http://weitzgroup.github.io/Virus_Microbe_Abundance/

Data are freely available Open source via Creative Commons Attribution 4.0 International License, see: http://creativecommons.org/licenses/by/4.0/legalcode

The original publication is:
C.H. Wigington et al. 2016. Re-examination of the relationship between marine virus and microbial cell abundances Nature Microbiology 1:15024. doi:10.1038/nmicrobiol.2015.24

Marine virus abundance data were aggregated from 25 studies. A total of 5,671 data points were aggregated. The data collection dates ranged from 1996 to 2012. Data were primarily collected from coastal waters in the northern hemisphere, predominately during the summer months, with the notable exceptions of long-term coastal monthly monitoring sites, that is, the studies USC MO, BATS and MOVE.

See the original publication for more information:
​C.H. Wigington et al. 2016. Re-examination of the relationship between marine virus and microbial cell abundances Nature Microbiology 1:15024. doi:10.1038/nmicrobiol.2015.24

Analyses of the data were performed using R version 3.1.1. Scripts and original data are provided at https://github.com/WeitzGroup/Virus_Microbe_Abundance

BCO-DMO Processing:
- modified parameter names to conform with BCO-DMO naming conventions;
- formatted dates to mm/dd/yyyy;
- replaced spaces with underscores.

Website: http://ecotheory.biology.gatech.edu

Coverage: Theory and modeling project; lab-based, Georgia Tech

Description from NSF award abstract:
Bacteria and their viruses (phages) make up two of the most abundant and genetically diverse groups of organisms in the oceans. The extent of this diversity has become increasingly apparent with the advent of environmental sequencing. However, the ongoing discovery of new taxonomic diversity has, thus far, out-paced gains in quantifying the function of and interactions among phages and bacteria. Improved quantitative understanding of how diverse groups of phages exploit bacterial hosts will improve predictions of microbial population dynamics, ecosystem functioning, and the large-scale dynamics of global biogeochemical cycles. This project will develop a theoretical framework for characterizing the effect of complex phage-bacteria interactions on marine ecosystem structure and function. The theoretical framework is grounded in the analysis of cross-infection assays of bacteriophages with their bacterial hosts, termed phage-bacteria infection networks (PBINs). Recent discoveries concerning the structure of PBINs will be combined with a novel eco-evolutionary dynamics modeling framework in the service of the following aims:

Aim 1. Develop theoretical methods to analyze PBINs that include quantitative infection data to characterize complex patterns of cross-infection found in marine ecosystems.

Aim 2. Establish eco-evolutionary multi-strain models that incorporate complex PBIN data to evaluate hypotheses regarding how cross-infection within PBINs affects community stability.

Aim 3. Utilize the multi-strain model to predict how PBINs influence: (i) the ratio of viral to bacterial population abundances; and (ii) the flux of carbon and nutrients at the ecosystem level.

The theory developed in this project will improve characterizations of phage- bacteria interactions in marine ecosystems and establish a framework to link phage-bacteria interactions with ecosystem function. First, the project will generalize preliminary findings of multi-scale structure within empirical PBINs by developing novel network theories that can be applied to quantitative infection data. Properties of marine PBINs will be analyzed to assess whether they are hierarchically organized, organized into modules, and/or possess multi-scale structure. The statistical structure of PBINs will be integrated with multi-scale coevolutionary models. These co- evolutionary models will be utilized to evaluate hypotheses regarding how cross-infection structure affects community stability. Finally, these coevolutionary models will be used to consider carbon and nutrient regeneration via viral lysis of bacterial hosts. PBIN structure will be varied to establish a link between cross-infection and key indices of ecosystem structure and function, with specific applications to Roseobacter and Synechococcus hosts. Analytical methods and large-scale simulations will be utilized to achieve these goals, closely linked to empirical datasets.