Marine bacterial, archaeal and protistan association networks reveal ecological linkages

• DOI: 10.1038/ismej.2011.24
• Publication Date: 2011-03-24T11:40:16Z
• Authors: Joshua A Steele; Peter D Countway; Li Xia; Patrick D Vigil; J Michael Beman; Diane Y Kim; Cheryl-Emiliane T...; Rohan Sachdeva; Adriane C Jones; Michael S Schwalbach; Julie M Rose; Ian Hewson; Anand Patel; Fengzhu Sun; David A Caron; Jed A Fuhrman
• Keywords: 570; 0303 health sciences; Bacteria; SAR11; Oceans and Seas; Marine Biology; Sequence Analysis, DNA; 15. Life on land; Plankton; cyanobacteria; Archaea; Polymerase Chain Reaction; California; co-occurrence patterns; 03 medical and health sciences; Alveolata; Ammonia; 13. Climate action; dinoflagellates; Seawater; 14. Life underwater; time series; stramenopiles; Stramenopiles

Abstract Microbes have central roles in ocean food webs and global biogeochemical processes, yet specific ecological relationships among these taxa are largely unknown. This is in part due to the dilute, microscopic nature of the planktonic microbial community, which prevents direct observation of their interactions. Here, we use a holistic (that is, microbial system-wide) approach to investigate time-dependent variations among taxa from all three domains of life in a marine microbial community. We investigated the community composition of bacteria, archaea and protists through cultivation-independent methods, along with total bacterial and viral abundance, and physico-chemical observations. Samples and observations were collected monthly over 3 years at a well-described ocean time-series site of southern California. To find associations among these organisms, we calculated time-dependent rank correlations (that is, local similarity correlations) among relative abundances of bacteria, archaea, protists, total abundance of bacteria and viruses and physico-chemical parameters. We used a network generated from these statistical correlations to visualize and identify time-dependent associations among ecologically important taxa, for example, the SAR11 cluster, stramenopiles, alveolates, cyanobacteria and ammonia-oxidizing archaea. Negative correlations, perhaps suggesting competition or predation, were also common. The analysis revealed a progression of microbial communities through time, and also a group of unknown eukaryotes that were highly correlated with dinoflagellates, indicating possible symbioses or parasitism. Possible ‘keystone’ species were evident. The network has statistical features similar to previously described ecological networks, and in network parlance has non-random, small world properties (that is, highly interconnected nodes). This approach provides new insights into the natural history of microbes.