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Co-occurrence patterns for abundant marine archaeal and bacterial lineages in the deep chlorophyll maximum of coastal California
Authors:J Michael Beman  Joshua A Steele  Jed A Fuhrman
Affiliation:1.Department of Biological Sciences and Wrigley Institute for Environmental Studies, University of Southern California, Los Angeles, CA, USA
Abstract:Microorganisms remineralize and respire half of marine primary production, yet the niches occupied by specific microbial groups, and how these different groups may interact, are poorly understood. In this study, we identify co-occurrence patterns for marine Archaea and specific bacterial groups in the chlorophyll maximum of the Southern California Bight. Quantitative PCR time series of marine group 1 (MG1) Crenarchaeota 16S rRNA genes varied substantially over time but were well-correlated (r2=0.94, P<0.001) with ammonia monooxygenase subunit A (amoA) genes, and were more weakly related to 16S rRNA genes for all Archaea (r2=0.39), indicating that other archaeal groups (for example, Euryarchaeota) were numerically important. These data sets were compared with variability in bacterial community composition based on automated ribosomal intergenic spacer analysis (ARISA). We found that archaeal amoA gene copies and a SAR11 (or Pelagibacter) group Ib operational taxonomic unit (OTU) displayed strong co-variation through time (r2=0.55, P<0.05), and archaeal amoA and MG1 16S rRNA genes also co-occurred with two SAR86 and two Bacteroidetes OTUs. The relative abundance of these groups increased and decreased in synchrony over the course of the time series, and peaked during periods of seasonal transition. By using a combination of quantitative and relative abundance estimates, our findings show that abundant microbial OTUs—including the marine Crenarchaeota, SAR11, SAR86 and the Bacteroidetes—co-occur non-randomly; they consequently have important implications for our understanding of microbial community ecology in the sea.
Keywords:Crenarchaeota   SAR11   SAR86   Bacteroidetes   community assembly   time-series
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