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ABSTRACT Hydrologic changes modify microbial community structure and ecosystem functions, especially in wetland systems. Here, we present 24 metagenomes from a coastal freshwater wetland experiment in which we manipulated hydrologic conditions and plant presence. These wetland soil metagenomes will deepen our understanding of how hydrology and vegetation influence microbial functional diversity. 

It is often important to determine the source of a microbial strain. Examples include tracking a bacterium linked to a disease epidemic, contaminating the food supply, or used in bioterrorism. Strain identification and tracking are generally approached by using cultivation-based or relatively nonspecific gene fingerprinting methods. Genomic methods have the ability to distinguish strains, but this approach typically has been restricted to isolates or relatively low-complexity communities. We demonstrate that strain-resolved metagenomics can be applied to extremely complex soil samples. We genotypically defined a soil-associated bacterium and identified it as a contaminant. By linking together snapshots of the bacterial genome over time, it was possible to estimate how long the contaminant had been diverging from a likely source population. The results are congruent with the derivation of the bacterium from a strain isolated in Germany and sequenced a decade ago and highlight the utility of metagenomics in strain tracking. 

Metagenomic and metatranscriptomic time-series data covering a 52-day period in the fall of 2016 provide an inventory of bacterial and archaeal community genes, transcripts, and taxonomy during an intense dinoflagellate bloom in Monterey Bay, CA, USA. The dataset comprises 84 metagenomes (0.8 terabases), 82 metatranscriptomes (1.1 terabases), and 88 16S rRNA amplicon libraries from samples collected on 41 dates. The dataset also includes 88 18S rRNA amplicon libraries, characterizing the taxonomy of the eukaryotic community during the bloom. Accompanying the sequence data are chemical and biological measurements associated with each sample. These datasets will facilitate studies of the structure and function of marine bacterial communities during episodic phytoplankton blooms.