Publications by authors named "Theodore Cosart"

Nitrogen fixing and denitrifying bacteria, respectively, control bulk inputs and outputs of nitrogen in soils, thereby mediating nitrogen-based greenhouse gas emissions in an ecosystem. Molecular techniques were used to evaluate the relative abundances of nitrogen fixing, denitrifying and two numerically dominant ribotypes (based on the > or =97% sequence similarity at the 16S rRNA gene) of bacteria in plots representing 10 agricultural and other land-use practices at the Kellogg biological station long-term ecological research site. Quantification of nitrogen-related functional genes (nitrite reductase, nirS; nitrous oxide reductase, nosZ; and nitrogenase, nifH) as well as two dominant 16S ribotypes (belonging to the phyla Acidobacteria, Thermomicrobia) allowed us to evaluate the hypothesis that microbial community differences are linked to greenhouse gas emissions under different land management practices.

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To thoroughly investigate the bacterial community diversity present in a single composite sample from an agricultural soil and to examine potential biases resulting from data acquisition and analytical approaches, we examined the effects of percent G+C DNA fractionation, sequence length, and degree of coverage of bacterial diversity on several commonly used ecological parameters (species estimation, diversity indices, and evenness). We also examined variation in phylogenetic placement based on multiple commonly used approaches (ARB alignments and multiple RDP tools). The results demonstrate that this soil bacterial community is highly diverse, with 1,714 operational taxonomic units demonstrated and 3,555 estimated (based on the Chao1 richness estimation) at 97% sequence similarity using the 16S rRNA gene.

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In order to retrieve phylogenetic information from distance matrices generated from large-scale clone libraries, and to explore OTU distribution among them, we have developed downstream applications for use with the already available DOTUR program. These programs enhance and ease data extraction, providing phylogeny to the already generated distance data.

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