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Consequences of Rising Atmospheric Carbon Dioxide Levels for the Belowground Microbiota Associated with White Oak

Dyntel Corp., Waterways Experiment Station, 3909 Halls Ferry Road, Vicksburg, MS 39180-6199;

Julia O. Stair and Jonas Almeida

Center for Environ. Biotechnol., Univ. of Tennessee, 10515 Research Dr., Suite 300, Knoxville, TN 37932-2575;

Richard J. Norby, Elizabeth G. O'Neill and David C. White

Environ. Sciences Div., Oak Ridge National Lab., Oak Ridge, TN 37831.

^* Corresponding author (dringelbe{at}aol.com).

ABSTRACT

The consequences for belowground microbiota under conditions of rising atmospheric CO₂ are largely unknown. In this research we examined the microbiota associated with white oak (Quercus alba L.). It was our hypothesis that an increase in CO₂ level would induce a change in the rhizosphere-associated microbial abundance and community composition. To provide an in situ estimation of microbial abundance and community composition, ester-linked polar lipid fatty acid (PLFA) technology was utilized. This technology, based on the quantitative measurement of membrane lipid fatty acids, has been utilized in the accurate identification and description of bacterial isolates and communities. Initial experiments demonstrated that a clear distinction in lipid patterns and microbial biomass existed between sterile roots and those of roots containing an associated viable microbiota. Statistical approaches were then used to determine what differences existed between individual PLFA and PLFA patterns obtained from white oak fine roots and bulk soils. An analysis of variance (ANOVA) showed significant differences to exist in the relative percentages of individual prokaryotic PLFA collected under ambient vs. elevated CO₂ and between those associated with fine roots and bulk soils. Multivariate statistics showed distinct differences in the patterns of prokaryotic PLFA detected in the rhizosphere vs. the surrounding bulk soil, but did not identify differences related to elevated CO₂ exposures. An artificial neural network recognized PLFA patterns unique to three different CO₂ exposures: 35, 50, and 65 Pa. Results of the three statistical tests were viewed as supportive of the hypothesis describing significant differences in individual PLFA and patterns of PLFA as a result of elevated CO₂ exposure.

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