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Title: Application of Nonlinear Analysis Methods for Identifying Relationships Between Microbial Community Structure and Groundwater Geochemistry

Abstract

The relationship between groundwater geochemistry and microbial community structure can be complex and difficult to assess. We applied nonlinear and generalized linear data analysis methods to relate microbial biomarkers (phospholipids fatty acids, PLFA) to groundwater geochemical characteristics at the Shiprock uranium mill tailings disposal site that is primarily contaminated by uranium, sulfate, and nitrate. First, predictive models were constructed using feedforward artificial neural networks (NN) to predict PLFA classes from geochemistry. To reduce the danger of overfitting, parsimonious NN architectures were selected based on pruning of hidden nodes and elimination of redundant predictor (geochemical) variables. The resulting NN models greatly outperformed the generalized linear models. Sensitivity analysis indicated that tritium, which was indicative of riverine influences, and uranium were important in predicting the distributions of the PLFA classes. In contrast, nitrate concentration and inorganic carbon were least important, and total ionic strength was of intermediate importance. Second, nonlinear principal components (NPC) were extracted from the PLFA data using a variant of the feedforward NN. The NPC grouped the samples according to similar geochemistry. PLFA indicators of Gram-negative bacteria and eukaryotes were associated with the groups of wells with lower levels of contamination. The more contaminated samples contained microbial communities thatmore » were predominated by terminally branched saturates and branched monounsaturates that are indicative of metal reducers, actinomycetes, and Gram-positive bacteria. These results indicate that the microbial community at the site is coupled to the geochemistry and knowledge of the geochemistry allows prediction of the community composition.« less

Authors:
; ; ; ; ; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
882104
Report Number(s):
PNNL-SA-49253
Journal ID: ISSN 0095-3628; MCBEBU; KP1301010; TRN: US0603185
DOE Contract Number:
AC05-76RL01830
Resource Type:
Journal Article
Resource Relation:
Journal Name: Microbial Ecology; Journal Volume: 51; Journal Issue: 2
Country of Publication:
United States
Language:
English
Subject:
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS; BACTERIA; CARBON; CARBOXYLIC ACIDS; CONTAMINATION; DATA ANALYSIS; FEED MATERIALS PLANTS; FORECASTING; GEOCHEMISTRY; NEURAL NETWORKS; NITRATES; PHOSPHOLIPIDS; SENSITIVITY ANALYSIS; TAILINGS; TRITIUM; URANIUM

Citation Formats

Schryver, Jack C., Brandt, Craig C., Pfiffner, Susan M., Palumbo, A V., Peacock, Aaron D., White, David C., McKinley, James P., and Long, Philip E. Application of Nonlinear Analysis Methods for Identifying Relationships Between Microbial Community Structure and Groundwater Geochemistry. United States: N. p., 2006. Web. doi:10.1007/s00248-004-0137-0.
Schryver, Jack C., Brandt, Craig C., Pfiffner, Susan M., Palumbo, A V., Peacock, Aaron D., White, David C., McKinley, James P., & Long, Philip E. Application of Nonlinear Analysis Methods for Identifying Relationships Between Microbial Community Structure and Groundwater Geochemistry. United States. doi:10.1007/s00248-004-0137-0.
Schryver, Jack C., Brandt, Craig C., Pfiffner, Susan M., Palumbo, A V., Peacock, Aaron D., White, David C., McKinley, James P., and Long, Philip E. Wed . "Application of Nonlinear Analysis Methods for Identifying Relationships Between Microbial Community Structure and Groundwater Geochemistry". United States. doi:10.1007/s00248-004-0137-0.
@article{osti_882104,
title = {Application of Nonlinear Analysis Methods for Identifying Relationships Between Microbial Community Structure and Groundwater Geochemistry},
author = {Schryver, Jack C. and Brandt, Craig C. and Pfiffner, Susan M. and Palumbo, A V. and Peacock, Aaron D. and White, David C. and McKinley, James P. and Long, Philip E.},
abstractNote = {The relationship between groundwater geochemistry and microbial community structure can be complex and difficult to assess. We applied nonlinear and generalized linear data analysis methods to relate microbial biomarkers (phospholipids fatty acids, PLFA) to groundwater geochemical characteristics at the Shiprock uranium mill tailings disposal site that is primarily contaminated by uranium, sulfate, and nitrate. First, predictive models were constructed using feedforward artificial neural networks (NN) to predict PLFA classes from geochemistry. To reduce the danger of overfitting, parsimonious NN architectures were selected based on pruning of hidden nodes and elimination of redundant predictor (geochemical) variables. The resulting NN models greatly outperformed the generalized linear models. Sensitivity analysis indicated that tritium, which was indicative of riverine influences, and uranium were important in predicting the distributions of the PLFA classes. In contrast, nitrate concentration and inorganic carbon were least important, and total ionic strength was of intermediate importance. Second, nonlinear principal components (NPC) were extracted from the PLFA data using a variant of the feedforward NN. The NPC grouped the samples according to similar geochemistry. PLFA indicators of Gram-negative bacteria and eukaryotes were associated with the groups of wells with lower levels of contamination. The more contaminated samples contained microbial communities that were predominated by terminally branched saturates and branched monounsaturates that are indicative of metal reducers, actinomycetes, and Gram-positive bacteria. These results indicate that the microbial community at the site is coupled to the geochemistry and knowledge of the geochemistry allows prediction of the community composition.},
doi = {10.1007/s00248-004-0137-0},
journal = {Microbial Ecology},
number = 2,
volume = 51,
place = {United States},
year = {Wed Feb 01 00:00:00 EST 2006},
month = {Wed Feb 01 00:00:00 EST 2006}
}