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High&#45;Resolution Three&#45;Dimensional Whole&#45;Organ Tomography of Microbial Infections
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Novel application of a statistical technique, Random Forests, in a bacterial source tracking study.

Amanda Smith1, Blair Sterba-Boatwright, Joanna Mott

  • 1Department of Life Sciences, Texas A&M University-Corpus Christi, 6300 Ocean Drive, Unit 5800, Corpus Christi, TX 78412, United States. Amanda.smith@tamucc.edu

Water Research
|June 23, 2010
PubMed
Summary

Random Forests (RF) and discriminant analysis (DA) identified sources of fecal contamination in Texas waters. RF analysis outperformed DA, revealing migratory birds as the primary source of Escherichia coli contamination.

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Area of Science:

  • Environmental microbiology
  • Water quality assessment
  • Statistical modeling in ecology

Background:

  • Texas water bodies, including Cow Trap and Cedar Lakes, are impaired for bacteria, impacting oyster harvesting.
  • Accurate identification of fecal contamination sources is crucial for water quality management.

Purpose of the Study:

  • To compare the effectiveness of Random Forests (RF) and discriminant analysis (DA) for bacterial source tracking (BST).
  • To determine the sources of Escherichia coli contamination in a Texas water body.

Main Methods:

  • Bacterial source tracking (BST) using antibiotic resistance analysis (ARA) of Escherichia coli isolates.
  • Application of two statistical techniques: Random Forests (RF) and discriminant analysis (DA).
  • Validation of isolate source classification using Kirby-Bauer disk diffusion and carbon source utilization profiles.

Main Results:

  • Both RF and DA classified over 90% of unknown isolates as nonhuman.
  • RF demonstrated higher average rates of correct classification (ARCCs) than DA (82.3% vs. 74.6%).
  • Migratory birds were identified as the predominant source of E. coli contamination.

Conclusions:

  • Random Forests is a powerful and effective tool for bacterial source tracking, outperforming traditional discriminant analysis.
  • The study provides critical data for managing fecal contamination in Texas water bodies.
  • This research marks the first application of Random Forests in BST.