Estimation of the Number of Microbial Species Comprising a Population

Abstract

The purpose of this research was to evaluate the appropriateness of using non-parametric estimators, specifically the Chao1, ACES, and Jackknife methods, for estimation of the number of unique species comprising a population. This research consisted of creating diverse populations, with a known number of species, and applying the aforementioned methods to samples drawn from the constructed populations. The analysis of the non-parametric methods was followed by the parametric fitting of several different distributions to the sample data, including the lognormal, gamma, and Weibull. These results were analyzed as well. Both types of methodologies were then applied to sample data from constructed wetlands, where little is known about the population size and composition. This research did not attempt to identify the underlying population distribution of the wetlands, but rather focused upon demonstrating that the use of parametric methods are more apt to provide better results in estimating the number of species in a natural population. This research discovered the use of the non-parametric methods is not an appropriate for species estimation. The use of these methods resulted in lower bounds, which were several standard deviations away from the true number of species, for the contrived populations. A parametric method was more accurate in representing the truth. Recommendations for further research are provided in this thesis.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2008
Accession Number
ADA481067

Entities

People

  • Melanie R. Slattery

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Computer Programs
  • Data Science
  • Demography
  • Distribution Functions
  • Estimators
  • Information Science
  • Microbiology
  • Microorganisms
  • Probability
  • Probability Density Functions
  • Probability Distribution Functions
  • Probability Distributions
  • Standards
  • Statistical Algorithms
  • Statistics

Readers

  • Regression Analysis.
  • Statistical inference.
  • Wetland-Land-Environmental Management.

Technology Areas

  • Biotechnology