| Microbialites are a product of trapping and binding of sediment by microbial communities, and are considered to be some of the most ancient records of life on Earth. It is widely held that microbialites are limited to extreme, hypersaline environments. However, literature also shows evidence of their occurrence in a wider range of environments. The goal of this thesis is to explore geochemical properties of aquatic environments in which microbialites have been found. We apply statistical techniques to distinguish any common traits in microbialite environments. These techniques ultimately could be used to address the question are microbialites restricted to hypersaline environments, other environments with specific characteristics, or are they more broadly distributed? A dataset containing hydrographic characteristics of several microbialite sites with data on pH, conductivity, alkalinity, and concentrations of several major anions and cations was constructed from previously published studies. Qualitative inspection of these data show that microbialites are not restricted to hypersaline environments, as they are present at Pavilion Lake, a freshwater body. In order to group the water samples by their natural similarities and differences, a clustering approach was chosen for analysis. K-means clustering with partial distances was applied to the dataset with missing values, and separated the data into 2 distinct but geochemically similar clusters. One of the clusters is formed by samples from atoll Kiritimati, and the second cluster contains all other observations. Then, the missing values were imputed by k-nearest neighbor method, producing a complete dataset that can be used for further multivariate analysis. We find that microbialites occur within environments spanning a range of salinities (as indicated by conductivity), pH values, and ionic compositions. Furthermore, pH seems to define geochemical profile of this dataset and its clustering. Clustering and imputation procedure outlined here can be applied to an expanded dataset on microbialite characteristics in order to determine if unique properties are associated with microbialite-containing environments, and can also serve as an outline for analysis of such datasets with missing data. |
