Utilizing novel diversity estimators to quantify multiple dimensions of microbial biodiversity across domains
1 Environmental Science, Policy, and Management, University of California, Berkeley, California 94720, USA
2 Integrative Biology, University of California, Berkeley, California 94720, USA
3 Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
4 Ecology Department, Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
5 Current address: Department of Microbiology, The Ohio State University, Columbus, Ohio 43210, USA
6 Earth and Planetary Science, University of California, Berkeley, California 94720, USA
7 Current address: Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado 80309, USA
8 Current address: Department of Microbiology and Immunology, School of Medicine, Stanford University, Stanford, California 94305, USA
9 Current address: Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
BMC Microbiology 2013, 13:259 doi:10.1186/1471-2180-13-259Published: 15 November 2013
Microbial ecologists often employ methods from classical community ecology to analyze microbial community diversity. However, these methods have limitations because microbial communities differ from macro-organismal communities in key ways. This study sought to quantify microbial diversity using methods that are better suited for data spanning multiple domains of life and dimensions of diversity. Diversity profiles are one novel, promising way to analyze microbial datasets. Diversity profiles encompass many other indices, provide effective numbers of diversity (mathematical generalizations of previous indices that better convey the magnitude of differences in diversity), and can incorporate taxa similarity information. To explore whether these profiles change interpretations of microbial datasets, diversity profiles were calculated for four microbial datasets from different environments spanning all domains of life as well as viruses. Both similarity-based profiles that incorporated phylogenetic relatedness and naïve (not similarity-based) profiles were calculated. Simulated datasets were used to examine the robustness of diversity profiles to varying phylogenetic topology and community composition.
Diversity profiles provided insights into microbial datasets that were not detectable with classical univariate diversity metrics. For all datasets analyzed, there were key distinctions between calculations that incorporated phylogenetic diversity as a measure of taxa similarity and naïve calculations. The profiles also provided information about the effects of rare species on diversity calculations. Additionally, diversity profiles were used to examine thousands of simulated microbial communities, showing that similarity-based and naïve diversity profiles only agreed approximately 50% of the time in their classification of which sample was most diverse. This is a strong argument for incorporating similarity information and calculating diversity with a range of emphases on rare and abundant species when quantifying microbial community diversity.
For many datasets, diversity profiles provided a different view of microbial community diversity compared to analyses that did not take into account taxa similarity information, effective diversity, or multiple diversity metrics. These findings are a valuable contribution to data analysis methodology in microbial ecology.