Title |
Utilizing novel diversity estimators to quantify multiple dimensions of microbial biodiversity across domains
|
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Published in |
BMC Microbiology, November 2013
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DOI | 10.1186/1471-2180-13-259 |
Pubmed ID | |
Authors |
Hannah M Doll, David W Armitage, Rebecca A Daly, Joanne B Emerson, Daniela S Aliaga Goltsman, Alexis P Yelton, Jennifer Kerekes, Mary K Firestone, Matthew D Potts |
Abstract |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 4 | 100% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 2 | 50% |
Scientists | 2 | 50% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United States | 10 | 10% |
Malaysia | 1 | 1% |
Indonesia | 1 | 1% |
Australia | 1 | 1% |
France | 1 | 1% |
South Africa | 1 | 1% |
Brazil | 1 | 1% |
Unknown | 80 | 83% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 27 | 28% |
Student > Ph. D. Student | 22 | 23% |
Student > Master | 13 | 14% |
Professor > Associate Professor | 9 | 9% |
Student > Doctoral Student | 8 | 8% |
Other | 9 | 9% |
Unknown | 8 | 8% |
Readers by discipline | Count | As % |
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Agricultural and Biological Sciences | 53 | 55% |
Environmental Science | 13 | 14% |
Biochemistry, Genetics and Molecular Biology | 5 | 5% |
Immunology and Microbiology | 4 | 4% |
Medicine and Dentistry | 3 | 3% |
Other | 7 | 7% |
Unknown | 11 | 11% |