Predicting microbial species richness.
Microorganisms are spectacularly diverse phylogenetically, but available estimates of their species richness are vague and problematic. For example, for comparable environments, the estimated numbers of species range from a few dozen or hundreds to tens of thousands and even half a million. Such estimates provide no baseline information on either local or global microbial species richness. We argue that this uncertainty is due in large part to the way statistical tools are used, if not indeed misused, in biodiversity research. Here we develop a powerful synthetic statistical approach to quantify biodiversity. It provides statistically sound estimates of microbial richness at any level of taxonomic hierarchy. We apply this approach to a large original 16S rRNA dataset on marine bacterial diversity and show that the number of bacterial species in a sample from marine sediments is (2.4 +/- 0.5 SE) x 10(3). We argue that our methodology provides estimates of microbial richness that are reliable and general, have biologically meaningful SEs, and meet other fundamental statistical standards. This approach can be an essential tool in biodiversity research, and the estimates of microbial richness presented here can serve as a baseline in microbial diversity studies.
Hong
Sun-Hee
SH
Department of Biology, Northeastern University, Boston, MA 02115, USA.
Bunge
John
J
Jeon
Sun-Ok
SO
Epstein
Slava S
SS
D001419:Bacteria / Q000235:genetics*; D044822:Biodiversity; D019015:Geologic Sediments / Q000382:microbiology*; D008404:Massachusetts; D015233:Models, Statistical; D010802:Phylogeny; D012336:RNA, Ribosomal, 16S / Q000235:genetics; D017422:Sequence Analysis, DNA; D017385:Sequence Homology; D013045:Species Specificity
D012336:RNA, Ribosomal, 16S