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Chapter 6 : Microbial Biogeography: Patterns in Microbial Diversity across Space and Time
Category: Genomics and Bioinformatics; Environmental Microbiology
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Microbes inhabit a wide range of habitats, from hot springs to the deep subsurface, and it is highly improbable that one will be able to observe similar biogeographical patterns across the full range of possible microbial habitats. This chapter primarily focuses on selected topics that are particularly relevant to researchers studying uncultivated microbes in natural environments in order to illustrate what one do, or do not, currently know about their biogeography. However, it is important to recognize that the "unknown unknowns" and "known unknowns" in microbial biogeography currently outnumber the "known knowns." For this reason, the chapter highlights key topics where the gaps in one's knowledge of microbial biogeography are particularly apparent. A few research topics that may be ripe avenues for future research are also highlighted in this chapter. Although the field of biogeography principally focuses on the spatial distribution of organisms, the temporal aspects of microbial biogeography may be particularly important. The entire chapter emphasizes on how studies in microbial biogeography are more difficult to conduct than comparable studies of plant or animal biogeography, largely due to the problems associated with surveying microbial communities. The study of microbial biogeography will help one to move beyond anecdotal studies and observations to build a predictive understanding of microbial diversity and the factors influencing this diversity across space and time.
Hypothetical dispersal capabilities of microbes that differ in population densities and stress tolerances. (A) High population density, stress tolerant; (B) high population density, stress intolerant; (C) low population density, stress tolerant; (D) low population density, stress intolerant. Across larger spatial scales, microbial dispersal rates should be directly related to population densities in the source population and the ability to withstand biotic and abiotic stresses associated with dispersal. Figure based on Martiny et al. (2006) .
Comparison of rarefaction curves (A) and rank-abundance curves (B) for bacterial, archaeal, and fungal clone libraries targeting the small-subunit (16S, 18S) rRNA gene. Libraries constructed from a single desert soil sample collected in Joshua Tree, CA. OTUs are defined at the ≤97% sequence similarity level. For the rank-abundance curve (B), only the 50 most abundant OTUs are shown. All three rarefaction curves fail to asymptote, indicating that we have not surveyed the full extent of taxonomic richness in the sample. The differences in the slopes of the rarefaction curves (A) are a result of differences in community evenness (evident in B), not necessarily differences in overall richness. Data are from Fierer et al. (2007a) .
Comparison of rarefaction curves from bacterial communities found in different environments. All data are from bacterial clone libraries targeting the 16S rRNA gene with OTUs defined at the ≥97% sequence similarity. Data are from Vasanthakumar et al. (2006) for the beetle gut-associated bacteria, Wani et al. (2006) for the soda lake sediment, Lawley et al. (2004) for the Antarctic soil, and Fierer et al. (2007b) for the stream sediment. The total number of clones (n) in each library is indicated in the legend.
Hypothetical changes in the total number of unique microbial taxa identified from surveys of different spatial scales. The gray line represents the predictions of Fenchel and Finlay ( Fenchel, 1993 ; Fenchel et al., 1997 ; Finlay, 2002 ); the black line represents the competing hypothesis that there is minimal overlap in species assemblages across habitats. The dashed line and the question mark indicate the high degree of uncertainty.
A comparison of published taxon-area relationships (TARs) from contiguous habitats (arctic diatoms and salt-marsh bacteria) and noncontiguous (island) habitats (treehole bacteria and ectomycorrhizal fungi). The TAR for arctic diatoms is from Azovsky (2002) and represents the number of diatom species in Arctic sediments versus area (m2). The TAR for treehole bacteria is from Bell et al. (2005) and represents bacterial genetic diversity (determined by DGGE fingerprinting) versus the volume (ml) of water-filled treeholes. The TAR for salt-marsh bacteria is from Horner-Devine et al. (2004a) and represents the number of bacterial OTUs in a salt marsh (99% sequence similarity) versus area (cm2). The TAR for ectomycorrhizal fungi is from Peay et al. (2007) and represents the number of ectomycorrhizal fungal species in “tree islands” of a given area (m2).