16Microbial Biodiversity and Biogeographyon the Deep Seafloor
Shingo Kato and Akihiko Yamagishi
Department of Molecular Biology, Tokyo University of Pharmacy and Life Science,
Hachioji, Tokyo,
Japan
1. Introduction
Microbes are widely distributed on and within the Earth (Gold, 1992; Whitman et al., 1998).
They have co-evolved with the Earth through their history and have acquired theirdiversity. Although most of microbes (estimated more than 99% of the total species numberspresent on Earth) are still uncultivated (Amann et al., 1995), vigorous surveys in naturalenvironments, from cold poles to hot deep-sea vents, by microbiologists revealed the wide
distribution of microbes. The accumulation of 16S rRNA gene sequence data and thedevelopment of useful bioinformatics tools allow us to image a big picture of microbial
biodiversity and biogeography in natural environments (Martiny et al., 2006). This will help
us to address some fundamental questions about microbial community: where they are;what species are present there; how they constitute communities; what are the factors that
control the diversity and distribution pattern of the communities; and how they have
evolved from the past to present and will evolve in future. Considering the wide
distribution and powerful metabolic function of microbes, they are likely to contribute to themaintenance of the whole ecosystem on Earth and to global climate change.
1.1 Biodiversity and biogeography“Microbial biodiversity” (or simply “biodiversity”, “microbial diversity”) includes
phylogenetic (genotypic) and physiologic (phenotypic) diversity of microbial communities.
In many previous papers (and also in this chapter), microbial biodiversity indicated thephylogenetic diversity that can be measured based on variation in nucleotide sequences ofgenes (16S rRNA gene have been used frequently). The biodiversity measures can be
distinguished into the measure of diversity within a single community (-diversity) and thatof the partitioning of diversity among two or more communities (-diversity). The
biodiversity can be measured qualitatively (based on the presence/absence of each taxon)and quantitatively (taken account for the abundance of each taxon). Furthermore, speciesbased (treated all taxa as equally) and divergence-based (taken account for the phylogenetic
distance between each taxon) measurements are used. The above classification and methods
for biodiversity measurements have been well summarized (Lozupone & Knight, 2008). To
avoid misinterpretation of results, it is needed to understand the principles of these diverse
measurements for microbial biodiversity. Because the microbial diversity potentially affects

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Changing Diversity in Changing Environmenton the ecosystem functioning (Duffy & Stachowicz, 2006; Prosser et al., 2007), it is important
to measure and interpret correctly the microbial diversity in natural environments.
Microbial biogeography is the descriptive and explanatory study of microbial biodiversity
over space and time. It aims to reveal where microbes live, and what kinds of microbes arepresent there and how many they exist. The scope of biogeography extends to understandthe underlying mechanism of generating and maintaining the distribution pattern ofmicrobial communities in natural environments. Traditional biogeography has focused onlarge eukaryotes, such as plants and animals. Recent development of molecular biological
techniques enabled us to approach the biogeography for microbes, including protists andprokaryotes in gene sequence level (Darling et al., 2000; Whitaker et al., 2003). This offers achallenge to the famous classical Baas Becking hypothesis ‘Everything is everywhere, but, theenvironment selects’ (Baas Becking, 1934). To assess how the environmental similarity (i.e.,
contemporary physicochemical condition) and geographic distance (i.e., historical event)affect on the distribution patterns of biodiversity of microbial communities or population, diversity are associated with physicochemical and geographic differences among eachenvironment. A variety of habitats in natural environments have been targeted for microbial
biogeography, such as soil (Cho & Tiedje, 2000; Fierer & Jackson, 2006), lake sediments(Yannarell & Triplett, 2005), ocean (García-Martínez & Rodríguez-Valera, 2000), deep-sea
sediments (Schauer et al., 2009) and deep-sea hydrothermal fields (Kato et al., 2010). Thesestudies have suggested that the distribution pattern of microbes in habitats is controlled by
not only environmental factors (e.g., pH, salinity and oxygen concentration) but also
geographic isolation. However, further data collection and reliable explanation are needed
to propose and evaluate theories regarding the generation and evolution of distribution
pattern of microbes in natural environments. While the study of microbial biodiversity andbiogeography seems to be descriptive, it is the first step and the essential base for theory
construction in microbial ecology (Prosser et al., 2007).
1.2 Seafloor microbial communities
Deep seafloor is seemingly an unrelieved, monotonous and poor environment, like a desert
where organisms are scarcely present. Actually, this notion is not always correct. There arevariable environments on the deep seafloor, such as hydrothermal vents, cold seeps, ironrich mats, out crops of young crustal rocks and aged ferromanganese crusts (hereafter, Mn
crusts). Furthermore, phylogenetically and physiologically diverse microbes (especiallyprokaryotes, i.e., the domain Bacteria and Archaea) thrive in these environments (e.g., Takai& Horikoshi, 1999; Inagaki et al., 2002; Santelli et al., 2008; Kato et al., 2009a; Nitahara et al.,2011). Following the Baas Becking hypothesis, these microbes may adapt to eachenvironment and should form a unique community structure. However, the hypothesis has
not been tested well for the microbial communities on the deep seafloor, especially nonhydrothermal and unsedimented areas far from land where organic inputs derived fromsurface photosynthetic ecosystems are not significant. Such deep seafloor accounts for alarge part of the surface area of Earth (Smith & Sandwell, 1997). Microbes on and within theseafloor are thought to play a role in geochemical cycling between oceans and Earth crusts(Edwards et al., 2005). Hence, understanding the microbial diversity and biogeography on
the deep seafloor is important for modeling the global relationship between microbes andEarth at present and can be applied for in past and future. Furthermore, recently, massive
sulfide deposits and Mn crusts on the deep seafloor have been focused on as mineral

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resources (Rona, 2003; Hoagland et al., 2010). There are diverse microbes on/in theseseafloor minerals (Kato et al., 2010; Nitahara et al., 2011). Microbial biogeography on the
deep seafloor will contribute ultimately to develop deep-sea mining techniques utilizing
microbes in future.

2. Analytical methods
The study of microbial biodiversity and biogeography starts upon collecting data of
microbial communities in the environments. In this chapter, we introduce the analysis
methods of microbial communities based on nucleotide sequences of genes, especially thesmall subunit ribosomal RNA gene (called 16S rRNA gene for prokaryotes) which is
generally used for such analysis of biodiversity and biogeography. 16S rRNA genes havesome merit for the analysis: 1) all prokaryotes have this gene; 2) its sequence length(approximately 1500 bases) is moderate and adequate for analysis; 3) there are someconserved regions that allow to design PCR primers; 4) there are some variable regions thatallow to affiliate the sequences in species-level; 5) enormous sequences have been depositedin public database and can be used conveniently; and 6) useful bioinformatics tools
specialized for this gene are available.
Recent rapid development of molecular biological techniques including gene amplificationand nucleotide sequencing enabled us to approach unexpected biodiversity of microbes innatural environments. Especially, next-generation sequencing techniques (e.g.,pyrosequencing) open new windows for approaching microbial biodiversity (Sogin et al.,2006). However, it is hard to gain biological meanings of the biodiversity resulted from shortsequences (~400 bases) that are produced by the next-generation sequencing. At least, nearlyfull-length sequences of 16S rRNA genes are needed to connect biodiversity to ecology. Itshould be noted that information on the gene sequences cannot be related to ecology
directly. To connect biodiversity to ecology, the determination of the whole-genome
sequence is not enough and information on function of microbes derived from the genesequences must be obtained by culture-dependent analysis. However, cultivation of all
microbes in an environment is impossible by now. Actually, most prokaryotes on Earth arestill uncultivated (Amann et al., 1995).
There are several steps to measure and compare the biodiversity of microbial communities
based on 16S rRNA gene analysis (Figure 1). The target environmental samples arecollected, genomic DNA is extracted from the samples, and then 16S rRNA gene sequencesin the genome DNA extracts are determined by PCR-cloning-sequencing analysis. In somecases, analyses of electrophoresis patterns, such as denaturing gradient gel electrophoresis(DGGE) and terminal restriction fragment length polymorphism (T-RFLP), without
sequencing process are also used for biodiversity measurement; however these analyses
mask valuable information on the biodiversity in contrast to sequencing analysis (Nocker etal., 2007). It should be noted that PCR-cloning-sequencing analysis alone is insufficient forthe determination of biodiversity because of the presence of methodological biases(Wintzingerode et al., 1997) in addition to relatively high cost performance regarding timeand money. For example, even if the same DNA extract was used, the diversity andcomposition of microbial communities determined using different primer sets weredramatically different from each other (Kato et al., 2011). Hence, for reliable assessment of
the biodiversity and distribution pattern of microbial communities, 16S rRNA genesequences used for comparative analysis should be obtained by the same method.

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Fig. 1. Flow chart of the recommended analysis steps for microbial diversity andbiogeography.
All of the 16S rRNA gene sequences collected are aligned into an alignment dataset. The
alignment process is very important to assess more accurate biodiversity and biogeography
because the wrong alignment dataset cause overestimation of biodiversity and of difference
among communities. Alignments have often been performed by multiple sequencealignment tools such as ClustalW (Larkin et al., 2007) and MUSCLE (Edgar, 2004). However,against the vast 16S rRNA gene datasets including one thousand sequences or more, it takes
an immense amount of time. Recently, improved alignment methods incorporating thesecondary structure of 16S rRNA genes and using a reference alignment have been provided
from several 16S rRNA gene database projects, such as RDP using Infernal (Cole et al., 2009),
Greengenes using NAST aligner (Desantis et al., 2006), and SILVA using SINA aligner(Pruesse et al., 2007). However, these methods, in particular NAST, have predicted higher
diversity as compared with the results from the pair-wise and multiple alignment methods(Schloss, 2010). The sequences with long insertions seem to be better aligned by Infernalbuilt in RDP than the other methods from our experience. It is known that several
prokaryotes, especially Archaea, have long insertions (including introns) in their 16S rRNA
gene (Burggraf et al., 1993; Itoh et al., 1998; Itoh et al., 2003). Chimera sequences have often

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observed in datasets. Such chimera sequences must be removed from the datasets before thefollowing analysis by using chimera-check tools such as Mallard (Ashelford et al., 2006) andBellerophon (Huber et al., 2004). NAST automatically checks chimera sequences in thedatasets and remove the chimeric part of the sequences. However, the sequence withrelatively longer insertion seems to be also recognized as a chimera sequence by NAST.
Until more improvement of the alignment method and chimera check function, we
recommend not using NAST for the following phylogenetic analysis. Overall, for alignmentof 16S rRNA gene sequences, SINA aligner built in SILVA, or MUSCLE in the case of lower
sequence numbers, is recommended for alignment. Finally, accuracy of the alignment
dataset should be confirmed by the naked eyes.
After the construction of a 16S rRNA gene alignment dataset, the sequences are assigned as
operational taxonomic units (OTUs) or phylotypes for each habitat. An OTU is a group ofsimilar sequences each other, which is defined based on the genetic distance thresholds. In
general, 97% (0.03 cut-off), 95% (0.05 cut-off) or 80% (0.20 cut-off) similarity threshold areused as species-, genus- and family-level taxonomic definition, respectively (Ludwig et al.,1998). For comparative analysis of communities, the same definition level of OTUs must be
used. Assessment of sequences to OTUs can be performed using DOTUR (Schloss &Handelsman, 2005) and its current version mothur (Schloss et al., 2009). A distance matrix
generated from the alignment dataset using ARB (Ludwig et al., 2004) or DNADIST in
PHYLIP package (Felsenstein, 1989) is needed for calculation using DOTUR. The matrix canbe generated by mothur itself.
-diversity measures can be calculated using the distance matrix generated from thealignment dataset: Chao1 species richness estimates, abundance-based coverage estimator of
species richness (ACE) and rarefaction curves (species-based, qualitative), Shannon andSimpson indices (species-based, quantitative), Phylogenetic Diversity (PD; divergencebased, qualitative) and  (divergence-based, quantitative), and so on (Lozupone & Knight,
2008). Species-based measurements of -diversity can be performed using mothur at once.
PD and  can be calculated by PHYLOCOM (Webb et al., 2008) and ARLEQUIN (Excoffieret al., 2005), respectively.
-diversity can be also measured using the distance matrix generated from the alignment
dataset: Sørensen and Jaccard indices (species-based), UniFrac and FST (divergence-based),and so on (Lozupone & Knight, 2008). These -diversity values provide measures of
distance between pairs of communities. Furthermore, the measured distance matrix can beused for multivariate statistical techniques such as clustering [e.g., unweighted pair groupmethod using arithmetic average (UPGMA)] and ordination [e.g., principal coordinate
analysis (PCoA)]. Several species-based measures of -diversity can be calculated using
SONS (Schloss & Handelsman, 2006), which has been incorporated into mothur. UniFrac(Lozupone & Knight, 2005; Lozupone et al., 2006), and its current version Fast UniFrac(Hamady et al., 2009), is an effective divergence-based method for -diversity (Lozupone etal., 2010) and can easily perform clustering and ordination analyses. For UniFrac analysis, aphylogenetic tree and definition data for OTUs and habitats are needed as input data. This
tree can be constructed from the alignment dataset by neighbor-joining (NJ) or maximumlikelihood (ML) method. NJ tree can be constructed using ARB or ClearCut (Sheneman etal., 2006). ML tree can be constructed using FASTTREE (Price et al., 2010) or PHYML(Guindon et al., 2010). Such clustering and ordination analyses can also be performed using
R (R Development Core Team, 2011). In addition, the shared OTU numbers and the sharedwww.intechopen.com

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Chao1 richness among communities can be calculated and viewed automatically in Venn
diagrams using mothur. Overall, our recommendation of the analytical steps is shown in
Figure 1.

3. Microbial biodiversity and biogeography of microbial communities on deep
seafloor
3.1 Data collection
In this chapter, we try to assess microbial biodiversity and biogeography on deep seafloorusing the recent useful bioinformatics tools as described above, though few data from nonhydrothermal and unsedimented deep-seafloor in open sea are available for biogeographicalanalysis. To investigate the distribution pattern of microbial communities on the seafloor inopen sea, several data were collected (Table 1) and used for the following analysis. The
locations where the samples were collected are shown in Figure 2. The samples of thecollected data are basaltic rocks, Mn crusts, sulfide deposits called as dead chimney whichwere collected from hydrothermally inactive vents, sandy sediments that were not organicrich, and overlying bottom seawater (Figure 2). Although the samples were mainly collectedon spreading ridges, they were collected far from hydrothermal vents and may not besignificantly influenced by hydrothermal activity. We analyzed and compared thecommunities as described above using the 16S rRNA gene sequences collected.
3.2 -diversity
-diversity for a microbial community is often indicated using Chao1 species richness
estimates, rarefaction curves and/or Shannon’s index value. However, we should not
simply compare these indicators of -diversity provided by the investigators because these
indicators are biased by the PCR primers, alignment software and OTU or phylotype
clustering methods used in the analyses. As is often the case, the sequences deposited into
public databases do not contain all clones in the libraries but only the representative OTUs.
Furthermore, the definition levels of OTUs are not always consistent; OTU0.03 (i.e., 97%
similarity level) are usually used, but OTU0.01 or others are also used in some cases. In
general, OTU0.03 or OTU0.05 is used as species or genus level definition, respectively. In thischapter, to compare-diversity for the communities for the collected data as impartially as
possible, the percentage of the number of OTU0.05 in total clone numbers, N0.05/Nt, wereused (Table 1). This comparison can roughly address the difference in the-diversity, even
if only representative sequences were deposited and several definition levels of OTU (<0.05
cut-off) were used.
The N0.05/Nt are summarized in Figure 3. The N0.05/Nt for the samples of dead chimneys
and seawater, except Asp, were <40%. In contrast, those for the samples of Mn crusts,
basaltic rocks, except Rh3, and sediments were 50% or higher. Noted that the N0.05/Nt forRe5 and Rj were very high (>90%) likely due to the small total clone numbers comparedwith the other rock or sediment samples (Table 1). The relatively high N0.05/Nt of Asp may
be due to contamination from seafloor sediment at the sampling. In fact, some phylotypes
recovered from Asp were closely related to those from seafloor rocks and sediments (Kato etal., 2009b). The N0.05/Nt did not correlate with the total clone numbers analyzed (r2 = 0.223)
when the data of Asp, Re5 and Rj were excluded (the plot is not shown). Thus, the differencein the -diversity associated with each habitat type is potentially meaningful for microbial

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Fig. 2. (A) Location of the samples of which data were used in this chapter, and photographsof on-site observation of (B) sulfide chimney, (C) Mn crust, (D) sandy sediment and (E)basaltic rocks. These photographs (B-E) were taken at the Southern Mariana Trough andTakuyo-Daigo Seamount.

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Table 1. List of the clone libraries used in this study.

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ecology. It is difficult to answer questions, like why the diversity of dead chimneys andseawater are lower than that of Mn crusts, basaltic rocks and sediments. Further datacollection and experimental investigations are needed to answer.

Fig. 3. The percentage of the number of OTU0.05 in the total clone number for each sample.

Measurement and comparison of -diversity is the first step in the study of biogeography.
Investigating what kinds of habitats represent high or low diversity within a community isimportant for understanding the mechanism how microbial communities acquire thediversity. It should be noted that unification of the methodological process including DNA
extraction and PCR primer sets in sequencing process (Wintzingerode et al., 1997), andalignment software, used region in the alignment dataset and distance calculation methods
in phylogenetic and statistic processes (Schloss, 2010) is important to compare fairly thediversity among communities.
3.3 -diversity
To compare the microbial communities for each sample based on the -diversity measures,
UniFrac was used here. The result of clustering analysis is shown in Figure 4A. In UniFrac
analysis, the jackknifing method can be used to assess confidence in the nodes of the
UPGMA tree. In the present case, all nodes, except the root, in the tree were not strongly
supported by jackknifing (<50%). The PCoA results are shown in Figures 4B to E. Figure 4B
is a three-dimensional image representing the first, second and third principal coordinate
axes. Based on the results of clustering analysis and PCoA and taken each habitat-type into
account, the samples were affiliated to six groups as shown in Figure 4. Group1 to Group3
represent the communities of basaltic rocks and Mn crusts and of each one of the sedimentand chimney, respectively. Group4 and Group5 represent communities of dead chimneys
and those of ambient bottom seawater, respectively. Group6 including As and Sdi is themost far from the other communities. Re5 was excluded from any groups due to theunambiguous behavior that may be caused by the small size of the clone libraries.
Using UniFrac, we can easily compare the samples and see the difference in communitylevel (Figure 4). The results indicated that seawater communities Group5 were clearlywww.intechopen.com

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distinguished from the other communities. However, the difference between Group4(representing dead chimney communities) and Group5 were not shown by the twodimensional image by the first and second axes (describing 10.81% and 7.79% of thevariation, respectively; Figure 4C). On the other hand, these communities were separated
along the third axis describing 6.93% of the variation. This means that the third mostinfluential factor for the community similarity, not the first and second factors, is the factor
separating the seawater and dead chimney communities. If certain environmental
characteristics in habitats (e.g., pH and availability of energy sources such as iron, sulfide
and ammonium as electron donors and oxygen, nitrate and sulfate as electron acceptors)
and physiological characteristics of microbes in the communities (e.g., life styles of freeliving and attachment) were correlated with the third axis, this traits would be the factor
causing the difference between seawater and dead chimney communities. Likewise, details

Fig. 4. The comparative results from (A) cluster analysis and (B-E) principal coordinate
analysis using UniFrac. (B) The 3D image based on the first to third principal coordinatesand (C-E) 2D images for the first vs. second, first vs. third, and second vs. third principal
coordinates are shown, respectively.

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of geographical and physicochemical characterization of the environments andphysiological characterization of the communities in the habitats will help us to address thefactors, for example, causing the difference among Group1 to Group4, causing the groupingof Group2 that contains Rh1, Mt and Sdt despite of the different habitat types (Mn crust,basaltic rock and sediment), and causing the difference between Group6 and the othergroups.
Geographic distance may be one of the factors affecting the biogeography of the microbialcommunities. For example, the genetic distance between pairs of populations ofSynechococcus or Sulfolobus in hot springs is related to the geographic distance despite thesimilar environmental characteristics of each habitat, which can be interpreted due togenetic drift caused by geographical isolation or to adaptation to the fluctuating
environment in the past time (Papke et al., 2003; Whitaker et al., 2003). For the deep seafloor
communities, the relationships between the community similarity and the geographic
distance are shown in Figure 5. For all habitat types (i.e., basaltic rocks, Mn crusts, deadchimneys, sediments and bottom seawater), the similarity between the communities seems
not to be related to the geographic distance (Figure 5A). For basaltic rocks and deadchimneys (Figure 5B and C), positive correlation between the community similarity andgeographic distance is also not observed. In such solid habitats, environmental
characteristics can be varied; for example, the gradient of oxygen concentration may occur
due to chemical and biological consumption. The environmental varieties will affect on the
microbial community diversity and composition in these habitats. This means that thecommunity traits would be dramatically biased by its sampling position (e.g., interior orexterior parts of a sample). Hence, it is difficult to show the clear relationship between thecommunity similarity and the geographic distance for solid habitats. For overlying bottom
seawater (Figure 5D), our result implies that the community similarity is related to thegeographic distance. Such correlation for marine microbial communities has been alreadyreported (García-Martínez & Rodríguez-Valera, 2000). Further data sampling from various
depths and locations in global oceans will provide a more clear view of oceanic
biogeography.

Fig. 5. The relationship between the community similarity and the geographic distance. The
results of (A) all habitat types integrated, (B) basaltic rocks, (C) dead chimneys, and (D)ambient seawater are shown, respectively. The similarities between pairs of communities
are calculated using UniFrac: the value 1 means that the two communities are the same. The
geographic distance between pairs of habitats is calculated using spDistN1 in sp package of
R software. Fitted line and coefficient regression value are shown in each figure.

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To address the relationship between the community similarity and the phylogeny of the
OTUs in each community, the OTUs in each community must be compared at nucleotidesequence level, neither the band pattern of DGGE nor the fragment sizes of T-RFLP. As
shown by the results of UniFrac (Figure 4), the community similarity on the deep seafloor
has positively related to the habitat types. How many OTUs were shared among each
community in genus- and family-levels (i.e., 0.05 and 0.20 cutoff) are shown in Venn
diagrams (Figure 5) depicted by mothur.
Among the solid samples (i.e., basaltic rocks, Mn crusts, dead chimneys and sediments), theintegrated community for the dead chimneys and that for the basaltic rocks contain many
unique OTUs0.05 (67.7% and 70.0%, respectively) in genus-level in contrast to that for
sediments or Mn crusts (39.6% and 41.9%, respectively) (Figure 6A). In family-level, over
80% of OTUs0.20 of each community were shared with others (Figure 6B). These uniqueOTUs0.05 potentially contain indigenous members for each habitat; for example, the uniqueclusters for basaltic rocks, Ocean Crust Clades I to VII defined by Mason et al. (2007), and forsulfide chimney, Cluster A to C defined by Kato et al. (2010). Unfortunately, it is unclear
whether and how these potential indigenous members play a role in the microbial
ecosystem and elemental cycling because they are not phylogenetically close to known
cultured species and their physiological characteristics are unknown (Mason et al., 2007;
Kato et al., 2010). Further cultivation effort and characterization is important to link thephylogeny of the OTUs to their function and significance in the environments.
Over 50% of the OTUs0.05 in the Mn crusts were shared with those in the basaltic rocks(Figure 6A). Furthermore, all of the OTUs0.20 in the Mn crusts were shared with those in thebasaltic rocks (Figure 6B). These results indicate that the bacterial members in thecommunities of the Mn crusts and basaltic rocks are phylogenetically close to each other,which is consistent with the UniFrac (divergence-based) result that the Mn crustcommunities clustered with some basaltic rock communities (Group2 in Figure 4). The
phylogeny of the shared and unique OTUs can be confirmed by phylogenetic analysis (suchas homology search against public databases and phylogenetic tree construction), although
this is not shown in this chapter. Although the physiology of OTUs (e.g., metabolic function,
growth rate and optimal growth temperature and pH) cannot be directly determined bytheir phylogeny, the physiological characteristics of OTUs may not be so different fromthose of certain cultured species that are closely related to the OTUs. The physiology of
OTUs inferred from their phylogeny will provide basal information for constructing
working hypothesis of the microbial ecosystem modeling and for preparing culture media
targeting these uncultured members.
Such comparative analysis using nucleotide sequences are also used to check crosscontamination among each habitat. The shared OTUs between the bottom seawatercommunity and others are shown in Figure 6C and D. In genus-level, approximately 6-8% of
the total OTUs0.05 of the Mn crusts, dead chimneys or basaltic rocks were shared with theseawater community (Figure 6C). In family-level, 83%-100% of OTUs0.20 of each community
were shared with others (Figure 6D), similar to the comparison among the solid samples(Figure 6B). Given that all of the OTUs0.05 detected in the seawater are indigenous in theseawater, these shared OTUs0.05 observed in the solid samples are potentially contaminantsfrom the seawater community. However, it is also possible that these shared OTUs0.05 aredifferent from each other in higher-similarity level (e.g., 97% or 99% similarity).
Microdiverse clusters at the level of >99% similarity have been reported for marineprokaryotes such as Pelagibacter (SAR11 cluster) (Acinas et al., 2004), Marine Group I

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Crenarchaeota (Durbin & Teske, 2010), and for Halomonas and Marinobacter (Kaye et al.,2011). Hence, we need to be careful in concluding the shared OTUs between target andreference environments to the contaminants from the reference environment.

Fig. 6. Venn diagrams comparing the OTU0.05 or OTU0.20 memberships found in the sandy
sediment, Mn crusts, dead chimneys, basaltic rocks and seawater samples. The comparative
results among the sandy sediment, Mn crusts, dead chimneys and basaltic rocks are shownin (A) OTU0.05 or (B) OTU0.20 levels. The comparative results among the seawater, Mn crusts,
dead chimneys and basaltic rocks are shown in (C) OTU0.05 or (D) OTU0.20 levels.

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4. Concluding remarks
In this chapter, we introduce the recent bioinformatics tools for assessing the microbial
diversity and biogeography. These useful tools allow us to analyze vast sequence data fastand correctly and to get the entire view of the biogeography of microbial communities innatural environments. We should use these tools for analysis of microbial biodiversity andbiogeography effectively. Furthermore, both nucleotide sequencing technology andbioinformatics are developing steadily. Microbiologists, especially who study not only
biogeography and biodiversity, but also evolution, ecology and biogeosciences, should
always try not to overlook these advancing techniques and to apply to their studies. We
applied the recent bioinformatics tools for actual data collected from deep seafloor
environments. Our results provide insight into the microbial diversity and biogeography of
the global deep seafloor in open oceans: for example, relationship between the community
similarity and habitat types or geographic distance, commonality and difference among thecommunities in community- and OTU- levels. For providing persuasive explanation
regarding the biogeography in the global deep seafloor, carefull collection of more
molecular biological and environmental data from more seafloor habitats in various
locations are needed.

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Changing Diversity in Changing Environment
Edited by PhD. Oscar Grillo

ISBN 978-953-307-796-3
Hard cover, 392 pages
Publisher InTech

Published online 14, November, 2011

Published in print edition November, 2011
As everybody knows, the dynamic interactions between biotic and abiotic factors, as well as the anthropic
ones, considerably affect global climate changes and consequently biology, ecology and distribution of life
forms of our planet. These important natural events affect all ecosystems, causing important changes on
biodiversity. Systematic and phylogenetic studies, biogeographic distribution analysis and evaluations ofdiversity richness are focal topics of this book written by international experts, some even considering
economical effects and future perspectives on the managing and conservation plans.

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Changing Diversity in Changing Environment, PhD. Oscar Grillo (Ed.), ISBN: 978-953-307-796-3, InTech,
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