to study the composition of phytoplankton in lakes (Paul
et al. 1990), microbial mats of volcanic deposits (Nanba
et al. 2004), soils (Selesi et al. 2005), and even communities
in subglacial Lake Vostok (Lavire et al. 2006).
Anoxygenic phototrophic bacteria play a prominent role
in the colonization of ecosystems. They belong to different
phylogenetic groups, and a way to analyze them is based on
the analysis of the gene encoding a protein PufM which is
the reaction center of photosynthesis enzyme (Achenbach
et al. 2001).
Oxidation of Methane (pmo)
Methane-oxidizing bacteria oxidize the methane produced
by methanogens or released from the deep earth. The gene
responsible for this function, pmo (particulate methane
monooxygenase), were used in PCR-sequencing approaches
(Cebron et al. 2007). In addition, the survey technique with
stable isotopes (stable isotope probing or SIP) was also used
to identify the 16S bacteria involved in this function
(Radajewski et al. 2000; Cebron et al. 2007).
Sulfur Cycle (dsrAB and aprA)
Bacteria of the sulfur cycle (reducing or oxidizing sulfur) are
important in habitats such as bacterial mats, symbiotic invertebrate tissues, sediments, and black smokers from oceanic
ridges. Several functional genes have been used to monitor
sulfur-metabolizing bacterial communities, such as dsrAB
genes (disulfite reductases) and aprA (coding the dissimilatory adenosine-5
0 -phosphosulfate (APS) reductase) (Meyer
and Kuever 2007). In addition, it has been shown that dsrAB
genes can confirm the phylogenetic links revealed by analysis of 16S rRNA genes (Wagner et al. 1998).
Other Genes Linked to a Function
Other functions can be listed as the assimilation of sulfur
(sulfur esterase), phosphate uptake (phosphatase), the synthesis of protective pigments, synthesis of protective squalene
lipids (squalene hopene cyclase), the synthesis of secondary
metabolites (NRPS, PKS), and the catabolism of xenobiotics
(diox), which have permitted or may permit diversity studies.
Metagenomic approaches (Sect. 18.3.3) are also increasingly yielding a mass of data to analyze. For instance, work
has been done on riboswitches (cf. Box 9.1) in microbial
communities (Kazanov et al. 2007).
8.4.3 How to Measure This Biodiversity?
Quantifying the biodiversity of an ecological community is
usually done by computing a number which increases with
biodiversity that is called a “biodiversity index.” The value
of this number may not have any meaning in itself, but a
comparison of the number of different communities must let
us know which harbors the largest diversity. Whatever the
method used, different indices can be calculated to analyze
the microbial diversity of the samples studied. As a first step,
some indices are presented to illustrate the process of computation, and they are discussed in the context of microbial
ecology. There are many indices of biodiversity in the literature, and it is not easy to make a wise choice in a particular
context. That is why, in a second step, an experiment-based
approach is presented. The idea consists in choosing an
index of biodiversity which takes into account the specificity
of the method used to acquire the data set.
Diversity indices are of two types: primary indices and
subindices (or composite). The primary indices are direct
measures of population parameters and do not require calculations confusing the number of species (richness) with the
frequency of each species and their identity. The number of
species or OTUs (Box 8.3) in a community and the frequency
of a given OTU in a community are examples of primary indices.
Secondary or composite indices are most often used in studies of
biodiversity indices such as Shannon, Simpson, and Nei
described below. It is important to distinguish between primary
and secondary indices because they have an impact on the
experimental devices that more or less facilitate statistical
analyses. In general, it is often easier to perform parametric
analyses (e.g., analysis of variance) on the primary indices than
on subindices, which require some refinements. Trends in
statistical processing of microbial biodiversity data demonstrate
this situation (Morris et al. 2002). For a given workload (e.g.,
Box 8.3: OTU (Operational Taxonomical Unit)
Philippe Normand
Bacterial taxonomy includes different levels from
phylum to subspecies, including order, family, genus,
and species, as discussed in Chap. 1. The issue of
diversity is studied at each of these levels, as long as
individuals can be identified in a particular group. In
the microbial world, it is often difficult to agree on the
belonging of isolates to a species. It is for this reason
that different authors have developed the concept of
OTU used in different softwares for individuals or
groups resulting from different aggregation procedures
(Sneath 2005).
The definition of an OTU would be “a group of
phylogenetically related organisms used in a study
without specifying its taxonomic rank.”
The concept of OTU has already led to a second
one, that of molecular operational taxonomic units or
MOTU (Floyd et al. 2002), to designate the OTUs
obtained based on molecular approaches.
8 Biodiversity and Microbial Ecosystems Functioning
275
et al. 1990), microbial mats of volcanic deposits (Nanba
et al. 2004), soils (Selesi et al. 2005), and even communities
in subglacial Lake Vostok (Lavire et al. 2006).
Anoxygenic phototrophic bacteria play a prominent role
in the colonization of ecosystems. They belong to different
phylogenetic groups, and a way to analyze them is based on
the analysis of the gene encoding a protein PufM which is
the reaction center of photosynthesis enzyme (Achenbach
et al. 2001).
Oxidation of Methane (pmo)
Methane-oxidizing bacteria oxidize the methane produced
by methanogens or released from the deep earth. The gene
responsible for this function, pmo (particulate methane
monooxygenase), were used in PCR-sequencing approaches
(Cebron et al. 2007). In addition, the survey technique with
stable isotopes (stable isotope probing or SIP) was also used
to identify the 16S bacteria involved in this function
(Radajewski et al. 2000; Cebron et al. 2007).
Sulfur Cycle (dsrAB and aprA)
Bacteria of the sulfur cycle (reducing or oxidizing sulfur) are
important in habitats such as bacterial mats, symbiotic invertebrate tissues, sediments, and black smokers from oceanic
ridges. Several functional genes have been used to monitor
sulfur-metabolizing bacterial communities, such as dsrAB
genes (disulfite reductases) and aprA (coding the dissimilatory adenosine-5
0 -phosphosulfate (APS) reductase) (Meyer
and Kuever 2007). In addition, it has been shown that dsrAB
genes can confirm the phylogenetic links revealed by analysis of 16S rRNA genes (Wagner et al. 1998).
Other Genes Linked to a Function
Other functions can be listed as the assimilation of sulfur
(sulfur esterase), phosphate uptake (phosphatase), the synthesis of protective pigments, synthesis of protective squalene
lipids (squalene hopene cyclase), the synthesis of secondary
metabolites (NRPS, PKS), and the catabolism of xenobiotics
(diox), which have permitted or may permit diversity studies.
Metagenomic approaches (Sect. 18.3.3) are also increasingly yielding a mass of data to analyze. For instance, work
has been done on riboswitches (cf. Box 9.1) in microbial
communities (Kazanov et al. 2007).
8.4.3 How to Measure This Biodiversity?
Quantifying the biodiversity of an ecological community is
usually done by computing a number which increases with
biodiversity that is called a “biodiversity index.” The value
of this number may not have any meaning in itself, but a
comparison of the number of different communities must let
us know which harbors the largest diversity. Whatever the
method used, different indices can be calculated to analyze
the microbial diversity of the samples studied. As a first step,
some indices are presented to illustrate the process of computation, and they are discussed in the context of microbial
ecology. There are many indices of biodiversity in the literature, and it is not easy to make a wise choice in a particular
context. That is why, in a second step, an experiment-based
approach is presented. The idea consists in choosing an
index of biodiversity which takes into account the specificity
of the method used to acquire the data set.
Diversity indices are of two types: primary indices and
subindices (or composite). The primary indices are direct
measures of population parameters and do not require calculations confusing the number of species (richness) with the
frequency of each species and their identity. The number of
species or OTUs (Box 8.3) in a community and the frequency
of a given OTU in a community are examples of primary indices.
Secondary or composite indices are most often used in studies of
biodiversity indices such as Shannon, Simpson, and Nei
described below. It is important to distinguish between primary
and secondary indices because they have an impact on the
experimental devices that more or less facilitate statistical
analyses. In general, it is often easier to perform parametric
analyses (e.g., analysis of variance) on the primary indices than
on subindices, which require some refinements. Trends in
statistical processing of microbial biodiversity data demonstrate
this situation (Morris et al. 2002). For a given workload (e.g.,
Box 8.3: OTU (Operational Taxonomical Unit)
Philippe Normand
Bacterial taxonomy includes different levels from
phylum to subspecies, including order, family, genus,
and species, as discussed in Chap. 1. The issue of
diversity is studied at each of these levels, as long as
individuals can be identified in a particular group. In
the microbial world, it is often difficult to agree on the
belonging of isolates to a species. It is for this reason
that different authors have developed the concept of
OTU used in different softwares for individuals or
groups resulting from different aggregation procedures
(Sneath 2005).
The definition of an OTU would be “a group of
phylogenetically related organisms used in a study
without specifying its taxonomic rank.”
The concept of OTU has already led to a second
one, that of molecular operational taxonomic units or
MOTU (Floyd et al. 2002), to designate the OTUs
obtained based on molecular approaches.
8 Biodiversity and Microbial Ecosystems Functioning
275