w a t e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 7 1 1 e1 7 2 5
Available online at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Toward the development of microbial indicators for
wetland assessment
Atreyee Sims a, Yanyan Zhang a, Shashikanth Gajaraj a, Pamela B. Brown b, Zhiqiang Hu a,*
a
b
Department of Civil and Environmental Engineering, University of Missouri, E2509 Lafferre Hall, Columbia, MO 65211, USA
Department of Biological Sciences, University of Missouri, Columbia, MO 65211, USA
article info
abstract
Article history:
Wetland assessment tools are being developed and employed in wetland monitoring and
Received 23 July 2012
conservation based on physical, chemical and biological characterization. In wetland
Received in revised form
biological assessment, various ecological functions have been described by biological traits
10 January 2013
of an entire species pool that adapts to different types of wetland environments. Since
Accepted 11 January 2013
microorganisms play a key role in wetland biogeochemical processes and respond quickly
Available online 23 January 2013
to environmental disturbances, this review paper describes the different macro indicators
used in wetland biological monitoring and expands the potential use of microbial in-
Keywords:
dicators in wetland assessment and management. Application of molecular microbial
Wetland assessment
technologies paves the path to an integrated measure of wetland health conditions. For
Microbial indicators
example, the ratio of ammonia-oxidizing archaeal and bacterial populations has been
AOA/AOB ratio
proposed to serve as a microbial indicator of wetland nutrient conditions. The microbial
Molecular microbial techniques
indicators coupled with physical, chemical and other biological parameters are vital to the
Wetland health
development of multi-metric index for measuring wetland health conditions. Inclusion of
Nutrients
microbial indicators will lead to a more comprehensive wetland assessment for wetland
restoration and management practices.
ª 2013 Elsevier Ltd. All rights reserved.
Contents
1.
2.
3.
4.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Concepts of bioindication and wetland biological assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1. Existing major biological indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.1. Vegetation as bioindicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.2. Birds as bioindicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.3. Invertebrates, fish and amphibians as bioindicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.4. Algae as bioindicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Expansion of biological indicators with an emphasis on microbial communities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Future research in wetland biological monitoring and assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.1. Development of a multi-metric index incorporating microbial indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
* Corresponding author. Tel.: þ1 573 884 0497; fax: þ1 573 882 4784.
E-mail address: [email protected] (Z. Hu).
0043-1354/$ e see front matter ª 2013 Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.watres.2013.01.023
1712
1712
1713
1713
1713
1714
1714
1714
1716
1716
1712
w a t e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 7 1 1 e1 7 2 5
4.2.
Roles of genetic, phylogenetic, and functional diversity in structuring and sustaining wetland microbial
communities through environmental change: use of microbial biodiversity to link environmental factors to
wetland ecosystem function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1717
4.3. Recent advances in molecular tools for the development of new microbial indicators . . . . . . . . . . . . . . . . . . . . . 1718
4.4. Microbial indicator research to meet the goal of the core elements in wetland management and protection . 1719
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1720
1.
Introduction
Wetlands are found at the interface of aquatic and terrestrial
ecosystems in a biome spanning from tundra to tropics
(USEPA, 1995). In the United States, wetlands are estimated to
comprise 110.1 million acres (44.6 million ha), dominated by
freshwater wetlands (95%) although marine and estuarine
systems also exist (Dahl, 2011). Wetlands such as marshes,
swamps and bogs constitute habitats of unique ecological
values. They act as biodiversity reservoirs, fostering countless
species of characteristic flora and fauna (Mitsch and Gosselink,
2000) and provide benefits such as flood control, shoreline
stabilization, nutrient sinks, stormwater runoff storage, water
purification, and recreational use (Johnston, 1991).
Wetlands vary extensively due to differences in soil texture, climate, landscape, hydrology, water quality, and flora
and fauna from one region to the other. Notably, wetland
hydrology is the major factor that governs the overall composition of wetlands. Any change in water volume and quality
due to natural and human activities may negatively impact
the wetland health conditions. Therefore, for water quality
management, the U.S. Environmental Protection Agency
(USEPA) has extensively employed the Total Maximum Daily
Load (TMDL) to define the maximum amount of a pollutant
(from point and nonpoint sources) that a water body can
receive as a means to return to compliance with water quality
standards (Muñoz-Carpena et al., 2006).
The escalation of industrial and agricultural practices,
together with the continuous growth of population and urban
development, has caused a substantial change to the environment. Despite continued wetland conservation efforts, it
has been estimated that about 50% of the wetlands in the
world have been lost and existing wetlands continue to be
endangered due to human activities (Zedler and Kercher,
2005). Excess nutrient input and accumulation of phosphorus (P) and nitrogen (N) results in eutrophication and changes
in wetland integrity and function. Wetlands and other aquatic
ecosystems receive millions of tons of sewage and pollutants
from industrial and agricultural effluents producing harmful
algal blooms (HABs) that cause human diseases and destroy
aquatic systems (Van Dohla, 2000). The impairment of the
wetland function is further weakened by potential climate
changes (Erwin, 2009). Among all ecosystems, wetlands have
been considered the most vulnerable to climate change as
flooding events flush nutrients, pollutants and toxic compounds into the wetland ecosystems (Williams et al., 1999).
The development of wetland monitoring and assessment
strategies is necessary to determine the impact of these
human activities on wetland health and improve wetland
management and protection. Numerous efforts have been
made by employing indicators to improve and diagnose the
ecological health of wetlands over space and time at the cellular, organism, habitat, and ecosystem levels (Niemi et al.,
2004). The US EPA has proposed a three-tier approach for
wetland monitoring and assessment (USEPA, 2006), which
includes geographic assessment (Level 1, also known as
landscape assessment), rapid field sampling and wetland
assessment (Level 2) and rigorous biological and physicochemical procedures (Level 3). Rapid assessment (Level 2)
tools are commonly used to gauge the ecological integrity of
wetlands and there are at least 16 rapid assessment methods
(RAMs) proposed by different states in the U.S. to assess
wetland conditions (Fennessy et al., 2004). The Ohio RAM
(ORAM) can evaluate the quality of wetlands in the field by
including ecological condition indicators and disturbance indicators in a total of six metrics (Fennessy et al., 2004). Rapid
assessment methods can deliver comprehensive, but mostly
qualitative data on wetland functions and values. Biological
assessments (Level 3) include more intensive site monitoring
to refine RAMs by collecting detailed biological and hydrogeomorphic (HGM) measurements using indices of biological
integrity (USEPA, 2006) and HGM classification (USDA, 2008).
2.
Concepts of bioindication and wetland
biological assessment
Various wetland assessment indicators have been developed
and employed to measure the changes in ecological condition.
There are primarily three types of indicators based on physical
(e.g., water depth, open water area and land uses), chemical
(e.g., total P and N, sediment chemistry) and biological (plant
species, composition and abundance of macroinvertebrates,
algae, etc.) characteristics (Fig. 1) (Asmus et al., 2009; Karr and
Dudley, 1981; Yagow et al., 2006). Bioindication uses higher
plants, animals, and microbial species to predict or indicate
the wetland water quality and health conditions. The
advantage of biomonitoring over chemical methods is that
these organisms are subject to and sensitive to fluctuating
environmental conditions. Chemical analyses provide direct
evidence of water quality but do not echo the altered and
fluctuating environmental conditions (Teiter and Mander,
2005). Although the adaptations of microorganisms to environmental changes are diverse and sophisticated, microorganisms play a key role in biogeochemical processes in
wetlands and respond quickly to environmental disturbances.
Given the current abundance of molecular technologies, it is
time to consider the development and use of microbial indicators for wetland biological assessment as microorganisms
and associated indicators/indices of biological integrity
w a t e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 7 1 1 e1 7 2 5
1713
Fig. 1 e Use of microbial species and population to expand the repertoire of biological indicators for wetland assessment.
provide the groundwork in assessing wetland health conditions. The use of microbial indicators such as the ratio of
ammonia oxidizing archaea (AOA) to ammonia-oxidizing
bacteria (AOB) may prove beneficial in determining the condition of oligotrophic wetlands. In this review, the major
biological indicators of wetland health are described. In
addition, the on-going efforts to apply multi-metric index
incorporating microbial indicators for biological assessment
of wetlands are highlighted.
2.1.
Existing major biological indicators
Biological measurements offer detailed information about the
biota and species richness in wetlands. These measurements
address the ecological quality by integrating biological community assessment with wetland water quality and the
physical and geomorphic characteristics. Biological assessments include a variety of indices that describe the structure
and abundance of wetland biological community. For
instance, the biotic, diversity and community comparison
indices assess the effects of pollutants on aquatic environments (Dziock et al., 2006). The tolerance and sensitivity of
organisms to pollutants is measured by the biotic index while
the effects of pollution on the community structure and
composition are estimated by the diversity and community
comparison indices, respectively (Dziock et al., 2006). Major
bio-indicators that are used for wetland monitoring include
wetland vegetation, birds, macroinvertebrates, fish and amphibians, and algae (Fig. 1).
2.1.1.
Vegetation as bioindicators
Despite the variation in vegetation community composition
with wetland types and geographic locations, wetland macrophytes are sensitive to water quality, which allows them to
be good indicators of wetland health (Zimmer et al., 2003). For
example, submerged aquatic vegetation (SAV) is an indication
of healthy wetlands, whereas turbid emergent macrophytes
and algal growth indicate less healthy wetland conditions
(Lougheed and Chow-Fraser, 2002). Sediment loading and
herbicides (Chaplin et al., 2004) reduce macrophyte seedling
(Gleason et al., 2003) and SAV biomass (Best et al., 1984;
McNair and Chow-Fraser, 2003; Ozimek et al., 1991). Thus records of such an overwhelming vegetation shift are vital in
understanding, conserving and maintaining ecological integrity (Frieswyk and Zedler, 2007). Currently there are a few
vegetation-based indices that have been developed for wetland assessment. These include floristic quality index (FQI)
based on the predisposition of plant species (Mushet et al.,
2002), wetland macrophyte index (WMI) for detecting wetland water quality impairment (Croft and Chow-Fraser, 2007),
and plant index of biotic integrity (PIBI) to evaluate the condition of wetland plant communities using vascular plants as
the indicator taxa group (Rothrock et al., 2008). While plant
species and population density are good indicators of environmental conditions, wetland vegetation is subject to change
due to human activities.
2.1.2.
Birds as bioindicators
Birds have been linked to impacts of human activities and the
changes in wetland water quality parameters such as the
dissolved oxygen (DO), nitrate and chloride (Getachew et al.,
2012). In the rapid assessment method, the Ohio RAM scores
reflect conditions important to wetland birds by determining
the bird assemblages in response to human-induced disturbances (Peterson and Niemi, 2007). For instance, heavy metal
pollution and salinity pose a lethal effect to water birds (Hart
et al., 1990; Zhang and Ma, 2011). On the other hand, bird
breeding was enhanced by increasing macrophyte biomass
1714
w a t e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 7 1 1 e1 7 2 5
(Lillie and Evrard, 1994) with minimal cattle grazing and
human disturbances (Casey et al., 1999). Studies also show
that high habitat heterogeneity produces high bird species
richness (Brown et al., 2009) and the index of ecological condition (IEC) uses birds to demonstrate the relationships with
anthropogenic disturbances (Howe et al., 2007). There is also
a positive relationship between breeding duck and pond
densities in the prairie wetland regions (Austin, 2002). The
abundance of dabbling ducks was positively associated with
turbidity and total dissolved nitrogen, but negatively with
chloride (Savard et al., 1994). The wetland bird species are also
influenced by emergent vegetation cover (Fairbairn and
Dinsmore, 2001). Nevertheless, preservation of birds is poor
and bird assemblages are so variable that they may not be
suitable for quantitative wetland assessment (VanReesSiewert and Dinsmore, 1996).
2.1.3.
Invertebrates, fish and amphibians as bioindicators
Invertebrate communities respond well to wet and dry regions
in wetlands (Batzer et al., 2004; Euliss and Mushet, 1999). An
index of size spectra sensitivity (ISS) spatially differentiates
macroinvertebrates under fluctuating environmental conditions (Basset et al., 2012) while a synthetic index of biological
soil quality (IBQS) helps to measure soil quality based on soil
macro-invertebrate communities (Nuria et al., 2011). Invertebrates are also used to assess heavy metal pollution
(Michailova et al., 2012) and nutrient contamination (Hann
et al., 2001; Zrum and Hann, 2002). Although there could be
no effect of heavy metals on the diversity of macroinvertebrates, heavy metal pollution causes chromosomal
aberrations in the polytene chromosomes of Chironomidae
larvae (Michailova et al., 2012). Chironomidae indicators
specify wetland disturbances best when identified at a species
level, with the presence of the dipteran larvae of the family
Chironomidae indicating poor water quality, although such
indicators can vary from wetland to wetland (Karr, 1993;
Plafkin, 1989). Fish are delicate indicators to predict environmental quality (Zimmer et al., 2001). When combined with
aquatic macrophyte communities, they provide a stronger
and more direct interconnection between macrophyte and
fish assemblages. When predicting the fish community, researchers using canonical correspondence analysis (CCA) and
co-correspondence analysis (COCA) to integrate the fish
community with the plant community data have shown that
aquatic plants are more reliable than water quality parameters alone (Cvetkovic et al., 2010). Amphibian species richness
is positively associated with wetland cover while land use and
nitrogen concentrations may limit amphibian growth with
changing plant diversity and richness (Houlahan et al., 2006).
2.1.4.
index) are designed by the presence of algal species indicate
organic pollution in water (Palmer, 1969). Phytobenthos (diatoms) and macrophytes are also used for ecological assessment through BrayeCurtis similarity analysis (Feio et al.,
2012). The prevalence of diatom communities in the Great
Lakes’ coastal waters has been used to determine water
quality (Reavie et al., 2006). However, no discernible difference
in the respiration/biomass ratio is observed between restored
and reference wetlands since plankton species reestablish
quickly in response to the change in wetland hydrology and
because multiple algal species may have similar functions
(Mayer et al., 2004).
In assessing wetland health conditions, algal samples are
easy to collect, but analysis is complicated by seasonal variation in biomass and community composition and differences among wetlands (Chow-Fraser, 1999; Mayer and
Galatowitsch, 1999). The presence of phytoplankton, mainly
algae in the unicellular and colonial forms, can be verified by
the occurrence of 50 or more algal cells in 1 mL of water. The
total cell numbers are calculated to produce a value of algal
genus index (Mccullagh and Nelder, 1989), and algal richness
is generally increased with a decrease in the salinity (Floder
and Burns, 2004). Algae density, volume, ash-free-dry-mass
(AFDM) and chlorophyll concentrations can be used as a surrogate of algal biomass, although these methods have disadvantages due to measurement variation (LaBaugh, 1995).
Chlorophyll and carotenoid photopigments are key indicators
to examine the effects of nutrient loading and community
structure changes (Niemi et al., 2004). Algal blooms are synonymous with eutrophication since significant changes in
algal communities are discernible as the concentrations of
phosphorus and organic matter increase (Best et al., 1984;
Ozimek et al., 1991). A direct link between algal biomass and
nutrient loading is the most widely used indicator of eutrophication in aquatic ecosystems (Dyhrman, 2008). Total
phosphorus (TP) and total nitrogen (TN) ratios are used to
deduce which nutrient regulates algal growth (Schneider and
Lindstrom, 2011). Cyanobacteria (blue-green algae) produce
potent toxins that affect human and animal health and are
candidate bioindicators (Codd, 2000).
Although periphyton (benthic algae) are abundant in wetlands, historically these organisms have been rarely used in
biological wetland assessment largely due to technical difficulties (McCormick and Stevenson, 1998). However, a new
index based on non-diatomaceous benthic algae (Periphyton
index of trophic status, PIT) has been developed that displayed
a positive correlation between filament width and TP concentration, making the genus Oedogonium a new eutrophication indicator (Schneider and Lindstrom, 2011).
Algae as bioindicators
Due to their species richness and ubiquity, algae are another
important biological indicator to determine the biological
integrity and ecological condition of wetlands (USEPA, 2002).
Assays such as algal growth potential (AGP) bioassay indicating the bioavailability of aquatic nutrients and the limiting
nutrient algal assay (LNAA) offer dependable information
about wetlands with limited nutrients (McCormick and
Stevenson, 1998; USEPA, 2002). Biotic indices (e.g., Myxophycean index, Chlorophycean index, Euglenophycean
3.
Expansion of biological indicators with an
emphasis on microbial communities
While the current bioindicators and wetland assessment tools
have been used to define wetland conditions, the preservation
of algae, macrophytes, fish and invertebrates in wetlands is
often too poor for experts to deliver definitive results. On the
other hand, hydric soil formation is one of the key features of
wetland development and leads to the production of an
w a t e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 7 1 1 e1 7 2 5
anaerobic zone where hydrophytic vegetation thrives. Wetland soil microbes play a vital role in carbon, nitrogen, and
sulfur cycling (Fig. 2) and also catalyze chemical transformation under alternating anoxic/anaerobic conditions
found within the wetland soils (Mitsch and Gosselink, 2000).
The overall wetland health is impacted by physical and
chemical properties of the soil, which are influenced by the
resident microbial communities. The development of molecular technology has allowed for identification and characterization of the composition and function of soil microbial
communities in wetlands. With this knowledge, it is logical to
consider how these microbial communities can be used to
expand the repertoire of biological indicators for wetland
health assessment (Fig. 1).
There are a number of potential advantages for using microbes as bioindicators. Firstly, microbial populations can
undergo rapid changes in composition and function in
response to changing environmental conditions. Secondly,
bacteria are extremely sensitive to even small fluxes of contaminants in the environment. Indeed, monitoring of aerobic
bacterial metabolic diversity has been suggested as a means to
detect the early signs of degradation in wetland ecosystems
(Merkley et al., 2004). In order to overcome the challenges
associated with microbial indicators (such as temporal population changes and accurate identification of microbes), the
design of assessment strategies should be matched to the
hydric soil characteristics in wetlands. Thus, rather than
monitoring the global microbial population, it is important to
determine soil microbial communities that are heavily
involved in wetland biogeochemical cycles.
Microbes are key contributors to nitrogen cycling in soil
environments where organic nitrogen and ammonia in the
soil are converted sequentially to nitrate and gaseous nitrogen. Nitrification, the sequential biological oxidation of
ammonia to nitrite and nitrate, is carried out by two phylogenetically distinct groups of obligate aerobes: ammoniaoxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB)
(Fig. 2). The chemolithotrophic AOB commonly belonging to
the Beta- and Gamma-proteobacteria, including Nitrosomonas
(Beta), Nitrosospira (Beta), and Nitrosococcus (Gamma) have
been identified in various surface flow/subsurface flow wetlands, floodplains, marshes and similar mesocosms (Ahn and
Peralta, 2009; Dorador et al., 2008; Gorra et al., 2007; Moin et al.,
1715
2009; Yan et al., 2005). Nitrosomonas and Nitrosospira are also of
prime importance in the brackish and freshwater wetland
sediments and bogs (Coci et al., 2005; Morales et al., 2006). The
recent discovery of ammonia-oxidizing archaea (Konneke
et al., 2005; Treusch et al., 2005; You et al., 2009) has prompted studies which explore the possibility of niche differentiation between the AOA and the more well-studied AOB (Moin
et al., 2009). AOA have been reported to be present in a variety of environments, including soils and sediments (Bernhard
et al., 2010; Treusch et al., 2005; You et al., 2009). The oligotrophic ammonia oxidation kinetics and cellular characteristics of the mesophilic crenarchaeon ‘Nitrosopumilus maritimus’
strain SCM1 with high specific affinity for ammonia (halfsaturation constant, Km ¼ 133 nM ammonium) suggest that
Nitrosopumilus-like oligotrophic AOA could compete with
bacteria and phytoplankton for ammonia (Martens-Habbena
et al., 2009). In addition, AOA are more persistent and more
abundant than AOB with the gene encoding a subunit of
ammonia monooxygenase (amoA) from AOA is expressed an
order of magnitude higher than amoA of AOB in natural wetlands (Sims et al., 2012b). In contrast, nitrification is mainly
driven by AOB in the constructed wetlands treating ammonialaden wastewater (Sims et al., 2012a). Similar results were
obtained in another study where AOA and AOB were quantified in the sediments of four nitrogen-rich wetlands in China,
the latter dominated ammonia oxidation in nitrification
(Wang et al., 2011). Taken together, these observations suggest
that the ratio of AOA to AOB may serve as a new biological
indicator for wetland assessment and management.
Denitrifying organisms constitute a phylogenetically
diverse group of prokaryotes, and some eukaryotes (RisgaardPetersen et al., 2006) where nitrate is subsequently reduced to
gaseous nitrogen (N2). Denitrification is a widespread feature
among soil bacteria and therefore denitrifying organisms can
be used as a representative for microbial biomass (Stenberg,
1999). The genetic diversity of denitrifiers has been explored
by using specific genes as functional markers of the nitrate
reduction processes (Hallin and Lindgren, 1999). A recent
study has shown that while bacterial community structures
varied significantly between restored and reference (natural)
wetlands, denitrifying microbial assemblages were similar
among reference sites, where the highest denitrification potential was found (Peralta et al., 2010). The results indicate
Fig. 2 e Part of wetland nitrogen, carbon and sulfur cycles that are mainly carried out by the microbes in hydric soils. The
ratio of AOA to AOB may serve as a new biological indicator with the high AOA/AOB indicating oligotrophic healthy
conditions in wetlands.
1716
w a t e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 7 1 1 e1 7 2 5
that wetland restoration efforts could benefit from increased
denitrification mediated by denitrifying bacteria in natural
wetlands.
Due to the presence of oxic and anoxic zones in hydric
soils, methane cycling is another important component of
wetland biogeochemistry (Fig. 2). Methane is produced in
anoxic environments by methanogenic archaea and consumed by aerobic methane oxidizing bacteria, the methanotrophs (Ritchie et al., 1997). Methane flux in wetlands is
a function of the relative activities of methanogenic archaea
and methanotrophic bacteria (Freitag et al., 2010). Although
the structure and function of methanogens in wetlands are
less studied, efforts have been made to identify relationship
between the composition of methanogenic assemblages and
the effectiveness of soil redevelopment. Although methanogenic assemblages remain relatively stable in wet and dry
seasons, shifts in Methanobacteriales populations are correlated with a general decline in hydrogenotrophic methanogenic activity with restoration age (Smith et al., 2007).
The number of methanotrophs and methanotrophic communities can be determined by PCR-DGGE using
methanotrophs-specific 16S rRNA primers (Ritchie et al.,
1997). Based on structures of internal membranes and carbon assimilation pathways, methanotrophic bacteria are
characterized into Type I (phylogenetically with g-Proteobacteria, proliferate under high oxygen and low methane conditions), Type II (phylogenetically with a-Proteobacteria,
proliferate under high methane and low oxygen conditions),
and Type X (phylogenetically with g-Proteobacteria, possess
characteristics of both Type I and Type II methanotrophs)
clusters (Hanson and Hanson, 1996). Notably, in the dry wetland area, the more stagnant hydrological conditions result in
the dominance of type II methanotrophs over type I methanotrophs while the relative abundance of type II methanotrophs increase in winter in the littoral wetland (Siljanen et al.,
2012). Taken together, these observations suggest that fluxes
of methanogens and methanotrophs and their seasonal
changes may serve as a new microbial indicator for wetland
assessment.
Finally, microbial sulfate reduction is an important biogeochemical process in the recycling of sulfur (Fig. 2). Favorable conditions for the growth of sulfate reducing bacteria
(SRB) include anaerobic conditions, copious amount of carbon,
and a continuous source of sulfate ions (Riefler et al., 2008). In
a constructed wetland study, the fractions of denitrifiers and
SRB in total bacteria were 2 and 40%, respectively, suggesting
the importance of SRB in sulfate reduction and overall biochemical processes in wetland soils and sediments (Park et al.,
2009).
4.
Future research in wetland biological
monitoring and assessment
4.1.
Development of a multi-metric index incorporating
microbial indicators
Ecological health of a system is characterized by the stable
condition with its innate potential being realized. Such a system is well preserved and has the capacity for self-repair
when it is discomposed with minimal external support
(Karr, 1993). Evaluation of wetland condition and complexity
cannot be met effectively by a single physical, chemical or
biological attribute but a combination of multiple attributes is
useful for wetland assessment. Wetland monitoring to characterize soil physical and chemical properties is considered
essential in wetland development and restoration (Anderson
et al., 2005; Siobhan et al., 2004). However, chemical assessment through wetland water quality measurements does not
account for various physical, chemical or biological stressors
(e.g., erosion, pollution, competition from invasive species)
and therefore cannot accurately measure biological integrity
of wetlands. Similarly, assessments with an emphasis on
physical attributes of wetlands, such as the hydrogeomorphic
approach, fail to determine the health of the wetland ecosystem due to chemical and biological stressors. The health
condition or biological integrity of a wetland is only reflected
by the balanced biological wetland community having a species composition, diversity, and functional organization
comparable to that of natural habitats. Because bacterial
communities are largely affected by soil pH, land use, and
restoration status in different types of wetlands (Hartman
et al., 2008), and because wetland microorganisms play a key
role in regulating biogeochemical fluxes in wetlands, the
development of a multi-metric index incorporating microbial
indicators will allow more accurate assessment of wetland
conditions.
The multi-metric index called the Index of Biotic Integrity
(IBI) was initially used to monitor fish assemblages (Karr, 1981)
but was later modified to include wetland invertebrates (Euliss
et al., 2001), amphibians (i.e., AmphIBI) (Miccachion, 2002),
plant assemblages (Kantrud and Newton, 1996), aquatic
macroinvertebrates (Plafkin, 1989), terrestrial invertebrates
(Kimberling et al., 2001) and coastal marine systems (Deegan
et al., 1997). Development of a multi-metric periphyton
index of biotic integrity (PIBI) has also been proposed, which
incorporates both microbial functional and structural deviations from the natural/reference wetlands into wetland
condition assessment (McCormick and Stevenson, 1998).
To develop a multi-metric index incorporating microbial
indicators, one of the promising sub-indices is the ratio of
oligotrophic organisms to copiotrophic organisms in wetlands, specially, the ratio of AOA to AOB. AOA are well adapted
to growth in the nutrient limiting (oligotrophic) environment
(Martens-Habbena et al., 2009; Sims et al., 2012b) with unique
ecological or niche separation (Erguder et al., 2009; GubryRangin et al., 2010; Santoro et al., 2008). The ratio of AOA
amoA gene copies to AOB amoA gene copies may serve as
a new biological indicator for wetland condition assessment
and restoration applications (Sims et al., 2012b). In a healthy
wetland, the increased relative abundance of AOA in the
wetland soils under low nutrients and low DO conditions
suggests that such niche differentiation may form the
grounds for the coexistence of AOA and AOB with the numerical dominance of AOA (Herrmann et al., 2011; Sims et al.,
2012b). The seasonal changing patterns in AOA and AOB
abundances in response to nutrient inputs suggest that AOA
prefers low-nutrient environments whereas AOB dominates
in high nutrient soils (Schleper, 2010; Verhamme et al., 2011).
On the contrary, there are more AOB than AOA in aerobic
w a t e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 7 1 1 e1 7 2 5
sludge samples with high nitrogen loading. The ratio of AOB to
AOA increased significantly to w4000 with increasing
ammonium loading, touting AOB as the main players in the
nitrification reactors (Ye and Zhang, 2011). In a constructed
wetland system treating wastewater containing high ammonium concentrations, more AOB were detected than the
archaeal counterpart (Sims et al., 2012a). Moreover, under
eutrophic conditions, AOB thrived better than AOA (Wei et al.,
2011). Apparently, soil NH4 þ eN is a major factor influencing
AOA and AOB populations (Gregory et al., 2010; Höfferle et al.,
2010) and the high ratio of AOA/AOB is an indication of oligotrophic healthy conditions in natural wetlands (Sims et al.,
2012b).
Similarly, certain bacterial phyla can be differentiated into
oligotrophic and copiotrophic bacteria that correspond to the
k-strategists (i.e., organisms having high affinities for the
substrate and capable of scavenging substrates at very low
concentrations) and r-strategists (i.e., organisms having very
high specific growth rate and capable of outgrowing oligotrophs at high substrate concentrations) (Rittmann and
McCarty, 2001) The ratio of oligotrophic bacteria to copiotrophic bacteria reflects the tolerance of the soil species to
environmental stress (Klappenbach et al., 2000; van Brüggen
and Semenov, 2000). The presence of acidophilic Acidobacteria in oligotrophic soils in comparison to greater abundance of b- Proteobacteria in eutrophic ecosystems reflects soil
trophic status (Hartman et al., 2008). A high ratio signifies
stable environmental conditions while a low ratio indicates
high nutrient-laden ecosystems or eutrophic conditions.
Hence, applying the oligotroph/copiotroph concept to wetland
soil microorganisms may help us better understand the
structure and function of soil microbial communities and
predict their ecological attributes. Other microbial indicators
should be developed and validated for wetland condition
assessment because of the complexity of microbial diversity
(Keller and Zengler, 2004) and their changes along with functional gene abundance over time (Bannert et al., 2011).
4.2.
Roles of genetic, phylogenetic, and functional
diversity in structuring and sustaining wetland microbial
communities through environmental change: use of
microbial biodiversity to link environmental factors to
wetland ecosystem function
The primary determinant components of biological integrity
include microbial genes, groups/species, and communities in
wetlands. Information about microbial community structure
and diversity has been noted as important for understanding
the relationship between environmental factors and ecosystem functions (Sutton-Grier et al., 2011; Torsvik et al., 1996).
However, soil microbial diversity and its role in wetland ecological functioning are still poorly understood, partly due to
the lack of effective methods for characterizing microbial
communities (Achtman and Wagner, 2008). More importantly,
horizontal gene transfer through the acquisition of genetic
information from distantly related organisms may produce
bacteria with genetic diversity and extremely dynamic genomes, resulting in effective changes in their ecological
functions (Ochman et al., 2000).
1717
Bacteria and Archaea are genetically diverse and cover
a dominant part of the global biogeochemical cycle (Brown
et al., 2009). Microbial species and genetic diversity are best
understood in a phylogenetic context that reflects the interaction between genetic and functional dimensions of biodiversity. Phylogenetic diversity of bacteria is most commonly
studied using 16S rRNA genes which are highly conserved
among all bacteria but contain hypervariable regions from
which the relatedness of species can be inferred. 16S rRNA
genes are therefore broadly used for phylogenetic studies of
Eubacteria and Archaea as the microbial taxonomic composition
is an important indicator of microbial ecology and function. For
instance, aerobic heterotrophic bacteria (AHB) in cyanobacterial mats play an important role in carbon cycling within
microbial mats (Abed et al., 2007). The contribution to ecological functioning of coastal microbial mats has been demonstrated by bacterial and archaeal genetic diversity using 16S
rRNA sequencing. The study concluded that the diverse and
unique microbial compositions were linked to salinity in these
marine ecosystems (Bolhuis and Stal, 2011). Habitat degradation has a negative impact on taxonomic diversity thereby
influencing ecological functions of alpine meadows (Wu and
Yang, 2011). Taxonomic studies of archaeal ammonia oxidizers also reveal the importance of niche specificity and pH
adaptation in nitrogen cycling (Gubry-Rangin et al., 2011).
While the use of 16S rRNA-based molecular techniques is
essential to examine microbial community structure and diversity, equally important is the determination of those microbes that are functionally active in soils in order to better
understand the concepts of functionality and redundancy.
This is particularly important considering the phylogenetic
diversity among denitrifying bacteria and suggests that functional genes may be a better bioindicator. For instance, coppercontaining nitrite reductase is encoded by nirK genes and heme
containing nitrite reductase is encoded by nirS genes and either
one or both genes can be found in denitrifiers. Indeed, heterogeneity of nitrite reductase genes (nirK and nirS ) from denitrifiers has been observed in different wetland soils (Prieme
et al., 2002). In contrast, molecular techniques targeting the
functional amoA gene (present in both AOB and AOA) encoding
the a-subunit of ammonia monooxygenase (AMO) have shown
that both organisms may be important in ammonia oxidation
and nitrogen cycling in wetlands (Schleper, 2010; Sims et al.,
2012a,b). A number of methods have been developed to
determine the functional diversity of microbial communities.
Phospholipid Fatty Acids (PLFAs) and Community Level Physiological Profiles (CLPP) have been used to describe the functional diversity of the microbial communities in wetland
restoration (Andersen et al., 2010). Microbial mass and diversity of biological soil crust were also investigated using PLFA
analysis and 16S rRNA analysis in an arid ecosystem (Zaadya
et al., 2010). The catabolic response profile (CRP) measuring
short term substrate induced respiration expresses the diversity of catabolic functions in situ (Degens et al., 2001). Furthermore, microbial communities with low catabolic
functional capabilities were sensitive to stress and environmental perturbations in non-vegetated areas (Schipper et al.,
2001). Length Heterogeneity Polymerase Chain Reaction (LHPCR) has been used to compare that the bacterial community
structure and diversity in hollows relative to hummocks in
1718
w a t e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 7 1 1 e1 7 2 5
a palustrine forested wetland (Ahn and Peralta, 2009). This
study revealed that there was a significant correlation between
physicochemistry and bacterial community structure in wetland soils. However, a disconnect between bacterial community structure and their enzyme activities has been noted in
other environments, indicating high functional redundancy
within microbial communities (Frossard et al., 2012; Rittmann
and McCarty, 2001).
Remarkably, new methodologies combine phylogenetic
and functional assessment of microbial communities by
generating phylogenies of protein-coding genes thus providing critical information about both the phylogeny and functional diversity of microbial communities (Von Mering et al.,
2007). In comparison to the traditional rRNA-based of functional profile approaches, this methodology could provide
a more accurate and quantitative picture of microbial community composition in wetlands. A limitation of this method
is that it remains unknown if the presence of genes reflects
accurately the microbial physiology of the community.
4.3.
Recent advances in molecular tools for the
development of new microbial indicators
There has been an inspiring and promising advancement in
using emerging molecular technologies in environmental
microbiology studies (Keller and Zengler, 2004). Such molecular techniques provide us with new culture-independent
perspectives about microbial structural, taxonomical and
functional diversity along with an understanding of microbial
adaptation to changing environments (Torsvik and Ovreas,
2002).
Automated image analysis techniques are emerging in
microbial community studies (Eickhorst and Tippkötter, 2008;
Ernebjerg and Kishony, 2012; Pohn et al., 2007). For instance,
flow cytometric analysis coupled with CTCþ (5-cyano-2,3ditolyl tetrazolium chloride reduction) cell sorting has shown
the promise to determine the active and redundant microbial
composition within soil communities (Whiteley et al., 2003).
Fluorescent in situ hybridization (FISH) quantifies microbes
with the help of fluorescent or confocal microscopy and can be
coupled to microautoradiography (MAReFISH) to determine
the abundance and activity of microbial communities at various phylogenetic levels (Wagner et al., 2006). FISH and gold
nanoparticle labeling revealed prokaryotic structural diversity
(Gerard et al., 2005). Raman-FISH along with secondary ion
mass spectrometry (SIMS) has also been used to elucidate the
role of microorganisms in biogeochemical cycling to global
scales (Musat et al., 2012). MAReFISH has been modified
slightly, resulting in STAR (substrate tracking autoradiography)eFISH to provide insights about the metabolism
of uncultivated microbial cells (Neufeld and Murrell, 2007).
Stable isotope probing (SIP) involves a stable isotope (e.g., 13C)
-labeled substrate to decipher key biogeochemical processes
(Wellington et al., 2003) in ammonia-oxidizing population
(Tourna et al., 2010) and bacterial diversity and functional
relationships (Monard et al., 2011). With the improvements in
imaging and spectroscopic techniques, SIP is often combined
with FISH and Raman microscopy to simultaneously investigate the taxonomical relationship and activity of microbial
communities (Huang et al., 2007). The RamaneFISH method
involves the incubation of soil samples with a substrate
labeled with 13C stable isotope and measurement using high
resolution scattered laser light by the chemical bonds of different cell biomarkers which overcomes many of the limitations associated with conventional SIP/MAReFISH techniques
(Huang et al., 2007).
The variety of culture-independent molecular tools continues to increase providing more information about the genetic and functional diversity of microbes in a particular
environment. For instance, microbes can be enumerated from
soil samples by the amplification, cloning and sequencing of
small subunit ribosomal RNA (SSU rRNA) genes using
sequence-specific cleaving activity of catalytic DNA (Suenaga
et al., 2005), thus providing taxonomic diversity. Polymerase
chain reaction (PCR)-based fingerprinting methods (Zhang
et al., 2009) and a modification of single-stranded conformational polymorphism (SSCP) (MacGregor, 2006) provide resolution and information about whole community structure and
unknown microbial communities, respectively. Other fingerprinting techniques comprised of denaturant gradient gel
electrophoresis (DGGE) (Bodelier et al., 2005), amplified rDNA
restriction analysis (ARDRA) (Walsh et al., 2002), T-RFLP (Lee
et al., 2012) and ribosomal intergenic spacer analysis (RISA)
(Martı́nez et al., 2007) describe species composition and
community diversity. DNA microarrays are slides to which
complementary sequences of DNA are attached in an orderly
fashion and can be used to study bacterial diversity (Martens
et al., 2007). Hybridization of fluorescently labeled mRNAs or
DNA from environmental samples to microarrays indicates
the presence of genes of interest, with the level of fluorescence indicating the quantity of the genes or cells (Yoder
and Kulik, 2003).
Major advances in molecular biology techniques, such as
next generation high-throughput DNA sequencing are just
beginning to allow robust exploration of taxonomic diversity in
soil communities. Sogin and coworkers used the 454 sequence
technology for tag-sequencing of the V6 variable part of the
bacterial 16S rRNA gene from deep sea samples to comprehend
the ‘rare biodiversity’ (Sogin et al., 2006). Microfluidic or lab-ona-chip (LOC) digital PCR is a rapid, sensitive and accurate fingerprinting procedure for microbial communities and genetic
diversity (Khanna, 2007). A new generation of functional gene
arrays (FGAs; GeoChip 3.0) has been developed to analyze genetic diversity and functional activity (He et al., 2010). Recent
interest has focused on the use of other sequencing methodologies, most notably Solexa/Illumina, with the cost per read less
than 1/100 of 454 pyrosequencing (Degnan and Ochman, 2012).
Cloning and sequencing of large genome fragments containing phylogenetic markers enable coupling phylogenetic
groups to particular functional genes via metagenomics
(Simon and Daniel, 2009). The genetic diversity and metabolic
activity of a temperate planktonic freshwater community was
studied using metagenomics (Oh et al., 2011). Shotgun
sequencing revealed the symbiotic and evolutionary diversity
of uncultivated bacteria and sponges (Thomas et al., 2010).
The distribution and expression of target functional genes
relevant to carbon, nitrogen, and sulfur cycles were studied
(Yergeau et al., 2007). Metagenomics analysis revealed the
genetic and functional adaptation modes of Prochlorococcus in
iron-depleted ocean (Ruscha et al., 2010). Structural and
w a t e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 7 1 1 e1 7 2 5
functional diversity of prokaryotes and eukaryotes were
defined via proteogenomics (Schneider and Riedel, 2010).
Metatranscriptomics determines cell regulation of microbial
activities by random sequencing of mRNA genes pooled from
microbial communities that respond to climate change and
fluctuations (Moran, 2009). Moreover, it can assess community
and functional diversity in soil microbes (Urich et al., 2008).
Table 1 lists the methods to determine genetic, phylogenetic
and functional diversity for microbial assessment of wetlands. Readers are referred to a recent review article about the
study of soil microbiota using different molecular methods in
order to link genetic and functional diversity in soil microorganisms (van Elsas and Boersma, 2011). These methods could
be used to develop quantitative microbial indicators such as
the ratio of AOA to AOB amoA genes for wetland assessment.
4.4.
Microbial indicator research to meet the goal of the
core elements in wetland management and protection
In this review, we summarize the rapid methods to determine
wetland health conditions using macro-indicators, and highlight the importance and challenges of using microbial indicators in wetland monitoring and assessment. Currently
there are few microbial indicators, especially quantitative
ones, because of the complexity of microbial diversity in
wetlands. One of the disadvantages of microbial indicators for
wetland assessment is that wetland specialists must have
expertise in many different areas, including molecular
microbiology. Furthermore, microbial indicators often require
the use of expensive instruments and equipment that are not
accessible to many users. However, with the continued
development of molecular tools and equipment, the
molecular-based enumeration and quantification can be
1719
rapidly accomplished and the microbial indicators will be
explored and applied to wetland assessment.
Microbial indicators serve the core elements of wetland
management and protection, as reflected by the important
relationship between wetland functions and bioindicators.
One of the best indicators of wetland health or function is its
biological integrity, an ability to support and maintain a balanced biological community comparable to that of natural
habitats. Therefore, bioindicators through the direct examination of one or more biological assemblages, such as microorganisms or vascular plants, are directly linked to
biological conditions and ecological functions of wetlands.
There is also an important relationship between different
bioindicators. For instance, plant functional diversity significantly affects the denitrifying population and denitrification potential through its interactions with soil
conditions leading to increased denitrification potential
observed at high functional diversity (Sutton-Grier et al.,
2011). In addition soil organisms play a key role in the
nutrient transformations that support plant growth. Therefore, both microorganisms and plants are good bioindicators.
Wetland assessment using microbial indicators entails on-site
field sampling that portrays the true wetland condition and is
not based on subjective judgments from the observation of
landscape characteristics. The assessments are verified with
other biological indicators in Level 3 assessment, as recommended by the US EPA, which suggests using a tiered
approach to wetland monitoring that includes a core set of
baseline indicators selected to represent each applicable
designated use, plus supplemental indicators selected according to site-specific or project-specific decision criteria
(USEPA, 2003; USEPA, 2006). Thus, the microbial indicators can
be used along with the already existing wetland biological
Table 1 e Methods to determine genetic, phylogenetic and functional diversity for microbial assessment of wetlands.
Microbial diversity
Genetic/phylogenetic
Functional
Methods
Genotypic analysis
Genomic hybridization
Multilocus sequence typing (MLST)
Multigene and whole genome analyses:
Shotgun, 454 pyrosequencing, and
Solexa/Illumina sequencing
Gene chip
Fluorescent in situ hybridization (FISH)
Small subunit (SSU) rRNA
gene or other functional
gene-based analysis
DNA fingerprinting: Length heterogeneity
polymerase chain reaction (LH-PCR), terminal
restriction fragment length polymorphism
(T-RFLP), amplified fragment length
polymorphism (AFLP), denaturant gradient gel
electrophoresis (DGGE), ribosomal intergenic
spacer analysis (RISA), amplified rDNA
restriction analysis (ARDRA)
Phospholipid-derived fatty acids (PLFA) profile
Community Level Physiological Profile (CLPP)
Enzyme activity
Chemical and microelectrode-based assay
Phenotypic analysis: morphology,
motility, physiology, cell lipid
and wall chemistry, catabolic
response
Methods to link genetic
information to microbial
function and activity
Functional metagenomics
Metatranscriptomics
Metaproteomics
Coupled FISH assays: Substrate
tracking autoradiography
(STAR), In situ reverse
transcription (ISRT), Catalyzed
Reporter Deposition (CARD),
microautoradiography (MAR),
Raman spectroscopy
Stable isotope probing (SIP)
Flow cytometric analysis
coupled with CTC þ cell
sorting and 16S rDNA analysis
1720
w a t e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 7 1 1 e1 7 2 5
assessment tools to fulfill the need for more precise wetland
assessment and restoration techniques.
The biological indicators of ecosystem integrity are
increasingly being sought out to predict wetland water quality, which enables the development and implementation of
quantitative biological criteria for water quality standards of
wetlands. For instance, measuring the relative amount of
ammonia oxidizing organisms in wetland soils becomes
important since the soil AOA/AOB can be used to infer
eutrophic or oligotrophic wetland conditions. Development
and implementation of numeric biological criteria require
detailed evaluation of the components of wetland communities to determine the structure and function of unimpaired
wetlands. These measures serve as reference conditions for
evaluating the integrity of other wetlands.
Microbial indicators have potential applications in wetland
restoration. Wetland restoration efforts led to decreased
bacterial diversity resulting in dominance of a few taxa,
especially the Acidobacteria and Proteobacteria, whereas an
increase in bacterial diversity is noted in terrestrial ecosystems (Hartman et al., 2008). This result suggests the incompleteness of the restoration. In contrast, successful
restoration showed similar microbial community composition and biomass between the restored sites (7e11 yrs) and the
reference sites (Card and Quideau, 2010). Sediment microbial
activities and physicochemistry have been used as progress
indicators of salt marsh restoration processes (Duarte et al.,
2012). These observations highlight the promise for microbial indicators to monitor wetland restorations. The incorporation of microbial indicators will help to develop a more
comprehensive wetland assessment which will prove advantageous for wetland restoration and management efforts.
To summarize, wetland health conditions require the
simultaneous assessment of its physical, chemical, and biological components. Maintaining wetland flora and fauna and
protecting biological diversity can be achieved by the development of multi-metric indices which incorporate microbial
indicators. Although microbial structureefunction relationships may vary in degree and kind with wetland biogeochemical processes (Hartman et al., 2008), the advantages
of integrating microbial monitoring are that these indicators
can accommodate environmental impacts from point and
nonpoint sources and human activities, overcome the weaknesses of individual parameter approaches, and assess trends
over long periods of time. The multi-metric index of biotic
integrity incorporating microbial indicators could play an
invaluable role in supporting ecologically based assessments
of wetland conditions and determining effective management
and restoration strategies.
references
Abed, R.M.M., Zein, B., Al-Thukair, A., de Beer, D., 2007.
Phylogenetic diversity and activity of aerobic heterotrophic
bacteria from a hypersaline oil-polluted microbial mat.
Systematic and Applied Microbiology 30 (4), 319e330.
Achtman, M., Wagner, M., 2008. Microbial diversity and the
genetic nature of microbial species. Nature Reviews
Microbiology 6 (0), 431e440.
Ahn, C., Peralta, R.M., 2009. Soil bacterial community structure
and physicochemical properties in mitigation wetlands
created in the Piedmont region of Virginia (USA). Ecological
Engineering 35 (7), 1036e1042.
Andersen, R., Grasset, L., Thormann, M.N., Rochefort, L.,
Francez, A.J., 2010. Changes in microbial community structure
and function following Sphagnum peatland restoration. Soil
Biology and Biochemistry 42 (2), 291e301.
Anderson, C.J., Mitsch, W.J., Nairn, R.W., 2005. Temporal and spatial
development of surface soil conditions at two created riverine
marshes. Journal of Environmental Quality 34, 2072e2081.
Asmus, B., Magner, J.A., Vondracek, B., Perry, J., 2009. Physical
integrity: the missing link in biological monitoring and TMDLs.
Environmental Monitoring and Assessment 159 (1e4), 443e463.
Austin, S., 2002. Response of dabbling ducks to wetland conditions
in the prairie pothole region. Waterbirds 25 (4), 465e473.
Bannert, A., Kleineidam, K., Wissing, L., Mueller-Niggemann, C.,
Vogelsang, V., Welzl, G., Cao, Z., Schloter, M., 2011. Changes in
diversity and functional gene abundances of microbial
communities involved in nitrogen fixation, nitrification, and
denitrification in a tidal wetland versus paddy soils cultivated
for different time periods. Applied and Environmental
Microbiology 77 (17), 6109e6116.
Basset, A., Barbone, E., Borja, A., Brucet, S., Pinna, M.,
Quintana, X.D., Reizopoulou, S., Rosati, I., Simboura, N., 2012.
A benthic macroinvertebrate size spectra index for
implementing the water framework directive in coastal
lagoons in Mediterranean and Black Sea ecoregions. Ecological
Indicators 12 (1), 72e83.
Batzer, D.P., Palik, B.J., Buech, R., 2004. Relationships between
environmental characteristics and macroinvertebrate
communities in seasonal woodland ponds of Minnesota.
Journal of the North American Benthological Society 23 (1),
50e68.
Bernhard, A.E., Landry, Z.C., Blevins, A., De La Torre, J.R.,
Giblin, A.E., Stahl, D.A., 2010. Abundance of ammoniaoxidizing archaea and bacteria along an estuarine salinity
gradient in relation to potential nitrification rates. Applied and
Environmental Microbiology 76 (4), 1285e1289.
Best, E.P.H., de Vries, D., Reins, A., 1984. The macrophytes in the
Loosdrecht Lakes: story of their decline in the course of
eutrophication. Journal of Limnology 22 (0), 868e875.
Bodelier, P.L.E., Meima-Franke, M., Zwart, G., Laanbroek, H.J.,
2005. New DGGE strategies for the analyses of methanotrophic
microbial communities using different combinations of
existing 16S rRNA-based primers. FEMS Microbiology Ecology
52 (2), 163e174.
Bolhuis, H., Stal, L.J., 2011. Analysis of bacterial and archaeal
diversity in coastal microbial mats using massive parallel 16S
rRNA gene tag sequencing. ISME Journal 5 (11), 1701e1712.
Brown, M.V., Philip, G.K., Bunge, J.A., Smith, M.C., Bissett, A.,
Lauro, F.M., Fuhrman, J.A., Donachie, S.P., 2009. Microbial
community structure in the North Pacific ocean. ISME Journal
3 (12), 1374e1386.
Card, S.M., Quideau, S.A., 2010. Microbial community structure in
restored riparian soils of the Canadian prairie pothole region.
Soil Biology and Biochemistry 42 (9), 1463e1471.
Casey, R.J., Paszkowski, C.A., Kendall, S.A., Ambrose, N.,
Gingras, B., 1999. Effects of Cattle Grazing Intensity on Water
Chemistry, Aquatic Invertebrates, Waterbirds, Songbirds and
Amphibians of Pothole Ponds of the Aspen Parkland, Central
Alberta. Institute for Wetland and Waterfowl Research
Publication, Ducks Unlimited Canada, Stonewall, Manitoba.
Chaplin, C.T., Bridgham, S.D., Pastor, J., 2004. pH and nutrient
effects on above-ground net primary production in
a Minnesota, USA bog and fen. Wetlands 24 (1), 186e201.
Chow-Fraser, P., 1999. Seasonal, interannual, and apatial
variability in the concentrations of the total suspended solids
w a t e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 7 1 1 e1 7 2 5
in a degraded coastal wetland of Lake Ontario. Journal of Great
Lakes Research 25 (4), 799e813.
Coci, M., Riechmann, D., Bodelier, P.L.E., Stefani, S., Zwart, G.,
Laanbroek, H.J., 2005. Effect of salinity on temporal and spatial
dynamics of ammonia-oxidising bacteria from intertidal
freshwater sediment. FEMS Microbiology Ecology 53 (3),
359e368.
Codd, G.A., 2000. Cyanobacterial toxins, the perception of water
quality, and the prioritisation of eutrophication control.
Ecological Engineering 16 (1), 51e60.
Croft, M.V., Chow-Fraser, P., 2007. Use and development of the
wetland macrophyte index to detect water quality
impairment in fish habitat of Great Lakes coastal marshes.
Journal of Great Lakes Research 33 (3), 172e197.
Cvetkovic, M., Wei, A., Chow-Fraser, P., 2010. Relative importance
of macrophyte community versus water quality variables for
predicting fish assemblages in coastal wetlands of the
Laurentian Great Lakes. Journal of Great Lakes Research 36 (1),
64e73.
Dahl, T.E., 2011. Status and Trends of Wetlands in the
Conterminous United States 2004 to 2009. U.S. Department of
the Interior; Fish and Wildlife Service, Washington, D.C, p.
108.
Deegan, L.A., Finn, J.T., Buonaccorsi, J., 1997. Development and
validation of an estuarine biotic integrity index. Estuaries 20
(3), 601e617.
Degens, B.P., Schipper, L.A., Sparling, G.P., Duncan, L.C., 2001. Is
the microbial community in a soil with reduced catabolic
diversity less resistant to stress or disturbance? Soil Biology
and Biochemistry 33 (9), 1143e1153.
Degnan, P.H., Ochman, H., 2012. Illumina-based analysis of
microbial community diversity. ISME Journal 6 (1), 183e194.
Dorador, C., Busekow, A., Vila, I., Imhoff, J.F., Witzel, K.P., 2008.
Molecular analysis of enrichment cultures of ammonia
oxidizers from the Salar de Huasco, a high altitude saline
wetland in northern Chile. Extremophiles 12 (3), 405e414.
Duarte, B., Freitas, J., Caçador, I., 2012. Sediment microbial
activities and physic-chemistry as progress indicators of salt
marsh restoration processes. Ecological Indicators 19 (0),
231e239.
Dyhrman, S.T., 2008. Molecular approaches to diagnosing
nutritional physiology in harmful algae: implications for
studying the effects of eutrophication. Harmful Algae 8 (1),
167e174.
Dziock, F., Henle, K., Foeckler, F., Follner, K., Scholz, M., 2006.
Biological indicator systems in floodplains e a review.
International Review of Hydrobiology 91 (4), 271e291.
Eickhorst, T., Tippkötter, R., 2008. Improved detection of soil
microorganisms using fluorescence in situ hybridization
(FISH) and catalyzed reporter deposition (CARD-FISH). Soil
Biology and Biochemistry 40 (7), 1883e1891.
Erguder, T.H., Boon, N., Wittebolle, L., Marzorati, M.,
Verstraete, W., 2009. Environmental factors shaping the
ecological niches of ammonia-oxidizing archaea. FEMS
Microbiology Reviews 33 (5), 855e869.
Ernebjerg, M., Kishony, R., 2012. Distinct growth strategies of soil
bacteria as revealed by large-scale colony tracking. Applied
and Environmental Microbiology 78 (5), 1345e1352.
Erwin, K.L., 2009. Wetlands and global climate change: the role of
wetland restoration in a changing world. Wetlands Ecology
and Management 17 (1), 71e84.
Euliss, N.H.J., Mushet, D.M., 1999. Influence of agriculture on
aquatic invertebrate communities of temporary wetlands in
the prairie pothole region of North Dakota, USA. Wetlands 19
(2), 578e583.
Euliss, N.H.J., Mushet, D.M., Johnson, D.H., 2001. Use of
macroinvertebrates to identify cultivated wetlands in the
prairie pothole region. Wetlands 21 (2), 223e231.
1721
Fairbairn, S.E., Dinsmore, J.J., 2001. Local and landscape-level
influences on wetland bird communities of the prarie pothole
region of Iowa, USA. Wetlands 21 (1), 41e47.
Feio, M.J., Aguiar, F.C., Almeida, S.F.P., Ferreira, M.T., 2012.
Aquaflora: a predictive model based on diatoms and
macrophytes for streams water quality assessment. Ecological
Indicators 18 (0), 586e598.
Fennessy, M.S., Jacobs, A.D., Kentula, M.E., 2004. Review of Rapid
Methods for Assessing Wetland Condition EPA/620/R-04/009.
U.S. Environmental Protection Agency, Washington, D.C.
Floder, S., Burns, C.W., 2004. Phytoplankton diversity of shallow
tidal lakes: influence of periodic salinity changes on diversity
and species number of a natural assemblage. Journal of
Phycology 40 (1), 54e61.
Freitag, T.E., Toet, S., Ineson, P., Prosser, J.I., 2010. Links between
methane flux and transcriptional activities of methanogens
and methane oxidizers in a blanket peat bog. FEMS
Microbiology Ecology 73 (1), 157e165.
Frieswyk, C.B., Zedler, J.B., 2007. Vegetation change in Great Lakes
coastal wetlands: deviation from the historical cycle. Journal
of Great Lakes Research 33 (2), 366e380.
Frossard, A., Gerull, L., Mutz, M., Gessner, M.O., 2012. Disconnect
of microbial structure and function: enzyme activities and
bacterial communities in nascent stream corridors. ISME
Journal 6 (3), 680e691.
Gerard, E., Guyot, F., Phillippot, P., Lopez-Garcia, P., 2005.
Fluorescence in situ hybridisation coupled to ultra small
immunogold detection to identify prokaryotic cells using
transmission and scanning electron microscopy. Journal of
Microbiological Methods 63 (1), 20e28.
Getachew, M., Ambelu, A., Tiku, S., Legesse, W., Adugna, A.,
Kloos, H., 2012. Ecological assessment of Cheffa wetland in the
Borkena Valley, northeast Ethiopia: macroinvertebrate and
bird communities. Ecological Indicators 15 (1), 63e71.
Gleason, R.A., Euliss, N.H.J., Hubbard, D.E., Duffy, W.G., 2003.
Effects of sediment load on emergence of aquatic
invertebrates and plants from wetland soil egg and seed
banks. Wetlands 23 (1), 26e34.
Gorra, R., Coci, M., Ambrosoli, R., Laanbroek, H.J., 2007. Effects of
substratum on the diversity and stability of ammoniaoxidizing communities in a constructed wetland used for
wastewater treatment. Journal of Applied Microbiology 103 (5),
1442e1452.
Gregory, S.P., Shields, R.J., Fletcher, D.J., Gatland, P., Dyson, P.J.,
2010. Bacterial community responses to increasing ammonia
concentrations in model recirculating vertical flow saline
biofilters. Ecological Engineering 36 (10), 1485e1491.
Gubry-Rangin, C., Hai, B., Quince, C., Engel, M., Thomson, B.C.,
James, P., Schloter, M., Griffiths, R.I., Prosser, J.I.,
Nicol, G.W., 2011. Niche specialization of terrestrial
archaeal ammonia oxidizers. Proceedings of the National
Academy of Sciences of the United States of America 108
(52), 21206e21211.
Gubry-Rangin, C., Nicol, G.W., Prosser, J.I., 2010. Archaea rather
than bacteria control nitrification in two agricultural acidic
soils. FEMS Microbiology Ecology 74 (3), 566e574.
Hallin, S., Lindgren, P.E., 1999. PCR detection of genes encoding
nitrite reductase in denitrifying bacteria. Applied and
Environmental Microbiology 65 (4), 1652e1657.
Hann, B.J., Mundy, C.J., Goldsborough, L.G., 2001. Snail-periphyton
interactions in a prairie lacustrine wetland. Hydrobiologia 457
(1e3), 167e175.
Hanson, R.S., Hanson, T.E., 1996. Methanotrophic bacteria.
Microbiological Reviews 60 (2), 439e471.
Hart, B.T., Bailey, P., Edwards, R., Hortle, K., James, K.,
McMahon, A., Meredith, C., Swadling, K., 1990. Effects of
salinity on river, stream and wetland ecosystems in Victoria,
Australia. Water Research 24 (9), 1103e1117.
1722
w a t e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 7 1 1 e1 7 2 5
Hartman, W.H., Richardson, C.J., Vilgalys, R., Bruland, G.L., 2008.
Environmental and anthropogenic controls over bacterial
communities in wetland soils. Proceedings of the National
Academy of Sciences of the United States of America 105,
17842e17847.
He, Z., Deng, Y., Van Nostrand, J.D., Tu, Q., Xu, M., Hemme, C.L.,
Li, X., Wu, L., Gentry, T.J., Yin, Y., Liebich, J., Hazen, T.C.,
Zhou, J., 2010. GeoChip 3.0 as a high-throughput tool for
analyzing microbial community composition, structure and
functional activity. ISME Journal 4 (9), 1167e1179.
Herrmann, M., Scheibe, A., Avrahami, S., Kusel, K., 2011.
Ammonium availability affects the ratio of ammoniaoxidizing bacteria to ammonia-oxidizing archaea in simulated
creek ecosystems. Applied and Environmental Microbiology
77 (5), 1896e1899.
Höfferle, S., Nicol, G.W., Pal, L., Hacin, J., Prosser, J.I., Mandic
Mulec, I., 2010. Ammonium supply rate influences archaeal
and bacterial ammonia oxidizers in a wetland soil vertical
profile. FEMS Microbiology Ecology 74 (2), 302e315.
Houlahan, J.E., Keddy, P.A., Makkay, K., Findlay, C.S., 2006. The
effects of adjacent land use on wetland species richness and
community composition. Wetlands 26, 79e96.
Howe, R.W., Regal, R.R., Hanowski, J., Niemi, G.J., Danz, N.P.,
Smith, C.R., 2007. An index of ecological condition based on
bird assemblages in Great Lakes coastal wetlands. Journal of
Great Lakes Research 33 (3), 93e105.
Huang, W.E., Stoecker, K., Griffiths, R., Newbold, L., Daims, H.,
Whiteley, A.S., Wagner, M., 2007. RamaneFISH: combining
stable-isotope Raman spectroscopy and fluorescence in situ
hybridization for the single cell analysis of identity and
function. Environmental Microbiology 9 (8), 1878e1889.
Johnston, C.A., 1991. Sediment and nutrient retention by freshwater
wetlands: effects on surface water quality. Critical Reviews in
Environmental Science and Technology 491 (5), 491e565.
Kantrud, H.A., Newton, W.E., 1996. A test of vegetation related
indicators in the prairie pothole region. Journal of Aquatic
Ecosystem Health 5 (3), 177e191.
Karr, J.R., 1981. Assessment of biotic integrity using fish
communities. Fisheries 6 (0), 21e27.
Karr, J.R., 1993. Defining and assessing ecological integrity:
beyond water quality. Environmental Toxicology and
Chemistry 12 (9), 1521e1531.
Karr, J.R., Dudley, D.R., 1981. Ecological perspective on water
quality goals. Environmental Management 5 (1), 55e68.
Keller, M., Zengler, K., 2004. Tapping into microbial diversity.
Nature Reviews Microbiology 2 (2), 141e150.
Khanna, K.V., 2007. Existing and emerging detection technologies
for DNA (deoxyribonucleic acid) finger printing, sequencing,
bio- and analytical chips: a multidisciplinary development
unifying molecular biology, chemical and electronics
engineering. Biotechnology Advances 25 (1), 85e98.
Kimberling, D.N., Karr, J.R., Fore, L.S., 2001. Measuring human
disturbance using terrestrial invertebrates in the shrub-steppe
of Eastern Washington (USA). Ecological Indicators 1 (2), 63e81.
Klappenbach, J.A., Dunbar, J.M., Schmidt, T.M., 2000. rRNA operon
copy number reflects ecological strategies of bacteria. Applied
and Environmental Microbiology 66 (4), 1328e1333.
Konneke, M., Bernhard, A.E., de la Torre, J.R., Walker, C.B.,
Waterbury, J.B., Stahl, D.A., 2005. Isolation of an autotrophic
ammonia-oxidizing marine archaeon. Nature 437 (7058),
543e546.
LaBaugh, J.W., 1995. Relation of algal biomass to chlorophyll-a in
selected lakes and wetlands in the north central United States.
Canadian Journal of Fisheries and Aquatic Sciences 52 (2),
416e424.
Lee, S.-H., Kim, S.-Y., Kang, H., 2012. Effects of elevated CO2 on
communities of denitrifying bacteria and methanogens in
a temperate marsh microcosm. Microbial Ecology, 1e14.
Lillie, R.A., Evrard, J.O., 1994. Influence of macroinvertebrates and
macrophytes on waterfowl utilization of wetlands in the
prairie pothole region of northwestern Wisconsin.
Hydrobiologia 279-280 (1), 235e246.
Lougheed, V.L., Chow-Fraser, P., 2002. Development and use of
a zooplankton index of wetland quality in the Laurentian
Great Lakes basin. Ecological Applications 12 (2), 474e486.
MacGregor, B.J. a., A.,, R., 2006. Single-stranded conformational
polymorphism for separation of mixed rRNAS (rRNA-SSCP):
a new method for profiling microbial communities.
Systematic and Applied Microbiology 29, 661e670.
Martens-Habbena, W., Berube, P.M., Urakawa, H., de la Torre, J.R.,
Stahl, D.A., 2009. Ammonia oxidation kinetics determine
niche separation of nitrifying Archaea and Bacteria. Nature
461 (7266), 976e979.
Martens, M., Weidner, S., Linke, B., de Vos, P., Gillis, M.,
Willems, A., 2007. A prototype taxonomic microarray targeting
the rpsA housekeeping gene permits species identification
within the rhizobial genus Ensifer. Systematic and Applied
Microbiology 30 (5), 390e400.
Martı́nez, C.E., Yáñez, C., Yoon, S.J., Bruns, M.A., 2007.
Biogeochemistry of metalliferous peats: sulfur speciation and
depth distributions of dsrAB genes and Cd, Fe, Mn, S, and Zn in
soil cores. Environmental Science and Technology 41 (15),
5323e5329.
Mayer, P.M., Galatowitsch, S.M., 1999. Diatom communities as
ecological indicators of recovery in restored prairie wetlands.
Wetlands 19 (4), 765e774.
Mayer, P.M., Megard, R.O., Galatowitsch, S.M., 2004. Plankton
respiration and biomass as functional indicators of recovery
in restored prairie wetlands. Ecological Indicators 4 (4),
245e253.
McCormick, P.V., Stevenson, R.J., 1998. Periphyton as a tool for
ecological assessment and management in the Florida
Everglades. Journal of Phycology 34 (5), 726e733.
Mccullagh, P., Nelder, J.A., 1989. Generalized Linear Models.
Chapman and Hall, London.
McNair, S.A., Chow-Fraser, P., 2003. Change in biomass of benthic
and planktonic algae along a disturbance gradient for 24 Great
Lakes coastal wetlands. Canadian Journal of Fisheries and
Aquatic Sciences 60 (6), 676e689.
Merkley, M., Rader, R., McArthur, J., Eggett, D., 2004. Bacteria as
bioindicators in wetlands: bioassessment in the bonneville
basin of Utah, USA. Wetlands 24 (3), 600e607.
Miccachion, M., 2002. Amphibian index of biotic integrity
(AmphIBI) for wetlands U.S. EPA Grant CD985875-01. Journal.
Michailova, P., Warchałowska-Sliwa,
E., Szarek-Gwiazda, E.,
Kownacki, A., 2012. Does biodiversity of macroinvertebrates
and genome response of Chironomidae larvae (Diptera) reflect
heavy metal pollution in a small pond? Environmental
Monitoring and Assessment 184 (1), 1e14.
Mitsch, W.J., Gosselink, J.G., 2000. Wetlands. West Sessex. Wiley,
UK.
Moin, N.S., Nelson, K.A., Bush, A., Bernhard, A.E., 2009.
Distribution and diversity of archaeal and bacterial ammonia
oxidizers in salt marsh sediments. Applied and Environmental
Microbiology 75 (23), 7461e7468.
Monard, C., Vandenkoornhuyse, P., Le Bot, B., Binet, F., 2011.
Relationship between bacterial diversity and function under
biotic control: the soil pesticide degraders as a case study.
ISME Journal 5 (6), 1048e1056.
Morales, S.E., Mouser, P.J., Ward, N., Hudman, S.P., Gotelli, N.J.,
Ross, D.S., Lewis, T.A., 2006. Comparison of bacterial
communities in New England Sphagnum bogs using terminal
restriction fragment length polymorphism (T-RFLP). Microbial
Ecology 52 (1), 34e44.
Moran, M.A., 2009. Metatranscriptomics: eavesdropping on
complex microbial communities. Microbe 4 (7), 329e335.
w a t e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 7 1 1 e1 7 2 5
Muñoz-Carpena, R., Vellidis, G., Shirmohammadi, A.,
Wallender, W.W., 2006. Modeling tools for TMDL development
and implementation. Transactions of the ASABE 49 (4),
961e966.
Musat, N., Foster, R., Vagner, T., Adam, B., Kuypers, M.M.M., 2012.
Detecting metabolic activities in single cells, with emphasis
on nanoSIMS. FEMS Microbiology Ecology 36 (2), 486e511.
Mushet, D.M., Euliss, N.H., Shaffer, T.L., 2002. Floristic quality
assessment of one natural and three restored wetland
complexes in North Dakota, USA. Wetlands 22 (1), 1126e1138.
Neufeld, J., Murrell, J., 2007. Witnessing the last supper of
uncultivated microbial cells with Raman-FISH. The ISME
Journal 1 (4), 269e270.
Niemi, G., Wardrop, D., Brooks, R., Anderson, S., Brady, V.,
Paerl, H., Rakocinski, C., Brouwer, M., Levinson, B.,
McDonald, M., 2004. Rationale for a new generation of
indicators for coastal waters. Environmental Health
Perspectives 112 (9), 979e986.
Nuria, R., Jérôme, M., Léonide, C., Christine, R., Gérard, H.,
Etienne, I., Patrick, L., 2011. IBQS: a synthetic index of soil
quality based on soil macro-invertebrate communities. Soil
Biology and Biochemistry 43 (10), 2032e2045.
Ochman, H., Lawrence, J.G., Groisman, E.A., 2000. Lateral gene
transfer and the nature of bacterial innovation. Nature 405
(6784), 299e304.
Oh, S., Caro-Quintero, A., Tsementzi, D., DeLeon-Rodriguez, N.,
Luo, C., Poretsky, R., Konstantinidis, K.T., 2011. Metagenomic
insights into the evolution, function, and complexity of the
planktonic microbial community of Lake Lanier, a temperate
freshwater ecosystem. Applied and Environmental
Microbiology 77 (17), 6000e6011.
Ozimek, T., Pieczynska, E., Hankiewicz, A., 1991. Effects of
filamentous algae on submerged macrophyte growth a laboratory experiment. Aquatic Botany 41 (4), 309e315.
Palmer, C.M., 1969. A composite rating of algae tolerating organic
pollution. Journal of Phycology 15 (1), 78e82.
Park, N., Lee, J., Chon, K., Kang, H., Cho, J., 2009. Investigating
microbial activities of constructed wetlands with respect to
nitrate and sulfate reduction. Desalination and Water
Treatment 1 (1e3), 172e179.
Peralta, A.L., Matthews, J.W., Kent, A.D., 2010. Microbial
community structure and denitrification in a wetland
mitigation bank. Applied and Environmental Microbiology 76
(13), 4207e4215.
Peterson, A.C., Niemi, G.J., 2007. Evaluation of the Ohio rapid
assessment method for wetlands in the Western Great Lakes:
an analysis using bird communities. Journal of Great Lakes
Research 33 (3), 280e291.
Plafkin, J.L., 1989. Rapid Bioassessment Protocols for Use in Rivers
and Streams: Benthic Macroinvertebrates and Fish EPA/444/489-001. U.S. Environmental Protection Agency, Washington, D.C.
Pohn, B., Gerlach, J., Scheideler, M., Katz, H., Uray, M., Bischof, H.,
Klimant, I., Schwab, H., 2007. Micro-colony array based high
throughput platform for enzyme library screening. Journal of
Biotechnology 129 (1), 162e170.
Prieme, A., Braker, G., Tiedje, J.M., 2002. Diversity of nitrite
reductase (nirK and nirS ) gene fragments in forested upland
and wetland soils. Applied and Environmental Microbiology 68
(4), 1893e1900.
Reavie, E.D., Axler, R.P., Sgro, G.V., Danz, N.P., Kingston, J.C.,
Kireta, A.R., Brown, T.N., Hollenhorst, T.P., Ferguson, M.J., 2006.
Diatom-based weighted-averaging transfer functions for Great
Lakes coastal water quality: relationships to watershed
characteristics. Journal of Great Lakes Research 32 (2), 321e347.
Riefler, G.R., Krohn, J., Stuart, B., Socotch, C., 2008. Role of sulfurreducing bacteria in a wetland system treating acid mine
drainage. Science of the Total Environment 394 (2e3),
222e229.
1723
Risgaard-Petersen, N., Langezaal, A.M., Ingvardsen, S.,
Schmid, M.C., Jetten, M.S.M., Opden Camp, H.J.M.,
Derksen, J.W.M., Pina-Ochoa, E., Eriksson, S.P., Nielsen, L.P.,
Revsbech, N.P., Cedhagen, T., vanDer Zwaan, G.J., 2006.
Evidence for complete denitrification in a benthic
foraminifera. Nature 443, 93e96.
Ritchie, D.A., Edwards, C., McDonald, I.R., Murrell, J.C., 1997.
Detection of methanogens and methanotrophs in natural
environments. Global Change Biology 3 (4), 339e350.
Rittmann, B.E., McCarty, P.L., 2001. Environmental Biotechnology:
Principles and Applications. McGraw-Hill Science Engineering,
New York.
Rothrock, P.E., Simon, T.P., Stewart, P.M., 2008. Development,
calibration, and validation of a littoral zone plant index of
biotic integrity (PIBI) for lacustrine wetlands. Ecological
Indicators 8 (1), 79e88.
Ruscha, D.B., Martiny, A.C., Dupont, C.L., Halpern, A.L.,
Venter, J.C., 2010. Characterization of Prochlorococcus clades
from iron-depleted oceanic regions. Proceedings of the
National Academy of Sciences of the United States of America
107 (37), 16184e16189.
Santoro, A.E., Francis, C.A., De Sieyes, N.R., Boehm, A.B., 2008.
Shifts in the relative abundance of ammonia-oxidizing
bacteria and archaea across physicochemical gradients in
a subterranean estuary. Environmental Microbiology 10 (4),
1068e1079.
Savard, J.P.L., Boyd, W.S., Smith, G.E.J., 1994. Waterfowl wetland
relationship in the aspen parkland of British Columbia comparison of analytical methods. Hydrobiologia 280 (1),
309e325.
Schipper, L.A., Degens, B.P., Sparling, G.P., Duncan, L.C., 2001.
Changes in microbial heterotrophic diversity along five plant
successional sequences. Soil Biology and Biochemistry 33 (15),
2093e2103.
Schleper, C., 2010. Ammonia oxidation: different niches for
bacteria and archaea? ISME Journal 4 (9), 1092e1094.
Schneider, S., Lindstrom, E., 2011. The periphyton index of
trophic status PIT: a new eutrophication metric based on nondiatomaceous benthic algae in Nordic rivers. Hydrobiologia
665 (1), 143e155.
Schneider, T., Riedel, K., 2010. Environmental proteomics:
analysis of structure and function of microbial communities.
Proteomics 10 (4), 785e798.
Siljanen, H.M.P., Saari, A., Bodrossy, L., Martikainen, P.J., 2012.
Seasonal variation in the function and diversity of
methanotrophs in the littoral wetland of a boreal eutrophic
lake. FEMS Microbiology Ecology 80, 548e555.
Simon, C., Daniel, R., 2009. Achievements and new knowledge
unraveled by metagenomic approaches. Applied Microbiology
and Biotechnology 85 (2), 265e276.
Sims, A., Gajaraj, S., Hu, Z., 2012a. Seasonal population changes of
ammonia-oxidizing organisms and their relationship to water
quality in a constructed wetland. Ecological Engineering 40 (0),
100e107.
Sims, A., Horton, J., McIntosh, S., Gajaraj, S., Mueller, R.,
Miles, R., Reed, R., Hu, Z., 2012b. Temporal and spatial
distributions of ammonia-oxidizing archaea and
bacteria and their ratio as an indicator of oligotrophic
conditions in natural wetlands. Water Research 46 (13),
4121e4129.
Siobhan, F., Mack, J.J., Rokosch, A., Knapp, M., Micacchion, M.,
2004. Integrated Wetland Assessment Program. Part 5:
Biogeochemical and Hydrological Investigations of Natural
and Mitigation Wetlands. Ohio EPA Technical Report WET/
2004-5. Ohio Environmental Protection Agency, Wetland
Ecology Group, Division of Surface Water.
Smith, J.M., Castro, H., Ogram, A., 2007. Structure and function of
methanogens along a short-term restoration chronosequence
1724
w a t e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 7 1 1 e1 7 2 5
in the Florida Everglades. Applied and Environmental
Microbiology 73, 4135e4141.
Sogin, M.L., Morrison, H.G., Huber, J.A., Welch, D.M., Huse, S.M.,
Neal, P.R., Arrieta, J.M., Herndl, G.J., 2006. Microbial diversity in
the deep sea and the underexplored "rare biosphere.
Proceedings of the National Academy of Sciences United
States of America 103, 12115e12120.
Stenberg, B., 1999. Monitoring soil quality of arable land:
microbiological indicators. Acta Agriculturae Scandinavica 49
(1), 1e24.
Suenaga, H., Liu, R., Shiramasa, Y., Kanagawa, T., 2005. Novel
approach to quantitative detection of specific rRNA in
a microbial community, using catalytic DNA. Applied and
Environmental Microbiology 71 (8), 4879e4884.
Sutton-Grier, A.E., Wright, J.P., McGill, B.M., Richardson, C.J., 2011.
Environmental conditions influence the plant functional
diversity effect on potential denitrification. PLoS ONE 6 (2),
e16584.
Teiter, S., Mander, Ü., 2005. Emission of N2O, N2, CH4, and CO2
from constructed wetlands for wastewater treatment and
from riparian buffer zones. Ecological Engineering 25 (5),
528e541.
Thomas, T., Rusch, D., DeMaere, M.Z., Yung, P.Y., Lewis, M.,
Halpern, A., Heidelberg, K.B., Egan, S., Steinberg, P.D.,
Kjelleberg, S., 2010. Functional genomic signatures of sponge
bacteria reveal unique and shared features of symbiosis. ISME
Journal 4, 1557e1567.
Torsvik, V., Ovreas, L., 2002. Microbial diversity and function in
soil: from genes to ecosystems. Current Opinion in
Microbiology 5 (3), 240e245.
Torsvik, V., Sorheim, R., Goksoyr, J., 1996. Total bacterial diversity
in soil and sediment communities - a review. Journal of
Industrial Microbiology 17 (3e4), 170e178.
Tourna, M., Freitag, T.E., Prosser, J.I., 2010. Stable isotope probing
analysis of interactions between ammonia oxidizers. Applied
and Environmental Microbiology 76 (8), 2468e2477.
Treusch, A.H., Leininger, S., Kletzin, A., Schuster, S.C., Klenk, H.,
Schleper, C., 2005. Novel genes for nitrite reductase and Amorelated proteins indicate a role of uncultivated mesophilic
crenarchaeota in nitrogen cycling. Environmental
Microbiology 7 (12), 1985e1995.
Urich, T., Lanzén, A., Qi, J., Huson, D.H., Schleper, C.,
Schuster, S.C., 2008. Simultaneous assessment of soil
microbial community structure and function through analysis
of the meta-transcriptome. PLoS ONE 3, 1e13.
USDA, 2008. Hydrogeomorphic Wetland Classification System: An
Overview and Modification to Better Meet the Needs of the
Natural Resources Conservation Service. United States
Department of Agriculture Natural Resources Conservation
Service.
USEPA, 1995. America’s Wetlands: Our Vital Link between Land
and Water. Office of Water, Oceans, and Watersheds.
USEPA, 2002. Methods for Evaluating Wetland Condition: Using
Algae To Assess Environmental Conditions in Wetlands. Office
of Water, U.S. Environmental Protection Agency Washington,
D.C.
USEPA, 2003. Elements of a State Water Monitoring and
Assessment Program. Assessment and Watershed
Protection Division, Office of Wetlands, Oceans and
Watershed, U.S. Environmental Protection Agency
Washington, D.C.
USEPA, 2006. Application of Elements of a State Water Monitoring
and Assessment Program For Wetlands. Wetlands Division,
Office of Wetlands, Oceans and Watersheds, U.S.
Environmental Protection Agency Washington, D.C.
van Brüggen, A.H.C., Semenov, A.M., 2000. In search of biological
indicators for soil health and disease suppression. Applied Soil
Ecology 15 (1), 13e24.
Van Dohla, F.M., 2000. Marine algal toxins: origins, health effects,
and their increased occurrence. Environmental Health
Perspectives 108 (1), 133e141.
van Elsas, J.D., Boersma, F.G.H., 2011. A review of molecular
methods to study the microbiota of soil and the mycosphere.".
European Journal of Soil Biology 47, 77e87.
VanRees-Siewert, K.L., Dinsmore, J.L., 1996. Influence of wetland
age on bird use of restored wetlands in Iowa. Wetlands 16 (4),
577e582.
Verhamme, D.T., Prosser, J.I., Nicol, G.W., 2011. Ammonia
concentration determines differential growth of ammoniaoxidising archaea and bacteria in soil microcosms. ISME
Journal 5 (6), 1067e1071.
Von Mering, C., Hugenholtz, P., Raes, J., Tringe, S.G., Doerks, T.,
Jensen, L.J., Ward, N., Bork, P., 2007. Quantitative phylogenetic
assessment of microbial communities in diverse
environments. Science 315 (5815), 1126e1130.
Wagner, M., Nielsen, P.H., Loy, A., Nielsen, J.L., Daims, H., 2006.
Linking microbial community structure with function:
fluorescence in situ hybridization-microautoradiography and
isotope arrays. Current Opinion in Biotechnology 17 (1), 83e91.
Walsh, K.A., Hill, T.C.J., Moffett, B.F., Harris, J.A., Shaw, P.J.,
Wallace, J.S., 2002. Molecular characaterisation of bacteria in
a wetland used to remove ammoniacal-N from landfill
leachate. Waste Management and Research 20 (6), 529e535.
Wang, S., Wang, Y., Feng, X., Zhai, L., Zhu, G., 2011. Quantitative
analyses of ammonia-oxidizing archaea and bacteria in the
sediments of four nitrogen-rich wetlands in China. Applied
Microbiology and Biotechnology 90 (2), 779e787.
Wei, B., Yu, X., Zhang, S., Gu, L., 2011. Comparison of the
community structures of ammonia-oxidizing bacteria and
archaea in rhizoplanes of floating aquatic macrophytes.
Microbiological Research 166 (6), 468e474.
Wellington, E.M., Berry, A., Krsek, M., 2003. Resolving functional
diversity in relation to microbial community structure in soil:
exploiting genomics and stable isotope probing. Current
Opinion in Microbiology 6 (3), 295e301.
Whiteley, A.S., Griffiths, R.I., Bailey, M.J., 2003. Analysis of the
microbial functional diversity within water-stressed soil
communities by flow cytometric analysis and CTCþ cell
sorting. Journal of Microbiological Methods 54 (2), 257e267.
Williams, K., Ewel, K.C., Stumpf, R.P., Putz, F.E., Workman, T.W.,
1999. Sea-level rise and coastal forest retreat on the west coast
of Florida, USA. Ecology Letters 80 (6), 2045e2063.
Wu, P., Yang, D., 2011. Effect of habitat degradation on soil mesoand microfaunal communities in the Zoigê Alpine Meadow,
Qinghai-Tibetan Plateau. Shengtai Xuebao/Acta Ecologica
Sinica 31 (13), 3745e3757.
Yagow, G., Wilson, B., Srivastava, P., Obropta, C.C., 2006. Use of
biological indicators in TMDL assessment and
implementation. Transactions of the ASABE 49 (4), 1023e1032.
Yan, L., Inamori, R., Gui, P., Xu, K.Q., Kong, H.N., Matsumura, M.,
Inamori, Y., 2005. Distribution characteristics of ammoniaoxidizing bacteria in the Typha latifolia constructed wetlands
using fluorescent in situ hybridization (FISH). Journal of
Environmental Sciences 17 (6), 993e997.
Ye, L., Zhang, T., 2011. Ammonia-oxidizing bacteria dominates
over ammonia-oxidizing archaea in a saline nitrification
reactor under low DO and high nitrogen loading.
Biotechnology and Bioengineering 108 (11), 2544e2552.
Yergeau, E., Kang, S., He, Z., Zhou, J., Kowalchuk, G.A., 2007.
Functional microarray analysis of nitrogen and carbon cycling
genes across an Antarctic latitudinal transect. ISME Journal,
1e17.
Yoder, C.O., Kulik, B.H., 2003. The development and application of
multimetric indices for the assessment of impacts to fish
assemblages in large rivers: a review of current science and
applications. Canadian Water Resources Journal 28 (2), 1e28.
w a t e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 7 1 1 e1 7 2 5
You, J., Das, A., Dolan, E.M., Hu, Z., 2009. Ammonia-oxidizing
archaea involved in nitrogen removal. Water Research 43 (7),
1801e1809.
Zaadya, E., Ben-Davida, E.A., Sherb, Y., Tzirkinb, R.,
Nejidatb, A., 2010. Inferring biological soil crust
successional stage using combined PLFA, DGGE, physical
and biophysiological analyses. Soil Biology and
Biochemistry 42 (5), 842e849.
Zedler, J.B., Kercher, S., 2005. Wetland resources: status, trends,
ecosystem services, and restorability. Annual Review of
Environment and Resources 30 (1), 39e74.
Zhang, L., Xu, Z., Patel, B., 2009. Culture-dependent and cultureindependent microbial investigation of pine litters and soil in
subtropical Australia. Journal of Soils and Sediments 9 (2),
148e160.
1725
Zhang, W.W., Ma, J.Z., 2011. Waterbirds as bioindicators of
wetland heavy metal pollution. Procedia Environmental
Sciences 10 (C), 2769e2774.
Zimmer, K., Hanson, M., Butler, M., 2003. Interspecies
relationships, community structure, and factors influencing
abundance of submerged macrophytes in prairie wetlands.
Wetlands 23 (4), 717e728.
Zimmer, K.D., Hanson, M.A., Butler, M.G., Duffy, W.G., 2001. Size
distribution of aquatic invertebrates in two prairie wetlands,
with and without fish, with implications for community
production. Freshwater Biology 46 (10), 1373e1386.
Zrum, L., Hann, B.J., 2002. Invertebrates associated with
submerged macrophytes in a prairie wetland: effects of
organophosphorus insecticide nutrients. Hydrobiologia 154
(0), 413e445.